From a0e4cb28955417810633361549f001dce82e132d Mon Sep 17 00:00:00 2001
From: d-a-bunin <142778107+d-a-bunin@users.noreply.github.com>
Date: Tue, 26 Mar 2024 11:12:01 +0300
Subject: [PATCH] Merge `unaligned-data` into `master` (#282)
---
.github/workflows/test.yml | 17 +-
CHANGELOG.md | 51 +
README.md | 1 -
docs/source/api_reference/datasets.rst | 1 +
etna/analysis/decomposition/plots.py | 48 +-
etna/analysis/decomposition/search.py | 3 +-
etna/analysis/decomposition/utils.py | 44 +-
etna/analysis/eda/plots.py | 58 +-
etna/analysis/eda/utils.py | 27 +-
etna/analysis/forecast/plots.py | 19 +-
etna/analysis/outliers/density_outliers.py | 2 +-
etna/analysis/outliers/hist_outliers.py | 2 +-
etna/analysis/outliers/median_outliers.py | 2 +-
etna/analysis/outliers/plots.py | 25 +-
.../outliers/prediction_interval_outliers.py | 4 +-
etna/analysis/utils.py | 11 +-
etna/auto/__init__.py | 4 +-
etna/commands/backtest_command.py | 16 +-
etna/commands/forecast_command.py | 28 +-
etna/datasets/__init__.py | 1 +
etna/datasets/datasets_generation.py | 66 +-
etna/datasets/tsdataset.py | 393 ++++--
etna/datasets/utils.py | 450 +++++-
etna/ensembles/direct_ensemble.py | 8 +-
etna/ensembles/mixins.py | 5 +-
etna/ensembles/stacking_ensemble.py | 7 +-
etna/ensembles/voting_ensemble.py | 7 +-
etna/models/__init__.py | 2 +-
etna/models/catboost.py | 6 +-
etna/models/holt_winters.py | 38 +-
etna/models/nn/deepar_native/deepar.py | 3 +-
etna/models/nn/deepstate/deepstate.py | 1 +
etna/models/nn/mlp.py | 4 +-
etna/models/nn/patchts.py | 2 +-
etna/models/nn/rnn.py | 3 +-
etna/models/nn/utils.py | 7 +-
etna/models/prophet.py | 53 +-
etna/models/sarimax.py | 28 +-
etna/models/statsforecast.py | 19 +-
etna/models/tbats.py | 24 +-
etna/models/utils.py | 105 +-
etna/pipeline/assembling_pipelines.py | 2 +-
etna/pipeline/autoregressive_pipeline.py | 35 +-
etna/pipeline/base.py | 196 ++-
etna/pipeline/mixins.py | 19 +-
.../decomposition/change_points_based/base.py | 4 +-
.../change_points_models/base.py | 10 +-
.../change_points_based/detrend.py | 11 +-
etna/transforms/decomposition/deseasonal.py | 22 +-
etna/transforms/decomposition/detrend.py | 12 +-
etna/transforms/decomposition/stl.py | 64 +-
.../feature_selection/feature_importance.py | 10 +-
etna/transforms/math/binary_operator.py | 3 +-
etna/transforms/math/differencing.py | 19 +-
etna/transforms/math/lags.py | 48 +-
etna/transforms/missing_values/resample.py | 25 +-
etna/transforms/outliers/base.py | 29 +-
etna/transforms/timestamp/date_flags.py | 130 +-
etna/transforms/timestamp/fourier.py | 229 ++-
etna/transforms/timestamp/holiday.py | 140 +-
etna/transforms/timestamp/special_days.py | 78 +-
etna/transforms/timestamp/time_flags.py | 121 +-
examples/101-get_started.ipynb | 947 ++++++++-----
examples/102-backtest.ipynb | 6 +-
examples/103-EDA.ipynb | 5 +-
examples/201-exogenous_data.ipynb | 4 +-
examples/202-NN_examples.ipynb | 5 +-
examples/203-ensembles.ipynb | 12 +-
examples/204-outliers.ipynb | 29 +-
examples/205-automl.ipynb | 1 -
examples/206-clustering.ipynb | 2 +-
examples/207-feature_selection.ipynb | 1 -
examples/208-forecasting_strategies.ipynb | 1 -
examples/209-mechanics_of_forecasting.ipynb | 1 -
examples/301-custom_transform_and_model.ipynb | 1 -
examples/302-inference.ipynb | 1 -
examples/303-hierarchical_pipeline.ipynb | 218 ++-
examples/304-forecasting_interpretation.ipynb | 1 -
examples/305-classification.ipynb | 843 ++++++-----
examples/quickstart.ipynb | 1 -
poetry.lock | 6 +-
pyproject.toml | 6 +-
tests/conftest.py | 24 +-
.../test_decomposition/test_plots.py | 68 +
.../test_decomposition/test_utils.py | 43 +-
tests/test_analysis/test_eda/test_plots.py | 51 +
tests/test_analysis/test_eda/test_utils.py | 102 +-
.../test_confidence_interval_outliers.py | 51 +-
.../test_analysis/test_outliers/test_plots.py | 43 +
tests/test_commands/conftest.py | 31 +-
tests/test_commands/test_backtest.py | 26 +-
tests/test_commands/test_forecast.py | 164 ++-
tests/test_commands/test_utils.py | 4 +-
tests/test_datasets/conftest.py | 121 ++
tests/test_datasets/test_dataset.py | 787 +++++++++--
.../test_datasets/test_datasets_generation.py | 111 +-
tests/test_datasets/test_utils.py | 614 +++++++-
tests/test_ensembles/conftest.py | 24 +
tests/test_ensembles/test_direct_ensemble.py | 71 +-
.../test_ensembles/test_stacking_ensemble.py | 65 +-
tests/test_ensembles/test_voting_ensemble.py | 94 +-
tests/test_models/conftest.py | 22 +
tests/test_models/test_autoarima_model.py | 63 +-
tests/test_models/test_base.py | 4 +-
tests/test_models/test_holt_winters_model.py | 39 +-
tests/test_models/test_inference/common.py | 4 +-
.../test_inference/test_forecast.py | 1072 +++++++++-----
.../test_inference/test_predict.py | 886 ++++++++----
tests/test_models/test_nn/conftest.py | 26 +-
.../deepar_native/test_deepar_native.py | 53 +-
.../test_nn/nbeats/test_nbeats_nets.py | 26 +-
tests/test_models/test_nn/test_deepstate.py | 44 +
tests/test_models/test_nn/test_mlp.py | 48 +-
tests/test_models/test_nn/test_patchts.py | 36 +-
tests/test_models/test_nn/test_rnn.py | 37 +-
tests/test_models/test_prophet.py | 121 +-
tests/test_models/test_sarimax_model.py | 69 +-
tests/test_models/test_simple_models.py | 120 +-
tests/test_models/test_tbats.py | 42 +-
tests/test_models/test_utils.py | 112 +-
.../test_autoregressive_pipeline.py | 157 ++-
tests/test_pipeline/test_base.py | 505 ++++++-
.../test_hierarchical_pipeline.py | 129 +-
tests/test_pipeline/test_mixins.py | 60 +-
tests/test_pipeline/test_pipeline.py | 881 +++++++++---
tests/test_pipeline/utils.py | 47 +-
.../test_base_change_points_model.py | 41 +-
.../test_deseasonal_transform.py | 9 +-
.../test_decomposition/test_stl_transform.py | 40 +-
.../test_transforms/test_inference/common.py | 19 -
.../test_inference/conftest.py | 106 ++
.../test_inference/test_inverse_transform.py | 1237 ++++++++++++++++-
.../test_inference/test_transform.py | 1192 +++++++++++++++-
.../test_math/test_differencing_transform.py | 28 +-
.../test_math/test_exog_shift_transform.py | 35 +-
.../test_math/test_lag_transform.py | 12 +
.../test_dateflags_transform.py | 310 ++++-
.../test_timestamp/test_fourier_transform.py | 291 +++-
.../test_timestamp/test_holiday_transform.py | 380 +++--
.../test_special_days_transform.py | 114 +-
.../test_timeflags_transform.py | 280 +++-
tests/test_transforms/utils.py | 30 +
tests/utils.py | 21 +
143 files changed, 12569 insertions(+), 3566 deletions(-)
create mode 100644 tests/test_analysis/test_outliers/test_plots.py
delete mode 100644 tests/test_transforms/test_inference/common.py
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 4bc7f741d..cd5ed01c1 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -19,12 +19,15 @@ jobs:
with:
python-version: "3.10"
- - name: Install Dependencies
+ - name: Install Poetry
+ uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+
+ - name: Install dependencies
run: |
- pip install poetry==1.4.0 # TODO: remove after poetry fix
- poetry --version
- poetry config virtualenvs.in-project true
- poetry install -E style --no-root
+ poetry install -E style -vv
- name: Static Analysis
run: poetry run make lint
@@ -62,7 +65,7 @@ jobs:
- name: Install dependencies
if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
run: |
- poetry install -E "all tests" -vv
+ poetry install -E "all jupyter tests" -vv
- name: PyTest with sharding
run: |
@@ -101,7 +104,7 @@ jobs:
- name: Install dependencies
run: |
- poetry install -E "all tests" -vv
+ poetry install -E "all jupyter tests" -vv
poetry run pip install "pandas${{ matrix.pandas-version }}"
- name: PyTest ("tsdataset transforms")
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 043f7a7e8..1820a977f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,6 +13,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
-
-
-
+- Add warning on trying to pass numeric timestamp if freq is not None and add `_cast_index_to_datetime` ([#214](https://github.com/etna-team/etna/pull/214))
+-
+-
+-
+-
+-
+-
+- Add `infer_alignment`, `apply_alignment`, `make_timestamp_df` into `etna.dataset.utils` ([#256](https://github.com/etna-team/etna/pull/256))
+-
+-
+-
+- Add `TSDataset.create_from_misaligned` constructor ([#269](https://github.com/etna-team/etna/pull/269))
+-
-
-
-
@@ -35,6 +48,38 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
-
-
-
+- Add ignoring of integer timestamp as a feature into native DL models ([#210](https://github.com/etna-team/etna/pull/210))
+- Update `pytorch_forecasting` models to handle integer timestamp ([#208](https://github.com/etna-team/etna/pull/208))
+- Update `datasets` module to work with integer timestamp ([#146](https://github.com/etna-team/etna/pull/146))
+-
+-
+- Add tests for `transform` on data with integer timestamp ([#153](https://github.com/etna-team/etna/pull/153))
+- Add tests for `models` on data with integer timestamp ([#188](https://github.com/etna-team/etna/pull/188))
+-
+- Update `DateFlagsTransform`, `TimeFlagsTransform`, `HolidayTransform`, `SpecialDaysTransform`, `FourierTransform` to work with external timestamp ([#169](https://github.com/etna-team/etna/pull/169))
+- Update `analysis` module to work with integer timestamp ([#161](https://github.com/etna-team/etna/pull/161))
+-
+- Update `StatsForecastARIMAModel`, `StatsForecastAutoARIMAModel`, `StatsForecastAutoCESModel`, `StatsForecastAutoETSModel`, `StatsForecastAutoThetaModel` to handle integer timestamp ([#197](https://github.com/etna-team/etna/pull/197))
+- Update `MRMRFeatureSelectionTransform` to handle integer timestamp ([#164](https://github.com/etna-team/etna/pull/164))
+-
+- Update deseasonality transforms (`STLTransform`, `DeseasonalityTransform`) to handle integer timestamp ([#174](https://github.com/etna-team/etna/pull/174))
+- Update `HoltModel`, `HoltWintersModel`, `SimpleExpSmoothingModel`, `SARIMAXModel`, `AutoARIMAModel` to handle integer timestamp ((#200)[https://github.com/etna-team/etna/pull/200])
+- Update detrend transforms (`LinearTrendTransform`, `TheilSenTrendTransform`) to handle integer timestamp ([#163](https://github.com/etna-team/etna/pull/163))
+- Update `ResampleWithDistributionTransform` to work with integer timestamp ([#165](https://github.com/etna-team/etna/pull/165))
+-
+- Update change point transforms (`ChangePointsSegmentationTransform`, `ChangePointsTrendTransform`, `ChangePointsLevelTransform`, `TrendTransform`) to handle integer timestamp ([#176](https://github.com/etna-team/etna/pull/176))
+- Update `BATSModel`, `TBATSModel` models to work with integer timestamp ([#195](https://github.com/etna-team/etna/pull/195))
+- Update `ProphetModel` to handle external timestamp ([#203](https://github.com/etna-team/etna/pull/203))
+- Remove checking frequency in `timestamp_column` of `ProphetModel` ([#222](https://github.com/etna-team/etna/pull/222))
+- Update `FourierTransform` to handle external datetime timestamp ([#223](https://github.com/etna-team/etna/pull/223))
+- Update `FoldMask` to work with integer timestamp, in `validate_on_dataset` method add validation on presence of `FoldMask` parameters in `ts.index`, add tests for `FoldMask` ([#226](https://github.com/etna-team/etna/pull/226))
+- Fix `FourierTransform` on integer index, add inference tests ([#230](https://github.com/etna-team/etna/pull/230))
+- Update outliers transforms to handle integer timestamp ([#229](https://github.com/etna-team/etna/pull/229))
+- Update pipelines to handle integer timestamp ([#241](https://github.com/etna-team/etna/pull/241))
+- Add `timestamp_range` and refactor code with it ([#244](https://github.com/etna-team/etna/pull/244))
+- Update CLI to handle integer timestamp ([#246](https://github.com/etna-team/etna/pull/246))
+- Update `ExogShiftTransform` to handle integer timestamp ([#254](https://github.com/etna-team/etna/pull/254))
+- Extend base `TSDataset` constructor to handle long format dataframes, update documentation and tutorials with this change ([#266](https://github.com/etna-team/etna/pull/266))
-
-
-
@@ -54,7 +99,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
-
-
-
+- Prohibit empty list value and duplication of `target_timestamps` parameter in `FoldMask` ([#226](https://github.com/etna-team/etna/pull/226))
+-
+-
+- Fix `DeseasonalityTransform` fails to inverse transform short series ([#174](https://github.com/etna-team/etna/pull/174))
-
+- Fix indexing in `stl_plot`, `plot_periodogram`, `plot_holidays`, `plot_backtest`, `plot_backtest_interactive`, `ResampleWithDistributionTransform` ([#244](https://github.com/etna-team/etna/pull/244))
+- Fix `DifferencingTransform` to handle integer timestamp on test ([#244](https://github.com/etna-team/etna/pull/244))
-
-
-
diff --git a/README.md b/README.md
index 0da0f8038..79a540d96 100644
--- a/README.md
+++ b/README.md
@@ -55,7 +55,6 @@ from etna.datasets import TSDataset
df = pd.read_csv("examples/data/example_dataset.csv")
# Create a TSDataset
-df = TSDataset.to_dataset(df)
ts = TSDataset(df, freq="D")
# Choose a horizon
diff --git a/docs/source/api_reference/datasets.rst b/docs/source/api_reference/datasets.rst
index f19310ed3..3e3ce9545 100644
--- a/docs/source/api_reference/datasets.rst
+++ b/docs/source/api_reference/datasets.rst
@@ -19,6 +19,7 @@ Basic structures:
:template: class.rst
TSDataset
+ DataFrameFormat
HierarchicalStructure
Utilities for dataset generation:
diff --git a/etna/analysis/decomposition/plots.py b/etna/analysis/decomposition/plots.py
index 8b0591c47..6be22ef0e 100644
--- a/etna/analysis/decomposition/plots.py
+++ b/etna/analysis/decomposition/plots.py
@@ -6,7 +6,9 @@
from typing import List
from typing import Optional
from typing import Tuple
+from typing import Type
from typing import Union
+from typing import cast
import matplotlib.pyplot as plt
import numpy as np
@@ -84,12 +86,12 @@ def plot_trend(
def plot_time_series_with_change_points(
ts: "TSDataset",
- change_points: Dict[str, List[pd.Timestamp]],
+ change_points: Dict[str, List[Union[pd.Timestamp, int]]],
segments: Optional[List[str]] = None,
columns_num: int = 2,
figsize: Tuple[int, int] = (10, 5),
- start: Optional[str] = None,
- end: Optional[str] = None,
+ start: Optional[Union[pd.Timestamp, int, str]] = None,
+ end: Optional[Union[pd.Timestamp, int, str]] = None,
):
"""Plot segments with their trend change points.
@@ -110,6 +112,11 @@ def plot_time_series_with_change_points(
start timestamp for plot
end:
end timestamp for plot
+
+ Raises
+ ------
+ ValueError:
+ Incorrect type of ``start`` or ``end`` is used according to ``ts.freq``.
"""
start, end = _get_borders_ts(ts, start, end)
@@ -147,8 +154,8 @@ def plot_time_series_with_change_points(
def plot_change_points_interactive(
ts,
- change_point_model: BaseEstimator,
- model: BaseCost,
+ change_point_model: Type[BaseEstimator],
+ model: Union[str, BaseCost],
params_bounds: Dict[str, Tuple[Union[int, float], Union[int, float], Union[int, float]]],
model_params: List[str],
predict_params: List[str],
@@ -156,8 +163,8 @@ def plot_change_points_interactive(
segments: Optional[List[str]] = None,
columns_num: int = 2,
figsize: Tuple[int, int] = (10, 5),
- start: Optional[str] = None,
- end: Optional[str] = None,
+ start: Optional[Union[pd.Timestamp, int, str]] = None,
+ end: Optional[Union[pd.Timestamp, int, str]] = None,
):
"""Plot a time series with indicated change points.
@@ -196,14 +203,18 @@ def plot_change_points_interactive(
Jupyter notebook might display the results incorrectly,
in this case try to use ``!jupyter nbextension enable --py widgetsnbextension``.
+ Raises
+ ------
+ ValueError:
+ Incorrect type of ``start`` or ``end`` is used according to ``ts.freq``.
+
Examples
--------
>>> from etna.datasets import TSDataset
>>> from etna.datasets import generate_ar_df
>>> from etna.analysis import plot_change_points_interactive
>>> from ruptures.detection import Binseg
- >>> classic_df = generate_ar_df(periods=1000, start_time="2021-08-01", n_segments=2)
- >>> df = TSDataset.to_dataset(classic_df)
+ >>> df = generate_ar_df(periods=1000, start_time="2021-08-01", n_segments=2)
>>> ts = TSDataset(df, "D")
>>> params_bounds = {"n_bkps": [0, 5, 1], "min_size":[1,10,3]}
>>> plot_change_points_interactive(ts=ts, change_point_model=Binseg, model="l2", params_bounds=params_bounds, model_params=["min_size"], predict_params=["n_bkps"], figsize=(20, 10)) # doctest: +SKIP
@@ -212,6 +223,8 @@ def plot_change_points_interactive(
from ipywidgets import IntSlider
from ipywidgets import interact
+ start, end = _get_borders_ts(ts, start, end)
+
if segments is None:
segments = sorted(ts.segments)
@@ -329,7 +342,7 @@ def stl_plot(
df = ts.to_pandas()
for i, segment in enumerate(segments):
segment_df = df.loc[:, pd.IndexSlice[segment, :]][segment]
- segment_df = segment_df[segment_df.first_valid_index() : segment_df.last_valid_index()]
+ segment_df = segment_df.loc[segment_df.first_valid_index() : segment_df.last_valid_index()]
decompose_result = STL(endog=segment_df[in_column], period=period, **stl_kwargs).fit()
# start plotting
@@ -360,7 +373,7 @@ def stl_plot(
def seasonal_plot(
ts: "TSDataset",
- freq: Optional[str] = None,
+ freq: Union[Optional[str], Literal["not_given"]] = "not_given",
cycle: Union[
Literal["hour"], Literal["day"], Literal["week"], Literal["month"], Literal["quarter"], Literal["year"], int
] = "year",
@@ -386,6 +399,7 @@ def seasonal_plot(
* if set, resampling will be made using ``aggregation`` parameter.
If given frequency is too low, then the frequency of ``ts`` will be used.
+ This option isn't supported for data with integer timestamp.
cycle:
period of seasonality to capture (see :class:`~etna.analysis.decomposition.utils.SeasonalPlotCycle`)
@@ -406,11 +420,21 @@ def seasonal_plot(
number of columns in subplots
figsize:
size of the figure per subplot with one segment in inches
+
+ Raises
+ ------
+ ValueError:
+ Resampling isn't supported for data with integer timestamp
+ ValueError:
+ Setting non-integer cycle isn't supported for data with integer timestamp
+ ValueError:
+ Value None for freq parameter isn't supported for data with datetime timestamp
"""
if plot_params is None:
plot_params = {}
- if freq is None:
+ if freq == "not_given":
freq = ts.freq
+ freq = cast(Optional[str], freq)
if segments is None:
segments = sorted(ts.segments)
diff --git a/etna/analysis/decomposition/search.py b/etna/analysis/decomposition/search.py
index ad607dde1..6c349ed95 100644
--- a/etna/analysis/decomposition/search.py
+++ b/etna/analysis/decomposition/search.py
@@ -1,5 +1,6 @@
from typing import Dict
from typing import List
+from typing import Union
import pandas as pd
from ruptures.base import BaseEstimator
@@ -9,7 +10,7 @@
def find_change_points(
ts: TSDataset, in_column: str, change_point_model: BaseEstimator, **model_predict_params
-) -> Dict[str, List[pd.Timestamp]]:
+) -> Dict[str, List[Union[int, pd.Timestamp]]]:
"""Find trend change points using ruptures models.
Parameters
diff --git a/etna/analysis/decomposition/utils.py b/etna/analysis/decomposition/utils.py
index 4bd6b4ad8..c4d2080bc 100644
--- a/etna/analysis/decomposition/utils.py
+++ b/etna/analysis/decomposition/utils.py
@@ -3,12 +3,15 @@
from typing import Callable
from typing import Dict
from typing import List
+from typing import Optional
from typing import Tuple
from typing import Union
+from typing import cast
import numpy as np
import pandas as pd
from typing_extensions import Literal
+from typing_extensions import assert_never
if TYPE_CHECKING:
from etna.datasets import TSDataset
@@ -145,7 +148,7 @@ def _get_seasonal_in_cycle_num(
cycle: Union[
Literal["hour"], Literal["day"], Literal["week"], Literal["month"], Literal["quarter"], Literal["year"], int
],
- freq: str,
+ freq: Optional[str],
) -> pd.Series:
"""Get number for each point within cycle in a series of timestamps."""
cycle_functions: Dict[Tuple[SeasonalPlotCycle, str], Callable[[pd.Series], pd.Series]] = {
@@ -164,6 +167,7 @@ def _get_seasonal_in_cycle_num(
if isinstance(cycle, int):
pass
else:
+ freq = cast(str, freq)
key = (SeasonalPlotCycle(cycle), freq)
if key in cycle_functions:
return cycle_functions[key](timestamp)
@@ -179,15 +183,17 @@ def _get_seasonal_in_cycle_name(
cycle: Union[
Literal["hour"], Literal["day"], Literal["week"], Literal["month"], Literal["quarter"], Literal["year"], int
],
- freq: str,
+ freq: Optional[str],
) -> pd.Series:
"""Get unique name for each point within the cycle in a series of timestamps."""
if isinstance(cycle, int):
pass
elif SeasonalPlotCycle(cycle) == SeasonalPlotCycle.week:
+ freq = cast(str, freq)
if freq == "D":
return timestamp.dt.strftime("%a")
elif SeasonalPlotCycle(cycle) == SeasonalPlotCycle.year:
+ freq = cast(str, freq)
if freq == "M" or freq == "MS":
return timestamp.dt.strftime("%b")
@@ -197,7 +203,7 @@ def _get_seasonal_in_cycle_name(
def _seasonal_split(
timestamp: pd.Series,
- freq: str,
+ freq: Optional[str],
cycle: Union[
Literal["hour"], Literal["day"], Literal["week"], Literal["month"], Literal["quarter"], Literal["year"], int
],
@@ -246,7 +252,7 @@ def _resample(df: pd.DataFrame, freq: str, aggregation: Union[Literal["sum"], Li
def _prepare_seasonal_plot_df(
ts: "TSDataset",
- freq: str,
+ freq: Optional[str],
cycle: Union[
Literal["hour"], Literal["day"], Literal["week"], Literal["month"], Literal["quarter"], Literal["year"], int
],
@@ -255,6 +261,8 @@ def _prepare_seasonal_plot_df(
in_column: str,
segments: List[str],
):
+ from etna.datasets.utils import timestamp_range
+
# for simplicity we will rename our column to target
df = ts.to_pandas().loc[:, pd.IndexSlice[segments, in_column]]
df.rename(columns={in_column: "target"}, inplace=True)
@@ -264,20 +272,34 @@ def _prepare_seasonal_plot_df(
# make resampling if necessary
if ts.freq != freq:
+ if ts.freq is None:
+ raise ValueError("Resampling isn't supported for data with integer timestamp!")
+ elif freq is None:
+ raise ValueError("Value None for freq parameter isn't supported for data with datetime timestamp!")
+
df = _resample(df=df, freq=freq, aggregation=aggregation)
# process alignment
if isinstance(cycle, int):
timestamp = df.index
num_to_add = -len(timestamp) % cycle
- # if we want align by the first value, then we should append NaNs to timestamp
- to_add_index = None
- if SeasonalPlotAlignment(alignment) == SeasonalPlotAlignment.first:
- to_add_index = pd.date_range(start=timestamp.max(), periods=num_to_add + 1, closed="right", freq=freq)
+
+ alignment_enum = SeasonalPlotAlignment(alignment)
+ # if we want to align by the first value, then we should append NaNs to timestamp
+ if alignment_enum is SeasonalPlotAlignment.first:
+ to_add_index = timestamp_range(start=timestamp[-1], periods=num_to_add + 1, freq=freq)[1:]
# if we want to align by the last value, then we should prepend NaNs to timestamp
- elif SeasonalPlotAlignment(alignment) == SeasonalPlotAlignment.last:
- to_add_index = pd.date_range(end=timestamp.min(), periods=num_to_add + 1, closed="left", freq=freq)
+ elif alignment_enum is SeasonalPlotAlignment.last:
+ to_add_index = timestamp_range(end=timestamp[0], periods=num_to_add + 1, freq=freq)[:-1]
+ else:
+ assert_never(alignment_enum)
+
+ new_index = df.index.append(to_add_index)
+ index_name = df.index.name
+ df = df.reindex(new_index)
+ df.index.name = index_name
- df = pd.concat((df, pd.DataFrame(None, index=to_add_index))).sort_index()
+ elif freq is None:
+ raise ValueError("Setting non-integer cycle isn't supported for data with integer timestamp!")
return df
diff --git a/etna/analysis/eda/plots.py b/etna/analysis/eda/plots.py
index de87e84ea..f08d19594 100644
--- a/etna/analysis/eda/plots.py
+++ b/etna/analysis/eda/plots.py
@@ -215,7 +215,7 @@ def plot_periodogram(
_, ax = _prepare_axes(num_plots=len(segments), columns_num=columns_num, figsize=figsize)
for i, segment in enumerate(segments):
segment_df = df.loc[:, pd.IndexSlice[segment, "target"]]
- segment_df = segment_df[segment_df.first_valid_index() : segment_df.last_valid_index()]
+ segment_df = segment_df.loc[segment_df.first_valid_index() : segment_df.last_valid_index()]
if segment_df.isna().any():
raise ValueError(f"Periodogram can't be calculated on segment with NaNs inside: {segment}")
frequencies, spectrum = periodogram(x=segment_df, fs=period, **periodogram_params)
@@ -233,7 +233,7 @@ def plot_periodogram(
lengths_segments = []
for segment in segments:
segment_df = df.loc[:, pd.IndexSlice[segment, "target"]]
- segment_df = segment_df[segment_df.first_valid_index() : segment_df.last_valid_index()]
+ segment_df = segment_df.loc[segment_df.first_valid_index() : segment_df.last_valid_index()]
if segment_df.isna().any():
raise ValueError(f"Periodogram can't be calculated on segment with NaNs inside: {segment}")
lengths_segments.append(len(segment_df))
@@ -244,7 +244,7 @@ def plot_periodogram(
spectrums_segments = []
for segment in segments:
segment_df = df.loc[:, pd.IndexSlice[segment, "target"]]
- segment_df = segment_df[segment_df.first_valid_index() : segment_df.last_valid_index()][-cut_length:]
+ segment_df = segment_df.loc[segment_df.first_valid_index() : segment_df.last_valid_index()][-cut_length:]
frequencies, spectrum = periodogram(x=segment_df, fs=period, **periodogram_params)
frequencies_segments.append(frequencies)
spectrums_segments.append(spectrum)
@@ -271,8 +271,8 @@ def plot_holidays(
segments: Optional[List[str]] = None,
columns_num: int = 2,
figsize: Tuple[int, int] = (10, 5),
- start: Optional[str] = None,
- end: Optional[str] = None,
+ start: Optional[Union[pd.Timestamp, int, str]] = None,
+ end: Optional[Union[pd.Timestamp, int, str]] = None,
as_is: bool = False,
):
"""Plot holidays for segments.
@@ -292,7 +292,11 @@ def plot_holidays(
* if str, then this is code of the country in `holidays `_
+ for datetime timestamp
+
+ - None for integer timestamp
+
df_exog:
- dataframe with exogenous data;
+ dataframe with exogenous data in a wide or long format: :py:class:`~etna.datasets.utils.DataFrameFormat`
known_future:
columns in ``df_exog[known_future]`` that are regressors,
if "all" value is given, all columns are meant to be regressors
hierarchical_structure:
Structure of the levels in the hierarchy. If None, there is no hierarchical structure in the dataset.
"""
- self.raw_df = self._prepare_df(df)
- self.raw_df.index = pd.to_datetime(self.raw_df.index)
self.freq = freq
self.df_exog = None
-
- self.raw_df.index = pd.to_datetime(self.raw_df.index)
-
- try:
- inferred_freq = pd.infer_freq(self.raw_df.index)
- except ValueError:
- warnings.warn("TSDataset freq can't be inferred")
- inferred_freq = None
-
- if inferred_freq != self.freq:
- warnings.warn(
- f"You probably set wrong freq. Discovered freq in you data is {inferred_freq}, you set {self.freq}"
- )
-
- self.raw_df = self.raw_df.asfreq(self.freq)
-
+ self.raw_df = self._prepare_df(df=df.copy(deep=True), freq=freq)
self.df = self.raw_df.copy(deep=True)
- self.known_future = self._check_known_future(known_future, df_exog)
- self._regressors = copy(self.known_future)
-
self.hierarchical_structure = hierarchical_structure
self.current_df_level: Optional[str] = self._get_dataframe_level(df=self.df)
self.current_df_exog_level: Optional[str] = None
if df_exog is not None:
- self.df_exog = df_exog.copy(deep=True)
- self.df_exog.index = pd.to_datetime(self.df_exog.index)
+ self.df_exog = self._prepare_df_exog(df_exog=df_exog.copy(deep=True), freq=freq)
+
+ self.known_future = self._check_known_future(known_future, self.df_exog)
+ self._regressors = copy(self.known_future)
+
self.current_df_exog_level = self._get_dataframe_level(df=self.df_exog)
if self.current_df_level == self.current_df_exog_level:
- self.df = self._merge_exog(self.df)
+ self.df = self._merge_exog(df=self.df)
+ else:
+ self.known_future = self._check_known_future(known_future, df_exog)
+ self._regressors = copy(self.known_future)
self._target_components_names: Tuple[str, ...] = tuple()
self._prediction_intervals_names: Tuple[str, ...] = tuple()
self.df = self.df.sort_index(axis=1, level=("segment", "feature"))
+ @classmethod
+ def create_from_misaligned(
+ cls,
+ df: pd.DataFrame,
+ freq: Optional[str],
+ df_exog: Optional[pd.DataFrame] = None,
+ known_future: Union[Literal["all"], Sequence] = (),
+ future_steps: int = 1,
+ original_timestamp_name: str = "external_timestamp",
+ ) -> "TSDataset":
+ """Make TSDataset from misaligned data by realigning it according to inferred alignment in ``df``.
+
+ This method:
+ - Infers alignment using :py:func:`~etna.datasets.utils.infer_alignment`;
+ - Realigns ``df`` and ``df_exog`` using inferred alignment using :py:func:`~etna.datasets.utils.apply_alignment`;
+ - Creates exog feature with original timestamp using :py:func:`~etna.datasets.utils.make_timestamp_df_from_alignment`;
+ - Creates TSDataset from these data.
+
+ This method doesn't work with ``hierarchical_structure``, because it doesn't make much sense.
+
+ Parameters
+ ----------
+ df:
+ dataframe with timeseries in a long format: :py:class:`~etna.datasets.utils.DataFrameFormat`;
+ it is expected that ``df`` has feature named "target"
+ freq:
+ frequency of timestamp in df, possible values:
+
+ - `pandas offset aliases `_
+ for datetime timestamp
+
+ - None for integer timestamp
+
+ df_exog:
+ dataframe with exogenous data in a long format: :py:class:`~etna.datasets.utils.DataFrameFormat`
+ known_future:
+ columns in ``df_exog[known_future]`` that are regressors,
+ if "all" value is given, all columns are meant to be regressors
+ future_steps:
+ determines on how many steps original timestamp should be extended into the future
+ before adding into ``df_exog``; expected to be positive
+ original_timestamp_name:
+ name for original timestamp column to add it into ``df_exog``
+
+ Returns
+ -------
+ :
+ Created TSDataset.
+
+ Raises
+ ------
+ ValueError:
+ If ``future_steps`` is not positive.
+ ValueError:
+ If ``original_timestamp_name`` intersects with columns in ``df_exog``.
+ ValueError:
+ Parameter ``df`` isn't in a long format.
+ ValueError:
+ Parameter ``df_exog`` isn't in a long format if it set.
+ """
+ if future_steps <= 0:
+ raise ValueError("Parameter future_steps should be positive!")
+ if df_exog is not None and original_timestamp_name in df_exog.columns:
+ raise ValueError("Parameter original_timestamp_name shouldn't intersect with columns in df_exog!")
+
+ alignment = infer_alignment(df)
+ df_realigned = apply_alignment(df=df, alignment=alignment)
+ df_realigned = TSDataset.to_dataset(df_realigned)
+
+ timestamp_start = df_realigned.index[0]
+ periods = len(df_realigned) + future_steps
+ timestamp_df = make_timestamp_df_from_alignment(
+ alignment=alignment,
+ start=timestamp_start,
+ periods=periods,
+ freq=freq,
+ timestamp_name=original_timestamp_name,
+ )
+ timestamp_df = TSDataset.to_dataset(timestamp_df)
+
+ if df_exog is not None:
+ df_exog_realigned = apply_alignment(df=df_exog, alignment=alignment)
+ df_exog_realigned = TSDataset.to_dataset(df_exog_realigned)
+
+ df_exog_realigned = df_exog_realigned.join(timestamp_df, how="outer")
+ else:
+ df_exog_realigned = timestamp_df
+
+ known_future_realigned: Union[Literal["all"], Sequence]
+ if known_future != "all":
+ known_future_realigned = list(known_future)
+ known_future_realigned.append(original_timestamp_name)
+ else:
+ known_future_realigned = "all"
+
+ return TSDataset(
+ df=df_realigned,
+ df_exog=df_exog_realigned,
+ freq=None,
+ known_future=known_future_realigned,
+ hierarchical_structure=None,
+ )
+
def _get_dataframe_level(self, df: pd.DataFrame) -> Optional[str]:
"""Return the level of the passed dataframe in hierarchical structure."""
if self.hierarchical_structure is None:
@@ -203,13 +300,74 @@ def fit_transform(self, transforms: Sequence["Transform"]):
transform.fit_transform(self)
@staticmethod
- def _prepare_df(df: pd.DataFrame) -> pd.DataFrame:
- # cast segment to str type
- df_copy = df.copy(deep=True)
+ def _cast_segment_to_str(df: pd.DataFrame) -> pd.DataFrame:
columns_frame = df.columns.to_frame()
- columns_frame["segment"] = columns_frame["segment"].astype(str)
- df_copy.columns = pd.MultiIndex.from_frame(columns_frame)
- return df_copy
+ dtype = columns_frame["segment"].dtype
+ if not pd.api.types.is_object_dtype(dtype):
+ warnings.warn(
+ f"Segment values doesn't have string type, given type is {dtype}. "
+ f"Segments will be converted to string."
+ )
+ columns_frame["segment"] = columns_frame["segment"].astype(str)
+ df.columns = pd.MultiIndex.from_frame(columns_frame)
+ return df
+
+ @staticmethod
+ def _cast_index_to_datetime(df: pd.DataFrame, freq: str) -> pd.DataFrame:
+ if pd.api.types.is_numeric_dtype(df.index):
+ warnings.warn(
+ f"Timestamp contains numeric values, and given freq is {freq}. Timestamp will be converted to datetime."
+ )
+ df.index = pd.to_datetime(df.index)
+ return df
+
+ @classmethod
+ def _prepare_df(cls, df: pd.DataFrame, freq: Optional[str]) -> pd.DataFrame:
+ df_format = DataFrameFormat.determine(df)
+ if df_format is DataFrameFormat.long:
+ df = cls.to_dataset(df)
+
+ # cast segment to str type
+ cls._cast_segment_to_str(df)
+
+ # handle freq
+ if freq is None:
+ if not pd.api.types.is_integer_dtype(df.index.dtype):
+ raise ValueError("You set wrong freq. Data contains datetime index, not integer.")
+
+ new_index = np.arange(df.index.min(), df.index.max() + 1)
+ index_name = df.index.name
+ df = df.reindex(new_index)
+ df.index.name = index_name
+
+ else:
+ cls._cast_index_to_datetime(df, freq)
+ try:
+ inferred_freq = pd.infer_freq(df.index)
+ except ValueError:
+ warnings.warn("TSDataset freq can't be inferred")
+ inferred_freq = None
+
+ if inferred_freq is not None and inferred_freq != freq:
+ warnings.warn(
+ f"You probably set wrong freq. Discovered freq in you data is {inferred_freq}, you set {freq}"
+ )
+
+ df = df.asfreq(freq)
+
+ return df
+
+ @classmethod
+ def _prepare_df_exog(cls, df_exog: pd.DataFrame, freq: Optional[str]) -> pd.DataFrame:
+ df_format = DataFrameFormat.determine(df_exog)
+ if df_format is DataFrameFormat.long:
+ df_exog = cls.to_dataset(df_exog)
+
+ df_exog = cls._cast_segment_to_str(df=df_exog)
+ if freq is not None:
+ cls._cast_index_to_datetime(df_exog, freq)
+
+ return df_exog
def __repr__(self):
return self.df.__repr__()
@@ -230,6 +388,15 @@ def __getitem__(self, item):
df = df.loc[first_valid_idx:]
return df
+ @staticmethod
+ def _expand_index(df: pd.DataFrame, freq: Optional[str], future_steps: int) -> pd.DataFrame:
+ to_add_index = timestamp_range(start=df.index[-1], periods=future_steps + 1, freq=freq)[1:]
+ new_index = df.index.append(to_add_index)
+ index_name = df.index.name
+ df = df.reindex(new_index)
+ df.index.name = index_name
+ return df
+
def make_future(
self, future_steps: int, transforms: Sequence["Transform"] = (), tail_steps: int = 0
) -> "TSDataset":
@@ -263,10 +430,8 @@ def make_future(
... "regressor_1": np.arange(80), "regressor_2": np.arange(80) + 5,
... "segment": ["segment_0"]*40 + ["segment_1"]*40
... })
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> df_regressors_ts_format = TSDataset.to_dataset(df_regressors)
>>> ts = TSDataset(
- ... df_ts_format, "D", df_exog=df_regressors_ts_format, known_future="all"
+ ... df, "D", df_exog=df_regressors, known_future="all"
... )
>>> ts.make_future(4)
segment segment_0 segment_1
@@ -278,23 +443,18 @@ def make_future(
2021-07-04 33 38 NaN 73 78 NaN
"""
self._check_endings(warning=True)
- max_date_in_dataset = self.df.index.max()
- future_dates = pd.date_range(
- start=max_date_in_dataset, periods=future_steps + 1, freq=self.freq, closed="right"
- )
-
- new_index = self.raw_df.index.append(future_dates)
- df = self.raw_df.reindex(new_index)
- df.index.name = "timestamp"
+ df = self._expand_index(df=self.raw_df, freq=self.freq, future_steps=future_steps)
if self.df_exog is not None and self.current_df_level == self.current_df_exog_level:
- df = self._merge_exog(df)
+ df = self._merge_exog(df=df)
# check if we have enough values in regressors
+ # TODO: check performance
if self.regressors:
+ future_index = df.index.difference(self.index)
for segment in self.segments:
regressors_index = self.df_exog.loc[:, pd.IndexSlice[segment, self.regressors]].index
- if not np.all(future_dates.isin(regressors_index)):
+ if not np.all(future_index.isin(regressors_index)):
warnings.warn(
f"Some regressors don't have enough values in segment {segment}, "
f"NaN-s will be used for missing values"
@@ -341,9 +501,9 @@ def tsdataset_idx_slice(self, start_idx: Optional[int] = None, end_idx: Optional
Parameters
----------
start_idx:
- starting index of the slice.
+ starting integer index (counting from 0) of the slice.
end_idx:
- last index of the slice.
+ last integer index (counting from 0) of the slice.
Returns
-------
@@ -419,8 +579,11 @@ def _check_regressors(df: pd.DataFrame, df_regressors: pd.DataFrame):
def _merge_exog(self, df: pd.DataFrame) -> pd.DataFrame:
if self.df_exog is None:
raise ValueError("Something went wrong, Trying to merge df_exog which is None!")
+
+ # TODO: this check could probably be skipped at make_future
df_regressors = self.df_exog.loc[:, pd.IndexSlice[:, self.known_future]]
self._check_regressors(df=df, df_regressors=df_regressors)
+
df = pd.concat((df, self.df_exog), axis=1).loc[df.index].sort_index(axis=1, level=(0, 1))
return df
@@ -430,9 +593,9 @@ def _check_endings(self, warning=False):
if np.any(pd.isna(self.df.loc[max_index, pd.IndexSlice[:, "target"]])):
if warning:
warnings.warn(
- "Segments contains NaNs in the last timestamps."
- "Some of the transforms might work incorrectly or even fail."
- "Make sure that you use the imputer before making the forecast."
+ "Segments contains NaNs in the last timestamps. "
+ "Some of the transforms might work incorrectly or even fail. "
+ "Try to start using integer timestamp and align the segments."
)
else:
raise ValueError("All segments should end at the same timestamp")
@@ -483,8 +646,7 @@ def segments(self) -> List[str]:
... periods=30, start_time="2021-06-01",
... n_segments=2, scale=1
... )
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> ts = TSDataset(df_ts_format, "D")
+ >>> ts = TSDataset(df, "D")
>>> ts.segments
['segment_0', 'segment_1']
"""
@@ -501,7 +663,6 @@ def regressors(self) -> List[str]:
... periods=30, start_time="2021-06-01",
... n_segments=2, scale=1
... )
- >>> df_ts_format = TSDataset.to_dataset(df)
>>> regressors_timestamp = pd.date_range(start="2021-06-01", periods=50)
>>> df_regressors_1 = pd.DataFrame(
... {"timestamp": regressors_timestamp, "regressor_1": 1, "segment": "segment_0"}
@@ -510,9 +671,8 @@ def regressors(self) -> List[str]:
... {"timestamp": regressors_timestamp, "regressor_1": 2, "segment": "segment_1"}
... )
>>> df_exog = pd.concat([df_regressors_1, df_regressors_2], ignore_index=True)
- >>> df_exog_ts_format = TSDataset.to_dataset(df_exog)
>>> ts = TSDataset(
- ... df_ts_format, df_exog=df_exog_ts_format, freq="D", known_future="all"
+ ... df, df_exog=df_exog, freq="D", known_future="all"
... )
>>> ts.regressors
['regressor_1']
@@ -543,8 +703,8 @@ def plot(
n_segments: int = 10,
column: str = "target",
segments: Optional[Sequence[str]] = None,
- start: Optional[str] = None,
- end: Optional[str] = None,
+ start: Union[pd.Timestamp, int, str, None] = None,
+ end: Union[pd.Timestamp, int, str, None] = None,
seed: int = 1,
figsize: Tuple[int, int] = (10, 5),
):
@@ -566,6 +726,11 @@ def plot(
end plot at this timestamp
figsize:
size of the figure per subplot with one segment in inches
+
+ Raises
+ ------
+ ValueError:
+ Incorrect type of ``start`` or ``end`` is used according to ``freq``
"""
if segments is None:
segments = self.segments
@@ -574,8 +739,12 @@ def plot(
k = len(segments)
columns_num = min(2, k)
rows_num = math.ceil(k / columns_num)
- start = self.df.index.min() if start is None else pd.Timestamp(start)
- end = self.df.index.max() if end is None else pd.Timestamp(end)
+
+ start = _check_timestamp_param(param=start, param_name="start", freq=self.freq)
+ end = _check_timestamp_param(param=end, param_name="end", freq=self.freq)
+
+ start = self.df.index.min() if start is None else start
+ end = self.df.index.max() if end is None else end
figsize = (figsize[0] * columns_num, figsize[1] * rows_num)
_, ax = plt.subplots(rows_num, columns_num, figsize=figsize, squeeze=False)
@@ -589,7 +758,7 @@ def plot(
@staticmethod
def to_flatten(df: pd.DataFrame, features: Union[Literal["all"], Sequence[str]] = "all") -> pd.DataFrame:
- """Return pandas DataFrame with flatten index.
+ """Return pandas DataFrame in a long format.
The order of columns is (timestamp, segment, target,
features in alphabetical order).
@@ -621,8 +790,8 @@ def to_flatten(df: pd.DataFrame, features: Union[Literal["all"], Sequence[str]]
2 2021-06-03 segment_0 1.00
3 2021-06-04 segment_0 1.00
4 2021-06-05 segment_0 1.00
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> TSDataset.to_flatten(df_ts_format).head(5)
+ >>> df_wide = TSDataset.to_dataset(df)
+ >>> TSDataset.to_flatten(df_wide).head(5)
timestamp segment target
0 2021-06-01 segment_0 1.0
1 2021-06-02 segment_0 1.0
@@ -666,9 +835,9 @@ def to_pandas(self, flatten: bool = False, features: Union[Literal["all"], Seque
Parameters
----------
flatten:
- * If False, return pd.DataFrame with multiindex
+ * If False, return dataframe in a wide format
- * If True, return with flatten index,
+ * If True, return dataframe in a long format,
its order of columns is (timestamp, segment, target,
features in alphabetical order).
@@ -694,8 +863,7 @@ def to_pandas(self, flatten: bool = False, features: Union[Literal["all"], Seque
2 2021-06-03 segment_0 1.00
3 2021-06-04 segment_0 1.00
4 2021-06-05 segment_0 1.00
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> ts = TSDataset(df_ts_format, "D")
+ >>> ts = TSDataset(df, "D")
>>> ts.to_pandas(True).head(5)
timestamp segment target
0 2021-06-01 segment_0 1.00
@@ -724,7 +892,7 @@ def to_pandas(self, flatten: bool = False, features: Union[Literal["all"], Seque
@staticmethod
def to_dataset(df: pd.DataFrame) -> pd.DataFrame:
- """Convert pandas dataframe to ETNA Dataset format.
+ """Convert pandas dataframe to wide format.
Columns "timestamp" and "segment" are required.
@@ -732,6 +900,7 @@ def to_dataset(df: pd.DataFrame) -> pd.DataFrame:
----------
df:
DataFrame with columns ["timestamp", "segment"]. Other columns considered features.
+ Columns "timestamp" is expected to be one of two types: integer or timestamp.
Notes
-----
@@ -751,8 +920,8 @@ def to_dataset(df: pd.DataFrame) -> pd.DataFrame:
2 2021-06-03 segment_0 1.00
3 2021-06-04 segment_0 1.00
4 2021-06-05 segment_0 1.00
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> df_ts_format.head(5)
+ >>> df_wide = TSDataset.to_dataset(df)
+ >>> df_wide.head(5)
segment segment_0 segment_1
feature target target
timestamp
@@ -778,11 +947,16 @@ def to_dataset(df: pd.DataFrame) -> pd.DataFrame:
2021-01-05 4 9
"""
df_copy = df.copy(deep=True)
- df_copy["timestamp"] = pd.to_datetime(df_copy["timestamp"])
+
+ if not pd.api.types.is_integer_dtype(df_copy["timestamp"]):
+ df_copy["timestamp"] = pd.to_datetime(df_copy["timestamp"])
+
df_copy["segment"] = df_copy["segment"].astype(str)
+
feature_columns = df_copy.columns.tolist()
feature_columns.remove("timestamp")
feature_columns.remove("segment")
+
df_copy = df_copy.pivot(index="timestamp", columns="segment")
df_copy = df_copy.reorder_levels([1, 0], axis=1)
df_copy.columns.names = ["segment", "feature"]
@@ -873,13 +1047,19 @@ def to_hierarchical_dataset(
def _find_all_borders(
self,
- train_start: Optional[TTimestamp],
- train_end: Optional[TTimestamp],
- test_start: Optional[TTimestamp],
- test_end: Optional[TTimestamp],
+ train_start: Union[pd.Timestamp, int, str, None],
+ train_end: Union[pd.Timestamp, int, str, None],
+ test_start: Union[pd.Timestamp, int, str, None],
+ test_end: Union[pd.Timestamp, int, str, None],
test_size: Optional[int],
- ) -> Tuple[TTimestamp, TTimestamp, TTimestamp, TTimestamp]:
+ ) -> Tuple[Union[pd.Timestamp, int], Union[pd.Timestamp, int], Union[pd.Timestamp, int], Union[pd.Timestamp, int]]:
"""Find borders for train_test_split if some values wasn't specified."""
+ # prepare and validate values
+ train_start = _check_timestamp_param(param=train_start, param_name="train_start", freq=self.freq)
+ train_end = _check_timestamp_param(param=train_end, param_name="train_end", freq=self.freq)
+ test_start = _check_timestamp_param(param=test_start, param_name="test_start", freq=self.freq)
+ test_end = _check_timestamp_param(param=test_end, param_name="test_end", freq=self.freq)
+
if test_end is not None and test_start is not None and test_size is not None:
warnings.warn(
"test_size, test_start and test_end cannot be applied at the same time. test_size will be ignored"
@@ -935,17 +1115,17 @@ def _find_all_borders(
else:
train_end_defined = train_end
- if np.datetime64(test_start_defined) < np.datetime64(train_end_defined):
+ if test_start_defined < train_end_defined:
raise ValueError("The beginning of the test goes before the end of the train")
return train_start_defined, train_end_defined, test_start_defined, test_end_defined
def train_test_split(
self,
- train_start: Optional[TTimestamp] = None,
- train_end: Optional[TTimestamp] = None,
- test_start: Optional[TTimestamp] = None,
- test_end: Optional[TTimestamp] = None,
+ train_start: Union[pd.Timestamp, int, str, None] = None,
+ train_end: Union[pd.Timestamp, int, str, None] = None,
+ test_start: Union[pd.Timestamp, int, str, None] = None,
+ test_end: Union[pd.Timestamp, int, str, None] = None,
test_size: Optional[int] = None,
) -> Tuple["TSDataset", "TSDataset"]:
"""Split given df with train-test timestamp indices or size of test set.
@@ -970,12 +1150,17 @@ def train_test_split(
train, test:
generated datasets
+ Raises
+ ------
+ ValueError:
+ Incorrect type of ``train_start`` or ``train_end`` or ``test_start`` or ``test_end``
+ is used according to ``ts.freq``
+
Examples
--------
>>> from etna.datasets import generate_ar_df
>>> pd.options.display.float_format = '{:,.2f}'.format
>>> df = generate_ar_df(100, start_time="2021-01-01", n_segments=3)
- >>> df = TSDataset.to_dataset(df)
>>> ts = TSDataset(df, "D")
>>> train_ts, test_ts = ts.train_test_split(
... train_start="2021-01-01", train_end="2021-02-01",
@@ -1004,13 +1189,13 @@ def train_test_split(
train_start, train_end, test_start, test_end, test_size
)
- if pd.Timestamp(test_end_defined) > self.df.index.max():
+ if test_end_defined > self.df.index.max():
warnings.warn(f"Max timestamp in df is {self.df.index.max()}.")
- if pd.Timestamp(train_start_defined) < self.df.index.min():
+ if train_start_defined < self.df.index.min():
warnings.warn(f"Min timestamp in df is {self.df.index.min()}.")
- train_df = self.df[train_start_defined:train_end_defined][self.raw_df.columns] # type: ignore
- train_raw_df = self.raw_df[train_start_defined:train_end_defined] # type: ignore
+ train_df = self.df.loc[train_start_defined:train_end_defined][self.raw_df.columns] # type: ignore
+ train_raw_df = self.raw_df.loc[train_start_defined:train_end_defined] # type: ignore
train = TSDataset(
df=train_df,
df_exog=self.df_exog,
@@ -1023,8 +1208,8 @@ def train_test_split(
train._target_components_names = deepcopy(self.target_components_names)
train._prediction_intervals_names = deepcopy(self._prediction_intervals_names)
- test_df = self.df[test_start_defined:test_end_defined][self.raw_df.columns] # type: ignore
- test_raw_df = self.raw_df[train_start_defined:test_end_defined] # type: ignore
+ test_df = self.df.loc[test_start_defined:test_end_defined][self.raw_df.columns] # type: ignore
+ test_raw_df = self.raw_df.loc[train_start_defined:test_end_defined] # type: ignore
test = TSDataset(
df=test_df,
df_exog=self.df_exog,
@@ -1072,7 +1257,7 @@ def add_columns_from_pandas(
regressors:
List of regressors in the passed dataframe.
"""
- self.df = pd.concat((self.df, df_update[: self.df.index.max()]), axis=1).sort_index(axis=1)
+ self.df = pd.concat((self.df, df_update.loc[: self.df.index.max()]), axis=1).sort_index(axis=1)
if update_exog:
if self.df_exog is None:
self.df_exog = df_update
@@ -1126,12 +1311,12 @@ def drop_features(self, features: List[str], drop_from_exog: bool = False):
self._regressors = list(set(self._regressors) - features_set)
@property
- def index(self) -> pd.core.indexes.datetimes.DatetimeIndex:
+ def index(self) -> pd.Index:
"""Return TSDataset timestamp index.
Returns
-------
- pd.core.indexes.datetimes.DatetimeIndex
+ :
timestamp index of TSDataset
"""
return self.df.index
@@ -1218,7 +1403,7 @@ def add_target_components(self, target_components_df: pd.DataFrame):
Parameters
----------
target_components_df:
- Dataframe in etna wide format with target components
+ Dataframe in a wide format with target components
Raises
------
@@ -1275,7 +1460,7 @@ def add_prediction_intervals(self, prediction_intervals_df: pd.DataFrame):
Parameters
----------
prediction_intervals_df:
- Dataframe in etna wide format with prediction intervals
+ Dataframe in a wide format with prediction intervals
Raises
------
@@ -1490,7 +1675,6 @@ def describe(self, segments: Optional[Sequence[str]] = None) -> pd.DataFrame:
... periods=30, start_time="2021-06-01",
... n_segments=2, scale=1
... )
- >>> df_ts_format = TSDataset.to_dataset(df)
>>> regressors_timestamp = pd.date_range(start="2021-06-01", periods=50)
>>> df_regressors_1 = pd.DataFrame(
... {"timestamp": regressors_timestamp, "regressor_1": 1, "segment": "segment_0"}
@@ -1499,8 +1683,7 @@ def describe(self, segments: Optional[Sequence[str]] = None) -> pd.DataFrame:
... {"timestamp": regressors_timestamp, "regressor_1": 2, "segment": "segment_1"}
... )
>>> df_exog = pd.concat([df_regressors_1, df_regressors_2], ignore_index=True)
- >>> df_exog_ts_format = TSDataset.to_dataset(df_exog)
- >>> ts = TSDataset(df_ts_format, df_exog=df_exog_ts_format, freq="D", known_future="all")
+ >>> ts = TSDataset(df, df_exog=df_exog, freq="D", known_future="all")
>>> ts.describe()
start_timestamp end_timestamp length num_missing num_segments num_exogs num_regressors num_known_future freq
segments
@@ -1578,7 +1761,6 @@ def info(self, segments: Optional[Sequence[str]] = None) -> None:
... periods=30, start_time="2021-06-01",
... n_segments=2, scale=1
... )
- >>> df_ts_format = TSDataset.to_dataset(df)
>>> regressors_timestamp = pd.date_range(start="2021-06-01", periods=50)
>>> df_regressors_1 = pd.DataFrame(
... {"timestamp": regressors_timestamp, "regressor_1": 1, "segment": "segment_0"}
@@ -1587,8 +1769,7 @@ def info(self, segments: Optional[Sequence[str]] = None) -> None:
... {"timestamp": regressors_timestamp, "regressor_1": 2, "segment": "segment_1"}
... )
>>> df_exog = pd.concat([df_regressors_1, df_regressors_2], ignore_index=True)
- >>> df_exog_ts_format = TSDataset.to_dataset(df_exog)
- >>> ts = TSDataset(df_ts_format, df_exog=df_exog_ts_format, freq="D", known_future="all")
+ >>> ts = TSDataset(df, df_exog=df_exog, freq="D", known_future="all")
>>> ts.info()
num_segments: 2
diff --git a/etna/datasets/utils.py b/etna/datasets/utils.py
index 9045cf82a..e745f713b 100644
--- a/etna/datasets/utils.py
+++ b/etna/datasets/utils.py
@@ -1,9 +1,12 @@
import re
from enum import Enum
+from typing import Dict
from typing import List
from typing import Optional
from typing import Sequence
from typing import Set
+from typing import Union
+from typing import cast
import numpy as np
import pandas as pd
@@ -20,11 +23,101 @@
class DataFrameFormat(str, Enum):
- """Enum for different types of result."""
+ """Enum for different kinds of ``pd.DataFrame`` which can be used.
+ This dataframe stores:
+
+ - Timestamps;
+ - Segments;
+ - Features. In this context, 'target' is also a feature.
+
+ Currently, there are formats:
+
+ - Wide
+
+ - Has index to store timestamps.
+ - Columns has two levels with names 'segment', 'feature'.
+ Each column stores values for a given feature in a given segment.
+ - List of columns isn't empty.
+ - There are all combinations for (segment, feature) in the columns.
+
+ - Long
+
+ - Has column 'timestamp' to store timestamps.
+ - Has column 'segment' to store segments.
+ - Has at least one more column except for 'timestamp' and 'segment'.
+
+ Currently, we don't check the types of columns to save compatibility, but it is expected that:
+
+ - Timestamps have type ``int`` or ``pd.Timestamp``. If it isn't, :py:class:`~etna.datasets.tsdataset.TSDataset`
+ makes conversion for you.
+ - Segments have type ``str``. If it isn't, :py:class:`~etna.datasets.tsdataset.TSDataset` makes conversion for you.
+ """
+
+ #: Wide format.
wide = "wide"
+
+ #: Long format.
long = "long"
+ @classmethod
+ def determine(cls, df: pd.DataFrame) -> "DataFrameFormat":
+ """Determine format of the given dataframe.
+
+ Parameters
+ ----------
+ df:
+ Dataframe to infer format.
+
+ Returns
+ -------
+ :
+ Format of the given dataframe.
+
+ Raises
+ ------
+ ValueError:
+ Given long dataframe doesn't have required column 'timestamp'
+ ValueError:
+ Given long dataframe doesn't have required column 'segment'
+ ValueError:
+ Given long dataframe doesn't have any columns except for 'timestamp` and 'segment'
+ ValueError:
+ Given wide dataframe doesn't have levels of columns ['segment', 'feature']
+ ValueError:
+ Given wide dataframe doesn't have any features
+ ValueError:
+ Given wide dataframe doesn't have all combinations of pairs (segment, feature)
+ """
+ columns = df.columns
+ is_multiindex = isinstance(columns, pd.MultiIndex)
+
+ if not is_multiindex:
+ if "timestamp" not in columns:
+ raise ValueError("Given long dataframe doesn't have required column 'timestamp'!")
+ if "segment" not in columns:
+ raise ValueError("Given long dataframe doesn't have required column 'segment'!")
+ if set(columns) == {"timestamp", "segment"}:
+ raise ValueError("Given long dataframe doesn't have any columns except for 'timestamp` and 'segment'!")
+ return DataFrameFormat.long
+ else:
+ expected_level_names = ["segment", "feature"]
+ if columns.names != expected_level_names:
+ raise ValueError("Given wide dataframe doesn't have levels of columns ['segment', 'feature']!")
+
+ if len(columns) == 0:
+ raise ValueError("Given wide dataframe doesn't have any features!")
+
+ segments = columns.get_level_values("segment").unique()
+ features = columns.get_level_values("feature").unique()
+ expected_columns = pd.MultiIndex.from_product(
+ [segments, features], names=["segment", "feature"]
+ ).sort_values()
+ if not columns.sort_values().equals(expected_columns):
+ raise ValueError("Given wide dataframe doesn't have all combinations of pairs (segment, feature)!")
+
+ return DataFrameFormat.wide
+
def duplicate_data(df: pd.DataFrame, segments: Sequence[str], format: str = DataFrameFormat.wide) -> pd.DataFrame:
"""Duplicate dataframe for all the segments.
@@ -65,8 +158,7 @@ def duplicate_data(df: pd.DataFrame, segments: Sequence[str], format: str = Data
>>> is_friday_13 = (timestamp.weekday == 4) & (timestamp.day == 13)
>>> df_exog_raw = pd.DataFrame({"timestamp": timestamp, "is_friday_13": is_friday_13})
>>> df_exog = duplicate_data(df_exog_raw, segments=["segment_0", "segment_1"], format="wide")
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> ts = TSDataset(df=df_ts_format, df_exog=df_exog, freq="D", known_future="all")
+ >>> ts = TSDataset(df=df, df_exog=df_exog, freq="D", known_future="all")
>>> ts.head()
segment segment_0 segment_1
feature is_friday_13 target is_friday_13 target
@@ -126,8 +218,8 @@ def __len__(self):
def set_columns_wide(
df_left: pd.DataFrame,
df_right: pd.DataFrame,
- timestamps_left: Optional[Sequence[pd.Timestamp]] = None,
- timestamps_right: Optional[Sequence[pd.Timestamp]] = None,
+ timestamps_left: Optional[Sequence[Union[pd.Timestamp, int]]] = None,
+ timestamps_right: Optional[Sequence[Union[pd.Timestamp, int]]] = None,
segments_left: Optional[Sequence[str]] = None,
features_right: Optional[Sequence[str]] = None,
features_left: Optional[Sequence[str]] = None,
@@ -290,3 +382,351 @@ def inverse_transform_target_components(
scale_coef = np.repeat((inverse_transformed_target_df / target_df).values, repeats=components_number, axis=1)
inverse_transformed_target_components_df = target_components_df * scale_coef
return inverse_transformed_target_components_df
+
+
+def _check_timestamp_param(
+ param: Union[pd.Timestamp, int, str, None], param_name: str, freq: Optional[str]
+) -> Union[pd.Timestamp, int, None]:
+ if param is None:
+ return param
+
+ if freq is None:
+ if not (isinstance(param, int) or isinstance(param, np.integer)):
+ raise ValueError(
+ f"Parameter {param_name} has incorrect type! For integer timestamp only integer parameter type is allowed."
+ )
+
+ return param
+ else:
+ if not isinstance(param, str) and not isinstance(param, pd.Timestamp):
+ raise ValueError(
+ f"Parameter {param_name} has incorrect type! For datetime timestamp only pd.Timestamp or str parameter type is allowed."
+ )
+
+ new_param = pd.Timestamp(param)
+ return new_param
+
+
+def determine_num_steps(
+ start_timestamp: Union[pd.Timestamp, int], end_timestamp: Union[pd.Timestamp, int], freq: Optional[str]
+) -> int:
+ """Determine how many steps of ``freq`` should we make from ``start_timestamp`` to reach ``end_timestamp``.
+
+ Parameters
+ ----------
+ start_timestamp:
+ timestamp to start counting from
+ end_timestamp:
+ timestamp to end counting, should be not earlier than ``start_timestamp``
+ freq:
+ frequency of timestamps, possible values:
+
+ - `pandas offset aliases `_ for datetime timestamp
+
+ - None for integer timestamp
+
+ Returns
+ -------
+ :
+ number of steps
+
+ Raises
+ ------
+ ValueError:
+ Value of end timestamp is less than start timestamp
+ ValueError:
+ Start timestamp isn't correct according to a given frequency
+ ValueError:
+ End timestamp isn't correct according to a given frequency
+ ValueError:
+ End timestamp isn't reachable with a given frequency
+ """
+ if start_timestamp > end_timestamp:
+ raise ValueError("Start timestamp should be less or equal than end timestamp!")
+
+ if freq is None:
+ if int(start_timestamp) != start_timestamp:
+ raise ValueError(f"Start timestamp isn't correct according to given frequency: {freq}")
+ if int(end_timestamp) != end_timestamp:
+ raise ValueError(f"End timestamp isn't correct according to given frequency: {freq}")
+
+ return end_timestamp - start_timestamp
+ else:
+ # check if start_timestamp is normalized
+ normalized_start_timestamp = pd.date_range(start=start_timestamp, periods=1, freq=freq)
+ if normalized_start_timestamp != start_timestamp:
+ raise ValueError(f"Start timestamp isn't correct according to given frequency: {freq}")
+
+ # check a simple case
+ if start_timestamp == end_timestamp:
+ return 0
+
+ # make linear probing, because for complex offsets there is a cycle in `pd.date_range`
+ cur_value = 1
+ cur_timestamp = start_timestamp
+ while True:
+ timestamps = pd.date_range(start=cur_timestamp, periods=2, freq=freq)
+ if timestamps[-1] == end_timestamp:
+ return cur_value
+ elif timestamps[-1] > end_timestamp:
+ raise ValueError(f"End timestamp isn't reachable with freq: {freq}")
+ cur_value += 1
+ cur_timestamp = timestamps[-1]
+
+
+def determine_freq(timestamps: Union[pd.Series, pd.Index]) -> Optional[str]:
+ """Determine data frequency using provided timestamps.
+
+ Parameters
+ ----------
+ timestamps:
+ timeline to determine frequency
+
+ Returns
+ -------
+ :
+ pandas frequency string
+
+ Raises
+ ------
+ ValueError:
+ unable do determine frequency of data
+ ValueError:
+ integer timestamp isn't ordered and doesn't contain all the values from min to max
+ """
+ # check integer timestamp
+ if pd.api.types.is_integer_dtype(timestamps):
+ diffs = np.diff(timestamps)[1:]
+ if not np.all(diffs == 1):
+ raise ValueError("Integer timestamp isn't ordered and doesn't contain all the values from min to max")
+
+ return None
+
+ # check datetime timestamp
+ else:
+ try:
+ freq = pd.infer_freq(timestamps)
+ except ValueError:
+ freq = None
+
+ if freq is None:
+ raise ValueError("Can't determine frequency of a given dataframe")
+
+ return freq
+
+
+def timestamp_range(
+ start: Union[pd.Timestamp, int, str, None] = None,
+ end: Union[pd.Timestamp, int, str, None] = None,
+ periods: Optional[int] = None,
+ freq: Optional[str] = None,
+) -> pd.Index:
+ """Create index with timestamps.
+
+ Parameters
+ ----------
+ start:
+ start of index
+ end:
+ end of index
+ periods:
+ length of the index
+ freq:
+ frequency of timestamps, possible values:
+
+ - `pandas offset aliases `_ for datetime timestamp
+
+ - None for integer timestamp
+
+ Returns
+ -------
+ :
+ Created index
+
+ Raises
+ ------
+ ValueError:
+ Incorrect type of ``start`` or ``end`` is used according to ``freq``
+ ValueError:
+ Of the three parameters: start, end, periods, exactly two must be specified
+ """
+ start = _check_timestamp_param(param=start, param_name="start", freq=freq)
+ end = _check_timestamp_param(param=end, param_name="end", freq=freq)
+
+ num_set = 0
+ if start is not None:
+ num_set += 1
+ if end is not None:
+ num_set += 1
+ if periods is not None:
+ num_set += 1
+ if num_set != 2:
+ raise ValueError("Of the three parameters: start, end, periods, exactly two must be specified!")
+
+ if freq is None:
+ if start is None:
+ start = end - periods + 1 # type: ignore
+ if periods is None:
+ periods = end - start + 1 # type: ignore
+ return pd.Index(np.arange(start, start + periods))
+ else:
+ return pd.date_range(start=start, end=end, periods=periods, freq=freq)
+
+
+def infer_alignment(df: pd.DataFrame) -> Union[Dict[str, pd.Timestamp], Dict[str, int]]:
+ """Inference alignment of a given dataframe.
+
+ Alignment tells us which timestamps for each segment should be considered to have the same integer timestamp after
+ alignment transformation.
+
+ For long dataframe the alignment is determined by the last timestamp for each segment.
+ Last timestamp is taken without checking is 'target' value missing or not.
+
+ Parameters
+ ----------
+ df:
+ Dataframe in a long format.
+
+ Returns
+ -------
+ :
+ Dictionary with mapping segment -> timestamp.
+
+ Raises
+ ------
+ ValueError:
+ Parameter ``df`` isn't in a long format.
+ """
+ df_format = DataFrameFormat.determine(df=df)
+ if df_format is not DataFrameFormat.long:
+ raise ValueError("Parameter df should be in a long format!")
+
+ return df.groupby(by=["segment"]).agg({"timestamp": "max"})["timestamp"].to_dict()
+
+
+def apply_alignment(
+ df: pd.DataFrame,
+ alignment: Union[Dict[str, pd.Timestamp], Dict[str, int]],
+ original_timestamp_name: Optional[str] = None,
+):
+ """Apply given alignment to a dataframe.
+
+ Applying alignment creates a new dataframe in which we have a new 'timestamp' column
+ with sequential integer timestamps.
+
+ For each segment we sort timestamps and assign them sequential integer values (with step 1)
+ in a way that timestamp in ``alignment`` gets value 0.
+
+ Parameters
+ ----------
+ df:
+ Dataframe in a long format.
+ alignment:
+ Alignment to apply.
+ original_timestamp_name:
+ Name for a column to save the original timestamps. If ``None`` original timestamps won't be saved.
+
+ Returns
+ -------
+ :
+ Aligned dataframe in a long format.
+
+ Raises
+ ------
+ ValueError:
+ Parameter ``df`` isn't in a long format.
+ ValueError:
+ There is a segment in ``df`` which isn't present in ``alignment``.
+ ValueError:
+ There is a segment which doesn't have a timestamp that is present in ``alignment``.
+ """
+ df_format = DataFrameFormat.determine(df=df)
+ if df_format is not DataFrameFormat.long:
+ raise ValueError("Parameter df should be in a long format!")
+
+ df_list = []
+ for segment in df["segment"].unique():
+ if segment not in alignment:
+ raise ValueError(f"The segment '{segment}' isn't present in alignment!")
+
+ cur_df = df[df["segment"] == segment].sort_values(by="timestamp")
+ reference_timestamp = alignment[segment]
+
+ if reference_timestamp not in cur_df["timestamp"].values:
+ raise ValueError(
+ f"The segment '{segment}' doesn't contain timestamp '{reference_timestamp}' from alignment!"
+ )
+
+ reference_timestamp_index = pd.Index(cur_df["timestamp"]).get_loc(reference_timestamp)
+ new_timestamp = np.arange(len(cur_df)) - reference_timestamp_index
+
+ if original_timestamp_name is not None:
+ cur_df.rename(columns={"timestamp": original_timestamp_name}, inplace=True)
+
+ cur_df["timestamp"] = new_timestamp
+ df_list.append(cur_df)
+
+ result = pd.concat(df_list)
+ return result
+
+
+def make_timestamp_df_from_alignment(
+ alignment: Union[Dict[str, pd.Timestamp], Dict[str, int]],
+ start: Optional[int] = None,
+ end: Optional[int] = None,
+ periods: Optional[int] = None,
+ freq: Optional[str] = None,
+ timestamp_name: str = "external_timestamp",
+):
+ """Create a dataframe with timestamp according to a given alignment.
+
+ This utility could be used after alignment of ``df`` to create ``df_exog`` with external timestamps
+ extended into the future.
+
+ For each segment we take ``start``, ``end``, ``periods`` and create sequential integer timestamps.
+ After that we map this sequential integer timestamps into external timestamps according to ``alignment`` in a way
+ that 0 translates into ``alignment[segment]`` timestamp and any other values are calculated based on ``freq``.
+
+ Parameters
+ ----------
+ alignment:
+ Alignment to use.
+ start:
+ Start timestamp to generate sequential integer timestamps.
+ end:
+ End timestamp to generate sequential integer timestamps.
+ periods:
+ Number of periods to generate sequential integer timestamps.
+ freq:
+ Frequency of timestamps to generate, possible values:
+
+ - `pandas offset aliases `_ for datetime timestamp
+
+ - None for integer timestamp
+
+ timestamp_name:
+ Name of created timestamp column.
+
+ Returns
+ -------
+ :
+ Dataframe with a created timestamp in a long format.
+ """
+ df_list = []
+ timestamp = timestamp_range(start=start, end=end, periods=periods, freq=None)
+ start = timestamp[0]
+ start = cast(int, start)
+ for segment, reference_timestamp in alignment.items():
+ if start < 0:
+ external_start_timestamp = timestamp_range(end=reference_timestamp, periods=-start + 1, freq=freq)[0]
+ else:
+ external_start_timestamp = timestamp_range(start=reference_timestamp, periods=start + 1, freq=freq)[-1]
+
+ external_timestamp = timestamp_range(start=external_start_timestamp, periods=len(timestamp), freq=freq)
+ cur_df = pd.DataFrame(
+ {"segment": [segment] * len(timestamp), "timestamp": timestamp, timestamp_name: external_timestamp}
+ )
+ df_list.append(cur_df)
+
+ result = pd.concat(df_list)
+ return result
diff --git a/etna/ensembles/direct_ensemble.py b/etna/ensembles/direct_ensemble.py
index 5725d51bd..21f3e11cb 100644
--- a/etna/ensembles/direct_ensemble.py
+++ b/etna/ensembles/direct_ensemble.py
@@ -4,6 +4,7 @@
from typing import List
from typing import Optional
from typing import Sequence
+from typing import Union
import numpy as np
import pandas as pd
@@ -33,8 +34,7 @@ class DirectEnsemble(EnsembleMixin, SaveEnsembleMixin, BasePipeline):
>>> from etna.models import ProphetModel
>>> from etna.pipeline import Pipeline
>>> df = generate_ar_df(periods=30, start_time="2021-06-01", ar_coef=[1.2], n_segments=3)
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> ts = TSDataset(df_ts_format, "D")
+ >>> ts = TSDataset(df, "D")
>>> prophet_pipeline = Pipeline(model=ProphetModel(), transforms=[], horizon=3)
>>> naive_pipeline = Pipeline(model=NaiveModel(lag=10), transforms=[], horizon=5)
>>> ensemble = DirectEnsemble(pipelines=[prophet_pipeline, naive_pipeline])
@@ -148,8 +148,8 @@ def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
def _predict(
self,
ts: TSDataset,
- start_timestamp: pd.Timestamp,
- end_timestamp: pd.Timestamp,
+ start_timestamp: Union[pd.Timestamp, int],
+ end_timestamp: Union[pd.Timestamp, int],
prediction_interval: bool,
quantiles: Sequence[float],
return_components: bool,
diff --git a/etna/ensembles/mixins.py b/etna/ensembles/mixins.py
index a2772b193..066e9f537 100644
--- a/etna/ensembles/mixins.py
+++ b/etna/ensembles/mixins.py
@@ -4,6 +4,7 @@
from copy import deepcopy
from typing import List
from typing import Optional
+from typing import Union
import pandas as pd
from typing_extensions import Self
@@ -52,8 +53,8 @@ def _forecast_pipeline(pipeline: BasePipeline, ts: TSDataset) -> TSDataset:
def _predict_pipeline(
ts: TSDataset,
pipeline: BasePipeline,
- start_timestamp: Optional[pd.Timestamp],
- end_timestamp: Optional[pd.Timestamp],
+ start_timestamp: Union[pd.Timestamp, int, str, None],
+ end_timestamp: Union[pd.Timestamp, int, str, None],
) -> TSDataset:
"""Make predict with given pipeline."""
tslogger.log(msg=f"Start prediction with {pipeline}.")
diff --git a/etna/ensembles/stacking_ensemble.py b/etna/ensembles/stacking_ensemble.py
index b7e614e6e..d8c06d46d 100644
--- a/etna/ensembles/stacking_ensemble.py
+++ b/etna/ensembles/stacking_ensemble.py
@@ -38,8 +38,7 @@ class StackingEnsemble(EnsembleMixin, SaveEnsembleMixin, BasePipeline):
>>> import pandas as pd
>>> pd.options.display.float_format = '{:,.2f}'.format
>>> df = generate_ar_df(periods=100, start_time="2021-06-01", ar_coef=[0.8], n_segments=3)
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> ts = TSDataset(df_ts_format, "D")
+ >>> ts = TSDataset(df, "D")
>>> ma_pipeline = Pipeline(model=MovingAverageModel(window=5), transforms=[], horizon=7)
>>> naive_pipeline = Pipeline(model=NaiveModel(lag=10), transforms=[], horizon=7)
>>> ensemble = StackingEnsemble(pipelines=[ma_pipeline, naive_pipeline])
@@ -256,8 +255,8 @@ def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
def _predict(
self,
ts: TSDataset,
- start_timestamp: pd.Timestamp,
- end_timestamp: pd.Timestamp,
+ start_timestamp: Union[pd.Timestamp, int],
+ end_timestamp: Union[pd.Timestamp, int],
prediction_interval: bool,
quantiles: Sequence[float],
return_components: bool,
diff --git a/etna/ensembles/voting_ensemble.py b/etna/ensembles/voting_ensemble.py
index bd7046496..bcce36f3a 100644
--- a/etna/ensembles/voting_ensemble.py
+++ b/etna/ensembles/voting_ensemble.py
@@ -32,8 +32,7 @@ class VotingEnsemble(EnsembleMixin, SaveEnsembleMixin, BasePipeline):
>>> from etna.models import ProphetModel
>>> from etna.pipeline import Pipeline
>>> df = generate_ar_df(periods=30, start_time="2021-06-01", ar_coef=[1.2], n_segments=3)
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> ts = TSDataset(df_ts_format, "D")
+ >>> ts = TSDataset(df, "D")
>>> prophet_pipeline = Pipeline(model=ProphetModel(), transforms=[], horizon=7)
>>> naive_pipeline = Pipeline(model=NaiveModel(lag=10), transforms=[], horizon=7)
>>> ensemble = VotingEnsemble(
@@ -214,8 +213,8 @@ def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
def _predict(
self,
ts: TSDataset,
- start_timestamp: pd.Timestamp,
- end_timestamp: pd.Timestamp,
+ start_timestamp: Union[pd.Timestamp, int],
+ end_timestamp: Union[pd.Timestamp, int],
prediction_interval: bool,
quantiles: Sequence[float],
return_components: bool,
diff --git a/etna/models/__init__.py b/etna/models/__init__.py
index e80dac0cf..b1823b65d 100644
--- a/etna/models/__init__.py
+++ b/etna/models/__init__.py
@@ -12,7 +12,7 @@
>>> from etna.models import LinearPerSegmentModel
>>>
>>> df = generate_ar_df(periods=100, start_time="2021-01-01", ar_coef=[1/2], n_segments=2)
->>> ts = TSDataset(TSDataset.to_dataset(df), freq="D")
+>>> ts = TSDataset(df, freq="D")
>>> lag_transform = LagTransform(in_column="target", lags=[3, 4, 5])
>>> ts.fit_transform(transforms=[lag_transform])
>>> future_ts = ts.make_future(future_steps=3, transforms=[lag_transform])
diff --git a/etna/models/catboost.py b/etna/models/catboost.py
index 979beec4d..02afc1b52 100644
--- a/etna/models/catboost.py
+++ b/etna/models/catboost.py
@@ -191,14 +191,13 @@ class CatBoostPerSegmentModel(
>>> from etna.datasets import TSDataset
>>> from etna.models import CatBoostPerSegmentModel
>>> from etna.transforms import LagTransform
- >>> classic_df = generate_periodic_df(
+ >>> df = generate_periodic_df(
... periods=100,
... start_time="2020-01-01",
... n_segments=4,
... period=7,
... sigma=3
... )
- >>> df = TSDataset.to_dataset(df=classic_df)
>>> ts = TSDataset(df, freq="D")
>>> horizon = 7
>>> transforms = [
@@ -331,14 +330,13 @@ class CatBoostMultiSegmentModel(
>>> from etna.datasets import TSDataset
>>> from etna.models import CatBoostMultiSegmentModel
>>> from etna.transforms import LagTransform
- >>> classic_df = generate_periodic_df(
+ >>> df = generate_periodic_df(
... periods=100,
... start_time="2020-01-01",
... n_segments=4,
... period=7,
... sigma=3
... )
- >>> df = TSDataset.to_dataset(df=classic_df)
>>> ts = TSDataset(df, freq="D")
>>> horizon = 7
>>> transforms = [
diff --git a/etna/models/holt_winters.py b/etna/models/holt_winters.py
index 2e0a7c015..c0df7508a 100644
--- a/etna/models/holt_winters.py
+++ b/etna/models/holt_winters.py
@@ -13,16 +13,18 @@
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.holtwinters.results import HoltWintersResultsWrapper
+from etna.datasets.utils import determine_freq
+from etna.datasets.utils import determine_num_steps
from etna.distributions import BaseDistribution
from etna.distributions import CategoricalDistribution
from etna.models.base import BaseAdapter
from etna.models.base import NonPredictionIntervalContextIgnorantAbstractModel
from etna.models.mixins import NonPredictionIntervalContextIgnorantModelMixin
from etna.models.mixins import PerSegmentModelMixin
-from etna.models.utils import determine_freq
-from etna.models.utils import determine_num_steps
from etna.models.utils import select_observations
+_DEFAULT_FREQ = object()
+
class _HoltWintersAdapter(BaseAdapter):
"""
@@ -197,9 +199,9 @@ def __init__(
self._model: Optional[ExponentialSmoothing] = None
self._result: Optional[HoltWintersResultsWrapper] = None
- self._first_train_timestamp: Optional[pd.Timestamp] = None
- self._last_train_timestamp: Optional[pd.Timestamp] = None
- self._train_freq: Optional[str] = None
+ self._first_train_timestamp: Union[pd.Timestamp, int, None] = None
+ self._last_train_timestamp: Union[pd.Timestamp, int, None] = None
+ self._train_freq: Optional[str] = _DEFAULT_FREQ # type: ignore
def _check_not_used_columns(self, df: pd.DataFrame):
columns = df.columns
@@ -227,9 +229,15 @@ def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_HoltWintersAdapter":
"""
self._train_freq = determine_freq(timestamps=df["timestamp"])
self._check_not_used_columns(df)
+ self._first_train_timestamp = df["timestamp"].min()
+ self._last_train_timestamp = df["timestamp"].max()
targets = df["target"]
- targets.index = df["timestamp"]
+ if pd.api.types.is_integer_dtype(df["timestamp"]):
+ # make index start with zero
+ targets.index = df["timestamp"] - self._first_train_timestamp
+ else:
+ targets.index = df["timestamp"]
self._model = ExponentialSmoothing(
endog=targets,
@@ -255,9 +263,6 @@ def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_HoltWintersAdapter":
**self.fit_kwargs,
)
- self._first_train_timestamp = targets.index.min()
- self._last_train_timestamp = targets.index.max()
-
return self
def predict(self, df: pd.DataFrame) -> np.ndarray:
@@ -277,7 +282,16 @@ def predict(self, df: pd.DataFrame) -> np.ndarray:
if self._result is None or self._model is None:
raise ValueError("This model is not fitted! Fit the model before calling predict method!")
- forecast = self._result.predict(start=df["timestamp"].min(), end=df["timestamp"].max())
+ start_timestamp = df["timestamp"].min()
+ end_timestamp = df["timestamp"].max()
+ start_idx = determine_num_steps(
+ start_timestamp=self._first_train_timestamp, end_timestamp=start_timestamp, freq=self._train_freq
+ )
+ end_idx = determine_num_steps(
+ start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._train_freq
+ )
+
+ forecast = self._result.predict(start=start_idx, end=end_idx)
y_pred = forecast.values
return y_pred
@@ -329,7 +343,7 @@ def forecast_components(self, df: pd.DataFrame) -> pd.DataFrame:
model = self._model
fit_result = self._result
- if fit_result is None or model is None or self._train_freq is None:
+ if fit_result is None or model is None or self._train_freq is _DEFAULT_FREQ:
raise ValueError("This model is not fitted!")
if df["timestamp"].min() <= self._last_train_timestamp:
@@ -400,7 +414,7 @@ def predict_components(self, df: pd.DataFrame) -> pd.DataFrame:
model = self._model
fit_result = self._result
- if fit_result is None or model is None or self._train_freq is None:
+ if fit_result is None or model is None or self._train_freq is _DEFAULT_FREQ:
raise ValueError("This model is not fitted!")
if df["timestamp"].min() < self._first_train_timestamp or df["timestamp"].max() > self._last_train_timestamp:
diff --git a/etna/models/nn/deepar_native/deepar.py b/etna/models/nn/deepar_native/deepar.py
index 2bceb0ae4..def98e768 100644
--- a/etna/models/nn/deepar_native/deepar.py
+++ b/etna/models/nn/deepar_native/deepar.py
@@ -181,7 +181,8 @@ def make_samples(self, df: pd.DataFrame, encoder_length: int, decoder_length: in
segment = df["segment"].values[0]
values_target = df["target"].values
values_real = (
- df.select_dtypes(include=[np.number])
+ df.drop(["segment", "timestamp"], axis=1)
+ .select_dtypes(include=[np.number])
.assign(target_shifted=df["target"].shift(1))
.drop(["target"], axis=1)
.pipe(lambda x: x[["target_shifted"] + [i for i in x.columns if i != "target_shifted"]])
diff --git a/etna/models/nn/deepstate/deepstate.py b/etna/models/nn/deepstate/deepstate.py
index a0463aefa..9aeb99c60 100644
--- a/etna/models/nn/deepstate/deepstate.py
+++ b/etna/models/nn/deepstate/deepstate.py
@@ -25,6 +25,7 @@ class DeepStateBatch(TypedDict):
decoder_real: Tensor # (batch_size, horizon, input_size)
datetime_index: Tensor # (batch_size, num_models , seq_length + horizon)
encoder_target: Tensor # (batch_size, seq_length, 1)
+ segment: Tensor # (batch_size)
class DeepStateNet(DeepBaseNet):
diff --git a/etna/models/nn/mlp.py b/etna/models/nn/mlp.py
index b13452068..cf8f0bbbd 100644
--- a/etna/models/nn/mlp.py
+++ b/etna/models/nn/mlp.py
@@ -111,9 +111,7 @@ def step(self, batch: MLPBatch, *args, **kwargs): # type: ignore
def make_samples(self, df: pd.DataFrame, encoder_length: int, decoder_length: int) -> Iterable[dict]:
"""Make samples from segment DataFrame."""
- values_real = (
- df.select_dtypes(include=[np.number]).pipe(lambda x: x[[i for i in x.columns if i != "target"]]).values
- )
+ values_real = df.drop(["target", "segment", "timestamp"], axis=1).select_dtypes(include=[np.number]).values
values_target = df["target"].values
segment = df["segment"].values[0]
diff --git a/etna/models/nn/patchts.py b/etna/models/nn/patchts.py
index 478b49831..dfc5fb281 100644
--- a/etna/models/nn/patchts.py
+++ b/etna/models/nn/patchts.py
@@ -197,7 +197,7 @@ def step(self, batch: PatchTSBatch, *args, **kwargs): # type: ignore
def make_samples(self, df: pd.DataFrame, encoder_length: int, decoder_length: int) -> Iterator[dict]:
"""Make samples from segment DataFrame."""
- values_real = df.select_dtypes(include=[np.number]).values
+ values_real = df.drop(["segment", "timestamp"], axis=1).select_dtypes(include=[np.number]).values
values_target = df["target"].values
segment = df["segment"].values[0]
diff --git a/etna/models/nn/rnn.py b/etna/models/nn/rnn.py
index e85ccaf67..ee14abe7b 100644
--- a/etna/models/nn/rnn.py
+++ b/etna/models/nn/rnn.py
@@ -135,7 +135,8 @@ def step(self, batch: RNNBatch, *args, **kwargs): # type: ignore
def make_samples(self, df: pd.DataFrame, encoder_length: int, decoder_length: int) -> Iterator[dict]:
"""Make samples from segment DataFrame."""
values_real = (
- df.select_dtypes(include=[np.number])
+ df.drop(["segment", "timestamp"], axis=1)
+ .select_dtypes(include=[np.number])
.assign(target_shifted=df["target"].shift(1))
.drop(["target"], axis=1)
.pipe(lambda x: x[["target_shifted"] + [i for i in x.columns if i != "target_shifted"]])
diff --git a/etna/models/nn/utils.py b/etna/models/nn/utils.py
index dd1a0e95d..ea861a3e6 100644
--- a/etna/models/nn/utils.py
+++ b/etna/models/nn/utils.py
@@ -14,9 +14,10 @@
from etna import SETTINGS
from etna.core import BaseMixin
from etna.datasets.tsdataset import TSDataset
+from etna.datasets.utils import determine_num_steps
+from etna.datasets.utils import timestamp_range
from etna.loggers import tslogger
from etna.models.base import log_decorator
-from etna.models.utils import determine_num_steps
if SETTINGS.torch_required:
import pytorch_lightning as pl
@@ -266,7 +267,7 @@ def fit(self, ts: TSDataset):
raise ValueError("Trainer or model is None")
return self
- def _get_first_prediction_timestamp(self, ts: TSDataset, horizon: int) -> pd.Timestamp:
+ def _get_first_prediction_timestamp(self, ts: TSDataset, horizon: int) -> Union[pd.Timestamp, int]:
return ts.index[-horizon]
def _is_in_sample_prediction(self, ts: TSDataset, horizon: int) -> bool:
@@ -275,7 +276,7 @@ def _is_in_sample_prediction(self, ts: TSDataset, horizon: int) -> bool:
def _is_prediction_with_gap(self, ts: TSDataset, horizon: int) -> bool:
first_prediction_timestamp = self._get_first_prediction_timestamp(ts=ts, horizon=horizon)
- first_timestamp_after_train = pd.date_range(self._last_train_timestamp, periods=2, freq=self._freq)[-1]
+ first_timestamp_after_train = timestamp_range(start=self._last_train_timestamp, periods=2, freq=self._freq)[-1]
return first_prediction_timestamp > first_timestamp_after_train
def _make_target_prediction(self, ts: TSDataset, horizon: int) -> Tuple[TSDataset, DataLoader]:
diff --git a/etna/models/prophet.py b/etna/models/prophet.py
index 5baccecef..82581ea71 100644
--- a/etna/models/prophet.py
+++ b/etna/models/prophet.py
@@ -8,6 +8,7 @@
from typing import Sequence
from typing import Set
from typing import Union
+from typing import cast
import pandas as pd
@@ -50,6 +51,7 @@ def __init__(
uncertainty_samples: Union[int, bool] = 1000,
stan_backend: Optional[str] = None,
additional_seasonality_params: Iterable[Dict[str, Union[str, float, int]]] = (),
+ timestamp_column: Optional[str] = None,
):
self.growth = growth
@@ -69,6 +71,7 @@ def __init__(
self.uncertainty_samples = uncertainty_samples
self.stan_backend = stan_backend
self.additional_seasonality_params = additional_seasonality_params
+ self.timestamp_column = timestamp_column
self.model = self._create_model()
@@ -131,8 +134,12 @@ def _select_regressors(self, df: pd.DataFrame) -> Optional[pd.DataFrame]:
)
if self.regressor_columns:
+ columns = deepcopy(self.regressor_columns)
+ if self.timestamp_column in columns:
+ columns.remove(self.timestamp_column)
+
try:
- result = df[self.regressor_columns].apply(pd.to_numeric)
+ result = df[columns].apply(pd.to_numeric)
except ValueError as e:
raise ValueError(f"Only convertible to numeric features are allowed! Error: {str(e)}")
else:
@@ -156,8 +163,11 @@ def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_ProphetAdapter":
prophet_df = self._prepare_prophet_df(df=df)
for regressor in self.regressor_columns:
- if regressor not in self.predefined_regressors_names:
- self.model.add_regressor(regressor)
+ if regressor in self.predefined_regressors_names:
+ continue
+ if regressor == self.timestamp_column:
+ continue
+ self.model.add_regressor(regressor)
self.model.fit(prophet_df)
return self
@@ -193,20 +203,42 @@ def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Sequen
y_pred = y_pred.rename(rename_dict, axis=1)
return y_pred
+ def _validate_timestamp(self, df: pd.DataFrame):
+ self.regressor_columns = cast(List[str], self.regressor_columns)
+
+ if self.timestamp_column is None:
+ if not pd.api.types.is_datetime64_dtype(df["timestamp"]):
+ raise ValueError("Invalid timestamp! Only datetime type is supported.")
+
+ else:
+ if self.timestamp_column not in df.columns:
+ raise ValueError("Invalid timestamp_column! It isn't present in a given dataset.")
+
+ if self.timestamp_column not in self.regressor_columns:
+ raise ValueError("Invalid timestamp_column! It should be a regressor.")
+
+ if not pd.api.types.is_datetime64_dtype(df[self.timestamp_column]):
+ raise ValueError("Invalid timestamp_column! Only datetime type is supported.")
+
def _prepare_prophet_df(self, df: pd.DataFrame) -> pd.DataFrame:
"""Prepare dataframe for fit and predict."""
if self.regressor_columns is None:
raise ValueError("List of regressor is not set!")
+ self._validate_timestamp(df)
df = df.reset_index()
prophet_df = pd.DataFrame()
prophet_df["y"] = df["target"]
- prophet_df["ds"] = df["timestamp"]
+
+ if self.timestamp_column is None:
+ prophet_df["ds"] = df["timestamp"]
+ else:
+ prophet_df["ds"] = df[self.timestamp_column]
regressors_data = self._select_regressors(df)
if regressors_data is not None:
- prophet_df[self.regressor_columns] = regressors_data[self.regressor_columns]
+ prophet_df[regressors_data.columns] = regressors_data
return prophet_df
@@ -341,14 +373,13 @@ class ProphetModel(
>>> from etna.datasets import generate_periodic_df
>>> from etna.datasets import TSDataset
>>> from etna.models import ProphetModel
- >>> classic_df = generate_periodic_df(
+ >>> df = generate_periodic_df(
... periods=100,
... start_time="2020-01-01",
... n_segments=4,
... period=7,
... sigma=3
... )
- >>> df = TSDataset.to_dataset(df=classic_df)
>>> ts = TSDataset(df, freq="D")
>>> future = ts.make_future(7)
>>> model = ProphetModel(growth="flat")
@@ -358,7 +389,7 @@ class ProphetModel(
daily_seasonality = 'auto', holidays = None, seasonality_mode = 'additive',
seasonality_prior_scale = 10.0, holidays_prior_scale = 10.0, changepoint_prior_scale = 0.05,
mcmc_samples = 0, interval_width = 0.8, uncertainty_samples = 1000, stan_backend = None,
- additional_seasonality_params = (), )
+ additional_seasonality_params = (), timestamp_column = None, )
>>> forecast = model.forecast(future)
>>> forecast
segment segment_0 segment_1 segment_2 segment_3
@@ -392,6 +423,7 @@ def __init__(
uncertainty_samples: Union[int, bool] = 1000,
stan_backend: Optional[str] = None,
additional_seasonality_params: Iterable[Dict[str, Union[str, float, int]]] = (),
+ timestamp_column: Optional[str] = None,
):
"""
Create instance of Prophet model.
@@ -467,6 +499,9 @@ def __init__(
parameters that describe additional (not 'daily', 'weekly', 'yearly') seasonality that should be
added to model; dict with required keys 'name', 'period', 'fourier_order' and optional ones 'prior_scale',
'mode', 'condition_name' will be used for :py:meth:`prophet.Prophet.add_seasonality` method call.
+ timestamp_column:
+ Name of a column to be used as timestamp. If not given, index is used.
+ Column is expected to be regressor containing datetime values.
"""
self.growth = growth
self.n_changepoints = n_changepoints
@@ -485,6 +520,7 @@ def __init__(
self.uncertainty_samples = uncertainty_samples
self.stan_backend = stan_backend
self.additional_seasonality_params = additional_seasonality_params
+ self.timestamp_column = timestamp_column
super(ProphetModel, self).__init__(
base_model=_ProphetAdapter(
@@ -505,6 +541,7 @@ def __init__(
uncertainty_samples=self.uncertainty_samples,
stan_backend=self.stan_backend,
additional_seasonality_params=self.additional_seasonality_params,
+ timestamp_column=self.timestamp_column,
)
)
diff --git a/etna/models/sarimax.py b/etna/models/sarimax.py
index 5398e0b2d..fd1ad92f3 100644
--- a/etna/models/sarimax.py
+++ b/etna/models/sarimax.py
@@ -15,6 +15,8 @@
from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper
from statsmodels.tsa.statespace.simulation_smoother import SimulationSmoother
+from etna.datasets.utils import determine_freq
+from etna.datasets.utils import determine_num_steps
from etna.distributions import BaseDistribution
from etna.distributions import CategoricalDistribution
from etna.distributions import IntDistribution
@@ -23,8 +25,6 @@
from etna.models.base import PredictionIntervalContextIgnorantAbstractModel
from etna.models.mixins import PerSegmentModelMixin
from etna.models.mixins import PredictionIntervalContextIgnorantModelMixin
-from etna.models.utils import determine_freq
-from etna.models.utils import determine_num_steps
from etna.models.utils import select_observations
warnings.filterwarnings(
@@ -34,6 +34,8 @@
module="statsmodels.tsa.base.tsa_model",
)
+_DEFAULT_FREQ = object()
+
class _SARIMAXBaseAdapter(BaseAdapter):
"""Base class for adapters based on :py:class:`statsmodels.tsa.statespace.sarimax.SARIMAX`."""
@@ -41,7 +43,7 @@ class _SARIMAXBaseAdapter(BaseAdapter):
def __init__(self):
self.regressor_columns = None
self._fit_results = None
- self._freq = None
+ self._freq: Union[str, None] = _DEFAULT_FREQ # type: ignore
self._first_train_timestamp = None
self._last_train_timestamp = None
@@ -64,12 +66,13 @@ def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_SARIMAXBaseAdapter":
self.regressor_columns = regressors
self._check_not_used_columns(df)
+ self._first_train_timestamp = df["timestamp"].min()
+ self._last_train_timestamp = df["timestamp"].max()
+
exog_train = self._select_regressors(df)
self._fit_results = self._get_fit_results(endog=df["target"], exog=exog_train)
self._freq = determine_freq(timestamps=df["timestamp"])
- self._first_train_timestamp = df["timestamp"].min()
- self._last_train_timestamp = df["timestamp"].max()
return self
@@ -86,11 +89,11 @@ def _make_prediction(
end_timestamp = df["timestamp"].max()
# determine index of start_timestamp if counting from first timestamp of train
start_idx = determine_num_steps(
- start_timestamp=self._first_train_timestamp, end_timestamp=start_timestamp, freq=self._freq # type: ignore
+ start_timestamp=self._first_train_timestamp, end_timestamp=start_timestamp, freq=self._freq
)
# determine index of end_timestamp if counting from first timestamp of train
end_idx = determine_num_steps(
- start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._freq # type: ignore
+ start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._freq
)
if prediction_interval:
@@ -206,7 +209,12 @@ def _select_regressors(self, df: pd.DataFrame) -> Optional[pd.DataFrame]:
result = df[self.regressor_columns].astype(float)
except ValueError as e:
raise ValueError(f"Only convertible to float features are allowed! Error: {str(e)}")
- result.index = df["timestamp"]
+
+ if pd.api.types.is_integer_dtype(df["timestamp"]):
+ # make index start with zero
+ result.index = df["timestamp"] - self._first_train_timestamp
+ else:
+ result.index = df["timestamp"]
else:
result = None
@@ -370,11 +378,11 @@ def forecast_components(self, df: pd.DataFrame) -> pd.DataFrame:
# determine index of start_timestamp if counting from last timestamp of train
start_idx = determine_num_steps(
- start_timestamp=self._last_train_timestamp, end_timestamp=start_timestamp, freq=self._freq # type: ignore
+ start_timestamp=self._last_train_timestamp, end_timestamp=start_timestamp, freq=self._freq
)
# determine index of end_timestamp if counting from last timestamp of train
end_idx = determine_num_steps(
- start_timestamp=self._last_train_timestamp, end_timestamp=end_timestamp, freq=self._freq # type: ignore
+ start_timestamp=self._last_train_timestamp, end_timestamp=end_timestamp, freq=self._freq
)
if start_idx > 1:
diff --git a/etna/models/statsforecast.py b/etna/models/statsforecast.py
index a2d20329f..758a745ca 100644
--- a/etna/models/statsforecast.py
+++ b/etna/models/statsforecast.py
@@ -13,6 +13,8 @@
from statsforecast.models import AutoETS
from statsforecast.models import AutoTheta
+from etna.datasets.utils import determine_freq
+from etna.datasets.utils import determine_num_steps
from etna.distributions import BaseDistribution
from etna.distributions import IntDistribution
from etna.libs.statsforecast import ARIMA
@@ -22,10 +24,9 @@
from etna.models.mixins import NonPredictionIntervalContextIgnorantModelMixin
from etna.models.mixins import PerSegmentModelMixin
from etna.models.mixins import PredictionIntervalContextIgnorantModelMixin
-from etna.models.utils import determine_freq
-from etna.models.utils import determine_num_steps
StatsForecastModel = Union[AutoCES, AutoARIMA, AutoTheta, AutoETS, ARIMA]
+_DEFAULT_FREQ = object()
class _StatsForecastBaseAdapter(BaseAdapter):
@@ -43,7 +44,7 @@ def __init__(self, model: StatsForecastModel, support_prediction_intervals: bool
Should model support prediction intervals.
"""
self.regressor_columns: Optional[List[str]] = None
- self._freq: Optional[str] = None
+ self._freq: Optional[str] = _DEFAULT_FREQ # type: ignore
self._first_train_timestamp: Optional[pd.Timestamp] = None
self._last_train_timestamp: Optional[pd.Timestamp] = None
self._model = model
@@ -140,7 +141,7 @@ def forecast(
:
DataFrame with predictions
"""
- if self._freq is None:
+ if self._freq is _DEFAULT_FREQ:
raise ValueError("Model is not fitted! Fit the model before calling predict method!")
start_timestamp = df["timestamp"].min()
@@ -153,11 +154,11 @@ def forecast(
# determine index of start_timestamp if counting from last timestamp of train
start_idx = determine_num_steps(
- start_timestamp=self._last_train_timestamp, end_timestamp=start_timestamp, freq=self._freq # type: ignore
+ start_timestamp=self._last_train_timestamp, end_timestamp=start_timestamp, freq=self._freq
)
# determine index of end_timestamp if counting from last timestamp of train
end_idx = determine_num_steps(
- start_timestamp=self._last_train_timestamp, end_timestamp=end_timestamp, freq=self._freq # type: ignore
+ start_timestamp=self._last_train_timestamp, end_timestamp=end_timestamp, freq=self._freq
)
if start_idx > 1:
@@ -213,7 +214,7 @@ def predict(
:
DataFrame with predictions
"""
- if self._freq is None:
+ if self._freq is _DEFAULT_FREQ:
raise ValueError("Model is not fitted! Fit the model before calling predict method!")
start_timestamp = df["timestamp"].min()
@@ -232,11 +233,11 @@ def predict(
# determine index of start_timestamp if counting from first timestamp of train
start_idx = determine_num_steps(
- start_timestamp=self._first_train_timestamp, end_timestamp=start_timestamp, freq=self._freq # type: ignore
+ start_timestamp=self._first_train_timestamp, end_timestamp=start_timestamp, freq=self._freq
)
# determine index of end_timestamp if counting from first timestamp of train
end_idx = determine_num_steps(
- start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._freq # type: ignore
+ start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._freq
)
if prediction_interval and self._support_prediction_intervals:
diff --git a/etna/models/tbats.py b/etna/models/tbats.py
index 954813547..97778cdd1 100644
--- a/etna/models/tbats.py
+++ b/etna/models/tbats.py
@@ -1,6 +1,7 @@
from typing import Iterable
from typing import Optional
from typing import Tuple
+from typing import Union
from warnings import warn
import numpy as np
@@ -11,14 +12,17 @@
from tbats.tbats import TBATS
from tbats.tbats.Model import Model
+from etna.datasets.utils import determine_freq
+from etna.datasets.utils import determine_num_steps
+from etna.datasets.utils import timestamp_range
from etna.models.base import BaseAdapter
from etna.models.base import PredictionIntervalContextIgnorantAbstractModel
from etna.models.mixins import PerSegmentModelMixin
from etna.models.mixins import PredictionIntervalContextIgnorantModelMixin
-from etna.models.utils import determine_freq
-from etna.models.utils import determine_num_steps
from etna.models.utils import select_observations
+_DEFAULT_FREQ = object()
+
class _TBATSAdapter(BaseAdapter):
def __init__(self, model: Estimator):
@@ -26,7 +30,7 @@ def __init__(self, model: Estimator):
self._fitted_model: Optional[Model] = None
self._first_train_timestamp = None
self._last_train_timestamp = None
- self._freq: Optional[str] = None
+ self._freq: Union[str, None] = _DEFAULT_FREQ # type: ignore
def _check_not_used_columns(self, df: pd.DataFrame):
columns = df.columns
@@ -49,7 +53,7 @@ def fit(self, df: pd.DataFrame, regressors: Iterable[str]):
return self
def forecast(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Iterable[float]) -> pd.DataFrame:
- if self._fitted_model is None or self._freq is None:
+ if self._fitted_model is None or self._freq is _DEFAULT_FREQ:
raise ValueError("Model is not fitted! Fit the model before calling predict method!")
steps_to_forecast = self._get_steps_to_forecast(df=df)
@@ -76,11 +80,11 @@ def forecast(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Itera
return y_pred
def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Iterable[float]) -> pd.DataFrame:
- if self._fitted_model is None or self._freq is None:
+ if self._fitted_model is None or self._freq is _DEFAULT_FREQ or self._last_train_timestamp is None:
raise ValueError("Model is not fitted! Fit the model before calling predict method!")
- train_timestamp = pd.date_range(
- start=str(self._first_train_timestamp), end=str(self._last_train_timestamp), freq=self._freq
+ train_timestamp = timestamp_range(
+ start=self._first_train_timestamp, end=self._last_train_timestamp, freq=self._freq
)
if not (set(df["timestamp"]) <= set(train_timestamp)):
@@ -134,7 +138,7 @@ def forecast_components(self, df: pd.DataFrame) -> pd.DataFrame:
:
dataframe with forecast components
"""
- if self._fitted_model is None or self._freq is None:
+ if self._fitted_model is None or self._freq is _DEFAULT_FREQ:
raise ValueError("Model is not fitted! Fit the model before estimating forecast components!")
if df["timestamp"].min() <= self._last_train_timestamp:
@@ -168,7 +172,7 @@ def predict_components(self, df: pd.DataFrame) -> pd.DataFrame:
:
dataframe with prediction components
"""
- if self._fitted_model is None or self._freq is None:
+ if self._fitted_model is None or self._freq is _DEFAULT_FREQ:
raise ValueError("Model is not fitted! Fit the model before estimating forecast components!")
if self._last_train_timestamp < df["timestamp"].max() or self._first_train_timestamp > df["timestamp"].min():
@@ -193,7 +197,7 @@ def predict_components(self, df: pd.DataFrame) -> pd.DataFrame:
return components
def _get_steps_to_forecast(self, df: pd.DataFrame) -> int:
- if self._freq is None:
+ if self._freq is _DEFAULT_FREQ:
raise ValueError("Data frequency is not set!")
if df["timestamp"].min() <= self._last_train_timestamp:
diff --git a/etna/models/utils.py b/etna/models/utils.py
index d16db1822..fd2dd0b00 100644
--- a/etna/models/utils.py
+++ b/etna/models/utils.py
@@ -3,64 +3,17 @@
import pandas as pd
-
-def determine_num_steps(start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp, freq: str) -> int:
- """Determine how many steps of ``freq`` should we make from ``start_timestamp`` to reach ``end_timestamp``.
-
- Parameters
- ----------
- start_timestamp:
- timestamp to start counting from
- end_timestamp:
- timestamp to end counting, should be not earlier than ``start_timestamp``
- freq:
- pandas frequency string: `Offset aliases `_
-
- Returns
- -------
- :
- number of steps
-
- Raises
- ------
- ValueError:
- Value of end timestamp is less than start timestamp
- ValueError:
- Start timestamp isn't correct according to a given frequency
- ValueError:
- End timestamp isn't reachable with a given frequency
- """
- if start_timestamp > end_timestamp:
- raise ValueError("Start train timestamp should be less or equal than end timestamp!")
-
- # check if start_timestamp is normalized
- normalized_start_timestamp = pd.date_range(start=start_timestamp, periods=1, freq=freq)
- if normalized_start_timestamp != start_timestamp:
- raise ValueError(f"Start timestamp isn't correct according to given frequency: {freq}")
-
- # check a simple case
- if start_timestamp == end_timestamp:
- return 0
-
- # make linear probing, because for complex offsets there is a cycle in `pd.date_range`
- cur_value = 1
- cur_timestamp = start_timestamp
- while True:
- timestamps = pd.date_range(start=cur_timestamp, periods=2, freq=freq)
- if timestamps[-1] == end_timestamp:
- return cur_value
- elif timestamps[-1] > end_timestamp:
- raise ValueError(f"End timestamp isn't reachable with freq: {freq}")
- cur_value += 1
- cur_timestamp = timestamps[-1]
+from etna.datasets.utils import determine_freq # noqa: F401
+from etna.datasets.utils import determine_num_steps # noqa: F401
+from etna.datasets.utils import timestamp_range
def select_observations(
df: pd.DataFrame,
timestamps: pd.Series,
- freq: str,
- start: Optional[Union[pd.Timestamp, str]] = None,
- end: Optional[Union[pd.Timestamp, str]] = None,
+ freq: Optional[str],
+ start: Optional[Union[pd.Timestamp, int, str]] = None,
+ end: Optional[Union[pd.Timestamp, int, str]] = None,
periods: Optional[int] = None,
) -> pd.DataFrame:
"""Select observations from dataframe with known timeline.
@@ -72,11 +25,17 @@ def select_observations(
timestamps:
series of timestamps to select
freq:
- pandas frequency string
+ frequency of timestamp in df, possible values:
+
+ - `pandas offset aliases `_
+ for datetime timestamp
+
+ - None for integer timestamp
+
start:
start of the timeline
end:
- end of the timeline
+ end of the timeline (included)
periods:
number of periods in the timeline
@@ -84,8 +43,13 @@ def select_observations(
-------
:
dataframe with selected observations
+
+ Raises
+ ------
+ ValueError:
+ Of the three parameters: start, end, periods, exactly two must be specified
"""
- df["timestamp"] = pd.date_range(start=start, end=end, periods=periods, freq=freq)
+ df["timestamp"] = timestamp_range(start=start, end=end, periods=periods, freq=freq)
if not (set(timestamps) <= set(df["timestamp"])):
raise ValueError("Some timestamps do not lie inside the timeline of the provided dataframe.")
@@ -94,32 +58,3 @@ def select_observations(
observations = observations.loc[timestamps]
observations.reset_index(drop=True, inplace=True)
return observations
-
-
-def determine_freq(timestamps: Union[pd.Series, pd.DatetimeIndex]) -> str:
- """Determine data frequency using provided timestamps.
-
- Parameters
- ----------
- timestamps:
- timeline to determine frequency
-
- Returns
- -------
- :
- pandas frequency string
-
- Raises
- ------
- ValueError:
- unable do determine frequency of data
- """
- try:
- freq = pd.infer_freq(timestamps)
- except ValueError:
- freq = None
-
- if freq is None:
- raise ValueError("Can't determine frequency of a given dataframe")
-
- return freq
diff --git a/etna/pipeline/assembling_pipelines.py b/etna/pipeline/assembling_pipelines.py
index e54d01b92..b7ff0d5df 100644
--- a/etna/pipeline/assembling_pipelines.py
+++ b/etna/pipeline/assembling_pipelines.py
@@ -63,7 +63,7 @@ def assemble_pipelines(
Pipeline(model = LinearPerSegmentModel(fit_intercept = True, ), transforms = [LagTransform(in_column = 'target', lags = [1], out_column = None, ), AddConstTransform(in_column = 'target', value = 1, inplace = True, out_column = None, )], horizon = 3, )]
>>> assemble_pipelines(models=[LinearPerSegmentModel(), NaiveModel()], transforms=[LagTransform(in_column='target', lags=[1]), [AddConstTransform(in_column='target', value=1), DateFlagsTransform()]], horizons=[1,2])
[Pipeline(model = LinearPerSegmentModel(fit_intercept = True, ), transforms = [LagTransform(in_column = 'target', lags = [1], out_column = None, ), AddConstTransform(in_column = 'target', value = 1, inplace = True, out_column = None, )], horizon = 1, ),
- Pipeline(model = NaiveModel(lag = 1, ), transforms = [LagTransform(in_column = 'target', lags = [1], out_column = None, ), DateFlagsTransform(day_number_in_week = True, day_number_in_month = True, day_number_in_year = False, week_number_in_month = False, week_number_in_year = False, month_number_in_year = False, season_number = False, year_number = False, is_weekend = True, special_days_in_week = (), special_days_in_month = (), out_column = None, )], horizon = 2, )]
+ Pipeline(model = NaiveModel(lag = 1, ), transforms = [LagTransform(in_column = 'target', lags = [1], out_column = None, ), DateFlagsTransform(day_number_in_week = True, day_number_in_month = True, day_number_in_year = False, week_number_in_month = False, week_number_in_year = False, month_number_in_year = False, season_number = False, year_number = False, is_weekend = True, special_days_in_week = (), special_days_in_month = (), out_column = None, in_column = None, )], horizon = 2, )]
"""
n_models = len(models) if isinstance(models, Sequence) else 1
n_horizons = len(horizons) if isinstance(horizons, Sequence) else 1
diff --git a/etna/pipeline/autoregressive_pipeline.py b/etna/pipeline/autoregressive_pipeline.py
index 0cdbc8443..7e7384784 100644
--- a/etna/pipeline/autoregressive_pipeline.py
+++ b/etna/pipeline/autoregressive_pipeline.py
@@ -6,6 +6,7 @@
from typing_extensions import get_args
from etna.datasets import TSDataset
+from etna.datasets.utils import timestamp_range
from etna.models.base import ContextIgnorantModelType
from etna.models.base import ContextRequiredModelType
from etna.models.base import ModelType
@@ -27,14 +28,13 @@ class AutoRegressivePipeline(
>>> from etna.datasets import TSDataset
>>> from etna.models import LinearPerSegmentModel
>>> from etna.transforms import LagTransform
- >>> classic_df = generate_periodic_df(
+ >>> df = generate_periodic_df(
... periods=100,
... start_time="2020-01-01",
... n_segments=4,
... period=7,
... sigma=3
... )
- >>> df = TSDataset.to_dataset(df=classic_df)
>>> ts = TSDataset(df, freq="D")
>>> horizon = 7
>>> transforms = [
@@ -106,12 +106,13 @@ def fit(self, ts: TSDataset, save_ts: bool = True) -> "AutoRegressivePipeline":
def _create_predictions_template(self, ts: TSDataset) -> pd.DataFrame:
"""Create dataframe to fill with forecasts."""
- prediction_df = ts[:, :, "target"]
- future_dates = pd.date_range(
- start=prediction_df.index.max(), periods=self.horizon + 1, freq=ts.freq, closed="right"
- )
- prediction_df = prediction_df.reindex(prediction_df.index.append(future_dates))
- prediction_df.index.name = "timestamp"
+ prediction_df = ts.to_pandas(features=["target"])
+ last_timestamp = prediction_df.index[-1]
+ to_add_index = timestamp_range(start=last_timestamp, periods=self.horizon + 1, freq=ts.freq)[1:]
+ new_index = prediction_df.index.append(to_add_index)
+ index_name = prediction_df.index.name
+ prediction_df = prediction_df.reindex(new_index)
+ prediction_df.index.name = index_name
return prediction_df
def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
@@ -172,21 +173,3 @@ def _forecast(self, ts: TSDataset, return_components: bool) -> TSDataset:
prediction_ts.add_target_components(target_components_df=target_components_df)
return prediction_ts
-
- def _predict(
- self,
- ts: TSDataset,
- start_timestamp: pd.Timestamp,
- end_timestamp: pd.Timestamp,
- prediction_interval: bool,
- quantiles: Sequence[float],
- return_components: bool = False,
- ) -> TSDataset:
- return super()._predict(
- ts=ts,
- start_timestamp=start_timestamp,
- end_timestamp=end_timestamp,
- prediction_interval=prediction_interval,
- quantiles=quantiles,
- return_components=return_components,
- )
diff --git a/etna/pipeline/base.py b/etna/pipeline/base.py
index ebcdbaff3..8066b8d05 100644
--- a/etna/pipeline/base.py
+++ b/etna/pipeline/base.py
@@ -1,4 +1,5 @@
import math
+import reprlib
import warnings
from abc import abstractmethod
from copy import deepcopy
@@ -9,6 +10,7 @@
from typing import List
from typing import Optional
from typing import Sequence
+from typing import Set
from typing import Tuple
from typing import Union
@@ -23,14 +25,14 @@
from etna.core import AbstractSaveable
from etna.core import BaseMixin
from etna.datasets import TSDataset
+from etna.datasets.utils import _check_timestamp_param
+from etna.datasets.utils import timestamp_range
from etna.distributions import BaseDistribution
from etna.loggers import tslogger
from etna.metrics import Metric
from etna.metrics import MetricAggregationMode
from etna.metrics.functional_metrics import ArrayLike
-Timestamp = Union[str, pd.Timestamp]
-
class CrossValidationMode(str, Enum):
"""Enum for different cross-validation modes."""
@@ -53,12 +55,18 @@ class FoldMask(BaseMixin):
def __init__(
self,
- first_train_timestamp: Optional[Timestamp],
- last_train_timestamp: Timestamp,
- target_timestamps: List[Timestamp],
+ first_train_timestamp: Union[pd.Timestamp, int, str, None],
+ last_train_timestamp: Union[pd.Timestamp, int, str],
+ target_timestamps: List[Union[pd.Timestamp, int, str]],
):
"""Init FoldMask.
+ Values of ``target_timestamps`` are sorted in ascending order.
+
+ Notes
+ -----
+ String value is converted into :py:class`pd.Timestamps` using :py:func:`pandas.to_datetime`.
+
Parameters
----------
first_train_timestamp:
@@ -67,23 +75,93 @@ def __init__(
Last train timestamp
target_timestamps:
List of target timestamps
+
+ Raises
+ ------
+ ValueError:
+ All timestamps should be one of two possible types: pd.Timestamp or int
+ ValueError:
+ Last train timestamp should be not sooner than first train timestamp
+ ValueError:
+ Target timestamps shouldn't be empty
+ ValueError:
+ Target timestamps shouldn't contain duplicates
+ ValueError:
+ Target timestamps should be strictly later then last train timestamp
"""
- self.first_train_timestamp = pd.to_datetime(first_train_timestamp) if first_train_timestamp else None
- self.last_train_timestamp = pd.to_datetime(last_train_timestamp)
- self.target_timestamps = sorted([pd.to_datetime(timestamp) for timestamp in target_timestamps])
+ if isinstance(first_train_timestamp, str):
+ first_train_timestamp = pd.to_datetime(first_train_timestamp)
+
+ if isinstance(last_train_timestamp, str):
+ last_train_timestamp = pd.to_datetime(last_train_timestamp)
+
+ target_timestamps_processed = []
+ for timestamp in target_timestamps:
+ if isinstance(timestamp, str):
+ target_timestamps_processed.append(pd.to_datetime(timestamp))
+ else:
+ target_timestamps_processed.append(timestamp)
+
+ self._validate_parameters_same_type(
+ first_train_timestamp=first_train_timestamp,
+ last_train_timestamp=last_train_timestamp,
+ target_timestamps=target_timestamps_processed,
+ )
- self._validate_last_train_timestamp()
- self._validate_target_timestamps()
+ target_timestamps = sorted(target_timestamps_processed)
- def _validate_last_train_timestamp(self):
- """Check that last train timestamp is later then first train timestamp."""
- if self.first_train_timestamp and self.last_train_timestamp < self.first_train_timestamp:
+ self._validate_first_last_train_timestamps_order(
+ first_train_timestamp=first_train_timestamp, last_train_timestamp=last_train_timestamp
+ )
+ self._validate_target_timestamps(last_train_timestamp=last_train_timestamp, target_timestamps=target_timestamps)
+
+ self.first_train_timestamp = first_train_timestamp
+ self.last_train_timestamp = last_train_timestamp
+ self.target_timestamps = target_timestamps
+
+ @staticmethod
+ def _validate_parameters_same_type(
+ first_train_timestamp: Union[pd.Timestamp, int, str, None],
+ last_train_timestamp: Union[pd.Timestamp, int],
+ target_timestamps: List[Union[pd.Timestamp, int]],
+ ):
+ """Check that first train timestamp, last train timestamp, target timestamps has the same type."""
+ if first_train_timestamp is not None:
+ values_to_check = [first_train_timestamp, last_train_timestamp, *target_timestamps]
+ else:
+ values_to_check = [last_train_timestamp, *target_timestamps]
+
+ types: Set[type] = set()
+ for value in values_to_check:
+ if isinstance(value, np.integer):
+ types.add(int)
+ else:
+ types.add(type(value))
+
+ if len(types) > 1:
+ raise ValueError("All timestamps should be one of two possible types: pd.Timestamp or int!")
+
+ @staticmethod
+ def _validate_first_last_train_timestamps_order(
+ first_train_timestamp: Union[pd.Timestamp, int, None], last_train_timestamp: Union[pd.Timestamp, int]
+ ):
+ """Check that last train timestamp is later than first train timestamp."""
+ if first_train_timestamp is not None and last_train_timestamp < first_train_timestamp: # type: ignore
raise ValueError("Last train timestamp should be not sooner than first train timestamp!")
- def _validate_target_timestamps(self):
- """Check that all target timestamps are later then last train timestamp."""
- first_target_timestamp = self.target_timestamps[0]
- if first_target_timestamp <= self.last_train_timestamp:
+ @staticmethod
+ def _validate_target_timestamps(
+ last_train_timestamp: Union[pd.Timestamp, int], target_timestamps: List[Union[pd.Timestamp, int]]
+ ):
+ """Check that all target timestamps aren't empty and later than last train timestamp."""
+ if len(target_timestamps) == 0:
+ raise ValueError("Target timestamps shouldn't be empty!")
+
+ if len(target_timestamps) != len(set(target_timestamps)):
+ raise ValueError("Target timestamps shouldn't contain duplicates!")
+
+ first_target_timestamp = target_timestamps[0]
+ if first_target_timestamp <= last_train_timestamp: # type: ignore
raise ValueError("Target timestamps should be strictly later then last train timestamp!")
def validate_on_dataset(self, ts: TSDataset, horizon: int):
@@ -95,29 +173,44 @@ def validate_on_dataset(self, ts: TSDataset, horizon: int):
Dataset to validate on
horizon:
Forecasting horizon
+
+ Raises
+ ------
+ ValueError:
+ First train timestamp isn't present in a given dataset
+ ValueError:
+ Last train timestamp isn't present in a given dataset
+ ValueError:
+ Some of target timestamps aren't present in a given dataset
+ ValueError:
+ First train timestamp should be later than minimal dataset timestamp
+ ValueError:
+ Last train timestamp should be not later than the ending of the shortest segment
+ ValueError:
+ Last target timestamp should be not later than horizon steps after last train timestamp
"""
- dataset_timestamps = list(ts.index)
- dataset_description = ts.describe()
+ timestamps = ts.index.to_list()
- min_first_timestamp = ts.index.min()
- if self.first_train_timestamp and self.first_train_timestamp < min_first_timestamp:
- raise ValueError(f"First train timestamp should be later than {min_first_timestamp}!")
+ if self.first_train_timestamp is not None and self.first_train_timestamp not in timestamps:
+ raise ValueError("First train timestamp isn't present in a given dataset!")
- last_timestamp = dataset_description["end_timestamp"].min()
- if self.last_train_timestamp > last_timestamp:
- raise ValueError(f"Last train timestamp should be not later than {last_timestamp}!")
+ if self.last_train_timestamp not in timestamps:
+ raise ValueError("Last train timestamp isn't present in a given dataset!")
+
+ if not set(self.target_timestamps).issubset(set(timestamps)):
+ diff = set(self.target_timestamps).difference(set(timestamps))
+ raise ValueError(f"Some target timestamps aren't present in a given dataset: {reprlib.repr(diff)}")
+
+ dataset_description = ts.describe()
- dataset_first_target_timestamp = dataset_timestamps[dataset_timestamps.index(self.last_train_timestamp) + 1]
- mask_first_target_timestamp = self.target_timestamps[0]
- if mask_first_target_timestamp < dataset_first_target_timestamp:
- raise ValueError(f"First target timestamp should be not sooner than {dataset_first_target_timestamp}!")
+ dataset_min_last_timestamp = dataset_description["end_timestamp"].min()
+ if self.last_train_timestamp > dataset_min_last_timestamp:
+ raise ValueError(f"Last train timestamp should be not later than {dataset_min_last_timestamp}!")
- dataset_last_target_timestamp = dataset_timestamps[
- dataset_timestamps.index(self.last_train_timestamp) + horizon
- ]
+ dataset_horizon_border_timestamp = timestamps[timestamps.index(self.last_train_timestamp) + horizon]
mask_last_target_timestamp = self.target_timestamps[-1]
- if dataset_last_target_timestamp < mask_last_target_timestamp:
- raise ValueError(f"Last target timestamp should be not later than {dataset_last_target_timestamp}!")
+ if dataset_horizon_border_timestamp < mask_last_target_timestamp:
+ raise ValueError(f"Last target timestamp should be not later than {dataset_horizon_border_timestamp}!")
class AbstractPipeline(AbstractSaveable):
@@ -178,8 +271,8 @@ def forecast(
def predict(
self,
ts: TSDataset,
- start_timestamp: Optional[pd.Timestamp] = None,
- end_timestamp: Optional[pd.Timestamp] = None,
+ start_timestamp: Union[pd.Timestamp, int, str, None] = None,
+ end_timestamp: Union[pd.Timestamp, int, str, None] = None,
prediction_interval: bool = False,
quantiles: Sequence[float] = (0.025, 0.975),
return_components: bool = False,
@@ -189,6 +282,8 @@ def predict(
Currently, in situation when segments start with different timestamps
we only guarantee to work with ``start_timestamp`` >= beginning of all segments.
+ Parameters ``start_timestamp`` and ``end_timestamp`` of type ``str`` are converted into ``pd.Timestamp``.
+
Parameters
----------
ts:
@@ -214,6 +309,8 @@ def predict(
Raises
------
+ ValueError:
+ Incorrect type of ``start_timestamp`` or ``end_timestamp`` is used according to ``ts.freq``
ValueError:
Value of ``end_timestamp`` is less than ``start_timestamp``.
ValueError:
@@ -462,9 +559,12 @@ def forecast(
@staticmethod
def _make_predict_timestamps(
ts: TSDataset,
- start_timestamp: Optional[pd.Timestamp] = None,
- end_timestamp: Optional[pd.Timestamp] = None,
- ) -> Tuple[pd.Timestamp, pd.Timestamp]:
+ start_timestamp: Union[pd.Timestamp, int, str, None],
+ end_timestamp: Union[pd.Timestamp, int, str, None],
+ ) -> Union[Tuple[pd.Timestamp, pd.Timestamp], Tuple[int, int]]:
+ start_timestamp = _check_timestamp_param(param=start_timestamp, param_name="start_timestamp", freq=ts.freq)
+ end_timestamp = _check_timestamp_param(param=end_timestamp, param_name="end_timestamp", freq=ts.freq)
+
min_timestamp = ts.describe()["start_timestamp"].max()
max_timestamp = ts.index[-1]
@@ -478,7 +578,7 @@ def _make_predict_timestamps(
if end_timestamp > max_timestamp:
raise ValueError("Value of end_timestamp is more than ending of dataset!")
- if start_timestamp > end_timestamp:
+ if start_timestamp > end_timestamp: # type: ignore
raise ValueError("Value of end_timestamp is less than start_timestamp!")
return start_timestamp, end_timestamp
@@ -487,8 +587,8 @@ def _make_predict_timestamps(
def _predict(
self,
ts: TSDataset,
- start_timestamp: Optional[pd.Timestamp],
- end_timestamp: Optional[pd.Timestamp],
+ start_timestamp: Union[pd.Timestamp, int],
+ end_timestamp: Union[pd.Timestamp, int],
prediction_interval: bool,
quantiles: Sequence[float],
return_components: bool,
@@ -498,8 +598,8 @@ def _predict(
def predict(
self,
ts: TSDataset,
- start_timestamp: Optional[pd.Timestamp] = None,
- end_timestamp: Optional[pd.Timestamp] = None,
+ start_timestamp: Union[pd.Timestamp, int, str, None] = None,
+ end_timestamp: Union[pd.Timestamp, int, str, None] = None,
prediction_interval: bool = False,
quantiles: Sequence[float] = (0.025, 0.975),
return_components: bool = False,
@@ -509,6 +609,8 @@ def predict(
Currently, in situation when segments start with different timestamps
we only guarantee to work with ``start_timestamp`` >= beginning of all segments.
+ Parameters ``start_timestamp`` and ``end_timestamp`` of type ``str`` are converted into ``pd.Timestamp``.
+
Parameters
----------
ts:
@@ -534,6 +636,8 @@ def predict(
Raises
------
+ ValueError
+ Incorrect type of ``start_timestamp`` or ``end_timestamp`` is used according to ``ts.freq``
ValueError:
Value of ``end_timestamp`` is less than ``start_timestamp``.
ValueError:
@@ -632,11 +736,11 @@ def _generate_masks_from_n_folds(
min_train, max_train = dataset_timestamps[min_train_idx], dataset_timestamps[max_train_idx]
min_test, max_test = dataset_timestamps[min_test_idx], dataset_timestamps[max_test_idx]
-
+ target_timestamps = timestamp_range(start=min_test, end=max_test, freq=ts.freq).tolist()
mask = FoldMask(
first_train_timestamp=min_train,
last_train_timestamp=max_train,
- target_timestamps=list(pd.date_range(start=min_test, end=max_test, freq=ts.freq)),
+ target_timestamps=target_timestamps,
)
masks.append(mask)
diff --git a/etna/pipeline/mixins.py b/etna/pipeline/mixins.py
index 72acb12b5..561c6c4fe 100644
--- a/etna/pipeline/mixins.py
+++ b/etna/pipeline/mixins.py
@@ -5,6 +5,7 @@
from typing import Dict
from typing import Optional
from typing import Sequence
+from typing import Union
import numpy as np
import pandas as pd
@@ -27,7 +28,9 @@
class ModelPipelinePredictMixin:
"""Mixin for pipelines with model inside with implementation of ``_predict`` method."""
- def _create_ts(self, ts: TSDataset, start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp) -> TSDataset:
+ def _create_ts(
+ self, ts: TSDataset, start_timestamp: Union[pd.Timestamp, int], end_timestamp: Union[pd.Timestamp, int]
+ ) -> TSDataset:
"""Create ``TSDataset`` to make predictions on."""
self.model: ModelType
self.transforms: Sequence[Transform]
@@ -37,7 +40,7 @@ def _create_ts(self, ts: TSDataset, start_timestamp: pd.Timestamp, end_timestamp
freq = deepcopy(ts.freq)
known_future = deepcopy(ts.known_future)
- df_to_transform = df[:end_timestamp]
+ df_to_transform = df.loc[:end_timestamp]
cur_ts = TSDataset(
df=df_to_transform,
@@ -51,25 +54,25 @@ def _create_ts(self, ts: TSDataset, start_timestamp: pd.Timestamp, end_timestamp
# correct start_timestamp taking into account context size
timestamp_indices = pd.Series(np.arange(len(df.index)), index=df.index)
- start_idx = timestamp_indices[start_timestamp]
+ start_idx = timestamp_indices.loc[start_timestamp]
start_idx = max(0, start_idx - self.model.context_size)
start_timestamp = timestamp_indices.index[start_idx]
- cur_ts.df = cur_ts.df[start_timestamp:end_timestamp]
+ cur_ts.df = cur_ts.df.loc[start_timestamp:end_timestamp]
return cur_ts
def _determine_prediction_size(
- self, ts: TSDataset, start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp
+ self, ts: TSDataset, start_timestamp: Union[pd.Timestamp, int], end_timestamp: Union[pd.Timestamp, int]
) -> int:
timestamp_indices = pd.Series(np.arange(len(ts.index)), index=ts.index)
- timestamps = timestamp_indices[start_timestamp:end_timestamp]
+ timestamps = timestamp_indices.loc[start_timestamp:end_timestamp]
return len(timestamps)
def _predict(
self,
ts: TSDataset,
- start_timestamp: pd.Timestamp,
- end_timestamp: pd.Timestamp,
+ start_timestamp: Union[pd.Timestamp, int],
+ end_timestamp: Union[pd.Timestamp, int],
prediction_interval: bool,
quantiles: Sequence[float],
return_components: bool = False,
diff --git a/etna/transforms/decomposition/change_points_based/base.py b/etna/transforms/decomposition/change_points_based/base.py
index 64d95d110..ecb0c02bf 100644
--- a/etna/transforms/decomposition/change_points_based/base.py
+++ b/etna/transforms/decomposition/change_points_based/base.py
@@ -81,7 +81,7 @@ def _fit_per_interval_models(self, series: pd.Series):
if self.intervals is None or self.per_interval_models is None:
raise ValueError("Something went wrong on fit! Check the parameters of the transform.")
for interval in self.intervals:
- tmp_series = series[interval[0] : interval[1]]
+ tmp_series = series.loc[interval[0] : interval[1]]
features = self._get_features(series=tmp_series)
targets = self._get_targets(series=tmp_series)
self.per_interval_models[interval].fit(features=features, target=targets)
@@ -113,7 +113,7 @@ def _predict_per_interval_model(self, series: pd.Series) -> pd.Series:
raise ValueError("Transform is not fitted! Fit the Transform before calling transform method.")
prediction_series = pd.Series(index=series.index, dtype=float)
for interval in self.intervals:
- tmp_series = series[interval[0] : interval[1]]
+ tmp_series = series.loc[interval[0] : interval[1]]
if tmp_series.empty:
continue
features = self._get_features(series=tmp_series)
diff --git a/etna/transforms/decomposition/change_points_based/change_points_models/base.py b/etna/transforms/decomposition/change_points_based/change_points_models/base.py
index 4f7b510c1..767c3aa40 100644
--- a/etna/transforms/decomposition/change_points_based/change_points_models/base.py
+++ b/etna/transforms/decomposition/change_points_based/change_points_models/base.py
@@ -3,13 +3,14 @@
from typing import List
from typing import Tuple
from typing import Type
+from typing import Union
import pandas as pd
from sklearn.base import RegressorMixin
from etna.core import BaseMixin
-TTimestampInterval = Tuple[pd.Timestamp, pd.Timestamp]
+TTimestampInterval = Tuple[Union[pd.Timestamp, int, None], Union[pd.Timestamp, int, None]]
TDetrendModel = Type[RegressorMixin]
@@ -35,10 +36,9 @@ def get_change_points(self, df: pd.DataFrame, in_column: str) -> List[pd.Timesta
pass
@staticmethod
- def _build_intervals(change_points: List[pd.Timestamp]) -> List[TTimestampInterval]:
+ def _build_intervals(change_points: List[Union[pd.Timestamp, int]]) -> List[TTimestampInterval]:
"""Create list of stable intervals from list of change points."""
- change_points.extend([pd.Timestamp.min, pd.Timestamp.max])
- change_points = sorted(change_points)
+ change_points = [None] + sorted(change_points) + [None]
intervals = list(zip(change_points[:-1], change_points[1:]))
return intervals
@@ -48,7 +48,7 @@ def get_change_points_intervals(self, df: pd.DataFrame, in_column: str) -> List[
Parameters
----------
df:
- dataframe indexed with timestamp
+ dataframe indexed with timestamp (datetime or integer)
in_column:
name of column to get change points
diff --git a/etna/transforms/decomposition/change_points_based/detrend.py b/etna/transforms/decomposition/change_points_based/detrend.py
index 830fe0196..318ae9c28 100644
--- a/etna/transforms/decomposition/change_points_based/detrend.py
+++ b/etna/transforms/decomposition/change_points_based/detrend.py
@@ -23,9 +23,14 @@ class _OneSegmentChangePointsTrendTransform(_OneSegmentChangePointsTransform):
@staticmethod
def _get_features(series: pd.Series) -> np.ndarray:
"""Convert ETNA timestamp-index to a list of timestamps to fit regression models."""
- timestamps = series.index
- timestamps = np.array([[ts.timestamp()] for ts in timestamps])
- return timestamps
+ timestamps = series.index.to_series()
+
+ if pd.api.types.is_integer_dtype(timestamps.dtype):
+ timestamps = timestamps.astype("float").to_numpy()
+ else:
+ timestamps = timestamps.apply(lambda ts: ts.timestamp()).to_numpy()
+
+ return timestamps.reshape((-1, 1))
def _apply_transformation(self, df: pd.DataFrame, transformed_series: pd.Series) -> pd.DataFrame:
df.loc[:, self.in_column] -= transformed_series
diff --git a/etna/transforms/decomposition/deseasonal.py b/etna/transforms/decomposition/deseasonal.py
index 142daa8dc..1fef9fabe 100644
--- a/etna/transforms/decomposition/deseasonal.py
+++ b/etna/transforms/decomposition/deseasonal.py
@@ -8,13 +8,15 @@
import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose
+from etna.datasets.utils import determine_freq
+from etna.datasets.utils import determine_num_steps
from etna.distributions import BaseDistribution
from etna.distributions import CategoricalDistribution
-from etna.models.utils import determine_freq
-from etna.models.utils import determine_num_steps
from etna.transforms.base import OneSegmentTransform
from etna.transforms.base import ReversiblePerSegmentWrapper
+_DEFAULT_FREQ = object()
+
class DeseasonalModel(str, Enum):
"""Enum for different types of deseasonality model."""
@@ -47,6 +49,7 @@ def __init__(self, in_column: str, period: int, model: str = DeseasonalModel.add
self.period = period
self.model = DeseasonalModel(model)
self._seasonal: Optional[pd.Series] = None
+ self._freq: Optional[str] = _DEFAULT_FREQ # type: ignore
def _roll_seasonal(self, x: pd.Series) -> np.ndarray:
"""
@@ -64,11 +67,16 @@ def _roll_seasonal(self, x: pd.Series) -> np.ndarray:
"""
if self._seasonal is None:
raise ValueError("Transform is not fitted! Fit the Transform before calling.")
- freq = determine_freq(x.index)
if self._seasonal.index[0] <= x.index[0]:
- shift = -determine_num_steps(self._seasonal.index[0], x.index[0], freq) % self.period
+ shift = (
+ -determine_num_steps(start_timestamp=self._seasonal.index[0], end_timestamp=x.index[0], freq=self._freq)
+ % self.period
+ )
else:
- shift = determine_num_steps(x.index[0], self._seasonal.index[0], freq) % self.period
+ shift = (
+ determine_num_steps(start_timestamp=x.index[0], end_timestamp=self._seasonal.index[0], freq=self._freq)
+ % self.period
+ )
return np.resize(np.roll(self._seasonal, shift=shift), x.shape[0])
def fit(self, df: pd.DataFrame) -> "_OneSegmentDeseasonalityTransform":
@@ -90,11 +98,13 @@ def fit(self, df: pd.DataFrame) -> "_OneSegmentDeseasonalityTransform":
ValueError:
if input column contains NaNs in the middle of the series
"""
+ self._freq = determine_freq(df.index)
+
df = df.loc[df[self.in_column].first_valid_index() : df[self.in_column].last_valid_index()]
if df[self.in_column].isnull().values.any():
raise ValueError("The input column contains NaNs in the middle of the series! Try to use the imputer.")
self._seasonal = seasonal_decompose(
- x=df[self.in_column], model=self.model, filt=None, two_sided=False, extrapolate_trend=0
+ x=df[self.in_column], model=self.model, period=self.period, filt=None, two_sided=False, extrapolate_trend=0
).seasonal[: self.period]
return self
diff --git a/etna/transforms/decomposition/detrend.py b/etna/transforms/decomposition/detrend.py
index 24831d433..5454e6255 100644
--- a/etna/transforms/decomposition/detrend.py
+++ b/etna/transforms/decomposition/detrend.py
@@ -41,12 +41,14 @@ def __init__(self, in_column: str, regressor: RegressorMixin, poly_degree: int =
@staticmethod
def _get_x(df) -> np.ndarray:
- series_len = len(df)
x = df.index.to_series()
- if isinstance(type(x.dtype), pd.Timestamp):
- raise ValueError("Your timestamp column has wrong format. Need np.datetime64 or datetime.datetime")
- x = x.apply(lambda ts: ts.timestamp())
- x = x.to_numpy().reshape(series_len, 1)
+
+ if pd.api.types.is_integer_dtype(x.dtype):
+ x = x.astype("float").to_numpy()
+ else:
+ x = x.apply(lambda ts: ts.timestamp()).to_numpy()
+
+ x = x.reshape(-1, 1)
return x
def fit(self, df: pd.DataFrame) -> "_OneSegmentLinearTrendBaseTransform":
diff --git a/etna/transforms/decomposition/stl.py b/etna/transforms/decomposition/stl.py
index bcbc51926..5bb8cb5d1 100644
--- a/etna/transforms/decomposition/stl.py
+++ b/etna/transforms/decomposition/stl.py
@@ -11,11 +11,15 @@
from statsmodels.tsa.forecasting.stl import STLForecast
from statsmodels.tsa.forecasting.stl import STLForecastResults
+from etna.datasets.utils import determine_freq
+from etna.datasets.utils import determine_num_steps
from etna.distributions import BaseDistribution
from etna.distributions import CategoricalDistribution
from etna.transforms.base import OneSegmentTransform
from etna.transforms.base import ReversiblePerSegmentWrapper
+_DEFAULT_FREQ = object()
+
class _OneSegmentSTLTransform(OneSegmentTransform):
def __init__(
@@ -80,6 +84,8 @@ def __init__(
self.model_kwargs = model_kwargs
self.stl_kwargs = stl_kwargs
self.fit_results: Optional[STLForecastResults] = None
+ self._first_train_timestamp: Union[pd.Timestamp, int, None] = None
+ self._freq: Optional[str] = _DEFAULT_FREQ # type: ignore
def fit(self, df: pd.DataFrame) -> "_OneSegmentSTLTransform":
"""
@@ -92,15 +98,24 @@ def fit(self, df: pd.DataFrame) -> "_OneSegmentSTLTransform":
Returns
-------
- result: _OneSegmentSTLTransform
+ :
instance after processing
"""
df = df.loc[df[self.in_column].first_valid_index() : df[self.in_column].last_valid_index()]
if df[self.in_column].isnull().values.any():
raise ValueError("The input column contains NaNs in the middle of the series! Try to use the imputer.")
+
+ self._first_train_timestamp = df.index.min()
+ self._freq = determine_freq(df.index)
+
+ endog = df[self.in_column]
+ if pd.api.types.is_integer_dtype(df.index):
+ # make index start with zero
+ endog.index = endog.index - self._first_train_timestamp
+
model = STLForecast(
- df[self.in_column],
- self.model,
+ endog=endog,
+ model=self.model,
model_kwargs=self.model_kwargs,
period=self.period,
robust=self.robust,
@@ -109,6 +124,30 @@ def fit(self, df: pd.DataFrame) -> "_OneSegmentSTLTransform":
self.fit_results = model.fit()
return self
+ def _get_season_trend(self, df: pd.DataFrame) -> pd.Series:
+ if self.fit_results is None:
+ raise ValueError("Transform is not fitted! Fit the Transform before calling transform method.")
+
+ start_timestamp = df[self.in_column].first_valid_index()
+ end_timestamp = df[self.in_column].last_valid_index()
+
+ # if all values are NaNs
+ if start_timestamp is None:
+ return pd.Series([], dtype=float)
+
+ start_idx = determine_num_steps(
+ start_timestamp=self._first_train_timestamp, end_timestamp=start_timestamp, freq=self._freq
+ )
+ end_idx = determine_num_steps(
+ start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._freq
+ )
+
+ prediction = self.fit_results.get_prediction(start=start_idx, end=end_idx).predicted_mean.values
+
+ index = df.index[df.index.get_loc(start_timestamp) : df.index.get_loc(end_timestamp) + 1]
+ season_trend = pd.Series(prediction, index=index)
+ return season_trend
+
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Subtract trend and seasonal component.
@@ -120,16 +159,11 @@ def transform(self, df: pd.DataFrame) -> pd.DataFrame:
Returns
-------
- result: pd.DataFrame
+ :
Dataframe with extracted features
"""
result = df
- if self.fit_results is not None:
- season_trend = self.fit_results.get_prediction(
- start=df[self.in_column].first_valid_index(), end=df[self.in_column].last_valid_index()
- ).predicted_mean
- else:
- raise ValueError("Transform is not fitted! Fit the Transform before calling transform method.")
+ season_trend = self._get_season_trend(df=df)
result[self.in_column] -= season_trend
return result
@@ -144,19 +178,13 @@ def inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:
Returns
-------
- result: pd.DataFrame
+ :
Dataframe with extracted features
"""
result = df
- if self.fit_results is None:
- raise ValueError("Transform is not fitted! Fit the Transform before calling inverse_transform method.")
- season_trend = self.fit_results.get_prediction(
- start=df[self.in_column].first_valid_index(), end=df[self.in_column].last_valid_index()
- ).predicted_mean
-
+ season_trend = self._get_season_trend(df=df)
for colum_name in df.columns:
result.loc[:, colum_name] += season_trend
-
return result
diff --git a/etna/transforms/feature_selection/feature_importance.py b/etna/transforms/feature_selection/feature_importance.py
index a56702b80..d1eef3053 100644
--- a/etna/transforms/feature_selection/feature_importance.py
+++ b/etna/transforms/feature_selection/feature_importance.py
@@ -229,13 +229,17 @@ def _fit(self, df: pd.DataFrame) -> "MRMRFeatureSelectionTransform":
instance after fitting
"""
features = self._get_features_to_use(df)
- ts = TSDataset(df=df, freq=pd.infer_freq(df.index))
- relevance_table = self.relevance_table(ts[:, :, "target"], ts[:, :, features], **self.relevance_params)
+ df_target = df.loc[:, pd.IndexSlice[:, "target"]]
+ df_target = df_target.loc[df_target.first_valid_index() :]
+ df_features = df.loc[:, pd.IndexSlice[:, features]]
+ df_features = df_features.loc[df_features.first_valid_index() :]
+ relevance_table = self.relevance_table(df_target, df_features, **self.relevance_params)
+
if not self.relevance_table.greater_is_better:
relevance_table *= -1
self.selected_features = mrmr(
relevance_table=relevance_table,
- regressors=ts[:, :, features],
+ regressors=df_features,
top_k=self.top_k,
fast_redundancy=self.fast_redundancy,
relevance_aggregation_mode=self.relevance_aggregation_mode,
diff --git a/etna/transforms/math/binary_operator.py b/etna/transforms/math/binary_operator.py
index 7a1277b8b..b5feab7f7 100644
--- a/etna/transforms/math/binary_operator.py
+++ b/etna/transforms/math/binary_operator.py
@@ -86,8 +86,7 @@ class BinaryOperationTransform(ReversibleTransform):
>>> df = generate_ar_df(start_time="2020-01-01", periods=30, freq="D", n_segments=1)
>>> df["feature"] = np.full(30, 10)
>>> df["target"] = np.full(30, 1)
- >>> df_ts_format = TSDataset.to_dataset(df)
- >>> ts = TSDataset(df_ts_format, "D")
+ >>> ts = TSDataset(df, "D")
>>> ts["2020-01-01":"2020-01-06", "segment_0", ["feature", "target"]]
segment segment_0
feature feature target
diff --git a/etna/transforms/math/differencing.py b/etna/transforms/math/differencing.py
index 51bb268ce..2622a9a37 100644
--- a/etna/transforms/math/differencing.py
+++ b/etna/transforms/math/differencing.py
@@ -9,11 +9,14 @@
import pandas as pd
from etna.datasets import TSDataset
+from etna.datasets.utils import timestamp_range
from etna.distributions import BaseDistribution
from etna.distributions import IntDistribution
from etna.transforms.base import ReversibleTransform
from etna.transforms.utils import check_new_segments
+_DEFAULT_FREQ = object()
+
class _SingleDifferencingTransform(ReversibleTransform):
"""Calculate a time series differences of order 1.
@@ -73,6 +76,7 @@ def __init__(
self._train_init_dict: Optional[Dict[str, pd.Series]] = None
self._test_init_df: Optional[pd.DataFrame] = None
self.in_column_regressor: Optional[bool] = None
+ self._freq: Optional[str] = _DEFAULT_FREQ # type: ignore
def _get_column_name(self) -> str:
if self.inplace:
@@ -93,6 +97,7 @@ def get_regressors_info(self) -> List[str]:
def fit(self, ts: TSDataset) -> "_SingleDifferencingTransform":
"""Fit the transform."""
self.in_column_regressor = self.in_column in ts.regressors
+ self._freq = ts.freq
super().fit(ts)
return self
@@ -194,12 +199,9 @@ def _reconstruct_test(self, df: pd.DataFrame, columns_to_inverse: Set[str]) -> p
result_df = df.copy()
# check that test is right after the train
- expected_min_test_timestamp = pd.date_range(
- start=self._test_init_df.index.max(), # type: ignore
- periods=2,
- freq=pd.infer_freq(self._train_timestamp),
- closed="right",
- )[0]
+ expected_min_test_timestamp = timestamp_range(
+ start=self._test_init_df.index[-1], periods=2, freq=self._freq # type: ignore
+ )[-1]
if expected_min_test_timestamp != df.index.min():
raise ValueError("Test should go after the train without gaps")
@@ -354,6 +356,7 @@ def __init__(
)
self._fit_segments: Optional[List[str]] = None
self.in_column_regressor: Optional[bool] = None
+ self._freq: Optional[str] = _DEFAULT_FREQ # type: ignore
def _get_column_name(self) -> str:
if self.inplace:
@@ -374,6 +377,7 @@ def get_regressors_info(self) -> List[str]:
def fit(self, ts: TSDataset) -> "DifferencingTransform":
"""Fit the transform."""
self.in_column_regressor = self.in_column in ts.regressors
+ self._freq = ts.freq
super().fit(ts)
return self
@@ -398,6 +402,9 @@ def _fit(self, df: pd.DataFrame) -> "DifferencingTransform":
result_df = df
for transform in self._differencing_transforms:
result_df = transform._fit_transform(result_df)
+ transform._freq = self._freq
+ transform.in_column_regressor = self.in_column_regressor
+
self._fit_segments = df.columns.get_level_values("segment").unique().tolist()
return self
diff --git a/etna/transforms/math/lags.py b/etna/transforms/math/lags.py
index 27f817fad..925ac2dac 100644
--- a/etna/transforms/math/lags.py
+++ b/etna/transforms/math/lags.py
@@ -7,12 +7,19 @@
import pandas as pd
from etna.datasets import TSDataset
-from etna.models.utils import determine_num_steps
+from etna.datasets.utils import determine_num_steps
from etna.transforms.base import IrreversibleTransform
+_DEFAULT_FREQ = object()
+
class LagTransform(IrreversibleTransform):
- """Generates series of lags from given dataframe."""
+ """Generates series of lags from given dataframe.
+
+ Notes
+ -----
+ Types of shifted variables could change due to applying :py:meth:`pandas.DataFrame.shift`.
+ """
def __init__(self, in_column: str, lags: Union[List[int], int], out_column: Optional[str] = None):
"""Create instance of LagTransform.
@@ -89,6 +96,7 @@ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
features = df.loc[:, pd.IndexSlice[:, self.in_column]]
for lag in self.lags:
column_name = self._get_column_name(lag)
+ # this could lead to type changes due to introduction of NaNs
transformed_features = features.shift(lag)
transformed_features.columns = pd.MultiIndex.from_product([segments, [column_name]])
all_transformed_features.append(transformed_features)
@@ -102,7 +110,12 @@ def get_regressors_info(self) -> List[str]:
class ExogShiftTransform(IrreversibleTransform):
- """Shifts exogenous variables from a given dataframe."""
+ """Shifts exogenous variables from a given dataframe.
+
+ Notes
+ -----
+ Types of shifted variables could change due to applying :py:meth:`pandas.DataFrame.shift`.
+ """
def __init__(self, lag: Union[int, Literal["auto"]], horizon: Optional[int] = None):
"""Create instance of ExogShiftTransform.
@@ -126,10 +139,10 @@ def __init__(self, lag: Union[int, Literal["auto"]], horizon: Optional[int] = No
self.horizon: Optional[int] = None
self._auto = False
- self._freq: Optional[str] = None
+ self._freq: Optional[str] = _DEFAULT_FREQ # type: ignore
self._created_regressors: Optional[List[str]] = None
self._exog_shifts: Optional[Dict[str, int]] = None
- self._exog_last_date: Optional[Dict[str, pd.Timestamp]] = None
+ self._exog_last_timestamp: Union[Dict[str, pd.Timestamp], Dict[str, int], None] = None
self._filter_out_columns = {"target"}
if isinstance(lag, int):
@@ -147,9 +160,9 @@ def __init__(self, lag: Union[int, Literal["auto"]], horizon: Optional[int] = No
self.horizon = horizon
self._auto = True
- def _save_exog_last_date(self, df_exog: Optional[pd.DataFrame] = None):
- """Save last available date of each exogenous variable."""
- self._exog_last_date = {}
+ def _save_exog_last_timestamp(self, df_exog: Optional[pd.DataFrame] = None):
+ """Save last available timestamp of each exogenous variable."""
+ exog_last_timestamp = {}
if df_exog is not None:
exog_names = set(df_exog.columns.get_level_values("feature"))
@@ -157,9 +170,11 @@ def _save_exog_last_date(self, df_exog: Optional[pd.DataFrame] = None):
feature = df_exog.loc[:, pd.IndexSlice[:, name]]
na_mask = pd.isna(feature).any(axis=1)
- last_date = feature.index[~na_mask].max()
+ last_timestamp = feature.index[~na_mask].max()
+
+ exog_last_timestamp[name] = last_timestamp
- self._exog_last_date[name] = last_date
+ self._exog_last_timestamp = exog_last_timestamp
def fit(self, ts: TSDataset) -> "ExogShiftTransform":
"""Fit the transform.
@@ -175,7 +190,7 @@ def fit(self, ts: TSDataset) -> "ExogShiftTransform":
The fitted transform instance.
"""
self._freq = ts.freq
- self._save_exog_last_date(df_exog=ts.df_exog)
+ self._save_exog_last_timestamp(df_exog=ts.df_exog)
super().fit(ts=ts)
@@ -211,8 +226,8 @@ def _fit(self, df: pd.DataFrame) -> "ExogShiftTransform":
def _get_feature_names(self, df: pd.DataFrame) -> List[str]:
"""Return the names of exogenous variables."""
feature_names = []
- if self._exog_last_date is not None:
- feature_names = list(self._exog_last_date.keys())
+ if self._exog_last_timestamp is not None:
+ feature_names = list(self._exog_last_timestamp.keys())
df_columns = df.columns.get_level_values("feature")
for name in feature_names:
@@ -226,11 +241,11 @@ def _estimate_shift(self, df: pd.DataFrame, feature_name: str) -> int:
if not self._auto:
return self.lag # type: ignore
- if self._exog_last_date is None or self._freq is None:
+ if self._exog_last_timestamp is None:
raise ValueError("Call `fit()` method before estimating exog shifts!")
last_date = df.index.max()
- last_feature_date = self._exog_last_date[feature_name]
+ last_feature_date = self._exog_last_timestamp[feature_name]
if last_feature_date > last_date:
delta = -determine_num_steps(start_timestamp=last_date, end_timestamp=last_feature_date, freq=self._freq)
@@ -273,7 +288,8 @@ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
feature = df.loc[:, pd.IndexSlice[:, feature_name]]
if shift > 0:
- shifted_feature = feature.shift(shift, freq=self._freq)
+ # this could lead to type changes due to introduction of NaNs
+ shifted_feature = feature.shift(shift)
column_name = f"{feature_name}_shift_{shift}"
shifted_feature.columns = pd.MultiIndex.from_product([segments, [column_name]])
diff --git a/etna/transforms/missing_values/resample.py b/etna/transforms/missing_values/resample.py
index b3d4ff390..c327b37bd 100644
--- a/etna/transforms/missing_values/resample.py
+++ b/etna/transforms/missing_values/resample.py
@@ -44,19 +44,27 @@ def _get_folds(self, df: pd.DataFrame) -> List[int]:
Here the ``in_column`` frequency gap is divided into the folds with the size of dataset frequency gap.
"""
in_column_index = df[self.in_column].dropna().index
- if len(in_column_index) <= 1 or (len(in_column_index) >= 3 and not pd.infer_freq(in_column_index)):
+
+ fail_datetime_timestamp = (
+ not pd.api.types.is_integer_dtype(in_column_index)
+ and len(in_column_index) >= 3
+ and not pd.infer_freq(in_column_index)
+ )
+ if len(in_column_index) <= 1 or fail_datetime_timestamp:
raise ValueError(
- "Can not infer in_column frequency!"
+ "Can not infer in_column frequency! "
"Check that in_column frequency is compatible with dataset frequency."
)
+
in_column_freq = in_column_index[1] - in_column_index[0]
dataset_freq = df.index[1] - df.index[0]
+
n_folds_per_gap = in_column_freq // dataset_freq
n_periods = len(df) // n_folds_per_gap + 2
in_column_start_index = in_column_index[0]
- left_tie_len = len(df[:in_column_start_index]) - 1
- right_tie_len = len(df[in_column_start_index:])
+ left_tie_len = len(df.loc[:in_column_start_index]) - 1
+ right_tie_len = len(df.loc[in_column_start_index:])
folds_for_left_tie = list(range(n_folds_per_gap - left_tie_len, n_folds_per_gap))
folds_for_right_tie = [fold for _ in range(n_periods) for fold in range(n_folds_per_gap)][:right_tie_len]
return folds_for_left_tie + folds_for_right_tie
@@ -111,6 +119,15 @@ def inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:
class ResampleWithDistributionTransform(IrreversiblePerSegmentWrapper):
"""ResampleWithDistributionTransform resamples the given column using the distribution of the other column.
+ This transform expects ``in_column`` to have non-NaN values separated by the same number of timestamps to form a cycle.
+ The cycle starts with a non-NaN value and each position has a number from 0 to cycle size - 1.
+
+ During ``fit'', the fraction of each cycle position in a total sum of values is calculated according to ``distribution_column''.
+ During ``transform`` the NaNs within ``in_column`` are filled using the learned distribution.
+
+ The most common application of this transform is to fill NaNs in ``in_column`` that come from data with a different frequency.
+ For example, a dataset has an hourly frequency, but an exogenous variable has only a daily frequency.
+
Warning
-------
This transform can suffer from look-ahead bias. For transforming data at some timestamp
diff --git a/etna/transforms/outliers/base.py b/etna/transforms/outliers/base.py
index fcbfddc14..0e65573f5 100644
--- a/etna/transforms/outliers/base.py
+++ b/etna/transforms/outliers/base.py
@@ -3,6 +3,7 @@
from typing import Dict
from typing import List
from typing import Optional
+from typing import Union
import numpy as np
import pandas as pd
@@ -37,7 +38,7 @@ def __init__(self, in_column: str):
reason="Attribute `outliers_timestamps` is deprecated and will be removed! Use `segment_outliers` instead.",
version="3.0",
)
- def outliers_timestamps(self) -> Optional[Dict[str, List[pd.Timestamp]]]:
+ def outliers_timestamps(self) -> Union[Dict[str, List[pd.Timestamp]], Dict[str, List[int]], None]:
"""Backward compatibility property."""
if self.segment_outliers is not None:
return {segment: outliers.index.to_list() for segment, outliers in self.segment_outliers.items()}
@@ -48,7 +49,7 @@ def outliers_timestamps(self) -> Optional[Dict[str, List[pd.Timestamp]]]:
reason="Attribute `original_values` is deprecated and will be removed! Use `segment_outliers` instead.",
version="3.0",
)
- def original_values(self) -> Optional[Dict[str, List[pd.Series]]]:
+ def original_values(self) -> Optional[Dict[str, pd.Series]]:
"""Backward compatibility property."""
if self.segment_outliers is not None:
return self.segment_outliers.copy()
@@ -64,6 +65,24 @@ def get_regressors_info(self) -> List[str]:
"""
return []
+ def fit(self, ts: TSDataset) -> "OutliersTransform":
+ """Fit the transform.
+
+ Parameters
+ ----------
+ ts:
+ Dataset to fit the transform on.
+
+ Returns
+ -------
+ :
+ The fitted transform instance.
+ """
+ self.segment_outliers = self.detect_outliers(ts)
+ self._fit_segments = ts.segments
+ super().fit(ts=ts)
+ return self
+
def _fit(self, df: pd.DataFrame) -> "OutliersTransform":
"""
Find outliers using detection method.
@@ -75,13 +94,9 @@ def _fit(self, df: pd.DataFrame) -> "OutliersTransform":
Returns
-------
- result: OutliersTransform
+ result:
instance with saved outliers
"""
- ts = TSDataset(df, freq=pd.infer_freq(df.index))
- self.segment_outliers = self.detect_outliers(ts)
- self._fit_segments = ts.segments
-
return self
def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
diff --git a/etna/transforms/timestamp/date_flags.py b/etna/transforms/timestamp/date_flags.py
index d65c9ff1c..6eabdcf17 100644
--- a/etna/transforms/timestamp/date_flags.py
+++ b/etna/transforms/timestamp/date_flags.py
@@ -8,6 +8,7 @@
import numpy as np
import pandas as pd
+from etna.datasets import TSDataset
from etna.distributions import BaseDistribution
from etna.distributions import CategoricalDistribution
from etna.transforms.base import IrreversibleTransform
@@ -47,6 +48,7 @@ def __init__(
special_days_in_week: Sequence[int] = (),
special_days_in_month: Sequence[int] = (),
out_column: Optional[str] = None,
+ in_column: Optional[str] = None,
):
"""Create instance of DateFlags.
@@ -84,6 +86,13 @@ def __init__(
* if don't set, name will be ``transform.__repr__()``,
repr will be made for transform that creates exactly this column
+ in_column:
+ name of column to work with; if not given, index is used, only datetime index is supported
+
+ Raises
+ ------
+ ValueError:
+ if all features aren't set in transform
"""
if not any(
[
@@ -106,7 +115,13 @@ def __init__(
f"week_number_in_year, month_number_in_year, season_number, year_number, is_weekend should be True or any of "
f"special_days_in_week, special_days_in_month should be not empty."
)
- super().__init__(required_features=["target"])
+
+ if in_column is None:
+ required_features = ["target"]
+ else:
+ required_features = [in_column]
+ super().__init__(required_features=required_features)
+
self.day_number_in_week = day_number_in_week
self.day_number_in_month = day_number_in_month
self.day_number_in_year = day_number_in_year
@@ -121,6 +136,12 @@ def __init__(
self.special_days_in_month = special_days_in_month
self.out_column = out_column
+ self.in_column = in_column
+
+ if self.in_column is None:
+ self.in_column_regressor: Optional[bool] = True
+ else:
+ self.in_column_regressor = None
# create empty init parameters
self._empty_parameters = dict(
@@ -148,6 +169,12 @@ def _get_column_name(self, feature_name: str) -> str:
def get_regressors_info(self) -> List[str]:
"""Return the list with regressors created by the transform."""
+ if self.in_column_regressor is None:
+ raise ValueError("Fit the transform to get the correct regressors info!")
+
+ if not self.in_column_regressor:
+ return []
+
features = [
"day_number_in_week",
"day_number_in_month",
@@ -166,90 +193,121 @@ def get_regressors_info(self) -> List[str]:
]
return output_columns
+ def fit(self, ts: TSDataset) -> "DateFlagsTransform":
+ """Fit the transform."""
+ if self.in_column is None:
+ self.in_column_regressor = True
+ else:
+ self.in_column_regressor = self.in_column in ts.regressors
+ super().fit(ts)
+ return self
+
def _fit(self, df: pd.DataFrame) -> "DateFlagsTransform":
"""Fit model. In this case of DateFlags does nothing."""
return self
- def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
- """Get required features from df.
-
- Parameters
- ----------
- df:
- dataframe for feature extraction, should contain 'timestamp' column
-
- Returns
- -------
- :
- dataframe with extracted features
- """
- features = pd.DataFrame(index=df.index)
- timestamp_series = pd.Series(df.index)
+ def _compute_features(self, timestamps: pd.Series) -> pd.DataFrame:
+ timestamps_no_nans = timestamps.dropna()
+ features = pd.DataFrame(index=timestamps_no_nans.index)
if self.day_number_in_week:
features[self._get_column_name("day_number_in_week")] = self._get_day_number_in_week(
- timestamp_series=timestamp_series
+ timestamp_series=timestamps_no_nans
)
if self.day_number_in_month:
features[self._get_column_name("day_number_in_month")] = self._get_day_number_in_month(
- timestamp_series=timestamp_series
+ timestamp_series=timestamps_no_nans
)
if self.day_number_in_year:
features[self._get_column_name("day_number_in_year")] = self._get_day_number_in_year(
- timestamp_series=timestamp_series
+ timestamp_series=timestamps_no_nans
)
if self.week_number_in_month:
features[self._get_column_name("week_number_in_month")] = self._get_week_number_in_month(
- timestamp_series=timestamp_series
+ timestamp_series=timestamps_no_nans
)
if self.week_number_in_year:
features[self._get_column_name("week_number_in_year")] = self._get_week_number_in_year(
- timestamp_series=timestamp_series
+ timestamp_series=timestamps_no_nans
)
if self.month_number_in_year:
features[self._get_column_name("month_number_in_year")] = self._get_month_number_in_year(
- timestamp_series=timestamp_series
+ timestamp_series=timestamps_no_nans
)
if self.season_number:
features[self._get_column_name("season_number")] = self._get_season_number(
- timestamp_series=timestamp_series
+ timestamp_series=timestamps_no_nans
)
if self.year_number:
- features[self._get_column_name("year_number")] = self._get_year(timestamp_series=timestamp_series)
+ features[self._get_column_name("year_number")] = self._get_year(timestamp_series=timestamps_no_nans)
if self.is_weekend:
- features[self._get_column_name("is_weekend")] = self._get_weekends(timestamp_series=timestamp_series)
+ features[self._get_column_name("is_weekend")] = self._get_weekends(timestamp_series=timestamps_no_nans)
if self.special_days_in_week:
features[self._get_column_name("special_days_in_week")] = self._get_special_day_in_week(
- special_days=self.special_days_in_week, timestamp_series=timestamp_series
+ special_days=self.special_days_in_week, timestamp_series=timestamps_no_nans
)
if self.special_days_in_month:
features[self._get_column_name("special_days_in_month")] = self._get_special_day_in_month(
- special_days=self.special_days_in_month, timestamp_series=timestamp_series
+ special_days=self.special_days_in_month, timestamp_series=timestamps_no_nans
)
for feature in features.columns:
features[feature] = features[feature].astype("category")
- dataframes = []
- for seg in df.columns.get_level_values("segment").unique():
- tmp = df[seg].join(features)
- _idx = tmp.columns.to_frame()
- _idx.insert(0, "segment", seg)
- tmp.columns = pd.MultiIndex.from_frame(_idx)
- dataframes.append(tmp)
+ # add NaNs in features
+ features = features.reindex(timestamps.index)
+
+ return features
+
+ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
+ """Get required features from df.
+
+ Parameters
+ ----------
+ df:
+ dataframe for feature extraction, should contain 'timestamp' column
+
+ Returns
+ -------
+ :
+ dataframe with extracted features
+ """
+ if self.in_column is None:
+ if pd.api.types.is_integer_dtype(df.index.dtype):
+ raise ValueError("Transform can't work with integer index, parameter in_column should be set!")
+
+ timestamps = pd.Series(df.index)
+ features = self._compute_features(timestamps=timestamps)
+ features.index = df.index
+
+ dataframes = []
+ for seg in df.columns.get_level_values("segment").unique():
+ tmp = df[seg].join(features)
+ _idx = tmp.columns.to_frame()
+ _idx.insert(0, "segment", seg)
+ tmp.columns = pd.MultiIndex.from_frame(_idx)
+ dataframes.append(tmp)
+ result = pd.concat(dataframes, axis=1).sort_index(axis=1)
+ result.columns.names = ["segment", "feature"]
+
+ else:
+ flat_df = TSDataset.to_flatten(df=df, features=[self.in_column])
+ features = self._compute_features(timestamps=flat_df[self.in_column])
+ features["timestamp"] = flat_df["timestamp"]
+ features["segment"] = flat_df["segment"]
+ wide_df = TSDataset.to_dataset(features)
+ result = pd.concat([df, wide_df], axis=1).sort_index(axis=1)
- result = pd.concat(dataframes, axis=1).sort_index(axis=1)
- result.columns.names = ["segment", "feature"]
return result
@staticmethod
diff --git a/etna/transforms/timestamp/fourier.py b/etna/transforms/timestamp/fourier.py
index db3afb2d0..981e2095c 100644
--- a/etna/transforms/timestamp/fourier.py
+++ b/etna/transforms/timestamp/fourier.py
@@ -3,29 +3,56 @@
from typing import List
from typing import Optional
from typing import Sequence
+from typing import Union
import numpy as np
import pandas as pd
+from etna.datasets import TSDataset
+from etna.datasets.utils import determine_freq
+from etna.datasets.utils import determine_num_steps
from etna.distributions import BaseDistribution
from etna.distributions import IntDistribution
from etna.transforms.base import IrreversibleTransform
+_DEFAULT_FREQ = object()
+
class FourierTransform(IrreversibleTransform):
"""Adds fourier features to the dataset.
+ Transform can work with two types of timestamp data: numeric and datetime.
+
+ Transform can accept timestamp data in two forms:
+
+ - As index. In this case the dataset index is used to compute features.
+ The features will be the same for each segment.
+
+ - As external column. In this case for each segment its ``in_column`` will be used to compute features.
+ It is expected that for each segment we have the same type of timestamp data (datetime or numeric)
+ and for datetime type only one frequency is used.
+
+ If we are working with external column, there is a difference in handling numeric and datetime data:
+
+ - Numeric data can have missing values at any place.
+
+ - Datetime data could have missing values only at the beginning of each segment.
+
Notes
-----
- To understand how transform works we recommend:
- `Fourier series `_.
+ To understand how transform works we recommend reading:
+ `Fourier series `_.
+
+ If we already have a numeric data then for a mode $m$ with a period $p$ we have:
- * Parameter ``period`` is responsible for the seasonality we want to capture.
- * Parameters ``order`` and ``mods`` define which harmonics will be used.
+ .. math::
+ & k = \\left \\lfloor \\frac{m}{2} \\right \\rfloor
+ \\\\
+ & f_{m, i} = \\sin \\left( \\frac{2 \\pi k i}{p} + \\frac{\\pi}{2} (m \\mod 2) \\right)
- Parameter ``order`` is a more user-friendly version of ``mods``.
- For example, ``order=2`` can be represented as ``mods=[1, 2, 3, 4]`` if ``period`` > 4 and
- as ``mods=[1, 2, 3]`` if 3 <= ``period`` <= 4.
+ If we have datetime data, then it first should be transformed into numeric.
+ During fitting the transform saves frequency and some datetime timestamp as a reference point.
+ During transformation it uses reference point to compute number of frequency units between reference point and each timestamp.
"""
def __init__(
@@ -34,6 +61,7 @@ def __init__(
order: Optional[int] = None,
mods: Optional[Sequence[int]] = None,
out_column: Optional[str] = None,
+ in_column: Optional[str] = None,
):
"""Create instance of FourierTransform.
@@ -49,8 +77,8 @@ def __init__(
``order`` should be >= 1 and <= ceil(period/2))
mods:
alternative and precise way of defining which harmonics will be used,
- for example ``mods=[1, 3, 4]`` means that sin of the first order
- and sin and cos of the second order will be used;
+ for example, ``order=2`` can be represented as ``mods=[1, 2, 3, 4]`` if ``period`` > 4 and
+ as ``mods=[1, 2, 3]`` if 3 <= ``period`` <= 4.
``mods`` should be >= 1 and < period
out_column:
@@ -60,6 +88,14 @@ def __init__(
* if don't set, name will be ``transform.__repr__()``,
repr will be made for transform that creates exactly this column
+ in_column:
+ name of column to work with:
+
+ * if ``in_column`` is ``None`` (default) both datetime and integer timestamps are supported;
+
+ * if ``in_column`` isn't ``None`` datetime and numeric columns are supported,
+ but for datetime values only regular timestamps with some frequency are supported
+
Raises
------
ValueError:
@@ -91,7 +127,21 @@ def __init__(
raise ValueError("There should be exactly one option set: order or mods")
self.out_column = out_column
- super().__init__(required_features=["target"])
+ self.in_column = in_column
+
+ self._reference_timestamp: Union[pd.Timestamp, int, None] = None
+ self._freq: Optional[str] = _DEFAULT_FREQ # type: ignore
+
+ if self.in_column is None:
+ self.in_column_regressor: Optional[bool] = True
+ else:
+ self.in_column_regressor = None
+
+ if in_column is None:
+ required_features = ["target"]
+ else:
+ required_features = [in_column]
+ super().__init__(required_features=required_features)
def _get_column_name(self, mod: int) -> str:
if self.out_column is None:
@@ -101,9 +151,78 @@ def _get_column_name(self, mod: int) -> str:
def get_regressors_info(self) -> List[str]:
"""Return the list with regressors created by the transform."""
+ if self.in_column_regressor is None:
+ raise ValueError("Fit the transform to get the correct regressors info!")
+
+ if not self.in_column_regressor:
+ return []
+
output_columns = [self._get_column_name(mod=mod) for mod in self._mods]
return output_columns
+ def fit(self, ts: TSDataset) -> "FourierTransform":
+ """Fit the transform."""
+ if self.in_column is None:
+ self._freq = ts.freq
+ self.in_column_regressor = True
+ else:
+ self.in_column_regressor = self.in_column in ts.regressors
+ super().fit(ts)
+ return self
+
+ def _validate_external_timestamps(self, df: pd.DataFrame):
+ df = df.droplevel("feature", axis=1)
+
+ # here we are assuming that every segment has the same timestamp dtype
+ timestamp_dtype = df.dtypes.iloc[0]
+ if not pd.api.types.is_datetime64_dtype(timestamp_dtype):
+ return
+
+ segments = df.columns.unique()
+ freq_values = set()
+ for segment in segments:
+ timestamps = df[segment]
+ timestamps = timestamps.loc[timestamps.first_valid_index() :]
+ if len(timestamps) >= 3:
+ cur_freq = pd.infer_freq(timestamps)
+ if cur_freq is None:
+ raise ValueError(
+ f"Invalid in_column values! Datetime values should be regular timestamps with some frequency. "
+ f"This doesn't hold for segment {segment}"
+ )
+ freq_values.add(cur_freq)
+
+ if len(freq_values) > 1:
+ raise ValueError(
+ f"Invalid in_column values! Datetime values should have the same frequency for every segment. "
+ f"Discovered frequencies: {freq_values}"
+ )
+
+ def _infer_external_freq(self, df: pd.DataFrame) -> Optional[str]:
+ df = df.droplevel("feature", axis=1)
+
+ # here we are assuming that every segment has the same timestamp dtype
+ timestamp_dtype = df.dtypes.iloc[0]
+ if not pd.api.types.is_datetime64_dtype(timestamp_dtype):
+ return None
+
+ sample_segment = df.columns[0]
+ sample_timestamps = df[sample_segment]
+ sample_timestamps = sample_timestamps.loc[sample_timestamps.first_valid_index() :]
+ result = determine_freq(sample_timestamps)
+ return result
+
+ def _infer_external_reference_timestamp(self, df: pd.DataFrame) -> Union[pd.Timestamp, int]:
+ # here we are assuming that every segment has the same timestamp dtype
+ timestamp_dtype = df.dtypes.iloc[0]
+ if not pd.api.types.is_datetime64_dtype(timestamp_dtype):
+ return 0
+
+ sample_segment = df.columns[0]
+ sample_timestamps = df[sample_segment]
+ reference_timestamp = sample_timestamps.loc[sample_timestamps.first_valid_index()]
+ return reference_timestamp
+
def _fit(self, df: pd.DataFrame) -> "FourierTransform":
"""Fit method does nothing and is kept for compatibility.
@@ -114,12 +233,18 @@ def _fit(self, df: pd.DataFrame) -> "FourierTransform":
Returns
-------
- result: FourierTransform
+ result:
"""
+ if self.in_column is None:
+ self._reference_timestamp = df.index[0]
+ else:
+ self._validate_external_timestamps(df)
+ self._freq = self._infer_external_freq(df)
+ self._reference_timestamp = self._infer_external_reference_timestamp(df)
return self
@staticmethod
- def _construct_answer(df: pd.DataFrame, features: pd.DataFrame) -> pd.DataFrame:
+ def _construct_answer_for_index(df: pd.DataFrame, features: pd.DataFrame) -> pd.DataFrame:
dataframes = []
for seg in df.columns.get_level_values("segment").unique():
tmp = df[seg].join(features)
@@ -132,6 +257,32 @@ def _construct_answer(df: pd.DataFrame, features: pd.DataFrame) -> pd.DataFrame:
result.columns.names = ["segment", "feature"]
return result
+ def _compute_features(self, timestamps: pd.Series) -> pd.DataFrame:
+ features = pd.DataFrame(index=timestamps.index)
+ elapsed = timestamps / self.period
+
+ for mod in self._mods:
+ order = (mod + 1) // 2
+ is_cos = mod % 2 == 0
+
+ features[self._get_column_name(mod)] = np.sin(2 * np.pi * order * elapsed + np.pi / 2 * is_cos)
+
+ return features
+
+ def _convert_regular_timestamps_datetime_to_numeric(
+ self, timestamps: pd.Series, reference_timestamp: pd.Timestamp, freq: Optional[str]
+ ) -> pd.Series:
+ # we should always align timestamps to some fixed point
+ end_timestamp = timestamps.iloc[-1]
+ if end_timestamp >= reference_timestamp:
+ end_idx = determine_num_steps(start_timestamp=reference_timestamp, end_timestamp=end_timestamp, freq=freq)
+ else:
+ end_idx = -determine_num_steps(start_timestamp=end_timestamp, end_timestamp=reference_timestamp, freq=freq)
+
+ numeric_timestamp = pd.Series(np.arange(end_idx - len(timestamps) + 1, end_idx + 1))
+
+ return numeric_timestamp
+
def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""Add harmonics to the dataset.
@@ -142,19 +293,51 @@ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
Returns
-------
- result: pd.Dataframe
+ result:
transformed dataframe
"""
- features = pd.DataFrame(index=df.index)
- elapsed = np.arange(features.shape[0]) / self.period
-
- for mod in self._mods:
- order = (mod + 1) // 2
- is_cos = mod % 2 == 0
-
- features[self._get_column_name(mod)] = np.sin(2 * np.pi * order * elapsed + np.pi / 2 * is_cos)
-
- return self._construct_answer(df, features)
+ if self._freq is _DEFAULT_FREQ:
+ raise ValueError("The transform isn't fitted!")
+
+ if self.in_column is None:
+ if pd.api.types.is_integer_dtype(df.index.dtype):
+ timestamps = df.index.to_series()
+ else:
+ timestamps = self._convert_regular_timestamps_datetime_to_numeric(
+ timestamps=df.index.to_series(), reference_timestamp=self._reference_timestamp, freq=self._freq
+ )
+ features = self._compute_features(timestamps=timestamps)
+ features.index = df.index
+ result = self._construct_answer_for_index(df=df, features=features)
+ else:
+ # here we are assuming that every segment has the same timestamp dtype
+ timestamp_dtype = df.dtypes.iloc[0]
+ if pd.api.types.is_numeric_dtype(timestamp_dtype):
+ flat_df = TSDataset.to_flatten(df=df)
+ timestamps = flat_df[self.in_column]
+ else:
+ self._validate_external_timestamps(df=df)
+ segments = df.columns.get_level_values("segment").unique()
+ int_values = []
+ for segment in segments:
+ segment_timestamps = df[segment][self.in_column]
+ int_segment = self._convert_regular_timestamps_datetime_to_numeric(
+ timestamps=segment_timestamps,
+ reference_timestamp=self._reference_timestamp,
+ freq=self._freq,
+ )
+ int_values.append(int_segment)
+
+ df_int = pd.DataFrame(np.array(int_values).T, index=df.index, columns=df.columns)
+ flat_df = TSDataset.to_flatten(df=df_int)
+ timestamps = flat_df[self.in_column]
+
+ features = self._compute_features(timestamps=timestamps)
+ features["timestamp"] = flat_df["timestamp"]
+ features["segment"] = flat_df["segment"]
+ wide_df = TSDataset.to_dataset(features)
+ result = pd.concat([df, wide_df], axis=1).sort_index(axis=1)
+ return result
def params_to_tune(self) -> Dict[str, BaseDistribution]:
"""Get default grid for tuning hyperparameters.
diff --git a/etna/transforms/timestamp/holiday.py b/etna/transforms/timestamp/holiday.py
index 453fb756f..91b99a278 100644
--- a/etna/transforms/timestamp/holiday.py
+++ b/etna/transforms/timestamp/holiday.py
@@ -3,7 +3,6 @@
from typing import Optional
import holidays
-import numpy as np
import pandas as pd
from pandas.tseries.offsets import MonthBegin
from pandas.tseries.offsets import MonthEnd
@@ -17,7 +16,10 @@
from etna.datasets import TSDataset
from etna.transforms.base import IrreversibleTransform
+_DEFAULT_FREQ = object()
+
+# TODO: it shouldn't be called on freq=None, we should discuss this
def bigger_than_day(freq: Optional[str]):
"""Compare frequency with day."""
dt = "2000-01-01"
@@ -26,6 +28,7 @@ def bigger_than_day(freq: Optional[str]):
return dates_freq[-1] > dates_day[-1]
+# TODO: it shouldn't be called on freq=None, we should discuss this
def define_period(offset: pd.tseries.offsets.BaseOffset, dt: pd.Timestamp, freq: Optional[str]):
"""Define start_date and end_date of period using dataset frequency."""
if isinstance(offset, Week) and offset.weekday == 6:
@@ -67,6 +70,7 @@ def _missing_(cls, value):
)
+# TODO: discuss conceptual problems with
class HolidayTransform(IrreversibleTransform):
"""
HolidayTransform generates series that indicates holidays in given dataset.
@@ -82,7 +86,13 @@ class HolidayTransform(IrreversibleTransform):
_no_holiday_name: str = "NO_HOLIDAY"
- def __init__(self, iso_code: str = "RUS", mode: str = "binary", out_column: Optional[str] = None):
+ def __init__(
+ self,
+ iso_code: str = "RUS",
+ mode: str = "binary",
+ out_column: Optional[str] = None,
+ in_column: Optional[str] = None,
+ ):
"""
Create instance of HolidayTransform.
@@ -95,14 +105,27 @@ def __init__(self, iso_code: str = "RUS", mode: str = "binary", out_column: Opti
`days_count` to determine the proportion of holidays in a given period of time.
out_column:
name of added column. Use ``self.__repr__()`` if not given.
+ in_column:
+ name of column to work with; if not given, index is used, only datetime index is supported
"""
- super().__init__(required_features=["target"])
+ if in_column is None:
+ required_features = ["target"]
+ else:
+ required_features = [in_column]
+ super().__init__(required_features=required_features)
+
self.iso_code = iso_code
self.mode = mode
self._mode = HolidayTransformMode(mode)
+ self._freq: Optional[str] = _DEFAULT_FREQ # type: ignore
self.holidays = holidays.country_holidays(iso_code)
self.out_column = out_column
- self.freq: Optional[str] = None
+ self.in_column = in_column
+
+ if self.in_column is None:
+ self.in_column_regressor: Optional[bool] = True
+ else:
+ self.in_column_regressor = None
def _get_column_name(self) -> str:
if self.out_column:
@@ -110,12 +133,12 @@ def _get_column_name(self) -> str:
else:
return self.__repr__()
- def _fit(self, df: pd.DataFrame) -> "HolidayTransform":
+ def fit(self, ts: TSDataset) -> "HolidayTransform":
"""Fit the transform.
Parameters
----------
- df:
+ ts:
Dataset to fit the transform on.
Returns
@@ -123,14 +146,20 @@ def _fit(self, df: pd.DataFrame) -> "HolidayTransform":
:
The fitted transform instance.
"""
+ if self.in_column is None:
+ self.in_column_regressor = True
+ else:
+ self.in_column_regressor = self.in_column in ts.regressors
+ self._freq = ts.freq
+ super().fit(ts)
return self
- def fit(self, ts: TSDataset):
+ def _fit(self, df: pd.DataFrame) -> "HolidayTransform":
"""Fit the transform.
Parameters
----------
- ts:
+ df:
Dataset to fit the transform on.
Returns
@@ -138,17 +167,49 @@ def fit(self, ts: TSDataset):
:
The fitted transform instance.
"""
- super().fit(ts=ts)
- self.freq = ts.freq
return self
+ def _compute_feature(self, timestamps: pd.Series) -> pd.Series:
+ if bigger_than_day(self._freq) and self._mode is not HolidayTransformMode.days_count:
+ raise ValueError("For binary and category modes frequency of data should be no more than daily.")
+
+ if self._mode is HolidayTransformMode.days_count:
+ date_offset = pd.tseries.frequencies.to_offset(self._freq)
+ values = []
+ for dt in timestamps:
+ if dt is pd.NaT:
+ values.append(pd.NA)
+ else:
+ start_date, end_date = define_period(date_offset, pd.Timestamp(dt), self._freq)
+ date_range = pd.date_range(start=start_date, end=end_date, freq="D")
+ count_holidays = sum(1 for d in date_range if d in self.holidays)
+ holidays_freq = count_holidays / date_range.size
+ values.append(holidays_freq)
+ result = pd.Series(values)
+ elif self._mode is HolidayTransformMode.category:
+ values = []
+ for t in timestamps:
+ if t is pd.NaT:
+ values.append(pd.NA)
+ elif t in self.holidays:
+ values.append(self.holidays[t])
+ else:
+ values.append(self._no_holiday_name)
+ result = pd.Series(values)
+ elif self._mode is HolidayTransformMode.binary:
+ result = pd.Series([int(x in self.holidays) if x is not pd.NaT else pd.NA for x in timestamps])
+ else:
+ assert_never(self._mode)
+
+ return result
+
def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Transform data.
Parameters
----------
- df: pd.DataFrame
+ df:
value series with index column in timestamp format
Returns
@@ -158,46 +219,39 @@ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
Raises
------
+ ValueError:
+ if transform isn't fitted
ValueError:
if the frequency is greater than daily and this is a ``binary`` or ``categorical`` mode
ValueError:
if the frequency is not weekly, monthly, quarterly or yearly and this is ``days_count`` mode
"""
- if self.freq is None:
+ if self._freq is _DEFAULT_FREQ:
raise ValueError("Transform is not fitted")
- if bigger_than_day(self.freq) and self._mode is not HolidayTransformMode.days_count:
- raise ValueError("For binary and category modes frequency of data should be no more than daily.")
- cols = df.columns.get_level_values("segment").unique()
out_column = self._get_column_name()
+ if self.in_column is None:
+ if pd.api.types.is_integer_dtype(df.index.dtype):
+ raise ValueError("Transform can't work with integer index, parameter in_column should be set!")
- if self._mode is HolidayTransformMode.days_count:
- date_offset = pd.tseries.frequencies.to_offset(self.freq)
- encoded_matrix = np.empty(0)
- for dt in df.index:
- start_date, end_date = define_period(date_offset, pd.Timestamp(dt), self.freq)
- date_range = pd.date_range(start=start_date, end=end_date, freq="D")
- count_holidays = sum(1 for d in date_range if d in self.holidays)
- holidays_freq = count_holidays / date_range.size
- encoded_matrix = np.append(encoded_matrix, holidays_freq)
- elif self._mode is HolidayTransformMode.category:
- encoded_matrix = np.array(
- [self.holidays[x] if x in self.holidays else self._no_holiday_name for x in df.index]
+ feature = self._compute_feature(timestamps=df.index).values
+ cols = df.columns.get_level_values("segment").unique()
+ encoded_matrix = feature.reshape(-1, 1).repeat(len(cols), axis=1)
+ wide_df = pd.DataFrame(
+ encoded_matrix,
+ columns=pd.MultiIndex.from_product([cols, [out_column]], names=("segment", "feature")),
+ index=df.index,
)
- elif self._mode is HolidayTransformMode.binary:
- encoded_matrix = np.array([int(x in self.holidays) for x in df.index])
else:
- assert_never(self._mode)
- encoded_matrix = encoded_matrix.reshape(-1, 1).repeat(len(cols), axis=1)
- encoded_df = pd.DataFrame(
- encoded_matrix,
- columns=pd.MultiIndex.from_product([cols, [out_column]], names=("segment", "feature")),
- index=df.index,
- )
- if self._mode is not HolidayTransformMode.days_count:
- encoded_df = encoded_df.astype("category")
- df = df.join(encoded_df)
- df = df.sort_index(axis=1)
+ features = TSDataset.to_flatten(df=df, features=[self.in_column])
+ features[out_column] = self._compute_feature(timestamps=features[self.in_column])
+ features.drop(columns=[self.in_column], inplace=True)
+ wide_df = TSDataset.to_dataset(features)
+
+ if self._mode is HolidayTransformMode.binary or self._mode is HolidayTransformMode.category:
+ wide_df = wide_df.astype("category")
+
+ df = pd.concat([df, wide_df], axis=1).sort_index(axis=1)
return df
def get_regressors_info(self) -> List[str]:
@@ -207,4 +261,10 @@ def get_regressors_info(self) -> List[str]:
:
List with regressors created by the transform.
"""
+ if self.in_column_regressor is None:
+ raise ValueError("Fit the transform to get the correct regressors info!")
+
+ if not self.in_column_regressor:
+ return []
+
return [self._get_column_name()]
diff --git a/etna/transforms/timestamp/special_days.py b/etna/transforms/timestamp/special_days.py
index 1b7c51dfa..ff9d5ca0f 100644
--- a/etna/transforms/timestamp/special_days.py
+++ b/etna/transforms/timestamp/special_days.py
@@ -7,6 +7,7 @@
import pandas as pd
+from etna.datasets import TSDataset
from etna.distributions import BaseDistribution
from etna.distributions import CategoricalDistribution
from etna.transforms.base import IrreversiblePerSegmentWrapper
@@ -31,7 +32,9 @@ class _OneSegmentSpecialDaysTransform(OneSegmentTransform):
`Time Series of Price Anomaly Detection `_
"""
- def __init__(self, find_special_weekday: bool = True, find_special_month_day: bool = True):
+ def __init__(
+ self, find_special_weekday: bool = True, find_special_month_day: bool = True, in_column: Optional[str] = None
+ ):
"""
Create instance of _OneSegmentSpecialDaysTransform.
@@ -41,6 +44,8 @@ def __init__(self, find_special_weekday: bool = True, find_special_month_day: bo
flag, if True, find special weekdays in transform
find_special_month_day:
flag, if True, find special monthdays in transform
+ in_column:
+ name of column to work with; if not given, index is used, only datetime index is supported
Raises
------
@@ -59,6 +64,8 @@ def __init__(self, find_special_weekday: bool = True, find_special_month_day: bo
self.anomaly_week_days: Optional[Tuple[int]] = None
self.anomaly_month_days: Optional[Tuple[int]] = None
+ self.in_column = in_column
+
self.res_type: Dict[str, Any]
if self.find_special_weekday and find_special_month_day:
self.res_type = {"df_sample": (0, 0), "columns": ["anomaly_weekdays", "anomaly_monthdays"]}
@@ -78,9 +85,17 @@ def fit(self, df: pd.DataFrame) -> "_OneSegmentSpecialDaysTransform":
df: pd.DataFrame
value series with index column in timestamp format
"""
- common_df = df[["target"]].reset_index()
+ if self.in_column is None:
+ if pd.api.types.is_integer_dtype(df.index.dtype):
+ raise ValueError("Transform can't work with integer index, parameter in_column should be set!")
+
+ common_df = df[["target"]].reset_index()
+ else:
+ common_df = df[[self.in_column, "target"]]
common_df.columns = ["datetime", "value"]
+ common_df = common_df.dropna()
+
if self.find_special_weekday:
self.anomaly_week_days = self._find_anomaly_day_in_week(common_df)
@@ -103,23 +118,34 @@ def transform(self, df: pd.DataFrame) -> pd.DataFrame:
pd.DataFrame with 'anomaly_weekday', 'anomaly_monthday' or both of them columns no-timestamp indexed that
contains 1 at i-th position if i-th day is a special day
"""
- common_df = df[["target"]].reset_index()
+ if self.in_column is None:
+ common_df = df[["target"]].reset_index()
+ else:
+ common_df = df[[self.in_column, "target"]].reset_index(drop=True)
common_df.columns = ["datetime", "value"]
+ common_df_no_nans = common_df.dropna()
- to_add = pd.DataFrame([self.res_type["df_sample"]] * len(df), columns=self.res_type["columns"])
+ to_add = pd.DataFrame(
+ [self.res_type["df_sample"]] * len(common_df_no_nans),
+ columns=self.res_type["columns"],
+ index=common_df_no_nans.index,
+ )
if self.find_special_weekday:
if self.anomaly_week_days is None:
raise ValueError("Transform is not fitted! Fit the Transform before calling transform method.")
- to_add["anomaly_weekdays"] += self._marked_special_week_day(common_df, self.anomaly_week_days)
+ to_add["anomaly_weekdays"] += self._marked_special_week_day(common_df_no_nans, self.anomaly_week_days)
to_add["anomaly_weekdays"] = to_add["anomaly_weekdays"].astype("category")
if self.find_special_month_day:
if self.anomaly_month_days is None:
raise ValueError("Transform is not fitted! Fit the Transform before calling transform method.")
- to_add["anomaly_monthdays"] += self._marked_special_month_day(common_df, self.anomaly_month_days)
+ to_add["anomaly_monthdays"] += self._marked_special_month_day(common_df_no_nans, self.anomaly_month_days)
to_add["anomaly_monthdays"] = to_add["anomaly_monthdays"].astype("category")
+ # add NaNs in features
+ to_add = to_add.reindex(common_df.index)
+
to_add.index = df.index
to_return = pd.concat([df, to_add], axis=1)
to_return.columns.names = df.columns.names
@@ -185,7 +211,9 @@ class SpecialDaysTransform(IrreversiblePerSegmentWrapper):
it uses information from the whole train part.
"""
- def __init__(self, find_special_weekday: bool = True, find_special_month_day: bool = True):
+ def __init__(
+ self, find_special_weekday: bool = True, find_special_month_day: bool = True, in_column: Optional[str] = None
+ ):
"""
Create instance of SpecialDaysTransform.
@@ -195,6 +223,8 @@ def __init__(self, find_special_weekday: bool = True, find_special_month_day: bo
flag, if True, find special weekdays in transform
find_special_month_day:
flag, if True, find special monthdays in transform
+ in_column:
+ name of column to work with; if not given, index is used, only datetime index is supported
Raises
------
@@ -203,13 +233,43 @@ def __init__(self, find_special_weekday: bool = True, find_special_month_day: bo
"""
self.find_special_weekday = find_special_weekday
self.find_special_month_day = find_special_month_day
+ self.in_column = in_column
+
+ if self.in_column is None:
+ self.in_column_regressor: Optional[bool] = True
+ else:
+ self.in_column_regressor = None
+
+ if in_column is None:
+ required_features = ["target"]
+ else:
+ required_features = [in_column, "target"]
super().__init__(
- transform=_OneSegmentSpecialDaysTransform(self.find_special_weekday, self.find_special_month_day),
- required_features=["target"],
+ transform=_OneSegmentSpecialDaysTransform(
+ find_special_weekday=self.find_special_weekday,
+ find_special_month_day=self.find_special_month_day,
+ in_column=self.in_column,
+ ),
+ required_features=required_features,
)
+ def fit(self, ts: TSDataset) -> "SpecialDaysTransform":
+ """Fit the transform."""
+ if self.in_column is None:
+ self.in_column_regressor = True
+ else:
+ self.in_column_regressor = self.in_column in ts.regressors
+ super().fit(ts)
+ return self
+
def get_regressors_info(self) -> List[str]:
"""Return the list with regressors created by the transform."""
+ if self.in_column_regressor is None:
+ raise ValueError("Fit the transform to get the correct regressors info!")
+
+ if not self.in_column_regressor:
+ return []
+
output_columns = []
if self.find_special_weekday:
output_columns.append("anomaly_weekdays")
diff --git a/etna/transforms/timestamp/time_flags.py b/etna/transforms/timestamp/time_flags.py
index c31c73fa0..5e5fa4267 100644
--- a/etna/transforms/timestamp/time_flags.py
+++ b/etna/transforms/timestamp/time_flags.py
@@ -6,6 +6,7 @@
import numpy as np
import pandas as pd
+from etna.datasets import TSDataset
from etna.distributions import BaseDistribution
from etna.distributions import CategoricalDistribution
from etna.transforms.base import IrreversibleTransform
@@ -23,6 +24,7 @@ def __init__(
half_day_number: bool = False,
one_third_day_number: bool = False,
out_column: Optional[str] = None,
+ in_column: Optional[str] = None,
):
"""Initialise class attributes.
@@ -52,9 +54,13 @@ def __init__(
* if don't set, name will be ``transform.__repr__()``,
repr will be made for transform that creates exactly this column
+ in_column:
+ name of column to work with; if not given, index is used, only datetime index is supported
+
Raises
------
- ValueError: if feature has invalid initial params
+ ValueError:
+ if all features aren't set in transform
"""
if not any(
[
@@ -71,7 +77,13 @@ def __init__(
f"at least one of minute_in_hour_number, fifteen_minutes_in_hour_number, hour_number, "
f"half_hour_number, half_day_number, one_third_day_number should be True."
)
- super().__init__(required_features=["target"])
+
+ if in_column is None:
+ required_features = ["target"]
+ else:
+ required_features = [in_column]
+ super().__init__(required_features=required_features)
+
self.date_column_name = None
self.minute_in_hour_number: bool = minute_in_hour_number
self.fifteen_minutes_in_hour_number: bool = fifteen_minutes_in_hour_number
@@ -81,6 +93,12 @@ def __init__(
self.one_third_day_number: bool = one_third_day_number
self.out_column = out_column
+ self.in_column = in_column
+
+ if self.in_column is None:
+ self.in_column_regressor: Optional[bool] = True
+ else:
+ self.in_column_regressor = None
# create empty init parameters
self._empty_parameters = dict(
@@ -103,6 +121,12 @@ def _get_column_name(self, feature_name: str) -> str:
def get_regressors_info(self) -> List[str]:
"""Return the list with regressors created by the transform."""
+ if self.in_column_regressor is None:
+ raise ValueError("Fit the transform to get the correct regressors info!")
+
+ if not self.in_column_regressor:
+ return []
+
features = [
"minute_in_hour_number",
"fifteen_minutes_in_hour_number",
@@ -116,66 +140,97 @@ def get_regressors_info(self) -> List[str]:
]
return output_columns
+ def fit(self, ts: TSDataset) -> "TimeFlagsTransform":
+ """Fit the transform."""
+ if self.in_column is None:
+ self.in_column_regressor = True
+ else:
+ self.in_column_regressor = self.in_column in ts.regressors
+ super().fit(ts)
+ return self
+
def _fit(self, *args, **kwargs) -> "TimeFlagsTransform":
"""Fit datetime model."""
return self
- def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
- """
- Transform method for features based on time.
-
- Parameters
- ----------
- df:
- Features dataframe with time
-
- Returns
- -------
- result: pd.DataFrame
- Dataframe with extracted features
- """
- features = pd.DataFrame(index=df.index)
- timestamp_series = pd.Series(df.index)
+ def _compute_features(self, timestamps: pd.Series) -> pd.DataFrame:
+ timestamps_no_nans = timestamps.dropna()
+ features = pd.DataFrame(index=timestamps_no_nans.index)
if self.minute_in_hour_number:
- minute_in_hour_number = self._get_minute_number(timestamp_series=timestamp_series)
+ minute_in_hour_number = self._get_minute_number(timestamp_series=timestamps_no_nans)
features[self._get_column_name("minute_in_hour_number")] = minute_in_hour_number
if self.fifteen_minutes_in_hour_number:
fifteen_minutes_in_hour_number = self._get_period_in_hour(
- timestamp_series=timestamp_series, period_in_minutes=15
+ timestamp_series=timestamps_no_nans, period_in_minutes=15
)
features[self._get_column_name("fifteen_minutes_in_hour_number")] = fifteen_minutes_in_hour_number
if self.hour_number:
- hour_number = self._get_hour_number(timestamp_series=timestamp_series)
+ hour_number = self._get_hour_number(timestamp_series=timestamps_no_nans)
features[self._get_column_name("hour_number")] = hour_number
if self.half_hour_number:
- half_hour_number = self._get_period_in_hour(timestamp_series=timestamp_series, period_in_minutes=30)
+ half_hour_number = self._get_period_in_hour(timestamp_series=timestamps_no_nans, period_in_minutes=30)
features[self._get_column_name("half_hour_number")] = half_hour_number
if self.half_day_number:
- half_day_number = self._get_period_in_day(timestamp_series=timestamp_series, period_in_hours=12)
+ half_day_number = self._get_period_in_day(timestamp_series=timestamps_no_nans, period_in_hours=12)
features[self._get_column_name("half_day_number")] = half_day_number
if self.one_third_day_number:
- one_third_day_number = self._get_period_in_day(timestamp_series=timestamp_series, period_in_hours=8)
+ one_third_day_number = self._get_period_in_day(timestamp_series=timestamps_no_nans, period_in_hours=8)
features[self._get_column_name("one_third_day_number")] = one_third_day_number
for feature in features.columns:
features[feature] = features[feature].astype("category")
- dataframes = []
- for seg in df.columns.get_level_values("segment").unique():
- tmp = df[seg].join(features)
- _idx = tmp.columns.to_frame()
- _idx.insert(0, "segment", seg)
- tmp.columns = pd.MultiIndex.from_frame(_idx)
- dataframes.append(tmp)
+ # add NaNs in features
+ features = features.reindex(timestamps.index)
+
+ return features
+
+ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
+ """
+ Transform method for features based on time.
+
+ Parameters
+ ----------
+ df:
+ Features dataframe with time
+
+ Returns
+ -------
+ :
+ Dataframe with extracted features
+ """
+ if self.in_column is None:
+ if pd.api.types.is_integer_dtype(df.index.dtype):
+ raise ValueError("Transform can't work with integer index, parameter in_column should be set!")
+
+ timestamps = pd.Series(df.index)
+ features = self._compute_features(timestamps=timestamps)
+ features.index = df.index
+
+ dataframes = []
+ for seg in df.columns.get_level_values("segment").unique():
+ tmp = df[seg].join(features)
+ _idx = tmp.columns.to_frame()
+ _idx.insert(0, "segment", seg)
+ tmp.columns = pd.MultiIndex.from_frame(_idx)
+ dataframes.append(tmp)
+ result = pd.concat(dataframes, axis=1).sort_index(axis=1)
+ result.columns.names = ["segment", "feature"]
+
+ else:
+ flat_df = TSDataset.to_flatten(df=df, features=[self.in_column])
+ features = self._compute_features(timestamps=flat_df[self.in_column])
+ features["timestamp"] = flat_df["timestamp"]
+ features["segment"] = flat_df["segment"]
+ wide_df = TSDataset.to_dataset(features)
+ result = pd.concat([df, wide_df], axis=1).sort_index(axis=1)
- result = pd.concat(dataframes, axis=1).sort_index(axis=1)
- result.columns.names = ["segment", "feature"]
return result
@staticmethod
diff --git a/examples/101-get_started.ipynb b/examples/101-get_started.ipynb
index 131faf2ee..91ce9e414 100644
--- a/examples/101-get_started.ipynb
+++ b/examples/101-get_started.ipynb
@@ -2,11 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"# Get started\n",
"\n",
@@ -15,11 +11,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"This notebook contains the simple examples of time series forecasting pipeline\n",
"using ETNA library.\n",
@@ -38,11 +30,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"!pip install \"etna[prophet]\" -q"
@@ -51,56 +39,31 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"\n",
- "warnings.filterwarnings(action=\"ignore\", message=\"Torchmetrics v0.9\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"## 1. Loading dataset \n\n
\n \n \n month \n sales \n \n \n \n \n 0 \n 1980-01-01 \n 15136 \n \n \n 1 \n 1980-02-01 \n 16733 \n \n \n 2 \n 1980-03-01 \n 20016 \n \n \n 3 \n 1980-04-01 \n 17708 \n \n \n 4 \n 1980-05-01 \n 18019 \n \n \n
\n
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " month \n",
+ " sales \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1980-01-01 \n",
+ " 15136 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1980-02-01 \n",
+ " 16733 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1980-03-01 \n",
+ " 20016 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 1980-04-01 \n",
+ " 17708 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 1980-05-01 \n",
+ " 18019 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n\n
\n \n \n timestamp \n target \n segment \n \n \n \n \n 0 \n 1980-01-01 \n 15136 \n main \n \n \n 1 \n 1980-02-01 \n 16733 \n main \n \n \n 2 \n 1980-03-01 \n 20016 \n main \n \n \n 3 \n 1980-04-01 \n 17708 \n main \n \n \n 4 \n 1980-05-01 \n 18019 \n main \n \n \n
\n
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " timestamp \n",
+ " target \n",
+ " segment \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1980-01-01 \n",
+ " 15136 \n",
+ " main \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1980-02-01 \n",
+ " 16733 \n",
+ " main \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1980-03-01 \n",
+ " 20016 \n",
+ " main \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 1980-04-01 \n",
+ " 17708 \n",
+ " main \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 1980-05-01 \n",
+ " 18019 \n",
+ " main \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n\n
\n \n \n segment \n main \n \n \n feature \n target \n \n \n timestamp \n \n \n \n \n 1980-01-01 \n 15136 \n \n \n 1980-02-01 \n 16733 \n \n \n 1980-03-01 \n 20016 \n \n \n 1980-04-01 \n 17708 \n \n \n 1980-05-01 \n 18019 \n \n \n
\n
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " segment \n",
+ " main \n",
+ " \n",
+ " \n",
+ " feature \n",
+ " target \n",
+ " \n",
+ " \n",
+ " timestamp \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1980-01-01 \n",
+ " 15136 \n",
+ " \n",
+ " \n",
+ " 1980-02-01 \n",
+ " 16733 \n",
+ " \n",
+ " \n",
+ " 1980-03-01 \n",
+ " 20016 \n",
+ " \n",
+ " \n",
+ " 1980-04-01 \n",
+ " 17708 \n",
+ " \n",
+ " \n",
+ " 1980-05-01 \n",
+ " 18019 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n\n
\n \n \n start_timestamp \n end_timestamp \n length \n num_missing \n num_segments \n num_exogs \n num_regressors \n num_known_future \n freq \n \n \n segments \n \n \n \n \n main \n 1980-01-01 \n 1994-08-01 \n 176 \n 0 \n 1 \n 0 \n 0 \n 0 \n MS \n \n \n
\n
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " start_timestamp \n",
+ " end_timestamp \n",
+ " length \n",
+ " num_missing \n",
+ " num_segments \n",
+ " num_exogs \n",
+ " num_regressors \n",
+ " num_known_future \n",
+ " freq \n",
+ " \n",
+ " \n",
+ " segments \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " main \n",
+ " 1980-01-01 \n",
+ " 1994-08-01 \n",
+ " 176 \n",
+ " 0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " MS \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n\n
\n \n \n segment \n main \n \n \n feature \n target \n \n \n timestamp \n \n \n \n \n 1980-01-01 \n 15136 \n \n \n 1980-02-01 \n 16733 \n \n \n 1980-03-01 \n 20016 \n \n \n 1980-04-01 \n 17708 \n \n \n 1980-05-01 \n 18019 \n \n \n
\n
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " segment \n",
+ " main \n",
+ " \n",
+ " \n",
+ " feature \n",
+ " target \n",
+ " \n",
+ " \n",
+ " timestamp \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1980-01-01 \n",
+ " 15136 \n",
+ " \n",
+ " \n",
+ " 1980-02-01 \n",
+ " 16733 \n",
+ " \n",
+ " \n",
+ " 1980-03-01 \n",
+ " 20016 \n",
+ " \n",
+ " \n",
+ " 1980-04-01 \n",
+ " 17708 \n",
+ " \n",
+ " \n",
+ " 1980-05-01 \n",
+ " 18019 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -466,11 +700,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"## 3. Forecasting single time series ",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -706,7 +904,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -717,8 +915,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "19:41:47 - cmdstanpy - INFO - Chain [1] start processing\n",
- "19:41:47 - cmdstanpy - INFO - Chain [1] done processing\n"
+ "11:47:57 - cmdstanpy - INFO - Chain [1] start processing\n",
+ "11:47:57 - cmdstanpy - INFO - Chain [1] done processing\n"
]
}
],
@@ -740,7 +938,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -749,9 +947,11 @@
"outputs": [
{
"data": {
- "text/plain": "{'main': 10.54389444925695}"
+ "text/plain": [
+ "{'main': 10.514961160817307}"
+ ]
},
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -762,7 +962,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -771,8 +971,10 @@
"outputs": [
{
"data": {
- "text/plain": "",
- "image/png": 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"
+ "image/png": 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tnn32WYoUKYKLiwv+/v6sXr2aLl26UKVKFV5++WU+/PBDOnfuDMDQoUOpWrUqDRs2JDAwkHXr1pn8FYqISH6y/vAFvog4AsC7fWpTpoiXyYlEREQKBl0zX8BUqVKFDRs2/Ov2WbNmXXX76tWrs2jRomvuLzAwkCVLluRYPhERKTwuJaYxfPoODAMG3FGOLrVKmR1JRESkwNDIvIiIiOQ4wzB44ZddnItLJTTQh1e7h5sdSUREpEBRMS8iIiI57seNJ1i69xzuVhc+HVAPb3dNBhQREclJKuZFREQkRx08F89b8/YC8EKnqtQsE2ByIhERkYInW8X8F198Qe3atfH398ff358mTZqwcOHCzPtbtWqFxWLJ8vH4449n2ceJEyfo2rUr3t7eBAUF8fzzz5ORkZFlm1WrVlG/fn08PDwICwtj8uTJ/8oyfvx4KlSogKenJ40bN2bTpk3Z+VJEREQkF6Sk23j65+2kZthpWSWQh5pWNDuSiIhIgZStYr5s2bK8++67bN26lS1bttCmTRt69OjBnj17MrcZOnQoZ8+ezfwYO3Zs5n02m42uXbuSlpbG+vXr+e6775g8eTKvvvpq5jaRkZF07dqV1q1bs2PHDp599lkeeeQRFi9enLnNtGnTGDFiBK+99hrbtm2jTp06dOzYkfPnz9/OcyEiIiK36d2F+9kfFU8JX3c+uKcOLmpDJyIikiuyVcx3796dLl26ULlyZapUqcLbb7+Nr68vv//+e+Y23t7eBAcHZ374+/tn3rdkyRL27t3Ljz/+SN26dencuTNvvvkm48ePJy0tDYAJEyZQsWJFPvzwQ6pXr86TTz5J3759+fjjjzP389FHHzF06FCGDBlCeHg4EyZMwNvbm2+++eZ2nw8RERG5Rcv3nWPy+mMAfHBPHQL9PMwNJCIiUoDd8mo0NpuNGTNmkJiYSJMmTTJv/+mnn/jxxx8JDg6me/fuvPLKK3h7ewOwYcMGatWqRcmSJTO379ixI0888QR79uyhXr16bNiwgXbt2mU5VseOHXn22WcBSEtLY+vWrYwaNSrzfhcXF9q1a3fVlmx/l5qaSmpqaubncXFxAKSnp5Oenp5l2/T0dAzDwG63Y7fbs/HM5DzDMDL/NTuLs7Hb7RiGQXp6OlarNc+Oe+X75Z/fN2IOnQ/npPPifHLznJyPT2XkjJ0ADG5SnqahRXXub0A/I+bTOXA+OifORefDHDf7fGe7mN+9ezdNmjQhJSUFX19ffv31V8LDHe1mBg4cSEhICKVLl2bXrl28+OKLHDhwILPHeVRUVJZCHsj8PCoq6rrbxMXFkZyczKVLl7DZbFfdZv/+/dfNPmbMGF5//fV/3b5kyZLMNxyucHV1JTg4mISEhMxZA2aLj483O4LTSUtLIzk5mdWrV/9r7YW8sHTp0jw/plybzodz0nlxPjl9TuwGfLHPhUtJLpTxNqhlP8qCBUdz9BgFmX5GzKdz4Hx0TpyLzkfeSkpKuqntsl3MV61alR07dhAbG8vMmTMZNGgQERERhIeH8+ijj2ZuV6tWLUqVKkXbtm05cuQIlSpVyu6hctyoUaMYMWJE5udxcXGUK1eODh06ZLkcACAlJYWTJ0/i6+uLp6dnXkfNwjAM4uPj8fPzw2LRtYd/l5KSgpeXFy1atMjT85Sens7SpUtp3749bm5ueXZcuTqdD+ek8+J8cuucfL02koOxh/B0c2Hiw3cSFuSbY/suyPQzYj6dA+ejc+JcdD7McWUG+Y1ku5h3d3cnLCwMgAYNGrB582bGjRvHl19++a9tGzduDMDhw4epVKkSwcHB/1p1/ty5cwAEBwdn/nvltr9v4+/vj5eXF1arFavVetVtruzjWjw8PPDw+Pf1e25ubv/65rTZbFgsFlxcXHBxMbeD35Wp9VfyXI9hGDz22GPMnDmTS5cusX37durWrZsHKc3h4uKCxWK56jnMC2YdV65O58M56bw4n5w8J7tOXeajpYcBeK17DaqXKZoj+y1M9DNiPp0D56Nz4lx0PvLWzT7Xt12l2u32LNeh/92OHTsAKFWqFABNmjRh9+7dWVadX7p0Kf7+/plT9Zs0acLy5cuz7Gfp0qWZ1+W7u7vToEGDLNvY7XaWL1+e5dr9wmrRokVMnjyZefPmcfbsWWrWrGl2pFtSoUIFPvnkE7NjiIjIdSSmZvDM1B1k2A061wxmwB3lzI4kIiJSaGRrZH7UqFF07tyZ8uXLEx8fz5QpU1i1ahWLFy/myJEjTJkyhS5dulC8eHF27drF8OHDadGiBbVr1wagQ4cOhIeH88ADDzB27FiioqJ4+eWXGTZsWOaI+eOPP87nn3/OCy+8wEMPPcSKFSuYPn068+fPz8wxYsQIBg0aRMOGDWnUqBGffPIJiYmJDBkyJAefmvzpyJEjlCpVirvuuuuWHm8YBjabDVfXW14bUUREConRv+0h8kIipQI8GdO7li4FExERyUPZGpk/f/48Dz74IFWrVqVt27Zs3ryZxYsX0759e9zd3Vm2bBkdOnSgWrVqPPfcc/Tp04e5c+dmPt5qtTJv3jysVitNmjTh/vvv58EHH+SNN97I3KZixYrMnz+fpUuXUqdOHT788EMmTpxIx44dM7fp378/H3zwAa+++ip169Zlx44dLFq06F+L4hU2gwcP5qmnnuLEiRNYLBYqVKhAamoqTz/9NEFBQXh6etKsWTM2b96c+ZhVq1ZhsVhYuHAhDRo0wMPDg7Vr12K32xkzZgwVK1bEy8uLOnXqMHPmzCzH27NnD926dcPf3x8/Pz+aN2/OkSNHANi8eTPt27enRIkSBAQE0LJlS7Zt25b5WMMwGD16NOXLl8fDw4PSpUvz9NNPA9CqVSuOHz/O8OHDsVgsenEoIuKE5u48w4ytp7BY4JP+dSni7W52JBERkUIlW8OvkyZNuuZ95cqVIyIi4ob7CAkJYcGCBdfdplWrVmzfvv262zz55JM8+eSTNzxejkpLggsH8/aYAMXDbmqzcePGUalSJb766is2b96M1WrlhRde4JdffuG7774jJCSEsWPH0rFjRw4fPkyxYsUyH/vSSy/xwQcfEBoaStGiRRkzZgw//vgjEyZMoHLlyqxevZr777+fwMBAWrZsyenTp2nRogWtWrVixYoV+Pv7s27duswV5ePj4xk0aBCfffYZhmHw4Ycf0qVLFw4dOoSfnx+//PILH3/8MVOnTqVGjRpERUWxc6ejpdGsWbOoU6cOjz76KEOHDs3551NERG7LyZgk/jtrNwBPtg6jcWhxkxOJiIgUPppLnR0XDsJXLfP+uENXgU/FG24WEBCAn58fVquV4OBgEhMT+eKLL5g8eTKdO3cG4Ouvv2bp0qVMmjSJ559/PvOxb7zxBu3btwcgNTWVd955h2XLlmWuQxAaGsratWv58ssvadmyJePHjycgIICpU6dmLtBQpUqVzP21adMmS7avvvqKIkWKEBERQbdu3Thx4gTBwcG0a9cONzc3ypcvT6NGjQAoVqwYVqsVPz+/Gy5qKCIieSvDZufZaTuIT82gfvkiPNO2stmRRERECiUV89lRogo8euPZBzmueBgkZ7+H+pEjR0hPT6dp06aZt7m5udGoUSP27duXZduGDRtm/v/w4cMkJSVlFvdXpKWlUa9ePcCxuGHz5s2vudLiuXPnePnll1m1ahXnz5/HZrORlJTEiRMnALjnnnv45JNPCA0NpVOnTnTp0oXu3bvrWn0RESf36YrDbD1+CT8PV8YNqIer1dyOLyIiIoWVKqfscPeG0nXz/rh2OyTfXK/BW+Xj45P5/4SEBADmz59PmTJlsmx3ZaFCLy+v6+5v0KBBXLx4kXHjxhESEoKHhwdNmjQhLS0NcFyWceDAAZYtW8bSpUv5z3/+w/vvv09ERITaXoiIOKlNkTF8vuIQAG/1qkm5Yt4mJxIRESm89HZ6AVapUiXc3d1Zt25d5m3p6els3rw5sxXg1YSHh+Ph4cGJEycICwvL8lGunKPtUO3atVmzZg3p6elX3ce6det4+umn6dKlCzVq1MDDw4MLFy5k2cbLy4vu3bvz6aefsmrVKjZs2MDu3Y5rMN3d3bHZbLf7FIiISA6JTUrn2anbsRvQp35ZetQtc+MHiYiISK7RyHwB5uPjwxNPPMHzzz9PsWLFKF++PGPHjiUpKYmHH374mo/z8/Nj5MiRDB8+HLvdTrNmzYiNjWXdunX4+/szaNAgnnzyST777DMGDBjAqFGjCAgI4Pfff6dRo0ZUrVqVypUr88MPP9CwYUPi4uJ4/vnns4zmT548GZvNRuPGjfH29ubHH3/Ey8uLkJAQwNFnfvXq1QwYMAAPDw9KlCiR68+XiIhcnWEYjPp1F2diU6hQ3JvXe9QwO5KIiEihp5H5Au7dd9+lT58+PPDAA9SvX5/Dhw+zePFiihYtet3Hvfnmm7zyyiuMGTOG6tWr06lTJ+bPn0/Fio6F+IoXL86KFStISEigZcuWNGjQgK+//jpzivykSZO4dOkS9evX54EHHshsj3dFkSJF+Prrr2natCm1a9dm2bJlzJ07l+LFHSsiv/HGGxw7doxKlSoRGBiYS8+OiIjcjOlbTrJgdxSuLhbGDaiHr4fGAkRERMymv8YFzLPPPsuzzz6b+bmnpyeffvopn3766VW3b9WqFYZh/Ot2i8XCM888wzPPPHPNY9WuXZvFixdf9b569epl6WcP0Ldv38z/9+zZk549e15z33feeWdmqzoRETHP4fMJjP5tLwDPdahKnXJFzA0kIiIigEbmRURE5BpSM2w8M3U7yek27qpUnMdahJodSURERP6kYl5ERESu6v1FB9hzJo6i3m583L8uLi4WsyOJiIjIn1TMi4iIyL9EHIxm4tpIAN7vW4eS/p4mJxIREZG/UzEvIiIiWVxISOW56Y51Sx5sEkK78JImJxIREZF/UjF/A1dbHE6ch86PiEjOstsNRs7YyYWEVKqW9OO/XaqbHUlERESuQsX8NVitVgDS0tJMTiLXk5SUBJDZEk9ERG7P5PXHWHUgGndXFz69tx6eblazI4mIiMhVqDXdNbi6uuLt7U10dDRubm64uJj3vofdbictLY2UlBRTczgTwzBISkri/PnzFClSJPPNFxERuXV7zsTy7sL9ALzctTpVg/1MTiQiIiLXomL+GiwWC6VKlSIyMpLjx4+bmsUwDJKTk/Hy8sJi0UrCf1ekSBGCg4PNjiEiku8lp9l4+uftpNnstKsexAN3hpgdSURERK5Dxfx1uLu7U7lyZdOn2qenp7N69WpatGih6eR/4+bmphF5EZEc8sa8vRyJTiTIz4OxfevozWMREREnp2L+BlxcXPD0NLcdj9VqJSMjA09PTxXzIiKS4xb9cZafN53AYoGP+9elmI+72ZFERETkBnQBtoiISCF25nIyL/6yG4DHWlSiaVgJkxOJiIjIzVAxLyIiUkjZ7AbDp+0gNjmd2mUDGNG+itmRRERE5CapmBcRESmkvlh1mI2RMXi7W/l0QD3cXfWyQEREJL/QX20REZFCaOvxS3y87BAAb/SoSYUSPiYnEhERkexQMS8iIlLIxKek88zU7djsBnfXKU2f+mXMjiQiIiLZpGJeRESkEDEMeG3uPk5dSqZsUS/e6lVTbehERETyIbWmExERKUQ2X7Aw93AUVhcL4wbUw99TLU9FRETyI43Mi4iIFBLHLyYx86jjT/8zbSvTIKSoyYlERETkVqmYFxERKQTSMuyMmLGLVLuFOyoUZVjrMLMjiYiIyG1QMS8iIlIIfLj0ALtOx+FlNfiwby2sLrpOXkREJD9TMS8iIlLArTxwni8jjgIwoJKdUgGeJicSERGR26ViXkREpACLik3huek7Abi/cTnqFjdMTiQiIiI5QcW8iIhIAWWzGzwzdTsxiWmEl/LnpY5VzI4kIiIiOUTFvIiISAH16fJDbIyMwcfdyucD6+HhZjU7koiIiOQQFfMiIiIF0PrDF/h0xSEA3uldi9BAX5MTiYiISE5SMS8iIlLAXEhI5ZlpOzAM6NewLD3qljE7koiIiOQwFfMiIiIFiN1uMHzaDqLjU6kc5Mvou2uYHUlERERygYp5ERGRAmTC6iOsOXQBTzcXxt9XH293V7MjiYiISC5QMS8iIlJAbDkWw4dLDgLw+t01qFLSz+REIiIikltUzIuIiBQAl5PSePrn7djsBj3qlqZfw3JmRxIREZFcpGJeREQknzMMg5EzdnEmNoUKxb15u1ctLBaL2bFEREQkF6mYFxERyee+XXeMZfvO4W514fOB9fH10HXyIiIiBZ2KeRERkXxs16nLjFm4D4D/61qdmmUCTE4kIiIieUHFvIiISD4Vl5LOk1O2k24z6FijJA82CTE7koiIiOQRFfMiIiL5kGEYjJq1mxMxSZQp4sXYPnV0nbyIiEghomJeREQkH5qy6QTzd53F1cXCZwPrEeDtZnYkERERyUMq5kVERPKZfWfjeH3uXgBe6FSV+uWLmpxIRERE8pqKeRERkXwkMTWDYVO2kZZhp3XVQB5pFmp2JBERETGBinkREZF85NU5ezganUhJfw8+7FcXFxddJy8iIlIYqZgXERHJJ2ZuPcUv207hYoFPB9SjmI+72ZFERETEJCrmRURE8oHD5xN4ZfYfADzbrgqNQ4ubnEhERETMpGJeRETEyaWk23hyyjaS023cVak4w1qHmR1JRERETKZiXkRExMm9MW8v+6PiKeHrzif962LVdfIiIiKFnop5ERERJzZ35xmmbDyBxQIf969LkL+n2ZFERETECbiaHUBERByS02z88PsxUtPtDGsdplXKheMXExk1azcA/2lVieaVA01OJCIiIs5CxbyIiMkMw2D+7rOMWbCf05eTAahRxp821UqanEzMlJph48kp20lIzaBhSFGGt6tidiQRERFxIirmRURMtPdMHK/P3cPGyBgArC4WbHaDGVtOqZgv5N5beIDdp2Mp4u3Gp/fWw9WqK+NERETkLyrmRURMEJOYxodLDvDzphPYDfBwdeHxlpVoWTWQ3v9bz7J954hJTFMf8UJq6d5zfLMuEoAP+tahdBEvkxOJiIiIs1ExLyKSh9Jtdn78/TgfLz1IXEoGAF1rl2JU52qULeoNQK0yAew+Hcvs7ad5qFlFM+OKCU5fTmbkjJ0APNysIu3CNUNDRERE/k3FvIhIHllzKJo35u7l0PkEAKqX8ue17uHcGVo8y3b3NCzL7tOxzNh6SsV8IZNus/P0z9uJTU6ndtkAXuxUzexIIiIi4qRUzIuI5LLjFxN5a/4+lu49B0BRbzdGdqzKgDvKX7Vf+N11SvPWvH3sOxvHH6djqVkmIK8ji0k+WnqQrccv4efhyuf31sfdVdfJi4iIyNWpmBcRySUJqRmMX3mYSWsiSbPZsbpYeLBJCM+2rUKAt9s1H1fE2532NUoyf9dZZmw5qWK+kIg4GM0Xq44A8G6f2pQv7m1yIhEREXFmKuZFRHKY3W7w6/bTvLdoP+fjUwFoXrkEr3YLp3JJv5vaR7+G5Zi/6yxzdp7hv12r4+Fqzc3IYrLzcSmMmLYDgPvvLE/X2qXMDSQiIiJOT8W8iEgO2nHyMqN/28OOk5cBCCnuzctdw2lXPQiL5d9T6q+lWVgJSgV4cjY2hWV7z6u4K8BsdoNnpu7gYmIa1YL9eLlruNmRREREJB9QMS8ikgPOx6Xw3qID/LLtFADe7laebBPGw80q3tKoutXFQu/6ZRi/8gjTt5xUMV+Afb7iMBuOXsTb3crnA+vj6aZZGCIiInJjKuZFRG5DaoaNb9cd47Plh0hMswHQu34ZXuxUjZL+nre173salGP8yiOsORRNVGwKwQG3tz9xPhuOXGTc8oMAvNWzJmFBviYnEhERkfxCxbyIyC0wDIPl+87z1vy9HLuYBECdckV4rXs49csXzZFjVCjhQ6MKxdh0LIZftp1iWOuwHNmvOIeLCak8M3U7dgP6NihL7/plzY4kIiIi+YiKeRGRbDp8Pp435u1j9cFoAAL9PHixUzV61yuDy1Vazd2Ovg3LsulYDDO2nOQ/rSpl67p7cV52u8GI6Ts5H59KpUAf3uhRw+xIIiIiks+omBcRuUmxyemMW3aI7zccI8Nu4G514aFmFXmyTRi+Hrnz67RrrVKM/m0Pxy4mseX4Je6oUCxXjiN566s1R4k4GI2Hqwvj76uPt7v+HIuIiEj26NWDiMgN2OwG0zaf5IMlB4hJTAOgXfWSvNy1OhVK+OTqsX08XOlaqxQztp5ixpaTKuYLgK3HL/HB4gMAjL67BtWC/U1OJCIiIvmRi9kBRESc2abIGLp/tpb//rqbmMQ0woJ8+e6hRkwc1DDXC/kr7mlYDoD5u86SmJqRJ8eU3BGblM7TP28nw27QrXYpBtxRzuxIIiIikk9lq5j/4osvqF27Nv7+/vj7+9OkSRMWLlyYeX9KSgrDhg2jePHi+Pr60qdPH86dO5dlHydOnKBr1654e3sTFBTE888/T0ZG1henq1aton79+nh4eBAWFsbkyZP/lWX8+PFUqFABT09PGjduzKZNm7LzpYiIXNfpy8k8OWUb/b7cwN6zcfh5uvJqt3AWPtOcllUC8zTLHRWKUqG4N4lpNhbsPpunx5acYxgGz8/cyenLyYQU92ZM71paA0FERERuWbaK+bJly/Luu++ydetWtmzZQps2bejRowd79uwBYPjw4cydO5cZM2YQERHBmTNn6N27d+bjbTYbXbt2JS0tjfXr1/Pdd98xefJkXn311cxtIiMj6dq1K61bt2bHjh08++yzPPLIIyxevDhzm2nTpjFixAhee+01tm3bRp06dejYsSPnz5+/3edDRAq55DQbnyw7SNsPVzFv11ksFhjYuDyrRrbioWYVcbPm/YQmi8WSOTo/Y+upPD++5Izv1h9jyd5zuFktfH5vffw83cyOJCIiIvlYtl6Vdu/enS5dulC5cmWqVKnC22+/ja+vL7///juxsbFMmjSJjz76iDZt2tCgQQO+/fZb1q9fz++//w7AkiVL2Lt3Lz/++CN169alc+fOvPnmm4wfP560NMd1qBMmTKBixYp8+OGHVK9enSeffJK+ffvy8ccfZ+b46KOPGDp0KEOGDCE8PJwJEybg7e3NN998k4NPjYgUJoZhMG/XGdp9FMEnyw6Rkm6nUYVizHuqGe/0qkVxXw9T8/WuXwYXi2Pa/7ELiaZmkezbfSqWdxbsB+C/XapTq2yAyYlEREQkv7vlBfBsNhszZswgMTGRJk2asHXrVtLT02nXrl3mNtWqVaN8+fJs2LCBO++8kw0bNlCrVi1KliyZuU3Hjh154okn2LNnD/Xq1WPDhg1Z9nFlm2effRaAtLQ0tm7dyqhRozLvd3FxoV27dmzYsOG6mVNTU0lNTc38PC4uDoD09HTS09Nv9anIdVeyOXPGwkbnxLnc7vnYdzaetxbsZ9OxSwCUCvDkxY5V6FKzJBaLxSnOcwlvV5pWKs6awxeZtvk4I9pVNjvSDennxCE+JYNhU7aSZrPTrlog991RxrTnROfEueh8mE/nwPnonDgXnQ9z3Ozzne1ifvfu3TRp0oSUlBR8fX359ddfCQ8PZ8eOHbi7u1OkSJEs25csWZKoqCgAoqKishTyV+6/ct/1tomLiyM5OZlLly5hs9muus3+/fuvm33MmDG8/vrr/7p9yZIleHt73/iLN9nSpUvNjiD/oHPiXLJ7PhLSYf5JFzacs2Bgwc1i0LaMQdvSCVhObmPhyVwKeosqWSyswcrPG45SJfUQOdzSPtcU5p8Tw4DvD7lwIsaFou4GbXzPsnCh+eseFOZz4ox0Psync+B8dE6ci85H3kpKSrqp7bJdzFetWpUdO3YQGxvLzJkzGTRoEBEREdkOaIZRo0YxYsSIzM/j4uIoV64cHTp0wN/feVsDpaens3TpUtq3b4+bm66xdAY6J84lu+cj3WZnyqaTfLriCHEpjgU4u9QsyQsdq1CmiFdux71lbdNt/Pp+BJeTMwio2ojmYSXMjnRd+jmB6VtOse33vVhdLHw5qBH1yhcxNY/OiXPR+TCfzoHz0TlxLjof5rgyg/xGsl3Mu7u7ExYWBkCDBg3YvHkz48aNo3///qSlpXH58uUso/Pnzp0jODgYgODg4H+tOn9ltfu/b/PPFfDPnTuHv78/Xl5eWK1WrFbrVbe5so9r8fDwwMPj39e9urm55YtvzvySszDROXEuN3M+1hyK5o25ezl0PgGA6qX8ea17OHeGFs+LiLfFzc2NHnXL8P2G4/y6I4o21UuZHemmFNafkwNR8bwx3zFjbGSHqjSqlLddEK6nsJ4TZ6XzYT6dA+ejc+JcdD7y1s0+17e9LLPdbic1NZUGDRrg5ubG8uXLM+87cOAAJ06coEmTJgA0adKE3bt3Z1l1funSpfj7+xMeHp65zd/3cWWbK/twd3enQYMGWbax2+0sX748cxsRkX86fjGRod9v4YFJmzh0PoGi3m683asm855qli8K+SvuaeBY1X7xnihik3T9mrNKSstg2JRtpGbYaVElkMdahJodSURERAqYbI3Mjxo1is6dO1O+fHni4+OZMmUKq1atYvHixQQEBPDwww8zYsQIihUrhr+/P0899RRNmjThzjvvBKBDhw6Eh4fzwAMPMHbsWKKionj55ZcZNmxY5oj5448/zueff84LL7zAQw89xIoVK5g+fTrz58/PzDFixAgGDRpEw4YNadSoEZ988gmJiYkMGTIkB58aESkIElIzGL/yMJPWRJJms2N1sfBgkxCebVuFAO/89w5zzTL+VAv2Y39UPL/tPM0DTSqYHUmu4rU5ezh8PoEgPw8+6lcHl/yywIGIiMifLh+/zNIXl3Iu9hxGZ8PsOHIV2Srmz58/z4MPPsjZs2cJCAigdu3aLF68mPbt2wPw8ccf4+LiQp8+fUhNTaVjx47873//y3y81Wpl3rx5PPHEEzRp0gQfHx8GDRrEG2+8kblNxYoVmT9/PsOHD2fcuHGULVuWiRMn0rFjx8xt+vfvT3R0NK+++ipRUVHUrVuXRYsW/WtRPBEpvOx2g1+3n+a9Rfs5H+/oYtG8cgle7RZO5ZJ+Jqe7dVd6zr85by8ztp5SMe+EZm07xYytp3CxwLgB9ShhcltDERGR7DAMg+2TtrN4xGLS4h3tww/MPkCtfrVMTib/lK1iftKkSde939PTk/HjxzN+/PhrbhMSEsKCBQuuu59WrVqxffv2627z5JNP8uSTT153GxEpnHacvMzo3/aw4+RlAEKKe/Ny13DaVQ/CYsn/I6Q965ZmzIJ97DoVy/6oOKoFO+8CnoXNkegEXp79BwBPt61Mk0r55xIOERGRuNNxzB06l8MLDwPgVcKL5AvJrH59NTX61MDFettXaUsO0tkQkQLjfHwqz03fSc/x69hx8jLe7lZe6FSVJcNb0D68ZIEo5AGK+3rQrrpjJtKMLadMTiNXpKTbGPbTNpLSbNwZWoyn2lQ2O5KIiMhNMQyDnT/s5IuaX3B44WGsHlbav9+ex3Y/htXHyoW9F9gzfY/ZMeUfVMyLSL6XmmFn2WkLHT5Zyy/bHMVt7/plWDmyFf9pFYaHq9XkhDnvnoZlAZi9/TTpNrvJaQTg7fn72B8VT3Efd8YNqIdV18mLiEg+kBCVwLRe05j94GxSLqdQ+o7SPLb9Me4aeRfexb0J7OHoxhIxOgJ7hl5zOJNst6YTEXEmqRk2Bk7axK5TVsBGnXJFeK17OPXLFzU7Wq5qWSWQQD8PouNTWbH/PB1rXL81p+SuBbvP8sPvxwH4sF8dSvp7mpxIRETk+gzDYM+0PSwYtoDkmGRc3FxoNboVTV9oiovrX2O+gd0DiV0cy8WDF9n10y7qDqprXmjJQiPzIpKvjVt2iF2n4vC2GrzXuwa/PnFXgS/kAVytLvSuVwaAGVtOmpymcDtxMYkXZ+4C4PGWlWhVNcjkRCIiIteXGJ3IzH4z+eXeX0iOSSa4XjCPbn2U5v9tnqWQB7B6WWky0tECPOL1CGzpNjMiy1WomBeRfGvbiUtMiDgCwIBKdnrXK1OoWoBdmWq/8kA05+NTTE5TOKVl2Hnq523Ep2ZQv3wRnutQxexIIiIi17Vv1j7+V+N/7J25FxdXF1q+1pJHNj5CyVrX7gzW4IkG+JT04XLkZXZ8uyPvwsp1qZgXkXwpJd3GyBk7sRvQvXYwdYoXvv6nYUF+1CtfBJvdYPb202bHKZTGLtrPzlOxBHi58em99XDTKr8iIuKkkmOSmXXfLKb3mU5SdBJBNYN4ZOMjtBrdCqvb9dcXcvN2o9moZgCsfnM1GSkZeRFZbkCvOkQkX3p/8QGORicS5OfBq12rmx3HNPc0KAfA9C2nMIzC94aGmZbtPcfEtZEAvN+3NmWLepucSERE5OoOzjvI/2r8j91TdmNxsdBsVDOGbhlKqfqlbnofDR9riF8ZP+JOxbH16625mFZulop5Ecl3Nh69yDfrHEXUe31qU8TbzeRE5ulWpxSebi4cPp/AjpOXzY5TaJy5nMzImTsBGNK0Ah20AKGIiDihlMspzBkyh5+7/0xCVAIlqpXg4Q0P0/adtrh6ZG8tdFdPV1q83AKAte+sJT0pPTciSzaomBeRfCUxNYORM3diGNC/YTlaVyvci435e7rRuabjXfUZW9VzPi9k2Ow8M3U7l5PSqVUmgJc6VzM7koiIyL8cXnyYL2p9wY7JO8ACTUY24dFtj1KmUZlb3me9h+pRpEIREqIS2PzF5pwLK7dExbyI5CvvLNjHyZhkyhTx4uVuhXd6/d/d08CxEN7cHWdITtMKs7ntk2WH2HzsEr4ernw+sB4erte/zlBERCQvpcanMvexufzU6SfiTsVRLKwYQ9YMocP7HXDzur3ZjFZ3Ky1edYzOr3t3HanxqTkRWW6RinkRyTfWHIrmp40nABjbtzZ+noV3ev3f3RlanLJFvYhPzWDxniiz4xRoaw5FM37VYQDe6V2LkOI+JicSERH5S+TKSL6o9QXbvtoGQKOnG/HYjsco37R8jh2jzgN1KFa5GEkXktj02aYc269kn4p5EckX4lLSeeHPXt4PNgmhaVgJkxM5DxcXC33/HJ2fsVU953PL+fgUhk/bgWHAvY3Kc3ed0mZHEhERASAtMY0FTy3g+zbfE3s8liIVijBo5SA6j+uMu497jh7LxdWFVqNbAbD+/fWkXFZ7XLOomBeRfOGNuXs5G5tCSHFvXaN8FX3qO4r59UcucjImyeQ0BY/NbvDs1B1cSEijakk/XusebnYkERERAE6sPcGEOhPY/LnjGvYGjzfg8V2PU6FVhVw7Zo3+NQgMDyTlcgobPt6Qa8eR61MxLyJOb9nec8zcegqLBT64pw7e7tlbfbUwKFfMm7sqFccw4JdtWggvp/1v5WHWH7mIl5uV8ffVw/MG/XhFRERyW3pyOoufW8y3Lb7l0pFL+Jfz5/4l99Pti254+Hnk6rFdrC60er0VAL9//DtJFzWQYAYV8yLi1C4lpvHSrN0APNKsIndUKGZyIufVr6Gj5/zMraew29VzPqdsPHqRj5cdBODNnjUJC/IzOZGIiBR2pzae4st6X/L7R7+DAXUfqssTu5+gUvtKOXugi4cIubASy55f4MBCOBoBp7bC+f1Ub+tLyB1+2JKSWP/Bupw9rtwUDW+JiFN79bc9XEhIpVKgD891qGp2HKfWsUYwfh6unLqUzO+RF7mrktYVuF0xiWk8PXU7dgN61y+TuTaBiIiIGTJSM1g1ehXrx67HsBv4lvKl+9fdqdK1Ss4fLPkyrj/0oG7ieTj57b/utgCDuwBdwGb7HOMdPyyevuDmDe4+4O7757//+NzN+2/3Xbn9GttYVa5ej54dEXFa83edZe7OM1hdLHzYr66mNt+Al7uVbnVK8/OmE8zYckrFfA54b+F+zsWlEhrow5s9apodR0RECrEzW88we9BsovdEA1D7/tp0GtcJr2JeuXPA5a9DehKLa3xCmy69cDPSIC0B0pIgLRHSEjDSEoj4v4UknjpPWNtSVG1S5s/7/vaRFAOxpzIf4/g3CTKSb5zB6nGVgv/Pz6/6psHf3yTwATefrI/x+PP+AkLFvIg4pej4VF6e7Zhe/0TLStQtV8TcQPlEv4Zl+XnTCRb+cZbXe9TAX+37btmhc/GZ3QHe71sbHw/9yRQRkbxnS7Ox+u3VrHl7DYbNwCfIh64TulK9V/XcO+jJTbDlG+wd3iUlutifhXERICjLZhag3BM1+bHjj+z4w5WnnnoS/zL+N3cMu+2vgj896W+FfuK/3jS46jYJ5/7xxkGCYxtb2rWPGdoaHpx9i0+K89ErExFxOoZh8H+/7uZSUjrVgv14um1lsyPlG3XLFSEsyJfD5xOYv+ss9zbKub6yhc17iw5gN6BjjZI0CNFaDSIikvfO7TrH7EGzidoRBUCNfjXoMr4L3iW8c++gtnSY+yyUroe9wRBYtPi6m4e2D6V8s/KcWHuCNe+soev4rjd3HBcrePo7PnJSRhqkJ/41A+DvbwB4BuTssUymBfBExOnM3nGaJXvP4Wa18FG/uri76lfVzbJYLNxzpef8FvWcv1Wbj8WwbN85rC4Wnu+oVogiIpK37Bl2Vr+9mq8afkXUjii8invRd1pf+k7rm7uFPMCG8RC9D7qPcxTcN2CxWGj9ZmsAtn29jcvHL+duvhtxdQevohBQFgKrQJn6ULE5VO0EIU3MzZbD9ApZRJxKVGwKr87ZA8DTbSoTXjqH360tBHrVL4PVxcK2E5c5fD7e7Dj5jmEYvLNgH+DoEBAW5GtyIhERKUyi90YzqckkVr68Enu6nWo9q/GfPf+hRr8auX/wS8dg1bvQ+AkoVeemH1ahVQUqtq2IPd3O6rdW514+yULFvIg4DcMwePGXXcSnZFCnbABPtMrh9iqFRJCfJ62rBgIwY6t6zmfX4j1RbD9xGS83K8Pb6RIPERHJG3abnXXvr+PL+l9yZssZPIt40uuHXvSb1Q/fknnwxrJhwILnwbs4tP5vth9+ZXR+x7c7iDkck9Pp5CpUzIuI05i6+SQRB6Nxd3Xhw351cLXqV9St6tvA0XN+1rbTZNjsJqfJP9JtdsYuOgDAI80rEuTvaXIiEREpDC4evMi3zb9l2QvLsKXaqNylMv/Z8x9q318bi8WSNyH2zoZDS6DLWMeq79lUrkk5wjqHYdgMIt6IyPl88i96pSwiTuFkTBJvzdsLwPMdqhIW5GdyovytTbUgivm4Ex2fSsTBaLPj5BvTt5zk6IVEivm482iLULPjiIhIAWfYDX7/5Hcm1JnAqQ2ncPdz5+5Jd3PvvHvxK52Hr4VSYmHhS1CtG1S7yQXsruLK6Pzun3YTvU+vP3KbinkRMZ3dbvDCzF0kptloGFKUh5pVNDtSvufu6kKvemUAmLFFU+1vRlJaBp8sOwTAU23C8FNbPxERyUWXjl7iu9bfsXj4YjJSMghtF8p//vgP9R6ql3ej8Vcsf9Ox6nvn925rN6UblKZaz2oYdoOI0Rqdz20q5kXEdN9vOMaGoxfxcrPywT11sLrk8R+wAuqeho5V7ZfvP0dM4nV6rgoAE9dEEh2fSvli3tzXOMTsOCIiUkAZdoPN/9vMF7W/4Pjq47j5uNF1QlfuX3I/AeVNaJ12aitsngit/8+xAvxtavV6KwD2TN/DuV3nbnt/cm0q5kXEVJEXEnl30X4ARnWpRoUSPiYnKjiqBftTq0wA6TaD2dtPmx3HqV1ISOXLiCMAjOxYVe0QRUQkV1w+fpkfOvzAgmELSE9MJ6RlCE/sfoKGjzXM+9F4AFsGzHsGStWGRo/myC5L1i5Jjf6OlfdXvroyR/YpV6dXKyJiGpvd4LnpO0hJt3NXpeLcr9HQHHdldH76lpMYhmFyGuf12fJDJKbZqFUmgG61SpkdR0REChjDMNg2cRtf1PqCyOWRuHq50mlcJwatGETRikXNC7ZxApzbA90+Aatrju221ehWWFwsHJhzgDNbzuTYfiUrFfMiYpqJa46y7cRlfD1cGdu3Ni6aXp/j7q5TGndXF/ZHxbPnTJzZcZzSsQuJ/LTxBACjOlfT96GIiOSouNNxTOk6hblD55IWn0a5u8rx+M7Hafx0Yyxm/s25fBJWvu0YkS9TP0d3XaJaCWrdVwvQ6HxuUjEvIqY4eC6eD5ccBOCVbtUpW9Tb5EQFUxFvdzqElwRgxpaTJqdxTh8sOUCG3aBllUDuCithdhwRESkgDMNg5/c7+V+N/3F44WGsHlbav9+ewasHU7xycbPDOXrKewY4rpXPBS1fa4nFauHwwsOcXK/XILlBxbyI5Ll0m53npu8kzWanddVA+jUsZ3akAu2eP5/f2TvOkJJuMzmNc9l58jLzdp3FYoEXO1UzO46IiBQQKZdTmNZzGrMHzSY1NpXSd5Tmse2PcdfIu3CxOkEJtn8eHFwInceCp3+uHKJYpWLUHVIXgJWvaHQ+NzjBd5KIFDZfrDrC7tOxBHi58W6f2uYs+FKINAsrQakAT2KT01m2T6vKXmEYBu8udCy+2KtuGcJL586LGRERKVxsaTam9pzKgd8O4OLmQpu32/Dw+ocJrB5odjSH1HhY8AJU6QTVu+fqoVq83AIXNxciV0QSuTIyV49VGKmYF5E89cfpWD5d7ujl/frdNSjp72lyooLP6mKhT33HQnjqOf+XVQej2XD0Iu5WF0Z0qGJ2HBERKQAMw2Du0LkcjziOu587D294mOb/bY6LM3VJWfE2pFyGLu9DLg+oFAkpQoNHGwCO0XktxpuznOi7SkQKutQMGyNn7CTDbtCpRjA96pY2O1Kh0beBo5hfcyias7HJJqcxn81u8N6fo/KD7grRmg0iIpIj1ry9hp3f78RitXDPjHso3cDJXuuc2Q6bvoRWo6BI+Tw5ZPP/NsfV05WT605yZMmRPDlmYaFiXkTyzKfLD7E/Kp5iPu681aumptfnoQolfGhUoRh2A2ZtU8/5X7efZn9UPP6ergxrHWZ2HBERKQB2T9mdeW141/91Jayjk/19sWXA3GcgqAbc+USeHdavtB8Nn2gIaHQ+p6mYF5E8sf3EJb5Y5Xg39p1eNSnh62FyosLnSs/5GYW853xKuo2PlhwA4D+twyji7W5yIhERye9OrD3BnCFzAGgysknm1HKnsvlrOLsLun8CVrc8PXSzl5rh5u3Gmc1nODj3YJ4euyBTMS8iuS4l3cZzM3ZiN6BH3dJ0qlnK7EiFUpdapfB2t3LsYhJbjl8yO45pvt9wjDOxKZQK8GTwXRXMjiMiIvlczOEYpvacii3NRvXe1Wn/XnuzI/1b7GlY8Rbc8TCUbZjnh/cJ8qHR040AR995w154BxVykop5Ecl17y8+wNHoRIL8PHj97hpmxym0fDxc6VrL8UbK9M2Fs99rbFI641c6ZogMb18FTzeryYlERCQ/S45JZkrXKSRfTKb0HaXp9UMvLC5OeBnhwhfA3QfavmpahLtG3oW7nzvndp5j36x9puUoSFTMi0iu2hQZwzfrHK1I3u1TS1OaTdbvDkfP+fm7z5KYmmFymrz3v1WHiU1Op2pJv8wV/kVERG5FRmoG03pN4+LBiwSUD+De3+7FzTtvp6/flP0LHH3lO70LngGmxfAu7k2TEU0Ax+i83WY3LUtBoWJeRHJNYmoGI2fsxDCgX8OytKlW0uxIhV7DkKJULOFDUpqNBbvPmh0nT52+nMy3648B8GLnqlidceRERETyhcwWdKuP4+HvwcD5A/EN9jU71r+lJsCC5yGsHdToZXYa7hx+J55FPbmw7wJ/TP3D7Dj5nop5Eck1Yxbu40RMEmWKePFKt3Cz4whgsVgy29QVtp7zHy05SFqGncYVi9G6apDZcUREJB9b/eZqdv2wy9GCbuY9BNV00r8rq8ZA0kXo+mGu95S/GZ4Bntw18i4AIkZHYM/Q6PztUDEvIrlizaFofvz9BABj+9bGz9MJp50VUr3rl8HFApuOxXDsQqLZcfLE/qg4Zm13vHkxqkt1tUUUEZFbtuunXax6bRUAXb/oSqX2lcwNdC1nd8HvX0CrF6FoBbPTZGr8dGO8S3gTcziGnd/vNDtOvqZiXvKM3W4wIeIID36zicV7ogp1a6yCLi4lnRdm7gLggTtDaBpWwuRE8nelArxoXjkQgJlbC8fo/HsL92MY0KVWMHXLFTE7joiI5FPH1xznt4d+A+CuF+6iwVAnbEEHYLfBvGchsCo0edLsNFm4+7rT9KWmAES8EYEtzWZyovxLxbzkiYsJqQyevJl3F+5n9cFoHvthK10/XcsSFfUF0ptz93I2NoWQ4t681Lma2XHkKq70nP9l2ylsBbw9zIYjF1l5IBpXFwvPd9T3o4iI3JqLhy4yrec0Rwu6PtVpN6ad2ZGubcs3cHordPskz3vK34w7nrgD32BfYo/Hsv2b7WbHybdUzEuu23Ishq6frmX1wWg83Vzo17AsPu5W9p6N49EfttLts7Us3XtORX0BsXzfOWZsPYXFAh/cUwcfD1ezI8lVtKtekgAvN87GprD28AWz4+QawzB4d6Gj/c29jcpTsYSPyYlERCQ/SrqY5GhBF5NMmUZl6PW9k7agA4g7C8vfgAaDoXxjs9NclZu3G83/rzkAq99aTUZK4euwkxNUzEuuMQyDr1cfpf9XvxMVl0JooA+zhzVlbN86rH2xDcNaV8LH3cqeM3EM/X6LivoC4FJiGi/N2g3AI80qckeFYiYnkmvxdLPSs25pAGZsKbg95+fvPsvOU7H4uFt5um1ls+OIiEg+dKUFXcyhGAJCAhjw2wDnbEF3xaKXwNUD2o02O8l11R9aH/9y/sSfjmfLl1vMjpMvqZiXXBGblM6jP2zl7QX7sNkN7q5Tmt+ebEa1YH8Aivq483zHaqx5sQ3/aZW1qO/++VqWqajPl177bQ/R8alUCvThuQ5VzY4jN3BPQ0fP+SV7z3E5Kc3kNDkv3Wbn/cUHABjaIpRAPw+TE4mISH5jGAZzH5nLiTUn8PD34L4F9+Fb0glb0F1xcAnsnQ0dx4BXUbPTXJerhystXm4BwNoxa0lLLHivRXKbinnJcbtOXabrZ2tYuvcc7lYX3upZk3ED6uJ7lenWxXzceaHTX0W9t7uVP07H8cj3W7j783Us36eiPr9YsPssv+08g4sFPuxXF083q9mR5AZqlPanWrAfaRl2ftt5xuw4Oe7nTSc4fjGJEr7uDG0eanYcERHJhyLeiGDXj7twcXWh3y/9CAwPNDvStaUlwvznILQ11OprdpqbUndIXYqGFiXxXCKbx282O06+o2JecoxhGHy/4Rh9v9jAqUvJlC/mzaz/3MX9d4bcsA3UlaJ+7YtteOLPon736Vge/k5FfX5wISGVl2f/AcB/WoVptfB8wmKx0O/P0fmC1nM+ITWDccsOAfBM28pau0FERLJt14+7iBgdATha0IW2c/I3hiPeg4RzTtNT/mZY3ay0eNUxOr9u7DpS41NNTpS/qJiXHJGQmsFTP2/n1Tl7SLPZ6VijJHOfakbNMgHZ2k8xH3de7FSNNS+05vGWWYv6HuPXsWK/inpnYxgG/521m5jENKoF++m65HymZ70yuFkt7D4dy/6oOLPj5JivVh/lYmIaFUv4MKBRebPjiIjkuozUDBKjE82OUWAcX32c3x52tKBr+mJT6j9S3+REN3BuD2wYDy2fh+JO2vf+GmrfV5viVYqTfDGZjeM2mh0nX1ExL7dt39k47v5sLfN2ncXVxcIr3cKZcH8DArxufWGQ4r4evNQ5a1G/61QsD03eQs/x61i5/7yKeicxe8dpluw9h5vVwkf96uLuql8r+UkxH3faVisJFJzR+fPxKUxccxSA5ztWxc2q70kRKdii90Xzv/D/8VHpj9g0fpNeI92miwcvMq2XowVdeN9w2r7T1uxI12e3w9xnoFgluOsZs9Nkm4urC61ebwXA+g/Wk3wp2dQ8+Yle4cgtMwyD6ZtP0nP8Oo5eSKRUgCfTHmvCw80q3nBa/c36e1H/WMtQvNys7DwVy5DJm1XUO4Go2BRem7MHgKfbVCa8tL/JieRWXOk5/+v206Rl2E1Oc/vGLTtEUpqNuuWK0LlmsNlxRERy1dHlR5nUZBKXjl7CnmFn4ZMLmfvoXDJS1errViRd+FsLusZl6Pl9T+dtQXfFtslwajN0+xhc3c1Oc0tq9KtBUM0gUmNT2fDRBrPj5Bsq5uWWJKVlMHLGLl74ZRepGXZaVQ1k/tPNaRCSO6tmFvf1YFTn6qx5sTWPtfhHUf+/9aw8oKI+rxmGwYu/7CIuJYPaZQN4olX+mtIlf2lZJZBAPw9iEtNYsf+82XFuy9HoBKZudrTaG9W5Wo69sSgi4oy2fr2Vnzr9RGpsKuWalqP1m62xuFjYPnE737f5noSoBLMj5iuZLegOx1CkQhEGzBmA223MNM0T8edg6Wiodz9UaGp2mltmcbFkjs5v/GQjSReSTM2TX6iYl2w7fD6enuPX8cu2U7hYHNNYvxl0B8V8cv+dwBK+Hozq8o+i/uRlhny7mV7/W88qFfV5Ztrmk0QcjMbd1YUP76mDq6Yy51uuVhd61y8DwMyt+bvn/PuLD2CzG7StFkTj0OJmxxERyRWG3WDpC0uZ9+g87Bl2at1XiweXP0iLl1swcP5APAI8OLn+JF81/IozWwpet5LcYBgGvz30GyfWnsAjwIOB8wc6dwu6Kxb/F6yu0P5Ns5Pctmq9qhFcL5i0hDTWjV1ndpx8Qa++JVvm7DjN3Z+v4+C5BAL9PPjpkTsZ1joMlzyefvT3ov7RFqF4urmw4+RlBquozxMnY5J4c95eAEZ2qELlkn4mJ5LbdU8Dx6r2Kw9Ecz4+xeQ0t2bbiUss/CMKFwu80Kma2XFERHJFelI60/tOZ/376wFoObolvX7oheufXTvCOoUxdNNQSlQrQfzpeL5t/i27ftxlZuR8IeL1CHZP2Z0/WtBdcXg5/DETOrwN3sXMTnPbLBYLrd9sDcCmzzdpZslNUDEvNyUl3cb//bqbZ6buICnNxl2VirPg6eY0qWTuyFcJXw/+26U6a15ow9DmFbMU9b2/WE/EwWgV9TnMbjd4YeYuEtNsNAwpysPNnLxNi9yUsCBf6pcvgs1u8Ou202bHyTbDMHh3wX4A+tQvS9VgvcEkIgVP/Nl4JreczP5f92N1t9Lrx160eq3Vvy4pKl6lOI9sfIQq3aqQkZLBrw/8ypLnl2C35f91UXLDzu93EvH6ny3oJnQltG0+eG2TngzzR0CF5lBngNlpckzlLpUp07gMGckZrH13rdlxnJ6Kebmh4xcT6fPFen7aeAKLBZ5uE8YPDzcm0M/D7GiZAv08+L+u4VmK+u0nLjPom030+WI9q1XU55gffj/OhqMX8XKz8sE9dbA6+6IwctPuudJzfuupfPfzsnzfeTYdi8HD1YURHaqYHUdEJMdF7YxiYuOJnNlyBu8S3jy4/EFq31f7mtt7+HswYM4Amv9fcwA2fLDBsbCbVgrP4ljEMX57xNGCrtmoZtR/2Mlb0F2x+n2IOwPdPsk3PeVvhsVioc1bbQDY8sUW4k4VnLa5uUHFvFzXoj/O0u3Ttew5E0cxH3cmD2nEiA5VnbaAu1LUr36hNY80q4iHqwvbTlzmQRX1OSLyQiJjFu4D4KXO1ahQwsfkRJKTutUuhaebC4fPJ7Dj5GWz49y0DJud9xY5RuWHNK1IqQAvkxOJiOSsQwsO8W2zb4k7GUfxqsV5+PeHKd+s/A0fZ3FxFEZ9p/XFzduNI4uPMLHRRKL3RedBaud34cAFpvWahj3dTvg94ZlFpNM7vw/WfQrNn4MSYWanyXEV21YkpEUItjQbq99ebXYcp6ZiXq4qLcPOG3P38viP24hPzaBhSFHmP92MllXywfVDQJCfJy93C2fNi615+B9Ffd8JG1hzSEV9dtnsBiNn7CQl3c5dlYrzwJ0hZkeSHObn6UbnmqUAmJ6Pes7P2naaQ+cTCPByU1cFESlwNn2+iZ+7/0xaQhoVWlfg4Q0PU6xS9q6PrtGvBg+te4iAkABiDscwsfFEDsw9kEuJ84crLehSLqVQ9s6y9PwuH7SgA0dP+XnDoWgINBtudppc8fdr57dP2s6lyEsmJ3JeKublX05fTqb/Vxv4Zl0kAI+1COXnR+/Ml6NdQX6evPKPon7r8Us8MGkT90zYwNpDF1TU36RJa4+y9fglfD1cGdu3dp4veih540rP+Xk7z5CcZjM5zY0lp9n4aOlBAJ5sHUaAs7cQEhG5SXabnYVPL2ThUwsx7AZ1H6rL/Yvux6vorb0eC64bzNDNQwlpGUJafBpTe0xlzTtrCuXroIyUDKb2nMqlI5coUjGftKC7YvsPcGLDnz3lneeS15wW0iKE0Pah2NPtrH5To/PXomJesli5/zxdP13D9hOX8fd05esHGzKqS3Xc8nnbscyi/oXWPNTUUdRvOX6J+ydtVFF/Ew6di+eDJY6C6ZVu1Slb1NvkRJJb7qxYnLJFvYhPzWDxniiz49zQt+sjiYpLoUwRLx5ootkiIlIwpManMrXHVDZ9tgmAtu+25e6Jd2N1t97Wfn0CfXhg6QPcMewOMGDF/61gZv+ZpCWm5UTsfMEwDOY8NIeT605mtqDzCconlw0mRMPSV6HOQKjYwuw0ue7K6PzO73dy8dBFk9M4p/xdoUmOybDZGbtoP0Mmb+ZyUjq1ywYw/+nmtA8vaXa0HBXk78mr3R1F/ZCmFXD/W1Hf78sNrDusov6f0m12npuxk7QMO62rBtLvz0XSpGBycbHQt4FjdH76FufuOX8pMY0vVh0B4LkOVfB0u70XuSIiziD2ZCzfNvuWQ/MP4erpyj0z7qHZi83+tWL9rbK6WenyeRe6fdUNFzcX9s7YyzdNv+Hyscs5sn9nt+q1Vfzx8x+4uLrQf1Z/Aqvnj0tIAVjyf47F7jq8ZXaSPFG2cVkqd62MYTMyuw1IVirmhXNxKQycuJH//fmieFCTEGY83oRyxQru6GuQvyevda/BmhdaM/guR1G/+dgl7pu4kf5f/s56FfWZvlh1hF2nYgnwcuPdPrVz7MWEOK++DcpiscD6Ixc5GZNkdpxr+nzlYeJTMqheyp+edcuYHUdE5Lad2XKGiY0mcm7XOXxK+jA4YjDhfcNz5VgNhjZg0MpB+AT5cG7nOb6+42uORRzLlWM5i53f78ycst3tq25UbFPR5ETZcHQV7JrmKOR9zG0NnZdav+EYnd89ZTfn95w3OY3zUTFfyK07fIGun65hU2QMvh6ufD6wHq/3qImHa+EY4Srp78nou7MW9ZuOxTBQRT0Ae87E8unyQwC8fncNSvp7mpxI8kLZot7cVcnxQuGXbc65EN7JmCR+2HAccHRW0BoOIpLf7Z+9n29bfEtCVAJBNYN4ZOMjlGmUu29Ulm9anqFbhlKqQSmSLiTxQ7sf2Py/zQXytc+xVX9rQfffZtQbUs/kRNmQngLzRkBIU6h7n9lp8lSp+qWo3rs6GBAxWqPz/6RivpCy2Q3GLTvE/ZM2ciEhjWrBfvz2ZFO61S5tdjRTXCnqVz9/laL+q99Zf+SC2RHzXGqGjeem7yTDbtCxRkl61C2c3xuF1T0N/uw5v+UUdrvzvaj7aOlB0mx2moYVp0XlEmbHERG5ZYZhsP6D9UzrPY2M5AzCOoXx0LqHKBJSJE+OH1AugCFrhlBrYC3sGXYWDFvAvMfmYcsHi6DerAsHLjCtt6MFXY3+NWjzZj5pQXfF2o/g8gnHoneFcIZkq9dbgQX2ztxL1A7nX88nL6mYL4QuJqQy+NtNfLzsIIYB/RuWY/awpoQG+podzXTBAX8V9YOahOBudWFTZAwDv95I/y83sOFI4Vl849Plh9gfFU8xH3fe7lVL0+sLmU41g/HzdOX05WR+P+pc3/d7zsQye8dpAF7qVF3fmyKSb9nSbcx7fB5Ln18KBjR8oiH3zr0XD/+8XaXczcuNXj/2ot3YdmCBbV9v47s235FwLiFPc+SGxOhEpnT5swVdk7L0nJxPWtBdEX0Q1nwEzZ6FwKpmpzFFUM0gag6oCcDKV1eanMa5qJgvZDYfi6Hrp2tZc+gCXm5WPrynDu/1ra2Fo/4hOMCT13vUJOKFVplF/cbIGO79+nf6f7mBjZExFMAZaJm2n7iUubDY2z1rUsK34LY+kavzdLPSvY5jNsaMrc411f7dhfsxDOhepzS1ygaYHUdE5JakXE5hSpcpbPtqG1ig4ycd6TK+Cy6u5rw8t1gsNH2+KfctuA+PAA9OrjvJ1w2/5syWM6bkyQkZKRlM6zmNS0cvUTS0KAPmDMDV09XsWDfPMBw95YuUg+bPmZ3GVC1fa4nFxcLBuQc5vem02XGchor5QsIwDL6MOMKAr34nKi6FSoE+zHmyKX3+XLVarq5UgFdmUf/g34r6+7/Zwn+3WOn31Uaen7GTCRFHWLIniiPRCaTb7GbHvi0p6Taem7ETuwE96pamc61SZkcSk9zz5++HhX+cJS4l3eQ0DmsPXWDNoQu4WS0836FwjlCISP53KfISk+6axNFlR3HzcWPAnAHc+cydTjHTKKxTGEM3DaVEtRLEnYrj2+bfsuunXWbHyjbDbjBnyBxOrj+JZxFPRwu6wHzSgu6KHVPg+Fro+hG4eZmdxlQlqpag9gO1AVj5ikbnr8hWMT9mzBjuuOMO/Pz8CAoKomfPnhw4cCDLNq1atcJisWT5ePzxx7Nsc+LECbp27Yq3tzdBQUE8//zzZGRkZNlm1apV1K9fHw8PD8LCwpg8efK/8owfP54KFSrg6elJ48aN2bRpU3a+nELjclIaQ7/fwpiF+7HZDXrULc1vTzajSkk/s6PlG6UCvHjj70W9qwtJGRa2n4xlxtZTvLtwP4/+sJW2H0ZQ/ZVFtPlwFUO/38K7C/czfctJth6/xOWk/NHD9YPFBzganUiQnwev313D7DhiorrlihAW5EtKup15O8+aHQe73WDMwn0A3Nc4hPLFC27HDREpuE5uOMnExhO5sO8CfmX8GLJmCFW7O9ebk8WrFOfh3x+mctfKZKRk8Ov9v7L0haXY89GAxcrXVvLHVEcLun6z+lGiWj5bXyXxIix5GWr1g0qtzU7jFFq+2hIXVxeOLDnCibUnzI7jFLI1zyQiIoJhw4Zxxx13kJGRwX//+186dOjA3r178fH5652uoUOH8sYbb2R+7u391wsum81G165dCQ4OZv369Zw9e5YHH3wQNzc33nnnHQAiIyPp2rUrjz/+OD/99BPLly/nkUceoVSpUnTs2BGAadOmMWLECCZMmEDjxo355JNP6NixIwcOHCAoKOi2npSCZMfJywz7aRunLyfj7urC6O41uLdROad45zc/ulLUP98+jB9mL6ZMtfoci0nh6IUEjkQncDQ6kaQ0G0ejEzkanchSzmV5fHEfdyoF+lIpyIfQEn/9W7aoF65W8yfKbIqMYdK6SADe7VOLIt7uJicSM1ksFvo1LMs7C/YzY+tJBjYub2qeubvOsOdMHL4erjzVJszULCIit+KPaX8we9BsbKk2gusFc+/ce/Ev4292rKvyDPBkwJwBrHx1JWvfWcv699dzfvd5ek/pjVdR5x4l3jF5B2veWgNA96+7U7F1PmpBd8XSV8CwQce3zU7iNIqGFqXuQ3XZ9tU2Vry8gkErBxX6miZbxfyiRYuyfD558mSCgoLYunUrLVq0yLzd29ub4ODgq+5jyZIl7N27l2XLllGyZEnq1q3Lm2++yYsvvsjo0aNxd3dnwoQJVKxYkQ8//BCA6tWrs3btWj7++OPMYv6jjz5i6NChDBkyBIAJEyYwf/58vvnmG1566aXsfFkFkmEYfLf+GG8v2Ee6zSCkuDfjB9anZhldX5oTPN2slPGBLrWCcXNzy7zdMAyi4lI4cj7xz+I+gSPRjv+fjU3hYmIaFxNj2HQsJsv+3K0uVCjhnVngVwr0JTTQl9BAH/w93f55+FyRmJrByBk7MQzo17AsbaqVzJPjinPrWa8M7y06wPYTlzl8Pp6wIHNm9KRm2PhgiWMm2OMtQymudRxEJB8xDIM1b6/JnB5c9e6q9P6pN+6+zv2muYvVhbZvt6Vk7ZLMGTKHw4sOM7HxRAbMGUBg9UCz411V5MpI5j46F4Dm/9ecuoPrmhvoVkSugR0/Qfdx4KtByr9r8XILdk7eyfGI40SuiCS0bajZkUx1WytAxMbGAlCsWLEst//000/8+OOPBAcH0717d1555ZXM0fkNGzZQq1YtSpb8q1Do2LEjTzzxBHv27KFevXps2LCBdu3aZdlnx44defbZZwFIS0tj69atjBo1KvN+FxcX2rVrx4YNG66ZNzU1ldTU1MzP4+LiAEhPTyc93TmuB72aK9luNmN8Sgb/N3sPC/c4RoU7hAfxbq8a+Hm6OfXXmZ9c75yU8HalRIUAGlfI+sZJYmoGxy4mcSQ6kcgLiRy9kMjRC0lEXkgkNcPOwXMJHDyXAHuy7i/Iz4OKJbwJLeFDaKCP498SPpQO8MzR3trvzN/HiZgkSgV48lLHyvnqeyW7PyNy84p6WmlZuQQrDkQzddMJXuxY5aYfm5Pn5fsNxzkZk0yQnwcPNC6rc32L9LPiXHQ+zJcX5yAjNYOFTyxk94+7AWj0bCPajGmDxWrJN+e+au+qDAodxIy+M4g5FMPExhPp8X0PKnetnOPHup1zcmH/Bab3no493U54v3CavdIs3zzHmTJScZ33LEbZxthq3Qsm53e231Pewd7UG1qPLeO3sOLlFZRtXrZAjs7f7PNtMYxbW5Pbbrdz9913c/nyZdauXZt5+1dffUVISAilS5dm165dvPjiizRq1IhZs2YB8Oijj3L8+HEWL16c+ZikpCR8fHxYsGABnTt3pkqVKgwZMiRLsb5gwQK6du1KUlISly5dokyZMqxfv54mTZpkbvPCCy8QERHBxo0br5p59OjRvP766/+6fcqUKVkuBcjPTifCNwetXEix4GIx6BFip2WwURhbUuYbdgMupcL5FAvnkuF8soXzyXAu2UJc+rVPnJvFINALSnoZBHlCkJfh+L8XeGSzOcGByxb+t8/xoP9Ut1G1SAFeql+ybVeMhUkHrPi7GYxuYMOax79PkjPgze1WEjMs9A+1cVdJfX+KSP6QEZdB5HuRJO5JBBco+2hZSnTKZ9du/01GbAaRY//8eixQamApgvoGOUUxlRGbwcEXDpJ2Lg2faj5UeqMSLu7mX8KYXVXOzqZq1BxWVXuTeC8tVH016THp7H18L0aaQegrofg3cM5LVW5HUlISAwcOJDY2Fn//a399tzwyP2zYMP74448shTw4ivUratWqRalSpWjbti1HjhyhUqVKt3q4HDFq1ChGjBiR+XlcXBzlypWjQ4cO132SzJaens7SpUtp3759lindf2cYBjO2nmbc/P2kZtgpFeDJuP61qVeuSN6GLSRu5pzkhPiUdCIvJDlG8aOvjOYncuxiEuk2OJMEZ5L+/Qc02N8jyyj+lf8H+3v86w9ufEo6736+AUjhvkblGN69eq59Pbklr85HYdXeZufX9yOISUzHJ+wO2lS9uamVOXVePlp6iMSMSEJL+DD6wSZOsb5EfqWfFeei82G+3DwHFw9eZHrP6SQeTsTD34NeP/citH3+nxJs62Nj6XNL2fblNs7+dJYiqUXo+nVX3H1y5pKBWzknGSkZ/NThJ9LOpVEktAiDlg/KfyvXA1w8jOvX87Hf9RTNWz964+3zgLP+nvLd7cvGjzeSND+J/i/3d4o3lHLSlRnkN3JLxfyTTz7JvHnzWL16NWXLXv8do8aNGwNw+PBhKlWqRHBw8L9WnT93zjEd/Mp19sHBwZm3/X0bf39/vLy8sFqtWK3Wq25zrWv1ATw8PPDw+Pd1lm5ubk71zXkt18qZlJbBy7/uYdZ2R8/F1lUD+ahfXYrm0C9Vubbc/t4p5uZGMT9vGlTM+i6+zW5w6lISR6ITOHI+0bEA35/X6V9MTCMqLpWouFTWH8l6bb63u/XPwt73z+vyfVi27xxnY1MoX8yb/3YNx80tH/Vf/Yf88rOc37i5Qa96ZZm0NpJft5+lY83S2Xz8rZ+XqNgUvt1wHIAXO1fDy1PXyucE/aw4F50P8+X0OTgWcYzpvaeTHJNMQEgAA+cNJKhmwbj22c3Nje4TulOqXikWPrmQfTP3cenwJfrP7k+RkCI5epybOSeG3WDO0Dmc/v00nkU9uW/BfRQpnXM58oxhwOIXwC8Ya6sXsTrZ7wRn+z3VfFRztn21jahtURxdcJRqPauZHSlH3exzna1X7YZh8NRTT/Hrr7+yatUqKla88cqQO3bsAKBUKUev6iZNmvD2229z/vz5zFXnly5dir+/P+Hh4ZnbLFiwIMt+li5dmjml3t3dnQYNGrB8+XJ69uwJOKb9L1++nCeffDI7X1K+d/h8PE/8uI1D5xOwulgY2aEqj7UIzdFrqcX5WF0shBT3IaS4D23+8bvrclJa5qJ7R//890h0AicuJpGUZuOP03H8cTrru30WC3xwTx18PPJvIS+5656GjmJ+2b5zXExIzbMF6MYtP0hKup0GIUXpEK5FGUXE+e38fie/PfIb9nQ7ZRqXYcCcAfiW9DU7Vo5r+FhDAsMDmd5nOlE7ovi64dfcM/MeKrSskKc5Vryygj3T9uDi5kL/Wf0pUTWfXsawazpErob7fgH3gnH5b27yCfSh8TONWfvOWla+upKqd1fFUgjrn2y9ch82bBhTpkxhzpw5+Pn5ERUVBUBAQABeXl4cOXKEKVOm0KVLF4oXL86uXbsYPnw4LVq0oHbt2gB06NCB8PBwHnjgAcaOHUtUVBQvv/wyw4YNyxw1f/zxx/n888954YUXeOihh1ixYgXTp09n/vz5mVlGjBjBoEGDaNiwIY0aNeKTTz4hMTExc3X7wmD29tOMmrWb5HQbQX4efHZvPRqHFjc7lpisiLc7DULcaRBSNMvt6TY7J2KSOHI+gaMXEjP/PRmTxINNQmhUsdg19igC1YL9qV02gF2nYpm94wwPN8v9Nj+Hz8czbfNJAEZ1rlbgptCJSMFi2A1WvrqSNW87WqKF3xNOz+964ublPKOZOS2keQiPbnmUab2mcXbbWX5o9wOdPu1Ew8cb5snv7O3fbmftO45Lfrt/3Z0KrSrk+jFzRVIMLP4v1OgNldvdeHsB4K7n7mLz55s5v/s8e2bsoWb/mmZHynPZKua/+OILAFq1apXl9m+//ZbBgwfj7u7OsmXLMgvrcuXK0adPH15++eXMba1WK/PmzeOJJ56gSZMm+Pj4MGjQoCx96StWrMj8+fMZPnw448aNo2zZskycODGzLR1A//79iY6O5tVXXyUqKoq6deuyaNGiLKvkF1Qp6TZen7uXnzedAKBpWHE+6V+PQD9NP5Vrc7O6OHrcBxa80QHJG/c0KMuuU7HM2HKSh5pWyPUXau8tOoDdgPbhJWlYQW82iYjzSk9OZ86QOeyZ5mhJ0+y/zWjzZptCMVIYUD6AIWuG8Nsjv/HHz3+w4D8LiNoRRZfPumB1z+aKvNkQuSKSeY/OA6DFKy2oO6hurh0r1y17DWzp0GmM2UnyFa9iXjR5rgmrXltFxOgIwvuG41LI1tXJ9jT76ylXrhwRERE33E9ISMi/ptH/U6tWrdi+fft1t3nyyScL3bT6YxcS+c9P29h7Ng6LBZ5uU5mn21bGWgj+WIiIue6uU4Y35+9jf1Q8f5yOo1bZgBs/6BZtORbD0r3ncLHAi52q5tpxRERuV+L5RKb2mMqp30/h4uZC96+658/e5rfBzduN3j/1JrhuMMteWsa2r7YRvSeafr/0y5VLDKL3RTO9z3TsGXZq3luTVq+3yvFj5Jnj62Hb99D1Q/C79tpfcnV3PnsnG8dt5ML+C+yesps6D9QxO1KeKlxvXeRzi/aco9tna9l7No7iPu58/1AjhrevokJeRPJEgLcbHWs4XmjM2Hoy145jGAbvLNgHQP87yhEW5JdrxxIRuR3Re6OZ2Hgip34/hWdRTx5Y8oB5hXx6CiReMOfYgMVioekLTRk4byAeAR6cXHeSrxt+zZmtZ3L0OInnE5nSdQopl1Mo17QcPb7pkX8vw8pIg3nDoUxDaPCQ2WnyJQ9/D+56/i4AIkZHYEu3mZwob6mYzwfSMuzMinThqak7SUjN4I4KRZn/dHOaV7659lAiIjnlngaODiZzdpwhJZf+YC7ec45tJy7j6ebCs+2q5MoxRERu15GlR5jUZBKXj12maKWiPLzh4by/Zjv+HGz7AabeB2ND4f1K8HEtmDEEfv8CTm1xFIx5qHKXyjyy8RGKVy1O3Kk4vm32Lbun7M6RfacnpzO1x1QuRzqe8wGzB+DqmY8X713/KVw4BN3HgYvKslvV6KlG+AT5cOnoJXZ+t9PsOHkqH3/3Fw7xKencN2kTu6IcP+CPtQxlZIequBWy60FExDk0DStBqQBPzsamsGzfObrVzl6buhvJsNkZu3g/AI80C6Wkv2eO7l9EJCds/Wor8/8zH8NmUL55efrP6o93iTxYgdwwIGoXHFwMBxfB6a2ABco1ghYjoWgInN4GpzbD0tfAlgpWDyhVx7FN2YZQ9g4IuH5r6dtVomoJHtn4CLMGzuLQgkPMum8WUTujaPtO21u+ptmwG8wZPCdzFsR9C+7Lm+c8t8QchdXvQ5NhEFz4Fm7LSe4+7jR9qSlLRixh9Zurqf1AbVwLSYemwvFV5mO+Hq6UK+rN4bOxfDSgHp1qlTE7kogUYlYXC33ql+XzlYeZvuVUjhfz07ec4mh0IkW93XisZWiO7ltE5HbZbXaWvbiMDR9uAKD2/bXpPrF77hYO6cmOlmUHFjqK+Pgz4O4HYW3hjqFQuT34/K0dW80+jn8z0iBqt6OwP7UJ9v0GGz533OdX2lHYl2vkKO5L1QW3nH3z1DPAkwG/DWDlKytZO2Yt68eu5/yu8/T5uQ+eRbJ/rBUvr2DP9D9b0P3an+JV8nEHJ8OA+c+BTxC0esnsNAVCw8cbsuGDDcSeiGXbxG00GtbI7Eh5QsW8k7NYLLzVI5xfXU/RtlqQ2XFEROjbwFHMrzkUzdnYZEoFeOXIfpPSMvh42UEAnmpTGT/PgtvOSUTyn7TENH69/1f2z3bMHmr1RitavNwid67XjjsLhxbDgUVwdBVkJEPRChDeA6p2gvJ3gav79ffh6g5lGzg+eNxxW/y5v4r7U1tgxduOfbu4QXCtv4r7sndAkfJwm1+bi9WFtu+0pWTtksx5aA6HFx3m60Zfc+9v91Ki2s33g9/+zXbWjnG0oLt74t153ss+x/3xCxxZAfdOA3cfs9MUCG5ebjT/v+YsGLaANW+vod5D9Qp0W8grVMznA74erhTXTFMRcRIVSvjQqGIxNkXGMGvbaYa1DsuR/U5aE0l0fCrlinlx353lc2SfIiI5If5MPD/f/TNnt57F6m6lx+Qe1Lq3Vs4dwG6Hszv+mj5/dgdYXKDcndB6FFTpBCWq3HZxjV9JqN7N8QGOdmjn9vxZ4G+GQ0tg4wTHfT5Bf5ua3whK173lwrPmgJoUr1KcqT2nEnMohomNJ9L7p95U6XbjdVGOLj/KvMf+bEH3agvqPJjPVytPvgSLRkH1ux1vzEiOqfdwPda9t47YE7FsmbCFJsObmB0p16mYFxGRbLunQVk2RcYwY8tJ/tOq0m2PTF1MSOXL1UcBGNmhKh6uudebWEQkO6J2RvFzt5+JOxWHdwlv+s/uT/mmOfCGY1oiHI2Agwvh4BJIiAKPAKjcznEddVg78C52+8e5Hqubo0gvXRcaDXXclnjBMWp/apOjwF/9AaQlgMUKJWtkHb0vFnrTbzCUql+KR7c8yvS+0zmx5gQ/3/0zbd5uQ7OXml3zb0j03r9a0NUaWItWo1vlyJdtqmWvOy6d6Pye2UkKHFcPV1q80oK5Q+eydsxaGgxtgLvvDWaw5HMq5kVEJNu61CrF6N/2cOxiEpuPXaJRxdt7wfnZisMkpGZQq0wA3XP4OnwRkVt1cP5BfhnwC2kJaZSoVoKB8wdSNLTore8w9pRj5P3gYsd18BkpUDwMavV1jL6Xv9NRYJvJp4RjxPjKqLHdBuf3/TU1/2gEbJ7ouM+7+J+F/Z+j92Xqg8e124n6BPnw4LIHWfjMQrZO2MqK/67g3M5z3D3pbtx9shZdCecSmNJ1CqmxqZRvVp67v7k7/7agu+LkJtj6LXQeC/76W5cb6gyqw9p313LpyCU2fb6JZi81MztSrlIxLyIi2ebj4UrX2qWYvuUUM7acvK1i/vjFRH7aeByAlzpXw8Uln79YE5ECYeOnG1k8fDGG3aBi24r0m9kv+wu32e1wZtufBfwix4J0FiuE3AVtXvlz+nzOXKqUa1ysjtXWg2tCwz97oSfFOFbSP7XZUaCu+xRS4xyXBgSF/1Xcl73D8WbF39quWd2tdPuiG8F1gln41EL2TNvDxQMXGTBnAAHlAwCwp9qZ2Wcml49dplhYMfr/2j//r05uS4e5z0DpenDHI2anKbCsblZavtaS2Q/OZt3YdTR8oiGeAQX3euV8/lMhIiJmuadhOaZvOcX83WcZfXcNfG7xhdYHSw6SbjNoXrkETcNufkEkEZHcYM+ws2j4IjZ/vhlwXIfb9YuuWN1u8vKf1AQ4utKxeN2hxZAYDZ5FoHIHaPqsY/q8V5Hcip83vIs5VtGv3N7xud0OFw78Vdyf2AhbvwMMx9d+pSVe2TugTAPwKkLDxxsSWCOQ6X2mE7Ujiq8afkW/mf0o1bgUx8cdJ3ZTLF7FvBi4YGD+bkF3xYbxEL0fHl3leINEck2tgbVY+85aLuy/wO+f/E6r11qZHSnXqJgXEZFb0jCkKBVL+BB5IZH5u8/Sr2G5bO9j16nLzN15BovFMSovImKm1LhUZg6YyeGFhwFoN7Ydd42868bTuy+fcBTvBxfBsTVgS4MSVaHuQMfoe9lGYC3AL7tdXCCouuOj/oOO21JiHaP3J/9cXO/3LyBlDGCBwKpQtiEhZRvx+NKWTBmylajt5/m+7fdUaFuB2PWxf7Wgq5yPW9BdcekYrHoXGj8BpfL5An75gIvVhVavt2Jm/5n8/tHvNH6qMV7FcqbzjrMpwL9VREQkN1ksFvo2KMv7iw8wc8upbBfzhmHw7kJHi6eedctQo3RAbsQUEbkpsSdimdJtCud3n8fVy5XeP/ameu/qV9/YbnMUqld6v5/fAy6uENIU2r8BVTo6FocrzDwDoFIbxwc4Ru9jjjhG7q+snr9jCn6GnUf7+nG+bTn2b/Hj9NHDlCrlTdPXuxDSwAcy0m7chs+ZGQYseN6xvkDr/5qdptAI7xtOydolObfrHOs/XE/bt9uaHSlXqJgXEZFb1qd+WT5ccoBNx2KIvJBIxRI337Yo4mA0649cxN3qwoj2N25PJCKSW85sOcPM3jNJiErAN9iXAb8NoMwdZbJulBLn6A1+cJGjhVvSRUeBVrkDtHzeUbR66k3Ja3JxgRKVHR/17nPclhoPp7dhObWZoFObKOazATc2Ou47PQXe//Oxrl6O59bT3/Gvh/81Pg+4+v3uvrff1u9W7Z3t+H4ZMAU8fM3JUAhZXCy0er0V03pNY+O4jdz57J34BN5aa0VnpmJeRERuWXCAJ80rBxJxMJqZW0/yfMebmypvs/81Kv9gkxDKFSsA10OKSL50ecNlfvz0RzKSMwiqFcTAeQMzF2IjJvKvxeuOrQN7umOBt/qD/pw+31DXP98ODz8IbQmhLbEAboZB2oVI1i+bS7M7auOanuhYWC8l1vFmSkospMY6/k26CDFH/3Z/LNgzrn4ci8ufBf6V4j8g+28O3MrsgJRYWPgSVOsG1bre1lMl2Ve1R1VKNSjF2a1nWffeOjp80MHsSDlOxbyIiNyWfg3LEXEwml+2nmZE+6pYb2I1+jk7TrM/Kh4/T1eGtXbylZxFpMBJT0rnxNoT7Ju9j2MTjoEBYZ3D6DulJx6xO2HpIsc18BcOgNUdKjSHju84ps8XDTE7fsFlsWApUo5Y7woYIc3ALRtt+gzD0b/9WsV/5ud/uz8mMuvnafHX3r+rV/ZnBuz4EdIS1FPeJBaLhdZvtmZKlylsHr+ZJs81wa/UtVsn5kcq5kVE5La0Cw+iiLcbUXEprD18gZZVAq+7fUq6jQ+XHATgiVaVKOqTj6+FFJF8wW6zc3bbWY4uO8rRpUc5ue4ktjQbAB4eKbR/woX6TVdi+fL/IPkS+ARC5Y7Q9hUIbXXd3uniJCwWcPd2fPgF39o+7LarvBlwtc8vOz5PinEsbvf3++3pWffZ6V0IKHu7X53corBOYZRtUpZTG06x5p01dPmsi9mRcpSKeRERuS0erlZ61CnNdxuOM33LyRsW8z9sOM7py8kE+3vyUNOKeZRSRAoTwzC4dPQSR5ce5eiyo0SuiCTlUkqWbfzL+dGlzzYq+8/HxWKH6FqO/t9VOkHp+ll6o0sh4WIFr6KOj1thGJCR8ldxb9ghSJ1azGSxWGjzVhu+b/s9277aRtPnm/51GU0BoGJeRERu2z0Ny/HdhuMs3XOOy0lpFPG++mh7bFI6n690tHwa0b4Knjfbt1lE5AaSLiYRuTwyc/T98rHLWe73CPCgYuuKhLYPJbRdKMVOf4UlYi4HSt5NaL83cStewZTcUoBYLODm5fi41dkBkuMqtqlIhVYVOLbqGKvfXk33L7ubHSnHqJgXEZHbVrNMANVL+bPvbBy/7TzDg00qXHW7/0UcJjY5nSolfenTQNMOReTWpSenc3Ldyczi/ez2s2D8db+Lmwvl7ipHaDtH8V66YWlcXP8cbV//OUS8i63V/7E/tiqh/mWufhARKRBav9mab5t/y45vdtDsxWYUDb3F2RdORsW8iIjkiHsalOWNeXuZseXUVYv5M5eT+XbdMQBe7FTtphbKExG5wrAbRO2I4sjSI0Qui+TE2hNkpGRdvTyoVpCjeG8fSkjzENx9rzJLaMu3sOT/oNlw7E2Hw4IFefQViIhZyjcrT5VuVfAr44ebdzYWVnRyKuZFRCRH9KxXhjEL97H7dCz7zsYRVsIry/0fLz1IWoadRhWL0aZakEkpRSQ/uRR5KXPkPXJFJMkXk7Pc71fazzFtvn0ooW1D8Q2+QR/vXdNh3nBo9Ci0fQ0yrtHKTEQKnAFzBmApYAMJKuZFRCRHFPNxp131kiz8I4oZW04xqlPlzPsORMXzy7ZTALzUuRoWS8H6YyoiOSM5JpnIlZGZC9ddOnIpy/3ufu5UbF2Riu0qEtoulBLVStz875N98+DXx6HuQOj0nuP6ZhEpNApaIQ8q5kVEJAfd07AsC/+IYvaO0zzXrlLm7e8t2o/dgM41g6lfvmBcpyYity8jNeOv696XHeXMljNZr3t3daHsnWWp2K4ildpXovQdpbHeysKZh5fDzCFQvTvc/ZlWqheRAkHFvIiI5JgWlQMJ8vPgfHwqKw9EA7AxMoYV+89jdbHwfMeqJicUETMZdoNzu85lTp0/vuY4GclZp7oHhgdmFu8hLUPw8PO4vYMeXw9T74PQ1tD7a0f7MRGRAkDFvIiI5BhXqwu965dlQsQRftl+mh5FYeySgwDc26gcoYE3uJ5VRAqc2BOxmYvWHV1+lKTopCz3+wb7ZraLC20Xil9pv5w7+Olt8FM/KNsQ+n0Hrldvmykikh+pmBcRkRx1T0NHMb/60EWKlbew61Qc3u5WnmlbxexoIpIHUi6nOK57/3P0PeZQTJb73XzcqNCqQuaq84Hhgbmzjsa5vfBjbwisCvf+7Oj9LSJSgKiYFxGRHFUp0Jf65Yuw7cRlZh1zXJc6tHkogbc7VVZEnNb5Pef54+c/HNe9bz6DYf/rwneL1UKZRmUyR9/LNi6L1T2Xp7pfPAI/9AT/snD/TPDIwdF+EREnoWJeRERy3D0Ny7HtxGUMLBT3cWdoi1CzI4lILklLTOObpt+QGpuaeVvxqsUzi/cKrSrgGeCZd4Eun4Tv7gbPAHjgV/DSopsiUjCpmBcRkRzXrXYpXp+7h5R0O0+2DsXXQ39uRAqqA3MOkBqbil9pP1q/1ZrQdqEElAswJ0z8Ofj+bsdq9Q/MBt9Ac3KIiOQB9eUQEZEc5+fpxtjeNelYxk7/hmXNjiMiuWj3lN0A1Hu4HvWG1DOvkE+KcUytT0+GB3+DgDLm5BARySMaKhERkVzRuWYwxgk7bla9byxSUCVdSOLI4iMA1BpYy7wgKXGOxe4SzsGQhVCsonlZRETyiIp5EREREbkle6bvwZ5hp1T9UpSoVsKcEGlJMKU/XDwKg+c6Vq8XESkEVMyLiIiIyC25MsW+1n0mjcpnpMK0++DsTnhwNpSqY04OERETqJgXERERkWy7fOwyJ9edBAvUHFAz7wPYMmDmQ3BsHdw3A8o1yvsMIiImUjEvIiIiItl2ZVS+YuuK+JXO4z7udjvM+Q8cXAT9f4LQlnl7fBERJ6BiXkRERESyxTAMdv9k0hR7w4AFz8HuGdBnIlTtlLfHFxFxEirmRURERCRbzu06R/TeaKweVqr3qZ53BzYMWPIybPkG7v4cavbJu2OLiDgZ9QsSERERkWy5MipfpWsVPAM88+7AEe/Bhs+h03tQ/4G8O66IiBNSMS8iIiIiN82wG/zx8x9AHk+xX/85rBoDbV6BOx/Pu+OKiDgpFfMiIiIictOOrzlO3Kk4PAI8qNylct4cdMu3sOT/oNlwaDEyb44pIuLkVMyLiIiIyE27MsW+ep/quHrmwfJLu6bDvOHQ6FFo+1ruH09EJJ9QMS8iIiIiNyUjNYO9M/cCUPu+2rl/wH3z4NfHoe5Ax3XyFkvuH1NEJJ9QMS8iIiIiN+XwosOkXErBr7QfIS1Dcvlgy2HmEKjeHbp/Ci562Soi8nf6rSgiIiIiN+XKFPsaA2rgYs3Fl5HH18PU+yC0NfT+Gqzqpiwi8k8q5kVERETkhlLjUjk49yCQy1PsT2+Dn/pB2YbQ7ztwdc+9Y4mI5GMq5kVERETkhvb9uo+MlAxKVCtBcL3g3DnIub3wY28IrAr3/gxuXrlzHBGRAkDFvIiIiIjc0JUp9jUH1sSSGwvRXTwCP/QE/7Jw/0zw8Mv5Y4iIFCAq5kVERETkuhKiEohcHglArYG1cv4Al0/Cd3eDhz888Ct4Fc35Y4iIFDAq5kVERETkuv6Y9geG3aDsnWUpVqlYzu48/hx8f7djtfoH54BvYM7uX0SkgNLSoCIiIiJyXX+fYp+jkmIcU+vTk2HIQggok7P7FxEpwFTMi4iIiMg1XTx0kTObz2CxWqjZPweL+ZQ4x2J3CecchXyxijm3bxGRQkDFvIiIiIhc0+4pjlH5Su0r4RPkkzM7TUuCKf3h4lEYPNexer2IiGSLinkRERERuSrDMHJ+in1GKky7H87udCx2V6pOzuxXRKSQUTEvIiIiIld1dutZYg7F4OrlSrWe1W5/h7YMmPkQHFsL902H8o1vf58iIoWUinkRERERuapdP+0CoFqPanj4edzezux2mPMfOLgI+v8Eoa1uP6CISCGmYl5ERERE/sVus7Nn6h4gB6bYGwYseA52z4A+E6FqpxxIKCJSuKmYFxEREZF/ObbyGAlRCXgV8yKsY9it78gwYOkrsOUbuPtzqNkn50KKiBRiLmYHEBERERHnc2Xhu/B+4Vjdrbe+o4ixsP4z6PQe1H8gh9KJiIiKeRERERHJIj05nb2/7AWg1sBat76j9Z/DqnegzStw5+M5lE5EREDFvIiIiIj8w6H5h0iLTyOgfADlm5a/tZ1s+RaW/B80Gw4tRuZsQBERUTEvIiIiIln9vbe8xcWS/R3smg7zhkOjR6HtazmcTkREQMW8iIiIiPxN8qVkDi04BNziFPt98+DXx6HuQMd18pZbeDNARERuSMW8iIiIiGTa98s+bGk2gmoFUbJWyew9+PBymDkEqneD7p+Ci15qiojkFv2GFREREZFMV6bY17ovm6Pyx9fD1PsgtBX0nghWdUAWEclNKuZFREREBIC4U3EcizgGQM0BNW/+gae3wk/9oGxD6Pc9uLrnTkAREcmkYl5EREREAPhj6h9gQPnm5SkSUuTmHrTnV5jcDYKqwb0/g5tXrmYUEREHFfMiIiIiAmRzir3dDsvfhBmDoWoXePA38PDL3YAiIpJJFzOJiIiICNF7o4naEYWLqwvhfcOvv3FKHMx6FA4ugnavQ9NntGq9iEgey9bI/JgxY7jjjjvw8/MjKCiInj17cuDAgSzbpKSkMGzYMIoXL46vry99+vTh3LlzWbY5ceIEXbt2xdvbm6CgIJ5//nkyMjKybLNq1Srq16+Ph4cHYWFhTJ48+V95xo8fT4UKFfD09KRx48Zs2rQpO1+OiIiIiPxp9xTHqHxY5zC8i3tfe8OLR2BiO8eCdwOnQ7NnVciLiJggW8V8REQEw4YN4/fff2fp0qWkp6fToUMHEhMTM7cZPnw4c+fOZcaMGURERHDmzBl69+6deb/NZqNr166kpaWxfv16vvvuOyZPnsyrr76auU1kZCRdu3aldevW7Nixg2effZZHHnmExYsXZ24zbdo0RowYwWuvvca2bduoU6cOHTt25Pz587fzfIiIyP+3d+fhUVRp+8fv7pAEIhCSIPsioiwhAQKiAqLIjuM4jqBsKgji8jMoxAFfFLcB9VVHxFcRRBF1FEFmUFQQiSgDYlBZwpKwG3YSkS0JIWuf3x9MWiK7JqnT3d/PdXlJV1c3T3JTyXm6Tp0CEHCMMd5m/qxT7Ld9Jb15vWQ80vDFUpMe5VQhAOC3Lmia/cKFC0s8fuedd1SjRg2tWrVK1157rY4eParp06dr5syZ6tKliyRpxowZat68uVasWKGrr75aixYtUmpqqr766ivVrFlTrVu31vjx4/XII4/oqaeeUkhIiKZOnapGjRrppZdekiQ1b95c3377rV5++WX17NlTkjRx4kQNHz5cd911lyRp6tSpmj9/vt5++239z//8zx/+xgAAAASKPUl7dCTtiEIqh6jpn5ueuoMx0nevSl89KV3WTerzllQxvPwLBQB4/aFr5o8ePSpJioyMlCStWrVKBQUF6tatm3efZs2aqUGDBkpKStLVV1+tpKQkxcbGqmbNmt59evbsqfvvv18pKSmKi4tTUlJSifco3mfkyJGSpPz8fK1atUpjx471Pu92u9WtWzclJSWdsd68vDzl5eV5H2dmZkqSCgoKVFBQ8Du/C2WvuDabaww0ZGIX8rATudiHTOxiUx5r318rSWrylyZS8G9qKjiuoAWj5N7wLxV1GCnPdWMld5BkQd1/lE0Z4AQysQt5OON8v9+/u5n3eDwaOXKkOnbsqJiYE/chTU9PV0hIiKpVq1Zi35o1ayo9Pd27z8mNfPHzxc+dbZ/MzEwdP35chw8fVlFR0Wn32bRp0xlrfu655/T000+fsn3RokUKCzvLtWGWSExMdLoE/AaZ2IU87EQu9iETuzidhyk02vDBBklSzmU5WrBggfe5ivmHdGXaK6pyfK9WX/L/tPd4G2nhl2d6K5/ldAY4FZnYhTzKV05Oznnt97ub+QceeEAbNmzQt99++3vfotyNHTtWCQkJ3seZmZmqX7++evTooapVqzpY2dkVFBQoMTFR3bt3V3BwsNPlQGRiG/KwE7nYh0zsYkse277YprWZaxVWI0y3PXKb3BVOLKnk2vODgv71N6lCsArv+kKtardSK8eqLBu2ZIBfkYldyMMZxTPIz+V3NfPx8fH6/PPPtXTpUtWrV8+7vVatWsrPz9eRI0dKnJ3PyMhQrVq1vPv8dtX54tXuT97ntyvgZ2RkqGrVqqpUqZKCgoIUFBR02n2K3+N0QkNDFRoaesr24OBgn/jH6St1BhIysQt52Ilc7EMmdnE6j40fbZQkxfSLUWil/46TVr8nfZ4g1Wsn3faegitf7Fh95cHpDHAqMrELeZSv8/1eX9Bq9sYYxcfH6+OPP9bXX3+tRo0alXi+bdu2Cg4O1uLFi73bNm/erF27dql9+/aSpPbt22v9+vUlVp1PTExU1apVFR0d7d3n5Pco3qf4PUJCQtS2bdsS+3g8Hi1evNi7DwAAAM4u/1i+Nn1y4hLF2EGxUlGBtGC09OkIKe526c55kp838gDgqy7ozPwDDzygmTNnat68eapSpYr3Gvfw8HBVqlRJ4eHhGjZsmBISEhQZGamqVatqxIgRat++va6++mpJUo8ePRQdHa077rhDL7zwgtLT0zVu3Dg98MAD3rPm9913n1577TWNGTNGQ4cO1ddff62PPvpI8+fP99aSkJCgwYMH64orrtCVV16pSZMm6dixY97V7QEAAHB2m+dtVsGxAkU0jlDdmIrSP/8q7UqS/jRRajfM6fIAAGdxQc38lClTJEmdO3cusX3GjBkaMmSIJOnll1+W2+1Wnz59lJeXp549e+r111/37hsUFKTPP/9c999/v9q3b6+LLrpIgwcP1t///nfvPo0aNdL8+fM1atQovfLKK6pXr57eeust723pJKlfv346cOCAnnjiCaWnp6t169ZauHDhKYviAQAA4PSK7y1/9cDKcr15vZSfI935qXRJR4crAwCcywU188aYc+5TsWJFTZ48WZMnTz7jPg0bNiyxUurpdO7cWWvWrDnrPvHx8YqPjz9nTQAAACgp55ccbf9yu5o336J2wVOkipdJQ+ZL1Ro4XRoA4Dz8ofvMAwAAwDelzF6v6zot07XXfi81u0X6y2QpxP5b9QIATqCZBwAACDS5maqRmqAGnTZoZ+hdatj3ZcnlcroqAMAFuKDV7AEAAODjDm5X0dQuqllliz6cdbMiB4+nkQcAH0QzDwAAECi2LZbevF55R3L01lsDVVi/q6rUqeJ0VQCA34FmHgAAwN8ZI333mvRBX5l6V2rmJ8N08GCkYgfGOl0ZAOB3opkHAADwZwXHpY/vkxY9JnV4UBkxr2jv2mwFhQapeZ/mTlcHAPidWAAPAADAX2Xuk2YNkn5OlfpMl2L7av2YRElSkxubqGJ4RYcLBAD8XjTzAAAA/mj3D9Ls2yV3BWnoQqlOnIzHaMOHGySJKfYA4OOYZg8AAOBvVv9TeudPUuSl0j1LpDpxkqSdy3Yqc0+mQsNDdfkNlztbIwDgD+HMPAAAgL8oKpAWjZO+nyq1HSL1flGqEOJ9ev0H6yVJ0X2jVaEiw0AA8GX8FAcAAPAHOYekOYOlnd9Jf5ootRtW4unCvEKl/itVElPsAcAf0MwDAAD4uowU6cMBUn62dOen0iUdT9ll28Jtyj2cqyp1qqjhdQ0dKBIAUJq4Zh4AAMCXpc6T3uouVax64vr40zTy0q9T7GMGxMgdxBAQAHwdP8kBAAB8kccjffOs9NGdUpMe0tAvpWoNTrtrXmaetny2RRJT7AHAXzDNHgAAwNfkZUlz75U2L5C6PiFdkyC5XGfcfePHG1WYW6jqzaqrVlytciwUAFBWaOYBAAB8yaGfpA8HSkf3SANmSU17nfMlxVPsYwfFynWWph8A4Dto5gEAAHzF9q+lOXdJYVHS8MXSxU3P+ZLs9GylLU6TdOJ6eQCAf+CaeQAAANsZIyVNlt7vI9Vte96NvCRtmL1BxmNU7+p6imwcWcaFAgDKC2fmAQAAbFaQK30+Ulr7odThQanbU5I76LxffvIUewCA/6CZBwAAsFXmPmn27SfuI3/Lm1LL2y7o5Qe3HtS+H/fJFeRSi9talFGRAAAn0MwDAADYaPePJxp5d5B01xdS3TYX/BbrZ544K9+4e2NdVOOi0q4QAOAgrpkHAACwzZr3pXdukCIaSsO/+V2NvDGGKfYA4Mc4Mw8AAGCLokJp0Tjp+ylSmzulG/4hVQj9XW+1f9V+Hdp6SBUqVVDTv5zfYnkAAN9BMw8AAGCDnEPSnMHSzu9ONPHt7pb+wD3h132wTpLU7C/NFFrl930gAACwF808AACA0zJSpVkDpLws6Y5PpEad/tDbeYo8SpmVIokp9gDgr7hmHgAAwEGuTfOlt7pJIVVOXB//Bxt5SdrxzQ5lp2erUmQlNe7RuBSqBADYhjPzAAAATjAeNd3/sSqs+ViKvlm6+XUppHRWnC9e+C76tmgFhZz/PekBAL6DZh4AAMABQZ/Fq2n6Jyrq/JiCrhv9h66PP1nB8QKl/jtVktRyUMtSeU8AgH1o5gEAABzgafInrTxWV207jlJQKTXykrR1/lblZ+UrvEG46neoX2rvCwCwC808AACAA0yzPynjp9Jr4osVT7GPGRgjl7v03x8AYAcWwAMAAPATxw8f19YFWyUxxR4A/B3NPAAAgJ/Y+O+NKsovUo3YGqoRU8PpcgAAZYhmHgAAwE8UT7Hn3vIA4P9o5gEAAPxA5p5M7fjPDklS7ACaeQDwdzTzAAAAfmDDrA2SkRp0aqDwBuFOlwMAKGM08wAAAH6AKfYAEFho5gEAAHzcgdQDSk9OlzvYrei+0U6XAwAoBzTzAAAAPm79zBNn5S/rdZnCosIcrgYAUB5o5gEAAHyYMcbbzDPFHgACB808AACAD9uTtEdH0o4opHKImv65qdPlAADKCc08AACADys+K9/8luYKDgt2uBoAQHmhmQcAAPBRRQVFSpmdIkmKGRjjcDUAgPJEMw8AAOCjfkr8STm/5OiiGhfp0q6XOl0OAKAc0cwDAAD4qOIp9i36t5C7AsM6AAgk/NQHAADwQfnH8rXpk02SpNiBrGIPAIGGZh4AAMAHbZ63WQXHChTROEJ1r6zrdDkAgHJGMw8AAOCD1n/w673lXS6Xw9UAAMobzTwAAICPOXbgmLZ9uU0SU+wBIFDRzAMAAPiY1DmpMkVGtdvWVvWm1Z0uBwDgAJp5AAAAH3PyFHsAQGCimQcAAPAhh9MOa/d3uyWXFNMvxulyAAAOoZkHAADwIRs+3CBJatSlkarUqeJwNQAAp9DMAwAA+AhjDFPsAQCSaOYBAAB8Rsa6DB1IPaCg0CA1v6W50+UAABxEMw8AAOAjis/KN7mxiSqGV3S4GgCAk2jmAQAAfIDxGO/18kyxBwDQzAMAAPiAnct2KnNPpkLDQ3V578udLgcA4DCaeQAAAB9QPMU+um+0KlSs4HA1AACn0cwDAABYrjCvUKlzUiUxxR4AcALNPAAAgOW2Ldym3CO5qlKnihpe29DpcgAAFqCZBwAAsFzxFPuYATFyBzF8AwDQzAMAAFgtLzNPWz7bIokp9gCAX9HMAwAAWGzjxxtVmFuo6s2qq1brWk6XAwCwBM08AACAxYqn2McOipXL5XK4GgCALWjmAQAALJWdnq20xWmSpNiBTLEHAPyKZh4AAMBSG2ZvkPEY1bu6niIujXC6HACARWjmAQAALHXyFHsAAE5GMw8AAGChg1sPat+P++QKcqnFbS2cLgcAYJkLbuaXLl2qP//5z6pTp45cLpc++eSTEs8PGTJELperxH+9evUqsc+hQ4c0aNAgVa1aVdWqVdOwYcOUnZ1dYp9169apU6dOqlixourXr68XXnjhlFrmzJmjZs2aqWLFioqNjdWCBQsu9MsBAACw0vqZJ87KN+7eWBfVuMjhagAAtrngZv7YsWNq1aqVJk+efMZ9evXqpf3793v/+/DDD0s8P2jQIKWkpCgxMVGff/65li5dqnvuucf7fGZmpnr06KGGDRtq1apVevHFF/XUU09p2rRp3n2+++47DRgwQMOGDdOaNWt088036+abb9aGDRsu9EsCAACwijGGKfYAgLOqcKEv6N27t3r37n3WfUJDQ1Wr1unvg7px40YtXLhQP/74o6644gpJ0quvvqobbrhB//jHP1SnTh198MEHys/P19tvv62QkBC1aNFCycnJmjhxorfpf+WVV9SrVy+NHj1akjR+/HglJibqtdde09SpUy/0ywIAALDGvpX7dGjrIVWoVEHNbm7mdDkAAAtdcDN/PpYsWaIaNWooIiJCXbp00YQJExQVFSVJSkpKUrVq1byNvCR169ZNbrdb33//vf76178qKSlJ1157rUJCQrz79OzZU88//7wOHz6siIgIJSUlKSEhocTf27Nnz1Om/Z8sLy9PeXl53seZmZmSpIKCAhUUFJTGl14mimuzucZAQyZ2IQ87kYt9yMQuZ8tj7ftrJUlN/txErlAXmZURjgn7kIldyMMZ5/v9LvVmvlevXrrlllvUqFEjbd++XY8++qh69+6tpKQkBQUFKT09XTVq1ChZRIUKioyMVHp6uiQpPT1djRo1KrFPzZo1vc9FREQoPT3du+3kfYrf43See+45Pf3006dsX7RokcLCwn7X11ueEhMTnS4Bv0EmdiEPO5GLfcjELr/NwxQZpfwzRZJ0/PLjrAlUDjgm7EMmdiGP8pWTk3Ne+5V6M9+/f3/vn2NjY9WyZUs1btxYS5YsUdeuXUv7r7sgY8eOLXE2PzMzU/Xr11ePHj1UtWpVBys7u4KCAiUmJqp79+4KDg52uhyITGxDHnYiF/uQiV3OlEfaV2lae3itKkVV0m2P3qag4CAHq/RvHBP2IRO7kIczimeQn0uZTLM/2aWXXqrq1atr27Zt6tq1q2rVqqWff/65xD6FhYU6dOiQ9zr7WrVqKSMjo8Q+xY/Ptc+ZrtWXTlzLHxoaesr24OBgn/jH6St1BhIysQt52Ilc7EMmdvltHhs/2ihJir41WhXDKjpVVkDhmLAPmdiFPMrX+X6vy/w+83v27NHBgwdVu3ZtSVL79u115MgRrVq1yrvP119/LY/Ho6uuusq7z9KlS0tcK5CYmKimTZsqIiLCu8/ixYtL/F2JiYlq3759WX9JAAAAZaLgeIFS/50qSWo5qKXD1QAAbHbBzXx2draSk5OVnJwsSUpLS1NycrJ27dql7OxsjR49WitWrNCOHTu0ePFi/eUvf9Fll12mnj17SpKaN2+uXr16afjw4frhhx+0fPlyxcfHq3///qpTp44kaeDAgQoJCdGwYcOUkpKi2bNn65VXXikxRf6hhx7SwoUL9dJLL2nTpk166qmntHLlSsXHx5fCtwUAAKD8bfl8i/Kz8hXeMFz1O9R3uhwAgMUuuJlfuXKl4uLiFBcXJ0lKSEhQXFycnnjiCQUFBWndunW66aab1KRJEw0bNkxt27bVsmXLSkxv/+CDD9SsWTN17dpVN9xwg6655poS95APDw/XokWLlJaWprZt2+rhhx/WE088UeJe9B06dNDMmTM1bdo0tWrVSv/617/0ySefKCYm5o98PwAAAByzYeYGSVLMgBi53C6HqwEA2OyCr5nv3LmzjDFnfP7LL78853tERkZq5syZZ92nZcuWWrZs2Vn3ufXWW3Xrrbee8+8DAACw3fHDx7V1wVZJTLEHAJxbmV8zDwAAgHNL/VeqivKLVLNlTdWIqXHuFwAAAhrNPAAAgAW8U+wHcskgAODcaOYBAAAclrknUzv+s0OSFDsg1tliAAA+gWYeAADAYes/XC8ZqeG1DRXeINzpcgAAPoBmHgAAwGFMsQcAXCiaeQAAAAcdSD2g9OR0uYPdanFrC6fLAQD4CJp5AAAAB6V8mCJJurz35aoUWcnhagAAvoJmHgAAwCHGGKXMPtHMM8UeAHAhaOYBAAAckrM5R0d3HFVI5RA1/XNTp8sBAPgQmnkAAACHHP7PYUlS81uaKzgs2OFqAAC+hGYeAADAAUUFRTq8/EQzHzuIe8sDAC4MzTwAAIAD0r5KU1FmkcJqhKlRl0ZOlwMA8DE08wAAAA4oXsU++rZouSswJAMAXBh+cwAAAJSz/GP52vLpFklSzABWsQcAXDiaeQAAgHKWcyBHDa5toNB6oap9RW2nywEA+CCaeQAAgHJW7ZJq6vdpPzV9ualcLpfT5QAAfBDNPAAAgEPcwQzFAAC/D79BAAAAAADwMTTzAAAAAAD4GJp5AAAAAAB8DM08AAAAAAA+hmYeAAAAAAAfQzMPAAAAAICPoZkHAAAAAMDH0MwDAAAAAOBjaOYBAAAAAPAxNPMAAAAAAPgYmnkAAAAAAHwMzTwAAAAAAD6GZh4AAAAAAB9DMw8AAAAAgI+p4HQBTjLGSJIyMzMdruTsCgoKlJOTo8zMTAUHBztdDkQmtiEPO5GLfcjELuThPDKwD5nYhTycUdyfFverZxLQzXxWVpYkqX79+g5XAgAAAADAr7KyshQeHn7G513mXO2+H/N4PNq3b5+qVKkil8vldDlnlJmZqfr162v37t2qWrWq0+VAZGIb8rATudiHTOxCHs4jA/uQiV3IwxnGGGVlZalOnTpyu898ZXxAn5l3u92qV6+e02Wct6pVq3IQWYZM7EIediIX+5CJXcjDeWRgHzKxC3mUv7OdkS/GAngAAAAAAPgYmnkAAAAAAHwMzbwPCA0N1ZNPPqnQ0FCnS8F/kYldyMNO5GIfMrELeTiPDOxDJnYhD7sF9AJ4AAAAAAD4Is7MAwAAAADgY2jmAQAAAADwMTTzAAAAAAD4GJp5AAAAAAB8DM08AAAAAAA+hmYeAAAAAAAfQzMPOIS7Qtph37592rZtm9Nl4Aw4TgAAF4rfHXZgjFX2aOb92NGjR5WWlqb9+/erqKjI6XIgKTk5WYMHD5bH45HL5XK6nICXkpKiTp06adasWZLEcWKJrKwsHTlyRNnZ2RwnwGl4PB6nSwh4jLHswxjLLoyxygfNvJ/asGGDevbsqZtuukkxMTGaOnUqn1I6bO3aterQoYPq1Kkjt/vXQ49cnLF27VpdeeWVyszM1Hvvvae8vDwFBQU5XVbAW79+vbp27aqePXsqOjpaDz/8sH788UenywpohYWFTpeAk2zbtk3Tp0/XoUOHnC4lYDHGsg9jLLswxio/NPN+KCUlRZ07d1bHjh01Y8YMDR06VI888gi/+B20du1adezYUQ888ICee+65Es/x6XH5W7t2rdq3b6+EhAStWrVKHo9H06ZNk8Qvfif99NNP6t69u6655hq99tprSkhI0JdffqkhQ4ZoyZIlTpcXkLZs2aLx48dr8+bNTpcCSVu3blXbtm1177336p133lFmZqbTJQUcxlj2YYxlF8ZY5czAr2RkZJirr77ajBo1yrstKyvL9OrVy6xZs8Zs377d/Pzzzw5WGHj27t1rIiMjTZ8+fYwxxhQUFJhHHnnE9OnTx7Rv39688847ZufOnQ5XGTiSk5NNpUqVzKOPPmqMMSYnJ8d07drV9OzZ0+HK8NRTT5m+ffuW2DZ69GjjcrlM48aNzeLFix2qLDBt3brVXHzxxcblcpm//e1vZtu2bU6XFNAyMzPNwIEDzdChQ80TTzxhXC6Xef75583Ro0edLi1gMMayD2MsuzDGKn8VnP4wAaVr//796t27t26//XbvtpdeeklfffWVdu3apePHj6t169Z6+umnFRsb62ClgWPnzp1q06aNMjIytHz5ck2YMEG5ublq2rSpgoKC9PTTTys5OVljx45VjRo1nC7X782bN0+jRo3SM888I4/Ho0qVKunvf/+7unfvrtmzZ6tfv35OlxiwDh06pNzcXBUVFcnj8Sg4OFixsbG68cYbFRQUpClTpqhVq1aKiopyulS/l5OTo2eeeUY9evRQXFycXn75ZRUWFio+Pl6NGzd2uryAdPz4ccXFxalhw4a69dZbVa1aNT388MOSpPvuu09Vq1Z1uEL/xxjLPoyx7MIYywFOf5qA0rdjxw7vn2fMmGFcLpf54IMPzN69e83cuXNN69atzSuvvOJghYFnyZIl5q9//asJCQkxPXr0MAcOHPA+N2nSJBMVFWWSkpIcrDBweTwek5GRYbp3726GDx9ujDGmqKjI4aoCi8fjMcYY8/TTT5saNWqYtWvXmqysLLN7925z8cUXm2nTppk5c+aYqKgos3XrVoerDQzHjh0zb7/9tnn//feNMca8++67pm7dumbUqFGcoXfQnj17Sjx+6aWXTjlDX1hYaPbt2+dEeQGBMZZ9GGPZizFW2aOZ9wN5eXkmNzf3tM+lpKSYFStWlNjWoUMHM2DAgPIoLWAVZ1LcpBhjTGJiohk1apRZsmSJMabkD7OaNWua8ePHl3udgeJ0x0hhYWGJx6+//roJDg42mzdvLs/SAtrpcunWrZupWrWqadOmjbnooovMvffe632uVq1aZubMmeVdZsA6cuRIicczZswwdevWNSNHjvQ29IWFhSYtLc2B6gLDmX6/FxQUeP98ckN/4MABM3r0aHPHHXeccVyAC8MYyz6MsezCGMtZTLP3cSkpKZowYYK2b9+uuLg4XXXVVRo6dKikE4tMREdHe/f1eDzKzc1V7dq1ddVVVzlVst/7bSbt2rXT3XffrW7duqlBgwa65JJLJElut1sej0cZGRmqX7++WrZs6WzhfupMx0hQUJD39jUul0t33nmn3nvvPU2aNEmvvPKKgoODnS7dr/02lyuuuELDhw9XYmKipk2bpgoVKqhatWq65ZZbJJ1YiC0yMlKXXnqpw5X7v6KiIgUFBSk8PLzE4yFDhkiSxo0bJ5fLpXvuuUdvvvmmVq5cqS+++EKVKlVisalSdLbf78W/P9xutxISEiRJY8eO1axZs7Ru3TqtWrVKoaGhTpbvFxhj2Ycxll0YYzmP1ex92JYtW3TNNdeocuXK6tq1qw4ePKixY8fqvvvuk3RiBc+Tbynkdrv1/PPPa/Xq1brxxhudKtuvnS6Txx57TPfcc48kqUmTJiV+gLndbk2dOlXHjh1TXFycU2X7rXMdI8W/7CXpoosuUqdOnfTZZ58pNzfXybL93ulyGTdunIYPHy5JuueeezR06FBvI19YWKh//vOf8ng8atiwoZOl+61du3bp008/lSQFBQWVWHH45MdDhgzRM888o7lz5+qmm27S66+/rkmTJiksLIxGvhSdz88uc2J2pSQpISFBrVq10q5du7RmzRq1atXKyfL9AmMs+zDGsgtjLEs4OS0Af8yzzz5revXq5Z1KdOjQIfP++++bypUrmyFDhpTYd+7cueahhx4ykZGRZvXq1U6UGxAuJJM5c+aY+Ph4U61aNbNmzRoHqvV/55tH8ZTVHTt2mCZNmphdu3Y5Um+gOFsugwcP9u7n8XjM6tWrzf3332+qVq3Kz64ysmnTJhMREWFatWplZs+e7d1+8hTW3z6+9tprTWRkpFm3bl251RlIzvdnV1FRkcnPzzfx8fHG5XKRRylijGUfxlh2YYxlB6bZ+7C0tDRlZmbK7T4xwSIiIkK33XabKlWqpLvuuku1a9fWs88+K0nKyMjQjh07tGzZshLTwlC6LiST/fv3a9OmTVq2bJliYmKcLNtvnW8eFSpUkDFG9erV08qVK1WlShWHK/dv58qlTp06evbZZ+VyueR2u9WgQQOtWLFCzZs3d7hy//PLL78oPj5e7du3V1FRkd544w15PB71799fLpdLxhjvGffiM5GjRo3SsmXLlJyczIrdZeR8f3a53W7l5ubqkksu0cqVK8mjFDHGsg9jLLswxrKEwx8m4A/497//bS699FLzzTfflNh+7Ngx88ILL5i4uDiTmprq3Z6VlVXOFQaeC82E+wOXrfPJY9OmTc4UF8DOJ5eNGzd6t5+82BdK144dO8ydd95plixZYrZt22ZuuOEG06VLF/Phhx969/ntGfqZM2eaVatWlXepAeVCf3axOnTpY4xlH8ZYdmGMZQeumfdhzZs3V7169fTee+8pNTXVuz0sLEy9e/fW5s2blZaW5t1euXJlJ8oMKBeaCfcFLlvnk8f27dsdrDAwnU8uP/30k3d7hQpMIisrDRs21Pjx43XdddepcePGmjRpkkJDQ/Xmm29q1qxZkk69NnjAgAFq06aNUyUHhAv92VV8ZgylhzGWfRhj2YUxlh346e/DmjdvrgcffFBff/21Jk2apNWrV3ufa9SokaKjo1mQqJyRiV3Iw07kYpcGDRpIOrHQ4OWXX65XX321REPv8Xg0ZswYTZw40eFKAwfHiPPIwD5kYhfysAOnO3xUQUGBgoOD1adPH1WqVEkPP/yw9u7dq5tvvllXXHGFPvjgA+3atYvrhMoRmdiFPOxELvaqUKGCioqK1LhxY7366qsaMWKEpk+frjfeeEMrVqzQd99953SJAYFjxHlkYB8ysQt5WMTpef44f4WFhSX+n5aWZh588EFjjDFfffWVufvuu014eLhp0aKFadasGSuqlgMysQt52Ilc7PXbbIz59Rr51NRUExERYapVq2aSk5MdqS/QcIw4jwzsQyZ2IQ+70MxbbP/+/Wb58uVm3rx53m35+fnGmBOLFtWuXdt7EBlzYpGo9PR0s3PnTnPw4MFyrzcQkIldyMNO5GK33zbwu3btMg899JDJyMjw7pObm2vuvfdeEx4ebtavX+9Inf5s48aN5oUXXjDZ2dnebcWL2HGMlA8ysA+Z2IU8fAPNvKXWrVtnWrRoYWJjY021atVMx44dvc9lZmaaypUrm7vvvrvEKsO/XXEYpYtM7EIediIX+5zPhysjR44s8Zpjx46Z1q1bmxUrVpRrrf7O4/GY7Oxs06hRI+NyuczYsWNNXl6e97mjR49yjJQxMrAPmdiFPHwLzbyFUlNTTVRUlHn00UfNxo0bzbJly0zNmjXNt99+691n8eLF3IqmHJGJXcjDTuRin9/z4UpxPuRUdu6//34zfPhwExYWZkaMGGGOHTvmfS4pKYnbMZYDMrAPmdiFPHwDC+BZ5tChQxo6dKiGDBmiZ555RtKJFYZbtGihvXv3avr06brxxhvVpUsXhysNHGRiF/KwE7nYZ+PGjbr++ut177336o477tAvv/yivn37avny5erYsaOqVKmiefPmqXPnziVWHC6+zRmrEJc+j8cjt9utrKwstWnTRp9++ql69+6tkJAQ/eMf/9Dbb7+tnj17cjvGMkQG9iETu5CHbyEFy0RGRurWW29Vu3btvNv+93//V99++62ysrJ05MgRPfbYY5o7d646dOjgPeBQdsjELuRhJ3KxS2l8uEIzX/qKv6c33HCD1q1bp65du+rjjz/WLbfcok8//VRut1vdunVzuEr/Rgb2IRO7kIePcXpqAH51uukq8+fPNw0bNjTz5s3zLibRuXNnc/3115d3eQGJTOxCHnYiFzu99NJLZunSpd7H48ePNyEhIaZdu3bm8ssvNzVr1jTLly83xjClvjycfCnDvHnzTFxcnPf7fv3115ugoCDTv39/rjstQ2RgHzKxC3n4Hk6LWCAnJ0cej0eFhYWnPHfZZZdpwYIFuummmxQRESFJuuqqq8q7xIBDJnYhDzuRi52K80hISFCnTp0kSQsWLNBbb72lOXPmaOHChdqyZYuaN2+ucePGSRKzJMpI8TGSm5vrPdtljFGjRo1Us2ZNud1uDRs2TFu2bNGLL76ozz77TMOHD1d+fr7DlfsPMrAPmdiFPHwb0+wdtmHDBo0aNUqFhYXat2+fRo0apV69eumSSy6RJDVp0sS7b/EBlp6erpiYGHk8HrlcLqZCljIysQt52Ilc7JOTk6OKFSuqsLDwlGsZiz9ciY6OljFG0okPV3744QcnSg0IZztGoqOjlZ+fr6ZNm+ro0aNasGCB2rRpo3r16mnEiBF65plnVLNmTae/BJ9HBvYhE7uQhx9wdF5AgNuyZYu5+OKLzciRI82cOXPMU089ZVwul+nTp4/57rvvTtm/oKDAjBs3ztSoUcNs2rTJgYr9H5nYhTzsRC72Wb9+venWrZvp3LmzadKkiZkyZYpJS0s762sGDx5sRowYYYqKipgyWcrOdowU393h9ttvN+3atTOrVq0q8dqsrCwnSvY7ZGAfMrELefgHmnkHPfTQQ6Z///4ltg0ZMsRUqlTJ9O3b16xcudK7fcmSJWbgwIGmdu3aZvXq1eVdasAgE7uQh53IxS58uGKfsx0jt9xyi9m2bZv5+eefzY4dOxyq0P+RgX3IxC7k4R+4SM5Be/fuVVRUlCQpKytL0ompkJ06ddL69ev18ccfS5Jyc3NVsWJFNWzYUF9//bXi4uIcq9nfkYldyMNO5GKXyZMnq2vXrnr55ZfVt29fPfnkkxo8eLAWLFigiRMnatWqVd59//Of/2jw4MGaPn26Fi5cqKZNmzpYuf862zGyYcMGTZ8+XRdffLEaNGjgZJl+jQzsQyZ2IQ8/4fSnCYFs1KhRpnbt2iY7O9sYY8z+/ftNRESESUxMNFOmTDFhYWFm165d3v1Pt2I0SheZ2IU87EQudunbt6954IEHjDHGZGZmGmOMmTBhgunRo4dp2rSpeeyxx4wxxhw/ftysWLHCjB071mzcuNGxegPB+Rwju3fvdrhK/0YG9iETu5CHf6CZd9DOnTtNhw4dTGhoqOnVq5cJCwszw4cPN8YY88svv5i6det6r1lB+SATu5CHncjFLny4Yh+OEeeRgX3IxC7k4R9Yzb6cbN68We+884727NmjVq1aqUePHmrZsqW+/PJLTZ48WR6PR7fffrsGDRokSdq1a5fCwsIUHh7ucOX+i0zsQh52Ihf7jRw5Ut9//72ioqJ0/fXXa+nSpRo0aJC6deumuLg4TZgwQbt27VL9+vUl6ZSV7vHHcIw4jwzsQyZ2IQ//5TLmv/eoQZlJTU1Vx44d1b17d0VFRWn+/PmKiorSfffdp3vvvVeS5PF4Stzn95FHHtGiRYuUmJio6tWrO1W63yITu5CHncjFPmcakGVnZ3sHZA0aNPAOyNasWaN+/fpp7ty5iomJcbh6/8Mx4jwysA+Z2IU8/JzTUwP8XVZWlunZs6cZM2aMd9uePXtMVFSUqVmzphk/fnyJ/ZcuXWpGjBhhqlSpYtasWVPO1QYGMrELediJXOyTkpJiqlWrZm699VZz3333mfr165vWrVubqVOnevcpKioq8ZoxY8aY1q1bmwMHDpR3uX6PY8R5ZGAfMrELefg/VrMvY263W4cOHVLr1q0lSTk5Oapbt666dOmimJgYffHFF/riiy9K7F9YWKikpCTva1C6yMQu5GEncrFLdna2EhISdM899+ijjz7SlClTlJSUpN27d+vJJ5/UhAkTJMl7ZmXZsmV68MEHNWXKFM2YMYMzK2WAY8R5ZGAfMrELefg/mvkyZIxRdna29u7dq71790qSwsLCtGfPHqWkpOjOO+9Udna25s6d631Nx44dNXHiRLVo0cKpsv0amdiFPOxELvZhQGYXjhHnkYF9yMQu5BEgnJsU4L8KCwtLPH7ttdeMy+UyQ4cONePGjTOVK1f2rhY5Z84cc8kll5hffvmFFYbLEJnYhTzsRC528ng8JiMjw9SpU8e8+OKL3u27d+820dHR5t133zUtW7Y0d999d4nXHT9+vLxL9XscI84jA/uQiV3II7BwZr6UbdmyRZMmTdL+/fu92+6//37NmDFD69ev18qVK/X4449r2rRpkqT09HRFREQoMjKSFYbLCJnYhTzsRC72KSoqkiS5XC7VqFFDjz76qMaMGaNhw4bp8ccfV/PmzdWxY0fdeeedevzxx/XVV1/p4MGDKiwslCRVrFjRyfL9DseI88jAPmRiF/IIPKRWirZt26b27dvr8OHDOnjwoBISElS9enW53W4NHjxY/fr1k8vlUmhoqPc1mzdvVuPGjZWXl6fQ0FC5XC4HvwL/QyZ2IQ87kYt9tmzZos8++0wDBw5U7dq1JZ0YkFWuXFmTJ0/Wvn379Pjjj2vMmDGSSg7IyKL0cYw4jwzsQyZ2IY8A5fTUAH+RnZ1thg4daoYMGWImT55sXC6XGT16dIkVhD0ej/fPGzduNCNHjjRVqlQx69atc6Jkv0cmdiEPO5GLfbZu3WoiIyONy+UyY8eOPWUl+uPHj5vc3NwS2+Lj403fvn3N8ePHS+SFP45jxHlkYB8ysQt5BC7OzJcSt9uttm3bKioqSv369VP16tXVv39/SdKYMWNUvXp176ddWVlZSkxM1Jo1a7R06VLFxsY6WbrfIhO7kIedyMUux44d03PPPaebbrpJ7dq1U3x8vAoLC71ZSCpx9mTTpk1644039O6772r58uVMrS8DHCPOIwP7kIldyCOAOf1pgj/Jzs4u8XjWrFnG5XKZv/3tb+aXX34xxpxYlCIjI8MUFBSYQ4cOOVFmQCETu5CHncjFHjk5OWby5Mlm1qxZxhhjZs+efdozLMYYk5mZaf7v//7PXHfdddwPuIxxjDiPDOxDJnYhj8BEM18GCgsLvVNZPvzwQ+9AbO/evWbUqFHm5ptvNjk5OQ5XGVjIxC7kYSdysQMDMntxjDiPDOxDJnYhj8BCM19GPB6PKSoqMsacGIgFBwebpk2bmgoVKpjVq1c7XF1gIhO7kIedyMUeDMjsxDHiPDKwD5nYhTwCh8sYY5ye6u+vir+1LpdLXbt2VXJyspYsWcK1KQ4iE7uQh53IxR7mxIfucrvdmj17tu644w5deuml2r59u3744QfFxcU5XWJA4hhxHhnYh0zsQh6BgQXwypDL5VJRUZFGjx6tb775RsnJyRxADiMTu5CHncjFHsULFhlj1K9fP02bNk3JyclavXo1mTiIY8R5ZGAfMrELeQQGmvly0KJFC61evVotW7Z0uhT8F5nYhTzsRC52YEBmL44R55GBfcjELuTh35hmXw6MMd6zK7ADmdiFPOxELvYoKirSO++8o7Zt26p169ZOl4P/4hhxHhnYh0zsQh7+jWYeAAAfwIAMAACczO10AQAA4Nxo5AEAwMlo5gEAAAAA8DE08wAAAAAA+BiaeQAAAAAAfAzNPAAAAAAAPoZmHgAAAAAAH0MzDwAAAACAj6GZBwAAAADAx9DMAwAAAADgY2jmAQAAAADwMf8fNyFkCxjfEIEAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -784,11 +986,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"### 3.3 Catboost ",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -1081,11 +1219,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"## 4. Forecasting multiple time series \n\n
\n \n \n timestamp \n segment \n target \n \n \n \n \n 0 \n 2019-01-01 \n segment_a \n 170 \n \n \n 1 \n 2019-01-02 \n segment_a \n 243 \n \n \n 2 \n 2019-01-03 \n segment_a \n 267 \n \n \n 3 \n 2019-01-04 \n segment_a \n 287 \n \n \n 4 \n 2019-01-05 \n segment_a \n 279 \n \n \n
\n
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " timestamp \n",
+ " segment \n",
+ " target \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2019-01-01 \n",
+ " segment_a \n",
+ " 170 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2019-01-02 \n",
+ " segment_a \n",
+ " 243 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2019-01-03 \n",
+ " segment_a \n",
+ " 267 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 2019-01-04 \n",
+ " segment_a \n",
+ " 287 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2019-01-05 \n",
+ " segment_a \n",
+ " 279 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
",
- "image/png": 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+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
- "df = TSDataset.to_dataset(original_df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"ts.plot()"
]
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "execution_count": 34,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1187,7 +1379,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 35,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1200,12 +1392,8 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "execution_count": 36,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1232,7 +1420,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 37,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1275,7 +1463,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 38,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1284,9 +1472,14 @@
"outputs": [
{
"data": {
- "text/plain": "{'segment_a': 6.146211495853116,\n 'segment_b': 5.912030620420795,\n 'segment_c': 11.833167344191251,\n 'segment_d': 5.026194101393465}"
+ "text/plain": [
+ "{'segment_a': 6.146211495853116,\n",
+ " 'segment_b': 5.912030620420795,\n",
+ " 'segment_c': 11.833167344191251,\n",
+ " 'segment_d': 5.026194101393465}"
+ ]
},
- "execution_count": 39,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -1297,7 +1490,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 39,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1306,8 +1499,10 @@
"outputs": [
{
"data": {
- "text/plain": "",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -1339,4 +1534,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/examples/102-backtest.ipynb b/examples/102-backtest.ipynb
index a66331a1d..b8fb8803a 100644
--- a/examples/102-backtest.ipynb
+++ b/examples/102-backtest.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "eb837376",
+ "id": "ba840de2",
"metadata": {},
"source": [
"# Backtest: validation on historical data\n",
@@ -206,7 +206,7 @@
"id": "f792fcd5",
"metadata": {},
"source": [
- "Our library works with the special data structure TSDataset. So, before starting the EDA, we need to convert the classical DataFrame to TSDataset."
+ "Our library works with the special data structure TSDataset. So, before starting the EDA, we need to convert the DataFrame into TSDataset."
]
},
{
@@ -216,7 +216,6 @@
"metadata": {},
"outputs": [],
"source": [
- "df = TSDataset.to_dataset(df)\n",
"ts = TSDataset(df, freq=\"D\")"
]
},
@@ -1618,7 +1617,6 @@
"source": [
"df = pd.read_csv(\"./data/example_dataset.csv\")\n",
"df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n",
- "df = TSDataset.to_dataset(df)\n",
"ts_all = TSDataset(df, freq=\"D\")"
]
},
diff --git a/examples/103-EDA.ipynb b/examples/103-EDA.ipynb
index f6bfd6617..a5c1ff6ca 100644
--- a/examples/103-EDA.ipynb
+++ b/examples/103-EDA.ipynb
@@ -158,8 +158,8 @@
}
],
"source": [
- "classic_df = pd.read_csv(\"data/example_dataset.csv\")\n",
- "classic_df.head()"
+ "df = pd.read_csv(\"data/example_dataset.csv\")\n",
+ "df.head()"
]
},
{
@@ -278,7 +278,6 @@
}
],
"source": [
- "df = TSDataset.to_dataset(classic_df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"ts.head(5)"
]
diff --git a/examples/201-exogenous_data.ipynb b/examples/201-exogenous_data.ipynb
index 15871baf6..70d6ee831 100644
--- a/examples/201-exogenous_data.ipynb
+++ b/examples/201-exogenous_data.ipynb
@@ -151,7 +151,9 @@
},
"source": [
"The next step is converting the data into the ETNA format.\n",
- "Code that allows us to do that is identical for target time series and exogenous data."
+ "Code that allows us to do that is identical for target time series and exogenous data. \n",
+ "\n",
+ "For demostrational purposes we will convert data into a wide format."
]
},
{
diff --git a/examples/202-NN_examples.ipynb b/examples/202-NN_examples.ipynb
index 21977385f..121ca8ccb 100644
--- a/examples/202-NN_examples.ipynb
+++ b/examples/202-NN_examples.ipynb
@@ -194,8 +194,8 @@
}
],
"source": [
- "original_df = pd.read_csv(\"data/example_dataset.csv\")\n",
- "original_df.head()"
+ "df = pd.read_csv(\"data/example_dataset.csv\")\n",
+ "df.head()"
]
},
{
@@ -314,7 +314,6 @@
}
],
"source": [
- "df = TSDataset.to_dataset(original_df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"ts.head(5)"
]
diff --git a/examples/203-ensembles.ipynb b/examples/203-ensembles.ipynb
index b2ee9450d..dcaae0585 100644
--- a/examples/203-ensembles.ipynb
+++ b/examples/203-ensembles.ipynb
@@ -88,13 +88,11 @@
}
],
"source": [
- "original_df = pd.read_csv(\"data/monthly-australian-wine-sales.csv\")\n",
- "original_df[\"timestamp\"] = pd.to_datetime(original_df[\"month\"])\n",
- "original_df[\"target\"] = original_df[\"sales\"]\n",
- "original_df.drop(columns=[\"month\", \"sales\"], inplace=True)\n",
- "original_df[\"segment\"] = \"main\"\n",
- "original_df.head()\n",
- "df = TSDataset.to_dataset(original_df)\n",
+ "df = pd.read_csv(\"data/monthly-australian-wine-sales.csv\")\n",
+ "df[\"timestamp\"] = pd.to_datetime(df[\"month\"])\n",
+ "df[\"target\"] = df[\"sales\"]\n",
+ "df.drop(columns=[\"month\", \"sales\"], inplace=True)\n",
+ "df[\"segment\"] = \"main\"\n",
"ts = TSDataset(df=df, freq=\"MS\")\n",
"ts.plot()"
]
diff --git a/examples/204-outliers.ipynb b/examples/204-outliers.ipynb
index 289971340..f688c68c7 100644
--- a/examples/204-outliers.ipynb
+++ b/examples/204-outliers.ipynb
@@ -193,8 +193,7 @@
}
],
"source": [
- "classic_df = pd.read_csv(\"data/example_dataset.csv\")\n",
- "df = TSDataset.to_dataset(classic_df)\n",
+ "df = pd.read_csv(\"data/example_dataset.csv\")\n",
"ts = TSDataset(df, freq=\"D\")\n",
"ts.head(5)"
]
@@ -350,19 +349,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "16:59:44 - cmdstanpy - INFO - Chain [1] start processing\n",
- "16:59:44 - cmdstanpy - INFO - Chain [1] done processing\n",
- "16:59:44 - cmdstanpy - INFO - Chain [1] start processing\n",
- "16:59:44 - cmdstanpy - INFO - Chain [1] done processing\n",
- "16:59:44 - cmdstanpy - INFO - Chain [1] start processing\n",
- "16:59:44 - cmdstanpy - INFO - Chain [1] done processing\n",
- "16:59:44 - cmdstanpy - INFO - Chain [1] start processing\n",
- "16:59:44 - cmdstanpy - INFO - Chain [1] done processing\n"
+ "15:33:17 - cmdstanpy - INFO - Chain [1] start processing\n",
+ "15:33:17 - cmdstanpy - INFO - Chain [1] done processing\n",
+ "15:33:17 - cmdstanpy - INFO - Chain [1] start processing\n",
+ "15:33:17 - cmdstanpy - INFO - Chain [1] done processing\n",
+ "15:33:17 - cmdstanpy - INFO - Chain [1] start processing\n",
+ "15:33:17 - cmdstanpy - INFO - Chain [1] done processing\n",
+ "15:33:17 - cmdstanpy - INFO - Chain [1] start processing\n",
+ "15:33:17 - cmdstanpy - INFO - Chain [1] done processing\n"
]
},
{
"data": {
- "image/png": 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",
+ "image/png": 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Z69Tz6/dIkgZlJgW2+4vteQ1MSV9YXWyvvc68zWrRNRd00zP//k7/u+WgJg5MP2MMhmHU6Ww/dLxcXq8ha/XzLTipU7/0NJ3tH393VL/4ny/08JTzNGZAar2Pteeo78IC1xm+unR7/Z3tFmV28l0csfdoWUOn1OEKFOvP3Nlub6Cz3T9LQVKMXVF23/TuXfyd7UEU2/0XhnSKOfO65pH+aeRddLYDAACEVN1p5E0MBAAAAAAAAGhlwXS2+zu3s5JjAkVkvz4p8ZLqn0a+dpd1Q6xWi1LiG79ue1FZlX728maVuzwa0beLrr24e2BfTKSv/7eigYLryWu2+027oJsk6d+7jqiw9MwF4eNlrsCa7DarRVUer/KLa+L3rzXuV1p1arH9g2+OKPdEhd75Kq/Bx/r+iO81PtMM6VXu6tfcatG5GQmSpG/yi1XeyK76xr5nUk1ne+VpLtg4eQp5qXZne+Onka89Lf6Z1HS2U2wHAAAIKW+t/38ZdLYDAAAAAACgHXNWNH7Ndv9U6KkJjlP29UnxdbbnOStUXOs+/dyN7GyXpJQE/7rtDRfbDcPQ3Ne2an9hmTI7ReuJGUP0/9l77zg57vr+/zXbr5/upGvqkuUiuXe5YGzkdiYBTPmamOAQAvyCSTBOICEBEqqBBxADITgkYEKIIRTjAJaN5S5b1ZJlq/d6vffdnd2d3x8zn5nZ2ZnZmdnT3en8ej4efli3O/vZz3ym3N6+3q/XO2waP+oSbS4wnO35bumzGqqwdG4FMjkFezuGi85ZxNg3VSf0/ugn+43IdquzfTxVKHiLxzqH3aPehbM949HZHgmH0FyTQGN1HJmcgtdPDbq/UGOyYuRHkmphQXXCWOO5VcLZ7kds9y7+xz0c+zMZiu2EEEIImbGYne2U2gkhhBBCCCGEEELIbMbsbJeLxDz2jqjC9NzKQrG9piyqi/B2UfJ+IskbhbN9xF2IPdwzhuf39yAWDuHf//SyvCh4wBCJ3eLxB8ZENHms4LmlcysAAMc0Id0N4fpfVFeOhXVCbDci23ss+zJqEyM/rjnOO4bciwyOaGJ7TpGQczlmwpkeDUuQJAmXLpoDANh+YtB1fIHX6H/AtNY24rYu2kcMiVh3tntIDRBkdPHfw3xmec92iu2EEEIImbGYzezs2U4IIYQQQgghhBBCZjN5YnsRF7CI/LYT2wFghYiStxHbhVAaDRWXCRs1Z3t3EWe7iFM/u6kSq1pqCp4X7mYnx346k9NF77qKQrF9Sb0mtvd6ENu1fu2L6suxUOuPfnLAENu7tUj5Wq3f+LhNjLx4rGMw6Zi4KWdzurAvfnZCPBfR1twQ2weK7g8AyBn/MfJ2Yns6a/SOF9RXBnC250Qsvhdnu9aznc52QgghhJCpJa9n++z8LEYIIYQQQgghhBBCCABgeMIQfTNFvgwTfceFUGpFRMkf7rER23Pene2iZ7s1et2KiFNfOrfS9vmoi9saUPu9A0BIyo84FyyZq4rmx/rGC56zclwTwBfXlWNhnSa2m2Lkxdot1gT8MZu+6cLZPiFn846LmRP948ia3Oxurn2x5kLkvnRxLQDg1RMDntpnivPBi5PcrbDBrte6KNjoHfXubBfFIF7OoTid7YQQQggh04P5YyZ7thNCCCGEEEIIIYSQ2cyw2dmeLRIjP+ocIw8Yru3RZKFQnPHRb7s8HgEAjMvuQqmId19aX277vJvbGjD1ay+PIWQTlR7U2b5gjhYjb3a2a4UDYq7jNjHyEyYBvn3Ivm/70Z78ubj1JM9YnO2rWmoQDUvoHU3nFQI44adHur7WdmK7NsdopFBs7xxK5hUPuGGI/x6c7VH2bCeEEEIImRbynO3U2gkhhBBCCCGEEELILGY4aRbbi8TIa33H5zmI7YmoGt2dlAvHEUJp1Ev/b10kdxfbj2jC8xKtt7rjOA771a/1CxdFAlaE2H68f9y1Nzpg9GxfXF9hcrarj2VzCvq09xJzHU0V7tuYKVq+06Fv+5He/NSAtEuBhBDLhcidiIb1uH0vUfJ2jnQn3Hu2K3nbAMCyeRWoikcwmspgZ9tQ0fHN43hxtov3Yow8IYQQQsgUk2PPdkIIIYQQQgghhBDyBiGvZ3sxsb1Iz/aEJuombURyPy7peFhEkrt/N6c7253E9qIx8uq+2/VrB4CW2gSiYQnpTA4dLv3jk3IWndrzi+qMnu2dw0mkMln0j6WRzSmQJPV5wL5nuydne68PZ7tNgYOfvu0Zm17rTkRdxG27nu3RcAjXnFUPAHjxQE/R8QFTD3pPznatZ7tN4cdsgGI7IYQQQmYsCp3thBBCCCGEEEIIIeQNgjlGPuMibudM7uy5VfbidEIXOAvFdiH8enElRyPqNm5C8lgqgy4Rze7obNfGKeJsn1Nuvz+RcEgXzo+7RMkLB3tVPII55VHMrYyhLBqGogDtg0n0aIkA9RUxvTe8Xc92T852HzHydk5w0bfdm7Nde72vNAJvPdsB4E1nzwPgXWzP2DjknWDPdkIIIYSQacIcCcWe7YQQQgghhBBCCCFkNmN2tjuJ0gAwOCHrvbXrK+yd7aJPtp27WQil0ZCXSHJVtHcTkoWrfU55FLUOYnmxcQY0sd3J2Q4Yse9H+5zF9uOmfu2SJEGSJKNve/84ukdU4XxeVQLlcXVOdj3bx83O9kGnGHmL2O5yzDI2aQLC2b63Y8TWXW9GF8kjJfZsdxLbV6hi+6snB/POQ8f5iIIND+J/vEgLgTMdiu2EEEIImbHkx8hP3zwIIYQQQgghhBBCCJkMFEXBH3Z34oEn9uKeH23Bn/5wMwbG0lAUBcNJQ3B1c7aLCPmasqgurFpJRETPdrsYee/OdjF+ykUoPdarCtxO/drN4zjF4/ePi57tzmL74nrN2a4J6nYY/drL9cf0vu0D47qzfV5VHJXxCADVmW9GUZS8GPnO4cIY+ZGkrI81R+sz7yYm6yK3SZxurkmgsTqObE7B66fce6Vncj4KJFyd7UreNoKFdeVYNq8C2ZyCDYd6i76HnPHRikCcQ4yRJ4QQQgiZWsx/UrBnOyGEEEIIIYQQQgg503n+QA8+8t/b8O8vHMELB3qw/mAv/rC7E2PprO5WB9x7tveOiH7tzsK0iJFP2gicunDrQ2yXXZztR3tHAThHyJvfS84qeWmWgiGtZ7sQru0Q41t7pZs5NaAK4yJyXv23cLZPoFtbu4aqOMpjmthuiZFPZ3P6GgFAh42zXcxhbmVcj753jZHPFfY4lyQJly+uAwBsPdrv+FrA7Gz34CQPO4vt4jG7Yy/c7S8eLB4lL1oRxDycQ3Gt8MMuZWE2QLGdEEIIITMWc3Q8Y+QJIYQQQgghhBBCyJnOpiN9AICLFtbiqqWq0Hq0b6wgujuTUxy/D+sZFWK7fYQ8YMTI2znb9UhzDy5pIcq6ubaPas72pfXFne1OYyW1ft5lsbDjGIu18Y+7xMhPyKpLvSoR0R8Tjvvfv96OPe3DAFRne4WIkbdEuE9YxPeOoWTBsRBi+7J5Fa6x7YKMTc92ALh6mXoObNTOCyf0NAIfzna7gg2nGHkAuEHv295b9LtY2SYW3wn2bCeEEEIImSbMbnbGyBNCCCGEEEIIIYSQM53XTg4CAP7kyoW4/fwmAMCx3jEMa2J7zCReyg5R8r2jauT63CoXsV3EyNsInH5i5OMukeQC3dk+z5vYbicCpzPCbe8sXS7VxfZxW3e8OnbhOG+7eD4W1ZXj1MAEHt/ZASDf2S5nlTwhWDjdw1rk+4ScLSiGONyjie1zKxDT3OZua5TRY+Tz9+/qZfUAgG3HB1zF6EzWfxqBfYy8s9h+1bI6xMIhtA1O6PvnRJBWBG7rcyZDsZ0QQgghM5b8nu1U2wkhhBBCCCGEEELImUs2p2Cn1pv74oVzdMf10V7D2V5vioYXUd1WRM/2eS7O9kTUuU+2ESPvwSUdVkV7N6H0mNZDfYmLs90sMvsVgQUttQlEQhJSmRw6hwuj3Z3GqauI4acfvAoNpuKEeVVxVJhc9OMpQ+ieSBvu+LoK9Xh0DOW/n3C2L51boRdIuMfI2zvbz2qoxNzKOFKZHHacGHR+vYf1EYj5pGyd7UreNmbKYxFcqaUtrC8SJS+KB+zGscIYeUIIIYSQaSKXFyM/jRMhhBBCCCGEEEIIIUTjd6+141O/eg0DY2lfrzvYPYKxdBYVsTDOaqjEsrmVAFSxelDrWW6Ohpcz9l+I9Y366dnu7GwXzm033CLJAbXXer+2Dm4920MhyTWS3hCTnecUCYewqE7txX7MoW+70ds8XwJdVF+On3zwSlRr8fLL5lYiEg7pzv0xU5T8uOZsL4+G0VSdAAB0WsT2A50jAIDl8yoNsd0hiQAwOdst4rQkSZ6i5P3EtkddnOTpIussWhuIBAbH+eS8tyIQLQ0othNCCCGETDFmgZ1aOyGEEEIIIYQQQgiZbkZTGXz60Z34xSun8IEfb8VYKlP8RRrCuXzBghqEQxJaahOIhiWkMzns18TbORWGgC47Otu1GHlXZ7uIkbdxtotIch/9v51c20e1/ukNVXFUxCO22+hjaUKxXRGB7NEpvbheE9s1N70VPY7eppDg3KZqPP7X1+PHH7gCK1uqAUCf87ipT/tYyugf31Kriu3tQxP686OpDA50q8frwgU1urjtVJAAuMfAr16uRslvchXbRQy9hwIJF6e9nLEvRhCsmq+uy26tt73jfDL+WxGkbAo/ZgMU2wkhhBAyY1HyerZTbieEEEIIIYQQQqadbBZ4/nngZz9T/5+dneIJIU48uv0URjWBfcfJQXzkv7e59to289qpQQBqhDyQ79TecXIAAFBTFtUF2Yxjz3bhbHcR200iubW/edYh0twOMRe7SHLAcJgvcXG162OJOdncN9I2vdbtEO9zrK+Is91hnIV15XjzOQ36z+ValPyoqWhiQlb/XRGPoLmmDEC+s33nqSEoCtBSk0BDdcJfjLxNgYPo2779xKBtEgFgiv53EMnNBO3ZDgCrWmoAAId7RjGRdukhn3OOo3ecj0sxwpkMxXZCCCGEzFjYs50QQgghhBBCCJlBPPoosGQJcOONwJ/8ifr/JUvUxwl5A5DLKfivDccAAP/v8oUoj4Xx0qFefO6x3Z5e/6rmbL94Ya3+mIhef03r5V5TFtEFWSendO+IJrZXOYvt8ajRj9wqcgrHvBex3SzcKjbfzx3RxPZlHsR2vZd4AMe1QPSFP+ozRt6JSuFsT9k426NhNNVozvZBQ2zfoUWsX7yoFoA3MTnjEt++bG4FGqriSGdy2H5iwPb1Yr8iHpztcZf5uPVsB9SEgrmVMeQUYF+ns7tdn48nZ3tYf+9sbvZ9x0uxnRBCCCEzFvZsJ4QQQgghhBBCZgiPPgq8613AqVP5j7e1qY9TcCdvAF461IvDPWOojEfw2T9aiX+7+1IAwG92tGE87R4nP5bK4ECXGj1+iSbSAobYLvqeVycMZ7ud2K4oiilG3qVnu0lsNrulszlF/57NS4x8PGyI9rKN096Ps93o/+4cI+/Wsx0AGrUe6qJvvdM4MQ8iMGA4280924WjuyIe0WPkO4eNGHmRQiCKJopF7avzcu65LkmSESV/2D5Kvpgj3YzbfNJFxpEkSXe3u0XJ+5lP3HQuuq3RmQrFdkIIIYTMWOhsJ4QQQgghhBBCZgDZLPDxj+uV8IOJSuh/pYu/1++7j5HyZNYjXO3vumwBKuMR3HD2PCysK0M6k8PLh5z7bQPAzrYh5BSgqTqhC8ZAoUitxsg7i9LDyYwumLrFyEfCId0FnZQNgdMs4PtxtltfKxgYV4X/BheXvT6WWy9xjz3b41F3F7nXOHqB0bPdENvFv8tiYTRVqzHyHTbO9osW1ObN2U1IzuTcnemrl4m+7f32r/exX7GwcJK7xcg7H/tVLaJv+5DjNm496K1QbCeEEEIImSbye7ZP40QIIYQQQgghhJA3MuvX6472bfPPxSV//Qi+esOfGc8rCnDypLodIbOU431jeHZ/NwDg/asXA1BdwDdp/b+f3dft+no9etwUIQ8YznZBdZ7YXihMin7tVfEIEqaoeDvE82Zne8b0JZsX4dYsptoJpSISPlZyL3FvYrIQblOyvWirx9F7Fdtjqtg+ao6R15zt5dGw7mzvGEpCURR0DE2gaziFcEjCBQtUB7hw0bvFyBdzgl++ZA4AYJeDwJ32EdvuqWe7y/Hy5Gx36UFvJRIOQdQYpDKzryiLYjshhBBCZiy5PLGdajshhBBCCCGEEDItdHTo/9y08AIoUgh7G5a6bkfIbOOFAz1QFOCa5fVYNq9Sf/zGc1Wx/fn93bY9zQWvWfp8C6xie01ZVBdUbcV2D/3aBQnNBZ40CZwZs7PdQ/9vs1Bq3wPcmyMdMMfI+483F7j1IwcMB7lXsb08rhYkjKfsY+RFCsGEnMXguIwdJwYBAGc3VqFcE+rd4vEFRoy8/ZpXl0X197E7jzJFeq2bEfNJ2YntmeLjCGf7vs4R22OljlNctDcj+rbbzelMh2I7IYQQQmYsZjc7tXZCCCGEEEIIIWSaaG7W/3lsTgsAIBOKuG5HyGyjY0iNEV/RUJn3+NXL6pGIhtAxlMTejhHH1+9sUx3LInpc0FiVQJnJoV5TFtWF0IxN1KPo115f4dyvXaALnHkx8saYYQ9iO1CkB7gP0VUI4LYisBDtI+5zEhHpjs52IUoXGUcgnO3Cza7+24iRT0TDWFinRsk/suUEdpwaBJCfUOApRl440x2c4CKFQFHsRXs9ht6Lsz3sXJDgpdf6orpyVMYjSGdyONwzaruNXtTg8RwS8f90thNCCCGETCFmgZ3OdkIIIYQQQgghZJq4/npgwQJAknC8tgkAkAmb4qslCVi4UN2OkFlKlya2N9Yk8h5PRMO4dvlcAMBz+52j5EeSqoA7z+JID4UkLK4v13+uTpic7TbirYiRd+vXLhACZ36MvNGzW5I8iu0exNu4p17iLuN4jH8v2rM94y5qWxHO9jEbZ3u5JoD/zc3nAAC+88xBPL2nCwBwiUlsjxZx2wNG4YRTEYC5r3nSIkgriuI5Zt/8HrbFER56todCElY2a33b2+yj5A2nvrd1jrkUWpzpUGwnhBBCyIyFPdsJIYQQQgghhJAZQDgMfPvbAIDjmrM9Hdac7UKse/BBdTtCZimdw6rY3lSdKHhORMm79W13i1tfNs+Ikq8x92y3dbaLGPnizvaE5mxPmgROEUfuVYwGgJg2TqnO9sno2a6LtrK9Q9qLc9tMpeZsH08bYvu4ENvj6nNvu7gF1yyvRyqTw+GeMQDART6d7XKRdY+FQ/rt1OraNyccRD0cN+H+d+3ZXmR9VmpR8k5922UPor0Zw9lOsZ0QQgghZMowu9ndel4RQgghhBBCCCHkNHPnnZj431+hq6oegClGfsEC4Fe/Au68cxonR8jpp8uD2P7qiQEMjKVtX6+LkzbO5iX1htheXRbR3cKlOtsTNs52MQ8vceSCWNjZKS0H6CVu7QOuKIr3nu1FnO1e4+gFQlAfSxlrJIT38pgqWkuShC++/Xx9HytiYZxlaifgVkQgyBRZd0mSdHd70lJIYF4vTzHyLk57I2bffZ1X6WL7kO3zGR9Oe8C+pcFswbfY3tbWhve9732or69HWVkZLrjgArzyyiv684qi4HOf+xyam5tRVlaGNWvW4ODBg3lj9Pf34+6770Z1dTVqa2vxwQ9+EKOj9pn/hBBCCHnjklPs/00IIYQQQgghhJCp58T1N+v/zixZCjz3HHD0KIV28oaga1gVua0x8gAwv7YM5zZVIacA6w/1FjxfLAZ8yVxDbK9KRHVxW0S+m+kZUcV8TzHywtmeFyPvTyQF3MVb4VQuJt4Czg5ws3O7mGgvnpezCnI2Xxj6iVsHVOEccHC2x4y0juXzKvH/vXk5AODyJXV5/e7d1kfgZd11QdqyPuYe7t5i5NVtsjkFWcsaeY3rX9VSAwDY0zFsa4KSRQ95rz3bI+zZDgAYGBjAtddei2g0iieeeAJ79uzBN7/5TcyZM0ff5utf/zq+853v4KGHHsLmzZtRUVGBW2+9FclkUt/m7rvvxu7du7Fu3Tr8/ve/x4svvogPf/jDk7dXhBBCCJkV5PJi5Km2E0IIIYQQQggh08mxvjH933JVNfDmNzM6nrwhGE1lMKr19G60cbYDwLlNVQCA7uFkwXPFxNJlmtheFY8gHJL0qPF01iVG3oez3Sze6s52jyIpYHKklxBLbh7HKrabndt2zn8z8ahxz7ETt7065AXC2T5q6tk+povtkbxt//qms/Ct91yEL739/LzH3Zz/Ai/r7iRIZ8zr48PZbn5fgZee7QCworESkgSMJDPoHS1MazCSGjz2bPfg/j9TiRTfxOBrX/saFi5ciIcfflh/bOnSpfq/FUXBgw8+iM985jN429veBgD4yU9+gsbGRjz22GO46667sHfvXjz55JPYunUrLr/8cgDAd7/7XbS2tuIb3/gGWlpaJmO/CCGEEDILMOvriqJ+1pAk738IEEIIIYQQ8oYmmwXWrwc6OoDmZuD66ymKEUJK4kTfuP7vDCPoyBuIziFVQK+MR1AZt5fWYpFCYVtgFjztnNsXLqjFjefMw/nzVTexEDAzNmLy0IQMAKir8NCzPSqiu03Odr13uH+xPWUnbmv7G/fjbLeMI2e8O7fN65eSc/o+6mP57dkeF852Y40mLDHygkg4hDsvXeA4J9mmOELgJXZd7EtStne2R0KSp+9G89Yok79Gesx+kfWJhkNIRMKYkLMFsfaAaX889JAHzIUEb3Cx/be//S1uvfVWvPvd78YLL7yA+fPn46Mf/Sg+9KEPAQCOHj2Kzs5OrFmzRn9NTU0NrrrqKmzcuBF33XUXNm7ciNraWl1oB4A1a9YgFAph8+bNeMc73lHwvqlUCqlUSv95eHgYACDLMmRZ9rfHU4yY30yfJzkz4flFpgOed2QqyWbzP8il0zJCPv4QIKRUeM8j0wHPu9kHjyUhZFp49FHg4x8HTp0yHluwAPj2txn3TAgJjNnZnnERlQiZbYh+7Y3Vzm5yN+euXMSZHIuE8PAHrjS20b7/srqSzeN7EbftxFsRTR/xESMfdYh/N8/Ri7gtXOvWcczie7EigGhYgiSpxpxUNgsgqj+XzSm6ecdLD3nAcK+Ppdxj5N3wFiPv3rMdcHa2y0X6vVsxn2Pmtc7mFL1Vp5fjFY+GMCFnHQpIFF9zcorInw34EtuPHDmC73//+7j//vvxD//wD9i6dSv++q//GrFYDPfccw86OzsBAI2NjXmva2xs1J/r7OxEQ0ND/iQiEdTV1enbWHnggQfw+c9/vuDxp556CuXl5X52YdpYt27ddE+BzGJ4fpHpgOcdmQqOHgvB3PVm7RNPgFo7mQ54zyPTAc+72cP4+HjxjQghZDJ59FHgXe/Kj4oCgLY29fFf/YqCOyEkECf6jc81diIgIbMV4WxvsunXLohp6TFu0eaShLxe305EXZzSfmLSRYx80s7Z7lEkBZx7redyip5y4a1nu/0amd3WxZzbkiQhFg4hlcmVFEcvqNDEdrOzfdwhRt4Jp/UxI56LuDjB9SQCh572Xl3kkiQhFgkhncnlrXX++ngQ2136rPtNEIjN4p7tvsT2XC6Hyy+/HF/5ylcAAJdccgl27dqFhx56CPfcc89pmSAAfPrTn8b999+v/zw8PIyFCxfilltuQXV19Wl738lAlmWsW7cON998M6LRaPEXEOIDnl9kOuB5R6aSTb/dA3QZTpxbb7vN8wc4QiYD3vPIdMDzbvYh0tkIIWRKyGZVR7smtMuhMMK5HEJQ1MckCbjvPuBtb2OkPCHEN3nOdsbIkzcQXSPC2e4stju5toH8CHEvMeBCCHdztsc8iMnCTZw0CZx+hVv1vezF5HTAXuLW3u+yxz7i5nFSmVyBKJ3vkPfas11do9E8Z7t9jLzbfAB3sV1fdy/Odtne2e61PzqgFgCkLQUJfo+Xfv7Idue0v2MWZ892lebmZqxcuTLvsfPOOw+//vWvAQBNTU0AgK6uLjQ3N+vbdHV14eKLL9a36e7uzhsjk8mgv79ff72VeDyOeLwwmiMajZ4xXz6dSXMlZx48v8h0wPOOTAlS/gfIcCSCaIRfCJKph/c8Mh3wvJs98DgSQqaU9ev16Ph0KII1f/F9NI304hc/+7T6vKIAJ0+q2735zdM3T0LIGUc6k0PbwIT+s1UsI2Q20yWc7S5ie1x3o7tErXuMbBROabt2DYYLvPh3ZPGoEG8L3c1+nO1CKLXum1m89eZslwpep46rCdEexeR4JIwRZAqd7Rl/YjIAVMYNZ7uiqDHrQlye1Bh5Dz3b9ePlUIxQLGK/YE6pfHE7b308FCO4OttzIiHB+zFTx5p9vzt8WcOuvfZa7N+/P++xAwcOYPHixQCApUuXoqmpCc8884z+/PDwMDZv3ozVq1cDAFavXo3BwUFs27ZN3+bZZ59FLpfDVVddFXhHCCGEEDL7UCyxl9YUTEIIIYQQQoiFjg79n12VdTgxpxlbF65CwUdp03aEEOKFtsEJmM3scm72CSaEONE57CFG3kPPdq9icsRBlM4fq7jwmrBztmf9iaSAs5jsV7x1WiO/keSGCOwg2oclTwkCgCGoZ3MKUpkcJkyucq8x8m497fW5eejZrh+vAmd7caHeil20vd5nPSQh5EG4d4q1V8fy6Wy3KfyYLfgS2z/xiU9g06ZN+MpXvoJDhw7hkUcewQ9+8APce++9ANQeAPfddx++9KUv4be//S127tyJ97///WhpacHb3/52AKoT/rbbbsOHPvQhbNmyBS+//DI+9rGP4a677kJLS8uk7yAhhBBCzlxyFnXd+jMhhBBC3Mlms/jsZz+LpUuXoqysDMuXL8cXv/jFvII2RVHwuc99Ds3NzSgrK8OaNWtw8ODBvHH6+/tx9913o7q6GrW1tfjgBz+I0dHRqd4dQogXTGmT6YiarKFIIcihiON2hBDiBREhX1Om3lvsHLeEzFY6h1MA3GPk3cT2dMafWBpxcLbncooumMY89WwvjAHP5Py57AFnMTltEly9iLeOor2pZ7sXnCLJ/Yr2QL6gPpbK6BHykmT0vC+GLmw7ONuzOUU3EbkVJTg52/1GtgP2ax24qMEikHvdn7z5hGdvz3ZfYvsVV1yB3/zmN/jZz36G888/H1/84hfx4IMP4u6779a3+dSnPoW/+qu/woc//GFcccUVGB0dxZNPPolEwrgB/c///A/OPfdcvOUtb0Frayuuu+46/OAHP5i8vSKEEELIrMCqrbMdHCGEEOKPr33ta/j+97+Pf/3Xf8XevXvxta99DV//+tfx3e9+V9/m61//Or7zne/goYcewubNm1FRUYFbb70VyWRS3+buu+/G7t27sW7dOvz+97/Hiy++iA9/+MPTsUuEkGJcfz2wYAEgSXkCuxDeIUnAwoXqdoQQ4oMTfeMAgLMaKgGo/YetiXSEzFa8xMjrYqKLG92rmBxziKQ3J0p4cckLsdjslNbdzX6E27C9CCzEbq/irZNoH6RnuzqffOE2HUBsD4cklGlFCePpLMZT6pjl0bBnd3xMSxmwayFgfdxt3fWoddm6PiWkEdj0bPftRs/Y95BX5+RvrDd8z3YAeOtb34q3vvWtjs9LkoQvfOEL+MIXvuC4TV1dHR555BG/b00IIYSQNxhWcZ3OdkIIIcQfGzZswNve9jbccccdAIAlS5bgZz/7GbZs2QJAdbU/+OCD+MxnPoO3ve1tAICf/OQnaGxsxGOPPYa77roLe/fuxZNPPomtW7fi8ssvBwB897vfRWtrK77xjW8wpY6QmUY4DHz728C73oV0NKY/nApHUSlpRTQPPqhuRwghDqw/2IPashguWFCjPyac7WfNq8S24wMAVMHdj9OSkDORbE5Bz6gXZ7v6u9U1Rt7j9SJ6c1vbNZjH9iLcxyMuzvYAwm2B+C+KCDzG4zu5//06/yfT2Q4AFfEwJuQsxtIZiCUv8xghD5j3y/67y4zpS063udkVRwDB9svObe/3eDmL/6aiD/Zs9y+2E0IIIYRMFQU922ffZzFCCCHktHLNNdfgBz/4AQ4cOICzzz4br732Gl566SV861vfAgAcPXoUnZ2dWLNmjf6ampoaXHXVVdi4cSPuuusubNy4EbW1tbrQDgBr1qxBKBTC5s2b8Y53vKPgfVOpFFKplP7z8PAwAECWZciyfLp2d1IQ85vp8yRnLlNyjv3RHwG/+hXGv/6Q/tBYZRWqFzQCX/2q+jzP8TcMvK8RvwyMp3HPj7agpiyKTX/3Zj0a+miP2kJm6dwyfduJZBqI5Rfv8Jwj04nX8y+bU/DzV07hisW1OLuxynXb7pEUsjkFIQmoiUuOY4egfnGVkjMF20yk1J8jIefXmxGafCqdP9Z4Mm1slMtCLtL/OhpSv1tLmuaU1GLSQ5L361Ros0nLfMa0+US97hfU+aQy2bztJ9Jp7X28jSOKFsaS6fxxkrL+vJ97kHC2D4+ldLNPeSzkeQxJ+9IyncnZvkbMCwCUbAYy7I+bKLIYT+f/3ZRMi/PH+zETazRhWiMxD8/rrB338ZRlnVP+zkMAiErquk6kC6+PmYrn6+M0z4MQQgghJDDs2U4IIYSUxt///d9jeHgY5557LsLhMLLZLL785S/r7eA6OzsBAI2NjXmva2xs1J/r7OxEQ0ND3vORSAR1dXX6NlYeeOABfP7zny94/KmnnkJ5eXnJ+zUVrFu3brqnQGY5p/0cC4dx4KMfA/aoPz71ne9hntDH1q49ve9NZiS8rxGvdE0AOSWCgXEZP33sCczVjLx7T4YBSOg/theAKkytffIPKHNQGXjOkemk2Pm3b1DC9/eGsbRKwX3nu/eQPjEKABFURRQ89YcnHbfb0ysBCKOjqwdrLb9r9w6qz02MjRY8Z8exEyEAIRw+cgxr1x7RHx9Kq3MJQcEfnnyi6Dh7+sScevX33dGlPtbf0+1pLgBw8rg6n30HD2OtfFB//PiIOp+snPI01u4eMZ/89941oD4+PjrsaZzhQXU+W7e9CuWE8X3hMW0+cnLC874BQDal3t+ee2mj9kgYmeS45zGGteOSzubw+ONrYU2fF88DwB+efLLgeUG7dtz3HTiMtWljnV/TjuPI0JDnOY0Oqfu06ZVtSB1V1+ioWJ9U0tM4vd3qfHbs3I05fbsK9kfyeB4CwOF2dR+OnjiJtWuPe3rNdDM+Pu5pO4rthBBCCJmxMEaeEEIIKY1f/OIX+J//+R888sgjWLVqFXbs2IH77rsPLS0tuOeee07b+37605/G/fffr/88PDyMhQsX4pZbbkF1dfVpe9/JQJZlrFu3DjfffDOi0eh0T4fMQqbyHKs40APseRUAsPq664s698jshPc14pfd7cPAjk0AgPkrL8dbzm2AnM3hb7c8A0DB/7v9Bvxg30sAgBvfsgZ1FbG81/OcI9OJ1/NvYPMJYO8+9MpR3H77La69uZ/e2w3s3IHFDTVobb3acbvw7i785OBrqK6tQ2vrlXnPxfd1A3t3oL7OfQzB0eeP4A9th9CycBFaW1fqj58cGAe2vYR4NIzW1luLjlO2vwcPH3gV5dXG+/ZvPgEc2Yf5Lc1obb2o6BgAsP/pQ3i2/QgWLFqM1tbz9Me3HhsAdm1FTWUFWluvKzpOaHcX/vtQ4RqFd3cB+17DvPo5BWtnx2/6tuPAUC/OO/9CtF42X398y7F+YNcrqK6qRGvrtZ72DQB+0rYF7ScGseqiS1UhfO9raJrrbS4A0Ds8js9uU++LN996W0FMe8dQEtj2IqJhCXfc0eo4zsFnDuGZ9iNoWZi/ztjZCRx4HQ1z69DaeoWnOf2iexsOj/Th/AsuQuvFauuvzUfV9anxuD4vP7Yb23rbsGzFOWi9YZn+ePvgBLBtPaIRb+choF5zjx3fh7kNTWhtvdjTa6YbkdBWDIrthBBCCJmxFDrbp2kihBBCyBnKJz/5Sfz93/897rrrLgDABRdcgOPHj+OBBx7APffcg6amJgBAV1cXmpub9dd1dXXh4osvBgA0NTWhu7s7b9xMJoP+/n799Vbi8Tji8XjB49Fo9Iz50v1Mmis5M5mKcyyHkOnfYZ7Tb3B4XyNekRVDdDzSN4HbolEc7BmGnFVQlYhgWUM1QpL2N3rI+d7Cc45MJ8XOv54xNR56JJnBqAzUVThv26tt21RT5jpmeUJ9Ts4pBdsp2u/keMTb7+N4VJXvsgrytlckNVUiGg55GqcioRbDpDI5fXvx+SDmcS6A0b88k7POx99Y5XGxRvnj5HyOk3BaHy11Ixbxtj6CCu3YpbLQXecV8YjnMSoSxt8+SiiMaNQiv0oivt19XmVx+3PI7zoDQFyLxs9C0l9jjONtfZyOO0JG+wCv8ymPq+ei9djPZLzO01vXekIIIYSQacCqrVt7uBNCCCHEnfHxcYRC+X/6h8Nh5HJqT72lS5eiqakJzzzzjP788PAwNm/ejNWrVwMAVq9ejcHBQWzbtk3f5tlnn0Uul8NVV101BXtBCAlKOmv0z0xl3CNyCSFEMJE27hcHu9Q+7bvbhwAAK5urIUkSImH184WcLd6ndzJpH5zAPT/aguf3dxffmBAXOoaS+r+P9Y25bts5rG7bVJNw3S4WVsXNdKbwuhC/k6Nhb7Kc6LedsVxjYmyrc9qJhNZ0O2WakxgzEnZ281sR75cucT76OJY1kjP+1idus1+AcU/yOh9BTZkqqvaOpjCu3QNFH3cvmN9Ptjn+ck7sn/uaJ7T3tO6Xcf74OGbhwrX2uz5xh/nIWfU72ojH42V+z9n4mZTOdkIIIYTMWKziOp3thBBCiD/+6I/+CF/+8pexaNEirFq1Cq+++iq+9a1v4c///M8BAJIk4b777sOXvvQlrFixAkuXLsVnP/tZtLS04O1vfzsA4LzzzsNtt92GD33oQ3jooYcgyzI+9rGP4a677kJLS8s07h0hpBjmL1ftvvgnhBA7JmRDCDnQNQJAi5YHsKqlBoDqZkwDyGSn9g/1tTs78MKBHgDAm89pmNL3JrOLrmGT2N47hksXzXHZNgUAaKwuIrY7CMmAca14FSejekFL/jWmi6VeRemIKpYmTdd1RvuCLRLyLtxGbYRbwL8IbIyTL7jKPosRxP47i9L+xPbl8yoAAId7RnFWQyUA1dnulXBIQggKcpAKChIA4/gXm1dcO4fMx8v8+iDidtp0DqUz3uZRbD5+j5d5rNn4mZRiOyGEEEJmLDnLZy/2bCeEEEL88d3vfhef/exn8dGPfhTd3d1oaWnBRz7yEXzuc5/Tt/nUpz6FsbExfPjDH8bg4CCuu+46PPnkk0gkjC8T/+d//gcf+9jH8Ja3vAWhUAjvfOc78Z3vfGc6dokQ4gPzl5nWL6MJIcQJs6hyqHsU2ZyCPbrYXg1ACD5ZZKx/uJ9mTg1MAAA6hiam9H3J7KMzz9k+7rqtEOabPIrtdr9zDZHcnyhtTY/QRU7PznYhtpud7SUIt1ZxO6iz3WG/YhFv6yOc7QUO+QAOcABY0VAFADjYPYqW2jIAQFnMu7MdACIhIJ2zF5Nlj2kCcYdzKOPRGW/G7pj5XR/H+ejFA97n45RGMBug2E4IIYSQGYtVXKfUTgghhPijqqoKDz74IB588EHHbSRJwhe+8AV84QtfcNymrq4OjzzyyGmYISHkdCJnKbYTQvxjjpFPZXI43jeGPR2a2D5fFdudXLenm/ZBTWwfTBbZkhB38sT23iIx8kPeYuSF8GjXXsGvEzjiMJb4fe7V2S5i5POd7dpcfDjbi4nkfh3pcib/3pH26Pw2xnGKNw/mbBdu9kNdo7hiSR0AoNxHjDwARCQgbTMnwJwm4D4vozgi30me9hmzDxQT2/0VaxSsc85/KwKRspCSZ99nUvZsJ4QQQsiMxRobn2OOPCGEEEIIIZ5J5TnbZ19/TELI6WHCIvI8s7cbo6kMYpEQls9TBSmjn/QUi+2ao30klcFIUp7S9yazh5GkjDFTUcnxIj3bhbO9sTruul3cQZBWH/MrJqvbZXLWGHm/MeCGWCraNQbpty32zSr+CyE3PknOdr892wt7v6v75rUYQbBkbjnCIQkjqQyO9KjnQ7mPGHlAdbbbzQkAMh4d5c7Odm9ivRm9Z3vWONf9tyGw77MulyD+z8bPpBTbCSGEEDJjsfZsZ4o8IYQQQggh3knT2U4ICYBVbP/Nq20AgHObqnRhRXfdTnGMfNuAER/fMUR3OwlGp+XcOdo7VvAdlJnRVAYAUJ2Iuo4r3NZuMeJ+ne1Ose3eY+SN7cRngYzHSHMzMYee7UGd7YUiedCe7RYROOdfBAbUooTF9eUAgNdPDQIAyn3GyIvltCu28FrgoDvJZaf18eMkL1xrv0UfTm50If5HfYj/s7lnO8V2QgghhMxYrDHy7NlOCCGEEEKId/K+XJ2FX2wSQk4PSc3xK2majh4hr/VrBwyBZSqd7ePpDAbGDTe7iJQnxC+dmlN9YZ3am3s4mcHguH1SQjan6MmLxfqSO/U1Bwyx1GtP8qijs11zkvuMAQcMwTSIUBp1EMn1WPvJ6tnutZe4o7PdXzGCmRValHz3SAoAUBGgZ7vdnAAjuj9SJLpfCNLJgiICfyI54BAj73N9nPqse+1BnzdWxD6SfjZAsZ0QQgghMxbrn+wU2wkhhBBCCPFOOi9GfvZ9sUkIOT0IZ/tZWmS8YGVLjf5vp37Sp5N2S592OttJUISzfUl9BZq1PuxHHaLkzed4MaFTiJuZnFLQCtGvA9yp/7vhbPcu2oc1gVcIuEGE0phDvLnfWHuzaG9OEwgas+84Hx/96AUrGqryfi6L+YyRl8Qc7GLkve1f3MHZHiSNQF9r03xkj3H2+nyE+G9JPPF73M1jzcbPpBTbCSGEEDJjKejZTq2dEEIIIYQQz5i/7KWznRDiFSG2X7igNu9xs7Nd9A12E9sVRcFf/nQb/upnr7pGdHulzeJk76CznQREiO1N1Qk9Otypb3s6gNhufZ36czBRWrakR6R99twGgIRFMPUq/JqJFenZ7tfZro6lmP7t13FtH9mf9lnUYGZFY36Bkd8YeTdnu9cCh0QRJ7mf425XIOG/Z7u9G91rD3r7+bBnOyGEEELIlFHYs51qOyGEEEIIIV7Jd7bPvi82CSGnh4m0eu84q6ESZZqgFZKA85pMMfKawOIWIz84IeOJXZ343Wvt6NFimUvBGhvfTmc7CYiIkW+uSWDp3AoAwNHecdtt5YxZbHcXFs3PO4mlnnu2OxS0+B0HMITppOaWlj1GmpspHv/uVbw1i+12jmuP49i4tvPG8ej8N3NWw+SI7XbOba9OcKNHur2T3FcagU3Bhu+e7VF7gVzE2kfnf/GoAAEAAElEQVQC9GyXs4XJD2c6FNsJIYQQMmMp7Nk+TRMhhBBCCCHkDCRNZzshJADC/VoRD+tOz2XzKlFmEp4iej9p53vLeNoQZw732LuG/SDE9qq4Gu3cMURnOwmGcLY31iSwuF4V252c7YZIKkGS3IVOs+DsJJJ77UkuertnSnSSA4azXQimGV249eGSdujZrjvtPc7HLPKmbR3XXtfHQQQuwdm+fF4lzIe43HeMvLqu1gIAwLhXFivYcIpa153xPsRto2e7sUa+ixocnO1+e78DRtEHYL9GZzIU2wkhhBAyY7H+zc6e7YQQQgghhHgnxZ7thJAAiBj5RDSs9zA+3xQhD5j7STv/nT6Wyuj/PtpbutjeNqCK65csngMA6Biks50Ew+xsX6KJ7ccczlE/IqckSY6idHBnu33vd18x8hZnu1fh14wh3Dr0kPc4Vjgk6T3k84sCfTquHeYj1svP+ggS0TAWzinXf/brbBdLYB8j780JLo5VOptD1uQ6EgUSfoos7I6ZIZL769le0ENenEN+0hFMx8Q63pkOxXZCCCGEzFgKne0U2wkhhBBCCPFKXmwoxXZCzliyOQV72ofzhJfTyYTmSC+LhvHeKxfirIZK/MlVi/O2iXpwto+ZnO1HekZLnpfo2X6FJra3D02w3RwJRJcmtjdWJ7BkriquHutziJH32ZvaSZSWfbrJI3pBi5O4HSRGPps/Fz8u6WLO9rB3YdpuLL/FCHb9yM1jBnG2A8AKU5T8ZPZs99rj3Byzn7c+AaL/4zbR/36LNUQP+WRBgoD/WHs1HUL992xrb0SxnRBCCCEzFuufzPwbmhBCCCGEEO+YY0Nn25eahLyR+PnWE2j9znr8x/ojU/J+47Ihtl++pA5P338Drlxal7eNEHzcne3GfWcynO3tWmz8ZZrYnpRzGByXSx6XvLFIZbLoHU0DAJqqE1hcpzrbhyZkDIylC7aXfTqKi/U29yza6/22reK2f4ezEF2F2C6EX1/9v039tvPmEyDWXqyBnQjsNZZcxJuXmiBg5axGs9juM0Y+lD+HvHl57HFuFttTefHv/pz/gH1Rg++e7XoP+dLXWZIkx5j8Mx2K7YQQQgiZsVgr1OlsJ4QQQgghxDtpxsgTMivY0z4MADjRb++8nWySwtnu4urUe7a7iO3j6cmLkc/mFL3P9pK5FZhbGQNgCPCEeKV7OAVAFSLrKmIoi4XRVJ0AAByz6dvu23FdJEbeqyjtdI0FETkNd7KIkTf60HvFXERg/r7ObxGBOlahUB68Z7sl3lyIyR5j0q2I1hkAUB736Wx3iZH3WuAQCYf0YqakSeD26ow3Yxsj77dne1SsczbvuGcCiP+Acw/4Mx2K7YQQQgiZsVgT8qYoMY8QQgghhJBZQX4v1Nn1pSYhbyS6NHFwqq5jc892J6IOEddmxk0x8if6x123LUbPSApyVkE4JKGhKo7mmjIAZ2bf9mO9Y7jpG8/jp5uOT/dU3pDoEfI1cUhapvXiejVK3q6gJT1p8eb+xEk797c6jn8nubiWU7qzXZ1L2EeMvHnedp8v4gGc9uZ7QtpntL1zz3bRS3wSYuRd7oF26DHyNvc6P+K04f42O9uFWO+/Z3vKVmz32rNdXYOcYhRpAMFi7fPnNLsSlyi2E0IIIWTGwp7thBBCCCGEBIfOdkJmBz0jqjg41WJ7mYvQJAQxNwF9LGU42zM5BSdLcOaLfu1N1QlEwiE016hO5I4z0Nm+5Wg/jvSO4dHtp6Z7Km8oxPXToSUkCDc7AFQlogCAiXShAChn/ImTToUofh3FYrtMzt7Z7tUBDgAJTTA1nO1CkPbf/xsozSmtbisVjiPW2Wdcv1W0TQdwgJs5p6kK82vLcOGCGl/CNmA42+0+c/kRp/XiiLx19u8kF9va9mz3HNdvjrU3Hy+RIODX2T47Y+T9NRwghBBCCJlCrE52a6w8IYQQQgghxJm0KXqWznZCzly6R6bW2e4tRl4VjKxCoJkxi3B5pGcMy+ZVOmztTrsmts+vVR3tLdr/24fOPGe7EN1ODZx5hQJnKusP9uDPHt6Kj914FqoSqizWpKUjAO4CoF+R0y4iXR3Hr2ivvl82pyCXUxAK5QvUQWLkhbNd7JMvl7RpW3Pf9lQAp71bvLnXIgKnY+a397uVRDSM5/72zZACaPW6s902Rt77mot9S8rGPVQvkAgQI5+XIOAzYSFPbJezqIxH8ufj09nulEhwpkNnOyGEEEJmLFZxnVo7IYQQQggh3sl3ts+uuE5C3ijkcgp6hNheQgy7H7w420VEc8bV2Z5/3ymlb7twtrfUqm5k4WwXIvyZhBDdukdSeWLaTGDHyUF88pev6efcbOGp3V3I5hR859mDeHxnBwCgqTquP+8mAMo5f+K2nZAMmMVkrz3bDRFTzAEw7gN+xG0RBS7ONyGUFusfbiYUknRXdqnOdnP/96DjOMX1B3GA240d5PVhDz3bvYjlcTtnu0+RHDDOtVKOlyRJ+lonbZz2ft3/7NlOCCGEEDLFWMV19mwnhBBCCCHEO2mTwD7bHESEvFHoH0/r7vGpuI7lbE5/P9cYeT0q2/kP9fG0GiMvHKJHekcDz6tdF9tVN3Jz7Znbs93sMm2bYcUC33/+EH657RR+tW12Rdzv6xwGoH7P9OqJQQBAoylGPi6c3zaFaX5j5OM20d3qzz4d8g5O8iBisnC2J2UtRl6M4bOvedRGvA3SQ95OBPa9PqaiBrNZx29Rw2Ri9GwvPI/87J+dsz1Ij3S7IhK/CQvmcVJyYQ95v0UJMZuxZgMU2wkhhBAyY2HPdkIIIYQQQoJj/qJ/tjmICHmj0DVsiMlTIbZPmASQRMxZPjD6STvPaVyLkV+uRccf6QnubNdj5OdoMfLC2X4G9mw3C6czLUr+ZL86nwNdI9M8k8lDURTs61T3p6Ysqj/enBcj7+y29R8j7+Bs99mT3CyqZvJiwLN57+MFowe4iJH372w3v6dZTBbr40fcdusl7j3e3CgGkm3a5pTibA9KRFLnIVzoZjI+1lx3tsvG+mQCFFm4x/UHOX/M8/Ev2gOzt2c7xXZCCCGEzFisTnaK7YQQQgghhHjH/GXvbPtSk5A3Ct2mOO/UFMTIi37tIcldjBECS8bF2S5i5C+YXwMAOFJSjLxadGB1tncNJ5E7w2LwzMLpqYHxaZxJIcJpP5vE9vahJEaSGURCEh5632UQGrZoSQA4R5IDJnHSo7hdLEbeqzgZDkl6KkS+KO1f3Bbire5szwnhNpjYnirV2e4mAkf89WxX52PnuA7QdL1EDGe7TYx8znuaQMJmnScrrt9vggBgL5DL2v5EfKYjiHNxtiUuUWwnhBBCyIyFPdsJIYQQQggJTt6Xq7PsS01C3ij0DBti+1Q628uiYUiSs1gl+vS6xciPaTHyq1qqAQA9IymMJGXfc1IUBW2aKD1fE9kbq+IISer7946eWf3FZVNxgHCSzwRGUxkMTajH51D3KLIlFDE8/noHLv/S09hwuHeypheYfR1qhPxZDZVYvbwe377rEnz0zctx8cJafRu3nu1pnyKnEHmtxTFBemULYTZj59z2KEoD5hj5bN54foXSmM11H0QEFuttbqkg+3Skm4sNzMdNF7V9iP+TRcSlZ7ufNAGjOKKwiMBPGoH5eImiJL8JC4BDjHyA89A8p9lWBEqxnRBCCCEzFsbIE0IIIYQQEhzzl712fWgJITMfc4y8PAXOdl1sjzn3aweAqGYP9hIj31xThrmVcQDAsV7/Tu59nSMYTmYQj4SwqK4cgCr2N1SpzuSTM8wdXoyZ6mxvM0XapzI5nOwPPrcndnWgdzSFJ3Z2TsbUSkJEyJ/bVAUA+KOLWvCp287NKyYxnLs2Pdt9OqVjWry5XOBs9+8otkuQSOsx4O7XqBkRuZ7U5lRyjHze5wv/znbb3u8+1ycUkozCBhunvd9+9JOBWAI7IdlPDLytkzzrP43AfEzEeROsZ3v++QP4c+rnjRV1vtbOZCi2E0IIIWTGUhgjPz3zIIQQQggh5EzE7stwQsiZhTlGfkqc7ZpALnr0OmE4253nJJzt5fEwls2tAAAc6R31Pad1e7oAANevmJs3L+FM/uUrpzyP9eSuDvz7C4cLkvSmkozZ2T6Dera3DeaL6/tLiJJvn0Fx9Hs1Z/u5zdWO2+g922UbR3JAx7U1SjyIyBlx7W3u39kunMlGjHwwZ7td/HsQEbjUmHRx3Eqdz2QRsYn9F8haYVIkVHxeRo90Q5DOBFgf87ZWsd1fGwIbZ3vAgg23FIkzGYrthBBCCJmx0NlOCCGEEEJIMBRFyfuyl2I7IWcmZmf7VMfIuyEEFrcY+XGtZ3tFLIJl81Sx/XCP/77tQmy/eWVj3uMfetNSAMCj29vQbVonJ7I5BX/zi9fwwBP7sLNtyPc8JgtzgULbDHW2A8DBEoRyc+/36SxsAAqd7XYYblu7+G9/wnSxnu1+RE7xnuYEiSA90hNOznYPwq8Zowe4IbqKzxpxPz3bXeLo/ayPvWg/jTHyome7jWtbj+734WxPmoo/gvRIN6+lrB97/4UW4vwptTgCMBW2zLLPpRTbCSGEEDJzsfw9Nt1/oBFCCCGEEHKmYBXA0pkcP08TcgaS52yfghj5pOcYedFL2s3Zront8TBWan3bf73tFMY1x7sX2gcnsLNtCJIE3HRuvth+2eI6XL54DtLZHH748tGiYx3rG9Pn9Pqp0sT2ruEktp8YCPRacyR472haTxOYbk5pArkQYA90+U8hANTfN+K8HRiX0TuanpwJBiApZ3GkR92P81yc7UYfaZsY+Zw/p7STc7eUGHk5U5oonbD0APcTaW7GKCQwzUeI/z5i7aM2a6SLtz56gNutdZD1mSzCLj3bMz7OI7u2Bvp++Vgfc9S++P2RDpIgYFOMkgkQaw+4t2w4k6HYTgghhJAZS4GzfXYVPRJCCCGEEHLasBPlpkKoI4RMLj1THiOvvkfxGHlNBHTp9yZi5CtiEbz7soVYMKcMbYMT+PYzBz3P5+m9qqv90kVzMK8qXvD8/3fDcgDAI5tOYDgpu44l4sQBYFeJzvaP/Pc23PlvG3Co27/729rnfqb0bRfO9iuX1gEIHgHfNZyE+eucUhzypXKoexQ5BZhTHkWDzfkjcHW2ZwI62y2/c3WRM0Bvc9nG2e5nHF3g1GPkA/Zst4m1TwcQyY1x1PkoihKoGCHmIkr7LSSYDMQhsUv8EI95caYbMfKF4rYfZztQGP0fRLS3Ff8DOO3zxrJp2XAmQ7GdEEIIITOWwp7tdOIQQgghhBDiBTtRbrZFdhIy03luXzeu//qz2HykL9DrFUVB98hMjZF3d7YrCjCuO9sjKIuF8YW3rQIA/HD9UezrHLZ9nRWnCHnBTec2YEVDJUZSGTyy+YTrWHvajfcsJUZeURRduN/d7m0/zFiFuFMzpG+7iH6/8ZwGAMCRnjHX5IJi4wims2+7ESFfDUlyFhfdoq39ird2fc1VMTlIz3bhbC+MSQ/mbFfHyQQUSq2O9FxOCTQfIbiK15qvCX892wvXWi9GmI6e7XqMvI2z3cfxN2LkC+P6fRdIWNbIWB8/66xdH+ZY+wBFH+pY9sUoZzoU2wkhhBAyYxHiekhStJ+nczaEEEIIIYScOYgvU8OmfqxTIdQRQgye2tOJk/0TeHZfd6DXD4zLeSJUOnv620F4FdtjmuCTcejZns5BdzdXxNWxbjq3EbetakImp+Aff7MLuSJ/5A8nZWzSChWcxPZQSMKHrl8GAPi/He2u45md7Qe6RgLHGPeMpHRR9niff1e6VcA+OQOd7WXRMNLZHI73+5+btff7ge5gcfSTwT7tmJ/b7NyvHTA7d53Fdq890qM27u9sTtGvB189yfWe7fn3AfP7eCGhO/ezeXPxK0gXuKRNjvsgjn2r29r8Hp7mI45btrAYYVqc7dr3l3ZCsp952Tvbg8XjW/vaB3H+6+K/6Z6px+KHgon/dLYTQgghhEwR4sN/SP+ZajshhBBCCCFeSGeML2WtX7QSQqaG0ZQqTBSLN3eia1h1tZvFldPtBkymvfVsF45Ypxh5bdchSfnC/T/98UpUxMLYdnwAT+zqdH2P5/f3QM4qWDavAsvnVTput7xBfW4s5d4Lfm+H4bCWswr2dwZzXJ80icnH+sZ8v97qBJ4JzvZUJqv3WV8wpwwrGtU1PRBgjdo1Z3uFdg5NZ4y8cLaf1+Tcrx0wC4CFBRhpn450q5MYCO7cjlj6bZvH9Sr+A/nOdrOwHfYplBqO9HyXNBBQJLcR2/0UAIj3zHNc+yyOmEwiLj3bZR/OdKuzPZtTdANSJKDYLoq1ghQj6G0W5MJz2u98jBQJ9mwnhBBCCJkSDGe7+HkaJ0MIIYQQQsgZRNr0ZXPcJtKWEHL6GdVE9uEJdxHYCSF+zp9Tpj92uq9j4Wz32rPdKWZciO0VsUhefHdzTRn+QnOif/uZA67u9ldPDAAAbjh7nutchOAmuxQi9I+l0akVL1y6qBYAsKvNfwQ8kN9j/UQAZ7uY56L68oLxpouOQXVtEtEQ6ipiWNGgOsEPdPl3pbcPqWL7tWfN1ceYLvOEaFdQ3Nmunu9uIqnvnu3maPM8MdmH2B4S7RqM9ZMDOJyFsz2Zyea55P26v637Zi4iCCK266K99n9J8lcAoB837fWKopgi8qcvRt6uuNFPdL81aSFoMQKQn0YQ9HglbNosBGmLAJiE+1n2mZRiOyGEEEJmLOJvsbAutlNtJ4QQQgghxAtm51vcFB9LCJk6xkp0tndr4vCCaRDbi8XIR4sI3Cnt4XIbh/yfX7cU1YkIDnSN4vGdHY7vIVydc8pjrnOxEzetiAj5JfXluGpZPYDgfdtP5TnbA8TIa6LbkvoKAMDJ/ul3tos+6y21ZZAkCWcLZ3u3f1d6mybcX3/2PIQkYGhCRo9WODKVjKcz6B1NAwCWuSQjAEVi5DP+nMD2zvbSnNuyjbM9GvEvSiflbF6BjF9BOmpx2ou5REISQj7GKoyRN9bYXJxTDGsiQV6CwHQ42/We7YWft/z0bNdj5DUneWkFEupYcjY/1cDX+WPzOTITMK4/7uF+fSZCsZ0QQgghMxYhrovP2ZTaCSGEEEII8Uba5Hyz9lglhEwNo1qs+fBEQLFdEyibqhO6KHa6Y+Qn9Bh5d+lAzEd26NmuO9vjkYLnasqiurv9wacPIOsURe8xLtsqANqxp10V289rrsYF82sAALsCi+2GwN47mtKPs1eE4LV0bunO9g2He9E/lg78eoEQ2+fXqoUdZzeqTvAgEfBt2v4sn1uhFxTsn4Yo+aQp8rq8SPGIm9tW9CX3KpLraTKm81EIk5GQ5EtMFgkS4pzJ5Qznth9nsti/pJzLm5ffGPnJ6P9tHkcX202tb/wQj+SvddDe75OF0K/t7ot+Ytet4nYpBRIxcX/MWMR2Xz3bnZ3tvuczS1sbUWwnhBBCyIxF/L0tPrexZzshhBBCCCHeEF9gxyMhxKOFX5ISQk4/utieDBgjrznbG6rjRuxy5vT+XZz06WzP5Jxi5NU/5Cvi9uN84NolqCmL4nDPGH7/ervtNmmPAlwxlz1gONtXmsT2/Z0jgYqQrD3W/UbJC+F1yVxViB4Yl30L9gCw4+Qg/uQ/NuP9P9pc8vclbdo+iRSFs5tUsf1IzxgGfIj5iqKgXXO2t9Saer8HiKMvlbRJDCzmunbrI+23x7Wbs92vKG2c2+oc8uLoA/RsB4yCmmjYn/APALFwfty+14IYK3HLNRs0ktxwttvFrU+js93mXiTulVEP4rR+PsqlxewD+edjOqBob+0hD5iKUHwe+4aqBC5fPEe/N8wWKLYTQgghZMYi/lgUfxIwRp4QQgghhBBvmL/Yj1u+jCaETA1jmoA6VKKzvaEqYQgm2dPbDsJ/z3YHV7oeI1/obAeAqkQUH7p+KQDgf7eetN1G701dRMzxEiO/p8Nwti+YU4aasijS2RwOBHBcn+xXxXUhVh3vG/P1eiG6zSmPobY8CgD48uN78MATe/Gjl45iw+FeDI4XF7jbNTf6rrZhvHCgx9ccrFid7S01CaxqqUYmp+B/X7E/PnYMjsv6OdRUkyjJIV8q5qKzYri5bYXr2qt4G7VxtqcDislR/Torzbktem4DRhGQl97hVqy91kt2tlt6tvuPJM/v2S7+HwogSk8GwtmezSkFiR0ZH872RIGzXSv4CPmL2Qfy11oUbcR8xvXbtVkwz8kP1541F7/6y2vwT3+0ytfrZjoU2wkhhBAyYxHiuvh87FAwTwghhBBCCLFg7tk+VSIdISQfc4x8EOdxl+Zsb6yO68JakISKpJy1dezaYcTIFxHbQ0V6tmtvV2kTIy+4eOEcAHCMQU97dM2KtckpsI2kT2WyONStOqtXtlRDkiScP78agP++7bmcogvTlyyqBQAc7/fnbJdNkeLLNHf7z7acxL+/cARf+P0e/Ml/bMYVX34aLx3sLTKOsfb//sIRX3OwIpzt8zVnuyRJuOeaJQCA/954PC/G2nUcbW3mVsaRiIaxQhPb93aO4IUDPfjGH/bjWK+/4oSg+HFdm/tIW69VvehjEpztfh3g1tQGczy5H7E9Gpb079ZGtaSNiE/hHyjcNz8FDfnzscbRB+v/XehsDzbOZGFeBmvxj3CCe1l3UUSQlK1FDQGOmWmtjcIRf+PELT3kzXMKch7NRii2E0IIIWTGYo2Rp7OdEEIIIYQQb5hFBjrbCZl65GxOvw4zOUV3+/pBONvnmZ3tPsX2431juOkbz+Pmb73oGrMumPAYIx/TLJwZp37r2u6Wu4j2ImLeKUJduFSLCXnmGGO7fTzYNYpMTkFNWRTNNQkAwPlalLxfsb1rJAk5qyASknDl0joAwZ3t0XAIX3r7BfjIDcvwoeuX4oPXLcUtKxsxtzIOOavguf3druOYhdeNR/rw2slBX/MwYzjby/XH/viiFtRVxNA2OIGn97rPpWAcEUevRUW/dnIQ9/xoC/71uUP4ytq9gecJACNJGZ99bBc2Hu5z3c4Qgt3PZXUb4xyyRoDrruuIN1HRVmzPBBOBI9YY+Yz3aHwzkiTp6yCSNoII0ub+3+q8Sot/t47jO47eUkwYtPf7ZGFeBuu9OmNylRfD6mz30+/dinmt9ePlc52t8zHPaboKG2Yavlbhn//5nyFJUt5/5557rv58MpnEvffei/r6elRWVuKd73wnurq68sY4ceIE7rjjDpSXl6OhoQGf/OQnkckE61lDCCGEkNmNYnG2U2snhBBCCCHEG2mTE88a10oIOf2MWQTk4Ql/34ErioLuYVVsbzT1bPcjtncNJ/G+H25G+1ASJ/rH0aH10nbDa8924Wx3jJHXNJkKhxh5wHC9W9dK4L1nu6Fw2Tn/zf3aRXTyJZqr/sldnRhOeo/5F/3am2sTWDZXFZKPB+zZHglLWNlSjU/ffh7+8Y6V+OxbV+IH778cf3PL2QCAg93ufc6thQX//uJhX/MQ5HIKOobyRXJAbSVw1xULAQA/3nDU01jtumivFjUsm1upR+WL4731WH9JPeYffvkY/nvTcXzwv7a6tgHwmowA5Avy1nNId/B6jMuOucbI+3WAS3lzCBrbDgCL69VCile1ogw/PbsFBfHvAXu2F8TRB3VcO/Rs9ysmTxbm6acsaUKy6bovhtXZbhToBDlmWtS+qWd70Lj+/Bj54HOajfg+41atWoWOjg79v5deekl/7hOf+AR+97vf4Ze//CVeeOEFtLe3484779Sfz2azuOOOO5BOp7Fhwwb813/9F3784x/jc5/73OTsDSGEEEJmFQqd7YQQQgghhARCfAEejYSML0npbCdkyrC6tf0IuoAqzgthZF5V3FbAc2NwPI33/3ALTvZP6I+1D024vEJF79leLEbeIgJaSWXV5ytcYuQrdLHd3vXvN0beaT4nNYF82bwK/bG3nNeAZfMq0D+WxkPPexepTw2owvqC2nIsmauKl37F9mKC14oGVcQ/VKTPudhXsf0TuzoD9UbvHklBzioIhyQ0VsXznnvf1YsRDknYdKQf+zqHi44lxPaWGlW0j0VC+MVHVuOnH7wKW/9xDeKREAbGZRzu8ZYGsOFwL+76wUZsO94PQC1CeXT7KQDAeDqLj/z3Nt2pbSXtwy2dV7AhW8V2fw5etxh53z3bRVGLliCRCihKA2qvbAB4YX+PNkYQZ7t9r3X/8fj5DvmgIrBV/DeO1fQIwJJkf/wBQzD3UrRh9EgXjv3gLnLzMZN9uOvt52M+p4O77WcjvlchEomgqalJ/2/uXPUCHRoawg9/+EN861vfwk033YTLLrsMDz/8MDZs2IBNmzYBAJ566ins2bMHP/3pT3HxxRfj9ttvxxe/+EV873vfQzpt35eFEEIIIW9cCnq2U2snhBBCfLFkyZKChDpJknDvvfcCYEIdIbMZc49ZvV8nne2ETBlWAXnYQRB0fH1a/V0bC6sFM3GfzvbvPHMI+7tG0FAV18XYzqHiznbRs728iLPd2kvaSkp7WETF2yHE9nQ2Z7tfXntuS5JU4AA2I8Y2u/Wj4RA+fft5AIAfvnRUjz8vhiheWFhXhsX1qnjfPjSRF69cjEwRQfAs7Xi1DyUdI/YBY79WtlRjzXmNUBTg3ke260kBg+NpPLmrs2j7AFGE0VSdKBDOWmrLcOuqRgDAr7edKrZraNfSE1pqDYf82Y1VuG7FXJTFwrhoYS0A4JVj/UXHevXEAP7iv17BpiP9+Mff7EIup2Db8QEc6xtHeSyM+bVlONo7hk/87w7kbL4w8tNPXI1ZL4zKBswR594EXLtrNagjXUTXF8atF4/Gt3KdJrbv0ZIegvVsN1zS5v/7d0o7ieQBx7Gsj9cUgtNBTL835p+Tftz7iWi+k9xPv/eC+YgUAXOMvO8EAVG0aYqRL8FtPxtxLitz4ODBg2hpaUEikcDq1avxwAMPYNGiRdi2bRtkWcaaNWv0bc8991wsWrQIGzduxNVXX42NGzfiggsuQGNjo77Nrbfeir/8y7/E7t27cckll9i+ZyqVQiqV0n8eHlZvBrIsQ5b9fUiZasT8Zvo8yZkJzy8yHfC8I1OJENvF5zY5k+G5R6YU3vPIdMDzbvYxncdy69atyJoiDHft2oWbb74Z7373uwGoCXWPP/44fvnLX6KmpgYf+9jHcOedd+Lll18GYCTUNTU1YcOGDejo6MD73/9+RKNRfOUrX5mWfSKEeMMsMogvZ1MBekYTQoIxmsr//e/kvnXC6uoWIpRXsf0VzQn8j3echxf29+Bg96gnZ7uILS4r5mzXquKLxciXu8TIV5jeYyyVQSwSy3ve3A6jGNFwCHI2qztA88ZxEO3XnNeAq5bWYfPRfnzjD/vxL//v4qLvozvb55SjviKGilgYY+ksTvZP6CJ5MTJFBMHa8hjmVcXRM5LC4e5RXaC2YhYov/KO8/D6qUEc6BrFp371Ov7kqkW4/xc70DWcwv93w3L8/e3n2o4BGAUWlQ4pBG9aMQ9rd3biQJd7rD0AnBosjKM3c8WSOdhytB+vHB/AXVcuchznQNcI/uzhrRjX5ravcwRP7u7E+oOqK/v285vxgWuX4J3f34Bn93Vj67F+XLWsPm8Mr8UagngkhFSmsPAjsLM9Wyi2+3WA6+0achaxPYDAeeXSOkRCku6SDxIjb3WkB3VKx8LqtS9biwgCOttTBcUI0yi2RyQgVXivlnPeneCiiCApZ6EoiiHUBygiEOdKOpszCf4+ixqi+euczSl6GmmQOc1GfIntV111FX784x/jnHPOQUdHBz7/+c/j+uuvx65du9DZ2YlYLIba2tq81zQ2NqKzsxMA0NnZmSe0i+fFc0488MAD+PznP1/w+FNPPYXy8nI/uzBtrFu3brqnQGYxPL/IdMDzjkwFmUwYgKRH8ezctQtre3dO55TIGxTe88h0wPNu9jA+7i9adDKZN29e3s9f/epXsXz5ctxwww16Qt0jjzyCm266CQDw8MMP47zzzsOmTZtw9dVX6wl1Tz/9NBobG3HxxRfji1/8Iv7u7/4O//zP/4xYLGb3tiyaJ8SFqTrHJtLq+JEQENW+UE+mWbz6RoT3telhaCyV9/PAaNLXMRhPqkmw0bAEWZZ1kWs8Vfx3qZzN6X3KVzVVYl/7EACgfWC86GvHNUd9RFLct1W0eONcrmA7WZZ1sb0s4n7uJaIhJOUcBseSqIzli39prUAohML3sBILhzCOLMaSKchyNO+5lKzuU9hmn/7+1rPxjoc24TevtuF9Vy7AhQtqXN/nZL/6ua65OoZMJoNFdeXY2zmCw93DWDwn7vpage40V7KO+3XWvAr0jKSwr2MQK5sqbLdJavf5sATMKQvju3ddhPf9aCse39mBx3d26Nv9attJfPzGpY4i33gq/1yzsqBW3a9jvWNFj0O7VozQWBm13fbiBdUAVGe701hyNocPPLwFQxMyLl5Yg8sW1eKHLx/HN5/aj+4R9bp6+0VNOKehHBfMr8YrxwfRNTQOWVbHFuOOawUv0ZD9flkRAudYMp23fVpzuktK8fNQ3S6rvc7YfiIlfid7m4sgLKmKpvj9bdwXQr7v6bEQcPHCGrxyfDDQXMzzScnqfCa0c8fvWGKNUtoaGevj73eVCBsQ6xN0nMlAvJ8oGBi3nEeiyAY55+teEIa6bU4BJlJp/VoPcsxE3cFEOmP6bOhvnAi081BW5540FW8qHvbnTMbrvvkS22+//Xb93xdeeCGuuuoqLF68GL/4xS9QVmZfqTQZfPrTn8b999+v/zw8PIyFCxfilltuQXV19Wl738lAlmWsW7cON998M6LRaPEXEOIDnl9kOuB5R6aSv92yDsgqeoz8ypWr0Hq1c+UzIZMN73lkOuB5N/sQQvN0k06n8dOf/hT3338/JEk6rQl1LJonpDin+xzbeUoCEEZXR5uWFBXCrr37sXZ072l9XzJz4X1tatnRp16Dgo3bXkO0fYfn158cBYAIcpk01q5di8H+EIAQXnl1ByJtr7q+9tQYIGcjKAsr2LXpefR2qXPZceA41q496vrasaRa9L7ppRdxMOG83WBKnV86k8XatWsLnk9lVYXn4L7dWNu3y3GcCNT3e/Lp59Bi0ZSHx9TntmzagPYidfc5rVj/uRdexAHLOEeOqWt35NBBrE0eKHjtFfNC2NoTwsd/ugn3X5CFm2n4QJv6Pif37cDa9h2IpdWxn3zpFSQPe+t9N66t8caXX8Ixh49FkXF13Kc27USi4zXbbfadULdpP3UCa9ceAwC8fZGEXx5Vz7trGnJ4rV9C72ga//LzP2DVHPv5vd6vnh+jw0O2x1Ic65P9Y/jd79fCyRSbyQE9o6rktHvrSzhu86fMeEYd61jfOH7+2FpU29Rt9qeAtsEIwpKC9zT2QUr3oSwc1vu8z4kp6N27CWv3ASOD6hps2fYqlBP5+/fqa68DCGN4oM92v6xkZXEOrceRKuPxoWH18Ve2bEKfh1+h/dp6JdOy/r7be9U1Hh4c8DQXwXHtGB86fBRr1x7GwSF1nNTEmK9xBHOzxn1pbHTE9xi7tPtaV28/1q5di23d6s+D/T2+xuqeAIAIxpOpvHEG+nt9jbNfu7edbO/A2rVt2Duo/jwxNhpofSYDOZUEIOGFl17CCe08yilATlGvjeeffQaVRf7MVwNG1O1/t/ZJHB1W92t8zP8xO6GdQwcOHQF6DqvjjA77GqdjXJ3PyNgE1q5di2TGmN+zTz+F6Cw2t3stnPcdI2+mtrYWZ599Ng4dOoSbb74Z6XQag4ODee72rq4uNDU1AQCampqwZcuWvDFELzixjR3xeBzxeGFVWDQaPWO+fDqT5krOPHh+kemA5x2ZCkQkkRDbpVCI5x2ZFnjPI9MBz7vZw0w5jo899hgGBwfxZ3/2ZwBwWhPqWDRPiDNTdY4devYQcPIIli1ZhHgkjJe7jmPR0mVoveXs0/aeZGbC+9r0ML69DTiwW/954fJz0PrmZZ5f/+qJQWDnFlRXlKO19Xr8fnAH9g5249yV56P1yoWur/3ltlPA63tw0aI63HHHFSjb34NfHn0VSlkNWltXO75OURR8fKNalNF6y1tQX+ns1O4dTeGftr+AnCLh9ttvhyQZCrUsy3hw17MAgGuuuBS3rGx0Ggbf3L8eo/0TuPSqa3Dpotq85/5px3OALOPGG96k95134mt7XsTwUBJXrb62wJ3+3K92At0dOH/leWi9bknBa68cTeHWb7+MU2MZ9Netwj2rF9u+Ryabw99sfgaAgnfdfhOaaxLYHTmA19YfQ2XjErS2nuc6R8E/bn8WyGRw05tvwNK59q71gS0nsf53e5GrakBr66W22+x+6gDQdgwrli1F6+3nAABuVxRcv7sLteVRrF5Wjy8+vg8/2XQCJ8Mt+GTrRfYT2tkJ7H8dDXPr0Np6RcHTuZyCB3Y+g6Scw4Wr34zF9fYVAsf7x4HNLyERDeHdf5x/Tph5+PgGHOgeRd3Zl9meG0d7x4DtL6M8HsW733YrAKCz6jC+/exhAMBdq5fhrWtWAAB+O/Aq9g/14JyVF6D1igUAjHveinNXAof2Y35zI1pb7QtEzXz74EvoT43jsquuxpVL6vTHv7rnRSCVxA3XXYfz5xf/HNs7msLnt7+AjOnakHe0Awd3oXHeXLS2XlZ0DMGhZw/h6bYjmL9oEVpbV+LFg73Anu2YU1Ptei070Xh8AE/+51YAQN2cGrS2Xu3r9WX7e/CjA6+islp97cCWk8DhvZjf3ITW1os9j9M2OIEv71gPJRRGa+utGN56Cji8B/ObvB0rQXpHO/73yC7U1s9Da+tliO/rBvbuQP2cWrS2XuVr30pFnHc1VRXoS43jsiuuxtXL1PMolckBm54GANx+682oSrj/LlYUBX+7Wb0X33DjW1BzcgjYF2y/Dj93GOvaDqN5wSJcuLwO2P865tXPQWvrlZ7HON4/jq++9hKUcAStrbdiYDwNbH0eAPDW1tsRDtCS4EzBa+F8SWL76OgoDh8+jD/90z/FZZddhmg0imeeeQbvfOc7AQD79+/HiRMnsHq1etGvXr0aX/7yl9Hd3Y2GhgYAakVldXU1Vq5cWcpUCCGEEDILETXJIUkBICHnrUicEEIIITb88Ic/xO23346WlpbT/l4smiekOKf7HMso6hefiWhU77WZyUk8r9/A8L42tUzI+X/AjqWzvtY/qzVUi0fDiEajiEdVN2pWKX4d7+lUe2tfuHAOotEoFtSpgm7nUMr1teZo4KryBKJRZ/mgLG7snxSOFPQAFjHy1eVx1/esjEcBTCCZLSxQFD2vKxKxovss1icnFRbpizbuZbGI7TjNc6L4u9vPxT/+ZhcefOYw3nrRAjTVFNr6u0bHkckpiIYlzK+rRDgkYek81bq66egAdrSN4LJFcxAqIjyJ/ttlcef9OqdJLRg43DPuuE1Wu8/Hovn79ceXGMUY77liEX6y6QSe2deDcRmoKS8cKwt1HHGu2bGkvgL7OkdwciiFs5rso/ZVtytQUxZ1bDUEAJcvrcOB7lG8enIYd1y0oOD5nKSd+xHjWP7Fm5bjp5tPYjgp4z1XLNYfT8TUczSjFJ4/Yn0SDsfdSlw737NK/jkk+pIn4t7uoeXmUycUQTQSQs5yPXtFzCmnXfdBxxFctnQuKmJhjKWziIX9j1EeV4+rnFUQjUaNNY56W2NjHCNqX90vcQ76G6csrm6b1uajiPWJBFufySAe0e7VMO7V6VxGf74sHkc0GrZ9bf44IaQyOWSlMBTtmohF/JuQ5lapJ+TQRMY0jr/1qUyof9elZPV4QdJaK0hAIu58rc8GvK6TL3P/3/7t3+KFF17AsWPHsGHDBrzjHe9AOBzGe9/7XtTU1OCDH/wg7r//fjz33HPYtm0bPvCBD2D16tW4+mq1OuaWW27BypUr8ad/+qd47bXX8Ic//AGf+cxncO+999r+EU4IIYSQNzY5zdou/k5VFKrthBBCSBCOHz+Op59+Gn/xF3+hP9bU1KQn1JmxJtSJRDrz8+I5QsjMJZ1RxZxYJKT3oU1lsm4vIYRMImOpTN7PwxMZhy3tEUKz6P8b065jvd+3CzvbVCfe+fNVUbS5Rm0B2zeWzhPUrUykjecSRcQgs7ieyRb+rS7E9vKYu9+vMq4+b10vwNhXse/u81G/OJAzhesjxom6jPPeKxbhkkW1GE1l8MAT9lnhpwYmAADza8t0J+f5LeoaH+wexbsf2oibvvk8jvWOuc5VrFfEJa9+RaPq5D85MJ53XMzo6+MyzqqWapzTWIV0Noffvd5eZBzn9RFu9uMu+5b2eLwuXzwHAPDK8QH7+WjVEeb5VCWieOzea/HYvdfmpQEkNGEzKRce95R2LsRd9suM+F2ZtpxD+vnjcxzAWJO0PoY/B7B4T/F6L8eq2HhXL6sH4H7+Ob9efY1YI79rIxDnSE5REyPkgOsjhG0xH32dI9PntDbu1cZ90XyP9LqP4jxKyll9vyIh/8e9sVoV2zuGk/o6+T1eYi6ZnKIeL80N5Xec2YyvlTh16hTe+9734pxzzsF73vMe1NfXY9OmTZg3bx4A4F/+5V/w1re+Fe985zvxpje9CU1NTXj00Uf114fDYfz+979HOBzG6tWr8b73vQ/vf//78YUvfGFy94oQQgghZzyKougx8mFdbJ+++RBCCCFnMg8//DAaGhpwxx136I+ZE+oEdgl1O3fuRHd3t74NE+oIOTMwi+0xBwGBEHL6GE2r4rEQKYYmZF+vN1/D5nGKXcdyNoe9HarYfoEmts8pj+qv7x5OOb52QhPiY5FQ0Vhgs1An52yETk0fFmK6ExVxVSwbtYjtuZyii1VehEWrKGnGi8AUCkn4u9vOBQC8fKjXdhshti+YY8SoX7CgBr/4yGq867IFqIiFcaxvHP/7yknH91EUBRlNqHITzuorYphTHoWiAId7Rm238SJ0SpKEd142HwDw6PZTtttYzzU7lmgC97E+5/7Fskch7woton13+5BtIUE6qz5mLY5YWFeOVS35rnqR3GJXROJlv/LG0gvT7MV2rwK3ef9LFaUj2vZCrPW7T3Zce9ZcAECZB3e1lZhljYLOx7y9nFVMxQjBxknp6zz9IrDdZy7zPdJr5LpI60jJOf34uxUMOdGspXR0DSUDr0/c1JQ9nc0hI47XLI6P94uvGPmf//znrs8nEgl873vfw/e+9z3HbRYvXoy1a9f6eVtCCCGEvAExC+vio1uOajshhBDim1wuh4cffhj33HMPIhHjawBzQl1dXR2qq6vxV3/1V44JdV//+tfR2dnJhDpCzhD0L8DDku78sgoIhJDTx6iWqd1SW4ajvWMYTpYmtsdcxGQzh7pHkc7kUBmPYHGdKgpLkoTmmgSO9Y2jfWgCixx6bgux3YsIFzUJxXZu8pT2UHnMfawKTYwX6yUw76cXIc+tqEgITPEi4zRUGVHJdgyMpQEAcyvzY5OvXFqHK5fW4bqz5uK+/92B5/Z168K9lYypP56beCtJElY0VGHLsX4c6h7VUwrMpDPeBLi3XzwfX1m7D9tPDKJ/LI26ivz5pz0IcEvqhdjuwdleRMhbMKcMcyvj6B1NYV/nMC5ZNCfv+VTGu7idcPn9JuZT7LgLDOE2X7g3hE5vwmI4JCEckpDNKbrIHtSRLlILROuBoA55M++5YiGO9I7ibRfP9/1aa8KGl3QFO6wFCbLHc9mKUYSkHrNMCQ7wycK4VxvnkX4OhSVIkre1SkSN81Ec/yDidpPmbO8ZTZkKqoIlCADq/VEc9wid7TpcCUIIIYTMSMzCuvgsyZ7thBBCiH+efvppnDhxAn/+539e8BwT6giZvZjjl706Ygkhk4eIRW+pVYUO32K7JtSI69drQsXOtiEAany4uXe4iJLvHEo6vla4jL2I7aGQpP+tnrH8sa7GDKtPVhRxtjvFyPsV24V4J9tE2nt1zepiq0NBgyHe2q/PDWfPQ0gC9nWOoG1wwnYbcxuAYjHeZ2lR8oe67Z3tugBXZL8aqhP6MbUWNQDe3MkiRt4tIt+ry1mSJNRVqH2Qx22c7XqigYfjnnBxtqd8O9sLhXtFCea61gXXEh3XEX2cfGd7Kc7tyngEX3r7BXrCgB/0zxMiHj+gsz0SkiA051Q2G7wYwTIf47PP9DmurVH7gDEvP0UAcVOLBC8FMU7UV8b14o8O7b7kd5xwSNL3K5XJzYgEgZmGL2c7IYQQQshUYf5b3RDbqbYTQgghfrnlllugOPwOZUIdIbOXlOmLaye3HiHk9DGq5agLkdtvz3bhrraK7cUSKnZpYvsFFie0iBJuH7IXgQGTs72IG10QCYdUV6pFnJ4wCZ8iJt4J3dmetojtpv30IsDFdLHdpWe7R7E9nclBUZQCB6oudDoIeXMqYrhk0RxsOz6A5/Z1431XL7aZi/GZrJjYvqJBFdsPdo/YPu/HVZyIhjAhZ5G0+T3gZX1En/RTAxOQsznbbf0Ipoaw7Rz/7kXIc0tu8R0jL5zEpvPXXEjiS2yPqOttjVv320s8Esp3tpsL6aaDqKWIIGj8uyRJiIVDunAbtGe7ONfE/bIUUXqysI2R153g3vcvz9ke4PWCcEhCY1Uc7UNJnBxQ20AEWZ94JAw5m9HmYzj1iQrLDgghhBAyI1Fg/EFj9Gyn2E4IIYQQQogXDJEh7NiHlhBy+tCd7TVBne3WGPlw3uNO6GL7AovYrjnsvTjbEx57OYtI44zFTT6mjRMJSUWF1wonZ7spStxL7LKb89+r6BoPG/tt55D3IkrfdG4DAOC5fd22z2dMxy9axOW6oqEKAHDQwdku3M5eopwTpv7PheMUj1tvrEogHgkhk1PQNmBfsOHHSe6WuOJHJE/YCOTW+TglETjOKVsokgL+XNfW8zFoz3bn2Pbpkfas+1VKD3mz+z+oaC+Ov9XZPq1ie7jwM5co2vAzL3MhSabEIoJG7ffQif5gznZ1PiJFwjheQcT/2QrFdkIIIYTMSBRbZ/v0zIUQQgghhJAzDfMX4IyRJ2TqGU0ZPdsBYHhC9lVAbhabAW8x8plsDns6hgGgoMd3k+awbx90Edv1nu3eZAMh8grXrWBMc/VXxMNFhfIqXWzPF0v9inh6dLOrs919LnHTfruN4yZ03niOKra/fLjXNtpciG4hCXkx/3acpTnbj/eNl+TYB0xCmauz3Xk+oZBUtG+7n2hp3UVu22s9v4WC6zguxWTWghXPY5kKEkQvccCfsGj07S5NJBex42JtSxG3J4OYfs0ryOWUksRt8z0tcDFCOL+IRJ6EmP1SsUshMWLkvZ9DhridNRUjBBO3Rd/2U/2qs91LGobTfMxOe8bIG3AlCCGEEDIjse/ZTrWdEEIIIYQQLxgxvJJrzC4h5PQgnNrNmtieUwwB3gtWl7BdH2ArB7pGkZRzqIiFsVQTRgXCYd857Bwjn/QZIy/mZHWBj2uR8OWx4l1s9Rh5h57t3sV2txh5rQd4sRh50/N26+yll/h5zVVoqk4gKeew8UifzRjCEVp8v+oqYgCAbE7Ji+a3juVFgBPOdrsCAK+uYtG3/XjfuO3zfoRga/y3GSFwexHyXPfLg2PfjN3vSnPRRRChtNSe7cY1NjNi0s3HNp3N+V5ju7HkbE4/5n6LCOIOzvYgYvJkUZVQ72kjSeOeFsSZrqdRmJztXu4bdjRp9/8R7T4b5PzJm49w6vvoQT/b4UoQQgghZEaS17Pd5jFCCCGEEEKIM+IL5zid7W9YcvwDaloR4nF9RUwXFoeT3sV2q3Dp5Tpef7AHAHDF0roC17QQWzrcnO1p4Wz3KrZrLldrjLzmUi/3INqLnu5uMfJe8BIjX0xgCoUkXVC1GyflYRxJknDjufMA2EfJ66KbB+E2GpYgggHsxGRfPdI9xMgXEzpF3/ajvU7O9gA9222KI1I+xjELgFZ892w3OXcF5n3y0s5AUBC3HjB223qN+elnfzowv286m/NUgOKE2QFeas/2bE5BJpub9mIEAKgtiwIABsfT+mMi/cPP8Y/brY+Pgg8zwtkuiJZyvBgjbwvFdkIIIYTMSMwudvZsJ4QQQgghxB9mV2jMRkAgs5ufbjqOi77wFLYdH5juqbxhEWJ7VSKCak18GZ7w3rddv4a1mGRdvHPp2f6iJra/acW8gudatBj5vrG0rXALGDHyXnu2C6FFtsbIa852IaS7UenQs10XtiPexJyYi7Pdj0veTbT3GnUtouSf399T8JwhuhWfiyRJSEScRXJfse2eYuSLOdtVsf24Q4y8H3E77tJr3dc4pqhtx3E8Cq92BS1BRWBr0kLQ2G3rOLKPc/l0YE1/8FKA4oR4TSk9262tH/TjNU3rAwBzyoXYbtzvgyQbGG0Nsvo9NmgRgSi2EgTq2a4XtmRL7iE/G+FKEEIIIWRGopj+jpQYI08IIYQQQogvDJEhrDsI6Wx/4/DErg6MJDPYdrx/uqfyhiGXU9A/ltb/PZ4WfcsjqC5TBWVfYrtFcCzWs308ncHWo2pxxQ3nFIrtteVRXbzpGrZ3txs92z0620U/acuc9H0vJUbep1CqC3fZwu8N/PSDNooagse2X7igFgDQNlgY2W+Ibt7EW6O3uct8PAiLCQ/O9mJR4EvmqjHyx5xi5H0I03qMvEtRgxdBWY+Rt1kfMZ+4x/M5buOS14+XT/HW2rfbaysDKxFrjLx+XUyPozgUkvJaWgTtRQ8ACe3cnpAzJfRsN7ZPyTmjqCGgA3wyqNGKqwbMznYRA+9jXpMaI29xtpfWs920znS261BsJ4QQQsiMREGhs50piIQQQgghhHjD3LPd+qU/mf0c7VGdp9Ze2uT0oCgKPvLTbbj0i+uw89SQ7uwGVOd2dUJztpcQIy8c7k7O9s1H+pHO5jC/tgzL5lYUPC9JElq0/vEdQ/ZiezLtr2e7EAIzueAx8oazPV8sNQRXb3PxEiPvRRCcDBFYiFIi2tqMIbr5c1snbZ3t3oXFhKuz3ZtLdYnmbD/ZP16wX0BAZ3sJsf+A2f3rHPvvuRWBzbEvVQQW16ufQgQzhrNdyZvPdDnbgfwUCf1YBZhPQ5UqAHcPp0ooRgghLFo/zJQY+fIYAIuzPYAz3ZzaUGov+klxtpvmo8fIs2e7DleCEEIIITOSvJ7tdLYTQgghhBDiC3Nscpxi+xuK8XQG7ZqYymM+Nfx003Gs29MFANh6rF8XjiMhCfFISI+RHwrgbI97dLa/cECLkD97nmNvaeFu7BgqdFwD/p3tQmixRrePixh5H852x57tHkU8a9y2GT8C5WSI9tZo67y5CNHNYzy+W09yP65r4dq2E+1THte6qTqBeCSETE5B+2BhwYa19YHrfFwSV7w67QHTftk5232Mo47l3rPdD9bzSA4oSgtxPlMg2k+j2G7at1Kc7UIA7hhKmlIa/IvJepGEPMNi5CdsnO0+xHJxbxyakPVrPaizvdHasz2Q2F7otJ/OdZ5pcCUIIYQQMiMxC+viAwu1dkIIIYQQQrxhFqqKiXRkdnGs14h45jE//RzpGcWX1+7Vfz41MIHRlCqqV8QjkCQJ1YkAMfIWEavYdSz6td9w9lzHMZtN4pYdfnu2G0Kgg7PdQ8920dd9LJ2BYvqjX48A9+xKzo/bFiiK4iu63S0JxKtr1trX2owuuvp0trv1NvcinCUihUKyPiePAm4oJKG2XKQ0FJ7LhqDsZ52d5+OnZ7u9sz2bt02QscRc/IikgI3YXmLPdnHu+Tnmp4uoKQHAKOzzL5I3awJwp1lsD7Bf5tYPpYwzWYhrZGBc1u9pRry993ktrlfbNhztHQt8HgoS0bBeBAAEE8lF7H9KziIjioamMa5/pkGxnRBCCCEzEiG2S5KpZztz5AkhhBBCCPGELlSZnO3pbI6fqd8AHOkd1f9Nsf30ksnm8IlfvIaknNOFiFMD4xjVxGYRkS6c7XYCpROFMfL5sdRmTvaP40jPGMIhCdec5SK212piu40rGQAm0urY3mPk1TkJ4UUwpjvbvcfI5xRD7Af8O9udihHMrRS8CEz6Ots5yT26k83R1lbRXkTuexXN3J3t3qPJhWvbLY7eV490O/HfR4GEW+JKKuN9v1znk9GiyT2L7YWtGsQYvmPk9fNRnVfQmHS9oCWXL9pPZ4x8VcJwXBtpD97uGWZ0Z/tw0nMrAzvM51KpceuTgejZns7k9HtaOoBYLto2HOsb08X6IAkCgqaaMv3fwXq2G/eidACn/myHYjshhBBCZiSioD0kSYbYzu8FCSGEEEII8YT5C/C4yaXq1O+ZTD4dQxP4z/VHfAmsk4Ho1w6oTj9y+nh6bzdeOzmI6kQEn7ljJQCgbXBCj0QXQrIQX4YnvPdst0Z7uznbhav90kW1en94O+oq4gCAgfG07fNJ3zHywk2e/8f6uNb73UuMfFk0rLeOGzVFyQeNkbfe48w/exGq4i7r7EfIc3Jc6w5V3z3bC69lIdx72a+EEMpcHPJe9kuMYyfa++u17hIj7yOOPuHW+10vOvN2PsdcnO1Be7aLayNo/LveHkHbP6+tDE4ni+pUx/XxvvHAvegBoFkTfzuHJkqKozenUQQtjphMKmJhfT0GtL7tGa3oItLdBTz/PODhd/PSearY3jYwgbG00ZokKE3Vcf3fgYoaTNdaZgYkCMw0uBKEEEIImZHoznbtPwBQQLWdEEIIIYQQL6RNcb7mL6/Zw3vq+NdnD+FLj+/Fr145NaXve6TXJLbzeJ9Wth7rBwD88cUtuGJJHQARI685u7WIdCGA+ym8sEZgu4nA6w/0AgDetGKe65hCmHQ6L4wYeX8Ct9XZPpLM3383JEnSRXkRPw8AKZ/iW9Qibgpk0756EYZiEXvR3vyYr97vFlEt4yPSHijibM8It6x3R7q7Q97LOM7if5D1cdsvbzHyxn4plt6D4hry7mx369nuM0beUvzhJ4XAjDi2slZYoTvkp9HZvlhzXB/vH/NdFGPG3LO9lHh8871xJsTIS5KE2vIYAGBwPA08+igyf/tJdV6vbgduvBFYsgR49FHXceZVxlERCyOnAId71MSaUnqki/UGSlvnpJw13ccoMQu4EoQQQgiZkYi/kSTJJLZTayeEEEIIIaQouZyS53iMho20KIqvU0fb4AQAoGvYPrL7dGEW263CI5lctp8YAABcumgO5s9RXZpDEzI6tZ7oFXqMvHvPdjmbwzef2o8XDvTojxXEyLuIwAe6RwAAlyya4zpfszBph3hPr07gaNje2d4zmgIAzKuKF7zGDrFOo8ngznZrbLdAiG/hkKRHu3sbp1Tntn1suyhM8CKQ54/jLG57ipF3GceaouA6johtt+m17ueYufai97Ff5sIQ63kdNEbePE7QeHNrMYEuAvsUS41rTHO2zwAxWTjbT/aPlyRuC/F3JJnBoOYAD+KQj5mOW9B1nmxqtTSTwT88C7zrXZCH1Ht0JKvd49ragHe9y1VwlyRJd7cf7xsH4K/nu5WmaiNGPpjYbo6RFwkdjJEXUGwnhBBCyIxEONvzY+T5RREhhBBCCCHFyItNjoQgSYa7PWUjkJDTQ68mOAoRYSpQFAVHetizfSpIZbLY3TYMQBXbK+MR1JarAsv+LlVYEb2NhbN9yEFs/82rbfjus4fwwNq9+mNpi7PbqZe4oiho1wo7Fswpgxt2zl0zfgVuvWe7RWzvHlbP/QbPYrsq4pQSI2+N7Rb46f8NuMeb+xEWnYojxPy8ilRuBRJ+orcnz9nuHCPvZ5y4S/GIUfTh3dkOFEb2GzHyHsV2m0j6wDHylqINWcSb+xRLxfaKAmRzSklO8skiL0bex7GyUhmP6PfIrhG1QCmISG52tuuO62kWgedozvaB//wvQFGQCannaTSn3ePE95v33ecaKS/6tmdzpfdIb6ox7sexiP9xjJYNJmf7NBc1zCS4EoQQQgiZkRg92wFJi4+n2E4IIYQQQkhxrGI7YBbZKL5OFb0jal/swQn7/ting76xtB7hDfB4n052tw8jnc2hriKGxfWq+DS/VhW793eqYruIR6+Oq9ffcEePbb/eX29TWw2MuDi7ow6O6/6xtC58Ntcm4Eax+4D/6PZ81y2giv9dI6rY3ljtTWwXve3HSunZHimci/lnr/ukFybZiMB+XM66SO7gbPcq3sZNApeZbE6Bpr9Nafx7mTbOhFvvdx8Oeev6AKZj5mGcaFiC0FXNbntF8X8O2bVqCOqULhDbA8bIm99XzuYCjzOZiPvdCZPYHtRp36y528XXfUF6tovjNiFnZ4TzH4BeeDU4rv7+l8Pqz9GscY+DogAnTwLr1zuOs2xuRd7PpexXY3WpMfLGNavfx+hs16HYTgghhJAZidnZHtKd7dM4IUIIIYQQQs4QzEKB7op1cWuSyUdRFPSNTb2z/UjPWN7Pdq5RMjlsPy4i5GshaXFswlmui+3xCPDoo6i+690AgOGu3oJ+vSf7x7H5qNr73SyEFsTIm3pAm3tTi3YFDVXxovHvbgKn3XsWIxISbnJjvNFUBuPprD4nL4gY+bG0SWzXChL89my3FhL47XHtJUbem+PaXiTXne0+nfZWJ7l5zb0IwcY4hSK5cF37csi7ifZenP8uaSspHwKuJEm2hQ3mgAO/x96uZ7tfUdG4XtWx5Fww0d6cgCBnc8Y1Oo1i8kLN2T6SymBMu9aDOu2bavLTOIKIwEKwz4u1n2bHtXC2D5ZVAQB6KmoBAHUTw4Ubd3Q4jrOkQGwPLm4315QaI2/cY/UYefZs1+FKEEIIIWRGIoR1SZJMPdupthNCCCGEEFIMs/NNiIB0tk8twxMZXVRzig4/HRztHc372dq/mkwer54YBJDfJ33BHFWEEnHoVccOAe96F2pOHAEADMc14cTUr/c3r7bpr88T2y0R2GYxy1xE0Tagiu3zi0TIm8ey67cNGOeL9xh59f6SMVXGd2kR8mVhBeWas78Yes/2SYmRt/bt9ud0dRPbA8XIW8bJ6DHy3uaTcHC254ntXnq220SkW8fystYJL6K9J2d78Rh5r8ded+2bRXLTsJ5j5G1E+3SJPdvFmvgpaMgbx7R9Us75Olani0Q0XJBaEXQ+zdX5aRxBxOSzGioBAIe7R2eE8x8wnO0DZdUAgLbqeQCAluHuwo2bmx3HWTqJzvamEp3tcyrUfeocShox8hTbdbgShBBCCJmRGM52GD3b+b0gIYQQQgghRbFzvunCBsX2KaFXc7UD0+Nsb9Gcfjzep4/tJ1Rn+yWLavXHRIy8oOLx3wKKguqUelxG4+XIQdIzk5X77sOj20/p20/IWb3I3LiOVQHQLBiaj6twtlvf2w696MbJ2e5TyBP9pDMmwbRrWO29XB3zNAQA+xh53ZHu1dkecRDbfe6Tq7Pdh5DnVOAk4pe99kx2drYbBQ5e+oAbvdad49/9xdEXro+fNgRu56Ifh7w6p0KRPGN2tnuN7Lc5ZrJYG78x8qYkCiB47/dQSEJ9hXoxdY8kfbUyOJ0srpscEbixxiq2+x9HiO2HekZ93zdOF7WiZ3t9IyBJaK9uAAC0DPcaG0kSsHAhcP31juNYxfZSnOTVZRGUaddKkPU5t0ktHNjbOTxjihpmEhTbCSGEEDIjEV8wSBJ0Zzt7thNCCCGEEFIcO1egW2QvmXx6R0xi+xT2bD/Sq4q65zSp0bVmQY7k0zeayhOJ/dAxNIGOoSRCEnDRglr98QUWd3lFtxoPXJ1Uj4sihTASV93vUBRsy1XiWN+4fn3mFEOcc4qRB/KP6ylfznZNlHQowvAbUR3Re7abne2q2F4T837uVerOduP+lArobLeK5H5FTmv8d9CxnFp3iJ+9Otud4+jVccIhCSEPEed6qoGbuO3F2e4i2vsRpt3SVvxGgdslNohhY5GQnvDidRxzq4agoqK1aMNPoYaV5lpVkO4YTPq+Lk4Xi7S+7QKv6QFWmi1ie5D9Wj7PcLaX2kN+spijOduHLr0SANBRPRcA0DLco24gzskHHwTCzu0/astj+lhAaT3SJUnC7Rc0YcGcMiybV1H8BRaWz6tENCxhJJnBsb5xAN7vY28EuBKEEEIImZEIXT1kipFnz3ZCCCGEEHIm0j2SRN9oqviGk4Tdl/GiVzOdzlND35ghsCflnK0wdTo40qPGyJ+jOdB4vO154UAPrvjy0/jqE/vyHj/RN54XZe7E9uODAFSnn4hAB4wYeUFVWhXCY7kMIll13ImoIS79+vy3AABuv6BJf0yIoVaxPRSS9P7Nds72BV6c7Q6R5AI/PckBQ9DKmGLoRIx8jQ9ne4WNs913jLzubM//4sCv2O7quA7Us93qbPfXs93OtW2ei1fxVh/HcuwVRfHn2Bdiu8055MeRHncoRjA/FvfpbDffZ8VyeR0DyD/XxHETx8t3/Lvl+Ad1tgNGr+2OoYmSxplMFtXl3+uCzqdpEpzti+srEA5JGEtn0akV+0z3+ujO9up6yL/4Fboq6wCYxPYFC4Bf/Qq4886iY5n7tpfai/5b77kY6z91Y97vLa/EIiGsaFAL+XaeGtTmQ2e7gGI7IYQQQmYkRs92o+CTPdsJIYQQQsiZxlgqg9sfXI/bv70ew8mpiRNP23wZHw/bCz/k9NBrKa7w2rf9hQM9uPd/tqN7JOn7PTPZHE70q26z85rVL8Tt+iGfiZwaGMdJbd8mg+89dwg5Bfjd6+3635mHe0bxlm89j7d+Zz0Gx93TCESE/KWLa/Met7rLK9LGnKOa2C6HDZHjlQXnAQDednELhGFRCIYpG7exXcS5n57thuBaxNnuWWzXerbbOdujti+xpTKuzitPbPcZJR51cLYbbn1volDM5G42k80p+vcU/nq254vSIk3BS/Q7YHZtl+bYd3K2Z3OKbnbwstZuMfJGP3E/MfvOsfZez0O7AgkRI+/HKS0KANR55Re9+O7ZXhAj772fvRXRFqR9KOk7feJ0sdjkbA+HJIQDOq6tzvYgzv9YJKTPJyuKI6ZZBBY92wfHZXTedBsUKYSYBNT/x78Bzz0HHD3qSWgHgKX1htgeKcHZLvCa9GDHec1qId+A1h7H633sjQBXghBCCCEzEqNnu8QYeUIIIYQQcsayu30YfWNpdI+k8KOXjk7Je8q2znb2bJ9KzDHygPe+7f+5/gge39mBf3vusO/3PDUwATmrIB4J6a7D2XC8M9kc3vFvG9D6nfXoHys9kn9P+zC2HO0HoLqwj2rR+0/t7oKcVXCsbxwf//kOXbSx41Uhti+ak/d4TVkUVQlDTK+oqdSrx2NZ9RxIhTUVWpKQTKgiSm15TO+lm9T6ttuJaoYQbAiURs/2fKepHXGTWG9XzO63v7noH1xqjHyFHiNvdrZnfc1FiHRWkVz2uU/F4uiB0mLSxVr5d7ZbY+T9Oa6dCi3M6+Vljco89H6PucRiW9/LPkbeX19yO7e92C0/8ebRsKSbPUQRQFAnubnYIptT9PtJIGe7llrRMTjh+3w+XZid7aX07W6uzi8SCupIF1HygumON58jnO3jaXQMqffE5rpyhP7kvcCb3+waHW/F3Ld9uh37K1uq8372eh97I0CxnRBCCCEzkjyxXRKPTeOECCGEEEIICcDu9iH93z986SiGPTqcS8HOEermIiSTT69FFC7mlBYMaNv98pWTGPGZhNCtCfzNNYmiDuYziZFkBj0jKYwkM3h0+6mSx/uvDcfyft54pA8A8OKBHv2xFw704F/WHXAcQyQInNNUVfDcfFOce9XHP6b+Q5Lyne3aH7nynHoA6rVaFlOP2YSczROv85ztloSK0VRGT03w1rO9MCZbkMspvgVc0T/YLEQbYrunIQBMToy82Dc5axWT/YmcdukB6jgmsd1L3HrE/hoUkfu+HeklOtsdRfuMca55GcutZ7ufY+a0PubHvB57u6h98U8/orQkSQUu+cno2S77PHesCPd3+2BSj7UvReCeDMxieyku++qyiF7AAQQXk89qyBfbS41bLxW9Z/uEjFMD6u+Llpri92g7lswgsV2k5gimez4zCa4EIYQQQmYkiilGXnxgobOdEEIIIYScaexuH9b/PZLM4OENx0/7e9r1FXZzERIVq0hXCgXOdo9FFkI4HUtn8ett/oRlUUiRiIYdI6xnAvc+sh2t317vqTc6kO92fmTLiZLaiw2MpfHYjjYAwPUr5gIANh7uw1gqg1eOq273j79lBQDgX587pDvgrQghziwSCcx92ytuu1ntyzt/PmI59djK4YjerzcdiwNQr08hPk6ks3lFMXbXsbjGRYR8TVkUlR568NrFZAv8OpwBw9lu37Pd+3Gq1MV2Y7/FfPz2j5cdYuT9iu0pq2hvGtdbT3J70V5E7nuNg3Z2tqvjenWWGqJ9/jgpLSVBkrzNyS1G3lfvd20+2ZyiR+vr4/hMNbDbt4wi+RrDGEvrJW+Jfw8aIy9nFYvYHiBGXivgOd4/Zow/zWJyXUVMv25LmYskSXoxQSlx9GdZnO3TXYxQo4ntOQXY3zUCAGiuTbi9xBGzs326neQrm/Od7dO9zjMJiu2EEEIImZEIYV2CuWf79M2HEEIIIYSQIAix/c5L5gMAHt54HGOn2dxu5y7UBYSAYns6k5vVrviOoQlc+sV1+OQvX5uU8fo0Z7v4W2bIY4y8ebv/2ngcOR/xXuYiC2u/4JnC3o5hPP56B/Z0DOOJnR2eXmMW24/0jGHrsYHA7//zrSeRyuSwqqUaH7vxLADApiP92HSkD3JWwcK6Mty3ZgXWnNcIwIiLtyKE6rit2G64FyvjEbUv77FjiDWpY6b/7SG9X69sEoKFsz0p5xyF3QKxfVB1TJrd9G7YxWQLgontwtmunqe5nILukeDO9lE7Z7vfnu2lxsg7iORmh7OXnsdOaSJGjHypzvZgMfJyVslrkWAWk73sVyIySc72qLFN4TELGJFv42w3F5h4IWZxtqdLjpHP5SVVBIqR18RoUcgSdJzJRJIk3d1eav/4Jm3/ShFul1uc7dPd0z4eCaNcu6fv0T4Her1PW8lztk9zPH5teQwtNUbRwHTH9c8kuBKEEEIImZEIYT1k+mNPAdV2QgghhBBy5pDKZHFQczR94uazcW5TFcZSWWzoPr1OIDtxwBo/7YdMNofW76xH67fXT2oP8M6h5KS6yUvhtZODGElm8LvX2ydlH3tHVVFEfLk+OFE8Rj6XUzCiiY2xcAhHe8fw4sGeIq8yMAtdQugxizwzAbNb/7evtXt6zZjFAf+zLScCv//Pt6qv/bNrluDiRbWIR0LoHU3h4ZePAQDetGIeJElCQ7XqOLdz7yqK4uq6zhPbRf/2cBjRSlUwSV94kd6vN2USgg3HcDbPIRwyOT2tRRTC2e4lQh6wj8kW+HVuA4bwI5zJA+Np/ZyrjnoaAgBQGVfXYywdPEbefM6b0w9km7YabjgVJom49VLj6I0YeY+O9Kg4XvbOdu8x8uYWAsZYouAj7lPYtjrkzXPystbmbQrORZ8FErbO9gAx8uax9J7toiAmEjBGPmvEyIckBHJuN1YnYK2DmG4xGTCi5EuNbDfE9uDjLJ9XkffzdBcjAEbfdlF02RJQbK+MR3DHBc24YH4NWgK64ycTc9/26Y7rn0lwJQghhBAyIzF6tqvudgDIzYzv4QghhBBCCPHEwa5RZHIKasqiWDCnDHdc0AwA6E2eZrHdztkuBJsAQvKJ/nEc6h7F4Z4x7DL1oC+FFw/0YPVXn8E3nto/KeOViuh3npRz2NlW+j72jari+nIt2nbQg7N9JJnRi47fc8UCAMCPNBHYC+Y+x0KIyebyXazTSSabw2M7DIF9w+E+vSjBDeF2Fi7Bx3d2YHC8ePGCHac0cfr6FfMQj4Rx2eI5AICXDvUCAN509jwAzpHbQP41ZCfkCbE9JOXHzFvFV0VR8tzSYtsJOevo6raOcWpQE9t9iDhOvbLN7+nF4QyYnO3aOdap9Wuvr4jBj9Zl17Pdb99us7hmLjIxYuS97VOxnu1+e61b19lvLLkQtx3n41EENju8zUUkxjge98shRj6TzUHcarwcs0g4pAvPZme7+Z7l3dleWEAiTgG/orT1uPkt1hDohTGmnu1BBeBoOISGqrj+cySUX4QzXSyunxxnu3DulzJOVSKKpmpDiJ4JYnutFiXfryXdNNcEF8q/d/el+N1fXec5EeN0cp4pSj46A87DmcL0HxlCCCGEEBvEH2mSJEF8dmPPdkIIIYQQciaxWxOmV7VUQ5IkPSbaxiw7qaRtxAHD2e4/Cv5wj9En1qmHtV8e3X4KigK8dLB3UsYrlW5TPG+p+5iUs7pAfJYWbeulZ7vo156IhvCh65chJKlFCY+/bsStH+0dwy6HYgAjRj6cJ3ZNZhpBKbx4sAe9oynUV8SwqqUa2ZyCtR6i5MVanj+/Buc1VyOdyeHR7W2+3z+TzekinhDnVi+r15+PhCRcs7xee945Ktssnto721UBqjIeyROt9Z7imgqYySl6cUU8HM57TydXt1nAAwxn+wKPznbznAti5H2K24CpZ7t2zxHXUWN13PE1dlTEVLF9JGlytvsUKM33O7N469cl7dSCYbLi6MVa+e61bnW2++xFHw5JesGB+dj7jet3ujaCtCGwS1mQA41T6LbXY+Sj/iQwazGKKCQJGiOfMsXIlyImN9cY1/hMEJIBYJEmtpc6nyZt30odZ3mDKW59BvQSF2K7IGiM/EzD3Ld9Joj/MwWuBCGEEEJmJIrubJcMZzu1dkIIIYQQMoMZTWXw3h9swrefPgjAiA5dpUVuiv7Op11stxHNhHPULGZ55UjPqP7vyRDbszkFLxzo0cYe89WX/HTRM2IW2/smZaxYJKSLoF56tguxvaYsisX1FbhX6yn+j4/tRNdwEmt3duDWf3kRd35/A4aTheOlTEUWeWL7DInq//U2VSD/44tb8I5L5gMAfrujeJS8cDtXxiO4+6pFAID/XH/Ed+GIeR2EoHb1ckNsv3TxHFQlVHEk4eD+BorHra9srsZ7r1yI+9acnfd4TBfb8x2zgOpOTpic7U6ubnM0NbJZtB3vAgC0tB0Bst7Wwynlwq+THDBcjRlNTOzSnO1mF64XKrX7UyqT08XooOI2YAjR6r/9iaVxJ2e7T1HaKUFAdzl77HWs9yMviKP3LwIb/dbtnO3exEm7MQBjnf3MKWZT+GHeT78R+WbRPhPQ2W4tkhDnktfiiMJxssYxLyFy2xwf7jca/3RxyUI1GWTp3IoiW7rTrDnS/Ub1WzlLS5KJhiXP6Rynk1otRl7QPEvE9jxn+wwoapgpzIyrkhBCCCHEQk7v2Q69N5VCZzshhBBCCJnBvHigBxuP9OHBZw7gYNeISWyvAQAkRE/hqRLbTSJDo/ZldtdQ0vd4h01i+9Zj/SXHku84OYABTXyekLN69PR00j1izOGVYwMl7WOfFhk7tyKm92z10rPdLLYDwF+/ZQUumF+DwXEZ7/2PTbj3ke1IZ3NIZ3J6TL0Z0dM5FgkhEpL0v6NmgrN9aFzGuj2qMPzOSxfgrRe2QJKAV44PoE2LQndiNKXuV2U8gnddtgCN1XG0DyXxi60nAQD7Oodx0zefx4NPH3AdxyzECbHqogW1enz7DVqEPIC8/ukF42SMdbYTdEIhCQ/ceSH+/LqleY9bRTyraF8WNURMJze2PsbmrcCSJWg7pRatzP/ER4ElS4BHH3VeAA1dBHbo2e5HnNTd+pYYed/Odk1sB4Ax7Xj7nU84ZKTimQsZ/EZ4OznSzZH/XnBKEBBrVbKz3ed8AHOhRWHPdq/rIxJarD3kU1qxhySpKRGe5mMTtZ/OE9v9FQCkbJ3tYbuXeJiT1rM9YAR8zJRk4beVgR0z0dm+sqUaL37yRnzr/11U0jjnz69BPBLCOY3VxTd2QSTJzJT1mWNytlcnInpR0ZnOorpyVGj3gZmy1jMBrgQhhBBCZiQiMl7Kc7ZTbCeEEEIIITOXvR2quK4owL88fUD/+fz56hfICd3ZfnqdQHaO0KYaVfzqGgkithsx8iPJDPZ3jpQ0v2f3dVvGH3XYcuroMfUOH0ll9GPnld3tQ3j45aPI5RT0as72uVVx1Ghftnvp2W4V26PhEP7l/12EeCSEIz1jMP85NJEuFIGFQBvXROCoQxz2dPB/r7Uhnc3h3KYqrGqpRlNNAlcuqQMA/P41d3f7qJbGUBGPIBEN647/7z13GN3DSXz4J9twpGcM/73xuGuBtliHSEjSe0XHIiG8/ZL5qIpH8NYLm/VtdTexTWWMEAbtIuTdEEKbmIf4vySpQnGZXYy8tWe7OKYP/QCpjk50V6nO/PnD3UBbG/CudxUV3B1j5LNGEYFX9J7t2ny7RIx8lb/exLFISN+30bR6vNMB1lmc83nibUCHfOH6BBPtCxzpeox8ac72tE+RHDDFrds42/32R58oEP8Np71XV7Gd+9/8+8vzODZ95MVtz3fPdsu1HzQC3pxCUWrPdiC/33dsBrmJF9WX68cxKE01CWz5hzV46H2XljTOcs3Z7rXY43Qzx+Rsb5klrnZALSi7dVUTquIRvcCBUGwnhBBCyAxF0Xu2G872GZAuSQghhBBCiCN72g2Bdu3OToynsyiLhrF0rvplZGIaY+SFs71zKGX7GjdEjHyTNkapMevP7uvJm98Rk5g/XYhe0/O0+Outx/zF5f/jb3bh87/bg9+93o6+MXWs+ooYasuCi+0AcFZDFb709vMxpzyKT956DhbWqV/YW4UuoPC4xy2x5dNFOpPDv79wBABw1xULdQHtrRe1AACe39/j+vqxtIiRV6+f/3fFQjTXJNA5nMQd330JJ/rHAaiJAqcGnF3yQjyzirdfecf5eO2fbsHieiMK2clNLPZH3cafwOTkbI9p4qQuYqbNPdvz30OIbOlwBJ1VcwEACTmJ+vEh44/o++5zjZS3cxObf/YVIy96tudEz3YtRt6nsx0AKrTjK9oGBOkhL7Y1n/N+HfJ6QUOBs91vHL1D/LsYx6f7O5NTdKE+yHwAsyhtcoD7LEYQLnLrfPTrwsd87AobgiQsGAUJxjh6jLzPohjr9eG3yEJQrjl/szlFTyIppWe7ud/3TImRn0xqyqMl9/++aGEtls6twA3nNEzSrErD/Lt8tvRrF3zzPRdh62fW6J8tCcV2QgghhMxQjJ7toLOdEEIIIYScEQg39OL6cv2xc5urdBetENPSp1n7TNk4FcUXon1jKV/ia/9YWo98f/flCwAAW3wK0WY6hiawt2MYkgS8/WJVbJ1uZ3s2p+jR763nNwHw15s+l1Owr1M99s/v70GvJqzMrYzrPVuFkO6G2Kba9AU9ALz78oXY/tmbce+NZ6E8qsbQ2jnbrWKpUxz2VPOLV06ibXACDVVx3HXlIv3xC+ar7RWKHf9RvWe7ui7xiOFu7xlJIR4JoUVzfe44Oeg4jjn+3YwkSQhZhE8h3iVt+sIHd7Zbe7bnO2YTMRtnuzVGvq8XAJAOR9FWrQpKLcM9+t/MUBTg5Elg/XrHeTiJwEFETuEgFfvSGbBnO2BEyYvj7deRDuRHdwsCx8hnHdbHp0O+QLTXY+T9ifaAgwM8QM/2/Nh2f87thCmWPengSPdK3GaNghx3ozjGNB8tQcbvdWrMyRoj788tXZWI6p8FxO/MkpzttTMvRn6mURGP4Nm/uQHfuevi6Z4KgHxne3Pt7BKl1QKx0hINZhu8KgkhhBAyIzF6thsx8tTaCSGEEELITGVwPI12rR/6t95zkZ7OtKrF6EEqvpg83dqnnShUVx5DNCxBUYDuEe/uduFqn19bhjdpPa23HO13jet24znN1X7xwlpcrsWIT7fY3j+WRjanQJKA2y9Qo8T97GPb4IQu8qw/2IMeU4y8cLaPpjJFixzsnO0C4QYXgqybs12Ic4bYN31/SKUyWXzvuUMAgI++eXnel/NL56pO8u6RlC6w2mHEyBuvfc/lC3WX/1ffeQHWrGwEUExs9+5IF4UxtjHy2tr7FfGsrmvrdSqE0Ak5a7QEsIhq8aTq4lfFdvV6nD9skwzQ0eE4D71vt+UcCuIktzrb9Rj5AM520c94LJVBLqcEiu+O2rjS9bj1iDex1Cn+3a+47ZQgkPEp3prPMzux3Y8InLBxtvt1bpvnkzdOoFh7557tJTvbRc/2gNepmJO+zgHc5JctngMA2Hi4TxsjeLx5izlGfhY62ycLSZI8tx843cypMH6Xz6YYeWIPr0pCCCGEzEjMPdtDUv5jhBBCCCGETCaKouC5/d14bl83OoYmAgnJezRX+8K6Mly2uA53XqK6wK87a56+jRDTpipG3ix6hEISGqpElLz3vu1CCF82rwIXLqhBLBJC72gaR3qDRb+Lfu03ndOg91cNEiP/vecO4WOPbJ+UiHQhjtdXxHDJolrEIyH0jaXzetW7cbDb6GHfO5rG+oM9+nhml3oxd7ub2C4ojxYX2wuc7dMYI/+/W0+iYyiJpupEnqsdUPdzbqXq/DvqstZjurM9oj8Wi4Twi4+sxmP3Xot3XLIAFy+sBeBNbPciVMVdnO1BnLdAYTy51XFdZiqkcHS2V6pO2XQkiu5KtVilacSmrUNzc+FjGk4icJD9Ej3bM1kFcjant1BoLMHZPpbK5J2zQWLk09lCUbr0GHl/or2xzvnnkIiRj4S8zScUkvQ55ce/B4iRjxjpCfo4PossQiFJ39Y8TpA2BHaFDWmf6wzYO9uDx8jnu/8zAXu2A8AVWkHZ7vYhAN6PuR1zK+N6YQWd7WcGNWWGs322xciTQnhVEkIIIWRGkmOMPCGEEDIptLW14X3vex/q6+tRVlaGCy64AK+88or+vKIo+NznPofm5maUlZVhzZo1OHjwYN4Y/f39uPvuu1FdXY3a2lp88IMfxOjo9DpRCZlMth4bwAce3ooP/HgrVj/wLK756rM45lNM3tuhCq7nNalO9q++8wKs/evrceuqRn0b4So83WK7U5xvk+aM6xr2LrYLIXz5vErEI2FcoomafmLWBdmcgg2H1RjsG89twPJ5qrO5YyipC6peSGWyePDpA/j96x149cSg73lY6R5R12NeVULdx0W1AIBNR7z1pj/YlX8/FCL9vKo4wiEJ1QlVRCzWt33Yg9guBNmkbYx8vuPaSTScKjLZHP7tucMAgHtvXG4bOSvc7Ud6nX+nCNd7hUlsB4DmmjJdZBf/39U25FiAYV0fNxK6KGnnbC8tRj6tiXdWcbJMuHPlnGPkfWyRWsSTDkfRVVkPAGgcNZ2nkgQsXAhcf73jPIrFyPvZLyEeytkcekZSUBTVaW2OT/ZKlXadDCeDi+1CjJTzxHbF1zhxk2BvLrzy69x2jpFXf474cKTraQQ2znY/fa4TNuME6UmesIttD+C0F+eiXRKBH3FbL46RS3e2W4skxPpEQv7d0pdrznaR3FhKz/ZQSNLbwZQyDpk65pQbv8ubayi2z3Z4VRJCCCFkRqKYY+R1Z/v0zYcQQgg5ExkYGMC1116LaDSKJ554Anv27ME3v/lNzJkzR9/m61//Or7zne/goYcewubNm1FRUYFbb70VyaQhxt19993YvXs31q1bh9///vd48cUX8eEPf3g6domQ04Lot52IhhAOSegYSuIZzYHtlT3t6hgrtdj4aDiElS3VeXGmQmwsJraPpjK44zvr8clfvuZrDgIn0aypOrizXQjjVy1VnXpBxPYT/eMYT2eRiIZwXnM1astjqK/QnM0+ihsOdI7qAtqh7tILf4SzfZ7mxr1m+VwA0AsDinFQm4NYX0F9hTqe0bc97TqOF2d7mQdnuzjuhrgbXGxfu7MDl31xHZ7Y6RxL7kTPaAqdw0mEQxLec8VC222WzS2ebjCW1pztiYjjNkvqK1CdiCCVyWF/54jtNvr6RL042wuduwI/cfRmrOKrVZwUQmies90iqsWEIB+OorNKiO3atSjuNQ8+CISd5+bkuA4icoq5Z3KK3q99XmUcoQDCZHVCPe9Hkpk88bXUGPmUT5FcHFdFUfdLkPYdI29f1BDEKW3rSNfn4ydGXhR0FI7jR5TWi35sYuRjPq4Lu3MxyDh2vejFP3072y0tJEqJkV8+rzLvfl5KjDwAtGiCLWPkzwzMRUcts6xnOymEVyUhhBBCZiRGjDxMPdupthNCCCF++NrXvoaFCxfi4YcfxpVXXomlS5filltuwfLlywGov1sffPBBfOYzn8Hb3vY2XHjhhfjJT36C9vZ2PPbYYwCAvXv34sknn8R//ud/4qqrrsJ1112H7373u/j5z3+O9vb2adw7QiaPtsEJAMB7r1yEe9+sXh+Huu0FOyf2ajHy5zVXO24TNznb3T7bvnyoF7vbh/Gr7acwVMQNbYdTHHSD1kfZ7GwfGpdd5yJE0GVa5PuVS1WBL4jYvl8raljRUIWwJsaJKHk/fdt3aZG8fl/nhOhh36CJ7deepe7jhsN9yHmo+BWC//uvWZz3+Nwq9Yv2Ws3dVszZ7kVsT7iJ7Zbj7uSs9cOj20+hbyyNT/3qdZwaGPf1WiFWlUXDjsL0Uq2Iw63YQvRsr4w7i+2hkISLNHf7qw5R8ikfYrKbsz2d1RzyHkR7M0LsLezZrr5XwuTOTTkUBuhi8u13oKuuCQDQKGLkFywAfvUr4M47XedhFRON/SqhZ3tWQduAeh8N2pu4ukxztk/IecK/n/7LQryVS4mRN+2/+doRceteRVdHZ7vuSPe/X6U60t16pPvr/V7YI90pUcV1PqY0h4JxAjj/84oRtFu336KYuKVISc4Ej5EPhSS9bztQevx7sybY+jlWZPqoKYviooW1WNlcTWf7GwDnTyiEEEIIIdOI+L5PkkBnOyGEEBKQ3/72t7j11lvx7ne/Gy+88ALmz5+Pj370o/jQhz4EADh69Cg6OzuxZs0a/TU1NTW46qqrsHHjRtx1113YuHEjamtrcfnll+vbrFmzBqFQCJs3b8Y73vGOgvdNpVJIpVL6z8PDqsAmyzJk2b9wOJWI+c30eZLJ5VS/KiI2VcV0wfVA54jn8yCdyel9u8+eV+74ugjUL+8VSBhPph1FpC1HVEe1ogCbjvTgpnPm2W7nhIgYD0HJm8u8SlXE7RicgCzL2HFyEO/5jy3482sW4+9vO8d2v45ra7NoThyyLOOClgpEQhLaBidwrGfYVx/SPZpIflZDhT6vJfVl2HIMONg5DFlu8DTOaycH9H8f7Bou+XrtGlJFwvryKGRZxnmNFaiIhzE4LuP1k/1Y1eJcQKEoin7s37yiHj/bXIaTmuhYGw9BlmU9Rr5vJOk618Fx1fleEZUctxN681gyXbDNhOYAD2vHXQgyE2kZYQS7r+1qU4/ZSCqD+/93B/77A5d7di2PJ9X9iYad92fxHFU8OtzjfL2JGPl4yH0fLpxfjfUHe7H9eD/uuqzFcT4xl/kIIpJ6raYy2YJtx5Pqz5Ei8ykcU4uPlzOQZRkTafW1UW2cmKbDjaczSGrH0voewhibXLAQXalyYDiF+r//BORlDcDq1aqjvcicoiHjvDCPPZEKsF+KFrWdyeJEn1p00lwTD/S7tFJzSw+MpYxzJ1L8WJkRUd8TKWPf0hlxP8x5GksyFR+NJVOIhdSfk7J2TCRv+xWG/TkkxG1J8TYfwBDJx5Ip/TUpcb17nA9gCNhjSWN9jHPN+1oLQXpkwrgPjafyz2cvCF1+3HS89HE8XKf6OBDXVlb/rCnqCcKS4vMcUv8/kVavU1FcIymF9wIvXLKgGs9qSTlezx0nGrUCLj/Hikwddve9X/zFFQCAXDaDXGGNHDkD8H4fIoQQQgiZgRg92yX2bCeEEEICcuTIEXz/+9/H/fffj3/4h3/A1q1b8dd//deIxWK455570NnZCQBobGzMe11jY6P+XGdnJxoa8gWwSCSCuro6fRsrDzzwAD7/+c8XPP7UU0+hvLx8MnbttLNu3brpngKZQvYcCwOQ0HlkL7IJBUAEe9sG8Pjja+HFVNk2BsjZCMrCCl7b8Bxed3iN+uW/+nXcE+ueQbnDN3PP7FTnAwD/++w2JA/7cya3d4YAhLBv906s7X5df7yjRwIQxp5jbVi79iSeOBmCooTw4s6juDB3uGCcznEgm4sgHlKwbf2z+lrMLw/j+KiE//y/53HFPO+f0V88oM4r138Sa9eeAACketU5vbzzEFakDnga5+U9xvrsOtGLtWvXep6DHa8fVOfVc/IQ1q49CABYWh7CrlQIP3r8ZbxlvvM+DqaAsVQEISjYu+VFLIqFcBIhSFCw8YVnEJKA8QF1/I3bX0O8Y4fjWH0j6n69tnUjunbbb9NxUh1r9/5DWJvMXy9x3Pdqx31Ye9/tO17HFfP839dGZaBzWD1JYyEFW44N4FM/ehI3tXg75idHASACJZN2PEad4+o2hzqHHa+34XF1XV7ZuB5HXZJ45QH1XNqwr00/v8y80q0+PzTQV/ScGUip8xpPyQXbvtqpjjPQ0+3r3Dt6Sn3d4aPHsXbtUezoU38eHR7C2rVrsW9Q/bm7bxC79vYDCKOz7VTevhxqV7c5euIUuoclABJ2VSVwangY+MMfPM3j5An1vNh/8AjWZg7pj+9s096/ox1r157yNFaHdvzGkylsem0/gBAm+tqxbp36ej/nXLv2/nsPH8MzE0cARIBsxtcaDw+q+7Z123Zkj6vnaVePev7sfG0HQqde9TROCGHkIOHJp55GjZYEvUubX2e7ev8sxlAaACJIydm8c3tYu863bt6Evr3e9iulXQPrN2xG7x6thcYRdV+PHT2s37eK0dmm3T/2HcDa8X3qfp1U96uj7STWrj3uaZzkmDqflzduwdB+dT6vaL9fhgf7PR+zrnZtPnv3Ye3o3uDjTABABKMTKf01mZyqmu+1/B4sxhHtGjtyXF2PiaS6rxteWo9DAczJ8rA6NwDo7ekq6ffVhLY2yf7Okn/vkdMH/4aYXYyPe0v1KUls/+pXv4pPf/rT+PjHP44HH3wQAJBMJvE3f/M3+PnPf45UKoVbb70V//Zv/5b3h/uJEyfwl3/5l3juuedQWVmJe+65Bw888AAiEWr/hBBCCFHJmXu2a49RayeEEEL8kcvlcPnll+MrX/kKAOCSSy7Brl278NBDD+Gee+45be/76U9/Gvfff7/+8/DwMBYuXIhbbrkF1dXODtGZgCzLWLduHW6++WZEo85RzmR28ZXdLwBI4a03XoOzGyvxjZ3PYDwr4co3vUXv4+3GYzvagdd3YdWCObjjjisdt1MUBZ/csg45Bbjmujehpa6yYJuknMXfbnkW0Jx6/eE5aG29ytf+/LRjKzA0gCsvuwS3n9+kP15/tB//fegVZKKVaG29Dr9/ZAeAbpRX27/Huj3dwGs7sKKpBnfccbX++Ouh/fjhy8eRrlmE1tZVnuf17YMvAxjD2264AtevUPuiJ/b34P+Ov4qJSDVaW68pOkY6k8PfbnkG+vqkJNy45la9h3AQ/rt9C9A3iDdfZaxXz5zj2LV2P/qiDWhtvczxtesP9QLbt2PJ3Er88VuvRfnebrz8yA7UV8bx1jveDADYkt2L7X0n0bJkBVrfcpbtOLmcgk9sUr+gf+utb9ETFqwceOYQnus4gpaFi9Hael7ec9bj/lj/duwf6sU5560Eenf7vq+9dKgPeGUbFteV40PXL8Fn/m8PnmyL4kv33KjHSLux/cQgsHMLqivL0dp6ve026UwOX3v9aaRyEi6//iY0WvreZ7I5fHzj0wCAO25dg7qKmN0wAICrxtL4wVefR9eEhOtvuhlVifx9Hdp6Eji8Fwuam9DaerHr3PvH0vjn7c8jq0i49bbb9bYHANDx8jHg6AEsXjgfra0XuI5jpu2lo3j85EE0taivy77eARzYicZ59WhtvRwNxwfw/b1bESurwJJljcCpozhr2RK0tp6rjzGw+QQeO74PUkUdcn2DCEnAe/74NkR8xFMfee4wnm47jOaFi9DaulJ//NCzh4ATR7BsSf7jbhztHcNXX3sZUjiKSG0t0NWLN122Cjdf3OT7d+nIK6fw2xN7UF3fiKuuWQ68tgmVZQm0tt7ged9+3bsNB4f7sPKCC9F6yXwAwA9PbAJGhnHVlZd7Tgn5+1eexoScw3U3vBkL56hFgmJ9li/1tj5DEzI+t+05KJBwy2236RHiX9vzIpBK4vrrrsVFC2o8zefHpzajbXwIF11yGdacpxZAbvi/3UBXG1aeczZa37zM0zg7/3AAL3Yew8IlS9GqpZnsW3cQOHUUy5fmn2tu/LRjK06MDeD8iy5B6wXqPXNiextwaDeaGxrQ2nqpp3G2r92HDV0nsHjZWWi9eUXgcdoHJ/CVHeuRlcJobb0Vsizju7ufBQBceakxRy+Ia2xug3qf+LtXngayOay56UYsmONfbU/KWXx/37OQswoWzfd3z7BySzaHNx3uw2WLagvub2T64d8QsxOR0FaMwOr21q1b8e///u+48MIL8x7/xCc+gccffxy//OUvUVNTg4997GO488478fLLLwMAstks7rjjDjQ1NWHDhg3o6OjA+9//fkSjUf2Pf0IIIYQQRXe2GzHy7NlOCCGE+KO5uRkrV+Z/IXzeeefh17/+NQCgqUn98rGrqwvNzc36Nl1dXbj44ov1bbq7u/PGyGQy6O/v119vJR6PIx4vFIqi0egZ8+XTmTRXUhpyNqf37F40twpV5XEsqivHsb5xHOtP2griVvZ3qf2mz59fW/S8iUdCmJBzyCBku+2rp0YgZxWUx8IYT2exq20YsiKhPOb9azw5q35uLovH8t5jgbYvXcMpRCIR7O1U489TmZztXI5pcehnNVTmPb96+Tz88OXjeOX4oOfrJCln9Uj6VQvm6K87p0kVm472jSMcjhSNKN/fPQQ5q6A6EUE4JGFgXMaJwRTOn+9NtLKjd1SNrG6qrdDn9aZzGoG1+7HtxACyCDmKy0f7kgCAsxurEI1GsWZVM96/egAXzK/Rx6qrVO+HI6ms43oNJ2W94Li+qgxRh/er0ASWVEYpGCttOe6JqHrOZBV1Tf3e1/Z3i/O6BndfvQT/9Lu9SGVyGM8AVeXFx8lqZdOJaNjxfaNRYGFdOY73jePkYBoL6qvynh/PGPGttZUJRF36LzfVRrGwrgwn+yewp3Mc12kFHYJMTptPLFJ0HSrLjPMwJ4X0tVTHgbZfxccxUxZTt5Vz6rEQxyWurU9VmXqeJOWc8R6WuZbF1X+f0q7NuZVxlCWKFwSZKdfGkLPIGzujeF8fYz5q8UMml0PHkHYfrTfuF37OuTkVaqHFSCqLnKQK07Go/X3SiZhm5suZ7q+iHXh53PtcYpEwJuRc3jjG8fK2PpUwCiByUhhR7RzKaBd6IuZ9PuL8kxVJf404n+M+jpc49mnTsRfXaZmf4x4rnI8YJ+5yvVtJiHFyhfPxM05lmXqQ05kcwmGxztpcfRx3sb15TuL3aXki5mscQTQaxfnza/DqiUFf+2Q/FnDzqsIWGWRmwb8hZhdej6X3kjcTo6OjuPvuu/Ef//EfmDNnjv740NAQfvjDH+Jb3/oWbrrpJlx22WV4+OGHsWHDBmzatAmAGhm3Z88e/PSnP8XFF1+M22+/HV/84hfxve99D+l0Osh0CCGEEDILEV80SXkx8tM2HUIIIeSM5Nprr8X+/fvzHjtw4AAWL14MAFi6dCmamprwzDPP6M8PDw9j8+bNWL16NQBg9erVGBwcxLZt2/Rtnn32WeRyOVx1lT+3LSEzkc6hJBQFiEVCqNdcs2c1qILfwe5RT2Ps7VRdL+c1VxXZErpom5Lto+FfOd4PALjh7HloqUkgk1Pw6olBT/MQiJ7AsUj+V3/CNTwhZ9E2OKELdhOyfSPR472qOL50bn7BwRVL6iBJwJHeMfRohQrFONwzimxOQU1ZNM+1vWBOGaJhCalMDm2DE0XH2a31fT9/fg3OaqjUxy4FsQ/mea1oqMS8qjiScg7bTww4vRSHtHNkRaM6l2g4hC+87Xy8+/KF+jY1ZeoXtYPjzn0/h7Tn4hFnYR8AyrTnxm2OmTjuor+zcNIKEd4vu9vV83plSzUkSUJMjJfx1tYg5XAeWlk2twIAcKS38DiOaf3ao2EJcRehXXC2du2eHCiMfU1n89fHDfMxSFquVX2do/6+WhfHQ9bmIUQ88XhCG29CzhpztTjWxVqKAiFrEoAXxP6nMvnnkNN9w42I1gM8k1XQpt1Pgrh/AaC6TBVJhydkYy4+HPsAENOa2os1Nv876mOsmL5GheN4nZN5O/M1I8R2P/MR50bKdN0H2S9x7JOmccTcfM3HZhw5wPkjrmnz+gQ5D+Om61Ucs4x22/MzjnVOuZyCrHa8IkUKwdy4ckkdAKAizmRnQmYrga7ue++9F3fccQfWrFmDL33pS/rj27ZtgyzLWLNmjf7Yueeei0WLFmHjxo24+uqrsXHjRlxwwQV5sfK33nor/vIv/xK7d+/GJZdcUvB+qVQKqZTxwV3Y9mVZ9tycfroQ85vp8yRnJjy/yHTA845MFXJG/VIFiqI727O5HM89MqXwnkemA553s4/pPJaf+MQncM011+ArX/kK3vOe92DLli34wQ9+gB/84AcA1KK2++67D1/60pewYsUKLF26FJ/97GfR0tKCt7/97QBUJ/xtt92GD33oQ3jooYcgyzI+9rGP4a677kJLC9015MxHCLzza8t0V/WKxko8vbcLB7tHPI3RNax+b7WwrrzotrrYkbEXuF85pgq7ly+pQzwSwmP/P3tvHiZHdZ/7v9X7TM+mGWlmtK9oF0hIgMaAzCqCBLEtiB1swHG4WRzZPzAJcbAdXxsnwfZ1DE7MtX0dAo5tvICFbbBYBAaxCbSAhNC+j5ZZNDOataf3+v1Rdaqqq6u7q6pHmh7N+3kePZrurj596tSp6uU97/vdfgrvHOnC5bPGWm5vhRDRzKJiWcCLqpAPvdEkXt13Wrt/MG7dl55B5fpVG8509VSX+zGnoRJ7W/uw5WgXVi0ab/X0DPa3KWM5p7ESkqEwt8/rwfSxYexv68cHJ3sKjuHOk4rYvmhiNXqjCWw5ekYTvN0wEEtiQN1/Y8kASZJwxayxePq9k3jzYAc+NNN6/A+qc0QI/1bUlCuLOM5EchuNxFhXleV3UAmxPWpxzOLqcRfikvjfrjhuRixsWDBBKf/h90oYTGQKkPnQxf/8Ivn0sRV4Zd9pHDk9kPVYvyq2V9gUqYSYFbEYH7HAxY745vVI8HslJFJyligdS9oX7Y2Yj4cm3vqE2K4e20Qqp+AY8GaOpSuxXXudHIsIHIiuPo+ybTIto089VhNqyiDKPDihSk1tyBDbbSywMGK1IMSNmGzVjnlxRCE8OeaQOO5ioYIdxDkUtRT/7bejLfYyjk/KubgtynZkiPYOFyMA1gs/nC5qAHTxX7Tl80taooGdRTq5+pRI6+Pkd3i+G7nryulIyzI+ddlU120QQkobx2L7L3/5S7z77rvYsmVL1mOtra0IBAKoqanJuL+hoQGtra3aNkahXTwuHrPiwQcfxNe//vWs+1988UWUlxf+ElMKbNiwYbi7QM5jOL/IcMB5R84273VIALzoPtMFSX277+npxfr164e1X2R0wmseGQ44784fIpFsd9254pJLLsHTTz+N+++/Hw888ACmT5+Ohx9+GJ/61Ke0bf7xH/8RAwMD+Ou//mt0d3fjiiuuwPPPP49QSP8B/+c//zk+97nP4dprr4XH48Ett9yC//iP/xiOXSJkyDmliu0TavQ5f4EqnNoVcYVQYKeOdSiH0AUodbu3HVPF9qljUOb34rfbT2HzkU5b/RDkE08aq0Pojfbj1X16eYhczvaBuCp0hrJ/Qrxsei32tvZh8xF7YvteNbJ+TkO2+3/FBeOwv60fz+9qxY0F2tp5UjHhLJhYjfZeJcK9GGe7cLWHA94s1+Hlqtj+xsFO3HdD9nNlWcb+NtXZXp871aBGFdCFoG5Fr/pYdSGxXRW5rI6Z2Umuibsp52L7QCyJIx2K+L1gQrXanhdAMsM1nI+YTXfy9HHC2Z5bbLfrCC0X46PO3cz+WC9CyUXI50Uilcw6V+069s0ENGe7Itqa3dtGITTXa2SnVTiLkAeG1tnuNwm9teEAygM+VwsNxUKT3mjSVV+U/mTP+YSDRANB0GKhipvjHvB6kEilMp3tQrT3OHFuD42zXTjki3e2W4j2rpzt2QkC7hIWPPB6JKTSMqKJNMJ+rxYj79jZ7tf7lDCkgjhNWTBSXxnCl1fPL7whIWTE4khsP378OO6++25s2LAh40v32eb+++/Hvffeq93u7e3F5MmTsXLlSlRVVZ2zfrghkUhgw4YNuP7661mngQw5nF9kOOC8I+eK5I4W4MBO1NXVQZI6AAAVlZVYtepDw9wzMprgNY8MB5x35x8inW24uOmmm3DTTTflfFySJDzwwAN44IEHcm5TW1uLJ5544mx0j5BhRxPbq/XoYyGc2hbbEw7iqfM42w+d7kfPYAJlfi/mT6jSBMb3mrsRS6ZsO/Ty9aehKoT9bf1486Au4EdziO19UVXotKgXf+n0Ovxk0zG8c6TLVp/2t+rOdjM3LhqP/3rjCF7e0553PxOpNPa0KNfURROrcVRdBFCMs13EcRtd7YLLpivRvx+c7IEsyxmOfECp9d4zmIAkATNUwdiKmnIbMfJ2xXZ/brHdHCOvibsunO17W3shy0q0vhgbKwEyH3bj1meqMfJHLMT2AYfOdrEYwcrZbtdpLwj6PeiLZZ8fMYftCMyLH+KaWKrWzDYs1hGLL8wCX67SEE4IWgilxv44FTmNTKxxFyEPAFXq+dwfS2rz24nLHtDdx4mkLpIWEyNvJdo7im33ezEQT2WMdTLt3NluKW47dNoD1sfenLBgqz9Wor2r/uROInAqkod8HnWsUwB0sd1pAoU2Rol0xrXTyX4RQkYfjsT2bdu2ob29HRdffLF2XyqVwmuvvYbvf//7eOGFFxCPx9Hd3Z3hbm9ra0NjYyMAoLGxEZs3b85ot62tTXvMimAwiGAw+wOv3+8fMT8+jaS+kpEH5xcZDjjvyNnGo36R8Xo8EF9pZIDzjgwLvOaR4YDz7vyBx5EUw2A8hfdPdGPZtFp4i6gXSnJzsltxR08wiEQz6xXxr6M/jq6BOGrVWu65cBIrHcxTs32LGiG/eHIN/F4PZo4Loy4cQOdAHB+c7MHSqbU29ih/bepGQ912QSIlI5FKZ4kJmtBp4Wy/ZPoYAIoo2xNJoLo8/7VOOMCtxPYlk2vQUBVEW28MbxzowLXzGrK2AYADbf2IJ9OoDPowtbZcq6F7pGMAyVQ6S/izg16vPVu0HKMe91RaRiyZzkouEGUGptSW50010MX2wjHytp3tVmKy6bgHLQRDu4h67QsnVmv3OXXKC+e0XWd7c1cE8WQ6Q2TrjzqMkQ/kiZF3KOJp0d1ZYrszh7zAb4omN4uKxjkk5oM5Rt08lo1F1Wy3jpF34uA1O9uLEtsNc7+zXzkv3aYHxFP6MYslMxc12GrHQgROpFy0Yzrmsiw7jqMHrJ3tSRdx9Pmc7W7i6I3XIXfO9qFZRADoCxtEEoVYb+G23EMsmdKOuUcCP4MRQvLi6Epz7bXXYufOndi+fbv2b9myZfjUpz6l/e33+/Hyyy9rz9m3bx+am5vR1NQEAGhqasLOnTvR3q5HVW3YsAFVVVWYP59RGoQQQghREKWxPBIgqfXe0s7LvhFCCCGEjGj+/cV9+MT/exu/235yuLty3qLVbB+ji0TlAZ8mGtlxTevim50Y+WyxQ7D1mOISXzZNEbIlScKlqrv67cP2HOSA0dme3Z/Gamtxzqo/+epl11eGMLuhArIM/ObdE3n70xtNaOM82yJG3uORcONCJT5+/U7rMpMA8IGoIT6xCh6PhIk1ZQj5PUikZBw/M5i3D7lo71MWW1g528sN4qcYCyOn1IUaUwrUma8uU0T73mgSqRxfapw6262Ol1aTXK3rXUzN9l0irn+CnioqREbnzvb850VjVQhlfi9SaRnHz2SWXnEaI68724cgRl47V4cmRl6MnxDwzE5pr0fSxFldbM/vbK93EyNvIdwCxv2y79g3R6Ebr6NO8Xs9WhmAjv642hd3Qqkx/tuVs92yZrsLMdmvC7cAMs5/J6K9Va11d7XN89R+d7Rfoh2LWutFjA9QnLPd2JbrGHnDdTPuYu4QQkYnjq4SlZWVWLhwYca/cDiMuro6LFy4ENXV1bjrrrtw77334pVXXsG2bdvwmc98Bk1NTVi+fDkAYOXKlZg/fz7uuOMO7NixAy+88AK+8pWvYO3atZbudUIIIYSMTtKy8iXUI0kQaY3iPkIIIYSQ0YJwl4robDL0iBh5syPzggalbrtwL+dCluWs+O58WIkdgqNqjPa88brA+aGZdQCA1/afLti2IF8ctDF2Ohzwap+1rWLJC7mKP/2haQCAR984kreO94E2ZQzHV4dyisk3LlQSLzfsbs0p5p5QBfWZ45Rj4/FImDFW+dtplLysfrfIFyPv8Uia+BeJZY+PELzLCojJVWX6+IkxNWNXbA/li5EXznZVwLKqX22XXS3qwgaD2O7c2W5PDJQkCdPUKPljnZlR8k5j5MttxcjbFduFwGld29x1jHwOZ7vymvnFdnPfi4mRN59nbmLkPR4JRsNvMc52AKgKKfO/QzjbncbIWywIEcK7o5rt/uy57sb5r6UIJMQCC/03DSdJHKKdomPbLRZauGnHaiGKm9rvVosaRCqB02Mf0hYiqWOtOdudnadGt704XsXUayeEjA6G/Crx0EMP4aabbsItt9yCFStWoLGxEevWrdMe93q9ePbZZ+H1etHU1ITbb78dd955Z97acIQQQggZfQhdXZIAyXQfIYQQQsho4US34vIUrmCSydGOAXzowZfx328ccfV8WZb1mu1msb1eFdvb8ou4ybSsJTDZ+VE/l6sUAAZVkcAoLK6YPQ4AsO3YGfRFc9f81vqTSmvuyVw12wVzx1fpTul4pvAmyzL64/mFzlsunoSxFQGc7B7Es++fytmnvXnqtQuWTavF2IoAeqNJbDrcabmNED2FoAoAs+qdi+3vn+jG0n95Cf/41A5tgYOV2K68lrLvAxZOaSF85YuQBxShRkTeRxL5xfYqlzHyxuMuhCG3zvZEKo39rcp4LphgiJG3EMbyYbdmOwCMUaP2+0yLEXRnuz3BrDxPzL6Tcg9AtnhnbsdtxLnZ2W4U8sTx7clRs90sZA5tjLwqcjrcL6NoXIyzHdAXpojyDs7TAzJF8lRa1s4LNyJwsfHmWiS5OOZpvT2fg1hyS2e7ELcd1Vq3iG1Xj7sjsd2ixEIiT/mSXOjO9uJrthvj39NpGWlZctWOWEgwEE9q++Qkqp8QMjopWmx/9dVX8fDDD2u3Q6EQHnnkEXR1dWFgYADr1q3LqsU+depUrF+/HpFIBKdPn8Z3vvMd+HyOyscTQggh5DxHVqPjJQl0thNCCCFkVJJKy2hRY6pFXXGSyUt72nCqJ4qfvn3M1fO7IwnNATveFK9+Qb0iDBcScY0igR1RMZ+zfVAVdMsMYvLUujCm1ZUjmZax6ZC1CG3E6MS0EhmM4tx8g9hudkpH4iltsatVzXZAEW4+c/l0AMCPNh7W3OJmhKA9S3WkW+H1SLhhgfIb4nM7Wyy3sYrHFy53J2L75iNd6BqI49dbT+C5D5TY+vocYnuFKvIOWMTIi2MfKnDcJUkyxJtni8CAmxh5a1cyoB93vX61s+9RzV0RxFNphANeTDKIp07Fe7s12wHDogZTgkC/ersimH9cBGU2arbbdboGTbHUWjsJdzXbzRHncQux3SyGmq8pxnM64PWgptzeuBixiu4G3Dm3AcBvEI2HzNk+oMTIO43v1sY4mbmgAXAmSlvWbHchbmsuaVFHPGWMkXfhkC+yhry1SK4u0nEh2ls72+33xzw+Gf0pwtle6H0wH43VIXgkpR2x0JEx8oSQQvAqQQghhJCSRLiDPJKkOdspthNCCCFkJPKDVw/hV1uaHT+vrTeKpPqh6BSd7ZYc71Kc/0c6BrTYYSeIH9LHVgSz3MmzGuyJuEaHuh1xIFcdaEAXCM2x5MLd/tqBwlHyRtHCqj8N1bqoPG98Vc5YciEue6T8Mem3XzYV4YAXe1v78GqOqPsBdb8qQ/mFwevmNwBQxHArhDhoFLfnjlcWRfxxbxvOqAJdIcyOXsCOsz13jHwhZzsAhIUIbBFHDzgX2+OpNJIWEdeALsy5dbYLV3hVmR+SpAtn/rPobC/PUWtdj5G36WxXxydiFbPvoD+AUbwzidIuHLyAPn5i/sWTqlPa0I75XAt6c4vt9VXBjONjFyuBE3C/X0Zn+6Sine2q2O7S2W5OD8gQXR050rOj9vUkAvtjHjA57cU5K0nKAiO7WMXIu6nZntdJ7qCdskD2go2Yi/4ELBa0uHW2G8coZnE9tN+OF5NrywEA+9RUForthJBC8CpBCCGEkJLEsma781KDhBBCCCHDSmtPFN96fi+++Jud2HG829FzjdHxp/tiWYIPURy4gq1Hzzh+vl6vPTuKeYr6Y3tbXzRvPfKYwc3nsSGeBIVz1TJGPjsmHQBWXKCK7fs7CrYvRB2vR7KsCTw2HNTii+eNr8wZS96nxXf78gp61eV+3HbpFADAzzZZJwzoonT+nyLHVQQzXju7nWxn8jVz6zG7oQJnIgl86/m9eds39+djSybilosn4aLJNVg6dYzltiK+PGLRJydiey4xWdBrV2w3zI2ohWDmkXTx02mNdUEsR5x40JcpZBZuRz1eNoQqbZzj1os+wnZrtuc5Xk6c9oA+X82LM4RI7Tbi3CwEG/sTNM0l82sYt3UTIQ/ki5F3u1/K9SEc8Bacv4WoUlM0xOIptwsaxNgmDPvoxHFtHSPvfHzMZUMSItLe41AAtoqRVx3gTmq/54t/d+Rst2pHmz/2a6QHLRYEuS3TYEyFEO1JkrO4fsH0sWEAwH5VbHfaF0LI6INXCUIIIYSUJGlDzXbxgSVXLCUhhBBCSKlirLH9r+v3OPo8c+JMJON2aw+j5M0YxfZtx6zd0Pk4maNeOwDUlgfg90qQZaC9L7drXnPL2hQYQsJ9ZxkjrzrbTWJ708w6+L0SmrsiWiR7of7kEhQ9HgmfvWomVl84HosmVuux5ElrkbPShsgpnPencsxRIU4WEqWFoGpVbxuwFu39Xg/+9WOLAAC/3HIcW48WngeinfrKIP794xfhd2svz+m6F33qtxTb1f2yIcSU5xCTBXad7UGfR1uMbBTurQQqTRx36GzX4/qtXdV2xXvdSV743CjziwSBzHEWCy9ylTIwU24nRt6us91CUFTaETHy9kVFZftMYdEqlrwsT2y8+XZDkWJ7PJVGOq2/J7kV232qcDxxTJkrp70RMf/dCq56mkNmVL/fKznqm1UqhB6T7r72e9JlDfB8znZHse15nO2OarZbxci76Y/PahGB8/4obWU725XrpfM5OWOskmyzr00421mznRCSH4rthBBCCClJZIOzXZCm1k4IIYSQEYYxmnvzkS68uLvN9nNPdGVGxzNKPpN0WsbxM/qYbD3m3tluJbZ7PBLqKxUxK99CB+OP+nbQY+Sz6yWLsgHl/kxhMRz0YdnUWgDAxhxR7Xp/VCEwj6D49yvn4JFPXgyf16OL7SZxsj9q31EcyuPWz+hTgTEyur+tFqbkqrl9ybRafGLZZADAl57eiU2HOvNGymsOeSfx73li5O20I45psWK7JEmGY5btBjWOjdnla5dc4xxwGCMfK7Dww4hwtpsXWugx8nbFduuyCIBxIYrNmu0WgiJgiFu3KdoLcjnbjc5/c4x8rnQBQImRd4NxvsYtShE4rZUthONi67UDeoy8wHH9eNMYJ5Lu6n/rCxKsxG0nzvbMOHrNje7QbR2ydLY7P15iAUkqLWcnLLhw7A8W6ZDXY+Qt5uEQONudHnfB9HGKs/2AWkaGMfKEkELwKkEIIYSQkkSssPdI0JwbMqi2E0IIIWRkYRZpvvncXtsRzCdN4voJiu0ZtPfFMkS/D072OI7aP9WtiOi5RKLGakVsb+vNJ7bbE5IFQsA0O9uNooXZ2Q4Y6rYXFNudiQyhHOJkvwNHcVmOuu+CqE1nuxBK07J1XXVd3M7et3+6cS7GlPuxv60ft/34bSz5xgbc9+SOHP2xF2sP6CKw2XEN6MfQVox8nnZkWUavurjBTgy31XhbCVROxXFBrgUk5prjhXBWs111tsesxXax6KEQYmys4vqdOtut3MSAIUbeoQAnjk0yLSNtEDv9Pl14Nc8l82sMZYw8kFm33Y3oauyT1aIlp1SZEibcRtqLuae5rV075LNj2x2J7SYxOZku3rVdVH8Mc1+05cYhrzvbLa5BLsYnI0HAxSICpU/6GLmdy4KZaoy8G9c/IWR0wqsEIYQQQkoSIatLkCC+8tHZTgghhJCRhhDDptaVY2xFAEc6BvDUthO2nntCdW0L8YjO9kxEhPzk2jLUVwaRSMnYcbzbURv5YuQBoEF1jtpyttsQXAFdEIibFmIIR6/XI1mKHitmjwUAbDrcaauGvF1BsczCoQgYxHYbjuKygHUbArvidrlBUM0bA24R3z0mHMBjn7kU189v0BZPPL+r1bo/QiS3EQOui8D5arbbEZOtnduAMtYp9cuOHbE9ZCG2W9UjtxIM7ZArHUG0Z79mu/0a6bojvdgYed2NnjJ9gRTJC/ZTKKyd7TFtkYU7IRhQBMWYhdO+kLPd45E0V7TbGHmfR4IwVseSxjnkMkZeONvHDIWzPfM4O+1L0DRH3YqlVgtV3DjJzedg0oVADljHrbtZSJCx0CKZOUZ2zwvAWGKhOEe6VUkD3SHvNGpfHyOniTNmhLNdwBh5QkghKLYTQgghpCQx1mzXnO2s2U4IIYSQEYYQ1sZWBHFn0zQAwKZDnbaeK2q2L506BgDFdjNCbJ9aG8ayacoYOY2SF4718dXWopUQs/I62x26XEM5aqQL4bTc77WsMTuvsQqSpIjQZyK5I9Kdugs1l7Q5Rt6B2B7MUdtaENXE2/zittcjaUKNG3F78eQa/PjOZVj3dx8CoIyV1XcIvR0bMfLCkR5zJv6b0UR7C8e1cLUHvB5bAm6ZhXBv5SLXa6w7+x6VM0beoXjvZOGHEMlzOdvtxsgbyx6YF384dbuGtPrW2SUfAOc1240CayKVtnQUhwL5xXbjfW5j5CVJshZvXQqUWs32s+FsLzZG3qVLOmAQgQXFiMliDon2nNZst5qLbhzpyrHPdMm7cchr/TE62120YxzLuGmBhN1yD1Z9KjZGvrEqlLHwhc52QkgheJUghBBCSElirNkuPrDQ2U4IIYSQkYb4YTzk92DhxCoAwL7WvoLPS6dlLeL80ulKrW5zrPxox+hsX6rWM996tMtRGwMFXLONNsR2UdPXrpM8pAkdmYKliL22ipAHFEerVa1uM04EYMA6DhjQxXY7NdvLDG7itMWHdi1G3kafwnlqbtvdNyHcptJy3jh6ezHyZ9/Z3hNR6rVXlfktF1qYKbOKcLYQFa0EQzvkcoW6FdvtiGZiMUJ2zXbltp15CCh9FkNojpIXC2OcnxtmZ7vq2HcZtw4oY2gl3prPkaDF2NVXBuGRgOljw1mP2SVoEm9lWXYdvX3zRRNwQX0FLp811nV/BFk12x3HyGfGtrupI27c3tJJ7kDcHmpnu5iLqbQMsY7I73FX21y05cb9r13zk8YFP87PC+O5aHbaO0810K+LblMaBJIkZZxfFNsJIYWw9ymFEEIIIeQck5aza7an6WwnhBBCyAhDCEdlfi9mN1QCAA6d7kc8mc77I/Dp/hjiqTS8HsngbM8t+I5GTmhiezkuUZ3t246dQTotw+OxJ4YIQdcc3SwQNdtbbTjbbdds9wtHqcnZLuZKDrEdUATbSDyFSCJb+BU4FSqsIskBoD/qIEbenymYmPchVyy5FeUBH85EEpYx8m7j6M0O9pjNGvKAXivcMtZe9MeBs92qnZ5BRWyvLnNWlzwjRt5iHgpxN+E0Rj5H3LoWrW1TvHfilLaqaZ9Oy4ZFH/YEckmSUO73YiCeyhLunUZLawtjDOdqMpXWFoE7dYCLCPhkWkYiJVu6rkVJBoHVefxfn16G031xjK927yQPmhb9JA3irZXAn4/PXjUTn71qpuu+GDGXUXArkmsx8i4EciB/jLyzmu2ZCQJJ4Wy3+R4lMDvJjaUcnNajN7rt3S6yENe8REpGKi3D65E0h7yTdvxeCZIEyLJ4n/C7Pma6+z+tteE2Rh4AZowLY3dLr9oXiu2EkPzwKkEIIYSQkkSLkfcYarbT2k4IIYSQEcagIa56Yk0ZKoI+JNMyjnQM5H2eiJBvrAphSm05AMXZzrI6OsLZPqW2HPPGV6HM70VvNIlvv7APRwuMLyBinJXxLM8hcOsx8rGc7Th3kls72wsJ/4B1fHh2f5zVpdbbzOyPcHJX2qiVbRStraLknTjbRX8iFk5yu2Pt9UjaOFs60pP2Hemas90i/j3qQLQXc8zstgYMkf2hwvXaAT1qPCNGPpU9NsU7202R5qaIbrvt2BHfyi3KGUQMc6kyaG9sAKDMYmGDG1FRWxhjXNRgEF+dxsgbX9uus92qr7PqK9E0s87xaxsJ+TNFYKOo7NYNPBSYY+SdiqV6jLxybXdbs10sDMoU252LyeYY+YT6m4bPcX+EkzxzEQHgRpTWne0JQ4kJZzHy2dd8N9Htxlj7mMlpX4yz3W0bRmYYnO1O68cTQkYfFNsJIYQQUpJkONvV+/jbMiGEEEJGGkYxTpIkzG6oAADsa8sfJX/ijBIZP3FMGRqqQpAk5Qfojv7ctbpHG0ax3e/1YMVsJcL4hxsP4arvvIqP/d838T+bjqKz31ooNwpxudzkIka+tSeac6GDU3E7lKO+ecSGsz1XfXUjTkUGK5c0APQ5iJH3eiRNYLGKf3cU266J0sW2k8+Rbn+BRFirJV6caF+eZ7/0hRZ2j1n2WFsddyt3rh1y1Vq3itbOR9zBuWG1qEGMuUeyN8YCq4UNmSK5s5rtxoUxsSJFab8hHcCqVrbx/Pd6JHgdOqDtklVLvFTEdlO6g+Na66Y570Ygz2gnpce2p1Sh3Emfgr7M/iTdurYN7ciynJFW4TxGXj/2xoUzThY2GLfVxHa3kf2msXbrSjc624uNkQeAGeMqtL/pbCeEFIJXCUIIIYSUJOK3TI8kMUaeEEIIISMWs1t5TqOo296b93lCbJ80pgwBnwf1lUEAwCnWbQegiM3tfYqILpz/3/vzJXjoExdhxexx8EjAe83d+OrvdqHpm3/EO4c7LdsAMoViM8LZPphIoTdqHd2eS5jMhdguahIsRX9yuewB3bFrJWhn9cem69aq/jdgqGdvs1Z2yEIA1vrkwAEuxEYrJ7k+1jZE8nyOdCciuVazPZ/4bz9Gvth2AOtjZlVH3L2zPUeMvNua7Xac7Vqigb5PfVF9wYedWvZZbVk4/wEHKRRaBHj2ogafSyHcGHNuNT7GOeBUaHaCOd5cjM/ZFPjtYL7eOBU5zTHybqLfje3oor272PagaYGKEP+dxsgHTaU6jO3YLZui90kkNqQzzmUnY+TxSNoYiWu+WADgPEVA7w9QTBqBhbO9iHOINdsJIU7gVYIQQgghJYlwDknQne1MkSeEEELISCNmcuLOEc721v68zzvZLcR2RUieWKPU5h3pYvvT753ATzcdLToOX8TsV4Z8Wo3fkN+Ljy2ZhP/5y0vx9peuxT/fNB8Ta8oQT6ax5WhXVhvC9Vqupg5YURbwokqNUW/LUbddCAR2f9TXBDyTKK0vzMgtbgtHs5U7WuCkTjZgHUkOGKLNbYvt1u2k0rIjp6JwpJvbSadlbd9CLsVbQVSrSV5YdK0I5o5/15Mr7Di3RYKAxSICGyUEjJRZjJHVcdeFR9lRSpg2p03j7HfolI87WPih1bRPpLTrg1bKwOYc1NvKFttjCaOoaE+cNMZta+04TLIwY3ReWwnBRrHd7gIeN+SM7h5mUdHn9WRcc5w6k8Wx1VzSLvfLLJK7jW03i/bJtLpYw2V/AOWYuV1EABgTG3Rnu5tFFuI6LM6PmEtne1bUftHtpF277I1MH2cU2xkjTwjJD8V2QgghhJQkWs12OtsJIYQQMoLJ6Wxvs+lsV0X2Cer/J0ew2J5IpfGPT72Pf/7dLnz7hX1FtWWMkLcSyusrQ7jriun4k4WNAIB+C8FVOzZ5nOQA0Fgt6rZbi+1WtbLzIQQBs7PdTox8eQ4hOqM/QxQjL8bMrtgu+m10ARv7AzhztpsXFGQ4k4t1tjuqta60YzWHnIj2YpytnO2DLp3tkQxnu0WMvOHvlBOxPVfNdodOeUfOdnUxQiota88bcFDKIKMti/PEKJLbdckHDaKk3k5xIp4Q7RKptOUCibJz5Wz3ZwqcQxG7PVSIBU6AG7E9U9x2HW1udra7jG03JwgktdIBzsRbv9ejieGxZErbL58LEdjYp7jLWHvAuBhFWSCjieRFpAik07Ie/e+wnZCFs93tohgAqAr5MbZCSRWis50QUgheJQghhBBSkhhrtosPLNTaCSGEEDLSEGKPcA/PaawEABzvGrSsAS0Qzu1JYxSRfeJ5ILYrLjrlA90PXj2EH2085Loto9ieDyHS9ccSWY/ZiW0H9Cj51p5cznZVxLPpQg1ZCHjG2+V5BNdcwnhGfxw6b3OK7VFlzOwKnXo9+Uwh1rif9sRtayd5RjtOnO2WYrub2u+Z7ciyLgrb2S8xjlYLJZw45AGgLODJaitm4eA1/u2kbHuuORTUnPJOne02jpdhDMV+9bkU260WbLgR34QoGbWIkbe7uMaMUVi0cieLY2vc9myQFSNfSmK7mlYCuHc3Z8fIOxOTA15lfMyivd/rLLY9Vxy9G/FWW6iVSGuivZsFGcb3oGISDcR1L5ZMIZnW0zOcpwhki/+As7h+pR19fIZqPs9Q3e0U2wkhheBVghBCCCElSZo12wkhhBByHiDcyyI6vDYcwDi1/vqBdusoeVmWcVJ1tk8ck+lsP9U9iGgihZf3tGkx3yOFmEnte/C5vfj9jlOu2rIrtosIcCs3sRDiCgmljVX5ne0xhyKecGUnUjJShjpJQszN52zP5fo24tjZHrAW/8X8qgzZEzqDOUR7IVT6vfZiirU4cdM+inH2eiRbEczhHDXSnYvk+pinDcfLOJ/tLLTIV4veiUMesK7ZronA/hxiu4OvUrmE6YDDGHknCz98Xo82Z8UYDTgsZSCwWmihO7fti+S6KGkRI+8y4l2IdrFkGkl1PmXUbDf07+yK7dYx6cMdIw+YxHaH/RHjm5bVEhYu63+bUxwSSeFILy4iXRxznwN3vMAobhcl2huuH5qL3MVcKzOUWTAuwCkm/j2jHZfO9lgypY13sfN5piq2F+OQJ4SMDniVIIQQQkhJIhuc7XrNdorthBBCCBlZDFpEg89pUNzt+1qto+Q7+uOIJdOQJGB8daazfdepXnzk+2/irp9sxXeKjGI/1+iCogf/64rpAICHX9rvqn77cVVsn1xQbFdEG6uFCRGbznYRI99aUGy3J+IZXdnGyHU7MfL2nO0Oa7bnqLUuRGr7zvZcjv3MBSeFsKq3bWzXjqsd0GPJzY50o0juxJEOmGLbDQKsnX3LVYsecBZrb9xu0Cre3Ku34fFI8KkLHJw524Vwn9kfJzXbk6m0toDa7rkhjr0YI/die/aCDafnBWAQ7yyOu1sRz7ygAMh0XYcM5/9ZjZHXarYr+zYUsdtDRVVIF9ud9sfohjamBxQTbQ4Yne3u2ollOdvdxL8bnNuiHZ/7dmLJtOvFCIC1Q15py2GKgGFBgrEdp8dMXCdaeqI40qF8Rih2Pt+xfBpuWNCAjy2ZWFQ7hJDzn+F/9ySEEEIIsUAI65k124exQ4QQQgghLrCKqxZR8vtarZ3tLT2Kq72+Mqj9CC2c7SfODGJfWx8A4I2DHWen02cJo9h1z/WzUR7w4vDpAWw+0uW4Lfsx8sLZni22DyaU+4Qwlws9Rj5m+bjmoHMobgOZjlk7MfK6GJk71cBp7WUrAT+dlrUFCrZrtudytjuM2Rfidi6R3K4gncvZ7jSOPujTayVHDPNIOPY9kj1xqTyfsz1pP9Ye0BdkDFrEpJuPu7jtxNmeS5g2C4d22rDqUy60Y6buV7/DBR8CqwUbVs7/Qmg1oA37Eks5b8eIEDWN16RczvazKXyXdoy8+5rtRoE2nkq7dm4HvLlEcncR6XqMvOpsLzK2PVGUSK470t3WtAeMDnldtPdIzvctaFjYoNWi9ziL6weAeeOrsGhiNSLxFJ7d2Qqg+Pk8f0IVfnTHMlygLpIkhJBcDP+7JyGEEEKIBcLgJBmc7cr9VNwJIYQQMnIQwl6ZQSDUnO1t1s52IXIa3X2Tass0we/yWXUAgIPt/eiOxIe+02cJ4QgN+r2oCPrwkcUTAAC/2NzsuK1T3YrLXMTs56JCq9me29mez0kO6GL7UMXIez0SvJLymdYo/Nrpj5WjOXd/bEaSB7IjyY0Obrsx8iGLaHNX/fHnd7bbHedcznaxwMFnM45ekiRNvDXOI30hjReSVFgU0sc5nVE+wNhWmc2FBFYLG+Ip6/HRxHYnzvYcsfaiLTs12+MuxHZz/Ht/LAFALwdhF6tFKW5ipY0ioCghoF3H3NZsV1+/37AIxG+IFDee/2c1Rt5vjpF3tmjobGJ873PaH+PCl0TKvXNbd7YX5/w3x/UnhWjvUEg2t5Uopma7Txfti3O269f8okR7w8IPEdfvph2vR8IP71iK2nDA0Pbwz2dCyOiAVxtCCCGElCQZNdsN91NrJ4QQQshIYtAgyAlmF3C2a8KbQXSpCvnxvT9fjIc/sRg/u+syzBir1BHdduzMWen32cBcv/m2S6cAANZ/0Opo0YAsy5o72CjKWJFPbB+0GyNfZTdG3v7PbMIU61RszxWxbsRxzXaLGPn+qDJeXo9ke7+s6ogbb9t1befaR9fO9iJFe2Nbxj45jX4PGxIUcrn/7bZlNdZavHmOOutD6WyP2xDbRRs+j6QtFCqEduxjIkZe+b/C5oIPQZmWIpA9PuZo/HwYj4fYn2JrQWsx8uo1ye/NdPAaF1yck5rtphj5kV6zXZIkTXA3OqXd1lrXara7jH83p0FoNduLipHXa7a7akeLf3cfsw/oySCDieJEe+NcLHbRx8SaMnz/k0u0a04pzGdCyOiAVxtCCCGElCQZNdsN3x9Zt50QQgghIwkrEW12QwUkCejoj6FrIFtkHoxbi3g3XTgBH10yEZIkYdm0MQCArSNIbDeLk4smVmP++CrEk2mse/ek7XZiybS2ALOQK13ET1vFyNut2d5QHQSgHK+khcjoRsTTxXaLGPl8NdstXOhZ/Uk6E5ONLmnxGdwYIW/HtQ3otabF/BU4FZLLc8TIi3Ycu6RNx16PbLd/vKzKETitIR/ye7TvNbnc9nYXJGhjneFszy+Qu6vZbi3c26nZ7sYJXK4tkFDGpzeqONsdx8hbLCDJNT75MB7bqEmUdh8jr0wCXWzPbMc4B85uzfbMGHmn5SfOJlUh9zHygD5uiVRai1t3HCM/RDXbdTd6SusT4C5GPujXj5nbWHtAd7ZHk7po73cxzkZnu3Dau3GSGxc2xIZg0ceHZo7FN/50HupDMq68YKzrdgghxAnD/+5JCCGEEGJBRs32jPuHpz+EEEIIIW4QIprRrVge8GmO7K6B7DrgQojLJ7oum1oLANh29NyK7Qfb+/Dp/96Mdw53On6uWQSWJAm3Xaa423+xudl2uSCjgFYodltztketaraL6O78Qt7YcBA+jwRZBk73Zx8vp+I2YBDbkxbO9jz7lCti3YhTZ7sQbtOyLig5rdcO6AJOtmvbmQPcSigF9EUNtp3kWqpBce0Y2zLWW3e6iECSJG3fIjnqyBczRrnEbXfO9vwx8nbEds0B7khsz9yv7ogito8pD+R8jmU7Fgs23JynPq8HPtUhmyVKu3a2Z5YkMI9P6Fw7200x6aUgtleXuY+RB3ThOJEyOredOcDF+KRlJfpdiMlu4+j1GHm1nSJi5I3itrv4d+Ek12P2g8W0k3Qf1w8YShok3I+zmT9bOglfXpLCgglVRbVDCCF2Gf53T0IIIYQQC/QYeTrbCSGEEDJyGbSIhAeM8ebZoqmd+s1LVWf79hPdmlhyLnjinePYuP801j7xLjoshOd8WEVTf2TxBJT5vTjQ3o/3T/TYaidicDgXiqcW8dMD8ZRWc1kwqMW25/95zOORUF+puNtbe7Kj5J3WJAesY+T1/uQWuIXz1yxEF9Mf4zyLqq50sTjBidguxjG7ZrvDWusWEeCAvjDBaTu5HPJOnMlanwzna1RzONs/7mUWcfQZfbJ7zAz13wW5nMm6s92+uKfXJbcW7oUglrcNF+dFeTAz+v+MWl7CsdhuUYog1z4VImSK7I+5OO5GCjnbgz49ASHgsi68HYwCJ6AvtCmF2G1jjLyxnr1dxJjGioiRN55HcUPtd6fiv5j/8WQasiwjkXbvbA9ZONvdHC9NtE/qtdb9Pufiv7GcRTHx78aUBTeJGIQQUgrwqkUIIYSQkiStxcizZjshhBBCRi561HQOsT2v4zq30DJjbBi14QDiyTQ+ONlbsB9vHOjANf/+Kt5tLs4Jf6RDqTPf0R/H/et22najA9Y1t6tCflw1ZxwA4NV9p221M2jDAS4wisURkwisJwgUFpQbqpW67W0WddvdiDBC540ZxNJBWzHynoxtrXDaH7/BvSvaFa5bEZ9uh1w12506yYXgal5Q4LZGerZor7bjQMissChH4LQWPaCPZ7Ex8sbof0Gu466J7S5qtudqy56z3fl5oTv2lfHRne3+nM+xbCdPzL5zsVQXJgF38fhW7YmFVmaxVJIkbW6eyxj5UnK2i+QXcz17uxgXhcTVie84Rt4w9rEiapsbF/XEU2nN2V5szfa4yxrygL6Yoas/rh/3IsT/aCKlj7OLdrTrShGLGgghZLjhVYsQQgghJYn43VaSYIqRp9pOCCGEkJGBLMua2BMyuaeF6NZvUUtcq9meR3SVJAlLpyru9m3Hugr25bE3j+Dw6QH8ZtsJe53PweGOAe3vDbvb8ORW++3p7t3MsVgxWxHbXztgT2y34/wXBA3ud/PCBjux7YLGKkVsz+9sdxEjbxADhQCbP0be2hltxI1D1SzeajHyIfsiZ8hCAAaMNdKdOtszj5djh7wQts01212I5OUWwn3MhWifqwyAGCM7cxHQx9oo2sdzOMm1GHlHNdutx1q4g+OpdMGFNm5EM3HMspztYWfOdnGeGBdH6M52Z25xXVAUMfLuHbyAPoYDOWLkAT25YLTGyFeVKcfPfVR/doy8U2e7z+uB0PnjxnYcOsCN+xBPppEsypE+NDXbp9aFAQDHOgeKim0PGs4NtwkCgGEuJlJF7RchhAwnvGoRQgghpCQRP95IkOBhjDwhhBBCRiCxZFpbQGh244YtnLKCSEJ1XBcQ3papYvvWAnXbU2kZm48qgvyh0/2FO56DeDKN410RAMBfXj4dAPD1Z3ahvS9bgLYiV6y0ENu3H+9Gz2CiYDtCqMznABdIkoRwwHphw6CDdkSM/NDVbFcmhrFmux4jn8/Zbl3P3LI/DsTkUEB3KAL6vKxw4Gw3x20LcqU75MIqAlxpx52zPVdku5ua7RELZ7uTcdbaMfcp7qxPxhh5UR5BE4FNIpUQHm0kv2vkiko3R2vnb8P5eRE2lElIpWXtelDj1tmesTjCeX8AY9y6KUa+WLFdXShhJbqG1LbPZpR20FBvGyjesT+UzBhbgSm15bjigrGuni/c3nFjLXEX+2VMcnBbk9w4nrFkGgn1fPW5iMc31lpPFFEjfVpdOQDgVE8UfVHlHHMzPqI/0URK64+7GHl9LrpJxCCEkFKAVy1CCCGElCQZNdst7ieEEEIIKXWMEeFmx6oWSx3PFtujNkRXAFg2rRYAsO3Ymbwu032tfehTXd2HTw/k3K4QzV0DSMtAOODFl1fPw0WTqjEQT+HxN4/aen4shzg5saYMM8eFkUrLeOtgR8F2Bh2KpZWqO9u8sCFic5yNbVjF/muOWQfirQg6iFrEyOdzN1vFY5vRRLOhcLY7qdmutZEpwka18bHrbFde0+gCBZyLpWHNJW1yyDsU7QHoCzbiRqe0s0UEQJ468hYlFvJhnCNCnCpcs91eH2VZzim8Gm8XqtvuxiktzsWBWBI9gwltsZLTmu1aXL+NmP1CiOMbzRKl3dVTF68vzrEsp3QqhVBSWdQTbDkJpHKf68WguaRLsGZ7WcCLV/7hKvzw9qWunm+MJdfj393H0RdT+12SJK2deFIXyd3FyIu5mEIyLRzpztupDQdQGVKuswfblQV4Tt4vBMZzo5j5k7GooYTmISGEOIFXLUIIIYSUJBk12w3fH53UBSWEEEIIGU6EcOnzSFk/0AuHq2WMvE0xeeHEKgR8HnQOxNGsOs6t2HykU/u7vS+mOdmcIoT66ePC8HokrL16FgDgp28f09ociCXxwckey+fni902R8lHEym8f6Lb8rOfE0c6kDuyXwhxdmq268crW/gqJkZeCLZK3LGs9idfjLwdZ7szcdvYrljooYvtzmPko6a+6SK5M2c7kCmWOnW2i+MaMR0zp7H2gF5H3tiW0zrrSp9yufad9ck4BuJ6IUSqXNHvdmu2Gx3rudoCCtdtd3NehA3jIyLkK4M+xwJnmUWqgVtHutG9q7QzxDHyxn1btw6YNg1lhw4oj/38p8C0acr9Q0wpx8gDgNcjQZKcC8mAPsYJo3jrytmuzEejSF6sc7sYkdzobC8mtl2SJExTo+T3q2K7m3ZChvejRBHjbIzHLyaJgBBChhNetQghhBBSkqRz1mwflu4QQgghhDgmX1y1cAxbOaWFM7hQ/eagz4uGKiXevKM/nnM7ESEvcOtuF/XaZ4ytAABcN68BM8eF0RdN4pebj6OjP4abv/8GbvrPN/Buc3a0fS5nO2AQ2/d3IJZM4bYfv40//f6b2Lg/u477oBqzb8eRDuRe2DCouovtiPYiTt0q9j/uQnzTararwoLRqW4rRj6R0uLDs/sjnIEOIuBNjnkxL53EyJepdn1jND7gXCQP+vRaycXEgItI8ngqnSEMO421BwzHP54dI+/EIa8tADC0I8uy47a8Hkmbb+KYCYdysc72WNIotmf2x+uR4PXoEd350MVbd+PTrYrtNWFnEfKAXoLDmI6Qq4xFITQ3cSJTlHYbty6eJxZuaCLnunXArbcCJ04glFT2PZBMACdPKvcPseBuFDiN/5eK2F4MYkzjRdRsB/RjFTcshirGuR1L6qK0mxh5o7M9oa6ecStKTxuriO0H2/oy+ugETfxPpoqaP0GL8aGznRAy0uBVixBCCCEliZzD2c6a7YQQQggZKeRzqFfkqdnuxLld7tdrHFshyzI2H1HE9kr1Nd3WbT8inO3qj/Qej4S/WTETAPDoG0fwF49t1oT8IxaCfj5n6fLpdQj4PDjZPYi//p9teK+5W2mnI7udwbgz8TbXWDuJow/nif0vxtkuBDxx/DxSfpHBKj48Z38cOdszhVutZnvIfox8KIfr3qlrW5Ikg+hqENsdivbGRQvGPjmNtQd0Edg4h3SHvJsYeb0/iZSsLSh20pY55SCXg1fEQ9sW29XxkSRr960xEjtvOy7Oi3JtUUMKXQNKWobTCHkg89hHNEe6yxh5g5vY2I77mu3KmIqSBAGfR4mKv/tuiNz8MjVGPpBKaPfhnnuGNFI+ZK7Zromc7uLxSwlxbBIp97XWAet4czeO9KChnWTKvbO9uky5DnX1x4sWpUXd9oG4adGHA8S52d4bM4yzi7h+g/O/2MUshBAyXPCqRQghhJCSRI+RR8b/FNsJIYQQ+3zta1+DJEkZ/+bOnas9Ho1GsXbtWtTV1aGiogK33HIL2traMtpobm7G6tWrUV5ejvr6etx3331IJrMFR5LNYB6RMV8suRPndlmOGtCCwx0D6OiPI+Dz4IaFjcp9Lp3tQvieMS6s3feRJRPQUBVEa28UH5zs1e7PJ0pbCYplAS8uVWvQG93sVs7/iANHOpBHbHewqCGXO16WZVeOWV1sT5v64ssbnWwU23Mdc93Zbv9nP7NQ3qfuZ9hBzfaQqe67wKmzHdCPifGYOXW2B3webQysHOlOjpeYQ5GiRftssd04Xm4i6bMd15n7pTnbbX6NMo6z1Vw01sPOh5vEB7FPg4YYeTdiu1U6gtP5IxDzVjxfiO7uxXbleeJrbcDrAV5/HThxQn/NhEFsFxsfP65sN0ToNdtLM0a+GPQYeVl3pLuJkTcsLClGtA9qcyiNZFrUbHfezvRxSqrM4Y5+g0PeXdS+iJEXuBmfOY2VAJQFfOK90UmShcAyRt6FaE8IIcPJyH/3JIQQQsh5iazFyCtfsjzq/9TaCSGEEGcsWLAALS0t2r833nhDe+wLX/gCnnnmGTz55JPYuHEjTp06hTVr1miPp1IprF69GvF4HG+99RZ+8pOf4PHHH8dXv/rV4diVEYcQwazi4PPFkgtxyI44WR6wFjgFwtW+eHIN5o2vAuDe2X64Q3meiJEHlB/J//Ly6QAU5/ziyTUAgD4LkVwXOa1/jloxe6z299gKJR7fqqZ9vnG1QgjGfeaa7Q7E9lyCfUZ9awdCqdnZLvpSaIGFxyNp45frmGuiopua7Vkx8vbFdr2NTBE26kLktJrXbkT7cDB7MUq+RR+F+mOcj27i6K1i5IXYKRVINTBTZlrckEtMFiJaKm1PvCq0eMTo9rXXjosEAUOM/Jhy5zHykiRpZQQimtjutmZ75rwW57zTOHqBZcx/S0vGfZcd34VgIoYLWw5kPtm0XTEEzc7280psV+Z6sbHk+sKS4mqSG0V7If67EclnqKkyRzsi2vXHfYx8uamPzvszvjqEmnI/kmkZu08pi+2Kcf7HDAkC58M8JISMLux/YiaEEEIIOYcYa7Yb/6eznRBCCHGGz+dDY2Nj1v09PT149NFH8cQTT+Caa64BADz22GOYN28e3n77bSxfvhwvvvgidu/ejZdeegkNDQ1YvHgxvvGNb+CLX/wivva1ryEQcO44HE1oorCFgJovltxuzXYg0wlqhRDbL5teqznS3TjbewYTWl346eMyHXGfUcX2Ky4Yiye3nsD2492WInkhEe/miybg0TeOYMUF4zBxTBkefukAei1r2tsTpgVWQrksy47a0Y6XKYkgs761E7FdVp+vxsiLNAObxzyWTFse83RadlVb2CzcinnpTmw3jZELkbzMIrbdzSKC8oAPZyKJjOPmNNYe0I9/JFac+K85263a8XnzphqYMacR5BJLxTyw7Wwv4NwOGOph58NNHLQQyBVnu+LqrnHhbAeU87ovltQWNripIQ/o8yRqXtTgYP4YMTuj/V4PMH58xn1/teVpfHrbMwikTdc/03bFYBQ4gfNLbB9fXQYA2NfWN2Qx8sWI9kFDbXPhbHfTn4k1ZQj6PIgl0zjSGXHdDjA0znZJkjCvsQqbDndix4luAO4SH0S5kpbuQW3x0fkwDwkhowuK7YQQQggpSYw12wHhcJfpbCeEEEIccuDAAUyYMAGhUAhNTU148MEHMWXKFGzbtg2JRALXXXedtu3cuXMxZcoUbNq0CcuXL8emTZuwaNEiNDQ0aNvccMMN+OxnP4tdu3ZhyZIllq8Zi8UQi8W02729iuMpkUggkUicpT0dGkT/hqKf/YOKOB3wSlnthVT3V180e0wGRS1fj1ywH6Iec180brnt5iOdAICLJ1dj6hjFLX6kcwDRWBxeB866A609AID6yiCCpn5JAP7yQ1MAAOWqqNA3mN0fsV++HPs1ttyH1/9hBSRJwmNvHQMA9Fq00x9NqPuePa5WlPkltS19rAfjKe1zpV+yMc6qON4fyzxeA4P6PJfSKSQS+QVIQJlbQqeLxJJIJBLoU+dKmd9TsC+KyJpA32AMiUQo4zGj0O1B2vY8DvjUOtLqfOwbVJ4X8tk/F7ySGomfSGU8Rxx3O+MssJpHEVV490n2+1QeUNrpjcT0dkR/PPbbUQ3yGDAc/6jWjv39CopxNrTTr86hkI1jb0SIwP2DcQxGY9piZY+cedyF0TSZtre/A1E1wjzH+SWcq5Ec1xxBJK485vPYO08BfRHKQCyJrv4oAKAq5HV1PRaLP8SxF8fL52AeAroDOaIes6i6uMEDZ+0IvMj8QuvzAInly4FZs4BTp7QoNwlAAqqrX5KAiROB5cuBIXoP9UDUoFfO16i64Mfrcr/MDOV7qVMumVqNx98CNh3q1MoZSHLKcV+E2zsSS2jXVo/D+QMAfjGHoglN/Jdk+9dnI9PryrG3rR97W5TPVW6PV2VAQkXQpy2Mc7NfADC3sQKbDnfimCr+O7k+CxaOr0BlyIf2vhjePNgBQLluFTN3hnP+kdEL5935id3jSbGdEEIIISUJa7YTQgghxXPZZZfh8ccfx5w5c9DS0oKvf/3ruPLKK/HBBx+gtbUVgUAANTU1Gc9paGhAa2srAKC1tTVDaBePi8dy8eCDD+LrX/961v0vvvgiysvLLZ5RemzYsKHoNjaflgB40d/dhfXr12c8tr9Heay1ozvrsTO9XgAStm3ehLZd+V+jo80DwIPtO3dj/ZnMjXviwMluHzyQcXrPO+j2AD7Ji3gyjZ//9jmMVXVaWQZeOiWhuV/CHbPSsDJ6b1H3pUoazOqvkRMnle32HTqG9euPZDzWfFLp6/49u7C+84O8+3WkXWnnyPFTWL/+RMZj+w8p7TQfOYj16w9YPt+6T0exfv1hAEB/AhA/i7360osotO6gO6Zs3xdN4A9/WK+lLnWp9/skGc8991zBvgiE+HL0+EmsX38cO7uUPkYH+vKOLwCk48r8eOW1N3G8OvOxSFLfrz9ueBF2zYGtJ5Qx3bX3ANYP7kOnOge3b3kbHbvttSFeO5WW8cyz6yEMly3tSlu7du6A/9R2W20N9Cj9eXvru0gfUz7/n1Ln+p4P3sf6th222olHlNd+7a130LVXaedos9LO4f17sb5vj612WiPKvp3p1+f/MXU+H9ibfe7l4oB6HjWfbNXaae5X2paT8YLH3ki/OkabtmxD/yEZ4ri/8vIGbXEAABw7rmyXlO1d1w72KP1JxKzP9Zg6pm9sehund+f+brbvqPK6J4/p510hxHk2EEtgz+HjADw4dWQ/1g/us/V8I8mo0s+Nb76N9l2yNg9379yBYMt22+2cUufLngOHsD5xAO2dSjs7t+tz0wkfdCpzQNB68gTWv9AMfOc7hZ/8wguOXy8XA+o1MJGS8ewf1rs6v+wwFO+lThlIABK8OHR6QF3AIWHz22/h1E5n7XR3KWOy5d3tONYvAfDg6KGDWB8v/L5jpE89V9/Z+i5Od3gASHh/x3vAcefzpyyptNXepyyKOXRgH9ZH9jpuBwDG+LzojynvRQf37cX6XnvXQyPx9sz5fLw5+73fDvMrPXgn6sG25m4AwAkH1418DMf8I4Tz7vwiEonY2o5iOyGEEEJKkjRrthNCCCFFc+ONN2p/X3jhhbjsssswdepU/PrXv0ZZWdlZe937778f9957r3a7t7cXkydPxsqVK1FVVXXWXncoSCQS2LBhA66//nr4/c5rBRvp3nwcOLgHkyc0YtWqxRmPTTrRg0d2vwMpUIZVq1ZkPPbP7/0RSCRx3VUf1qLfc7H1D3vxzulmTJk+C6uuvyDjsd0tvcC2t1FXEcTHbr4KAPDDI29hX1s/piy8BFfNHodEKo0v/3YXnm1WagGPnXcJrphVl/U6+146CBw8jKWzp2DVqvl59/n3zXtQMy57n3/VthXo7sKyJYux6qL8ccieXW34xaEdKK+uxapVl2Y89tKT7wPtrVi8cD5WfWhq3nYA4Mw7zXimeW9Gn06cGQS2vo6gz4ObVq8q2EZfNIH//e4rSMsSrlt5A4Kqa/ZIxwDw7psoC/qxatUNBdsBlDn29s9fAgDUjK3HqlUXI7mjBdi3ExPq67Bq1bK8z//xsbfRNtiLC5cqx9DI6b4YsGUjAODm1TfajiXf+9IBvNpyBBOmTMOqVXNx/7aXAaRww7VXYWqtvQUysWQa929R9uuq665HZciv9Rd9vfjQZcuy+puLZ7u3Y29POy6YuwCrLlNSE35ycjPQ043ll1yMlfMbCrSg8Ku2rTjW34V5Fy7GqguVOfe7rveAztO4+KJFWLVskq12TnUP4sEdryMBr3acf92+DTjTiWVLLsKqxRNstePd1YafH9yBsGFebzl6Bti5BWMqw1i16gpb7QDA78+8h/09pzFnwSJcNb8e2PwqAODmVX8CnyFaunnjYTx/4iCSadi6rr1+oAPY/S5qq6uwalVT1uM/PvY2WgZ7sWRp/uO59dk9QMtxzJ09C6uum2Vrn3oGlfMsJUvwhGuArh5ccckSrFqUXQqlED85uRknm7ux8KKLccOCBjx24h2gtweXXbIU182rt93OwT8exMunDmPCZOXa98iht4CBfly+/FJ8aGb2tbIQwb3teGz/du32zBnKOQcAeOYZ4ItfBE6e1J8waRLwzW8CN9/s+LXyEYkn8aWtfwQAXHv9Dfh5yzagpxuXLF2CGxc6H28zQ/le6oafntiEPa19SKSVa+DVH16BC+orHLXxbPd27Olux9z5CxE70QO0ncKCeXOxasV0R+2s63wX+3s6MG/hhdg+eBzo78VllyzD1XPsXQ+N7A8exHuv6iL0ooXzsaqp8PugFS/2vY/jHygLJy9atBCrLp3suI3pLX34+f/dpN2ee8HMrM8idqg62Il3frJNb2f2LKy61t51w4rhnn9kdMJ5d34iEtoKQbGdEEIIISWJ2dnOmu2EEEJI8dTU1GD27Nk4ePAgrr/+esTjcXR3d2e429va2rQa742Njdi8eXNGG21tbdpjuQgGgwgGg1n3+/3+EfPj01D0VSSKlwd9WW1VhxVb+UAsmfWYqOFcWR4s2IcKVdCMpuSsbYXIYHz9mfUV2NfWj2NdUSRlD/72iffw2v7T+msns9sBgGNnBgEAFzRU5u1TTVg57gPxVNZ2cbWWeDhUeGxFO/2x7HZiSfvtAEC12tZgIq1tn5CViOrygNdeG179J7RYWkKF+pwUFFEz6LPXjkBNN0dcHW8xV8IWc8VMuVrXOpGWsrZNS2rEvs+DQMB+reuKUEDrj8frQ0SNyh4TDtneL59PhkdSFs0mZY/2PFETOhwM2G5LCPWxFLTniBrh5SH77YTF+ZE0tiPmj/12atQ1L/FkGvB44fd6tP44aaeyXJmLkYy5qJynoUDhY2+kPCj2B5AlZfGH1yOhLJR57S1Tt0vK9q5rYk6HcpwbopZyynCMrRA14ssc7FeVpDtkW3qUc3RsZZmra3E4qJwn8bSy33G1Q07mDwCUB9VzQ52LbuahkbJg5nkZ8hvGec0a4CMfAV5/HWhpUWq0X3kl4HVWZ94OYY/eZlryGN6v3O1XLobrfb9p5ljsae3TbrvZL7GoKiVLUC8bjs9TACjzK3MxBQkJtaFQwN24zG7MXLAY8jvvj2CGYfFBmcv+zJ1QA79X36+gy/5cObseYysC6OhXSqoUs19GRtLnTnL+wHl3fmH3WNoMkyKEEEIIObcISd3sbE9TayeEEEJc09/fj0OHDmH8+PFYunQp/H4/Xn75Ze3xffv2obm5GU1NipuxqakJO3fuRHt7u7bNhg0bUFVVhfnzc7ubiYKo8SpqBxupUIWggXgKsmExYTKV1sUcqzx3E+Vq24PxVNZjQjA1vv7MccqP64dOD+C+p3bgtf2nEfJ7MLFGSTroU+u3mjlyegAAMH1sfqd9WBWCByzaEaJr0GI8zIjx6bdoZ1AdVzvjY+xTX1Rva9BibPLh9UhajeyIYay1fbKb164iNo+q9YRFmyEb/SlT9zticcxFPeCAw/6I1x1MpDAQ18dJCJZ2kCRJaydqqF3v5LgLxD4OGPZRtBny2W8nrI2Vvk/ivBTH0w7lhlz2SCyV2R8H7Yj+DBbZHwAoU7cfTKQQU/sS8Ga3IeZCMp31kCUxdU7mmtOiPXGdytlOQhx3+/sV8Hm0mvAiJrum3J1gUW46TwrtVy7EcRHHKe7ynBeI/RNknateL3DVVcBttyn/nwWhHQB8Xo9Wjz6WTLu+dpQqTabUATf7ZZzriZT78RHnQCyRQlL9QcPntZc6YmbG2Ex3vt/inLfL1Dr9/dztcQ/4PJhVX1l0Oz6vB6sX6Yk358s8JISMHnjVIoQQQkhJIrNmOyGEEFI0//AP/4CNGzfi6NGjeOutt/Cxj30MXq8Xt912G6qrq3HXXXfh3nvvxSuvvIJt27bhM5/5DJqamrB8+XIAwMqVKzF//nzccccd2LFjB1544QV85Stfwdq1ay2d6ySTwURuATWsineptKyJkcbn5HqemXzCq7jPKEqLWPp1757As++3wOeR8PhnLsXiKTUArEXydFpW4tJRWGyvCKnCtkU7QqyyI1JVhvKI7Q6Fcm1hg6EtbSGCTcHe2I6xTzGxTw6FUrG5EGydLCAoMwjjZnTx35lAZ2yzX12U4PdKjgVFq765EZOFyG8UpYVY6kwkF8fe0B9NdLU/Rn6vRxN/xGIEbb8ctGO9iMB5O4A+1tFECvGU0oaVQCUE+JTNr1GaSJ6jPwH1/kQB9T6Wyr0AIB8iuUF87asN209osGpHLLSIuxRLxTiIc9XtOaa3l/n6xYilxSL6EkukEU/mnkMjkUun18JYRcPNOIvrce9gQktGcNOOOAfiqTSS6jx0e9zNpWWKmT/Tx+olQoppZ954XWx3uwgFAP7UUI7jfJmHhJDRA69ahBBCCClJ0upvN9k12ym2E0IIIXY5ceIEbrvtNsyZMwcf//jHUVdXh7fffhvjxil1Qh966CHcdNNNuOWWW7BixQo0NjZi3bp12vO9Xi+effZZeL1eNDU14fbbb8edd96JBx54YLh2aUShO18txPaA7hg2irdCpJQkez9a64JStvA6mEhmbAPoznYhGH315vlYPqMOFXkc6V2ROAYTKUgSMLlA/W4rYVvgRKSqUKOv+6PJrM9/+RYxWLYVyu6T1dgUImyxb0LAcyq8+T3KPgmxflBbGFG4P+UW7mitPy5dt2UB3b0r9i8c9Nmu+S4I5RXbHYjS/uxFJNECIrAV1s52545rY1tifDTR3sF+ifPemEQRc+GQB5SYd9FWvoQFIaLZd7bnn0NG4TBvO9o4Ozs3wqYFJ2PK3Ynt5oVI+iICd852sdgjVqQD3CxqDqeoKI5NLJlyvRihVKku82PBBD1y3emiDwC4QHVs72vt08fHRTu6sz2txa2LVAGnhIM+NFaFtNv+Io7XUDjbAWD+eH2cixHtL54yRkvZOV/mISFk9MCa7YQQQggpSXLXbB+mDhFCCCEjkF/+8pd5Hw+FQnjkkUfwyCOP5Nxm6tSpWL9+/VB3bVQwmCdG3uOREA54MRBXnMRjK5SkgGg8rT3HjtAphNeohcvZyr09Y5weQXvr0km4Y/lUALqQ3B/Lbkc4ncMBX8Ef0jX3d9RKbLfvbBcCeVJ1/huFWifCNGDct+Kc7UIozXS2uxOodGe78xh5XWTNFjvdxtqXGcoRnBbx3WXO47uFMGkUk6Mu+mSOAAf0hQmOnO1iEYllbLszEbg84MOZSEJzpbuJkS83iP+yLEOSJE20d9wftQ70YCKVVwDWYuRle+JerMAigoBPaSdeQL13K04az8mgz+PoHDViLrHh1pEujos414uNkTcfIzfi7VChOduT6bylCEYqTTPq8MHJXgDuxFsh1u861aulurgRtwNqKYC+aBLJdHHOdgCYWR9Ga29UbdudaA8AdeEAKoM+9MWSWeUNnGAU24sRySVJwv2r5uLHrx3GNXPrXbdDCCHDwfnz7kkIIYSQ8wohqnuyarZTbSeEEELIyCAazy8OWonATuuRh/zZzl2tLYsY+YqgD3931Ux8bMlE/MtHF2qCfkUw07VrRAiVdvoUNtSiT5tWSeZz+psp93u1xZa90UTmfuVZxGCFMf5duOStIvbttmOMJHcrbmtie9J5jLwQESOJ3M72Ymq2725RxKk5jZX5nmKJEEaFgCzLstYnJ2KyiH/PcKS7qP0uyjVEYtkOeaex7eL4R2KmGHkX+5WW9bkTdTifBSKNYDCRynvcnddst+lsLxQj77LEgriGAO5d7UD2gg23IrlWsz2pXNP0NIuR72yvCikLak73xYrer1LEWLfdjZg8b3wVJAlo7Y2irQhxe2qdkgjzP5uOorM/DsB9zXYgs257MaK9JElaCZlJY8pctzNviJztAHDThRPwu89dgfHV7vtDCCHDgaOr3w9+8ANceOGFqKqqQlVVFZqamvDcc89pj0ejUaxduxZ1dXWoqKjALbfcgra2tow2mpubsXr1apSXl6O+vh733XcfksnsLweEEEIIGd1k12xXxXabPxIRQgghhAw3QnDM5cy0riWu/G1XwLNyAOttWQu4//gnc/HQJxZnvIZVRLq5HaMIlgtRax3IdBMDzpztHo+kRdubXfKa2B6w97NW2CBwajXSXYjtYYsFCU72yYjoelSLkVfatCO4aoK2xTF32x9jrfU9LX0AMgUUp+1ETW5iwKEobYqRzxDtHTnks53tbmq/A0C5evzF4piYi0UExuMr9k0kFDiNW8+o2Z7Hta072+21WyhuXbRXKEbetbPdMA415c7TFQT6go0UZFl2fW7oNdtTGfvsViQ3j8dw1mwXC2p2t/S6XqhTylw6vQ5jyv2YXFsGr4vY9nDQh+lq1PrhjgEA7o7XbZdOwZ9eNAHJtIxkWsTIF+FsN9Rt9xU5f77/yYvxwj0rMKve+eIqwZhwAOOrlWj782n+EEKIExxd/SZNmoRvfvOb2LZtG7Zu3YprrrkGH/nIR7Br1y4AwBe+8AU888wzePLJJ7Fx40acOnUKa9as0Z6fSqWwevVqxONxvPXWW/jJT36Cxx9/HF/96leHdq8IIYQQMuIRvwUJt5UeI09nOyGEEEJGBkLQzeWg1V3g2c52uy5XrX53vhh5f2GRXMS291k522P2ne1Bn0erRWt0gCtil7Na2aJP/aY+6RHw9mLkjS550ZaTsRFYJRG4jaY2xsjLsuwo1t5ci9qIW8HMKODvUZ3t812I7eaa7cbyBk5Ecs2RbiHau3K2W9ZId+lsV1Mb3Ij/Xo+kifzm2u9Oxf+qMt2VnC9GPui4ZrsQpXPFyNt1trus2W5Y1FMbHgpnexLJtKwlpzlPfVCd7Yl05jx0eM4LSilGXo9J7zkvxfaKoA8v3fthPPu5K22VZbFi/oTM66Cb8Qn4PHj4E4vxl5dPz+ibW4zlYIqJfweU2vZuUkzMXDWnHj6PhAvqKwpvTAgh5yGOruo333xzxu1//dd/xQ9+8AO8/fbbmDRpEh599FE88cQTuOaaawAAjz32GObNm4e3334by5cvx4svvojdu3fjpZdeQkNDAxYvXoxvfOMb+OIXv4ivfe1rCATcf4AihBBCyPmFENXFV0fhbKfWTgghhJCRghZXnUNADWtOWWPEtbNa4vmE10EH8e9WLnuBEM3tONslSUI46EPPYAL9sQQAxe2WSMna5zi7IlVlyIeWnkxne8ogctpdkODxSAgHfOiPJdEfS2JcZVB3ktt0xwPWYxR3uIBAIDZPy8rYOImRL/PnXmDhVvwXbfZFkzjdr9Rsd+NsD2lu63TG/z6P5MiBKRZSROLFifaas109ZilDDLjzmu1qskE86Vr8F32KJuJZCxKc9mea6rg92hnR56HFGDt2thcQXYWz96w52w3nQDEx8mWGdIR4ESK50dkuFiJIknuR0+yMdlMDfKhYMKEaALDzZI/muD6farYDQF1FsKjnL5hQjWffb9Fuu00i8Hgk/PNN87BoUhW6Iwk0qk5wN8w0CNqlcrz+7WML8U9/MhfVRaRREELISMb1EqpUKoUnn3wSAwMDaGpqwrZt25BIJHDddddp28ydOxdTpkzBpk2bsHz5cmzatAmLFi1CQ0ODts0NN9yAz372s9i1axeWLFli+VqxWAyxWEy73durrLBNJBJIJBKWzykVRP9KvZ9kZML5RYYDzjtyrkipP87Iam68+CkjPgLe/8n5A695ZDjgvDv/4LEcvRRyqVcElR+ljeKtiJS272z3qc/L42y3U2s9kEdsV4XpsM0FABWa2G5YRJA0iKV2ne3BbLe9UWB2GgHfH0tq+6eL2y6c7RaR5FYiZz6MQxBNprTjZ+e4a2kGQ+hsF0KvGOvKkM9VDV/zQgAhJDuN7ja6kgFdAPY6FO3DJtE+5mIemtsaiCVdi/+APkaas91lDXkhtp/ui+FMRKkDbbXow+/a2V4gRr6Qs12043ichyhG3nBtNC6OcHtuxJJpza0f8HpcO6VL0dl+vGtQu+98crYPBQtMzvZia6R/bMmkYruE8VUhhPweRBPpomPkhwpJkii0E0JGNY7F9p07d6KpqQnRaBQVFRV4+umnMX/+fGzfvh2BQAA1NTUZ2zc0NKC1tRUA0NramiG0i8fFY7l48MEH8fWvfz3r/hdffBHl5eVOd2FY2LBhw3B3gZzHcH6R4YDzjpxt2k97AHiwa9cHuGQcEI0OApDwxptv4kTxKWeEOILXPDIccN6dP0QikeHuAhkmdMeq9Y/hFcLZbnBuRzTHtdOa7UnIspwhAEUcuKWtItK1dkSMvM3YWyGSG/dLiFSAfXGpIuTPascoMDsRcCuCPrQhZhEj70Swz16QoEdlOxM8fJLijpVlZZ4MOkg0CJnqmRsRAmcxbmJAcbW7ERP1yO3M+He3LnKzs92psC3qrIsFI9GEe4ezfvxT2uIRp459pZ3MxRIx7dg7a6e63I8x5X6ciSRwoK0PgPVxF80mZQBvvAGsWAF4c+97oTktFpYkCjnbk+6c7cYFMMU427U5lEhq54XPIzmu3W2c08Kt73TxiBGzIz7gKy4GvBjGhAOYWFOGk90U23NhFttLwUnu8Ui4s2ka3jnciblDEAFPCCGkeByL7XPmzMH27dvR09ODp556Cp/+9KexcePGs9E3jfvvvx/33nuvdru3txeTJ0/GypUrUVXlPNLqXJJIJLBhwwZcf/318Pu5uosMLZxfZDjgvCPnil+1bQV6unDhokVA6/sIl5ehKxZFU9OHsGRKzXB3j4wSeM0jwwHn3fmHSGcjo49oAWe7lcBd6DlmhEialhVh0yhqCjHPWYx8toA7oLZj29mu1VrXUx2Mblm7Im5lgfFxIgabFwA4GRu9DeFINtT/dhnbLknKWEQTiltW70/hn+o0x26+GHmH4r95vrmp125sJ1pkRLo5sUHfL4cCuXC2xzL74/c6F13LtbIPSd2N7rA/gB6RL86rQZdjBADTxoZxprkb+4TYbhZK161D4Cv/Bvzp15FKA1i9GqirA773PWDNGss2dUG5yJrt2kILd6kGgCIGu0UT22N6jLwbkVyMQyyZ1uZPwGW9diBbrA3kWfhwLpg/oSpTbC8BMbmUqKsIorEqhNbeKIDSWYzwpVXzhrsLhBBCDDgW2wOBAGbNmgUAWLp0KbZs2YLvfe97+MQnPoF4PI7u7u4Md3tbWxsaGxsBAI2Njdi8eXNGe21tbdpjuQgGgwgGs+ur+P3+EfPj00jqKxl5cH6R4YDzjpxtZDU43q/+kOGRlC+1Xp+Xc4+cc3jNI8MB5935A4/j6KWQiGZVA7xQ9LwZ43bRRCrjtXSXfOGff3SB3Kpmuxojb9PZri8iMNaidy5OVliI7REXIrmxT8Lh7KYdq8URhSK381Hm9yKaSKMvmnTktNei2q1i5F3WyTYfF7die8gUca/FyLuMEh9QExtcO9sN7Rj74zSyHQDGqbWf2/tiBVMr8hE2ReQX06dpdWG819yN/W39AEzzcN064NZbEagcB8AQI3/yJHDrrcBTT1kK7pqzvVDN9gJiu+5sd3euAsCYIYiRjxhi5J0u1gCAqjIf/F4JiZSMA1bj7BBJkrT2APe134eKBROqsGG38vt8MfH45zMLJlRpYvtwHy9CCCGlSdFLsdLpNGKxGJYuXQq/34+XX35Ze2zfvn1obm5GU1MTAKCpqQk7d+5Ee3u7ts2GDRtQVVWF+fPnF9sVQgghhJxHpGXlxweP+mVfGE/S8nD1iBBCCCHEGUJwzCUwmwVg5TlqzXabIrDf69F+/DfHimtuaRsCUziYKW4aiWjOdntiu+ZIj1o72+0iFgD0RbMXIzh1AZuF8ogW3W3fh2K1OCJWhGP2gvoKAMD7J7odxciLbSyd7S5j7b0eKcOxOc+t2O4z1WwX7maHQrLYR1lNbHDtbFePWTSRRiota4s+3Iiu46uVGvYt3YOGWvTO28mOyHd3zAC9bnvXgFKzXTuGqRRw992ALCOYUs7DpAzIgDKoAHDPPcp2JuzWbI8ViJEfipSFYmLkywyLGoy11p0S9HmxdOoYAMCGPYoo7eZYGTHW/R5up/SCCdXa38Pdl1LFGCVfTM12Qggh5y+O3h3uv/9+vPbaazh69Ch27tyJ+++/H6+++io+9alPobq6GnfddRfuvfdevPLKK9i2bRs+85nPoKmpCcuXLwcArFy5EvPnz8cdd9yBHTt24IUXXsBXvvIVrF271tK5TgghhJDRi/iJVyysFyvs01TbCSGEEDJCEEJjLgG1wsIBHkmobnQHYmBZjhreTtzboi+ynN3OgFaz3WaMfDAzJhtwJ7yJdvoMor1wAzt1tleahPJBF+2IxQZGsT1ehGN2mSrgbT7S5SjW3izWGtGd7S7izdV98HokXNBQ4fj5gD7XhYDs1tlujNMfiCUN4rb7SPJIPKnVWnfjSB9fEwIAtPREDUkNztsxOq4BGPrkJka+POO2Jpa+/jpw4oTSbiIGQEkO6w8oCwYgy8Dx48p2JgotbBCvkcjjbE+mlMUNgHOBO2y4ztQU4Wyvr1J+a+6NJnGqR4lJdyuSr5itpANs3HcaQPFR60ZRe7jFW6OQTLHdmvlckEAIIaQAjt4d2tvbceedd2LOnDm49tprsWXLFrzwwgu4/vrrAQAPPfQQbrrpJtxyyy1YsWIFGhsbsW7dOu35Xq8Xzz77LLxeL5qamnD77bfjzjvvxAMPPDC0e0UIIYSQEY9wVEl0thNCCCFkBJJKy5oQmyv62sopHY3bdzgLzPWtBREHbZX5vdrnrQFTlLxw3lc4jJE3OtL1aGr7+1VpEW0fdeAAt+qTqNnuJLY9qw0LZ7sb8e2SaYrYvulwpyaSO4mRjw6hs93Y7sxxYVfCr1XfYi6d7V6PpAnrkXjKsF/O2gn6PPCpE3sglnJdQx4AJqjO9tbeKAbVRTFu2tFriYsYeff134WzXaCdXy0t2n1V8Qgm9iopo+83zMpswLCdoFBag5jr8TzOduNjjp3thoUWtUXUbK8K+TFbXTSy6VCn0heXQumKCxSxXZz7bhbXGDEK7MVE0g8F46tDWlw/67VbQ2c7IYSQQjiq2f7oo4/mfTwUCuGRRx7BI488knObqVOnYv369U5elhBCCCGjECGqeyQgDT1O3hxrSgghhBBSiogoZiC3MGwWgAF3MenlphrQ5rbKbUSlS5KEcMCHvlgS/bEk6g2PDcTstwPo8e8ZiwhcOIorrMYn7k6YNNeRH3SxqEGL2o8ZHfvunNsAsGRyNbweCS09Ue0+O/0xHm9ZljNqLMfVWHA3opl4bbf12gH9+Gox8kXUNi8PeBFLpjGYSOnzx6EwKUkSygNe9EaTGDDEibvpz7jKIHweCcm0jONdg2o7bsR21dkuFiQknC/8EJjFds11O358xv0Xn9qHk1X12DZhLq7av1l/wLQdYD9GPl/NduNjjp3tAaOz3b3YDgDLptVif1s/3jzYofTFpbA9f3wVxlYE0NGvxPUXK5Abx2S4xVtJkrBgQjXeONhB13YOJo0pw7Vz6zGYSGkLEwghhBAjfAclhBBCSEnCmu2EEEIIGckYXea5XL1CvDU6pQdVMdBJvLlWmzhhdrY7i0rXasjHrNsJ22ynwmK/3DnbFVGjz9CO6xh50wIAfSGCc2f7QNxqv5z/xBYO+rBwoh5PLEn22gmpfU7L2e7iYpztQjh2W6/d2EbUJCQXI0oPxJKune2AftwisZQmJDt12gOK276hSomSP9IxoPTHxXE3O9sHi1iQUF3uzxD/NBH3yiuBSZO0mlxLW/YCALaNn6s8LknA5MnKdiYKnauasz2P2C7c8V6PBJ9DMVkcd69HQlXIkU8sC1Gq4UB7PwBn1x8jHo+EK1V3u9LO0MXIl4LALZzbpdCXUkSSJDz6F5fgib9anrG4iRBCCBHwHZQQQgghJYkQ1bNqttPZTgghhJARgBDQAj4PPB7rH+crLMTbQRfx5kK8Mwr86bSsxVPbdW8LR3pfLJFxv+ZstxkjXxFUxL/MuHXnNbdFf4zO9qhLF7BYKCD65KSevdYfQ+y/SFvSI7fdiXiXTa/V/i73e20JOcZ9N5cO0Gu2O//Jb0qtEpN+2Yw6x8819013trtfjGCc126d7cZ2BuLJoiLbASVyGwAOq2K7q0UEIiEhbnb/u+vTVIO7XVtk4fUC3/ue8rckYekpRWzf0XgBkh71dR5+WNnOhF6zPb+zPZFKA6kU8OqrwC9+ofyvJivEiyivIBbGjCkPFC1sLptam3G7GJF8xeyxQ9IOAPi9kuHv4f95foG66MdNugIhhBBCKLYTQgghpESRzc529VMLxXZCCCGEjAQ0oTuPeBG2qNkuakE7iTcXNY4jBuF10OByL9bZrtdst9uOKmxb1Gx3IihWWNRIH3Qrtpva0mPk7TtnRRtpWT++8QL1rQtx6TRdDLTbF7/Xo4l1EZPYXowD/N8/vhjr/78rsXhyjePnCsTxHRwCIVkXyd3XbAcMzvZ4sqhYewAYX6MsSDjS0e+6nTq1DnlnfwyAoWa7ywUb08fqYnuGuL1mDfDUU8DEibig6wRCXhkDgTLsXXipcv+aNZbtiYUxuYRyIbbHOs8A06YBV18NfPKTyv/TpgHr1hVVXmHe+Cr8+SWT8Q8rZzt+rpnJtWWorwxm9d0Nmc724kTpUnO2Xz+vAR9dPAF/++GZw90VQgghZEQy/O/mhBBCCCEWyIaa7cr/Usb9hBBCCCGljB0HtqWYHHdRs93kJgYyRVi7Il6FVpM8s/a705rtWmR7vDhnu2inPyNG3nmt9Yw+xZJIptKaA7zcxTgb+6QJky4Fs0um1WpJTmUB+22YHeQCsV9BF27ZiqAP8ye4j5AH9OMi3NHi/6Jqm8eTQ+Nsj6W089KtWDpBdbafOKPWbHfRTn2l0kZ7XwyyLOv75nIBgLFue9b5tWYNcPQovM8+g2kVyheprY/8LKfQDhiPmXV/hBM7fuIUcOJE5oMnTwK33orYCy8BcOds93okfPOWC/Hnl05x/FwzkiRh2bQx2u1iHOljK4JDFrdudLO7GaOhpizgxcN/vgSrLxw/3F0hhBBCRiTD/25OCCGEEGKBcLCL6EBJyryfEEIIIaSUsVOHWYjt0UQaSVUkLaZm+2COOPpcMfZmwoFscRsw1mx3GCNvdLa7cIBrixGiQ+tsN9a2dyLaezySFkc/oIntxcXIV5f7MaehEgBQ7rfvsi+zKB2g9Me9o3goKMvhbHfTH2OMfDG16MMZor37dgA9Rl58JXHjtB+nOq3b+2KIp9JFtQUA08aWa39bzkOvF7jiCsyoUsX2rfuzYt+NFKzZrl5O4l416QESBvzKuIidiX33YaWNYZqHRpYaouSLdaRfPaceAFBd5i+qHaPAXgrOdkIIIYQUB9/NCSGEEFKSmGu2e7Sa7cPUIUIIIYQQB9iJzw4baqAL97ibmuRCeDW62SNqHL3bmuSCdFrW65s7jZGPWYntDvqjutHjqbQmIg+6qLWu9EkX20UbkuTc6WqOoy9U39oOom67E+FfuL6znO1F1MoeCsTiEq1mu+ZIdy5yijSCM5FEUe2UG8ojFFsfXcTIC9y40eurFLG9sz+GiKFkg9t62UZnez7hdoaypgNbD7RBNsW+G9HSEXK0Fdi5HQCQ8CqC8//3p/dh6ed/hlOVak1zWUa8s0vZtgRc25cYnO3FCtt//eEZ+Kcb5+JvPjyjqHZEPzyS4uQnhBBCyMhm+D/xEEIIIYRYkFWzXRPbqbYTQgghpPSxEwcf8Hk0MapfdY8LF7mrGHmj2O5QIAd0cTsjtt0g5lYEbcbIC2e7oR03tbKNTnrhbtfG1aHYblxIoI2N36ulKLlpBwBiWix58bWgG6tCtp8j5oe5Zrsmtg+TW1b0SxzvaBGO9KmqiHyko19zW7sRyUUaQX8sWXR99AnVZrHdeTt14SA8krKI+GS3EkfvkQC/153oaktsf+YZTKmQ4U2n0Fo5Fier1Prjauy7ENyTqTRS6urmXAtjgp0dAHRn++ZJCxD1h7Bt4jxtm5gvkLeNc8m88VXaQoZizlMAqAr58bcfnonxpnngFHHdp6udEEIIOT/gOzohhBBCSpK0JrYj43+ZYjshhBBCRgAirrqQWzVsqpM+6KImebmFs11zgDuIJje7tgEgov7tceACF/sUiac04U53gNvfL68htr1PiO0uY+QrDO5msaChzGYsvhExRqIeve4Cdi8qXjuvHv915zI88NEFtp9TnjNGvvj+FIM4LrFkGum0rNf/dtGfmfUVAICD7f16PL4LcXLSGEUYPXS639WiDyPjazIXRLjZL69HwtgKxd3e3BVR++N84YegutyPMeXKAhfL8UmlgC9+EUEvsKD9CABg28T5ymPiu9U99wCplHa8gNwLJPz1ilAf9/qRhoTOcA0A4NgYvd63EOJLQUz2ez1YPLkGQGmI/4Bes91fAs5/QgghhBQP39EJIYQQUpKIuHiP6UcnxsgTQgghZCQQtSmam93kUVc120VN6mxnuxPR3ipGfkBtJxzw2RYDxT4pzy/OAW4eH7cx8pWGSPrjqsDptA3AGJGv9EOvb+3+JzZJknDd/AbUV9p3tmu10RPJjPtLxdkOKBHyxcS2zxqni+3RIpztCyZUAwB2nerVRHu3MfJ14UDG2LoV7UWUvFFsL4alU5Wo9Cm15dkPvv664mAHcHHLXgDA1km6Cx2yDBw/Drz+eobYnisCPrD8UgBA3OdHd1klUh6l70fGTFA2kCTEGhThvVgn+VBx7Tyl1vrUOovxGQb86riUyvgQQgghpDicL+ElhBBCCDkHCAe7uWY7je2EEEIIGQkM2nTQiqj0gVgSyVRac0o7cW5rLmeD8Crc204EZeEiHzDUkRbCu5M4+qDPC79XQiIloz+aRFXI71osrQz50dYby3K2u2lnyZQavNfcjR9sPAzAndieFSM/TOJ2meZsT2fcrzvbS0BsT6SLcpLPGBeGJAG90SROnlHi1t3E0S+YUAUAOHy6XxPw3YrkkiRhfHUIxzqLE8mVhRW9uthe5PH6j9uWoL03hmljw9kPtrRofy5uPQAAeL9xtuV2YjFCwOuBJ0ct8YBaJiLh9eN0hV4P/eiYCdqXt/idfwEcLg1nOwB85vLpuPKCcZjdUDHcXQGgL2Sgs50QQgg5P+A7OiGEEEJKEiGqSxA125XbrNlOCCGEkJGAXUevUbwdNNRHdyLileWLkXcitlvEyAtROWyzXrsgW5R26Ww39Umrt+4iAv5vVswEAOw43g3AmetfEDbsVzotG2Lkz+1PbHrpgExn+3CJ/wKvR9JeezCRKqrWesjv1SLgd7f0Kve5iAGvrwphbEUQaRnYcaLbdX8E46v1BAK3x72+UnW2C9HexVw0Uh7wWQvtADBej3e/sO0gAGB3w3TEvL6s7ewkNRjnVsu0Odrfx8aMByZNAp56CrHFF6vtlEZsu9cjYU5jpeuo/qEm4JPU//nTPCGEEHI+wHd0QgghhJQk2TXbpYz7CSGEEEJKGbsObCHe9kV1sV1yUB8dMESKW8bI2xelrWLkI4YYeSeI+Pc+kwPcqThZqcXIJwDoixic1mwHgJXzGzDDIEi6i5HXxX8htAPOatEPBWXqOEa3vQe8+qpSlxvuFzUMJcKlPRhPIVpkf4QTXSy2cONsB4CFExV3e0tPVO2j++M1obpM+9u9s90UI382RekrrwQmTgQATOptx5hIDxJeP/bUz1AelyRg8mTgyiv18zTPOBvj5U9970fa3x3hMejbcwBYs0YrZ8CYdGvobCeEEELOL/iOTgghhJCSxFyzXWKMPCGEEEJGEIM2RWGjwB1VI8HL/V5HDkw9Rt4otie1tuxiro8O6DXXnQrTxnh8wCi2O2tHc7abYuTLAs5/0vJ4JPz1ihna7TK/c3e88XgJFzBwjkXFdetQ9rOfAAAiT/8euPpqYNo0YN26Ya/ZDuiJAdEine0AMKs+M/bbrVNaRMlr7bgU7QFgfI3ubHcbRz+uSmnjZPdgUe3YwusFvvUtAMp3qotalCj5HeNn6zW7Hn4Y8HoNizVyj3OG2N4by3jsWLeymKEUFn2UMkJkD1BsJ4QQQs4L+I5OCCFkdJJKKQ6QX/wiwwlCSod0Vs32zPsJIYQQQkoZITIWEtvDai30gXjKICQ7ExStYuR1Z/vQxMhXOIyR1xzpqkguHOlORU7xun2mGHk3QjkAfOziiRinuopdxcgHxBilEFO/Q3gkwJejvvWQs24dcOutKDvTCQCI+FXh9+RJyLfe6npRw1Ai5nw0oTvb3YrJWWK7y3YWTKjObKeI8RlvcLa7TTQQzvaUusK4mFh7W9x8s/L/hAm4qGU/AGBH4wVa7DvWrAFgrwyBxyNp8/2UulhAcKRjAABKYtFHKeNXx8XP8SGEEELOC/iOTgghZPSxbp3i/Lj6auCTn8xwgpDSQTY52/UY+eHqESGEEEKIfUSkeyGRsSLoB6AI3MKN7lR4E/XLrWLknTjSrWLkB2JqOw7FdrNw79rZbhbtXSwiMBL0efF3Vym120VEuRO0xREGZ3vA5zk3taBTKeDuuwFZRnlCcRBH/YpoC1lGwqMfo+EUOcX8HUyk9EUWLsXtmaZj5DZu3exsL8ZJPsHobHfZHyG26/05R4sjdu7E4rs/AwDYsfw64MgRTWgHYKtmO6DPr1M9mWL7sU5FbHdbNmK0IBztQTrbCSGEkPMCvqMTQggZXahOEJw4kXn/yZPK/RTcSwZZq9kuYuSV++lsJ4QQQshIQHf0FoqR18Vbu9HzZso1Z7sukg+6ENvDmtieHUcfdihuV2SJ7e6c7ZWGdmRZRiThfL/MfOby6Xju7ivxt1fNKLyxCTFGkXjy3LvIX39d+x5TportPSFdjI55dbF9WGu2C7E9nkJUi5EfXmf75DHl2lwy9tEN4zNqtrvrT31VKOP2WY2RN+L14sIbrwQAHBqQ0WsohQDYj38XMein1Nj4SWOUMTnSodSgp7M9PwHN2X6OEjEIIYQQclbhJx5CCCGjB4MTBAC6yqqwfs7liHt8uo36nnsYKV8iCAe7HiMvarZTbCeEEEJI6TNo04FtdIBHXQrJQpzPiJHXIuntO9Ir1G3jqbQmug1oor0zZ7vZJR+16ZjNakd1tvdFk0ik5CGL3Z43vsqVSG48Xue8LnVLi/bn7I5mAMAHDTO1++Jev/b3cNaCFvPROKfdLkioKQ9gbEVAu+3WSe7xSJhncLcXM38mZIjt7toZVzFMznYAdRVBTRz/4H+eziirFre5gESIxS2qs/2SabUArJztw1fOoJQJsGY7IYQQcl7Bd3RCCCGjB4MTBAC+c+Ud+LuP3o9n5ykr+yHLwPHjynZk2Mlds32YOkQIIYQQ4oDeaAJA4VrnQrztiyYxGBcuYHfO9lgyrYnRg6oj3ZmzXd9WuNv1mu3unO19Jme7032rDCkCcl9Ud/4Dzt3/Q4WeRJDSBcVz5UoeP177c/GpfZDkNI7XNOJ0eQ0AIOZTRGm/pIjLw8XsBsWN/vN3mrUxKkZMnmGIki9mrBdkiO3u26kq82FMuTIva8r9Bba2JuDzaG0o/TmH83ndOly0XfnOu/2/n8woq2Z3TguROJFSrjdLp44BABw1ie10tlvj90rq/xwfQggh5HyA7+iEEEJGDwYnCAC0VI0FAJysqs+7HRke0qaa7ZJWs51qOyGEEEJKn66BOACgLhzMu51wmB5o69Mi253WIze6zoUg7aZmu8/r0URIIbK7rdkuHOmiHbu1oLPa0ZzkCS0twOeRhk3ECwf0/bLrAh4yrrwSmDQJkCRUxgc1d/u7E+cCAOKq2B4sImJ/KPjsVbNQ5vdi27Ez2n3FiNvGKPliROkFE6r1doo4ZpIk4ZFPXYzvfvyijEh5p9RXFl/73THPPAPceisWH9oBANgxfrZyv1pWLbZ5C4DC56n58WXTFLG9oz+Ovmji3Kc+jDAunV6HcZVBXD23vvDGhBBCCCl5+ImHEELI6MHgBAGAgYDyw0hfsDzvdmR40Gu2I+N/OtsJIYQQMhIQYnttOJB3u4unjoEkAUc7Izh+RolkduraNgpaQpAWYrvTtsy11ouu2R4VznZ3wrQQ7fuLqGk/lGTGyLtbQOAarxf43veUvyUJF5/cCwB4d8JcQJK0GPnAMEd3N1aH8FcrZmTcV4xIPsvobC9irBcMUYw8AHxo5lisuXhSUW3UV+kLcc5ZzfYvfhGQZVzYsh8A8P74C5T71e9esV//BkDh89TsyJ5WF9bi/o91RlizvQDzJ1Rh85euxW2XThnurhBCCCFkCOAnHkIIIaMHgxMEAPoCisjeG1J/vJEkYPJkZTsy7Ogx8srxYs12QgghhIwU0mlZd7ZX5Bfbq0J+zGmoBAC8ceA0AOfOdo9H0gRoIbYPuqy1HjbVWi+2Znu/VrNdxMg7+ymq0iDaC+E/NIzObWMt+pi6T+dUUFyzBnjqKWDiRCw5pYjt702cC0yahNh3HwJQGm7iv1kxA+MqFTHZIylpBG6ZOUTO9ln1FagLBxAOeF3Hvw8lYnyAcxgjf/IkAGBh2yF40im0VI1DW4VSbx2yjFifEgNfaA4Z53xl0IeQ34updWEAwFuHOrBxn3ItM7r3SSbiey4hhBBCRj7D/+mbEEIIOVeYnCDC2d4bDOuFwR9+WNmODDvmGHnxf5rWdkIIIYSUON2DCe2zzJjy/GI7oNc73n68G4A757aIi48kkhn/OxXuRUx6vxYjrzrbHcbIGx3gsiwPibNdCPZOovGHGm0xQjylOe3Pubi9Zg1w9Cgu/rf7AQDvT12IxMFDiK/4MIDScBOHgz78/fVKRHlF0FeUsHiBKrZ7pOLG2u/14LdrL8fvP3/Fua2RnoNMsf3cHrNwIqqVIdjReIF2f8ynLEIoWLPdcBzGqvsxTRXbv/38PvTFklg8uQY3LGgY0n4TQgghhJQiw//pmxBCCDmXGJwgmtgeCiuO96eeUh4nJYE5Rh6MkSeEEELICKFrIAYAqAr5bAmfl0xTnKXic44bsV2I6pEsZ7u7+HdRq10X292L5PFUWru/kIhnpiqkiH/dkQRO9ynjOpwx8hWGRQe/234KwDms2W7E68WMVVejKuRDNA3saR/QjlkpONsB4M+WTcbnr5mFf75pflHtTKgpw99fPxv33zgvK77cKZNryzHTEEs/nBhd38Mxpy9sPQDAECUPIOZTFgcVmtMBw3EQ8fHT6pTkuGRaRpnfi4c+sRi+Io8XIYQQQshIgJ94CCGEjD5UJ0h/ZQ0AoPfCpcCRIxTaSwyRFi9l1Wyn2k4IIYSQ0qazX0TIBwtsqSCc7QKnbnRAF9XNNdsdi+2aSJ4wteMuRn4glkI0YRDbHQrB46tDmDkujGRaxq+2HAfgbnyGipDfo43pH/e2AwCqy4YnktzjkbBkijJ33j12Bj9+/TAAlIyY7PVI+PuVc/BnyyYX3dbnr70gqw78SKfe4GwPniuxfeJE7QvWRaeUuu07xisJBJAkxGrqlP4UOE/9Rme7ep2bNjas3feVm+ZhuuE2IYQQQsj5DMV2Qggho5IkJMTU3/z6AmWMji9BctVsJ4QQQoh7vvnNb0KSJNxzzz3afdFoFGvXrkVdXR0qKipwyy23oK2tLeN5zc3NWL16NcrLy1FfX4/77rsPyWTyHPd+5NCp1muvDReOkAeASWPK0FClC29uxOQyVQyPxFOQZVmLOHccI6/FvwvRXjnOFQ5j5IUjvWsgjlhSaUuSMh2xdpAkCbddOgUA8IpaB3o4ne2SJOE//nwJ7lg+FXcsn4q/vHw67r7ugsJPPEtcrIrtP3rtMF4/0IGA14P7bpgzbP0h9qkfjprt3/qW8r8k4aIWXWxPS8p5GbvqGgCFSxFkOtuV/Vg+ow71lUF8bMlEfFI9ZwkhhBBCRgPOvikRQggh5wkiYhEAegcTw9gTkgu9Znvm/3S2E0IIIe7YsmULfvSjH+HCCy/MuP8LX/gC/vCHP+DJJ59EdXU1Pve5z2HNmjV48803AQCpVAqrV69GY2Mj3nrrLbS0tODOO++E3+/Hv/3bvw3HrpQ8TsV2SZKwbGot/rCzBYDLmu1+ESOfRDSR1lKCnDvSlXZEfLyo3e7UIT+1rhySBPQMJtDSHQWguGXd1O5ec/EkfPv5fVoc/XCK7QBw3fwGXDe/NGpRXzy1BgDQ0qOM8d9dPRMzSsTZTvJTX6XHyIfOVfT/zTcr5dPuvhtzTh1DMBFDb6gCR+ctwYxvfAkxeRrQfrygsz1o4WwfVxnEO1+61tU5TgghhBAykqGznRBCyKikP647sXqjCa0+OCkd0lrNduXHGvGjDWu2E0IIIc7p7+/Hpz71Kfz4xz/GmDF6ZHlPTw8effRRfPe738U111yDpUuX4rHHHsNbb72Ft99+GwDw4osvYvfu3fjZz36GxYsX48Ybb8Q3vvENPPLII4jH48O1SyVNlxojL2oZ22HZNP24uBLbVTE8mkhpbnQ3bYUDIv49iVRa1iLgww6d7SG/F5PHKDWcPzjVA8B9bfPacAB/srBRuz2cMfKlxuLJNVrZpeljw/jbD88c3g4R2wyLsx3Qyqr5X34JC6qV133/f54G1qxBLKmc7wVrthvF9kr9OkehnRBCCCGjETrbCSGEjEqEUwcAEinlR0T+aFdayJqzPTNGns52QgghxDlr167F6tWrcd111+Ff/uVftPu3bduGRCKB6667Trtv7ty5mDJlCjZt2oTly5dj06ZNWLRoERoadCfvDTfcgM9+9rPYtWsXlixZkvV6sVgMsVhMu93b2wsASCQSSCRKO1VI9K+Yfnb0DQIAakI+2+0snlil/e33OH/9oE/5rNQ3GEdvJKbe50E6lUQ6le+ZmZT5lXZ6B+PoGYhq9wektOM+zRxXjuauCN4/fgaA4t51O64fXzoBv99xCoCyr6U+j/IxFHNMEPICS6fU4N3mbvzvm+bCizQS6gIJUtoEPEA44MVAPAWfJJ/VOW055y6/HIt69uLdTc1493g3Vl/YiKi6UMfnyd8fr0FTH+PgOkdGD0N5nSPEKZx/ZDjgvDs/sXs8KbYTQggZlfTHMmuM9kUTFNtLDBmiZrtyW8TIU2snhBBCnPHLX/4S7777LrZs2ZL1WGtrKwKBAGpqajLub2hoQGtrq7aNUWgXj4vHrHjwwQfx9a9/Pev+F198EeXl5W5245yzYcMG18/94KAHgAetzQexfv0BW89JyUDA40U8LWHPzu3wnXzP0Wt2timvuf2D3Uie3AXABx9SWL9+vaN2jp+SAHix/0gznn3+KAAfPJKMl158AU5Nq54+pU9v7TkBQEIyHnXcH4EsA+NCXpyOSmg/dQLr1ze7aqeUKGaOGbmlHrhhDNCz7x2s3zckTZJzxISQB4cTEg5u34TTu8/+65nnXPq0cr6/9sExrJcO4/gp5Zzdt3sX1nd+kLOdlpPKdgCwf+dWJI6etS6TEc5QXecIcQPnHxkOOO/OLyKRiK3tKLYTQggZlfRHM8X23mgio2YeGX5EXLz4TVeLkWeOPCGEEGKb48eP4+6778aGDRsQCp27zzr3338/7r33Xu12b28vJk+ejJUrV6KqqirPM4efRCKBDRs24Prrr4ff73fVxi9atwCdZ3D5ssVYddF42897NbITz+9uwyduvBzT6sKOXnPrs3vwzunjmDx9FpbNrQd2vIPqcBlWrVrhqJ2+rSfw22O7UV3XgMsunw1sexMVQT9Wr77BUTsAEH3vJF5etwutUQ8AGWOqKrBq1eWO2xHEJpzEF9ftwkeuuMjRuJYaQzHHyPnBtden0BNNZkTKnw1yzbn5nQP46cNv4lTUi+tvuB6Pn9gCnOnBsovzX7veW78Xb7YpC15uvv4qTKkdGYuoyLmD1zkynHD+keGA8+78RCS0FYJiOyGEkFHJgMnZ3jOYzLElGS7MNduFs51aOyGEEGKfbdu2ob29HRdffLF2XyqVwmuvvYbvf//7eOGFFxCPx9Hd3Z3hbm9ra0Njo1Iju7GxEZs3b85ot62tTXvMimAwiGAwWzzy+/0j5senYvp6JqJ8tmyoLnPUxsN/vgT98SSqQs5fNxxS6ibHkkBcTREvD/oc70NVuXLcIokU4mnlA1jYRTsAMHd8DQClbBMAhALeoo7/Jy6dhpsXT0J54Pz4OWsknQ/k7OD3+1FxDnVq85yb1VCNqpAPvdEk/uW5/XjveA88ErBwUk3euWmsMT9+57vwX70C8DIpjmTD6xwZTjj/yHDAeXd+YfdYes5yPwghhJCSxBwj3xtlPZ1SQpZlQ8128T9rthNCCCFOufbaa7Fz505s375d+7ds2TJ86lOf0v72+/14+eWXtefs27cPzc3NaGpqAgA0NTVh586daG9v17bZsGEDqqqqMH/+/HO+TyOBzoE4AKA2HHD0PI9HciW0A0C5WhJpMJHEYFwp0h52USapIqg8ZyCWwoBav7ncZbmlmfUVGbdDvuLFuPNFaCekFJAkCRdNrgEAPPGO4lT/hxvmYG5jngSSdesQeOT7AIDy+CDKr78GmDYNWLfuLPeWEEIIIaQ04TcUQgghoxKzs713kGJ7KWHU0yWTs12m2E4IIYTYprKyEgsXLsy4LxwOo66uTrv/rrvuwr333ova2lpUVVXh85//PJqamrB8+XIAwMqVKzF//nzccccd+Pa3v43W1lZ85Stfwdq1ay3d66OddFrGmYgitteFz934CEE8Ek8hoortZS5E8lq1z81dEfSon5Ergu5+PqoI+jChOoRTPVEAQNBPzwchpcbiyTV4/UAHAGDVokZ89sMzc2+8bh1w660ILP8zAMDYgW7l/pMngVtvBZ56Cliz5iz3mBBCCCGktOC3HEIIIaOSAfUHSEFflDHypYTRvS4c7VrNdmrthBBCyJDy0EMP4aabbsItt9yCFStWoLGxEesMDkWv14tnn30WXq8XTU1NuP3223HnnXfigQceGMZeDx8H2/vxyt52nOwetFwE2DOYQEr9wDImfO4iJMsMYrtwtrtxgS+cUIW6cAA9gwn8cU+763YEsxoqtb+DQ+BsJ4QMLZfPGgsAuKC+At++9SLte1cWqRRw992ALCOQUhbijI10K4+Ja+E99yjbEUIIIYSMIuhsJ4QQMiphjHxpY/zZf+IyDQAAn31JREFUWo+RV/5njDwhhBBSHK+++mrG7VAohEceeQSPPPJIzudMnToV69evP8s9K30GYkl87P++qS3UrCn348GPLcKNi8Zr24gI+cqQ75yKy8LZHk2kEFHj3904231eD1YuaMAvNh/HH3a2AADCQff7cUF9BV7bfxoAEPTR80FIqbF8Rh2e/rsP4YKGyvwpFq+/Dpw4AQAoiytpFfX9XfrjsgwcP65sd9VVZ7HHhBBCCCGlBb/lEEIIGZVkx8jT2V5KGAV1PUaeznZCCCGEDC9vHuxAXzSJgNcDn0dCdySBX2w5nrFN14CIkHdWr71Ywqr7/OSZQUQSqrPd704kv3GhsnhALFANu4yRBxSxXRBy2R9CyNllyZQxhctFtLRof/7J/k34+Psv4m/e+U3e7QghhBBCRgMU2wkhhIxKxA+HZeoPfnS2lxaZNdsz/2fNdkIIIYQMF6/sU2LVP3nZFPz6b5sAAHtbejO26RqIAQBqz7HYftn0OpT5vTjcMYBX9ylO8nIXznYAaJpZh+oyPQK/mBj5Cxp0sZ3OdkJGMOP1BI9xkW58+7n/wJKW/Xm3I4QQQggZDfBbDiGEkFGJcLZPqAkBAHoHKbaXEpk125X/9ZrtFNsJIYQQcu6RZRmv7FVE7Kvn1mNOQyUkCWjvi6GzP6Zt19GvONtrw8Fz2r/qcj8+dvFEAMDmI0q0c5lLkdzv9eD6+Q3a7bBL0R4AZo0z1mznz1CEjFiuvBKYNElfBW1GkoDJk5XtCCGEEEJGEfyWQwghZFTSr4ntZQCg1d0kpYExKt6jxcgrt6m1E0IIIeSckkoBr76K3Y/9Gq29UZT5vbhsei3CQR+m1JYDAPa19mmbixj5sRXn1tkOAJ9umpZx262zHQBWLWrU2ykiRr663I9xlcrCA8bIEzKC8XqB731P+dssuIvbDz+sbEcIIYQQMoqg2E4IIWRU0h9T6lg2VqnOdsbIlxSs2U4IIYSQkmDdOmDaNODqq/HKf60DAFx++F2EnvkdAGBuo+La3mMhtp/rGHkAmNNYiQ/NrNNuFyO2Xz5rLCpVkb0iWJx4Juq209lOyAhnzRrgqaeAiRMz7580Sbl/zZrh6RchhBBCyDDCbzmEEEJGJSJGfrzqbGeMfGkhp/W/Paaa7YyRJ4SQ0Ycsy5B5/SfnmmeeAW69FThxAgDwx5mXAACu2f26cv+6dZjbWAUgs2575zCK7QDw6Q9N0/4uK0JsD/q8+OgSRVCb3VBZYOv8LJtWCwCYpCYBEEJGMGvWAEePAq+8AjzxhPL/kSMU2gkhhBAyanGfA0YIIYSMYLSa7dXC2c4Y+VJChrFme6aznWILIYSMPh5+6QD++40jeOqzH8KcxuJEP0Js88UvavVrusqq8N6EOQCAqw9tUR6/5x7Me3YTAGCvwdku6rfXDUOMPABcN68BE2vKcLJ7ENVl/qLa+urN83H78qmY3VBRVDufu3oWVs5vwIIJVUW1QwgpEbxe4KqrhrsXhBBCCCElgSNn+4MPPohLLrkElZWVqK+vx0c/+lHs27cvY5toNIq1a9eirq4OFRUVuOWWW9DW1paxTXNzM1avXo3y8nLU19fjvvvuQzJJkYMQQsi5o9/kbO9jjHxJYYyKF9UAPVL2Y4QQQs5/Ovtj+MHGQ+iLJfHMjlPD3R0ymjh5Uvtz4/SLIUsezGs7jPF9nYoIf/w45h5XfhPZ39aHZEqJ5hEx8nXh4LnvMwCvR8L3P7kEf3n5dFw3r6GotvxeD+Y0VmplfdwS8HmwcGJ10e0QQgghhBBCSKnhSGzfuHEj1q5di7fffhsbNmxAIpHAypUrMTAwoG3zhS98Ac888wyefPJJbNy4EadOncIaQ4xQKpXC6tWrEY/H8dZbb+EnP/kJHn/8cXz1q18dur0ihBAyqrnvyR2449F3kM6hysqynOVsjybSiCVT56yPJD+ZNdvF/1LWY4QQQs5/fv5OM+JJRcTceqxrmHtDRiMxrw//71Lld41rhKtdZUpPG8r8XsSSaRztjACpFDrP9AMAave+D6SG5/Plkilj8NWb5yPkL67WOiGEEEIIIYSQ/DgS259//nn8xV/8BRYsWICLLroIjz/+OJqbm7Ft2zYAQE9PDx599FF897vfxTXXXIOlS5fisccew1tvvYW3334bAPDiiy9i9+7d+NnPfobFixfjxhtvxDe+8Q088sgjiMfjQ7+HhBBCRhXxZBpPbjuB1w904GT3oOU20URac0c3qGI7APQxSr5kEIK6JOkiu0cT24etW4QQQs4xsWQK/7PpmHZ7+/FuJFT3MCFnjWeeybj50BWfwp6GGaiN9ODT72Y+5pkwXittsPd3LyE9bTrORJTEpLrbPwFMmwasW3dOuk0IIYQQQggh5NxTVM32np4eAEBtbS0AYNu2bUgkErjuuuu0bebOnYspU6Zg06ZNWL58OTZt2oRFixahoUGPMrvhhhvw2c9+Frt27cKSJUuyXicWiyEWi2m3e3t7AQCJRAKJRGnH/or+lXo/i+FIxwDGlAdQU15cLTjinNEwv0jpUerzrq03qv3dPRBFY2X2tal7QHlPkSQg5JFREfShP5ZEV98gqoOO1qGRs0Q8rswvjyRpc01OK86wVCpVsvOPnH+U+jWPnJ9w3uk8/e5JdPTH0FAVRCyRRvdgAjuau3DRpOrh7pojeCxHEKmUUqv9O98BAGyetAA/uuwWAMC/Pf991A90K9tJEjBpEnDllZjXtQvbj3dj70/X4YqOM0h6lZ9aaiM9QH8XcOutwFNPAYbUP0IIIYQQQggh5weuxfZ0Oo177rkHl19+ORYuXAgAaG1tRSAQQE1NTca2DQ0NaG1t1bYxCu3icfGYFQ8++CC+/vWvZ93/4osvory83O0unFM2bNgw3F04K3THgK+/68XkCuDeRYxfHi7O1/lFSptSnXcnBgDx9vbSq6/jcFX2Nu2DyjYBj4znnnsOftkLQMLzL2/E1Mpz11eSm+4YAPgAOa3NtUOHDgLw4ljzcaxffyzf0wkZckr1mkfOb0byvDs9CNQGAa+LNWzRFNASUUpi//qI8h59WU0Eh/okdA968LPn38LJCSMr5iQSiQx3F4hdXn9dq9Ue8/pw7+ovQJY8+LP3N+BPDmzSt5Nl4OGHAa8Xc+srAAB7x01DZ7myEKQiFkEwpaYmSRJwzz3ARz4CeBnrTgghhBBCCCHnE67F9rVr1+KDDz7AG2+8MZT9seT+++/Hvffeq93u7e3F5MmTsXLlSlRVWagoJUQikcCGDRtw/fXXw+8//5zfbx3qRPrdbehM+LBq1Q3D3Z1Rx/k+v0hpUgrzbvPRLjzzfiv+ceUFqAxl9uH1Ax3A++8CABZdfAk+PHtc1vN3neoFtr+NmvIQVq36MP7v4bdwpq0fi5Zehitm1Z2TfSD5aemJ4n+/+xq8Xi+uv/4abNiwAXMuuADPNh/GxEmTsGrVwuHuIhkllMI1j4w+RvK8O9U9iG/8YS9e2nsaiydX47/vXIrKkLOvnbf+6B3sONGj3S7ze/DV26/GL7ecwK4NBzAYHo9VqxYPcc/PLiKdjYwAWlq0Pw/UTsGJmkZURfvx1Zf/X+Z299yjOdXnth8BAOypn46XZ10KAKiLdOvbyjJw/Lgi5F911VnsPCGEEEIIIYSQc40rsf1zn/scnn32Wbz22muYNGmSdn9jYyPi8Ti6u7sz3O1tbW1obGzUttm8eXNGe21tbdpjVgSDQQSDwaz7/X7/iPnxaST11Qk9MaVeYn8sCY/XB69HGuYejU7O1/lFSpvhnHffe/kwNh/twiXT6nDL0kkZj3VH9ZSNwSQs+yg2CYd88Pv9qC4LAAAiCZnnUong8YoYef0Y+nzqxxZJ4nEi5xy+15LhYKTNu19vOY7//ftdGEwob7Tbj/fgb37+Hh7/zKUIB+199TzaMYAdJ3rgkYCpdWFIEvAXH5qGsVXluGzmWGDDAWxr7oHP54MkjZzvHiPpOI56xo/X/hwIhAAA9f1dqIwPZm73kY9of84daAdQhZPV9fi3q+8CACxoO5zdtkHIJ4QQQgghhBByfuAo1E+WZXzuc5/D008/jT/+8Y+YPn16xuNLly6F3+/Hyy+/rN23b98+NDc3o6mpCQDQ1NSEnTt3or29Xdtmw4YNqKqqwvz584vZFzIMdPXHtL97B1mHkBBybjjQ3gcAaOuLZj3W2R/X/h6IJS2fL+6vUH/4rypT/u+N8jpWKshqOrAEXUgR67nkkZUcTAgho4KugTi+/NudGEykcOm0WvznbUtQGfJhy9Ez+F8/2YpY0l7JqZf2KAuxm2bW4ZV/uAp//PurcGfTNADAoonVCHg96OiPobmLsezkLHHllcDEiQCAiF8R28sThs+ckgRMnqxsp1I9eTwmdStl8aqi/finVx7Dd//w3ey2DUI+IYQQQgghhJDzA0fO9rVr1+KJJ57A7373O1RWVmo11qurq1FWVobq6mrcdddduPfee1FbW4uqqip8/vOfR1NTE5YvXw4AWLlyJebPn4877rgD3/72t9Ha2oqvfOUrWLt2raV7nZQ2XQO6qNUzmMCYcGAYe0MIGQ10DcRxJqKI4h198azHOw3Xpf4cYru4PxxQxXY1ip6LhkqHtKqoGwNThIMxTbWdEEJKjt9tP4lESsaCCVX41d8shyRJmDSmDHc8uhmbDnfiZ283464rphdsR4jt181ryHos5Pdi0aRqbDt2BluPnsHUuvCQ7wch8HqBb30LABBRne3lcVVsF2kKaq12jSuvxHf+/gFs99fiz3e8gJpof2abkgRMmpQh0BNCCCGEEEIIOT9w5Gz/wQ9+gJ6eHlx11VUYP3689u9Xv/qVts1DDz2Em266CbfccgtWrFiBxsZGrFu3Tnvc6/Xi2WefhdfrRVNTE26//XbceeedeOCBB4Zur8g5oyuSKbYTQsjZ5tBp/cfLDkO6hqDTcF9fNJezXXHXhTVnuyq209leMqRVPd1jiAgWf6aptRNCSNF0R+L4zbYTORemOeWpbScAAB9fNllbHLVkyhh8adU8AMCPNh5CNJHf3d4diWPL0TMArMV2AFg2dQwAYOuxriHpNyGW3HwzACAyTil1FxbO9kmTgKee0mq1a3i9WP7lz+FvN69DTWwg87FcAj0hhBBCCCGEkPMCR8522YaTLBQK4ZFHHsEjjzySc5upU6di/fr1Tl6alChmZzshhJxtDrUXENttONtFjHxlyJfxfy5xnpx7hHvdWI7XQ2c7IYRk8MHJHvi9HsxprHT83P/840E8+sYR/OydY/jpXZdppVXcsKelF7tO9cLvlfCnF03IeOyWpRPxn388gJaeKJ7cdgJ3LJ+as51X951GKi1jbmMlJteWW26zVBXbhShPyNlk4MtfBZ4/gPJlS4DPvaI403MJ5mvWKEL83XcDJ07o90+apAjtZoGeEEIIIYQQQsh5gSNnOyFmjLWRh0psjyVTeOtQB+LJ9JC0Rwg5vzhYSGw33JerZrsWIx9UfixljHzpIfR0jye7ZjuotRNCCHqjCdz6w7dw8/ffwL7WPsfP39PSCwB4r7kbn3lsMyJx9wvOfqO62q+d25BVViro8+JvPzwTAPDDVw/l/Yy/IU+EvOCSabWQJOXzQGtPNOd2hAwFkYTyoSN8wQzgqqsKO9PXrAGOHgVeeQV44gnl/yNHKLQTQgghhBBCyHkMxXZSFGfD2f7oG0fwyR+/g5+8dXRI2iOEnF9kxshn12w33tdXwNmux8gr//fS2V4yyFrNdmOMPJ3thBAiONDWj2gijXgyjXt+tR2xZHZE+4G2vpwi+pEOJera55Gw5egZfOyRt/Dgc3vwzI5TeGVfO17Z164J8vlIpNL47faTAIBbl06y3OYTl0zG2IogTnYP4un3TlhuE0+msXHfaQDAdfNzi+1jwgFcNKkGALBxf3vB/hFSDJG4cl6VBxwkP3i9ijB/2232BHpCCCGEEEIIISMaiu2kKM6chZrtu04qP+rtdeHQIYSc/xw6rdfB7BqII5HSHXKyLKNzQHe29+cQz4WzvUL94ZTO9rNLJJ7E/evex+sHTtt+jqjLbkiR15ztFNsJIcNFc2cEv9rSjC89vROfe+JdvH+ie9j6ctiw+GxPSy8efulAxuOPv3kE1z/0Gr74m51Zz43Ek2hRXeE//vQyVAR92NfWhx9tPIzP/+I9fOaxLfjMY1uw6j9exyv78gvaG/edRkd/HGMrAvjwnHGW24T8XvzNihnK671+xHKbd450oj+WxLjKIC6cWJ33Na9SX+fVffbfVwhxQyShiO0iDYkQQgghhBBCCDFDsZ24Jp2WcSaiC1NDJbafOBMBALT2Dg5Je4SQ84doIoXj6jVCYEzYiMRTiCZ08b1wjLxwtitiO2u2nx3WvXsSv9h8HP/+4n7Lxw+29+H+de/jZLd+3ddrthtj5IWz/Sx2lhBCcnCkYwDX/Pur+OJvduKJd5rx7Pst+LMfbsLvVFf3ueaw6kyfOS4MAPjhxkN4/oMWAMBbBzvwjT/sAQC8ceC0lhYiONqhvJfWlPtx9Zx6vHTvh/GtWxbhU5dNwcVTarBwYhWm1pVDloF/fOr9jPdaM2Ih1epF4+H35v56+YlLJ8PrkXCwvV/7vG9k85EuAMCHZ4/LKCFixdVz6tV968hYdEfIUCOSIcoCFNsJIYQQQgghhFhDsZ24pmcwgZRB8eiJDI3YfvyMIrawBiMhxMyRjgHIMlAV8mFsRRAAcLpPd7KbxYD+AjHyFarYXhkSMfJ0tp8Nth5VBBSjmG7k0TeO4Bebj+NXW45r96W1GHl9O/G3WTQihJBzwdPvnkAyLWNybRn+ZsUMXD1nHGLJNO7+5XZ86/m9GZ+LzwXC2X778qm4dekkyDLwtz97F3/9P1vxd0+8q/XnTCSBE2cyr79HOxWhfvpYRahvrA7hE5dMwb9+bBHW/d3lePbzV+KFe1ZgVn0FTvfF8KV1O3Nee0XizIIJ+d3oVSE/LpqkbPPGgY6sx0Xd+fnjqwru+6KJ1agLB9AXS2LbsTMFtyfELZGY6mx3EiNPCCGEEEIIIWRUQbGduKbTJGoNhbN9IJbUxLKWnigFFUJIBqJe+8z6CoyrVMT2jn5dbDf+DeR2qg+IH06DjJE/F2xVhZCO/hjiyWwH4vEuRQQyLpwQl3/rmu1nq6eEkPOdQ6f78YNXD+VMPsmFLMv4/Y5TAIB/WDkH96+ah//69CX4u6tmAgB+8Ooh/PX/bEXfOVy0dVgVuWeMq8C/fHQh/nrFDHg9El7c3YbuSAIXTqrGnIZKAMDOkz0ZzxX12oXYbkXI78XDn1gMn0fC87tase5dawe/8b25EFdeoMS/v24hth9oV9qZrfY5Hx6PhBWzGSVPzj56zXY62wkhhBBCCCGEWEOxnbjG7CAdCrHdGA8diafQ5/CHUELI+c2hdkUcmDWuAmMrAgCAjn79WtSp/h1WfxDN5WzXY+SV7caElbYG4qmc7mvijtaeqOaolGWgrTc7tUSM+RnD+0o+ZztrthNC3PK13+/Ct57fiy8/nV3HPB/vn+jB0c4IQn4PrpvXAADweiT845/Mxff+fDGCPg9e3tuONf/3LZw6B+8jqbSMY53K5+YZY8MI+b340qp5ePbzV+BDM+swb3wVfnTHUlw8dYzWfyOaUJ9HbAeAhROr8YXrZwNQYurN9Mf02u8izj4fV14wFgDw5qGOjCSAaCKFY6rbfnZDYdEeMNZtz19TnpBiGIhnlh4ihBBCCCGEEELM8Bsjcc1ZEdu7Mn+cbO2Jao5TQggxuufEj/RGN3vngPL3lLow9rT0YiCWhCzLGXW/Af2HUxEfX13mR9OMOmw63IlfbW7GvSvnnPV9GS1sPdaVcftU9yAm15Zrt2VZ1sR24/uK0NNZs50QMlTEk2lsUcta/Hb7KVw7rwE3XTgeP3rtMB5/8yjWXjMLdyyfavlc4Wq/bl5Dluj2kcUTMa0ujL/+6VYcaO/Hd17ch+9+fHFWG4+9eQT/+ceDmD42jAsnVSOaSGHH8R4cbO9HSr3oLZhQhR/evhTjwvm/pp04E0E8lUbQ58HEmjLt/nnjq/DEXy3Xbl84qRq/2Ax8kOVsV95PpxUQ2wHgz5ZNwv95YR8One7HYDyVUbtaRNmPrQigpjxQsK2LJtegIuhDdySBXad6cOGkGgDK+3taVt6PRXJNIVZcMA4eCdjb2oeWnkGMry4r/CRCHEJnOyGEEEIIIYSQQtDZTlwjRBFRN3loxPZIxm3WbSeEGNHE9nEVGCti5PuMMfLKdWlanSLmJtMyYhax5QOxbJfSp5ZPAQD8cstxJFLZzyHu2Ho0s5Zui+m63tEf16LlxWIJwOBsN3xSEcI7S4wQQtyw82QPogn9+v6V336Azz3xHr753F609kbxz7/9AD97+1jW81JpGc++r4jtf3rRBMu2L5pcg3//s8UAgE2HOi2vU7/cfBxdA3FsO3YGj715FL/YfBy7W3oRT6WRSstIpWW8f6IHn/qvdyxTQIwIZ/r0sWF4PFLO7RZNVGqkv3+iO6NPdmLkBfWVIYytCCItA/va+jIeE+/LM8bZc6P7vR4sn1EHIDNK/kCbiJCvyFogl4sx4QAWT64BAGxklDw5Swixnc52QgghhBBCCCG5oNhOXNOliiIifnIoah2LqGEBxXZCiCCdlg1ie9gQI29wtqtiu9E5bVW3XYuRD+g/nK6c34ixFQG098Xw8p62od+BUco2tV57hfoj9amezOu8MW65KyNGXvlfgtHZLh6j2E4Icc7mI4qr/dq59bhwUjV6BhP4w84WeD2SFkn+ld9+gF9vOZ71vLbeGKpCPnxY3c6KpVPHwOeR0GIonyGIJlI4qL6Hff1PF+AvL5+Oz141Ez+8/WK8dt/V2Pyla/HCPSswaUwZjnQM4M7HtqE3bvUqCrrInV8sn91QiYDPg95oEs3qotbuSBxnIsrn9ml1hcV2AJg/oQoAsPtUb2Y/RHkXG/XaBStmK1Hyrx/QBfL9qoh/gY167UaumlMPANhiWthFyFAxSGc7IYQQQgghhJACUGwfwazf2YK7Ht+SUeP2XNKpvu60sYqo1RdLZtRedIOo2e5TFRWzA5IQMno51TOIaCINv1fClNpyLVUjo2a7ughoXEVQE3cHTHXbk6m05mysMLiUAj4PPr5sMgDg5+80n70dGUUMxJLY3aIIMysXKDWOW7ozr+tGsb17MKG9j8iWNdvVGHkGDxBCXPDOkU4AwIdmjcVDn1iMypAPY8r9+Oldl+Kxv7gEf/GhaQCA+5/emZG2JCLk/2RhI4K+3IJbWcCLCydVq6+VWULjQFs/UmkZteEA7myaiq/ePB9f/JO5+JOF4zGlrhz1VSHMaazEL/5qOcZXh3C4YwDrjub+qna4Q9Rczy9yB3wezBuvCOWibrtwtTdWhWy7deerbexuyYyjNybO2OWKWYrYvu3YGUTUsi77hbPdgWgPKBH3z37+CvyfWy909DxC7DKgie10thNCCCGEEEIIsYZi+wjmRxsP4eW97fjj3vZhef0zmtiuO2KKdbeLHzYXqpGXrb2D+TYnhIwiRGTu1LowfF6PJrafNsTIC2d0XUVAE9L7TWL7QCyl/W0WGW67dAokSYm2PaqKEcQ92493I5WWMbGmDEumjAEAtJic7ScNYrssK45LQHe2eyQ62wkhxZNKy1pZi8um12LmuAq8dt/VeOufrsWHZo6FJEn43zfPx/IZtUilZTy17QQAIBJP4llVbP/I4okFX+cyNSL9ncOdGfcLkXr++Kq8MemTa8vxg9uXAgB2dkmW6SyAXivdTgz8hern6p0nM8V2sWDWDrmc7Qfb9cQZu0wfG8bEmjIkUrK2KOFAuztn+/jqMiycWJ03Sp8Qt8gytAUhYTrbCSGEEEIIIYTkgGL7CEbEU7YWqOl4thDO9vrKkPbjQzF122VZ1vbpkmmKKMMYeUKIQNSvnVBTBgAGZ3t2zfa6iiAqQoqQbhYq+tUfTQNeDwK+zLfBybXl+PBsJSL44z/ahO9u2M/rUBEIYWvZtDGYUB0CAJwyOduNYjugL5gQgrqVJkWtnRBnvLqvHV/7/S7EkqnCGw8RXQNxy7rlw8Well70x5KoDPo0p/eYcABlBgFNkiTcdukUAMBv3j2BdFrGMztOoS+WxNS6cjSpQno+Lp1eCwDYfDTT2S5EaiFa5+OiSdWYMTaMpCzhpT3Wi2rFArRCMfIAsGiSXrcdMNZrt+8iF872va19WgJJMpXG0U6lLSfOdkmSsEJ9r31mxykMxlNaxP0FDc6c7YScTRJpffFfOWu2E0IIIYQQQgjJAcX2EUokntTE7rZhEts1B2k4gOoyP4DixPbuSEJzoC6bpvxQyRh5Qs4upSSEFEII6aJW+9hK5f+uSBzJlJIr3qkK73XhgOZaz3a2qw6loLVD6b4b5mBcZRDtfTH8x8sHcN13N9oq1zGSxtIukXgS331xH/a19rl6/tZjiti0bOoYjK9WFklkOdtNdY3Fe5ts6WxXY+TPw7Em5GzywLO78fhbR7Fx3+nCGw8Br+xtx9J/2YB/+s1OV8/fsLsNP9p4qOjyREbeVp3my6aNgTePC3rl/EZUBn04cWYQ7xzp0sqKfPLSKbbc00unjoFHAo51RjIWa4mSGkK0zockSbjpwkYAwDPvt2Q93hdNoF1NdZlhQ+QW0fYfnOxFOi0bIuidudFDfg8i8RSOqQL78TODSKRkhPweTFQXwtnl48smAQD+8H4L3m0+A1kGasr9GKcupCOkFIgbytaU+elsJ4QQQgghhBBiDcX2EYpRnBhusb02HECVKrZ3FyG2C1f7uMogptUpP/4Nl2ufkNFAdySOy7/5R/zdz7cNd1dsIRzswtFeFw7CIymibFckjnRa1q5LYyuCqMxRs12Uu8hVp3bBhGq8+cVr8J+3LUF1mR/9sSQOqnG9ufjNthOY9eXn8Mq+4Snrcbb40cbD+I8/HsQ3nt3t6vm7VCfnkiljNCHmTCSBwbjurj3VY+1s12u2U2wnpBh6ownNBX2sM1Jg66Hhey8fgCwDv9p6HM++f8rRcxOpNO755Xt48Lm9+D8v7LPcRpZlxwucNqtx5ZdOz+9OLwt4cdNF4wEA/7Z+D94/0YOA14Nbl06y9TpVIb/mXhc14tNpGXtalEVLdpztAHCzKra/dbhLW0gmEM70sRX6gtd8zBpXgZDfg/5YEvvb+3DktHC22xfbvR4JcxtF3Xbl2n5IjZCfMbbCcYz74sk1mD++CrFkGt96fi8AYHZ9Zd6IfULONaLyUJnfm3eRDiGEEEIIIYSQ0Q3F9hHKCYPY3toby7Pl2UGWZc19WDtEzvbjZ5QfgCePKUOjGjfcHUkgmjj7kacv72nDzhM9Z/11CCmGf3l2N1Z8+5WM2PRieONgB071RPHSnvYR4cru1MR2xdHu9UioDSt/d/TF0RtNIKm6IGvDes32PpPY/qrq7Jydpy5swOfBzRdNwJxGZZtCi5rW72zJqPFbqqTTMg6292Hduyfw83eO5XWNptMyfvOusj/vNp9BIpXOua0VsWRKE84njSlDVZkP5Wpcs9HdLmLlp9QqtYM7BzJrtht1F/E7t6y28dO3jznuFyGlTjyZxs/fOYbTfUNzrf/gpP75RnzWOptsO3YG2493a7e//PQHGQ7vk92D+NufbsOyf9lg+dlrT0svBtQFOT/ceAi/234y4/GjHQNoevCPWPzABtzx6Dv47ob9BT9/ptMythwVYnttwX0QwrqocX7jokbUOXBcX6YK+kLgP34mgv5YEgGfx7abfFpdGJPDMlJpGet3ZrrbtQh5mzHwPq8HiyfXAAD+/P+9rS0gm+6gzjqQXbf9kNrOzHrn0e+SJOGTlymR/e+r84AR8qTUEGJ7rjQkQgghhBBCCCEEoNg+Yjlh+LG0fRjc35F4CvGkInAMmdiu1mqcXFuOqpAuypztesnHuyL4X/+zFX/x2OYhjSslZChJp2X8cstxNHdF8PqBoYkBFvW048m0JnCWMnqMvC54GOu2i8crQz4EfB49Rt5Qsz1lEJBvubiwS7GhSln401ZgUZMQLjYf6SrZhQunugfR9M2Xcd13X8O9v96BLz/9QZaAY+SdI13awq5IPIU9qpPRLu3qmAV8HlSX+SFJEsarC6lEiZBIPKkJ8osmKjHHZ7Jqtutqu6Sq7fFkGn/x31vwz7/9AL/cctxRvwgpdR5/6wi+/PQH+Nrvdw1Je0ZBW9TFPpv89xtHAAAfWzIRiyZWo2cwgc898S5+/NphfOv5vbj+uxvx/K5WdPTH8cTmY1nP36K+N4X8yteUf3zqfbzXrNzXF03gf/3PVrT2RtEzmMDrBzrwHy8fwA9ePZS3Tx+c6sGZSAJlfq92rcnHxVPGZIjin7psqr2dVxGC/juq2C7E6TkNlfB57X/9unis8ln79zsy0wEOq+85duq1C/7lowsxb3wVuiMJxJNpeCRg8phy288H9Ah84Ww/qDrbZzmo127ko0smIhzQRcx8i+AIGQ5i6nq+sgDFdkIIIYQQQgghuaHYPkIxOtvb+2LnXCQW4kjQ50F5wKuJ7b1D4mwvhyRJaKzKFGWcsvtUL2783ut4bX9+YfJIxwBkWXFT7m11JiYRcq440jmg1R7f15o/0twuop42oAixZ4t0WsYr+9q164ZbhKO/LofY3mmKma8MZcfIbzrUiZaeKKrL/Lh2Xn3B12yoVNrKt6gpmkhpAtbpvhiOnqOYZqdsOtSJtt4YAj6PlgiQ75onFiUIxOIMu7T3KWPWUBXUBPMJapS8mG/C1V4Z9GFqnSL6dJnEdo+Fs33XqV7sa1MimV8vcI0nZKTxx71KOYpX9rUPSbrP+yfPndh+vCuC5z5QFvH8zYdn4KFPLEbQ58HWY2fwr+v34AevHkIknsI09Xx/Ze/prAVK29T3prVXzcLVc8Yhlkzjz364Cd96fi++8KsdONjej4aqIJ74q8vwtx+eCQB4ZscppHN8Fk6k0vjKbz8AAFw1ZxwCvsJffyRJwi2qu/2C+gpcMm2Mo3G4ZJoith9s70drT9RRvXYjF9fJkCRlAcJzO1sgy4pDX9SRn+XAUT6rvhLPfO5yfGX1PFQEfbjiAntjYSS3s92ZQ15QEfThI0smarfpbD97/OAHP8CFF16IqqoqVFVVoampCc8995z2eDQaxdq1a1FXV4eKigrccsstaGtry2ijubkZq1evRnl5Oerr63HfffchmUyaX+q8Ip5SPniEA9alhwghhBBCCCGEEIBi+4jFKLan0jI6B85tlLxwwdaFA5AkaYic7co+TRqjiDEiSr61150I+MstzdjT0osfv34473bGOON3Dnfl2ZIQ+xw63Y+P/3AT3jrUMSTtGZ2J+4ZgUUh/LKn9WA7ooufZ4I972/GZx7bgq7/7oKh2dGd7QLtP/N3RH9NE2jpVSBYx8v0Gsf2pbYoL+k8vmoCQv7BLqb5KEdvzxcgfPq0s2BG8c7izYLvDgRDZ1iyZiLVXzwKg1/01MxBLaq73q+eMA6BEQztBpAE0VIa0+8zOdiG6T6gp0xYAdGo125XnWNVsN/L24U6mkpDzhkg8qZ1rkXgKm4bgemJ8/zjRNZhTlB4KfvLWUaRl4IpZYzG3sQqz6ivww9uXYs3FE7FmyUSsuXgivvvxi/Dc3SsQ8nvQ2hvVFs4ASpmi/7+9+w6Pqsz+AP69UzLplfQCAQIh9E5o0quKggVB0V2xIBbAdV17X/y5NqxrwbaKXVQUEATpvZMQeiAhvfdk2v39MXNvZlJnAplLwvfzPPusJJPJOzNn7uTe855zpI09Q2IDseyW/pjQIxRGs4j3N53Bnyk5cNOo8MFtgzC8SwcsmhAHb50GGcVVOJDW8DHq7Y2nceRCCfw8tHjmmp4OP5a/j4jF/WO74o2b+zk9RzzQyw0DYvwBAE/9koRk6+eto/PaJf46YLz1GLzgqwO4+YNdmPPRLhRU6JEQ7osbB0Y7dX8atQrzR3XGgacm4tM7Bjv1swAQH+YDQbBs9D2TVy5XtndpYWU7AMwZEiP/NyvbW09UVBRefvll7N+/H/v27cO4ceMwY8YMJCdbOmgsXrwYq1atwvfff4/NmzcjMzMTM2fOlH/eZDJh+vTp0Ov12LFjBz7//HN89tlnePrpp5V6SC4hVbZ7srKdiIiIiIiImsBkext1oc7MzZwS1ybbC63J/UBroktOtldegsp269xeOdnewscmzSjdf74IxiZm+tpWzkuzNYku1v92nseec4V4bd3JS3J/R+yS7WVN3NIxh9KKYZtvsd10cqlJLdaPZtSfzesok1mUjzvBDVa265EvJdutxyWpjXyZtY18abUBa5OzAdTO422OI23kT+Xavx6X63HE9hgrtUdOzW+4ynVNUjYq9SbEdvDCPdbK0b3nnGuRL21QkJ5DAAj3s2ymkuItw5psjwyoTbZLr7OI+pXttvmuATH+8NZpUFptdLrFfXuTlFGC+Z/va3TzBLUdu1MLYTDVvs/+PJbTxK2bV1yplzfaqARAbzIj9xLNgq/LbBbx3T7LhqY7R8XKXx8bH4LXb+qH12/uh9dv6oeZA6Lg4abG8C4dAFiq2yXphVXILauBVi2gb7Q/fN21+Pj2QfjwtoGI8HOHIABLr+8tzx9316oxqWcoAOCXQ/at1gHgQFoR3v3rNABLG3Xpb0tHeLip8Y/J3dHLgbbzDXnxut7QqgWsP5aDzdYOHM4m2wHgjZv64P6xXaFVC9hzzhIf03uH44cFifDz1LZobW4aFdQq5zYQAICnmwax1s+P8a9tRmm1ESoB8tdaolekH567tidemNHTbkwMXVrXXHMNpk2bhri4OHTr1g0vvfQSvL29sWvXLpSUlGD58uV4/fXXMW7cOAwcOBCffvopduzYgV27dgEA1q1bh2PHjuHLL79Ev379MHXqVLzwwgt49913oddf/qOIWqp2Zjsr24mIiIiIiKhxPGtso6TKdnetCtUGM7JLq9EbzV8MXH00C3tSC/HYtHjoNC3foV9YYUmqB3pZLopJF/ucrWyvNphwMqcMJrOIDOtjkuZHhsvJ9oaTgAaTGU/9nISoAA/MH9XZrkrVZBbllp2VehOSMkvlC7N12c6E32NNJjlbwUSOWZuUhR1nCvDY1B7tfvbh4QvFACybPXJKq+0Sji1xNKNY/u/MkmqUVhvg696yi+yAfQt5oOXjGhwhze5OL6xEtcHkUEV5XUWVenlzQICXTWW7tc17flkN3K3tcKU2897uUmW75bi0+kgWqg1mxIV4o0+UY8mTEGtVdk5Z48/PGWtlX3SgB9ILq+QZvZebdGvCLSbQE52siZFz+RUwm0Wo6iRdpA4ANwyMQr9of2jVAnLLanChqEreENUcaYOC1B0AACL8Lc+n1EmhtrLd3SbZbnm9zNY9UrbHY63K8hqrVQJeur43Xv3jBDYcz8WOM/ktToi1B59uP4c/U3LgrVPjzdn9lV4OXYRtpyzdUGICPZFWWIk/U3Lw4nW9Wvx3ibTJqWOQJ8yiiPTCKqQVVjaadD6RXYYPtpzB3KEdMbCjc63TM4qrUFpthJtahVFdOzR7+zHdg7HxeC42ncjFgjGWTT3SZ1OvSD+7z4pJPcMwulswCir0iLSOo5Bc2zcCPx3IwOqjWXjmmgS7mehP/ZwEk1nEdf0icE3fCKcez8VKiPDFw5O64+U1x+XuG/Fhzlduu2stSf/r+kfgnY2n0SvSD3eOjFXsb9URXTrgbJ5lY09MoCduHhzdos91W7cP73QJVkaOMplM+P7771FRUYHExETs378fBoMBEyZMkG8THx+PmJgY7Ny5E8OGDcPOnTvRu3dvhIaGyreZPHkyFixYgOTkZPTv3/BnT01NDWpqajf4lJZazs8MBgMMhpZv0nYFg8EgJ9vdNarLfr3U9kgxxdgiV2HMkasx5khJjD9SAuOufXL09WSyvQ2q1BvlNrt9ovyxJ7WwyRbHtp75NRl5ZTVICPfFTYOdaz1pS65stybZnW0jfy6/Al/tPo/v919AsU01vEoAwq3JmOZmtu8+W4hv9loSQj/sv4DnZ/TC6G6WVptn88pRbaitZt+TWtBosj3T5v4LK/Q4nVuOOLaxbBUvrU5BemEVwvzccd+Yrkovp9XojWa5ZSwA/JGcjXmJnVp8fyaziKQMy/1p1QIMJhEns8swyDoTtiWkNsWdgjxxrqCyVWe2S7O7zSJwrqAC8WHOV/YVWFvIB3hqobVJpEhVcPvOF+EPa9W6VLXto5NmtluulP58KAOAJYHsaJIi1Fea2d5UZbsl2X7jwGgs23AKGcVVuFBUiagAx5LSrpJmk2yPCvCARiWgymBCTlm1XHEOWDZBSdX5Urv9nhF+OJRejL3nCh1Otuc6Utlu3WQV6e+JIOvmLenzRZrZbvtS9Y/xx/Te4RgZ1wE9wn2R2CXImmwvwN2juzj3hLQjaYWWxNeGlFzUGE0XtZmOlCUl2xdNiMNTPychp7QGSRml6O3ABqGSSgN8PTR2xzepK0rvSD8UVerlZPuQ2PqfHydzynDLR7tQWKHHn8dy8Ov9I+WNOY6Q5nd36uBpl/BuzJhuIQCSse98kbyBbK+1hfygBhL97lp1vUQ7AIzo2gGBXm4oqNBj+5kCXGXzt2ByZik0KgFPO9E+/lK6a1RnbEzJxZ5zhegY5Amfi9gk1zXE57LYTPPE9B6YNTAKHQM97Ta/0eXv6NGjSExMRHV1Nby9vbFy5UokJCTg0KFDcHNzg7+/v93tQ0NDkZ1t+dsqOzvbLtEufV/6XmOWLl2K5557rt7X161bB0/Py+vvpIbozZbjaXFeNlavXq3waqi9Wr9+vdJLoCsMY45cjTFHSmL8kRIYd+1LZWXDnWHrYrK9DZKSEz7uGnQL9XY42Z5bWo08a+vQH/ZfaDDZXmM0wU2tajYRJSX75cp2J5LtJ3PKMP2trXKb1ABPrdyab3rvcDmRFmZNymQ38tjO5pfL/32uoBLzPtmDT+4YhHHxoXaJTsDS1rmxRIxUOS91CdidWshkexMMJjM0KqHBGLlQVIkVu9PwtxGxCPaxbwVaYzTJHRk+3pqKO4Z3gqdb+zwEncgug95Yu9lj9dGsi0q2n8krR5XBBE83NQZ1CsSWk3k4keN8st1gMkOrVsFoMuOANdl+Td8IvL3xdOtWttu0LD6T27Jke3655T7qtpiVZrZLieQRXYNw67COAGzayNcYIYqiPKNe2pTjiBBrori8xojyGqM8B96WlGzvG+2P3pGWpPSe1ELFk+1Gk1lOeFUbTHKleXSgJ7RqFWICPXE2vwKpeRV2yfYzeeUwi4C/pxZRAZavD+4UgEPpxdh3vggzBzjWgl/qBhDaQGV7lrWyPcO2st1bqmzXQxRFuZOB7Zx2nUaFd+cOkP+d2CUIALA3tVCO7yvR+QJL/JfVGLHjdAHGxocovCJqiRzr/HJBAMZ2D8HobsFYk5SN9Sk5zSbb31h/Ess2nMKouA54YUYvOUkuzWvvE+WH1PwKbEeB3OXC1pm8csz5aDcKK/RQqwSUVhsx/4t9WHnfcIcTxGes1c5dQxyb3x0TZBlpcTa/AttP5WNq73Dst1a2O/P5plWrMK13GL7clYZfD2XKyfY1SZYE4HBrMl4JapWA12/ui3/+cMTllfWtxV2rbnQDK13eunfvjkOHDqGkpAQ//PADbr/9dmzevLlVf+djjz2GJUuWyP8uLS1FdHQ0Jk2aBF9f5/8edCWDwYA1n2wAAMR1jsG0aQkKr4jaG4PBgPXr12PixInQalu+GYvIUYw5cjXGHCmJ8UdKYNy1T1KHtua0z0xXOyclLKMCPBEqtTh2INmelFk7L3nPuUKcy69Apw5eSM2vwH83ncHhC8U4mVOGgR0D8N09iU0m3AvL7WcjO5Ns33++CAaTiJhATzxzTQLGdA9pcG5kbRv5hh+bNJv2liExKK7UY01SNr7fdwHj4kPlee39Y/xxMM2S+DKZxQZ/j5RknJgQhlWHM7E7tVBO1rVUTmk1vtp1HpEBHugd6Y9uod4OVXk54lx+BdYkZWNk1w6ID3VtMu9gWhHmfrwbIT46zB3aETcMjJKrmkxmEfd9dQBHLpRABPDolHi7n00vrIQ07rmwQo8Vu9Mwf1Rnl67fWaJcWetcq9ZD1hbyXUO8cTq3HHtSC1FQXiO3N3fEH8nZqKgxYuaAKLkysVeEH3qE+1iS7U7Obd9xJh93f7EfgzoF4K5RnVGhN8FHp8GY7sGWZHsrVrbn2ybb88qbuGUT92FNtkvHHIntpo4hsYH4eN5guZ2tlBgvrzagqNKAUuvsdmdmy3rrNPDWaVBeY0RuaTW8g+2TSAaTGefyaxNMQ2MD5WS7o0np1rDmaBYe+uYQnry6B+YldpI/N7x1GgRYO5LEWpNcZ/MrMNym5fNp6+aBrsHecuwP7BiIj7amYt+5QqQXVmLVkUz0i/aXZy43REruS59TQG1le1mNEWXVBmSWSJXtHgj0tLy2BpMob5AA7Ge219UjzBf+nloUVxpw5EIJwv3c8fuRLLtjU1PMZhHbz+TjwPli3DIkWt5c0ZZU6U12G1pWH81isr2Nkqrae0X4IcDLDRN6hGJNUjb+PJaDJRO7Nfpzqw5nYtmGUwCArafyMenNLVg4pivuHdNZbiPfO9IfRusOlrrJdoPJjNs/2YP88hr0CPfFstn9cNvy3TidW44Hvj6IpTN7223IaYx07OgS7FiyHQDGdA/B2fxUbDqRh+FdOuBkjuU+nG1hf23fSHy5Kw3rkrNRVp0AH3ct1lqT7VN7hTl1X5daVIAnVtw1TNE1EAGAm5sbuna1dJYaOHAg9u7di2XLluHmm2+GXq9HcXGxXXV7Tk4OwsIs75+wsDDs2bPH7v5ycnLk7zVGp9NBp6v/969Wq20TF5+kNvLe7m5tYr3UNrWV9wO1H4w5cjXGHCmJ8UdKYNy1L46+lldmCVgbd6HIcpE0KsADoVJCuokWxxKpDbXkpwMXUFZtwG3Ld+Pbfek4nl0GswjsPVeEQmvlemMK5cp2+2R7qQPJ9nMFlsTU2O7BGN8jtMEEOFDbejivvAYGk7ne96Vke58oP3nW5+aTeag2mOSNBTcPioaXmxql1cYGk5PlNUaUWRNwM6wVR3tSC+QkT0vkllZj9oe78NbG03j0x6OY9tZWjPy/v+RqLWcZTWYcyyzFN3vScNvy3Rjz6ib839rj+Ntne1FRY2zxOluyjsd+OopKvQnnCirx0uoUDH95IzYet1xoW7EnTU4KH8+qv9snNd8St1Le+sMtZ1FtMF2y9RVW6PHFznOo0l+a+9x1tgBTl23F8Jc34tfDmU7FxKG0YgDAtF5h6BXpC7MIrDuW4/DP/5GcjXv+tx9LvjuMv47n4qg1ed87yg/drV0XnEm2l1QasOTbwyivMWLTiTz87dO9AIABHQMQ6W/ZsJFTViPPlK3LZBax8XiOnLhwVu4lSbZbjjl1K9u7hnijV6QvxnYPxid3DIaHW237bB/32jbyqdZOGBF+7k7PlpVmjuc0cJw9X1ABo1mEl5saEX7ucmvmHWcKXPr+tFVSacBTvyRBbzLjt8NZAGqTa9GBnnIC3XZuuy0pYRYXWpswG9TJkvg6mVOO0f/5C6+sPYF5y/dgXXLjMSFtArNNYHvpNPC1vi4ZxVVyhXtkgAc83NTwsL42heV6ubK9qc0uKpWAYbGW6vbl287imre34aXVKXjkhyON/gwAFFXo8dGWsxj32ibctnwP3vjzJK55ZxuOWN9rbUl6kX3idH1KToOfmXR5+n5fOno+vRZ3f7EP3+6zjMYZFWfZxDI2PgQqATiWVSp3gagrObMEj/xwGAAwe3A0RsV1gN5oxht/nsSkN7bIP9cr0hfR1m4baXWS7YfTi3GhqAr+nlp8eecQdAv1wQe3DYKbRoVNJ/Iw8v/+wt1f7MMP+y/gVE5Zo58V0vHduWS7pQp95aEMzP5oFwDLKJC6x/rmDOoYgM7BXiirMeKjLWeRXliJoxklUAnApITQ5u+A6ApkNptRU1ODgQMHQqvVYsOGDfL3Tpw4gbS0NCQmJgIAEhMTcfToUeTm5sq3Wb9+PXx9fZGQ0H4rvvXWj1NPN45nISIiIiIiosaxsr0Nqq1s95AT0rmOVLZbq5v6RfvjUHoxfjyQgYzialwoqkJUgAeevjoBT/+SjOzSaqTmVzRahVtjNOGYNZka7G3fRr6sxmjXurghUmKnuTmgQV5ucFOroDeZkVVcjZgg+ypuKdneKcgLvSL8EOqrQ05pDXaeKUCydWNB32h/DLS23d6dWoCECPt2hVILeR93DUbGdYCbWoWc0hpsPZWPoko9grx0GBnXeOVmXfnlNZjz8W6k5lcgws8dHYO8cDSjBNml1bjlw9146fpeuHFQ/fb9jdl0IhcPfXPIrmOAIACeWjXyy2vwyY7zcNWU4s93nsfx7DL4e2qxZGI3fL0nHSlZpbj3fwfwfzf0xn/WHpdvK1Wm2Tpv3WQxsYel80BmSTWe+jkJ3cN8UFZtRHJmCZIzS9E1xBuv3NDHoSo6iSiKuPfL/diTWoiskup6VfXOKKk04PnfjuHHAxfkrz349UF8tzcdr97YF2F+zVe+HrYm7PpG+0OnVSMpoxRrkrJxy5CYZn/2ZE4Zlnx7SP73M78my0njPlF+cnveEzllEEXRoar7p35JQnZpNWICPWEyi3LyZVDHAAT76KBRCTCaReTWmd1dYzTh462p+GrXeWRaO0CsfnAU4oIdf20q9Zb265KLrWyvm4DRadT47YFRDf6M1Ea+vMaIs9b2xrHBjle1S0J93HE2r0KePW9LruQMsVSBD+oUCLVKQFphJYb9ewNmDojEgjFdHYqbS+XVdSfkzQlJmSUwmUU5IRsdUPvaSRX+qY0k220TZh28dXKnBlG0zH1PK6zEwhUH8OG8QRjb3b6SulJfu5HJto08AET4e6A0uwxXv7UNRrMIjUpAiLX6PdDLDRnFVSis1ENE85XtADC8axDWJmdj9dHaxP+fKTk4llla75ifVVKFV/84id+OZKLGOurBR6eBr4cWGcVVuPG/O/HqjX0Vafe871whjGYRwzoHOfVzUgv5+DAf5JXVoKBCj91nC5367HJUdkk11iZlyRXSo7sFoxvHrrSYySzizT9PoUJvstuQJb12gV5u6Btt6dCz+2xBvW4Z1QYT7vnfflQbzLiqWzBeur43VAKw6kgWXvjtmBwbnYO94OOuRUyg5e+ouhs0dp4pAAAM7xIk/+3XL9ofn/9tCN788yR2pxZi3bEceY2BXm745u5h9V77sy1Itg/tHIjOwV44m1eBFOvflg3Nk2+OSiXgkUndseCrA/hoa6r8/h4SG+hUVxmi9uqxxx7D1KlTERMTg7KyMqxYsQKbNm3CH3/8AT8/P9x5551YsmQJAgMD4evriwceeACJiYkYNszSlWHSpElISEjAbbfdhldeeQXZ2dl48sknsXDhwgYr19sLqbLdq52OviIiIiIiIqJLg5XtbZBtG/kwX6myvflkuzTHfNGEOPi4a5BRXIUfD1yAIACv39QPk3qGyZWMUmKqIf/beR5ZJdUI9dVhhLX1sK9HbSsFqVVzY85ZK5ybS7arVIKcWEzJtq+U1hvNcqVm52AvqFQCxvewVC59sj0VZTVGuGlUcltnwDK3va5Ma1VluLXatW+0ZSbqvE/24KFvDuHW5bvx5a7zTa5TYrS2YT2dW45wP3d8e08ivr57GHY/Ph5TeoZBbzLjkR+O4IPNZxy6v9O5Zbh/xUGUVBngo9MgsXMQHhzXFZv/MRYvz+oDAFi+7RzKmm8mcNGyS6rx+roTAIB/TYnHvMRO+PX+EZjcMxR6kxmLvz2M0mojulgTmRnFVfWqeqWEXlyoN+61diL4fv8FvPh7CpZtOIU/U3KRVVKNrafyce0723Egrcjh9a08mCG/vj/uvwBjC6s6S6oMuHX5bvl9MWdoDBZNiIObRoVtp/Px/G/Jzd5HabVBTij3jfbHFGv72h2n81FQ3nQHiuJKPe76Yh8q9CYM6xyIMF93pBVWyu/d3pF+6BLsDbVKQHGlAXllzXe0+OVQBn49nAm1SsCy2f3w6/0jkNg5CG4aFSb1DINaJcibdqT3g+S7ven4zx8n5EQ7AJzNdy5ZnlunGvxMbgXMjVRFNkVqRd/B2/G5u962yXZr/DnTQl4SKle21z/OnrJuLJGOVX4eWrwyqw86BXmirMaIz3eex9Vvb21xZwtnHblQjC93W45ZGpWASr0JZ/PKkWZNuknJNsBSPQrUT7afkivb7RNpr9/UF49OicfGh6/CxoevwvTe4TCYRNzzv/1IthlTAtS+7p5u6npz7qWqXSlhO7xrB7nDiTQmwLayXdXMhhLbVvbTe4djck/LZ8G7f52ud9uHvjmEHw9cQI3RjJ4Rvlg6szd2PT4eaxaNwrj4ENQYzVj07SF5w4Gr7D1XiBs/2InZH+7CXV/sa7SKuSFpNp+Fk6yPfU1S1iVfY05pNa59ZxueXXUML/6eghd/T8Edn+xxqOuHKIpYczQL05ZtxdLVKS3uHiOKItIKKrE2KUvewHU5KazQ4+eDGfhubzq+25uOfeeaft9vPpmLjOIq+HlocXtiR/joNOgS7GXXQn2Q9b/3na//mbjvXBEuFFWhg7cb3prdH2qVAEEQcG3fCGx4+CrcntgRKgGY3NPyOSS9/3NKa+w6y+ywJtsT64yFSOwShG/vScT6xaNxz1WdMaRTIDy0ahRW6PFenfdXcaVe3uTT2YlNTTqNGusWjcbqB0fh/2b1xoPjumLRhMZb5jdlSq8w9I/xR5XBhA+2nAUATO0V3qL7ImpvcnNzMW/ePHTv3h3jx4/H3r178ccff2DixIkAgDfeeANXX301Zs2ahdGjRyMsLAw//fST/PNqtRq//fYb1Go1EhMTceutt2LevHl4/vnnlXpILiEl2z11rGwnIiIiIiKixnGLdhtk10bemgQqrjSg2mBqtD1yUYVevng/oGMArukbgRW70wAA94zuIlcRde7gha2n8nGmkYRaabUB71gvsC6e0E1u2axVq+DlpkaF3oSSKoPcXr4us1nE+cLaivTmJET44lhWKY5llsoXiwFLVZZZtCRyQqwzmyf2CMWK3WnYap152iPMB1q1yi7ZXrcSWJoHL1XzTu8djr3niqDTqBAT6IlTueV48uckuKlVuGlw0xXpSZmlSM4shbdOgxV3DUO09aK2l06D9+YOwJt/nsRbG0/j9fUncU3fCET4N14dXFJlwF1f7Ed5jRFDYgPx5Z1D4aap3RsTFeCBj7aexZELJfjjggo3N/M8iqKIwxdK0D3Ux67NtqNeXpOCCr0J/WP8cZO1Ml+rVuHtWwbg3i/3Y+PxXAgC8OqNfXHXF/uRX16DU7nl6BftL9+HVGHXKcgLM/pFIqukWp4T7qZRIT7MF52DvbB09XGcyCnD7A924bO/D25yJjRgqUT/9+oU+d+5ZTXYejq/XqVtc8prjLjj0z04mlGCQC83fDRvkJzwGBATgHmf7MFBa3v4piRdKIEoWl6jDt46dPDWoXekH45mlODVdSexdGbvRn/2v5vP4nxBJaICPPDe3IHYdbYA9311AICl+rZTkGVjSacgT5zJq8Dx7LImZ0yXVhvw7K+WDQL3j+2K/jGWx7PirqGoMZrl40W4n7ulpXdJFYDaJI80FuDmQdEortLjj+Qc+T3jKKmFfFSAB7JLqlFlMCGrtBqRTcR/QwoqGm4j3xSpIwBQ29kjtoPjFZcSaTNCQ23kT1s3VsSF1CamZw2MwvX9I7H9TD5e+j0Fx7PLMPvDXXjp+t7y+6cxq49m4aOtZ3Ft3wjcNqxjk11C6hJFEU/9kgxRBK7rF4GM4irsPVeEIxdK5ISsbYcQqco/rbASBpMZWrXKbgZ9XIj9c9Unyh99ovzlf785ux+Kq/TYfroAPx/MQM8IP/l70saEUF/3et0XnpiegLtGd5ZbUdvOdJc+Owor9NBpLY+9uWR71xBvPHV1AnQaFeYOjcGJnDL8kZyD1UlZOJ1bhq7W16a4Uo+91uTn13cNw7DOgXZr+2jeINz5+V5sOpGH//xxHB/cNqjJ3wtY5qUv+vYgiioN6BPph77R/hgbH1Jvg0FTyqoNWPztIUj55/XHcrDtVD6evTYBNw9uvhtGmjXpHBPohcQuQfh6Tzp+P5qFoZ2DMMX6uXk8uxRmEXbHZGdIFdS5ZTWIDvTAwJgArEnKRmZJNU7klCE+zLfBn6sxmpCcWYq3N5zCXyfyAFhaolcbTHj22p4OdeYQRREH0orw1a40bDyRi+JKyw4zX3fLZ22vSL9m7qH1HU4vxmc7zuH3o1nQG2s3e6kEYNM/xtbrzCP5apfl77AbB0bhyasT8NTVCVAJAlQ27RwGdQrER1tTG0zc7zxr+XtndLdg+Hnaz4/yddfiuRm98K+pPeTPfX9PLXx0GpTVGHGhqBJdQ3xQbTBhv3Vz2/AuDXdViAv1wWNTewCwHEuvfnsbfjuShX9N7SF37ZA2mUX4uctdRRylUauQEOFbrxOFswRBwL+mxOPmD3fJX7P925HoSrZ8+fImv+/u7o53330X7777bqO36dixI1avXn2pl3ZZq7Ee0lnZTkRERERERE1hZXsbZNtG3s9DC501CVu3gtSWVBnbKcgTvu5azBkSA41KQJ8oPyyZWFtB1Dm46cr2/246g+JKA7qGeOOGgfbtTKVW8iVNzG3PKatGtcEMtUpAVEDzybaEcMuF12N1ZoCn5tVWqUoX6xO7BMnzfgEgwZr46R3lB51GhYIKfb1qxayS2sp2ALh9eCfseXw8kp+bjHWLR+NvIzoBAB796Qge/PogPtmWiuPZ9eeRA5AvhA+NDaxXPatSCVg8sRuGxAaixmjGG+tPNvm4H/7uEFLzKxDp74H35g6wS7RL9/cva6v07TkCzteZv2pLFEU8/Usyrnt3O657dzsKrUlLRxlNZqyxzup++uoEuySAm0aF9+YOwIPjuuK1G/uif0wAulm7I5zMsZ8pnmozPsBNo8KjU+Lx5uz+eHN2f7xyQ1/8fWQsxnQPwU/3DceY7sHQm8z4yrohpClSy+yuId6YO9SSmPph/4VmfsqewWTG3z/bi4NpxdaZtUPtKgv7xfgDsMRLcWXTz9/B9GIAlqp2yVNXW2ZZfrM3DQebqNiXqp8fGh+HQC83TO0VhtHdLPNse0X6yc999zBL8rDuc1zXB5vPoKjSgC7BXrh/XFf564Ig2G3MCbcmvrPqVLYft86FHxsfjI7WzTFZTifbLbeP8POQu1mcaUHVcGNt5Jui06igsT5nUrK9cwsq20PkZHvzle0SlUrAqLhg/LhgOKb2CoPBJOKfPxyR2yTXZTaLeH3dCdz31QEcTCvGc6uOYca723HIGk+SSr2x0arrvLIaHE4vhkoAHp/eA70j/QEARzNKkG793Ii2qWwP9XGHu1YFo1mUP1dsZ9CHN9P6XqtWYZa1rXXdqtsc6yYLaTNUXSE+7gj380C4n4fdMUVKthdU6GG2Zp8dyMfizpGxuHVYRwiCgPgwX0zuGQpRBN79q7aTyI4zBRBFoFuoNxK7BNVL9KpVAp6Y1gMqAfgjOQf7G6gkruvlNSn4IzkHe1IL8fG2VDzw9UEMfelPPPnz0UY/K+p64bdj8jiXHxcMx5BOgagymPDoj0fx7K/JzXbqkI7/HYM8MbxLEDoGeaK40oAHvz6IQS+uR69n/sC171iO/x9Zq30bczy7FLM/3Ilr39mGa9/Zhrkf78LSNSlY/O0hHEovhq+7Bv/7+1C8Obu/3O5+m3WDm63U/Apc/9529HrmD8x8bwf+OpEHrdpScS0IlrEk/3agwj2zuArXvLMNs97fiZ8OZqC40gA3tQodvHUorTbi1uW7HX6eAUvy/71Np3HXF/suSWV8YYUe//zhMGa8ux0rD2ZAbzSjR7gvxseHIMRHB7MIbDmV1+DPXiiqxMYTltnHt1g/uzRqld37AYD8WXQypxwllfZ/X8kV6U2MHrDdYCcIAqKkVvKFlvf8gbQi6I1mhPjoHDo+9or0w5DYQBjNIj7feU7++plcy/PZJcT5DU2X0tDOQRgfb9lsNyDG36UjPIio/dGbLMdkzmwnIiIiIiKipjDZ3sZU6o1yhWdUgCcEobYFdE4D84QlSdYWvz2tFWC9Iv2w+Z9j8e3diXaJXKn159kG5irnlFbjk+2pAIBHp8TXq7j0dSDZLrWQjw7wgNaBik2pyulYZp1kewMtod21ark9MQD0irT8rE6jli9W7zxbYHc/WdaZ7dLFWEEQEOLrDo1aBUEQ8PTVCbh1WAxEEfj1cCae/+0Ypry5tcGL+/vOWRIzAzsF1PuedN//mmpJkP944AJOZDecKD1fUIE/U3KhUQn44LaBjSYXh3ftgOFdAmEWBfx6uOGWwaIo4vnfjuF/1lb4J3LKcOvHu5tNGNtKza9AjdEMTzc1+tpUtUrctWosmdRdniUrzXA9ZZMIrjaYkGl9rpvraOCl0+CuUZ0B1CZIG3tsH245I7fMfmFGL3km+vpjOfWSEk35bPs57EkthI/OkkiqW13n666VN4ccb+R1kxy2Jkf72TxXQ2IDMWtAFEQRePLnJLmi15bJLCIpwxJXUvWpIAh4eWZvzOgXgYcmxMm37R7q2+xaskuqsXyb5f36zynxTb7fIqzxL71GgGWThZTMjw/zrR1Z4Wyy3boJKNhXJ48ZaMncdqmNfJATbeQFQZArLIus8dDc+IqGSB1E6m5oMplF+bHUrQKXeOk0eHfOAIy0jtzYcrJ+4k0URTz07SG8tdHSNWR673D4umuQnFmKWe/vwBc7z0EURaRklWLSG1sw4fXNDY5ZOGftHhEZ4IEQH3f0ibIc749cKJbHbkQH1CbbLV0SLM+HVM0uJfK7WmfQN2dQR0vnkKSMEru21Lk2le3OCPSUKttrYLbmmB1ZR133j7W8X345lCE/9q3WpOfIrsGN/lxcqA9uHGjpPvDymqaTwZtP5uHznZbjz5KJ3TAvsSM6d/BChd6EL3elYcqbW3HD+zvw88EMGBpJmK8/loPv9tWOcxnYMQDf3D1M3gT32Y5zuOG/O/HOxlPYcjLPrmpaInctCPSEVq3CD/cOx0Pj4xDqa0lI601m+FjfBy+tTsFn1s/xhny67Rx2nS3EkQslOHKhBNtPF+CDzWexJikbKgF4Z84A+T0kfd5ubSDZ/sXOcziYVgyDSZQ3Dq15aDTeuqU//n29pbvHR1tT8Z8/TjT5HK88mIGkjFLoNCrcMDAKP9ybiKTnJmPjP65C3yg/FFcaMPvDXZj7seV/DY0OkOw4k4+py7bilbUnsP5YDm75cJccGy2x6UQuxr+2Cd/ts2zuuq5fBH5ZOAJrHhqF5XcMxq3DOgKonYde17d70yGKlmrypmacd/DWyX/r2L7vy2uMcveRxEYq0hsSE2j5LJPixnZeu6PvtfkjYwEAK3anoVJvGRlzpgXz2lvLczN64uo+4XhsWg+ll0JEbZw8s93Jjh1ERERERER0ZWGyvY3JsFYf+rhr5EpyR5JgUtKyl02b30h/j3otxaXK9rTCynrVdKsOZ6LaYEb/GH9M6FG/Rbe0nqMXivHK2uP4n03Fk+RcQW11syN6WCvbM4qr7JKnjc1fnpAQKv+37WOVWqPWvegtVelG+DVcZS8IAl6Y0Qsr5g/F4gnd0MnaCvav4/YJM1EU5crOwZ0CG308A2ICMLVXGMwi8Mra4w3eRkpcDOgY0Gx73OnWeeCbT9ZPdgDAG+tP4tPt5wAAiybEoYO3DseySjH7w134eOtZ7D1X2GgSSCJ1FegR7luv4q4hcXJle21C9UJRJUTRMkPbkZnb0mt3vqCywaR5tcGEh787jH+vPg5RtFz4T+wShJ4RvogP84HeaMaqI5nN/h7AsuHijT8tnQaeuiYBvaMafs6lNsnHG6lMBizJD6myXaqGlzw2LV5OoH5p3fxg60xeOaoMJni6qeX3IQBE+HtgmU0VKQB0D7N8v7EqaQB488+TqDaYMbBjACbZvC8aIlUw21a2nyuwbLLw0KoRE+gp3ya7gerupuTaVDhLSRhnk+2iKCK/BW3kAdi189Y42FGjrsY2NKUVVqLGaIabRmVXMV6XSiVgTHdLgndPav1W0MmZpVh1OBNatYD/3NAH784dgI3/GIPpfcJhMls6U9zzv/2Y9f4OuQL9j+TsevcjJcylBHpvOdlegvIaS0Ks7uOXN1hZf1aq1He0OjU60AMhPjoYTKK80QSwbSPv3OsVKM1srzDIle0OHHbq6R3lh5FdO8AsWjpKiKIoH1ttN2U1ZNHEOOg0Kuw9V4Q/U3IbvE1RhR6PfH8YAHDH8E54cHwcnp/RCxsevgor5g/FtN5h0KgE7DtfhEXfHsIDKw42mFRevs1SaT5/ZKw8zkWlEvDg+Dj899aB8HRT41B6MV5ddxLzPtmDxd8esvt5k1nEBWuFsjSPO9hHh8UTu2Hbo+Pw44JEbHlkLI48OwkLx3YBADy76hi+25fe4OPaY+3Q8sjk7vj0jsF4eWZvzBkag0EdA/DyzD5ypw3L82j5792pBXYbLQBgk7Vl/Os39cX+Jyfg/VsHyt0fbhkSg+dn9AQAvLfpDJZtONXgWgDI3QUemdwdr97YF4M6BcJNo4KvuxZf/H0oekb4orjSgO2nC7D9dAH+88eJBhPoRy+U4NaPd+NsXgU6eLuhY5AnMkuqcctHu5BZXFXv9s05nl2K+746gKJKA+LDfPDjgkS8Obu/XUcTKQG+62wBzHU2WBlMZnyz1/IazB3asdnfJ81t32vTSn5vaiFMZhExgZ6ICmj8+FOXFCd1k+3OJOzH9whFxyBPlFQZ8KO1k0xtst35DU2XWlSAJ96ZM6DJv8eIiBwhtZFvyRguIiIiIiIiunIw2d7GSBdHbS+shliTGQ21OJZIbeR7NjMPM9zX0lbYYBLltsMS6YLs1F5hDVY/Scn2V9edxHubzuCpX5JxOte+8rZuMqg5fh61FcW2reRTrTPl6ybbx8WHwF2rgq+7Rm61DdReRN5Z56K3tEGhqTajgiBgeNcOeGhCHG4f3gmApULOVlphJfLLa+CmVqF3Mwnyf0zuDrVKwIbjufVaRAO11ZejujadEAJqk0ZHMkrqtYcvrtTj3U2WFsovXNcLiyZ0w1fzhyLQyw3Hs8vw4u8puPG/OzH/831N/g7peZda+jenocr21PzaNseOVM75eWrljQ1HG6huf/LnJPx0MANqlYDnZ/TEE9Mt1WuCIMjjDd7fdAb//OEwnlh5tF4c2nrht2Oo1JswqGMAbhgQ1ejteoRbHldD1eSiKGLN0SxMeG0z8spq4KPT1HuvdfDW4ZHJ3QEAH209Wy/xJlUo9orwg7qZ7KKU0EnJKkVpdf3NCKdzy+Rk2mNT45t9zuU28jaV7SlZlsfZPcwHKpWAUL8WVrZbE9QhPu5ysq2xNuiNKasxyhW9wY20JW+M7dx2qfLXWdJM8ZzSarvXbUNKDgCgf7R/s6+ZlEjdc66wXmcD6dg6Ki4YN1pnunfw1uGdW/pbXz9g3bEcVOpN8qaHBlt3F9gfX2ODvOCt08Bo/X1hvu524wOA2mOodExtaAZ9UwRBwCBrNw/bVvLSfHtnK9uDvGor26WnurmZ7Y2Rxkp8u/cCTueW40JRFbRqAUM7N52AC/fzwN+tlbvv/HW6wST5v1enILesBl2CvfCodaQHUPt58d7cgdjxr3FYMrEb3NQqrE3Oxvf77MdblFUb5I4oDSVcp/QKw9qHRuPJ6T0wvU84AMsmiyKbY31OaTX0JjM0KgER/vYbKbRqFQZ2DESM9bj7j0ndcfdoS9eQl9ccr7fRKre0Gqn5FRAE4NZhHTE2PgSzh8Tg39f3xg8LhuOmwdF2t+8W6o0QHx2qDWYcsHntzxdUIDW/AhqVgIkJoQ0ef+YldsKT1uP2m3+ewgebz9S7jdksysn2hpKmfp5a/HDvcPz31gFYNrufvDlvcwPdI9YmZ8EsAsM6B2LDw2Pw3T2J6BTkiQtFVbju3e347Uhmsy3tJUUVetz1xT5U6k0Y0TUIqx4YiYEd66+vb5Q/PLRqFFTocbLOZ9Bfx3ORV1aDDt5umNjMZigADb7HpL9DGpuz3pgYuY18JSpqjPLfIcO7NP83h0StEvA3699DH21NRY3RhDN5l0cbeSKiS0kvVbZzZjsRERERERE1gcn2NkZqIRpvk0gOa2KeMGC5oC+1XW8u2a5SCYjtIM1tr02IGU1muSKzsQuy4XIrdsDf05J4r9veXK5sD3K8Cquhue1SO/q6yfYO3jr8uGA4vr93uF1SqU+UPzzd1CiuNNglS6W22RH+jiWEpMe+71yRXTvfvdaESa9I33rJrLq6BHtjem9L4mT1Ufvnx2gyyzNYR3VrvNWxJNzPHRGeIkSxNkkv+etELkxmEd1DfXCbtZ1t9zAf/Hr/CDw8sZvcnWDrqbwGE7YSqYV/3dbqjelmTdJlllSjzHq/5/Kd62gAAL2tbdiPZBTbfd1gMmON9Xl7f+4AzEvsZJfMmdEvEm5qFTKKq/Ddvgv4ancanv8tpcHfselELlYfzYZaJeCF63o1WbkvVbY3VE3+0dazWPDVAWSXVqNjkCc+un0QPBu4KHfDwGh4aNW4UFQlb4CRHL1QbH3cTW/WACzJwNgOXjCLlurGur7ekw6zCEzoEYJBDlT2SZ0dMm0S6dKoBGmTgfT+zimtrlel2ZQ8a2V7sF1lu3OzkqUW8t46TbPvr7ps237WPV44StrQVG0wo7TaKH99TZKlunyqtcNEUxLCfeGt06Cs2lhvDIU03qLu3GVBEHDPVV3wye2D0SXYC3eNisXK+0YAsGygKii3b2t/vk7nEJVKkMdpAJYq9LqkxHxqncr2ujPomyIlGvfZVN1Kn0chzraR97I814UVeohoeWU7YOl0EuyjQ355DZ7+Jdm61oAG35t13TkyFm4aFQ6nF9dr2Z9TWo2VBzMAAK/c0KfRarcQX3c8OD4OD0+ytIR/blUy0gpqq663n86H0SwitoNXo8fGmCBPzB/VGe/OGYCEcF8YzSJWJ9V+bpwvkDbgeTS74UMQBPxzcnd08HZDYYUe20/bb9jYbT2W9AjzlTfPNXd/0niErTb3JVW1D+oUAB/3xu9n/qjO8kaF//xxQt6YIzmdV46SKgM8tOpGP3883NSY0iscM/pF4mrrhoRNJ+p3I5A+V2cNiIKfhxahvu5YcdcwdA72Qm5ZDe5fcRC3f7oXB9KKmky6m8wi7v/6ANILqxAT6Il3bhnQ6AYeN40Kg62bbOp21fnBWg0+c0CU3SifxkjvscPpxfLfHvJxw8lkuzSzfeeZAjyx8iiMZhFRAR5NdudoyI2DohHso0NaYSXe/euMfPzpehm0kSciulSkynbObCciIiIiIqKmMNnexuxooN2nVDmYXWeesERKlkb4uSPIgRbMtXPbaxNiSZmlKKsxwtddI1eP1fXg+Dj8+/re2PLIWDx7jaVF7KrD9tViUpLcmaRr3bntFTVGuZV1Q8mznhF+dlXtgKXCT6qMk6rBymuMKLMmzsIaaSNfV7dQbwR5uaHKYMJha3IUAPaftyQpHG1ZOrmnJTn357Ecu68fyShBWbURfh7aZivkJT38Lc/vX8ftEwxS++MJCfYt/6MCPPHA+Dh8fPtgxAR6wizWtuqtSxTF2mS7g5Xtfp5ahFirj09ZK5ilTRaxDnY0AIDe1iTh0Qv2le0H04pRoTchyMsNE3rUrwgM9tHhizuH4J9TuuPBcV0BADtO5zfYjv4d64zsO4Z3ajSuJVLS+UROmV1lstFkxsdbLTOQ7xwZiz8WjbZr+W7Lw00ttxNfk2S/0UKq4O/jQLIdgPw7djQwD1j62ox+kQ7dV7h1s0l+eY2cyDlurWyXnpdgbx1UAmA0iyio00WhKdKc8xAfnXxsySurQUlV4xs86pJ+nzPz2iXelyDZ7q5Vy8lHaRZ5Tmm1/L6Z0iu82fvQqFUYaG0FbdtK3nYjU2NJs7HxIdjw8Bg8MT0BYX7u8mar7XVe+1R5E1Jt0qyPddMKgAaTadJrciK7DJV6Y7Mz6Bsy2Fp1u/98kbwRQxofEOpkJ4JAa2V7QYUe0tusJTPbActxf7a1GltKTEqtz5vTwVuH663vH+n9Lfli5zkYzSKGdApssKK5rvmjOmNIbCAq9CYs+e6QfPyQktJXObCxCgCu7RcBAPj1UO2IjLRCy7E1xsFjq0atwjTrZq9fD9uP2pDisLnKf1ujulmS7badFv6yJrvHdK8/bqauBWO6YGDHABjNYr3Kf6nqv1+0v0MdKaRj6/bTBagx1ra1b2y2eYS/B1Y/OAqLJsTBTa3ClpN5mPneDkx/axt+P2J/fJasTc7B9tMF8HRT46N5gxDg1fQxKbGB43RBeQ02Wj+vZzXRTcVWl2AvBHhqUWM0IymzBMWVennDVt1NOs3pH+2PMF93lNUY8bM1lpy9D8CykUnqTvD2xlMwi4CPTuN09xEiosuVySzCYLb8DcKZ7URERERERNQUJtvbENsLxrZtQ0P9mq5sl5J4PR1M3nbuIM3wra1slxLUwzoHNVo9F+Stw5yhMYgO9MTEhFC4a1VIza9AUoblgrDZLNpUtjuRbK9T2S7dR6CXG/w9HU++1Z3bLrXD9tFp7BJyTREEAcOs97PjdO3Fc6myXUqmNWd0tw7QqgWcza+wm1+99WRtW9jmqhQlCQGW5OiWU/lyoktvNGOzNZHTUEJaIre2bqA6GrAkRQsq9FAJqLeBoSl1W8lLr1lHJzoa9I70B1DbXl2yzVrBP7xrh0Yr0Yd1DsJ9Y7piyaTu6B7qA6NZxJ8p9hsbLhRVYt/5IggC5NbKTekY5AV3rQrVBrNcwQdYKjpzy2oQ4KnFo1Pim628nmKtgl6TlC1vRDGazHLixNFNFlI81022F1bo5er7xpL+dQV5ucFNo4Io1h5HpA4QUkW/Rq1CiI/zc9vlNvK+Ovi4a+VOHINf/BPxT63Bs78mN3sfUmW7s/PaAcDbpo187EXMEg6Vx3VY1iLNTO8f49/kGApbDb3fjmZY5qn7eWgd3tAizc3eatMuWxRFOS472hxfbeMppoFke49wXwR5uSG/XI/5n+9zaAZ9Q/fhoVWjtNqIU7nlEEXRZma7s5XtUht5vTyzvYWF7QCA2UNi7CrjRzownkMitZL/IzlbngNepTfhq91pdt9vjlol4LUb+8Jbp8G+80X4bl86RFGUk+1j45tPSgPANX0tyfY95wrlzy9ptExHJ16va633sy45x27Wupxsj3U82T7C+nwmZVpGmVQbTPJn7FgHku1Abbv/FbvT7DYy7bNuYpNaqDcnIdwXIT46VBlMdu+xpmabu2vVWDShG9YuGoVZA6Kg06hwLKsUC1ccqFeNDgBfW+eszx8Z69BnonSc3n22QH5svx7OhNEsondk/Y2BjREEwa6DxK6zhRBFSwcKZ7tH+Hu6YdMjY/DGzX0xsGMAPLTqeiMCHHVt3wgkdg6SRz50DvFu8eYYIqLLTaW+9jOSle1ERERERETUFCbb25DGLhg310Zeag07yMFEsFTpaNvqWbro7OhsUC+dBuOtSd5fDlna7eaUVaPGaJktK81hd4RU2X46twx6o1lud+xMK3qgtgX8ntRCGE1meT51uIMt5CVSBdjOs5bEeFGFXp5B7Wiy3cddKydCbavbt522zmt3sPoSAGK9LdW7hRV6HLFurNidWoDyGiM6eOvQ16aytS4pqbL7bP2kAgAkW5O2XYK9nWrfHRdqqYo9aW1J3Vjb/6ZI7a8ziqvs2mVvsVZQOjLTHrBPbttaZR1xMDQ20KGEoFoloHto/bntUjvgGf0iHWoHPC4+BG5qFc7mVciV/6dyy1FjNMNHp3F4I4oUPylZpXYznKXXsluot8MVhoIgyG3iM4urUFJpQEax5f1hmwyqndvecBeNuvRGM4qsHQWkRL2UWNSbzKg2mLFid5pdwq8h+eVSsr0Fle02LcOd6axQV2id4+yao5Z4muZAVbtkqE2yXdpoIW2WGNY5sMkxBrakhPG20/ny/eSV1aBSb4JKAKIDbCvbm062e7pp8M6cAVCrBHktnTt4ObzZB7BUkPeL9gdgSZCW1xjlC9RSC35HScn2Sr0JSdbjWUtntgNApL+HnPT189Cil4ObWQBL7I+K6wCzCHy6/RwA4McDF1BcaUCMdVOZo6IDPbF4oqWd/BvrT+JQejGyS6vhrlU5nNyO9PfAoI4BEEXgtyOWimSpjXxDr21jBsQEINLfA+U1RrkjSmGFHiesm6Mc7dACWN7X8WE+EEXgpwMXsOtsAWqMZoT7uaNbqGPdEab1DoefhxYZxVXYYrOBRKpsd2QUBmA5jkldAqSNDIBjs807B3vjtZv6Yvfj4+VNDU//kmQ31z6nCtidWgSVYNnE4YieEb7w0WlQWm2Uu8RInxk3DHSsql0ibTp4ff1JPPzdoWYfU1PctWpc3z8KPy4YjpQXpjj1mtsSBAEvXNcTWrXlPdrlIjY0ERFdbir1lg5oapUAnQN/4xMREREREdGVi2eNbUhjc307WVsGpxVWyi2OJWaziL3WObpDHLyg31me2W5JauuNZvmid2Ij89obIlXP/XYkC2azKCfJowM9oXGgJawk0t8Dvu4aGEwiTuWWyfO/pdnyjkqI8IWvuwZlNUYkZZYiy1oZ6GgLeYl0cfvA+WJUG0xyK+nOwV4OtemXSIkaqeK6vMaIg2nFAIBRcY4/z2oVMKKL5bWVEidSAn9Cj5AmE3hDYy2P5ciFElTp6yc8peRAcy3W65Iq20/mlKHaYEKmdWODM+MDfNy18sYPqTtDSaUBR6zt+0c6+BxN7W1Jtm85lYfymtp521IL5Wv7OtZqHait8j5u3YRQUmnA+mTLc+1o4sTHXSu/vquts+elVvm9Iv0cTrgG++jkZNYum80SO+SNMY7HEFA7kz2rpFqeKR7p72E3uzlcSjiXOVbZnmdNkmvVAgI8Lffz7+t7Yddj47H9X+PQwVsHvcksJ1Ubk18utZFXrrJd2iyQU1aNgvIa7E61PM9THJjXLukT5Q+dRoWCCr3c0WJnC16vIbGBcNOokFVSLW+Kko6vUQGedps+YgI94W997hvrLJHYJQhPX50g/9uZee0SqZX8vnNFcvW/j7vGofnotnzdNfJs+a/3WKqIVRf5l8pdoztDoxJwff9IpzYRAJbREJa1pOGxn47ggy1nAFhGTzh7X7cOi0FUgAdyy2rw0DeHAFg+z53ZyCS3krcev6SK+xgnNp+pVAKu7mvfSl76OyEuxNvp95k0K/3F31PwjLVTxZjuwQ5XOLtr1fLx86vd5wFYOmKkFVZCECzdIxwlbeb5y2ZuuzOzzf093fDijF4I8nLDqdxyfLKtdoTAjhxLII6LD0WEv2N/N2jUKrkt//pj2UjKKEFyZim0akH+G8lR4+NDoFYJqDaYUWH9vHbm+NNauob44MFxcQCc+9uFiOhyJ20c9HRTs2sHERERERERNYnJ9jZErs7qan/BOMTHHf1j/CGKta2NJSdzy1BcaYCnm9rhij4pwZlfXoPSagMOXyhGlcEyI9vRSjXAcrHdx12D7NJq7DlXWDuv3cmKdEEQ5Or25MxSuVq6s5OJM7VKwFB5fmq+3IY33MkWrLEdvBDm6w69ddaylDB1tHOARKr833++CAXlNdh1pgBGs4iOQZ5OtXAGamf+rj6ahcIKfe289iZayANAdKAHwnzdYTSLOJhWf2671Lpfev4d1U2ubC9Dan4FRNFSfR/UzHzbuvpYY1ZKRu88mw+zaKmeczTZ0T3UB7EdvKA3muU5uadzy5CSZUl4THUiWRFvndueYq1sX3UkE3qTGfFhPujpxHMkJUjWWqvtj2QUA3B8XrtEStDatpK3HfngDOn53HwyT25DL82pl4Q5Wdkubf4J9tbJFykFQUCYn7tcpQvUjmFoTG1lewuS7dYRER5aNUJ9nHuv25LbyJdUWzYQiZbuC868V900KgyIsTzm3amFqDGa5FbZzlSoumvVGGKtRJXGKsgjOupsaBEEAc/P6IW/j4hF/+jGj1HzEjviFmu1rrOxAwADrevZeaYAyZmW96uzLeSl9f64YDhm9KtNRGouMts+rHMQ9j05QZ4v7YyrugUjIdwXVQYTvt6TjvTCKvjoNC1qu63TqPHI5O4Aatu/O9pCXjKtdzjUKgFHLpTgy13ncc5a2e7MiA6gdjPchuO5KKs2yG3XHd2UZ2vBmK645yrLKA6p0t6Ree225lhbyW88novM4irstx4Tuof6wNdd29SP2hnRtQPUKgFn8yqQXljZotnmfp5aPDbNEitv/nkKGcVVqDaYsCfXcgybO8yxqnaJtEnxrY2ncc072wBYPpubm/deV1yoD3Y9Nh5rF43C2kWjsPOxcU5vqmotD4yPw/4nJ+C6fo5vXiMiutzJyXYnNsURERERERHRlYnJ9jaipNLQ5AVjKWG4+qh9sl26gD6wYwC0DlaT+7hr5fbTZ/Mq5Nnkw7oEObWrX6dRY0pPy7pe+j1Frpzr2IJWzgnhliTkYz8dlSvxnJn7LpESWm9tOIVv9ljm7jrbRl4QBLlC7p7/7cdPBy1t8sc5mTSJ9PdAQrgvzCKwbMMpvLQ6BYBzM4UlY7p1gIdWjVO55bjqlb+QUVwFd61Knqfb1GORqu52NTC3PcUac47OkpbEWSvbc0prMP2trQAsHRicrQrpbW2BL7XHl1vIO9FmXxBqE+prkywbI349ZImh0XHBTiU8pAp/KRlt2w7Ymcc2MSEUGpWA49llWHnwAg6lFwMAejuZbJfiUKrczC21VDoLgqUtuTNuGBgFlQCsPJiBD7acBVBbyS+Rk+0OzmzPtc5aD24k6Sq1Rd5/vn7s2ZKS7cEtaSNvTbZ36uDlcNeAhkiJ4y92nZerd6c60UJeIiUzfzmUiTVHs1FtMKODt87panKps8NW63sitYnNTNf2jcDT1yQ0+fgFQcC/r++FTf8YgzkOtsi2NSDGH34eWmSXVstV26FOtpCXhPi4Y9ns/vhq/lBM7hkqz/S+GP6ebk51VJEIgoBv7hmG9+cOwL1XdcG4+BC8eH0vOa6cdU2fCLuNOWO6Ofe50cFbh0nWrihP/pyEkirLmIboAOeS7QnhvugSbNmENHXZVqyyfq62JNmuVgl4bGoPvH5TX7hpVPB11zT72VNXl2BvJHYOglkE7vx8H763HludbW/u56HFQOuGlu/3X5Bnm3cJ9nJqtvmsAZEY3CkAVQYTpryxBfd8dRCVJgFR/u4Y7cTnDwBc1y8CV3ULhpebWp5tPqeFMR3so0N8mC/iw3wR7mRXntYWZLOpioioPbCtbCciIiIiIiJqCpPtbcSu1IImLxhLSZ/dqQV28613n7VWqzl5wbqztTry271p+PWwJZncktmgd4/uDH9PLY5mlGClNSntzNxuiZQAMJlFaNUCEjsHYVQ355PSMwdEoW+0P6oNZmRKle1+zldfSknOKoMJfh5aLJ3ZG5N7Ot/OdYI1afLFzvNIza9AB28d/jYi1un7CfbR4au7hiI+zAdl1lbpI7sGw8OBi0ND5DnS9nPbK/VGpFqrZZ1tI+/rrsX8kbHwcdfAbE0uNDU7vjFSpbdU2b7Nmlh0dkOC9P7463gePt9xDj9bk+3X9nOujW+8dX75haIq3PLhLhxKL4ZaJWCGk9V8/p5u8uaMxd8eRlKGJXnfJ9LfqfsZFhsEQQBO55Yjt7RaTrr3jPCFv6dzienhXTrg0SnxACCPWIivU9ke3sJke0gjs+OlWcz7zhfBLAVKAwouoo28VHnex4lZ3Q2RXntRBDQqAQM7BuDGQc7NXAZq2yzvSS3Eom8PAbAcT5xNUkkJv22n81FabcB5qbL9IubSC4LQ4k0JPu5afDV/qF13hpCL6CQAWKqUP7htkNyRRCm+7lpM7R2Of02Nxyd3DHb6/W5LZU1MA5aEtzPt3yWv3tgX/5oaL7fb7xbqDS8nk/+CIODJqxPg667BhaIq+b0qjRZpiZkDorDx4avw+4OjWrQZ4ZEp3eHnoUVKVqnchUTakOMMqXPIWxtO4dEfjwBwfqyGIAj4v1l90DnYC2U1Ruw4Y/lb6uZBUU6PDwjy1uHzvw/B0Wcn488lo/HbAyOd2jBGRETKkGa2e+qYbCciIiIiIqKmtaw0i1rEYDI7XF1elzTXt7GZo9GBnugZ4YvkzFKsP5aD2UNiIIoidlurlZ1NVnQO9sbu1EJ5Zq4gtKziOi7UB78sHIH5n+/DqVxL+3dn5nZLJvcMxYr5Q+HtrkH3MB/oNC276OHnocXP9w3H/vNF+HLXeWSXVmNSgvNJ8skJYfiuYzq6hnjjkcndW5QEBCwdCd7ZeAoigFuHdsQ/Jne3m5HtjAExAVj1wEh8uj0Va5Kycf+4rg793FBrsv1gWjFqjCb5uT2eXQZRtCRKgxtJljblyasT8Pi0HjhfWIlzBRVOt9kHLMkolWBJ7g5fugGZJdXQqAQMc3LjR69IX3QM8sT5gkq5Ktldq2q2zX5d/p5uCPdzR1aJJbGtEoAHxnVt0fPznxv7otuWs/hmbxryy/WI8HOXk2eO8vPUomeEL5IySvHsqmSYzZavO9ouua67R3fGsaxS/GLdjFCvsl2a2V5aAzjwtsmzJuUbS7b3jPCFu1aF4koDzuaXo2uIT73b6I1mueV2S9rIT0oIxdd3DUPPSOc2jNQ1tHMQvrsnEVq1gB7hvk7N2bY1sGMAPpo3CJ9uT5Xb/4/t7nzirUe4D+JCvHEqtxy/H8mSZ7a3ZDPTpdIr0g8r7xuBr3afx7d70zFzAFtKN2RkXAesun8kQlpY+e+l0+Deq7rg7lGdcTC9GBFOdmeRjO0egt2PT8CqI5lYeSAD3cN85O4VLRXlZIW9rQExAdj48FX49+rj+PHABahVQosq7ecldkRJlQHvbzojV/63ZLNg52Bv/Ln4Kmw/k48vd55DakY2ZrdgfIBEpRIaPMYREdHlqbaynZdMiIiIiIiIqGk8c3SRg2nFuPWTfZiYEIpXbujTaBVacaUeq45kIdhbJ1dnGUxmbDphqfJqqjpraq8wJGeWYk1SNmYPiUFqfgXyy2vgplE5PQv6+v6R2H46HyE+OvSO8sPY7iEtav8OWNrG/3TfcDyxMgmp+S1LugqCgOEtSPY3dl+DOgXKVbUt4eepxQ8Lhl/0WnqE++L7exPh465Ft9CLvwivVatw9+guuHt0F4d/pkuwN4K83FBQocfjPyUhwFOLC0VVOGCd4e7svHZbKpWA2A5eLU4Aeuk0uHFgNL7bny53IhgSG+h01aQgCPjkjsH45VAmjl4oxsmcctw8ONrpalAA+PuIWHy3Lx2Te4bhlqExiHRwdnxdfh5a/GNydzw4Pg47zuQjOtD5NvsA8LfhsfjHD4ftRki0dI6vVM1ZXm2EINR2uJBIbYuzS6vldshNqa1sbziBp1Wr0C/aH7vOFmLfuaIGE1Fv/HkSuWU1CPRyqzdD3hEqldDoJiVntSTxV5cgCJiYEIqJCaE4nVuOc/kVGN/DuVbi0v3cMDAKS9ccx/f70uVZ2c7O7r7U1CoB8xI7YV5iJ0XXcblzdmREQ1TWDgsXw8NNjZsGReOmQS1PIl9KQd46vHZTX9w+vCMq9aYWtUrXqFVYPLEbZvSLwIu/pyC/vAajurWsklylEjAqLhjDOvlj9erV8Pds2WY4IiJqeyrYRp6IiIiIiIgc5HSmacuWLfjPf/6D/fv3IysrCytXrsR1110nf18URTzzzDP46KOPUFxcjBEjRuD9999HXFycfJvCwkI88MADWLVqFVQqFWbNmoVly5bB29u5mbVtyTf7LkBvMuP3o1k4k1eOj+YNQnSgJ0RRxIWiKhy5UIK/TuRi1eFM1Bgt5anvzx2Aqb3D8daGUzhXUAk/D22Tc1Cn9ArHq+tOYseZfJRUGeSq9n7R/k5XYQ6JDcSWf45t+QOuw8ddi7du6X/J7q89Gdjx4hN4F0MQLJXivx/Jwo8HLth9T60ScG1f51qtX2r/d0MfPHl1DyRllOJMXjnGtKAKGLBsKlgysdtFr+eu0Z1x1+jOF30/EjeNCmO6O59slcwaGIXOwV548uckJGeWwk2twuCLSAq7a9VYfsfgBr8nVeJWG8yoNNZ+3WQWsSYpC4GebnabYuRkexMVvIM6BmLX2ULsPVeE2XVmhe89V4j/bj4DAPj39b3h496+El1dQ7ydntVu6/r+kfi/tcdxIK0YgOX9KrXNJ2rL+rRg7EhdnYO98UkjxzIiIqLmSJXtXky2ExERERERUTOcTrZXVFSgb9+++Pvf/46ZM2fW+/4rr7yCt956C59//jliY2Px1FNPYfLkyTh27Bjc3S3VjXPnzkVWVhbWr18Pg8GAv/3tb7j77ruxYsWKi39ElyGTGdhgnT/q5abG8ewyjHl1E9zUKphEEXprcl0S7KNDXlkNHv7+MIoqDXj3r9MALMmmplqMdw3xltsK/+vHIyittrRPHXoJqjGpfXt8Wg906eCFGpMlFoO83NAnyh+9Iv1aNHv3UvNx1yKxS9Alq1Bub/rHBOCXhSPwy6FMBHhpW+01c9eqEejlhsIKPUosY9SRlFGCJ35OwuH0YgDAg+PjsGh8HFQqAbllTbeRB2pnMu8/Xyh/rVJvRHJmKRZ/ewiiCNw4MEru9EG1QnzdcVW3YPx1Ig8AEBXg0eJRJURERERUq7LGOrOdbeSJiIiIiIioGU6fOU6dOhVTp05t8HuiKOLNN9/Ek08+iRkzZgAAvvjiC4SGhuLnn3/G7NmzkZKSgrVr12Lv3r0YNGgQAODtt9/GtGnT8OqrryIiQtkq2tZwqlRASZURHbzd8PPCEVi44iAOpxejymzZLa9RCYgP90G/aH9c3z8KfaP8cMene7HtdD4eX3kUgKWCcXqf8GZ/199GxOLxlUexJqm2pfTQWCYoqWmR/h5YMqm70sugi6BRqzBrYFSr/54wX3cUVuhRrBewYk86nvstBWYRcNeqUG0w460Np5CUUYJQX3eczi0H0HgbeQAY0DEAggCcK6jEZ9tTsepIFg6mFcFsbVMfFeCBp69JaPXH1VbdMDBaTra3dNQHEREREdmTKts9WNlOREREREREzbik27RTU1ORnZ2NCRMmyF/z8/PD0KFDsXPnTsyePRs7d+6Ev7+/nGgHgAkTJkClUmH37t24/vrr691vTU0Nampq5H+XlpYCAAwGAwwGw6V8CJecwWDA4QLLHObx8SEI9dbi+7sGI6ukWk4mBXu7QWfT5l00m/DGjb0x87+7kF5UhXA/dzw5tZtDj/XGAeHo0sEDK/akY3VSNjp469A7wuuyf56oZaTXla8vuUqorxuOZQFnygR8tuYEzCIwtWconpjWHdvPFODJX45ho7WTBwD4uGsQ6efWaIx6qIHuId44nlOOZ1cdk78e4qND3yg/LB7fFe5qxnhjruoaAD8PDUqqjOgY4N5unyce60hJjD9SAuOu/eFr2YaYTKg8lw4A8MrJBEwJgJpJdyIiIiIiImrYJU22Z2dbqqlDQ0Ptvh4aGip/Lzs7GyEh9vOJNRoNAgMD5dvUtXTpUjz33HP1vr5u3Tp4el7e82nNInCkyHJiHlhxHqtXn3P4Z+d1BDa5qTAitBzb/lrv1O8d5wkMHwgIMGDTn+uc+llqe9avdy4+iFqqplgFQIWNGQLMMCPO14zJPhnYvy0D7gDu7wFsz1HBRwvEeIvo6mvE1o1NH4M6aVU4DhUCdSKGh5oxqIOIAJ0RQAVO7c/EKVc8sDZscKAKf2aooCk6h9WrU5VeTqvisY6UxPgjJTDu2o/Kykqll0CO+Okn4KGHUNnvRqDneHh+9QXw4nxg2TKggTF6RERERERERG1iANljjz2GJUuWyP8uLS1FdHQ0Jk2aBF9fXwVX1rwdp3JRvusQfN01eODmCU7P072jdZZF7YTBYMD69esxceJEaLVapZdDV4DUTWexI+c0zBCgUQl46/aR6BribXeb+5y8z0kmM5YUVSEm0BNqlXDpFnuFmGIWkV5chZgADwhC+3z+eKwjJTH+SAmMu/ZH6s5Gl7GffgJuuAEQRVQOtoxB8jRUAxkZlq//8AMT7kRERERERFTPJU22h4WFAQBycnIQHl47XzwnJwf9+vWTb5Obm2v3c0ajEYWFhfLP16XT6aDT6ep9XavVXvYXn/48UQAAmNAjBJ7u9R8D0aXQFt4L1D5EBtR2E5k/shN6RAZc9H1qtUC3cB4fL0bXUDell+ASPNaRkhh/pATGXfvB1/EyZzIBDz0EiJZZbxVam2S7KAKCACxaBMyYwZbyREREREREZMe5MutmxMbGIiwsDBs2bJC/Vlpait27dyMxMREAkJiYiOLiYuzfv1++zcaNG2E2mzF06NBLuRzFmc0i1h2zbCyY3DO0mVsTEV3+uoX6AAACdSIWXBWr8GqIiIiIiC6BrVuBCxfkf1Zak+1ehmrLF0QRSE+33I6IiIiIiIjIhtOV7eXl5Th9+rT879TUVBw6dAiBgYGIiYnBokWL8OKLLyIuLg6xsbF46qmnEBERgeuuuw4A0KNHD0yZMgV33XUX/vvf/8JgMOD+++/H7NmzERERccke2OUgr7wGfh5aFFdWY0SXIKWXQ0R00fpG+2P5vAG4kLQHnm5tYhIJEREREVHTsrLs/qkxm6BVifDUVzd5OyIiIiIiIiKnK9v37duH/v37o3///gCAJUuWoH///nj66acBAP/85z/xwAMP4O6778bgwYNRXl6OtWvXwt3dXb6Pr776CvHx8Rg/fjymTZuGkSNH4sMPP7xED+nyEerrjt8fGI6n+pug01zSJgJERIoZHdcB/uz6TkRERETthc0YPAD4/OcX8OpQE8acP9jk7YiIiIiIiIicLkscM2YMROscs4YIgoDnn38ezz//fKO3CQwMxIoVK5z91W2WD8fzEREREREREV2eRo0CoqKAjAx5brsdQbB8f9Qo16+NiIiIiIiILmsstyYiIiIiIiKiK5daDSxbZvlvQbD/nvTvN9+03I6IiIiIiIjIBpPtRERERERERHRlmzkT+OEHIDLS/utRUZavz5ypzLqIiIiIiIjosuZ0G3kiIiIiIiIionZn5kxgxgxgyxagtBT4/Xdg9GhWtBMREREREVGjWNlORERERERERARYEusjR1r+e+RIJtqJiIiIiIioSUy2ExEREREREREREREREREROYnJdiIiIiIiIiIiIiIiIiIiIicx2U5EREREREREREREREREROQkJtuJiIiIiIiIiIiIiIiIiIicxGQ7ERERERERERERERERERGRk5hsJyIiIiIiIiIiIiIiIiIichKT7URERERERERERERERERERE5isp2IiIiIiIionVq6dCkGDx4MHx8fhISE4LrrrsOJEyfsblNdXY2FCxciKCgI3t7emDVrFnJycuxuk5aWhunTp8PT0xMhISF45JFHYDQaXflQiIiIiIiIiC47TLYTERERERERtVObN2/GwoULsWvXLqxfvx4GgwGTJk1CRUWFfJvFixdj1apV+P7777F582ZkZmZi5syZ8vdNJhOmT58OvV6PHTt24PPPP8dnn32Gp59+WomHRERERERERHTZ0Ci9ACIiIiIiIiJqHWvXrrX792effYaQkBDs378fo0ePRklJCZYvX44VK1Zg3LhxAIBPP/0UPXr0wK5duzBs2DCsW7cOx44dw59//onQ0FD069cPL7zwAh599FE8++yzcHNzU+KhERERERERESmuTSbbRVEEAJSWliq8kuYZDAZUVlaitLQUWq1W6eVQO8P4IiUw7sjVGHOkJMYfKYFx1/5I567SuaySSkpKAACBgYEAgP3798NgMGDChAnybeLj4xETE4OdO3di2LBh2LlzJ3r37o3Q0FD5NpMnT8aCBQuQnJyM/v371/s9NTU1qKmpqfd7CwsLYTAYWuWxXSrSe7CgoIDvQWoVjDFyNcYcuRpjjpTE+CMlMO7ap7KyMgDNn8u3yWS79OCio6MVXgkRERERERGRY8rKyuDn56fY7zebzVi0aBFGjBiBXr16AQCys7Ph5uYGf39/u9uGhoYiOztbvo1tol36vvS9hixduhTPPfdcva/HxsZe7MMgIiIiIiIicpnmzuXbZLI9IiIC6enp8PHxgSAISi+nSaWlpYiOjkZ6ejp8fX2VXg61M4wvUgLjjlyNMUdKYvyREhh37Y8oiigrK0NERISi61i4cCGSkpKwbdu2Vv9djz32GJYsWSL/22w2o7CwEEFBQTyPpyseY4xcjTFHrsaYIyUx/kgJjLv2ydFz+TaZbFepVIiKilJ6GU7x9fXlG4xaDeOLlMC4I1djzJGSGH+kBMZd+6JkRTsA3H///fjtt9+wZcsWu/PpsLAw6PV6FBcX21W35+TkICwsTL7Nnj177O4vJydH/l5DdDoddDqd3dfqVs9f7vgepNbGGCNXY8yRqzHmSEmMP1IC4679ceRcXuWCdRARERERERGRAkRRxP3334+VK1di48aN9dq4Dxw4EFqtFhs2bJC/duLECaSlpSExMREAkJiYiKNHjyI3N1e+zfr16+Hr64uEhATXPBAiIiIiIiKiy1CbrGwnIiIiIiIiouYtXLgQK1aswC+//AIfHx95xrqfnx88PDzg5+eHO++8E0uWLEFgYCB8fX3xwAMPIDExEcOGDQMATJo0CQkJCbjtttvwyiuvIDs7G08++SQWLlxYr3qdiIiIiIiI6ErCZHsr0+l0eOaZZ3gBgloF44uUwLgjV2PMkZIYf6QExh1dSu+//z4AYMyYMXZf//TTT3HHHXcAAN544w2oVCrMmjULNTU1mDx5Mt577z35tmq1Gr/99hsWLFiAxMREeHl54fbbb8fzzz/vqofhUnwPUmtjjJGrMebI1RhzpCTGHymBcXdlE0RRFJVeBBERERERERERERERERERUVvCme1EREREREREREREREREREROYrKdiIiIiIiIiIiIiIiIiIjISUy2ExEREREREREREREREREROYnJdiIiIiIiIiIiIiIiIiIiIicx2U5EREREREREREREREREROQkJtuJiIioXauoqFB6CURERERERETkIJ7HExFRW8JkO9EVShRFmM1mpZdBV5AzZ87gwIEDSi+DrjAnTpzAwoULkZmZqfRSiIiIiIguCs/jydV4Hk9K4Hk8ERG1NUy2X4by8vJQXFys9DKoHTt58iQefvhhzJgxAy+99BIKCwuVXhK1c4cOHcKAAQN4kk4udfjwYfTv3x9ffPEFNmzYoPRy6Apz4sQJPPHEE7j99tvx5ptv4vDhw0oviYiIWhnP5ak18TyeXI3n8aQEnseTkngeT0QtxWT7ZebYsWOIjo7Gvffei7KyMqWXQ+3Q0aNHMWrUKKSlpSE8PBzPPvss3nvvPaWXRe3Y4cOHMWLECMyfPx/z589Xejl0hTh8+DASExNx//33Y9GiRVi+fDlycnKUXhZdIZKTkzFs2DCcPHkS7u7ueO2113DnnXfiww8/VHpp1I5lZWXh2LFjSi+D6IrFc3lqTTyPJ1fjeTwpgefxpCSex5MSeB7ffjDZfhnJzs7G/PnzMXz4cKxfvx533303T9LpkkpNTcU111yDO++8Ez/88AM+/PBDPPbYY8jOzobRaFR6edQOnTx5EkOHDsWSJUvw2muvwWAw4LfffsPy5cvx+++/cwYXtYoDBw5g9OjRWLx4MV555RUMGjQIhw8fRmpqKgCw9Sa1qvLycixZsgT33HMPvv/+e3zwwQfYvn07zp49i2eeeQYvv/yy0kukdiglJQVdu3bFfffdh+PHjyu9HKIrDs/lqTXxPJ5cjefxpASex5OSeB5PSuB5fPvCZPtlQhRFHDx4ELGxsXjllVfw22+/Ye3atTxJp0vGZDLh+++/x7Rp0/Cvf/1L/npmZiYOHTqEESNG4P7778fvv/+u4CqpPTEajXjnnXfg7e2NAQMGAACuu+46PPHEE3jhhRdw7bXX4s4778ShQ4eUXSi1KxUVFRg5ciTmz5+Pl156CQAwZ84c9OvXD0899RRMJhNUKv75Q61HEAQUFRWhf//+AICqqirExMRg3Lhx6NmzJ1atWoU//vhD4VVSe5KXl4d7770XkyZNwqlTp7BgwQKkpKQovSyiKwbP5ak18TyeXI3n8aQEnseT0ngeT67G8/j2h59SlwlBEDBw4EDMnz8fgwYNQmJiot1JemlpqXxbURQVXCm1VWq1GrNnz8a8efPg6+sLAHjxxRfx+eef46qrrsKcOXOwa9cuvPnmm2zRRJeERqPBwoULMXPmTLzyyiuIiYkBAHzzzTdISUnBzp07sWXLFrz11lsKr5TaEy8vLyQlJeG1114DYLlACQC33HIL0tLScOTIEQD8LKXWIYoiysrKkJ6ejqysLACAh4cH0tPTcfz4cdx2220oKyvDypUrFV4ptSepqamIjY3Fs88+i3379uHEiRO47777eKJO5CI8l6fWxPN4cjWex5MSeB5PSuJ5PCmB5/HtjyDyU+qyYjaboVKp5P/fsWMHpk+fjilTpuDDDz+Eu7s7li9fjri4OIwfP17p5VIbJIoiBEFAfn4+XnjhBUydOhVTpkwBACQlJaFPnz5Yu3YtJk2apPBKqb04fvw4XnzxRRQXF+O1115D9+7d5e/9/PPPmDVrFk6cOIGuXbsquEpqb6TPUUlpaSkSEhIwa9YsLFu2TMGV0ZXg9ddfxz/+8Q/Mnz8f4eHheP311zFnzhx88MEH+Pbbb/H4449j37598PPzY4UGXbTS0lKcOnUK/fv3h0qlQkZGBgYNGoT4+Hi899576NGjBwDLRUu1Wq3waonaL57LU2vieTy5Gs/jSQk8jycl8TyeXInn8e2PRukFXMnOnTuHnTt3IicnB2PHjkXXrl3h5eUFo9EIjcby0gwfPhyrV6/GtGnTcM8998DDwwNffvkljh07pvDqqS1oLMbMZjM6dOiApUuXwtPTE6IoQhRFmM1m9O/fHxEREUovndoo25gbM2YMunTpgvj4eDz77LM4ffo0OnfuDKD2YpHBYED37t0RHBys8MqpLWvsWCf9QWoymeDr64tHH30Uy5Ytwx133CG3BiO6WHWPe3FxcViyZAm8vLzw0UcfISgoCE8++SQeffRRAEBOTg78/f0REBCg8MqpvfD19cXAgQMBAHq9HpGRkdi/fz8GDhyI++67D++99x66dOmCN998E5GRkZg7d67CKyZq+3guT62J5/HkajyPJyXwPJ6UxPN4UhrP49sfVrYr5OjRoxg3bhx69OiBo0ePIioqCt27d8f777+P4OBgu5N0ANiyZQvGjBmDgIAArF+/Xp6bRNSY5mJMmnckCIL8M0888QQ2bNiA3377DR06dFBw9dQWNRRz3bp1w3vvvYfQ0FDo9Xq4ubnZ/cwjjzyCpKQkfPfdd/Dx8VFo5dSWOXKsk3aA7t27F9dffz2eeuop3HPPPQqvnNqDuvEXGRmJHj164N1330VISAjKysrg5uYGnU4n/8wDDzyAnJwcfPHFF9DpdHafw0SOKCkpgUqlgo+Pj3zR25Z0HpGVlYUBAwYgISEBQUFB+PXXX3Ho0CHEx8crtHKi9oHn8tSaeB5PrsbzeFICz+NJSTyPJyXwPP4KIJLLlZeXiyNHjhTvv/9+saqqSjQYDOKHH34ojho1SuzTp4+YnZ0tiqIoGo1GURRFsaamRrz33ntFHx8fMTk5WcmlUxvhaIxJjh07Jj755JOir6+vePjwYYVWTW1ZS2LuiSeeEH19fcWjR48qtGpq6xyNO4PBIP/MrbfeKvbo0UM0GAyi2WxWaunUDjQWfyNHjhR79+4tZmVliaIoiiaTSRRFUUxJSREXLVok+vr6ikeOHFFy6dSGJScni4MHDxbffPNNsaSkpNHbSecR586dEwVBEAMDA8UDBw64aplE7RbP5ak18TyeXI3n8aQEnseTkngeT0rgefyVgcMlFFBVVYX8/HyMHTsW7u7u0Gg0+Nvf/obnnnsOPj4+mDlzJoqKiqBWqyGKIg4dOoStW7diw4YNSEhIUHr51AY4GmMAkJqaiscffxwrV67Eli1b0KdPH4VXT22RMzF35swZLF68GN9++y02b96MXr16Kbx6aqscjTuNRgOTyQTAshv5999/h0aj4U5kuiiNxd/zzz8PX19fzJo1C0VFRVCpVCguLsa6detw6NAhbN68Gb1791Z6+dQGpaWl4ZZbbsGZM2fw0Ucf4ZtvvkFpaWmDt1Wr1aiqqsJrr70GLy8vbN26lW03iS4BnstTa+J5PLkaz+NJCTyPJyXxPJ5cjefxVw4m2xXg5+cHf39/7NixQ/6aRqPBmDFj8Pjjj8NoNOLtt9+W20n06NEDW7ZsweDBgxVcNbUlzsRYbGwsXnjhBaxduxZ9+/ZVcNXUljkTc126dMHLL7+MDRs2oF+/fsotmto8R+MOAFQqy588Q4YMQWxsrCLrpfbFmeOev78/5s6di59++onHPWoRs9mM9evXIyoqCjt37sSwYcPwn//8x+5EXawzHSwjIwO7d+9mko/oEuK5PLUmnseTq/E8npTA83hSEs/jyZV4Hn9lYbJdAWq1GiNHjsTWrVuxdetW+euCIGDatGno168f1q1bJ+/U8/HxQWBgoFLLpTbI0RiT9OrVC1FRUUosldoJZ2OuX79+iImJUWKp1I44E3fc/U6XmrPHvaCgIAQEBCixVGoHVCoVRo8ejXvvvRfdunXDxx9/jKuuuko+US8pKal3nIuOjsbGjRsxZMgQhVZN1P7wXJ5aE8/jydV4Hk9K4Hk8KYnn8eRKPI+/sjDZrgCVSoUnnngClZWV+Oc//4n9+/fLbXEAYPz48SgqKpJbNRE5y9EYKy4uVm6R1K4w5kgJ/DwlJfG4R64WFxeHa665Rv637Yn6t99+K++M/9///oeSkhLodDp4eXkptVyidol/e1Br4t8W5GqMOVICP0tJSTzukavxPP7KwWS7AvR6Pfz9/fHXX38hPz8fDzzwAH766ScYDAaIooitW7ciKCgIOp1O6aVSG8UYI1djzJESGHekJMYfKUm6IGR7or5ixQrMnz8fixcv5sUholbCYz+1JsYXuRpjjpTAuCMlMf5ISTyPb98Ese5QALqkpFltEpPJBLVajczMTFRXVyMwMBA33XQT8vLykJOTg169emHv3r3466+/OAuEHMIYI1djzJESGHekJMYfXQ7MZrM8txIADAYDtFotAOCuu+7C8uXL4eXlhc2bN2PAgAFKLZOo3eCxn1oT44tcjTFHSmDckZIYf3Q54Hn8lYPJ9lZiNBqh0WjkN5PZbIbZbIZGo8H58+cxfPhw/Otf/8IDDzyAiooKHDhwANu2bUNISAiuuuoqdO3aVemHQJc5xhi5GmOOlMC4IyUx/kgpjV0YysvLw6lTpzB8+HC7ry9atAj/+9//sHXrViQkJCi1bKJ2gcd+ak2ML3I1xhwpgXFHSmL8kVJ4Hn9lY7K9FaSkpODtt99GXl4eIiIiMHv2bCQmJgIAMjIy0L17d9x66614//33IYqi3c4WIkcwxsjVGHOkBMYdKYnxR0pw5MLQU089hXvvvVf+ma+//hpz587F3r17MXDgQAVXT9T28dhPrYnxRa7GmCMlMO5ISYw/UgLP4wngzPZLLjk5GSNGjIAoiggODkZOTg5Gjx6Njz/+GJWVlcjOzsbChQvx3nvvQRAEHtDJaYwxcjXGHCmBcUdKYvyRElJSUvDggw/ixhtvxOLFi7Fz506oVCpoNBpkZGSgZ8+euOaaa3DPPffY/dzMmTNx/vx5nqATXSQe+6k1Mb7I1RhzpATGHSmJ8UdK4Hk8SVjZfgnV1NRg7ty5iIiIwFtvvQUAyMrKwrhx43D27Fn8+9//xsMPP1yvnQSRoxhj5GqMOVIC446UxPgjJSQnJ2PUqFG4+eabIQgCCgsL8eOPP+L999/HnDlzkJKSgu+++w5Lly7lRSGiVsBjP7Umxhe5GmOOlMC4IyUx/kgJPI8nWxqlF9CeGAwGnDp1ChMnTgRgaR8RHh6OESNGoHPnznjkkUcQHx+P6dOnK7xSaqsYY+RqjDlSAuOOlMT4I1erqanBM888g1tvvdXuwtDhw4excOFClJSU4OGHH8aAAQN4YYiolfDYT62J8UWuxpgjJTDuSEmMP3I1nsdTXdxOcQnpdDp07NgRe/fuRWlpqTyT4ZdffsGiRYtw++23Y+nSpaisrFR6qdRGMcbI1RhzpATGHSmJ8UeuJl0Y6tmzJwD7C0MTJkzAI488gt9//50n6EStiMd+ak2ML3I1xhwpgXFHSmL8kavxPJ7qYrL9EjCbzQAArVaL8ePHIzk5GVdffTUef/xx9OzZEzfccAMmTpyIyZMnIyMjAwaDQeEVU1vDGCNXY8yREhh3pCTGHymFF4aIlMNjP7Umxhe5GmOOlMC4IyUx/kgpPI+nuthG/iIUFxfD398fKpUKRqMRGo0GDz30EAICArBx40acPHkSL730Eh566CEAljegr6+vwqumtoQxRq7GmCMlMO5ISYw/UorZbIZKpZIvDH3zzTe4+uqrMXLkSLz11lu47bbbMHHiRBQUFGDTpk28MER0CfHYT62J8UWuxpgjJTDuSEmMP1IKz+OpMYIoiqLSi2iLUlJSMH36dNx66614/vnnAQB6vR5ubm7ybQwGA7RarfzvBQsW4OzZs/j555/h4eHh8jVT28IYI1djzJESGHekJMYfKUG6MARAvjAEAF988QU2btyI8vJyjBo1Sr4wtHLlSjz77LPYsmUL/Pz8lFo2UbvBYz+1JsYXuRpjjpTAuCMlMf5ICTyPp+awsr0F0tPTMWfOHGg0GqxcuRIajQZPP/003Nzc7N5o0v/v2LEDK1aswIoVK7B161Ye0KlZjDFyNcYcKYFxR0pi/JES6l4Y0mg08oWhefPmYd68efUuDK1btw5hYWF2F4+IqGV47KfWxPgiV2PMkRIYd6Qkxh8pgefx5Agm250kiiK+/vprREREYNGiRdi+fTu+/vprAMDTTz8NjUYDk8kEtVoNQRBgNpuRn5+PAwcOYMuWLejTp4/Cj4Aud4wxcjXGHCmBcUdKYvyREnhhiEhZPPZTa2J8kasx5kgJjDtSEuOPlMDzeHIUk+1OEgQB8+bNQ2hoKCZOnIi+ffsCAL7++muIoohnnnkGarVant2gUqlw7bXXYuzYsfDx8VF49dQWMMbI1RhzpATGHSmJ8UeuxgtDRMrjsZ9aE+OLXI0xR0pg3JGSGH/kajyPJ2dwZvslkJWVhQ8++ADffvstZs+ejWeeeQYA8PPPP+Oaa66BWq1WeIXU1jHGyNUYc6QExh0pifFHrS07Oxt//PEHbr/9duTm5uK9996rF2/ShSFJWVkZLwwRtSIe+6k1Mb7I1RhzpATGHSmJ8Uetjefx5ChWtjsgKysL6enpKCoqwoQJE+SDtNlshiAICA8Px9133w0A+OabbyCKIkpKSrBs2TJcuHABERERSi6f2gDGGLkaY46UwLgjJTH+SGlhYWG4/fbbAQAhISG45557AFjiDQCeeeYZqFQquwtDPEEnujg89lNrYnyRqzHmSAmMO1IS44+UxvN4chST7c04cuQIrr32Wuh0OuTk5CA8PBxPP/00Jk+ejMDAQJjNZgBAREQE7rnnHoiiiOeffx7+/v7Yu3cvD+jULMYYuRpjjpTAuCMlMf5ICbwwRKQsHvupNTG+yNUYc6QExh0pifFHSuB5PLWUqvmbXLny8vJw8803Y+7cuVizZg2OHTuGvn374oUXXsBbb72FvLw8u/YQ4eHhSE1NhY+PD7Zt24aBAwcquHpqCxhj5GqMOVIC446UxPgjJRw5cgSJiYm47bbbcPPNN6NXr174+uuvUVhYCJVKBVEUIYqifGHopptuwvPPP4/PP/+cF4aILgEe+6k1Mb7I1RhzpATGHSmJ8UdK4Hk8XQwm25uQl5eH6upqzJw5E507d0ZERAS++eYbXHvttfjpp5/w2WefobKyEoIgAACWL1+OVatWYdOmTUhISFB49dQWMMbI1RhzpATGHSmJ8UeuxgtDRMrjsZ9aE+OLXI0xR0pg3JGSGH/kajyPp4vFZHsTDAYDjEYjKisrAQBVVVUAgJdffhljx47F+++/j9OnT8u3v/rqq3HgwAH0799fkfVS28MYI1djzJESGHekJMYfuRovDBEpj8d+ak2ML3I1xhwpgXFHSmL8kavxPJ4uliCKoqj0Ii5nQ4YMgbe3NzZu3AgAqKmpgU6nAwAMHjwYXbt2xddffw2TySTPbyByBmOMXI0xR0pg3JGSGH/kSocPH8bVV1+NFStWYNSoUaiqqoKHhwcA4KGHHsKqVavw888/o0+fPgCAnJwcVFZWIjY2VsllE7U7PPZTa2J8kasx5kgJjDtSEuOPXInn8XSxWNluo6KiAmVlZSgtLZW/9sEHHyA5ORlz5swBAOh0OhiNRgDA6NGjUVFRAQA8oJNDGGPkaow5UgLjjpTE+COl9e3bF+Hh4XjmmWcAAB4eHqipqQEALFu2DEFBQVi6dCkAwGQyITQ0lCfoRBeJx35qTYwvcjXGHCmBcUdKYvyR0ngeTxeLyXarY8eOYebMmbjqqqvQo0cPfPXVVwCAHj16YNmyZVi/fj1uvPFGGAwGeTZDbm4uvLy8YDQawQYB1BzGGLkaY46UwLgjJTH+SAm8MESkLB77qTUxvsjVGHOkBMYdKYnxR0rgeTxdahqlF3A5OHbsGEaPHo158+Zh0KBB2L9/P/72t78hISEB/fv3x7XXXgsvLy/cd9996NOnD+Lj4+Hm5obff/8du3btgkbDp5GaxhgjV2PMkRIYd6Qkxh8p4dixY1i8eDHy8vKQk5ODV155BXPnzpUvDD3wwAO48cYbsWLFCvmE3PbCkFqtlme+EZHzeOyn1sT4IldjzJESGHekJMYfKYHn8dQarviZ7YWFhbjlllsQHx+PZcuWyV8fO3Ysevfujbfeekv+WllZGV588UUUFhbC3d0dCxYsQEJCghLLpjaEMUauxpgjJTDuSEmMP1JCQxeG3n77bezevRv9+/dHZWUlNmzYgPvuuw/e3t71Lgz16tVL6YdA1Kbx2E+tifFFrsaYIyUw7khJjD9SAs/jqbVc8Vt/DAYDiouLccMNNwAAzGYzVCoVYmNjUVhYCAAQRRGiKMLHxwf/93//Z3c7ouYwxsjVGHOkBMYdKYnxR65WWFiIxYsXY+7cuXj99dcBAHPmzMGBAwfw6aefon///vD09MQ111yDMWPG2F0Y2rNnDy8MEV0CPPZTa2J8kasx5kgJjDtSEuOPXI3n8dSarvhke2hoKL788kvExcUBAEwmE1QqFSIjI3H+/HkAgCAIEAQBpaWl8PX1lb9G5AjGGLkaY46UwLgjJTH+yNV4YYhIeTz2U2tifJGrMeZICYw7UhLjj1yN5/HUmhghgHxAN5vN0Gq1ACxvqtzcXPk2S5cuxccffwyj0QiAB3VyDmOMXI0xR0pg3JGSGH/kStKFoVGjRgGwXBgCgMjISPkkXBAEqFQqlJaWyj/HmCO6tHjsp9bE+CJXY8yREhh3pCTGH7kSz+OpNV3xle22VCoVRFGU3zzSG+zpp5/Giy++iIMHD0Kj4VNGLccYI1djzJESGHekJMYfuYqjF4Z0Oh0efPBBaDQanqQTtRIe+6k1Mb7I1RhzpATGHSmJ8UeuwvN4ai08QtUhHdQ1Gg2io6Px6quv4pVXXsG+ffvQt29fpZdH7QBjjFyNMUdKYNyRkhh/5Eq8MER0eeCxn1oT44tcjTFHSmDckZIYf+RKPI+nS43RUof0ptJqtfjoo4/g6+uLbdu2YcCAAQqvjNoLxhi5GmOOlMC4IyUx/sjVeGGISHk89lNrYnyRqzHmSAmMO1IS449cjefxdClxZnsjJk+eDADYsWMHBg0apPBqqD1ijJGrMeZICYw7UhLjj1yl7oWhF198kReGiBTCYz+1JsYXuRpjjpTAuCMlMf7IVXgeT5eSIIqiqPQiLlcVFRXw8vJSehnUjjHGyNUYc6QExh0pifFHrrRv3z4MGTIESUlJSEhIUHo5RFcsHvupNTG+yNUYc6QExh0pifFHrsTzeLoUmGwnIiIiIiK6RHhhiIiIiIiIiKjt4Hk8XSwm24mIiIiIiIiIiIiIiIiIiJzEme1EREREREREREREREREREROYrKdiIiIiIiIiIiIiIiIiIjISUy2ExEREREREREREREREREROYnJdiIiIiIiIiIiIiIiIiIiIicx2U5EREREREREREREREREROQkJtuJiIiIiIiIiIiIiIiIiIicxGQ7ERERERERERERERERERGRk5hsJyIiIiIiIiIiIiIiIiIichKT7URERERERERERERERERERE76f5qYDKaI59j2AAAAAElFTkSuQmCC",
"text/plain": [
""
]
@@ -469,7 +468,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f26b5895fbaa462188c8d844cf933fff",
+ "model_id": "9877feebe9f8469a88d93efa49cfe816",
"version_major": 2,
"version_minor": 0
},
@@ -673,7 +672,7 @@
" metrics = {\"MAE\": MAE(), \"MSE\": MSE(), \"SMAPE\": SMAPE()}\n",
" results = dict()\n",
" for name, metric in metrics.items():\n",
- " results[name] = metric(y_true=test, y_pred=forecast)[\"segment_c\"]\n",
+ " results[name] = metric(y_true=test, y_pred=forecast)[segment]\n",
" return results"
]
},
@@ -686,8 +685,8 @@
"source": [
"def test_transforms(transforms=[]):\n",
" \"\"\"Run the experiment on the list of transforms\"\"\"\n",
- " classic_df = pd.read_csv(\"data/example_dataset.csv\")\n",
- " df = TSDataset.to_dataset(classic_df[classic_df[\"segment\"] == segment])\n",
+ " df = pd.read_csv(\"data/example_dataset.csv\")\n",
+ " df = df[df[\"segment\"] == segment]\n",
" ts = TSDataset(df, freq=\"D\")\n",
" train, test = ts.train_test_split(\n",
" train_start=\"2019-05-20\",\n",
diff --git a/examples/205-automl.ipynb b/examples/205-automl.ipynb
index e50936021..9a422ab84 100644
--- a/examples/205-automl.ipynb
+++ b/examples/205-automl.ipynb
@@ -585,7 +585,6 @@
}
],
"source": [
- "df = TSDataset.to_dataset(df)\n",
"full_ts = TSDataset(df, freq=\"D\")\n",
"full_ts.plot()"
]
diff --git a/examples/206-clustering.ipynb b/examples/206-clustering.ipynb
index ccc07f4a0..d061c5413 100644
--- a/examples/206-clustering.ipynb
+++ b/examples/206-clustering.ipynb
@@ -84,7 +84,7 @@
" tmp[\"segment\"] = f\"{2*i}{j}\"\n",
" tmp[\"target\"] = np.random.normal(2 * i, sigma, len(tmp))\n",
" df = df.append(tmp, ignore_index=True)\n",
- " ts = TSDataset(df=TSDataset.to_dataset(df), freq=\"D\")\n",
+ " ts = TSDataset(df=df, freq=\"D\")\n",
" return ts"
]
},
diff --git a/examples/207-feature_selection.ipynb b/examples/207-feature_selection.ipynb
index 67e75eeba..922d04a80 100644
--- a/examples/207-feature_selection.ipynb
+++ b/examples/207-feature_selection.ipynb
@@ -110,7 +110,6 @@
}
],
"source": [
- "df = TSDataset.to_dataset(df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"ts.plot(4)"
]
diff --git a/examples/208-forecasting_strategies.ipynb b/examples/208-forecasting_strategies.ipynb
index c1ffa2b5b..67425ffe8 100644
--- a/examples/208-forecasting_strategies.ipynb
+++ b/examples/208-forecasting_strategies.ipynb
@@ -101,7 +101,6 @@
],
"source": [
"df = pd.read_csv(\"data/example_dataset.csv\")\n",
- "df = TSDataset.to_dataset(df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"\n",
"ts.plot()"
diff --git a/examples/209-mechanics_of_forecasting.ipynb b/examples/209-mechanics_of_forecasting.ipynb
index e6f94699b..e2b5308f4 100644
--- a/examples/209-mechanics_of_forecasting.ipynb
+++ b/examples/209-mechanics_of_forecasting.ipynb
@@ -180,7 +180,6 @@
}
],
"source": [
- "df = TSDataset.to_dataset(df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"ts.plot()"
]
diff --git a/examples/301-custom_transform_and_model.ipynb b/examples/301-custom_transform_and_model.ipynb
index f09a5eeb0..156780aea 100644
--- a/examples/301-custom_transform_and_model.ipynb
+++ b/examples/301-custom_transform_and_model.ipynb
@@ -201,7 +201,6 @@
"source": [
"df = pd.read_csv(\"data/example_dataset.csv\")\n",
"df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n",
- "df = TSDataset.to_dataset(df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"\n",
"ts.head(5)"
diff --git a/examples/302-inference.ipynb b/examples/302-inference.ipynb
index dbcf08e9c..b34c8a328 100644
--- a/examples/302-inference.ipynb
+++ b/examples/302-inference.ipynb
@@ -195,7 +195,6 @@
}
],
"source": [
- "df = TSDataset.to_dataset(df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"ts.plot()"
]
diff --git a/examples/303-hierarchical_pipeline.ipynb b/examples/303-hierarchical_pipeline.ipynb
index 394be802b..586d17788 100644
--- a/examples/303-hierarchical_pipeline.ipynb
+++ b/examples/303-hierarchical_pipeline.ipynb
@@ -167,7 +167,7 @@
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
- "100 15664 100 15664 0 0 16533 0 --:--:-- --:--:-- --:--:-- 16646\n"
+ "100 15664 100 15664 0 0 8240 0 0:00:01 0:00:01 --:--:-- 8270\n"
]
}
],
@@ -1286,22 +1286,6 @@
"hierarchical_df.head()"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, the dataframe could be converted to ETNA wide format."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "hierarchical_df = TSDataset.to_dataset(df=hierarchical_df)"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -1317,7 +1301,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -1326,7 +1310,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -1335,7 +1319,7 @@
"HierarchicalStructure(level_structure = {'total': ['Hol', 'VFR', 'Bus', 'Oth']}, level_names = ['total', 'reason'], )"
]
},
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -1358,7 +1342,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -1457,7 +1441,7 @@
"2006-05-01 10027 46793 3126 26947"
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -1477,7 +1461,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1486,7 +1470,7 @@
"'reason'"
]
},
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -1515,7 +1499,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -2110,7 +2094,7 @@
"2006-05-01 15 47 "
]
},
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -2133,7 +2117,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -2218,7 +2202,7 @@
"4 2006-05-01 1958 city NSW Hol"
]
},
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -2264,7 +2248,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -2896,7 +2880,7 @@
"2006-05-01 2988 3164 1396 630 "
]
},
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -2911,7 +2895,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -2920,7 +2904,7 @@
"HierarchicalStructure(level_structure = {'total': ['Hol', 'VFR', 'Bus', 'Oth'], 'Bus': ['Bus_NSW', 'Bus_VIC', 'Bus_QLD', 'Bus_SA', 'Bus_WA', 'Bus_TAS', 'Bus_NT'], 'Hol': ['Hol_NSW', 'Hol_VIC', 'Hol_QLD', 'Hol_SA', 'Hol_WA', 'Hol_TAS', 'Hol_NT'], 'Oth': ['Oth_NSW', 'Oth_VIC', 'Oth_QLD', 'Oth_SA', 'Oth_WA', 'Oth_TAS', 'Oth_NT'], 'VFR': ['VFR_NSW', 'VFR_VIC', 'VFR_QLD', 'VFR_SA', 'VFR_WA', 'VFR_TAS', 'VFR_NT'], 'Bus_NSW': ['Bus_NSW_city', 'Bus_NSW_noncity'], 'Bus_NT': ['Bus_NT_city', 'Bus_NT_noncity'], 'Bus_QLD': ['Bus_QLD_city', 'Bus_QLD_noncity'], 'Bus_SA': ['Bus_SA_city', 'Bus_SA_noncity'], 'Bus_TAS': ['Bus_TAS_city', 'Bus_TAS_noncity'], 'Bus_VIC': ['Bus_VIC_city', 'Bus_VIC_noncity'], 'Bus_WA': ['Bus_WA_city', 'Bus_WA_noncity'], 'Hol_NSW': ['Hol_NSW_city', 'Hol_NSW_noncity'], 'Hol_NT': ['Hol_NT_city', 'Hol_NT_noncity'], 'Hol_QLD': ['Hol_QLD_city', 'Hol_QLD_noncity'], 'Hol_SA': ['Hol_SA_city', 'Hol_SA_noncity'], 'Hol_TAS': ['Hol_TAS_city', 'Hol_TAS_noncity'], 'Hol_VIC': ['Hol_VIC_city', 'Hol_VIC_noncity'], 'Hol_WA': ['Hol_WA_city', 'Hol_WA_noncity'], 'Oth_NSW': ['Oth_NSW_city', 'Oth_NSW_noncity'], 'Oth_NT': ['Oth_NT_city', 'Oth_NT_noncity'], 'Oth_QLD': ['Oth_QLD_city', 'Oth_QLD_noncity'], 'Oth_SA': ['Oth_SA_city', 'Oth_SA_noncity'], 'Oth_TAS': ['Oth_TAS_city', 'Oth_TAS_noncity'], 'Oth_VIC': ['Oth_VIC_city', 'Oth_VIC_noncity'], 'Oth_WA': ['Oth_WA_city', 'Oth_WA_noncity'], 'VFR_NSW': ['VFR_NSW_city', 'VFR_NSW_noncity'], 'VFR_NT': ['VFR_NT_city', 'VFR_NT_noncity'], 'VFR_QLD': ['VFR_QLD_city', 'VFR_QLD_noncity'], 'VFR_SA': ['VFR_SA_city', 'VFR_SA_noncity'], 'VFR_TAS': ['VFR_TAS_city', 'VFR_TAS_noncity'], 'VFR_VIC': ['VFR_VIC_city', 'VFR_VIC_noncity'], 'VFR_WA': ['VFR_WA_city', 'VFR_WA_noncity']}, level_names = ['total', 'reason_level', 'region_level', 'city_level'], )"
]
},
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -2946,7 +2930,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -3578,7 +3562,7 @@
"2006-05-01 2988 3164 1396 630 "
]
},
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -3597,7 +3581,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -3606,7 +3590,7 @@
"['total', 'reason_level', 'region_level', 'city_level']"
]
},
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -3624,7 +3608,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -3633,7 +3617,7 @@
"'city_level'"
]
},
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -3651,7 +3635,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -3750,7 +3734,7 @@
"2006-05-01 10027 46793 3126 26947"
]
},
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -3853,7 +3837,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -3870,7 +3854,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -3887,7 +3871,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -3902,7 +3886,7 @@
" [0, 0, 0, ..., 0, 1, 1]], dtype=int32)"
]
},
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -3921,7 +3905,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -4553,7 +4537,7 @@
"2006-05-01 2988 3164 1396 630 "
]
},
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -4575,28 +4559,28 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.3s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.7s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.0s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.0s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.4s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.7s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.8s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.0s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.0s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s finished\n"
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.3s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.6s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.8s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.8s finished\n",
+ "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s finished\n"
]
}
],
@@ -4710,7 +4694,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -4727,7 +4711,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@@ -4753,7 +4737,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -4789,7 +4773,7 @@
" [0. , 0. , 0. , 0.04254726]])"
]
},
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@@ -4808,7 +4792,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -4816,20 +4800,20 @@
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.3s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.4s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.4s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s finished\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n",
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- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s finished\n"
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s finished\n"
]
}
],
@@ -4858,7 +4842,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -4877,7 +4861,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -4913,7 +4897,7 @@
" [0. , 0. , 0. , 0.04470731]])"
]
},
- "execution_count": 31,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -4932,7 +4916,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -4947,13 +4931,13 @@
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s finished\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s finished\n"
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s finished\n"
]
}
],
@@ -4981,28 +4965,28 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
+ "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.3s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.4s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.4s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.3s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.5s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.5s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s finished\n",
- "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s finished\n"
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.0s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s finished\n"
]
}
],
@@ -5030,7 +5014,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
@@ -5301,7 +5285,7 @@
"VFR_WA 17.416115 15.014164 15.509316 14.953232"
]
},
- "execution_count": 34,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -5314,7 +5298,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
@@ -5327,7 +5311,7 @@
"dtype: float64"
]
},
- "execution_count": 35,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -5366,7 +5350,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
@@ -5382,7 +5366,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
@@ -5481,7 +5465,7 @@
"2006-05-01 5 5 5 5"
]
},
- "execution_count": 37,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -5508,7 +5492,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
@@ -5581,7 +5565,7 @@
"4 2006-05-01 5 Hol"
]
},
- "execution_count": 38,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -5603,7 +5587,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
@@ -5702,7 +5686,7 @@
"2006-05-01 5 5 5 5"
]
},
- "execution_count": 39,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -5721,7 +5705,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
@@ -5736,7 +5720,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
@@ -5745,7 +5729,7 @@
"'df_level=city_level, df_exog_level=reason_level'"
]
},
- "execution_count": 41,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -5764,7 +5748,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
@@ -6396,7 +6380,7 @@
"2006-05-01 2988 3164 1396 630 "
]
},
- "execution_count": 42,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@@ -6416,7 +6400,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
@@ -6543,7 +6527,7 @@
"2006-05-01 5 10027.0 5 46793.0 5 3126.0 5 26947.0"
]
},
- "execution_count": 43,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
@@ -6561,28 +6545,28 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.5s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.7s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.7s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s finished\n"
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.5s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.5s finished\n",
+ "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s finished\n"
]
}
],
diff --git a/examples/304-forecasting_interpretation.ipynb b/examples/304-forecasting_interpretation.ipynb
index 7d0596afd..eb2df6054 100644
--- a/examples/304-forecasting_interpretation.ipynb
+++ b/examples/304-forecasting_interpretation.ipynb
@@ -194,7 +194,6 @@
"source": [
"df = pd.read_csv(\"data/example_dataset.csv\")\n",
"df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n",
- "df = TSDataset.to_dataset(df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"\n",
"ts.head(5)"
diff --git a/examples/305-classification.ipynb b/examples/305-classification.ipynb
index b6c15cbb4..1d6ea20cc 100644
--- a/examples/305-classification.ipynb
+++ b/examples/305-classification.ipynb
@@ -3,11 +3,7 @@
{
"cell_type": "markdown",
"id": "cb9e5d62",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"# Classification\n",
"\n",
@@ -17,11 +13,7 @@
{
"cell_type": "markdown",
"id": "d3ed2b1d",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"This notebook contains the simple examples of timeseries classification using ETNA library.\n",
"\n",
@@ -41,11 +33,7 @@
"cell_type": "code",
"execution_count": 1,
"id": "832a3c88",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"!pip install \"etna[classification]\" -q"
@@ -55,11 +43,7 @@
"cell_type": "code",
"execution_count": 2,
"id": "4a2ba68a",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"import warnings\n",
@@ -71,11 +55,7 @@
"cell_type": "code",
"execution_count": 3,
"id": "c085ebe2",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"import pathlib\n",
@@ -94,11 +74,7 @@
{
"cell_type": "markdown",
"id": "d56594f0",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"## 1. Classification ",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -253,11 +205,7 @@
{
"cell_type": "markdown",
"id": "ae38212d",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"### 1.2 Feature extraction ",
- "image/png": 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AYDAYYDAY3PpcjYXwuZvq53cXj5d7eLzcx2PmHh4v9/B4uYfHy308Zu7xxfHiuSEiIiIiIvIerwZczGYzpk+fjiFDhqBnz55O1/n000/RrVs33HLLLbL5c+bMwe23347Q0FCkpaXhL3/5C0pLSzFt2jQAQF5eHmJjY2XviY2NRXFxMSoqKhASEuKwr3nz5mH27NkO89PS0hAaGlrXj9kopKen+7oJAaWm43WiSIFD1xW4M8EMjcpz+80rByI0QKjXQ6Wexe+X+3jM3MPj5R4eL/fweLmPx8w9DXm8ysvLG2xfRERE9WEyW/DOhmMY1DEat3eNrfkNREREfsCrj21TU1Nx+PBh7Nixw+nyiooKrFy5Eq+++qrDMum8fv36oaysDPPnzxcDLnUxa9YszJw5U5wuLi5GQkICkpOTERERUeftBjKDwYD09HSMHDkSarXa183xe7U5XhaLBX99zfrgJKlPV0wY0sEj+z5yqRh/XbQL4cFB2Pfy7R7Zprfx++U+HjP38Hi5h8fLPTxe7uMxc48vjpeQ4U2+NW/ePKxevRrHjh1DSEgIbrnlFvzzn/9Ely5dAADXrl3D66+/jrS0NJw7dw6tWrXCPffcg7lz5yIyMlLcjpD9L/Xll1/i4YcfFqe3bNmCmTNn4siRI0hISMArr7yCJ5980uufkYiovr7NPI9/bzuNf287jRNvjoZa5fFhiImIiDzOawGXKVOmYO3atdi2bRvi4+OdrvPtt9+ivLwcjz/+eI3bGzRoEObOnQudTgetVou4uDjk5+fL1snPz0dERITT7BYA0Gq10Gq1DvPVanWTfyjAY+Ce6o7XwQuF4uuCMoPHjuu2k9cAACWVxoA7V/x+uY/HzD08Xu7h8XIPj5f7eMzc05DHi+fFP2zduhWpqam46aabYDQa8fe//x3Jyck4evQowsLCcOnSJVy6dAnvvvsuunfvjrNnz2Ly5Mm4dOkSvv32W9m2li5dilGjRonTUVFR4uucnByMHTsWkydPxooVK/DLL79g4sSJaN26Nce9JCK/d+pKmfh61+kC/OGGVj5sDRERUe14POBisVgwdepUrFmzBlu2bEFiYqLLdT/99FPcddddaNWq5h/NrKwsNG/eXAyYJCUlYf369bJ10tPTkZSUVL8PQFRPFXqT+LpDyzCPbddi8dimiIiIiMiHNmzYIJtetmwZYmJikJmZiaFDh6Jnz5747rvvxOWdOnXCm2++iUcffRRGoxFBQbbbuKioKMTFxTndz+LFi5GYmIj33nsPANCtWzfs2LEDCxcuZMCFiPxeYbne100gIiJym8fzMVNTU7F8+XKsXLkS4eHhyMvLQ15eHioqKmTrnTx5Etu2bcPEiRMdtvHjjz9iyZIlOHz4ME6ePIlFixbhrbfewtSpU8V1Jk+ejNOnT+OFF17AsWPH8PHHH+Prr7/GjBkzPP2RiGp0+kqpGGipMNgCLmql5/7ELGDEhYiIiKgxKioqAgBER0dXu05ERIQs2AJY779atmyJm2++GZ999hkskl46GRkZGDFihGz9lJQUZGRkeLD1RETeceG67TmSykkJRSIiIn/k8QyXRYsWAQCGDRsmm7906VJZreDPPvsM8fHxSE5OdtiGWq3GRx99hBkzZsBisaBz585YsGABnn76aXGdxMRErFu3DjNmzMAHH3yA+Ph4LFmyhD21qMH9dCgXz67YhxHdYrHkiYEol2S46E1mj+3HzHgLERERUaNjNpsxffp0DBkyBD179nS6ztWrVzF37lxMmjRJNn/OnDm4/fbbERoairS0NPzlL39BaWmpOO5lXl4eYmPlA03HxsaiuLgYFRUVTksx63Q66HQ6cVoY98dgMMBgMIjzhdfSeRQYeO4CW2M9f0JwJb657d8lnaQzo9lsCvjP3FjPXVPB8xe4eO4Cm6/OX33255WSYrXx1ltv4a233nK6bNSoUbI6xK4MGzYM+/fvd6t9RJ42d+1RAMDPv+fjepkeZTqjuMzowYALa4oRERERNT6pqak4fPgwduzY4XR5cXExxo4di+7du+ONN96QLXv11VfF1/369UNZWRnmz58vBlzqYt68eZg9e7bD/LS0NISGhjrMT09Pr/O+yLd47gJbYzp/RjPw3G7r46n3BhkRVFUoouCaCoA1s2X37l0o+N1HDfSwxnTumiKev8DFcxfYGvr8lZeX1/m9Hg+4EDU11yR1ZU9eKZVluBg9mJbCcAsRERFR4zJlyhSsXbsW27ZtQ3x8vMPykpISjBo1CuHh4VizZg3UanW12xs0aBDmzp0LnU4HrVaLuLg45Ofny9bJz89HRESE0+wWAJg1axZmzpwpThcXFyMhIQHJycmIiIgQ5xsMBqSnp2PkyJE1tov8C89dYGuM56+gTA/s3gIAWFsYh5dSuqBjqzBsqTyMM/svAQBuvnkwBnd0XXYxEDTGc9eU8PwFLp67wOar8ydkedcFAy5E9VRpsGWxhKhVKNPbMlwMJs+FSczMcCEiIiJqFCwWC6ZOnYo1a9Zgy5YtSExMdFinuLgYKSkp0Gq1+OGHHxAcHFzjdrOystC8eXNotVoAQFJSEtavXy9bJz09HUlJSS63odVqxfdLqdVqpze5ruaT/+O5C2yN6fwFBdnuqTdnX8Xm7Ks48/ZYWZEHi0JZp8+rN5rx4aYTuK1LKwxo7x8Bm8Z07poinr/AxXMX2Br6/NVnXwy4ENVTl9hwZOeXAAB0RjPKdZIMFw+WFGO8hYiIiKhxSE1NxcqVK/H9998jPDwceXl5AIDIyEiEhISguLgYycnJKC8vx/Lly1FcXCz2smvVqhVUKhV+/PFH5OfnY/DgwQgODkZ6ejreeust/O1vfxP3M3nyZHz44Yd44YUX8NRTT2HTpk34+uuvsW7dOp98biIiZ9KO5DvM0xlNMEgqRhjNdbu3/m/GGfxr00n8a9NJnHl7bJ3bSEREVFsMuBDVk0kSCdEbzXYZLh4MuHhsS0RERETkS4sWLQJgHZNSaunSpXjyySexb98+7N69GwDQuXNn2To5OTno0KED1Go1PvroI8yYMQMWiwWdO3fGggUL8PTTT4vrJiYmYt26dZgxYwY++OADxMfHY8mSJUhJSfHuByQicsN/M844zDOaLLIOjHWtHnEiv7SuzSIiIqoTBlyI6klvtF0E6owmFJYbbMs8WFKMGS5EREREjYOlhgu7YcOG1bjOqFGjMGrUqBr3NWzYMOzfv9+t9hER+Zo14GKRTRMREQUCpa8bQBTopAEXvdGMMwVl4nSlweTsLXVS0003ERERERERUaBxdqtrMJuhk9xr17WkmIW1IoiIqIEx4EJUT9KyYfZjuJRLyovVFy8TiYiIiIiIqLFxFhQxmix25bp5R0xERIGBAReierLPcDFIet6U6z2X4XK1ROexbRERERERERH52sXCChx3Ms6KwWRGheR+2pPjoxIREXkTAy5E9aSzy3CR1pat8GDA5VJRhfia5cWIiIiIiIgo0P199SGn8w0msyzDZdGWU1i970JDNYuIiKjOGHAhqgeLxWJXUswEk9kWDCnzYEkx6Xalr4mIiIiIiIgC0WUXlRyMZgsKywzi9Llr5Zj59QHsO3e9oZpGRERUJwy4ENWD0WyRDfCnN5plARhPZbhYLBb8dsZ2YWlihgsREREREREFOE2Q88dSs388ghKdYwfGez/e6db2pbfO7LhIREQNgQEXonqQjt8CWEuKSS/iPDWGy4XrFbJpM8vXEhERERERUYDTqpw/lvr1ZIHH92V//05EROQNDLgQ1YP9BduVEh0KyvTitKcCLvaY4UJERERERESBLkilaLB9nbtW3mD7IiKiposBF6J6kJYPA4Avdp2VTVcYPBNwUdhdgzIVmoiIiIiIiKh60nvnnKtlPmwJERE1FQy4ENWDroaUZE+N4WI0yQMsZgZciIiIiIiIqAk670amivSeXavmIzAiIvI+/toQ1YPeVH3ARWc0weKB8l9Gu0FbWFKMiIiIiIiImqLxy36r9bqVkqoTOgPHcCEiIu9jwIWoHuxLitkzWwCjm9koheV6PLg4A1/uOSfOO3SxSL5dZrgQERERERFRE3Tycmmt16002gIuNXWYJCIi8gQGXIjqoUxXc8mwmsqO2Vu09RT2nLmGWasPifNmfHVAtg4zXIiIiIiIiIiqJ81qWZCW7cOWEBFRU8GAC1E95BZVOJ3/4MB48XW53ujWNgvLDDWuY2KGCxEREREREQU4hcK725dmuJwpKMfpK7XPjiEiIqoLBlyI6iG3sNLp/PjmoeLrbzMvuLVNcy2yV8zMhCYiIiIiIqImRKOyPsIa0S2m1u+ptBu35Xp5zR0ciYiI6oMBF6J6uFSV4dI2KkQ2XxNk+9PKOFXg1jZrk7vCkmJERERERETUlDwyuB0AwJ3bYZ1RXga8uJIBFyIi8i4GXIjqwGKx4LMdOUg/mg8AaBMVLFuuVtn+tG7t3NKtbRslA/kZXQzqx5JiREREREREFOjcubcND1YDqF1VCIF9hovOwHIRRETkXQy4ENXBij3nMWftUVy4bs1wiQxRy5aP7BaLP99s7X1jf4FXk0MXi8TXepMZZicXoO5cYBIRERERERH5o5OXaz+mSrg2CADgTv/DSoM8w4WdF4mIyNsYcCGqAyHQIogItgVctEFKtI4KRphGBQAoNxjd2vapK2Xia53BjBlfZzmsw4tEIiIiIiIiCnRXS/W1XjciRAi41P5+WGeUd4BkeW4iIvI2BlyI6kChUMimDZIASOeYZlCrlAhWWwMulXp5jxp36IxmfJ91yWE+Ay5ERERERETUmLxzf2/88txtLpcL99i1DbiYzRbo7QMuZpYUIyIi72LAhagODHZjq0jDL8L4Lcqqme6GRrRBtj/LDYdzna7DkmJERERERETUmDw4MAGdWjVzOQ6q0PGxtjGTKV/uE18L99nf7L1Qv0YSERHVgAEXojqwD7hIAyCaqoCLcDHobmxEmr3ybtpx2bJgtdJhHSIiIiIiIqJAtvjRAeLraXfcIFvWJyEKT97SQezUWNuyYOsP5YmvhdJiO08V1LOlRERE1WPAhagOjCb5Bd6jg9uLr4NU1qtAoeqYfTZKcaUBW49fgdHk2C3HYrHAKAmmJLYMky1vpnW/Zi0RERERERGRP1JVRVH6tYsS54VWjYcq+D51CN64qweUYqfG2t0Pt40K8UwjiYiI3MCAC1EdSDNcokLV6BYXIU4L2SeKqkJj9peCj3+6B098tgefbD/tsF37zJVbOrWQTYdVBVycxGqIiIiIiIiIAsaVEp14DyxUigBsHQ3tCQGX2hZ8uFhYAQBY/Gj/erSSiIjIPR4PuMybNw833XQTwsPDERMTg3vuuQfZ2dmydYYNGwaFQiH7b/LkybJ1zp07h7FjxyI0NBQxMTF4/vnnYTQaZets2bIF/fv3h1arRefOnbFs2TJPfxwKEOlH8/HEZ3twuaSyQfanl2S4hGmCEBFiuyC8Xq4HYMtwse98k3W+EACwet9F2fxrZXocvFgkm7c5+7JsWhgfhiXFiIiIiIiIKJC9s+GY+FqrlgRcgl0FXKz/l1Z8yC2qwPH8Eod1Kw0m8fW5a+X1bSoREVGteTzgsnXrVqSmpmLXrl1IT0+HwWBAcnIyysrKZOs9/fTTyM3NFf975513xGUmkwljx46FXq/Hzp078fnnn2PZsmV47bXXxHVycnIwduxYDB8+HFlZWZg+fTomTpyIjRs3evojUQB4+r97sfX4Fcz+4WiD7E+a4aJU2sZrAYBrZQYAgG2O7WJQGigJUtrWAIDJX2Ti3o93yuYdzy+VTQvvYcCFiIiIiIiIAlluka3DZIjaVkZMGODenpjhUnU/bDJbkDRvE5IXbsPJy/J758vFOvF1p1bNZMtqW5KMiIioLpx3G6iHDRs2yKaXLVuGmJgYZGZmYujQoeL80NBQxMXFOd1GWloajh49ip9//hmxsbHo27cv5s6dixdffBFvvPEGNBoNFi9ejMTERLz33nsAgG7dumHHjh1YuHAhUlJSPP2xKEBcqEoZ9jZZwEUhD5xcK7Ne2DnLcLlw3dazRrqNcwXl2HPmWo37Ferb1naQQCIiIiIiIiJ/JK0UIe3EqHERcBHuh4X+hzO/zhKXnbxcgs4xtsDKySu2rJfbu8YgLiIYecXWAI/RbIFaJb+PJyIi8hSPB1zsFRVZSyRFR0fL5q9YsQLLly9HXFwc/vjHP+LVV19FaGgoACAjIwO9evVCbGysuH5KSgqeffZZHDlyBP369UNGRgZGjBgh22ZKSgqmT5/usi06nQ46na2XQ3FxMQDAYDDAYDDU63MGKuFzN5bPrzeYvPpZhG3rJenJCsiPn9linbaYrQEVo8ksLldYbEGWcr2treOW7KrV/oVrwkp9YHxnG9v3qyHwmLmHx8s9PF7u4fFyH4+Ze3xxvHhuiIjIXyR1bIH1h/Ic5kvHc5FSVgVcjFURl++zLonLiipsv2/FlQY8tWwvAECtspax//qZJAydv9n6fpMFkoQaIiIij/JqwMVsNmP69OkYMmQIevbsKc4fN24c2rdvjzZt2uDgwYN48cUXkZ2djdWrVwMA8vLyZMEWAOJ0Xl5etesUFxejoqICISEhDu2ZN28eZs+e7TA/LS1NDPY0Venp6b5uQj1Zv8rXi4qxfv16r+/t8tUCCBX54lSlVfu0/TmtX78e2RcVAFQ4f/481q8/i4JKYM5+2zrFZRViWy9cr92fYnFREQAF9vy2F5WnAifLJfC/Xw2Px8w9PF7u4fFyD4+X+3jM3NOQx6u8nHXsiYjIPwiBkzt7t5bNl2a7pA7vJL5uHqoGAFwv0ztsS2+0dW48JSkvZqgafzUuMti2rsmMEDDiQkRE3uHVgEtqaioOHz6MHTt2yOZPmjRJfN2rVy+0bt0ad9xxB06dOoVOnTrZb8ZjZs2ahZkzZ4rTxcXFSEhIQHJyMiIiIry2X39mMBiQnp6OkSNHQq1W+7o5dfbXjDQAgDo4FGPG/MFr+xGOV3hkc6C4CN1bh+Pjp25CeHAQ8qPO4q2fsjFhSHuMGdUF57flYO25E2gbH48xY3rib98eApArbiuhRTjGjLlF1v6atGoZjTOl19Gnbz+M7um8JJ8/aSzfr4bEY+YeHi/38Hi5h8fLfTxm7vHF8RIyvMm35s2bh9WrV+PYsWMICQnBLbfcgn/+85/o0qWLuE5lZSWee+45rFq1CjqdDikpKfj4449lnc7OnTuHZ599Fps3b0azZs3wxBNPYN68eQgKst3mbdmyBTNnzsSRI0eQkJCAV155BU8++WRDflwiIqeMVcEQtYuMFgCIi7AFSqLDNACA6+WOAZf95wvxWJL1dYXe5LBcWkLscnElIkN4nUJERN7htYDLlClTsHbtWmzbtg3x8fHVrjto0CAAwMmTJ9GpUyfExcVhz549snXy8/MBQBz3JS4uTpwnXSciIsJpdgsAaLVaaLVah/lqtbrJPxRoLMfg/PWKBvkcQk+cv6V0QXS49fv29NBOuKN7HBJbhEGpVCAoyNpjRqFQQq1WQ6mUX0QOuaGV2Fa1SiH2vHFl4q2JOJprfUhiqdpmoGgs36+GxGPmHh4v9/B4uYfHy308Zu5pyOPF8+Iftm7ditTUVNx0000wGo34+9//juTkZBw9ehRhYWEAgBkzZmDdunX45ptvEBkZiSlTpuDee+/Fr7/+CgAwmUwYO3Ys4uLisHPnTuTm5uLxxx+HWq3GW2+9BQDIycnB2LFjMXnyZKxYsQK//PILJk6ciNatW3PcSyLyOX3VuKZBSsfxVP5wQ0scvliEP/ZpI84TxnYRxkONClWjsNxaSmz1vot474E+UCgUKNUZHbYnzZoZuXAbzrw91nMfhIiISMLjAReLxYKpU6dizZo12LJlCxITE2t8T1ZWFgCgdWtrGmlSUhLefPNNXL58GTExMQCspRYiIiLQvXt3cR370lHp6elISkry4KehQDRywVacuFyKZ4Z2xKwx3Ty23cJyPRamZyO2DNBXBUeCJEEUhUKBTq1sg/QJl3MWWNdV2V1EGk22lOeB7aORcbrA5b6z/zEK2iAVHvt0NwDAZA6ccmJEREREJLdhwwbZ9LJlyxATE4PMzEwMHToURUVF+PTTT7Fy5UrcfvvtAIClS5eiW7du2LVrFwYPHoy0tDQcPXoUP//8M2JjY9G3b1/MnTsXL774It544w1oNBosXrwYiYmJeO+99wAA3bp1w44dO7Bw4UIGXIjI53RVZcCEQIrUsvE3w2g2QxtkK/2lrrr/Nlus98T9EqKwOfuKuNxktiBIpcCBC4XebTgREVE1XOdt1lFqaiqWL1+OlStXIjw8HHl5ecjLy0NFRQUA4NSpU5g7dy4yMzNx5swZ/PDDD3j88ccxdOhQ9O7dGwCQnJyM7t2747HHHsOBAwewceNGvPLKK0hNTRUzVCZPnozTp0/jhRdewLFjx/Dxxx/j66+/xowZMzz9kSjAnKiq1/rvbadR6CTVuK4mfZGJzzPO4YsTKhzLKwFQfeqzUuhBUxUbkaYwA4BBEjTRS4IvzggXmcL+jDVkwxARERFR4CgqKgIAREdHAwAyMzNhMBgwYsQIcZ2uXbuiXbt2yMjIAABkZGSgV69eshJjKSkpKC4uxpEjR8R1pNsQ1hG2QUTkSwWlOgBAi2aOlUhUSoUs2AIAaklgxmAyi5UnbPOs0xX66u+vAWtnYSIiIm/weIbLokWLAADDhg2TzV+6dCmefPJJaDQa/Pzzz3j//fdRVlaGhIQE3HfffXjllVfEdVUqFdauXYtnn30WSUlJCAsLwxNPPIE5c+aI6yQmJmLdunWYMWMGPvjgA8THx2PJkiXsqUUyuUWViArVoKjcgFe+P4x7+7fF8C4xddrWnpxr1m1W2AIn9kEUKSHeYq66kFMq5Ouu2XcRb/2pFwBbSrTgg4f74q+rsgAAoRrbRaaQJWN/YUlEREREgclsNmP69OkYMmQIevbsCQDIy8uDRqNBVFSUbN3Y2Fjk5eWJ60iDLcJyYVl16xQXF6OiosJpKWadTgedTidOC+P+GAwGGAwGcb7wWjqPAgPPXWBrTOfvcnElACA6NKh2n8dsu28ur9RBb5SP1VJeqcMbPxzHV3svivPm/amH023nXClGQvPQOra8bhrTuWuKeP4CF89dYPPV+avP/rxSUqw6CQkJ2Lp1a43bad++vUPJMHvDhg3D/v373WofNT56o+veK8UV1j+O99Kz8eOBS/jxwKU612oNVitRaZDvq7p9C4S/CLPd30aFwXZxaL+dvglRuLlDNPacuYZNzw0T55+qyt75b8YZjBvUzo3WExEREZE/Sk1NxeHDh7Fjxw5fNwUAMG/ePMyePdthflpaGkJDHR9OpqenN0SzyAt47gJbYzh/J86rAChwLvsw1l89VOP61n6H1sdYP21Mx8XL1vcLNqSl46u9tsdc9yeaEJp3AOvXH6iaY1uW/ssWxDVsvMW270Zw7poynr/AxXMX2Br6/JWXl9f5vR4PuBB5W9b5QvztmwN4PKk9Hk/qgK/3nne5bkmldbC8S4UV9dqnyWxxCLYAcDoYn0AYlE+Is+icvL/SYEKwWiUrKaZRKdEmKgRfT3Ycj+j01TIAEEuaEREREVHgmjJlCtauXYtt27YhPj5enB8XFwe9Xo/CwkJZlkt+fj7i4uLEdfbs2SPbXn5+vrhM+L8wT7pORESE0+wWAJg1axZmzpwpThcXFyMhIQHJycmIiIgQ5xsMBqSnp2PkyJFQq9V1+PTkKzx3ga0xnb9/n8kASkswZPBNGHpDy1q952970mEyWzB0+O1458hOALZ78qHDbwf2bhOnbxnYF2N6txan/5qRJr4ePORWdG9t+zetITSmc9cU8fwFLp67wOar8ydkedcFAy4UcD7efBInL5fite+P4PGkDjhYzYB4V0uFcgiuS3/VxvPfHnA6f1BiC5fvEfYo5LV0bxOB1fsvytZZs/8i/nxzO5TrrNkuHzzcF3+4oVW1Y8MIzGYLlMr6fS4iIiIiangWiwVTp07FmjVrsGXLFiQmJsqWDxgwAGq1Gr/88gvuu+8+AEB2djbOnTuHpCRrp5ykpCS8+eabuHz5MmJirCVz09PTERERge7du4vr2FcNSE9PF7fhjFarFcfNlFKr1U5vcl3NJ//HcxfYGsP5Eypla9RBtf4sKoUCJligVAWhqELeAdJkkd9HN28W7HK7FoXKZ8evMZy7poznL3Dx3AW2hj5/9dlXzU91ifzM5RJbTekynRFd42y9Up68pYNs3fPXrelfinrGJVbvu+gwr0OLUESGuv7jE2Ih1ZXZqzSYUFJpQF5V7do+8VGIDtPUqk0nqsqLEREREVFgSU1NxfLly7Fy5UqEh4cjLy8PeXl5qKiwZmVHRkZiwoQJmDlzJjZv3ozMzEyMHz8eSUlJGDx4MAAgOTkZ3bt3x2OPPYYDBw5g48aNeOWVV5CamioGTCZPnozTp0/jhRdewLFjx/Dxxx/j66+/xowZM3z22YmIBELZbZU7N+xVqx6+WOSwaHP2Zdl0RLDr+/XalAcnIiKqCwZcKOBog2xf2wXpx1FZNVDevf3a4o27esjWLavKHPFGIkhNgRH7kmLCxeTQG1uJ67QK1+Kr32wl0cKDq086eyKpvfjaYOIFIhEREVEgWrRoEYqKijBs2DC0bt1a/O+rr74S11m4cCHuvPNO3HfffRg6dCji4uKwevVqcblKpcLatWuhUqmQlJSERx99FI8//jjmzJkjrpOYmIh169YhPT0dffr0wXvvvYclS5YgJSWlQT8vEZEzpqoUF3cqNwir/nryqsOy2T8elU1Hhsjvr9tG2Uop8n6aiIi8hSXFKOBo1Srx9ac7csTXOQXW8U0+fWIgJny+F4DtIkpRj5JirjJUhPFhXBE66ViqiopduG7tsdiymS1QE6JWYfHW0+J0eDU9cABg3KD2+DzjLACguMJQfcOJiIiIyC9VlwEtCA4OxkcffYSPPvrI5Trt27d3KBlmb9iwYdi/f7/bbSQi8jYh4KJyI+Ai3Nv/t+q+uDr2GS5fTLgZt7+3FQBk46gSERF5EjNcKOBEhjgPSuw/VwgAuKNbLF4Y1QWALeCy4Uhenfe3J+ea0/k1lfQSLhn1RjN+PHBJvCBcve8i+iZEAbBeYNrGmQE0QdX/SXaJCxdfFzHgQkRERERERAHKVBV8VrpRUsyd6hX2HRo7tmqGfu2iAAAGlhQjIiIvYcCFAo6r66sljw8UX2uqBp03mGruPViTLcev1O2NVReNP/9+GVO/lPcqFHrwmGvRu9HeHV2tg6Iy4EJERERERESBylwV83Arw8UuOCPc+zsTrHZcpvbgswIiIiJnGHAhnxPSiGvL2eB2wWol7ugWI04LF1HO0oTXHrxUq/3kFlXgsU93Y/+56261T1DdJaMwKKC0ec4uBp0RMnwYcCEiIiIiIqJAJZYUcyPDxX7NpE4tnK733gN9HIIzgLRzJjNciIjIOxhwIZ/6dEcOer2xEQfOF9b6Pc4ujMKD1bKLKbHXSlVwJi4iWFw2ZeV+ZOeV1Lif178/gu0nrmLXaeclxWpSXVq0suovL3XlPnFeqKZ2QyqFB1vXq2kMGSIiIiIiIiJ/JZYUc+PJlP1tttZJWe6kji1w34B4p+8PUlk3MP2rLLc7fxIREdUGAy7kU3PXHkW53oQXvzvosMxisaBM5xhUcJa10iJMI5sWLqKMLgbhKyjToSZpR/Nl0+HaIDxyc4JtHzWkPVfXScdZyvTwLjFO1nQkjPMiDTyVVBpw4HxhrQZgJSIiIiIiIvI1c9X9epAbERf7rBWtWoWWzbSyeWFa150ZM8/YKlik1WOsVyIiIlcYcCG/4CyIMuOrLPR4fSNO2g1OX6E3Oazrqo6rqzRhZ2XJajLvvl5444/d8NceRvRqG4Gvnhlc7fqu4i1tIoOdzn9xVJdatcNZubS7PvwVd3/0K37+/XKttkFERERERETkS0KGSzXDsDiw77uoUSnxt+QbZfPCtCqX7y+RdOosZJluIiLyAgZcyC8YnQxY978s61grn+88I84zmy3Ye9ZxTBX74IYYlKgKrNgHXpwFbWoSorZetHWMAFZPHowB7aOrXd9Vhst7D/YVBweUiqgam6UmaifBpJyrZQCAHw/UbnwaIiIiIiIiIl+xWCwoLLcGPKorx23PvrOlQgEo7aIw1WW4SDmrPEFERFRfDLiQXzDaBUQ2H7NlakhrsmZKBrDv3jpCfH1DbDPZ+9VVJcWEoIR9wKW8DgGXYLXrXjLOKFzkuESGqMWePFLqWnbrEUqKOQtSOdsuERERERERkT+5cL1CfO3Ovbb9XfbPv+fjRL58jNazBWX1aRoREVG9MOBCfkFvFzxYkH5cfK1V276m0hqrL4/thm8mJ2FI5xaYentn2fttWSDW7V4vl6cKP/fNAVwr07vVxmC1e38urjrpaIIUYq1agVJR+941QjDJWRk2k5MgDBEREREREZE/uVpqG1e1TVRIrd9nf8erM5hxNLdYNu/XkwUu3/+CpJS3cG9NRETkSQy4kF8w2tXYCpJc+Hy0+RTMZgvMZgv+sz1HnB8bEYybOkRjxcTB6BwTLnu/tOzWmau23i339G0jvn7jhyOy95y8XIpZqw/i/LVyALZMEoE2yM0MFxcRF7VKCbNdJor9vqojDCgoBJOk2TvMcCEiIiIiIiJ/d+qK9T59UGL1pbrt2XecTGwZBoNRfh/cIkzj8v3P3tZJfB2irl3pMSIiIncw4EJ+wWA3iL19Ddd9566juFKepdI5Rl5GTEpaUux6ue2C7ObEFuLrw5eKZO+5f/FOfLnnPJ7+716YzBZx/BdBbevAClz1lWkVroVdgotbKdTqquDMjwcu4f9+OSErv7b1+BW32khERERERETU0Eqq7u9bhWvrva3YyGDZ9LsP9nG5rkKhwOCO1iCPfelxIiIiT2DAhfxCuUE+psrNTnq5XC21BU6SOrZwWC4lBCVOXSkTy5N1ahUmy6Sxz1gRBuw7lleCSoPjGC8JzWuf5gy4LikWqgmCxS4TpbbjtwCARpL98176cTHTBQDUHPSPiIiIiIiI/JwQ7NC4cS/sypTh8hLjHVuGVbu+tCIGERGRpzHgQn5BGn+o0Jvw9W/nZcuDVEqU6Yzi9Dv39652e2ql7au9/cRVANYAizRz5uYOzV2+//7FGQ7zgty8EHQVcAHgkOFiH4Cpjn02TJnedlxclTEjIiIiIiIi8hdCRQl3yms70yYqGJEhatm8kBoqSAgBFyPHQCUiIi9gwIX8zur9F1BgV5fVYDKLWScdW4YhITq02m2ogxwDDxYA9/ZvK5t25Xe7QfdaNnNdA9YVhcuiYoDJLuJitI/AVKNb6wjZdHGFrdSas8wcIiIiIiIiIn9SUXXv6k61B3s3xjbD3Ht6Qmm3iWBN9QGXoKrKEHpmuBARkRcw4EJ+J+NUgcM8vdEMXVUPGG0txjtxdtH2e24xQjVBeD6lCwBAZ6jdxdWdvVtj1aSkWq0rVV2ySWyEvE6tyY2eNeHB8rFkiiQBlxtjw2u9HSIiIiIiIiJf+GjzKQDAluOXa1jTtbQZt6F1ZAiC7CIuNWa4BAkZLgy4+NKZq2W4btfZloioMXBvFHCiBuBsAHmd0QThWkhbi5Tj6urACu/XGWuXDfLhuP61Ws9edeW9HhnUHmevleP0lTIA7mW4uBp7BgDMbpQmIyIiIiIiImpoH285Kb4+f62i3ttT2d1715Q1I4x9amBJMZ/JLarAsHe3AADOvD3Wt40hIvIwZriQ35FmcIRVpQLrjbaSYsHqmr+2zi6wesdHApAGXGruzVKb4I4r1Y2mMqJ7LDY9N0ycti8x5k6bCiUZLu4EboiIiIiIiIga2jsbssXXk2/rVO/tqVTujWUqPC8wmJnh4isvfndIfH2uoNyHLSEi8jwGXMhvlFcN/i6MfdKzbQT6JEQBAL7eewGZZ68DcMzwcEYalIgMUeOTxwZg2fibZe+vTcClbfOQ2n8AO+6MX29yIzPFPuByrqBMfM2UaCIiIiIiIgoUcXbltmurRxvb2Kb2GS41CRICLkZ2WASs1T82Z18Wn8l4m9lswbbjV8TpU1dLG2S/REQNhQEX8hvdX9sIvdEMvcmayXJH11gxuLDp2GUs23kGgOP4J85oJVkwQUoFknvEITpMI1tWm5JiavvR99ygqDbHRc6dDJcgu+ydAxeKxNdMiSYiIiIiIiJ/JtybA0Bd72BVStv9dpDbGS7W9Y3McAFgzTgav/Q3dH9tY4Psr8Bu3JarJboG2S8RUUNhwIV8SnqRBABXSnU4fLEYAKAJUkLjpKTXkM4ta9yudAwX+ws4IYijr0WGi7sXblLKur+1zi4WVmDCst+w98y1ht85ERERERERUQ3u7N1afF3XqthKSVZLTWO22BOeQ7jT8bEx+3RHjvi6pNJQzZqeIZSLF1wt1btYk4goMDHgQj5lH5S4UqJD1vlCANbMFGflw+IigmvcrjQLxH4f7pQUm3tPzxrXccXNrGa3vPWnXi6X/XLsMu5fnOG9nRMRERERERHVkUFSCrtbXHidthFUjx6OQrDGndLeTcW1Mu8HP/R2pdCvljLDhYgaFwZcyKcUdlGJE/kl4utyvclphkuoJsitfXRq1Uw2LWS4HLxQhMMXi5y9RdS/XXO39iVX8wVgSo9YAMDtXWPc2vK4Qe18kkFDRERERERE5I5yvVGW1aCvGjule+sI3FKLChbOKOtxQyxkuJiZ4YLz1+QD1hdVeD/DxWAXcClgwIWIGhkGXMin7Ae323fuuvg6VKNyGnAJ0bj3tW0ZLh/zRTq+y53/t8OtbbmjNhku7z7QB+/c3xsLH+zr9vb/Pqab+40iIiIiIiIiaiBGkxnJC7fhtvmbxQftQobD/QPi67xd+2cJ7hAzXDiEC36zK0duHwzxBvvy7iwpRkSNDQMu5FP2nVI2H7sivr5/QLyYjSIVrHYsM1adW+16zMSEy0uSLUjLdmt7tSX9aOHB1qyc3vGRsnXCg9V4cGACIkPVbm9/wq2J9WkeERERERERkVcVVhhw4XoF8ot1yLlaBgDQG63ZLs46WNaW/Xiw7r3X+n8zS4o5VBCpTen1urJYLDCbLQ4Bl0tFFV7bJxGRL3g84DJv3jzcdNNNCA8PR0xMDO655x5kZ9seaF+7dg1Tp05Fly5dEBISgnbt2mHatGkoKpKXdlIoFA7/rVq1SrbOli1b0L9/f2i1WnTu3BnLli3z9MchL7MvKSZcNPVvF4UWzbROx3CpbUmxX567DTNH3og/9Wsrmx/fPEQ2/a9NJ3G2oMydZteK9ALw0yduwoRbE/Gfxwd6bPsKhQI3xDSreUUiIiIiIiIiH6jQ20qJlVe9Fh641yfgUp8xU4XsmIIGGK/E11789iAe/HcGdEaT0+X2A9gvSDvutbY89MkujPpgGyrs9nn6SplDpg0RUSDzeMBl69atSE1Nxa5du5Ceng6DwYDk5GSUlVkfaF+6dAmXLl3Cu+++i8OHD2PZsmXYsGEDJkyY4LCtpUuXIjc3V/zvnnvuEZfl5ORg7NixGD58OLKysjB9+nRMnDgRGzdu9PRHIi+yv0a6WGjt2SCUUg3VOAZcokJqlw3SqVUzTLvjBoeMGPsgDwAUVxhrtU13BKlsf16JLcPw6p3dERsRXM073KdWuf4TtlgseHdjNpbvOuvRfRIRERERERHVRpnedq+tq3rQLpQU01RzP1sTaSDHXcL4Lz8euIQl20/XeTuB4Ku957En5xp+PnrZ6XL74Mfes9eRnVfidN36MJjM2JNzDcfzS3H0UrHD8lfWHHb6PvuAEBFRIHBv9PFa2LBhg2x62bJliImJQWZmJoYOHYqePXviu+++E5d36tQJb775Jh599FEYjUYEBdmaFBUVhbi4OKf7Wbx4MRITE/Hee+8BALp164YdO3Zg4cKFSElJ8fTHIi8J1apQonMMdmSdL7QudxJwqc/geK446+0xvEurem0zSNLO+qQ7V0ddTY+gY3kl+HDzSQDAAwPjnWYLEREREREREXmL9EG6MFaHwWjtYVmfDJcb48Jl00/e0gHLdp7B5Ns61fhe6fgv/1j3Oyb+oWOd2+HPpM85SnUGp+s4C2gUlnsu82fJ9tPIOl+IN+/pJc6b99Mxh/WclXfbdCwfk/6bidl398Ajg9p7rE1ERN7m8YCLPaFUWHR0dLXrREREyIItAJCamoqJEyeiY8eOmDx5MsaPHy9mJ2RkZGDEiBGy9VNSUjB9+nSX+9HpdNDpdOJ0cbE1qm4wGGAwOP/xaeyEz+2Lz19YbkB+sc7lcoPBgOAgx0CFN9paVul4QfF/D/V22Jc7x8tssl24mIxGGAyeD7qoJden8c1DcOG6rfbp+YJS8XXO5RJ0ahXm8f3XxJffr0DFY+YeHi/38Hi5h8fLfTxm7vHF8eK5ISKihrT37HXxderKfRjbeyx09chw2Th9KNbsv4hn7QIrr4zthvsHxKNb64gat+GNTpz+6NMdOeJrV8PV2Ge4AIAnR7b5x7rfAQCtwrUOy25OjMaeHGspsdu7xTgsn7JyP4xmC15ec5gBFyIKKF4NuJjNZkyfPh1DhgxBz549na5z9epVzJ07F5MmTZLNnzNnDm6//XaEhoYiLS0Nf/nLX1BaWopp06YBAPLy8hAbGyt7T2xsLIqLi1FRUYGQEPk4HYB1fJnZs2c7zE9LS0NoaGhdP2ajkJ6e3uD7vFoJVPcVXL9+PXZeUABQOcyvP/l+d+zaI9tPC60Fv6S7Lk9Xm+N1ssi2n19+TkeIF/7aiguVECoDmnTlkBZpS/91L4TPtGnLVmQ3fLzF1hYffL8CHY+Ze3i83MPj5R4eL/fxmLmnIY9XeXl5g+2LiIiaNpPZ+aP7+ozh0iUuHC+N7uowP0ilRM+2kbXahn2V8XH/2YXnU7qgX7vmbrfHn63ac158bXIRcak0mB3mOcs2qa+lv55xmKcNUuKxwe3xxa6zTityKOszUA8RkQ95NeCSmpqKw4cPY8eOHU6XFxcXY+zYsejevTveeOMN2bJXX31VfN2vXz+UlZVh/vz5YsClLmbNmoWZM2fK9p+QkIDk5GRERNTcC6IxMhgMSE9Px8iRI6FW125sFE/JzisB9megRZgGo3rEYoXkYgAAxowZg455JVj/UYbD/PpaXbAPW49fFacrIjsAsO1fr1BjzBjH0nTuHK/fzlzH/x39DQCQkpKMZlrP/7mtvroPx4usn6Ntq+bIPVsoLmvfuSuQcwIAkHTLrejZtuG/4778fgUqHjP38Hi5h8fLPTxe7uMxc48vjpeQ4U2+t23bNsyfPx+ZmZnIzc3FmjVrZGNWOht3EADeeecdPP/88wCADh064OxZ+Xh98+bNw0svvSROHzx4EKmpqfjtt9/QqlUrTJ06FS+88ILnPxARkR0hsOI435pVUZ+SYvWx/lCubHrnqQKM+89u/D53lE/a4y0DOzTHuWvWjhYFpc7LhDkrKebqvHmaWqUUx6U1mMwwmy2y7KMgFQMuRBSYvBZwmTJlCtauXYtt27YhPj7eYXlJSQlGjRqF8PBwrFmzpsabzEGDBmHu3LnQ6XTQarWIi4tDfn6+bJ38/HxEREQ4zW4BAK1WC63WMY1RrVY3+YcCDX0MLBYLvj+YB8Cawprcs7Us4LJ8wiCo1Wr0jHfsYeKJdr59X28kzdtk299uebDHbLZUu5/aHC+txvbnFazRQK32/BgqGsk2e8c3x15JwKVUL7lIUip9+h3n35j7eMzcw+PlHh4v9/B4uY/HzD0Nebx4XvxHWVkZ+vTpg6eeegr33nuvw/LcXPkDwZ9++gkTJkzAfffdJ5s/Z84cPP300+J0eLhtXIPi4mIkJydjxIgRWLx4MQ4dOoSnnnoKUVFRDhUGiIg8zdlYqQCgryoppq5DSTFPcDZou7PSWoFOIamAsSD9OKbdcYPDOkIgZuKtiVhSVYLMaPJMhotFkimjVAD2CU/Xy/W4IaYZAGDRllP4fOcZfP7Uzbipg3VIAum4uBaLxWVHBCIif+PxgIvFYsHUqVOxZs0abNmyBYmJiQ7rFBcXIyUlBVqtFj/88AOCg4Nr3G5WVhaaN28uBkySkpIcSkulp6cjKSnJMx+EvOZSYQVuedsW7CjXmzD0hpaydfq2iwJg7dnXsWUYTl8t82gbWkc6D8oJXGQ+u0V6MaD00nWktOZt+xbysniLt54SX7tK5SYiIiIi3xg9ejRGjx7tcnlcXJxs+vvvv8fw4cPRsaN8cOfw8HCHdQUrVqyAXq/HZ599Bo1Ggx49eiArKwsLFixgwIWIvM5ZpoTFYoHBaL0/1foow6Wp3B4bTDVnquQWWceB7SGpiPHDgUsY0T3W1VtqTfocwtkxT+rYAtfLbWPLletNeGBxBubc3QOPJ3VAkORByp6caxjUsUW920RE1BA8/uuWmpqK5cuXY+XKlQgPD0deXh7y8vJQUWH9R1zoZVVWVoZPP/0UxcXF4jqmqkHGf/zxRyxZsgSHDx/GyZMnsWjRIrz11luYOnWquJ/Jkyfj9OnTeOGFF3Ds2DF8/PHH+PrrrzFjxgxPfyTysHH/2eUwT6FQyAIGaknq6KJHB+CVsd0woH1zLB1/U4O00RO9W1TSgIuXemJIj1N1vYMYcCEiIiIKXPn5+Vi3bh0mTJjgsOztt99GixYt0K9fP8yfPx9Go1FclpGRgaFDh0Kj0YjzUlJSkJ2djevXrztsi4jIk3ROAi5GswVFFdaH7L4qKfbZkwN9st+GVq6XP9dwlnGUV1wJAIiLsHVK/eHAJY/sX19DwCcqVI2iCsdSZ699fwTvbDgmm1feCDOQiKjx8niGy6JFiwAAw4YNk81funQpnnzySezbtw+7d+8GAHTu3Fm2Tk5ODjp06AC1Wo2PPvoIM2bMgMViQefOnbFgwQJZqnxiYiLWrVuHGTNm4IMPPkB8fDyWLFmClBTHcTfIv5wpkA/W2qtqYLs5d/fEE5/tAQCoJT0ZusSFo0tcOCb+Qd6br77u7dcWq/dfdLps5sgb6719aZBF5bWAi1Ly2vU+GHAhIiIiClyff/45wsPDHUqPTZs2Df3790d0dDR27tyJWbNmITc3FwsWLAAA5OXlOVQciI2NFZc1b+5Yvlen00Gn04nTwrg/BoMBBoOtJ7LwWjqPAgPPXWALpPNXWqFzmHfmSrHYwbFVWJBPPscfOkXj76O74K2fsmXzvd2Whjx3p6+U4eff5WX4i8t0iAqVlxYtLLcGPCK08ucJdW3jhesVGPvhTmhUSix+pG+160aHBCHRrlKH4OMtpxCqsZVQr9QZfP6dD6S/PZLjuQtsvjp/9dmfV0qKVWfYsGE1rjNq1CiMGlXzYGXDhg3D/v373Wof+Z85d/cAYJcRovR+bc659/TEgA7N8fKaw+K8CbcmYmzv1uhdFQSqD2mMxVulRtWSHkHV9Q4yMuBCREREFLA+++wzPPLIIw6lmGfOnCm+7t27NzQaDZ555hnMmzfP6diVtTFv3jzMnj3bYX5aWhpCQx0fjKWnp9dpP+R7PHeBLRDO38liwP6x0+qN2wCoEK21YNsvab5oFgDg/BUFAPk4q/Zl672lIc7dsuNK2Be1+SktHZG2hEdYLEBRuQqAAnt2bof0XNX1WLy6V4VygwLlMOHhJb85LL8x0oybWllwqlgB5cUstDcDrh5NSjN0du7JhD7HP55rBMLfHjnHcxfYGvr8lZeX17ySCx4PuBC5K0xr/RoGqxs2nThMG4S7+rSRBVxiwrXo386xp19ddGplHfwtWK302uBuGlmGixL39G2D/2U5pv8yw4WIiIgoMG3fvh3Z2dn46quvalx30KBBMBqNOHPmDLp06YK4uDjk58t7OAvTrsZ9mTVrliyQU1xcjISEBCQnJyMiwlbj32AwID09HSNHjoRarXa2KfJTPHeBLZDOX9I/twCQl4zq3W8A8HsWoiOaYcyYIT5pFwAEZ1/BFyflHXjHjBnj1X025LnbUHwAKJD/+z9k6DC0iw6FzmjGuCV7cPBisbjs7tEjMXvfZnG6rsfirxnVB9HGDrwRfxkmr16i6ZCH6V8frPZ9N3TviTE3JdSpTZ4SSH97JMdzF9h8df6ELO+6YMCFfCq5e6wYmOjfrjlG9YhD+5bOU0q9IchuNPvoMI2LNd0XolHh0BvJ1Y6tUl/SMmJBSgUWPNiXARciIiKiRuTTTz/FgAED0KdPnxrXzcrKglKpRExMDAAgKSkJL7/8MgwGg3iDmp6eji5dujgtJwYAWq3WaXaMWq12epPraj75P567wBYI5+9qqeP4HEaL9R42SKX0aftbNAt2mNdQ7WmIc6dUOj6HMMF6zH89fVkWbAGAqLAQ2bS32tcsROOw7YjQmp/D6IwNd35qEgh/e+Qcz11ga+jzV599+WaEMmrSYiOsN3APDozHJ48PhKqqfJhSqcDixwZg1uhuDdYW+2uQFs08F3ABgPBgNYLVqppXrCO1XYaLUqnA039IdFiPJcWIiIiI/EtpaSmysrKQlZUFwDqeZVZWFs6dOyeuU1xcjG+++QYTJ050eH9GRgbef/99HDhwAKdPn8aKFSswY8YMPProo2IwZdy4cdBoNJgwYQKOHDmCr776Ch988IEsg4WIqCFVVo3fElTNGKQNwX4sk8bGAsdnADqDdRB7Z1X+lUoF/tSvrbebBa2TUujBQTU/MynVGb3RHCIir2DAhRpc86reC3f2buPjljgOZh8RHFgXXdKLDm1VSbaHb27nsB4zXIiIiIj8y969e9GvXz/069cPgHU8ln79+uG1114T11m1ahUsFgv+/Oc/O7xfq9Vi1apVuO2229CjRw+8+eabmDFjBj755BNxncjISKSlpSEnJwcDBgzAc889h9deew2TJk3y/gckoiavW+sIh3mHLxYBAFROMjAaUkRIYN37u0spedYRF2HN5qk0WoNdFQaT0/c8NcTaebN1pGP2j6dcKdE5zKvN04qrpY7vIyLyVywpRg1Ob7T2qvBm5kdtCdk1glBNYP1JfLP3gvhaOJ5CiTYpk7MuLERERETkM8OGDYOlhmu0SZMmuQyO9O/fH7t27apxP71798b27dvr1EYiovpoWVVB4s0/9RTHTi2sMACwlsT2pXBt4w24nL9WjrUHcwEAf0u+EWsP5iKvuFLMcCmqOgeCyKrgkzCurq7qmY1A+K2qzdi04cFBKKl0nY1ye9cYh3muxvO9pVMLDOncEvM3ZuPC9Yoa901E5C+Y4UINTkghdvWj2pAUCgWk13lhWt8Hgdwh9FAB5Gm4m567De890Ae3dm4JADCZzQ7vJSIiIiIiIvKWcr31frVFmBZ3VD1o/75qzFH7zo8NTeOktFVjoDea8fR/94rTrSNDoK3qnKmren5QWC4PuAhxFG3VM4VrZXr8dMgasLFYLHj8sz24b9HOGitnmM0WsQrHlOGdZcsOvpGMtVNvRZ+EKIf39W/XHPf0bYMWdmPqvvtAH/RqGwkA2Hr8ith+IiJ/1zh/YcivVVb1ltDWok5nQ5Cm2gZahsvfkruIr6UBrI6tmuG+AfFiXVyjiRkuRERERERE1HCEgEuIRoXfzlyTLfN1houvAz7eMmv1IRzLKxGng9UqcdyUShcZLkJpda3kmcKzK/bBbLbAYLJg+4mr2HeuECcul6A6JTqjOD7MlNs7o29VcOXBgfGICFajZ1XwxJ5CocD7D/dD5qsjsW7areJ8TZASIRrbc6P5G7Kr3T8Rkb9gwIUanD9luADyAeUDLcPlz5LxWtQqx+MpXMRyDBciIiIiIiJqCBeul+Pr386juOrBfqhGJXa8FPhjwMNgMmNh+nHsP3fd102ps+/2XZBNB6uVYvlxIUOkqEIPAOgSG46E6BB8NK4/AMcB7fNLKmEw2c5bcYVjqTCjbLlB3E6wWoUVEwfhvQf64OWx3Wvdfo3kuYYmSCl7zrFkR06tt0NE5EuB1Z2fAp7FYpEEXPwvuBHsJ1k3tRUq6e0hZLNICRexHMOFiIiIiIiIGsKYD7ajWDKOR4haBfv4iq8zXJxZtOUUPvjlBD745QTOvD3W183xiOoyXMYNaocnbukgrmtfhSRp3iZxfBcAKKm0ZcYs33UWr/zvMFRKBT578ibcdmMrFFctj6h6T5g2CPcNiHervdJhYjQqpSwAQ0QUKPgvFzUog8kCIdnCH4MbSj+86KtOsFqFZ27riMcGt0fryBCH5UFK6584M1yIiIiIiIioIRTbDZoeqlFBAfm9tj92wPzf/ou+bkK9OLvvD1YrxYDL6z8cxrmCcjFTJSJE3gfb2bg20vJjJVXntaBUh1f+d1jc55QV+wAA+cWVAIBm2rr37W4VHmxrj0oJTVBgPaMhIgKY4UINTNojolkwv36eMGt0N5fLhAASx3AhIiIiIiIiXwjROGa4tIsO9U1jqnH6apmvm1AvQjURKW2QSgxuGUwWDJ2/WcwukmavADWXeSsst5Yiu+XtTbL5ZosFZToj/r7aGoTJqcdxjAxRY+3UW6ENUjp0iLUveUZE5K/4rxU1KKGnS7g2yC9rtjY2HMOFiIiIiIiIGsrvucUO80LVQVAo5Pf/8X4QcFn59CBfN8GjMk4VOMwL0agcAhXCOLYRwWqH9atzrdyAC9fLobMbj6dMb0KP1zciryrDJSrUve3a69k2EjfEhgMAwiVt1BnNsLBcOhEFAAZcqEEdvWS9+IoIqd8PMNUOx3AhIiIiIiKihnD4YhFGf7DdYX6IRgWjWf6QPtQPSorVp/SVP5r4372y6S6x4UhsEeYwNovAPsOlJr/nFuO5rw/UuN7rf+zu1narExsRjHfu7y1Ob8m+4rFtExF5CwMu1GByrpYhdaW1tufFwgoft8ZRsLrx/Tkww4WIiIiIiIgawvYTVx3mKRXWsUGEAdsFaj8oD2WfqRHIzl8rd5j3yeMDoFQqXD7rcLcjbPrRfOzOuVbjenERjuPL1sf9/ePF10IWDRGRP/P9Lxw1CRmnCjD83S2+bka1GuM4JxzDhYiIiIiIiBpCqMYxk8JVKXG1H5QY79yqma+b4DGv/3DEYV5I1fnwVIYLYM2aqckNsZ49rkqlAqN6xAFwPk4NEZG/YcCFGsQ3med93YQaGRthFogtw6Xx9NwhIiIiIiIi/2N2UspaAes9aerwTrL5QSrfP45qHqbBumm3+roZHqFxcjxbhGkBAFoXGS7uDkIfE65Fs+Cay7BFeaGEvBAcKtMZPb5tIiJP8/0vHDUJ9j/k9/Zv66OWOJox4kYAwJ9vTvBxSzyPY7gQERERERGRtxlMZmx2Nr5GVSLLzJFdZLPVKt9nuABAjzaRftOW+ohupgEAtIkMxudP3Yxtzw8XnwcEuwisKBTufe5Kg0kMeCR3j8WWvw1zup43gmlCoKdUZ8KGw3m49+NfcfJyqcf3Q0TkCY1rhDDyW2q7H9w37+nlo5Y4mnJ7Z9ycGI2+CVG+borHCRkujTF7h4iIiIiIiPzDv7eewrbjjgEX4ZG+fWkx+2cEvjSqZ2v8eOCSr5tRL/qq8WgeS+qA225sJVumVTuWFPvDDS3d3ofOaIbeZN3PU7cmIirU85ksrhir9rt46yk00wahVGfEM1/sxS/PDcO7G7OxZv9F/PLcbQh28lmJiBqa//zCUaN2qbBCNh3ipLarr6iUCiR1auFXbfIUYQwXE8dwISIiIiIiIi/5YtdZp/OlSRRje7cWX/tTwMVZOS5TgHVaFAISzrJ1VHaZLMFqJf7z+EC396EzmnG9TF+1H6VDcOPJWzrg348NcHu7tfHz75fF16VVWTYXCyugN5rx4eaTuFhYgTfX/e6VfRMRuct/fuGoUZP+OFLDYYYLEREREREReZurW85Kg2080YTmoeLrID8q46VxUnJLyBjxZ/M3HkOHl9bhxW8PYuORfAC2ZwBSuUWVsul20aF1zgS5Xm4AYA1SSUvHB6uVeOOuHkipGtze055P6eIwz2wGiisN4vQXu85i07F8r+yfiMgdDLgQNWIqpfVP3NnghURERERERET1ZTCZcaVEV+N6IZKH/M6ySnzF2eDxQuksf1VcacBHm08BAL7aex4VBhMAwNmdv7BMEKqp/+gCQSoFFAoFosOsY8csnzCo3tuszoD2zQFYAzsCo9mM4gqDbL2nlu3FRbsKK0REDc1/fuGoUevYMkx8Pf/+3j5sSdPCDBciIiIiIiLyptzCSpfL1k27VXwtfVjuTxkuzgIuBj8PuBSVG5zOd9bux5Pay6a7xIa73K6zTJIZI250mCeUhNv36kgcnZOCgR2iq21vfQkl4KUZU2YLUFJpdFj3yMUir7aFiKgmDLhQgxBqbH41aTAeGJjg49Y0HSqO4UJERERERERetPDn407n58wbgx5tIsVpaekuvxrDJQADLq7a56wUWpuoENmYLe1bhjqsI0gd3hn/vK+XOH1n79b464gbHNaTjhXjiYyZmoS4KIFWUOaYWVWuNzlZk4io4fjPLxw1amVVAZfYiGAft6RpUTHDhYiIiIiIiLzEZLZgzf6LTpcp7AZrlwVclP7zOMrZA3qD0b/voV3d47saeyZMYwtYjO7Zutpt3z/A1knWVWAsPFhdUxM9ytWYM+9syHaYZ19CjYioofnPLxw1WiazBWVVFzBhWu/3fCAboaQYx3AhIiIiIiIiT9t07HKt15WO2+JPJcUeGdTOYZ6/j+HiKsMlWOM8MCENKsXV0BFW6LgptfX5YbLpqJCGDbg4axMAHMsrEV93bGUtZV/JgAsR+RgDLuR170vSi8ODGXBpSMxwISIiIiIiIm+5eL281uv6a0mxjq2aOczz95JiRhdlw8fd7Bg8AgCdJPMlxEVQxhlLVefN9i3CcHOibZwWpYsAiC8NaNccADNciMj3/OcXjhqtjzafFF87G4yOvEfIcDGZ63exqDPygoWIiIiIiIjkVCppEMX2EL55qGMGhFJSYkztRxkuzvhzwMViscDo5B7/oYEJiArVOH3PHd1icEunFnhu5I3u7UvyevZdPRDfPATz7+/t1jY85e6+bVwumzS0o1hRZeOR/IZqEhGRU3z6TV4nTVe1r+FK3iX0OnHV+6U2vsu8gC6vbMDag5c81SwiIiIiIiJqBFSSe/wfptyK0KrsiZs6RDusK30c4E8ZLs64GgvF177ccw6Js9bjqWV7HZZpqungGqxWYeXTgzH1jhtqtZ+YcC0AIKVHnDivW+sI7HjxdjwwMMHV27xqpiRY9OywTrJl9/Zvi5bNrMGmA+cLUVRuaNC2ERFJ+fcvHDUK3VpHAACGdWnl45Y0PUKGS9rRuvfweO6bAwCAKSv3e6RNRERERERE1DhIM0G6tY7AD1NuxTO3dcTb9zlmQShgi7j40xguzuQVV/q6CQ5MZgtmrT4EACiqcAwoxEZoPbavtBlD8dWkwRjdM67mlRtIXKStM+/4IR1ky6LDNLIOvgVluoZqFhGRA48HXObNm4ebbroJ4eHhiImJwT333IPs7GzZOpWVlUhNTUWLFi3QrFkz3HfffcjPlz8QPnfuHMaOHYvQ0FDExMTg+eefh9FolK2zZcsW9O/fH1qtFp07d8ayZcs8/XHIA4TB2e7tH+/jljQ918psF2EmjuNCREREREREHpJXVIkVu88CAO7t1xYA0DmmGWaN7oboMMfSVrIMF6V/9f+1H2/WHzscluuN1S7vFR/lsX1FhWowqGMLv6pSog1SIeu1kTjwWjJiwoNly6JDNXhkkG38Go7jQkS+5PFfuK1btyI1NRW7du1Ceno6DAYDkpOTUVZWJq4zY8YM/Pjjj/jmm2+wdetWXLp0Cffee6+43GQyYezYsdDr9di5cyc+//xzLFu2DK+99pq4Tk5ODsaOHYvhw4cjKysL06dPx8SJE7Fx40ZPfySqp1Kd9aIgzI2B2cgzynS2CzKOw0JERERERESe8voPh3E8vxQAxPEzqiMdZ93fBl1fNWkwht7o31U5KvTV39M3hWcuUaEaRDoZHyhIpURUqAbtW4QCACoZcCEiH6r5F9FNGzZskE0vW7YMMTExyMzMxNChQ1FUVIRPP/0UK1euxO233w4AWLp0Kbp164Zdu3Zh8ODBSEtLw9GjR/Hzzz8jNjYWffv2xdy5c/Hiiy/ijTfegEajweLFi5GYmIj33nsPANCtWzfs2LEDCxcuREpKiqc/FtVDbpE1FTc2IriGNcnTpBexOoMZLsbPIyIiIiIiInKLdHDy2gRcAP8Kskj1aBOJ/z51Mzq8tM7XTXGp3EnApWOrMJy+Yu3g7O/j4niLdNzg4CBr0KlC759j8BBR0+DxgIu9oqIiAEB0tHXAtMzMTBgMBowYMUJcp2vXrmjXrh0yMjIwePBgZGRkoFevXoiNjRXXSUlJwbPPPosjR46gX79+yMjIkG1DWGf69Oku26LT6aDT2eo4FhcXAwAMBgMMhqY5oJbwub31+XVGM66WWo95q7CggD/O3j5enmaUZLWUVurQTFO/C1x3P3egHS9/wGPmHh4v9/B4uYfHy308Zu7xxfHiuSEiIk/44cAl2XQzbc3ZFdpqBnWnmpU5KSnWspm2yQdcVJKOpsFVWT7McCEiX/JqwMVsNmP69OkYMmQIevbsCQDIy8uDRqNBVFSUbN3Y2Fjk5eWJ60iDLcJyYVl16xQXF6OiogIhISEO7Zk3bx5mz57tMD8tLQ2hoaF1+5CNRHp6ule2e7USAIKgVliQseVn+FH5z3rx1vHytOyzSgiVA9N+3oSWdUgyClGpUGGynrj169fXqR2Bcrz8CY+Ze3i83MPj5R4eL/fxmLmnIY9XeXl5g+2LiIgarw83nZBNh2pqfrz0hxtaYnDHaHRrHeGtZnlMYsswXzfBgbOSYnlVFUUAQK1qJA9c3BQiKaUWorY+/+AYLkTkS14NuKSmpuLw4cPYsWOHN3dTa7NmzcLMmTPF6eLiYiQkJCA5ORkREf7/g+8NBoMB6enpGDlyJNRqxzqY9bX/XCGwfw9iI0MwduxQj2+/oXn7eHnagZ+ygUvWQQyThgzFDbHN3N7GwuM7cKbA+nBmzJgxbr030I6XP+Axcw+Pl3t4vNzD4+U+HjP3+OJ4CRne5Hvbtm3D/PnzkZmZidzcXKxZswb33HOPuPzJJ5/E559/LntPSkqKrITztWvXMHXqVPz4449QKpW477778MEHH6BZM9s138GDB5GamorffvsNrVq1wtSpU/HCCy94/fMRUeMWHiz/3arNw/4glRKrJiV5q0ke8crYbvjHut/RJz7S101x4Kyk2Llrto4UTS3D5clbOmDZzjN4eWw3cV6IuqqkGAMuRORDXgu4TJkyBWvXrsW2bdsQHx8vzo+Li4Ner0dhYaEsyyU/Px9xcXHiOnv27JFtLz8/X1wm/F+YJ10nIiLCaXYLAGi1Wmi1Wof5arW6yT8U8NYx0FWVzWwW3LiOcaB8Z3olRAGwBlxMUNapzcFqW2+Run7mQDle/oTHzD08Xu7h8XIPj5f7eMzc05DHi+fFf5SVlaFPnz546qmncO+99zpdZ9SoUVi6dKk4bX8v88gjjyA3Nxfp6ekwGAwYP348Jk2ahJUrVwKwBtiSk5MxYsQILF68GIcOHcJTTz2FqKgoTJo0yXsfjogavfBg+eMknbFxjJkRVFWeymC2+LgljsqdlBQbN6gdVu4+BwBQN7GSba/d2R1/GdYJMZIxXEJYUoyI/IDH/zW2WCyYMmUK1qxZg02bNiExMVG2fMCAAVCr1fjll1/EednZ2Th37hySkqw9HZKSknDo0CFcvnxZXCc9PR0RERHo3r27uI50G8I6wjbIP5TprD9ytRtAjzzt7j5txdfpR/PqtI0gSU8lkx9edBIREREFotGjR+Mf//gH/vSnP7lcR6vVIi4uTvyvefPm4rLff/8dGzZswJIlSzBo0CDceuut+L//+z+sWrUKly5Zx1ZYsWIF9Ho9PvvsM/To0QMPP/wwpk2bhgULFnj98xFR47Yl+4psenjXGB+1xLOCqrJEjCb/CyAdzS1xmDd9xA3i65hwxw7GjZlSqZAFWwAgOKgqw8VJNhARUUPxeMAlNTUVy5cvx8qVKxEeHo68vDzk5eWhoqICABAZGYkJEyZg5syZ2Lx5MzIzMzF+/HgkJSVh8ODBAIDk5GR0794djz32GA4cOICNGzfilVdeQWpqqtira/LkyTh9+jReeOEFHDt2DB9//DG+/vprzJgxw9MfiepB6IERqql5AD3yPKVk8Lh/bTpZp22olLZ/JnRGXrQQERERNZQtW7YgJiYGXbp0wbPPPouCggJxWUZGBqKiojBw4EBx3ogRI6BUKrF7925xnaFDh0Kj0YjrpKSkIDs7G9evX2+4D0JEjUphuV42/cHDfdGplfvlq/2RUBrNYPK/zob/+uWEw7yY8GCsnDgI66f9ocmVFHMmWMOSYkTkex5PO1i0aBEAYNiwYbL5S5cuxZNPPgkAWLhwoVhjWKfTISUlBR9//LG4rkqlwtq1a/Hss88iKSkJYWFheOKJJzBnzhxxncTERKxbtw4zZszABx98gPj4eCxZsgQpKSme/khUDwfOFwIAwmoxgB75pyBJ0KbSYEaoppqViYiIiMgjRo0ahXvvvReJiYk4deoU/v73v2P06NHIyMiASqVCXl4eYmLkPcqDgoIQHR2NvDxrZnNeXp5DxYHY2FhxmTRjRqDT6aDT6cRpYdwfg8EAg8EgzhdeS+dRYOC5C2z+cP4yzxTIpkd1a9Vovk9KWAMteqPJ45/Jk+du6vCOeCKpPQwGA25qH+mx7QY6bVXArKzS4NfnjxoWz11g89X5q8/+PP4U3GKpuRdAcHAwPvroI3z00Ucu12nfvj3Wr19f7XaGDRuG/fv3u91Gahh6oxmfZ1jHD1HUPH4eNYAKvUmsaVpbZsnfNOugEhERETWMhx9+WHzdq1cv9O7dG506dcKWLVtwxx13eG2/8+bNw+zZsx3mp6WlITQ01GF+enq619pC3sVzF9h8ef5+v64AYL2vnHeTERs2/OSztnja4avWz5Z/+UqNz6Tqyp1zd6kM+PGcEmMSzBAe4XWNNKNz5XH8uvm4V9oXyC6cUwJQ4tjJ01hvqluVj5rw387AxXMX2Br6/JWXl9f5vUw7IK8pqbRFAgvK9NWsSQ3l84wzmHxbJ7feIx23pbEMhEhEREQUaDp27IiWLVvi5MmTuOOOOxAXFycb8xIAjEYjrl27hri4OABAXFwc8vPzZesI08I69mbNmoWZM2eK08XFxUhISEBycjIiIiLE+QaDAenp6Rg5ciTUarVHPiM1DJ67wOYP589wIBc4dgg3d2iO+++6ySdt8BblkXx8fuIAIptHY8yYmz26bXfPXYXehN5zrWMXHy20lQubMrofUnrEerRtjcWpTaeQfvEU2ia0w5gx3T26bX/426O64bkLbL46f0KWd10w4EJeM22VLfuouIJpe/7g7Z+OoVvrCNx2Y6tav0dau1YafCEiIiKihnPhwgUUFBSgdevWAICkpCQUFhYiMzMTAwYMAABs2rQJZrMZgwYNEtd5+eWXYTAYxBvU9PR0dOnSxWk5MQDQarXiuJlSarXa6U2uq/nk/3juApsvz9/Za9YxejvFNGt036EQjfXz5BbpvPbZanvubng1zen8oKCgRnfcPUVbVc7ebIHPzx/5H567wNbQ568+++KIWuQ1v5601XU18kG9z9iXc3visz21ep/FYsH4pXvwe26xbB4RERER1V9paSmysrKQlZUFAMjJyUFWVhbOnTuH0tJSPP/889i1axfOnDmDX375BXfffTc6d+4sjlnZrVs3jBo1Ck8//TT27NmDX3/9FVOmTMHDDz+MNm3aAADGjRsHjUaDCRMm4MiRI/jqq6/wwQcfyDJYiIhq8vr3h9HhpXUo1RkBAGU6a6npyJDGN8BnUNUYIBcLK1BQqqthbd8Y0N55wJwAVdUYtHwGRUS+xIALecU3e8/Lpufd28tHLSHpoPfuKNObsDn7imyeiQEXIiIiIo/Yu3cv+vXrh379+gEAZs6ciX79+uG1116DSqXCwYMHcdddd+HGG2/EhAkTMGDAAGzfvl2WfbJixQp07doVd9xxB8aMGYNbb70Vn3zyibg8MjISaWlpyMnJwYABA/Dcc8/htddew6RJkxr88xJR4BLGZr3rwx0AgAqDNfAS6ub4oIFArbI9Jjt0sciHLQFahTtmG1Y3n2zPP9bsv4icq2XQG82o0HMsWiJqWCwpRh73fdZFPP/tQXG6RZgGN3WI9mGLmjalQgHA/UCJszCNmUO4EBEREXnEsGHDqs0e3rhxY43biI6OxsqVK6tdp3fv3ti+fbvb7SMisnf6ShkAoLzqAXZjD7gEKX3bR1mjctx/QnSID1oSOITzZ7EAw9/dIs4/NncUgtWN7/tKRP6JGS7kcX9dlSWb3vS3YT5pB1nVNcPFWTaLmRkuRERERERETYY0MPzHPtZyhd9nXQIAhDTKgIvt/nndoUs+bAlQrjc6zNMGNb5j7kkqF88/hGAhEVFDYMCFPKZcb8Ss1Qdl88I0KkSGcEAqX3J2wVGbsVjMTmqeMuBCRERERETUdOiMtjIHzUPl9/bH80oaujleFxMRLL7+cs95GE2+K/NQ5qQUVrCaj/GqIw2YSfFZBhE1JP5LTR4zf2M2vtwjH7vlnfv7+Kg1JAhykoasr8VFo8lJwMXZPCIiIiIiImqcDpwvFF8rFQroJQGY2txXBpq2UfKSXUUVBp+0w2Ayy4614FJhpQ9aEzhcfSXnb8xu2IYQUZPGgAt5RFG5AUt/PeMwf3jXVg3fGJKxjuEiZzDVHDhxXlLMI00iIiIiIiKiAPD6D0fE1xaLBb+euipO3923rS+a1KAqDL4ZcL3cxUDv18r0DdySwGJ0MfDs1uNXfJqtRERNCwMu5BH7zl13mLf/1ZEI1QT5oDUk9fofuzvMc9ZTxp6z6xSm4RIRERERETUd8c1tGR8GswXjl/4mTg/u2MIXTfI66TiolT4LuDiO30L1o6vFcxAiIk9gwIXqzWKxYO3BXNm8G2OboXmYxkctIilhYEOp2gRcnGa4MMWFiIiIiIioyQhS2h4bGZrIA2uVLODi/c9cUulYtqykkgEXT/NVthIRNT0MuFC9bcm+gu/2XZDNYyKEf6tdhouTMVx4YomIiIiIiJqES4UV2HAkT5z+JvNCNWs3HtIMF28/pF+05RR6vZGGv67aL5ufvHCbV/fbWDkWVLfxVbYSETU9DLhQvWWcLnCYx8fy/u2JpXtqXMdZ+TDGW4iIiIiIiJqGxz+r+b6xMZJmuFS4GEvFU/654RgA4PusS7haqnO6zsyRN3q1DU3FxiP5vm4CETURDLhQvTUPdSwd1jxU7YOWkCthGpVsOudqWY3vMTnLcGFJMSIiIiIioibh5OVSl8s+eWxAA7akYQWpbI/KDl0sarD9/vK7NSBgsevpOO2OGxqsDY3Zil1nfd0EImoiGHCherM4yWdp2Uzrg5aQK1q1quaV7DjLcHE2j4iIiIiIiBoXZ+OKSCX3iGugljQ8aYbL/I3ZDbbfF787BAAwOuno+OXTg9GxVRhWPj2owdoTkBSORcV6x0cCcD6+LRGRNzDgQvVWpnMczO2Wzi190BJyRenkoqMmJifDvDDgQkRERERE1Pg15QHGpWO4DOvSqsH3bzTZ7rsXPtQHAJDUqQU2PTcMt3TisxZ39U2IAiDPHLJYLFh3MBenrrjO4gKAnaeu4nJxpTebR0SNEAMuVG9lOuuF2Oiecfhf6hC8NLorxt3czsetIimVk790+zRle87Kh10q9J8Ljf3nruPuj37F3jPXfN0UIiIiIiKiRkVvdNIDr4mQdlhMaB7q1X3d3jXGYZ5e0vtxbC9mZbijc6tmDvOClNYHIgbJM47lu88hdeU+PLZkt8ttbT1+BeP+sxs3v/ULsvNKUFxRfdYXEZGAAReqt9KqDJfe8VHomxCFybd1kqXgku+pnGS41DQci7Nsllf+d9hTTaq38ct+w4Hzhbh/cYavm0JERERERNSo5BbZOtu9PKabbFlMeOMuIR6kst0/64zezfRxVj7MKAm4qFV8tuKOpE4t8O4DffDds0kYN6gdFj7URzyG0uO69sAlAMClokocyyt2uq2/fXNAfJ3y/jYMeGsz0i/yfBBRzRhwoXorrbQGXJpp3R8nhBqG0kkArC4ZLv6ksJy9S4iIiIiIqOnIK6rEuWvlDbKvByQd2/pUlWQS3N23cWddSDuQejvTx2hXy7vSYMK1Mj0Aa2kzRR3Kgzd19w+Ix4D20XjrT73wp37xYgDNICnVVlB1jAFg1PvbnW5H46RUyNpzfO5FRDUL8nUDKPCV6a0BlzAtv07+ylnGUU3xFJOfj9eiDVJC14TT3ImIiIiIqOmwWCwYPO8XAMC8mxp235Ehatn0s8M6N2wDGph0DBe9s8FNPUg6XgsAFFUYcOF6hXWZn3eCDBSqqpJiQqfS3acLcPJy9WO3AECbqGBcLKzwatuIqHFihgvVm1BSjAEX/+W8pFj1F29CBkyI2j97cAT7abuIiIiIiIg8TdrZLLdhklxEiS3DxNdP3tIB0WGahm1AA1NLMhu8WfnBbLY4lLMqqTSi0mAtY9YnPtJr+25K1FUBNKPZ+jf0zPJM2XJXx1ntJMNFrWQQjIhqxoAL1VuZTigpxoCLv3JeUqz69wgdeVpHBnuhRfWTW1SBIsmAdex1QkREREREjVmF3jaWiMHccGWmFApAE2R7dBQb4X/3h542a7RtzBpvJrj89assFFeVaBcYzWaUV53rCLvMIqqboKrAiVBSzKFMnIuybdJnDgKDWVGr7BgiatoYcGmi0o/mY9qX+1Fi9+NeF2U668UAM1z8V5DTkmKOERezpPeO0JNHqVRg/JAOAFxehzS4oe9slk3/+ZNdPmoJERERERGR91UYbAGXhqw09e9HB8im/eWe0JtuvaElpo+4AUDNlSHq48eqgdsBICZcC8BaYkw416EaVnXwBHXVGC7CeDnaIPmjUJ3kb0tgsVhw5FKxw3wAeP67wx5uIRE1Ngy4NEGF5Xo8/d+9+OHAJWw4kl+vbRlNZlwvtw42Fh7MgIu/Gtk9FgDQPNTWQ8b+wrGo3IAh/9yEF749IFuuUijw55vbAQCiQ/0jddxgV+e2oQaOJCIiIiIi8gVpZ0lDAwxlKTyk7tlWXm6pCcRbAADxzUMBeDfgIiVkERlMZrGkmL+W9w40wpi2BWXWZ1f2ActKJwGXnw7nudze4UvFOJFfgislOs81kogaFQZcmoCDFwrx8CcZ2HvmGgDgeL4t/fFa1Q9OXZ2+WoZyvQnNtEFoHx1ar22R90y9/QYseLAPfphyqzjP/rLxy9/OIbeoEl/vvQBAnuEi1C4tdJJS6wvSGsJERERERESN3avf23rVezvgYjZbxE5u9tkATSHDBQCEIhHeGsNFZ5Q/5NdIyl4JJcVCNOzU6glCSbHtJ64iv7hSHIdYUOnkD+r0Fdtzs+6tIxyqhoxcuA03vfmzF1pLRI0BAy5NwP9tOoldp6/h/sUZAKzjXwgOXCiq17YvXrduKyE6VPwRI/+jCVLi3v7xiJOMx2Kxu6YoKJX3zjAJGS5KW28bk9lS7yCdJ7RvweAeERERERE1DQfOF2JPzjVx2tsBF71k4BJtE82yELIitp+46pXtP7pkt2w6qCqj6P2fj2PJ9tMAmOHiKdJgyaZjlx2CaJVGxwyXS0WV4us1qbdg2fibvddAImp0+IS8CUg/aisb9vGWk/jrqixxOq+40sk7au9q1UP6VlX1Rsm/KSXdkYTUaL3RjPfSsrH1+BXZusJ4LiqFQqwnCwAn8ksaoKXVK9c7XhARERERERE1NhcLK3D3R7/K5pm8XOVKJ4noaNixEpfr+dzEmd/OXJdNBymtx3nnqQIUV5WPC9Hw2HuCtCqcxQLERsifXxVXGHAsrxiWqhULy/VYufucuFwbpBJL7BER1Qb/9W7kLhZWyKbf2ZAtmy6zS6V0l/D+cC1TXQOBNAvWbLHAbLbgmS/24v82nZSVmgMcS4r1bxcFoP5l6OrLYrGImVVERERERESN2SEnVSm8VOUKV0t1WPZrDq5UdaxUKODwoFnRREZxuVpqu+8tqedzk9rQBDk+ngtlSTGPsEgKqv99zSHkF1u/338f0xWA9e9p1Pvbse5QLgDg/DXH5w2uMr28VXKOiAIbAy6N3Ns/Hat2eVkdMwXMZgve//k41lcNJNaMAZeAoJBluACr91/E5uwrTtcVMmBUVe+JDtMA8P04LqeulIqBxMWP9hfnG0wNMHIkERERERFRA3LW4c0bGS4Xrpdj4D9+xhs/HsWIBVsBWLNbhHvIe/q2QXhwEO4bEO/5nfshk9l2f2lpgGfqzUPVDvOCWVLMqzq0CJONSfSf7TkAgNNXbZ1R7+7bBoDz8wMApZXeD8YRUeDxeMBl27Zt+OMf/4g2bdpAoVDgf//7n2y5QqFw+t/8+fPFdTp06OCw/O2335Zt5+DBg/jDH/6A4OBgJCQk4J133vH0R2kUOtYwuHhdSzOtPZSL938+IdaRDWPAJWAIWS4WiwUbDue6XE+IXyir3qANsl7s6Y2+DWwUllsDPgnRIbi9a6w4v1zHMmNERERERNR4zPgqC39fc8hhvjc61T+17DeHedKOlQsf6ot9r44UO+I1dtL+fGYPR1zynZQoax0V4jCPY7h4hqusrOgwDVpH2Ma51VZlGRVUZTdFBAfh7Xt7AwDaRoWgUyvH52vZflBynYj8j8cDLmVlZejTpw8++ugjp8tzc3Nl/3322WdQKBS47777ZOvNmTNHtt7UqVPFZcXFxUhOTkb79u2RmZmJ+fPn44033sAnn3zi6Y8T8EI01f9AV9Qx4HLS7kelmZYXAoFCGMfFgup76ggXlUKARkhx9nUmiTCAY4haBU2QUqwpXKpnzxIiIiIiImo81uy/6HS+NzJc7EtMA0BkiK1Xv0JhLTXdVEgzXIwePuAnr5TJptNmDEX76FCH9cL4nMUjgpTOAy4hGhVCJUFFobqH8MxjRPdY8ZlakEqJtalJmN1f/tyhpNK3FUCIyD95PC1h9OjRGD16tMvlcXFxsunvv/8ew4cPR8eOHWXzw8PDHdYVrFixAnq9Hp999hk0Gg169OiBrKwsLFiwAJMmTar/h2gk0o7k1VhSzGi21Kl3jH1mTLNgZrgECmvAxVJjLx2xpFjVxYlQu1fv64BLVYaNcLEfqlVBX25GeQPU1SUiIiIiImoIxmruuzwdcMk8e93p/PAQ52WUmgLpcxJPj9Nhv7120aFOy4clOAnCkPtULgIuoZogWRCxuCp4IgRctHbj6gSplIjSAisn3IRxn1ozwiZ8vrdJZX4RUe349Cl5fn4+1q1bh88//9xh2dtvv425c+eiXbt2GDduHGbMmIGgIGtzMzIyMHToUGg0tn/QUlJS8M9//hPXr19H8+bNne5Pp9NBp9OJ08XFxQAAg8EAg6FxRaWNJjMmfZFZq3VNFrj9+XML5YOIBQcpGt0xdEb4jAH9WauuNfR6A8wW5xfxJ/OKoDcYxdUNBgOEsRIrdcZaf35vHK9KnXVbQUrrdy5Mo0JhuQGFZZUwGIJreLf/axTfsQbE4+UeHi/38Hi5j8fMPb44Xjw3RESBwb6j26OD2+F6mQHrDuXCbPHswPX3LdrpdH5pE+69Lw2KmDxcUsy+0kiwWoVgtWP2EEuKeUaQynVJMWnSljBekhCANLiIbN7UoTk6tAjFmYJyAMCzyzOxatJg2Zi5RNS0+TTg8vnnnyM8PBz33nuvbP60adPQv39/REdHY+fOnZg1axZyc3OxYMECAEBeXh4SExNl74mNjRWXuQq4zJs3D7Nnz3aYn5aWhtDQxtVzoFgPVHd6u0eZcbTQ+stiNAPp6em12q7ZAqw+o8T2PPnFwImjh7H+imNt2caqtsfLH1nMKgAK/LJpM/LzlXBWWfChRduREm8GoELB1StYv349Lp63rvv78RNYX5nt1j49ebyyChQAVCgtKsT69eth0lk/z+K1u3BHGzPUSqCGSnoBIZC/Y77A4+UeHi/38Hi5j8fMPQ15vMrLyxtsX0REVHc6gzzgktSxJfafsz4I9kZJMWeM3hgsJkDIAi5mz1Z5KHFSnUEYM1VK7SJQQO5p62R8HMA6Ros0+FVQpofFYsHm7CsAgG8zL+DdB/o4fa90bNvdOdcw7j+78eWkwR5sNREFMp8GXD777DM88sgjCA6W90qfOXOm+Lp3797QaDR45plnMG/ePGi12jrvb9asWbJtFxcXIyEhAcnJyYiIiKjzdv1RztUyIPNXp8umDu+IKcM6ocvr1pt7kwUYOXIk1Oqa04X3nr2O7bscB9O76/Yk9E2IqlebA4HBYEB6enqtj5c/mpX5Cwx6E24bNgxby44BhVcd1inQKdCzVy/g1FHExcZgzJj+OLghG9vyzqJdh0SMGdWlVvvyxvEyHMgFjh9CbEwLjBkzEH/NSAMA/HJJiZ1XghAVqsG2vw2t1bZ0RrNDmrCvNYbvWEPi8XIPj5d7eLzcx2PmHl8cLyHDm3xv27ZtmD9/PjIzM5Gbm4s1a9bgnnvuAWD9brzyyitYv349Tp8+jcjISIwYMQJvv/022rRpI26jQ4cOOHv2rGy78+bNw0svvSROHzx4EKmpqfjtt9/QqlUrTJ06FS+88EKDfEYiqrvfc+X/XkeGqBFU1R2/oQIunVo1a5gd+aF7+rXBh5tPAvD8GC4Xrlc4zHM2Po5K6V/3qoFqYIdotI4MRm5RpWy+QqHAKcl4OnqjGaW1LFXeKiIYlyTbyzhdgKulOrRsVvdnltUxmMz4ePMp3NalVZN49kYU6HwWcNm+fTuys7Px1Vdf1bjuoEGDYDQacebMGXTp0gVxcXHIz8+XrSNMuxr3BQC0Wq3TgI1arW50DwXOXde5XNYyPARarQZqlQIGkwVGc83HYE/ONVgsFlwudZ5SPDCxZZNKnwzk74yy6jwFBQWJr53ZduIaACC3SAe1Wo1gjfWfC5NF4fZn99Txyi2qwIo95wFYewDZb7PCYEZFUSWuV5oQE159ebHdpwvwyJLdmDHyRqQO71zvtnlaIH/HfIHHyz08Xu7h8XIfj5l7GvJ48bz4j7KyMvTp0wdPPfWUQ8Z/eXk59u3bh1dffRV9+vTB9evX8de//hV33XUX9u7dK1t3zpw5ePrpp8Xp8PBw8XVxcTGSk5MxYsQILF68GIcOHcJTTz2FqKgojn1J5OfK7MpORYWqxcG/PZl4YqmmXNbLY7t5bkcBpnNMOFqEaVBQpvd4STFDLcdFdTXYO7nv2WGd8Nr3R2pc7+TlUvH1/Pt7u1zvg4f6Yti7W2TzLhd7L+CyNfsKFv58HAt/Po4zb4/1yj6IyHN8Fi7/9NNPMWDAAPTp4zw9TyorKwtKpRIxMTEAgKSkJGzbtk1Wgzo9PR1dunRxWU6sqdmdU+ByWfOqwbyEHhTGGq4ddEYTHvx3Bh76ZBeOXnLsFZncPbZJBVsCndBjI6+oEtWd+g1H8gAAx/JKAABBVb1rlu08483mVev+RRnYf64QAKCpykxx9tU7dKEIFosF45fuwaj3t0FnNDms89Vv52E0WzB/o3vl0YiIiIg8YfTo0fjHP/6BP/3pTw7LIiMjkZ6ejgcffBBdunTB4MGD8eGHHyIzMxPnzp2TrRseHo64uDjxv7CwMHHZihUroNfr8dlnn6FHjx54+OGHMW3aNLFUMxH5L2nJooHtm6NLXLg4FoUnEy6kgZ1BidGyAcbjmzsvxdRUxERYO/HZD3JfX9Lt9WxrrbZicXJ37izrherG1bEc0F7+DPGoJLPszt5t7FcXdWgZhlNvjZHNu16ur0cLq3emwJaJ4+z5BhH5F4//611aWoqsrCxkZWUBAHJycpCVlSW7MSguLsY333yDiRMnOrw/IyMD77//Pg4cOIDTp09jxYoVmDFjBh599FExmDJu3DhoNBpMmDABR44cwVdffYUPPvhAVi6sKTObLVh/KM/l8uhQa8BFeGBtrKFzRWmlLaVy12l5IOfdB/rg348NqGNLyZde/6Hm3h1SwqCNGh+W4LpYaEu9FmrcfjSuv8N6By8U4deTBdicfQXH8kqw7NczDusUVTTdASCJiIgo8BQVFUGhUCAqKko2/+2330aLFi3Qr18/zJ8/H0aj7do9IyMDQ4cOhUajEeelpKQgOzsb169fb6imE1EdLNlxGgDQsWUYvn32FqhVSvGhsfT5//lr5Rg2fzP+m3HGre0fzy/BXR/uwKIt1rJZwWolvnomSRYMcDauSFMiPKP39Fg2wjEe26s1vp18CwDnWUuuBnsn97nKFloxcRD+fHM7hFYNBJtbaC0TplBY/yaqo1Iq8PNMWznza2XeC7iEaW0FivKLXFe0ISL/4PGSYnv37sXw4cPFaSEI8sQTT2DZsmUAgFWrVsFiseDPf/6zw/u1Wi1WrVqFN954AzqdDomJiZgxY4YsmBIZGYm0tDSkpqZiwIABaNmyJV577TWmxVf54cAl2YNpe51jrHVYQ9QqFMIAnRlYdygPxToTHk/q4LB+uaTHy4ELReLruIhg3N23DbNbAtTlEh1iI6ovuyV1Z+/WWLTlFCJDfFOKxL6GcXiw9Z+vMb1a497+bbF630Vx2aGLRQjT2m4O9uRcwzO3dRKnDSYzfjl22cstJiIiAgpKdbh30U7c0TUWr/2xu6+bQwGqsrISL774Iv785z/Lxp6cNm0a+vfvj+joaOzcuROzZs1Cbm6umMGSl5eHxMRE2bZiY2PFZc6qA+h0Ouh0toc5wrg/BoNBVmFAeC2dR4GB5y4wCJn9p6+W2c6VxdoJ7nKFQpz31rqjOFNQjte+P4I/D2xb6+1/8PNxHLxQhINV9/gRwWqH70RT/44Iz+j1BqNHjoX4t2eyPmNp1zwYKphhMJhlwXKBxWRq8ufAUyxmx57GBoMBKgBz/tgVJpMJX2deFMftsVjgcE6c/dvZvnkwRvWIxYYj+bhaUuG181Whs21358nLaB1R+7914u9eoPPV+avP/jwecBk2bFi1NUABYNKkSS6DI/3798euXbtq3E/v3r2xffv2OrWxsfv1pHwQ9MWP9sf/bTqJI1XlwOIirQ/ZI0PUyC2qRKlBgelfHwQADO8Sg4ToUNn7KwyO6Yqz7+qBJ27p4IXWU0PRuJme3KyqR0VZLQeR87RVe+TlM5pJenjYB4EOnC/EJklA5Zdjl3GuoBzxzUOgVCpw5792eLexRER+bO+Za3hnYzZeHNUFA9pH+7o5AWXr8Su4eL0C4wa1q9X6O05cxdd7z+NsQTk++zUH44d0cLjOIqqJwWDAgw8+CIvFgkWLFsmWSTul9e7dGxqNBs888wzmzZvndOzK2pg3bx5mz57tMD8tLQ2hoY7f3/T09Drth3yP587f2e531q9fDwD46ZgSgBKnShTi+Tt70TpPul5trDskfxx0uURX9X7H/TZVJUUqAArs+W0vKk95Lsvl7NnzAJQ4ffoU1q8/AQCoNAD2j+g2/ZKO4KadZOQxWZcVAOQHU/r9Lsi1/R05Wy5l/29nyVXre3dnHUV0wWFPNNfBTydt7XtpzREE5x6ABbagINUOf/cCW0Ofv/Ly8jq/1+MBF/KtcwXlslTUH6fcil7xkViYfsJh3ahQ60PqPMn3x1lwpVzvOK9VuHcGAqOGow5SVDuGS6hGhXK9CYMSrQ/jhABHud4Ek9kiq+3bEFrYDT4nbbt9wKXASSrv0Pmb8dSQRLz2x+7Izi+Rb8tiYaYWETUJJrMF9y/OAAAs2nIKS55gwKW2LBYLnvhsDwBrve8uceEu163QmzB5eSa2Hr8im3/ycikDLuQWIdhy9uxZbNq0SZbd4sygQYNgNBpx5swZdOnSBXFxccjPz5etI0zHxcU53casWbNkgZzi4mIkJCQgOTlZtn+DwYD09HSMHDkSarVvMqCpbnjuAsNfM9IAAA8NjMeYMdYMyVf3bwJg7QAnnL81BftwtNDa6XLMmDFOt+XM20e3IbeoUpxuHqrGmDHJ2Fp5GKv3X3J7e43RF5f2IKekEH369sPons7/zXSH8LfXJj4eyL+ELjfegDHDbZUYRo8yYnP2Fcz85hAA4J6xo6HkE3WPKMu8AJw6CgC4tXMLPJnUDrfd2EpcfvyXk/jl0mlx+k99W2PMmF6ybbj6tzP755P4Nf80ImITMGZMD6+0/90F2wHYKtl8caklCsr0+DE1CcFqRuVqwt+9wOar8ydkedcFAy6NyIbDuZi8fJ9sXruqm/oOLUMdHjKHB1u/pJfKbT/gFU6CK+V6x4yGPglR9W0u+ZjeaMahC4Uul/dNiMLOUwV4ZHB7APKaoR9tPoknbunQoOXF7BPnpLWFa9uOz37NQetIxzJqBpMFmiBeyBJR43b4YhEe+neGOL3DLiOWqldYbkspr2lQ1EeW7MK+qlIwUqv3X8Thi0UoN5jw4qiunm4iNTJCsOXEiRPYvHkzWrRoUeN7srKyoFQqERMTAwBISkrCyy+/DIPBIN6gpqeno0uXLk7LiQHWEs/OsmPUarXTm1xX88n/8dz5t06twnDqShn+1D9ePE/SsUSE86dSKmXzasu+s6VKqYBarcaMkV0AKPDMbZ2a/PdDrbI+yFYoVR4+FtZ7T02QfLvN1WqoVLaH51qtxuGdVDcVksdayycOdljer100AFvA5aGb27s85/b/dpqq/iy/zryIdx7o64nmyhSW63H+unzYgMyq68xZ/zuKD52Ma0vO8XcvsDX0+avPvhhwaUSEWpNSwjgWL43uBqPJgukjbhSXCYOCHSiwPWjOLapEnwT5NpwFYVq7MfYH+af84uoHWhMu5oXB5bRBSqhVChhMFixIP46cq2VY+FBfbzdTdLagTDZ96w0txdfuBH7eXP+7wzyd0QRNkHsl1oiIAs3Law6hTPKbXmlwrGVNrp27ZksJ1hmrP3bOgi0A8OOBS+Lr+/q3RfvmvJ5qykpLS3HypO36PScnB1lZWYiOjkbr1q1x//33Y9++fVi7di1MJhPy8vIAANHR0dBoNMjIyMDu3bsxfPhwhIeHIyMjAzNmzMCjjz4qBlPGjRuH2bNnY8KECXjxxRdx+PBhfPDBB1i4cKFPPjMR1Z7wW6OV3Kc8ODABy3aeka0n7ZeWnVdSbQamVLlOfp/fr531342E6FAsaMD7PH8mDFpvcjaifT0I99rOsldGdo9F+xahYqUJ8oz2NWQYC2MdC7RuPB+Qdk71RjWQez761eWytQdz8eE4j+6OiDyATxgbuaCqcToSW4bh0ydvQq/4SHGZ0FPTYLH9GKw9eAn2SiodM1yY1hq47uzdulbrCReVwsWCQqGQXUis2X8RJZUNN2DVlVJrgOgPN7TE3Lt7YJgk/VfarrrgQ0ciagqcBQlqGnePgKulOkz7cj/ultzsFpbrsePEVZTWY1yzU1fKal6JGrW9e/eiX79+6NevHwDreCz9+vXDa6+9hosXL+KHH37AhQsX0LdvX7Ru3Vr8b+fOnQCsmSirVq3Cbbfdhh49euDNN9/EjBkz8Mknn4j7iIyMRFpaGnJycjBgwAA899xzeO2111yOp0lE/kMvBlxsGQ/CGGIqhfPf75T3t9V623qT/Lrg7Xt7uVi76VJWlZ02eijgcq1Mj3//rsSGI9bSjionZa1DNUHY8rdheOf+Ph7ZJ1nd0S0Gc+7uge9Thzhd3qFlGO4fEC9Ou9Mh8+k/dBRflzp5flZfZwpsnX7u6x/vsPz8tbqPM0FE3sEMl0bsL8M6Vbvcvq444DxT4G/fHJBND+vSymEdChxCZlNNMs9eB2DLcAGs47hIS6qM+89u/Dj1Vs820AVjVZ7uAwMTcFefNrJlBsnNQpfYcLF83gcP98VnO3Jw4EJRtdvWGR2zuIiIGhv7ByuA9QGCWsVOFNX566r9+PVkgWzeu2nZOH+tAn+4oSW+mDBItqy2vWBvsOtJSU3PsGHDqg161hQQ7d+/P3bt2lXjfnr37o3t27e73T4i8i2ho4T0wa+6qkOlWvIsODrM/bJTl0sqHebZj5lJtnths4cCLgt+PoGjhUoA1vtPV8OIcnxRz1MoFHg8qUO167z+x+74NvMCAMeS5tUJ0aigUSmhN5lRqjciMtQ7JY/+1K8t3nuwD77bd0E2/811v2PxYwO8sk8iqhtmuDQi9s9RavtgXUp4qC2bJ7m4uDG2GWZIypJR4HE3vVW6vn1a7aGL1QcyPMlotn7B1U7af9uNrdA2KgQpPWLxfEoXcX5MeDBGdIt1ur3BHW0p2sxwIaKmQO8kw8XZPLI5ebnUIdgCAOevWetobz/hOA6ONOtl8aOub347tmLAhYiInDtyqQhFFdaObtJ7MCEAIL1tb9HMvYCLzmjCrf/cLJv3bA2dNZsq4V7YUxkuaUcvy6a/3nvBxZrkC+HBajye1B6DO0ajay1L8wmEcv5l9ch+drntqmd70+64wenyNlEhHt8nEdUPAy6NiMEu4hKiqT6BydmD6DK96x+HG2ObIW3GbeiTEFWn9pF/ULrZW0bas0PoUeULhqq7iiAnbQgPVmP7C8Px78cGormkh1ewWukyFbiw3ICYcGsvLma4EFFTUGlw/LeOAZfqzfgqy+33/FA1TotapcConnE1ZhwTERGZzRbZPcmzy/eJr7VqxwwXacDFYLRN9GwbUeO+TtuVtNww/Q/4W3IXF2s3bULAxWR273rp28wLOHih0GH+9XJ5SW6jk+xj8q05d/fEqklJTp87VEcoc+7pgEuF3iSOwSgEV5+5raNsneIGLPVORLXDgEsjYv/QpKaHyK//sbvDvAq943sigq0/HAse7Fv3xpHfcDfgUi75TgT5sOyMkOHiqg3CuEIayYVRdXVXb4wNR7Da2lOEGS5E1BRcLdU7zOs3Nx1f7jnng9YEhgvX3auJffhiEV7932EAto4CL4zqisOzU9CxVZjH20dERI3D45/twYC5P4sPTs9JxmSQjuEi3N+YLQrxYb2042V0WM1lwS6X6GTTXeMiPD7Id2MhHJdXvz9S62uCHSeu4m/fHMBdH7oe6Fzw+h971Kt95D+aiQEXz3bmvFo1lq0mSInwqn28kNIV66bdivn39wYA5BZVeHSfRFR/DLg0IvYBlz7xUdWu39JJjdZyJwEX4YGBs/FdKPC4ezEtzXry5djKQrk7tbL6f7bUQfISaM4GiQaAx5Pai8Gby8WONYyJiBqbVuHOH8LMWn2ogVsSOEJryBa2l3HKsfwYYL0J3/TcMCx8yDoA7qOD29W7bURE1HjsOHkVpToj1h3MxakrpbJl0lLhkSFqCLdzQraE9DmA0WTG6Sul2Hgkz+W+nvhsjwdb3rhJxzN95ovMWr3n99xil8t6x8szkBKiWQqqsRAyXITSss99fQCj3t/mNMPcHULApVUzrTi2j0qpQI82kWjb3Pr9yS3k8wwif8OASyNiX1Lslk4tql0/WO14+svtfgzMZgsqquYJ2QAU2NzNcJGed18GXITvd02DO0vLnqlVSpcXOAM7RIvp9M+u2Od0HSIiX7v93S3o8NI6XCqsf8+18qobwDl39xCzV6l60rr5XWLD0b21Y6kW6cDmO046jukidU/fttjyt2GYfVdPzzWSiIgCmnQA+1mrD+GO97bKlkvvb1RKBcKrfsPPFFgzLqTPAfRGM25/byue+SITGw7nOuwr63yhbJrXA9VTSgIuRy65DqRIHbnkepxTjV2ZKo2Kz1gaCyHDZfLyTCz7NQff7buAY3kl6PrqBmw/ccXpe/RGMw5dKIK5mjGChAx1Z2M1tYm0BlwuFVXIrkeJyPcYcGlE7DNcFDU8WHe2vNyu3uRPh209Y5wFaCjwuDsMy5ieceJrb/6EWywWfJt5weUFanVjuEjZlxSTZrh0juEAxUQUOHaeuorTV62B4Xc3ZtdrW1uyL4v1n2/p1BLFlZ4f0LMxkpamXDvtVqcDqErrsZ++WuqwXEqhUKBDyzCWbiEiItH8Da5/47VOSiQXVVh/w8d9+htMZgsqJaXE9569Lr6evHwfTGYLMs9eF8uN5xXJe8I/OSSxXm1v7KQZLrX96T4sCczYP0i3r74QE1FzCTgKDBGSijBv/HhUtuyxT51nlf1zwzH88cMdeOX7wyiuNODdjdk4nl8iW6egKsPFWYWauMhgANYS6dPrMO4gEXkPn6A3InoPDLgmLSlmNlvw25lr4jQzXBoHdzJcBrRvLgtweLPXxJbsK/jbNwcw9l87nC431jLDRfoQS6NSIjrM1hPknr5trPOrGduFiPxPYbm+Sfba2n6i+myJ2rJYLHhy6W/idJuoYI9stynIqQp43du/LdQqpayzivBbImQCWywWXKmqi39XnzZY/ZdbGri1REQUiHKLXJcDWj5xULXvfe37wygsdz1gds/XN+K+RTvF8qHS66ll42/CjBE3uNnapkUlKWdd2/voHm1s2bDf7rsgWyY916/d2Z3PWBqRunSl+XRHDgBg5e5zeGfDMXy4+SSSF26TrXNVDLg4ZrhIvz/fZ11yOiYzEfkGnzo2EmazRdZbom1U7WqB/vPeHri5lRnf/2UwANtDA53RhBELtmLZzjPiump3UyPILymddM1J6ui8/FympIeUtx2tptbt0UvFuFR1cRpUwxgu0uvgIJUS44d0wD1922Dxo/0xaWgnzL2nJ9JnDPVIm4nI+55cugd956Tj6f/u9XVTGpzOYPtdX73/otvv/+X3fAx+6xc8/Mkucd4fbmiJUE0QWkfKgy7fZV7AqPe34VyBe4PEN2bbjl8Rr62m33EjAODZYR0BAA8OjEdI1U2ucHO76/Q1VFads3/e1xv92zVv6CYTEVEACqqmQ1lUDeOorth9DtfK9C6XC/f3q/dZryNKqjJch3dphWFdYmqsitHUSR+B1DbgIl3rf3bXb9Lg2G1dWtWnaeRnCitcBz5rY/muc07n20qK1ZwNVa5nBjuRv+AT9EZCOrB5XEQw1tSyV+W9/drikc5mRARbL+SEf6D//MkusYwJAIzpFef0/RR4VE4uFBNbhWHXrDtqfK/ZBz3My/VGjPnXdnFaE1T9hW5wkK2Xh0alRKgmCO8/3A+jeraGJkiJxwa3R/sWYQ7vO2BXz5iI/MOWbGvN459/v+zjljS83CL5uC2uBmQXHM8vQeqKffj31lN48N8ZeGn1IeQVV2J3ji1btV9CFADgf6lDZO997psDOJZXgrnr5CUQmrLHJYMKC/XyO8eE4+icFPzzvt5iwKXSYILBZMaf/2MLbIVo2GOViIhqJ0zjehyVyBoCLgBwvdx1wMXef3edAQBc9MDYcE2BrLNfLWNTJZKyrUI519/OXMPAf/wMY1WJsS8n3oROrVjuujFpE1l9BrmzcVrimzvvKC0dl+lKNSXF7LFkMJH/YMClkSitGnslSKlAxqzbERPhXrkQ4cFApcEMvdGMfecK5cvVHEyvsXCW4WIyWcT6n9XxZrzFvveP4MxVeW/r+Oah1W6neZgGr93ZHXPv7uHWAy9pD3Ai8k8zv8oSyws2ZhaLBTtPXcUeSaAEAL6zK0thb8ZXWVh3KBfzfjqGPTnXxPJWUq2qrg9iI4Lx+h+7Oywv0/FGDbDVyxaESwYVDtUEQaFQiL2GNx+7jAXpxxu0fURE1HiUVPPbG1FDwCVcG4TrZbXvWX/4orWqwPH86sccIytVHcZwkQazYsKtD8mf+SJTLA0FAN1bO44JR4Ft5sgbq13uLMjZTOv8OdsNL/+E7DzrWC5XS1yXFLOXYleOjIh8hwGXRiA7rwSnLluzUcK0QXVKCw6V1H4sqXS8YAvR8KvSWDi7UDSYrQ8wNTWUjWsW7JnAW6XBhNyiCuw8ZRuf4MRl5xf99mMT1abO7VO3JuKxpA41rvfM0I7ia+HBGRH5D/ueYKv3X0TG6eqzPBqD385cx7j/7EaBXYkQZ7/PUkcuuS7NKHhgQLz4+q4+bRyWB7F8KAA4HHtnx6WoqnTEe+nHsWjLqQZpFxERNT6l1fy+13Tv0yYqpE5juU6S3AeRa/KAS+2es0jPh7ZqvDf7Di3aIGbCNjYxEcHY+dLtLpc7K/1ndJL1IrjrQ+vYtkKmuqsMl5sTo8XXepMZxTXcLxBRw+BddYD7LvMCUt7fhkc/3Q3AdYS8JsFqpTj2hbM0xNBq0pwpsDgrKdYnPgoAsOOl4bL5T97SQTZ9X/941NeZq2Xo+uoGJM3bhHH/2Y0t2Y5lgkySCw+DF3uzvzS6K+be3cNr2/eVI5eKsP5QLn49eRUvfnsQe89cq/lNRH6oyEkt5Nre7AaqvKJKPPjvDKfLosNq7tlWne0vDJc9uAlzcs0Q7qHAeqCT/vasrmWZVsHonizDSkREtVemq3vHr/wS6ziXapUCjye1r3H98Krf/gcH1v++rimoS8DFaLLdy649mAtAPs6o/Xap8WgTFYKtzw/D+w/1xZDO8nFyLznJcKkuc19nNMuy1RNcVPp4/6G+sum8qrFvici3GHAJcLvsevrWpsarMwqFQsxycfaAK6QWWQUUGOxLiv1lWCc8Mqgd8P/s3Xd8E/UbB/BPZtO9d0spq+wCZZUlsyxxgT8FFAQFB7gnigj6U1Rwoag/Jw5AxYGICJSNUFahjAJltZTRAd0zzbjfH2kud8klbZrVtM/79eJFcne5XC5pcnfP93keAGG+/LJifdvyG/5O698GwzrZ1tzvf3su8e4/8O1hk+ZuSrXhpEOldlzARSQSYWSXcACAXNpyvg4nrvgXj60+iulfHcTPR65gyudpWLj+JBgX9OAhxBZXS0xPTLjfD65QUlXn0L+ltzad4d2XS8VQyHTfT+U15kuONLRNT4/uhNgg/omah8D3XlOPI1oafeA/3M8DfdoENrC0weJJXfHefxIdtVmEEEJaoEobynnqm7D7e8rRLsTQp/Ljqb0xb0R73rIZV0rZ8mVRAcK9IwiflHPu3NgQiUYga6FWZTinlYjonKwliwv2xh29o00GLT+6+ihbNrimvrcPN8NlSpJpEPTSDUMVkDbBwgEX479loZLChBDnazlXGFup/HJ+9DrMr+FGWuYEeOlGzhaUm0bEqflry2E8muaBQW3NlpAxHsUjFovw+MgOvGklAqmxlghdFPxo23neff0BaaVSjRU7zpssb0+K+guOdWotr66uuzJ30fXHA7k4k1fh5K0hxDbbzxaYTNt4PM8FW6KzJTMfvd9IxfKtWQ57DuOTpFAfD7x3dy8AQGGF+RFrlkoSAIBUYnqZQKgEaTX1cAFg2J+WgvGzB8ebTJs2II6yggkhhFiloZKhjRHgJUNSnKG00LCOoXh+bGcMaq8bZS8SAXes3MfOp9+qxpFxzpM9LAxCPZpbgt/Sr0Kt0fJ6dXSOMO3VMr1Dy+9HSIA37+huMu33o9cw7cuDmL/mKABDNtTbd/XAm3fyl/eQilFSrbvW0qdNQKOfV2gANSHE+Sjg4sZyi6qx9/xN3rQIv4Ybn5sTWt/QLU8g1dGLAi4thnH2snHGi6VlAdORPYetLFelFQgIGGe96Eewv/n3GRy45NhyWNzyOqPf3+3Q53IGS003axuRGUBZMKQ52Xfhpsm0349dc8GW6CzekAkAWLnzok1/KzV1Giz95wzmrT6KOqMsPuOMPy3DsL/PlkasGa/HmNBoSyGVNpQ1aUn0+0sqNn+o/PSYjibTWlK2JCGEEMdjGMbqDJc9zw3DuBj+73V0gCd6xPhjzUMDkPr0MPh76TJWP7y3V/3z2GVzWx1u34wIf/ODW+/6dD+eXXccD3x7mDddaECMgi6ttAphfgosnNiFN+2bfdkAgO1nC/FnxjV2AHWvNgHwkEqw5DZDuXOlWovVB3MBNFzObsP8wextfdYbIcS16KzQjS3954zJtLhgb4ElG0dfRqRE4AuaSoq1HCZZKxZ+vIXmGTdhK7QyZbUxB/v6DJe1h3KtWndTcAMupdUqkybd7ub4lVKz84TKM3Gt2peNvv/dhv0CF7kJcbZD2cU4nFMCwLQ2satwsz1/P9r0wM99Xx/E/3Zfwt8n8/Br+lXevE7h/JGQKg2DYB9dBmpRpfmMwoYCLg1lwOhV16nBMAyullS36gCsfsSh1MKgBF8Fv/za6ocGOHSbCCGEtDw1Kg3M/UTf3itKcHqkvwJJIfwHBdYHWAZ1CEFHzrGEzMLAAdKwAC/Db72iEY3u/zU6j9JoGZM+HQpJ6z2+am0eGtoO254ZBsB0EPOTP2Wwt/W9mGckx2HvCyMQV18+TD/A+sjlEovP0zMmgC1JVlpjXQUSQohj0K+vG6tRmY5CDfRqeu11fS33qvoRNtyLDFRSrOUwLikmsTLgYtznxdoRWY255qdUa1AoUNrOEYz3h9KBPWOcobDcfADsibXHzJYk0mgZLP7rNIqq6jDn+yOO2jxCGkWl0fIaxw9qH2zSeNIV/DgX2DOulOLUtTLc+vFe7MwqbPQ63vz7NNI5J03GWSu1Rt9BGq0W3vVlP6pVGrNBEO53V/doP5P5IzuHNWr7SqpV+GDbeQx5Zyd+OHC5UY9pidRa3f5sqKnt3GHt4C2X4I/HBmFwhxBnbBohhJAWpLJWdy4lFgH/vjgCO58bjt8fG4QxXcMxf0QHs48zTqhUmBkgKaPMS5vo++gB/PJijVWlVJsc23nSpZVWJT7EBz4eUlTXaZB5vVxwmej6PiwikQixQV4IN6pcM7BdkNDDeALqB1CXUYYLIc0C/fq6sWqBsh9eHk2vxaovg6Ev1+LHaZzrTTVeWwzji0eWBj0JxWK6R/tj+d2J7AU9/UlCYzVmxLRSpcVNCyO5HcnVDbnNaexI84ZqQO89J5y9cq7A0N+lqq557gPSMuy7cBNzvj9iMajKDUIM6RCCMD8FXr/dtA6ysyVwRox6e0hx68f/4tS1csz69nCDGSaA7u/4y73ZvGkqo1GP5UZ1l9UaBl4eujNzjZbBxhPCPWz0z+8pk2D1gwPh4yGFTCLC7ueHY/fzw9ErNkDwcWO7hfPun80vx4rtut5Zi/7MbPA1tVT6jCCh3jdcL0/ogqOLxqB3m0BnbBYhhJAWRj94zdtDiphAL8SHeKNPm0B8OaMvL1PFmNTo58lcwMVDIOCy6NauTd/gVsaDk9XSlLKhhRVKk/MzBV1aaVUkYhG6RZkOhtKb2r+NSV9F44DLp9OTGnwefTYWlRQjpHmggIsbiw3yMplmS+kv/QFEcX0T9OKqOjwzphPGdYvA0I40arOlMM5asTR619w1/ilJMbilUygA0wyX6jo1Vh+8jAKji6kXCivwa/pVwR4uxkqq6zBhxd4Gl7OXh4e1Y283xwyX6jo1blm2C8+vO97gskIZR9yTA/3JmFbL4PG1x/DyHycBNG3EFiFNMf2rg0g9XYD+b23nBSm4gQd9g0iRCPixvkxTkJdccFln4j6v8TZUNSLbr1ogmKnSavHAt4fwxsbTYBgGl4uqePO1DAMvzm/742uPIeNKKZ7+OQP/+TyN7TVSp9GtWy4Vw99Lhr8eH4J/nhyGuGBvi+VGpyTFsrdDfDx43/u+NgzicHea+pJikkaUYvFoRIkRQgghRIj+2N3a31zj8QAeMuHfK+Nj/DZBXpg9JN6q52rNuOfO5s6XGhoY97XRYBuv1nt41WqF+PL7/3B7L/t4mB5Hhhst35ieyv7150r68yhCiGvRFTY39dOhXPx2VFf3Pcrf8GVtPDLWGkIXDJ4Y1RGf358EKV2MbTGs6eFiiVyi+7wYByje2nQGr/xxClO/PMCbPvr9PXhu3XGsz7je4LofW31UcPrswY45OXhpfGfI6z/jtQKl+lyt66ItyC2uxjqjXg9CKupP2rhBUu5FbX2ZnGNXSvDX8etYczAXKo3WZRewSevWaeE/AIBPdpxHt9e24EhOMQDDyKwOoT7ssn6eMrbU5c1K63pH2UsFJ6hinA1X0UC2H8MwOJtfYTI9K78Cu7Ju4Ot/s5F2qQg5RdWQS8ToEe0PAJg2oI3Jb3D65RL8cewaDuUU40yerjRBWf3vv5+n7iw+PsQbHcJ80BBuXfGoAP5oOl8nD8EsLK/FM79k4Gy+cLkFZ2IzXBooKUYIIYTYQl8twMfK31zj03NL/UUOvzKavZ1bXG3V87R23GMhcxe9Gxqw99W/hoDL06M6QEHjNFodP6O/70jOMbeXQDUZ4wwXoUw1Y/ogzvUyy31bCSHOQVfR3dRLv59kb0/qFYWYQE+IRMDQTk3PRDH+Eh9Wn8FAWhbjg3OLGS4W1qMvs2LcBHDzqXwAwKUbVSaPaSyhUeDvTO6BBRM6N3mdlohEInh7CAeQXKmiVoV7OH0sgIZH9utLDRr32tFb8tdpAEB+meGCdY1KY1IOiQIwxFk+2XEey7eeQ51ai7c2nQEAttdQKGd0l0QsQnSgrr7xhcJKh2zL1sx8DHt3J6/HClfq6QL2tlLF/xupUFoe8LB8axYmf7bfZDr3t3fZliwAusDH97P746N7e+HxUR1NHnPiail7Wz/a8pMdFwAAV4qtO8ka0jEEfgopBrYLMrkIY+3FH1vNWnUYvx+9htnfHnbq8wrRUMCFEEKIE+gHTAhddLXEOMPFXEkxgH88RazTLcoPMYH6/hrCy9QJnDfNSI4TXPax4e0Ep5OWzccogy3Y28PsPACQGf2BG5ccExLup1tnkYtKsxNC+Cjg0gJ4yiTY8/wInFw81uxFVmtFB3jiu1n97LIu0ryYlBSz8ONtKT1afxCgHwWsp9E2rteIkL5x5mvgD2wX7NCyV/oML+OLqK608UQeDmYX86a9t/UcXv7jpNlMnKo63Si5MD/DQdztvaLY2/qSgfpMFwDIK601CTRxL+gS4kjLt55jbwfVn3wUlOsCghFGo7t6xgQAAHaeveGQbZn7Qzpyi6sx/asDJvPKjepvG//NVAn0VeNaufOi4HRugFn/bSyViBHoLcftvaLhp5CZPOZPTqagSqMFwzDYmdW0feKrkOHQK6Ox5qGBJjWfuSeD9rT+2DV8uuuCyXR9I9HrZeb7+ziL/juyoR4uhBBCiC2e//UEACDjSqlVjzP+eaqus5xpe1ui7nzg+bEJVj1PaycSifBgfQk2c6e5heWmmddtBMq/k9aLW03mlQldEOJjKJXsJVBSzIdz/D+6S1ijnkMfuMlrBsfRhBAKuLQIkf4KiMUiwci4NbgXYBdN6tqoKDpxP8YZLeKmZrjU17U3DrgY37fkseHtefeDOQcexppa+qyx9BfVVFrhgMvxK6UmvRUcTagn0+e7L2LNwVz8kHZZ8DGGDBfDhdLFk7qZLLc10zBSf/GGTJMMl+aU6UNajtwiy2Uswv08UFajwtv/nAUAhBkFXErqA4bf7Ms2eaw91aq0UGu0WL71PM6W6r4bblTwT6aNL2wY/w3p5ZfVYvEG883nuX2XCuufw5qsCqVay/t7vaUJ2akKmQRisQjLpvTkTZc1oTlsQ7RaBk/9nIF3N2fh1LUydrpxiTZX07+fjenhQgghhDSVUP/FxjA+VOD+pgpZMbU3jiwcjXkjOjTp+Voz/XmocS/So7klaPvS3xj9/m6Tx3jKJZRZRFjcgaOzBrflXfcQuo53a89IJLcLxpOjOuLLGX0b9Ry+nCDNW5vONNhbyN5qBapmENKaUbuuFqBjuK9d1vN8Smf8eCAXAFAjUNKJtAzWBC4aleFilELdUB8Drn5tgwAYRn37Cozkntq/DaqUajaV21H0PVzUGtPXnH65GJM/S4NULMK47hF45Jb26F7fX8GRGAshL+PR9nr6DBd/Txk2Pj4EGi2DQG9+IOtGhRJ/n8xj76ddKjIphyS0Hwix1S3Ld1qcX1ajwvL6sloAEOHHP1E9V2DogcIwDDYcv44V28/js/uS0MlOv4V669Kv4n97swFI8AyA9Bx+mbFtZwp5980FDBb8fsIk+2R0lzC0CfLGN/uyUcW50HK1RFcOzJpsPqVaw/vNjgtu+ojKxNgA3n1H9LTayPnu2X3uBgK95YgO8MT5An6ZuPdTz+FGRS2eTUlAiI/zL1hcuqkLsEcHOPa3hxBCCAH4/RebYnCHhh/vit/TlkAf3NIaDSx8oT47SYiXXAIZlSUl9bgZ01KJmPe36C1QTlAhk2Dt3IFWPQc3wPfFnksYnhCKQe1t+15pLKVag1Hv7ca10hoce3WMyfUHQlojuw/b27NnDyZNmoSoqCiIRCKsX7+eN/+BBx6ASCTi/Rs3bhxvmeLiYkyfPh1+fn4ICAjAgw8+iMpK/on4iRMnMHToUCgUCsTGxuLdd9+190tp1hLqLyx1j/ZDb6MLJE3l72W42J0QYd8LV6T5sJTRAgCJMY0LJOibOKtsuDBv3CfI26gR4bez+mHpXT2wYmpvh2dcsRkuAjV40y4WAdBl72w8kYfbPvnXoduiV2uhvJm5Os368kRecim6R/uzF1AXTuzCLnO1xDTL4NQ1fpNqtZlMH0JswY3hCjV0L6tR4YcDhuytCH9+hsuKqb3Z29dKa/DkTxm4eKMKC9efsnnb9l+4ybu/gNMrDQAyGiizZy4rbN+FIpNpkxKjEFn/2s4VmPajsaaM1ZXiatRwAiMdBfZrY8mNAj2HjEoa2sO/5w3Bp2VbsjD47R3452Qe9pznB6VWbD+PtYeu4IFvD9l9GxqjuL7+taOD/YQQQlo3/Xn9I7e0b2BJy2Ykt7XD1hAh+vNn4wwXS6W5veRSh2QKE/ekNBrExC3D7m1jpRoubjlmR/W8FPLE2mO4VqobOPbb0atOe15CmjO7/wJUVVUhMTERK1euNLvMuHHjkJeXx/5bu3Ytb/706dORmZmJ1NRUbNy4EXv27MHcuXPZ+eXl5UhJSUFcXBzS09OxbNkyLF68GF988YW9X06z5e+pC47MG97Brheidz43HKsfGoAukX52WydpXiwdGALAzw8ns7ctZaHqS9409cL84A7BkIhFCPbm1i/lH2yEOTENW18irU6jxf92X0Tq6QIUVSrxZ8Y1k2VtaFNjFUujy801tdePlvc2qgV7Z+9o9nZBecN1XevUlOFC7Mu4v5O+HjZXeQ0/08q4pNjAdsHoUZ9dxq11bo/09QcaaNSuj1WbC2joM1wYhsH10hrM+f4I9hkFcfTiQ7wtllCUWVHGau/5m7w+MPf2b9PoxxqTC1wYOJtfLrBk053OM13fR9vPswEOY8bBYGcoq1bhj2O6734vufkmxIQQQogtblQokVWfveth48V5od9wYh+GkmL86QoLxwhecgkUUv78tXOsy1ggLcc9/dtALhVjYs9IAMCtPQ09VnvZaQA1wO8Hs+jPTFwptlzO2ZKiSiXyG9EPZvuZAmzhlCu3puJJY1Uq1YKDRglpzuxeUmz8+PEYP368xWU8PDwQEREhOO/MmTPYvHkzDh8+jL59dbUKP/74Y0yYMAHLly9HVFQUVq9ejbq6OnzzzTeQy+Xo1q0bMjIy8P777/MCMy1Rea0Kj685hkM5ulGn9m4iHh/ijfgQb7uukzQvDX1kuJkTli65yyyU4GoMfTBnYLtgtryVcYaLPrDoDPoRSOuOXMGmk/kAdP0kCgSaIAK6kd/944Mcuk1rD+WanafPfmEYBpeLqtEmyAtisYiX4cLFLdd2sBGj1tVaXSNu6uVE7KXS6OBbKHBx2eikIEGgTFincF+cvFaGk1cNtcqNA4xNUWcmiKlXU6eb3z8+COcFRowpVVpcK63B7Z/sw81K3fdG6ukCk+UAXXaPpX5X1mS4hPh4YMHvupIWMonIpuMCoYs1heVKdBY+ZDOrsAaY88NRzB/ZESoNg0h/BdqGeKOiViUYQJGIRc2qb9ScH46wnwcKuBBCCHGU1zeeZm83pWpA5whfnM2vaHhBYhP9oBuGYXCluBq+CikCvOSotVCG3UsuQYivHFn1h4Jtg72Q3D4YKpVwWWjSskUHeOLYq2PY48oIfwVy3p5o9+dZemcP3PPFAfb+4g2Z+PqBfiirVsHPU9roc3uGYdD/re3QaBl880BfjOwcLrjchcIKPPjdEd40c6XPm0qrZXDbx//iakkNVs3qh4+2n8djIzo0qW8laf6q69R46LsjuKdfLG7vFd3wA5oxlwyD2LVrF8LCwpCQkIBHH30URUWGchtpaWkICAhggy0AMHr0aIjFYhw8eJBdZtiwYZDLDaNDx44di6ysLJSU8GustzSLN2Ri9zlD2Q0ayUKsZc0FdOO0aS6hElzWNGZjm8NxNsc4SODMgIv+gFkfbAFgNtgCgL2g6igMw/BKDc0ezM8G0I+mX5d+FcOX78I7m3VNxvWNvI0vQMulYrauKzczwJz5a45hwop/qfEdsRvjg+++bYPwxh3dedNKqw3LfDe7v2CKfXyIrkfJ//ZcYqfpe584UmV9n6MwX4Xg/JPXyrB005kGvxtWPzQAXnIpAr3MZ7hIrQiaqLVaHK7vL9MhzLZyoELHFNb0/dL7+ZIYu87dxJTP0zD1ywOY+8MRFJTXosfirYLLVyrVFjP6uA2FndEAlFtKzfh3iRBCCLEXbpnNTuHWlwSlFiHOoT9/3namELcs24m7P08DAIT4mj+W85JL0T3KUKpbqFcpaV28PRof8GiqAe2CsfzuRPZ+bnE1vtp7CYmvb8V//z7T6PVU1WnY6gSzVx3BqWu6gW55ZTWYt/oojuQUY/+Fmxj9/h6Tx+aV1iK7vheiPRRX1+HSzSrUabSY9tVBHMwuxsxvXFNymDje42uOYf/FIjz5U4bdKy04m9PPIseNG4e77roL8fHxuHjxIl5++WWMHz8eaWlpkEgkyM/PR1hYGH8jpVIEBQUhP193ITQ/Px/x8fyLj+Hh4ey8wMBAwedWKpVQKg0XQsrLdW+eSqVym5EGO8/ym/SKobVp2/WPdZfX72otYX8xWv5FLUuvRa3WmJ0vYnQX4lUaw2fQ+OK8fvrBi4aTicdHtMP2szfw8vhOUKlU6NvGH3+f0GW4KDjfSBKxCB5ixmn7OqvAutFhpVVKh2ybfp15pYaDlN3PDsWRy6W85Wrq1Kirq2ObNf5vzyU8N6YDW1JMJjLdd5H+HrhRocS5+pFwdydFY106v2Razxg/nLiq+248k1eOjMtF6N0mwG6vz95awt+kM7lyf+WX8g+8VSoV7k2KwqhOwdh6ugCLN57lzR8UHyC4nRECJ7eXi6pRWFZlMYjREB8PKe/Cvp6k/m+por7cWbC38KHT6oO5iAkQDsZwtQ/xhEqlgpeFIzAxGv7u02/vjwcMmXCfTUu06b2VMKYB1rzSKry/9SYm9YxA2+CGM2BVKhWKavknk+cKKvHSb8fNPqZaqUZNnfnyA1eLKhHp74E7PzuAdiHe+HRaL6dl3/l6iB369+KKv0n6viSEENdjGAYl9QNNXhzXGcFNaGhPiejOwR18omWA84WVuHijEl0j/QR79QG6DJeOnEztU9fLBJcjxN6GJxgyP7JvVrGBlq//zcart3Zt1DrKjMo8f/1vNj64pxcmfLQXJdUqtkKJkM2Z+dicmY+f5g7EwHbBTXgFfMVVwmWHqRpHy7Sdc837to/34dyblitoNWdOD7jce++97O0ePXqgZ8+eaN++PXbt2oVRo0Y59LmXLl2KJUuWmEzfunUrvLy8HPrc9rApV4ySav7o08z0AyhqfKDarNTUVNtX0oq48/7KLBEBMGQ/bNq0SWAp3VfDsWPHILoiPJr4YrluuTPXSvDX35sgEQE1asNjAcN++m3XEfY5w8rP4eG2wPH9O3EcgD8D3NNOhA5+DLIyT7LLeUu0+Oeff2x4pday7uvwwNET8C4wfwHRVn+m7gEghb+cQcb+nThv9L5dzMnF819f5k37++9NqFVJAIiwb88unDK69iyuFgMQo6o+m6fmRi7v8QDYYIvezn/TkBfQ/Pu5uPPfpCu4Yn8dLzJ8hjv7a3nfPX4M4CGWQKnVHTTHejNmvptg8reg9+OGbWhvQ/ux2jrd344xDSPC1q2puFqgm59z1vA99XxPNb7JkqBIqXvc1dKG6xzv37UNEhGgqxwi/L1TdKNQ4PXzlxVpVbzt9ZAw7PeqLWZ1EoEBsOpc/Wv87RQA4MvdF/B2f9MsFJVWVyJSX3krrxooqTN9XTuz+P1sugVqkVmiO6a5UVmHjEt5MN7/IjBgIMKG1D2QiBhcuinFpZvVWL3+HwQ5tMWXYfuLzxzEpixHPpeOM/8mq6upBnZzsWfPHixbtgzp6enIy8vDH3/8gTvuuIOdzzAMXnvtNXz55ZcoLS3F4MGD8dlnn6Fjx47sMsXFxXj88cfx119/QSwWY/Lkyfjoo4/g42MYLX/ixAnMmzcPhw8fRmhoKB5//HG88MILznyphBAjOzgXle7oHWVhSfNU1HPRKYQyiV749QQSYwLMPsZTLuFlDjshQZcQALpyw+/dnYhn1x03KWF88UYl2oc2nE1XYhTkuFxUhZo6DRskFjI8IRS7sgwDbb9Py7FLwMVc75YtmQUY193Kusek2UuMDcDx+oosDZX8bu5cXiehXbt2CAkJwYULFzBq1ChERESgsJCfxaFWq1FcXMz2fYmIiEBBAb8uuv6+ud4wALBgwQI888wz7P3y8nLExsYiJSUFfn7Nv0n8k6+aluK4Y/xoBHk3fUSvSqVCamoqxowZA5mM0lwb0hL2l+/5m/ji7FH2/oQJE0yWeTJN91nr1asXJtQ3djOm0TL45q2dqFSq0SlpKBIifHWldA7vZpcZPXo0tm3bhs4JCUDOBQDA3beNh8ToqHVS/f87s27gu/PHAACdogIxYUL/Jr9Oa+lfc2NFxLXHhJROdt8O/Wcsvmsv4OQpxIX6Y8KEgQi6VIzPzxrqo4aGR+LPU/zvweGjU8Ac2AEAmDh2DPyMSrIdF2UhY/9l9v6oAb1wU3IVB7LNl2L87IwEn9ybiLHdhOu2ulpL+Jt0Jlfur6xtF4Bzl9A3LgCrHuhr0hz2y5w0nKnPvvrxkWGICvAUXE9oTgn+d9a0wX233v0wMqFptXw1WgZPpukuePeK9UfGFf4oxM59h0B64QRQVY3hgwegc/dalNWqMWNgG/S5XIKpX5lujzmTJhq+cwsDcvD25nMmy8RERWLChETetBUX9uHiDUOWkI+XJyo4jSwj/L0xYcKQRm+HOfqtC9p9Ce9vu8BOr9GIMHRkCiqVakT4eeCTnZeQEOGD7/ddRlZBJXY9OxS+HlIkvNZw4OCbGX0wuH0wblQqMWSZrhTB1Srd78LL4xNwvbQGozqH4Yu92dh7oQgBbbviox0XAOgCPl2TBqGPAzPv9L8HozqHYtKtvR32PIBr/ib1Gd7E9aqqqpCYmIjZs2fjrrvuMpn/7rvvYsWKFfjuu+8QHx+PV199FWPHjsXp06ehUOgy6qZPn468vDykpqZCpVJh1qxZmDt3LtasWQNA936npKRg9OjR+Pzzz3Hy5EnMnj0bAQEBLb73JSHN2fVSQznUCL+GM2SF1KrNl+Mk9iNUXrW8RgWN1vzFQG+5FHJOTz4/hcsvvZFWJMRXeGTSqPd24/yb4832fPw1/SpW7c826bl46lo5uizabPb5JveJwZxh8byAy6FG9I1tjAOXhNfzyI/pAIAVU3vjtsSmBa1J81NpVIZcpdHavXe5s7j8W//q1asoKipCZKTuom5ycjJKS0uRnp6OpKQkAMCOHTug1WoxYMAAdplXXnkFKpWKPTlNTU1FQkKC2XJiAODh4QEPD9MvHplM5nYX6rpE+qFntD/CA+zT4N4d94ErufP+ksmkRvfNv45eccFm58sABPvIUalUQ6nVrUcr4peEEUl0z6XvJzi5TwwUHuYDhH5ehr/PAC95s97HZTUah25fUZVuX0b4e0Imk6FHLP+7TahH49Uyw0gUb08PyGT8LICYIP73RYCPAmvnJiNxyVaU1zc0bxPkhVyjxuXPrDuJc71imvxanMGd/yZdwRX763x9sODWnlHw8TT9LQ7yMXw3xIWaHwTRp63wSCmlxvL3mSUqTjmrshrT0lbLt11kM8MCvBUY0skQgExsE2R2vVP7t8HaQ7m8adxt7BoVIPg4b4Xp+7N+3mCcK6jE5M/2AzDttxLgbd/vzLgQ09FvKR/tw81KJVZO64MVOy/y5vV9ayc2PzW0wfV+Mq03RnbVHfP5M6YXMBJjAzH3lg4AgL9PFQAowlv/8FNMiqvVDv38RvgpkF9ei6fHJDjt78SZf5P0Xdl8jB8/HuPHC5dKYBgGH374IRYuXIjbb78dAPD9998jPDwc69evx7333oszZ85g8+bNOHz4MNv/8uOPP8aECROwfPlyREVFYfXq1airq8M333wDuVyObt26ISMjA++//z4FXAhxpfqL+P3aBja5LE6tyr1H/7oLobdHpdFCYyFtxdtDyrtI+OG9vRywZYQIk0nMf6dU12ng72l6Abu6To3n1gnnyjeUaXBLQig6GfWSvFlZh4paFfacu4lRXcKgkJlWKLCkolaFN/8+g58OX7G43BNrj2FSz0gqL9ZCcAcYAsD2M4Vum8lk9zBRZWUlMjIykJGRAQDIzs5GRkYGcnNzUVlZieeffx4HDhxATk4Otm/fjttvvx0dOnTA2LFjAQBdunTBuHHjMGfOHBw6dAj79u3D/Pnzce+99yIqShe1nDZtGuRyOR588EFkZmbi559/xkcffcTLXmlpTl7lj7b9ckYS3pnS00VbQ9yZpBE/RAcWjMLGx4cgPsRyQE9efxCprO/dYtLDpf6HWT/fS275R9ab05zYw8ofZFvtfG64VcsXmaklai+FFbr1h/npLkwHeMmx6YmhiK4f9a8UGNF268f/AtCdFBhnDwAwyRjwU+ga93FPFX55ONnkce6eykmah9PXdSOluPWsuZ4Z0wlecgmm9o+1uB5zB+tVRv1Xcm5W4cVfT+DhH46w30XmVNYHHMVm/nYu3axil/E1GqHoJZci1Mwosu7RlrNnhZrU69Zp+hp9FTIkxQWyzzWuG//AM8DTvhfRhUbb3qzU9cH75Yjwic/WzALB6VztOIEcHw/TcT8DOKUHwsyM+L1RqRScbi/68gtSCyerhDhadnY28vPzMXr0aHaav78/BgwYgLQ0XcPmtLQ0BAQEsMEWQJddLBaLcfDgQXaZYcOGQS43BLXHjh2LrKwslJSYz3AlhDiOVsvg1fW6kp3RZjJ6G6NWRRkuzmBcnQHQnd8aH16+O6UnFk/qiu3P3gIAEHMe1zvW/MBgQuxNbiEj4GyecKaz/lyHSyQCgo0q6gyI5w82G989Arf2iIRYLDL5W7n/60OYt+YoPtp+vrGbzvr632xesOXJUR3x2qSumCSQzVJD34VOd+paGdq/vAm9Xt+K3CL7lCsWKh/3yI/peOi7IwJLN392z3A5cuQIRowYwd7XB0FmzpyJzz77DCdOnMB3332H0tJSREVFISUlBW+88QYv82T16tWYP38+Ro0axdYiXrFiBTvf398fW7duxbx585CUlISQkBAsWrSoxY7SYhgGkz75lzfNQ+rci9Gk5RALFaE1EuGvQIR/w6nt+ouFdWYDLrqLVrX104UuZHJ5eRg+10Kp247UUHAJAL6b3R+f7ryAg9nF7IVHR9mcmQ8A8PHgjob3wysTu+Cx1UehtDCizUMqFhzhERPIP6Hjrlsvwl+BR4e3x2e7+KPXd2YVYkRCmFWvgRC9Xw5fwbX60hltzfytJcUFIXPJWKtGJwV7yzGkYwj+zLjOC7gUltdi+PJd7P39F3UNTQe1DxZMSS6tbwzp7ymDJyfY8erEznjj77OICfTEpZu6A0ChIEGUvwI3Kky/EyZ0j8SYLuHIK6vF0n/OYHQXfmm+zhHCwSdPC8Hp7c/eghsVShSU1eJ/ey6x0/3tHHAxV9INAHafuyE4PfV0wwEXD5lh/4tEIlx8awKyb1bilT9O4ZFb2vOWNVd+o7Dcsd+/+hIh0kb8XhLiKPn5uuOA8HD+90Z4eDg7Lz8/H2Fh/N9mqVSKoKAg3jLx8fEm69DPE6oOoFQqoVQa/s70ZehUKhVUKkOpB/1t7jTiHui9c608TknQqyXVVr8P+uVjAhU4nVfJm0bsT6sxvZhbq9JAZTQALkAhwZ2JugExKpUKGrXh2JTRanjfofR+uSd3ef+EPrN6M745hFOvjTaZXl5jenw9rEMIrpTU8AabfvifHkh+R1dG/teHByAxxh8ajRoaja5MM1dGfS+Oz3ZdxJzBcSYD17guFFZCIhax12UycvmDQnpE+eCWTqHoHeOHv45f5827UVZt8dwFcJ/3zl3M+f4INFoGpdUqfLAtC+/e1d3mdf7GGdTXI9oPJ+tL22nqvz8B579/tjyf3QMuw4cPB2MhtXLLli0NriMoKIitO2xOz549sXfvXqu3zx2VC5Q3MTcqlpCGCI3QaSr9hUt9YGVLfZBATx+A0We4NJRGys1w+ev4dXw81bG1880Z3SUM287oekk9l9IJl4uqMW1AG/RuEwhvuQRTPk9DsQMzXFRasBd3ufWdAUPQSijDxbCM8H5uE+zFu++jP+Ax+sp+cVxnHL1cgoOcuquzvj2MnLcnNmr7CTG2sH4UJyAcsNCzNhW8X9sgeNevr5pTZ+9CYSVvuZnfHAIAPDW6I54areu9xDAMVBoGcqkYpfUNIAO85LyL7P71fyN7zhcZtl/gRMHcd1tg/YiwMD8Ffpprmj0W4CXHlKQY/Jp+lTfdS2Z+H/kpZPBTyNA+1Ad39Y7G78eu6aZ72veQzjhA2xgnr+mycbsEaHGmVPg4xTjwLhGL0CHMFz8LZNf5KYSDSJVK0+Mie9JnuEjEdKxFWqelS5diyZIlJtO3bt0KLy8vk+mpqQ33biLNE713rnGlEtBfisnILcGmTZuatJ47w0shqRVjTLS2yesgDcsoEgHgH+tV1dYh98oVcIvGZB0/jBrOmLWzpYbHbU/dCu54Gvrbc2/N/f3LqwbMXe5VqrX4YO0/SPDnXwS4VsV/TIgHg1F++VhTKIb+cz4xVoNDe7aja4AYpXUi5Gbsw7UThnUEyCUorRM+n/vxz62Iqx9rdqZEhCIlMCRCtw11GmBhugRKjQhT4jXoGcSgrMjwvABQlHUYm+rbS35Uf9rw3AEJVIwI67fsRFvhcWwmmvt75y7yygyflV2Z17BJkWth6cY5lqN7z9v7MujjVYKT9d+fqtJC9n1z9vtXXd307B2X93AhDSuqMo00N5QpQIg53HjLgvGdbVqXcYbL+6n85s/55brRW8r6FE+FrPEZLq7UNcofgAgnr5VixqC2vIt++outxuWL7OnoTcObZDxKRB9MMb6gzGVuP/spZLweLfoL30JZTwcFmtypNVpUqzSoqFXbVP6AtD7csnSedigX+NTojvh2Xw5eGt8Zqw9eBgBUcfqwmBv28cWeS2zA5eU/TmLtoSv46N5ebINHf08ZL7tOKBtGKKDJDbh0j/bDqWvlGN/IWrPLpvTEf+/ojvMFlWw2q0zauMDTe/9JZAMu9s5wEYlEeGhIPL76N9vqxw4OZ3C+XMQGLrisydA1HgWnD4Y7uoSK/nuXMlyIK0VE6L5DCgoK2F6X+vu9evVilyksLOQ9Tq1Wo7i4mH18REQECgr42Wf6+/pljC1YsIBXqrm8vByxsbFISUmBn5+hVKJKpUJqairGjBlDvYHcDL13rrXtTCFwMgMA8Oe8QehkptyqOfr3b/ptY/AAvX8OJzqVj2/PneBNUzMiRERGATfyEOIjx9iu4Xj41s68wUNtrpXjszMHAAC3ThgHqURMf3tuzp3ev7ePbwUADIwPxLzh7TFj1RHox8Z/fU5mkuVyLLcUOKEbpHZowXAEeukGjrW5WIQHVuka1Lfv0AkTRrbHhAm6wWvGg+XGjWPw14k8PPfbKRjrntQfQzuEoLBCiSff1WXIJPftgQndIzDmw3+h1OgGmv6aLcH+YkX98bgSL49PwN1J0YKD9p5M073GDFUEHpvQx+L+cKf3rjkrr1HhsbUZAAwZSBVqMSZMGGvTeveev4mi3AsAynH34AR0ifDFd+d1n7vkxASMGRjjkvdPn+XdFBRwcQNCvSIs1WQkxBLuxcTJSbY1QtcH/urMpKze9flBfJRsqKnZUIaLF2e+pXRTR5k9OB5bT+dj1qC2CPCSQaNlIDX6W9Nn4ThyhLWSszuNB/x7ynXbU1Vn/oKjpf3M3a/6g5Y7e0dj1f4cdIk0XEQZ2jEEe8/f5D22qk6D+746iJPXyvD7Y4PQpw3VIibWs9TEsbGeGt0JT4zsCLFYBK/6v8ms/AoAwKI/T+H7tMuCj+Nmwaw9pEtZfvKnDHZaoJeM14BWKOAihFti64mRHRHs44FuUZb7t+iJRCIoZBIkcMqLWUgUNnmsnr0DLgBwa2JUkwIu/nJGMNgC8EuKNaQ35zvmjdu7QanWYtuZQofUadZqGaxLv4JB7UOg1ugzXCjgQlwnPj4eERER2L59OxtgKS8vx8GDB/Hoo48CAJKTk1FaWor09HQkJSUBAHbs2AGtVosBAwawy7zyyitQqVTsCWpqaioSEhIEy4kBgIeHB6/cs55MJhM8yTU3nTR/9N65RmGlLrN2dJdwdIsJamBp8+j9cw6tQOtjLQNsOJEHAJg/ogMeGBxvskyP2EAkxvjDz1MGhYecd9xG7517c6f3TyQSY2hCOI4uHIPeb+iyA5Rqrcn2/31KNxgjIdwXYf6GEtDDO0cgyl+B62W1GN4lvMHXPaVfHBb9dYZ33gUA+y+WYGSXSHyy6yw77ceDV9Ah3A9XSvhVPa5zyi52jQpAoI/lwZ61KqbR74c17x3DMMi4UoqO4b4WqzS0Jg99eUgXnONoF+rN7tOJK/Yi83o5fns0GUlxjft9O3ipCLO/P8rej/D3QoC3ocXBwPYh7Pqd/bdny3PRVXs3UCTQK6IxfTgIEcK9gGTrp0j/RfvzYeEGynrHr+rKzAR4yS0uJ5WI0SZIV6piy1PDbNw66y2a1BV7XxiBQG/dAbFxsAUwBCmUai3UDmomz71MaXzhVSiYYpxtYikDjvv+628/PrIDFk7sglWz+rHz3rs7ES+MS0D6wtFsVlStSsOWDFp3hF8CiZDGsrZsmDn630H9BfxdWTdwLLfEbLClMbzkUl6j9KY0TQ/zUyApLrDBALOxppYKbReqOyEa1y2ygSWt522hlwyXcfPMQNPrtCxrMnQj/BWYN6I9ktsFY3JSDLtPHZHh8mv6Vbz420kMfXcn1NTDhThJZWUlMjIykJGRAQDIzs5GRkYGcnNzIRKJ8NRTT+G///0vNmzYgJMnT2LGjBmIiorCHXfcAQDo0qULxo0bhzlz5uDQoUPYt28f5s+fj3vvvRdRUbqmstOmTYNcLseDDz6IzMxM/Pzzz/joo494GSyEEOfKKaoCALQNNi3RR5ofVQPnfELnjPrp6+cNxvez+9vt+JcQaw1opztO15c6FnKjQonv6s+hhD7vfz0+BH9YMeDy10cGmUzTD+Jae8hQeupobikO55SYLMsVZGG7P7q3FwA4ZDAWAGw8kYc7P92P+78+6JD1uyPjYAsAnCuoRHFVHXJuViHzui4jZPJnaY1ep77Xj15UgCfvulWP6ICmbKrLUYjODdys1GW49G8bhEM5xQjwco9IOmmeRJwwi60HfvosjwOXTMtP6am1QG6xbsRCY0Zg73puOLSMaWaJszS0T7w5IxuqlBr4e9l/O9WcYxzjkmJC5Zh2Pjcc36fl4L9/nwFgOcNF6PUF+3jgoaHteNPC/BR4bHgH9jmr6jSo4YxSKa12XA8bQqwR4Gk4CL/z0/0Wl23oK08qETVYUkxIdIAnrtX3WwrztRBtaCStmewQIX8/PhRlNSpE+CsaXthKnpyAyzuTe2DF9gu4VlqD9qHeeGBwPN755yy6Rvrhs/uS0OcNQz1dXwtf9dZm6D4/1lD6Uv/dtiWzAFotY7fBJ6XVdXjhN0OpEP3upwwX4mhHjhzBiBEj2Pv6IMjMmTOxatUqvPDCC6iqqsLcuXNRWlqKIUOGYPPmzVAoDH/vq1evxvz58zFq1CiIxWJMnjwZK1asYOf7+/tj69atmDdvHpKSkhASEoJFixZh7ty5znuhhBCWRstga6ZuJHn7MB8Xbw1pDH2/UnMsDdCgQAtxldSnh2HH2ULMHNSWnbZiam88sfYYAF1PWA+pBAzDYOk/Z9hlhCp5BPt4INin8ec4kWbOSw5cKjKZ9sbG0xbXpR9cJiTcT/c8GVdKBUuc2eqnw7rgkFCQobXqGxeII5dNg2Tcc0Fr1an5Qb52od4I9JJjUPtgRAV4Qi4VQ+XgktKOQAEXN6DvixEVoED6wtFWj5olhIv7G2Trz9HM5Dh8l3YZo7uEmV2GWx4rMca/wXWKxSKIbd4yx5FLxZBLxKjTaFFZp4a/AwKgnIpG0BqluHgajTgf3CEYcqkYQzqGsNMsjSC35frhL0cMmUzG20WIJcMTQrEr6wabwWZPk5Oi8fIfJwXnfTa9Dx5dbUhPZhhddsTFG8I9kKRiMbpH+7Elwhpb/mzD/MFI+u82TOwRafYEwxrW/HV5yiUm3wv2wu23cmvPKNzTrw1v/h29ouAhlUAuFWPRrV3x+sbTuH9ALADhMmRf3J9k04kQN+Pm3ws3MaxTaJPXxZV20fTkD9B9HghxpOHDh4Ox8HsqEonw+uuv4/XXXze7TFBQENasWWPxeXr27Im9e/c2eTsJIba7dKMSt6/cB4VMghsVugoWMYHUE9EdNDR63lUDBQmxpGO4Lzoa9Yea2COSDbhUKXUBl11ZN/D70WvsMo0dcGYJtzfvG7d3w6t/ZgIA7v3igFXr2fj4EIvXP7kDel9ZfwqPj+yASP/Gfa8yDINHfkyHlgE+vy9JcKDVvgvC5witmaqRAwOtKXd906iqU3B9xZk1cwZatW3NDf0yuFBJVR3eTz2HnJtVFpcrru/hsj7jOoJ9PHgj7AmxFi/gYmNcIyHCr3495ld06IZhXqgdRn43B/oG4H8ctX9Zrf0Xi/DPVcNBxROjOvLmG2e4pHTVNbz1VRh+0CxmuDRhm/T9Yj7ddZGdRvEWYo2yGl2t8vkjO9h93R5SCRLMNJsd3yPSpOTe3vM3sbG+5raxMD8PzB/REfNHdMCG+YMbnY0R7OOBnLcnYuX0PnYJKNgrkGCrUF8PzB/RAU+P7iR47OGrkLGl0GYPicdf84fguZSOJssButeU0k24QXdjDepgCCxfLq62aV1cCjMBK4kd+g0RQgghWi2Dke/tRkWtmg22ALqyKaT5u7N3tMX59uhPSIgzSMQieNUf9+oHHN0wutitsKLfojncc6h2oYZMPi8rBon1iPZH92jLA3a5PWjXHMzF7FVHGrXukqo69HtzG7ZkFiD1dIHg4KvU0wW8+44qJ+9uauoMGVD92povMVdeq8KFwopGVW74jlMSPMJP0WIyAyng4kJvbDyNFdvP4+7/Na623czkOAdvEWkNuOVyRDZmkugPLi3VtV1/WfejKhWLWswXp97yreesfoxSrcHEFXvxzM8ZgvNnrko33E6OMznIMO7zoM9m8VUYLoZa2s/2Ggm/1egAhBBzdmUVsmnYjuqJkVVQYXbepieG8u7XqjSQmdmOaf3bwFMuwXNjE9AzJoD3dwUAU/vH2r6xFux9cST+mj8EvWIDHPo81nhubAKeHC0cRDHWI8YfXnLhQSHm9rk1/D1luKuP7qJHtUC5g6ZaueOCyTSFTAwvyigmhBBiJY2WwavrT+G3dMPArPJaleCysYHUw8UdBHnLMbGH+V55VIKUuBN9M/t5a3RVAI4b9c9IimtcnxZLRCIRnhrdEff0jUVyu2A8WT+ItJpTovyXh5N5j3l0eHtIxSJ0ifTD3Ukx+HpmX6uf90xeeaOW23o6n23dAAD3GfVoWfJXJuZ8zw/e/HXiutXb09KculaGcwW6ShHrHknGLw8n4+nRnQSXZRhg9Pt78NamM4Lz9YwDWb89Ztr/x11RwMWF0urrF3JHuQjRX2C9nwIuxA54ARcbvwH0n806tdZiSQygac2nW6J9F24i83o5fj92rcFlw/xMSxMZp/jqG4Z7cy5yqtTmA2CLJ3VDiI8HFt3atbGbbBaN8iCN8fluQ2aUM09IN8wfDADw95JhzZwB7PRKpRq19X8j3EySR4e3R6xRybNwP35W3qt2+LuxJMhbjh6NKL3oDtY+1A+jOhvKTdrrvdd/11XZMeAiVIc43E9htx4xhBBCWo8tmfn44cBlPLvuODtt+dYsk+U+mdbbZCAVab4sHRNQCVLirhiGweqDhib2gV4yzBthn4oET43uhHem9IRYLGJ7rXD1jw9Cx/o+VrMGt8WL4zrj+Gsp2PTEECy7O1HwWogQ4/Oz79NyGnyMUOb+zrOF7O1v95muI7/M8nXb1uDWj/9lb/t7yiASiRq8Tv3Vv9no9Mo/WC9w/YthGOQUGSo+HV+UYlKdwp3RL4MLcb9AquuELxwwDMM2EDI3apQQa9izh4s+VVSl0eJ/ey5ZXLYlHYi+cUd3AE1rjl1TZwhSNBSkElq/uQwB7sVMxkIHiI7hvjj8yijMHhLf0Kaylt7VQ3B6rYXADiF6fpxyd84IuLx3dyLO/Xc8esYEsNOS2wWzt7edLmBHPyXG+GN4QijahXpj+oA2xquCD+dg/LmUTvQ7bIW+cYH4coZhZJrMTheV9CdIlUr7NE7kprlzP57XS2vssn5CCCGtC/f3Q3+s/+MBwwXNQe2D8d7dibi1Z5TTt400naWxg1RSjLirQqPB38cWpSAu2HyT+qby8+SfQ03ooSsznPrMLch5eyJem9QNgO4439qqKA8aXddY9Gcmiqvq8MuRK3jz79MmJa3KalSYv+aYyXpmrToMwPw1mh8PXKYBpxz6UveBnJ7Gob4eGN0l3GTZOo0WT/2cgS2Z+bzp89Ycxej39wAAQnzkDumP7Eot5wqoG6rlpNNdL60VXGb7GUOU1bh3AyFNwb2gZGuJL/2orMM5JXj7n7MWl62042hkVxsQHwRAd4Dy8+HcBpbm23/xJntbaRSwKKvmlxsI9pGbPN74Pcu+adrHoKH+Kta+7/rXa6y2gQaShAD8/kI1dY75zAR76/5WogM8MTkpxmTEqEgkYoMuJ66VYe953d+hXCLGqln9sePZ4YgRKOvB/Vsx/nslDeN+1dijpBhgqP3cUAPbxiqtMXzvHlgwir19W6Lleu2EEEKIEP0xBgDUqkyrAKyZMxCTk2KcvVnERpYyXKikGHEn3BJQK3cayuqOSHBcD8lQH/5A0sX1ARZ74Q6uA4AZ3xzEC7+ewJd7s/HPKcNFfqUGWPjnacF1RPnrBsRfLREedHWttAYP/5AuOK+lUmm0uHSjEgzDoMKoNGZE/f7ini97yyX4amZfvHWn8IDdh39IZ68hlVWrsOmk4b3hlnhrKSjg4kLF1YYPVF6Z8B/1+cJK9ra9ei+Q1k3E6+FiG+PyVnrPjhGu49hScJvAvfjbyUY/Tqvlp+waX3y+UsIPnng3YjS9UE8Je/ezjw/xxq09TesWU8CFNAb3QoM9y0BxfTe7P1K6huO72f3MLnNbL91I0pucxpCqRjTxS4nWItzPA/cNpLKe1uL+3kjslOWov6ih0donAFZcpfs8+CqkCPNTYNMTQzF7cLxdyi4SQghpfU5dK2Nvl9bU4cNt59n7707u6YpNInYgsTBgzdw5MSHNEbc3Y/ZNQzmnFVN7O+w5O4b78u4H+1hfKcSSb2fxzwFPXTP0ctlY33tFq2Xw5jEJNmfye9G+d3ciAKC6/tpGVr753qDbzxZa7F/c0jy37jhGvrcb284UopQzOPitO3sIfu+pNLpz63v6xeLT6X0Q6W9aFu5yke6a17r0K7zpLTFTkH4ZXEjJuVi5/phwA6ZQTkkhBWW4EDvg9XCx8TtNqPYlADw0tJ1tK27mmlpv+UYlP2XXeIT2TaP5jSlfFOkvUOPSzhEXkUiED+7pZTK9VtV6DjZI03Gz26oclOHSPdofX8zoiw5hvmaXaR+qqxHMHWh6fyOCKBPbaLH3uWGCtYdJ45krh9jU9djrXKegXPe9qz/e6hrlh0WTura4lHZCCCHOwT2+3511Ax9tNwRcEmMDXLBFxB4sZbHY6xiHEGcJqQ946DPyOkf48qoS2FuQN79yh72zwhQyCeab6T3zz6l8/Hv+JspqVShTmT7viPqek6XVKtSptSaD4b+f3R+dwn3Y+/oATmvwZ4buta7Yfh7f7Mtmp08TKMUNAN4eumvWErEIE3pEwldhej1r7Id7cL20Bv/9+wxv+uqHBtprs5sNCri4ELf/wW9Hrwou8+r6UwCAPm0CnLFJpBUQ8W7b9kMXEyjc0Kqlp1U3OeBiVCPVOEOEW0IQADxkTXse4xqp9iB0IkEZLqQxuAEXW4O8tvD35J9EJIT78gY1WGJr+UUC2KuNl70zXPT9x9qF2L9eNSGEkNZFo2Wg1hhGdrz0Oz8T3ty5E2n+LAZcWuDIbNKyGV8In9DDtJqFo7w4rrND1vvc2AScfn2s4LxPd11Aea1wpYUATxl7reNmpRK/puuuzQ7uEIzvZvfHsE6h2Pr0LfCtH2y8aH2mA7a++dl0Mo+9XV2nxrf7cswu+9G9vRDlr8CyKYm86f3NlKaft+Yo775CJka/toFN39hmigIuLsIwDOqM6sEbN3PalVXIjpBRN6LsCSGNwb1uaOs1RONanHoSsQhfz+wrOK8lMA64NDbwYJzRYnz/hwOXbdqucD/d+3FHb/v3HhC64KxUU8CFNIxbRsyVZbmMAy6ahpodEbuyU3yEveBhr+Oi8wW6sgHdo/3tsj5CCCGtV/bNStQZpWAm1JfSGdIhxGx1ANL8WR5QSAEX4l70mQh6ziiL98E9ibg7KQazh7R12HN4yaV4bHh7k+n7LxbhqZ9PmExfO2cgxGIRW8ngemkN20vkjl7RuKWToa/NY/UZNBUtqDexJY+tNgRFGqpscnuvaOxfMMoki3PhxK545BbT9+NYbil7e+dzw3H2jfEtcoAj/eK7yK5zN0ym/Xb0KqIDPHE6rxzDOoVi+dYsdt7YbhHO3DzSgont+EVmrnmgWARIW3AtW7nRayuvVTWq5N+Lv/F/5PWBmh1nC/DCr/wRcP3bBqJDqA+ssWH+EJy+Xo7hDmp4N3twPHKLq5BTVI0LhZVUUow0ij7D5ae5A+HnwFT1hhgHXCzV4ib2Z68Al6GkmG3ru3SjEiPf283ev6efaT8sQgghxBonOf1b9HKLdfXqH76lZZdcbum459C+HlLeRdfW1NOBtAzGpcud0T/jzt4xuLN3jMOfp0OY8DWUU9fLefe/mtEXye2DAQCxQZ64VlqDD7adw7VSXUmxAfHBvOWHJ4Tinc1nAejOb31acADd+Dyrqs7wfXdbYlSj16OQSfDS+M54cVwCalVadFm0mTc/yFuO+BZcZaDlfkKauUqBdLbnf+VcjDWqZ/efvnQhgNgHd2SVo643ikSiFtn0Ss8k4FKjhoXWEaxLN6p492vqdAfns1cd4U2f3l6DxQ/0MxvQMifcT+HQPhOLJumaSN/2yb8AqKQYaRx9wMXVB6UKoxJ9/eJbXtpyc6a1U8BFUl+bzJaAS0lVHS/YIhGL2FrWhBBCSFNlcEbt6ukz2l056ITYjpvh4uUh4QVclGoKuBD34mFUsaOpJdObo+BGHtP7cQbj6R+z70IROy3Qm/+d3ZETyPnxwGXBzI2Wwri3cGm1ir393n8SjRdvkEgkgqdcgjlD4/HlXkMvGEUL+twJadmvrhkb1lE3Ar0xJ/gf3tOr0XXmCWlIkLccb9/VA+/dnQgPacNZGU3ljLRUVzEOhJTVqMwsadmVkmrB6cEKyxcSJ/fRjQyZkeya8kyK+s8NnVyQxqhS6i40uLqMhkgkQvdoP/b+Q0NopKkzGZdNbSr9T4stAZc/jl3j3W8b7NWif7MIIYQ4h7keAQDgJXfceRdxPO7pn7dRdkCIjxyEuJMjOSW8+9wL6u5OX2a9Idygk0xgoKuvUZCcW8Hl7X/OgmnB5anXHMwVnB7hp7DpnOluo0SCn+YmN3ld7oDOLl3E30uGQy+PwtanhzW4bGyQlxO2iLQm9/Zvg8lJjk3nNG6yvmJqb4c+n7OlLRjJnjiV1zbtAGXZlizB6SENJKksv7snfns02WEN5xriUZ8pUKvSUAo9sUirZZpNhgsATOlj+N5zdQCotQirHzCSYqfSqPoMF1t6uBj3zxrSIcSmbSKEEEIAQ1av8ehxAPCi4w63xj3l8eL0v3jrzh7oFkV94Ih7MT4W7tGCehl2DPNFbJAnAFjsKxwXbLjOmp5bYnY5rtdv78be7rl4K0qr65q4lc3X+6nn8NH284LzHhXoj2ONcF/+ha42wS37WjcFXFwozE+BIG85+2VgjnHdeUKas9FdwgDwM1y6RPhaVevRHUT6e6JnjO7A5Fx+RZPWUVxVJxiw8G9gkJRIJEJSXJDLLhjr+9Us+jMTvV9PRW6RcKYOaZ2+/jcb936RhiqlGrvOFbLTm0PARcL5XjIuDUgcY/NTw7BmzgCM726fgIs9ergYZ+c9PqqjTdtECCGEAEBVfcBl6V09TOZ5NaLfI2m+1FrDsQM3w2XagDau2BxC7OZ/9yfxmsO7O4lYhL0vjETO2xOREGGo+96vbSCW9FGjT5sAPDGyAwK8DBddFt3ajbeOdqHCfUXuH2ioMFKhVOPHA5ftvPWut4ITbHl4GL8ihK3l6/29DNe2Rzio73BzQlcbmoH7BlguC9SuBTcRIi1LuJ8HPrsvCQAg5fRwefuubuYe4tYOXCoGACxtREqpuUwQc+mazZk+4FKpVKNSqcb8tUddvEWkubhaUo03Np7GgUvF+PnwFRzj1DL3bAalNLiJdzJpy+0z1ZwEecsxqH0IRHZqGia2Q8ClgpOVeGThaOrfQgghxC70ARd/Txmv3j/Az4og7oc7mFBBwTPi5o4vSkGAlwzvTO6Bsd0irO4d6y64rRm0WgYBHsDPc/rjmZQE3nLDOhmy3RUyMb6aIZwZY3w+s3zrOTtubfMTHeiJsd3C2fsjOtseJDm5OAXfzuqHL8zs45aEAi7NwNxh7bDpiaF47+5ErJrVD4GcqF+PaP8W++VHWgZuBlZsoKEOPrekmPEJR0u0/2KRxfmVZmo6/3vhpiM2x6GMm5uduFrmoi0hzc1jqw3BtzqNFh/vuAAAeGJkB1dtEo+Ec5BMPTvckz0yXPQB8CdHdaRgCyGEELvRN1L39pDinSk9efMos9a9cXvwGJfOJsTd+HvJkLEoBff0a9kZWtyexcZl1IyXG9Q+GHKJGLueG4F2oeavX8UEGioUtaRSbEL8FDK8cXt3AECbIC+79ID2VcgwIiGsVZyLu76+B4FIJELXKD90jdI18z366hhcKa7Bqv05eGhovIu3jhDLfnt0EEa/vxsAP6ulfagPnhrVASW5Wa3iyzSvrNbsvJKqOmw4fp29P21AGzaz5WJhpcO3zd70PVxIy1RQXouPd5zHfQPj0DnCr+EHcHCDb9zPtnHTQVfxpJNltyeuD5odyilu8jpUal2wRi5QY58QQghpqipO3zrj0r/2yvQkrsHNaqEBsYS4n1oLARcA+H52f1TVaRps6fDviyOxeEMmVu3PafEDiyP9FQjzU+DQy6Pgo6DwgbXoTLMZEolEaBPshUWTuiIqwHJ/F0JcjRvh5wZWRCIR5g1vhz4hTR+F7E4slRSb+uUBvLYhE4AurXXOUEMtzEs3qwAAye2CAQCTetqnz4EjnStwvyARabzH1xzDjwdyMe7DvVCbKYUnJLv+s6x3rbSGvd1cTky5fWTowod7unjD8P1Tpzb9fObcrMLLf5zE5aIqk3l6qvo67BR0I4QQYi/VdWoUlCsB6I43gry5/QG6umqziJ1wL8JK6BiSELdTXWc54CKViBvdP7tzfW+Y349da7C0vDsxriCQGBsAQNd/3EtOARdrUcCFEGITCeeCVWtOlb9eWsv7sb1aUo2a+h/1s/kV7PSSqjrEh3ijTZAX7/HJ7YNxYnEK3pti2mSzuTliNLKczjlaFm7mwJHLJY1+3JXiat79jCul7O3uUdZlyjhKS0/7bg2iAgzNGpVq0xOn6V8dxJqDuXjkR/O9pVQa3Xe1tBX/ZhFCSGtXVqPCvNVHkXq6wC7rW7nzAnvb20MKf08ZPp3eB1/N6IvZQ6hqhbub3CcGiTH+eGx4e0gkdPJDiLuIrh/EPqRDSANLNl7PmAD29qHspmfdNycVtSr8dvQqe/+PxwZRvyob0ZkmIcQm3BE+0lZ88PnBtnPotPAfXLpRiQuFFRjyzk7csXKfySgBdf39GclxvOmeMgn8FDK3GHVvXB6KYSxn+BD3ouCUjHtj42kUlpsvl8dVVqPi3dePImoX4o0B9Rlcrhbmp0Dq08Ow/6WRrt4U0kSTekaxt5UCGS76zKozeeVm16HP3JK34t8sQghpzbRaBolLtuLvk3mY8/0Ru6xz5c6L7G0/T91I4Ak9IjG6a7i5hxA34imX4M/5Q/DCuM6U4UKIG/nlkWS8PKEzXh7fyW7r7BhuKCX2J6d0fHN2/Eop2r70Nx5fe0xw/it/nMILv54AoKsC0LtNoDM3r0WigAshxCbcUkGSVl6eRaVhMOr93diQofvRzSqowI0KJW+ZaQN0jel8jOo6K9yoL8oL4xJMpjWUokvcR63KcBE783o5Rr23u1GPq6hVC05/aXxnu2yXvXQM96VynW5MKhHDo773inEt5pKqOva2zEIwhTJcCCGkddt6Ot8u62EYBj8cuMzL6gVgl8bCpPmikqSEuI/oAE/MHdberj1FZRIxRnYOAwCsOZiLE1dL7bZuR2AYBrev3AcA+Ov4dZzk9F3V4/YcVmtpMK090JkmIYTYEcMAK3YYSgqU1tTx5vdvGwQA8DIKuLjTidm0/m1MphlnNxD3lHPTtO9FhVI4kGJsfcY1wemNrYVLSGPpAy7GGS6930g13I41PypLVZ/hIqOACyGEtEpXS2p497l956zxy5EreHX9KdxRfyELANbOGWjTtpHmjwbuEEK8Oddz/j6R58ItaZhxr9XzhRUmy4T4eDhrc1oNOtMkhNiNCDTax9hxoxFv+kwW45HZ7lSOTajsWXktBVxaAuODMT3jvj1Czpop4eTvRQEXYl/l9dlU3Atmh40+o4dyitnSYcbUWn3AxX2+dwkhhNhPudFAofEf7mnSel787STvvp9CiuT2zaOMKnGcR4e3x6TEKHx+Xx9XbwohxEXaBBkCr6sP5rpwSxpmfJ5kPFi2Tq1FcRW/MguxHQVcCCF2ox91TAyMT8Q86huPDesYyptu7kK3uyirpoBLSzBr1WHB6Q9+d8Rsn54/M66h7Ut/sxfBjfsTUYYLcZSZ3xxC5vUyaLQMvtxzyWT+Xyeu485P92FLJr90DFtSTEy/WYQQ0hoZX2wqr1VjZ1ahzettE+xl8zpI8+ftIcXHU3tjXPdIV28KIcRFuOO6mnNz+YLyWpNrUkv+Oo17/peGa6U10GgZLFx/EvoqYmKRLqhMbGf3M809e/Zg0qRJiIqKgkgkwvr169l5KpUKL774Inr06AFvb29ERUVhxowZuH6d32Sobdu2EIlEvH9vv/02b5kTJ05g6NChUCgUiI2Nxbvvvmvvl0IIsZKcAi4N0gelIvwVvOl960uNuatyM/07iPswF1ABdBcmFq4/JbjMc+uO8+5P7BGJnjH+7H0KuBBHmrjiX7R/eRO2ni4AADx8Szt23tM/H8ex3FI8/EM67zHVdbrvK3fqnUUIIcR+hErh/nPSupIw1wXKkMUGUsCFEEJaA27FEpWZrPrmYPWBy+xt7vW6g9nF+Hj7efx8+Ap+OXIVANAuxBsnF4/FC2NNe/YS69n9TLOqqgqJiYlYuXKlybzq6mocPXoUr776Ko4ePYrff/8dWVlZuO2220yWff3115GXl8f+e/zxx9l55eXlSElJQVxcHNLT07Fs2TIsXrwYX3zxhb1fDiGkESb21I3umTU43sVb0rzJJWLEh3gLzhvWMcTJW2NfVY3s80Gar6wCQy3XTuE+JvNXH8xFvze3m0zXZwvo+Sik6McJIHo24xE/xD29eWd3s/NCvD1MsqyM5ZfVAgDC/RQWlyOEENIyCQVcfjlyFWfMlEcVcijbtNxqmyAKuBBCSGvADbiU1ahMSsY3BwzD8PoLf3hPL978nw5fwct/GLJfqus08PaQCpaQJ9aTNryIdcaPH4/x48cLzvP390dqaipv2ieffIL+/fsjNzcXbdoYGjH7+voiIiJCcD2rV69GXV0dvvnmG8jlcnTr1g0ZGRl4//33MXfuXPu9GEJIo3x8b2+8dWcPGsluwbpHkhEf4m22GZm7/6g151EdpHHGfbiXvb12zkAMX7YLFUaBtJuVSiz4/QQeHtYebUO8odWaZrx0ifDDuO4afP1vNhJjA9z+s02an5Gdw8zOC/GVQyuQibX/wk0cu1KKWYPboqiqDgAQ5kfNIQkhpDXSB1xeGt8Zb/9zlp0+/qO9yHl7YqPWcbPStN59DAVcCCGkVagxCrAUlNciLlh4cK2rbDjOrybVt22gxeW9PGigpD25vJZCWVkZRCIRAgICeNPffvttBAcHo3fv3li2bBnUasNFn7S0NAwbNgxyuZydNnbsWGRlZaGkpMRZm04IqScWiyjY0oD2oT4mwZb+9VkAdyfFuGKTbNI10o933zjLwRjDMFi58wKmfXkAWfkVFpclzldTxz9gDPKWY9uzt2D+iA4my649dAXDl+/CqWtlmP7VQd68wR2CIRaL0K9tEP59cQR+njvQodtNWicvufnxQsHeHgjylptMf/iHdCzbkoUn1h6DPh7jp6DfLUIIaY30AZfEmACsfmgAb15hRa3Fx566VoaR7+3Cf/8+AwC4LTEK4vqxJX3jLF/MIoQQ0jIktwvm3b/UDHvyfvNvNnt7QHwQwnzNZ/f7eEjxXAqVErMnu2e4WKO2thYvvvgipk6dCj8/w8W7J554An369EFQUBD279+PBQsWIC8vD++//z4AID8/H/Hx/NJF4eHh7LzAQOEDHaVSCaXSMBKlvFyXMqxSqaBStc6Gz/rX3Vpfv7Vof1mnpe+vL+/vjTk/HGvUsp4SxmQ/LJ/SHVtPF+DupGiTfdXc99mKe3vi96PXkXGlFPsvFaO2zvL36Nf7crBsyzkAwK0f78XpxWPssh3usr+aC3P766bRxQW1Wo0gTwlGdArGJzsvQMjJqyVIu1TEmyYRGdYd7iMDoIVK5b7ZT/T5sp4z9pmnhEHncB+cLag0mdcr2hdpdabPrc/W2nbG0BRZzGhc/vl0xWeMPs+EkNausEJ3TSDUV26SiZtzs9riRamnfs7ApRuGC2tdIv1wZ+9o1Kg06GI0IIkQQkjLdHffWHh5SPH1v9k4fqUUn+26iBEJ5rPwXSGQMwjt46m9AQA9Y/xx4moZb7nUp4ehY7ivU7etNXBZwEWlUuE///kPGIbBZ599xpv3zDPPsLd79uwJuVyOhx9+GEuXLoWHR9PLPyxduhRLliwxmb5161Z4ebXu9F/jUm/EMtpf1mnJ++uDgcDlSuDDU+a/ThUSBlu3bBacFwpg17ZMk+nusM8SABwsFwMQ48SpTGwqPmV22V8zdcsBumyYTZs22XVb3GF/NSfG+2tPngiAIYVY//4wDNArWIwbNSL4yhicLTMkxi74w/Rze+PGDbu/t80Bfb6s5+h9NjceKAgH3jlh+O59pZcaO7dtwfky/ufZnH/++ceBW2gdZ37GqqurnfZcxDZt27bF5cuXTaY/9thjWLlyJYYPH47du3fz5j388MP4/PPP2fu5ubl49NFHsXPnTvj4+GDmzJlYunQppFKXjrsjxGbltSrM+PoQ+rQJxKJJXQWX+TX9Kj7ddQGrHxqASH9PMAyDzafyUVGrC8JH+HtCaVQW5nppjcXnvVDID/ZX16kxwkKpS0IIIS2PRCzCbYlROHq5BMevlOJQdjFKqup4QQ5XyyvVDar8bnZ/hNX3rnzrzh6Y9uUBlNf/Dg5qH0zBFgdxyZG2Pthy+fJl7Nixg5fdImTAgAFQq9XIyclBQkICIiIiUFBQwFtGf99c3xcAWLBgAS+YU15ejtjYWKSkpDS4DS2VSqVCamoqxowZA5mMSms0hPaXdVrT/lIGnceX/+ZALdDTwt9bgQkTbmnUetxtn+354xTSb15Hh06dMWFYvNnlfi44ggvlhuaiEyZMsMvzu9v+cjVz++vX79IB6LJVfpnbH71jA9h5+krmj63JwNkyQ3aAXrC33NATIzQMEyb0cdTmOx19vqznzH2mVGvxzoltAID7B7bBAxM7AwBOXivDJ6cPWnooAPt9D9nCFZ8xfYY3af4OHz4MjcZwMfjUqVMYM2YM7r77bnbanDlz8Prrr7P3uYPINBoNJk6ciIiICOzfvx95eXmYMWMGZDIZ3nrrLee8CEIcJDWzABlXSpFxpRSPjWjPlu7NLarGsGU7ecsmL92BoR1D0C3KH5/vvggA8FVI4eMhhZdMgv7xQTiUrTtOvVJsPihdadTbDgAeGtLOXi+JEEKIm9Fwrv/0fiMV598cD5mE370j/XIxLhdV487e0U7rb8owDK6U6H7PYgI92endo/1x/LUUxC/QDZKsMiotTuzH6QEXfbDl/Pnz2LlzJ4KDgxt8TEZGBsRiMcLCdCNHkpOT8corr0ClUrEnp6mpqUhISDBbTgwAPDw8BDNkZDJZq7+QQvvAOrS/rNMa9teLE7qiY4QfnvnluMk8L7nU6tfvLvvMQ6b7GdFCZHZ7v9xzCfsvFfOm2fu1ucv+ai64++t6aQ32XtAFW16/vRv6twsVfMzA9iFIPWMacIkJ8sKbd3bH4g2n8dCwdi3yfaDPl/Wcsc9kMl16/J8Z1/HMmAT2+Xw8G5cN3ZzeU2d+xprT6yaWhYbyv4/ffvtttG/fHrfcYhjE4eXlZXbA2datW3H69Gls27YN4eHh6NWrF9544w28+OKLWLx4Ma8fJiHuZPe5G/hy7yX2furpAkzt3wYA8O3+bMHH7D1/E3vP32Tv94zxB6DrRfnLw8n4aNt5fLDtHHuBSsj3aTnsbT+FFI8Mbw9/L/pOJYSQ1spHwb+s/t3+HDw01BCIr1KqMfmzNAC636rP7ktyynaVVqtQXR9MifL35M3jBn3CfZteRYpYJm54EetUVlYiIyMDGRkZAIDs7GxkZGQgNzcXKpUKU6ZMwZEjR7B69WpoNBrk5+cjPz8fdXW60bFpaWn48MMPcfz4cVy6dAmrV6/G008/jfvuu48NpkybNg1yuRwPPvggMjMz8fPPP+Ojjz7iZa8QQoizecmFS9h4yhoubeOu5BJ9mTDzfRDe3HSGd9/bzH4irvHPqXz2dttgb7PLTR/QBu9M7oGZyXG86cevlGJc90gceHkUhnYUDtYQ4iiTEqPw1cy+ZtP3k6iBMWkh6urq8OOPP2L27Nm8E+XVq1cjJCQE3bt3x4IFC3gl49LS0tCjRw+21yUAjB07FuXl5cjMNC0LSYg7KKtRYeY3h3A2v4Kdxi0D5uPRuDGlvh78QEl0/QjgvDJ+X7u1h3Lx5Z5LYBgGn+ww9LU7sXgsHhvewertJ4QQ0nLMMD43vlqG41dKMeSdHfgt/SoOZht6nnLPux1txHu72NueAtdf3pncAx3DfPDKxC5O26bWxu4ZLkeOHMGIESPY+/ogyMyZM7F48WJs2LABANCrVy/e43bu3Inhw4fDw8MDP/30ExYvXgylUon4+Hg8/fTTvGCKv78/tm7dinnz5iEpKQkhISFYtGgR5s6da++XQwghjeYlF/5KNReIaQmkYt1Fn11ZN/BsSkKjHuPvSSMBmxNuUnO/tkFml1PIJLinXxv8mXEN36WZ9hQgpLloF+KNzhG+CPCSISrAE+mXSwAA9/aLxZHLJSb19wlxB+vXr0dpaSkeeOABdtq0adMQFxeHqKgonDhxAi+++CKysrLw+++/AwDy8/N5wRYA7P38fPMn/UqlEkqlkr2vL0OnUqmgUqnY6frb3GnEPbjze3fo0g2TaeU1dexrKa1SmswXMrlPJO/1Byh0g4iKKpXs9EqlGgt+PwkAGNA2gB0tvPTObi7dd+78/rV29N65N3r/3Jej3rsQLyk2PJaM2z7VZbF4ycT4bn82rpbU4Nl1x9G/LX/w1+6z+RjUvuFKT43x+E/HcbNSie8e6Au51JBPodJoUVpterzGdVevSNzVK9Ls/ObGVX97tjyf3QMuw4cPB8OY9jDQszQPAPr06YMDBw40+Dw9e/bE3r17rd4+QghxFG8PQ2DlvoFt8OOBXADCIwpaiqsluhGFJ6+VNfoxKoE+N8R1jl8tBQA8MKhtoz6r8SHms2AIaQ6kEjE2PTEUIhFws7IOvx+9BgB4anQnFFbUYuoXB/DU6E4u3kpCrPP1119j/PjxiIqKYqdxB5v16NEDkZGRGDVqFC5evIj27ds3+bmWLl2KJUuWmEzfunUrr0eMXmpqapOfi7iWO753Gy6LYVyoI+10DjZBV2Ls9EX+fLmYQZ2WXzP/me5qVF84jE2GhBVcrgAAKS7fKMemTbra9terddMA4Nkf97HrLbl4HJvyTcsIO5s7vn9Eh94790bvn/ty1Ht3V1sRfs+R4JcjV8BwhjQeyinhLTdzVTo+SjbtB2at8jpgc6bu9+nL3zYjntP3vqwO4F7u1/+mtQTO/tvjZo5by+k9XAghpKWSig0nd1P7GwIuxk3TWpK4EMOFF4ZhIBKJUFajQpVSjagAT8HH3KhQYtLH/+K/d3RHIqc5O3GNGxW6kaD6WuYN6RkTgMWTumLxX6cduVmE2ERcn30X6uuBvS+MQHmtChH+CkT4K3Bi8VhIxM5pWEmIPVy+fBnbtm1jM1fMGTBgAADgwoULaN++PSIiInDo0CHeMgUFBQBgtu8LACxYsIBXXaC8vByxsbFISUmBn58fO12lUiE1NRVjxoyh3kBuxp3fuwVvbAfAb/J7rkyMYaNGY/e5m0hPO8FOXzghAVoGeOufLHbaiIQQPHpPH5P15hZX4/1T/6JKLcKTaVKcXTIGe87fBI4fAwCcLzccz993+1iXDqhy5/evtaP3zr3R++e+HP3eeZ27gd9zjvGCLeYo2vfDyATbSnHf8+UhAKUAgM49+2JUlzB23pm8CiBdl3EjFgETJkyw6bmaA1f97emzvJuCAi6EEGIn3DTO2CBDIGLHWdNG4y3F3KHt8L/duhGFGi0DqUSEfm9uQ51ai0Mvj0KYn0LwcSevleHeLw7gzBvjnLm5REBFrW6ETYAVTV8fGByPAe2C8eJvJxpdSo4QV+F+HwOgYAtxO99++y3CwsIwceJEi8vpe2hGRupKRCQnJ+PNN99EYWEhwsJ0J+Kpqanw8/ND165dza7Hw8MDHh6mTVRlMpngSa656aT5c7f3rqC8li3r9ejw9jibV46dWboSYz8evIr3Us+xy/7v/iSM7RYBhmFwe+8YhPspcK20BiE+csikpsGSUH/+b0Xn11Jx38A2JssN7RgCP2/h41tnc7f3jxjQe+fe6P1zX45673rGmi/NbezhH48h523Lx3SWKNUaHM0tZe+X12khk8lw4FIRfjlyBcMTDMGX/97Ro0V9Vp39t2fLc7XcYdeEEOJknSN88ejw9vjvHd3hKWu5ZcS4PDivU61lUKfWok6tBaBrGFdWbb7mZY1KY3YecZ5KpS7g4uNh3cFEl0g/bJg/BLd0sm10DiGEEPO0Wi2+/fZbzJw5E1KpYazcxYsX8cYbbyA9PR05OTnYsGEDZsyYgWHDhqFnz54AgJSUFHTt2hX3338/jh8/ji1btmDhwoWYN2+eYECFkOZu5U5dDbDoAE+8OK4zOoYbaqhwgy2AoWegSCRCeP0AoOgAT3gIBFsAwNfDdCyqPludq7+FfneEEEJapzBfy8dVr0ywX3P6h747wrv/3f4cAMC9XxzA70ev4eX63mOD2gdj2gDTgQPEOSjgQgghdiISifDiuM64b2Bciy4jxiXljBRXabRYtT+bvS8CUFBR64KtItaoqNUFxXwVlPRKCCHNzbZt25Cbm4vZs2fzpsvlcmzbtg0pKSno3Lkznn32WUyePBl//fUXu4xEIsHGjRshkUiQnJyM++67DzNmzMDrr7/u7JdBWrHU0wV4Z/NZaG3s4ccwDNYduQoA6BTuAwB4cEi82eWt7TknEjUu+/Ghoe2sWi8hhJCWr6HfkDA/fkBGrdE2+bn2nr/Ju595nV/2Sj+gMtiHBte4El1dIYQQB/H1kKJCqUavFtynhBtYUmkYvLXpLHtfJNIFYUjzpi8p5iMwspMQQohrpaSkgGFML1THxsZi9+7dDT4+Li6uRTVLJc1XVn4FgrzlCOWM8i2pqsOc73UjcTuE+mByUkyT13/qWjmbHf3KRN1I4XCB0rVdI/0wZ1i84LyGpD49DGM+2GN2/pnXx7m0dwshhJDma/qANlh9UJcZ+esjyZjyeRo7L9jbA/88ORTjP9oLACioUCLaTM/bpjh1rcxkWrC33G7rJ9ZrHUOwCSHEBdbPH4zpA9rgk2m9Xb0pDiMRi6AfzGE8SkOjZaCxcTQjcaw6tRbK+hJwfoqWU9uVEEIIIc5zuagKYz/cgxHLd/Gmn7puuACUfbMK+y7cxJ2f7kNWfoXVz/HahlPs7Q5hhlJio7uE85bbMH8w7uzdtMBOx3BfyCT8UcoenB6NFGwhhBBiTpivIdDvbTSYMchbji6Rfmgfqsu+PJvX9GbsQm79+F+7ro/YjgIuhBDiIO1DffDmnT0QE+jV8MJuTJ/l0v+t7bzptWot1BRwadb05cQAwIdKihFCCCGkCY7VN++tVKpRzjm2KOX08lt7KBfTvzqIY7mlGPvhHqvKqdSptbwGwVxfzeyL//SNgVgE/PHYIEhtLOs7oUck7/5jwzsAABQyunRCCCHEPG8PQ1DeW84/tw720WWb9G4TCAA4KZCRYo5Gy6CmTpfhyS3P2btNgMXHGQ8gIM5FV1cIIYTYRCYWoU5gek2dmjJcmjl9fVcvuQQSMR2QEUIIIcR65wsNGSt5pbXwi9BlzXKDL0VV/KPF/+25hHkjOli9/s1PDTWZ/+6URLw7JdGqbTbnpfGd0S7EB1kF5fCUSTF/ZAcMaBdkdU8YQgghrYuckxHpo5CiX9tAHM4pAWAo79UmSDcY93ppTaPXO+Xz/bhQUIlnUzqhS6QfO33NQwPx2e6LWLH9vODjBsQHW/0aiP3QMA1CCCE2MTeSsKZOA7XGEHCZlBjlrE0iDdBqGRRW1LL9W3wpu4UQQgghTbRy50X29uTP9rO3y2pUQosDAH5Iu9zo9V+8UQUA6BLph84Rfg0sbZtIf088ObojPp2ehPf+kwiJWISB7YKb1BOGEEJI66E/twaAQC8Zgr0NPc3010wi/XW/Jb8cuYr8stoG18kwDI7llqJCqcbiv07jni8OAADEIl3m5TNjOgk+buHELhjdNVxwHnEOCrgQQgixiblU1RqVls1w6Rzhi4+nttxeNu7iZqUS+dXAqxtOo/+b2/HTYV1TP1/q30IIIYSQJiiqVPLuVyrVUKp1pU8sBVzyyxu+0KR3obASAJAY49+ELSSEEEIcLzbIUEpeJBKhTbBpafmoAE/29qt/njKZb6xCqRac7uMhhai+me7Gx4fAUyZBzxh/fDWjL/59cQQeGtrO2s0ndkZDWgkhhNjEQyrcQLRGpYFKq6vPLaX6oc3CmA/3oVIpBXANAPDjAV3AxY8yXAghhBDSBEv/OWsyrdeSVPRuE4D9F4tM5nnJJaiur0Wv1mgt9lypUqrR7bUt7P32oT522GJCCCHE/iZ0j8CNW7tiQHwQAGD+yA7IK6vFHb0MlT6iOQGX1NMFDa6zpEqoeLsu4KLXPdofZ94Y19TNJg5CV1gIIYTYhPtjz1VTp4amvqSYRNxwQmWVUg25VAyZjc1OibCKWhXbs8UYtxYsIYQQQpquuKoO3h4SswNSWhqVRmsyrUal4QVbFk/qik7hvqhRadA50g+D394BQFd+JbC+rr2QP45d491vH0Z9VAghhDRPUokYDw6JZ+/7KWQmVT7igr0Q5uuBwgpdduiZvHKL5+Il1cKZotzyZaR5oqtahBBCbOLlIXxBoValhbq+pJi0gYbs5bUqdF+8BRNX7LX79hGdYjOjYwAgKS7QiVtCCCGEtEzXSmvQ541UJCzc7OpNcYrqOjX+zLgOAJiZHGd2uVBfBQZ1CMGoLuG80b0NXTAybircpw0drxBCCHFfIpEIe14Ywd5vqI+LuQwXUAGRZo8CLoQQQmxiLsNFrWWw9pCuZJXETMBl97kbyCurweHsYjAMcK6g0mHb2drVqkxHoOr1axvkxC0hhBBCWqb/fJ7G3j5xtdR1G2KjnWcL8cmO81ALZK/opZ4uQNdFhnJfCrkE3aKER+mO6x4hOP3YlRKz669UqvHprou8aQFe5rNhCCGEEHegkEnQPVr3e2muR4ue8aDJWYPbAgDmUI+WZo8CLoQQQmziJRfOcLlaUo3d524AACrrRzB+Mo2fUjvzm0NIXroDSrXhhF5bnxVD7GvdkStm53Eb/BFCCCGkaa5xMjJOXC1z4ZbYZtaqHpfZSAABAABJREFUw1i+9Rx+Oqw7dqhUqrF8Sxayb1bh9PVyaLUM5nx/hPeYO3pFY+PjQ9CT09heLhUjfeFoswNvnvwpw+w2PPpjOnv74Vva4d8XR5hdlhBCCHEnkf66bM/KBjI9S6oNAZfOEb54ZUIXpC8cjcdHdnDo9hHbUQ8XQgghNvE2k+Gy9/xN9ra+vvetPaOQGBOAoe/u5C3LTaVNzy2hjAsH+OrfbMHp+18a6eQtIYQQQlqmblF+yLxeDgB4a9MZ3DfQfJmt5krDGfhy6pouaPTm32ew9lAuPtl5AQDw2qSuJo/T16D3lhuOC3c9NxzBPh4NPifDMDhfWImYQE9e1ozeuG4RiAmkwSGEEEJaBt/6ayiXbliu8KEPuMxMjsPi27pBJBI16neVuB5luBBCCLEJ98TaHG7Oikxi+tOzqz4TBgDu5pTjMKbRMpQBYwe3J0Zi8aSuuPDmeERxaqkTQgghpGl2ni1kgy0AUF2nceHWNN0ezjHZ5aJqAMCOswW8ZZb8dZp3/9Hh7dnbi2/rhsQYf3w2vY/ZY4zZg3VNhSP9FQCA1QdzkfLBHsFgCwAKthBCCGlR2of5ADA/KFKvuEoFAAj0lkMkosYt7oQyXAghhNjEXIYLl5RTSkImMT1QKKs239BdT6NlMOzdnRCJgG3P3AKFTFfKTMsAp/PK0S06EFKBYA4xtXxKD8hkMldvBiGEENIiaLUMZq067OrNsIvv0nLY22mXirBy5wUUlCvNLh/lr8ALYxPY+wkRvvhz/hCLz3FPv1h8sy8btSpdUGrh+lNml/1seh+E+tJoXkIIIS1HV07Ps7nfH4FCJsEbd3SHvyf/HL2kvodLkDf1MHM3dGWKEEKITYwPCoRws1qEgiJ1Gn7WikqjxYxvDmHZlrPstOfXHce10hpcLanB1ZJqdnpaoQi3f3oAHV75B8u3ZDXlJbQKwfUHaaOjzTfAJYQQQoj1LjRQEsQeNFoG76eew6vrT6GsWuWw59mVdYN3f1kDx1b7Xhpp9ahb/YWj0hoVlGrhTCBfDynOvjEO43tEWrVuQgghpLlLbhfM3t56ugAbjl/H+mPXeMtUKtXYnJkPAAj0ooCLu6GACyGEEJtwR2eYw22WKhcKuBidbO88W4g9525g5c6L7LTfOQcgtSpD0CC30rDuT3ZeQGkjsmVaI/170DuYAi6EEEKIPelHoALAXX2i2dvGF09s8fvRq1ix/Tx+OHAZia9vRZXScqPdpmAY68u2NqXESaCXrP75gH0XbprM7xTug5NLxrLZzIQQQkhLopBJIJfyr4t8uusC7/7qA5fZ2xRwcT8UcCGEEGITP0XDJcW4QRahkmLcAApgWvfcuG+LvgQFANQYXW9YfTC3we1pjfT7TEa//IQQQojd7MwqxMxvD7H3X51oaCj/1M8ZdnkOhmHw/K8neNO6vbYFdWr7DqI4nFMCACYXgQDghXEJyHl7IuYOa8dOe3p0pyY9DzfbefaqIybznxzVtPUSQggh7qJvXCDvfkG5Ej9ygiyf7TYMPo0OpL6r7oYuuxBCCLEJt4fLxJ6RmD6gjckynnLDCEVutovetdIa3n2VxnABoU6txYFLRbz53ABNkZK/voZKX7RW+n0mp19+QgghxGYqjRanr5dj1reH2d/YSYlRCDSqs15YUYuaOg1vsIi1Np7IE5zeaeE/+HLPpSav19iurEIAwK09InHuv+N58x4comt0//KELsh5eyKyl07Ak6M72u25uSb2pDJihBBCWrbu0f4m0/Q9zS4UVqK0vnxoj2h/tA32cuq2EdvRZRdCCCE28eCMgvzv7d0hVFmiXag3e7sxpSe4GS5rDl7GtK8O8ubX1F+0qKnT4GqV9aUsWhuNlkFdfRCLMlwIIYQQ69SqNFh/7Bre2nQGl4uqAAB3fbofE1bs5S3HLSem9+KvJzDqvV3o/OpmHMoubtLzP772mNl5b24606R1GlNrtPh0l240bb/4IMilYiyc2IWd7yHll/dqSikxS/T17LtGNlyqlhBCCHF3ye2DTaZ1jvBFpVKN0e/vZqeteyTZ7r+5xPHosgshhBCbeHLqa3vKJfBTyHjzk+IC8ZSVpSGKObXQF/912mR+rUqDK8XV6PnGdiu31jYqjdakvJk74I6qpQwXQgghxDov/34ST/2cgS/2XMIty3aholaFk9fKeMtE+iswIiEMADChRwQ7fWfWDVwvqwUA/Od/aU16/vacgSu/PpJsMr8pvVeMnc2vYG8P6RACAHhoaDu8O7knvp3Vz+b1G5vYg5/F8vbkHtj+7C34ReD1EUIIIS1Nv7ZBEIuA6ABPfDa9DwBAyzC4VsKv/kH9zNwTXXYhhBBikzA/BZ5L6YRXJnSBQibBw7e0583/7dFB8PfiB2He/08iogPM1yEtrKi1+Jy1Kg2GvruTN+2+gYZSZmqN/RvDMwyDe784gFuW77SpLIi9tsUaW0/ns7cFyrITQgghxILfj13j3e+xeCvv/ssTOiNtwSj2/tuTe6JzhK9dnlut0eLiDV1WzcppfZAUF4jhCaG8ZfRlR2yhX0eAlwyxQYbSJf/pF8sGkuzpw3t78e57yaVoH+oDH4+GewMSQggh7s7HQ4qjr47B5qeGsuXFsm9W4VxBRQOPJO6ALrsQQgix2fyRHTGnvomqv6cMgwTSY7nu6hODfS+NxLIpPdlpPh5S+HvqAjNrD12x+Pj3tp4zmbbo1m7s7Sql/QMil25WIf1yCa4U1+BKcbXd199YV4qr0f+t7fh4+3nsPFuICR/txSmjUbbGnv75OHtboIUOIYQQQpooyFuOB4e0403zU8iw+alhJssGG/V3aYw1h3LZ2z4KKUQiEVbN6o/fHxvETt9Z33vFFtfr++l1i3JOSS+ZhH8pItBocA4hhBDS0gV4yeGrkCHCXwEAUGkYi2VEifuggAshhBCX6RUbwN7295TBS964dNn8cn4GzKsTO0MuFUNSH02oVds/4FJeYxg9Wl6rtvv6G2vF9vO4UaHEe6nnMGvVYZzOK8etH/8LpZnXTCNkCCGEEMd4enQn/PviCPb4w9jQjiG8++W1KquyVK8UV2PRn5nsfW5/kz5tAtnbh3Oa1huG6+LNSgBAxzD7ZOY0RoSf7gLTt7P6QSqhSxOEEEJaJ+NBCHrcKh7EvdBRDSGEEJfh1iOVS8WoVDYtkDGkPqPGo75ellJl/5Ji5wsr2dslnB4zzlZnplzaeqNyJ3rHckscuTmEEEJIq/Xk6I7wkpsvgbVsSiLvvkrDQKUxBFyul9YgcclW3PO/NJMecfsv3OSVTx3aMQShvh68ZR6pL+O69tAV3KhQNvl1qDRaHLykC9pYKvlqb6nPDMOBBaMcUrKMEEIIcSfGvc0AYOHEri7YEmIPFHAhhBDiMtyMFolYhIomZo4E1ZfoYAMuDshweeHXE+ztE1dL7b7+xtp7/qbg9Pwy4Qstf5809G95cHCcQ7aJEEIIaamMAyF67TiN7M2J8Ffg3Sk98fZdPdhp+oETWi2DQW/vQFmNCgezi/Hncf7AiWlfHeTdf2ViF5P160uxAsBTPzetBIlao0XHV/5BxpVSdpudhVtGhRBCCGnNRnXhDz6ID/HmDVAl7sXuAZc9e/Zg0qRJiIqKgkgkwvr163nzGYbBokWLEBkZCU9PT4wePRrnz5/nLVNcXIzp06fDz88PAQEBePDBB1FZWclb5sSJExg6dCgUCgViY2Px7rvv2vulEEIIaSJ9iYiGeHICLhozFzS45gyNN5k2LkaLgPq63x5S3fqUavtnuHDluqiHy7XSGhSbya5ZseM8Np/KQ9dFm/HjgcsAdPv0SH2ZkYUTu+CZ0R2dtq2EEEKII5kLhNjbnZ/uE5z+3t2JgtON/advLO7uG8ver6s/Rimv5Te612eYAMDFG/xzX0D42Gpaf0OpkX0Xihq1PcYOZvPLkbUJ8mrSegghhBDSdLclRvHu/zR3oIu2hNiD3QMuVVVVSExMxMqVKwXnv/vuu1ixYgU+//xzHDx4EN7e3hg7dixqaw31+KdPn47MzEykpqZi48aN2LNnD+bOncvOLy8vR0pKCuLi4pCeno5ly5Zh8eLF+OKLL+z9cgghhDTB7CHxiAv2wsdTe1tcTiE1BFyKKs2XwnhpfGfsfWEEukf7s9N8PaQ49/oYjI81BFc8ZLqfted/PYHPdl1s6uazNFoGhUb9YgBgfcZ1q2qw2wu3XIhCxv8J12gZPPLjUVTXadjyYpduVKK6TgMvuQSzBsdDLqXEVkIIIe5v+5kCtHt5E1I+2O3w5zp+tYy9rb8Y4i2XoDenh0pDuD1equrLpxqXUb10s4q9/fF2/oBEQNdY15i/lwxPjNINpugc0bTeK2WcHnUdwnzQM8bfwtKEEEIIcQRuL7MHh8QjvJGDWEnzZL7gbBONHz8e48ePF5zHMAw+/PBDLFy4ELfffjsA4Pvvv0d4eDjWr1+Pe++9F2fOnMHmzZtx+PBh9O3bFwDw8ccfY8KECVi+fDmioqKwevVq1NXV4ZtvvoFcLke3bt2QkZGB999/nxeYIYQQ4hrdo/2x+/kRDS4n5lyAEGpE7+MhxQOD2rI1yrklxx4f1QEiEb9Jrb6k2Jm8cpzJK8fcYe3MNrLVU2u0Zhu1PvtLBtZnXMfrt3czmXfxRiU6OLGxLAD8czKPvX3itbG4VlqDM3nleGz1Ud5y10prAACf1gedukX5QSIWQWv/SmuEEEKI0y3bkgUAOFdQiZuVSoT4eDTwiKaL8lfgelkt7uodjddu64a4YC/c3iu6yetbfTAXL43vjD8zrvOml1brMljLqlX460Se0EMFTekTgxXbz+PSzSoo1Ro227cxfjp8Fa9uOA0A8JRJsO2ZWxr9WEIIIYTYl1wqRp1ai2GdQl29KcRGTh3qmp2djfz8fIwePZqd5u/vjwEDBiAtLQ0AkJaWhoCAADbYAgCjR4+GWCzGwYMH2WWGDRsGudwwymfs2LHIyspCSQk1ByaEEHe19wVDkObhYe1w/LUUPDc2gZ3GrZfOrVuup9bws04a6rVSWFGLfm9uwyM/pAvOX19/MWTRn5km8y4UVplMc7T/7bnE3pZLxYgP8UZibIDJcgqZBGU1KvxRn+nSx4pRuIQQQkhzFx9iOB54fE3Tepc0ln5QxrQBbeDvKcOzKQnoEObT5PUp1RqUVtehwCiD9lxBJX44cBknr5WZlFkVOubRiwn0RLC3HHVqLY5fKTO7nLFKFdhgCwAMaBfU6McSQgghxP52Pjcc3z7QD7dQwMXt2T3DxZL8fF3j3vDwcN708PBwdl5+fj7CwviNgqRSKYKCgnjLxMfHm6xDPy8wUPjCklKphFJpKMdSXl4OAFCpVFCpVIKPaen0r7u1vn5r0f6yDu0v67X2fRbhK0P6yyNwILsYt3QKhVaj5mVlSAB8dX9vnLxWjgndwkz2F7ccBwDc+el+nH8jxezzpWcXoaRahc2Z+cgvqURwAyNk2wZ7oX2oN7afvYGbFTVQqVT4M+M6IvwVGBDv2AsVxn1p9K/Z38N07ISHRIQrNyvY+z2ifHm/da3182Ut2l/Wo31mHVfsL3pvSEtQx/lNTLtUBIZhTLJe7aFWpWH7ttmaRRPsLUdRVR2+3ZeDb/flsNN7twnAsdxSAMCr60/hyfoSYRN6ROClcV3wXmoW5g5rZ3a9YrEIXaP8sPf8TeQUVaF/I49Hzpfx99fLE7pY94IIIYQQYlfRAZ6IDvB09WYQO3BqwMXVli5diiVLlphM37p1K7y8WndzwNTUVFdvgluh/WUd2l/Wa037zEsqQbVad9K/adMmdvr2HPOPaQdgR2oWe1+/vzr5i3GujB+A+GvjJpipGIa0AhF0YRzgl03bEVc/YPVksQiHbohgnAgqrqtCyY1KAGIcyTiJGxdO4KNM3U/pBwPVaKB6WZOVKoG3jksA6J7glkgtb18Z/5zfKK3ArSvT2Pt1OenYdNkwvzV9vuyB9pf1aJ9Zx5n7q7q62mnPRWyzePFik3OXhIQEnD17FgBQW1uLZ599Fj/99BOUSiXGjh2LTz/9lDe4LTc3F48++ih27twJHx8fzJw5E0uXLoVU6t6ngcX15bf0HFXmc92RK+ztYB/THirWGNIxxKSMGADcNyCODbgAwEf1/Vv6xgWhTbAXPrrXcj88AGgb7K0LuNxsOPu2sKIW/d/cDv3xDwAkhPuiU7hzy6QSQgghhLRUTj3SjoiIAAAUFBQgMjKSnV5QUIBevXqxyxQWFvIep1arUVxczD4+IiICBQUFvGX09/XLCFmwYAGeeeYZ9n55eTliY2ORkpICPz+/pr8wN6ZSqZCamooxY8ZAJjOfqk50aH9Zh/aX9VrjPrvqm41lW8+jX9tATJjQz6rHGu+vjn0rMeHj/bxlcrwS8PjI9oKPz9l1Cbh0AQDQM2kABrcPBgA8+epWweUvVYhwT98YHC26hqi2HeHpKQUyzwEA2vcZii6RjrlYcd83h6HUGEpmPn3HIHSLMvxu/V2Wga2nDb+dRUp+5GfSxAkAWufnyxa0v6xH+8w6rthf+gxv4h66deuGbdu2sfe5gZKnn34af//9N9atWwd/f3/Mnz8fd911F/bt2wcA0Gg0mDhxIiIiIrB//37k5eVhxowZkMlkeOutt5z+WuyptJqfqXU2v8IhARdPuWF/+3jYdup8/Eqp4PTOkb7IXjoBvV5P5TWwn5wU0+h1xwXrBg9eLmo4oPqwURnVYG85/pg3qNHPRQghhBBCLHNqwCU+Ph4RERHYvn07G2ApLy/HwYMH8eijjwIAkpOTUVpaivT0dCQlJQEAduzYAa1WiwEDBrDLvPLKK1CpVOzJaWpqKhISEsyWEwMADw8PeHiYpoLLZLJWf1GA9oF1aH9Zh/aX9VrTPntkeEckRPgjKS6wya9Zv7+6Rpv+BqzYeRHPjO0s+Lic4hr2tloravD57x8YB7lUl/Xy6e5LvHnZxTXo2UZXxoNhGLy39Ryul9bg3Sk92frvTVVYwR/JGxnozdvWD+7pjW6vbRF8bEK4r8nrak2fL3ug/WU92mfWceb+ovfFvUilUsEBZWVlZfj666+xZs0ajBw5EgDw7bffokuXLjhw4AAGDhyIrVu34vTp09i2bRvCw8PRq1cvvPHGG3jxxRexePFiXj9Md6LRMsiuz+ToGeOPE1fLGhVoaAqlWlfXdFD7YJtLluUIbKOvQoqEcF+IRCJesCXSX2Gxb4uxtsG6njZ/n8zD3y/9jfSFowXLpGblV/CyaQBgaMcQeMndO+OJEEIIIaQ5se0KkIDKykpkZGQgIyMDAJCdnY2MjAzk5uZCJBLhqaeewn//+19s2LABJ0+exIwZMxAVFYU77rgDANClSxeMGzcOc+bMwaFDh7Bv3z7Mnz8f9957L6KiogAA06ZNg1wux4MPPojMzEz8/PPP+Oijj3jZK4QQQtyDRCzC6K7hCPR23IWfkqo6k2nltYam8gBQW39RRX9xRcgrE7vAVyF8UeLJnzJwJKcYAJBXVotPdl7A78eu4fjVxjewNae6Ts27H2S0r7w9pJjcR3gk7PNjE2x+fkIIIa5x/vx5REVFoV27dpg+fTpyc3MBAOnp6VCpVBg9ejS7bOfOndGmTRukpelKSqalpaFHjx68EmNjx45FeXk5MjMznftC7CT9cgnav2woqZlcn5l6sbASxVV1qFWZ/w1vit/SrwIAOkfYXg3B+Pjh7BvjcHLxWMFBGXlltVatu22IN+/+O5vP4p+TebhSzA/yfLsv2+SxHamUGCGEEEKIXdl9KMuRI0cwYsQI9r4+CDJz5kysWrUKL7zwAqqqqjB37lyUlpZiyJAh2Lx5MxQKBfuY1atXY/78+Rg1ahTEYjEmT56MFStWsPP9/f2xdetWzJs3D0lJSQgJCcGiRYswd+5ce78cQgghbuaWTqHYfe4Gb9q10hqTgM7STWd495UqXQPeokp+cOauPtFIu1iE23pFQSGToEe0v9nnfmvTGfz+2GD8cyqfnZZXVgPAfPZlY6g1DHtbKhZBJnBx5r3/JOLVW7ug1+v8XhA9Y81vLyGEkOZrwIABWLVqFRISEpCXl4clS5Zg6NChOHXqFPLz8yGXyxEQEMB7THh4OPLzdb9B+fn5vGCLfr5+njlKpRJKpZK9ry9Dp1KpoFIZsjD0t7nTHG3yZ/yyob2jdYGQ349dw+/HrmFAfCB+nG1deVJzUk8X4mh9Nkh0gIfNr/Ou3lH4Lk0XMBvTJQwSaKGqP/YAdL/vaq3h996a5wvylPDu/3LkKn45ogsWvTw+AbMGxQEAZBLTLJ27+0Q69T0ktnPF3x6xD3rv3Bu9f+6L3jv35qr3z5bns3vAZfjw4WAYxux8kUiE119/Ha+//rrZZYKCgrBmzRqLz9OzZ0/s3bu3ydtJCCGkZfrgnl54bt1x7Dhr6GnyfVoO3p2SyFuOm90CGDJcblYqedMHxAfhvbsT2VIiljJxrpXqSpRxAz7Gdeb19py7gV/Tr+LBIfFIjA0wmX+5qAprDuXiwSHxUGkMF2T0Jc2EcMuPxAZ5Ys1DAxHmqzC7PCGEkOZr/Pjx7O2ePXtiwIABiIuLwy+//AJPT0+HPe/SpUuxZMkSk+lbt26Fl5eXyfTU1FSTaY5QpwG4p6/PdFfjcuYR3rSD2SX4469N8JCYPNxqT6YZ1qu9dgqbNp2yaX3dNcDd8SKotMBAn+vYtOk6b36fYDEO3dD9xs/upMGmTZuEViNId/otfGr/1j9ZOJF5GgcLxfCTAYAIvYO1UGmBpBAGabu2CT6ONH/O+tsj9kfvnXuj98990Xvn3pz9/lVXN71kLRVrJYQQ0qIEecvx9cy+2H3uBp78KQNlNSr8cuSqScBFLhGjljOyVJ/hUlnLL991Z+8YXt12L3nDV3FiAg0XwrRmBiE8/XMGiqrqsOH4dWQsGoMAL34gZ/JnabhZqcTFwkrUcEqkWAq4cLezbbA3YoNML4wRQghxTwEBAejUqRMuXLiAMWPGoK6uDqWlpbwsl4KCArbnS0REBA4dOsRbR0FBATvPnAULFvBKNZeXlyM2NhYpKSnw8zOU1lKpVEhNTcWYMWMc2huoqKoOr/55GqlnDAMpUp8ajLbB3qioVWPp8R285fsNGcH7HW6KHw7kAjjL3n9g8gSb1qd3h4V5/SqU+P5ALm7rGYmO4T5Wr/upA1vNztuYqzt2uVFfqWz6Ld0hyzvh8PeOOIaz/vaI/dF7597o/XNf9N65N1e9f/os76aggAshhJAWRyQSYXhCGJ4fm4CF63UjUi/dqES7UB/2drlRYKVSqbuv5GSTPDCorUmAw1ugsewrE7rgzU1nUFGrxoXCCqw5mMvO45YD01NptCji9JU5nFOCMV35ZV/0mTYHLxVDZVRSzJLbEqOw+VQ+nhrdyeJyhBBC3EtlZSUuXryI+++/H0lJSZDJZNi+fTsmT54MAMjKykJubi6Sk5MBAMnJyXjzzTdRWFiIsLAwALqRgX5+fujatavZ5/Hw8ICHh2nDdZlMJniSa266vdz/zX6cL6zkTesYEQAACBJ43pvVasSHWb89ZTUq5NysQmJsAF7/2xBs6RUb4JST+6ggGV6aYP59aUjnCF+cza9o1LLdYwKRlef49444Fr1/7oveO/dG75/7ovfOvTn7/bPlucwPkyWEEELc3H0D49jbI9/bzd5+L/UcezspTtdf5cTVUrzyx0l8uy8HABDq64HFt3UzWWdskBeeHcMPZuib9lbXabBsSxZvnlCGS0k1v0/MMz9n4OClIsHXIDWqt641X7UTALD87kTsXzCSfV2EEELc03PPPYfdu3cjJycH+/fvx5133gmJRIKpU6fC398fDz74IJ555hns3LkT6enpmDVrFpKTkzFw4EAAQEpKCrp27Yr7778fx48fx5YtW7Bw4ULMmzdPMKDSXBkHW0YkhPLuX3prAnY/PxxdInXZN8/8kgFNQz+WAh5fewy3r9yHHWcLeNPXzhlo9bpcYdWs/vjl4WT2/of39BJc7q7e0Wgf6u2krSKEEEIIaX0ow4UQQkjrw7kO0zXSD+mXS7CNU6oEAG5UKGHO46M6YkjHEEz5PA1zh7VD92h/xAV74XJRtcmFIX0D3Oo6Nf44dg2TEqMw9/t03jIVSjXu+eIA5o/ogEEdgpF9s4qdV2LUA+Y/fWMtvjS5VIwQH/e5kEYIIUTY1atXMXXqVBQVFSE0NBRDhgzBgQMHEBqqCzh88MEHEIvFmDx5MpRKJcaOHYtPP/2UfbxEIsHGjRvx6KOPIjk5Gd7e3pg5c6bFXpruYHCHEN59sViEuGBvzB0Wj6d/Po4rxTW4XFTFZrU21p76/muzVx1hp93aMxKejSgl2hxE+CsQ4a/AxseH4NiVUtzeKwpP/ZzBzp/aPxYvjesCfy8ZNQ0mhBBCCHEgCrgQQghpdZRqQ0+UIR1D8MOBy1avo3ebQFx8y1DTPTZQF3C5aRSo0Y+yfWPjGaw9lIuNx/OQcaVUcJ2f7LyAT3ZeMPucH0/tbVJ6jBBCSMv0008/WZyvUCiwcuVKrFy50uwycXFxVjVfb452PHsLL0v1/uQ4weXu7B2D/+2+hLP5FbhcVG1VwKVOrRWc/tZdPazb2Gage7Q/ukf7AwA+m94Hj64+CgB4684evF5vhBBCCCHEMaikGCGEkBbtuRRD+a8Nx68DADqE+bLT9OXAbBXprwAAk94w5bUqdHxlE9Ye0vV1STNTOqwxJiVGQSFzj5G2hBBCiD20C/XB6dfHYkpSDL55oC88pOZ/B9sG60pl5RRVmV1GyNJ/zphMe/iWdvBTuHed9/E9IvHT3IE4/MpoCrYQQgghhDgJBVwIIYS0aOO6R7C3n1h7DBlXSnG5/kLM06M72e1iirmSI//bfYnX9L6p7uoTbfM6CCGEEHfkJZdi+d2JGNnZcpZniK8cALDkr9ONXvepa2Vs/za9wR2C8dK4zlZvZ3M0sF0wQn2p1CghhBBCiLNQwIUQQkiL5uPBD6hM+/IA2yOlV5sAuz3PEKOa8iE+8gYfs/+lkbg7KaZR639gUNumbBYhhBDSapzJq2Bvt33pb1y8UWlhaZ1Zqw6zt6P8Fch5eyJWPzSQMkIIIYQQQkiTUMCFEEJIi+aj4Lcrq67T4Gy+7oKMPijSNy7Q5HHfze5v1fOM6sIfddsl0s9kmfsH8uvORwV44rXbujVq/T1jAqzaHkIIIaS1mdyHP4hh3Id7kHq6AG1f+hvrj10zWZ5hGNzg9F5TaW3PSCWEEEIIIa0bBVwIIYS0aF4Wep5EB3gCAD6/P4nXjP6u3tG4pVOoVc8jEYt4/WLiQ7x58z+b3gdLBIIr3nIJ+rcN4k1LjA3AhB6GUmhv3N64oAwhhBDSmt3TL5Z3X6VhMOf7IwCAp37OgFqj5c0vqVbx7ksoq4UQQgghhNiIAi6EEEJaNLFY+OLJ82MTEOCly3AJ8fHAw8PasfMUZvqxNMTf01C+zNuDn1kztlsExGIRfn9sEDxlEiy6tSsAQCQS4fsH+dk0fgopPp2ehBfGJWBGchzuM8qMIYQQQogpiViE3x5NNjt/74Wb7G2VRovM62XsfbEIWH53okO3jxBCCCGEtHzShhchhBBC3NuS27rhtQ2ZvGl39OY3ofeSG34SPS1kxVjCrfcuMwr06AM/fdoE4tSSsZBw5ntI+eMfnh+bAAB4bHiHJm0HIYQQ0lolxQXh4lsT0P7lTSbzFv5xCvteGonqOjW6LtrCTveWS3DoldEmgyUIIYQQQgixFmW4EEIIafFmDmqLgy+P4k0zbmrvxclqUcia9vPoy+kXIxEb1rHuEf5oW4lRMEYkEuHDe3qhfag3/pw3mPq1EEIIITaQiEU4viiFvZ9SXzZUIhah3YK/ecEWAKiq01CwhRBCCCGE2AUdVRJCCGkVwv0UkIpFUGsZdI7whYeUn8XCDZbIJE0LuIzvHolfO15F/7ZBkHOyVsJ8PRp87B29o02ybgghhBDSNP5eMmQvnQCRSITcompsPV2A3OJqwWU3Pj7EyVtHCCGEEEJaKgq4EEIIaTXentwTn+w4j//e0d1kHrf/ilKtNZnfGHKpGD88OAAAsCUzn53OLVdGCCGEEOfQl/oM8xMe+NA3LhC/PjrImZtECCGEEEJaOLoCRAghpNWYkhSDKUkxgvOknKwWqVHJr6ZoE+TF3uaWKyOEEEKIcynM9GYb1z3CyVtCCCGEEEJaOurhQgghhNSbNbgtpGIRUrrafgGmU7gvBsQHYWjHEAq4EEIIIS729OhOAIB5I9pj9/PD8fHU3nhwSLyLt4oQQgghhLQ0lOFCCCGE1Ft0a1e8OK6z2ZGw1pCIRfj54WQ7bBUhhBBCbPXEqA54+JZ27G98XLC3i7eIEEIIIYS0RJThQgghhNQTiUR2CbYQQgghpHmh33hCCCGEEOIMFHAhhBBCCCGEEEIIIYQQQgixEQVcCCGEEEIIIYQQQgghhBBCbEQBF0IIIYQQQgghhBBCCCGEEBtRwIUQQgghhBBCCCGEEEIIIcRGFHAhhBBCCCGEEEIIIYQQQgixEQVcCCGEEEIIIYQQQgghhBBCbEQBF0IIIYQQQgghhBBCCCGEEBtRwIUQQgghhBBCCCGEEEIIIcRGFHAhhBBCCCGEEEIIIYQQQgixEQVcCCGEEEIIIYQQQgghhBBCbEQBF0IIIYQQQgghhBBCCCGEEBtJXb0BrsQwDACgvLzcxVviOiqVCtXV1SgvL4dMJnP15jR7tL+sQ/vLerTPrEP7yzq0v6xD+8t6tM+s44r9pT/u1R8HE9IQc+dM9Pfuvui9c2/0/rkveu/cG71/7oveO/fmqvfPlvOmVh1wqaioAADExsa6eEsIIYQQQghxnoqKCvj7+7t6M4gboHMmQgghhBDSWjXlvEnEtOLhbVqtFtevX4evry9EIpGrN8clysvLERsbiytXrsDPz8/Vm9Ps0f6yDu0v69E+sw7tL+vQ/rIO7S/r0T6zjiv2F8MwqKioQFRUFMRiqi5MGmbunIn+3t0XvXfujd4/90XvnXuj98990Xvn3lz1/tly3tSqM1zEYjFiYmJcvRnNgp+fH33pWIH2l3Vof1mP9pl1aH9Zh/aXdWh/WY/2mXWcvb8os4VYo6FzJvp7d1/03rk3ev/cF7137o3eP/dF7517c8X719TzJhrWRgghhBBCCCGEEEIIIYQQYiMKuBBCCCGEEEIIIYQQQgghhNiIAi6tnIeHB1577TV4eHi4elPcAu0v69D+sh7tM+vQ/rIO7S/r0P6yHu0z69D+Iu6MPr/ui94790bvn/ui98690fvnvui9c2/u+P6JGIZhXL0RhBBCCCGEEEIIIYQQQggh7owyXAghhBBCCCGEEEIIIYQQQmxEARdCCCGEEEIIIYQQQgghhBAbUcCFEEIIIYQQQgghhBBCCCHERhRwIYQQQgghhBBCCCGEEEIIsREFXNzc0qVL0a9fP/j6+iIsLAx33HEHsrKyeMvU1tZi3rx5CA4Oho+PDyZPnoyCggLeMk888QSSkpLg4eGBXr16WXzOCxcuwNfXFwEBAXZ+NY7nrP2Vk5MDkUhk8u/AgQOOfHl258zPF8MwWL58OTp16gQPDw9ER0fjzTffdNRLcxhn7bPFixcLfsa8vb0d+fLszpmfsS1btmDgwIHw9fVFaGgoJk+ejJycHAe9Msdx5j775Zdf0KtXL3h5eSEuLg7Lli1z1MtyGHvsr+PHj2Pq1KmIjY2Fp6cnunTpgo8++sjkuXbt2oU+ffrAw8MDHTp0wKpVqxz98uzOWfsrLy8P06ZNQ6dOnSAWi/HUU0854+U5hLP22e+//44xY8YgNDQUfn5+SE5OxpYtW5zyGknL5MzfkxMnTmDo0KFQKBSIjY3Fu+++66iX1Wo46/3btWsXbr/9dkRGRsLb2xu9evXC6tWrHfnSWjw6h3dvdI7svujc073ReZ37ao3nSxRwcXO7d+/GvHnzcODAAaSmpkKlUiElJQVVVVXsMk8//TT++usvrFu3Drt378b169dx1113maxr9uzZuOeeeyw+n0qlwtSpUzF06FC7vxZncPb+2rZtG/Ly8th/SUlJdn9NjuTM/fXkk0/iq6++wvLly3H27Fls2LAB/fv3d8jrciRn7bPnnnuO99nKy8tD165dcffddzvstTmCs/ZXdnY2br/9dowcORIZGRnYsmULbt68Kbie5s5Z++yff/7B9OnT8cgjj+DUqVP49NNP8cEHH+CTTz5x2GtzBHvsr/T0dISFheHHH39EZmYmXnnlFSxYsIC3L7KzszFx4kSMGDECGRkZeOqpp/DQQw+53QVxZ+0vpVKJ0NBQLFy4EImJiU59jfbmrH22Z88ejBkzBps2bUJ6ejpGjBiBSZMm4dixY059vaTlcNbvSXl5OVJSUhAXF4f09HQsW7YMixcvxhdffOGw19YaOOv9279/P3r27InffvsNJ06cwKxZszBjxgxs3LjRYa+tpaNzePdG58jui8493Rud17mvVnm+xJAWpbCwkAHA7N69m2EYhiktLWVkMhmzbt06dpkzZ84wAJi0tDSTx7/22mtMYmKi2fW/8MILzH333cd8++23jL+/v7033+kctb+ys7MZAMyxY8ccteku4aj9dfr0aUYqlTJnz5512La7iqP/JvUyMjIYAMyePXvstu2u4Kj9tW7dOkYqlTIajYadtmHDBkYkEjF1dXX2fyFO5Kh9NnXqVGbKlCm8aStWrGBiYmIYrVZr3xfhRLbuL73HHnuMGTFiBHv/hRdeYLp168Zb5p577mHGjh1r51fgXI7aX1y33HIL8+STT9p1u13JGftMr2vXrsySJUvss+Gk1XPU78mnn37KBAYGMkqlkp324osvMgkJCfZ/Ea2Ys445GYZhJkyYwMyaNcsu203oHN7d0Tmy+6JzT/dG53XuqzWcL1GGSwtTVlYGAAgKCgKgiwCqVCqMHj2aXaZz585o06YN0tLSrFr3jh07sG7dOqxcudJ+G+xijtxfAHDbbbchLCwMQ4YMwYYNG+yz0S7kqP31119/oV27dti4cSPi4+PRtm1bPPTQQyguLrbvC3ABR3/G9L766it06tTJ7UeuOWp/JSUlQSwW49tvv4VGo0FZWRl++OEHjB49GjKZzL4vwskctc+USiUUCgVvmqenJ65evYrLly/bYctdw177q6ysjF0HAKSlpfHWAQBjx4616e+6OXDU/mrJnLXPtFotKioqWs1+JY7nqN+TtLQ0DBs2DHK5nJ02duxYZGVloaSkxE5bT5x1zKl/LvrusR86h3dvdI7svujc073ReZ37ag3nSxRwaUG0Wi2eeuopDB48GN27dwcA5OfnQy6Xm9RqDQ8PR35+fqPXXVRUhAceeACrVq2Cn5+fPTfbZRy5v3x8fPDee+9h3bp1+PvvvzFkyBDccccdbh10ceT+unTpEi5fvox169bh+++/x6pVq5Ceno4pU6bY8yU4nSP3GVdtbS1Wr16NBx980NZNdilH7q/4+Hhs3boVL7/8Mjw8PBAQEICrV6/il19+sedLcDpH7rOxY8fi999/x/bt26HVanHu3Dm89957AHT9N9yRvfbX/v378fPPP2Pu3LnstPz8fISHh5uso7y8HDU1NfZ9IU7iyP3VUjlzny1fvhyVlZX4z3/+Y7ftJ62XI39PzH0/6ucR2znrmBPQ9Xc7fPgwZs2aZcsmk3p0Du/e6BzZfdG5p3uj8zr31VrOl6ROf0biMPPmzcOpU6fw77//2n3dc+bMwbRp0zBs2DC7r9tVHLm/QkJC8Mwzz7D3+/Xrh+vXr2PZsmW47bbb7P58zuDI/aXVaqFUKvH999+jU6dOAICvv/4aSUlJyMrKQkJCgt2f0xkcuc+4/vjjD1RUVGDmzJkOfR5Hc+T+ys/Px5w5czBz5kxMnToVFRUVWLRoEaZMmYLU1FSIRCK7P6czOPp7/+LFi7j11luhUqng5+eHJ598EosXL4ZY7J7jNeyxv06dOoXbb78dr732GlJSUuy4dc0P7S/rOWufrVmzBkuWLMGff/6JsLCwJj8XIXrOOmYhjuGs92/nzp2YNWsWvvzyS3Tr1s2hz9Va0Dm8e6NzZPdF557ujc5T3FdrOV9yzysmxMT8+fOxceNG7Ny5EzExMez0iIgI1NXVobS0lLd8QUEBIiIiGr3+HTt2YPny5ZBKpZBKpXjwwQdRVlYGqVSKb775xl4vw2kcvb+EDBgwABcuXLBpHa7i6P0VGRkJqVTKHkgCQJcuXQAAubm5tm28izjzM/bVV1/h1ltvNRmF4U4cvb9WrlwJf39/vPvuu+jduzeGDRuGH3/8Edu3b8fBgwft9TKcytH7TCQS4Z133kFlZSUuX76M/Px8tklnu3bt7PIanMke++v06dMYNWoU5s6di4ULF/LmRUREoKCgwGQdfn5+8PT0tO+LcQJH76+WyFn77KeffsJDDz2EX375xaTcASFN4ejfE3Pfj/p5xDbOOubcvXs3Jk2ahA8++AAzZsywdbMJ6Bze3dE5svuic0/3Rud17qs1nS9RwMXNMQyD+fPn448//sCOHTsQHx/Pm5+UlASZTIbt27ez07KyspCbm4vk5ORGP09aWhoyMjLYf6+//jp8fX2RkZGBO++8026vx9Gctb+EZGRkIDIy0qZ1OJuz9tfgwYOhVqtx8eJFdtq5c+cAAHFxcTa+Cudy9mcsOzsbO3fudNtyYs7aX9XV1SZZGRKJBIBu9Jg7cfZnTCKRIDo6GnK5HGvXrkVycjJCQ0Ntfh3OYq/9lZmZiREjRmDmzJl48803TZ4nOTmZtw4ASE1Ntfm3w9mctb9aEmfus7Vr12LWrFlYu3YtJk6c6JgXRFoNZ/2eJCcnY8+ePVCpVOy01NRUJCQkIDAw0PYX0ko583hg165dmDhxIt55551WUR7S0egc3r3RObL7onNP90bnde6rVZ4vMcStPfroo4y/vz+za9cuJi8vj/1XXV3NLvPII48wbdq0YXbs2MEcOXKESU5OZpKTk3nrOX/+PHPs2DHm4YcfZjp16sQcO3aMOXbsGKNUKgWf99tvv2X8/f0d+dIcwln7a9WqVcyaNWuYM2fOMGfOnGHefPNNRiwWM998841TX6+tnLW/NBoN06dPH2bYsGHM0aNHmSNHjjADBgxgxowZ49TXaw/O/ptcuHAhExUVxajVaqe8Pntz1v7avn07IxKJmCVLljDnzp1j0tPTmbFjxzJxcXG853IHztpnN27cYD777DPmzJkzzLFjx5gnnniCUSgUzMGDB536em1lj/118uRJJjQ0lLnvvvt46ygsLGSXuXTpEuPl5cU8//zzzJkzZ5iVK1cyEomE2bx5s1Nfr62ctb8YhmE/c0lJScy0adOYY8eOMZmZmU57rfbirH22evVqRiqVMitXruQtU1pa6tTXS1qO/7N35mFSVWf+/95au6o3ml5plmZHURTErY1BVGxCo07UZGbyxCUxGccEM6NkjDFjnLiNJjExzxgy5pcY0ZiMiRoNIiqtoKiAKNqI7NANDU3ve+3r749b99atqltVdzm3qgvfz/P4JHRVnXvuuedu7/d83zdX95Ph4eFobW1t9IYbboh+9tln0eeeey7qdDqjv/3tb3O6v6cauTp+mzZtijqdzujdd9+dsJ2BgYGc7u+pBL3DFzb0jly40LtnYUPvdYXL5/F9iQSXAgeA7H9PPfWU+B2v1xv97ne/G62oqIg6nc7oNddcE+3q6kpo55JLLpFtp729XXa7hfqwlqvxWrt2bfT000+POp3OaFlZWfT888+PPv/88zncUzbkcn51dnZGr7322mhJSUm0trY2+o1vfKMgX+RyOWbhcDg6ZcqU6I9+9KMc7R17cjle//d//xddtGhRtLi4OFpdXR29+uqro/v27cvRnrIjV2PW19cXvfDCC6PFxcVRp9MZvfzyy6Pbt2/P4Z6ygcV4/dd//ZdsGw0NDQnb2rx5c3ThwoVRm80WnTlzZsI2CoVcjpeS7xQCuRqzdOfsTTfdlLudJU4pcnkP3rVrV/Tiiy+O2u326OTJk6OPPPJIjvby1CVXx++mm26S/fySSy7J3c6eYuTy3JNSqO/w4w16Ry5c6N2zsKH3usLl8/i+xEWj0SgIgiAIgiAIgiAIgiAIgiAIgiAIzVANF4IgCIIgCIIgCIIgCIIgCIIgCJ2Q4EIQBEEQBEEQBEEQBEEQBEEQBKETElwIgiAIgiAIgiAIgiAIgiAIgiB0QoILQRAEQRAEQRAEQRAEQRAEQRCETkhwIQiCIAiCIAiCIAiCIAiCIAiC0AkJLgRBEARBEARBEARBEARBEARBEDohwYUgCIIgCIIgCIIgCIIgCIIgCEInJLgQBEEQBEEQBEEQBEEQBEEQBEHohAQXgiAIgiAIgiAIgiAIgiAIgiAInZDgQhAEQRAEQRAEQRAEQRAEQRAEoRMSXAiCIAiCIAiCIAiCIAiCIAiCIHRCggtBEARBEARBEARBEARBEARBEIROSHAhCIIgCIIgCIIgCIIgCIIgCILQCQkuBEEQBEEQBEEQBEEQBEEQBEEQOiHBhSAIgiAIgiAIgiAIgiAIgiAIQickuBAEQRAEQRAEQRAEQRAEQRAEQeiEBBeCIAiCIAiCIAiCIAiCIAiCIAidkOBCEARBEARBEARBEARBEARBEAShExJcCIIgCIIgCIIgCIIgCIIgCIIgdEKCC0EQBEEQBEEQBEEQBEEQBEEQhE5IcCEIgiAIgiAIgiAIgiAIgiAIgtAJCS4EQRAEQRAEQRAEQRAEQRAEQRA6IcGFIAiCIAiCIAiCIAiCIAiCIAhCJyS4EARBEAmsXbsWHMeJ/xUVFaG+vh7Lly/H//zP/2BsbCzh+11dXfjhD3+ISy+9FKWlpeA4Dm+//bZs20uXLk1oW/jvS1/6Usp3/X4/7rrrLtTX18PhcOCCCy5AS0tLwnc8Hg/WrFmDpqYmTJo0CaWlpVi0aBH+93//F+FwmNmYEARBEARBEARBSFH73vTWW2/h5ptvxty5c+F0OjFz5kx8+9vfRldXl2z7W7duxcUXXwyn04m6ujr827/9G1wuV8J39uzZg69+9auYOXMmnE4nqqqqsGTJErzyyiuybf7617/G6aefDrvdjsmTJ2P16tVwu91sBoQgCIIAAFjy3QGCIAhifHL//fdjxowZCAaD6O7uxttvv43bb78dv/zlL7Fu3TqcddZZAIADBw7gpz/9KebMmYMFCxZg27ZtGdudMmUKHn744YS/1dfXp3zvG9/4Bl544QXcfvvtmDNnDtauXYvm5mZs3rwZF198MQCgra0N3/ve93D55Zdj9erVKCsrwxtvvIHvfve72L59O55++mlGo0EQBEEQBEEQBJGK0vemu+66C4ODg/jqV7+KOXPmoK2tDb/+9a+xfv16tLa2oq6uTmyztbUVl19+OU4//XT88pe/xIkTJ/Doo4/i0KFDeO2118TvHTt2DGNjY7jppptQX18Pj8eDF198EVdffTV++9vf4pZbbhG/e9ddd+FnP/sZvvKVr+Df//3fsXfvXjz++OPYs2cP3njjjdwNGEEQxCkOF41Go/nuBEEQBDF+WLt2Lb75zW/iww8/xLnnnpvw2aZNm3DllVeipqYG+/btg8PhwNjYGILBICZOnIgXXngBX/3qV7F582YsXbo0pe2lS5eiv78fn332WcY+7NixAxdccAF+/vOf4z/+4z8AAD6fD2eeeSZqamqwdetWAEB/fz96enpwxhlnJPz+5ptvxlNPPYVDhw5h9uzZOkaDIAiCIAiCIAgiFbXvTVu2bMHFF18MkymebGbLli245JJL8J//+Z948MEHxb83NzejtbUV+/fvR1lZGQDg97//Pf7lX/4Fb7zxBpqamtL2KxwOY/HixfD5fNi/fz8APivBtGnT8LWvfQ3PPPOM+N1f//rX+N73vod169bhqquuYjIuBEEQn3copRhBEAShmMsuuww//vGPcezYMTz77LMAgNLSUkycOFFVO6FQKMUOL+WFF16A2WxOWJFVVFSEb33rW9i2bRuOHz8OAKiqqkoRWwDgmmuuAQDs27dPVb8IgiAIgiAIgiD0IvfetGTJkgSxRfjbxIkTE95bRkdH0dLSguuvv14UWwDgxhtvRElJCf76179m3LbZbMbUqVMxPDws/m3btm0IhUL453/+54TvCv9+7rnnNO0nQRAEkQoJLgRBEIQqbrjhBgDAxo0bNf3+4MGDKC4uRmlpKerq6vDjH/8YwWAw4TuffPIJ5s6dm/CCAQDnn38+AN5in4nu7m4AvCBDEARBEARBEASRa5S8N7lcLrhcroT3lt27dyMUCqW4Zmw2GxYuXIhPPvkkpR23243+/n4cOXIEjz32GF577TVcfvnl4ud+vx8A4HA4En7ndDoBADt37lS5dwRBEEQ6qIYLQRAEoYopU6agvLwcR44cUf3bWbNm4dJLL8WCBQvgdrvxwgsv4MEHH8TBgwfxl7/8RfxeV1cXJk2alPJ74W8nT55Mu41AIIBf/epXmDFjBs477zzVfSQIgiAIgiAIgtCLkvemX/3qVwgEAvinf/on8W9dXV0AkPZ96N133035+/e//3389re/BQCYTCZce+21+PWvfy1+Pm/ePADA+++/j0svvVT8u9BWZ2enml0jCIIgMkCCC0EQBKGakpISjI2Nqf7dk08+mfDvG264Abfccgt+97vf4Y477sCFF14IAPB6vbDb7Sm/LyoqEj9Px2233Ya9e/fi1VdfhcVCtzmCIAiCIAiCIPJDpvemLVu24L777sM//uM/4rLLLhP/LrzrpHsfknsXuv322/GVr3wFJ0+exF//+leEw2EEAgHx83POOQcXXHABfvrTn2Ly5Mm49NJLsW/fPnznO9+B1WrN+H5FEARBqINSihEEQRCqcblcKC0tZdLW97//fQDAm2++Kf7N4XCItncpPp9P/FyOn//85/jd736HBx54AM3NzUz6RxAEQRAEQRAEoYV070379+/HNddcgzPPPBO///3vEz4T3nXSvQ/JvQuddtppWLZsGW688UasX78eLpcLV111FaLRqPidF198EWeffTZuvvlmzJgxA1dddRX+8R//EYsWLUJJSYneXSUIgiBi0NJfgiAIQhUnTpzAyMgIZs+ezaS9qVOnAgAGBwfFv02aNEnW1i7Y6+vr61M+W7t2Le666y7ceuutuOeee5j0jSAIgiAIgiAIQgvp3puOHz+OpqYmlJeXY8OGDSmCjJBKTHj3kdLV1SX7LpTMV77yFfzrv/4rDh48KKYTmzx5Mt577z0cOnQI3d3dmDNnDurq6lBfX4+5c+dq3U2CIAgiCXK4EARBEKr44x//CABYvnw5k/ba2toAANXV1eLfFi5ciIMHD2J0dDThux988IH4uZS///3v+Pa3v41rr70Wa9asYdIvgiAIgiAIgiAIrci9Nw0MDKCpqQl+vx9vvPGGbJ2WM888ExaLBR999FHC3wOBAFpbW1PeheQQUoSNjIykfDZnzhx88YtfRF1dHfbu3Yuuri4sW7ZMza4RBEEQGSDBhSAIglDMpk2b8MADD2DGjBn4+te/ruq3o6OjKbb4aDSKBx98EEDii8hXvvIVhMNh/L//9//Ev/n9fjz11FO44IILRFcMwOc+/ud//mcsWbIEf/rTn2Ay0a2NIAiCIAiCIIj8Iffe5Ha70dzcjM7OTmzYsAFz5syR/W15eTmWLVuGZ599NqH+yx//+Ee4XC589atfFf/W29ub8vtgMIhnnnkGDocD8+fPT9vHSCSCH/zgB3A6nbj11lu17ipBEASRBKUUIwiCIGR57bXXsH//foRCIfT09GDTpk1oaWlBQ0MD1q1bJxawByCKJnv27AHAvwy89957ACCm9/r444/xta99DV/72tcwe/ZseL1evPTSS3j//fdxyy234JxzzhHbu+CCC/DVr34Vd999N3p7ezF79mw8/fTTOHr0KJ588knxe8eOHcPVV18NjuPwla98Bc8//3zCPpx11lk466yzjBkggiAIgiAIgiA+9yh9b/r617+OHTt24Oabb8a+ffuwb98+sY2SkhJ8+ctfFv/90EMP4aKLLsIll1yCW265BSdOnMAvfvELNDU14Utf+pL4vX/913/F6OgolixZgsmTJ6O7uxt/+tOfsH//fvziF79IqM3y7//+7/D5fFi4cCGCwSD+/Oc/Y8eOHXj66acxbdo04weKIAjicwIXlVbQIgiCID73rF27Ft/85jfFf9tsNkycOBELFizAlVdeiW9+85speYY5jkvbnnCbaW9vx1133YUPP/wQ3d3dMJlMOP300/Ev//IvuOWWW1La8Pl8+PGPf4xnn30WQ0NDOOuss/DAAw8kOGHefvttXHrppWm3/V//9V/4yU9+omb3CYIgCIIgCIIgsqL2vWn69Ok4duyYbFsNDQ04evRowt/ee+893HXXXfj4449RWlqKf/zHf8TDDz+c0OZzzz2HJ598Ert378bAwABKS0uxePFifO9738PVV1+d0t9f/epXOHz4MEwmE84//3z853/+Z8b3KYIgCEI9JLgQBEEQBEEQBEEQBEEQBEEQBEHohBLdEwRBEARBEARBEARBEARBEARB6IQEF4IgCIIgCIIgCIIgCIIgCIIgCJ2Q4EIQBEEQBEEQBEEQBEEQBEEQBKETElwIgiAIgiAIgiAIgiAIgiAIgiB0QoILQRAEQRAEQRAEQRAEQRAEQRCETkhwIQiCIAiCIAiCIAiCIAiCIAiC0AkJLgRBEARBEARBEARBEARBEARBEDqx5LsD+SQSieDkyZMoLS0Fx3H57g5BEARBEARBGEo0GsXY2Bjq6+thMtHaKyI79M5EEARBEARBfN7Q8970uRZcTp48ialTp+a7GwRBEARBEASRU44fP44pU6bkuxtEAUDvTARBEARBEMTnFS3vTcwFl4cffhh/+9vfsH//fjgcDlx00UX46U9/innz5onf8fl8+P73v4/nnnsOfr8fy5cvx29+8xvU1taK3+no6MB3vvMdbN68GSUlJbjpppvw8MMPw2KJd/ntt9/G6tWrsWfPHkydOhX33HMPvvGNbyjua2lpKQB+4MrKyvTvvEaCwSA2btyIpqYmWK3WvPWDyA4dq8KBjlXhQMeqcKBjVTjQsSoccn2sRkdHMXXqVPE5mCCyoeWdia5BcWgs9EHjF4fGQh00Xjw0DvqhMeShcVAHjRdPIY+Dnvcm5oLLO++8g1WrVuG8885DKBTCj370IzQ1NWHv3r0oLi4GANxxxx149dVX8fzzz6O8vBy33XYbrr32Wrz//vsAgHA4jJUrV6Kurg5bt25FV1cXbrzxRlitVvz3f/83AKC9vR0rV67Erbfeij/96U9466238O1vfxuTJk3C8uXLFfVVsMSXlZXlXXBxOp0oKysruMn3eYOOVeFAx6pwoGNVONCxKhzoWBUO+TpWlBqKUIqWdya6BsWhsdAHjV8cGgt10Hjx0Djoh8aQh8ZBHTRePKfCOGh5b2IuuLz++usJ/167di1qamqwc+dOLFmyBCMjI3jyySfx5z//GZdddhkA4KmnnsLpp5+O7du348ILL8TGjRuxd+9evPnmm6itrcXChQvxwAMP4K677sJPfvIT2Gw2PPHEE5gxYwZ+8YtfAABOP/10vPfee3jssccUCy4EQRAEQRAEQRAEQRAEQRAEQRAsMLyGy8jICABg4sSJAICdO3ciGAxi2bJl4ndOO+00TJs2Ddu2bcOFF16Ibdu2YcGCBQkpxpYvX47vfOc72LNnDxYtWoRt27YltCF85/bbb0/bF7/fD7/fL/57dHQUAK+2BYNB3fuqFWHb+ewDoQw6VoUDHavCgY5V4UDHqnCgY1U45PpY0ZwgCIIgCIIgCIIwDkMFl0gkgttvvx1f+MIXcOaZZwIAuru7YbPZMGHChITv1tbWoru7W/yOVGwRPhc+y/Sd0dFReL1eOByOlP48/PDDuO+++1L+vnHjRjidTm07yZCWlpZ8d4FQCB2rwoGOVeFAx6pwoGNVONCxKhxydaw8Hk9OtkMQBEEQBEEQBPF5xFDBZdWqVfjss8/w3nvvGbkZxdx9991YvXq1+G+h+E1TU1Pea7i0tLTgiiuuKNh8dp8X6FgVDnSsCgc6VoUDHavCgY5V4ZDrYyU4vAmCIAiCIAiCIAj2GCa43HbbbVi/fj22bNmCKVOmiH+vq6tDIBDA8PBwgsulp6cHdXV14nd27NiR0F5PT4/4mfC/wt+k3ykrK5N1twCA3W6H3W5P+bvVah0XwYjx0g8iO3SsCgc6VoUDHavCgY5V4UDHqnDI1bGi+UAQBEEQBEEQBGEcJtYNRqNR3HbbbXjppZewadMmzJgxI+HzxYsXw2q14q233hL/duDAAXR0dKCxsREA0NjYiN27d6O3t1f8TktLC8rKyjB//nzxO9I2hO8IbRAEQRAEQRAEQRAEQRAEQRAEQeQK5g6XVatW4c9//jP+/ve/o7S0VKy5Ul5eDofDgfLycnzrW9/C6tWrMXHiRJSVleF73/seGhsbceGFFwIAmpqaMH/+fNxwww342c9+hu7ubtxzzz1YtWqV6FC59dZb8etf/xo/+MEPcPPNN2PTpk3461//ildffZX1LhEEQRAEQRAEQRAEQRAEQRAEQWSEucPlf//3fzEyMoKlS5di0qRJ4n9/+ctfxO889thjuPLKK3HddddhyZIlqKurw9/+9jfxc7PZjPXr18NsNqOxsRHXX389brzxRtx///3id2bMmIFXX30VLS0tOPvss/GLX/wCv//977F8+XLWu0QQBEEQBEEQBEEQBEEQBEEQBJER5g6XaDSa9TtFRUVYs2YN1qxZk/Y7DQ0N2LBhQ8Z2li5dik8++UR1HwmCIAiCIAiCIAiCIAiCIAiCIFjC3OFCEARBEARBEARBZGbLli246qqrUF9fD47j8PLLLyd87nK5cNttt2HKlClwOByYP38+nnjiCfHzwcFBfO9738O8efPgcDgwbdo0/Nu//RtGRkYS2uno6MDKlSvhdDpRU1ODO++8E6FQKBe7SBAEQRAEQRCfO5g7XAiCyB872gdRVWLDzOqSfHeFIAiCIAiCyIDb7cbZZ5+Nm2++Gddee23K56tXr8amTZvw7LPPYvr06di4cSO++93vor6+HldffTVOnjyJkydP4tFHH8X8+fNx7Ngx3HrrrTh58iReeOEFAEA4HMbKlStRV1eHrVu3oqurCzfeeCOsViv++7//O9e7rImWvT040ufC186bhnKnNd/dIQiCIAiCIIiMkOBCEKcI63adxL/9H59i7/oLp+HOptPopZQgCIIgCGKcsmLFCqxYsSLt51u3bsVNN92EpUuXAgBuueUW/Pa3v8WOHTtw9dVX48wzz8SLL74ofn/WrFl46KGHcP311yMUCsFisWDjxo3Yu3cv3nzzTdTW1mLhwoV44IEHcNddd+EnP/kJbDab0bupm5+s24POYS/ObajAudMn5rs7BEEQBEEQBJERSilGEKcIgtgCAM9u78DDr+3LY28IgiAIgiAIPVx00UVYt24dOjs7EY1GsXnzZhw8eBBNTU1pfzMyMoKysjJYLPy6um3btmHBggWora0Vv7N8+XKMjo5iz549hu8DC2ZWFwMA2vrdeetDOBLFwZ4xRfVKWdE57MWIJ5iz7QXDERzK8T52DHjg8ucuvZ0vGMaRPpdh7feO+dA35jes/fZ+NwJhY9oOR6I40D2GSMSY4+/yh9Ax4DGkbcDY8yUajeJQzxgCoYgh7QdCERzuHTOkbQAYdAcwbNy0xLEBN9wGnceR2LU3bNC89ARCaDfw3tI94sOgO2BY+0oY8wVxfNC4c08J+bi/KIE/94y7J+SaaNTY88Voekbzf76whBwuBHGKcugUunEQBEEQBEF83nj88cdxyy23YMqUKbBYLDCZTPjd736HJUuWyH6/v78fDzzwAG655Rbxb93d3QliCwDx393d3bLt+P1++P3x6Nzo6CgAIBgMIhhUFtAUvqf0+5lomOjAuwCO9IwxaU8Lj715GL95pw2PfXUBrjxrkqrfahmLQXcAS3/+DmZVl+CVVY2qtqeV+9bvw7MfHMcfbjwHX5xTZfj2Tgx5cdlj7+KC6RX4483npf0ey7l05/Of4pVPu/HCv16As6eU625PSigcwYpfvQsAeOc/lsBuYbu2dXfnCK594gOcU2nCSgPOg9+83YbH3jqMR645A9edM5l5+9/4ww60Hh/Bm3dcjMkTHEzbHvYEcemj72BGpRPrb7tI/DurudOytxff/b9WfOsLDfjhl+bpakuO/1q3F//34Qms/cZifGFWJfP2r3tiOwbGzGi+wosJjDOPt/W5sfx/3sclc6vw+xvOYds4gGe2d+CBV/fjnuZ5uKmxgXn73332Y7xzqB+v3nYR5tSkHxwtc8kbCGPZL9/BBIcFm1Z/ERzH6e6vFm76ww7s7hzBW3d8EZPKi3S1pfWcemjDfqzd1oHfXr8Il82r1tUHltzz8h78dWcnnr35XFwwg72DluX9Swl/+qADP1m/Hz9aMQ/fvIj9+aIVJePgC/LnS6ndgs2rvwiTKT/nSzJ6jh0JLgRxirLz2FC+u0AQBEEQBEFo5PHHH8f27duxbt06NDQ0YMuWLVi1ahXq6+uxbNmyhO+Ojo5i5cqVmD9/Pn7yk5/o2u7DDz+M++67L+XvGzduhNPpVNVWS0uLrr4AgKeHA2DGtj1HsCF0SHd7Wth+0ATAhBe37ILpxCdZvy+HmrE44QaCYQv2d4/hhXUb4MzBW/tH+/l9fO6tjzB2yJiV/FIOjnCIRs346Ogg1r+6AdliKyzm0q4jZgAc/vj6VnTWs10B7A4CA27+QP3x5ddRr+5UycrOfv48ODLGMRmLZN47zB//l97bDUf3Lubt7z9pRijCYe26t7Goiu3Yd7qBQMiCAz0uvLhuAxxJ54ve8Xqrkx/7N3cdxVmRI7rakuOjA/zY/9+bH2LkANtzLxwBTgxbAHD406ubMaOUafP4bIgfm+1H+vDqqxvAWlN4u50fm/Xb96F6iL0rc0+HGdEoh2defReNtdnnpZq5NOADXH4LXP4Q/vzya6iw6+mpdg52mREMc/jDus1YVMnm3FN7Tn0Yu7/8ddNO+I4Yf39RyieH+H79ueUDDEwxzhVixDVbjneOxs6XD/ahdnj8uZgzjcOwHxjzWTDmC+HPf38NE/N0viTj8Wh3h5HgQhCnMN/900588wszcGZ9ORw2c767QxAEQRAEQSjA6/XiRz/6EV566SWsXLkSAHDWWWehtbUVjz76aILgMjY2hi996UsoLS3FSy+9BKs1XsOvrq4OO3bsSGi7p6dH/EyOu+++G6tXrxb/PTo6iqlTp6KpqQllZWWK+h8MBtHS0oIrrrgioT9aKDnUj78d/Rh+axmamy/K/gMDWD/cCgz0Ilxchebmc1X9VstYfNIxDHzKH7eZCy/COdMmqOuwBp7t+hAYGQJXXofm5oWGb895sA/Y+wlCUQ4LGpeiYaK8QsFyLv2mbSvgdsFeNQ3NzWfoaiuZrhEf8NEWAMDk087BijPlzy+tuHeeAA7txUiAw0VLLsWEErYukdee2wX09SDomIjm5vOZtg0AP9r5FoAwyqbORfOls5i2LT1fZi26CAunTgDAbu4c3nQY6GjDYMiKFSuamDsVnjrxATA6AvOESWhuPptp22O+EPDBJgBA1cwz0XzeNKbtY3c3sP9T+MMczv3iZagt0+egSObdl/YA3Z3wWIy5/v/3nncAnx/Fk2aiOYN7SctcOtTjAj7ZCgBoWHABLp7N3r2khHs+2QQEQyidPAfNl83W1ZbWc+qvvTuBoQGgrBbNzYt09YElz3TuAEaHYa6YjObms5i3z/L+pYSPN+wHujrgNpeiufkLhm9PKUrG4cSQF/iYd4k2nHl+Tpy2ShBc3logwYUgTmE27O7Ght3dWDRtAl767vi54BIEQRAEQRDpEdJ3mUyJaYnMZjMikfjq0NHRUSxfvhx2ux3r1q1DUVFisKuxsREPPfQQent7UVNTA4BfYVhWVob58+fLbttut8NuT11aaLVaVQcMtPwmmTm1fOqnowMemM2WvKSZCMcWvh7pc2veHzVjEUJ8H4/0e3HBLONTsARiO9nW78lJYCgcje/jsUEfZtdmTvHFYi4ZuY/BaDwN39FBH/P2/ZLaLR3DAVRXKBM/leKL1Sc50ueGxWJhKipEo1F4g/wOtA94mY9NIBLv69FBH86bmdi+3rkjjP2YL4RhXwQ1jEUFX5AfeyPmZcgbnziGzEuJWeHYkB9TKtlaaIR5eXTAA85khsXMNlWfN1YUSenYq5lLgah0XnpxaQ6uq3IEw/wYHu5jN7/UnlOhSG7vL0oJxe7tRveLxf1LCdHYs8OxAQ9gMsPK+HzRS6Zx4Ezx2i3tgz5cNk7miZ7jNr5GnyAI1fiCYXzjqR0Zv/NJx7CqNp/dfgxfeGTTKVVAjCAIgiAIYjzhcrnQ2tqK1tZWAEB7eztaW1vR0dGBsrIyXHLJJbjzzjvx9ttvo729HWvXrsUzzzyDa665BgAvtjQ1NcHtduPJJ5/E6Ogouru70d3djXCYDyI1NTVh/vz5uOGGG7Br1y688cYbuOeee7Bq1SpZUWU8Uj+hCFYzB38ogq5RX176IBTL7h3zY9RnfC52v6Q498Ee44ppJ2wzFhA/OuBGKGx8yhfpPhpZyD5hm7F9NGJ7Xkk1e0PaD8bbbzOgyLfQ/xFvEAOMixYHwhEINZyPGPB+KR2bI33GjQ0AHDbw2Lb1u5kXu06YNwaMjS9o7LwX2g+EI/wKeObtx4VG1hh9TVCKcP86kKN7iWwfYveUjkEP/KFwlm/njmBsbNr63IgUaKF5KYKwFYpE0TGoPRVWPghFcv9MYDQkuBBEgfPixyfw9oE+Zu2NeIO45+XP0DnsxUOv7mXWLkEQBEEQBBHno48+wqJFi7BoEZ9eY/Xq1Vi0aBHuvfdeAMBzzz2H8847D1//+tcxf/58PPLII3jooYdw6623AgA+/vhjfPDBB9i9ezdmz56NSZMmif8dP34cAO+IWb9+PcxmMxobG3H99dfjxhtvxP3335+fndaAxWzCtFi6qXYDgmJKCEjFgRwsSPIHcy+4CPsYDOcmUCPdx1wt8hJEniFPEAMuf5Zvq0MaeDZif3wBY0UFI/svDTy39buYBza9Bo99gqBjRPux8QmEIjgxxPbcy6kYZfDYs24/FI6IQsDxIU/COcACo68JSghJxM5jA+z3USnC/SUcifLui3GC4P7xBsM4OcJe0Ms14XD82lpoi6dDkcLtezoopRhBFDidCld6RKNRRdbwp7ceFf9/uPBFfoIgCIIgiHHJ0qVLEY2mf9iqq6vDU089pfn3Ag0NDdiwYYOmPo4XZlSV4EifG+39Llych7ze/rB05aUbi6ZVGLs9yQrgnDlcQon7OLO6xODtGRsIlt9m4j5WlrBzeSU7CSKRKNP0d4YHzpOcChfOZFdvQtq2LxhB57AXU9PU7NGCVIxqM9hdlIuxb6gsZte2ZGyOD/HuAruFXW3X5L6zJtklsgy1zNr2Sa4H0SjQ3u/G6ZPYpeozet4oISC5d4UjUbT1uTG/nm06QkX9SFq0MLeWbeo5rQST7u1TKthdl/KBVLQoNJdISBJ8NOI6ng/I4UIQBc6wN57WwGE14/lbG2W/J33ByITLH5K0R5cIgiAIgiAIIr/MrOYDkEakUlJCMJRbN4b0ub3fFWDuxpDfZm5XY/uTxlSJeKh/m7lxcRixWjqXQX8jxwZgn5ZL2vdjg56E4C4LfAa7OAx1F0najkSBo/3GOWiMcbgYd+1NmZcGtt835seI1/h0lMkEQ4nX1VwJ+Cn9COfe0aiEYAE7QuQIR8bnOCtBKhb1uwIY9rBNbZkPKJpKEAWO9OXEbjVhcZoVd1LbfiZs46ywFkEQBEEQBPH5ZkYVL7i050lwCYRzm1s8eaHUwZ7cpjHL9T4aUTckmXAkmhBcY72P3qRUPaxFEW8gPl4dg56EACbr9ln33ZMU2Gadlks69nzKIsZjb6CLIxiOJM7LXrZ9T04hxbr/UjGqZ5R9jSsja8QYPTap14TcB8D94cQ+5EtwCeShZpcSxqsQpJVEh0t+npe0klw77lQ4HhRZJYgCxyRJE2biuLTWdaXFyaQPBpPKHfo6RxAEQRAEQRA6EQSXo/kSXHJewyX3QTJfjh0uycFOo7eZ/C7EentG74+0/ZABdRASAtsGjw3zwLbRTgVJ/7tGfAkZIfSSMm+Yj42xQcxkUaGNudCYeF1i6YRL7nuhXROUkOz2ypvgIhU2xqngMp6EIK2EpYJLjpyjrAgl1fY6FY4HCS4EUeBIr0uZ0gT7FDpchiSry5SmISMIgiAIgiAIo5gZE1yOD3mZpwtSgnSbRqQsSib5GfyAwUEyOfeH0YGa5H00OriS7PZnvb0UFwfz9hOD/Czbj0ajCcHnzmEv3AxFhZSV/oxdHEY7CZIFHZb1BeTEIpbnnpHzBkhM+QUYK+iM+kLod7FzwiWPveHOrjwEkJPvVUbfS9KRUD+rl69xNR5IdJcVfoBfKlq4/CH0jhmfjpQVoaQC0uRwIQgi7yTeRBMVl8UNFahwWgEkrlrLxKAkV2LyqgyCIAiCIAiCyDXVpXYU28wIR6I4PsR2Zb8SgkmFh1mnLEpGcLiUO/jn+EMGB8mSg3JjvhD6DA7UJLt4jHe4JO5j57A3JeCqByEwXBSrgWlU4NlqijJvPxCOiCujhf6zTN8nBJ7FsTFIEDF67I1oX2jbZjaB49in1/MZOG8AwBsTdISxMUrsMmLsk+dlW58rwSGgl+R5k4+AvuAsEdLGHx/0pohwuUB6D/UGw+ga9eW8D3JInTcD7kDC4uNCJHn+FpJoEYokL4oorJRocpDgQhAFjtQen7waxsxxKLKa+e8pdLgMSm4ybn8IvWM+9I6TGyJBEARBEATx+YPjOEwX6rjk4SVcECTKiiwAcicOLJhcDgA40D1mqONE+j4xpYJPKZyrfczd9vh9LLaZUeG0IhoF2vrZbVOoZXH6pDIAbF0QQNxJMNnJ/5tl8NYnSTsl9J/l8RCC/kLbg+5AwjunXrxJ7bOvn5PcPnvBpcxhEc8FlsdWaF+cN30upu6C5LFnOW9C4YgYEBfbZzj2wrycWVUCm9kEfyiCk8NeZu0nz5t8BL+DIf5YV5bYUFViBwAcykFNsGRyfQ9ViiAE2S3GCIa5ppDTcgkOF6FiwniZI3ogwYUgChzpaq1Vl85O+IzjIAouih0ukoffjXt7cP5Db+H8/34Le0+OMugtQRAEQRAEQahHqOPCcuW9UvxJQT/D01+FhO2VwmziMOoLoWfUOMeJsD2zicNpdaUAcrePZ9QLAoXBrqHY9uxWM2bXlAAwxqkg7E+/K4BhD0OnQix4O6WYD0oZEfS3GHT8hcBzZbENkyc42Lcf6/+Z9eVi20aICkL7TOeN6LIwY1Z1bF4yHXt+3tc5o7CaOfiCEZwcYS8qSMeeFT5JnENs34BztsRuEe8vRgiNQt87Bj2K6+qyIhCOOagsJsyt5edXrtOKhcIRMQ2+eA8dB8H0cCQKYR3D3Nrc3PeMJhxzidSXFwEoLNFCEIumTeTV4eNDnoLPuEOCC0EUOMLLw5cX1uMbF01P+MzEcaJar9Thks5G+Yf327V3kiAIgiAIgiB0INRxacux4BKNRsXVufPrc7NSWQjKlRVZMb2SDz4YWexYeE+wW0zxoG+O9nH+JD4YybpuSMr2ZPbRiOBtZbHdkGCX0P7UEkFwcTNzPQltO6RBf0NSNxkjdgli1Ly6UlhMHDwBtimLhKCf4Dgz4rg6rGbMFuclu2ucmNbKDPFawrb/iW68YwPsalxJU/4JQqYRQmCRzVgRtqHSiVK7BZEocLQ/tykxhViRzWwSRQWjU1QmI03bJd5Dx4GwIU1zdvokfmwKSaCQQ3CJzKsrvP0RUorVlhWhrMiCaDQ/C2xYQoILQRQ4Qv7jK+bXwWRKrOFit5pgFxwuCtRhXzAMd5pcxi/sPKGzpwRBEARBEAShjRnVgsMltwEEaVFdo1IWJSOKA1aTGDgxVHCJiR92iwmzYoHHXO1jXbkdlcU2AMYGV6T7ONuAfRScBA6bWTKG7IO39c4ozCYOLj8715M08Gxk36WCjhFiV2mRBQ0xUYFV+8FwRLwGnDGZP/+PDXgSgrV6EN7RpfPGiLRZNlNctGY574X2p1cVi3W2OgbZtO+TilE1xs0bh9WEWdXC2LAXdBw2M2YacF4pQRC/bJb4veRAjlOKSQW48eRwkQpBp9Xl5t5uNEINl7k5cqqyROi71cxJ7tGF0385SHAhiALHL7mJJlNkMYsOFyUpxYay2N73nBzR0EOCIAiCIAiC0MeMKv4FPNcrhKWB1fmSlGIsUxYlI6a/spgxpyYWJOs2UnCJby93DpfcbtPo7fkMdokILg6HGZjGuO6NnMuivd+NEGNRwSl1Ehgk6LB2KkgXLc6qLoHTZkYoEsWxATbXIUGoKzJKVIjNG6spasi8FNp3SgUj1vNSMm9OjviYOeESzlkDHS7S8yrXjgNBLLRKHC4HDbyXyCEIGxyHnKWsVEJQIgSddqo4XGLPJfNix7pn1I9RXzCfXVKMMFfNJpMhjrN8QIILQRQ4woNCkVVGcLGaxBouSlKKCS9yk8qLsLihIuXzlf/zXsFcsAmCIAiCIIhThxmV/Ark7lF2ATclSFfnzq4pEVMWdTNMWZSM1I0hOlwMFSPi7xNCYLB71AeXkSm+JNs0wlWRaXtCMKe93y2uqtWLJ8CPlTQ4zGq1dDQahUdwKpjBfDW+NPA8eYIDRVYTguEojg+xqfXhkTpoDHQSOG0W5iujhbZNXFI6OkbtC/PGaYsLdZ3DXvHvuts3cN4AkpRlBogKokPEasYEpw1VJbwTjlW9J6kDxYgV9VIxKl8r9qUOlzm18Wv7iDd3MR2hD1Zz/PxhXeNKC/EAPycubCj0uiHC/ayi2IbqUjsA4+ujsUKoP2M1cTlb+GE0JLgQxDhhxBvEdf+7FX94T12tFOlqrWSKrGYUqXC47O0aBQCcP2MiXvzORbhn5emwmRMvE0+8fURV/wiCIAiCIAhCL+VOKybGUk8dHchdAEFYnWvi+Gfr6QYUV04mXuA9Me++Ua6aeH0TM8qdVlSV8IEaI9O+JDpOjB9Tn2Qf6yc4YLeYEAhHcHyQkVPBQIdLMBwVA2k2EzCT8XhJA88mE4eZVQY5FSQujhNDXmaBzbgTgn0NImnfOY5jPlelYtfEYpt4jWMtKthMEsHFABeHMyEdHaO+SxwuADBTOLZ9bBwa8WNrwcyqEnAcMOQJYsDFKFWfRIzKxTVOjkA4Lt6XFVnF+lK5rOMiCC52swnFdovYh3y7XAT3qtXMoarEhnKHteDrhggOF6vJlDdXlVakApgRaT/zAQkuBDFOeHb7Mew8NoT71+9V9Tt/RoeLWVLDJbvDRbCqlzusAIBvf3Emdt/XhBsbG8TvnGC00okgCIIgCIIg1DCjSqjjkkPBJSl9rxGrxJMRgrB2ixnTK52wmU3wBMLoHDbmOVxYmGWPvU/MrsnlPppysvrbL9lHs4kTg7fMnBCxdy2pqMBqtbRX0kZC4JyVy0IS9AfA/Hj4JCv9JxbbMMHJBzZZiwoJabkMCvozd9Akjz3jeSmt4TKjiq9vM+AOYMit310QjkTF66MRQqPU4QJIxr6XzbH1BOJCncPGu7sAA+aOZF629bkNTUeZjHj/ii2inSvWccmh4BJOuocyPo5aiQsuJkPE1HwgpIE0mzjMysF9nCXxGi5x4bytz8XMhZoPSHAhiHFCSFKQ8+6/7cbLn3Qq+p0vFM/7KiA8lCw7vVZ0uPgVOFykqzAE7BYz7r1yvvhvYcUbQRAEQRAEQeQSUXDJ4apHMVhkFsSIXNYbMcFiNomOhoMGBcniDhdBVMrhPlrjwRWWdUNStpeyj4ydChKXSFWJDWVFFmarpYWgucXEwSwpfm5E3wH2x98TSHSJiCuvDRAtZoopi/xM0iZJxRwgPjasXCJiDRdh7GsYu5eCQg0XPuVaXFTQ375UCOTTcsUDvNGo/iBpshjFel76jG5fcl5Nm+iE1czBGwzj5EjuFrAGJDVcAEgck7kLwgdDMXde8v0l7w6XWL+S7u2FIlDIIYgTFjNXgA6XuFg0dSK/0MQfiuCkQQtNcgEJLgQxTii2x0WO/9vRgdv/0qrod37J6jCBLT+4FH/910ZcPKdKfDj82esHsPfkaMa2BBeMVHABAIvZhDuXzwMAquFCEARBEARB5IXx4XDJgRsjmJgyWAiSGbUqOTlFcU4cJ5J9nDzBAYfVzLRuSMr2DN5H6cI1juOYtp8a9OfPg94xNgWRU1wWBhU/N0q08ElcKCV2CybFUhaxcNCkG5sjfW5DRQXWNWhieg7T4vBC2wAfi2ioLBZrXHWN6K9x5ZXU/gEk89KAc1bavhHuJYvZhOmVgiCVv/uXeC/pzqXDJSb6mZMdLvkWXPixsZg5ALlZaGA0Qkox3uFSWAKSVCwymzjxea+QjwcJLgQxDgiFI3jw1X0pf1din5NzuFSX2nH+jIkAeOu2QPP/vJvw22g0ihv/sAMr/+ddBEIRyUNH6qWhtMgCAMwK+BEEQRAEQRCEGoSV/W35EFxSHC7G9cGflOJrXiwNzEGDgmTi9iy5dPHEt2kycczrkmTaHsA+uGbkavx423zfS4usqCllV2cnRRAxyKngtPHvkywD58FwRFypnjL2DALbvqSUYg2VxTCbOLj8IfSM6q/1keKyYHzuxVOK8WPEMh2dtO8cx8FqNmFapZNZ+8nzXuj7sQG3GCzX1b7Bzq50QmMuA8jJgsu8mOBilFtSDn9SH1g73LQiTSkG4JSoGyKKFpI6KMcGPOI8GM+EJH0HTg3HEQkuBDEO+LRzRPbvcgXbhtwBvLjzBLyBMILhiHhRlTpcpCS7VaQc7HFhy8E+7Dk5io5BT8oDnxThRhQMF24ORYIgCIIgCKJwmRELuB0dyENKsdizdkLKIo8xzm8hQFWU5HA5aFAaGGl6LyAeGDw24GES2My4TYsxK/tTthdM3EfWToXktFwsg3dyaZ9ZimJeSS0LAJheWQwTB4z5QugbYygq2IQV7uyKt0tr5CQ7FVgIs2LKr1jbNosJDRMZigrJ8yZ2Hhzt9zBJr+cVBRf+30znTZIYBYBpGqPk2ER9edwJ1zHo0d1+eveS/r5HIlExe4hR9X+UkCy4zK4pAcfxdXz6ZWJNhvbBnHj+Hx9kU+NKK8kpxU6FuiGhSDwtV11ZEYptZoQjUXQMjn8RSSixYDEnCqzkcCEIQhfpxJJemQfcH720G99/fhceeHWv+KICpBdWpA9AyUhv9r968yDWf9rF/0amLUFpNuqliyAIgiAIgiAy0TCRfwEf9gSZFH1WQjApYCVNWWTUCt1kAWRubXxFsBE1TpJTmE0qL4LTZkYoEsWxAf2BTdlthuQFEOMcLon7OKOqGBwHjHiD6Hfpn0txFwf71ey+QOqiuLhAxdDFEWu/yGrG1JiowGKOe5JSos2u5gXEtn637sCmMO4mLrU+D0uXhTRrxExD3Et8+5MnOFBkNSEQjjBJrycKLkJKMYbzJrmoPWBMyjIhniF1wrEU6xxJqfo6h70J6dK0II3TOA2qz6OEYFINMqGeDJA7l4sobMTOz+oSO8qKLIhEc7t4IrVfiQ4Xad2QToNSWxpN3OFiAsdxzB1zRiKIRULcsZD6ng4SXAhiHBBK4xq58vH3EEl6CH3ts24AwJ8/6EhYEZBOtBFurnJIV4YIYgsgL94IN0gSXAiCIAiCIIh84LCZUS/UZ8hRWjF/UlAGYF+DImWbSTUap1Y4+SBsKIJjDFZ2p2wvKd0Wx3GGpr8Jybj0jU63k7yPRVYzplawcSoEQhExHUqKy6LPlfI+pxZPQMZJYIhTwRJvn+EcF4LXQkqxyRUO2Cz8fNYb2JQG/TkuMVDX1sfABRFLp+2QcRexEHSSj63JxGFmFbuxF9q3Jjlcjg/pdxdkcriwTSkmmZcM09El97+yxI4KpxXRKNDWr699aRp2wakoCI25rF2S7NAEJI7JHNVxSXbZjBchQBgbq4W/bkjrhhRqGitpDRdAej4WgMNFIhYBuamXZzTMBZctW7bgqquuQn19PTiOw8svv5zwucvlwm233YYpU6bA4XBg/vz5eOKJJxK+4/P5sGrVKlRWVqKkpATXXXcdenp6Er7T0dGBlStXwul0oqamBnfeeSdCIaotQRQmmUSMEW88VUGy3V2aD1N4wEwmU82VX7YclP278CAjRUwpFipMeyVBEARBEARR+AhpxdpzJLgkB4sA41PDJLsxTCZODJIdMmBVsl+mJqSR+yhd/S1XxJ5Fiq9027QbkJbLKwlcC4H5KRWO+GrpYZ2iQlItCyBR0NGLJ4NTwQgHjdnEifWYDvfpm8+Zgv7HhzwI6lwrmOxAAdimupFL6T2LkagQjkTj16/Y1KkstqHcwYsKeq+hcg4XljWuPEmp7gCpEMiwfQOvCUKNKgCiO2fAHciZQzM5nRcgqeOSI7EjEBZcVsYcR60I7lUhwA/kpn6ZkYTDiXVQxoOwpRTBvWsxx/pezae/G/IEZUstFALMBRe3242zzz4ba9askf189erVeP311/Hss89i3759uP3223Hbbbdh3bp14nfuuOMOvPLKK3j++efxzjvv4OTJk7j22mvFz8PhMFauXIlAIICtW7fi6aefxtq1a3Hvvfey3h2CyAmBDILLmC8umOzrSnwgFR7QitK4WwBg1JcquESjUdzxl9a0xbPOnFye8jdRcImQw4UgCIIgCILID8IK1HadK5CVIhewMjK3eCgcd0tIHexzavgg2YFu4wQQ6fZmMUzdk2570m1Or3IyrRuSjC/JNQSwO45C22YTB2ssWGQxm8S5yip4WySTUuzYoP6CyLKCDkPHkaGB7UDq2FSX2lEaS1nUpzMzkFjDxWB3UYLYychdJHWwCJoFx3HMRQXpcRVEBRY1rowUo4DUuksAO/eiT0YILLZbRIdmrlbtJxesB4A5sRSV+XK4AGyPo1aE+6zcvb1QXRXJDhejnaMsiTtc+L47bGZMnuAAUBgOHTmYCy4rVqzAgw8+iGuuuUb2861bt+Kmm27C0qVLMX36dNxyyy04++yzsWPHDgDAyMgInnzySfzyl7/EZZddhsWLF+Opp57C1q1bsX37dgDAxo0bsXfvXjz77LNYuHAhVqxYgQceeABr1qxBIJAbpZggWJIupRiQ6HAZ9sbn9wSnNf7ikKZ+CwBx9ZBAy94etPe78dInnar6KCjNlFKMIAiCIAiCyBczYul2cuVwCcqkZJmVK/eHJAA+ry4WJOtlHySTEyOMdLj4gvEVz8Lqb7slXlvAiCCcnKjEah/l0loBkuLwOtuXCzzXltlRYrcgHInimM46CLKBZ0ZjE41G46KCTRrYZLPCXS7oL02J1+OTzwKhp31hbHrH/Bj16RMVkuuU8O0L7h92zivp+kxWQWVR7JL0vbTIiroyNjWuZMUowdnVq98JJ3dsZzNydglCXXJtXCPvHXIk1ykBgHl1MYdLz5ghbsJkAuFUYYNlykKtBJNSigGF5QiRQ6zhEovdzZbcg3JxrPUQSnLnAIUlGMmR8xouF110EdatW4fOzk5Eo1Fs3rwZBw8eRFNTEwBg586dCAaDWLZsmfib0047DdOmTcO2bdsAANu2bcOCBQtQW1srfmf58uUYHR3Fnj17crtDBMGATCLGD178VPz/bn/8oWnMF8La948CQMZVYF9ZPCXh3//yzEe44rEtqvtoo5RiBEEQBEEQRJ4RFhO15WjFo5zDRQgWdQzqr4OQjFRwkW5TqOvQbsB++4Op6bakxbVZB2rkxI+EbRrhqsm0jwY4UAB2LhG5wDYvKhjnoBH63jXig8uvPXV7YvFw9rU45JwE0vZ7dJZxkRO7yoqsqCm1A2DohJAL+usUFeLuHxMkMUwDHC5J5zEjoVFOjGqojDnh/CH06nTCZXKOGeH+Ydm+UuTcJdMri8FxfCaUwRykNsvkcGnr11/jSm+/ZOuzFazDhd8nweHSUFkMi4mDJxBG14gvn13LiuhwMedm4UcusGT/Clsef/xx3HLLLZgyZQosFgtMJhN+97vfYcmSJQCA7u5u2Gw2TJgwIeF3tbW16O7uFr8jFVuEz4XP0uH3++H3xy/Ko6OjAIBgMIhgUN/KBD0I285nHwhlGHWsvH6+vWK7GTdeMA3/u6Vd/Gxf16i4vRFPfP6GI1E8v/NESt+UEM5yU5Nri4vyF+9AKFwQc5XOq8KBjlXhQMeqcKBjVTjk+ljRnCAKHSFN09EBNyKRqOiQMAq5osNCyqIxXwjHBjziimEWCMXdLSYuIfAgrV0TjUbT1m/Us02pANJQWQyziYPLH0LPqB91sVQ4TLeXFKidXVOCt/b3GpI+RG4fheDayREf3P4Qiu3awiPxOiLJgWdGDpq0ge0S7DoxwrD9eHC43GlFVYkd/S4/2vpcOGvKBE1tC+nEgMQ02NLAs575LKb8Sha7BMHFq9PhIhP0F9rvHfPjSJ8bi6ZVaG9fZuynVxbDFAuI97n8qCnVdu7JiTkAOxdH2varS/D+4QFDRAu7xYyGymK097txpNeF2jJtYxOJROGLibAOmXRx7f1uhCNRMXCtte/p5mWuUiTJiR1FVjPqyx3oHPaivd+NyhJ7TvogFTamxmpc+YJ8jaupMXdjLgmGE4u0A4l1Q/pdflQZPDYsiUSiEMJ8wj5ZzSZMq3Sirc+NI30u1MdSdI1HhBou5lPI4ZIXwWX79u1Yt24dGhoasGXLFqxatQr19fUJrhYjePjhh3Hfffel/H3jxo1wOnN/gifT0tKS7y4QCmF9rD7u5wCYUW8P4rTgISSfmq+s3wCzCfigm/+eHBs2bEjb/uX1Jrx1Mr2hrWlyBBs745/LtXV0DAAsGHG5M25rvEHnVeFAx6pwoGNVONCxKhxydaw8Hp3LfQkiz0ypcMBq5uALRnByxIspFca+x8kFrIQ6CJ90DONwr4ut4BKUd39MrXDCbOLgDYYNEEBSt2mzmNAw0Ym2fjcO97rYbk/cx9yt/pbbx4piGyqLbRhwB9DW58aCKal1LJXgk6lRAhi0Wl6SGIFZ+2lEhVnVxeh3+XG4V7vg4pWkj5MKiDOr+VX2I94gBtwBzYHNbE4C3YJLmsD5rOoSbD3CUFSwJQbEp0504tiAB0d63ZoFl0x9B4C2Ppc+USGdGBUryn6oR1/6w3SCzqxqXnA53OfCRbOrNLUtdV5J26+f4IDdYoI/FMHxQQ+mJ6VnV0r6cyrHDpdYENtuTryfzKwuRuewF239bpw7faKxfZC5hwo1rg70jOFwnytPgovQr/j8d9jMmFLhwPFBLw71uApKcAlL3HDSc3p2dQna+vj7+BfnVOeja4oQFoULddAAPpXqvNpSTK/Mf7xeCzkVXLxeL370ox/hpZdewsqVKwEAZ511FlpbW/Hoo49i2bJlqKurQyAQwPDwcILLpaenB3V1dQCAuro6seaL9HPhs3TcfffdWL16tfjv0dFRTJ06FU1NTSgrK2O1m6oJBoNoaWnBFVdcAavVmrd+ENkx6lgFWk8Chz5DbU01mpsX49+3bUz4/MJLLkd1qR2d77UD7Ydk22hubk7bfjOAOT/emPbzKy48Cxtf/CxjW3tOjuKxz7bDai9Cc/MlWfYo/9B5VTjQsSoc6FgVDnSsCodcHyvB4U0QhYrFbML0ymIc6nXhcK/LeMElnJpSDOADZ590DDNPdSEEApODpDaLCVMrHDg64EF7v9sgwSW13kBbP78y9uI52gKbmbdnjCMk8zZT93GgfRBH+lyaBRexKLwtMbwiBFeHPEEMugOYWGzT1H5CYXhJFiXmablkVuN/0D6oKzicLvBcZI0HNo/0ag9segN8ujNnmpRivT7oSlkkHNt07eudq5nmzrEBDw73udA4q1Jf20nHdUqFE7aYqNA55MU0jcHMdILOvFqhRgijVHfJokVNCd7c16tvXkpSQUr7bzZxmFldgn1dozjS59IuuAQzz8vjQ3w6yuSxY41cnRKAd4q+e6gfR3NQC03oQ+r1nhdcjvS6cOm8GsP7ka5f1qR7+7zaUhwf9OJgz5jmcy8fSLPYJNRBqSkB9vaM+7RcwVj/zRLH0eKGiXjjjiX56pJuciq4CKm7TKbECW02mxGJ5ZpbvHgxrFYr3nrrLVx33XUAgAMHDqCjowONjY0AgMbGRjz00EPo7e1FTQ1/Yra0tKCsrAzz589Pu3273Q67PfVGbrVax0UwYrz0g8gO62MVQbxYpFy7I/4I6ida4Qumf1jM1p/H/uls3PGXXSl/n1NTgjJn/Lx45NoFsm057PwLQigcLah5SudV4UDHqnCgY1U40LEqHHJ1rGg+EKcCs2tKcKjXhSN9biydZ+y25FbnCn0A2K9Ulkt9JTCjqlgUXFgGgtKl+JpVXYIW9Bi2jyljmlQ3pERjiq9M25Tbxx16RYU0Kb8cNjMmT+BT9xzudeH8GdpWknulgXOJ4CItPK8nvZ43TR0UFvUMvGmC/gB/vI8PenG4z4ULZmqbz+kcLlNjTrhgmE8ZN6NGm9iV3mWhv/5POBIVry9yYtem/b262k+X6s5s4jCzqhj7u8dwpM+lW3BJFhXm1vJj0znsxZgviNIibc8dwtxxpht7PfMyGL/OJjt8ZlUXY1/XKA73unD56bVyP8/efppUd1UlNpQVWTDqC6G9343TJxm78Nsv1iBL7IeQmrM9B4KLXFpOIH69z5cQIKQUSxZc5taW4s19vTig06GVa0KR9A4XYPyn5QpHBAHM2DSxuSR9jiGNuFwutLa2orW1FQDQ3t6O1tZWdHR0oKysDJdccgnuvPNOvP3222hvb8fatWvxzDPP4JprrgEAlJeX41vf+hZWr16NzZs3Y+fOnfjmN7+JxsZGXHjhhQCApqYmzJ8/HzfccAN27dqFN954A/fccw9WrVolK6gQxHgn+WL/+xvPxZmT4zffMR+/QsLlly/KOSf2wpeJyRNSH6SWnV6LZ751PqS1+K6YL/9QIVz4hBsmQRAEQRAEQeSDXKZlkcs/L+2DUQ4Xu0yAekaVUF+A8TbTpPgyqmCtXAF7IF43BODTHRmxzSID9jFd0J95+0mB7YZKJyyxNHNdo9oLIqdzQrAQFdP1HUgUjDS3LwS2k9rnnXD8+2+bjqBytlocxwY94jVCLT6Jy0KuDgqgb96kS3UHxN1kTNxLSe1PcNpQU8qfx4cMcEcxmZcxZ5TcvDTymiCko9TbvlLSLRiYnkvBJSQfSBcdjTrOfz2kc7jMZZQSL9eEwxkcLshd3SCtCDFRrSkOxyPMBZePPvoIixYtwqJFiwAAq1evxqJFi3DvvfcCAJ577jmcd955+PrXv4758+fjkUcewUMPPYRbb71VbOOxxx7DlVdeieuuuw5LlixBXV0d/va3v4mfm81mrF+/HmazGY2Njbj++utx44034v7772e9OwRhKH1jftz1wqe452U+nZcldhNaNr8W67/3RVF0cftDCf+bzF//tTHrtoqsqaf7krlVmFTuQFVJfMVPuqJpwo0oSIILQRAEQRAEkUfyEbBKdpxI+6AnZVEy6Wq4AMCMamOCZHGRJ1cunvT7OLum2JBt+tI6XPRvz6dAVGDSflLfrWYTGmKigh4nRLr+C4G6YwMeze+ASsQoPSnRMrU/MxZU1hNoTBf0ry2zo8RuQTgSxbEBbe1L01rJpVsC9B3XdGIRwNYlIte+UNfqYLf2oHXW+jyjfoz5gtraDsg7i6TtszlnjWlfKekEl5kSwYXl/UuOdC4bcRzy5nCRF4IEweVA9xiiUWPHhiWhSPwanVh4nj/WfWN+jHi1nS+5QKzhYmIuU+QN5inFli5dmnFS1tXV4amnnsrYRlFREdasWYM1a9ak/U5DQ0NBFe4mCDkufPithFyLybmhi2O5XF0xocUVSBVclp9RiwoF+YDlHoScsfYXN1Tgp9ctwLy69JZW4SYdChfOTYcgCIIgCII49WCRzkcpwTTpUKZWOGAzm+ALRnByxMuslkzGlGKVfOBEz4p9NducGQvU9I75MeoLokxjaqB020sXCN7eNmicqyaNcHZ0wI1QOJJQ2F0pCTVWkmC5Wj5d+0diBZGXzFVfEDkYjogri5ODw5PKiuC0meEJhHFswCPui6q+Z3ASxFe4GxnY7tUluKRrn+M4zKouxq4TIzjS58KcWJBWDfF5Y0pJBydc406O+OD2h1CsIb2eIrHLAAcKwAet3z3Ur6uOS7p5X+6worrUjr4xP470ubFw6gTNbWd2pbkRjUbBcepX3GcaG6OEbDnS1SCbPIFPuecPRdA16sPkCQ7D+pDuHircXwbdAV01rrQSSONwmVldDLOJw6gvhJ5RP9N6aUYixBUtJi5hzpYWWVFXVoTuUR+O9LlwzrSKfHUxI8I8IYcLQRC6iUSiCWILkGqrF/IWewLpHS5yDwlyyL20CflWOY7DP503LePDinAjCkWihq+CIAiCIAiCIIh0CKu/B9wBDLkDhm4rXcDKYjZhehUvsrAMnKUr7g7EHS4dAx6EGLrO022zrMiK2jLe/c5S3MrscDHaVZO4j/XlDjisZgTDUXQMejS1nSl4y8JBk7EOik5BR5rWKjmwbTJxYlBUa/sZA9vV8VofHpmFhYrazxDY1tt3INux1TdXM4lFE5w2MQuF5rGXCDrJzDJ4bIQ6Lgc1pmWKRKLwxURSWdFCp+ieScScUVUMjgNGvEH0u7TdX5S5i3JXsN5mSQxiW8wmTJvI37/aDe5HOpeN02YRhZ581HEJpanhUmQ1i85BrfM3H4Qi6VNyzTLIOcoSUTCiGi4EQehlQOblMPmlwxkTXPZ18Rf6YwOpLwEOm7LVLvIOF2ViDZB44QtGKK0YQRAEQRAEkR9yGagRAvVWGXHAiNQwQhA2OfUVwDsO7BYTQpEoTgx52W8z1/uYo+0B6V08iaKCvtRQmQSRzmGvGABX3758AW5A/3gJfee4NAKY3vYD6YPmFcU2VMZWtbfpHHtZ90+1fkdYusLzgP7aCJnmDaA/7VcmQWdmVQk4DhjyBDHg8jNvX0zLpDFg7ZfUxZGvQRMLIOsUo+TmZZHVjKkxx6IhQmNs3rT1uVIW4LImkCadF2BcTbD0fZATAvJX0F0Uo2T6NS82fwtKcAnHHS7J6BUoc0G8/6eOTHHq7AlBFBjS1UQCyQ+KJXb+32u3HsXLn3SK+ZqrS+N1VpQ6XJILRAKpK/UyIf1ukNKKEQRBEARB6GLLli246qqrUF9fD47j8PLLLyd87nK5cNttt2HKlClwOByYP38+nnjiiYTv+Hw+rFq1CpWVlSgpKcF1112Hnp6ehO90dHRg5cqVcDqdqKmpwZ133olQSNtq8vEEi9XrSogHi9K7MViuVM7k/jCZOMwQcu9rrBuRaZtyK+GN3cf0wUg9dUOSiUajaevUSLepXVTg3+vkFrNNLLZhgtOKaBRo0xjYFNNyGXB8fJJaFnKpk/QG/T1i340RFTwZxn5GzIE26A5qdsJ5xMB86iJHvWKXJ0PQH9AfjM7UvsNmlojWOsUumfaFFGt9Y35NY+/N4LwC9AeQM4lFgLHXhKkTnbCZTfCHIjg5zE44lyOduwSI30Pb+7U5+xT3IU1KMSC/QkAwTUoxILGOS6Eg1HCRd7jkru6dVoT+k8OFIAjdCKuspCS/WElfQn72+n4A/ENBvSTHplR8yYTcywVUXMukN6JgiBwuBEEQBEEQenC73Tj77LPT1q1cvXo1Xn/9dTz77LPYt28fbr/9dtx2221Yt26d+J077rgDr7zyCp5//nm88847OHnyJK699lrx83A4jJUrVyIQCGDr1q14+umnsXbtWtx7772G75/R5CoPfrr884AxtWT8ovtDPhAoCi4sBZBgegHECMeJuD05F085XzckFInKuvu1EAxHIZSZNWIfxdRNMsFVjuP0u0QyuDhmxtrud/kx4lFfEDmby2K2zjor2QLbekWFTO07bRZU2PgDr8UJEY5ExYB1tnRuWlJux90zacZevL4Y46BhJSrItV9ij7sQtbgEhL7bLKaMAWTNDpcM5xSgPxVgpmuCWSKcG33/yiS4TK8UBJc83kN1OpX0EAgJKazSCy4Hx7EjJJl4Si72TsVcEIqkd+gUKiS4EESeEHOSSm7ypqRVRdKbkicYXyUhzdl8+ek1irYnt0ruwhmVivtrNnEQrn2UUowgCIIgCEIfK1aswIMPPohrrrlG9vOtW7fipptuwtKlSzF9+nTccsstOPvss7Fjxw4AwMjICJ588kn88pe/xGWXXYbFixfjqaeewtatW7F9+3YAwMaNG7F37148++yzWLhwIVasWIEHHngAa9asQSBgbO0To8lVHvxMASsWBdGTyeTEACSCi440SVJ490f6FF/S9DesyLQ9vhg523GVLnTLtI9GpA8C9M/VTKvlS+wWTIoVddYStMwa9E8qIM66fb21RLIFzmsdfJ+1CEbSjBRyx7ah0gmLiYMnEEb3qE99+xkEC0C/qODLKiroTVnGX6vSpSmfV6c9LVOmOQ/E52WHRiec0vaNuiYYce+QI14YPjWIzfpekg5/hrRmsxlf69UgOCrkxmZeHd+vQz1jBVO/OHMNl9j5MuiRXfg9HhBTiqnIwjPeOXX2hCAKDOFCVxkrhgckWmeBRHXX448/rPaNxfOszoldPLORbBH/6uIpMKlUj4WLH6UUIwiCIAiCMJaLLroI69atQ2dnJ6LRKDZv3oyDBw+iqakJALBz504Eg0EsW7ZM/M1pp52GadOmYdu2bQCAbdu2YcGCBaitrRW/s3z5coyOjmLPnj253SHG5MrhIqZDkQkCCClZBtwBzSmLksmUbgtgHyQLRaKIKHB/HBv0iOKTXrLtI4tC83Lb47eZ2amkRVRQmp5Iu0skfQ0XQJ/TKlvKr4bKYphNHFz+EHpG1df6yJY2i5nLIk37tbHEEFral76by80bq9kkFtfW03664xpPr+fWJSrIpaKTtq89ZVnmuSO6BHo0jE0WMaqurAjFohNO/bVQsUiq05WWvn3jHS7RaDRjOi/h/nV8yMvs2i5HpkULghBwYsgrm3LfSDI5bxoqi2Ezm+AJhNFpcNo3VoQzOERqSu0otVsQiQJHDU4hpxVRADuFHC7Kqm0TBMEcuRUhY75EG7j0cV+4WTptZvG3QKqQkon7rj4D/7VuT+x3anvMv2gGQhFKKUYQBEEQBGEwjz/+OG655RZMmTIFFosFJpMJv/vd77BkyRIAQHd3N2w2GyZMmJDwu9raWnR3d4vfkYotwufCZ3L4/X74/fHA6ujoKAAgGAwiGFSWskj4ntLva6Ghgk+re3zIA5fHB7vCuoZqEVJ8mRBJ2R8rB9SXF+HkiA8HuoaxuKEi5fdqx8Lr579nNcn/ZuoEfr/b+lxMxtflj9fzMSOM5CYnOkwotpvh9odxuGdE8WKvTHgDwj5GZfdhhhDE7hllMpfcXn4+2y0m2fpFk8ttMHHAmD+Ek0Nu1ChM2SzgiY1humM2vTLmQOkdU70foXBEfA+0cPz/Jrcxo8qJ9w4DB7pHEAzWqWrf5eWFwiKrSbZvHIBpFQ60D3iwv2sYlU7lGRIAwBObz7Y0Y9NQwY9Ne78bXp9f9epmob6NlUudS8FgELVO/o36kIaxH/Pw86bIakI4HEJYJh48s6oYR/rcONg9gsYZE1S17/LFxt4iP/ZVDjMcVhO8wQjaekbFALlS3JJ5CaSO//SJ2uclEBcVLDJjDwCzqni1a3/3qPqxj52z6cYG4AWD3Z2j2H9yRJxHShHG3mbhZNufFru/nBzxYdjlhc3EzyPF13FhXqa5xk2v5MdG69grIRiOiKkUTdHU+1dFkQlOmxmeQBjtvcrml5brcaZ7aJmNwwSHFcPeIA52jeD0SaWK29VLvF/yx2hmlRP7e1zYe3IYdaVWTdvIxbOQgC92XzVz8tubUe3EpydGcaBrGDMr1Z0velEyDkIWn2g0nJPxUoqevpDgQhB5QnC4SFe0jPoSXwAiMiusHDYLTBry8wLATRdNjwsuagq4xBDslqwKWBIEQRAEQRDyPP7449i+fTvWrVuHhoYGbNmyBatWrUJ9fX2Cq4U1Dz/8MO67776Uv2/cuBFOp1NVWy0tLay6lUI0CjjNZnjCHJ55+Q1MVheLVEzfgBkAh92tnyDakfpsXgYTTsKElzdtR09teneE0rHYd9QEwITOjqPYsKEt5XNXEAAsODniw8uvbECahf2KEdoDgDc3viG7KKvSYobbz+H5N97Fwkr9Tvcj7fw+th85hA2+gymfDw9wAMzYeegkWlqOA9A3l3q8AGCBKRrGhg0bZL8z0W5Gv4/Dn9dvwtxydft4spefI3t3t8LS+UnK5/0+fvtHesew/tUNULOA1xfmfwsAW7e8DaspdSy8Pfx4bd/bjg2RI6r6/klsrD2jI2nHpjjCH69X3t6BkQPqxuZwbD4fO3IQG7wHUj6PRAGryYxgGHj25ddR40htIxNDY/zYf7xjG/r2pn5eG4srfnasL+3+paPbAwAWmDPMm+gov3+bd+5D9ZA61+DHJ/mxH+zrTtt+pc2ME0EOz7+xBQsmqhv74yf5vh05uA+V1anzRjj3O4fVX0siUcAX5Ofl+1s2Qy4ezZeesWDPiUG8+uoGVQs+9w3zYxPwutOOjd3P799r73+M8DF1YyNcZ0+muc4CQInFDFeIw7N/34ipMZ1Z6XWob5Cfl59+/BG8R1L71hUbm32dQ6rnpVL8kmvH5jdbZI/vRKsZngCHF97YgjNVzC811+OhEX4sPvnoQ7gOpW6jwmLGMDi82PIezqnKXSaVk938HNj32W5s6P005XNnKHbd2/IRfDLHUA1GPgsJtI8BgAU+n1d2ThXFzpfXt8o/y+SCTOMwEruWf/jBdvTLXMvzhcej3RFEggtB5AmfWJzShKb5tdi4twc3NTYkfEfO0e60mlNqvWih2K7+9LdSSjGCIAiCIAjD8Xq9+NGPfoSXXnoJK1euBACcddZZaG1txaOPPoply5ahrq4OgUAAw8PDCS6Xnp4e1NXxq9zr6urEmi/Sz4XP5Lj77ruxevVq8d+jo6OYOnUqmpqaUFZWpqj/wWAQLS0tuOKKK2C1alsZqoSnOz/AJ8dHUH/aOWheoG5lv1L+t30b4BpD44Xn4Yuzq1I+/xj7sX9bB4onzUTzl+alfK52LHa8sg/oOo7T585G8+WzUz6PRqP46WebMeoLYf75XxTT9mila8QHfLQFNosJK1c2y37nbe9udLR2YcLUeWheOlPX9gCg5a+fAn3dOPvM+WhOev8BgDm9Ljx1cCsGQhYsW3Yp3nzzTV1zaV/XGNC6DSUOO5qbl8p+5++Dn2DTgT5UzzwDzRdMU9W+MEe+kGaOhCNR/Gz3W/CHIljQuBQNE5ULl/0uP7DjHXAcsKJpmexYTGwbxAvtH2GMK0Zz8xdV9d33SSdwcA8m11WhuXmx7Hf2WA7is3ePoqhmOpqbT1fV/vo/twL9vVh01ploPn+q7Hf+39Ft2Nc9hqlnnIvLT1NWn1Tgv1o3Awji8qVLxBRZAsFgEGMb+ADfYIDDZVcsT5u+S47dnSPArg9QVuxAc/MS2e8EWk/izRc/Q8hZiebm81T1/ejbbcCxw5jVMBXNzWfIfudN96c48Wk3Jkw7Dc1LZqhq/48ndwAjwzh34VlA5y7Zc+jRvZsx5AlizuKLcUa9sus7EEv5tf0tAMCVX2qSjS34gmH8Yvdb8IQ4nL/kclSrcI5Z9vYA+3ahtqoCzc3ny37n2Dtt+OjNw7BMnILm5gWK2waAbev2Al0ncMZpc9B86SzZ7zzb9SE+PDqEunmLcMX8KlXX8Z/v2wJ4fbjk4kYsnDoh5XNvIIyff/oW3CEOF16yDBOLbamN6GTYEwR2bAYAXNn8JVn32MaxT3His25UzTgdzV+YnrVNLff2Xxx4F/B68cUvNOKcaRNSPn8vsAftOztRNmUOmi9LvecZxXM9HwHDgzhn0UI0nz0p5fNjxW34+M3DME1QP78EcvUsBAA7jg4Cn32EspISNDd/IeXz41vasaPlEMwVk9HcfJahfUlGyTj8dO8WwO/DF7/wBZw1pTyn/cuE4PLWAgkuBJEnpEXs/vf6xeh3+VFblmjtkyvQVWQ1qVoVlcy9V87HCztPYFWaB4tMxAUXcrgQBEEQBEEYhZC+y2RKDJCYzWZEYnmuFy9eDKvVirfeegvXXXcdAODAgQPo6OhAY2MjAKCxsREPPfQQent7UVPDBzJbWlpQVlaG+fPny27bbrfDbk8NjFmtVtUBAy2/UcOc2lJ8cnwERwe9hm1HeO512Gyy25hbxwcp2/o9GfugdCyCsed/hz3992dUl2DX8WEcH/bjjCkTs7aZiTD41Dp2iynt9mbXlgHoQvtA5n1UirCPzjT7OLu2HGYTB7c/jEFfLK+7jrkUjjn77VZz2jbm1JZi04E+tA+on0vCe12Zwy77Wyv42jv7u8dwbNCH2bXKg0nBCJ/ZwGE1w2bjg7LJYzFvEt/eiSEvwjCpEhUCEX5snLb04zunlp/jWo6/L5aKuqRI/vzh2y/Fvu4xHB30qW7fI4x9cZHsb0ssQLnDghFvCMeHA5ivQlSIj036eTO3jh/7I33qx8YfW8RYXJR+7OfWlgHo1jYvY2Nf6rDBDflzaHZNCT48OoRjQz4sbFCeLm4sEI9TlDqLZAt1W61WNFQWo73fjbYBH+onKk9HGBTH3pJh7LXPy0CI739JhrGfXVOKD48O4ahk7JVeh7yxxbVlTvl5abVaMXmCA53DXhwb8qN2AnuLZpSLpcziAEeRvNg1U6gTNKTu3FNzPRYW6zrtae6htWUAOtGmYY7rQQhppbvXnl4/AQBwuM+tu19GPwsBAMfx132rWf5ervRZxUgyjUMo9lxQlOHZJx/o6Yu6BJkEQTBjxMs/PJfYLTCbuBSxBQBk9BbYLCZVdVuSufniGdjw719EZYm63MRAPKVYgAQXgiAIgiAIXbhcLrS2tqK1tRUA0N7ejtbWVnR0dKCsrAyXXHIJ7rzzTrz99ttob2/H2rVr8cwzz+Caa64BAJSXl+Nb3/oWVq9ejc2bN2Pnzp345je/icbGRlx44YUAgKamJsyfPx833HADdu3ahTfeeAP33HMPVq1aJSuqFBpCYWMjCw9nKjos7cORPjZF7KUu+HTMrOKDc239+rcpiAXpCtgDkqLvzPdRfps2i0l0gbQx2KY/lH1MZ+nYx2zFzwHpGKqbq9mKbwNAdakdpUWxgsgqC4j7shSdB/QVVxfmlzND+1oLlIcjUbEgd7rx4TjpOapx7DP1PTY2/S4/RlSm/VZybIX2D6vsOxAvPK9oXqoce6HvNotJVmwRmFvLt3+gZ0xT+0r7HpVLDaKg/Uxjr/WczUX7SvBnKFYvMCN2L2lndG2XQzhHrRb5eTKrhu+D2jmoF+Hebk1TN0qYu4f7XGJ9kfGMIFikOx+F+dbW55Zd2J1vwrE+WUynjkxx6uwJQRQYfWN8IbhMRRnlarhYzSY88GXecvxvObRcAvGbdTCUesP56OggHn/rUEHcjAiCIAiCIPLNRx99hEWLFmHRokUAgNWrV2PRokW49957AQDPPfcczjvvPHz961/H/Pnz8cgjj+Chhx7CrbfeKrbx2GOP4corr8R1112HJUuWoK6uDn/729/Ez81mM9avXw+z2YzGxkZcf/31uPHGG3H//ffndmcNgrUQIEcwthI6XbBe6MPxIY8YXNaDUOfRniFQxzJIpkSMkAYGWQRqxH00SABJ3V5mgQfQJ955FYgWWttXEvTnOE4SfFY3XmpEhd4xP0Z92kSFIiWCjsrAs/R8yxTYFgRKtWMvilEZ2i6xW1AXWziptf9KgvJtGkQFQdjMKOhoFIyFOZ9JSAOAebGUh4fUCi4K2m+oLIbFxMEdCKN71KeufXHep0/6o1VojEajknmf/bpq1IIBcbFAGkEBkNxLGIj3Wvsxu5qfI239bjHonguCouAiL1BMrXCiyGpCIBTBsUHtdTxyhShYpNmfaROdsJo5eINhnBzx5rJrihCOR7r+FyKUUowg8sRv3uYLGtbIOFsE5AQXm9mEy06rxa7/akK5I7dWO+ElxS8jqnzliW0AgNqyIvzjefL5eQmCIAiCIAiepUuXZgyg1dXV4amnnsrYRlFREdasWYM1a9ak/U5DQ4NhRXnzTXzFpgvhSDTjSmutZHO4VBbbUO6wYsQbRFufW1XKIjmUCCAsg2R+weFiTb+9aROdsJg4eAJhdI36MHmCysrmydtU4jipLkELenCkz41KFcW8ZbenYB9nxwLP3aM+uPwhlKiod6kksK1VHPQqCPoD/Hh90jGsOnjrUdB+WZEVNaV29I75caTXhUXTKpi2L13hHo1GFWdz8EoEl8xzKda+RodLthRts2tK0D3qw5E+FxY3KB8bJUJdQ6UTZhOHMX8IvWOpKcgztq/GQaNWCFQ4L+fEBBetDpdM7VvNJkyrdKKtz43DvS5MKld+XVI0L2Pz5uiAW9WiUn8oItbiVSJ2GSW4BLPcu4D4vaR71Ae3P6Spzm82AlmcNpMrHLBZeGHjxJAHDZXs06vJEcwiBJlMHObWluLTEyM41DMmHq/xSjaHi8VswvTKYhzqdeFInxtTKpTXEssFcYfLqSO4kMOFIPLAiaG4Qn5OhgfWdA4XADkXW4D4TdIfTP/A0VEA6j9BEARBEARR+EypcMJmMcEfiuDksDErNsV0KGmCMgnuAgapYYTn7ExBXqaCiwL3h9VswvQqdmlfhH3M5OIRxTSm+5g+/FHutKIqlnJZzT6GwhFRlFMaXFXjVPApcKAA2tMTKXFZANqdEEpcIjOqimHigFFfCH0uv+K24ymzTDBlCNLNrNbmcFES9Ackgo7G9jOd63aLGdNi6fVUtx9Q4LKIHdd2le4CpWMzr05wuKib90rEKCDef7VjEz+v0o9NfbkDDqsZwXAUx4eU31+kzisj0gwqRRQ6MjhcJjhtmFjM14ZSm45QCdFoNOuiBbOJE11oRo2FHEJtGUuG8ZlTExMMu3Ob7kwL4Vh9v0yCRS7SsGolGMl+PAqNU2dPCKKAEC5w0yudaJyVvjjdP583LeVv6XJf5gLhJSVTDZdMLzIEQRAEQRAEwQppoMawtCwK8uDP0hjQlUNJui1B/BhwB8S6kNq3l12MAHK/j3FXQm5EJQCYXaM+6OeTpFrOFByeWV0MjuPreA64A4rbV+qy0JyyTGlgW6sTQkENl0RRQfnxVi4WxQVKVaKCyrFR76DJLtQBkmOron1pWqtM7U+e4IDdYkIgHMFxFQsnlc7L6ZXFsJo5uPwhnBxRnvZL8bzXmI5OSX0bk4kTxTo1taSEvlvNXFqhHojPy85hr9gflii5dwHGphULRaKi2yeT8KPVaaWHbCnFAGBeHd+vgyodWvkgm8MFMD6NnR7I4UIQBBNc/hCAzOnEAODMyeX46J5lCX/LdNM2mrjDJf5AEI1G8fInneK/M1n1CYIgCIIgCIIlWgtiKyFhdW6GZ3CmDhcFAkiJ3SLWgTyqM0gmiB9FWZ7hc72P0roh3pDe7anbRzXBKCFQynGZ96fIasaUCofm9rMF5eOOIHV1dtSkzQLUB+o8CgLbgDZRQanLYsoEh+iE69TgVFAsiGitEZNF0NFSVNwvEQKziwrqz22lYpTNYhID+ge7lQetlR7buMNFY+0ipfNejeCicM5XlthR4bQiGuXPW9YoFVymx1J46b2XZOpDtn5oPY56iAsu6fs1N5YSrxAEFyVF52dpWFSQC6LRKAkuBEHo568fHsdtf/4EAFBWlD1HpmBtF8in4CK8REgf4Fr29uD2v7RKvqMzyTJBEARBEARBKMTIlbFCyhEgm8OFXR+UujFYrUoW03tl2V6u91GoGwIAPTqzxfkM3EepIJKt9shsDe17FAaGp1Y4YDOb4AtG0KkivZ7ytFnxeklKiUSi4rFW7BJRMzYKg/4JTrg+5YFTpe0Lfe8Y9CSkk8rafjCkrH0tYpTEMaFYVFAx9krFKEBb0DouRmVx3ml0uChN1ScK+iqus8K8yeTqSm7fiPtXQIGgAMRT7rFI35hMUJIZRZHDJQ8pxTLd24W5297vFoX78UoorMDhUs3vD4vUoCwJSRYJZBKMCo1TZ08IokD4wYufiv9fTTFGgVGdaQP0YIu9pEhXKuztGk34jiWDJZMgCIIgCIIgWKK1ILYSAgqDRULAUm3KIjnEdFtZ3BisgmRKU4ppLfouu00FReyl2+z16nu/UJLCTLo9VSv9VQSetbixlLogLGYTplfxabnUBC2V1LIA4mNzbNCT8C6YsW1JgDJb8FnL2HgVBs0T2lexgl7psa0utaPUbkEkChwbUJGWS6F7aVaN+r4LQp3NYsoYgAW0pQtU6owCgHmxoPUBFYKL2vo5fWN+VekVVTvHVFz31IhRLK+ryYyHlGJCH0xc5tocszXWuNJDMEt9NgCYVF6EUrsFoUjUkPFhifDskSlFmvDcMOAOYEhFakujCUkWt5xK8UQSXAgij5QocLgkoybnL2vkHC5lRdaE7/xy40F81jmS034RBEEQBEEQn0+MzEmuNB3KlAqnppRFcsQdJ7kJksUFnszBQSHtUL/LjxEPm7oxStNMdesVXIQxzSLwCNs7NuBJWJmdCTWBZ00py1S0P0tDAXGlgefaMjtK7BaEI1EcU1hc2yNxWRRlcxdpcVko7Lvm9hUGzjmO09i+uhou3aM+jPmUnXtKjyugUWhUKAQCwBwNDhel8760yIraMt4Jp7T/SuvbAFIh0A2lOoDR56xSlKTDBIwVXPwKRA1Ae40rPQQVFJnnOA5z64T5O75cIckoqeFSbLegvpwvazCe0oqFIvH7bTaBuJAgwYUg8kiJ3Zr9S0noLYypB+HFT/rymSwaDbgDuPLx93LaL4IgCIIgCOLzycyqEnAcMOQJYsDlZ9q28MxrNnEZgwDSlEV6gxjKU4oJrhpW28scGiixWzApFqjRk/YlGo2qdtX06kwppnRMJ5UXwWkzIxSJKnYqqAk8a1ktr6TofHL72lwimRcCchyn2gkhjI3dYoIpSxBNWOHeNeIT651mbV+D2GWUqKBv7DO3X+6worpUEBWUzR0tLgs17gJxXipxuMQC1od7XYodgF4xLVf2BapqhcxAOAKhG9nGfnqVEyaOr8E7qjAMY/S8UYraGi7DniBz14Mo+mTpg7TGVa7SXSlJKQYAc2v5Y6SmBlE+CIkCkrI0fONKcJE4XPJZQoE1p86eEEQBUqrQ4fL0zeeL/z+/KcUEh0t8tVKuLJ8EQRAEQRAEkYzDZsbkCbFADeO0LEGFK4QBdrVkhEBpdocLnz6qvc+t63lc6fYANquxpU75bNsUtteTo5RivKig7jiqCWwLbXcOe+FWKiqocXFoSZulpX2FgTqfCrGo3GkVa5cqrROjLp2bUMNFvaigxqmgtf5PNmarPPfUpFubXlkMEweM+kLodykLuKsRFaZNdMJu4esLHR9UKGQqTHUHqJ+X0vo22Y6t3WLGtIn8tVbpdUhLmsE2Bukok1F6/3LYzKKYzrqOi9AHJfcXLbWKtBKORCUpuLIJLupT4uUDJTVcAGPrBmlFWsPlFDK4kOBCEPlEqeByydxqfPML0wEAd33pNAN7lBnhZiS9ICrN4UsQBEEQBEEQRmBUACGeDiV7BEBLDYpkEtwfWdJfTZ3Ir7x2B8Lo0+HsUer+ANisxk4UXJTVT+j36XvnMHIf1QRXK4ptqCy2AVCevkdpcW9AstLfqBo0KkVFjwpBAVBfj0lN0F9wwg17ghhUuIrfqzDlF6B+3kjTWikSdGrigpEShLFR0naR1YypMVFBsXtJRd/NJk4cH6VBazX9F8deZd+tZk7Ranqh/R6FTjs1IuzkCgfsFhMCoQhODCmv/6MEpQ4XIJ5W7ChjwUXsg5JFCxoEY61IU0Zmu78LgsuhcS64CAJSphRpgNThMn5q0gjuHKuZA8edOooLCS4EkUdK7MpruNx75Xx8/OMrcOlpNQb2KDOCWi5dfeEnwYUgCIIgCILII0alZYkHrIypz5GyPUkQSMnK6ykVcZeLVpTWNwHYuHgEtwnHZQ901ZbZUWw3IwIOxxSujJfdppp9FIL+KtNmFSkI+vPtqxtDNYFtoSDyoDugQlTQ4iRQNt/EviscG7XnkJrAttQJp7h9FYKOVCyKKHAqSN+hFYlpWh0uCuY8oMElomLsAWCOgULmbLXzUoWYA8Sve4odLiquCWYTJ9bHMmrBgBrBhXUdF+EealXicNEgGGslUXBR5rQ8PuRVXNsrHyip4QJInETjyeGi0J1TaJDgQhAFAsdxmBhbEZUvzFyq4BIYxzcdgiAIgiAI4tTHKIdLQEU6FLWr8+VQk24LiAfJ9KSBUZpuC2C0j8H4mGZbycpxHGYJ+6hHVFKxj1odLkpqWQDq8+erSTvltFkk6fXYOyGkY6NEVFBTfwZQv8JdTdosQNp/9u1Pm+iE1czBF4zg5Eh2K4Q0rZUqd5HqdG7KFniqFrsCyoU6QP2xVePsEsbm2IA7IfV5OtSKRfHUhoq+LjqjFF8TVNZGUkpARUrM+L3EoEULKtJy5qKGi5qaIbVldjisZoQjUcUp8fJBWKjhkmUhg3CuHx/yiOdZvhHEImuW+jOFxqm1NwQxzknOF6vE1j6eEBRnwfIHUEoxgiAIgiAIIr8Y5XAJKiz4C8RTFg15ghjQmOJLECMAZQGqmSrdGLLbjD3Lqwm4dwxqD9SoSe8FSEUePYKL8n2UujiU1PpQk9aKb19l4XkVDhRAXdAyEomnsFMSfG6odMJi4uAJhNE96sv6fTViEaB+hbv6lGVqU6KFFLdvMZvE4uNK2vfEjqvNYlK0qltIa3RsQNm551Hr4lCbzk2laKH22KqZOzWldpTaLYhE+fHJhhoxB5CmFFPqcAlpat8oh6YSd4lR6bwCKu6hsyU1roRzzyiEe7uJy+6q4DjOMAcQS5Q6XKpKbCgrsiAaHT/7I4hFZgXpWwsJElwIIoe4koojNp1Rm6eeaCOeUiz+NxJcCIIgCIIgiHwiBKw6h70JK8f1ElBRw0VLyqJkpE4MJXnMWdZUUeL+qC6xo7SID2weHdAWqFHjNgGAmVX6BRchwKpkmw2VxTCbOLj8IfSMZhfO1KT8AjQ4aDTWQVEyB30SN4CS4LDVbEJDpfJaH2pTN82WOBWUpO5Rk1JM2r7SsfepqOGS2H72uar2uNaU2lHusCIciSpye6kVArXWQVGblutIryurkCmtb6NkfDiOw0wVDh1vQN1xFcSIkQCXEs+RbV+jg4a1wyWowuEiHP/2fndCNhO9qKkjU1FsEzO66HE0KupXWLi3K7sPFYLgEq/hknmfOI4zTOTTSjCsrO+Fxqm1NwQxzhn2BMX/f2Njg+IL/HghLrjEH4CphgtBEARBEASRTyYW21DhtCIaZZsSRU2wCFCfsigZNeIHIAnU6UrxJYgRygKb8cCszn1UWVtCX9o05a4am8WEBhUFxLUGV9v73QgpEBW0CjpK5oRUnCxS7DhSEdhWmVJsUnkRnDYzguEoOhSk7lHvLtJWP8eI9tWKRRzHYW4t3/6h3uzFu7XWcDk54oNbiaigcuwbKp2ikNk7llnIDIQjEGL+iuv/aJiXSs+pcocV1SXKhQDtIqwyV51SAiruJ/UTHLBbTAiEI0zTZqlJawZI6/EYKwQIAX6l/WKRvtNolDpcAONEPq3ExSJyuBAEoYFhTwD/vWEfAN66eN/VZ+S5R+qxyDhcTgyN3zyWBEEQBEEQxOcDFkXrk/GryD8P6A9ixIu7qwvUnRjyMkjxldt9VBrgF9KmtfW7FdUNkd2m2n1UsfrXq7KWxeQJDhRZTQiGozg+pKDWh8paHGqKn8cDwyaYFAa61KyMVuvi4DhOktqIfeBcrRNOa0o0NWOvVLAA4mnFDnRnF1zUCjoTnDZUqRAVfCrFNLvFjGkKhUxfIB5sUCxk1ihPiaZWCAQSr0NZ2xccNArbn1FVDI4DRrxB9LsCivuUDTULBswmDjMNEDvULloQjqPRQoDg/slW70RAEFyOjmPBRY1oYcTzkh7UHo9CgQQXgsgRP3jhU7z2WTcAYE5NqaI0AeMNOYfLwZ7Ui7RgBSUIgiAIgiCIXCCtvcEKNTVcAP0pvtSm26ostqHcEXP2aHbVxLapcCU8s31UuL1pEx0wcVHFdUNktxnUto9GOBVMJg4zq1S0rzHor0SEUysoSNtX5f5REdgWU6IpCpyrSw0lOOGA7PNXmtZKeR0UFWKRynRrADCvjhdcDvYocLgI7asSFQR3VPb21daIAZSLgZ4g77CxmjnFGUFUOVxU1OYRUFNLSu01ochqxtQKZ6x9hmKHWneJAUF4NWnNAHWCsR6CalOKVY//lGKhsHqHC8vnJT2Qw4UgCF1s3Nsj/v/a8qI89kQ7ouAiWVymdTUdQRAEQRAEQbBCbQ0CJcRruOTG/SHUjFAquLDIxR7fZm7qDajdntVsQnXs1UnrNgMqUooB2tJmqQk8axMt2ItwatOhAeoCdfH2lblzAJVil8q0VtL2s50v0rTZStsXXBAD7gCG3JmdCmpTfgH8ok1AfsFj+vY1jI2CdIGa5o5C94IWMUpwpbX1ZXfCaRKjVNSS0nZesXd2BEL8OFgVuxeVu4SU90GtwyU3zgshpZjSe7tw/LtGfPAEsqfcywfComg1Dpe2PhfTmj1aEWu4FFjJhWycWntDEAXCWZPL890FTcg5XITVAf/ztUUpfyMIgiAIgiCIXGDEylhhhbBSAURtyqJk4g4XFUFSvSm+VKbbEgM1/S5NKb7UungAoNbBb0e7q0bbPqpLKcZ+pT+gXtCRinDZXCJaAs9CQLTf5ceIpD5ppvaVikWAVkFHw9grDPqrab/YbkF9bGFltmPr05RSjO97x6Ana9BXz9iocnapEbsUznstfW+Y6ITVzMEbDOPkSOZUfWqdUYDKeanhvDKiiPl4cLj41dZBi43z0X6PohpXWlHrXp3gjLvjjvaPz5T68Rou2fdpSoUDNrMJ/lAEJ4ezp7Y0GnK4KGTLli246qqrUF9fD47j8PLLL6d8Z9++fbj66qtRXl6O4uJinHfeeejo6BA/9/l8WLVqFSorK1FSUoLrrrsOPT09CW10dHRg5cqVcDqdqKmpwZ133olQaHwqjQSRzLRKZ767oIm44BJ/uRJWLZxZX4Ynbzo35XOCIAiCIAiCMJq4EOBm9iyqdnWumpRFcgjBqSIVq97FVeM5SmM2NRao8QUj6NQQqFErfgBAjYP/X+2ikrqUYoJToXfMj1FfFlFBQz0IpYHNSCQqOoJUORWUigoaAtsldgvqynhRQamgo81l4cpaQFwQHbQ4XLL13RMbG5vFpChFj4DSFfqegHr3T2WJHVUldgDAoSwuF2HstcxLI+rzAMrHRouYYzGbML1SmQtFjwOlY9CTdXGpMHecBoldSgmE4nNYbR+ynXuK+6AydZdQ4yoQjiiqcaWVoOheVX5uT68a32nFRNFCwT5ZzCaxLs14qOMSFNw5VMMlM263G2effTbWrFkj+/mRI0dw8cUX47TTTsPbb7+NTz/9FD/+8Y9RVBRPsXTHHXfglVdewfPPP4933nkHJ0+exLXXXit+Hg6HsXLlSgQCAWzduhVPP/001q5di3vvvZf17hCEIdSXO/LdBU2YORnBRXITPW1SGYB4/kiCIAiCIAiCyAX1ExywW0wIhCI4PshmBara/POAvpXKfpWprxK2p7OIvV1hcNBiNmF6lfZ6A1r2sU6vw0VlGrOyIitqy/jAtlInhCZRoS9zYNMXkrgsVLlQlIlwamtNCCidc/F0aMpFhYbKYphNHFz+EHrH/Bm/q0WMmqUwbZaW4wooP/+1OKOAuMslWx0XPanujg64M7oLpPVttMz7nlE/xjIImWLReZVjr1S00CLo1JXZYTdFEYpEcWwg8/1FT6o7rbW45FC7YGBGVTE4Dhj1hdDvypwSTynBWFozpX2Q1rhimR40GSGGZVHgBhGYIQou+Rco5Ig7XJSJFsJ9wuh6OUoIh5W7cwoJ5nuzYsUKPPjgg7jmmmtkP//P//xPNDc342c/+xkWLVqEWbNm4eqrr0ZNTQ0AYGRkBE8++SR++ctf4rLLLsPixYvx1FNPYevWrdi+fTsAYOPGjdi7dy+effZZLFy4ECtWrMADDzyANWvWIBBgc2EgCCOpK/QaLhLBRci3aLOYYI19HopQSjGCIAiCIAgid5hNnFj0mVUAQW06FECfAKK2uDsQDzJqdfZoctXoWI2tZR9rYoLLYQW1JWS3aeA+aglsT69ywsQBY74Q+jKICtK0VkUGiHBaitoDyms9aAnK2ywmNEzkBT2lY68lrVV7f2ZRQasYpXbeqKnhAgBza4U6LlkEFw1B/0llRXBYzQiGo+jIIFoHwhEIlxo1abPKiqyoKY0JmRmEBS3nFKBe7FLTPsdxip12WvovzJvOYS/cfjaZe8Q4jcIFA0VWM6ZWKDv3lBIIh1X1AVDuQtODsEBYaX0bIF7HpX2cphRTm5ZLbzpSlgjxQ+spllJM+VIDBkQiEbz66qv4wQ9+gOXLl+OTTz7BjBkzcPfdd+PLX/4yAGDnzp0IBoNYtmyZ+LvTTjsN06ZNw7Zt23DhhRdi27ZtWLBgAWpra8XvLF++HN/5znewZ88eLFq0KHnTAAC/3w+/P/5AMzo6CgAIBoMIBjNbhY1E2HY++0AoQ+uxSl65VOU0F+bxjvIXwrcP9uHHL+/GD5rmxF/sImEIj6yRKOD3B2DK4wWTzqvCgY5V4UDHqnCgY1U45PpY0ZwgTmVmVRdjX9coDve6cPnptdl/kIVASF06FL4PynP9J6Ml3daUCidsMWdP55BXdepiTXVjanK7j7WxQKdQN6Q8lrZNCeFIVFKLR90+bj0yYEh6IrvFjGkTnTg64MHhPhdqyuQX4wlt2y0mmEwcwgrLAiWLcOlWPAtOAq2BbaXFz9XUcAGAmdUlaOt340ifC1+YXZW9fRX9nxxzwvlDEZwY8oppglLa1ihGKT03tAo68+p4weVAlpRiWto3mTjMqinGZ538NVQQsFPaDsSFKi2CVO+YH0d6XVg4dYLsd7ScU4DE2WVAKj2AryV13M0ZIjRWFNtQWWzDgDuA9n43zmRQ81etwwXg52/HoAdH+lxonFXJrA9qrvdK6yzpIe5eVR6vmlEliLX5FyjkUO9wYV83SCtq+14o5FRw6e3thcvlwiOPPIIHH3wQP/3pT/H666/j2muvxebNm3HJJZegu7sbNpsNEyZMSPhtbW0turu7AQDd3d0JYovwufBZOh5++GHcd999KX/fuHEjnM7819RoaWnJdxcIhag9Vr4QID3d3nlrI9sO5Yjd/RwAM6JR4I/bOzDQ2Q6Af5B4+603Y4ILv5/rN7wGFfdVw6DzqnCgY1U40LEqHOhYFQ65OlYez/hcmUcQLGBdeDigsrAuoLxOgRxa0m2ZTRxmVhVjf/cYDveNaRBc1AfEdLl4NOxjkZlP6dM96sfhPhcWN1Qo/q0Q8OO3yd7hoiV9kND+0QEPjvS6cNEseVHBp6E+DKBchNNSfwaQjI0BgWeAn19v7uvJOPbStFZqBCNTzAknCLNpBRcNLgggPjbHhzzwBcNpf6+lsDoQd7gcUphSTIsg8lnnqCIHitXMqRKjAf7YbmsbyDh3fBrqzwDA7Gp+bNoUzku17dcKqQ2NuibUlGCgfRCHe11MBBe/hpSYs6qLsWk/Q4eLhkULuXC4qK0tA0hTio3XGi5CmjSFgst4crgIjiOV15PxTs4dLgDwD//wD7jjjjsAAAsXLsTWrVvxxBNP4JJLLjF0+3fffTdWr14t/nt0dBRTp05FU1MTysrKDN12JoLBIFpaWnDFFVfAalW+WofIPVqPVe+YH/jwHQDAPc3z0NzYYFQXjWV3N5459Kn4T27CZOA4L3KubP4SQuEI7v5wEwBgWVMTnCry9bKGzqvCgY5V4UDHqnCgY1U45PpYCQ5vgjgVUbryXilBLSuEJSmL1Kb4UlvAXmBWTQn2d4/hSK8bl52m/HfRaFTfCmRNNVy07ePM6mJ0j/pxRKXg4pfUQdEiKikN3moRFd7a35s5sK2xloVSEc4rFJ3X6HA5PqhMVFBTwwVQlrLMLxHStLhQ9nWN4kifC8sg74TTmvKrqsSGcocVI94g2vvdOH2SfJxJ67yZE6vh0jXiw4g3iHKH/HODKEapFi2yX0M9sXmjVowCJMdWSfsq+z4z1na/K4BhTwATnDbZ72mtzyM47YxwuAD8dXVHTHBhgVaHC8By0YK6Gi5AolMpGo2C49i7HoIaAvxC7bIhTxBD7gAqiuXnV74QRAuLwn0S7uNDniAG3QFMzOP+kMOFAVVVVbBYLJg/f37C308//XS89957AIC6ujoEAgEMDw8nuFx6enpQV1cnfmfHjh0JbfT09IifpcNut8Nut6f83Wq1jotgxHjpB5EdtccqFOVrCxXbzPj2ktlGdctw7EkPy8INFACcdhuC0totJsu4mM90XhUOdKwKBzpWhQMdq8IhV8eK5gNxKiNN58UiUCOmolIRlJGmLDo+6MHkcuVBjHgBe5WCi8aVqtKgtV1FcFAIbA64A6oDT8I+anEObD0yqNpVI+yj2cQpDkQJ2wOAY4MeBEKRtAFDrfUmlBwzrUFzIC7CHe514bLTMosKavteXWpHqd2CMX8IRwfcOK2OraigRDiV1rdRH9jOnnpKS2F1gK/1Mau6GB93DONwr4u54FJWZEV9eRFOjvhwqGcM506fKN++1sLzCtwFWvsOALNrShW0r63vxXaLODaHe10ZxkbbeSU6XDLcX4LhiBg81jrvWYkdQQ0uDtbpvLSIPtMri+M1rlx+1JSyr38cEsdG+TOC02ZBXVkRukd9aB9wjz/BRaVo4bCZMXmCA53DXhzudeH8GfLnSy7QcjwKgZz6dWw2G8477zwcOHAg4e8HDx5EQwO/4n/x4sWwWq146623xM8PHDiAjo4ONDY2AgAaGxuxe/du9Pb2it9paWlBWVlZiphDEOMBj0Zb6XjDlPRQITyIWkwcTCYOFlP8kqKlaCdBEARBEARBaGVGVTE4DhjxBtHvCuhuT0s6FCFlEaBdAFGTbgvQHqjza0y35bRZMHkCv9xbbdoXLSnMAGBWlbZizqKIpXJ7tWV2lNgtCEeiODYg70KJRKLwxdpXnZZLwTHzaHSgANLAqQIHjRZRoUZJ+1prcfBt94z6MeaTrzvmib2H2iwm1auilZwv4vu7Vf0aZSXtezWmzQKAubE6Lgcz1HER3Euq03IJzq6Yu0AOrWIUEHcvdAx4REEgGT2CjpLzSmv7VUV8MNvlD6Fn1C/7HY9UCFSdqk9ZDRqlaBE7hOvGyREf3P6Q/j5oSGtWZDVj6kRt13ulaBGjgHhasaPjMK2YILgoTSkGsBf5tHKqOlyYCy4ulwutra1obW0FALS3t6O1tRUdHR0AgDvvvBN/+ctf8Lvf/Q6HDx/Gr3/9a7zyyiv47ne/CwAoLy/Ht771LaxevRqbN2/Gzp078c1vfhONjY248MILAQBNTU2YP38+brjhBuzatQtvvPEG7rnnHqxatUrWwUIQ+UZr0b3xhiVJcd7XxeeOFW5UZhMHQZMJpXmAIgiCIAiCIAgjKLKaMbWCD9SwCCBoCVgB2oMYQiBTtRghBOr60gdJ5fDHtmfi1AVpAElgU7UAEttHjS4e9aKStjEVnApA+qCfL6Q9uCqkbuoa8cGVJrCptbA6oKwOgj6ngnKHjtqxKSuyoqaUj+ukS7mmVcwBEt1F6c4Xr44Fk6rcSxr6L9RxOZimjou0vo3a8WmodPLuAn+IT4sug1b3DADUlRWh2GZGKIOQqUvQUTD2Ws8riwlomOjI2L7QttnEqV6tL5xTRwfcTGIpWtJFVhTbUBlzbrRlSHeovA+xej9q76ESt6oRBDTWDJlRPX7ruAg1XNSIFuOljosw39W4UAsB5nvz0UcfYdGiRVi0aBEAYPXq1Vi0aBHuvfdeAMA111yDJ554Aj/72c+wYMEC/P73v8eLL76Iiy++WGzjsccew5VXXonrrrsOS5YsQV1dHf72t7+Jn5vNZqxfvx5msxmNjY24/vrrceONN+L+++9nvTsEwQSfjgfC8YTZlHjJ6HfxD2HShwnhZS1EDheCIAiCIAgix7Cs4yKuztUqgGh0uKgNws6sKgHHAcOxXOxqt2e3mFWnX9O7j2pdPEIas45Y3RCjtwdkXy0vTWtVpLL9cqcVVSW8qJCuToyeRXvS45PVqaDHQaPAxaGl/7OzCHp6+i444UZ9obROOK01XACp4KrA/aPF4RITXA50ywsugXAEwqu42rRZdosZDZWZ66zoEYuk7qh01w4x5Zcuh0uGsRfPK/XHdmZV5vpCUiFQ7TW1vtwBh9WMYDiKjkGP6r4lo/n+xdD1INRKUZOWM6EP48zhIhz/tnEouIg1XEzK94l13TutaHHnFALMBZelS5ciGo2m/Ld27VrxOzfffDMOHToEr9eL1tZW/MM//ENCG0VFRVizZg0GBwfhdrvxt7/9LaU2S0NDAzZs2ACPx4O+vj48+uijsFjyV6CbIDKhZ/XQeMKc5qFB+jAkXOBDYRJcCIIgCIIgiNyipOC2UnLtcNHqxhBysQPqAifi9nQFlXOzj9UlNpQWWRCJAscGlAcj9exjttW/XokjyaQhUJRNtBKcBNqKk5eI6fUG0ohwWmtZANkDdaFwRAz46nKhGCBGSZ1w2ZwKevre1udKm2ZbT/vzYoLLoV55wcUXiLsjtPU/7piTQ29sI5t7QZfzSom7SI+go/CaoKVtPh2lcP/SH9APakiJCTBetKD1HqrR0aiUoNgvdddtIaVYu0HOGz2ENaTlYvm8pIe44EIOF4IgVKLnxjueSE4pJiB90I07XCilGEEQBEEQBJFbjHC4qA1YKUlZJIfW+iaAshRSyQj1R9S6M4DsAfF0iPuoUgDhU3ypP7Z+HfuYbUz1pD5KaD9L8FZLnY8iqxlTKngRLptTwakjKN/W70JERlTwSeoDael/1rHRETRPaD+LU0GLGDV1ohM2swn+UASdQ1759nWmc+M4oN8VEDNOyLVtMXGqr11AdneBT0f9GWn7Rsx74bgeH0rvhIu3r37BdlaRVId7hm+f3f3Lr9mhmX/BRaj1Y1gNF40B/ulV8ZRiau7tuUBPDZfOYW+CYzPXhMnhQhCEVsRijQUuuKS7UUpfYARRhlKKEQRBEARBELkmvrqcRf55bQLIjKpimGIpi9K5C+TQ886gpEh6yvY0ih9APFBzYsirLsVXUHuKLy2uGh8LF0+vW1ZU0FPLAsielkuPC0LafjZRQYtgNG2iE1YzB18wgs7hVFHBE4jXpdEiIGYbGz0pv/j2laXN0jL2ZhMnroTP1n8tgo7DZsa0WFFxuToueh0o2eaNcGy19F3aftp5ryMde1WJDWVFFkSj6ets6Kn/MzOLI0BvKnlWRcyj0Whc7NDocGHhevDrXLSQqcaVHrSmFJta4YTZxMEbDKNnVL7GUb4QHS4qagdNLLZhgtOKaJQXz/NFUKzhQoILQRAqES4gtgK/gKR7WJY+bAmFriilGEEQBEEQBJFrhEBN57AXbp2BmvgzvLrX5iKrGVNjAVE1qWG0ptsCtDlc9GyvstiGckcsUJOjfdTjcNGyvWkTnbDEgmtdo76Uz3WnVjLaxZFFhNPTf4vZhOmV6YPPPokYpbaWBRAfm2MDHjFwnNA+I3dRdkHHqGOrT6wT6rgc6kltX487B0gUGuXwBvX1fXZNXOyScwl4dPSf47iMxzYYjogLQzUJLjEhrXfMj1FfMOVzo+eNUoKSWIzWGmRHB9xiMXPN/dDocJngtKGqxAYgfY0rFv2yqkwpZrOYMDXmHEwn6OULLQ4XjuMUpeEzGnK4EAShGa2WxfFGuhcV6coiSilGEARBEASRnS1btuCqq65CfX09OI7Dyy+/nPA5x3Gy//385z8Xv3Pw4EH8wz/8A6qqqlBWVoaLL74YmzdvTmino6MDK1euhNPpRE1NDe68806EQuxXjI4XKoptqCwWAjX6AiJa06EA0lXcGhwnetwfqmq4aN9etsCmEdvM9fasZpOYQkZuXIWV/nqD/scGPKK4l9i+PlFhVra0WTrTXmcKDutJCwUAtWV2lNgtCEei6BhMPYfEsbFqq+Ob7XzxMkqblb64ekhX+3Nr+fYPyDpc9LUtXLu6R30YM0BUaKgshsXEwR0Io9sAITOTMOuVuPGKNKT9Ki2yoLbMDiDdNUHnOStx/+hJWSW9nqhdMFBf7oDDakYwHEXHoPJ6WXIENC5aALI7ofSgdTEFIKnjMs4El3As/qamhgug7VmFNYJAaNFwPMYzp9beEMQ4Ja6gF/Ypl+5FRfqQLqjT9/59T076RBAEQRAEUYi43W6cffbZWLNmjeznXV1dCf/94Q9/AMdxuO6668TvXHnllQiFQti0aRN27tyJs88+G1deeSW6u7sBAOFwGCtXrkQgEMDWrVvx9NNPY+3atbj33ntzso/5IluwUyl+jUWHAak4oEVw0R6c6hz2JqRzyrg9He4Pfpvq8+wL+1ikqYh93FEhl+JLfnvaHTXSbcrto96UX5PKi+C0mRGKRHFsIDWwyWq1fDZRgUVwOBkx7ZTGvvM1e9KPPau+nxzxyTrh9IpRmfoejUZ1H1vB4XKwW0Zw0emeKXdYUV3KiwpyorVed5HVbEJDJe8ANOK8ynTtFVJ+mThtwXZp+5kEHa19n17lhIkDxnwh9I1pT1kldYWpXTBgMnFi6jS9rgc9ixZY1mNLJhAL8Gu5t8+o4vvVnscUXHJoLTyvZbEGawSxiBwuBEGoRnB7WAs8pVi6G6X0gaI39mDQenw4F10iCIIgCIIoSFasWIEHH3wQ11xzjezndXV1Cf/9/e9/x6WXXoqZM2cCAPr7+3Ho0CH88Ic/xFlnnYU5c+bgkUcegcfjwWeffQYA2LhxI/bu3Ytnn30WCxcuxIoVK/DAAw9gzZo1CASU1xYpNFgV/Q1oLDrM90EoKq5CcAlqrzcysdiGiSqdPX4d9U0AjY4TYR81OE6mxYqR+4IRnByRL0aesj0ddWqAzPvo1Rl45kWF9O2zquGSToRjFtiWST2ld2yAzCuv9dZwSUxZJNO+TkEn07wJhCMQ9EKtab/m1cUEl56xFCeEXrEIQMY0Q3pT3QHSmlOZziv2heelgoiWVHfS9mUFHZ3z3m6J1+dRkx4yGeHeZTZxqh0PgLYFA7L9YLCIwAjBJaSjZsiM6vHqcInVcFF5vFnW7NFK3OFS2PHSZLT5LwmCUIVwAbEWeEqxdKtA9DxIEwRBEARBEJnp6enBq6++iqefflr8W2VlJebNm4dnnnkG55xzDux2O37729+ipqYGixcvBgBs27YNCxYsQG1trfi75cuX4zvf+Q727NmDRYsWpWzL7/fD74+vrB0dHQUABINBBIOp6WXkEL6n9PusmVHJ51g/1DOqqw9CsMiEiOp2pk/k+3C41wVUKxsLIVhnRlRTv2dWOTHoDuBA1wjm1Tizft8dSxdkNXGatifuY8+Y4t97xX1UNqbSuWS1Ag2VDhzqdeNA1whqS6xZf+/xs9nHQzL76PLy/7abtbUNADMqndjdOYKDXSO4bG5lwmduoe/mxPNP6bZKbRwqnFYMeYI42DWCM+rLEj4XjoWFUz+/AaChoggAcKhXbmx4QddhNekYG37sD3annsfC3M009tnGa0ZVMfpdARzoGsZptYnni5Dyy2rSdh2bWm4HxwFDniC6h91imkMAGPPG27NouLYAwJRyO8wmDqO+EE4MulBXViR+5vLy1+8iC6dp3gDAjCoHtrWlGfvYvLRpHBuAv1YBwEGZa7Qg6Fg4bdfB6RP5sWjrc8HnDyQEoMc8sbGxmlW1LR3DTPcXl4+f93quCTOrinF0wIODXSM4b1q5pjbcPn4/bRr7IVz3ko+P2rkUCPPHkouG1d9DK/njeLjXxfxZwq/jXjt1guD+yt6vXD4LifV2IurGelpFbH/63SnnCyuyjUMwJMwTbee8kejpDwkuBJEDAhqLco030q0Mqy935LgnBEEQBEEQnx+efvpplJaW4tprrxX/xnEc3nzzTXz5y19GaWkpTCYTampq8Prrr6OiogIA0N3dnSC2ABD/LaQdS+bhhx/Gfffdl/L3jRs3wunMHsSX0tLSour7rBgc5gCYsau9Bxs2bNDczpjLDIDDh9u3omu3ut+6gwBgQfeoH/6wsrEYHuO3t3PHNvTtVd9fq9cEwISN23bB0vlJ1u9/3M2P01B/r6Zx6vcBgAWHe8ew/tUNUBKn8fj4fdz2/hYcKsr6dRFh/Jwhfh9feedDuA5lTyu2+wS/j71dndiw4bjyDcbodgGABfs6B1PG6OMuvu3BPu3zLDzEt7Fl10FMc+9P+Oz4SX5fD+39DBv64xNQzXlVYTZjCBxebHkfx6rj4xWNAt4Afyy2bnkbn9nSt5EOfxgALBjyBPHXv2+AVP/6ZIDfL8/oiOaxGR7k2/j48MmUY7f3KD82J48fw4YN7RnbSTde4vmy/VNYT7YmfNY7yI/Npx9/BN8RbbU0KmxmDPo5/PmVNzFLonUN+wHAAhMXRcsbr2tqGwCq7Gb0eDn8af1mnD4h3scdvfy4jQ4NJIy9mnnjj7Wx9bMj2BA6lPBZewc/bu2H9mODa5+mvo/18e1/uL8DG8xHEz/z8GO/Y+t7OKohzBCOAmbODH8ogj+//BoqJdeZo2MAYEE05Nc0L1taWtA3wvd999HU6+anx/VdbwAAY/z4vvXRXlQMfKapiR4vAFiASFjTfo7Gzt+PD3Viw4aOlM+VziXher/1vXdxWOWxHIydJ+39LryyfgNYlvfo6OTH+OD+fdgwou5mK/Tr2IAbr7y6AUpMGbl4FnLFzpvt297HiWLlv4tEAStnRiAUwZ9efg1VKu7Lakk3Dm2x63nbkUPY4D9oXAc04PFor2NEggtB5ICQmJPw1HS4TK4gwYUgCIIgCMIo/vCHP+DrX/86iorib8LRaBSrVq1CTU0N3n33XTgcDvz+97/HVVddhQ8//BCTJk3StK27774bq1evFv89OjqKqVOnoqmpCWVlZRl+GScYDKKlpQVXXHEFrNbsLgTWnD3sxRP73sVAwISm5VdoLsT6k12bgWAQly5ZgjmxItVq+MW+tzHgDqDXC9x8TfaxuO/TzUAgiMsu+aJYo0EN3e8fxbbXD4KbMAnNzWcr+j7aD6JhymQ0Ny9Qvb1wJIqf7n4LgVAEZzUuFVPhZOL7H7QAiKJp2WUJq/LTkTyXDtgOY9c7bbBXN6C5eX7W3x948zBwvA2zZzSgufl0JbuVgMsfwi92b8JYkMMXLr0C5Y74MTz2Thtw9DBmTZ+K5uYzVLcNAKY9PXj1uV3w2yagufnChM+e7foQGB7CBecuwooz6zSdV1uDe9D2USdKJ89B87LZ4t/9wTCi298CAFy5ogkldm2hoccObEHXiA8zFzbi3IYK8e/ejzuBg3swua4Kzc2LNbU9r8+NJw+8j8GgBStWNCWkgNq+bi/QdQJnzJuD5stmyf4+23j1bD2Gra8dAMrr0Ny8MOGzn+9/F/B6ccnFjVg0dYKm/r/YvxNbDg2getZZaD5vivj3owNu4OP34bRZ0dy8XFPbAPD66C68tqcHFQ2no/kL08W/D2zvAI7sR8Nk/jqgZd6UHR7Ai0d3wm0uQXPzxQmfvTz4MTDQj8ULz0Lz4sma+j7lxAj+dPgDDEeK0Ny8NOGz/9jBXyO+dIWya4QcT7RtxcFeF6aeeR6Wzq0W/769bRD47CNUlpegufkLituTjuFibxi/2bsFAwETljVdkZBy8rM3DgInjmLerBloXjFPU989H3di00t7EC6uQnPzuZra2N89BrRuQ7HDnjK+SpjdM4anDm7DQNCacO6pnUt3fvgmgAiaLr8U9RPUxYwikSh+/tkmeAJhnHHBJWJdGRa8MvQJMNCHhWctSDg3lfbrkU/fgj9232vIcN/L5bPQA7vfBgIBXPLFL4opB5Xy2/at2N/jwpQzzsNl86qz/0Al2cZhy0ufAb0nMf+009C8ZAbz7etBcHlrgQQXgjCAhzfsAzjg7hX8Q72QUkxL/ufxRLqX1eoSe457QhAEQRAE8fng3XffxYEDB/CXv/wl4e+bNm3C+vXrMTQ0JAohv/nNb9DS0oKnn34aP/zhD1FXV4cdO3Yk/K6npwcAXyNGDrvdDrs99dnOarWqDhho+Q0LplVa4LCa4Q2G0TUWxMxq9WIJEHepO4tsmvZjVk0JBtoH0e3lFI1FIMS/MxQX2TVtb+4kPv1Me79H0e9DUT6I5rRbNG3PCj79zf7uMRwb8mFWbeb0N6FwRCzsW6JyH4Xxm1vHz3Xl+8j/r9OubS5WWK2YVF6ErhEfjg35sbgsHlyLZT5Csca2AeC02DFr6/fAYrEkiApC/ZlSR+JYqTmv5tSWAejE0cHE8XIF4o6IUoddsyg5u6aEH5tBHxpnx9sPxrLb6BmbWbVlsJg4uANhDHjDmCTJquAPC+dK9vbTjde8DOeLLyg/9mqYU1uGLYcGcHTQm9BGMMKPtcNm1nV9nDepDK/t6cGRpP4HYmOfPOfVzJvT6vmx6Rj0AiZzQnFxXzB2Dju0XReB+LWqzxWAJwRRyAyGI2LspMxRpLn92bUlONjrwrFBn/zY2DRe86xWTHY4UGK3wOUP4eRoAHMk4nhAxbxMx9y62DWhT9k1To5IrFy33aJtjs2uK4eJ4wXnYV8ENUnCl5K5FI1GJfdQbefRzOpifNY5iqNDPsyrn6D69+kQ7gtFGufBjNh97/iwH7Oz3PeA3DwLCTVcijRcc2fXlmJ/jwvHkq5VrEk3DpHYs4jdqu14GIme/hR29JcgxiEjniB+u6UNv32nDSOeWK7CWD5F6ylWBEqg3Dm+LooEQRAEQRCnCk8++SQWL16Ms89OdCwIaQ5MSQ5qk8mESMxd3djYiN27d6O3t1f8vKWlBWVlZZg/P7s7oFAxmThxNayeor96F00JxWh7vcreAXQXsY8JS+397ng+90zbEwvYaw8LzMpQOD2ZgKRPRhSxl8PHYh/TFPj26CysDgANlcUwmzi4/CH0jPoTPvOwKE5eI194WqjfYjObNIstQPx4pGvfoaPvVrMJDZVO+fZjY+PUMfazYteIowOp54tQw0Vf++nGRn/bAEQhObl4N4uxqSsrQrHNjFAkimMDSe0zOLalRVbUlPLC/lFJ/4W2AaDIpmNeph17/ecUx3FpzysW1wSh792jPrj8IU1tCNdarfcuu8UsOha1Fq0XxHUgfaaUbAhjwbqge0iosayxXzOq+GtHu47nC9YI423WkFUn3bUqV8T7fmrFS0lwIQjGhKPxG4s/ViRMEFwKPaUYAKz/3sWYWZVo55Ra63/z9XNy3SWCIAiCIIiCw+VyobW1Fa2trQCA9vZ2tLa2oqMjni99dHQUzz//PL797W+n/L6xsREVFRW46aabsGvXLhw8eBB33nkn2tvbsXLlSgBAU1MT5s+fjxtuuAG7du3CG2+8gXvuuQerVq2SdbGcSqQLBCslGo3qDloJQYxub/bvhiNRUeCxW7QF6yZPcMBuMSEQjuD4UPaNCg4Ku56AvopAjT8YD2prDcAJQtqAO4Ahd0DxNvXsYzqRh0Xg2WYxiSlpUtpnGLw92u9JEBXigWd976fi8e+TDzwX6RQVxLE3IHBeX+6Aw2pGMBxFx2A8T380GmVybNPOm5jNQk/bAMR38hTBJah/3iSKCont+xiMDSAJWkv674vNGxOn/RoBSIRgA84pIL0QwGLelDutqIplEEme90oRawjrWPAr3kM1ih1CHwD991DWQkBAXBCtT3Bp68+PQCGH4HCxaBAt9D4v6SV0ii5QL/zoL0GMMyISwUVQzoOhUyOlGACcObkcm/5jacLfyorigsu50ytAEARBEARBZOajjz7CokWLsGjRIgDA6tWrsWjRItx7773id5577jlEo1F87WtfS/l9VVUVXn/9dbhcLlx22WU499xz8d577+Hvf/+76IYxm81Yv349zGYzGhsbcf311+PGG2/E/fffn5udzCOzdK6MlboxtAZl1DhcpMEprW4M3tkjH6CWQxRcdLyjqHGcCNuzmDjNrgqnzYLJsVoAyrbJwuHCB9eSg1GsAs8z0wQVWbQ/eYIDRdZUEY5V4FmoNXSoRz7w7NQ5NukEHRb9lzrhpGMfCEcQEVMO6XfQdA57xf4CbMQiAJgeC/r2uwIY8Qbj7TNwRgHpr6FxQUdfbENOcJEKFtL0empJF6hndc7OqjH2mjC7RnBo6rt/6Yk/zUojdiruAwPBJX5/Yesk0ZuBRrhmt41Dh4tFwz7Fz3U3opJ4Zq4QFptoceeMZ06tvSGIcUBYYp0UbjLBSPzl4lSkssQm/n+z5MEoEsn9xZogCIIgCKIQWLp0KaLRaMp/a9euFb9zyy23wOPxoLxcPkf4ueeeizfeeAMDAwMYHR3Ftm3bsGLFioTvNDQ0YMOGDfB4POjr68Ojjz4Ki+XUL+Wpd8UmCwFECLj2+ZA1xZcgDOjZHqBuVTJTMaLPlTVQw2J7AGSD5Om3qV9UmpVmTL2MXRxGpOUymTjMrEoNnLIKDM+t5dvuHPZizBcP+vtYOQnSjA27wHbqsfUF4ueqnvYrS+yocFoRjSYGzlkcVwAosVsypuViNTYp7iJGgk5GwUXnvBECyEOeIAYlTjhmY5NOCGTUf1b3L10uoTT7qLgPsXue2cRpThUlFX1YCgGi4KJTCMqXI0SOsI60XDOri8FxwIg3iH5Xducoa8JCvJQcLgRBZEKaq1J4wI9bOk+9U+6mxoaE/ZJe4MN5UMcJgiAIgiAIQlp3Q0ugJmF1rsZneD5lkQnhKJc1xZfw3mDW4f4A4gKIIoeLkG5LYwozAJhZVQKOA4aTApuy24vto95ArRZXjZ59FLZ3fNAjBvoBdsFb8ZhJ9kea1kpPLQtAXjBi5bKY4LSJQf9DkjnHov4MkLjyWgq7sU+tQSS0bTFxut/f5VwirMQoIC5aHB2QE1z09j0upkphNfayggujeeOwmUUnnDQoLqRz0yuSSmtXSReZGu0uUooouLBwLyqoz5WpD3rSRDVUOsUaV71j/uw/UIhYw0Wjo0IQ/XvH/BiVCM35IhqNSlKKqd+nIqsZUyqUO0dZE9KRDm08c+pFfwkiz0hXrwmruIQHzhL7qbea8KvnTk34t0kquJDDhSAIgiAIgsgD06ucMHHAmD+EPg2BGiHFhcXEJTzfqkGasihbSpS4+KHvFV2dw0Wob6J9m+kCm7LbY7SPqurGCK4aHftYXWJHaZEFkWi6wDb71ez+UASCTshqNf6RhMAzu6D/vDo+rdjB7rF4+0JKMUaB7b4xf2LarCBjd5EBDhRp+wljz7B94foiTW3E2l2ULFr7GLk4hL6398fTGBky9gYc22kTnbCaOXiDYXSN+iTts6nPw8rhokcwFGuQjfoS3GuK+xDW77KxW8yYFqtxxdJNEtCZUqysyIraMn11dlgiXfSt1U0026B6OUoQBDA9i03GI6fW3hDEOEDO4eLyhQAAxaeQ4PLOnUvx/K2NOHNyYoqLhJRi5HAhCIIgCIIg8oDeQA0rh7pQ2Dqb4OILMV4ZrcDZIwROWYk8SvdRTwF7NdsD2Ig8HMfJrvb2MaplIYgK0tXSUicNs3oTMoFnvYIIEK/jcqAnLriwSvlVYregrqwIQFLgnFHheeG4tknOF1ap4qTtS+cqK3cRAEyvTJ+WS2/7DZXFsJg4uANhdMdEhWA4IorRTqu+2MbUibwo7vKH0OfiRXEfw3kpJ8yyat9qNqGhMtVNyEqMEvp+bMAjpr9Sg/AbPde9cocV1TH3mpZaJXGXTX7dPnLoTSkGqBP+jUa60FmrS0SNc5Q1oVO0BAMJLgTBGEGdBfgH/Gg0ikO9/MNnsV3/g8N4oaGyGOdNn5jydzM5XAiCIAiCIIhxgJ4AQiDMB870pGQBJMGi/tw4XGZUFcPEAaO+eBAz7TYZpNsClK+MZe3iOT6UmOJLdpuM9lEuuCa6RHQGnsuKrGJaLiF4KwTNbWaT7lW/0tXyyaICCyeBUMflUE9qSjEWDhq51f6sAucNlXEnnJCyiKUYlWnesGg/Ux0Up03fvLSaTZhWmShaeyXnm95Ud3aLGVMq+PbbYwF9VqnoAPl54wmE2LWf8Zqgr/1J5UVw2swIRaI4NuBR/XvRXaL7/qW8XlZKHxjUzwKMqZcSDPHXQT3uGzXCv9GwcLjkU0CilGIEQShCUGcB3sL+wxd3Y8jDr1QqLTp1HC7pMEkdLuoXYxCMGXQH8OmJYdzyzEe464VP890dgiAIgiCInDFLR6DGzyAHPgDMrOIDitlWCLMqKF9kNWNqzNmTLfe+X3TVsHFoZBO2WO1jZbEN5Q6+GHl7FiFLdPHo3Ec58c4IUeFISuBZf8hmeiUvwo1JRDiWLgs5hwvL9pPHPhqNioFzvYFtu8Wc4lRgKUYJfW/vd4uLEVkVVgfSpOVi2f+kdHRCujITpy9YLTA9STBimepOrjYSq5RfQNw5lnhNiM1Lnf3nOE5XAFx0lzASa7UtWtBfwwXQJ/qkQ3S4MEi5Ni4cLmF2Dhctbia9xFOKkeBCEEQGpK4OTyCMv3x0XPz3qZRSLB0JDhdKKZZXguEILv7pJlz96/excW8P/vLRcQxkWelIEARBEARxqpCu4LYShLQ5ugNWkj5kSvHFyokBSAJBWQUQRg4XhcIWq+1JU3zlapuZ0hMxCd4mtc8y8CwV4ZKdCiz6PicmuPSN+THkDvDtM3RxiIHzWN8D4QiEV24Wab+Si8OzFIvqJzhgt5gQCEdwfJB3KrAqrA7Ip+XyMkp1B0hE677UecNx+oOjM5MEF5bnlHCN6Bz2imPO8rySd16xE3T0iB2sFgzoERWCjPpgRKorQXDRE+CPCxT5F1yki771Olw6h71w+0NM+qWUuMPl1JIoTq29IYhxQFCiLg97E4uLlTusue5OzpFe3ymlWH759tMfiavjBK56/D389aPjCGnIBUsQBEEQBFFI6ElFEmAULJpW6QSHKFySlEVysCjuLiBXqFt2m4yL2HcOe8UV3rLbY7iPSlc9s3LViMG1fhciyU4FA4KrLAPPQKr4yKqwOsDXWZk8wQEAOBhzubAcm1k1yX2Pv8cwbd8AMcps4jAzqQYFy/bl0nKxFHTiDpdY2wzdOUBqSjSWYzOx2IYJTt4J19bP/rxKPqdC4Yjo6mDTfmqNGKWwcHAAOkUfVmnNYn3oGY3XuNILiwUVYp2dQY/4vJAvhLib2cRpFkIrim2oLLYByO4cZY0Qm6KUYgRBZEQqMggrfARqSoty3Z2cw3GcKLpEyOGSV9452Jfyt5MjPvzghU/xpw868tAjgiAIgiCI3CEERLpHfXCpXLHJKiWL3WJCVewVIFPgjJX4Acin0pHdJiMBZGKxDRNjgZpM6UhY7qPSQKCw0ltvaq6pFQ7YzCb4ghF0DnsRjUbjTggWToJq+aA/i6A5kCrCsQxsA8C8Ot7lIgouBgT9jw244Q+Fxb5bTJzugDKQ6ghjKUbx7SeKg/GxZxOOE0SLowPsRYsUhwvD4wrICC4xMY2Fc4njuJQ6KyzPK2He9Lv8GPEE4ZME3dmmGczfggFRVBjwiCKO6j7oPEelNa5YpbtiIUjVltlRYrcgHIni2EB+67iEJIKLHvKVJk2IoeqtVzbeOLX2hiDGAVLnwIdHBxM+03sBLBSE/SSHS/7IlLICALYdGchRTwiCIAiCIPJDucOK6qRi5EoJhGNFyxmIA7UO/rksU4ovlinFcp1uC1Am8hiSNk2xi0ffNi1mE6bH6vEc6XPBH4pAeNxm6XARVkuzTH0ESFPbGSPoCHVcDvYkts8ipVh1qR2lRRZEonzgl7VYFBej2AsWCe0bJOgIokVbclouhunW+sb8GPEGmY+N0PdjAx6EI1EDx559jZhiuwX15byafrjPJbYNsBLOlaWjlIOV4DKpvAhOmxkhDaKC0AemoigDISASiYoChZ76MhzH6aoTx5Iwo6LzSuuxsSYYS4l2qsVLSXAhCAZEo1G8faAXJ4e94sUbAN491C/+//9ompuPruUFwcaY7HAJhSN4bXcXfv9uG3pGffno2ueGnceGMn5+iqXHJAiCIAiCkEWp2yOZQCiWcoSJ4ML/b0aHC6PUV0A8ONU1ktnZExdAcpPGzIi0adJi5MlEo1FDxvVwr0sMagNsRIvk1dIsBQsgXuA7XiMmwrT9ubX82BxIcriwCGwnFxAXXRbMHChxJ9yYL8hcjEqpz8O4fdEl0udGMBwR0yWxEC1Ki6yoLYuJ1n3xec9q3tRP4J1jgXAEJ4e9zNtPdo4xb19y3fMxrm/TUFkMs4mDyx9Cz6i6OqxCajO7TrEj8dxTJ7gEGaUUA9jWcQlK6p1YdTuAtD1fsIadw0VZqk7WhMP6BbDxCIXcCIIB7x7qxzee+hAXPbIp7QP/jRdNz22n8ohZEFySXKe/3dKG7/zpYzz46j7802+35aFnnw/GfEF85YnM48viIZAgCIIgCGK8o7WOS0BMOaL/malGjcOFgRgxwWlDVYmQ4itTGjNBjGDoOMm4PXYOlykVTtgsJvhDEXQOeWW/E4pExeLqLJ1DR/rigojVzCatFR/YjAfvjKrhIohw3iAvxLFyWcQdLmMIhCJiAJC9C8XF3AUhdcK19bklYhGbcJl03khT0bF2ibT3u5kLgUDi2HsYpxQzmzg0VPLOsbZ+N/OUZcmBeqH/7GsjuZjXt7FZTOLYqL1/sarhAmgXOwIMBX2WQkBIUnPZqnMVqp46cSwJR9jUQMnX/gQZCUbjDRJciILh+KAH//X3z/KeH1GO7W3x9EzpcluyuqkXAmJKsSSHy18+PC7+/6MDnpz26fPEa7u7s35nf9copXwjCIIgCOKUZ1a1vmCRjUGgvi4muBzJsELYx1D8AJBSqFt2mwxFnllJaZnkt8fObWI2cZhZlXl1sV9SU4HJPlanBp5ZvuNJnRCsA9vJIpwRgW0TBwx7gjg+FH/PY1cHJS7oCX1n5VLg248HdOPuIguTtmdUFYPjgBFvEP2ugKT/7NoH+LRcgqPNxLE5zwD5sWd1XAGpQ8cFj0FCY1vMCcfcvVSTes4acU1Qe//yM0opBihzL8oRYOpw4QVdJg4XSbxO74IKJQsNckHc4cKmZs/RAXdCqQSjCYsp3k4tieLU2hvilOb6Jz/A09uO4T+e35XvrqQgVWLTBbFPtYtHJoTh+PKa99E5zK848wXDKSnGjKRj0INQhM/RKc2n+nkgILk5rrp0Fvbd/yVsufPShO8c6XPjN5sP57prBEEQBEEQOUWzw4VRwV8AqImlFBNSFskhuD/0FncXyLbfoXBEfG8pYuH+qI6n+EoXqBEdLoz2MVtef79ktT/LtGlGBZ6lwVvWLgggcbxYt19kNaOhkg+ct3YMA+DfCVmcP0DifGYdNE9o3wAxqshqxtSKuFMh7oRgMzb1ExywWfi0XILgySqtFZAYcGftvAKAGdVxhw7r82pyhQN2iwmBUATHBz3MXSizDRajdN+/WLpLtC5aYFHDpSYuKgZC+oQAIVbCcfodFdL6T5E8LmYVXDt6HS6TJzjgsJoRDEfRMZi7BdKCCEYOF4LIE8dijojPOkfz3JNEHms5iMc3xQPXHpng/sqzJuWyS3lHuFCOeINY/ZdWjHiC+P/snXl8FPXdxz+zd3Zz3wkECAn3fQiioFA5JGq9W5V6VJTaaq0PPrb1qVK1ttaj1qO21lqsbbFae1irqERQQUCQI4DcuTlyX5vdTfZ+/pid2Znd2WSTzMxulu/79eJFdnZ25jfHzsz+Pr/P5zv7Z+U4HcHuLze7qttwya8/x2snNHjyoxOY8tMPcfhslyrrjge2HGvm/0426pFk0GJUwI4s5FflJ9RsFkEQBEEQhOpwncx1bY6ITnQpuHnl6Kg364Ac3l0g7QCRs6A8IHRj9L0+QB4BZER6oGPT64v4zC/7NvYTddMr6HSUo/N5bKDjsd3uwtnAoDJlRrPbZS+sDoj3V09A/JKz/VwdlwOnO/lly9Xpz3X6VrfY4XB5+OXLhdC9pIzYFXRjyS3oaDUMxgR+6x1pYH/zynreCM5LJfYN51SraXPILuhoNQzvoOHi7uRcPicEnGp3oKvHJeuyATkcmvI6XPwDGEDrkjHWLD/VxNe4qm8fWuKN2xt0Uwz1+jQq0wydhkGP24uGGNYo9soUyaXRMPx9rirCs4oS8A6XBCs0nFhbQ5wTZFoMsW4CT4/Li+c3nxRNa5S40L54wyy1mhQXCC/0e+o6sPlYk6QQpRR/+aIOAPBVhwbrd9TB5wf+ve+MauuPNULBJUnwA/q1b58Xi+YQBEEQBEHEjII0E8wGLTw+Pz+AKxq4Diu5irj2l0EvZ3F3QDxiX3p9QcFFjhHIbEdNP44Tmbex333qlnd9ZoMOI9JZu9JXZ9lBgEq4LKpabLArEE9UKjg+Sgg6EwJ1XA6c5jr95YnMAtiOTb2W7djkOgIVcRIIa3HI5MQSLV8hQYcTFY42dANQ5rysa7Ojq4d16JlkjRTj3HHKupe47ywg377PSTYi1aSDzw8cCex7Wb+zg3S4yFnDZVSmBVoNA7vLi0arM+rPySn6CGtcDbW+COfAlOO+p9dqMIaLtoxhHRcuUkwnw/NKf85RJeAcOlqZnrfiBRJciGGHHBdsuXj5s6qwac0Sgosmwaxx/aFhxBFrco1ii5a8VFPYtES7eEeCewjmyE8L7oslE3Kx4Y75ajeJIAiCIAgiZrAdNQPvQJAzfx6AYNRo3wKI3GJEXZtd0tnDiR8GrUa23yr9FVeWexujX5/8LpHDZ+R3EozOYkdLO1xe1LayooKcHc8lueFOBTmXPy4guBwNdGzLFZkFADqtBmMCkWX8vlegY7u+zYHu3oCDRgFBp6pFEMsl4/K5Tt8j3L6Xcd/kphiRYmRFhWMKiApjsll3zumOHv63rBIOHe68AeS7BjEME3ZNkFOM4u4bzd1OWCPEUUrhktGhadBpMDrgoKpujd71IKfgAsgnBHD3QznECaB/4V8N5HK4AP3fV5XA4wsIhAnWbxo/PdcE0Qe1ggu7XKPM5OBYY3i8WYstetU/UQm90Mv1QBMtf9pRGzatzeZStQ2xolVw/t1wXhGWTc4Xvc89jHO8LrGvCIIgCIIgEonBdCDIWXQYiKbeCFffRJ7OusK0vrPY+fXJ+JxeqvI2js1OBsMAHQ432iR+g8kt8ADBzrVDCnT667XBjs1DCgg63PegttXO1xKSs/0T8lnBhevslXPZQLD93L6RUyzKTzXBEnDCHWuQ373Eff+PN3bzkUZKxHJxjjY5zxuGYTA2ZN/L2facZCOSjTr4/WwdF7mXH37eyCcyA8HrXnDfyHe9STXpkZdqBDAwB4Wc9VMAcazcgNsg1z00d+BtkMLlkbdAeywEilCCgoUcNXvUdbj4fH5w5W+ohks/bN26FVdccQUKCwvBMAzeeeediPPeddddYBgGzz33nGh6e3s7Vq1ahdTUVKSnp2P16tWw2cQH++DBg1i0aBFMJhOKiorw1FNPyb0pRBzx9Kbj/N+6OMr1a+4Of7BvCUxbPjkPJTkW3L9svNrNijmhowWUPmSvbqvGq9uqAQBdDjev8AuROlaJSLudFZZGZ5nxy2unh920RqQn4ccrJ/Kvf/ru4QFlsRIEQRAEQQw3hBn00eLmY0fkEgf6Ljwsd9yWKItdYrt5MULGzkGunoFa25hk0PIRX1KdQ3ykmALRUNxvC6VEBW75Zhk7zgtSg/F6HQ637Msfk2URDY6UM1IMkNj3MjpohE4FJY4t14kp/E0qp6DDxXJxv4PlXDYQFBWC+15eQYeLROPaL+d5Gf6dUvi8lHnfD8qhKbPYwW1jpBpkUrhljO4C5He4xFu7hoJSDhc1+ok8gr47nUzHJF6QfWvsdjtmzJiBl156qc/5/v3vf+OLL75AYWFh2HurVq3C4cOHUV5ejvfeew9bt27FmjVr+PetViuWL1+O0aNHY+/evXj66afxyCOP4JVXXpF7c4g4IVtQt8U1gGKXStMskWHJ2Xgn5Kdg8/2L8f1LxqndrJgTOlrA5ZE+ZnJcwDsdLjz+/lE8/v5R2JwePPbeEcn5pKLeEhHOydNXraMVU8Sul0+PtyjaJoIgCIIgiFhS0k+clxTyx6Gwbahvc0SI+FLAcdJHHZeg+KFAbFKE4spKbqPUqGdFIsUCnWsccnY8Sy1fzo5zoQjHIWf7DToN33EOyDvSHwjfN3J3nCu5/AyLAVmC32caRt7vQXFIioGcggUQFFM55BYVQtsvrxhlgbA2ulKCCL98hQSdgTg7nHwNMrkdLoOI5VTASTKUfqRgfRu5ozTVKzIfClcDRY6YtDHZZmgYoLvXww8mVxLOnQMAOnK49M3KlSvx+OOP4+qrr444z5kzZ/D9738fGzZsgF6vF7139OhRfPjhh3j11Vcxf/58LFy4EC+++CLefPNNnD17FgCwYcMGuFwurF+/HlOmTMENN9yAe++9F88++6zcm0PEAe8dPIuK08G8zR4Vi69Hwuvz4/xfbMaZzp6w97gCh3I/AA4nQm+qzgiCC2enHgpt9mBUmK3Xg4OnOyXnU+NmEQ9wDpcsizHiPKEPmftPdeKTY8043titaNsIgiAIgiBigbBDJNqOmmAkizwdAPmpRj6yqK5NHXGAG5Ve1dzX+uTrEhiTZYGGAay9HsmYZW6dSkQ1STpcFBR4OJRyuCi2fAUFHQAYH6jjAii/b2R3cYTuexkdNEAwEglg9w3DyNe5mJ1sQIox2P+g9Hkjt9AYKrjIuXyTXouiDLPgtbzHVenv7GAcFG6Za5ANRfSRqw3CGlcNXYMfTMv1QcklRo0NHJ9WmxNdjujr7MiJR0aHi1GnxahM9vsSya0qJ2KHS2IJLqr3CPt8Ptx888144IEHMGXKlLD3d+7cifT0dMydO5eftnTpUmg0GuzatQtXX301du7ciYsuuggGQ3CEwIoVK/Dkk0+io6MDGRkZkut2Op1wOoMPflYr60Rwu91wu2PzxeDWL/yfCHK6owf3vLFfNM3mZI+X3emBxajuKcwdo6YuOxr7cUzMLko9Z49pqDLd45TeD3WtVuSlmIb0QNXUGcykbrf1RPxBZe2N7fdcLVqsrAiYYdZF3F49IxbA3jtwFi9sPoniLDM23bdQ8TaGQtfA4QMdq+EDHavhg9rHis4J4lxkVKYFWg0Dm9ODJqsT+Wmmfj8jt8OFiyw6eLoLlc12lOamiN7nHSeyRnz14XBxy7t9QKBjM9OMujYHqprtyE0R72e5I8WAvvPzlVhflsWAtCS9IsW9AYnR8kqPxpd5+RPyUvAeGgDIPwAxzJ0j+75RzmXBLj8Zu2vaAch/3jAMg+IcCw6elr/GCiAWi5RYvhrHlqtlJfe+H5mRBINWE6xdJLNQF4zzGoS7RKZrH3d8WmwuODxRtkHmeyhX46qqxY6qFhsKA3GSA4UTo+SKr0o26lCQZkJDVy8qW2yYM1q6P1pJvAGXiFwOkZKcZNS2OVDVbMMFJdmyLDMSHsEg7HgqHyEHqgsuTz75JHQ6He69917J9xsbG5GbmyuaptPpkJmZicbGRn6e4uJi0Tx5eXn8e5EElyeeeAKPPvpo2PRNmzbBbDZLfEJdysvLY92EuKPeBoSepl09Hox7eBMA4LbxXszKUr/+xOZPtoa1K5TTB3eg8St12hNv2KxaAMGL/b4DBwGEP9gse247xqf5cPfkwcfE7Wtl+GWXf7IVze3idXO4vX78972NSLBYSBF+P/D8F+z2dzScwsaNdZLzsc8+wfO3OlCcsKbNgY0bNyrf0AjQNXD4QMdq+EDHavig1rFyOMKLZxNEomPQsR011S12VDbbohJc5B4hDLCdGAdPd0mKA71uBRwuIRFfwhH1vYH6Jkp0Kte1OVDZYsOCkizRe0psY58OF7f8jhqGYVCam4y9dR0AVOjYlrlzWDgaX6thZIvV4RgncLjIfW6ZDTqMSE/iUybkjixT070k974BWIcZJ7iYZD5vRmWaodcyvDtA7vaPyVJWcCnNTcYngRhtuZet07JResebuhVZPnfe1LU74PL4oronBR2a8nxHUk165KUa0WR1ojk85EWSYHSXvPfQqsB9fNG4nEEtI1jDRb5rX2luMhq6elHVHBvBRU6HC8Buz+ZjzarEpHGRYgwjX/vjBVUFl7179+L555/Hvn37ZLVPRsuDDz6ItWvX8q+tViuKioqwfPlypKamqt4eDrfbjfLycixbtiwsYu1cZ3dtO3BoT8T3/3RCi5/8bLlq7eGO1dzzLwD27+an/++ycahps+Of+87y075+eZlq7Yo3NjR8iVpbB/96/MTJQPVxyXlPdGlQVnbpoNbT3evBA09+CoC9SE+bPQ+6U0eAXmn30cWXLENqUmJ+x9rtLjy16QR8YM/BeTMmoeyC0ZLz+v1+3L9LumNvxaUrVb/R0TVw+EDHavhAx2r4oPax4hzeBHGuUZKTjOrAyNiF4/ofsemUOX8eEAsgYetTwOEyOovNYrc5PWjudiIvNSg0KRG3BbDbuOVYs2rbyO3TM5096HF5RQJFrwIOF4AdLc8JLnJ3PCcbdchPNfFpCrILYgrGWgFsHVN++TKP9AfYkfa84CK7qMA64bgC1PK7l4T1beQXXMT1c+RdPususPDCptz7ZkxIpJj831llxa6S3KDgIvfyc1OMSDbqYHN6UNdmF4makZDbXQKw+7DJ6kRTT3TXDCXaUJqbjE1HmgZUSyYUpYSgbSdbVYngkoK7ZsnlEBlMjN1g4evPJJjYAqgsuGzbtg3Nzc0YNWoUP83r9eL+++/Hc889h9raWuTn56O5uVn0OY/Hg/b2duTns4We8/Pz0dTUJJqHe83NI4XRaITRGF7XQK/Xx0VnRLy0I55w+/r/0sVin3n94gvZPZeMR7O1lxdcfrtq9jl9LI0hDxm9nr5dSIPdV0+9e5S/kQNAR48Xrj7qwrj9moQ9Lt/725f8Dz8A+PrMkYPaVpefQWqM9hFdA4cPdKyGD3Sshg9qHSs6H4hzldLcZJQfaYq6A4F7xtTL2mHFdir2FfElZ0ejUafF6CwLalrZEcFiwYUTP5TpVJaM+FJgGzMtBmSY9ehwuFHVYsPUEWnh61PQqSB3xzO3fE5wkbvjfHSWmRcVlOh4HpVphlGngdPjU6SmaWku27EJyN+xLXTCAco6XJQ4b8YqLOiU5iQHBReZl5+WpEd2sgGtNhdMeg00Mne+loYIjXIjrHGjRFxcSW4yDpzqRGWzLSrBhXMiyT1gYEdVW/SCi1f+670cQoBL5houQFDIlhpooAacaCHXwFk+jlSF7ZFbLIonVN2im2++GQcPHkRFRQX/r7CwEA888AA++ugjAMCCBQvQ2dmJvXv38p/bsmULfD4f5s+fz8+zdetWUQZ1eXk5JkyYEDFOjBiecLbzvrA7owyRlBFutJSQ3FQTXrvtPLy55nyUTStQvU3xROjNa3ctKwQsnZSHj9deDItMDyFvfnlK9Pr+tw+gVaJAZ4qJfdh3uNQ/V5TG7/fj7T2nRGLL/y4f329Uxub7L0ayRA0kW2/i7SOCIAiCIAiuoybakbFyR7IA4RFfQoKOE3UEECUdLkAkF48y2xipjotS2ygcLa9E563ICSFz562wILISDhSthuGPhyJOAsX3vXJOiMK0JL7NSuwbkcNFAUGnJFcdh47Sx1WZfaPONWHA9y8F6mU1RRkppug9dAhRVx6vuoMp1CAoWsgUKRb4vjRae2FTuL+Vr6mTgA4X2e+wNpuNF1MAoKamBhUVFaivr0dWVhamTp0q+qfX65Gfn48JEyYAACZNmoRLL70Ud955J3bv3o3t27fjnnvuwQ033IDCwkIAwE033QSDwYDVq1fj8OHDeOutt/D888+L4sKIxIDLFu4Lh6v/eeRGKATNL87k/14yMRfnj82S+sg5hSbEmr71BJuX+o25I1GamyzbiJUUCcFACnPgoSoW54rSfHS4CQ/846BompSQEkpJTjJ+dtWUsOmxEDAJgiAIgiCUpnSAIzaVqOHCRRbZXV7ewcChRIF3IPJIVadbqbgtdn1nu3rDnisV28YcaZFHqfWp4XDhMCvRORzYX2a9MoEnUwtZl1GWxSD7ssX7RhkHDQBoGPnPG42G4V0oShzXMQpGigHKn/ec4KLEcc2wGPjzUcnvlFLLH8j9y+/38+4SuSPFAAzA4SK/k4T7/rR0O9HV4+5nbmn4SDEZO/i543Oq3RFVH6bcyF3DJc2sR3Yymw6ltGuHF4tkricWD8guuOzZswezZs3CrFmzAABr167FrFmzsG7duqiXsWHDBkycOBGXXHIJysrKsHDhQrzyyiv8+2lpadi0aRNqamowZ84c3H///Vi3bh3WrFkj9+YQMaYp5IeIVAEqrsiSmjgFDpeXvzVH9fUPV0YHiuF1y+SiCC3EGQnuoa0nBjc/pfmiui1sWoopuriYEenmsGmdg3xwIQiCIAiCiGe4jprmbiesvf0/77gUcEdwkUUAUNUsHqHLuTFMMhcCj+TsUcptkm42IDuZ7disFoxC9vv9wXUqVOw8dNSzUts4MsPMd2QON5cFEHQqyF1YneP+5ePxsyun4No5I2VfttipIL9Dh1u+EvVtQpcvN6mmYCepIi4OhV0ixdmcM0qZIB6u/Up8p4Rxbko6u6JxdnBiCyCv2MFdZ9t6g9fWPtuhgMsmxaRHfiAac7B1XJQQgnKSjUgx6eDzA7VtyheaD8Ub6BOVU7QozR2Yq2qwuPk4tMSLFJNdOl68eHGYPbovamtrw6ZlZmbijTfe6PNz06dPx7Zt2wbaPGIY4fH68MQHx0TTxueliKKT2PmiP9/kgssDnleciQwFRu4kKpkR9pXP5x+U6yUaAcWo0/APtInocPFJXG+TTdFd2jPM4cJMm8015DYRBEEQBEHEG6kmPfJSjWiyOlHVbMOsUX1HUbsUKKwLsFEd1S12VDZ3Y+G4bH56sL6JMnFbYQ4XhcQPgO0cbLW1o7KlG9NGsm4Ht9cP7rFV9ti0iC4eZbZRq2FQmpOMIw1WPrpYTkrzkvn1KNExPyFQAyJVgbYDbNT2zQvGKLLs7GQDMi0GtNtdUQ8yGwjcvlFi2QAwIT8FOABFzhuAjTZqtTkVWX5JTjI0DODzA8kKuFC4WCal9v34/GTsrm1XZPlmgw6jMs2ob3cosnzhdby/vhO3oH9MzgEDuSlGWIxa2J1e1Lc5MHlkeH1sIa7AIGU5BRcgWOOqstmG2f3cx6VwK1CfjWHYKMX99Z2oarZjYn6qbMuOhqDDRV5H0xfV7YrXceEcLvoEdLgoc5UnCBmQckHkpIRf1LmLi5pwHf1KPAAnBtLHJF2igx8A3D4fjJqB78u+nDJXzijAfw404PYLR+PL2k4AQE8C1nBps4cLJMKCqH0h9TAoVQOHIAiCIAgiESjNTUaT1YnKaAQXBUbnAgFx4EiTaKQy6/5QNm6ryepEd6+bf/5Tan0Au427atpFLh5hQoDcI9i5vPmaVju8Pj8fq6LkNj58+WR8dqIlasf9QMhNMeFnV06BTquR/fwDgLJpBTjaYMWlU/NlX7bSMAyDJ66ZhmMN3RgniLiSi6kjUnH/svEYlyf/sgHgm+cVoc3mwg3zihRZ/o9WTsRHXzViyYRc2ZdtMerwy2unw+H0IC3C7/qhsGRiLr5z8VhcPD5H9mUDwJ2LxsKg1eKG85TZ949dOQV7ajswsyhd9mWPzjTDoNWgx+3Fmc4eFGWGJ1VwuATuEznrpzAMg5IcCw6etqKq1Y7JI6MbtCD7PTTHgs8rWwcddcVHisncwV+SwwouahSaD0XuGi5A5NpocuMOuHPkikOLJ0hwIeIWYWH6BWOzYHN6RAUEOTxe9SPFuLzISAICEc7UEakRRwh6vH5EWY6Fx+/3o+JUZ8T3n7xmKorcp3HnRcX46ixb46TiVBe+NjFPkR8usaItIJBcPD4HN84bhe5eN2YERhL2R2pScKdnJxvRanOSw4UgCIIgiISlJCcZ2yvbBhTLImeHFRAUB4SdMh6fHz6F3B9pSXrkpBjR0u1EVYud7wxUylEDSG+jU6FOQAAYkZ4Eo04Dp8eHU+0OvpaFUpFiABttrITYwqGUQwRgI49+ctlkxZavNCum5GPFFGXEIoZh8P1LximybID9zbXuCuX2/exRGYMa9R8t35irjFgBsG7CB1dOUmz5o7Msiu77xRNysVgBoQsAdFoNxuZYcKyxGyeauqMSXHQaRrbauRwl2QHBJYp7qNvD3tRkv4cOUQjgBmzrZY6wUkugkELuGi5AcLCGeg6XxOmj40i8LSISBq4wfbJRh7+tOR/v3nOhpDXWHYNIsQ4HK7hkmClOTAqpC/2icZFHqrgHIZq9uKWy3zaMS/PDbNDxhete/qwK4x/6ACt+vTUmxcyUgBNIvnPRWFw6NR/Xzy2KOm9Y6NDi3GPkcCEIgiAIIlEZSOFhRR0uACoFnTLC51JlIr4CWeyC7eYGtylRL6GvbTTqNLLXxmCLkUcWeZSqCUEQBKEWE/LZuLvjTd19zsfdu5TowI5UE0yyHYo5XIYmBPD7Rye/wwVQXqCQQkmHS12bY1D9ddHCLTsRHS705EHELdxDOfeAzDAMkvThgovHp77DhRNcyOEijU5itIClj8J6rkFcwJ8tPxHxvT/eOlf02hySMXu8qRsfH20a8DrjES5SLCu57wxVKRiG4R+ALpmYG1geCS4EQRAEQSQmXIdIdTSdRQp1WnHiR0u3k3fNC90fSsRflUoIIIo6XPiOGjvfmRJ0myjTBSE1ujgo8lAMNEEQw5vxgfpCJxr7EVwUEjoAYGzAPVjdGoVL1KOQSzRwra9vd4iiKqPFrVR9tkC7qlvZOjtq4vHK73ApSDPBbNDC4/Ojrs0h23JDUUIsihdIcCHilqDgEnxANgs67bmLSSwcLs3dvQAG18l9LiB1oU/qo7DeYI6h8MadZRE7jS6ZlBey7sT8keXx+tDhYAWXTMvg3FZf/t9SfP6jJZhYwD7AtXZTpBhBEPGNtdeNf+49nTBOReLcZevWrbjiiitQWFgIhmHwzjvviN5nGEby39NPPy2a7/3338f8+fORlJSEjIwMXHXVVaL36+vrcdlll8FsNiM3NxcPPPAAPJ7Eq2sXDbwQ0O4Q5dxLwXVayS0QpJj0yEtlf0Nw4oBT4KaR2/0BSI+8VVIAKUg1IUmvhdvrR30721ETLGCvzHM5J2SptY0EQRBqwgsuTX0PGFDKnQkEr7PVLfZ+RQWl2pGTYkSKUQefH6htHbgQ4FYoLrQoIwkGrQa9bh/OdPbIuuz+8PqCMXJywdbsUT4mjROLdDLX1IkH6MmDiFt6JAQXYcc555iIRQ2Xow3sqIKJAVsnIUbqQt+Xlf8PW6sHvhLBKv707Xl9zposUSAmERT0f+0/A78fSDHpBi24pJn1GJlhRnoS+3lupCVBEES88lz5Sdz/9gF85y97Y90UghgSdrsdM2bMwEsvvST5fkNDg+jf+vXrwTAMrr32Wn6ef/7zn7j55pvx7W9/GwcOHMD27dtx00038e97vV5cdtllcLlc2LFjB15//XX86U9/wrp16xTfvngkN8WIZKMOXp8fdW19j9B1K9hpxbsxAuKA061ccXfR+oQOF66gvAJxWxoNg5JccYyZkgXsgQjb6FZuGwmCINRkQkBwqWyx9dkHplT9MQAoykyChvGjx+1Dg7U34nx+v18xpw3DMHxs5WCEAG6wr9wOF51WgzHZbG2dSpXruARruCjjyFUyJs3DO1wS7z6deFtEJAzcKChhR72wk5zrRPeobdfzAY1WNnaJU3wJMVIOl76s/H/aUTvgjv7CNBP/99QRqTiwbjlmj0rHo1+fEjbvyIyksGkaBUYPqs0P/3EQAFsMdaj20eRAfSSb89wc8UoQRPzTZnPijte/xPrtNQCAz060xLhFBDE0Vq5ciccffxxXX3215Pv5+fmif//5z3+wZMkSjB07FgDg8Xjwgx/8AE8//TTuuusujB8/HpMnT8Y3vvENfhmbNm3CkSNH8Ne//hUzZ87EypUr8bOf/QwvvfQSXK5zz9Uq7KjprwNByVgW3nES4nBRKvqKW19dW9DZo9Y6w7dRmS4IoYvH7/eHrDMx3e4EQZw7jMxIQpJeC5fHh7r2yM4Ol4LXWr1Wg5xAN0xf91BhgomStWQGIwRwDhclHBWhgynUgo/lknmbpAYyyA0nHibCgOhQImf8EESM4SPFdNIOF3NAcFGygJMU1oAuoNcyyKAaLpJIXej7+7HaZnMiLSn6/cnVZXnkislgGAZpZj3+9b0LJeeVEsaUiGtQE+6HJADMGJk+5OWlBASX7l5yuBAEEX/4/X5c+vw2tHQPnzpTe+s6UJSZhNwUU/8zE0Q/NDU14f3338frr7/OT9u3bx/OnDkDjUaDWbNmobGxETNnzsTTTz+NqVOnAgB27tyJadOmIS8vGLe6YsUKfPe738Xhw4cxa9assHU5nU44ncHvmtVqBQC43W643dE9J3DzRTu/mozNSsKBU5040WjF0onZkvP4fH6+w4jxeYe0HVL7ojiLHQxU2dQNt9sNey8rfhl1jCL7LNushcWghd3lRVVTF0pzk9HrYgfZ6Bi/IusszmJH+p4MbKMjsI0GrWZA64v2XCpKM4BhAGuvB42ddmQnG/nfk1rGF5fn4kCJ5+9VPEL7i4X2w9CJl31YmmvBoTNWHD3TiVHp0vH2PU72WqvTyH8/cbvdyEvyo6mHwYnGLlxQnC45n10wiFPj90Lu3cbdQ082WQe8jZzzUQv5731jMrn7nlX0zKT0eeMKOEg1Mm/T6Ezxs8pg6Ws/ON3suaLVxP77JcVQ2kSCCxG3NHRxdVKCUUkFaUlYc9FYmHQafmSrR+UaLtbAYMCcZOOw77RXipEZ5rBpwhEWRp1GVBwUYIu/j82Jfh1cLMHEgtR+551fnIkbzivCm1+e4qe9sasOyybn9fGp+Ea4/9ZdMXnIy0sxBh0ufr+fzm2CIOKKymabpNhS02pHcaCAZzxR2WzDtb/bAQCo/kUZNAk4aotQl9dffx0pKSm45ppr+GnV1Wwk6yOPPIJnn30WY8aMwa9+9SssXrwYJ06cQGZmJhobG0ViCwD+dWNjo+S6nnjiCTz66KNh0zdt2j923N0AAQAASURBVASzOfwZry/Ky8sHNL8auNsZAFpsPXACYxzHpOfxAdxP5U+3bEaSDL+ahfuipYttw8HaZmzcuBGVVnZ97t4ebNy4cegrkyBTr4XdxeDtj7ZhRpYfjS1aAAy+OrAfzCn5f091tbHbuO/kGWzcWI9Dgf3eY7MOahujOZcyDVq0ORls+O8WjEvzo72T3cb9e3aj+8SAVxm3xOP3Kp6h/cVC+2HoxHofJrk0ADR4//N98NZJX7cPdwSutfZuRe4neUlsv84ne48it+Ow5Dw2N8DdQz/e9BHkfgzuDNxP9lc1YOPG0wP6bG09uw8rTx7HRrv0M8Bg6W5h27Xn+Gls3FjHT1f6vKmsYbeppqoKG90nZVtuowMAdDje2IX339+IoXYRSe2HPYF91tHeptjzz1BwOAZeJ4iDBBcibqluZW1rnI2N4//KJgEAtle1AQA8PnUdLjY3e5XJTpEeUUAAqxcWo6bVjn/sDd78hA6XjT9YhI0HG3DLgjGY8dgmAEDrAEctD6SQqU6rwUOXTxYJLp8cH95RNMJRI9nJQz8XuUgxnx9wuLywSNS9IQiCiBWdEWInVz6/Fcd+tlLl1vTPiaZu/u/PK1tx0fgBjCggCAnWr1+PVatWwWQKOqZ8gWfgn/zkJ3xdl9deew0jR47E22+/je985zuDWteDDz6ItWvX8q+tViuKioqwfPlypKb2P9AFYEcElpeXY9myZdDr48sRrj/SjPfqK9BrSENZ2QLJebp73cCuTwAAl69cMaRC71L7Yo61F789shXtLg0uWb4MKbXtwOF9yExPjdimobLFcQinDjQgfdQElF08Fr+v3QnYunHB/POwaJy002cojGuy4bUTO9Dm1mPlyuXwf9UEHD+IvJxMlJWdF/VyBnIu/bttHz490Yqckqkom1eEp45uBXp7cfHCCzFjZNpQNynmxPP3Kh6h/cVC+2HoxMs+PPt5LXZ/dAJIK0RZ2QzJeXRHmoBjB5CTlYGysr5r3Q4Ut9uNL9/4GADgScqKeC1vtPYCe7ZCp2Fw+WVlsrYBACa12vHq8e1oc2lx6aXLBzSw6aO3DgCtTZg+dQrKzh8la7tGn7XiL5VfoN1rQFnZEtXOmy/ePQI0nsaECeNQtqREtuW6PD48dWgznF5gzqKvIT91cK79vvZDz74zQOVhFOTmoqxsthzNlhXO5T0YqEeNiFs6HWznSqTOZC7jz62Cw8Xu9IBhAD0D2AL93IMtUn4uYNJr8cz1M1DdYsO++k4AYmGkJCcZ379kHABgyYQcfHK8Bd29A6sdwtX4iTZX2zyEH8rxiN3JOnyS9Noh128RLsfr88Pm9JDgQhBEXMHFwoRPV3fQRbQI405vWb8bJx5fqUgdCOLcYNu2bTh+/Djeeust0fSCggIAwOTJQaer0WjE2LFjUV9fD4CtA7N7927R55qamvj3pDAajTAaw5+/9Xr9gDsMBvMZpZlQyHa8V7c4oNXqJDtq/M7gd9hsMsriUhPuixGZOiQbdbA5PThrdcHrZ68PJr1Wsf01Pj8VONCAmrYe6PV6uAK/ocwmgyLrLMlPhYZh3dMdvT54/Ow+TDLoBrW+aM6lcXkp+PREK7+NTg+7jRaFtjFWxOP3Kp6h/cVC+2HoxHofTgrcvypb7BHb4Q2U6jbqlLmf5CWx19Xq1shtAMP25em1GkXaMDY3FXotgx63Dy0Oj2TCSiS47kPTIO9FfTG+gD0+HQ43ul1+pBjY5St93vjB3l+Nenm3Sa8HRmeaUd1qR127E0VZKUNcXvh+8DPs+arXKXOuDJWhtIl++RFxC9e5khSho5wrvsUViFIKl8eHWT8rx/xfbIbP54c9MMg200yCS3/oNMIYMenjmGxiL2DdAyzWHnS4RCekJFqcy1dnuwAEnSlDhWEYJBu5Oi4DOxYEQRBy0+v2oqbVLngdn8JKJKwh19FDZ7pi1BIiEfjjH/+IOXPmYMYM8WjWOXPmwGg04vjx4/w0t9uN2tpajB49GgCwYMECHDp0CM3Nzfw85eXlSE1NFQk15xKjMs3QaRj0uL1osPZKzuMSFHFV4hmSYRiUCIrrclG5Jr1yP89Lctj4xSq+iD23TmUGJRl1WozOCqxTsI1KFHLmCC3wq/Q2EgRBqMmEfLbDu6bVzl/fQnEHosf1Cl1r89iyHmi1udDpcEnOw8WfKzXYSKfVYEzg/lI5wAL13IBtvUb+tpkNOoxIZ3eQkoXmQ/EE+kTlGIgbSknIfVVuuLbrFDgesSbxtohIGLjOlUgPyFxhduEoUiVo6OqBy+NDd68HNqcHNg+7XnK49I/wgh/pxxXXyW8bpMNFyR9t8czB02znnU7Gm2qyoI4LQRBELLnlj7ux5JlP8Z+KMwAiO1wAcXxXvBB6TzvdMfj8XyJxsdlsqKioQEVFBQCgpqYGFRUVvDsFYKMM3n77bdxxxx1hn09NTcVdd92Fn/70p9i0aROOHz+O7373uwCA66+/HgCwfPlyTJ48GTfffDMOHDiAjz76CA899BDuvvtuSRfLuYBeq8GY7KAQIIVL4c4iACjNYTsxKptt/O+eaAcSDWp9AoHH7/cL1qmkyBPYxhb1txGg3wsEQSQW+akmpBh18Pj8ooFJQrgBAwatMtc9oxbIT2WfHyJ1wqtyD+WFAOn9EAmu/1CvU2ZAbknIfUgNvLxooYDgInhWUQJP4HhotYk1QBogwYWIY3rcfY9I4hRQu9ODg6c74fcr43RxuIKdPDanJ1AADMggwaVfohFczAb2+P7lizrJ96Xocrj58yPVNHiLn8szvEZMC3lnP9sJefn0AtmWmRJwyzQLRnvuqGzFZyeGd70bgiCGF3anB7tr2wEAj/33COxOD36x8SgAYF5xJt6/dyGKA52lAFA9wB9aatDdK64584M3K2LTECKu2bNnD2bNmoVZs2YBANauXYtZs2Zh3bp1/Dxvvvkm/H4/brzxRsllPP3007jhhhtw880347zzzkNdXR22bNmCjIwMAIBWq8V7770HrVaLBQsW4Fvf+hZuueUWPPbYY8pvYBxT2k8HghqdRSW5QceJGu6PUZkWaDUM7C4vGq29cLqVXye/jSo5XLiOobNdvbA5PQOq+UgQBBHvMAyD8QGXy4mmvu9fqojpke6hCos+0bQhEtz+0SvUNs5NqpRAIYWSDpdQ56jccGKRPsESaQASXIg4xOvz4+MjTWjo7AEQ2VqvDyigj/z3CL7+m+0ofnBjREvjUOgQLLO714PdLWx7skhw6RfhBT/SD9bdNWynWqvNGfVyf77xCP93mnnwgktoh9hwoabVzhaiA1CQliTbco81sqPEH/0vu39dHh9uenUXbl2/G12O4bmvCIIYftS1Bd0gbXYXXtxSiYYu9pqXaTZgSmEa/v29C/h5etzx58rrkLhmehR25BLDj8WLF8Pv94f9+9Of/sTPs2bNGjgcDqSlSRf81uv1eOaZZ9DU1ASr1Yry8nJMmTJFNM/o0aOxceNGOBwOtLS04JlnnoFOd27XahOKHVKo0VlUKnB/8E4MBaOvDDoNRmexOfeVzTY+8kXJdUpvo3L7NN1sQHYy+xvtaEOw0K2S20gQBKEm4/MCgkujtMNbjQEDY/mISulBT5yLRB2Hy0AjxZQVXJQWKKTw+oIxqHKjtIDERbxpKVKMIJTnzztrccef9+BsoHMlUg0XncQF8i87o3dJRIPb68NNf9jFv95e1cb/TVnA/aMTOVyk99dN80eJXrfZnKhr63u08vbKtj7fj8RDl03CpIJU/vWGXfV9zB2/HDjVyf9tVyD+60xA7BQKUm326AUxgiCIoeBwia9rwh8s3A+3dLMBK6bkAQBqW+MvrqtNYhBBXXv8tZMgzlW4DpH+HC5KdcgAwtgRO+/cVtqJwY0IPtkkEFwUdbgE93Nwfcr+hhob2MbDgtpZ5HAhCCJRGJ/HXuOOR4jUdfGCgnKOgf464XnRRwWHy0Cjuzg3iFJtE0ZpqoVHQdGCu483dzthVWDAMicWKXm+xgp68iDijn/tOyN6HUnYkLKcyV3U8lebTohef3ailf971qh0WdeViDCCwxFpdMOyyXn83z6fH3Me/xgXP/2pKNYqlHnFmQCAOxYWD6g9dywaiw9+sIh//Wz5CeyqbkOPK3JtgHik3R50XY0KjFSUg+e+ORNA8CFOWPS5q4ccLgRBqIM95JpsMQSfAzw+n2A6O0L/+c0n8UQgckyItdeNb7+2G//ef1qhlkamLXCdXi24Twmv3QRBxBa+oybC6Fw1IllGZZqh1zLocXv5wUZKCwOc0HSsUeD+UCF2psnq5IVotbbxSMDhomGUGfVLEAQRCyYEHC4nIwkuakRiZvfjElXRZdNmd6FjAM/YXNt0CnXwc/eg0x09fdaglBMla7ikmvTITQnU7FHA5RJ0uCTefZoEFyKueOy/R3BIMBoJACzGSA6X8C+k3IXsX/6sSvS6voMd+X/x+GyMzrJIfYQQwF34gcg/roSCWq8neEOKNGIDAC+QjM4e+jH45itf4J439g15OWoidLVcPr1QtuXy9uQmGx781yFsOdbMv9dJkWIEQahET4jDxSa45jEI3vuTBELM77dWo83mxJ931uL9gw0AgFe31eCT4y34n7cOKNxiMX6/H6cCbpayaQWYMZKNguKiGb/zlz1Y+uxn2FHVGnEZBEEoCycEtNqckrGpLhXiUPRaDf974vBZVhxQ2v3BbTe3PqXXmZakR06go4YTQNTeRqNOC4ZJvI4cgiDOTbgaLnXtDsmBo8FITOWutZzD5VS7Q1JUcKoguFiMOhSmmQAMLL5L6UixLIsB6WY9/H6gRiUXvpI1XABhTJr8dTP5Gi4KuqFiReJtETGsWb+9Jmwa99AcilSkmJxfUqFYwHE6ILjMHCmdo02I8UQjuAimN1uDESx9RbZxYkykuLmBslkgLAwHbIHOyNULi2W9qY7OMvPW2r/trsfP3gvWyqGR2QRBqIUj5Mej8PojtJtbjOIaFP/Yexrr/nMYd7+xDy6PT5G6btHQ0u1Ec7cTWg2DyQWpSE1ia411BpyCdW0OVDbbJJ8zCIJQB4tRh4JAR41U7Ica+fNAsMbJyUDxYyXrmwDBThNufQyjfIxHrLdR6fURBEGoSXayEZkWA/x+6UgvNdwl2ckGpJh08PmBWok4eLcKsWaAIJpzQIKLsh38DMMIXLTqxIrxDheF9jcfk6aEwyWQXkAOF4JQkEh2u0gjkqTscm4ZC9KeCYgrUmTI7KRJVHz+YGeSlEDGTeeO5QP/CI5CjnTBrWuzo6aVvakPVnBZd/nksGnlR5rQ1EeMmZw0W3vx5IfH+BHQA4VzuIR2Ng4Vi1GHKSNSJd/riFHHJUEQ5x6hkWLCAvTCH0c5yUbRfLVtDsHfdpFw361A5nAkGgI16HKSjUgyaJFuZp8ZuGhGe0A0NxvO7aLlBBFr+BGbfXRYKT3isiSXHSXMjUhWOm6Li2ARrk9p90foNpoUr1Oj7j4lCIJQGy4C/IREKkiwfopy13aGYfqshRYUfdRxNA5ECOAHVCh4f+cGGlS3yu8IkcKjsGhROghhK1q8XmXFolhCTx9E3PDC5pMDml8nURCKu7DLgTDCZGLAtskxYwQ5XKKBK97VH1yn2Je1Hf1+9kf/PMj/7cfgRgffvrAYF43PEU278897sO4/Xw1qeQPl/rcP4HefVuEbv985qM87nGxnZHKEuL2hkB4YiR0KOVwIglCL0EixGsGPle8tKeX/XlCSJZrvdEdQcFn+662iH1+tNvWuYa2BOgXZKazQkhUYpNHSzU7nruGRIlMJglCHvkagOlUo+AsEOzE4+nJ4y0GqSY+81KBYrfT6gGDHE4dR4XUWpiWJBmWpsY0EQRBqMoGPAg8XXNRyaAaL1oeLCi4VRA1gcFFXvMNFp1wHPzfQQIkILimUrOECCI+1/IKLR+G2xxISXIi44S9f1IVNWy4oqB6KlD1RTocLNwK1ONuCSybl8tPTDH5MjeACIMREG5dikrD6RzqWwkV293ok54kGqaiZjw43DXp5A6GivhNAcBT0QOGK2ScbpcWRoZAaQXDpoBouBEEowO8+rcJLn1SKptVHcP89f8NMFAtqd+UH4oA4Ql2DwjpU7XYn1IIXXAIOnJEZSQCCghD3fGEhhwtBxJSSqEbnqtNhxaGGG0O4TlXWl6vuNmo0DO/kUWN9BEEQajMuILhI1b1V6/7FO1wkBi1wbVD6+jsUh4vUAG654PZNtUqCS7CGizLbxG1PXbtD1kHuQNCdo+TxiBWJt0XEsGXGyHT+79LcZLxw4yw8+82ZEeeXspy5ZBRcbHxskxaZluBIsLEplLkeLV5/dPtKqnhmpGPpFETPrZyaP7iGATh4umvQnx0q6ZahCSUNXWzcXX6asZ85B06k6Aw143gIgjg3ONXuwJMfHsPTHx3n77kbdtXhr1/US86/dJJ4EEaGWRzv2dhHLGSbig4XLtpsRDortBQG/m/o6oXX50evm72/yR0LSRDEwOCip6RruLDPsOoLLio4TnKFgou664vFOtVYH0EQhJpMCCSwcLWqhDjVcpf04XpQrQ5a4Fp/qsMRsURBKGpEinH39uo2B9Qo2ai0wyUv1Yhkow5enx91EjV7hgKXbEMOF4JQEK5GxB9vnYvy/7kIX59RiOQ+OiOkFFC3Z3BXM5/Pj1WvfoHv/20/Xtx8Er//rAqHzwQ75BeMDcaWnJdDgku0ROtwkboRuyMo51zEw19Wz+Nz8QfDZdMLJKfLfQORInMI7XZ6vDjSYAUQ7MSTk0XjsiWnyz2SgSAI4pDgPmsLOPceeicY7XjvJeP4v88bkxEmUGg1DPJTgy4XTsiQolNFl97RwDV6fGD0YaqJFdltvR44BHFpZgN1AhJELOE7atrDO2pcHva10h1WFqMOBQK3nhpuDLEYofz68lNNsAiud4no4iEIglCT8bnsM+aZzp6wgZF8DTKlBwxwLo5WG3wh/T5Ovg6asp3o2ckGpJp08PvFEcR9wQkuSkaKjcwww6DTwOXxoV0Fkz0nWihVw4VhmOAgFZljxfhIMYWft2JB4m0RMWxpsrJXorxUU1TFG6Uu3i6vF0cbrHB6gj+anvrwGJ7ddFxyGfvqO7CjqhX/3Hca2yvb8N8DZ/Gr8hN44oNjeO5jtqZMp8ONyYWp+PHKibh6ViEmppHgEi12Z3SRX1JqdkSHS+DmPdQ85qevmy45XdgBqBSZlr4FF2sfbpKdVW3w+9kYNmG0jlx8fUYhNtwxP2y6nHF9BEEQALC7pp3/m3O4jMkKXtfuXFTM/x3JMPnJ/y7G9xaX9Luu7ijvR0PF4/Xhi+o2AMB5YzIBAMkmViiyOT1wuNjnE62GoU5AgogxOclGpJp08PmB2pABNy6VRucCIQKIRMyu3IjECBXWxzCMKFZMjXWqvU8JgiDUJM0crMd1IsTlooaDAwCKMpJg0GrQ6/bhTGeP6D21Ys0YhglGm0UhBPj9/mANFwX3j1bDYGygr6apR3nnhtIOFyAosEnVvRsKHj7ijRwuBKEIbq8PbYF89dBM9khIKaDv7D+Llc9vwwNvs4XV2+0u/PbTKrywpTJM+Xd7fbjmtztw0x924YF/HAxbFqe0LhrHFle/6+ISPHXNVCSg8KoYp9p7+p8J0scyUgc/Fyk21I4qs0GHd++5MGx6tE6O443duOLFz1F+ZOB1X4QxOKGi1MZDDZj+yCa8tr0m7HM9Li+e38wKgeeNyVQkIoFhGFxYmo2flE0STeceTAiCIOTiTztq+b8rm7tx4FQnX+h49cJipJiC8YvuCI7JJIMWy6f0Hy9p6/XA7/fjt59W4tPjzf3OP1gaunrR6/bBoNNgYiDuIdnIbpPN6eGv+WaDNqrBJQRBKIdQCAgt+st3Fqnw4C92YyRm3FbpObCNBEEQasI5qU+G1HFRS+zQaTUYk20GEB7NyQ9a0Kp3vY9GCPAIfk8oKbgAQYGiKbousSHB10FRISZNOYdL4v0uoq5jIi5o6XbC72ddK9HGLUkpoJyy/u6BswCCaikQHjXSYY8uz/26OSOjmo8I586L2NHJ1/ezD6XcSlLxcD6fH2cDheaH6nABgjEvQpxRCi5PfHAUh8504c4/7xnweoUj7Vq6xR7T723YBwB49L9HJNe5v74TAJDVj0tmqNx24Ri8/K3ZeGDFBAAUKUYQhLz4Qywrd/11H658aTsfmbhkQq7o/YmBH5VSRHM97O51Y2dVG5768Dhue+3LQbQ4OrhR8qMyzdAEnlOSjey9pqvHjc4edvCHxUD1WwgiHiiN0IHgUqmGCyAuKq+G8y03xcjHNqvltFN7G0dnmcH9VCQ3IUEQiQgnuByPILioGVEZWsfFrZLoAwxMCBAO6lU67oxrlxoOF060UCpSDBAKWwrVcEnAke2Jt0XEsIQrdJubYuI7KPqjr/ouANBs7RWNiA3NZn78/aNRrUfpju1E5r6l4/HmmvPx+NVT+5xPanSBVKTYxq8a+L/leIDITQ0WnV8+mS3GHK2w4BmC40P42aY+ijyH8s+9p/m/My3GPuYcOnqtBpdOLcCEwINcpIg3giCIwWDrJ+LLEnCFbLhjPq6YUYg7BPFioUQTF2PtdaNNMNCizSZfoHJ3rxuXv7gNj793hI9HFdZk4LYFAK757Q4AgNlII64JIh6IFJGh1ghhINT9oW7El2qCi8oxZkadFqMDEZUkuBAEkYiMz2OvqyebpN0lSjs4gOC1PeweyjtclBcbBiIECAf1Kr1/SnNVFFxUKDwvPNahNXuGgkeFOLRYQU8fRFzQHOh0zkuNvhN5VKa5z/f31XeIOs+53HQOzgXTH1nJJLgMFr1Wg/PHZvVr5Zes4SIhfOyp7eD/liMewGzQ4fMfLcGXP1nKF2OOVnARCn7eAd5whPM3dTvR5XDjw68asLcuWM9A6n5jF5zDmZZwd44ScMX2yOFCEISc9FfEPiVQ9+TC0my8eOMsjOvD4RLqeBRen7kfO3/fcxofftXIT5/z+MeyZRB/erwFX52x4tXPa3AiMMowNyUouEgNEGmzReeyJQhCWSI6XDwqdljlBmtXGWVwcEe1zhxOjFApUky4jSqtU+1tJAiCUBOuAzy0WLyqAwYi1E9Rsw3cfqhusfXbLyMcRKp0Bz93D2pyhDv75cargsNldJYZOg0Dh8vLD5iXAz4OLQEFF8ozIOKCxi5OcImufgsAFKYn9fn+kYZuAMEvbU+IwyUa9FqmXycNMXSkfsye7ggPuxQWm5cr43FkBivccRndTk/k86TT4cIvPziGa+eMFK2/q8ctalt/CLND73tzP6SeC0I7EGtDHqSUdrhwcPslUk0dgiCIwdCf4DI2O7nP94WEjl5+8trpmDkqHXkpRvR6fDjv8Y/R4/bi/UMNovk+/KoRdy8pjb7RERA6FffVsQMDclKC12ipWi1dPX1vP0EQ6sA5Papb2RGbnNPe5WWfB9XoLMpJNiLTYkC73YVUkzq/O7gaU6lJ6qxvdJYFRp0GTo+PF9SVZkJ+Cj4+2qzaNhIEQajJmEBR9rNdPeh1e/n+g6C7RE2HS+xEn6JMMwxa9v5ytrMHRX0MzOY69w1ajeK1FEtyksEwgMPLoN3uQr5BuYHcatRB0Ws1GJ1lRlWLHZXNtn77Y6PFSzVcomfr1q244oorUFhYCIZh8M477/Dvud1u/OhHP8K0adNgsVhQWFiIW265BWfPip0G7e3tWLVqFVJTU5Geno7Vq1fDZhMrpgcPHsSiRYtgMplQVFSEp556Su5NIVSkKVDHYiCCS38X7+ONVtz11738a4dLHF9yYWlWv+sw6aiorRpIXVx3VLWGTRP+CI221k+0cPEGfTk5nvv4JN788hSuf3knegRuk/Yo6wFxCEdeRBqEkRQiuLy+s1b0OsOsjsPFoOM6HkhwIQhCPjoc7HWTi0MQ8rtVs6OOFwXCf1CunJqPEelJ0Gk1SDbqMGtUuuTn5BptJhwgwOVoCwUXAHjq2umi1yMz5PmRQhDE0CjKSIJBq0Gv28fXggSCsSNqRXz96hsz8NBlk1Ccben/AzLwzbmjcM+SUnzn4hJV1qfXavDcN2fiZ1dNRXayOoOGbr1gDO5eUoJbLxijyvoIgiDUJMtiQIpJB78fqGtz8NPVFDvGBlwc7XaXqE9ETdFHq2H4e2d/dVy4e7vS9VsAdgDtiIAoUdUqb92TULwquURKI8SwDgVuYK9Ok3gBXLJvkd1ux4wZM/DSSy+FvedwOLBv3z48/PDD2LdvH/71r3/h+PHj+PrXvy6ab9WqVTh8+DDKy8vx3nvvYevWrVizZg3/vtVqxfLlyzF69Gjs3bsXTz/9NB555BG88sorcm8OoRJNAYdLflr0gkt/F5PdNe2i1z0hkWKhHetSy9MmoMoaj0hdXI81dofV3eEK2l8za8SAOuOigXe49CEsCG8sm481839zHYfREk0EWYfDBbugxkHoA1O6zIJTJAxaVvhxU6QYQRAywl03swRuvdFZZqycmo+LJ+QMaFnCgRHF2Zaw+8PoLOmRbm0DFMsjwbl0AaC7l71uhwou3zivCK/ddh5+c9MsPLBiAv707XmyrJsgiKGh02owJpu9Rgif89TsLAKAJRNycceisaoN9Eoz6/G/KyaIaqsozcppBbj5/NGqrS83xYQHVkzk3ewEQRCJBMMwGBsQGmpaBfcvFSMxzQZdUFRokWiDSjW0ohUCuHu7WgXaSwLHR+5C86F4+EgxZberJEIM61DwJnANF9n9tStXrsTKlSsl30tLS0N5eblo2m9+8xvMmzcP9fX1GDVqFI4ePYoPP/wQX375JebOnQsAePHFF1FWVoZnnnkGhYWF2LBhA1wuF9avXw+DwYApU6agoqICzz77rEiYIYYPTd0Dr+HS30UytCBvaA2X0NfpZgNaQ4roDrQ2BzE4hBfX71w8Fr//rBoAK5IJo7U4wUWJfGtO0HC6IwsLZoP0egeaxe+J4rzy+VnRaVdNG2aPyhCJL/+zdDzOH5s5oHUOFj05XAiCUAAuUizDoseN80bhs+PNePfuhUgbonsvXeLzkUaMdwxBcPH7/WAYBk6PFw0SOca5KeHPM0sm5g56fQRBKEdpbjJONNlQ2WzD4gns9zTYYZV4HQAEQRBEYlCcbcGB012oaQ06XDjHgBoOTYCN5jzT2YPKZhvOG8P2Uag9aIGrl9Kvw8WrnhgFsO367GQrqhUWXNQSLZRxuHCRYonncIl5oGlXVxcYhkF6ejoAYOfOnUhPT+fFFgBYunQpNBoNdu3ahauvvho7d+7ERRddBIMgA2/FihV48skn0dHRgYyMDMl1OZ1OOJ3BDnWr1QqAjTpzu2OXpc2tO5ZtUJpetxdaDRPxwtbQyXZWZJv10e8Hn6fPt7kvLkd3j4tftt3pQU9IxNhN543EC59UiaZ5fX5Re86FYxULPjkedIvkJRug1zJwe/3o7nEi2RC8aTic7H43aPs/BgM9VtzzSK/bE/EzkVweX1S14JIJwYi6ow3d+M6G/fjB10pw7ewR4cvxRldP6OVPK1F+lN03l0/LBwD838oJ+PYFo+Hx9H3+y4UW7Db3uL1wu91o7nbCbNDKWtuIvlfyse1kK0akJ/H2brmhYzV8iPdj1WZj7/upJh0eu2Ii/JdPAMMMvb05yYawZdw4dwR+sfEY/3pcrgUnm+3oEjwXDIS/7zmNR98/hsun5ePdAw2SInpRujHqZat9rOL1nCCIWCGVQe/kI1mo4DpBEAQRnxQHah5KOVzUiBQDWFFh64kWVDXHsA1RCgFuXghSZzAF95tcPYeLOoJLZbN820MOF4Xo7e3Fj370I9x4441ITU0FADQ2NiI3VzwCUKfTITMzE42Njfw8xcXFonny8vL49yIJLk888QQeffTRsOmbNm2C2Rx7q3Go+ydRcHmBn+7VIs0A/HimdEdzc6cWAIOv9n6B9mOSs0gulzuFGfjhR99f0L0VB2FpOoDyMww21mvgC8z/P1M9yDYBdQ0nAIh/VLncHmzcuDFsWYl6rGKF0xO8FFUdPwwtNHCDwUcfb0GuIOb+aK0GgAZn6mqxcWN1VMuO9ljVnmEAaFFVW4+NG2sl56k5y56nofxpZz1mIdieXx7QosHB4Mf/PoykxgNh8zc1sdvRb9uPBoWo2tNnAWhQdfwINnYe7vezcuEMfM/sTi/+b/0HeLtGiyStHw/P8sIicxkZ+l4Njbpu4Nmv2O/S8wuUFeToWA0f4vVYVdSw18HWs5GvuQPhwjwNjnUyWGA6i40bz4a9P8KsxRkHe/1mnN0ANKg/2yJ5j++Pn+xkv2f/2h++HgDQwI892zYPeLlqHSuHw9H/TARxDsGP2BR0FvGdMip1FhEEQRDEQCnO4SLFgh3gLpXvX3wnfEv4PVQ1l02UUVfcoGy1os4450214jVc1BEtxgb2c6vNiS6He8jJBICghksCOopjJri43W584xvfgN/vx+9+9ztV1vnggw9i7dq1/Gur1YqioiIsX76cF3xigdvtRnl5OZYtWwa9Xp1C2Gqyr74Tjt274egBlq+4VNIq9pN9WwCPB0u/djHGZEU3Mtvt9eGB3R8DYOPAOhx9j9gsHjcRZRcV4wcPbxJNX77kIpTmJqOy2YZXju0QvednNCgrWxFcZ4Ifq1jxg53BY/Kdq5Zg08tfoNfmwvwLFmFSQQr/3s53jwANpzF54jiULem7yOdAj1X3ntN4t/4IkjNzUVY2W3KeXx3fBqBH8r2VK1fyuds/3vMxEHCGTDzv4jC3wZtNe4CudlwyMQebj7WI3rtz4Rhsr2rDkYZu0fSvOtjvzbzZM1E2o6Df7ZGTZ499hkarE8ddGQCs6PEyyBg/F0snyROPQ98refj91hrgq5MAgLKyMkXWQcdq+BDvx2rz24eAxgbMnTYRZReOGfLyyhCM+ZJi/aldOOPoAgBMKR6BExUN0JtTYBxbimSjDvOLo49pFN6zpPj7d87HjJFpUS9P7WPFObwJgmDhO2ok8udJcCEIgiDileIsseDi8/l5UUGtOK9SCbFDzToyQPA+3uFwo83mRFaydKmCYIF2lRwugVjjM529cLg8MBvk74L3+/284KK0wyXZqENBmgkNXb2obLFhzmhps8NAUKvtsSAmggsnttTV1WHLli0isSM/Px/Nzc2i+T0eD9rb25Gfn8/P09TUJJqHe83NI4XRaITRGP7F0+v1cdEZES/tkBuNNuga6fUyyDCFbyOnwptNxqj3gU43sPoqTq9fctmpFhP0ej0mjcjA72+eg+JsC5b/eisA9ssv9ZlEPVbxwKjsFJgCtVI8YET7ubmbzdvPSTFFvf+jPVbZKSYAQFePR3J+v9+P1j5qtdjcQGbA8iEsd7L5RCsmFKaL5uXS7lKTgrGI375wDH68ciKMOi3u+sveMMGFIyXJoPq5l5ZkQKPVidq24Kjouo5e2dtB36uhYRfWH9JoFX3ApWM1fIjXY2UN1KXKSo7+ej4UhPXA8tNZV/OxJhvu2lABAKj+RRk0UTzo+/39P3vMLc4eVBvVOlbxeD4QRCzhBsa0211ot7uQaTEI8ucTrwOAIAiCSAzGZLPPtK02F6y9bpGjRDUXR8DhcqazBz0uL5IMWkEspzptSDJoMSI9CWc6e1DVYu9XcFFLCMq0GGDR+WH3MKhusWPqiOgHZEWLsO60TqP8dpXkJKOhqxdVzfIILlwcmlrHRE1U3yJObDl58iQ+/vhjZGVlid5fsGABOjs7sXfvXn7ali1b4PP5MH/+fH6erVu3ijKoy8vLMWHChIhxYkTsEBavt/aGu1D8fr/gwhf9jxrhKNZoitv3uKTjzJIEnTArpuRjfF4KUgL1KSbmp0h+hpCXpZPYSMArZxYCAEyBvOzekGPGjZoYlyf/cUk3s+LHvvpO3Pfm/rBOtTa7Cw6Jc4g7f4RF7b2Cz9p6PXjwXwdxoikooJzpYF0ywjooN84bBWNgu+2uyHFQSoyK6A+jnr1VWHuD7dpT24FT7RRLE08Iawx19VCNBiK+4VypGWZDP3PKg/AhfsbI9LD3O6P8zkjdBzievm46tv1wyYDbRhBEbDEbdBiRzmbYcvnv5HAhCIIg4p0Ukx45Kay4UNtq5+9dgHoOlyyLAelmPfx+oDpQSyY4aEG9e2iwvkjkWLFYxIXmBSLy5Sw0L0RYS1KrwiCR0ijr5USLx8cek0R0uMh+ltlsNlRUVKCiogIAUFNTg4qKCtTX18PtduO6667Dnj17sGHDBni9XjQ2NqKxsREuFztyfNKkSbj00ktx5513Yvfu3di+fTvuuece3HDDDSgsZDtjb7rpJhgMBqxevRqHDx/GW2+9heeff14UF0bED+8daOD/tvaEdyR7fH5w/dNG7eAKU0bSW0pyLLhv6TgAgMPthdMT3lFiNoSv8+3vLsDXZxTid9+aM6j2EAPj2W/OwPM3zMQT10wDwI5QAIDekOPFCXZZFvk76LKTg8t8p+IsWrqdovcr6jslP2cKiBG97mBbR2UGa0L99tMq/G33KVz7Ozaubld1G850soKLMKcyP83E/81ZYgFgXkjMTZJB/R/+UtmrHx9twvUv7xQ91BHq887+M7jype3YXdOOL2vb+elN1t4Ytoog+qfTwT73pcuQ/RsNpzuCAvHXJobHIf5tdz02H20Kmx7KEx8clZw+rzgT188tQlFm7GsCEgQxcEpC6rgEC+sO7rcJQRAEQahBcXYwViwWggvDMHz/BVccPhbCRjRCAF/DRUUhKC+JXWd/9WUGi9jhorxowdWlkWt7PNwxUcGdozayb9GePXswa9YszJo1CwCwdu1azJo1C+vWrcOZM2fw7rvv4vTp05g5cyYKCgr4fzt2BGtnbNiwARMnTsQll1yCsrIyLFy4EK+88gr/flpaGjZt2oSamhrMmTMH999/P9atW4c1a9bIvTmEDDRagzUvOhzhkUzCm4JeN7gLRGG6SXL6+LwUfvRsS7cTdme44CLVmTwxPxUv3DiLv3kRypJq0uPKmSN49wZ3THrd4s58JUcbjsoUH+set/hc+byyNewzv1s1m3elOAXnsdDtwtHd64Hb68Pf95zmpwkfglIEbpefXjGZ/1sbUo8gRSKST2m4bQyl0dqLLcf676AklOO+typw4FQnvvH7nThwuoufvqOyLYatIoi+2VHZirpARGG6Sg6XyYVsfO30kWkw6DRhP0Kf/ug4Vr++B1391IP76xf1ktP/cMtceRpKEERMCM2gJ4cLQRAEMRzg6oRUt9h5Z4ley0QVlSsXke6hagobJRK1ZEIZTLLOUOEEF1UcLmoILrI7XKiGS9QsXry4z3zraLKvMzMz8cYbb/Q5z/Tp07Ft27YBt49QH6HieqojPILI7R26Cr9kQi5ONIV/4VNMOozOYkeb1rc5YOsN7wiPVGCXiB3cj1vhuQEo++M3dJlCsefAqU78aUctACDVpOOjtVZOK8CTHx4LzB8UaKQEFwC48ZUvkJsazBP93uJSNHT1YnJhqug8FP7thx9ZFgPa7KxYOV6BOLX+kBIlOe766z58fUYhmqy9uH5uEa6bM1LFlhGR4M4Xgog3tle2YtWru/jXGSo5XJ64ejqKMs24bFoBAOBva87nnYdCDp/twgWlA6vBcvjRFbAYY1IWkSAImSjJZTusuA4Ep0f9ThmCIAiCGChSDhe162GE3kO5dvTVjyA30Tlc1N8/XKSYGg6X0MG6SsCJa/XtDvS6vaI6mYPBEwMRTC1oyA6hOMKO6/q2cMGFuxgzzOBVTY2GwZqLxoZNN+g0GJnBXuHOdvWg20l1DYYD3A2wsSsYi+Tz+Xn1Ww17rNDhcuVL2/m/rw0IClysGXeD4c5zn88Pe4SM/+ON3dhd0wEA+Pt3FiDNrMcLN87CXReXhM37jbnseu69ZBxev30ebpo/Ch+vvWiomzUo+rtvv3vgLHbVtON/3z4gEp6I2LHxUAO+OtPV/4xxis3pwUlB3SMicRCKLQCQlqSO4JJm1uPBlZMwPVC/Zc7oDNT+8jIsmZAjmk/oFAvF7/dLCv4kthDE8IcfndsSkj9PDheCIAgijhkjEFxiEeUFCMSOGLpEuairM509Ees3uz2xixSrbXXw4oKccDVQNAxUcTXlpBiRYtLB5wefWDAUEtnhQk+QhKJsPtqEilOd/OtWm3jUdUu3E9/5614AbCf6YN0mWobhs/9Cp3NxJd29Hnx2okX0/oQYuAWI/uHqpzzxwTF+mkvohFLhxh1JOJg9KgMf3rcI5WsvBhAUZnZUsZFjwoL3oW6PbqcHrTZ228blJqMvfnH1NGz/8ddwQUk2po5Iwy+unobS3Nicr84B1GmhYu3qEqn+RX27A5e/+LnKrZGP6363A8t+vRX76jti3RRCRqRczjqVR+CFMiYkOvSd/Wciztvr9vE/IGeNSgcAZCcbI85PEMTwgYvION3Rg163l++0UnN0LkEQBEEMFC5SrLbVzv9uV7NYPRCM86putcPr8/O1UtQUXLKSjcgw6+H3A9Wt0m4SVwzcFBlGtu6vy+vDqY6e/j8wQDiHi06lGijCmj1yuHY8MairoxaJt0VEXLH69T2i1x0OF+xOD9+Z/cN/HMD+QDHyoVyMtRoGPomOnHaHWzR69qkPj/N/r79tLj76n9g4Boi+OdJgDZsm7PRX6sZ92wVj+L9/XX5Ccp4etxcT81ORGXC4cKr+bz+tAgD87L0j/LxPXzc94ujtFFPfI6J1Wg1GpCdF3XYlcbqjF1yk6jQRymHux8I7XAWwY42su+Wa3+4IE8qJ4UtLQHDmeGDFhBi1JEioYFLZYkO73YW739iHN3eL67VwsahJei1+/605uHHeKLx++3mqtZUgCOXIshiQznXUtARjWQzaoUVlEARBEISSjMoyg2HYwZ1nO9mEELUdLiMzzDDoNHB5fDjV7gi6RGMk/EQSAjwxiBTTMEBxViByTYFYMU6w0KkoIkUT3xYtXnK4EIQ81LbZcf4vNuO6l3fA7/fznWrA0C56ZoNWlF2Ym8J2oMwvzpRc7vVzRuJrE/MGvT5CWYQF5DlxzuUZeq2f/njoskn837tq2nH9yzvg8fpQmGbip18yMTfi53vdXvx9z2n+NcMw+OLBS1D+PxeJbiDJRl3MR3UPBKcn6PYpygyKQNNHpoXN29lPwWlCXnr6iXA7LVE3a7hx6/rd2HioIdbNIGTgRKP4oZy7V8eSnBDBxevz44G3D+D9gw348b8Oid47FIgbmzYyDbmpJjxxzTRMKQy/DhIEMfwQjtisarEFc/B1idcBQBAEQSQORp2Wj9E/3sgOXFVb6NBqGN5pc6wxOHhWH6tosxa75Pu880bl/TM2EHdWKVOheSGxECzkdLi4A5FoagpGajF8evyIhKC6xY5upwdfnbGistkmKrDkHkBsEccDKyZgRlE6vnX+aD77DwDe+/5CPPfNmfjG3CLJz43N6TvOiYgtZkNQcOnqcaPX7cVvtpwEMLTouf4IFUG+rO1AbZuDd9d88INFyArpnLtofDD//7efVIYtM8mgxbi8FKQKHC2p/bhb4o1bA86fi8fn4P5l7Ih0rYZBskTdgiZrb9g0YnAcbbDiLztr4fP5Ud/mwBMbj4bt394Q9xH3sM1xRgHbstJIZe4+9eExiTmJ4URtqx1v7K4TTeMiP2OJ2Rg+ev3A6U7JeTmHzsg4cR8SBCEvXB2Xk03dqtYNJAiCIIihUJzN3r+ON7Ed4LGoP8ZFcx5pCA6qjpXDJZKThHPeqN25z9WXUcThwgkWKgoucjlcfD4/uKAitSLR1GR49foRww6TXhPWIchh7XWjuzdY72IgdSI47l5SiruXlAIAvL7g53NTTbhq1gj+9fLJedh0pIl/vbA0e8DrItTDj6B41uFw4b2DDXh9J9tRp/TDw3ljMvBlbbBuhMvj4+uySAkML900C9Me2QQAeGFLuODCkWzSoSPg/kgxqVMkWi6umT0SE/NTUZhuQopJD4ZhC04/9t8jYfMebejGlTPVb2MisvL5bQCA/x5oQLvDhcpmGw6c7sSbaxYAYB9Qej1icSIv1YTTApHl9DAUXKy94S6pWhkK8hGxZfEzn4ZNG58X+8EPc0dnwqjTYGJ+CmxOD6pa7CLRr8flRZKBFWVaA/XFuEhJgiASi5JctkNE1FlENVwIgiCIOGdstgVbT7TgRCBBJiaCS0DsOHI26HBRW3DhhIBIzgt3DCLFAKAkWzmHS7DovHrbJBRcfD4/NIMUe9yCPlxyuBDEACnpw0lid3rRbg/muQuLog+Gvj4+d0yG6PU0iSgkIn4QHsvT7T2obQ1aQpUucPb7m+eKXve4vbxoyHW6CYkknoTWaEkxBuczSSwn3plcmIp0swFaDYMrZ47AyAwzkiWcOofPdsWgdYnN7tp2/qHxi+p2fnp1qx2hpas0DPDyt2bzr5u7xTUzhgMDqRlEDE/mjM7AH26Zi9FZlv5nVpj8NBM+/9HX8Mad5yM5cD23CwSXtsBzSlWLDa9+XgMAyEwmwYWQh61bt+KKK65AYWEhGIbBO++8I3qfYRjJf08//XTYspxOJ2bOnAmGYVBRUSF67+DBg1i0aBFMJhOKiorw1FNPKbhVwxeuA+GooJYgCS4EQRBEvDMmywwg6DiIhTsz9B6q1zKD7ogfLFz/Y02rXVRygCNmgovA4eKXqD09FPgaLiru66KMJBi07OD6M52DH+ApPEZqtl8t6AmSUJTigJLL5TkKaejqgcQ1cND4+rhwGXXBDu54yIwn+kZ4LKtabMgX1FCJ5JiSi9CRy22CIs9SDhcpRmWasfn+i0XThAKMKUF+vEs9yJ1skn/UxrmIL4qLY3XggXpyQSo/zeXx4dKpBfje4hIA4vo7w4VIbebqZxDDD1eIg/VrE3OxbHL81FHLSTHCYtQhWSJerN3uQq/bi0t+9Rk/bVJ+ath8BDEY7HY7ZsyYgZdeekny/YaGBtG/9evXg2EYXHvttWHz/vCHP0RhYWHYdKvViuXLl2P06NHYu3cvnn76aTzyyCN45ZVXZN+e4U5pTgoAiDoP9AkYcUEQBEEkFsUBoYFzO6gtKADBWE7uHhqLNozISIJRp4HL68Op9vCEBE6cUHswxZgsMzQMYO318BHFchGLGi46rQZjssUi32DgauoAiRkplnhbRMQV3Jf/xnmjwt5TM+rGKLig0ki1+EcouLTbXaLOuhyVBTMuis6o04hqDgl55voZoteXTs1HbopJNE3ohDFGWM5wo7YtvBhdo7UXniG61Qg2Sq8/uKi7TIsBN80fhTFZZvxtzfkAgiJzX1GN9W0OvLP/jOyjbIZKpDaXH22SnE7EP1094pi4UAdgvGAxhLerzebCxIc/FE1bMjFXrSYRCc7KlSvx+OOP4+qrr5Z8Pz8/X/TvP//5D5YsWYKxY8eK5vvggw+wadMmPPPMM2HL2LBhA1wuF9avX48pU6bghhtuwL333otnn31WkW0azozISBL9TojF6FyCIAiCGCihA5xj0ec1NscCYandWLRBq2H4Qd9SQoCLd7ioe2836rUoygwIFM3hfShDgRPZ1K9L03d8WzQkusMlPn/xEgkD9+W3GHVgGIjib6QU56Gwdtl4bDvZglsWjAl7z6gnwWU4IbzwhtZzUCMO7sUbZ+H7f9sPAPjH3tMAgLSkyHVXrpszEk9/dAxNVna0gpQTJjUBHS5SNl2ArWWzdtl4lVuTWEQz8sURiD1KMmjxi6unid4zBa55dqcHfr8fDCN+gHF7fbjo6U8AsK6/C2JY16rd7oJWw/DfsVDBJcWoQ7fTA7vTI/VxYhgQKrjEq9PULSEW//ZTcW2uj9deHDYPQahBU1MT3n//fbz++uth0++880688847MJvNYZ/buXMnLrroIhgMQQfvihUr8OSTT6KjowMZGRlhn3E6nXA6g/chq5WNB3G73XC7w+tsScHNF+388cLYLDOONQUjWeRo/3DdF/EC7b8gtC8GBu0vFtoPQyfe92GORQe9luEdA3qNMm3taz9oAYxIT+IHVst1Dx0oJdkWHGvsxvHGLlxUmil6z+lmf09q4FelbcL9VZxlRl2bA8cbuzB3lHxueaeLXYeWUff8LA7E2J1s6u53vZHOmx4nO8hUwwBerwfeOAznGMo+JcGFUBSvQG21GHSwCTrM5Ha4FGWa8eVPloZ1LALiSLFY5FkSA+M3N83Gret3AwCsPR78bfcp/r11l09WfP1XzCjEn3fW4svaDn5aah+CCwCkmvS84GKREFyE9U4SxeGyYGwWv4/euHM+bvrDLgDAC5tPkuAySJq7e+HzAS2B2iv5qSY0WntF85zt7EFhehJf2NssUROIc/X9p+Is3j/YgMpflIneb+wKLlN4LX5jVz3MBi2umjVCng3qhx6XF7N/Vg4AqHmiDAzDhMVPLZmYi3cPnOUFJiI+abM58aN/HsL5YzNx+4XFeH7zSfR6vPjhiokix9bPr56KZZPzY9jSyMwoSscnx1tE04T3AYD9IUkQseD1119HSkoKrrnmGn6a3+/Hbbfdhrvuugtz585FbW1t2OcaGxtRXFwsmpaXl8e/JyW4PPHEE3j00UfDpm/atElS1OmL8vLyAc0fa5I8GvAhED4PNm7cKNuyh9u+iDdo/wWhfTEwaH+x0H4YOvG8D7MMWjT2sH1hbS1Nst6/Qom0H1L8wXuox9WraBsi4e1kAGjx2b7jGGE9KnqvupZtX3XlSWzsPaFam8rLy6HpZte95cvDyGg9JNuyT3ax2+uw21Xd390t7Hr3HD+FjRtro/pM6HnT4QQAHRj4Y3KuRIPDMXijAAkuhKJwDhctw8Bi1CoquACQFFsAcaSYMUHcBYnMxeNz8NS10/HDfx7E+4ca+OnzizORl2rq45PyERoflt6P4CJ0UaVICC7CCJ1EOQe/t6QUOq0GE/NTML84S/Te+s9r8M99p/Gbm2bztl6ibzxeH+b9fDOAoKNqbI4lTHD5/t/24x93LeAFCEnBRXD+enx+eLw+6AJis9fnx2ZBPBfnIjvb2YP/+zf78LegJEuV75owJ9/h8sJi1PFWb52GwWvfPg/HGrrx7oGz6HWT4BLPPFt+Ah8fbcLHR5tgNujw/OaTANh6RH/aUcvPt2r+6Bi1sH/uXlIKvx9826VIkvi+EYQarF+/HqtWrYLJFLw2v/jii+ju7saDDz4o67oefPBBrF27ln9ttVpRVFSE5cuXIzU1ulGZbrcb5eXlWLZsGfT6vp+h4olKUyX2f1INALAkmVBWNnRX23DdF/EC7b8gtC8GBu0vFtoPQ2c47MP3OivQeLQZADBq5AiUlU3r5xMDp7/9cIA5jqM76gAAackWlJUtlL0N/eE/1IgP/34QLlMGysrmi9775B+HgOYGTJ08CWULxyjeFuH+sh9swpZ3jsBryUFZ2RzZ1vF5ZRtwZC/S01JRVrZAtuX2x+izVvyl8gt0+AwoK1vS57yRzpv6dgew73MY9TqUla1QusmDgnN5DwYSXAhF8QkcLkkhHdihnYjfvnCMYu0QxkFRpNjwIDvFEDZtQUmWxJzKsO1kq+j1qKy+R3QKnVNSDhdRDZcEOQdNei3uvWSc5HuPvXcEAPDyp1V48rrpajZrWNHr9uKBfxzEBSVZuHh8Dj+di2Bq6OoN+8zeug78Y+/pYKSYPvx884XUZbE7vUgzs+fda9tr8Pj7wdE+xxu7Rf8DwP76Tlw6VXkXgrB+THevBxajDs6AsDJ1RBoWjctBXRs7qsThokixeGZ/fSf/99921/N//2FbTQxaMzj0Wg0WlGRFFFzUcFgShBTbtm3D8ePH8dZbb4mmb9myBTt37oTRKI7pmzt3LlatWoXXX38d+fn5aGoS18DiXufnS1/njUZj2DIBQK/XD7ijaTCfiSXj8oPRtQadRta2D7d9EW/Q/gtC+2Jg0P5iof0wdOJ5H47NSQYCgotJr1O0nZH2w7j84KAMo14bk301oYC9j1e3OqDT6USDsr1g/zYZlN0/oej1ekG77PKuO1BsXq+V95mlP8YHtqfd7obN5UeGJbz/LpTQ84bRsH3EWg0Tt9+robQrMXr9iLjF42NHKms1DO92CeWeJaX46L6L8NBlynVkpJuDX34SXIYHoUXnAWDNRWMl5lSGOaPFERujM/t2aQhj69LN4RdlYQzNpyGRNYkMjQbvm3cPnMV/D5zFg/86JHJ7cBi0GkgZ9370z4PoCQgQUg6X0HonNoFYIRRbAODtQJ2iE01BwaU1ihoyciB0PXJOG87hwgmTnFhPkWLxTbs9GBt26ExXDFsyNGaNSscYCYF92eQ83L6wWOITBKE8f/zjHzFnzhzMmDFDNP2FF17AgQMHUFFRgYqKCj6O4a233sLPf/5zAMCCBQuwdetWUQZ1eXk5JkyYIBkndq5TGigCC9BvBoIgCGL4IEyViNX9qzQ39vfQ4mwLGIYdwNhqc4necweiq/UxKDPAFZlv6OoV/QYeKt5A3R6tykXnzQYd38dV1WIb1DK4PuJYHA81SMytIuIGvoaLhuHrDYRi0mswIT9F0QuEsANcEyF2jIgvQuOMJhWkwmxQz5T3f2WTRK8zk/tW7IUPFFKCy7QRwRGTK6bEZ/2CoaKT+A4Lo9SUpMPuwnMfn+BrnwwXGjqDDpYXJEbVXzVrBLIswVHG4wIPsQtKsviaQVKjSWxO8fWWE2BC66MAgF7LHreTzcEHpcrmwT00DRS7oJ07q9oAAE4320buO8UJSpHuIUR8IKzTMpwx6rTYfP9i7H1oKS4sDboqJ+SlxLBVRKJis9l4sQQAampqUFFRgfr6oEvMarXi7bffxh133BH2+VGjRmHq1Kn8v/Hj2fppJSUlGDlyJADgpptugsFgwOrVq3H48GG89dZbeP7550WRYUSQsTkWfqAD1X0kCIIghgvxILiUCActxOgeatJrUZTBDp4KFQLcXk5wUb9PMN1sQHagT6l6kAKFFB5Bn6vajM1hz7nB9h14YiQWqQU9RRKKwtdw0Wgijk5W42YgjBTjOvOI+CbLYsDIjCToNAx+df0M/OamWaquPzkkFizZ2LdTQ3geZ5jDO8DHZFvw21WzcfuFxVi7PDELyr9yS3gWqRqd5E6PF7N+Vo7nPj6JR/57WPH1yUlTd1BwCY2xA4DVC4vx0k2zkGzU4ZfXTMP3AxFu2yvb8OHhRgBAUUZ4Ee95YzJFr//6BZul29AV7qLx+vxwe334R8DpAgDlR5rQbO0VRX4pgXB0z0/fPYwuh5uPDuOcLWkBAbNFJdcNMXB63V44JcS81BDB9dVb5qrVpCGh1TDISjbiz7cHc5+loiIJYqjs2bMHs2bNwqxZ7DPO2rVrMWvWLKxbt46f580334Tf78eNN944qHWkpaVh06ZNqKmpwZw5c3D//fdj3bp1WLNmjSzbkGiY9FqMDNxXyeFCEARBDBeKc4KCS6wcA5kWAzIDgwFj6VooiSAEuLyxc7gAQUFqsI4QKby+2IkWnKNp8A6XwPFIUMGFfj0SihL88gMTC1JEGe8caijfwguqV+EOREIeNBoG/71nIdw+n2S8mNLkpYqzy/s7bbiaG4C04AIAZdMKUDatYMhti1eEdWo4elQodH6yKXiDP3CqU/H1yUlbHyLCnNEZMOg0mD82Cwd/uhwaDYOtJ8Lj6CYVhBcwvrA0C6/fPg+3rt8NAPjzzjo8duVUSeHb50fYcs909mDeLzbjrotL8OOVEwe6WVFztEFchK6+3cHXrclPY7/3EwNZwHVtDtidHur4jkMiOcsm5qdid207/3rKiOiKbccLwh8uJj11vBLys3jx4n6F7TVr1kQtjowZM0ZyedOnT8e2bdsG1cZzkZKcZJxq7yGHC0EQBDFsyEk2Itmog83piemAgZIcC9rtrpi2oTQ3GZ8cbwkTAjhHRcwEl9xk7KpplzVNghMtdDFw7XCCy6AdLlx/cQzargb0FEkoStAipsHPr5qGq2YWYumkXNE8Bp26NR4S86ucmGRYDDERWwDW8vnmmvP51/3FmXEFz8flJp+zdUtCXUGAfIKLy+MLq0vCUd/u4P+eWpgmOU+80maLHMNk6w1urybQ8RsaV5efakJRZni9CYZhcPH4HFw+nRX4Foxlo5GEgktRZtAZU9vmgBQvf1bV3yYMGr/fH1ac/P1DDfjtp+w6C9LY9mVaDHynF1fnhYgvXtlaDYCtVfXHW+fismkFOPTI8rBrIedaGo4YVX5WIQgidnB1XMjhQhAEQQwXGIbhY8WMMRY7gNjeQzknSagQ4I6xw4V7vqhqtsu2TK8gVUht+P08WIcLJ4DFoO1qkJhbRcQFfr8fRwKjl3UaBpMLU/HcDbNwUaBjmkPtCzGVcCGi5fyxWfi/solYOTU/TCgM5TsXjcXPr56KV4ZJZI4SSAouMkSK+Xx+XPrcVix99jPJAnPbK4NRXB8eboTHOzxiA30+P6pbxQ9bwn1Y1x7+IJaeJHZPPfL1yX2u46qZIwAA9kBMF3c8JuSl4NP/XcKv72fvHQEAXKpifSFhkXUOocAjzCHmfjRQJKS6fFnbjsau3n7nO3C6EwA7suqSSXl4adVspJj0sIREMQ5HMXrJhByYDVqsnJqYtbcIgghnQj5bsylVwrlLEARBEPHKuDy2Azw01ldNuLqHsbyH8lFXEQQXgy42nYIluUMTKKSIZQ0Xbj+f7uhB7yAG2nLunESt4UK5HIRi/HPfGf5v4RcoVG1XW31nyONCDIA1F5VENZ9Oq8Gq+aMVbk18o5TDxeby8MLE5qNNuDIgIgDAqXYHNuyqF83/wVeNuGJG4ZDXqzQ7q9vQbnfBYtDCHhBCCtNNKM1NxsZDjfiWxPkkrEcFACv6EUiyAoX5Dp7uQo/Li9e21wAAGq290GoYWIxakYiVbtbj5vNH4y+Bmi9KcfB0J77/t/0AWNHdJVH/Y2ZROv+3QacBnJCsE0Iow8HTnbj+5Z3QaRhU/qKsz3k58ex/lorrUyXpg9eEFKNuWLpE/njreXB5fTANY3cOQRAD44oZhWixObF8cl6sm0IQBEEQUfM/S8ejJCcZXxf8Xlab6+cWwe7y4rIYRqlzzouzXb2iSGqXlxMnYuRwCQgUdW12uL0+WZw2sazhkmUxIC1Jj64eN6pb7JhcOLD4aM7hokvQCNfE3CoiLnj4na/4v8U56OJOC9WthqS3EIQiJEuMpHHL4DYRjpbodAQjpU61O7Diua1h8wsjxuKZg6e7AABfmxTs0PH5gaevm4HfrpqNtcvHh30mRbCPZ49KB9OPZS87OViL6KqXtmPzsWYAwZpDofVQCtKS8NiVU/jXg3lwqzjViTV/3oOvznRJvt/Y1Yuv/2Y76gIxZnNGZWB+caZonlGZZr6GCyBwuHiUrwlEsOyuYWuveHz+PmtM1LbacbqjBwAwfaQ40s8scLTkpIjrYg0XNBqGxBaCOMcw6bX43uJSlOamxLopBEEQBBE1RZlm3L2kNGyQnppYjDrcvaQUYwRpBWqTYTEgy8IOPKxuCaZGxDpSrCDVhCS9Fm6vX7Y+C87hoo9BHRSGYYJuokG4drwxdOeoAQkuRL9sOtyIL6rbBvy5AkFnmbDTNXSEK+UjE0RioNdqcOO8UaJpbk/fxYCjQRgj9bfd9eh1e7GjqhWLnvqEr0nCMMC1s0cCALp7pWu9xButNrbQeKHgWunz+2Ex6lA2rUCybpBG8DAiVbslFM7hAgDHm7rD3g91Jd16wWgwDIP/3H0hACBvEJ3kV720HZuONOG17bWS79/22m7Ra7NBK+rQHpVp5tfPYQy8Tw4X9Xj8/aP83y2BczWU/fUdWPzMp/xrru4Oh1kQKZY9TAUXgiAIgiAIgiCGF5zLRSgEeGIcKabRMCjJZYWowRaaD8Xr5WK5YtOvWpIz+O3h+ol1MRCL1IB6uok+abL2Ys1f9uKGV76Azxd9x+mGXXV8BND0kWmYNyY4enl+cSZGZgQ7ZYZzEV2CIMT85LJJotcuGRwuwliyY43d+P1n1fjWq7tE89z7tXH8dWXLsaYhr1MNWrrZTmzhyP8+jAQ8z31zJsZkmfG9xaX9zisl2gDAnYuKAUDkkLl+zkikm1mBhnO+SNXMiZbPTjTj7jf24aVPKkXTjzWKhZ+vzyxEhyNYz2XppDxkWMS1ajiHi1T0GCE/nQ5xfZ1TEUZgXf3bHaLXoTVaMszB4zhcHS4EQRAEQRAEQQwv+HopAiHAzRVpj2GElZQQNBRiWcMFADlc+oAEF6JPhJ1gHY7wAsdS+P1+/OTfwTixV2+ZK8rky7AY8PmPvoZfXjMN18wagdmjMuRrcB8sGpcNALjtgjGqrI8gzkWSjTrsf3gZXv7WbADyR4oBwDsVZxCq/xamm9AbiJs60WRDlyB6LF7hHC7C2C/uAawvrpo1Ap8+sIQv6jsY7r1kHADAKdi3QlciF11mc3r6jJPqi1abC+8fbMDTHx3np53p7Amb78qZI/h4NQDwI3x9FCmmLp+daBG9vvZ3Owd1HmQKBJdcElwIgiAIgiAIglABKSGAGwwaqxouAFCaEy4EDQVPDGu4AMH+i0E5XHyxramjNIm5VYRsCKN8mqzSkSIfftWIi5/+BPvrOwCER49khoxU5rhh3ig8+82ZqkWK/fHW8/Dx2otQFsPiXQRxLpBhMSDVxObGDlVw8fn8+Mfe06Jps4rSkZ0svq4UpCWJRkbs7CMG0efz83biWOH0eLGjim1jdrIRPymbhHG5yXj8qqmyr+u97y8Uvf7DLXOREjg+woiu1QvH8n9zDhefH+h1D31fTf3pR/D6/Dhy1iqazj0YXjdnJD9N6p7ARVE6ZWgL0T976zrCprXZxYMuQl2vf/r2eWGfETqVhMIiQRAEQRAEQRCEUkhFXbljHCkGBJ03VYLaMkMh1i4RTtiqabXzbYkWr48ixYhzGLsgToYbje30eHH/3w/gwX8dgtfnx11/3Yu6Ngf+L+BqCY0e0cXQrifEoNNQ8UuCUAl9oNOcs+0Olle2VePPO+tE07qdHlHtEYtBi8mFqbj9wmJ+2uGzXfB4ffjFxqN4+bMqfrrf78e1L+/A0mc/U90tUd1iw4//eRBVLTb8RbBNOSlG3HnRWJSvvVhUKF4upo4QFzKfOzroKpxcmAoAGJmRhDRzsLiiWRD1OJBYsUjRkzanB4/+93CY1fj1b88DADzy9Sm4ZtYILBibhTsEwg8HJ8KExpERylDTGv4D4GyIO6k75Ly4eHxO2GcyBOdUpMEXBEEQBEEQBEEQcsIJAbVtdn6wpTsw2DCWkWK886bZNugkCSEeb2wdLiMzzDDoNHB6fDjTEZ5m0RdcX1GiRopJh7sTRAC7K9gh6XCxnSu//6wa/9zHjjg/f2ywNgt3sdhf36leAwmCiEu4h5ih1Nzw+/345QfHwqaf7exBbRsr7L5++zzMGJnG1x75xdXT8H//PoQXt1Tila3VvINj2eQ8lOQkY+OhRv4aNeGhD/G1ibn43bdm8w4KJSl7YRt63T68+eUp0fT8VPlFlr5ISwp2gv/i6mnISTZi6aQ80TwaDYNkow42pwc2pyfq+ht2V2Rx5s8763Dt7JGiaVkBp1KyUYdnvzkz4me5US+HQxwyhDJ0BiL5CtJMaOjqBQB8erwF00em8/MIhbgNd8wX1QPi4JxUQNA1RRAEQRAEQRAEoSSFaUlI0mvR4/aivt2BsTnJfIRVLAWX0VlmaDUMbE4PmrudyBtiXwDvEomRaKHVMBibbcGxxm5UtdgwKssc9We9fBxafAzSl5vE3CpCNoQOF5uTFV+2HGvmpwlrtYwOfLHe2F3PTxsbsPERBHFuoQ90kEtFikU7koPr6OUYlcleY4Sd7vPGZPJiCwBcPqMAXL+vMC7rwKlOVJzqxN1v7BMtc8uxZvxhazUefucrzP/FxzjdIV0cXA6kornKpuWLnCVqoBE8jKUl6fHI16dgYaDGlRCLkRWhbL3RO1zszr5dQx8fbRK9NumjE7qWTMjlPy9VB4aQl84eNj7sZ1cGI+5Cc3m58yLLYsCFpeHnDxCsBQQAyUblRU2CIAiCIAiCIAiNhuH7I6ta7PD7/XzfRCwFF6NOy/dryFHHxRMHosVg67h4+OORmA4XElyIPjnZHIxvcbg8cLg8qDjVyU8TjnDlRq9yF7F0sx5/WT1fnYYSBBFXGLRcpJgPbq8P1/1uBx565xD+vucUpj+6CW99WS/K+PyqncGGXfUiMaarR1z4/rerZoetJ8kg7sRNNeklHSO//bQKV720XbKtb355Cn/5og5NVicWPvlJxFisodDd65ac/oNLxsu+Likev2oqRqQnYdsPl0T9GW4/1rZFny/bX/xY6DGNdiTOmOygeH/hL7egx6VuHNy5BudwKc6x4OnrpgMIHrvdNe34+EgTf6yTTZGdK0LBJZY/bAiCIAiCIAiCOLcQCgFenx9cV0OsO/i5doXGbQ8GvoZLDLcpWJdmgIKLL7ZxaEpD+Q5ERHZUtWLDrqBbxe704vUddRHn73F5Udtq53P7/vXdCzAiPUnxdhIEEX/otcEaLl9Ut2FPXQf21HUAYK8pP/rnIVSc6sQT10yH3+/HH45rgePHcPBsN76oasMT107nRRsAePYbM8Lstg9fPlly3aEiDBA+2uKCkiy+aP3pkKzR+96qwAs3zhrYBvfDJ8dbwqa9/K05mJCvTl2pb50/Gt86f/SAPjO5MA0HTnfhRFP0dVO4TvgR6Ul49da56HC4cNMfdonmsRi0fFxltA+G6UliF1Btmx2TClKjbhcRPT6fH90B90pakh6pgX1v7XWjptWOb/x+JxgGuGMhWzMpuY+oMIsh+J4MEcUEQRAEQRAEQRBRwdVLqWy2iWrLxnogWEmuBR8fldvhEjvRQrifBwJXfybWx0MpEnOriCGz8VADbvrDLn6UK8A6XJqsvRE/s7umHYuf+RTc4PAsS3SZ/wRBJB76QJFzl9cXFg3G8bfdp+D0eNFqc/HT/rXvDM529eLW9bvRFYg1mjM6A9fMHomskKLbqwMdvqFoBbUkJMpK4JKJufjr6vn4eO3FWCgRhfTugbOiOEU5uPdv+0WvH7psEi6dmi/rOuQm08J2tHcPKFKMnddi1GJSQSouKMnGBz9YJJon3WzAT8om4QeXjENBWnSifFqI4NJhd0WYkxgqwjo8yUYdv+9PtTuwq5oVKf1+4P2DDQDE0X2hCOPrxuepIy4SBEEQBEEQBEEInSQuQdR5rDv4S5VwuMRQcCkJRLdVttiijo8H4kMsUhLZz7KtW7fiiiuuQGFhIRiGwTvvvCN63+/3Y926dSgoKEBSUhKWLl2KkydPiuZpb2/HqlWrkJqaivT0dKxevRo2m/hEPHjwIBYtWgSTyYSioiI89dRTcm/KOc33NuwLm2Z3emHQRT5l2kI6wFL6iBkhCCKx4Wy6Lo8PP/zHwYjztdlcOB2hJgcn+HLuBo2GwTfmskXXL59eEHGZHQKhuPoXZaL3JhWk4qnrpkOjYVCam4yizGBRt1TBNetogzLF2Sfmp6D2l5fhjkVjFVm+nJgD7gSHK3rBxcYLLsF9OS4w4oUjxaTDnReNxf8siz5OLT2kzk3o/YaQD0fAfaTVMDDqNBiZwYpirTYX3j/UwM93NiCkXjt7ZJ/L2/Hjr+HD+xYhP21oBSEJgiAIgiAIgiCihXNeVDXbRLVlYx4pNkhHiBQeH7tdsRQtxmYng2HY/pv2AfxOpxouA8Rut2PGjBl46aWXJN9/6qmn8MILL+Dll1/Grl27YLFYsGLFCvT2BkdAr1q1CocPH0Z5eTnee+89bN26FWvWrOHft1qtWL58OUaPHo29e/fi6aefxiOPPIJXXnlF7s0hBLi83n7z+TnOG5MhGtlKEMS5hVEXXYHsdrsL7Tbpm/K++g4AEBWVf/La6Xj3ngvx9HUzIi6zze7k/2aYYLE8APjBJaXISg6670oE75kNOiyekAMAOC6I0TpwqhMPv/MVuhzSdViiwaRnb7cvyhxVpiSWQDSb3Smul+L3A4fPWtHrDq+j8sfPawCIY6ZCH/4GI8anmMSCy7/2nR7wMojo4EUzgxYMw2BkhhmzR6UDALadbA2bf0ph39FuhelJmJhP8W8EQRAEQRAEQajHmGwzNAzQ7fTgbGCQp17LgJGKwVARznnTZHXCGqHWa7TEg8MlyaDly0kMREQih8sAWblyJR5//HFcffXVYe/5/X4899xzeOihh3DllVdi+vTp+POf/4yzZ8/yTpijR4/iww8/xKuvvor58+dj4cKFePHFF/Hmm2/i7NmzAIANGzbA5XJh/fr1mDJlCm644Qbce++9ePbZZ+XeHEKAx+tHa7ez/xkBjKPoEII4pzFL1FHhWDopl/+71eZER4/0Q8bf97Cd6ulJwSgxhmEwfWS6ZJ0WjlAXa7Yg3jA3pA7MJZPy+L+Neg0KAqPwW7uDItCVL23HX76ow6P/PRxxnUK2nmjBqXYH/9rt9aHXzY7eyE0ZPqP8zQHRxB7icNnfxuCq332Bu/66F3anB9/4/U48sfEo/vftA9hd0w4AovjJ0AfaUPEkGkIfwmpa7QNeBhEddgmX0oUS0Xsc2ckUH0oQBEEQBEEQRHxh1GkxKpBowSVY6DSxr6yRlqRHTgr7G6q6ZWi/a7k6KNoYbxfvJhrA9nDunHg4JkqgauZTTU0NGhsbsXTpUn5aWloa5s+fj507d+KGG27Azp07kZ6ejrlz5/LzLF26FBqNBrt27cLVV1+NnTt34qKLLoLBEOyEW7FiBZ588kl0dHQgIyNDcv1OpxNOZ1AwsFrZL5zb7YbbPTRVcShw645lG6LB6fagpTvYiTarKA37T3VJzqtj4n97BsNwOVYEHat4QK9lRMXpOFZOyYPD5cGOqnbc9tqX/S4nxagZ9HF0u93wI9iGkWkG0bIKUoKd/7ZeD9ID7otWW2/YOv+1/wz+d1kp/3Akxe7adtyyfg/0WgZHHlkGALAKBCUt4xs256QpoGnZet2i79OuZlb8+PR4C97+sg67a9p5oYWDQeTvnlaG+0NtmwPrt1Xh5vNHDWk5icxgr4FWB/ucZDZo+c9OK4w8iCIjafDfT4JF7fsVHS+CIAiCIAjiXKA0Nxm1bQ4cbWATLOIlvqo0Jxkt3U5UNtswsyh90MuJB4cLwLp2Pj3eMiiHS6zbrhSqCi6NjY0AgLy8PNH0vLw8/r3Gxkbk5uaK3tfpdMjMzBTNU1xcHLYM7r1IgssTTzyBRx99NGz6pk2bYDabJT6hLuXl5bFugoDwU6Pu1BnU2xgADK4e40WWsR37wfbI6Rg/PP7gl6SmthYbN1ar1VjVia9jRfQFHavYoYcWboTfPI8dqoCzi0G0JsszNSewsed41Ou9pFCDzWc1uHaMFxs3bsTxM1og0I6dn34s8Qn2etfd40RDXSUALQ6fFF7DgtfDrz//KR6eHR6lxfHhKQaAFm6vH3/510ZkmYBOJ7sMDfz4+KMPEWMHc9Qc7WC3paGlAxs3buSn5yZpcCygte+sOAKp45jktoo+I9yHTU2NIe9FxwW5GnzZysDtY3fgY+8fg+v0VyiI/e07rhnoNfCr9sA57LDxx8nlBTSMFj5/+Mn7xWebkaDPyKqj1v3K4XD0PxNBEARBEARBDHNKcpLx8dFmHGtkB9z3VZdaTUpyLdhZ3YaqlqHVcYmXWK6gw2UAgktgcK5OGx/HRG7OqarmDz74INauXcu/tlqtKCoqwvLly5GaGrt8cbfbjfLycixbtgx6/cCjVpTgBzs3hU3LyctHvbMTcLrw7bKFcLg8ePU4Ozo9P92M0x3BwtdjxoxBWdlEtZqrGvF4rAhp6FjFnicOfwaHlR0tv3JKHj443AQAuPjC+Ti7+zTQ2hjVci6YOxNl0wuiXu+lPj/q2h0Yk2UGwzD4wnMEf/vyNKaPSEVZ2flh8/PXO40WF86dhnfqvkJSeg7KyuaI3wfQ6mRwVD8OO6rb8Jdvz+ULy3Mc/PA4cLoOAPDYfh2WTcrF/cvGAfu2w2zU47LLVkS9HbEmt64DLx/7EjqTBWVlCwGw36uN6zfz82QWjAJOh9dTeeH2xXw8GyDeh9k5eSgrG3gtmzKw8WyTHwmKZk8d1OH4Y8sHvKxzgYFcA//yRT0+r2zDr66fBs+xFuD4IYzIy0JZWdBt/Ktjn6HRGh4revllZbK3/VxD7fsV5/AmCIIgCIIgiESGK1B/rJFzuMRH535poI7LQBwhUvAOlxg7d0oGsT0eLxcplpij91QVXPLz8wEATU1NKCgIdp41NTVh5syZ/DzNzc2iz3k8HrS3t/Ofz8/PR1NTk2ge7jU3jxRGoxFGY3gUjF6vj4sO2Xhph88XHgEEAF4/4PKwXwhLkgEmY7CtM4rSecFlYn4K1lxUEhfbohTxcqyI/qFjFTvYGiBsB+3o7GQA7HU6yWhAfqCoWjRkpSQN+BiOLwhGTv64bDIm5Kfi8hmFfS5HyzDITmXb1dnjxr5TVnzzlS/C5nt5K1sYfuPhFtw4Txxp1e4Q1zspP9qMCYGC4UkG7bA6F9PMrGDicHsjtrtJUOuGY8HYLIzKjhxB1dHjHvR+0OuBC0uzsL2yDQDg82NY7dNY0N81cH99Bx57/xgA4KlNJ/G33acAAMkm8eeSDMHv83/vWYi/fVmP88Zk0P6XEbXuV3TMCIIgCIIgiHMBTgjodLCRunEjuOSyv5eH7nBh+2jjxeFyprMHPS5vnzV3OTxxIhYphapnWnFxMfLz87F5c3B0rNVqxa5du7BgwQIAwIIFC9DZ2Ym9e/fy82zZsgU+nw/z58/n59m6dasog7q8vBwTJkyIGCdGRE+PWzoux+31wxVQIA1aDVJMQb3u4csm45rZI/DuPRfiw/suQlEmZbwQxLmOReD+mFIYdBEyDHD3ktKw+e9ZPBaFaSZcMaMQD6yYwE9PTxpa52Bakh63XVgcsbj3E9dMg07D4MUbZyHTzAo1HXYXblm/u8/ldveG10FotYU7AH7zSSUAtibGcMJiZNvrcIpFpMBtAABwtrMHoWRaDGHThKxdNn5I7Xrttnmi1zf9IVwUI6Ln958F4z85sQUALCHnq/AxeNrINPzi6mm4etZIpZtHEARBEARBEAQxKDgnCUe8dO6X5FoAAHVtDn5g+2CIlxoumRYDMsxsv020IhIfKZagDhfZBRebzYaKigpUVFQAAGpqalBRUYH6+nowDIP77rsPjz/+ON59910cOnQIt9xyCwoLC3HVVVcBACZNmoRLL70Ud955J3bv3o3t27fjnnvuwQ033IDCwkIAwE033QSDwYDVq1fj8OHDeOutt/D888+L4sKIweNwSQsuB053whm4EBh1GhSkJeGBFROw7vLJyE8z4dlvzMT0kekqtpQgiHjG7gp21E8fmcbXeRiXm4xMiwGvffs80fwFaSZ8/qOv4YUbZuL2C4N1uixGZc2YN84bha8eXYGlk/N4saDN7uKvd5Fwe8VuQL/fL4pWDCU0fize4dprd3lFzkePYLMbunoBAFfMKOSn9SW4lE3Lx6JxOUNqV2ju7o6qNpxqp5oUgyVSjrEm5MFX2vtKEARBEARBEAQRn6SZ9aKBl4Y4cbjkp5pgMWjh9flR324f9HKChedjv10DreMSdLjEvu1KIPtW7dmzB7NmzcKsWWw++9q1azFr1iysW7cOAPDDH/4Q3//+97FmzRqcd955sNls+PDDD2EyBbPeN2zYgIkTJ+KSSy5BWVkZFi5ciFdeeYV/Py0tDZs2bUJNTQ3mzJmD+++/H+vWrcOaNWvk3pxzkp4Igkunww1/oMfFqGNHvt69pBS3LyyWnJ8giHObhs5e/u/RWRZU/HQ59jy0FCkmduTDkgm52HDHfH4ek14LjYYBwzBIMmjx0GWTcPuFxSjJsSjeVpOevablpLAPY6Fiyws3zkJKiPDT3St2fpxq70FNK/uwNC43WeTSAYC5o4eXAzNZsL1C56NQZ+rqYV0+QgdTYR9xcePzIkeNDYSN9y4SvT50pkuW5Z6LNHf3Sk4P/Q74/SS5EARBEARBEAQxvCjNDfYnxEukGMMwfH2ZodRxCRaej71LhBdcotweLg4tUR0usg+3Xbx4cZ8/yhmGwWOPPYbHHnss4jyZmZl44403+lzP9OnTsW3btkG3k4iMw+3pd55II2IJgiA4Hrp8En7y769w5yJWlE01hUeDCTv1QyO37lg0VtkGSmDSa1GYZsLZLnEn9IUlWRiTbRF17IfGhz2z6TgAIC/ViPK1FwMAZoxMx+2vf4nF43Nw39JxCrdeXkx6DRgG8PtZtxLnNPJKGH8MWg0eWDEBW4414+YFo8Pe/+U10/Dh4UasuUieYzq5MBW7f3IJ5v2cjSj99HgzyqYV9PMpQoo2W3gdHgC4oCRL9PoX10zDzX/cPeRIOIIgCIIgCIIgCLUoyUnGF9XtAAB9HAgTHKU5yTh4ugtVLUNxuMRHDRcgWC8n2u3xxEkcmlIMr3wTQhVCI8VGpCfhTEhOPwkuBEH0x43njcLsURn8SAcphLWguJohsWZkhlkkuBx8ZDlSTXr8+pszsfTZz/jpLd1BwWVnVRvePXAWANBkDU5fOC4bhx9dETcjaQYCwzCwGHSwOT2wO71AwJzikRhToddpcPvCYsnaPABww7xRuGHeKFnbl5tiwg8vnYCnPjyOv+85jYcunywp6hF9I1V36N17LsS0EWmiaReUZOPgT5crHvFHEARBEARBEAQhF8L+iHj6XS6HwyVeargAA98eT2AkpzaOjomcJOZWEUOCixQbm23BP797Ab63pCRsnnhQTwmCiG80GgaTClL7fKgZkZGEMVlmFFn8mF2Url7j+iA1KdihrNcyfJRYqHAkFFx+v7WK/3tmyHbE00PdQOFcR3Zn0PnolRBcjDHaxqmFQVGgvo3quAwUj9eHDgcbC/fbVbMBAKvmj8L0kelgmPD7PIktBEEQBEEQBEEMJzjnBRBfv825dg0pUiwguGjjoYZLYHtqWu28mNIXnFikT9D+5dgfESLu4DrWUpP0mDM6Az6KbScIQiGMOi0+vPdC3D/NC6M+PhwuB04HY8MYhpHseAaAFoEzQOgSePyqqco1TmW4yLcb//AFXtteA0BacImV63HRuGz+7za7dDQWEZl2B7vPNAywYko+dv3fJVh3xeQYt4ogCIIgCIIgCEIexA6X+Onc52rLVLXYBl0vM54cLiPSk2DUaeDy+nC6o6ff+d1eTiyKfduVgAQXIgyuODI3svnKmYWxbA5BEAmOVsMggqYRE2aPSuf/dnkij8xoszn5B5x2QR2MvFSTYm1TG3Mg5q2714NH/3sEQIQaLjESXBiG4UWX1u7waCyib1q72fM202KAVsMgL9UEoy4+hE+CIAiCIAiCIIihUpBm4vs348nhMjrLAp2GgcPlRUNIDdlo8cSRaKHRMBg7ANcO73CJo2MiJ4m5VcSQ4Gq4cBckysQnCOJc4pGvT+H/vnRKfsT5fH6gI+AQsPayzsDvXDQWOSlGZRuoImZDeISUS0JwMcawrldRphkAcKzRGrM2DFdOd7AxbNnJiXPOEgRBEARBEARBcDAMw8d36eOoHrVeq8GoLPa3bFXL4GLF4snhAgTdRNFsj5ur4RInbZeb+DnTiJjT2NWLt76sR2cgzz1J0NF2/7Lx/N9PXDNN9bYRBEGoRUFaEmqeKMPfv7MAv+jnetfV48Z3/7oXtkAU43cuDq95NZxJlqjZYXOHPxBJCTNqwdXMOdLACi7/qTiDn/z7UFS5sec6e+s7AACzRmXEuCUEQRAEQRAEQRDKwAkBhjhzU5QOsY6LxxdfokVJDhuTNjCHS3y0XW7i60wjYso3X9mJH/3zEJ788BgAwCyop3DJpDz+b875QhAEkagwDIN5xZnItBj6nO/ql7bjg68a+deppsQqKp4Ucr2vb3fA6g6fz2KM3X2hMC0JALC9sg12pwc/eLMCG3bV4/1DDTFrk1qcbOrG3rqOQX++usUOABgdGFlFEIS6bN26FVdccQUKCwvBMAzeeecd0ftcHbHQf08//TQAoLa2FqtXr0ZxcTGSkpJQUlKCn/70p3C5xDWtDh48iEWLFsFkMqGoqAhPPfWUWptIEARBEAQRcyYVpAAA0pLiK8GnZACOECl4h0uciBYDcrj4uDi0xJQmEqtniBg0bTYn6tocomkN1mCGoEkf/ALE2wWKIAgiVnBRYhy6OBsxM1SS9GIh5QdvHYRNQnCJpcNFGOH2q00n+L+brYld08Xr82PZr7cCAL78ydIBR9l9dqIF5UeaAKBfYZEgCGWw2+2YMWMGbr/9dlxzzTVh7zc0iIXjDz74AKtXr8a1114LADh27Bh8Ph9+//vfo7S0FF999RXuvPNO2O12PPPMMwAAq9WK5cuXY+nSpXj55Zdx6NAh3H777UhPT8eaNWuU30iCIAiCIIgYc9P80dBpNCibVhDrpogYusMlvkSLEsH2+P1+MH0U6/UG3DnxIhbJDQkuBADg5j/uDpu2p7ad/1s4yjmRCkITBEHIxaSC1Fg3QXZCHY1fnbUCCH8giqXDZURGEv/3+u01/N9++GPRHNWoFowaOtPZM2DB5aPDQWeWto8HYYIglGPlypVYuXJlxPfz88V1xP7zn/9gyZIlGDt2LADg0ksvxaWXXsq/P3bsWBw/fhy/+93veMFlw4YNcLlcWL9+PQwGA6ZMmYKKigo8++yzJLgQBEEQBHFOkGzU4faFxbFuRhhBR4h9UJ+PtxouxdkWMAw7MLXV5urzN6rHG19tl5v4kMCImMNl3wvJMAdHvApzDrOSaSQsQRBEKK/ffl6smyA7oZFikYilwyXZqMOjX58SNv1E0+BGCQ0XDp7u4v9utw/MzeP3+/FlTXBQxezRVMOFIOKdpqYmvP/++1i9enWf83V1dSEzM5N/vXPnTlx00UUwGILP7ytWrMDx48fR0TH4SEKCIAiCIAhiaIwN1Dxp6Xaiq0ciSqIfgg6X+BAtTHotijLYuOr+XDseXixKTGmCHC5ERJ65fgb/d6bFgIWl2dBrGeQkD2wULUEQRKLw/r0L8dmJFhxt6MZ/D5zlp08fmYbclMRz/+kjPPyMSDchyaDjH6JiXdtr5dR8/PTdw6Jp/9h7Go9dOSWmYpCSCAdK3P6nPTj86ApYjNFta7fTg5OBY/fmmvNRnG1RpI0EQcjH66+/jpSUFMnoMY7Kykq8+OKLvLsFABobG1FcLB7RmZeXx7+XkREuuDqdTjidQSHXamWvN263G253dJ0B3HzRzp/I0L4YGrT/gtC+GBi0v1hoPwwd2ocstB8GRjT7y6QF8lKNaLI6cbyhE7OK0ge0Do+XjeXy+7xxc1zGZptR3+7AicYuzB2VGnE/uD1s2+GPn7aHMpR2JWYvBDEgXNxJHsKMojT+b4Zh8Nc75qvVJIIgiLhkSmEaphSm4ZGQzv1YCw5K0dId7HAbmZGE0x09ANhaXht/cBE27KqDXquBPsa1ayJZlc929vI27UTimY+O44+f14im/XlnHb67uCSqz3fY2YLaZoMW54/Nkr19BEHIz/r167Fq1SqYTNLi/pkzZ3DppZfi+uuvx5133jmkdT3xxBN49NFHw6Zv2rQJZrN5QMsqLy8fUlsSCdoXQ4P2XxDaFwOD9hcL7YehQ/uQhfbDwOhvf6UxGjRBg39v3omG3IHFYvc6tQAYbN+2FSeT+p1dFTTdGgAabP7yMNJbD/HTQ/dDt51t++4vdqJZ3L0SNzgcjv5nigAJLgRe2HyS/3tstgXVrWx2oEmXmB2IBEEQQyXVJL59TsxPvPotAESOiUyLgRdc0s16AMCq+aNj0q5QIhXjc3ulBxSEcqrdgfIjTbhx3qioY9Rihcfrw28+qQyb3tnjinoZ7QHBRRgdShBE/LJt2zYcP34cb731luT7Z8+exZIlS3DBBRfglVdeEb2Xn5+PpqYm0TTudWiNGI4HH3wQa9eu5V9brVYUFRVh+fLlSE2N7n7ndrtRXl6OZcuWQa/XR/WZRIX2xdCg/ReE9sXAoP3FQvth6NA+ZKH9MDCi3V97fEdxYtcpWApKULZi/IDW8X97NwNeL762ZDFGZQ5sUIxS2PeexpZ3jsCXnIOysjkR98MvDn8GOJ24aOFCTB0Rn/0pnMt7MJDgQoiKDJv0wY4mTZxkABIEQcQbqUniB6b7lo6LUUuU5buLS9Bqc+KGeUWoabXzdUPSk+LvAfvqWSPw7/1nsHbZeDxbfgIA4HB5ovrs13/zOTocbrTbXfjfFROUbOaQsbu8ktMHMkiCF1ws8XccCYII549//CPmzJmDGTNmhL135swZLFmyBHPmzMFrr70GTUgU5IIFC/CTn/wEbreb/5FbXl6OCRMmSMaJAYDRaITRGO4c1Ov1A+5gGcxnEhXaF0OD9l8Q2hcDg/YXC+2HoUP7kIX2w8Dob3+NCwzerG1zDHi/cnVQTEZD3ByTCQVsWlJ1i13UptD9wI2NNBnj93waSrsSszINMSAcgs6beB/ZSxAEEQ+kmoI33vfvXYj0BHUK5KQY8cKNs3BBSTaSBII853CJJ35+9VT8ZfU83LOkFBPzUwCI72990eFgs1k/r2xVrH1y0RNhm2zO6MQlALxTqTAtTnznBHGOYrPZUFFRgYqKCgBATU0NKioqUF9fz89jtVrx9ttv44477gj7/JkzZ7B48WKMGjUKzzzzDFpaWtDY2IjGxkZ+nptuugkGgwGrV6/G4cOH8dZbb+H5558XOVgIgiAIgiCI2FCaw0Zg91dkXgovX3g+fgbMlwS252xXL+x9/Eb1+FjFJZ7aLifkcCFECDvUCIIgCGlSk4K3zxRj/IkPSiC8P6TFocPFbNBh0bgcAMEoNLszOsGFw2IMbuM/957Gv/efwexR6VgxNR8GrQbj8lLka/AgsYe4dh65YjIe+e8R1LdHny9b28ZGh47JtsjaNoIgBsaePXuwZMkS/jUngtx6663405/+BAB488034ff7ceONN4Z9vry8HJWVlaisrMTIkSNF7/n97A/wtLQ0bNq0CXfffTfmzJmD7OxsrFu3DmvWrFFoqwiCIAiCIIhoKQnUHK1vd8Dp8cIYZXKB3+/nHS7xJFqkmw3ITjag1eZCdYsdE/Oko8683kDbY1wPVilIcCFELBqXjc8rW5EboQAxQRAEASQLRBZhJ30iYxI4IOO99oc50FaHy4NPjjXjvwfP4rErpyLZGP7Y093r5v/mRCW/34/73z4AgHW9vLCFrZly4vGVMOhi+0AodLi8dNNsXvyqaol+RFRdGyvOjM6Kj5xfgjhXWbx4MS+MRGLNmjURxZHbbrsNt912W7/rmT59OrZt2zaYJhIEQRAEQRAKkptiRIpRh26nB7WtDkzIj26QH+duAQCdJr5Ei7E5yWi1taOypTui4OImhwtxLnHT/FFITdLjaxNzY90UgiCIuEUreCiwSHTiJyJCh0uWJb4FlxQTe0xOtfdg7d9Z4WRXdTu2//hrYfOe6ezh/z50hq1R4/T4JJfbbnchP80kd3MHBBeTVpxtwWXTC1DbyrpVGrt64ff7wTD9P7BynxmTRQ4XgiAIgiAIgiCIWMEwDMbmJuPAqU5UtdiiFlw8AsFFq40v0aI0Nxm7a9pR1WyPOA8fhxZnbZeL+JLAiJiQEugs3HDHfKSY9Lhx3ijkpca2Q4kgCCKe4Tr0AcAYY8eDWpgFDpfMOBdcclPYe9ivPz7BTzvT2YMdVeE1Ws50BAWXJqsTTo8XvW7pKLJWm1PmlvbPX76ow4W/3MI7WByBSDFOAMtNNQame6Oq49Jqc6I6ILhE+zBPEARBEARBEARBKMNg6riIHS7xJVqU9LM9fr8f7kCkmDbO2i4X50YvERERn88PW6DzZnwcZNMTBEEMB6YUpuLWBaPx45UTo3IUJAKF6UngnoVKcuLbGZETIRbzq4CDRchpgeACALZeD3rd0g6XFptT9GCrBg+/8xXOdPbg0f8eAQB09bARaJzoZzbo+IETTdb+BSHO3VKUmYTsZIoPJQiCIAiCIAiCiCUluezv64HERIscLnEmWpQG6tJE2h7hT2p9nMWhycW5kYNCRMTu8oCLjhaO2CYIgiAiwzAMHr1yaqyboSrZyUZs+sFCbPn0UxTEOFarPyK5NFu6g4KE3+/HgdNd+Om7h0Xz2J1eeCPUVPj2a18iSa/FP797ASYXpsrX4CiwBoQWLgJtRHoS/15uqhHdLR40d/fyD7cRlxOoWZOeFN8uJYIgCIIgCIIgiHOBoTpctHE2CJQboFnbZofHGz6Y0S2YFm9xaHKRmDISETVc/Ihey5wzsTgEQRDE4BidZUZeUv/zxZq8VGnnxrsHzsIXeDD95QfHcNVL28PmsTk9fKSYlAOkx+3FP/edlrG10cE9UHOOnBEZwQPBCUzNUThcrD3sfT81iQZZEARBEARBEARBxBpu0Fx1i53/vdofnkDReQ0DaOLM4VKYloQkvRZurx+nQhIlALFYlKgOl8TcKiJqbL1sx0uyUXfOxOIQBEEQiU2KSc//nW7W8w+wTVYn/r3/DADg91urJT9rc3rQExBckgzSj0l1bZGL/ylFc3cvTrU7+JozIodLIEKtydrb73I4h0uqYB8RBEEQBEEQBEEQsWFUphl6LYMetxdnu8IFCin4ovNxKFhoNIwgJi38t7PHG79xaHIRf0eFUJXugMMlmeLECIIgiARhYn4K8lNNmDoiFRXrluP+ZeP59/6x9zSaJYQJLiatq8eNzUebAAB6rfRjUpvdpUCrw+GcNgArFi166hPsrmkHIO1wOXi6C1e8+Dl+s+VkxGV2OkhwIQiCIAiCIAiCiBd0Wg3GZLECRbSxYp44LzpfksPVcZEQXHzBSDFdnLZ/qFAv+zlOd8DhkmKkjheCIAgiMTDptfj0gcX8w+eMonT+PYtRi1tf+1I0//3LxuPgmS40dPWioasHdW0OAGx8l07DiAoSAkC7SoLL0QZr2DTOfTMyw8xPsxjZx7n3DzUAAA6d6cJN542UXGZtwJ0zMmMYZMMRBEEQBEEQBEGcA5TkJONksw1VLXYsntD//EGHS3wKFlxdmqpWO4pCSqxyv6/jMQ5NLsjhco7h9/vxyfFm/O7TKrTZnMFIMXK4EARBEAmESa/lHSqF6Um4etYIAIDfHy5kpJv1fETXmY4evhbK41dOxX+/vxC3XTBGNH9dmyOq+K6hIpV3y8E5cgBgbKAooZBtJ1sBAC6PD09/dIx3xnBiUrHEZwiCIAiCIAiCIAj14WKwo3a4BESLeC06XyKoSxMK13ZdhESJRCBxt4yQ5M0vT+Hbr32JJz88hu9u2Aebk40WSTGS4EIQBEEkLksm5gIAOhzh7pTUJD2ykw0A2EixM52s0FGal4xJBal45OtTMG9Mpugze2o7FG4x0G5zRnzPpNfyf6+cWhD2fmNAENqw+xRe+qQK3/j9TgCAw8U6ZChSjCAIgiAIgiAIIj4I1jyJTnCJe4dLbjBSzC8OjIDHy0aKxWvb5YAEl3OMX206wf+9u6adjxQjhwtBEASRyHADCxq6wp0pxdkWmA3s+109bjQEChWOFBSmf/32eXjpptm4YkYhAKCmNboH4aHARZdlWgyi6fd+rVT0WqthoA8Z2cTVajlyVuzmcXpYwcWgo0dAgiAIgiAIgiCIeKA0JwUAUBW1w4UVLeK1hsvoLDM0DGBzemB1i9/zxLlYJAf0a/scozNkZO/j7x8FACSTw4UgCIJIYLg6J1IOlwn5KbAYWcdIVYsNPj8rSGQnG/l5kgxaXDa9ABPz2Qfh6tZwa7TctAUEl2/NH4Ujj63A3UtK8N3FJbhv6fiweU06rej1W3tPAwDc3uBwIr/fD5eHfTA3kuBCEARBEARBEAQRF3Ax0W12FzqiqBkadLjE5+86o06L0VnsNjX1iIUVj5cixYgEI7TwL8fWky0qt4QgCIIg1IMTVHrdPtH01759How6Le9w4TJmM80GyQJ+Y7Mt/HwbDzXgqzNdsrfV7fXB7vSIHC5mgw4PrJiIH106UbJdqxcVi1632934wU4d3v+qUTDNBScvuIgFGoIgCIIgCIIgCCI2WIw6FAbqdEYTK8bXcIljl0hJDie4iKdz7hxyuMiM1+vFww8/jOLiYiQlJaGkpAQ/+9nP4PeLR2GuW7cOBQUFSEpKwtKlS3Hy5EnRctrb27Fq1SqkpqYiPT0dq1evhs2mfMRHrDjT2YPqKLP8BopU/jtBEARBJAopxvCaJfdeMg6Lx+cACAoy3IOrSS/9iDQywwwAqDjVie9t2IcrfvO57G29/uWdmPv4x6gJuGgyBU6bSHxvcSlevWUuyv/noojzfFHdDqebjRQzRtg+giAIgiAIgiAIQn1K+Lon/ff9xnsNFyC4PREdLnHc9qESk1/bTz75JH73u9/hN7/5DY4ePYonn3wSTz31FF588UV+nqeeegovvPACXn75ZezatQsWiwUrVqxAb28we33VqlU4fPgwysvL8d5772Hr1q1Ys2ZNLDZJFS785RZ87VefobWPIrp94Q+tUiRg7bLweBKCIAiCSBRyUsSiRV6qEWuXjQfDsA95oY6PJqv0vTY7RVxPxe/v+/46UPx+PypOdaLH7cWxxm4AQFZIDRcpDDoNlk7Ow7i8FFw9a4TkPJ+daIYrUKDQkMD2bYIgCIIgCIIgiOFGSQ4rUFRGUceFEy3i2+HCCS7i6XwNlwT+TRqTLduxYweuvPJKXHbZZRgzZgyuu+46LF++HLt37wbAdjY899xzeOihh3DllVdi+vTp+POf/4yzZ8/inXfeAQAcPXoUH374IV599VXMnz8fCxcuxIsvvog333wTZ8+ejcVmKYrbG4xA+bKmPerP9bi8OHLWipZuJ9/JIoVJT9EiBEEQROKSZBDf50KLxo8LjL7h6Ak4QULJsoS7Tay9niG2Lki3M3xZo7PMA1rGr785E3+747yw6Z8cb+Ej1cjhQhAEQRAEQRAEET+U8g6X/uuFeodBpFhpRIcLRYopwgUXXIDNmzfjxIkTAIADBw7g888/x8qVKwEANTU1aGxsxNKlS/nPpKWlYf78+di5cycAYOfOnUhPT8fcuXP5eZYuXQqNRoNdu3apuDXq0Cvo+Gnujt7hct3LO1D2wjac9/OPcbShW3KeX39zxpDbRxAEQRDDiVCHR26qKbrP6TTIMIvjyQbrPJUitEDi0km5fIzZQOBqzQAAw7APsy2C5weq4UIQBEEQBEEQBBE/DMjhEqiDoo9jlwi3PV0uBt2CQYp8HJo2cQUXXSxW+uMf/xhWqxUTJ06EVquF1+vFz3/+c6xatQoA0NjIFnjNy8sTfS4vL49/r7GxEbm5uaL3dTodMjMz+XlCcTqdcDqDnQ1WqxUA4Ha74Xa75dm4QcCtu6822HqC7fZ6vVG39/BZK//3VS9tl5xnYq4lpts/nIjmWBHxAR2r4QMdq+HDcD9Wf/72HNzy2l4AgF7D9Lkd180eEfH9TIsBHY7ge02dDoxK77/OSjQ0dzlEr7+/ZOyg9neyQBPy+4GF47Lw6YlWfprG78UwPYwJh9rfq+H6/SUIgiAIgiCIRIZzhJzqcKDX7e0zjWg4OFzSkvTISTagxeZCTasdmSlJAAA33/b4FYuGSkwEl7///e/YsGED3njjDUyZMgUVFRW47777UFhYiFtvvVWx9T7xxBN49NFHw6Zv2rQJZvPAR4/KTXl5ecT32noB7nBt23cEr392FIvyfTgvJ3JuvNMb/Exf7Px8K05EN7CXCNDXsSLiCzpWwwc6VsOH4XqsztgB7r7osHVj48aNIXME75nzdXXYuLFOcjlOhxZA8MH2R2/uxv9Ol44gGyiHOxgA7IP16GQ/qvd9jtpBP0MHt2dBUiM+Fbz+eNNHiONn83MStb5XDoej/5kIgiAIgiAIglCV7GQDUk06WHs9qGm1Y1JBasR53cOk8HxJjgUtNheqWuyYU5wNAPDy7pz4bvtQiIng8sADD+DHP/4xbrjhBgDAtGnTUFdXhyeeeAK33nor8vPzAQBNTU0oKCjgP9fU1ISZM2cCAPLz89Hc3CxarsfjQXt7O//5UB588EGsXbuWf221WlFUVITly5cjNTXySaw0brcb5eXlWLZsGfR6veQ8lc02YP8OAMAnDawCWFepxU9vXR5xuWc7e4Dd28Km56caMTIjCXvqOgEAK5ZdgtwUeUbmJjrRHCsiPqBjNXygYzV8GO7Hqq7NgacOfg4AyM3OQFnZPNH7/27bh09PtOL2C0bjqpUTIi7nj/Vf4LQ96CA9ZWdwweJlSDcPfZ/07j8DHDuMRaVZWH/rnEEvx+12Q/PFFvj87EPs7deVYbdzPzYfa8EdC8fg8hXjh9xWQh7U/l5xDm+CIAiCIAiCIOIHhmFQmpuMffWdqGy29Sm4DAeHCwCMzbHgi5oOVLcG69JwYlG8t30oxERwcTgc0ITYhrRaLXwBhau4uBj5+fnYvHkzL7BYrVbs2rUL3/3udwEACxYsQGdnJ/bu3Ys5c9gOiS1btsDn82H+/PmS6zUajTAaw4UFvV4fFx1Hkdrx6rZqUTRY6Gci4YV0przZqEN1a3B0Y7LJGBfbP5yIl3OG6B86VsMHOlbDh+F6rFLMwWcAg04btg3P3zAbn1e2YunkXOj7qHGik8jJbeh2ISdt6G5Zay/7LJSVPPR7c7YRaO5l/9br9fjjbfPg9fmhYdiHeSK+UOt7NRy/uwRBEARBEARxLlCSwwouVS1913HharjEex0Uro5LVUtQcOHEIj1FisnLFVdcgZ///OcYNWoUpkyZgv379+PZZ5/F7bffDoDtBLjvvvvw+OOPY9y4cSguLsbDDz+MwsJCXHXVVQCASZMm4dJLL8Wdd96Jl19+GW63G/fccw9uuOEGFBYWxmKzFMHa68bj7x8d1GedHp/kdLNBi2rBiW7UJ+4JThAEQRAcwgxcKTdKmlmPy6YXhE0PRSd4MCxIM6Ghqxen2nswfWT6kNvYamcHS2RYDENe1m3jvfigLRP/u2IiPy2RRxERBEEQBEEQBEEMZ7g6LpXNfQsu3mFSB2VstgWAWHBxe9n+6kT+bRoTweXFF1/Eww8/jP9n777jo6jTP4B/tqf3TkIIvUjvgohHFRUVFUU8O+gpeh6e+rPXs593tpNTzw6KWBEUiYKAgJSQ0CGUAIH0smmbbJ3fH7M72U02dXezJZ/368WL7Ozs7He/s9nszDPP89x1110oKSlBSkoK7rjjDjzxxBPSOg8++CDq6uqwePFiaLVaTJ48GevWrUNQUGOzkeXLl2PJkiWYNm0a5HI5rrrqKrzxxhveeEke02DsfE34lgIukcGOJ5k0St/+5SQiInKHEHVjwCU9NrTT27lkWDJ2nqpAz5gQDE2NxNp9hSiubnDHEPH7MbGxfXKk683VeoQC31wzgRkNREREREREfsBZRogzJov/9HABgDMVOhjNFqgUcilY5OvZOa7wSsAlPDwc//73v/Hvf/+7xXVkMhmeeeYZPPPMMy2uExMTgxUrVnhghL6jweA8aNIe+haCNVEharxy9TA8/+NhvH/TGJYVISKibkFlVwqsV2zny3/dMCEdCeEajE6Pxn9+OwEAWLOvALdOznBpfFU6o1RCNDky2KVtERERERERkX+xZbicLK2F2SK0mAXiLz1ckiI00MgF6C3A6fI69E0Ih8nsH8EiV3gl4ELtpzOaOv3YljJcokNUuGZMGq4Zk9bpbRMREfmj8CAlahpMmNI/vtPbUMhluHioWHosTCN+ldpzRuvy2A4XNfZrG9Mr2uXtERERERERkf9IjQ6GWiGH3mRBgbYeaTHOLxT0lwwXmUyGhGAgvw44XmINuEgZLoFbcSlwX1kA+N/veZj97y0t3t9WYkpLAZf4MNfLlBAREfmjDfdPxW9/n+q2DBL7ni81DUaXtmWr03th/3hmuBAREREREXUzSoUcGda+J631cTH7UR+UxGAxwHKiVHw9Jos4dl8PFrmCARcf9uyaQ63eLwiAyew8qLJixxks+mQ3ACA2VA21NWo4qW8sFoxnZgsREXVP8eEa9IrrfP+WpgYlRyAiSMxyKaxyrY+L7Qv1gKRwl8dFRERERERE/qdPgq3RfMsBF3/JcAHsAi7W412ppFgAZ7iwpJif+z6nAFeNTnVYVlqjxyPf7pdun9cjEu/eOBoapaLpw4mIiMhF6bGh2H+uCrnFNeif2PlgyelysTFihhsDQkREREREROQ/+saLfVxazXCRerj4ftAi0Vq84TgzXMjbBEFo13rOop0lNY5X2IYHKRlsISIi8pBhqZEAgEMF1W2s2boKnViSLC5M4/KYiIiIiIiIyP/0SWg74OKvGS6CIPjV2DuLARcfVWcwO10+tkkT3eTI5v1YbKlZNn2tv6hERETkfkkR4t/i8lqDS9upqNMDAGJCVS6PiYiIiIiIiPxPH2uGS2slxaQMF4XvBy3ig8TgSp3BjKLqBruSYr4/9s5iwMVHnSqrc7q8aeKL3Ek0UG9y7OuSwsa7REREHhMTpgYAlNd1PuDy6Lf7kV9RDwCIDlG7ZVxERERERETkX2wBl0qdEeW1eqfr+FOWiEIO9IwRz02fKKmzG3vghiUC95X5uSNFNc2WfXrbODQtNKY3Wpqt98PeAofbwWqWEyMiIvKU2FAxQFKpEwMuVToj3vz1GPIrdO3exvIdZ6SfU6J4oQQREREREVF3FKxWoIf1mPBEqfML8s3WPigKPwi4AI1BpOMlNTCZ/WvsncGAi48q0NY73L5xYjou6BePMI3SYbnB3Dzg8ukfpx1uhzDgQkRE5DG2jJQKa4bLv37JxT8zc3Hlf7Z2eFs3n98LQSr+3SbqDjZv3ozLLrsMKSkpkMlk+O677xzul8lkTv+98sor0joVFRVYuHAhIiIiEBUVhdtuuw21tY7lJ/bt24cLLrgAQUFBSEtLw8svv9wVL4+IiIiIOqlvG31c/CnDBQB6x4UCEANItnJoKpYUo65WXO3Y+N5WSmzh+J5QKxp3W9MMF9ub1h4zXIiIiDwnNqwx4HKitBYfbTsFACirNUCra7vMWIOxsW/bnRf28cgYicj31NXVYfjw4Xj77bed3l9YWOjw74MPPoBMJsNVV10lrbNw4UIcPHgQmZmZWLNmDTZv3ozFixdL91dXV2PmzJlIT09HVlYWXnnlFTz11FN49913Pf76iIiIiKhz2urjYrb2QVH4SVmuPvFiwOV4SS2MUg8X/xh7ZyjbXoW8obTGsUbfthNlAICZQ5Jw8JlZ+Mfaw/ho2ykYzGaH9XQGU7NtBfNKWSIiIo+JCdUAAKrqjVj43g6H+/afq8IF/eJbffzzPx6Wfg7R8G82UXdx8cUX4+KLL27x/qSkJIfb33//PS666CL07t0bAHD48GGsW7cOu3btwpgxYwAAb775JubMmYNXX30VKSkpWL58OQwGAz744AOo1WoMGTIEOTk5eO211xwCM0RERETkO9qb4eIvWSK9420ZLrXonyi+Nn/JzumMwA0l+bl6o2MgZf6YNOlnlUIOjVLcdXqjBb8dLcGE53/FptzSZo8DALWSu5mIiMhTIoNVsH1XLGqSoXrnp1ltPv6T7Y2lQEN4kQQROVFcXIy1a9fitttuk5Zt374dUVFRUrAFAKZPnw65XI4dO3ZI60yZMgVqtVpaZ9asWTh69CgqKyu77gUQERERUbv1sQtQOGOrcOQvfVBsJcVKavSo0BkBAEo/yc7pDGa4+CidQQycDE+LwoSMGNwyKcPhflvAxWC24OYPdwEAbvpgJzY/cFGzbTkrM0ZERETuoZDL0DMmBKfKdc3uqzM0vxCiNYGcVk1Enffxxx8jPDwc8+bNk5YVFRUhISHBYT2lUomYmBgUFRVJ62RkOB5HJCYmSvdFR0c3ey69Xg+9vjHbvrq6GgBgNBphNBrbNV7beu1dP5BxLlzD+WvEuegYzpeI8+A6zqGI89Axrs5XekwQAOCcth7VdQ3N2kUYTGKFI5kg+PQ+sY0tSCEgMVyD4ho9jhRWAQBksPjF2DuDARcfZQu4LJ3RHxf2b16KRGO9AlbX5ERO0wyXHlHBGJAU7qFREhEREQAMSYl0GnABgHqDudV+alEhKmh1vvtFk4i874MPPsDChQsRFBTk8ed64YUX8PTTTzdbvn79eoSEhHRoW5mZme4alt/jXLiG89eIc9ExnC8R58F1nEMR56FjOjtfggCEKBXQmWT49PufkRrqeP+p03IAchw/dhQ/6o64PlAPy8zMRKRcjmLIrVk7MhzPPYof63x37Dqd8+P79mDAxUfVW3uxtNR/JT5MrBdf0qTXi33AZev//QnxYRqWFCMiIvKwwSkRWLu/ULqdHhuC09YAzBPfH8Ar1wx3WD/7TCUe+GofhvaIRESQGHD5759Hd+mYicg/bNmyBUePHsXKlSsdliclJaGkpMRhmclkQkVFhdT/JSkpCcXFxQ7r2G437RFj8/DDD2Pp0qXS7erqaqSlpWHmzJmIiIho15iNRiMyMzMxY8YMqFSqdj0mUHEuXMP5a8S56BjOl4jz4DrOoYjz0DHumK9PC3Yi64wWyQNGYs6wZIf7Nn61HygtxJDBgzBnUi83jNgz7Odhl+U4cnfkwyKIZdDOGzIYc85P9/IIW2bL8u4MBlx8lC1zJaSFK2KTIsWr2zbnljosr7c+rm9CGHpEBXtwhERERGQza0gSXvn5KACgV2wIfr1/Kvo88iMAYFXWWYeAywe/5+GZNYcAiE0QZdayu71im1y2REQE4H//+x9Gjx6N4cMdA7cTJ06EVqtFVlYWRo8WA7YbNmyAxWLB+PHjpXUeffRRGI1G6WA/MzMTAwYMcFpODAA0Gg00Gk2z5SqVqsMnDDrzmEDFuXAN568R56JjOF8izoPrOIcizkPHuDJffRPCkXVGi1MVDc22YYF4EKlWKv1if6hUKvRPcrxwR6Py7bG7MjamPvgoW6ZKSyVIUqKclxNosD2OTXeJiIi6TN+EMOnnijoDFHIZPrx5rLRs3YEi6WdbsMVGsLZaC9XwbzdRd1JbW4ucnBzk5OQAAPLy8pCTk4MzZ85I61RXV2PVqlW4/fbbmz1+0KBBmD17NhYtWoSdO3di69atWLJkCa677jqkpKQAAK6//nqo1WrcdtttOHjwIFauXInXX3/dIYOFiIiIiHyP7RjzRElts/ts/bqVClmXjskVfePDHG4r5P4z9o5iwMUHWSyClOESpnGehJQU6Tx7pZ4BFyIiIq+qbhDLgkaGNF4R8+7mE20+rqW/+UQUmHbv3o2RI0di5MiRAIClS5di5MiReOKJJ6R1vvjiCwiCgAULFjjdxvLlyzFw4EBMmzYNc+bMweTJk/Huu+9K90dGRmL9+vXIy8vD6NGjcf/99+OJJ57A4sWLPfviiIiIiMgltoDLcScBF5PFAsC/ghZ9EhwDLio/ChZ1FI/sfVB5nQFmiwCZDIgNVTtdJ0yjRLhGiRq9SVoWF6aRSooFtdKcl4iIiDxPJW+8riU7Xwu9yQyN0vnfZ6Vchogg302nJiL3mzp1KgRbilsLFi9e3GpwJCYmBitWrGh1G8OGDcOWLVs6NUYiIiIi8o4+1oyQvLI6mC2CQ3BFynDxo4BLQrjG4Vy2Qh64eSCB+8r8WHF1AwAxgKJUtLyLokIdT8wYTGa7DBfuWiIioq702nyxv8I/rjwPADAgKRy948W+LIIAnK2sR2WdweljEyOCIPejL8tERERERETkOT2ig6FRymEwW5BfoXO4z2QNuPhT0EImk6G3XZZLIGe4+M9e6UYKtPUAgKQI531abJpeJdtgsrCHCxERkZfMG5WKvU/OxMLx6QAAtVKOX/52ofT3vN5gxpkmX5RtWurNRkRERERERN2PQi5DRpx4Ad+JUseyYv6Y4QI49nHxp3JoHcWAiw86WVYHANIvVUua/lIZTBbU6a0BF5YUIyIi6nKRwY7Zp3K5DCHWv8l1ehMe+nqf08e11JuNiIiIiIiIuqeW+riYzLYMF/8KWvRJaDzXrfSj7JyOCtxX5sdsGS5pMa2ffJHLmv9SVdUbAQBBzHAhIiLyCbaLIM5U6HCkqMbpOimRzHAhIiIiIiKiRrY+LoGY4eJvY+8IBlx8UG2D2Dyorea5zgKBu05VAIB0NS0RERF5V4317/oDXzVmtyQ3CbAkMeBCREREREREdlrMcLFYAPhjhotdwIU9XKgrVVtPzIS3EXCRofkbc/+5KgDs4UJEROQrnPVt2fZ/f0LOEzOk29Eh6q4cEhEREREREfm4xgyXOgiCIC2XMlz8LGjRMyYEKuuYWVKMutS+s1oAQFiQstX1Wgti9rFL0SIiIiLfER2igkwmc+j3EhnS+kUWRERERERE1L30jg+FTCa2kCirNUjLTRZbDxf/OrWvUsgxMCkCABATGrgXHfrXXukGKuoMKKnRAwDUitZ3j8yuh0vWY9Md7juvR6T7B0dEREQd9twV5zncrtSJ/dZkMhmeumwwrhmdiin94r0xNCIiIiIiIvJRQSoFUqPFHt/2fVz8tYcLALyxYCT+++fRGJQc7u2heEzrKRTU5U6X10k/944PbXVd+zp99lfJAuIvJBEREXnfDRPSse1EGX7cX9TsvpsnZXhhREREREREROQP+saHIb+iHsdLajGhdywA+wwX/wu4ZMSFIiOu9XPe/o4ZLj5Ga73qFQD6J7Ye6dMoG3efskk2TJCKu5aIiMhXBHJ9WiIiIiIiIvKMvtZG88dLAiPDpTvg0b+XWSwC/v7VfrxzSI6qeiP+mXkUADClf9ulRYKbZLHYVRhjhgsREZEPMVks3h4CERERERER+Rlbn277kmK248umF+CTb2BJMS/Lzq/E93sLAcgx5vmN0vLYdjQOahpUkctkMAtihFPFXzgiIiKfoTc2BlzCg/j1i4iIiIiIiNpmy3A5YZfhYjIzw8WXee2s/Llz53DDDTcgNjYWwcHBGDp0KHbv3i3dLwgCnnjiCSQnJyM4OBjTp0/HsWPHHLZRUVGBhQsXIiIiAlFRUbjttttQW1vb9Kl8Wk2DyenyEHXbGSrNAy5uGRIRERG5WVRI44UUH9w81osjISIiIiIiIn9hy3ApqGpAnV48j+zPPVy6A68EXCorKzFp0iSoVCr89NNPOHToEP75z38iOjpaWufll1/GG2+8gWXLlmHHjh0IDQ3FrFmz0NDQIK2zcOFCHDx4EJmZmVizZg02b96MxYsXe+MldZre5LzEiO0XqDX3Te+HcI0Si6f0BgDIZPwlIyIi8kV/ndYP0wYm4Ju7zsfYXjHeHg4RERERERH5gehQtVQJ6WRpHQD2cPF1Xqlp8dJLLyEtLQ0ffvihtCwjI0P6WRAE/Pvf/8Zjjz2Gyy+/HADwySefIDExEd999x2uu+46HD58GOvWrcOuXbswZswYAMCbb76JOXPm4NVXX0VKSkrXvqhOaingYv29aVVaTAiyn5gh1evTKOUwtLA9IiIi8p6esSH4HzNbiIiIiIiIqIP6xIehvK4CJ0prMTQ1EiazeP6XGS6+ySsBl9WrV2PWrFm45pprsGnTJvTo0QN33XUXFi1aBADIy8tDUVERpk+fLj0mMjIS48ePx/bt23Hddddh+/btiIqKkoItADB9+nTI5XLs2LEDV155ZbPn1ev10Ov10u3q6moAgNFohNFo9NTLbZWuweB0+V+m9Gr3mIwWMwBgTHoUNh4tQ3SIymuvJ9DZ5pXz6/u4r/wH95X/4L7yH9xX/qOr9xXfE0RERERE/qVPQhh2nqrAcWsfl8YMF/bw9kVeCbicPHkS77zzDpYuXYpHHnkEu3btwr333gu1Wo2bbroJRUVFAIDExESHxyUmJkr3FRUVISEhweF+pVKJmJgYaZ2mXnjhBTz99NPNlq9fvx4hISHueGkdllUkA+DYi+WFsSbk7t6M3A5u609hgCFRjouS6/Hjjz+6bYzUXGZmpreHQO3EfeU/uK/8B/eV/+C+8h9dta90Ol2XPA8REREREblHn/hQAMCJUjHgIvVwUTDDxRd5JeBisVgwZswYPP/88wCAkSNH4sCBA1i2bBluuukmjz3vww8/jKVLl0q3q6urkZaWhpkzZyIiIsJjz9ua4m2ngbyjDsuuuGS2VCaso65zx6CoRUajEZmZmZgxYwZUKpW3h0Ot4L7yH9xX/oP7yn9wX/mPrt5XtgxvIiIiIiLyD30TwgDASYYLAy6+yCsBl+TkZAwePNhh2aBBg/D1118DAJKSkgAAxcXFSE5OltYpLi7GiBEjpHVKSkoctmEymVBRUSE9vimNRgONRtNsuUql8trJCJOTXi3BQc3HSL7Fm+8Z6hjuK//BfeU/uK/8B/eV/+iqfcX3AxERERGRf+kTLwZcTpXXwWS2NGa4MODik7xS6G3SpEk4etQxqyM3Nxfp6ekAgIyMDCQlJeHXX3+V7q+ursaOHTswceJEAMDEiROh1WqRlZUlrbNhwwZYLBaMHz++C16Fe+iNYpMjGZxEXoiIiIiIiIiIiIio2+oRFYwglRxGs4BT5XXScma4+CavBFz+9re/4Y8//sDzzz+P48ePY8WKFXj33Xdx9913AwBkMhnuu+8+PPfcc1i9ejX279+PG2+8ESkpKbjiiisAiBkxs2fPxqJFi7Bz505s3boVS5YswXXXXYeUlBRvvKxOaTCJDe/Tw7w8ECIiIiIiIiIiIiLyKXK5DL3jxJPHR4pqpOXMcPFNXikpNnbsWHz77bd4+OGH8cwzzyAjIwP//ve/sXDhQmmdBx98EHV1dVi8eDG0Wi0mT56MdevWISgoSFpn+fLlWLJkCaZNmwa5XI6rrroKb7zxhjdeUqfp9GLAZUCkgKWXDEfvBO/0kiEiIiIiIiIiIiIi39M3IQyHCquRaxdwUcq9kktBbfBKwAUALr30Ulx66aUt3i+TyfDMM8/gmWeeaXGdmJgYrFixwhPD6zJ1BhMAQKMQcPF5SayrTURERERERERERESSvgnMcPEXDIN5WZ3eFnDx8kCIiIiIiIiIiIiIyOf0iRcDLrnF9hkuDLj4IgZcvKzOWlKMARciIiIiIiIiIiIiasqW4XK6QgcAkMnE3i7kexhw8aLDhdX4/XgZAAZciIiIiIiIiIiIiKi5XnEhkMsAQRBvM7vFdzHg4kXrDxZLP4coBS+OhIiIiIiIiIiIiIh8kUapQM+YEOk2+7f4LgZcvOiMNQUMADLCvTgQIiIiIiIiIiIiIvJZtj4uAKCU87S+r+Ke8aLSWj0A4KV5Q6BgUJKIiIiIiIiIiIiInLD1cQEAJU8m+ywGXLyowWAGAASr2MCFiIiIiIiIiIiIiJxzzHBhwMVXMeDiRfVGa8BFzYALERERERERERERETnXxy7DhT1cfBcDLl7UYA24BCkZcCEiIiIiIiIiIiIi5/qyh4tf4J7xIluGS5CKu4GIiIiIqDvZvHkzLrvsMqSkpEAmk+G7775rts7hw4cxd+5cREZGIjQ0FGPHjsWZM2ek+4uKivDnP/8ZSUlJCA0NxahRo/D11187bKOiogILFy5EREQEoqKicNttt6G2ttbTL4+IiIiI3CwyRIW4MA0AZrj4Mp7p96IGowUAEMQeLkRERERE3UpdXR2GDx+Ot99+2+n9J06cwOTJkzFw4ED89ttv2LdvHx5//HEEBQVJ69x44404evQoVq9ejf3792PevHmYP38+srOzpXUWLlyIgwcPIjMzE2vWrMHmzZuxePFij78+IiIiInK/PvGhANjDxZcpvT2A7sxWUiyYARciIiIiom7l4osvxsUXX9zi/Y8++ijmzJmDl19+WVrWp08fh3W2bduGd955B+PGjQMAPPbYY/jXv/6FrKwsjBw5EocPH8a6deuwa9cujBkzBgDw5ptvYs6cOXj11VeRkpLigVdGRERERJ7SNyEMO/IqmOHiwxhw8ZLyWj1q9SYAgIYlxYiIiIiIyMpisWDt2rV48MEHMWvWLGRnZyMjIwMPP/wwrrjiCmm9888/HytXrsQll1yCqKgofPnll2hoaMDUqVMBANu3b0dUVJQUbAGA6dOnQy6XY8eOHbjyyiubPbder4der5duV1dXAwCMRiOMRmO7xm9br73rBzLOhWs4f404Fx3D+RJxHlzHORRxHjrGk/OVERsMAFDIfH9/+PP7xpUxM+DiJd9mnwMAJEcGISZE7eXREBERERGRrygpKUFtbS1efPFFPPfcc3jppZewbt06zJs3Dxs3bsSFF14IAPjyyy9x7bXXIjY2FkqlEiEhIfj222/Rt29fAGKPl4SEBIdtK5VKxMTEoKioyOlzv/DCC3j66aebLV+/fj1CQkI69DoyMzM7tH4g41y4hvPXiHPRMZwvEefBdZxDEeehYzwxX7U1AKCEpaEaP/74o9u37wn++L7R6XSdfiwDLl5y2+QMlNToMX9MGtRKZrgQEREREZHIYhF7PV5++eX429/+BgAYMWIEtm3bhmXLlkkBl8cffxxarRa//PIL4uLi8N1332H+/PnYsmULhg4d2qnnfvjhh7F06VLpdnV1NdLS0jBz5kxERES0axtGoxGZmZmYMWMGVCpVp8YRKDgXruH8NeJcdAznS8R5cB3nUMR56BhPz9fQUWXolxCG5Migtlf2In9+39iyvDuDARcvkclkeGTOIAD+mVZFRERERESeERcXB6VSicGDBzssHzRoEH7//XcAwIkTJ/DWW2/hwIEDGDJkCABg+PDh2LJlC95++20sW7YMSUlJKCkpcdiGyWRCRUUFkpKSnD63RqOBRqNptlylUnX4QLkzjwlUnAvXcP4acS46hvMl4jy4jnMo4jx0jKfma9rgZLdv05P88X3jyniZWkFERERERORD1Go1xo4di6NHjzosz83NRXp6OoDGMgdyueMhnUKhkDJkJk6cCK1Wi6ysLOn+DRs2wGKxYPz48Z58CURERERE3RIzXIiIiIiIiLpYbW0tjh8/Lt3Oy8tDTk4OYmJi0LNnTzzwwAO49tprMWXKFFx00UVYt24dfvjhB/z2228AgIEDB6Jv376444478OqrryI2NhbfffcdMjMzsWbNGgBiRszs2bOxaNEiLFu2DEajEUuWLMF1112HlJQUb7xsIiIiIqKAxgwXIiIiIiKiLrZ7926MHDkSI0eOBAAsXboUI0eOxBNPPAEAuPLKK7Fs2TK8/PLLGDp0KN5//318/fXXmDx5MgCxzMGPP/6I+Ph4XHbZZRg2bBg++eQTfPzxx5gzZ470PMuXL8fAgQMxbdo0zJkzB5MnT8a7777b9S+YiIiIiKgbYIYLERERERFRF5s6dSoEQWh1nVtvvRW33npri/f369cPX3/9davbiImJwYoVKzo1RiIiIiIi6hhmuBAREREREREREREREbmIARciIiIiIiIiIiIiIiIXMeBCRERERERERERERETkIgZciIiIiIiIiIiIiIiIXMSACxERERERERERERERkYsYcCEiIiIiIiIiIiIiInIRAy5EREREREREREREREQuUnp7AN4kCAIAoLq62qvjMBqN0Ol0qK6uhkql8upYqHXcV/6D+8p/cF/5D+4r/8F95T+6el/ZvvfavgcTtaUzx0z8DGrEuXAN568R56JjOF8izoPrOIcizkPHcL5E/jwPrhw3deuAS01NDQAgLS3NyyMhIiIiIuo6NTU1iIyM9PYwyA/wmImIiIiIuqvOHDfJhG58eZvFYkFBQQHCw8Mhk8m8No7q6mqkpaUhPz8fERERXhsHtY37yn9wX/kP7iv/wX3lP7iv/EdX7ytBEFBTU4OUlBTI5awuTG3rzDETP4MacS5cw/lrxLnoGM6XiPPgOs6hiPPQMZwvkT/PgyvHTd06w0UulyM1NdXbw5BERET43Zuvu+K+8h/cV/6D+8p/cF/5D+4r/9GV+4qZLdQRrhwz8TOoEefCNZy/RpyLjuF8iTgPruMcijgPHcP5EvnrPHT2uImXtREREREREREREREREbmIARciIiIiIiIiIiIiIiIXMeDiAzQaDZ588kloNBpvD4XawH3lP7iv/Af3lf/gvvIf3Ff+g/uKAhHf1404F67h/DXiXHQM50vEeXAd51DEeegYzpeou86DTBAEwduDICIiIiIiIiIiIiIi8mfMcCEiIiIiIiIiIiIiInIRAy5EREREREREREREREQuYsCFiIiIiIiIiIiIiIjIRQy4EBERERERERERERERuYgBFzd44YUXMHbsWISHhyMhIQFXXHEFjh496rBOQ0MD7r77bsTGxiIsLAxXXXUViouLHda59957MXr0aGg0GowYMcLpcwmCgFdffRX9+/eHRqNBjx498I9//MNTLy0gddX+euqppyCTyZr9Cw0N9eTLCyhd+bv1888/Y8KECQgPD0d8fDyuuuoqnDp1ykOvLPB05b768ssvMWLECISEhCA9PR2vvPKKp15WQHLHvtq7dy8WLFiAtLQ0BAcHY9CgQXj99debPddvv/2GUaNGQaPRoG/fvvjoo488/fICSlftq8LCQlx//fXo378/5HI57rvvvq54eQGlq/bVN998gxkzZiA+Ph4RERGYOHEifv755y55jeSb/OFz4u2330avXr0QFBSE8ePHY+fOnQ73FxUV4c9//jOSkpIQGhqKUaNG4euvv25zu2fOnMEll1yCkJAQJCQkYMqUKRgzZow0F7Nnz8all17qMOZAnYu2vj+dOnXK6XHJH3/8Ia0TCO+lEydO4Morr5Q+I+fPn9/su6Yzgfhe6uxcdPa9NHjwYJ9972zevBmXXXYZUlJSIJPJ8N133zVbRxAEPPHEE0hOTkZwcDCmT5+OY8eOtblt+/dOWFgYkpKSHOZhy5YtDmNesmRJQM6DOz6DAN//HGrPHH7zzTeYOXMmYmNjIZPJkJOT065tV1RUYOHChYiIiEBQUBDi4+Md5mHfvn24+eabMXToUCiVSlx22WUBOQ//+Mc/cP755yMkJARRUVFO13H2XurTp4/fvm+MRiMeeughDB06FKGhoUhJScGNN96IgoKCNrcdSJ9BrsyDuz6D3IkBFzfYtGkT7r77bvzxxx/IzMyE0WjEzJkzUVdXJ63zt7/9DT/88ANWrVqFTZs2oaCgAPPmzWu2rVtvvRXXXntti8/117/+Fe+//z5effVVHDlyBKtXr8a4ceM88roCVVftr7///e8oLCx0+Dd48GBcc801Hnttgaar9lVeXh4uv/xy/OlPf0JOTg5+/vlnlJWVOd0OOddV++qnn37CwoULceedd+LAgQP4z3/+g3/961946623PPbaAo079lVWVhYSEhLw2Wef4eDBg3j00Ufx8MMPO+yHvLw8XHLJJbjooouQk5OD++67D7fffjtPDndAV+0rvV6P+Ph4PPbYYxg+fHiXvsZA0VX7avPmzZgxYwZ+/PFHZGVl4aKLLsJll12G7OzsLn295Dt8/XNi5cqVWLp0KZ588kns2bMHw4cPx6xZs1BSUiKtc+ONN+Lo0aNYvXo19u/fj3nz5mH+/Pmtvq/NZjMuueQSGAwGbNu2DR9//DH++OMPJCUlSXNhMBiwefNmPPDAA9KYA3EubNo6jgSAX375xeHYZPTo0dJ9/v5eqqurw8yZMyGTybBhwwZs3boVBoMBl112GSwWS4vbDcT3Umfnwqaj76WpU6fi/vvv99n3Tl1dHYYPH4633367xXVefvllvPHGG1i2bBl27NiB0NBQzJo1Cw0NDS0+pul7Z+DAgairq8N1110nzcN1112HqKgoacybN28OuHmwcfUzCPD9z6H2zGFdXR0mT56Ml156qd3bBYCFCxfi4MGDyMzMxPDhwyGXyzFlyhRpHi699FIolUrce++9mD59Og4cOBCQ82AwGHDNNdfgL3/5S6vrffjhhw6fQf/3f//nt+8bnU6HPXv24PHHH8eePXvwzTff4OjRo5g7d26r2w20z6DOzoONOz6D3EogtyspKREACJs2bRIEQRC0Wq2gUqmEVatWSescPnxYACBs37692eOffPJJYfjw4c2WHzp0SFAqlcKRI0c8NvbuyFP7q6mcnBwBgLB582a3jb278dS+WrVqlaBUKgWz2SwtW716tSCTyQSDweD+F9INeGpfLViwQLj66qsdlr3xxhtCamqqYLFY3PsiuglX95XNXXfdJVx00UXS7QcffFAYMmSIwzrXXnutMGvWLDe/gu7DU/vK3oUXXij89a9/deu4u6Ou2Fc2gwcPFp5++mn3DJz8nq99TowbN064++67pdtms1lISUkRXnjhBWlZaGio8Mknnzg8LiYmRnjvvfda3O6PP/4oyOVyoaioSFr2zjvvCBEREYJerxcEwXEuLrzwQuHOO+8MyLmw19L3p7y8PAGAkJ2d3a7tCIL/vZd+/vlnQS6XC1VVVdI6Wq1WkMlkQmZmZovbDcT3Umfnwp4r7yVfe+/YAyB8++23DsssFouQlJQkvPLKK9IyrVYraDQa4fPPP29xW229d5rOw6RJkwS5XB5w82DPnZ9BguB/7yV7HXnNhw4dEgAIu3btkpb99NNPgkwmE86dO9dsHhYsWCDIZLKAmwd7H374oRAZGdmp5/Tn943Nzp07BQDC6dOnW1wn0D6DnGnPPNhz92eQK5jh4gFVVVUAgJiYGABihNBoNGL69OnSOgMHDkTPnj2xffv2dm/3hx9+QO/evbFmzRpkZGSgV69euP3221FRUeHeF9DNeGp/NfX++++jf//+uOCCC1wbcDfmqX01evRoyOVyfPjhhzCbzaiqqsKnn36K6dOnQ6VSufdFdBOe2ld6vR5BQUEOy4KDg3H27FmcPn3aDSPvfty1r6qqqqRtAMD27dsdtgEAs2bNculztLvz1L4i9+uqfWWxWFBTU8P9SRJf+pwwGAzIyspyeG65XI7p06c7PPf555+PlStXoqKiAhaLBV988QUaGhowderUFre9fft2DB06FImJidKyWbNmobq6GgcPHpReA9A4FyUlJQE5Fx0xd+5cJCQkYPLkyVi9enWr6/rbe0mv10Mmk0Gj0UjrBAUFQS6X4/fff29x24H4XursXHREa+8lX3rvtEdeXh6KioocxhcZGYnx48e3Or623jtN56GmpgYWiyXg5qEjOvIZBPjfe6mztm/fjqioKIwZM0ZaNn36dMjlcuzYsaPZPJSXl0MQhICbh464++67ERcXh3HjxuGDDz6AIAjSfYHwvqmqqoJMJmuxrBoQeJ9BLT13W/PQER39DHIFAy5uZrFYcN9992HSpEk477zzAIi1eNVqdbM3SGJiIoqKitq97ZMnT+L06dNYtWoVPvnkE3z00UfIysrC1Vdf7c6X0K14cn/Za2howPLly3Hbbbe5OuRuy5P7KiMjA+vXr8cjjzwCjUaDqKgonD17Fl9++aU7X0K34cl9NWvWLHzzzTf49ddfYbFYkJubi3/+858AxFqi1DHu2lfbtm3DypUrsXjxYmlZUVGRw5c/2zaqq6tRX1/v3hfSDXhyX5F7deW+evXVV1FbW4v58+e7bfzkv3ztc6KsrAxms9np3wL75/7yyy9hNBoRGxsLjUaDO+64A99++y369u3b4rZb+htju8/ZXNTV1QXkXLRHWFgY/vnPf2LVqlVYu3YtJk+ejCuuuKLFkw3++F6aMGECQkND8dBDD0Gn06Gurg5///vfYTabW/2OGIjvpc7ORXu09V7ytfdOe9jG0Nbvp7PHtfTeKSgoaDYPBoMBCoUi4OahPTr6GQT453ups4qKipCQkOCwTKlUIiYmxul7qb6+HnK5PODmob2eeeYZfPnll8jMzMRVV12Fu+66C2+++SaAwHjfNDQ04KGHHsKCBQsQERHR4nqB9hnUVHvnoT068xnkKgZc3Ozuu+/GgQMH8MUXX7h92xaLBXq9Hp988gkuuOACTJ06Ff/73/+wcePGZg2hqH08ub/sffvtt6ipqcFNN93k0ecJZJ7cV0VFRVi0aBFuuukm7Nq1C5s2bYJarcbVV1/tcKUEtY8n99WiRYuwZMkSXHrppVCr1ZgwYQKuu+46AOLVfdQx7thXBw4cwOWXX44nn3wSM2fOdOPoyB73lf/oqn21YsUKPP300/jyyy+bHaRT9+TNz4ktW7YgLCxM+rd8+fJ2P/bxxx+HVqvFL7/8gt27d2Pp0qWYP38+9u/fDwC4+OKLpe0OGTKkXdvkXDiKi4vD0qVLMX78eIwdOxYvvvgibrjhBrzyyitO1/fH+YuPj8eqVavwww8/ICwsDJGRkdBqtRg1apT0HbG7vJc8NRdA2+8lf5yv9ujofP3nP//hPNjp6GcQELjvpTvvvNNh221ZuXIl56GJxx9/HJMmTcLIkSPx0EMP4cEHHwyYzyCj0Yj58+dDEAS888470vLu9hnkrnmw6cxnkKuUHttyN7RkyRKsWbMGmzdvRmpqqrQ8KSkJBoMBWq3WIZJYXFyMpKSkdm8/OTkZSqUS/fv3l5YNGjQIAHDmzBkMGDDA9RfRjXh6f9l7//33cemllzaLPlP7eHpfvf3224iMjMTLL78sLfvss8+QlpaGHTt2YMKECW55Hd2Bp/eVTCbDSy+9hOeffx5FRUWIj4/Hr7/+CgDo3bu3215Hd+COfXXo0CFMmzYNixcvxmOPPeZwX1JSEoqLix2WFRcXIyIiAsHBwe5/QQHM0/uK3Ker9tUXX3yB22+/HatWrWpWuo+6J29/TowZMwY5OTnS7cTERGg0GigUCqd/C2zPfeLECbz11ls4cOCAdOA8fPhwbNmyBW+//TaWLVuG999/X8qMtJV6TUpKws6dO5ttFwA++ugjbNu2rdlchIaGBuRcdNb48eORmZnZbLm/vpcAYObMmThx4gTKysqgVCoRFRWFpKQk6Ttid3kvdXYuOsv2XvLF90572MZQXFyM5ORkh/GNGDECQMffO1lZWdi+fbvDPKjVapjN5oCbh85q6TMI8M3PIXd55pln8Pe//91hWVJSEkpKShyWmUwmlJWV4dChQ9i1a5fDPAQHB8NisQTcPHTW+PHj8eyzz+Ivf/mLX79vbEGG06dPY8OGDQ5ZHd3hM8imo/PQWa19BrkDLwd2A0EQsGTJEnz77bfYsGEDMjIyHO4fPXo0VCqVdFIQAI4ePYozZ85g4sSJ7X6eSZMmwWQy4cSJE9Ky3NxcAEB6erqLr6L76Kr9ZZOXl4eNGzeynFgndNW+0ul0zbIjFAoFADGzjNrW1b9XCoUCPXr0gFqtxueff46JEyciPj7e5dfRHbhrXx08eBAXXXQRbrrpJvzjH/9o9jwTJ0502AYAZGZmdmp/d1ddta/IdV25rz7//HPccsst+Pzzz3HJJZd45gWR3/CVz4ng4GD07dtX+hceHg61Wo3Ro0c7PLfFYsGvv/4qPbdOpwPQPEtVoVBI38F69Oghbdd2zDNx4kTs37/f4QTV+vXroVKp8Pvvvzudi4SEhICci87KyclxOKnq7+8le3FxcYiKisKGDRtQUlKCuXPnAug+76XOzkVnZWdnw2Qy+eR7pz0yMjKQlJTkML7q6mrs2LFDGl973juCIODWW2+FTCbDr7/+2mwewsPDIZfLA24eOqvpZxDg259D7pKQkOCwbUB8L2m1WmRlZQEQ52HevHkQBAHfffdds3mIjY2V3mc2gTAPnZWdnQ2NRoPVq1f77fvGFmQ4duwYfvnlF8TGxjrc3x0+gzo7D53l7DPIrQRy2V/+8hchMjJS+O2334TCwkLpn06nk9a58847hZ49ewobNmwQdu/eLUycOFGYOHGiw3aOHTsmZGdnC3fccYfQv39/ITs7W8jOzhb0er0gCIJgNpuFUaNGCVOmTBH27Nkj7N69Wxg/frwwY8aMLn29/q6r9pfNY489JqSkpAgmk6lLXl8g6ap99euvvwoymUx4+umnhdzcXCErK0uYNWuWkJ6e7vBc1LKu2lelpaXCO++8Ixw+fFjIzs4W7r33XiEoKEjYsWNHl75ef+aOfbV//34hPj5euOGGGxy2UVJSIq1z8uRJISQkRHjggQeEw4cPC2+//bagUCiEdevWdenr9Wddta8EQZB+10aPHi1cf/31QnZ2tnDw4MEue63+rqv21fLlywWlUim8/fbbDutotdoufb3kO3z9c+KLL74QNBqN8NFHHwmHDh0SFi9eLERFRQlFRUWCIAiCwWAQ+vbtK1xwwQXCjh07hOPHjwuvvvqqIJPJhLVr17a4XZPJJJx33nnCzJkzhZycHGHdunVCUFCQoNFoHOYiMzNT2L59uzTmq6++WkhOTg6ouRCEtr8/ffTRR8KKFSuEw4cPC4cPHxb+8Y9/CHK5XPjggw+kbfj7e0kQBOGDDz4Qtm/fLhw/flz49NNPhZiYGGHp0qWtbjcQ30udnQtB6Nx7CYAQHBzss++dmpoa6XEAhNdee03Izs4WTp8+La3z4osvClFRUcL3338v7Nu3T7j88suFjIwMob6+vsXtNn3vXHrppYJMJhMWLlzo8Bq2b98ujblv375CUlKS8MEHHwTMPAiCez6DBMH3P4faM4fl5eVCdna2sHbtWgGA8MUXXwjZ2dlCYWFhq9uePXu2MHLkSGHHjh3CFVdcIcjlcmHatGkOryErK0vIzs4WLrvsMiElJUVISkoS3n333YCah9OnTwvZ2dnC008/LYSFhUnPU1NTIwiCIKxevVp47733hP379wvHjh0T/vOf/whKpbLZZ7Y/vW8MBoMwd+5cITU1VcjJyXF4/qbnF+0F2mdQZ+dBENz3GeRODLi4AQCn/z788ENpnfr6euGuu+4SoqOjhZCQEOHKK69s9kFz4YUXOt1OXl6etM65c+eEefPmCWFhYUJiYqJw8803C+Xl5V30SgNDV+4vs9kspKamCo888kgXvbrA0pX76vPPPxdGjhwphIaGCvHx8cLcuXOFw4cPd9Er9X9dta9KS0uFCRMmCKGhoUJISIgwbdo04Y8//ujCV+r/3LGvnnzySafbSE9Pd3iujRs3CiNGjBDUarXQu3dvh+egtnXlvmrPOtSyrtpXLX1G3nTTTV33Ysmn+MPnxJtvvin07NlTUKvVwrhx45r93c7NzRXmzZsnJCQkCCEhIcKwYcOETz75pM3tnjp1Srj44ouF4OBgIS4ursW5aPovLCws4Oaire9PH330kTBo0CAhJCREiIiIEMaNGyesWrWqzdfkb++lhx56SEhMTBRUKpXQr18/4Z///KdgsVja3G4gvpc6OxedeS/5+ntn48aNbf7ttFgswuOPPy4kJiYKGo1GmDZtmnD06NE258v+vdPe9w0AQS6XB9Q8uOMzqKXX5W/vpQ8//NDpOk8++WSr2y4vLxcWLFgghIWFtTgPsbGxTpcH0jzcdNNNTh+3ceNGQRAE4aeffhJGjBghhIWFCaGhocLw4cP9/n2Tl5fX4muwve6WBNJnkCvz4K7PIHeSCQI7QhMREREREREREREREbmCPVyIiIiIiIiIiIiIiIhcxIALERERERERERERERGRixhwISIiIiIiIiIiIiIichEDLkRERERERERERERERC5iwIWIiIiIiIiIiIiIiMhFDLgQERERERERERERERG5iAEXIiIiIiIiIiIiIiIiFzHgQkRERERERERERERE5CIGXIiIiIiIiIiIiIiIiFzEgAsREREREREREREREZGLGHAhIiIiIiIiIiIiIiJyEQMuRERERERERERERERELmLAhYiIiIiIiIiIiIiIyEUMuBAREREREREREREREbmIARciIiIiIiIiIiIiIiIXMeBCRERERERERERERETkIgZciIiIiIiIiIiIiIiIXMSACxERERERERERERERkYsYcCEiIiIiIiIiIiIiInIRAy5EREREREREREREREQuYsCFiIiIiIiIiIiIiIjIRQy4EBERERERERERERERuYgBFyIiIiIiIiIiIiIiIhcx4EJEREREREREREREROQiBlyIiMjBRx99BJlMJv0LCgpCSkoKZs2ahTfeeAM1NTUO62/evBlz585FWloagoKCkJSUhNmzZ2Pr1q3Ntj116lSHbdv+zZ49u9m6x44dw3XXXYfU1FSEhIRg4MCBeOaZZ6DT6aR1Tp065XR7tn+LFi1y/wQREREREVG358njJovFgmXLlmHEiBEICwtDYmIiLr74Ymzbtq3Zuu05bgI6dixGRESdp/T2AIiIyDc988wzyMjIgNFoRFFREX777Tfcd999eO2117B69WoMGzYMAJCbmwu5XI4777wTSUlJqKysxGeffYYpU6Zg7dq1zb7Ap6am4oUXXnBYlpKS4nA7Pz8f48aNQ2RkJJYsWYKYmBhs374dTz75JLKysvD9998DAOLj4/Hpp582G/u6deuwfPlyzJw5051TQkRERERE5MATx00PPPAAXnvtNdxwww246667oNVq8d///hcXXnghtm7dinHjxgFo/3GTTXuOxYiIyDUyQRAEbw+CiIh8x0cffYRbbrkFu3btwpgxYxzu27BhAy699FIkJCTg8OHDCA4OdroNnU6H3r17Y8SIEVi3bp20fOrUqSgrK8OBAwdaHcPzzz+PRx99FAcOHMCQIUOk5TfddBM++eQTVFRUIDo6usXHT58+Hbt27UJxcTGCgoLa87KJiIiIiIjazVPHTSaTCREREbjkkkuwatUqad28vDz07t0b9957L15//XUAHTtuau+xGBERuYYlxYiIqN3+9Kc/4fHHH8fp06fx2WeftbheSEgI4uPjodVqnd5vMplQW1vb4uOrq6sBAImJiQ7Lk5OTIZfLoVarW3xsYWEhNm7ciHnz5jHYQkREREREXc6V4yaj0Yj6+vpmx0IJCQmQy+UOwZvOHDe1dSxGRESuYcCFiIg65M9//jMAYP369Q7Lq6urUVZWhiNHjuCRRx7BgQMHMG3atGaPz83NRWhoKMLDw5GUlITHH38cRqPRYZ2pU6cCAG677Tbk5OQgPz8fK1euxDvvvIN7770XoaGhLY7viy++gMViwcKFC118pURERERERJ3T2eOm4OBgjB8/Hh999BGWL1+OM2fOYN++fbj55psRHR2NxYsXS+t29LipPcdiRETkGvZwISKiDklNTUVkZCROnDjhsHz+/Pn4+eefAQBqtRp33HEHHn/8cYd1+vTpg4suughDhw5FXV0dvvrqKzz33HPIzc3FypUrpfVmz56NZ599Fs8//zxWr14tLX/00Ufx3HPPtTq+5cuXIzk5GX/6059cfalERERERESd4spx02effYZrr70WN9xwg7Ssd+/e2Lp1K3r37i0t68hxU3uPxYiIyDUMuBARUYeFhYWhpqbGYdmLL76I+++/H/n5+fj4449hMBhgMpkc1vnf//7ncPvPf/4zFi9ejPfeew9/+9vfMGHCBOm+Xr16YcqUKbjqqqsQGxuLtWvX4vnnn0dSUhKWLFnidFy5ubnIysrC3/72N8jlTOIkIiIiIiLv6exxU3h4OIYMGYKJEydi2rRpKCoqwosvvogrrrgCW7ZsQVxcnLRue4+bOnIsRkREnceACxERdVhtbS0SEhIclo0YMUL6+YYbbsCoUaNw880346uvvmp1W/fffz/ee+89/PLLL9KX/C+++AKLFy9Gbm4uUlNTAQDz5s2DxWLBQw89hAULFiA2NrbZtpYvXw4ALCdGRERERERe15njJpPJhOnTp2Pq1Kl48803pXWnT5+OIUOG4JVXXsFLL70EoPPHTTbOjsWIiMg1vPyXiIg65OzZs6iqqkLfvn1bXEetVmPu3Ln45ptvUF9f3+r20tLSAAAVFRXSsv/85z8YOXKkdNBgM3fuXOh0OmRnZzvd1ooVKzBgwACMHj26vS+HiIiIiIjI7Tp73LR582YcOHAAc+fOdVi3X79+GDRoELZu3Sot6+xxk42zYzEiInINAy5ERNQhn376KQBg1qxZra5XX18PQRCapdA3dfLkSQBAfHy8tKy4uBhms7nZuraGjk1T7gFgx44dOH78OLNbiIiIiIjI6zp73FRcXAwALR4P2R8Ldea4yZ6zYzEiInINAy5ERNRuGzZswLPPPouMjAwpsFFSUtJsPa1Wi6+//hppaWlSCn11dTX0er3DeoIgSM0c7Q9E+vfvj+zsbOTm5jqs//nnn0Mul2PYsGHNnnPFihUAgOuvv96FV0hEREREROQaV46b+vfvD0AsF2Zvz549OHr0KEaOHCkta+9xU0eOxYiIyDXs4UJERE799NNPOHLkCEwmE4qLi7FhwwZkZmYiPT0dq1evRlBQEADg4osvRmpqKsaPH4+EhAScOXMGH374IQoKCrBy5Uppe3v27MGCBQuwYMEC9O3bF/X19fj222+xdetWLF68GKNGjZLWfeCBB/DTTz/hggsuwJIlSxAbG4s1a9bgp59+wu23346UlBSHsZrNZqxcuRITJkxAnz59umaCiIiIiIio23P3cdPo0aMxY8YMfPzxx6iursbMmTNRWFiIN998E8HBwbjvvvukddt73NSRYzEiInKNTBAEwduDICIi3/HRRx/hlltukW6r1WrExMRg6NChuPTSS3HLLbcgPDxcuv/tt9/GF198gSNHjkCr1SI6OhoTJkzAAw88gAsuuEBaLy8vDw899BB27dqFoqIiyOVyDBo0CIsWLcLixYshk8kcxrFz50489dRTyM7ORnl5OTIyMnDTTTfhwQcfhFLpeL3Azz//jNmzZ+ONN97APffc46GZISIiIiIiEnnquAkQy4y9+uqr+OKLL5CXlwe1Wo0LLrgAzz77LEaMGOGwbnuOmzp6LEZERJ3HgAsREREREREREREREZGL2MOFiIiIiIiIiIiIiIjIRQy4EBERERERERERERERuYgBFyIiIiIiIiIiIiIiIhcx4EJEREREREREREREROQiBlyIiIiIiIiIiIiIiIhcxIALERERERERERERERGRi5TeHoA3WSwWFBQUIDw8HDKZzNvDISIiIiLyKEEQUFNTg5SUFMjlvPaK2sZjJiIiIiLqblw5burWAZeCggKkpaV5exhERERERF0qPz8fqamp3h4G+QEeMxERERFRd9WZ46ZuHXAJDw8HIE5cREQEAMBoNGL9+vWYOXMmVCqVN4dHncD957+47/wb95//4r7zb9x//stb+666uhppaWnS92Citjg7ZvIn/Jx0L86n6ziH7sX5dC/Op+s4h+7F+XQd57BzXDlu6tYBF1tKfEREhEPAJSQkBBEREXwT+iHuP//FfeffuP/8F/edf+P+81/e3ncsDUXt5eyYyZ94+3ct0HA+Xcc5dC/Op3txPl3HOXQvzqfrOIeu6cxxEws3ExERERERERERERERuYgBFyIiIiIiIiIiIiIiIhcx4EJEREREREREREREROQiBlyIiIiIiIiIiIiIiIhcxIALERERERERERERERGRixhwISIiIiIiIiIiIiIichEDLkRERERERERERERERC5iwIWIiIiIiIiIiIiIiMhFDLgQERERERERERERERG5iAEXIiIiIiIiIiIiIiIiFzHgQkTUhCAI0BlM3h4GERERERERkVfUG8zeHgIRkV9iwIWIqIln1xzG4Cd+xoFzVd4eChEREREREVGXenPDCQx5ch2+3J3v7aEQEfkdBlyIiJr4YGseAODSN3/Hha9sxJ4zlV4eEREREREREZHnbSiQ4Y2NJ2ARgKxTPBYmIuooBlyIiFpxulyHuz7b4+1hEBEREREREXnUyt1n8f1phXRbW2/w4miIiPwTAy5ERG0wmC3eHgIRERERERGRx5wur8MTqw8BAIakhAMAtDqjN4dEROSXGHAhImqDIAjeHgIRERERERGRS/ad1aKqhSDKHyfLYRGAXmEC7p/RDwBQVc+ACxFRRzHgQkTUhkqdESZmuRAREREREZGf+mbPWcx9ayvu/SLb6f17z1YBAPpECIgKVgFgwIWIqDMYcCEistNSNkvfR3/C6fK6Lh4NERERERERkWuqdEb8Y+1hAMCm3FKcrdQ1W2e/NeCSFiYg0hpwYUkxIqKOY8CFiMiO3tRyJsuFr/yGOr2pC0dDRERERERE5Jp/Zh5FeZ1Buv1d9jmH+/UmM44UVQMAeoY2ZrjUG83Qm8xdN1AiogDAgAsRkZ22ruD5KutsF42EiIiIiIiIyDUHzlXhsz9OAwCuHp0KAPhmzzmH6g6HC2tgNAuIDlEhRgOEaZSQycT7WFaMiKhjlN4eABGRL3lu7SHp5+vH98SKHWcc7re0UHKMiIiIiIiIyFvyyurwt5U5qG4SICmvM8AiAHOHp+CpuUOwdl8hTpbVISdfi5E9owEA+85qAQBDe0RAJquHXC5DZLAKWp0RVTojEsKDuvrlEBH5LWa4EBHZWbOvUPr5iUsHN7tfreTHJhEREREREfmWb/ecRU6+FifL6hz+VdUbERGkxKOXDEKYRonZ5yUBELNcbPZZ+7cM7REpLbOVFdMyw4WIqEOY4UJEZEetkMNgFvu4BKkUWHRBBt7bkifdr1EqvDU0IiIiIiIiIqcOFYo9WBZdkIGZQ5Ic7kuPCUFChJilMm9UD3ybfQ4/7CvAY5cOgkapcMhw0Z8UHxMZogbKdW2W3SYiIke8VJuIyI4t2JIQrgEAPDJnkMP9Srmsy8dERERERERE1JpDBWLAZcbgJIztFePwzxZsAYDz+8QhMUIDrc6IdQeKUKc34XhJLQDHDJdIa4YLe7gQEXUMAy5ERFYNRrP0c3mdAQAgk8mQFhMsLbcFZIiIiIiIiIh8QWWdAQVVDQCAgcnhra6rkMtw3dieAIBn1xzC5txSWAQgKSJIuvAQsCsppjN4aNRERIGJARciIqvSGr30s62uLQB8f/dk6WeDiQEXIiIiIiIi8h22cmI9Y0IQEaRqc/2/TO2DgUnhKKs14O+r9gIAhqVGOqwTFcIMFyKizmDAhYjISm9qzHB5eu4Q6eeYUDUuG54CgAEXIiIiIiIi8i22cmKDkyPatX6QSoF/XzcCaoUcdQbxOHh4WpTDOiwpRkTUOQy4EBFZNRjFYEpihAZxYRqH+9QK8eOSJcWIiIiIiIjIl9gyXIaktC/gAgADkyLw4OwB0m37/i1AY8BFq2PAhYioI5TeHgARka/QW7NXglSKZvepldaACzNciIiIiIiIyIdIGS4dCLgAwK2TMnCosBpnynUYlxEDoPF4NypEDQDQMsOFiKhDGHAhom6rVm+CyWxBVIgahVX1uOqdbQAAjbJ58p+GARciIiIiIiLyMQ1GM46X1gLoeMBFLpfhtfkjpNtGo13AxVZSTGdwfZBERN0IS4oRUbckCAIuevU3jHgmE7V6E77Zc066T6NsnuGiUsgAAEZrSTFBEPDp9lM4cK6qawZMRERERERE1MSx4lqYLQKiQ1RIighy23YjQ9jDhYioM5jhQkTdksFsQWmNHgCw76xWKicGOM9wUVp7uJgsAgDgu5xzePz7gwCAUy9e4unhEhERERERETVzqFC8CHBwSgRkMpnbtmvLcGFJMSKijmGGCxF1S/UGs/SzTm+GUt74xVSpaP4l1Xa/2Rpw2X6i3MMjJCIiIiIiImqd1L8luWPlxNpin+FisR4HExFR2xhwIaJuSWcfcDGaobALuPxxsqLZ+rb7bSXFcotrpfv2n2VZMSIiIiIiIup6hwqtAZcO9m9pS6Q1w0UQgJoGk1u3TUQUyBhwIaJuSWdo/MJYVW90CLg4o7KWFDNbBBjNFuTka6X71uwraPP5DpyrwtM/HISWDQeJiIiIiIjIDSwWAYcLawAAg5Mj3bptjVKBYJXY35R9XIiI2o8BFyLqluwzXJ5efRCCXYb05L5xzda3BWS+2JWP73McAywNRnOz9Zu69M3f8eHWU3hq9cFOjpiIiIiIiIio0TltPWr1JqgVcvSOD3X79qNCbH1ceOEgEVF7MeBCRN1ScbVe+tlkzVqx6Rkb0mx9+x4vf1+11+E++/JibTlgra9LRERERERE5MyLPx3BX7/IdjhOdeZMhQ4AkBYTLFVlcCdbWTGtjhkuRETtxYALEXVLiz7Z7XDb/ouss4aArZUc236yvN3PKwhsNkhERERERETObcotxbJNJ/B9TgF2OOkvaq8x4NL8okF3sGW4sKQYEVH7Kb09ACIib1PIZTCaGwMh1Q3Nv0wqWwm4jMuIafG+nw8W4dfDxdJtJ7EcIiIiIiIiIpjMFvxj7SHp9qbcEkzu17zktU2+LeAS7ZmAi5ThwoALEVG7McOFiLo9s0WA3tTYh+XPE3o1W0fZSnp2SmRQi/fd8WkWvtx9VrptYYYLEREREREROfHl7rMOJas35ZZKPxdVNeAfaw+hsKpeWpZfKf7c01MZLsFqAECVjj1ciIjaiwEXIup2nJX1MpjEkmLzRvXAxD6xze5vraSYoY26uo7P3e5ViYiIiIiIqJuoaTDitcyjAIB7p/WDXCb2Cy3QikGVp1YfxHtb8vDfTSelx9j3cPEEW0kx9nAhImo/BlyIqNuxLx9mo7cGXFq6Mqi1kmIGU/ujKMxwISIiIiIioqbe23wSZbUG9I4LxT1/6ovhaVEAgM25pTinrcf6Q0UAgCNF1dJjzloDLqkeKikWEcweLkREHcUeLkTU7TTYlQ+zsQVcVC2UDmOGCxEREREREXlCg9GMT/84DQD4+6wBUCnkmNo/AdlntNiUW4r8Sp3UD/SYteRYnd6E8jqx1Feap0qKhbCHCxFRRzHDhYi6nUrrl1L7GIreKAZhWspkaSkQAwBGU0cCLoy4EBERERERUaMf9hagUmdEj6hgzBqSBAC4cEA8AOD3Y2X4fGe+tG55nQFltXqctfZviQxWSc3t3a2xhwsDLkRE7cWACxF1OyfL6gAAPaIb69x2JsMlTCMmCbaU4eIsuJIS5ZnaukREREREROR/BEHAx9tPAQBumJAuHXsO7RGJ6BAVavQmVNQZkBIZhB7W48nc4hqP928B7DNcDB57DiKiQMOACxF1K+sOFOGRb/YDAHrHhUnLDVLAxXmGi7PMl4ggMeBibCHgoneS+RKsVnRswERERERERBSwsvO1OHCuGmqlHNeOTZOWK+QyXNAvXrq9cEI6BiWHAxDLiuXbAi4e6t8CQMqcYQ8XIqL2Y8CFiLqVOz/LQmFVAwAgWNUY/NBb+7ooO5DhYmsgaGihpFiDsXmvmNZ6wRAREREREVH38sm2UwCAucNTEBOqdrjvwv5iwEWtkOO6sWnolygGXHKLa5BfKQZcenqofwvQGHDRsqQYEVG7Kb09ACIib1Er5ZDLAIvQmI3SkR4uEUHWgEsLGS4NxubLzRb2cCEiIiIiIiKgtEaPtfsLAQA3TezV7P6Lhybh1yPFGJ8Ri9gwDQYkNma4RASLp/RSPRhwsZUU05ssaDCaEaRixQYiorYw4EJE3UbTYIcYcJHBIgh2JcXan+Eit67akQwXZ2XGiIiIiIiIqPvZnFsKo1nA0B6RGJoa2ez+ELUS/1k4WrrdL1Esi320uAZJEUEAgLRoz/VwCdMooZDLYLYIqKo3MuBCRNQOLClGRN1GncHkcFulkENuDaRIGS4t9HBxFoipqhe3ZzI7z1r5Ludcs2Ut9XshIiIiIiKi7uVsZT0AYHByRLvW7xMfBrlM7KlyvLQWAJDmwQwXmUzGsmJERB3U4YDL5s2bcdlllyElJQUymQzfffeddJ/RaMRDDz2EoUOHIjQ0FCkpKbjxxhtRUFDgsI2KigosXLgQERERiIqKwm233Yba2lqHdfbt24cLLrgAQUFBSEtLw8svv9xsLKtWrcLAgQMRFBSEoUOH4scff+zoyyGibqS0Ru9wW2MtKQYAZ6wNB5Vy5x+LGmXz5VeMSAEAmAXnAZd//3Ks2bKWsmGIiIiIiIioezmnFY9De7QzSyVIpUCv2FAAYgUHmQzoEeW5DBcAiLIGXCp1Bo8+DxFRoOhwwKWurg7Dhw/H22+/3ew+nU6HPXv24PHHH8eePXvwzTff4OjRo5g7d67DegsXLsTBgweRmZmJNWvWYPPmzVi8eLF0f3V1NWbOnIn09HRkZWXhlVdewVNPPYV3331XWmfbtm1YsGABbrvtNmRnZ+OKK67AFVdcgQMHDnT0JRFRN3H/l3sdbquVcihkjhktqhYyXNROAi7pdl9024sBFyIiIiIiIgKAc1oxw6UjQRNbWTEASAwP8niZrxTr2E6X13n0eYiIAkWHe7hcfPHFuPjii53eFxkZiczMTIdlb731FsaNG4czZ86gZ8+eOHz4MNatW4ddu3ZhzJgxAIA333wTc+bMwauvvoqUlBQsX74cBoMBH3zwAdRqNYYMGYKcnBy89tprUmDm9ddfx+zZs/HAAw8AAJ599llkZmbirbfewrJlyzr6soioG8jJ1zrcFgRBKilm4yyw0pJoawNBUwtlwmYMTkTmoWKHZSwpRkRERERERABwzlpSrL0ZLgDQPzEcPx8UjzPTYjyb3QIAA5PC8fvxMhwurPH4cxERBQKP93CpqqqCTCZDVFQUAGD79u2IioqSgi0AMH36dMjlcuzYsUNaZ8qUKVCr1dI6s2bNwtGjR1FZWSmtM336dIfnmjVrFrZv3+7hV0RE/mjFjjPNlq3KOgt5kwwXtZNeLQBgcVI2LDpUbb3P+XOGaZrHtLX1rHtLRERERETU3VksAgq0DQA6luHSPzFc+tmT/VtsBlr7yxwpqvb4cxERBYIOZ7h0RENDAx566CEsWLAAERHiB3RRURESEhIcB6FUIiYmBkVFRdI6GRkZDuskJiZK90VHR6OoqEhaZr+ObRvO6PV66PWNPRyqq8U/FkajEUajUfrZ/n/yL9x//svT++6Rb/c3W1ZVb0RiuAZV9Y3L5LA4HUPPKA3SooMhkwFnKsQHqOVipKVWb3L6mNqG5su0OiPq6vUdyqTxB/zd81/cd/6N+89/eWvf8b1CRETkG8pq9TCYLZDLgKTIoHY/ziHgEt0FAZck8fmOFNVAEATIZM7LcBMRkchjARej0Yj58+dDEAS88847nnqaDnnhhRfw9NNPN1u+fv16hIQ4/pFqWhqN/Av3n//y3L5r/nEngwC5qQFA4xfGnX9sQ0Hz2AwA4G/9AZkM0BoAlRzYtHGjtN2nP/4JY+MdU13OFMjhLJFw7U/roPFsmV2v4e+e/+K+82/cf/6rq/edTqfr0ucj51544QV88803OHLkCIKDg3H++efjpZdewoABA6R13n33XaxYsQJ79uxBTU0NKisrpaoBNhUVFbjnnnvwww8/QC6X46qrrsLrr7+OsLDG+v779u3D3XffjV27diE+Ph733HMPHnzwwa56qURE1AJb/5akiCCoWqi04ExGXCiUchlMFqFLMlz6JoRBIZdBqzOiuFrfoeCQL8s6XYmNR0pw77R+AXdBpM2Xu/JhtFiwcHy6t4dC1K14JOBiC7acPn0aGzZskLJbACApKQklJSUO65tMJlRUVCApKUlap7jYse+B7XZb69jud+bhhx/G0qVLpdvV1dVIS0vDzJkzpTEajUZkZmZixowZUKlUHX3p5GXcf/7L0/vuwV2/QN+kYf1tkzKw92wVCk5VSssuunAK+iWENX24U6U1ejy5ZxMAYMUJBZ68aSY+3n4ax0pq8ezcwfj43C6gStvscdOmz0BEcGC9P/m757+47/wb95//8ta+s2V4k3dt2rQJd999N8aOHQuTyYRHHnkEM2fOxKFDhxAaGgpADI7Nnj0bs2fPxsMPP+x0OwsXLkRhYSEyMzNhNBpxyy23YPHixVixYgUAcX/PnDkT06dPx7Jly7B//37ceuutiIqKknpjEhGRd9gCLikdKCcGiH1HByVHYP+5Kgywy3bxlCCVAr3jQnGspBaHi6qbBVxqGozYm1+FSX1j/Sb7RRAE3P9lDk6V69AzNgTzx6R5e0hut/tUBR78eh8A4KIBCR1+nxFR57k94GILthw7dgwbN25EbGysw/0TJ06EVqtFVlYWRo8eDQDYsGEDLBYLxo8fL63z6KOPwmg0SgegmZmZGDBgAKKjo6V1fv31V9x3333StjMzMzFx4sQWx6bRaKDRaJotV6lUzQ50nS0j/8H95788te+ctGDBoil98Nh3BxyWhQap2/38QZrGjVoEwGCR4bkfjwIALh+ZinqjxenjZAplwL4/+bvnv7jv/Bv3n//q6n3H94lvWLduncPtjz76CAkJCcjKysKUKVMAQDrO+e2335xu4/Dhw1i3bh127dol9cd88803MWfOHLz66qtISUnB8uXLYTAY8MEHH0CtVmPIkCHIycnBa6+9xoALEZGXnasUAy49ojt+Ivyt60fiWHEthqZGuntYTg1MjsCxklocKazBRQMa2wQYTBZc/94O7D9XhfdvHIPpgxNb2YrvOFhQjVPlYtbvHyfLAy7gYrEIeHbtYen2gXNVDLgQdaEO58zV1tYiJycHOTk5AIC8vDzk5OTgzJkzMBqNuPrqq7F7924sX74cZrMZRUVFKCoqgsFgAAAMGjQIs2fPxqJFi7Bz505s3boVS5YswXXXXYeUlBQAwPXXXw+1Wo3bbrsNBw8exMqVK/H66687ZKf89a9/xbp16/DPf/4TR44cwVNPPYXdu3djyZIlbpgWIgo0BnPz4EdUiBqRTTJNglTtr/WlkDtevfPKz0eln+sNZugMJqePMzkZCxEREXVfVVVVAICYmJh2P2b79u2IioqSgi0AMH36dMjlcuzYsUNaZ8qUKVCr1dI6s2bNwtGjR1FZWdlsm0TkGfUGM77NPgutzuDtoZAPsWW49OjEifD02NAuDW409nFxzJR9/ddc7D8n/g07WOBbWbQWiwBjC8feP+wrkH7ecbKiq4bUZX7YV4C9+Vrptq/tG6JA1+EMl927d+Oiiy6SbtuCIDfddBOeeuoprF69GgAwYsQIh8dt3LgRU6dOBQAsX74cS5YswbRp06Raw2+88Ya0bmRkJNavX4+7774bo0ePRlxcHJ544gmHq7DOP/98rFixAo899hgeeeQR9OvXD9999x3OO++8jr4kIuqm1Ep5s4CLpgO1W5sGXHadavyiJpfJ0GA0O32cyeIk3YaIiIi6JYvFgvvuuw+TJk3q0LFMUVEREhISHJYplUrExMSgqKhIWicjI8NhncTEROk+W/UAe3q9Hnq9XrptK0NnNBphNBrbPT5fYRuzP47dF3E+O85ktuDO5TnYdKwM147pgSfn9AfAOXQXf35P5lfUAQCSItQ+M/6W5rNfvNgr5nBBtXTfrlOV+M9vJ6R1TpfXuvw6TGYL9CYLQjWuF+S554u9+PVICeYOT8biyRnoHS+W7BQEAWv3NgZczmnrkVdSjdROZBo54+33ZIPRjBd/OgIASI8JwekKHQ6c0/rMe6yjvD2fgYBz2DmuzFeHP8GmTp0KwVltHqvW7rOJiYmR6gq3ZNiwYdiyZUur61xzzTW45ppr2nw+IiJ7d1/UB9dbm8Y1D7i0P8NF2STgYh9HkcnE9GpnzAy4EBERkdXdd9+NAwcO4Pfff/f2UAAAL7zwAp5++ulmy9evX4+QEM83Z/aUzMxMbw8hoHA+2+/rPDk2F4kXdf209ywmKk9DJuMcups/zueRfAUAGQpyD+DH0v3eHo6DpvNZqQcAJY6X1mD1mh9htAAv71NAEGSIUgvQGmTYd+IcfvzxjEvP+8UJOXaXyvDAcDMSXYh/mCzA+kMKWAQZvt5TgG/2nMPkRAFXZVhwphY4q1VCLRcQHwSc08nw3ve/YXyCe4/TvfWezDwnQ2GVAtFqARcn1mBZhQJZJ0vw448/emU87uKPv+O+hnPYMTqdrtOPdXsPFyIiX6M3NWaaPHbJINw2OUNq5qdQOAZNXMlwMVsaAyxymazFgEtLac1ERORfth4vw7nKeswfG1h1v6nrLFmyBGvWrMHmzZuRmpraoccmJSWhpKTEYZnJZEJFRQWSkpKkdYqLix3Wsd22rdPUww8/7FDKubq6GmlpaZg5cyYiIiI6NEZfYDQakZmZiRkzZrCHkRtwPjtm+Y4z2LxdvNJcpZCh2gj0HDYR+fu3cw7dxFffk4VVDaioM6BHVDAig5VOm8k/umcDABMun3EB+iaEdf0gnWhpPgVBwGuHNqK6wYR+oy/Al1lnUaHPR1p0MJ6+bBBu/WQPdLJgzJkzxaXnf+bF32AUDJCnDMGciemd3s7BgmpYdvyB8CAlxvWKxq9HSrGlWIYRg/tCpzADOI0ZQ5KRGhWM/27JQ31EGubMcU/FHE+8J8tr9fhi9zlcOiwJ6TEtX/wgCAJeeW0LgAb836XnYcagRCz7xwZoDTJMuHA6YkLVLT7WV/nq77g/4Rx2ji3LuzMYcCGigFfb0NhL5ZZJGQ5fds1mx6tY5PLmX4RbopA1Dbg0bquiziD1jRnXKwY77cqNMcOFiCgwLHxf7JMxOCUC5/Xomqa1FBgEQcA999yDb7/9Fr/99luzsl/tMXHiRGi1WmRlZWH06NEAgA0bNsBisWD8+PHSOo8++iiMRqN0gJ2ZmYkBAwY4LScGABqNBhqNptlylUrl1wfp/j5+X8P5bNvm3FI8+6PY4/GBWQOQfaYSvxwuwbY8LdLAOXQ3X5rPslo9Zr2+FfXWEtMRQUo8cdkQXD26MbBeVW9ErV48Tk2PD4dK5Vun55zN58DkCOzMq8B3e4uwYmc+AOClq4ZJwaKi6gZAroBK0eF20QDEY+jyOrHPUW5JnUv782iJWK5tWGok/nfzOKzcdQYPfb0fb248iVC1WNXisuE9EKxW4L9b8rDrVKXb3z/ufE9+9McJLNt0Ah9uO423rx+Fyf3inK534FwVzmobEKSS47IRqQhRK5EeG4LT5TrklupwQVSoW8bjDb70O+6vOIcd48pcde5TkIjIj2Qearyys2lWitGF4EfT4Ix9IOXRb/fDaA3mvLVwJHKemIH0WPFKlNIaPYiIyL/Zl9G1Nb1tTeahYnyy/ZQHR0T+5O6778Znn32GFStWIDw8HEVFRSgqKkJ9feN7qaioCDk5OTh+/DgAYP/+/cjJyUFFhXgRx6BBgzB79mwsWrQIO3fuxNatW7FkyRJcd911SElJAQBcf/31UKvVuO2223Dw4EGsXLkSr7/+ukMGCxG537HiGty9fA/MFgHzRvXAXVP74ML+8QCAzcfKvDw68rS9+VrUG82wHS5WN5jwf1/vQ9bpSmmdc5Xi531MqBohat8KtrRkUFI4AOCDrXmwCMDc4Sk4v28c4sM10CjlsAhAobah09s/XlIr/Xy4sMalsdqaxA9JES+IuXZsT9wxpTcAoM5gRqhagakD4jEmPRoKuQxnK+uRX9H58kGeduBcFQAxUHfThzvxaQvfKX86UAgAmNo/QXpfnWedA9ucEJHnMeBCRAFvxc6W68jalwFzlcku4FJnaCxjplEqEBWiRkqkWIT2RFmd256TiIi8w3bVans0GM1Y9MluPPH9QZworW37ARTw3nnnHVRVVWHq1KlITk6W/q1cuVJaZ9myZRg5ciQWLVoEAJgyZQpGjhyJ1atXS+ssX74cAwcOxLRp0zBnzhxMnjwZ7777rnR/ZGQk1q9fj7y8PIwePRr3338/nnjiCSxevLjrXixRN1Neq8etH+9Cjd6Ecb1i8MK8oZDJZLiwfwIAYM8ZLewS8CkA5RaLf+svGZaCI8/OxiXDkmGyCFiyYg/Ka8WL72wXa/SIck+j9q4wMLmxrGSYRolHLxkEAJDJZOhhbTifX9n5oEVucWOQ5WhxDUwulOJuDLg0jvnB2QMxY3AiAGD2eckIUikQqlFiqDVLeUdeRfMNecEPewvw9sbj0sU9giDgcKH4esb1ioHZIuDx7w9i41HHsqKCIOCnA0UAgIuHNpYNHWydAwZciLoOAy5EFPCKqsSrbG6f3Lxch8mN5b3sM1xmD2n8gmPrC1PdYAQAPP7dAbc9JxEReYd9uUqLRUCd3gSzRcCq3fnSFayvrT+Kmf/ahFPljYH2SmupDOreBEFw+u/mm2+W1nnqqafaXCcmJgYrVqxATU0Nqqqq8MEHHyAszLEPwLBhw7BlyxY0NDTg7NmzeOihh7roVRJ1T//3zX7kV9SjZ0wIlv15NDRKsXxRz9gQZMSFwmQRkFvd/jLG5H9sgYMBiWEIUinw0lXD0Ds+FIVVDbhvZQ7MFgEF/hhwsWa4AMB90/shMSJIup0WLVZzcCVLxD7DxWCyIK+TFyqaLY0BCvuAi0Iuw5sLRuKt60fiiUsHS8sn9I4FAOw4Wd6p53Mng8mCB77ai1d+PorsfC0AsUJGeZ0Bchnw8a3jcMOEngCAl9cdhcXuHMSxklqcLK2DWiHHnwYmSMuHSAGXqq57IUTdHAMuRBTQ9p3VosRawmtUevNa5U17uLjCPngTEdyYFq621rDlFSVERIGjRt8YcNl6ogxDnvwZfR75EQ98tQ9XvbMNf1+1F29sOI7c4lq8vyVPWreq3uiN4RIRURf5w3rS9l/XDm/WoNpWVuywlgGXQGYLuPRLFAMUYRol3lk4GsEqBbYcK8MHv+c1ZrhE+0/AZXBKBIb2iMSkvrG46fxeDvelxbie4XKsxLGM2KHCzh0/nyqvg85gRrBKgYw4x4sQglQKXDosBZEhjb0ZxveOAQBsO1Hepf1Wj5fUYPAT6/DCT4elZQcKqtBgFDN79lgv4LHNQ6+4UASrFbh/xgCEa5Q4XFiNH/YVSI/9ab+Y3TK5XxzCgxpfn62sWl5ZHer0TK8j6goMuBBRQNtplxacGNG8Aax9kOTjW8e59FwNduVl6vTiz0q5rFmvFyIi8n/2GS6f/dG8dOVXWWeln7W6xqyWjpQiIyIi/9JgNKPG+vehb0J4s/ttAZcjWplDLzAKHGaLIGVq9E9sfA8MSArHE5eJWRX/zDwqZVOk+FGGi0apwA/3TMZnt42HSuF4OrExw6XtvnYtsZViO6+HmJHR2T4utn4ng5LDm/VwdWZsrxiEqBU4p63HS+uOdOo5O2P13kLoDGas3JUvZarssevzs/uU+LNtHgZbS7pFh6qx2NqP5rXMXBitpdds/Vtmn9dYbQMA4sM1SAjXQBCAI0W8CJSoKzDgQkQBLTmy8QvsedbarPbsr2CxHQB1ln3AxZb+rFbyY5aIKBDVdKAAf3V947pLv9zrieEQEXWJD7edxrLDctQbGDx2ptSaWa9WyhER1LwR+vjeMVApZKjQy3CilH0dA1F+hQ56kwUapRw9Y0Ic7rtubBrO7xOLBqMFe8+KQQF/KilmI5M1D2KkWgMuZzuZ4aLVGaTfn8uGpQCAVBasow5J/VuaH/87E6ZR4oV5QwEA724+iZW7Wu4B21k/7i/EzR/ulF4j0FjCTKszSlkstiALAGSdqXTo3zLIrofOrZMzEBemxulyHV5dfxTvbzmJI0U1UMhlmDEosdnzD2EfF6IuxTOBRBTQDGbxYPCCfnFS/WR77uzhYrQrT2b7wtRSwIVXtBER+bdafftLg9mX1zCYLDjvyZ+x+ViZJ4ZFRORR727Jw2GtHLvtrsKmRrZSxgnhGqcnpUPUSky0li/6LqewS8dGXcNWTqxvQliz7AqZTIYX5w1DkKrxGDHVj0qKtaaxpFj7MlyMZgtu/3gX/pWZC6Cxf0tKZBDG9BJ/RzpbUuxgQfP+LW25fEQP/HVaPwDAo98ekEoDusuyTSfw29FSrNghBnMajGapRwsA/H68DIIgIOtM42draY0eZyvrpYDLYLuAS6hGibsv6gsA+O+mk3hurViW7Pw+sYhuUsoQaAw+2bJ/iMizGHAhooBWbxDTa50FWwDAbLF49Pm1usYTcvbHXF1ZG5aIiNyvIxkuhVUNDrdr9SapvxcRkb+objCirFYskVhU3dDG2t1TaY04LwnhzUsZ21w3Jg0A8GXWWYcMeQoMtoCLfTkxez1jQ/D3mQOk2/6Y4eKMraRYaY2+Xe/rA+eq8MvhEryx4RgKtPVSObF+ieEYmBQOmUzcVlmtHjUNRvwrM7ddGS+CIEjN4dub4WJz3/R+uGx4CkwWAW9vPN6hx7bldLl48c36Q2KflZx8LQymxnMRW4+XIb+iHqU1eqgUMimbZevxMpy0Vs+wz3ABgOvH98TMwYkYlByB6YMSccukXnjm8vOcPr+tTNuBc12b4bLnTCVe/OkISvg3g7qZ5jmuREQBxPZlL1jtPODS0hfh9npg1gC88vNRAOKBVYldijAADLUrY6ZSyKUvVSaLgBZiQERE5AdqXWw6GhuqRkXbqxER+YxTZY0lsJoGkklkOxaIbyXg8qeB8YhWC6jUGfHD3gJcYw3AUGBoDByEtbjOLZMykFdWh1CN0mk2gj+KClEhTKNErd6Es5U6pz2M7GnrxQsTBQH4OussKqz97volhCFUo0R6TAhOletwuLAa32afwzd7zmHzsVJ8e9ekVrdbWNWASp0RSrkM/ZNa3gfOyGQy3Hx+L/ywtwAnrBk3HdVgNOPmD3ZBrpNjjnWZVmdAlfX1HiyoxtlKnZRBMzApHEeKarDrVAW2nRCzn4ekRGJMejQOF1bj8135MFsERIeomvWk1SgVePfGMe0a17DUKADA0eIaNBjNCFJ59mRESU0DXvrpKL7eI/Y0VCtkWGoXaCQKdAy4EFFAszUnDlY5v5L4xom9UKc348IBnevfcvdFfVHTYMKyTSekZnX2YsMav0Cr7QIuzHAhIvJvrjZVjQ1T45ibxkJE1BXy7AIuRdX6VtbsvkqlkmJBLa6jkMswOcmCH84o8PH2U7h6dKrT8mPkn2wZLgNaubBPIZfhH1cO7aohdQmZTIbU6GAcKapBfkV9s4CLxSJAbldircquEsSqrLNSSTJboGpQcgROlevw3pY8bM4tBQBkn9GisKreoU9rU7ZyYn0TwlqsctGaXrFipk5BVUOnAhO7TlXgj7xKyCBDg9EMlUolZbfYrD9YjB0nxctuFk5Ixxu/HkNpjR7/+z0PADAmPRpjekXj/d/zsNdadmxQcoRLnxPJkUGID9egtEaPgwVVGJ0e0+ltteRUWR2+2XMWO09VIPuMFnq7DJ7TFZ3r7UPkr1jLgIgCmi37pKWWKWqlHH+d3g8j0qI6/RxK6xfHSl3zev72JcXsgy/u7B1DRERdr8HoWknKqGCVm0ZCRNQ1TpYyw6UtJdVtZ7gAwIQEAWqlHAfOVTv0cSD/ZjJbpN8TVysp+KNUa1mxs5WOJ9e/yjqL8576WQqcAJAyPgDgTIUO20+IGR/9rPNmK59le4wtVrPuQFGrY+hsOTGbmFA1wjXiten5nQgSZFn7WwmQSaXAmgYb1uwrwB5rr5aJvWMxqU8sAOCYNatmdHo0RqVHOzymaTmxjpLJZBhuzXLJyXd/H5cqnRGXv70Vb2w4jj9OVkBvsmBYaiTuvLAPAOBsO3v7EAUKBlyIKGCZ7DJOfj7Y+hczV8jlLV9pkmN3APWfhaOkn5nhQkTUvbX2t4OIyBc5ZLgw4OJUaa0tw6X1gEuYCrh0aBIA4ONtpzw9LOoip8p1MJgtCFYpAqY3S0fYslTym5xcX7OvADqDGdvtGtHbB1wAwHZ43DehMcPFfrtLZ/QHAPzURsDlmLWk26DkzgW8ZDIZ0uPEwNGp8s4HXADgeIk14GL97JzQW8wq2WPN/ogL06BPfCgm9Y1z2Mbo9GgkhAehZ0yItMzVgAsAjEgTg1B7XQzyanUGfJV11qEvyyfbT6Gq3oj02BA8f+VQ/LJ0Cr6/exJmnyd+zp1jwIW6GQZciChgrdydL/0cqvFcBUVFO1N7h6RESlfmmCyuXRlNRETeIzhJm7x1UkabjxvuQjYlEZG32QdcCtkA2amSGnFeEiJaD7gAwJ/H9wQAfJ9TgH//kgsLL8jye8es5cT6JYZ1ywsr0qwZLk0zQw5Zy3xprX1axJ/FgMvE3rHSsuTIIEQEiRnAQ1IaAwwvzhuGK0b2ACCW7Cqtabmk4YlSMeDSJ75j/VvspceGAgBOl9e1saYjs0VA9hmt3VgcM1wm941zCJyM7x0DmUzmEHBJiwlGQoRYknC0XZZLZwNI9mzfQ/ee1ba6XkuKqxvw3JpDOP/FDfj7qr24/v0daDCaUW8w40Nr4HjpjP64fnxP9E0Ih0wmkwKPxTUNUnl1ou6AARciClj2X3bsv8i5265TLbc9bvo9WykXP3aZ4UJE5L9s9cHt3XR+epuPe/v6kTi/Tyw+uLl9DU6JiHyFIAgOAZc6vRk1Dc3L6XZ3thPB8WEt93CxOa9HBP4yVSy38+9fjuGez7NRbzB7dHzkWbnW7IruWE4MANKsGRn5diXFSmv0KLH+XlTUNQZcbBkuU/rHo7+1b4stuwUAUqKC8dJVQ/Ha/OGY1DcOqdEhGJYaCUEA1h9ynuVisTR+TvWOD+3067D1cTnVwYBLbnENavUm6fZxa/DHFrjpGRuKmYMTpfsnWM9RpEQFo3ecON4xdr1VbAEXpVzmMDedNaxHlHU8OlTa7Yv2qGkwYu5bv+P93/OgM5ghlwHHS2rx4k9H8OXufFTUGZAWE4xLhiY7PC4uTA2NUg5BAAqrmOVC3QcDLkQUsJIiGg90Hrt0sMee57i11qozTcMqCmsExmRmwIWIyF+9teG49PO0gQkY1ytGuqrT5sqRPfD5ogkOy1KjQ7Bi0QT8aWAiiIh8XVFVA+qsJw9La/Wo1ZsgkwEauSDdT43MFgFlteJJzPZkuADAQ7MH4uWrhkGlkGHt/kI8/+NhTw6RPCy3RMxwsQUQuhuppFhF44n1Q4WNF6nY9zy1BVyiQlS4fXJvAMAF/RxLa107tifmjUqVbl98nngyv6U+Lue09dCbLFAr5FI/mc5ozHDpWEmx3dZyYiFqBQC7kmLW7aTHhGDmkMbvgBN7NwZX5o5IAQDMGpIkLbuwfzyCVQpM7hcHjVLR0ZfRTGSISgrstJblIggCcotrHLLuMg8Vo7haj6SIIHx481j87+axAICPtp3Ca5m5AIDFU/pAqXA8zSyTydAjWnxfsKwYdSeeq7FDRORlZdYayvfP6I+YUHUba3eeStn+dHGlNeDCDBciIv81MDkc66y9wWwHnADw8a3jsOloKa4dm4Y+8aFQKuTolxAmNUElIvIX+RU6XPTqbzi/bxw+uXUcTpWJJwx7RAXDrNehUAcUVDVIDa5JvHrfbBEgkwGxHTj2mD82DUqFDEu/3CudsCX/Y7YIUm+M7vp7YQtyVNUbodUZEBWilsqJAY4lxaqtAZfIYBXmDE3G+X1jkRzZet+bi89LwkvrjmDbiXJU1hkQ3eT3zNakvldciHShY2f0sgZcOprhssf6+3vJ0CSsyjqHMxU6VNUbpQyfXrGhiAhW4oYJPWERHMue3fOnfrhmTJpD75+0mBBseegihKrdd+p2eFoUTpbVYW9+FaYOSHC6zv9+z8Nzaw/jrql98ODsgQCANfsKAQDXjk3DRQPFx900MR0fbz+Nqnoj4sLUuGZ0qtPt9YgKxsnSOpzVMuBC3QczXIgoYNUbxZT8EA/2bwEAlaLlj9KwJl+OFAprhgt7uBAR+S1bEH9Ek54sF/aPxxOXDcaApHDpCj93NDklIupqx0pqYLII2JxbisKqeuSViYHjjNgQRKltGS48eWbPVk4sNlTd7CrvtgxLjQIAnCmvc9onjLrGseIah9J5HfHj/kKcraxHRJASY+x6b3QnYRol+lhLeW0/UQ6g5QwXbb0YfIkMFnu2pEa3HSTpFReKgUnhMFsEZB4ubnb/CesFLr3jXMswspUUO1dZ36G+I1nWgMvFQxKhUQjSZyggvs7IEBVkMhmeu2Ionr9yKGR2vWAVcplDsMUmLkyDYLXr2S02w1MjAbSc4VKrN+GtjWIm98fbTqG6wYgqnRFbjomv49JhjSXDHp4zSCp1dsukDASpnI8z1ZrhcpYZLtSNMOBCRAHLaBa/HKkVnm1YqG7lgOqWSb0cbtsyXEzMcCEi8lt1ejGg356GrFMHxANo/W8FEZGvsX3OAWIpmcYrx0MRbb2ovEDLkmL2SmrE+YgLa185MXup0cGQyYA6g9mhzwV1nco6A+a+tRXTX9uEtzYc61BFAotFwJsbjgEAbpvcG+HWxu/d0YX9xeyHTdZAw6GCKuk+rc4gBRSr7DJcOqK1smInrYFhV/q3AEB8uAbBKgUsAnC2sn1lxUpqGnCmQgeZTLwgJ8kaO/nVGhiyBXG8bbj1YqG9+Vqnwd2Pt52C1hoYqzOYsXJnPn4+VASjWcCAxHCH7K0glQLLbx+Pl68ehjum9G7xOW2ZTywpRt0Jj/yIKGAZTOIXiNYyUNxBrWx5+/dM6+dwmz1ciIj8n62nQUg7rji8cmQP/GfhKGz4+4WeHhYRkdvU2TV+Xn+wGHmlYsAlIzYEURr2cHHGluGSYNdHsr2CVAqp/+SZio71jSD32HK8DPVGM8wWAa+uz8WCd/9AcbXje/zznWfw5q/Hmp2o/ulAEXKLaxEepMTNTS64624utF5osim3FDqDSQrWAoDRLKDOIAZzOx1wGSr2OPn9WBlqGowO9520fk71bscFMa2RyWRItwZI2tvHxVZObEBiOMKDlEgMFt8jG4+Kgaeesa4FgdxlUHIEVAoZyusMzTJO6vQmvL/lJIDGfjofbTuF1TkFAByzW2wSI4Iwf0xaq1l9tsydc1p+tlH3wYALEQUsW4ZLR1P6O6q1gE7T+5Ry8bbZIuBspQ71BrOzhxERkQ+zlVpoz0kxmUyGOUOTXWreSkTU1WrtAi5/nCzH/nPiVeq94kIQZctwYUkxB7Y+DQnhHc9wAcR+DQADLt6yyXpifEx6NMI0Suw8VYFn1hyS7q83mPHYdwfwz8xch95sFouAN34Vs1tunZTR4QBCoBmfEQONUo7Cqgas2VsIQQDiwtTSRYqVdQY0GM1oMIrH6pEhHZuvfglh6B0fCoPZgg1HShzuO1Eq7pc+Lma4AB3v47L7lBhwGdNLLCeXFOyYyZMe4xvfA4NUCqncbY6155DNJ9tPo1JnRK/YECy7YTRiQ9U4p63H78fLAACXOAm4tEePaFvAhX8zqPtgwIWIApYt4KLycEmxjmxfaV03+0wlJr+0EXd+luWpYRERkQccK66Rfm56oEpEFCh0dhcFmSwCCq3ZLBmxoVLAhRkujmwZLvGdDLj0tAVc2nlFPbmPxSJIJbCWzuiPZTeMBtCYtQCIfY1sZcYOnGssk/XzwSIcLa5BuEaJWydldOGofVOQSoEJvWMBAMs2nwAADE6JREyI+MFRqTOg2hqEkMua9zxti0wmwxxrWbGf9jeWFavVm1BcLf4OuprhAgDpcR3LcMk6I75XRlv79yQ2aceS7iMlxYDGMa4/1NgHR2cw4T1rdss9f+qHUI0SN0xIl+4fnBzR6Xm19XAp1DZ0qFQfkT9jwIWIAlZjDxfPftS11dzP2bpP/SBeLWX7Yk9ERP7hqne2ST+/sWCkF0dCROQ5tpJi9t9z1Uo5kiODpJJihS0EXARBwLbjZbj941148Ku9nh+sjyh1McMl3c8zXNbsK8AN7+9Avh+O/3BRNcpq9QhRKzC6VzSGp4mNxQurGqDViT11jhQ2XnBxsKCxEfyP1l4iCyekdzhbI1Bd2F8sK2Yr8TU4OQJR1rmp1BkdyonJO3AsbTP7PLGs2G+5JdAZxM8qW9nDuDC1W7KMOpLhIggCjhWL2TXnpYjvnaQQx8BCuo+UFAOAq0alAgDWHShEea34ufX5znxU1BmQHhuCy0ekAABumJAuZSZdOrxz2S0AkBAeBKVcBpNFaFamjyhQMeBCRAGr3ihemadR+c5HnbITXyiJiMh3VDc0ltmZ1CfWiyMhIvIcW0kx24lTQGz6rJDLpAyXWr2pWQ+FP06W48r/bMP17+/AL4dL8OXus92mCbzLGS62nhF+GLAAgLc3nsDvx8scynD5qsOF1Zj71u/ItF7hb7sI7vw+sdAoFQgPUiEtRrwq/1ChGFw5XNQYZDlo1wjelgVj63lBjX1cbAanRCDamuGi1Rk63b/FZkhKBNJigtFgtOA3ayk4Wzkxd2S3AOhQD5fSGj1q9SbIZY2/xzEax16vvXwow+W8HpEYnhoJo1nAV1lnYTBZpN4td17YRyrJHh+uwQMzB2B0ejTmj0nr9PMp5DIkR4k9qlhWjLoL3zkLSUTkZpV14hc525c7TzmvR2S711XI+bFLRBQoPN0jjIjIW2wZLhN6x0jN3DPixCu0NQogIkgsA2QrK2YwWfDiT0ew4L0/kJOvhUYply40KrNeQR3oSmrEuUgID+rU4209XPwxQ0RnMCHXWnIz81Axtp0o8/KIWvfj/kLsO1uFpV/moLi6QerfYh9gHJQk9rk4bM1saZrhIggCSqobcE5bD5kMGJba/mPCQNc7LlQqIwWIAZLoUGuGS50BWp1rAReZTIaLbWXFrBlGJ93YvwVozHDJr9DBZK2c0ZKTZWIWTFpMCDRKBQCxXFpv62dmsErR6UCsp1w/vicA4POdZ/Bd9jkUVjUgIVyDeaN6OKy3aEpvfP2X8xEX5tr4U6PEz7ezlf73+UbUGTxKJKKAZbt6IsLDjQv/Oq1fu9cVBNYsJSLyV/wMJ6Luos7awyVMo8LFQ8XyPUNSGk8oJ0eKQYWCqgaU1DRg3jtbsWzTCQgCcO2YNPz+0J+kK8TLarpLwMW1DBdbSbGi6gY0GM1trO1bDhZUO/RmeG7NYZ/u1VBuzbqqaTDhga/2IcuapXJh/wRpHVtj8cOFYnDliF2GS02DCfkV9dhj7dsxIDEc4UEsJ2Yjk8mk4FWwSoFesaGIknq4NJYUc+U43VZWbMPhYmh1BpywBj16x7knwyUpIghqpRwmi4ACbetlsGyl02xBaRtb8KdnTAhkMt+qdHHZ8BSEa5Q4Va7Ds2vFrLTbJmdIASN362ENwJ2rZIYLdQ8MuBBRQPrjZLn0s6d7uIR0oNHfkaKatlciIiKfpDc1XuG40HplIBFRILJluIRqFHhw1kC8Nn84br+gsSG4LeulqKoez689jAPnqhEVosKyG0bjpauHIT5cI10RXdYNSorV6U3QWYNUne3hEhOqRqhaAUEAzvrZScm9+VoAwLheMQgPUuJQYTW+3nPWu4NqRaXde3JzbilMFgEZcaFSOSjAMeBSUqNHpc4IuQzomyCe0D9YUIU9Z7QAgJE9o7tu8H5i5hAxIDIqPQoKuQzR1h4u9iXFolyoRDEiNQp94kNRZzDjbytzcKLEmuGS4J4MF7lcJgVBc85qW73oxpZd0zTY09cacEn3oXJiNiFqJa4YKWaz1DSYEB6klLJePKFHlDXgwpJi1E0w4EJEAen/vt4n/axUdO3VJJcMFdOb5w5P6dLnJSJqL4tFwPGSGmZsdFC9ofGK46fmDvHiSIiIPEsKuKiVCFYrMG9UqsNFRknWDJeNR0rx/d4CAMAnt46TrjoH0Bhw6QYZLrbsllC1AqGa9l+MZU8mk/ltWbEca8DlwgHxuPdPYvb/qz8f9dlMHVtfoYFJ4dIy+3JigNjoHQCOFddi/1mxZ0vv+DCMtgZXDhZUI9ua4TKqZ5Snh+x3Luwfj09uHYdXrxkOoLHMd4XOCK3Uw6VzvyuAGBCJkFe0AAEAAElEQVR5Y8FIaJRybDxaKl3Y6K4MFwDoZc1YuffzbIz9x6/4x9pDTjO38mzZNU3KmV01qgcuHZaMOy7s7bYxuZN9gOXGiekezdKylZjzt2AyUWcx4EJEAclobvwipOzivikvXz0My24YhRevGtqlz0tE1F4vrTuC6a9txrubT3p7KH5FZz1xpFbKoWL/FiIKYLVShovzE6JJEWIwZd3BIggCcMmwZAxLjXJYJy5MPMHaHXq4lLpYTszGdiX8GT8LuOw9qwUAjEiLwo3np6NHVDBKavT4LvucdwfWgkqdGHB5aPZADEgUgy4zhyQ6rJMaHYwwjRIGswU/7i8EIAZohvQQAzF7z2qxzxqIGZXODBdnpvSPR3KkeKLdFnDR6gyorneth4vNkJRIPHv5edJtlULm0DvGVX+Z2gdje0VDrZCjrFaP97bkYc2+gmbrnZTKmTkGXJIjg/DW9aMwOj3GbWNyp0HJEbhseAp6x4XilkkZbT/ABSwpRt0NjxSJKOB5uqRYU6EaJWafl9yhUmNERF3pv9ZAyws/HfHySPyLznoCMkTtmfrWRES+Qif1cHH+fdbWwwUAFHIZ7p/Rv9k6UoZLNwi4lNSIPR4SwoPaWLN1Pa0ZLqfL/SfgUl6rR36FeBJ1aGokNEoFbpnUCwDw/u95PplNW1EnnvBPjAjCl3dMxNd/mYjz+8Q5rCOXy6QMmHUHxcbsg5IjMCRFDLhsPV4GvcmCqBBVsxPt1Fx0qBhcqbQvKRbc+ZJiNvPHpmH+mFQAQJ/4MCjdeOw/qmc0Vt15PvY9NRN3XtgHAPDGr8ccslyMZosUIO0d777smq7y5oKR2PD3qdLntaekRomfbee09T75mUDkbgy4EFHA6+qSYkREFJhsTaQ1Sn6FJqLAVmvXw8WZJLuAyzWjU52eaIyzZnuU1wZ+Dxd3ZbjYAi7+lOFiy/LoEx+KCGtJovlj0xCqVuB4SS025ZZ6c3jNWCyClOESE6pGZIiqxQwEWx8XWwByYFI4BiZFQCYDbOfcR6ZF+VxDdF9k69dSWWeE1jr/rma42Dxz+Xm4f0Z/PGOX7eJOQSoF7rqoDyKDVThRWoe11ownQPxdNVsEhKgVSIzwbNDCnyVFBkEuE/shlnWDvwlEPFokooBkf9WErwdceIUHEZF/sJUUKa4O/Ku1iaj7EgShsYdLCxkutmbSaqUcf53ez+k6saHdp6RYiZsCLmlSwKXO5TF1FVv/luFpUdKyiCAVrh0r9of43+95XhhVy2oaTFKGgi3roiW2gIvNwOQIhGqUyLDLaBnVk+XE2sO+pJgtwyXCTQGXIJUC90zrh3EZnivdFRGkwm2TxbJbb9pluZwsFX9XM+JCGXhrhVopR2KEGKjPr/SfgDJRZzHgQkQBRxAEFFQ1SLdVXdjDJSKo42XE9CaLB0ZCROScvzXi9SVbj5d5ewhERB6nN1mkq/dbCrikRgfjzQUj8cmt46QeDU3ZMly6w9XMx4prAcDl/hHpseKJ/DMVOr+5KMu+f4u9Wyb1glwGbDlWhqPWhua+oMKaXRGmUUKjbL1E6KDkcOnn8CAlUqyZXeelRErL2b+lfaJDxOBKncEsfSa4K8Olq9w8qRcigpQ4VlIrXYSTVyb+7vtjObGuZgso81iEugMGXIgo4Hyy/bT084DEcMjlXXelSUxo63VonX2pzCvznyvYiMj/fbbjtMPtBqMZhwqq8fA3+1Bc3dDCowgAellPhC0Y19PLIyEi8hxbOTEACFG1fEL6suEpmNA7tsX74609AUpr9X4TPOgMQRCw50wlANdPvveICoZMBjQYLSj1g8wgQRCw15bhkhrlcF9aTAhmn5cEAPhwq+9kuVTUiSf728puAYABSeGwJS0MSoqQMhhsfVzkMsfMHmpZRJAKtsPys9YMh6gQ/wq4iFkuvQEAb204DkEQHDJcqHVp0WLA5WxlvZdHQuR5DLgQUcB5cvVB6ecf7pncpc/dVpM+Z1/ELn59i08dhBBRYFM0KXdQWqPHpW9uwec787H0yxxU1RulE0fkqMgakBqXwatZiShw2cqJhagVLl24FBsmXohkMFkcgjiBJq+sDhV1BqiVculEfGeplXKkWDOGzpT7/lXg+RX1qNQZoVbIm5XfAoDrx6UDgE/1cam0BlxiQtpu2B6iViLDerHFQLtsl7HW0lUj0qIQ1kIWGDmSy2XSxYe2DDp/y3ABxCyXELUCR4tr8PvxMpy0XjzZJ54Bl7ZIPar84LONyFUMuBBRQFN3cWNjVRsBl5aOWZ/+4ZAHRkNE1FzTwHCdwSQd+B44V42nVh/EvP9sw1dZZ70wOt9Wb22aGxPKpqhEFLhq2+jf0l4haiVC1GKGTCCXFcs6LV6kMDw1ss0SVe1hOyl52g9OSm4+JgZSBqdEOD3uGpUeBYVchsKqBhRW+cZV7bYMl7YqE9iM7y0GV8b2auwPMqpnNJbfPh5vXj/K/QMMYNFN5twfAy6RwSrMH5MGAHh/Sx4zXDqgZ6w1mMySYtQNMOBCRORGakXrVwGykR4ReVvTz6mPt52SfrZYBHybfQ4A8Mn2UyBHhwqrAQDBrZTYISLydzprcNkdV+7Hhdn6uPh+eazOsgVc3NXLIz3WehW4j5+UPFlaixd/OgIAmDE40ek6IWolBiaJmSF7Tmu7amitsvVwaXryvyUPzxmE5bePx6XDkh2WT+obhx5RrvXs6W6i7bKKVAqZFJD1N7dM6gWZTMzcsn22MeDSNinDxcc/24jcgQEXIgpY3qix31ZJMYZbiMibMg8V49X1uQ7LPt+ZL/1cY1fyZd/Zqi4blz+w77cVpOJXaCIKXI0ZLq6fDI2zlhUrqwn8gMuY9Jg21myfdGsJq9Plvtvnsd5gxl8+24NavQnjMmJwx5TeLa47qqcYiPKVcqUdKSkGiH07JvWN44VzbhBt17MlMljlt3OaHhuKmXZBxvhwDcKD/C9bp6vZergUVtXDaLZ4eTREnsWjRSIKOCmRQQCA670QcFG1keFCRORNiz7Z7e0h+K3nfzws/TzYSZ16IqJA0djDxY0ZLnWBWVJMqzPgWEktAGBUzyi3bLOXNcPllA+XFHvsuwM4WlyDuDAN3lowstWLzkalRwHwnYCLraRYezNcyH2i7IJcEX5YTszebZMbg4y9md3SLvHhGmiUclgEoEDrGyUGiTyFARciCihGswUFVWJTY40XrkBuq4dLcAtp0356cQ8RUbcxzq52e1vZjERE/swWcHFHSbFYW8DFyxkuVfVGrD9YhA+35uHFn45gx8lyt2w3+4wWgHjC1fZaXeXrGS5FVQ34es9ZyGTAW9ePREJEUKvr2zJcDp6rht5k7oohtqrSWlIslgGXLtc0w8Wfje0VjWGpkQCA3vEMuLSHTCZjWTHqNni0SEQBZf+5xhI4tj/mXUkpbz1y8uzl56F3fChevmqYw3JeFUPkX/acqcT/fb0P5e2oSa8zmKTyLP5GEARvD8FnRASLJx6nDUzw8kiIiDyrTi+eFA91Q8Al3lZSzPr3Um8y41BBdZf/fVn4/h9Y/GkWnv7hEJZtOoG/f7XXLdvdfboCgPv6twCNPVwqdUZU6Yxu2257CIKArNOV+Of6oy0GpYqrxYvbkiOCMKF3bJvb7BkTgphQNQxmCw6cq3breDujnBkuXmOf4RLl5wEXmUyGJy8bgrG9onH9uHRvD8dvMOBC3QUDLkQUUBoMjVdNBXmhqXFbGS694kKx4f6pmD82zWG5ycKTmkT+ZN5/tuGLXfl44vuDba47962tGP1sJmoauvakiTvoTZ2vr1zTYETW6cqACdrYPqcVbQTWiYj8nS3DJdQNDa3jwq0ZLtaAy78yj2HOG1uwem+By9tuL73JjIMF4on+iwbEAwDOVdbD5IYeAo39W9wXcAnVKBFvnbdTXZjlsmp3Pi585Tdc9c42vLnhOB74ap/T9WxN56Pa2QNFJpNJ5dayfaCsmNTDhQGXLhdt957x9wwXABidHo1Vd56PodZMF2pbGgMu1E0w4EJEAcV2cnCYl770tBVwaYnJHBgnJIm6m7X7C2FpJWBqNFtwvKQWepMF2064p3xJR1gsAkqqG2CxCFi7r9DhvumDElt4VKP2BFxaCqhc/94OXPXONvx6uKR9g/VxFgZciKibqDVYAy5uyHCx9XAprxVPcv96uNj6f9f9bThbWQ9BAELUCrx/01ioFDJYBKCkhTJnB85V4bi1L0trjGYLcvK1AMQTr+7U2MelawIuFouAJ74/iDMVOoRYA21nKnROM3S1OluGSPtPmI+0lhXzhT4uUg+XdgaMyH1iQgOnpBh1ji3gks+Ai1N5ZXXIsmZOkn9jwIWIAoqtLrBG2bUfb7YGylePSe3U400W16+wIyLvsF3p6Uy9sTHrrtALzSEf+XY/xj3/K+74LAt3r9jjcF97apfrja3XWi+r1WPySxvx6s9Hm91nK/G4dn9hs/v8ETNciKi70LmxpJjtb01Zrd6hwbwtM6QrnLE2n+8ZEwKFXIZEa8+RQmvfR3vVDUbMe2cbrlm2DcY2MmAOnKtCg9GCiCAl+sSHuXXMjX1cxLEXVzdg0osb8OyaQ259HpuCqnrUG81QKWTY+eh0KcPmWHFNs3Ur68SM3fZmuACNfVxsPW+8xWi2oLpBDCKxh0vXs3/PRDLg1S31lAIuXX9c5MssFgH/3XQCM/+1CVcv286AVABgwIWIAortamx1Fwdcvv7L+Vj/tym4aEDnavszw4XIf5W10selzu7KUG9UDvxiVz4AIPNQcbP7VEoZnrvivFYf32Bs/WTT+1vycE5bj7c2HgcAGEwWvPLzEew61XhlljfKO3qCmQEXIuomPFNSzOBwsv2cth6FVV1zws1WusZ2oi850hZwaf78hdoGGEwWVOqMOFLYPNhgb1XWWQDABf3iIXfz34amGS4/HyzCOW09fvTQRQx5ZeLz9IwJQZhGiQGJ4QCAY8XNM32kDJeQ9mcoDE+LhEIuQ2FVA3LytVizrwC/He36DNhK69jlMiCCGRZdLtBKilHHsYdLc+W1etzwvx144acjMJoFCAKk7EnyXwy4EFFA0VtPDmqUXXuCL1itQH/rgUl7XTumsY8Le7gQ+a/ahublNmy+zT4n/WwwW/DpH6fxzA+HfKKvSZBSAXUbZRD3ntW2en/T+vf/+iUXb288gWuWbZeWdbLSos9hwIWIugtbGSl3lhSr1Zvw+/Eyh/u6KsvFliVia0afHBkMAChykuFSbncRRWvlr6rqjfh2j/g3/oYJ7m+Y3TTDZWeeeCFDcXWDW3rPNHWyVAy49LZm6vRLFP8/6izDRSdmuHSkJFeIWomBSeKx0hVvb8WSFdm4+cNdOGTtrdNV7LNz+Pe869kH6Rhw6Z7SYsTP36p6I6p0/tff0hP+9Usutp0oR7BKIQW7jxa1HvAn3xcgh8BERCJvlRTrjJeuHoYvFk8A0PykJRH5rqbBktcyc5uts+5AEQ4XVuPldY2ltqrqjXj8uwP4YGueVFLFE4qqGtDr/9Zi0Se7W12vzmCGStn6yYZ7Ps9uNYNn87FSh9vv/Hai2TpnAqRkgNm63xUynqAhosBWZ+3hEuaGgEtEkFIK7v98sAgAEGU96dpVAZczFdbsDWsQw5bhUqBtHnApq2ssE9pawOWrrLOoN5oxIDEcE3rHuHO4AIBeUsClDoIgSAGXpr1n9uZr8fnOMx26kKOkpgHXvbsd3+w5Ky07WSp+L+kdJz6v7UKyXKcBF3GOOlJSDACmDRQrAchlQLj1vbXuQNeWHW3s38KT/d4QxQyXbi9ErZQC8fmVzHIBgN2nxL81r14zHAvGiRflHmHAxe/5/hlJIqIOsKWmyv3khJitaZyRGS5EfiO7SYr3thPlDrezTlfgzs+ycPHrWxyW219Ja2hHM/rOem6tWN/dWRkxe0azBap2pJ+Mee6XFst+5DopNdKUL2TzuMP6g+J8nq0MjAASEVFL6tzYw0UmkyEuTDzJavv8/LM1I6TLM1ys37uTrAGXourmn+ftyXCxWAR8uv0UAODG89Mh88BxR09rNk5ZrQEHC6odgiz2pdDuX7UXD3+zHwc7kCny+7Ey/HGyAss2NV4kcbLMluFiC7iIGS7OS4qJV6XbN0Bvj3um9cPaeydj75Mz8fTlQwAA69v4ruJutmBRDPu3eIVaKZdKFUYx6NVt2bJcWFYMaDCapQvxRqVHYUCS2Bv4aHHXZv+R+zHgQkQB5b0teQD8p0mz0prKbmbAhchv1BuaN5K3z1Lbmef8BI39CZKGNprRu+J4O7NnFDJZuz97bv5wV7NlTV9DS0Ekf8g4bA9bLeXtJ8tbX5GIyM+5s4cLAMRar2YGxKva51vL6h4sqIbO0HJZTnewWATppF7TkmLOMlzKaxszXPIr6lFa0zzLc9OxUpwq1yE8SIkrRvTwxLARGaySggJfZZ11uM82bpPZIvVeKa5u/lpaUlUvBkyOl9RK+7p5STExw6WoukFa36azGS4qxf+zd9fRbZxZH4B/QsvMGGM4DjjM3HCaNmm2u2lTzjaFFLPbfuUtbtuUIWVm2nLTNG4YHGwYncSOHTtmkEms74/RjGakkSzbYt/nnJ5KMyNpNIpHmve+914pBqZFI1KlwPT+SZBJJThR0YRzlj413sBmuFDAxXeGpMcgVCHjsqlI98P2caHG8MCxC2oYTWYkRIQgJUrFlV4srWvjynuSwBQcV8CEEALYXQwEAn7AJVhmgRMS7P7z81G7ZZtOWktrqTXi56LjvOa7bR4MuLjaTyo6TIEh6TFQyqTonRTR4ddpsKm77GjQTG8MrnNblKrrM74JIcSftbixhwsALsMFAIZnxiA9NhQpUSoYTWYcLG10y2s4UtWkhdZggkwqQVoME2hhS4qJ9nBpEQZYbLNcTlY04bX1hQCAy0dkuO0YiWEDRD8eKBMsZ/f7QqOGmzjh6LeHGHUb8/mazEzQS6M3otwyKSTHMggepVJwx+l0lbC0TT1XlqvzQYuYMCXG5DCl2NrLyHUnCrj43sc3jEbB/dMFgVjSvbABF8pwAQ6fZ74DB/eIgkQiQWy4EkmRzN+GWElHEjgo4EIICRofbi/ibj84b4AP98R1cqn1NGygLBdCAoJYBsk/P9mLkU/mQ2swCsqR8PGDwhq950qK/XywvN1t0mNDsXxyT+QkhGPDv6fg25vGCdZP6ZvY7nP8sF84AORoFlZnM/gOn2/Eoz8fRWOrHgdKGzDluY1YZ6n/7wtDM2IAAE8tGuyzfSCEEG9o0bmvpBgArl4/AIzIioVEIsGIrFgAzvukuAObPdEjJpQro5kawwQSqprsG9DXWDJc2OzM/SUNAJim9XNe3oLZL2/BXyUNkEsluHpclkf3ne3jwk5wYGc+s8ER/mBlR5pP84Mzh843oLi2BWYzM6EgnheI6MM1bxb+7qm3vFZX+6DMyk0GYC3Z6Q11bggWka5RyqUdzo4iwSWDAi6cw2WWgEt6DLesXwp77qWASyCjgAshJGjwZ1vfOLmnD/fEdXKZteazIchmgRMSjJwFD2qadfj5QDm+2Xve4TauPE9nmM1mnKpsavd5H790II49Phvb/m86NwCWHhuGWJuZnoN7RGPV4iFOn6vGJrDUKlJqDQAMps4Flxa8vg0f7ShG3uPrsHD1dpyrbcXyT/d16rncgS0pFkVNXgkhQcxsNvMyXNxTUiwhkh9wibP8nwm47C2uc8trOHLOMqDHzqgGgITwEChkErsG9IC1h8vE3gkAmICQWqPHrZ//hRMVTVDIJJiVm4xPl43hskE8hQ24AEyj+QV5aQCAC5aSYvxyPI1trpeeUfMmgBw838iVE8tJjBD0o+lryX7lz7LW6I1clm5XB81nDkwBAOw9V2f3m8JTqIcLIb6XEcucjw+db8S2whof741vWTNcorll/SngEhQo4EIICRoKS/DipgAJtgDCgIu+k4OShBDvaa90oau1dk1uLiH4wY5zmPUSM/PWkacvG4xrxmUjTNn+jGWFTIrZg1KcbmM7WOHuDBd/UtVkLTvT1Rm9hBDiz7QGE5d17a4MFzZrQiaVIC+DGVSyZrg0wOTB74mSWkvAJd4acJFKJUiOYrJc+P3VAKDWkgEx05J9ceh8A55bexI1zVr0TAjHngdn4J1rRmJcr3iP7TMrO8G6zwPTotHHEgC5IJbh0oHSyvwMl8PnG3C2mslg6WUTQGJLlBbySoqxE9xkUkmXS2z2iAnFoB5RMJmBDceruvRcrqKSYoT4Xl5GNDLjwtDYpsdV7+/CLZ/t40oVdidtOiN3fh2Sbg249EuJAgCcqFDbPcZoMnu89xlxDwq4EEKCRrOWmW3lymCiv+CXFDNShgshfs1kMnMzIx157JdjLj2Xu4MQz/5xCoB4uTPWpD4JLj+fXCYRbZa8v6Qeu4uY2chqm8GdI2XidfiDoYcLO/sMQKf63RBCSKBo4QXPw930m5ptUj8oLYr7nZ6bFgWlXIrGNj3O17c5e3iXsBkuWbwMF2af2ICLsI9LjSXjZXROHKJUcmj0Jny68xwA4LFLB3q1FFIWL8NlVHYc14Om3LLPnQ648LJhimtbuQzOnok2ARfLLOtTldbfFuzvoJhQhSAbprNm5TKTO/7wUslQdv9tM3sJId4TppTjl9sn4rrx2ZBJJfj9SAWe+f2E6LZGkxnf7D2POu8kwXnVsQtqmMxAYmQINwkAEGa48Pv8ms1m3Pr5Pgx7PB9nqh1f8xH/QAEXQkjQcHf5A2+QSSVgr1Uow4UQ/6QzmLD4zR0Y+vg6HL9gnWm0+Z6pLj2e/dHM5+4MF5W8/Z90Ee3MVB6VHcvdVsgkkMuszxmlksNkMmPRGzvw97cLUKXWCMo4AsAjPx0VfV52IMeR3UV1eDH/FHQG/z0HHrS8h7RoVUAF9QkhpKPY8pChChlk0q4PqAPARQOScPv03njs0kHcMoVMihhLicaONHzvKDYokRVvG3BhghdseS6AmW3M9q9JjAzBsEzr9+L8wamY1Kf9/mbulM3b59E5cUixBIlqmrXQGUyCkmIdOYa2224+VQ0AyEkQTihgM2qqm7Tc7HN3BywuGpAEANh5tlYwGeWTgmJMe34Tpjy3EVOe24gnf3VtQkt76iw9euKohwghPhUdqsCjlwzE21eNAAD8ebxSNNvxy90lePCnY3jruMyu51agO3y+AQAwhFdODGAmd0klTL+sal7Zy98OX8AfRyuhNZjw68EL3txV0gkdDrhs2bIFCxYsQFpaGiQSCX788UfBerPZjEceeQSpqakIDQ3FjBkzUFhYKNimrq4OS5cuRVRUFGJiYrBs2TI0Nwujc4cOHcKkSZOgUqmQkZGBVatW2e3Lt99+i/79+0OlUmHw4MFYs2ZNR98OISSAmc1mrDl8gUuDZ1Mr2xtU9Ddyy8VsMJTdISQYPf37cew7Vw+1xoAX1jGZJHnp0ciKD8cVozPbfTxbc52vKz2b3tt6FmuPCGeCJvLq4zvSXmmYtywXPAC4xsK/3zkJAFN+RWOw9mi593+H8PXeUpf32Zkr3t2JV9cX4rk/hDPb/OlcfsJSQ/mSoT18vCeEEOJZzR6YwKRSyPCvWf0wNCNGsDzMkknJ9gTxhJJapj9JZpwwe0Msw6W2hRnYUsqliAiRY7gl4BKmlOGhiwd4bB8diQlTIjc1CgkRSoztGYf4cCWUcinMZqBSrelySbEelowZNhPVNsMlPETObcP2cWEnW7irvGb/lCiEK2Vo4ZXWMZvNeHV9IYpqWnCuthXnalvx3rYil8u2OlNHPVwI8StT+iUiMkSO2hYdDolky3+3j+mNWdkmwdf7yrjlDa06lDd4LjvSGw6XMRP5BtkEXFQKGbItJR7Za5BmrQFP8ALPm095pwwj6bwOB1xaWlqQl5eH1atXi65ftWoVXn31Vbz11lvYtWsXwsPDMXv2bGg01h8yS5cuxdGjR5Gfn49ff/0VW7ZswfLly7n1arUas2bNQlZWFvbt24fnnnsOjz76KN555x1umx07duCKK67AsmXLsH//fixcuBALFy7EkSNHOvqWCCEBavOpatz6+V+Y/sJmHC1v5F0g+s8gnSvYsmJdGYAlhHjOh9uLudvnLAM3SZa077MupHOzder5jJ3McDlS1ognfzuOmz8TNo9vL167dEwmF0RxRMnLkmFnNbNBD63eBI3eOqts08nqjuy2wxlpK774iws2v7u1CJe9sZ07pqEiJc18hZ3Ry6+vTAghwajFi7+nQy0Zg206zwRc1Bo96i0BgkybDBc2W6RCbR2wq7VkPySEKyGRSPC3kekYlR2LZxYP4TJivO2bm8fhz5VTEBPG7BMbKCqsauLeG2Bf5tMZtqSYbanR7Phwu237JjNZLqcsJUu5kmJuyhCRSSUYkh4DADhQ0gCAKXNW06yDUibFNzeNQ0IE81pnnJRNdUWbzsj9lqGACyH+QSGTYlJf5ly04YQwiHCmulmQKf/K+tNQa/Q4UNqAKc9twswXN6OhnXLP/uxwWQMA8esLflkxAHh1fSEq1VqkWK5BD5Q2BPR77w46HHCZO3cunnzySSxatMhundlsxssvv4yHHnoIl156KYYMGYJPPvkE5eXlXCbM8ePHsXbtWrz33nsYM2YMJk6ciNdeew1fffUVysvLAQCff/45dDodPvjgAwwcOBBLlizBHXfcgRdffJF7rVdeeQVz5szBPffcgwEDBuCJJ57A8OHD8frrr3fyUBBCAs0hXk39//x0FC2WHi7+NCvaFXIZM7CpD7IUWUKChUJmX1KFLfNhW/tdDL8mL6uzDYKrm61p5fwghm2G3DXjsrjbl49Ix1OLBrf73PyADFuXnQ3C6Iwm7ge/rdzUqHafWytSLkxrMOK3Q8J0+L9KGjD9hc0AADdVsnELrsSOHwWBSGB7+umnMWrUKERGRiIpKQkLFy7EyZMnBdtoNBqsWLEC8fHxiIiIwOLFi1FZWSnYpqSkBPPnz0dYWBiSkpJwzz33wGAQzgLftGkThg8fjpCQEPTu3RsfffSRp98eCWDcBCYvlE8MVTDfMa0eCriU1DIZIAkRSrvrAzaAUt5gn+ESH8FkjfaICcW3N4/HJSKZqt4SESIXBDfYgMsuSz81lqsZLiaTGU2WDJcJva0Blx4xoaLfcX2TmUG/05YMF7a0mLsyXABgaGYMAGsJ0j3FzHvLy4jG6Jw49Eli9qGwiwEXNrtFKZdy2VWEEN+b1o8pLbjRJuDyw19MRsuk3vFIDjWjvlWPlV8fwNJ3d6KxTY8WnRH72yld7K9adQau9+bgHvYBl37JzPXV1tM1eHvzGXywrQgA8PTiweibHAGTGdh2ugYAMyFv/NPr8b5lm64yu7n0dXfl1l9RRUVFqKiowIwZM7hl0dHRGDNmDAoKCrBkyRIUFBQgJiYGI0eO5LaZMWMGpFIpdu3ahUWLFqGgoACTJ0+GUmn9YTF79mw8++yzqK+vR2xsLAoKCrBy5UrB68+ePduuxBmfVquFVmsdqFCrmfQtvV4PvV7P3eb/nwQW+vwCV2c+OwUvZFxa3wqVnPnhHCILrH8DbEkxjU4fUPvNR397gYs+u/alRYdyTXfZuIZSJoFer8fFg1PwxuazTh+vkpnx2Q0j8efxKhTVtGJzYQ10BkPnjjmv11NdcxvkMMFsts8guWt6L3xSwDT5Vbh6TuQFbUxGI/R6PaRm5nmNJjOueHen6MOiQ8V/Tq66bBDu/Z7JPG5u00IptT7/ii8P4Fxtq+jjAODn/aWAyG/9PknhXv+3ajKZcbSc+c2olJrd9vq++tujv3X/sHnzZqxYsQKjRo2CwWDAAw88gFmzZuHYsWMID2dmmd9999347bff8O233yI6Ohq33XYbLrvsMmzfvh0AYDQaMX/+fKSkpGDHjh24cOECrrnmGigUCvz3v/8FwFyfzZ8/HzfffDM+//xzrF+/Hv/85z+RmpqK2bNn++z9E//FBj+80ROR7Yml8VBJMfZ7JjMuzG4dG7io4E2cqLFkuMRH+G/2Q5olULTrLBOUiAlToKFV73KGS4vOwH3dj+8VD6mE+fq3LSfGYpefrWEyfOu5kmLuO0bDLKXm9lsyXPZaAi4js+MAAH2SI1BwtpYrOdYZZrMZr/7JlLpPjw3lJpYQQnxvqiXgcrisEVVNGiRFqmAymfHDfibgcvmIHjgir8I7J2T48zgTlJFJJTCazDha1sgFbALJnuJ6mMxAclQIVzmBr58lw2XLqWpssfTZmpWbjGn9krDjdA1OVTZj88lqXDwkDf9dcxzljRo8u/YEZg9MRnqs/Xeeq/44WoH7vz+MuYNS8OD8AdS7sgvceuQqKph64snJyYLlycnJ3LqKigokJQn/GORyOeLi4gTb5OTk2D0Huy42NhYVFRVOX0fM008/jccee8xu+bp16xAWJvwHmZ+f7/B5iP+jzy+waAxAoVqCATEd++zKqiUAmItBpVGDUjUASHB4bwGq3dNX0SuMehkACTZt3oJC8WudgEF/e4GLPjtxRjNQUsf8jfKdO1uINdpTOF9mPQ/x9YkyoVDNRIW3b1oPhRQYBuBArRSAFIcOH0F09eEO78/hOuvr3fX+BuyokmJMohSNrVrBPm78cx0WZUtQUClFf2Mx1qwpdvEVmJ+Gx44ewZqaw2ASB53/XGxtqAE/aToj3Iw7BhqhvHAAUokMJrMEa9f9iRhLmxmjGVh3zPlz/vvbg9CZ7AdDDG3NXu3Z12oAnj9k/fwP7tmJGjd/v3j7b6+11XGgi3jP2rVrBfc/+ugjJCUlYd++fZg8eTIaGxvx/vvv44svvsD06dMBAB9++CEGDBiAnTt3YuzYsVi3bh2OHTuGP//8E8nJyRg6dCieeOIJ/N///R8effRRKJVKvPXWW8jJycELL7wAABgwYAC2bduGl156iQIuRJQ3S/SqFMz3macyXM7VMUGCLJFSWakxzABXVZMGBqMJcpmUKykWH95+XzRfYUuhHbb0OhiUFo1tp2vQojNCbzS1Wz5UrWE+X6VMirhwJXonReBUZTNyEsQvQnISmJJixbVswMW9JcUAa4bLqaomNGsN2FtcDwAYlc2UZO2dxOxDV0qKvbv1LL7eWwqpBHh4fm7XdpgQ4laJkSEYkh6NQ+cbselkNf4+MgO7iupQ1tCGSJUc0/slwnTOjIm947HtdC1mDEjCsMxYPPfHSRyx9EEJJPUtOjzwPXMdOL2/eLBobM84pEar0KozYlR2LMbkxOOqsUwFgyl9k/Du1iJsPlWN3UV12HGmFgCgM5jw7NqTeO2KYZ3ar7KGNvz724No0hjw+a4SFJytxatLhtn1mCGu6Vahqvvvv1+QFaNWq5GRkYFZs2YhKopJ19Lr9cjPz8fMmTOhULgvTZZ4B31+gen5dYV4b08RRiSY8OmtF7n82bX9VQacPgoA0MtUMIHJYLt+8dyAmrX09NHNUOu1GDd+Igb1aL80jz+iv73ARZ+dc81aA8w7N9gtzxuUi3njslC6pQgoKRSsWzYhC7FhSjyfzyy/ZL71nLSu6RAO1FVgQO5AzBub2eH90R+8AJxkfqDvqGIGVXZV2w+uLJg/F5d04jx4Z8E6AMDYEUMxb0gqDEYT7t39p9PHzB8zAAd/t5ZCio6OxsIFYwEAD+xbjxadEROmTEWWZYZxs9YAiBxTPrFgCwDExsZi3rzRLr+frrrps/2o1Vr71VyzaA5C5B2uyCvKV397bIY38S+NjczgaVwcM6N737590Ov1gsoB/fv3R2ZmJgoKCjB27FgUFBRg8ODBgklos2fPxi233IKjR49i2LBhKCgoEDwHu81dd93l+TdFApI3e7iwZZ1adV1vhi6mxEmGS0J4CBQyCfRGM6qatEiLCUWtpWxngh9nuKRamtizpUQH9ojiysqo2/RcOTRH2EyYqFA5JBIJJvdJxKnKZtF+cwC4QMz5+jZoDUY0cBku7vveSopUoUdMKMoa2rDxRBWXTTMikzkfsgGXzpYUyz9Wiad/PwEAePjiXExzMMBJCPGdaf2SLAGXKvx9ZAa+/+s8AODiIWkIUcggkQBvXjkUh8qbMTonjgvMssHnQGEymXHX1wdQ1tCGnIRwPDBvgOh2MWFK7LhvOsxmQGpTZ3lkdixCFTJUNWlx73cHAQDjesZjZ1EtfjlYjuvGZzs8pzvbr399cwBNGgP6p0SivlWHs9UtWPTGdnz+z7EYnRPXuTfcjbn1V1RKSgoAoLKyEqmpqdzyyspKDB06lNumqkpYl89gMKCuro57fEpKil19YvZ+e9uw68WEhIQgJMT+B4hCobC70BVbRgIHfX6B5cs9pQCAfTXSDn12Rt5s7go1c4EUqZILyhEGAoVl8M4slQb8v1v62wtc9NmJK7ogfnEfoVJCoVBg4fB0PJ9fiJ6J4ThbzQwQqJRy8PrLC85JcvbvHZJOHe9/f+daVkxXz4NDs+It/yba3/baCTn4Ly/gIpFY31uIQoYWnREmWM9vRm3n+1VJJZ07bp3xV0k9NpysFiyLCHX/rGdv/+3R37n/MZlMuOuuuzBhwgQMGjQIAJPVr1QqERMTI9jWtnKAWMY/u87ZNmq1Gm1tbQgNtW8E7koZ5kBCpTM7pqmNyWAIlUtFj5k7j6dKzvyWb9F45t9WUQ3zHZ4REyL6/MmRITjfoEFpbTMSw+WobmLKi8WEyj3676UrxzApQngOz4xRISJEjmatAbVNbYgKcT4poK65DQAQGcK8xzun98TMAYkYlhEtuj/RIRLu+c9UqlFn6XMTFSJz6zHKS49CWUMb3t1yBgDQNykCYQrmGGXHMlk9pXWtaGrVcJlRLGfHU6s34v/+dxBmM3Dl6HQsHdWDzgXtoHNm19Ex7LhJvePwynpg86lq3PjxHmy0/Aa/dEgydxxlMGF0VjRgMqJfEvP7payhDVWNLW4tc+gpTRo9Xt94FptPVUOlkOK1fwyByoXSz0abJFAZgDE5sdh0qgbFta2QSyX478JcvL7pDP73Vzme+PUovrlxtGAScnv/Jt/dVoSdZ+sQppTh9SV5iAqV457vjmBzYQ3u/no/flkxDpGq7ncN0ZW/YbcGXHJycpCSkoL169dzARa1Wo1du3bhlltuAQCMGzcODQ0N2LdvH0aMGAEA2LBhA0wmE8aMGcNt8+CDD0Kv13MXhfn5+ejXrx9iY2O5bdavXy+YnZWfn49x48a58y0RQrxAo+/c4JtW5HFNGs/MkPMkhZS5MDIYqTkZIf7EYDThsjd2iK4LtVzsp8eG4eB/ZsFkMmPYE0xpKKlEItokHrDWq39h3SlcPS4LIXL31ciPDlXg1SuGISa08z+G1909GXUtOoelRWxN6Zvo9D3UWZrrfrOnFA9dzJTw6Git/hC5lDue3kxe/PlAueA+NdglnrJixQocOXIE27Zt8/WuAOhYGeZAQqUzXXPoHFP6srq8FGvWnHO4nTuOZ0UZ81qHT5zCmtYTXX4+WyfKmJKQZScPYE35Abv1SiOz/vdNBahIMONkMbM/588cxxq15+sTd+YYlrUA/GGc8sLDUEIKQILf129GdqTzx7OlSU3aFkGJzt+POH5MrFyGZq0E/1u3FWXVzGudOLQX+uIO775DyiZmvw5ZygMlStTc/pnNQJhMhlajBJ/++Ad6OPiJInY8d1dLUNciQ4zSjJGSYvz+uxt3OsjRObPr6Bi6zmQGIuQyNGuNyLf0aRkSZ0LFkQJUMkVN7I5ngkqGGo0EH/24Hv1i/Hcs5UIr8HupFEfrJTCYmYuZxZl6nPlrK8508jnjddYy06MSjDhUsBF5EuAXqQwHShvxxCdrMTLR/piI/Zs82SjB28eZc/sl6Toc3bUJADAvFjgSIkNZgwY3vb0eV/Xp/KS5QNWVUswdDrg0Nzfj9OnT3P2ioiIcOHAAcXFxyMzMxF133YUnn3wSffr0QU5ODh5++GGkpaVh4cKFAJi6wXPmzMGNN96It956C3q9HrfddhuWLFmCtLQ0AMCVV16Jxx57DMuWLcP//d//4ciRI3jllVfw0ksvca975513YsqUKXjhhRcwf/58fPXVV9i7dy/eeeedTh8MQohv6E2dO3G/9OcpN++Jb8gsKaKGTh4HQohnsCU6xPBnV0aHKtDGqz8vAZARZz9rHLCWNWnTG/HmpjO4a0Zf9+wsmMa5U/omduk5+ia3M1LDk5cejY+uH+XStu9tK+IFXDp2rgtTyqwBF3gv4jIgVXgsPl3mvVJmpPu47bbb8Ouvv2LLli1IT0/nlqekpECn06GhoUGQ5cLP6E9JScHu3bsFz+dqVYCoqCjR7BbAtTLMgYRKZ3bMnl+PA+WlGNivN+bN6G233p3H83h+IbZUFCEtIxvz5vXv0nPZatMZ0ViwHgBwxYIZorOf85sP4ezhCqT0GoB5E7LxVlEB0NiE6eNHYXKfBLfuD19XjmFDqx6rDm3k7i+eMwXrvziIuoomDB4+GpPa2W/t/nLg5BFkpCRg3rwRLr3mny2HUHqoAnFZ/aEvKQagx5zpk9HHUurLHVJKGvDju9bz2aJJeZiXZ62a8mn5buwraUBqv2GYNyRV8Fhnx/P9t3YCUOOGyX2wYEpPt+1vMKNzZtfRMeycxsRSfLP3PCb2jsclQ1K5xvGOjucfTQex5kglwtL7Y97kHEdPK0pnMHFlvTzt6g/24GAdUwKtV2I4rh2XiStGZXTpOQfWteL7l7dBJpHgqasmIyOWucasizmLl9efxh+VYbj7HxMQqWKG/R0dw/2lDbj/o30wmo2YNygZj/99iCAzJievAUve2409NVIM7JON6mYdjpWr8bcRPbB8UseOeSDqSinmDgdc9u7di2nTpnH32R/j1157LT766CPce++9aGlpwfLly9HQ0ICJEydi7dq1UKlU3GM+//xz3HbbbbjooosglUqxePFivPrqq9z66OhorFu3DitWrMCIESOQkJCARx55BMuXL+e2GT9+PL744gs89NBDeOCBB9CnTx/8+OOPXBo+IcS/qTV6/HGkAomRITB3YjKCRm8MyGwWMXJZxzNcPt15DnXNOtw5o4+ndouQbq9SrXG4TqUQluxQyHiBAIkEfx+ZgbL6NkzqIwyAKHn9P17+s7BDARez2QyJBA7PmXKpF9M/wNQT7ky/rI5muMjbaQDsKW02TZwH94jxyX6Q4GQ2m3H77bfjhx9+wKZNm5CTI7xoHTFiBBQKBdavX4/FixcDAE6ePImSkhIuo3/cuHF46qmnUFVVhaQkpidBfn4+oqKikJuby23Dn8XObuOsKkBHyjAHkkDff29ptQTFo8KUTo+XO45neAjzeJ3R7PbP5kwtUzorOlSBpGjxAbUelgGqC41aKBQK1FkawidHh3nl30pnjmFClBwqhRQavQkyqQSZ8ZGIsfRTada3fxxbLJ9vdDufL1+vpEgAFSiqbUOjhimvkhjl3mOUlxkHuVQCg6U3zdheCYLn75MciX0lDSiqbXP4urbH80BpAw6VqaGUSXHl2Gz6++8gOmd2HR3DjrluQk9cN8FxYNT2eA5Oj8WaI5U4XtncoePcpjPiyg9242BpA64Zl4WHL86FwkPXGxq9EX+VMn1mPv/nGIzvFe+WfsO9k6Px5tIRCA+RoWeStan9zVN748cD5SiubcXqzUV42DLhjcU/hicq1Ljx0/1o1RkxqU8CXloyDEqbygWjeyXitul98Or6QnxUUMItf25dIVRKBZZNDO6gS1f+fjsccJk6dSrMTkZHJRIJHn/8cTz++OMOt4mLi8MXX3zh9HWGDBmCrVu3Ot3m8ssvx+WXX+58hwkhPrG3uA7P/XESTy4chD42M6armjQY/dT6Tj+3Rm/E5W8VdHUX/QY7AOuoBJEts9mMh39k8v6n9U/EkPQYT+0aId1aYaXj5qy2mSAyXrBDAkAhk+LeOfazdZU2DddL61qRIdLMV4zOaHIaoG5s824Q2tHMVjOcB48dBVwm903EllPVdssFgSQvxpQ0Nudk28+OkK5YsWIFvvjiC/z000+IjIzkeq5ER0cjNDQU0dHRWLZsGVauXIm4uDhERUXh9ttvx7hx4zB27FgAwKxZs5Cbm4urr74aq1atQkVFBR566CGsWLGCC5jcfPPNeP3113HvvffihhtuwIYNG/DNN9/gt99+89l7J/6tVcuco8O9UEYx1PIarbqOBeJdUWxpvJ7tZPZybhqTsbWnuB5msxm1zUzAJT7Cf3sBSCQSpEWH4mxNC3rEhEIukyLaUkq0sa39WvNqy2+FqA7U4mdngB8obeB+h7BBHndRKWQYkBqFw2WNSI1WoUeMMAOvt+U3R2GV499mtj7ZUQwAuDgvFQkR7u/BRgjxrcE9mEDD0bJGp9sdKWtEqFKGXokRMJnMWPnNARwsbQAAfFJwDmeqm7H6yuGI8UAfmP0lDdAZTEiKDHFbsIU1Z5B9D3OVQoZHLxmI6z7cg492FOPykenon2KfnVzRqMG1H+xGY5seI7Ji8fbVIxyWib59em+UN7ShUq3BqOw4qNv0eG9bEZ749RgiVXL8fWTXsnWCFV05EkI84vYv92NXUR0uf9s+MPLh9mKHj3MW0GVtP12Dww6+VIdnxri6i34jypLm2awVXiSJDUqeqW7GvnP13P3SujbP7hwh3Rg7y1KM7UAD/8ezs9/RtkkohVVNLu+Pq0FZb0mMbH/w4r+LBgOw9rwBHJcUu3psluhyqUQYzPIW/jn4tzsmevGVSXfw5ptvorGxEVOnTkVqair339dff81t89JLL+Hiiy/G4sWLMXnyZKSkpOD777/n1stkMvz666+QyWQYN24crrrqKlxzzTWCiW85OTn47bffkJ+fj7y8PLzwwgt47733MHv2bK++XxI4WnTMgHx4iFvbvYpi+5q1dTDz0RVFNUzd9ex4x5Maxvdiym8du6BGUU0L970fF+6/ARcASI1hqoewZUrZgIvalYCLJUMlKtT1z7dnAhPsOG0JdkSGyD0yG3yY5TpuZHac3aAkG3A57WLApaZZi18PXQAAXDsu2237SAjxHwMtQfPi2lbu3Gbrk4JiXPzaNlz0wmb88+M9ePDHw/j9SAUUMglWzuyLMKUM20/X4qr3d8Hk5Nqvs3YV1QIAxvR0b7DFman9kjBnYAqMJjMe+fGo3Rhbi9aAZR/vQaVai77JEfjgulHc97EYhUyK5y/Pw6fLxuCOi/rgwfkDcKOlnNh9/zskGJ8iVp7/FUUI6ZYuNDKleBpa7b/4UqJUdstYOqMZ7V3iNGuFs7in9ktEr8QIZMSGYkFeWof31dciLBe0/BJpG05U4oaP9uLRBbm4boI1TfOiFzYLHnuhkQIuhHgKP8BxxehMfLnbmkatctIo3lncuNmmFGKVWuvy/rRXisvbfaCMLrwcW0teYzDi0te3YVyvBMQ7GMjSiQSU+iZHCAI0zq5TXvmzEBEqudtS28sbmPPr2J5xGJgW3c7WhHSMKxNMVCoVVq9ejdWrVzvcJisry65kmK2pU6di//79Hd5H0j2xv7O9EXAJVTKD9rYlHN3hXK0lwyXecYZLYmQI+qdE4kRFEzc4H6mSO5zl6y9So5nsDzZDls1WcSngYtmmIxku2QnCoFVMuGdKJN00pReaNAasmGbfO4itmFBU0wK90dRuwOebvaXQGU3Iy4hBXkaMJ3aXEOJjseFK9IgJRVlDG46WqTGuV7xg/RubTmPV2pPc/T+PV3G3n75sCP42Ih0zc5NxyevbcKRMjfLGNqTHulZ5wFU7zzIBl7E949z6vO15ZEEuNp2qwu7iOuwvbcDgVCZobTKZcdfXB3G0XI34cCXev3YUF7R3lUQiwQPzBqCophV/Hq9E/rFKjMiK9cTbCGiU4UII8Tr2S0eMK7X9+QGXUIUMH10/Gg9fzAQm4gMwXTzEUqamuKYV2ff9hvmvbsUtn/0FAHj0l2NOH/vkb8c9vn+EdFdaA3M+um9uf9xv08xX6qRfirNMFLb5I+v4Bdcb8Z2panG6Xixg4UmOBozvnzuAuy239LYxm4GD5xvx1uYzeGqN+HlrWv9E9E0Wlil7/9pRNuXa7I97WUMbfjlYjpf+PIUnfj0Go5tmp32z9zwAeKWZJiGE+AtrSTEvBFwUzGu06txfErPIUlKsvXP4hN7MxICfDpQBABID4FpixoBkRIcqMDOX6d3UoZJiXIaL6wNskSqFIKs1zgNldwCgR0woXvrHUC6bhS8tWoUwpQwGkxnnalvbfa78Y5UAgH9QqRtCgtqgHkyWy9FyYQWU1zcUcsGWO6b3xvp/TcHfR6YjXCnDv2b2xd9GpAMABqRGcdmC7PeGu2j0RuwvaQAAjMmJd76xm6XFhGLeoFQAwPd/neeWv7e9GPnHKqGUS/HONSNdLm1tSyKRYHp/5jvI9tgTBgVcCCFuV1jpvETO70cqHK5zpYYzf4Z4hCrwE/XYcjkfbC8CABwtV4sO2DZYGnna8vYgKyHdBfu3FSKXIkqlcJpdweds5vqkPom4Z3Y/7n5pvetZao/9ctTp+o42o+8qE+99sqXDXlkylBu8AoS9bRzJig/DW1cNR5hSjnV3T+GW94gJRUZcmF0ZNlsTntmA27+0zt5nA2Vdwb/gKq5pf2CHEEKChTXDxXs9XNoclJrsimJLhkuWk5JiADChNzMIdqaa2d6f+7ew5gxKwYFHZmJ6/2QAQHRYZ3q4dOwaih+48kSfg/ZIJBJeWTHn15r1LTquP8O0/ome3jVCiA+xfVyO8ErOn69vxSvrCwEwE+dWzuqHXokRWPW3PBx9fA5uv6iP4DnY81uxGwIu649XYk9xHQDgYGkDtAYTEiJC0CvR+xO4LhvOBJV+OXgBWoMJWiPw3rZiAMDjlwzsclYKG+w6UtboUuZ2d0MBF0KI2818aYvgPr9MwMqvDzh9rCslBfgZLi6M5fk9ZzPlWQdLGzD08XzRdV/vLXX3LhFCAK55Ltssvb3yFf+a2RcZcaFYNslxSSuZVIIV03rjrauGA3AcSBVzsp1gtgfKDtv54LqR3G1+SbErx2Ti5JNzcOnQHoLtFdL2f2q+sXQ45lhmYAHAJZbSkP9ZkAtAGLRxJejlqEeMq8oa2jDt+U3cfbGGlIQQEqzYHi4RXunhYgm4uCHD5ek1x3H/94dhMpnRqjOg0lKys70Ml9E58ZDzvmfiw/0/wwUQ9o7zdIYLAPTkHcfYMM+UFGtP70Qm4FJY6byPy7bTNTCZgX7JkVz5NUJIcBqcHgMA2HSqGrXNzHn/jU1noDeaMb5XPG6e0qvd52BLTxZ1cZJVUU0L/vnJXlz57k4cLW/EzrNM4GVsT/u+VN4wrlc8UqJUaGzTY9PJauyqkqC+VY/MuDAuw6cr+iZHQi5lnrPc0lKAWFHAhRDiVmKR7aXv7QQAGE1mfL+/zOnjXWmaye91UtmB/gf+ypWg0aNOZrafrXateSQhxHUavREFlvKHCZbyIsp2Ai63X9QHW++djqRIx32qWBEhzGBFi9b1bIz2Jg7dJlLz3J1untKLm00LCDNcAIjWvGdLijny8MW5dv1RXvh7Hjb+eypmDWQCHTJe0Mb2WkUsYNWVTB+90YQJz2wQLLtmXFann48QQgKJ2WxGi2ViU5g3ergo2AyXrmUmNrbp8faWs/hydwn2l9ZzJadiwhTtZmNEhMgxlNfjIxAyXGxxPVwcNI3m4wIuHejhAvg+wwUAelvKjhacrXU6m3rzqWoAwJR+lN1CSLAb3yse/VMi0dCqx2O/HENZQxu+tUxIvdMmk8WRnEQ24NK1cZWNJ6pgNgN6oxl3fnUAWwqZc9GYnt4tJ8aSSSVYOIyZDPftX2XYUM5cUy2f3BPydq5rXaFSyNDX0l/r8HkqK2aLAi6EELc6Umbfj+CvkgbsO1fHpVY641JJMa376zz7kisld9jan2LYi1VCiPvw64OzJbLcOS+JLdXCns+qm7T425s7sHrj6U4/510zXLuo6KiPbxiNv49Mx23ThQEdV1LHnWUFxYQpRBvcK2RSwcAO/ylse7g88/sJu8d3JuBytroZL+afQp8HfxcsD1fKfDIjjRBCfEFrMEFvZM7tkV4o28uWFHPl978z5+ut39m/H67AOa6cmGslXPilMAOxH2RURzJcLCXFokM7X1Is1kcBl1m5KVDKpNhxphZf7xHP8DeZzNaAS18KuBAS7BQyKZ77Wx6kEuDng+W4+dN90BvNGNszzuVAR47lu6LYhf5QzrDnHgA4XdWMfefqAQDjesZ16Xm74rLhTMBl86ka1OskSIhQuiW7heWohw6hgAshxM1W/WE/+AUAi98swJJ3drb7eL3ReSmYnWdr8d0+a9Ovy934ZeErXR3Mo7FAQtyPbeDbIyaUK6uiUrovuMk+Z4vOgGatAaOe+hN7z9XjuT9Oip4Hq9TWNO1JfRLs1gNwy0wlMVP6JmLV3/Lsysu4UsLMWUDZ5GINNJmTk9yOM7V2yzpTUmz6C5vxqqXWM19LFwcBCSEkkPCzyCOU3iwp1rVzbRmvH9rvRypw1lKHP6ed/i2sibzv1YQAzHDhSoq1MgGXmmYtHv35KE5WCEuRmkxmNHUyw6Unr/9AbLiPSoolRXB98B775Zhog+vjFWpUN2kRqpBhZHbX+hMQQgLD4PRo3Di5JwDgsKWXy50X9XX58dmWgHJpXSsMDsaj2ptoptEbsdNSHeHBeQO45QkRSvSylEP0hb7JkVyfGwC4blwWVG6csDtIpIcOYVDAhRDiVlsLa7jbceHiFyzJUSH4bNkYri8Cn6OAi8FowjtbzgiCNkPSo/HM4iFd3GPfczaY6AopRVwIcTu2vEkYL8jy5tLhUCmkeOTi3C4/f7gleNGsMeAvy+wn1sBH/sCawxcEyxa9sYO7PTrbd7Ok+FKi2y+dJncWcHGx54zUSQ+Xkjr7mWgvrDvp0vOazWbc//0hvOji9oQQEuzYrMuIELlLPQa7is3SNpjM7U66cuY8L+BS1tCGXw8y36HZ7fRvYeWlx3Df94HSw4WPDbg0aQ0wmcz4clcJPtpRjNdtsmZbdAbuu7ejPVwy4sK4Msi+KikGAMsm5mBcz3i06Y246+sDdv9u2Bnm43vFi5Y6JYQEp7tn9OUy8cbkxGFcL9fLeKVEqaBSSGEwmQXfJwBzvfDPj/dgynObRIPYrJ1na6E1mJAarcI/J+XguvHZAIBJfRJ9ni3PZrmoZGZcOdq9E5a5gEu5faWb7o4CLoQQj7lvTn/R5bmpUZjYJ0G0dwlbxsDW9/vL8N81wuyZO6b3cakcl7/r6vfvJwXncP/3h10q70MIcQ3bW4U/A2hkdhyOPjYHN4iUweqoCEupFoPJDKNN5EFnNOHdrWcFy8oarD/+xQbBEr04I/fD60bhitEZouXAbEkkEodBF9seMI44CiprDeIzotefqHLpeU9WNuHL3aV4dUPny7gRQkgwadZYAy7eEMqb1NCVsmK2A2THLjADP9kulhRTyqVYOiYTSZEhGBWAWRFRlvJgZjOTpXTEUtrFth+B2vL5KmVShIhMfHMmRC5DRhyTMeTLLCCpVIIX/p6HKJUcB0sb8LzNpInNJ6l/CyHdkUohw+orh2P+4FQ8tWhQhx4rlUq47wvbzLldRXX483gVSupa8Y93CnDofAOq1Bqs/PoABjyyFl/tLgFgDfZO7ccEWB6aPwBvXTUCD7thol5X/WNUBv4xsgeu6GVCZAezG9szICUKUglTHruSV5GBUMCFEOJBsy0Nj22xA4dig2g6g/jsNrYWM583akt7Q3v1ltvrR9DYpseXu0u4Bt+EkK4rtvzYzogLFSx3V5A3nFeqparJ/seps75N8wenAgD6WZoUetu0/kl4+rIhLqejOzpm7Myv9jg65M0ax/28Pt91rt3ndfR9w6f0UJk2QgjxR2y5KW/9xlbKpNx3RGf6b7HYHi62GaCuZrgAwIPzc7H7wRlIimo/e9PfhMhlUCmY7yu1Rs8FnM7VtAomZKkt1xxRofJOzbh+aH4urhufjVE+zrRNiwnFs5YqB29vPos/jzMTLdRteq5nAvVvIaT7yU2Lwuqlw9E7qePXSI4CLu9tLQLABOYbWvW48t1dmP7CZny/vwxagwmP/nIUJbWtdr2j5DIp5gxKcVj1xZvClHI8eelADI13/wTdUKUMvZOYkmlUVkyIriIJIR5x1dhMRIcpEBtmH0Fnm3TxJzdnWWZMOSonINacMSJIAi7tldV5RaSvgJiuXKgSQoTYkmLRoZ75kSyTSrhSKv/3v8NOty3hNXAc3yse2Qnh2PPgDPxy+0SsmMrUK350wQBHD/c5rUhgIzs+DCtnulZbubzBGpDiZ8WwpW/EPPjDkXaf11k5xp9vm4BxPePx3S3jXNpHQggJBk1sSTEv/caWSKzfhe7IcLlqXJagZHG2iz1cggFbVqy0vhWldczxaNIaUNei47bhAi6dnOE8MzcZj14yEAo/mIwwd3AqbpjAZNre+/0RFFRKsPDNnTCYzMhJCEeWi9lNhBACWAP0xbyJvmerm7H+RCUA4H83j8e4nvFo1jL9N/MyYjAsMwYavQk3f7YPZ6tbIJNKML63eK/NYDYoje3jQmXF+Hz/TUkICSr9U5jZBHMGMjOwe4o0CGPTGNt4AYL0WGYW+cHzjfirhJmZVKnWYFthDcxms2idYXc2+/Kl9kqBbS2sFl0+d5Awg0gupVM6Ie7CZj90tORGR4S7WLJl4RvbudvPX54HAEiMDIFSLsVdF/XG06MMmJWb7JF99JQJvRMgd3HAhl9OjT1dnq1uxtXv7+7SPjgqaZYdH4Yh6TH4cvlYDEmP6dJrEEJIIPF2STHAWlasVec4iN4e9nuif0okJvdhZhfHhCl82mvE29iAy86zdYLlxbxJG2xJscgO9m/xV/fP648RWbFo0hjw1VkZSuvbkBwVgqcvG+zrXSOEBJieCfYZLh9uL4bZDFzUPwmD06Px4fWjcPeMvnjx73n44ZbxePkfQ6FSSLmswhGZsZ0OaAcyax8XynDho9E5QohbsRkqChkzc/jlfwzFqOxYjMyy1kMWu4gLD2Eutj7dVYrL3tiBxlY9Jj67AVe9vwtbC2vwzpazdo+JCZKLBdv+DbYclc1JixGWOpLLAr+fDSH+wvZc5gkRIa4FjfmzU2NEsgbDAjDZz1Ffl/awMZJbP/8LJXXMIBJbRsUW/7iJqXWw3tVAGCGEBBu2pJg3B4zYDJfOZmqrNXquPG+PmFBcPMS3ZTd9hf3Mdp4Rlhjml2W2ZrgEx/ecQibF61cOQ2KEEjKJGTdNysGGf03F2J6uN8smhBDAmuHCBlwaWnX4bt95AOD6VqoUMtw5ow8uG54OqVSCrPhw/HtWP+45umvvKDbgcrC0AdtP12DH6Rru90R3FhzftIQQv8E2vVdYZoVnxIXh25vHY+2RCuw9tw+AeJkC29T06mYN91yvri/E6Sph08d3rh6B+IgQt++/L7Dv0xH+zDQ+CrAQ4n7bCmvw5G/HuB/bSh9muOiNJrtzY4g8ODL7OntczTBj08kqnKho4pZp9OKlKK98dyfW3jXZ4XMt/2Sv4P7CoWnYcKIKD87z3/JshBDiSWypRm9muIQpu1ZSrMxSTiwuXInwEDkuyUuDRm/EyOzYdh4ZXNgMlwOlDYLlwgwXtodLcExaA4DU6FCsvWMC1q7Lx99n9YFCQUNchJCOy05gSlCWNbRBazDis53n0KY3YkBqFMb1chzEvX5CDvKPVWJ/aYNdBZLuIjctChIJUNWkxdL3dgEAxvWMx5fLx/p4z3yLMlwIIW7Fzgq3bTSslFuDA2Kz5pQ2wQN+K5e9luaHfLMGBs+XmaO+Ne2xPcaGdgI3hJD23fPdQZyoaOL6jihlngtwtBdweeQn+z4kjhrQB5oeNhl6rjp0vhHXfbjHbvk3N43Dh9ePEizjB2XE2Aa7H744Fwf/M6tb1l4mhBDA+z1cAH5Jsc4FXNj+LWx5YqlUgiWjMzvVNDmQsQEXneW6YnhmDADbDBfm8w22kjdRoQpEBNdbIoR4WWJECMKVMpjNwO6iOry56QwA4KbJPSFx0vdRJpXg02VjsOv+i0TL6XcHESFy3D6tN/qnRKJ/SiQkEqDgbC3KeWWhuyMKuBBC3Mpahkd4euHHFAakCi+AXrg8z257g6lzQYhA1Nn3anvMOhu4IYRYXWjUCO5rDZ1v4tse2xnE/7tlvOD+l7tLPfba3jRKZJZxmNL1wTx+yTCxAbnoUAVG58RhWr8kTHQxWNLm4HmcXVARQkiwY8vYRnoz4NLFkmLn65kMjs4G8oOFbdbKvMFMaTXxDBfKAiGEED6JRIKcRKas2D3fHkKLzojhmTG4JC+t3ccq5VLEhnefnmFiVs7qh7V3TcbauyZjVFYcAGDtkQof75VvUcCFEOJWbKNp23JXRl5Qgb2wunlKL4zIisXFeanYcFLYGL69MlvBpLOZKeyxZnWnY0aIt+wTybBzF36Gyw0TcjAsI8ZuG9u/80CUY6mJzNeRkmK3TOntdP2zi4dwt2cNTOZu905yPMvszc1nBPcP/mcW5DL6WUwI6d6aNIFXUsw2w6W7iuYFXCJVcozvxUxAKK4R6+FC6SCEEGIrO565ZqlQayCVAE8sHARpkFQX8KY5ltJqFHAhhBA3Ygf9bctd8ZMv2BnE983tj//dMh4hchlqmoXNi4NhkNFVnenF8p8FuahvFR4zynAhxP16JtoHC9wlXGktV3bt+CxIpRJs+vdU9E1mAgWT+yZys1EBIFB/74uVQQvpQMClvffNn9WcwOvtZTKLB6HNZjNeXV8oWBYdRPXsCSGks9geLl7NcLFkPIplHrqijAu4hLltnwIRP8MlNzWK60fQ2KZHg+WaIRh7uBBCiLvwJ4ldMy4bA9Oifbg3gYsNuOw5V4eqJk07WwcvCrgQEmSMJjNWfn0AH24v8urrnqhQo9cDa9BmKQdgW+4qK975RVCMzQ9/ZwGXwT2C64vP9li54voJOZjaL0mwjAIuhHSNQeRv6N+z+nns9fizcdnZptkJ4bhlai8AwJZT1dhyypr9t++hmR7bF0+SipTp6kiGy7T+SU7X84/j7IEpmG7ZXqsXPyf+cbTS5dcmhJDuxFpSzHsD8qGWspFtnS0p1sCUzKIMF17AJS0KYUo5kiKZSQhsWTFrDxcqKUYIIbbY7PiECCXuntnXx3sTuNJiQpGXEQOzuXtfd1HAhZAgs/lUFb7fX4bHfjnm1dd9Y+MZGE3W2cQKm6yNQT2i8cqSoXY9ClhXj80Q3HfUN+GK0Zl479qRXdxb/zK2Z3yHts+IYy4obYcwO1uajBDC4PdvSY8Nxe3TeyOelzHhbklRKu42PwARIrdmvvxuScWODJEHbG1gsQwXsWWODOoRjXvniAe+Pr5htOC4yKQS3DOb2VbrIHB/82f7BPcfmNff5X0hhJBgxmZAeLekGPNarTpDpx7PlhTrQQEX7nZuahQAa3mcc7VMWTHKcCGEEMfmDkrFHRf1wQfXjaLs9y6ay5UVu+DjPfEdCrgQEmT49Y/NDsqpeILtrDSxGc2XDu2BEVn2zZMBQCYVno7EBsr+OTEHT182GMm8QcpgsGxijujyV5YMFdy/bnw2nrlsML69iQlaJUYKB4J1lOFCSId9t+88Ln19G6qaNFzKc0ZcKLb933T8y4PZLQAQzmscHyIIuFhv7y2uAwAsGt7Do/viSWLfBx0JuABAn6RIu2WxYQpM6Ztot5w9fo4C9/zje8/sflg+uVeH9oUQQoIVW1IswosZECpLb8c2Xcd/xzZp9GhoZYII/PKS3ZFthgtgrTBQXGPJcNFQDxdCCHFEKZdi5cy+GJIe4+tdCXhswGXn2TrUt+ja2To4UcCFkCDDL09V68UT25mqZsH9js4IkNsMvokNlIV5cbadNylkUlw1NtNuudwmCJUeG4olozOREs0EnPIyYnDvnH5cfwN+SbEPthXh1s/3iZZIIoRY/fvbgzh4vhEvrjsFjaUEVahC1s6j3CMsxPo6/Ibt/GyX+tbAHxwRzXARCcI4Y7v1sMwYbPr3NNFtQyyfn1jg3mw2c8tfu2IYVkzr3aH9IISQYMYGXLxZcirM0s+sTd/xDJeyBia7JSZM4dUyaP6IvfaSSyVcWZzsBJsMF0tJsejQ4LymIoQQ4h+y4sMxIDUKRpMZ+ce6Z1kxCrgQEmT4M4m3n67x2uvGhFkvco4/PgfSDs5etokt4Pk/TtltE6b0ziCoL/BLCLHkNmXZ4kTKCd06tTcuHcrMfOeXFHv812NYc7gCa49WuHlPCQke/MBuYVUz17DXWwEXNuOPLRPIMokkJ56oaPLGLnmEWLCooxkutt8RIzJjER0mPrimsgSsdAYTTCYzSmpbce93B3G6qgmHyxq57WqbtR3aB0IICWZmsxlNlh4uESHeC15wARddx3u4lFnKiXX3/i0A0CcpApcOTcPdM/ty1xVchkttC0wmM5oow4UQQoiXsFku6451zzEpmtpASBDRG034pKCYu69u03vttdl0/i9vHIvQTgRGbKufsTPW+II54JJmUwYhNkxhN6Pb0QAlWx5HI9JslG1+Sgixp+GVL9l3rh51rUxWYIiXAi5RKgUOPzpLkJkIAFqRv+XOnFf9xQ0Ts7H9dA12W8qjAR0PuEhszoghCsdzhvifn85owpXv7cT5+jZ8s/e8YDtqe0UIIVYavYnrx+iLkmKtnQi4sP1b0mPC3LpPgUgqleCVJcMEy6w9XFrx1pYzMJmZSSWOJiwQQggh7jIzNxkv5p/C1sIatOoMXM+27oIyXAgJEj/uL0OfB3/H1kJrVktLJy5cOkNrMKLIkqpuO1PbVa5Ul/HWrHNfuGJ0BmbmJuPZxYNR/Mx87H9kFiQ2B0WsDwJgbTb6Qr59VhAhxDHb3lN7ipiAQLgXgxuRKgU32MQSy2a7d7Zn+8l4UqRKgW9uHod/z+rLLetoFqRtBFosK9C6zvrzVqM3cgNytuYPTu3YPhBCSBBr0jKTpyQS734PWkuKdSbgwvQm6UEZLqIyLRkutS06rFp7EgDwwLz+Tr9DCSGEEHfonxKJHjGh0BpM2Fboveo7/oICLoQEibu+PmC3rEXrneyGZo2By1BJi+7cBc/fR6S3u00wR8TDlHK8e81I/GOUtZeLqxku/H4Pxy+oBeueWnMcl72xXTT7hZDuzvbv4nQ104sqLjzEF7vDYUuNsXomhCMjLvBn7948xdqcPquD78f27KdykuEil0q43labT1WLbtMzIZzrh0UIIQS8cmJyu0k/nsROqOpMSbHzVFLMqSiVAvG8SRzXjc/G1eOyfbdDhBBCug2JRIKZuckA0C37uFDAhZAg1qL1ziD70XLrIH+HZy1bRIcqIIXz+i7BXFJMTHiIMMDk6NCaecft9yPC+phNGgP+KmnA70cuuH3/CAl0trNp95c0AACSo3wbcJFIJFzNW0AYVA1kcpkUW++dhj/umoz4iI4dY9sMP2ezcyUSCbf+zq8OiG5Ds6EJIUSILUMbGeLdCU5syczOlBQrqWMyXDJiA39Sgqf0SowAAEztl4iH5g/w8d4QQgjpTtiAy4YTVVzZ0u4iOK7gCSGivJXhwl7sdNVTo4yYlZvkcH1qTPeajZyXES2476ikGD9OFeWg5ra6jXq5EGLLUebXRQMcn4e8hV8WK1gCLgCQEReGfimRHX6c7enPWYYL4LzHC2DfN4wQQrq7Zst1Q6SXG6qzGewdzcY2m80oqWWuQdjm8MTeQxcPwF0z+uC1K4ZBLgue3xOEEEL83+icOESp5Kht0WF/Sb2vd8er6BuXkCBgchApdlcgpD1s5smwzJiuPY8ciAl1fJGXFNm9Ai5hSjn6Jkdw9x2VFNMarI2/HQVlDN1sNgEhrnBUL35EVpyX98QeP8gSEkQBl86SwPUMF2a982PG1v0nhBDC4EqKOZi84ylsSbGOZrjUt+rRZAkSBUPZTU8Zkh6Du2b09XogjRBCCFHIpJjWn5nMmH9cvKzYiQo17v76ABau3o5SL41hegNdwRMSBNgLJFsFZ2u98vp6IzPgHxtm3+i5o77ZV+ZwnULmvXrS/uJUZTN321G5ttzUKO62zmiCWWTqdjDNkCfEXfy5t5EySDNcOss2ltxeQKW9gMyiYe33DSOEkO6kSaMHwPRw8SZrSbGOZWOfq20BAKREqaBSdK+yw4QQQkigmDFAvI9Lk0aPGz/Zizkvb8UP+8twoLQBj/1yzBe76BF0BU9IEGhs0ztcJzb47m56I/Ma8k72b+HjZ3EMTIvCgrw07r6im6fByxxkr1w2vAd3W2cwoaZZZ7eNshsGqwhp1hpwxTs78fGOYtH1Gr1JdLk/4AcMlN383AeIlRRzPrjWXsmxmDCa6UsICS4mkxk7z9biqd+Odao5LVtSzOsZLpaAC/872ZXrFzaTP5OyWwghhBC/NbVfIhQyCc5Wt+BMtXVC8VubzyD/WCUkEiYoI5NK8OfxSuw4XePDvXUfuoInJAhUN2u529eOy8LNU3px9/nlpjyFzXBRuGEWNj9oc9XYLMEsu+4+6OiopJhcJsW147IAMAGXV9afst9G2r2PHemePthWhIKztfjPz0e5ZbXNWhwsbQAAtImUL3l0Qa63ds8pwbmPMlxESop1LsPlsuE9kBCh5Bo4EkJIMPjfvvOY+OwGLHlnJ97dWoQHfzjc4edotmTMO+oH6ClhlgC6zmiCwWjChhOVyHtsHdYeueD0cWz/lkzq30IIIYT4rUiVAmN7xgMA/uRNCNlwohoA8Mxlg/HetSOxdEwmAODJ347DGAQl8b37a4oQ4hHXvL+Lu/3YpYNQ36LDW5vPAAC0ehNC5FJIHDVcdwODJcNF4YYMF7lUAi3vNn+3HZXU6i4c9WcBgDDL4OzrG09jZFas3frA/7oipONqecFo1vhnNkBrMOGlf+RBY2ACLjMGJOOBef2RGRfmNw1lI1X8gAuVSrErKdZOhottQCYvIwYfXz8KMWFKmM1mj34nEkKIt72YfwrljRqEKmRo0xtR26Lr8LmO7Yfiq5JiANCqN2Ld0UqoNQasPVKBOYNSHT7unCXDJYsyXAghhBC/NjM3GVsLa5B/rBI3TemFC41tOH5BzWW3AMBdM/rih/1lOHZBjZfyT0Ehk2JPcR0G9ojC/XMH+PgddJx/jCoQQrqkxWaWdkyYAmxs4uGfjmDMf9ejusl+4NFdXt94GoB7Sn7JeaWvFDIpunmMRcBRhgsAxIdb++f8VVJvt97khdJyhPgbfrlFtjwJm/V399cHuXIkUgnQMzHCb4ItABCmpOw+PtuzX3sZLrZZQc/9bQhiLH3GKNhCCAkmZrOZy3b/ccUEAIDRZLa7PmgP2xMyIsS7JReZiWHMbY3OyH03F9U6b5xLGS6EEEJIYGCDKvtK6lHbrMVGS3bL0IwYxEeEAADiwpW4Y3ofAMwY40t/nsK20zXYciowS4zRFTwhAU5rsF5M9YgJBcAMJkWHMhdLPx8sR1WTFp8UFHtsHxIimEGsNjc0oF401NqzRS6T2JWR6c6cjbkmRoZwt8WyL73Ry4cQf1PXag24nKlusVv/9uazAMD9yPMn/IABlRSzz3Bsr0dLi9bafPnr5WPRNznSI/tFCCG+1qIzQmeZTJARF8oF6dVOejyKYXu4RHq5pJhEIuHKirXqjDhnCaQUVTc7/f16ro75Xs+KD/f8ThJCCCGk09JiQjEwLQpmM7D+RBU2nqwCAEzrlyTY7prxWRjbMw5p0SosyEvDE5cOxMv/GOqDPe46KilGSAAym81QawyIDlXgQEkDt/yj60dxt20H3TtTA7GxTY8bPtqD6f2TsGJab4fbqS0z4vi9Yzpr+aQcfFRQAkDYz4UAmXGOLygT2hkwNvpvb3BCPMJsNqOhVcfdv+PL/fj4htGi2+alR3trt1zGD7K0l83RHdhnuDgvKRapss7QpoAVISSY1bcw33UqhRRhSjmiQuWoadZBrdEjDaEuP0+ThgnQRHg54AIwZcVadEaoNXpcaGwDwFxf1LXoRCdFaPRGVKqZrB4qKUYIIYT4v5m5yTharsZvhy5gT3EdAGB6f2HAJUQuw1fLx/li99yOrkAJCTAmkxk5969B3mPrsOVUNVp01lm8vRIjuNvLJ/cUPI4/27e957/jy/14fUMhPt5RjH3n6vHcHyehcZC9Yjab0WiZRR7LK2vVWSpeXX6zGVRSjIefxWJL1U4/AyopRrqbZ9aewKHzjdz9YxfUmPHiZtFth4v0PfI1Ja+8IgUMxHq4OD8mcwencLeDoekiIYQ4UmcJuMSHM78ToyxZ7o2tHcxwsUygivRyDxfA2selsLJZMGmsqMY+OxUASi1lxyJVcsSEebcEGiGEEEI6bmYuU1Zs86lqtOqMSIoMwcC0KB/vlefQFTwhAeZsTTN3+9bP/4K6jbk4mtA7XlByxTYjJdPFdPv9pfX4+WA5nl93ClVNGm75iYom0e1bdUboLOkTsW644OEPMkokkm5fa9/V3g3tzYCnkmKku2HLhfE1OiivEu6DwaX2CEqKUQ8Xu6zN9oLMV47O5G5nJ1C5GUJI8GIDLrHhzO/wKEuGH5uB7iprSTHvBzDCFMz38IkKtWD5WQcBF7bsWFZ8WLe/ViCEEEICQW5qFNcGAQCm9ksM6u9wuoInJMC08hpgNmsNaGIvjtppcOnqgLuUd8I7zJsdXlgpHnDZd87aoD20nQEwVyh4A4vOmsR3F65+/7Q3A55meJPuRK3p2KxelR9mkChl1vOpggIuduew9oLMEokEO+6bjvy7J7dbcpEQQgIZG3CJs81w6WAPlyZLgMYXJcVUlgwX2wlejjJczlkyXDKpnBghhBASECQSCWYMsJYQsy0nFmzoCp6QAMNmtADA8MwYLv2/vYsjRyXBbPFLT53lNZm+57tDotvf9z/rcndEp/lZOjKp9xt3+hupi8e0vQFZireQ7uRIWaPT9ZP7JuKK0Rnc/fayJXwhKtR67iviZTZ2Vyabk5grWT9pMaHokxzpqV0ihBC/wAVcLJnm0ZaAi7rDARdLDxcfZH2GWb6HT1oCLmGWAExRtXjApaSWWe6svyEhhBBC/MvMXKbss0ImwYTeCT7eG8+igAshAYY/c/v4hSb8fuQCAPEB94t4EeMtp2pcynLR6K3d1Ztc6PsyLNNzvQ8iVQr8c1JPjMiKxaMLcj32Ov5sWv9EAO3P4FPInAdmPtt5zm37RIi/0xpMTtcnhCsFA/b+GHDpl2INFLTXIL47MPK+vxbkpQV1+jkhhHREXStbUozppRhlmazkSran2Wzm/mNLikX5YLIT28OlqkkLABjfixmE4We47C2u40qOsRkuWfGU4UIIIYQEivG94nHL1F54cuEgn5Qw9abuPXWckABzpKwRvx4q5+636Y1cU+hdZ2vttn/nmpF44tdj+GhHMXYX12HdsUrMHphitx3fxhNVHdondqD/nxNzOvQ4Zx65OBdFNS0YmRULiUSC/90y3m3PHWievmwI8tJjsCAvzel27ZXMcVQDm5BgpOVl9PWICUVZQ5tgfaRKDrmfly/kB1mkfrh/3sYvKfbKP4b6bkcIIcTP1FsyXOLZgIuLJcVOVjRhwevbcPPknrhpSi8uG9oXJcXYgAtrSr9E/Hm8EkW1LTCZzChraMM/3tkJpUyKP+6ajBK2hwuVFCOEEEIChlQqwf/N6e/r3fAKynAhpINMJjMMRuezpz2htlmLi1/bhjWHK0TXXzU2y26ZTCoRzPT+/q/z7b6Os4usVp0148VsNsNkMmNPMdPDZUzP+Haf21U3TMzBEwsH0QxmMGUhbprSC2m85mJiVAoZXvpHnpf2ihD/drrKWoLr4xtGoWeisORIhEoOeQAFMQJoVz2GH3ChABQhhFjVtggzXKwlxZxnqm88WQWdwYQv95Ry/VtkUolbejJ2VJjNa47vFQ+FTAKdwYTyxjb8cbQCRpMZbXoj7vv+EM7XMxMpMinDhRBCCCF+iAIuhHSA0WTG398uwJTnNqFN51pPFHf5bGeJ0/UX56WKLq9u0nC32xu0B8D1hBGz71w9d/vq93dj7itbUW8pY2A7oEm8LzFC5XT92iMV0Bq8+++WEF94ft0p7nbvpEhs+NdUwfqIEIVfZrU40pf6kFAfKkIIccAuw0XlWobLGcvkhOomLQ6ebwDA9G/xxYQnfoaLVMKU0s2KZ64timpa8MdR64SzHWdqoTOaoJBJkBrd/rUNIYQQQoi3UcCFkA4oqmnG3nP1KGtow/n6Vq++Nv9CQ4yj2WgG3ihVuLL9EgHvbStyuI59LoPRhG2na3CysgmtlsBTd29u7w/a6+Ny82f78Nzak17aG0I8z2w2Q8fL4jNayo60J8KmpJi/+u7mcbjzoj64ckymr3fF54wUcSGEEFF1bIZLmE2GSzs9XM5UW7NB1x2tBMAEXHyBH3BJiwmFQiZFTgITcNlTXI+9lklf10/I5rbLiA0LqMkThBBCCOk+/H+0gRA/wjZyBNpvyuxuMwYkCe7/cttE7H94JnffUcClT1IEd9s2u8FkMqPBkqECAHoHpdKSIpn+IC2WZpoakfceFeQNrwKBKwPI37lQVo6QQPHAD0eQ99g6lDW0Ye2RCgx+9A9MeGYDt37+EPHMv8gQOS4b1gMAMCo71iv72hkjs+Nw98y+UARAcMjTKOBCCCHi6iy/5eO4Hi5M0ETtJMPFbDbjTLW1v9+GE0zAxVcTqPjXMVmWMmE9LQGXz3aeg9kMDO4RjQfnDUBeejQAIIP6txBCCCHET9GUdEI6gD+T2tsBlyatsNTXYMvFxrc3j4NMKnE42H7HRX3w7lYma8V2n5d9vAcbT1bj3WtGYmZuMirVGrGnQFy4ElVNWi7gIlZOLUROA4K+pnRhUDaQ+lYQ0p4vdzOlFue9slW0dMqqxUNEHxcXrkR2Qjj2PzyTsvMChNFMARdCCLFlMJq47784m5JizgIutS06wfdmfStz21ffiWG8DJfMOCbQkm0JuLAZPLNykyGXSfHSP4biyd+OC7JdCCGEEEL8CY2QEtIB/ICFzssBF35vlR68XiyjsuMwPNPxDO1IlQL3zO4HANDohYGSjSerAQA3frIXALiAS3psKL67eRyGZsRgYu8E9LH0D2Abato+DwBqcO8HFHLhZ3DV2Ew8u3iwYJmUPicShBzVqQ93UBqFnRUbG64MiNJiBJjePwnRoQpM6Zvo610hhBC/0dCmh9kMSCRAjF1JMcd9GU9b+rekRqsEwQ7flRSzvi6b4cKWFGPNGpgCAOiZGIEPrhuFSX3o+4AQQggh/ommdRLSDoPRhFOVzeifEikIshy7oMa4XvFe248m3kXTkwsHdeixbPZJe1k5FY1MybSUKBVGZsfhxxUTAAD3f3+Yed3fjiMnIRyZlMLvl+RS4cBxbJgS0/oLS9FRrWsSLGxLJHZEarTKjXtCvCEiRI69D82gLD1CCOGpt2R/xIQquN94UZaAS7PWAIPRJDqxgO3f0i8lEmYzsPkUMwkrwkclggUlxeKEJcUAJgjTNznC7nGEEEIIIf6IpnUS0o7Hfz2Gea9uxbKP9+Cznee45U/8egwnK5q8th/NlnJeT1w60G4QvT0hlouYnw6U41Sl+D6v+OIv3Pf9IQBAss1g5IVGaxPqZR/vRZtIhgvxPduSYjKpxC4IQxkuJFgUVja3vxHPqr8x5cVumdoLKgc9r4h/U8iklE1JCAlqeqMJ3/91XvDb2xm23FaspZwYICwL1uQgy+VMFdO/pXdihGACmV+UFLNkuCRGhiDcsnxWbjKd/wkhhBASMCjgQkg7PilggiwbT1ZjV1GdYN29/zvklX0wmszYdroGAJAaHdrO1vZUvP4qs17aAgAobxBeyP126AJ3UZYaJQy4bLKUHmO12vRwee5v4n0SiHfZlhSTSSR2GS2U4UKChVzWsX/Lfx+ZgeJn5uP/5vT30B4RQgghXbPm8AWs/OYgHv/lmEvbswGXuDBrwEUhk3KBCrVGvOQmm+HSKykC4/kBF1+VFONnuMQzmS0SiQSjcuIgl0pwSV4Pn+wXIYQQQkhnUEkxQjogMkQuaF5/sLQBWoMRIXLPzpZmL4oAwGDqeOPgEJvZ3CaTGdNf2ORw+5gwYTmBnonhOFvdwt2vaNRwt6f1S8TlIzM6vE/E/eyyWaQUcCHB63yd89m/8wenemlPCCGEEPc4Y+mtcuh8o0vb17VaAi68DBeAKSvWojM67HHGBVwSIzAwLRqRKjmaNAaf9XBhM1ziw5WCfXhlyTDUNmvRM5HKiRFCCCEkcFCGCyEdwA+2sPjBB4+9Lq8cwOS+CR1+fIhc+Kfe84E10Ogd93OZZzNQ+fZVIwT3X994mrv9xlLhOuI7tiXF5FKJXb8DCriQYKA3mvDPT/Y63ea/iwZ7aW8IIYQQ9yhr0Fj+3+YwO4Wvrlk84BJt6eOibrO/dmnTGVFmyXTvlRgOmVTCZbkkRoZ0fue7YEh6DEbnxOGGiTmC5dGhCgq2EEIIISTgUIYLIV2kNzpvRO8OrTrmYql/SiTClB3/s+1oeQDbC5s+yZG4emwWPrX0sDltmX03sXcCQpXUC8Ff2JUUE8twofrXJAj8dKC83W2iw3zT+JcQQgjpLH7J31MVTRiZHed0e4cZLipLwEUkaFNU0wKzmcloZx/30PxcjM6Jx4K8tC7tf2eFKmX45qZxPnltQgghhBB3owwXQtqRkxDudL3e2PESXx3VomV6poR3Ms0/JkzZ/kYWsQ4GKZ9YOMhuGTWe9i+2GS5SicQuwCKlDBcSBP797UHudpSPGvwSQkhXbdmyBQsWLEBaWhokEgl+/PFHwfrKykpcd911SEtLQ1hYGObMmYPCwkLBNhqNBitWrEB8fDwiIiKwePFiVFZWCrYpKSnB/PnzERYWhqSkJNxzzz0wGMSbqRPfKuMFXE5UNLW7fX2Lo5JizHejWEkxfjkxthF9RlwYlk3M6fS1BiGEEEIIsXJ7wMVoNOLhhx9GTk4OQkND0atXLzzxxBMwm62D0mazGY888ghSU1MRGhqKGTNm2F081NXVYenSpYiKikJMTAyWLVuG5uZmwTaHDh3CpEmToFKpkJGRgVWrVrn77ZBuzmw2o94yc8wRYyd6qnR0H46VM3WcwzqZTdI/JdLlbb+4cazDdSlRKsH9qibPl1MjrpPLpHh0QS53XyGX2gVYbEuMEeKvTE7Orb2TrFl484ekYf2/puCOi/pwyy7x0QxdQgjpiJaWFuTl5WH16tV268xmMxYuXIizZ8/ip59+wv79+5GVlYUZM2agpcXaV+/uu+/GL7/8gm+//RabN29GeXk5LrvsMm690WjE/PnzodPpsGPHDnz88cf46KOP8Mgjj3jlPRLXmUxmXGi0BlxOWgIuZrMZ//x4Dxa9sR1ag1HwmFpLwCU2zL6HCwConQZcnE8qI4QQQgghneP2gMuzzz6LN998E6+//jqOHz+OZ599FqtWrcJrr73GbbNq1Sq8+uqreOutt7Br1y6Eh4dj9uzZ0Gisg7dLly7F0aNHkZ+fj19//RVbtmzB8uXLufVqtRqzZs1CVlYW9u3bh+eeew6PPvoo3nnnHXe/JdKNqTUGNLQKL1RUCikGpEZx9z1dUuz1Dafx6gamZ0pnAy5SqQQ/rZjQ7nazByYL3put+AjhxVybzuhgS+Ir/VKsn59SZh9coQwX4u+OlDUi77F16PnAGmw+VQ2dwYQmm5IoIzJjudv3zemPXokRWDjUGmSZNTDZa/tLCCGdNXfuXDz55JNYtGiR3brCwkLs3LkTb775JkaNGoV+/frhzTffRFtbG7788ksAQGNjI95//328+OKLmD59OkaMGIEPP/wQO3bswM6dOwEA69atw7Fjx/DZZ59h6NChmDt3Lp544gmsXr0aOp3zSUXEu6qbtYLMeTbgcramBX8er8L+kgYcOt8oeAw7MSwuQrykmHiGCxOw60W9UQghhBBCPMLtAZcdO3bg0ksvxfz585GdnY2//e1vmDVrFnbv3g2AmaHz8ssv46GHHsKll16KIUOG4JNPPkF5eTmXRn/8+HGsXbsW7733HsaMGYOJEyfitddew1dffYXycqZu++effw6dTocPPvgAAwcOxJIlS3DHHXfgxRdfdPdbIt2Y7SAfwMwge+YyazNmg4czXF7IP8Xd3niyutPPk5cRI7hf9PQ8FD8zX7BMKXce0MmKDxPc93wxNdJRcl6QRSGzP8WLxGAI8SsXv7aNGyC67sPduPXzfRj11J94/JdjeOXPQpjNZny9txQA8OiCXK5Xi1Ju/ffe2eA0IYT4C61WCwBQqazZxVKpFCEhIdi2bRsAYN++fdDr9ZgxYwa3Tf/+/ZGZmYmCggIAQEFBAQYPHozkZGsgevbs2VCr1Th69Kg33gpxEVtOjK0Ge6JCDbPZjG2FNdw2f52rFzymrtkScHGU4SJyLXOmylpSjBBCCCGEuJ/bi7SOHz8e77zzDk6dOoW+ffvi4MGD2LZtGxcIKSoqQkVFheDCIDo6GmPGjEFBQQGWLFmCgoICxMTEYOTIkdw2M2bMgFQqxa5du7Bo0SIUFBRg8uTJUCqtPy5nz56NZ599FvX19YiNtc5+ZWm1Wu7iBWCyZABAr9dDr9dzt/n/J4HF3Z9fQ7N9yaxQhQy5KeHolRiOM9Ut0Gj1Xvv3ojOYuvRaU/omYPMp5qKNrd296V+TMPWFrQAAKcxOnz/MpmeLyeR8+46gvz03MVmzjiRm+38vf5U04KPtZ7EwL9Wtdbrp8wtc/vTZ2c7cNZuBP49XAQA+2F4EABieYc3iGpQWye23xGzNNlRK/eP9eIM/fX6kY3z12dG/lcDABk7uv/9+vP322wgPD8dLL72E8+fP48KFCwCAiooKKJVKxMTECB6bnJyMiooKbht+sIVdz64T48o1UyAJlPNkaQ0TCBmcFoWjF5qg1hhQWtuMzSeruG32FtfhhvGZ3P06S4ZLZIhE8P4ilMwkhIYWnWC5yWTGWcvrZMWFdOqYBMrx9Gd0DN2Ljqd70fHsOjqG7kXHs+voGHZOV46X2wMu9913H9RqNfr37w+ZTAaj0YinnnoKS5cuBWD9YS/2w59/YZCUlCTcUbkccXFxgm1ycnLsnoNdJxZwefrpp/HYY4/ZLV+3bh3CwoQz9/Pz811+z8T/uOvzK2oCbP9MDG3NWLNmDdpaZAAk2LFzF84eNKO4WYIhcWa4v2KT9fWv62vEmjVrOv1MugYp2MQ24fMwr1F1oQxr1pQ6fHz5eevjAUDd1Nyl/RFDf3tdU9oMsJ/n4YMHID2/H//sJ8F7J63Bskd/OY61u47iil7uL4dHn1/g8ofP7pkDzHnVmZs/3cNtc/7gdpQdYpa36AH23/6+3TtRe9xju+mX/OHzI53j7c+utbXVq69HOkehUOD777/HsmXLEBcXB5lMhhkzZmDu3LmC3pie0JFrpkDi7+fJ9WUSADIotA1ICJGgsk2CT37ZiO2FUrDfe7tOV+K339ZAIgF0RkCjZ7739m7bhMO8eVHFVcxznSktx5o157nlDVrmMVKYcXjnZhzrwnWLvx/PQEDH0L3oeLoXHc+uo2PoXnQ8u46OYcd05brJ7QGXb775Bp9//jm++OILDBw4EAcOHMBdd92FtLQ0XHvtte5+uQ65//77sXLlSu6+Wq1GRkYGZs2ahagoZsasXq9Hfn4+Zs6cCYVC4atdJZ3k7s9vS2ENcOQvwbK0pDjMmzcK757bifJWNYaPHIkbP90PAHjy0lz8Y2S66HOZzWasPVqJ/imRyElwvUnlnQXruNsPXj23E+/Cql91C+a8uh2LhqZi3jxrWTT2NXr3zMa8ef0dPn73L8exo8oakHnm78MxpW9il/aJRX977nH8QhOeP8yUEYnO6Id5U3tiHoBhxyqx4suD3HYnm0Mwb940t70ufX6By58+u9dObwfaWpxu02JgRofiw5WYP38Wt7xVZ8ADezcAACZPnIhBPRz3owom/vT5kY7x1WfHZisQ/zdixAgcOHAAjY2N0Ol0SExMxJgxY7gqACkpKdDpdGhoaBBkuVRWViIlJYXbhi3tzF/PrhPjyjVTIPHX8+QXu0ux5kgFXl8yFDFhCuz99ThQUopRub2QXNeKNUcqcdqcDK2pBrFhCjRrDVDrgbzx05AeG8qUINu9FUq5FAsvnguJxBo9UR6vwhdnDiAkMhbz5o3hlu87Vw/8tQdpsWFYMH9Sp/bbX49nIKFj6F50PN2LjmfX0TF0LzqeXUfHsHO6ct3k9oDLPffcg/vuuw9LliwBAAwePBjnzp3D008/jWuvvZb7YV9ZWYnU1FTucZWVlRg6dCgA5sd/VVWV4HkNBgPq6uoEFw/sxQL/Odh1YkJCQhASEmK3XKFQ2P2DE1tGAoe7Pr82AzODUCoB2FYtJyubmee39As4V2ctO/by+tPISYzE+F7xgoseAPjfvvP417fMVOwz/50HmYupMMlRIahUa/HkwkFdfk/902Jw9LHZCFPK7PYPACb3TXL6GlP6JeHz3daAy4yBaQ637Sz62+uaEKX12GUnRnDHUqUUHlOD0eyR40yfX+Dyh8/udLXzYAtfdJhwf8Ol1qm9IUrfvxdv84fPj3SOtz87+ncSeKKjowEAhYWF2Lt3L5544gkATEBGoVBg/fr1WLx4MQDg5MmTKCkpwbhx4wAA48aNw1NPPYWqqiqugkB+fj6ioqKQm5sr+noduWYKJP62/x8XlOBsTQvWnajG0jFZuKBmyrilx4UjPESBNUcqsclSCnhSn0Scq23BwfONOFTehJykKDRpmVmX8eFKQZltAIiNYHr/qDUGwXuuaGJKY6THhnb5WPjb8QxEdAzdi46ne9Hx7Do6hu5Fx7Pr6Bh2TFeOlX1H5S5qbW2FVCp8WplMBpOJKV2Tk5ODlJQUrF+/nluvVquxa9cuwYVBQ0MD9u3bx22zYcMGmEwmjBkzhttmy5Ytgnpq+fn56Nevn2g5MUI6o1nD9DlJjLRedNa3Mv/m5JaAyeZT1kb2Nc06LH1vF5MZY+PxX49xt8vq21x6/fP1rai0XHxN7uOeTJLwELldsGXrvdPwztUjMGNAkoNHMWbmWksBTuqT4Jb9Ie5lhrXMyNR+1s9TLhOel/Um95cTI6QrTlc1dWj7oekxgvsyqQQJESFQyCTomeh6FiEhhPhKc3MzDhw4gAMHDgBgel0eOHAAJSUlAIBvv/0WmzZtwtmzZ/HTTz9h5syZWLhwIWbNYrL7oqOjsWzZMqxcuRIbN27Evn37cP3112PcuHEYO3YsAGDWrFnIzc3F1VdfjYMHD+KPP/7AQw89hBUrVogGVYh3mExmnLdcD/x1rgEAUNbATOLqERuKfimRgu0n9UnAsEzmGnd/CbN9bQtzjRAbJgy2AECUihkgULcZBMvP1zNBmvTYwC0NRwghhBDi79wecFmwYAGeeuop/PbbbyguLsYPP/yAF198EYsWLQIASCQS3HXXXXjyySfx888/4/Dhw7jmmmuQlpaGhQsXAgAGDBiAOXPm4MYbb8Tu3buxfft23HbbbViyZAnS0pgZ9VdeeSWUSiWWLVuGo0eP4uuvv8Yrr7wiSH8npKuatcxFSkKE/QWp3BJYlIpkiqw/Xmm3rLHNGhz87fAFl16fH8xJivLcRXFGXBhmDUwRzXrhk0gk+OKfYzArNxnP/S3PY/tDOs9gtAZcVArrKV5hk1FlNHm2/nt3V1TTgvoWna93I6Ccrmru0Pbf7y8T3JdIJNh+3zQc/M8sqBQyB48ihBD/sXfvXgwbNgzDhg0DAKxcuRLDhg3DI488AgC4cOECrr76avTv3x933HEHrr76anz55ZeC53jppZdw8cUXY/HixZg8eTJSUlLw/fffc+tlMhl+/fVXyGQyjBs3DldddRWuueYaPP744957o8ROVZMWOiMz+WV/ST0AoMwSDOkRE4r+KcLSbZP6JGJ4FhNw+cuyfX0r8zsjLtw+4BIdZgm4aITNXtkgT3psqFveByGEEEIIsef2kmKvvfYaHn74Ydx6662oqqpCWloabrrpJu7CAQDuvfdetLS0YPny5WhoaMDEiROxdu1aqFQqbpvPP/8ct912Gy666CJIpVIsXrwYr776Krc+Ojoa69atw4oVKzBixAgkJCTgkUcewfLly939lkg31mTJcInnBVym9WMyTeQyZgC7TWe0e5xGb11mNpu5rBjWs2tP4Japvdp9fZWcGTTMig/zmwHE8b0TML43ZbcEAiU/q8UmlqY3UsDFU0pqWzHt+U1QKaQ48UTX+i51J2FK60+SOy/qg1fWFwIApvdPwoYTVXbbi8WHQ+T+cZ4khBBXTJ06FWaz4+/jO+64A3fccYfT51CpVFi9ejVWr17tcJusrCysWbOm0/tJ3K+03tqE9WxNC0rrWqG2XHekxYQiTCFDmFKGVp0RfZMjkBKtwvDMGADAsXI1NHoj6lqY6wuxgEuUivlO1RlM0OiN3HVEWQMbcKEMF0IIIYQQT3F7wCUyMhIvv/wyXn75ZYfbSCQSPP74405nVsXFxeGLL75w+lpDhgzB1q1bO7urfslsNqNNbxQMPBHfYbNS4sKsdfsuGsCU1VJYBrN3F9fZPe6bvefx+KWDUKnWYPGbOxAjkurvCo2BCdz0S45sZ0tCGLmpUZiZm4y0aJUgY4kyWrxnxxmmpKBGz8xcNRhNKGtoQ1Y8lblyxmApcxcil2Lp2Ewu4BKqFA+iPL1osNf2jRBCCHGn87yACwCssWS/R4cqEBHCXAf2TY7EgdIGTLKUFe4RE4rEyBBUN2lx6HwjimuYvmdiAZdwpZzrQalu03MBF8pwIYQQQgjxPLeXFCNdc9//DmPo4/koqW1tf2PicWzAZUBqFMb3ikdcuBJXjs4EgHab3u8vacArfxaiplnX4VI5rI0nmJJi/pLdQvyfVCrBu9eMxGOXDhIsN1DAxWu0Bmt/nOomLfo/vBZTntuE3w6JlxLU6I0UEAPQpmOOW15GDOJ4QWqFVIK3rx5ht328SKlHQgghJBCU1gn7Of5q+Y3QI8YaCFk6JhM9E8JxxegMAMykRTbL5d/fHsSnO88BYCbb2JJKJYgKZSaMsdczJpOZ6yPJfx1CCCGEEOJeFHDxM1/vLYXOYMJ72876elcIgAZLbeTYMCW+uHEs/np4JqSWQItC5jzgYjKb0aIzOF7vwgArWw6AbYpJSGcZqYSY1xwrV3O3Rz31JxfsemfLGbttq5u0GP3Un7jxk71e2z9/VVJnnWgg55XDk0mlmD0wBQPThANK7Z2DCSGEEH9VavnOS41mSmofLmsEwJQTY10+MgMb/j0VvZOsme7DM5k+LiV1rZBKgDsu6oPFI9JFXyNKJezjUt3M9I2RSSXc6xJCCCGEEPejgIufMjmp50y8p8EyIyyaV1KMZWhnAFtnMOGPo5WCZXMGptg9tzNtll4w/McR0hmx4fb/holnsE1sbUWq7D+D1RtPQ60xiPYo6W6eXXsCALC7SFimkY29HOUFsgBrWUdCCCEk0LA9XC7JSxMsb6/U19R+SZBImO2+uWkcVs7s6zDrPtqS4aJuYyaAsWXMUqJUgokNhBBCCCHEveiXlp9o1hrw8Y5i7j5Vl/EPjZZm9zGh9gOlMSJBGL51xyrtlkWHKrg6y9VN7WetsAEXKilGuoqdEUk8j19SjC9SZd+bq0VrzYKjsmLi2J5mV1jKObKatY4zCAkhhBB/xvZSmd4/CeG8XmVpMc4zT/qlRGLHfdPx58opGJkd53TbqFDm+5MtKUb9WwghhBBCvIMCLn7AbDbjkte24T8/H+WWuVJuingem4Ui1vReAuflbI6VN9ot0xlNSIjoQMBFxwRcHDWNJsRVEomELrC9pLyhTXS5WMCFP8P0w+1FHtsnf3em2trnamo/pjnwoB5MCbHFw5lSKf9ZkIsPrhvJbScWCCeEEEL8ncFowoVGDQAgOyEceRkx3Lo0F3qrpEaHujQZy7akGBtw6UG/BwkhhBBCPIoCLn7g0Z+P4mxNi2BZk4Zm7vqazmCy9nARKcdk24T86rFZgvtRIoOBU/slItYSvHFUdohPY8lwCaUMF+IGn9ww2te7EFTEAuNf7ylBYVWzyNbWTA0+HS8b5snfjnfLYHtNsxYXvbCZu//G0uEAgG9vGo9N/56KwenRAJhMv+n9k/H9rePx1KJBGJ3jfGYvIYQQ4o8uNGpgNJmhlEuRGBEiyEJ2ZzN7tqQYm7FvzXAJc9trEEIIIYQQexRw8bHjF9T4uOCc3fJzdS0iWxNvOl/fCpOZCXYkRoTYrTeYhGWD7p/XH89fnofcVGZWdkOrfY+WBUPSuGwVNpjiTBsFXIgb0QW2+zz+yzGMfOpPVFhmqLL+73+HHT5GrGRYq04YXGcHQ7qThau3C+6zgalQpQzZCeF22w/PjMXSMVmQSJxnGRJCCCH+qLSO6aWSHhMKqVSC4Vkx3Dp3BlzYyV/WDBfL61KGCyGEEEKIR1HAxccq1BrR5UfK1KhysI54x75z9QCArPgw0YE9g1E4eBqmlONvI9KRncAMate1CDNY7ryoD6RSCVRyJnhyz3eHUFTjPLDG9XChkmLEDRQyGqB2lw+2F6GuRYdX1p+CRm/EN3tLcbKiyeljtAb7IGuLTrjs670lbt1Pf6bRG3H9h7sFQaZXrxjmwz0ihBBCPK+UDXzEMdcMIzLjEKaUISEiBAkik7w6i81wKbOUOi2jHi6EEEIIIV5BARcfYwffxdz3veOZ0sTzthTWAADG90oQXT8yW7wJucLSk6HMpo9DeAjzWasU1j+7WS9thjOtWspwIe5jGzh0JcuKOFep1uLNTWdw73eHMPvlLU631RpMdstabBq/r954BhtPVLl1H/3VzwfKsfFktWBZZhxlYRFCCAlu7ESDDEvgIzpMgZ9vm4DvbxkPqdR9k2Mm9GauYTacqEKTRs9dm2RQxjMhhBBCiEdRwMXHZE5+VG/oJoNu/shsNiP/WAUAYExP8T4BV43NwiV5aQCAuHAlt1wpE/+zmjsoFQDAT4zRW+5sLazGm5vOwGy2rmzTGVFryZJJjVZ18p0Q4lh36RXVrDXgSFmjRwJM5+tb8cvBcofr48KVeOySgQCAnw6UCwIsLVoDl0nHd/1HewAwAbE1hy+gsc2+PGEwYEuc8EWq7PvcEEIIIcGEKynGC3z0TopEZrx7AyF56dHomRgOjd6ET3eeg9ZgglQCpNB1BSGEEEKIR1HAxcf0RvsZz3yGdtYTzzhT3QyNnjn2g3pEi26jkEnx6hXD8P2t45F/92RuuVIu/LO686I+2PvQDGRYZm6HKoTrv95Tgqvf341n157AmsMV3PKqJqakXJhShpgwJQhxh3vn9ONut3f+CQa/HirHoP/8gYtf24ZbPtvn9uevatICTiaj/nfRYEGG2sD//MEFVveKBFtYOoMJz/x+Ard+/hfu+HK/2/bXn+iN9j1tKJuPEEJIsCtlM1ziPFvaSyKRYPHwdADAe1uLAAApUSouG58QQgghhHgG/dryMV07A57dZQa6v6lSawEwjSvba145PDMW8bx6y7YBl/gIpaAe81VjswTr+U22D5c1crerm5h9SIx0Xy1nQm6d2hsRIUwWgU6kxFWwue0La7DCtnyVu5yvs290//zlebhjem/MHphsd07442glAOfHv7FNj492FAMANp/yzH77mu2EgtHZcUhzY7NgQgghxB+xGS7eKO21cFgPANbekulUTowQQgghxOMo4OJjepEBt2UTc7gBOtuAS2ldKxpadXaPIe7FlvDpTCkv25Ji7OA2a0h6DD64bqToY0N4A7NcwMWNzTMJAaxBwfYCvsQ12QnCwYueieH424h0rJzVDxKJBDatc3Cqsglnq5tx4yd7ATAlP9bxsuQA4LOd5zy6z/6gWSf8fnvzquE+2hNCCCHEOzR6I5MdC3DZ757UIyYU43rGc/fTY2liAyGEEEKIp1HAxcdu/fwvAMDgHtF4++oRmNYvETdN6YlIyyB9q946IFXbrMWkVRsxedVGn+xrd8LOLLdtfO8K29nsIXL7Ejm5qeJlykKV1m3v/e4QAOd9fgjpDIWM+TcV7BkuTSI9QtyhUq3hbov1HHnrqhGC+7alsyQApr+wmbt/pFyNPkkRgm1eWV/ohj31byW1rdzt0dlxgkxBQgghJBix1xZhShliwxReec3LhvfgbveggAshhBBCiMdRwMXHDCZmIO5wWSNmD0zBh9ePRlKkCmEhzMB7q45p8qwzMM0OAUCtMeCp345hwjMbsLuozjc7HuR2WY7rhUZNO1vas81wGdcr3m4blUL8Ty+MF3BpsjTX3kWfMXGz7pLh4qmSjPW8LEOlTMr1e2LF2Ayg2JbO+uFAmeC+0WSGxDYNBsJzCdv3JVg0aw34/QjTs+qRi3Px9U1jfbxHhBBCiOfxy4mJffd7wtzBqdy1B2W4EEIIIYR4nv3UXOI1zgbQwhTMR9NmCbhMXrURFbxZ1e9aGh/+/e0CFD8z34N72b2N7RnX4cdIbTJS4sLtG96rbBpDSySA2Qw88tNRPPLTUfRMCOfWvXbFsA7vAyHOyCwX+CZTcA3i23pqzXGPPO8NH+7hbp+pbrFbHxsm/JvX2xznsyKPsZUdH4YWnZErLdikNSBK5Z2ZsN5w4oKauz2hd4LXBp0IIYQQXyqtZzJcMuK8F/iICJHjlim98c3eUkzum+i11yWEEEII6a4ow8WHnM0uZ0tLNWsNOFauFgRbiOflZcQAAK4dl93hx9Y2W2e/R4mUGwKEvVoAoF9ypOD+2RrrgOysgckd3gdCnJGyAZfgjrfgt0MX3P6cRTUtKHeQ+XbF6Ew8fdlgKGyy3GYMSOrw61Q3aaHVG7n7Wn1wZSOpLeXeYsIU6JcS2c7WhBBCSHCoaGQCLmkx3s00uXNGH2y/bzpSoynDhRBCCCHE0yjDxYdatUaH69jSUs//cRKFVc0Ot7NtyE66rqJRg4OlDQCAsE4c3zPV1s/LNtuFZTub+0RFk8PnE+sBQ0hXsP8ujcEecfGAac9vcrhuxbReSI+1b4CbGh2K/Q/PxHPrTuKLXSUuvU6LTvj9UNGoQUKEMmgyQW74aC8AICs+vJ0tCSGEkODBTsxKoL5lhBBCCCFBizJcfMhZfxA24OIs2ALYN2gnXffFrnPc7XBlx4MdV4zO5G4Hx9AoCTZsSbFg6wviiq6UUWvvsbalAvliw5VIjlR1+rUXvL4Nd351oNOP9ydagzWY1CuBAi6EEEK6jxpLwEWs5DAhhBBCCAkONFrvQ78dLudu90wUDjpFulirv03nOEuGdF1nskvmDEpx2+s/cnGu256LEBabJGHshgGXdccqOv3YsoY2p+udBVwAIDNevIyH3JJxtHxyTyRGOp7x+vPBcofrAklxTSt3+z+XDPThnhBCCCHeVdfC9GZLiKCACyGEEEJIsKKAiw+t3niGu/3eNSMF65KiHA+6jcmxNnJv0xtR36JzuC3puIPnG7nb0k7+hSwZlQEAuHtmX4fbXDc+GwAwOjvO4TbU2JJ4gqwblxQ7fsFx+b72lNRZAwW2fZgAQNVOxqFtAPfTZaPRMzEcXy4fCwB4YN4A7H7gok7vX6B4+c9T3O3oUNcmFxBCCCHBoNZy3RZPJcUIIYQQQoIWNQDxAzKpBD0TIwTLUqLES89cMy4LSZEh2FVUxy2radYiltLS3YY/qJqbGtWp53hq0WD8c1IOetl8rnz3ze2PYZkxmNwnER/tKMYr6wvttolU0Z8ocT8pV1LMxzviQY6CSaYuvOkWrYG7HaqUQWsQNrKXy5wHXJS89csn98SkPonY8K+pgm2CpUeLM1QKkxBCSHdVSyXFCCGEEEKCHo3m+lDvpAicrmrGc38bYrdOLOBS/Mx8AECTRo/z9W34ak8pAKCxTe/ZHe1G2nRGFNW0AAD2Pzyz04OfMqkEvZMinW6jUshw6dAeAIC7ZvTB4uHpyIgLRUOrHsOeyAdg7eVDiDtJu0GGy4fbi7jbn9wwGtd8sBtA1wIah8us2W8DUqJQcLa2Q4/nBxqmdDJ7zWA0tRvY8Xc/HWBKo90+vbeP94QQQgjxHo3eiGbL5I2EcMpwIYQQQggJVoE9ahPAfj98AaermgFAtGZ/crQw4DK2p7XsVKRKgWcWD8GQ9GgAQEMrBVzcpUlrPZbeLHUjkUiQGR8GiUSC2HAl/jWzL+6d08/lXj6EdITMEnPoSraHvztRYS0dFh5iDVxKu5BA8tqG09ztF/6e1+HH88uQOevV4ozGJqvGn+08W4uCM7U4U93MLWvmZQnNHui+fleEEEKIv6uzlBOTSyWICqV5j4QQQgghwYoCLj4SxRvMDxVptGyb4fK3ERl227ABAcpwcY/3txVh9FPrATCZJdKujMx20e0X9cGtU2n2N/EMtqRYIAdcTlc146IXNuHH/WWi67/bdx4A8ND8AYLlnX3LBqM10DGlbyLSYkLxzGWDO/Qc/AwXldxx9tpt0xz/7bfpjB16TV85W92MJe/sxBXv7sRFL2xGRaMGADO7lzUwrXMlGwkhhJBAxAZc4sKV3aKEKCGEEEJId0UBFx+JDbPW7VWJBFySbQIul+Sl2W0TZcl+aNJQwMUdnvj1GHe7NUAGNQnpDGtJMR/vSBfc+91BnKluwV1fH7Bbt+VUNXd7f2kDkiKt59POllHj92t586rhAIAlozOx+4GLMHtgMh5dkNvuc4TwgizO+pg4q+seCAEXk8mMS17fLlh2tJwpx6azHEeFTEKDTYQQQrqVmmYtACA+gsqJEUIIIYQEMwq4+Eh8hPOAi4yXXbEgL010cI4tT6M3Bu4sdUKI98n8NMNl19lafLrzHMwu7Bc/s2/H6RrBuuWf7uVua/UmZMSFIdVSptHghoALP3CSFKXC21ePxHUTctp9DoXMel53FnCpUGscriupa233dXzt4PkGQekwwJpVpbdE+ZQB3oeGEEII6ajaZibDJd7JxApCCCGEEBL4aMTDR/gZLu2JdlDjV2EZsNIF8jR1P5UcRTPPSPCSWs78/hRw2VpYjX+8sxMP/3gER8rU7W7PDzRf+d4uwTqN3npOZIMclwxlsgQNnTxfspkZcqlEEBDvCH5CBz/4Ykurd5zFsu9cfade25uaNAa7ZacqmzDwkbWY8twmAEBLAGTqEEIIIe7ElhTjT7wjhBBCCCHBhwIuPqKUS3HV2EzMGZiCXonhotu89I88jMmJw10z+oquV8iFM4aJ+9w0uZevd4EQj9l+uhYA8PmuEh/vidU/P7ZmpZQ1MFkcJpFslFadAWUNbS5nerCBabklSNL5DBcmQBDiJDOlfdYgi8JJhoeOF0xaOFRYTjIpAILBLVr7gMvTv5+gIAshhJBurabFUlIs3P+/ywkhhBBCSOeJp04Qr3hyofOGy4uGpWPRsHSH69kBOwq4dJ3tAGF0qMJHe0KI9+wuqvP1LnBsz2MP/HAYvxwsx58rp3A9rTaerML1H+6xe+zUfoncbTYwwrppSk8AgNyS1mMwde58WVTTAgBQdCHgkh4bCoApp+UscLNgSCq+3F2CnonhWDQ8HT8eKOfW6Qz+f75XU18xQgghxE5dM2W4EEIIIYR0B5ThEsCUMurh4i4D//OH4H4UBVxIELt/bn8AwOjsOB/viRU/8WTd0Up8sasETRoDPtt5DgBQ26wVDbYAQKTK+vfK1kcHmLJdA9OiudsAYOxkhst1ltduaO18MEGlkOHwo7Nw8D+znDaMH987AWvvmoSfb5uIiBDhvIhACLgcLmtsd5v5g1O9sCeEEEKI/6htoR4uhBBCCCHdAWW4BDA2w6VVZ1++hbhOrGwRZbiQYNY3ORIA0Kr3j3OH2aaXzPf7y7jbEokELVoDimtbHD6+slEDvdEEhUwqCLjwyzHKpP4RoOYHh5zpnxIFwL7XSyD07HKlB482AAJHhBBCiDvVNjMlxeIo4EIIIYQQEtQowyWApcUw5WnO1brWy8AVtgOf3cH2MzV2y6JCKRZJglekivn33SzS3NwXzte3OVyXGReGic9uwOI3Cxxus7u4DjNe3AwAeGbtcW755SOtJRnZwIWhEwGLsgbH++dptr1e/D3DxWw242h5+xkutqXfCCGEEHcymsz4bOc5nKlu9vWucLgMlwjq4UIIIYQQEswo4BLAYsOYmdIavXsGrm78ZC/mvrLV7wf03O1YuXA2dt/kCOQkhPtobwjxvAg24CLS3NwXnFTXQll9G+pdKON1rrYVZrMZ20/XcsuSIlXcbZnUEnDpREmxsz4crGH3m+XvGS46o8mlLCKt3r/fByGEkMD22+ELeOjHI3j8l2O+3hUOm4WbQD1cCCGEEEKCGgVcApjS0nTZHQESvdGE/GOVOFHRxNXfFyu1FWwqGjXYX9LA3d/9wEX4467JCJHLfLdThHgY2xekyU8yXJydwxradA7X2WrROQ4+yy2ZIr8euoDGNscBnDc2ncayj/YIAtkaHwYHbHvO+HtAnH+snPUI0vp54IgQQkhg23KqGgBQqdb4eE8YrToD2iy/LaikGCGEEEJIcKOASwBjAy7uqIXPXpQATPDlhXUnkfuftVh3tKLLz+3Pxj69Hmst7/GmyT2RFKVy2syakGCgtAQfOpPt4QnOzmGbeeem9hzlNWt/4tKBgnVyXqbIyq8POHyOVWtPYv2JKlz+lrWE2dubz3C375ndz+X9cYfAC7gwg0lSCfDJstH49faJ2PTvqXbbad2UmUkIIYTYYjJemZLBaieTLLyJzW5RyqXcxBdCCCGEEBKcKOASwNhBU3cMwC37eC93u6FVj4+2F0OjN2H5p/vw66HyoOztYvueokJda2ZNSKBjg4omP/m7dnYOO1vd4nDd8MwYLvAMCHutzBucKtiWH3BZf6IKAHMO4PcS4fd3OcwL3uw9V8/dXjGtt8P98YQ+yRGI5p2b9H6cGWI2m1FnqU+vUsigUsgwqEc00mJCkREXiiHp0dy2VLaREEKIpxTVtOBCI5PZovaTbF62f0tCuJImdxFCCCGEBDkKuAQwd2a48H24vQhNvN4Ot32xH1sL7RvLB7pWm/JDUSqabUa6Bzb2YDbbBx59oaZZ26nHvXftKIC3+2yJtLhwpV1DWtvm8wajCTn3r0G/h9ZyGX5tIlkXe4rruNszc5M7tZ9dESKXYfeDF+H/5vQH4PkMl0PnG7B642kYjCYYTWbsKa5zuU/Yii/+wtxXtgIAQhXWsoxKuRTrV07Fj7dOwPe3jsfi4el4zCYDiRBCCHEXNrsFYPrVGfxgskJdC/NbJ476txBCCCGEBD0aYQ5gcikzgNjVWeq2FyG7iurstjlX2wIgsUuv42/KebPhASA9NsxHe0KId0l5MyvNZudN672B/VucPTAZfxytdOkxsWEKxIUrcXFeKr7/qwyAtWxIRpz937Jt83k2EwMArvlgN4qfmS/a2+Vf3xzkbt8ytZdL++ZuIXIZQhWWALuHB40ueX07ACZgUtOsxRubzuDacVl47NJB7T52zWFrCUqVQtgHi50gMDwzFsMzY924x4QQQojQttPCiWL8iWS+UmMpKRYfHtLOloQQQgghJNBRhksQ6OoEdVcaZ7MXCcHk9Y2nBfeHZ9EgIOke+AEXfygrxja7D3ehprlcKsGgHlF479qRAID/LLBmSrB/0yFy+682hUwYcOFnBkZaXpcNNgBAchQzIMLvoRIb5rtZqQq5+0pIOsIvo7a/tAFvbGJ61/x0sLzdx5pses3IZVQuhRBCiPcZTWYUnKkVLPOHsmLsRI/4cMpwIYQQQggJdhRwCWDsmKkZXRswVWvabyb5yvrCLr2GP/rpgHAQMZp6uJDugjcWbvJ9vAWtlpmnrjSRHZIejV9vn4QRWXEAmL/bnpZ+IGwQpabJvkSZTCr8utPxMkWm9k8CIMx6YQMtaTEqbllipO9mpbI9uzzZw+Xf3x3hbjfzvhcaWp1/R5ytbsbwJ/MFy87Vtrp35wghhBAXHClrhFpjQGSIHEmW7221SAarN+iNJjRavkNrLeVT46mkGCGEEEJI0KOASxDo6gR1dZtrs742W/ocBIO/SqxNsK8YnYFt/zfNh3tDiHdJBQEX30dcNJZACb8MVVq0SnTbAalRdsuUNhktZ2ta7LaxzbjQ6q2BiyaRoDObSbKnmDlXXD4i3aWAkKcovZDhwj9uYv1sHHl1fWG7QRlCCCHEG9hyYmN7xSPOkk3iqwyXGz7ag0mrNqC0rhW1bEmxCCopRgghhBAS7CjgEsA603fhZEUTDpY2oJg3sOZKhgsAXPvB7o6/oB9avfE0LntjB3d/Zm4y9W8h3YptDxdfY8tRSSUSPLt4MMb3isddM/qKbvt/c/vbLVPIhF9lYoERudS2pJg1oCAWLNDZZJL0S4l0sPfeEeLhgMtfNcLjs/OstZdXn6QIp4+1PVaEEEKIr+w4wwRcJvSKR5SKyV73VYbLgdIGqDUGfLyjGLWWLNo4KilGCCGEEBL0KOASBFwdLz1Y2oDZL2/Bpau3Y+rzm7hZ3exFyNCMGDy6IJfbflAP+5nkgeqZ30/g9i/3w2w247k/TgrWTe6T6KO9IsQ3/K2Hi9HMBlyAf4zKxBc3jkVCpHBAIjJEjmcuG8wNnvBdaGwT3M+Isw+gynklxUIVMkHgorFND7PNcdAbhffnDk518d14BhtU8lRwY0+14wi+s6B8pVqDNYcr7JZfMTrDLftFCCGEuEqjN3KZqRP7JCDKUi7YFxkuZrMZzZaSqd/sLcX5eqbUZgKVFCOEEEIICXq+q49CukxiacTg6nhp/rFKwf2SulYMTIvmBtPiw5W4bkIOHv3lGAAmAPPUwsG4dPV2u+cKJK06A97azDR/Ht8rXrBuVm4y5DKKO5LuhZ8d5/twizXDRcbLQgmRywTbHPzPLEil4kGBmmad4P6VYzLttuGXFDOazILHNLbp7QIZRpMZp6uauPsRSt9+XXq6pFiok7dXLdITh/VJQbHgftHT83CutlU06EUIIYR40sHSBugMJiREhKBXYgSiLF9uao0e3s5TbdEZuWs0tcbABX3iwqmkGCGEEEJIsKOR5gBmHTR1bcg0ymZEbdNJpidLoyXDJcqmaXxeeoxdGR2TP3TY7iD+YOEvB8sF63zZk4EQX/G3DBf2tMLfL35flpFZsQ6DLbaun5CNpaNFAi68x+uMJqz44i/ufl2LDlVq+6DCv789xN1WKX37dan0cIbLvhrH789kBgwOXpcN/APAfXP7QyKRIDshXBA8I4QQQrxhTzFTDnNMThwkEgmvpJj3M1yaHWTVxFNJMUIIIYSQoEcBlwDGjk26Ol5qu91zf5xEq86AuhZLwEXFBB++Xj4W98zuh8XD06FSyPD+tSO5x2g92LDZU2qarQOpO87UCtaFhchsNyck6PHHws1+8CdtLSnGz3Cxfj0V17a6/FxT+yWJBmds+7zYmrRqo92y01XNvP3x7bnC3RkuBqOJK3Xiii2F1aLL+YGVSrWmy/tFCCGEdNauIibgMio7FgAQzZUU834Pl2Yt85qhChlUCutvkHgqKUYIIYQQEvRoen8A488sdoXY4NqoJ/9Ei45pHs1muIzpGY8xPa2lt6b2S+Jua/RGhCoDK0hxrgODtYR0B36X4cKVFLMu42e48IOmYv45MQfvbSsCAKjk4oGVzmRcKOVSwPlLew0bMNLojTCazF3OIFn4xnYcKVPj7hl90aazDkTFhyu5xr58N3y0F8XPzLdbHhtmzYzs6HcSIYQQ4i4Gown7zjH9W0bnMNcxXA+XNgMQ4d39YUuIxUcoMalPAr7cXYpQhQxhPi5RSgghhBBCPI8yXIKAq8OlTSKp7WywBYBoM2qAGahUWPofDHsiH0+vOd7hffSlExVNDtfpDb4fbCbE2/g9XPwi4MJmuDjp4eLMv2b1w3Xjs3Hd+GyMyo4T3UYhcz0YwJYfy4pn+pDMHpjs8mM9hc34qWnWodcDa3CutqVLz3ekTA0AeOnPU3hrSxG3fHr/JEcPEdhxugb5xyrxScE5bpnZLzoCEUII6Y6OlqvRqjMiSiXnSiL7NMPFct0VqVLguvE5UMqlGNQjyuv7QQghhBBCvI+m2AQwa0kxx4NcZrMZO8/WYUBqJC40tjl9PrmTAUmlTAq9kQnOvL3lLBYO64EBqYFx0VDiJMPFU/0QCPFnEokEEglTZtAf2jKxf4aOSoq1J1Qpw6OXDHS6jUzq+vMpZFIYTEZusOSi/r4PuChtjsfDPx3FJzeMttvuk4Ji1DTrsHJm3069zrJJOfh233mn2+gMJlz53i675deNz+7UaxJCCCFdxfZvGZUdx2WBsuWS1Q76qXgSW1kgMoQJAK1fOcXh5DZCCCGEEBJcKOASwNihSWfjpT8fLMedXx0QLOuVGI4z1fazo49fUDt8Hn4mDCBensxfldY7CbgEYE8aQtxBKpHAaDY7Ddh6Q2OrHv/7ixngl/ECLu31XOkoeQdKcLXpmfNdoaWHi7NgtLfYBlxCFfbHp75Fh0d+OgoA+MeoDPSICe3w6yREhAju56VH4+D5RozOYTKHzte3YuKz9v1uFg9PR1Z8eIdfjxBCCHEHrn9LjjXT1VpSzPsZLk2WrJoIS9AnIy7M6/tACCGEEEJ8g0qKBTCJC2OAvx66YLfsvWtHiW57y9TeLr92F9sHeI3ZbOYyXKb0TbRbzzbVJKS7Yf+GjzkJtHrDE78d427zz2ld7VFiy5XzpSPuDv64Yx8OnW/ExhNVgmX8QHibTZCcz1mQLYzXo+uJSwdi+eRegvUfbi8WfVxMGM3aJYQQ4hsmk5nLcBnNC7hYS4p5f6IYW8o5IoTmNxJCCCGEdDe+H0UiXeZsgrrtrO5IlRw5CeFYtXiI3bY5Ca7PTtbo/S8z5KnfjuHSNwqgM1oHFKuatGiyDEK+ddUIbtvJfRPx9GWDsXRslk/2lRBf0xuZv5HrPtzjdHC+K7YV1mCvZQDEke945av4QZbYMAUSI5lsi6cWDeryvnQlgNOR/i+eYpvhcqFRg+s/2iNYZuTVh9M7KZdY1iBeXjI9RgUVr3fOwmE9EKpkXldjyfppcZDdaLt/hBBC2rdlyxYsWLAAaWlpkEgk+PHHHwXrm5ubcdtttyE9PR2hoaHIzc3FW2+9JdhGo9FgxYoViI+PR0REBBYvXozKykrBNiUlJZg/fz7CwsKQlJSEe+65BwZD4GSrt+d0dTMaWvUIVcgwKC2aWx7lw4ALV1JMRQEXQgghhJDuhn4BBjRmEFBstrLWYMTGE9Wob9UJlqdFMyVm2GaSLJVIeRq+KJVccLFy+5f7MTAtCh9eNwpyP5j9DQDvbmUaP29WSHDPI/l260N5M7d7JoTjitGZXts3QvyZWqMX/H24Q6vOgKveZ/p8HPzPLG6WqTP8oIhEIsGeB2fAZDJD6oZsF6mLKS6f3DAa13ywW7BM3oH+L56idOE8a3Ax4FLdpBVdLpNKIZVKsOWeaTCYTIhUKaBSMP8u2KCco2wfV/aPEEKIUEtLC/Ly8nDDDTfgsssus1u/cuVKbNiwAZ999hmys7Oxbt063HrrrUhLS8Mll1wCALj77rvx22+/4dtvv0V0dDRuu+02XHbZZdi+fTsAwGg0Yv78+UhJScGOHTtw4cIFXHPNNVAoFPjvf//r1ffrKWw5seFZMYIJAGwPF53BBA/NLXGIy3ChgAshhBBCSLdDIyQBjB0/FEtw+Wbvedz82T7sPCucXR5tKfvCLxuTGReGhy/Odfpa98zpjxFZsVxPgLoWHbYW1mD7mdrOvwE3Kq2z9mn5tcTxwPH0/kmQSyVYOKyHN3aLkG6LX96K//fJZxsslogERdwRbAEcB1z4i++Y3huTRUoPdqUcmbs4CmgYeIEVVzNcapuZQHzPxHAUPzOfW84GvDLjw9AzMQIAEGoJuLRaRqoc9bOhDBdCCOm4uXPn4sknn8SiRYtE1+/YsQPXXnstpk6diuzsbCxfvhx5eXnYvZuZGNDY2Ij3338fL774IqZPn44RI0bgww8/xI4dO7Bz504AwLp163Ds2DF89tlnGDp0KObOnYsnnngCq1evhk6nE33dQLPHEnAZnR0vWB4RIudKqLZ5OeDSbAm4RFJJMUIIIYSQbscjIyRlZWW46qqrEB8fj9DQUAwePBh79+7l1pvNZjzyyCNITU1FaGgoZsyYgcLCQsFz1NXVYenSpYiKikJMTAyWLVuG5uZmwTaHDh3CpEmToFKpkJGRgVWrVnni7fgtZ2OAvxwoF13O/viP4s023/CvKVg6xnlpravHZuF/t4xHf5vMmIZW/7hQm7TKvokzH9ur5d1rRmL3gzMwNCPGC3tFiP+K4s24dFaWsLN0BuuAv1oj3qyWbU7PknkwspEQoRTt48Qvu9gjVrzJPBug8CVHgSeNQTzgojM4/lD/+QnzfXy2ukWw3LYEJWA9JuWNbdDojZThQgghXjR+/Hj8/PPPKCsrg9lsxsaNG3Hq1CnMmjULALBv3z7o9XrMmDGDe0z//v2RmZmJgoICAEBBQQEGDx6M5ORkbpvZs2dDrVbj6NGj3n1DHnK+npnY0S8lQrBcIpFw1zxtXq4qZi0pRj3OCCGEEEK6G7dPuamvr8eECRMwbdo0/P7770hMTERhYSFiY63NyVetWoVXX30VH3/8MXJycvDwww9j9uzZOHbsGFQqFQBg6dKluHDhAvLz86HX63H99ddj+fLl+OKLLwAAarUas2bNwowZM/DWW2/h8OHDuOGGGxATE4Ply5e7+235N5FxtcHp0dgt0jthRBbzOSRHqfD85XmICJF3qCSYu8sOecvrVw4HwMzgjgtX+nhvCPG9mDClR2uaCwIuIqMcD/14GGsOVwiWebLOuUQiwcc3jEb2fb8JlocqZNAbmf1jy2eN6xmPgrPW7L05g1M8tl9dpdUbuYa8goCLkwwXW2NzYrGzqB5XjsmwWxcXxpwvzWZmQIv/Gnxd6ZFDCCFE3GuvvYbly5cjPT0dcrkcUqkU7777LiZPngwAqKiogFKpRExMjOBxycnJqKio4LbhB1vY9ew6MVqtFlqttfykWq0GAOj1euj14pMofImdABahlNrtX5RKjoZWPdqM8Oq+q9uYfQqVS/zymHUF+36C7X15Ex1D96Lj6V50PLuOjqF70fHsOjqGndOV4+X20a1nn30WGRkZ+PDDD7llOTk53G2z2YyXX34ZDz30EC699FIAwCeffILk5GT8+OOPWLJkCY4fP461a9diz549GDlyJADmgmPevHl4/vnnkZaWhs8//xw6nQ4ffPABlEolBg4ciAMHDuDFF1/sNgEXtvyO2PCXo0Gxf8/ux93+24j0Dr/mwfMNgvt7iutw6VDflucyOXivrK33TkNylMpLe0NIYOAPkJtFzyJds7e4nrstluHy2c4Su2Up0d7/Ow0PsfanYstnvX3NCAx5dB23TZQfz07V8gJbBpP1tqPz4oXGNrtl71w1HB/88AeuGGn/ncAPyM94cQuuGive+8ofyq4RQkiwee2117Bz5078/PPPyMrKwpYtW7BixQqkpaUJslrc7emnn8Zjjz1mt3zdunUICwvz2Ot2VlWDDIAEh/fuRO1x4TqzllnXapAgP9++x6OnlFYwr3vy6EGsuXDAa6/rTd48nsGKjqF70fF0LzqeXUfH0L3oeHYdHcOOaW0VL4/vCrcHXH7++WfMnj0bl19+OTZv3owePXrg1ltvxY033ggAKCoqQkVFheAiITo6GmPGjEFBQQGWLFmCgoICxMTEcMEWAJgxYwakUil27dqFRYsWoaCgAJMnT4ZSac1WmD17Np599lnU19cLMmqCFTu+ZdsHARAOwgHMYNiO+6a71LjamfIGjeD+ZztL8OTCwV16zq5qbya3LwZxCfF3/PFxRwHazjhSpsb3By7g053nuGXqNtdmBSRFhrhtPxwZnROH3UXW7D82yAIAqdFM+Sx/DrDYauJlKR0sbeBuGxx8pp+LBLpClTLkRLrWL+cA7zX4nPWMIYQQ0nFtbW144IEH8MMPP2D+fKbf1pAhQ3DgwAE8//zzmDFjBlJSUqDT6dDQ0CDIcqmsrERKCpOdmZKSwvV84a9n14m5//77sXLlSu6+Wq1GRkYGZs2ahaioKHe+zS4zm81YuetPAGZcPHs6UmwmWX1duRelZ+vQZgBmzpwJhcI73/Gvnd4ONLdg6vgxGNszziuv6S16vR75+flePZ7Bho6he9HxdC86nl1Hx9C96Hh2HR3DzmGzvDvD7QGXs2fP4s0338TKlSvxwAMPYM+ePbjjjjugVCpx7bXXcqnrYqnt/NT3pKQk4Y7K5YiLixNsw8+c4T9nRUWFaMDFlfT4QEqzMljK4Jhhv78avbCEz82Tc5AQJu/y+xIbjvP1sWptryySyQi9ycudMkmHBdLfXrDR6rpeIoR9/KK3dtqtq23SCJ5fLMCzaGgqUiMVHv/8Lx2SIgi4hPCavUeGWMt+RKmsmS/+/G/yrq/24+cV4wAAj/5yjFv+zO/HMbWP/QCP2WwNjPRNihD9/nPmSBnzvbkwLxU/HrwAAAhTyjBrQKJfH6dgRufOwOWrz47+rQQG9vwslQpL/8pkMpgsGY0jRoyAQqHA+vXrsXjxYgDAyZMnUVJSgnHjmO+GcePG4amnnkJVVRV3fZWfn4+oqCjk5uaKvnZISAhCQuwnQSgUCr+7SG/S6LnfFYlRYVAohOWPYyylhNuM3t3/Fp3R8voqvztm7uKP/x4CDR1D96Lj6V50PLuOjqF70fHsOjqGHdOVY+X2gIvJZMLIkSPx3//+FwAwbNgwHDlyBG+99RauvfZad79ch3QkPT4Q0qxqNAAgh8FgwJo1awTrThRJAVgv0A4cP4M1usIuv6bRxKTH89m+trc16wFn/5R9vX+kYwLhby8YtLZY/5Y3bNyEJPF+8W6x/8QZrNFbzz8aI8D/m+0RZsbU0FL8/nup53bC4lClBIB1MEahawR7rty6aSOiLEmTizMl+PCUDLPTTX50DrE/zx2vaOLtn3X9meoW0f0+U2p9/5elNgi2cfy3Z/+6CvV5/C0HkEuAIXEGHNyxEQddfh/EE+jcGbi8/dl1JTWeuFdzczNOnz7N3S8qKsKBAwcQFxeHzMxMTJkyBffccw9CQ0ORlZWFzZs345NPPsGLL74IgKkSsGzZMqxcuRJxcXGIiorC7bffjnHjxmHs2LEAgFmzZiE3NxdXX301Vq1ahYqKCjz00ENYsWKFaFAl0DS0MgHEELmU68XGx2atirST8yg2AzXCg/3pCCGEEEKIf3L7L8DU1FS72VIDBgzA//73PwDW1PXKykqkpqZy21RWVmLo0KHcNlVVVYLnMBgMqKurE6THs+nw/Ofgv4YtV9LjAynNqqSuFU/s3waZTIZ582Zzy8sb2nC8YKtg21GD+mDetF5dfs07C9bZLZs3b16Xn7crLjRqgL1bHK739f4R1wTS314wWH1mByramgEAkyZPQa/E8C49H/v5idlZJcVHK2ZzfWOqm7TA7s3c+tTEWMybN7pLr++qxj2lwFmmwPvi4Wm4eHAqrv94HwBg3uyZiLKUXZwH4OYWHWLDFFy/LF8TO/8CwJw5cyGVSvDZhT3Yw+udI3buO5FfCJwvAgDcsHguJBJJu397Yq87afQwzB0k/l1LvIvOnYHLV59dV1LjiXvt3bsX06ZN4+6z1ynXXnstPvroI3z11Ve4//77sXTpUtTV1SErKwtPPfUUbr75Zu4xL730EqRSKRYvXgytVovZs2fjjTfe4NbLZDL8+uuvuOWWWzBu3DiEh4fj2muvxeOPP+69N+pBjZaypY7KJrPLWw3e+y43mcxo1jIBl0gKuBBCCCGEdDtu/wU4YcIEnDx5UrDs1KlTyMrKAgDk5OQgJSUF69ev5wIsarUau3btwi233AKASX1vaGjAvn37MGLECADAhg0bYDKZMGbMGG6bBx98EHq9nrtIzc/PR79+/Rz2b+lIenwgpFkpLftnhjDN6edDxXbbLp/SGwpF1z/u2QOT8cdRYaBLLpf7dEDSLNE5Xe/vnyMRCoS/vWDA79chlcnsjvm2whrUtmjRojVi1sBkJER0bRbskQvNGJnNlLjSmYR/s3KZ1GufuURqnf36wt+HYd85a4AiPDREUIokOSYw/h1+ubcM103IQaJND5zv9l/AFaOFTe7VWqbEyV0z+gh6oAEd+9tLjQ2nv1M/Q+fOwOXtz47+nfiPqVOnivZiZKWkpODDDz90+hwqlQqrV6/G6tWrHW6TlZXlR9ma7sUGXGLCxP9dsxMp2rxYXbhFZ02niQihgAshhBBCSHcjbX+Tjrn77ruxc+dO/Pe//8Xp06fxxRdf4J133sGKFSsAABKJBHfddReefPJJ/Pzzzzh8+DCuueYapKWlYeHChQCYjJg5c+bgxhtvxO7du7F9+3bcdtttWLJkCdLS0gAAV155JZRKJZYtW4ajR4/i66+/xiuvvCLIYOkubK/T5DLhx/rnyilu+7G/anEe/rNAmMGkNfi2WbLOyes/OG+AF/eEkMAh5QVJbXuqGIwmXPX+Ltz51QE88MNhXPfhbtuHd1h9q7VnQKtOOOoh1tPFY2xOmKG8AItS5vavRLdydD77ak8p9hbXYc3hCsHy+78/bLdtm5459qEiZVc6oqsBOEIIIcQd2JJiMaFK0fVRlgwTb5YUY8uJKWQSQa84QgghhBDSPbh9ys2oUaPwww8/4P7778fjjz+OnJwcvPzyy1i6dCm3zb333ouWlhYsX74cDQ0NmDhxItauXQuVSsVt8/nnn+O2227DRRddxKXJv/rqq9z66OhorFv3/+zdeXgUVdr38V9nXzshQBIgIYR930WCgoBARMQFHdxh3HVwZtR51OEZd19HH9dxRkbHGRUdURR3BYUoAiogEInsILKELQkQspC1k673j043aRIgSW/p5Pu5Li7SVaerTt2Vpavuus9ZolmzZmnYsGFq166dHnroId16663uPqRm61RFJYZO3FD857VD1T0+ym37jIkI1g3npOrRWpMzV1RZ6x0z2VvqS/jcdE6Kbh7TTYnmsHreAaC86kTS4+SER2W188+UfaJ0V9ifQJWkAwVlTusCA7xXIXdyaqd3YrQm9k1Qu6gQp6qf5ujm0alK75eo385do12HSxzLt+UU64pXVtX7nvfWZisxJlzDU9poxutrHBU94SEN/53dtV2kdh0pcVrWOS7iFK0BAPCegjJb1WzMGSpcSr2YcDkxnFjzGZYUAAAA3uORGueLLrpIF1100SnXm0wmPfbYY6cdOzguLk7vvPPOafczcOBAfffdd6dt0xqcfAPxdBUfnlBRVS3Jd8NTFJXbbuS2jw61zQ0hqX9HszrEeHAWcMDP9e1gdty0PznhYqlyT8VJUIBJad3a6rtfjtT8nrCpnXyRpGAvVpZYTzrWgACT/j1juNf27wqTyaTObSP02Z3nqv/Dixv0nvs/tFW5PHhRX6fh08KCGp5wWfTH0er94FeO1yv/PN6rSTIAAE7lRIXLmYYU897fLXuFC8OJAQAAtE7UOPsxxxNTJ90brZ1w8cZNsQqLb4cUsw+bc7i4Qm/fOFzpSVZd0C/Bp30C/En1ScNsVVS7Z6DzoZ3bOG50WGr9Xqo6qYKmU6z3kqPn90nw+j7drSk3cIpOSnKFNaLCJSw4UPdM7Ol4HdGI9wIA4ElnnMMlrCbh4tUhxWx9IuECAADQOvEp0I/ZUynGSRmX3KIKx9dB3ki4+HgOl71HSx1fn50ap6PJ1jrz2ABwVvu3xslVH/VVyVVUVSu0AVURMeFBKqy5q2EynZgXxVJ9Yh+WkxIujRneylXJcRFa87/nOxJBrcW6vflOrxs7h0tOUbnja18OIQkAQG0FpbYhxWIj6p/DJSbc+wmXE0OKcakNAADQGnFX2o85ClxOqnDJKz5xY2xApxiP9+Pkm7MnP70OoHn7YedRp9f1JVz2HCmts6w+VbWSNwEmk4IDbb+oas8Lc6Cg3Ok93p6sPt4c5vdJg3O6t21U+/3HnOfNCQtuXMzvS+8lSRrVra3fxw4A0HLYK1xiTjmkmC3pUVZd9wGThjpWUqmHPt2kDfsLGtT+eDkJFwAAgNaMhIsfM8m5emXRxkP67pfD+u6XI5Kk+y/orXgvTBp/+PiJipqXlv6iQY8u0bYc1yfZbqzpw5O8vk+gJXjh6x1Or2tXo9jZh8c4k9rJmqBAk44ctz15+szi7br2P6s159udemX5r07v6ejHw3v5yt0Tep65US21KwElNahaqbbYiBDteWqK3rllZKPeBwCAJznmcDnDkGKGTCqpbFqZy+cbDuqtVXv1fMaOMzcWc7gAAAC0diRcWgBD0s/7CvS7eT/p+tfWOJb36RDtlf3PfH2NSmpK559dskMlldX6f19s9cq+a7t1TFev7xPwW6d5yLO+Cpf6ltXZpOGcrAkwmbR0W57j9Q87j+qZxdsdr2PCg3X1iGRdPaJzAzsNu1MNm3j/Bb2V9dDEM77fG9WPAAB42pkqXMKCAxUaZPubWVTetISLvUp088GGPVBW7BhSrHUNXwoAAAAbEi5+7MSQYoZ+rqfEvbFj9Lti95ESp6HETJ6fOkaS7djt+zJzUQM02MlzP721ao+KaqpYKqur67RvyFxNn+11/pOydk/+aX8PXTyoo56cNlAhQfwpaiz7UG0nm9g3QbERIeqZEHXK9958bqpX580BAMBTHBUu4fXP4SJJ5pqhvYqaOJHLoULbUKiHiyuchm4+FfuQYlEMKQYAANAqcZfLj9lvtxmqO4+LJCV4cDixp68YWGfZ6l0nJmU2eSnjUlFldRw7NxCBhjv5d8ZDn27Wv2qG+qovuVJRVTcJc7Klh5z/pDx4UV89ekm/U7aPZKiNJqs+xTj03eNtiZb20aGnfG9yXIRH+gQAgCdVWw394d31eqHW0F4FZbahS081pJgkmWuqX4oaODzqyXIKT8yDtvVQ8Rnb24dhZUgxAACA1omEiz+rldOo7+ZbUhvPzYswfXiy00WEYdjma7DLL6mo721uV1p54iZwRAgXNYArvtyUI6n+OVwaUuFS23f3jbMNFXaaocuYTLbpUuIi6yzrGHMiyR4bUf+Tvl3aRujyYcx3BQDwP5sOFOqznw9qzrc7VVVtVbmlWuUW2+eTmNMlXNxU4SJJWxowrNjxmiHFzHzOAQAAaJX4FNgCGIZUZqn79Pmpxvh3l9oTT1ZWV8ta65H5U42j7G6lNX0ICQpQYIBJ1jM/hA/gFAJrKtPqm6+lsQkXexXF0JQ2p2zDk59NFxMRrBX3jtOYZ751LKv9Oz+inqHc7hzXXX+a1NNrFYgAALjT9hxbdUmV1dChwnLH3CyBASZFn+YzhX3Y4doVLsXlFl332hrtyy+VZBuK+ZkrBmpU93ZO77VaDeUW1Uq4HGp4woUhxQAAAFonKlz8mKlWicsrNUMB2d1+XjeP77/2kEQVVVanG7J9O5g9vn9Jevor2wTcEQwnBjRKfcMQ2heVVtZ9ArS+JExtzyzZUe9y+xBX9ak6xbBYaJjkOOcqxsCAE38T6vudGBoUQLIFAOC3tuacSHbsyy9VQZktgRITHnzav2/mcFviI7+00rFsxY4j+nlfgfJLKpVfUqkDBWVauPFQnfceLal0qvzdcrDwjP0sss/hEsr8kgAAAK0RCRc/Vvu6orjc+Qbpnyf39mpfqqoNVVhO3JDdl192mtbusedIiT77+aCkE0+uAXBdUa3fJ2d1sVWoVFRZVVJRpV2Hj9f7nle/29Po/RTWuvGBxjv55lLtl2H1JFxS2tUdhgwAAH9hr3CRpOz8UhWU2hIusWeorO/W3vbwx+YDJ97/U/YxSdJlQzrpjrG2B9VOvp6SpJya4cTCaypHdx0pqffBlNqO11TSMHQqAABA60TCxY/5+jnloFpPUxeUWZwm1W7qpJSNcfj4iXlimHwbcJ8Xv7ZVq5zfO15d2tpu0ldUVSv9bys0/rnl2rC/oFHbe2ragDrLQoIC9JvhyS73FScE1Mq4hNczpFi/jt6pPAQAwBNqJ1z2HStVQc2DG6ebv0WShqfESpLW7T0mo6bE155wGdOznSMhU9/1y6FC20NkPROj1T46VIbh3I/6OIYU4/oEAACgVSLh4sd8PTTM8C4n5mb4w7vrtXDDiTJ8+4WGp5RUVOk3r6xyvE5tF+HR/QEtjXGa2eyPHLfdwPhmW55CasZHr6yyav8x202HxZtznLd10vhktZOxknTViM7625WDHa/vGNtNPz040THPC9wj0HT6IcV40hYA4K8OF1foaMmJytjs/DLHkGJnqnAZ2ClGASZDucUV2n+sTBVV1dp8wDY82dDObRyT2xeV1U245NTM39LBHOYYMvlM87jYK2X4uwsAANA6kXCB2yzZkuv4uqzy1LPXG4ahBev2aWsDJp08lUUnjbEcGxHS5G0BsNmZd9zpZ7djTJhCg2w37mvP0WQ6qb5uR67zMGP15YJ7d4h2fB0WFMhTnx5QO+71VbgQcwCAvzq5qiQ7v1SFpSfmcDmd8JBAJdeMqrlub742HShSZbVVbSND1DkuQtE1QxMX1TOk2KGaIcUSY8LUt6ZSdMvBU1/DVFsNldZ8lopmyGMAAIBWiYSLHztVfcs53dt6Zf+/HZV6ynXV9c3IXePrrXm694MNmvzid9p9pKRJ+y4/aQLviX0TmrQdoLU61Y9oQdmJp0ffvHGEo8Kl9hxNJydUrCdtrL5tBwWc+HOz71hpI3uLhgisVVkUVivh0i4qVMNT2tSbhAEAwB9sy7ElOTrXVMfuyy91fGZpyINX3aJtH07W7jmm9TXDiQ3pHCuTySRz+GkqXGoSLh1iGlbhUrvKPzKUv7sAAACtEQkXP1bfU+TL7x2rV68f7pX9X9A/UamnmIS52nrqhEvtp8ImPr+8SfsOCTxx8NePTNG4XvFN2g7QWg1NaVPv8jLHU5lB6pEQrVD7kGLVJypfTv7VU2Zxrmg7OQEjyel3xYBOMU3pMurx0e9GOb6uPYdLcOCJP+8Zd4/RgtvTfD4MJQAATWWvcJnQx/aQVX5JpQ7UDHV6pgoXSepqtn02Wbcn3zF/y5DOts9CZkeFy6nncKld4bLtUPEpr3WKa7YREhTgqBIGAABA68L4In7s5GF9JCmlbf0JEE/p1j6q3iqVqupTJ1yCaiVLqqyGSiqqGj3pfe0biw9c1KdR7wUg3XhOqqqthp5ZvN1puX0YDHs1hL3C5dtth080OunG/clPhNZ3DyIwwKR5N5+t5TsO6+oRnV3tPmoM7XwicVZ76pyAk6pdSLYAAPzZ9lxbwmV4lzb6eP1+HSu1aOOBQklSbMSZEy6pNRUuO3KPK6+4QtKJv6HmmoRNucWqiqpqp0TJIUeFS7i6tI1UeHCgyizV+tP7Wdqee1xd20fqH1cNcfzdtVe4mJm/BQAAoNWiwgUuCQ6s/yZefU99GYahTQcKZal2Hg5sZ97xOm3PxFKT0Bndox1PjwFNEBIUoJvOrTssoL1axT4kVV7NZLEHCsocbU7+qbffuDiTc7q30/9e2MeRxIF71U6yVNSqOgoLJt4AAP9VbTW0oybh0isx2jGs2K6ah74aknCJCpa61lTbFpRaFBhg0qBkW8VtdGiQ41mS4lrzuBiGUSvhEqbAAJNjTrpPsg5q66EiLdxwSHuOnnj47HjN+5k3DQAAoPXiLow/awYPLAcF1v8tlFNUrte+3+20bMmWXF30j+/1t69/cVpeUlklS7VVxmnmfTlZZZXtZqKZySiBJgsKqPtLxD4Brb0S7WhJZZ02JxdLvLd2n9Preyb2dFMP0Ri1K/86tQl3fE11CwDAn2Xnl6rcYlVoUIC6tI1Uck3CxX7pEBt+5jlcJGl4Sqzj696J0YoIsSVFAgJMjgRJ7ardY6UWVdbMGxlvDpUk/X58d43u0U43npOqpJq/tbsOn0i42BM2UVS4AAAAtFp8EvRjzeEeWnA9N2ztHv9ii9MT9B9m7q+33R1v/6So0CCltI3QO7eMbNB+7RUuPCkPNF1gPT+/N7+1zrau5hdMfUmZ2rnRcku1Mvcec1r/+/Hd3dhLNNTQzrGOr9O6ttXjl/ZX78Ro33UIAAA32J5jm/+xZ0K0AgNMjoSLXUwDKlwkaVhKrN7PPCDJeUhOyfYQV3F5lYpqVbjY529pFxXqqKgf3ztB43vb5pHJKy7X/mNl+vXwcU2QbVlxzZBi0aE8FAYAANBakXDxY80g3+I0hM2Z1H7iurbCMosKyyw6UFDW4PlcSiptFzP2YY8ANN7pKh/s1RKBAXWTmpW1hgXMrRlyrKHbhfstvmuMvtqUo1vGnEhwm0wmXT8yxYe9AgDAPbblnBhOTJJjSDG72PCGJlxOJFmG1qp2kWzzuBwoKHOqcMmpNZxYfbq1j5LkXOFynAoXAACAVo9Pgi3IC1cO8nUX6qi2Go6n6NtFhZ6xfX5JZYMSLgU1wx61aeATbQAax55MrW/UwArLiYSLfXJYu5euan6/h1q6XonRjptQAAC0NNtrEi69T5VwiWjYkGKd24Sre3yU9h8r1ciubZ3W2Se5Lyo/kXCxz9+SeIqES9f2tjlhdh05MR9lcc37o5nDBQAAoNViPCY/dnJ1x2VDknzUk1OzV6I01LSXVzZoLpdjpbZ5Jdo08AILQOPYEy31V7icmJDdPryfJMWHGUrvl+DxvgEAgNZj+xkqXMwNrCYxmUx65+aztegPo9UhxrnyPrpmXsiishPXLg2tcPm1doWLfUgxKlwAAABaLRIufiz4FBPWe9PG/YWnXV9S6+l3+6STdl/8/tw67Q8XV9Q7SffJjtkrXCJJuACeYB9SbFyv9nXWvb062/F1VbW1znoAAAB3MAxDlw7ppMn9E9U70SzJlgCxV9BHhwYpqBHXRPHmMHWtSZTUZg63JUiKG1HhktrOVuGSX1KpgpqHwYoZUgwAAKDV45Ogn0s0hymnnjkUvKW+Sbdrq51wqbI635jt3ymm3vccKig/4/Bjx0rsFS4MKQZ4gv2pzjE96yZcaqs9n0uQ73PAAACgBTGZTPrD+T2clgUFBqhjbJj25Zcpxk3XAmZ7hUuthEtOUZmkU1e4RIYGqUNMmA4VluvXwyUalhKiXw/bhhdLMNf/HgAAALR83B7zc76emzr4pDus143s7PT6eMWJoYfKKhv2JPyBgtI6y37YeUQ/7DzieG0fUqyhYzYDaJy84gpJJypdTqWq1pBiyZFnHg4QAADAVfZhxWLdlXAJrzukmKPCxRxe73ukE/O4/Hr4uCqrrFq355gk6ezUtqd8DwAAAFo2Ei5+zsf5Fp1c4NIj3nni5toVLh+t31/n/ZEhgXWWHShwrtgpq6zWtf/5Udf+50eVVlapqtqq/cdsT5xR4QJ41pmq2Cy1Klwu7cLwYgAAwPMcCZdw9zx8ZZ8Hxl7hYhjGGedwkU7M47LrcIl+3l+gMku12kaGqGdC3WHLAAAA0DqQcIFLTr4Va6m26jfDkhyvP//5oCRp95ESFZRadLIV942rs6ywzLldaWVVra+rNeudnxyv21DhAnjUGfItstRUuAzrHKsIBqkEAABe0DnOVlnirvkcT1S42K5DisqrVFppq9Q/1RwuktS1Zh6XXYePa9WvRyVJI7u1lcnXwxAAAADAZ7g9Bpec072dfsoucLxOMIfppnNTtSDTVs2y8UChJOmX3GJHm74dzHrskn6SpLb1zNXy929+UVW1VYcKy9U2MkS3ndfNsa6yyqrFm3Mdr+0XRwA8o74bBuHBtso0q9XQRz/ZftaDArmxAAAAvOPyoZ20M++4rk9Lccv2TszhYnvQ61DhiWr6sOC6Ffl2XWsqXH49fNxRHZPWleHEAAAAWjMSLnDJrHHdFRcZoqpqQ3nF5bpwQAeZTCZdPaKz3l2TrZE1FxwPfrrJ8Z7/d1l/De3cxvH6D+O76/Uf9mhsr/b6YsMhSdI/l/3qWD9zVBfH16OeWuq0/zMNdwTA/aqstqHDLvvnD/p5vy2pGhxIwSQAAPCOeHOYnps+yG3bM4fXDClWU+HimL8l5tTzt0hSt3hbwiU7v1T7aoY8HtWNhAsAAEBrRsLFz/m6XD0sOFA3nJNaZ3mC2Va5Um6xleLnFlU41oWcdGP2nkm99Ifze+i173c7Ei61Pf7FFnd2GYCLLNWGisotjmSLJAWR/AQAAH7qRIWLLeHSkPlbJKmDOUxhwQEqt1glGUowhyq1ZpgxAAAAtE48kgyPCA2yld7P+zFb2UdLndbFR9cdRiwoMEARIfWX6y/ZklvvcgC+M2fpTqfXDFUOAAD8lSPhUmYfUqxhCZeAAJNS20U5Xqd1Zf4WAACA1o6ECzwiLPjEt9b5zy9zWhdvrv/CJTykcQVXHc9wAQTAc349fFxnp8Y5XhfXjHkOAADgb+xDipVZqlVZZVVOzRwuZ0q4SFK39icqWkZ1a+eZDgIAAMBvkHCBR9grXCTb8EN2r/92+Cnfc6oKl/qM7x2vT+48p2mdA3BGn8w6/c9X57hIWY0TP9u2oTQAAAD8T1ToiQe/isstDZ7DRZK6tq9V4cL8LQAAAK0eCRd4xC95xfUuT+t66qe+kttE1Lu8az3jIL96/TDFR1PhAnjC7ed10+Dk2NO26RwXrirriYRLda2vAQAA/ElQYIAj6VJUXtXgOVykExUunWLDlRxX//UMAAAAWg8SLvCIckt1vcvDT1PF0i2+/gkmdx0pcXp957juCgrkWxfwlKCAU489Pm1oJ0nSu2v2OSVZale7AAAA+BtzmC3hUlxucSRcEhuQcEnvl6grhyfr0Yv7ebR/AAAA8A/ctfZzzXVOxoB6Ojakc+xp3xPRwDlczrQdAK4JPCnh8sZvz1JoUICenz5IbSJCJEnbc4u1YX+how0VLgAAwJ+Zw4MlSQcLylRcYZubLvEUc0/WFhYcqP+7YqAm9E3waP8AAADgH0i4wCNuPDe1zrLw4IbP0dIjPuqU62IjgpvUJwANc3KFy7je8dr0aLqmDU1Scpv6xzKnwgUAAPgzc5jtGmNH7vGa10GKDG3YA2EAAACAHQkXP/fgRX0lSbeN6erjnjjr1r5uwiQmvOGJktMNGRYa1PDEDYDGCwysW6EWXPMzGRVW/89xtdWjXQIAAPAoc7gtubI91zYXZYeY+h8yAQAAAE6HhIufS++XqJ8fmqTZF/bxdVfOqGNswy9ath4qOuW6k4c7AtB0V4/oLEm6qVZVWuBpxipsc4oKMypcAACAP3NUuOTYEi4Nmb8FAAAAOBkJlxYgxk+G2AoJaty32/je8fUur6rmxi7gLk9c2l8f/W6UZo3r7lhWfZrkyZie7etdTsIFAAD4M/scLruOlEiSOpBwAQAAQBOQcIHXBJ9mmDA7+4XNGzecpaevGFhvm6RTzCEBoPECAkwa2rmNIkNPDNV3utxJcGCABnSKqbO82krCBQAA+K/oMNuQYvbPNFS4AAAAoCmYBRDNyor7xqmwzKJ2UaGSpAl94vX11jxJ0n9mDFfX9pFqExniyy4CLVJwwImEqHGGapWNBwrrLJs+LEkq3+72fgEAAHiD+aR56qhwAQAAQFNQ4QKPeeakCpUP1u0743uCAwMcyRZJiquVXOncNkJd20e5r4MAHAJqzY3UmGKVmPBg/femEbr9vNQzNwYAAGimzOHOzyJ2iKGqHgAAAI1HwgUe85vhyTo7Nc7xOq+4otHbiIs8kXwJacCQZABc15j5WArLLBrdo32DhgwEAABorqhwAQAAgDtwhwweFV3rwuV0E3GfSklFlePr0GC+XQFvYDoWAADQ2pjDnRMuzOECAACApuAONjzKHHaiNP+Rqf0a/f5eidGOr0ODAk/TEoC7VFiqG9w25qSbEwAAAP6odoVLVGiQ04NjAAAAQEORcIFHRddKuIzs2rbR7++ZcCLhEhLEtyvgDV3bR552/YQ+CY6vX7hykKe7AwAA4HG153ChugUAAABNFXTmJkDT1X4yLCjQdJqW9TPVeksoCRfAo5bcPUYrdx7RtKFJp213wzld9PXWXElSRAh/RgAAgP+rXeHC/C0AAABoKu6UwaNqV7gEBzQ+YVI7RRMU0PiEDYCG65kQ7VRVdiq1fxaDA0mEAgAA/1f7uiXRTMIFAAAATcOdMnhU7QqXwCZUuPTuYK7ZTpBMJhIuQHNQ++c6uAk/1wAAQFqxYoWmTp2qjh07ymQy6ZNPPnFabzKZ6v33zDPPONrk5+fr2muvldlsVmxsrG666SYdP37caTsbNmzQ6NGjFRYWpuTkZD399NPeODy/ExQYoIgQ25yRVLgAAACgqUi4wKNqDwPWlCHBokKD9PPDk7Tmfye4s1sAXNApNtzxdZXV8GFPAADwXyUlJRo0aJDmzJlT7/pDhw45/Xv99ddlMpl0+eWXO9pce+212rx5szIyMvTFF19oxYoVuvXWWx3ri4qKNGnSJKWkpCgzM1PPPPOMHnnkEb366qsePz5/ZB9WLDEm/AwtAQAAgPoxpBg8qku7CEm2svy2kSFN2kZMePCZGwHwmtqTyjLUHwAATTN58mRNnjz5lOsTExOdXn/66acaN26cunbtKknaunWrvvrqK61du1bDhw+XJP3jH//QhRdeqGeffVYdO3bUvHnzVFlZqddff10hISHq16+fsrKy9PzzzzslZmDTJjJEOUXl6tSGhAsAAACahgoXeNSwlDh9eEea3r8tjSHBgBbCZDLp71cP0R/O76EBnWJ83R0AAFq83NxcLVy4UDfddJNj2apVqxQbG+tItkjShAkTFBAQoB9//NHRZsyYMQoJOfHgU3p6urZv365jx4557wD8xP0X9NLN56ZqVLe2vu4KAAAA/BQVLvC4YSlxvu4CADe7eFBHX3cBAIBW480331R0dLSmTZvmWJaTk6P4+HindkFBQYqLi1NOTo6jTWpqqlObhIQEx7o2bdrU2VdFRYUqKiocr4uKiiRJFotFFovFPQfkRfY+N6Tv53Rto3O6tpGs1bJYqz3dNb/UmHiifsTQvYinexFP1xFD9yKeriOGTeNKvEi4AAAAAEAz9vrrr+vaa69VWJjnJ3N/8skn9eijj9ZZvmTJEkVERHh8/56SkZHh6y60KMTTdcTQvYinexFP1xFD9yKeriOGjVNaWtrk93o84fLUU09p9uzZ+uMf/6i//e1vkqTy8nL96U9/0vz581VRUaH09HT985//dDxtJUnZ2dm644479O233yoqKkozZ87Uk08+qaCgE11etmyZ7rnnHm3evFnJycl64IEH9Nvf/tbThwQAAAAAXvHdd99p+/bteu+995yWJyYmKi8vz2lZVVWV8vPzHfO/JCYmKjc316mN/fXJc8TYzZ49W/fcc4/jdVFRkZKTkzVp0iSZzWaXj8fbLBaLMjIyNHHiRAUHMzekq4in64ihexFP9yKeriOG7kU8XUcMm8Ze5d0UHk24rF27Vv/61780cOBAp+V33323Fi5cqAULFigmJkZ33nmnpk2bph9++EGSVF1drSlTpigxMVErV67UoUOHNGPGDAUHB+uvf/2rJGn37t2aMmWKbr/9ds2bN0/ffPONbr75ZnXo0EHp6emePCwAAAAA8IrXXntNw4YN06BBg5yWp6WlqaCgQJmZmRo2bJgkaenSpbJarTr77LMdbf7yl7/IYrE4LrAzMjLUq1eveocTk6TQ0FCFhobWWR4cHOzXF+n+3v/mhni6jhi6F/F0L+LpOmLoXsTTdcSwcVyJVYAb++Hk+PHjuvbaa/Xvf//b6cN8YWGhXnvtNT3//PMaP368hg0bpjfeeEMrV67U6tWrJdnK1bds2aK3335bgwcP1uTJk/X4449rzpw5qqyslCS98sorSk1N1XPPPac+ffrozjvv1BVXXKEXXnjBU4cEAAAAAG5x/PhxZWVlKSsrS5LtgbKsrCxlZ2c72hQVFWnBggW6+eab67y/T58+uuCCC3TLLbdozZo1+uGHH3TnnXfqqquuUseOtrnWrrnmGoWEhOimm27S5s2b9d577+nFF190qmABAAAA4D4eq3CZNWuWpkyZogkTJuj//b//51iemZkpi8WiCRMmOJb17t1bnTt31qpVqzRy5EitWrVKAwYMcBpiLD09XXfccYc2b96sIUOGaNWqVU7bsLe56667TtmnhkwAyURC/o3z5784d/6N8+e/OHf+jfPnv3x17vheaT7WrVuncePGOV7bkyAzZ87U3LlzJUnz58+XYRi6+uqr693GvHnzdOedd+r8889XQECALr/8cv397393rI+JidGSJUs0a9YsDRs2TO3atdNDDz2kW2+91XMHBgAAALRiHkm4zJ8/Xz/99JPWrl1bZ11OTo5CQkIUGxvrtDwhIUE5OTmONrWTLfb19nWna1NUVKSysjKFh4fX2XdjJoBkIiH/xvnzX5w7/8b581+cO//G+fNf3j53rkz+CPcaO3asDMM4bZtbb731tMmRuLg4vfPOO6fdxsCBA/Xdd981qY8AAAAAGsftCZd9+/bpj3/8ozIyMhQWFubuzbukIRNAMpGQf+P8+S/OnX/j/Pkvzp1/4/z5L1+dO1cmfwQAAAAAnJ7bEy6ZmZnKy8vT0KFDHcuqq6u1YsUKvfTSS1q8eLEqKytVUFDgVOWSm5urxMRESVJiYqLWrFnjtN3c3FzHOvv/9mW125jN5nqrW6TGTQDJREL+jfPnvzh3/o3z5784d/6N8+e/vH3u+D4BAAAAAM8JcPcGzz//fG3cuNExAWRWVpaGDx+ua6+91vF1cHC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"
+ "image/png": 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b7Dliq25JjotQUCAj9+L0XnvttUa1NwyjzrKUlBQtWrTotO8bO3as1q9f36h9oeXZUKvCpTF61AwpZnfRwI5u6U96v0Q9+eU2x+uHp/bTFa+sVEGpRUdLKtUuqu48Qi3Vl5tyHF//+aMNSo6L0IjUOB/2CAAAuIIrQQAAAMAN9jjmb4nwcU8A4IRyS7V+yS2WZKskaYweNRUuktQjPkq9EqNP07rhurSLVO+abY3oEqdhKW2U3Mb2u/OXVjas2JcbD0mSEs1hslQbuu2/6xzzgQEAAP9DwgUAAABwA3vC5VTztwCAL2zLKVaV1VDbyBB1iGnccIfmsGDHe9w9v8itY7qqbWSI7p7YU5ItoSNJO/OK3bqf5mz/sVL9vL9QASZpwe1pGpgUo2OlFj306WZfdw0AADQRCRcAAADADfbWDClGhQuA5mRjreHEmjI3ynUjU9Q7MVpXndXZrf2aNjRJmQ9OVFq3tpKk7jXVNL/ktZ4Kl69qhhM7q0uckuMi9MSlAyRJWfsK6h1GEAAANH/M4QIAAAC4ARUuAJqjjfsLJEkDGjl/i92scd01a1x3N/aofj3ibUOMtaYhxRbVDCd24QBb9VCPhCgFmKTCMovyiiuUYG5cRRIAAPA9KlwAAAAAFxmGob1HqXAB0PxsPFAkyVbh0pzZhxRrLRUuOYXl+im7QJJ0Qf9ESVJYcKAjab8jt/UMrQYAQEtCwgUAAABw0eHiCpVZqhVgkpLakHAB0DyUW6r1S82N+4FJzTvh0q0m4XLkeIUKSit93BvP+2qTrbpleEobp0qWngm2Sp8drajSBwCAloSECwAAAOCiPTXVLZ3ahCskiI/YAJqHbTnFqrIaahsZog4xzXt4qqjQIHWs6ePOVlDl8vkGW8Jlcs1wYnY9a+ay2ZFDhQsAAP6Iq0EAAADARczfAqA5+jTrgCTbcGImk8nHvTmz7jXVHS19WLF1e/KVufeYggNNmnJSwqWHvcIlj4QLAAD+iIQLAAAA4KK9JFwANDMZW3L1xg97JEnXjUzxbWcayDGPi58Pp2Wptur+DzZozrc7613/Us3yaUOSlHhS5VGvxJqkU+5xGYbh2Y4CAAC3C/J1BwAAAAB/Zx9SLKUt87cA8L19+aX60/tZkqQbz0nVxL4Jvu1QAzkSLh6o7sgtKld+SaX6dDC7fdsnW7b9sN5bt0+S1K19pC7of6KKZdOBQi3bflgBJumOsd3qvLdL20gFBZh0vKJKBwvL1Sk23OP9BQAA7kOFCwAAAFDLx+v36+KXvm/UHAJUuABoLqqqrZr1zk8qKq/SkM6x+vPk3r7uUoP1qJm/xN1zuBiGoRmvrdHUf3yvnV4Yquvznw86vv7fjzfpcHGF47W96uWigR3VpV3dvxkhQQHq2t62fEcuw4oBAOBvSLgAAAAANfYcKdGfP9yoDfsL9czibQ16j2EY2nvEVuHSpR0VLgB867udR7Rhf6HMYUF66ZqhCgnyn8v+HgnRCjBJhwrL3Zp02XWkRNtzi1VlNZSxJc9t261PaWWVMrbkSpLio0OVX1Kp//14o6qthn7cdVRfbc6RJM0a1/2U23DM45JDwgUAAH/jP5+8AAAAAA+yWg3d/+EGVVRZJUmLN+c26EnooyWVKq6okskkJbUh4QLAt5ZvPyxJmjKwo98NR2UOC9b43vGSpPlrst223RU7Dtf7tScs3ZanMku1kuPCNfeGEQoONCljS676P7xYV766WoYhTeyb4JirpT697AkXP5/LBgCA1sjtCZeXX35ZAwcOlNlsltlsVlpamr788kvH+vLycs2aNUtt27ZVVFSULr/8cuXm5jptIzs7W1OmTFFERITi4+N17733qqqqyqnNsmXLNHToUIWGhqp79+6aO3euuw8FAAAArci7a7P14+58hQcH6qwubSRJLy/bdcb32YcT6xgTrrDgQI/2EQDOxJ5QGNurvY970jRXj+gsSfrwp/0qt1S7ZZu1kyzr9uarpKLqNK1dYx9ObOrAjurb0aw/TeolSSqzVCs8OFCje7TTw1P7nnYbPWuGVmNIMQAA/I/bEy5JSUl66qmnlJmZqXXr1mn8+PG65JJLtHnzZknS3Xffrc8//1wLFizQ8uXLdfDgQU2bNs3x/urqak2ZMkWVlZVauXKl3nzzTc2dO1cPPfSQo83u3bs1ZcoUjRs3TllZWbrrrrt08803a/Hixe4+HAAAALQCBwvK9OQi2xBi96b30gNTbDfDPs06oP3HSk/73j01w4mltKW6BYBvZR8t1a4jJQoKMGlUt7a+7k6TjO0Vrw4xYTpWatHimuG3isst+nj9fhWXWxq9vYqqaq3elS9JiggJlKXa0Kpfj7rcz6pqqwzDcFpWXG7RtzUVRlMHdZQk3Tamq969ZaS+umu0Nj4ySf+96ewzVkPahxTbmXdcVqtx2rYAAKB5CXL3BqdOner0+oknntDLL7+s1atXKykpSa+99preeecdjR8/XpL0xhtvqE+fPlq9erVGjhypJUuWaMuWLfr666+VkJCgwYMH6/HHH9f999+vRx55RCEhIXrllVeUmpqq5557TpLUp08fff/993rhhReUnp7u7kMCAABAC2YYhv7y8UYdr6jS0M6xmjmqiwIDTDqne1v9sPOo/r1ilx69pP8p32+vcElpW3fyYwDwpuU7bPOTDEtpo+iwYB/3pmkCA0y68qxk/e3rX/TOj9ka3aO9rn/tR20+WKQbzumih6f2a9T21u05pjJLteKjQzWpX4LeXp2t5TsOa0LfhCb38YedR3TD3LUKDQxQcly4wi0B0sYcHbdYVVllVff4KPWuGTLMZDIprZHJr5S4CIUEBajMUq39x8rUmYQ+AAB+w+0Jl9qqq6u1YMEClZSUKC0tTZmZmbJYLJowYYKjTe/evdW5c2etWrVKI0eO1KpVqzRgwAAlJJz48JOenq477rhDmzdv1pAhQ7Rq1Sqnbdjb3HXXXaftT0VFhSoqKhyvi4qKJEkWi0UWS+OflGnO7MfT0o7LW4if9xBr1xA/3+McuI4Yeg+xrt+nWQf17fbDCg406YlL+spaXSVrtXTruV30w86jmr92n+4Y00XmUFtx+Mnx23XYNsZ+cpvQZh/b5t4/AK5Z7hhOLN7HPXHNlWcl6+/f/KIfd+fr8pdXavcRW2L7y405euiivjKZTA3eln04sdE92uu8nvGOhIsrvt6aq8oqW3Jly6FiSQHKfH+DY/3UgR0b1ceTBQUGqFv7KG09VKTtucUkXAAA8CMeSbhs3LhRaWlpKi8vV1RUlD7++GP17dtXWVlZCgkJUWxsrFP7hIQE5eTYSoVzcnKcki329fZ1p2tTVFSksrIyhYfXPzHgk08+qUcffbTO8iVLligiomV+gMnIyPB1F/wa8fMeYu0a4ud7nAPXEUPvIdYnFFukv2YFSjJpUscq7Vi3Qjtq1hmG1DkyUNklVj349re6qLNVUt34bdhle/+R3du0qGirV/vfWKWlpx8eDYD/qqiq1sqaobLO6+mf87fYdYgJ1/je8fp6a552HylRfHSojldUKaeoXBsPFGpgUmyDt2VProzp2U5p3doqONCk7PxS7TlSoi7tmlaZmH3U9rv09+O7q1+HKC349idtL43Q/oJyBQWYdPHgjk3abm29EmwJl80HCzXRhWocAADgXR5JuPTq1UtZWVkqLCzUBx98oJkzZ2r58uWe2FWjzJ49W/fcc4/jdVFRkZKTkzVp0iSZzWYf9sz9LBaLMjIyNHHiRAUH+2cpuS8RP+8h1q4hfr7HOXAdMfQeYl3XH9/7WaVVueqTGK1nbjpbwYHOUxwGp+Zq1rs/a/XRED1xXZpWrfi2TvweyloqqUrTJp6rXjVDyDRX9gpvAC3Puj3HVFppGzqrT4fm/buoIWakddHXW/PUISZM79wyUs8u3q6FGw9pyebcBidc8orKtS2nWCaTdG73dooKDdLwlDit2nVUy3ccbnLCZW++LeEyIjVOI7vEqmKXVZMnj9aW3FIFBZiU2sTt1nZ217b6JOugXvtut34zPFmdYut/sBQAADQvHkm4hISEqHv37pKkYcOGae3atXrxxRd15ZVXqrKyUgUFBU5VLrm5uUpMTJQkJSYmas2aNU7by83Ndayz/29fVruN2Ww+ZXWLJIWGhio0NLTO8uDg4BZ706ElH5s3ED/vIdauIX6+xzlwHTH0HmJts3LnES3alKvAAJOe+c0gRYTV/Zw4eUAndf/mV+3MO673f8pRspzjV1BaqcKyKklSt4QYBQcHevMQGo3zDrQsBwvK9P3OI+oRH6WMLbZr5PN6tndpOKvmYkzP9vrwjjR1bRelNpEhmtQvQQs3HtLizTn6n/ReDdrGil+OSJL6d4xR26hQx3ZX7TqqFTsOa+aoLo3ul9VqKLsm4ZISdyKxYjKZNDg5ttHbO5XfDEvSgnX79FN2ge5d8LPevulsBQT4/3kFAKClCzhzE9dZrVZVVFRo2LBhCg4O1jfffONYt337dmVnZystLU2SlJaWpo0bNyovL8/RJiMjQ2azWX379nW0qb0Nexv7NgAAAIAzWVJzc/KKoUnq3ymm3jYBASbdfl43SdLcVXtVWW1bXm6p1oJ1+3TNv3+UJCWYQxUe0ryTLQD8y/GKKt3+30xd9I/vNGveT3r6q23amVfsWG8Yhm5/O1P3fbBBl/1zpeau3CNJOq+Xfw8nVtuwlDi1iQyRZJuXJijApF/yjjvmzjqdqmqr3l2TLck2nJidfbi1H349osKyxs9rlVNUrsoqq4ICTOoYG9bo9zdUUGCAnps+WOHBgVr561G9tWqPx/YFAADcx+0Jl9mzZ2vFihXas2ePNm7cqNmzZ2vZsmW69tprFRMTo5tuukn33HOPvv32W2VmZuqGG25QWlqaRo4cKUmaNGmS+vbtq+uvv14///yzFi9erAceeECzZs1yVKfcfvvt2rVrl+677z5t27ZN//znP/X+++/r7rvvdvfhAAAAoIVavatmroMz3Jy8ZHBHdYoN15HjlVp60KQXv9mpc55aqns/2KAth4oUFhyg28Z080aXAbQSZZXVuvnNtfpqc442HSjSwo2H9M9lv+rGuetkqbbNJ/VTdoE27C9UcKBJcTVJidiIYI3u3nISLrXFhAcrrVtbSXJU85zO37/5RZl7jykqNEhXndXZsbxPh2j1TIhSucWqDzP3N7ofe2vmb0lqE66gQM8+w5raLlL/e2FvSdKTX25zzB0DAACaL7d/OsjLy9OMGTPUq1cvnX/++Vq7dq0WL16siRMnSpJeeOEFXXTRRbr88ss1ZswYJSYm6qOPPnK8PzAwUF988YUCAwOVlpam6667TjNmzNBjjz3maJOamqqFCxcqIyNDgwYN0nPPPaf//Oc/Sk9Pd/fhAAAAoAU6VlKpbTm2J8VHpMadtm1wYIBuHdNVkvTl/kC9tGyXjpZUqmNMmO6/oLdWzz5fN56b6vE+A2gdKqqqddvbmVq9K1/RoUF6+oqBemBKH8VFhig7v1SfrD8gSY6Kh0sHd1LmAxP0/f3jtPRPYxUT0XKHDpxUM3n8kjMkXFbuPKJ/fLtTkvTXaQOUHBfhWGcymXT9yBRJ0tur98owjEb1ITu/RJLUua3r87Q0xHUjU3R2apwqqqyavzbbK/sEAABN5/Y5XF577bXTrg8LC9OcOXM0Z86cU7ZJSUnRokWLTrudsWPHav369U3qIwAAAFq3H3fnS5J6JkSpXVTduVtONn14sl5Z/qsOFZZrWOdY3XhuV6X3S/D4080AWp8HPt6kFTsOKzw4UG/ccJaGd7Elhauthp78cpte+nanzu3RTos2HpIkzRzVRSaTSUltIk632RZhYt9EPfjpZv2UfUx5xeWKj647pNfR4xX643tZMgzpqrOSdfGgjnXaXDY0Sf/31XbtOlKiH3Ye1bk92undNdl6bsl2ndu9na5P66KhnWPrnQvHXuGSEuedeJtMJs1I66Ifd+fr06yD+p9JvZjLBQCAZowrRAAAALQ69uHERnZt26D24SGB+uj2s/W/g6s0/5YRmjKwA8kWAG539HiFPqqpYPnX9cMcyRZJuj4tRXGRIdp7tFQ3v7lOlmpDw1LanHIOqpYoMSZMA5NiZBjS978cqbfNqyt26XBxhXrER+nhqf3qbRMVGqRpQztJslUKLdmco798vFFHjlfqk6yDuvzllZr+r1UqKq87x8ve/JqES1vvJbjO7xOv6NAgHSgo05o9+V7bLwAAaDyuEgEAANDqNDbhIkntokKVEO6pHgGAtGhTjqqthvp3MmtMT+e5WCJCgnTLaNvwhpsPFkmSZqSleL2Pvja0cxtJ0qYDRXXWlVuq9f66fZKk+y/orfCQwFNuxz6s2Ndbc/WH+etlNaSLB3XUFcOSFBIUoLV7junjnw7UeZ99HpXOXqpwkaSw4EBdOKCDJDmGlAMAAM0TCRcAAAC0Ko2ZvwUAvOmzLNvN9EsGdap3/Yy0FLWpmaOlfXSoJvfv4LW+NRcDaip6Nh0orLNu0cZDOlZqUafYcI3rHX/a7fRIiFZa17ayGlK5xarzerbXc9MH6dnfDNJ96b0kSUu25NR5396jtjlcUrw0h4vdpUNs3xMLNx5SuaXaq/sGAAANR8IFAAAArcqPu23VLQ2dvwXwhJdfflkDBw6U2WyW2WxWWlqavvzyS0lSfn6+fv/736tXr14KDw9X586d9Yc//EGFhc43mLOzszVlyhRFREQoPj5e9957r6qqqpzaLFu2TEOHDlVoaKi6d++uuXPneusQ0Uj7j5Vq7Z5jMpmkiwbVn0iJDA3SXRN6SpJuG9NVIUGt75LePoTa5oOFslqdJ7x/e/VeSdLVI5IV2IB5Tm4ZkypJ6tfRrDnXDlVwzVCRE/smSJJW78pXQWmlo31BaaWKym0/Y96scJGks1Pj1CEmTMXlVVq6Lc+r+wYAAA0X5OsOAAAAAN60epdt/PvGDCcGuFtSUpKeeuop9ejRQ4Zh6M0339Qll1yi9evXyzAMHTx4UM8++6z69u2rvXv36vbbb9fBgwf1wQcfSJKqq6s1ZcoUJSYmauXKlTp06JBmzJih4OBg/fWvf5Uk7d69W1OmTNHtt9+uefPm6ZtvvtHNN9+sDh06KD093ZeHj3p8/vMhSfYb66cev3DmqC6a3D9R7aNbZ8K4W/tIhQUHqKSyWruPlqhb+yhJtgTMT9kFCg40afpZyQ3a1vjeCVr0h9Hq2j5SYcEnhh9LaRup3onR2pZTrKXb8jRtaJIkaW/NcGLx0aGnHa7MEwICTLpkcCe9svxXfbz+gGOIMQAA0Ly0vsdhAAAA0Ko1Zf4WwN2mTp2qCy+8UD169FDPnj31xBNPKCoqSqtXr1b//v314YcfaurUqerWrZvGjx+vJ554Qp9//rmjgmXJkiXasmWL3n77bQ0ePFiTJ0/W448/rjlz5qiy0vZE/iuvvKLU1FQ999xz6tOnj+68805dccUVeuGFF3x56DiFT+3DiQ2ufzix2uLNYTKZzlzB0RIFBQaoTwezJOdhxd5enS1JSu+XqPjosAZvr29Hs1OyxW5STZXLks25jmV7820Jl5S23q1usZs21Pa9sWx7ng4UlPmkDwAA4PRIuAAAAKDVyGf+FjRD1dXVmj9/vkpKSpSWllZvm8LCQpnNZgUF2QYpWLVqlQYMGKCEhARHm/T0dBUVFWnz5s2ONhMmTHDaTnp6ulatWuWhI2nZDMPQP775RVe9ukob9he4ddvbc4q1LadYwYEmTe6f6NZtt0Qnz+NSVG5xTCZ//cgUt+xjUj/beVi+47BjzpS9R2zzt3SO8+78LXY9E6I1smucLNWG/rpoq0/6AAAATo8hxQAAANBqrGH+FjQjGzduVFpamsrLyxUVFaWPP/5Yffv2rdPuyJEjevzxx3Xrrbc6luXk5DglWyQ5Xufk5Jy2TVFRkcrKyhQeXnfYqoqKClVUVDheFxUVSZIsFossFksTj7Tp7Pv0xb5rs1oN/b9F2/TfH/dJki5/eaX+cmFvXXNWksuVJoZh6JVlv0iSxvRop8hgk8ePt7nEtal6J9iGEduwv0AWi0Wf/LRPZZZqdW8fqSFJ0W45rp7tw9UxJkwHC8u1bGuOzu8Tr91HjkuSktuE1Ymht2L5vxf00qUvr9LCDYd01bBcjezq/PDA5oNFenftft07qYdiwoO90idP8Pfv0eaKuLoPsfQM4upexLPpXIkZCRcAAAC0GszfguakV69eysrKUmFhoT744APNnDlTy5cvd0q6FBUVacqUKerbt68eeeQRj/fpySef1KOPPlpn+ZIlSxQR4ZthlCQpIyPDK/sxDKnYIh0qMymvTAoJkGJCpKyjJq3KC5BJhjpHSXuPS498vlWLVm/W1d2sasD87I7tbz5mUnCg1NNsm/D9070B+vaQbds9TTlatGiRB4/Qmbfi6m4FJZIUpJ+z8/XFwkV6bVOgJJP6RRTpyy+/dNt+uocH6GBhgN7I+EkVu636eadtP/nZ27Vo0Tantt6M5aj4AH2fG6D75q/VvYOqFVjz/WcY0jMbAnWg1KSyvL06v5PhtT55ir9+jzZ3xNV9iKVnEFf3Ip6NV1pa2uT3knABAABAq8H8LWhOQkJC1L17d0nSsGHDtHbtWr344ov617/+JUkqLi7WBRdcoOjoaH388ccKDj7xtHpiYqLWrFnjtL3c3FzHOvv/9mW125jN5nqrWyRp9uzZuueeexyvi4qKlJycrEmTJslsNrt4xI1nsViUkZGhiRMnOh2/J1RUWXXVv9do08GietcHmKQnL+uvywZ31Gs/7NWzGb9ozeEAjR3aS7ecm3rG7RuGoee/3ql/b98tSeqVEKW+HaL17aFDkqTHL+mnK4cnue+ATsObcfUES7VVf9uyVOVVVkV0P0t7V69XYIBJf75qvFurF+N25WvFG+u07XiIeg4foeObMiVV6OLxozQoyTasmS9iOarUool/+16Hyiw61ra/ZozsLEnasL9QB1b/KEkyYjvpwgsHeqU/nuDv36PNFXF1H2LpGcTVvYhn09mrvJuChAsAAABaBeZvQXNntVodw3kVFRUpPT1doaGh+uyzzxQW5jwJeFpamp544gnl5eUpPj5eku3pRbPZ7KiQSUtLq1MtkZGRccp5YiQpNDRUoaF1b1gHBwf79ELdG/tfs/eINh0skskkdWkbqW7to1RZbVVuYbmqrFbdM7GXpgzsIEm6Y1wPxUaGavZHG/V8xk6N7NZeQzu3OeW2DcPQEwu36j/f25It4cGB2p57XNtzbUNUPTy1r65LO3PSxt18fV6bKjhY6pMYrZ/3F+qpr3ZIksb1aq8ObaLcup+07u3VtV2kdh0p0eX/+lGllba5XLrFm+vEzZuxbB8TrP9J76UHPtmkF7/ZqUuHJKltVKjezzzoaLMjt8Qvz+3J/PV7tLkjru5DLD2DuLoX8Ww8V+JFwgUAAACtAvO3oDmZPXu2Jk+erM6dO6u4uFjvvPOOli1bpsWLF6uoqEiTJk1SaWmp3n77bRUVFTmesmvfvr0CAwM1adIk9e3bV9dff72efvpp5eTk6IEHHtCsWbMcCZPbb79dL730ku677z7deOONWrp0qd5//30tXLjQl4fuVQWllXp1xS4N6BSjyQM6nLbtyl+PSJIuG9xJz185+IzbvuqsZP2w84i+2HBIv39nvRb9YbRiIupenFdWWfXwZ5v17ppsSdJjl/TTJYM76d012fos66CmD0/Sb8/xfrLF3/XvFKOf9xdqd81E9lcMS3b7PoICA/TebWn64/z1Wvmr7W9IdFiQYus5z9529YjOeufHbG05VKRnl2zX/17YR5/9fCLh8uvh46qoqlZoUKAPewkAQOtDwgUAAACtwqpfGU4MzUdeXp5mzJihQ4cOKSYmRgMHDtTixYs1ceJELVu2TD/+aBsWyD7kmN3u3bvVpUsXBQYG6osvvtAdd9yhtLQ0RUZGaubMmXrsscccbVNTU7Vw4ULdfffdevHFF5WUlKT//Oc/Sk9P9+qx+sqKHYd17wc/K7eoQgEmaf6taaetbrPfUE/r1rDfESaTSU9OG6AN+wuVnV+qwY8vkUm2m/Tp/RJ157juigoL0qx5PylrX4FMJunJywboqhG24Z9uP6+bbj+vm8vH2Vr17xTj+DouMkTje8d7ZD/to0P135vO1t++3qF/LN2pYSltZDI1cNIeDwoMMOnRS/rpN6+s0vy1+yRJZZZqdY+PUl5RuYrKq/RrXon6dvT+UIAAALRmJFwAAADQKqzelS+JhAuah9dee+2U68aOHSvDOPNk1ykpKWecYH3s2LFav359o/vn755fsl1/X7pTkhQaFKCKKqv+ON9WhdImMqRO++MVVdqwv1BSwxMukhQdFqyXrhmiq15drdLKahmyVbR8/vNBff7zQUWEBKq0slrmsCA9P32wJvRNcMvxQRpQK+Fy6eBOCgkK8Ni+AgNM+tOkXrpqRGe1ref7x1fO6hKnSwd31CdZB/XuGlvS5eoRnbV4U47W7MnXtpwiEi4AAHiZ5z6RAAAAAM3E0eMV2p5rm7/lbOZvAVq0ffmljmTLzLQU/fDn8eraLlKHCsv1Pwt+rjeZtXZ3vqqthjrHRSipTUSj9jcwKVaZD0zUmr+crzV/OV+fzjpHUwZ0kMkklVZWa0CnGC38w2iSLW7WIyFKESG24bJ+MzzJK/vsFBuusODmNUTX7Av7OOIQEhSgy4d2Uu8O0ZKk7TXzlgEAAO+hwgUAAAAt3prdtuqWXgnRasv8LYDfyCuu0E9HTEq3GmrorBkf/rRfkjS6Rzs9ekl/SdI/rhmiy/65Ut9sy9Mdb/+k69NSlNa1rQICbEND2edvGdWI6pbawkMCFV5z0zs+Okxzrh2qnXnF2nigUBcO6MA8Gh4QGhSo12aepeJyi/p0aL1VHAnmMN09oaeeWLRV04Z0UmxEiHon2uKxlYQLAABeR8IFAAAALd7qXfb5W6huAfyF1Wrogr//oOLyQF16qFhDupw5GWK1Go6EyxXDTlQ99OsYo0em9tP/frxRX23O0Vebc9Q5LkKvXDdMfTuaGz1/S0N0j49W9/hot20PdbnzfPmzm0en6qzUOPVOtH2/2Stcth0q8mW3AABolRhSDAAAAC2apdrquJnK/C2A/wgIMGl4Sqwkac2e/Aa9Z82efO3LL1NUaJAm9U10WnfN2Z31+Z3n6rqRnRUdFqTs/FLd/namso+WakvNjWlu4MMfmUwmDU6OdQx31ivBlnDJK67Q0eMVvuwaAACtDhUuAAAAaDEMw9D23GJt2F+ojfsLteFAobYeKlJllVWSNIL5WwC/MqJLnL7dfkQ/7j6m28aeuf0HmbbqlosGdnAM8VXbgKQYDUgaoP+Z1EsX/eN7ZeeX6up/r5ZhSD3ioxQfHebmIwC8LzI0SCltI7T3aKm25xRrVHeG0gQAwFtIuAAAAKBFMAxDv393vb7YcKjOuuiwIF11VjLztwB+5uzUNpKkdXuPqdpqKLBmzpX6lFRUadFG289/7eHE6hMbEaJ/XjtUV7y8SgcKyiQ1ff4WoDnqlRCtvUdLtTWnWKO6t/N1dwAAaDVIuAAAAKBF+HZ7nr7YcEhBASYNS2mjgUkxGpAUq0FJMeocFyGT6dQ3agE0T30SoxUaaKiovEpbDxWpf6cYp/WllVX6alOOyi1W7cw7rtLKanVpG6FhKW3OuO2BSbF64KI+eujTzZKktG7clEbL0buDWUu25Gp7DvO4AADgTSRcAAAA4Peqqq3666JtkqSbzk3V7Av7+LhHANwhKDBA3aINbSkw6cfd+U4Jl5KKKs14fY0y9x5zes/lQ5ManGC9fmSK9uWXanvucZ3Xs71b+w74Up9E2zwu23KKfdwTAABaFxIuAAAA8Hvz1+7TzrzjahMRrN+N6+7r7gBwo+5mQ1sKpNW7juqmc1MlSeWWat385jpl7j0mc1iQRqS2VUVVtaLDgnR9WkqDt20ymfSXKX091HPAd3p3MEuStucUn3E4PgAA4D4kXAAAAODXisst+tvXOyRJfzy/h2LCg33cIwDu1N1sSJLW7M6X1Wqoymrotv9matWuo4oKDdJbN52twcmxvu0k0Mx0jotQeHCgyizV2n3kuLrHR/u6SwAAtAoBvu4AAAAA4IpXlv+qI8cr1bVdpK4d2fAn2wH4h6QoKTIkUIVlFm3LKdajn2/W8h2HFRYcoNd/exbJFqAegQEm9etoq3L5KbvAt50BAKAVIeECAAAAv3WwoEz/+W63JOnPk3srOJCPt0BLE2iShqXESpIe+nST5v2YLZNJmnPNUI1IjfNt54BmbFiXNpKkzD3HztASAAC4C1ekAAAA8FvPLt6uiiqrRqTGaWLfBF93B4CHjOhiS6ys22u7cXzPhJ46vw8/88DpDE+x/9zk+7gnAAC0HiRcAAAA4Jc2HSjUR+sPSJIemNJHJhMTAgMt1dmpbRxfp/dL0Kxx3X3YG8A/DEux/dz8erhEx0oqfdwbAABaBxIuAAAA8DuGYej/LdwiSbp0cEcNTIr1bYcAeFT/jmad1aWNhqe00XPTBysggAQrcCZxkSHq1j5SkpS5l2HFAADwhiBfdwAAAABoqGqrobV78vVp1kGt3pWvkKAA3XtBb193C4CHBQUGaMHto3zdDcDvDE+J06+HS7Ru7zFNYOhNAAA8joQLAAAAmrWKqmr9sPOIFm/KVcbWXOXXGhbl9vO6qVNsuA97BwBA8zWsSxu9t26fMpnHBQAAryDhAgAAgGbrUGGZpv7jex05fiLJEhsRrAl9EjS5f6LG9473Ye8AAGjehtfM4/Lz/kJVVFUrNCjQxz0CAKBlI+ECAACAZmv59sM6crxSMeHBumRwR13QL1EjUuMUFMhUhAAAnElqu0i1jQzR0ZJKbTpQpGE1CRgAAOAZXKkCAACg2craVyBJuubsznrskv4a1b0dyRYAABrIZDJpaE2ShWHFAADwPK5WAQAA0GzZEy6DkmJ92g8AAPyVfVixdXuO+bgnAAC0fCRcAAAA0CyVVlZpR26xJGlwcqxvOwMAgJ8a3sWWcPkpu8C3HQEAoBUg4QIAAIBmadOBIlkNKdEcpsSYMF93BwAAv9S1XZQk6cjxClmqrT7uDQAALRsJFwAAADRLWftsQ58MSo7xcU8AAPBf5vBgx9dFZRYf9gQAgJaPhAsAAACapZ/3FUqSBjGcGAAATRYYYFJ0WJAkqZCECwAAHkXCBQAAAM1S1r4CSdLgpFif9gMAAH8XU1PlQsIFAADPIuECAACAZudwcYUOFJTJZJIGJDGkGAAArrAnXApIuAAA4FEkXAAAANDs/FxT3dK9fZSiw4JP3xgAAJxWbITtbylzuAAA4FkkXAAAANDs/Ly/QBLzt6DlevnllzVw4ECZzWaZzWalpaXpyy+/dKwvLy/XrFmz1LZtW0VFRenyyy9Xbm6u0zays7M1ZcoURUREKD4+Xvfee6+qqqqc2ixbtkxDhw5VaGiounfvrrlz53rj8AA0M44Kl1ISLgAAeBIJFwAAADQ79vlbSLigpUpKStJTTz2lzMxMrVu3TuPHj9cll1yizZs3S5Luvvtuff7551qwYIGWL1+ugwcPatq0aY73V1dXa8qUKaqsrNTKlSv15ptvau7cuXrooYccbXbv3q0pU6Zo3LhxysrK0l133aWbb75Zixcv9vrxAvCtmPAQSczhAgCApwX5ugMAAABAbYZhOIYUG0LCBS3U1KlTnV4/8cQTevnll7V69WolJSXptdde0zvvvKPx48dLkt544w316dNHq1ev1siRI7VkyRJt2bJFX3/9tRISEjR48GA9/vjjuv/++/XII48oJCREr7zyilJTU/Xcc89Jkvr06aPvv/9eL7zwgtLT071+zAB8hwoXAAC8g4QLAAAAmpXdR0pUVF6lkKAA9UqM9nV3AI+rrq7WggULVFJSorS0NGVmZspisWjChAmONr1791bnzp21atUqjRw5UqtWrdKAAQOUkJDgaJOenq477rhDmzdv1pAhQ7Rq1Sqnbdjb3HXXXafsS0VFhSoqKhyvi4qKJEkWi0UWi/dv1Nr36Yt9t2TE1X38JZZRIbYBTo6VVjT7vkr+E1d/Q1zdh1h6BnF1L+LZdK7EjIQLAAAAmhX7/C39O5oVHMgIuGi5Nm7cqLS0NJWXlysqKkoff/yx+vbtq6ysLIWEhCg2NtapfUJCgnJyciRJOTk5TskW+3r7utO1KSoqUllZmcLDw+v06cknn9Sjjz5aZ/mSJUsUERHR5GN1VUZGhs/23ZIRV/dp7rHcl2uSFKhf9uzXokXZvu5OgzX3uPor4uo+xNIziKt7Ec/GKy0tbfJ7SbgAAACgWfl5X6Ek5m9By9erVy9lZWWpsLBQH3zwgWbOnKnly5f7tE+zZ8/WPffc43hdVFSk5ORkTZo0SWaz2ev9sVgsysjI0MSJExUcHOz1/bdUxNV9/CWWpk05em/XBoWZ43ThhSN83Z0z8pe4+hvi6j7E0jOIq3sRz6azV3k3BQkXAAAANCtZNfO3DCbhghYuJCRE3bt3lyQNGzZMa9eu1Ysvvqgrr7xSlZWVKigocKpyyc3NVWJioiQpMTFRa9ascdpebm6uY539f/uy2m3MZnO91S2SFBoaqtDQ0DrLg4ODfXqh7uv9t1TE1X2aeyzbRdt+5ovKq5p1P0/W3OPqr4ir+xBLzyCu7kU8G8+VeDFGAwAAAJqNiqpqbTloe5qIhAtaG6vVqoqKCg0bNkzBwcH65ptvHOu2b9+u7OxspaWlSZLS0tK0ceNG5eXlOdpkZGTIbDarb9++jja1t2FvY98GgNbDHG67cVRQyjj+AAB4EhUuAAAAaDa2HSpWZbVVsRHB6hznu/kiAE+bPXu2Jk+erM6dO6u4uFjvvPOOli1bpsWLFysmJkY33XST7rnnHsXFxclsNuv3v/+90tLSNHLkSEnSpEmT1LdvX11//fV6+umnlZOTowceeECzZs1yVKjcfvvteumll3Tffffpxhtv1NKlS/X+++9r4cKFvjx0AD4QU5NwKSwj4QIAgCeRcAEAAECz8fP+AknSoKRYmUwm33YG8KC8vDzNmDFDhw4dUkxMjAYOHKjFixdr4sSJkqQXXnhBAQEBuvzyy1VRUaH09HT985//dLw/MDBQX3zxhe644w6lpaUpMjJSM2fO1GOPPeZok5qaqoULF+ruu+/Wiy++qKSkJP3nP/9Renq6148XgG/FRtgSLhVVVpVbqhUWHOjjHgEA0DKRcAEAAECzYZ+/ZRDDiaGFe+211067PiwsTHPmzNGcOXNO2SYlJUWLFi067XbGjh2r9evXN6mPAFqOqNAgBQaYVG01VFhmIeECAICHMIcLAAAAmo2faxIuQ0i4AADgNiaTiWHFAADwAhIuAAAAaBYKyyz69XCJJGlgUoyPewMAQMtiT7gUlJJwAQDAU9yecHnyySd11llnKTo6WvHx8br00ku1fft2pzY5OTm6/vrrlZiYqMjISA0dOlQffvihU5v8/Hxde+21MpvNio2N1U033aTjx487tdmwYYNGjx6tsLAwJScn6+mnn3b34QAAAMDNFm/O0fWv/agF6/bJUm11LN+4v1CSlBwXrrZRob7qHgAALZKZChcAADzO7QmX5cuXa9asWVq9erUyMjJksVg0adIklZSUONrMmDFD27dv12effaaNGzdq2rRpmj59utPYwtdee602b96sjIwMffHFF1qxYoVuvfVWx/qioiJNmjRJKSkpyszM1DPPPKNHHnlEr776qrsPCQAAAG5SWWXVA59s0ne/HNG9H2zQ+c8t13trs1VZZdXP+wskSYOSYn3aRwAAWqJYR4VLpY97AgBAyxXk7g1+9dVXTq/nzp2r+Ph4ZWZmasyYMZKklStX6uWXX9aIESMkSQ888IBeeOEFZWZmasiQIdq6dau++uorrV27VsOHD5ck/eMf/9CFF16oZ599Vh07dtS8efNUWVmp119/XSEhIerXr5+ysrL0/PPPOyVmAAAA0Hx8uemQDhdXKDYiWEEBJmXnl+r+Dzfq79/sVGiw7VmgwczfAgCA2zGHCwAAnufxOVwKC21DQ8TFxTmWjRo1Su+9957y8/NltVo1f/58lZeXa+zYsZKkVatWKTY21pFskaQJEyYoICBAP/74o6PNmDFjFBIS4miTnp6u7du369ixY54+LAAAADTBmyv3SJJuGJWq7+4brwem9FH76FAdKCjTrpr5W0i4AADgfrERtoRLEQkXAAA8xu0VLrVZrVbdddddOuecc9S/f3/H8vfff19XXnml2rZtq6CgIEVEROjjjz9W9+7dJdnmeImPj3fuaFCQ4uLilJOT42iTmprq1CYhIcGxrk2bNnX6U1FRoYqKCsfroqIiSZLFYpHF0rI+cNiPp6Udl7cQP+8h1q4hfr7HOXAdMfQeX8d604Ei/ZRdoOBAk34ztIOCTFbNHJmsK4d11PuZB/Ta93tkDg9W7/iIZvn94Ov4uYM/9x0A4Bp7hUsBCRcAADzGowmXWbNmadOmTfr++++dlj/44IMqKCjQ119/rXbt2umTTz7R9OnT9d1332nAgAEe68+TTz6pRx99tM7yJUuWKCIiwmP79aWMjAxfd8GvET/vIdauIX6+xzlwHTH0Hl/Fet7OAEkBGtimWmu/+8ZpXTtJ9/e1ff1NxmKv960x/Pl7tbS01NddAAD4CEOKAQDgeR5LuNx5552Oye6TkpIcy3/99Ve99NJL2rRpk/r16ydJGjRokL777jvNmTNHr7zyihITE5WXl+e0vaqqKuXn5ysxMVGSlJiYqNzcXKc29tf2NiebPXu27rnnHsfroqIiJScna9KkSTKbza4fdDNisViUkZGhiRMnKjg42Nfd8TvEz3uItWuIn+9xDlxHDL3Hl7E+WlKpe9eukGTVn6eN9Mthw1rC96q9whsA0Po4KlxKSbgAAOApbk+4GIah3//+9/r444+1bNmyOsN+2Z+qCwhwnj4mMDBQVqtVkpSWlqaCggJlZmZq2LBhkqSlS5fKarXq7LPPdrT5y1/+IovF4rjgzcjIUK9eveodTkySQkNDFRoaWmd5cHCw3140n0lLPjZvIH7eQ6xdQ/x8j3PgOmLoPb6I9Yfr96qyyqpBSTE6q2t7r+7b3fz5e9Vf+w0AcB0VLgAAeF7AmZs0zqxZs/T222/rnXfeUXR0tHJycpSTk6OysjJJUu/evdW9e3fddtttWrNmjX799Vc999xzysjI0KWXXipJ6tOnjy644ALdcsstWrNmjX744Qfdeeeduuqqq9SxY0dJ0jXXXKOQkBDddNNN2rx5s9577z29+OKLThUsAAAA8D1LtVVvr94rSZo5qotvOwMAQCsVGxEiiYQLAACe5PaEy8svv6zCwkKNHTtWHTp0cPx77733JNmeqlu0aJHat2+vqVOnauDAgXrrrbf05ptv6sILL3RsZ968eerdu7fOP/98XXjhhTr33HP16quvOtbHxMRoyZIl2r17t4YNG6Y//elPeuihh3Trrbe6+5AAAADggiWbc3WosFztokI0ZWAHX3cHAIBWiQoXAAA8zyNDip1Jjx499OGHH562TVxcnN55553Tthk4cKC+++67RvUPAAAA3vXmyj2SpKtHdFZoUKBvOwMAQCtVO+FiGIZMJpOPewQAQMvj9goXAAAAwG7LwSKt2ZOvoACTrj07xdfdAQCg1YqNsCVcqq2GjldU+bg3AAC0TCRcAAAA4DH26pb0/olKjAnzbWcAAGjFwoIDFRJkuw3EsGIAAHgGCRcAAAB4xLGSSn2SdUCSdMOoLr7tDAAAUGzNsGIFpSRcAADwBBIuAAAAcLtyS7XueT9LFVVW9eto1rCUNr7uEgAArZ59HpciKlwAAPCIIF93AAAAAC1LuaVat/43Uyt2HFZYcIAentqPiXkBAGgG7AkXhhQDAMAzSLgAAADAbcoqq3XLW+v0/c4jiggJ1Ou/PUsjUuN83S0AACApNqJmSDESLgAAeAQJFwAAALhFaWWVbpy7Vqt35SsyJFBv3DCCZAsAAM2ImQoXAAA8ioQLAAAAXHa8oko3vrFWa/bkKyo0SG/eeJaGpZBsAQCgOYkND5EkFZSScAEAwBNIuAAAAMAluUXluuPtTP2UXaDo0CC9ddMIDencxtfdAgAAJ2EOFwAAPIuECwAAAJrsm625uveDDcovqZQ5LEj/velsDUqO9XW3AABAPWLCbbeBCssqfdwTAABaJhIuAAAAaLRyS7We+nKb5q7cI0nq28Gsf1wzRN3aR/m2YwAA4JRiImwVLkVlVT7uCQAALRMJFwAAADTKL7nF+v2767Utp1iSdNO5qbrvgl4KDQr0cc8AAMDp2P9WV1ZZfdwTAABaJhIuAAAAaLBPsw7o/g83qNxiVdvIED07fZDG9Yr3dbcAAEADBAWYJEkWKwkXAAA8gYQLAAAAGuRAQZnu+2CDKqqsGt2jnZ6bPkjx0WG+7hYAAGigoEBbwqXaavi4JwAAtEwBvu4AAAAA/MPTX21TRZVVI1Lj9OYNI0i2AE305JNP6qyzzlJ0dLTi4+N16aWXavv27U5tcnJydP311ysxMVGRkZEaOnSoPvzwQ6c2+fn5uvbaa2U2mxUbG6ubbrpJx48fd2qzYcMGjR49WmFhYUpOTtbTTz/t8eMD0HwFBdhuA1mqSbgAAOAJJFwAAABwRuuzj+nTrIMymaSHLuqrgJohSQA03vLlyzVr1iytXr1aGRkZslgsmjRpkkpKShxtZsyYoe3bt+uzzz7Txo0bNW3aNE2fPl3r1693tLn22mu1efNmZWRk6IsvvtCKFSt06623OtYXFRVp0qRJSklJUWZmpp555hk98sgjevXVV716vACaD/uQYtUMKQYAgEcwpBgAAABOyzAMPf7FFknS5UOT1L9TjI97BPi3r776yun13LlzFR8fr8zMTI0ZM0aStHLlSr388ssaMWKEJOmBBx7QCy+8oMzMTA0ZMkRbt27VV199pbVr12r48OGSpH/84x+68MIL9eyzz6pjx46aN2+eKisr9frrryvk/7N33+FRldkDx7/TMukVUkgCJPQQegdBUJogimXtBdcu6Kq7rmXVtazLrt11LevPgorYBRURiAIC0ksIEAidkN57m3J/f9yZSUJ6Munn8zw+kplb3nkzSebe855zXFwYOnQosbGxvPrqq9UCM0KI7kOvU9fdmiXDRQghhGgVkuEihBBCCCHqtToulX2Jebi76HhkzqD2Ho4QXU5+fj4A/v7+jscmT57Ml19+SU5ODlarlS+++IKysjKmT58OwPbt2/H19XUEWwBmzpyJVqtl586djm2mTZuGi4uLY5s5c+aQkJBAbm5uG7wyIURHY+/hYpYeLkIIIUSrkAwXIYQQQghRpzKThX/9fBSAey7sR5C39G0RwpmsVisPPvggU6ZMITo62vH4V199xbXXXktAQAB6vR53d3dWrlxJ//79AbXHS2BgYLVj6fV6/P39SUtLc2wTERFRbZugoCDHc35+fjXGU15eTnl5uePrgoICAEwmEyaTyQmvuGns52yPc3dlMq/O0+nm0moBwGSxdugxd7p57SRkXp1H5rJ1yLw6l8xn87VkziTgIoQQQggh6vTB1tMk55US4uPKnVMj23s4QnQ5ixcv5tChQ2zdurXa40899RR5eXn88ssv9OjRg1WrVnHNNdewZcsWhg0b1mrjWbp0Kc8++2yNx9evX4+7u3urnbchMTEx7Xburkzm1Xk6y1wmFwPoKS4pZc2aNe09nAZ1lnntbGRenUfmsnXIvDqXzGfTlZSUNHtfCbgIIYQQQohaZRSW8fbGEwA8Oncwbi66dh6REF3LkiVLHM3uw8LCHI+fPHmS//73vxw6dIihQ4cCMGLECLZs2cJbb73Fu+++S3BwMBkZGdWOZzabycnJITg4GIDg4GDS09OrbWP/2r7N+R5//HEefvhhx9cFBQWEh4cze/ZsvL29W/6im8hkMhETE8OsWbMwGAxtfv6uSubVeTrbXB5PL+LFuG3oDC7MmzejvYdTp842r52FzKvzyFy2DplX55L5bD57lndzSMBFCCGEEELU6tX1xyiusDAi3JfLRvRq7+EI0WUoisL999/PypUr2bRpU42yX/YVdVpt9ZabOp0Oq9UKwKRJk8jLy2Pv3r2MGTMGgA0bNmC1WpkwYYJjm7/97W+YTCbHRXZMTAyDBg2qtZwYgNFoxGg01njcYDC064V6e5+/q5J5dZ7OMpeuRnWMZqvSKcbbWea1s5F5dR6Zy9Yh8+pcMp9N15L50ja8iRBCCCGE6G7iUwr4cs85AJ6+dAharaadRyRE17F48WKWL1/OihUr8PLyIi0tjbS0NEpLSwEYPHgw/fv35+6772bXrl2cPHmSV155hZiYGBYuXAjAkCFDmDt3LnfeeSe7du3i999/Z8mSJVx33XX06qUGSG+44QZcXFy4/fbbOXz4MF9++SVvvPFGtQwWIUT3YtCpt4EsVqWdRyKEEEJ0TRJwEUIIIYQQ1aTklXLXp3tQFLh0eAhj+vi395CE6FLeeecd8vPzmT59OiEhIY7/vvzyS0BdUbdmzRp69uzJggULGD58OJ988gkff/wx8+bNcxzns88+Y/DgwVx88cXMmzePCy64gPfee8/xvI+PD+vXr+f06dOMGTOGP//5zzz99NPcddddbf6ahRAdg862gMJskYCLEEII0RqkpJgQQgghhHDIKCzjxvd3kpRbSt8Ad/6+YGh7D0mILkdRGr7ROWDAAL799tt6t/H392fFihX1bjN8+HC2bNnSpPEJIbouvU4NuJhs5QmFEEII4VyS4SKEEEIIIQDIKa7g5vd3cTqrmFBfN1bcOZGeXjV7OQghhBCic9LbekMpClilrJgQQgjhdBJwEUIIIYQQ5JeauOXDnSSkFxLkbWTFnRPo5evW3sMSQgghhBPZM1xAslyEEEKI1iABFyGEEEKIbq643MxtH+3iUHIBAR4ufHbHRPoEeLT3sIQQQgjhZAZt5W0gi2S4CCGEEE4nARchhBBCiG6swmzl9o93sy8xDx83A8vvmED/QM/2HpYQQgghWoFOWyXDxSIBFyGEEMLZJOAihBBCCNGN/XgghR2ncvAy6vn09vEMCfFu7yEJIYQQopXoqwRczBYpKSaEEEI4mwRchBBCCCG6sZ8OpgJw+9QIhof5tu9ghBBCCNGqtFoN9piLlBQTQgghnE8CLkIIIYQQ3VR+qYktxzMBuHR4SDuPRgghhBBtQa9TbwWZJOAihBBCOJ0EXIQQQgghuqlf4tMxWRQGBnnSP9CrvYcjhBBCiDZgsKW4WKSHixBCCOF0EnARQgghhOim1tjKic0bJtktQgghRHehswVcTFbp4SKEEEI4mwRchBBCCCG6oYIyE1uOZwEwXwIuQgghRLdhsJUUM0uGixBCCOF0+vYegBBCCCGEaHu/xKdTYbEyINCTAUFSTkwIIYToLuwZLmbJcBFCtKOicjPpBWWUmSxYrTA4pHNckyiKwuq4VHRaDXOHBqO1/U4Vwk4CLkIIIYQQ3ZCUExNCCCG6J8lwEUK0t9T8Uma9upmicrPjsYUje/HSVdHtOKqGpReU8cg3cWw+lgnA6N6+PHtZNMPCfNp5ZKIjkYCLEEIIIUQ3U1hmYvMxWzmx4RJwEUIIIboTvc6e4SIBFyFE4+WXmPh0xxnO5ZSSVlCGr7uBf181HFeDrsnH+iE2haJyMy46LT7uBrKLylkVm8KMgT1aYeTOsTEhg4e+jCWvxIRRr0Wn1bAvMY/L3trKP68YxvXje7f3EEUHIT1chBBCCCG6mQ1HM6mwWOkf6MlAKScmhBBCdCuOkmIWKSkmhGgcRVG445PdvLz+GF/uOcdvxzL5PjaFX49kNOt49mz7pxdEsftvM1kyoz8Af//xCPkVThu20+SXmnjg8/3klZiIDvXmpwcuYMOfpzN3aDCKAh/9frq9hyg6EMlwEUIIIYToZn4+nA5IOTEhhBCiOzJobSXFJMNFiG7PbLGSnFdKL183R7nB2qzcn8zuM7m4GXTcc2E/9ibmsvlYJjtPZzc5Y/5cTgkHkvLRamBudDAASy4awIaEDA4lF7D8hJZz646x9WQOidnFjv1mDw3mtWtHNut1ttSHW09TWGZmYJAn3907BRe9Old/mTOQtYfTSM0ra5dxiY5JAi5CCCGEEN1ImRm2nMgGYL4EXIQQQohux5HhIgEXIbo1s8XKXZ/uZcPRDAw6Df16ejJjcCB/njUQfZXgS0GZiX+uOQrA/Rf3577p/Vl7KJXNxzLZcSq7yeddeygNgAkRAfTwNALgotfy6jUjufTNrRzLh2Nbz9TYb+X+ZB6fN5hAL9dmvNrmyy8x8eFWNYPlTxcPdARbAEJ83AAoLDdTWGbCy9XQpmMTHZOUFBNCCCGE6EYO5WqoMFvp19ODgUGe7T0cIYQQQrQxg05Kigkh4LnV8Ww4qpYEM1kUjqYV8s6mk/zflurlsV6LOUZWUTmRPTy444JIAMZHBABwLL2I7KLyJp33J1s5sXnDgqs9PjDIi+cWDCHITeGKkSG8cd1INv5lOpsfmcEgWxnkvWdym/5CW+j9racoLDczONiLS6Krj9nDqMfbVc1nSM2XLBehkoCLEEIIIUQ3Eput3mSZPywEjUbTzqMRQgghRFuzr1yXDBchuq+Pt53hk+1n0Wjg3ZtGs/XRGTw6dzCgBliOpRcCsO1EFp9sPwvAM5cNdWR3+Hu4OIIgu07nNPq8yXmlxJ7LQ6OBOecFLwCuGh3KEyMtvHjVMC4fGUpEDw96B7gzIdJfPdeZxp/LGfJKKvjo9zMAPDhzAFptzeunXr5qlktKXmlbDk10YBJwEUIIIYToJgrLzBzJUy8S5jWx1rIQQgghugZHSTGLBFyE6I5+ikvl2R8PA/Do3MHMjQ4hzM+dey6M5KLBgVRYrPz5qwN8uuMst3y4C4tVYf7wEKYN7FntOPYgSENlxSxWhYzCMixWhZ9t2S3j+/o3qTTY2L7qufa0UYbLvsRc/rE6nkvf3EpRuZkhId7MjqoZIILKgItkuAg76eEihBBCCNFNbEzIxKxoiOzh7liRJoQQQojuxVFSzColxYTobj7dfoanfziMosC1Y8O5e1qk4zmNRsPSK4cx69XfOJicz8HkfAAWjOjFS1cPr3GsiZEBfLL9LDvryHCpMFv5bl8Sb286SWJOCS46LVrb0v/5TVz8Na6vHwCHU/IpKjfjaWy9W9obj2Zw27Ldjq89jXqeWRBVa3YLQIiPGjhKlQwXYSMBFyGEEEKIbmLt4XQA5g4NlnJiQgghRDels93xlAwXIboOi1UhJa+UcH/3Wp9XFIVXY47x5oYTANwwoTfPXx5d45ogyNuVZy4bysNfHQDg4VkDuf+i/rVeO4yPULNOjqYVklNcgb+Hi+O5o2kF3L5sD8lVghAVFitYwNWgZe7Q2rNF6hLi40aYnxtJuaXsT8xl6oCeDe/UTPYA0vAwH+6b3o9pA3vi7lL3LXRHSTHJcBE2EnARQgghhOjiFEXh/S2n2ZCQCcAl0UHtPCIhhBBCtBeDVjJchOgqTBYrq/Yn8/amk5zOKuaZBVEsmhJRY7tv9yU7gi31BVEArhgVilGvI8DThYmRAXWeu4enkQGBnhzPKGLX6RzmVunJ8u+fj5KcV0qgl5G7pkVy3fje5JVUcDa7hCBvI4HejS8nZje+rz9JucnsPp3TqgGXI6kFAFwzNpy50Q1n4jgyXPIlw0WoJOAihBBCCNGFFZWb+es3B1hzMA2ASYFWBgV5tvOohBBCCNFe9I6SYpLhIkRndiy9kDs+3kNiTonjsTc3nODacb1xc9E5HssoKOM5W8+Wh2cN5IGLB9R7XI1G0+iSXxMjAzieUcSOU9mOgEtyXimbjqkLvb66exJ9e3gAammuML/aM3AaY2xff77bn8zuVu7jcjRNDbgMCfFu1PYhPrYMlzzJcBEqbXsPQAghhBBCNM/eszkMe2YdV72zjc93JVJQZqr2/ImMQi7/71bWHEzDoNPwzKWDuTbSKuXEhBBCiG5MLyXFhOj0UvJKufXDXSTmlNDD08jjlwwm3N+N7OIKvtid6NhOURSeXHWIgjIzw0LVElnOZM+A2XEq2/HYl7sSURSY0j/AEWxxhvERah+X/edyqTC3ToZeTnEF6QXlAAwKblzPy16+aoZLSl4piiK/V4UEXIQQQgghOq2V+5MpLDOz92wuj393kPEv/MKDX+xn6/EsfopL5fL//s7JzGKCvI18cdckbpzQG4m1CCGEEN2bPcPFZJGSYkJ0RnklFdzy4S5S88voH+hJzEPTuPvCftxzoRpMeW/zKUdA4qeDqayPT0ev1fDi1cPR65x7K3hCpD9ajdrH5eeDqZgtVr7ccw6A68f3duq5+vX0xM/dQJnJyuGUfKce2+6orZxYnwB3PI2NKwwVbCspVm62kltiamDrlrFaFdILJJOmo5OAixBCCCFEJ3XgnHqhMX94CP0DPSkzWVkVm8JNH+xk8Yp9FFdYmBjpz+r7pzKmj187j1YIIYQQHYHO1sPFIiXFhOh0LFaFOz/Zw4mMIoK9Xfn4j+PxszWrv2p0GIFeRlLzy/hmbxKfbD/Do9/EAbB4Rv9Gl8hqih6eRu6apgZ6/vptHJ9sP0t6QTkBHi7MjgpuYO+m0Wg0jOnjD8DuMzlOPbbdkbRCAAY3MrsFwKjX0cNT/R6k5LVOHxeLVWF1XArz/rOFCf/8lce/i8MsQfMOSwIuQgghhBCdUJnJ4mjo+NjcwcQ8NI1Vi6dw08TeeLuqq7HumhbJ8tsn0NPL2J5DFUIIIUQHYrCXFJOAixCdzu8nsth9JhdPo56P/zieUF83x3OuBh13TYsE4G+rDvL094cprrAwIcKfxTP6t9qY/jx7IKN7+1JYZua51fEAXD02DBe9828728uK7TrdSgEX2/XV4OCmBafsfVxS852ffRKXlMec1zezZMV+jtoCQp/vOsc9y/dRWmGpd9+c4gqkylnbk4CLEEIIIUQnFJ9agNmqEODhQpifGxqNhpHhvvxj4TB2/W0m2x67iCfmDXF62QAhhBBCdG72kmLSw0WIzue7fUkAXDEqtNYeIzdM6I2fuwFFUZvUP3f5UFbcObFVgh92Bp2W/1w/yrHoC+D6cc4tJ2Zn7xmz81ROq5RFPJqmBlyamg0UYisrlprv3AyXb/YmcfW72zmRUYS3q54HZw7g9WtHYtRr+eVIOjd9sJPicnOt+3628ywT/rWJdUlSU7qtyRW4EEIIIUQnFHcuD4AR4b5ozmvM4mrQ0avKajchRMeydOlSxo0bh5eXF4GBgSxcuJCEhIQa223fvp2LLroIDw8PvL29mTZtGqWllRfyOTk53HjjjXh7e+Pr68vtt99OUVFRtWPExcUxdepUXF1dCQ8P58UXX2z11yeE6Nj0tpJiZquUoxGiMykqN7PucDoAV44OrXUbdxc97940hiUz+vPrny/klkl9HWUEW1OYnzsv/WEEWg3Migqibw+PVjnP0F4++LkbKCw3c8B2PeQsZouVY+nq56ghIY0vKQY4rr1S8pyT4aIoCv9YHc9fvj5AhdnKxYMD2fLXi3hw5kAWjgpl+R0T8HbVs/dsLi+sOVJj/6TcEl74SX08JlnbaqXORO0k4CKEEEII0QkdSFL7t4wI823fgQghmuy3335j8eLF7Nixg5iYGEwmE7Nnz6a4uNixzfbt25k7dy6zZ89m165d7N69myVLlqDVVl7C3XjjjRw+fJiYmBhWr17N5s2bueuuuxzPFxQUMHv2bPr06cPevXt56aWXeOaZZ3jvvffa9PUKIToWe/arlBQTzpKUW8J/NxznbHZxwxuLZlt7KI1Sk4WIHh6MDPetc7sJkQH8Zc4ggrxd225wwJyhwfz+2EW8ef2oVjuHTqthcv8eAGw5nuXUY5/OKqbCbMXDRUe4n3uT9nV2hkvsuTze33oagD9dPID/u2UsPu4Gx/Pj+vrz7s1jAFixM5GNCRmO5xRF4clVhyixlRszKxpe33DSKeMSjeP0gIus1hJCCCGEaH0HHBkuPu07ECFEk61du5ZFixYxdOhQRowYwbJly0hMTGTv3r2ObR566CEeeOABHnvsMYYOHcqgQYO45pprMBrVnkxHjhxh7dq1vP/++0yYMIELLriAN998ky+++IKUlBQAPvvsMyoqKvjwww8ZOnQo1113HQ888ACvvvpqu7xuIUTH4MhwkYbLwkne+OU4L68/xsxXf+O5H+PJK6lo7yF1SSv3q+XErhwVWiPDvaMI8XHD1aBr1XNMG2APuGQ69bhHbP1RBgV7oW1iVlCILcMl1UkZLiv3JwNw2YhePDRrYK3jmdyvB7dN6QvAo9/EkVus/tz9cCCFTQmZuOi0vHRVNACrYlMc/WlE69M3vEnT2FdrjRs3DrPZzBNPPMHs2bOJj4/Hw0NNJ7Ov1nr88cd588030ev1HDhwoMZqrdTUVMeKr9tuu4277rqLFStWAJWrtWbOnMm7777LwYMH+eMf/4ivr2+1VV1CCCGEEF1NfomJU1nqCkLJcBGi88vPVzPW/P39AcjIyGDnzp3ceOONTJ48mZMnTzJ48GBeeOEFLrjgAkC9pvL19WXs2LGO48ycOROtVsvOnTu54oor2L59O9OmTcPFxcWxzZw5c/j3v/9Nbm4ufn5+NcZSXl5OeXm54+uCAvXi3GQyYTKZnP/iG2A/Z3ucuyuTeXWezjiXWtTMlnKTpcOOuzPOa2fQWvN6MFn9O2ayKHz4+2lW7k/iw1vGEB3atD4YnUlrzaWiKJzKKqG3vxuGKr0YU/PL2HYyG4BLhwV12Z+NxszrxL6+gJoFkl1QgrebodrzGxMyMVsUZkUFNunch5PyABgY5Nnk+Q30UG+xp+SVtPh7U2G28kOsunhm4Yjgeo/38MX9+C0hk1NZxVz33nb83A0cTFE/uy2eHsn8oT35bJOV2Gwt/1pzhPdvGd2isXUnLfk+Oj3gsnbt2mpfL1u2jMDAQPbu3cu0adOA6qu17AYNGuT4t3211u7dux0XEG+++Sbz5s3j5ZdfplevXtVWa7m4uDB06FBiY2N59dVXJeAihBBCiC4tLjkPgD4B7vh5uNS/sRCiQ7NarTz44INMmTKF6Gh1FeKpU6cAeOaZZ3j55ZcZOXIkn3zyCRdffDGHDh1iwIABpKWlERhY/UaCXq/H39+ftLQ0ANLS0oiIiKi2TVBQkOO52gIuS5cu5dlnn63x+Pr163F3b1p5DWeKiYlpt3N3ZTKvztOZ5vL0OS2g5dTpM6xZc6q9h1OvzjSvnYkz59VihePpOkDDdZEWNqVqSSsxceP727k/ykKv1mnl0WE4cy7NVvjipJbdWVrCPBRuG2ihh60q2C/JGhRFRz8vhbjtG4lz2lk7pobmNdBVR0aZhre++YURAZXlEVNK4MUDOhQ0PBhtJqIJrVi2HFF/N1qyzrJmzZkmjTenHEBPan4pq39aQ0va5hzM0ZBXqsPboJB3bBdrjte//cIQeD1LR0J6ZWWocA+FsKKjxMQc5dJwiMvR8NvxLJ7/5GfG9JByko1RUlLS7H2dHnA5X0darSWEEEII0RU4yolJdosQnd7ixYs5dOgQW7dudTxmtTWyvvvuu7ntttsAGDVqFL/++isffvghS5cubbXxPP744zz88MOOrwsKCggPD2f27Nl4e7f9SmWTyURMTAyzZs3CYDA0vINoFJlX5+mMc3l60ynWJp0gNDycefOGtvdwatUZ57UzaI15PZ5ehGXnNjyMOp5bNIviCguLlu3lQFI+H5xy57M/jiOyZ9eLujh7LnOKK1j8eSx7svIASCrW8PoRVx68uD8Hk/JZn5IOWPnjRUOZNzasxefrqBo7r3uUo3y6I5FSnz7MmxflePy+FbEoqP1MYnL8+PYPE9E1Mvrxz8O/AeVcdfFExvRp2n1ls8XK8/t/waJoGD/tYgK9jE3av6o1n8cCGfxhfF8WzB3U0OYAjBmfQ0JaET5uBvzcDYyP8Meo1zrmc9HE3ny4/RyfndQzdvQwLokObvb4ugt7lndztGrApaOt1upo6fGtSdJvW0bmr+3IXLeMzF/7k+9By8kcNt3+xFwAont5NWneZK5bpivMX2cee1e0ZMkSR7P7sLDKmychISEAREVFVdt+yJAhJCYmAhAcHExGRka1581mMzk5OQQHBzu2SU9Pr7aN/Wv7NuczGo2OPjFVGQyGdr3p2d7n76pkXp2nM82l0aDeCrIqmg4/5s40r52JM+f1eJa6CnxwsDcuLi64uMAnf5zA9f+3g/jUAu5dEcuvf76ww/YcaSlnzOWJjCJu/3g3Z7NL8DLqeW7hUJbvSGTv2Vye/+moY7uxffxYODocg6HV18+3u4bm9cKBgXy6I5Ftp3Ic28WeyyPmSAZaDbi76DmcUsh3sWncMKF3g+fLKCwjvUC9Zzw0zK/J31ODAQK9XEkrKCOz2Eyov2eT9rfLLzGxMSELgKvG9G70OKYMCGLKgKA6n//r3MHklVv5bl8yD319EINezyXDQpo1xu6iJT/XrfoT2tFWa3XU9PjWJOm3LSPz13ZkrltG5q/9yfeg5WQOG0dRYPdJtWxDceJh1uQdbvIxZK5bpjPPX0tS44XzKIrC/fffz8qVK9m0aVONhWR9+/alV69eJCQkVHv82LFjXHLJJQBMmjSJvLw89u7dy5gxYwDYsGEDVquVCRMmOLb529/+hslkclw0xsTEMGjQIKkIIEQ3pret9jZbpayMaLkEW6PxwcGVtZt83A18/MfxjHvhF05lFVNQZsbHTQJntdl2Iot7lu+loMxMmJ8bHy0ax4AgLy4d3ouX1yXwfWwKMwYHcs3YMEaG+3bZwFVTTewXgF6r4Wx2CWezi+kT4MHL69TPTVeODiMqxJvnVsfz0rqjzB8Wgo97/e+/f/+s7hsd6o2Xa/PeqyG+asAlNa+UkeG+jsc/2X6GpNxSbp7Yh3D/+u8/rz6YQoXFyuBgL6J6OS+zWKfV8NLVI0CB7/Yns+Tz/bxitrJwVKjTziEqtVrApSOu1upo6fGtSdJvW0bmr+3IXLeMzF/7k+9By8kcNk1qfhkFOzaj02q4/co5uLnoGr2vzHXLdIX5a0lqvHCexYsXs2LFCr7//nu8vLwcWfw+Pj64ubmh0Wh45JFH+Pvf/86IESMYOXIkH3/8MUePHuWbb74B1OunuXPncuedd/Luu+9iMplYsmQJ1113Hb169QLghhtu4Nlnn+X222/n0Ucf5dChQ7zxxhu89tpr7fbahRDtT69Tb9iaLNZ2HonoCo7WEnAB6OllxN1FR0mFhdziCgm4nKfcbOGzHYn8c80RzFaF0b19ee+WsfTwVLNMDTotj88bwuPzhrTzSDsmT6Oe0X382HU6h3uX72NMHz+2nsjCoNPwp4sHEOzjyhe7EzmWXsRT3x/ipT8Mx6iv/bppY0IG3+5LQqOBZy+LbvaYevm6sT8xj5T8MsdjP8Wl8vT36gK5D7eeZuGoUB64aAC9A2oPvHy3LxmAK0c7PxCi02p46Q8j0Gg0fLsviYe+iqWwzMTNk/o6/VzdndMDLh15tVZHTY9vTV35tbUFmb+2I3PdMjJ/7U++By0nc9g48WlqivmgIC+8PVybdQyZ65bpzPPXWcfd1bzzzjsATJ8+vdrjH330EYsWLQLgwQcfpKysjIceeoicnBxGjBhBTEwM/fr1c2z/2WefsWTJEi6++GK0Wi1XXXUV//nPfxzP+/j4sH79ehYvXsyYMWPo0aMHTz/9NHfddVerv0YhRMel12kBsEiGi3CCo6nqYo7BITUXMvu5u1BSUUpuSQV96Vx9XL7bl0RSbilLZvRH25IO6OcpKDPx8e9n+Hj7WbKK1BJWC0b04qWrh+NqaPxCKgFXjw5j1+kc4lMLiLe9D28Y39uRRfLMgqHc8P5OfjiQwqmsIv5z3Sgie1Yv9VVYZuJv3x0E4I9TIprcu6WqcD/1vJ/vSuTS4SGYLFYe+y4OgL4B7pzJLuGbvUmsOZjKs5cN5eoxYdUyluJTCth7Nhe9VsPCka2TeaJmugzHy1XPsm1neOr7wxSWm7lvev9mHe9sdjHLd5zlpol96BPQuX7GW5PTAy6yWksIIYQQovXEnssHYESVNHUhROeiKI27yfnYY4/x2GOP1fm8v78/K1asqPcYw4cPZ8uWLU0anxCia7OXFDNZ2ibgkl9qwsuod+pNa9Ex5JeaHKv5BwZ51Xje38OF5Dw14NKZHEzK589fH0BR1Eyd68c33AOkMfJLTVz7v+2OrKAQH1funhbJrZP7SqmwZrhmXDhTBvRg87FMNh/LpKTCwgMXD3A8P7l/D96/ZSyPfHOAQ8kFXPrmVh6aOZCbJ/XB1aAjq6icp78/REp+GX0C3PnL7MY1qK/LjRN6s3J/Eicyirj63W0EeBgpLDMzMtyXr++ZxKHkfP7181F2ns7hkW/i2HI8ixeuiHaUMPtk+xkA5kQHE+jdvIV1jaHVavj7gii8XfX8Z8MJXlybgItOyx1TI5t0nPwSE7d8uIuz2SVsOJrB6vunNqn6Qlfm9ICLrNYSQgghhGg9B87lATAy3Kd9ByKEEEKITskecLFYW7+k2LLfT/PMj/G4u+gYEOTFxAh/HpkzyJFlIzo3e/+WUF+3WkuG+Xm4AJBTbGrTcbWE1arw1PeHsK+N+Pfao8wdGux4Lc1VZrJw5yd7OJpWSE8vI3+bN4T5w0MwyM9Ci4T6unH9+N51BsVmRgXx85+m8eCX+9lxKocX1hzh/a2nmDagJz/GpVBmUn8P/uvK4S0OFoT7u/PNPZO58f2dJOaUcC6nFE+jnv9cNwqDTsuo3n6suHMi7/52kldjjvHDgRRySyr4+LbxFJSZWBWrlhNbNLlvi8bRGBqNhodnq7+LX405xj9+OoLRoOPmiX0oKjdTXG4mqJ6gj8Wq8MAX+zmbrfaHPJlZzNKfj/Dc5c0vydaVtEpJscaQ1VpCCCGEEE1jsSocTJYMFyGEEEI0n72Hi7kNSoptTMgEoKTCwoFzeRw4l8eFg3oyuV+PVj+3qF9hmYmCMjO9fFybnV2RkKaWcRoUXDO7BcDP1qg8t7jzZLh8szeJ2HN5eLjoCPF140RGES+uS2DplcOafUyLVeHBL2LZdToHL6Oej28b79SG6KJ+wT6ufHbHRL7dm8Qbvx4nOa+Ur/cmATAizIc/zx7EpH4BTjmXGnSZxM0f7OJYRiEvXBFdrV+LTqth8Yz+TIjw56YPdrLleBYfbD2NgkKZycqQEG/GtqCsWVPdf1F/Sk0W3tl0kqdWHeL1mGNk235e37x+FAtG9Kp1v1fWJ/DbsUxcDVr+PGsQL6w5wifbzzJjcCAzBgW22fg7KqcHXIQQQgghROs4lVlEUblZXSUaWPuFrRBCCCFEffRadUW9ydL6GS5nsosBePHq4XyxK5F9iXkkZpcwuV8DO4pWVWG2MveN30nOK8Xfw4VhoT7cfkEE0wb2bNJxjtgyXAbXGXCxZbh0kpJieSUV/GvtUQAemjWQ4WG+XPO/7XyxO5HrxoU3e8HTJ9vPsPZwGi46Lf+7ZYwEW9qBTqvhmnHhXD6qF1/sOseR1ALmDQth6oAeTi/nFujtyuoHLiCjsJxQX7datxnb15+nLx3KEysP8uK6o/i4qT8riyb3adPychqNhr/OGURphYVl2844gi0AH2w9XWvAZeX+JN7edBKAf181nMtHhpKSX8pHv5/hka/jiHloWoszwjo7yVsTQgghhOgkYm3lxKJDfdBJHXQhhBBCNINBZy8p1roZLiaLlaTcUgCmDejJ0F5qOdRzuSWtel7RsP3n8kjOU783OcUV/HYsk6e+P9Tk49hLitWV4eJvu+naWTJclq45Sk5xBQODPLl1cl/GR/hz5ahQFAWe+v5Qs39m1hxMBeAvcwZKdlc7M+p13Dq5L/+6ajjTBvZsteCGQaetM9hid/34cOYODcZkUcgqKsfHzcBlI0JbZTz10WjUni5f3zOJ1fdfwKa/TEev1RB7Lo+jtiw2u00JGTzydRwAd0+L5PKR6ngfnTuY/oGeZBWV8+2+pDZ/DR2NBFyEEEIIITqJA0l5AIyUcmJCCCGEaCadI8OldQMu53JKsFgV3Aw6gryNhPu72R4vbdXzioZtPZENwPzhIXx510QAzmaXUFjW+F4riqI4Ai5DQmrP2LCvcs/tBBkuK/cn8eWec2g08Pzl0Y7eKo/NG4yXUU9cUj5f7j7X5OPml5jYl5gHwCXRIc4csujkNBoN/7pqGMG2XinXjgtvt6bzGo2GcX39iQ71oW8PD2YOCQKo9p4/cC6P+z7bh9mqcPnIXjw6d7DjOVeDjhsnqH101h9Ob9vBd0AScBFCCCFEq1MUhW/3JnHI1n9ENE9ckq1/S5hv+w5ECCGEEJ2Wvo0yXOzNlPsEuKPRaAjzU/sYSIZL+9tyIguAiwcHMiEygBAf9YbvUVsApTGSckspKjdj0GmI6OFR6zb+7vYMl8YHctrD8fRCnvhOzfD508UDmBBZ2c8j0MuVh2cPBODFdUebnK2z5UQmFqtC/0BPwv3dG95BdCu+7i58/Mfx3De9H/df1L+9h+Nw7fhwAFbuT6bcbOF0VjG3LdtNSYWFqQN68NLVI9CeV3FhztBgAHafzSGzsLzJ51QUhWW/n+bOT/Zwzbvbmf3abzzy9YGWv5h2IAEXIYQQQrS67aey+fPXB7j2f9s5mVnU3sPplMpMFo6kqindw8N82nk0QgghhOis9LabZK3dw+V0ltq/pW+AejM+3B5wkQyXdlVogsMpamBl6gC1Z4s9QyU+paDO/c5nD870D/RyZIOcz8/DADS/h0tCWiEXvbyJ+z/fX6O0kbOUVJi577N9lJosXNC/B/dfNKDGNjdP7MPgYC/ySky8uC6hScffeDQTgBmDmtYfR3Qfg4K9+OvcwXi5Gtp7KA7TBvQkxMeVvBITn+1I5JYPd5JTXEF0qDfv3DQGF33Nn/levm6MCPNBUSAmvulZLl/vTeKZH+OJiU9n15kcjqUXOfqAdTYScBFCCCFEq9uUoF5oFFdYuHf5XkoqzO08os7nSGoBJotCgIcLYX711wMWQgghhKiL3lZSzNzKGS72G2V9bdkP9pJiWUXllJksNbYvqTDz3b4kWZzTyhLy1IBbVIg3Pb2Mjn8DjsU9jbH7TA4Aw0LrbgDv596yHi7f7kviVFYxPx5IYe7rW7j70z3kObk82Svrj3E8o4hALyOvXzey1j6Jep2W5y6PBuCL3Yl8uTuR+NQCymu+jauxWhV+O5YBwIxBgU4dtxCtSafV8IcxYQA8tzqeczml9Alw56NF4/E06uvcb060muWy9nBak86XUVjGCz8dAeDGCb1564bRfHbHBJZeOayZr6B91T1DQgghhBBOsvmYGnDRauBYehFPrjzEK9eMaLUmhV3RgXN5AIwI95V5E0IIIUSzGdqopJg9wyWih5rZ4uNmwMuop7DcTFJuCf0DKxutW60Kiz/bx0bbIp2oEC8GGTXMtlgxdJxF313CUVvAZdrAyoyLIc0IuGw5rpYlm9K/7ibw/lV6uFitSo0SRA05aCunOzDIk+MZRaw7nI6HMZ5XrxnZpOPU5URGER9vOwPAi1cPp4ensc5tx0f4c+XoUL7bl8yj3x4EQIOO5ck7uHBQIHOjgxnaq3oW+qGUfLKKKvBw0TG2r79TxixEW/nD2HDe3HgCRYEeni588sfxjiBtXeYODebFtQlsO5FFfqkJH7fqv8DNFiv6WjLinv0xnvxSE9Gh3jx72dBat+lMOvfohRBCCNHhZRSUcTStEI0G3rphNDqthu/2J/P5rqY3nezODkj/FiGEEEI4ga6NSoo5MlxsJcU0Gg1h/rWXFXt380k2JmRi0GnQazXEpxay8oyO9fEZDZ6ntMLCnjM5rR5A6gqsVoWj+faAS2WgZEiIGvxKSC9s1DxmFpY7gjP1BVx83dWbrVYFCsualuFutSqO/o+vXzuKL+6cCMB3+5LZn5jbpGPV5R8/xWO2Klw8OJDpjchA+fuCodw6qQ8jw33xczegoCEuuYA3N5xgwZtb2ZhQ/f1qz/K/YECPWkswCdGRhfu7c/XoMHp6Gflo0Xj6BNTeq6mqyJ6eDAzyxGxV2HC0elmxjQkZDHpqLa+ur16WLyY+nZ/iUtFpNfzryuGdPtgCEnARQgghRCuzr36L7uXDJcNCeGTOIACe+eGwY9WaaFhlhov0bxFCCCFE89n7bbRmgKLCbCU5Vw2qVG2obi+Lei63xPHYjlPZvGzri/H85dHs+ttM5g9Ty9JsPZnd4LleWBPP1e9u54q3f3fcoBeVDiblc/MHO/l8VyJH0gopNGlwd9Extk9lxkWfAA/cXXSUmayOzKT6bDupfr6PCvGuNyvEqNc5yg81tY/L2ZwSCsvNGPVaBgR5MiEygKtGV5Y4UpSmvX/NFiuPf3eQuz7Zw5bjmWw8msEmW5Dvb/OHNOoYPm4Gnr08mlWLp7Dr8Rk8N8bMv64YyuR+AVgVePSbuGolz+wBmMYEc4ToiF76wwh2PXExw5rQQ3TuUFtZsUOVZcUsVoV/rI7HYlV4c+MJttt+tyfnlfL4d2rG2J1TI4kO7RrXuhJwEUIIIapQFIUnVx3k4a9iZZWck2w5rq7smjpAXf1219RIZg4JosJi5d7P9pJfamrP4XUK+aUmTtkufiXDRQghhBAtUZnh0nqfdRNzSrAq4OGiq1aCJtxPzXBJsgVj8koqeODz/VgVuHJUKNeOC8ffw4UrRoYAOG7K1WebbZu4pHwu++9Wlq45glU+xzu8/ssxthzP4vHvDnLzR3sAmBDhVy3jQqfVMChYzXKJb0RZMfuCKvvn+/r4eahZLjlN7ONy0BY8GxLi7QgSPjp3EB4uOvYn5vF9bEqTjvfy+mN8viuR9fHp3PzBLu78RJ2L26ZEENnTs0nHsvNxgatGh/LhonH06+lBRmE5T31/GFBL6sXaFkxNH9SznqMI0bE1tZy1vY/Lb8cySclTf9d/H5vMyUz1elZR4C9fHyAtv4zbl+0mq6icwcFePDhzgHMH3o4k4CKEEEJUcSKjiOU7EvluXzI7Tzd8gSfqZ7UqbD1hvyBTLzS0Wg2v/GEE4f5uJOWW8si3B5Fr4vodTlEvOMP93fCz1cIWQgghhGgOew8Xs7X1SoqdsS0U6RPgUe1mXbi/LcMlR81wWXsojYzCcvoGuPOPK6Id247t44dWo5CUV0Zidgl1KSwzccp2E292VBBWBf63+RSfbD/TGi+r0ykzWfjdlo3iZtA5ynpNraUMWGP7uCiKwlZbwOWCRgRc/N1tfVyaGHCxZysNq7LiPdDblftm9Adg6c9HKDM10LXeZu2hNN797SQA84YF4+6iw2xVCPBwYclF/Zs0rtq4GnS8es1IdFoNPx5I4dYPdzHr1d9QFIgO9SbEx63F5xCis4gK8WZ4mA9lJit3fbqHgjITb/x6HIAlM/oT7u9Gcl4ps179jaNphfT0MvLhonG4GnTtPHLnkYCLEEIIUcWGo5V1d1ftT27HkXQNR9IKyCqqwN1Fx+g+vo7HfdwNvHPjGFz0WjYmZLEjQ5rA1+dIaiEAQ4K923kkQgghhOjs9FpbSbFWzHCx92+pWk4MKjNc7CXF7Nkpl40Mxd1F79jOw6inry3pwB4wqI09CyLU1433bhnL05dGAfDiugRHUKc723EqmzKTlRAfVzb/dQbXjg1jsI+VBcNDamxbX8Alr6TCMZ8nM4tIKyjDRa9lXCMawdsXCzW1pJi99PCw80oM3X5BBEHeRtILytl1OqfB45zKLOIvXx8A4I4LInj7xjFse+will45jM/unIC3q6GBIzTOiHBfFtuCQb8dy8RsVZjSP4DXrx3llOML0VloNBreumE0/h4uHEouYMGbWzmbXUIPTxfum9GPV/4wEo0GCsvNuBq0vH/LWHr5dq2gpARchBBCiCp+rRJw+flgWqNXTYna2csNTIwMwKivvmIlOtSH+20XJQdzJOBSH/uFr/1CWAghhBCiuRwlxVozwyXbnuHiXu3xcH9bwCWnFEVRHAGXyf0CahxjoI8aEPr9RN0BlzjbTXl7j7tFk/syPsKfkgoLj30X1+Q+H1VZrAr7EnMxW1pvnlrbxqOVPUR6ehn5x+VR3BtldTSzryqqjoBL7Lk8pr+8iekvb+K7fUmO7Jbxff0btSLdrxkZLlar4shwOb+ng6tBx/gI9f0Sl5RX73EUReHPXx+gqNzM+L7+PHrJYAB83V24fnxvBjt5MdP9F/XnytGhXBIdzLf3TuazOybSP7B55cqE6MzC/d1596YxGHQaztqyFO+5sB/uLnrGR/jzyJxB9PB04fVrRzEi3Ld9B9sKJOAihBBC2OSXmNh7NhcAX3cDheVmfj2S0cBeoj7n92853xTb42eLNC26IO7qJOAihBBCCGex98NozX6FZ7LUG2x9z8twCfNTVzHnl5rYl5hHVlE5Rr2WUb19axxjoI8a6Nh+MrvOniyVWRDq/lqthhevGo6rQcvvJ7L5cve5Zr+GL3ef48q3t7Fkxf5O+TlVURQ2JqifxWc0oofI4GAvNBpILygnu6gcgG0ns7jx/3aQV2LCYlV4+KsDvPvbKaBx5cSgSsClpPF9G8/mlFBYbsao1zIgqGbAYoStgbc94FaXTccy2Z+Yh6tBy5s3jHK891uLQafl1WtG8s5NYxjTx69VzyVERzc+wp/nL48GIMTHlZsm9nE8d9/0/uz+20zm2vq9dDUScBFCCCFsfjueicWqMCDQkxvG9wZg5f6kdh5V51VSYWb3aTWANW1g7Rd5Q3t5Y9BpKDZrOGdrniqqM1msHE8vAipXHgohhBBCNJcjw8WitFog4XRW7SXFPIx6/G0lpr7eowZDxvX1r5EJDdDHE9wMWrKLK0hIL6z1PAdsGQ72G/CgBnn+PGsQAEt/Pkq5uXkZ65sS1IVXaw+n8fmu5gdu2svJzGISc0pw0WmZUkvPlvN5GPX0sWUgbTuZzf9tPsWij3ZTXGFhcr8Abp2k3ixNKygD4IJGHBPA30PNpmlKhou9VNyQEO9agyTDw3yB+gMuiqLwxi9q34ibJ/YhyNu10ecXQjjHdeN78+VdE/nq7kk1MuKq9vfqaiTgIoQQQtjYU+4vGhLIFaNCAdiUkElOExs8dhdf7znHnNc2O9L9z7fzdA4VFiuhvm5EnnexbWfU6xgS4gXAgQZWqHVXpzKLqbBY8TLqHatChRBCCCGay6CrvMnVGlkuZSYLKfnqQpq+ATU/A4bbPs/8eCAFgEm1lBMD0GthXF81S6C2smLZReUk2RbsRIdVLzv1xwsiCPZ2Jb/UxOZjdZckq4uiKI7Md4DnVh/mREbtQZ+Oyh4wmhDpj4dR38DWqqhe6uKe+z/fzwtrjlBhtjIrKogPF43jmcuG8sDFAwAI9nZt9EKg5vRwsV9fnN+/xW5oL2+0GjX4k2ELAJ1v8/EsYs+p2S13TevX6HMLIZxrQmSAo5xkdyEBFyGEEAL1YtN+UXLRoEAGBHkRHeqN2arwU1xKO4+u48kpruDZH+NJSC/kkW/iaq1tvcV2cTt1QI96V6+MsK1Qiz0nAZfaxKeq8zI4xAuttuuuAhJCCCFE29BXyRgwt0LA5VxOCYoCnkY9PTxdajwfZrvxVlyhZp7U1r/FblKk+py910tVcbab8pE9PWo0PtdpNcwbpjaGtwd2muJsdgnZxRW27JAAykxW7v88tlP1d9xgW0w2Y1Bgo/cZ3buyDFZ0qDfPL4zmnRtH42rQodFoeHjWQFbcOYHld0xo9OdS/2b0cKksFVd7wMXDqHf0Rqlt0ZaiKLz+yzEAbpzQh55exkafWwghWkoCLkIIITqMU5lF5Dehtq8zxZ7LJbfEhLer3lFvd+FINctl5f7kdhlTR/bfDScoKjcDan+RZdvO1Nimsn9L/TWj7SUgJMOldkdS1dWU0r9FCCGEEM6gr3KjvLaAy+PfHeThr2KbXW7MXk6sbw/3WhfdhPtVrnT2NOrrvKkOMCnSH4Cdp7IxnbfAx35Tfngd+y8YoQZcfjmSTmlF0wIle2zZLcPCfHjt2pEEeLhwJLWAn+JSm3Sc9lJYZmL3mRwAZgxufMDllkl9efWaEax5YCqr75/KzRP7VAvQAUzu16NJjeCbmuFitSqODJfoet4b9rJiB21l5aracjyL/Yl5GPVa7r4wstFjFUIIZ5CAixBCiA5h+8lsLnrlN8b/8xce/iqWvWdz27Q55a9H1BVgFw4KdFxUXDaiF1oN7EvM42x2cZuNpaM7l1PCpzvOAHClrfTaazHHSM2v7MGSml/K8YwiNBqY0r/uVYsAI8PVC6n41IJm19juyo6kFgAScBFCCCGEc1QNuFgs1T9vl5ksfL4rke/2JZNf2ryFUOm2Ek+hvrWXQq1aInVChH+NG/pVDQn2wt/DheIKC/uqlPgCiLPdaLffeD/fyHBfwvzcKKmwOLI9GsteTmxMHz8CvVwd5Ybjarm535GYLVZW7k/iire3YbIoRPTwqNFHpz4uei1Xjg5zlBZzBnvPnrxGLqw7lVVEYbkZo17LgKC6AzvD61m09cXuRACuH9+bQC/p3SKEaFsScBFCCNEiSbklPPvjYS58aSNrDzV/xde3+9Tm9OVmK9/tS+aqd7ZxyRtb+HT7GQrLWj/rxX4RdnGVFWCB3q6OBpOr9ktZMbtXY45hsihMHdCDl/8wgjF9/CiusPDcj/GObbYcV8uJDQ/zxde9ZimJqnr7ueGhVzBZFEc2h6gkARchhBBCOJOuSsDFZK2eNVJuttb676YoKFOzoH3cDLU+X7WWf139W+y0Wg0XDlSzpTcmZDoeVxTFcaN9RHjtWRAajYYFI3oBTS8rtvesmh1iz3wfGqp+DjucUtCk47Q2s8XKX785wJCn1jL4qZ+JenodD315gBMZRXi56vnbvCHtPUR83dX3QV5JRaN6Bn21R70unBgZgKGeYJw90BaXlFdtoV652cJvtveKPVAmhBBtSQIuQgghmuVIagEPfRnLhS9t4qPfz3A2u4RnfohvVoaCyWIlJj4dgGcWRPGHMWG4GrQcTSvkqe8PM+Gfv/LCT/FkFpY7+2UAkJxXytG0QrQaHBd0dvYP6atik9s046ajOpySz6pYtcTao3MHo9VqeOGKaHRaDT8fSmPDUfX7uPmYepFz4YAeDR5To9HQx1Od2/2JuQ1s3b1kFJaRVVSBVgODgrzaezhCCCGE6AI0Go0jy8V8XoZL1bJdze1XUmDLjDm/r4pdeJUMl4YCLgDTB6mfz+39FgHSC8rJLCxHp9UQFVJ32akFw9WAy4aEjEYv4sovNXEsvQio7GkytJd6jiOpBVhboe9Nc/3jpyN8tSeJUpOFMpOVCosVP3cDj8wZxO+PXcTMqKD2HiJ+tsVXVqXyvVGX0goLX+4+B8Atk/rUu+2QEC8MOg25JSaScisz7befzKa4wkKgl7HecnVCCNFa9O09ACGEEJ3LvsRc/vPrcTZVWWE2pX8AJzKKSCso46s9Sdw8sf4Px+fbfjKb/FITPTxduHlSX3RaDU/Oj+K7/Ul8tjORExlF/N+W03yy/Sw3TujD3RdGEuTtvNRwe3bL6N5+jhrDdnOGBuNmOMTprGIOJOUzMtzXaeftjP69NgFFUcut2WsqDw725o4LIvjf5lM8/f1hJkQEsPWEmuEydWD9/Vvs+ngqxOdB7Lm8Vhp552TP+OnbwwM3F107j0YIIYQQXYVep8FsVTCfl+FS4ZQMF1vApY4Ml97+7kSHeuOi0zIkuOEM3mkDeqLVwNG0QlLySunl6+b4zDgg0LPez0hDQrzo19ODk5nFfLM3ievH98bVUP9nqn22BUB9A9wdzdYje3hg1GsprrBwJruYyJ6N72HSWj7ZfsbRR/HVa0YwPkLtd9PTy4hR33E+Nxp0Wrxc9RSWmckpqahxvVXVDwfUUnbh/m5MH1R/7xmjXsegYC8OJRcQl5TvyJz65Yi6AGxmVBBabc0eQkII0dokw0UIIUSjncsp4fr3drApIROtBuYPD+HHJRfw2R0TWTyjPwDvbDxR7UKtMX62lSKbPTTYUeLAx93AbVMiiHloGh8tGsfIcF/KzVY+/P00U1/cyN+/P1StZ0hLbLQFXGprKOlh1DN7qLoybNX+ZKecr7P6/UQWm49lYtBp+MvsQdWe+9PMAYT6upGUW8riFfvIKzHhadQ3OkDV15a8IQGX6qScmBBCCCFag16r3g46P8Ol6uf45me4qCXFvF1rX+Or12lZff9Uvr13cqNuiPt5uDDKlmliX/T1pa1Hhz3IUJeqZcWe/TGewU+t5YJ/b2Dnqew699nn6N9SeWy9TsvgkI5TVuz3E1k888NhAP46dxBXjg4jzM+dMD/3DhVssbP3ccktrqhzG0VRWLbtLAC3TOxbrfRdXaqWFbMf45d49dpu1pD2z+4RQnRPEnARQgjRaNtPZlNutjIwyJMNf57OWzeMZpitWeE1Y8MJ9DKSkl/GN3uTGn1Mi1Vh/WF1FdIl0cE1ntdoNMwYHMjK+ybz6e3jGdvHjwqzlY+3n+XiV35j1+mcFr2m0goLv9uyMS4eUvsqqoW2smI/HkipVmahO7FaFf7181EAbpzQh94B7tWed3fR88xlQ4HKC+FJ/eqvu1xVb1tJsbPZJeTUcyHW3dgDLlEScBFCCCGEE+l1tpJi1rpLirVWhoudRtP47IPpAyvLih04l8fGhEx0Wg1/nBLR4L43TOjN+Ah/vIxqACgpt5TnVsfXWS54zxl7wMWv2uNDbY3kD6XUbNLe1t797SRWBa4cHcq9F/Zr7+E0yF5WLLek7pJie87mciS1AFeDlj+MDWvUcUfYrkXjbP18DiUXkFZQhruLrlHl6oQQojVIwEUIIUSjxSXnATB9UCB9e3hUe87VoOMe24f9tzaeaHRgYtfpHLKLK/B1NzAxsu4PxRqNhqkDevL1PZNYcecERob7UlJh4fZluzmU3PyLnu2nsig3Wwn1dauzR8bU/j3o4elCdnEFW23N4LubNYdSOZicj4eLjiUX9a91m1lRQcyqUid6WiP6t9i56yGyhxrEiT0nfVzsKjNcpH+LEEIIIZzH0cPlvJJi5U7JcKm/h0tz2DPRt57I4uX1CQBcPrJXjWuS2gR6ufLV3ZOIe2Y2Wx+dgbuLjsMpBfx6JKPGtiaL1ZFxPbZv9YBLtK2PS3w7Z7hYrAr7E/MAuP2CiCYFrtpL1QwXRVE4lVnEuZwSCstM5JVUsPlYJq/Yvq8LR4bi61532bGq7Bkue8/m8vuJLGLi0wC1DF1DpeOEEKK1SMBFiPPklVTw8vrjnGz/LGEhOhz7yqHhYbU3H7xhQm96eBpJzitl5b7Gld+ylxObNSSoUdkQGo2Gyf168PmdExnf15/CcjO3friLk5lFjXwV1dkvtGYM7lnnxYpep3WUIvhk+5k6V8N1VSaLlZfWqRdAd1/Yjx6exjq3feayobi76NBpNVw4sP66y+cbYSs/Fmu7gOzuykwWTmYWA1JSTIiuZunSpYwbNw4vLy8CAwNZuHAhCQkJtW6rKAqXXHIJGo2GVatWVXsuMTGR+fPn4+7uTmBgII888ghms7naNps2bWL06NEYjUb69+/PsmXLWulVCSE6k7pKilXLcDE1N8PFVlKsgQyXpogK8aanl5GSCgtbjmeh1cD9Fw1o0jE0Gg1hfu7cMqkvAG/8erzG5/ojqQWUmix4u+rpf16fFnuGy+GUgna9HjiaVkBRuRlPo57BjeiB0xH4uqvvhZySCl7/5TgXvfIbU1/cyLBn1jPyuRhu+XAXO06plQvs35/GGBzsxcwhgVRYrNy2bDdf7D4HUG0RmBBCtDUJuAhRhcli5Z7le/nfltO8E69jZwtLFQnRlZSbLY7V9iNsK4nOp2a5RALw340nMDeQ5WK1Kqw9pK5CumRYzXJi9XFz0fH+orFEh3qTXVzBze/vJDmvaT1dFEVx9G+5eHD9H8qvHReOTqthY0Imr/1yvEnn6exW7k/mbHYJPTyN3H5B/WUbQn3d+Pbeyay4Y0KNsmMNsZcE2C99XAA4kVGExarg624g2Nu1vYcjhHCi3377jcWLF7Njxw5iYmIwmUzMnj2b4uLiGtu+/vrrtS4IsFgszJ8/n4qKCrZt28bHH3/MsmXLePrppx3bnD59mvnz5zNjxgxiY2N58MEHueOOO1i3bl2rvj4hRMdXV0mxaj1czC3LcPFxYsBFq9U4yoqBmgUR0YjsltrcOTUCN4OOg8n5bEyonuWyxZbNPj4ioEZ/mUHBXui0GnKKK0grKGvWuZ1hr63HzKjevo3qc9IR+NsyVmLi0/nPBvVaykVfeUuyT4A7l43oxX+uH0VUr8YHkTQaDW/dOJqZQ4KoMFvJKCxHq6m9N6cQQrQVCbgIUcVzP8Y7VlWYFA13f7bf0XxNiO4uIa0Qk0XBz91AmJ9bndvdMKE3AR4uJOaUsCo2pd5j7kvMJaOwHC+jnin9G19+ys7b1cDHt40nsqcHKfll3Pz+TrKKyhu9/9G0QlLyy3A1aBus8Ts42JsXFkYD8J9fjzepT01n99kOtXnlHVMj8DDW3vy0qiEh3kyopzxcXUbaAi4HzuVhtXavLKLaxNvLiQV7d4pSEUKIxlu7di2LFi1i6NChjBgxgmXLlpGYmMjevXurbRcbG8srr7zChx9+WOMY69evJz4+nuXLlzNy5EguueQSnn/+ed566y0qKtReWO+++y4RERG88sorDBkyhCVLlnD11Vfz2muvtcnrFEJ0XI6SYuctkKqoI8PFalWIS8qrFpCpjaIoVXq4NPy5sSnsN9G1GuoscdsYAZ5GbpnUB4A3fqme5fKbrRfh9EE9a+znatA5sl4OJ7dfSYy6esx0ZH62kmJ7z+aiKHDN2DCO/eMSjj4/l0PPzuG3R2bwn+tHcZmtqkBTGPU63r5xtKMf6MTIAEcJMyGEaA8ScBHCZvmOs3y64ywaDbxxzXAGeFspLrdw64e7OJFR2N7DE6LdHbCVExsW5lvvzV93Fz13TlOzXN5qIMvlZ1t2y8VDAjHqm1djN8DTyPLbJxDq68aprGJu+WAX+aV1N2OsaoMtu2VKvx6NqvF73fje3Dtd7VPz+HdxbDvZ9fu5HErO50BSPgadhqvHNK55ZXMNDPLEqNdSUGbmVFbNVd4dyY5T2fxzzRHHDYXWUNm/pXOUihBCNF9+vvo31t/f3/FYSUkJN9xwA2+99RbBwTWzQLdv386wYcMICqrM0JwzZw4FBQUcPnzYsc3MmTOr7Tdnzhy2b9/eGi9DCNGJ6G2lfM/PcKlaUqxqhst3+5O57L+/89bGE/Uet8xkxWQrU+bMHi4AFw0O5PKRvXhi3hAizyv31VR3TovEzaDjQFI+v5/IBiC/1MTeRDWYceHAmgEXqF5WrL3YM1zG9vFvYMuOo2oAJLKHB39fMBRQg1iejVjQ1RAXvZY3rx/F2zeO5pVrRrT4eEII0RLOXW4gRCe1/WQ2z/ygXpj+ZfYg5g0LpuyMlc+SfYlLLuCm93fx9T2TCPdvWnkcIbqSOFuZp+GhtfdvqermiX34328nOZ1VzOq4VBaOCq2xjaJULScW0qKx9fJ149Pbx3PN/7YTn1rAn77Yz7Lbxje43/r4dKBpKeePzB7EuZwSVselcvene/nqzobP05l9visRgDlDg+vt3eIMBp2W4WE+7D6TS+y5PPoHtuxCujU9+2M8R1ILSMwu4Z2bRrdKBkplwMXL6ccWQnQcVquVBx98kClTphAdHe14/KGHHmLy5Mlcfvnlte6XlpZWLdgCOL5OS0urd5uCggJKS0txc6uZsVpeXk55eWW2aEGB+rvIZDJhMrVekLku9nO2x7m7MplX5+msc2mrKEZZRfWf7ZIqi0lKyiufO52pLkI8mppf72vNLlRLbem0Ggwaa7PnpbZ51QEvXxVd4/Hm8DFquWp0L5bvPMfyHWeY0NeHzQnpWKwKkT3cCfYy1HqOwcHq59NDyXnt8j1PzS8jOa8UrQaGhng0eQzt9X71dVUXtxl0Gl65ehguWqVVxjBrsFo1oS1eX2f92e/oZF6dS+az+VoyZxJwEd1eYnYJ9322F7NVYcGIXtw3vR9msxlXHfzfzaO56cM9HM8o4uYPdvLVPZMI9JI6+qJ7Opisrr4dHtZwwMXDqOeOqZG8tC6B/2w4zoIRvWrUF45Lyic5rxR3F12dK8iaIrKnJx//cTyX/fd3NiVkcjqruN66zonZJRw4l4dWowYTGkur1fDyH0aQml/G3rO53PnpPu7u1+Lhd0jF5Wa+t5WFu2FC7zY558hwX1vAJbfVM2qaq6jcTEKaegNy7eE0Pt52hkVT6u9t01SKonAkVb2xIRkuQnRtixcv5tChQ2zdutXx2A8//MCGDRvYv39/m49n6dKlPPvsszUeX79+Pe7u7bf4KCYmpt3O3ZXJvDpPZ5vL4iIdoGHHjl3kJ1RmuezL0qCGNuDAoXjW5KoLE+PPaAEtJ86ls2bNmjqPm1YCoMdVa+Xnn39u8Thbc15DSwH0rI9P44tVyaw5p77GcENRna+xMF/dZ8/J+uehtdi/P73cFTb/ur7Zx2nr96vJChN6aonyU0g8sJXEA216+lbV2X72OwuZV+eS+Wy6kpKSZu8rARfRrRWVm7nzkz3klpgYFurDi1cNr7ZK2d/DhU9vn8DV727jTHYJt3ywi6/umeT01GghOrqSCjPH0tWbvyPCfRu1zy2T+vDe5lOcyizmgS/289LVw3F3qfyzYy8nNmNwYKPKeTXG0F4+TOnfg83HMvk+NpkHZw6sc9sf49RAwuR+Pejp1bTMDVeDjv+7ZSxXvP07Z7NLeD9Bx+UVFgyGrvW74YcDKRSVm4no4cGkZvRkaY6R4X7AafYn5rXJ+ZojLikPq6LWD7cq8MKaI4zu48fwMF+nnSM1v4z8UhN6rYYBQR0300cI0TJLlixh9erVbN68mbCwyiDzhg0bOHnyJL6+vtW2v+qqq5g6dSqbNm0iODiYXbt2VXs+PV3N3LSXIAsODnY8VnUbb2/vWrNbAB5//HEefvhhx9cFBQWEh4cze/ZsvL3bPgBsMpmIiYlh1qxZXe7vbHuSeXWezjqXH57bSVJxPiPHjOHiKtneZfuT4bgaZInoN4B5tl4pO3+Mh9QkcPVk3rwpdR53X2IeHNhFgLc78+ZNbfb42mpe1+XuJPZcPnl+gzl1/BxQzq2zxjJ1QO39JQvLTLwZv5HcCg2Tp8/C171tv+d7fjoKxxOZEd2befOGNHn/9ny/1p6v2Xl11p/9jk7m1blkPpvPnuXdHBJwEd2Wxarw4BexJKQX0tPLyHu3jMHNpeZN32AfV5bfPoGr393O0bRCvt6TxO0XOHclsxAdXXxKAVYFAr2MBHk3LsvLy9XA8wujefjLWH6KS+V0ZjHv3TKGMD93FEXh50OpAI7mhs6ycGQvNh/LZNX+ZP508YA6Sz39eEANuCwY0bxyZv4eLny0aBxXvr2Ns0UmPtmRyJKL6w7wdEYrdqrlxK4fH95mTdtH9vYF4GhaIaUVllp/L7e3WFt5vbnRwVisCusOp7N4xT5W3z8VHzfnfIi1lxPr19Oz2f2NhBAdl6Io3H///axcuZJNmzYREVH9s+Vjjz3GHXfcUe2xYcOG8dprr7FgwQIAJk2axAsvvEBGRgaBgerN0piYGLy9vYmKinJsc/4K7JiYGCZNmlTn2IxGI0ZjzYUIBoOhXS/U2/v8XZXMq/N0trk02Hq4oNFWG7e1SqvfCiuO58rN6mO5JaZ6X2eJSc2W8XFzccp8tPa83jihD7Hn4nhv6xkKy8y4GrRMHhCIoY4FYf4GAyE+rqTml5GUX05Pn7bN/Ntv+xw6LrJHi+als71fOzKZy9Yh8+pcMp9N15L50ja8iRBdj6IoPPHdQX45ko6LXsv/bh5DiE/tq/wA+vbw4OaJfQD1xrMQ3c2BJHs5Md8m7XfZiF6suHMiAR4uxKcWcNl/f2fHqWyOpBZyNrsEo17LjEGN75/SGHOGBuNm0HEmu8Qx7vMdSy/kaFohBp2GuUOb3z8msqcnD16s1hPbciKr2cfpiA4m5XMwOR8XnZarx4S32Xl7+bgS6GXEYlU4lFL796+9xdqyb0aF+/Hi1SMI83PjXE4pj34Th6Io9e/cSNK/RYiubfHixSxfvpwVK1bg5eVFWloaaWlplJaWAmpmSnR0dLX/AHr37u0IzsyePZuoqChuvvlmDhw4wLp163jyySdZvHixI2Byzz33cOrUKf76179y9OhR3n77bb766iseeuih9nnhQogOQ29r4mK2Vv/sUmG2Ov5dbqr8d5nZAkBuSQUWa92fdwpsPWC83TrH+t5Lh/fCy1VPYZkaUZoYGdBg9n2Yn3rvIDmvtNXHV1VxudlRcnZsH782PbcQQojGk4CL6HYURWHpz0f5cs85tBp449qRjO7d8IeVgbaSLsczClt7iEJ0OHFJeQCMaET/lvONj/Dnh/svIDrUm5ziCm56fyd/W3UQgAsH9sTD6NyLMQ+jnllRaoPgVfuTa93Gnt1y4cCe+LSwDMDkfmqprX2JeZSZLC06VkeyYpea3TI3Ohh/D5c2O69Go2GkrWxdbAcsK6YoimNl4cjevvi4GXjrhtEYdBrWHk5j2bYzjm1LKyysjkvhrk/2MOSptSz9+UijzyP9W4To2t555x3y8/OZPn06ISEhjv++/PLLRh9Dp9OxevVqdDodkyZN4qabbuKWW27hueeec2wTERHBTz/9RExMDCNGjOCVV17h/fffZ86cOa3xsoQQnYheq94OMlvqCbiYKz/bllWo/1YUyCupqPO4BaW2gEsnKcPt5qLjylGhjq8b01sy1FcNuCTltm3AJfZcHharQi8fV3r51r1gVAghRPvqHEsOhHCitzed5L3NpwD415XDuWRY41a3DwhSVxkfTy/CalXQatumvI4QHcFBW6bIsGYEXEC9KPn67sk89l0c38emOPpzXDLMueXE7BaO6sUPB1JYHZfCk/OHoNdVri9QFKVKObFeLT5X3wB3fAwK+SbYdzaXyf1rr/fcmRSVm/khVg1W3TChd5uff2RvX9bHp7P/XG6bn7shKfllZBaWo9NqiO6l/jyMCPfliXlDePbHeP655gh6nZa9Z3JYH59OSUXljYoPtpxm0eS+9WZU2lVmuEjARYiuqDnZcLXt06dPnwabNk+fPp39+/c3+XxCiK6tzgwXS+0ZLqVVFhblFFcQ4Fl7D8QCW6ZIZwm4AFw/oTcfbz8LNDLgYs9wcXLApaDMxJmsYoaF+tQo51tSYeafa9TFOxPbqLeiEEKI5pEMF9GtfLrjLC+tSwDgyflDuGZc48vk9A1wx0WnpdRkafPUYSHaU36piVNZxUDTS4pV5eai4/VrR/L4JYPRaMDDRcfFQ4KcNMrqpg7oib+HC1lFFfx+Mrvac4eSCziTXYKrQctMJ5xfo9EwwEe9UN123rk6q+9jkymusBDZ04MJEf5tfv5R4WrWYUfMcLGPaUiIV7X+Mosm92Xu0GBMFoWnVh1iVWwKJRUWwvzcuG96P0aE+2K2KtUyYOpSUmHmdHax7TwScBFCCCGE81VmuFirPV49w6VKSbEqAZfs4kZkuHSSkmIAg4O9efrSKJ6cP4TInp4Nbh/qq/ZtcfZ9gUe+PsBl//2d//x6otrjiqLwyNdxHE4pwN/DhYdmda2+kUII0dV0nr+AQrTQ97HJPP39IQDuv6g/d0yNbNL+ep2WyJ4eHE0r5Fh6IeH+bdscT4j2cihZzW4J83NrcWkpjUbD3Rf2Y+qAnui0mlZb+WbQaZk/LIRPd5zl+/3J1Vaq/XBAzdy4eEiQ08qZDfBR2JMF2091/oCLoiis2KmWE7thfO8aq+vawvAwH7QaNZsko6CMQG/XNh9DXWJtWTf2smd2Go2Gf189nMScErKKypk3LITLRvZiVLgvGo2GX+LTueOTPazYkciSGf3xque9n5BWiKJAD08jPb1qXz0qhBBCCNESelvFBtN5GS4mS+1BltIq2S459QVcyjpXSTG7P14Q0eht7T1cknJLnHb+MpOFTQmZALz2yzGCvI1cN743iqLw2i/H+elgKgadhndvGiP3IoQQooOTgIvoFjYezeDhrw6gKHDLpD483MwVIQOCvGwBl6JWW5kvREcTZysnNqIF2S3ni+rV+qv2F47qxac7zrLucBolFWbcXfRYrQqr41IBuMwJ5cTsBnirF6oHzuVRXG52el+athSXlM/hlAJc9FquGh3WLmPwMOoZEuLN4ZQC/r02gZf/MLxdAj+1ibX3bwmv2fvLx83Amj9NrXW/iwYH0q+nByczi/ly97l6g/6V/Vu8Wj5gIYQQQoha2EuKWZqT4VJUXudxC0ptJcXcOlfApSmqlhRTFMUpn1P3nc2l3GxFqwGrAk+sPMiR1AI2HcvkbLYa2Hn+8mjGt0P2uRBCiKaRkmKiyzNbrDyx8iAWq8LCkb14ZsHQZn8gGhiophcfSy905hCFcMguKufx7w5y7/K9PL86ng+2nmbtoVQOnMurdyVZa4pLygPUrIPOZHRvP8L93SiusBATnw7AnrO5pOaX4WXUN6o+c2MFuEKYrytmq8LuMzlOO257+HyXmt0yLzoYvxZmNLXEY5cMRqfV8O2+JN797VS7jaMqk8XqCECO6u3bpH21Wg132oIsH249XW316Pns/VuipJyYEEIIIVqJPcOlvh4u1TJcKhpXUiy/E5YUa6pQW8P64gqL4/W21O8nswC4fGQo14wNw6rAx9vPcja7BA8XHY/MGcR149u+t6IQQoim67p/AYWwWR+fTmp+GQEeLvzrquEtanY/MFhdbSwBF9EazuWUcMuHuzht65dyPq0Gnpg3pMnl8FrKfoN5WCcLuGg0Gi4fEcp/N57g+9gULh8Zyo8HUgCYPTQYV4OugSM0zYRIf5L2pbD9ZDbTBwU69dhtJb/ExA+2ObphQp92HcvUAT15ZkEUT31/mH+vPUpEDw/mRge365gS0gopN1vxdtUTEeDR5P0Xjgrl5fXHSMkv46e4VBaOCq11u0Mp6s+c9G8RQgghRGvR62w9XOopKVYtw8VcGXDpiiXFmsLVoKOHp5GsonKSckvxdXchIa2QWz/cxUVDAnn60qgmX2tsPaGWJr6gfw8uH9kLnVZLUm4JC0eGcsmwYNxd5PadEEJ0FpLhIro8e4Pi68f3bvEN1oFBasDlREYRlvM+mArREoeS87nynW2cziom1NeNJ+cP4a5pkcwfHsKo3r4EeRuxKvDPNUfY0YZ9QrKKyknOK0WjgWGhnSvgAmpZMYDNxzLJKCxjzUFbObGRzisnZjfJlt7fmfu4vLflJCUVFgYHezGub82SWW3t5kl9uXWSGvh56MtYRz+h9rLfVk5sRLhvs4L3rgYdiyarr+e9zadQlJp/R/5v8yn2J6rnaWoWjRBCCCFEYxlsJcXM52Xd1lVGrLEZLgWODJeuG3CByrJiSbmlAKw5mEpaQRkrdiZyxdvb6lxEV5v8EhMHbVUFpvTvgV6nZemVw/j09glcNSZMgi1CCNHJSMBFdGlHUgvYdToHnVbDTRNbvlq7t787Rr2WcrOVcznOa5Anuretx7O47r0dZBaWMzjYi+/um8wdUyN5Yt4Q3rphNCvvm8LOJ2Zy1Wg1tfyBz/eTWVh33WRnOmjLbons4VFvk++Oqn+gF9Gh3pitCn9beYjs4gr8PVyY3C/A6eeaEKkGXA4l5zuttEBbyioq56PfzwDw0KyBHaZnylOXRjFtYE9KTRZu/3g36QVl7TaWWHsgJNy32ce4cUIf3Aw64lML+P1E9eDcd/uSeGHNEQAenTuYPs3IohFCCCGEaAydbfGIyXJ+hkvl1/bgi9WqVAvE5BTVl+Fi6+HSCa8dmiLMVlYsOU8NuBy0LQzSaNT7EAve3EpCWuMqY2w/lY1VgX49PQj2cW2dAQshhGgzEnARXdrHtuyWudHBTvngotNq6NdT+rgI5/k+Npnblu2iqNzMxEh/vrpnEkHetb9Xn184lP6BnmQUlvPQl7FtkmV1wNG/xbfVz9VaFo5UyzbZ+7jMGxaMQef8P3/B3q5E9vDAqsCu052vj8u7m9TslmGhPsyOCmrv4TjodVr+e8MoBgR6kl5Qzh0f76m2wrIt7T+XC8Co3s3P/vHzcOGasWEAvLelsjfNxoQM/vpNHAC3XxDBPRe2belAIYQQQnQveq36efj8a4qKKqXD7BkuVcuJQd0lxRRFqZLh0rWzMuwZLsm5pSiK4ijD/PYNoxkW6kNRuZlv9yU16ljbbP1bLujfo3UGK4QQok1JwEV0eOsPp/HR76epMNfdYLg2ucUVrNyfDMCiyX2dNp6BQWrA5XhGkdOOKbqnD7ee5k9fxGKyKMwfHsLHfxxf70owdxc9b984GjeDjq0nsnh74wmnjCO7qJxV+5NrvYltz3AZ3sn6t1S1YEQvqiZrLBju/HJidhNtmTP2i6bOIr2gjE93nAXgz7M7TnaLnbergQ9uHYe/hwsHk/N55JsDbT6G/BITpzLV0hAjWpDhAnD7BZFoNWqpuyOpBexLzOW+5fswWxUWjuzF3+YN6XDfAyGEEEJ0LXp7hou1+nV2bRkuZabq29RVUqzUZHH0hOnyGS6OkmIlpBWUkVVUjk6rYcbgQG6YoDa3P5Ja0KhjbT2hXjtMkYCLEEJ0CRJwER3a4ZR87lm+l2d/jOcP725rUhmvL/eco9xsJSrEm7F9nNeLYICtj4tkuIiWeHvTCZ5bHQ+oAcE3rxuFUd9wj6GBQV48vzAagNd+OcZOJ2RSPP39YR78Mpbr3tterVSZoigc6AIBlyBvV0cJsWBvV8b19W+1c9nPs/1k5+rj8tbGE5SbrYzt48eFA3u293Bq1TvAnf/dPAa9VsPquNQ27+diz/bqE+COv4dLi47VO8CdS6JDAHjhpyP8cdluSk0Wpg3syYtXj2hWfxghhBBCiKbQ2zK+LZbzM1xq9nApNVVfmJVbUoG1lmz7glK1nJhOq8HdpWX9Uzu60ColxezZLQODvHA16BgcrN4zOJLa8D2D1PxSTmUWo9XAhEjnlz0WQgjR9iTgIjosq1XhqVWHsH+OO5CUz/z/bGH94bQG97VYFT7drq7WXjS5r1NXCg90BFwkw0U0z5u/HufFtQkAPDRzIH9fENWkG6xXjwnj6jFqP5eHvoqjoO4Syg3KKa5gfbz6M3UgKZ8r3v6dExnqhUFqfuVKraiQzhtwAfjjlAg0Glg0pW+r3syeaLtIOppWSHZR2/TZaamk3BI+35UIwJ9nD+rQmRXj+vozf7gaqPjw99Nteu7Yc3kAjGxhdovdXdPUkmFbT2SRV2JiRLgv79w4Ghe9fDQTQgghROsz6NTPfOYaJcUqAy7lZiuKojgy4d0MahDFYlUoKKvZs9D+mLervkN/pnQGR0mxvNLKqgCh6jXToGAvNBq1R2JDvTe3HlezW4aH+eLj1rWzgoQQoruQq3rRYX2zN4l9iXl4uOj49t5JjAj3paDMzF2f7uUfq+MxWeouMfbLkXSS80rxczdw2Ujnlg8aZAu4nMwswlzPGIQ4n6IovBZzjFdijgHwyJxB/GnmgGZdjDx3+VAGBHqSWVTBpye0ze7n8kNsMiaLQmRPD/oGuJOUW8qVb29jx6nsaiu13Dr5CrWLhwRx6Jk53D2tdfti9PA0On5HOCP7qC28+esJTBaFKf0DmNSv46+qu21KBACrD6Q2eAHrTPsTbf1bnBRwGRHuy/gINdsqsqcHHy0ah4exa9c6F0IIIUTHobOXFDvvmrbivK/LzVZHpou3mx4v2+eV2sqKVfZv6fqBA3uGS16Jie2n1Oz2YbaqAO4uevoGeABwNK3+smJf7j4H0GGzzIUQQjSdBFxEh5RXUsG/1h4F4MGZAxnTx5+v757EHReoN9re33qaa/63vc4SYx9vOwPAdeN742pw7o3iMD833Aw6KsxWzjahxJno3hRF4dWYY7zx63EAHrtkMItn9G/28Sr7uWg5lq/lu/0pzTrON7ZGjrdM7MN3901hdG81sHnzBzt5b/NJoHKlVmfnYWyblXaTOlFZsdNZxY73wMOzBrXzaBpnZLgvI8N9qbBYWbEzsU3OqShKZYZLb+eVqFx65TDumhbJZ3dMaHGZMiGEEEKIpjDYS4rVk+EC1QMubgYd/p7qZ5bsoloCLo4Ml64fcPFyNTgyUvaeVRfmVC3DPCREXYR1tJ6yYnvO5LDnbC4uOi032vq+CCGE6Pwk4CI6pBfXJZBTXMHAIE8WTekLgItey5OXRvHezWPwdtWzPzGPWa/9xkvrjlJYJZ05Ia2QbSez0Wrgpol9nD42rVZD/0BPAI5LHxfRCIqi8OK6BN7coDa5f3L+EO65sF+LjzsgyIsHLlKDNm9uPOm4EGqsI6kFHEouwKDTcPnIUPw9XFhx50TmDwvBZFHYl5gHwPDwrhFwaSv2gMu2k1ntPJKGvfHLMSxWhYsGBzLGib2uWttttr8Ly3eerXFToDUk5pSQW2LCRad1XDw7Q7+enjwxbwghPm5OO6YQQgghRGNUZrhUD7icn/FSbrI4eri4GnQE2BaJ5BTXzDS293DxduseWbv2LBdQS7QNCq78nDg42BtQr7nq8u5v6gK3q8aEEujt2kqjFEII0dYk4CI6nNhzeY5+As9fHu1YeWM3e2gwPz0wlfER/pSZrLy18SQXvrSJj7edwWSx8vH2M+p2UcHVPgA504AgNeAifVxEY3z0+xne2aR+mP77gijumOq8slY3TQjH10UhNb+M5TvONmnfb/eqmQ0zhwThZ7twcjXoePP6UdVKbzmrZ0V3MTEiAI0GTmYWk1FQ1qh9kvNKWfTRLjYfy2zl0VU6ll7I9wfUzKiHZw1ss/M6wyXRIQR6GcksLGfNwdRWP589uyWqlzdGfecuryeEEEIIAaC3BVws1oZLitl7uLgadPh7GIE6Sop1owwXqOzjAmqApernxCEhtoBLWu2LNI+lF/LLkQw0GrjTideHQggh2p8EXESHYrEqPLnqIIoCV44KZUJk7f0Ewv3d+fKuibx38xgie3qQU1zB3384zOzXNrNyXzIAt07u22rjHGjr0XBMMlxEI3xjC2w8MmeQo/+Es7gadMwNUy+K3tp4olq2V31MFiurYtWflavHhFV7TqvV8Pi8Ifz3hlE8dWkUUbaLBdE4Pu4GhvZS58xez7kh/7f5FJsSMnn6+0NYm9mPp6ne+OU4igLzhgUT3cnKxrnotdxsy2D8yFZCsjXtt2V7jert2+rnEkIIIYRoC/aFjaYGSoqVmSyU2R5zq5rhUltJsdLuFXAJqxJwGRZW/fP0YFu2y4mMwlr7z9qzW+YODSayp2crjlIIIURbk4CL6FBW7DzLoeQCvFz1PD5vSL3bajQaZg8NZt2D03h+YTQ9PF04nVVMqcnC4GAvJkb6t9o4BwbZS4pJhouoX36piSO2Rol/GBvWwNbNMz5QIbKHO7klJv5vy+lG7fNbQiZZRRX08DQyrY4GjZcO78XtF0S0Sd+TrmZSZOP7uFitCj8fUrM0zmSXsPl462e5ZBSWsfZwGgAPXDyg1c/XGq6f0BsXnZYD5/LYZ2to31r22/u3SLaXEEIIIboIe0kx83nBgBolxcxWymwZLm4uVXq42DJcsovKHb1VC8q6b0mxYectYArzc8PLqMdkUTiZWf2+QWp+KT/Eqpnmdzuh1LQQQoiORQIuosPIKirnpXUJAPxl9iB6ehkbtZ9Bp6503vTIDB64qD8DAj352/whrXqTeECgulrlVFZRratVhLDbcyYHRYHIHh4EerVOXV6dBh6aqd40f3/LKbKKatZTPt/Xe88BcMWoXjXK9omWm9yvBwDbGhFw2ZuYS3pB5ffsk+1NKw3XHCv3JWOxKozu7euoL93Z9PA0ctnIXgAs+/1Mq52n3GzhSIoaNB0V3nn63AghhBBC1Megs5cUq57hUl5LhktlDxdtlR4uFVitCn/433bmvL6Z5LzS7p3hcl7ARaPRMNjW++9oavXKGOsOpWG2Kozt4ycLeoQQoguSu2yiw/j3z0cpKDMztJd3s5rdexr1PDx7EDEPX8jUAbWv2HeWUF83PFx0mCwKZ7OLW/VconPbdToHgPERrZdxBTAnKpDhYT6UVFj474YT9W6bXVTOr0cyALhqTOtk3XR34yL80Wk1JOaUOFb81eWnODW7ZXxf9T2yMSGj3t8riqLw/Op47vtsb71NOOvb/6s9asDtmrHhTd6/I1lkKx255mAqafmN65fTVPEpBVRYrPh7uBDuL83thRBCCNE16LS2kmKW6gEX+4JCT6OapVJutlJmqtrDpTLgsv9cLqcyiympsPDzwVRHDxcf9+4ScHEH1HK39rLjVdkXNtkrHthtOZ4FwMVDglp5hEIIIdqDBFxEhxCXlMfXtj4Xz10e7Uhv7qi0Wg39HX1cpKyYqNvONgq4aDQaHp07GIDPdp6t9yb/DwdSMFsVhoX6dNrsho7O06hnXF81G8Ie3KhN1XJid18YyYUDe6IosHxH3VkuPx1M5YOtp1lzMI35/9nCk6sOklNL09K67EvM5WRmMW4GHfOHhzR6v44oOtSH8X39MVsVPtvp/MygqsGpUeG+Ul5PCCGEEF1GXRku9h4uXq5qwKVqhotblYBLdnEF6w6nO/ZbfzidglJbSbFukuESFeLNTRN78+T8Ibjoa95eG2LrhXmkSoZLhdnKDlufx6kDerTNQIUQQrQppwdcli5dyrhx4/Dy8iIwMJCFCxeSkJBQ67aKonDJJZeg0WhYtWpVtecSExOZP38+7u7uBAYG8sgjj2A2m6tts2nTJkaPHo3RaKR///4sW7bM2S9HtAFFUXj2x3gArhgVypg+naNky8BAtY9LQlphA1uK7qq43Myh5HwAJth6erSmKf17cEH/HpgsCq/9cqzO7b6xBTdbq6eMUN08sS8An+9KpNxsqXWbfbZyYl5GPRcM6MGtk9Xsvq/2JFFaUXOf4nIzL/x0BID+gZ5YFVi+I5HpL23ko99PN6rE4Ve71e///OEheHWBi+FFU/oCsGJnomP1pTPY/zZ9vksNuMjPixBCCCG6Er0jw6Xy86PZYsUef7EHXMrN1iolxXQEeKilv3OKy1l7KM2x7+6zOZzOUrO0u0sPF61Wwz8WDuOWSX1rfb6ypFhlhsv+xFyKKywEeLgQFSKL34QQoityesDlt99+Y/HixezYsYOYmBhMJhOzZ8+muLhmeZTXX3+91tWiFouF+fPnU1FRwbZt2/j4449ZtmwZTz/9tGOb06dPM3/+fGbMmEFsbCwPPvggd9xxB+vWrXP2SxKt7IcDKew9m4ubQedYod8Z2FOGj2dIwEXUbl9iLmarQqivW7WGiq3pkTmDAFi5P7nWYGB8SgGHUwpw0WlZMLxXm4ypu5o9NIhgb1eyiiocZcPO99NB9fGZUUEY9TouHBhIb3938ktN/HAgucb2/914gtT8MsL93Vh9/wV8cddEhoR4U1Bm5tkf45n3xpZ6G8gXl5tZHac26Ozs5cTsZkcF0cvHleziCn48kOKUY1qtCk99f4hl286g0cC/rhzG3OjOnQ0khBBCCFGVvaqEuUqGS9XyYvaFOeVmC2UVVTJcPNUMl/SCchJzSjDqtQwO9kJRIDmvFOg+GS4NGRTkhUYDGYXlZNv6bG49oZYTm9K/B9oOXtlDCCFE8zg94LJ27VoWLVrE0KFDGTFiBMuWLSMxMZG9e/dW2y42NpZXXnmFDz/8sMYx1q9fT3x8PMuXL2fkyJFccsklPP/887z11ltUVKhlU959910iIiJ45ZVXGDJkCEuWLOHqq6/mtddec/ZLEq2otMLCv34+CsB90/sR7NM6TcVbw4AgNcNFSoqJutj7t0xo5XJiVY0I92XesGAUBR79No6f4lIdH+4Bvt2nZjfMjArEz1YOQLQOg07LTRN7A7Bs2xkUpXq5BqtV4eeD6qrAecPUm/k6rcaxz8fbzlbb51RmEe9vOQXA05cOxdWgY2JkAKvvv4AXrojGz93A8Ywi/rhsNxkFtfczWXMwleIKC30D3B0lzzo7vU7LTZPUzKBVsTWDVE1ltSr8bdVBlu9IRKOBf181nOvG927xcYUQQgghOhJ7SbGqARd7OTGoWlLMSplJfdzNRUfAedcQFw7syWUjqy/k8naTgAuAh1FPRA8PoHKh1WZb/xYpJyaEEF1Xq+d55uer5XT8/StvOJaUlHDDDTfw1ltvERwcXGOf7du3M2zYMIKCKhuIzZkzh3vvvZfDhw8zatQotm/fzsyZM6vtN2fOHB588ME6x1JeXk55eeWNx4ICNa3TZDJhMpma9fo6Kvvr6eiv652NJ0nNLyPU15VFk8I7zHgbM3+RAWrGwpmsYopLy2ut2Soa1lneq81hr807prdPq72+2ubvTzP6EROfTuy5PBav2AfA4GAvJkX6870tA2DhiJAuOeftob738NWje/GfDSeIS8pn9+ksRoX7Op7bl5hHWkEZHkYdk/pWvkeuGBHCK+uPEZ9awM6TmYzp44eiKPz9+0OYLAoXDujBhf39qp3vmtG9mDOkJ7cu28PhlEIe/fYA/7txVI0s0i93JwJw1aheNcp0tqeW/h6YHKEGj+KS8qmoqGh2rxWLVeFv3x/m230paDXw7yujWTgiuEv9rHTl37ltoSvMX2ceuxBCCOdxZLhUKSlWUeXfHkZbSbEqPVyMei2uBh3uLjpKbFkvc6ODGRnuy4trK0vJS4ZLpUWT+/L094d5/ZfjzBgUyMGkPACmDujZvgMTQgjRalo14GK1WnnwwQeZMmUK0dHRjscfeughJk+ezOWXX17rfmlpadWCLYDj67S0tHq3KSgooLS0FDe3muV7li5dyrPPPlvj8fXr1+Pu7t60F9dJxMTEtPcQ6pRbDu/E6gANswKL2RDT8crB1Td/igKuOh1lFvhk1Vp6dc23UJvpyO/V5jBZYf9Z9f1dcjaONRlxrXq+8+fvT1GwO0vLsXwNqSUajqYVctRWYszLoFB0YjdrTrbqkLqdut7DI/207MrU8u/vdnDLgMqL2O/OaAEtg71M/Hre779R/lp2ZGh5ceVObh1oJS5Hw5YTOnQahQs80vj5559rPdelPeFoqo6NCVk88/FaJgRWrljMKIU9Z/VoUPDOOcqaNUdb/qKdrLm/B8xW0Gt0FJaZ+eS7n+nZjAp+igIrTqrfKw0KN/W34pISy5qU2GaNqaPrar9z21pnnr+SkpL2HoIQQogOwKBTFwxaqma42AIuLnotrnodAGVVeri4uaiP+Xu4UFJRil6r4eLBQfi4GxgY5Omo/tBderg0xvXje/PxtjOczCzmzk/2YFVgQKBnp6ruIYQQomla9a/g4sWLOXToEFu3bnU89sMPP7Bhwwb279/fmqeu1eOPP87DDz/s+LqgoIDw8HBmz56Nt3fXalZmMpmIiYlh1qxZGAwdc3XJQ1/FYbKmMbaPL0/cNK7ZK5JbQ2Pnb1nyTmLP5RMyaDTzhlVmayWkFfLsT0eZ0NePP13cvy2G3Gl1hvdqc+w6k4N55x56erpw65WzWu39Xd/83W37f3ZROdtP5bDjdA6HUgq4ZWJvFowKbZXxdEcNvYd7Jxdwxbs7iMvVMW7qDHp6GbFaFZa+shko547Zo5k5JLDaPn1SClj4jrpP9MRpvPjRHqCMu6ZGsmjWgHrHYwk8zcsxx/khycg9V0wmxHYx9/L648Bppg3syQ1XjHbSq3cOZ/we+DhpB3HJBfQYWP33cWOtO5zOrh0H0Gk1vHr18GYdozPoqr9z20pXmD97hrcQQojuTW/LcDFVyWox2UqKuei0uBrUgEy5yUqZqbKHC0CAhwtJuaVM6heAj7v693Du0GCOpZ9Ar9U4thNqYOvJ+VHctmy3YwHcBVJOTAghurRWC7gsWbKE1atXs3nzZsLCwhyPb9iwgZMnT+Lr61tt+6uuuoqpU6eyadMmgoOD2bVrV7Xn09PTARwlyIKDgx2PVd3G29u71uwWAKPRiNForPG4wWDotBfNDWnP11ZmsvDdvmSKyk2M6eNHdKgPRtsqmb1nc1h9MA2NBp65LBoXl47ZS6Kh+Rsc7E3suXxOZZVgMBhQFIWv9pzj6e8PU262Ensuj3tnDHCkY4u6dbWfw32J6g2t8ZEBbfL+rm/+gv0MXDHGkyvGSB+K1lTX92BU3wBG9/ZlX2IeX+1L4cGZA9l7Npe0gnI8jXpmDAnGcN5F6cg+AYzt48ees7ncumwvyXllhPi4cv/MgRgM9f8+uXfGAH5NyGR/Yh5PrIrn09vHY7EqrIxVy8ldP753h/1Za8nvgWFhvsQlF3AkrYiFo5t2DEVReG/rGQDuuTCSy0eHN2sMnUlX+53b1jrz/HXWcQshhHAuva2HS10ZLkZHhoulRsAloocHB5LyWTC8snfLvOEhvLnxBOH+7h1qMWVHMH1QT6YO6MEWW/+WaVJOTAghujSn3wVWFIX777+flStXsmnTJiIiIqo9/9hjj3HHHXdUe2zYsGG89tprLFiwAIBJkybxwgsvkJGRQWCguuo3JiYGb29voqKiHNusWbOm2nFiYmKYNGmSs1+SaCJFUVh3OJ0X1sRzLqfU8biLXsuIMB/G9PFn87FMAK4dG050qE97DbXFBgR5AXAsvYjicjNPrTrEd/vVps0aDZgsCr+fyGL20K65UlrUbdeZHAAmRPg3sKXoDm6d3Jd9ibF8tjOR+6b3Z42taebFQwJxrWMF4C2T+7LnbC5Juerv0SfnR+Hu0vCfbZ1Wwyt/GMG8/2xh64kslu9MpJePKxmF5fh7uHDR4KAGj9EZDbP9LTmYnN/kfX8/kU1cUj6uBi1/nBLR8A5CCCGEEJ2cXqtmsJgsVQIuVTJcjFUyXOwlxeyfW5+YP4S50SHMGVr5uXJwsDcr7phIgGfHXEzZnjQaDU/Oj2Lef7bgZtAxIVKuEYUQoitzepfvxYsXs3z5clasWIGXlxdpaWmkpaVRWqreMAoODiY6OrrafwC9e/d2BGdmz55NVFQUN998MwcOHGDdunU8+eSTLF682JGhcs8993Dq1Cn++te/cvToUd5++22++uorHnroIWe/JNEECWmF3PTBTu5ZvpdzOaUEehmZOSQIfw8XKsxWdp/J5d3fThKfWoCnUc+fZw9q7yG3yMAgTwD2n8vlsv9u5bv9yei0Gv46dxA3T+wDwCZbcEl0HyaLlb1ncwGYEBHQzqMRHcEl0SH09DKSWVjOmoOp/GwLuMwbFlLnPnOHBtPDU/2bN6V/QJNKXEX29OSvcwYDsHTNEd7aeAKAK0aF4qJ3+p/+DsEevD+UnI+iKA1sXZ19fq4b15sAz5qZsEII4WxLly5l3LhxeHl5ERgYyMKFC0lIqGw4nZOTw/3338+gQYNwc3Ojd+/ePPDAA+TnVw8qJyYmMn/+fNzd3QkMDOSRRx7BbDZX22bTpk2MHj0ao9FI//79WbZsWVu8RCFEB6ezlRQzWytLitkzXAx6TZUeLhZKK6oHXAK9XJkbHVwjk2VSvwAG2hYliuoGBXvx3b2T+eruSY1aRCWEEKLzcvpv+XfeeQeA6dOnV3v8o48+YtGiRY06hk6nY/Xq1dx7771MmjQJDw8Pbr31Vp577jnHNhEREfz000889NBDvPHGG4SFhfH+++8zZ84cZ70U0QR5JRW8FnOM5TsTsVgVXPRa7poayb3T++Fh1KMoCqezitlzNpc9Z3JISCvkjxdE0NOrc9/Ysn+YTC8oJ51ygryNvHn9aMZH+LMxIYNPtp9l09EMFEWRtOpu5FByPiUVFnzdDQwI9Gzv4YgOwEWv5cYJvXn9l+O8sOYImYXleLjouHBg3eUEXPRa/r4gihU7E/nnFcOa/Dtk0eS+rDucxs7TOexLzAPgmrFdt1TWwCAvXHRaCsrMJOaU0CfAo1H77U/MZfupbPRaDXdOi2zlUQohhOq3335j8eLFjBs3DrPZzBNPPMHs2bOJj4/Hw8ODlJQUUlJSePnll4mKiuLs2bPcc889pKSk8M033wBgsViYP38+wcHBbNu2jdTUVG655RYMBgP//Oc/ATh9+jTz58/nnnvu4bPPPuPXX3/ljjvuICQkRK6bhOjmDDp1EU61kmJ1ZLiUmdTH3VykN0tLjAj3be8hCCGEaAOtUlLMGfv06dOnRsmw802fPp39+/c3+XzCeaxWhW/2JrH05yPklpgAdVX2E/OG0DvA3bGdRqMhsqcnkT09u9QNv0AvIz08jWQVlTN1QA9eu3akY0X6pMgAjHotKfllHEsvYlCwrPTpLnadVsuJjevrj1YrgTahumFCb97aeILMwnIALh4SVGc5MbsFI3qxYESverepi1ar4eU/jGDO65spqbAwIsynS/8ectFrGRziRVxSPgeT8xsdcHl700kAFo4KJdS39h5wQgjhbGvXrq329bJlywgMDGTv3r1MmzaN6Ohovv32W8fz/fr144UXXuCmm27CbDaj1+tZv3498fHx/PLLLwQFBTFy5Eief/55Hn30UZ555hlcXFx49913iYiI4JVXXgFgyJAhbN26lddee00CLkJ0c/YeLlVLipnsGS46La62rOiqPVxcDV0zU1oIIYRwJsljFM0Wn1LAU98fcpROGhjkyTMLhjK5f492Hlnb0Wg0/O/mMSTmFHP5iNBqN9ddDTom9QtgU0ImmxIyuvSNTlGdPeAi/VtEVYFerswfFsIqW/P6+sqJOUu4vztLrxzGMz8c5v6LBrT6+dpbdKiPI+By6fCGA1XH0guJiU9Ho4F7LuzXBiMUQoja2UuF+fvX/dkhPz8fb29v9Hr1Em779u0MGzaMoKDKHgpz5szh3nvv5fDhw4waNYrt27czc+bMaseZM2cODz74YJ3nKS8vp7y83PF1QUEBACaTCZPJ1OTX1lL2c7bHubsymVfn6axzqVjVIIrZanWMvaRc/b+LToO9Cm1ZhdnRw0WvUdrsdXbWee3oZF6dR+aydci8OpfMZ/O1ZM4k4CKarLDMxGsxx/l4+xksVgUPFx0PzhzIoil9HWnJ3cmYPn6M6eNX63MzBgWyKSGTjQkZ3C0387oFi1Vh1xl7wEX6t4jqFk2JYFVsCl6ueqYPqrucmDNdPjKUy0eGtsm52tvwUB9WAAeT8hvcFuBdW3bLnKhg+kv5PyFEO7FarTz44INMmTLF0d/yfFlZWTz//PPcddddjsfS0tKqBVsAx9dpaWn1blNQUEBpaSlubjUz+5YuXcqzzz5b4/H169fj7u5e4/G2EhMT027n7spkXp2ns81lTjmAnooKs6O6SGy2BtBRVJDP0cO5gI6k1HRKKzSAhm2bN3HYpW3H2dnmtbOQeXUemcvWIfPqXDKfTVdSUtLsfSXgIppk95kcFn+2jwxbSZz5w0J48tIhhPhIGZbazBgUyN85zJ4zuRSUmfB2NbT3kEQrO5pWQGGZGU+jniEhktUkqhsZ7suHi8bi72FssJyYaLroUB9A7aPUUO+sczklfH9AzTa6b4YExIUQ7Wfx4sUcOnSIrVu31vp8QUEB8+fPJyoqimeeeabVx/P444/z8MMPVzt/eHg4s2fPxtvbu9XPfz6TyURMTAyzZs3CYJDP0s4i8+o8nXUu0wvKeHbfZqwaLfPmqSUGLXGpcOwgQT0DGD8mjOUn4jB6+aLkq5lul86dhVcbXdN21nnt6GRenUfmsnXIvDqXzGfz2bO8m0MCLqLR4pLyuO2j3RSVm+kb4M5zl0czrZ6GzwJ6B7gT2cODU1nF/H48i0tasYSQxapwNK2AqBDvJjfX7m7KzRYMWm2r9FexlxMb08cPfTfM+BINu2hwUMMbiWYZGOSFi05LQZmZxJySevu4/N+WU1isChf078HwMN+2G6QQQlSxZMkSVq9ezebNmwkLC6vxfGFhIXPnzsXLy4uVK1dWu1AODg5m165d1bZPT093PGf/v/2xqtt4e3vXmt0CYDQaMRqNNR43GAzteqHe3ufvqmRenaezzaWbUe3XYrEq6PV6NBoNFkW9PjIadHi6qaks+aVmxz6ebq4Y9G17jdPZ5rWzkHl1HpnL1iHz6lwyn03XkvmSu4GiUU5kFHLrh7soKjczMdKftQ9Ok2BLI00fFAjApoTMVj3P098fYv5/tvL5rnOtep7O7lRmEaOfi+Hez/aiKErDOzSRPeAyXvq3CNHmXPRaBtsyyw4m111WLLOwnC93q78r75su2S1CiLanKApLlixh5cqVbNiwgYiIiBrbFBQUMHv2bFxcXPjhhx9wdXWt9vykSZM4ePAgGRkZjsdiYmLw9vYmKirKsc2vv/5abb+YmBgmTZrUCq9KCNGZVF0cZraq10Umi/p/g06LUa9mY+eVqDXsdVoNBp0s7BNCCCEaIgEX0aBzOSXc9P4ucktMDA/z4f1bx0kpnCaYMVgNTG1MyGiVG/yglrH6fFciAF/ukYBLfT7ZfpbiCgvrDqez7nB6wzs0gaIojoDLxEgJuAjRHuxlxeoLuHz0+2nKzVZGhPsyqZ/0WhJCtL3FixezfPlyVqxYgZeXF2lpaaSlpVFaWgpUBluKi4v54IMPKCgocGxjsajNq2fPnk1UVBQ333wzBw4cYN26dTz55JMsXrzYkaFyzz33cOrUKf76179y9OhR3n77bb766iseeuihdnvtQoiOQV8l299iC7hUmNXfLy56La4G9XZRQZkacHEz6KSSghBCCNEIEnAR9cosLOfmD3aSVlDGgEBPlt02Hk+jVKJrivER/rgZdGQUlhOf2vz6f/X5189HsX1G5sC5PM7lNL+xU1dWbrawKjbZ8fU/foqnzGRx2vFPZhaRXVyBUa9lWKiv044rhGi8YVX6uNSmpMLMpzvOAmp2i9w4EEK0h3feeYf8/HymT59OSEiI478vv/wSgH379rFz504OHjxI//79q21z7py6uEan07F69Wp0Oh2TJk3ipptu4pZbbuG5555znCciIoKffvqJmJgYRowYwSuvvML777/PnDlz2uV1CyE6Dn2VbBWTRS0vVmH7v7FKhot9zaA9ACOEEEKI+smdc1Gn/FITt3y4izPZJYT5ufHp7RPw93Bp72F1Oka9jin9A/jlSAabEjIZ2svHqcf//UQWmxIy0Ws1RPb04Fh6ET/GpXDf9P5OPU9XsP5wOnklJoK9XdFoICm3lPc2n+KBiwc45fgbjqolPUb39sOljWsbCyFUlQGXAhRFqRFQWbU/hcIyM30C3Jk1RPrpCCHaR0NZz9OnT29UZnSfPn1Ys2ZNg8fav39/k8YnhOj69NoqJcUsNUuKnR9gkSoXQgghROPIHUFRqzKThT8u282R1AJ6eBpZfvsEgn1cG95R1Kqyj0tGA1s2jdWqsPTnIwDcNLEPt01R63+vPpDq1PN0FfaeDdeMDeOJeUMAeHvTCZLzSlt8bItVcayav3xkrxYfTwjRPAODvHDRackvNXEup/rPtqIofLL9DAA3T+yDVivZLUIIIYTonnRaDfZ1KfYeLuVmNcPFRV+Z4WLnJgEXIYQQolEk4CJq9en2s+w9m4u3q55Pbx9P3x4e7T2kTm36ILWPy96zueTbmg46ww8HUjiUXICnUc/9F/Vn7tBg9FoN8akFnMwsctp5uoJzOSVsPZEFwB/GhnPp8BDGR/hTZrKydM2RWvcxW6y8+etxHvh8P/ml9X/fNhzN4FxOKT5uBi4fGer08QshGsdFr2VQsBdQs4/LrtM5HE0rxM2g4w9jwttjeEIIIYQQHYbBluVitqqBFntpMYNOi/G8DBc3Fwm4CCGEEI0hARdRQ2GZibc3nQDgyflRDAnxbucRdX5hfu4MCPTEqsCWE5lOOWaZycJL6xIAuHd6PwI8jfh5uHDBgB6AZLmc7+u9SQBM6R9AuL87Go2GZxYMRauB1XGp7DiVXW37rKJybv5gF6/EHOOHAymOn4m6fLztDADXjQuXixEh2tmwMLWsWFxyXrXHP7FloS0cFYqPu6GthyWEEEII0aHobNm+9pJiFfVkuLjq5RpHCCGEaAwJuIgaPth6mtwSE5E9PbhytKzUd5YZg9WyYhuPOifg8un2syTnlRLs7cofbaXEABYMV8tZ/RiX0qja392Bxarw9R61nNi143o7Ho/q5c0NE9Svn/nhMGbbiq59iblc+p+tbD+VjYtO/TW57PczpOWX1Xr8ExmFbD2RhVajlnYTQrSvyj4ulRkuafllrDuUBsAtk+TnVAghhBBCr7MFXKz2Hi6VAZcaPVxkUZkQQgjRKBJwEdXkFFfw/pbTAPx51iD0OnmLOIu9rNhvxzKwWlsWCMkrqeDNDccBeHj2wGoZFbOGBuGi03Iio4iE9MIWnaer2HI8k9T8MnzcDMyOqt4k+8+zBuHjZuBoWiErdiXy6Y6zXPu/7aQVlNGvpwc/PXAB4/r6UW628savx2s9/sfb1FXzFw8JItzfvdVfjxCifpUBlwJH4HnFrkTMVoXxff0lc1MIIYQQAtA7MlzUQIsjw0WnwUWndfR4AXAzyL0BIYQQojHkL6ao5t3fTlJUbmZoL28uiQ5u7+F0KWP7+OPhoiOrqIJfj2aw41Q2K/cn8dbGE/xt5UGe+eEwZSZLo4711sYTFJSZGRTkxVWjw6o95+1qcAR3fjyQ4vTX0Rl9ZctuuWJUKK7nNXv083DhL7MHAvDsj/E8teoQJovCJdHBfL/kAgYEefHXuYMdxzl1Xm+cgjIT3+5Ty5XdNrlvK78SIURjDAzywkWnJb/UxLmcUirMVlbsTATglsmS3SKEEEIIATgWWNozXKqWFNNoNBj1lbeMzr+OEkIIIUTtJOAiHNLyyxx9KP4yZxBarab+HUSTuOi1jv4qd36yh+ve28FDXx7gpXUJfLYzkWXbzvCNrc9IfZLzSh0ZFY/NG+you1vVghG2smIHUrt9WbHsonJi4tMBuHZc7U2yrx/fm8HBXlisCloNPDFvMG/fOBpPox6AcX39uXhwIBarwisxx6rt+/WeJEoqLAwM8mRSv4DWfTFCiEZx0WsZFOwFwMHkfH4+lEpWUTmBXkbmDJXFBEIIIYQQAIbze7jYMl0MtkBM1T4ubhJwEUIIIRpFAi7C4c0Nxyk3WxnX14/pA3u293C6pGvHhaPXanDRa+kb4M7kfgFcNTrMkZGy7nBag8f4bm8SFRYr4yP86/w+XTwkEDeDjsScEg5W6WHQHa3cn4zJojA8zKfOMkJ6nZY3rx/FlaNCWXHnRO6a1g+Npnog6y9zBqHRwE9xqRxMUufUalX4dPsZAG6Z1LfGPkKI9hNtKyt2MDmfT7erQeobJ/Rx3EAQQgghhOjudI4eLueVFLNltlTt4yIZLkIIIUTj6Nt7AKJjOJtdzJe71bJLj8wZLDeOW8lFg4M4/NwcWz3cyjk+lVnEpoTf2H4ym/xSEz5uhjqPscbW9Pnq0WF1fp/cXfRcPCSQ1XGp/HggheFhvk59HZ2FoiiO9/U1Y2vPbrEbEOTFq9eOrPP5ISHeLBwZysr9yby47iif3j6B345lcia7BC9XPVeMCnXm0IUQLTQs1IfPUUsrJueVYtBpuH5C/b8HhBBCCCG6E4O2ekkxU30ZLi4ScBFCCCEaQ5Z5CgBe/+U4ZqvC9EE9GR/h397D6dKMel2NQElkT08GBHpitipsSsioc9/TWcUcSS1Ap9Uw67zm7+e7dLhaVuynuFSs1u5ZVmxfYh7HM4pwNWi5bGSvFh/voZkDMeg0bDmexbYTWSyzleC7Zmw4HkaJXwvRkQyzZbgk55UCMDc6hEAv1/YckhBCCCFEh2IvT20PtNhLihlry3DRS8BFCCGEaAwJuLSjonIzSbkl7T0MEtIKWRWbDMBfZg9q59F0X7OHqgGU9YfT69zm50OpAEzuF4Cfh0u9x5s+qCeeRj0p+WXsS8x13kA7ka9s2S3zhoXg7Vp31lBj9Q5w54bxvQF4ctUhfjuWiUYDt0ySJtxCdDQDgz1xqVI+7Fb5ORVCCCGEqEZv+6xksS3Qc5QUqzXDRW4fCSGEEI0hfzHbycr9SYx+PoZ/rD7S3kPhlfUJKArMHxbiqHkv2t7sKLWR86aEDMpMllq3+fmgWk5s3rCQBo/natAx25YFszou1Umj7DxMFis/HVRf97UNlBNriiUXDcDdRceprGIAZgwKpE+Ah9OOL4RwDqNex6BgLwCiQrwZ08evnUckhBBCCNGxGOw9XCy2gIvt//aSYlUzXNykh4sQQgjRKBJwaScDg7yoMFvZmJBBcbm53cax81Q26+PT0WrgoVkD220cAoaH+RDs7UpxhYVtJ7NqPJ+YXcLB5Hy0GhyBlIYsGKGW0Vodl1pnEKerOpicT1G5GT93A+P6Oq9MXk8vI7dfEOH4etHkvk47thDCuS4eEgjAfTP6SW8yIYQQQojz2EuKmc/PcNHXzHAxSsBFCCGEaBQJuLSTqBBv+ga4U24LurQHq1XhHz+pGTbXje9N/0DPdhmHUGk0mnrLitnLiU2MDCDA09ioY07p3wM/d8P/s3fn4U2UaxvA76RN0zVdgLYUCpR9h8paRRaBlkUURTkIokdQXEBF/JCDC4LLQXFFRTkcF/QILrigIgIFhLKUrbQsBcpWKFtaaGnTNUmT+f5IM02atE2bpEna+3ddXnZm3sy880xaMnnmeV/cKFJjzIdJVhM5jdWBzDwAwIB2YZBKHftF62ND26NLRBCGdGyOIR2bO3TfROQ4T9/RCQdeHCnOaUVERERElWRSw1dC5RVztxjncmGFCxERUf0x4eIiEokEYyuGhTIOE9XQfkm9gmNXChAk98Y8Vre4hYQehmHFEk9ki+PoGm08bnifjLVhODEjH28pVky9BZEKX1zILcHU/+7HCz8dQX6JxnGddlPGhMvAGMdVtxgpfGXY/NxQfPvoIIcnc4jIcbykEoQrfF3dDSIiIiK3ZKxw0dpQ4cKECxERkW2YcHGhcT0NX5xvP5WDUk3DDvdUoinHO5tPAQBm39ERzW2smCDnGhgTBoWvN3KLNWYT3V++WYIjl/IhkQBjKpIytrq1Y3MkzhuK6YMNE0b/eOgyRr2/ExuOXoUgCLW82jPp9AIOViRcBrdv5uLeEBERERERuR/vijlcdHrzChe5MeFiWuHiw4QLERGRLZhwcaGerRRoHeqHUq0OOxp4WLGVO88jW6VGdJgfHrmtXYMem6on85JiZDfjsGKVlU+bKqpbBrYLQ4uguifHgnxleH1iT/z0RBw6hgfiRpEGc9amYuzyXVj4yzGs3Z+F41cKxCeaPN3JayoUqssRKPdGt5YKV3eHiIiIiIjI7RiHDtPqzCtcjOtNK1xMhxcjIiKi6vFfTBeSSCQYVzE8lHG4qIZwraAUq5LOAQAWju1m9iGKXC+hYh6XzenZYgXKxmOG+VvG1WE4MWv6twvDn88MwdxRneDjJcUpZSG+O5CFF389hjs/3o2er27GxBV7kHT6un0n4WLG4cT6twsVy+SJiIiIiIiokvFeSVfNkGKmSRZfDilGRERkEyZcXGxsT8PwUNtPZqNM2zDDii3blIEyrR4D2oWKxyf3MbRzC8i9pcjKK0FGdiGuFZTicFbFcGIOuF5yby/MHdUZu/81AisfvAVPDe+A2zs1R7CfDBqdHmmX8jHz64NmFTaexpnztxARERERETUGsoohxcorhhLT6PRm6zmHCxERUd15u7oDTV3f6BBEBfviakEZkk5fR3wd5+eoqyOX8vFr6hUAwMvju0Mi4dP/7sbfxxu3d2qOrSdzsPl4NhR+hl/T/m1DEeHAyZ/Dg3wxpmdLjKmYS0gQBFzKK8Xbm0/hz6PX8NSaw/j4gViMtbOqpqEJgoADFwwJl0ExnL+FiIiIiIjIGi9p5ZBigiCICRdjhYtxLheAFS5ERES2YoWLi0kkEvELbeOwUc4iCAJe33ACAHBvbCv0iQ5x6vGo/oyJty0nlPjrmKHSZGxP5yY+JBIJ2jTzx/J/9MXdfaNQrhcw57tUbDh61anHdbSzOUXIK9bAVyZFr1bBru4OERERERGRW5KZDCmm0wuoGNEaci9DcsU0ycIKFyIiItsw4eIGxvUyfLm+9WQO1OXOG1Zs4zElDl28CV+ZFPPHdHHacch+I7uGQyoB0q+qcPCioVrDEcOJ2cLbS4r3J/fFvbGtoNMLeOa7VPyWdqVBju0I+yuGE7ulTaj4ZBYRERERERGZ864YOkyr14vVLQAg8zYOKVZ5P+Xnw4QLERGRLfhtpBuIjQ5FhEKOInU5dp+5YbWNXi+gVGNfMuY/SecAALOGdkDLYD+79kXO1SxQjgHtDPOPCAIQ2yYEUSENd828pBK8c38f3N+vNfQC8NwPafjl8OUGO7499nP+FiIiIiIioloZhxTT6QRoyisTLj5ehvWmFS5yPsxGRERkE/6L6QakUok4XNTGY5YTlecWqTHx0z0Y8OZWZOWW1OsY568X4ejlAnhJJXgorq1d/aWGYTqfz3gXzKPiJZXg7Um9MWVANPQC8Py6I0ipqLZxV4Ig4EBmLgDO30JERERERFQTmVjhUjl/i0RiuBcEKpMsvjIp538lIiKyERMubmJsxXBRiSeUZk+WZKvKMGXVPhy9XIAidTm+Tr5Qr/2vTzPMw3F7p+ZoHii3u7/kfPHdI8SfG2o4saqkUgn+fU8vjO/dEoIAfLnngkv6YausvBJkq9SQeUkQ2ybE1d0hIiIiIiJyW4FybwCAqlQrfg/h41WZXDFWuHD+FiIiItsx4eIm+rcLQ4sgOVRl5dhzzjCs2OWbJZj8n2ScySkSPwj9eOgSSjTlddq3IAjiHBwT+7ZybMfJaaLD/PH+5D748B990TrU32X9kEolmD28IwBgS7oSN4rULusLABSpyyEYZ3OswjicWJ/WIWbl70RERERERGQuMtgXAKAsKINWZ7jHMp0H01jhwoQLERGR7ZhwcRNeUgnGVAwh9dexa8i8UYzJK5NxMbcE0WF+2PjM7WjXzB+FZeVYn3q1TvtOvZSPi7kl8JN5YbRJ1QS5v3tvaY2Jsa5PknWPUqBPdAi0OgHrDtU+l8vxKwUoKNU6vB97z95Az1c3Y8kfJ6xu33+e87cQERERERHZIjzIkHDJLiwzq3AxMj7ExofZiIiIbMeEixsZ28uQcNl0XInJ/0nG1YIydGgRgHWP34o2zfzx4GDD3CvfJF+o9gl/a35LNVS3JPSIQEBFpQxRXU0b2AYA8N2BLOj11b//tqQrcefHu3H/yr0o1egc2oc/jl4DAKzeewFbT2RbbD9woWL+lvacv4WIiIjc19KlSzFgwAAEBQUhPDwcEydOREZGhlmbsrIyzJ49G82aNUNgYCAmTZqE7Gzzzz9ZWVkYP348/P39ER4ejvnz56O83LwafseOHbjlllsgl8vRsWNHrF692tmnR0QewljhkqNSVyZcTCpc+rYJQf+2oZgyMNol/SMiIvJETLi4kYHtwtAswAeqsnJcL1Sja2QQfng8TvwQdH+/aPjJvHBKWYiDF27atE+tTo8NFV9S3+0GlRLkue7s0xJBcm9k5ZVg77lcq220Oj2W/nUKAHA6uwivbUh3aB8OXsgTf/7XL0eRazK82dX8UlzKK4VUAvRrG+rQ4xIRERE50s6dOzF79mzs27cPiYmJ0Gq1iI+PR3Fxsdjmueeewx9//IF169Zh586duHr1Ku69915xu06nw/jx46HRaLB37158/fXXWL16NRYtWiS2yczMxPjx4zFixAikpaVh7ty5ePTRR7F58+YGPV8ick8RCsP8rtmqMqjLDQ/LyUwqXALl3vjpyVsxa2gHl/SPiIjIEzHh4ka8vaQY37slAKBPdAi+nzXYbIL7YH+ZOLzU18kXbNrn3nO5yC3WoFmAD27v2Nzhfaamw9/HG/fcYnj/rT1w0Wqb7w9kIfNGMYJ8vSGRAN8duIQ/jtRtCLzq5BVrcDanCADQvnkAbhRp8OKvx8RqrwMV87f0bBUsznlERERE5I42bdqEf/7zn+jRowf69OmD1atXIysrCykpKQCAgoICfPHFF3j//fdxxx13oF+/fvjqq6+wd+9e7Nu3DwCwZcsWnDhxAt9++y369u2LsWPH4vXXX8eKFSug0WgAACtXrkRMTAzee+89dOvWDXPmzMF9992HDz74wGXnTkTuo3mgHBIJUK4XoFSVATCvcCEiIqK647+kbmZ+Qhd89EAs1j46CCH+PhbbH4ozDCu2+bgSyoKyWvf3+xElAODO3i3h7cXLTfaZOsgwrNiW9GzkFJq//wrLtPhw6xkAwAsJXTBnREcAwIu/HENWbondxzZWt3QMD8THU2Mh85Jgc3o2fjlsGDJvf0XCZWA7zt9CREREnqWgoAAAEBZm+ByTkpICrVaLUaNGiW26du2KNm3aIDk5GQCQnJyMXr16ISKico7GhIQEqFQqpKeni21M92FsY9wHETVtMi+p+JDn5Zul4joiIiKqPz4G7maCfGW4q09Utdu7tVRgYLswHLiQh7UHsjBvdOdq26p1QOJJwzjPHE6MHKFrpAKxbUKQmpWPdYcuY3ZFUgUAViWdR26xBjHNAzBlYBtIACSfy8Whizfx9HeHse6JW+16WupgRUJlQLsw9IgKxtxRnfHO5gws/j0dgzs0w4FMzt9CREREnkev12Pu3Lm47bbb0LNnTwCAUqmEj48PQkJCzNpGRERAqVSKbUyTLcbtxm01tVGpVCgtLYWfn59Ff9RqNdTqymFbVSoVAECr1UKr1dpxpvVjPKYrjt2YMa6O4+mxDA/ywfVCNS7eMIwmIPNyj3Px9Li6K8bVcRhL52BcHYvxrD97YsaEiwd66Na2hoTL/izMGdGx2i+xj+VJUKrVo20zf8RGhzRsJ6nRmjqwDVKz8vH9wSw8OawDpFIJslVl+O+u8wCABWO6iE9FLX8gFmM/TMKRywV4d0sGXhzXrd7HNVa4DIwxzM/y+ND22HYyG4ez8vHUtyk4d90w5vmAdpy/hYiIiDzH7Nmzcfz4cezevdvVXQEALF26FEuWLLFYv2XLFvj7+7ugRwaJiYkuO3Zjxrg6jsfGslQKQIrU01kApCguKMDGjRtd3SuRx8bVzTGujsNYOgfj6liMZ92VlNR/tB4mXDxQQo9IhAfJkVOoxqZ0ZbUVMYduSAAAd/dtBYlE0pBdpEbszt5ReG3DCVzKK8WuszcwrHMLvL/lNMq0evRrG4qEHpFi21Yhflh2Xx888W0KViWdR1yHZhjRJbzOxyxWl+P4VcPTlQMqhgzz9pLi/cl9Me6jXThy2TAMR9fIIKtD8RERERG5ozlz5mDDhg1ISkpC69atxfWRkZHQaDTIz883q3LJzs5GZGSk2ObAgQNm+8vOzha3Gf9vXGfaRqFQWK1uAYCFCxdi3rx54rJKpUJ0dDTi4+OhUCjqf7L1pNVqkZiYiNGjR0MmkzX48RsrxtVxPD2WyeUnkH7wMtTegQBKENGiGcaN6+/qbnl8XN0V4+o4jKVzMK6OxXjWn7HKuz6YcPFAMi8ppg5qgw+3nsE3ey9YTbjkFqmRkW9IskzsW/0QZUR15efjhUm3tMbqvRfw3f4sRCp8sS7lEgDgxXFdLZJ7Y3pG4qG4tvgm+SKe//EI/nr2dkQofOt0zNSsfOj0AqKCfdE6tPLJynbNA/DS+G546dfjAICBMZy/hYiIiNyfIAh4+umn8euvv2LHjh2IiYkx296vXz/IZDJs27YNkyZNAgBkZGQgKysLcXFxAIC4uDi8+eabyMnJQXi44YGWxMREKBQKdO/eXWxT9Un1xMREcR/WyOVyyOVyi/UymcylN+quPn5jxbg6jqfGsmWw4f7qSr5hjk65zMutzsNT4+ruGFfHYSydg3F1LMaz7uyJF2dD81BTB7aBt1SCQxdv4viVAovtG49nQw8JerVSoH2LQBf0kBqzBwa2AWCYI+ilX49BLwBjekSiX1vrCY8Xx3VD18gg5BVrsPCXYxAEoU7HO1AxnNgAKwmVqQPbYFQ3w9jkptU1RERERO5q9uzZ+Pbbb7F27VoEBQVBqVRCqVSitNQwaXVwcDBmzpyJefPm4e+//0ZKSgoeeeQRxMXFYfDgwQCA+Ph4dO/eHdOnT8eRI0ewefNmvPzyy5g9e7aYMHniiSdw/vx5vPDCCzh16hQ+/fRT/Pjjj3juuedcdu5E5F4iFIa/FxqdHgDE4aGJiIiofvgvqYcKV/hibK+WAIB3NmdgR0YOlAVl4hfZvx+9BgCY0Luly/pIjVeXyCD0axsKnV7AoYs34S2V4IUxXapt7yvzwkcPxMLHS4rtp3Lwy+ErdTrewcyKhEs7y4SLRCLBf6b3w64XRuC2js3rdiJERERELvDZZ5+hoKAAw4cPR8uWLcX/fvjhB7HNBx98gDvvvBOTJk3C0KFDERkZiV9++UXc7uXlhQ0bNsDLywtxcXF48MEH8dBDD+G1114T28TExODPP/9EYmIi+vTpg/feew+ff/45EhISGvR8ich9RQSbjz5Q3RyxREREZBsOKebBHo5riz+OXMXO09ex8/R1AECwnwydwgORdqkAEgi4sxef+CfnmDqwDVIu3jT8PKhNrZVUnSOC8OyoTnhncwaW/JGOIZ2a2zS0mFanR+olw3GqGzLMSypBdJjrJnElIiIiqgtbqn19fX2xYsUKrFixoto2bdu2rXVy6+HDhyM1NbXOfSSipiEiiAkXIiIiR+K/pB6sf7swvHVvL4zv3RIdwwPhJZWgoFSLQxVfgncJFtAiyHL8ZSJHGN+7JVqF+KF5oA+eGdnJptc8PrQ9erUKhqqsHC/aOLTY8SsFKNPqEeIvQ0cOj0dEREREROQwxiHFjHw4pBgREZFdWOHi4aYMbIMpFfNplGl1OH+9GBnZKmTdKEZQ3ikX944aM1+ZF/6aezsEPRDsb9tEUt5eUrx7fx9M+Hg3tp3Kwfq0K7izZ0SNrzlYMX9L/7ZhkEoldvebiIiIiIiIDMICfCDzkkCrMzwMxwoXIiIi+/Bf0kbEV+aF7lEK3BPbGk8Nb49mtY/WRGQXha/M5mSLUZdIw9BiALD49xPIKVTX2P5ApnE4sdD6dZKIiIiIiIiskkgkCDcZVkzGChciIiK78F9SImpwxqHFCkq1WPT7CVQ3spheL+DQRUOFy4B21udvISIiIiIiovozHVaMFS5ERET24b+kRNTgvL2keOf+3pB5SbDt1HWk3LA+VNjZ60XIL9HCT+aFnq2CG7iXREREREREjV9kcGWFC+dwISIisg//JSUil+gaqcAzdxiGFvs5U4qr+aUWbQ5kGqpbYtuEsLSdiIiIiIjICUyHFGOFCxERkX34LykRucwTwzugZ5QCJToJHv82FUXqcrPtBy9wODEiIiIiIiJnilCwwoWIiMhR+C8pEbmMzEuKTx7ogyCZgFPZRZj7fSp0+soJXQ5WVLgMjGHChYiIiIiIyBkigyvncJF5WR/umYiIiGzDhAsRuVSrED882kUHH28ptp7MwdubTgEALt8swdWCMnhLJYhtE+LaThIRERERETVSEWZDinm5sCdERESez+EJl6VLl2LAgAEICgpCeHg4Jk6ciIyMDHF7Xl4enn76aXTp0gV+fn5o06YNnnnmGRQUFJjtJysrC+PHj4e/vz/Cw8Mxf/58lJebDze0Y8cO3HLLLZDL5ejYsSNWr17t6NMhogbQLgh4+54eAIBVSefxw8EscTixHq2C4e/j7cruERERERERNVrhCs7hQkRE5CgO/5d0586dmD17Nvbt24fExERotVrEx8ejuLgYAHD16lVcvXoV7777Lo4fP47Vq1dj06ZNmDlzprgPnU6H8ePHQ6PRYO/evfj666+xevVqLFq0SGyTmZmJ8ePHY8SIEUhLS8PcuXPx6KOPYvPmzY4+JSJqAHf2bom5ozoBAF769ThW770IABjYLtSV3SIiIiIiImrUIoMrEy4cUoyIiMg+Dn9sfNOmTWbLq1evRnh4OFJSUjB06FD07NkTP//8s7i9Q4cOePPNN/Hggw+ivLwc3t7e2LJlC06cOIGtW7ciIiICffv2xeuvv44FCxZg8eLF8PHxwcqVKxETE4P33nsPANCtWzfs3r0bH3zwARISEhx9WkTUAJ4d2Qnnrxfj9yNXceRSPgBgQDvO30JEREREROQsgXJvBPh4oVijg5wVLkRERHZx+jg9xqHCwsKq/9K0oKAACoUC3t6G7iQnJ6NXr16IiIgQ2yQkJODJJ59Eeno6YmNjkZycjFGjRpntJyEhAXPnzq32OGq1Gmq1WlxWqVQAAK1WC61WW+dzc2fG82ls59VQGL+GUzXWb97dDRdzi3HksuFvR9/WQbwONeB71fV4DezHGDYcxto+jSF+ntx3IiJynohgX5y/XgyZFxMuRERE9nBqwkWv12Pu3Lm47bbb0LNnT6ttbty4gddffx2zZs0S1ymVSrNkCwBxWalU1thGpVKhtLQUfn5+FsdaunQplixZYrF+y5Yt8Pf3r9vJeYjExERXd8GjMX4NxzTW90UA+fleaOkvIHnHVhf2ynPwvep6vAb2YwwbDmNtH0+OX0lJiau7QEREbui+fq3x6+Er6Bsd4uquEBEReTSnJlxmz56N48ePY/fu3Va3q1QqjB8/Ht27d8fixYud2RUAwMKFCzFv3jyz40dHRyM+Ph4KhcLpx29IWq0WiYmJGD16NGQymau743EYv4ZTXaynuLBPnoTvVdfjNbAfY9hwGGv7NIb4GSu8iYiITD01vCOeGt7R1d0gIiLyeE5LuMyZMwcbNmxAUlISWrdubbG9sLAQY8aMQVBQEH799Vezm9bIyEgcOHDArH12dra4zfh/4zrTNgqFwmp1CwDI5XLI5XKL9TKZzGNvmmvTmM+tITB+DYextg/j53q8BvZjDBsOY20fT46fp/abiIiIiIjIEzh8cE5BEDBnzhz8+uuv2L59O2JiYizaqFQqxMfHw8fHB7///jt8fX3NtsfFxeHYsWPIyckR1yUmJkKhUKB79+5im23btpm9LjExEXFxcY4+JSIiIiIiIiIiIiIioho5POEye/ZsfPvtt1i7di2CgoKgVCqhVCpRWloKoDLZUlxcjC+++AIqlUpso9PpAADx8fHo3r07pk+fjiNHjmDz5s14+eWXMXv2bLFC5YknnsD58+fxwgsv4NSpU/j000/x448/4rnnnnP0KREREREREREREREREdXI4UOKffbZZwCA4cOHm63/6quv8M9//hOHDx/G/v37AQAdO5qPD5qZmYl27drBy8sLGzZswJNPPom4uDgEBATg4YcfxmuvvSa2jYmJwZ9//onnnnsOy5cvR+vWrfH5558jISHB0adERERERERERERERERUI4cnXARBqHH78OHDa20DAG3btsXGjRtr3Vdqamqd+kdERERERERERERERORoDh9SjIiIiIiIiIiIiIiIqKlhwoWIiIiIiIiIiIiIiMhOTLgQERERERERERERERHZiQkXIiIiIiIiIiIiIiIiOzHhQkREREREREREREREZCcmXIiIiIiIiIiIiIiIiOzEhAsREREREREREREREZGdvF3dAVcSBAEAoFKpXNwTx9NqtSgpKYFKpYJMJnN1dzwO49dwGGv7MH6ux2tgP8aw4TDW9mkM8TN+7jV+DiaqjavvmRrD7507Ylwdh7F0DsbVORhXx2EsnYNxdSzGs/7suW9q0gmXwsJCAEB0dLSLe0JERERE1HAKCwsRHBzs6m6QB+A9ExERERE1VfW5b5IITfjxNr1ej6tXryIoKAgSicTV3XEolUqF6OhoXLp0CQqFwtXd8TiMX8NhrO3D+Lker4H9GMOGw1jbpzHETxAEFBYWIioqClIpRxem2rn6nqkx/N65I8bVcRhL52BcnYNxdRzG0jkYV8diPOvPnvumJl3hIpVK0bp1a1d3w6kUCgV/oezA+DUcxto+jJ/r8RrYjzFsOIy1fTw9fqxsobpwl3smT/+9c1eMq+Mwls7BuDoH4+o4jKVzMK6OxXjWT33vm/hYGxERERERERERERERkZ2YcCEiIiIiIiIiIiIiIrITEy6NlFwux6uvvgq5XO7qrngkxq/hMNb2Yfxcj9fAfoxhw2Gs7cP4ETU8/t45B+PqOIylczCuzsG4Og5j6RyMq2Mxnq4hEQRBcHUniIiIiIiIiIiIiIiIPBkrXIiIiIiIiIiIiIiIiOzEhAsREREREREREREREZGdmHAhIiIiIiIiIiIiIiKyExMuREREREREREREREREdmLCxU5Lly7FgAEDEBQUhPDwcEycOBEZGRlmbcrKyjB79mw0a9YMgYGBmDRpErKzs8XtR44cwQMPPIDo6Gj4+fmhW7duWL58udk+rl27hqlTp6Jz586QSqWYO3euzX1csWIF2rVrB19fXwwaNAgHDhww237u3Dncc889aNGiBRQKBSZPnmzWv+pkZWVh/Pjx8Pf3R3h4OObPn4/y8vI69bkpx++ZZ55Bv379IJfL0bdvX4vtFy5cgEQisfhv3759NvfdlLvHOikpCRMmTEBUVBQkEgnWr19v0UYQBCxatAgtW7aEn58fRo0ahTNnztS676bwXnVm/Br6vVqdxnANfvnlF8THx6NZs2aQSCRIS0uzad95eXmYNm0aFAoFQkJCMHPmTBQVFZmd9z//+U/06tUL3t7emDhxosU+mnL83nzzTdx6663w9/dHSEiI1TbW3sPff/+9Tfu3xtPjrdVqsWDBAvTq1QsBAQGIiorCQw89hKtXr9a6b/7NtS9+7vI3l6gm7v47CtT+GX748OEWv0dPPPFE3YPhQI0hrgCQnJyMO+64AwEBAVAoFBg6dChKS0vrFgwH8PR4Vvf3XiKRYN26dfULip08PaYAoFQqMX36dERGRiIgIAC33HILfv7557oHw4EaQ1zr+72IM7h7PJ157+5MjSGu9b2fdDRPj6U99zpNBRMudtq5cydmz56Nffv2ITExEVqtFvHx8SguLhbbPPfcc/jjjz+wbt067Ny5E1evXsW9994rbk9JSUF4eDi+/fZbpKen46WXXsLChQvxySefiG3UajVatGiBl19+GX369LG5fz/88APmzZuHV199FYcPH0afPn2QkJCAnJwcAEBxcTHi4+MhkUiwfft27NmzBxqNBhMmTIBer692vzqdDuPHj4dGo8HevXvx9ddfY/Xq1Vi0aFGd+txU42c0Y8YM/OMf/6ixzdatW3Ht2jXxv379+tncf1PuHuvi4mL06dMHK1asqLbNsmXL8NFHH2HlypXYv38/AgICkJCQgLKysmpf01Teq86Kn1FDvler0xiuQXFxMYYMGYK33367Tuc+bdo0pKenIzExERs2bEBSUhJmzZolbtfpdPDz88MzzzyDUaNGWd1HU46fRqPB/fffjyeffLLGdl999ZXZe9ha4spWnh7vkpISHD58GK+88goOHz6MX375BRkZGbjrrrtq3C//5hrUN35G7vA3l6gm7v47WttneKPHHnvM7Pdo2bJldkTFfo0hrsnJyRgzZgzi4+Nx4MABHDx4EHPmzIFU2vBfPXh6PKOjo83en9euXcOSJUsQGBiIsWPHOiBCdefpMQWAhx56CBkZGfj9999x7Ngx3HvvvZg8eTJSU1PtjE79eXpc7f1exNHcPZ7Ovnd3lsYQ1/reTzqap8fS3nudJkEgh8rJyREACDt37hQEQRDy8/MFmUwmrFu3Tmxz8uRJAYCQnJxc7X6eeuopYcSIEVa3DRs2THj22Wdt6s/AgQOF2bNni8s6nU6IiooSli5dKgiCIGzevFmQSqVCQUGB2CY/P1+QSCRCYmJitfvduHGjIJVKBaVSKa777LPPBIVCIajV6nr3uanEz9Srr74q9OnTx2J9ZmamAEBITU21aT915W6xNgVA+PXXX83W6fV6ITIyUnjnnXfEdfn5+YJcLhe+++67avfVVN6rphwZP1Oueq9Wx9Ougam6xOzEiRMCAOHgwYPiur/++kuQSCTClStXLNo//PDDwt13313rfptK/Ex99dVXQnBwcL2OaS9PjrfRgQMHBADCxYsXq23Dv7nVsyV+ptztby5RTdztd7S2z/B13Z+reGJcBw0aJLz88ss27a+heWI8q+rbt68wY8YMm/bfEDwxpgEBAcI333xj9rqwsDDhv//9r03HaAieFldHfC/iTO4WT1POundvCJ4WV1Pu9nnak2NpVNd7ncaOFS4OVlBQAAAICwsDYMg4arVasyeOu3btijZt2iA5ObnG/Rj3UV8ajQYpKSlmx5ZKpRg1apR4bLVaDYlEArlcLrbx9fWFVCrF7t27q913cnIyevXqhYiICHFdQkICVCoV0tPT693nphK/urjrrrsQHh6OIUOG4Pfff3fIPgH3irUtMjMzoVQqzfoXHByMQYMG1di/pvBetUV941cXznqvVsfTrkF9JScnIyQkBP379xfXjRo1ClKpFPv376/3fptK/Opi9uzZaN68OQYOHIgvv/wSgiA4bN+NId4FBQWQSCTVDssG8G9uTWyJX1009N9copq40++oLZ/hjdasWYPmzZujZ8+eWLhwIUpKSuw6tqN5WlxzcnKwf/9+hIeH49Zbb0VERASGDRvmsPsie3laPKtKSUlBWloaZs6cadexHckTY3rrrbfihx9+QF5eHvR6Pb7//nuUlZVh+PDhdh3fkTwtrg3xvYg93CmetmiIe3dH8LS4urPGEEtH3+t4Om9Xd6Ax0ev1mDt3Lm677Tb07NkTgGF8UB8fH4s3XEREBJRKpdX97N27Fz/88AP+/PNPu/pz48YN6HQ6sy89jMc+deoUAGDw4MEICAjAggUL8O9//xuCIOBf//oXdDodrl27Vu2+lUql1f0at9VHU4qfLQIDA/Hee+/htttug1Qqxc8//4yJEydi/fr1dpfpuVusbWHsg7XrUdN7rim8V21R3/jZwpnv1ep44jWoL6VSifDwcLN13t7eCAsLa1LvYWd77bXXcMcdd8Df3x9btmzBU089haKiIjzzzDN277sxxLusrAwLFizAAw88AIVCUW07/s21ztb42cIVf3OJauJuv6O2fIYHgKlTp6Jt27aIiorC0aNHsWDBAmRkZOCXX36x6/iO4olxPX/+PABg8eLFePfdd9G3b1988803GDlyJI4fP45OnTrZ1Qd7eGI8q/riiy/QrVs33HrrrXYd21E8NaY//vgj/vGPf6BZs2bw9vaGv78/fv31V3Ts2NGu4zuKJ8bVmd+L2Mvd4mkLZ967O4onxtVdNYZYOvJep7FghYsDzZ49G8ePH7drkt3jx4/j7rvvxquvvor4+HibX7dr1y4EBgaK/61Zs8am17Vo0QLr1q3DH3/8gcDAQAQHByM/Px+33HKLOM7u2LFjxf326NGjXudlC8bPXPPmzTFv3jwMGjQIAwYMwFtvvYUHH3wQ77zzjs37qI4nxtoWfK/axx3fq9VprNfgiSeeMNu3szB+ll555RXcdtttiI2NxYIFC/DCCy847D3s6fHWarWYPHkyBEHAZ599Jq7n31zbODp+rvibS1QTT/0dnTVrFhISEtCrVy9MmzYN33zzDX799VecO3euPqfgcJ4YV+NcDY8//jgeeeQRxMbG4oMPPkCXLl3w5Zdf1uscHMUT42mqtLQUa9eudavqFk+N6SuvvIL8/Hxs3boVhw4dwrx58zB58mQcO3asPqfgcJ4YV1u+F3EVT4ynJ2BcHcfTY1ndvU5TxwoXB5kzZ444kXHr1q3F9ZGRkdBoNMjPzzfLTGZnZyMyMtJsHydOnMDIkSMxa9YsvPzyy3U6fv/+/ZGWliYuR0REQC6Xw8vLC9nZ2WZtqx47Pj4e586dw40bN+Dt7Y2QkBBERkaiffv2AIDPP/8cpaWlAACZTCae14EDByz2a9xWV00tfvU1aNAgJCYm2rUPd4y1LYx9yM7ORsuWLc3617dvXwBN971qi/rGr74c8V6tjqdeA1u89tpr+L//+z+zdZGRkRaT/JaXlyMvL69JvYdtYS1+9TVo0CC8/vrrUKvVZsMj1JWnx9v4AfrixYvYvn272RNL/Jtbu7rGr76c+TeXqCbu+Dtq62f4qgYNGgQAOHv2LDp06FCnfjiap8bV+Bmze/fuZm26deuGrKysOvXBkTw1nqZ++uknlJSU4KGHHqrTsZ3FU2N67tw5fPLJJzh+/Lj4sEOfPn2wa9curFixAitXrqxTPxzNU+MK1P69iCu4YzxtYcu9uyt5alzdkafHsqZ7nSbPhfPHNAp6vV6YPXu2EBUVJZw+fdpiu3Gio59++klcd+rUKYuJjo4fPy6Eh4cL8+fPr/WYdZ3cbM6cOeKyTqcTWrVqVeNEfNu2bRMkEolw6tSpatsYJ8XNzs4W1/3nP/8RFAqFUFZWZnOfm2r8TFU3Ka41jz76qBAbG2tT26rcPdamUMPEce+++664rqCgoNaJ45rKe9WUI+NnqqHeq9Xx9Gtgqi6T9J04cUIAIBw6dEhct3nzZkEikQhXrlyxaP/www8Ld999t8X6pho/U1999ZUQHBxsU9s33nhDCA0NrdP+TTWGeGs0GmHixIlCjx49hJycHJv2xb+5leoTP1Ou/ptLVBN3/x2tz2f43bt3CwCEI0eO2HQMZ/D0uOr1eiEqKkp4+eWXzV7Xt29fYeHChTYdw5E8PZ5V9ztp0iSb9utMnh7To0ePCgCEEydOmL0uPj5eeOyxx2w6hjN4elytqev3Io7k7vE05ax7d2fw9Liaqu/9pKM0hljae6/T2DHhYqcnn3xSCA4OFnbs2CFcu3ZN/K+kpERs88QTTwht2rQRtm/fLhw6dEiIi4sT4uLixO3Hjh0TWrRoITz44INm+6j6hk1NTRVSU1OFfv36CVOnThVSU1OF9PT0Gvv3/fffC3K5XFi9erVw4sQJYdasWUJISIigVCrFNl9++aWQnJwsnD17Vvjf//4nhIWFCfPmzatxv+Xl5ULPnj2F+Ph4IS0tTdi0aZPQokULiw/StfW5qcZPEAThzJkzQmpqqvD4448LnTt3FvunVqsFQRCE1atXC2vXrhVOnjwpnDx5UnjzzTcFqVQqfPnll7Xu2xp3j3VhYaH4OgDC+++/L6SmpgoXL14U27z11ltCSEiI8NtvvwlHjx4V7r77biEmJkYoLS2tdr9N5b3qrPgJQsO/V6vTGK5Bbm6ukJqaKvz5558CAOH7778XUlNThWvXrtW47zFjxgixsbHC/v37hd27dwudOnUSHnjgAbM26enpQmpqqjBhwgRh+PDhYl8YP0G4ePGikJqaKixZskQIDAwUj1NYWCgIgiD8/vvvwn//+1/h2LFjwpkzZ4RPP/1U8Pf3FxYtWlTjfmvi6fHWaDTCXXfdJbRu3VpIS0szO77xd98a/s21L36C4D5/c4lq4u6/o7V9hj979qzw2muvCYcOHRIyMzOF3377TWjfvr0wdOhQB0ap7jw9roIgCB988IGgUCiEdevWCWfOnBFefvllwdfXVzh79qyDomS7xhBPQTD8uyCRSIS//vrLAVGxj6fHVKPRCB07dhRuv/12Yf/+/cLZs2eFd999V5BIJMKff/7pwEjVjafHVRDq/72IM7h7PJ157+5MjSGu9b2fdDRPj6U99zpNBRMudgJg9b+vvvpKbFNaWio89dRTQmhoqODv7y/cc889Zr/Mr776qtV9tG3bttZjVW1jzccffyy0adNG8PHxEQYOHCjs27fPbPuCBQuEiIgIQSaTCZ06dRLee+89Qa/X17rfCxcuCGPHjhX8/PyE5s2bC88//7yg1Wrr1OemHL9hw4ZZ7VNmZqYgCIYvVLp16yb4+/sLCoVCGDhwoLBu3bpa91sdd4/133//bfV1Dz/8sNhGr9cLr7zyihARESHI5XJh5MiRQkZGRq3n3hTeq86MX0O/V6vTGK7BV199ZbXNq6++WuO+c3NzhQceeEAIDAwUFAqF8Mgjj4jJAqO2bdta3TfjZ6j6sfa6v//+WxAEQfjrr7+Evn37CoGBgUJAQIDQp08fYeXKlYJOp6txvzXx9Hgbn/qqKW7V4d9c++LnLn9ziWri7r+jglDzZ/isrCxh6NChQlhYmCCXy4WOHTsK8+fPFwoKCuwNjV08Pa5GS5cuFVq3bi34+/sLcXFxwq5du+obErs0lnguXLhQiI6OtutziaM0hpiePn1auPfee4Xw8HDB399f6N27t/DNN9/YExa7NYa41vd7EWdw93g6897dmRpDXOt7P+lonh5Le+51mgqJIAgCiIiIiIiIiIiIiIiIqN6kru4AERERERERERERERGRp2PChYiIiIiIiIiIiIiIyE5MuBAREREREREREREREdmJCRciIiIiIiIiIiIiIiI7MeFCRERERERERERERERkJyZciIiIiIiIiIiIiIiI7MSECxERERERERERERERkZ2YcCEiIiIiIiIiIiIiIrITEy5ERERERERERERERER2YsKFiIiIiIiIiIiIiIjITky4EBERERERERERERER2YkJFyIiIiIiIiIiIiIiIjsx4UJERERERERERERERGQnJlyIiIiIiIiIiIiIiIjsxIQLERERERERERERERGRnZhwISIiIiIiIiIiIiIishMTLkRERERERERERERERHZiwoWIiIiIiIiIiIiIiMhOTLgQERERERERERERERHZiQkXIiIiIiIiIiIiIiIiOzHhQkREREREREREREREZCcmXIiIiIiIiIiIiIiIiOzEhAsREREREREREREREZGdmHAhIqJarV69GhKJRPzP19cXUVFRSEhIwEcffYTCwkKL16SkpODOO+9EZGQkAgMD0bt3b3z00UfQ6XRm7X744Qc8+OCD6NSpEyQSCYYPH15tP86cOYMpU6agdevW8Pf3R9euXfHaa6+hpKTE0adMRERERERUJ/W5b0pMTMSQIUPg7++P0NBQ3Hfffbhw4YJFu6KiIsydOxetW7eGXC5Ht27d8Nlnn1m027ZtG2bMmIHOnTvD398f7du3x6OPPopr164545SJiKgKb1d3gIiIPMdrr72GmJgYaLVaKJVK7NixA3PnzsX777+P33//Hb179wZgSLbceuut6NSpExYsWAB/f3/89ddfePbZZ3Hu3DksX75c3Odnn32GlJQUDBgwALm5udUe+9KlSxg4cCCCg4MxZ84chIWFITk5Ga+++ipSUlLw22+/Of38iYiIiIiIamPrfdOGDRtw991345ZbbsFbb70FlUqF5cuXY8iQIUhNTUWLFi0AADqdDgkJCTh06BBmz56NTp06YfPmzXjqqadw8+ZNvPjii+KxFyxYgLy8PNx///3o1KkTzp8/j08++QQbNmxAWloaIiMjXRITIqKmQiIIguDqThARkXtbvXo1HnnkERw8eBD9+/c327Z9+3bceeedCA8Px8mTJ+Hn54dZs2bh66+/xrVr1xAWFia2HTZsGNLS0lBQUCCuu3TpElq1agWpVIqePXuiefPm2LFjh0Uf/v3vf+Oll17C8ePH0aNHD3H9ww8/jG+++QZ5eXkIDQ11/MkTERERERHZoK73TT169IBGo0F6ejp8fHwAAEeOHMEtt9yCuXPn4r333gMArFu3DpMnT8YXX3yBGTNmiPu877778Oeff+LixYsIDw8HACQlJWHIkCGQSisHtUlKSsKwYcPw0ksv4Y033nB2GIiImjQOKUZERHa544478Morr+DixYv49ttvAQAqlQq+vr4ICQkxa9uyZUv4+fmZrYuOjja7GaiOSqUCAERERFjsUyqVijcoRERERERE7qbqfVNeXh5OnDiBe+65x+xepk+fPujWrRu+//57cd2uXbsAAFOmTDHb55QpU1BWVmZW7T906FCL+6uhQ4ciLCwMJ0+edMapERGRCSZciIjIbtOnTwcAbNmyBQAwfPhwqFQqPP744zh58iQuXryIlStX4pdffsHChQvrdQzj3C4zZ85EWloaLl26hB9++AGfffYZnnnmGQQEBDjkXIiIiIiIiJzB9L5JrVYDgMUDaQDg7++Pq1evQqlUAgDUajW8vLwsHjLz9/cHYBjSuSZFRUUoKipC8+bN7T4HIiKqGedwISIiu7Vu3RrBwcE4d+4cAOCxxx5Deno6/vOf/+Dzzz8HAHh5eeGTTz7BE088Ua9jjBkzBq+//jr+/e9/4/fffxfXsyyeiIiIiIg8gel9U0REBEJCQrBnzx6zNrm5uThx4gQA4MqVK4iMjESXLl2g0+mwb98+DBkyRGxrrHy5cuVKjcf98MMPodFo8I9//MPBZ0RERFWxwoWIiBwiMDAQhYWFAAzJlQ4dOiAhIQFff/01fvjhB0yYMAFPP/001q9fX+9jtGvXDkOHDsWqVavw888/Y8aMGfj3v/+NTz75xEFnQURERERE5DzG+yapVIrHH38c27Ztw8KFC3HmzBmkpKRg8uTJ0Gg0AIDS0lIAwNSpUxEcHIwZM2YgMTERFy5cwKpVq/Dpp5+atbMmKSkJS5YsweTJk3HHHXc4/wSJiJo4VrgQEZFDFBUViRM1vvXWW1i+fDnOnDmDwMBAAMDkyZMxYsQIzJ49G3feeSe8vev2T9D333+PWbNm4fTp02jdujUA4N5774Ver8eCBQvwwAMPoFmzZo49KSIiIiIiIgcyvW967bXXcOPGDSxbtgxvvfUWACA+Ph4zZ87EypUrxXupyMhI/P7775g+fTri4+MBAAqFAh9//DEefvhhsV1Vp06dwj333IOePXuKIw8QEZFzscKFiIjsdvnyZRQUFKBjx44AgE8//RR33HGHxQf/u+66C1evXsWFCxfqfIxPP/0UsbGxYrLFdJ8lJSVITU2td/+JiIiIiIicrep9k4+PDz7//HNcvXoVSUlJyMjIwObNm1FQUACpVCq2AwwT358/fx6pqanYvXs3rly5gsGDBwMAOnfubHGsS5cuIT4+HsHBwdi4cSOCgoIa5iSJiJo4VrgQEZHd/ve//wEAEhISAADZ2dnQ6XQW7bRaLQCgvLy8zsfIzs5GaGioQ/dJRERERETUUKreNxlFREQgIiICAKDT6bBjxw4MGjTI4gE2Ly8v9O3bV1zeunUrAGDUqFFm7XJzcxEfHw+1Wo1t27ahZcuWjj4VIiKqBitciIjILtu3b8frr7+OmJgYTJs2DYDhCavExETk5uaK7XQ6HX788UcEBQWhQ4cOdT5O586dkZqaitOnT5ut/+677yCVStG7d2/7ToSIiIiIiMhJrN03WfPuu+/i2rVreP7552vc3/Xr1/H222+jd+/eZgmX4uJijBs3DleuXMHGjRvRqVMnh50DERHVjhUuRERks7/++gunTp1CeXk5srOzsX37diQmJqJt27b4/fff4evrCwD417/+hQcffBCDBg3CrFmz4Ofnh++++w4pKSl44403IJPJxH0mJSUhKSkJgOGmobi4GG+88QYAQ9n80KFDAQDz58/HX3/9hdtvvx1z5sxBs2bNsGHDBvz111949NFHERUV1cDRICIiIiIismTrfdO3336Ln3/+GUOHDkVgYCC2bt2KH3/8EY8++igmTZpkts9hw4YhLi4OHTt2hFKpxKpVq1BUVIQNGzZAKq18nnratGk4cOAAZsyYgZMnT+LkyZPitsDAQEycOLFBYkBE1FRJBEEQXN0JIiJyb6tXr8YjjzwiLvv4+CAsLAy9evXCnXfeiUceecRiTODNmzdj6dKlSE9Ph0qlQpcuXTB79mw8/vjjZu0WL16MJUuWWD3uq6++isWLF4vLBw4cwOLFi5Gamorc3FzExMTg4YcfxgsvvABvbz5DQERERERErlPX+6YDBw5g/vz5OHbsGEpLS9GlSxc8+eSTmDVrFiQSidm+582bh99++w1XrlyBQqHA6NGj8frrr6N9+/Zm7dq1a4eLFy9a7V/btm3rNZ8mERHZjgkXIiIiIiIiIiIiIiIiO3EOFyIiIiIiIiIiIiIiIjsx4UJERERERERERERERGQnJlyIiIiIiIiIiIiIiIjsxIQLERERERERERERERGRnZhwISIiIiIiIiIiIiIishMTLkRERERERERERERERHbydnUHXEmv1+Pq1asICgqCRCJxdXeIiIiIiJxKEAQUFhYiKioKUimfvaLa8Z6JiIiIiJoae+6bmnTC5erVq4iOjnZ1N4iIiIiIGtSlS5fQunVrV3eDPADvmYiIiIioqarPfVOTTrgEBQUBMAROoVC4uDek1WqxZcsWxMfHQyaTubo7ZAdey8aD17Lx4LVsPHgtGwdXXUeVSoXo6GjxczBRbepzz8S/U7VjjGzHWNmOsaodY2Q7xsp2jFXtGCPbMVa2cXac7LlvatIJF2NJvEKhYMLFDWi1Wvj7+0OhUPAPiofjtWw8eC0bD17LxoPXsnFw9XXk0FBkq/rcM7n6/e0JGCPbMVa2Y6xqxxjZjrGyHWNVO8bIdoyVbRoqTvW5b+LAzURERERERERERERERHZiwoWIiIiIiIiIiIiIiMhOTLgQERERERERERERERHZiQkXIiIiIiIiIiIiIiIiOzHhQkREREREREREREREZCcmXIiIiIiIiIiIiIiIiOzEhAsREREREVEDS0pKwoQJExAVFQWJRIL169dbtDl58iTuuusuBAcHIyAgAAMGDEBWVpa4vaysDLNnz0azZs0QGBiISZMmITs722wfWVlZGD9+PPz9/REeHo758+ejvLzc2adHRERERNQkMeFCRERERETUwIqLi9GnTx+sWLHC6vZz585hyJAh6Nq1K3bs2IGjR4/ilVdega+vr9jmueeewx9//IF169Zh586duHr1Ku69915xu06nw/jx46HRaLB37158/fXXWL16NRYtWuT08yMiIiIiaoq8Xd0BIiIiIiKipmbs2LEYO3ZstdtfeukljBs3DsuWLRPXdejQQfy5oKAAX3zxBdauXYs77rgDAPDVV1+hW7du2LdvHwYPHowtW7bgxIkT2Lp1KyIiItC3b1+8/vrrWLBgARYvXgwfHx/nnSARERERURPEhAsREREREZEb0ev1+PPPP/HCCy8gISEBqampiImJwcKFCzFx4kQAQEpKCrRaLUaNGiW+rmvXrmjTpg2Sk5MxePBgJCcno1evXoiIiBDbJCQk4Mknn0R6ejpiY2Mtjq1Wq6FWq8VllUoFANBqtdBqtV9sX9sAAQAASURBVDb139jO1vZNEWNkO8bKdoxV7Rgj2zFWtmOsascY2Y6xso2z42TPfplwISIiIiIiciM5OTkoKirCW2+9hTfeeANvv/02Nm3ahHvvvRd///03hg0bBqVSCR8fH4SEhJi9NiIiAkqlEgCgVCrNki3G7cZt1ixduhRLliyxWL9lyxb4+/vX6TwSExPr1L4pYoxsx1jZjrGqHWNkO8bKdoxV7Rgj2zFWtnFWnEpKSur9WiZciIiIiIjstOm4EhqdHnf1iXJ1V6gR0Ov1AIC7774bzz33HACgb9++2Lt3L1auXIlhw4Y57dgLFy7EvHnzxGWVSoXo6GjEx8dDoVDYtA+tVovExESMHj0aMpnMWV31aIyR7Rgr2zFWtWOMbMdY2Y6xqh1jZLumGKtsVRl+Sb2K6YPbIFBuW7rC2XEyVnnXBxMuRERERER20Or0eOLbFADAbR2aoVmg3MU9Ik/XvHlzeHt7o3v37mbru3Xrht27dwMAIiMjodFokJ+fb1blkp2djcjISLHNgQMHzPaRnZ0tbrNGLpdDLrd8D8tksjrfzNbnNU0NY2Q7xsp2jFXtGCPbMVa2Y6xqxxjZrqnEalXSOfx74ykAQJFGjxfHdavT650VJ3v2KXVgP4iIiIiImhx1uV78+WYJx1om+/n4+GDAgAHIyMgwW3/69Gm0bdsWANCvXz/IZDJs27ZN3J6RkYGsrCzExcUBAOLi4nDs2DHk5OSIbRITE6FQKCySOUREREREDenkNZWYbAGArSeyXdgbx2GFCxERERGRHTQmCRfTn4lqUlRUhLNnz4rLmZmZSEtLQ1hYGNq0aYP58+fjH//4B4YOHYoRI0Zg06ZN+OOPP7Bjxw4AQHBwMGbOnIl58+YhLCwMCoUCTz/9NOLi4jB48GAAQHx8PLp3747p06dj2bJlUCqVePnllzF79myrVSxERERERM50JrsQr204gWdGdsKBzDyzbTdLNBAEARKJxEW9cwxWuBARERER2UFdrhN/LtGUu7An5EkOHTqE2NhYxMbGAgDmzZuH2NhYLFq0CABwzz33YOXKlVi2bBl69eqFzz//HD///DOGDBki7uODDz7AnXfeiUmTJmHo0KGIjIzEL7/8Im738vLChg0b4OXlhbi4ODz44IN46KGH8NprrzXsyRIRERERAfj3xpPYdeYG7l+ZjBxVGQDgsdtjIJUYRgu4Xqh2cQ/txwoXIiIiIiI7mFa1FKqZcCHbDB8+HIIg1NhmxowZmDFjRrXbfX19sWLFCqxYsaLaNm3btsXGjRvr3U8iIiIiIkcp1VY+rHYlvxQAEB3mj2aBclwvVCOnUI1wha+ruucQrHAhIiIiIrKD6RwuRWVMuBAREREREVkTHeov/rz1pGGewfAgX7QINAx32xgqXJhwISIiIiKyg1prknBhhQsREREREZFVJRqdxboIhRzhCiZciIiIiIgIgEZXedOwJV3pwp4QERERERG5L2sPqEUoTCpcigwJF72+5qF33RkTLkREREREdjCtcPk747oLe0JEREREROS+qiZcpBKgRZAcLYIqK1we++YQRr6/E9mqMld00W51TrgkJSVhwoQJiIqKgkQiwfr16y3anDx5EnfddReCg4MREBCAAQMGICsrS9xeVlaG2bNno1mzZggMDMSkSZOQnZ1tto+srCyMHz8e/v7+CA8Px/z581Febn5BduzYgVtuuQVyuRwdO3bE6tWr63o6RERERER2Uev0tTciIiIiIiJq4oqrJFzCAuSQeUkRXpFwOXo5H4knspF5oxgPf3kAOg+sdKlzwqW4uBh9+vTBihUrrG4/d+4chgwZgq5du2LHjh04evQoXnnlFfj6+optnnvuOfzxxx9Yt24ddu7ciatXr+Lee+8Vt+t0OowfPx4ajQZ79+7F119/jdWrV2PRokVim8zMTIwfPx4jRoxAWloa5s6di0cffRSbN2+u6ykREREREdWbaYVL79bBLuwJERERERGR+yosM0+4eEslAIAhnVrASyrB4ax8cduIruEQBM9LuHjX9QVjx47F2LFjq93+0ksvYdy4cVi2bJm4rkOHDuLPBQUF+OKLL7B27VrccccdAICvvvoK3bp1w759+zB48GBs2bIFJ06cwNatWxEREYG+ffvi9ddfx4IFC7B48WL4+Phg5cqViImJwXvvvQcA6NatG3bv3o0PPvgACQkJdT0tIiIiIqJ60ZhUuBy9XICCUi2C/WQu7BEREREREZF7+fHgJVzJLzVbV5FvQcfwQMwe3gEfbT8LAJB5SbBgTNeG7qJDOHQOF71ejz///BOdO3dGQkICwsPDMWjQILNhx1JSUqDVajFq1ChxXdeuXdGmTRskJycDAJKTk9GrVy9ERESIbRISEqBSqZCeni62Md2HsY1xH0REREREDUGt1ZktT/9iv4t6QkRERERE5J5e+PmoxbpuLRXiz3NHdRZ/DpTXuU7EbTi05zk5OSgqKsJbb72FN954A2+//TY2bdqEe++9F3///TeGDRsGpVIJHx8fhISEmL02IiICSqUSAKBUKs2SLcbtxm01tVGpVCgtLYWfn59F/9RqNdRqtbisUqkAAFqtFlqt1r6TJ7sZrwGvhefjtWw8eC0bD17LxoPX0v2UqM2vxdHLBbVeH1ddR75viIiIiIioIe08fR3NAnzM1v33of74KeUSXp3QQ1wnNZa7AChWmz/U5kkcmnDR6w3DKdx999147rnnAAB9+/bF3r17sXLlSgwbNsyRh6uzpUuXYsmSJRbrt2zZAn9/fxf0iKxJTEx0dRfIQXgtGw9ey8aD17Lx4LV0D7uVEqzL9LJYv3HjRpte39DXsaSkpEGPR0RERERETVfmjWI8/OUBi/Uju4ZjdPcIK68wMB222dM4NOHSvHlzeHt7o3v37mbrjfOrAEBkZCQ0Gg3y8/PNqlyys7MRGRkptjlwwPxCZGdni9uM/zeuM22jUCisVrcAwMKFCzFv3jxxWaVSITo6GvHx8VAoFFZfQw1Hq9UiMTERo0ePhkzGcc89Ga9l48Fr2XjwWjYevJbuo0yrw7OvbbO6bdy4cTW+1lXX0VjhTURERERE5Gxnsgst1j07spNZNYupF8Z0wbJNGfj3Pb2c3TWncWjCxcfHBwMGDEBGRobZ+tOnT6Nt27YAgH79+kEmk2Hbtm2YNGkSACAjIwNZWVmIi4sDAMTFxeHNN99ETk4OwsPDARie/lMoFGIyJy4uzuLJwcTERHEf1sjlcsjlcov1MpmMX1i4EV6PxoPXsvHgtWw8eC0bD15L18stqb7M3dZr09DXke8ZIiIiIiJqKAWl5kMahwfJ8dzoztW0Bp4c1gF3922FqGBfZ3fNaeqccCkqKsLZs2fF5czMTKSlpSEsLAxt2rTB/Pnz8Y9//ANDhw7FiBEjsGnTJvzxxx/YsWMHACA4OBgzZ87EvHnzEBYWBoVCgaeffhpxcXEYPHgwACA+Ph7du3fH9OnTsWzZMiiVSrz88suYPXu2mDB54okn8Mknn+CFF17AjBkzsH37dvz444/4888/HRAWIiIiIqKaFanLXd0FIiIiIiIit3U1v8xsuWUtiRSJRIJWIdZHr/IUdU64HDp0CCNGjBCXjUN0Pfzww1i9ejXuuecerFy5EkuXLsUzzzyDLl264Oeff8aQIUPE13zwwQeQSqWYNGkS1Go1EhIS8Omnn4rbvby8sGHDBjz55JOIi4tDQEAAHn74Ybz22mtim5iYGPz555947rnnsHz5crRu3Rqff/45EhIS6hUIIiIiIqK6KNEw4UJERERERFSdPWdvmC23bRbgop40nDonXIYPHw5BEGpsM2PGDMyYMaPa7b6+vlixYgVWrFhRbZu2bdvWOtno8OHDkZqaWnOHiYiIiIic4PLNUld3gYiIiIiIyC0dvJCHAxfyzNa1b9H4Ey5SV3eAiIiIiMgTPbXmsKu7QERERERE5JZOXFVZrGvfItAFPWlYTLgQERERETlA95YKAIC/j5eLe0JERERERORaucUaAMDdfaPEdTEcUoyIiIiIiKqyNsTu4PbNcOKa5VNcRERERERETcmlvBJ8tO0MAMO8LZ9OuwWX8krQs5XCxT1zPiZciIiIiIjqKO1Svvjz/IQukHlJEN89El/uyXRdp4iIiIiIiFxAEARIJBJxedJne8Wfw/xlGNerpSu65RIcUoyIiIiIqI50+soKl+FdWmDW0A6QmtxgEBERERERNQUpF/PQ742t+DnlMgBD8iWnUC1uDwuUu6prLsGECxERERGRHXpEBbu6C0RERERERC4xZ20q8oo1eH7dEQDA5ZulZtubBfi4olsuw4QLEREREVEdqMt12H32BgCga2SQxXYr07sQERERERE1SkpVmdny0csFZssKX1lDdsflmHAhIiIiIqqDl389jg+3GiaA1JtkVziiGBERERERNXbrDl3C3Sv24Ou9FwBYPnCWftU84dIq1K+BeuYevF3dASIiIiIiT7KuYmxiADidXeTCnhARERERETWcK/mlmP/TUQDAkUv5mDaojdl2nV5A+lUVAODxoe1xd99WCOOQYkRERERERERERERERJX+OHLVbDn1Ur7Zcm6RGlfyDXO4DOvcAt2jFA3VNbfBhAsRERERUT3NuC3G1V0gIiIiIiJqEDszrpst/3VMaba8KV2J/BItACDYv2nN3WLEIcWIiIiIiOrpto7NXN0FIiIiIiIip9PrBRzOumm2LvGkecJl0W/p4s8h/k1rKDEjVrgQEREREdko5WKe2XKQb9N8aouIiIiIiJqW7MIyqMv18JZKENsmBABwKc8wfFiQ3LKuI9ivad4rMeFCRERERGSjR78+ZLYcaOXGQoDQUN0hD5aUlIQJEyYgKioKEokE69evN9v+z3/+ExKJxOy/MWPGmLXJy8vDtGnToFAoEBISgpkzZ6KoqMiszdGjR3H77bfD19cX0dHRWLZsmbNPjYiIiIgaoQs3SgAArUL9EBHka7YtpkWARfsAH68G6Ze7YcKFiIiIiJq0gxfyMPK9Hdhz9katbW9WjEdsFOTLEXqpfoqLi9GnTx+sWLGi2jZjxozBtWvXxP++++47s+3Tpk1Deno6EhMTsWHDBiQlJWHWrFnidpVKhfj4eLRt2xYpKSl45513sHjxYqxatcpp50VEREREjVNWXjEAoG2zADQLNB8u7P7+0VBUuTeSSCQN1jd3wjtEIiIiImrSpn+xH2VaPaZ9vh/n/z0OUqntNwZMuFB9jR07FmPHjq2xjVwuR2RkpNVtJ0+exKZNm3Dw4EH0798fAPDxxx9j3LhxePfddxEVFYU1a9ZAo9Hgyy+/hI+PD3r06IG0tDS8//77ZokZIiIiIqLaZFZUuLQN80dogHnCpV0zf6QuikenlzZC38QL/nmHSERERERNWplWL/6cV6JB80B5tW0lEkAwuYEIsDKkGJGj7NixA+Hh4QgNDcUdd9yBN954A82aNQMAJCcnIyQkREy2AMCoUaMglUqxf/9+3HPPPUhOTsbQoUPh41N5Q5yQkIC3334bN2/eRGhoqMUx1Wo11Gq1uKxSqQAAWq0WWq3Wor01xna2tm+KGCPbMVa2Y6xqxxjZjrGyHWNVO8bIdu4cq2OX8wEAncL9oauSVZFJBOh15Qj2k4mjAjjzHJwdJ3v2yztEIiIiImqyDmfdNFu+UaSuMeFya4dm2HM2FwDg4yWFzIsj9JJzjBkzBvfeey9iYmJw7tw5vPjiixg7diySk5Ph5eUFpVKJ8PBws9d4e3sjLCwMSqUSAKBUKhETE2PWJiIiQtxmLeGydOlSLFmyxGL9li1b4O/vX6dzSExMrFP7pogxsh1jZTvGqnaMke0YK9sxVrVjjGznbrHSC8DhC14AJCi8cAzXSyUAKudoObQ/GdnpgC8MbQBg48aNTu+Xs+JUUlJS79cy4UJERERETY4gCPh4+1m8n3jabH2pRlfj6zTlhmqYNyb2xN19o5zWP6IpU6aIP/fq1Qu9e/dGhw4dsGPHDowcOdJpx124cCHmzZsnLqtUKkRHRyM+Ph4KhcKmfWi1WiQmJmL06NGQyWTO6qpHY4xsx1jZjrGqHWNkO8bKdoxV7Rgj27ljrK4VlOH8jWKU7kuB3FuKR+4dg8NZ+Vh95pDYZtSIYejQIgBt+6rwyNcpeHZkR4wbGO20Pjk7TsYq7/pgwoWIiIiImpxdZ25YJFsAQF2ut9K6UrHakJBpHeqHIF/rH+yFJj5mMTlH+/bt0bx5c5w9exYjR45EZGQkcnJyzNqUl5cjLy9PnPclMjIS2dnZZm2My9XNDSOXyyGXW1Z5yWSyOt/M1uc1TQ1jZDvGynaMVe0YI9sxVrZjrGrHGNnOXWKlLtdh6LtJ4nJUiB/8feWICDavfA7yl0Mmk6Fv22Y4/MpoSCS2z4tpD2fFyZ59cgwEIiIiImpybhSpra6vLeFSqjUkXKzN3dJA9xTURF2+fBm5ublo2bIlACAuLg75+flISUkR22zfvh16vR6DBg0S2yQlJZmNQZ2YmIguXbpYHU6MiIiIiMjUvvN5ZsstKoZfblZlGGZf78o0Q0MlW9wVEy5ERERE1ORIq7kJUGtrHlKsWF0OAPCTedXYjqg2RUVFSEtLQ1paGgAgMzMTaWlpyMrKQlFREebPn499+/bhwoUL2LZtG+6++2507NgRCQkJAIBu3bphzJgxeOyxx3DgwAHs2bMHc+bMwZQpUxAVZRjuburUqfDx8cHMmTORnp6OH374AcuXLzcbMoyIiIiIqDpJp6+bLbcIMiRaQvzMK0D8fHh/ZMSECxERERE1OQcu5FldX1OFy6ELecgpNFTG+POGgux06NAhxMbGIjY2FgAwb948xMbGYtGiRfDy8sLRo0dx1113oXPnzpg5cyb69euHXbt2mQ33tWbNGnTt2hUjR47EuHHjMGTIEKxatUrcHhwcjC1btiAzMxP9+vXD888/j0WLFmHWrFkNfr5ERERE5Hmy8swnj28e6AMAkEol8JJWPsTm6837IyPO4UJERERETc7V/FKr6/U1TMCy+I908Wd/H36MJvsMHz4cQg3vt82bN9e6j7CwMKxdu7bGNr1798auXbvq3D8iIiIiomxVmdmyscIFMFS55BZrABgSMGTAChciIiIialIEQcCOjOvVbLP+GmVBGY5fUYnLxie7iIiIiIiIGqNsVRmOXi4wW2eWcPF3/GT1jQEfzSMiIiKiJqWmYcMEWM+43PlxZYWAj5cU3l58bomIiIiIiBofQRDw4q/H8N2BSxbbokL8xJ/DAnxw7npxQ3bNIzDhQkRERERNSmFZebXb9FZyMaUaHW4UacTl72YNrnH/1Q8SRURERERE5J405XpcvlkCvSBYTbYAQN/oEPHnUH9W/VvDhAsRERERNSlF6hoSLlXGFCvT6tBt0SazddUNTyyRcNxiIiIiIiLyPFqdHmOXJ9VasRLkWzmMmGm1C1ViwoWIiIiImpSiGipcqlanXMorsWjTKSLIwT0iIiIiIiJynWv5ZdUmW1Y+2A/f7ruIaYPamK1/ZmQn7Dufi4mxrRqiix6DCRciIiIi8hiCINhdSVKo1gIAmgfKcaNIDQAI8vVGYVk5hCoVLsUancXrA+X8CE1ERERERI3H9Yr7IlMh/jIsvacXxvSMxJiekRbbwwJ8sGnu0Ibonkep82yfSUlJmDBhAqKioiCRSLB+/fpq2z7xxBOQSCT48MMPzdbn5eVh2rRpUCgUCAkJwcyZM1FUVGTW5ujRo7j99tvh6+uL6OhoLFu2zGL/69atQ9euXeHr64tevXph48aNdT0dIiIiIvIQn+44i1teT8Tp7EK79nP5ZikAoF0zf3GdXm9ItFTJt6CkyvBjpmMWExERERERNQa5JgmXYD8Zdi8YgbRF8Rjbq6ULe+WZ6pxwKS4uRp8+fbBixYoa2/3666/Yt28foqKiLLZNmzYN6enpSExMxIYNG5CUlIRZs2aJ21UqFeLj49G2bVukpKTgnXfeweLFi7Fq1Sqxzd69e/HAAw9g5syZSE1NxcSJEzFx4kQcP368rqdERERERB5g2aYM3CzR4tfUK3btJ7ugDADQKSJQXBcaYJjwUV8l4WJa4fLFw/3x9YyBdh2biIiIiIjI3dwo0gAAhndpgf0vjkTrUP9aXkHVqfN4CGPHjsXYsWNrbHPlyhU8/fTT2Lx5M8aPH2+27eTJk9i0aRMOHjyI/v37AwA+/vhjjBs3Du+++y6ioqKwZs0aaDQafPnll/Dx8UGPHj2QlpaG999/X0zMLF++HGPGjMH8+fMBAK+//joSExPxySefYOXKlXU9LSIiIiLyEDcKLcvd68KYRPH38cZn027BpZslSLl4E5dvlkIvCEg6fR1f7cnES+O7o0RjqHC5rWMzjOwWYXffiYiIiIiI3I1xqOWIIF/4yrxc3BvPVucKl9ro9XpMnz4d8+fPR48ePSy2JycnIyQkREy2AMCoUaMglUqxf/9+sc3QoUPh4+MjtklISEBGRgZu3rwpthk1apTZvhMSEpCcnOzoUyIiIiIiNyK1cw6XlTvPAQBuFmswtldLzBraQdzny+uP46EvD+DvjOsY/9EuFKsrkzO1EXsl1NSKiIiIiIjIvRiHFGse5FNLS6qNw2f8fPvtt+Ht7Y1nnnnG6nalUonw8HDzTnh7IywsDEqlUmwTExNj1iYiIkLcFhoaCqVSKa4zbWPchzVqtRpqdeUTkSqVCgCg1Wqh1WptPENyFuM14LXwfLyWjQevZePBa9l4NNVrqSnXmyzpHXL+RerKz4BC1clbAKjL9SgsNXx29POW1nrM8nJDNYwAoda2rrqOTe19Q0REREREtbtekXBpFiB3cU88n0MTLikpKVi+fDkOHz4MiZ1PHjrD0qVLsWTJEov1W7Zsgb8/x6VzF4mJia7uAjkIr2XjwWvZePBaNh5N7VrmlgHGj66XLl3Cxo0X7dibYT9tdNewceNVAEC2Ugprxd9px08C8MKN7CvYuPFSjXu9qTbsW6/XY+PGjTb1pKGvY0lJSYMej4iIiIiI3N/p7CIAQLvm/I7cXg5NuOzatQs5OTlo06aNuE6n0+H555/Hhx9+iAsXLiAyMhI5OTlmrysvL0deXh4iIyMBAJGRkcjOzjZrY1yurY1xuzULFy7EvHnzxGWVSoXo6GjEx8dDoVDU44zJkbRaLRITEzF69GjIZDJXd4fswGvZePBaNh68lo1HU72WL/92AsBlAEDLVq0xblzPeu3nWkEZkJwEABh+2yAMigkDAGwpOorUXMtK6VbtOgCXL6BbxxiMG9Ol1n0vPpwEqVSKceMSamzrqutorPAmIiIiIqKmIaewDJfyStCvbZjV7SWacpy7bki49GwV3JBda5QcmnCZPn261XlVpk+fjkceeQQAEBcXh/z8fKSkpKBfv34AgO3bt0Ov12PQoEFim5deeglarVa8AU1MTESXLl0QGhoqttm2bRvmzp0rHisxMRFxcXHV9k8ul0MutyyLkslkTeoLC3fH69F48Fo2HryWjQevZePR1K5lWGDlZzilSl3vcy/TlVUuSLzE/XhJrU9tWKo1DGUW4OtT6zG9vQ1Dikkgsbl/DX0dm9J7hoiIiIiIgNHvJ6GgVIufnohD/3aWSZek0zcgCEB4kBzhQb4u6GHjUueES1FREc6ePSsuZ2ZmIi0tDWFhYWjTpg2aNWtm1l4mkyEyMhJduhieCOzWrRvGjBmDxx57DCtXroRWq8WcOXMwZcoUREVFAQCmTp2KJUuWYObMmViwYAGOHz+O5cuX44MPPhD3++yzz2LYsGF47733MH78eHz//fc4dOgQVq1aVa9AEBEREZH7igqu/OC/52wu9HoBUqltQ9jml2gg9/aCn48XtLrKuWA0Op34c3W7Kig1zHkS4ONVj14TERERERG5lvGeZuvJHKsJl/cTMwAAI7tFWGyjurP+KF8NDh06hNjYWMTGxgIA5s2bh9jYWCxatMjmfaxZswZdu3bFyJEjMW7cOAwZMsQsURIcHIwtW7YgMzMT/fr1w/PPP49FixZh1qxZYptbb70Va9euxapVq9CnTx/89NNPWL9+PXr2rN/wEkRERETkvkq1OrNljUnipCY3izW49a3tGP/xLuj1Ako1lftp1yxA/Lm6+QdvlmgAAP5yhxaGExEREREROZ0gCOLPmnK92bLRxVzDPI+P3h7TYP1qzOp85zh8+HCrF6Y6Fy5csFgXFhaGtWvX1vi63r17Y9euXTW2uf/++3H//ffb3BciIiIi8kylGvMEi1anh6+s9qqT8zeKUaLR4fz1YuSVaFBsknBp3yJQ/Lm6Wpk9Z3MB2FbhYszZCLD9szIREREREZGzGO9nAODLPZk4cCEX65+6Dd5ehjqMMq0O6nLDvVaLIMupOKju6lzhQkRERETU0KpWuKRm5dv0usIybeU+NDoUqw3zrAxoF2rWTquvTJLsWzjSYj/+PqxwISIiIiIiz1FQqsWDX+w3W3f8igozvj6Esor7K1XF/ZJEAgTynschmHAhIiIiIrdXqik3W37oywM2vU5VVvm69KsqPLXmMACgSG2ewCkzSeg0D/Sx2E+AnHO4EBERERGRe7two1gcRvnUNZXVNkmnr+PrvRcAAKqK+V0UvjKb58ikmjHhQkRERERur2qFi62e+S5V/HnJH+nizyer3HycuFq5bCyvN8UKFyIiIiIicmdHL+dj+Ls78HDFw2npV60nXADgQm4xAEMVDAAo/Hi/4yhMuBARERGR2yvV6mtvVAvjzYQ1V/JLa3wtK1yIiIiIiMidbT2ZAwA4cCEPZVodTimrT7gYqUoNIwIE+8mc2remhKkrIiIiInJ7pRrLChe9Xqix7F2vN5+8vsTKPmwVwAoXIiIiIiJyY5EKX/HnlIs3ka1SV9t2w9Fr6BQehLAAw3DKTLg4DitciIiIiMjtlVkZUqy8SkKlKo2u+qoY7zqOT+zvwwoXIiIiIiJyXyUm816ev1GM/IoK/+4tFfj7/4ajV6tgcXthWTle23ACyedyARjmcCHHYMKFiIiIiNxCysU8/DfpPHRWEimmNw9G1tqZUpdXn3DZ8687anztV/8cYLYcIK+9wkUCQxJHqLlbREREREREDmc6KkB+sQb5JRoAwJK7eyCmeQBkXpYPnR25nA+AFS6OxLERiIiIiMjlynV6TPosGQDg7SXBI7fFmG23NodLuV4PoPrKE3W59SHE3p/cBxEm5fYAkLzwDiz4+RheGtcNADC0cwuz7XJvPqdERERERETuq8RkVID8Ui3ySwwVLqH+hmSKtQfWzuYUGdpUDC1G9uOdIxERERG5XJlJNcqa/Vlm28p1epy8ZjnhY20VLtbmfQGsDw/WMtgP38wYiC6RQQAAL6kEc0Z0ROtQP+xbOBISSd2GICMiIiIiImpIpvc/uUVqqMoMCZdgP0My5Z7YVhavMQ7T3CrErwF62DSwwoWIiIiIXE5t8jTW+F4tzbalX7VMtgC1z+Fyo0hjdb2fj20fgf8voQv+L6GLTW2JiIiIiIicLbdIjU3pSmReL8aUgdHoGB6EUo0Ofj5eKFZXDsOclVciDnUcUlHh8uDgtgj0leH/1h2x2G+rUCZcHIUVLkRERETkMupyHR7/3yH865dj4jqfKsN3md44mKqtwsU4ZnFYlfJ4axUuRA0tKSkJEyZMQFRUFCQSCdavX19t2yeeeAISiQQffvih2fq8vDxMmzYNCoUCISEhmDlzJoqKiszaHD16FLfffjt8fX0RHR2NZcuWOeFsiIiIiKghPPN9Kl769Tg+352Jsct34djlAvRavBlv/XXKbEixC7klAIBAuTdkXob7K28vKe61UuUCAK1Z4eIwTLgQERERkcv8lHIZm9OzkXgiW1xnWu0CAOduFFt9rVZnOa+LqeKKkvouEUGYPrituN5PxoQLuV5xcTH69OmDFStW1Nju119/xb59+xAVFWWxbdq0aUhPT0diYiI2bNiApKQkzJo1S9yuUqkQHx+Ptm3bIiUlBe+88w4WL16MVatWOfx8iIiIiMj59pzNFX/W6gTsPXcD5XoBSaevmw0plldsePjMWN1iJJVaHyqZFS6OwyHFiIiIiMhl3t2cYbHOdD4XACisGHt4Yt8orE+7Kq4Xai5wEStjAuReyFAWiuurVtA4Cqd5oboYO3Ysxo4dW2ObK1eu4Omnn8bmzZsxfvx4s20nT57Epk2bcPDgQfTv3x8A8PHHH2PcuHF49913ERUVhTVr1kCj0eDLL7+Ej48PevTogbS0NLz//vtmiRkiIiIicn+ClRug7adyABiGEAvytfyqPzrUv9b9Bvl6w9/GYZepdqxwISIiIiKXuVmitVhXVqXC5T87zwMAiqoMLVbbkGKVCRdv6E1uTpoHyuvVV1vVkgcisoler8f06dMxf/589OjRw2J7cnIyQkJCxGQLAIwaNQpSqRT79+8X2wwdOhQ+PpXD6iUkJCAjIwM3b950/kkQERERkcNYm6Nyf2YeAMO90pX8UovtTwzvUOt+Wzj5/qipYeqKiIiIiFymZbAvrhWUma0r0+qg0wso1eoQKPdGQakhKbP1ZA4kksrKFn0tJS7FakPiJkDubZasqTqnC5E7evvtt+Ht7Y1nnnnG6nalUonw8HCzdd7e3ggLC4NSqRTbxMTEmLWJiIgQt4WGhlrsV61WQ61Wi8sqlQoAoNVqodVaJkitMbaztX1TxBjZjrGyHWNVO8bIdoyV7Rir2jFGtqspVmezC2p87eWbhoTL1IGt4e/jjVs7hOHWmJBa4x4d6udx18bZ7yl79suECxERERG5TNVkCwCUaHS465PduHCjGP8a101c//iw9lh36LI4HnFNCRdBEPDZzrMAgAAfL7QIkuOUybBiRO4sJSUFy5cvx+HDhyFp4LHqli5diiVLllis37JlC/z9ax+SwlRiYqKjutVoMUa2Y6xsx1jVjjGyHWNlO8aqdoyR7azFKuWGBIAXOioEBHoLSMuzPnhVZOkFtPUCCk8DG09bbh8WKcVOpRReEgG+XsBtAUps3LjRwWfQMJz1niopKan3a5lwISIiIiKXyCm0TLYAQPpVFTJvFAMAXll/XFy/IKErYqND8cS3KQAAnd7qywEYJpMs0xoa+Pt449/39MKrv6dj1tD2Duo9kfPs2rULOTk5aNOmjbhOp9Ph+eefx4cffogLFy4gMjISOTk5Zq8rLy9HXl4eIiMjAQCRkZHIzs42a2NcNrapauHChZg3b564rFKpEB0djfj4eCgUCpv6r9VqkZiYiNGjR0Mmk9X+giaIMbIdY2U7xqp2jJHtGCvbMVa1Y4xsV1Osru6+AJw5je7toiDzliAt76rVfYy5YxhimgdUe4x4nR4Z2UXoGhkEqQQN/oCPIzj7PWWs8q4PJlyIiIiIyCVyVGqr6y/mFltdL5VKMKZnJGReEmh1gkWFy1/HriHpzA28dncPXMmvfCIpyNcb0WH++PKfAxzXeSInmj59OkaNGmW2LiEhAdOnT8cjjzwCAIiLi0N+fj5SUlLQr18/AMD27duh1+sxaNAgsc1LL70ErVYr3ogmJiaiS5cuVocTAwC5XA653HIcb5lMVueb2fq8pqlhjGzHWNmOsaodY2Q7xsp2jFXtGCPbWYtVTsUcLlGh/lCX66y9DLFtQtApMrjGJIpMBvRt2zjmbXHWe8qefTLhQkREREQuUd1Ngr6WWedD/X2QU6iGrkrDJ9ccBgB0bxkkzvti2B+nsSf3U1RUhLNnz4rLmZmZSEtLQ1hYGNq0aYNmzZqZtZfJZIiMjESXLl0AAN26dcOYMWPw2GOPYeXKldBqtZgzZw6mTJmCqKgoAMDUqVOxZMkSzJw5EwsWLMDx48exfPlyfPDBBw13okRERERkl9/SrmDF32chgSGJ0jLYF7lF1h9ee+z29h5ZsdKYMOFCRERERC5x4mr9yrS9pIYbiOryKJdulmJV0nlxOSygYZ7e4m0N1cWhQ4cwYsQIcdk4jNfDDz+M1atX27SPNWvWYM6cORg5ciSkUikmTZqEjz76SNweHByMLVu2YPbs2ejXrx+aN2+ORYsWYdasWQ49FyIiIiJynme/TzNbjgz2NXt4LUIhR3bF6AFx7c0f2qGGx4QLEREREbnEK7+l1+t10oontnTVZFxyVOZzw9zdN6pex6kvgRU1ZIPhw4fX6b1y4cIFi3VhYWFYu3Ztja/r3bs3du3aVdfuEREREZGbah4oR27F8GIAML5XFHKL1WgT5o/QAB8X9owAJlyIiIiIyMNIpYb/VzdU2LWCyoRLgI8XZF7ShugWERERERGR0wX5eiPQt/Jr/c4RgZgysLsLe0SmePdJRERERC4xKCbM5rYzbosRf/aqqHDRVzPZi9KkwkXK8YuJiIiIiKgRCZB7I0hemXDpEhnkwt5QVUy4EBEREVGDEwQBWp3eYr2Pt+XH0x5RCiyaUPnEljGJcsNkosgLN4rFn00rXJhvISIiIiKixiTQx9tsDpfOEUy4uBMmXIiIiIiowX264xwOZ+UDAAaaVLrsemGERdtnR3YyW5ZKDVmUJ749jG+SLwAAVu48J27XlFcmcm7v1MJRXSYiIiIiInK5ALkX+kaHAgDCg+QIkHPWEHfCq0FEREREDe6dzRniz5NuaYX5CV0Q5OuNCIUvukYG4ZSyUNwe7Ccze62XSdnKot/S8VBcu2orWd6b3MexHSciIiIiImogVYdRlntL4e0lRWSwL3a9MALB/rJqXkmuwgoXIiIichqhmknNiUz5eEsxoF0YukYqAADfzxqM3q2Dxe1VbyKqJlfmrzuC/BKtxX4fuz0GvjIvx3e4OhX94rueiIiIiIgcoVhTbrYcaFLNEh3mD4UvEy7uhhUuRERE5BRanR73fbYXkcG++M/0/q7uDrkxHy/zpEiIvw8GxYTh6OUCAEC7ZgFm272k5hmXdSmXre43NMDHgb0kIiIiIiJqWMVqndkyhw9zf7xCRERE5BSpWfk4crkARy4XQBAESDh7OVVD5mX53jB9v1StUpHa+F7qGRVceyMiIiIiIiI3VaQ2r3BhwsX9cUgxIiIicor953PFny/llXJ4MaqWj7flR9KakipSqW0JlwHtwurdJyIiIiIiIlcrsRhSrAGHTKZ6qXPCJSkpCRMmTEBUVBQkEgnWr18vbtNqtViwYAF69eqFgIAAREVF4aGHHsLVq1fN9pGXl4dp06ZBoVAgJCQEM2fORFFRkVmbo0eP4vbbb4evry+io6OxbNkyi76sW7cOXbt2ha+vL3r16oWNGzfW9XSIiIjISX4/Uvnv/9B3/sbcH9Jc1xlya9YSLl41fErNLiirdZ/v3Ncbfj68GSEiIiIiIs/FChfPU+eES3FxMfr06YMVK1ZYbCspKcHhw4fxyiuv4PDhw/jll1+QkZGBu+66y6zdtGnTkJ6ejsTERGzYsAFJSUmYNWuWuF2lUiE+Ph5t27ZFSkoK3nnnHSxevBirVq0S2+zduxcPPPAAZs6cidTUVEycOBETJ07E8ePH63pKRERE5ASdI4PMln9Lu1pNS2rq5FYSLpEK32rbK1W1J1yiw/zt6hMREREREZGr5RVrzJZ1eo4c4e7qnBIbO3Ysxo4da3VbcHAwEhMTzdZ98sknGDhwILKystCmTRucPHkSmzZtwsGDB9G/v2EC3Y8//hjjxo3Du+++i6ioKKxZswYajQZffvklfHx80KNHD6SlpeH9998XEzPLly/HmDFjMH/+fADA66+/jsTERHzyySdYuXJlXU+LiIjIo5VqdPh813mM7hGBrpEKl/aloFQLvV7An0evWWzLL9EgxJ8TmZM5Hy/LSpR/DGiDtEsFGNalRZ329djtMbiSX4p+bUMd1T2bScB5ioiIiIiIyHGu3Cw1Wx7cvpmLekK2cnoNUkFBASQSCUJCQgAAycnJCAkJEZMtADBq1ChIpVLs378f99xzD5KTkzF06FD4+FR+IZOQkIC3334bN2/eRGhoKJKTkzFv3jyzYyUkJJgNcVaVWq2GWq0Wl1UqFQDDUGhardYBZ0v2MF4DXgvPx2vZePBaeo5Ptp/Fih3n8V7iaZx5Pd5iuzOvpapUCz8fL8i8pCgsK8ctb26vtu17WzKwaHxXh/ehKWmMv5dS6CzORwLgrXu6A6jbuT47oj3kMi9Ar4NWr3NkN2tVXm7opyDU3mdXXcfG9L4hIiIiImrMfk29jO8OZAEAnhreAf+8tR1aBMld3CuqjVMTLmVlZViwYAEeeOABKBSGp22VSiXCw8PNO+HtjbCwMCiVSrFNTEyMWZuIiAhxW2hoKJRKpbjOtI1xH9YsXboUS5YssVi/ZcsW+Ptz2Al3UbVKijwXr2XjwWvp/lYkV/6TXtOcZo6+loVa4OVD3mjpJ+BffXX46LgXUMNT/v/bl4XosvOI8HNoN5okz/+9rHzP7t29C2fr9J6o/iPstsTN9e+SnVQawNg3W+cWbOjrWFJS0qDHIyIiIiKiuruQW4znfjgiLrcK9UN4DcMuk/twWsJFq9Vi8uTJEAQBn332mbMOUycLFy40q4pRqVSIjo5GfHy8mBAi19FqtUhMTMTo0aMhk8lc3R2yA69l48Fr6TmeTd4i/jxu3DiL7c66lv/30zEA13CtVIJx48aZ9aM6WfIYjI5rh9ahzLrUR2P4vdTrBSDZkGiQeUkw5a4E+Mpsn+De2vvMTybFp1NjMaSj60rsbxSp8UrKTgDWfw9Nueo6Giu8iYiIiIjIfZ3LKTZbjgrh/bOncErCxZhsuXjxIrZv326WzIiMjEROTo5Z+/LycuTl5SEyMlJsk52dbdbGuFxbG+N2a+RyOeRyy7IrmUzmsV9YNEa8Ho0Hr2XjwWvpWWq6Vo6+lr8dqZynRepl28eKb/dfwrf7L+H4kgQ89MV+jOoegaeGd3RYn5oKT/691JTrxZ/3vzgKQQ6Y12fz3GFo08y1Fcve3pXnZeu1aejr6KnvGSIiIiKipuR8rnnCJaZZgIt6QnUldfQOjcmWM2fOYOvWrWjWzPwpw7i4OOTn5yMlJUVct337duj1egwaNEhsk5SUZDbGdGJiIrp06YLQ0FCxzbZt28z2nZiYiLi4OEefEhERkduLCq4sLRYEwSV9uF6orr2RiS93Z+JwVj6WbcpwUo/IXZXrKxMTvjLHfBwNkNteIUNEREREROTOzl03T7i0dfHDZWS7Ot/hFhUVIS0tDWlpaQCAzMxMpKWlISsrC1qtFvfddx8OHTqENWvWQKfTQalUQqlUQqPRAAC6deuGMWPG4LHHHsOBAwewZ88ezJkzB1OmTEFUVBQAYOrUqfDx8cHMmTORnp6OH374AcuXLzcbDuzZZ5/Fpk2b8N577+HUqVNYvHgxDh06hDlz5jggLERERJ7Fy6ty3hS1SfWAs2TeKEa5zvw4GdmFFu2CfKuvenk/8bTD+0WewbTCxVvqqISLU6cmJCIiIiIiajCmCZdbOzSDRFL9XKnkXup8h3vo0CHExsYiNjYWADBv3jzExsZi0aJFuHLlCn7//XdcvnwZffv2RcuWLcX/9u7dK+5jzZo16Nq1K0aOHIlx48ZhyJAhWLVqlbg9ODgYW7ZsQWZmJvr164fnn38eixYtwqxZs8Q2t956K9auXYtVq1ahT58++Omnn7B+/Xr07NnTnngQERF5JInJRPVF6nKnHuvrvRcw4t0dWLXrPPx9KqsKDmTmWrTd8687xJ+7t6x+vjStzvlJInIfK3eeF3/28XZMwkXuoP3Yg/dARERERERkr9MFEqRdKgAA/PPWdvjP9H4u7hHVRZ0fBRw+fHiNQ5XYMoxJWFgY1q5dW2Ob3r17Y9euXTW2uf/++3H//ffXejwiIqLGKPNGMbQ6PUL9fXD5Zom4vqisHM0DLecsc5RXf08HACzblGH2BfOes4aEy/AuLbAj4zoAIMCn8qNG15ZBOHHN+oTdb/55Eovv6uGkHpO7WbnznEP2I/OSwEsqQeeIID7xRUREREREHk8QBKw4Uflg48JxXSH35vDJnoRjLxAREXmgHFUZRry7w+o2Z1e4mDJ9ziLtUj4AIDY6FAvHdkOwnwxeUgm+nTkIpVoddmTkVLuf1XsvMOFCdRYW4IO//284ZF6ur24hIiIiIiKyV2GZ+f08ky2ehwkXIiIiD2SsILGmRKNrwJ5YO345ukQGictDOjUHAHx3IKva1/zz1nbO7hY1Ii2C5LheqMYdXcPh78OPs0RERERE1Dhkq9Tiz48Pa+/CnlB98XFAIiIiD7N2fxZe+PlotduLNc6tcPGV1fzx4YlhHayuf/OeynnWfn7yVkwb1EZMtHhLORxUU5BbpMbty7aLy7FtQuq1n1+fuhWv3NkdL4/v7qCeERERERERuZ5SVQYAaN88AAvHdnNxb6g+mHAhIiLyMC/+eqzG7SVqx1a4XCsoxaxvDmH3mRsQBAHlOsv52vxklWXOoQE+VvfTMtgPKS+PwoGXRqJf21C8eU8vKPxkAAB1ud6hfSb73ChS48+j11Cmte+9dOhCHlb8fRZ6veE9s+VENi7llYrbl9RzGLnWof6YOSQGAXJWtxARERERUeOhrKhwaRXi6+KeUH0x4UJERE3elfxSvLclAzmFZa7uikM4usLlo21nseVENh78Yj+UqjKU680TLnd0Dce80Z1t2lezQDnCgyo/OMq9DR9F1OWuHQaNzL36Wzpmrz2MD7eesWs/961MxjubM/Dz4ctWt7cM9rNr/0RERERERI1JdkWFS4SCCRdPxccCiYioyTqdXYjnfkhD+lUVAOBsThE+e7Cfi3tVs5vFmlrblDp4DpfLN0vEn+PfTxJ/XvvYIGw6rsT8hC7w8ZbiSn4pRnYLr9O+KxMurHBxJ38euwYA+HJ3Jv41tqvd+8tQFgIAhCrFUbUNT+dpODAeERERERHZI69ECwBoVs3IEeT+mHAhIqIm618/HxWTLQCw5+wNF/bGNgPe3Gp1/d19o6AXgD+OXMVvaVfwsI2T0AuCgMs3S9E61A8SifnXxdtPZePj7WeRmpUvritUV1bP3NqhOW7t0FxcXlyP4aHkFUORqbVMuLgjLwfNrWOsitJXybj4mgxF19gIgmDxO0VERERERFQT47DO/j6N916psWtcjxUSERHVQW6VahFVmXMnm7dXmVZnMZwXAAzr3ALLp8TiesWQaIez8nGtoNSinTUrd57H7cv+xqc7zpmtv1mswYzVh8ySLc7AIcXcm+MSLoaEWtWEi8yLH0WJiIiIiIiMSipGrPBjwsVj8S6XiIianN/SruC5H9IQ6WFjouZXlBZX1b5FgMX29Csqq21NaXV6vL3pFADgnc0ZZtuuF6nr2806MSZcNDpWuLiLS3mVQ8gFyB3zIV9XkSg0Pq1FREBSUhImTJiAqKgoSCQSrF+/3mz74sWL0bVrVwQEBCA0NBSjRo3C/v37zdrk5eVh2rRpUCgUCAkJwcyZM1FUVGTW5ujRo7j99tvh6+uL6OhoLFu2zNmnRkRERET1ZBwi3K8RjwbQ2DHhQkRETc6z36fh19Qr2J+ZZ7FN48ZzieRVqcj5ftZgTB3UBrNHdARQ+aU2AOiqTpZhxc6M6zYfq6p37utd6/5tYUy4lHFIMbdx+7K/xZ9D/BwzbrBWZ0y48DoTGRUXF6NPnz5YsWKF1e2dO3fGJ598gmPHjmH37t1o164d4uPjcf165d/uadOmIT09HYmJidiwYQOSkpIwa9YscbtKpUJ8fDzatm2LlJQUvPPOO1i8eDFWrVrl9PMjIiIiorozPqTGChfPxTlciIioSdFbGZLLVImmHD7e7jk5XX5JZRJk9ogOGNy+GQa3byau6x6lwJkcw5PNulrOEwDeqqhuseZmDQmXc/8e57ChpgLkho8iRW4+nJutjl8pwD+/OohnR3XC9MFtXd0du2kdVHlUXrGflIs3HbI/osZg7NixGDt2bLXbp06darb8/vvv44svvsDRo0cxcuRInDx5Eps2bcLBgwfRv39/AMDHH3+McePG4d1330VUVBTWrFkDjUaDL7/8Ej4+PujRowfS0tLw/vvvmyVmiIiIiMg9lBjncGGFi8dihQsRETUph7Msv/D970P9xZ+LNe475FFeRcLFx1uK50d3sdj+6oTKSeutzfViSlOux9mcomq3G+e3GdUtAh/8ow+WTTJUtDQP9HFYsgUAQv0Nya2bJTVX1HgCQRBw58e7caNIjVfWH3d1dxyivkOA5ZdoMPW/+8Rl4/tx5+nqq6oaA4nEcb8bRKY0Gg1WrVqF4OBg9OnTBwCQnJyMkJAQMdkCAKNGjYJUKhWHHktOTsbQoUPh41P5IEFCQgIyMjJw8yYToERERETupqziOwlfH35t76lY4UJERE3KDStzk9zRNRwh/jLkl2hRonbfSgtj1cnwzi0gtZL0CAvwwZCOzbH77I1aK3msxcHUW38Zql+uF6lxT2xrAECXyCAE+8nq0/Vqhfgb9pdfooUgCB79hXVuLcOweYLyKhUtZfUcYu+/u85j77lccXnD0Wvwkx0xa8MxiYlqt2HDBkyZMgUlJSVo2bIlEhMT0bx5cwCAUqlEeHi4WXtvb2+EhYVBqVSKbWJiYszaREREiNtCQ0MtjqlWq6FWV/4boVIZ5gTTarXQaq3PJVaVsZ2t7Zsixsh2jJXtGKvaMUa2Y6xsx1jVjjGynfEhUB+JwHjVwNnvKXv2y4QLERE1KWorXyB7SSXwk3khH1qUuHGFS2FFMkhRQ9LDWH1SW4WLtaGicgrLEB7kCwAoqjiWn6zyqZo+0SF16q8tjBUuGp0eJRqdOMSYJ8qvUqVTrtPD28uznkp6fcMJs+X6VrhczS+zWLcu5TI6RwTidLahsuqeW1rVa9+eQhAAD84fkpsYMWIE0tLScOPGDfz3v//F5MmTsX//fotEiyMtXboUS5YssVi/ZcsW+Pv712lfiYmJjupWo8UY2Y6xsh1jVTvGyHaMle0Yq9oxRrUrKPICIMGRwweRm+Hq3rg/Z72nSkpK6v1az/1Wg4iIqB52n7lhdf21AsMXxOvTrjglseAIxtLimioDvCsSLjp9zZUJGiuJp0//PofFd/Uwm/9lyV0969NVm/n7eMHHSwqNTo/8Uq1HJ1wylOZDtBWrdQj296yEy9fJF82WS7W6elUeVde6WG14D0/u3xovj+9Wny4SNSkBAQHo2LEjOnbsiMGDB6NTp0744osvsHDhQkRGRiInJ8esfXl5OfLy8hAZGQkAiIyMRHZ2tlkb47KxTVULFy7EvHnzxGWVSoXo6GjEx8dDoVDY1G+tVovExESMHj0aMpljKyMbC8bIdoyV7Rir2jkrRikXb+I/uzLxyK1tEWcyx6In4/vJdoxV7Rgj2710eBsAHUbcfhs6RQa7ujtuy9nvKWOVd3147rcaRERE9bAu5bLZ8uT+rc2Wd1WTkHEHxuobf5/qEy62VrhoKipcQv1luFliKJUtrqhquXKzFAAg85KgY3igfZ2uhUQiQYi/DDmFatws1qBViJ9Tj+dMVeehKdKUI9jfs28mBMHwXpF72zb819HL+dDpBZSVW6+MuZJveG/NGtoe/j78GEpUV3q9XhzuKy4uDvn5+UhJSUG/fv0AANu3b4der8egQYPENi+99BK0Wq14I5qYmIguXbpYHU4MAORyOeRyucV6mUxW55vZ+rymqWGMbMdY2Y6xqp2jY/TdoSv4O+MG/s64gROvJTSqzzl8P9mOsaodY1QzQRBQqjXcqwf5yxkrGzjrPWXPPj3rsUsiIiI7LN96xmx59SMD8PKd3QEAY3oYnvQd0aVFg/fLFpuOX8PnuzMBAH41JFy8vQwJlxJ1zUNBrUo6DwDw9/HGv8Z2BWCYr+X7A1kY+s7fAACtThATOM5kOo+LJyutMhxdsRvPB1SdrpFBAICWwb7iujKtbfO4FJRocdcne3DPp3ux8ZiyxraN6UsIovoqKipCWloa0tLSAACZmZlIS0tDVlYWiouL8eKLL2Lfvn24ePEiUlJSMGPGDFy5cgX3338/AKBbt24YM2YMHnvsMRw4cAB79uzBnDlzMGXKFERFRQEApk6dCh8fH8ycORPp6en44YcfsHz5crMKFiKipkqnF3DkUr7FHHZVZeWW4JfDl2ucI/FyxQNLAHC9sOa5EomIqqPVCeKIE5zz0nMx4UJERE1CkbocH2w9LS6/dW8vDO8SDoWv4cv+7lEKsZ07euLbw+LPNVW4SCuGfnpz40kcuZRvtY2qTIvf0q4CMFQcdG9pOPcdGdfxr1+OOajHtgupmMelaoWIp6k6/4+7vpdqIlR8j/Du/X1gzLWpbZzH5ez1otobVQhgwoUIhw4dQmxsLGJjYwEA8+bNQ2xsLBYtWgQvLy+cOnUKkyZNQufOnTFhwgTk5uZi165d6NGjh7iPNWvWoGvXrhg5ciTGjRuHIUOGYNWqVeL24OBgbNmyBZmZmejXrx+ef/55LFq0CLNmzWrw8yUicjfvbcnA3Sv2WMxhV1XCh0mY9+MRrEu5VG2bvOLKz7Gv/JZudb5EIqLamD7Ex4SL5+LdLhERNQlVnzSLbWM+lEpgxdwhhWW2f0l+JrsQoQE+aB5oOfSKM/nUMBG76c3dI6sP4vAroy3aHL9cYLZsWs1gatItra2ud7RQscLFsxMuxRrz944nVriUV8z94yWVwFfmhRKNzuYKl5vFtl+/AHnjvHlwfj0YNSbDhw+HIFT/tPQvv/xS6z7CwsKwdu3aGtv07t0bu3btqnP/iIgaK43OUFn96Y5zAAxz2C25u/p5C0srHj75+9R1/GNAG6ttslVl4s9Jp6+j00t/4cfH4zAwJsyBPSeixs7490YqEeDjzToJT8UrR0RETcJbf500Ww4PMk+SBPoaEi62ViX8eOgSRn+QhP5vbEWZjRUAjlLT/Cym390F+Vp/ruLSzRKz5fYtLOdpWT6lL968p/obT0cKFStcPG9IsWxVGS7fLIEgCOIwbUYXckuqeZX7Mr61vKQSlOsMC6U2vr+vF9k2fEaIvwzeNSQNiYiIiJzp7aNeGLD07zq/zvhgSlVF6nKLSmcAmPyf5Dofg4iaNuNDfD68XfJovHxERNToFZRqsTk922xdSJXJzIMqKlyKbKxweeGno+LPXV/ZhFd/O25nL6tXdbxo7xrmVTGtsqiuVdUqHi+pBHf1iTJbN6F3FHwbqIQ5LMCQcLmQW9wgx3OUv45dw6B/b8OQt//GV3suWGzPr0PFh7swrXDRVFRLvbYhvdbX7Tx9HQutDEf3xsSe6NfWvJos+V8jHdBT91d9WpSIiIic6e+MHHy07Uy1VYQ3ysw/JddUPf7Rtso5ILU6y/1pyvX4fNd5i/VGNVUyEhFVdfyKYTSK5tYHoSAPwYQLERE1etaGOpJIzG+0jBUuhy7erPbGaM/ZG8gpLLO67evki3b2snobj18zW76vf3S1bReO7Sb+fCG3BOeszKthrYrHtOJn0Z3dIa0hqeNonSIMFTZXTCYbdUd7zt7A2ZxCcfnJNZXz6uw8fd2i/XuJpy3WubPT2YW4lGe4Bl4mvx97zubW+tqHvzxgdb2vzAv/mzkQ38wYiJlDYrB13lD41TAHEREREZG9HvnqIN5PPI0dGZafz3RWKsWrqwq/XqjG+yaf5zTllhUuv6Zexodbz1isN1KqrN87EBFZk3zOcO/VScFkrSdjwoWIiBo9dZWbo9fv7mHRRmryBfOZHMskxd5zNzDt8/2YvNIwNIC1J+HU5c4ZWmzO2lSzZeN8M9b0bBWMVJN5W+5ZsceijWkC6uk7OgIAOoZXDit2a8dm9e5rfSh8DdVGDT00W11k5ZZg2uf7Mer9JDEh18IkSWUtsQU47z3hDKZJEy8HJdx8ZVL4+3hjaOcWeOXO7ugYHuSQ/RIRERFZY/p5MttKssPaZzOFn8xiHQDsO2/+0Eny+VyzhI2qTIuVOyurW3xllvcHeR5Y8UxErnPymgoAEBPEhIsnY8KFiIgaPdMbqzu6hmN6XDuLNqYTx5+zknDZUjEk2YXcErT715/icEumfku76oDe1mzaIOsTdZoKNrlpVFkZIu1GxY3fc6M64/n4LgCA/u0qh31qGxZgbzfrxK9i6DJb5wpxhSv5ldU3MQs34o73dmCQySSol02qcwaYxPKtv06ZPRlpL0EQ8Nj/DmPlSanVJzTtca2g8ksJL6kE/7y1HQBAUc1Tn6Z9qo6vN6tZiIiIqOFcyqucQ09r5bNSmdbyM3x1CZdDF/Is1r296RQAw+ef8R/tQuYNw5C4/xffGV0iFRbtVaW2DVfsDJvTldh2Mrv2hkTkNm4UGe7Vg32YcPFkTLgQEVGjZ1r+/859va22ad8iUHwqbXO6Es9+n2r2VJzEhgf+X/jpKEqtTJjpSG/e06vWNrUNB/bnUcMQZe2a+4vrOoYHYdmk3lj76KAGH/LJ18f9Ey5VE2znrxdjw9FrFu1mj+iAjx6IFZe/2nMBH207g7RL+Q7pR16xBjtO38DJfClOXius/QX1ZJpwqS2vYy35aNRQ8wARERERAUCWScLlRqHaYnvVyncACJRb/7xi+sCN0f+SL0JVpsXRywXiUKwAEB7ki/v7tQZgeJiodagfAMNckq5QpC7H4/9LwcyvD1kdTpiI3FNuseHvVqD1PDB5CCZciIio0TPeWHWNDEKzQHm17e7u0woAsD7tKn5Lu4rnfkgTt5VbmSTTmg1Hr+KUUuWUxMuDg2uvbqnNL6lXxJ+bV4nF5AHRuLVjc7uPUVdihYum+i/uXa2wzLab5eFdwtEy2M9i/REHJVxKTN5Xpl8oOJqXVCI+7VmkLkeJpvobdWtfXBj5+TStj5q2JGaJiIjIecwSLkXWEi6Wn9GrVg3r9AKSz+WK1SsA8OqE7mjXzB+lWh0OZuaZVTcDhqFmHxjYBt/OHIR9L44Uh+tVuSjhct0k2XQxt7iGlkTkLko05WIVHhMunq1p3QUTEVGTZLzZqm64AKOAKnOj7D1XOW6zrU+Gzf/pKMZ8uAtv/XWyjr207qrJk3UPWxkKrTqLJ3QHYEgymVrwS7r48+D2DTtXS3WMCZcyrQ6v/XECz/94pMZhqlyh0MrQbNb0b2sYTmzKgGiz9a/+no6nv0vFvZ/uQUGpFn9n5FideLU2pu/D/+3Pcsi8N8evFGDkezvM1oX6+5hNIDtj9cFqX19TH2KaB1a7rbFzt/cwERFRU2CaaLCWcLE2pJi2yoNViSey8cB/9+HcdUOiYuMzt+OR22IwKMbw2XndocvIUKrMXtM8UA4vqQRDOjVHsJ9MHOJXZeNDO46Wa3LupokjInI/JZpyXM0vRW7FcGJybynk/Mbeo/HyERFRo3eiYuK51iGWlQemrA0nYPyCu6Yv3BeM6Wqx7uvkiw75MjzZJOlTU3VOVcYxpLU1DPfkqInR7WUcwqxIXY4v92Ti58OX8cpvx3E623lDZtWVscLlnthWOPJqfLXtJBUlDhIrpQ5/HLmKw1n5GPLWdjzy1UG8tyWjzv0wrTQ5dDEfPV/dXOM1tsWTa1LELxSMwgJ8IPOq/Ji473wedmTkWH292soXF0ZBtcz/QkRERORIpkN4GedCMGX8fN4qxBdfPNwfAFBe5bPUmSqfQY1zPRrnPNyUrsRH28+atWke5GO2bEy4uGJIsV1nrmPpX6fE5czrTLgQubOn16Zi2Dt/Y+fp6wAM92KsnPdsTLgQEVGjln61AP/ZeR4A4ONd8z97gVa+HC6qSLQUqa3fLH332GA8ObyD1Q9Exkk17WEcwzWmeQDCAnxqaV3JmMSo+hRflwhDxcHIruF2981RrM3z8e2+LMR/kOSC3lhnfB8E+Xoj2E+Ggy+NgryG91PiCWW12worknjfJF+sez/U5km8cr2A/BL7buRNxx8HgH+NtUwgAsAPBy9ZXW9taA4j06QNERERkbOZJjhuFlsmXIxDocq9vcTPKZoqFS6yKp/xQvwNyRPjMGHWNAswfzBK4dvwCZccVRl+TrmM6V8cQMrFm+L6s9eL6rSfz3edx+SVyXjg8wMo4fQvRE5VrC7HtlM50OoEvLz+OAAgLIDjiXm6Ot8FJyUlYcKECYiKioJEIsH69evNtguCgEWLFqFly5bw8/PDqFGjcObMGbM2eXl5mDZtGhQKBUJCQjBz5kwUFZn/A3D06FHcfvvt8PX1RXR0NJYtW2bRl3Xr1qFr167w9fVFr169sHHjxrqeDhERNWJ6vYDxH+0Wl0d3j6ixvdRK1mTw0m1o968/se98ntXXNA80JEHeua+Pxbav9lyoQ2+tK674gv22jnUb/st0mC5r/nlbO7v65UiKGqogrE1W+v/snXd4FOXaxu/tm15JgxBC7xB6VyG0gL2hKOpB8XjseOzIp9iO6PFg92A9KtgVGyIBlE4CgdA7ISEJ6WWTTbbv98fszM7MzpYkm7Z5ftfFRXbm3dnp8857P8/9tAc6nuACMD7dB5bPxKiekZLtNUrvxeIbm5EBpZewtivVGfDINwe5iKiWcve03pLTNx8vk/x9sagX4cW6jyAIgiAIorXgCxx1kv0Wpv+lVck5wUWcLcy3fZ3aL5bLXO4RFeyyvIxhCfjtgSkugV2cpVgbCi6PfX8Ij3x70GX6kaJan5ehN1rw4vrjyD5fhX35NcirozB7gmhN9p53HWeIaUKgJdExabLgotfrMWLECLzzzjuS81euXIk333wT77//PrKyshASEoLZs2fDYDBwbRYuXIijR48iMzMTv/76K7Zt24YlS5Zw83U6HWbNmoWUlBTk5OTg1VdfxbPPPovVq1dzbXbt2oWbbroJixcvxoEDB3DVVVfhqquuwpEjR5q6SQRBEEQAUaU34ccDhTCYrTCIIu+ne8nqEBfM9MSSab1x8/ieXKSbViX9SOXXcfgquwA3rd7jU6Rbqc6AM2V1nIVUiLpp1kzs+ogFF9YiLVTTcayeZDIZHpzRT3LexqPuM0XaEtZSLlTjFBOC1UpB1hEb/QgAKTGuL+RSXKxtmqAkJXjMf2sHvt9fiNs+zm7SsopqGlElEfnJt0N7Zv5g7m+T1YZHvnF9iednuFw1Mglv3pTWpPUgCIIgCIJoKTUNJiz8cA+2n67gptVLWAKzGS5alQJqJdPnEVuKsQLM1Wnd8dFtY7npbKAVn8l9YzEkKcJleltbitUbLfjrpHTwzbkKvc+1ZKr0JvDL0FGGC0G0LuckLP9G94xqhzUh/EmTR1vmzp2LuXPnSs6z2+1YtWoVli1bhiuvvBIA8NlnnyE+Ph7r1q3DggULcPz4cWzYsAF79+7FmDGMX+Zbb72FjIwMvPbaa0hKSsKaNWtgMpnw8ccfQ61WY8iQIcjNzcXrr7/OCTNvvPEG5syZg0cffRQA8PzzzyMzMxNvv/023n///WbtDIIgCKLzc+f/9mJ/QQ0e/vog7r2sj2CeVF0NPlIZLmL6xoXiofR+mD88STDdnXWS0WLjLLOe+OEwAOC9v866tW0CgDNldUh/nbHTmtI3FgAQ0kSBhM1waTRbBaKPM1OjY2UhjE+Nlpz+3C/HcMfk1DZeG1dYSzlxTZK5QxOx6ThT2+TX+6dw01+6ehgufe0vr8s1W5pWWF1KcGkOtQ1mTP7Xlib/3gYJAYzNcBkQH4ZVCxix5cWrhyI1JsQPa0oQBEEQBOGdzcfLsPNMpWBao9kKs9Um6Kez/RatUg6lnM1wEfbHTA7BJTZULchckXqXCHfTp45wBOJUt9D61Vey8yrdzrPbgeHPbsTau8ZjUp9Yj8sRC0QkuBBE62KxudbDnDU4Dqf2tdyenGg//GqsnZeXh5KSEqSnp3PTIiIiMH78eOzevRsAsHv3bkRGRnJiCwCkp6dDLpcjKyuLazNt2jSo1c7ogdmzZ+PkyZOorq7m2vB/h23D/g5BEATRNdlfUMP9/c6fZ7m/H07v7/W7vhSR3/DgVBexBYDbeh5P/XAYpTqDYNr7W89KtmX5+xf7ub93nGGi9ILV3i2qBOvjEFxsdudLo9HqzNSID9e4/W57kNqtaYPzfBGpNSmrM2DX2QocLmSsGMSCy9Vp3fHF4vE48MxMgc1Er1jftsdT/RMp9CbP7et9FGQuVDf41E6p8H5NsFlUGl6W18LxKZjU1/MLfSAiA9luEARBEER7cL5SujC8OMuF7XtpeJZiJjeWYlIBVXFhGsm2YlhLILYeY2tis9mReazMa7vlPx0FwGQ5z3x9K9ZkudYTFNcGJMGFIFoXi8NlY2q/WNw9rTdWXjvcY70oonPgVz+RkhIm6jE+XuiRHx8fz80rKSlBXJzQ0kWpVCI6OlrQJjU11WUZ7LyoqCiUlJR4/B0pjEYjjEbnw06n0wEAzGYzzOa289UkpGGPAR2Lzg8dy8AhkI7l3yYle92OK4fHY8Wvx9zOf+WaIbDbrDDbXAe9NW70kB8OFCEnvxqbHp4imO5pXc6UuRa21CplTToOSjhf/uobjNAq7PjtAvPSqJTLoFV0rOMqt0u/rAJAha5BUBdk+c/HsOtsFX68Z4KLAOJPbDY7xr24WTAtVC132W/jezE2EuLp14/ujm9zijz+ht5gatJxqGt0tQDjU6lrgCYyyOtyrFb3b8/89bFLRFyJ11dvYNZJrWjaORqImC3O7TeZzbDb3Mc2tdf9tasfI4IgCCIwyatwI7gYLdCo5NAqFZDLZbwMFwVUCs+WYlKCy/f3TMKusxVY/tNRGC02jE6Rtv6JCWWEmap6z303f/BFVj6+zC6QnDcwIQwnSuoAOAO4Xvn9BE6X1ePpH49g4fgUQfsaUV9zc5Ec9UYLolQdKzueIAIFiyPDrkdUMJ7MGASA+uuBQMcxcG8DXn75ZTz33HMu0zdu3IjgYN+81onWJzMzs71XgfATdCwDh85yLJkAM+lH25bMP3xaxj+HAa8dll7GiSOHsP6iaw0LAKgzO3/7qhQrTutkOFrNvKTlVzVg/fr1gnVjPrvD9ffPHj+M9WWHfNkEAIx1gAwK2CHDR+s2oc4EbL3IvGTJYfPy+20PEz0nvd/HvPQnruhpxYzuTGf0y71Mu4zXN2NivA3RGmB4tP8zXk7VygAIlbSio9lYf9q3718slMNbMvFf23cgP8z3dTqaxyyzT5gdZyWKmP6+6U8k+tClOV8HSO3vjGSr4Nw4WeS6D375bT34iS97y5k29TVVHe68amv45/GG3zfAjdOggLa+vzY0+JbdRBAEQRCdCXGGS7hWCZ3BgnMVesxZtQ0T+8Tiw9vGcDUeNUpnhouLpZgja0UtkcGeHB2MG6N7YubgBFTWG91mNbN1/vQmKwxmK2cx3BqwmStSDE4M5wSX+HAtAKC6wb0IJM5wMdtleD3zNJ6/ergf1pQgCDFshovKB2cBovPgV8ElISEBAFBaWorExERuemlpKUaOHMm1KSsTpjpaLBZUVVVx309ISEBpaamgDfvZWxt2vhRPPvkkli5dyn3W6XRITk7GrFmzEB4e3pRNJVoBs9mMzMxMzJw5EyqKnujU0LEMHDrTsTxSpMNPB4sBuEZ3zRkSj4yMET4v57XDeyTnTZkwFtP6ubdJGjG+DqEaJXpEBWHMS1sAOLMIMjIy8ODujdznWbPnQCkxGmu32/H4vs1c9B3LgjnT0LuJtltP798MvcmKd48p0C1UDYB5uQrSqJCRMbtJy2pt9EYLntzrvqbIzwUK/PuuWQDA7ceSRhl+PM+8vK67ZwKGJPn3WV66Kx84dlIw7YYr5vpkPQcAIafKsePzAx7bxPcbgYxR3X1ep79+OAKUFGNwlA3BYZE4XFwnmD9q3CSk9Yz0upysvCrgyD6X6bfPmSj4/qAKPX5+Y6egzdTLZiIy2Hk/qNtXCJw5hu6J8cjISPN5WwKR2kYzntz7JwBgztw5bms7Ae13f2UzvAmCIAgiECjVGRAVrEZRdSM3bXRKFKobTNAZLPjfrvPQm6zYdJwZP2L72BqVAiqltKUYK8CoPTzHo0PUnKgiRbhWCZVCBrPVjiq9CUk+ZCA3h5fXH/c4f2j3CPxwgMm4Vjhq0Ng8WPOKa7gAwPojpXj+6hasJEF0UXadqUB8hBZ9urm3CGMz7NiaUkRg4FfBJTU1FQkJCdi8eTMnsOh0OmRlZeGee+4BAEycOBE1NTXIycnB6NGjAQBbtmyBzWbD+PHjuTZPP/00zGYz9wKamZmJAQMGICoqimuzefNmPPTQQ9zvZ2ZmYuLEiW7XT6PRQKNx9axXqVQdfiCxK0HHI3CgYxk4dIZjefX70iJJTIgay+YP9nn9R/SMRvqgOK4QerBagQZH3YywII3H5QxLdhZ+r20UWjaJv2eVKRCkcn0M640WF7EFAHrEhEIl0d4T/Hof5Tw7A4PZ1uGOp5aXCTIoMRy/PzgV417chLI6pxWop3X+63QlRqbE+HWdFAphJOLMwfHQaty/WItJH5KELxarcctHWW7bPPnjUdw0vpfPy2TPRY0CKKwwuMxvtAIKhRJyL6KQ1U3mjVotvNb7J0biibkD8a/fnUUbZQqFoA17mgWplR3uvGprVLwxCpVK5VFwEbRrw/3W1Y8RQRAdm/0F1QjXKtE3rgnpn0SXZeeZCtz6URZuHNuTq2O34/HLkBgRhGveZQJGKuudfclqvQlvbmFqKWpVcqjk0pZirAAjleHiKzKZDFHBapTVGVtVcPlst7MOS4ha4VLv75YJKVj5xwkYzDbOLsxqcy+41DiyX3pEBaHQIWLxA20IgvCN4xd1uPlD5j3w/L/muW3HXo++1M4kOg9NfnrU19cjNzcXubm5AIC8vDzk5uaioKAAMpkMDz30EF544QX8/PPPOHz4MBYtWoSkpCRcddVVAIBBgwZhzpw5uOuuu5CdnY2dO3fivvvuw4IFC5CUxBQhvvnmm6FWq7F48WIcPXoUX3/9Nd544w1BdsqDDz6IDRs24N///jdOnDiBZ599Fvv27cN9993X8r1CEARBdCqOFbuP2N7++GWCYubekMtl+PC2sdzn2UOcmZNRTXjZePmaYR7ns4XG+eTkV+GF31xryDw4ox9CNP6LkTC6Ke7ZnvCzRtj9LBP1OesM7r1s95yr9Ps6sb83qU8Mzr2UgQ8WjWnyMqZ4yIhqDiU6ZtAgXCUs2trPUVjx59xiDHjmd7y+kcnM+WbfBXyz74LLcmxuXrSldJq7p/XGhN5OMVH8kq53DHCE+vEcJQiCILoeFfVGXPPuLqS/vo0bPCcITzz4VS5sduDL7AIuKyU8SMX0Kx0dyYOFtVz7v0453VYig5yBETa7sH/DWor5EjjhCTYDplLfOnVcLFYbJw49f+UQbH3sMnx/jzAIWa2U4+2bRgEA9pyrwrt/nQG/RJ+J916w51wlPtieBwACCzTq4xFE09l3vor7Wyzq8mHvXUofXRSIzkGTnx779u1DWloa0tIYy4ilS5ciLS0Ny5cvBwA89thjuP/++7FkyRKMHTsW9fX12LBhA7RaLbeMNWvWYODAgZgxYwYyMjIwZcoUrF69mpsfERGBjRs3Ii8vD6NHj8YjjzyC5cuXY8mSJVybSZMmYe3atVi9ejVGjBiB7777DuvWrcPQoUObvTMIgiCIzkml3ij4fP/0vrh0QDc8lN4PwermvSD89sAU/OPSPnjhqqG4dEA3TOoT4zEVWMz84YmCz3ZR6n6jyVVwufa93fgy2zk4ftmAbvju7xPx8Mz+TVz7zoeCp66w0YRXpQmtts5XNCC/Urog6p5zVThVWueyn1sCa6kwIjnSa8aIr8waHI/ZQ+IF08QCRmF1Azft5d+PY9HH2TA6/MarHS/s4Wo7lmUMBABcNTIJPaMZUfH7/YUwW+14c8sZbDtVjse+O4THvjvkcr7ZIb2fpK4XmUyGNXdO4D6bRS8M9SZmUMyfomCnhd6TCIIgfOKPoyWY/tpfOHihhpt2nlf0/KfconZYq47HpzvzcO+a/YJBcYJBZzCjot7oMj3E0ZdRS0SLn7jotGJdOD6ZsxQDhP0b9u+W1lRgBZfqVhJcCqsbYbXZoVHKsXB8CmJDNRidEo3XrhdaKYdqnX20lRtOwsJTXNgAI4vVhls+dGZl3zU1lftbJ2EzRhCEe/aer8KGoyXc54p69/cA9nokwSWwaPKb8aWXXupxMEMmk2HFihVYsWKF2zbR0dFYu3atx98ZPnw4tm/f7rHN9ddfj+uvv97zChMEQRABj94oHEyODFbj0zvGtWiZQ5IiMCQpAgCatawQ0cC1OKuEn+HSaLLitY3CWiEyGfBJC7ehM8EXNNhowsdmD8R/t57jppfXG3DvWtcMIJZZ/9mGqGAVbpvUCw+lt1ykqnR0jCOC/GejEB6kwmvXj0CvJ37jptU0mBATymSrbD5eisX/24dbJ6Tg+auGctu/4UgJrhzZncsm0SiACb2jufT0ZesOu/zWoo+zub/rDGYEqZ2RilJduSXTeqNvnLSoqJDLOJsKdxkuJLgQBEEQvnL35zkAgMX/24d9y9IBAOU8G9HcghosHJ/SLuvWkXj2F6bfM753NBZN7NW+K9PB4EePswSrFVzW9H3T++E2Xl8IAFc8PjXMjqhgNay8SAmz1cZldZj9YCkGAGEOoaPOzxlblfVG5FXoueX2igkR9KWvTuuOktpGTOgdI1gPFp3BuT63fpSN3x6Ygls+yuKKd8eGqnHtqB5IidJiwYd7UdFKghFBBCLHinW4/v3dgmklOgMSIrSS7S2cpRjVcAkk6GgSBEEQnZ4q0UuAPwfIm4s4I8JoFgsuzs8vrT+Oj3bkCeb7I8Llvsv6Sk4fnOjf4vL+5lx5PQC4FKevN1pRUNXg8bvVDWas2nTaL5ku5x3ZNKmxIS1azts3OwvJS0VK6gwWbD1VDoPZihW/MgMrn+/JF0Ranitn1oW1WNEKy8sIBqmkEL/oi3dPRJAKT2UM8rgMs+NlwGy1w2qz4/XMU8jOq0KDQ/AMUSs8fb3L4cdkK4IgiIClot6Ib/ddgM1mF9RtK9G51inryhwpqvXeqIuRnVftMo0f/HFJ/24YkiTs854uZQSXYCXzkOZbhrG2PoDTZkvdwgHQUA3zTlJv8I/gUttgxh9HS3DNe7tw3fu78faWMwCA/gnCmkcKuQz3Te+HMb0YS9hwrfDdKI+XTXbsog7rcouw5xwjYCnlMnx6xzgoFXIuEKfOYOGCxfyZTd4W/HboIpatO+zR0okg/Mmnu/Jcpm06VopPdua5OAUATrsx8bsv0bmhUESCIAii01MlshQL13a8x9ttnwgj7AqrGzCsB5NBs/NshUt7ubiASTMQv2SyLJvneWC9vTlbLm0bpm9CdKDRYhN4TzcH9uU4soUCXnSwmvubfbG/c0oqPnSIbA99nctZqvBFi1qefcPne/IxqU8MlymlEb3/3zaxF/44Wup2HcQv+uJXZY0PEZzs4MNPuUX4YX8Rimoa8ebm05g1mLFIowwXgiAIojk8+t0hBKkVKKtziizFNY3tuEYdj/MVngNOuiJHi11FKHGtEXFWbnEtc44FO7pbCrkMCrkMVptdMBBqsroKMs2BzSypN/rHkuvvX+RgN69uYU4+IzpNdGSyeFsPFvF+efjrg9zfB5bPRJhDoAnXKiGX2WGzy1DdYMLzvx7DyZI6/PbA1Bb3s9uKe9fuBwAM6x6BG8f2bOe1IboCp0rrXaa9/ScjjkaHqHHlSKFtNpvh0lILQ6JjQRkuBEEQRKfhQlUDXvj1GPdywSIuRNkRMlwA4O+X9OH+zuV5lAPAPWv2c39L+XL7I8NF/JI4ONKGUytmYlJf/xZyb02evXww93dNg/Nl9dIB3Tx+r0znOePDHcU1jVwEH5tNEtpCAS/ZUV8FcAppy+Y7t4vvX9/Is5rjb2+V3oQbV+/hPmtE77hpPaM8rkOdl8jKprw0v/PnGRTxBsI2HmOEHiqoShAEQfhKTIha8Dm3oEbw7L5Ya+h0kfT+hr/9F6pJcOHzXU4htp9mApbGpDj7QOK+iLvaN0G8Zmyf+0BBNU45MmDMFv9aivkrw4UvtvCZ3Nez4OJrH+2fs/pzYgvAlAwIcnQR6wwWrD9cgrPleuxxsx4dGW9Z8gThD+x2O+fWIEV2nqsVosUh8CrkNEQfSNDRJAiCIDoN7/x5Bh/uyMO17+0SWCuILcX6x4eJv9ou3HNpH++N4FqEHPBPSvGlA7phIM9i4Lb+Nsj8kDnTWswblggAWHvXeG7a7ZNTcXUaEwVU0+A8zrdP6sX9LRUNtDa7oMm/f7a8HpP+tQUjntsIu93uFFxaKCRE8waVDvDElX5u6qWwnCypk5yuUsggfv/3lqHCFkRlEQ9iBTVBcLG5Gf+iDBeCIAjCV/Qm4QB0iEYpsBRrMFkFdSa6Ivz6f7YuLj7xsdrs+Oe3zoyM0b2cgkuIKCJlSPcIyWUE87osrG3Y37/Yj2vf2wWD2QqTo2/e0gwXtg/pLfCFpdFkxYYjJWgw+X7u3zSuJ1JiPNvfKhVyvLtwlMv0uy/pjfhwDfe5l4SNLmtjyxdEO8vpyO/vuhPfCMKfnC2v555d84Yl4vrRPQTz9xfUuHzHShkuAQkJLgRBEESn4au9F7i/1x0owo8HCnHLh1korBbaTkSJoibbC63Kt8cs3zOaxR+Ci1Ihx28PTMWgxHCM7RXlYkPV0Xhn4SicfnEuJvURZuDEhjLHky+sJUUGcX8P7xGJR2cPEHzn/a1nm/z7mY5MDaPFhow3d6DBxGSbtFRw4UdH8rNZgiWWyxfEWAsEMVLrw68ZJCXklNcLM37EZ1xTssK68/Y9H6rhQhAEQbjjRIkOG4+WAGAGlQ2i2nZ6o0UguABASW3XruNi4GW9+sNqNlDgZ7rPGBiHbqFOwUDcR3pm/iDcMKYH/iEKggpSOntCKl4/rc5gwcELNVwwlFrZsv3OZkmLa+lJYbfbkfHmdvz9ixx8sSffp+VP6B2NZ68Y7L0hgIxhiVgyrTf3ed6wRDw5dxASwp2FvKOCXd+h2GygQl6WlbGTiBdsXx7oPOtMdG5Ye77k6CC8s3AUFk9NFcw/WaLjgvpYzFTDJSDp4EMvBEEQBCFNpd6Eh78+iB1nKrgXr6tGJuHHf0xq5zVzolbI4en9mI26Mku8APgrpVghl+G3+6dgzd/GeFyXjoJUJGGwmnnTq3ZYbCnkMkEW041jkyUH+6UyhzzBz/I4flHH/d1SSzF39nDBElklvhRn5duOSSFltyYetBJHJkaFeBdcRjhqDs0YFCc5nzJc0CmuMYIgiLbGYrVhzqrtWPJ5Dk6U6FDVYHJpU6U3obxO+Kwqr/NsD2owW7H1VLlAmAgk+M97i7v00i4IW7tlxsA4fHT7WEFBeLHgEhemxcrrRmBaf2HfKJjXBdOKsoSz8qqclmKKlgWTsPZcvliKnS2v54rZHy7SeWkNxIZqsPbOCdAofV/HeJ64kurIZonkiSyRwa79Qa2COff4AW66Rv/UpGltdLwMb/EgN0H4G7vdjrMOO7F7LukLAOgXJ3TesNmBQyKrcS7DhSzFAgo6mgRBEESn5HSZq93S3y/t47WWRVsik8mg9fASxEZaSQ3S+jPARS6XdWgrMW+wGSKsvQIrShx5bja+uXsirhvVQ3Kw//ucwib9jruooqa8yErB3/fBPGFIbHsBeBdTALhEBbPcP70vZgyMwyOzBqB/vDDLhR/hxyAcuEmN9WxvBgCXDWSElvxKaQ9sElwIgiAIKbJ4nvX5lQ34T+YplzaldQauJh/7DKuo9yy4rNp0Grd9nI0Vvx7z49oydIT6MfznPdkhOWEDcBIiGPGAXxDeXV9EnMnLjzOJEGV1nK/QOy3FWpjhEuZYH18G+0t5ll1BElnyYmGxW5hGkOHsC/xsltGO2jdRPJFFMsPF0V3l1++r7SyCS6Nzv1fUuwq9BOEPDhRUY92BIpTVGdFgskIuA65zWIkp5DKsf2Aqvl4yAfOGM/bZ+0T1aM02O9eWCBxIcCEIgiA6BeKXjCMSkV/xYVqXae2NJ1sxvdGCAwXVkh7lLfWMDiRYP1v25Y4VLUI1SoxLjYZcLsMlElkdu842raCn1MvwfEfH2F/83+VO2wc2c8cdrJWarzwyawA+un0stCoF1t07WTBP7AUuHkfyVgMGcJ6TW0+VS85vqfVaoGF3MW4jCCHbtm3D5ZdfjqSkJMhkMqxbt46bZzab8fjjj2PYsGEICQlBUlISFi1ahOLiYsEyqqqqsHDhQoSHhyMyMhKLFy9Gfb2wWOuhQ4cwdepUaLVaJCcnY+XKlW2xeQTBsfNMBfd3TYMJf51kniMZwxLwye1jAQAnS+phtzMDTgMSwgF4z3Bh7UPXZjW9bpsURosVP+UWoaLeiKvf3YX017fCaGn77Jl/fnsQc9/YLqhdR4KLE3a/sOIAv8h7nJt3AXHmRkqo8xkdJZpntNi4/d3iGi6spZjBu0DBFxj1LoEyQE2DcBkxzbBQ5gtPY1OjAQitb6UEF62EpVinEVx4+93b/YQgmsvV7+7CQ1/nYvxLmwEw9yH+dTU4KRzje8dgssM2+92/zgjuCVYbc79RUg2XgIJGcwiCIIhOQaXee1SSVBp8e6OVsI1iRZgGkxVXv7uLm779scu4vymj2An7snu0mBHZpCy+pF6wk6Ola424Q+pl+KqR3Zu0DHewotHUfk5hSCrDhU9LigWLrcmkXtz5+GK/5s4ajcXXmkUEQTDo9XqMGDEC77zzjsu8hoYG7N+/H8888wz279+PH374ASdPnsQVV1whaLdw4UIcPXoUmZmZ+PXXX7Ft2zYsWbKEm6/T6TBr1iykpKQgJycHr776Kp599lmsXr261bePIFj4mZFny/XcwPLK60Yg2jFozE6LDVUjPoypySGuP8ZSZzDjeVFWS1NtRKVYtek0HvwqF7P/sw25F2pwpqwep0rqvX/Rj+iNFnyXU4jjF3XYftopVLWH8NNRYTNc2H4/P8NlbC/pTPfIIKeQEB+uQSjvlUEsMhjMVi7DxRerV0+ENiHDhZ+B0SjRb6vUC6+HnjHBTV6f8b2jMWtwPB6Y0U8yUEaqL8dmuOw974zKr2kUvpfZOqjlHd/6jAQXojWQOvfdZcbNG5YIpVwGg9mGB7/K5aaz9Vwp4DKwoFBEgiAIolNQ7YPg0hFts8SCy/zhidh1thIGs8nF5ik52vniVFFHae8s4s6nr5kU7/x5FndMTkUsr5iqJ+okBI7e3UJ8+q439i2bCV2jGUm8gvPeMlxaEs2qVMjRKyYY5x2DXOIXd/GrgavlmCue0tyjglUudh0EQXhm7ty5mDt3ruS8iIgIZGZmCqa9/fbbGDduHAoKCtCzZ08cP34cGzZswN69ezFmzBgAwFtvvYWMjAy89tprSEpKwpo1a2AymfDxxx9DrVZjyJAhyM3Nxeuvvy4QZgiiNcmv0nN/Z51jsk8TI7QI1SgFfR+AqTHRjRVc3AyQvvvXWXy0I08w7VixDiOSI1u0nv/bdR6AMMjndFkdhjlqmLUFR4pqub8v1jotnGx2phaOkgbkXDJc+IE47qyFtSo5RvSIwMHCWiya0BOoO87N04hEBqPFxg2Aqn3IAPZEiKOvpzda8fXeArz95xk8ML0frh+TDIA5psvWHcHolChU8jNcJASaMt710D0yCA+n92/y+qgUcqxeNEYwTQZn/07qXUorER9U67DqMlttuPytHdAo5fjhH5M7nCUSP8OlSm+E1WZvtXXceqocq44oMHCsHgOSIlvlN4iOxaHCGnyz74LL9AdnSF+bEcEqXD8mGV9mF+BkidMe3eIQeDva9UO0DHpaEwRBEJ2CKi+Ci1Sh8I4A36opIkiFVTeO5KZ58ia32Mg6gkUcXeiuM9oz2jXSb8wLm/DvjSd9+p3Pdue7TJPKpmkOEUEql0GlELVvtWGeyhjIDT4BwNwh8T59b929k7F8PmNhJn5xF1uK+dLB9xR19fXdEzuk4EkQgURtbS1kMhkiIyMBALt370ZkZCQntgBAeno65HI5srKyuDbTpk2DWu2M4J49ezZOnjyJ6mqhhzhBtAZ2ux35Fc4Ml4OFjKDQN46p0xIdouayXACmxoQnwaXBZMGH28+5TBd74vuCyWLDNe/uxO2fZMNut0sGH5wsda0Z2Joc5gkuX2YLB/KMZCsGAKhmBRdHIZbesSF4OL0/Xr9hBILc9K1kMhl+/MdkHH52FpZMTRXMk4v6Lw0mC1fEuqUZLmyku8Vmw6+HLuJCVSMe/e4QZ/W68Vgpvtp7AY9+d0jwXiBV06/AEUSTPigOmx+5RNA39Mc6uiNI6RrBz4pe+ZUNOFFSh4OFtcgVFQL3BX9kpnmCX8PFZvf+PtkS7vz8APLqZHjom0Ot9huEK3a7He/+dQabjpW26e+eKavHFW/vxBd7hJaWV45M4uq3SPHAjL4AgFKdgbvPsP+ryFIsoKAMF4IgCKJTUN0g3UEe0SMCr10/Aikx/slE8Df8DJdP7xgLpULORcv9nFvs7mtcZB3h+iJ4qLBWst1/bhyBn3OLEapV4p0/z3LT39pyBjeN6ynILhFzrNhZE2ho93CuRpCUl7W/CPYxU2doUgT2Pp2O4ppGfLrrPK4dmYgTe4u8fi8yWI3UWOa6EA8iieuL+NK9t3iwi+jnGDgjCKJ1MBgMePzxx3HTTTchPJypb1FSUoK4uDhBO6VSiejoaJSUlHBtUlOFg4vx8fHcvKgo12hwo9EIo9E58KfTMfdDs9kMs9k33362na/tuyJdZR9V6k2ok4jWT+sRwW1779hgbiA0OUqLSEdIfUWdQXDemc1mPPXTCck+0t68Siwa736QS4qjRTrsL6gBABy6UCXZ5sRFXZseo3Pl7gUevcEItdxz/zCQz6ujxTocLdZxWe+hajm3nf+4pBcA79utVbjuI5moTySoT2K3tmxf2pj+l9lqRyOvnt6J4hoM6x6ByjoDN+23Qxe5v+sNFpffPV/B2NulRAdBARvMZv+IFUum9MLvh0tww5juLr9pNpslM1xKa5lr81yZs/+85XgJhif53h/cc64Kf/ssB0/MGcBkHbUChbzsOgAoqdEjUtu6cefHS+oC8vrzB61xf9p1thIrNzDBdaefn+W35Xrj4x1nXabFhWmweFKKx+2L0iqgkMtgsdlRXF2PhHCt09XAZnPZR3Queaa191NLlkuCC0EQBNEpcBeRtGBcT/SLD2vjtfEdfuYAW9STrYXxtUQKMuFKiBfrLZbRKdEYnRItGfk66V9bcP5f89x+N7/S+UL2/JVDkRwdDLu9db10g33McGHPm6TIIDyVMQhmsxknmvgbDSbPGS5W8QQJpOwtWCi7hYH2AtEamM1m3HDDDbDb7Xjvvfda/fdefvllPPfccy7TN27ciODgptUMENuiEa4E+j46USMD4Pq8i6o9ifXrmUEyjUEO1nxDV3wOx3RnAShxsboO69evB8A8tzZuzMS6g9J9gmPnS7B+vfdgBD77yp3rdusHuyF1Fz90vpxbhwYLoJQBPj6+m8Wh0859IWbDxk2I8DEOJFDOq5/y5ThRI8P8njasPiHc8Qezd+Hi4eYvm91HhReE+7ysph7sufDXpo1oicuP3gyww24lFdXccr/N3IULcXYcLHGeg/x6e1W6eu68Y9l3glnP2uJzWL/edbC3JTwzDJCZTmP9+tMu84Ikou7zK+vw22/rkVnkXP/MA2fQ33jK5998LEsBs02G5387gdiqI81ed0/sPC48tuu37MC5yNYKanPem8THjhDiz/vTdt419Otv61t0vTaFn/crwH9mDI604e5BeuQd2I68A56/G6FSoMoow/e/b0GEGiitZpaVs28v6k4Lz89AuZe3Nq21nxoaGrw3cgMJLgRBEESnQEpwSQjX4qZxrRMR5S8UMr7gwjx2z5br3TXHUxkD8dL6E1g2b1Crr1tnoVesMHtJyjqMj8aN37bJYvPJi9ud/7e/8VbDhWVwUniLf8NbjRYf9BYSEwiiHWDFlvz8fGzZsoXLbgGAhIQElJWVCdpbLBZUVVUhISGBa1NaKrTZYD+zbcQ8+eSTWLp0KfdZp9MhOTkZs2bNEvy+t/XOzMzEzJkzoVJRfScpOvM+Kq5pxNbTFbhmZBI0Ks/qQ/7Wc8DxM5g3NAEF1Q04XKTDtaOScMvVQ7k2iqOl2PnVQQBAxrRxSI0NxmuHt6PWJMOgcZege7gKV72xBZFR0QBqJH9HpglBRsaUJm3HiczTwBmmFozOLP2UqzbJMHX6LAB2jHrxT/SICsKfS6c26Xeawgf5e4BqneS8ydMu9doH6sznlRi90YIHX9gCANhUEQZAOPB1TcZMhDejfpx4H6mPl2H32lxuvsEmB2CHWinH/HkZLdgCoN5owVP7mG1QaEIAPbMNQfG9kTF3AM5sOQPkuQYK2RUqZGTMFkx799wuAPWYM2UMLunfNlbKZrMZx7/dxH0e1TMS+wtqYLTK8EFBFI4WOzOyys0azJ17qc9BOA/u3sj9nZHRsv3sjleObQNggFYlh8FsQ+qgEchIS2qV3+Jvz5w5cyGnehwutMb9qWhHHpDHCIVTp89sk5qSjSYr6ndvFkwb1jcZGRlDfPr+58XZqMqvwaojSshljN0dAEyaOB7jekUDCKx7eWvS2vuJzfJuDiS4EARBEB2WC1UN2Hy8FDeO7ckJLkpHCi4A/P5g673w+gsTz5uYLfauUshc7DBeu34EAOCuqb0xd2giekS5t7/qaogzQd65eZTH9teM6oFnfjrqMr3eaEG0Ujo0lLU7uawNawG5q+Eyrlc0ss87rU1aUkAxWOOImhTXcBG1u3m8d+HyujE98O9M3yMXuzq+iFgE4QlWbDl9+jT+/PNPxMTECOZPnDgRNTU1yMnJwejRowEAW7Zsgc1mw/jx47k2Tz/9NMxmM/cimpmZiQEDBkjaiQGARqOBRuNaG0ClUjX5ZbY53+lqdLZ9ZLfbcf3qbJTVGVGht2DpTM+Fu4+XMDZII3pG4rG5A7H+cAkWTUyBSuUcipg+OAEAI7j0T4xAJG/AbNYbO5H1xKU4rZMDuhrBsof3iMCE3jFYve0cag3mJu/HvMpGweekCC1qGs0uQQqHiuuw7VQFAKCwuhF2maLFxdTdwRZG//X+KZj/1g7BPLtM7vM2drbzSoqDeTXc33ZR2EdCuBYx4U3LuBPD7qM5w5Lw8e1K2GzAnZ/t4/roQSpFi/dhEC+7op7XF6tutEClUqGqQTp7uLbRAsgVXKb1q3+cwMlS5lpKjApp02MbpHB2aGJDNYgNVaOi3iQQWwCgusGMD3cV4B+X9vW6TJvIprY1tqfeaEFxLWPZNrlPLDafKENFg/f7xOHCWqzNLsAjs/ojNtT3Ojn897s6sx2xoa1nS9zZ8ef9qVLvvIbqTHbEhrf+tVFYy4xLaJRyTO4bi+oGE26f3NvnbcoYloR9+TUAnGILAGjVapdlBMK9vC1orf3UkmW2rnkhQRAEQbSAK97egWd/OYY3t5zmarjwsxcigzt+56OR99LOCgdaUTTopqWXcMX1ZDIZkqODyaKJh9hSrLsXMSpEo8SJ5+fg/y4fjH3L0rnpvxx0XzOHFSRCfKyr4g/4NVz4GU2rF43222+EuMlwsYvUgP4+2PIlRgTh6HOzvbYjCMI36uvrkZubi9zcXABAXl4ecnNzUVBQALPZjOuuuw779u3DmjVrYLVaUVJSgpKSEphMzPNw0KBBmDNnDu666y5kZ2dj586duO+++7BgwQIkJTERvDfffDPUajUWL16Mo0eP4uuvv8Ybb7whyGAhiKZw79r9nCjgS5HiwmpG1OgdG4qUmBDcc2kfl2dtsFqJH/4xCZ/cPhbdI4NcAi0+5xUl5gcrvHrdCNw5halRpGs0uwzieoPtW666cSTeWDASa++aIIiOHtEjAgBw+yd78fHOPJfv+RuL1cYVTo8L10DcFTT4qWZHZ2F/fjX3d6OoH9OS7F8xMpkM0wfGo1esUMAJ8pK95Qt8a9pKXrY+2++srHd/Lj36LSNCluoMgtqEka1YX1AKfg2XEI0S3Xk1EW8al4w1d45HtzBGmPhuX6FPy6wzOAfJw1qp711QyWQTRYeoMaQ7cy1fqPJuD/Te1jP4MrsAP3motSnm54PFgmC6ajdW2IT/Ka5xCufuLMj9zcVa5je7RwXh49vH4sd/TMagRN/vSX+bkoqZg+NdpispKyqgIMGFIAiC6LBUNzBFynacruA6UHzris4gSuh5tTPY9RUPCGhV9Dj2BJulwcJas3lCq1Lgjsmpgsi0Ag8vWawg4Wu9GH/AHzTqGxeKky/MwZHnZvv1RZrddxab3VmQUcS3f5/o8/JCNEq8ePVQzB4Sjx//MQl940Kx+lb/CUQE0ZXYt28f0tLSkJaWBgBYunQp0tLSsHz5chQVFeHnn39GYWEhRo4cicTERO7frl27uGWsWbMGAwcOxIwZM5CRkYEpU6Zg9erV3PyIiAhs3LgReXl5GD16NB555BEsX74cS5YsafPtJQKD9YdLuL99ycAs0TER5gkRWo/tRvWMwmUD4wC49u/e/stpucQf1OzdLYSzlLLZndmqvsJmHEQGq3DlyO7oFRuCvnHOot89Y0Ikv8eKIv6mrM4Im53Zr7EhGqxZPF4gAPGzpgOdnPwqvLHZWU9EL6pFN7gJg5u+olEK+5tBfijWo5DLJGtKsP1O9ly6Jq07rh/dA1/eNYFrsy63GAcv1OBbUc3HqDYOOAvidY2D1Qqk8K6L8akxmNw3Fv+5YSQA1+PkjkazU0DTmyxNFkt9ge33J0cHo1cMI6adr/AuuFx0ZMWU1DZ6aclgMFvxwJfCoh2VJLi0GXzBZeWGE/h6bwEsrXyvLGWfa+Gen2uekLIFVErUSyI6L2QpRhAEQXR4tCq5U3BpJQuH1kIckQe4Fij3RwRdIKNRKqBRymF0CAbNLWT/0Y48PDN/sOQ8NtJQLO60JvwaLmqF3LGdfv4N3rnVYLJA7bBUY0/BKX1jMdbhFewrC8enYOH4FABMdhZBEM3j0ksvdck24+NpHkt0dDTWrl3rsc3w4cOxffv2Jq8fQXjDW40Ck8WZseFNcPEVVnRYe9d4qBRyqBTg6jPoGs1N8u9nBRd+IMdN43pi++kKqBQyxIVJ2wl5ykqQ4p0/z6BbmAY3jEn22O5YMeMVnxobArlchkl9Y5G7fCbSX9+Ks+V6t4ETnY0LVQ3Ir2zAlH6xkvMLqxtw7Xu7BdP4GRHjUqNx41jP+7I5aEQBUOKM9OaiVMhdjh177lU5sqWuH5OMiX1iXL575Ts7Xaa19XsDP8NFJgP68URJNus8MZK5vqXee6Ro4AkzNjtzfCP8LCSx2Sw9o4M5kSi/0n0dTZYyHXPPYjP5vLHlRJnLtGq9Cb8duogPd5zDHZNTccWI1qkb09UxmK0oqjFwn7PyqpCVV4VwrQpzhyX6/fdsNjt+PXwRhwprAbTsuTYgwdVdQCnvXOMchGfoaBIEQRAdBr3RgiNFtS7T6wwWnHL4Fj+VMQhqhRyLJqa09eo1C6li5TbR+7K/XugCmeeuYIoQRoc0PfsjfVAc93edwSzZhrMUa8sMF564o2olIVGpkEPliJbSS5yLnSBJrFPRGbLuCIIgmotZFDXsLSq9rM4Au52pbRDtZxsk/vOaFVlqG6Wf8Xx0BjOMFuZ5WO8YxA/VOAd6M4Yl4t2Fo/DTvVM4myQxTclwOVNWj1f/OInHvjvkte3+AsZCa1TPSG6aTCaD2pF5YQwQwWXqyj9xy0dZ3PaK8WZV99VdE5Ac3bL6LVK4ZLj4KQNdJSFMsoIDKyRJCYWJbgZz27qvoebtBpsd6MMXXBz2YqwI5KvtHT/DBXAKT/5Cb7TgxfXHAQA9o4O4DJeLOgMMZveikN1uR7lDaGGzGADAarPj4a9z8eH2cy7f+VnCeqy83oh71+7HgYIafLbrfEs2hXDDpzvzMPCZDZL344OFruMJ/uC7nEI88OUBfLLzPAAI7PWaSv84CcGFMlwCChJcCIIgiA7Dwg+zMP+tHVjxyzF8vdfp132ixFmUcWyvaBx6dhZWXDm0PVaxyYhfKADXDBcSXLxzzageWHXjSPz+4NQmf5fNxgCA69/fLdnmf7vzAQC2Nqx0zreqENuyTOzNRDn28FKvxhdY+5Ur33YW37WDKroTBEEQTUMcROJNeGAHLOPDtV6zYcQ8lN7P43z+MzQyiBFzaho8Cy5Himox4rmNmPDSZpyv0HMWZKEiq9KMYYkYnBTutmB2UzJc+IEeVi8C1cHCGgBAWs8owXS1IygjUDJcWHLOSwsuh4t0br+jVcmbfC75itji1x+WYgAT/CJGb2SuJZ1DJAzn+XZ9vYSxFVN3kKx+vr5js9nRi2cpFu+wVGIFF5PV5pOd04YjJYLP/q69sem4U7Qb1j0S0SFqyGRMhrdOFHxlt9vxXU4h8iv1qGkwc1l0/AyXrHOV+PFAEV747bgg+9RgtmLLSSbDZUxKJEKUzLzjF53ncGmdU7gh/MezvxxzmRYfztyzj110fw9pCd+I7P2aUrdFjFRGF9VwCSw6xh2cIAiCIADkXqgBAHy8Mw+Pf39Ysk1cmKbTCxTiF25fPNC7OmqlHFeldede7JoCvzgvX7yTQiyGtSb86FxxlPCbN6Xh3sv6CLy8W0oFb4CoDTeTIAiC6CCYrTZ8tCMPZ8o8Pwv5WKw2boCxQVSfobzO6FFEOFfO2PckNSMK+KH0/gj3ULONb6vEZgfsOlvhcZl7zlXCbmdqBGafr+IEjFA3fp4qN9HGTclw4WcjeBNM2PoSfMsmwGmny2bmdGb42QXnKvSY+fpW/JRbJGjDz3afPzxRUAOlNe201CJhxF+/JdXNL6ppxJ3/28tlLYXzMlxYy1lPmRjthc1ux+CkcDw6ewBWXjuce4fhi1NSwWZ8/jxZhre2nBFME4sgLeWow55vSFI4Zg+Jh0wm4/rdDUbh+v15sgz//PYgLnn1L4HIwlqLAUwtRJZyXpsqvQkmiw0qhQxrF4/FxDim3SFehkVlvckni1Ci+aTGhiDz4Wl4dyFTV/L4RR0MZiuOFNVy+76ldYLsdjvyKoSWdEOSWlZL6tM7xgrqUUmJs0TnhY4mQRAE0WnoHhnUalFtROAyKNGZsi1VoBBgCuYCQMZQ//v9uoP/Is8vAgwA3cI0eHT2wFaxzACcggtZYBEEQXQd3v3zLJ7/9Rhu/O8ej+1+yi3CkaJa6I0WTF35J+76bB8AZ1Q+wAxOW2x2nC2vl1xGUU0jHnXYaI1OiZJs4w1PNdv4A7yT+jJZoZ/vyfc4sMkWwwaAUt7f7gSX3rFC4YMVgHwtDA4A/KespwF0k8WGYkeR7p4xwme/JoAyXPjFxL/MLsDpsno8+FWuoM2FakZ4+uufl+Ltm0eBP04q7i/5E5lMJqgV6a8AL3fjvJuOlzl+Fwjl1/VzrIPe2PEEF/byuveyvriBV0dHo5RzmTDeBJetJ8tdpul8sAP0hTNldbj2vV1YvY2x/rp1QgrX1w123DPE1+/JEuc9LPOYM/Om3mjhLIf523S+soH7m7Mk1ighk8kQqmJ2ECv4AExmIFuzh09LBQDCSXJ0MPrFh6FPNyb7qrzOiEUfZ2P+Wzvw66GL+HD7OQx8ZgNy8qWz6nyhusEsuH+pFDIkR7XsPe3SAXGC+qJS9oNE54UEF4IgCKLdKNMZ8FNukYsnuDvevjmtldeICETCtCquM7v1VLlkNC5rQxLehGK7LYUvHvLrubQm7EAUuweoW08QBNF1+H5/IQDhoLeYveer8OBXuZj/1g5k5VXiYq0Bm46XwWSxcRkuSRFajHTUGeFHcu85V4mr392JI0W1+B+vbsGE3q7FwH3BU/+QH7Rw59TeAJh6GFK181hKeCLLRYfdWbBa4TbTeFiPCPzrmmHc5xHJkQAAo491KgBh5iybzbDvfBU+3H5OIA6dLa+H3c6sTzeRlRmbeSEWXPIr9Vj+0xF8sScfZZ3EtqjSS3aQ1WbnjqFUn0xq4Nqf8EUWf2W4eMtuCNMoBX1CVvSR2taRjnOwrblrSi+EaZS4f7q01Z9MJnPWcTF5vj5MEte1ztD84/rFnnxc8fYObD1VjvTXtwkG1Ufy6iGxGe/iewRfCN18okwwj8144QtC5yudWQ5sfUQ2eybCTakqfj0YAPhs93mMXLERh1up1kigoxddG2xWYESQijsPs/OqAAD/23UeL/x2HCarDe/8KcysagrscU8I1+L9W0bj67sn+iUQlF+/iVwvAgsSXAiCIIh2Y8EHe/DgV7n479azaPTwggwwnqZiT2uC8JUZA+O4v7efFkbW8V92okP8W9TXG69eNxz3T++LYd0jWu033r9lFPc3G03JQgkuBEEQXQO73Y6CKtfIbDEneN73/AH+gqoGLuI+WKPEcMdzi2//dM8XOThQUIPr398tsF6Z0Du6Wes8tpf77/EHxkPUCk6UqPZQfJvNIAGc4ou77BaWBeN6cn/36cYM6jWleL2Z15a1BLvu/d144bfj2Mx7Js99YzsAIDkq2CX7VOOoLfLED4eRySso/79d+fhsdz6WrTuChR9ktZkFVUmtAT8eKGxWxo07OzZ23/BFhrYKRuHDz3DxVw0Xb25SYVqhsKRRSQ/ThWmU+PC2MX5Zp6by2Oz+OLB8pkv2FR92oNtbhotZ4rxpSYbLsnVHcKiwFrd9nM1Nu31SL7x/y2gMTHDaNXEZLqJ7H/+cPFMmzNgrcwgldTxB6KDDAhsAGrgMF2bZEWrpg11SKzzvl/90FDqDBU/8cMjzxhGSvPDbccHnod2Z4yyTyZAYIbSf5tdzaclrT4Ejs6lnTDDmDE3AKD+NS/CfQUo5DdEHEnQ0CYIgiHaD9fZ+beMpbDxW4rFt//gwj/M7OsF+emkjmkevWGeBzw+35wnmnSp1etlHtGGGCwBcPyYZj8wa0KrWXsN7RHJ/r/j1KADv0ZYEQRBEYPFdTqHg8/iXNkvaq/Atm7addtZEefevM077HLUCiY66LPxi1/U8+52/HIWkP1w0Bhpl8/pAL187DIunpErO40cCy2QyzhqUzViV4mKNM8qcE1w81Ilh+eneyfjk9rHo64iiboqwwY/mL6puFBQUL6phBCB+Js+wHq4BGH3jnH3ge9fu5/7mDxSfLqsXFApvTea/tQMPf30Qa7PyPbZ7ZcMJ3Lt2v0CYqaiTFsRyC2oAOM8htVLe7POmJfDFDn9luNhEfS5xFLs4k0dcS4bltkm9ECvKfmpLvNWXYEVQb9lW0hku/qvhctO4nnj2iiGYMzRBMJ2r4SIK8uNfR3WiTJtSNsOFt34/7C/CqdI61BstuNUh8rB1d6LcHB53QjDV7GgaP+UWYfq//8KX2QWC6f1498jESKHgwj/em0+U4SvRd30l3yG4pPjZ7jk61Bnsp1JSJFwgQVc3QRAE0SEQ+zeLUbopXNrReWLuQADAv68f0c5rQrBM52W7/G/XeTzy7UEAzY/A7ejwixVfqGIGd8hSrHWg/UkQREdlf4FQXKk3WvDWltMu7fjWN2uznANTP+wvwjM/HQHARP5rHQPT/Gh2NgMEcAo33aOcz6CmEhemFfjbeyIqmBm0cjewabfbBQOrFx3ZLt4yXADGSuyygXHcgHKTMlx4g8s3f5iFmz5w1s9hLY74ItHLPAszFn79OQ1vgFa8redFBZ1bC3Y/7jxb6bZNncGM9/46i98OXeTEN0Box8TnxfVMxHq9Y8A7zIfj0hrwRZbWquEitrYNF4l+Gje/64s42J6wYtWtH2V7zH6ql7AP0zX6zyquW5i06hGscZfh4j4rTirDpdFsxfy3duCRb3K5Y8lmuISrpLPH3Tk5qDvp+2178eBXuVzAJgBM7ReLuUMTBIXnEyM8P3NWbXJ97vlCiY55ZrTkmSZFqEaJDxeNwepbR3PCHREYkOBCEARBdAo6q6fp3y/pgyPPzcbcYW1XjJ2Q5ooRSQCEkYb/9/NR7u/IoLa1E2tXHLugNTNrCIIgiI5DvUQB7r9OlmP+W9s58QEQFpYXw84LUSud9Rp4gkt8uNblO/4YnPrl3onoFeo5M5PNcKl2k+FitNhg4Q10s+2aUvSYtZti7a98QTzwvPe8U/jSKOVoNFlx5ds7ADBZtiqJiPcRvKyXId2dA4usUMPakrIZM60Jf9vj3AxsA8C/fj/B/b3zjDNTarPI2pTlUGEtqvQm1BuZbWovcaF1BBf/ZLiEa9s2C7up8G3BPGW5SNnKfZldgMLqBonWTced4OJLhgtLfDizjP9uY2otsdt255RUhGqUMFls+OOoM6OMHShXyoFYnj1xd0fQk94kLSiplTQk21yC1Qp89rdxeO+W0YJaKoN44ovk95ppVciKgpGt4IaQPjges4YkeG9IdCro6iYIgiDaBXfpvJP7xmDZvEEYJ/LtVnTigWFx9ORw3ovz2zentfXqdFnYaFw2MtUisjTgp3QHGg+lC4uc2h2KS+e9qgiCIIim4K5my5EiHeeH//XeAvx8sNilzf3T+yKBJ6YEa5TObA+za40SljCt0i+DxAMTwvDwMCuOPZuO60b3EBSyZ2EzXGrdZLiIrYJYRqX47sPvFFx8z3AxWd0LRWarDd/su4Bih5DFikZilAo5/nvraGZ5vN9mM1zY+gWF1a0vuLB1DAD3drkXqhqwhpcddfwiY916pKgWJx02rsN7RODR2QOQ93IGZ9W2P7+aO05h7SW48LbJXzVk+GfAq9cNx+eLxwnmiwdwVQqZZJbE6Cacq+0BP1Ok3mhBld6Euz/fhy0nSt2247Nyw8km/6aURW43N7Zr7PlaL85wqXMVXAY4ar+U1xlxsLCWsxRLiQ3BdaN7uF02IBSZUxw1b8QiD4s7cY0QsvtsJca8sEkwrdFslQwcu3VCCnpJ1Bpir7vmZlOx54C45hJBuIOuboIgCKJdeOKHw5LTJ/eNxZ1Te7v4rxqaEE3Y0flw0Rg8M38wDv7fLMwfntTeq9NlcA4OMedSnsh6o6efPXk7EhN7xwAAencLEUzvxDpmh4fK5BAE0Z78lFuEx747yEWaS9n4sJTXGXGosAaPf8/0zcRJxb1iQgS2ViFuLMUazUIhQirjpSWoFHK8dv0IQSF7lqgQzxku7gSnsb2aILhwWT1NEFw8iDNGi40TIADPwUVaid+udWzrkCRHhksrCy4mi03Qd5LKmgJcMwbqHPv+891MzZcrRybh5/um4N7L+kImkyEtORIAcP+XB3CkqBaAMFjpsgHOc+/7eya2fEM8wM9w8Ze9D18UuH5MMib1icVqh4AGADEigUAmk0kOxPePD3WZ1lGpbTDjX78fxx9HS/G3T/dx0+12Oyr1rgIH4N4O0BONEvWU4sLdZLg4zqlX/ziJkloD8ir0mLNqG/QiMSQmRI1VN47kPv/t071cNku4VulSlB0Q2sb14r1PsIKLu/sPZbh4x2C24qYP9rjcV9yJVWqlHBsemsYJ0QCQFKHFQIeIVqk3ugTdsVTpTcir0KOszuDyezrHM1SckUYQ7iCDOIIgCKLN8VSw+7aJvQAASrmwE3WkSNeaq9SmxIVr3RaBJVoPbrDCYsOTPxzCl9kXhPMD+KWHjdg8V67H4cJaEgMIgiACmIu1jXjkm4Ow2Oy4UNWIL5dM4KK6V143HGEaJb7fX4hNDnsnjVLOPRNjQ9X4askEPPXjEWTnVQEAYkLVGJEcia/3MW16xgTzBADnYKVBNHAZHdx2maORXmq4iKPaASbLgrXj8gVtMyzFzG4G9gAmAOTERWf/1p3tEAAXCzez1cYJGUMd21Bc23qCS6PJiste+wslvBo/dQYzzFabiw2aOJqftQnbk8fUfLk6rbtgfh9Hhkuj2YrXNp4CAIRqnIOa7ywchaPFOozuGSWwDmoN+Bku7jJ4moq4hgvgHPwHmGtOjDiL6pM7xnYqG9jaRjNOlda7TNebrG4Fy9TYEMnpnpC6rnvFSC+Hf2x/PliEzGOlOFFS59LuizvHIzpEjXnDE/HboYuo0jvvKZHBasnjwL8P8gWZWIeY5jbDRemfcyyQufvzHJdp3cI0WHHFELff0aoUeO6KIbj2vd0AgCHdIxATooZCLoPVZkdFvQkJIuGsttGMK9/ZwdW7DNUokfNMOjSOY1THZbjQMDrhG34fWbBarXjmmWeQmpqKoKAg9OnTB88//7xgcM1ut2P58uVITExEUFAQ0tPTcfq0sHBRVVUVFi5ciPDwcERGRmLx4sWorxfesA8dOoSpU6dCq9UiOTkZK1eu9PfmEARBEH7CYLbiH2ty8M3eCy4vEWdenIs//3kpzr2Uwb2ADOvu2X+VIJoKawWyets5F7EF8J9Xd0eEv213fLqXZ2/ReV7eCYIgCN9Ym1XA1Ss5VFgDu93ODUz26RaKucMSOYECYDJHqhxR5w/P7I++cWGI4EXxJkRoBXaoV4xI4gUx8AQXkRBx+Yi2q1/HZkS4K05dIlGb5j83jmzSIDab4XKuXM+JUd7wluFSqnNGUbsblAWctqjswG4NL5Mn1THAbDDbBAO//uRMWb1AbAGAXw9dxISXNqNaLxS52O1gj0m9wYLKeiPyHXZkaT2FWUUpEhnG/EHNYLUSY3tFt7rYAgj7S/4TXFwVF/6yo0M8C5O9u4XgsgFxflmX1qQPL4u6ttEsee5XirIGXrraaQ/YnHNXz8uySusZif/eOtrt/uT/9qZjZThUWCvZjhVMpKzJukdqJQUy/rpfM4pxLxjRI4LLkuJnuPDHRslSzDtbT5ULPivkMmQ9OcNrfVS+9df4VOb+wR7TUtG9zGixYv5b2zmxBWDEvJx8Z80t1oqso9dSIjoOfr+6X3nlFbz33nt4++23cfz4cbzyyitYuXIl3nrrLa7NypUr8eabb+L9999HVlYWQkJCMHv2bBgMzpN+4cKFOHr0KDIzM/Hrr79i27ZtWLJkCTdfp9Nh1qxZSElJQU5ODl599VU8++yzWL16tb83iSAIgvADy386gvWHS/DY94dcXiiVCjlSY0MEL1K3TEjBY3MGtPVqEgGMVSrEkEcgCy58i4yKeiOX4dKJgiUJgiAIH7Db7fiFV4dFb7KiusHMDfixg+D8sWu73c7ZgWkd0bz850Z8mBZDksJx//S++L/LB6NHVDDXrtHkHFRlxY53bh6Ff10zDAvHp7TCFkrDPsOlLIYq64248zPG1iiKVycl1U0kvDs0vEzYG/6726fveMpwMZhtAtsad2IR4DwejZzgwogc4VolwoOUUDoOaE2jtKVaS2lwk31TqTdhw9ESybastVO90YKjxUwmT+9uIQIxDwCSJQQXcf3DtiJYUMPFX5ZirtP4yxZbionxZAfYkfj0Dmdtmke/O4QzZc6AaTYrjK3fEh+uwYaHpuKmccl49vLBADwLju5g72vx4Rr8+I/JmO2h8DjfOiz7fJVLAKBSLkO4Vsmdn93CXI9LYkQQ+nRztXbj17LqFROC7Y9dhjV3TeDqALHbtutMBUbzapGoldQRbyrhWqVP4iu/NhJ7XrD1dc5XCm2lL1Q1CMQWlq+yL3D2Y5ThQjQVv58pu3btwpVXXol58+YBAHr16oUvv/wS2dnZAJjO3KpVq7Bs2TJceeWVAIDPPvsM8fHxWLduHRYsWIDjx49jw4YN2Lt3L8aMGQMAeOutt5CRkYHXXnsNSUlJWLNmDUwmEz7++GOo1WoMGTIEubm5eP311wXCDEEQBNEx+GZfIfc3/6XtnZtHSbZXKuT4x6V9m1VAkSCkEEdmiglkwYXvEd0zOhh2R44Lveb5FxKwCIJob3QGC847MglCNUrUGy24UNXAZbhIFQKvaTRztlCs7Q7fNisyWAWZTIZHZjkDYbh2fEsxx98DE8MkByVbE06QkBi03X66gvt7QEIYbp/UC3Hh2iZnTGiaYT0qHtTlU91gEsy/ebxrbRoWcQ2XYkfGTkKEFjKZDJHBKlTUmwSZL/5E52HQ352lWFyYBufK9TBb7bjosDuTqn+RIlHg2lvWR2sR1AoZLlLwlx3jZVulbLM6IsnRwbh1Qgo+38PU6jHxxMaKehO6RwbhcGENACAhIoirqcFmgTRHcHHe17wPbT4wvR9+O3TR7fx9y9KhUsi5rDepTJYQjRIhGiV++MckXPPuLm76oMQwQTtWROQyXBzvvst/PiqwKPMlw8VitWHJ5zk4VqxDr9hgDO8RiSCVAndf0ttvdYY6Exarb77IceFaPJTeD8FqBXc8BieGIye/GseKdbhypNPakK391T0yCEU1TuHl54PFMFqsePOmNO5eTTVcCF/x+9U5adIkrF69GqdOnUL//v1x8OBB7NixA6+//joAIC8vDyUlJUhPT+e+ExERgfHjx2P37t1YsGABdu/ejcjISE5sAYD09HTI5XJkZWXh6quvxu7duzFt2jSo1c6b4OzZs/HKK6+guroaUVGuxe+MRiOMRmcEiU7HRFmYzWaYza3TMSF8hz0GdCw6P3QsAwd/HUtxYboKHdORiQhSYtagWJ+XT+dU86HrEpg1qBt+2F/kdn5qtLZT7J/mHMsQlXNQKUglh9XKvNTa7fZOsc2dBbPZwvvbDKXM/UBbe12TdLwJIrBhByDVSjkGJIQhJ78aeRV6brAozFEbY1xqDBcMU9tgRqgjatdpXeW8f0nZbnHt+JZibJZMOwQwBKmZ9ZHKcOEPbhstNswZ2jyrM/F2GcxWr9vqKcOlkFfk/q2b0pA+KN7rbzearbDb7Sh2DAp2j2QitiOCGMGltpUyXHQelivu57NZB93CnOLK498fBiBtxxOmVWHW4HhsPFbKTRuR7HttHX/CF1xCWnEwmz9QHhnseQC3OUJEe+FOHCqvMyIqWIVnfzkGQGi1F8xlgTRdWBJn7nliQEIYbhyTzNWiEhMpqjkl9xBFM6pnFIZ2D8eRIh2uG90Dj8yWdmUIcdx7GoxCmz0WtRcRd9WmU/h67wVcdAisJToD9pxj7Azf2Hwa2x+7TDJDLJC5d3pfn9s+lN5f8HlwEiPyHbsorA3LimDdwjQCwQUAdp+txJubnSUw2iv7juh8+P1MeeKJJ6DT6TBw4EAoFApYrVa8+OKLWLhwIQCgpIRJN42PF3Ym4uPjuXklJSWIixN6VCqVSkRHRwvapKamuiyDnScluLz88st47rnnXKZv3LgRwcFd6ybVkcnMzGzvVSD8BB3LwKGlx/JkjQyA8wVm4187ASggs5qxfv16L99mHlXTEmw+tCW80ZWvS8bSwbXr8/gIC+pMMpzYuxUn2nytmk9Tj+Ud/WX45JQClbV1OHz4CAAFSktL6LryI8z7NHOObfjjD0gEkrvQ1tdkQ0NDm/4eQRBtSwNvALJHVBBy8qvx0Ne53Hw2w+WatO44VqzDxzvzUN1gglLBDC5qRcXZ3cFaipmtdlisNshkMi6iPagdBBd2faTW28Ab3PUkHHhDnOFSXNOI3l4yeTzVcGFFk+ToIFw+IsnjcoJEohH73SSH4MIMFutbMcPF/XLFIo+zhouCy7JiEduJsaxeNAbbT5fj1o8YZ5S0ZNfxnLZAy9vPwb48xJtJOM+WKC7MNeuns1IpqufDwgouLMO7OwU1VhBtUYaLj+JYkETWUrBagdeuH+EyfVxqtOBz71ihBeEP90xGo8mKCMd2SQW0BGuEGS69YoKRe6GGm6/0kOGSeawUqzaddjsfYDIw7r3MdwGis2EX+fF9sXg8pvSLbfby2FpkWXlVuFjbiMQI5v7JWjRGh6iRHB2EC1WNuPuS3vjv1nPQGSx458+z3DIUbVBLiggM/C64fPPNN1izZg3Wrl3L2Xw99NBDSEpKwm233ebvn2sSTz75JJYuXcp91ul0SE5OxqxZsxAeTsWZ2xuz2YzMzEzMnDkTKhWl6XVm6FgGDv46lt9/lgOgkvvcf1gacPwQosJCkJExxeN3H9y9EQAwfcxgZHiwWiA8Q9clw6w5Nry+6Qw+3HEeAPDmjcMxd6h7v+eOSHOPZerFOnxyajeg1GDI0D74Nu84EhISkJExsvVWtovRYLLgsewtAIDZs2d5tHpor2uSzfAmCCIwYQcgg9UKxIS41iBgB/jkchnuubSPQ3Axc5YqrODyUHp/3PTBHtw0Llnyd/gDlwaLTWBR2S6Ci1poucWHXwPDkzWWNzSi7bpYa/AquHjKcCl22GzFeqnhAQBanthjMFuRV8HUIOAEF4eQUdNoRpjr11sMWzBaCnHdGHbgPFithEYpB79Ouic7njEp0RiUGI4+3UIQ1U6WYjLemdyaGS5KhRy5y2cC8J7l0Jl4cEZf7DlbiRvHJiMyWIVdZyuRk1+N8jojkiKdwtI9l/bh/g5SNd9STO/IHPG13g4/2210ShReunoY+saFSg6ip8SEIPPhaYgIVmHXmUpM7BMjmK9Wyr0euxCRmCR2w7J5qC+55USp4PPUfrECe0QAyBfVIgk0xPfzlogtAGMpNrZXFPaer8YvB4uxZBpzHlbpmXtYVLAaXy2ZiK0ny3HNqO74MqtA8MxQkthCNAG/P0EeffRRPPHEE1iwYAEAYNiwYcjPz8fLL7+M2267DQkJzKBGaWkpEhOdqbylpaUYOXIkACAhIQFlZWWC5VosFlRVVXHfT0hIQGmp8AbEfmbbiNFoNNBoXDszKpWqSw8+dTToeAQOdCwDh5YeS5lM2BmtanBEX2q9L3dC72jsOVeFeSN60PnkB7r6dalSAcvmD8Gy+UNQb7R06rTwph7LyBDmRVdvtEIhZ65JhVzepc8Hf6OyO1/EmOPj/fxq62uSjjdBBDbO7AKlV6uimBA1NEq5oI4IK5ZM7BODvU+nu60voREJAO7mtRXiovJ86njZGS2x3JLKcPGGpwwXNhvFF8FFqZBDpZDBbLXjQEENfnXUouguyHBhltkcwaVKb0JEkMpt9LY4wyUhXMvVxnPNcHGKfuKMB3cZLgAj4v3+4NQmr7s/sfEi6ltbOBRbWPEZlBiO4w7bo9sn9WrV9fAno1Oicfi5WdA4Ms4qfjjECS6sONI7NgS9eNkibNadVP0lbzgtxXw7Vvxj+s9ZAzAgwfPV0i+emX9VWneP7dzB1XBxrGejyDbNZncvuJyvYDKSrxqZBJPVhvun98PcN7YL2hwqrG3WenUW6ozOewsrULYEmUyGIUkR2Hu+WnDfYjNcooJV6B4ZxNXT6h4VDB3PfmzdvZNbvA5E18HvPaGGhgbI5cLFKhQK2GxMRyM1NRUJCQnYvHkzN1+n0yErKwsTJ04EAEycOBE1NTXIycnh2mzZsgU2mw3jx4/n2mzbtk2QtpeZmYkBAwZI2okRBEEQ7YPBbMXWU+WCaTn51QB8K4i55s4JOPrcbHQL8/4yShBNoTOLLc1Bo3L625sdIXZU5J0gCCJwsNvt+HTXeQDMYLdYcFl962jBZ7lc5tK/4tck6RamcVtYXiaTcQJEo8nKiS4apbzJxej9ASe4SAza8i2tnr18SLN/Q1wc3pt916HCGkFdEndM9TFqmz022047+9WT+jJR9+yxfm/rOZyubdr+P1dej1HPZ+Jvn+5124a1Yrt/el/sW5aOLf+8hLPnqW1wn+Eihm+l1RHhWxi1x3nM8tVdE/D54nH46d7JWDZvULutR3NgxRYA6OYQE8vrDZzoIM5GYbNO9M2o4VLvZpnu4GfmtcV7QJjjfK9tNMNut7tk8bhLgKszmLH7HOMOsWhSL7y7cDT6xTmz6djMmgp++piPWKw2HC2uhc1mh81md7Ht6kjUObJLwrRKjwJlU2DPgUaTc+ezNVzEmXWsoA0wz5ghSeSMRPiO3wWXyy+/HC+++CJ+++03nD9/Hj/++CNef/11XH311QCYjtlDDz2EF154AT///DMOHz6MRYsWISkpCVdddRUAYNCgQZgzZw7uuusuZGdnY+fOnbjvvvuwYMECJCUx3qY333wz1Go1Fi9ejKNHj+Lrr7/GG2+8IbAMIwiCINqf7Lwql2m/HWai8txFTfJRyGU+d6IJgnAPf6Boxa9M0VIZSHHxJ7Q/CYJoT1ZvO4dMxwB/iEYpyCboHx+KWUNcnSCMogyMpkT1swNXRotTcPFWRL61COIsxaQyXJhBu6vTunORy/6gplG6XgXLFW/v5Ipde+LKEb5Fz7PH5pOd5wEA14/uwdX/mDGQqYGrN1nx/fmmDfN8m1MIAC4BUnzYDJe4cC1iQzUIVitxx+ReAFz3Az/DZUJvYR0MT5ZiHYGOMvYcEazC1H7dMCI50mOdj44OK+ieK9fjdFkdAGdGCwsrzDXPUox1TfDVUszZztfvtISYUOZd12ixocFkddlGNsPlr5Nl2Hi0hJv+yDcHub97xTDZQPzzgBWpWCGnKbyx+TTmvbkDf/vfXkx4eTPu+izH+5faCdYOMsyPYwFS2ZCspWaUSNSZ1t8phodplZBRpBrRBPx+537rrbdw3XXX4R//+AcGDRqEf/7zn7j77rvx/PPPc20ee+wx3H///ViyZAnGjh2L+vp6bNiwAVqt09NxzZo1GDhwIGbMmIGMjAxMmTIFq1ev5uZHRERg48aNyMvLw+jRo/HII49g+fLlWLJkib83iSAIgmgBfAsCcVQb2wklCKL1USnoJYEgCCJQMVttePWPk9znELUwIrhndLDk98RB/FqV70MEbIbH498fxvKfjgJon/otAKBVerIUYwbtvNkHNZWm2JOlD4rD81cOwZkX52LVjSO56YkRWq7otjfEYhZ/wHhS31h8fPsYAEBJg1P08AW+MGdxE3JfqjO6tI0MYs6vnWcqBZH2zgwXBd6+eRTeuimNmyfOEupotEZWy51TUgEAN43rerUoWcFl19lKvLT+BADX2jiseGCy2Nyef+5gM1xCfay3E6R2nn9i4ac1CFYruXtqld7kkoFns9tR22DG7Z/sxZLPc3C2vB4A8BdP/IySuD+w91mz1S55z/PEW1vOML9xshxldUZsOl4Kq4daMu0Je3zDtP4Tatl9Z+TtNzaDT2x5yL9my+qank1EdG38/rQLCwvDqlWrkJ+fj8bGRpw9exYvvPAC1GpnZ08mk2HFihUoKSmBwWDApk2b0L9/f8FyoqOjsXbtWtTV1aG2thYff/wxQkOFBemGDx+O7du3w2AwoLCwEI8//ri/N4cgiC5Cg8mCOoMZhwprcL4isIvPtTWF1Yy/dY+oIPSIEr7sp8Z6LjRKEIT/kBzkIA2GIAii06M3WjBqRSYsvEEzjUrOFVIHgD5uiru/fM0wweemZKiwmco5+dXYdZaxv/HFLrY10DoGUg1mqyDiu6LeiHMVzCBmmB8i2h+Y3pf725OlmHjg+JIBcbh1Yi8oFXKBrREbve4LYjFLbIk0fWA84sM0sEOG4xfrPC6rttGMi7WNeGPTafy4v4ibXioxqHisWIfcCzUAgLTkSG76+N7RUDv6Fj/lFnPTG4xOS7HYUA0uH5HEzTM3cUC9rVk4vie6RwZxIok/eGLuQPzwj0lYcWXz7ew6K1KW0K6WYs7PDU0UD5pqKcanrayFY0KYfVBRb0SD2bWGy568Su4zey0mRTDB6K9cO0wyq2JcajRXb0nXyCzTYrXhq+wCr2MZwWrXe3x5BxUT2Ppb/sxG0qqF4rzdbncKd6LfUSnkuG50DwDAtP7d/LYORNeAPFoIgujyVOtNSHs+UzDtyHOzu1x9h9aCrddy1cjunM0FS0qMdLQlQRD+R0pwIb2l9egotiQEQQQ+Px4oQp1ROJDXaLIKarj0jZMWXKYPjMfsIfH44yjTR2tKwfu3bkrDzR9mCabFtlPNPVaMsNkBk9UGjVIBi9WGiS9v5uqW+aNvv3TWAKTEhOCRbw96zHAxiKzaNLxnMH8f84uHeyNYFJEvNcg8ICEUpXVGnC3XY0Jfl9kcN3+wB0eLdS7Ti2saBXUL7HY7FqzeDQAYmBCGZF6mVLBaiWtH98CX2QVchDgAVDsKUPOjxWcPicf+ghpMd1ifdVQig9XY8fhlfrUOUirkGNWza9YZjg31LriolXIo5TJYbHY0GK0Ib0I2A2cp5uO1beNdlm2VjRcdokZRTaMgw2XesET8dvgirDYg65zTfvtwUS0Ap5A0gidwAsCv90/BV3sL8HB6f2w/XYEqvQlVehM+2ZmHAxdqkJ1XBaVchjMvZbhdn14xITh2UXjtF9c2IiFC6+Yb7UclW1vFT/VbAKGl2IYjJXjih0OceC51Hr18zTCk9YzEZQM69r2L6Hh07HxOgiAIP1BeZ/SYniwWAQBg73nXuiNE08nJr+L274jkSIxIjhDMl+qEEwTROigkbDLIi5ggCKJzc6JEh2XrjrhMbzBZOcsnAEjxkEnBH+BsynNhUt9YjOslrNER214ZLrzBU4OjGHJhdSMntgBo0kCuJ1ghQedJcBFF6ifxRAx+UfFBib7bnImtmKQGB9k24to8fOx2u6TYAjCCC5/8ygboHJZsT0sUbw8PYn6vnif4sdY7ceHOfv77t4zGriem+9UaqLWgvpH/kCp0Hiph5cVmfTXFCg8A9I5sKl8zXKy8aJi2Os6shXal3sTZ7bHXrt1uR4nOec2xYiV7zYmv8aHdI/DCVcMQE6rhrLo/35OP/247x9VNtdjsWLXplNv1kUuMApf4UGuqPaiqZ/aHL3VffYUTXExW/P2LHEGmotQ9VaWQY+H4FME9nCB8gQQXgiACmiNFtRj74ib0ffp3t20MFtfUZfHLBtE8bv9kL/f3wIQwl7TyWKrhQhDtCg0pEARBdG5yC2q4vx+Y0Y/7W2+yCCy0PNUvUTchq0XM9WN6CD5HtZPgolLIuVplrFVMnshaJ9lNHZumwmYO1XgQXPi1Gj5fPA6T+8ZwnwcnhSM1NgTzhiXihjHJPv+u2ArI3eAg4Nm6y2B2P09sLcRG3I/oEYGp/VwtdVgRq7C6AXa7HQazlcv8ieP1+2UyWYev30L4H3H9TkBaHGGFwgaTFUU1jZj5+lZ8viff6/KdlmK+ZauwAnFbamqszWJFvZG7N7H7wGqzczWmAKbOi9FihckhmHoSKFnh97ucCy7zVm06DZubuiz1BldRq6OOfbAZLtF+HDMI4tlPivGndRlB0NlEEETAUlzTiPlv7eA+nymrl7RTkHrYnizx7HtMeOdibaOgA5kYocXIZGE6vT/TgwmCaDpGCcGZIAiC6Dyw2QQ3jknG0pn98ebm0wCYiGC5XIYdj18Gi9XuUgyYT8sEl2T0jw/Dle/sBCCdTdlWaJUKmK0Wrm//hWjA1l9Wtuy+9FTDhX2+RgarXISKiCAV/vznpU3+XbHAIiW4KB2iEz+zR0yd0f1663h9d5vNjiMOwWVo9wjJ9uw6/HG0FC+tP45FE3sBYM4pT+cc0TWQyiKROm+DuQwXK77Ycxqny+rxzLojuHVCCmw2O+Ru7it6U9MsxXrFhiDz4WltWmuKzc4oqTVwdrPswL7NLrzmqvQmgSDiabvCHdeXu2u9zmBBRLDrNVhvdBVcOmJB+JMldThazNx//JnhwmZDSm0zWcoT/oTOJoIgAo6bVu/B7nOVLtPdRXpJ+S9/tjsfK64c6vd160o88OUB7u9n5g+GUiHHjIFxGJcazaU8u+s8EwTRNpwpq2/vVQgoyIWEIIi2pqyOsYJh7Zs+uWMsPtqeh/+7nCnQ3SPKu8igbWEtA36dAYuHgf7WRqtWoM5oQYPJil8OFmPziTLBfH9lWLCDtbWNZuw6W4FJfWJd2jQ6bM38WSfClxouvmS41ElEuLOwNmlZ5yqx6ONszppsmBfBBQA+2J6H045+RVyYhqy5CEmkzlv23G4wWQQ18G7+YA+OXdRhwdieeGLuQG56ld6EgqoGroaLr5ZiANAv3ncbP38QqmFEjzKdc4A/RM3WnLKjjjcW0WCyosJhoxWiVngUsMO9CJqVeqOk4KKTuP5rPYjH7cH5Cj1mr9rGfY7xZ4aL4558UcJGLUTdNnV9iK4B5XQSBBFQVOlNkmILwHRopGBfOnpEBeGqkUmttm5djb3nq7m/bxzL2CXI5TKsvXM8bhjTAyuvHd5eq0YQhIPqDvaCRRBdiW3btuHyyy9HUlISZDIZ1q1bJ5j/ww8/YNasWYiJiYFMJkNubq7LMgwGA+69917ExMQgNDQU1157LUpLhbXpCgoKMG/ePAQHByMuLg6PPvooLJam+eQTHRfWAoq1bb1sQBy+uHN8k+yz/jY5FbGhatw+qVeL10dcr68tYQf/9SYL3vvrrGDeoMRwv/1OTKgG84YlAgA+3pEn2Ya1LG6pmMVHXMMlTML+Ru0lw8Vms+Puz3MAAN0jgzC1n1AsYgWXmz7YI6gD4zbDRbQOf50sByC0EyMIPpKCi8ppKcbWBQKAXWcrUdNgxvtbmevZaLHiTFk9Mt7Yjqve2cmJEx05M4G9RsrrmXu1ViXnhBSb3e4igBRUNQi+5w5v11iVw46LD9+u7O2b0/DIzP4AAJ2hY70P/HVSKJZHh/jvfhLkRlQJUimgJNtDwo/Q2UQQREBR0+DasWCRsg4DnD6mt05IwX3T+wrmnSjR4d2/znAdE6J58DvBSoUcK68bgRvG+u5ZTRBE62B0c18kCKL10ev1GDFiBN555x2386dMmYJXXnnF7TIefvhh/PLLL/j222+xdetWFBcX45prruHmW61WzJs3DyaTCbt27cL//vc/fPrpp1i+fLnft4doH7gC5S0Y4O4WpkH2U+l49oohzV7G7w9OxQtXDcXlw9sveIntb9YbLCisbuDWa9WNI/G/v431628tmdYbAJCdVwWrRK0EtoaLPwWXYJHgolW5Dud4y3DJyqvisltDNUqXoua1jWZsPVUO8Sb1d5MVEOZmoJuf9UQQfEIl6q04M1ysLuc5n3e2nEH661tRohNmJ0iJjx0F9hrJyWeCEYPVSk5wYWq4CMWO/Eqm9pSn+i0AMCYlmvtbLSEUSAkueqOz3z9nSAJ6OmwWO5rgcrRYJ/jsT0sxd1mHTcmSIghfoDOKIIiAYNeZCvxy6CKmD4xz24ZN7RdT50hFDtUquY4N2wm69aNslNcZUdtoxpNzB/l5rQObpAgtimsNmNLX1WaBIIiOQXsVN+4KtJ+pDtFZmDt3LubOnet2/q233goAOH/+vOT82tpafPTRR1i7di2mT58OAPjkk08waNAg7NmzBxMmTMDGjRtx7NgxbNq0CfHx8Rg5ciSef/55PP7443j22WehVtM9oLPD2tR0C9O2aDkttXkdlBju1yyS5sAKLhX1Ri5qPCkyqFXWa0hSOEI1SugMFpwuq8PABOdvNJgsWPRxNgBpUaS5iJcVJDEwzdZwsbgpmM0PQDNbbYgSWQ7pDGb8lFskmNY7NsRtnR+pQeFl8wbhlgkpku0JQkpQcdZwscDkRix84ddj+FAio0whlyHcizjRnogzVYJUCsgddnsGs43LJEuM0OJircGZ4eJFABiX6hRchvWIgEohQ0FlA3rFhmDX2UpJwaXEYaPFZnOw+03KYr09OVehF3zu6af6W4B7Ebwji3ZE54TOKIIgOizHL+oQF6ZBTKj3iL2bP8wCAHyZXeC2TUFVA+x2u4ufMJvhEqZVQeN4mbDa7LBYbZxNw4H8muZsQpeGfc97fM5Azw0JgmgX+sWF4rXrR7T3ahAE0UxycnJgNpuRnp7OTRs4cCB69uyJ3bt3Y8KECdi9ezeGDRuG+Ph4rs3s2bNxzz334OjRo0hLS3NZrtFohNHo9JrX6ZhIU7PZDLPZt0EZtp2v7bsi/tpHVXrmWEVo5QG7v33dVyFqph9/upQ5Z9VKOYIU9lbbL71ignGkWIeCinr0iQnipn+z9wL3t0Yh89vv22zOgegHp/dBXIjSZdkKh9xvNFskf7e2gXdtG8yIDVGJ5psRpGIGZdkaEm/eONztNmiVrsLOpN5RUMAGs7ljOgTQ/cl3/LGvLukXi62nK7jPWoXr8th38LpGE+rdZFtIiS0AEBmkgtVqgbWdk7bd7SvxOL5WJYfdzlwbNbzrkRVciqpZwUXhcb9HauVYmt4X7209h/SB3bB4cgosNjuW/XQMAFCuaxR83263487/7QXAiAtmsxnBKmZcpLbB9+d7S/D1fDKYhTZrQRLnTHNRwnlfYgNEAUAl99+92h/Qfco3Wns/tWS5JLgQBNHhOFNWh1s+zOZShf82ORXL5g1yG3ln8VAUctbgeNQZLNh9rhJP/XgYz/5yFKdeEEaT1jgiOsI0SkHEQ73R+aC3U6xyk2FTkylahCA6Ho/M7I/7Z/Rr79UgCKIFlJSUQK1WIzIyUjA9Pj4eJSUlXBu+2MLOZ+dJ8fLLL+O5555zmb5x40YEBzctyjQzM7NJ7bsiLdlHZhvQaGb6WXt3/IWjAd7l8ravaivkAORYvf08ACBIbsXvv//eautja2B+b8uufWg863xXWH+GmQ4AF8ursH79er/83oliGQDmXSW14STWrz/p0ia/kGmTl38B69fnu8zfUeJcRq3eiIiqE+APC50qq0dhVR0AGRb2NmFgpB1n92/HWZclMdSaAPGw0qE923C6E5yLdH/ynZbsq9kRwDYoYAfzLr939w7kBwnbVBQz18yhoydRawaaUv1AZTP67RrzB+J9lVcH8K8RU0M9jh09AkCBwrJqADJoFHYYdFUA5Dh5oQyADPXV5V63KwXAy6MBme4Yfv+dEVpqSpl9mXP0FNbrT3BtT9fKUFzLXPvjoxqwfv16lDQw61ZZ19Cm+9Db+VRRrQAc58vf+lv9um5MQhFzPFK1DSiuZc61Rn1dhzqPWOg+5RuttZ8aGhqa/d1O8BgkCKKr8dDXuQJf1o935mFKvxhMH8gMEBwpqsWDXx3Ao7MHYM7QRDz94xG3y1Ir5Wjgpc6bLDZYbXZnoTqbHXkVjI9xz5hggf/pyBXOm7a7wpOENBarDQ0O72oSXAii4/DewlHIPFaKuxze8wRBEGKefPJJLF26lPus0+mQnJyMWbNmITzcN2sms9mMzMxMzJw5EypVx7V6aU/8sY/K6oxA1lbIZcA18+e22Baso+Lrvtr763HsrXBml9SaZMjIyGi19dphOopjOUVITB2AjEudz9WP3t8DgMmykWtCkJExxS+/V7LzPH7KPwUAmDdPersK/jqD9RfOIT4xCRkZw13mn9lyBsg7BwAw22W4+eoMDB1bix1nKvGfzWcAAA0W5jyaOW0iRnqpxWKy2LA8Z5Ng2jWXz3VxFOhI0P3Jd/y1r85qTuKjnYwAOG/WDHQT1Zw68scpbC89j+4pqVDWGYGyEvSPC8UpR70hMb1jg3GughkITY6PRkaGf2s0NQd3++p0aT1WHdnFfU7sFo0RwxLxzbljsCm1AIyICtEiNTkKx2pK0AgNABP69UpGRkbT62oV7cjDluLTiIzrjoyMYQCYMY+c308CKMCVIxLx2nXM9LI6I14+uBUGqwxz5rT+M8TX8+nVE9uBxkZ8s2Qc0vxcD8put+Pdc7tQUmvAytsuwR2f5uB0eT3+tWAsxvWK9r6ANoLuU77R2vuJzfJuDjQKRhBEh6NUZ3SZ9q/fT3CCy/y3dgAA/v7Ffhx5bja+3nfBpT3L6JQonCypE0xrMFk4v+EL1Q0wmG1QK+VIiQ5228lwV3iSkIafHeSt4B9BEG3H3GGJmDsssb1XgyAIP5CQkACTyYSamhpBlktpaSkSEhK4NtnZ2YLvlZaWcvOk0Gg00Ghc7VxVKlWTX2ab852uRkv2kd7MBChFBKmg0QR+PR5v+yosyHUftOb5Fx/OhOlXNZgFv1Opd1qQ6AwWv62DQuHMxHe3TI2amW61S7epNTgD0WYOjodKpcLo1Fj0T4zgBBeWuIhgr+uuUgHZT89AcY0BV72zE8O6R3Sa2lB0f/Kdlu4r/rkbGaqFSiUcimSv3ZwLtejmsBMflBTuVnB5ML0/HvwqFwAQE6LpUMdRvK8iQ4X1tUI0SqiUzPaz78whGiXCg5jvVDpqr0QEqZu1XbFhzH2phnfveefPM/hsD2O93i8+jJseE8YEm9rsQG5RHeLDtegVG4JXNpyAWiHHwzP74+MdeWgwWXDvZX39JqR6O59Mjro2odrWOba/3D8FJosNkcFqfPv3SWgwW5AYEeT9i+0A3ad8o7X2U0uW6b8KbgRBEH4iSKKQ2alS6c7W0P/7Q3L66JQoPHfFENw+qRf0RqEHqN5oxXt/ncW+81XIr2QiY1JjQqBUuL8lWlqY4WK32/HAlwew5LN9sNsDP1umzlEXR6uSuy2ySRAEQRBE8xk9ejRUKhU2b97MTTt58iQKCgowceJEAMDEiRNx+PBhlJWVcW0yMzMRHh6OwYMHt/k6E/6lpoEZ2I8M7hwD3K2NuMj0i1cPbdXfY6P0y+uFwWKVemGdFH9xSf9uAIBwD9njKgUzIOouO58tpJ0cHYRXrnVmwIRpVbhsQDdB26gQ386ruDAtRiZHIvvpGfj27xN9+g7RteAHL0q967PuEwcv1GDTcSYoYFCi+4zK+HCniBER1LEHo0NF16tWpeCCPFlHCK1KgRC10uP3fCXGcd2y1zoAvPqH034wOdppDapVKbh39RtX78GNq3ejSm/Ce3+dxRubT6OszoAVvx7DaxtP4UhR8yP9m4rB4VCiUbXOOEKwWsk9NyOCVR1WbCE6N5ThQhBEh6Ogyr1PYqPJezW86BA13r45jXtw1osEly+zC/DG5tMAgHsu7QMAKKszwBOF1c33bgSADUdK8PPBYgDA0WIdhnaPaNHyOjq1bF0cym4hCIIgCEnq6+tx5owzojwvLw+5ubmIjo5Gz549UVVVhYKCAhQXM/2HkyeZAZOEhAQkJCQgIiICixcvxtKlSxEdHY3w8HDcf//9mDhxIiZMmAAAmDVrFgYPHoxbb70VK1euRElJCZYtW4Z7771XMouF6FzUNLCR0NTfAoQ2tuN6RWPh+JRW/T1OcKlzCiwNJgsMvGLxVpv/Aq36xYdh09Jp6CaKmOejcgSQucvOr6xnzplHZg5AtEhQuXl8Cv48WQ4AUMplCNM0bbgoLsz9ehFdG/51IJUlERfm+jzqFRPidnl8MUah6Lj2dQBchBS7HRDHeWpVCheBpbnv0ex1faiwFjUNJsgg3D89o4W12JIitDjvCEIt1Rm593gAOHHR6RSy82wFhvVomzEMoyPDRUOBm0Qnhs5egiA6HCpep+mpjIEAmEwJvdGCaa/+6fG7j84egJxl6YIoBb1IpDlYWMP9/d5fTAnI6gbP0Wd6kxW1Xtq4Y8+5StyzZj/3ecWvx5q1nM4Em+HiKQKPIAgikOkK2YxEy9i3bx/S0tKQlpYGAFi6dCnS0tKwfPlyAMDPP/+MtLQ0zJs3DwCwYMECpKWl4f333+eW8Z///Afz58/Htddei2nTpiEhIQE//PADN1+hUODXX3+FQqHAxIkTccstt2DRokVYsWJFG24p4S/2nKvEy+uP41QpMwhW08hmuJDgAggzXMRiQmuQFMm8b5wt13P3fFbQABgBaPWtY/z6m33jwhDh4XgrHZHzUtn5ZqsNh4tqAQC9u7kOZoeonZkHUSHqDl2HhehceKuHmjEs0SXDKkSjwN8v6SOYFhOixq/3T0FEkAofLhqD4T0icEsrC6stRSGyLLfa7ZCLri2NUu6SoddUwZOFf++buvJPjFixUTBfLLj06RYq+FzGq6V77KIzq2Xf+Wqfft9ut+OLPfnIzqvyeZ3F33cKLq7ZUATRWaCRMIIgOhz8DlnfOKYDYLMBf54sE0SQifnXNcOwYFxPl+k3j+uJz/fkc5/rDRaXNr6QV6nHyODIJn0n61wlnll3RDCtuZ2PzkSdgTJcCIIgCMITl156qUdh7vbbb8ftt9/ucRlarRbvvPMO3nnnHbdtUlJSsH79+uauJtFBMFttuHfNflTqTfj10EXsfGI6FwwURZZiAISCi692WC1hcGI4tCo5qvQmnC6rR//4MK7+QlKEFjufmN7mogWb4VLTaMb17+/CzMHxWDKNGbQ+UFCDeqMFUcEqDE1yjVQP4QtWdE4RfsRq81wPNUSjxPNXDcWUV5zBlcFqBZ6YOxC5F6qx5xzz/nz7pF6cU0T64HikD45vvZX2I8/MH4znHUGXdgnBRatSuAouzQxc5AsudbxxD61KjqfnDUZMqDCbKDVWKL6W8cZbjhU7BRdfHT+2n67AMsf4x7EVsxGsbtp2sGILu84E0Vmhs5cgiA5F1rlK7u9uYRqM6hkFADBZbThfoRe0vWtqKvf3F4vHS4otAPD0vEF4+Zph3Od9+b5FZ4i56p2dnJ+oL1htdty4eg9Ouyn2F8g88u1BAGjS/iIIgiAIgiCk2XqynBvML6ppxC8Hi/Hi+uMAyFKMpTcvUrstrGjUSjlGpzDvKuw7TKWjnktMqKZdMkRYp4CDhbXYe74aL60/wc07Vsxkt4zpFc3VkOATouFnuNA5RfgPcZaHFFpRbRf2M1+ICFJ3zoyHxVOc4xY2OyQEF7lA8ASaX8NFLNywfLVkIm6d4JoNdP/0fkgfFMd9FgguvAyX4ppGn35/66ly7u+fcot9+g4fvuBCGS5EZ4YEF4IgOgzldUbcuHoP93nXE9MFGRKvbTwlaM+PXPNkG6BVKXCTGzGGZfWto7m/75jcy227I440fF9oMEln0oxMjvR5GZ2Ri7WNXDTNiZI6L60JgiACB3JfIQiitThSLOyD3v/lAe5v/kB5V6YPzyaLrW/T2oxPjQEA7HFksFdwgkv7ZIioxcUheBTXMlZByVHBkvND2tiSjeg63D+9H5IitHh09gC3bYJEggubGcEXEMSiTGfEZre71HDRKP1Xw0Umk7mI8Etn9nc7BhERrMKHt43FgPgwAMDXewu4eWd4gaM6g4W7v4k5X6HHNofQsuN0BTf9s935TbbYNVqsju0QWs0TRGeDBBeCIDoMLzmi9ADg75f0gUohh0IukxzAUivkWDiOidBQymUuqbBSXNK/m+T0B2b0w6whCdznpzMGYZibovZiSzObzY4zZfWwSRTEbDBJZ3f4EODTqcl4Yzv3983jPQtdBEEQBEEQhHcuVLmPLhYPVHZVZDIZnr9yCGJC1PjHZX3b5DfHp0YDALLOVcFut6OwmjlObH2XtkbpYYCyyBGh3j1Ket34gou7KHmCaA5JkUHY+cR03OvhuhSLKcGObJaQABNcrDa7S/abVuVaw6Ul1+D6B6cKPt84Ntnrd8KDmN87VerenWP6a39JTr/0tb+w6ONsZB4rxclSZ8Dl8Ys6LjPTV4xmJsNFq1RQHSmiU0OCC0EQHQK73Y4fDxRxn6cPdKa1KiUUig9vG4OIYBWyn5qBrKdm+JRe3D8+VHK6uCCdUuG0BxBTJhJcPth+Dumvb8X1/93t0tad4LK/oMbrunZmqh1+4gDw3BVD2nFNCIIgCIIgAoMLDv/8HhKD5YEwCOkvbp3YCznPzER/R7R2azO8RyQAJrNFZ7Bwgou7LJLWRuUhw6XIsW7dI7WS84N555G6DSzZiK6Ft8Fzse0YK7TwMz86s7gcFcxknVw2IA4K0b7QKBXcfJbYFmTJdY8MEuzPuDCNh9YMnu4dLDqDRWAZbrPZce17u7jP7/51BgAwIjmSG2Opa2L9XDbDRUP1W4hODp3BBEF0CMRRe8nRzpdJiyh75NM7xmKaI1slLlzrUvjNHe5eHKT8Ud35Pl90pOIDQEmtAS//zvgi5+RX488TZQCAj3fkYdWmU24txQC41KMJROYNS/Sp40YQBEEQBEF4prCKEVym9ot1mddZ6xoEAlqVnMteN5qtuFDlXhhrC6T63qylD1uDwV32jZL3XaqdQLQ3rAAYqubXcOm875brH5yKNxaMxKKJKZCLLcVUcvSKETp2RAa3zNbvsdkDEKJW4PPF43zKFOEHTYpZwMuQya9s4P4uqmlEDq8+7omLTHbLyB4RnGBW76PgYrXZ8fSPh/HFHsbSrC3qcBFEa0J5ogRBdAhqGp2ppk/OHYjECOeLgNj2c3Jf1xdNX1ArpF8cpGxF3YkzJ0ucheOWfL5PMO+OT/fikZn98e9MptaMuBje0pn98bpj3oXqBvTywQats2Gz2aGQy5gO07xB7b06BEEQ7UbTHKsJgiDcU1zTiIs6JuhnSt9u+DL7gmB+Z4767uzIZDIEqRTQm6xoNFudGS7R7ZXh4jqwarIyFj1spr4vdmeU4UK0N3KHkskPjuzM2XyJEUG4cmR3AK7jBFqlQiB4+oO7L+mDxVNSfV5utQfrr1smpOBESR1yL9TgbHk9BiQwGYTi7JVGR/ZLz5gQrrZYvdE3wWXbqXKsyXLWjyHRl+js0FOUIIh2R2+04Iq3dwJgbL/uvqSP27Z940KbnTXhLi1VKhOFX3DyybkD0S8u1NHWmUJ7qLDW5Xus2AKAE1cA4LXrR+CBGf24z098f7gJa955OHChBlZHRlKsj5lHBEEQBEEQhHs+2pEHux0YlxotaXvbmQchAwF2/9c2mlFaxxam7zgZLgazDaW1jNiiUcoRE+I9cl5cdJsg2gt+DZdAEZfF9mnsPeQ+R42b5fMH++V3miLieKr/1D8+DH26MeMh2XlV3PTaRumsmJ7RwVwNGr2PgovYjv1irfu6ZQTRGSDBhSCIdmfHmQru73Ct5879pqWXNPt31G46HIMTw12mmXk2ZuNSo/HYnIEAgH28lFkp+sZJ14m5bnQPwWe2aGUgYLPZsWrTKSz5bB/e++ssN50i4wiCIAiCIFrOCUeG9Q1jkhEXpnHx4w+UQcjOCjtYeq5cD7udOR7RPogarYFUhovRbOXePbpHBnm0F3pkZn8M7xGBheN7tto6EkRT4NdbDRRxWZzhwtpnPZTeDz/dOxm3T+rV5uv0+g0jEcmrIzOsewTCtErMHZoAtVKO+cMTAQBrswqgMzBCC/u/mJSYYE4o05ss+GDbOSxbd5izN5TC7MjEc36mXHGic0OjYQRBtDv8yAg2cqI14AsAI3pEAADumpqKSRIWZcO7R3B/qxRyWBwdAKvN7tIZ4NMoiszwhKcOR2fiu/2FWLXpNDYeK8Wm46UAwNXYIQiC6ErI4N0jmyAIoim8vvEkdp6pBMBkTcjlMnz794mY5xj8AgJnELKzwtbQOV3G1C9IjvYsarQmUhkujWar1/otLPfP6Ief75uCMC9BcATRVgRihouLpZhju5QKOUYkR3J2am3JuNRo5C6fxX1OitQi+6l0vLtwFADg0gHdEBmsgslqQ2mtAXa7Hd/uKwTgWrMqOcopuBTVNOLF9cfxxZ4CnKtogDtqGtxbmhFEZ4QEF4Ig2p2c886sEf7Lo5grRiS16Hf4hddun9wL5/81D0/Pk07X5XfsVAo550cKuE+dBbxnrux+cjr399nyeq/r3FEoqGrA57vPw2B2FZTe33rWZdq1o7q3xWoRBEEQBEEENG9uOcP9zdYFSYkJQVpyJDedHfAn2getw7b4dCnTt+8R1T71WwD3lmJOwUXb1qtEEC1CILgEyL1OrKdo3Viftyd6oxVBagUnHstkMs5q8JNd57HhSAkXbDk6JQq3TUwBADx3xRAEqRWcpdjGo6XcMk0W94GrNR7GWAiiM9LxrmqCILoUVpsdX+9zFv6Uyoy4dlQPRAWr8H+Xt8zLlJ/hkhThObqL31apkGHuUKcQ9My6I9h83NlxeHT2AJ/XIZH3u/mVzgiPjUdL0OuJ33D/lwd8XlZbMv/tXXjmp6OS4opUrZaEcHqZIwiCIAiCaAlWmzAbOp7Xv+IPPHbEwbquBBt1f+BCDYD2q98CSNdhmL1qGz7Yfg4AkODlHYggOhr8+1ugZPOJa7h0xALxUsXuWcFlbVYBHvn2IDc9XKvC/10+BPuWpeM2hx1aiIbZplzHfRGAZPAmS02DUHB5ZGb/5q46QXQIlN6bEARBtB78bJE7JveSbPPa9cNhsdklI7aaAj/DhR8p462tSi4XvNT+fqQEW06UcZ/HpUa7fP+B6X2REBGE5T8dwfNXDRXMG9UzEvsLagRZM0s+zwEA/HKwGG/dlObjFrUdjWYmGmXX2Uo8lC6cV1TtmtXTuxWt4QiCIAiCILoClfVGwWf+IF0wr28aKDY7nRV2ELi8jjleaT2j2m1dgt2cCzoDM3ga0061ZQiiuSjlfMElMMRlseVgR9qu3t1CcK5cj5mD413msYILICxyb7XbIZfLBIGYUuMtBot7wYUdF3o6YxDumNwLyhaO/RBEe0OCC0EQ7Uodr9Dak3MHSbaRyWSSBSCbCj9yJNhLOjJf3JGKFDPy0mG7SWR4jEuNwZR+sbhmVHeXSJzIYOZFh99JUchlXBSj0WLtkFEuABOhojOYEe7wdW40WVFc6yq4xIbSyxxBEF2bACnTRRBEO1JW5xRc1tw5XjBPqyTBpaPA7+t3C9PgypEts0FuCeFBKgyNsuFItfRgJX/AlCA6A6mxIUiM0CIiSAV1gAzCizNcOlLmzjd3T8Sec5WYNTjBZV64m9pO5XVGl2mhatfhZjaIUwq2hktEsIrEFiIgaJWzuKioCLfccgtiYmIQFBSEYcOGYd++fdx8u92O5cuXIzExEUFBQUhPT8fp06cFy6iqqsLChQsRHh6OyMhILF68GPX1wnoHhw4dwtSpU6HVapGcnIyVK1e2xuYQBNGKVOmZB2tihFZg49UaqJuQ4dItzCmiRAZ7fjGJDXMVXMK0zPKlOk+RjhedkloDAOC693YJLCNOXKzz+Hvticliw43/3cN93nyiFHa7q4VYexUKJQiCIAiCCBTK6pi+4pCkcEzuGyuYxx+Q0gZIXYPOCl/wSozQtns/+JJE94p/hJf3GoLoaKiVcmx99DL8ev+Udr+2/IW4hktrj4M0hdhQDeYPT5JcJ3fC0IgeES7TJDNcTB4sxRwZLpEkChMBgt+v6urqakyePBkqlQq///47jh07hn//+9+IinKm1a5cuRJvvvkm3n//fWRlZSEkJASzZ8+GwWDg2ixcuBBHjx5FZmYmfv31V2zbtg1Llizh5ut0OsyaNQspKSnIycnBq6++imeffRarV6/29yYRBNGKnK/UAwBSYtq2uKS3gnvRIWqsf2Aqtj16GYId0Rm3O/xIxYRILCsq2H2GR0/Htr6eeQoAsC+/WjD/ynd2wmx1H/3R1ohr2x2/qOP+P1bM/D2ht9NW7cYxyW22bgRBEARBEIFKqY6JGo73UhtP20Ezo7sKfDugUC9BXW1BnzD3ggsNZhIdmWXzGMeLl64eJpiuVsoDKutBLhKOOkvmjtQYRbhWicVTertMl8qm82QppnMILmFusmgIorPh997AK6+8guTkZHzyySfctNTUVO5vu92OVatWYdmyZbjyyisBAJ999hni4+Oxbt06LFiwAMePH8eGDRuwd+9ejBkzBgDw1ltvISMjA6+99hqSkpKwZs0amEwmfPzxx1Cr1RgyZAhyc3Px+uuvC4QZgiA6Nmz6aVsUWbfwskjc+RvzGZwULvj8wIx++HTXeZd2MpkMOcvSMfqFTQCAecMTkRztviBlcY3TgiuvQi/Z5pl1R/Cva4d7Xce24GStayTRUz8extqsAu5zeJAKC8f3RHZeFf7visFtuXoEQRAdhgAJvCQIooNwqLAWANDDSxF2f1jvEs2Hn+HSEQQXhRz4aNEoLP5sv8s8shQjOjJ3Tu2Na0f1QFSA1xpyEVw6UIaLJ0ziSEwAj8waIBnMmtotxGVao9kGd6M+eiMjxnSEeyhB+AO/X9U///wzxowZg+uvvx5xcXFIS0vDBx98wM3Py8tDSUkJ0tOdVZcjIiIwfvx47N69GwCwe/duREZGcmILAKSnp0MulyMrK4trM23aNKjVzhvx7NmzcfLkSVRXC6PFCYLouFTUM5ZisRJ1UPyN1ebsIDQnQiY6RI1r0roLpj2VMRAAEBOqwfEVc3Dmxbl4+6Y0j+nOiyb24v4uqGqQbPPV3gtNXr/WQm9xncYXWwDGQu3Fq4chc+klXEYQQRAEQRAE0TzK6gz49VAxAEh66fMH+QPFZqezwrfZCdV2jH7wtH6xyBjmet5EesjCJ4iOQKCLLYBrDZdOI7hIZLiEubnn9YsLdZlmMLvPcNEbmUGHjnIPJYiW4vcz+dy5c3jvvfewdOlSPPXUU9i7dy8eeOABqNVq3HbbbSgpKQEAxMfHC74XHx/PzSspKUFcXJxwRZVKREdHC9rwM2f4yywpKRFYmLEYjUYYjc5iTjodY4VjNpthNptd2hNtC3sM6Fh0fnw5luV1Ruw+V4WTDnuqyCBlqx/7vrFO27Lm/tYPB4q4v9cuHosxKZHcspQywG5zteASExvsfCmrrndaKW5/dBqmvrqNN6+x3SM8zGYz9D7sKqWMrt2ODt1jAwc6lh0XvtWCxWyG2cMtvL2OI503BNE5ePG346gzWJAaGyKwbmWZ2CcGc4YkoH9CWDusHcGHL7i4KyrdHiRFuGZGhdNgJkG0O+LYz85iKfb3S/pgy4kywTR39zwpazCDWXqgxG63o97ECC4hGrLIJAIDvz9tbTYbxowZg5deegkAkJaWhiNHjuD999/Hbbfd5u+faxIvv/wynnvuOZfpGzduRHBw29aPINyTmZnZ3qtA+AnxsTTbgM9Py2G0AidqhZ2KC2dPYH398VZfpydGAMFKYP369c36/thucuwtZ9b95IHdKD/W9GUw/Qzm9vvv3w4CkKF/hA37d2xBjxAFCvVMxEvaC1vwxkSJ9JI2psHivQN4/ORprG882QZrQ7QUuscGDnQsOx5W3v19Y2Ymgn3oabf1cWxokM6sJAii7XFXP9hstWHTsVIAwMvXDJPMzFbIZXj/1tGtuXqEj2g7mKUYyz2X9sEXWfncIGeoRhlQdTAIorMizkrUdJIMl3Gp0ch+agZeXH8cP+UyGZjuMlwA4LrRPfBdTiH32V2GS4PJCrvD/b0j3UMJoiX4/UxOTEzE4MFC//5Bgwbh+++/BwAkJDBpraWlpUhMTOTalJaWYuTIkVybsjKhamqxWFBVVcV9PyEhAaWlpYI27Ge2jZgnn3wSS5cu5T7rdDokJydj1qxZCA8Pl/wO0XaYzWZkZmZi5syZUKk6TmQQ0XTcHcvHfziCg1XFkt+ZPDYNc4dKX7sdiZ5FOlz9/h4AwNxZ6YhpZsrzP7M2AgAsyiAABqQkJSAjYyTmzrWj/3Ln4FtGRkaL17klmM1mPLjiTwDAzEFxyDxeJtlu3IhByJiY0parRjQRuscGDnQsOy5mqw1Ls5h6XjNnzvTold9ex5HN8CYIon15+feT+CRbgYj+5Zg1NEkw78kfDkNvsiImRI1xvVyzW4iORaim41mKAYzl8c7Hp3N1JqXqLBAE0fYoOmkNFwCIC9fiypFJnOASEey+D7vy2uG4fVIvrMnKx5fZF9AoIbjY7XY8/HUuAEAuE9plEkRnxu+9gcmTJ+PkSWGU86lTp5CSwgzEpaamIiEhAZs3b+YEFp1Oh6ysLNxzzz0AgIkTJ6KmpgY5OTkYPZqJ2tmyZQtsNhvGjx/PtXn66adhNpu5l9TMzEwMGDBA0k4MADQaDTQa1zoRKpWKBiw6EHQ8Agf+sbTb7fjhgLTYAgDzRvRw8TLtiAxNjsLUfrEID1IhPiK42Z7ZC8f3xJqsAlysZSzFwoKc+6pXTDDOVzIRyHKFssPslwEJ4bh2dA/8/QumAOflI5Lwy8FiTO4bg1smpkJFnaNOAd1jAwc6lh0QudMqQaX07fi09XGkc4Yg2p9TpXX4eFc+ABn2nKtyEVw2HmVstG+f1AvyDtIPJNyTGuusVdDRorP5NVvITowgOgZy0RiCqpNlnk0fGI9l8wYhv7IBA+Ld21rK5TIM7R6BRIe9ocFsA0Td0JOlddjoyOi02akmGRE4+P2J+/DDD2PSpEl46aWXcMMNNyA7OxurV6/G6tWrATAXz0MPPYQXXngB/fr1Q2pqKp555hkkJSXhqquuAsBkxMyZMwd33XUX3n//fZjNZtx3331YsGABkpKYzujNN9+M5557DosXL8bjjz+OI0eO4I033sB//vMff28SQRB+4FRpvdt5o1OiOoyo4A2VQo7PF49v8XLEUc8V9Sbu725hGk5w+ffGk3hszsAW/15zaTA5Lc0WTUpBSa2z5sydU1Lx1k1p7bFaBEEQHZLO8SQjCKK9OX7RmWlmF80zW23QGZj+18IJlD3cGRjAq6OjUnSsJwH/HctT1iVBEG2HXFzDpRNluLDcObW3z23ZrBWD2SoQXIwWK/7hCOYkiEDD74LL2LFj8eOPP+LJJ5/EihUrkJqailWrVmHhwoVcm8ceewx6vR5LlixBTU0NpkyZgg0bNkCr1XJt1qxZg/vuuw8zZsyAXC7HtddeizfffJObHxERgY0bN+Lee+/F6NGjERsbi+XLl2PJkiX+3iSCIPwA/8WS5cAzM7G/oBppPaWz0gKZEFH0W59uIdzf90/vh0UfZwMAlwHTXhwuYo5bt1A14sK0nLcqAEQ3006NIAiCIAiiK5NXoef+1huF9fqqG5ggHLmMBsg7C7Ghzj6xrrH96y+6g84ngugY8DNcZDJA2UmCT5uLVsUISmJLsQ1HSnCO9zwkiECiVXJK58+fj/nz57udL5PJsGLFCqxYscJtm+joaKxdu9bj7wwfPhzbt29v9noSBNF27DhT4TItKkSNGYPi22Ft2p+EcK3g89KZ/bm/p/Xvhv7xoThVWo/gdvZa3naaOW5pPSMBAN1CNejdLQQWqx2JEVoP3yQIgiAIgiCkEAguJuEAVLXeDICxguosGeBdHZlMhjsm98IvB4tx5cgk719oJ0hwIYiOAf/erlbIA95GS+vIcDGabYLpG4+WSjUniICg8+WtEQTR5jSYLHj461z84fCTbg7f5RT6cY06P/NHJAo+h2mFL0DXjOoBADBahJ2StqZMZwQADO8eAYDxYd3w4DRsfHgalJ3Ma5YgCIIgCKIjcK7cKbjw7VsBoFLP9L2iPBQiJjoe/3f5EGQ/lY648I4bkJQUGdTeq0AQBJisFpbOaCfWVIIcQaQNogyX3As17bA2BNE2BP6VTRCEV+x2O/aer0KpTtq+6s3NZ/DjgSLc/XlOk5e74UgJ/jxRxk17dPYAaFVyPHfFkBatc2dHo1RgcGK4h/nM7bm9BZc6h80FPyJOrZRzUSoEQRCENHaXygwEQRCMh/2JEqfVboObDJeYEE2brhfRcuQdNCNp+fzBGNUzEndf0qe9V4UgCAAKnuKi6gJBjKyduthCs8ZhoZkcTWIwEXi0iqUYQRCdi6/3XsATPxwGABxbMRvBauGt4WhxbZOWZ3cU+th2ugJ//+KAYN4/Lu2Du6f1puwIAG/elIZl6w7jgen9XOZplIyg8ceREtjt9nZJM7ba7Nh8ohwAEK6lxwVBEARBEERLyb1QA7PVKciKBZcqxwBUVAhluBD+4W9TUvG3KantvRoEQTjgW4oFev0WAAjlBBfn885ksXGWmqtvHYN/bzyJOybTfYoIHGjEkyAIPPnjYe5vvsUBi/hF0BM7z1Rg+PObsatUhkNFOsG80SlRkMlkJLY46BsXiq+WTMSkvrEu89jUYpPVhh8PFLX1qgEAyuuM3N9sDReCIAiCIAiiedjtdry5+TQAoJuj0Dp/AAoAqvUOwSVYDYIgCCLw4Asu8gCv3wLwBBeehWZtI5PNKZMB/ePD8OFtYzFZYlyEIDorNOpJEASG94jk/t5fUC2YZ7PZkZNfDV+554scGMw2fH1OgTe3nBXMUykCvzPhLzQ8L9cNR5pfO6clVDle+GWwIzGi4/pREwRBdBQCvegp4V+2bduGyy+/HElJSZDJZFi3bp1gvt1ux/Lly5GYmIigoCCkp6fj9OnTgjZVVVVYuHAhwsPDERkZicWLF6O+vl7Q5tChQ5g6dSq0Wi2Sk5OxcuXK1t40QgKjxYob/rsbu85WQqWQ4fHZ/QG41nBhLVfYASqCIAgisAjhOYoYLL4Ht3ZW2OdZPc9SjLUTC9eqBAIUQQQKJLgQBAH+423f+Wq899dZFFY34FRpHfot+13Q1mrz7EnvabDphauGtmQ1uxR8waXR3D6dsGpHJyieLFUJgiAIwu/o9XqMGDEC77zzjuT8lStX4s0338T777+PrKwshISEYPbs2TAYnDX3Fi5ciKNHjyIzMxO//vortm3bhiVLlnDzdTodZs2ahZSUFOTk5ODVV1/Fs88+i9WrV7f69hFCfj9cgr3nmSCmUT2jMKx7BADXTHKDo99H9fIIgiACE369pwZj4AsubA0Xg9kGqx04W67Hi+uPAwCigsk+kwhMKGyGILo4VXoTjhQ5a7T8fLAYPx8sxisbTki215ssCNe6fygmRQZx6aFi+saFtWxluxAa3ku2oZUFl9oGMw4V1SBcq8KAhDDuBZ/NcAmlPhBBEARB+J25c+di7ty5kvPsdjtWrVqFZcuW4corrwQAfPbZZ4iPj8e6deuwYMECHD9+HBs2bMDevXsxZswYAMBbb72FjIwMvPbaa0hKSsKaNWtgMpnw8ccfQ61WY8iQIcjNzcXrr78uEGYI/2C22nDiYh2GJIW7FFDfcaaC+3vRxF4I1jD9LZ3BggWrd+O9haMRFaKGwWwDAGhVFBtJEAQR6JistvZehVYnROMc2zBagblv7YSj7C8iyD6TCFBIcCGILs7TPx6GxUvWCp96g3vBpbbBjOMXdZLzYkM1zVq/rgo/w4WNhmwt7vp8H7LzqrjPp16YC7VSzglnQQrfzw+CIAiCIFpOXl4eSkpKkJ6ezk2LiIjA+PHjsXv3bixYsAC7d+9GZGQkJ7YAQHp6OuRyObKysnD11Vdj9+7dmDZtGtRq54DG7Nmz8corr6C6uhpRUVEuv200GmE0Ouu46XRM385sNsNslg6qEcO287V9oPDOX+ewavMZDE4Mw2vXDkO/+FDGGu6X4/gupxAA8My8gZg1KBbV9Y3c9/acq8L7f53BP2f1Q4OJ2WdqhazL7T93dNXzqTnQvvIO7SPfoX3lOy3ZV4G+f+Vg7OXNVjsaLODEFgCI1CoDfvubA117vtHa+6klyyXBhSC6MDn51fi9ifVBcvKrkRQp7TH10Y5z3N89Quwo1DORfb27heCDRWMkv0NIwxdcAMbjNLKVoj/4YgsA9F/2O+6f3pfLdAmiJwVBEESTsZNWTbSAkhKmfxYfHy+YHh8fz80rKSlBXFycYL5SqUR0dLSgTWpqqssy2HlSgsvLL7+M5557zmX6xo0bERwc3KTtyMzMbFL7zs4XBxQAZDh2sQ7//GIH7h5kQ4UB+OqAszMVXn4E69cfARPv5Jx++ORZrLecRn6hHIAcp08cw/qao229CR2arnY+tQTaV96hfeQ7tK98x/d95bz/r1+/vnVWpgOhlilghgwna4XZn/XVZV1i+5sLXXu+0Vr7qaGhodnfpWE0guiinCmrx7Xv7fKpbUK4FiU6xi/86R8P4/IRSS5tDhXW4M0tZ7jPjw63QtZzFIp1Jvz9kj7+WekuhEYp9O3+eOd5LJ3Zv81+/y3esdSShThBEARBdBmefPJJLF26lPus0+mQnJyMWbNmITw83KdlmM1mZGZmYubMmVCpuoY3aaPJin9mbwHAqK1WTTgyMiYhK68KOLAPABCkkuOqyzMAMPsoaO8WNFqZwaddZXL8a9GliCg/ClRVYkzacGSkdW+XbelodMXzqbnQvvIO7SPfoX3lO03dVw/u3sj9nZGR0Zqr1iF49cR26KsbsalIGFg6amBvZMxuu3GOzgJde77R2vuJzfJuDiS4EEQX5bHvDgo+XzEiCT8fLBZM+/bvE9EjKgiJEUHo9cRvAIDJfWMll7f0G+fy5g1LAFCIuUMT6OHQTDQi3+43N59uFcHF5oOdHAkuBEEQBNG2JCQkAABKS0uRmJjITS8tLcXIkSO5NmVlZYLvWSwWVFVVcd9PSEhAaWmpoA37mW0jRqPRQKNxtYJVqVRN7tc15zudlZNlDTBbnf2qSr0JKpUKZfVOO4oPbxsr2B8aBdDIK9X32A9s5gsQolV3mX3nK13pfGoptK+8Q/vId2hf+Y6v+4q12GK/E+iEapjh5yqjMMMlJTa0S2x/c6Frzzdaaz+1ZJlUiY8guij8DAqFXIaXrhmGzY9cgq2PXop/XTMMz181FGN7RSMxgrEPu3tabwDAkeJa7ntWmx13/m8fZv9nG86U1XPTl6b3baOtCFzElmKtxbKfjnhtE6QkXxyCIAhfkHlvQhA+kZqaioSEBGzevJmbptPpkJWVhYkTJwIAJk6ciJqaGuTk5HBttmzZApvNhvHjx3Nttm3bJvCgzszMxIABAyTtxIjmo3PUvosKZl7Oq/QmXKxt5IKSrh3VwyVwSS3q7u05VwWjmVFgtEqKeCEIgghUorpYsfgwrXS8f48oabt6gujskOBCEF2QPecqsfc8U7dj0cQUnHphLkI1SvTpFoqUmBAsGNcTt05IEXxnfwFTuP1ClbPA55ubT2PT8VKcLK0TtE2K0LbyFgQ+MlnrD9tZrDaszSoQTLvvMlexjDJcCIIgCML/1NfXIzc3F7m5uQCAvLw85ObmoqCgADKZDA899BBeeOEF/Pzzzzh8+DAWLVqEpKQkXHXVVQCAQYMGYc6cObjrrruQnZ2NnTt34r777sOCBQuQlMTYv958881Qq9VYvHgxjh49iq+//hpvvPGGwDKM8A86AyO4pMSEAABsduDKt3dy84PVrh2qlFDXoBaD2QYACJJoTxAEQQQG/711NLqFafCfG0e096q0CUOSIiSn94hqWm04gugskOBCEF2Mb/ddwILVe2Bx+BVcMSIJCrn3wX2rhPXUG5tPS7ZVKujW0lLiwzTo3S2kVX/jXIXeZZqUbVkImU8SBEEQhN/Zt28f0tLSkJaWBgBYunQp0tLSsHz5cgDAY489hvvvvx9LlizB2LFjUV9fjw0bNkCrdQa2rFmzBgMHDsSMGTOQkZGBKVOmYPXq1dz8iIgIbNy4EXl5eRg9ejQeeeQRLF++HEuWLGnbje0C1DoyXKJD1FyWS1mdkZvfPyHM5TtX9bIJon5VChkMFkeGi4r60wRBEIFKWs8oZD81A1en9WjvVWkTnr1iiOT07pGU4UIEJjSMRhBdAJvNDjsY67Bv9xUK5sWF+ZaN8vicgbhx9R4AwIYjJZgzVNr3e0QP6cgFomkoFXL88dA09Hv691b7jZMlwsykZy8fDLlchsVTUvHRjjxueoSaLMUIgiAIwt9ceumlsNvdP2NlMhlWrFiBFStWuG0THR2NtWvXevyd4cOHY/v27c1ez67KvWv2o7CmEZ8vHodwrXcPb12jBQAQrlUiJlSD6gZGgAlSKXDf9L64cUyyy3dCVcD2f05D+qqdqKg3wmy142KNAYDQ/pcgCIIIPNrC1aKj8uLVQxGkUlA2JxGwUNgMQXQBbv04C9P//RcMZisq9UbBvLhw16KoUvSIdqZ6/v2LHMk2D6f3x9q7JjR/RQkBqlbOFKppNAs+3z45FQDwzPzB+P6eSdz0JMryJQiCaDIkVRNE50VnMOO3wxdx8EINXt94yufvAEB4kArxvP719/dMwr2X9YXaTX2+EI0S2U/NQHQI4+dvsjKWYloVDUIRBEEQgcnC8Sm4ZlTXyO4huiYkuBBEgFNnMGPnmUrkVzbgiz35OFsutJHy9WVO7cPg/5R+sQjRUOJcZ8BgtuL5X45xn1+/Qegdm5YciWtGdcdjs/uBgk4IgiAIguhKnOfZrp4o0fn0HZ0jkCUiSIXbJzFBLFHBKgyQsBITI5fLOMGFhaJ+CYIgiEDiulHdAQCzB8e185oQROtDI6MEEeD8mxeV98Jvx7m/L+nfDU9mDPR5OVJReZcN6IY/T5Zzn8lrunNgt9sx8JkN3OdrR/VwiS6Ry2V4/YaRMJvNWL/+uHgRBEEQhARd2BmCIAKKPJ7gUmew+PQdnYG1FFNh5uB4fHrHWMSEaHyqlQgA0cFCwUXrJiOGIAiCIDojyzIGIEhXgIeukq7nQhCBBPXiCCLA+XTXecnp//vbOAxMCPd5OeFaoT57pKgWZqtd1Ma7vzXRNFZeNxwAMDI50m/LPHChRvDZncUFQRAEQRBEV4QvuLBWYd5gM1zCg5g+86UD4jCsCbUNo0KE/WiyFCMIgiACiRCNEmO62REeRONGROBDo2wEEcCcLa+XnN6cTBRxQbcdZyrQYBJG/MWECiPziJYTH64FAJgdft7+oExnEHz2VLCXIAiCIAiiq8G3FNM1+pbhUssKLs0MQBJbipHgQhAEQRAE0TkhwYUgApg7PtkrOf2T28e1eNmV9UY0mKzc5yl9YxGsJpdCf6NSMEKXPwUXcWZSTn6135ZNEARBEATR2cmrbOD+rjOYYbN5Dk4xWWw4fpGp9dIzJrhZvxnFsxRTK+Q+W5ERBEEQBEEQHQsaHSWIAKagyvmyuG9ZOhqMViRHB7lkq/jKZ38bh0UfZwMAMo+V4rzjZfSLxeMxvnd0y1eYcEGtYHRxsUjSEnadrRR8XjCup9+WTRAEQRAE0dnhZ7jY7IDeZEGYm8yVzcdLsfh/+wAAsaFqDGqCZS8fvuAyrX+3Zi2DIAiCIAiCaH8ow4UgApStp5zF7OcOTUBsqAY9Y4KbLbYAzMvfJ3eMBQBObAEYz2mVgm4nrQG7X00W/2W4fJldIPh8/Zgefls2QRAEwUB2jQTROanWmzh7MBadQdpW7I1NpzmxBQAyhiVC3szMFK3aaSH2VMbAZi2DIAiCIAiCaH8ow4UgApTbHJkoAPDuwlF+W27v2BCXaWQl1npwgosfLcVYkiK0ePHqYc32GicIgiAIggg0TpcxNRATI7QwW22oqDehzmAGEOTS9j+bTnF/r7hyCG5qQdbwnCEJ+HbfBVw3ugd6dwtt9nIIgiAIgiCI9oVGSQkiwIkIUrUoq0VMz2hXX+rkKNcXUMI/qJX+r+HSIyoIhdWNeHvhKIzqGeW35RIEQXR1/Pm8JQiifdhfwNS2G9EjEqdK61BRb4Ku0TXDRZzFdsOY5BZlfHcL0+Dn+6Y0+/sEQRAEQRBEx4A8gAgiQBmUyPhHr7pxpF+XKx5M+uufl0JJdmKtBvvibnZYiu04XYGlX+e6WF00hXojM2gQpiHNnSAIgiAIgs/+fEZwGZUSibAgJgtYqt9V0+CcNj41GlqVwqUNQRAEQRAE0fWgUVKCCBDe33oW7/x5Bna7HTabHccv6gAw0XKtSS8JizHCf3CCi5WJorzloyz8cKAIr/5xolnLs9vtqHP4kLsr/koQBEEQBNGVOFGiw88Hi2G327kMl9EpUYh0CC7VDSaX75TXG7m/P7p9bNusKEEQBEEQBNHhofBmgggAqvUm/Ot3ZgB+ztAEWG1Oi4OUGFcLMH/RPZKsxFobfg0XvnXF+YqGZi2v0Wzlzo9QLT0CCIIgCIIg5qzaDgDQGy2oqDdBpZBhSFIEYkLUAIAqvavgUlzTCADoHx+KUMoaJgiCIAiCIBxQz5AgAoAL1c7B96LqRpgc9lOJEdpWzWJQyMmrvrVR8+zaLDwhjS+qNYV6R3aLXAaEqMn6giAIgiAIguWD7ecAAEO7R0CrUiDajeDy4fZzeOG34/j/9u48Lqqq/wP4ZwaGYQdBVhcEkdz3BNLMBcGi0rLHXCqz1DQs0efRsp9mamqaZrmUrfqYW/rk06I+IiJqKqIhuO+i5AK4scg6zJzfH8SVEZARZmGGz/v18uXce8+995zvYebOnXPPOYDhe5MTERERkXlhgwuRmSsoVmPjn39Jy7fzirDzVCYAINDT0aDnbtfYxaDHJ0Bhfb9RS6XWSK/VGoEb2QWQQQZvF1udj5fzd4OLo9KakzsTERlQzZrFicjYyn+/unQzDwDQI7AhAMDNsbTB5fY97QaXssYWAPBwZIMLEREREd1n8DlcPvnkE8hkMkRHR0vrCgsLERUVBXd3dzg6OmLQoEHIyMjQ2i8tLQ2RkZGwt7eHp6cnJk+ejJKSEq00u3fvRufOnaFUKhEYGIhVq1YZujhEdc787Wew5mCatLxs1wVsPX4DAHAzt6iq3Wplw5gQDOjoi5nPtzHI8ek+RbkeLqqS+z/f5atKEDpvF0LmxaGk3A8F1Sl7QpPztxARERFVPj/LwE6NAEAaUux2XhFS/srCx1tOITtfpZXW01n3B1+IiIiIyPIZtIfL4cOH8fXXX6N9+/Za6ydOnIitW7di06ZNcHFxwfjx4/Hiiy9i//79AAC1Wo3IyEh4e3vjwIEDuHHjBl577TUoFArMnTsXAJCamorIyEiMHTsWa9euRVxcHEaNGgUfHx9EREQYslhEdcqqA5e1li/+/WQeAIx9qrlBzhkS4I6QAHeDHJu0WZcbtq24XMPK9axC6XVekRou9nIcvnwHLnYKBHk5VXm885m5AIAWXobt/URERERkDh4cLsxaLkNAQwcAgJuDUkozdfNxnL6RgyNpd7XSs4cLEREREZVnsB4u9+7dw/Dhw/Htt9+iQYMG0vrs7Gx8//33+Oyzz9CnTx906dIFK1euxIEDB3Dw4EEAwI4dO3Dq1CmsWbMGHTt2xNNPP43Zs2dj+fLlKC4u/UK8YsUK+Pv7Y9GiRWjVqhXGjx+Pl156CYsXLzZUkYjqnPKTqFema7MGD91OdZ9MJpPmcckuuP9EZfkfB+4Vl+BaVgH+sSIB4Yv3PvR4ZUNiePNpTCIiIqIKDS5uDjbSsKtlc7jcvleM0zdyAABH0rK00nMOFyIiIiIqz2A9XKKiohAZGYmwsDB8/PHH0vqkpCSoVCqEhYVJ61q2bImmTZsiISEBISEhSEhIQLt27eDl5SWliYiIwLhx43Dy5El06tQJCQkJWscoS1N+6LIHFRUVoajo/hBLOTmlX5pVKhVUKlVVu5GRlNUB60J3BcVq6fXPbwVj0NeJWtsb2FqZJJ6sS/0qm2ol4vPKG1MSL96Ei939IcKKioohl1c+P8vN3NKeMa521jrVD+vScrAuLQfr0jyUqFRQqap+tslU9ci/G6L7hBCY9NNRrXXu5XqsNHK1AwBcyyqo8hiebHAhIiIionIM0uCyYcMGHDlyBIcPH66wLT09HTY2NnB1ddVa7+XlhfT0dClN+caWsu1l2x6WJicnBwUFBbCzs6tw7nnz5mHmzJkV1u/YsQP29va6F9CIhADWXZTDzgp40V/3eRrMWWxsrKmzUGepNUDSbRlkAB73EEjIkAGwAgD8dXQ/HnxL79yx3eh5LI91qR9FJaX1qtZU3qNp0qbjeCVQjbK/hV+3/g9Kq8qPdfK8HIAcGWkXsG3beZ3zwLq0HKxLy8G6rKtKP7N3xsXBSYfpsoxdj/n5+UY9H1FdtvvsTaTnFGqtK5u3BQC8XWzh39ABqbfyHtxVwh4uRERERFSe3htc/vrrL0yYMAGxsbGwta1bQ9ZMnToVkyZNkpZzcnLQpEkThIeHw9nZ2YQ5q1p6TiGiD5Y+1f7pyD5oYG9TzR7mS6VSITY2Fv369YNCwQm9KzPy30nYd+E2AGDNBeDlro2BS1cBAJGRz+BuwzTM3HIGANDVzxXPPNPNJPlkXerXhIQd1aaJzbQHUNqDL6RnH/i4VPz8LShWY0JCHACge9eOeKaDT7XHZV1aDtal5WBd1m1ln9lhfftqPSn/IFPVY1kPb6L67kjaXYxcVfEBwRKN9kNu3QPdH9rg0pBzuBARERFROXpvcElKSkJmZiY6d+4srVOr1di7dy+WLVuGmJgYFBcXIysrS6uXS0ZGBry9vQEA3t7eOHTokNZxMzIypG1l/5etK5/G2dm50t4tAKBUKqFUVvxCrFAo6uQPFsUlGmQV3P/Cn3q7EJ4uDibMkXHU1fowNSGE1NhS5qc/SxtbxvQMgEKhwMgezfFkkCcKVRq0beRiimxqYV0aT0bO/eESey7ci8ufRGptT72Vh94Ld0vLns52j1Q3rEvLwbq0HKzLus3KWrf6MXY98m+G6pvrWQWY+FMKRnZvhv5t7z9ssi4xTXrdyNVOGjYsK1972L0gL6cqjx3Z3geu9nxPEREREdF9VQ8sXUN9+/bF8ePHkZKSIv3r2rUrhg8fLr1WKBSIi4uT9jl79izS0tIQGhoKAAgNDcXx48eRmZkppYmNjYWzszNat24tpSl/jLI0ZcewBOGL9+C5Zfuk5eW7L5owN2Rqn++sevin8kNNBXo61YnGFqpbhn5zUGvZ3dFye8sREZmSrPIptIjIROZvP4PE1DsYu+aI1vq8ohLpdaMG9x/Ye/A7UhO3+0NPR7b3QUNHGzjYWCF2Yk8sH9YZMr7piYiIiKgcvfdwcXJyQtu2bbXWOTg4wN3dXVr/5ptvYtKkSXBzc4OzszPeeecdhIaGIiQkBAAQHh6O1q1b49VXX8WCBQuQnp6OadOmISoqSuqhMnbsWCxbtgxTpkzBG2+8gV27dmHjxo3YunWrvotkEoUqNS7f1h5je++5myhRa2Btpfd2MqrjcgpV+CLufoPLh8+2xqwtp6TlZ9p5myJbZEYqjk/O4S+IiIjI8mWW6wVc3qkb94fX6+rXAFG9A7E8/gJmDdC+l/Ur1+DyVJAHFv2jA/KL1XBz4MMrRERERFSRSX65X7x4MZ599lkMGjQIPXv2hLe3NzZv3ixtt7KywpYtW2BlZYXQ0FC88soreO211zBr1iwpjb+/P7Zu3YrY2Fh06NABixYtwnfffYeIiAhTFEmvcgpVaDm98snOl8VfMHJuqC5Ye/D+kAdDuzXFGz38tcaLbt/Y1QS5IlPxdq5+fqwb2QVVbuvb0hNezmxwISIiIsslRGkPcFvF/Vve3MLS4cLUGoGrd0u/Kw3t1gTv9m2Bp4I8sPGtUDT3cNQ6TvneL652CtgqrNjYQkRERERV0nsPl8rs3r1ba9nW1hbLly/H8uXLq9zHz88P27Zte+hxe/XqheTkZH1ksU6ZsL7qMn2+8zze7OEPJ1uOFVyfJF25I72eGNYCANCvtRfWH0qD0loOBXs91Su9W3pg/aG/AADtG7tg87gnkK9S48S1bAz7NhEA8O3eVIzu6Q8fl4pzWn03oiuHvyAiIiKLlXTlLkauPISJ/YKQV6SW1p/PvIfOTRvgZm4R1BoBK7kMswe0fegIAkprK/Rp6Ymz6bnoHtjQGNknIiIiIjPGX2nrGI1GIP7sTWn57V7NcXHuMxjfO1Ba1+6jHbh6N7+y3ckC/ZhwGTtPl85n9EtUd3j+3bth+rOtMKqHP9aMCjZl9sgElNZW0mshAGsrOZxtFXiieUP0a+0FAPhhfypC5+3Ch7+eAADYWJd+3O+c1JONLURERGSR5m07jWeX/oFBXx1ATmEJZv5+Cocu339w6Zs9lwAA1//uCezlpNRpuObvR3TFnsm94KA0yvOKRERERGTG2OBSx6TeztNa/kfXJrCSy/CviMe01veYH4/sApUxs0ZGdjevGG+vTcL0X09K69r4Okuv7W2sMe3Z1ni8mZspskcmZKuwwtu9mgMA/i+ylda24cFNtZZXJ1zB0b+yUFyiAQB46TAcGRER1Z6AMHUWiOqdr/dewolrOVVu33U2E3+cv4lXvivtEezjWrEncGVkMhnn0SQiIiIinfBbYx3zxc77E6PHRPeEf0MHaXnyA40uMSfTjZYvMr5Os2Ox7fj9On6nTyCHDiMApWORT+nfEidnRiAkwF1rW6cmDSqk/2Zv6dOcchngYMMnM4mIiMxFbm4uoqOj4efnBzs7OzzxxBM4fPiwtF0IgQ8//BA+Pj6ws7NDWFgYzp8/r3WMO3fuYPjw4XB2doarqyvefPNN3Lt3z9hFMbiyOVuqIpcBxSUavPr9IeQXlw4z5uPCB1GIiIiISL/4620dodEI5BeX4Lej16V1j3k7aaV5cHLGKf85Vu2NBZmnM+kVn8z7Z/hjlaSk+uDA+33Q2ud+7yZbRemQYpUNa+Fir8CwB3q5XLxZ+qOKk60CcjmHEyMiMiR+ypI+jRo1CrGxsfjxxx9x/PhxhIeHIywsDNeuXQMALFiwAEuWLMGKFSuQmJgIBwcHREREoLCwUDrG8OHDcfLkScTGxmLLli3Yu3cvxowZY6oiGUxuUYn0+sVOjSpsb+pmX2Ede/4SERERkb6xwaWOWBZ/AW1nxDw0TbB/xaGjUm/lVZKSzFluoQr9P/9Da92Dw0RR/eLraodforpLy7fvFT00/aDO2j8ynEnPBQB4OCn1nzkiIiIyiIKCAvz8889YsGABevbsicDAQHz00UcIDAzEV199BSEEPv/8c0ybNg0DBgxA+/btsXr1aly/fh2//PILAOD06dPYvn07vvvuOwQHB6NHjx5YunQpNmzYgOvXrz88A2YmK690uGVbhRxvl5v/EgBe6NQIuYUlFfZ58IE2IiIiIqLa4tgydcD1rAJ8FntOa92Dw4cBQICHI57v4KvVCyYztwgBHo4GzyMZz+Yj16TXbRs549eoHrBir4R6z8ZajsebNcDhy3fRv63PQ9N2btoAr4b44dSNHCRduSutb+7h8JC9iIiIqC4pKSmBWq2Gra12Lww7Ozvs27cPqampSE9PR1hYmLTNxcUFwcHBSEhIwJAhQ5CQkABXV1d07dpVShMWFga5XI7ExES88MILFc5bVFSEoqL7D3fk5JT2vFapVFCpdJtDsiydrun14WZOPgCggb0N7B+4y535bEtEbyzGrrM3tda72FoZNY/lmSJG5oqx0h1jVT3GSHeMle4Yq+oxRrpjrHRj6DjV5rhscKkDei6Ir7Auoo1XpWmXDO2Ej55vgzf/fRjJaVnIyuebz9KUr9MJfYPY2EKSr1/tioycQrQqN7xYZWQyGWYPbIu9527itR8OSevv5vHzgoiIyFw4OTkhNDQUs2fPRqtWreDl5YX169cjISEBgYGBSE8vnevPy0v7vsHLy0valp6eDk9PT63t1tbWcHNzk9I8aN68eZg5c2aF9Tt27IC9fcVhuR4mNjb2kdLXxqm7MgBWkKsKcGBPHMpudfv4aBC/MwY97IFdD9z+Xjp9HNsyjxktj5UxZozMHWOlO8aqeoyR7hgr3TFW1WOMdMdY6cZQccrPz6/xvmxwMbGiEjVKNBXnYXFzqHroHzcHGzSwL+3+fiY9B/3behssf2R8GbmlY24P7OiLfq0rb3ij+snNweaRhr7o0MRVa/mtpwL0nCMiIiIypB9//BFvvPEGGjVqBCsrK3Tu3BlDhw5FUlKSwc45depUTJo0SVrOyclBkyZNEB4eDmfnhz/0UUalUiE2Nhb9+vWDQqEwVFa1z5lyHThzAn7e7hjwbFf8K3EHAKB58wA8ExEEANh+9zAOXb7f+7fvk8Ho1qzisM1Gya8JYmSuGCvdMVbVY4x0x1jpjrGqHmOkO8ZKN4aOU1kv75pgg4uJnbxeeeW52un2h/L5zvOY0LcFZDL2grAUe/4e6iA4wN3EOSFz52KnwIS+LfBF3HkAQN9WbMAjIjKais/TED2y5s2bY8+ePcjLy0NOTg58fHzw8ssvIyAgAN7epQ9dZWRkwMfn/nCjGRkZ6NixIwDA29sbmZmZWscsKSnBnTt3pP0fpFQqoVRWfPhLoVA88s1sTfapqbSs0mHQGjgqtc5pbW0lLdvaaN/+ejrbm/yHDGPGyNwxVrpjrKrHGOmOsdIdY1U9xkh3jJVuDBWdK2vTAAAtyklEQVSn2hxTrsd80CPILVQh6P/+hxe/PCCtuzDnaUSHtcDsgW0hr2YYqfzi+5M+3uWwYhbjyu08XMsqAAA0dXu0IRuIKvNqqB/aN3bBjOdamzorREREVEMODg7w8fHB3bt3ERMTgwEDBsDf3x/e3t6Ii4uT0uXk5CAxMRGhoaEAgNDQUGRlZWn1iNm1axc0Gg2Cg4ONXg5DuZNXjB/2pQIAgjydtLa5l+sdPLandm9fV3vdew4TEREREemCPVxMZG1iGorVGmnZwcYK1lZyRIcF6bT/xwPbIuyzvQCAv+7kP9IwQ2RcxSUa/H70Oto1doEMQFGJBm0buWilUWsEPo05i+/+uCSt69qsgZFzSpaooaMSv43vYepsEBHVGzKZDBDs3kL6ERMTAyEEHnvsMVy4cAGTJ09Gy5YtMXLkSMhkMkRHR+Pjjz9GixYt4O/vj+nTp8PX1xcDBw4EALRq1Qr9+/fH6NGjsWLFCqhUKowfPx5DhgyBr6+vaQunRxsOp+FeUQmCvBylIVQXvNQecacz8EqIn5TuicCGmD+oHd77+TgAwNWeT40SERERkX6xwcVE3ujuj0/+d0Za3jAm9JH2Dyz35NaA5ftx+ZNIveWN9GvSxhRsOXZDa13y9H5oUK6R7L2fj+E/SVel5ZnPt4HS2spoeSQiIiKiuic7OxtTp07F1atX4ebmhkGDBmHOnDnSEAdTpkxBXl4exowZg6ysLPTo0QPbt2+Hra2tdIy1a9di/Pjx6Nu3L+RyOQYNGoQlS5aYqkgGsfvvIXlHPNEMtorS79CDuzbB4K5NKqR9vkMjfBpzFr6udlBYccAHIiIiItIvNriYiI21HJ2buuJIWhYAoF1jl4fvUIl3+7bAkr/nZth67AYi2/tUswfVVE6hCit2X8Sh1Dt4s4c/nm6nW6zVGlGhsQUAZm89BW9nW7wa6gcfFzutxhYAGNipkV7yTURERETma/DgwRg8eHCV22UyGWbNmoVZs2ZVmcbNzQ3r1q0zRPbqjAuZ9wAAHRq7VpvWzsYK+97rA+tqhnAmIiIiIqoJNriY0PxB7TFhQwpeC/WrPnEloss1uEStO4LI9uzlom+LdpzFf5Ku4kZ2obTuzyt3sWlsKPaeu4kgLyc81+H+cAx5RSVIu5OPBvY2SM8pRNqd/EqPu/nINQDAl7svVtg2OeIxuNhxeAMiIiIiourculeEO3nFkMmA5h6OOu1T1guGiIiIiEjf2OBiQi28nLBtwpM13l9e7qmsx7ycHpKyftNoBGSyv8dUfwT9P9+LM+m5lW77x4oE6fU765OrPVaPwIb4YkhHRP+Ugj/O36oy3bGPwuFsy8YWIiIiIiJdnM8o7d3SpIE97GzYkEJEREREpsVBa83cF0M6AgDslby5qMw765MR8ME2+E/dhl9Trum8X2ZuYZWNLTUR6OkId0clvhzeGQ0dbSpN8/7TLdnYQkREZCFEFetVag0KVWqj5oXIkmXklPZEb9zAzsQ5ISIiIiJiDxez17iBPQAgM6fIxDmpe5LT7uL3o9el5QkbUtC/rfdDJ6NPuHgbO06lo6GjUmv9u31bYFK/INy6V4SuH+98pHworeX4Z3gQAMDJVoE9k3ujRC1w7FoW3vvPMVzPLoR/QweMfar5Ix2XiIiIzM9LKxJwMfMePnmhjamzQmQR7uQVAwDcHCp/qImIiIiIyJjY4GLmPP5uGLiWVYDtJ9LRp6UnbKzrR8elrcduYNeZTMx5oa00DvP+C7fw5e4LmPp0K7zw5YEK+1y+lY/HvCsffk0IgQkbkpGZe7/xqqGjEn9OC9NaPjO7P8avOwI7G2ssGdIRx69lw8lWgWW7LqBnUEMM6NgImbmFuJiZh2B/twrDmTkoS992T7bwwIGpfVFcooHCipN2EhERWYKqrujXswrwxCe7pGVPJyVuGCdLRBbtbn5pg0sDeza4EBEREZHpscHFzLmXG55q7Jok/LNfEN7p28KEOTKO349el+ZO+fnIVUS08UKQlxOW7roAAHh26T4p7ZMtGkrzplzLut/g8kvyNSzddR6dmzbA3BfbYeGOs1qNLQAw6kn/Cue2VVjhuxGPS8vtG7sCABYN7iCt83SyhaeTrU5lqS8NZERERPXZPzce1Vru1NQVN06YKDNEFqSsh0sD9nAhIiIiojqADS5mrqy3RJlFsefwRGBDtPR2wt38YmnIMXN14MItXLqVh+HBTaVeIoUqdYWJ6mNOZiDmZEalx3ipS2PIZDLsPXcT6xLT0D2wIeJOZyL6pxQAwMWbediUdLXSfUd2b6a3shAREVH9EnMyHe4ONjiXcQ8Jl25L66N6cxhRIn3JylcBANzsORciEREREZkeG1ws0KCvDsDP3R5Xbufjn/2C8Li/G0IC3E2drUd2+14Rhn2XCAD44/xNfP1qVwDAkbS7Oh8jrJUnnu/gizlbTwMAdp7OxGPTtj90n/+MDYWPqx2cbK0fOt8LERERUVW6f7ILJRpR6bYhjzc1cm6ILBd7uBARERFRXcKxjCzUldv5AEp7vAz55iDazojBu+uTIUTlN/510aoDl6XX5Xuv3Cw37NfzHXxhb6PdKDK63DBgy4Z1hkwmw6f/6IDKDO7aWGvZx8UWXZu5oZGrHZxt+ZQcERER1UxVjS3N3O3RyNXOyLkhslycw4WIiIiI6hL2cLEADR2VuHWv6KFp7hWV4Lej1/Hy403QPbChkXJWOz/sS9VafuHL/ZjULwgTNqQAAAZ09MUXQzpJ209ez8alm3l4roMv/i+ytda+TwV5VDi+lVyGd/u2QFc/N0z5+RgAYN3oED2XgoiIiKjUzkk9EehZOpecWm3izBBZiNt5bHAhIiIiorqDDS4WYMOYEKzcn4qo3oF4buk+6aajMqdv5Ji8wSXudAZ8Xe3QyscZR9LuYtS//8RHz7fBlZu5UBaUphFCIK9Y+5eI5LQsvPr9IWn5MW8nre1tfF3QxtelyvO2beSME9dyAAAb3wpF4wZ28HW1wwudbXH8WjZCAtzh39BBT6UkIiKi+ujvKecq5cueLUR6dTevWOr97tfQvOeuJCIiIiLLwAYXCxDo6Yg5L7QDADjaWj+0weXizTwUqtToNCsWBSo1Nr4Vim7+bgbP4y/J1/DV7ouIaOOFJbsuACidkH7l/ssAgHfXJ/+d0ho9e+ZhY9L1ao/5Rnf/atOUt3RoZyyOPYeJ/YK0GlYUVnLMHtj2kY5FREREpIvHmzXA4ct30cbXGfY2/OpNpE+7z2UCKB2qj8MBExEREVFdwLs+C9OlaQNp/pbKrD+UhvWH0qTlwV8n4M9pYXB3sMHmI9fQsakrmns46i0/QghsPnIN/9x0FABwNiNX2lbW2PKg/kv2S6/dHGywaHAHjFx5GM3c7XH577J9ObwzbBWPNqG9f0MHLBnaqfqERERERHrw+hPN8NHzbVCoUsNK/pCuL0T0yK7czsPEn0rvMdo0qrqXOxERERGRMbHBxcKMejIAm5OvScsNHW2Q+EEY1h1Kw/RfTlS6z3v/OYah3Zrin5uOwtnWGkdnhEP2sPEwHsHsLafxw/7U6hNW4YVOjdD7MU+cmBkBBxsrrD/0Fxo3sEPPSuZkISIiIqpLgv/uRfyoD4kQUfWWxF2QXg/q3MiEOSEiIiIiuo8NLhamta8zzs95GnvO3kTqrTz0b+sNK7kMGo2ocp+4M5m4da907OOcwhL4T92GP6b0RhO3Rx8HOSu/GL8kX8NzHXxx9W6BTo0tOyb2xLmMXAS42eGZZQek9SEBbninTyAAwFFZ+qc6LLjpI+eJiIiIyBRcOYk3kcGcvJ4NAPjh9a7o09LLxLkhIiIiIirFBhcLpLCSI6y19k2HtZV2jxVHpTUSP+iLNjNiAABHr2ZrbX9yQTx2TnoKgZ66Dy825JsEHLx0BwDw0e+ntLZZyWUY0zMAxSUavBLih0KVGu4ONvB0tgUABHk5QaVSYUr7Ejj4tcOQYD8orfk0KBEREZkXlfr+Qy6ezkoT5oTIshSq1NAIIc2FlJ5TCABo5ProD4kRERERERkKG1zqiUGdG2Pjn1chlwHLh3WGt7Mt5NWMJR722R4c+yhcpwko9567KTW2PKhHYEOsGRWsUz4bOQDPdGsCBRtbiIiIyMw1bmBn6iwQWYQStQb9P9+L2/eK8e2IrmjoaIOsfBUAwNvF1sS5IyIiIiK6T67vA86bNw+PP/44nJyc4OnpiYEDB+Ls2bNaaQoLCxEVFQV3d3c4Ojpi0KBByMjI0EqTlpaGyMhI2Nvbw9PTE5MnT0ZJSYlWmt27d6Nz585QKpUIDAzEqlWr9F0ci2GrsMKvUd3x37e7w9fVTmpsifvnU1rpLs19Bt2auUnL7T/agWtZBVUe92ZuEfaeu4kRKw9VmeaDZ1rVMvdERERE5sVWIWdvXSI9+fPKXVy+nY/cohIM+eYgwj7bCwCwt7GCsy2fISQiIiKiukPvDS579uxBVFQUDh48iNjYWKhUKoSHhyMvL09KM3HiRPz+++/YtGkT9uzZg+vXr+PFF1+UtqvVakRGRqK4uBgHDhzAv//9b6xatQoffvihlCY1NRWRkZHo3bs3UlJSEB0djVGjRiEmJkbfRbJozT0cMS2ytEHkX+FBkMtlmPtiW6003+69VOm+Qgj8c9NRvPbDIYgqpoiJaOOFlt5Oes0zERERUV01e2BbONla46cxoabOCpHFiD+bWen6/GI1ZLKH99onIiIiIjImvT8OtH37dq3lVatWwdPTE0lJSejZsyeys7Px/fffY926dejTpw8AYOXKlWjVqhUOHjyIkJAQ7NixA6dOncLOnTvh5eWFjh07Yvbs2Xjvvffw0UcfwcbGBitWrIC/vz8WLVoEAGjVqhX27duHxYsXIyIiQt/Fsmgju/vjyRYeCPIqna8l0NMJJ2dGSPO7pN7Kq7DPnnM3MeKHir1aNr/9BDo3bWDYDBMRERHVUa+G+OHVED9TZ4PIIgghcDYjF1/vqfwBsCdbNDRyjoiIiIiIHs7g/a+zs0snY3dzKx2mKikpCSqVCmFhYVKali1bomnTpkhISEBISAgSEhLQrl07eHndn/g9IiIC48aNw8mTJ9GpUyckJCRoHaMsTXR0dJV5KSoqQlFRkbSck5MDAFCpVFCpVLUuqzkLcLfVGrLNRg6sGNYRY9el4GZuoVZ8hBCVNrb8d2wI2vo41jiWZfvV97qwBKxLy8G6tBysS8vBurQMpqpH/t2QuRn+bWKl60MC3DAtsrWRc0NERERE9HAGbXDRaDSIjo5G9+7d0bZt6TBV6enpsLGxgaurq1ZaLy8vpKenS2nKN7aUbS/b9rA0OTk5KCgogJ1dxUlK582bh5kzZ1ZYv2PHDtjb29eskBYsowAArHHqRi7++/s2xF2Xw0kh4GANAPfHJHdTCkzvpEba0X1IO1r788bGxtb+IFQnsC4tB+vScrAuLQfr0jIYux7z8/ONej6i2pDJZGjbyAV7zt0EAMhlgObvoYynPt0Kj3HoYiIiIiKqYwza4BIVFYUTJ05g3759hjyNzqZOnYpJkyZJyzk5OWjSpAnCw8Ph7OxswpzVTSq1BnNTdgIAphyq/E8loKEDJvRpjmfaedf+fCoVYmNj0a9fPygUilofj0yHdWk5WJeWg3VpOViXlsFU9VjWw5vIXDRucP9Bup5BHth9trTxxc3BxlRZIiIiIiKqksEaXMaPH48tW7Zg7969aNy4sbTe29sbxcXFyMrK0urlkpGRAW9vbynNoUPaQ1ZlZGRI28r+L1tXPo2zs3OlvVsAQKlUQqlUVlivUCj4g0UlqgvJ+N6B+FfEYwY4L+vDUrAuLQfr0nKwLi0H69IyGLse+TdD5ibQ01F63dzDUWpwcXdkgwsRERER1T1yfR9QCIHx48fjv//9L3bt2gV/f3+t7V26dIFCoUBcXJy07uzZs0hLS0NoaCgAIDQ0FMePH0dmZqaUJjY2Fs7OzmjdurWUpvwxytKUHYP0Y+4L7arcFsQu/EREREREZEBDuzVFWCsvzHy+DTyd7j88Z29j8OlIiYiIiIgemd4bXKKiorBmzRqsW7cOTk5OSE9PR3p6OgoKCgAALi4uePPNNzFp0iTEx8cjKSkJI0eORGhoKEJCQgAA4eHhaN26NV599VUcPXoUMTExmDZtGqKioqQeKmPHjsWlS5cwZcoUnDlzBl9++SU2btyIiRMn6rtI9Vp4Gy+pG//nL3fEj292k7Y9FeRhqmwREREREVE9YKuwwncjumLEE80Q2d4HANCukYuJc0VEREREVDm9Pxb01VdfAQB69eqltX7lypV4/fXXAQCLFy+GXC7HoEGDUFRUhIiICHz55ZdSWisrK2zZsgXjxo1DaGgoHBwcMGLECMyaNUtK4+/vj61bt2LixIn44osv0LhxY3z33XeIiIjQd5HqtYaOSux7r4/Wur2Te8PbxRY21npvryMiIiIiIqpU4wb2SPygL5xs2buFiIiIiOomgwwpVtm/ssYWALC1tcXy5ctx584d5OXlYfPmzdLcLGX8/Pywbds25Ofn4+bNm1i4cCGsrbW/WPfq1QvJyckoKirCxYsXtc5BhtPU3Z6NLUREREREBqRWqzF9+nT4+/vDzs4OzZs3x+zZsyGEkNIIIfDhhx/Cx8cHdnZ2CAsLw/nz57WOc+fOHQwfPhzOzs5wdXXFm2++iXv37hm7OHrj5WzL4cSIiIiIqM7ir+ZERERERER1zPz58/HVV19h2bJlOH36NObPn48FCxZg6dKlUpoFCxZgyZIlWLFiBRITE+Hg4ICIiAgUFhZKaYYPH46TJ08iNjYWW7Zswd69ezFmzBhTFImIiIiIyOLx0SAiIiIiIqI65sCBAxgwYAAiIyMBAM2aNcP69etx6NAhAKW9Wz7//HNMmzYNAwYMAACsXr0aXl5e+OWXXzBkyBCcPn0a27dvx+HDh9G1a1cAwNKlS/HMM89g4cKF8PX1NU3hiIiIiIgsFHu4EBERERER1TFPPPEE4uLicO7cOQDA0aNHsW/fPjz99NMAgNTUVKSnpyMsLEzax8XFBcHBwUhISAAAJCQkwNXVVWpsAYCwsDDI5XIkJiYasTRERERERPUDe7gQERERERHVMe+//z5ycnLQsmVLWFlZQa1WY86cORg+fDgAID09HQDg5eWltZ+Xl5e0LT09HZ6enlrbra2t4ebmJqV5UFFREYqKiqTlnJwcAIBKpYJKpdIp72XpdE1fHzFGumOsdMdYVY8x0h1jpTvGqnqMke4YK90YOk61OS4bXIiIiIiIiOqYjRs3Yu3atVi3bh3atGmDlJQUREdHw9fXFyNGjDDYeefNm4eZM2dWWL9jxw7Y29s/0rFiY2P1lS2LxRjpjrHSHWNVPcZId4yV7hir6jFGumOsdGOoOOXn59d4Xza4EBERERER1TGTJ0/G+++/jyFDhgAA2rVrhytXrmDevHkYMWIEvL29AQAZGRnw8fGR9svIyEDHjh0BAN7e3sjMzNQ6bklJCe7cuSPt/6CpU6di0qRJ0nJOTg6aNGmC8PBwODs765R3lUqF2NhY9OvXDwqFQucy1yeMke4YK90xVtVjjHTHWOmOsaoeY6Q7xko3ho5TWS/vmmCDCxERERERUR2Tn58PuVx7yk0rKytoNBoAgL+/P7y9vREXFyc1sOTk5CAxMRHjxo0DAISGhiIrKwtJSUno0qULAGDXrl3QaDQIDg6u9LxKpRJKpbLCeoVC8cg3szXZp75hjHTHWOmOsaoeY6Q7xkp3jFX1GCPdMVa6MVScanNMNrgQERERERHVMc899xzmzJmDpk2bok2bNkhOTsZnn32GN954AwAgk8kQHR2Njz/+GC1atIC/vz+mT58OX19fDBw4EADQqlUr9O/fH6NHj8aKFSugUqkwfvx4DBkyBL6+viYsHRERERGRZWKDCxERERERUR2zdOlSTJ8+HW+//TYyMzPh6+uLt956Cx9++KGUZsqUKcjLy8OYMWOQlZWFHj16YPv27bC1tZXSrF27FuPHj0ffvn0hl8sxaNAgLFmyxBRFIiIiIiKyeGxwISIiIiIiqmOcnJzw+eef4/PPP68yjUwmw6xZszBr1qwq07i5uWHdunUGyCERERERET1IXn0SIiIiIiIiIiIiIiIieph63cNFCAGgdHJJMj2VSoX8/Hzk5ORwUigzx7q0HKxLy8G6tBysS8tgqnos+95b9j2YqDo1uWfi51T1GCPdMVa6Y6yqxxjpjrHSHWNVPcZId4yVbgwdp9rcN9XrBpfc3FwAQJMmTUycEyIiIiIi48nNzYWLi4ups0FmgPdMRERERFRf1eS+SSbq8eNtGo0G169fh5OTE2QymamzU+/l5OSgSZMm+Ouvv+Ds7Gzq7FAtsC4tB+vScrAuLQfr0jKYqh6FEMjNzYWvry/kco4uTNWryT0TP6eqxxjpjrHSHWNVPcZId4yV7hir6jFGumOsdGPoONXmvqle93CRy+Vo3LixqbNBD3B2duYHioVgXVoO1qXlYF1aDtalZTBFPbJnCz2K2twz8XOqeoyR7hgr3TFW1WOMdMdY6Y6xqh5jpDvGSjeGjFNN75v4WBsREREREREREREREVEtscGFiIiIiIiIiIiIiIioltjgQnWGUqnEjBkzoFQqTZ0VqiXWpeVgXVoO1qXlYF1aBtYjWTL+fVePMdIdY6U7xqp6jJHuGCvdMVbVY4x0x1jppi7HSSaEEKbOBBERERERERERERERkTljDxciIiIiIiIiIiIiIqJaYoMLERERERERERERERFRLbHBhYiIiIiIiIiIiIiIqJbY4EJERERERERERERERFRLbHAhvZo3bx4ef/xxODk5wdPTEwMHDsTZs2e10hQWFiIqKgru7u5wdHTEoEGDkJGRoZXm3XffRZcuXaBUKtGxY8cK59m9ezcGDBgAHx8fODg4oGPHjli7dq0hi1avGKsey7tw4QKcnJzg6uqq59LUb8asSyEEFi5ciKCgICiVSjRq1Ahz5swxVNHqHWPWZUxMDEJCQuDk5AQPDw8MGjQIly9fNlDJ6h991OXRo0cxdOhQNGnSBHZ2dmjVqhW++OKLCufavXs3OnfuDKVSicDAQKxatcrQxatXjFWXmzdvRr9+/eDh4QFnZ2eEhoYiJibGKGUky2HM68jGjRvRsWNH2Nvbw8/PD59++qlOedy0aRNatmwJW1tbtGvXDtu2bdPavnnzZoSHh8Pd3R0ymQwpKSk6l19XlhCn119/HTKZTOtf//79dQ+CjiwhVhkZGXj99dfh6+sLe3t79O/fH+fPn9c9CDqo69d9IQQ+/PBD+Pj4wM7ODmFhYRViMGfOHDzxxBOwt7c32P2aJcSpWbNmFd57n3zySc0C8hCWEKsjR46gX79+cHV1hbu7O8aMGYN79+7VLCBVMFacbty4gWHDhiEoKAhyuRzR0dE653H58uVo1qwZbG1tERwcjEOHDmlt/+abb9CrVy84OztDJpMhKyvrkePwMJYQo169elV4340dO/bRg1ENS4jVxYsX8cILL0j3FIMHD65wTa6tun5/ZKxrHhtcSK/27NmDqKgoHDx4ELGxsVCpVAgPD0deXp6UZuLEifj999+xadMm7NmzB9evX8eLL75Y4VhvvPEGXn755UrPc+DAAbRv3x4///wzjh07hpEjR+K1117Dli1bDFa2+sRY9VhGpVJh6NChePLJJ/VelvrOmHU5YcIEfPfdd1i4cCHOnDmD3377Dd26dTNIueojY9VlamoqBgwYgD59+iAlJQUxMTG4detWpcehmtFHXSYlJcHT0xNr1qzByZMn8X//93+YOnUqli1bJqVJTU1FZGQkevfujZSUFERHR2PUqFH8oV6PjFWXe/fuRb9+/bBt2zYkJSWhd+/eeO6555CcnGzU8pJ5M9Z15H//+x+GDx+OsWPH4sSJE/jyyy+xePFirb/pyhw4cABDhw7Fm2++ieTkZAwcOBADBw7EiRMnpDR5eXno0aMH5s+fX8MoVM8S4gQA/fv3x40bN6R/69evr0E0Hs7cYyWEwMCBA3Hp0iX8+uuvSE5Ohp+fH8LCwrTKUFt1/bq/YMECLFmyBCtWrEBiYiIcHBwQERGBwsJCKU1xcTH+8Y9/YNy4cXqLy4MsIU4AMGvWLK333jvvvKOnCN1n7rG6fv06wsLCEBgYiMTERGzfvh0nT57E66+/bpZxKioqgoeHB6ZNm4YOHTronL+ffvoJkyZNwowZM3DkyBF06NABERERyMzMlNLk5+ejf//++OCDD2oZjcpZQowAYPTo0VrvuwULFtQiKpUz91jl5eUhPDwcMpkMu3btwv79+1FcXIznnnsOGo1GDxEqVdfvj4x2zRNEBpSZmSkAiD179gghhMjKyhIKhUJs2rRJSnP69GkBQCQkJFTYf8aMGaJDhw46neuZZ54RI0eO1Eu+SZuh63HKlCnilVdeEStXrhQuLi76zj6VY6i6PHXqlLC2thZnzpwxWN5Jm6HqctOmTcLa2lqo1Wpp3W+//SZkMpkoLi7Wf0Go1nVZ5u233xa9e/eWlqdMmSLatGmjlebll18WERERei4BlTFUXVamdevWYubMmfrJONVLhrqODB06VLz00kta65YsWSIaN24sNBpNlfkZPHiwiIyM1FoXHBws3nrrrQppU1NTBQCRnJz8sCLqhTnGacSIEWLAgAG6FE+vzC1WZ8+eFQDEiRMnpO1qtVp4eHiIb7/9tvoC11Bduu5rNBrh7e0tPv30U2ldVlaWUCqVYv369RXSG/N+zRzj5OfnJxYvXqxzGfXF3GL19ddfC09PT637jWPHjgkA4vz58zqW+tEZ43vaU089JSZMmKBTfrp16yaioqKkZbVaLXx9fcW8efMqpI2PjxcAxN27d3U6dk2ZY4we5Xj6ZG6xiomJEXK5XGRnZ0tpsrKyhEwmE7GxsTqdoybq0v2RMa957OFCBpWdnQ0AcHNzA1DaSqlSqRAWFialadmyJZo2bYqEhIRan6vsPKRfhqzHXbt2YdOmTVi+fLn+MkxVMlRd/v777wgICMCWLVvg7++PZs2aYdSoUbhz545+C0ASQ9Vlly5dIJfLsXLlSqjVamRnZ+PHH39EWFgYFAqFfgtBAPRXlw9eBxMSErSOAQARERG1vt5S1QxVlw/SaDTIzc3l9x6qFUNdR4qKimBra6u1zs7ODlevXsWVK1eq3K+ufmaZa5x2794NT09PPPbYYxg3bhxu376tc95qytxiVVRUBABax5bL5VAqldi3b5/O+XtUdem6n5qaivT0dK39XFxcEBwcbDHvPWPH6ZNPPoG7uzs6deqETz/9FCUlJTqUtnbMLVZFRUWwsbGBXH7/J0k7OzsAMMv3Xk0UFxcjKSlJ69xyuRxhYWEmfe+Za4zWrl2Lhg0bom3btpg6dSry8/NrdW5dmFusioqKIJPJoFQqpTS2traQy+UW8b7T5f7ImNc8NriQwWg0GkRHR6N79+5o27YtACA9PR02NjYVxsDz8vJCenp6jc+1ceNGHD58GCNHjqxNlqkShqzH27dv4/XXX8eqVavg7Oysz2xTJQxZl5cuXcKVK1ewadMmrF69GqtWrUJSUhJeeuklfRaB/mbIuvT398eOHTvwwQcfQKlUwtXVFVevXsXGjRv1WQT6m77q8sCBA/jpp58wZswYaV16ejq8vLwqHCMnJwcFBQX6LQgZtC4ftHDhQty7dw+DBw/WW/6pfjHkdSQiIgKbN29GXFwcNBoNzp07h0WLFgEoHZe8KlV9ZtXmHqG2zDVO/fv3x+rVqxEXF4f58+djz549ePrpp6FWq3XO36Myx1iV/cAzdepU3L17F8XFxZg/fz6uXr360OPWRl277pcd31Lfe8aO07vvvosNGzYgPj4eb731FubOnYspU6boVugaMsdY9enTB+np6fj0009RXFyMu3fv4v333wfw8Pd0bRjze5oubt26BbVaXafee+Yao2HDhmHNmjWIj4/H1KlT8eOPP+KVV16p1bmrY46xCgkJgYODA9577z3k5+cjLy8P//rXv6BWqy3ifafL/ZExr3lscCGDiYqKwokTJ7BhwwaDnic+Ph4jR47Et99+izZt2hj0XPWRIetx9OjRGDZsGHr27Kn3Y1NFhqxLjUaDoqIirF69Gk8++SR69eqF77//HvHx8RUmSKPaM2RdpqenY/To0RgxYgQOHz6MPXv2wMbGBi+99BKEEHo/X32nj7o8ceIEBgwYgBkzZiA8PFyPuaNHYay6XLduHWbOnImNGzfC09Ozxuei+s3Q3+/Gjx+PZ599FjY2NggJCcGQIUMAlD5tmZaWBkdHR+nf3Llz9Z4HfTHXOA0ZMgTPP/882rVrh4EDB2LLli04fPgwdu/erfdylDHHWCkUCmzevBnnzp2Dm5sb7O3tER8fj6efflrryXt9MuV1f+3atVpx+uOPP2qcB0Mz1zhNmjQJvXr1Qvv27TF27FgsWrQIS5culXpTGYI5xqpNmzb497//jUWLFsHe3h7e3t7w9/eHl5eXRb73/vjjD604rV27tsZ5MCRzjdGYMWMQERGBdu3aYfjw4Vi9ejX++9//4uLFizUpgk7MMVYeHh7YtGkTfv/9dzg6OsLFxQVZWVno3Lmz2b/vKrs/MvU1z9qoZ6N6Y/z48diyZQv27t2Lxo0bS+u9vb1RXFyMrKwsrdbMjIwMeHt7P/J59uzZg+eeew6LFy/Ga6+9po+sUzmGrsddu3bht99+w8KFCwGUTlyp0WhgbW2Nb775Bm+88YbeylLfGboufXx8YG1tjaCgIGldq1atAABpaWl47LHHal8IAmD4uly+fDlcXFy0Jhpcs2YNmjRpgsTERISEhOilHKSfujx16hT69u2LMWPGYNq0aVrbvL29kZGRobUuIyMDzs7O0rANpB+GrssyGzZswKhRo7Bp06YKQ3wQ6crQ1xGZTIb58+dj7ty5SE9Ph4eHB+Li4gAAAQEBaNCgAVJSUqT0ZUM/VPWZVZN7BH2wpDgFBASgYcOGuHDhAvr27atzHnVlzrHq0qULUlJSkJ2djeLiYnh4eCA4OBhdu3Z9lBDoxNTX/eeffx7BwcHStkaNGklPNWdkZMDHx0drv44dO9a2yDViSXEKDg5GSUkJLl++bJD7IXOO1bBhwzBs2DBkZGTAwcEBMpkMn332GQICAmocj6oY63taVbp27ar1GeXl5QWlUgkrK6s6c92zpBiV/U1euHABzZs3f6R86MKcYxUeHo6LFy/i1q1bsLa2hqurK7y9vc36fVfV/ZHJr3k1mvmFqAoajUZERUUJX19fce7cuQrbyyZH+s9//iOtO3PmTI0mW4+PjxcODg5i2bJless/lTJWPZ46dUocP35c+vfxxx8LJycncfz4cXHnzh29lqm+MlZdxsTECADiwoUL0rqUlBQBQJw9e1Y/hannjFWXkyZNEt26ddNad/36dQFA7N+/v/YFIb3V5YkTJ4Snp6eYPHlypeeZMmWKaNu2rda6oUOHPnSiU3o0xqpLIYRYt26dsLW1Fb/88ot+C0H1hjG/pz/o1VdfFaGhoQ9NM3jwYPHss89qrQsNDdWaDL5MamqqACCSk5N1Ov+jsKQ4lfnrr7+ETCYTv/76q0750JUlxurcuXNCLpeLmJgYnfKhi7p83S+bQHjhwoXSuuzsbINMIFwdS4pTmTVr1gi5XK73e1tLjNX3338v7O3t9TopvDG/p5V51EnOx48fLy2r1WrRqFEjrQnhy8THxwsAeo2PEJYVozL79u0TAMTRo0d1OoeuLDFWcXFxQiaTiTNnzuh0Dl3U5fsjY17z2OBCejVu3Djh4uIidu/eLW7cuCH9y8/Pl9KMHTtWNG3aVOzatUv8+eefIjQ0tMKX5fPnz4vk5GTx1ltviaCgIJGcnCySk5NFUVGREEKIXbt2CXt7ezF16lSt89y+fduo5bVUxqrHBxnyC3x9Zay6VKvVonPnzqJnz57iyJEj4s8//xTBwcGiX79+Ri2vJTNWXZZ96Zo5c6Y4d+6cSEpKEhEREcLPz0/rXFRz+qjL48ePCw8PD/HKK69oHSMzM1NKc+nSJWFvby8mT54sTp8+LZYvXy6srKzE9u3bjVpeS2asuly7dq2wtrYWy5cv10qTlZVl1PKSeTPWdeTmzZviq6++EqdPnxbJycni3XffFba2tiIxMfGh+du/f7+wtrYWCxcuFKdPnxYzZswQCoVCHD9+XEpz+/ZtkZycLLZu3SoAiA0bNojk5GRx48YNxunvOOXm5op//etfIiEhQaSmpoqdO3eKzp07ixYtWojCwkK9xckSYiWEEBs3bhTx8fHi4sWL4pdffhF+fn7ixRdf1GOU6v51/5NPPhGurq7i119/FceOHRMDBgwQ/v7+oqCgQEpz5coVkZycLGbOnCkcHR2lOsrNzWWc/o7TgQMHxOLFi0VKSoq4ePGiWLNmjfDw8BCvvfaa3mJUxtxjJYQQS5cuFUlJSeLs2bNi2bJlws7OTnzxxRd6jJLx4iSEkN4TXbp0EcOGDRPJycni5MmTD83fhg0bhFKpFKtWrRKnTp0SY8aMEa6uriI9PV1Kc+PGDZGcnCy+/fZbAUDs3btXJCcn6+23L3OP0YULF8SsWbPEn3/+KVJTU8Wvv/4qAgICRM+ePfUSn/LMPVZCCPHDDz+IhIQEceHCBfHjjz8KNzc3MWnSJD1FqFRdvz8y1jWPDS6kVwAq/bdy5UopTUFBgXj77bdFgwYNhL29vXjhhRcq3CQ99dRTlR4nNTVVCCHEiBEjKt3+1FNPGa+wFsxY9fggNrjonzHr8tq1a+LFF18Ujo6OwsvLS7z++utsBNUjY9bl+vXrRadOnYSDg4Pw8PAQzz//vDh9+rSRSmr59FGXM2bMqPQYfn5+WueKj48XHTt2FDY2NiIgIEDrHFR7xqrLqt63I0aMMF5hyewZ6zpy8+ZNERISIhwcHIS9vb3o27evOHjwoE553LhxowgKChI2NjaiTZs2YuvWrVrbV65cWem5Z8yYUZvQaDH3OOXn54vw8HDh4eEhFAqF8PPzE6NHj9b6wUVfzD1WQgjxxRdfiMaNGwuFQiGaNm0qpk2bVuXDYTVV16/7Go1GTJ8+XXh5eQmlUin69u1boYd6Vfff8fHxtYiMNnOPU1JSkggODhYuLi7C1tZWtGrVSsydO1fvDZ1CmH+shCjtpebm5iZsbGxE+/btxerVq2sTkkoZM066pKnM0qVLRdOmTYWNjY3o1q1bhc+2qs6vr+/05h6jtLQ00bNnT+Hm5iaUSqUIDAwUkydPFtnZ2bUNTQXmHishhHjvvfeEl5eXUCgUokWLFmLRokVCo9HUJiwVGCtONb0/MtY1T/Z3MIiIiIiIiIiIiIiIiKiG5KbOABERERERERERERERkbljgwsREREREREREREREVEtscGFiIiIiIiIiIiIiIioltjgQkREREREREREREREVEtscCEiIiIiIiIiIiIiIqolNrgQERERERERERERERHVEhtciIiIiIiIiIiIiIiIaokNLkRERERERERERERERLXEBhciIiIiIiIiIiIiIqJaYoMLERERERERERERERFRLbHBhYiIiIiIiIiIiIiIqJbY4EJERERERERERERERFRL/w/vEzhnL/vp/QAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -746,22 +638,12 @@
"source": [
"print(\"Number of segments:\", len(ts.segments))\n",
"ts.plot(n_segments=10)"
- ],
- "metadata": {
- "collapsed": false,
- "pycharm": {
- "name": "#%%\n"
- }
- }
+ ]
},
{
"cell_type": "markdown",
"id": "4d4fdce3",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"Dataset consists of 4k segments of 1-4 years length. As the plot suggests, the behavior of the segments are different across the dataset, and it might be hard to predict all of them accurately. Let's try to evaluate the SMAPE on the backtest using some baseline model."
]
@@ -770,11 +652,7 @@
"cell_type": "code",
"execution_count": 26,
"id": "75132e0d",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"pipeline = Pipeline(model=NaiveModel(), transforms=[], horizon=30)"
@@ -783,11 +661,7 @@
{
"cell_type": "markdown",
"id": "8b293b2b",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"It takes about 2 minutes even for naive model to evaluate the performance on this dataset, imagine how long it takes for more complex one."
]
@@ -796,31 +670,27 @@
"cell_type": "code",
"execution_count": 27,
"id": "4d37dc70",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.5s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 3.0s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 4.9s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 4.9s finished\n",
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.7s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 3.6s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 5.4s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 5.5s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.2s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 7.5s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 10.8s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 10.8s finished\n",
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 3.5s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 7.2s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 10.7s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 10.7s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 2.7s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 5.4s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 8.0s remaining: 0.0s\n",
- "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 8.1s finished\n"
+ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 5.5s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 8.3s remaining: 0.0s\n",
+ "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 8.4s finished\n"
]
}
],
@@ -834,9 +704,6 @@
"metadata": {
"slideshow": {
"slide_type": "slide"
- },
- "pycharm": {
- "name": "#%% md\n"
}
},
"source": [
@@ -847,11 +714,7 @@
"cell_type": "code",
"execution_count": 28,
"id": "9be354ae",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"from etna.analysis import metric_per_segment_distribution_plot\n",
@@ -862,16 +725,14 @@
"cell_type": "code",
"execution_count": 29,
"id": "0cf82f66",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [
{
"data": {
- "text/plain": "",
- "image/png": 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"
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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -885,16 +746,14 @@
"cell_type": "code",
"execution_count": 30,
"id": "19c3dfd9",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [
{
"data": {
- "text/plain": "",
- "image/png": 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"
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -907,11 +766,7 @@
{
"cell_type": "markdown",
"id": "9316f3f0",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"Most of the segments can be forecasted with SMAPE less than 10, however there is a list of segments with SMAPE greater than 20 which we want to catch and analyze separately."
]
@@ -920,11 +775,7 @@
"cell_type": "code",
"execution_count": 31,
"id": "5c3709f5",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -943,11 +794,7 @@
{
"cell_type": "markdown",
"id": "7a658091",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"### 2.2 Loading pretrained analyzer \n\n
\n \n \n segment \n SMAPE \n fold_number \n \n \n \n \n 412 \n D137 \n 57.452473 \n 1.0 \n \n \n 4072 \n D86 \n 52.144064 \n 1.0 \n \n \n 734 \n D166 \n 45.620776 \n 1.0 \n \n \n 3387 \n D4047 \n 29.501460 \n 1.0 \n \n \n 1310 \n D2178 \n 29.205434 \n 1.0 \n \n \n 4061 \n D85 \n 22.579621 \n 1.0 \n \n \n 1205 \n D2083 \n 22.547771 \n 1.0 \n \n \n 3333 \n D4 \n 20.994039 \n 1.0 \n \n \n 357 \n D132 \n 15.903925 \n 1.0 \n \n \n 2778 \n D35 \n 14.327464 \n 1.0 \n \n \n
\n
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " segment \n",
+ " SMAPE \n",
+ " fold_number \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 412 \n",
+ " D137 \n",
+ " 57.452473 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 4072 \n",
+ " D86 \n",
+ " 52.144064 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 734 \n",
+ " D166 \n",
+ " 45.620776 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 3387 \n",
+ " D4047 \n",
+ " 29.501460 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 1310 \n",
+ " D2178 \n",
+ " 29.205434 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 4061 \n",
+ " D85 \n",
+ " 22.579621 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 1205 \n",
+ " D2083 \n",
+ " 22.547771 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 3333 \n",
+ " D4 \n",
+ " 20.994039 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 357 \n",
+ " D132 \n",
+ " 15.903925 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 2778 \n",
+ " D35 \n",
+ " 14.327464 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
",
- "image/png": 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1s2bN0kMPPdTp+cWLFys9PXJ3ZVZUVERsX9GQ6PM7lmSbe7LN15CM807GOUvJO28p+eaebPNtLxJzX77HIsmmwjS/BuUGTnAskob1qNfChQvDvv/2kvVYG/Nubm6O8kgAAACA2OQJlilLxsyYdKddxTmp2lPfqqoDTQRjwiSkwZiioiJJUk1NjYqLi83na2pqNGzYMHOdvXv3dnidx+PRwYMHzdcXFRWppqamwzrG4+OtYyw/mgceeEDTpk0zHzc0NKhPnz4aPXq0srOzuzPVk+J2u1VRUaFrrrlGDocj7PuLtESf37Ek29yTbb6GZJx3Ms5ZSt55S8k392Sbb3uRnPvHb34mbd2ufxtRqvvKzw7rvrqSrMf6yHkbmeEAAAAAOjLKlCVjzxgpUKpsT32rqvY16fy+edEeTkIKaTCmtLRURUVFWrJkiRl8aWho0IoVK3TXXXdJksrKylRXV6fVq1drxIgRkqSlS5fK5/Pp4osvNtf56U9/KrfbbZ4sV1RUaMCAAcrLyzPXWbJkiaZOnWruv6KiQmVlZV2OLyUlRSkpKZ2edzgcET0pj/T+Ii3R53csyTb3ZJuvIRnnnYxzlpJ33lLyzT3Z5tteJOa+7WCLJOnMwqyov8/JeqyNeSfj3AEAAIAT4Q2WKbMncTDmw68O0DcmjLpdAK+xsVHr1q3TunXrJElVVVVat26dtm/fLovFoqlTp+rnP/+5/v73v+uTTz7RLbfcopKSEt10002SpEGDBunaa6/VHXfcoZUrV+qDDz7QlClTNH78eJWUlEiSbr75ZjmdTk2cOFEbNmzQn//8Zz311FMdslp++MMfatGiRXrsscf02Wef6cEHH9THH3+sKVOmnPq7AgAAgJDZGvwyf3qPjCiPBAAAAACOzm0GY5KvZ4wUCMZIIhgTRt3OjPn444911VVXmY+NAMmtt96quXPn6r777lNTU5MmTZqkuro6XX755Vq0aJFSU1PN18ybN09TpkzR1VdfLavVqnHjxunpp582l+fk5Gjx4sWaPHmyRowYoZ49e2rGjBmaNGmSuc6ll16q+fPna/r06frJT36is846S2+88YbOPffck3ojAAAAEHouj1c7awN9Skp7EYwBAAAAEJu8wZ4x9iTsGSNJ/YPna1sIxoRNt4MxI0eOlN/v73K5xWLRzJkzNXPmzC7Xyc/P1/z584+5n6FDh+q999475jrf/va39e1vf/vYAwYAAEDU7DjYLJ9fynDa1Cuzc7lYAAAAAIgF7qTvGZMpKVDZwOfzy5qk70M4JWfOFQAAACKiav/hrBiLhS/zAAAAAGKTN8nLlPXJS5PdalGL26uaQ63RHk5CSs5PFgAAACKian+jJPrFAAAAAIhtHjMYk5w3kdltVvXtkS5JqtpHqbJwIBgDAACAsDEyY/r3JBgDAAAAIHZ5vMndM0Y6fN5G35jwIBgDAACAsDEyY0p7EYwBAAAAELuSvUyZJJUawRgyY8IieT9ZAAAACLuq4B1VlCkDAAAAEMvcwcwYW5KWKZOk0p6Zkg7fVIfQIhgDAACAsGhyeVTT4JJ0+A4rAAAAAIhFRmaMI4nLlBnnbVWUKQsLe7QHAAAAgFPz6a56zfjbp2pu857wa/x+vxoO2fTrLR/KYgnPyUZb8M6yvHSHctOdYdkHAAAAAISCJxiMSebMmP7B8tLbDjbr2ieXh2y7GSl2/eIbQzSgKCtk24xHBGMAAADi3GtrdmnN9rqTeKVFe5rDn34+ol9e2PcBAAAAAKfC4zUyY5K3mFRBVop656VpZ22LPqs+FNJtv/rxDk2/fnBItxlvCMYAAADEuUaXW5L0nQv66IbzSk7oNR6vRytXrNRFF18kuy18XwmtFmlY39ywbR8AAAAAQoHMGMlisehvky/Tpj2hC8S8s3mvXnq/itJnIhgDAAAQ95pcgfJkg0uydflZPU/oNW63W/Wb/brsjB5yOBzhHB4AAAAAxDyvL1Bm2Z7EwRhJ6pGZosvPSgnZ9iwWEYwJSt6cKwAAgATR1OaRJKU7bVEeCQAAAADEJ6NMmT2Jy5SFg9GHZvvBZrmDfUWTFZ8sAACAONfkCgRjMlNIegYAAACAk2GUKUv2zJhQK8xKVZrDJo/Pr521LdEeTlQRjAEAAIhzjcEyZekEYwAAAADgpHiMMmU2gjGhZLVadHrPQHZM1f7GKI8mugjGAAAAxLnmNiMzhjJlAAAAAHAyjMwYG5kxIdc/GIzZsi+5+8YQjAEAAIhzRpmydCeZMQAAAADQXT6fX/5ALEZ2K5fMQ63UCMbsJxgDAACAONYULFNGzxgAAAAA6D6373BjecqUhZ4RjKkiMwYAAADxyuvzq8Ud7BnjpEwZAAAAAHSXN1iiTJLslCkLudJeRs8YgjEAAACIU0a/GEnKIDMGAAAAALrN7W0fjOGSeagZPWOqG1rNMtvJiE8WAABAHDNKlNmsFqXY+WoHAAAAAN1FZkx45aY7lZfukCRtPZC82TGcsQMAAMSxpmBmTIbTJouFkwYAAAAA6C5PsGeMxSJZCcaEhdk3JolLlRGMAQAAiGNGijclygAAAADg5HiCZcoclCgLm/69MiVJVfsIxgAAACAOGWXKCMYA8eX555/X0KFDlZ2drezsbJWVlemf//ynuby1tVWTJ09Wjx49lJmZqXHjxqmmpqbDNrZv366xY8cqPT1dBQUFuvfee+XxdKzBvWzZMp1//vlKSUnRmWeeqblz50ZiegAAAHHFKFNmIysmbMiMIRgDAAAQ18iMAeJT79699d///d9avXq1Pv74Y33961/XjTfeqA0bNkiS7rnnHv3jH//Qq6++qnfffVe7d+/WN7/5TfP1Xq9XY8eOVVtbmz788EP97ne/09y5czVjxgxznaqqKo0dO1ZXXXWV1q1bp6lTp+oHP/iB3nrrrYjPFwAAIJa5vYEyZXYbwZhw6R8MxmxJ4mAMZ+0AAABxrH3PGADx44Ybbujw+JFHHtHzzz+vjz76SL1799ZLL72k+fPn6+tf/7ok6eWXX9agQYP00Ucf6ZJLLtHixYu1ceNGvf322yosLNSwYcP08MMP6/7779eDDz4op9OpOXPmqLS0VI899pgkadCgQXr//ff1xBNPqLy8POJzBgAAiFVGZoydzJiwKe0VDMbsa5Tf70/KnqcEYwAAAOIYZcqA+Of1evXqq6+qqalJZWVlWr16tdxut0aNGmWuM3DgQPXt21eVlZW65JJLVFlZqSFDhqiwsNBcp7y8XHfddZc2bNig4cOHq7KyssM2jHWmTp0aqakBAIAT4Pf79erqndrSrpeGz+fVV9us2rD4c1mtJ3fjVVF2iv7jkn6y2yiOdDwes0wZ71W4nN4jEIxpaPXoYFObemSmRHlEkcdZOwAAQBxrJjMGiFuffPKJysrK1NraqszMTL3++usaPHiw1q1bJ6fTqdzc3A7rFxYWqrq6WpJUXV3dIRBjLDeWHWudhoYGtbS0KC0trdOYXC6XXC6X+bihoUGS5Ha75Xa7T23CCBvj2HCMkgvHPTlwnJPDh18d0H1/WX+UJVYt2b31lLbdLz9Nl5/Z45S2kQxaXYGfMbs1sj9vyfQzbpNUkpOq3fWt+qK6Xtn98qI9pC6F63gQjAEAAIhjjfSMAeLWgAEDtG7dOtXX1+svf/mLbr31Vr377rtRHdOsWbP00EMPdXp+8eLFSk9Pj8KI0B0VFRXRHgKigOOeHDjOie03m6ySrDojy68+mf6QbHPdAYvq2ixa9uFKNXwemm0msq2HJMmuNlerFi5cGPH9J8vPeKYCn/W/vfORagpi93PZ3Nwclu1y1g4AABDHmtsoUwbEK6fTqTPPPFOSNGLECK1atUpPPfWUvvOd76itrU11dXUdsmNqampUVFQkSSoqKtLKlSs7bK+mpsZcZvzfeK79OtnZ2UfNipGkBx54QNOmTTMfNzQ0qE+fPho9erSys7NPbcIIG7fbrYqKCl1zzTVyOBzRHg4ihOOeHDjOie/LvY3aWPmhLBbpNxOvUL8egZsfTvXYT/z9ai3/4oDOGXKexgwvCfWwE87H22qlT1cpOzNDY8ZcHrH9JtvP+ErvJn2+coeyS87UmNFnRXs4XTKyw0ONs3YAAIA4ZmbGOPlaB8Q7n88nl8ulESNGyOFwaMmSJRo3bpwkafPmzdq+fbvKysokSWVlZXrkkUe0d+9eFRQUSArcUZmdna3Bgweb6xx5Z2dFRYW5jaNJSUlRSkrn+t0OhyMpLhDEO45TcuK4JweOc+L6/YodkqRrBhXqzKKcTstP9tg77YHzA68sfHZOhCVQ9tlus0bl/UqWn/EzCrIkSdsOtsT0fMM1Ns7aAQAA4lizWaaMnjFAPHnggQd03XXXqW/fvjp06JDmz5+vZcuW6a233lJOTo4mTpyoadOmKT8/X9nZ2br77rtVVlamSy65RJI0evRoDR48WN/73vc0e/ZsVVdXa/r06Zo8ebIZTLnzzjv17LPP6r777tPtt9+upUuX6pVXXtGbb74ZzakDAICgA40u/XXNLknSD67oH9Jtp9gDjejbPL6QbjdReX2Bkll2qyXKI0lspb0yJElV+5uiPJLoIBgDAAAQxxpdlCkD4tHevXt1yy23aM+ePcrJydHQoUP11ltv6ZprrpEkPfHEE7JarRo3bpxcLpfKy8v161//2ny9zWbTggULdNddd6msrEwZGRm69dZbNXPmTHOd0tJSvfnmm7rnnnv01FNPqXfv3nrxxRdVXl4e8fkCAIDO/vDRdrV5fDqvd44uPD20zcydBGO6xe0LvE92G8GYcOrfMxiMOdAkn88va5IFvzhrBwAAiGNNwcyYdCeZMUA8eemll465PDU1Vc8995yee+65Ltfp16/fcRvMjhw5UmvXrj2pMQIAgPBpdXv1vx9tlSRNvKK/LJbQXpR2BIMKbV6CMSfC6w1kxtis1iiPJLH1zkuXw2ZRm8en3fUt6p2XHu0hRRSfLgAAgDjW3BYIxmSSGQMAAADEjb+v2639jW0qyUnVdecWhXz7ZMZ0j8fIjEmyTI1Is1kt6tcjeUuVcdYOAAAQxxrNzBi+1gEAAADxwO/368X3t0iSvn/Z6XLYQn+/vNMWyJwnM+bEeOgZEzGlPTP05d5Gvb52l3bVtnS5XmF2qq4aWBDBkYUfZ+0AAABxrLkt0DOGzBgAAAAgPqzZXqvPaxqV4bTpOxf2Dcs+jMwYN5kxJ8RrBGPoGRN2Z/TKVIVq9NqaXXptza4u17v0jB4EYwAAABA7zMyYFHrGAAAAAPFg1dZaSdIVZ/VSTpojLPtw0jOmW9xeIzOGrh7h9h+X9FV1fYt5LtuVgUXZERpR5BCMAQAAiFN+v5/MGAAAACDOrNkWCMac3y83bPugZ0z3eOkZEzG989L15Pjh0R5GVBDqAwAAiFMuj89Mp093khkDAAAAxDq/368124PBmL55YdsPwZjuMXrG2AjGIIwIxgAAAMSppnZp3RlOMmMAAACAWLfjYIv2N7bJYbPo3NNywrYfhy0YjKFM2QnxBMuUGe8bEA58ugAAAOJUkytQoizdaZOVO7gAAACAmGdkxQwuyVGqI3zZ7WTGdA+ZMYgEgjEAAABxqqktkBmTTlYMAAAAEBcOlyjLDet+nGTGdIsn+D7ZbQRjED4EYwAAAOKUUaYsM4V+MQAAAEA8MIIxI/qFr1+MRGZMdxmZMXYyYxBGBGMAAADiVFObUaaMzBgAAAAg1jW3ebRpzyFJ0vl9wxyMCWbGuMmMOSFes0wZl8sRPny6AAAA4tThzBiCMQAAAECsW7+zXl6fX0XZqSrJTQvrvsiM6R6jTJmDMmUIo5AHY7xer372s5+ptLRUaWlpOuOMM/Twww/L7/eb6/j9fs2YMUPFxcVKS0vTqFGj9MUXX3TYzsGDBzVhwgRlZ2crNzdXEydOVGNjY4d11q9fryuuuEKpqanq06ePZs+eHerpAAAAxCwjGJNOmTIAAAAg5q3eFuwX0y837PsygjEugjEnxGNmxhCMQfiEPBjz6KOP6vnnn9ezzz6rTZs26dFHH9Xs2bP1zDPPmOvMnj1bTz/9tObMmaMVK1YoIyND5eXlam1tNdeZMGGCNmzYoIqKCi1YsEDLly/XpEmTzOUNDQ0aPXq0+vXrp9WrV+uXv/ylHnzwQb3wwguhnhIAAEBMMoIxGWTGAAAAADFvbbBfTLhLlEmSgzJl3WIEY4z3DQiHkJ+5f/jhh7rxxhs1duxYSdLpp5+uP/7xj1q5cqWkQFbMk08+qenTp+vGG2+UJP3+979XYWGh3njjDY0fP16bNm3SokWLtGrVKl1wwQWSpGeeeUZjxozRr371K5WUlGjevHlqa2vTb3/7WzmdTp1zzjlat26dHn/88Q5BGwAAgERl9IzJcJIZAwAAAMQyv9+vNdvrJEnDIxCMMcuUEYw5IR4vmTEIv5AHYy699FK98MIL+vzzz3X22WfrX//6l95//309/vjjkqSqqipVV1dr1KhR5mtycnJ08cUXq7KyUuPHj1dlZaVyc3PNQIwkjRo1SlarVStWrNA3vvENVVZW6sorr5TT6TTXKS8v16OPPqra2lrl5XX+peZyueRyuczHDQ0NkiS32y232x3qt6ITYx+R2Fc0JPr8jiXZ5p5s8zUk47yTcc5S8s5bit+5/2nVTr335f4Ozw0pydadX+t/zNfF23y/2tek59/dohZ3IAjz5d4mSVKaw9rtOcTb3E9Vss3XcOS8k23+AAAAsWLbgWYdbGqT02bVuadlh31/Ths9Y7rD6wu8T3aCMQijkAdjfvzjH6uhoUEDBw6UzWaT1+vVI488ogkTJkiSqqurJUmFhYUdXldYWGguq66uVkFBQceB2u3Kz8/vsE5paWmnbRjLjhaMmTVrlh566KFOzy9evFjp6eknM92TUlFREbF9RUOiz+9Ykm3uyTZfQzLOOxnnLCXvvKX4mnubV5qx0ia/On5pXrxxr9IPfKaeqcffRrzM95UtVn1Q0zltvm53lRYu3HJS24yXuYdKss3XYMy7ubk5yiMBAABITmuCJcrOPS1bKfbwZ7an2AnGdIc7WKbMbqVMGcIn5MGYV155RfPmzdP8+fPN0mFTp05VSUmJbr311lDvrlseeOABTZs2zXzc0NCgPn36aPTo0crODn9E2u12q6KiQtdcc40cDkfY9xdpiT6/Y0m2uSfbfA3JOO9knLOUvPOW4nPun1Ufkn9lpTJT7Pq/o8+SJL2wvEq761vV99wLNfLsXl2+Nt7mu2D+Oqlmr24YWqQR/QI3nmSm2DV6UIHSulmqLN7mfqqSbb6GI+dtZIYDAAAgstZEsF+M1L5njD8i+4t33uD7ZLeRGYPwCXkw5t5779WPf/xjjR8/XpI0ZMgQbdu2TbNmzdKtt96qoqIiSVJNTY2Ki4vN19XU1GjYsGGSpKKiIu3du7fDdj0ejw4ePGi+vqioSDU1NR3WMR4b6xwpJSVFKSkpnZ53OBwRPSmP9P4iLdHndyzJNvdkm68hGeedjHOWknfeUnzNfUddoATpmQWZ+v5lgbJkH22p1e76am2vdZ3QPOJlvvWtHklS+bnFun5oSUi2GS9zD5Vkm6/BmHcyzh0AACAWrNlWJ0k6v19kgjFOMmO6xU2ZMkRAyPOumpubZT0inctms8kX/ECXlpaqqKhIS5YsMZc3NDRoxYoVKisrkySVlZWprq5Oq1evNtdZunSpfD6fLr74YnOd5cuXd6h7XVFRoQEDBhy1RBkAAEhMVfsDfVP698wwnyvtFfj31uCyRFHfHPjek5vmPM6aAAAAAGJFo8ujz6oDGcqRyowxgzFen/x+smOOxxssU2YjGIMwCnkw5oYbbtAjjzyiN998U1u3btXrr7+uxx9/XN/4xjckSRaLRVOnTtXPf/5z/f3vf9cnn3yiW265RSUlJbrpppskSYMGDdK1116rO+64QytXrtQHH3ygKVOmaPz48SopCdwFevPNN8vpdGrixInasGGD/vznP+upp57qUIYMAAAkPiMYc3r7YEzw31UJFoypbW6TJOWmk90AAAAAxIs122rl80slOakqyjmBppYhYJQpkyhVdiI8Zs8YgjEIn5CXKXvmmWf0s5/9TP/5n/+pvXv3qqSkRP/n//wfzZgxw1znvvvuU1NTkyZNmqS6ujpdfvnlWrRokVJTD/8ymjdvnqZMmaKrr75aVqtV48aN09NPP20uz8nJ0eLFizV58mSNGDFCPXv21IwZMzRp0qRQTwkAAMQwI/ulNMGDMX6/X3XBzJi8DDJjAAAAgHgxf8V2SdLXBhREbJ8p9sPBmDavz8yUwdF5vMEyZTbeJ4RPyIMxWVlZevLJJ/Xkk092uY7FYtHMmTM1c+bMLtfJz8/X/Pnzj7mvoUOH6r333jvZoQIAgARQdYxgzO76FrW6vUp1dK+5fSxqbvOqLXiCkEdmDAAAABAXth1o0lsbqyVJt112esT262wXVGjz+KTObbTRjpfMGEQAoT4AABC36lvcOtAUKN3VvkxZjwynslLt8vulbQeaozW8kDJKlDltVqUlQHAJAAAASAYvf7BVfr/0tbN76ezCrIjt12q1mIGFNo8vYvuNV0YpNzJjEE58ugAAQNwySpQVZKUoM+Vwwq/FYkm4UmVGibLcdIcsFu7WAgAAAGJdfbNbr3y8Q5J0xxX9I75/o2+M20sw5njIjEEkEIwBAABxywi0tM+KMSRqMCYvnX4xAAAAQDz446rtam7zamBRli47s0fE92/0iXGRGXNcHl/gPbIRjEEYEYwBAABxywi09D9GMGZrggRjjDJlufSLAQAAAGKe2+vT3A+2SpImXl4alex2IxhDmbLj8wTLlDlsBGMQPgRjAABA3DKCMaVJkRkTCMaQGQMAAADEvjfX71F1Q6t6Zqbo34aVRGUMzmCZsjbKlB2XJ1imzGblcjnCh08XAACIW1sPnECZsgOJEYypbdczBgAAAEDs8vv9evH9LZKkW8v6KcVui8o4jMwYesYcn1GmzE5mDMLIfvxVAAAAosfn8+u2uau0oupAp2Wt7sAX5qOVKTMCNPsOuTTwZ//scvter033f/z2SY+vOCdNr95Zpp6ZKSe9jRNxuEwZmTEAAABALFtRdVCf7mpQqsOqCZf0i9o4zMwYypQdl1GmzE7PGIQRwRgAABDTdta26N3P93W5vG9+uvr16ByMyU516Py+uVqzvc4M2hydRW7fyZ+cVO1v0sdba3XtuUUnvY0TUR/MjMkjMwYAAACIaS++VyVJGnd+b+VnRO9mKnrGnDivWaaMYAzCh2AMAACIaVv2N0qS+vfK0O9uu6jT8oLsFPMk40iv/J8y7alv7XLbHo9H77zzjq666irZ7d3/WvSjV/+llVUH1dzm6fZru6uWnjEAAABAzNuyr1FLPquRJN1+eWlUx2I0o6dnzPEZPWMcNrp6IHwIxgAAgJi2dX+g58tZBZnqk5/erdfabdZjvsbtdqtHqtQ7L00OR/czTvKDgZEmVySCMfSMAQAAAGLdbz+okt8vXT2wQGf0yozqWMiMOXFGzxgyYxBOhPoAAEBMqwoGY0p7RvdE5mgyUgL3tTS1ecO+rzp6xgAAAAAxrbapTX9ZvVOSNPGK6GbFSJLTbpNEMOZE0DMGkUAwBgAAxLSqA82SpNKe3cuKiYTMlMDJTSQzY+gZAwAAAMSm+Su3q9Xt0+DibJX17xHt4chJmbITZpQps1u5XI7w4dMFAABiWlWwZ0wsZsakG5kxrvBmxnh9fjW0GmXKyIwB4t2sWbN04YUXKisrSwUFBbrpppu0efPmDuuMHDlSFoulw3933nlnh3W2b9+usWPHKj09XQUFBbr33nvl8XQMDi9btkznn3++UlJSdOaZZ2ru3Lnhnh4AAEnJ5fFq7odbJUl3XFkqiyX6GRZGmTI3wZjj8hrBGFv0jxsSF8EYAAAQs1wer3bVtkiSTo/JzBgjGBPezJiGFrf8gXMDesYACeDdd9/V5MmT9dFHH6miokJut1ujR49WU1NTh/XuuOMO7dmzx/xv9uzZ5jKv16uxY8eqra1NH374oX73u99p7ty5mjFjhrlOVVWVxo4dq6uuukrr1q3T1KlT9YMf/EBvvfVWxOYKAECyWPCvPdp3yKXC7BSNHVIS7eFIkpw2esacKCNgRZkyhJM92gMAAADoyo6DzfL5A0GPXpkp0R5OJ+nOYJmytvAGY2qD/WIyU+xy2LiXBoh3ixYt6vB47ty5Kigo0OrVq3XllVeaz6enp6uoqOio21i8eLE2btyot99+W4WFhRo2bJgefvhh3X///XrwwQfldDo1Z84clZaW6rHHHpMkDRo0SO+//76eeOIJlZeXh2+CAAAkGb/fr/95b4sk6dZLTzczUqLNGIeLYMxxeSlThgggGAMAAGLWln2Bu8RP75keE2n+R8qIUGaM0S+GrBggMdXX10uS8vPzOzw/b948/eEPf1BRUZFuuOEG/exnP1N6eiBLsLKyUkOGDFFhYaG5fnl5ue666y5t2LBBw4cPV2VlpUaNGtVhm+Xl5Zo6dWqXY3G5XHK5XObjhoYGSZLb7Zbb7T6leSJ8jGPDMUouHPfkwHGOjoqNe/Xcu1/JG2zqfjwen19f7mtSmsOqbw8vCcnxCsWxNypuudo8fIaOw+gZ4/dF9r3iZzw2het4EIwBAAAxa+uBQDAmFvvFSFKGMxiMaQtvz5i6YGZMHv1igITj8/k0depUXXbZZTr33HPN52+++Wb169dPJSUlWr9+ve6//35t3rxZr732miSpurq6QyBGkvm4urr6mOs0NDSopaVFaWlpncYza9YsPfTQQ52eX7x4sRkIQuyqqKiI9hAQBRz35MBxjqxnNtj0ZUP3bwa7uKdHHy4L7bE6lWO/e4dVklUbP/9CC1s3H3f9ZOb22CRZ9O6yd5QThdMufsZjS3Nzc1i2SzAGAADErKr9RjAmI8ojObqMlGCZsjBnxtSRGQMkrMmTJ+vTTz/V+++/3+H5SZMmmf8eMmSIiouLdfXVV+urr77SGWecEbbxPPDAA5o2bZr5uKGhQX369NHo0aOVnZ0dtv3i1LjdblVUVOiaa66Rw8HfimTBcU8OHOfoeOqL9yU164Frz9aAoqwTeo3TZtWwPjkhKyscimP/6Vuf693qrerbr1RjrhsQknElIp/PL39lIBgy+ppR6pERuWgMP+OxycgODzWCMQAAIGYZZcpKe8bm3dhGmbLmMGfG1JIZAySkKVOmaMGCBVq+fLl69+59zHUvvvhiSdKXX36pM844Q0VFRVq5cmWHdWpqaiTJ7DNTVFRkPtd+nezs7KNmxUhSSkqKUlI69+hyOBxcIIgDHKfkxHFPDhznyNrfGPj+/fVBRTqr8MSCMeFyKsc+NZjJ7/GLz88xtLXrqZOW4ozKe8XPeGwJ17EgGAMAAKKmpqHVzH45mq/2xUeZssYQZ8Z4vD6t31VvnhRs3BO4K4fMGCAx+P1+3X333Xr99de1bNkylZaWHvc169atkyQVFxdLksrKyvTII49o7969KigokBQob5Gdna3Bgweb6yxcuLDDdioqKlRWVhbC2QAAkFhcHq8aWgPf73tmdr5BIZ44g1k6bq/vOGsmN6/vcG8guzX2epUicRCMAQAAUdHQ6tbXf7XshPqtlPaI7TJlzSEOxjzx9ud67p2vOj1PZgyQGCZPnqz58+frb3/7m7KyssweLzk5OUpLS9NXX32l+fPna8yYMerRo4fWr1+ve+65R1deeaWGDh0qSRo9erQGDx6s733ve5o9e7aqq6s1ffp0TZ482cxsufPOO/Xss8/qvvvu0+23366lS5fqlVde0Ztvvhm1uQMAEOsOBLNi7FaLctLi+2Yopz0QjHF5CMYci8d3+P2xEYxBGBGMAQAAUbHjYLOa2ryyWy3q16PrMmQjBxQoJ0YzQowyZU1tXvl8fllD9MX94621kqSi7FQz4JOd5tAN5xWHZPsAouv555+XJI0cObLD8y+//LK+//3vy+l06u2339aTTz6ppqYm9enTR+PGjdP06dPNdW02mxYsWKC77rpLZWVlysjI0K233qqZM2ea65SWlurNN9/UPffco6eeekq9e/fWiy++qPLy8ojMEwCAeLS/0SVJ6pHpDNn3+2gxgjFtBGOOyeM9nBkTqp4/wNEQjAEAAFFRH2xKX9ozQxXTvhbl0Zwco0yZJDW7vcpMCc1Xq60HAuXZ5nxvhIb1yQ3JNgHEDr/ff8zlffr00bvvvnvc7fTr169TGbIjjRw5UmvXru3W+AAASGZGZky8lyiTDgcWCMYcm6ddmbI4j78hxhHqAwAAUVEbDMbEcx+UVIfV/LIeqlJlTS6PahoCd+PFank2AAAAIFHtC2bGJEIwxsiMoWfMsRllyhw2iywWojEIH4IxAAAgKmqbA3ec5cZxHxSLxWJmxzSGKBhTtT+QFZOf4YzZ8mwAAABAompfpizepRhlygjGHJNRpox+MQg3gjEAACAq6oLBmLw4DzgYfWOa27wh2Z5Roqy0J1kxAAAAQKTtPxQ4T+mVAJkxlCk7Md5gmTK7lUvlCC8+YQAAICqMMmV5cZwZI0npKTZJIcyM2RcIxpxOiTIAAAAg4vYnUpkyIxjjPXa/umRnlCmz28iMQXgRjAEAAFFRZ/aMie9gTKaZGROiYEwwM6Z/L4IxAAAAQKSZwZis+D5PkQ73jCEz5tg8ZmYMwRiEF8EYAAAQFXVmz5j4LlOW7jQyY0JTpszoGUOZMgAAACDyDjQGzlMSIjPGDMaE5lwlURk9YyhThnDjEwYAAKKiNkF6xpiZMaEqU7afMmUAAABAtCRSmTKzZ4yXzJhjMTJjbGTGIMwIxgAAgKhIlDJlGcFgTCh6xtQ2tZnvy+k90095ewAAAABOnMfr08HgTWM9MuP7PEWSUoKZMW4PPWOOxUvPGEQIwRgAABAVhzNj4vskJ91p9Iw59dR/o19MUXaquV0AAAAAkXGwuU1+v2SxSPlxfp4itStTRmbMMbm99IxBZBCMAQAAEefz+VXfEsgAif8yZYGeMU0hyIzZSr8YAAAAIGr2HwrcMJaf7pTdFv+XTc0yZR6CMcfi9dEzBpHBLZcAACDiDrV6FPy+q5w4D8YYGSxNbScejNl2oEl/Wb3TvAPLsGZ7rSSptBfBGAAAACDSEqlfjERmzIlyeylThsggGAMAACLOKFGW7rQpxW6L8mhOTWawZ0yT68TLlP1i4Sa9taGmy+VnF2Se8rgAAAAAdM+BpmAwJiv+S5RJkrNdZozf75fFQrDhaA5nxvD+ILwIxgAAgIhLlH4xkpR+EmXKPq9plCRdP7RYhdmpHZblpDn0rQv6hG6AAAAAAE6IUaYs0TJjpEBfFKedYMPReILBGBvBGIQZwRgAABBxdc2BfjG5cV6iTGqXGXOCZcrcXp92HGyWJP1kzCCV5KaFbWwAAAAATlzClSlr1/emzevrEJzBYZ5g+ehE6BOE2MYnDAAARFxdSwJlxji7V6ZsZ22LPD6/Uh1WFR2RFQMAAAAgevYFgzE9MuP/PEU6IjPGQ9+Yrnh8wZ4xZMYgzAjGAACAiKttSpzMmIxulinbur9JknR6jwxZ+bIPAAAAxIz9jYlVpsxmtZilt9q8BGO6QmYMIoVPGAAAiLi6YM+YhAjGmJkxJxaM2RIMxpT2zAjbmAAAAAB03/5DgcyYXgkSjJEkhy0YjCEzpkveYM8YMmMQbgRjAABAxNUGe8YkQpmyDLNnzImVKava3yiJYAwAAAAQaw40JVbPGOlw3xgXwZgueYLBGBvBGIQZwRgAABBxtWZmTCIEYw6XKfP7/cddf+v+ZknS6QRjAAAAgJjh8/l1wChTlhX/5ykGpz1wvuKmTFmXjJ4xRhYREC4EYwAAQMTVtxiZMQlQpiyYGePx+U+oDnNVsExZf4IxAAAAQMyob3GbGRI9MhInMybFHrj8S5myrhk9Y2xWLpUjvPiEAQCAiDMyYxKhTFm6w2b+u8l17FJlrW6vdte3SKJMGQAAABBL9jcGSpTlpDnktCfOJVOzZwyZMV0yMmPoGYNwC8tvll27duk//uM/1KNHD6WlpWnIkCH6+OOPzeV+v18zZsxQcXGx0tLSNGrUKH3xxRcdtnHw4EFNmDBB2dnZys3N1cSJE9XY2NhhnfXr1+uKK65Qamqq+vTpo9mzZ4djOgAAIMRqmwKZMbkJkBljt1mV6gh8pWpyeY657rYDzfL7paxUu/Iz4j8QBQAAACSKfcFgTI/MxPqebgSW3GTGdMnIiCIYg3ALeTCmtrZWl112mRwOh/75z39q48aNeuyxx5SXl2euM3v2bD399NOaM2eOVqxYoYyMDJWXl6u1tdVcZ8KECdqwYYMqKiq0YMECLV++XJMmTTKXNzQ0aPTo0erXr59Wr16tX/7yl3rwwQf1wgsvhHpKAAAgxOoSqGeMJGU4A6XKmtqOHYxpX6LMYuGLPgAAABAr9hv9YjITp0SZdDgY4yIzpkveYJkyOz1jEGb2UG/w0UcfVZ8+ffTyyy+bz5WWlpr/9vv9evLJJzV9+nTdeOONkqTf//73Kiws1BtvvKHx48dr06ZNWrRokVatWqULLrhAkvTMM89ozJgx+tWvfqWSkhLNmzdPbW1t+u1vfyun06lzzjlH69at0+OPP94haAMAAGJLm8enprZAOa9E6BkjBfrGHGhqO26ZMiMYQ4kyAAAAILbsPxTIjOmVYMEYh42eMcfjNjNjEqc8HWJTyIMxf//731VeXq5vf/vbevfdd3XaaafpP//zP3XHHXdIkqqqqlRdXa1Ro0aZr8nJydHFF1+syspKjR8/XpWVlcrNzTUDMZI0atQoWa1WrVixQt/4xjdUWVmpK6+8Uk7n4Ttqy8vL9eijj6q2trZDJo7B5XLJ5XKZjxsaGiRJbrdbbrc71G9FJ8Y+IrGvaEj0+R1Lss092eZrSMZ5J+OcpeSdtxSZue9tCGTCWi1Smi2673Oo5pseLFM29U9rleG0dbleTfAEr29eWtQ/X8n2OU+2+RqOnHeyzR8AAOBEHWgKfFfvmWhlygjGHJc32DPGRpkyhFnIgzFbtmzR888/r2nTpuknP/mJVq1apf/6r/+S0+nUrbfequrqaklSYWFhh9cVFhaay6qrq1VQUNBxoHa78vPzO6zTPuOm/Tarq6uPGoyZNWuWHnrooU7PL168WOnp6Sc54+6rqKiI2L6iIdHndyzJNvdkm68hGeedjHOWknfeUnjn/uctVklW9Uzxa9Gif4ZtP91xqvNN8wTmtKO25YTWd1d/roULN5/SPkMl2T7nyTZfgzHv5ubmKI8EAAAgNu0/lNhlytyUKesSPWMQKSEPxvh8Pl1wwQX6xS9+IUkaPny4Pv30U82ZM0e33nprqHfXLQ888ICmTZtmPm5oaFCfPn00evRoZWdnh33/brdbFRUVuuaaa+RwJEZZlvYSfX7HkmxzT7b5GpJx3sk4Zym+5t3c5pErhHc4edweLVu2TCNHjpTdEfKvCVr+xQF9WPmJLBZp9vgLdNkZPUK+j+4I1bH++jVerd1RpxM5v8nPcGhQUVbUe8bE0+c8FJJtvoYj521khgMAAKCj/Y3BzJisxArGpNjJjDkej9kzhjJlCK+QX2UpLi7W4MGDOzw3aNAg/fWvf5UkFRUVSZJqampUXFxsrlNTU6Nhw4aZ6+zdu7fDNjwejw4ePGi+vqioSDU1NR3WMR4b6xwpJSVFKSmdf6E6HI6InpRHen+RlujzO5Zkm3uyzdeQjPNOxjlLsT/vZZv36ge/+9i8iyd07NLH74d4mx3d9bUzNHLg0f9eR8OpHmuHw6ErB8TOfLoj1j/noZZs8zUY807GuQMAAJwIIxjTIyOxypSZPWPIjOmSl8wYREjIgzGXXXaZNm/uWHbj888/V79+/SRJpaWlKioq0pIlS8zgS0NDg1asWKG77rpLklRWVqa6ujqtXr1aI0aMkCQtXbpUPp9PF198sbnOT3/6U7ndbvOksqKiQgMGDDhqiTIAABLN39ftDkMgJvyuPLuX7rnm7GgPAwAAAEAEeH1+fbKrXi63N9pD6aA4J019exxuW7C/MVimLMEyY5xkxhyXUcLNbiMYg/AKeTDmnnvu0aWXXqpf/OIX+vd//3etXLlSL7zwgl544QVJksVi0dSpU/Xzn/9cZ511lkpLS/Wzn/1MJSUluummmyQFMmmuvfZa3XHHHZozZ47cbremTJmi8ePHq6SkRJJ0880366GHHtLEiRN1//3369NPP9VTTz2lJ554ItRTAgAgJq2oOihJmnvbhbrirF4h2abb7dY///lPXXfddWG7g56miAAAAEDyeGbpF3ry7S+iPYxOHDaL3vyvK3R2YZb8fr/2BTNjeiVazxgyY46LzBhESsiDMRdeeKFef/11PfDAA5o5c6ZKS0v15JNPasKECeY69913n5qamjRp0iTV1dXp8ssv16JFi5SammquM2/ePE2ZMkVXX321rFarxo0bp6efftpcnpOTo8WLF2vy5MkaMWKEevbsqRkzZmjSpEmhnhIAADFnZ22zdtW1yGa16MLT80MW4PBZLbJaAgETgiYAAAAATtU7m/dJkkpyUpXmtEV5NAG1zW4dbGrTi+9t0exvnadGl8fMHOmZYMEYB5kxx2VUnLBZ6RmD8Ap9Z15J119/va6//voul1ssFs2cOVMzZ87scp38/HzNnz//mPsZOnSo3nvvvZMeJwAA8WrFlkBWzJDTcpSREpY/5wAAAABwSlrdXm3cXS9J+vP/KVOf/PTjvCIyVm+r1bjnP9Qba3fr3vKBanR5JEkZTlvMBIxCxcyMIRjTJU8wa8hBmTKEGeE+AADi0MpgibKLS/OjPBIAAAAAOLpPd9XL7fWrZ2aKeuelRXs4phH98jS8b67avD79b+VW7Q+WKEu0fjGSlBLMjHFTpqxLhzNjCMYgvAjGAAAQh1ZUHZAkXdyfYAwAAACA2LRme60k6fy+ubJYYutC9w8u7y9J+t+PtmlnbbOkxCtRJklOypQdl8cb7Blj41I5wotPGAAAcaamoVVbDzTLYpEuOJ1gDADEm1mzZunCCy9UVlaWCgoKdNNNN2nz5s0d1mltbdXkyZPVo0cPZWZmaty4caqpqemwzvbt2zV27Filp6eroKBA9957rzweT4d1li1bpvPPP18pKSk688wzNXfu3HBPDwAA05ptdZKk8/vlRXcgR1F+TqFOy01TbbNbL75XJUnqkeGM8qhCz2GUKSMzpktGZoydzBiEGUXmAQAIsXc279Wj//wsbF92W9u8kqTBxdnKTnWEZR8AgPB59913NXnyZF144YXyeDz6yU9+otGjR2vjxo3KyMiQJN1zzz1688039eqrryonJ0dTpkzRN7/5TX3wwQeSJK/Xq7Fjx6qoqEgffvih9uzZo1tuuUUOh0O/+MUvJElVVVUaO3as7rzzTs2bN09LlizRD37wAxUXF6u8vDxq8wcAJAe/398uMyb2gjF2m1W3X16qhxds1IbdDZISs0yZkRnjIjOmS15f4L2hTBnCjWAMAAAh9ofKbfqs+lDY9zNqUGHY9wEACL1FixZ1eDx37lwVFBRo9erVuvLKK1VfX6+XXnpJ8+fP19e//nVJ0ssvv6xBgwbpo48+0iWXXKLFixdr48aNevvtt1VYWKhhw4bp4Ycf1v33368HH3xQTqdTc+bMUWlpqR577DFJ0qBBg/T+++/riSeeIBgDAAi7XXUt2nvIJbvVoqG9c6I9nKP69wt668mKz3XIFcgsTcgyZTajZ4w/yiOJXUZmjMNGMAbhRTAGAIAQ21nbIkmaPnaQhvbODcs+UuxWnXtabJ7QAAC6p76+XpKUnx8oPbl69Wq53W6NGjXKXGfgwIHq27evKisrdckll6iyslJDhgxRYeHhwHx5ebnuuusubdiwQcOHD1dlZWWHbRjrTJ06NfyTAoAY5fJ4lWK3RXsYSWHN9jpJ0uCSbKU6YvM9z0p1aPxFffQ/wTJlvTITsEyZ2TPGG+WRxC6jZ4zNSkcPhBfBGAAAQsjv92tXXSAYM3JAgc4syIzyiAAAsczn82nq1Km67LLLdO6550qSqqur5XQ6lZub22HdwsJCVVdXm+u0D8QYy41lx1qnoaFBLS0tSktL6zQel8sll8tlPm5oCJRtcbvdcrvdpzBThJNxbDhGyYXj3n2rttbq1rkf697RZ+u2S/tFezgnJJ6P86qqA5Kk83rnxPT4/+Oi3vrtB1vl9fmVm2aPmbGG6tjbFAg0tLq9MTO3WOP2BgJVFr8v4u9RPP+MJ7JwHQ+CMQAAhFBDi0eNwRT303I7X+QCAKC9yZMn69NPP9X7778f7aFIkmbNmqWHHnqo0/OLFy9Wenp6FEaE7qioqIj2EBAFHPcTt2SXRW6vTf/4aJMK6zZEezjdEo/HedknNkkWWQ5UaeHCLdEezjFdXWzV+oMWNXy5Wgu3RXs0HZ3qsd+0zyLJpj01e7Vw4cLQDCrB7D8Q+Kz+a+0a+bZFp5xbPP6MJ7Lm5uawbJdgDAAAIbSjNvAHu0eGU2nO2EzFBwDEhilTpmjBggVavny5evfubT5fVFSktrY21dXVdciOqampUVFRkbnOypUrO2yvpqbGXGb833iu/TrZ2dlHzYqRpAceeEDTpk0zHzc0NKhPnz4aPXq0srOzT36yCCu3262Kigpdc801cjgc0R4OIoTj3n1fLv1S2r5F2fm9NGbMiGgP54TE63FudXv1oxVLJfl12w0j1Tsvtm9UGxPtARxFqI695dNq/e+X65Wdm68xYy4K4QgTx4vbP5IaG3TRhRfoqgG9IrrveP0ZT3RGdnioEYwBACCEjBJlsX6yAQCIHr/fr7vvvluvv/66li1bptLS0g7LR4wYIYfDoSVLlmjcuHGSpM2bN2v79u0qKyuTJJWVlemRRx7R3r17VVBQIClwR2V2drYGDx5srnPkHbAVFRXmNo4mJSVFKSmdmxc7HA4uEMQBjlNy4rifuFZP4I73Frc37t6zeDvOa3ceksfnV6+sFJ3eK0sWC43RT9apHvu0lEAfHLdPcfUZiiSPL/D/VGf0fs7i7Wc80YXrWBCMAQAghHbVBoIxpxGMAQB0YfLkyZo/f77+9re/KSsry+zxkpOTo7S0NOXk5GjixImaNm2a8vPzlZ2drbvvvltlZWW65JJLJEmjR4/W4MGD9b3vfU+zZ89WdXW1pk+frsmTJ5vBlDvvvFPPPvus7rvvPt1+++1aunSpXnnlFb355ptRmzsARFNzm7fD/xE+a7bXSpLO75tLICbKnPZAU/o2I+KATry+wHtjt/JZRXhZoz0AAAASyU4jGEO/GABAF55//nnV19dr5MiRKi4uNv/785//bK7zxBNP6Prrr9e4ceN05ZVXqqioSK+99pq53GazacGCBbLZbCorK9N//Md/6JZbbtHMmTPNdUpLS/Xmm2+qoqJC5513nh577DG9+OKLKi8vj+h8ASBWtASDMC1ugjHhtmabEYzJi/JI4LQFLv+6vQRjuuLxBrLm7DYulSO8yIwBACCEdtUFesb0zqPJMQDg6Pz+4zeGTU1N1XPPPafnnnuuy3X69et33Ea8I0eO1Nq1a7s9RgBIRE1tnsD/XQRjwsnv92vN9jpJ0vn9CMZEm9MeyPYgM6ZrHl/gu5mNzBiEGeE+AABCyOgZQ2YMAAAAEFuM8mQtwaAMwmNnbYv2N7rksFk05LScaA8n6TltNkkEY47FGwzGUKYM4UYwBgCAENpJzxgAAAAgJpk9Y9zeE8pSxMkx+sUMLslRqsMW5dHA6BlDmbKuGe+N3UYwBuFFmTIAAEKkyeVRXbNbEsEYAAAAINYYwRi/X2p1+5TmJFBwMh5fvFkvvV8lXxfxLOPC9vl9cyM3KHTJCMaQGdO1w5kx5C0gvAjGAAAQIkaJsuxUu7JTHVEeDQAAAID2mtuVJ2tq8xCMOUmvrd2lprZj992xWqTRg4siNCIciyOY7eEiM6ZLRgCRnjEIN4IxAACEyM7aZknSaXnpUR4JAAAAgCM1twsgtBwnmICuGdUA/njHJerdRUWArFS7ctOdkRwWutA+M8bv98tiIeBwJCMzxkGZMoQZwRgAAEJkV7BfTFcnJAAAAACip30ApplgzElp8/jU6ApkGA0qziLgEgdSbIczwDw+PwGHo/AEgzFkxiDcCMYASDoHGl16Z/M+eX0+5Wek6OqBBbK2+4Nb3+zWmu21uuKsnrLbYq9eqNfn19ubalTX3Bb+fXm9Wl9jUdPqnbLZkieF/2Tn/c7mfZKk03IJxgAAAACxxO/3q+mIMmXovrqWwHmo1SJKM8cJIzNGCgTTHDF4nSPaPGZmDO8NwotgDICk8+PXPlHFxhrz8f/ccoGuGVxoPv7vRZv0x5U79NT4Ybpx2GnRGOIxLfq0WpPnr4ngHm3605aNEdxfrDj5effJp0wZAAAAEEtcHp/87RrOU6bs5BglynLSHB1uakTsap8J0+bxKSMlioOJQX6/3yxTRmYMwo1gDICk4vH69MGX+yVJBVkp2nvIpc3VDR2CMZv2HJIkba4+FJUxHs/qbbWSpP49M9S/V0ZY9+Xz+bV3b40KCgqT6ov2qcw7N92pm4aVhGlkAAAAAE5Gk8tzzMc4MQebApkxeZQnixt2m1VWi+TzS23BRvU4zMiKkSR7El33QHQQjAGQVD7d3aDmNq9y0hwaf1FfPb3kC+0M9vkwGI+PfD5WbNxTL0m6a+QZ+vYFfcK6L7fbrYULF2rMmOFyOJInBT1Z5w0AAAAkqiN7xLS4yYw5GUa57Nx0zpPiidNuVavbpzYPwZgjedsHYyhThjDjEwYgqazYckCSdOHp+eobLCW1q+5w0KXV7dX+Rlen52OF3+83M3cGFWdHeTQAAAAAEB+ODL4cGZzBiakNlikjMya+GL1QyIzpjMwYRBLBGABJZWXVQUnSJf3zzSbru9plwOxuF4DZFYOZMXvqW1Xf4pbdatFZhZnRHg4AAAAAxAXKlIVGrZkZQzAmnqTYg8EYMmM68bQLUBGMQbhRpgxA0vD6/Fq5NRCMubi0h5lWvbOuRT6fX1arpUNpsppDrWrz+OS0x07cetOeBknSGb0ylWK3RXk0AAAAABAfWo4sU0ZmzEmpMzNjKFMWT5zBzBg3mTGdtM+MsRGMQZjFzhVGAAizTXsadKjVo8wUuwYVZ6koJ1VWS+DOkP1NnUuT+f3SnvrYyo4xgjGDirOiPBIAAAAAiB9NRwRfjnyME1PbFMiMycsgMyaeOMmM6ZLRM8ZmtchiIRiD8CIYAyBpGCXKLjg9T3abVQ6bVUXZqZIOlyQ7sjRZrJUqM/rFDKRfDAAAAACcsOa2jmXJWtooU3Yy6BkTn8yeMQRjOjGyhShRhkggGAMgaXz41QFJgRJlhtPyAn1jjPJkO2ubO7xmZ8wFY4zMGIIxAAAAAHCimsmMCYm6YM8YypTFFyMzxkWZsk6MzBiCMYgEgjEAkkJ9s1vLP98nSfra2b3M53vnpUs6XJ7M+H9O2uF+MrGiuc2jqgNNkihTBgAAAADdcWQwhp4xJ6c2GIzJJTMmrhjBGDeZMZ24vcFgjI3L5Ag/e7QHAACRsOCT3Wrz+jSwKEuDSw5nlZyWG8iMObJM2UWl+arYWBOSMmXNbR4t+NceuTyn9mW/uqFVfr/UM9OpgqzUUx4XAAAAACQLoyyZ3WqRx+fvVLYMJ6bOKFOWQWZMPDHLlJEZ0wmZMYgkgjEAksJra3ZJksad37vD84fLlDXL7fWpuqFVknRxMBhzZNmy7vL7/Zr6p3VavLHmlLbT3uCSnJBtCwAAAACSgVGWrEemUzUNLsqUnQS/36+6FnrGxKMUOz1juuLxBd4TG8EYRADBGAAJb+v+Jq3eViurRbpxWEmHZb2DwZhddS2qrm+Vzx9I3x3aO9d8/lT8vnKbFm+skdNm1dWDCmQ5xb/tDptVEy8vPbWNAAAAAECSMcqS9cxMUU2DizJlJ6Gh1WNmEeTSMyauOIOZMW4yYzrxBMuUOShThgggGAMg4b22NpAVc8VZvVSQ3bG8V/syZTuCWTCn5aapT37g+er6Vnm8vhOqHVq1v0nPLP3C/FLv90tLP9srSXpgzEDddhlBFAAAAACIhiZXoCxZz8yUwGPKlHVbXbBfTLrTphS7LcqjQXc4yYzpkicYYCQzBpFAMAZAwvtoywFJ0tghxZ2WlQSDMU1tXm3c3SApkC1TkJVq1hKuOeQygzbHMu+jbWY5tPZGDSrQ9y89/RRmAAAAAAA4Fc3uw5kxksiMOQm1zZQoi1dG1oeLYEwnnmC2kN1GMAbhRzAGQMIzGjP2ykrptCzVYVOvrBTtO+TSiqqDkgKZMTarRSW5adp+sFm7altOKBhj1By+emCBRg4skCSl2KwaO7RYllOtTwYAAAAAOGnNZmZMIJBgZMrgxNUGM2MoURZ/zMwYypR1YpTes5MZgwggGAMg4bW6A182UhxHLzV2Wm6a9h1yacmmGvOx8f/tB5v1/ZdXqkemU8+OP++Y+zHuphhxep6+d0m/UA0fAAAAAHCKmtuOyIxxkxnTXUaZMjJj4o8RjHF7/FEeSew5XKaMnjEIPz5lABKekX6e5jh6TdtL+veQJPn8ksUiXVia3+H55javdhxs0ZufVB9zP0YjPAd/wAEAAAAgphjBl55ZgUCC2+unf0Y31TYFypSRGRN/nDYjM4Yg5JE8vuC1HMqUIQLIjAGQ8FyewJeN1C6CMfdfO0DfvaiP3F6/ctIcZjmz/7r6TH3z/NP0xtpdeqzic31WfUjn9Ox6P+7g3RT8AQcAAACA2NJklik7XL66pc1rZgzg+MiMiV9mmTICkJ14vEZmDNdyEH78xQGQ8IwyZV0FYywWi/r1yNCZBZkd+spYLBb1yU/XpWcGIjCfVTcecz+Hm77xqxUAAAAAYolRMSEnzWH2hmh20zemO2qbA5kxeWTGxB0zM4ZgTCdGmTKqnCAS+JQBSHhGOnpXZcqOZ2BRliwWae8hlxrdXa/nDt5N4SQYAwAAAAAxpSkYjEl32pXuDJwbNrko2dQdtcHMmFwyY+KOmRnjpWfMkQ73jCEzBuHHFUMACc3t9ckb/MOa6ji5X3kZKXb1y0+XJO1q6vqPs9vMjOEPOAAAAADEkhYzGGNTutPe4TmcmDojMyaDzJh44yAzpkteH9dyEDkEYwAkNCMrRuq6TNmJGFScLUna1dz1Om7KlAEAAABAzHF7fWoLnq8FgjGBc8PmNsqUdQeZMfHrcGYMwZgjGVVO7GTGIAK4YgggobUGgzEWi5RyCo0ZjWDM7mNkxnjMMmX8AQcAAACAWNHcLgMm3WlXeoqt0/M4PjMzhmBM3DGCMW4yYzrxmmXKuEyO8Av7p+y///u/ZbFYNHXqVPO51tZWTZ48WT169FBmZqbGjRunmpqaDq/bvn27xo4dq/T0dBUUFOjee++Vx9PxjoVly5bp/PPPV0pKis4880zNnTs33NMBEGdc7sAXjRS7VRbLyQdJDmfGHKNMmc+4m4I/4AAAAAAQK4xyZHarRU67VemOQJkygjHdY2TG5KVTpizeGDeNkhnTmSf4nji4sRYRENYrhqtWrdJvfvMbDR06tMPz99xzj/7xj3/o1Vdf1bvvvqvdu3frm9/8prnc6/Vq7Nixamtr04cffqjf/e53mjt3rmbMmGGuU1VVpbFjx+qqq67SunXrNHXqVP3gBz/QW2+9Fc4pAYgzRpmytFMoUSZJg4qzJEk1LV3XWDXuMKHOKAAAAADEjqZgOTKjPJmRGdNEmbIT5vJ4zeAVZcrij1mmjMyYTjxmZgzXchB+YQvGNDY2asKECfqf//kf5eXlmc/X19frpZde0uOPP66vf/3rGjFihF5++WV9+OGH+uijjyRJixcv1saNG/WHP/xBw4YN03XXXaeHH35Yzz33nNraAlH4OXPmqLS0VI899pgGDRqkKVOm6Fvf+paeeOKJcE0JQBwyypSdSr8YSTotN03ZqXZ5/RZ9ta/pqOt4gk3fnPSMAQAAAICYYWTGpDvtwf/bOjyP4zNKlNmsFmWn2qM8GnSX0xb4zBOM6czro2cMIidsvz0nT56ssWPHatSoUfr5z39uPr969Wq53W6NGjXKfG7gwIHq27evKisrdckll6iyslJDhgxRYWGhuU55ebnuuusubdiwQcOHD1dlZWWHbRjrtC+HdiSXyyWXy2U+bmhokCS53W653e5TnfJxGfuIxL6iIdHndyzJNvd4mu+hlsDPfKrdesrjPbsgQx9vr9eGXXVmpkx7Zu1Vvy8u3psTEU/HOpSSdd5S8s092ebbXrLNPdnmazhy3sk2fwAAJKnJFcyMCWbEpAXLlJEZc+IONgVujs5Nc5xSCXBEh5kZQ5myTtzB/r92bqxFBIQlGPOnP/1Ja9as0apVqzotq66ultPpVG5ubofnCwsLVV1dba7TPhBjLDeWHWudhoYGtbS0KC0trdO+Z82apYceeqjT84sXL1Z6evqJT/AUVVRURGxf0ZDo8zuWZJt7PMx3U51Fkk1tLU1auHDhKW0r1WWVZNWSVRuUvveTTsvrG22SLFq1olL7Np7SrmJOPBzrcEjWeUvJN/dkm297yTb3ZJuvwZh3c3NzlEcCw/Lly/XLX/5Sq1ev1p49e/T666/rpptuMpd///vf1+9+97sOrykvL9eiRYvMxwcPHtTdd9+tf/zjH7JarRo3bpyeeuopZWZmmuusX79ekydP1qpVq9SrVy/dfffduu+++8I+PwCIJc1uIzMmEIzJSCEzpruMfjG59IuJS0Y/FDJjOvMGq5yQGYNICHkwZseOHfrhD3+oiooKpaamhnrzp+SBBx7QtGnTzMcNDQ3q06ePRo8erezs7LDv3+12q6KiQtdcc40cjsT745Xo8zuWZJt7PM3XsXGvtGmdevXI1ZgxF5/Stqrf36L33/pS1pxCjRkzvNPyX2x4V3K59LUrLtc5JeH/nRIJ8XSsQylZ5y0l39yTbb7tJdvck22+hiPnbWSGI/qampp03nnn6fbbb+/QP7O9a6+9Vi+//LL5OCUlpcPyCRMmaM+ePaqoqJDb7dZtt92mSZMmaf78+ZIC5zujR4/WqFGjNGfOHH3yySe6/fbblZubq0mTJoVvcgAQY8wyZcGMmLRgUKaZYMwJM8qU5dEvJi6RGdM1IzOGnjGIhJAHY1avXq29e/fq/PPPN5/zer1avny5nn32Wb311ltqa2tTXV1dh+yYmpoaFRUVSZKKioq0cuXKDtutqakxlxn/N55rv052dvZRs2KkwMnLkScwkuRwOCJ6Uh7p/UVaos/vWJJt7vEwX3fgb6rSnfZTHusZBYHSZNsOthx1W0ad0dSU2H9fuisejnU4JOu8peSbe7LNt71km3uyzddgzDsZ5x6rrrvuOl133XXHXCclJcU8/znSpk2btGjRIq1atUoXXHCBJOmZZ57RmDFj9Ktf/UolJSWaN2+e2tra9Nvf/lZOp1PnnHOO1q1bp8cff5xgDICkcmSZsoxg75hmypSdsMOZMQRj4lFKMBjjJhjTiXEtx0GZMkRAyD9lV199tT755BOtW7fO/O+CCy7QhAkTzH87HA4tWbLEfM3mzZu1fft2lZWVSZLKysr0ySefaO/eveY6FRUVys7O1uDBg8112m/DWMfYBgBIUmswHT3VYTvlbZ3eI0OStPVAs3zBP9btGem+/AEHAAChsGzZMhUUFGjAgAG66667dODAAXNZZWWlcnNzzUCMJI0aNUpWq1UrVqww17nyyivldB6+cFZeXq7NmzertrY2chMBgChrOaJMWTqZMd12ODOGGzvikXGdgjJlnXl8ZMYgckKeGZOVlaVzzz23w3MZGRnq0aOH+fzEiRM1bdo05efnKzs7W3fffbfKysp0ySWXSJJGjx6twYMH63vf+55mz56t6upqTZ8+XZMnTzYzW+688049++yzuu+++3T77bdr6dKleuWVV/Tmm2+GekoA4lirO/BFIy0EwZjTclNltfjl8vhU3dCqktyOWXjGH3CHlWAMAAA4Nddee62++c1vqrS0VF999ZV+8pOf6LrrrlNlZaVsNpuqq6tVUFDQ4TV2u135+fkd+myWlpZ2WKd9L868vLxO+3W5XHK5XOZjo7Sd2+2W2+0O6RwROsax4RglF477iWsIZnWk2q1yu91KCfbPaGyN/d9tsXKcDxxqlSRlp9qiPpZkEcpjb/UHro24PF6O3xHa3IEMOav8UXlvYuVnHB2F63iEPBhzIp544gmzwaTL5VJ5ebl+/etfm8ttNpsWLFigu+66S2VlZcrIyNCtt96qmTNnmuuUlpbqzTff1D333KOnnnpKvXv31osvvqjy8vJoTAlAjDLugEpxnHqAxG6zqmeKtLdVqtrf1CkYY6T72m3cTQEAAE7N+PHjzX8PGTJEQ4cO1RlnnKFly5bp6quvDtt+Z82apYceeqjT84sXL1Z6enrY9ovQqKioiPYQEAUc9+P7ZLtVklX7du/UwoXb9fk+iySbduyp0cKFC6M9vBMS7eP86ZeB97BmxxYtXPhVVMeSbEJx7Pe2SJJdza1tcfOZj5QvqwKf7a1VW7Rw4ZdRG0e0f8bRUXNzc1i2G5FgzLJlyzo8Tk1N1XPPPafnnnuuy9f069fvuL8cRo4cqbVr14ZiiAASVCjLlElSrzS/9rZaVLW/SZed2dN83u/3m03fKFMGAABCrX///urZs6e+/PJLXX311SoqKupQ1lmSPB6PDh48eNw+m8ayo3nggQc0bdo083FDQ4P69Omj0aNHKzs7O5RTQgi53W5VVFTommuuoTdUEuG4n7h1/9ws7dqmQWf315jRZ8uxca/+98t1SsvK05gxF0d7eMcUK8f5jT+skfbtV9nwIRpzQe+ojSOZhPLY76pr0SPr3pPfYtOYMdzI3t6Kf2yUqndqwFlnaszVZ0Z8/7HyM46OjOzwUItKZgwARIqRGROKMmWS1Cs18P+q/U0dnve26yHjIDMGAACE2M6dO3XgwAEVFxdLCvTQrKur0+rVqzVixAhJ0tKlS+Xz+XTxxReb6/z0pz+V2+02T+4rKio0YMCAo5Yok6SUlBSzNHR7DoeDCwRxgOOUnDjux9fqCZyvZaY65XA4lJ0e+D3X6vbFzXsX7eNc3xIo5dQjKy1u3rNEEYpjn54SuDbS5vXJbrfLYuG6hcGvwHuR4rBH9bMd7Z9xdBSuY8Ht2wASmivYMyY1BGXKJKlXauBL/JHBGCMrRgqUMwMAADiWxsZGrVu3TuvWrZMkVVVVad26ddq+fbsaGxt177336qOPPtLWrVu1ZMkS3XjjjTrzzDPNssyDBg3StddeqzvuuEMrV67UBx98oClTpmj8+PEqKSmRJN18881yOp2aOHGiNmzYoD//+c966qmnOmS+AEAyaGkLBBLSnYGb9NKC/28O9orA8dU1B/on5KVzsTgeOe2B6xR+/+F+twjwBK/n2LixFhHAFUMACa01xJkxBcE2MVuPDMb4fOa/yYwBAADH8/HHH2v48OEaPny4JGnatGkaPny4ZsyYIZvNpvXr1+vf/u3fdPbZZ2vixIkaMWKE3nvvvQ5ZK/PmzdPAgQN19dVXa8yYMbr88sv1wgsvmMtzcnK0ePFiVVVVacSIEfrRj36kGTNmaNKkSRGfLwBEU1Nb4Lww3RkoEJOREgzGuLxRG1O8qW1ukyTlZTijPBKcDCMYIx3ud4sAIzjlsHKZHOFHmTIACa0lxD1jCoKZMdsPNsvj9ZlZMJ52mTH8AQcAAMczcuRI+f1d35n61ltvHXcb+fn5mj9//jHXGTp0qN57771ujw8AEkmLGYwJnBemOwKXw5rbCMacCJ/Pr/qWQGZMLpkxcal9b9s2j0/pxNRMRjDGZuXGWoQfVwwBJDQjMyYlRMGYbGeg5JnH59fO2hbzeePOEpvVIit/wAEAAAAgZjR1Uaasxe2Vj5JNx9XQ6pbxNuWmcRU/HtmtFhltYto8ZMa05w1WOrFT5QQRQDAGQEJrCfaMCVWZMqtFOj0/XZJUsbFGG3c3yO/3m8EYO4EYAAAAAIgpLV2UKZMOV1NA12qD/WIyU+wdyl0hflgsFjmD2TEugjEdGD2A7VQ5QQTwKQOQ0FrNMmWh+3V3es8MSdIjCzdpzNPv6W/rdptlytqn/gIAAAAAos/MjAkGYVLttk7L0DWjXwwlyuKbEUijZ0xHXp8RjOHmWoQfVw0BJDRXMBgTqswYSbr5ot4aXJyt7NTAXVVb9jeZX2YcpLUCAAAAQEw5smeM1Wox/91C35jjqgsGY/JoNBLXjMyYNoIxHZiVTriegwggGAMgobWYmTGhC8aU9e+hhT+8Qv9+QR9JksvjPZzWSmYMAAAAAMSUZiMY47CbzxnBmGaCMcdV2xQoU0ZmTHwzMmPoGdORkRljIzMGEcBVQwAJrTXYMyaUZcoMKY7DX2TMzBj+eAMAAABAzPD5/IeDMe16xRj9Y5opU3ZctWTGJASCMUfn8dEzBpHDpwxAQgtHZozBaQts0+XxyeMLBmNoZggAAAAAMaPVczjzxciGaf9vMmOOr645kBmTR2ZMXHNQpuyoPJQpQwRx1RBAQmsNYzCmY2YMDd8AAAAAINY0uQLnhBaLlGo/fF6YFgzGGMvRNSMzJpfMmLhm9owhM6YDr4/rOYgcgjEAEporWKYsLSyZMYFfoa72ZcroGQMAAAAAMaMlmPmS5rDJ2u5ia0awTFmLmzJlx0NmTGKgTNnR0QMYkcSnDEDC8vr8ZvpteDNjvPIE/3gTjAEAAACA2NEcDLa0L1EmHc6MoUzZ8Zk9YzLIjIlnRjDGCD4ggMwYRJI92gMAgHAxSpRJUqoj9EGSo2XGUGMUAAAAAGKHUYYs3dnxEliGEYwJc5kyv9+vfY0u6SSvf7s9HtW3SXsPueSwRydwtL/RJYkyZfHOuIaxv9GlvQ2tkqRUp03Zqcmd8WT0ALYRjEEEEIwBkLBa2gdj7OHIjAlss33PGDJjAAAAACB2GGXKOmfGBC6JhTszZvL8NVr4SfUpbsWuGavfDcl4TgVlyuKbkRnz//6+Qf/v7xskBQIQL95yga4aWBDNoUWVx2dczyEYg/AjGAMgYRmZMU67tUNt4FBpnxlj3EnBH28AAAAAiB1NbUcvU5ZulikLX88Yn8+vJZv2SpKsFsliObnzRb/PJ4s1ujf+nV2YpbMLs6I6Bpya0YML9eFX+82bSX1+v7w+v979fF9yB2OC74ctyj9jSA4EYwAkrFZ3IECSFoZ+MdLhnjEuj/dwwzf+eAMAAABAzDicGdNFmbIwZsbsaWiVy+OT3WrRZw9fe1INwt1utxYuXKgxY8rlcJCZgpM3/qK+Gn9RX/Pxn1Zu149f+0Rb9jdFcVTRZ9xcS88YRAJXDQEkLCMzJhz9YiQpJfhFuq1dzxjKlAEAAABA7OgqM8YoU9YUxsyYqn2Bi9x9e6SfVCAGCKfSnhmSpKr9jVEeSXR5g2XK6AGMSOAvAYCEdTgYE+7MGJ88XsqUAQAAAECs6apnTEaKrcPycDAucvcPXvQGYklpr8Dncmdti1ye8PZOimVGzxgyYxAJBGMAJKyWYDAmXGXKnLbAdts8PrUZZcq42wkAAAAAYoZRhiztiDJlxnliOMuUGeWfSgnGIAb1ykxRZopdfr+0/UBztIcTNR7KziOC+JQBSFhGz5gUMmMAAAAAICkZZcgyOmXGBIIzzeEsU2YGYzLDtg/gZFksFjNQmMx9Y4yeMTYyYxABBGMAJKxWMzMmPL/qnO16xhhprQ7upAAAAACAmNFVmbI0Z/gzY4xgTP9eZMYgNhmfzapkDsYEM2PoAYxI4FMGIGG1RKxnjFdtnmBmjJ07KQAAAAAgVjS5gsGYlI5lytLDXKaszePTjoOB0k/0jEGsMjJjqvYlZzDG7/ebN9eSGYNIsB9/FQCITy4jGGMPUzAmuF2316+2YJkyaowCAAAAsWfan9fpn59WR3UMdqtFPxp9tr5/WWlUx5FsWtyBMmRHZsaEu0zZ9oPN8vkD5dF6ZaWEZR/AqTKDMUmaGROMw0gK/I4Gwo1gDICEZWTGpDnDE4xx2g8HXppdgS/w9IwBAAAAYkubx6fX1u6K9jAkSX/7126CMRFmZsY4O14CM8uUucKTGWP2i+mVIYuF80TEpv7BfkbJ2jPGHbyxVpLsXM9BBBCMAZCwWt2BP6qpYeoZk9IuGNMY/AJPjVEAAAAgthxsapMUKEHzzo9GKhrXxdfvrNfk+WtU1+yO/M6TXFc9YzKCwZlmt1d+vz/kAZMt+xolSaXBi91ALDq9Z7okaX+jSw2tbmWnOqI8osjytkuNodIJIoFgDICE1RrmnjF2q0UWi+T3H05ttxOMAQAAAGLK/kaXJKlHhlN9e6RHZQzGuUltc1tU9p/MmoNlyo6smGA89voCZadTQlze2syMoV8MYlhWqkO9slK075BLW/c3aWjv3GgPKaI83sPBGHrGIBK4agggYbWEORhjsVjM7JhGo0wZf7wBAACAmLIvGIzpmRm9vh256U5JUn2Lu8Od2Ag/owxZxhFlytpnyoSjVJlR9qk/wRjEuGTuG+PxtStTxvUcRADBGAAJyyxTFuI7nNpzBjNhmoxgjJ1fqwAAAEAs2X8oGIyJYhP13PRA6R+/PxCQQeQ0d1GmzGGzmudzze7QB2PIjEG8MAKGW/YlXzDGCI5bLZKVYAwigKuGABKWUQogzRm+X3Upwawboykkd1IAAAAAsWV/Y6A0WM9MZ9TG4LBZlZUSyMygVFlkNQVLSh8ZjJEOlyprDt5cFyqHWt3aFwwCnk4wBjEumTNj3MFgDCXnESl80gAkrHD3jJEOZ8YYZcqcZMYAAAAAMcXoGdMrimXKJCk3I5AdU0cwJqJazMyYzm2TM4xgTFtoM2O27m+WFAgA5qQlV0N0xJ9kDsZ4gz1juLEWkcJVQwAJKxLBmBRHsExZ8G4ru5VfqwAAAEAsORADPWMkKS/YN6a2iTJlkdLm8ckTvPM97RiZMcb5XKhs2d8oiRJliA/9ex0Oxvj9ydXTyugZYyMYgwjpfFsAAMS5//1om/60cru2Bu/qiERmjNEzxm7jDzgAAAAQS8wyZVnRK1MmSblGMIbMmIhpbhdkOVqZsoxg6biWEGfGGBkG/XtmhnS7QDj0zc+Q1RKo+LGv0aWCrNRoDylijGCtgzJliBA+aQASykdbDuj//e1TbdjdoKbgF+rSHuG7G8noGeMOprY6+QMOAAAAxBSjTFmPjGhnxhhlysiMiRSj/JjTZj3qxdY0R3jKlBnBmNJeZMYg9jntVvXJT5ckbdmXXKXKPMFrOWTGIFLIjAGQMA42temHf1orn1+64bwS/fsFvVWYnaqzC7PCts+UI77QkxkDAAAAxJb9sVamjMyYiDEyY9JTjl4tId3sGRPaMmVmMIYyZYgTpT0ztO1As6r2N+mS/j2iPZyIMcqUOQjGIEIIxgBIGD9fsFE1DS7175WhR8cNOWqDxlAzesYY6BkDAAAAxA6vz6+DTbFSpiyQGVNLZkzEGBkv6V2Urk4PlikLZWaM3+9X1T6jTBnBGMSH0p4ZWrZ5nxlITBZGmTIbN9YiQrhqCCAh+P1+vfv5PknSIzdFJhAjdS5L5rTzBxwAAACIFQeb2uTzSxaLlJ8e3WCMkRlTR2ZMxDS5gsGYlKOfH6aHoUzZ/sY2HXJ5ZLFIfXukh2y7QDgZgcNkK1PmDQZjuLEWkcInDUBC2HfIpQNNbbJapOF9cyO2XzJjAAAAgNhllCjLT3fKHuX+jnkZlCmLtBZ3sEyZ8+iZMRlmZkzoypRt2dcoSeqdl6YU+9H3C8Sa0p6ZkqSq/Y1RHklkub2BMmV2ypQhQrhqCCAhbNzTICmQWpvaRQp6OByZGUPPGAAAcCKWL1+uG264QSUlJbJYLHrjjTc6LPf7/ZoxY4aKi4uVlpamUaNG6YsvvuiwzsGDBzVhwgRlZ2crNzdXEydOVGNjx4so69ev1xVXXKHU1FT16dNHs2fPDvfUgJhyoDFYoizK/WIkKS9YpqyOMmURY2S8pHVxjpjmDH1mzOF+MZkh2yYQbqW9Apkx2w82yxMMUCQDIzPGRjAGEUIwBkBC2LTnkCRpUHF2RPd75J1ORwZnAAAAjqapqUnnnXeennvuuaMunz17tp5++mnNmTNHK1asUEZGhsrLy9Xa2mquM2HCBG3YsEEVFRVasGCBli9frkmTJpnLGxoaNHr0aPXr10+rV6/WL3/5Sz344IN64YUXwj4/IFYYmTE9MqNbokw6XKaMzJjIaQ6WKcs4XpkyV+iDMfSLQTwpzk5Vit0qt9evXXUt0R5OxHi8gWCMg2s5iJDINFUAgDDbFMyMiXQwxmk/MjOGP+AAAOD4rrvuOl133XVHXeb3+/Xkk09q+vTpuvHGGyVJv//971VYWKg33nhD48eP16ZNm7Ro0SKtWrVKF1xwgSTpmWee0ZgxY/SrX/1KJSUlmjdvntra2vTb3/5WTqdT55xzjtatW6fHH3+8Q9AGSGRGMCYWMmNyg5kxtc1u+f1+WSzciR1uRvmxtC7KlBm9ZJrdoQvGbDEzYwjGIH5YrRaV9szQZ9WHtGV/k/r1SI7Pr4fMGEQYwRgACcEIxgyOeGZMx+CLgzJlAADgFFVVVam6ulqjRo0yn8vJydHFF1+syspKjR8/XpWVlcrNzTUDMZI0atQoWa1WrVixQt/4xjdUWVmpK6+8Uk7n4YyA8vJyPfroo6qtrVVeXl6nfbtcLrlcLvNxQ0PgO5bb7ZbbTWmlWGUcG45RZzX1gTu889PtUX9/Mh2Bc4U2j08Nza1Kd3bvksz8lTvk9vp0a1k/Sclx3Jd9vk+vr92t4PXSbjOyVNLs1qO+TynBGE1ja9tRl/v9fj255CszwHIiVlYdkCT1yUsJybFJhuOMo4v0se+Xn6bPqg/psbc2688rt0dkn5J0TnGW7vxa/xNa9+NttfrDih1mebFTVdMQyDi2WaL3M8bPeGwK1/EIeTBm1qxZeu211/TZZ58pLS1Nl156qR599FENGDDAXKe1tVU/+tGP9Kc//Ukul0vl5eX69a9/rcLCQnOd7du366677tI777yjzMxM3XrrrZo1a5bs9sNDXrZsmaZNm6YNGzaoT58+mj59ur7//e+HekoAYlyr22t+OY52ZgyprQAA4FRVV1dLUofzI+Oxsay6uloFBQUdltvtduXn53dYp7S0tNM2jGVHC8bMmjVLDz30UKfnFy9erPT09JOcESKloqIi2kOIOf/60irJqv27qrRw4ZaojsXvl2wWm7x+i157c7Hyu5Gs0+aVHlxpk18WpdRsUHa7qmuJfNwfXG1Tbdup3/B2aO8OLVy4rdPzX+y3SLJp2+59WrhwYaflOxqlX3/S/UtnVvm189OVWvj5yYz26BL5OOPYInXs7YcCPw+f7m7Qp7sbIrJPSVq0oUbOfZ+p6AS+Zjz+iU3bGkN/E6yn8eBRfwdEEj/jsaW5uTks2w15MObdd9/V5MmTdeGFF8rj8egnP/mJRo8erY0bNyojI5Dids899+jNN9/Uq6++qpycHE2ZMkXf/OY39cEHH0iSvF6vxo4dq6KiIn344Yfas2ePbrnlFjkcDv3iF7+QFLhbbOzYsbrzzjs1b948LVmyRD/4wQ9UXFys8vLyUE8LQAz7oqZRXp9feekOFWZHtvzAkT1j7KS2AgCAOPbAAw9o2rRp5uOGhgb16dNHo0ePVnZ2ZG96wYlzu92qqKjQNddcI4fDEe3hxJS//n61tO+ALhsxVGPOPy3aw9EvNryrvYdcGn7x5Tqn5MR/pnbUNsu/8n1J0hnDynTh6XkJf9yrG1pVW7lcVos0fcxAneypVprTptGDC5V5lL4xBdtq9bsvVqnNnq4xY67otHzRhhrpk3+pX366bru07wnv86zCTF10ev7JDfgIiX6c0bVIH/ur2rz62oYas7xfJMxbuUNf7G1SVulQjRnR+5jrtrq9+tGKpZL8unf0Wcroovxgd9msVl09sJd6ZUWnnCU/47HJyA4PtZAHYxYtWtTh8dy5c1VQUKDVq1fryiuvVH19vV566SXNnz9fX//61yVJL7/8sgYNGqSPPvpIl1xyiRYvXqyNGzfq7bffVmFhoYYNG6aHH35Y999/vx588EE5nU7NmTNHpaWleuyxxyRJgwYN0vvvv68nnniCYAyQZIwSZQOLsiNed5nMGAAAEGpFRUWSpJqaGhUXF5vP19TUaNiwYeY6e/fu7fA6j8ejgwcPmq8vKipSTU1Nh3WMx8Y6R0pJSVFKSueLEQ6HgwsEcYDj1NmBpkCZkaKc9Jh4b/LSndp7yKXGNn+3xlPX6jP/vaOuVZe2e22iHvdPdu+XFDjPu/2KM8Kyj975mZKkvQ0u2Wx2WY+I+OxrDHx+zj0tR9+/PDxjOFGJepxxfJE69g6HQ/9+Ub+w76e9mka3vtj7lf6185BuvuTYc1y365A8Pr96ZaXoP686K+H6bvEzHlvCdSzCftWwvr5ekpSfH7gjYPXq1XK73R3qHw8cOFB9+/ZVZWWlJKmyslJDhgzpkJZfXl6uhoYGbdiwwVyn/TaMdYxtAEgeG4PBmEiXKJOO1jOGYAwAADg1paWlKioq0pIlS8znGhoatGLFCpWVlUmSysrKVFdXp9WrV5vrLF26VD6fTxdffLG5zvLlyzvUvK6oqNCAAQOOWqIMSEQHGtskST0zo3PH85Fy0wMXd2qb27r1OmMekrrVvySerd5WK0k6v19u2PZRmJ0qi0Vq8/p08CjHpDrYT6IoJzVsYwCS2fl9A99HVm+vPe665u+EvrkJF4hB8gh5Zkx7Pp9PU6dO1WWXXaZzzz1XUqA2sdPpVG5ubod1j6x/fLT6yMayY63T0NCglpYWpaWldRpPtJtRJnpDpkSf37Ek29xjab6f1xzS2xsDvxfOLkgP65iONm+b5YimcT5PTLwvoRJLxzqSknXeUvLNPdnm216yzT3Z5ms4ct7JNv9Y1tjYqC+//NJ8XFVVpXXr1ik/P199+/bV1KlT9fOf/1xnnXWWSktL9bOf/UwlJSW66aabJAUqA1x77bW64447NGfOHLndbk2ZMkXjx49XSUmJJOnmm2/WQw89pIkTJ+r+++/Xp59+qqeeekpPPPFENKYMRJzf79eBpsA1gB6ZzuOsHRl56YFx1HUzGLO/8fC1jC37kiMYs2a7ceE1fMFjp92qnpkp2nfIpT11rZ2CdrvrWiRJxQRjgLAY3jdXkvTl3kbVN7uVk951NsKabeH/nQCEW1iDMZMnT9ann36q999/P5y7OWGx0owy0RsyJfr8jiXZ5h7t+a7Ya9Gft1jl9VuU6fDLvf1fWlj9r7Dvt/28N+8NNLgzvLN0idLD+ps1OqJ9rKMlWectJd/ck22+7SXb3JNtvgZj3uFqRInu+/jjj3XVVVeZj40+Lbfeeqvmzp2r++67T01NTZo0aZLq6up0+eWXa9GiRUpNPXxBcN68eZoyZYquvvpqWa1WjRs3Tk8//bS5PCcnR4sXL9bkyZM1YsQI9ezZUzNmzNCkSZMiN1Egiupb3HJ7AzdPxUwwJsPIjOlecHz/ocPBmKokyIxxebzasCtwA224L7wW56QGgjH1LRrSO6fDsur61uA6nW/4BXDqemamqF+PdG070Ky1O2o1ckDBUdfz+/1as71OknR+P4IxiF9hu2Q4ZcoULViwQMuXL1fv3ocbMBUVFamtrU11dXUdsmNqamo61DZeuXJlh+0dWdu4q/rH2dnZR82KkaLfjDLRGzIl+vyOJdnmHgvzPdDUph8/tlxev09XDeipR248J+zN1o42b9/6PZr/1SfmOmOuHa10Z+JEY2LhWEdDss5bSr65J9t820u2uSfbfA1HzjtcjSjRfSNHjpTf7+9yucVi0cyZMzVz5swu18nPz9f8+fOPuZ+hQ4fqvffeO+lxAvHMyCbJTrUrxR6aRs+nKjeYGdPdMmXtM2O2HWiS19f1749E8OmuBrV5feqR4VS/HuG9ebYoO1XrVW+WJGtvTz1lyoBwG9E3T9sONGvN9rougzE7a1u0v9Elh82iIaflHHUdIB6E/Iqh3+/X3Xffrddff13Lli1TaWlph+UjRoyQw+HQkiVLNG7cOEnS5s2btX379g71jx955BHt3btXBQWBH8KKigplZ2dr8ODB5joLFy7ssO2KigpzG0cTK80oE70hU6LP71iSbe7RnO/vPvpKLW6fhpyWo99+/6KI1gttP++0lI532KWnpiRk35hk+2wbknXeUvLNPdnm216yzT3Z5msw5p2McweQvPYdCvaLCfNNW92RFyzBU9fdzJh2PWPcXr921baoODtxf6evDZYoG943L+zneiW5gRt6jcCLwefzq6bByIwhGAOEy/B+eXpt7S7z5/5ojLKFg0tylOqIjeA6cDJCfsVw8uTJ+sMf/qD58+crKytL1dXVqq6uVktLoM5mTk6OJk6cqGnTpumdd97R6tWrddttt6msrEyXXHKJJGn06NEaPHiwvve97+lf//qX3nrrLU2fPl2TJ082gyl33nmntmzZovvuu0+fffaZfv3rX+uVV17RPffcE+opAYgxdc1t+v2HWyVJd3/9zKg2bktxdPw1arfSRA4AAACIBUY2yZF9QKLpZDNj9rXLjJGkLfsbQzamWGT2i+mXG/Z9GVkve4L9YQz7G13y+PyyWqSCGAroAYnm/GDfmHXb6+TrIuvvcL+Y3AiNCgiPkGfGPP/885ICafftvfzyy/r+978vSXriiSfMmsYul0vl5eX69a9/ba5rs9m0YMEC3XXXXSorK1NGRoZuvfXWDin6paWlevPNN3XPPffoqaeeUu/evfXiiy+qvLw81FMCEGbLP9+nXXUt+u5FfY+53tLPavTWpzXaeqBJTW1eDSrO1jWDCyM0yqNLaZcFY7daohoYAgAAAHDYgWAAo1cMBWPyzGBMdzNjAnPJTXeortmtqv1Nuqx/4vZNWLOtTlJkGnUbWS9HZsYYjwuyUmVPwOoHQKwYUJildKdNh1wefbG3UQOKsjqtY/aLicDvBCCcwlKm7HhSU1P13HPP6bnnnutynX79+nUqQ3akkSNHau3atd0eI4DY4fb69J/z1qjR5dHwvrkaWHT0/k2f7qrXnf+7Rm1en/lctLNipI6ZMYlYngwAAACIV0Zpr56ZzuOsGTmHy5R1LzPmQHAuF/TL19ubalS1vynkY4sVu+taVN3QKpvVoqG9w98bojgnUKbsyJ4x9IsBIsNus+q83rmq3HJAa7bXdgrGNLd5tHFPoO/h+f0IxiC+ceUQQFR9sqtejS5P4N8764+6TpPLo7v/uFZtXp8uLs3XveUD9MR3ztN15xZFcqhH5bQdrlVqt5EVAwAAAMQKI5ukRwxlxphlyppOPBjT5vGpviWQSXNRaeBCZCIHY1YHyxENKs5SujPk9xB30j4zpv0NxtX1LR2WAwgfoySh8fPf3vqd9fL6/CrMTlEJP4+Ic+H/qwYAx7Biy0Hz35v2HDrqOjP+tkFV+5tUnJOqOf8xQnkZsXNnG5kxAAAAQGyKxZ4xRmZMQ6tHHq/vhMpfHWgKzMNutWhYn0AwZsu+xA3GmP1iIlSOqCA78Plo8/h0sKnNDN4ZmTFG5gyA8DF+3o2f//ba/06IdnUU4FRx5RBAVK2sOmD+e1Mw7bS919bs1F/X7JTVIj01fnhMBWL0/9m77/imrvN/4B9JlmTLtrwnHpiN2SMBk4SNHeJs2iQkBUoGX6hpC7QJpSUDaEKbJiFJQ8avSSEDSiCBDCABs5eBYGz2shm28cJTnrLG/f2hgY23rWFJn/fr5ReW7tG955xrbF0993kOAJmkfjCGbwqIiIiIiLqKW12wTJmPh9T8fVlN29aNKaowjCPAS4aeQZ4AgNzyGtRqdJbvYBdg67Uh5G4S889I/XVjbgdjeCc+kbUNi7odaL6zjKMt15AisjYGY4jIbnR6ASeu377r4UK+qkFa+NVblVj63VkAwILJfXB3jL/N+9ia+pkxbmL+SiUiIiIi6iqKKoyZMd5dJzPGTSKG0t1QpKSt68aYy615yuHvKYPS3Q2CAGSVVFutn/ZSq9HhfK6hfLUtP3g1rxtTLxiTzzVjiGzG31OGmEBDsDnNGJAFDGuTp5kyY4ylzIgcGT85JCK7OZ+rQoVaCy+5GyRiEcqqNQ0WTVz87WlU1+kwuoc/kib0smNPm8fMGCIiIiKirkcQBHMQI6gLlSkDYM72L61uW2bMrcrbQSWRSIQeQV4AgGtFzheMOXuzHBqdgEAvOSL9bVcezBRwyat3PZqnMqwZE+7LYAyRLTRVqiyrpBrFVXWQScQYEO5jr64RWQyDMURkN8eMJcrujvE3p9ubSpVdLqjAL9dLIZWIsOrJoZCIu2agQy6VmL/nmjFERERERF1DVZ0Oaq0eQNdaMwYAfBXGYExV+zJjTKW0ehjvHr9e7HzBmNtrQ/jadG0IUymyvDJDAEavF+plxnDNGCJbMGW+1A/GmL4f0E0J93qfvxA5Kjd7d4DIVWh1euw4V4AqtbbD+xCLRRjXJwhBXSjNvjOOXSsBYAjGeLu74XJBJS7kVWBivxBsPnkTADC+b3CXXjCxfmZMWxbfJCIiIiIi6zOVKPOUSeAh61of4PkpDOvGlLUxM6bYuPaNKcPHVMrnalEVIltI2ihU1eLAlSLo9ULzjbqYn8/mAwCGR9t2bYg7y5QVV9VBoxMgEgHBTnL9TdTVmTJj0rPKsPGXbADAtjN5DbYROToGY4hsZOVPF/HZoWud3s/EfsH472/vskCP7M9U99O0Fsz36bk4n6eCTi9gS1oOAGDa8G52619bSCUiiESAIAAylikjIiIiIuoSzOusdLGsGADwM2XGtHPNGFOGT0zQ7cyYcS1cLi34Oh1HMos70VP7sfUHr+bMGGMwxhSUCfKSswICkY30CfGGl9wNlWotXvr2dINtDMaQs2AwhsgGdl8oMAdixvUJglsHSm6V12hw4kYprhdXWbp7dqHR6VFkvMMr2l+BilpDxtCFPBWOZBahQKWGj4cUE/oF27ObrRKJRJBJxFBr9cyMISIiIiLqIu4s7dWV+BozY9q6Zox5LN6GsZgyY64VVQHNBGPUWh1OXDfc/Da2TxCkXbTsc1N6BnthpI0zY0xrxpjWMM0rN5QrC/PtulUaiJyNRCzC648NxI+nciHUS+gL9XHH5Niu/dkQUVsxGENO41R2Gb5IuQGdXm/zY+v1Am7mirF70xmIm3iTu+/yLQDA7Hu649WHBnToGBfzVbj/3YNtTmXv6kx3gYlEhprJ/cO8AQDXi6qwYut5AMBDQ8Igd+taJQWaInczBmMc6AKHiIiIiMiZ3TLe+NXV1osBbmfGlLU1M6bC0C7A0zCW7gGGYExptQZVzVwenr2pQp1OjwBPGT6ffZdN119xROHGMmW5ZTUQBMGcIROmbKEOHBFZ3CNDu+GRoV27QgpRZzAYQ05jyeYzOG9c/N0+xEgtymt264BwJf4ytV+H917/DbteLzQZ9HEkJcbFKv0UMkjEIgR7u6ObrwdultXgckElAOBXIyLt2cU2k7lJAGghc2NmDBERERFRV2BaMyawC6734WfOjOlYmTJPuRtCle7IV9XiVm3TrzGVhB4W5cdATBsEKw1zq9bqUVatMQdjTBkzRERElsBgDDmFC3kqnM9TQSoR4aWEfrD1e029Xo/z588jNjYWYnHjD+TdxCI8MLhzWR4+HoY37HoBqFBrzY8dVYnxTjV/z9tlA/7727tw8Iohiygm0BNDI33t0bV2kxuDMMyMISIiIiLqGu4MYHQlfp6mNWNar3qg1elRYgzamMqUAYbrJUMwpulrkJPGYMzwaN9O9tY1uEslCPCUobiqDnnltcg3lSljMIaIiCyIwRhyClvSbgIAJvULwQtje9j8+BqNBtvLzuGBMdGQSq0TJHGXSuAhlaBGo0NZdZ3DB2OKqxoHY/qGeqNvqLe9utRh5mAM14whIiIiIuoSio03fwV1wTVj2lOmrKS6DoJgKO/sr6gXjAnyRMrVYhTWNBOMuVEGgItet0eoj7sxGFODXFOZMq4ZQ0REFsRPDsnhaXV6czDm8eHOXVfSr50LPXZlpjJlAZ5d7+KovUzlyWQMxhARERERdQldOTPGtx3Xdaagkr9C1uDmrx6BhnVjmipTlltWg3xVLSRiEQZH+Figx64hzLhujCEzxhiMYWYMERFZEDNjqJFajQ43y2ra/TqtRouCGuDqrSq4SW33o3Uquwy3KtTwU0gxvm+wzY5rD74KGXLLa9tcW7grayozxlHdzoxhmTIiIiIioq7AFIwJ6ILBmPqZMYIgtLimS3NBpRhjMKapzBhTibL+Yd5QyPixT1uZAi955TXmYEyoksEYIiKyHP5VpgbUWh0mv7MfOaXtD8YYuOGN9MMW7VNbPTQk3OkXUPfzNNxB1ZZ09q6upMpwUeEcwRjDWkBSZsYQERERWdTpnDLUavT27kaXp9VqkakCfrleCjc3XuYDwK0KUxCj611vmIIxGp2AqjodvOTNn7PbQaWG44iplxkjCEKDbSxR1jGhxmDM+VwV6nR6iERACIMxRERkQXyXRg3svViInNIaSMSiFt8QNk2Apk4DqUwKwLYZAn4KKX47prtNj2kPvuY7qJynTJkzBGNMQUApM2OIiIiILGrh1+nIvFVl7244CDe8f+4Xe3eiywn07nqZMR4yCeRuYqi1epRW1bUcjKkwXDfdmRkT6a+Am1iEOj1wo6QavUNvX1eZMmMYjGmfcF9D4CUtuwyAYc6d/YZPIiKyLQZjqIFvTxrWXnnhvh74y9R+7XqtRqPB9u3b8cADCVZbxN7V+Xo435oxzhCMMZcpE/ONOhEREZElRforILTejAQBlZVV8PLyNKz0TgCAsb2DoHTvmtemfgoZ8lW1KKvWINK/+XbNlSmTSsQYFeOPw5nF+N/xHLzysC8AQ9nxc7nlABiMaa9QpWHNGNPNj1wvhoiILI3BGDIrqarD3ouFAIDHh3ezc2+oKfVrCzs6UzAmwLPr3anWXrczYxiMISIiIrKktbPvtncXHMLtG+Pu5Y1xDsJXIUW+qvX1QG+ZgjHejW9imz0mCoczi/F1ag4WxPeF0l2Kc7nl0OgEBHrJEOnvYZW+O6s7gy9cL4aIiCyNnxyS2Y+ncqHVCxjUzQd9Qrzt3R1qgq+CmTFdkZxlyoiIiIiIqB1MN9q1Fowprmy6TBkA3NcrECEeAqrUOmz8JRsAkHrDUKJsWJQfRMySapfQO4Ix4b4MZhERkWUxGEP49OBV/G5dKj7alwmAWTFdmbNkxuj1gjmgdOdClI7IlBnjxmAMERERERG1gZ+n4Ua71tYDNZUpC2oiGCMWizA+TA8AWHP4OrQ6PU7eKAPAEmUd4S6VNLhZ8M7gDBERUWexTJmLu5Rfgb9vu2B+LHMT46Eh4XbsEbWkrW/Yu7ryGg10ekP1b1OAyZGZ0teDvflmnYiIiIiIWufbxsyY5taMMRkZKCC5QIqbZTX46Ww+TmYZMmOGR/larrMuJFTpbq7iwDVjiIjI0hiMcXGbT+YAAEZE++HRoeEYFOHb7Js8sj8fj7a9Ye/qio1vbr3d3cxZJY5szrieGNDNB+P6BNm7K0RERERE5AD8FK3faKfXC+YyZc1VFJBJgGfujsS/917Fv3ZcQmGFGm5iEQZH+Fq8z64gzMcd5/NUALhmDBERWZ7jfwpKHabTC9iSdhMAMGdsD8yI646hkb727RS1qC1v2B2B6U6jACdYLwYAvORuSBgQCnepxN5dISIiIiIiB9CWNWPKazTQGisKtFTe+Zm7IyFzEyOrpBoAEBuuhIeM1yYdEeZ7OwDDNWOIiMjSGIxxYYczilBYoYavQooJfYPt3R1qA9Mb9kq1FnVavZ1703ElVYZUez8nCcYQERERERG1x+0yZc3faGcqUaZ0d4PcrfngSoCXHI8Pu732K9eL6bgwn9sBmGAlq4YQEZFlMRjjwkwlyh4eEu4UpaJcgdJDCpFxjfjyGsfNjil2sswYIiIiIiKi9jBVPbhVoW62zS3TejHerQcFnr03xvz9MK4X02Gm0mSBXrIWA2BEREQdwU/gXVSlWoufz+UDAB4fHmHn3lBbScQiKN1Npcocd92YEmPdY38GY4iIiIiIyAX1D1NCLAIu5KlwKb+iyTam9WLasq5rnxBvzIqLRv8wJcb3YeWLjuob6t3gXyIiIktys3cHyD5+OpOHWo0ePQI9MSTCx97doXbwU0hRXqNpMZ29qyupNgVjmPZNRERERESuJ9zXA/cPDMX2M/n49OBV/OvXQxq1MZUpC2pDMAYAlj0y0KJ9dEUDu/ng23lxiA7wtHdXiIjICTEzxkVtPnkTAPD48G4QmepekUPwbcNCj11dCcuUERERERGRi3v+vh4AgO/Tc1FYUdtouykYE+DF6yZbGhHt36ZsJCIiovZiMMYF5ZRWI+VqMQDg0XqL/JFjMNUWdugyZVUsU0ZERERERK5teJQfhkf5ok6nx1cpNxptL6poe5kyIiIi6voYjHFB36fnAgBG9/BHhJ/Czr2h9vIzZsaUOXCZMlPtY3/e4UVERERERC7MlB3z5dEbqNXoGmwzZcYwGENEROQcuGaMnZzKLsO/91yxy7HTssoAAI8Pj7DL8alzfIyZMdZaM0anF/DmjovILKxsto1eL6CwUIzvS9IgFre/zN3VIsO+WaaMiIiIqGmvvfYali1b1uC5vn374uLFiwCA2tpa/OlPf8KGDRugVquRkJCADz/8ECEhIeb2WVlZmDdvHvbu3QsvLy/MmjULK1euhJsbLwOJuor42BBE+Hkgp7QG357MwTOjos3bbgdjeN1ERETkDPgu3E6Kq9TYdaHQbsf3krth6sBQux2fOu52Zox1ypR9uDcDn+y/2oaWYqD0VoePI5WI0M3Xo8OvJyIiInJ2AwYMwK5du8yP6wdRFi5ciG3btmHTpk3w8fHB/Pnz8fjjj+Pw4cMAAJ1Oh8TERISGhuLIkSPIy8vDzJkzIZVK8cYbb9h8LETUNDeJGLPvicGKrefx2aFrmH5XlPmGtyJjRYFAb2bGEBEROQMGY+ykb6gS/5w2yG7HHxLpC293qd2OTx3nZ86MsXww5vi1EqzadRkA8LvxPREd0HQZO51Oh9Onz2Dw4EGQSCQdOlafEG8EMN2eiIiIqFlubm4IDW18A1V5eTk+++wzrF+/HhMnTgQArFmzBv3798fRo0cxevRo7Ny5E+fPn8euXbsQEhKCoUOHYsWKFVi8eDFee+01yGS8056oq3jyrki8m3wZV29VYd/lQkzsFwJBEMyZMUG8biIiInIKDMbYSTdfDzx5V5S9u0EOyNeYGZN8vgCxr/xs0X2rtXroBeDx4d3w0v39mm2n0WjgWXAaD4yIgFTKoB4RERGRNVy5cgXh4eFwd3dHXFwcVq5ciaioKKSmpkKj0WDy5Mnmtv369UNUVBRSUlIwevRopKSkYNCgQQ3KliUkJGDevHk4d+4chg0b1uQx1Wo11Gq1+bFKpQJgeP+n0TjumoXOznRueI4ck1wMPDGyGz47fANzvkiFzE0MQRCg1uoBAEq5qMlzy/PuGnieXRfPvWvgee6arHU+GIwhcjCDI3wgdxNDrdWjuk7X+gvaqU+IF1Y8MtDi+yUiIiKiths1ahTWrl2Lvn37Ii8vD8uWLcN9992Hs2fPIj8/HzKZDL6+vg1eExISgvz8fABAfn5+g0CMabtpW3NWrlzZaK0aANi5cycUiqazpqnrSE5OtncXqIMi1YCHRIIaHaCtd50X6Slg366dLb6W59018Dy7Lp5718Dz3LVUV1dbZb8MxhA5mOgAT5xYOhll1daJ0IYo3SFzE1tl30RERETUNlOnTjV/P3jwYIwaNQrR0dHYuHEjPDyst+7ekiVLsGjRIvNjlUqFyMhIxMfHQ6lUWu241DkajQbJycmYMmUKM9cd2KMPaFFyRznqMKU73CRNX5/xvLsGnmfXxXPvGnieuyZTdrilMRhD5IC83aVc84eIiIjIhfj6+qJPnz7IyMjAlClTUFdXh7KysgbZMQUFBeY1ZkJDQ3H8+PEG+ygoKDBva45cLodc3nh9CqlUyg8IHADPk2PzlUrh69X+YCvPu2vgeXZdPPeugee5a7HWueDt70RERERERF1cZWUlMjMzERYWhhEjRkAqlWL37t3m7ZcuXUJWVhbi4uIAAHFxcThz5gwKCwvNbZKTk6FUKhEbG2vz/hMRERERuTpmxhAREREREXUxf/7zn/HQQw8hOjoaubm5ePXVVyGRSDB9+nT4+Pjgueeew6JFi+Dv7w+lUonf//73iIuLw+jRowEA8fHxiI2NxYwZM/Dmm28iPz8fS5cuRVJSUpOZL0REREREZF0MxhAREREREXUxOTk5mD59OoqLixEUFIR7770XR48eRVBQEABg1apVEIvFmDZtGtRqNRISEvDhhx+aXy+RSLB161bMmzcPcXFx8PT0xKxZs7B8+XJ7DYmIiIiIyKUxGENERERERNTFbNiwocXt7u7uWL16NVavXt1sm+joaGzfvt3SXSMiIiIiog7gmjFERERERERERERERERWxGAMERERERERERERERGRFTl8MGb16tXo3r073N3dMWrUKBw/ftzeXSIiIiIiIiIiIiIiIjJz6GDM119/jUWLFuHVV1/FyZMnMWTIECQkJKCwsNDeXSMiIiIiIiIiIiIiIgLg4MGYd955By+88AJmz56N2NhYfPzxx1AoFPjvf/9r764REREREREREREREREBcOBgTF1dHVJTUzF58mTzc2KxGJMnT0ZKSoode0ZERERERERERERERHSbm7070FFFRUXQ6XQICQlp8HxISAguXrzY5GvUajXUarX5sUqlAgBoNBpoNBrrddbIdAxbHMsenH18LXG1sbvaeE1ccdyuOGbAdccNuN7YXW289bna2F1tvCZ3jtvVxk9ERERERNRVOGwwpiNWrlyJZcuWNXr+u+++g0KhsFk/vv/+e5sdyx6cfXwtcbWxu9p4TVxx3K44ZsB1xw243thdbbz1udrYXW28JqZxV1dXAwAEQbBnd8iBmH5WTDeyUdek0WhQXV0NlUoFqVRq7+6QjfC8uwaeZ9fFc+8aeJ67JtN7X0tfNzlsMCYwMBASiQQFBQUNni8oKEBoaGiTr1myZAkWLVpkfnzz5k3Exsbi+eeft2pfiYiIiIi6koqKCvj4+Ni7G+QAKioqAACRkZF27gkRERERkW1Z+rrJYYMxMpkMI0aMwO7du/Hoo48CAPR6PXbv3o358+c3+Rq5XA65XG5+7OXlhezsbHh7e0MkElm9zyqVCpGRkcjOzoZSqbT68WzN2cfXElcbu6uN18QVx+2KYwZcd9yA643d1cZbn6uN3dXGa3LnuAVBQEVFBcLDw+3dNXIQ4eHhNr1moo5x1d9xro7n3TXwPLsunnvXwPPcNVnruslhgzEAsGjRIsyaNQsjR47E3XffjXfffRdVVVWYPXt2m14vFosRERFh5V42plQqnfo/l7OPryWuNnZXG6+JK47bFccMuO64Adcbu6uNtz5XG7urjdek/riZEUPtYa9rJuoYV/0d5+p43l0Dz7Pr4rl3DTzPXY81rpscOhjz5JNP4tatW3jllVeQn5+PoUOH4ueff0ZISIi9u0ZERERERERERERERATAwYMxADB//vxmy5IRERERERERERERERHZm9jeHXAlcrkcr776aoN1a5yJs4+vJa42dlcbr4krjtsVxwy47rgB1xu7q423Plcbu6uN18RVx03kavh/3TXxvLsGnmfXxXPvGnieXYtIEATB3p0gIiIiIiIiIiIiIiJyVsyMISIiIiIiIiIiIiIisiIGY4iIiIiIiIiIiIiIiKyIwRgiIiIiIiIiIiIiIiIrYjCGiIiIiIiIiIiIiIjIilw+GLNy5Urcdddd8Pb2RnBwMB599FFcunSpQZva2lokJSUhICAAXl5emDZtGgoKCszbT506henTpyMyMhIeHh7o378/3nvvvQb7yMvLw9NPP40+ffpALBZjwYIFbe7j6tWr0b17d7i7u2PUqFE4fvy4eVtJSQl+//vfo2/fvvDw8EBUVBT+8Ic/oLy83CnGBwD/93//h549e8LDwwNBQUF45JFHcPHixVb36wxjNxEEAVOnToVIJMJ3333ntOMdP348RCJRg6+5c+e2ul9nGDsApKSkYOLEifD09IRSqcTYsWNRU1PjdOO9fv16o/Ns+tq0aVOz+3T0cQNAfn4+ZsyYgdDQUHh6emL48OH49ttvW92vM4w9MzMTjz32GIKCgqBUKvHEE0806J8jjffAgQN46KGHEB4e3uzvZUEQ8MorryAsLAweHh6YPHkyrly50uq+nWHsmzdvRnx8PAICAiASiZCenu6049VoNFi8eDEGDRoET09PhIeHY+bMmcjNzW1137Ya++bNmzFlyhTz/724uDjs2LGj1f615Wf49ddfx5gxY6BQKODr69vqPolcTVf/HQdY772LK3OG896e922urKufa2u+Z3VlznDe2/N+3ZU5+rnuzLUKWY/LB2P279+PpKQkHD16FMnJydBoNIiPj0dVVZW5zcKFC/Hjjz9i06ZN2L9/P3Jzc/H444+bt6empiI4OBhfffUVzp07h7/97W9YsmQJPvjgA3MbtVqNoKAgLF26FEOGDGlz/77++mssWrQIr776Kk6ePIkhQ4YgISEBhYWFAIDc3Fzk5ubirbfewtmzZ7F27Vr8/PPPeO6555xifAAwYsQIrFmzBhcuXMCOHTsgCALi4+Oh0+la3LczjN3k3XffhUgkconxvvDCC8jLyzN/vfnmm63u2xnGnpKSgvvvvx/x8fE4fvw4fvnlF8yfPx9iceNf044+3sjIyAbnOC8vD8uWLYOXlxemTp3a7H4dfdwAMHPmTFy6dAk//PADzpw5g8cffxxPPPEE0tLSWty3o4+9qqoK8fHxEIlE2LNnDw4fPoy6ujo89NBD0Ov1DjfeqqoqDBkyBKtXr262zZtvvon3338fH3/8MY4dOwZPT08kJCSgtra2xX07w9irqqpw77334p///Ger+3P08VZXV+PkyZN4+eWXcfLkSWzevBmXLl3Cww8/3GXGfuDAAUyZMgXbt29HamoqJkyYgIceeqjV3ztt+Rmuq6vDr3/9a8ybN6/V8RK5oq7+O86a711cmaOf9/a+b3NlXf1cW/M9qytzhvPenvfrrszRz3VnrlXIigRqoLCwUAAg7N+/XxAEQSgrKxOkUqmwadMmc5sLFy4IAISUlJRm9/O73/1OmDBhQpPbxo0bJ/zxj39sU3/uvvtuISkpyfxYp9MJ4eHhwsqVK5t9zcaNGwWZTCZoNJpG25xhfKdOnRIACBkZGW06homjjj0tLU3o1q2bkJeXJwAQtmzZ0qb9O+J427O/ljji2EeNGiUsXbq0Tfu7kyOO905Dhw4Vnn322Tbt38QRx+3p6Sl88cUXDV7n7+8v/Oc//2nTMUwcbew7duwQxGKxUF5ebm5TVlYmiEQiITk5udX9d7Xx1tfU72W9Xi+EhoYK//rXv8zPlZWVCXK5XPjf//7Xrv072tjru3btmgBASEtLa/M+HXm8JsePHxcACDdu3GjX/m0xdpPY2Fhh2bJlzW5v78/wmjVrBB8fnxaPSURd73ecLd+7uDJHO++dfd/myrraua7P2u9ZXZmjnff6OvJ+3ZU58rk26ei1ClmOy2fG3MlU3svf3x+AIYKp0WgwefJkc5t+/fohKioKKSkpLe7HtI+OqqurQ2pqaoNji8ViTJ48udVjK5VKuLm5NbkNcNzxVVVVYc2aNYiJiUFkZGS7jueIY6+ursbTTz+N1atXIzQ0tF3HcMTxAsC6desQGBiIgQMHYsmSJaiurm738Rxt7IWFhTh27BiCg4MxZswYhISEYNy4cTh06FCbjuFo471Tamoq0tPTzRl9beWI4x4zZgy+/vprlJSUQK/XY8OGDaitrcX48ePbdTxHG7tarYZIJIJcLje3cXd3h1gsbtPPeVcab1tcu3YN+fn5Dfrn4+ODUaNGtdi/pjja2DvLGcZbXl4OkUjU7rJdthq7Xq9HRUVFi20s+TNMRLd1pd9xtn7v4soc7bx39n2bK+tK57ot+PfeMhztvFPHOcO57ui1CllO40/rXZher8eCBQtwzz33YODAgQAMNXJlMlmjH9KQkBDk5+c3uZ8jR47g66+/xrZt2zrVn6KiIuh0OoSEhDQ6dnNrphQVFWHFihWYM2dOo22OPL4PP/wQL730EqqqqtC3b18kJydDJpO1+ViOOvaFCxdizJgxeOSRR9q1f0cd79NPP43o6GiEh4fj9OnTWLx4MS5duoTNmze3+ViOOParV68CAF577TW89dZbGDp0KL744gtMmjQJZ8+eRe/evZvdvyOO906fffYZ+vfvjzFjxrT5OI467o0bN+LJJ59EQEAA3NzcoFAosGXLFvTq1avNx3LEsY8ePRqenp5YvHgx3njjDQiCgL/85S/Q6XTIy8trcf9dbbxtYepDU3PSXP+a4ohj7wxnGG9tbS0WL16M6dOnQ6lUtvl1thz7W2+9hcrKSjzxxBPNtrHUzzAR3dbVfsfZ8r2LK3PE896Z922urKud67bg3/vOc8TzTh3jDOe6o9cqZFnMjKknKSkJZ8+exYYNGzq8j7Nnz+KRRx7Bq6++ivj4+Da/7uDBg/Dy8jJ/rVu3rt3HVqlUSExMRGxsLF577bVG2x15fM888wzS0tKwf/9+9OnTB0888US7apg64th/+OEH7NmzB++++267++qI4wWAOXPmICEhAYMGDcIzzzyDL774Alu2bEFmZmab9+GIYzfVXv6///s/zJ49G8OGDcOqVavQt29f/Pe//23xtY443vpqamqwfv36dmfFOOq4X375ZZSVlWHXrl04ceIEFi1ahCeeeAJnzpxp8z4ccexBQUHYtGkTfvzxR3h5ecHHxwdlZWUYPnx4k+si1eeI47UUVxu7o49Xo9HgiSeegCAI+Oijj9r1WluNff369Vi2bBk2btyI4OBgAIaM1PpjP3jwYIf7QETNc9TfcZZ47+LKHPG8d+Z9mytzxHNNncfz7joc/Vx35lqFLIuZMUbz58/H1q1bceDAAURERJifDw0NRV1dHcrKyhpEOgsKChqVjTp//jwmTZqEOXPmYOnSpe06/siRI5Genm5+HBISArlcDolEgoKCggZtmzp2RUUF7r//fnh7e2PLli2QSqVONT4fHx/4+Pigd+/eGD16NPz8/LBlyxZMnz691WM76tj37NmDzMzMRhH2adOm4b777sO+ffucarxNGTVqFAAgIyMDPXv2bPXYjjr2sLAwAEBsbGyDNv3790dWVpbTjbe+b775BtXV1Zg5c2abj+uo487MzMQHH3yAs2fPYsCAAQCAIUOG4ODBg1i9ejU+/vhjpx07AMTHxyMzMxNFRUVwc3ODr68vQkND0aNHD4cab1uY+lBQUGD+/216PHTo0Dbtw1HH3lGOPl7Txc2NGzewZ8+edt1pZquxb9iwAc8//zw2bdrUoJTCww8/bP57CwDdunUz3/ncmZ9hIrqtK/6Os9V7F1fmqOcd6Nj7NlfWFc91W1jiPasrc9TzTu3n6Oe6M9cqZAX2XbLG/vR6vZCUlCSEh4cLly9fbrTdtBjTN998Y37u4sWLjRZjOnv2rBAcHCy8+OKLrR6zvYvrzZ8/3/xYp9MJ3bp1a7CoYnl5uTB69Ghh3LhxQlVVldON7061tbWCh4eHsGbNmhb37ehjz8vLE86cOdPgC4Dw3nvvCVevXnW68Tbl0KFDAgDh1KlTLe7b0ceu1+uF8PBwYenSpQ1eN3ToUGHJkiWN9ufo471zv9OmTWvTfh193KdPnxYACOfPn2/wuvj4eOGFF15ocd+OPvam7N69WxCJRMLFixcbbevq460PLSyG+tZbb5mfKy8vb9NiqI4+9vrasiCoM4y3rq5OePTRR4UBAwYIhYWFbd6fLce+fv16wd3dXfjuu+/a3Lf2/AyvWbNG8PHxadO+iVxJV/8dZ833Lq7M0c97U1p63+bKuvq5rs/S71ldmaOf9/ra8n7dlTnDue7otQpZj8sHY+bNmyf4+PgI+/btE/Ly8sxf1dXV5jZz584VoqKihD179ggnTpwQ4uLihLi4OPP2M2fOCEFBQcJvfvObBvu484c8LS1NSEtLE0aMGCE8/fTTQlpamnDu3LkW+7dhwwZBLpcLa9euFc6fPy/MmTNH8PX1FfLz8wVBMPyhHDVqlDBo0CAhIyOjwfG1Wq3Djy8zM1N44403hBMnTgg3btwQDh8+LDz00EOCv7+/UFBQ0OK+HX3sTWnpD6mjjzcjI0NYvny5cOLECeHatWvC999/L/To0UMYO3Zsi/t1hrELgiCsWrVKUCqVwqZNm4QrV64IS5cuFdzd3YWMjAynHK8gCMKVK1cEkUgk/PTTTy3uz1nGXVdXJ/Tq1Uu47777hGPHjgkZGRnCW2+9JYhEImHbtm1OPXZBEIT//ve/QkpKipCRkSF8+eWXgr+/v7Bo0SKHHG9FRYX5dQCEd955R0hLSxNu3LhhbvOPf/xD8PX1Fb7//nvh9OnTwiOPPCLExMQINTU1Le7bGcZeXFwspKWlCdu2bRMACBs2bBDS0tKEvLw8pxtvXV2d8PDDDwsRERFCenp6g+Or1eoW922rsa9bt05wc3MTVq9e3aBNWVlZi/1ry8/wjRs3hLS0NGHZsmWCl5eXea4qKipa3DeRq+jqv+Os+d7FlTn6eReE9r1vc2Vd/Vxb8z2rK3OG896e9+uuzNHPdWeuVch6XD4YA6DJr/pZFzU1NcLvfvc7wc/PT1AoFMJjjz3W4BfUq6++2uQ+oqOjWz3WnW2a8u9//1uIiooSZDKZcPfddwtHjx41b9u7d2+zYzBFuB15fDdv3hSmTp0qBAcHC1KpVIiIiBCefvrpNt2R4+hjb25MzQVjHH28WVlZwtixYwV/f39BLpcLvXr1El588UWhvLy81f06+thNVq5cKURERAgKhUKIi4sTDh486NTjXbJkiRAZGSnodLpW9+cs4758+bLw+OOPC8HBwYJCoRAGDx4sfPHFFy4x9sWLFwshISGCVCoVevfuLbz99tuCXq93yPE297d31qxZ5jZ6vV54+eWXhZCQEEEulwuTJk0SLl261Oo8OsPY16xZ02SbV1991enG29J7rb1797a4b1uNfdy4ca2es6a05Wd41qxZHRo7kavo6r/jBMF6711cmTOc9/a8b3NlXf1cW/M9qytzhvPenvfrrszRz3VnrlXIekSCIAggIiIiIiIiIiIiIiIiqxDbuwNERERERERERERERETOjMEYIiIiIiIiIiIiIiIiK2IwhoiIiIiIiIiIiIiIyIoYjCEiIiIiIiIiIiIiIrIiBmOIiIiIiIiIiIiIiIisiMEYIiIiIiIiIiIiIiIiK2IwhoiIiIiIiIiIiIiIyIoYjCEiIiIiIiIiIiIiIrIiBmOIiIiIiIiIiIiIiIisiMEYIiIiIiIiIiIiIiIiK2IwhoiIiIiIiIiIiIiIyIoYjCEiIiIiIiIiIiIiIrIiBmOIiIiIiIiIiIiIiIisiMEYIiIiIiIiIiIiIiIiK2IwhoiIiIiIiIiIiIiIyIoYjCEiIiIiIiIiIiIiIrIiBmOIiIiIiIiIiIiIiIisiMEYIiIiIiIiIiIiIiIiK2IwhoiIiIiIiIiIiIiIyIoYjCEiIiIiIiIiIiIiIrIiBmOIiIiIiIiIiIiIiIisiMEYIiIiIiIiIiIiIiIiK2IwhoiIiIiIiIiIiIiIyIoYjCEiolatXbsWIpHI/OXu7o7w8HAkJCTg/fffR0VFRYP2eXl5+Mtf/oIJEybA29sbIpEI+/bta3b/dXV1eOONN9CvXz+4u7sjJCQEiYmJyMnJadT25MmTePjhh+Hv7w+FQoGBAwfi/ffft/SQiYiIiIiImtXea6Tdu3fj2WefRZ8+faBQKNCjRw88//zzyMvLa3L/R44cwb333guFQoHQ0FD84Q9/QGVlZaN2V65cwVNPPYWIiAgoFAr069cPy5cvR3V1dYN2b7zxBkaPHo2goCC4u7ujd+/eWLBgAW7dumW5SSEioha52bsDRETkOJYvX46YmBhoNBrk5+dj3759WLBgAd555x388MMPGDx4MADg0qVL+Oc//4nevXtj0KBBSElJaXafGo0GiYmJOHLkCF544QUMHjwYpaWlOHbsGMrLyxEREWFuu3PnTjz00EMYNmwYXn75ZXh5eSEzM7PJoA0REREREZG1tfUaafHixSgpKcGvf/1r9O7dG1evXsUHH3yArVu3Ij09HaGhoeZ9pqenY9KkSejfvz/eeecd5OTk4K233sKVK1fw008/mdtlZ2fj7rvvho+PD+bPnw9/f3+kpKTg1VdfRWpqKr7//ntz29TUVAwdOhRPPfUUvL29ceHCBfznP//Btm3bkJ6eDk9PT9tNGhGRi2IwhoiI2mzq1KkYOXKk+fGSJUuwZ88ePPjgg3j44Ydx4cIFeHh4YMSIESguLoa/vz+++eYb/PrXv252n6tWrcL+/ftx6NAh3H333c22U6lUmDlzJhITE/HNN99ALGZyJxERERER2Vdbr5Heeecd3HvvvQ2uY+6//36MGzcOH3zwAf7+97+bn//rX/8KPz8/7Nu3D0qlEgDQvXt3vPDCC9i5cyfi4+MBAF9++SXKyspw6NAhDBgwAAAwZ84c6PV6fPHFFygtLYWfnx8A4Ntvv23U97i4OPzqV7/Cjz/+iKeeesryk0NERA3wkywiIuqUiRMn4uWXX8aNGzfw1VdfAQC8vb3h7+/f6mv1ej3ee+89PPbYY7j77ruh1WobpdObrF+/HgUFBXj99dchFotRVVUFvV5v0bEQERERERF1VlPXSGPHjm10Q9nYsWPh7++PCxcumJ9TqVRITk7Gb37zG3MgBgBmzpwJLy8vbNy4sUFbAAgJCWmw37CwMIjFYshkshb72b17dwBAWVlZu8dIRETtx2AMERF12owZMwAYyoi1x/nz55Gbm4vBgwdjzpw58PT0hKenJwYPHoy9e/c2aLtr1y4olUrcvHkTffv2hZeXF5RKJebNm4fa2lqLjYWIiIiIiKiz2nKNVFlZicrKSgQGBpqfO3PmDLRabYNsGwCQyWQYOnQo0tLSzM+NHz8eAPDcc88hPT0d2dnZ+Prrr/HRRx/hD3/4Q6PSY4IgoKioCPn5+Th48CD+8Ic/QCKRmPdDRETWxWAMERF1WkREBHx8fJCZmdmu1125cgWAoVTZvn378Mknn2DNmjWora3F/fffj9OnTzdoq9Vq8cgjjyAhIQHffvstnn32WXz88ceYPXu2RcdDRERERETUGW25Rnr33XdRV1eHJ5980vxcXl4eAEN2y53CwsKQm5trfnz//fdjxYoVSE5OxrBhwxAVFYWnnnoKv//977Fq1apGry8oKEBQUBDCwsIwduxYZGVlYf369ejXr19nhkpERG3ENWOIiMgivLy8UFFR0a7XVFZWAgAqKiqQlpaGyMhIAIa0/l69euHNN980p/VXVlaiuroac+fOxfvvvw8AePzxx1FXV4dPPvkEy5cvR+/evS04IiIiIiIioo5r6RrpwIEDWLZsGZ544glMnDjR/HxNTQ0AQC6XN3qNu7u7ebtJ9+7dMXbsWEybNg0BAQHYtm0b3njjDYSGhmL+/PkN2vr7+yM5ORm1tbVIS0vD5s2bzddkRERkfQzGEBGRRVRWViI4OLhdr/Hw8AAA3HPPPeZADABERUXh3nvvxZEjRxq1nT59eoN9PP300/jkk0+QkpLCYAwREREREXUZzV0jXbx4EY899hgGDhyITz/9tME203WPWq1u9Lra2lrzdgDYsGED5syZg8uXLyMiIgKA4YY1vV6PxYsXY/r06QgICDC3l8lkmDx5MgDgwQcfxKRJk3DPPfcgODgYDz74YOcHTERELWKZMiIi6rScnByUl5ejV69e7XpdeHg4gMYLTgJAcHAwSktLW21rurip35aIiIiIiMiemrtGys7ORnx8PHx8fLB9+3Z4e3s32G4qT2YqV1ZfXl6e+boIAD788EMMGzbMHIgxefjhh1FdXd1gfZmmjBkzBmFhYVi3bl27xkZERB3DYAwREXXal19+CQBISEho1+sGDRoEqVSKmzdvNtqWm5uLoKAg8+MRI0YAQKO2pprJ9dsSERERERHZU1PXSMXFxYiPj4darcaOHTuaXBdm4MCBcHNzw4kTJxo8X1dXh/T0dAwdOtT8XEFBAXQ6XaN9aDQaAIBWq221n7W1tSgvL2/TmIiIqHMYjCEiok7Zs2cPVqxYgZiYGDzzzDPteq23tzceeOABHDlyBBcvXjQ/f+HCBRw5cgRTpkwxP/fEE08AAD777LMG+/j000/h5uaG8ePHd3wQREREREREFtLUNVJVVRUeeOAB3Lx5E9u3b2+2xLKPjw8mT56Mr776qsF6M19++SUqKyvx61//2vxcnz59kJaWhsuXLzfYx//+9z+IxWIMHjzYfOzq6upGx/r2229RWlqKkSNHdnrMRETUOq4ZQ0REbfbTTz/h4sWL0Gq1KCgowJ49e5CcnIzo6Gj88MMPcHd3N7f9+9//DgA4d+4cAMPFw6FDhwAAS5cuNbd74403sHv3bkycOBF/+MMfAADvv/8+/P398de//tXcbtiwYXj22Wfx3//+F1qtFuPGjcO+ffuwadMmLFmypEG6PhERERERkS209RrpmWeewfHjx/Hss8/iwoULuHDhgnkfXl5eePTRR82PX3/9dYwZMwbjxo3DnDlzkJOTg7fffhvx8fG4//77ze1efPFF/PTTT7jvvvswf/58BAQEYOvWrfjpp5/w/PPPm6+Rrly5gsmTJ+PJJ59Ev379IBaLceLECXz11Vfo3r07/vjHP9pmsoiIXJxIEATB3p0gIqKube3atZg9e7b5sUwmg7+/PwYNGoQHH3wQs2fPblTrWCQSNbu/O//0nDx5EosXL0ZKSgrEYjEmTpyIf/3rX43uFtNoNHjjjTewZs0a5ObmIjo6GklJSViwYEHnB0lERERERNRG7b1G6t69O27cuNHkvqKjo3H9+vUGzx06dAiLFy/GyZMn4e3tjSeeeAIrV65sdN11/PhxvPbaa0hLS0NxcTFiYmIwa9YsvPTSS3BzM9yDXVRUhL/97W84cOAAsrOzodFoEB0djcTERPztb39DYGCghWaFiIhawmAMERERERERERERERGRFXHNGCIiIiIiIiIiIiIiIitiMIaIiIiIiIiIiIiIiMiKGIwhIiIiIiIiIiIiIiKyIgZjiIiIiIiIiIiIiIiIrIjBGCIiIiIiIiIiIiIiIitiMIaIiIiIiIiIiIiIiMiKGIwhIiIiIiIiIiIiIiKyIjd7d8Ce9Ho9cnNz4e3tDZFIZO/uEBERERFZlSAIqKioQHh4OMRi3pdFreM1ExERERG5GmtdN7l0MCY3NxeRkZH27gYRERERkU1lZ2cjIiLC3t0gB8BrJiIiIiJyVZa+bnLpYIy3tzcAw6QqlUo796bzNBoNdu7cifj4eEilUnt3h5rAc2R7nHPHxvNnW5xvx8DzZFvONt8qlQqRkZHm98FErXG2ayYTZ/u/7Qg45/bF+bcvzr9tcb7ti/Nve5xzy7PWdZNLB2NMafZKpdIpLiw0Gg0UCgWUSiX/43VRPEe2xzl3bDx/tsX5dgw8T7blrPPNclPUVs52zWTirP+3uzLOuX1x/u2L829bnG/74vzbHufceix93cRC0URERERERERERERERFbEYAwREREREREREREREZEVMRhDRERERERERERERERkRQzGEBERERERERERERERWRGDMURERERERERERERERFbEYAwREREREREREREREZEVMRhDRERERERERERERERkRQzGEBERERERERERERERWRGDMURERERERERERERERFbEYAwREREREREREREREZEVudm7A0RERERErmLr6VxU1+lwb69AhPt62Ls7RERERETkgM7klEMkAgZ287F3V6gdmBlDRERERGQj/+/AVbz0zWmcvVlu764QEREREZEDUmt1eOr/peCJT1JQqdbauzvUDgzGEBERERHZSE5pDQAgwk9h554QEREREZEjKq3SoKpOh+o6HdKySu3dHWoHBmOIiIiIiGygSq1FSVUdACDCnyXKiIiIiIio/cpq6szf/3KdwRhHwmAMEREREZENmLJifDykULpL7dwbIiIiIiJyRKVVGvP3v1wrsWNPqL0YjCEiIiIisoHskmoAQCSzYoiIiIiIqIPK62XGpGWXQqPT27E31B4MxhARERER2UBOqSEYE+HL9WKIiIiIiKhjSqtvZ8bUavQ4n1dhx95QezAYQ0RERERkA9nGMmXMjCEiIiIioo4qqxeMAYDUG1w3xlEwGENEREREZAPmzBg/ZsYQEREREVHHlFUbypTJ3Awf7Z+4UWbH3lB7MBhDRERERGQD2SXMjCEiIiIios4xZcbc0zMAAJCaVQpBsGePqK0YjCEiIiIisgFmxhARERERUWeV1RgyY+7tHQSZmxglVRrcqrVzp6hNGIwhIiIiIrKy8hoNVLVaAECEHzNjiIiIiIioY0qNmTEhSjmGRvgCAK5WiOzYI2orBmOIiIiIiKzMlBUT4CmDQuZm594QEREREZGjKjcGY3w9ZBjZ3Q8AcFXFYIwjYDCGiIiIiMjKckoN68VE+LNEGRERERERdVxptaFMma9Ciru6+wNgZoyjYDCGiIiIiMjKsktM68WwRBkREREREXWMIAgoqzFmxiikGB7lB5EIuFUrQnFVnZ17R61hMIaIiIiIyMpMmTGRfsyMISIiIiKijqnR6FCn1QMA/BQy+CikCFW6A7h9Axh1XQzGEBERERFZmWnNGGbGEBERERFRR5UZ14uRSkRQyCQAgBClHACQr1LbrV/UNgzGEBERERFZmXnNGAZjiIiIiIiog0zBGB8PGUQiwzoxpsyYAlWt3fpFbcNgDBERERGRFQmCYC4ZEOnPMmVk8Nprr0EkEjX46tevn3n7+PHjG22fO3dug31kZWUhMTERCoUCwcHBePHFF6HVahu02bdvH4YPHw65XI5evXph7dq1thgeEREREVlBWbVhXRg/hdT8nCkzpoCZMV2em707QERERETkzMqqNaiq0wEAuvkyM4ZuGzBgAHbt2mV+7ObW8PLshRdewPLly82PFYrbwTydTofExESEhobiyJEjyMvLw8yZMyGVSvHGG28AAK5du4bExETMnTsX69atw+7du/H8888jLCwMCQkJVh4dEREREVlaWY0hM8a3XjDGlBmTz8yYLo/BGCIiIiIiK8o2rhcT7C2Hu1Ri595QV+Lm5obQ0NBmtysUima379y5E+fPn8euXbsQEhKCoUOHYsWKFVi8eDFee+01yGQyfPzxx4iJicHbb78NAOjfvz8OHTqEVatWMRhDRERE5IBKjZkxvgqZ+TlmxjgOlikjIiIiIrIirhdDzbly5QrCw8PRo0cPPPPMM8jKymqwfd26dQgMDMTAgQOxZMkSVFdXm7elpKRg0KBBCAkJMT+XkJAAlUqFc+fOmdtMnjy5wT4TEhKQkpJixVERERERkbWY1ozx9WhcpoyZMV0fM2OIiIiIiKyI68VQU0aNGoW1a9eib9++yMvLw7Jly3Dffffh7Nmz8Pb2xtNPP43o6GiEh4fj9OnTWLx4MS5duoTNmzcDAPLz8xsEYgCYH+fn57fYRqVSoaamBh4ejQOEarUaavXtuypVKhUAQKPRQKPRWG4C7Mw0FmcaU1fHObcvzr99cf5ti/NtX5x/6yqpNARclO4S8xwHKgwf8Reo1Kirq4NIJLJb/9pDrdFhz6Vb8PGQIjpAgSAvOTJvVSEtuwzp2WXoGeSF/xsbY5e+Wevnl8EYIiIiIqJmCIKAqk6+D2dmDDVl6tSp5u8HDx6MUaNGITo6Ghs3bsRzzz2HOXPmmLcPGjQIYWFhmDRpEjIzM9GzZ0+r9WvlypVYtmxZo+d37tzZYM0aZ5GcnGzvLrgczrl9cf7ti/NvW5xv++L8W8fZDDEAMQqyr2L79kwAgEYPAG5Qa/X45oef4CltaQ9dx483xNiV23zhrihPAZGVF2zYo9vqZ6RbEoMxRERERERNUNVqsOB/6dhzyQ1ePfPx8LDIDu3HtGZMpJ/zfZBNluPr64s+ffogIyOjye2jRo0CAGRkZKBnz54IDQ3F8ePHG7QpKCgAAPM6M6Ghoebn6rdRKpVNZsUAwJIlS7Bo0SLzY5VKhcjISMTHx0OpVHZscF2QRqNBcnIypkyZAqnUQT6xcHCcc/vi/NsX59+2ON/2xfm3rh/WpQG3bmHU0EF44K4IAIY5fzV1D6q0IgwadR/6hXrbuZet0+r0WPHWAQB1CPNxR1GlGhqdAE+5BEMifDAs0hcjonxxX+9Au/TPlB1uaQzGEBERERHdIaOwAnO+SMXVoioAQPKFwg4HY25nxjAYQ82rrKxEZmYmZsyY0eT29PR0AEBYWBgAIC4uDq+//joKCwsRHBwMwHAHqlKpRGxsrLnN9u3bG+wnOTkZcXFxzfZDLpdDLpc3el4qlTrlByrOOq6ujHNuX5x/++L82xbn2744/9ahqtUCAAK83RvMr68MqNICRVXaLjHvH+3LxM9n8/DawwMwLMqv0faDmQUoqqxDgKcMB16aALFIhOIqNQI85ZCI7V9mzVpz2HweEBERERGRC9p5Lh+Prj6Cq0VV8JRJAABpWWUd2pcgCMgxZcb4s0wZ3fbnP/8Z+/fvx/Xr13HkyBE89thjkEgkmD59OjIzM7FixQqkpqbi+vXr+OGHHzBz5kyMHTsWgwcPBgDEx8cjNjYWM2bMwKlTp7Bjxw4sXboUSUlJ5mDK3LlzcfXqVbz00ku4ePEiPvzwQ2zcuBELFy6059CJiIiIqIPKqg01lH09GgYLfGQCACBfVWvzPt1Jq9Pjw70ZOJVTjic+ScGXKdchCEKDNt+m3gQAPDw0HFKJGBKxCMHe7l0iEGNNDMYQEREREQHQ6wW8k3wZc75MRaVai1Ex/vg+KQ4iCMgpq0VBBy5siirrUKvRQyQCwnwYjKHbcnJyMH36dPTt2xdPPPEEAgICcPToUQQFBUEmk2HXrl2Ij49Hv3798Kc//QnTpk3Djz/+aH69RCLB1q1bIZFIEBcXh9/85jeYOXMmli9fbm4TExODbdu2ITk5GUOGDMHbb7+NTz/9FAkJCfYYMhERERF1UqkpGKOQNXje1/gwv9z+wZjTN8tRoTZk8Gh0Al7+/hwWfp2OWo0OAFBerUHyeUMp3WnDI+zWT3tgmTIiIiIicnmqWg0WfZ2OXRcKAQC/HdMdf0vsD+h1CFcAN6uBE9dLkTg4rF37NWXFhCndIXPjfVB024YNG5rdFhkZif3797e6j+jo6EZlyO40fvx4pKWltbt/RERERNS1CIKA8po6AICvomFmjK/cmBnTBYIxh64UAQDuHxCKkd39sPKni/guPRe1Gj1WPzMcP5zORZ1Oj36h3hgQ7jxrErYFgzFERERE5BJW783Ahl+ycEeGPABAVaOBqlYLmZsYKx8bhGkjjIth6nWI8RZws1qE1BvtD8Zkc70YIiIiIiKygKo6HTQ6w8WM3x2ZMT6mzJguUKbsUIYhGHNv70D8ZnQ0+oUq8ezaX/DzuXy8/P1ZnM9VAQB+NSICIpFzlyW7k8Vvz/voo48wePBgKJVKKJVKxMXF4aeffjJvr62tRVJSEgICAuDl5YVp06ahoKCgwT6ysrKQmJgIhUKB4OBgvPjii9BqtQ3a7Nu3D8OHD4dcLkevXr2wdu1aSw+FiIiIiJzEiesl+NeOS8guqUFOaeMvVa0WYT7u+GZunDkQYxLjbbjgSb1R0u7jmjJjIrheDBERERERdUJZtSErRuYmhru04cf6Pl2kTFmVWou0rFIAwH29AwEYgjLvPjUUIhGw/lgW0rPLIBGL8MjQbvbsql1YPDMmIiIC//jHP9C7d28IgoDPP/8cjzzyCNLS0jBgwAAsXLgQ27Ztw6ZNm+Dj44P58+fj8ccfx+HDhwEAOp0OiYmJCA0NxZEjR5CXl4eZM2dCKpXijTfeAABcu3YNiYmJmDt3LtatW4fdu3fj+eefR1hYGOsfExEREVEDaq0Of9l8BgDwyNBwzL4nplEbEYC+od5wl0oabeuhNARjzuWqUFOng4escZvmZJcwM4aIiIiIiDqvzLhejJ9C2iijxFdmLFNm58yY49dKoNEJiPDzQJT/7WugBwaFYfnDA/Dy9+cAAOP7BCHIW26vbtqNxYMxDz30UIPHr7/+Oj766CMcPXoUERER+Oyzz7B+/XpMnDgRALBmzRr0798fR48exejRo7Fz506cP38eu3btQkhICIYOHYoVK1Zg8eLFeO211yCTyfDxxx8jJiYGb7/9NgCgf//+OHToEFatWsVgDBERERE18NG+TGQUViLQS4ZlDw9otNhla/xkQIi3HAUVapzKKcPoHgFtfq0pMybSj5kxRERERETUcaZgjK9H4+sZU2ZMeY2m3TeQWZKpRNl9vQMbBYxmxHWHqlaL/xy8ijlje9ije3Zn1TVjdDodNm3ahKqqKsTFxSE1NRUajQaTJ082t+nXrx+ioqKQkpKC0aNHIyUlBYMGDUJISIi5TUJCAubNm4dz585h2LBhSElJabAPU5sFCxa02B+1Wg21Wm1+rFIZ6tNpNBpoNBoLjNi+TGNwhrE4K54j2+OcOzaeP9vifDsGnqf2ySisxOq9GQCAv03tC0+pqF1zp9FoIBIBQyOV2HH+Fo5fLcKIyLYvMpldYgjGhCllXeKcdYU+EBERERFR+5XVGMqU+SikjbZ5SACFTILqOh3yVbWICfS0dfcAAIeuGIIx9/QKbHJ70oReSJrQy5Zd6lKsEow5c+YM4uLiUFtbCy8vL2zZsgWxsbFIT0+HTCaDr69vg/YhISHIz88HAOTn5zcIxJi2m7a11EalUqGmpgYeHk3febhy5UosW7as0fM7d+6EQuE8pSOSk5Pt3QVqBc+R7XHOHRvPn21xvh0Dz1Pr9ALw/jkJNDoRYn31EGWnYXtOWof2pajOByDBzycuI7rqYpuPn10iASDC5bSjKL7QoUNbVHV1tb27QEREREREHVBar0zZnUQiQzb/teJq5JXX2CUYU1hRi0sFFRCJgDE9mw7GuDqrBGP69u2L9PR0lJeX45tvvsGsWbOwf/9+axyqXZYsWYJFixaZH6tUKkRGRiI+Ph5KZdvvcOyqNBoNkpOTMWXKFEiljf9Tkv3xHNke59yx8fzZFufbMfA8td3/fsnGtaMXoJBJ8NFz9yHct/2lwkzz/dSku7Hls1TcVMtw//0TIBaLWn1tvqoWuqMHIBGLMP2R++EmEbf6GmszZYYTEREREZFjKa82ZMY0VaYMAEJ93HGtuBoFdlo35rCxRNmAcCX8PdtXGtpVWCUYI5PJ0KuXId1oxIgR+OWXX/Dee+/hySefRF1dHcrKyhpkxxQUFCA0NBQAEBoaiuPHjzfYX0FBgXmb6V/Tc/XbKJXKZrNiAEAul0Mub7wwkFQqdaoPM5xtPM6I58j2OOeOjefPtjjfjoHnqWUFqlr8a8cVAMCf4/siOqhzN94MivSDu1SM8hotssrU6B3i3XofKioAAGE+7vBw7xqLU/JnhoiIiIjIMZkyY3w9m35PH+JtuObIL1c3ud3aDl0pBgDc2yvILsd3BDa5PU+v10OtVmPEiBGQSqXYvXu3edulS5eQlZWFuLg4AEBcXBzOnDmDwsJCc5vk5GQolUrExsaa29Tfh6mNaR9ERERE5Npe/f4cKtRaDIn0xawx3Tu9P6lEjCERvgCA1BulbXpNdqmhJFikn/OUwyUiIiIiIvsoMwVjWsiMAYD88hqb9am+I5mGzJh7m1kvhqwQjFmyZAkOHDiA69ev48yZM1iyZAn27duHZ555Bj4+PnjuueewaNEi7N27F6mpqZg9ezbi4uIwevRoAEB8fDxiY2MxY8YMnDp1Cjt27MDSpUuRlJRkzmqZO3curl69ipdeegkXL17Ehx9+iI0bN2LhwoWWHg4REREROZgd5/Lx87l8uIlF+MfjgyBpQ0mxthjZ3Q8AcKKNwZicEsNFUIRf+8ujERERERER1VdmLFPW1JoxABCiNGbGWKBMmV4v4KN9mTiZ1bZrn9yyGuSV10IiFmF4tG+nj++sLF6mrLCwEDNnzkReXh58fHwwePBg7NixA1OmTAEArFq1CmKxGNOmTYNarUZCQgI+/PBD8+slEgm2bt2KefPmIS4uDp6enpg1axaWL19ubhMTE4Nt27Zh4cKFeO+99xAREYFPP/0UCQkJlh4OERERETkQVa0Gr3x/FgDwwtge6B9muXUBR0b7A8hsc2ZMTqkhGBPpz8wYIiIiIiLqnLIaY2ZMM8GYUKUpM6bzwZjkCwX4588XERPoib1/Ht9qe9M1Uv8wbyhkVlkZxSlYfGY+++yzFre7u7tj9erVWL16dbNtoqOjsX379hb3M378eKSlpXWoj0RERETknDb+ko0ClRrdAxT446TeFt33sChfAMC1oioUV6oR4NXyOjCmMmXMjCEiIiIios4qNWbG+CqaLlNmycyY9OwyAIZrn+yS6lZvMDNl0AyP8uv0sZ2ZTdaMISIiIiKyhT0XDesOzojrDnepxKL79lXI0DvYC0Db1o1hZgwREREREVlKeXXLmTEhxsyYWxVqaHX6Th3r7M1y8/eHMopabX8yqwwAgzGtYTCGiIiIiJxCpVqLX66XAADG9w2yyjFM68a0FozR6QXklnHNGCIiIiIi6jxBEG6XKfNoOjMmwFMGN7EIegG4Vanu1LHO1A/GXGk5GFOr0eF8rqH9iGgGY1rCYAwREREROYUjGUXQ6ARE+nugR6CnVY5hutOrtWBMvqoWWr0AqUSEEG93q/SFiIiIiIhcQ4VaC51eANB8ZoxELEKwt6FUWW5Zx0uV5ZTWoMyYhQMAhzOLzMduytmb5dDoBAR6yXkjWisYjCEiIiIip7Dv8i0AwPg+wRCJRFY5xsju/gCA0zfLodbqmm2XXWJYL6abrwfEYuv0hYiIiIiIXENOiSHr3kMqabEcc4SfoUTyTWOWfkeYSpT1C/WGt9wNZdUanMstb7b97fVifK12HeYsGIwhIiIiIocnCAL2XzIGY6xUogwAugcoEOApQ51W36CO8p24XgwREREREVnKlrQcAMA9vQJabBfhb8hMMd0c1hGmEmVDI30xuqfheAdbKFV28kYZAGA4S5S1isEYIiIiInJ4GYWVuFlWA5lEjLieLV+gdIZIJDJfZLRUqsx08cM0fSIiIiIi6ow6rR6bT94EADx5V1SLbSONmTGWCMYM7OaD+3oHAmh+3RhBEOplxjAY0xoGY4iIiIjI4e0zZsWM6uEPhczNqscaaQzGnLjefDDGlBljKhNARERERETUGkEQ8OOp3AZlxvZcLEBxVR2CvOWY0EoVAFNmfnZpx4IxgiCYKwAMjvDBvb0MwZjUG6WoqWtcpvlmWQ0KK9RwE4swOMKnQ8d0JQzGEBEREZHD23e5EAAwro/1SpSZjKiXGSMITS9kabr4YWYMERERERG11f7Lt/D7/6XhVx8dQXGlGgCw4ZdsAMCvRkTATdLyx/lRpmBMScfWjLlZVoPSag2kEhH6hnojJtAT3Xw9UKfT49i14kbtT2aVAQBiw5UtrmVDBgzGEBEREZFDq1Jr8cs1Q5bK+L7BVj/ewG4+kEnEKK6qw43ipu84u8nMGCIiIiIiaqcLeRUAgLzyWiz4Oh05pdU4cNlQBeCJkZGtvj7SuGbMzbIaaHX6dh/flBXTJ8QbcjcJRCKROTtmzeHrWLAhDaPe2IWp7x3E9+k3kXq9BABLlLWVdWs4EBERERFZ2ZHMYtTp9Ij090DPIE+rH89dKsGgCB+k3ijFiRul6B7Y8JganR555YZgjOliiIiIiIiIqDU3iqvM3x+8UoQZnx2HXgBGxfgjJrD1a50Qb3fIJGLU6fTIK681ly1rK9N6MYO63S45dm/vQHx9Ihv7jUEhAChQqfHHDenmx8OifNt1HFfFzBgiIiIicmj7LhlKlI3vEwyRSGSTY94uVVbSaFteWS30AiB3EyPIS26T/hARERERkeO7bgzGTOxnyPi/VmR4/ORdrWfFAIBYLEI3Y6nkjqwbczrHEIwZWC8YM7FfMO7q7odB3Xzwu/E9sf75UfhzfB/4eEjNbUzXR9QyZsYQERERkcMSBAH7Lhnu0BrfymKWlmS62DicUYytp3MbbMsorARgWC/GVsEhIiIiIiJyfKYyyPMn9kKYjzvWHcuCt9wNUweGtXkfEX4euFZUhZySGqBn248tCIK5TFn9zBhPuRs2zR3ToO2YXoGYNaY7Np7IgZ9CyvLMbcRgDBERERE5rMxblbhZVgOZRIy4ngE2O64pGJNVUo3569OabNPekgBEREREROS6ajU65JXXAgC6B3jilYdiEeApw9AoX3jIJG3eT5TxOiSrpH2ZMTfLalBarYGbWIS+od6ttvd2l+K5e2PadQxXx2AMERERETksU1bMqB7+UMhs99Y20EuOl+7va15M805SiRjzxrXjNjQiIiIiInJppuCJt7sb/BRSiEQiLIrv2+79mG4Ka2+Zsl3nCwAAfUK84S5te/CH2o7BGCIiIiJyWKZgzLg+titRZvK78b3wu/G9bH5cIiIiIiJyPteN68N0D/DsVLnjSGPJsOx2ZMYczijC37ddAAA8OKTtJdGofcT27gARERERUUdUqbU4fq0EADC+b7Cde0NERERERNRx14uNwZhAz07tJ9LfAwCQXVrTpvYX81WY+2UqtHoBDw0Jx9yxzPC3FgZjiIiIiMiuBEGAIAjtfl1KZjHqdHpE+HmgZ1DnLliIiIiIiIjs6XqxIZOle0Dn1p40rRlzq0KNmjpdi20LVbX47X9/QYVai7tj/PHWrwdDLO54Vg61jMEYIiIiIrKbwopajF65G0nrT7Y7ILPvciEAYHzfoE6l8RMREREREdnbDWNmTHRA52408/GQwltuWJ0kp5V1Yzal5iBfVYseQZ74z4yRkLtxrRhrYjCGiIiIiOxm2+k8FKjU2H4mH9+l32zz6wRBMK8XM74PS5QREREREZFju15kmcwYkUiECGN2THYrwRjTOjWPDe0GH4W0U8el1jEYQ0RERER2s+diofn7FVsvoLSqrk2vy7xVhZzSGsgkYozpFWCt7hEREREREVmdWqtDbrlhjZfOZsYAQKSfcd2YkpbXjckxrisTYVxnhqyLwRgiIiIisouKWg2OXi0GAIT5uKOkqg7/+Olim16775IhiHN3jD8UMjer9ZHIWl577TWIRKIGX/369TNvr62tRVJSEgICAuDl5YVp06ahoKCgwT6ysrKQmJgIhUKB4OBgvPjii9BqtQ3a7Nu3D8OHD4dcLkevXr2wdu1aWwyPiIiIiNohu6QGggB4yiQI9JJ1en+mdWOySlrOjDFlzkT6dS4bh9qGwRgiIiIisotDV4qg0QmICfTEv6cPAwB8fSIbx4wBmpbsv2wsUdY3yKp9JLKmAQMGIC8vz/x16NAh87aFCxfixx9/xKZNm7B//37k5ubi8ccfN2/X6XRITExEXV0djhw5gs8//xxr167FK6+8Ym5z7do1JCYmYsKECUhPT8eCBQvw/PPPY8eOHTYdJxERERG1rP56MZZYDzPSVKashWCMVqdHXnktACCCwRibYDCGiIiIiOxi1wVDdsukfsEY2d0f0++OAgD8dcsZqLW6Zl/3XdpNHMk0BGwYjCFH5ubmhtDQUPNXYGAgAKC8vByfffYZ3nnnHUycOBEjRozAmjVrcOTIERw9ehQAsHPnTpw/fx5fffUVhg4diqlTp2LFihVYvXo16uoM5f4+/vhjxMTE4O2330b//v0xf/58/OpXv8KqVavsNmYiIiIiaux6sSFoEhPY+RJlABBpLDuWXdp8mbJ8VS10egFSiQjB3nKLHJdaxmAMEREREdmcTi+YS41N7B8MAPjL/f0Q6CVD5q0q/L/9Vxu9RhAEfLgvAwu+TodOL+Dx4d3QM8jLpv0msqQrV64gPDwcPXr0wDPPPIOsrCwAQGpqKjQaDSZPnmxu269fP0RFRSElJQUAkJKSgkGDBiEkJMTcJiEhASqVCufOnTO3qb8PUxvTPoiIiIioa7idGWOZDBVT2bGckmoIgtBkG9N6Md18PSAWdz4bh1rHAttEREREZHPp2WUorqqDt7sb7uruDwDwUUjx8oOx+OOGdPx7bwYeHBJuvjNMpxew7Mdz+CLlBgDghftisGRqf4uk8BPZw6hRo7B27Vr07dsXeXl5WLZsGe677z6cPXsW+fn5kMlk8PX1bfCakJAQ5OfnAwDy8/MbBGJM203bWmqjUqlQU1MDD4/GC7Wq1Wqo1WrzY5VKBQDQaDTQaDSdG3QXYhqLM42pq+Oc2xfn3744/7bF+bYvzn/HXL1VCQCI8HVv99w1NechXlIAQIVai1uqavgpGq9Dc/1WBQAgvAPHdHbWmg8GY4iIiIjI5vZcNCxEPq5PEKSS28naDw8JxzepOTh4pQhLvzuDr54bBbVWjz9uSMOOcwUQiYClibF47t4Ye3WdyCKmTp1q/n7w4MEYNWoUoqOjsXHjxiaDJLaycuVKLFu2rNHzO3fuhELhfLXEk5OT7d0Fl8M5ty/Ov31x/m2L821fnP/2uZAtASBC3pVT2F5wqkP7uHPOlVIJVBoRvt66C1FNFBTYly0GIIZQUYTt27d36JjOqrq6+bV2OoPBGCIiIiKyud3G9WIm9294175IJMLfHx2I+FUHcDijGGsOX8e2M3lIvVEKmUSMVU8OReLgMHt0mciqfH190adPH2RkZGDKlCmoq6tDWVlZg+yYgoIChIaGAgBCQ0Nx/PjxBvsoKCgwbzP9a3qufhulUtlswGfJkiVYtGiR+bFKpUJkZCTi4+OhVCo7Pc6uQqPRIDk5GVOmTIFUKrV3d1wC59y+OP/2xfm3Lc63fXH+269Oq8fCo7sAAE8+MBEhSvd2vb65Of8y9zhO3ChDiXdPzJ3at9Hr9m0+C+TkIm5wHzwwrkfnBuFkTNnhlsZgDBERERHZVE5pNS7mV0AsMmTG3Ck6wBN/nNwbb/58Ccu3ngcAKN3d8J+ZIzGqR4Ctu0tkE5WVlcjMzMSMGTMwYsQISKVS7N69G9OmTQMAXLp0CVlZWYiLiwMAxMXF4fXXX0dhYSGCgw3rLiUnJ0OpVCI2Ntbc5s67HJOTk837aIpcLodc3ngBV6lU6pQfqDjruLoyzrl9cf7ti/NvW5xv++L8t11OeRX0AuAhlaCbv1eHSzHfOedzx/XC81+cwJojNzCpfyju7R3YoH1uWS0AIDrQi+fqDtaaD3HrTYiIiIiILGfPRUNWzMhof/h5Nq5dDAAv3NcDfUO8AQDhPu74Zt4YBmLIqfz5z3/G/v37cf36dRw5cgSPPfYYJBIJpk+fDh8fHzz33HNYtGgR9u7di9TUVMyePRtxcXEYPXo0ACA+Ph6xsbGYMWMGTp06hR07dmDp0qVISkoyB1Pmzp2Lq1ev4qWXXsLFixfx4YcfYuPGjVi4cKE9h05ERERE9VwvrgIARAcoLLom5uTYEDwzKgoA8KdN6SitqmuwPae0BgAQ4We/ErmuhpkxRERERGRTphJlE/sHN9tGKhHj01kj8X36Tfx6ZGS7U/WJurqcnBxMnz4dxcXFCAoKwr333oujR48iKMiQLbZq1SqIxWJMmzYNarUaCQkJ+PDDD82vl0gk2Lp1K+bNm4e4uDh4enpi1qxZWL58ublNTEwMtm3bhoULF+K9995DREQEPv30UyQkJNh8vERERETUtJwSw/okUf6WX59vaWIsUq4W4+qtKvx1yxl8+MxwiEQiaHR65JUbgjGRfs63LmBXxWAMEREREdlMlVqLlMxiAMDkFoIxABDpr8D8ib1t0S0im9uwYUOL293d3bF69WqsXr262TbR0dGtLrY6fvx4pKWldaiPRERERGR9xcaMlSDvxqViO8tDJsH7Tw3DYx8exk9n85F8vgDxA0KRX14LvQDI3MQI9LL8calpLFNGRERERDZzKKMIdTo9ovwV6BnkZe/uEBERERER2ZWpfJh/MyWcO2tgNx/MGN0dAPDzuXwAQHapIRsnwtcDYrHlSqNRyxiMISIiIiKb2WMsUTapf7BF6yETERERERE5opJqDQDAV2GdYAxwuyrBwStFEAQBOSWGEmXduF6MTTEYQ0REREQ2odcL2H3RGIzpF2Ln3hAREREREdnf7cwYqdWOMaK7HzykEtyqUONCXgVyTJkxXC/GphiMISIiIiKbOHOzHEWVanjJ3XB3jL+9u0NERERERGR3JcZgjJ8VM2PkbhKM6RkAADhw5RZySg2ZMZH+zIyxJQZjiIiIiMgmdl8oAACM7RMImRvfhhIREREREZVWW3fNGJOxfYIAAAcu3w7GMDPGttzs3QEiIiIicg0sUUZERERERNSQKRhjzcwY4HYw5sT1Uni7G8ICEVwzxqYsfkviypUrcdddd8Hb2xvBwcF49NFHcenSpQZtxo8fD5FI1OBr7ty5DdpkZWUhMTERCoUCwcHBePHFF6HVahu02bdvH4YPHw65XI5evXph7dq1lh4OEREREVlAXnkNzuWqIBIB4/sG2bs7REREREREdldTp0OtRg8A8LNyZkz3AAUi/T1Qp9Oj2FgajcEY27J4MGb//v1ISkrC0aNHkZycDI1Gg/j4eFRVVTVo98ILLyAvL8/89eabb5q36XQ6JCYmoq6uDkeOHMHnn3+OtWvX4pVXXjG3uXbtGhITEzFhwgSkp6djwYIFeP7557Fjxw5LD4mIiIiIOmmPMStmeJQfArzkdu4NERERERGR/ZUYs2JkEjE8ZRKrHkskEmFs79s3xsndxAjitZlNWbxM2c8//9zg8dq1axEcHIzU1FSMHTvW/LxCoUBoaGiT+9i5cyfOnz+PXbt2ISQkBEOHDsWKFSuwePFivPbaa5DJZPj4448RExODt99+GwDQv39/HDp0CKtWrUJCQoKlh0VEREREnbDngiEYM7FfsJ17QkRERERE1DWUGjNU/DylEIlEVj/e2D5BWHcsC4AhK8YWx6TbrL5mTHl5OQDA39+/wfPr1q3DV199hdDQUDz00EN4+eWXoVAYFgxKSUnBoEGDEBJyu554QkIC5s2bh3PnzmHYsGFISUnB5MmTG+wzISEBCxYsaLYvarUaarXa/FilUgEANBoNNBpNp8bZFZjG4AxjcVY8R7bHOXdsPH+2xfl2DI54nuq0ehzJLAIA3NfT36H67ojz3RJnGQcRERERkTMoqbLNejEmY3oGwE0sglYvIMJPYZNj0m1WDcbo9XosWLAA99xzDwYOHGh+/umnn0Z0dDTCw8Nx+vRpLF68GJcuXcLmzZsBAPn5+Q0CMQDMj/Pz81tso1KpUFNTAw+PxvXuVq5ciWXLljV6fufOneZAkDNITk62dxeoFTxHtsc5d2w8f7bF+XYMjnSeMlVAjcYNXlIBmScP4poD3nzlSPPdkurqant3gYiIiIiIjEqNZcr8rbxejIm3uxTDo/xw/HoJ14uxA6sGY5KSknD27FkcOnSowfNz5swxfz9o0CCEhYVh0qRJyMzMRM+ePa3WnyVLlmDRokXmxyqVCpGRkYiPj4dSqbTacW1Fo9EgOTkZU6ZMgVQqtXd3qAk8R7bHOXdsPH+2xfl2DI54nt7bnQHgKsb1C8ODiYPt3Z12ccT5bokpM5yIiIiIiOzPnBljo2AMAMwcE40zN8sRP6DpJUTIeqwWjJk/fz62bt2KAwcOICIiosW2o0aNAgBkZGSgZ8+eCA0NxfHjxxu0KSgoAADzOjOhoaHm5+q3USqVTWbFAIBcLodc3nhRIqlU6hQX1ybONh5nxHNke5xzx8bzZ1ucb8fgSOfp6LVSAIb6xI7S5zs50ny3xBnGQETO40ZxFd7eeRmDI3zw/H097N0dIiIimyutNpQR9lPY7n36g4PD8eDgcJsdj24TW3qHgiBg/vz52LJlC/bs2YOYmJhWX5Oeng4ACAsLAwDExcXhzJkzKCwsNLdJTk6GUqlEbGysuc3u3bsb7Cc5ORlxcXEWGgkRERERdValWov07DIAwJiegfbtDBERdQl6vYC1h6/h/ncP4odTufj3ngx7d4mIiMguSo2ZMf42WjOG7MvimTFJSUlYv349vv/+e3h7e5vXePHx8YGHhwcyMzOxfv16PPDAAwgICMDp06excOFCjB07FoMHG8pWxMfHIzY2FjNmzMCbb76J/Px8LF26FElJSebMlrlz5+KDDz7ASy+9hGeffRZ79uzBxo0bsW3bNksPiYiIiIg66Pi1Ymj1AqIDFIj0d541+oiIqGM0Oj1++/kxHM4oNj9XXqOBVqeHm8Ti94sSERF1aSXVti9TRvZj8Xc6H330EcrLyzF+/HiEhYWZv77++msAgEwmw65duxAfH49+/frhT3/6E6ZNm4Yff/zRvA+JRIKtW7dCIpEgLi4Ov/nNbzBz5kwsX77c3CYmJgbbtm1DcnIyhgwZgrfffhuffvopEhISLD0kIiIiIuog04dtzIohInIe14qqsP5YFrQ6fYPnC1W1+NeOiyhU1Tb72gNXinA4oxjuUjFefSjW/LyqVmu1/hIREXVV5swYBmNcgsUzYwRBaHF7ZGQk9u/f3+p+oqOjsX379hbbjB8/Hmlpae3qHxERERHZzuGMIgDAPb0C7NwTIiKylNd+OIf9l2+hvEaDeeN7mp//y+Yz2HOxEGqNHksfjG3ytdeKqgEAk/uHYPY9MXgn+TIqarUoq67jB1FERORySozBGD+WKXMJzAEmIiIiIqu4VaHGxfwKAMyMISJyJpeMv9v/c/AqqtSGjJazN8ux56Jh3ddLBRXNvvZaURUAoEeQFwDA17hgsWkBYyIiIldSWs3MGFfCYAwRERERWcWRTENWTGyYkhcXREROorpOi3xjGbKSqjp8dfQGAODfe66Y22QUVjb7+mvFhsyYHoGeAABfD8Pfh/KaOqv0l4iIqKsSBMF8M4Lp5gRybgzGEBEREZFVHDGuF3Nvb2bFEBE5i+vGMmMm/+/AVaRllWLHuQKIRIbn8sprUalueg2Y6+bMGGMwxvjhUxkzY4iIyMVU1+lQpzWsv8ab11wDgzFEREREZHGCIOCQcb2YMT25XgwRkbO4XmwIpgzq5oMofwWKq+owe+0vAIAHBoYh0MvwYdLVW42zY2q1wK1KQwZMd1NmjLFGPoMxRETkakzrxcjdxPCQSuzcG7IFBmOIiIiIyOKySqpxs6wGUokId8f427s7RERkIaY1X3oFeyFpQk8AtwMpSRN6oadxLZimSpUVGqqbIdBLDqW7ISPG18OUGcMyZURE5FrqrxcjMqWXklNjMIaIiIiILO6wsUTZsCg/KGRudu4NERFZiikYExPoiceHR6CbrwcAYHL/EMSGK9EruIVgTI3hgyZTiTKgXpmyGmbGEBGRazFlxvgpWKLMVTAYQ0REREQWd9hYouyenlwvhojImZjWfOke6AmpRIw3fzUY4/sGYWlifwBoORhTawzGBNYPxrBMGRERuab6mTHkGnibIhERERFZlF4v4EimIRhzb2+uF0NE5ExMa8bEBBgCKvf0CsQ9vW4H3s3BmCbWjLlVY/i3QWaMsUxZKcuUERGRiympMtyIYMoSJefHzBgiIiIisqjzeSqUVmvgKZNgcISvvbtDREQWoqrVoKjSEDTpHqhoso0pGHOjuBp1Wn2DbabMmJhAL/Nzpg+gylmmjIiIXEwZM2NcDoMxRERERGRRpqyY0T0CIJXw7SYRkbMwlSgL9JLD273pu3hDle7wlEmg0wu4YcyiAQBBEFBozIyJCWxizRiWKSMiIhfDNWNcD6+OiYiIiMiiDmUUAwDG9OJ6MUREzuSaMRgT00xWDACIRCL0bGLdmMIKNer0IkjEIkT533797TVjWKaMiIhcC9eMcT0MxhARERGRxdRp9fjlWgkA4F4GY4iInMr1omoADTNbmtIrqHEw5prxtRG+HpC53f4owrRmjKpWC62uYVkzIiIiZ2bOjGEwxmUwGENEREREFpOWVYoajQ6BXnL0CfFq/QVEROQwrhUZgivdWwnGmDNjbtULxhQ3nVXj43G73JmqVmuRftrCumM38OjqwyiuVNu7K0RE5KBKqwwlOv1ZpsxlMBhDRERERBZzOMOwXsyYngEQiUR27g0REVnStWJjZkxAK5kxTZQpay6rxk0ihrfcDYDjlCqrUmuxcvtFpGeXYe+lW/buDhEROagS49890/pp5PwYjCEiIiIiizmcaVgvhiXKiIicz3XTmjFBbQvGXL1VBb1eMHxvfG33gMbrzfh6Gj6EKqvRWKyv1vTjqVxUqg1ZPEXMjCEiog4QBAGlVVwzxtUwGENEREREFlFRq0F6dhkAYEyvAPt2hsiB/OMf/4BIJMKCBQvMz40fPx4ikajB19y5cxu8LisrC4mJiVAoFAgODsaLL74IrbZhmad9+/Zh+PDhkMvl6NWrF9auXWuDEZEzKq2qQ7kxWBLt33IwJtpfAalEhBqNDrnlNQCA68asmh5NlDjz9TB8COUomTH/O55l/v5WBYMxRETUfpVqLbTGGxb8WKbMZTAYQ0REREQWcfxaCXR6Ad0DFIjwa3znMxE19ssvv+CTTz7B4MGDG2174YUXkJeXZ/568803zdt0Oh0SExNRV1eHI0eO4PPPP8fatWvxyiuvmNtcu3YNiYmJmDBhAtLT07FgwQI8//zz2LFjh03GRs7FlNkS5uMOD5mkxbZuEjG6G0uZZRRWQqPTI7vUEJTpHthEZoyxPEtZtXUzY/ZeLMSvPjqCdcdudHgfZ2+W41ROufkxgzFERNQRpvViPKSSVv+ukvNws3cHiIiIiMg5HL9eAgCI68msGKK2qKysxDPPPIP//Oc/+Pvf/95ou0KhQGhoaJOv3blzJ86fP49du3YhJCQEQ4cOxYoVK7B48WK89tprkMlk+PjjjxETE4O3334bANC/f38cOnQIq1atQkJCglXHRs7nurnMWMtZMSa9gr1wpbASKZnFEIlE0OkFyMQCQrzljdr6eFg3GFNercHyrefx7ckcAMCZm+WYEhuCYG/3du9rvTErxlvuhgq1lsEYIiLqENN6MSxR5lqYGUNEREREFpGeVQYAGBblZ9+OEDmIpKQkJCYmYvLkyU1uX7duHQIDAzFw4EAsWbIE1dXV5m0pKSkYNGgQQkJCzM8lJCRApVLh3Llz5jZ37jshIQEpKSlWGA05u+vFbVsvxsS0bswnB65i1n+PAwCCPQCRSNSorak8izXKlN0orkL8u/vx7ckciERAoJccaq0e/2//1Sbbq7U6JK0/iXlfpZrXhTGpVGvxfdpNAMBv7+kOALjFNWOIiKgDTOvF+BnXTSPXwMwYIiIiIuo0rU6P08ayLcOjfO3bGSIHsGHDBpw8eRK//PJLk9uffvppREdHIzw8HKdPn8bixYtx6dIlbN68GQCQn5/fIBADwPw4Pz+/xTYqlQo1NTXw8PBodFy1Wg21+vaHyyqVCgCg0Wig0TjG4uptYRqLM43J2jILKwAAUX7ubZq3hwaF4HBGEQpUtaio1UKt1WNkYNM/R95yQ3mWkiq1xc/Jx/syUKBSI9pfgTenDUSlWovnvjiJr47dwPP3RCHAq2Gmzj9+uoRtp/MAAKoaDf7fb4ZB5ma4j3VLag6q6nSICVBgamww/r0nA7cqah3i54g/8/bF+bctzrd9cf7b5pbKUL7Tx13a6bninFueteaSwRgiIiIi6rRLBRWo0ejg7e6GHoFe9u4OUZeWnZ2NP/7xj0hOToa7e9NlkubMmWP+ftCgQQgLC8OkSZOQmZmJnj17Wq1vK1euxLJlyxo9v3PnTigUzrcWVHJysr274DBOX5MAEKHo2gVsLz/fptfM6gagW8Pnmprz3FwRAAnOZ9zA9u3XOt1XE60e+O6kod+JoRXIP3sEggBEeUqQVaXHX7/ci0ei9eb2F0pFWHPREBiSigQczizGzNU78XRPPVIKRdieLQYgwmCvCpw6dgCAG8prtPhh63a4OUjdEf7M2xfn37Y43/bF+W/ZEePfvpqyW9i+fbtF9sk5t5z6GemWxGAMEREREXVamrFE2dBIX4jFjUvQENFtqampKCwsxPDhw83P6XQ6HDhwAB988AHUajUkkoYLuY4aNQoAkJGRgZ49eyI0NBTHjx9v0KagoAAAzOvMhIaGmp+r30apVDaZFQMAS5YswaJFi8yPVSoVIiMjER8fD6VS2cERdz0ajQbJycmYMmUKpFKWB2mLpWl7AGjxePx96B3c/qB7S3Nem3YT3904B0+/IDzwwAgL9Rj46Ww+ao6dRqhSjj88ORYS498nRa9bmPNVGlJuueGNmWMR4ClDUaUayz9IAVCHGaMiMb5vEP7vqzSkFolxtVqOUuN6Nn1DvPDKM3fB290Nr5zcBY1OwMh7JyDct+n/U10Ff+bti/NvW5xv++L8t82F5CvAjWsY0Ls7HnigX6f2xTm3PFN2uKUxGENEREREnWYKxgyL9LVrP4gcwaRJk3DmzJkGz82ePRv9+vXD4sWLGwViACA9PR0AEBYWBgCIi4vD66+/jsLCQgQHBwMw3A2pVCoRGxtrbnPnnZbJycmIi4trtm9yuRxyeeMF1qVSqVNe3DvruCytvFqDilrD+ikxQUpIpY1/RtuqqTkP9DYEMsprtRY9Hz+cNpTse2x4BNzltxdInjIgDIO6XcWZm+X43fp0dA/wxPk8FYqr6tAv1Bt/e3AA3KUSvPkrHRZtPIXSag0CPGVYMKUPnrorElKJIQ0m2NsdN8tqUFKjQ3SQY/wc8Wfevjj/tsX5ti/Of8vKagx/V4O83S02T5xzy7HWPDIYQ0RERESdlpZdCgAYFuVn554QdX3e3t4YOHBgg+c8PT0REBCAgQMHIjMzE+vXr8cDDzyAgIAAnD59GgsXLsTYsWMxePBgAEB8fDxiY2MxY8YMvPnmm8jPz8fSpUuRlJRkDqbMnTsXH3zwAV566SU8++yz2LNnDzZu3Iht27bZfMzk2LJKDKU6grzl8JB1PBDTHF+F4QOPsmrL1WcvqlRj36VbAIBpwxvWShOJRPjjpN54/osTOJlVhpPGGwrkbmL8e/owuBuDTY8Pj4BCJkFWSTWm3x0Fb/c7g0hy3Cyrwa0KNYiIiNqjuKoOAODnKWulJTkTBmOIiIiIqFPKqutw9VYVAEOZMiLqHJlMhl27duHdd99FVVUVIiMjMW3aNCxdutTcRiKRYOvWrZg3bx7i4uLg6emJWbNmYfny5eY2MTEx2LZtGxYuXIj33nsPERER+PTTT5GQkGCPYZEDMwVjovyts26Qj4fhg6iy6jqL7fOH9Fxo9QKGRPigV7B3o+2T+gfjw2eG42Zpjfm5kd390DukYdv7B4Y1e4wgL0Pg81YlgzFERNQ+pcZgTACDMS6FwRgiIiIi6pT07DIAQEygJ+/sIuqgffv2mb+PjIzE/v37W31NdHR0qwu+jh8/HmlpaZ3tHrmQA5dvQaPTY1L/EPNz2aWGYEykn3XWRfEzZsaoarXQ6QXz2i6d8e3JHACG7JamiEQiPDCo+UBLWwR5G4MxzIwhIqJ2KjEGY/x5/eRSxPbuABERERE5NlMwhuvFEBE5tqJKNZ5d+wv+78tUFNfL9rB+Zszt8l/lNZ0vVXYxX4VzuSpIJSI8NCS80/trDoMxRETUUcXMjHFJDMYQERERUaekGWvtD4vytWs/iIioc3acy4dWL0CrF3Apv8L8fLYxGBNhpWCMm0QMb7mhcIclSpVtPZUHABjfN9iqdxwzGENERB2h0enNNx8wM8a1MBhDRERERB2m1wu3M2Oi/OzbGSIi6pSfzuSbv7/YRDDGWpkxAOBjLFVWZoHMmMOZRQCAKbEhrbTsHK4ZQ0REHVFqvPFAJAJ8FQzGuBIGY4iIiIiow64VV6G8RgO5mxh9QxsvkExERI6hpKoOKVeLzY8vFxiCMTq9gJtlhkXurRmM8TN+GNXZzJiKWg1O55QDAMb0DOh0v1rCzBgiIuqI0irDjQe+HlKLrJNGjoPBGCIiIiLqMFOJssERPpBK+NaSiMhRJZ/Ph04vwPSZ0CVjMCZfVQuNToBUIkKI0t1qx/c1ZcZUdy4z5pfrJdDpBUT5KxDhZ73gEQAE1wvGCIJg1WMREZHzKK4yBPFZosz18IqZiIiIiDosLasUAEuUERE5uu3GEmUPGxe8v5xfAb1eQFaxcb0YP4VV79718bBMMOZIhiG7x9pZMQAQaCxTptbqUaHWWv14REQAkHmrEhfyVPbuBnVCSZUhCzTAU27nnpCtMRhDRERERB1myowZFulr134QEVHHlVdrcDjDsM7K7yb0glQiQlWdDjfLaszrxURasUQZUC8zpoU1YwRBwLu7LuOR1Yfx30PXzIsf13ck0xCMibNBMMZDJoG33A0AS5URUccIgoAT10ua/H3WlFqNDtM+OoKHPziEjMJKK/eOrMUUjGFmjOthMIaIiIiIOqS6TouL+Ya78pgZQ0TkuJIvFECrF9Av1Bt9QrzRM8gLAHApvwLZpcZgjJ+HVfvQ2poxgiDg79su4N1dV3AquwzLt57H6Dd2429bzqDKmJVSWlWH88a7xW0RjAG4bgwRtY0gCCitavz77bND1/Crj1Ow7MdzbdpPytVilFVroNEJeCf5kqW7STZSXGkMxngxGONqGIwhIiIiog45k1MOvQCE+bgj1Md66wgQEZF1bT+TBwCYOjAMANA31BuAYd2YLGNmTJSVM2NaKlNmCsR8dugaAGBmXDT6hXqjRqPDumNZWPnTBQDA0auGrJjewV4I9rbN3yVTMKaQwRgiakZaVime+CQFw1Yk453ky+bnbxRX4a2dhoDKqeyyNu1r78VC8/fbz+S3+XXUtZgzYxQMxrgaN3t3gIiIiIgcU5rx4m9YlK9d+0FERB1XUavBwSu3AAAPDAoFcDsYc7leMMb6ZcqMmTF3lOrJLqnGO8mXsSXtJgDgjccG4elRURAEAT+fzce8dSex7lgWnhwZZS5RZov1YkyYGUNE9f3veBY+P3IdIUp3dA9QoKiyDtuMAW8AeH/3FUT4euDXIyOwZPMZ1Gr0AICskmpodXq4SZq/b14QBOwxBmOiAxS4UVyNN3dcxLrnR3e4v60dk6yjpJplylwVgzFERERE1CFpWaUAgGGRLFFGROSoTlwvhUYnIMpfgd4hhiBMX+O/l/IrUGQspWLtzBhfc2aM4XiXCyqwem8Gtp7Og04vALgdiAEAkUiEqYPC8MjQcHyfnouXvz8LVa0hkDOmV6BV+1ofgzFEZFKl1uKN7RdQUavFxfwK7Dc+LxIB04ZHQOkuxX8PX8Nft5xBWnYpjmQWw10qhl4A6rR63CyrQXSAZ7P7z7xViZzSGsjc4GPw6gABAABJREFUxPjPzJFIfP8gDmcU49CVItzbu/2/9w5euYUXvjiBv9zfD7+9J6aDo6aOKDH+bQ1gmTKXY/HQ58qVK3HXXXfB29sbwcHBePTRR3HpUsMahrW1tUhKSkJAQAC8vLwwbdo0FBQUNGiTlZWFxMREKBQKBAcH48UXX4RWq23QZt++fRg+fDjkcjl69eqFtWvXWno4RERERNQEQRBwMqsMADCUmTFERA7LVNorrsftbBJTZkxGYSWKKg1BBmtnxvh5GoIxhSo1Vmw9j6nvHcT36bnQ6QWM7ROEjf8XZw7E1PfXB/rDS+6G9OwyXL1VBZEIGB3DzBgisr3v0m+iolaLKH8FVj4+CHPH9cSsuGhs+/19eOvXQ/Dyg/3xyNBwaPUC/nc8GwDwpyl90SPQEIC5WlTV4v5NWTGjewSgT4g3nhkVDQB4ffsFpN4oNQeu20IQBLz58yXUavQ4eKWoI8OlTjCXKWNmjMuxeDBm//79SEpKwtGjR5GcnAyNRoP4+HhUVd3+hbJw4UL8+OOP2LRpE/bv34/c3Fw8/vjj5u06nQ6JiYmoq6vDkSNH8Pnnn2Pt2rV45ZVXzG2uXbuGxMRETJgwAenp6ViwYAGef/557Nixw9JDIiIiIqI75JbX4laFGm5iEQaG+9i7O0RE1EGmYMzonv7m57r5esBTJoHW+MGe0t3NvKaLtfh4GD6QylfV4rND16DTC4iPDcHW39+LL569G3fH+Df5uhClOxZM7m1+PDDcBz4K6/a1viAvYzCmksEYIlcmCAK+OHIDgGFdq+l3R+EvU/th2SMDERuuBGDI6PvntMG4q7shq3xwhA9m39MdPYKMwZhbLQdj9l40lJSc0DcIADB/Yi8oZBJcyFNh2kdHMGz5TizamI5aja7V/qZkFuPMzXIAQLExMEC2U8xgjMuyeJmyn3/+ucHjtWvXIjg4GKmpqRg7dizKy8vx2WefYf369Zg4cSIAYM2aNejfvz+OHj2K0aNHY+fOnTh//jx27dqFkJAQDB06FCtWrMDixYvx2muvQSaT4eOPP0ZMTAzefvttAED//v1x6NAhrFq1CgkJCZYeFhERERHVYypR1j9MCQ+ZxM69ISKijqio1eBsrgoAMKpeNolIJEKfUG+kGTMgowKsmxUDAIH1SrV0D1Bg2SMDMa5PUJteO2tMd2w6kYNLBRU2XS8GYGaMpRy9WoxwHw+b/KwRWcOxayW4VFABD6kEvx4Z2Ww7d6kEn/32Lvx4KhcJA0LhJhEjxpgZc62ostnXqWo1+OV6CQBgQt9gAECglxxfPjcKnx26ikNXiqCq1WLzyZuY1C8EiYPDWuzvR/szzd+XVjMYY0t6vWCe8wBPuZ17Q7Zm9TVjyssNUVZ/f8NdLKmpqdBoNJg8ebK5Tb9+/RAVFYWUlBSMHj0aKSkpGDRoEEJCQsxtEhISMG/ePJw7dw7Dhg1DSkpKg32Y2ixYsKDZvqjVaqjVt98gqVSGN50ajQYajaa5lzkM0xicYSzOiufI9jjnjo3nz7Y4346hq5ynVOPF4JAIpd37Yk1dZb4txVnGQUSWccJY1ibKX4FwX48G2/rVD8ZYuUQZAPgqZFj28ABodHr8ZnQ03KVtD/RLJWJ8PGME1h29gTlje1ixl40xGNN5hzOK8MynxxAT6Ik9fxoHkUhk7y4RtdsXKdcBAI8N79ZqJqHSXWouMQYAMYFeAIBrLZQpO3SlCFq9gB6BnugeeHtdmRHRfhgRPQJanR6Lvz2Db0/mIC2rtMVgzNmb5Q1Kk5nWLyHbqKjVmkvKmUp0kuuwajBGr9djwYIFuOeeezBw4EAAQH5+PmQyGXx9fRu0DQkJQX5+vrlN/UCMabtpW0ttVCoVampq4OHR8I0kYFjPZtmyZY2e37lzJxQK57n7Ijk52d5doFbwHNke59yx8fzZFufbMdj7PO09IwEgAoqvY/v2a3btiy3Ye74tpbq62t5dIKIuxFyirEfjEmB9QrzN30f62eZ6edaY7h1+bUygJ5Y+GGu5zrSRKRhTUqWGTi9AImYgob3e330FgOGD6Iv5FegfprRzj4jaJ6+8BjvOGdbCnhkX3UrrxsyZMS2UKdtrXC9mQr/gJre7ScQY0zMA357MQXp2WYvH++TAVQDA2D5BOHD5FirUWtRp9ZC5WXw1C2pCcZUheO8ld4PcjRUGXI1VgzFJSUk4e/YsDh06ZM3DtNmSJUuwaNEi82OVSoXIyEjEx8dDqXT8P/YajQbJycmYMmUKpFJGVrsiniPb45w7Np4/2+J8O4aucJ7qtHq8+MseAHr89sFxiHbikiJdYb4tyZQZTkQEAEevGrIcR/doXNqrb2i9YIwNMmMcVYCnHGIRoBcMH7AFe7vbu0sO5cT1Ehy7VmJ+vPtCAYMx5HDWH8uCTi9gVIw/+oW2/+e3hzEYk1tei5o6XYMSwDml1ThwuQjJFwzBHlOJsqYMi/IFAJy5WQ6NTg+ppHFwJbukGttO5wIAXkroi8MZRdAZy2aFKC3/++uLlOv4MuUGPp4xAj2DvCy+f0dUwvViXJrVgjHz58/H1q1bceDAAURERJifDw0NRV1dHcrKyhpkxxQUFCA0NNTc5vjx4w32V1BQYN5m+tf0XP02SqWyyawYAJDL5ZDLG9fik0qlTnFxbeJs43FGPEe2xzl3bDx/tsX5dgz2PE/n88tQp9XDTyFFzxClS5QTcZb/F84wBiKyjEq1FmeNizePaioYE8JgTFtIxCL4e8pRVKnGrQoGY9rrg70ZAAwZRrcq1Nh1oRDzJ/a2c6+I2k6t1eF/x7MAdDy7z89TBl+FFGXVGlwvrkL/MCWq67R4+j/HGmS5+CqkuCvGr9n9xAR6wsdDivIaDS7mVWBQhE+jNv87ngW9ANzXOxADu/nATyFFUWUdiistH4zZc+kWXv3hHAQB2H46D7+fxP/bAFDMYIxLs3j+mSAImD9/PrZs2YI9e/YgJiamwfYRI0ZAKpVi9+7d5ucuXbqErKwsxMXFAQDi4uJw5swZFBYWmtskJydDqVQiNjbW3Kb+PkxtTPsgIiIiIutIyyoFAAyN9HWJQAwRkSPJK6/By9+dxfF62QZNOXG9BDq9gEh/D3TzbXxDY4CXHN0DFJCIRQ0CM9QY143pmDM55dh36RbEImD108MBAKdyyjiP5FC2n8lDUWUdQpXumBIb0voLmmEuVWZcN2b3hUKkZ5dBIhZhRLQfFk7ug83zxrRY1kokEmFIpC8AID27tMk2+y7dAgBMG264cd4UEDAtKG8pBTXAnzadgWBYGgVXCistun9HZsqMCWAwxiVZPBiTlJSEr776CuvXr4e3tzfy8/ORn5+PmpoaAICPjw+ee+45LFq0CHv37kVqaipmz56NuLg4jB49GgAQHx+P2NhYzJgxA6dOncKOHTuwdOlSJCUlmTNb5s6di6tXr+Kll17CxYsX8eGHH2Ljxo1YuHChpYdERERERPWkGe/QGxbV/J15RERkH3/bchZfHr2Bp/9zFOuO3Wi2nblEWUzjrBiTr54fhS2/G4NQH2Z7tMQUjNmSdhPl1Ro798ZxrDZmxTw8JBx3x/hjUDcfCMLttTGIHMHnRwy/Z58ZFdVkWbC2ujMYs8f4/+D5e2Pw7bwx+OPk3ujRhjJfw4zBmLSsskbbblWocT7PULL23t6BAAA/hSEgYMrWsISKWi0+uyRBpVprDvZcLqiw2P4dHcuUuTaLB2M++ugjlJeXY/z48QgLCzN/ff311+Y2q1atwoMPPohp06Zh7NixCA0NxebNm83bJRIJtm7dColEgri4OPzmN7/BzJkzsXz5cnObmJgYbNu2DcnJyRgyZAjefvttfPrpp0hISLD0kIiIiIioHtPFnakuNRERdQ0ns0rNH+Bp9QL+tuUsXvvhHLQ6faO2R68WA2h6vRiTCD8FBkf4WqWvziTOOIffp+di7L/24tODV6FpYs7ptqNXi/HzuXwAwO8m9AIATOpvWAtj14WCZl9H1JWcyi5DenYZZBIxnro7qlP7Mq0bc/VWFbQ6PfZeMvwun9S/fdk2Q43vz+uXNzM5eMWQFTOwmxKBXoYgcoCXMTPGgsGYpd+fQ0GNCCFKOf4zcyQA4GpRVZN/i1wRgzGuzeJrxgim/LMWuLu7Y/Xq1Vi9enWzbaKjo7F9+/YW9zN+/HikpaW1u49ERERE1DHZJdXIKqmGWAR+QEdE1MW8s/MyAOCJkRGI8lfgrZ2XsfbIdahqNXjniaHmdhW1Gpwxrxfjb4+uOpW543qgf5g3Vm6/iEsFFfj7tgtIySzG6meGw13afEkhwFCq64O9V5BfXmt+blyfICyK72u1/l4vqkI3P49O3cXfGceuFuPZtb8AAB4ZGo4+xjJ4k/uH4N1dV3DwShFqNbpW547I3r5IMWTFJA4OM2fIdVRMoCHr5VpRJdKyy1BWrYGPhxTD23nz01Dj+/OrRVUoq66Dr+L2B/77LxuCMWN7B5mfMwUELJUZk3y+ANvPFkAMAR88NQTDIn3hIZWgRqNDVkl1m7J7nB2DMa7NPn95iYiIiMgh/XzWcBfrqJgA+HhwMXgioq4iJbMYhzKKIJWI8IdJvTF/Ym989MxwSMQibD55E5tP5gAw3ED56vfnoNMLiAn0RISfws49d3wikQjj+wZj+x/vw8rHB0HuJsbui4V44YsTqKnTNfma7JJqLPw6HQ99cAg7zhXgVE65+ev9PRm4UVzVoH2hqhbncsvbdANsS748moXxb+3DE5+kQK1tum/WdOxqMWav/QXVdTrc1zsQ/5w22LxtQLgSoUp31Gh0SDFmbhF1VcWVavx4OhcAMDMuutP7q1+mzJQdNr5vENzaGTT185SZ91U/O0avF3DwShEAYGyfesEYheUyYypqNXj5u7MAgAnhAoZG+kIsFqFXsCEAc7mA68YAtwNfDMa4JotnxhARERGR89p+Ng8AMHVQqJ17QkREJoIg4J3kSwCA6XdHmQMsUweF4Q8FlVi16zJe/u4sRkT7YfeFQmxOuwmJWIS/PzrQnt12OhKxCNPvjkL3AE889/kvOHilCL9dcxz3DwxFTmkNckqrjf/WoLzm9toyjw3rhsRBYRCLgQ/2ZOBkVhm2ns5DkrF8l1anx68+TkFWSTXu6x2IJVP7IzZc2e7+XSkX4aNjhp+TtKwyvLHtApY9YpmfAY1Oj0v5FXCXiuEll8LHQwoPWcPMlh9P5WLxt6fNgZj/zBzZIPtFJBJhUv9grDuWhV3nCzChb7BF+kZkDV+fyEadVo/BET4YalynpTO6Bxp+b5dWa/BDuiHI094SZSZDI31xragK6dllGG/8f3QuV4WSqjp4yiQYXm/dR1NAoKSdwZis4mr8edMpFFWq8af4vnhgUCje3nkZ+apaRPp54P6I22vE9A72wpmb5cgorADAa4iSKjWA2yXiyLUwGENEREREbZJXXoO0rDKIREDCAF5IERHZS3mNBt+m5uBIZjH0ggC1VodfrpdC7iY2f4BvMn9iLxzOKMLx6yWYveYX3CipBgAsmdoP9/QKtEf3nV5czwB88ezd+O2aX3DsWgmOXStpst2YngFYMrU/BkX4mJ8rqqjDyawy/Hgq13wud10oQJbxvB28UoRDGQfx5MhILH9kIGRubbtrPresBmsui6HTCxge5YuTWWX4POUGRnT3x8NDwjs5YmDJ5jP4JjXH/FgkAib0DcaMuGgMjfDFKz+cw4+nDB8wNxWIMZncPwTrjmVhz8VCCIIAkUjU6b4RWcOG49kAgJlx3S3yc6qQuSHMxx155bXIK6+FRCzCuHrlxNpjWJQvtqTdNK/zCAAHjOvFjOkV2OD3hp+5TJm6zfv/6UweXvrmNCrUWgBA0vqTGBbla87EWf5wLFSXj5nb9zaWImRmjEFJpSkzpnOl7cgxMRhDRERERG1iKlE2MtoPIUp3O/eGiMj1ZJdU48N9GfguLRc1msYlpmaN6d7o97NELMKqp4Zi6rsHcLXIUPrq0aHheO7eGJv02VWN7O6P9S+M+v/s3Xd8FHX+x/HXbnoP6QkphN4hgEKUQ6QK2PH01AO7p4d6yp36485eTk/P82yn51nQU+8sZwUUokiRLhJ6b6GkEEJ62+zO749NFiIJhGSTzWbfz8eDh2Z2duYz30k2mfnM5/Pl5YW78PYykdgpkMROAXQODyCxUyCdOwUQ7HfyLZmJ/eL40+cb2ZZTwo7cEnrGhvDeyiwAfjk0kcoaG1+tP8x/1xyge0wwN/+i62ljqbRY+e1/MimrMdEnLoT3bx7BK9/v4uXvd/F//9tAZbWV4koLOUWVnJUaccYPXOzILeF/tW3wwgJ8KK2qwWozWLgtj4Xb8vAym7DaDLzMJmac3507x3RvdL6aEV0j8TabyC6q5HBRJZ3DA84oFpG2kFtc6ZhDcVJ/5z2glBoVRHbt/FFndelEWGDzWgLXVeqsP1joSGo65ovpWT/BE1mbEDhWZqEpnpq3lX8u2QPA0JROnNMtkteX7HEkfi4f0pmR3SOZt+P4e3rG1rUpK/n55jxSQbk9GROpNmUeSckYEREREWmSrzfakzGT+se7OBIREc8069ON/LDL3vO/V2wIVwxNdNysC/DxYkK/hlvadA4P4JkrBjLjg3X0TwjlqcsHquKgDQxMDOf16cPO6D1hgT6c1zOab7fmMWf9YS4bksgPu/IxmeCusT1Iigjk7NQIHvx8E68u2s3VZycT1EBSp47VZnDPh5lsPlxCkLfBq9cOJsDXi3vG9+SnrGMs332U+/63wbH+28v38dlvz2Fg7STgTfHCtzsxDLigXxyvTRuKYRjszS/jvZVZfLz2ACWVNXSNCuJvVw0+bTunAF8veseHsOlQMZlZhUrGSLu0vrYCpEdMyCl//s5UalQQy3fb50sa27t5LcoAeseF4uttprDcws68UuLD/Plp/zGAk6ptIhyVMadvU7bxYJEjEfOb87ryhwm98PEy86uzk/nr/O3klVTywJS+J72vR4y9MmZPfhk1VtsZz4PTkZRX11BpsQHHq5LEsygZIyIiIiKnlVdcyZr99jYrFzjxCUAREWm67bVPFb/wq8FcPCjhjBIqF/SPZ/n/dSIq2A8vsxIx7dlFgxL4dmseX23IprzaXgE1umc0SRH2OSV+dVYSby7dw76j5cxevu+k1nR1DMPgsa828/WmHHy8TNzYs8aR3PAym3jx6jTu+TCTSouVuLAADh0r56esQmZ+tJ45d448qY3YocIKHv5iE1kF5fz9qjT6JoSyNbuYuRuzMZng7vE9APvcL12jg3noor78YWJP1mUVMiS500lzyDRmcFI4mw4Vsy7rGFMG6gEQaX82HCwCYOAJLQadITUqyPH/Y/s0f84kX28zAzqHsXb/MS588Qf6JIRSYzPoEhlIcmRgvXXrkjHHyqtP2xrw3yv3AXDJ4ARmTerjWN45PIDnrxrs+NpiqV9lk9gpAH8fM5UWG1kF5XSNDm72sbm7o7Utyny9zQQ18TNROhbPTUWKiIiISJPN35yDYdhvkCToKVURkTZXVlXDkRJ7T//RPWOaVdkSG+qvRIwbGNcnFn8fM3vzy3h35X4Afj0ixfG6j5eZu8f1BOCfi3dTVNFwe6HXFu/hnRX29/916gC6/+y+cVSwH/++aTgf33YOL12dxhvXnUVUsB+78kr5W8bxHkOGYfDBqiwmPr+Eb7fmsSO3lKteX8HqvQU8X7velAHx9I4LPSmGQF9vzu0e1eREDEBakn1y8br5J0Tam/UHCwEYeJpKrzPVJ97+M9Q1OqjFCYvfT+hJ77gQqq02RyXPz1uUAXQKsldXWm0GxRU1jW6vqNzCF5n2eZ+mp6c0ul5DzGYT3WPsx7Mzz7PnjSkoO96iTBWqnknJGBERERE5ra9r54uZPEBVMSIirlA3gXunQJ9mzyMg7iHIz9vRoqi6xkbn8ABG96r/lPxFgxLoERNMcWUNb/6w96RtfPrTQf7yzTYAHrywb5N+f0cE+fL05QMA+NfSPfx3dRbPLdjOlBd/4I+fbaS0qoahKZ0YltKJksoafv3mKhZsybVXxYzr0dLDdhicHA7AxkNFWKw2p21XxBkMw2DjIXtlzCAnV8ac0y2Sv0wdwKvXDnXCtqL45u5RfP27X/Cb87oyulc0N488eY4pP28vx/xVR8uqGt3ex2sPUFVjo098KEOSO51xPD1rW5Xt9PB5Y+qSMRFqUeax1KZMRERERE7paGkVK/fY+1drvhgREdfYf7QMgOTIoNOsKR3BRYPimbsxG4BrhiefVNHkZTYxc3xPbn//J976YS9XnZXkaEG2ZMcR7vvEPg/MraO6ctPI1JPaBjVmXN9Yfjk0kY/XHuT/Pt3oWO7vY+beib25/pwuWKw27vjgJ77dmgfAJYMS6F57o9UZUiODCAvwoajCwrbsEgY4+Ya3SEscKKigsNyCr5e5wWqwljCZTFx1VrJTt9knPtRRcdOYiCBfSqtqOFbe8LwxNpvBe7VVetNGpDSroqNHrP0zYkeuKmNAyRhPpsoYERERETmlBVtysRkwoHOYo1+9iIi0rf1H7ZUxXSL1OewJRveKISbEjxB/b64cltTgOhP7xdEvIZTSqhomv7CUL9cfZtOhIm5/by01NoNLBifwfxf0PuN9P3RRX3rHhdAp0IeLBiXw7BUDWXrfGG4amYqX2YS/jxev/XooN56byqDEMH4/oVdLD7ces9nEoNr2T5kHjjl12yItVdeirE98CL7eHeO2al1ioG4+k5/7YVc++46WE+LnzSWDE5q1jx5qUwYoGSNKxoiIiIjIacyrfTJ3klqUibSKp59+GpPJxN133+1YVllZyYwZM4iMjCQ4OJipU6eSm5tb731ZWVlMmTKFwMBAYmJiuPfee6mpqd/vfdGiRQwZMgQ/Pz+6d+/O7Nmz2+CIpDXsq03GpCgp7hH8fbyYc+dI5t89iugQvwbXMZtN/OPaIQxMDKOowsJd/1nH1FeXU1Zt5dzukTx7xSDMzZgjKMTfh2/uHsW6hybw0tVp/HJY0kkxeHuZeeiivnxxx8hWeVBjcG0yZl1WodO3LdISG2qTMR2pYqsuMdBYZcy/a6tipg5NJMiveU2WetZWxuw+UorVZjRrGx3BUSVjPJ6SMSIiIiLSqMLyalbsVosykdayZs0a/vnPfzJw4MB6y++55x6++uorPv74YxYvXszhw4e5/PLLHa9brVamTJlCdXU1y5cv55133mH27Nk89NBDjnX27t3LlClTOP/888nMzOTuu+/m5ptvZv78+W12fOI8WQX2NmUpalPmMWJC/UmobT3WmJTIIP53+zncNbYHXmaTY06H13491K2f2k+rnTcms3bicZH2Yv1B+3wxAxPDXRuIEzkqY8pOTsZkF1Xw3Vb7wyC/HpHS7H0kdgrA38dMdY3NMQeaJzpSYp+XJyq44SS7dHzu+5tZRERERFpdxpZcamwGveNCSI3SDUARZyotLeXaa6/lX//6F506HZ8Mt6ioiDfffJO//e1vjBkzhqFDh/L222+zfPlyVq5cCcCCBQvYsmUL7733HoMHD2bSpEk8/vjjvPLKK1RX22+mvPbaa6SmpvLcc8/Rp08f7rjjDq644gqef/55lxyvtMy+/NrKGLUpk5/x8TIzc3xPPq1Nyrx749mE+Pu4OqwWGVx7o3tPfhmFjTytL9LWrDaDTYfsyZhBHTAZU9BAm7Kv1h/GZsDZXSLoXttqrDnMZpPj/TtyS5q9HXe3N9/epk2/yz1X82rLRERERMQjfL0pB4DJA1QVI+JsM2bMYMqUKYwbN44nnnjCsXzt2rVYLBbGjRvnWNa7d2+Sk5NZsWIFI0aMYMWKFQwYMIDY2FjHOhMnTuT2229n8+bNpKWlsWLFinrbqFvnxHZoP1dVVUVVVZXj6+LiYgAsFkuTJwB3B3XH4i7HVFVjI7uoAoDOYb5uE/eJ3G3M3VHfuCD6xqUCJ4+zu41/sK+JLpGB7Dtaztp9RxnVI8rVIbWIu42/u2ut8d6ZW0p5tZVAXy9SOvl1mPMZ5u8FwNHSypOO6bOfDgFw4cDYJh9vY+PfPSqITYeK2XKokDE9I1satlvam2+vck0Kd+73jz5jnK+1xlLJGBERERFpUHGlhaU7jwAwWfPFiDjVf//7X3766SfWrFlz0ms5OTn4+voSHh5eb3lsbCw5OTmOdU5MxNS9XvfaqdYpLi6moqKCgICT2x899dRTPProoyctX7BgAYGBHe8pzoyMDFeH0CS5FWAzvPE1G6xa/B2mM58GpN1wlzHvqNxp/KNMZvZh5uOFayjd2THmmHCn8e8InD3eq/JMgBfxfjXM/+Zrp27blQ7WHte2fYeYN++AY3lOOWzN8cbLZOB9eCPz5m08o+3+fPxNRfb9fLtuJ10rtjshcvdSZoFj5fZb8dt//IF9Xs7fhz5jnKe8vHXa6SkZIyIiIiIN+m5rLharQY+YYLrHhLg6HJEO48CBA/zud78jIyMDf39/V4dTz6xZs5g5c6bj6+LiYpKSkpgwYQKhoaEujMy5LBYLGRkZjB8/Hh+f9t/OadGOI5C5jq4xoUyZku7qcJrF3ca8o3HH8S9YlcWPc7ZRHhDD5MlDXR1Oi7jj+Luz1hrv1V9thd0HOG9AFyZP6uW07bqa37Y8/rM7E5+gcCZPHuFY/reMncBezusZwy8vSWvy9hob/84Hi/jsn6s4XOXHpEmjMbnzkwXNsC6rEH5cTVyoH5ddNMGp29ZnjPPVVYc7m5IxIiIiItKgrzfan66f1F9VMSLOtHbtWvLy8hgyZIhjmdVqZcmSJbz88svMnz+f6upqCgsL61XH5ObmEhdn/3mMi4tj9erV9babm5vreK3uv3XLTlwnNDS0waoYAD8/P/z8Tp5U1sfHp0Ne3LvLcR0stLeOS40Kcot4T8VdxryjcqfxH9rF3sZow6FivL29O8SNW3ca/47AGeO9cFsue46UERnsy+p9xwAYlNypQ53HmDB75WtBucVxXIZh8FXttcBlQxKbdbw/H/+BSRH4eps5Vm7hULHF4+ajzKr9Xd4tJrjVvn/0GeM8rTWOSsaIiIiIyElKq2rsT2IDkzRfjIhTjR07lo0b67f6uOGGG+jduzf3338/SUlJ+Pj48N133zF16lQAtm/fTlZWFunp9qqI9PR0nnzySfLy8oiJiQHsrSlCQ0Pp27evY5158+bV209GRoZjG+I+9h+1t8pI1oS/4kF6x4Xi622msNxCVkE5KZGedeNWXO9AQTk3vfMjxs+65A1KDHdJPK0lItAXgGNl1Y5lP2Ud4+CxCoJ8vRjXJ7axt54RX28zAzqHsXb/MX7af8zjkjF780sBPO64pT4lY0RERETkJN9vy6O6xkZqVBC949SiTMSZQkJC6N+/f71lQUFBREZGOpbfdNNNzJw5k4iICEJDQ7nzzjtJT09nxAh7+5AJEybQt29fpk2bxjPPPENOTg4PPPAAM2bMcFS23Hbbbbz88svcd9993HjjjSxcuJCPPvqIuXPntu0BS4vtP2qf8LeLbkaLB/H1NtM7LoQNB4vYfLhYyRhpc6v2FmAYEB3iR8/YYI6WVpOWHE5KB0uMRwTbkzFl1VYqLVb8fbz4fN1hACb2iyPA13mTmwxJDrcnY7KOMXVootO26w72HLH/Lk+NCnZxJOJKSsaIiIiIyEm+yDwEwAX94zpEWxARd/P8889jNpuZOnUqVVVVTJw4kX/84x+O1728vJgzZw6333476enpBAUFcd111/HYY4851klNTWXu3Lncc889vPDCCyQmJvLGG28wceJEVxyStEBdZUxKRMe6AShyOv0SwthwsIhNh4qYrEpdaWNr99vbkl2e1plZk/u4OJrWE+LnjY+XCYvVoKCsmugQP+ZuzAbgkrTOTt1XWnInYK99/pQO5r+rs5i7MZtgP2+C/bzpEhXEbed1w8tsv5bam29PxnSNVmLZkykZIyIiIiL1HDxWzsJteQBc4WFPrIm4yqJFi+p97e/vzyuvvMIrr7zS6HtSUlJOakP2c6NHj2bdunXOCFFcxGozOHCsNhmj1ibiYfp3DgVg0+HWmUhZ5FTW7i8AYEhKJxdH0rpMJhOdAn3JK6mioKya9QcKKSirJirYl3O7RTp1X0OS7WO5LaeYsqoagvw6xq3p0qoaHvpyM9U1tnrLU6OCmDwgHpvNOJ6M0e9yj2Z2dQAiIiIi0r68vyoLmwEju0fRLVpl9CIirnS4sAKL1cDXy0xcqL+rwxFpU/0TwgDYfKgI4+cTd4g4SUW1lZkfZbJgc45jWVGFhR259jk+hnbwZAxARJC9VVlBWTX/WroHgF+dlYy3l3NvHceF+ZMQ5o/NgPUHC526bVeqa/HcOTyAxy/pxy96RAGwdGc+AIeLKqiqseHjZSKxk6pcPZmSMSIiIiLiUFVj5cM1BwD49YgUF0cjIiJZBfaqmKSIAEerExFP0SsuBC+ziaNl1eQUV7o6HOmgvtmczac/HeJPn2/CarMn/X7Ksrco6xIZSFSwnyvDaxN1yZhvt+byU1Yhvl5mpp/TOtcCabXJrY7UquzrTfa2bhcPTmBaehduPDcVgGW77MmYuqqYlMgg/S73cErGiIiIiIjDvI3ZFJRVEx/mz7g+Ma4OR0TE4+07evwGjoin8ffxokeMvUp30yG1KpPWsTvP/jl7pKSKlXuOArB2nz0ZMzQlwmVxtaW6ZMx/VmcBcGlaAjEhrVONmZYUDsBPtXPyAG5d+VZeXcP3244AMLm/fW6rs1Mj8DabyCooJ+toOXuO2L/HUtWizOMpGSMiIiIiDu+u2A/ANWc7vy2BiIicuayjtfPFRKqtiXim/p3trco2HSpycSTSUe0+Uur4/y8yDwGwtjZRMKxLx29RBhBZm4yxWO1JkZt/0bXV9lU3B8+6A4UYhsFHaw4w7IlveW3x7lbbZ2tavP0IFRYriZ0CHPNcBfl5k5YcDsCy3fnH54uJVjLG0+kKW0REREQA+02OdVmF+HiZ+NXZya4OR0REOF4Z00WVMeKh+ifYb25uPqxkjLSOuqoFgK835VBeXUPmgULAM+aLAehUm4wBGN0rmp6xIa22r34Jofh6mSkoq+beTzZw3/82cLSsmr/O314vMdZeHS2toqjc4vh63ib7XEOTB8RjMh1vQXZud/u8MT/symdPXTJGlTEeT8kYEREREQHg37VVMZP6xxMd0vF7Y4uIuIO6m4TJqowRD3W8MkZtysT5rDaDvbVJ7yBfL0oqa3h10W4qLFZC/b3pHh3s4gjbRuQJyZhbW7EqBsDP24t+tRUkn6w9CEDn8ABqbAZPzt3aqvtuqYpqK+OfX8IvnlnI2v0FVFqsLNyaC8Ck/nH11h1Zm4xZviuf3Xn2JFNXD/l+ksYpGSMiIiIiFJVb+GK9vS3DtPTWmaxTRETOzN78MnbmleJlNjGw9oa0iKfpEx+KyQQ5xZUcKalydTjSwRw6VkF1jQ1fb7OjMvyfS/YA9nZaZg+ZbL1LbcXGgM5hpHeLbPX9DU22Vxz5eJl47peDePems/E2m1i4LY/FO460+v6ba2deCQVl1RRX1vDrN1bzzDfbKau2khDmz+DauXDqDEoKJ8jXi2PlFg4VVgCaM0aUjBERERER4OO1B6i02OgdF8IwD2nHICLS3tXNXTCyexSRwapYFM8U5OftaO2jVmXibLvz7RULqZFBXJbWGYDqGhuAR/1NPLJ7FK9eO4Q3rx9Wr9VWa7n+3C78cmgi7988gqlDE+kWHcx153QB4Ik5W6ix2lo9hubYmXu8jVqFxcpby/YCMOlnLcoAfLzMjOh6PLEV4u9drwJJPJOSMSIiIiIezmYzeG+lvUXZ9PQubXIBJiIip2YYBl9kHgbg0rQEF0cj4lp1rco2H1arMnGu4+2jguiXEFpvgvUhHpSMMZlMTBoQT0yIf5vsL7FTIM/+chBnp0Y4lt01tgcRQb7szCvl5e93YbMZbRLLmdhZ+/1y1bCkem3JJg+Ia3D9unljwN6iTNdZomSMiIiIiIf7YVc++46WE+LnzSWDdcNPRKQ92HioiL35Zfj7mBnft+GbPCKeon9C3bwxqowR56qbWL1b7Y3ySwbZq2O8zKaT2k5J6woL8GHm+J4A/P3bnVz08g8s35Xv4qjq21WbjOmbEMpLV6cx4/xu3HhuKmlJDSfuRvY4IRmjFmWCkjEiIiIiHu/dFfaqmKlDEwny83ZxNCIiAvD5OntVzPi+cQTrs1k8XN1k35vUpkycbM+R45UxAFOHdiY80IfxfWIJ9NVnb1u7dngyf5zcmxA/bzYfLuaaN1bxzDfbnL6f8uoaDOPMK2925ZUA0CMmGG8vM/dO7M1DF/VtdG6hHjHBRIfY24xqvhgBJWNEREREPNrBY+Us3JYLwK9HpLg4GhERAbDaDL7aUNuiTBWLIvSLt1fGHCiooLC82sXRSEey+4i9MqZrdDBgb5+1ctZY/nHtEFeG5bFMJhO3jurGontHc+3wZABmL9/n1JZln607SN+H5jNnQ/YZva/SYiWroByA7rHBTXqPyWTiiqGJmE0wqmf0GccqHY+SMSIiIiIe7INVWdgMOLd7JN1jmnZRISIirWv57nyOlFQRHujDL3ro5o1IWKCPo8XPqr0FLo5GOoriSgtHSqoA6s0V4+/j1Wilg7SNyGA/Hrm4Hz5eJsqrrRwqrHDatj/+8SAA32/PO6P37c0vw2ZAqL830cF+TX7fHyb0YuMjE9X2TgBQvZ2IiIiIm8gvreK/q/azbLeZZZ9vxmxu+XM1X2/KAWDaiC4t3paIiDjHF5n2qpgpA+Lx9dYzlCIAv+gRxZ78MpbsOMLEfppHSVpuT21VTHSIH6H+Pi6ORn7Ox8tM16hgtueWsDOvhKSIwBZvs9Ji5cf9xwDIOlp+Ru/dWTtfTI/YEEympifrvMwmtYIWB30niIiIiLRzWw4X8/ayvXyx/jDVNTbAzIq8Q07bfkKYP+P6xDhteyIi0nwHCsqZW9s65ZLBnV0cjUj78Yse0byzYj9Ldh7BMIwzuhkq0pC6+WK6RWsuj/aqR6w9GbMjt5QxvWNbvL2fso7VXk/haDnWVLvqkjHqJiAtoGSMiIiISDtktRl8tzWXt5btZeWe4+04BiaGkmg6Ru9evfDy8nLKvsb2icHbS09ei4i4mmEYzPp0IxUWK2d3ieCsLp1cHZJIu5HeLRIfLxMHCirYf7ScLpoMW1poz8/mi5H2p2dsCJDNjtySJq1fY7VRYzPw92n4OmnF7qOO/88rqaKi2kqAb9OuqXbl2WNQa2dpCacnY5YsWcKzzz7L2rVryc7O5rPPPuPSSy91vH799dfzzjvv1HvPxIkT+eabbxxfFxQUcOedd/LVV19hNpuZOnUqL7zwAsHBx7/ZN2zYwIwZM1izZg3R0dHceeed3Hfffc4+HBEREZFWUVxp4ZlvtrG/kfL4vfllHDxm743sZTYxqX8cN45MZUB8MPPmzWPyeV3x8VE7BRGRjuTDNQf4YVc+ft5m/nLFQD35L3KCID9vhqZ0YuWeApbsPKJkjLTY7trKmK76Xmq3esba7wXvzC1tdJ1tOcV8tf4wP+0vZP3BQkzA69OHcW73qJPWXX5CMgbs1TG94kKaFEtdDErGSEs4PRlTVlbGoEGDuPHGG7n88ssbXOeCCy7g7bffdnzt51d/0qNrr72W7OxsMjIysFgs3HDDDdx666188MEHABQXFzNhwgTGjRvHa6+9xsaNG7nxxhsJDw/n1ltvdfYhiYiIiDiVxWrjt+/9xA+78k+5XliAD9cMT2baiBQSwgPs77VY2iJEERFpY9lFFTw5dytgn+w3VTcHRU7yix7R9mTMjnymp3dxdTji5uoqY7rp5nq71SPWnijZlVeKzWZgNtd/SKG6xsav31hFfml1veW3vPsj/75pOENTjleYllbVsP5AIWCfJ+hISRX7j5Y1KRljsdrYd7SsXkwizeH0ZMykSZOYNGnSKdfx8/MjLq7hyda2bt3KN998w5o1axg2bBgAL730EpMnT+avf/0rCQkJvP/++1RXV/PWW2/h6+tLv379yMzM5G9/+5uSMSIiItKuGYbBA59t4odd+QT6evGnKX0IbKA0PsDHm/N6Rje5bF5ERNzX0dIq/vDxekqqahiUFM6NI1NdHZJIu3Rez2ienb+dFbvzqa6x4eutNqvSPFabwd7am+vdopSMaa9SIgLx9TJTYbFy8FgFyZGB9V7/YdcR8kuriQjy5b6JvRiYGM5TX29l6c58rn97Nf+5ZQT9O4cBsGZfATU2g+SIQAZ0DmPuxuwmzxuz/2g5FqtBkK8XCWH+Tj9O8RwumTNm0aJFxMTE0KlTJ8aMGcMTTzxBZGQkACtWrCA8PNyRiAEYN24cZrOZVatWcdlll7FixQpGjRqFr6+vY52JEyfyl7/8hWPHjtGpU8N9dauqqqiqqnJ8XVxcDNifMO0IT5nWHUNHOJaOSueo7WnM3ZvOX9vSeLeNfy7Zy4c/HsBsguevHMiYXtGnWNuGxWKrt0TnqW11tPHuKMch0lFUWqy8tWwv//h+N6VVNfh6mXn2ioF4mdWeTKQhfeNDiQzy5WhZNeuyjjG8a6SrQxI3dehYhSOh17lTgKvDkUZ4e5npGh3EtpwSduSWnJSM+Wp9NgAXD0rgV2cnA/D6tGFMf2sVa/YdY/pbq/nktnS6Rgc75os5p1sknYLs95Sbmoypmy+mW0ywWohKi7R5MuaCCy7g8ssvJzU1ld27d/PHP/6RSZMmsWLFCry8vMjJySEmJqZ+kN7eREREkJOTA0BOTg6pqfWfFIqNjXW81lgy5qmnnuLRRx89afmCBQsIDAxs4B3uKSMjw9UhyGnoHLU9jbl70/lrWxrv1rPuqInZO+yVLpelWKncvYZ5u5u3LZ2nttVRxru8vGkXnCLS+iotVi59ZRnbcuw3ePolhPLIxf1qJysWkYaYzSZG9ojii8zDLNl5RMkYabbd+fb5P1Ijg5QAb+d6xobYkzF5JYzrG+tYXlFtZcFm+73iiwYlOJYH+Hrx5vVnce2/VrHxUBE3v/Mjn/72HJbvtreITu8WSUW1FaDR+Tt/blee5osR52jzZMyvfvUrx/8PGDCAgQMH0q1bNxYtWsTYsWNbdd+zZs1i5syZjq+Li4tJSkpiwoQJhIaGtuq+24LFYiEjI4Px48drQt92Sueo7WnM3ZvOX9vSeLeudVmF3Pv2j4CN69KTeWBy72ZtR+epbXW08a6rDBcR11u7/xjbckoI8ffm0Yv7cengzif1wheRk/2iRzRfZB5m6c587p3o6mjEXe2qnYy9W4zm52rvesbaEyA7a89ZnYXb8iirtpLYKYAhyeH1Xgv19+Gt68/i0leWsSe/jJvf+ZHNh+1/B6d3i3QkV5paGbOzdv0eMXpgQlrGJW3KTtS1a1eioqLYtWsXY8eOJS4ujry8vHrr1NTUUFBQ4JhnJi4ujtzc3Hrr1H3d2Fw0YJ+rxs/P76TlPj4+HeLiuk5HO56OSOeo7WnM3ZvOX9vSeDtf1tFybv8gk+oaG+P6xPDQRf1b/ASezlPb6ijj3RGOQaSjyKydRHh0rxguH5Lo2mBE3MioHlEAbDxUREGZfa4IkTO1Jdt+Y75PnPs/nN3R9aitGN2RW1Jv+ZfrDwH2qpiGWodFh/jxxnXDuOLV5fy4/5h9WzHBxIT4U11jbwV98Fg5Vptx2muzukSQKmOkpVw+09nBgwc5evQo8fHxAKSnp1NYWMjatWsd6yxcuBCbzcbw4cMd6yxZsqRez+uMjAx69erVaIsyEREREVcoKrdww+zVHC2rpn/nUF74VZpaIYiIiCMZMygxzLWBiLiZmFB/esWGYBiweu9RV4cjbmprbTKmb4KSMe1dXfvOXXmlWG0GAMWVFr7ffgSAiwYmNPrePvH266+6XM053eytDePDAvDxMmGxGmQXVZxy/1abwe4jdZUxSsZIyzg9GVNaWkpmZiaZmZkA7N27l8zMTLKysigtLeXee+9l5cqV7Nu3j++++45LLrmE7t27M3Givba0T58+XHDBBdxyyy2sXr2aZcuWcccdd/CrX/2KhAT7D9c111yDr68vN910E5s3b+bDDz/khRdeqNeCTERERMTVjpVVc+M7a9h9pIz4MH/evO4sgvxcXpgsIiIuZhiGIxkzOCncpbGIuKOhXewP4v6UVejaQMQtVVqsjjZVfeKVjGnvkiMC8fM2U1Vj40BtW7H5m3KorrHRPSaYPvGnbh02rm8sT146gG7RQVx1VjIAXmYTSZ3s84dnnWbemKyCcqpqbPh6m0mK6DhzjotrOD0Z8+OPP5KWlkZaWhoAM2fOJC0tjYceeggvLy82bNjAxRdfTM+ePbnpppsYOnQoS5curdc+7P3336d3796MHTuWyZMnM3LkSF5//XXH62FhYSxYsIC9e/cydOhQfv/73/PQQw9x6623OvtwRERERJol62g5U19dztr9xwjx9+at688iNtTf1WGJiMgpVFqsbD5cxKo9R/luay6bDxe1yn6yiyo5UlKFl9lE/86qjBE5U2m1ScyfalsPSceWV1xJUbnl9Cs20a68UmpsBuGBPsSH6e/z9s7LbKJbtL0ipa5V2ZfrDwNwcSMtyn7umuHJfPf70fUqoZIj7YmV/aeZN2bxdvt0GoOTwtXhQFrM6Y9mjh49GsMwGn19/vz5p91GREQEH3zwwSnXGThwIEuXLj3j+ERERERaW+aBQm6avYajZdV0Dg/g7RvOcpTXi4hI+2S1GUx+YSl78sscy7zMJr6beR5dopw7wfP62qqY3nEh+Pt4OXXbIp5gSIq9MmbjoSKqa59Yl47FajNYuC2Pd1fsY+nOfJIjAvn+D6OdcjN8S+1E7n3jQ5t0I19cr2dsMFuyi9mZV8qBY3tZujMfsM8X01wptVUu+09TGfPtVnsyZnyf2GbvS6SO+mSIiIiIONGCzTnc9d91VFps9EsI5e3rzyJGFTEiIu3e2v3H2JNfhrfZRHJEIEfLqimqsLB4xxGnJ2PUokykZbpGBREe6ENhuYWt2cUM0s9Sh1JRbeWil39wtBIDe6uog8fKSYls+efxluzjyRhxDz1qH2ybvXwfR0qqALjj/O6ktuD3c13LsQOnqIwpqrCwco99bqpxfZWMkZbTowMiIiIiTjJ72V5+895aKi02RveK5qPfpCsRIyLiJuZvzgHsLU8W/mE0t47qCsDy3flO31ddMkY3kEWax2QyHW9VlqVWZR3NugPH2JVXSoCPF78Z1ZWU2nZSe46UneadTeOojElQMsZd1HUZqEvE/G5sD34/oWeLtlmX2Ntf0Pj31eIdR6ixGXSPCW5R4kekjpIxIiIiIi1ksdp4Ys4WHvlqC4YBV5+dzBvThxHkpyJkERF3YBgGC7bYkzET+tmffD2nWyQAK/cUYLU13or7TFltBhsP2eeiUWWMSPOlJdtbla3LKnRtIOJ027Lt84L8okcUsyb3oX+CfW6t3UdKT/W2JjEMg63ZSsa4mz7xx1s+3zuxF/eM79niFnN1Sb79R8sxDIMDBeVMf2s1n6875FgnY0suAOPUokycRHcIRERERJrpWFk1/1mTxb9X7Ce7qBKA+y7oxe3ndVP/aRERN7Itp4QDBRX4eZsZ1TMagAGdwwj286aowt4GqX/nMKfsa2deCeXVVoL9vB0TEovImRtSm4xRZUzHsy3HnizpXdtGrGu0vSJhtxMqYw4eq6CkqgZfL7M+g91IYqdAnrliIMF+3kweEO+UbSbXtikrqazhaFk1d3+Yydr9x1i15ygDE8NI7BTIou2188WoRZk4iZIxIiIiImdoZ24Jby/fx6c/HaTSYgMgKtiXhy7qx8UtmERSRERco65F2S96RBPoa79M9vYyMzw1gu+25bF8d77TkjGZtU/xD+gc5pSJqEU81aCkMEwm+831vJJKYkLUGraj2JZjr4zpE2evhqhLxuxxQmXM5toWZT3jgvHxUsMgd3LlsCSnbs/fx4vYUD9yi6t44LNNrN1vT+xW1di4/38buGtsD0oqa4gK9lUlqziNPnVERETEIy3ansd/Vmed0b9/r9zPtDdXMf75JXywKotKi42+8aH89ZeDWPZ/Y5SIEZEme/XVVxk4cCChoaGEhoaSnp7O119/7Xh99OjRmEymev9uu+22etvIyspiypQpBAYGEhMTw7333ktNTU29dRYtWsSQIUPw8/Oje/fuzJ49uy0Oz+0s2GxvQ1LXoqxOem2rsuW7jzptX+sPFgIwODncadsU8UQh/j70qp1HQq3KOg6rzWB7bTLGURkTZa9g2ZPftMqYGquNgqqGX9tS16IsXi3K5Hh1zDe1D2XMHN+TIF8v1uw7xv/9byMAY3rH6OEJcRpVxoiIiIjHyTxQyPVvr2n2+80me6n6jeemcnZqhFqSicgZS0xM5Omnn6ZHjx4YhsE777zDJZdcwrp16+jXrx8At9xyC4899pjjPYGBgY7/t1qtTJkyhbi4OJYvX052djbTp0/Hx8eHP//5zwDs3buXKVOmcNttt/H+++/z3XffcfPNNxMfH8/EiRPb9oDbsQMF5WzJLsZsOrkn/DndogBYvbcAi9XmlKeoMw/Y54sZlBje4m2JeLq05HC25ZTwU9YxJvaLc3U44gT7jpZRVWMjwMfLcaO8rjLmSEkVxZUWQv19TrmNJ7/ezns/eXMocAd/mtK33t/qWw4rGSPHJUcEsWafvSLmgn5x3DmmO50CfXjwi80cKqwAYHxffbaI8ygZIyIiIh6n7iIsNtSPgWd4M6xrdBC/Hp5CUkTg6VcWEWnERRddVO/rJ598kldffZWVK1c6kjGBgYHExTV8A2DBggVs2bKFb7/9ltjYWAYPHszjjz/O/fffzyOPPIKvry+vvfYaqampPPfccwD06dOHH374geeff17JmBMsqJ2c96wuEUQE+dZ7rXdcCJ0CfThWbmHDwUKGpkS0aF/l1TVsr50LQS1PRFouLbkT/1l9gHX7C10dijjJtmx7VUzPuBBHNUKIvw8xIX7klVSx50jZKT8/DcPg6032z/U3fthHYUUNT18+AO/aZPrWusqYBOe0nhT3lhplv6aLCvbjz5cPwGQyce3wFL7akM3qvQX4+5gZ2T3KxVFKR6JkjIiIiHicrIJyACb1j+eRi/u5OBoR8XRWq5WPP/6YsrIy0tPTHcvff/993nvvPeLi4rjooot48MEHHdUxK1asYMCAAcTGHq/kmDhxIrfffjubN28mLS2NFStWMG7cuHr7mjhxInfffXejsVRVVVFVdby3S3Gx/aaVxWLBYrE443DbhbpjsVgsfLMpG4BxfaIbPMbhqRF8szmXpTuOMDDB3hLJMIxmVUWu3nMUmwGxIX5EBnp1qDE9nRPHXNpeRx3/up/JDYcKKa+sardzgHTU8W8pwzBYtruAfgkhdAq0J8M3HyoEoFdMUL3xSo0KJK+kip05RfSLC2p0m9tzSjhaVo2XyQDMfLL2IAWlVTx9eT9MmBzVDt2j/HU+Wok7fb9fNjiePUdKuebsJEJ8TY6Y/3xpX257bx3j+sTgbbJhqZ0ntL1ypzF3F601lkrGiIiIiMfJKrD3m05WdYuIuNDGjRtJT0+nsrKS4OBgPvvsM/r27QvANddcQ0pKCgkJCWzYsIH777+f7du38+mnnwKQk5NTLxEDOL7Oyck55TrFxcVUVFQQEBBwUkxPPfUUjz766EnLFyxYUK9NWkfx2bwMftznBZjwyt7MvHmbT1onpNwEeDF3zQ6887bxZZaZA6Umbuhlo1eYcUb7++9uM2Cma0AF8+bNc8oxuJuMjAxXh+DROtr42wwI9PKi3GLjrf99Q1KwqyM6tY42/i21ocDEm9u96B1m4/a+9pvdS7bZPyetR7OYN2+/Y13vcvvyBSs34Hs4s9Ftfn/Y/pndI9RgZFwN7+wws3D7Ec55+ntSQ+wJmkg/g6ULdS5am7t8v5/nD4c2ZHFoQ/3ld3YHLEXMm7fTJXE1h7uMuTsoLy9vle0qGSMiIiIep64yRskYEXGlXr16kZmZSVFREZ988gnXXXcdixcvpm/fvtx6662O9QYMGEB8fDxjx45l9+7ddOvWrdVimjVrFjNnznR8XVxcTFJSEhMmTCA0tOP017dYLGRkZGCL74fBdvrEhTDt8vQG1+19pIyPX1zG7hIzz20yY9TmXz7cH8BXM9KJDvFr0j6ra2w8/MxiwMLtU84ivWukk47GPdSN+fjx4/HxOfV8D+J8HXn8Py/4icU78vFN7Mfkc1JcHU6DOvL4t8Si/20EstlebCbt3NHEh/nz7NYlQCWXjRnO8NTjrSFzl+9n2dfbMYfFMXny4Ea3+em/fwLy6RVuMPOqcYzPLuPROVvZkl3CzmJ7ReOQrrGn3Ia0jL7f257G3PnqqsOdTckYERER8SiGYbD/qD0ZkxKpZIyIuI6vry/du3cHYOjQoaxZs4YXXniBf/7znyetO3z4cAB27dpFt27diIuLY/Xq1fXWyc2198ivm2cmLi7OsezEdUJDQxusigHw8/PDz+/k5IKPj0+HvLj/bvtRACYNiG/0+HrGhxEb6kdusb1926WDE9iaXcL23BLu+3Qz7954Nmbz6VuW/bA7j8IKC1HBfpzbI9YxF4Kn6ajfS+6iI47/ud2jWLwjn5V7j3Hred1dHc4pdcTxby7DMPhhd0Ht/8PXm/O4engyBwsrAeif2KneWPWIsz8QsO9oRaNjWF1jc0zG3jPMwMfHh+Hdopl7VxSZBwr594r9rNpbwNXDk3Ue2oC+39uextx5WmsclYwRERERj1JUYaGksgaAJFXGiEg7YrPZ6s3XcqLMzEwA4uPjAUhPT+fJJ58kLy+PmJgYwN6aIjQ01NHqLD09/aRWWBkZGfXmpfFklVZYttuejLmgf1yj65lMJh6/pD/fbs3l1yNSGJgYzq68Ei56aRk/7Mrn1cW7mXF+/RvANpvBluxiesWFOOaw+Gr9YQAuHBjvsYkYkdZwTjf75Nqr9hzFYrW123ljpL6t2SUcKTn+O++zdYcYmtIJgPgwf8Jr55Cp0z3a3oNu79EyrDajwc/R9QcLKa+20inQh4TAGsdyk8lEWnIn0pI7tcahiIg0mX5DiYiIiEepq4qJDfXD38fLxdGIiKeaNWsWS5YsYd++fWzcuJFZs2axaNEirr32Wnbv3s3jjz/O2rVr2bdvH19++SXTp09n1KhRDBw4EIAJEybQt29fpk2bxvr165k/fz4PPPAAM2bMcFS23HbbbezZs4f77ruPbdu28Y9//IOPPvqIe+65x5WH3m5sOWbCYjVIjQqiR8ypJ5qY0C+OZ64YxMDEcAC6x4Tw6MX9APhbxg6ez9hBXrH9ae5lu/K58KUfuPClH7hx9hpqrDYqLVYWbLFXKV00KL71DkrEA/WND6VToA9l1VY2HCx0dTjSREt2HgFgaEonfLxMbMsp4fPMQwD0jgs5af2E8AB8vc1U19g4dKyiwW3+sDMfgHO6RqKct4i0R6qMEREREY+i+WJEpD3Iy8tj+vTpZGdnExYWxsCBA5k/fz7jx4/nwIEDfPvtt/z973+nrKyMpKQkpk6dygMPPOB4v5eXF3PmzOH2228nPT2doKAgrrvuOh577DHHOqmpqcydO5d77rmHF154gcTERN544w0mTpzoikNudzYU2O/UTewXh8l05nftfjkskZV7jvLpukO88N1OXvl+Fz1jQ9iSfbzH+NKd+TwzfztDksMpraqhc3gAaUl6MlvEmcxmE+ndIpm3MYdlu44yNCXi9G8Sl1uyw56MuWhgPJFBvizYkst/Vx8AoHf8yXOUeZlNpEYGsT23hN35pSQ30G542a7aZEy3CMg72IrRi4g0j5IxIiIi4lGOJ2OCXByJiHiyN998s9HXkpKSWLx48Wm3kZKSclIbsp8bPXo069atO+P4Oroqi5Utx+qSMbHN2obJZOLZXw7ivF7RvLdyP2v2HWNLdjHeZhO/HpFCr7gQZn26kdeX7HE8AHDhwPgmzS8jImfmnG5RtcmYfO4a28PV4chplFXVsGaffb6YUT2jiQ31Z8GWXGpsBtBwZQxAt5jaZExeKef3iqn3WkmlhXUHCgE4p1skG/JaL34RkeZSMkZEREQ8StZRVcaIiHi65XsKqLKZiA31Y1Bt67Hm8DKbuGRwZy4Z3Jkth4tZu7+AkT2iSY2yJ/yzCsp5ddFux4MAFw1KcEb4IvIz53a3zxuzLquQimorAb5qRduerdxzFIvVICkigNSoIBLCAwjx93bM69ingcoYgK5R9paSe/LLTnpt9d4CrDaDlMhAEjsFsKH1whcRaTbNGSMiIiIeZX+B/eItpYHWBiIi4hkWbLE/Mj2+T4zTKlX6JoQyLb2LIxED8IcJvTivZzQAqVFB9Eto+AajiLRMl8hAEsL8qbbaHBUX0n7VtSgb1SMak8mEv48Xk/vb59Py9TLX+xw9Uddo+/I9R0rrLa+usTF3QzZwPDEnItIeqTJGREREPMqBAvuEn0mqjBER8Ug1VhvfbTuejGlNXmYTL16dxivf72Jcn9hmzU0jIqdnMpk4p3sUn6w9yLLd+YyqTYJK+7Rkp31ulxPP05VnJfLR2gMMTemEj1fDz453i7ZXxuzKK2PToSJKKmtYvjuf/6w+QH5pFYAjAS4i0h4pGSMiIiIeo6rGyuEiezJGlTEiIp5pzb5jHCu3EOhtcFaXTq2+v7AAH/44uU+r70fE053bPZJP1h5k+a6jrg5FTuFAQTl788vwNps4p1ukY/nQlAjm3DmSuFD/Rt9bVxmTX1rFhS/9UO+1mBA/bhyZyoS+sdTU1LRO8CIiLaRkjIiIiHiMQ8cqMAwI9PUiMsjX1eGIiIgLzN+cA0D/TkajT1+LiPs5p5u9PdWmw0X8d3UWS3flszuvlCcvG8DQlNZPvErTLK2tihmS3IkQf596r/VLCDvle0P8fbh4UALfbc0l2N+bYD9vEjsFcuWwJCb0i9Vnuoi0e0rGiIiIiMfYXzuBcnJEoFrFiIh4IMMwWFCbjBkYYbg4GhFxpthQf7rHBLMrr5T/+3SjY/knta2vpH3IPHAMgOFdI5r1/hevTnNmOCIibUopYxEREfEYB05IxoiIiOfZeKiIw0WVBPp60StMyRiRjuaXQxMB6BUbwpje9jmhdueVuTIk+ZkNB4sAGJgY7tpARERcQJUxIiIi4jH2H7UnYzRfjIiIZ6prUTaqRxS+XodcHI2IONtvzuvGded0wd/Hi02Hili4LY/dR0pdHZbUKq+uYUduCQADE0/dkkxEpCNSZYyIiIh4jCxVxoiIeLRvNtmTMeP7xLg4EhFpLf4+XsDxyd6PllVzrKzalSFJrc2Hi7EZEBvqR2yov6vDERFpc0rGiIiIiMfIqq2MSY4McnEkIiLS1nbllbD7SBk+XibO7xXl6nBEpJUF+nrTOTwAQNUx7cT6A4WAWpSJiOdSMkZEREQ8gmEYqowREfFg8zfnAnBOtyhC/H1cHI2ItIW66hglY9qHuvliBqlFmYh4KCVjRERExCMcKa2iwmLFbMLxlKSIiHiOuvliLugf5+JIRKStdIsOBmD3kTIXRyIAGw4WAqqMERHPpWSMiIiIeIQDtVUx8WEB+HrrTyAREU9yqLCCDQeLMJlgXJ9YV4cjIm2kW0xtMiZPlTGuVlRuYV9ty+CBqowREQ+lOxEiIiLiEfbXXvylRKpFmYiIp1m2Kx+AtKRwokP8XByNiLSV7o7KGCVjXG3jIXuLspTIQMIDfV0cjYiIaygZIyIiIh5B88WIiHiuH/cVAHB2aqSLIxGRttQtxj5nTFZBOZUWq4uj8Wzra1uUDeisqhgR8VxKxoiIiIhHyKqtjElWZYyIiMf5cf8xAM7q0snFkYhIW4oO9iPE3xubcbxKWlyjbr6YQZovRkQ8mJIxIiIi4hFUGSMi4pmOllaxp3by7qEpSsaIeBKTyUQ3tSprFzYctLcp03wxIuLJlIwRERERj7C/NhmTEhHk4khERKQt1VXF9IgJ1jwFIh7IkYzJUzLGVfJKKskuqsRsgv5qUyYiHkzJGBEREenwyqtrOFJSBagyRkTE06ytTcYM6xLh4khExBXq5o1RZYzrbKytiukeE0yQn7eLoxERcR0lY0RERKTDO1BQAUBYgA9hgT4ujkZERNrSmn0FgOaLEfFU3R1tyspcHInn2njInowZ0DnctYGIiLiYkjEiIiLS4e0/ar/4VlWMiIhnqbRY2VR7E3BYiipjRDxRt5jjc8bYbIaLo/FMdXM31lUpiYh4KqcnY5YsWcJFF11EQkICJpOJzz//vN7rhmHw0EMPER8fT0BAAOPGjWPnzp311ikoKODaa68lNDSU8PBwbrrpJkpL65eTbtiwgV/84hf4+/uTlJTEM8884+xDERERkQ6i7gIwOVLJGBERT7L+QCEWq0FMiB9JEQGuDkdEXCA5IhBvs4nyais5xZWuDscjHS60V6l3DtfnsIh4NqcnY8rKyhg0aBCvvPJKg68/88wzvPjii7z22musWrWKoKAgJk6cSGXl8V+I1157LZs3byYjI4M5c+awZMkSbr31VsfrxcXFTJgwgZSUFNauXcuzzz7LI488wuuvv+7swxEREZEOwJGMUWWMiIhH+bF2vpizukRgMplcHI2IuIKPl5mU2gdyNG+Ma2QX2e/5xYcpGSMins3ps2ZNmjSJSZMmNfiaYRj8/e9/54EHHuCSSy4B4N133yU2NpbPP/+cX/3qV2zdupVvvvmGNWvWMGzYMABeeuklJk+ezF//+lcSEhJ4//33qa6u5q233sLX15d+/fqRmZnJ3/72t3pJGxERERE4noxJUTJGRMSj/Fg7X8zQFM0XI+LJukUHs/tIGbvzSvlFj2hXh+NRDMM4IRnj7+JoRERcq03njNm7dy85OTmMGzfOsSwsLIzhw4ezYsUKAFasWEF4eLgjEQMwbtw4zGYzq1atcqwzatQofH19HetMnDiR7du3c+zYsTY6GhEREXEXWUdVGSMi4mlsNoO1J1TGiIjnqps3ZkPtHFLSdo6WVVNdY8NkgjglY0TEwzm9MuZUcnJyAIiNja23PDY21vFaTk4OMTEx9V739vYmIiKi3jqpqaknbaPutU6dGn7qqaqqiqqqKsfXxcXFAFgsFiwWS3MPq92oO4aOcCwdlc5R29OYuzedv7bVUcfbajM4cMyejEkI83X74+uo56m96mjj3VGOQ6QpduaVUlxZQ6CvF33iQ1wdjoi40Pm9Ynh10W6+Wn+YmeN7kthJD+i0lbr5YmJC/PDxatNnwkVE2p02Tca42lNPPcWjjz560vIFCxYQGNhxfhFnZGS4OgQ5DZ2jtqcxd286f22ro413QRVYrN54mQzWLfue9R1kyoCOdp7au44y3uXl5a4OQaTN1FXFpCWH460bgCIe7ezUCM7pFsny3Ud5eeEunp460NUheYzDhZovRkSkTpsmY+Li4gDIzc0lPj7esTw3N5fBgwc71snLy6v3vpqaGgoKChzvj4uLIzc3t946dV/XrdOQWbNmMXPmTMfXxcXFJCUlMWHCBEJDQ5t/YO2ExWIhIyOD8ePH4+Pj4+pwpAE6R21PY+7edP7aVkcd71V7C+CnH0mKCOLCKSNdHU6LddTz1F51tPGuqwwX8QQbDxUCMCgx3KVxiEj78PsJPVn+6go+XnuQ287rRpeoIFeH5BGyi+yVMQnhalEmItKmyZjU1FTi4uL47rvvHMmX4uJiVq1axe233w5Aeno6hYWFrF27lqFDhwKwcOFCbDYbw4cPd6zzpz/9CYvF4rgozsjIoFevXo22KAPw8/PDz8/vpOU+Pj4d4uK6Tkc7no5I56jtaczdm85f2+po432oyN6iNDkyqEMdV0c7T+1dRxnvjnAMIk21sXZuiAGdw1wciYi0B0NTIhjdK5pF24/w4nc7+dtVg10dkkfILlJljIhIHafXapeWlpKZmUlmZiYAe/fuJTMzk6ysLEwmE3fffTdPPPEEX375JRs3bmT69OkkJCRw6aWXAtCnTx8uuOACbrnlFlavXs2yZcu44447+NWvfkVCQgIA11xzDb6+vtx0001s3ryZDz/8kBdeeKFe1YuIiIgIQFaBvS1TSkTHaUkqIiKnVmmxsj2nBIABiUrGiIjdzPE9Afg88xC78kpdHI1nOFQ7Z0x8mCpjREScnoz58ccfSUtLIy0tDYCZM2eSlpbGQw89BMB9993HnXfeya233spZZ51FaWkp33zzDf7+xz+U33//fXr37s3YsWOZPHkyI0eO5PXXX3e8HhYWxoIFC9i7dy9Dhw7l97//PQ899BC33nqrsw9HRERE3Nz+o/ZkTLKSMSIiHmN7TgkWq0FEkC+dw/U0tojYDUwMZ3zfWGwG/P3bHa4OxyNk1yZj9FksItIKbcpGjx6NYRiNvm4ymXjsscd47LHHGl0nIiKCDz744JT7GThwIEuXLm12nCIiIuIZDtRWxiRHKhkjIuIpNtS2KOvfOQyTyeTiaESkPZk5vicZW3KZsyGbO8YU0zvO/ecQbs8cbcqUjBERcX5ljIiIiEh7sr9AlTEi0v68+uqrDBw4kNDQUEJDQ0lPT+frr792vF5ZWcmMGTOIjIwkODiYqVOnkpubW28bWVlZTJkyhcDAQGJiYrj33nupqampt86iRYsYMmQIfn5+dO/endmzZ7fF4bncpoP2ZMxAzRcjIj/TJz6UKQPiAXg+Q9UxranGaiO32J6MSVCbMhERJWNERESk4yqqsFBYbgGUjBGR9iUxMZGnn36atWvX8uOPPzJmzBguueQSNm/eDMA999zDV199xccff8zixYs5fPgwl19+ueP9VquVKVOmUF1dzfLly3nnnXeYPXu2oz002OfvnDJlCueffz6ZmZncfffd3HzzzcyfP7/Nj7et1VXGaL4YEWnI3eN6YDLB/M25bKxN3orz5ZZUYTPAx8tEVLCfq8MREXE5JWNERESkw6prURYV7EuQn9O7s4qINNtFF13E5MmT6dGjBz179uTJJ58kODiYlStXUlRUxJtvvsnf/vY3xowZw9ChQ3n77bdZvnw5K1euBGDBggVs2bKF9957j8GDBzNp0iQef/xxXnnlFaqrqwF47bXXSE1N5bnnnqNPnz7ccccdXHHFFTz//POuPPRWV2mxsiO3BIABqowRkQb0iA3hkkEJAPwtY7uLo+m46uaLiQvzx2xWy0gREd2VEBERkQ4rSy3KRMQNWK1WPv74Y8rKykhPT2ft2rVYLBbGjRvnWKd3794kJyezYsUKRowYwYoVKxgwYACxsbGOdSZOnMjtt9/O5s2bSUtLY8WKFfW2UbfO3Xff3WgsVVVVVFVVOb4uLi4GwGKxYLFYnHTErWvjgUKsNoPIIF+iAr0ajLtumbscU0egMXctjf/JZoxO5asN2Xy//Qirdx8hLTm81fblqeN/4GgpAHGh/m167J463u2Fxr/tacydr7XGUskYERER6bD2H1UyRkTar40bN5Kenk5lZSXBwcF89tln9O3bl8zMTHx9fQkPD6+3fmxsLDk5OQDk5OTUS8TUvV732qnWKS4upqKigoCAkydTfuqpp3j00UdPWr5gwQICA9vvZ2mNDbxr+z4szTEBXsT6VNabh6chGRkZrR+c1KMxdy2Nf33DIs2sOmLmvv+s4q5+VrxauX+Mp43/94fsn8e20qPMmzevzffvaePd3mj8257G3HnKy8tbZbtKxoiIiEiH5aiMiQxycSQiIifr1asXmZmZFBUV8cknn3DdddexePFil8Y0a9YsZs6c6fi6uLiYpKQkJkyYQGhoqAsja9xXG7L5v083ce+EntxwTgqLP90EHGbM4O5MHtu9wfdYLBYyMjIYP348Pj4+bRuwh9KYu5bGv2GDCiuY8vJy9pVa2WDuyp8m926V/Xjq+P84ZytkHWBYn25MntCjzfbrqePdXmj8257G3PnqqsOdTckYERERaTeKKiys2VtAtdVGjc3AarNhtYHVZv/aMM5sez/tPwaoMkZE2idfX1+6d7cnC4YOHcqaNWt44YUXuOqqq6iurqawsLBedUxubi5xcXEAxMXFsXr16nrby83NdbxW99+6ZSeuExoa2mBVDICfnx9+fidPsuzj49NuL+5/zCrCYjV46pvt9IoPY/Nh+3wxg5IjThtzez6ujkpj7loa//q6RPvwtysH85t/r2X2iizSUiK4ZHDnVtufp41/Tol9DrPEyCCXHLenjXd7o/Fvexpz52mtcVQyRkRERFyu0mLl3RX7eOX73RRVOL83a2qUkjEi0v7ZbDaqqqoYOnQoPj4+fPfdd0ydOhWA7du3k5WVRXp6OgDp6ek8+eST5OXlERMTA9hbU4SGhtK3b1/HOj9vC5ORkeHYRkdRUlkDgGHAnR/8RGmV/euBiWGuDEtE3MTEfnH8dnQ3/rFoN//3v430iguhd1z7rAR0N9lFFQAkhPm7OBIRkfZByRgRERFxGavN4NOfDvJ8xg4OF1UCkBQRQHxoAF5mE95eJswmE95mE2azCbPpzPfRJSqItKROTo5cRKRlZs2axaRJk0hOTqakpIQPPviARYsWMX/+fMLCwrjpppuYOXMmERERhIaGcuedd5Kens6IESMAmDBhAn379mXatGk888wz5OTk8MADDzBjxgxHZcttt93Gyy+/zH333ceNN97IwoUL+eijj5g7d64rD93pSirtSXw/bzPFtYmZmBA/YkN1809Emub3E3qx4WARP+zK5/5PNvDFHSNdHVKHkF1o//s+PqzhakwREU+jZIyIiIi0OcMw+G5rHs/M38aO3FLA/sTcPeN7cvmQRLyak3UREXEjeXl5TJ8+nezsbMLCwhg4cCDz589n/PjxADz//POYzWamTp1KVVUVEydO5B//+Ifj/V5eXsyZM4fbb7+d9PR0goKCuO6663jssccc66SmpjJ37lzuueceXnjhBRITE3njjTeYOHFimx9va6qrjPnTlD68tHAXR0qqGNBZVTEi0nReZhN/vmwAo579ns2Hi7HaDP092kKVFitHy+xtyhLClRwXEQElY0REROQ0CsureXb+dnbmlTptm0XlFrbn2nv6hwX4cMf53ZmWnoK/j5fT9iEi0p69+eabp3zd39+fV155hVdeeaXRdVJSUk5qQ/Zzo0ePZt26dc2K0V3UVcZ0iw7mzeuG8fTX27hpZKqLoxIRd9O5UwA+XiYsVoPc4koSwlXN0RLZtVXvAT5ehAVoDgsREVAyRkRERE5h1Z6j3P1hpuNiypn8vM3cODKV287rpgs0ERFpttLaypgQf28GJobzwS0jXByRiLgjL7OJ+LAAsgrKOXisQsmYFsourJ0vJtwfk0lVRiIioGSMiIiINKDGauOlhbt4aeFObAakRgVx19ju+Hk7p3LFBAxN6USM+vmLiEgL1bUpC/bT5a2ItEzncHsy5lBhORDR7O1U19jYkVtCv4RQj01E1M0HqaSWiMhx+mtVRERE6skuquQPn2xi9b4CAKYOSeSxS/oRpJtcIiLSzthsBqXVdZUxqrIUkZbp3MmeODh0rKJF23n5+128+N1O/jJ1AFedleyM0NzO4drKmPgwPXwlIlJHd1VERETEYf1REw+9spyiihqC/bx54tL+XJrW2dVhiYiINKi0ugbDsP9/iL8ub0WkZTrXVnEcKmxZMmbpziMAfLn+sMclY/JKKvlw9QHeXbEfgPgwVcaIiNTRX6siIiJu6kBBOSt2H2Vol050iw5u0nsKy6v5akM2h45VUFheTWG5hWO1/y0srya3xAuoYVBiGC9enUZKZFDrHoSIiEgL1LUo8/Uy4+/jnFaaIuK5EmsrYw62oDLGajPYll0CwOq9BZRW1eBndkp47Y5hGDw7fzufrD2IrTYxXlheTU3tF1HBvkzsF+fCCEVE2hclY0RERNyIYRis2lvA28v2krEl13HRc36vaG44N5Vf9IhqsC/1rrwS3l62j//9dJBKi63R7ZswuHlkKvde0Adf7w561SgiIh1GSaUFUFWMiDiHM9qU7c0vpcJiBcBiNfhh5xHG9opySnztzXMLdvCPRbtPWj40pRPTRqQwaUCc0+acFBHpCPQXq4iIiBuotFj5av1h3l62jy3ZxY7lveNC2J5bwvfbj/D99iP0iAnmhnNTuSytM37eZpbsPMJby/axZMcRx3v6xIeS3jWS8EAfOgX6EB7oS3igDyG+Zjat+YGrJvbER4kYERFxA3WVMUrGiIgzJIYHAvY2ZYZhNPiQ0+lsPlxc7+vvt3XMZMw7y/fx8ve7AHjowr6c0z0SgCBfb5IiAl0ZmohIu6W/WEVERNqxvJJK3l+Zxfur9pNfWg2Av4+ZqUMSueHcLnSPCWFffhmzl+/j4x8PsDOvlD9+tpFn5m8jItCXPfllAJhMML5PLDeOTGV4akSDF5YWi4X9mvtYRETcyPHKGP0CE5GWiwvzx2SCqhob+aXVRIf4nfE26pIxXaOD2HOkjO+352EYvZ0dqkvN25jNI19tBmDm+J7cODLVxRGJiLgHJWNERETaoU2Hinhr2V6+Wn8Yi9Xeiyw+zJ/p6V24+uwkwgN9Het2iQrikYv7MXNCTz7+8SCzl+/lQEEFheUWgv28ueqsJK5L70JypJ5QExGRjkWVMSLiTL7eZuJC/ckuquTgsfJmJmOKALj+nC48/fU28kqq2FI7h0xHYBgGD32xGcOAaSNSuHNMd1eHJCLiNvQXq4iISDthtRlkbMnlrWV7Wb23wLF8SHI4N5ybygX94/Dxarx9WKi/DzeNTOX6c7qweEceheUWxveN1dPCIiLSYdUlY4L9dGkrIs7ROTyA7KJKDhVWkJbc6YzeaxiGozImLakT53aPImNLLot25PPz2hHDMPh+ex7JEUF0jwl2UvStb9/RcvJLq/D1NvPghX2b1cpNRMRT6S9WERERF6i0WDlQUM7e/DL25pex72gZS3fmc7B2slBvs4nJA+K54dwuZ3wR6GU2MaZ3bGuELSIi0q4cr4zRgwci4hydOwXw4/5jHKr9u/xMHC6qpLDcgrfZRM+4YMb0jqlNxhwhNbH+uh//eJD7/rcBkwmmDIjnrrE96Bkb4qSjaD2ZB44B0D8hFF/NMykickaUjBEREXGivJJK3li6lw0HCxt83WazTwh6uKgCwzj59U6BPlwzPJlpI7oQF+bfusGKiIi4ueNzxujSVkSco3N4AGD/m/1MbamtiukeE4yftxeje0UDsP5gEaUnPCtlsdp4ceFOAAwD5mzIZu7GbO4Z15O7xvZo4RG0rsysQgAGJ53ZA2MiIqJkjIiIiFMcLa3in0v28O6KfVRabE16T4ifN12igugSFURqVBA9Y4MZ2zuWAF+vVo5WRESkY6irjAlVMkZEnCSxk32exYPNqIypmy+mX0IYAPFhAfSJD2VrdjFbCo+38/rsp0McPFZBVLAfr08fyuuL9/DN5hxeXriL69K7EBbYfqv9Mg8UAjA4OdylcYiIuCP9xSoiItICx8qq+dfSPcxevo/yaisAg5PC+fWIFPx9Gi7bjw31JzUqiMggX/VYFhERaYHjlTHt98aliLiXzp1qK2OalYyxV8b0Swh1LBvfN5at2cV8sd/MTcfKSY4M4eXvdwHwm1FdGZLcidemDeWCvy9hW04Jczdmc83wZCccifNVWqxsya6bEyfctcGIiLghJWNERESaoarGyisLd/HWsn2UVtmfyh3QOYyZ43syule0kiwiIiJt4PicMbq0FRHnOLFNmWEYZ/R3/ZYGkjG3jurKgs05bMsp4eZ3f+Ka4SlkFZQTGeTLtSOOJ10uS+vMU19v47N1B9ttMmZLdjEWq0FkkC+JtUkrERFpOs20JSIi0gxPzt3Kiwt3UVpVQ5/4UF6fNpQv7ziX83vHKBEjIiLSRo4nY1QZIyLOUZeMKa2qobiipsnvO1ZW7Zhnpu8JyZhgP2/emJZGuK/Bnvxynpi7FYBbRnUl0Pd4IvniwQmYTLBm3zEOFJQ741Cc7vh8MeG65hERaQYlY0RERM7QrrxS3l+VBcAzVwxk7p0jmdAvThckIiIibay4tk1ZsCpjRMRJAny9iAzyBeDAsaYnRerad6VEBp6UII4N9ee2PlbH/FadAn2YNiKl3jrxYQGc0y0SgC8yDzU7/tbkmC9GLcpERJpFyRgREZEz9JdvtmG1GYzrE8OVw5Iwm5WEERERcYW6VqFqUyYizlTXgquu0qUpNh8uAuq3KDtRfCC8dm0avWJDeGBKX4L8Tv7cunRwZwA+XXcIwzDONOxmsdkM3lm+j02Hik67riMZkxzeukGJiHRQSsaIiIicgdV7C8jYkouX2cT/Tert6nBEREQ8Wl2bslAlY0TEiTrXJWOONT0Zs/GQvTKmb3zDyRiAs7p0Yv49o5g6NLHB1y/oH4e/j5k9R8rY2ITkiDN8vSmHh7/czNWvr2TPkdJG1ztaWkVWbfu0gYnhbRKbiEhHo2SMiIhIExmGwZPz7D2erzorie4xIS6OSERExHMZhnFCZYzmjBER56mbN6aplTFVNVYWbc8D4KwuEc3eb4i/D+P7xgHw6U9t06rs2625AJRU1XDrv9dSUtv+8efWHywEoFt0EGEB+swVEWkOJWNERESaaM6GbNYfKCTQ14u7x/VwdTgiIiIerbzaitVmb+OjNmUi4kx1yZiDTZwzZumOfEoqa4gN9WtRMgbgsrQEAD5cc4D5m3MaXMdmM7j/kw1c8PclFJU3nDxpCqvNcCSRAn292JVXysyP1mOzndwiLTOrEIDBSZ2avT8REU+nZIyIiEgTVNVYeWb+NgBuHdWVmBB/F0ckIiLi2epalHmZTQT4eLk4GhHpSBI7BQJNr4z5asNhACYPiG/xfJKjekRzXs9oKixWbntvLa98v+uk+WNeX7qHD388wLacEhZuz232vjIPHONYuYVQf2/+fdPZ+HqZydiSy43vrOGpr7fy5g97+Wr9YVbuOcrKPQWA5osREWkJPT4kIiLSBO+tzOJAQQXRIX7c8ouurg5HRETE49W10gnx98ZkatnNTxGRE9XNGXOwCXPGVFqsfLvFnhC5cGBCi/ft7WXmjeuG8ficLby7Yj/Pzt/O1uxiHrqoLzEh/qzcc5RnvtnmWH/13mNcltbwHDSn891We1XMeb1iGJoSwROX9ee+TzawaPsRFm0/0uB70pLCm7UvERFRMkZEROS0iiosvLRwJwAzx/ckyE+/PkVERFytuLJuvhj9XhYR50qOCMTHy0RhuYXtOSX0imt8rsjvt+VRVm2lc3gAQ5xUNeLjZeaxS/rTIzaER77czJwN2SzafoTfjOrKOyv2YzOga3QQe46U8eO+gmbvZ+E2ezJmbO8YAK4clkRyRCDrsgrJK6kkr6SKI7X/8oor6ZsQSu9TjIWIiJya/moVERE5jX98v4vCcgs9YoL55dDmPXUmIiIizlVXGRPsp4mkRcS5gvy8Gd0rhowtuXyeeYj7L+jd6LpzNmQDcOHAeKdX6U0bkUL/hFAe/nIzGw4W8VzGDgB6xYbw1g1nce7TC9mZV8qxsmo6Bfme0bYPFVawLacEswnO6xntWD6iayQjukY69ThERMROc8aIiIicwsFj5by9fB8A/zepN95e+tUpIiLSHpRWqTJGRFrPZWmdAfhi3aEGJ7QHKKuq4bttzmtR1pC05E58/ttzefaKgUQF+xER5Murvx5C5/AAukUHAbCmGdUxdVUxQ1M6nXEiR0REmscld5QeeeQRTCZTvX+9ex9/yqCyspIZM2YQGRlJcHAwU6dOJTe3/oRkWVlZTJkyhcDAQGJiYrj33nupqalp60MREZEO4EhJFf9be5CPfjxw0r8HPt9EdY2NEV0jGFNbvi8iIiKuV1LbpixUyRgRaQVjescQ4ufN4aLKRpMd327NpdJiIyUykP6dQ1stFrPZxC+HJbFy1hiW3T+GrtHBAJydGgE0Mxmz1X6f7Xxd44iItBmX/dXar18/vv322+OBeB8P5Z577mHu3Ll8/PHHhIWFcccdd3D55ZezbNkyAKxWK1OmTCEuLo7ly5eTnZ3N9OnT8fHx4c9//nObH4uIiLgvi9XGdW+tZkt28SnX+9PkvpocWEREpB2pa1MW4q82ZSLifP4+XkwaEMdHPx7k88xDDG+gddf/fjoEwEUDE9rkWsHby4y31/Gvz+oSwX9WH2D1vmNntJ2KaivLdx8FYGzvWGeGKCIip+CyZIy3tzdxcXEnLS8qKuLNN9/kgw8+YMyYMQC8/fbb9OnTh5UrVzJixAgWLFjAli1b+Pbbb4mNjWXw4ME8/vjj3H///TzyyCP4+qq8UkREmuafi3ezJbuYEH9vhqV0anCd83pGMyAxrI0jExERkVOpq4xRmzIRaS2XDu7MRz8eZO6GbB65uB9+J2RC5m3MZsmOI3ibTVw2pLNL4juri70yZvOhIsqrawj0bdrn4ZwNh6mqsdE5PICescGtGaKIiJzAZX+17ty5k4SEBPz9/UlPT+epp54iOTmZtWvXYrFYGDdunGPd3r17k5yczIoVKxgxYgQrVqxgwIABxMYez95PnDiR22+/nc2bN5OWluaKQxIRETezI7eEF7/bBcDjl/Tn0jTXXESJiIjImVMyRkRa2/CukcSF+pNTXMn3245wQX/7Q8UFZdU89MUmAH47uhvdol2T0EjsFEB8mD/ZRZWsyyrk3O5Rp33P5sNFPPTFZgCuHJak6n8RkTbkkr9ahw8fzuzZs+nVqxfZ2dk8+uij/OIXv2DTpk3k5OTg6+tLeHh4vffExsaSk5MDQE5OTr1ETN3rda81pqqqiqqqKsfXxcX2ljQWiwWLxeKMQ3OpumPoCMfSUekctT2NuXtrzfNntRnc+/F6qq02zu8VxeR+0R7/faKfF/eg89S2Otp4d5TjEAEoVpsyEWllXmYTFw9O4PUle/h83SFHMuaxrzaTX1pNz9hgZozp7rL4TCYTZ3WJ4Mv1h1mzr+C0yZgjJVXc8s6PVFisjOoZzYzzu7VRpCIiAi5KxkyaNMnx/wMHDmT48OGkpKTw0UcfERAQ0Gr7feqpp3j00UdPWr5gwQICAwNbbb9tLSMjw9UhyGnoHLU9jbl7a43z9/1hE+sPeuHvZTA6KIevv/7a6ftwV/p5cQ86T22ro4x3eXm5q0MQ7Ncln376Kdu2bSMgIIBzzjmHv/zlL/Tq1cuxzujRo1m8eHG99/3mN7/htddec3ydlZXF7bffzvfff09wcDDXXXcdTz31VL35OBctWsTMmTPZvHkzSUlJPPDAA1x//fWtfoxtoa4yJthPlTEi0nouHdyZ15fs4ZvNOVzw9yX0Swjj88zDmE3wzBWD6rUuc4WzUo8nY06lusbG7e+t5XBRJV2jgnjp6jS8vcxtFKWIiIAL25SdKDw8nJ49e7Jr1y7Gjx9PdXU1hYWF9apjcnNzHXPMxMXFsXr16nrbyM3NdbzWmFmzZjFz5kzH18XFxSQlJTFhwgRCQ0OdeESuYbFYyMjIYPz48fj46Omw9kjnqO1pzN1ba52/fUfLuO/lFYCNBy/sx5XDEp22bXemnxf3oPPUtjraeNdVhotrLV68mBkzZnDWWWdRU1PDH//4RyZMmMCWLVsICgpyrHfLLbfw2GOPOb4+8QEyq9XKlClTiIuLY/ny5WRnZzN9+nR8fHz485//DMDevXuZMmUKt912G++//z7fffcdN998M/Hx8UycOLHtDriVlDgqY9rFZa2IdFB94kO4eFACX204zLacErbllABwyy+6Mjgp3LXBAWfXzhvz0/5CLFYbPo0kWP67Josf9x8jxN+bf103jLAA9/+7RkTE3bSLv1pLS0vZvXs306ZNY+jQofj4+PDdd98xdepUALZv305WVhbp6ekApKen8+STT5KXl0dMTAxgf1oxNDSUvn37NrofPz8//Pz8Tlru4+PTIS6u63S04+mIdI7ansbcvTnz/NlsBn/6YitVNTbO7R7JNSO6qE/yz+jnxT3oPLWtjjLeHeEYOoJvvvmm3tezZ88mJiaGtWvXMmrUKMfywMDARh82W7BgAVu2bOHbb78lNjaWwYMH8/jjj3P//ffzyCOP4Ovry2uvvUZqairPPfccAH369OGHH37g+eef7xDJmNIqe2VMqNqUiUgrMplMvHh1Go9c3I+lO4+wdGc+XiYT94zv6erQAOgRE0xYgA9FFRY2HSoiLblTg+utyyoE4OaRXV02x42IiKdzST3iH/7wBxYvXsy+fftYvnw5l112GV5eXlx99dWEhYVx0003MXPmTL7//nvWrl3LDTfcQHp6OiNGjABgwoQJ9O3bl2nTprF+/Xrmz5/PAw88wIwZMxpMtoiIiNR5f3UWq/cWEODjxdOXD1QiRkREXK6oqAiAiIiIesvff/99oqKi6N+/P7NmzarXZm7FihUMGDCg3lyaEydOpLi4mM2bNzvWGTduXL1tTpw4kRUrVrTWobSpujZlqowRkbYQEeTLJYM789dfDuIvVwzE38e17cnqmM0m0rtGAjB/c26j69VV9PRNcP/OMCIi7solf7UePHiQq6++mqNHjxIdHc3IkSNZuXIl0dHRADz//POYzWamTp1KVVUVEydO5B//+Ifj/V5eXsyZM4fbb7+d9PR0goKCuO666+qV8IuIiPzcwWPlPD1vKwD3X9CLpIiOM1+YiIi4J5vNxt133825555L//79HcuvueYaUlJSSEhIYMOGDdx///1s376dTz/9FICcnJx6iRjA8XVOTs4p1ykuLqaioqLB+TqrqqqoqqpyfF3X2s5isWCxWJxwxM5TXGGPJ8CbM46tbv32dkwdmcbctTT+rtXa4z+5fwzfbM7hy8xD3DOmK2Zz/QfOLFYbu/LsyZhuUf4d/vtA3++upfFvexpz52utsXRJMua///3vKV/39/fnlVde4ZVXXml0nZSUFObNm+fs0EREpIMyDINZn26krNrKsJROTE/v4uqQREREmDFjBps2beKHH36ot/zWW291/P+AAQOIj49n7Nix7N69m27durVaPE899RSPPvroScsXLFhQb84aVzMMKK7wAkysWb6UXc1skJCRkeHUuOT0NOaupfF3rdYa/2or+Hl5cbioklc++ppuPyt+ySkHi9UbP7PB+uWL2OghzQH0/e5aGv+2pzF3nhMr0p1J9dwiItLhFVVY+ONnG1m6Mx9fbzN/uWLgSU+LiYiItLU77riDOXPmsGTJEhITE0+57vDhwwHYtWsX3bp1Iy4ujtWrV9dbJzfX3p6mbp6ZuLg4x7IT1wkNDW2wKgZg1qxZzJw50/F1cXExSUlJTJgwgdDQ9tPaptJixbryOwAunjThjFuVWSwWMjIyGD9+vOZSaiMac9fS+LtWW4z/CssmPl13mCOBXbhzcv35lOdsyIb1G+nTOZwLpwxvlf23J/p+dy2Nf9vTmDtfXXW4sykZIyIiHdra/QXc9Z9MDhVW4G028cQl/TVhpYiIuJRhGNx555189tlnLFq0iNTU1NO+JzMzE4D4+HgA0tPTefLJJ8nLyyMmJgawPw0ZGhpK3759Hev8vJtARkYG6enpje7Hz8+vwXk4fXx82tXF/bFKKwAmE4QH+Tf7IYv2dlyeQGPuWhp/12rN8b9sSCKfrjvM15tzefSSAfh6H58mele+/QnvPvGhHnX+9f3uWhr/tqcxd57WGkfz6VcRERFxP1abwcsLd3LlP1dyqLCC5IhAPrn9HK48K8nVoYmIiIebMWMG7733Hh988AEhISHk5OSQk5NDRUUFALt37+bxxx9n7dq17Nu3jy+//JLp06czatQoBg4cCMCECRPo27cv06ZNY/369cyfP58HHniAGTNmOJIpt912G3v27OG+++5j27Zt/OMf/+Cjjz7innvucdmxO0tJZQ0Awb7eqnYVEQHO6RZFdIgfheUWluw4Uu+17Tn2+WJ6xYa4IjQREamlZIyIiHQ4OUWV/PqNVfx1wQ6sNoNLBicw966RDE4Kd3VoIiIivPrqqxQVFTF69Gji4+Md/z788EMAfH19+fbbb5kwYQK9e/fm97//PVOnTuWrr75ybMPLy4s5c+bg5eVFeno6v/71r5k+fTqPPfaYY53U1FTmzp1LRkYGgwYN4rnnnuONN95g4sSJbX7MzlaXjDnT9mQiIh2Vl9nERQMTAPhi/eF6r22rTcb0jm8/7SZFRDyR/nIVEZEOw2ozmLPhMI98uZlj5RYCfb14/JL+XD6kMyaTnpoVEZH2wTCMU76elJTE4sWLT7udlJSUk9qQ/dzo0aNZt27dGcXnDkodyRi14hARqXNpWgJvLdtLxpYcSqtqCPbzprSqhoPH7JWXveNUGSMi4kpKxoiIiNsrqrDw0ZoDzF6+j0OF9guN/p1DefFXaXTV/DAiIiIdTkmlBVBljIjIiQZ0DiM1Koi9+WV8symHK4YmOlqUxYb6ER7o6+IIRUQ8m/5yFRERt7U3v4zZy/by8dqDlFfbJ/LtFOjDded04fbR3fDz9nJxhCIiItIa1KZMRORkJpOJK4Ym8uz87by3cn+9ZEyvOLUoExFxNf3lKiIirS6/tIo3f9jLsbLqM3qfzWbjwAEzyz7fjNlcf5qzw0WV9Sam7BUbwg3nduHStM74+ygJIyIi0pEVOypj1KZMROREVw5L4u/f7iDzQCEbDxaxPacYUIsyEZH2QMkYERFpVUt3HuGeD9eTX1rVzC2YWZF3qNFXx/aO4caRqZzTLVLzwoiIiHgIVcaIiDQsOsSPSf3j+XL9Yd5buZ99R8sA+8NrIiLiWvrLVUREWoXFauOvC7bzz8V7AOgZG8zFgxLOKGFitVrZvn07vXr1wsurfrWLj5eJ8X3jSI0KcmrcIiIi0v7VJWOClYwRETnJtPQUvlx/mC/WH8KntsNAL1XGiIi4nP5yFRERp9t/tIy7/pvJ+gOFAFw7PJkHL+x7xu3DLBYL88q2Mfm8rvj4qA2JiIiIJ/ty/WH+uXg3VptBTnElAKFqUyYicpJhKZ3oHRfCtpwSKrHhZTbRPSbY1WGJiHg8JWNERMSpPl93iAc+30RpVQ2h/t48c8VALugf7+qwRERExM39df52sgrK6y3rFq2biyIiP2cymZiWnsKfPtsEQJfIQM2rKSLSDigZIyLiRpbvyufv3+6krLrG1aE0yGK1sSO3FICzunTi779Ko3N4gIujEhEREXe3L7+MrIJyfLxMvD59GN5mE50CfemXEOrq0ERE2qVLB3fmqXnbKK2qoXe8PitFRNoDJWNERNzE2v0F3PjOGiotNleHckpmE9w5pgd3jumOt5fZ1eGIiIhIB7Bk5xEAhqVEcH6vGBdHIyLS/gX5eXPN8GReX7KHEakRrg5HRERQMkZExC1szynhhrftiZjzekZzw7ldXB1So1Iig0iNCnJ1GCIiItKBLN5uT8aM6hnt4khERNzH/Rf0ZmK/OAYnhbs6FBERQckYEZF270BBOdPfWkVxZQ1DksN59ddDCPTVx7eIiIh4huoaGyv2HAVgVM8oF0cjIuI+vMwmhqZ0cnUYIiJSS/1jRETasfzSKqa/tZrc4ip6xgbz1vVnKREjIiIiHuXH/QWUV1uJCvajT5zmPRARERER96RkjIhIO1VSaeH6t1ezN7+MzuEBvHvjcMIDfV0dloiIiEibWrIjH4BRPaIwm00ujkZEREREpHmUjBERaYcqLVZufXctmw4VExHky79vOpu4MH9XhyUiIiLS5pbs0HwxIiIiIuL+1OtGRKQJ8kurWLz9CJkHCqmxGa2+v115JazZd4wgXy/eueFsukYHt/o+RURERNqbvJJKtmQXAzCyh+aLERERERH3pWSMiEgDbDaDTYeL+H7bERZuz2PDwUKM1s/B1OPrZeZf04cxIDGsbXcsIiIi0k4srW1R1r9zKFHBfi6ORkRERESk+ZSMcZGNB4t45ftdTt2mzbCRk2NmblEmZpM60LVHOkdtrzljXmMzyDxQSH5pVb3l/RJCGdk9imC/tvnoPL93DP07KxEjIiIinmvJTnuLsvPUokxERERE3JySMS5ypLSSbzbntMKWzWwoyGuF7Yrz6By1veaNebCfNyO7R3F+72hG94ohNlRztoiIiIi0FZvNYOlOe2XMqB5KxoiIiIiIe1MyxkV6xobwxKX9nbpNq9XKpk2b6N+/P15eXk7dtjiHzlHba+6Yd40KYliXCHy9VcEkIiIi4iqvXDOEZbvyGZLSydWhiIiIiIi0iJIxLpLYKZBfj0hx6jYtFgvz8jcy+ewkfHx8nLptcQ6do7anMRcRERFxT2azifRukaR3i3R1KCIiIiIiLaZHvkVERERERERERERERFqRkjEiIiIiIiIiIiIiIiKtSMkYERERERERERERERGRVqRkjIiIiIiIiIiIiIiISCtSMkZERERERERERERERKQVKRkjIiIiIiIiIiIiIiLSipSMERERERERERERERERaUVKxoiIiIiIiIiIiIiIiLQiJWNERERERERERERERERakZIxIiIiIiIiIiIiIiIirUjJGBERERERERERERERkVbk7eoAXMkwDACKi4tdHIlzWCwWysvLKS4uxsfHx9XhSAN0jtqexty96fy1LY23e9B5alsdbbzr/u6t+ztY5HQ62jVTnY72s+0ONOaupfF3LY1/29J4u5bGv+1pzJ2vta6bPDoZU1JSAkBSUpKLIxERERERaTslJSWEhYW5OgxxA7pmEhERERFP5ezrJpPhwY/F2Ww2Dh8+TEhICCaTydXhtFhxcTFJSUkcOHCA0NBQV4cjDdA5ansac/em89e2NN7uQeepbXW08TYMg5KSEhISEjCb1bFYTq+jXTPV6Wg/2+5AY+5aGn/X0vi3LY23a2n8257G3Pla67rJoytjzGYziYmJrg7D6UJDQ/WD187pHLU9jbl70/lrWxpv96Dz1LY60nirIkbOREe9ZqrTkX623YXG3LU0/q6l8W9bGm/X0vi3PY25c7XGdZMehxMREREREREREREREWlFSsaIiIiIiIiIiIiIiIi0IiVjOhA/Pz8efvhh/Pz8XB2KNELnqO1pzN2bzl/b0ni7B52ntqXxFumY9LPd9jTmrqXxdy2Nf9vSeLuWxr/taczdh8kwDMPVQYiIiIiIiIiIiIiIiHRUqowRERERERERERERERFpRUrGiIiIiIiIiIiIiIiItCIlY0RERERERERERERERFqRkjEiIiIiIiIiIiIiIiKtSMmYM/TUU09x1llnERISQkxMDJdeeinbt2+vt05lZSUzZswgMjKS4OBgpk6dSm5uruP19evXc/XVV5OUlERAQAB9+vThhRdeqLeN7OxsrrnmGnr27InZbObuu+9ucoyvvPIKXbp0wd/fn+HDh7N69ep6r+/evZvLLruM6OhoQkNDufLKK+vF15i77rqLoUOH4ufnx+DBg096fd++fZhMppP+rVy5ssmxO0t7P09LlizhoosuIiEhAZPJxOeff37SOoZh8NBDDxEfH09AQADjxo1j586dp912VlYWU6ZMITAwkJiYGO69915qampaHPPpePKYu9PPRmM6wvn79NNPmTBhApGRkZhMJjIzM5u07YKCAq699lpCQ0MJDw/npptuorS0tN5xX3/99QwYMABvb28uvfTSJm33VDx5vJ988knOOeccAgMDCQ8Pb3Cdhn5e/vvf/zZp+87k7ufJYrFw//33M2DAAIKCgkhISGD69OkcPnz4tNt2xe8STx7vjvB7RMQZ2vvnAJz+WgtgxYoVjBkzhqCgIEJDQxk1ahQVFRVnNhhtxN3HvLHPR5PJxMcff9y8QWlD7j7+ADk5OUybNo24uDiCgoIYMmQI//vf/858MFygI4x/c+/vuEp7H/PWvG/gah1h7Jt7Deoq7j7mLbm+kcYpGXOGFi9ezIwZM1i5ciUZGRlYLBYmTJhAWVmZY5177rmHr776io8//pjFixdz+PBhLr/8csfra9euJSYmhvfee4/Nmzfzpz/9iVmzZvHyyy871qmqqiI6OpoHHniAQYMGNTm+Dz/8kJkzZ/Lwww/z008/MWjQICZOnEheXh4AZWVlTJgwAZPJxMKFC1m2bBnV1dVcdNFF2Gy2027/xhtv5KqrrjrlOt9++y3Z2dmOf0OHDm1y/M7S3s9TWVkZgwYN4pVXXml0nWeeeYYXX3yR1157jVWrVhEUFMTEiROprKxs9D1Wq5UpU6ZQXV3N8uXLeeedd5g9ezYPPfRQi2M+HU8d8zru8rPRmI5w/srKyhg5ciR/+ctfzujYr732WjZv3kxGRgZz5sxhyZIl3HrrrY7XrVYrAQEB3HXXXYwbN+6Mtt0YTx7v6upqfvnLX3L77befcr2333673s+LM5JgZ8rdz1N5eTk//fQTDz74ID/99BOffvop27dv5+KLLz7ldl31u8RTx7uOu/8eEXGG9v45cLprLbAnYi644AImTJjA6tWrWbNmDXfccQdmc/u89Hb3MU9KSqr3uZidnc2jjz5KcHAwkyZNcsIItS53H3+A6dOns337dr788ks2btzI5ZdfzpVXXsm6detaODqtz93Hv6X3d1yhvY95a983cKWOMPbNvQZ1FXcf85Ze30gjDGmRvLw8AzAWL15sGIZhFBYWGj4+PsbHH3/sWGfr1q0GYKxYsaLR7fz2t781zj///AZfO++884zf/e53TYrn7LPPNmbMmOH42mq1GgkJCcZTTz1lGIZhzJ8/3zCbzUZRUZFjncLCQsNkMhkZGRlN2sfDDz9sDBo06KTle/fuNQBj3bp1TdpOW2pv5+lEgPHZZ5/VW2az2Yy4uDjj2WefdSwrLCw0/Pz8jP/85z+NbmvevHmG2Ww2cnJyHMteffVVIzQ01KiqqnJazE3hKWN+Inf82WiMu52/E53JeG/ZssUAjDVr1jiWff3114bJZDIOHTp00vrXXXedcckll5xxzKfjKeN9orffftsICwtr1j5dxZ3PU53Vq1cbgLF///5G12kvv0s8ZbxP1JF+j4g4Q3v7HDjdtZZhGMbw4cONBx54oEnba4/cccx/bvDgwcaNN97YpO23N+44/kFBQca7775b730RERHGv/71rybtoz1xt/F3xv0dV2tvY36i1rpv0F6429ifyF3/NnbnMa9zptc3crL2+XiOGykqKgIgIiICsGcsLRZLvaene/fuTXJyMitWrDjlduq20VzV1dWsXbu23r7NZjPjxo1z7LuqqgqTyYSfn59jHX9/f8xmMz/88EOL9l/n4osvJiYmhpEjR/Lll186ZZst1Z7OU1Ps3buXnJycevGFhYUxfPjwU8a3YsUKBgwYQGxsrGPZxIkTKS4uZvPmza0a8895ypififb4s9EYdzt/zbVixQrCw8MZNmyYY9m4ceMwm82sWrWqzeLwlPE+EzNmzCAqKoqzzz6bt956C8MwXB1ShzhPRUVFmEymRlvEQfv5XeIp430m3On3iIgztKfPgaZca+Xl5bFq1SpiYmI455xziI2N5bzzznPadVZbcLcx/7m1a9eSmZnJTTfd1KJ9u4o7jv8555zDhx9+SEFBATabjf/+979UVlYyevToFu3fFdxt/Nvi/k5ra09j3hRtcd+grbjb2HcEHWHMnX1944m8XR2AO7PZbNx9992ce+659O/fH7D3S/X19T3pmzI2NpacnJwGt7N8+XI+/PBD5s6d26J48vPzsVqt9W6e1O1727ZtAIwYMYKgoCDuv/9+/vznP2MYBv/3f/+H1WolOzu7RfsPDg7mueee49xzz8VsNvO///2PSy+9lM8//9ylJWzt7Tw1RV0MDZ3LxuKre19D7zlxm23Bk8a8Kdrrz0Zj3PH8NVdOTg4xMTH1lnl7exMREdFmPzOeNN5N9dhjjzFmzBgCAwNZsGABv/3tbyktLeWuu+5yWUwd4TxVVlZy//33c/XVVxMaGtroeu3hd4knjXdTuNvvERFnaG+fA0251tqzZw8AjzzyCH/9618ZPHgw7777LmPHjmXTpk306NGjRTG0Nncc859788036dOnD+ecc06L9u0K7jr+H330EVdddRWRkZF4e3sTGBjIZ599Rvfu3Vu0/7bmjuPfmvd32kJ7G/OmaM37Bm3JHcfe3XWEMXfm9Y0nU2VMC8yYMYNNmza1aFLhTZs2cckll/Dwww8zYcKEJr9v6dKlBAcHO/69//77TXpfdHQ0H3/8MV999RXBwcGEhYVRWFjIkCFDHH2MJ02a5Nhuv379mhxTVFQUM2fOZPjw4Zx11lk8/fTT/PrXv+bZZ59t8jZagzuep6Zo7nlqCxrz+trrz0ZjOur5u+222+ptu73QeJ/swQcf5NxzzyUtLY3777+f++67z+U/L+5+niwWC1deeSWGYfDqq686lrfX3yUa7/rc7feIiDO44+dA3RwNv/nNb7jhhhtIS0vj+eefp1evXrz11lvNOoa25I5jfqKKigo++OADt62Kcdfxf/DBByksLOTbb7/lxx9/ZObMmVx55ZVs3LixOYfgMu44/k25v9OeueOYdxQa+7bn7mPe2PWNnDlVxjTTHXfc4ZjoOTEx0bE8Li6O6upqCgsL62U2c3NziYuLq7eNLVu2MHbsWG699VYeeOCBM9r/sGHDyMzMdHwdGxuLn58fXl5e5Obm1lv35/ueMGECu3fvJj8/H29vb8LDw4mLi6Nr164AvPHGG1RUVADg4+NzRnH93PDhw8nIyGjRNlqiPZ6npqiLITc3l/j4+HrxDR48GGj4PMXFxbF69ep626r7fvj5cbUWTxvz5nL1z0Zj3PX8NcVjjz3GH/7wh3rL4uLi6k1AClBTU0NBQUGb/Mx42ng31/Dhw3n88cepqqqq14ahrbj7ear7w3n//v0sXLiw3lNM7fF3iaeNd3O1198jIs7QHj8HmnKtVfc3ZN++feut06dPH7Kyss4ohrbmrmN+ok8++YTy8nKmT59+RvtuD9x1/Hfv3s3LL7/Mpk2bHA8ZDBo0iKVLl/LKK6/w2muvnVEcruKu4w+nv7/TXrXHMW+Kptw3aO/cdezdmbuP+amub6QZXDlhjTuy2WzGjBkzjISEBGPHjh0nvV43+dInn3ziWLZt27aTJl/atGmTERMTY9x7772n3eeZTvB2xx13OL62Wq1G586dTznB4XfffWeYTCZj27ZtTdpHY5PLNuTmm2820tLSmrSuM7X383QiTjEp3F//+lfHsqKiotNOClc36XJubq5j2T//+U8jNDTUqKysdFrMDfHUMT+RO/xsNMbdz9+JzmQyvy1bthiA8eOPPzqWzZ8/3zCZTMahQ4dOWv+6664zLrnkkjOO+ec8dbxP9PbbbxthYWFNWveJJ54wOnXqdEbbd4aOcJ6qq6uNSy+91OjXr5+Rl5fXpG256neJp473idz594iIM7T3z4HTXWvZbDYjISHBeOCBB+q9b/DgwcasWbOatI+25u5j/vPtTp06tUnbbS/cffw3bNhgAMaWLVvqvW/ChAnGLbfc0qR9uJK7j39DzvT+Tltr72N+ota6b+Aq7j72J2ruNWhb6whj3tLrGzmZKmPO0IwZM/jggw/44osvCAkJcfTwCwsLIyAggLCwMG666SZmzpxJREQEoaGh3HnnnaSnpzNixAjAXlY2ZswYJk6cyMyZMx3b8PLyIjo62rGvuqxlaWkpR44cITMzE19f35OetDrRzJkzue666xg2bBhnn302f//73ykrK+OGG25wrPP222/Tp08foqOjWbFiBb/73e+455576NWr1ymPfdeuXZSWlpKTk0NFRYUjvr59++Lr68s777yDr68vaWlpAHz66ae89dZbvPHGG2c2yE7Q3s9TaWkpu3btcny9d+9eMjMziYiIIDk5GZPJxN13380TTzxBjx49SE1N5cEHHyQhIYFLL7200e1OmDCBvn37Mm3aNJ555hlycnJ44IEHmDFjRr2nyZsT8+l46piDe/1sNMbdzx9AQUEBWVlZHD58GIDt27cD9qdNGnuav0+fPlxwwQXccsstvPbaa1gsFu644w5+9atfkZCQ4Fhvy5YtVFdXU1BQQElJieMYmvv0k6eON0BWVpbjvVar1RFf9+7dCQ4O5quvviI3N5cRI0bg7+9PRkYGf/7zn51WZXMm3P08WSwWrrjiCn766SfmzJmD1Wp17D8iIgJfX98Gt+uq3yWeOt7QMX6PiDhDe/8cON21lslk4t577+Xhhx9m0KBBDB48mHfeeYdt27bxySeftMaQtZi7j3mdXbt2sWTJEubNm+fM4Wl17j7+vXv3pnv37vzmN7/hr3/9K5GRkXz++edkZGQwZ86c1hgyp3L38Yfm399xlfY+5q1538DV3H3sofnXoK7i7mPekusbOQVXZ4PcDdDgv7ffftuxTkVFhfHb3/7W6NSpkxEYGGhcdtllRnZ2tuP1hx9+uMFtpKSknHZfP1+nIS+99JKRnJxs+Pr6GmeffbaxcuXKeq/ff//9RmxsrOHj42P06NHDeO655wybzXba7Z533nkNxrR3717DMAxj9uzZRp8+fYzAwEAjNDTUOPvss42PP/74tNttDe39PH3//fcNvu+6665zrGOz2YwHH3zQiI2NNfz8/IyxY8ca27dvP+2x79u3z5g0aZIREBBgREVFGb///e8Ni8XS4phPx5PH3J1+NhrTEc7f22+/3eA6Dz/88Cm3ffToUePqq682goODjdDQUOOGG24wSkpK6q2TkpLS4Laby5PH+7rrrmvwfd9//71hGIbx9ddfG4MHDzaCg4ONoKAgY9CgQcZrr71mWK3WJoysc7n7eap7YuxU490YV/wu8eTx7gi/R0Scob1/DhjG6a+1DMMwnnrqKSMxMdEIDAw00tPTjaVLlzZ3SFpdRxnzWbNmGUlJSS75e6ElOsL479ixw7j88suNmJgYIzAw0Bg4cKDx7rvvtmRY2kxHGP/m3t9xlfY+5q1538DVOsLYN/ca1FXcfcxbcn0jjTMZhmEgIiIiIiIiIiIiIiIircLs6gBEREREREREREREREQ6MiVjREREREREREREREREWpGSMSIiIiIiIiIiIiIiIq1IyRgREREREREREREREZFWpGSMiIiIiIiIiIiIiIhIK1IyRkREREREREREREREpBUpGSMiIiIiIiIiIiIiItKKlIwRERERERERERERERFpRUrGiIiIiIiIiIiIiIiItCIlY0RERERERERERERERFqRkjEiIiIiIiIiIiIiIiKtSMkYERERERERERERERGRVqRkjIiIiIiIiIiIiIiISCtSMkZERERERERERERERKQVKRkjIiIiIiIiIiIiIiLSipSMERERERERERERERERaUVKxoiIiIiIiIiIiIiIiLQiJWNERERERERERERERERakZIxIiIiIiIiIiIiIiIirUjJGBERERERERERERERkVakZIyIiIiIiIiIiIiIiEgrUjJGRERERERERERERESkFSkZIyIiIiIiIiIiIiIi0oqUjBEREREREREREREREWlFSsaIiEg9s2fPxmQyOf75+/uTkJDAxIkTefHFFykpKam3/nfffceNN95Iz549CQwMpGvXrtx8881kZ2eftO0FCxZw00030b9/f7y8vOjSpUuDMTzyyCP1Yvj5v2XLltVb/6OPPmLEiBGEh4cTGRnJeeedx9y5c502JiIiIiIiIs1xptdXS5Ys4eKLLyYpKQl/f3/i4uK44IILTroGAhg9enSD10sXXHBBWx2eiIicAW9XByAiIu3TY489RmpqKhaLhZycHBYtWsTdd9/N3/72N7788ksGDhwIwP33309BQQG//OUv6dGjB3v27OHll19mzpw5ZGZmEhcX59jmBx98wIcffsiQIUNISEhodN+XX3453bt3P2n5H//4R0pLSznrrLMcy1566SXuuusupkyZwtNPP01lZSWzZ8/mwgsv5H//+x+XX365E0dFRERERETkzDX1+mrHjh2YzWZuu+024uLiOHbsGO+99x6jRo1i7ty5JyVaEhMTeeqpp+otO9W1loiIuI7JMAzD1UGIiEj7MXv2bG644QbWrFnDsGHD6r22cOFCLrzwQmJiYti6dSsBAQEsWbKEkSNHYjYfL7ZcsmQJ5513Hn/605944oknHMsPHz5MdHQ0Pj4+XHjhhWzatIl9+/Y1Ka4DBw6QkpLCzTffzOuvv+5Y3rNnT8LDw1m1ahUmkwmA4uJiOnfuzJgxY/jiiy9aMBoiIiIiIiLNd6bXVw0pLy+na9euDB48mG+++caxfPTo0eTn57Np06ZWPQYREXEOtSkTEZEmGzNmDA8++CD79+/nvffeA2DUqFH1EjF1yyIiIti6dWu95QkJCfj4+DRr3//5z38wDINrr7223vLi4mJiYmIciRiA0NBQgoODG72YERERERERcbWGrq8aEhgYSHR0NIWFhQ2+XlNTQ2lpaStFKSIizqJkjIiInJFp06YB9vlfGlNaWkppaSlRUVFO2+/7779PUlISo0aNqrd89OjRfPPNN7z00kvs27ePbdu2MWPGDIqKivjd737ntP2LiIiIiIg4W2PXV8XFxeTn57Nt2zb++Mc/smnTJsaOHXvS+3fs2EFQUBAhISHExcXx4IMPYrFY2iR2ERE5M5ozRkREzkhiYiJhYWHs3r270XX+/ve/U11dzVVXXeWUfW7evJkNGzZw33331auAAXjxxRfJz8/nrrvu4q677gIgKiqK7777jvT0dKfsX0REREREpDU0dn115ZVXMn/+fAB8fX35zW9+w4MPPlhvnW7dunH++eczYMAAysrK+OSTT3jiiSfYsWMHH374YZsdg4iINI2SMSIicsaCg4MpKSlp8LUlS5bw6KOPcuWVVzJmzBin7O/9998HOKlFGdhL9nv16kViYiIXXnghJSUlPP/881x++eUsXbqU7t27OyUGERERERGR1tDQ9dXTTz/N73//ew4cOMA777xDdXU1NTU19dZ588036309bdo0br31Vv71r39xzz33MGLEiFaPXUREmk5tykRE5IyVlpYSEhJy0vJt27Zx2WWX0b9/f9544w2n7MswDD744AP69+/PwIEDT3r9l7/8JVlZWcyePZsrrriCG264gUWLFlFdXc2f/vQnp8QgIiIiIiLSWhq6vho8eDDjx4/nxhtvJCMjg9WrV3P99defdlu///3vAfj2229bI1QREWkBJWNEROSMHDx4kKKiopMqTg4cOMCECRMICwtj3rx5DSZrmmPZsmXs37+/waqYPXv28M0333DxxRfXWx4REcHIkSNZtmyZU2IQERERERFpDY1dX53I19eXiy++mE8//ZSKiopTbi8pKQmAgoICp8YpIiItp2SMiIickX//+98ATJw40bHs6NGjTJgwgaqqKubPn098fLzT9vf+++9jMpm45pprTnotNzcXAKvVetJrFovlpDJ+ERERERGR9qSh66uGVFRUYBhGo+2i6+zZsweA6Oho5wQoIiJOo2SMiIg02cKFC3n88cdJTU11VKqUlZUxefJkDh06xLx58+jRo4fT9mexWPj4448ZOXIkycnJJ73evXt3zGYzH374IYZhOJYfPHiQpUuXkpaW5rRYREREREREnKmh66u8vLyT1iv8f/buPDyq8vz/+HtmMpMFyApJCAQIsu+bQlQoKiQCWlFqK6JSRakWbIFWKf0pZdHiUkRbUb5UBdtCXdpqqyAkoAhIWCXsIEsgbAmyJEPINpmZ3x9hBmLYMsxkkszndV25YM555sx97qxz7nM/T14e//73v0lMTCQ2NhYAq9VKSUlJhXFOp5MXXngBuHpxR0REql+QvwMQEZGa6YsvvmD37t2UlZWRm5vLl19+SXp6Os2bN+d///sfISEhAIwYMYL169fz2GOPsWvXLnbt2uU+Rv369Rk6dKj78datW/nf//4HwL59+8jPz3e/WejatSt33313hRiWLl3KqVOnLjlFGZTf7fXYY4/xzjvvcMcdd3Dfffdx9uxZ3nrrLYqKipg0aZI3UyIiIiIiIuKRa31/NWjQIJo2bUrv3r2JjY0lOzubefPmcezYMT788EP38b799luGDx/O8OHDadWqFUVFRXzyySd88803jB49mh49evjrVEVE5DIMzotvJRYRkYA3f/58Hn30Ufdji8VCdHQ0nTt35q677uLRRx+tsB5MixYtOHTo0CWP1bx5cw4ePHjZY19s5MiRzJ8/v8K24cOH8+9//5ucnByio6Mv+byysjLmzJnDu+++y759+wC48cYbef7557ntttuu5ZRFRERERER8oqrvr2bPns0HH3zA7t27ycvLIyoqij59+vDMM8/Qt29f97isrCwmTpzIhg0byMnJwWg00r59e5544glGjx6NwWCo1vMUEZGrUzFGRERERERERERERETEh7RmjIiIiIiIiIiIiIiIiA+pGCMiIiIiIiIiIiIiIuJDKsaIiIiIiIiIiIiIiIj4kIoxIiIiIiIiIiIiIiIiPqRijIiIiIiIiIiIiIiIiA+pGCMiIiIiIiIiIiIiIuJDQf4OwJ8cDgfHjh2jQYMGGAwGf4cjIiIiIuJTTqeTs2fPkpCQgNGo+7Lk6vSeSUREREQCja/eNwV0MebYsWMkJib6OwwRERERkWp1+PBhmjZt6u8wpBbQeyYRERERCVTeft8U0MWYBg0aAOVJDQ8P93M0vmOz2UhLSyMlJQWz2ezvcGo15dJzyp33KJfeoTxeH+XPO5TH66P8VZ3VaiUxMdH9d7DI1QTKeyYX/VzxjPLmGeWt6pQzzyhvnlHeqkb58pxy5xlf5s1X75sCuhjjarMPDw+v028sbDYbYWFhhIeH6xv6OimXnlPuvEe59A7l8foof96hPF4f5c9zmm5KrlWgvGdy0c8VzyhvnlHeqk4584zy5hnlrWqUL88pd56pjrx5+31TlSY8mzFjBjfeeCMNGjQgNjaWoUOHsmfPngpjiouLGTNmDDExMdSvX59hw4aRm5tbYUx2djZDhgwhLCyM2NhYnnnmGcrKyiqMWbFiBT169CA4OJhWrVoxf/78SvHMnj2bFi1aEBISQu/evVm/fn1VTkdERERERERERERERMTnqlSM+frrrxkzZgxr164lPT0dm81GSkoK586dc48ZP348n332GR9//DFff/01x44d47777nPvt9vtDBkyhNLSUtasWcP777/P/PnzmTx5sntMVlYWQ4YM4bbbbiMzM5Nx48bx+OOPs3TpUveYDz/8kAkTJvCHP/yBb7/9lq5du5KamsqJEyeuJx8iIiIiIiIiIiIiIiJeVaVpypYsWVLh8fz584mNjWXTpk3069eP/Px83n33XRYuXMjtt98OwLx582jfvj1r166lT58+pKWlsXPnTpYtW0ZcXBzdunVj+vTpTJw4kSlTpmCxWJgzZw5JSUnMnDkTgPbt27N69WpmzZpFamoqAK+99hpPPPEEjz76KABz5sxh0aJFvPfee/zud7+77sSIiIiIiIiIiIiIiIh4w3WtGZOfnw9AdHQ0AJs2bcJmszFgwAD3mHbt2tGsWTMyMjLo06cPGRkZdO7cmbi4OPeY1NRUnnrqKXbs2EH37t3JyMiocAzXmHHjxgFQWlrKpk2bmDRpknu/0WhkwIABZGRkXDbekpISSkpK3I+tVitQPr+czWbzMAs1n+vc6vI5Vhfl0nPKnfcol96hPF4f5c87lMfro/xVnXIlIiIiIiLiHx4XYxwOB+PGjeOWW26hU6dOAOTk5GCxWIiMjKwwNi4ujpycHPeYiwsxrv2ufVcaY7VaKSoq4syZM9jt9kuO2b1792VjnjFjBlOnTq20PS0tjbCwsGs469otPT3d3yHUGcql55Q771EuvUN5vD7Kn3coj9dH+bt2hYWF/g5BREREREQkIHlcjBkzZgzbt29n9erV3ozHpyZNmsSECRPcj61WK4mJiaSkpBAeHu7HyHzLZrORnp7OwIEDMZvN/g6nVlMuPafceY9y6R3K4/VR/rxDebw+yl/VuTrDRUREREREpHp5VIwZO3Ysn3/+OStXrqRp06bu7fHx8ZSWlpKXl1ehOyY3N5f4+Hj3mPXr11c4Xm5urnuf61/XtovHhIeHExoaislkwmQyXXKM6xiXEhwcTHBwcKXtZrM5IN7AB8p5Vgfl0nPKnfcol96hPF4f5c87lMfro/xdO+VJRERERETEP4xVGex0Ohk7diyffPIJX375JUlJSRX29+zZE7PZzPLly93b9uzZQ3Z2NsnJyQAkJyezbds2Tpw44R6Tnp5OeHg4HTp0cI+5+BiuMa5jWCwWevbsWWGMw+Fg+fLl7jEiIiIiIiIiIiIiIiI1QZU6Y8aMGcPChQv573//S4MGDdxrvERERBAaGkpERASjRo1iwoQJREdHEx4eztNPP01ycjJ9+vQBICUlhQ4dOvDwww/zyiuvkJOTw3PPPceYMWPcXStPPvkkb775Js8++yyPPfYYX375JR999BGLFi1yxzJhwgRGjhxJr169uOmmm3j99dc5d+4cjz76qLdyIyIiIiIiIiIiIiIict2qVIx5++23Aejfv3+F7fPmzePnP/85ALNmzcJoNDJs2DBKSkpITU3lrbfeco81mUx8/vnnPPXUUyQnJ1OvXj1GjhzJtGnT3GOSkpJYtGgR48eP54033qBp06a88847pKamusf87Gc/4/vvv2fy5Mnk5OTQrVs3lixZQlxcXFVzICIiIiIiIiIiIiIi4jNVKsY4nc6rjgkJCWH27NnMnj37smOaN2/O4sWLr3ic/v37s3nz5iuOGTt2LGPHjr1qTCIiIiLiWyVldsxGI0ajwd+hiIiIiIifFdvsfH+2xN9hXJeysjJOFcORM0UEBdmq9FxLkJG48BAfRSYitVWVijEiIiIiIj905Ewhd/9lNTe2iGbuI738HY6IiIiI+FFJmZ3b/7SCY/nF/g7FC4KYtnmVR8/81R2tmTCwjZfjEZHaTMUYEREREbkuf884xJlCG+m7cjlzrpSoehZ/hyQiIiIifnKyoNRdiAk1m/wczfVwYrfbMZlMwLV3f9sdTkrtDrYeyfNZZCJSO6kYIyIiIiKXdOD7AvaeKCClQxwGw6XfgJaU2flo42EAnE7IOHCKwZ0bV2eYIiIiIlKDuJY5CA4ysmv6nX6OxnM2m43FixczeHAqZrP5mp/36eajjPswkzL71Zd7EJHAYvR3ACIiIiJS8+w6buXHb37DL/6+iffXHLzsuCXbczhTeGEO7dX7TlZDdCK1m91u5/nnnycpKYnQ0FBuuOEGpk+fXmGNTqfTyeTJk2ncuDGhoaEMGDCAvXv3VjjO6dOnGTFiBOHh4URGRjJq1CgKCgoqjNm6dSt9+/YlJCSExMREXnnllWo5RxERkUBlOr+GYpnD4edIRKSmUTFGRERERCrItRbz2PwNFJSUATDji918l3v2kmP/sfYQAD2aRQKweq+KMSJX8/LLL/P222/z5ptvsmvXLl5++WVeeeUV/vKXv7jHvPLKK/z5z39mzpw5rFu3jnr16pGamkpx8YX590eMGMGOHTtIT0/n888/Z+XKlYwePdq932q1kpKSQvPmzdm0aROvvvoqU6ZMYe7cudV6viIiElhc9xZcprG6zgs6X4yxO9QZIyIVaZoyEREREXE7V1LGY/M3cDy/mJaN6pEQEcrqfScZ90EmbzzQjXdXZ/HN/pPc2qohKR3j2XDwDCajgT/d35WUWSvJPl1I9qlCmsWE+ftURGqsNWvWcM899zBkyBAAWrRowT//+U/Wr18PlHfFvP766zz33HPcc889APztb38jLi6OTz/9lAceeIBdu3axZMkSNmzYQK9evQD4y1/+wuDBg/nTn/5EQkICCxYsoLS0lPfeew+LxULHjh3JzMzktddeq1C0EREREe8JMpXf+27TNGUi8gMqxoiIiIiI26tL97DjmJWYehbm//wmQixG7nx9FTuPWxk4a6V73D/XH+af68vXihnYPo6WjerTvVkkGw6e4Zv9J2kW08xfpyBS4918883MnTuX7777jjZt2rBlyxZWr17Na6+9BkBWVhY5OTkMGDDA/ZyIiAh69+5NRkYGDzzwABkZGURGRroLMQADBgzAaDSybt067r33XjIyMujXrx8Wi8U9JjU1lZdffpkzZ84QFRVVKbaSkhJKSkrcj61WK1A+b77NZqs0vq5xnWMgnKs3KW+eUd6qTjnzTHXnzVZW/jqGanxNX/A4b047AGV2R60+/6rS96fnlDvP+DJvvvpcqBgjIiIiIgDsO1HA389PO/b6A93c3S0v3deZ0X/fBMDt7WL5cdcEPtiQzdoDpwF4OLk5ALe0asiGg2dYve8kD9yYyNtf7+fQyUJeuLcTZpNmxxVx+d3vfofVaqVdu3aYTCbsdjsvvvgiI0aMACAnJweAuLi4Cs+Li4tz78vJySE2NrbC/qCgIKKjoyuMSUpKqnQM175LFWNmzJjB1KlTK21PS0sjLCxwOt7S09P9HUKtpLx5RnmrOuXMM9WVt1PFAEHY7XYWL15cLa/pS1XN2+48A2DiTF5+nTj/qtL3p+eUO8/4Im+FhYVePyaoGCMiIiIi5/1x8S7sDicD2sfSt3Uj9/aUjvH855c3U88SRNv4BgAM7d6EDQdPc7bYxi2tGgLQt3VDXl+2lzX7TjL5vzvchZ07O8dzW9vYSq+Xk1/M2WIbreMaVMPZidQcH330EQsWLGDhwoXuqcPGjRtHQkICI0eO9GtskyZNYsKECe7HVquVxMREUlJSCA8P92Nk1cNms5Gens7AgQMxm83+DqfWUN48o7xVnXLmmerOW/bpQti8mqCgIAYPTvX56/mKp3mLOnCKt3dtIqx+fQYPvsWHEdYs+v70nHLnGV/mzdUd7m0qxoiIiIgIq/Z+z5e7TxBkNPD7we0r7e/RrPId9De2iK7wuEvTSOoHB3Gm0OYuxACs3nuyUjGmpMzOsLfX8H1BCV8/05/GEaFeOhORmu+ZZ57hd7/7HQ888AAAnTt35tChQ8yYMYORI0cSHx8PQG5uLo0bN3Y/Lzc3l27dugEQHx/PiRMnKhy3rKyM06dPu58fHx9Pbm5uhTGux64xPxQcHExwcHCl7WazOaAuDgTa+XqL8uYZ5a3qlDPPVFfezEEXXqMufJ6qmreQ89OD2h114/yrSt+fnlPuPOOLvPnq86D5IkREREQCwObsM3y15wTH8ooottn535ZjPPLeevr8cTmD3ljF+A+3AOVTjrVsVN+j1zCbjPRpGQOA0QB3dSm/iLx678lKYz/fcpyjeUWUljlYn3Xaw7MSqZ0KCwsxGiu+FTOZTDgcDgCSkpKIj49n+fLl7v1Wq5V169aRnJwMQHJyMnl5eWzatMk95ssvv8ThcNC7d2/3mJUrV1aY8zo9PZ22bdtecooyERERb3BSvnC9wc9x+IvJWH7mZQ6nnyMRkZpGnTEiIiIiddjRvCKm/G8H6Tsv3B1vNMDF7w1zrMUARIaZ+fUdra/r9R69pQVHzhQy9vZW3HJDQxZtO86e3LPkWouJCw8BwOl08t43We7nbDmczz3dmlzX64rUJnfffTcvvvgizZo1o2PHjmzevJnXXnuNxx57DACDwcC4ceN44YUXaN26NUlJSTz//PMkJCQwdOhQANq3b8+dd97JE088wZw5c7DZbIwdO5YHHniAhIQEAB588EGmTp3KqFGjmDhxItu3b+eNN95g1qxZ/jp1ERGROi/ofDHGrmKMiPyAijEiIiIidVBRqZ33vsli9lf7KCy1E2Q00KJhPQ6ePEeZw0lCRAjDejalf9tGnC0u4/S5Uro0jSAyzHJdr3tLq4YsGdfP/bhzkwi2Hsln9d6TDOvZFIB1WafZcezCHLxbjuRd12uK1DZ/+ctfeP755/nlL3/JiRMnSEhI4Be/+AWTJ092j3n22Wc5d+4co0ePJi8vj1tvvZUlS5YQEhLiHrNgwQLGjh3LHXfcgdFoZNiwYfz5z39274+IiCAtLY0xY8bQs2dPGjZsyOTJkxk9enS1nq+IiAQW5/kahMEQmL0xQaby87bZHX6ORERqGhVjREREROoQu8PJvzYdZlb6XnfHy40tonjx3s60iWtAaZmDXGsxCZGh7ikUfOnWVg3ZeiSfVXu/dxdj3ltd3hWT3DKGjAOn2H40H5vdgdmkGXQlMDRo0IDXX3+d119//bJjDAYD06ZNY9q0aZcdEx0dzcKFC6/4Wl26dGHVqlWehioiIlJlgd4PEnR+KlJ1xojID+kdr4iIiEgtlbYjh/EfZrJizwmcTif7ThRw/5w1TPz3NnKsxTSJDGXWz7ry4ehk2sQ1AMASZCQxOqxaCjEAfVs3AmD1vlM4HE6yTxWSvqt8yrSp93QkPCSIkjIHe3LOVks8IiIiIlI9ArMvRmvGiMjlqTNGREREpBbam3uWp/+5mZIyB59sPkrLhvU4kldEaZmD+sFBjBvQmof6NCfEbPJrnD2aRxJqNnGyoIR1WaeZtew7nE7o16YRbeIa0DUxklV7T5J5OI9OTSL8GquIiIiIXD+ne54y/8bhL+bz05SVaZoyEfkBdcaIiIiI1DIlZXZ+9UEmJWUObmhUj/rBQRw4eY7SMgf92jRi6fh+PN63pd8LMQDBQSb6tIwGYOR761mfdZr6wUH8ZmAbALo2jQRgy+E8P0UoIiIiIuI96owRkctRZ4yIiIhILfOnpXvYddxKdD0L/xzdhxCzif9uPkpkmIW7ujSucYul3tq6EV/t+Z5Su4PGESG89/Mbad84HICuiZEAbDmS578ARURERMRrXCWImvUXafXRmjEicjkqxoiIiIjUIpsOneGvq7IAeHlYF2IbhADwcHILP0Z1Zakd43gtbQ+tYusz95FexIWHuPd1TSyfmmzviQIKSsqoH6w/T0VERESk9goyXeiMcTqdNe5GKRHxH73bFREREalF/pt5FIB7uiUwsEOcn6O5Nk2jwtj43EBCzMZKb0ZjG4TQJDKUo3lFbDuST/INMX6KUkRERES8wb1kTIAWIYKMF87b7nC6izMiIlozRkRERKQWWbX3JABDOjf2cyRVE2oxXfYNuas7JlPrxoiIiIhILWe6qBijdWNE5GIqxoiIiIjUEodPF5J18hwmo6FOdZB0bRoJQObhM/4NRERERES8oLwAEaCNMe41Y0DFGBGpSMUYERERkVrC1RXTo1kkDULMfo7Ge3q1iAJg5XcnOX2u1M/RiIiIiIh47uJpyex2FWNE5AIVY0RERERqiVV7vwegb+tGfo7Eu3o0i6JjQjhFNjvzv8nydzgiIiIich3ca8b4Nwy/MRkunqbM4cdIRKSmUTFGREREpBYoszv4Zl95Z0zf1g39HI13GQwGftm/FQDz1xykoKTMzxGJiIiIiHjGaDTgWjZG05SJyMVUjBERERGpBbYcycdaXEZ4SBBdzq+xUpfc2Smelg3rYS0uY+G6Q/4OR0REREQ85Co/GAJ10RggyFR+yVXFGBG5mIoxIiIiIrWAa4qyW1s3xGSse29sTUYDv/hRSwDeWZVFSZndzxGJiIiIiCcCfZoygKDzf69rzRgRuZiKMSIiIiK1wKq9rinK6tZ6MRe7t3tT4sNDOHG2hH9vOurvcEREREREPOK6ecqmNWNE5CJVLsasXLmSu+++m4SEBAwGA59++mmF/QaD4ZIfr776qntMixYtKu1/6aWXKhxn69at9O3bl5CQEBITE3nllVcqxfLxxx/Trl07QkJC6Ny5M4sXL67q6YiIiIjUaDn5xUz811a+zT4DwK2t6tZ6MRezBBl5vG8SAP+3cj9ldr15FREREaltnOcnKgvgWcown5+mzK5pykTkIlUuxpw7d46uXbsye/bsS+4/fvx4hY/33nsPg8HAsGHDKoybNm1ahXFPP/20e5/VaiUlJYXmzZuzadMmXn31VaZMmcLcuXPdY9asWcPw4cMZNWoUmzdvZujQoQwdOpTt27dX9ZREREREaqQl23Po/6ev+HDjYZxOGJncnMToMH+H5VPDb2pGVJiZQ6cKWbw9x9/hiIiIiIhUmaszpkzTlInIRYKq+oRBgwYxaNCgy+6Pj4+v8Pi///0vt912Gy1btqywvUGDBpXGuixYsIDS0lLee+89LBYLHTt2JDMzk9dee43Ro0cD8MYbb3DnnXfyzDPPADB9+nTS09N58803mTNnTlVPS0RERKTGeX3ZdxTbHPRoFsn/G9Kens2j/R2Sz9ULDuLnNycxa9l3vL1iP3d3aRzQi7+KiIiI1DZOd/0hcP+Gc60ZU6ZpykTkIlUuxlRFbm4uixYt4v3336+076WXXmL69Ok0a9aMBx98kPHjxxMUVB5ORkYG/fr1w2KxuMenpqby8ssvc+bMGaKiosjIyGDChAkVjpmamlpp2rSLlZSUUFJS4n5stVoBsNls2Gy26znVGs11bnX5HKuLcuk55c57lEvvUB6vj/LnHVfKY1Gpnb0nCgB442ddiA8PCZh8P3hjE+au3M+u41a+2HqM49ZiPthwmKHdEhh9fhoz0NehJ5QrEREREd9zd8ZomjIRuYhPizHvv/8+DRo04L777quw/Ve/+hU9evQgOjqaNWvWMGnSJI4fP85rr70GQE5ODklJSRWeExcX594XFRVFTk6Oe9vFY3JyLj+dxYwZM5g6dWql7WlpaYSF1e0pPwDS09P9HUKdoVx6TrnzHuXSO5TH66P8ecel8njACnZHEOFmJ9+u/tIPUfnXTTFGvjpuZMw/N+M8f1flm8u/o7F1F6Yf3GSpr8NrV1hY6O8QREREpI5zdcYEcnOz1owRkUvxaTHmvffeY8SIEYSEhFTYfnFHS5cuXbBYLPziF79gxowZBAcH+yyeSZMmVXhtq9VKYmIiKSkphIeH++x1/c1ms5Gens7AgQMxm83+DqdWUy49p9x5j3LpHcrj9VH+vONKeZy35hDs2MONN8QyeHB3P0XoPz2txdz22ipsdmhU30Khzc65EjtxHftwU4vy6dr0dVh1rs5wEREREfEdrRkjIpfis2LMqlWr2LNnDx9++OFVx/bu3ZuysjIOHjxI27ZtiY+PJzc3t8IY12PXOjOXG3O5dWgAgoODL1nsMZvNAfEGPlDOszool55T7rxHufQO5fH6KH/ecak87jh+FoBuiVEBmeOmMWbm/fwmDp46x309mvD7/2zj08xjrNp3hltaV+yO1tfhtVOeRERExNeclBcgArgxRmvGiMglGX114HfffZeePXvStWvXq47NzMzEaDQSGxsLQHJyMitXrqwwp3V6ejpt27YlKirKPWb58uUVjpOenk5ycrIXz0JERETEP7YdyQegc9MIP0fiP7e2bshDfZoTZgnitnblfyeu2HPCz1GJiIiIiFxZkElrxohIZVUuxhQUFJCZmUlmZiYAWVlZZGZmkp2d7R5jtVr5+OOPefzxxys9PyMjg9dff50tW7Zw4MABFixYwPjx43nooYfchZYHH3wQi8XCqFGj2LFjBx9++CFvvPFGhSnGfv3rX7NkyRJmzpzJ7t27mTJlChs3bmTs2LFVPSURERGRGiW/yMaBk+cA6NI00r/B1BD9WjfCaIDdOWc5mlfk73BERERE5DK0ZgyYjOfXjNE0ZSJykSoXYzZu3Ej37t3p3r187vIJEybQvXt3Jk+e7B7zwQcf4HQ6GT58eKXnBwcH88EHH/CjH/2Ijh078uKLLzJ+/Hjmzp3rHhMREUFaWhpZWVn07NmT3/zmN0yePJnRo0e7x9x8880sXLiQuXPn0rVrV/71r3/x6aef0qlTp6qekoiIiEiNsv1oeVdMYnQo0fUsfo6mZoiqZ6F7s/Ibd9QdIyIiIiI1maYpE5FLqfKaMf3798fpvHJVd/To0RUKJxfr0aMHa9euverrdOnShVWrVl1xzP3338/9999/1WOJiIiI1CZbjuQB6or5odvbxbLp0Bm+2n2CEb2b+zscEREREbkCQwCvGuMqxtjUGSMiF/HZmjEiIiIi4pmth8s7Y7oG8Hoxl9K/bSMAvtl3imKb3c/RiIiIiMilaJoysASVX3K12dUZIyIXqBgjIiIiUsNsVWfMJXVoHE5ceDBFNjvrsk77OxwRERERkUsym1SMEZHKVIwRERERqUG+P1vCsfxiDAbo1ESdMRczGAz0bxMLwDf7Tvo5GhERERG5FCflrTEB3BiD5XwxprRMxRgRuUDFGBEREZEaxNUVc0Oj+tQPrvLyfnVenxuiAVh34JSfIxERERERuTTXNGUlKsaIyEVUjBERERGpQdJ25AJwY4soP0dSM/VOigFg+zErBSVlfo5GRERERH7owpoxgdsbc2GaMqefIxGRmkTFGBEREZEaothmZ/G24wD8uGsTP0dTMyVEhpIYHYrd4eTb7Dx/hyMiIiIiUomrM0bTlInIxVSMEREREakhlu86wdmSMppEhtI7Kdrf4dRYru6Y9Vln/ByJiOdatGiBwWCo9DFmzBgAiouLGTNmDDExMdSvX59hw4aRm5tb4RjZ2dkMGTKEsLAwYmNjeeaZZygrq9gxtmLFCnr06EFwcDCtWrVi/vz51XWKIiISoNQLAsGuYozd7udIRKQmUTFGREREpIb4ZPMRAO7ploDRGLjTOlyNq1C1/uBpP0ci4rkNGzZw/Phx90d6ejoA999/PwDjx4/ns88+4+OPP+brr7/m2LFj3Hfffe7n2+12hgwZQmlpKWvWrOH9999n/vz5TJ482T0mKyuLIUOGcNttt5GZmcm4ceN4/PHHWbp0afWerIiISIBxdcZomjIRuZhWhRURERGpAU4VlLBiz/cA3NdDU5RdSZ+W5Z0x245aKWns52BEPNSoUaMKj1966SVuuOEGfvSjH5Gfn8+7777LwoULuf322wGYN28e7du3Z+3atfTp04e0tDR27tzJsmXLiIuLo1u3bkyfPp2JEycyZcoULBYLc+bMISkpiZkzZwLQvn17Vq9ezaxZs0hNTa32cxYRkcDgPL9oTAAvGYPZVH7ymqZMRC6mYoyIiIhIDfD51uOUOZx0bhJBq9gG/g6nRmsaFUpCRAjH8os5eDaA3+VLnVFaWso//vEPJkyYgMFgYNOmTdhsNgYMGOAe065dO5o1a0ZGRgZ9+vQhIyODzp07ExcX5x6TmprKU089xY4dO+jevTsZGRkVjuEaM27cuMvGUlJSQklJifux1WoFwGazYbPZvHTGNZfrHAPhXL1JefOM8lZ1yplnqjtv7ikznc5a/bm6nrydr8VQVFpWq3NQFfr+9Jxy5xlf5s1XnwsVY0RERERqgP9sPgrA0O7qirkag8FA75YxfLL5KPusKsZI7ffpp5+Sl5fHz3/+cwBycnKwWCxERkZWGBcXF0dOTo57zMWFGNd+174rjbFarRQVFREaGloplhkzZjB16tRK29PS0ggLC/Po/Goj17RxUjXKm2eUt6pTzjxTXXk7eBYgiKKiIhYvXlwtr+lLnuQt66gBMJF1KJvFiw96PaaaTN+fnlPuPOOLvBUWFnr9mKBijIiIiIjfHc8vZsvhPIwG+HHXBH+HUyv0Tormk81H2a9ijNQB7777LoMGDSIhwf/f/5MmTWLChAnux1arlcTERFJSUggPD/djZNXDZrORnp7OwIEDMZvN/g6n1lDePKO8VZ1y5pnqztvmw3nM2r6esLAwBg/u6/PX85XryVvumkN8lr2H2PgEBg/u4qMIaxZ9f3pOufOML/Pm6g73NhVjRERERPxs9b6TAHRNjKRRg2A/R1M79D6/bszBAiix2fWmRWqtQ4cOsWzZMv7zn/+4t8XHx1NaWkpeXl6F7pjc3Fzi4+PdY9avX1/hWLm5ue59rn9d2y4eEx4efsmuGIDg4GCCgyv/HDKbzQH1fRZo5+styptnlLeqU848U115M5nKLzcaDIY68XnyJG+hlvIclDmoEzmoCn1/ek6584wv8uarz4PRJ0cVERERkWu2au8pAPq1bnSVkeLSIiaMqDAzdqeBvSfO+TscEY/NmzeP2NhYhgwZ4t7Ws2dPzGYzy5cvd2/bs2cP2dnZJCcnA5CcnMy2bds4ceKEe0x6ejrh4eF06NDBPebiY7jGuI4hIiLiG04ADAHcwGwJKr/karM7/ByJiNQkKsaIiIiI+JHDCWsOnC/GtGno52hqD4PBQNu4+gDsyT3r52hEPONwOJg3bx4jR44kKOjCpAURERGMGjWKCRMm8NVXX7Fp0yYeffRRkpOT6dOnDwApKSl06NCBhx9+mC1btrB06VKee+45xowZ4+5sefLJJzlw4ADPPvssu3fv5q233uKjjz5i/PjxfjlfERGRQOEqxpSqGCMiF1ExRkRERMSPDhdAflEZDUKC6No00t/h1Cpt4hoA8F1ugZ8jEfHMsmXLyM7O5rHHHqu0b9asWdx1110MGzaMfv36ER8fX2EqM5PJxOeff47JZCI5OZmHHnqIRx55hGnTprnHJCUlsWjRItLT0+natSszZ87knXfeITU1tVrOT0REApOzvDGGAG6MwWwqv+RaUqZijIhcoDVjRERERPxod37529RbbmhIkEn3yVRFm/OdMd+dUDFGaqeUlBScritWPxASEsLs2bOZPXv2ZZ/fvHlzFi9efMXX6N+/P5s3b76uOEVERKRqLCZNUyYilekdv4iIiIgf7c4r/3Osr6YoqzJ3MUadMSIiIiI1hus2A0MALxrjnqZMnTEichEVY0RERET85GyxjYPnlzvp17qRf4OphVrHlhdjTpwt4cy5Uj9HIyIiIiJSTsUYEbkUFWNERERE/GTtgTM4MNAiJozE6DB/h1Pr1A8OIjq4/N7L3Tln/RyNiIiIiIDWjAFNUyYil6ZijIiIiIifrNp3EoC+rWL8HEntlRBW/m7/u1wVY0RERESkZlBnjIhciooxIiIiIn6SceA0ADffoGKMpxqfbyhSZ4yIiIhIzeBUa8yFYow6Y0TkIirGiIiIiPjB8fwiDp4qxICTm1pE+TucWsvVGbMnx+rnSERERETkYgFci8FsUmeMiFQW5O8ARERERAJRxv5TADStB+GhZj9HU3vFu6cpK8DpdGIwBPLbfhERERH/c/o7gBrAtWaMtbiMNs99AUC7+Ab868mb3V0zIhJ49N0vIiIi4geuYkybCL1dvR5xIWA2GSgoKeNoXpG/wxERERGR8wL5Jpm48BCaRIYC5d0xpWUOth7J58DJAj9HJiL+pM4YERERET9Yc74Y0zpcxZjrYTJCy4b12JNbwJ6cszSNCvN3SCIiIiIBzak/b7EEGfnytz/iVEEpAENnf8OJsyXYypQckUCmzhgRERGRanb4dCFH84oIMhpoqWLMdWsdWx+A3Tln/RyJiIiIiDjPT1QWuH0x5YKDTCREhpIQGUqI2QRAqd3u56hExJ9UjBERERGpZmv2nwSgS9MIgk1+DqYOaBtXXoz5LlfFGBERERGpeVzrxJSqM0YkoFW5GLNy5UruvvtuEhISMBgMfPrppxX2//znP8dgMFT4uPPOOyuMOX36NCNGjCA8PJzIyEhGjRpFQUHFORO3bt1K3759CQkJITExkVdeeaVSLB9//DHt2rUjJCSEzp07s3jx4qqejoiIiEi1c60X0ycp2s+R1A2t3cUYzcEtIiIi4nfn6w0BvGRMJWbT+WKM3eHnSETEn6pcjDl37hxdu3Zl9uzZlx1z5513cvz4cffHP//5zwr7R4wYwY4dO0hPT+fzzz9n5cqVjB492r3farWSkpJC8+bN2bRpE6+++ipTpkxh7ty57jFr1qxh+PDhjBo1is2bNzN06FCGDh3K9u3bq3pKIiIiItXG6XS614vp0zLKz9HUDUkx9QA4ePIcTk1SLiIiIiI1jKszxlamYoxIIAuq6hMGDRrEoEGDrjgmODiY+Pj4S+7btWsXS5YsYcOGDfTq1QuAv/zlLwwePJg//elPJCQksGDBAkpLS3nvvfewWCx07NiRzMxMXnvtNXfR5o033uDOO+/kmWeeAWD69Omkp6fz5ptvMmfOnKqeloiIiEi1OHDyHCfOlmAJMtI9MZIvd/s7otqvaVQoJqOBIpudXGsJ8REh/g5JREREJGC5bo0xBPyqMRdYTOW5UGeMSGCrcjHmWqxYsYLY2FiioqK4/fbbeeGFF4iJiQEgIyODyMhIdyEGYMCAARiNRtatW8e9995LRkYG/fr1w2KxuMekpqby8ssvc+bMGaKiosjIyGDChAkVXjc1NbXStGkXKykpoaSkxP3YarUCYLPZsNls3jj1Gsl1bnX5HKuLcuk55c57lEvvUB6vj/LnuVV7cgHonhiBifI3Y8qjZ1x5MzjtNI0M5dDpQvbl5hMTpoV4LkdfayIiIiLVz90Zo2KMSEDzejHmzjvv5L777iMpKYn9+/fz+9//nkGDBpGRkYHJZCInJ4fY2NiKQQQFER0dTU5ODgA5OTkkJSVVGBMXF+feFxUVRU5OjnvbxWNcx7iUGTNmMHXq1Erb09LSCAsL8+h8a5P09HR/h1BnKJeeU+68R7n0DuXx+ih/Vffv3UbASCP7SXf+lMfrk56eTpijPK+frVjHqV2aquxyCgsL/R2CiIiI1HFOrRlTiXvNGE1TJhLQvF6MeeCBB9z/79y5M126dOGGG25gxYoV3HHHHd5+uSqZNGlShW4aq9VKYmIiKSkphIeH+zEy37LZbKSnpzNw4EDMZrO/w6nVlEvPKXfeo1x6h/J4fZQ/z9jsDn7/7VeAnVGDb6FtbKjyeB0u/jr8lv3sysimfuOWDL6zrb9Dq7FcneEiIiIiUn0srmKMOmNEAppPpim7WMuWLWnYsCH79u3jjjvuID4+nhMnTlQYU1ZWxunTp93rzMTHx5Obm1thjOvx1cZcbq0aKF/LJjg4uNJ2s9kcEBdAAuU8q4Ny6TnlznuUS+9QHq+P8lc1m4+c5lyJnagwM12bRWO3lwHK4/Uym820im0AwKHTxcrlFSg3IiIi4mtO1KX8Q2bXNGXqjBEJaEZfv8CRI0c4deoUjRs3BiA5OZm8vDw2bdrkHvPll1/icDjo3bu3e8zKlSsrzGmdnp5O27ZtiYqKco9Zvnx5hddKT08nOTnZ16ckIiIi4pFVe78H4NbWjTAaNW+DNyU1rA9A1skCP0ciIiIiIgAGzVPmFqzOGBHBg2JMQUEBmZmZZGZmApCVlUVmZibZ2dkUFBTwzDPPsHbtWg4ePMjy5cu55557aNWqFampqQC0b9+eO++8kyeeeIL169fzzTffMHbsWB544AESEhIAePDBB7FYLIwaNYodO3bw4Ycf8sYbb1SYYuzXv/41S5YsYebMmezevZspU6awceNGxo4d64W0iIiIiHjfyr0nAejbuqGfI6l7khrVAyD7dCFlepMrIiIi4jdONcZU4lozxmZXckQCWZWLMRs3bqR79+50794dgAkTJtC9e3cmT56MyWRi69at/PjHP6ZNmzaMGjWKnj17smrVqgrTgy1YsIB27dpxxx13MHjwYG699Vbmzp3r3h8REUFaWhpZWVn07NmT3/zmN0yePJnRo0e7x9x8880sXLiQuXPn0rVrV/71r3/x6aef0qlTp+vJh4iIiIhP5BWWsvVIHqBijC80Dg8hOMiIze7kaF6Rv8MRERERCXjqi7nAcn6ashJNUyYS0Kq8Zkz//v1xXqHEvXTp0qseIzo6moULF15xTJcuXVi1atUVx9x///3cf//9V309EREREX/7Zt8pnE5oHVufxhGh/g6nzjEaDbSIqcee3LNknTxH85h6/g5JREREJCCp96OyC50xKsaIBDKfrxkjIiIiIhfWi+nbupGfI6m7khqWF2CyTp7zcyQiIiIigct1E7eWjLnA1RlTqs4YkYCmYoyIiIiIjzmdTladXy+mXxtNUeYrrnVjVIwRERERkZrEYiqvTKkzRiSwqRgjIiIi4mN7cs9yNK8IS5CR3kkx/g6nzkqKUTFGRERExN9c05SpM+YCdcaICKgYIyIiIuJzy3bmAnBrq4aEWkx+jqbuUmeMiIiIiNRErjVjStUZIxLQVIwRERER8bH088WYgR3i/BxJ3eZaM+ZoXhHFNrufoxEREREJUOdbYwyoNcZFnTEiAirGiIiIiPhUrrWYLUfyAbijfayfo6nbYupZaBAShNMJ2acL/R2OiIiIiAhwoTNGa8aIBDYVY0RERER8aNmu8q6YbomRxDYI8XM0dZvBYHB3xxz4XlOViYiIiPiD83xrjNaMuUCdMSICKsaIiIiI+NQyTVFWrVo1qg/AruNWP0ciIiIiEthUi7nA4u6Mcfo5EhHxJxVjRERERHzkXEkZ3+w/BagYU116NI8CYOOh036ORERERCQwOVVvqESdMSICKsaIiIiI+Myqvd9TWuageUwYrWPr+zucgHBTUjQA3x7K05zcUqMdPXqUhx56iJiYGEJDQ+ncuTMbN25073c6nUyePJnGjRsTGhrKgAED2Lt3b4VjnD59mhEjRhAeHk5kZCSjRo2ioKCgwpitW7fSt29fQkJCSExM5JVXXqmW8xMREdE8ZRe41oxZf/A0h7W2oUjAUjFGRERExEfSXFOUtY/DoDej1aJVo/pEhJopstnZeUxTlUnNdObMGW655RbMZjNffPEFO3fuZObMmURFRbnHvPLKK/z5z39mzpw5rFu3jnr16pGamkpxcbF7zIgRI9ixYwfp6el8/vnnrFy5ktGjR7v3W61WUlJSaN68OZs2beLVV19lypQpzJ07t1rPV0REAos6YyprHhPm/v+S7Tl+jERE/CnI3wGIiIiI1EU2u4Plu04AmqKsOhmNBm5sEcWyXSfYcPA0XRMj/R2SSCUvv/wyiYmJzJs3z70tKSnJ/X+n08nrr7/Oc889xz333APA3/72N+Li4vj000954IEH2LVrF0uWLGHDhg306tULgL/85S8MHjyYP/3pTyQkJLBgwQJKS0t57733sFgsdOzYkczMTF577bUKRRsRERFf0K1IF7SJa0DvpGjWZZ3mbEmZv8MRET9RMUZERETEBzL2nyK/yEbD+hZ6tYj2dzgBpVeLaHcx5vG+Lf0djkgl//vf/0hNTeX+++/n66+/pkmTJvzyl7/kiSeeACArK4ucnBwGDBjgfk5ERAS9e/cmIyODBx54gIyMDCIjI92FGIABAwZgNBpZt24d9957LxkZGfTr1w+LxeIek5qayssvv8yZM2cqdOK4lJSUUFJS4n5stZZ3mNlsNmw2m9dzUdO4zjEQztWblDfPKG9Vp5x5prrzVlZWXmxwOp21+nPl7bx1aRJeXowpKq3VebkcfX96TrnzjC/z5qvPhYoxIiIiIj7wxfnpB1I6xmMy6r7A6nRji/ILzBsPnsHpdGqKOKlxDhw4wNtvv82ECRP4/e9/z4YNG/jVr36FxWJh5MiR5OSU//yIi6vYVRcXF+fel5OTQ2xsbIX9QUFBREdHVxhzccfNxcfMycm5ZDFmxowZTJ06tdL2tLQ0wsLCKm2vq9LT0/0dQq2kvHlGeas65cwz1ZW3bacNgIn8vDwWL15cLa/pS97K29Ej5XnZvS+LxYv3e+WYNZG+Pz2n3HnGF3krLPTN2k4qxoiIiIh4md3hJH1n+cXQQZ3i/RxN4OnUJILgICOnzpVy4OQ5wkPMvJb+HXd1acwtrRr6OzwRHA4HvXr14o9//CMA3bt3Z/v27cyZM4eRI0f6NbZJkyYxYcIE92Or1UpiYiIpKSmEh4f7MbLqYbPZSE9PZ+DAgZjNZn+HU2sob55R3qpOOfNMdefNsusE7+zJJDIqksGDe/v89XzF23k7kXGIxYf3EB2bwODBXbwQYc2i70/PKXee8WXeXN3h3qZijIiIiIiXbTh4mpMFpUSEmunTMsbf4QSc4CATXRMjWZ91mm/2neSTzUfZnJ3HugOnWP6bH6lTRvyucePGdOjQocK29u3b8+9//xuA+PjyIm5ubi6NGzd2j8nNzaVbt27uMSdOnKhwjLKyMk6fPu1+fnx8PLm5uRXGuB67xvxQcHAwwcHBlbabzeaAujgQaOfrLcqbZ5S3qlPOPFNdeTOaTOX/Ggx14vPkrbxFhJb/fi202etEXi5H35+eU+4844u8+erzYPTJUUVEREQC2JLzU5QN7BCH2aQ/t/zhpvPr9Ly4aBebs/MAOHDyHLuOn/VjVCLlbrnlFvbs2VNh23fffUfz5s0BSEpKIj4+nuXLl7v3W61W1q1bR3JyMgDJycnk5eWxadMm95gvv/wSh8NB79693WNWrlxZYc7r9PR02rZte8kpykRERMR3woLLi1TnSux+jkRE/EVXB0RERES8yOFwuosxmqLMf3qdXzempMyByWigdWx9AD7fesyfYYkAMH78eNauXcsf//hH9u3bx8KFC5k7dy5jxowBwGAwMG7cOF544QX+97//sW3bNh555BESEhIYOnQoUN5Jc+edd/LEE0+wfv16vvnmG8aOHcsDDzxAQkICAA8++CAWi4VRo0axY8cOPvzwQ954440K05CJiIh4m9NZ/q+6kSuqF1w+QVFBSZmfIxERf1ExRkRERMSLNh8+Q461mPrBQdzaWuuT+EvP5lGYTeUXAKbc3YGn72gNwKJtx3G6rhCI+MmNN97IJ598wj//+U86derE9OnTef311xkxYoR7zLPPPsvTTz/N6NGjufHGGykoKGDJkiWEhIS4xyxYsIB27dpxxx13MHjwYG699Vbmzp3r3h8REUFaWhpZWVn07NmT3/zmN0yePJnRo0dX6/mKiIgI1D9fjDlXqmKMSKDSmjEiIiIiXjTvm4MApHSMIzjI5N9gAliDEDOzH+zB2eIyhvVsyrmSMkLMRg6dKmTHMSudmkT4O0QJcHfddRd33XXXZfcbDAamTZvGtGnTLjsmOjqahQsXXvF1unTpwqpVqzyOU0REpOrKb3xRX0xF9SznizHqjBEJWOqMEREREfGSw6cLWbztOACP39rSz9FISsd4hvVsCpRPC3F7u1gAPtNUZSIiIiI+p1nKKnJ3xmjNGJGApWKMiIiIiJe8s+oADif0bd2QDgnh/g5HfuCuLuXraCzaqqnKRERERHxFf2ZdWr3g8q75IpudjQdPs+nQGY7lFfk5KhGpTpqmTERERMQLTp8r5cONhwF48kc3+DkauZTb2sYSajZx5EwRW47k0y0x0t8hiYiIiNRZBk1UVkH9kAuXYX8yJwMAowFW/PY2msWE+SssEalG6owRERER8YK/Zxyi2OagU5Nwbr4hxt/hyCWEWkzc3r58qrKlO3L8HI2IiIhI3aTGmEsLDjLx6C0taB4TRvOYMCwmIw4nHDx1zt+hiUg1UTFGRERE5DrlWouZtyYLgNH9bsCgCbJrrJQOcQAs25nr50hERERE6ib3NGX6k7iSP9zdka+fuY2vn7mNdo0bAFDmcPg5KhGpLirGiIiIiFyHMruDpxduJq/QRvvG4QzuFO/vkOQK+reNJchoYO+JAg6e1F2IIiIiIuIfQcbyapXNrl4ikUChYoyIiIjIdZiZ/h3rD56mfnAQb43oQZBJf17VZBGhZnq3jAZg2S51x4iIiIh4m/P8RGVqjLky1/uGMhVjRAKGrhaIiIiIeGjFnhO8vWI/AC8P60JSw3p+jkiuxYD25VOVpWmqMhERERHxE7OpvFylacpEAkeVizErV67k7rvvJiEhAYPBwKeffureZ7PZmDhxIp07d6ZevXokJCTwyCOPcOzYsQrHaNGiBQaDocLHSy+9VGHM1q1b6du3LyEhISQmJvLKK69UiuXjjz+mXbt2hISE0LlzZxYvXlzV0xERERHxiM3uYMr/dgAwMrk5Q7o09nNEcq1cxZiNB09z5lypn6MRERERqVtca8ZoGcUrCzKWX5bVNGUigaPKxZhz587RtWtXZs+eXWlfYWEh3377Lc8//zzffvst//nPf9izZw8//vGPK42dNm0ax48fd388/fTT7n1Wq5WUlBSaN2/Opk2bePXVV5kyZQpz5851j1mzZg3Dhw9n1KhRbN68maFDhzJ06FC2b99e1VMSERERqbL/fHuEg6cKialn4dk72/k7HKmCxOgw2sU3wOGEL3ef8Hc4IiIiIhKA3J0xdnXGiASKoKo+YdCgQQwaNOiS+yIiIkhPT6+w7c033+Smm24iOzubZs2aubc3aNCA+PhLL3C7YMECSktLee+997BYLHTs2JHMzExee+01Ro8eDcAbb7zBnXfeyTPPPAPA9OnTSU9P580332TOnDlVPS0RERGRa1ZSZufPy/cB8FT/G6gXXOU/qcTPUjrEsTvnLMt25TKsZ1N/hyMiIiJSZ7j6PAxaNeaKTEbXNGXqjBEJFD6/cpCfn4/BYCAyMrLC9pdeeonp06fTrFkzHnzwQcaPH09QUHk4GRkZ9OvXD4vF4h6fmprKyy+/zJkzZ4iKiiIjI4MJEyZUOGZqamqFadN+qKSkhJKSEvdjq9UKlE+vZrPZrvNMay7XudXlc6wuyqXnlDvvUS69Q3m8PoGevwVrszmaV0Rcg2B+1jPB4zwEeh6v1/Xkr3+bGP785T6+/u57rOeKCbWYvB1ejaSvNREREakumqbsyoJM5RMWqTNGJHD4tBhTXFzMxIkTGT58OOHh4e7tv/rVr+jRowfR0dGsWbOGSZMmcfz4cV577TUAcnJySEpKqnCsuLg4976oqChycnLc2y4ek5OTc9l4ZsyYwdSpUyttT0tLIywszOPzrC1+2LUknlMuPafceY9y6R3K4/UJlPw5nfCvLCNZZw1EWJwcKjAABvo1LOTL9KXXffxAyaOveJI/pxNigk2cKrHz8j/TuKlRYNyRWFhY6O8QREREpI5zOgPj76rrZVZnjEjA8Vkxxmaz8dOf/hSn08nbb79dYd/FHS1dunTBYrHwi1/8ghkzZhAcHOyrkJg0aVKF17ZarSQmJpKSklKhWFTX2Gw20tPTGThwIGaz2d/h1GrKpeeUO+9RLr1Debw+gZa/L7bnsHrtVgCOFpa/aWoaGcKUR27FElTlJfjcAi2P3na9+TtU7wCzlu9jjy2GKYNv8kGENY+rM1xERETE19QZc2WuzhibXcUYkUDhk2KMqxBz6NAhvvzyy6sWOnr37k1ZWRkHDx6kbdu2xMfHk5ubW2GM67FrnZnLjbncOjQAwcHBlyz2mM3mgLgAEijnWR2US88pd96jXHqH8nh9AiF/50rK+OMX3wEwonczOjeJ4GRBCQM7xFMv1Ds3kQRCHn3J0/w90Ls5b3y5j42H8jh0poRWsfV9EF3Noq8zERERkZrBbDrfGaNpykQChue3cl6GqxCzd+9eli1bRkxMzFWfk5mZidFoJDY2FoDk5GRWrlxZYU7r9PR02rZtS1RUlHvM8uXLKxwnPT2d5ORkL56NiIiIBLq/fLmPHGsxidGhPH9XBx64qRljb29N2/gG/g5NrlNceAi3tyv/+/PDDdl+jkZERESkbjGg1pgrCTKe74zRNGUiAaPKxZiCggIyMzPJzMwEICsri8zMTLKzs7HZbPzkJz9h48aNLFiwALvdTk5ODjk5OZSWlgKQkZHB66+/zpYtWzhw4AALFixg/PjxPPTQQ+5Cy4MPPojFYmHUqFHs2LGDDz/8kDfeeKPCFGO//vWvWbJkCTNnzmT37t1MmTKFjRs3MnbsWC+kRURERAR2HMvn3dUHAPjDXR0JMQfGIu+B5IEbmwHw72+PUlqmuxJFRERErpeWjLk2QeqMEQk4VZ6mbOPGjdx2223ux64CyciRI5kyZQr/+9//AOjWrVuF53311Vf079+f4OBgPvjgA6ZMmUJJSQlJSUmMHz++QqElIiKCtLQ0xowZQ8+ePWnYsCGTJ09m9OjR7jE333wzCxcu5LnnnuP3v/89rVu35tNPP6VTp05VPSURERGRCjYcPM3clQdYtisXpxNubxfLgA5x/g5LfKB/20bEhQeTay0hfWcuQ7o09ndIIiIiIrWak/JqjNaMubIg4/lijDpjRAJGlYsx/fv3x3mFEveV9gH06NGDtWvXXvV1unTpwqpVq6445v777+f++++/6rFERERErtWXu3N5bP5G9+N+bRox477OfoxIfCnIZOT+nom8+dU+/pt5VMUYEREREakWQabyCYvK7CrGiASKKhdjREREROqqolI7z3+6A4DUjnE8k9qWVrFaG6au69u6IW9+tY8dx6z+DkVERESk1tM0ZdfG7O6M0TRlIoFCxRgRERGR8/7y5V6O5hXRJDKUWT/rRphFfyoFgnbx4QAczSsiv8hGRKjZzxGJiIiISF3n6oyxqTNGJGAY/R2AiIiISE2w78RZ/rrqAAB/uLuDCjEBJCLMTJPIUAD25Jz1czQiIiIitZurM8agRWOuKMh0vjPGrs4YkUChYoyIiIgEPIfDyf/7ZDs2u5MB7WNJ6Rjv75CkmrWLL5+ObneOpioTEREREd8zG8+vGeNQZ4xIoFAxRkRERALe21/vZ13WaULNJv5wd0d/hyN+0K5xeTFm13F1xoiIiIhcD1dpQX0xV+bqjLGpM0YkYKgYIyIiIgFt06EzvJb+HQBT7+lIYnSYnyMSf2jfuHzdmF3H1RkjIiIi4g2apezKXGvGlGnNGJGAoWKMiIiIBKz8Ihu/+udm7A4nP+6awP09m/o7JPGTdvHlxZg9OWdxaKoIEREREY85nfpb6loEGc+vGaO/PUUChooxIiIiErD+tHQPR/OKaBYdxov3dtIiowGsRUwYwUFGimx2sk8X+jscERERkVpPf1lf2YVijKYpEwkUKsaIiIhIQDpVUMJHGw8DMOO+zjQIMfs5IvGnIJORNnHl68bsztFUZSIiIiKeUp/HtTFrmjKRgKNijIiIiASkf6zNpqTMQecmEdx8Q4y/w5EaoH3j8mLMzuNn/RyJiIiISO2nrvMrCzKV58dmV2eMSKBQMUZEREQCTrHNzt8yDgLwRL+WeqMowIV1Y3YfV2eMiIiIiMfU6HFNgoznO2O0ZoxIwFAxRkRERGocm92B3YdvSj7ZfJRT50ppEhnK4E7xPnsdqV3aNXZNU6bOGBERERFPOc9XY3S705WZz3fGlKkzRiRgqBgjIiIiNca5kjJeX/YdXaemcf+cNRSV2t37HA4nxTb7FZ59bRwOJ39ddQCAR29pQZBJfw5JufbnO2OyTxdSUFLm52ikrpsyZQoGg6HCR7t27dz7i4uLGTNmDDExMdSvX59hw4aRm5tb4RjZ2dkMGTKEsLAwYmNjeeaZZygrq/i1u2LFCnr06EFwcDCtWrVi/vz51XF6IiIichWu9yE2rRkjEjB09UFERERqhCXbj9P/Tyt4fdleCkvtfJudxzP/2oLT6eR4fhE/nr2abtPS+PPyvZSUeVaUcTqdzPhiFwe+P0eD4CB+dmOil89CarOoehbiw0MA2JOjqcrE9zp27Mjx48fdH6tXr3bvGz9+PJ999hkff/wxX3/9NceOHeO+++5z77fb7QwZMoTS0lLWrFnD+++/z/z585k8ebJ7TFZWFkOGDOG2224jMzOTcePG8fjjj7N06dJqPU8REQkszvO1Bc0EfGVm4/nOGIc6Y0QCRZC/AxARERH5as8JxizcjN3hpHlMGD/tlcjry77j863HiQwzk74zl1xrCQCvpX/Hf749wov3duaWVg0vebwz50pxAlFhZvd6MOWFmN38dVUWAM/d1Z4GIeZqOT+pPTo1CSfHWszyXSfo2Tza3+FIHRcUFER8fOWpEvPz83n33XdZuHAht99+OwDz5s2jffv2rF27lj59+pCWlsbOnTtZtmwZcXFxdOvWjenTpzNx4kSmTJmCxWJhzpw5JCUlMXPmTADat2/P6tWrmTVrFqmpqdV6riIiIlKRyV2MUWeMSKBQMUZERET8avvRfMYs+Ba7w8m93Zvw8rAuWIKMRIaZ+X+fbOcfa7MBaB1bn5E3t+DPy/dy8FQhI95Zx+h+LfltSlssQeXNvntzzzL7q338b8sxHE5oEBJEYlQYYRYTNruDLUfyAZg+tBM/u7GZ385Zaq6f9GzKsl0nWLg+m6dvb02oxeTvkKQO27t3LwkJCYSEhJCcnMyMGTNo1qwZmzZtwmazMWDAAPfYdu3a0axZMzIyMujTpw8ZGRl07tyZuLg495jU1FSeeuopduzYQffu3cnIyKhwDNeYcePGXTamkpISSkpK3I+t1vIuMZvNhs1m89KZ11yucwyEc/Um5c0zylvVKWeeqe68ldnLu9idTmet/lz5Om8GZ3lHjK3MUavz5KLvT88pd57xZd589blQMUZERET85lheEY/O30BhqZ1bWzV0F2IARvRuzu7jZ/n72kP0bd2Q2SN6EB5i5p5uCbz0xW4WrMtm7soDrPzue5pEhnIsv5hdxytOLXW2uIydP9g2/Z6OPNynebWdo9QuAzvEkxgdyuHTRfxn8xFG9NbXivhG7969mT9/Pm3btuX48eNMnTqVvn37sn37dnJycrBYLERGRlZ4TlxcHDk5OQDk5ORUKMS49rv2XWmM1WqlqKiI0NDQSnHNmDGDqVOnVtqelpZGWFiYx+db26Snp/s7hFpJefOM8lZ1yplnqitv23INgIkTubksXry4Wl7Tl3yVt+wCgCDOniusE3ly0fen55Q7z/gib4WFhV4/JqgYIyIiIn705lf7+P5sCe3iG/DWQz3chRiXafd05Oe3tCApph7G8238DULMvHhvZ/q1acTEf29ld85ZduecdT/nzo7xjL29Fa1i65N9upCjZ4ooKbNTanfSsmE9OjWJqNZzlNrFZDTw85uTmP75Tt5bncXwG5u5v/ZEvGnQoEHu/3fp0oXevXvTvHlzPvroo0sWSarLpEmTmDBhgvux1WolMTGRlJQUwsPD/RZXdbHZbKSnpzNw4EDMZk1lea2UN88ob1WnnHmmuvNm3XCEDw/sJC4ujsGDu/v89XzF13nbnXOWmdsyCLIEM3hwf68fv7rp+9Nzyp1nfJk3V3e4t6kYIyIiIn5RVGrns8xjAEy+uwPhl1i/xWAwcEOj+pd8fmrHeLo2jeTzrccIswQRHxFM69gGJEZfuHO7TVwD2sQ18M0JSJ31015NmZX+Hfu/P8fXe7/ntrax/g5JAkBkZCRt2rRh3759DBw4kNLSUvLy8ip0x+Tm5rrXmImPj2f9+vUVjpGbm+ve5/rXte3iMeHh4Zct+AQHBxMcHFxpu9lsDqiLA4F2vt6ivHlGeas65cwz1ZU3k6l8mlej0VgnPk++yltosAUAm91ZJ/Lkou9Pzyl3nvFF3nz1eTBefYiIiIiI9y3edpyzJWU0iw6jT1KMR8eIjwjh8b4tebB3M25vF1ehECPiqQYhZn52YyIA763O8nM0EigKCgrYv38/jRs3pmfPnpjNZpYvX+7ev2fPHrKzs0lOTgYgOTmZbdu2ceLECfeY9PR0wsPD6dChg3vMxcdwjXEdQ0RExBecaEH6axF8flYAm93h50hEpLqoGCMiIiJ+8dHGw0B5F4KmgZKa5uc3twBg1d6TnCwoufJgEQ/89re/5euvv+bgwYOsWbOGe++9F5PJxPDhw4mIiGDUqFFMmDCBr776ik2bNvHoo4+SnJxMnz59AEhJSaFDhw48/PDDbNmyhaVLl/Lcc88xZswYd2fLk08+yYEDB3j22WfZvXs3b731Fh999BHjx4/356mLiEiAMOhP/Csym8ovy5aWqRgjEihUjBEREZFqd/DkOdZlncZogGE9m/o7HJFKEqPDaBdfPsXd+qzTfo5G6qIjR44wfPhw2rZty09/+lNiYmJYu3YtjRo1AmDWrFncddddDBs2jH79+hEfH89//vMf9/NNJhOff/45JpOJ5ORkHnroIR555BGmTZvmHpOUlMSiRYtIT0+na9euzJw5k3feeYfU1NRqP18REQkcTjXGXBPXepllDicOh5ImEgi0ZoyIiIhUu483lXfF9GvTiMYR/luoWuRK+rSMYXfOWdYeOMXgzo39HY7UMR988MEV94eEhDB79mxmz5592THNmzdn8eLFVzxO//792bx5s0cxioiIXA8Dao25ErPpQn5K7Q5CjCY/RiMi1UGdMSIiIlKtyuwO/rXpCAA/7ZXo52hELq9Py2gA1h445edIRERERGoP9XhcG1dnDGjdGJFAoWKMiIiIVKsVe74n11pCVJiZAe3j/B2OyGXdlBQDwHe5BVo3RkRERORanZ+nTGvGXJnZeOGyrNaNEQkMKsaIiIhItVq4PhuA+3slVrgbTKSmia5n0boxIiIiIuITRqPBPVVZqTpjRAKCroCIiIhItTmaV8SKPScAGH5TMz9HI3J1fVqWd8doqjIRERGRa+OapkydMVdnNpVfmrWVaXI3kUCgYoyIiIhUmw/XZ+Nwws03xJDUsJ6/wxG5Kq0bIyIiIuIZA6rGXI1rpoBSu93PkYhIdahyMWblypXcfffdJCQkYDAY+PTTTyvsdzqdTJ48mcaNGxMaGsqAAQPYu3dvhTGnT59mxIgRhIeHExkZyahRoygoKKgwZuvWrfTt25eQkBASExN55ZVXKsXy8ccf065dO0JCQujcuTOLFy+u6umIiIhINSmzO/hw42EAHuytrhipHbRujIiIiEjVONXkcc1cnTGl6owRCQhVLsacO3eOrl27Mnv27Evuf+WVV/jzn//MnDlzWLduHfXq1SM1NZXi4mL3mBEjRrBjxw7S09P5/PPPWblyJaNHj3bvt1qtpKSk0Lx5czZt2sSrr77KlClTmDt3rnvMmjVrGD58OKNGjWLz5s0MHTqUoUOHsn379qqekoiIiFSD5btPkGstoWF9Cykd4v0djsg10boxIiIiIh5SY8xVWVzFGK0ZIxIQqlyMGTRoEC+88AL33ntvpX1Op5PXX3+d5557jnvuuYcuXbrwt7/9jWPHjrk7aHbt2sWSJUt455136N27N7feeit/+ctf+OCDDzh27BgACxYsoLS0lPfee4+OHTvywAMP8Ktf/YrXXnvN/VpvvPEGd955J8888wzt27dn+vTp9OjRgzfffNPDVIiIiIivOBxO3l2dBcBPeia62/FFagOtGyMiIiJy7ZxqjblmweffF9lUjBEJCEHePFhWVhY5OTkMGDDAvS0iIoLevXuTkZHBAw88QEZGBpGRkfTq1cs9ZsCAARiNRtatW8e9995LRkYG/fr1w2KxuMekpqby8ssvc+bMGaKiosjIyGDChAkVXj81NbXStGkXKykpoaTkwvQSVqsVAJvNhs1mu97Tr7Fc51aXz7G6KJeeU+68R7n0DuXx+rjyduxMAav253H0TBE5Z0swGw08m9qGiFBzhfF/X5vN+qzThJiNPNArQXk/T1+H16e68te9aTjzgczsM7X+c1Xb4xcREZHaQ40xV3dhmjIVY0QCgVeLMTk5OQDExcVV2B4XF+fel5OTQ2xsbMUggoKIjo6uMCYpKanSMVz7oqKiyMnJueLrXMqMGTOYOnVqpe1paWmEhYVdyynWaunp6f4Ooc5QLj2n3HmPcukdymPVFdth5xkDG743sjvjGxw/eJv17d7DPNXegav5JbcIXt1qAgzc1dTGljVfsaX6w67R9HV4fXydv++LAILYeSyfzz5fjKkWN3YVFhb6OwQRERGp49QXc+1cMwZomjKRwODVYkxNN2nSpArdNFarlcTERFJSUggPD/djZL5ls9lIT09n4MCBmM3mqz9BLku59Jxy5z3KpXcoj1e29Ug+3x7O42xxGedKyihzOHE44ciZQr7Zf7rCnVtdm0bQuUk40fUsvPvNQfZZYVVxE14Z1onCUjuPzNuIzWHllhtiePGRHhiNukfORV+H16e68udwOHl911cUlJTRuldf9xoytZGrM1xERETE1wwG/d1/NWZTeY7UGSMSGLxajImPL1+MNzc3l8aNG7u35+bm0q1bN/eYEydOVHheWVkZp0+fdj8/Pj6e3NzcCmNcj682xrX/UoKDgwkODq603Ww2B8QFkEA5z+qgXHpOufMe5dI7lMfKPt54mGf/vZUrTfXcIiaM1iEF/HZYX9omRLq392oRw6PzN/DpluNsOWol+3QhdoeT8JAg/vTTrgQHWy5/0ACmr8PrUx3565gQzrqs0+zOPUfnxGifvpYv6etMREREfE1Lxlw7i9aMEQkoXp1kISkpifj4eJYvX+7eZrVaWbduHcnJyQAkJyeTl5fHpk2b3GO+/PJLHA4HvXv3do9ZuXJlhTmt09PTadu2LVFRUe4xF7+Oa4zrdURERKTqPrqoEHPzDTEMv6kZo/u15Jf9b+Dp21sxaVA70sb3I+3Xt3BXMwctG9Wr8Px+bRrxwtBOAGSdPIfd4aRJZCh/Ht6dxhGh/jglEa/o1CQCgB3H1FkiIiIici3UF3N1WjNGJLBUuTOmoKCAffv2uR9nZWWRmZlJdHQ0zZo1Y9y4cbzwwgu0bt2apKQknn/+eRISEhg6dCgA7du358477+SJJ55gzpw52Gw2xo4dywMPPEBCQgIADz74IFOnTmXUqFFMnDiR7du388YbbzBr1iz36/7617/mRz/6ETNnzmTIkCF88MEHbNy4kblz515nSkREROqmnces/OfbI0D5HVgtYuqR2jGeiDAzp8+V8t7qLGav2IfTCY8kN2fqjztedmqBKy0CPvymZsSHh2AtttGrRTRNIlWEkdqvU5PyKW23H833cyQiIiIiNZsaY65dsDpjRAJKlYsxGzdu5LbbbnM/dq3BMnLkSObPn8+zzz7LuXPnGD16NHl5edx6660sWbKEkJAQ93MWLFjA2LFjueOOOzAajQwbNow///nP7v0RERGkpaUxZswYevbsScOGDZk8eTKjR492j7n55ptZuHAhzz33HL///e9p3bo1n376KZ06dfIoESIiInXZugOneHT+BgpL7RW2P/fpdnq1iGLToTOUnL8b62qFmGtxW7vY64pXpKbplFDeGbPzuBW7w4lJax+JiIiIXJLz/DxlWjLm6tQZIxJYqlyM6d+/v/uH6qUYDAamTZvGtGnTLjsmOjqahQsXXvF1unTpwqpVq6445v777+f++++/csAiIiIBLmP/KR6bv4Eim50bW0TRs3k0xTY7GftPsSf3LGv2nwLK7/z/Rb8buKtLYy22KfIDLRvVJ8RspLDUTtbJc7SKre/vkERERESklnOtGVNqVz+RSCCocjFGREREao/N2Wd4dP56im0O+rZuyF8f6UWI2eTev+u4lW/2naRjQgR9WkarCCNyGSajgQ6Nw/k2O48dx/JVjBERERG5Cr2zuDp1xogEFqO/AxARERHf+P5sCU/941uKbQ5+1KZRpUIMQPvG4TzetyXJN8SoECNyFZ2alE9VpnVjRERERK5O7y+uzqI1Y0QCijpjRERE6qAyu4On//ktOdZibmhUj9kjelQqxIhI1bjWjdl+1ArAtiP5hFqMtIpt4M+wROq0n89bz74TBfz1kV60bxzu73BEROQaXGF1A/kBy/nOmHnfZPHfzKNXHBtkNPKrO1ozpEvj6ghNRHxAxRgREZE66NWle1h74DT1LCb+7+Ge1A/Wr3yR69WxSfmF4O3H8nlx0U7+uiqLqDAzG/7fAIJMajgX8YXjecUcOVPEyYISf4ciIiJVpL6Yq2vZqB4AZwptnCm0XXX8+2sOqhgjUovpyoyIiEgdc/DkOeauOgDAq/d31V37Il7SOrYBFpORs8Vl/HVVFlD+xjn7dCEtG2kNGRFfiKpnBrimC1QiIlIzOFFrzLV6uE9zejSL4lxJ2RXH7TxuZepnOzldWFpNkYmIL6gYIyIiUsfMX3MQpxP6t23E4M66a0rEWyxBRto1bsDWI/mEhwQRYjZx4mwJ3+WeVTFGxEei61kAOHNOF59ERGodtcZclcFgcK9LeCXhoeU3J+SpGCNSq2k+BRERkTokv8jGRxsPAzDq1iQ/RyNS9/zuznY82LsZi37Vl76tGwHwXW6Bn6MSqbsiw8qLMadVjBERqTW0Zoz3uW9OKLThcCjBIrWVOmNERETqkI82HKaw1E6buPrc2qqhv8MRqXNubtWQm89/b7WJK++G2ZN71p8hidRp0eeLMboTWESk9jGoNcZrIsPKO2PsDidni8uIOP9YRGoXdcaIiIjUEWV2B/PXHATgsVuSMBj05kfEl9rEl6/HtFfFGBGfiTp/J/BprRkjIlJrqG/D+4KDTNSzmAA4oxsURGotFWNERETqiLSduRzNKyK6noWh3Zv4OxyROq9NXHkx5sD35ygtc/g5GpG6Ker8nb9aM0ZEpPbRvWHe5Z66U8UYkVpL05SJiIjUAQUlZby8ZDcAI3o3I8Rs8nNEInVfQkQI9YODKCgp4+Cpc+7ijIh4j6szZk/uWWZ8savKz0/pEEfP5tHeDktERK5Aa8b4RnQ9C0fzinh3VRZLo3Oq9twwCyNvbqH3iSJ+pmKMiIhIHTDtsx0cOlVIk8hQHu/b0t/hiAQEg8FA67j6bM7OY0/OWRVjRHwgISIUgO/PlvB/Xx+o8vMXbT3O6om3ezssERG5Auf5icrUGONdjSNC2HY0n0Xbjnv0/NjwYO7t3tTLUYlIVagYIyIiUsst2X6cjzYewWCAmT/tSkSoFnMUqS5t4xqwOTtP68aI+Ejb+AbMuK8zB74vqNLzim0O/r72EMfyinA4nBiNuiQoIlLdNE2Zd/1uUDtuiK1Pmb1q0+Ou3neKXcetHD1T5KPIRORaqRgjIiJSC50rKWPtgVNsOnSGBeuyAXjyRzfQp2WMnyMTCSytz3fD7FExRsRnht/UrMrPsdnLizEOJ+QV2Yg+P92ZiIj4nqYp842Wjeoz8c52VX7eK0t2s+u4lZMFWmtGxN+M/g5AREREKtube5b/fHuEtB05rDtwimKb3b0vJ7+YlFkrGfX+Rt5asZ/8Ihudm0QwfkAbP0YsEpjani/G7M2t2l37Ihd76aWXMBgMjBs3zr2tuLiYMWPGEBMTQ/369Rk2bBi5ubkVnpednc2QIUMICwsjNjaWZ555hrKysgpjVqxYQY8ePQgODqZVq1bMnz+/Gs7I/8wmI5Fh5Z2iJwtK/ByNiEhgMmiishohpn4woN+HIjWBOmNERERqEIfDyZyV+5mZ9h12x4Vbylo2rMc7I3sRHxHC43/bwNG8Iho1CKZ/m0b0ahHFXV0SsATpHguR6tYmrj4AB0+do9hm16KoUmUbNmzg//7v/+jSpUuF7ePHj2fRokV8/PHHREREMHbsWO677z6++eYbAOx2O0OGDCE+Pp41a9Zw/PhxHnnkEcxmM3/84x8ByMrKYsiQITz55JMsWLCA5cuX8/jjj9O4cWNSU1Or/VyrW0w9C3mFNk4WlGhNJxERCVgN65d3h55SZ4yI36kYIyIiUgM4nU5255zlpS928/V33wPQtWkEBoOBg6fOceDkOYbO/ob2jcPZftRKdD0L/37yZprFhPk5cpHA1qhBMJFhZvIKbez/voCOCRH+DklqkYKCAkaMGMFf//pXXnjhBff2/Px83n33XRYuXMjtt5cvPj9v3jzat2/P2rVr6dOnD2lpaezcuZNly5YRFxdHt27dmD59OhMnTmTKlClYLBbmzJlDUlISM2fOBKB9+/asXr2aWbNmBUQxpmH9YPZ/f04Xn0RE/ERrxtQMDc93xpw6p84YEX9TMUZERMSP8gttzFr2HUt35HA8vxiA4CAj0+7pyE97JWIwGPj+bAm/+PtGvs3OY13WaSwmI3Mf7qlCjEgNYDAYaBPbgPUHT/Nd7lkVY6RKxowZw5AhQxgwYECFYsymTZuw2WwMGDDAva1du3Y0a9aMjIwM+vTpQ0ZGBp07dyYuLs49JjU1laeeeoodO3bQvXt3MjIyKhzDNebi6dDqMtfFp83ZecSFh1Ta36lJOGEWvSUWEfE2pxaNqVFiznfG5FpLWJ91utL+6HoWWsXWr+6wRAKS/vIUERHxk83ZZxi7cDNH84oACDEbubVVI36b2oZ28eHucY0aBPPP0X2Y/OkOFm07zh/v60yvFtH+CltEfqBNfH3WHzzNhoNnGNqtCQbdBirX4IMPPuDbb79lw4YNlfbl5ORgsViIjIyssD0uLo6cnBz3mIsLMa79rn1XGmO1WikqKiI0NLTSa5eUlFBScuHOWavVCoDNZsNms1XxLP0rOqz87e5732Tx3jdZlfZ3S4zg49G9K2xznWNtO1d/U948o7xVnXLmmerOm93uAMDpdNTqz1Vd+XqLDC6fzjq/yMZP/y/jkmPmjezJra1irut16kq+/EG584wv8+arz4WKMSIiIn7wj7WHmPrZDmx2J81jwnh+SAdubd3wsutNBAeZePknXZhxX2eMRl3oFalJejWP5h9rs1m4LpvjeUW8cG9nmkRWvsgt4nL48GF+/etfk56eTkhI5Y4Nf5oxYwZTp06ttD0tLY2wsNrVkdmoEBLrmSixV9xe5oTTJQb2Hs9j8eLFl3xuenp6NURY9yhvnlHeqk4580x15W3PEQNgIjv7MIsXH6qW1/SluvD1dnOckX35ld9H5pdCicPAopXrsX7nnY6mupAvf1HuPOOLvBUWFnr9mKBijIiISLVbtjOX5z7dDsCQzo2ZMawz4SHma3quCjEiNc+PuyZw6FQhs7/ax1d7viflta/568he3HxDQ3+HJjXUpk2bOHHiBD169HBvs9vtrFy5kjfffJOlS5dSWlpKXl5ehe6Y3Nxc4uPjAYiPj2f9+vUVjpubm+ve5/rXte3iMeHh4ZfsigGYNGkSEyZMcD+2Wq0kJiaSkpJCeHj4JZ9Tkz11iW0HT51j4OvfYAwyM3hwxbVzbDYb6enpDBw4ELP52n43i/LmKeWt6pQzz1R33rJWHGDx4X00a9aMwYM7+Pz1fKUufb0Nvsz2cR9uZdH2HNq178Dg5ObX9Rp1KV/VTbnzjC/z5uoO9zYVY0RERKpR1slzjP8wE4CH+zRn2j0dNaWRSC1nNBr49YDWDOkSz8R/b2PToTM8/v5G/j7qJno215SCUtkdd9zBtm3bKmx79NFHadeuHRMnTiQxMRGz2czy5csZNmwYAHv27CE7O5vk5GQAkpOTefHFFzlx4gSxsbFA+V2B4eHhdOjQwT3mh50f6enp7mNcSnBwMMHBwZW2m83mOnNxIMRSPne+3eG87DnVpfOtTsqbZ5S3qlPOPFNdeTMaje5/68LnqS5/vVlcMzMYvPe5qsv58jXlzjO+yJuvPg9GnxxVREREKjlVUMIv/r6RsyVl3Ngiiufv6qBCjEgd0iq2AQse703f1g0pLLXz8/c2sO1Ivr/DkhqoQYMGdOrUqcJHvXr1iImJoVOnTkRERDBq1CgmTJjAV199xaZNm3j00UdJTk6mT58+AKSkpNChQwcefvhhtmzZwtKlS3nuuecYM2aMu5jy5JNPcuDAAZ599ll2797NW2+9xUcffcT48eP9efp+F2Qq/91bZtcC0yIivqS3OjWf6fzMCzb9ThSpFirGiIiI+FCutZgZi3cx6I1V9HpxGd/lFhDbIJjZD/bAEqRfwyJ1TYjZxNyHe3FTUjRnS8r45cJN/g5JaqlZs2Zx1113MWzYMPr160d8fDz/+c9/3PtNJhOff/45JpOJ5ORkHnroIR555BGmTZvmHpOUlMSiRYtIT0+na9euzJw5k3feeYfU1NRLvWTAcF14KnM4/ByJiEjd5NR1/Voj6PzvRLt+J4pUC01TJiIidVpBSRkTPsykaVQYz9/V3uedKCVldvIKbZwqKOWTzUf4W8YhSsou/GHbNq4Br/ykC7HhNWvBZhHxnlCLib8+3Iuu09I4fLqI/CIbEaGabkCubMWKFRUeh4SEMHv2bGbPnn3Z5zRv3vyyC9C79O/fn82bN3sjxDoj6Pz0OQ4nOBxOrccmIuJlTsqrMfrpWvNduEFBFTSR6qBijIiI1FlOp5OJ/95K2s7yxYs7JoQzrGfTy463O5xsOZKHrcyBJchIXHgICZGXXuDYdfz8IhtHzhSxau9Jlu3K5dvsM5XuBOvVPIpHbm5Bn5bRxDZQEUYkEESEmWlY38LJglKOnCkkIjTC3yGJyHmuacqg/OKTRcUYERGf0DRlNZ/ZVH6DgqbuFKkeKsaIiEid9d43B1m09bj78ZTPdnBzqxgaR1QusHyXe5Zn/rWVLYfzKmwf2CGOJ390A5FhZjL2n2Jzdh7H8orIsRZzPL+IYlvldm6T0UBkqJkbYuvzy/438KM2jbQ2jEgAahIVdr4YU0THBBVjRGqKoIuKL3bdCSwi4nWapqz2UGeMSPVSMUZEROqkDQdPM2PxLgCev6sD/9tyjC2H83j2X1v53aB2rN1/kpVZRrYu2UNxmZOPNx6h1O6gnsVEXEQIpWUOjuYVkb4zl/TznTWXE13PQucmEQzsEMdt7WJpHB6iKU9EhKZRoWw5nMeRM0X+DkVELmK66He0zeEgFJMfoxERqbsMmqisxtOaMSLVy+vFmBYtWnDo0KFK23/5y18ye/Zs+vfvz9dff11h3y9+8QvmzJnjfpydnc1TTz3FV199Rf369Rk5ciQzZswgKOhCuCtWrGDChAns2LGDxMREnnvuOX7+8597+3RERKQWKiq1M/7DTMocTn7cNYHHbmnBj9o0YsifV7Fq70lW7V19fqSRr3Mu/M66o10sL97bmfiI8qnE9p0oYO7K/Xyy+SgGDHRvFknvljG0bFiP+IgQEiJCiQ0PJsSsizgiUlnTqPIuvCNnCv0ciYhczHx+zRgAu6ZlERHxOv1krT1cU3fa9PtQpFp4vRizYcMG7Ha7+/H27dsZOHAg999/v3vbE088wbRp09yPw8LC3P+32+0MGTKE+Ph41qxZw/Hjx3nkkUcwm8388Y9/BCArK4shQ4bw5JNPsmDBApYvX87jjz9O48aNSU1N9fYpiYhILfP2in0cOVNEQkQIM+7rjMFgoFVsff7fkPZM/u8O6llM9GgWibnwe1q2bInDaeDGFlHc2Sm+wnRirWLr88pPujLlxx0xGgwquohIlTSNKv8bV50xIjWL0WjAYCifRkfTsoiI+I5maq75TOdvUNC0nSLVw+vFmEaNGlV4/NJLL3HDDTfwox/9yL0tLCyM+Pj4Sz4/LS2NnTt3smzZMuLi4ujWrRvTp09n4sSJTJkyBYvFwpw5c0hKSmLmzJkAtG/fntWrVzNr1iwVY0REAtzBk+eY8/UBACbf3YF6wRd+1T2S3II7O8UTHWbB6bCzePFiBqe2wWw2X/GYYRbN6ikiVXehM0bFGJGaJshowGZ3UqZpWUREvE+LxtQaQVozRqRa+fTqUmlpKf/4xz+YMGFChTuNFyxYwD/+8Q/i4+O5++67ef75593dMRkZGXTu3Jm4uDj3+NTUVJ566il27NhB9+7dycjIYMCAARVeKzU1lXHjxl0xnpKSEkpKStyPrVYrADabDZvNdr2nW2O5zq0un2N1US49p9x5j3J5eU6nkz/8dzuldgd9W8Vwe5uYSnmKCjHhdNiVx+uk/HmH8nh9anr+4uuXF3qPnCmsMTHWlDhE/C3IaMRmt1OmaVlERHxGjTE1n2uasjK7bk4QqQ4+LcZ8+umn5OXlVVjL5Tj823UAAQAASURBVMEHH6R58+YkJCSwdetWJk6cyJ49e/jPf/4DQE5OToVCDOB+nJOTc8UxVquVoqIiQkNDLxnPjBkzmDp1aqXtaWlpFaZKq6vS09P9HUKdoVx6TrnzHuXyglPFsPmUgZ1njOw/a8BkcNKvfi5ffPHFVZ+rPF4f5c87lMfrU1PzV2oHCOJscRn/+t9iwmpAk11hodavEYGLFyxWMUZExNv0k7X20O9Dkerl07eE7777LoMGDSIhIcG9bfTo0e7/d+7cmcaNG3PHHXewf/9+brjhBl+Gw6RJk5gwYYL7sdVqJTExkZSUFMLDw3362v5ks9lIT09n4MCBV52KR65MufSccuc9ymVFOdZihvxlDdbiMve236a05ee3trji85TH66P8eYfyeH1qQ/5e2rGCU+dK6XDjrXRo7P+/N12d4SKBzuS6E1jTlImI+IxBi8bUeK41YzRNmUj18Fkx5tChQyxbtszd8XI5vXv3BmDfvn3ccMMNxMfHs379+gpjcnNzAdzrzMTHx7u3XTwmPDz8sl0xAMHBwQQHB1fabjaba+wbeG8KlPOsDsql55Q771Euy338bRbW4jJaxITx2K1J/KhNI5rH1Lvm5yuP10f58w7l8frU5Pw1jQ7j1LlScs7a6NrM/zHW1DyJVDfNkS8i4jtaMqb2MOvmBJFqZfTVgefNm0dsbCxDhgy54rjMzEwAGjduDEBycjLbtm3jxIkT7jHp6emEh4fToUMH95jly5dXOE56ejrJyclePAMREanpbHYH/1yfDcBvUtrySHKLKhViRER8rWlU+Y1CR84U+TkSEblYkOtOYK0ZIyIiAczkujlBvw9FqoVPijEOh4N58+YxcuRIgoIuNN/s37+f6dOns2nTJg4ePMj//vc/HnnkEfr160eXLl0ASElJoUOHDjz88MNs2bKFpUuX8txzzzFmzBh3V8uTTz7JgQMHePbZZ9m9ezdvvfUWH330EePHj/fF6YiISA21dEcO358toVGDYFI7xvs7HBGRSi4UY7RWi0hNYlJnjIiIzzjPrxqjWcpqPq0ZI1K9fFKMWbZsGdnZ2Tz22GMVtlssFpYtW0ZKSgrt2rXjN7/5DcOGDeOzzz5zjzGZTHz++eeYTCaSk5N56KGHeOSRR5g2bZp7TFJSEosWLSI9PZ2uXbsyc+ZM3nnnHVJTU31xOiIiUkP9PeMQAMNvTMQS5LNmTxERjzWNCgPUGSNS0wSZXBefNC2LiIi3aZqy2iNIa8aIVCufrBmTkpKC8xI/eRMTE/n666+v+vzmzZuzePHiK47p378/mzdv9jhGERGp3fbknGVd1mlMRgPDezfzdzgiIpekacpEaqYgTcsiIuJzBtQaU9MFac0YkWql24hFRKRW+vvagwAMbB9H44hQ/wYjInIZTSM1TZlITaQ7gUVEfEc/WWsPrRkjUr1UjBERkVrnaF4RH204AsDIm1v4NxgRkStocr4z5mxxGflFNj9HIyIuWjNGRMT3tGZMzee6OUFrxohUDxVjRESk1nk9/TtK7Q6SW8bQp2W0v8MREbmsMEsQMfUsgLpjRGoSs9aMERHxGa0ZU3u4pu20qRgjUi1UjBERkVpl34mz/Pvb8q6YZ+9si0G3W4lIDad1Y0RqHldnjE3TsoiI+IzeqdV8Jt2cIFKtVIwREZFa5U9Lv8PhhJQOcXRvFuXvcERErqppVBigYoxITaJpWUREfMepVWNqDbNrDTXdnCBSLVSMERGRWiPzcB5LduRgNMBvU9v6OxwRkWvi6ow5fFrTlInUFFozRkTE9zSJQc2n34ci1UvFGBERqRXsDid/+O92AO7r0ZQ2cQ38HJGIyLVJiCwvxuTkF/s5EhFxCTo/LUuZXdOyiIh4na7r1xpB7mnK9EkTqQ4qxoiISK3wwYZsthzJp0FwEM/eqa4YEak94sJDAMixqhgjUlME6U5gERGf0/qeNd+F34e6OUGkOqgYIyIiNd6pghJeWbIHgN+ktCG2QYifIxIRuXbxEeU/s06oGCNSY5i0ZoyIiM/oJ2vtEaQ1Y0SqVZC/AxAREbmc0jIH3+We5c0v95FfZKND43Ae6tPc32GJiFRJXHgwACfOluBwODEadZeoiL+ZNU2ZiIjP6S+emk9rxohULxVjRESkxiksLWPG4t18uPEwpWUXLpJMH9qJIJOaOkWkdmlUPxiDofxN7slzJeruE6kBdPFJRMR3nM7zP1tVjanxzFozRqRaqRgjIiI1yvaj+fzqg80c+P4cAOEhQXRpGsn9vZrSs3mUn6MTEam6IJORhvWD+f5sCSesKsaI1ASuOfJ18UlExPuc+tFaa7huTrCpU1SkWqgYIyIiNcbSHTmMXfgtNruTuPBgXv1JV/q2bqiFH0Wk1osPD+H7syXk5BfTqUmEv8MRCXiuTlub5sgXEfEZg1pjarwgraEmUq1UjBERkRohY/8pnv7nZmx2JwPax/HqT7oQVc/i77BERLwiLjyEbUfzyT1b7O9QRISLO2N0J7CIiLfpsn7tEWTStJ0i1UnFGBER8bvtR/N54m8bKS1zkNIhjrdG9NDaMCJSp8SFBwOQm69ijEhNcGFaFl18EhHxFU1wUPO5bk4o0zRlItVCxRgREfELu8PJN/tO8p9vj7BkRw7FNge9k6L58/DuKsSISJ0TH16+TkyOVcUYkZpAa8aIiPiO1oypPVw3Jzic4HA4MRpVQRPxJRVjRESkWjmdTpbuyGVm2h72nihwb+/VPIq/juxFiNnkx+hERHwj7nwxJtda4udIRAQurBnz5lf7GNo9gVaxDfwckYhI3aPL+jXfxTdCLlyfzfCbmrkLNCLifbr1WEREqs2+EwUMfWsNT/5jE3tPFNAgJIiH+zTn0zG38PGTyYSHmP0dooiIT8RFuIox6oyRcm+//TZdunQhPDyc8PBwkpOT+eKLL9z7i4uLGTNmDDExMdSvX59hw4aRm5tb4RjZ2dkMGTKEsLAwYmNjeeaZZygrK6swZsWKFfTo0YPg4GBatWrF/Pnzq+P0arz6wRfuS/zdv7f5MRIRkbrHqVVjao3gICPm8+vGPPfpdtZlnfJzRCJ1mzpjRETEIzuPWck4cIoQs5H6wUHE1AumaVQojSNDCA6q3N2ybGcu4z7MpKCkjDCLiVG3JvF435ZEhKoAIyJ1X3y4ijFSUdOmTXnppZdo3bo1TqeT999/n3vuuYfNmzfTsWNHxo8fz6JFi/j444+JiIhg7Nix3HfffXzzzTcA2O12hgwZQnx8PGvWrOH48eM88sgjmM1m/vjHPwKQlZXFkCFDePLJJ1mwYAHLly/n8ccfp3HjxqSmpvrz9P1uRO9mrPjue7YczuNMYam/wxERqZO0ZkzNF2I28af7u/LrDzIByCu0+TcgkTpOxRgREbkmTqeTY/nFbDx4mgVrs1l/8PQlxxkNMKhTY/7fkPYkRIaSX2Rj7sr9vLViP04n3JQUzZvDuxN7/sKkiEggiAsPBuBMoY1im11TMgp33313hccvvvgib7/9NmvXrqVp06a8++67LFy4kNtvvx2AefPm0b59e9auXUufPn1IS0tj586dLFu2jLi4OLp168b06dOZOHEiU6ZMwWKxMGfOHJKSkpg5cyYA7du3Z/Xq1cyaNSvgizGx4SFMvqs9w97O0LoxIiJepjVjapd7ujXhww2HWbP/FGX6nSjiUyrGiIjIZZ0rKWPZrlwWbT3OhoOnOXPRXTImo4F+rRtiNhk5W1zGyYISjpwposhmZ9G243y15wQpHeJI35nLuVI7AI8kN+f5uzpgNmmWTBEJLBGhZoKDjJSUOThhLaFZTJi/Q5IaxG638/HHH3Pu3DmSk5PZtGkTNpuNAQMGuMe0a9eOZs2akZGRQZ8+fcjIyKBz587ExcW5x6SmpvLUU0+xY8cOunfvTkZGRoVjuMaMGzeuuk6tRjMZy/8e0YUnERHfMGjVmFrDtU6M3eHwcyQidZuKMSIi4lZss/OvTUfYdiSf706cZddxK8W2C3+MBRkNtIqtz8AOcYzo3Zz4iIrdLU6nk53HrfzhvzvYeOgMn2YeA6BtXAN+dUdrhnRpXK3nIyJSUxgMBuIjQjh0qpDcs8UqxggA27ZtIzk5meLiYurXr88nn3xChw4dyMzMxGKxEBkZWWF8XFwcOTk5AOTk5FQoxLj2u/ZdaYzVaqWoqIjQ0NBKMZWUlFBSUuJ+bLVaAbDZbNhsdWzqEkf5zSI2u8N9bj/8V66N8uYZ5a3qlDPPVHfeXBf0HQ5Hrf5cBdLX2/laDMWlZR6fbyDly9uUO8/4Mm+++lyoGCMiIgBk7D/F//tkGwdOnquwvUVMGHd1SWBAhzjaxTe44tQ6BoOBjgkRfPSLZP797RFW7T3Jvd2b0L9tIwyaMFhEAlxcg/JiTE6+1o2Rcm3btiUzM5P8/Hz+9a9/MXLkSL7++mu/xjRjxgymTp1aaXtaWhphYXWriHjsHEAQhUXFLF68uMK+9PR0v8RU2ylvnlHeqk4580x15e3QQSNgZN++fSwu/a5aXtOXAuHr7fT35Z+zzC1bCcvZcl3HCoR8+Ypy5xlf5K2wsNDrxwQVY0REAp7T6WTa5zuZ981BABo1CGb4jYm0iW9Au/gG3NCofpULKUajgft7JXJ/r0QfRCwiUjvFne8mzLWqGCPlLBYLrVq1AqBnz55s2LCBN954g5/97GeUlpaSl5dXoTsmNzeX+Ph4AOLj41m/fn2F4+Xm5rr3uf51bbt4THh4+CW7YgAmTZrEhAkT3I+tViuJiYmkpKQQHh5+fSdcw+w7UcDLW9cQZLYwePBtQPldkOnp6QwcOBCz2eznCGsP5c0zylvVKWeeqe68bfx8F+QcpnXrVgy+o5XPX89XAunrbXF+JtvOnKB9x04Mvsmz9/GBlC9vU+4848u8ubrDvU3FGBGRAPfu6ix3IWZE72Y8e2c7IkL1y19ExNviGgQDKsbI5TkcDkpKSujZsydms5nly5czbNgwAPbs2UN2djbJyckAJCcn8+KLL3LixAliY2OB8rsCw8PD6dChg3vMpTo+XMe4lODgYIKDgyttN5vNde7iQEiwBYAyu7PSudXF860OyptnlLeqU848U115M5xfk8tkNNaJz1MgfL2Zg8pnwHBiuO5zDYR8+Ypy5xlf5M1XnwcVY0REAtia/SeZ8cVuAP5wdwcevSXJzxGJiNRdrnW2cqwlVxkpgWDSpEkMGjSIZs2acfbsWRYuXMiKFStYunQpERERjBo1igkTJhAdHU14eDhPP/00ycnJ9OnTB4CUlBQ6dOjAww8/zCuvvEJOTg7PPfccY8aMcRdTnnzySd58802effZZHnvsMb788ks++ugjFi1a5M9TrzGCzk+QX+Zw+jkSEZE6SlNV1xqu34l2/U4U8SkVY0REAtSRM4WMXbgZu8PJfd2b8PObW/g7JBGROi0uXNOUyQUnTpzgkUce4fjx40RERNClSxeWLl3KwIEDAZg1axZGo5Fhw4ZRUlJCamoqb731lvv5JpOJzz//nKeeeork5GTq1avHyJEjmTZtmntMUlISixYtYvz48bzxxhs0bdqUd955h9TU1Go/35ooyKQLTyIivuDUj9VaJ8hU3s2kGxREfEvFGBGRALTlcB6j/76R0+dK6ZgQzh/v61zldWFERKRqVIyRi7377rtX3B8SEsLs2bOZPXv2Zcc0b9680jRkP9S/f382b97sUYx1nen8XcA2h8PPkYiI1E16h1l7uLtF7fqdKOJLRm8fcMqUKRgMhgof7dq1c+8vLi5mzJgxxMTEUL9+fYYNG1ZpUcns7GyGDBlCWFgYsbGxPPPMM5SVlVUYs2LFCnr06EFwcDCtWrVi/vz53j4VEZE66dPNR7n//zLItZbQKrY+cx/pRYjZ5O+wRETqvPiLijFO3TIq4ndB59c0cDrBoTuBRUS8xol+ptY2Jk3dKVItvF6MAejYsSPHjx93f6xevdq9b/z48Xz22Wd8/PHHfP311xw7doz77rvPvd9utzNkyBBKS0tZs2YN77//PvPnz2fy5MnuMVlZWQwZMoTbbruNzMxMxo0bx+OPP87SpUt9cToiInWC3eFkxhe7GPdhJqVlDu5oF8snv7yZJpGh/g5NRCQgxIaXr+NRbHNgLSq7ymgR8TXXNGWgi08iIr6gyRdqD/P5aco0daeIb/lkmrKgoCDi4+Mrbc/Pz+fdd99l4cKF3H777QDMmzeP9u3bs3btWvr06UNaWho7d+5k2bJlxMXF0a1bN6ZPn87EiROZMmUKFouFOXPmkJSUxMyZMwFo3749q1evZtasWZr/WETkEqzFNn79z818ted7AH7Z/wZ+k9LWffeLiIj4XojZRGSYmbxCGznWYiLCzP4OSSSgBV30d5AuPomIeI8agGsfdcaIVA+fFGP27t1LQkICISEhJCcnM2PGDJo1a8amTZuw2WwMGDDAPbZdu3Y0a9aMjIwM+vTpQ0ZGBp07dyYuLs49JjU1laeeeoodO3bQvXt3MjIyKhzDNWbcuHFXjKukpISSkhL3Y6vVCoDNZsNms3nhzGsm17nV5XOsLsql55Q776lqLo/nFzPqb5vYe+IcwUFGZtzbkbu7NMZhL8Nh92WkNZu+Jq+P8ucdyuP1qY35i2sQTF6hjaNnCmgZE1Ltr1+bciXiaxfflGJzOAhF07aKiHiTQavG1BpaM0akeni9GNO7d2/mz59P27ZtOX78OFOnTqVv375s376dnJwcLBYLkZGRFZ4TFxdHTk4OADk5ORUKMa79rn1XGmO1WikqKiI09NJT7syYMYOpU6dW2p6WlkZYWJhH51ubpKen+zuEOkO59Jxy5z3XksucQnh7l4m8UgMRZidPtCvFdGQzi49oIV8XfU1eH+XPO5TH61Ob8tfYaCQkGrZtWs/Z76r/9QsLC6v/RUVqKNeaMQB2u+4EFhHxFv1ErX3UGSNSPbxejBk0aJD7/126dKF37940b96cjz766LJFkuoyadIkJkyY4H5stVpJTEwkJSWF8PBwP0bmWzabjfT0dAYOHIjZrOkwrody6TnlznuulsuiUjtbjuSz8dAZ3s/MJq/URsuGYcwb2ZMErQ/jpq/J66P8eYfyeH1qY/4G+/n1XZ3hIlJ+4clgKJ9ORxefRES8T2vG1B5BWjNGpFr4ZJqyi0VGRtKmTRv27dvHwIEDKS0tJS8vr0J3TG5urnuNmfj4eNavX1/hGLm5ue59rn9d2y4eEx4efsWCT3BwMMHBwZW2m83mWvMG/noEynlWB+XSc8qd9/wwl3aHk/fXHGRm2h7OlV6Yf6xbYiTv/fxGoutZ/BFmjaevyeuj/HmH8nh9lL9rpzyJVBRkNGCzOylzaFoWERFvca0Zo1pM7eGapsymTlERn/J5MaagoID9+/fz8MMP07NnT8xmM8uXL2fYsGEA7Nmzh+zsbJKTkwFITk7mxRdf5MSJE8TGxgLlU0+Eh4fToUMH95jFixdXeJ309HT3MURE6rKzxTb2W+Hva7M5kldCvWATEaFmPttyjC1H8gGIDw+hV4soereM4Sc9mhJq0RzoIiIiIj9kchVjdPFJREQCmGuaMrtuThDxKa8XY377299y991307x5c44dO8Yf/vAHTCYTw4cPJyIiglGjRjFhwgSio6MJDw/n6aefJjk5mT59+gCQkpJChw4dePjhh3nllVfIycnhueeeY8yYMe6ulieffJI333yTZ599lscee4wvv/ySjz76iEWLFnn7dERE/GbjwdN8sOEwZXYHZpOR/CIbu3KsHD5dBATBjt2VntMgJIhJg9rzwI2JGI26D0lERETkSsxGI8U4NC2LiIhXlf9M1TRltYfZpDVjRKqD14sxR44cYfjw4Zw6dYpGjRpx6623snbtWho1agTArFmzMBqNDBs2jJKSElJTU3nrrbfczzeZTHz++ec89dRTJCcnU69ePUaOHMm0adPcY5KSkli0aBHjx4/njTfeoGnTprzzzjukpqZ6+3RERKqV0+nk1LlSXlmym482HrnsuEiLk+5JjWgdF06xzc7pc6XE1LMw5rZWxIaHVGPEIiIiIrWXyX3xSXcCi4h4i1PX82sdk7F8zRh1ior4lteLMR988MEV94eEhDB79mxmz5592THNmzevNA3ZD/Xv35/Nmzd7FKOISE3ybfYZXl+2l3UHTlFSVvFCwLAeTWnfuAGldgchQSbaNW5A64ZhrFmRzuDBPTT3v4iIiMh1cM2RrzuBRUS8z6DWmFojyD1NmX4fiviSz9eMERGRS/v+bAm/+/dWlu8+UWlfh8bhTB/akZ7Noyvts9ls1RGeiIiISJ3nmiNfdwKLiHiPOmNqH/fvQ3WKiviUijEiIl50PL+IV5bsITwkiLG3t6ZRg+DLjnUVYoyG8g6YUX2TiKkXjCXISHhIkO4iEhEREfGxoPPTsuhOYBERCWSuNWP0+1DEt1SMERHxAqfTyb82HWHa5zs5W1wGwL+/PcrTt7fiZzcmEhlmqTD+q90nWL77BEFGA5/88hY6N43wR9giIiIiAS1Ia8aIiHidE13Qr21ca8bY1Ckq4lMqxoiIeCC/yMaOo/lsO/+x9Ug+2acLAeiaGInD4WTb0XxmfLGbGV/spk1cfW5t1Yhf/KglkWFmpn62A4DHbk1SIUZERETETzRNmYiI72iyh9pDa8aIVA8VY0REquD0uVKe/Mcm1medrrTPYjIybmBrRvdtidFg4F/fHmHuygPsO1HAd7nlHx9syKZXi2gOnioktkEwT9/eyg9nISIiIiIAZk1TJiLidVozpvZRp6hI9VAxRkTkGlmLbTzy3jq2H7UC0DQqlM5NIujUJILOTSLo2jSSiDCze/xPeyXy016JnCwoYUPWaf5v5QEyD+ex8rvv4f+zd9/xVZb3/8ffZ2cnBEhCIMEgyF6CIA4cIEGtitqvRf2qrQO1oFX6U0uLA9tK1bq1+rWtWis466oDiaDgQBAEkT1kkwGE7OTM+/dHcg5EVsaZyev5ePDQc+7r3Pd1f+6ccd+f+3Ndkqad10fJcbbDbgsAAACh56+McZOMAYCgM4nSmFhhpVIUCAuSMQCg+rshiyrqZDOb5LBalBRnDZycS1KNy6PrXvpWq3ZVqGOiXa9OOlknZCY3ad2dkhw6d2AX5ffP0nvf79JT8zapb5cUTRjSNVS7AwAAgCawBiYs5k5gAAgW/+V8himLHf45YzzcnACEFMkYAO3a1r3VemvZTr21bKeKKuoCz8fbLOrbJVk9M5K0dV+NVu0qV43Lq5Q4q/593cgmJ2IOZjabdPHQbrp4aLdg7gIAAABaiDljAABgzhggXEjGAGh31uyu0Pvf79Zn60q0vrgy8LzVbJLXMGQYUq3bq++2l+m77WWB5Z2THXruf4epX3ZKBHoNAACAYLNxJzAABJ1/zhgKY2KHv1LU7aVSFAglkjEA2pW3lu3U7/6zMnDCbTZJp/XqrF8Mz9HYfhmyW8xyeX3aUVqj1bsrtLmkSt3SEzQ0J03Hd06S2czPSQAAgLYiUBlDMgYA0I5ZqIwBwoJkDIB2wTAM/e3zzXr4k/WSpDNO6KxLTuyqM07orLQEe6O2DqtFPTOS1TOj+UORAQAAIHYwZwwABJ/RMGsMc8bEDmtDpSjJGCC0SMYAaLMMw9DGkirNW1uiuWuKtLxhyLGbzjhed+b3psoFAACgnbMyZwwABB8fqTHHf3MClaJAaJGMAdAm7Nxfo4I1xaqs86ja5dHmkiot27Zf+2vcgTYWs0l3n99Xvzw1L4I9BQAAQLSwMGcMAISMiVljYsaBmxOoFAVCiWQMgJjm9Rl68astemTuBtW6vYcsd1jNOrlHR43tl6mxfTPUJTU+Ar0EAABANLIyZwwABB2fqLGHOdSA8CAZAyDmlNe69c2P+7RqV7nmryvR6t0VkqQhOWnq2yVZCXaruqTGaVj3DuqfnSq71RzhHgMAACAaWfxzxnAnMAAEHXPGxA7mjAHCg2QMgJiyc3+NLvnb1yqpdAaeS3ZYNe28vpp4Ug7zwAAAAKDJbA2/Heev36PyWo+8Pq827jBp82ebZTFbmrSO9CS7LhveTQ5r09oDQFtnGFzQjzX+OWOqnB498enGFq3D5/PKXh3MXgFtD8kYADGj2unR9f9aqpJKp7qkxum0np00sFuqxg/IUkZyXKS7BwAAgBiTFFd/Srxwwx4t3LCn4VmLPt65uVnrSYu36YLB2UHuHQAA4ZHkqP8+dHp8euzTDS1eT1a8RZOC1SmgDSIZAyAm+HyGfvPaCq0rqlSnJIf+c/Mpyk5j/hcAAAC03KTTj5fDalFdw9yDPp9P27dvV25urszmYw91+9Wmvdq6r0Z7q5zHbAsA7YW/LsbEOGUxIzMlTjMvGahVu8pb9PryWrc+WFmoSneQOwa0MSRjAMSE/1v4oz5dWyy71aznrx5GIgYAAACtltsxQXf/rF/gsdvt1kcfbdV55/WTzWY75uv/35vfa+u+GtU2JHMAAIhVl4/IbfFrd5TW6IOVhXIzBRtwVMxqDSDqeX2GXl60VZI048L+OjG3Q2Q7BAAAAEiKt9XPE1PH1ScACPBPGUNdTPvhsNVfYnb7mDMIOBqSMQCi3jc/7lNheZ1S42265MSuke4OAABAq8ycOVMnnXSSkpOTlZGRoQkTJmj9+vWN2tTV1Wny5Mnq2LGjkpKSdOmll6q4uLhRm+3bt+v8889XQkKCMjIydMcdd8jj8TRq8/nnn+vEE0+Uw+FQz5499dJLL4V699qVuIaLT3VUxgAA2jH/zQmGTHJ5uEEBOBKSMQCi3n++2ylJumBwFzmslgj3BgAAoHUWLFigyZMn65tvvlFBQYHcbrfGjRun6urqQJvbb79d//3vf/Xmm29qwYIF2r17ty655JLAcq/Xq/PPP18ul0tff/21/vWvf+mll17SPffcE2izZcsWnX/++TrrrLO0YsUK3Xbbbbr++uv1ySefhHV/27IDlTEkYwDA78CcMRHtBsIoznbgWk0dyRjgiJgzBkBUq3Z6NGdVkSTpkhO7Rbg3AAAArTdnzpxGj1966SVlZGRo2bJlGj16tMrLy/XPf/5Ts2fP1tlnny1JevHFF9W3b1998803OvnkkzV37lytWbNGn376qTIzMzVkyBD98Y9/1F133aX77rtPdrtdzz33nPLy8vTII49Ikvr27asvv/xSjz32mPLz88O+322Ro+HiU62LZAwAoP2yWcyymk3y+AzmUQOOgsoYAFHtk9VFqnF5ldcpUUNz0iLdHQAAgKArLy+XJKWnp0uSli1bJrfbrbFjxwba9OnTR7m5uVq0aJEkadGiRRo4cKAyMzMDbfLz81VRUaHVq1cH2hy8Dn8b/zrQeoHKGO4CBoAA/5whFMa0Lw6G7gSOicoYAFHt7e92SZIuGdpVJmqcAQBAG+Pz+XTbbbfp1FNP1YABAyRJRUVFstvtSktLa9Q2MzNTRUVFgTYHJ2L8y/3LjtamoqJCtbW1io+PP6Q/TqdTTqcz8LiiokKS5Ha75Xa7W7GnscG/j03d14brTqpxto/4HElz44Z6xK35iFnLhDtuPl99gtrr88X0seLvrXnirBZVO72qqnURs2bib61lQhm3UB0LkjEAotaPe6r01ea9kqQJQ7tGuDcAAADBN3nyZK1atUpffvllpLsiSZo5c6ZmzJhxyPNz585VQkJCBHoUGQUFBU1qt36PSZJFOwuL9dFHH4W2UzGgqXFDY8St+YhZy4QrboWFZklmrVm9Wh+VrgrLNkOJv7emMTwWSSYt/GqRtidHujexib+1lglF3GpqaoK+TolkDIAotbawQte8sESGIZ3as6Ny0tvPyT8AAGgfpkyZog8++EALFy5Ut24H5sbLysqSy+VSWVlZo+qY4uJiZWVlBdosWbKk0fqKi4sDy/z/9T93cJuUlJTDVsVI0rRp0zR16tTA44qKCuXk5GjcuHFKSUlp+c7GCLfbrYKCAp1zzjmy2WzHbG9eXaxXNn2vpNR0nXfeiDD0MDo1N26xZvp7azR3TfEhz+emJ+jlXw1Tgr1ll1baetxCgZi1TLjjNqfie2lfsfr376/zTs4N+fZChb+35nly05cq3VOjQUOH67QTMiLdnZgSK39rK3eW6zdvrFS109PoeavZpN+e00uXnhjeG6lDGTd/dXiwkYwBEHGVdW4t2LBHP+wsV5LDqjibRU/O36jKOo/6ZCXr0cuGRLqLAAAAQWMYhm655Ra98847+vzzz5WXl9do+bBhw2Sz2TRv3jxdeumlkqT169dr+/btGjVqlCRp1KhR+vOf/6ySkhJlZNRf8CgoKFBKSor69esXaPPTao2CgoLAOg7H4XDI4XAc8rzNZovqiwPB1tT9TYq3S5KcXl+7is+RtMW/k4o6t15fuvOwy/bXlOuH3dU6rVenVm2jLcYt1IhZy4QrbmZz/RiOVqulTRwn/t6aJt5Wf5nZbZiIVwtF+9/aB6uKtXN/7WGXvb2iUBNHHhfeDjUIRdxCdRyCnoyZOXOm3n77ba1bt07x8fE65ZRT9OCDD6p3796BNmeeeaYWLFjQ6HU33nijnnvuucDj7du36+abb9Znn32mpKQkXXPNNZo5c6as1gNd/vzzzzV16lStXr1aOTk5mj59un75y18Ge5cAtJLXZ+jNZbv08hqz3tqzTAkOq8wmk+rcXlXWefT9zjK5vcYhrzvpuA76xzUnKTU+er+IAAAAmmvy5MmaPXu23nvvPSUnJwfmeElNTVV8fLxSU1N13XXXaerUqUpPT1dKSopuueUWjRo1SieffLIkady4cerXr5+uuuoqPfTQQyoqKtL06dM1efLkQDLlpptu0tNPP60777xT1157rebPn6833nhDH374YcT2va2Jt1kkSbUuJituC0qrXVq5s0xnnNA5MF/l+qJKSVJmikOzrh8ZaHvXf37Qsm37VVJZF5G+AtHM0KHn92j74homUqtz853YFiz+cZ+6pSeoa9qBauq1hfXVIneN76Nz+tXfDLRqV4Vue32F9lQ6D7seNBb0ZMyCBQs0efJknXTSSfJ4PPr973+vcePGac2aNUpMTAy0u+GGG3T//fcHHh88/rDX69X555+vrKwsff311yosLNTVV18tm82mBx54QJK0ZcsWnX/++brppps0a9YszZs3T9dff726dOmi/Pz8YO8WgBZatHmf/vThGq3eXSHJrHXl+w7brkenRJ3as5PcXp/2VbvUo3Oibh97guIaTnABAADaimeffVZS/U1qB3vxxRcDN5c99thjMpvNuvTSS+V0OpWfn6+//e1vgbYWi0UffPCBbr75Zo0aNUqJiYm65pprGp1j5eXl6cMPP9Ttt9+uJ554Qt26ddM//vEPzpeCyP9btc7ti3BPEAx/+Xit3li6U6f17KRzB9YP97ds235JUv/sVPXMODAJQvf0BC3btl/FFVx8Ao7EFOkOIKz4Tmw7tu+r0S+e/0aS9KcJA9Rwf4LW7K5Pxpzeq1PgO9HaUAlXXFEnwzACNzPg8IKejJkzZ06jxy+99JIyMjK0bNkyjR49OvB8QkJCYCzjn5o7d67WrFmjTz/9VJmZmRoyZIj++Mc/6q677tJ9990nu92u5557Tnl5eXrkkUckSX379tWXX36pxx57jJMLIMK8PkOfrC7SP774Ud9tL5MkJTmsOiPDqTNOGiyXr354DofNojibRf26pKhnRlJkOw0AABAmhnHsO4bj4uL0zDPP6Jlnnjlim+7dux9z0vgzzzxTy5cvb3Yf0TTxgQtP3AXcFrzRMBzZl5v26stNexst69ul8WzUGSlxkuovPiG6eX2Gdu2vle8wn71Wi0ld0+K5eBhkgVAT13Yl8J3o4Tsx1hUd9N02/d1VjZZZzaZG1/AyUuorsmtcXlU5PUqOY3Sbown5nDHl5eWSpPT09EbPz5o1S6+88oqysrJ0wQUX6O677w5UxyxatEgDBw5UZmZmoH1+fr5uvvlmrV69WkOHDtWiRYs0duzYRuvMz8/XbbfddsS+OJ1OOZ0H7lrxT8TjdrvldrtbtZ/RzL9vbXkfw4VYHl2V06O3vtulfy3aHhhD0mYx6X+GddWvT++uZV8v0DkDOh923EVi2nT8HQYHcWwd4hccxLF1iF/zESsguBiSpW0Z1aOjFv1YX8k/rt+B6xFJcVb978ndG7XNbLj4xLAs0e/mV5Zp7priIy6/6Yzj9btz+4SxR0Db5LD6vxOpjIl1Ht+BY3jw96Ekjemb0WgUmwS7VckOqyqdHhVXOEnGHENIkzE+n0+33XabTj31VA0YMCDw/BVXXKHu3bsrOztbK1eu1F133aX169fr7bffliQVFRU1SsRICjz2j6d8pDYVFRWqra1VfHy8fmrmzJmaMWPGIc/PnTu30TBpbVVBQUGku9BmEMvG9julhUVmLSo2qdZbf+dLotXQqZmGTs/yKcWyVcu+3iqJ2AUTsQwO4tg6xC84iGPrEL+mq6mpiXQXgDYlMGeM26s3lu5o8XqG5qSpV2bysRsipPwXn5698kSdO7DLUdtmNlTGLN5Sqqmvr2i0bGy/TJ13jNcjtEoq6nT9y0u1p9KpwvL6O7yTHY0vgRmqv6Hw5UVbVRKECqdemcm66YweVNnoQGUMkWhf4u3134lLt+1XWgu/ExPtVuX3z5TVYg5m19BMnoZ5nftkJev5q4cfs31GikOVezy6/4M16pRoDzyfEm/Tb8b0UoeDnmvvQpqMmTx5slatWqUvv/yy0fOTJk0K/P/AgQPVpUsXjRkzRps3b9bxxx8fsv5MmzZNU6dODTyuqKhQTk6Oxo0bp5SUlJBtN9LcbrcKCgp0zjnnHLYiAU3X3mLp9RlaW1ipxVtL9c2PpVpTWCmvz5DFbJLdalZ6gk1xNou+214mj6/+g7pHpwT98pTumjA4O/BFLLW/2IUSsQwO4tg6xC84iGPrEL/m81eGAwiOhIaLuz5DuvOtlS1eT5LDqunn95XF3PjSZUZKnEb36sTF3TBxN1x8aspFwLxO9XPi7q1y6u3luxot+2R1kc4dkMVxi6DPN+zRyp3lgcfXjOquGRcNaNTGMAzlP75QG4qrDjmGLVXldOu4jomHPN+jc6KGdU8/zCuAtiOp4Ttx7poSzV1T0uL1TBiSrVN7dmr0nNlk0um9OgWGiERo+W9OsDUxKZbXKUmb91Rr4YY9hyzr1iFe15/eI6j9i2UhS8ZMmTJFH3zwgRYuXKhu3bodte3IkSMlSZs2bdLxxx+vrKwsLVmypFGb4uL6klL/PDNZWVmB5w5uk5KSctiqGElyOBxyOByHPG+z2drFCXx72c9waKuxrHN7tWpXub7bvl9LtuzXki37VFHnOWJ7/1BkUn1J/w2j83TmCRkym4980tFWYxcJxDI4iGPrEL/gII6tQ/yajjgBwZXksOoP5/UNDG3VEhuKK7Vzf61+9/YPh10++4aROuX4ToddhuDyX3yyWo6dROnbJUXP/e8w7Sg9UHFoyNADH61Ttcur8lq30hK4EzhSSqtdkqQxfTL0//J764TDVJ6ZTCb9/erhKlhTrCZM5XVUX23eq8/X79Ezn20+YptOSY7A0IZ2q1l/OK+vxvTNPGL7WGaoPqDkI9uXicO7acX6LUrtmCGzufmVLW6vT19s3Kt3V+zWuyt2H7J8VI+OenXSycHoKo7Bf3OCrQnfh5J0/0X9dcrxHeX1Hfgw/XxDib7atE+7y5hb7WBBT8YYhqFbbrlF77zzjj7//HPl5eUd8zUrVqyQJHXpUl/GO2rUKP35z39WSUmJMjIyJNUPP5GSkqJ+/foF2vx0ssqCggKNGjUqiHsDtA9Oj1dPzduk57/4US5P47E9kxxWjchL16geHTXsuA6Kt1nk9Rmqc3u1v8at8lq3+nVJUb/stltdBgAAABzJDaN76IbRLb/jc9u+av3l43Wq/cm8Mz/sLNe+alejG6AQWm5P/UUkexPvBB4/IOuQ5/72+WaV1bhVXOEkGRNB+xuSMXmdEtW3y5HPVbt3TAzKHdvjB2Tp8r9/oxqXV/2zUxpVuZXXurV8e5n2VjWeX+hPH65VRd2R53KLt1l1Zu/OjeZmAKJZj86J+uUJPp133oktvgHouQWb9c1PbnCorPNo2bb92rGf4XbDxe3135zQtO/D7LR4XXta4xyAxWzSV5v2qbiSZMzBgp6MmTx5smbPnq333ntPycnJgTleUlNTFR8fr82bN2v27Nk677zz1LFjR61cuVK33367Ro8erUGDBkmSxo0bp379+umqq67SQw89pKKiIk2fPl2TJ08OVLbcdNNNevrpp3XnnXfq2muv1fz58/XGG2/oww8/DPYuATFvX5VT//lup+avK5HdalGHBJs6JTl0XMcEdUxy6Ml5G7WuqFJS/d06J+amaVj3Djq5R0f1z05hrE4AAAAgRLp3TNSz/zvskOcnz/5OH64sVLXzyJXqCC63vzLmKJX+x5KZHKeyGrdKKuvUO4t5gCLFXxkTrnkKctIT9OVdZx9x+c79NdpbVd8nj9ena15Yoi17q3X7698fdb135PfW5LN6BrWv4XBgzhhKY9A8N51xvG46o/EUFuuLKpX/+EK+D8PI08zKmMPxz622p8J5jJbtS9CTMc8++6wk6cwzz2z0/Isvvqhf/vKXstvt+vTTT/X444+rurpaOTk5uvTSSzV9+vRAW4vFog8++EA333yzRo0apcTERF1zzTW6//77A23y8vL04Ycf6vbbb9cTTzyhbt266R//+Ify8/ODvUtAzNpUUqUn5m3UnFWFgRLDI0lPtOtPEwYwtjEAAAAQBZLs9afrXHwKH08z5ow5kowUh9YXV6q4iReffIZUUumUzdq4MspkMqlTkp1zsxbaX1Of+EiPkkmju3VIULcOCYHHMy8dpLeW7ZRxhPHR9la5tLawQos274vNZEykO4A2JdFRXx1W7fQeoyWCJVAZ04Lh5vwyU+oLKppTGVNR51ad68BxtlnMYUuqh0tIhik7mpycHC1YsOCY6+nevfshw5D91Jlnnqnly5c3q39AW1dR59bG4iq9uXSH3li6Q/7hGgd3S9XPh3VTvN2qshqXisrrtHVftbaX1mhA11T9/ry+6pR06JxKAAAAAMIvsWEi5CouPoWNx+ufsLj1dwJ/sXGPHNajX8TaX12nJ5ZbtO+bw18juWJkrh64eGCL+9Ke7fNXxkTpUHEXDs7WhYOzj7h89e5ynf/kl/p+R5n++/2hc2f4JTosOuOEjEbDokUTcokIhqSG70OX1yeXxyf7MT5b0Xoen78ypjXJmPrvw6LyuqN+jvl9vn6P3luxK7BtSTrl+I6afUPbmico6MkYAOFjGIY+XlWkxT/u06Y9VdpYXKWSysZ3YI3tm6nbxvbSgK6pEeolAAAAgOZKCtwJTGVMuLi8rb/4lNVw8em9Fbv13mEmoD5U/dXqg6+lG6of5mn+2hLp4hZ3pV3zzxkTLZUxzXVCZrLibGZVOj265dWj34QcjUOZHeM+baBZ/DcnSPXfiXZrbL6vY0kwbk7onOyQySQ5Pb5jfo79lP870dwGM7okY4Ao5PMZKq1xKT3BLvNBv8o9Xl+gZH5PpVO/+89KzVtXcsjrM1McGtg1VTedcbyGH5cetn4DAAAACA7/xSeSMeHj8bX+4tNlw3O0obhSlXXHPm6G4ZOzYp/+evVoHZ954Oa5shqXhtxfoKKKOlU7PY0uRKJpSgPJmJZNIh5pNotZ917QX+8fJaFX4/bq+x1l+r8Fm3XdaXmKs1nC2MOmaXuXUREJNotZdqtZLo9PVU5Pmxu2Khq5gjBsZ5zNomnn9tFn6/Y0+TXnDczSVaOOa/E2YwHf6EAU8fkMffhDoR77dIN+3FMtu8Wsbh3iZTabVFxRp8o6jzolOdQrI0kbiiu1r9olu9WsK0bkqn92inpmJOn4jCSlxMXmD04AAAAA9Q4MU0YyJlwCc8a0Yoz83I4Jev7q4U1q63a79dFHHyk3PaHR82kJdqUn2lVa7dKWvdWMctBM+6tdqmhIhmU0VCrFostH5OryEblHXO71GRr5wDztrXJq1a7yKLsRs/691AZvakeEJDmsKvW4VO3iOzEcApUxrRwCcdLo4zVp9PHB6FKbQTIGCKOyGpe8PkOJDqusZpMq6jwqr3VrQ3Gllm8v02frSrS+uDLQ3uX16ce91Y3WsbfKqb1V9UOR9clK1hMTh6p3VnJY9wMAAABAaPnHyOfCU/gEJixuRWVMsOR1SlRptUs3vbIs8LfQVCnxNj146SDldUoMUe+i2/c7yyTVx7At36hoMZs0NDdNBWuKtWRraZQlY4DgSnRYVFpNtWi4BGPOGBweyRggxLbsrdYH3+/WJ2uKtGpXxTHbJzusuv70HvrlKcepos6tHaU1MlQ/9Fhagl279tdqY0mVJOmCwV3ksEZfKTIAAACA1jlQGeONcE/aD3dgjPzIX3wa1r2Dlm3br537a1v0+g++361bxvQKcq/CzzCMRtVhbrdHdR6pss4j20FvjaXb9uvFr7ZqT6UzMF/M4G5tv6JoSE59MuahOevVJytZZ/fJjHSXJB2YM8bEQGUIkkQ734nhFE03J7Q1JGOAEPl+R5n+9vkmzV1TfNTJ6xLtFnXrkKATu6dpSE6a8vtnKS2hfvzL1ASbcn5Sst4pyaHBOWkh7DkAAACASEt01N90xV3A4eHzGWq4ETgqkjF35PfW2L6Zcnl8zXrdf77bqXeW79KehtEUYplhGPrF/32jJVtLf7LEqru+nX/M15/Wq3NoOhZF8vtn6a9z18swpFtfXaGHfz5II/LS1THJEemuAUGVHMc8auHkH7YzGr4P2xqSMUCQVNa59dJXW7V8R5nWFlaosLwusOyMEzrrvIFZGts3U2kJdtW6vXJ7fEqOs7ZqMiwAAAAAbVNgmDIuPIWF23cg6RENdwLbLGaNyGv+sFOb91TVJ2MqYz8ZU17rPkwi5vAsZpMuG95N+f2zZDaZlBxn1eBuaaHtYBTomZGkdX8cr8v+7xt9v6NMN8/6Tn27pOjj35we0X4F7keN/FsJbQTzqIVXoDKmlXPG4FAkY4Ag2FBcqZteWaYf9xyY38VqNunCIdm6+Yzj1Suz8ZwuSQ6rxI0qAAAAAI7Af+GppNKpC5/+ssmv69clRX++eKAsXEBpFv9dwJJkM8fuDXOdk+tPNNtCMmZXWf0QbR0T7fp62tmS6ocp+2TOHOWPHy+b7cAlLbPJ1G7v4HZYLXrsssH65Yvfantpjbb+ZN5ZoC3wfyc+8elGvfLNtia9xm4x687xfVqU2G7v3P7KGGv7/FwNJZIxQCsYhqF3lu/SH95ZpVq3V11S43Tj6B7ql52qPl2S2/RkgQAAAABCJzMlTgl2i2pcXq3cWd7k163cWa7vtu9XvD18p/uG4VN5mUX/3PGNTKYDF256ZSRp5iUDY+Iiuf8uYCk6KmNaKpCMaQPDlBWW1Y820bVDfGCuVLPhk9UsOaxm2Zg/NaBH5yS98+tTNOxPn6rW7ZXXZ0Q0IWs0jNUeu+8kRJsenRIl1Sdp/Ynaprj+X98qr3NSqLrVZHaLSbePPUGn9OwU6a40iaehWtTGjR1BRzIGaKGte6t193ur9MXGvZKk03p20hMThzA2KwAAAIBWS3JY9clto7WxpLLJr1mxo1xPztuoDcVVIezZkZikqopGz3y/o0wuj09dO8SHfOsOq1n/e3J3dWrh+Zj7oMqYWB6WpXPD/pdUOJs930xLmU0KyfDbu8vrL7h2SY0L+rrbIn/lgCRVuzzcHIo25dYxvTSqR0fVebxNam8Y0oz/rtH20hp9v6MstJ1rojveWqkLh2QfdpnP69PmbWatmbtR5iB8nvrnpG4p/3ciUysEH8kYoIm8PkN7q5z6bq9Jn731gz5aXSyXxye7xawpZ/fU5LN6MhQAAAAAgKDJSU9QTnpCk9uf3SdTo3t1UnmtO4S9OpTH69XSpUs1fPhwWS311QpfbdqnF77aove/3x22fny2rkSn9Tr2XccdEx268uTcQLWFdNBdwBaTTKbYPa/zV8bUur06YfrHYdmmzWLSY78YovGtuPDn9+aynXr7u506rmOitu6rH24rOy30yby2wGE1y2o2yeMzVO2MbDLGn9qM5fcSoovNYm52VcmgbmlaubMsNB1qBsOQ7vrPSu0qq9Wzn28+SkuzPt29JSjbNJmk607Nk8N29GSKSSbl98/SwG6pjZ73+OeMieFK0WhFMgbtntdnaH1Rpb7bvl8rd5ZpX5VLVU6Pql0eVdV5VOX0qsrpVp3bf1eRRVKhJOn0Xp10/0UDlNdQLgkAAAAAkTT8uPCPje92u1W32dDZvTvLZqu/ADz6hM7qlGzXvipXyLfvMwzNWrxd3+8s1/dNHNItMyVO5w/qEnjsnzPGGsPzxUj11REn90jXNz82beL7YHB7DU2ZvTyo6/x26/7A/3dvRkKyPTOZTEp0WFVe61Z1hCc5N4xjtwFCrXOyQ2P6Zka6G5Kk5xOH6+MfCnWkt4bP59OWLVuUl5cncyu/h37YVa4lW0r1jy+bltj5dG2x5tw2utFz/qE77VTGBB3JGLQb2/ZV6/0Vu1Xl8shhtcjt9en7HWX6fkeZql1NK3OUpKx4QxcMy9PYflkakZfOnR4AAAAA8BM2i1m/PrNn2LZ3Zu8Mfb6+5JjtFmzYox/3VKu4oq7R8+42dBfwqzecrMowXYz3+Qz96qVvtXx7WVDWZzGblN8/UzkdEuTy+pQab9Olw7oFZd3tQVJDMqbK2fRrHKEU++8mIDiGde+gYd07HHG52+3WRx9t1nnn9g7c1NBSlXVu/X3hj8f8Hiivdevt73Yd8n0oSW6f/wYF3sXBRjIGbZrH69P8dSV6ZfF2Ldyw54jtkh1WDclN09CcNGWnxSvRYVVSnFVJDqsS7Q3/dVjkMBuaV/CJzss/odUfjgAAAACA4DjjhM4644TOx2z3+3d+0I97qlVR13goN//4+LY2cBewyWQK6xBVb998iipqg5P8sVlNSrBzqaqlEh31Q+9FvDImolsH2rfkOJumjut9zHbFFXV6+7tdqqjzyDCMRjebHximLPa/E6MN33CIaYZhaNHmfVq+o0weryGvYchhNSvBbtH+GrfeXLpDheUHMryn9+qk3pnJcnp8MmSof3aqTsztoJ4ZSU2a78XtDu/YywAAAACA4PEnKX6aPPBXxtjaQGVMuJlMJqUmcLNiNEh01F/mq4pwMsaPgUSA6OX/PvT6DNW4vIHPD+nA0J18JwYfyRjEJJ/P0Hfb9+vhT9Zr8Zajj4XbIcGmy4bn6IqRuerekbldAAAAAKC9Somvvwzy08oYj69tzBmD9i2p4WJqxCtjGiaNIRkDRK84m1k2i0lur6GKOnejZIx/mLK2UC0abUjGIKrtLqvV3NVFWrW7Qnsqndpb5dSeSqf2VbvkbfhgsFvMyh+QpeQ4qywmk1wen6pdHhmGdE6/TI0fkKU4myXCewIAAAAAiDT/ncCVP03GUBmDNiDRHh3JGADRz2QyKTnOptJqlyrrPOqSemCZ28MwZaFCMgZR5cuNe/X9zjJt31ejtUUVWrmz/IhtbRaTLj2xm24Z00td0+LD2EsAAAAAQCxKiT/8MGUuxsdHG3BgmDJvhHtSzySSm0A0S4mzqrTapYran1aLNtyg0IQpHdA8JGMQFVwen+7772rNXry90fMmkzQst4NO79VZXVLj1DnZoU5JDnVOdqhjkp1yOQAAAABAk6XEHWGYMi9DsiD2JTnqRwV57dvtWvTjPg3plqpbx/SKcK8ARKvADQo/+U50N3wncoNC8JGMQcQVldfp1leXa8nWUplM0gWDstUzI0ndOyZo1PEdlZEcF+kuAgAAAADagOS4w194CtwFzDBliGE56QmSpG37arRtX40WbtijJ+dvksNqVpLFomGn1albR1vI+9EwZQxzxgBRzj9050+rRflODB2SMYiITSWVev3bHfpy0z6tLayQJCU7rHri8iE6u09mhHsHAAAAAGiLUuPrL4OUVbu1cmdZ4PnNJdWSJCtDsiCGXTWqu3LSE1Tt9Gj17grNXrxdtW6vnB6fnB6TCtaW6FenJUe6mwCiRHJDtejaogr12JkYeL6qrj45Q7Vo8JGMQcgVlddp854qeX2Gat1evbVspwrWFDdqMzgnTY/8zyD1zOBHAQAAAAAgNPx3AVc6Pbrw6a8OWc6FJ8Qyh9Wi/P5ZkqRLTpRuP+cElde69cIXm/XPr7bpzWW7dM0pPWQOcdLRkBHS9QMIDv934v8t+FH/t+DHQ5bznRh8JGMQdE6PV0u37teCDXu0YP0erS+uPGy7c/pl6oLB2RrVo6M6JzvC3EsAAAAAQHvTOdmhCUOy9e3W/Ycss5hN+sVJORHoFRAaSQ6rkhxWnd6rk/751TatKazUX+eu153j+0S6awCiwIShXbV0W6nq3L5DluWmJ2hQt9QI9KptIxmDFimprNPSrfu1o7RGheV12lPpVLXLo6q6+lLYWrc30NZkknp0SpTdapHZJA3smqrrT++hnhlJEdwDAAAAIHIWLlyohx9+WMuWLVNhYaHeeecdTZgwIbDcMAzde++9+vvf/66ysjKdeuqpevbZZ9Wr14GJmEtLS3XLLbfov//9r8xmsy699FI98cQTSko68Dt75cqVmjx5sr799lt17txZt9xyi+68885w7ioQVUwmkx6fODTS3QDCanj3DuqeZGhblUn/t/BHTTwpV7kdE0K2vQNzxjDsHxDNRh3fUfN+e2aku9GukIzBEbm9Pm0srtLq3eXaXlqjaqdX1U6Pvt9ZpnVFh6928euc7NDoXp11Ru/OOq1nJ6Un2sPUawAAACD6VVdXa/Dgwbr22mt1ySWXHLL8oYce0pNPPql//etfysvL09133638/HytWbNGcXFxkqQrr7xShYWFKigokNvt1q9+9StNmjRJs2fPliRVVFRo3LhxGjt2rJ577jn98MMPuvbaa5WWlqZJkyaFdX8BAJHjsJo1daBXrxZ11jdb9mvWkm26eGhXpcbb1CU1PujbMxilDAAOi2RMO2MYhkqrXdq6r1o799cq3mZRxySH7BazftxbpU0lVdq8p/6/W/fWyOU9tEzNr1+XFPXKTFJ2Wrwykh1KdFiVYLeoR6ck9e2SzB0QAAAAwBGce+65Ovfccw+7zDAMPf7445o+fbouuugiSdLLL7+szMxMvfvuu5o4caLWrl2rOXPm6Ntvv9Xw4cMlSU899ZTOO+88/fWvf1V2drZmzZoll8ulF154QXa7Xf3799eKFSv06KOPkowBgHZofP9MfbNlf6P5IWZdP1Kn9uwUku1xVQgAGiMZ08bsqXTq262lWrKlVMt3lKmy1q06t1cV1Rb9/rt5crp98viafotCssOqftn1SZfkOJsSbBbldkzQaT07qWMS87wAAAAAwbZlyxYVFRVp7NixgedSU1M1cuRILVq0SBMnTtSiRYuUlpYWSMRI0tixY2U2m7V48WJdfPHFWrRokUaPHi27/UCVen5+vh588EHt379fHTp0COt+AQAi6/yBXfTO90Xatb9WtS6Pql1e/ff73UFPxhiqv+7EPboA0BjJmBi3c3+NlmypT74s2VqqH/dUH6GlSfIcmMclOzVOOekJcnp82lvlVJ3bp7xOCeqZkaTjOyfp+Iwk9eycpK5p8TKb+fYEAAAAwqWoqEiSlJmZ2ej5zMzMwLKioiJlZGQ0Wm61WpWent6oTV5e3iHr8C87XDLG6XTK6XQGHldUVEiS3G633G53a3YrJvj3sT3sazARt5Yhbs1HzFrGH69Em/TWpBGSpC827tW1L3+nt5btVMGa4kbtzSbp+tOO07WnHtei7RkN45R5Pd6YPlb8vTUP8Wo5YtcyoYxbqI5FzCdjnnnmGT388MMqKirS4MGD9dRTT2nEiBGR7tYx7dxfowUb9sjnM+T1GXJ5fdpX7dK+Kpeq6jxye31y+wy5PT55fD65vIY8Xp/cXp883vr2tS6v9lW7Gq3XZJJ6ZyZrZF66hh+XrsyUOFnk05JvvtI5Z52pxHi7OiTYFWezRGbHAQAAAEStmTNnasaMGYc8P3fuXCUkhG6y52hTUFAQ6S7EJOLWMsSt+YhZyxwcN7dPSrVbVO7SIdeWJOmRueu1af3aFlW3/FholmTS8hXLpR2xP4EMf2/NQ7xajti1TCjiVlNTE/R1SjGejHn99dc1depUPffccxo5cqQef/xx5efna/369YfcJRZt1hVW6g/vrGr1eqxmkwZ0TdXIvHSNyEvX8O7pSk2wNWrjdru1M0Hq3jFBNpvtCGsCAAAAEA2ysrIkScXFxerSpUvg+eLiYg0ZMiTQpqSkpNHrPB6PSktLA6/PyspScXHju539j/1tfmratGmaOnVq4HFFRYVycnI0btw4paSktG7HYoDb7VZBQYHOOecczp2agbi1DHFrPmLWMkeK25hzPNpdVntI+9+8vlKb9lTrzS2tu5F35PBhGts3uq/PHQ1/b81DvFqO2LVMKOPmrw4PtphOxjz66KO64YYb9Ktf/UqS9Nxzz+nDDz/UCy+8oN/97ncR7t3RZabEaVy/TFnMJpnNJtktZqUn2tUxya6UOJvsFrOsFpOsFrPsFpOsZrNsVrNs5vrnbBaTbBaz8jolKtER04cRAAAAwEHy8vKUlZWlefPmBZIvFRUVWrx4sW6++WZJ0qhRo1RWVqZly5Zp2LBhkqT58+fL5/Np5MiRgTZ/+MMf5Ha7AyeoBQUF6t279xHni3E4HHI4Dp0b0maztauLA+1tf4OFuLUMcWs+YtYyP41bB5tNHZLiD2n34M8H6YWvtsrj9bV4W5kpcTqjT6Zstti/ZsXfW/MQr5Yjdi0TiriF6jjE7Ceiy+XSsmXLNG3atMBzZrNZY8eO1aJFiw77mmga/7hPZoKeuXxwENZkHLPvjDsYPMSy5Yhd8BDL4CCOrUP8goM4tg7xaz5iFT2qqqq0adOmwOMtW7ZoxYoVSk9PV25urm677Tb96U9/Uq9evZSXl6e7775b2dnZmjBhgiSpb9++Gj9+vG644QY999xzcrvdmjJliiZ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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -1280,11 +1155,7 @@
{
"cell_type": "markdown",
"id": "62687cb5",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"It took only about 15 seconds to analyze the dataset and suggest the set of possible bad segments for weasel-based analyzer, which is much faster than using any baseline pipeline."
]
@@ -1292,11 +1163,7 @@
{
"cell_type": "markdown",
"id": "5f840ef5",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"However, there might be false-positives in the results. "
]
@@ -1305,16 +1172,112 @@
"cell_type": "code",
"execution_count": 44,
"id": "652392f3",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "metadata": {},
"outputs": [
{
"data": {
- "text/plain": " segment SMAPE fold_number\n1234 D2109 1.387898 1.0\n2972 D3674 1.384460 1.0\n3706 D53 1.294982 1.0\n3534 D418 1.281990 1.0\n2167 D295 1.258861 1.0\n2457 D321 1.177998 1.0\n2446 D320 1.123942 1.0\n3242 D3917 1.010831 1.0\n346 D131 0.487805 1.0\n1348 D2211 0.000000 1.0",
- "text/html": "\n\n
\n \n \n segment \n SMAPE \n fold_number \n \n \n \n \n 1234 \n D2109 \n 1.387898 \n 1.0 \n \n \n 2972 \n D3674 \n 1.384460 \n 1.0 \n \n \n 3706 \n D53 \n 1.294982 \n 1.0 \n \n \n 3534 \n D418 \n 1.281990 \n 1.0 \n \n \n 2167 \n D295 \n 1.258861 \n 1.0 \n \n \n 2457 \n D321 \n 1.177998 \n 1.0 \n \n \n 2446 \n D320 \n 1.123942 \n 1.0 \n \n \n 3242 \n D3917 \n 1.010831 \n 1.0 \n \n \n 346 \n D131 \n 0.487805 \n 1.0 \n \n \n 1348 \n D2211 \n 0.000000 \n 1.0 \n \n \n
\n
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " segment \n",
+ " SMAPE \n",
+ " fold_number \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1234 \n",
+ " D2109 \n",
+ " 1.387898 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 2972 \n",
+ " D3674 \n",
+ " 1.384460 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 3706 \n",
+ " D53 \n",
+ " 1.294982 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 3534 \n",
+ " D418 \n",
+ " 1.281990 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 2167 \n",
+ " D295 \n",
+ " 1.258861 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 2457 \n",
+ " D321 \n",
+ " 1.177998 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 2446 \n",
+ " D320 \n",
+ " 1.123942 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 3242 \n",
+ " D3917 \n",
+ " 1.010831 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 346 \n",
+ " D131 \n",
+ " 0.487805 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 1348 \n",
+ " D2211 \n",
+ " 0.000000 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
",
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"
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -1351,11 +1312,7 @@
{
"cell_type": "markdown",
"id": "ee1102ff",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
+ "metadata": {},
"source": [
"That's all for this notebook. Remember, that this is an experimental feature, and it might change the interface in the future!"
]
@@ -1382,4 +1339,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/examples/quickstart.ipynb b/examples/quickstart.ipynb
index 0c05e8ee2..392e15af5 100644
--- a/examples/quickstart.ipynb
+++ b/examples/quickstart.ipynb
@@ -53,7 +53,6 @@
"df = pd.read_csv(\"data/example_dataset.csv\")\n",
"\n",
"# Create a TSDataset\n",
- "df = TSDataset.to_dataset(df)\n",
"ts = TSDataset(df, freq=\"D\")\n",
"\n",
"# Choose a horizon\n",
diff --git a/poetry.lock b/poetry.lock
index 91e727359..dd8298bc2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -6251,11 +6251,11 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
[extras]
all = ["optuna", "prophet", "pytorch-forecasting", "pytorch-lightning", "pyts", "sqlalchemy", "statsforecast", "torch", "tsfresh", "wandb"]
-all-dev = ["GitPython", "Sphinx", "black", "click", "click", "codespell", "flake8", "flake8-bugbear", "flake8-comprehensions", "flake8-docstrings", "isort", "jupyter", "mypy", "myst-parser", "nbconvert", "nbqa", "nbsphinx", "optuna", "pep8-naming", "prophet", "pydata-sphinx-theme", "pytest", "pytest-cov", "pytest-shard", "pytorch-forecasting", "pytorch-lightning", "pyts", "semver", "semver", "sphinx-design", "sphinx-mathjax-offline", "sqlalchemy", "statsforecast", "torch", "tsfresh", "types-PyYAML", "types-setuptools", "wandb"]
+all-dev = ["GitPython", "Sphinx", "black", "click", "click", "codespell", "flake8", "flake8-bugbear", "flake8-comprehensions", "flake8-docstrings", "ipywidgets", "isort", "jupyter", "mypy", "myst-parser", "nbconvert", "nbqa", "nbsphinx", "optuna", "pep8-naming", "prophet", "pydata-sphinx-theme", "pytest", "pytest-cov", "pytest-shard", "pytorch-forecasting", "pytorch-lightning", "pyts", "semver", "semver", "sphinx-design", "sphinx-mathjax-offline", "sqlalchemy", "statsforecast", "torch", "tsfresh", "types-PyYAML", "types-setuptools", "wandb"]
auto = ["optuna", "sqlalchemy"]
classification = ["pyts", "tsfresh"]
docs = ["GitPython", "Sphinx", "jupyter", "myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx-design", "sphinx-mathjax-offline"]
-jupyter = ["black", "jupyter", "nbconvert"]
+jupyter = ["black", "ipywidgets", "jupyter", "nbconvert"]
prophet = ["prophet"]
release = ["click", "semver"]
statsforecast = ["statsforecast"]
@@ -6267,4 +6267,4 @@ wandb = ["wandb"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.0, <3.11.0"
-content-hash = "94b8eb777cfc0d1a9487eadb3e9bd7b73fbed0ddfe190e8acf8f1b106a850944"
+content-hash = "a337c82112d96af5cf8d4b54ec95cf6c3c2d4e57e933487e1fe585d0cf81ee42"
diff --git a/pyproject.toml b/pyproject.toml
index 38ab77b5b..f37aa127b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -130,7 +130,7 @@ statsforecast = ["statsforecast"]
release = ["click", "semver"]
docs = ["Sphinx", "nbsphinx", "sphinx-mathjax-offline", "myst-parser", "GitPython", "pydata-sphinx-theme", "sphinx-design", "jupyter"]
tests = ["pytest-cov", "pytest", "pytest-shard"]
-jupyter = ["jupyter", "nbconvert", "black"]
+jupyter = ["jupyter", "nbconvert", "black", "ipywidgets"]
style = ["black", "isort", "flake8", "pep8-naming", "flake8-docstrings", "mypy", "types-PyYAML", "codespell", "flake8-bugbear", "flake8-comprehensions", "types-setuptools", "nbqa"]
all = [
@@ -152,7 +152,7 @@ all-dev = [
"pytest-cov", "pytest", "pytest-shard",
"black", "isort", "flake8", "pep8-naming", "flake8-docstrings", "mypy", "types-PyYAML", "codespell", "flake8-bugbear", "flake8-comprehensions", "types-setuptools", "nbqa",
"click", "semver",
- "jupyter", "nbconvert",
+ "jupyter", "nbconvert", "ipywidgets",
"pyts", "tsfresh",
"statsforecast"
]
@@ -202,7 +202,7 @@ filterwarnings = [
"ignore: TSDataset freq can't be inferred",
"ignore: You probably set wrong freq. Discovered freq in you data",
"ignore: Option `fast_redundancy=False` was added for backward compatibility and will be removed in etna 3.0.0.",
- "ignore: Some regressors don't have enough values in segment",
+ "ignore: Some regressors don't have enough values",
"ignore: Segments contains NaNs in the last timestamps",
"ignore: Given top_k=.* is bigger than n_features=.*. Transform will not filter",
"ignore: Given top_k=.* is less than n_segments=.*. Algo will filter data without Gale-Shapley run.",
diff --git a/tests/conftest.py b/tests/conftest.py
index 552e7784a..e028f64c3 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8,6 +8,7 @@
from etna.datasets import generate_const_df
from etna.datasets.hierarchical_structure import HierarchicalStructure
from etna.datasets.tsdataset import TSDataset
+from tests.utils import convert_ts_to_int_timestamp
@pytest.fixture(autouse=True)
@@ -111,7 +112,7 @@ def generate_df():
@pytest.fixture
-def simple_df() -> TSDataset:
+def simple_tsdf() -> TSDataset:
"""Generate dataset with simple values without any noise"""
history = 49
@@ -188,6 +189,11 @@ def example_tsds(random_seed) -> TSDataset:
return tsds
+@pytest.fixture
+def example_tsds_int_timestamp(example_tsds) -> TSDataset:
+ return convert_ts_to_int_timestamp(ts=example_tsds, shift=10)
+
+
@pytest.fixture
def example_reg_tsds(random_seed) -> TSDataset:
periods = 100
@@ -218,6 +224,11 @@ def example_reg_tsds(random_seed) -> TSDataset:
return tsds
+@pytest.fixture
+def example_reg_tsds_int_timestamp(example_reg_tsds) -> TSDataset:
+ return convert_ts_to_int_timestamp(ts=example_reg_tsds, shift=10)
+
+
@pytest.fixture()
def outliers_tsds():
timestamp1 = np.arange(np.datetime64("2021-01-01"), np.datetime64("2021-02-01"))
@@ -346,6 +357,12 @@ def example_tsdf(random_seed) -> TSDataset:
return df
+@pytest.fixture
+def example_tsdf_int_timestamp(example_tsdf) -> TSDataset:
+ ts = convert_ts_to_int_timestamp(example_tsdf, shift=10)
+ return ts
+
+
@pytest.fixture
def big_daily_example_tsdf(random_seed) -> TSDataset:
df1 = pd.DataFrame()
@@ -700,6 +717,11 @@ def product_level_constant_hierarchical_ts(product_level_constant_hierarchical_d
return ts
+@pytest.fixture
+def product_level_constant_hierarchical_ts_int_timestamp(product_level_constant_hierarchical_ts):
+ return convert_ts_to_int_timestamp(product_level_constant_hierarchical_ts)
+
+
@pytest.fixture
def product_level_constant_hierarchical_ts_with_exog(
product_level_constant_hierarchical_df, market_level_constant_hierarchical_df_exog, hierarchical_structure
diff --git a/tests/test_analysis/test_decomposition/test_plots.py b/tests/test_analysis/test_decomposition/test_plots.py
index 7127bf441..29e7def47 100644
--- a/tests/test_analysis/test_decomposition/test_plots.py
+++ b/tests/test_analysis/test_decomposition/test_plots.py
@@ -1,7 +1,10 @@
import pytest
+from ruptures.detection import Binseg
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import TheilSenRegressor
+from etna.analysis import plot_change_points_interactive
+from etna.analysis import plot_time_series_with_change_points
from etna.analysis import plot_trend
from etna.analysis import seasonal_plot
from etna.transforms import ChangePointsTrendTransform
@@ -47,3 +50,68 @@ def test_plot_stl(example_tsdf, period):
)
def test_dummy_seasonal_plot(freq, cycle, additional_params, ts_with_different_series_length):
seasonal_plot(ts=ts_with_different_series_length, freq=freq, cycle=cycle, **additional_params)
+
+
+@pytest.mark.parametrize(
+ "ts_name, params, match",
+ [
+ ("example_tsdf", {"start": 10}, "Parameter start has incorrect type"),
+ ("example_tsdf", {"end": 10}, "Parameter end has incorrect type"),
+ ("example_tsdf_int_timestamp", {"start": "2020-01-01"}, "Parameter start has incorrect type"),
+ ("example_tsdf_int_timestamp", {"end": "2020-01-01"}, "Parameter end has incorrect type"),
+ ],
+)
+def test_plot_time_series_with_change_points_fail_incorrect_start_end_type(ts_name, params, match, request):
+ ts = request.getfixturevalue(ts_name)
+ change_points = {"segment_1": [10, 100], "segment_2": [20, 200]}
+ with pytest.raises(ValueError, match=match):
+ plot_time_series_with_change_points(ts=ts, change_points=change_points, **params)
+
+
+@pytest.mark.parametrize(
+ "ts_name, params, match",
+ [
+ ("example_tsdf", {"start": 10}, "Parameter start has incorrect type"),
+ ("example_tsdf", {"end": 10}, "Parameter end has incorrect type"),
+ ("example_tsdf_int_timestamp", {"start": "2020-01-01"}, "Parameter start has incorrect type"),
+ ("example_tsdf_int_timestamp", {"end": "2020-01-01"}, "Parameter end has incorrect type"),
+ ],
+)
+def test_plot_change_points_interactive_fail_incorrect_start_end_type(ts_name, params, match, request):
+ ts = request.getfixturevalue(ts_name)
+ params_bounds = {"n_bkps": [0, 5, 1], "min_size": [1, 10, 3]}
+ with pytest.raises(ValueError, match=match):
+ plot_change_points_interactive(
+ ts=ts,
+ change_point_model=Binseg,
+ model="l2",
+ params_bounds=params_bounds,
+ model_params=["min_size"],
+ predict_params=["n_bkps"],
+ **params,
+ )
+
+
+@pytest.mark.parametrize("alignment", ["first", "last"])
+def test_seasonal_plot_datetime_timestamp(alignment, example_tsdf):
+ seasonal_plot(ts=example_tsdf, cycle=10, alignment=alignment)
+
+
+@pytest.mark.parametrize("alignment", ["first", "last"])
+def test_seasonal_plot_int_timestamp(alignment, example_tsdf_int_timestamp):
+ seasonal_plot(ts=example_tsdf_int_timestamp, cycle=10, alignment=alignment)
+
+
+def test_seasonal_plot_int_timestamp_fail_resample(example_tsdf_int_timestamp):
+ with pytest.raises(ValueError, match="Resampling isn't supported for data with integer timestamp"):
+ seasonal_plot(ts=example_tsdf_int_timestamp, freq="D", cycle=10)
+
+
+def test_seasonal_plot_int_timestamp_fail_non_int_cycle(example_tsdf_int_timestamp):
+ with pytest.raises(ValueError, match="Setting non-integer cycle isn't supported"):
+ seasonal_plot(ts=example_tsdf_int_timestamp, freq=None, cycle="year")
+
+
+def test_seasonal_plot_datetime_timestamp_fail_none_freq(example_tsdf):
+ with pytest.raises(ValueError, match="Value None for freq parameter isn't supported"):
+ seasonal_plot(ts=example_tsdf, freq=None, cycle=10)
diff --git a/tests/test_analysis/test_decomposition/test_utils.py b/tests/test_analysis/test_decomposition/test_utils.py
index d800d06ae..27b08a1b9 100644
--- a/tests/test_analysis/test_decomposition/test_utils.py
+++ b/tests/test_analysis/test_decomposition/test_utils.py
@@ -31,61 +31,76 @@ def test_get_labels_names_linear_coeffs(example_tsdf, poly_degree, expect_values
@pytest.mark.parametrize(
- "timestamp, cycle, expected_cycle_names, expected_in_cycle_nums, expected_in_cycle_names",
+ "timestamp, freq, cycle, expected_cycle_names, expected_in_cycle_nums, expected_in_cycle_names",
[
(
- pd.date_range(start="2020-01-01", periods=5, freq="D"),
+ pd.date_range(start="2020-01-01", periods=5, freq="D").to_series(),
+ "D",
3,
["1", "1", "1", "2", "2"],
[0, 1, 2, 0, 1],
["0", "1", "2", "0", "1"],
),
(
- pd.date_range(start="2020-01-01", periods=6, freq="15T"),
+ pd.date_range(start="2020-01-01", periods=6, freq="15T").to_series(),
+ "15T",
"hour",
["2020-01-01 00"] * 4 + ["2020-01-01 01"] * 2,
[0, 1, 2, 3, 0, 1],
["0", "1", "2", "3", "0", "1"],
),
(
- pd.date_range(start="2020-01-01", periods=26, freq="H"),
+ pd.date_range(start="2020-01-01", periods=26, freq="H").to_series(),
+ "H",
"day",
["2020-01-01"] * 24 + ["2020-01-02"] * 2,
[i % 24 for i in range(26)],
[str(i % 24) for i in range(26)],
),
(
- pd.date_range(start="2020-01-01", periods=10, freq="D"),
+ pd.date_range(start="2020-01-01", periods=10, freq="D").to_series(),
+ "D",
"week",
["2020-00"] * 5 + ["2020-01"] * 5,
[2, 3, 4, 5, 6, 0, 1, 2, 3, 4],
["Wed", "Thu", "Fri", "Sat", "Sun", "Mon", "Tue", "Wed", "Thu", "Fri"],
),
(
- pd.date_range(start="2020-01-03", periods=40, freq="D"),
+ pd.date_range(start="2020-01-03", periods=40, freq="D").to_series(),
+ "D",
"month",
["2020-Jan"] * 29 + ["2020-Feb"] * 11,
list(range(3, 32)) + list(range(1, 12)),
[str(i) for i in range(3, 32)] + [str(i) for i in range(1, 12)],
),
(
- pd.date_range(start="2020-01-01", periods=14, freq="M"),
+ pd.date_range(start="2020-01-01", periods=14, freq="M").to_series(),
+ "M",
"quarter",
["2020-1"] * 3 + ["2020-2"] * 3 + ["2020-3"] * 3 + ["2020-4"] * 3 + ["2021-1"] * 2,
[i % 3 for i in range(14)],
[str(i % 3) for i in range(14)],
),
(
- pd.date_range(start="2020-01-01", periods=14, freq="M"),
+ pd.date_range(start="2020-01-01", periods=14, freq="M").to_series(),
+ "M",
"year",
["2020"] * 12 + ["2021"] * 2,
[i % 12 + 1 for i in range(14)],
["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", "Jan", "Feb"],
),
+ (
+ pd.Series(np.arange(5, 10)),
+ None,
+ 3,
+ ["1", "1", "1", "2", "2"],
+ [0, 1, 2, 0, 1],
+ ["0", "1", "2", "0", "1"],
+ ),
],
)
-def test_seasonal_split(timestamp, cycle, expected_cycle_names, expected_in_cycle_nums, expected_in_cycle_names):
- cycle_df = _seasonal_split(timestamp=timestamp.to_series(), freq=timestamp.freq.freqstr, cycle=cycle)
+def test_seasonal_split(timestamp, freq, cycle, expected_cycle_names, expected_in_cycle_nums, expected_in_cycle_names):
+ cycle_df = _seasonal_split(timestamp=timestamp, freq=freq, cycle=cycle)
assert cycle_df["cycle_name"].tolist() == expected_cycle_names
assert cycle_df["in_cycle_num"].tolist() == expected_in_cycle_nums
assert cycle_df["in_cycle_name"].tolist() == expected_in_cycle_names
@@ -110,6 +125,14 @@ def test_seasonal_split(timestamp, cycle, expected_cycle_names, expected_in_cycl
pd.date_range(start="2020-01-01", periods=4, freq="Y"),
[np.NaN, 12.0, 6.0, 3.0],
),
+ (
+ pd.date_range(start="2020-01-01", periods=4, freq="Y"),
+ [np.NaN, 12.0, 6.0, 3.0],
+ "Q",
+ "mean",
+ pd.date_range(start="2020-01-01", periods=4, freq="Y"),
+ [np.NaN, 12.0, 6.0, 3.0],
+ ),
],
)
def test_resample(timestamp, values, resample_freq, aggregation, expected_timestamp, expected_values):
diff --git a/tests/test_analysis/test_eda/test_plots.py b/tests/test_analysis/test_eda/test_plots.py
index 8e19bda25..6f1cd9ad0 100644
--- a/tests/test_analysis/test_eda/test_plots.py
+++ b/tests/test_analysis/test_eda/test_plots.py
@@ -2,9 +2,13 @@
import pandas as pd
import pytest
+from etna.analysis import distribution_plot
from etna.analysis.eda import acf_plot
+from etna.analysis.eda import plot_holidays
+from etna.analysis.eda import plot_imputation
from etna.analysis.eda.plots import _cross_correlation
from etna.datasets import TSDataset
+from etna.transforms import TimeSeriesImputerTransform
def test_cross_corr_fail_lengths():
@@ -133,3 +137,50 @@ def test_acf_nan_begin(df_with_nans_in_head):
ts = TSDataset(df_with_nans_in_head, freq="H")
acf_plot(ts, partial=False)
acf_plot(ts, partial=True)
+
+
+@pytest.mark.parametrize(
+ "ts_name, params, match",
+ [
+ ("example_tsdf", {"start": 10}, "Parameter start has incorrect type"),
+ ("example_tsdf", {"end": 10}, "Parameter end has incorrect type"),
+ ("example_tsdf_int_timestamp", {"start": "2020-01-01"}, "Parameter start has incorrect type"),
+ ("example_tsdf_int_timestamp", {"end": "2020-01-01"}, "Parameter end has incorrect type"),
+ ],
+)
+def test_plot_holidays_incorrect_start_end_type(ts_name, params, match, request):
+ ts = request.getfixturevalue(ts_name)
+ holidays = pd.DataFrame(
+ {
+ "holiday": "Example",
+ "ds": [3],
+ "upper_window": 3,
+ }
+ )
+ with pytest.raises(ValueError, match=match):
+ plot_holidays(ts=ts, holidays=holidays, **params)
+
+
+@pytest.mark.parametrize(
+ "ts_name, params, match",
+ [
+ ("example_tsdf", {"start": 10}, "Parameter start has incorrect type"),
+ ("example_tsdf", {"end": 10}, "Parameter end has incorrect type"),
+ ("example_tsdf_int_timestamp", {"start": "2020-01-01"}, "Parameter start has incorrect type"),
+ ("example_tsdf_int_timestamp", {"end": "2020-01-01"}, "Parameter end has incorrect type"),
+ ],
+)
+def test_plot_imputation_fail_incorrect_start_end_type(ts_name, params, match, request):
+ ts = request.getfixturevalue(ts_name)
+ imputer = TimeSeriesImputerTransform(in_column="target", strategy="constant", constant_value=0)
+ with pytest.raises(ValueError, match=match):
+ plot_imputation(ts=ts, imputer=imputer, **params)
+
+
+def test_distribution_plot_datetime_timestamp(example_tsdf):
+ distribution_plot(example_tsdf, freq="D")
+
+
+@pytest.mark.parametrize("freq", [1, 24, 1000])
+def test_distribution_plot_int_timestamp(freq, example_tsdf_int_timestamp):
+ distribution_plot(example_tsdf_int_timestamp, freq=freq)
diff --git a/tests/test_analysis/test_eda/test_utils.py b/tests/test_analysis/test_eda/test_utils.py
index e5bb0b9e5..c4ab2a0ed 100644
--- a/tests/test_analysis/test_eda/test_utils.py
+++ b/tests/test_analysis/test_eda/test_utils.py
@@ -1,86 +1,104 @@
+import numpy as np
import pandas as pd
import pytest
from etna.analysis.eda.plots import _create_holidays_df
from etna.datasets import TSDataset
from etna.datasets import generate_ar_df
+from tests.utils import convert_ts_to_int_timestamp
-def test_create_holidays_df_str_fail(simple_df):
- with pytest.raises(ValueError):
- _create_holidays_df("RU", simple_df.index, as_is=True)
+@pytest.fixture
+def simple_tsdf_int_timestamp(simple_tsdf) -> TSDataset:
+ return convert_ts_to_int_timestamp(ts=simple_tsdf)
+
+
+def test_create_holidays_df_str_fail_as_is(simple_tsdf):
+ with pytest.raises(ValueError, match="Parameter `as_is` should be used with"):
+ _create_holidays_df("RU", simple_tsdf.index, as_is=True)
+
+
+def test_create_holidays_df_str_fail_int_timestamp(simple_tsdf_int_timestamp):
+ with pytest.raises(ValueError, match="Parameter `holidays` should be pd.DataFrame for data with integer timestamp"):
+ _create_holidays_df("RU", simple_tsdf_int_timestamp.index, as_is=False)
-def test_create_holidays_df_str_non_existing_country(simple_df):
+def test_create_holidays_df_str_non_existing_country(simple_tsdf):
with pytest.raises((NotImplementedError, KeyError)):
- _create_holidays_df("THIS_COUNTRY_DOES_NOT_EXIST", simple_df.index, as_is=False)
+ _create_holidays_df("THIS_COUNTRY_DOES_NOT_EXIST", simple_tsdf.index, as_is=False)
-def test_create_holidays_df_str(simple_df):
- df = _create_holidays_df("RU", simple_df.index, as_is=False)
- assert len(df) == len(simple_df.df)
+def test_create_holidays_df_str(simple_tsdf):
+ df = _create_holidays_df("RU", simple_tsdf.index, as_is=False)
+ assert len(df) == len(simple_tsdf.df)
assert all(df.dtypes == bool)
-def test_create_holidays_df_empty_fail(simple_df):
+def test_create_holidays_df_empty_fail(simple_tsdf):
with pytest.raises(ValueError):
- _create_holidays_df(pd.DataFrame(), simple_df.index, as_is=False)
+ _create_holidays_df(pd.DataFrame(), simple_tsdf.index, as_is=False)
-def test_create_holidays_df_intersect_none(simple_df):
+def test_create_holidays_df_intersect_none(simple_tsdf):
holidays = pd.DataFrame({"holiday": "New Year", "ds": pd.to_datetime(["1900-01-01", "1901-01-01"])})
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert not df.all(axis=None)
-def test_create_holidays_df_one_day(simple_df):
+def test_create_holidays_df_one_day(simple_tsdf):
holidays = pd.DataFrame({"holiday": "New Year", "ds": pd.to_datetime(["2020-01-01"])})
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert df.sum().sum() == 1
assert "New Year" in df.columns
-def test_create_holidays_df_upper_window(simple_df):
+def test_create_holidays_df_upper_window(simple_tsdf):
holidays = pd.DataFrame({"holiday": "New Year", "ds": pd.to_datetime(["2020-01-01"]), "upper_window": 2})
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert df.sum().sum() == 3
-def test_create_holidays_df_upper_window_out_of_index(simple_df):
+def test_create_holidays_df_upper_window_out_of_index(simple_tsdf):
holidays = pd.DataFrame({"holiday": "Christmas", "ds": pd.to_datetime(["2019-12-25"]), "upper_window": 10})
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert df.sum().sum() == 4
-def test_create_holidays_df_lower_window(simple_df):
+def test_create_holidays_df_lower_window(simple_tsdf):
holidays = pd.DataFrame({"holiday": "Christmas", "ds": pd.to_datetime(["2020-01-07"]), "lower_window": -2})
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert df.sum().sum() == 3
-def test_create_holidays_df_lower_window_out_of_index(simple_df):
+def test_create_holidays_df_lower_window_out_of_index(simple_tsdf):
holidays = pd.DataFrame(
{"holiday": "Moscow Anime Festival", "ds": pd.to_datetime(["2020-02-22"]), "lower_window": -5}
)
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert df.sum().sum() == 2
-def test_create_holidays_df_lower_upper_windows(simple_df):
+def test_create_holidays_df_lower_upper_windows(simple_tsdf):
holidays = pd.DataFrame(
{"holiday": "Christmas", "ds": pd.to_datetime(["2020-01-07"]), "upper_window": 3, "lower_window": -3}
)
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert df.sum().sum() == 7
-def test_create_holidays_df_as_is(simple_df):
+def test_create_holidays_df_as_is(simple_tsdf):
holidays = pd.DataFrame(index=pd.date_range(start="2020-01-07", end="2020-01-10"), columns=["Christmas"], data=1)
- df = _create_holidays_df(holidays, simple_df.index, as_is=True)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=True)
assert df.sum().sum() == 4
-def test_create_holidays_df_non_day_freq():
+def test_create_holidays_df_as_is_int_timestamp(simple_tsdf_int_timestamp):
+ holidays = pd.DataFrame(index=np.arange(7, 11), columns=["Christmas"], data=1)
+ df = _create_holidays_df(holidays, simple_tsdf_int_timestamp.index, as_is=True)
+ assert df.sum().sum() == 4
+
+
+def test_create_holidays_df_hour_freq():
classic_df = generate_ar_df(periods=30, start_time="2020-01-01", n_segments=1, freq="H")
ts = TSDataset.to_dataset(classic_df)
holidays = pd.DataFrame(
@@ -105,30 +123,44 @@ def test_create_holidays_df_15t_freq():
assert df.loc["2020-01-01 01:00:00":"2020-01-01 01:45:00"].sum().sum() == 4
-def test_create_holidays_df_several_holidays(simple_df):
+def test_create_holidays_df_int_timestamp():
+ classic_df = generate_ar_df(periods=30, start_time=0, n_segments=1, freq=None)
+ ts = TSDataset.to_dataset(classic_df)
+ holidays = pd.DataFrame(
+ {
+ "holiday": "Christmas",
+ "ds": [3],
+ "upper_window": 3,
+ }
+ )
+ df = _create_holidays_df(holidays, ts.index, as_is=False)
+ assert df.sum().sum() == 4
+
+
+def test_create_holidays_df_several_holidays(simple_tsdf):
christmas = pd.DataFrame({"holiday": "Christmas", "ds": pd.to_datetime(["2020-01-07"]), "lower_window": -3})
new_year = pd.DataFrame({"holiday": "New Year", "ds": pd.to_datetime(["2020-01-01"]), "upper_window": 2})
holidays = pd.concat((christmas, new_year))
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert df.sum().sum() == 7
-def test_create_holidays_df_zero_windows(simple_df):
+def test_create_holidays_df_zero_windows(simple_tsdf):
holidays = pd.DataFrame(
{"holiday": "Christmas", "ds": pd.to_datetime(["2020-01-07"]), "lower_window": 0, "upper_window": 0}
)
- df = _create_holidays_df(holidays, simple_df.index, as_is=False)
+ df = _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
assert df.sum().sum() == 1
assert df.loc["2020-01-07"].sum() == 1
-def test_create_holidays_df_upper_window_negative(simple_df):
+def test_create_holidays_df_upper_window_negative(simple_tsdf):
holidays = pd.DataFrame({"holiday": "Christmas", "ds": pd.to_datetime(["2020-01-07"]), "upper_window": -1})
with pytest.raises(ValueError):
- _create_holidays_df(holidays, simple_df.index, as_is=False)
+ _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
-def test_create_holidays_df_lower_window_positive(simple_df):
+def test_create_holidays_df_lower_window_positive(simple_tsdf):
holidays = pd.DataFrame({"holiday": "Christmas", "ds": pd.to_datetime(["2020-01-07"]), "lower_window": 1})
with pytest.raises(ValueError):
- _create_holidays_df(holidays, simple_df.index, as_is=False)
+ _create_holidays_df(holidays, simple_tsdf.index, as_is=False)
diff --git a/tests/test_analysis/test_outliers/test_confidence_interval_outliers.py b/tests/test_analysis/test_outliers/test_confidence_interval_outliers.py
index 2ac0eac3b..99f07b9fc 100644
--- a/tests/test_analysis/test_outliers/test_confidence_interval_outliers.py
+++ b/tests/test_analysis/test_outliers/test_confidence_interval_outliers.py
@@ -7,6 +7,25 @@
from etna.datasets import TSDataset
from etna.models import ProphetModel
from etna.models import SARIMAXModel
+from tests.utils import convert_ts_to_int_timestamp
+
+
+@pytest.fixture
+def outliers_tsds_int_timestamp(outliers_tsds) -> TSDataset:
+ return convert_ts_to_int_timestamp(ts=outliers_tsds, shift=10)
+
+
+@pytest.fixture
+def outliers_tsds_with_external_timestamp(outliers_tsds) -> TSDataset:
+ df = outliers_tsds.to_pandas(flatten=True)
+ df_exog = df.copy()
+ df_exog["external_timestamp"] = df["timestamp"]
+ df_exog.drop(columns=["target"], inplace=True)
+ df_wide = TSDataset.to_dataset(df.drop(columns=["exog"])).iloc[1:-1]
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq="D", known_future=["external_timestamp"])
+ ts = convert_ts_to_int_timestamp(ts=ts, shift=10)
+ return ts
@pytest.mark.parametrize("column", ["exog"])
@@ -45,28 +64,46 @@ def test_get_anomalies_prediction_interval_interface(outliers_tsds, model, in_co
@pytest.mark.parametrize("in_column", ["target", "exog"])
@pytest.mark.parametrize(
- "model, interval_width, true_anomalies",
+ "model, interval_width, ts_name, true_anomalies",
(
(
ProphetModel,
0.95,
+ "outliers_tsds",
{"1": [np.datetime64("2021-01-11")], "2": [np.datetime64("2021-01-09"), np.datetime64("2021-01-27")]},
),
(
SARIMAXModel,
0.999,
+ "outliers_tsds",
{"1": [np.datetime64("2021-01-11")], "2": [np.datetime64("2021-01-09"), np.datetime64("2021-01-27")]},
),
+ (
+ SARIMAXModel,
+ 0.999,
+ "outliers_tsds_int_timestamp",
+ {"1": [20], "2": [18, 36]},
+ ),
),
)
-def test_get_anomalies_prediction_interval_values(outliers_tsds, model, interval_width, true_anomalies, in_column):
+def test_get_anomalies_prediction_interval_values(model, interval_width, ts_name, true_anomalies, in_column, request):
"""Test that `get_anomalies_prediction_interval` generates correct values."""
- assert (
- get_anomalies_prediction_interval(
- outliers_tsds, model=model, interval_width=interval_width, in_column=in_column
- )
- == true_anomalies
+ ts = request.getfixturevalue(ts_name)
+ predicted_anomalies = get_anomalies_prediction_interval(
+ ts, model=model, interval_width=interval_width, in_column=in_column
+ )
+ assert predicted_anomalies == true_anomalies
+
+
+def test_get_anomalies_prediction_interval_values_prophet_with_external_timestamp(
+ outliers_tsds_with_external_timestamp,
+):
+ ts = outliers_tsds_with_external_timestamp
+ predicted_anomalies = get_anomalies_prediction_interval(
+ ts, model=ProphetModel, interval_width=0.95, in_column="target", timestamp_column="external_timestamp"
)
+ true_anomalies = {"1": [19], "2": [17, 35]}
+ assert predicted_anomalies == true_anomalies
@pytest.mark.parametrize(
diff --git a/tests/test_analysis/test_outliers/test_plots.py b/tests/test_analysis/test_outliers/test_plots.py
new file mode 100644
index 000000000..ae1bd7c12
--- /dev/null
+++ b/tests/test_analysis/test_outliers/test_plots.py
@@ -0,0 +1,43 @@
+import pytest
+
+from etna.analysis import get_anomalies_density
+from etna.analysis import plot_anomalies
+from etna.analysis import plot_anomalies_interactive
+
+
+@pytest.mark.parametrize(
+ "ts_name, params, match",
+ [
+ ("example_tsdf", {"start": 10}, "Parameter start has incorrect type"),
+ ("example_tsdf", {"end": 10}, "Parameter end has incorrect type"),
+ ("example_tsdf_int_timestamp", {"start": "2020-01-01"}, "Parameter start has incorrect type"),
+ ("example_tsdf_int_timestamp", {"end": "2020-01-01"}, "Parameter end has incorrect type"),
+ ],
+)
+def test_plot_anomalies_fail_incorrect_start_end_type(ts_name, params, match, request):
+ ts = request.getfixturevalue(ts_name)
+ anomaly_dict = {"segment_1": [10, 100], "segment_2": [20, 200]}
+ with pytest.raises(ValueError, match=match):
+ plot_anomalies(ts=ts, anomaly_dict=anomaly_dict, **params)
+
+
+@pytest.mark.parametrize(
+ "ts_name, params, match",
+ [
+ ("example_tsdf", {"start": 10}, "Parameter start has incorrect type"),
+ ("example_tsdf", {"end": 10}, "Parameter end has incorrect type"),
+ ("example_tsdf_int_timestamp", {"start": "2020-01-01"}, "Parameter start has incorrect type"),
+ ("example_tsdf_int_timestamp", {"end": "2020-01-01"}, "Parameter end has incorrect type"),
+ ],
+)
+def test_plot_anomalies_interactive_fail_incorrect_start_end_type(ts_name, params, match, request):
+ ts = request.getfixturevalue(ts_name)
+ params_bounds = {"window_size": (5, 20, 1), "distance_coef": (0.1, 3, 0.25)}
+ with pytest.raises(ValueError, match=match):
+ plot_anomalies_interactive(
+ ts=ts,
+ segment="segment_1",
+ method=get_anomalies_density,
+ params_bounds=params_bounds,
+ **params,
+ )
diff --git a/tests/test_commands/conftest.py b/tests/test_commands/conftest.py
index 0663ab8a8..16d75f31b 100644
--- a/tests/test_commands/conftest.py
+++ b/tests/test_commands/conftest.py
@@ -183,6 +183,16 @@ def base_timeseries_path():
tmp.close()
+@pytest.fixture
+def base_timeseries_int_timestamp_path():
+ df = generate_ar_df(periods=100, start_time=10, n_segments=2, freq=None)
+ tmp = NamedTemporaryFile("w")
+ df.to_csv(tmp, index=False)
+ tmp.flush()
+ yield Path(tmp.name)
+ tmp.close()
+
+
@pytest.fixture
def base_timeseries_exog_path():
df_regressors = pd.DataFrame(
@@ -201,7 +211,24 @@ def base_timeseries_exog_path():
@pytest.fixture
-def empty_ts():
- df = pd.DataFrame({"segment": [], "timestamp": [], "target": []})
+def base_timeseries_int_timestamp_exog_path():
+ df_regressors = pd.DataFrame(
+ {
+ "timestamp": np.arange(10, 130).tolist() * 2,
+ "regressor_1": np.arange(240),
+ "regressor_2": np.arange(240) + 5,
+ "segment": ["segment_0"] * 120 + ["segment_1"] * 120,
+ }
+ )
+ tmp = NamedTemporaryFile("w")
+ df_regressors.to_csv(tmp, index=False)
+ tmp.flush()
+ yield Path(tmp.name)
+ tmp.close()
+
+
+@pytest.fixture
+def small_ts():
+ df = pd.DataFrame({"segment": ["segment_0"], "timestamp": [pd.Timestamp("2020-01-01")], "target": [1]})
df = TSDataset.to_dataset(df=df)
return TSDataset(df=df, freq="D")
diff --git a/tests/test_commands/test_backtest.py b/tests/test_commands/test_backtest.py
index 8ab83e2b3..8f9a5248e 100644
--- a/tests/test_commands/test_backtest.py
+++ b/tests/test_commands/test_backtest.py
@@ -68,7 +68,7 @@ def backtest_with_stride_yaml_path():
@pytest.mark.parametrize("pipeline_path_name", ("base_pipeline_yaml_path", "base_ensemble_yaml_path"))
-def test_dummy_run(pipeline_path_name, base_backtest_yaml_path, base_timeseries_path, request):
+def test_backtest(pipeline_path_name, base_backtest_yaml_path, base_timeseries_path, request):
tmp_output = TemporaryDirectory()
tmp_output_path = Path(tmp_output.name)
pipeline_path = request.getfixturevalue(pipeline_path_name)
@@ -88,7 +88,29 @@ def test_dummy_run(pipeline_path_name, base_backtest_yaml_path, base_timeseries_
@pytest.mark.parametrize("pipeline_path_name", ("base_pipeline_yaml_path", "base_ensemble_yaml_path"))
-def test_dummy_run_with_exog(
+def test_backtest_with_int_timestamp(
+ pipeline_path_name, base_backtest_yaml_path, base_timeseries_int_timestamp_path, request
+):
+ tmp_output = TemporaryDirectory()
+ tmp_output_path = Path(tmp_output.name)
+ pipeline_path = request.getfixturevalue(pipeline_path_name)
+ run(
+ [
+ "etna",
+ "backtest",
+ str(pipeline_path),
+ str(base_backtest_yaml_path),
+ str(base_timeseries_int_timestamp_path),
+ "None",
+ str(tmp_output_path),
+ ]
+ )
+ for file_name in ["metrics.csv", "forecast.csv", "info.csv"]:
+ assert Path.exists(tmp_output_path / file_name)
+
+
+@pytest.mark.parametrize("pipeline_path_name", ("base_pipeline_yaml_path", "base_ensemble_yaml_path"))
+def test_backtest_with_exog(
pipeline_path_name, base_backtest_yaml_path, base_timeseries_path, base_timeseries_exog_path, request
):
tmp_output = TemporaryDirectory()
diff --git a/tests/test_commands/test_forecast.py b/tests/test_commands/test_forecast.py
index 262bb53e0..be19d2df8 100644
--- a/tests/test_commands/test_forecast.py
+++ b/tests/test_commands/test_forecast.py
@@ -47,6 +47,22 @@ def start_timestamp_forecast_omegaconf_path():
tmp.close()
+@pytest.fixture
+def int_start_timestamp_forecast_omegaconf_path():
+ tmp = NamedTemporaryFile("w")
+ tmp.write(
+ """
+ prediction_interval: true
+ quantiles: [0.025, 0.975]
+ n_folds: 3
+ start_timestamp: 112
+ """
+ )
+ tmp.flush()
+ yield Path(tmp.name)
+ tmp.close()
+
+
@pytest.fixture
def base_forecast_with_folds_estimation_omegaconf_path():
tmp = NamedTemporaryFile("w")
@@ -64,8 +80,16 @@ def base_forecast_with_folds_estimation_omegaconf_path():
tmp.close()
+def test_forecast(base_pipeline_yaml_path, base_timeseries_path):
+ tmp_output = NamedTemporaryFile("w")
+ tmp_output_path = Path(tmp_output.name)
+ run(["etna", "forecast", str(base_pipeline_yaml_path), str(base_timeseries_path), "D", str(tmp_output_path)])
+ df_output = pd.read_csv(tmp_output_path)
+ assert len(df_output) == 2 * 4
+
+
@pytest.mark.parametrize("pipeline_path_name", ("base_pipeline_yaml_path", "base_ensemble_yaml_path"))
-def test_dummy_run_with_exog(pipeline_path_name, base_timeseries_path, base_timeseries_exog_path, request):
+def test_forecast_with_int_timestamp(pipeline_path_name, base_timeseries_int_timestamp_path, request):
tmp_output = NamedTemporaryFile("w")
tmp_output_path = Path(tmp_output.name)
pipeline_path = request.getfixturevalue(pipeline_path_name)
@@ -74,24 +98,26 @@ def test_dummy_run_with_exog(pipeline_path_name, base_timeseries_path, base_time
"etna",
"forecast",
str(pipeline_path),
- str(base_timeseries_path),
- "D",
+ str(base_timeseries_int_timestamp_path),
+ "None",
str(tmp_output_path),
- str(base_timeseries_exog_path),
]
)
df_output = pd.read_csv(tmp_output_path)
assert len(df_output) == 2 * 4
+ assert df_output["timestamp"].dtype == "int" # int timestamp in forecast
-def test_omegaconf_run_with_exog(base_pipeline_omegaconf_path, base_timeseries_path, base_timeseries_exog_path):
+@pytest.mark.parametrize("pipeline_path_name", ("base_pipeline_yaml_path", "base_ensemble_yaml_path"))
+def test_forecast_with_exog(pipeline_path_name, base_timeseries_path, base_timeseries_exog_path, request):
tmp_output = NamedTemporaryFile("w")
tmp_output_path = Path(tmp_output.name)
+ pipeline_path = request.getfixturevalue(pipeline_path_name)
run(
[
"etna",
"forecast",
- str(base_pipeline_omegaconf_path),
+ str(pipeline_path),
str(base_timeseries_path),
"D",
str(tmp_output_path),
@@ -102,16 +128,26 @@ def test_omegaconf_run_with_exog(base_pipeline_omegaconf_path, base_timeseries_p
assert len(df_output) == 2 * 4
-def test_dummy_run(base_pipeline_yaml_path, base_timeseries_path):
+def test_forecast_omegaconf_with_exog(base_pipeline_omegaconf_path, base_timeseries_path, base_timeseries_exog_path):
tmp_output = NamedTemporaryFile("w")
tmp_output_path = Path(tmp_output.name)
- run(["etna", "forecast", str(base_pipeline_yaml_path), str(base_timeseries_path), "D", str(tmp_output_path)])
+ run(
+ [
+ "etna",
+ "forecast",
+ str(base_pipeline_omegaconf_path),
+ str(base_timeseries_path),
+ "D",
+ str(tmp_output_path),
+ str(base_timeseries_exog_path),
+ ]
+ )
df_output = pd.read_csv(tmp_output_path)
assert len(df_output) == 2 * 4
@pytest.mark.parametrize("pipeline_path_name", ("base_pipeline_yaml_path", "base_ensemble_yaml_path"))
-def test_run_with_predictive_intervals(
+def test_forecast_with_predictive_intervals(
pipeline_path_name, base_timeseries_path, base_timeseries_exog_path, base_forecast_omegaconf_path, request
):
tmp_output = NamedTemporaryFile("w")
@@ -182,21 +218,42 @@ def pipeline_dummy_config():
return {"horizon": 3}
-@pytest.mark.parametrize("forecast_params", ({"start_timestamp": "2020-04-09"}, {"start_timestamp": "2019-04-10"}))
-def test_compute_horizon_error(example_tsds, forecast_params, pipeline_dummy_config):
+@pytest.mark.parametrize(
+ "forecast_params,tsdataset_name",
+ [
+ ({"start_timestamp": "2020-04-09"}, "example_tsds"),
+ ({"start_timestamp": "2019-04-10"}, "example_tsds"),
+ ({"start_timestamp": 100}, "example_tsds_int_timestamp"),
+ ({"start_timestamp": 109}, "example_tsds_int_timestamp"),
+ ],
+)
+def test_compute_horizon_fail_too_small(forecast_params, tsdataset_name, request, pipeline_dummy_config):
+ tsdataset = request.getfixturevalue(tsdataset_name)
with pytest.raises(ValueError, match="Parameter `start_timestamp` should greater than end of training dataset!"):
- compute_horizon(
- horizon=pipeline_dummy_config["horizon"], forecast_params=forecast_params, tsdataset=example_tsds
- )
+ compute_horizon(horizon=pipeline_dummy_config["horizon"], forecast_params=forecast_params, tsdataset=tsdataset)
+
+
+@pytest.mark.parametrize(
+ "forecast_params,tsdataset_name",
+ [({"start_timestamp": 100}, "example_tsds"), ({"start_timestamp": "2019-04-10"}, "example_tsds_int_timestamp")],
+)
+def test_compute_horizon_fail_wrong_type(forecast_params, tsdataset_name, request, pipeline_dummy_config):
+ tsdataset = request.getfixturevalue(tsdataset_name)
+ with pytest.raises(ValueError, match="Parameter start_timestamp has incorrect type"):
+ compute_horizon(horizon=pipeline_dummy_config["horizon"], forecast_params=forecast_params, tsdataset=tsdataset)
@pytest.mark.parametrize(
"forecast_params,tsdataset_name,expected",
(
+ ({}, "example_tsds", 3),
({"start_timestamp": "2020-04-10"}, "example_tsds", 3),
({"start_timestamp": "2020-04-12"}, "example_tsds", 5),
({"start_timestamp": "2020-02-01 02:00:00"}, "example_tsdf", 4),
({"start_timestamp": "2023-06-01"}, "ms_tsds", 4),
+ ({}, "example_tsds_int_timestamp", 3),
+ ({"start_timestamp": 110}, "example_tsds_int_timestamp", 3),
+ ({"start_timestamp": 112}, "example_tsds_int_timestamp", 5),
),
)
def test_compute_horizon(forecast_params, tsdataset_name, expected, request, pipeline_dummy_config):
@@ -208,30 +265,47 @@ def test_compute_horizon(forecast_params, tsdataset_name, expected, request, pip
@pytest.mark.parametrize(
- "forecast_params,expected",
+ "forecast_params,tsdataset_name",
+ [({"start_timestamp": 100}, "example_tsds"), ({"start_timestamp": "2019-04-10"}, "example_tsds_int_timestamp")],
+)
+def test_filter_forecast_fail_wrong_type(forecast_params, tsdataset_name, request):
+ tsdataset = request.getfixturevalue(tsdataset_name)
+ with pytest.raises(ValueError, match="Parameter start_timestamp has incorrect type"):
+ filter_forecast(forecast_ts=tsdataset, forecast_params=forecast_params)
+
+
+@pytest.mark.parametrize(
+ "forecast_params,tsdataset_name,expected",
(
- ({"start_timestamp": "2020-04-06"}, "2020-04-06"),
- ({}, "2020-01-01"),
+ ({"start_timestamp": "2020-04-06"}, "example_tsds", pd.Timestamp("2020-04-06")),
+ ({}, "example_tsds", pd.Timestamp("2020-01-01")),
+ ({"start_timestamp": 100}, "example_tsds_int_timestamp", 100),
+ ({}, "example_tsds_int_timestamp", 10),
),
)
-def test_filter_forecast(forecast_params, expected, example_tsds):
- result = filter_forecast(forecast_ts=example_tsds, forecast_params=forecast_params)
- assert result.df.index.min() == pd.Timestamp(expected)
+def test_filter_forecast(forecast_params, tsdataset_name, expected, request):
+ tsdataset = request.getfixturevalue(tsdataset_name)
+ result = filter_forecast(forecast_ts=tsdataset, forecast_params=forecast_params)
+ assert result.df.index.min() == expected
@pytest.mark.parametrize(
- "forecast_params,pipeline_path_name,expected",
+ "forecast_params,tsdataset_name,pipeline_path_name,expected",
(
- ({"start_timestamp": "2020-04-10"}, "base_pipeline_with_context_size_yaml_path", 4),
- ({"start_timestamp": "2020-04-12"}, "base_pipeline_with_context_size_yaml_path", 6),
- ({"start_timestamp": "2020-04-11"}, "base_ensemble_yaml_path", 5),
+ ({"start_timestamp": "2020-04-10"}, "example_tsds", "base_pipeline_with_context_size_yaml_path", 4),
+ ({"start_timestamp": "2020-04-12"}, "example_tsds", "base_pipeline_with_context_size_yaml_path", 6),
+ ({"start_timestamp": "2020-04-11"}, "example_tsds", "base_ensemble_yaml_path", 5),
+ ({"start_timestamp": 110}, "example_tsds_int_timestamp", "base_pipeline_with_context_size_yaml_path", 4),
+ ({"start_timestamp": 112}, "example_tsds_int_timestamp", "base_pipeline_with_context_size_yaml_path", 6),
+ ({"start_timestamp": 111}, "example_tsds_int_timestamp", "base_ensemble_yaml_path", 5),
),
)
-def test_update_horizon(pipeline_path_name, forecast_params, example_tsds, expected, request):
+def test_update_horizon(pipeline_path_name, forecast_params, tsdataset_name, expected, request):
+ tsdataset = request.getfixturevalue(tsdataset_name)
pipeline_path = request.getfixturevalue(pipeline_path_name)
pipeline_conf = OmegaConf.to_object(OmegaConf.load(pipeline_path))
- update_horizon(pipeline_configs=pipeline_conf, forecast_params=forecast_params, tsdataset=example_tsds)
+ update_horizon(pipeline_configs=pipeline_conf, forecast_params=forecast_params, tsdataset=tsdataset)
pipeline_conf = remove_params(params=pipeline_conf, to_remove=ADDITIONAL_PIPELINE_PARAMETERS)
pipeline = hydra_slayer.get_from_params(**pipeline_conf)
@@ -243,7 +317,7 @@ def test_update_horizon(pipeline_path_name, forecast_params, example_tsds, expec
"pipeline_path_name",
("base_pipeline_with_context_size_yaml_path", "base_ensemble_yaml_path"),
)
-def test_forecast_start_timestamp(
+def test_forecast_with_start_timestamp(
pipeline_path_name,
base_timeseries_path,
base_timeseries_exog_path,
@@ -273,8 +347,42 @@ def test_forecast_start_timestamp(
assert not np.any(df_output.isna().values)
+@pytest.mark.parametrize(
+ "pipeline_path_name",
+ ("base_pipeline_with_context_size_yaml_path", "base_ensemble_yaml_path"),
+)
+def test_forecast_with_start_timestamp_int_timestamp(
+ pipeline_path_name,
+ base_timeseries_int_timestamp_path,
+ base_timeseries_int_timestamp_exog_path,
+ int_start_timestamp_forecast_omegaconf_path,
+ request,
+):
+ tmp_output = NamedTemporaryFile("w")
+ tmp_output_path = Path(tmp_output.name)
+ pipeline_path = request.getfixturevalue(pipeline_path_name)
+
+ run(
+ [
+ "etna",
+ "forecast",
+ str(pipeline_path),
+ str(base_timeseries_int_timestamp_path),
+ "None",
+ str(tmp_output_path),
+ str(base_timeseries_int_timestamp_exog_path),
+ str(int_start_timestamp_forecast_omegaconf_path),
+ ]
+ )
+ df_output = pd.read_csv(tmp_output_path)
+
+ assert len(df_output) == 4 * 2 # 4 predictions for 2 segments
+ assert df_output["timestamp"].min() == 112 # start_timestamp
+ assert not np.any(df_output.isna().values)
+
+
@pytest.mark.parametrize("pipeline_path_name", ("base_pipeline_with_context_size_yaml_path", "base_ensemble_yaml_path"))
-def test_forecast_estimate_n_folds(
+def test_forecast_with_estimate_n_folds(
pipeline_path_name,
base_forecast_with_folds_estimation_omegaconf_path,
base_timeseries_path,
diff --git a/tests/test_commands/test_utils.py b/tests/test_commands/test_utils.py
index 66df69cab..2545d72ad 100644
--- a/tests/test_commands/test_utils.py
+++ b/tests/test_commands/test_utils.py
@@ -125,9 +125,9 @@ def test_estimate_max_n_folds_invalid_method_name(pipeline_without_context, exam
)
-def test_estimate_max_n_folds_empty_ts(pipeline_without_context, empty_ts):
+def test_estimate_max_n_folds_small_ts(pipeline_without_context, small_ts):
with pytest.raises(ValueError, match="Not enough data points!"):
- _ = estimate_max_n_folds(pipeline=pipeline_without_context, ts=empty_ts, method_name="forecast", context_size=1)
+ _ = estimate_max_n_folds(pipeline=pipeline_without_context, ts=small_ts, method_name="forecast", context_size=1)
def test_estimate_max_n_folds_negative_context(pipeline_without_context, example_tsds):
diff --git a/tests/test_datasets/conftest.py b/tests/test_datasets/conftest.py
index 203d0546d..50afa5772 100644
--- a/tests/test_datasets/conftest.py
+++ b/tests/test_datasets/conftest.py
@@ -1,5 +1,8 @@
+import numpy as np
+import pandas as pd
import pytest
+from etna.datasets import generate_ar_df
from etna.datasets.hierarchical_structure import HierarchicalStructure
@@ -19,3 +22,121 @@ def tailed_hierarchical_structure():
level_names=["l1", "l2", "l3", "l4"],
)
return hs
+
+
+@pytest.fixture
+def df_aligned_datetime() -> pd.DataFrame:
+ df = generate_ar_df(start_time="2020-01-01", periods=10, n_segments=2, freq="D")
+ return df
+
+
+@pytest.fixture
+def df_exog_aligned_datetime() -> pd.DataFrame:
+ df_exog_1 = generate_ar_df(start_time="2020-01-01", periods=15, n_segments=2, freq="D")
+ df_exog_1.rename(columns={"target": "exog_1"}, inplace=True)
+
+ df_exog_2 = generate_ar_df(start_time="2020-01-01", periods=10, n_segments=2, freq="D")
+ df_exog_2.rename(columns={"target": "exog_2"}, inplace=True)
+
+ df = pd.merge(left=df_exog_1, right=df_exog_2, how="outer")
+
+ return df
+
+
+@pytest.fixture
+def df_aligned_int() -> pd.DataFrame:
+ df = generate_ar_df(start_time=10, periods=10, n_segments=2, freq=None)
+ return df
+
+
+@pytest.fixture
+def df_exog_aligned_int() -> pd.DataFrame:
+ df_exog_1 = generate_ar_df(start_time=10, periods=15, n_segments=2, freq=None)
+ df_exog_1.rename(columns={"target": "exog_1"}, inplace=True)
+
+ df_exog_2 = generate_ar_df(start_time=10, periods=10, n_segments=2, freq=None)
+ df_exog_2.rename(columns={"target": "exog_2"}, inplace=True)
+
+ df = pd.merge(left=df_exog_1, right=df_exog_2, how="outer")
+
+ return df
+
+
+@pytest.fixture
+def df_misaligned_datetime() -> pd.DataFrame:
+ df = generate_ar_df(start_time="2020-01-01", periods=10, n_segments=2, freq="D")
+ df = df.iloc[:-3]
+ return df
+
+
+@pytest.fixture
+def df_exog_misaligned_datetime() -> pd.DataFrame:
+ df_exog_1 = generate_ar_df(start_time="2020-01-01", periods=15, n_segments=2, freq="D")
+ df_exog_1.rename(columns={"target": "exog_1"}, inplace=True)
+ df_exog_1 = df_exog_1.iloc[:-3]
+
+ df_exog_2 = generate_ar_df(start_time="2020-01-01", periods=10, n_segments=2, freq="D")
+ df_exog_2.rename(columns={"target": "exog_2"}, inplace=True)
+ df_exog_2 = df_exog_2.iloc[:-3]
+
+ df = pd.merge(left=df_exog_1, right=df_exog_2, how="outer")
+
+ return df
+
+
+@pytest.fixture
+def df_exog_all_misaligned_datetime() -> pd.DataFrame:
+ df_exog_1 = generate_ar_df(start_time="2020-01-01", periods=20, n_segments=2, freq="D")
+ df_exog_1.rename(columns={"target": "exog_1"}, inplace=True)
+ df_exog_1 = df_exog_1.iloc[:-3]
+
+ df_exog_2 = generate_ar_df(start_time="2020-01-01", periods=20, n_segments=2, freq="D")
+ df_exog_2.rename(columns={"target": "exog_2"}, inplace=True)
+ df_exog_2 = df_exog_2.iloc[:-3]
+
+ df = pd.merge(left=df_exog_1, right=df_exog_2, how="outer")
+
+ return df
+
+
+@pytest.fixture
+def df_misaligned_int() -> pd.DataFrame:
+ df = generate_ar_df(start_time=10, periods=10, n_segments=2, freq=None)
+ df = df.iloc[:-3]
+ return df
+
+
+@pytest.fixture
+def df_exog_misaligned_int() -> pd.DataFrame:
+ df_exog_1 = generate_ar_df(start_time=10, periods=15, n_segments=2, freq=None)
+ df_exog_1.rename(columns={"target": "exog_1"}, inplace=True)
+ df_exog_1 = df_exog_1.iloc[:-3]
+
+ df_exog_2 = generate_ar_df(start_time=10, periods=10, n_segments=2, freq=None)
+ df_exog_2.rename(columns={"target": "exog_2"}, inplace=True)
+ df_exog_2 = df_exog_2.iloc[:-3]
+
+ df = pd.merge(left=df_exog_1, right=df_exog_2, how="outer")
+
+ return df
+
+
+@pytest.fixture
+def df_aligned_datetime_with_missing_values() -> pd.DataFrame:
+ df = generate_ar_df(start_time="2020-01-01", periods=10, n_segments=2, freq="D")
+ df.loc[df.index[-3:], "target"] = np.NaN
+ return df
+
+
+@pytest.fixture
+def df_aligned_int_with_missing_values() -> pd.DataFrame:
+ df = generate_ar_df(start_time=10, periods=10, n_segments=2, freq=None)
+ df.loc[df.index[-3:], "target"] = np.NaN
+ return df
+
+
+@pytest.fixture
+def df_aligned_datetime_with_additional_columns() -> pd.DataFrame:
+ df = generate_ar_df(start_time="2020-01-01", periods=10, n_segments=2, freq="D")
+ df["feature_1"] = df["timestamp"].dt.weekday
+ return df
diff --git a/tests/test_datasets/test_dataset.py b/tests/test_datasets/test_dataset.py
index b6d3562af..298b047e4 100644
--- a/tests/test_datasets/test_dataset.py
+++ b/tests/test_datasets/test_dataset.py
@@ -2,6 +2,7 @@
from copy import deepcopy
from typing import List
from typing import Tuple
+from unittest.mock import patch
import numpy as np
import pandas as pd
@@ -10,15 +11,19 @@
from etna.datasets import generate_ar_df
from etna.datasets.tsdataset import TSDataset
+from etna.datasets.utils import DataFrameFormat
+from etna.datasets.utils import apply_alignment
+from etna.datasets.utils import infer_alignment
+from etna.datasets.utils import make_timestamp_df_from_alignment
from etna.transforms import AddConstTransform
from etna.transforms import DifferencingTransform
from etna.transforms import TimeSeriesImputerTransform
-@pytest.fixture()
+@pytest.fixture
def tsdf_with_exog(random_seed) -> TSDataset:
- df_1 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-02-01", "2021-07-01", freq="1d")})
- df_2 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-02-01", "2021-07-01", freq="1d")})
+ df_1 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-02-01", "2021-07-01", freq="D")})
+ df_2 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-02-01", "2021-07-01", freq="D")})
df_1["segment"] = "Moscow"
df_1["target"] = [x**2 + np.random.uniform(-2, 2) for x in list(range(len(df_1)))]
df_2["segment"] = "Omsk"
@@ -32,7 +37,19 @@ def tsdf_with_exog(random_seed) -> TSDataset:
classic_df_exog.rename(columns={"target": "exog"}, inplace=True)
df_exog = TSDataset.to_dataset(classic_df_exog)
- ts = TSDataset(df=df, df_exog=df_exog, freq="1D")
+ ts = TSDataset(df=df, df_exog=df_exog, freq="D")
+ return ts
+
+
+@pytest.fixture
+def tsdf_int_with_exog(tsdf_with_exog) -> TSDataset:
+ df = tsdf_with_exog.raw_df
+ df_exog = tsdf_with_exog.df_exog
+ ref_point = pd.Timestamp("2021-01-01")
+ df.index = pd.Index((df.index - ref_point).days, name=df.index.name)
+ df_exog.index = pd.Index((df_exog.index - ref_point).days, name=df_exog.index.name)
+
+ ts = TSDataset(df=df, df_exog=df_exog, freq=None)
return ts
@@ -310,6 +327,179 @@ def ts_with_prediction_intervals(ts_without_target_components, prediction_interv
return ts
+def test_create_ts_with_datetime_timestamp():
+ freq = "D"
+ df = generate_ar_df(periods=10, freq=freq, n_segments=3)
+ df_wide = TSDataset.to_dataset(df)
+ df_wide.index.freq = freq
+ ts = TSDataset(df=df_wide, freq=freq)
+
+ pd.testing.assert_index_equal(ts.index, df_wide.index)
+ pd.testing.assert_frame_equal(ts.to_pandas(), df_wide)
+
+
+def test_create_ts_with_int_timestamp():
+ df = generate_ar_df(periods=10, freq=None, n_segments=3)
+ df_wide = TSDataset.to_dataset(df)
+ ts = TSDataset(df=df_wide, freq=None)
+
+ pd.testing.assert_index_equal(ts.index, df_wide.index)
+ pd.testing.assert_frame_equal(ts.to_pandas(), df_wide)
+
+
+@pytest.mark.filterwarnings(
+ "ignore: Timestamp contains numeric values, and given freq is D. Timestamp will be converted to datetime.",
+ "ignore: You probably set wrong freq. Discovered freq in you data is N, you set D",
+)
+def test_create_ts_with_int_timestamp_with_freq():
+ df = generate_ar_df(periods=10, freq=None, n_segments=3)
+ df_wide = TSDataset.to_dataset(df)
+ ts = TSDataset(df=df_wide, freq="D")
+
+ assert ts.index.dtype == "datetime64[ns]"
+
+
+def test_create_ts_with_exog_datetime_timestamp():
+ freq = "D"
+ df = generate_ar_df(periods=10, start_time="2020-01-05", freq=freq, n_segments=3, random_seed=0)
+ df_exog = generate_ar_df(periods=20, start_time="2020-01-01", freq=freq, n_segments=3, random_seed=1)
+ df_exog.rename(columns={"target": "exog"}, inplace=True)
+
+ df_wide = TSDataset.to_dataset(df)
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq=freq)
+
+ expected_merged = pd.concat([df_wide, df_exog_wide.loc[df_wide.index]], axis=1).sort_index(axis=1, level=(0, 1))
+ expected_merged.index.freq = freq
+ pd.testing.assert_index_equal(ts.index, df_wide.index)
+ pd.testing.assert_frame_equal(ts.to_pandas(), expected_merged)
+
+
+def test_create_ts_with_exog_int_timestamp():
+ df = generate_ar_df(periods=10, start_time=5, freq=None, n_segments=3, random_seed=0)
+ df_exog = generate_ar_df(periods=20, start_time=0, freq=None, n_segments=3, random_seed=1)
+ df_exog.rename(columns={"target": "exog"}, inplace=True)
+
+ df_wide = TSDataset.to_dataset(df)
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq=None)
+
+ expected_merged = pd.concat([df_wide, df_exog_wide.loc[df_wide.index]], axis=1).sort_index(axis=1, level=(0, 1))
+ pd.testing.assert_index_equal(ts.index, df_wide.index)
+ pd.testing.assert_frame_equal(ts.to_pandas(), expected_merged)
+
+
+@pytest.mark.filterwarnings(
+ "ignore: Timestamp contains numeric values, and given freq is D. Timestamp will be converted to datetime.",
+ "ignore: You probably set wrong freq. Discovered freq in you data is N, you set D",
+)
+def test_create_ts_with_exog_int_timestamp_with_freq():
+ df = generate_ar_df(periods=10, start_time=5, freq=None, n_segments=3, random_seed=0)
+ df_exog = generate_ar_df(periods=20, start_time=0, freq=None, n_segments=3, random_seed=1)
+ df_exog.rename(columns={"target": "exog"}, inplace=True)
+
+ df_wide = TSDataset.to_dataset(df)
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq="D")
+
+ assert ts.index.dtype == "datetime64[ns]"
+
+
+def test_create_ts_missing_datetime_timestamp():
+ freq = "D"
+ df = generate_ar_df(periods=10, start_time="2020-01-01", freq=freq, n_segments=3, random_seed=0)
+
+ df_wide = TSDataset.to_dataset(df)
+ df_wide_missing = df_wide.drop(index=df_wide.index[3:5])
+ ts = TSDataset(df=df_wide_missing, freq=freq)
+
+ expected_df = df_wide.copy()
+ expected_df.iloc[3:5] = np.NaN
+ expected_df.index.freq = freq
+ pd.testing.assert_index_equal(ts.index, df_wide.index)
+ pd.testing.assert_frame_equal(ts.to_pandas(), expected_df)
+
+
+def test_create_ts_missing_int_timestamp():
+ df = generate_ar_df(periods=10, start_time=5, freq=None, n_segments=3, random_seed=0)
+
+ df_wide = TSDataset.to_dataset(df)
+ df_wide_missing = df_wide.drop(index=df_wide.index[3:5])
+ ts = TSDataset(df=df_wide_missing, freq=None)
+
+ expected_df = df_wide.copy()
+ expected_df.iloc[3:5] = np.NaN
+ pd.testing.assert_index_equal(ts.index, df_wide.index)
+ pd.testing.assert_frame_equal(ts.to_pandas(), expected_df)
+
+
+def test_create_ts_with_int_timestamp_fail_datetime():
+ df = generate_ar_df(periods=10, freq="D", n_segments=3)
+ df_wide = TSDataset.to_dataset(df)
+ with pytest.raises(ValueError, match="You set wrong freq"):
+ _ = TSDataset(df=df_wide, freq=None)
+
+
+def test_create_datetime_conversion_during_init():
+ classic_df = generate_ar_df(periods=30, start_time="2021-06-01", n_segments=2)
+ classic_df["categorical_column"] = [0] * 30 + [1] * 30
+ classic_df["categorical_column"] = classic_df["categorical_column"].astype("category")
+ df = TSDataset.to_dataset(classic_df[["timestamp", "segment", "target"]])
+ df_exog = TSDataset.to_dataset(classic_df[["timestamp", "segment", "categorical_column"]])
+ df.index = df.index.astype(str)
+ df_exog.index = df.index.astype(str)
+ ts = TSDataset(df=df, df_exog=df_exog, freq="D")
+ assert ts.index.dtype == "datetime64[ns]"
+
+
+def test_create_segment_conversion_during_init(df_segments_int):
+ df_wide = TSDataset.to_dataset(df_segments_int)
+ df_exog = df_segments_int.rename(columns={"target": "exog"})
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+
+ # make conversion back to integers
+ columns_frame = df_wide.columns.to_frame()
+ columns_frame["segment"] = columns_frame["segment"].astype(int)
+ df_wide.columns = pd.MultiIndex.from_frame(columns_frame)
+
+ columns_frame = df_exog_wide.columns.to_frame()
+ columns_frame["segment"] = columns_frame["segment"].astype(int)
+ df_exog_wide.columns = pd.MultiIndex.from_frame(columns_frame)
+
+ with pytest.warns(UserWarning, match="Segment values doesn't have string type"):
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq="D")
+
+ assert np.all(ts.columns.get_level_values("segment") == ["1", "1", "2", "2"])
+
+
+def test_create_from_long_format_with_exog():
+ freq = "D"
+ df = generate_ar_df(periods=10, start_time="2020-01-05", freq=freq, n_segments=3, random_seed=0)
+ df_exog = generate_ar_df(periods=20, start_time="2020-01-01", freq=freq, n_segments=3, random_seed=1)
+ df_exog.rename(columns={"target": "exog"}, inplace=True)
+ ts_long = TSDataset(df=df, df_exog=df_exog, freq=freq)
+
+ df_wide = TSDataset.to_dataset(df)
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ ts_wide = TSDataset(df=df_wide, df_exog=df_exog_wide, freq=freq)
+
+ pd.testing.assert_index_equal(ts_long.index, ts_wide.index)
+ pd.testing.assert_frame_equal(ts_long.to_pandas(), ts_wide.to_pandas())
+
+
+@patch("etna.datasets.utils.DataFrameFormat.determine")
+def test_create_from_long_format_with_exog_calls_determine(determine_mock):
+ determine_mock.side_effect = [DataFrameFormat.long, DataFrameFormat.long]
+
+ freq = "D"
+ df = generate_ar_df(periods=10, start_time="2020-01-05", freq=freq, n_segments=3, random_seed=0)
+ df_exog = generate_ar_df(periods=20, start_time="2020-01-01", freq=freq, n_segments=3, random_seed=1)
+ df_exog.rename(columns={"target": "exog"}, inplace=True)
+ _ = TSDataset(df=df, df_exog=df_exog, freq=freq)
+
+ assert determine_mock.call_count == 2
+
+
def test_check_endings_error():
"""Check that _check_endings method raises exception if some segments end with nan."""
timestamp = pd.date_range("2021-01-01", "2021-02-01")
@@ -389,162 +579,335 @@ def test_categorical_after_call_to_pandas():
@pytest.mark.parametrize(
- "borders, true_borders",
+ "ts_name, borders, true_borders",
(
+ # datetime timestamp
(
+ "tsdf_with_exog",
("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01"),
("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01"),
),
(
+ "tsdf_with_exog",
("2021-02-03", "2021-06-20", "2021-06-22", "2021-07-01"),
("2021-02-03", "2021-06-20", "2021-06-22", "2021-07-01"),
),
(
+ "tsdf_with_exog",
("2021-02-01", "2021-06-20", "2021-06-21", "2021-06-28"),
("2021-02-01", "2021-06-20", "2021-06-21", "2021-06-28"),
),
(
+ "tsdf_with_exog",
("2021-02-01", "2021-06-20", "2021-06-23", "2021-07-01"),
("2021-02-01", "2021-06-20", "2021-06-23", "2021-07-01"),
),
- ((None, "2021-06-20", "2021-06-23", "2021-06-28"), ("2021-02-01", "2021-06-20", "2021-06-23", "2021-06-28")),
- (("2021-02-03", "2021-06-20", "2021-06-23", None), ("2021-02-03", "2021-06-20", "2021-06-23", "2021-07-01")),
- ((None, "2021-06-20", "2021-06-23", None), ("2021-02-01", "2021-06-20", "2021-06-23", "2021-07-01")),
- ((None, "2021-06-20", None, None), ("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01")),
- ((None, None, "2021-06-21", None), ("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01")),
+ (
+ "tsdf_with_exog",
+ (None, "2021-06-20", "2021-06-23", "2021-06-28"),
+ ("2021-02-01", "2021-06-20", "2021-06-23", "2021-06-28"),
+ ),
+ (
+ "tsdf_with_exog",
+ ("2021-02-03", "2021-06-20", "2021-06-23", None),
+ ("2021-02-03", "2021-06-20", "2021-06-23", "2021-07-01"),
+ ),
+ (
+ "tsdf_with_exog",
+ (None, "2021-06-20", "2021-06-23", None),
+ ("2021-02-01", "2021-06-20", "2021-06-23", "2021-07-01"),
+ ),
+ ("tsdf_with_exog", (None, "2021-06-20", None, None), ("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01")),
+ ("tsdf_with_exog", (None, None, "2021-06-21", None), ("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01")),
+ # int timestamp
+ (
+ "tsdf_int_with_exog",
+ (31, 50, 51, 58),
+ (31, 50, 51, 58),
+ ),
+ (
+ "tsdf_int_with_exog",
+ (35, 50, 51, 58),
+ (35, 50, 51, 58),
+ ),
+ (
+ "tsdf_int_with_exog",
+ (31, 50, 51, 55),
+ (31, 50, 51, 55),
+ ),
+ (
+ "tsdf_int_with_exog",
+ (31, 50, 53, 58),
+ (31, 50, 53, 58),
+ ),
+ ("tsdf_int_with_exog", (None, 50, 53, 58), (31, 50, 53, 58)),
+ ("tsdf_int_with_exog", (35, 50, 53, None), (35, 50, 53, 181)),
+ ("tsdf_int_with_exog", (None, 50, 53, None), (31, 50, 53, 181)),
+ ("tsdf_int_with_exog", (None, 50, None, None), (31, 50, 51, 181)),
+ ("tsdf_int_with_exog", (None, None, 51, None), (31, 50, 51, 181)),
),
)
-def test_train_test_split(borders, true_borders, tsdf_with_exog):
+def test_train_test_split(ts_name, borders, true_borders, request):
+ ts = request.getfixturevalue(ts_name)
train_start, train_end, test_start, test_end = borders
train_start_true, train_end_true, test_start_true, test_end_true = true_borders
- train, test = tsdf_with_exog.train_test_split(
+ train, test = ts.train_test_split(
train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end
)
assert isinstance(train, TSDataset)
assert isinstance(test, TSDataset)
- assert (train.df == tsdf_with_exog.df[train_start_true:train_end_true]).all().all()
- assert (train.df_exog == tsdf_with_exog.df_exog).all().all()
- assert (test.df == tsdf_with_exog.df[test_start_true:test_end_true]).all().all()
- assert (test.df_exog == tsdf_with_exog.df_exog).all().all()
+ pd.testing.assert_frame_equal(train.df, ts.df.loc[train_start_true:train_end_true])
+ pd.testing.assert_frame_equal(train.df_exog, ts.df_exog)
+ pd.testing.assert_frame_equal(test.df, ts.df.loc[test_start_true:test_end_true])
+ pd.testing.assert_frame_equal(test.df_exog, ts.df_exog)
@pytest.mark.parametrize(
- "test_size, true_borders",
+ "ts_name, test_size, true_borders",
(
- (11, ("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01")),
- (9, ("2021-02-01", "2021-06-22", "2021-06-23", "2021-07-01")),
- (1, ("2021-02-01", "2021-06-30", "2021-07-01", "2021-07-01")),
+ # datetime timestamp
+ ("tsdf_with_exog", 11, ("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01")),
+ ("tsdf_with_exog", 9, ("2021-02-01", "2021-06-22", "2021-06-23", "2021-07-01")),
+ ("tsdf_with_exog", 1, ("2021-02-01", "2021-06-30", "2021-07-01", "2021-07-01")),
+ # int timestamp
+ ("tsdf_int_with_exog", 11, (31, 170, 171, 181)),
+ ("tsdf_int_with_exog", 9, (31, 172, 173, 181)),
+ ("tsdf_int_with_exog", 1, (31, 180, 181, 181)),
),
)
-def test_train_test_split_with_test_size(test_size, true_borders, tsdf_with_exog):
+def test_train_test_split_with_test_size(ts_name, test_size, true_borders, request):
+ ts = request.getfixturevalue(ts_name)
train_start_true, train_end_true, test_start_true, test_end_true = true_borders
- train, test = tsdf_with_exog.train_test_split(test_size=test_size)
+ train, test = ts.train_test_split(test_size=test_size)
assert isinstance(train, TSDataset)
assert isinstance(test, TSDataset)
- assert (train.df == tsdf_with_exog.df[train_start_true:train_end_true]).all().all()
- assert (train.df_exog == tsdf_with_exog.df_exog).all().all()
- assert (test.df == tsdf_with_exog.df[test_start_true:test_end_true]).all().all()
- assert (test.df_exog == tsdf_with_exog.df_exog).all().all()
+ pd.testing.assert_frame_equal(train.df, ts.df.loc[train_start_true:train_end_true])
+ pd.testing.assert_frame_equal(train.df_exog, ts.df_exog)
+ pd.testing.assert_frame_equal(test.df, ts.df.loc[test_start_true:test_end_true])
+ pd.testing.assert_frame_equal(test.df_exog, ts.df_exog)
@pytest.mark.filterwarnings("ignore: test_size, test_start and test_end cannot be")
@pytest.mark.parametrize(
- "test_size, borders, true_borders",
+ "ts_name, test_size, borders, true_borders",
(
+ # datetime timestamp
(
+ "tsdf_with_exog",
10,
("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01"),
("2021-02-01", "2021-06-20", "2021-06-21", "2021-07-01"),
),
(
+ "tsdf_with_exog",
15,
("2021-02-03", "2021-06-20", "2021-06-22", "2021-07-01"),
("2021-02-03", "2021-06-20", "2021-06-22", "2021-07-01"),
),
- (11, ("2021-02-02", None, None, "2021-06-28"), ("2021-02-02", "2021-06-17", "2021-06-18", "2021-06-28")),
(
+ "tsdf_with_exog",
+ 11,
+ ("2021-02-02", None, None, "2021-06-28"),
+ ("2021-02-02", "2021-06-17", "2021-06-18", "2021-06-28"),
+ ),
+ (
+ "tsdf_with_exog",
4,
("2021-02-03", "2021-06-20", None, "2021-07-01"),
("2021-02-03", "2021-06-20", "2021-06-28", "2021-07-01"),
),
(
+ "tsdf_with_exog",
4,
("2021-02-03", "2021-06-20", None, None),
("2021-02-03", "2021-06-20", "2021-06-21", "2021-06-24"),
),
+ # int timestamp
+ (
+ "tsdf_int_with_exog",
+ 10,
+ (31, 171, 172, 181),
+ (31, 171, 172, 181),
+ ),
+ (
+ "tsdf_int_with_exog",
+ 15,
+ (31, 169, 172, 181),
+ (31, 169, 172, 181),
+ ),
+ (
+ "tsdf_int_with_exog",
+ 11,
+ (33, None, None, 170),
+ (33, 159, 160, 170),
+ ),
+ (
+ "tsdf_int_with_exog",
+ 4,
+ (33, 170, None, 181),
+ (33, 170, 178, 181),
+ ),
+ (
+ "tsdf_int_with_exog",
+ 4,
+ (33, 170, None, None),
+ (33, 170, 171, 174),
+ ),
),
)
-def test_train_test_split_both(test_size, borders, true_borders, tsdf_with_exog):
+def test_train_test_split_both(ts_name, test_size, borders, true_borders, request):
+ ts = request.getfixturevalue(ts_name)
train_start, train_end, test_start, test_end = borders
train_start_true, train_end_true, test_start_true, test_end_true = true_borders
- train, test = tsdf_with_exog.train_test_split(
+ train, test = ts.train_test_split(
train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, test_size=test_size
)
assert isinstance(train, TSDataset)
assert isinstance(test, TSDataset)
- assert (train.df == tsdf_with_exog.df[train_start_true:train_end_true]).all().all()
- assert (train.df_exog == tsdf_with_exog.df_exog).all().all()
- assert (test.df == tsdf_with_exog.df[test_start_true:test_end_true]).all().all()
- assert (test.df_exog == tsdf_with_exog.df_exog).all().all()
+ pd.testing.assert_frame_equal(train.df, ts.df.loc[train_start_true:train_end_true])
+ pd.testing.assert_frame_equal(train.df_exog, ts.df_exog)
+ pd.testing.assert_frame_equal(test.df, ts.df.loc[test_start_true:test_end_true])
+ pd.testing.assert_frame_equal(test.df_exog, ts.df_exog)
@pytest.mark.parametrize(
- "borders, match",
+ "ts_name, borders, match",
(
- (("2021-01-01", "2021-06-20", "2021-06-21", "2021-07-01"), "Min timestamp in df is"),
- (("2021-02-01", "2021-06-20", "2021-06-21", "2021-08-01"), "Max timestamp in df is"),
+ ("tsdf_with_exog", ("2021-01-01", "2021-06-20", "2021-06-21", "2021-07-01"), "Min timestamp in df is"),
+ ("tsdf_with_exog", ("2021-02-01", "2021-06-20", "2021-06-21", "2021-08-01"), "Max timestamp in df is"),
+ ("tsdf_int_with_exog", (1, 50, 51, 181), "Min timestamp in df is"),
+ ("tsdf_int_with_exog", (31, 50, 51, 200), "Max timestamp in df is"),
),
)
-def test_train_test_split_warning(borders, match, tsdf_with_exog):
+def test_train_test_split_warning_borders(ts_name, borders, match, request):
+ ts = request.getfixturevalue(ts_name)
train_start, train_end, test_start, test_end = borders
with pytest.warns(UserWarning, match=match):
- tsdf_with_exog.train_test_split(
- train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end
- )
+ ts.train_test_split(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end)
@pytest.mark.parametrize(
- "test_size, borders, match",
+ "ts_name, test_size, borders, match",
(
(
+ "tsdf_with_exog",
10,
("2021-02-01", None, "2021-06-21", "2021-07-01"),
"test_size, test_start and test_end cannot be applied at the same time. test_size will be ignored",
),
+ (
+ "tsdf_int_with_exog",
+ 10,
+ (31, None, 50, 60),
+ "test_size, test_start and test_end cannot be applied at the same time. test_size will be ignored",
+ ),
),
)
-def test_train_test_split_warning2(test_size, borders, match, tsdf_with_exog):
+def test_train_test_split_warning_many_parameters(ts_name, test_size, borders, match, request):
+ ts = request.getfixturevalue(ts_name)
train_start, train_end, test_start, test_end = borders
with pytest.warns(UserWarning, match=match):
- tsdf_with_exog.train_test_split(
+ ts.train_test_split(
train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, test_size=test_size
)
@pytest.mark.parametrize(
- "test_size, borders, match",
+ "ts_name, test_size, borders, match",
(
+ # datetime timestamp
(
+ "tsdf_with_exog",
None,
("2021-02-03", None, None, "2021-07-01"),
"At least one of train_end, test_start or test_size should be defined",
),
(
+ "tsdf_with_exog",
+ None,
+ (10, "2021-06-20", "2021-06-26", "2021-07-01"),
+ "Parameter train_start has incorrect type",
+ ),
+ (
+ "tsdf_with_exog",
+ None,
+ ("2021-02-03", 10, "2021-06-26", "2021-07-01"),
+ "Parameter train_end has incorrect type",
+ ),
+ (
+ "tsdf_with_exog",
+ None,
+ ("2021-02-03", "2021-06-20", 10, "2021-07-01"),
+ "Parameter test_start has incorrect type",
+ ),
+ (
+ "tsdf_with_exog",
+ None,
+ ("2021-02-03", "2021-06-20", "2021-06-26", 10),
+ "Parameter test_end has incorrect type",
+ ),
+ (
+ "tsdf_with_exog",
17,
("2021-02-01", "2021-06-20", None, "2021-07-01"),
"The beginning of the test goes before the end of the train",
),
(
+ "tsdf_with_exog",
17,
("2021-02-01", "2021-06-20", "2021-06-26", None),
"test_size is 17, but only 6 available with your test_start",
),
+ # int timestamp
+ (
+ "tsdf_int_with_exog",
+ None,
+ (33, None, None, 181),
+ "At least one of train_end, test_start or test_size should be defined",
+ ),
+ (
+ "tsdf_int_with_exog",
+ None,
+ (pd.Timestamp("2020-01-01"), 170, 176, 181),
+ "Parameter train_start has incorrect type",
+ ),
+ (
+ "tsdf_int_with_exog",
+ None,
+ (33, pd.Timestamp("2020-01-01"), 176, 181),
+ "Parameter train_end has incorrect type",
+ ),
+ (
+ "tsdf_int_with_exog",
+ None,
+ (33, 170, pd.Timestamp("2020-01-01"), 181),
+ "Parameter test_start has incorrect type",
+ ),
+ (
+ "tsdf_int_with_exog",
+ None,
+ (33, 170, 176, pd.Timestamp("2020-01-01")),
+ "Parameter test_end has incorrect type",
+ ),
+ (
+ "tsdf_int_with_exog",
+ 17,
+ (31, 170, None, 181),
+ "The beginning of the test goes before the end of the train",
+ ),
+ (
+ "tsdf_int_with_exog",
+ 17,
+ (31, 50, 176, None),
+ "test_size is 17, but only 6 available with your test_start",
+ ),
),
)
-def test_train_test_split_failed(test_size, borders, match, tsdf_with_exog):
+def test_train_test_split_failed(ts_name, test_size, borders, match, request):
+ ts = request.getfixturevalue(ts_name)
train_start, train_end, test_start, test_end = borders
with pytest.raises(ValueError, match=match):
- tsdf_with_exog.train_test_split(
+ ts.train_test_split(
train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, test_size=test_size
)
@@ -569,41 +932,52 @@ def test_train_test_split_pass_prediction_intervals_to_output(ts_with_prediction
assert sorted(test.prediction_intervals_names) == sorted(ts_with_prediction_intervals.prediction_intervals_names)
-def test_dataset_datetime_conversion():
+def test_to_dataset_datetime_conversion():
classic_df = generate_ar_df(periods=30, start_time="2021-06-01", n_segments=2)
classic_df["timestamp"] = classic_df["timestamp"].astype(str)
df = TSDataset.to_dataset(classic_df[["timestamp", "segment", "target"]])
- # todo: deal with pandas datetime format
assert df.index.dtype == "datetime64[ns]"
-def test_dataset_datetime_conversion_during_init():
- classic_df = generate_ar_df(periods=30, start_time="2021-06-01", n_segments=2)
- classic_df["categorical_column"] = [0] * 30 + [1] * 30
- classic_df["categorical_column"] = classic_df["categorical_column"].astype("category")
- df = TSDataset.to_dataset(classic_df[["timestamp", "segment", "target"]])
- exog = TSDataset.to_dataset(classic_df[["timestamp", "segment", "categorical_column"]])
- df.index = df.index.astype(str)
- exog.index = df.index.astype(str)
- ts = TSDataset(df, "D", exog)
- assert ts.df.index.dtype == "datetime64[ns]"
-
-
def test_to_dataset_segment_conversion(df_segments_int):
"""Test that `TSDataset.to_dataset` makes casting of segment to string."""
df = TSDataset.to_dataset(df_segments_int)
assert np.all(df.columns.get_level_values("segment") == ["1", "2"])
-def test_dataset_segment_conversion_during_init(df_segments_int):
- """Test that `TSDataset.__init__` makes casting of segment to string."""
- df = TSDataset.to_dataset(df_segments_int)
- # make conversion back to integers
- columns_frame = df.columns.to_frame()
- columns_frame["segment"] = columns_frame["segment"].astype(int)
- df.columns = pd.MultiIndex.from_frame(columns_frame)
- ts = TSDataset(df=df, freq="D")
- assert np.all(ts.columns.get_level_values("segment") == ["1", "2"])
+def test_to_dataset_on_integer_timestamp():
+ classic_df = generate_ar_df(periods=30, freq=None, n_segments=2)
+ df = TSDataset.to_dataset(classic_df)
+ assert pd.api.types.is_integer_dtype(df.index.dtype)
+
+
+def test_size_with_diff_number_of_features():
+ df_temp = generate_ar_df(start_time="2023-01-01", periods=30, n_segments=2, freq="D")
+ df_exog_temp = generate_ar_df(start_time="2023-01-01", periods=30, n_segments=1, freq="D")
+ df_exog_temp = df_exog_temp.rename({"target": "target_exog"}, axis=1)
+ ts_temp = TSDataset(df=TSDataset.to_dataset(df_temp), df_exog=TSDataset.to_dataset(df_exog_temp), freq="D")
+ assert ts_temp.size()[0] == len(df_exog_temp)
+ assert ts_temp.size()[1] == 2
+ assert ts_temp.size()[2] is None
+
+
+def test_size_target_only():
+ df_temp = generate_ar_df(start_time="2023-01-01", periods=40, n_segments=3, freq="D")
+ ts_temp = TSDataset(df=TSDataset.to_dataset(df_temp), freq="D")
+ assert ts_temp.size()[0] == len(df_temp) / 3
+ assert ts_temp.size()[1] == 3
+ assert ts_temp.size()[2] == 1
+
+
+def simple_test_size_():
+ df_temp = generate_ar_df(start_time="2023-01-01", periods=30, n_segments=2, freq="D")
+ df_exog_temp = generate_ar_df(start_time="2023-01-01", periods=30, n_segments=2, freq="D")
+ df_exog_temp = df_exog_temp.rename({"target": "target_exog"}, axis=1)
+ df_exog_temp["other_feature"] = 1
+ ts_temp = TSDataset(df=TSDataset.to_dataset(df_temp), df_exog=TSDataset.to_dataset(df_exog_temp), freq="D")
+ assert ts_temp.size()[0] == len(df_exog_temp) / 2
+ assert ts_temp.size()[1] == 2
+ assert ts_temp.size()[2] == 3
def test_size_with_diff_number_of_features():
@@ -648,17 +1022,37 @@ def test_make_future_with_imputer(ts_diff_endings, ts_future):
assert_frame_equal(future.to_pandas(), ts_future.to_pandas())
-def test_make_future():
- timestamp = pd.date_range("2020-01-01", periods=100, freq="D")
- df1 = pd.DataFrame({"timestamp": timestamp, "target": 1, "segment": "segment_1"})
- df2 = pd.DataFrame({"timestamp": timestamp, "target": 2, "segment": "segment_2"})
- df = pd.concat([df1, df2], ignore_index=False)
+def test_make_future_datetime_timestamp():
+ df = generate_ar_df(periods=20, freq="D", n_segments=2)
ts = TSDataset(TSDataset.to_dataset(df), freq="D")
ts_future = ts.make_future(10)
assert np.all(ts_future.index == pd.date_range(ts.index.max() + pd.Timedelta("1D"), periods=10, freq="D"))
assert set(ts_future.columns.get_level_values("feature")) == {"target"}
+def test_make_future_int_timestamp():
+ freq = None
+ df = generate_ar_df(periods=20, freq=freq, n_segments=2)
+ ts = TSDataset(TSDataset.to_dataset(df), freq=freq)
+ ts_future = ts.make_future(10)
+ assert np.all(ts_future.index == np.arange(ts.index.max() + 1, ts.index.max() + 10 + 1))
+ assert set(ts_future.columns.get_level_values("feature")) == {"target"}
+
+
+def test_make_future_with_exog_datetime_timestamp(tsdf_with_exog):
+ ts = tsdf_with_exog
+ ts_future = ts.make_future(10)
+ assert np.all(ts_future.index == pd.date_range(ts.index.max() + pd.Timedelta("1D"), periods=10, freq="D"))
+ assert set(ts_future.columns.get_level_values("feature")) == {"target", "exog"}
+
+
+def test_make_future_with_exog_int_timestamp(tsdf_int_with_exog):
+ ts = tsdf_int_with_exog
+ ts_future = ts.make_future(10)
+ assert np.all(ts_future.index == np.arange(ts.index.max() + 1, ts.index.max() + 10 + 1))
+ assert set(ts_future.columns.get_level_values("feature")) == {"target", "exog"}
+
+
def test_make_future_small_horizon():
timestamp = np.arange(np.datetime64("2021-01-01"), np.datetime64("2021-02-01"))
target1 = [np.sin(i) for i in range(len(timestamp))]
@@ -673,19 +1067,6 @@ def test_make_future_small_horizon():
assert len(train.make_future(1).df) == 1
-def test_make_future_with_exog():
- timestamp = pd.date_range("2020-01-01", periods=100, freq="D")
- df1 = pd.DataFrame({"timestamp": timestamp, "target": 1, "segment": "segment_1"})
- df2 = pd.DataFrame({"timestamp": timestamp, "target": 2, "segment": "segment_2"})
- df = pd.concat([df1, df2], ignore_index=False)
- exog = df.copy()
- exog.columns = ["timestamp", "exog", "segment"]
- ts = TSDataset(df=TSDataset.to_dataset(df), df_exog=TSDataset.to_dataset(exog), freq="D")
- ts_future = ts.make_future(10)
- assert np.all(ts_future.index == pd.date_range(ts.index.max() + pd.Timedelta("1D"), periods=10, freq="D"))
- assert set(ts_future.columns.get_level_values("feature")) == {"target", "exog"}
-
-
def test_make_future_with_regressors(df_and_regressors):
df, df_exog, known_future = df_and_regressors
ts = TSDataset(df=df, df_exog=df_exog, freq="D", known_future=known_future)
@@ -852,6 +1233,16 @@ def test_to_flatten_simple(example_df):
assert np.all(expected_df.values == obtained_df.values)
+def test_to_flatten_simple_int_timestamp():
+ flat_df = generate_ar_df(periods=10, freq=None, n_segments=3)
+ sorted_columns = sorted(flat_df.columns)
+ expected_df = flat_df[sorted_columns]
+ obtained_df = TSDataset.to_flatten(TSDataset.to_dataset(flat_df))[sorted_columns]
+ assert np.all(expected_df.columns == obtained_df.columns)
+ assert np.all(expected_df.dtypes == obtained_df.dtypes)
+ assert np.all(expected_df.values == obtained_df.values)
+
+
def test_to_flatten_with_exog(df_and_regressors_flat):
"""Check that TSDataset.to_flatten works correctly with exogenous features."""
df, df_exog = df_and_regressors_flat
@@ -906,6 +1297,14 @@ def test_to_flatten_raise_error_incorrect_literal(df_and_regressors):
_ = ts.to_flatten(ts.df, features="incorrect")
+def test_to_pandas_simple_int_timestamp():
+ df = generate_ar_df(periods=30, freq=None, n_segments=3)
+ df_wide = TSDataset.to_dataset(df)
+ ts = TSDataset(df=df_wide, freq=None)
+ pandas_df = ts.to_pandas(flatten=False, features="all")
+ pd.testing.assert_frame_equal(pandas_df, df_wide)
+
+
@pytest.mark.parametrize(
"features, expected_columns",
(
@@ -1314,3 +1713,215 @@ def test_target_quantiles_names_deprecation_warning(ts_with_prediction_intervals
DeprecationWarning, match="Usage of this property may mislead while accessing prediction intervals."
):
_ = ts_with_prediction_intervals.target_quantiles_names
+
+
+@pytest.mark.parametrize(
+ "ts_name, params, match",
+ [
+ ("tsdf_with_exog", {"start": 1}, "Parameter start has incorrect type"),
+ ("tsdf_with_exog", {"end": 1}, "Parameter end has incorrect type"),
+ ("tsdf_int_with_exog", {"start": "2020-01-01"}, "Parameter start has incorrect type"),
+ ("tsdf_int_with_exog", {"end": "2020-01-01"}, "Parameter end has incorrect type"),
+ ],
+)
+def test_plot_fail_incorrect_start_end_type(ts_name, params, match, request):
+ ts = request.getfixturevalue(ts_name)
+ with pytest.raises(ValueError, match=match):
+ ts.plot(**params)
+
+
+@pytest.mark.filterwarnings("ignore: You probably set wrong freq. Discovered freq in you data is N, you set D")
+def test_check_timestamp_type_warning():
+ match = "Timestamp contains numeric values, and given freq is D. Timestamp will be converted to datetime."
+
+ df = generate_ar_df(periods=10, start_time=5, freq=None, n_segments=3, random_seed=0)
+ df_exog = generate_ar_df(periods=20, start_time=0, freq=None, n_segments=3, random_seed=1)
+ df_exog.rename(columns={"target": "exog"}, inplace=True)
+ df_wide = TSDataset.to_dataset(df)
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+
+ with pytest.warns(UserWarning, match=match):
+ TSDataset(df=df_wide, freq="D")
+
+ with pytest.warns(UserWarning, match=match):
+ TSDataset(df=df_wide, df_exog=df_exog_wide, freq="D")
+
+
+@pytest.mark.parametrize(
+ "df_name, freq, original_timestamp_name, future_steps",
+ [
+ ("df_aligned_datetime", "D", "external_timestamp", 1),
+ ("df_aligned_int", None, "external_timestamp", 1),
+ ("df_misaligned_datetime", "D", "external_timestamp", 1),
+ ("df_misaligned_int", None, "external_timestamp", 1),
+ ("df_misaligned_datetime", "D", "new_timestamp", 1),
+ ("df_misaligned_datetime", "D", "external_timestamp", 3),
+ ("df_misaligned_datetime", "D", "external_timestamp", 100),
+ ],
+)
+def test_create_from_misaligned_without_exog(df_name, freq, original_timestamp_name, future_steps, request):
+ df = request.getfixturevalue(df_name)
+ ts = TSDataset.create_from_misaligned(
+ df=df, df_exog=None, freq=freq, original_timestamp_name=original_timestamp_name, future_steps=future_steps
+ )
+
+ alignment = infer_alignment(df)
+ expected_raw_df = TSDataset.to_dataset(apply_alignment(df=df, alignment=alignment))
+ pd.testing.assert_frame_equal(ts.raw_df, expected_raw_df)
+
+ timestamp_df = make_timestamp_df_from_alignment(
+ alignment=alignment,
+ start=expected_raw_df.index[0],
+ periods=len(expected_raw_df) + future_steps,
+ freq=freq,
+ timestamp_name=original_timestamp_name,
+ )
+ expected_df_exog = TSDataset.to_dataset(timestamp_df)
+ pd.testing.assert_frame_equal(ts.df_exog, expected_df_exog)
+
+ assert original_timestamp_name in ts.known_future
+ assert ts.freq is None
+
+
+@pytest.mark.parametrize(
+ "df_name, df_exog_name, freq, known_future, original_timestamp_name, future_steps",
+ [
+ ("df_aligned_datetime", "df_exog_aligned_datetime", "D", ["exog_1"], "external_timestamp", 1),
+ ("df_aligned_datetime", "df_exog_misaligned_datetime", "D", ["exog_1"], "external_timestamp", 1),
+ ("df_aligned_int", "df_exog_aligned_int", None, ["exog_1"], "external_timestamp", 1),
+ ("df_aligned_int", "df_exog_misaligned_int", None, ["exog_1"], "external_timestamp", 1),
+ ("df_misaligned_datetime", "df_exog_aligned_datetime", "D", ["exog_1"], "external_timestamp", 1),
+ ("df_misaligned_datetime", "df_exog_misaligned_datetime", "D", ["exog_1"], "external_timestamp", 1),
+ ("df_misaligned_int", "df_exog_aligned_int", None, ["exog_1"], "external_timestamp", 1),
+ ("df_misaligned_int", "df_exog_misaligned_int", None, ["exog_1"], "external_timestamp", 1),
+ ("df_misaligned_datetime", "df_exog_misaligned_datetime", "D", ["exog_1"], "new_timestamp", 1),
+ ("df_misaligned_datetime", "df_exog_misaligned_datetime", "D", ["exog_1"], "external_timestamp", 3),
+ ("df_misaligned_datetime", "df_exog_misaligned_datetime", "D", ["exog_1"], "external_timestamp", 100),
+ ],
+)
+def test_create_from_misaligned_with_exog(
+ df_name, df_exog_name, freq, known_future, original_timestamp_name, future_steps, request
+):
+ df = request.getfixturevalue(df_name)
+ df_exog = request.getfixturevalue(df_exog_name)
+ ts = TSDataset.create_from_misaligned(
+ df=df,
+ df_exog=df_exog,
+ freq=freq,
+ original_timestamp_name=original_timestamp_name,
+ future_steps=future_steps,
+ known_future=known_future,
+ )
+
+ alignment = infer_alignment(df)
+ expected_raw_df = TSDataset.to_dataset(apply_alignment(df=df, alignment=alignment))
+ pd.testing.assert_frame_equal(ts.raw_df, expected_raw_df)
+
+ expected_df_exog = TSDataset.to_dataset(apply_alignment(df=df_exog, alignment=alignment))
+ timestamp_df = make_timestamp_df_from_alignment(
+ alignment=alignment,
+ start=expected_raw_df.index[0],
+ periods=len(expected_raw_df) + future_steps,
+ freq=freq,
+ timestamp_name=original_timestamp_name,
+ )
+ expected_df_exog = expected_df_exog.join(TSDataset.to_dataset(timestamp_df), how="outer")
+ pd.testing.assert_frame_equal(ts.df_exog, expected_df_exog)
+
+ expected_known_future = sorted(set(known_future).union([original_timestamp_name]))
+ assert ts.known_future == expected_known_future
+
+ assert ts.freq is None
+
+
+@pytest.mark.parametrize(
+ "df_name, df_exog_name, freq, original_timestamp_name, future_steps, expected_known_future",
+ [
+ (
+ "df_misaligned_datetime",
+ "df_exog_all_misaligned_datetime",
+ "D",
+ "external_timestamp",
+ 1,
+ ["exog_1", "exog_2", "external_timestamp"],
+ ),
+ ],
+)
+def test_create_from_misaligned_with_exog_all(
+ df_name, df_exog_name, freq, original_timestamp_name, future_steps, expected_known_future, request
+):
+ df = request.getfixturevalue(df_name)
+ df_exog = request.getfixturevalue(df_exog_name)
+ ts = TSDataset.create_from_misaligned(
+ df=df,
+ df_exog=df_exog,
+ freq=freq,
+ original_timestamp_name=original_timestamp_name,
+ future_steps=future_steps,
+ known_future="all",
+ )
+
+ alignment = infer_alignment(df)
+ expected_raw_df = TSDataset.to_dataset(apply_alignment(df=df, alignment=alignment))
+ pd.testing.assert_frame_equal(ts.raw_df, expected_raw_df)
+
+ expected_df_exog = TSDataset.to_dataset(apply_alignment(df=df_exog, alignment=alignment))
+ timestamp_df = make_timestamp_df_from_alignment(
+ alignment=alignment,
+ start=expected_raw_df.index[0],
+ periods=len(expected_raw_df) + future_steps,
+ freq=freq,
+ timestamp_name=original_timestamp_name,
+ )
+ expected_df_exog = expected_df_exog.join(TSDataset.to_dataset(timestamp_df), how="outer")
+ pd.testing.assert_frame_equal(ts.df_exog, expected_df_exog)
+
+ assert ts.known_future == expected_known_future
+ assert ts.freq is None
+
+
+@pytest.mark.parametrize(
+ "df_name, freq, original_timestamp_name, future_steps",
+ [
+ ("df_misaligned_datetime", "D", "external_timestamp", 0),
+ ("df_misaligned_datetime", "D", "external_timestamp", -3),
+ ("df_misaligned_int", None, "external_timestamp", 0),
+ ("df_misaligned_int", None, "external_timestamp", -3),
+ ],
+)
+def test_create_from_misaligned_fail_non_positive_future_steps(
+ df_name, freq, original_timestamp_name, future_steps, request
+):
+ df = request.getfixturevalue(df_name)
+ with pytest.raises(ValueError, match="Parameter future_steps should be positive"):
+ _ = TSDataset.create_from_misaligned(
+ df=df,
+ df_exog=None,
+ freq=freq,
+ original_timestamp_name=original_timestamp_name,
+ future_steps=future_steps,
+ )
+
+
+@pytest.mark.parametrize(
+ "df_name, df_exog_name, freq, known_future, original_timestamp_name, future_steps",
+ [
+ ("df_misaligned_datetime", "df_exog_misaligned_datetime", "D", ["exog_1"], "exog_1", 1),
+ ],
+)
+def test_create_from_misaligned_fail_name_intersection(
+ df_name, df_exog_name, freq, known_future, original_timestamp_name, future_steps, request
+):
+ df = request.getfixturevalue(df_name)
+ df_exog = request.getfixturevalue(df_exog_name)
+ with pytest.raises(
+ ValueError, match="Parameter original_timestamp_name shouldn't intersect with columns in df_exog"
+ ):
+ _ = TSDataset.create_from_misaligned(
+ df=df,
+ df_exog=df_exog,
+ freq=freq,
+ original_timestamp_name=original_timestamp_name,
+ future_steps=future_steps,
+ known_future=known_future,
+ )
diff --git a/tests/test_datasets/test_datasets_generation.py b/tests/test_datasets/test_datasets_generation.py
index 350ff96ee..d1a08b4e4 100644
--- a/tests/test_datasets/test_datasets_generation.py
+++ b/tests/test_datasets/test_datasets_generation.py
@@ -1,4 +1,5 @@
import numpy as np
+import pandas as pd
import pytest
from etna.datasets.datasets_generation import generate_ar_df
@@ -21,7 +22,102 @@ def check_not_equal_within_3_sigma(generated_value, expected_value, sigma, **kwa
return abs(generated_value - expected_value) <= 3 * sigma
-def test_simple_ar_process_check():
+@pytest.mark.parametrize("generate_method", [generate_ar_df, generate_periodic_df, generate_const_df])
+@pytest.mark.parametrize("freq", [None, "D"])
+@pytest.mark.parametrize("periods, n_segments", [(5, 1), (6, 2)])
+def test_generate_method_columns_set(generate_method, freq, periods, n_segments):
+ expected_columns = {"timestamp", "segment", "target"}
+ df = generate_method(periods=periods, n_segments=n_segments, freq=freq)
+ assert set(df.columns) == expected_columns
+
+
+@pytest.mark.parametrize("freq", [None, "D"])
+@pytest.mark.parametrize("periods, patterns", [(5, [[0, 1], [0, 2, 1]])])
+def test_generate_from_patterns_df_columns_set(freq, periods, patterns):
+ expected_columns = {"timestamp", "segment", "target"}
+ df = generate_from_patterns_df(periods=periods, patterns=patterns, freq=freq, start_time=None)
+ assert set(df.columns) == expected_columns
+
+
+@pytest.mark.parametrize("generate_method", [generate_ar_df, generate_periodic_df, generate_const_df])
+@pytest.mark.parametrize("freq", [None, "D"])
+@pytest.mark.parametrize("periods, n_segments", [(5, 1), (6, 2)])
+def test_generate_method_periods(generate_method, freq, periods, n_segments):
+ df = generate_method(periods=periods, n_segments=n_segments, freq=freq)
+ assert df["timestamp"].nunique() == periods
+
+
+@pytest.mark.parametrize("freq", [None, "D"])
+@pytest.mark.parametrize("periods, patterns", [(5, [[0, 1], [0, 2, 1]])])
+def test_generate_from_patterns_df_periods(freq, periods, patterns):
+ df = generate_from_patterns_df(periods=periods, patterns=patterns, freq=freq, start_time=None)
+ assert df["timestamp"].nunique() == periods
+
+
+@pytest.mark.parametrize("generate_method", [generate_ar_df, generate_periodic_df, generate_const_df])
+@pytest.mark.parametrize("freq", [None, "D"])
+@pytest.mark.parametrize("periods, n_segments", [(5, 1), (6, 2)])
+def test_generate_method_segments(generate_method, freq, periods, n_segments):
+ df = generate_method(periods=periods, n_segments=n_segments, freq=freq)
+ assert df["segment"].nunique() == n_segments
+
+
+@pytest.mark.parametrize("freq", [None, "D"])
+@pytest.mark.parametrize("periods, patterns", [(5, [[0, 1], [0, 2, 1]])])
+def test_generate_from_patterns_df_segments(freq, periods, patterns):
+ df = generate_from_patterns_df(periods=periods, patterns=patterns, freq=freq, start_time=None)
+ assert df["segment"].nunique() == len(patterns)
+
+
+@pytest.mark.parametrize("generate_method", [generate_ar_df, generate_periodic_df, generate_const_df])
+@pytest.mark.parametrize(
+ "start_time, expected_start_time, freq",
+ [
+ (None, 0, None),
+ (1, 1, None),
+ (None, pd.Timestamp("2000-01-01"), "D"),
+ ("2020-01-01", pd.Timestamp("2020-01-01"), "D"),
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), "D"),
+ ],
+)
+@pytest.mark.parametrize("periods, n_segments", [(5, 1), (6, 2)])
+def test_generate_method_start_time(generate_method, start_time, expected_start_time, freq, periods, n_segments):
+ df = generate_method(periods=periods, n_segments=n_segments, freq=freq, start_time=start_time)
+ assert df["timestamp"].min() == expected_start_time
+
+
+@pytest.mark.parametrize(
+ "start_time, expected_start_time, freq",
+ [
+ (None, 0, None),
+ (1, 1, None),
+ (None, pd.Timestamp("2000-01-01"), "D"),
+ ("2020-01-01", pd.Timestamp("2020-01-01"), "D"),
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), "D"),
+ ],
+)
+@pytest.mark.parametrize("periods, patterns", [(5, [[0, 1], [0, 2, 1]])])
+def test_generate_from_patterns_df_start_time(start_time, expected_start_time, freq, periods, patterns):
+ df = generate_from_patterns_df(periods=periods, patterns=patterns, freq=freq, start_time=start_time)
+ assert df["timestamp"].min() == expected_start_time
+
+
+@pytest.mark.parametrize("generate_method", [generate_ar_df, generate_periodic_df, generate_const_df])
+@pytest.mark.parametrize("start_time, freq", [("2020-01-01", None), (pd.Timestamp("2020-01-01"), None), (10, "D")])
+@pytest.mark.parametrize("periods, n_segments", [(5, 1), (6, 2)])
+def test_generate_method_timestamp_start_time_fail(generate_method, start_time, freq, periods, n_segments):
+ with pytest.raises(ValueError, match="Parameter start_time has incorrect type"):
+ _ = generate_method(periods=periods, n_segments=n_segments, freq=freq, start_time=start_time)
+
+
+@pytest.mark.parametrize("start_time, freq", [("2020-01-01", None), (pd.Timestamp("2020-01-01"), None), (10, "D")])
+@pytest.mark.parametrize("periods, patterns", [(5, [[0, 1], [0, 2, 1]])])
+def test_generate_from_patterns_df_start_time_fail(start_time, freq, periods, patterns):
+ with pytest.raises(ValueError, match="Parameter start_time has incorrect type"):
+ _ = generate_from_patterns_df(periods=periods, patterns=patterns, freq=freq, start_time=start_time)
+
+
+def test_generate_ar_df_values():
ar_coef = [10, 11]
random_seed = 1
periods = 10
@@ -37,7 +133,7 @@ def test_simple_ar_process_check():
@pytest.mark.parametrize("add_noise, checker", [(False, check_equals), (True, check_not_equal_within_3_sigma)])
-def test_simple_periodic_df_check(add_noise, checker):
+def test_generate_periodic_df_values(add_noise, checker):
period = 3
periods = 11
sigma = 0.1
@@ -58,7 +154,7 @@ def test_simple_periodic_df_check(add_noise, checker):
@pytest.mark.parametrize("add_noise, checker", [(False, check_equals), (True, check_not_equal_within_3_sigma)])
-def test_simple_const_df_check(add_noise, checker):
+def test_generate_const_df_values(add_noise, checker):
const = 1
periods = 3
sigma = 0.1
@@ -78,7 +174,7 @@ def test_simple_const_df_check(add_noise, checker):
@pytest.mark.parametrize("add_noise, checker", [(False, check_equals), (True, check_not_equal_within_3_sigma)])
-def test_simple_from_patterns_df_check(add_noise, checker):
+def test_generate_from_patterns_df_values(add_noise, checker):
patterns = [[0, 1], [0, 2, 1]]
periods = 10
sigma = 0.1
@@ -147,3 +243,10 @@ def test_generate_hierarchical_df_convert_to_wide_format(periods, n_segments):
hierarchical_df = generate_hierarchical_df(periods=periods, n_segments=n_segments)
level_names = [f"level_{idx}" for idx in range(len(n_segments))]
TSDataset.to_hierarchical_dataset(df=hierarchical_df, level_columns=level_names)
+
+
+@pytest.mark.parametrize("start_time", ["2020-01-01", pd.Timestamp("2020-01-01")])
+@pytest.mark.parametrize("periods,n_segments", ((2, [1, 2]), (2, [2]), (4, [3, 4]), (4, [3, 3])))
+def test_generate_hierarchical_df_start_time_fail(start_time, periods, n_segments):
+ with pytest.raises(ValueError, match="Parameter start_time has incorrect type"):
+ _ = generate_hierarchical_df(periods=periods, n_segments=n_segments, freq=None, start_time=start_time)
diff --git a/tests/test_datasets/test_utils.py b/tests/test_datasets/test_utils.py
index 318ace939..0dc7b3a4e 100644
--- a/tests/test_datasets/test_utils.py
+++ b/tests/test_datasets/test_utils.py
@@ -1,3 +1,5 @@
+from typing import Optional
+
import numpy as np
import pandas as pd
import pytest
@@ -5,12 +7,19 @@
from etna.datasets import TSDataset
from etna.datasets import duplicate_data
from etna.datasets import generate_ar_df
+from etna.datasets.utils import DataFrameFormat
from etna.datasets.utils import _TorchDataset
+from etna.datasets.utils import apply_alignment
+from etna.datasets.utils import determine_freq
+from etna.datasets.utils import determine_num_steps
from etna.datasets.utils import get_level_dataframe
from etna.datasets.utils import get_target_with_quantiles
+from etna.datasets.utils import infer_alignment
from etna.datasets.utils import inverse_transform_target_components
+from etna.datasets.utils import make_timestamp_df_from_alignment
from etna.datasets.utils import match_target_components
from etna.datasets.utils import set_columns_wide
+from etna.datasets.utils import timestamp_range
@pytest.fixture
@@ -106,8 +115,8 @@ def test_torch_dataset():
assert len(torch_dataset) == 1
-def _get_df_wide(random_seed: int) -> pd.DataFrame:
- df = generate_ar_df(periods=5, start_time="2020-01-01", n_segments=3, random_seed=random_seed)
+def _get_df_wide(freq: Optional[str], random_seed: int) -> pd.DataFrame:
+ df = generate_ar_df(periods=5, n_segments=3, freq=freq, random_seed=random_seed)
df_wide = TSDataset.to_dataset(df)
df_exog = df.copy()
@@ -117,7 +126,7 @@ def _get_df_wide(random_seed: int) -> pd.DataFrame:
df_exog["exog_2"] = df_exog["exog_1"] + 1
df_exog_wide = TSDataset.to_dataset(df_exog)
- ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq="D")
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq=freq)
df = ts.df
# make some reorderings for checking corner cases
@@ -127,13 +136,23 @@ def _get_df_wide(random_seed: int) -> pd.DataFrame:
@pytest.fixture
-def df_left() -> pd.DataFrame:
- return _get_df_wide(0)
+def df_left_datetime() -> pd.DataFrame:
+ return _get_df_wide(freq="D", random_seed=0)
+
+
+@pytest.fixture
+def df_left_int() -> pd.DataFrame:
+ return _get_df_wide(freq=None, random_seed=0)
@pytest.fixture
-def df_right() -> pd.DataFrame:
- return _get_df_wide(1)
+def df_right_datetime() -> pd.DataFrame:
+ return _get_df_wide(freq="D", random_seed=1)
+
+
+@pytest.fixture
+def df_right_int() -> pd.DataFrame:
+ return _get_df_wide(freq=None, random_seed=0)
@pytest.mark.parametrize(
@@ -157,6 +176,7 @@ def df_right() -> pd.DataFrame:
@pytest.mark.parametrize(
"timestamps_idx_left, timestamps_idx_right", [(None, None), ([0], [0]), ([1, 2], [1, 2]), ([1, 2], [3, 4])]
)
+@pytest.mark.parametrize("dataframes", [("df_left_datetime", "df_right_datetime"), ("df_left_int", "df_right_int")])
def test_set_columns_wide(
timestamps_idx_left,
timestamps_idx_right,
@@ -164,9 +184,13 @@ def test_set_columns_wide(
segment_right,
features_left,
features_right,
- df_left,
- df_right,
+ dataframes,
+ request,
):
+ df_left_name, df_right_name = dataframes
+ df_left = request.getfixturevalue(df_left_name)
+ df_right = request.getfixturevalue(df_right_name)
+
timestamps_left = None if timestamps_idx_left is None else df_left.index[timestamps_idx_left]
timestamps_right = None if timestamps_idx_right is None else df_right.index[timestamps_idx_right]
@@ -361,3 +385,575 @@ def test_inverse_transform_target_components(
inverse_transformed_target_df=inverse_transformed_target_df,
)
pd.testing.assert_frame_equal(obtained_inverse_transformed_components_df, inverse_transformed_components_df)
+
+
+@pytest.mark.parametrize(
+ "start_timestamp, end_timestamp, freq, answer",
+ [
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02"), "D", 1),
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-11"), "D", 10),
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), "D", 0),
+ (pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-19"), "W-SUN", 2),
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-15"), pd.offsets.Week(), 2),
+ (pd.Timestamp("2020-01-31"), pd.Timestamp("2021-02-28"), "M", 13),
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2021-06-01"), "MS", 17),
+ (0, 0, None, 0),
+ (0, 5, None, 5),
+ (3, 10, None, 7),
+ ],
+)
+def test_determine_num_steps_ok(start_timestamp, end_timestamp, freq, answer):
+ result = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
+ assert result == answer
+
+
+@pytest.mark.parametrize(
+ "start_timestamp, end_timestamp, freq",
+ [
+ (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-01-01"), "D"),
+ (5, 2, None),
+ ],
+)
+def test_determine_num_steps_fail_wrong_order(start_timestamp, end_timestamp, freq):
+ with pytest.raises(ValueError, match="Start timestamp should be less or equal than end timestamp"):
+ _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
+
+
+@pytest.mark.parametrize(
+ "start_timestamp, end_timestamp, freq",
+ [
+ (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-06-01"), "M"),
+ (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-06-01"), "MS"),
+ (2.2, 5, None),
+ ],
+)
+def test_determine_num_steps_fail_wrong_start(start_timestamp, end_timestamp, freq):
+ with pytest.raises(ValueError, match="Start timestamp isn't correct according to given frequency"):
+ _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
+
+
+@pytest.mark.parametrize(
+ "start_timestamp, end_timestamp, freq",
+ [
+ (2, 5.5, None),
+ ],
+)
+def test_determine_num_steps_fail_wrong_start(start_timestamp, end_timestamp, freq):
+ with pytest.raises(ValueError, match="End timestamp isn't correct according to given frequency"):
+ _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
+
+
+@pytest.mark.parametrize(
+ "start_timestamp, end_timestamp, freq",
+ [
+ (pd.Timestamp("2020-01-31"), pd.Timestamp("2020-06-05"), "M"),
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-06-05"), "MS"),
+ ],
+)
+def test_determine_num_steps_fail_wrong_end(start_timestamp, end_timestamp, freq):
+ with pytest.raises(ValueError, match="End timestamp isn't reachable with freq"):
+ _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
+
+
+@pytest.mark.parametrize(
+ "timestamps,answer",
+ (
+ (pd.date_range(start="2020-01-01", periods=3, freq="M"), "M"),
+ (pd.date_range(start="2020-01-01", periods=3, freq="W"), "W-SUN"),
+ (pd.date_range(start="2020-01-01", periods=3, freq="D"), "D"),
+ (pd.Series(np.arange(10)), None),
+ (pd.Series(np.arange(5, 15)), None),
+ (pd.Series(np.arange(1)), None),
+ ),
+)
+def test_determine_freq(timestamps, answer):
+ assert determine_freq(timestamps=timestamps) == answer
+
+
+@pytest.mark.parametrize(
+ "timestamps",
+ (
+ pd.to_datetime(pd.Series(["2020-02-01", "2020-02-15", "2021-02-15"])),
+ pd.to_datetime(pd.Series(["2020-02-15", "2020-01-22", "2020-01-23"])),
+ ),
+)
+def test_determine_freq_fail_cant_determine(timestamps):
+ with pytest.raises(ValueError, match="Can't determine frequency of a given dataframe"):
+ _ = determine_freq(timestamps=timestamps)
+
+
+@pytest.mark.parametrize(
+ "timestamps",
+ (
+ pd.Series([5, 4, 3]),
+ pd.Series([4, 5, 3]),
+ pd.Series([3, 4, 6]),
+ ),
+)
+def test_determine_freq_fail_int_gaps(timestamps):
+ with pytest.raises(ValueError, match="Integer timestamp isn't ordered and doesn't contain all the values"):
+ _ = determine_freq(timestamps=timestamps)
+
+
+@pytest.mark.parametrize(
+ "start, end, periods, freq, expected_range",
+ [
+ ("2020-01-01", "2020-01-10", None, "D", pd.date_range(start="2020-01-01", end="2020-01-10", freq="D")),
+ ("2020-01-01", None, 10, "D", pd.date_range(start="2020-01-01", periods=10, freq="D")),
+ (None, "2020-01-10", 10, "D", pd.date_range(end="2020-01-10", periods=10, freq="D")),
+ ("2020-01-01", None, 10, "MS", pd.date_range(start="2020-01-01", periods=10, freq="MS")),
+ (10, 19, None, None, np.arange(10, 20)),
+ (10, None, 10, None, np.arange(10, 20)),
+ (None, 19, 10, None, np.arange(10, 20)),
+ ],
+)
+def test_timestamp_range(start, end, periods, freq, expected_range):
+ result = timestamp_range(start=start, end=end, periods=periods, freq=freq)
+ np.testing.assert_array_equal(result, expected_range)
+
+
+@pytest.mark.parametrize(
+ "start, end, periods, freq",
+ [
+ ("2020-01-01", "2020-01-10", None, None),
+ ("2020-01-01", None, 10, None),
+ (None, "2020-01-10", 10, None),
+ ("2020-01-01", 20, None, "D"),
+ (10, "2020-01-10", None, "D"),
+ (10, 20, None, "D"),
+ (10, None, 10, "D"),
+ (None, 20, 10, "D"),
+ ],
+)
+def test_timestamp_range_fail_type(start, end, periods, freq):
+ with pytest.raises(ValueError, match="Parameter .* has incorrect type"):
+ _ = timestamp_range(start=start, end=end, periods=periods, freq=freq)
+
+
+@pytest.mark.parametrize(
+ "start, end, periods, freq",
+ [
+ ("2020-01-01", "2020-01-10", 10, "D"),
+ ("2020-01-01", None, None, "D"),
+ (None, "2020-01-10", None, "D"),
+ (None, None, 10, "D"),
+ (None, None, None, "D"),
+ (10, 19, 10, None),
+ (10, None, None, None),
+ (None, 19, None, None),
+ (None, None, 10, None),
+ (None, None, None, None),
+ ],
+)
+def test_timestamp_range_fail_num_parameters(start, end, periods, freq):
+ with pytest.raises(ValueError, match="Of the three parameters: .* must be specified"):
+ _ = timestamp_range(start=start, end=end, periods=periods, freq=freq)
+
+
+@pytest.mark.parametrize(
+ "df_name",
+ [
+ "df_aligned_datetime",
+ "df_aligned_int",
+ ],
+)
+def test_infer_alignment_fail_wrong_format(df_name, request):
+ df = request.getfixturevalue(df_name)
+ df_wide = TSDataset.to_dataset(df)
+ with pytest.raises(ValueError, match="Parameter df should be in a long format"):
+ _ = infer_alignment(df_wide)
+
+
+@pytest.mark.parametrize(
+ "df_name, expected_alignment",
+ [
+ ("df_aligned_datetime", {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-10")}),
+ ("df_aligned_int", {"segment_0": 19, "segment_1": 19}),
+ ("df_misaligned_datetime", {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-07")}),
+ ("df_misaligned_int", {"segment_0": 19, "segment_1": 16}),
+ (
+ "df_aligned_datetime_with_missing_values",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-10")},
+ ),
+ ("df_aligned_int_with_missing_values", {"segment_0": 19, "segment_1": 19}),
+ ],
+)
+def test_infer_alignment(df_name, expected_alignment, request):
+ df = request.getfixturevalue(df_name)
+ alignment = infer_alignment(df)
+ assert alignment == expected_alignment
+
+
+@pytest.mark.parametrize(
+ "df_name",
+ [
+ "df_aligned_datetime",
+ "df_aligned_int",
+ ],
+)
+def test_apply_alignment_fail_wrong_format(df_name, request):
+ df = request.getfixturevalue(df_name)
+ df_wide = TSDataset.to_dataset(df)
+ with pytest.raises(ValueError, match="Parameter df should be in a long format"):
+ _ = apply_alignment(df=df_wide, alignment={})
+
+
+@pytest.mark.parametrize(
+ "df_name, alignment",
+ [
+ ("df_aligned_datetime", {}),
+ ("df_aligned_datetime", {"segment_0": pd.Timestamp("2020-01-10")}),
+ ("df_aligned_datetime", {"segment_1": pd.Timestamp("2020-01-10")}),
+ ],
+)
+def test_apply_alignment_fail_no_segment(df_name, alignment, request):
+ df = request.getfixturevalue(df_name)
+ with pytest.raises(ValueError, match="The segment .* isn't present in alignment"):
+ _ = apply_alignment(df=df, alignment=alignment)
+
+
+@pytest.mark.parametrize(
+ "df_name, alignment",
+ [
+ ("df_aligned_datetime", {"segment_0": pd.Timestamp("2020-01-20"), "segment_1": pd.Timestamp("2020-01-10")}),
+ ("df_aligned_datetime", {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-20")}),
+ ],
+)
+def test_apply_alignment_fail_no_timestamp(df_name, alignment, request):
+ df = request.getfixturevalue(df_name)
+ with pytest.raises(ValueError, match="The segment .* doesn't contain timestamp .* from alignment"):
+ _ = apply_alignment(df=df, alignment=alignment)
+
+
+@pytest.mark.parametrize(
+ "df_name, alignment, original_timestamp_name, expected_columns",
+ [
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-10")},
+ None,
+ {"timestamp", "segment", "target"},
+ ),
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-05")},
+ None,
+ {"timestamp", "segment", "target"},
+ ),
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-10")},
+ "original_timestamp",
+ {"timestamp", "segment", "target", "original_timestamp"},
+ ),
+ (
+ "df_aligned_datetime_with_additional_columns",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-10")},
+ None,
+ {"timestamp", "segment", "target", "feature_1"},
+ ),
+ (
+ "df_aligned_datetime_with_additional_columns",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-10")},
+ "original_timestamp",
+ {"timestamp", "segment", "target", "feature_1", "original_timestamp"},
+ ),
+ ("df_aligned_int", {"segment_0": 19, "segment_1": 19}, None, {"timestamp", "segment", "target"}),
+ (
+ "df_aligned_int",
+ {"segment_0": 19, "segment_1": 19},
+ "original_timestamp",
+ {"timestamp", "segment", "target", "original_timestamp"},
+ ),
+ ],
+)
+def test_apply_alignment_format(df_name, alignment, original_timestamp_name, expected_columns, request):
+ df = request.getfixturevalue(df_name)
+ result_df = apply_alignment(df=df, alignment=alignment, original_timestamp_name=original_timestamp_name)
+
+ assert len(result_df) == len(df)
+ assert set(result_df.columns) == expected_columns
+
+
+@pytest.mark.parametrize(
+ "df_name, alignment",
+ [
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-10")},
+ ),
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-05")},
+ ),
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-05"), "segment_1": pd.Timestamp("2020-01-10")},
+ ),
+ (
+ "df_misaligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-07")},
+ ),
+ ("df_aligned_int", {"segment_0": 19, "segment_1": 19}),
+ ("df_aligned_int", {"segment_0": 19, "segment_1": 14}),
+ ("df_aligned_int", {"segment_0": 14, "segment_1": 19}),
+ ("df_misaligned_int", {"segment_0": 19, "segment_1": 16}),
+ ],
+)
+def test_apply_alignment_doesnt_change_original(df_name, alignment, request):
+ df = request.getfixturevalue(df_name)
+ result_df = apply_alignment(df=df[::-1], alignment=alignment, original_timestamp_name="original_timestamp")
+
+ check_df = result_df.drop(columns=["timestamp"])
+ check_df = check_df.rename(columns={"original_timestamp": "timestamp"})
+ pd.testing.assert_frame_equal(check_df.loc[df.index], df)
+
+
+@pytest.mark.parametrize(
+ "df_name, alignment, expected_timestamps",
+ [
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-10")},
+ list(range(-9, 1)) + list(range(-9, 1)),
+ ),
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-05")},
+ list(range(-9, 1)) + list(range(-4, 6)),
+ ),
+ (
+ "df_aligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-05"), "segment_1": pd.Timestamp("2020-01-10")},
+ list(range(-4, 6)) + list(range(-9, 1)),
+ ),
+ (
+ "df_misaligned_datetime",
+ {"segment_0": pd.Timestamp("2020-01-10"), "segment_1": pd.Timestamp("2020-01-07")},
+ list(range(-9, 1)) + list(range(-6, 1)),
+ ),
+ ("df_aligned_int", {"segment_0": 19, "segment_1": 19}, list(range(-9, 1)) + list(range(-9, 1))),
+ ("df_aligned_int", {"segment_0": 19, "segment_1": 14}, list(range(-9, 1)) + list(range(-4, 6))),
+ ("df_aligned_int", {"segment_0": 14, "segment_1": 19}, list(range(-4, 6)) + list(range(-9, 1))),
+ ("df_misaligned_int", {"segment_0": 19, "segment_1": 16}, list(range(-9, 1)) + list(range(-6, 1))),
+ ],
+)
+def test_apply_alignment_new_timestamps(df_name, alignment, expected_timestamps, request):
+ df = request.getfixturevalue(df_name)
+ result_df = apply_alignment(df=df, alignment=alignment)
+
+ np.testing.assert_array_equal(result_df["timestamp"], expected_timestamps)
+
+
+@pytest.mark.parametrize(
+ "alignment, start, end, periods, freq, timestamp_name, expected_timestamp",
+ [
+ (
+ {"segment_0": pd.Timestamp("2020-01-01")},
+ 0,
+ 9,
+ None,
+ "D",
+ "external_timestamp",
+ timestamp_range(start="2020-01-01", periods=10, freq="D"),
+ ),
+ (
+ {"segment_0": pd.Timestamp("2020-01-01")},
+ 2,
+ 11,
+ None,
+ "D",
+ "external_timestamp",
+ timestamp_range(start="2020-01-03", periods=10, freq="D"),
+ ),
+ (
+ {"segment_0": pd.Timestamp("2020-01-01")},
+ -2,
+ 7,
+ None,
+ "D",
+ "external_timestamp",
+ timestamp_range(start="2019-12-30", periods=10, freq="D"),
+ ),
+ (
+ {"segment_0": pd.Timestamp("2020-01-01")},
+ 0,
+ None,
+ 10,
+ "D",
+ "external_timestamp",
+ timestamp_range(start="2020-01-01", periods=10, freq="D"),
+ ),
+ (
+ {"segment_0": pd.Timestamp("2020-01-01")},
+ None,
+ 9,
+ 10,
+ "D",
+ "external_timestamp",
+ timestamp_range(start="2020-01-01", periods=10, freq="D"),
+ ),
+ (
+ {"segment_0": pd.Timestamp("2020-01-01"), "segment_1": pd.Timestamp("2020-01-03")},
+ 0,
+ 9,
+ None,
+ "D",
+ "external_timestamp",
+ pd.concat(
+ [
+ timestamp_range(start="2020-01-01", periods=10, freq="D").to_series(),
+ timestamp_range(start="2020-01-03", periods=10, freq="D").to_series(),
+ ]
+ ),
+ ),
+ ({"segment_0": 10}, 0, 9, None, None, "external_timestamp", timestamp_range(start=10, periods=10, freq=None)),
+ ({"segment_0": 10}, 2, 11, None, None, "external_timestamp", timestamp_range(start=12, periods=10, freq=None)),
+ ({"segment_0": 10}, -2, 7, None, None, "external_timestamp", timestamp_range(start=8, periods=10, freq=None)),
+ ({"segment_0": 10}, 0, None, 10, None, "external_timestamp", timestamp_range(start=10, periods=10, freq=None)),
+ ({"segment_0": 10}, None, 9, 10, None, "external_timestamp", timestamp_range(start=10, periods=10, freq=None)),
+ (
+ {"segment_0": 10, "segment_1": 12},
+ 0,
+ 9,
+ None,
+ None,
+ "external_timestamp",
+ pd.concat(
+ [
+ timestamp_range(start=10, periods=10, freq=None).to_series(),
+ timestamp_range(start=12, periods=10, freq=None).to_series(),
+ ]
+ ),
+ ),
+ ],
+)
+def test_make_timestamp_df_from_alignment_format(
+ alignment, start, end, periods, freq, timestamp_name, expected_timestamp
+):
+ df = make_timestamp_df_from_alignment(
+ alignment=alignment, start=start, end=end, periods=periods, freq=freq, timestamp_name=timestamp_name
+ )
+
+ assert set(df.columns) == {"timestamp", "segment", timestamp_name}
+ np.testing.assert_array_equal(df[timestamp_name], expected_timestamp)
+
+
+@pytest.fixture
+def example_long_df() -> pd.DataFrame:
+ df = generate_ar_df(periods=10, start_time="2020-01-01", n_segments=2, freq="D")
+ return df
+
+
+@pytest.fixture
+def example_long_df_exog() -> pd.DataFrame:
+ df = generate_ar_df(periods=10, start_time="2020-01-01", n_segments=2, freq="D")
+ df.rename(columns={"target": "exog_1"}, inplace=True)
+ df["exog_2"] = df["exog_1"] + 1.5
+ return df
+
+
+@pytest.fixture
+def example_long_df_no_timestamp() -> pd.DataFrame:
+ df = generate_ar_df(periods=10, start_time="2020-01-01", n_segments=2, freq="D")
+ df.rename(columns={"timestamp": "renamed_timestamp"}, inplace=True)
+ return df
+
+
+@pytest.fixture
+def example_long_df_no_segment() -> pd.DataFrame:
+ df = generate_ar_df(periods=10, start_time="2020-01-01", n_segments=2, freq="D")
+ df.rename(columns={"segment": "renamed_segment"}, inplace=True)
+ return df
+
+
+@pytest.fixture
+def example_long_df_no_features() -> pd.DataFrame:
+ df = generate_ar_df(periods=10, start_time="2020-01-01", n_segments=2, freq="D")
+ df.drop(columns=["target"], inplace=True)
+ return df
+
+
+@pytest.fixture
+def example_wide_df(example_long_df) -> pd.DataFrame:
+ wide_df = TSDataset.to_dataset(example_long_df)
+ return wide_df
+
+
+@pytest.fixture
+def example_wide_df_exog(example_long_df_exog) -> pd.DataFrame:
+ wide_df = TSDataset.to_dataset(example_long_df_exog)
+ return wide_df
+
+
+@pytest.fixture
+def example_wide_df_not_sorted(example_wide_df_exog) -> pd.DataFrame:
+ example_wide_df_exog = example_wide_df_exog.iloc[:, ::-1]
+ return example_wide_df_exog
+
+
+@pytest.fixture
+def example_wide_df_no_index_name(example_wide_df) -> pd.DataFrame:
+ example_wide_df.index.name = None
+ return example_wide_df
+
+
+@pytest.fixture
+def example_wide_df_wrong_level_names(example_wide_df) -> pd.DataFrame:
+ example_wide_df.columns.set_names(("name_1", "name_2"), inplace=True)
+ return example_wide_df
+
+
+@pytest.fixture
+def example_wide_df_no_features(example_long_df_no_features) -> pd.DataFrame:
+ wide_df = TSDataset.to_dataset(example_long_df_no_features)
+ return wide_df
+
+
+@pytest.fixture
+def example_wide_df_exog_not_full(example_wide_df_exog) -> pd.DataFrame:
+ wide_df = example_wide_df_exog.iloc[:, :-1]
+ return wide_df
+
+
+@pytest.mark.parametrize(
+ "df_name, expected_format",
+ [
+ ("example_long_df", DataFrameFormat.long),
+ ("example_long_df_exog", DataFrameFormat.long),
+ ("example_wide_df", DataFrameFormat.wide),
+ ("example_wide_df_exog", DataFrameFormat.wide),
+ ("example_wide_df_not_sorted", DataFrameFormat.wide),
+ ("example_wide_df_no_index_name", DataFrameFormat.wide),
+ ],
+)
+def test_determine_format_ok(df_name, expected_format, request):
+ df = request.getfixturevalue(df_name)
+ determined_format = DataFrameFormat.determine(df=df)
+ assert determined_format is expected_format
+
+
+@pytest.mark.parametrize(
+ "df_name, error_match",
+ [
+ ("example_long_df_no_timestamp", "Given long dataframe doesn't have required column 'timestamp'"),
+ ("example_long_df_no_segment", "Given long dataframe doesn't have required column 'segment'!"),
+ (
+ "example_long_df_no_features",
+ "Given long dataframe doesn't have any columns except for 'timestamp` and 'segment'",
+ ),
+ (
+ "example_wide_df_wrong_level_names",
+ "Given wide dataframe doesn't have levels of columns \['segment', 'feature'\]",
+ ),
+ ("example_wide_df_no_features", "Given wide dataframe doesn't have any features"),
+ (
+ "example_wide_df_exog_not_full",
+ "Given wide dataframe doesn't have all combinations of pairs \(segment, feature\)",
+ ),
+ ],
+)
+def test_determine_format_fail(df_name, error_match, request):
+ df = request.getfixturevalue(df_name)
+ with pytest.raises(ValueError, match=error_match):
+ _ = DataFrameFormat.determine(df=df)
diff --git a/tests/test_ensembles/conftest.py b/tests/test_ensembles/conftest.py
index 804e406e9..09c45a7ff 100644
--- a/tests/test_ensembles/conftest.py
+++ b/tests/test_ensembles/conftest.py
@@ -15,6 +15,7 @@
from etna.models import CatBoostPerSegmentModel
from etna.models import NaiveModel
from etna.models import ProphetModel
+from etna.models import SARIMAXModel
from etna.pipeline import HierarchicalPipeline
from etna.pipeline import Pipeline
from etna.reconciliation import BottomUpReconciliator
@@ -41,6 +42,13 @@ def prophet_pipeline() -> Pipeline:
return pipeline
+@pytest.fixture
+def sarimax_pipeline() -> Pipeline:
+ """Generate pipeline with SARIMAXModel."""
+ pipeline = Pipeline(model=SARIMAXModel(), transforms=[], horizon=7)
+ return pipeline
+
+
@pytest.fixture
def naive_pipeline() -> Pipeline:
"""Generate pipeline with NaiveModel."""
@@ -106,6 +114,14 @@ def voting_ensemble_pipeline(
return pipeline
+@pytest.fixture
+def voting_ensemble_pipeline_int_timestamp(
+ catboost_pipeline: Pipeline, sarimax_pipeline: Pipeline, naive_pipeline_1: Pipeline
+) -> VotingEnsemble:
+ pipeline = VotingEnsemble(pipelines=[catboost_pipeline, sarimax_pipeline, naive_pipeline_1])
+ return pipeline
+
+
@pytest.fixture
def voting_ensemble_hierarchical_pipeline(
naive_pipeline_top_down_market_14: HierarchicalPipeline, naive_pipeline_bottom_up_market_14: HierarchicalPipeline
@@ -136,6 +152,14 @@ def stacking_ensemble_pipeline(
return pipeline
+@pytest.fixture
+def stacking_ensemble_pipeline_int_timestamp(
+ catboost_pipeline: Pipeline, sarimax_pipeline: Pipeline, naive_pipeline_1: Pipeline
+) -> StackingEnsemble:
+ pipeline = StackingEnsemble(pipelines=[catboost_pipeline, sarimax_pipeline, naive_pipeline_1])
+ return pipeline
+
+
@pytest.fixture
def stacking_ensemble_hierarchical_pipeline(
naive_pipeline_top_down_market_14: HierarchicalPipeline, naive_pipeline_bottom_up_market_14: HierarchicalPipeline
diff --git a/tests/test_ensembles/test_direct_ensemble.py b/tests/test_ensembles/test_direct_ensemble.py
index 04f8b5693..fa75902fb 100644
--- a/tests/test_ensembles/test_direct_ensemble.py
+++ b/tests/test_ensembles/test_direct_ensemble.py
@@ -16,6 +16,7 @@
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts_with_prediction_intervals
from tests.test_pipeline.utils import assert_pipeline_forecasts_without_self_ts
+from tests.test_pipeline.utils import assert_pipeline_predicts
@pytest.fixture
@@ -113,13 +114,13 @@ def test_fit_saving_ts(direct_ensemble_pipeline, simple_ts_train, save_ts):
assert direct_ensemble_pipeline.ts is None
-def test_forecast(direct_ensemble_pipeline, simple_ts_train, simple_ts_forecast):
+def test_forecast_values(direct_ensemble_pipeline, simple_ts_train, simple_ts_forecast):
direct_ensemble_pipeline.fit(simple_ts_train)
forecast = direct_ensemble_pipeline.forecast()
pd.testing.assert_frame_equal(forecast.to_pandas(), simple_ts_forecast.to_pandas())
-def test_predict(direct_ensemble_pipeline, simple_ts_train):
+def test_predict_values(direct_ensemble_pipeline, simple_ts_train):
smallest_pipeline = Pipeline(model=NaiveModel(lag=1), transforms=[], horizon=1)
direct_ensemble_pipeline.fit(simple_ts_train)
smallest_pipeline.fit(simple_ts_train)
@@ -141,22 +142,56 @@ def test_forecast_raise_error_if_no_ts(direct_ensemble_pipeline, example_tsds):
assert_pipeline_forecast_raise_error_if_no_ts(pipeline=direct_ensemble_pipeline, ts=example_tsds)
-def test_forecasts_without_self_ts(direct_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_without_self_ts(
- pipeline=direct_ensemble_pipeline, ts=example_tsds, horizon=direct_ensemble_pipeline.horizon
- )
-
-
-def test_forecast_given_ts(direct_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_given_ts(
- pipeline=direct_ensemble_pipeline, ts=example_tsds, horizon=direct_ensemble_pipeline.horizon
- )
-
-
-def test_forecast_given_ts_with_prediction_interval(direct_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_given_ts_with_prediction_intervals(
- pipeline=direct_ensemble_pipeline, ts=example_tsds, horizon=direct_ensemble_pipeline.horizon
- )
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "direct_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "direct_ensemble_pipeline"),
+ ],
+)
+def test_forecasts_without_self_ts(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_without_self_ts(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "direct_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "direct_ensemble_pipeline"),
+ ],
+)
+def test_forecast_given_ts(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_given_ts(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "direct_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "direct_ensemble_pipeline"),
+ ],
+)
+def test_forecast_given_ts_with_prediction_interval(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_given_ts_with_prediction_intervals(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "direct_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "direct_ensemble_pipeline"),
+ ],
+)
+def test_predict(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_predicts(pipeline=ensemble, ts=ts, start_idx=20, end_idx=30)
def test_forecast_with_return_components_fails(example_tsds, direct_ensemble_pipeline):
diff --git a/tests/test_ensembles/test_stacking_ensemble.py b/tests/test_ensembles/test_stacking_ensemble.py
index 198bd33db..bcda4c56e 100644
--- a/tests/test_ensembles/test_stacking_ensemble.py
+++ b/tests/test_ensembles/test_stacking_ensemble.py
@@ -19,6 +19,7 @@
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts_with_prediction_intervals
from tests.test_pipeline.utils import assert_pipeline_forecasts_without_self_ts
+from tests.test_pipeline.utils import assert_pipeline_predicts
HORIZON = 7
@@ -303,7 +304,7 @@ def test_forecast_sanity(weekly_period_ts: Tuple["TSDataset", "TSDataset"], naiv
def test_multiprocessing_ensembles(
- simple_df: TSDataset,
+ simple_tsdf,
catboost_pipeline: Pipeline,
prophet_pipeline: Pipeline,
naive_pipeline_1: Pipeline,
@@ -314,8 +315,8 @@ def test_multiprocessing_ensembles(
single_jobs_ensemble = StackingEnsemble(pipelines=deepcopy(pipelines), n_jobs=1)
multi_jobs_ensemble = StackingEnsemble(pipelines=deepcopy(pipelines), n_jobs=3)
- single_jobs_ensemble.fit(ts=deepcopy(simple_df))
- multi_jobs_ensemble.fit(ts=deepcopy(simple_df))
+ single_jobs_ensemble.fit(ts=deepcopy(simple_tsdf))
+ multi_jobs_ensemble.fit(ts=deepcopy(simple_tsdf))
single_jobs_forecast = single_jobs_ensemble.forecast()
multi_jobs_forecast = multi_jobs_ensemble.forecast()
@@ -377,22 +378,56 @@ def test_forecast_raise_error_if_no_ts(stacking_ensemble_pipeline, example_tsds)
assert_pipeline_forecast_raise_error_if_no_ts(pipeline=stacking_ensemble_pipeline, ts=example_tsds)
-def test_forecasts_without_self_ts(stacking_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_without_self_ts(
- pipeline=stacking_ensemble_pipeline, ts=example_tsds, horizon=stacking_ensemble_pipeline.horizon
- )
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "stacking_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "stacking_ensemble_pipeline_int_timestamp"),
+ ],
+)
+def test_forecasts_without_self_ts(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_without_self_ts(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
-def test_forecast_given_ts(stacking_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_given_ts(
- pipeline=stacking_ensemble_pipeline, ts=example_tsds, horizon=stacking_ensemble_pipeline.horizon
- )
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "stacking_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "stacking_ensemble_pipeline_int_timestamp"),
+ ],
+)
+def test_forecast_given_ts(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_given_ts(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
-def test_forecast_given_ts_with_prediction_interval(stacking_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_given_ts_with_prediction_intervals(
- pipeline=stacking_ensemble_pipeline, ts=example_tsds, horizon=stacking_ensemble_pipeline.horizon
- )
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "stacking_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "stacking_ensemble_pipeline_int_timestamp"),
+ ],
+)
+def test_forecast_given_ts_with_prediction_interval(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_given_ts_with_prediction_intervals(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "stacking_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "stacking_ensemble_pipeline_int_timestamp"),
+ ],
+)
+def test_predict(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_predicts(pipeline=ensemble, ts=ts, start_idx=20, end_idx=30)
def test_forecast_with_return_components_fails(example_tsds, naive_ensemble):
diff --git a/tests/test_ensembles/test_voting_ensemble.py b/tests/test_ensembles/test_voting_ensemble.py
index 132989a44..4ba56753c 100644
--- a/tests/test_ensembles/test_voting_ensemble.py
+++ b/tests/test_ensembles/test_voting_ensemble.py
@@ -20,6 +20,7 @@
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts_with_prediction_intervals
from tests.test_pipeline.utils import assert_pipeline_forecasts_without_self_ts
+from tests.test_pipeline.utils import assert_pipeline_predicts
HORIZON = 7
@@ -117,37 +118,24 @@ def test_forecast_prediction_interval_interface(example_tsds, naive_pipeline_1,
assert (segment_slice["target_0.975"] - segment_slice["target_0.025"] >= 0).all()
-def test_predict_interface(example_tsds: TSDataset, catboost_pipeline: Pipeline, prophet_pipeline: Pipeline):
- """Check that VotingEnsemble.predict returns TSDataset of correct length."""
- ensemble = VotingEnsemble(pipelines=[catboost_pipeline, prophet_pipeline])
- ensemble.fit(ts=example_tsds)
- start_idx = 20
- end_idx = 30
- prediction = ensemble.predict(
- ts=example_tsds, start_timestamp=example_tsds.index[start_idx], end_timestamp=example_tsds.index[end_idx]
- )
- assert isinstance(prediction, TSDataset)
- assert len(prediction.df) == end_idx - start_idx + 1
-
-
-def test_vote_default_weights(simple_df: TSDataset, naive_pipeline_1: Pipeline, naive_pipeline_2: Pipeline):
+def test_vote_default_weights(simple_tsdf, naive_pipeline_1: Pipeline, naive_pipeline_2: Pipeline):
"""Check that VotingEnsemble gets average during vote."""
ensemble = VotingEnsemble(pipelines=[naive_pipeline_1, naive_pipeline_2])
- ensemble.fit(ts=simple_df)
+ ensemble.fit(ts=simple_tsdf)
forecasts = Parallel(n_jobs=ensemble.n_jobs, backend="multiprocessing", verbose=11)(
- delayed(ensemble._forecast_pipeline)(pipeline=pipeline, ts=simple_df) for pipeline in ensemble.pipelines
+ delayed(ensemble._forecast_pipeline)(pipeline=pipeline, ts=simple_tsdf) for pipeline in ensemble.pipelines
)
forecast = ensemble._vote(forecasts=forecasts)
np.testing.assert_array_equal(forecast[:, "A", "target"].values, [47.5, 48, 47.5, 48, 47.5, 48, 47.5])
np.testing.assert_array_equal(forecast[:, "B", "target"].values, [11, 12, 11, 12, 11, 12, 11])
-def test_vote_custom_weights(simple_df: TSDataset, naive_pipeline_1: Pipeline, naive_pipeline_2: Pipeline):
+def test_vote_custom_weights(simple_tsdf, naive_pipeline_1: Pipeline, naive_pipeline_2: Pipeline):
"""Check that VotingEnsemble gets average during vote."""
ensemble = VotingEnsemble(pipelines=[naive_pipeline_1, naive_pipeline_2], weights=[1, 3])
- ensemble.fit(ts=simple_df)
+ ensemble.fit(ts=simple_tsdf)
forecasts = Parallel(n_jobs=ensemble.n_jobs, backend="multiprocessing", verbose=11)(
- delayed(ensemble._forecast_pipeline)(pipeline=pipeline, ts=simple_df) for pipeline in ensemble.pipelines
+ delayed(ensemble._forecast_pipeline)(pipeline=pipeline, ts=simple_tsdf) for pipeline in ensemble.pipelines
)
forecast = ensemble._vote(forecasts=forecasts)
np.testing.assert_array_equal(forecast[:, "A", "target"].values, [47.25, 48, 47.25, 48, 47.25, 48, 47.25])
@@ -184,7 +172,7 @@ def test_predict_calls_vote(example_tsds: TSDataset, naive_pipeline_1: Pipeline,
def test_multiprocessing_ensembles(
- simple_df: TSDataset,
+ simple_tsdf,
catboost_pipeline: Pipeline,
prophet_pipeline: Pipeline,
naive_pipeline_1: Pipeline,
@@ -195,8 +183,8 @@ def test_multiprocessing_ensembles(
single_jobs_ensemble = VotingEnsemble(pipelines=deepcopy(pipelines), n_jobs=1)
multi_jobs_ensemble = VotingEnsemble(pipelines=deepcopy(pipelines), n_jobs=3)
- single_jobs_ensemble.fit(ts=deepcopy(simple_df))
- multi_jobs_ensemble.fit(ts=deepcopy(simple_df))
+ single_jobs_ensemble.fit(ts=deepcopy(simple_tsdf))
+ multi_jobs_ensemble.fit(ts=deepcopy(simple_tsdf))
single_jobs_forecast = single_jobs_ensemble.forecast()
multi_jobs_forecast = multi_jobs_ensemble.forecast()
@@ -254,26 +242,60 @@ def test_save_load(load_ts, voting_ensemble_pipeline, example_tsds):
assert_pipeline_equals_loaded_original(pipeline=voting_ensemble_pipeline, ts=example_tsds, load_ts=load_ts)
-def test_forecast_raise_error_if_no_ts(stacking_ensemble_pipeline, example_tsds):
- assert_pipeline_forecast_raise_error_if_no_ts(pipeline=stacking_ensemble_pipeline, ts=example_tsds)
+def test_forecast_raise_error_if_no_ts(voting_ensemble_pipeline, example_tsds):
+ assert_pipeline_forecast_raise_error_if_no_ts(pipeline=voting_ensemble_pipeline, ts=example_tsds)
-def test_forecasts_without_self_ts(voting_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_without_self_ts(
- pipeline=voting_ensemble_pipeline, ts=example_tsds, horizon=voting_ensemble_pipeline.horizon
- )
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "voting_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "voting_ensemble_pipeline_int_timestamp"),
+ ],
+)
+def test_forecasts_without_self_ts(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_without_self_ts(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
-def test_forecast_given_ts(voting_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_given_ts(
- pipeline=voting_ensemble_pipeline, ts=example_tsds, horizon=voting_ensemble_pipeline.horizon
- )
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "voting_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "voting_ensemble_pipeline_int_timestamp"),
+ ],
+)
+def test_forecast_given_ts(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_given_ts(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
-def test_forecast_given_ts_with_prediction_interval(voting_ensemble_pipeline, example_tsds):
- assert_pipeline_forecasts_given_ts_with_prediction_intervals(
- pipeline=voting_ensemble_pipeline, ts=example_tsds, horizon=voting_ensemble_pipeline.horizon
- )
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "voting_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "voting_ensemble_pipeline_int_timestamp"),
+ ],
+)
+def test_forecast_given_ts_with_prediction_interval(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_forecasts_given_ts_with_prediction_intervals(pipeline=ensemble, ts=ts, horizon=ensemble.horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, ensemble_name",
+ [
+ ("example_tsds", "voting_ensemble_pipeline"),
+ ("example_tsds_int_timestamp", "voting_ensemble_pipeline_int_timestamp"),
+ ],
+)
+def test_predict(ts_name, ensemble_name, request):
+ ts = request.getfixturevalue(ts_name)
+ ensemble = request.getfixturevalue(ensemble_name)
+ assert_pipeline_predicts(pipeline=ensemble, ts=ts, start_idx=20, end_idx=30)
def test_forecast_with_return_components_fails(example_tsds, voting_ensemble_naive):
diff --git a/tests/test_models/conftest.py b/tests/test_models/conftest.py
index 129e1a2be..7e1a456c8 100644
--- a/tests/test_models/conftest.py
+++ b/tests/test_models/conftest.py
@@ -1,6 +1,7 @@
from copy import deepcopy
import numpy as np
+import pandas as pd
import pytest
from etna.datasets import generate_ar_df
@@ -33,6 +34,15 @@ def dfs_w_exog():
return train, test
+@pytest.fixture()
+def dfs_w_exog_int_timestamp(dfs_w_exog):
+ shift = 10
+ train_df, test_df = dfs_w_exog
+ train_df["timestamp"] = np.arange(len(train_df)) + shift
+ test_df["timestamp"] = np.arange(len(test_df)) + len(train_df) + shift
+ return train_df, test_df
+
+
@pytest.fixture
def ts_with_non_convertable_category_regressor(example_tsds) -> TSDataset:
ts = example_tsds
@@ -71,3 +81,15 @@ def ts_with_non_regressor_exog(example_tsds) -> TSDataset:
df_exog_wide = TSDataset.to_dataset(df_exog)
ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq=ts.freq)
return ts
+
+
+@pytest.fixture
+def ts_with_external_timestamp() -> TSDataset:
+ df = generate_ar_df(periods=100, start_time=10, n_segments=2, freq=None)
+ df_wide = TSDataset.to_dataset(df)
+ df_exog = generate_ar_df(periods=100, start_time=10, n_segments=2, freq=None)
+ df_exog["target"] = pd.date_range(start="2020-01-01", periods=100).tolist() * 2
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ df_exog_wide.rename(columns={"target": "external_timestamp"}, level="feature", inplace=True)
+ ts = TSDataset(df=df_wide.iloc[:-10], df_exog=df_exog_wide, known_future="all", freq=None)
+ return ts
diff --git a/tests/test_models/test_autoarima_model.py b/tests/test_models/test_autoarima_model.py
index 8e10d1196..d434a57a5 100644
--- a/tests/test_models/test_autoarima_model.py
+++ b/tests/test_models/test_autoarima_model.py
@@ -1,6 +1,7 @@
from copy import deepcopy
import numpy as np
+import pandas as pd
import pytest
from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper
@@ -50,24 +51,46 @@ def test_save_regressors_on_fit(example_reg_tsds):
assert sorted(segment_model.regressor_columns) == example_reg_tsds.regressors
-def test_select_regressors_correctly(example_reg_tsds):
+def test_select_regressors_correctly_datetime_timestamp(example_reg_tsds):
+ ts = example_reg_tsds
model = AutoARIMAModel()
- model.fit(ts=example_reg_tsds)
+ model.fit(ts=ts)
+ for segment, segment_model in model._models.items():
+ segment_features = ts[:, segment, :].droplevel("segment", axis=1).reset_index()
+
+ segment_regressors_expected = segment_features[ts.regressors].astype(float)
+ segment_regressors_expected.index = segment_features["timestamp"]
+ segment_regressors = segment_model._select_regressors(df=segment_features)
+
+ pd.testing.assert_frame_equal(segment_regressors, segment_regressors_expected)
+
+
+def test_select_regressors_correctly_int_timestamp(example_reg_tsds_int_timestamp):
+ ts = example_reg_tsds_int_timestamp
+ model = AutoARIMAModel()
+ model.fit(ts=ts)
for segment, segment_model in model._models.items():
- segment_features = example_reg_tsds[:, segment, :].droplevel("segment", axis=1)
- segment_regressors_expected = segment_features[example_reg_tsds.regressors]
- segment_regressors = segment_model._select_regressors(df=segment_features.reset_index())
- assert (segment_regressors == segment_regressors_expected).all().all()
+ segment_features = ts[:, segment, :].droplevel("segment", axis=1).reset_index()
+ segment_regressors_expected = segment_features[ts.regressors].astype(float)
+ segment_regressors_expected.index = pd.Index(np.arange(len(segment_regressors_expected)), name="timestamp")
+ segment_regressors = segment_model._select_regressors(df=segment_features)
-def test_prediction(example_tsds):
- _check_forecast(ts=deepcopy(example_tsds), model=AutoARIMAModel(), horizon=7)
- _check_predict(ts=deepcopy(example_tsds), model=AutoARIMAModel())
+ pd.testing.assert_frame_equal(segment_regressors, segment_regressors_expected)
-def test_prediction_with_reg(example_reg_tsds):
- _check_forecast(ts=deepcopy(example_reg_tsds), model=AutoARIMAModel(), horizon=7)
- _check_predict(ts=deepcopy(example_reg_tsds), model=AutoARIMAModel())
+@pytest.mark.parametrize("ts_name", ["example_tsds", "example_tsds_int_timestamp"])
+def test_prediction(ts_name, request):
+ ts = request.getfixturevalue(ts_name)
+ _check_forecast(ts=deepcopy(ts), model=AutoARIMAModel(), horizon=7)
+ _check_predict(ts=deepcopy(ts), model=AutoARIMAModel())
+
+
+@pytest.mark.parametrize("ts_name", ["example_reg_tsds", "example_reg_tsds_int_timestamp"])
+def test_prediction_with_reg(ts_name, request):
+ ts = request.getfixturevalue(ts_name)
+ _check_forecast(ts=deepcopy(ts), model=AutoARIMAModel(), horizon=7)
+ _check_predict(ts=deepcopy(ts), model=AutoARIMAModel())
@pytest.mark.filterwarnings("ignore: Error fitting ARIMA")
@@ -98,12 +121,14 @@ def test_prediction_with_params(example_reg_tsds):
_check_predict(ts=deepcopy(example_reg_tsds), model=deepcopy(model))
+@pytest.mark.parametrize("ts_name", ["example_tsds", "example_tsds_int_timestamp"])
@pytest.mark.parametrize("method_name", ["forecast", "predict"])
-def test_prediction_interval_insample(example_tsds, method_name):
+def test_prediction_interval_insample(ts_name, method_name, request):
+ ts = request.getfixturevalue(ts_name)
model = AutoARIMAModel()
- model.fit(example_tsds)
+ model.fit(ts)
method = getattr(model, method_name)
- forecast = method(example_tsds, prediction_interval=True, quantiles=[0.025, 0.975])
+ forecast = method(ts, prediction_interval=True, quantiles=[0.025, 0.975])
assert forecast.prediction_intervals_names == ("target_0.025", "target_0.975")
prediction_intervals = forecast.get_prediction_intervals()
@@ -120,10 +145,12 @@ def test_prediction_interval_insample(example_tsds, method_name):
assert np.allclose(segment_slice["target_0.025"], segment_intervals["target_0.025"])
-def test_forecast_prediction_interval_infuture(example_tsds):
+@pytest.mark.parametrize("ts_name", ["example_tsds", "example_tsds_int_timestamp"])
+def test_forecast_prediction_interval_infuture(ts_name, request):
+ ts = request.getfixturevalue(ts_name)
model = AutoARIMAModel()
- model.fit(example_tsds)
- future = example_tsds.make_future(10)
+ model.fit(ts)
+ future = ts.make_future(10)
forecast = model.forecast(future, prediction_interval=True, quantiles=[0.025, 0.975])
assert forecast.prediction_intervals_names == ("target_0.025", "target_0.975")
diff --git a/tests/test_models/test_base.py b/tests/test_models/test_base.py
index bc6cc0f49..fea869b25 100644
--- a/tests/test_models/test_base.py
+++ b/tests/test_models/test_base.py
@@ -171,14 +171,14 @@ def test_deep_base_model_forecast_fail_not_enough_context(deep_base_model_mock,
_ = DeepBaseModel.forecast(self=deep_base_model_mock, ts=ts_mock, prediction_size=horizon)
-def test_deep_base_model_forecast_loop(simple_df, deep_base_model_mock, ts_mock):
+def test_deep_base_model_forecast_loop(simple_tsdf, deep_base_model_mock, ts_mock):
ts_after_tsdataset_idx_slice = MagicMock()
horizon = 7
raw_predict = {("A", "target"): np.arange(10).reshape(-1, 1), ("B", "target"): -np.arange(10).reshape(-1, 1)}
deep_base_model_mock.raw_predict.return_value = raw_predict
- ts_after_tsdataset_idx_slice.df = simple_df.df.iloc[-horizon:]
+ ts_after_tsdataset_idx_slice.df = simple_tsdf.df.iloc[-horizon:]
ts_mock.tsdataset_idx_slice.return_value = ts_after_tsdataset_idx_slice
future = DeepBaseModel.forecast(self=deep_base_model_mock, ts=ts_mock, prediction_size=horizon)
diff --git a/tests/test_models/test_holt_winters_model.py b/tests/test_models/test_holt_winters_model.py
index 1fd30b556..2d69a7033 100644
--- a/tests/test_models/test_holt_winters_model.py
+++ b/tests/test_models/test_holt_winters_model.py
@@ -40,19 +40,22 @@ def test_holt_winters_fit_with_exog_warning(model, example_reg_tsds):
model.fit(ts)
+@pytest.mark.parametrize("ts_name", ["example_tsds", "example_tsds_int_timestamp"])
@pytest.mark.parametrize(
"model",
[
HoltWintersModel(),
+ HoltWintersModel(seasonal="add", seasonal_periods=7),
HoltModel(),
SimpleExpSmoothingModel(),
],
)
-def test_holt_winters_simple(model, example_tsds):
+def test_holt_winters_simple(ts_name, model, request):
"""Test that Holt-Winters' models make predictions in simple case."""
horizon = 7
- model.fit(example_tsds)
- future_ts = example_tsds.make_future(future_steps=horizon)
+ ts = request.getfixturevalue(ts_name)
+ model.fit(ts)
+ future_ts = ts.make_future(future_steps=horizon)
res = model.forecast(future_ts)
res = res.to_pandas(flatten=True)
@@ -64,6 +67,7 @@ def test_holt_winters_simple(model, example_tsds):
"model",
[
HoltWintersModel(),
+ HoltWintersModel(seasonal="add", seasonal_periods=7),
HoltModel(),
SimpleExpSmoothingModel(),
],
@@ -171,6 +175,14 @@ def seasonal_dfs():
return df.iloc[:-9], df.iloc[-9:]
+@pytest.fixture()
+def seasonal_dfs_int_timestamp(seasonal_dfs):
+ train_df, test_df = seasonal_dfs
+ train_df["timestamp"] = np.arange(len(train_df)) + 10
+ test_df["timestamp"] = np.arange(len(test_df)) + 10 + len(train_df)
+ return train_df, test_df
+
+
def test_check_mul_components_not_fitted_error():
model = _HoltWintersAdapter()
with pytest.raises(ValueError, match="This model is not fitted!"):
@@ -236,17 +248,23 @@ def test_components_names(
@pytest.mark.parametrize(
"components_method_name,in_sample", (("predict_components", True), ("forecast_components", False))
)
-@pytest.mark.parametrize("df_names", ("seasonal_dfs", "multi_trend_dfs"))
+@pytest.mark.parametrize("df_names", ("seasonal_dfs", "multi_trend_dfs", "seasonal_dfs_int_timestamp"))
@pytest.mark.parametrize("trend,damped_trend", (("add", True), ("add", False), (None, False)))
-@pytest.mark.parametrize("seasonal", ("add", None))
+@pytest.mark.parametrize("seasonal, seasonal_periods", (("add", 7), (None, None)))
@pytest.mark.parametrize("use_boxcox", (True, False))
def test_components_sum_up_to_target(
- df_names, trend, seasonal, damped_trend, use_boxcox, components_method_name, in_sample, request
+ df_names, trend, seasonal, seasonal_periods, damped_trend, use_boxcox, components_method_name, in_sample, request
):
dfs = request.getfixturevalue(df_names)
train, test = dfs
- model = _HoltWintersAdapter(trend=trend, seasonal=seasonal, damped_trend=damped_trend, use_boxcox=use_boxcox)
+ model = _HoltWintersAdapter(
+ trend=trend,
+ seasonal=seasonal,
+ seasonal_periods=seasonal_periods,
+ damped_trend=damped_trend,
+ use_boxcox=use_boxcox,
+ )
model.fit(train, [])
components_method = getattr(model, components_method_name)
@@ -264,10 +282,7 @@ def test_components_sum_up_to_target(
)
@pytest.mark.parametrize(
"df_names",
- (
- "seasonal_dfs",
- "multi_trend_dfs",
- ),
+ ("seasonal_dfs", "multi_trend_dfs", "seasonal_dfs_int_timestamp"),
)
def test_components_of_subset_sum_up_to_target(df_names, components_method_name, in_sample, request):
dfs = request.getfixturevalue(df_names)
@@ -326,7 +341,7 @@ def test_prediction_decomposition(outliers_tsds, model):
"model, expected_length",
[
(HoltWintersModel(), 3),
- (HoltWintersModel(seasonal="add"), 4),
+ (HoltWintersModel(seasonal="add", seasonal_periods=7), 4),
(HoltModel(), 2),
(SimpleExpSmoothingModel(), 0),
],
diff --git a/tests/test_models/test_inference/common.py b/tests/test_models/test_inference/common.py
index 9d18acb33..cc70f799d 100644
--- a/tests/test_models/test_inference/common.py
+++ b/tests/test_models/test_inference/common.py
@@ -24,7 +24,7 @@ def _test_prediction_in_sample_full(ts, model, transforms, method_name):
model.fit(ts)
# forecasting
- forecast_ts = TSDataset(df, freq="D")
+ forecast_ts = TSDataset(df, freq=ts.freq)
forecast_ts.transform(transforms)
prediction_size = len(forecast_ts.index)
forecast_ts = make_prediction(model=model, ts=forecast_ts, prediction_size=prediction_size, method_name=method_name)
@@ -44,7 +44,7 @@ def _test_prediction_in_sample_suffix(ts, model, transforms, method_name, num_sk
model.fit(ts)
# forecasting
- forecast_ts = TSDataset(df, freq="D")
+ forecast_ts = TSDataset(df, freq=ts.freq)
forecast_ts.transform(transforms)
prediction_size = len(forecast_ts.index) - num_skip_points
forecast_ts.df = forecast_ts.df.iloc[(num_skip_points - model.context_size) :]
diff --git a/tests/test_models/test_inference/test_forecast.py b/tests/test_models/test_inference/test_forecast.py
index 685dbc30f..4604c41b4 100644
--- a/tests/test_models/test_inference/test_forecast.py
+++ b/tests/test_models/test_inference/test_forecast.py
@@ -50,6 +50,7 @@
from tests.test_models.test_inference.common import _test_prediction_in_sample_full
from tests.test_models.test_inference.common import _test_prediction_in_sample_suffix
from tests.test_models.test_inference.common import make_prediction
+from tests.utils import convert_ts_to_int_timestamp
from tests.utils import select_segments_subset
from tests.utils import to_be_fixed
@@ -66,14 +67,13 @@ class TestForecastInSampleFullNoTarget:
@staticmethod
def _test_forecast_in_sample_full_no_target(ts, model, transforms):
- df = ts.to_pandas()
+ forecast_ts = deepcopy(ts)
# fitting
ts.fit_transform(transforms)
model.fit(ts)
# forecasting
- forecast_ts = TSDataset(df, freq="D")
forecast_ts.transform(transforms)
forecast_ts.df.loc[:, pd.IndexSlice[:, "target"]] = np.NaN
prediction_size = len(forecast_ts.index)
@@ -84,47 +84,55 @@ def _test_forecast_in_sample_full_no_target(ts, model, transforms):
assert not np.any(forecast_df["target"].isna())
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (AutoARIMAModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
],
)
- def test_forecast_in_sample_full_no_target(self, model, transforms, example_tsds):
- self._test_forecast_in_sample_full_no_target(example_tsds, model, transforms)
+ def test_forecast_in_sample_full_no_target(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_in_sample_full_no_target(ts, model, transforms)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
],
)
- def test_forecast_in_sample_full_no_target_failed_nans_sklearn(self, model, transforms, example_tsds):
+ def test_forecast_in_sample_full_no_target_failed_nans_sklearn(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(ValueError, match="Input contains NaN, infinity or a value too large"):
- self._test_forecast_in_sample_full_no_target(example_tsds, model, transforms)
+ self._test_forecast_in_sample_full_no_target(ts, model, transforms)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (MovingAverageModel(window=3), []),
- (NaiveModel(lag=3), []),
- (SeasonalMovingAverageModel(), []),
- (DeadlineMovingAverageModel(window=1), []),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
DeepStateModel(
ssm=CompositeSSM(seasonal_ssms=[WeeklySeasonalitySSM()]),
@@ -134,39 +142,49 @@ def test_forecast_in_sample_full_no_target_failed_nans_sklearn(self, model, tran
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=1, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=1, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (
+ NBeatsInterpretableModel(input_size=1, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (NBeatsGenericModel(input_size=1, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_in_sample_full_no_target_failed_not_enough_context(self, model, transforms, example_tsds):
+ def test_forecast_in_sample_full_no_target_failed_not_enough_context(
+ self, model, transforms, dataset_name, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(ValueError, match="Given context isn't big enough"):
- self._test_forecast_in_sample_full_no_target(example_tsds, model, transforms)
+ self._test_forecast_in_sample_full_no_target(ts, model, transforms)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
],
)
- def test_forecast_in_sample_full_no_target_failed_nans_nn(self, model, transforms, example_tsds):
+ def test_forecast_in_sample_full_no_target_failed_nans_nn(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(ValueError, match="There are NaNs in features"):
- self._test_forecast_in_sample_full_no_target(example_tsds, model, transforms)
+ self._test_forecast_in_sample_full_no_target(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="This model can't make forecast on history data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
(
DeepARModel(
dataset_builder=PytorchForecastingDatasetBuilder(
@@ -180,6 +198,7 @@ def test_forecast_in_sample_full_no_target_failed_nans_nn(self, model, transform
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -196,11 +215,15 @@ def test_forecast_in_sample_full_no_target_failed_nans_nn(self, model, transform
lr=0.01,
),
[],
+ "example_tsds",
),
],
)
- def test_forecast_in_sample_full_no_target_failed_not_implemented_in_sample(self, model, transforms, example_tsds):
- self._test_forecast_in_sample_full_no_target(example_tsds, model, transforms)
+ def test_forecast_in_sample_full_no_target_failed_not_implemented_in_sample(
+ self, model, transforms, dataset_name, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_in_sample_full_no_target(ts, model, transforms)
class TestForecastInSampleFull:
@@ -210,60 +233,70 @@ class TestForecastInSampleFull:
"""
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (AutoARIMAModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
],
)
- def test_forecast_in_sample_full(self, model, transforms, example_tsds):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="forecast")
+ def test_forecast_in_sample_full(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="forecast")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
],
)
- def test_forecast_in_sample_full_failed_nans_sklearn(self, model, transforms, example_tsds):
+ def test_forecast_in_sample_full_failed_nans_sklearn(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(ValueError, match="Input contains NaN, infinity or a value too large"):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="forecast")
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="forecast")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[2, 3])],
+ "example_tsds",
),
],
)
- def test_forecast_in_sample_full_failed_nans_nn(self, model, transforms, example_tsds):
+ def test_forecast_in_sample_full_failed_nans_nn(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(ValueError, match="There are NaNs in features"):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="forecast")
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="forecast")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (MovingAverageModel(window=3), []),
- (NaiveModel(lag=3), []),
- (SeasonalMovingAverageModel(), []),
- (DeadlineMovingAverageModel(window=1), []),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
DeepStateModel(
ssm=CompositeSSM(seasonal_ssms=[WeeklySeasonalitySSM()]),
@@ -273,26 +306,32 @@ def test_forecast_in_sample_full_failed_nans_nn(self, model, transforms, example
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=1, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=1, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=1, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=1, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_in_sample_full_failed_not_enough_context(self, model, transforms, example_tsds):
+ def test_forecast_in_sample_full_failed_not_enough_context(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(ValueError, match="Given context isn't big enough"):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="forecast")
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="forecast")
@to_be_fixed(raises=NotImplementedError, match="This model can't make forecast on history data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
(
DeepARModel(
dataset_builder=PytorchForecastingDatasetBuilder(
@@ -306,6 +345,7 @@ def test_forecast_in_sample_full_failed_not_enough_context(self, model, transfor
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -322,11 +362,13 @@ def test_forecast_in_sample_full_failed_not_enough_context(self, model, transfor
lr=0.01,
),
[],
+ "example_tsds",
),
],
)
- def test_forecast_in_sample_full_not_implemented(self, model, transforms, example_tsds):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="forecast")
+ def test_forecast_in_sample_full_not_implemented(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="forecast")
class TestForecastInSampleSuffixNoTarget:
@@ -337,14 +379,13 @@ class TestForecastInSampleSuffixNoTarget:
@staticmethod
def _test_forecast_in_sample_suffix_no_target(ts, model, transforms, num_skip_points):
- df = ts.to_pandas()
+ forecast_ts = deepcopy(ts)
# fitting
ts.fit_transform(transforms)
model.fit(ts)
# forecasting
- forecast_ts = TSDataset(df, freq="D")
forecast_ts.transform(transforms)
forecast_ts.df.loc[forecast_ts.index[num_skip_points] :, pd.IndexSlice[:, "target"]] = np.NaN
prediction_size = len(forecast_ts.index) - num_skip_points
@@ -356,33 +397,40 @@ def _test_forecast_in_sample_suffix_no_target(ts, model, transforms, num_skip_po
assert not np.any(forecast_df["target"].isna())
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (AutoARIMAModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (NaiveModel(lag=3), []),
- (SeasonalMovingAverageModel(), []),
- (DeadlineMovingAverageModel(window=1), []),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[2, 3])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -393,25 +441,31 @@ def _test_forecast_in_sample_suffix_no_target(ts, model, transforms, num_skip_po
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=50, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=50, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=50, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=50, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_in_sample_suffix_no_target(self, model, transforms, example_tsds):
- self._test_forecast_in_sample_suffix_no_target(example_tsds, model, transforms, num_skip_points=50)
+ def test_forecast_in_sample_suffix_no_target(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_in_sample_suffix_no_target(ts, model, transforms, num_skip_points=50)
@to_be_fixed(raises=NotImplementedError, match="This model can't make forecast on history data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
(
DeepARModel(
dataset_builder=PytorchForecastingDatasetBuilder(
@@ -425,6 +479,7 @@ def test_forecast_in_sample_suffix_no_target(self, model, transforms, example_ts
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -441,13 +496,15 @@ def test_forecast_in_sample_suffix_no_target(self, model, transforms, example_ts
lr=0.01,
),
[],
+ "example_tsds",
),
],
)
def test_forecast_in_sample_suffix_no_target_failed_not_implemented_in_sample(
- self, model, transforms, example_tsds
+ self, model, transforms, dataset_name, request
):
- self._test_forecast_in_sample_suffix_no_target(example_tsds, model, transforms, num_skip_points=50)
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_in_sample_suffix_no_target(ts, model, transforms, num_skip_points=50)
class TestForecastInSampleSuffix:
@@ -457,33 +514,40 @@ class TestForecastInSampleSuffix:
"""
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (AutoARIMAModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (NaiveModel(lag=3), []),
- (SeasonalMovingAverageModel(), []),
- (DeadlineMovingAverageModel(window=1), []),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[2, 3])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -494,25 +558,31 @@ class TestForecastInSampleSuffix:
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=50, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=50, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=50, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=50, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_in_sample_suffix(self, model, transforms, example_tsds):
- _test_prediction_in_sample_suffix(example_tsds, model, transforms, method_name="forecast", num_skip_points=50)
+ def test_forecast_in_sample_suffix(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_suffix(ts, model, transforms, method_name="forecast", num_skip_points=50)
@to_be_fixed(raises=NotImplementedError, match="This model can't make forecast on history data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
(
DeepARModel(
dataset_builder=PytorchForecastingDatasetBuilder(
@@ -526,6 +596,205 @@ def test_forecast_in_sample_suffix(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ TFTModel(
+ dataset_builder=PytorchForecastingDatasetBuilder(
+ max_encoder_length=21,
+ min_encoder_length=21,
+ max_prediction_length=5,
+ time_varying_known_reals=["time_idx"],
+ time_varying_unknown_reals=["target"],
+ static_categoricals=["segment"],
+ target_normalizer=None,
+ ),
+ trainer_params=dict(max_epochs=1),
+ lr=0.01,
+ ),
+ [],
+ "example_tsds",
+ ),
+ ],
+ )
+ def test_forecast_in_sample_suffix_failed_not_implemented_in_sample(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_suffix(ts, model, transforms, method_name="forecast", num_skip_points=50)
+
+
+class TestForecastOutSample:
+ """Test forecast on of future dataset.
+
+ Expected that NaNs are filled after prediction.
+ """
+
+ @staticmethod
+ def _test_forecast_out_sample(ts, model, transforms, prediction_size=5):
+ # fitting
+ ts.fit_transform(transforms)
+ model.fit(ts)
+
+ # forecasting
+ forecast_ts = ts.make_future(future_steps=prediction_size, tail_steps=model.context_size, transforms=transforms)
+ forecast_ts = make_forecast(model=model, ts=forecast_ts, prediction_size=prediction_size)
+
+ # checking
+ forecast_df = forecast_ts.to_pandas(flatten=True)
+ assert not np.any(forecast_df["target"].isna())
+
+ @pytest.mark.parametrize(
+ "model, transforms, dataset_name",
+ [
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (
+ DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
+ (
+ MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
+ ),
+ (
+ DeepARModel(
+ dataset_builder=PytorchForecastingDatasetBuilder(
+ max_encoder_length=5,
+ max_prediction_length=5,
+ time_varying_known_reals=["time_idx"],
+ time_varying_unknown_reals=["target"],
+ target_normalizer=GroupNormalizer(groups=["segment"]),
+ ),
+ trainer_params=dict(max_epochs=1),
+ lr=0.01,
+ ),
+ [],
+ "example_tsds",
+ ),
+ (
+ TFTModel(
+ dataset_builder=PytorchForecastingDatasetBuilder(
+ max_encoder_length=21,
+ min_encoder_length=21,
+ max_prediction_length=5,
+ time_varying_known_reals=["time_idx"],
+ time_varying_unknown_reals=["target"],
+ static_categoricals=["segment"],
+ target_normalizer=None,
+ ),
+ trainer_params=dict(max_epochs=1),
+ lr=0.01,
+ ),
+ [],
+ "example_tsds",
+ ),
+ (
+ DeepStateModel(
+ ssm=CompositeSSM(seasonal_ssms=[WeeklySeasonalitySSM()]),
+ input_size=1,
+ encoder_length=7,
+ decoder_length=7,
+ trainer_params=dict(max_epochs=1),
+ ),
+ [SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
+ ],
+ )
+ def test_forecast_out_sample_datetime_timestamp(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_out_sample(ts, model, transforms)
+
+ @pytest.mark.parametrize(
+ "model, transforms, dataset_name",
+ [
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (
+ DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
+ (
+ MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
+ ),
+ (
+ DeepARModel(
+ dataset_builder=PytorchForecastingDatasetBuilder(
+ max_encoder_length=5,
+ max_prediction_length=5,
+ time_varying_known_reals=["time_idx"],
+ time_varying_unknown_reals=["target"],
+ target_normalizer=GroupNormalizer(groups=["segment"]),
+ ),
+ trainer_params=dict(max_epochs=1),
+ lr=0.01,
+ ),
+ [],
+ "example_tsds",
),
(
TFTModel(
@@ -542,11 +811,55 @@ def test_forecast_in_sample_suffix(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_in_sample_suffix_failed_not_implemented_in_sample(self, model, transforms, example_tsds):
- _test_prediction_in_sample_suffix(example_tsds, model, transforms, method_name="forecast", num_skip_points=50)
+ def test_forecast_out_sample_int_timestamp(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ self._test_forecast_out_sample(ts_int_timestamp, model, transforms)
+
+ @pytest.mark.parametrize(
+ "model, transforms, dataset_name",
+ [
+ (ProphetModel(), [], "example_tsds"),
+ ],
+ )
+ def test_forecast_out_sample_int_timestamp_not_supported(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ with pytest.raises(ValueError, match="Invalid timestamp! Only datetime type is supported."):
+ self._test_forecast_out_sample(ts_int_timestamp, model, transforms)
+
+ @to_be_fixed(raises=Exception)
+ @pytest.mark.parametrize(
+ "model, transforms, dataset_name",
+ [
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (
+ DeepStateModel(
+ ssm=CompositeSSM(seasonal_ssms=[WeeklySeasonalitySSM()]),
+ input_size=1,
+ encoder_length=7,
+ decoder_length=7,
+ trainer_params=dict(max_epochs=1),
+ ),
+ [SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ ],
+ )
+ def test_forecast_out_sample_int_timestamp_failed(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ self._test_forecast_out_sample(ts_int_timestamp, model, transforms)
class TestForecastOutSamplePrefix:
@@ -582,40 +895,47 @@ def _test_forecast_out_sample_prefix(ts, model, transforms, full_prediction_size
assert_frame_equal(forecast_prefix_df, forecast_full_df.iloc[:prefix_prediction_size])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepARModel(
@@ -630,6 +950,7 @@ def _test_forecast_out_sample_prefix(ts, model, transforms, full_prediction_size
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -646,16 +967,22 @@ def _test_forecast_out_sample_prefix(ts, model, transforms, full_prediction_size
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_out_sample_prefix(self, model, transforms, example_tsds):
- self._test_forecast_out_sample_prefix(example_tsds, model, transforms)
+ def test_forecast_out_sample_prefix(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_out_sample_prefix(ts, model, transforms)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepStateModel(
@@ -666,13 +993,15 @@ def test_forecast_out_sample_prefix(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
)
],
)
- def test_forecast_out_sample_prefix_failed_deep_state(self, model, transforms, example_tsds):
+ def test_forecast_out_sample_prefix_failed_deep_state(self, model, transforms, dataset_name, request):
"""This test is expected to fail due to sampling procedure of DeepStateModel"""
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(AssertionError):
- self._test_forecast_out_sample_prefix(example_tsds, model, transforms)
+ self._test_forecast_out_sample_prefix(ts, model, transforms)
class TestForecastOutSampleSuffix:
@@ -718,66 +1047,76 @@ def _test_forecast_out_sample_suffix(ts, model, transforms, full_prediction_size
assert_frame_equal(forecast_gap_df, forecast_full_df.iloc[prediction_size_diff:])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
],
)
- def test_forecast_out_sample_suffix(self, model, transforms, example_tsds):
- self._test_forecast_out_sample_suffix(example_tsds, model, transforms)
+ def test_forecast_out_sample_suffix(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_out_sample_suffix(ts, model, transforms)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
],
)
- def test_forecast_out_sample_suffix_failed_rnn(self, model, transforms, example_tsds):
+ def test_forecast_out_sample_suffix_failed_rnn(self, model, transforms, dataset_name, request):
"""This test is expected to fail due to autoregression in RNN.
More about it in issue: https://github.com/tinkoff-ai/etna/issues/1087
"""
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(AssertionError):
- self._test_forecast_out_sample_suffix(example_tsds, model, transforms)
+ self._test_forecast_out_sample_suffix(ts, model, transforms)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
],
)
- def test_forecast_out_sample_suffix_failed_deepar(self, model, transforms, example_tsds):
+ def test_forecast_out_sample_suffix_failed_deepar(self, model, transforms, dataset_name, request):
"""This test is expected to fail due to autoregression in DeepAR."""
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(AssertionError):
- self._test_forecast_out_sample_suffix(example_tsds, model, transforms)
+ self._test_forecast_out_sample_suffix(ts, model, transforms)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepStateModel(
@@ -788,38 +1127,45 @@ def test_forecast_out_sample_suffix_failed_deepar(self, model, transforms, examp
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
)
],
)
- def test_forecast_out_sample_suffix_failed_deep_state(self, model, transforms, example_tsds):
+ def test_forecast_out_sample_suffix_failed_deep_state(self, model, transforms, dataset_name, request):
"""This test is expected to fail due to sampling procedure of DeepStateModel"""
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(AssertionError):
- self._test_forecast_out_sample_suffix(example_tsds, model, transforms)
+ self._test_forecast_out_sample_suffix(ts, model, transforms)
@pytest.mark.parametrize(
- "model,transforms",
+ "model, transforms, dataset_name",
(
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
),
)
- def test_forecast_out_sample_suffix_failed_nbeats(self, model, transforms, example_tsds):
+ def test_forecast_out_sample_suffix_failed_nbeats(self, model, transforms, dataset_name, request):
"""This test is expected to fail due to windowed view on data in N-BEATS"""
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(AssertionError):
- self._test_forecast_out_sample_suffix(example_tsds, model, transforms)
+ self._test_forecast_out_sample_suffix(ts, model, transforms)
@to_be_fixed(
raises=NotImplementedError,
match="This model can't make forecast on out-of-sample data that goes after training data with a gap",
)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
(
DeepARModel(
dataset_builder=PytorchForecastingDatasetBuilder(
@@ -833,6 +1179,7 @@ def test_forecast_out_sample_suffix_failed_nbeats(self, model, transforms, examp
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -849,11 +1196,13 @@ def test_forecast_out_sample_suffix_failed_nbeats(self, model, transforms, examp
lr=0.01,
),
[],
+ "example_tsds",
),
],
)
- def test_forecast_out_sample_suffix_failed_not_implemented(self, model, transforms, example_tsds):
- self._test_forecast_out_sample_suffix(example_tsds, model, transforms)
+ def test_forecast_out_sample_suffix_failed_not_implemented(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_out_sample_suffix(ts, model, transforms)
class TestForecastMixedInOutSample:
@@ -865,7 +1214,7 @@ class TestForecastMixedInOutSample:
@staticmethod
def _test_forecast_mixed_in_out_sample(ts, model, transforms, num_skip_points=50, future_prediction_size=5):
# fitting
- df = ts.to_pandas()
+ df = ts.to_pandas().loc[:, pd.IndexSlice[:, "target"]]
ts.fit_transform(transforms)
model.fit(ts)
@@ -873,7 +1222,7 @@ def _test_forecast_mixed_in_out_sample(ts, model, transforms, num_skip_points=50
future_ts = ts.make_future(future_steps=future_prediction_size)
future_df = future_ts.to_pandas().loc[:, pd.IndexSlice[:, "target"]]
df_full = pd.concat((df, future_df))
- forecast_full_ts = TSDataset(df=df_full, freq=ts.freq)
+ forecast_full_ts = TSDataset(df=df_full, df_exog=ts.df_exog, freq=ts.freq, known_future=ts.known_future)
forecast_full_ts.transform(transforms)
forecast_full_ts.df = forecast_full_ts.df.iloc[(num_skip_points - model.context_size) :]
full_prediction_size = len(forecast_full_ts.index) - model.context_size
@@ -886,33 +1235,40 @@ def _test_forecast_mixed_in_out_sample(ts, model, transforms, num_skip_points=50
assert not forecast_full_df["target"].equals(original_target)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -923,25 +1279,31 @@ def _test_forecast_mixed_in_out_sample(ts, model, transforms, num_skip_points=50
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=55, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=55, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=55, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=55, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_mixed_in_out_sample(self, model, transforms, example_tsds):
- self._test_forecast_mixed_in_out_sample(example_tsds, model, transforms)
+ def test_forecast_mixed_in_out_sample(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_mixed_in_out_sample(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="This model can't make forecast on history data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
(
DeepARModel(
dataset_builder=PytorchForecastingDatasetBuilder(
@@ -955,6 +1317,7 @@ def test_forecast_mixed_in_out_sample(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -971,11 +1334,15 @@ def test_forecast_mixed_in_out_sample(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
],
)
- def test_forecast_mixed_in_out_sample_failed_not_implemented_in_sample(self, model, transforms, example_tsds):
- self._test_forecast_mixed_in_out_sample(example_tsds, model, transforms)
+ def test_forecast_mixed_in_out_sample_failed_not_implemented_in_sample(
+ self, model, transforms, dataset_name, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_mixed_in_out_sample(ts, model, transforms)
class TestForecastSubsetSegments:
@@ -1011,31 +1378,32 @@ def _test_forecast_subset_segments(self, ts, model, transforms, segments, predic
assert_frame_equal(forecast_subset_df, forecast_full_df.loc[:, pd.IndexSlice[segments, :]])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
(
TFTModel(
dataset_builder=PytorchForecastingDatasetBuilder(
@@ -1051,26 +1419,38 @@ def _test_forecast_subset_segments(self, ts, model, transforms, segments, predic
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_subset_segments(self, model, transforms, example_tsds):
- self._test_forecast_subset_segments(example_tsds, model, transforms, segments=["segment_2"])
+ def test_forecast_subset_segments(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_subset_segments(ts, model, transforms, segments=["segment_1"])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepStateModel(
@@ -1081,17 +1461,19 @@ def test_forecast_subset_segments(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
),
],
)
- def test_forecast_subset_segments_failed_deep_state(self, model, transforms, example_tsds):
+ def test_forecast_subset_segments_failed_deep_state(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(AssertionError):
- self._test_forecast_subset_segments(example_tsds, model, transforms, segments=["segment_2"])
+ self._test_forecast_subset_segments(ts, model, transforms, segments=["segment_1"])
@to_be_fixed(raises=AssertionError)
# issue with explanation: https://github.com/tinkoff-ai/etna/issues/1089
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -1106,11 +1488,13 @@ def test_forecast_subset_segments_failed_deep_state(self, model, transforms, exa
lr=0.01,
),
[],
+ "example_tsds",
),
],
)
- def test_forecast_subset_segments_failed_assertion_error(self, model, transforms, example_tsds):
- self._test_forecast_subset_segments(example_tsds, model, transforms, segments=["segment_2"])
+ def test_forecast_subset_segments_failed_assertion_error(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_subset_segments(ts, model, transforms, segments=["segment_1"])
class TestForecastNewSegments:
@@ -1141,24 +1525,30 @@ def _test_forecast_new_segments(self, ts, model, transforms, train_segments, pre
assert not np.any(forecast_df["target"].isna())
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepARModel(
@@ -1174,6 +1564,7 @@ def _test_forecast_new_segments(self, ts, model, transforms, train_segments, pre
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -1191,6 +1582,7 @@ def _test_forecast_new_segments(self, ts, model, transforms, train_segments, pre
lr=0.01,
),
[],
+ "example_tsds",
),
(
DeepStateModel(
@@ -1201,35 +1593,43 @@ def _test_forecast_new_segments(self, ts, model, transforms, train_segments, pre
trainer_params=dict(max_epochs=1),
),
[],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_forecast_new_segments(self, model, transforms, example_tsds):
- self._test_forecast_new_segments(example_tsds, model, transforms, train_segments=["segment_1"])
+ def test_forecast_new_segments(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_forecast_new_segments(ts, model, transforms, train_segments=["segment_1"])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_forecast_new_segments_failed_per_segment(self, model, transforms, example_tsds):
+ def test_forecast_new_segments_failed_per_segment(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(NotImplementedError, match="Per-segment models can't make predictions on new segments"):
- self._test_forecast_new_segments(example_tsds, model, transforms, train_segments=["segment_1"])
+ self._test_forecast_new_segments(ts, model, transforms, train_segments=["segment_1"])
diff --git a/tests/test_models/test_inference/test_predict.py b/tests/test_models/test_inference/test_predict.py
index 623cd6391..bdf19ab4d 100644
--- a/tests/test_models/test_inference/test_predict.py
+++ b/tests/test_models/test_inference/test_predict.py
@@ -48,6 +48,7 @@
from tests.test_models.test_inference.common import _test_prediction_in_sample_full
from tests.test_models.test_inference.common import _test_prediction_in_sample_suffix
from tests.test_models.test_inference.common import make_prediction
+from tests.utils import convert_ts_to_int_timestamp
from tests.utils import select_segments_subset
from tests.utils import to_be_fixed
@@ -63,57 +64,61 @@ class TestPredictInSampleFull:
"""
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (AutoARIMAModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_predict_in_sample_full(self, model, transforms, example_tsds):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="predict")
+ def test_predict_in_sample_full(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="predict")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
],
)
- def test_predict_in_sample_full_failed_nans_sklearn(self, model, transforms, example_tsds):
+ def test_predict_in_sample_full_failed_nans_sklearn(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(ValueError, match="Input contains NaN, infinity or a value too large"):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="predict")
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="predict")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (MovingAverageModel(window=3), []),
- (NaiveModel(lag=3), []),
- (SeasonalMovingAverageModel(), []),
- (DeadlineMovingAverageModel(window=1), []),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
],
)
- def test_predict_in_sample_full_failed_not_enough_context(self, model, transforms, example_tsds):
+ def test_predict_in_sample_full_failed_not_enough_context(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(ValueError, match="Given context isn't big enough"):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="predict")
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="predict")
@to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -128,6 +133,7 @@ def test_predict_in_sample_full_failed_not_enough_context(self, model, transform
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -144,16 +150,23 @@ def test_predict_in_sample_full_failed_not_enough_context(self, model, transform
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[2, 3])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -164,21 +177,28 @@ def test_predict_in_sample_full_failed_not_enough_context(self, model, transform
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_predict_in_sample_full_failed_not_implemented_predict(self, model, transforms, example_tsds):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="predict")
+ def test_predict_in_sample_full_failed_not_implemented_predict(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="predict")
@to_be_fixed(raises=NotImplementedError, match="It is not possible to make in-sample predictions")
@pytest.mark.parametrize(
"model, transforms",
[],
)
- def test_predict_in_sample_full_failed_not_implemented_in_sample(self, model, transforms, example_tsds):
- _test_prediction_in_sample_full(example_tsds, model, transforms, method_name="predict")
+ def test_predict_in_sample_full_failed_not_implemented_in_sample(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_full(ts, model, transforms, method_name="predict")
class TestPredictInSampleSuffix:
@@ -188,39 +208,41 @@ class TestPredictInSampleSuffix:
"""
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])]),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (AutoARIMAModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (NaiveModel(lag=3), []),
- (SeasonalMovingAverageModel(), []),
- (DeadlineMovingAverageModel(window=1), []),
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_predict_in_sample_suffix(self, model, transforms, example_tsds):
- _test_prediction_in_sample_suffix(example_tsds, model, transforms, method_name="predict", num_skip_points=50)
+ def test_predict_in_sample_suffix_datetime_timestamp(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_suffix(ts, model, transforms, method_name="predict", num_skip_points=50)
@to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -235,6 +257,7 @@ def test_predict_in_sample_suffix(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -251,16 +274,23 @@ def test_predict_in_sample_suffix(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[2, 3])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -271,21 +301,163 @@ def test_predict_in_sample_suffix(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_predict_in_sample_full_failed_not_implemented_predict(self, model, transforms, example_tsds):
- _test_prediction_in_sample_suffix(example_tsds, model, transforms, method_name="predict", num_skip_points=50)
+ def test_predict_in_sample_suffix_datetime_timestamp_failed_not_implemented_predict(
+ self, model, transforms, dataset_name, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
+ _test_prediction_in_sample_suffix(ts, model, transforms, method_name="predict", num_skip_points=50)
- @to_be_fixed(raises=NotImplementedError, match="It is not possible to make in-sample predictions")
@pytest.mark.parametrize(
- "model, transforms",
- [],
+ "model, transforms, dataset_name",
+ [
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[2, 3])], "example_tsds"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
+ ],
)
- def test_predict_in_sample_suffix_failed_not_implemented_in_sample(self, model, transforms, example_tsds):
- _test_prediction_in_sample_suffix(example_tsds, model, transforms, method_name="predict", num_skip_points=50)
+ def test_predict_in_sample_suffix_int_timestamp(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ _test_prediction_in_sample_suffix(
+ ts_int_timestamp, model, transforms, method_name="predict", num_skip_points=50
+ )
+
+ @pytest.mark.parametrize(
+ "model, transforms, dataset_name",
+ [
+ (ProphetModel(), [], "example_tsds"),
+ ],
+ )
+ def test_predict_in_sample_suffix_int_timestamp_not_supported(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ with pytest.raises(ValueError, match="Invalid timestamp! Only datetime type is supported."):
+ _test_prediction_in_sample_suffix(
+ ts_int_timestamp, model, transforms, method_name="predict", num_skip_points=50
+ )
+
+ @to_be_fixed(raises=Exception)
+ @pytest.mark.parametrize(
+ "model, transforms, dataset_name",
+ [
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (
+ DeepStateModel(
+ ssm=CompositeSSM(seasonal_ssms=[WeeklySeasonalitySSM()]),
+ input_size=1,
+ encoder_length=7,
+ decoder_length=7,
+ trainer_params=dict(max_epochs=1),
+ ),
+ [SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ ],
+ )
+ def test_predict_in_sample_suffix_int_timestamp_failed(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ _test_prediction_in_sample_suffix(
+ ts_int_timestamp, model, transforms, method_name="predict", num_skip_points=50
+ )
+
+ @to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
+ @pytest.mark.parametrize(
+ "model, transforms, dataset_name",
+ [
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (
+ DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
+ (
+ MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [LagTransform(in_column="target", lags=[2, 3])],
+ "example_tsds",
+ ),
+ (
+ DeepARModel(
+ dataset_builder=PytorchForecastingDatasetBuilder(
+ max_encoder_length=1,
+ max_prediction_length=1,
+ time_varying_known_reals=["time_idx"],
+ time_varying_unknown_reals=["target"],
+ target_normalizer=GroupNormalizer(groups=["segment"]),
+ ),
+ trainer_params=dict(max_epochs=1),
+ lr=0.01,
+ ),
+ [],
+ "example_tsds",
+ ),
+ (
+ TFTModel(
+ dataset_builder=PytorchForecastingDatasetBuilder(
+ max_encoder_length=21,
+ min_encoder_length=21,
+ max_prediction_length=5,
+ time_varying_known_reals=["time_idx"],
+ time_varying_unknown_reals=["target"],
+ static_categoricals=["segment"],
+ target_normalizer=None,
+ ),
+ trainer_params=dict(max_epochs=1),
+ lr=0.01,
+ ),
+ [],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
+ ],
+ )
+ def test_predict_in_sample_suffix_int_timestamp_failed_not_implemented_predict(
+ self, model, transforms, dataset_name, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ _test_prediction_in_sample_suffix(
+ ts_int_timestamp, model, transforms, method_name="predict", num_skip_points=50
+ )
class TestPredictOutSample:
@@ -297,7 +469,7 @@ class TestPredictOutSample:
@staticmethod
def _test_predict_out_sample(ts, model, transforms, prediction_size=5):
train_ts, _ = ts.train_test_split(test_size=prediction_size)
- forecast_ts = TSDataset(df=ts.df, freq=ts.freq)
+ forecast_ts = deepcopy(ts)
df = forecast_ts.to_pandas()
# fitting
@@ -317,32 +489,34 @@ def _test_predict_out_sample(ts, model, transforms, prediction_size=5):
assert not forecast_df["target"].equals(original_target)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
],
)
- def test_predict_out_sample(self, model, transforms, example_tsds):
- self._test_predict_out_sample(example_tsds, model, transforms)
+ def test_predict_out_sample(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -357,6 +531,7 @@ def test_predict_out_sample(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -373,16 +548,23 @@ def test_predict_out_sample(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -393,29 +575,36 @@ def test_predict_out_sample(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_predict_out_sample_failed_not_implemented_predict(self, model, transforms, example_tsds):
- self._test_predict_out_sample(example_tsds, model, transforms)
+ def test_predict_out_sample_failed_not_implemented_predict(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="This model can't make predict on future out-of-sample data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_predict_out_sample_failed_not_implemented_out_sample(self, model, transforms, example_tsds):
- self._test_predict_out_sample(example_tsds, model, transforms)
+ def test_predict_out_sample_failed_not_implemented_out_sample(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample(ts, model, transforms)
class TestPredictOutSamplePrefix:
@@ -428,8 +617,8 @@ class TestPredictOutSamplePrefix:
def _test_predict_out_sample_prefix(ts, model, transforms, full_prediction_size=5, prefix_prediction_size=3):
prediction_size_diff = full_prediction_size - prefix_prediction_size
train_ts, _ = ts.train_test_split(test_size=full_prediction_size)
- forecast_full_ts = TSDataset(df=ts.df, freq=ts.freq)
- forecast_prefix_ts = TSDataset(df=ts.df, freq=ts.freq)
+ forecast_full_ts = deepcopy(ts)
+ forecast_prefix_ts = deepcopy(ts)
# fitting
train_ts.fit_transform(transforms)
@@ -452,32 +641,34 @@ def _test_predict_out_sample_prefix(ts, model, transforms, full_prediction_size=
assert_frame_equal(forecast_prefix_df, forecast_full_df.iloc[:prefix_prediction_size])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
],
)
- def test_predict_out_sample_prefix(self, model, transforms, example_tsds):
- self._test_predict_out_sample_prefix(example_tsds, model, transforms)
+ def test_predict_out_sample_prefix(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample_prefix(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -492,6 +683,7 @@ def test_predict_out_sample_prefix(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -508,16 +700,23 @@ def test_predict_out_sample_prefix(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -528,29 +727,38 @@ def test_predict_out_sample_prefix(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_predict_out_sample_prefix_failed_not_implemented_predict(self, model, transforms, example_tsds):
- self._test_predict_out_sample_prefix(example_tsds, model, transforms)
+ def test_predict_out_sample_prefix_failed_not_implemented_predict(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample_prefix(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="This model can't make predict on future out-of-sample data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_predict_out_sample_prefix_failed_not_implemented_out_sample(self, model, transforms, example_tsds):
- self._test_predict_out_sample_prefix(example_tsds, model, transforms)
+ def test_predict_out_sample_prefix_failed_not_implemented_out_sample(
+ self, model, transforms, dataset_name, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample_prefix(ts, model, transforms)
class TestPredictOutSampleSuffix:
@@ -563,8 +771,8 @@ class TestPredictOutSampleSuffix:
def _test_predict_out_sample_suffix(ts, model, transforms, full_prediction_size=5, suffix_prediction_size=3):
prediction_size_diff = full_prediction_size - suffix_prediction_size
train_ts, _ = ts.train_test_split(test_size=full_prediction_size)
- forecast_full_ts = TSDataset(df=ts.df, freq=ts.freq)
- forecast_suffix_ts = TSDataset(df=ts.df, freq=ts.freq)
+ forecast_full_ts = deepcopy(ts)
+ forecast_suffix_ts = deepcopy(ts)
# fitting
train_ts.fit_transform(transforms)
@@ -588,32 +796,34 @@ def _test_predict_out_sample_suffix(ts, model, transforms, full_prediction_size=
assert_frame_equal(forecast_suffix_df, forecast_full_df.iloc[prediction_size_diff:])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
],
)
- def test_predict_out_sample_suffix(self, model, transforms, example_tsds):
- self._test_predict_out_sample_suffix(example_tsds, model, transforms)
+ def test_predict_out_sample_suffix(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample_suffix(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -628,6 +838,7 @@ def test_predict_out_sample_suffix(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -644,16 +855,23 @@ def test_predict_out_sample_suffix(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -664,6 +882,7 @@ def test_predict_out_sample_suffix(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
),
(
DeepStateModel(
@@ -674,29 +893,38 @@ def test_predict_out_sample_suffix(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_predict_out_sample_suffix_failed_not_implemented_predict(self, model, transforms, example_tsds):
- self._test_predict_out_sample_suffix(example_tsds, model, transforms)
+ def test_predict_out_sample_suffix_failed_not_implemented_predict(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample_suffix(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="This model can't make predict on future out-of-sample data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_predict_out_sample_suffix_failed_not_implemented_out_sample(self, model, transforms, example_tsds):
- self._test_predict_out_sample_suffix(example_tsds, model, transforms)
+ def test_predict_out_sample_suffix_failed_not_implemented_out_sample(
+ self, model, transforms, dataset_name, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_out_sample_suffix(ts, model, transforms)
class TestPredictMixedInOutSample:
@@ -708,21 +936,23 @@ class TestPredictMixedInOutSample:
@staticmethod
def _test_predict_mixed_in_out_sample(ts, model, transforms, num_skip_points=50, future_prediction_size=5):
train_ts, future_ts = ts.train_test_split(test_size=future_prediction_size)
- train_df = train_ts.to_pandas()
- future_df = future_ts.to_pandas()
+ train_df = train_ts.to_pandas().loc[:, pd.IndexSlice[:, "target"]]
+ future_df = future_ts.to_pandas().loc[:, pd.IndexSlice[:, "target"]]
train_ts.fit_transform(transforms)
model.fit(train_ts)
# predicting mixed in-sample and out-sample
df_full = pd.concat((train_df, future_df))
- forecast_full_ts = TSDataset(df=df_full, freq=ts.freq)
+ forecast_full_ts = TSDataset(df=df_full, df_exog=ts.df_exog, freq=ts.freq, known_future=ts.known_future)
forecast_full_ts.transform(transforms)
forecast_full_ts.df = forecast_full_ts.df.iloc[(num_skip_points - model.context_size) :]
full_prediction_size = len(forecast_full_ts.index) - model.context_size
forecast_full_ts = make_predict(model=model, ts=forecast_full_ts, prediction_size=full_prediction_size)
# predicting only in sample
- forecast_in_sample_ts = TSDataset(train_df, freq=ts.freq)
+ forecast_in_sample_ts = TSDataset(
+ df=train_df, df_exog=train_ts.df_exog, freq=ts.freq, known_future=ts.known_future
+ )
forecast_in_sample_ts.transform(transforms)
to_skip = num_skip_points - model.context_size
forecast_in_sample_ts.df = forecast_in_sample_ts.df.iloc[to_skip:]
@@ -732,7 +962,7 @@ def _test_predict_mixed_in_out_sample(ts, model, transforms, num_skip_points=50,
)
# predicting only out sample
- forecast_out_sample_ts = TSDataset(df=df_full, freq=ts.freq)
+ forecast_out_sample_ts = TSDataset(df=df_full, df_exog=ts.df_exog, freq=ts.freq, known_future=ts.known_future)
forecast_out_sample_ts.transform(transforms)
to_remain = model.context_size + future_prediction_size
forecast_out_sample_ts.df = forecast_out_sample_ts.df.iloc[-to_remain:]
@@ -748,32 +978,34 @@ def _test_predict_mixed_in_out_sample(ts, model, transforms, num_skip_points=50,
assert_frame_equal(forecast_out_sample_df, forecast_full_df.iloc[-future_prediction_size:])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
],
)
- def test_predict_mixed_in_out_sample(self, model, transforms, example_tsds):
- self._test_predict_mixed_in_out_sample(example_tsds, model, transforms)
+ def test_predict_mixed_in_out_sample(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_mixed_in_out_sample(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -788,6 +1020,7 @@ def test_predict_mixed_in_out_sample(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -804,16 +1037,23 @@ def test_predict_mixed_in_out_sample(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -824,29 +1064,38 @@ def test_predict_mixed_in_out_sample(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_predict_mixed_in_out_sample_failed_not_implemented_predict(self, model, transforms, example_tsds):
- self._test_predict_mixed_in_out_sample(example_tsds, model, transforms)
+ def test_predict_mixed_in_out_sample_failed_not_implemented_predict(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_mixed_in_out_sample(ts, model, transforms)
@to_be_fixed(raises=NotImplementedError, match="This model can't make predict on future out-of-sample data")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_predict_mixed_in_out_sample_failed_not_implemented_out_sample(self, model, transforms, example_tsds):
- self._test_predict_mixed_in_out_sample(example_tsds, model, transforms)
+ def test_predict_mixed_in_out_sample_failed_not_implemented_out_sample(
+ self, model, transforms, dataset_name, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_mixed_in_out_sample(ts, model, transforms)
class TestPredictSubsetSegments:
@@ -881,39 +1130,41 @@ def _test_predict_subset_segments(self, ts, model, transforms, segments, num_ski
assert_frame_equal(forecast_subset_df, forecast_full_df.loc[:, pd.IndexSlice[segments, :]])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_predict_subset_segments(self, model, transforms, example_tsds):
- self._test_predict_subset_segments(example_tsds, model, transforms, segments=["segment_2"])
+ def test_predict_subset_segments(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_subset_segments(ts, model, transforms, segments=["segment_1"])
@to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -928,6 +1179,7 @@ def test_predict_subset_segments(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -944,16 +1196,23 @@ def test_predict_subset_segments(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -964,13 +1223,19 @@ def test_predict_subset_segments(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[SegmentEncoderTransform()],
+ "example_tsds",
+ ),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_predict_subset_segments_failed_not_implemented_predict(self, model, transforms, example_tsds):
- self._test_predict_subset_segments(example_tsds, model, transforms, segments=["segment_2"])
+ def test_predict_subset_segments_failed_not_implemented_predict(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_subset_segments(ts, model, transforms, segments=["segment_1"])
class TestPredictNewSegments:
@@ -1004,23 +1269,24 @@ def _test_predict_new_segments(self, ts, model, transforms, train_segments, num_
assert not forecast_df["target"].equals(original_target)
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (MovingAverageModel(window=3), []),
- (SeasonalMovingAverageModel(), []),
- (NaiveModel(lag=3), []),
- (DeadlineMovingAverageModel(window=1), []),
+ (CatBoostMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticMultiSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (MovingAverageModel(window=3), [], "example_tsds"),
+ (SeasonalMovingAverageModel(), [], "example_tsds"),
+ (NaiveModel(lag=3), [], "example_tsds"),
+ (DeadlineMovingAverageModel(window=1), [], "example_tsds"),
],
)
- def test_predict_new_segments(self, model, transforms, example_tsds):
- self._test_predict_new_segments(example_tsds, model, transforms, train_segments=["segment_1"])
+ def test_predict_new_segments(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_new_segments(ts, model, transforms, train_segments=["segment_1"])
@to_be_fixed(raises=NotImplementedError, match="Method predict isn't currently implemented")
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
(
DeepARModel(
@@ -1036,6 +1302,7 @@ def test_predict_new_segments(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
),
(
TFTModel(
@@ -1053,16 +1320,23 @@ def test_predict_new_segments(self, model, transforms, example_tsds):
lr=0.01,
),
[],
+ "example_tsds",
+ ),
+ (
+ RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
),
- (RNNModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
(
DeepARNativeModel(input_size=1, encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)),
[],
+ "example_tsds",
),
- (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), []),
+ (PatchTSModel(encoder_length=7, decoder_length=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
(
MLPModel(input_size=2, hidden_size=[10], decoder_length=7, trainer_params=dict(max_epochs=1)),
[LagTransform(in_column="target", lags=[5, 6])],
+ "example_tsds",
),
(
DeepStateModel(
@@ -1073,35 +1347,43 @@ def test_predict_new_segments(self, model, transforms, example_tsds):
trainer_params=dict(max_epochs=1),
),
[],
+ "example_tsds",
),
- (NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
- (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), []),
+ (
+ NBeatsInterpretableModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)),
+ [],
+ "example_tsds",
+ ),
+ (NBeatsGenericModel(input_size=7, output_size=7, trainer_params=dict(max_epochs=1)), [], "example_tsds"),
],
)
- def test_predict_new_segments_failed_not_implemented_predict(self, model, transforms, example_tsds):
- self._test_predict_new_segments(example_tsds, model, transforms, train_segments=["segment_1"])
+ def test_predict_new_segments_failed_not_implemented_predict(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_predict_new_segments(ts, model, transforms, train_segments=["segment_1"])
@pytest.mark.parametrize(
- "model, transforms",
+ "model, transforms, dataset_name",
[
- (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])]),
- (AutoARIMAModel(), []),
- (ProphetModel(), []),
- (SARIMAXModel(), []),
- (HoltModel(), []),
- (HoltWintersModel(), []),
- (SimpleExpSmoothingModel(), []),
- (BATSModel(use_trend=True), []),
- (TBATSModel(use_trend=True), []),
- (StatsForecastARIMAModel(), []),
- (StatsForecastAutoARIMAModel(), []),
- (StatsForecastAutoCESModel(), []),
- (StatsForecastAutoETSModel(), []),
- (StatsForecastAutoThetaModel(), []),
+ (CatBoostPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (LinearPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (ElasticPerSegmentModel(), [LagTransform(in_column="target", lags=[5, 6])], "example_tsds"),
+ (AutoARIMAModel(), [], "example_tsds"),
+ (ProphetModel(), [], "example_tsds"),
+ (ProphetModel(timestamp_column="external_timestamp"), [], "ts_with_external_timestamp"),
+ (SARIMAXModel(), [], "example_tsds"),
+ (HoltModel(), [], "example_tsds"),
+ (HoltWintersModel(), [], "example_tsds"),
+ (SimpleExpSmoothingModel(), [], "example_tsds"),
+ (BATSModel(use_trend=True), [], "example_tsds"),
+ (TBATSModel(use_trend=True), [], "example_tsds"),
+ (StatsForecastARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoARIMAModel(), [], "example_tsds"),
+ (StatsForecastAutoCESModel(), [], "example_tsds"),
+ (StatsForecastAutoETSModel(), [], "example_tsds"),
+ (StatsForecastAutoThetaModel(), [], "example_tsds"),
],
)
- def test_predict_new_segments_failed_per_segment(self, model, transforms, example_tsds):
+ def test_predict_new_segments_failed_per_segment(self, model, transforms, dataset_name, request):
+ ts = request.getfixturevalue(dataset_name)
with pytest.raises(NotImplementedError, match="Per-segment models can't make predictions on new segments"):
- self._test_predict_new_segments(example_tsds, model, transforms, train_segments=["segment_1"])
+ self._test_predict_new_segments(ts, model, transforms, train_segments=["segment_1"])
diff --git a/tests/test_models/test_nn/conftest.py b/tests/test_models/test_nn/conftest.py
index f801b7365..99d961a8e 100644
--- a/tests/test_models/test_nn/conftest.py
+++ b/tests/test_models/test_nn/conftest.py
@@ -1,5 +1,6 @@
from typing import Tuple
+import numpy as np
import pandas as pd
import pytest
@@ -38,8 +39,25 @@ def wrapper(horizon: int) -> Tuple[TSDataset, TSDataset]:
@pytest.fixture()
-def df_with_ascending_window_mean():
- segment_1 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
- ts_range = list(pd.date_range("2020-01-03", freq="1D", periods=len(segment_1)))
- df = pd.DataFrame({"timestamp": ts_range, "target": segment_1, "segment": ["segment_1"] * len(segment_1)})
+def example_make_samples_df():
+ target = np.arange(50).astype(float)
+ timestamp = pd.date_range("2020-01-03", freq="D", periods=len(target))
+ df = pd.DataFrame(
+ {
+ "timestamp": timestamp,
+ "target": target,
+ "segment": ["segment_1"] * len(target),
+ "regressor_float": timestamp.weekday.astype(float) * 2,
+ "regressor_int": timestamp.weekday * 3,
+ "regressor_bool": timestamp.weekday.isin([5, 6]),
+ "regressor_str": timestamp.weekday.astype(str),
+ "regressor_int_cat": timestamp.weekday.astype("category"),
+ }
+ )
return df
+
+
+@pytest.fixture()
+def example_make_samples_df_int_timestamp(example_make_samples_df):
+ example_make_samples_df["timestamp"] = np.arange(len(example_make_samples_df)) + 10
+ return example_make_samples_df
diff --git a/tests/test_models/test_nn/deepar_native/test_deepar_native.py b/tests/test_models/test_nn/deepar_native/test_deepar_native.py
index efeb94d2e..7cdf1073a 100644
--- a/tests/test_models/test_nn/deepar_native/test_deepar_native.py
+++ b/tests/test_models/test_nn/deepar_native/test_deepar_native.py
@@ -54,8 +54,10 @@ def test_deepar_model_run_weekly_overfit(
assert mae(ts_test, future) < eps
-@pytest.mark.parametrize("scale, mean_1, mean_2", [(False, 0, 0), (True, 3, 4)])
-def test_deepar_make_samples(df_with_ascending_window_mean, scale, mean_1, mean_2):
+@pytest.mark.parametrize("df_name", ["example_make_samples_df", "example_make_samples_df_int_timestamp"])
+@pytest.mark.parametrize("scale, weights", [(False, [1, 1]), (True, [3, 4])])
+def test_deepar_make_samples(df_name, scale, weights, request):
+ df = request.getfixturevalue(df_name)
deepar_module = MagicMock(scale=scale)
encoder_length = 4
decoder_length = 1
@@ -63,26 +65,41 @@ def test_deepar_make_samples(df_with_ascending_window_mean, scale, mean_1, mean_
ts_samples = list(
DeepARNativeNet.make_samples(
deepar_module,
- df=df_with_ascending_window_mean,
+ df=df,
encoder_length=encoder_length,
decoder_length=decoder_length,
)
)
- first_sample = ts_samples[0]
- second_sample = ts_samples[1]
-
- assert first_sample["segment"] == "segment_1"
- assert first_sample["encoder_real"].shape == (encoder_length - 1, 1)
- assert first_sample["decoder_real"].shape == (decoder_length, 1)
- assert first_sample["encoder_target"].shape == (encoder_length - 1, 1)
- assert first_sample["decoder_target"].shape == (decoder_length, 1)
- np.testing.assert_almost_equal(
- df_with_ascending_window_mean[["target"]].iloc[: encoder_length - 1],
- first_sample["encoder_real"] * (1 + mean_1),
- )
- np.testing.assert_almost_equal(
- df_with_ascending_window_mean[["target"]].iloc[1:encoder_length], second_sample["encoder_real"] * (1 + mean_2)
- )
+
+ assert len(ts_samples) == len(df) - encoder_length - decoder_length + 1
+
+ num_samples_check = 2
+ df["target_shifted"] = df["target"].shift(1)
+ for i in range(num_samples_check):
+ df[f"target_shifted_scaled_{i}"] = df["target_shifted"] / weights[i]
+ expected_sample = {
+ "encoder_real": df[[f"target_shifted_scaled_{i}", "regressor_float", "regressor_int"]]
+ .iloc[1 + i : encoder_length + i]
+ .values,
+ "decoder_real": df[[f"target_shifted_scaled_{i}", "regressor_float", "regressor_int"]]
+ .iloc[encoder_length + i : encoder_length + decoder_length + i]
+ .values,
+ "encoder_target": df[["target"]].iloc[1 + i : encoder_length + i].values,
+ "decoder_target": df[["target"]].iloc[encoder_length + i : encoder_length + decoder_length + i].values,
+ "weight": weights[i],
+ }
+
+ assert ts_samples[i].keys() == {
+ "encoder_real",
+ "decoder_real",
+ "encoder_target",
+ "decoder_target",
+ "segment",
+ "weight",
+ }
+ assert ts_samples[i]["segment"] == "segment_1"
+ for key in expected_sample:
+ np.testing.assert_equal(ts_samples[i][key], expected_sample[key])
@pytest.mark.parametrize("encoder_length", [1, 2, 10])
diff --git a/tests/test_models/test_nn/nbeats/test_nbeats_nets.py b/tests/test_models/test_nn/nbeats/test_nbeats_nets.py
index aef8a5112..e95583403 100644
--- a/tests/test_models/test_nn/nbeats/test_nbeats_nets.py
+++ b/tests/test_models/test_nn/nbeats/test_nbeats_nets.py
@@ -33,18 +33,26 @@ def nbeats_interpretable_net():
)
-def test_make_samples(example_tsdf):
+@pytest.mark.parametrize("df_name", ["example_make_samples_df", "example_make_samples_df_int_timestamp"])
+def test_make_samples(df_name, request):
+ df = request.getfixturevalue(df_name)
module = MagicMock()
- df = example_tsdf.to_flatten(example_tsdf.df)
- segment_1_df = df[df.segment == "segment_1"]
- sample = list(NBeatsBaseNet.make_samples(module, df=segment_1_df, encoder_length=-1, decoder_length=-1))[0]
+ ts_samples = list(NBeatsBaseNet.make_samples(module, df=df, encoder_length=-1, decoder_length=-1))
+ first_sample = ts_samples[0]
- assert sample["target"] is None
- assert sample["target_mask"] is None
- assert sample["segment"] == "segment_1"
- assert tuple(sample["history"].shape) == (len(segment_1_df),)
- np.testing.assert_allclose(segment_1_df["target"].values, sample["history"])
+ assert len(ts_samples) == 1
+
+ expected_first_sample = {
+ "history": df["target"].values,
+ }
+
+ assert first_sample.keys() == {"history", "history_mask", "target", "target_mask", "segment"}
+ assert first_sample["history_mask"] is None
+ assert first_sample["target"] is None
+ assert first_sample["target_mask"] is None
+ assert first_sample["segment"] == "segment_1"
+ np.testing.assert_equal(first_sample["history"], expected_first_sample["history"])
@pytest.mark.parametrize(
diff --git a/tests/test_models/test_nn/test_deepstate.py b/tests/test_models/test_nn/test_deepstate.py
index 07f6c9901..40d63b7a0 100644
--- a/tests/test_models/test_nn/test_deepstate.py
+++ b/tests/test_models/test_nn/test_deepstate.py
@@ -1,9 +1,13 @@
+from unittest.mock import MagicMock
+
+import numpy as np
import pytest
from etna.metrics import MAE
from etna.models.nn import DeepStateModel
from etna.models.nn.deepstate import CompositeSSM
from etna.models.nn.deepstate import WeeklySeasonalitySSM
+from etna.models.nn.deepstate.deepstate import DeepStateNet
from etna.transforms import StandardScalerTransform
from tests.test_models.utils import assert_model_equals_loaded_original
from tests.test_models.utils import assert_sampling_is_valid
@@ -47,6 +51,46 @@ def test_deepstate_model_run_weekly_overfit_with_scaler(ts_dataset_weekly_functi
assert mae(ts_test, future) < 0.001
+@pytest.mark.parametrize("df_name", ["example_make_samples_df", "example_make_samples_df_int_timestamp"])
+def test_deepstate_make_samples(df_name, request):
+ df = request.getfixturevalue(df_name)
+
+ # this model can't work with this type of columns
+ df.drop(columns=["regressor_bool", "regressor_str"], inplace=True)
+
+ ssm = MagicMock()
+ datetime_index = np.arange(len(df))
+ ssm.generate_datetime_index.return_value = datetime_index[np.newaxis, :]
+ module = MagicMock(ssm=ssm)
+ encoder_length = 8
+ decoder_length = 4
+
+ ts_samples = list(
+ DeepStateNet.make_samples(module, df=df, encoder_length=encoder_length, decoder_length=decoder_length)
+ )
+
+ assert len(ts_samples) == len(df) - encoder_length - decoder_length + 1
+
+ num_samples_check = 2
+ df["datetime_index"] = datetime_index
+ for i in range(num_samples_check):
+ expected_sample = {
+ "encoder_real": df[["regressor_float", "regressor_int", "regressor_int_cat"]]
+ .iloc[i : encoder_length + i]
+ .values,
+ "decoder_real": df[["regressor_float", "regressor_int", "regressor_int_cat"]]
+ .iloc[encoder_length + i : encoder_length + decoder_length + i]
+ .values,
+ "encoder_target": df[["target"]].iloc[i : encoder_length + i].values,
+ "datetime_index": df[["datetime_index"]].iloc[i : encoder_length + decoder_length + i].values.T,
+ }
+
+ assert ts_samples[i].keys() == {"encoder_real", "decoder_real", "encoder_target", "datetime_index", "segment"}
+ assert ts_samples[i]["segment"] == "segment_1"
+ for key in expected_sample:
+ np.testing.assert_equal(ts_samples[i][key], expected_sample[key])
+
+
def test_save_load(example_tsds):
model = DeepStateModel(
ssm=CompositeSSM(seasonal_ssms=[WeeklySeasonalitySSM()], nonseasonal_ssm=None),
diff --git a/tests/test_models/test_nn/test_mlp.py b/tests/test_models/test_nn/test_mlp.py
index 939401337..362e18782 100644
--- a/tests/test_models/test_nn/test_mlp.py
+++ b/tests/test_models/test_nn/test_mlp.py
@@ -1,3 +1,4 @@
+import math
from unittest.mock import MagicMock
import numpy as np
@@ -5,7 +6,6 @@
import torch
from torch import nn
-from etna.datasets.tsdataset import TSDataset
from etna.metrics import MAE
from etna.models.nn import MLPModel
from etna.models.nn.mlp import MLPNet
@@ -49,37 +49,33 @@ def test_mlp_model_run_weekly_overfit_with_scaler(ts_dataset_weekly_function_wit
assert mae(ts_test, future) < 0.05
-def test_mlp_make_samples(simple_df_relevance):
+@pytest.mark.parametrize("df_name", ["example_make_samples_df", "example_make_samples_df_int_timestamp"])
+def test_mlp_make_samples(df_name, request):
+ df = request.getfixturevalue(df_name)
mlp_module = MagicMock()
- df, df_exog = simple_df_relevance
-
- ts = TSDataset(df=df, df_exog=df_exog, freq="D")
- df = ts.to_flatten(ts.df)
encoder_length = 0
decoder_length = 5
ts_samples = list(
- MLPNet.make_samples(
- mlp_module, df=df[df.segment == "1"], encoder_length=encoder_length, decoder_length=decoder_length
- )
+ MLPNet.make_samples(mlp_module, df=df, encoder_length=encoder_length, decoder_length=decoder_length)
)
- first_sample = ts_samples[0]
- second_sample = ts_samples[1]
- last_sample = ts_samples[-1]
- expected = {
- "decoder_real": np.array([[58.0, 0], [59.0, 0], [60.0, 0], [61.0, 0], [62.0, 0]]),
- "decoder_target": np.array([[27.0], [28.0], [29.0], [30.0], [31.0]]),
- "segment": "1",
- }
- assert first_sample["segment"] == "1"
- assert first_sample["decoder_real"].shape == (decoder_length, 2)
- assert first_sample["decoder_target"].shape == (decoder_length, 1)
- assert len(ts_samples) == 7
- assert np.all(last_sample["decoder_target"] == expected["decoder_target"])
- assert np.all(last_sample["decoder_real"] == expected["decoder_real"])
- assert last_sample["segment"] == expected["segment"]
- np.testing.assert_equal(df[["target"]].iloc[:decoder_length], first_sample["decoder_target"])
- np.testing.assert_equal(df[["target"]].iloc[decoder_length : 2 * decoder_length], second_sample["decoder_target"])
+ assert len(ts_samples) == math.ceil(len(df) / decoder_length)
+
+ num_samples_check = 2
+ for i in range(num_samples_check):
+ expected_sample = {
+ "decoder_real": df[["regressor_float", "regressor_int"]]
+ .iloc[encoder_length + decoder_length * i : encoder_length + decoder_length * (i + 1)]
+ .values,
+ "decoder_target": df[["target"]]
+ .iloc[encoder_length + decoder_length * i : encoder_length + decoder_length * (i + 1)]
+ .values,
+ }
+
+ assert ts_samples[i].keys() == {"decoder_real", "decoder_target", "segment"}
+ assert ts_samples[i]["segment"] == "segment_1"
+ for key in expected_sample:
+ np.testing.assert_equal(ts_samples[i][key], expected_sample[key])
def test_mlp_forward_fail_nans():
diff --git a/tests/test_models/test_nn/test_patchts.py b/tests/test_models/test_nn/test_patchts.py
index 3db1e83bc..c5d5bb17c 100644
--- a/tests/test_models/test_nn/test_patchts.py
+++ b/tests/test_models/test_nn/test_patchts.py
@@ -55,24 +55,34 @@ def test_patchts_model_run_weekly_overfit_with_scaler_medium_patch(ts_dataset_we
assert mae(ts_test, future) < 1.3
-def test_patchts_make_samples(example_df):
- rnn_module = MagicMock()
+@pytest.mark.parametrize("df_name", ["example_make_samples_df", "example_make_samples_df_int_timestamp"])
+def test_patchts_make_samples(df_name, request):
+ df = request.getfixturevalue(df_name)
+ module = MagicMock()
encoder_length = 8
decoder_length = 4
ts_samples = list(
- PatchTSNet.make_samples(rnn_module, df=example_df, encoder_length=encoder_length, decoder_length=decoder_length)
+ PatchTSNet.make_samples(module, df=df, encoder_length=encoder_length, decoder_length=decoder_length)
)
- first_sample = ts_samples[0]
- second_sample = ts_samples[1]
-
- assert first_sample["segment"] == "segment_1"
- assert first_sample["encoder_real"].shape == (encoder_length, 1)
- assert first_sample["decoder_real"].shape == (decoder_length, 1)
- assert first_sample["encoder_target"].shape == (encoder_length, 1)
- assert first_sample["decoder_target"].shape == (decoder_length, 1)
- np.testing.assert_equal(example_df[["target"]].iloc[:encoder_length], first_sample["encoder_real"])
- np.testing.assert_equal(example_df[["target"]].iloc[1 : encoder_length + 1], second_sample["encoder_real"])
+
+ assert len(ts_samples) == len(df) - encoder_length - decoder_length + 1
+
+ num_samples_check = 2
+ for i in range(num_samples_check):
+ expected_sample = {
+ "encoder_real": df[["target", "regressor_float", "regressor_int"]].iloc[i : encoder_length + i].values,
+ "decoder_real": df[["target", "regressor_float", "regressor_int"]]
+ .iloc[encoder_length + i : encoder_length + decoder_length + i]
+ .values,
+ "encoder_target": df[["target"]].iloc[i : encoder_length + i].values,
+ "decoder_target": df[["target"]].iloc[encoder_length + i : encoder_length + decoder_length + i].values,
+ }
+
+ assert ts_samples[i].keys() == {"encoder_real", "decoder_real", "encoder_target", "decoder_target", "segment"}
+ assert ts_samples[i]["segment"] == "segment_1"
+ for key in expected_sample:
+ np.testing.assert_equal(ts_samples[i][key], expected_sample[key])
def test_save_load(example_tsds):
diff --git a/tests/test_models/test_nn/test_rnn.py b/tests/test_models/test_nn/test_rnn.py
index 40db42f71..a458fa59c 100644
--- a/tests/test_models/test_nn/test_rnn.py
+++ b/tests/test_models/test_nn/test_rnn.py
@@ -45,24 +45,37 @@ def test_rnn_model_run_weekly_overfit_with_scaler(ts_dataset_weekly_function_wit
assert mae(ts_test, future) < 0.06
-def test_rnn_make_samples(example_df):
+@pytest.mark.parametrize("df_name", ["example_make_samples_df", "example_make_samples_df_int_timestamp"])
+def test_rnn_make_samples(df_name, request):
+ df = request.getfixturevalue(df_name)
rnn_module = MagicMock()
encoder_length = 8
decoder_length = 4
ts_samples = list(
- RNNNet.make_samples(rnn_module, df=example_df, encoder_length=encoder_length, decoder_length=decoder_length)
+ RNNNet.make_samples(rnn_module, df=df, encoder_length=encoder_length, decoder_length=decoder_length)
)
- first_sample = ts_samples[0]
- second_sample = ts_samples[1]
-
- assert first_sample["segment"] == "segment_1"
- assert first_sample["encoder_real"].shape == (encoder_length - 1, 1)
- assert first_sample["decoder_real"].shape == (decoder_length, 1)
- assert first_sample["encoder_target"].shape == (encoder_length - 1, 1)
- assert first_sample["decoder_target"].shape == (decoder_length, 1)
- np.testing.assert_equal(example_df[["target"]].iloc[: encoder_length - 1], first_sample["encoder_real"])
- np.testing.assert_equal(example_df[["target"]].iloc[1:encoder_length], second_sample["encoder_real"])
+
+ assert len(ts_samples) == len(df) - encoder_length - decoder_length + 1
+
+ num_samples_check = 2
+ df["target_shifted"] = df["target"].shift(1)
+ for i in range(num_samples_check):
+ expected_sample = {
+ "encoder_real": df[["target_shifted", "regressor_float", "regressor_int"]]
+ .iloc[1 + i : encoder_length + i]
+ .values,
+ "decoder_real": df[["target_shifted", "regressor_float", "regressor_int"]]
+ .iloc[encoder_length + i : encoder_length + decoder_length + i]
+ .values,
+ "encoder_target": df[["target"]].iloc[1 + i : encoder_length + i].values,
+ "decoder_target": df[["target"]].iloc[encoder_length + i : encoder_length + decoder_length + i].values,
+ }
+
+ assert ts_samples[i].keys() == {"encoder_real", "decoder_real", "encoder_target", "decoder_target", "segment"}
+ assert ts_samples[i]["segment"] == "segment_1"
+ for key in expected_sample:
+ np.testing.assert_equal(ts_samples[i][key], expected_sample[key])
@pytest.mark.parametrize("encoder_length", [1, 2, 10])
diff --git a/tests/test_models/test_prophet.py b/tests/test_models/test_prophet.py
index 01b688b7f..cb461d504 100644
--- a/tests/test_models/test_prophet.py
+++ b/tests/test_models/test_prophet.py
@@ -6,7 +6,8 @@
from prophet import Prophet
from prophet.serialize import model_to_dict
-from etna.datasets.tsdataset import TSDataset
+from etna.datasets import TSDataset
+from etna.datasets import generate_ar_df
from etna.models import ProphetModel
from etna.models.prophet import _ProphetAdapter
from etna.pipeline import Pipeline
@@ -22,7 +23,7 @@ def _check_forecast(ts, model, horizon):
res = res.to_pandas(flatten=True)
assert not res["target"].isnull().values.any()
- assert len(res) == horizon * 2
+ assert len(res) == horizon * len(ts.segments)
def _check_predict(ts, model):
@@ -31,7 +32,7 @@ def _check_predict(ts, model):
res = res.to_pandas(flatten=True)
assert not res["target"].isnull().values.any()
- assert len(res) == len(ts.index) * 2
+ assert len(res) == len(ts.index) * len(ts.segments)
def test_fit_str_category_fail(ts_with_non_convertable_category_regressor):
@@ -48,14 +49,60 @@ def test_fit_with_exogs_warning(ts_with_non_regressor_exog):
model.fit(ts)
-def test_prediction(example_tsds):
- _check_forecast(ts=deepcopy(example_tsds), model=ProphetModel(), horizon=7)
- _check_predict(ts=deepcopy(example_tsds), model=ProphetModel())
+def test_fit_int_timestamp_fail(example_tsds_int_timestamp):
+ ts = example_tsds_int_timestamp
+ model = ProphetModel()
+ with pytest.raises(ValueError, match="Invalid timestamp! Only datetime type is supported."):
+ model.fit(ts)
+
+def test_fit_external_timestamp_not_present_fail(example_tsds):
+ ts = example_tsds
+ model = ProphetModel(timestamp_column="unknown_feature")
+ with pytest.raises(ValueError, match="Invalid timestamp_column! It isn't present in a given dataset."):
+ model.fit(ts)
+
+
+def test_fit_external_timestamp_not_regressor_fail():
+ df = generate_ar_df(periods=100, start_time=10, n_segments=1, freq=None)
+ df_wide = TSDataset.to_dataset(df)
+ df_exog = generate_ar_df(periods=100, start_time=10, n_segments=1, freq=None)
+ df_exog["target"] = pd.date_range(start="2020-01-01", periods=100)
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ df_exog_wide.rename(columns={"target": "external_timestamp"}, level="feature", inplace=True)
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, known_future=[], freq=None)
+
+ model = ProphetModel(timestamp_column="external_timestamp")
+ with pytest.raises(ValueError, match="Invalid timestamp_column! It should be a regressor."):
+ model.fit(ts)
-def test_prediction_with_reg(example_reg_tsds):
- _check_forecast(ts=deepcopy(example_reg_tsds), model=ProphetModel(), horizon=7)
- _check_predict(ts=deepcopy(example_reg_tsds), model=ProphetModel())
+
+def test_fit_external_timestamp_not_datetime_fail():
+ df = generate_ar_df(periods=100, start_time=10, n_segments=1, freq=None)
+ df_wide = TSDataset.to_dataset(df)
+ df_exog = generate_ar_df(periods=100, start_time=10, n_segments=1, freq=None)
+ df_exog["target"] = np.arange(100)
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ df_exog_wide.rename(columns={"target": "external_timestamp"}, level="feature", inplace=True)
+ ts = TSDataset(df=df_wide.iloc[:-5], df_exog=df_exog_wide, known_future="all", freq=None)
+
+ model = ProphetModel(timestamp_column="external_timestamp")
+ with pytest.raises(ValueError, match="Invalid timestamp_column! Only datetime type is supported."):
+ model.fit(ts)
+
+
+@pytest.mark.parametrize(
+ "ts_name, timestamp_column",
+ [
+ ("example_tsds", None),
+ ("example_reg_tsds", None),
+ ("ts_with_external_timestamp", "external_timestamp"),
+ ],
+)
+def test_prediction(ts_name, timestamp_column, request):
+ ts = request.getfixturevalue(ts_name)
+ _check_forecast(ts=deepcopy(ts), model=ProphetModel(timestamp_column=timestamp_column), horizon=7)
+ _check_predict(ts=deepcopy(ts), model=ProphetModel(timestamp_column=timestamp_column))
def test_prediction_with_cap_floor():
@@ -95,10 +142,18 @@ def test_forecast_with_short_regressors_fail(ts_with_short_regressor):
_check_forecast(ts=deepcopy(ts), model=ProphetModel(), horizon=20)
-def test_prediction_interval_run_insample(example_tsds):
- model = ProphetModel()
- model.fit(example_tsds)
- forecast = model.forecast(example_tsds, prediction_interval=True, quantiles=[0.025, 0.975])
+@pytest.mark.parametrize(
+ "ts_name, timestamp_column",
+ [
+ ("example_tsds", None),
+ ("ts_with_external_timestamp", "external_timestamp"),
+ ],
+)
+def test_prediction_interval_run_insample(ts_name, timestamp_column, request):
+ ts = request.getfixturevalue(ts_name)
+ model = ProphetModel(timestamp_column=timestamp_column)
+ model.fit(ts)
+ forecast = model.forecast(ts, prediction_interval=True, quantiles=[0.025, 0.975])
assert forecast.prediction_intervals_names == ("target_0.025", "target_0.975")
prediction_intervals = forecast.get_prediction_intervals()
@@ -112,10 +167,18 @@ def test_prediction_interval_run_insample(example_tsds):
assert np.allclose(segment_slice["target_0.025"], segment_intervals["target_0.025"])
-def test_prediction_interval_run_infuture(example_tsds):
- model = ProphetModel()
- model.fit(example_tsds)
- future = example_tsds.make_future(10)
+@pytest.mark.parametrize(
+ "ts_name, timestamp_column",
+ [
+ ("example_tsds", None),
+ ("ts_with_external_timestamp", "external_timestamp"),
+ ],
+)
+def test_prediction_interval_run_infuture(ts_name, timestamp_column, request):
+ ts = request.getfixturevalue(ts_name)
+ model = ProphetModel(timestamp_column=timestamp_column)
+ model.fit(ts)
+ future = ts.make_future(10)
forecast = model.forecast(future, prediction_interval=True, quantiles=[0.025, 0.975])
assert forecast.prediction_intervals_names == ("target_0.025", "target_0.975")
@@ -185,6 +248,7 @@ def test_getstate_not_fitted(prophet_default_params):
"_is_fitted": False,
"_model_dict": {},
"regressor_columns": None,
+ "timestamp_column": None,
**prophet_default_params,
}
assert state == expected_state
@@ -199,6 +263,7 @@ def test_getstate_fitted(example_tsds, prophet_default_params):
"_is_fitted": True,
"_model_dict": model_to_dict(model.model),
"regressor_columns": [],
+ "timestamp_column": None,
**prophet_default_params,
}
assert state == expected_state
@@ -363,6 +428,9 @@ def test_predict_components_names(
assert set(components.columns) == expected_columns
+@pytest.mark.parametrize(
+ "timestamp_column,timestamp_column_regressors", [(None, []), ("external_timestamp", ["external_timestamp"])]
+)
@pytest.mark.parametrize("growth,cap", (("linear", []), ("logistic", ["cap"])))
@pytest.mark.parametrize("regressors", (["f1", "f2"], []))
@pytest.mark.parametrize("custom_seas", ([{"name": "s1", "period": 14, "fourier_order": 1}], []))
@@ -371,13 +439,27 @@ def test_predict_components_names(
@pytest.mark.parametrize("weekly", (True, False))
@pytest.mark.parametrize("yearly", (True, False))
def test_predict_components_sum_up_to_target(
- prophet_dfs, regressors, use_holidays, daily, weekly, yearly, custom_seas, growth, cap
+ prophet_dfs,
+ regressors,
+ use_holidays,
+ daily,
+ weekly,
+ yearly,
+ custom_seas,
+ growth,
+ cap,
+ timestamp_column,
+ timestamp_column_regressors,
):
train, test, holidays = prophet_dfs
if not use_holidays:
holidays = None
+ if timestamp_column is not None:
+ train[timestamp_column] = train["timestamp"]
+ test[timestamp_column] = test["timestamp"]
+
model = _ProphetAdapter(
growth=growth,
holidays=holidays,
@@ -385,8 +467,9 @@ def test_predict_components_sum_up_to_target(
weekly_seasonality=weekly,
yearly_seasonality=yearly,
additional_seasonality_params=custom_seas,
+ timestamp_column=timestamp_column,
)
- model.fit(df=train, regressors=regressors + cap)
+ model.fit(df=train, regressors=regressors + cap + timestamp_column_regressors)
components = model.predict_components(df=test)
pred = model.predict(df=test, prediction_interval=False, quantiles=[])
diff --git a/tests/test_models/test_sarimax_model.py b/tests/test_models/test_sarimax_model.py
index 8b4209203..a15a4d010 100644
--- a/tests/test_models/test_sarimax_model.py
+++ b/tests/test_models/test_sarimax_model.py
@@ -3,6 +3,7 @@
from unittest.mock import patch
import numpy as np
+import pandas as pd
import pytest
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper
@@ -55,19 +56,39 @@ def test_save_regressors_on_fit(example_reg_tsds):
assert sorted(segment_model.regressor_columns) == example_reg_tsds.regressors
-def test_select_regressors_correctly(example_reg_tsds):
+def test_select_regressors_correctly_datetime_timestamp(example_reg_tsds):
+ ts = example_reg_tsds
model = SARIMAXModel()
- model.fit(ts=example_reg_tsds)
+ model.fit(ts=ts)
+ for segment, segment_model in model._models.items():
+ segment_features = ts[:, segment, :].droplevel("segment", axis=1).reset_index()
+
+ segment_regressors_expected = segment_features[ts.regressors].astype(float)
+ segment_regressors_expected.index = segment_features["timestamp"]
+ segment_regressors = segment_model._select_regressors(df=segment_features)
+
+ pd.testing.assert_frame_equal(segment_regressors, segment_regressors_expected)
+
+
+def test_select_regressors_correctly_int_timestamp(example_reg_tsds_int_timestamp):
+ ts = example_reg_tsds_int_timestamp
+ model = SARIMAXModel()
+ model.fit(ts=ts)
for segment, segment_model in model._models.items():
- segment_features = example_reg_tsds[:, segment, :].droplevel("segment", axis=1)
- segment_regressors_expected = segment_features[example_reg_tsds.regressors]
- segment_regressors = segment_model._select_regressors(df=segment_features.reset_index())
- assert (segment_regressors == segment_regressors_expected).all().all()
+ segment_features = ts[:, segment, :].droplevel("segment", axis=1).reset_index()
+ segment_regressors_expected = segment_features[ts.regressors].astype(float)
+ segment_regressors_expected.index = pd.Index(np.arange(len(segment_regressors_expected)), name="timestamp")
+ segment_regressors = segment_model._select_regressors(df=segment_features)
-def test_prediction(example_tsds):
- _check_forecast(ts=deepcopy(example_tsds), model=SARIMAXModel(), horizon=7)
- _check_predict(ts=deepcopy(example_tsds), model=SARIMAXModel())
+ pd.testing.assert_frame_equal(segment_regressors, segment_regressors_expected)
+
+
+@pytest.mark.parametrize("ts_name", ["example_tsds", "example_tsds_int_timestamp"])
+def test_prediction(ts_name, request):
+ ts = request.getfixturevalue(ts_name)
+ _check_forecast(ts=deepcopy(ts), model=SARIMAXModel(), horizon=7)
+ _check_predict(ts=deepcopy(ts), model=SARIMAXModel())
def test_prediction_with_simple_differencing(example_tsds):
@@ -75,9 +96,11 @@ def test_prediction_with_simple_differencing(example_tsds):
_check_predict(ts=deepcopy(example_tsds), model=SARIMAXModel(simple_differencing=True))
-def test_prediction_with_reg(example_reg_tsds):
- _check_forecast(ts=deepcopy(example_reg_tsds), model=SARIMAXModel(), horizon=7)
- _check_predict(ts=deepcopy(example_reg_tsds), model=SARIMAXModel())
+@pytest.mark.parametrize("ts_name", ["example_reg_tsds", "example_reg_tsds_int_timestamp"])
+def test_prediction_with_reg(ts_name, request):
+ ts = request.getfixturevalue(ts_name)
+ _check_forecast(ts=deepcopy(ts), model=SARIMAXModel(), horizon=7)
+ _check_predict(ts=deepcopy(ts), model=SARIMAXModel())
def test_prediction_with_reg_custom_order(example_reg_tsds):
@@ -91,12 +114,14 @@ def test_forecast_with_short_regressors_fail(ts_with_short_regressor):
_check_forecast(ts=deepcopy(ts), model=SARIMAXModel(), horizon=20)
+@pytest.mark.parametrize("ts_name", ["example_tsds", "example_tsds_int_timestamp"])
@pytest.mark.parametrize("method_name", ["forecast", "predict"])
-def test_prediction_interval_insample(example_tsds, method_name):
+def test_prediction_interval_insample(ts_name, method_name, request):
+ ts = request.getfixturevalue(ts_name)
model = SARIMAXModel()
- model.fit(example_tsds)
+ model.fit(ts)
method = getattr(model, method_name)
- forecast = method(example_tsds, prediction_interval=True, quantiles=[0.025, 0.975])
+ forecast = method(ts, prediction_interval=True, quantiles=[0.025, 0.975])
assert forecast.prediction_intervals_names == ("target_0.025", "target_0.975")
prediction_intervals = forecast.get_prediction_intervals()
@@ -113,10 +138,12 @@ def test_prediction_interval_insample(example_tsds, method_name):
assert np.allclose(segment_slice["target_0.025"], segment_intervals["target_0.025"])
-def test_forecast_prediction_interval_infuture(example_tsds):
+@pytest.mark.parametrize("ts_name", ["example_tsds", "example_tsds_int_timestamp"])
+def test_forecast_prediction_interval_infuture(ts_name, request):
+ ts = request.getfixturevalue(ts_name)
model = SARIMAXModel()
- model.fit(example_tsds)
- future = example_tsds.make_future(10)
+ model.fit(ts)
+ future = ts.make_future(10)
forecast = model.forecast(future, prediction_interval=True, quantiles=[0.025, 0.975])
assert forecast.prediction_intervals_names == ("target_0.025", "target_0.975")
@@ -236,6 +263,7 @@ def test_components_names(dfs_w_exog, regressors, regressors_components, trend,
assert sorted(components.columns) == sorted(expected_components)
+@pytest.mark.parametrize("dfs_name", ["dfs_w_exog", "dfs_w_exog_int_timestamp"])
@pytest.mark.parametrize(
"components_method_name,predict_method_name,in_sample",
(("predict_components", "predict", True), ("forecast_components", "forecast", False)),
@@ -256,7 +284,7 @@ def test_components_names(dfs_w_exog, regressors, regressors_components, trend,
@pytest.mark.parametrize("concentrate_scale", (True, False))
@pytest.mark.parametrize("use_exact_diffuse", (True, False))
def test_components_sum_up_to_target(
- dfs_w_exog,
+ dfs_name,
components_method_name,
predict_method_name,
in_sample,
@@ -268,8 +296,9 @@ def test_components_sum_up_to_target(
concentrate_scale,
use_exact_diffuse,
regressors,
+ request,
):
- train, test = dfs_w_exog
+ train, test = request.getfixturevalue(dfs_name)
model = _SARIMAXAdapter(
trend=trend,
diff --git a/tests/test_models/test_simple_models.py b/tests/test_models/test_simple_models.py
index 1be64e4f6..a955a839e 100644
--- a/tests/test_models/test_simple_models.py
+++ b/tests/test_models/test_simple_models.py
@@ -85,45 +85,45 @@ def test_sma_model_fit_with_exogs_warning(example_reg_tsds):
@pytest.mark.parametrize("model", [SeasonalMovingAverageModel, NaiveModel, MovingAverageModel])
-def test_sma_model_forecast(simple_df, model):
- _check_forecast(ts=simple_df, model=model(), horizon=7)
+def test_sma_model_forecast(simple_tsdf, model):
+ _check_forecast(ts=simple_tsdf, model=model(), horizon=7)
@pytest.mark.parametrize("model", [SeasonalMovingAverageModel, NaiveModel, MovingAverageModel])
-def test_sma_model_predict(simple_df, model):
- _check_predict(ts=simple_df, model=model(), prediction_size=7)
+def test_sma_model_predict(simple_tsdf, model):
+ _check_predict(ts=simple_tsdf, model=model(), prediction_size=7)
-def test_sma_model_forecast_fail_not_enough_context(simple_df):
+def test_sma_model_forecast_fail_not_enough_context(simple_tsdf):
sma_model = SeasonalMovingAverageModel(window=1000, seasonality=7)
- sma_model.fit(simple_df)
- future_ts = simple_df.make_future(future_steps=7, tail_steps=sma_model.context_size)
+ sma_model.fit(simple_tsdf)
+ future_ts = simple_tsdf.make_future(future_steps=7, tail_steps=sma_model.context_size)
with pytest.raises(ValueError, match="Given context isn't big enough"):
_ = sma_model.forecast(future_ts, prediction_size=7)
-def test_sma_model_predict_fail_not_enough_context(simple_df):
+def test_sma_model_predict_fail_not_enough_context(simple_tsdf):
sma_model = SeasonalMovingAverageModel(window=1000, seasonality=7)
- sma_model.fit(simple_df)
+ sma_model.fit(simple_tsdf)
with pytest.raises(ValueError, match="Given context isn't big enough"):
- _ = sma_model.predict(simple_df, prediction_size=7)
+ _ = sma_model.predict(simple_tsdf, prediction_size=7)
-def test_sma_model_forecast_fail_nans_in_context(simple_df):
+def test_sma_model_forecast_fail_nans_in_context(simple_tsdf):
sma_model = SeasonalMovingAverageModel(window=2, seasonality=2)
- sma_model.fit(simple_df)
- future_ts = simple_df.make_future(future_steps=7, tail_steps=sma_model.context_size)
+ sma_model.fit(simple_tsdf)
+ future_ts = simple_tsdf.make_future(future_steps=7, tail_steps=sma_model.context_size)
future_ts.df.iloc[1, 0] = np.NaN
with pytest.raises(ValueError, match="There are NaNs in a forecast context"):
_ = sma_model.forecast(future_ts, prediction_size=7)
-def test_sma_model_predict_fail_nans_in_context(simple_df):
+def test_sma_model_predict_fail_nans_in_context(simple_tsdf):
sma_model = SeasonalMovingAverageModel(window=2, seasonality=7)
- sma_model.fit(simple_df)
- simple_df.df.iloc[-1, 0] = np.NaN
+ sma_model.fit(simple_tsdf)
+ simple_tsdf.df.iloc[-1, 0] = np.NaN
with pytest.raises(ValueError, match="There are NaNs in a target column"):
- _ = sma_model.predict(simple_df, prediction_size=7)
+ _ = sma_model.predict(simple_tsdf, prediction_size=7)
def test_deadline_model_fit_with_exogs_warning(example_reg_tsds):
@@ -195,13 +195,13 @@ def test_deadline_get_context_beginning_fail_not_enough_context(
@pytest.mark.parametrize("model", [DeadlineMovingAverageModel])
-def test_deadline_model_forecast(simple_df, model):
- _check_forecast(ts=simple_df, model=model(window=1), horizon=7)
+def test_deadline_model_forecast(simple_tsdf, model):
+ _check_forecast(ts=simple_tsdf, model=model(window=1), horizon=7)
@pytest.mark.parametrize("model", [DeadlineMovingAverageModel])
-def test_deadline_model_predict(simple_df, model):
- _check_predict(ts=simple_df, model=model(window=1), prediction_size=7)
+def test_deadline_model_predict(simple_tsdf, model):
+ _check_predict(ts=simple_tsdf, model=model(window=1), prediction_size=7)
def test_deadline_model_fit_fail_not_supported_freq():
@@ -212,61 +212,61 @@ def test_deadline_model_fit_fail_not_supported_freq():
model.fit(ts)
-def test_deadline_model_forecast_fail_not_enough_context(simple_df):
+def test_deadline_model_forecast_fail_not_enough_context(simple_tsdf):
model = DeadlineMovingAverageModel(window=1000)
- model.fit(simple_df)
- future_ts = simple_df.make_future(future_steps=7, tail_steps=model.context_size)
+ model.fit(simple_tsdf)
+ future_ts = simple_tsdf.make_future(future_steps=7, tail_steps=model.context_size)
with pytest.raises(ValueError, match="Given context isn't big enough"):
_ = model.forecast(future_ts, prediction_size=7)
-def test_deadline_model_predict_fail_not_enough_context(simple_df):
+def test_deadline_model_predict_fail_not_enough_context(simple_tsdf):
model = DeadlineMovingAverageModel(window=1000)
- model.fit(simple_df)
+ model.fit(simple_tsdf)
with pytest.raises(ValueError, match="Given context isn't big enough"):
- _ = model.predict(simple_df, prediction_size=7)
+ _ = model.predict(simple_tsdf, prediction_size=7)
-def test_deadline_model_forecast_fail_nans_in_context(simple_df):
+def test_deadline_model_forecast_fail_nans_in_context(simple_tsdf):
model = DeadlineMovingAverageModel(window=1)
- model.fit(simple_df)
- future_ts = simple_df.make_future(future_steps=7, tail_steps=model.context_size)
+ model.fit(simple_tsdf)
+ future_ts = simple_tsdf.make_future(future_steps=7, tail_steps=model.context_size)
future_ts.df.iloc[1, 0] = np.NaN
with pytest.raises(ValueError, match="There are NaNs in a forecast context"):
_ = model.forecast(future_ts, prediction_size=7)
-def test_deadline_model_predict_fail_nans_in_context(simple_df):
+def test_deadline_model_predict_fail_nans_in_context(simple_tsdf):
model = DeadlineMovingAverageModel(window=1)
- model.fit(simple_df)
- simple_df.df.iloc[-1, 0] = np.NaN
+ model.fit(simple_tsdf)
+ simple_tsdf.df.iloc[-1, 0] = np.NaN
with pytest.raises(ValueError, match="There are NaNs in a target column"):
- _ = model.predict(simple_df, prediction_size=7)
+ _ = model.predict(simple_tsdf, prediction_size=7)
-def test_deadline_model_context_size_fail_not_fitted(simple_df):
+def test_deadline_model_context_size_fail_not_fitted(simple_tsdf):
model = DeadlineMovingAverageModel(window=1000)
with pytest.raises(ValueError, match="Model is not fitted"):
_ = model.context_size
-def test_deadline_model_forecast_fail_not_fitted(simple_df):
+def test_deadline_model_forecast_fail_not_fitted(simple_tsdf):
model = DeadlineMovingAverageModel(window=1000)
- future_ts = simple_df.make_future(future_steps=7, tail_steps=100)
+ future_ts = simple_tsdf.make_future(future_steps=7, tail_steps=100)
with pytest.raises(ValueError, match="Model is not fitted"):
_ = model.forecast(future_ts, prediction_size=7)
-def test_deadline_model_predict_fail_not_fitted(simple_df):
+def test_deadline_model_predict_fail_not_fitted(simple_tsdf):
model = DeadlineMovingAverageModel(window=1000)
with pytest.raises(ValueError, match="Model is not fitted"):
- _ = model.predict(simple_df, prediction_size=7)
+ _ = model.predict(simple_tsdf, prediction_size=7)
-def test_seasonal_moving_average_forecast_correct(simple_df):
+def test_seasonal_moving_average_forecast_correct(simple_tsdf):
model = SeasonalMovingAverageModel(window=3, seasonality=7)
- model.fit(simple_df)
- future_ts = simple_df.make_future(future_steps=7, tail_steps=model.context_size)
+ model.fit(simple_tsdf)
+ future_ts = simple_tsdf.make_future(future_steps=7, tail_steps=model.context_size)
res = model.forecast(future_ts, prediction_size=7)
res = res.to_pandas(flatten=True)[["target", "segment", "timestamp"]]
@@ -287,10 +287,10 @@ def test_seasonal_moving_average_forecast_correct(simple_df):
pd.testing.assert_frame_equal(res, answer)
-def test_naive_forecast_correct(simple_df):
+def test_naive_forecast_correct(simple_tsdf):
model = NaiveModel(lag=3)
- model.fit(simple_df)
- future_ts = simple_df.make_future(future_steps=7, tail_steps=model.context_size)
+ model.fit(simple_tsdf)
+ future_ts = simple_tsdf.make_future(future_steps=7, tail_steps=model.context_size)
res = model.forecast(future_ts, prediction_size=7)
res = res.to_pandas(flatten=True)[["target", "segment", "timestamp"]]
@@ -311,10 +311,10 @@ def test_naive_forecast_correct(simple_df):
pd.testing.assert_frame_equal(res, answer)
-def test_moving_average_forecast_correct(simple_df):
+def test_moving_average_forecast_correct(simple_tsdf):
model = MovingAverageModel(window=5)
- model.fit(simple_df)
- future_ts = simple_df.make_future(future_steps=7, tail_steps=model.context_size)
+ model.fit(simple_tsdf)
+ future_ts = simple_tsdf.make_future(future_steps=7, tail_steps=model.context_size)
res = model.forecast(future_ts, prediction_size=7)
res = res.to_pandas(flatten=True)[["target", "segment", "timestamp"]]
@@ -411,10 +411,10 @@ def test_deadline_moving_average_forecast_correct(df):
pd.testing.assert_frame_equal(res, answer)
-def test_seasonal_moving_average_predict_correct(simple_df):
+def test_seasonal_moving_average_predict_correct(simple_tsdf):
model = SeasonalMovingAverageModel(window=2, seasonality=2)
- model.fit(simple_df)
- res = model.predict(simple_df, prediction_size=7)
+ model.fit(simple_tsdf)
+ res = model.predict(simple_tsdf, prediction_size=7)
res = res.to_pandas(flatten=True)[["target", "segment", "timestamp"]]
df1 = pd.DataFrame()
@@ -434,10 +434,10 @@ def test_seasonal_moving_average_predict_correct(simple_df):
pd.testing.assert_frame_equal(res, answer)
-def test_naive_predict_correct(simple_df):
+def test_naive_predict_correct(simple_tsdf):
model = NaiveModel(lag=3)
- model.fit(simple_df)
- res = model.predict(simple_df, prediction_size=7)
+ model.fit(simple_tsdf)
+ res = model.predict(simple_tsdf, prediction_size=7)
res = res.to_pandas(flatten=True)[["target", "segment", "timestamp"]]
df1 = pd.DataFrame()
@@ -457,10 +457,10 @@ def test_naive_predict_correct(simple_df):
pd.testing.assert_frame_equal(res, answer)
-def test_moving_average_predict_correct(simple_df):
+def test_moving_average_predict_correct(simple_tsdf):
model = MovingAverageModel(window=5)
- model.fit(simple_df)
- res = model.predict(simple_df, prediction_size=7)
+ model.fit(simple_tsdf)
+ res = model.predict(simple_tsdf, prediction_size=7)
res = res.to_pandas(flatten=True)[["target", "segment", "timestamp"]]
df1 = pd.DataFrame()
@@ -801,13 +801,13 @@ def test_sma_model_predict_components_sum_up_to_target(example_tsds, method_name
(("forecast", [[44, 4], [45, 6], [44, 4]]), ("predict", [[44, 4], [45, 6], [46, 8]])),
)
def test_sma_model_predict_components_correct(
- simple_df, method_name, expected_values, window=1, seasonality=2, horizon=3
+ simple_tsdf, method_name, expected_values, window=1, seasonality=2, horizon=3
):
"""Testing that correct lag used as a component."""
model = SeasonalMovingAverageModel(window=window, seasonality=seasonality)
- model.fit(simple_df)
+ model.fit(simple_tsdf)
to_call = getattr(model, method_name)
- forecast = to_call(ts=simple_df, prediction_size=horizon, return_components=True)
+ forecast = to_call(ts=simple_tsdf, prediction_size=horizon, return_components=True)
target_components_df = forecast.get_target_components()
np.testing.assert_allclose(target_components_df.values, expected_values)
diff --git a/tests/test_models/test_tbats.py b/tests/test_models/test_tbats.py
index e5ab17157..108479a08 100644
--- a/tests/test_models/test_tbats.py
+++ b/tests/test_models/test_tbats.py
@@ -15,6 +15,7 @@
from tests.test_models.test_linear_model import linear_segments_by_parameters
from tests.test_models.utils import assert_model_equals_loaded_original
from tests.test_models.utils import assert_prediction_components_are_present
+from tests.utils import convert_ts_to_int_timestamp
@pytest.fixture()
@@ -26,7 +27,6 @@ def linear_segments_ts_unique(random_seed):
@pytest.fixture()
def sinusoid_ts():
- horizon = 14
periods = 100
sinusoid_ts_1 = pd.DataFrame(
{
@@ -45,7 +45,12 @@ def sinusoid_ts():
df = pd.concat((sinusoid_ts_1, sinusoid_ts_2))
df = TSDataset.to_dataset(df)
ts = TSDataset(df, freq="D")
- return ts.train_test_split(test_size=horizon)
+ return ts
+
+
+@pytest.fixture()
+def sinusoid_ts_int_timestamp(sinusoid_ts):
+ return convert_ts_to_int_timestamp(ts=sinusoid_ts, shift=10)
@pytest.fixture()
@@ -71,6 +76,15 @@ def periodic_dfs():
return df.iloc[:40], df.iloc[40:]
+@pytest.fixture()
+def periodic_dfs_int_timestamp(periodic_dfs):
+ shift = 10
+ train_df, test_df = periodic_dfs
+ train_df["timestamp"] = np.arange(len(train_df)) + shift
+ test_df["timestamp"] = np.arange(len(test_df)) + len(train_df) + shift
+ return train_df, test_df
+
+
@pytest.fixture()
def periodic_ts(periodic_dfs):
horizon = 10
@@ -147,14 +161,17 @@ def test_not_fitted(model, method, linear_segments_ts_unique):
method_to_call(ts=Mock())
+@pytest.mark.parametrize("ts_name", ["sinusoid_ts", "sinusoid_ts_int_timestamp"])
@pytest.mark.parametrize("model", [TBATSModel(), BATSModel()])
@pytest.mark.parametrize("method, use_future", (("predict", False), ("forecast", True)))
-def test_dummy(model, method, use_future, sinusoid_ts):
- train, test = sinusoid_ts
+def test_dummy(ts_name, model, method, use_future, request):
+ horizon = 14
+ ts = request.getfixturevalue(ts_name)
+ train, test = ts.train_test_split(test_size=horizon)
model.fit(train)
if use_future:
- pred_ts = train.make_future(14)
+ pred_ts = train.make_future(horizon)
y_true = test
else:
pred_ts = deepcopy(train)
@@ -168,14 +185,16 @@ def test_dummy(model, method, use_future, sinusoid_ts):
assert value_metric < 0.33
+@pytest.mark.parametrize("ts_name", ["example_tsds", "example_tsds_int_timestamp"])
@pytest.mark.parametrize("model", [TBATSModel(), BATSModel()])
@pytest.mark.parametrize("method, use_future", (("predict", False), ("forecast", True)))
-def test_prediction_interval(model, method, use_future, example_tsds):
- model.fit(example_tsds)
+def test_prediction_interval(ts_name, model, method, use_future, request):
+ ts = request.getfixturevalue(ts_name)
+ model.fit(ts)
if use_future:
- pred_ts = example_tsds.make_future(3)
+ pred_ts = ts.make_future(3)
else:
- pred_ts = deepcopy(example_tsds)
+ pred_ts = deepcopy(ts)
method_to_call = getattr(model, method)
forecast = method_to_call(ts=pred_ts, prediction_interval=True, quantiles=[0.025, 0.975])
@@ -375,6 +394,7 @@ def test_arma_with_seasonal_components_not_fitted(periodic_dfs, estimator, metho
@pytest.mark.filterwarnings("ignore:.*not fitted.*")
+@pytest.mark.parametrize("dfs_name", ["periodic_dfs", "periodic_dfs_int_timestamp"])
@pytest.mark.parametrize(
"estimator",
(
@@ -407,8 +427,8 @@ def test_arma_with_seasonal_components_not_fitted(periodic_dfs, estimator, metho
),
)
@pytest.mark.parametrize("method,use_future", (("predict_components", False), ("forecast_components", True)))
-def test_forecast_decompose_sum_up_to_target(periodic_dfs, estimator, params, method, use_future):
- train, test = periodic_dfs
+def test_forecast_decompose_sum_up_to_target(dfs_name, estimator, params, method, use_future, request):
+ train, test = request.getfixturevalue(dfs_name)
model = _TBATSAdapter(model=estimator(**params))
model.fit(train, [])
diff --git a/tests/test_models/test_utils.py b/tests/test_models/test_utils.py
index 9277dd8c7..7f1fa0121 100644
--- a/tests/test_models/test_utils.py
+++ b/tests/test_models/test_utils.py
@@ -1,63 +1,9 @@
import pandas as pd
import pytest
-from etna.models.utils import determine_freq
-from etna.models.utils import determine_num_steps
from etna.models.utils import select_observations
-@pytest.mark.parametrize(
- "start_timestamp, end_timestamp, freq, answer",
- [
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02"), "D", 1),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-11"), "D", 10),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), "D", 0),
- (pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-19"), "W-SUN", 2),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-15"), pd.offsets.Week(), 2),
- (pd.Timestamp("2020-01-31"), pd.Timestamp("2021-02-28"), "M", 13),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2021-06-01"), "MS", 17),
- ],
-)
-def test_determine_num_steps_ok(start_timestamp, end_timestamp, freq, answer):
- result = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
- assert result == answer
-
-
-@pytest.mark.parametrize(
- "start_timestamp, end_timestamp, freq",
- [
- (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-01-01"), "D"),
- ],
-)
-def test_determine_num_steps_fail_wrong_order(start_timestamp, end_timestamp, freq):
- with pytest.raises(ValueError, match="Start train timestamp should be less or equal than end timestamp"):
- _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
-
-
-@pytest.mark.parametrize(
- "start_timestamp, end_timestamp, freq",
- [
- (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-06-01"), "M"),
- (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-06-01"), "MS"),
- ],
-)
-def test_determine_num_steps_fail_wrong_start(start_timestamp, end_timestamp, freq):
- with pytest.raises(ValueError, match="Start timestamp isn't correct according to given frequency"):
- _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
-
-
-@pytest.mark.parametrize(
- "start_timestamp, end_timestamp, freq",
- [
- (pd.Timestamp("2020-01-31"), pd.Timestamp("2020-06-05"), "M"),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-06-05"), "MS"),
- ],
-)
-def test_determine_num_steps_fail_wrong_end(start_timestamp, end_timestamp, freq):
- with pytest.raises(ValueError, match="End timestamp isn't reachable with freq"):
- _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
-
-
@pytest.fixture()
def df_without_timestamp():
df = pd.DataFrame({"target": list(range(5))})
@@ -65,38 +11,42 @@ def df_without_timestamp():
@pytest.mark.parametrize(
- "timestamps",
- (
- pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])),
- pd.to_datetime(pd.Series(["2020-02-01"])),
- ),
+ "timestamps, freq, start, end, periods",
+ [
+ (pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])), "D", "2020-02-01", None, 5),
+ (pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])), "D", "2020-02-01", "2020-02-05", None),
+ (pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])), "D", None, "2020-02-05", 5),
+ (pd.to_datetime(pd.Series(["2020-02-01"])), "D", "2020-02-01", None, 5),
+ (pd.Series([6, 7]), None, 5, None, 5),
+ (pd.Series([6, 7]), None, 5, 9, None),
+ (pd.Series([6, 7]), None, None, 9, 5),
+ (pd.Series([6]), None, 5, None, 5),
+ ],
)
-def test_select_observations_without_timestamp(df_without_timestamp, timestamps):
+def test_select_observations(timestamps, freq, start, end, periods, df_without_timestamp):
selected_df = select_observations(
- df=df_without_timestamp, timestamps=timestamps, freq="D", start="2020-02-01", periods=5
+ df=df_without_timestamp, timestamps=timestamps, freq=freq, start=start, end=end, periods=periods
)
assert len(selected_df) == len(timestamps)
@pytest.mark.parametrize(
- "timestamps,answer",
- (
- (pd.date_range(start="2020-01-01", periods=3, freq="M"), "M"),
- (pd.date_range(start="2020-01-01", periods=3, freq="W"), "W-SUN"),
- (pd.date_range(start="2020-01-01", periods=3, freq="D"), "D"),
- ),
-)
-def test_determine_freq(timestamps, answer):
- assert determine_freq(timestamps=timestamps) == answer
-
-
-@pytest.mark.parametrize(
- "timestamps",
- (
- pd.to_datetime(pd.Series(["2020-02-01", "2020-02-15", "2021-02-15"])),
- pd.to_datetime(pd.Series(["2020-02-15", "2020-01-22", "2020-01-23"])),
- ),
+ "timestamps, freq, start, end, periods",
+ [
+ (pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])), "D", None, None, None),
+ (pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])), "D", "2020-02-01", None, None),
+ (pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])), "D", None, "2020-02-05", None),
+ (pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])), "D", None, None, 5),
+ (pd.to_datetime(pd.Series(["2020-02-01", "2020-02-03"])), "D", "2020-02-01", "2020-02-05", 5),
+ (pd.Series([6, 7]), None, None, None, None),
+ (pd.Series([6, 7]), None, 5, None, None),
+ (pd.Series([6, 7]), None, None, 9, None),
+ (pd.Series([6, 7]), None, None, None, 5),
+ (pd.Series([6, 7]), None, 5, 9, 5),
+ ],
)
-def test_determine_freq(timestamps):
- with pytest.raises(ValueError, match="Can't determine frequency of a given dataframe"):
- _ = determine_freq(timestamps=timestamps)
+def test_select_observations_fail(timestamps, freq, start, end, periods, df_without_timestamp):
+ with pytest.raises(ValueError, match="Of the three parameters: start, end, periods, exactly two must be specified"):
+ _ = select_observations(
+ df=df_without_timestamp, timestamps=timestamps, freq=freq, start=start, end=end, periods=periods
+ )
diff --git a/tests/test_pipeline/test_autoregressive_pipeline.py b/tests/test_pipeline/test_autoregressive_pipeline.py
index 9e8036670..f88aa3277 100644
--- a/tests/test_pipeline/test_autoregressive_pipeline.py
+++ b/tests/test_pipeline/test_autoregressive_pipeline.py
@@ -28,6 +28,7 @@
from etna.pipeline import AutoRegressivePipeline
from etna.transforms import AddConstTransform
from etna.transforms import DateFlagsTransform
+from etna.transforms import FourierTransform
from etna.transforms import LagTransform
from etna.transforms import LinearTrendTransform
from tests.test_pipeline.utils import assert_pipeline_equals_loaded_original
@@ -35,6 +36,7 @@
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts_with_prediction_intervals
from tests.test_pipeline.utils import assert_pipeline_forecasts_without_self_ts
+from tests.test_pipeline.utils import assert_pipeline_predicts
DEFAULT_METRICS = [MAE(mode=MetricAggregationMode.per_segment)]
@@ -261,44 +263,6 @@ def test_get_historical_forecasts_sanity(step_ts: TSDataset):
assert np.all(forecast_df == expected_forecast_df)
-@pytest.mark.parametrize(
- "model, transforms",
- [
- (
- CatBoostMultiSegmentModel(iterations=100),
- [DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(7, 15)))],
- ),
- (
- LinearPerSegmentModel(),
- [DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(7, 15)))],
- ),
- (SeasonalMovingAverageModel(window=2, seasonality=7), []),
- (SARIMAXModel(), []),
- (ProphetModel(), []),
- ],
-)
-def test_predict_format(model, transforms, example_tsds):
- ts = example_tsds
- pipeline = AutoRegressivePipeline(model=model, transforms=transforms, horizon=7)
- pipeline.fit(ts)
-
- start_idx = 50
- end_idx = 70
- start_timestamp = ts.index[start_idx]
- end_timestamp = ts.index[end_idx]
- num_points = end_idx - start_idx + 1
-
- # create a separate TSDataset with slice of original timestamps
- predict_ts = deepcopy(ts)
- predict_ts.df = predict_ts.df.iloc[5 : end_idx + 5]
-
- result_ts = pipeline.predict(ts=predict_ts, start_timestamp=start_timestamp, end_timestamp=end_timestamp)
- result_df = result_ts.to_pandas(flatten=True)
-
- assert not np.any(result_df["target"].isna())
- assert len(result_df) == len(example_tsds.segments) * num_points
-
-
def test_predict_values(example_tsds):
original_ts = deepcopy(example_tsds)
@@ -345,69 +309,150 @@ def test_forecast_raise_error_if_no_ts(example_tsds):
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "example_tsds",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
(
+ "example_tsds",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
- (SeasonalMovingAverageModel(window=2, seasonality=7), []),
- (SARIMAXModel(), []),
- (ProphetModel(), []),
+ ("example_tsds", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds", SARIMAXModel(), []),
+ ("example_tsds", ProphetModel(), []),
+ (
+ "example_tsds_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds_int_timestamp", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecasts_without_self_ts(model, transforms, example_tsds):
+def test_forecasts_without_self_ts(ts_name, model, transforms, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 3
pipeline = AutoRegressivePipeline(model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_without_self_ts(pipeline=pipeline, ts=example_tsds, horizon=horizon)
+ assert_pipeline_forecasts_without_self_ts(pipeline=pipeline, ts=ts, horizon=horizon)
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "example_tsds",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
(
+ "example_tsds",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
- (SeasonalMovingAverageModel(window=2, seasonality=7), []),
- (SARIMAXModel(), []),
- (ProphetModel(), []),
+ ("example_tsds", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds", SARIMAXModel(), []),
+ ("example_tsds", ProphetModel(), []),
+ (
+ "example_tsds_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds_int_timestamp", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecast_given_ts(model, transforms, example_tsds):
+def test_forecast_given_ts(ts_name, model, transforms, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 3
pipeline = AutoRegressivePipeline(model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_given_ts(pipeline=pipeline, ts=example_tsds, horizon=horizon)
+ assert_pipeline_forecasts_given_ts(pipeline=pipeline, ts=ts, horizon=horizon)
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "example_tsds",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
(
+ "example_tsds",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
- (SeasonalMovingAverageModel(window=2, seasonality=7), []),
- (SARIMAXModel(), []),
- (ProphetModel(), []),
+ ("example_tsds", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds", SARIMAXModel(), []),
+ ("example_tsds", ProphetModel(), []),
+ (
+ "example_tsds_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds_int_timestamp", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecast_given_ts_with_prediction_interval(model, transforms, example_tsds):
+def test_forecast_given_ts_with_prediction_interval(ts_name, model, transforms, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 3
pipeline = AutoRegressivePipeline(model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_given_ts_with_prediction_intervals(pipeline=pipeline, ts=example_tsds, horizon=horizon)
+ assert_pipeline_forecasts_given_ts_with_prediction_intervals(pipeline=pipeline, ts=ts, horizon=horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, model, transforms",
+ [
+ (
+ "example_tsds",
+ CatBoostMultiSegmentModel(iterations=100),
+ [DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds",
+ LinearPerSegmentModel(),
+ [DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds", SARIMAXModel(), []),
+ ("example_tsds", ProphetModel(), []),
+ (
+ "example_tsds_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds_int_timestamp", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds_int_timestamp", SARIMAXModel(), []),
+ ],
+)
+def test_predict(ts_name, model, transforms, request):
+ ts = request.getfixturevalue(ts_name)
+ pipeline = AutoRegressivePipeline(model=model, transforms=transforms, horizon=7)
+ assert_pipeline_predicts(pipeline=pipeline, ts=ts, start_idx=50, end_idx=70)
@pytest.mark.parametrize(
diff --git a/tests/test_pipeline/test_base.py b/tests/test_pipeline/test_base.py
index 2d770ab45..535eb9094 100644
--- a/tests/test_pipeline/test_base.py
+++ b/tests/test_pipeline/test_base.py
@@ -10,44 +10,509 @@
from etna.datasets import TSDataset
from etna.distributions import BaseDistribution
+from etna.pipeline import FoldMask
from etna.pipeline.base import BasePipeline
@pytest.mark.parametrize(
- "ts_name, expected_start_timestamp, expected_end_timestamp",
+ "first_train_timestamp, last_train_timestamp, target_timestamps",
[
- ("example_tsds", pd.Timestamp("2020-01-01"), pd.Timestamp("2020-04-09")),
- ("ts_with_different_series_length", pd.Timestamp("2020-01-01 4:00"), pd.Timestamp("2020-02-01")),
+ (None, pd.Timestamp("2020-01-05"), [pd.Timestamp("2020-01-06")]),
+ (None, pd.Timestamp("2020-01-05"), ["2020-01-06"]),
+ (None, "2020-01-05", [pd.Timestamp("2020-01-06")]),
+ (None, "2020-01-05", ["2020-01-06"]),
+ (None, "2020-01-05", ["2020-01-06", "2020-01-07"]),
+ (None, "2020-01-05", ["2020-01-07", "2020-01-06"]),
+ (None, 5, [6]),
+ (None, 5, [6, 7]),
+ (None, 5, [7, 6]),
+ ("2020-01-01", "2020-01-01", ["2020-01-06"]),
+ ("2020-01-01", "2020-01-05", ["2020-01-06"]),
+ ("2020-01-01", "2020-01-05", ["2020-01-06", "2020-01-07"]),
+ (1, 1, [6]),
+ (1, 5, [6]),
+ (1, 5, [6, 7]),
],
)
-def test_make_predict_timestamps_calculate_values(ts_name, expected_start_timestamp, expected_end_timestamp, request):
+def test_fold_mask_init_ok(first_train_timestamp, last_train_timestamp, target_timestamps):
+ _ = FoldMask(
+ first_train_timestamp=first_train_timestamp,
+ last_train_timestamp=last_train_timestamp,
+ target_timestamps=target_timestamps,
+ )
+
+
+@pytest.mark.parametrize(
+ "first_train_timestamp, last_train_timestamp, target_timestamps",
+ [
+ (pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-01"), [pd.Timestamp("2020-01-02")]),
+ (5, 1, [2]),
+ ("2020-01-05", "2020-01-01", ["2020-01-02"]),
+ ],
+)
+def test_fold_mask_init_fail_wrong_order_first_last_training_timestamps(
+ first_train_timestamp, last_train_timestamp, target_timestamps
+):
+ with pytest.raises(ValueError, match="Last train timestamp should be not sooner than first train timestamp"):
+ _ = FoldMask(
+ first_train_timestamp=first_train_timestamp,
+ last_train_timestamp=last_train_timestamp,
+ target_timestamps=target_timestamps,
+ )
+
+
+@pytest.mark.parametrize(
+ "first_train_timestamp, last_train_timestamp, target_timestamps",
+ [
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-05"), []),
+ (None, pd.Timestamp("2020-01-05"), []),
+ (1, 5, []),
+ ("2020-01-01", "2020-01-05", []),
+ ],
+)
+def test_fold_mask_init_fail_wrong_fail_empty_target_timestamps(
+ first_train_timestamp, last_train_timestamp, target_timestamps
+):
+ with pytest.raises(ValueError, match="Target timestamps shouldn't be empty"):
+ _ = FoldMask(
+ first_train_timestamp=first_train_timestamp,
+ last_train_timestamp=last_train_timestamp,
+ target_timestamps=target_timestamps,
+ )
+
+
+@pytest.mark.parametrize(
+ "first_train_timestamp, last_train_timestamp, target_timestamps",
+ [
+ (
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-05"),
+ [pd.Timestamp("2020-01-06"), pd.Timestamp("2020-01-06")],
+ ),
+ (None, pd.Timestamp("2020-01-05"), [pd.Timestamp("2020-01-06"), pd.Timestamp("2020-01-06")]),
+ (1, 5, [6, 6]),
+ ("2020-01-01", "2020-01-05", ["2020-01-06", "2020-01-06"]),
+ ],
+)
+def test_fold_mask_init_fail_wrong_fail_duplicated_target_timestamps(
+ first_train_timestamp, last_train_timestamp, target_timestamps
+):
+ with pytest.raises(ValueError, match="Target timestamps shouldn't contain duplicates"):
+ _ = FoldMask(
+ first_train_timestamp=first_train_timestamp,
+ last_train_timestamp=last_train_timestamp,
+ target_timestamps=target_timestamps,
+ )
+
+
+@pytest.mark.parametrize(
+ "first_train_timestamp, last_train_timestamp, target_timestamps",
+ [
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-05"), [pd.Timestamp("2020-01-02")]),
+ (None, pd.Timestamp("2020-01-05"), [pd.Timestamp("2020-01-02")]),
+ (1, 5, [2]),
+ ("2020-01-01", "2020-01-05", ["2020-01-02"]),
+ ],
+)
+def test_fold_mask_init_fail_target_timestamps_before_train_end(
+ first_train_timestamp, last_train_timestamp, target_timestamps
+):
+ with pytest.raises(ValueError, match="Target timestamps should be strictly later then last train timestamp"):
+ _ = FoldMask(
+ first_train_timestamp=first_train_timestamp,
+ last_train_timestamp=last_train_timestamp,
+ target_timestamps=target_timestamps,
+ )
+
+
+@pytest.mark.parametrize(
+ "first_train_timestamp, last_train_timestamp, target_timestamps",
+ [
+ (None, 5, [pd.Timestamp("2020-01-06")]),
+ (None, 5, ["2020-01-06"]),
+ (None, "2020-01-05", [6]),
+ (None, 5, [6, "2020-01-07"]),
+ (None, "2020-01-05", ["2020-01-06", 7]),
+ (1, 5, ["2020-01-06"]),
+ (1, "2020-01-05", [6]),
+ (1, "2020-01-05", ["2020-01-06"]),
+ ("2020-01-01", 5, [6]),
+ ("2020-01-01", "2020-01-05", [6]),
+ ("2020-01-01", 5, ["2020-01-06"]),
+ (1, 5, [6, "2020-01-07"]),
+ ("2020-01-01", "2020-01-05", ["2020-01-06", 7]),
+ ],
+)
+def test_fold_mask_init_fail_mismatched_types(first_train_timestamp, last_train_timestamp, target_timestamps):
+ with pytest.raises(ValueError, match="All timestamps should be one of two possible types: pd.Timestamp or int"):
+ _ = FoldMask(
+ first_train_timestamp=first_train_timestamp,
+ last_train_timestamp=last_train_timestamp,
+ target_timestamps=target_timestamps,
+ )
+
+
+@pytest.mark.parametrize(
+ "ts_name, horizon, fold_mask",
+ [
+ (
+ "example_tsds",
+ 1,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]),
+ ),
+ (
+ "example_tsds",
+ 1,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]
+ ),
+ ),
+ (
+ "example_tsds",
+ 1,
+ FoldMask(
+ first_train_timestamp="2020-01-03", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]
+ ),
+ ),
+ (
+ "example_tsds",
+ 10,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2020-01-05", target_timestamps=["2020-01-10"]),
+ ),
+ (
+ "example_tsds",
+ 10,
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp="2020-01-05",
+ target_timestamps=["2020-01-10", "2020-01-12"],
+ ),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=15, target_timestamps=[16]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=10, last_train_timestamp=15, target_timestamps=[16]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=12, last_train_timestamp=15, target_timestamps=[16]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 10,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=15, target_timestamps=[20]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 10,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=15, target_timestamps=[20, 22]),
+ ),
+ ],
+)
+def test_fold_mask_validate_on_dataset_ok(ts_name, fold_mask, horizon, request):
+ ts = request.getfixturevalue(ts_name)
+ fold_mask.validate_on_dataset(ts=ts, horizon=horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, horizon, fold_mask",
+ [
+ (
+ "example_tsds",
+ 1,
+ FoldMask(
+ first_train_timestamp="2019-01-01", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]
+ ),
+ ),
+ (
+ "example_tsds",
+ 1,
+ FoldMask(
+ first_train_timestamp="2021-01-01", last_train_timestamp="2021-01-05", target_timestamps=["2021-01-06"]
+ ),
+ ),
+ (
+ "example_tsds",
+ 1,
+ FoldMask(
+ first_train_timestamp="2020-01-01 01:00",
+ last_train_timestamp="2020-01-05",
+ target_timestamps=["2020-01-06"],
+ ),
+ ),
+ (
+ "example_tsds",
+ 1,
+ FoldMask(first_train_timestamp=5, last_train_timestamp=15, target_timestamps=[16]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=5, last_train_timestamp=15, target_timestamps=[16]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=210, last_train_timestamp=215, target_timestamps=[216]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]
+ ),
+ ),
+ ],
+)
+def test_fold_mask_validate_on_dataset_fail_not_present_first_train_timestamp(ts_name, fold_mask, horizon, request):
ts = request.getfixturevalue(ts_name)
+ with pytest.raises(ValueError, match="First train timestamp isn't present in a given dataset"):
+ fold_mask.validate_on_dataset(ts=ts, horizon=horizon)
- start_timestamp, end_timestamp = BasePipeline._make_predict_timestamps(ts=ts)
+
+@pytest.mark.parametrize(
+ "ts_name, horizon, fold_mask",
+ [
+ (
+ "example_tsds",
+ 1,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2021-01-05", target_timestamps=["2021-01-06"]
+ ),
+ ),
+ (
+ "example_tsds",
+ 1,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2021-01-05", target_timestamps=["2021-01-06"]),
+ ),
+ (
+ "example_tsds",
+ 1,
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp="2020-01-05 01:00",
+ target_timestamps=["2020-01-06"],
+ ),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=10, last_train_timestamp=215, target_timestamps=[216]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=215, target_timestamps=[216]),
+ ),
+ ],
+)
+def test_fold_mask_validate_on_dataset_fail_not_present_last_train_timestamp(ts_name, fold_mask, horizon, request):
+ ts = request.getfixturevalue(ts_name)
+ with pytest.raises(ValueError, match="Last train timestamp isn't present in a given dataset"):
+ fold_mask.validate_on_dataset(ts=ts, horizon=horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, horizon, fold_mask",
+ [
+ (
+ "example_tsds",
+ 1,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2020-01-05", target_timestamps=["2021-01-06"]
+ ),
+ ),
+ (
+ "example_tsds",
+ 1,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2020-01-05", target_timestamps=["2021-01-06"]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=10, last_train_timestamp=15, target_timestamps=[216]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=15, target_timestamps=[216]),
+ ),
+ ],
+)
+def test_fold_mask_validate_on_dataset_fail_not_present_some_target_timestamps(ts_name, fold_mask, horizon, request):
+ ts = request.getfixturevalue(ts_name)
+ with pytest.raises(ValueError, match="Some target timestamps aren't present in a given dataset"):
+ fold_mask.validate_on_dataset(ts=ts, horizon=horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, horizon, fold_mask",
+ [
+ (
+ "ts_with_nans_in_tails",
+ 1,
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp="2020-01-31 22:00",
+ target_timestamps=["2020-01-31 23:00"],
+ ),
+ ),
+ ],
+)
+def test_fold_mask_validate_on_dataset_fail_not_enough_future(ts_name, fold_mask, horizon, request):
+ ts = request.getfixturevalue(ts_name)
+ with pytest.raises(ValueError, match="Last train timestamp should be not later than"):
+ fold_mask.validate_on_dataset(ts=ts, horizon=horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, horizon, fold_mask",
+ [
+ (
+ "example_tsds",
+ 3,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-10"]
+ ),
+ ),
+ (
+ "example_tsds",
+ 3,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2020-01-05", target_timestamps=["2020-01-10"]),
+ ),
+ (
+ "example_tsds",
+ 3,
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp="2020-01-05",
+ target_timestamps=["2020-01-06", "2020-01-10"],
+ ),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 3,
+ FoldMask(first_train_timestamp=10, last_train_timestamp=15, target_timestamps=[20]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 3,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=15, target_timestamps=[20]),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 3,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=15, target_timestamps=[16, 20]),
+ ),
+ ],
+)
+def test_fold_mask_validate_on_dataset_fail_not_enough_horizon(ts_name, fold_mask, horizon, request):
+ ts = request.getfixturevalue(ts_name)
+ with pytest.raises(ValueError, match="Last target timestamp should be not later than"):
+ fold_mask.validate_on_dataset(ts=ts, horizon=horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, start_timestamp, end_timestamp, expected_start_timestamp, expected_end_timestamp",
+ [
+ ("example_tsds", None, None, pd.Timestamp("2020-01-01"), pd.Timestamp("2020-04-09")),
+ ("example_tsds", pd.Timestamp("2020-01-05"), None, pd.Timestamp("2020-01-05"), pd.Timestamp("2020-04-09")),
+ ("example_tsds", "2020-01-05", None, pd.Timestamp("2020-01-05"), pd.Timestamp("2020-04-09")),
+ ("example_tsds", None, pd.Timestamp("2020-04-05"), pd.Timestamp("2020-01-01"), pd.Timestamp("2020-04-05")),
+ ("example_tsds", None, "2020-04-05", pd.Timestamp("2020-01-01"), pd.Timestamp("2020-04-05")),
+ (
+ "example_tsds",
+ pd.Timestamp("2020-01-05"),
+ pd.Timestamp("2020-04-05"),
+ pd.Timestamp("2020-01-05"),
+ pd.Timestamp("2020-04-05"),
+ ),
+ ("example_tsds", "2020-01-05", "2020-04-05", pd.Timestamp("2020-01-05"), pd.Timestamp("2020-04-05")),
+ (
+ "example_tsds",
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-04-09"),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-04-09"),
+ ),
+ ("ts_with_different_series_length", None, None, pd.Timestamp("2020-01-01 4:00"), pd.Timestamp("2020-02-01")),
+ ("example_tsds_int_timestamp", None, None, 10, 109),
+ ("example_tsds_int_timestamp", 15, None, 15, 109),
+ ("example_tsds_int_timestamp", None, 100, 10, 100),
+ ("example_tsds_int_timestamp", 15, 100, 15, 100),
+ ],
+)
+def test_make_predict_timestamps_ok(
+ ts_name, start_timestamp, end_timestamp, expected_start_timestamp, expected_end_timestamp, request
+):
+ ts = request.getfixturevalue(ts_name)
+
+ start_timestamp, end_timestamp = BasePipeline._make_predict_timestamps(
+ ts=ts, start_timestamp=start_timestamp, end_timestamp=end_timestamp
+ )
assert start_timestamp == expected_start_timestamp
assert end_timestamp == expected_end_timestamp
-def test_make_predict_timestamps_fail_early_start(example_tsds):
- start_timestamp = example_tsds.index[0] - pd.DateOffset(days=5)
+@pytest.mark.parametrize(
+ "ts_name, start_timestamp",
+ [("example_tsds", 10), ("example_tsds_int_timestamp", pd.Timestamp("2020-01-01"))],
+)
+def test_make_predict_timestamps_fail_wrong_start_timestamp_type(ts_name, start_timestamp, request):
+ ts = request.getfixturevalue(ts_name)
+ with pytest.raises(ValueError, match="Parameter start_timestamp has incorrect type"):
+ _ = BasePipeline._make_predict_timestamps(ts=ts, start_timestamp=start_timestamp, end_timestamp=None)
+
+
+@pytest.mark.parametrize(
+ "ts_name, end_timestamp",
+ [("example_tsds", 10), ("example_tsds_int_timestamp", pd.Timestamp("2020-01-01"))],
+)
+def test_make_predict_timestamps_fail_wrong_start_timestamp_type(ts_name, end_timestamp, request):
+ ts = request.getfixturevalue(ts_name)
+ with pytest.raises(ValueError, match="Parameter end_timestamp has incorrect type"):
+ _ = BasePipeline._make_predict_timestamps(ts=ts, start_timestamp=None, end_timestamp=end_timestamp)
+
+
+@pytest.mark.parametrize(
+ "ts_name, start_timestamp",
+ [("example_tsds", pd.Timestamp("2019-01-01")), ("example_tsds", "2019-01-01"), ("example_tsds_int_timestamp", 8)],
+)
+def test_make_predict_timestamps_fail_early_start(ts_name, start_timestamp, request):
+ ts = request.getfixturevalue(ts_name)
with pytest.raises(ValueError, match="Value of start_timestamp is less than beginning of some segments"):
- _ = BasePipeline._make_predict_timestamps(ts=example_tsds, start_timestamp=start_timestamp)
+ _ = BasePipeline._make_predict_timestamps(ts=ts, start_timestamp=start_timestamp, end_timestamp=None)
-def test_make_predict_timestamps_fail_late_end(example_tsds):
- end_timestamp = example_tsds.index[-1] + pd.DateOffset(days=5)
+@pytest.mark.parametrize(
+ "ts_name, end_timestamp",
+ [("example_tsds", pd.Timestamp("2021-01-01")), ("example_tsds", "2021-01-01"), ("example_tsds_int_timestamp", 120)],
+)
+def test_make_predict_timestamps_fail_late_end(ts_name, end_timestamp, request):
+ ts = request.getfixturevalue(ts_name)
with pytest.raises(ValueError, match="Value of end_timestamp is more than ending of dataset"):
- _ = BasePipeline._make_predict_timestamps(ts=example_tsds, end_timestamp=end_timestamp)
+ _ = BasePipeline._make_predict_timestamps(ts=ts, start_timestamp=None, end_timestamp=end_timestamp)
-def test_make_predict_timestamps_fail_start_later_than_end(example_tsds):
- start_timestamp = example_tsds.index[2]
- end_timestamp = example_tsds.index[0]
+@pytest.mark.parametrize(
+ "ts_name, start_timestamp, end_timestamp",
+ [
+ ("example_tsds", pd.Timestamp("2020-01-10"), pd.Timestamp("2020-01-09")),
+ ("example_tsds", "2020-01-10", "2020-01-09"),
+ ("example_tsds_int_timestamp", 20, 19),
+ ],
+)
+def test_make_predict_timestamps_fail_start_later_than_end(ts_name, start_timestamp, end_timestamp, request):
+ ts = request.getfixturevalue(ts_name)
with pytest.raises(ValueError, match="Value of end_timestamp is less than start_timestamp"):
- _ = BasePipeline._make_predict_timestamps(
- ts=example_tsds, start_timestamp=start_timestamp, end_timestamp=end_timestamp
- )
+ _ = BasePipeline._make_predict_timestamps(ts=ts, start_timestamp=start_timestamp, end_timestamp=end_timestamp)
class DummyPipeline(BasePipeline):
@@ -77,9 +542,15 @@ def params_to_tune(self) -> Dict[str, BaseDistribution]:
[
(None, None),
(pd.Timestamp("2020-01-02"), None),
+ ("2020-01-02", None),
+ (10, None),
(None, pd.Timestamp("2020-02-01")),
+ (None, "2020-02-01"),
+ (None, 10),
(pd.Timestamp("2020-01-02"), pd.Timestamp("2020-02-01")),
(pd.Timestamp("2020-01-05"), pd.Timestamp("2020-02-03")),
+ ("2020-01-02", "2020-02-01"),
+ (12, 100),
],
)
def test_predict_calls_make_timestamps(start_timestamp, end_timestamp, example_tsds):
diff --git a/tests/test_pipeline/test_hierarchical_pipeline.py b/tests/test_pipeline/test_hierarchical_pipeline.py
index 88b9a74e3..88bfa4e43 100644
--- a/tests/test_pipeline/test_hierarchical_pipeline.py
+++ b/tests/test_pipeline/test_hierarchical_pipeline.py
@@ -15,10 +15,12 @@
from etna.models import LinearPerSegmentModel
from etna.models import NaiveModel
from etna.models import ProphetModel
+from etna.models import SARIMAXModel
from etna.pipeline.hierarchical_pipeline import HierarchicalPipeline
from etna.reconciliation import BottomUpReconciliator
from etna.reconciliation import TopDownReconciliator
from etna.transforms import DateFlagsTransform
+from etna.transforms import FourierTransform
from etna.transforms import LagTransform
from etna.transforms import LinearTrendTransform
from etna.transforms import MeanTransform
@@ -27,6 +29,7 @@
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts_with_prediction_intervals
from tests.test_pipeline.utils import assert_pipeline_forecasts_without_self_ts
+from tests.test_pipeline.utils import assert_pipeline_predicts
@pytest.mark.parametrize(
@@ -498,26 +501,39 @@ def test_save_load(model, transforms, reconciliator, product_level_constant_hier
),
)
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "product_level_constant_hierarchical_ts",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=[1])],
),
(
+ "product_level_constant_hierarchical_ts",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=[1])],
),
- (NaiveModel(), []),
- (ProphetModel(), []),
+ ("product_level_constant_hierarchical_ts", NaiveModel(), []),
+ ("product_level_constant_hierarchical_ts", ProphetModel(), []),
+ (
+ "product_level_constant_hierarchical_ts_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=[1])],
+ ),
+ (
+ "product_level_constant_hierarchical_ts_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=[1])],
+ ),
+ ("product_level_constant_hierarchical_ts_int_timestamp", NaiveModel(), []),
+ ("product_level_constant_hierarchical_ts_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecasts_without_self_ts(model, transforms, reconciliator, product_level_constant_hierarchical_ts):
+def test_forecasts_without_self_ts(ts_name, model, transforms, reconciliator, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 1
pipeline = HierarchicalPipeline(reconciliator=reconciliator, model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_without_self_ts(
- pipeline=pipeline, ts=product_level_constant_hierarchical_ts, horizon=horizon
- )
+ assert_pipeline_forecasts_without_self_ts(pipeline=pipeline, ts=ts, horizon=horizon)
@pytest.mark.parametrize(
@@ -530,24 +546,39 @@ def test_forecasts_without_self_ts(model, transforms, reconciliator, product_lev
),
)
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "product_level_constant_hierarchical_ts",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=[1])],
),
(
+ "product_level_constant_hierarchical_ts",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=[1])],
),
- (NaiveModel(), []),
- (ProphetModel(), []),
+ ("product_level_constant_hierarchical_ts", NaiveModel(), []),
+ ("product_level_constant_hierarchical_ts", ProphetModel(), []),
+ (
+ "product_level_constant_hierarchical_ts_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=[1])],
+ ),
+ (
+ "product_level_constant_hierarchical_ts_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=[1])],
+ ),
+ ("product_level_constant_hierarchical_ts_int_timestamp", NaiveModel(), []),
+ ("product_level_constant_hierarchical_ts_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecast_given_ts(model, transforms, reconciliator, product_level_constant_hierarchical_ts):
+def test_forecast_given_ts(ts_name, model, transforms, reconciliator, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 1
pipeline = HierarchicalPipeline(reconciliator=reconciliator, model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_given_ts(pipeline=pipeline, ts=product_level_constant_hierarchical_ts, horizon=horizon)
+ assert_pipeline_forecasts_given_ts(pipeline=pipeline, ts=ts, horizon=horizon)
@pytest.mark.parametrize(
@@ -560,28 +591,84 @@ def test_forecast_given_ts(model, transforms, reconciliator, product_level_const
),
)
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "product_level_constant_hierarchical_ts",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=[1])],
),
(
+ "product_level_constant_hierarchical_ts",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=[1])],
),
- (NaiveModel(), []),
- (ProphetModel(), []),
+ ("product_level_constant_hierarchical_ts", NaiveModel(), []),
+ ("product_level_constant_hierarchical_ts", ProphetModel(), []),
+ (
+ "product_level_constant_hierarchical_ts_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=[1])],
+ ),
+ (
+ "product_level_constant_hierarchical_ts_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=[1])],
+ ),
+ ("product_level_constant_hierarchical_ts_int_timestamp", NaiveModel(), []),
+ ("product_level_constant_hierarchical_ts_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecast_given_ts_with_prediction_interval(
- model, transforms, reconciliator, product_level_constant_hierarchical_ts
-):
+def test_forecast_given_ts_with_prediction_interval(ts_name, model, transforms, reconciliator, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 1
pipeline = HierarchicalPipeline(reconciliator=reconciliator, model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_given_ts_with_prediction_intervals(
- pipeline=pipeline, ts=product_level_constant_hierarchical_ts, horizon=horizon, n_folds=2
- )
+ assert_pipeline_forecasts_given_ts_with_prediction_intervals(pipeline=pipeline, ts=ts, horizon=horizon, n_folds=2)
+
+
+@pytest.mark.parametrize(
+ "reconciliator",
+ (
+ TopDownReconciliator(target_level="product", source_level="market", period=1, method="AHP"),
+ TopDownReconciliator(target_level="product", source_level="market", period=1, method="PHA"),
+ BottomUpReconciliator(target_level="market", source_level="product"),
+ BottomUpReconciliator(target_level="total", source_level="market"),
+ ),
+)
+@pytest.mark.parametrize(
+ "ts_name, model, transforms",
+ [
+ (
+ "product_level_constant_hierarchical_ts",
+ CatBoostMultiSegmentModel(iterations=100),
+ [DateFlagsTransform(), LagTransform(in_column="target", lags=[1])],
+ ),
+ (
+ "product_level_constant_hierarchical_ts",
+ LinearPerSegmentModel(),
+ [DateFlagsTransform(), LagTransform(in_column="target", lags=[1])],
+ ),
+ ("product_level_constant_hierarchical_ts", NaiveModel(), []),
+ ("product_level_constant_hierarchical_ts", ProphetModel(), []),
+ (
+ "product_level_constant_hierarchical_ts_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=[1])],
+ ),
+ (
+ "product_level_constant_hierarchical_ts_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=[1])],
+ ),
+ ("product_level_constant_hierarchical_ts_int_timestamp", NaiveModel(), []),
+ ("product_level_constant_hierarchical_ts_int_timestamp", SARIMAXModel(), []),
+ ],
+)
+def test_predict(ts_name, model, transforms, reconciliator, request):
+ ts = request.getfixturevalue(ts_name)
+ horizon = 1
+ pipeline = HierarchicalPipeline(reconciliator=reconciliator, model=model, transforms=transforms, horizon=horizon)
+ assert_pipeline_predicts(pipeline=pipeline, ts=ts, start_idx=1, end_idx=2)
@pytest.mark.parametrize(
diff --git a/tests/test_pipeline/test_mixins.py b/tests/test_pipeline/test_mixins.py
index d4ea952aa..23da104c7 100644
--- a/tests/test_pipeline/test_mixins.py
+++ b/tests/test_pipeline/test_mixins.py
@@ -18,8 +18,8 @@
from etna.pipeline.mixins import ModelPipelinePredictMixin
from etna.pipeline.mixins import SaveModelPipelineMixin
from etna.transforms import AddConstTransform
-from etna.transforms import DateFlagsTransform
from etna.transforms import FilterFeaturesTransform
+from etna.transforms import FourierTransform
def make_mixin(model=None, transforms=(), mock_recreate_ts=True, mock_determine_prediction_size=True):
@@ -36,26 +36,31 @@ def make_mixin(model=None, transforms=(), mock_recreate_ts=True, mock_determine_
return mixin
-@pytest.mark.parametrize("context_size", [0, 3])
@pytest.mark.parametrize(
- "start_timestamp, end_timestamp",
+ "ts_name, start_timestamp, end_timestamp, context_size, expected_start_timestamp",
[
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-10")),
- (pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-10")),
+ ("example_reg_tsds", pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), 0, pd.Timestamp("2020-01-01")),
+ ("example_reg_tsds", pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-10"), 0, pd.Timestamp("2020-01-05")),
+ ("example_reg_tsds", pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), 3, pd.Timestamp("2020-01-01")),
+ ("example_reg_tsds", pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-10"), 3, pd.Timestamp("2020-01-02")),
+ ("example_reg_tsds_int_timestamp", 10, 10, 0, 10),
+ ("example_reg_tsds_int_timestamp", 15, 20, 0, 15),
+ ("example_reg_tsds_int_timestamp", 10, 10, 3, 10),
+ ("example_reg_tsds_int_timestamp", 15, 20, 3, 12),
],
)
@pytest.mark.parametrize(
"transforms",
[
- [DateFlagsTransform()],
+ [FourierTransform(period=7, order=1)],
[FilterFeaturesTransform(exclude=["regressor_exog_weekend"])],
- [DateFlagsTransform(), FilterFeaturesTransform(exclude=["regressor_exog_weekend"])],
+ [FourierTransform(period=7, order=1), FilterFeaturesTransform(exclude=["regressor_exog_weekend"])],
],
)
-def test_predict_mixin_create_ts(context_size, start_timestamp, end_timestamp, transforms, example_reg_tsds):
- ts = example_reg_tsds
+def test_predict_mixin_create_ts(
+ ts_name, context_size, start_timestamp, end_timestamp, expected_start_timestamp, transforms, request
+):
+ ts = request.getfixturevalue(ts_name)
model = MagicMock()
model.context_size = context_size
mixin = make_mixin(transforms=transforms, model=model, mock_recreate_ts=False)
@@ -63,27 +68,30 @@ def test_predict_mixin_create_ts(context_size, start_timestamp, end_timestamp, t
ts.fit_transform(transforms)
created_ts = mixin._create_ts(ts=ts, start_timestamp=start_timestamp, end_timestamp=end_timestamp)
- expected_start_timestamp = max(example_reg_tsds.index[0], start_timestamp - pd.Timedelta(days=model.context_size))
assert created_ts.index[0] == expected_start_timestamp
assert created_ts.index[-1] == end_timestamp
assert created_ts.regressors == ts.regressors
- expected_df = ts.df[expected_start_timestamp:end_timestamp]
+ expected_df = ts.df.loc[expected_start_timestamp:end_timestamp]
pd.testing.assert_frame_equal(created_ts.df, expected_df, check_categorical=False)
@pytest.mark.parametrize(
- "start_timestamp, end_timestamp, expected_prediction_size",
+ "ts_name, start_timestamp, end_timestamp, expected_prediction_size",
[
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), 1),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02"), 2),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-10"), 10),
- (pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-10"), 6),
+ ("example_tsds", pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), 1),
+ ("example_tsds", pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02"), 2),
+ ("example_tsds", pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-10"), 10),
+ ("example_tsds", pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-10"), 6),
+ ("example_tsds_int_timestamp", 10, 10, 1),
+ ("example_tsds_int_timestamp", 10, 11, 2),
+ ("example_tsds_int_timestamp", 10, 19, 10),
+ ("example_tsds_int_timestamp", 15, 20, 6),
],
)
def test_predict_mixin_determine_prediction_size(
- start_timestamp, end_timestamp, expected_prediction_size, example_tsds
+ ts_name, start_timestamp, end_timestamp, expected_prediction_size, request
):
- ts = example_tsds
+ ts = request.getfixturevalue(ts_name)
mixin = make_mixin(mock_determine_prediction_size=False)
prediction_size = mixin._determine_prediction_size(
@@ -97,9 +105,9 @@ def test_predict_mixin_determine_prediction_size(
"start_timestamp, end_timestamp",
[
(pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-10")),
(pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-10")),
+ (10, 10),
+ (15, 20),
],
)
def test_predict_mixin_predict_create_ts_called(start_timestamp, end_timestamp, example_tsds):
@@ -117,9 +125,9 @@ def test_predict_mixin_predict_create_ts_called(start_timestamp, end_timestamp,
"start_timestamp, end_timestamp",
[
(pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-10")),
(pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-10")),
+ (10, 10),
+ (15, 20),
],
)
def test_predict_mixin_predict_inverse_transform_called(start_timestamp, end_timestamp, example_tsds):
@@ -137,9 +145,9 @@ def test_predict_mixin_predict_inverse_transform_called(start_timestamp, end_tim
"start_timestamp, end_timestamp",
[
(pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-10")),
(pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-10")),
+ (10, 10),
+ (15, 20),
],
)
def test_predict_mixin_predict_determine_prediction_size_called(start_timestamp, end_timestamp, example_tsds):
diff --git a/tests/test_pipeline/test_pipeline.py b/tests/test_pipeline/test_pipeline.py
index a04a8e594..200268c49 100644
--- a/tests/test_pipeline/test_pipeline.py
+++ b/tests/test_pipeline/test_pipeline.py
@@ -36,6 +36,7 @@
from etna.transforms import DateFlagsTransform
from etna.transforms import DifferencingTransform
from etna.transforms import FilterFeaturesTransform
+from etna.transforms import FourierTransform
from etna.transforms import LagTransform
from etna.transforms import LogTransform
from etna.transforms import ReversibleTransform
@@ -45,6 +46,7 @@
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts
from tests.test_pipeline.utils import assert_pipeline_forecasts_given_ts_with_prediction_intervals
from tests.test_pipeline.utils import assert_pipeline_forecasts_without_self_ts
+from tests.test_pipeline.utils import assert_pipeline_predicts
from tests.utils import DummyMetric
DEFAULT_METRICS = [MAE(mode=MetricAggregationMode.per_segment)]
@@ -523,11 +525,12 @@ def test_get_historical_forecasts_columns(ts_fixture, catboost_pipeline, request
@pytest.mark.parametrize(
- "n_folds, horizon, expected_timestamps",
+ "ts_name, n_folds, horizon, expected_timestamp_indices",
[
- (2, 3, [-6, -5, -4, -3, -2, -1]),
- (2, 5, [-10, -9, -8, -7, -6, -5, -4, -3, -2, -1]),
+ ("example_tsdf", 2, 3, [-6, -5, -4, -3, -2, -1]),
+ ("example_tsdf", 2, 5, [-10, -9, -8, -7, -6, -5, -4, -3, -2, -1]),
(
+ "example_tsdf",
[
FoldMask(
first_train_timestamp=pd.Timestamp("2020-01-01"),
@@ -543,23 +546,42 @@ def test_get_historical_forecasts_columns(ts_fixture, catboost_pipeline, request
5,
[-8, -3],
),
+ (
+ "example_tsdf_int_timestamp",
+ [
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=740,
+ target_timestamps=[744],
+ ),
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=745,
+ target_timestamps=[749],
+ ),
+ ],
+ 5,
+ [-11, -6],
+ ),
],
)
-def test_backtest_forecasts_timestamps(n_folds, horizon, expected_timestamps, example_tsdf):
+def test_backtest_forecasts_timestamps(ts_name, n_folds, horizon, expected_timestamp_indices, request):
"""Check that Pipeline.backtest returns forecasts with expected timestamps."""
+ ts = request.getfixturevalue(ts_name)
pipeline = Pipeline(model=NaiveModel(lag=horizon), horizon=horizon)
- _, forecast, _ = pipeline.backtest(ts=example_tsdf, metrics=DEFAULT_METRICS, n_folds=n_folds)
- timestamp = example_tsdf.index
+ _, forecast, _ = pipeline.backtest(ts=ts, metrics=DEFAULT_METRICS, n_folds=n_folds)
+ timestamp = ts.index
- np.testing.assert_array_equal(forecast.index, timestamp[expected_timestamps])
+ np.testing.assert_array_equal(forecast.index, timestamp[expected_timestamp_indices])
@pytest.mark.parametrize(
- "n_folds, horizon, expected_timestamps",
+ "ts_name, n_folds, horizon, expected_timestamp_indices",
[
- (2, 3, [-6, -5, -4, -3, -2, -1]),
- (2, 5, [-10, -9, -8, -7, -6, -5, -4, -3, -2, -1]),
+ ("example_tsdf", 2, 3, [-6, -5, -4, -3, -2, -1]),
+ ("example_tsdf", 2, 5, [-10, -9, -8, -7, -6, -5, -4, -3, -2, -1]),
(
+ "example_tsdf",
[
FoldMask(
first_train_timestamp=pd.Timestamp("2020-01-01"),
@@ -575,49 +597,69 @@ def test_backtest_forecasts_timestamps(n_folds, horizon, expected_timestamps, ex
5,
[-8, -3],
),
+ (
+ "example_tsdf_int_timestamp",
+ [
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=740,
+ target_timestamps=[744],
+ ),
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=745,
+ target_timestamps=[749],
+ ),
+ ],
+ 5,
+ [-11, -6],
+ ),
],
)
-def test_get_historical_forecasts_timestamps(n_folds, horizon, expected_timestamps, example_tsdf):
+def test_get_historical_forecasts_timestamps(ts_name, n_folds, horizon, expected_timestamp_indices, request):
"""Check that Pipeline.get_historical_forecasts returns forecasts with expected timestamps."""
+ ts = request.getfixturevalue(ts_name)
pipeline = Pipeline(model=NaiveModel(lag=horizon), horizon=horizon)
- forecast = pipeline.get_historical_forecasts(ts=example_tsdf, n_folds=n_folds)
- timestamp = example_tsdf.index
+ forecast = pipeline.get_historical_forecasts(ts=ts, n_folds=n_folds)
+ timestamp = ts.index
- np.testing.assert_array_equal(forecast.index, timestamp[expected_timestamps])
+ np.testing.assert_array_equal(forecast.index, timestamp[expected_timestamp_indices])
@pytest.mark.parametrize(
- "n_folds, horizon, stride, expected_timestamps",
+ "n_folds, horizon, stride, expected_timestamp_indices",
[
(2, 3, 3, [-6, -5, -4, -3, -2, -1]),
(2, 3, 1, [-4, -3, -2, -3, -2, -1]),
(2, 3, 5, [-8, -7, -6, -3, -2, -1]),
],
)
-def test_backtest_forecasts_timestamps_with_stride(n_folds, horizon, stride, expected_timestamps, example_tsdf):
+def test_backtest_forecasts_timestamps_with_stride(n_folds, horizon, stride, expected_timestamp_indices, example_tsdf):
"""Check that Pipeline.backtest with stride returns forecasts with expected timestamps."""
pipeline = Pipeline(model=NaiveModel(lag=horizon), horizon=horizon)
_, forecast, _ = pipeline.backtest(ts=example_tsdf, metrics=DEFAULT_METRICS, n_folds=n_folds, stride=stride)
timestamp = example_tsdf.index
- np.testing.assert_array_equal(forecast.index, timestamp[expected_timestamps])
+ np.testing.assert_array_equal(forecast.index, timestamp[expected_timestamp_indices])
@pytest.mark.parametrize(
- "n_folds, horizon, stride, expected_timestamps",
+ "n_folds, horizon, stride, expected_timestamp_indices",
[
(2, 3, 3, [-6, -5, -4, -3, -2, -1]),
(2, 3, 1, [-4, -3, -2, -3, -2, -1]),
(2, 3, 5, [-8, -7, -6, -3, -2, -1]),
],
)
-def test_get_historical_forecasts_timestamps_with_stride(n_folds, horizon, stride, expected_timestamps, example_tsdf):
+def test_get_historical_forecasts_timestamps_with_stride(
+ n_folds, horizon, stride, expected_timestamp_indices, example_tsdf
+):
"""Check that Pipeline.get_historical_forecasts with stride returns forecasts with expected timestamps."""
pipeline = Pipeline(model=NaiveModel(lag=horizon), horizon=horizon)
forecast = pipeline.get_historical_forecasts(ts=example_tsdf, n_folds=n_folds, stride=stride)
timestamp = example_tsdf.index
- np.testing.assert_array_equal(forecast.index, timestamp[expected_timestamps])
+ np.testing.assert_array_equal(forecast.index, timestamp[expected_timestamp_indices])
@pytest.mark.parametrize(
@@ -642,47 +684,180 @@ def test_backtest_fold_info_format(ts_fixture, n_folds, request):
@pytest.mark.parametrize(
- "mode, n_folds, refit, horizon, stride, expected_train_starts, expected_train_ends, expected_test_starts, expected_test_ends",
+ "ts_name, mode, n_folds, refit, horizon, stride, expected_train_start_indices, expected_train_end_indices, expected_test_start_indices, expected_test_end_indices",
[
- ("expand", 3, True, 7, None, [0, 0, 0], [-22, -15, -8], [-21, -14, -7], [-15, -8, -1]),
- ("expand", 3, True, 7, 1, [0, 0, 0], [-10, -9, -8], [-9, -8, -7], [-3, -2, -1]),
- ("expand", 3, True, 7, 10, [0, 0, 0], [-28, -18, -8], [-27, -17, -7], [-21, -11, -1]),
- ("expand", 3, False, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
- ("expand", 3, False, 7, 1, [0, 0, 0], [-10, -10, -10], [-9, -8, -7], [-3, -2, -1]),
- ("expand", 3, False, 7, 10, [0, 0, 0], [-28, -28, -28], [-27, -17, -7], [-21, -11, -1]),
- ("expand", 1, 1, 7, None, [0], [-8], [-7], [-1]),
- ("expand", 1, 2, 7, None, [0], [-8], [-7], [-1]),
- ("expand", 3, 1, 7, None, [0, 0, 0], [-22, -15, -8], [-21, -14, -7], [-15, -8, -1]),
- ("expand", 3, 2, 7, None, [0, 0, 0], [-22, -22, -8], [-21, -14, -7], [-15, -8, -1]),
- ("expand", 3, 3, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
- ("expand", 3, 4, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
- ("expand", 4, 1, 7, None, [0, 0, 0, 0], [-29, -22, -15, -8], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("expand", 4, 2, 7, None, [0, 0, 0, 0], [-29, -29, -15, -15], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("expand", 4, 2, 7, 1, [0, 0, 0, 0], [-11, -11, -9, -9], [-10, -9, -8, -7], [-4, -3, -2, -1]),
- ("expand", 4, 2, 7, 10, [0, 0, 0, 0], [-38, -38, -18, -18], [-37, -27, -17, -7], [-31, -21, -11, -1]),
- ("expand", 4, 3, 7, None, [0, 0, 0, 0], [-29, -29, -29, -8], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("expand", 4, 4, 7, None, [0, 0, 0, 0], [-29, -29, -29, -29], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("expand", 4, 5, 7, None, [0, 0, 0, 0], [-29, -29, -29, -29], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("constant", 3, True, 7, None, [0, 7, 14], [-22, -15, -8], [-21, -14, -7], [-15, -8, -1]),
- ("constant", 3, True, 7, 1, [0, 1, 2], [-10, -9, -8], [-9, -8, -7], [-3, -2, -1]),
- ("constant", 3, True, 7, 10, [0, 10, 20], [-28, -18, -8], [-27, -17, -7], [-21, -11, -1]),
- ("constant", 3, False, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
- ("constant", 3, False, 7, 1, [0, 0, 0], [-10, -10, -10], [-9, -8, -7], [-3, -2, -1]),
- ("constant", 3, False, 7, 10, [0, 0, 0], [-28, -28, -28], [-27, -17, -7], [-21, -11, -1]),
- ("constant", 1, 1, 7, None, [0], [-8], [-7], [-1]),
- ("constant", 1, 2, 7, None, [0], [-8], [-7], [-1]),
- ("constant", 3, 1, 7, None, [0, 7, 14], [-22, -15, -8], [-21, -14, -7], [-15, -8, -1]),
- ("constant", 3, 2, 7, None, [0, 0, 14], [-22, -22, -8], [-21, -14, -7], [-15, -8, -1]),
- ("constant", 3, 3, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
- ("constant", 3, 4, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
- ("constant", 4, 1, 7, None, [0, 7, 14, 21], [-29, -22, -15, -8], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("constant", 4, 2, 7, None, [0, 0, 14, 14], [-29, -29, -15, -15], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("constant", 4, 2, 7, 1, [0, 0, 2, 2], [-11, -11, -9, -9], [-10, -9, -8, -7], [-4, -3, -2, -1]),
- ("constant", 4, 2, 7, 10, [0, 0, 20, 20], [-38, -38, -18, -18], [-37, -27, -17, -7], [-31, -21, -11, -1]),
- ("constant", 4, 3, 7, None, [0, 0, 0, 21], [-29, -29, -29, -8], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("constant", 4, 4, 7, None, [0, 0, 0, 0], [-29, -29, -29, -29], [-28, -21, -14, -7], [-22, -15, -8, -1]),
- ("constant", 4, 5, 7, None, [0, 0, 0, 0], [-29, -29, -29, -29], [-28, -21, -14, -7], [-22, -15, -8, -1]),
+ ("example_tsdf", "expand", 3, True, 7, None, [0, 0, 0], [-22, -15, -8], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "expand", 3, True, 7, 1, [0, 0, 0], [-10, -9, -8], [-9, -8, -7], [-3, -2, -1]),
+ ("example_tsdf", "expand", 3, True, 7, 10, [0, 0, 0], [-28, -18, -8], [-27, -17, -7], [-21, -11, -1]),
+ ("example_tsdf", "expand", 3, False, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "expand", 3, False, 7, 1, [0, 0, 0], [-10, -10, -10], [-9, -8, -7], [-3, -2, -1]),
+ ("example_tsdf", "expand", 3, False, 7, 10, [0, 0, 0], [-28, -28, -28], [-27, -17, -7], [-21, -11, -1]),
+ ("example_tsdf", "expand", 1, 1, 7, None, [0], [-8], [-7], [-1]),
+ ("example_tsdf", "expand", 1, 2, 7, None, [0], [-8], [-7], [-1]),
+ ("example_tsdf", "expand", 3, 1, 7, None, [0, 0, 0], [-22, -15, -8], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "expand", 3, 2, 7, None, [0, 0, 0], [-22, -22, -8], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "expand", 3, 3, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "expand", 3, 4, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
+ (
+ "example_tsdf",
+ "expand",
+ 4,
+ 1,
+ 7,
+ None,
+ [0, 0, 0, 0],
+ [-29, -22, -15, -8],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ (
+ "example_tsdf",
+ "expand",
+ 4,
+ 2,
+ 7,
+ None,
+ [0, 0, 0, 0],
+ [-29, -29, -15, -15],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ ("example_tsdf", "expand", 4, 2, 7, 1, [0, 0, 0, 0], [-11, -11, -9, -9], [-10, -9, -8, -7], [-4, -3, -2, -1]),
+ (
+ "example_tsdf",
+ "expand",
+ 4,
+ 2,
+ 7,
+ 10,
+ [0, 0, 0, 0],
+ [-38, -38, -18, -18],
+ [-37, -27, -17, -7],
+ [-31, -21, -11, -1],
+ ),
+ (
+ "example_tsdf",
+ "expand",
+ 4,
+ 3,
+ 7,
+ None,
+ [0, 0, 0, 0],
+ [-29, -29, -29, -8],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ (
+ "example_tsdf",
+ "expand",
+ 4,
+ 4,
+ 7,
+ None,
+ [0, 0, 0, 0],
+ [-29, -29, -29, -29],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ (
+ "example_tsdf",
+ "expand",
+ 4,
+ 5,
+ 7,
+ None,
+ [0, 0, 0, 0],
+ [-29, -29, -29, -29],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ ("example_tsdf", "constant", 3, True, 7, None, [0, 7, 14], [-22, -15, -8], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "constant", 3, True, 7, 1, [0, 1, 2], [-10, -9, -8], [-9, -8, -7], [-3, -2, -1]),
+ ("example_tsdf", "constant", 3, True, 7, 10, [0, 10, 20], [-28, -18, -8], [-27, -17, -7], [-21, -11, -1]),
+ ("example_tsdf", "constant", 3, False, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "constant", 3, False, 7, 1, [0, 0, 0], [-10, -10, -10], [-9, -8, -7], [-3, -2, -1]),
+ ("example_tsdf", "constant", 3, False, 7, 10, [0, 0, 0], [-28, -28, -28], [-27, -17, -7], [-21, -11, -1]),
+ ("example_tsdf", "constant", 1, 1, 7, None, [0], [-8], [-7], [-1]),
+ ("example_tsdf", "constant", 1, 2, 7, None, [0], [-8], [-7], [-1]),
+ ("example_tsdf", "constant", 3, 1, 7, None, [0, 7, 14], [-22, -15, -8], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "constant", 3, 2, 7, None, [0, 0, 14], [-22, -22, -8], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "constant", 3, 3, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
+ ("example_tsdf", "constant", 3, 4, 7, None, [0, 0, 0], [-22, -22, -22], [-21, -14, -7], [-15, -8, -1]),
+ (
+ "example_tsdf",
+ "constant",
+ 4,
+ 1,
+ 7,
+ None,
+ [0, 7, 14, 21],
+ [-29, -22, -15, -8],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ (
+ "example_tsdf",
+ "constant",
+ 4,
+ 2,
+ 7,
+ None,
+ [0, 0, 14, 14],
+ [-29, -29, -15, -15],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ ("example_tsdf", "constant", 4, 2, 7, 1, [0, 0, 2, 2], [-11, -11, -9, -9], [-10, -9, -8, -7], [-4, -3, -2, -1]),
+ (
+ "example_tsdf",
+ "constant",
+ 4,
+ 2,
+ 7,
+ 10,
+ [0, 0, 20, 20],
+ [-38, -38, -18, -18],
+ [-37, -27, -17, -7],
+ [-31, -21, -11, -1],
+ ),
+ (
+ "example_tsdf",
+ "constant",
+ 4,
+ 3,
+ 7,
+ None,
+ [0, 0, 0, 21],
+ [-29, -29, -29, -8],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ (
+ "example_tsdf",
+ "constant",
+ 4,
+ 4,
+ 7,
+ None,
+ [0, 0, 0, 0],
+ [-29, -29, -29, -29],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
(
+ "example_tsdf",
+ "constant",
+ 4,
+ 5,
+ 7,
+ None,
+ [0, 0, 0, 0],
+ [-29, -29, -29, -29],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
+ (
+ "example_tsdf",
None,
[
FoldMask(
@@ -705,6 +880,30 @@ def test_backtest_fold_info_format(ts_fixture, n_folds, request):
[-8, -1],
),
(
+ "example_tsdf_int_timestamp",
+ None,
+ [
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp=740,
+ target_timestamps=[744],
+ ),
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp=747,
+ target_timestamps=[751],
+ ),
+ ],
+ True,
+ 7,
+ None,
+ [0, 0],
+ [-15, -8],
+ [-14, -7],
+ [-8, -1],
+ ),
+ (
+ "example_tsdf",
None,
[
FoldMask(
@@ -727,6 +926,30 @@ def test_backtest_fold_info_format(ts_fixture, n_folds, request):
[-8, -1],
),
(
+ "example_tsdf_int_timestamp",
+ None,
+ [
+ FoldMask(
+ first_train_timestamp=11,
+ last_train_timestamp=740,
+ target_timestamps=[744],
+ ),
+ FoldMask(
+ first_train_timestamp=18,
+ last_train_timestamp=747,
+ target_timestamps=[751],
+ ),
+ ],
+ True,
+ 7,
+ None,
+ [1, 8],
+ [-15, -8],
+ [-14, -7],
+ [-8, -1],
+ ),
+ (
+ "example_tsdf",
None,
[
FoldMask(
@@ -758,31 +981,66 @@ def test_backtest_fold_info_format(ts_fixture, n_folds, request):
[-28, -21, -14, -7],
[-22, -15, -8, -1],
),
+ (
+ "example_tsdf_int_timestamp",
+ None,
+ [
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp=726,
+ target_timestamps=[730],
+ ),
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp=733,
+ target_timestamps=[737],
+ ),
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp=740,
+ target_timestamps=[744],
+ ),
+ FoldMask(
+ first_train_timestamp=None,
+ last_train_timestamp=747,
+ target_timestamps=[751],
+ ),
+ ],
+ 2,
+ 7,
+ None,
+ [0, 0, 0, 0],
+ [-29, -29, -15, -15],
+ [-28, -21, -14, -7],
+ [-22, -15, -8, -1],
+ ),
],
)
def test_backtest_fold_info_timestamps(
+ ts_name,
mode,
n_folds,
refit,
horizon,
stride,
- expected_train_starts,
- expected_train_ends,
- expected_test_starts,
- expected_test_ends,
- example_tsdf,
+ expected_train_start_indices,
+ expected_train_end_indices,
+ expected_test_start_indices,
+ expected_test_end_indices,
+ request,
):
"""Check that Pipeline.backtest returns info dataframe with correct timestamps."""
+ ts = request.getfixturevalue(ts_name)
pipeline = Pipeline(model=NaiveModel(lag=horizon), horizon=horizon)
_, _, info_df = pipeline.backtest(
- ts=example_tsdf, metrics=DEFAULT_METRICS, mode=mode, n_folds=n_folds, refit=refit, stride=stride
+ ts=ts, metrics=DEFAULT_METRICS, mode=mode, n_folds=n_folds, refit=refit, stride=stride
)
- timestamp = example_tsdf.index
+ timestamp = ts.index
- np.testing.assert_array_equal(info_df["train_start_time"], timestamp[expected_train_starts])
- np.testing.assert_array_equal(info_df["train_end_time"], timestamp[expected_train_ends])
- np.testing.assert_array_equal(info_df["test_start_time"], timestamp[expected_test_starts])
- np.testing.assert_array_equal(info_df["test_end_time"], timestamp[expected_test_ends])
+ np.testing.assert_array_equal(info_df["train_start_time"], timestamp[expected_train_start_indices])
+ np.testing.assert_array_equal(info_df["train_end_time"], timestamp[expected_train_end_indices])
+ np.testing.assert_array_equal(info_df["test_start_time"], timestamp[expected_test_start_indices])
+ np.testing.assert_array_equal(info_df["test_end_time"], timestamp[expected_test_end_indices])
def test_backtest_refit_success(catboost_pipeline: Pipeline, big_example_tsdf: TSDataset):
@@ -841,9 +1099,10 @@ def test_forecast_pipeline_with_nan_at_the_end(ts_with_nans_in_tails):
@pytest.mark.parametrize(
- "n_folds, horizon, stride, mode, expected_masks",
+ "ts_name, n_folds, horizon, stride, mode, expected_masks",
(
(
+ "example_tsds",
2,
3,
3,
@@ -862,6 +1121,26 @@ def test_forecast_pipeline_with_nan_at_the_end(ts_with_nans_in_tails):
],
),
(
+ "example_tsds_int_timestamp",
+ 2,
+ 3,
+ 3,
+ CrossValidationMode.expand,
+ [
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=103,
+ target_timestamps=[104, 105, 106],
+ ),
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=106,
+ target_timestamps=[107, 108, 109],
+ ),
+ ],
+ ),
+ (
+ "example_tsds",
2,
3,
1,
@@ -880,6 +1159,26 @@ def test_forecast_pipeline_with_nan_at_the_end(ts_with_nans_in_tails):
],
),
(
+ "example_tsds_int_timestamp",
+ 2,
+ 3,
+ 1,
+ CrossValidationMode.expand,
+ [
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=105,
+ target_timestamps=[106, 107, 108],
+ ),
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=106,
+ target_timestamps=[107, 108, 109],
+ ),
+ ],
+ ),
+ (
+ "example_tsds",
2,
3,
5,
@@ -898,6 +1197,26 @@ def test_forecast_pipeline_with_nan_at_the_end(ts_with_nans_in_tails):
],
),
(
+ "example_tsds_int_timestamp",
+ 2,
+ 3,
+ 5,
+ CrossValidationMode.expand,
+ [
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=101,
+ target_timestamps=[102, 103, 104],
+ ),
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=106,
+ target_timestamps=[107, 108, 109],
+ ),
+ ],
+ ),
+ (
+ "example_tsds",
2,
3,
3,
@@ -916,6 +1235,26 @@ def test_forecast_pipeline_with_nan_at_the_end(ts_with_nans_in_tails):
],
),
(
+ "example_tsds_int_timestamp",
+ 2,
+ 3,
+ 3,
+ CrossValidationMode.constant,
+ [
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=103,
+ target_timestamps=[104, 105, 106],
+ ),
+ FoldMask(
+ first_train_timestamp=13,
+ last_train_timestamp=106,
+ target_timestamps=[107, 108, 109],
+ ),
+ ],
+ ),
+ (
+ "example_tsds",
2,
3,
1,
@@ -934,6 +1273,26 @@ def test_forecast_pipeline_with_nan_at_the_end(ts_with_nans_in_tails):
],
),
(
+ "example_tsds_int_timestamp",
+ 2,
+ 3,
+ 1,
+ CrossValidationMode.constant,
+ [
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=105,
+ target_timestamps=[106, 107, 108],
+ ),
+ FoldMask(
+ first_train_timestamp=11,
+ last_train_timestamp=106,
+ target_timestamps=[107, 108, 109],
+ ),
+ ],
+ ),
+ (
+ "example_tsds",
2,
3,
5,
@@ -951,12 +1310,30 @@ def test_forecast_pipeline_with_nan_at_the_end(ts_with_nans_in_tails):
),
],
),
+ (
+ "example_tsds_int_timestamp",
+ 2,
+ 3,
+ 5,
+ CrossValidationMode.constant,
+ [
+ FoldMask(
+ first_train_timestamp=10,
+ last_train_timestamp=101,
+ target_timestamps=[102, 103, 104],
+ ),
+ FoldMask(
+ first_train_timestamp=15,
+ last_train_timestamp=106,
+ target_timestamps=[107, 108, 109],
+ ),
+ ],
+ ),
),
)
-def test_generate_masks_from_n_folds(example_tsds: TSDataset, n_folds, horizon, stride, mode, expected_masks):
- masks = Pipeline._generate_masks_from_n_folds(
- ts=example_tsds, n_folds=n_folds, horizon=horizon, stride=stride, mode=mode
- )
+def test_generate_masks_from_n_folds(ts_name, n_folds, horizon, stride, mode, expected_masks, request):
+ ts = request.getfixturevalue(ts_name)
+ masks = Pipeline._generate_masks_from_n_folds(ts=ts, n_folds=n_folds, horizon=horizon, stride=stride, mode=mode)
for mask, expected_mask in zip(masks, expected_masks):
assert mask.first_train_timestamp == expected_mask.first_train_timestamp
assert mask.last_train_timestamp == expected_mask.last_train_timestamp
@@ -964,39 +1341,182 @@ def test_generate_masks_from_n_folds(example_tsds: TSDataset, n_folds, horizon,
@pytest.mark.parametrize(
- "mask", (FoldMask("2020-01-01", "2020-01-02", ["2020-01-03"]), FoldMask("2020-01-03", "2020-01-05", ["2020-01-06"]))
-)
-@pytest.mark.parametrize(
- "ts_name", ["simple_ts", "simple_ts_starting_with_nans_one_segment", "simple_ts_starting_with_nans_all_segments"]
+ "ts_name, horizon, mask, expected_train_start, expected_test_start, expected_test_end",
+ [
+ (
+ "simple_ts",
+ 1,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2020-01-02", target_timestamps=["2020-01-03"]),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-03"),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=15, target_timestamps=[16]),
+ 10,
+ 16,
+ 16,
+ ),
+ (
+ "simple_ts",
+ 3,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2020-01-02", target_timestamps=["2020-01-03"]),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-05"),
+ ),
+ (
+ "simple_ts_starting_with_nans_one_segment",
+ 3,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2020-01-02", target_timestamps=["2020-01-03"]),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-05"),
+ ),
+ (
+ "simple_ts_starting_with_nans_all_segments",
+ 3,
+ FoldMask(first_train_timestamp=None, last_train_timestamp="2020-01-02", target_timestamps=["2020-01-03"]),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-05"),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 3,
+ FoldMask(first_train_timestamp=None, last_train_timestamp=15, target_timestamps=[16]),
+ 10,
+ 16,
+ 18,
+ ),
+ (
+ "simple_ts",
+ 1,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2020-01-02", target_timestamps=["2020-01-03"]
+ ),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-03"),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=10, last_train_timestamp=15, target_timestamps=[16]),
+ 10,
+ 16,
+ 16,
+ ),
+ (
+ "simple_ts",
+ 3,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2020-01-02", target_timestamps=["2020-01-03"]
+ ),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-05"),
+ ),
+ (
+ "simple_ts_starting_with_nans_one_segment",
+ 3,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2020-01-02", target_timestamps=["2020-01-03"]
+ ),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-05"),
+ ),
+ (
+ "simple_ts_starting_with_nans_all_segments",
+ 3,
+ FoldMask(
+ first_train_timestamp="2020-01-01", last_train_timestamp="2020-01-02", target_timestamps=["2020-01-03"]
+ ),
+ pd.Timestamp("2020-01-01"),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-05"),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 3,
+ FoldMask(first_train_timestamp=10, last_train_timestamp=15, target_timestamps=[16]),
+ 10,
+ 16,
+ 18,
+ ),
+ (
+ "simple_ts",
+ 1,
+ FoldMask(
+ first_train_timestamp="2020-01-03", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]
+ ),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-06"),
+ pd.Timestamp("2020-01-06"),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 1,
+ FoldMask(first_train_timestamp=13, last_train_timestamp=15, target_timestamps=[16]),
+ 13,
+ 16,
+ 16,
+ ),
+ (
+ "simple_ts",
+ 3,
+ FoldMask(
+ first_train_timestamp="2020-01-03", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]
+ ),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-06"),
+ pd.Timestamp("2020-01-08"),
+ ),
+ (
+ "simple_ts_starting_with_nans_one_segment",
+ 3,
+ FoldMask(
+ first_train_timestamp="2020-01-03", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]
+ ),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-06"),
+ pd.Timestamp("2020-01-08"),
+ ),
+ (
+ "simple_ts_starting_with_nans_all_segments",
+ 3,
+ FoldMask(
+ first_train_timestamp="2020-01-03", last_train_timestamp="2020-01-05", target_timestamps=["2020-01-06"]
+ ),
+ pd.Timestamp("2020-01-03"),
+ pd.Timestamp("2020-01-06"),
+ pd.Timestamp("2020-01-08"),
+ ),
+ (
+ "example_tsds_int_timestamp",
+ 3,
+ FoldMask(first_train_timestamp=13, last_train_timestamp=15, target_timestamps=[16]),
+ 13,
+ 16,
+ 18,
+ ),
+ ],
)
-def test_generate_folds_datasets(ts_name, mask, request):
+def test_generate_folds_datasets(
+ ts_name, horizon, mask, expected_train_start, expected_test_start, expected_test_end, request
+):
"""Check _generate_folds_datasets for correct work."""
ts = request.getfixturevalue(ts_name)
pipeline = Pipeline(model=NaiveModel(lag=7))
mask = pipeline._prepare_fold_masks(ts=ts, masks=[mask], mode=CrossValidationMode.expand, stride=-1)[0]
- train, test = list(pipeline._generate_folds_datasets(ts, [mask], 4))[0]
- assert train.index.min() == np.datetime64(mask.first_train_timestamp)
- assert train.index.max() == np.datetime64(mask.last_train_timestamp)
- assert test.index.min() == np.datetime64(mask.last_train_timestamp) + np.timedelta64(1, "D")
- assert test.index.max() == np.datetime64(mask.last_train_timestamp) + np.timedelta64(4, "D")
-
-
-@pytest.mark.parametrize(
- "mask", (FoldMask(None, "2020-01-02", ["2020-01-03"]), FoldMask(None, "2020-01-05", ["2020-01-06"]))
-)
-@pytest.mark.parametrize(
- "ts_name", ["simple_ts", "simple_ts_starting_with_nans_one_segment", "simple_ts_starting_with_nans_all_segments"]
-)
-def test_generate_folds_datasets_without_first_date(ts_name, mask, request):
- """Check _generate_folds_datasets for correct work without first date."""
- ts = request.getfixturevalue(ts_name)
- pipeline = Pipeline(model=NaiveModel(lag=7))
- mask = pipeline._prepare_fold_masks(ts=ts, masks=[mask], mode=CrossValidationMode.expand, stride=-1)[0]
- train, test = list(pipeline._generate_folds_datasets(ts, [mask], 4))[0]
- assert train.index.min() == np.datetime64(ts.index.min())
- assert train.index.max() == np.datetime64(mask.last_train_timestamp)
- assert test.index.min() == np.datetime64(mask.last_train_timestamp) + np.timedelta64(1, "D")
- assert test.index.max() == np.datetime64(mask.last_train_timestamp) + np.timedelta64(4, "D")
+ train, test = list(pipeline._generate_folds_datasets(ts=ts, masks=[mask], horizon=horizon))[0]
+ assert train.index.min() == expected_train_start
+ assert train.index.max() == mask.last_train_timestamp
+ assert test.index.min() == expected_test_start
+ assert test.index.max() == expected_test_end
@pytest.mark.parametrize(
@@ -1220,44 +1740,6 @@ def test_pipeline_with_deepmodel(example_tsds):
_ = pipeline.backtest(ts=example_tsds, metrics=[MAE()], n_folds=2, aggregate_metrics=True)
-@pytest.mark.parametrize(
- "model, transforms",
- [
- (
- CatBoostMultiSegmentModel(iterations=100),
- [DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(7, 15)))],
- ),
- (
- LinearPerSegmentModel(),
- [DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(7, 15)))],
- ),
- (SeasonalMovingAverageModel(window=2, seasonality=7), []),
- (SARIMAXModel(), []),
- (ProphetModel(), []),
- ],
-)
-def test_predict_format(model, transforms, example_tsds):
- ts = example_tsds
- pipeline = Pipeline(model=model, transforms=transforms, horizon=7)
- pipeline.fit(ts)
-
- start_idx = 50
- end_idx = 70
- start_timestamp = ts.index[start_idx]
- end_timestamp = ts.index[end_idx]
- num_points = end_idx - start_idx + 1
-
- # create a separate TSDataset with slice of original timestamps
- predict_ts = deepcopy(ts)
- predict_ts.df = predict_ts.df.iloc[5 : end_idx + 5]
-
- result_ts = pipeline.predict(ts=predict_ts, start_timestamp=start_timestamp, end_timestamp=end_timestamp)
- result_df = result_ts.to_pandas(flatten=True)
-
- assert not np.any(result_df["target"].isna())
- assert len(result_df) == len(example_tsds.segments) * num_points
-
-
def test_predict_values(example_tsds):
original_ts = deepcopy(example_tsds)
@@ -1304,69 +1786,150 @@ def test_forecast_raise_error_if_no_ts(example_tsds):
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "example_tsds",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
(
+ "example_tsds",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
- (SeasonalMovingAverageModel(window=2, seasonality=7), []),
- (SARIMAXModel(), []),
- (ProphetModel(), []),
+ ("example_tsds", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds", SARIMAXModel(), []),
+ ("example_tsds", ProphetModel(), []),
+ (
+ "example_tsds_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds_int_timestamp", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecasts_without_self_ts(model, transforms, example_tsds):
+def test_forecasts_without_self_ts(ts_name, model, transforms, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 3
pipeline = Pipeline(model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_without_self_ts(pipeline=pipeline, ts=example_tsds, horizon=horizon)
+ assert_pipeline_forecasts_without_self_ts(pipeline=pipeline, ts=ts, horizon=horizon)
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "example_tsds",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
(
+ "example_tsds",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
- (SeasonalMovingAverageModel(window=2, seasonality=7), []),
- (SARIMAXModel(), []),
- (ProphetModel(), []),
+ ("example_tsds", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds", SARIMAXModel(), []),
+ ("example_tsds", ProphetModel(), []),
+ (
+ "example_tsds_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds_int_timestamp", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecast_given_ts(model, transforms, example_tsds):
+def test_forecast_given_ts(ts_name, model, transforms, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 3
pipeline = Pipeline(model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_given_ts(pipeline=pipeline, ts=example_tsds, horizon=horizon)
+ assert_pipeline_forecasts_given_ts(pipeline=pipeline, ts=ts, horizon=horizon)
@pytest.mark.parametrize(
- "model, transforms",
+ "ts_name, model, transforms",
[
(
+ "example_tsds",
CatBoostMultiSegmentModel(iterations=100),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
(
+ "example_tsds",
LinearPerSegmentModel(),
[DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
),
- (SeasonalMovingAverageModel(window=2, seasonality=7), []),
- (SARIMAXModel(), []),
- (ProphetModel(), []),
+ ("example_tsds", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds", SARIMAXModel(), []),
+ ("example_tsds", ProphetModel(), []),
+ (
+ "example_tsds_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds_int_timestamp", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds_int_timestamp", SARIMAXModel(), []),
],
)
-def test_forecast_given_ts_with_prediction_interval(model, transforms, example_tsds):
+def test_forecast_given_ts_with_prediction_interval(ts_name, model, transforms, request):
+ ts = request.getfixturevalue(ts_name)
horizon = 3
pipeline = Pipeline(model=model, transforms=transforms, horizon=horizon)
- assert_pipeline_forecasts_given_ts_with_prediction_intervals(pipeline=pipeline, ts=example_tsds, horizon=horizon)
+ assert_pipeline_forecasts_given_ts_with_prediction_intervals(pipeline=pipeline, ts=ts, horizon=horizon)
+
+
+@pytest.mark.parametrize(
+ "ts_name, model, transforms",
+ [
+ (
+ "example_tsds",
+ CatBoostMultiSegmentModel(iterations=100),
+ [DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds",
+ LinearPerSegmentModel(),
+ [DateFlagsTransform(), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds", SARIMAXModel(), []),
+ ("example_tsds", ProphetModel(), []),
+ (
+ "example_tsds_int_timestamp",
+ CatBoostMultiSegmentModel(iterations=100),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ (
+ "example_tsds_int_timestamp",
+ LinearPerSegmentModel(),
+ [FourierTransform(period=7, order=1), LagTransform(in_column="target", lags=list(range(3, 10)))],
+ ),
+ ("example_tsds_int_timestamp", SeasonalMovingAverageModel(window=2, seasonality=7), []),
+ ("example_tsds_int_timestamp", SARIMAXModel(), []),
+ ],
+)
+def test_predict(ts_name, model, transforms, request):
+ ts = request.getfixturevalue(ts_name)
+ pipeline = Pipeline(model=model, transforms=transforms, horizon=7)
+ assert_pipeline_predicts(pipeline=pipeline, ts=ts, start_idx=50, end_idx=70)
@pytest.mark.parametrize(
diff --git a/tests/test_pipeline/utils.py b/tests/test_pipeline/utils.py
index 689106f79..734562d7f 100644
--- a/tests/test_pipeline/utils.py
+++ b/tests/test_pipeline/utils.py
@@ -3,11 +3,13 @@
from copy import deepcopy
from typing import Tuple
+import numpy as np
import pandas as pd
import pytest
from lightning_fabric.utilities.seed import seed_everything
from etna.datasets import TSDataset
+from etna.datasets.utils import timestamp_range
from etna.pipeline.base import AbstractPipeline
@@ -68,7 +70,9 @@ def assert_pipeline_forecasts_without_self_ts(
else:
expected_segments = ts.segments
assert forecast_ts.segments == expected_segments
- expected_index = pd.date_range(start=ts.index[-1], periods=horizon + 1, freq=ts.freq, name="timestamp")[1:]
+
+ expected_index = timestamp_range(start=ts.index[-1], periods=horizon + 1, freq=ts.freq)[1:]
+ expected_index.name = "timestamp"
pd.testing.assert_index_equal(forecast_ts.index, expected_index)
assert not forecast_df["target"].isna().any()
@@ -89,9 +93,9 @@ def assert_pipeline_forecasts_given_ts(pipeline: AbstractPipeline, ts: TSDataset
else:
expected_segments = to_forecast_ts.segments
assert forecast_ts.segments == expected_segments
- expected_index = pd.date_range(
- start=to_forecast_ts.index[-1], periods=horizon + 1, freq=to_forecast_ts.freq, name="timestamp"
- )[1:]
+
+ expected_index = timestamp_range(start=to_forecast_ts.index[-1], periods=horizon + 1, freq=ts.freq)[1:]
+ expected_index.name = "timestamp"
pd.testing.assert_index_equal(forecast_ts.index, expected_index)
assert not forecast_df["target"].isna().any()
@@ -116,12 +120,41 @@ def assert_pipeline_forecasts_given_ts_with_prediction_intervals(
else:
expected_segments = to_forecast_ts.segments
assert forecast_ts.segments == expected_segments
- expected_index = pd.date_range(
- start=to_forecast_ts.index[-1], periods=horizon + 1, freq=to_forecast_ts.freq, name="timestamp"
- )[1:]
+
+ expected_index = timestamp_range(start=to_forecast_ts.index[-1], periods=horizon + 1, freq=ts.freq)[1:]
+ expected_index.name = "timestamp"
pd.testing.assert_index_equal(forecast_ts.index, expected_index)
+
assert not forecast_df["target"].isna().any()
assert not forecast_df["target_0.025"].isna().any()
assert not forecast_df["target_0.975"].isna().any()
return pipeline
+
+
+def assert_pipeline_predicts(
+ pipeline: AbstractPipeline, ts: TSDataset, start_idx: int, end_idx: int
+) -> AbstractPipeline:
+ predict_ts = deepcopy(ts)
+ pipeline.fit(ts)
+
+ start_timestamp = ts.index[start_idx]
+ end_timestamp = ts.index[end_idx]
+ num_points = end_idx - start_idx + 1
+
+ predict_ts = pipeline.predict(ts=predict_ts, start_timestamp=start_timestamp, end_timestamp=end_timestamp)
+ predict_df = predict_ts.to_pandas(flatten=True)
+
+ if ts.has_hierarchy():
+ expected_segments = ts.hierarchical_structure.get_level_segments(predict_ts.current_df_level)
+ else:
+ expected_segments = predict_ts.segments
+ assert predict_ts.segments == expected_segments
+
+ expected_index = timestamp_range(start=start_timestamp, periods=num_points, freq=ts.freq)
+ expected_index.name = "timestamp"
+ pd.testing.assert_index_equal(predict_ts.index, expected_index)
+
+ assert not np.any(predict_df["target"].isna())
+
+ return pipeline
diff --git a/tests/test_transforms/test_decomposition/test_change_points_based/test_change_points_models/test_base_change_points_model.py b/tests/test_transforms/test_decomposition/test_change_points_based/test_change_points_models/test_base_change_points_model.py
index bc0bbd4e4..418a81c62 100644
--- a/tests/test_transforms/test_decomposition/test_change_points_based/test_change_points_models/test_base_change_points_model.py
+++ b/tests/test_transforms/test_decomposition/test_change_points_based/test_change_points_models/test_base_change_points_model.py
@@ -1,20 +1,43 @@
import pandas as pd
+import pytest
from etna.transforms.decomposition.change_points_based.change_points_models import BaseChangePointsModelAdapter
-def test_build_intervals():
+@pytest.mark.parametrize(
+ "change_points, expected_intervals",
+ [
+ (
+ [],
+ [
+ (None, None),
+ ],
+ ),
+ (
+ [pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-18"), pd.Timestamp("2020-02-24")],
+ [
+ (None, pd.Timestamp("2020-01-01")),
+ (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-18")),
+ (pd.Timestamp("2020-01-18"), pd.Timestamp("2020-02-24")),
+ (pd.Timestamp("2020-02-24"), None),
+ ],
+ ),
+ (
+ [10, 20, 30],
+ [
+ (None, 10),
+ (10, 20),
+ (20, 30),
+ (30, None),
+ ],
+ ),
+ ],
+)
+def test_build_intervals(change_points, expected_intervals):
"""Check correctness of intervals generation with list of change points."""
- change_points = [pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-18"), pd.Timestamp("2020-02-24")]
- expected_intervals = [
- (pd.Timestamp.min, pd.Timestamp("2020-01-01")),
- (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-18")),
- (pd.Timestamp("2020-01-18"), pd.Timestamp("2020-02-24")),
- (pd.Timestamp("2020-02-24"), pd.Timestamp.max),
- ]
intervals = BaseChangePointsModelAdapter._build_intervals(change_points=change_points)
assert isinstance(intervals, list)
- assert len(intervals) == 4
+ assert len(intervals) == len(expected_intervals)
for (exp_left, exp_right), (real_left, real_right) in zip(expected_intervals, intervals):
assert exp_left == real_left
assert exp_right == real_right
diff --git a/tests/test_transforms/test_decomposition/test_deseasonal_transform.py b/tests/test_transforms/test_decomposition/test_deseasonal_transform.py
index 5d18413ce..e47df4a81 100644
--- a/tests/test_transforms/test_decomposition/test_deseasonal_transform.py
+++ b/tests/test_transforms/test_decomposition/test_deseasonal_transform.py
@@ -137,16 +137,17 @@ def test_inverse_transform_multi_segments(ts_name, model, request):
pd.testing.assert_frame_equal(df_inverse_transformed, df)
+@pytest.mark.parametrize("horizon", [1, 3])
@pytest.mark.parametrize("model_decompose", ["additive", "multiplicative"])
-def test_forecast(ts_seasonal, model_decompose):
+def test_forecast(horizon, model_decompose, ts_seasonal):
"""Test that transform works correctly in forecast."""
transform = DeseasonalityTransform(in_column="target", period=7, model=model_decompose)
- ts_train, ts_test = ts_seasonal.train_test_split(test_size=3)
+ ts_train, ts_test = ts_seasonal.train_test_split(test_size=horizon)
transform.fit_transform(ts_train)
model = NaiveModel()
model.fit(ts_train)
- ts_future = ts_train.make_future(future_steps=3, transforms=[transform], tail_steps=model.context_size)
- ts_forecast = model.forecast(ts_future, prediction_size=3)
+ ts_future = ts_train.make_future(future_steps=horizon, transforms=[transform], tail_steps=model.context_size)
+ ts_forecast = model.forecast(ts_future, prediction_size=horizon)
ts_forecast.inverse_transform([transform])
for segment in ts_forecast.segments:
np.testing.assert_allclose(ts_forecast[:, segment, "target"], ts_test[:, segment, "target"], atol=0.1)
diff --git a/tests/test_transforms/test_decomposition/test_stl_transform.py b/tests/test_transforms/test_decomposition/test_stl_transform.py
index 49d9f2eaf..95aff1beb 100644
--- a/tests/test_transforms/test_decomposition/test_stl_transform.py
+++ b/tests/test_transforms/test_decomposition/test_stl_transform.py
@@ -8,6 +8,7 @@
from etna.transforms.decomposition.stl import _OneSegmentSTLTransform
from tests.test_transforms.utils import assert_sampling_is_valid
from tests.test_transforms.utils import assert_transformation_equals_loaded_original
+from tests.utils import convert_ts_to_int_timestamp
def add_trend(series: pd.Series, coef: float = 1) -> pd.Series:
@@ -44,6 +45,13 @@ def df_trend_seasonal_one_segment() -> pd.DataFrame:
return df
+@pytest.fixture
+def df_trend_seasonal_one_segment_int_timestamp(df_trend_seasonal_one_segment) -> pd.DataFrame:
+ df = df_trend_seasonal_one_segment
+ df.index = pd.Index(np.arange(10, 10 + len(df)), name=df.index.name)
+ return df
+
+
@pytest.fixture
def df_trend_seasonal_starting_with_nans_one_segment(df_trend_seasonal_one_segment) -> pd.DataFrame:
result = df_trend_seasonal_one_segment
@@ -61,6 +69,12 @@ def ts_trend_seasonal() -> TSDataset:
return TSDataset(TSDataset.to_dataset(classic_df), freq="D")
+@pytest.fixture
+def ts_trend_seasonal_int_timestamp(ts_trend_seasonal) -> TSDataset:
+ ts = convert_ts_to_int_timestamp(ts=ts_trend_seasonal, shift=10)
+ return ts
+
+
@pytest.fixture
def ts_trend_seasonal_starting_with_nans() -> TSDataset:
df_1 = get_one_df(coef=0.1, period=7, magnitude=1)
@@ -91,7 +105,12 @@ def ts_trend_seasonal_nan_tails() -> TSDataset:
@pytest.mark.parametrize("model", ["arima", "holt"])
@pytest.mark.parametrize(
- "df_name", ["df_trend_seasonal_one_segment", "df_trend_seasonal_starting_with_nans_one_segment"]
+ "df_name",
+ [
+ "df_trend_seasonal_one_segment",
+ "df_trend_seasonal_starting_with_nans_one_segment",
+ "df_trend_seasonal_one_segment_int_timestamp",
+ ],
)
def test_transform_one_segment(df_name, model, request):
"""Test that transform for one segment removes trend and seasonality."""
@@ -104,7 +123,9 @@ def test_transform_one_segment(df_name, model, request):
@pytest.mark.parametrize("model", ["arima", "holt"])
-@pytest.mark.parametrize("ts_name", ["ts_trend_seasonal", "ts_trend_seasonal_starting_with_nans"])
+@pytest.mark.parametrize(
+ "ts_name", ["ts_trend_seasonal", "ts_trend_seasonal_starting_with_nans", "ts_trend_seasonal_int_timestamp"]
+)
def test_transform_multi_segments(ts_name, model, request):
"""Test that transform for all segments removes trend and seasonality."""
ts = request.getfixturevalue(ts_name)
@@ -118,7 +139,12 @@ def test_transform_multi_segments(ts_name, model, request):
@pytest.mark.parametrize("model", ["arima", "holt"])
@pytest.mark.parametrize(
- "df_name", ["df_trend_seasonal_one_segment", "df_trend_seasonal_starting_with_nans_one_segment"]
+ "df_name",
+ [
+ "df_trend_seasonal_one_segment",
+ "df_trend_seasonal_starting_with_nans_one_segment",
+ "df_trend_seasonal_one_segment_int_timestamp",
+ ],
)
def test_inverse_transform_one_segment(df_name, model, request):
"""Test that transform + inverse_transform don't change dataframe."""
@@ -126,11 +152,13 @@ def test_inverse_transform_one_segment(df_name, model, request):
transform = _OneSegmentSTLTransform(in_column="target", period=7, model=model)
df_transformed = transform.fit_transform(df)
df_inverse_transformed = transform.inverse_transform(df=df_transformed)
- assert df["target"].equals(df_inverse_transformed["target"])
+ pd.testing.assert_series_equal(df_inverse_transformed["target"], df["target"])
@pytest.mark.parametrize("model", ["arima", "holt"])
-@pytest.mark.parametrize("ts_name", ["ts_trend_seasonal", "ts_trend_seasonal_starting_with_nans"])
+@pytest.mark.parametrize(
+ "ts_name", ["ts_trend_seasonal", "ts_trend_seasonal_starting_with_nans", "ts_trend_seasonal_int_timestamp"]
+)
def test_inverse_transform_multi_segments(ts_name, model, request):
"""Test that transform + inverse_transform don't change tsdataset."""
ts = request.getfixturevalue(ts_name)
@@ -139,7 +167,7 @@ def test_inverse_transform_multi_segments(ts_name, model, request):
transform.fit_transform(ts)
transform.inverse_transform(ts)
df_inverse_transformed = ts.to_pandas(flatten=True)
- assert df_inverse_transformed["target"].equals(df["target"])
+ pd.testing.assert_series_equal(df_inverse_transformed["target"], df["target"])
@pytest.mark.parametrize("model_stl", ["arima", "holt"])
diff --git a/tests/test_transforms/test_inference/common.py b/tests/test_transforms/test_inference/common.py
deleted file mode 100644
index 5a38de52d..000000000
--- a/tests/test_transforms/test_inference/common.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from typing import Set
-from typing import Tuple
-
-import pandas as pd
-
-
-def find_columns_diff(df_before: pd.DataFrame, df_after: pd.DataFrame) -> Tuple[Set[str], Set[str], Set[str]]:
- columns_before_transform = set(df_before.columns)
- columns_after_transform = set(df_after.columns)
- created_columns = columns_after_transform - columns_before_transform
- removed_columns = columns_before_transform - columns_after_transform
-
- columns_to_check_changes = columns_after_transform.intersection(columns_before_transform)
- changed_columns = set()
- for column in columns_to_check_changes:
- if not df_before[column].equals(df_after[column]):
- changed_columns.add(column)
-
- return created_columns, removed_columns, changed_columns
diff --git a/tests/test_transforms/test_inference/conftest.py b/tests/test_transforms/test_inference/conftest.py
index 813ee4063..8d12b42e5 100644
--- a/tests/test_transforms/test_inference/conftest.py
+++ b/tests/test_transforms/test_inference/conftest.py
@@ -76,6 +76,61 @@ def ts_with_exog(regular_ts) -> TSDataset:
return ts
+@pytest.fixture
+def ts_with_exog_to_shift(regular_ts) -> TSDataset:
+ df = regular_ts.to_pandas(flatten=True)
+ periods = 120
+ timestamp = pd.date_range("2020-01-01", periods=periods)
+ feature = timestamp.weekday.astype(float)
+ df_exog_common = pd.DataFrame(
+ {
+ "timestamp": timestamp,
+ "feature_1": feature[:100].tolist() + [None] * 20,
+ "feature_2": feature[:105].tolist() + [None] * 15,
+ "feature_3": feature,
+ }
+ )
+ df_exog_wide = duplicate_data(df=df_exog_common, segments=regular_ts.segments)
+ ts = TSDataset(df=TSDataset.to_dataset(df).iloc[5:], df_exog=df_exog_wide, freq="D")
+ return ts
+
+
+@pytest.fixture
+def ts_with_external_timestamp(regular_ts) -> TSDataset:
+ df = regular_ts.to_pandas(flatten=True)
+ df_exog = df.copy()
+ df_exog["external_timestamp"] = df["timestamp"]
+ df_exog.drop(columns=["target"], inplace=True)
+ ts = TSDataset(
+ df=TSDataset.to_dataset(df).iloc[1:-10], df_exog=TSDataset.to_dataset(df_exog), freq="D", known_future="all"
+ )
+ return ts
+
+
+@pytest.fixture
+def ts_with_external_timestamp_one_month(regular_ts_one_month) -> TSDataset:
+ df = regular_ts_one_month.to_pandas(flatten=True)
+ df_exog = df.copy()
+ df_exog["external_timestamp"] = df["timestamp"]
+ df_exog.drop(columns=["target"], inplace=True)
+ ts = TSDataset(
+ df=TSDataset.to_dataset(df).iloc[1:-10], df_exog=TSDataset.to_dataset(df_exog), freq="M", known_future="all"
+ )
+ return ts
+
+
+@pytest.fixture
+def ts_with_external_int_timestamp(regular_ts) -> TSDataset:
+ df = regular_ts.to_pandas(flatten=True)
+ df_exog = df.copy()
+ df_exog["external_timestamp"] = np.arange(10, 110).tolist() * 3
+ df_exog.drop(columns=["target"], inplace=True)
+ ts = TSDataset(
+ df=TSDataset.to_dataset(df).iloc[1:-10], df_exog=TSDataset.to_dataset(df_exog), freq="D", known_future="all"
+ )
+ return ts
+
+
@pytest.fixture
def positive_ts() -> TSDataset:
periods = 100
@@ -163,6 +218,57 @@ def ts_to_resample() -> TSDataset:
return ts
+@pytest.fixture
+def ts_to_resample_int_timestamp() -> TSDataset:
+ df_1 = pd.DataFrame(
+ {
+ "timestamp": np.arange(24, 144),
+ "segment": "segment_1",
+ "target": 1,
+ }
+ )
+ df_2 = pd.DataFrame(
+ {
+ "timestamp": np.arange(24, 144),
+ "segment": "segment_2",
+ "target": ([1] + 23 * [0]) * 5,
+ }
+ )
+ df_3 = pd.DataFrame(
+ {
+ "timestamp": np.arange(24, 144),
+ "segment": "segment_3",
+ "target": ([4] + 23 * [0]) * 5,
+ }
+ )
+ df = pd.concat([df_1, df_2, df_3], ignore_index=True)
+
+ df_exog_1 = pd.DataFrame(
+ {
+ "timestamp": np.arange(24, 216, 24),
+ "segment": "segment_1",
+ "regressor_exog": 2,
+ }
+ )
+ df_exog_2 = pd.DataFrame(
+ {
+ "timestamp": np.arange(24, 216, 24),
+ "segment": "segment_2",
+ "regressor_exog": 40,
+ }
+ )
+ df_exog_3 = pd.DataFrame(
+ {
+ "timestamp": np.arange(24, 216, 24),
+ "segment": "segment_3",
+ "regressor_exog": 40,
+ }
+ )
+ df_exog = pd.concat([df_exog_1, df_exog_2, df_exog_3], ignore_index=True)
+ ts = TSDataset(df=TSDataset.to_dataset(df), freq=None, df_exog=TSDataset.to_dataset(df_exog), known_future="all")
+ return ts
+
+
@pytest.fixture
def ts_with_outliers(regular_ts) -> TSDataset:
df = regular_ts.to_pandas()
diff --git a/tests/test_transforms/test_inference/test_inverse_transform.py b/tests/test_transforms/test_inference/test_inverse_transform.py
index 0326a915b..9f47b5e2d 100644
--- a/tests/test_transforms/test_inference/test_inverse_transform.py
+++ b/tests/test_transforms/test_inference/test_inverse_transform.py
@@ -19,6 +19,7 @@
from etna.transforms import DeseasonalityTransform
from etna.transforms import DifferencingTransform
from etna.transforms import EventTransform
+from etna.transforms import ExogShiftTransform
from etna.transforms import FilterFeaturesTransform
from etna.transforms import FourierTransform
from etna.transforms import GaleShapleyFeatureSelectionTransform
@@ -58,11 +59,895 @@
from etna.transforms import TrendTransform
from etna.transforms import YeoJohnsonTransform
from etna.transforms.decomposition import RupturesChangePointsModel
-from tests.test_transforms.test_inference.common import find_columns_diff
+from tests.test_transforms.utils import assert_column_changes
+from tests.test_transforms.utils import find_columns_diff
+from tests.utils import convert_ts_to_int_timestamp
from tests.utils import select_segments_subset
from tests.utils import to_be_fixed
+class TestInverseTransformTrain:
+ """Test inverse transform on train dataset.
+
+ Expected that inverse transformation creates columns, removes columns and changes values.
+ """
+
+ def _test_inverse_transform_train(self, ts, transform, expected_changes):
+ # prepare data
+ train_ts = deepcopy(ts)
+ test_ts = deepcopy(ts)
+
+ # fit
+ transform.fit(train_ts)
+
+ # transform
+ transformed_test_ts = transform.transform(deepcopy(test_ts))
+
+ # inverse transform
+ inverse_transformed_test_ts = transform.inverse_transform(deepcopy(transformed_test_ts))
+
+ # check
+ assert_column_changes(
+ ts_1=transformed_test_ts, ts_2=inverse_transformed_test_ts, expected_changes=expected_changes
+ )
+ flat_test_df = test_ts.to_pandas(flatten=True)
+ flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
+ flat_inverse_transformed_test_df = inverse_transformed_test_ts.to_pandas(flatten=True)
+ created_columns, removed_columns, changed_columns = find_columns_diff(
+ flat_transformed_test_df, flat_inverse_transformed_test_df
+ )
+ pd.testing.assert_frame_equal(
+ flat_test_df[list(changed_columns)], flat_inverse_transformed_test_df[list(changed_columns)]
+ )
+
+ @pytest.mark.parametrize(
+ "transform, dataset_name, expected_changes",
+ [
+ # decomposition
+ (
+ ChangePointsSegmentationTransform(
+ in_column="target",
+ change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
+ out_column="res",
+ ),
+ "regular_ts",
+ {},
+ ),
+ (
+ ChangePointsTrendTransform(in_column="target"),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ ChangePointsLevelTransform(in_column="target"),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LinearTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
+ (TheilSenTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
+ (STLTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
+ (DeseasonalityTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
+ (
+ TrendTransform(
+ in_column="target",
+ change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
+ out_column="res",
+ ),
+ "regular_ts",
+ {},
+ ),
+ # encoders
+ (LabelEncoderTransform(in_column="weekday", out_column="res"), "ts_with_exog", {}),
+ (
+ OneHotEncoderTransform(in_column="weekday", out_column="res"),
+ "ts_with_exog",
+ {},
+ ),
+ (MeanSegmentEncoderTransform(), "regular_ts", {}),
+ (SegmentEncoderTransform(), "regular_ts", {}),
+ # feature_selection
+ (FilterFeaturesTransform(exclude=["year"]), "ts_with_exog", {}),
+ (FilterFeaturesTransform(exclude=["year"], return_features=True), "ts_with_exog", {"create": {"year"}}),
+ (
+ GaleShapleyFeatureSelectionTransform(relevance_table=StatisticsRelevanceTable(), top_k=2),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ GaleShapleyFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, return_features=True
+ ),
+ "ts_with_exog",
+ {"create": {"month", "year", "weekday"}},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=True
+ ),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=False
+ ),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(),
+ top_k=2,
+ return_features=True,
+ fast_redundancy=True,
+ ),
+ "ts_with_exog",
+ {"create": {"weekday", "monthday", "positive"}},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, return_features=True, fast_redundancy=False
+ ),
+ "ts_with_exog",
+ {"create": {"weekday", "monthday", "positive"}},
+ ),
+ (
+ TreeFeatureSelectionTransform(model=DecisionTreeRegressor(random_state=42), top_k=2),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ TreeFeatureSelectionTransform(
+ model=DecisionTreeRegressor(random_state=42), top_k=2, return_features=True
+ ),
+ "ts_with_exog",
+ {"create": {"year", "month", "monthday"}},
+ ),
+ # math
+ (
+ AddConstTransform(in_column="target", value=1, inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts", {"change": {"target"}}),
+ (
+ BinaryOperationTransform(
+ left_column="positive", right_column="target", operator="+", out_column="target"
+ ),
+ "ts_with_exog",
+ {"change": {"target"}},
+ ),
+ (
+ BinaryOperationTransform(
+ left_column="positive", right_column="target", operator="+", out_column="new_col"
+ ),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ LagTransform(in_column="target", lags=[1, 2, 3], out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {},
+ ),
+ (
+ LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ LambdaTransform(
+ in_column="target",
+ transform_func=lambda x: x + 1,
+ inverse_transform_func=lambda x: x - 1,
+ inplace=True,
+ ),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LimitTransform(in_column="target"), "regular_ts", {}),
+ (LimitTransform(in_column="target", lower_bound=-50, upper_bound=50), "regular_ts", {"change": {"target"}}),
+ (LogTransform(in_column="target", inplace=False, out_column="res"), "positive_ts", {}),
+ (LogTransform(in_column="target", inplace=True), "positive_ts", {"change": {"target"}}),
+ (
+ DifferencingTransform(in_column="target", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (DifferencingTransform(in_column="target", inplace=True), "regular_ts", {"change": {"target"}}),
+ (MADTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (MaxTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (MeanTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (MedianTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (
+ MinMaxDifferenceTransform(in_column="target", window=7, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (MinTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (
+ QuantileTransform(in_column="target", quantile=0.9, window=7, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (StdTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (SumTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (
+ BoxCoxTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "positive_ts",
+ {},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="per-segment", inplace=True),
+ "positive_ts",
+ {"change": {"target"}},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "positive_ts",
+ {},
+ ),
+ (BoxCoxTransform(in_column="target", mode="macro", inplace=True), "positive_ts", {"change": {"target"}}),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ # setting clip=False is important
+ MinMaxScalerTransform(in_column="target", mode="per-segment", clip=False, inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ # setting clip=False is important
+ MinMaxScalerTransform(in_column="target", mode="macro", clip=False, inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (YeoJohnsonTransform(in_column="target", mode="macro", inplace=True), "regular_ts", {"change": {"target"}}),
+ # missing_values
+ (
+ ResampleWithDistributionTransform(
+ in_column="regressor_exog", distribution_column="target", inplace=False, out_column="res"
+ ),
+ "ts_to_resample",
+ {},
+ ),
+ (TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {"change": {"target"}}),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ (TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {"change": {"target"}}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {"change": {"target"}}),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="running_mean"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ # outliers
+ (DensityOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
+ (MedianOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
+ (
+ PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel),
+ "ts_with_outliers",
+ {"change": {"target"}},
+ ),
+ # timestamp
+ (
+ DateFlagsTransform(out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {},
+ ),
+ (HolidayTransform(out_column="res", mode="binary"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (HolidayTransform(out_column="res", mode="category"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {},
+ ),
+ (SpecialDaysTransform(), "regular_ts", {}),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ TimeFlagsTransform(out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog", {}),
+ (
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
+ "ts_with_binary_exog",
+ {},
+ ),
+ ],
+ )
+ def test_inverse_transform_train_datetime_timestamp(self, transform, dataset_name, expected_changes, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_inverse_transform_train(ts, transform, expected_changes=expected_changes)
+
+ # It is the only transform that doesn't change values back during `inverse_transform`
+ @to_be_fixed(raises=AssertionError)
+ @pytest.mark.parametrize(
+ "transform, dataset_name, expected_changes",
+ [
+ (
+ ResampleWithDistributionTransform(
+ in_column="regressor_exog", distribution_column="target", inplace=True
+ ),
+ "ts_to_resample",
+ {"change": {"regressor_exog"}},
+ ),
+ (
+ ResampleWithDistributionTransform(
+ in_column="regressor_exog", distribution_column="target", inplace=True
+ ),
+ "ts_to_resample_int_timestamp",
+ {"change": {"regressor_exog"}},
+ ),
+ ],
+ )
+ def test_inverse_transform_train_fail_resample(self, transform, dataset_name, expected_changes, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_inverse_transform_train(ts, transform, expected_changes=expected_changes)
+
+ @pytest.mark.parametrize(
+ "transform, dataset_name, expected_changes",
+ [
+ # decomposition
+ (
+ ChangePointsSegmentationTransform(
+ in_column="target",
+ change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
+ out_column="res",
+ ),
+ "regular_ts",
+ {},
+ ),
+ (
+ ChangePointsTrendTransform(in_column="target"),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ ChangePointsLevelTransform(in_column="target"),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LinearTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
+ (TheilSenTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
+ (STLTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
+ (DeseasonalityTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
+ (
+ TrendTransform(
+ in_column="target",
+ change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
+ out_column="res",
+ ),
+ "regular_ts",
+ {},
+ ),
+ # encoders
+ (LabelEncoderTransform(in_column="weekday", out_column="res"), "ts_with_exog", {}),
+ (
+ OneHotEncoderTransform(in_column="weekday", out_column="res"),
+ "ts_with_exog",
+ {},
+ ),
+ (MeanSegmentEncoderTransform(), "regular_ts", {}),
+ (SegmentEncoderTransform(), "regular_ts", {}),
+ # feature_selection
+ (FilterFeaturesTransform(exclude=["year"]), "ts_with_exog", {}),
+ (FilterFeaturesTransform(exclude=["year"], return_features=True), "ts_with_exog", {"create": {"year"}}),
+ (
+ GaleShapleyFeatureSelectionTransform(relevance_table=StatisticsRelevanceTable(), top_k=2),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ GaleShapleyFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, return_features=True
+ ),
+ "ts_with_exog",
+ {"create": {"month", "year", "weekday"}},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=True
+ ),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=False
+ ),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(),
+ top_k=2,
+ return_features=True,
+ fast_redundancy=True,
+ ),
+ "ts_with_exog",
+ {"create": {"weekday", "monthday", "positive"}},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, return_features=True, fast_redundancy=False
+ ),
+ "ts_with_exog",
+ {"create": {"weekday", "monthday", "positive"}},
+ ),
+ (
+ TreeFeatureSelectionTransform(model=DecisionTreeRegressor(random_state=42), top_k=2),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ TreeFeatureSelectionTransform(
+ model=DecisionTreeRegressor(random_state=42), top_k=2, return_features=True
+ ),
+ "ts_with_exog",
+ {"create": {"year", "month", "monthday"}},
+ ),
+ # math
+ (
+ AddConstTransform(in_column="target", value=1, inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts", {"change": {"target"}}),
+ (
+ BinaryOperationTransform(
+ left_column="positive", right_column="target", operator="+", out_column="target"
+ ),
+ "ts_with_exog",
+ {"change": {"target"}},
+ ),
+ (
+ BinaryOperationTransform(
+ left_column="positive", right_column="target", operator="+", out_column="new_col"
+ ),
+ "ts_with_exog",
+ {},
+ ),
+ (
+ LagTransform(in_column="target", lags=[1, 2, 3], out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {},
+ ),
+ (
+ LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ LambdaTransform(
+ in_column="target",
+ transform_func=lambda x: x + 1,
+ inverse_transform_func=lambda x: x - 1,
+ inplace=True,
+ ),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LimitTransform(in_column="target"), "regular_ts", {}),
+ (LimitTransform(in_column="target", lower_bound=-50, upper_bound=50), "regular_ts", {"change": {"target"}}),
+ (LogTransform(in_column="target", inplace=False, out_column="res"), "positive_ts", {}),
+ (LogTransform(in_column="target", inplace=True), "positive_ts", {"change": {"target"}}),
+ (
+ DifferencingTransform(in_column="target", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (DifferencingTransform(in_column="target", inplace=True), "regular_ts", {"change": {"target"}}),
+ (MADTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (MaxTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (MeanTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (MedianTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (
+ MinMaxDifferenceTransform(in_column="target", window=7, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (MinTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (
+ QuantileTransform(in_column="target", quantile=0.9, window=7, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (StdTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (SumTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
+ (
+ BoxCoxTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "positive_ts",
+ {},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="per-segment", inplace=True),
+ "positive_ts",
+ {"change": {"target"}},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "positive_ts",
+ {},
+ ),
+ (BoxCoxTransform(in_column="target", mode="macro", inplace=True), "positive_ts", {"change": {"target"}}),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ # setting clip=False is important
+ MinMaxScalerTransform(in_column="target", mode="per-segment", clip=False, inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ # setting clip=False is important
+ MinMaxScalerTransform(in_column="target", mode="macro", clip=False, inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (YeoJohnsonTransform(in_column="target", mode="macro", inplace=True), "regular_ts", {"change": {"target"}}),
+ # missing_values
+ (TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {"change": {"target"}}),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ (TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {"change": {"target"}}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {"change": {"target"}}),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="running_mean"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ # outliers
+ (DensityOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
+ (MedianOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
+ # timestamp
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res"),
+ "regular_ts",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {},
+ ),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ # TODO: fix after discussing conceptual problems
+ # (
+ # HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ # "ts_with_external_timestamp_one_month",
+ # {},
+ # ),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog", {}),
+ (
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
+ "ts_with_binary_exog",
+ {},
+ ),
+ ],
+ )
+ def test_inverse_transform_train_int_timestamp(self, transform, dataset_name, expected_changes, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ self._test_inverse_transform_train(ts_int_timestamp, transform, expected_changes=expected_changes)
+
+ @pytest.mark.parametrize(
+ "transform, dataset_name, expected_changes",
+ [
+ (
+ ResampleWithDistributionTransform(
+ in_column="regressor_exog", distribution_column="target", inplace=False, out_column="res"
+ ),
+ "ts_to_resample_int_timestamp",
+ {},
+ ),
+ ],
+ )
+ def test_inverse_transform_train_int_timestamp_non_inplace_resample(
+ self, transform, dataset_name, expected_changes, request
+ ):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_inverse_transform_train(ts, transform, expected_changes=expected_changes)
+
+ @pytest.mark.parametrize(
+ "transform, dataset_name, error_match",
+ [
+ # outliers
+ (
+ PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel),
+ "ts_with_outliers",
+ "Invalid timestamp! Only datetime type is supported",
+ ),
+ # timestamp
+ (DateFlagsTransform(out_column="res"), "regular_ts", "Transform can't work with integer index"),
+ (
+ HolidayTransform(out_column="res", mode="binary"),
+ "regular_ts",
+ "Transform can't work with integer index",
+ ),
+ (
+ HolidayTransform(out_column="res", mode="category"),
+ "regular_ts",
+ "Transform can't work with integer index",
+ ),
+ (
+ HolidayTransform(out_column="res", mode="days_count"),
+ "regular_ts_one_month",
+ "Transform can't work with integer index",
+ ),
+ (TimeFlagsTransform(out_column="res"), "regular_ts", "Transform can't work with integer index"),
+ (SpecialDaysTransform(), "regular_ts", "Transform can't work with integer index"),
+ ],
+ )
+ def test_inverse_transform_train_int_timestamp_not_supported(self, transform, dataset_name, error_match, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ with pytest.raises(ValueError, match=error_match):
+ self._test_inverse_transform_train(ts_int_timestamp, transform, expected_changes={})
+
+
class TestInverseTransformTrainSubsetSegments:
"""Test inverse transform on train part of subset of segments.
@@ -140,7 +1025,7 @@ def _test_inverse_transform_train_subset_segments(self, ts, transform, segments)
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(),
top_k=2,
- fast_redundancy=GaleShapleyFeatureSelectionTransform,
+ fast_redundancy=False,
),
"ts_with_exog",
),
@@ -150,7 +1035,7 @@ def _test_inverse_transform_train_subset_segments(self, ts, transform, segments)
(AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts"),
(
BinaryOperationTransform(
- left_column="positive", right_column="target", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
),
@@ -161,6 +1046,10 @@ def _test_inverse_transform_train_subset_segments(self, ts, transform, segments)
"ts_with_exog",
),
(LagTransform(in_column="target", lags=[1, 2, 3]), "regular_ts"),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False),
"regular_ts",
@@ -239,12 +1128,44 @@ def _test_inverse_transform_train_subset_segments(self, ts, transform, segments)
(PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel), "ts_with_outliers"),
# timestamp
(DateFlagsTransform(), "regular_ts"),
- (FourierTransform(period=7, order=2), "regular_ts"),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
+ (FourierTransform(period=7, order=2, out_column="res"), "regular_ts"),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ ),
(HolidayTransform(mode="binary"), "regular_ts"),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(HolidayTransform(mode="category"), "regular_ts"),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(HolidayTransform(mode="days_count"), "regular_ts_one_month"),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ ),
(SpecialDaysTransform(), "regular_ts"),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(TimeFlagsTransform(), "regular_ts"),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog"),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
@@ -360,7 +1281,7 @@ def _test_inverse_transform_future_subset_segments(self, ts, transform, segments
(AddConstTransform(in_column="positive", value=1, inplace=True), "ts_with_exog"),
(
BinaryOperationTransform(
- left_column="positive", right_column="target", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
),
@@ -371,6 +1292,10 @@ def _test_inverse_transform_future_subset_segments(self, ts, transform, segments
"ts_with_exog",
),
(LagTransform(in_column="target", lags=[1, 2, 3]), "regular_ts"),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False),
"regular_ts",
@@ -473,12 +1398,44 @@ def _test_inverse_transform_future_subset_segments(self, ts, transform, segments
(PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel), "ts_with_outliers"),
# timestamp
(DateFlagsTransform(), "regular_ts"),
- (FourierTransform(period=7, order=2), "regular_ts"),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
+ (FourierTransform(period=7, order=2, out_column="res"), "regular_ts"),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ ),
(HolidayTransform(mode="binary"), "regular_ts"),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(HolidayTransform(mode="category"), "regular_ts"),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(HolidayTransform(mode="days_count"), "regular_ts_one_month"),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ ),
(SpecialDaysTransform(), "regular_ts"),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(TimeFlagsTransform(), "regular_ts"),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog"),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
@@ -514,19 +1471,15 @@ def _test_inverse_transform_train_new_segments(self, ts, transform, train_segmen
inverse_transformed_test_ts = transform.inverse_transform(deepcopy(transformed_test_ts))
# check
- expected_columns_to_create = expected_changes.get("create", set())
- expected_columns_to_remove = expected_changes.get("remove", set())
- expected_columns_to_change = expected_changes.get("change", set())
+ assert_column_changes(
+ ts_1=transformed_test_ts, ts_2=inverse_transformed_test_ts, expected_changes=expected_changes
+ )
flat_test_df = test_ts.to_pandas(flatten=True)
flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
flat_inverse_transformed_test_df = inverse_transformed_test_ts.to_pandas(flatten=True)
created_columns, removed_columns, changed_columns = find_columns_diff(
flat_transformed_test_df, flat_inverse_transformed_test_df
)
-
- assert created_columns == expected_columns_to_create
- assert removed_columns == expected_columns_to_remove
- assert changed_columns == expected_columns_to_change
pd.testing.assert_frame_equal(
flat_test_df[list(changed_columns)], flat_inverse_transformed_test_df[list(changed_columns)]
)
@@ -605,10 +1558,10 @@ def _test_inverse_transform_train_new_segments(self, ts, transform, train_segmen
(AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts", {"change": {"target"}}),
(
BinaryOperationTransform(
- left_column="positive", right_column="target", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
- {"change": {"positive"}},
+ {"change": {"target"}},
),
(
BinaryOperationTransform(
@@ -622,6 +1575,11 @@ def _test_inverse_transform_train_new_segments(self, ts, transform, train_segmen
"regular_ts",
{},
),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {},
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
@@ -721,19 +1679,54 @@ def _test_inverse_transform_train_new_segments(self, ts, transform, train_segmen
"regular_ts",
{},
),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{},
),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {},
+ ),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{},
),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog", {}),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
@@ -818,6 +1811,10 @@ def test_inverse_transform_train_new_segments(self, transform, dataset_name, exp
(PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel), "ts_with_outliers"),
# timestamp
(SpecialDaysTransform(), "regular_ts"),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
],
)
def test_inverse_transform_train_new_segments_not_implemented(self, transform, dataset_name, request):
@@ -854,19 +1851,15 @@ def _test_inverse_transform_future_new_segments(self, ts, transform, train_segme
inverse_transformed_test_ts = transform.inverse_transform(deepcopy(transformed_test_ts))
# check
- expected_columns_to_create = expected_changes.get("create", set())
- expected_columns_to_remove = expected_changes.get("remove", set())
- expected_columns_to_change = expected_changes.get("change", set())
+ assert_column_changes(
+ ts_1=transformed_test_ts, ts_2=inverse_transformed_test_ts, expected_changes=expected_changes
+ )
flat_test_df = test_ts.to_pandas(flatten=True)
flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
flat_inverse_transformed_test_df = inverse_transformed_test_ts.to_pandas(flatten=True)
created_columns, removed_columns, changed_columns = find_columns_diff(
flat_transformed_test_df, flat_inverse_transformed_test_df
)
-
- assert created_columns == expected_columns_to_create
- assert removed_columns == expected_columns_to_remove
- assert changed_columns == expected_columns_to_change
pd.testing.assert_frame_equal(
flat_test_df[list(changed_columns)], flat_inverse_transformed_test_df[list(changed_columns)]
)
@@ -917,14 +1910,14 @@ def _test_inverse_transform_future_new_segments(self, ts, transform, train_segme
(AddConstTransform(in_column="positive", value=1, inplace=True), "ts_with_exog", {"change": {"positive"}}),
(
BinaryOperationTransform(
- left_column="positive", right_column="weekday", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
- {"change": {"positive"}},
+ {},
),
(
BinaryOperationTransform(
- left_column="positive", right_column="weekday", operator="+", out_column="new_col"
+ left_column="positive", right_column="target", operator="+", out_column="new_col"
),
"ts_with_exog",
{},
@@ -934,6 +1927,11 @@ def _test_inverse_transform_future_new_segments(self, ts, transform, train_segme
"regular_ts",
{},
),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {},
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
@@ -1080,19 +2078,54 @@ def _test_inverse_transform_future_new_segments(self, ts, transform, train_segme
"regular_ts",
{},
),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{},
),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {},
+ ),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{},
),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog", {}),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
@@ -1184,6 +2217,10 @@ def test_inverse_transform_future_new_segments(self, transform, dataset_name, ex
(PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel), "ts_with_outliers"),
# timestamp
(SpecialDaysTransform(), "regular_ts"),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
],
)
def test_inverse_transform_future_new_segments_not_implemented(self, transform, dataset_name, request):
@@ -1262,19 +2299,15 @@ def _test_inverse_transform_future_with_target(
inverse_transformed_test_ts = transform.inverse_transform(deepcopy(transformed_test_ts))
# check
- expected_columns_to_create = expected_changes.get("create", set())
- expected_columns_to_remove = expected_changes.get("remove", set())
- expected_columns_to_change = expected_changes.get("change", set())
+ assert_column_changes(
+ ts_1=transformed_test_ts, ts_2=inverse_transformed_test_ts, expected_changes=expected_changes
+ )
flat_test_df = test_ts.to_pandas(flatten=True)
flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
flat_inverse_transformed_test_df = inverse_transformed_test_ts.to_pandas(flatten=True)
created_columns, removed_columns, changed_columns = find_columns_diff(
flat_transformed_test_df, flat_inverse_transformed_test_df
)
-
- assert created_columns == expected_columns_to_create
- assert removed_columns == expected_columns_to_remove
- assert changed_columns == expected_columns_to_change
pd.testing.assert_frame_equal(
flat_test_df[list(changed_columns)],
flat_inverse_transformed_test_df[list(changed_columns)],
@@ -1392,10 +2425,10 @@ def _test_inverse_transform_future_with_target(
(AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts", {"change": {"target"}}),
(
BinaryOperationTransform(
- left_column="positive", right_column="target", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
- {"change": {"positive"}},
+ {"change": {"target"}},
),
(
BinaryOperationTransform(
@@ -1409,6 +2442,11 @@ def _test_inverse_transform_future_with_target(
"regular_ts",
{},
),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {},
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
@@ -1572,12 +2610,13 @@ def _test_inverse_transform_future_with_target(
"ts_to_resample",
{},
),
- (
- # this behaviour can be unexpected for someone
- TimeSeriesImputerTransform(in_column="target"),
- "ts_to_fill",
- {},
- ),
+ # this behaviour can be unexpected for someone
+ (TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="running_mean"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"), "ts_to_fill", {}),
# outliers
(DensityOutliersTransform(in_column="target"), "ts_with_outliers", {}),
(MedianOutliersTransform(in_column="target"), "ts_with_outliers", {}),
@@ -1588,20 +2627,60 @@ def _test_inverse_transform_future_with_target(
"regular_ts",
{},
),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{},
),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {},
+ ),
+ (SpecialDaysTransform(), "regular_ts", {}),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{},
),
- (SpecialDaysTransform(), "regular_ts", {}),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog", {}),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
@@ -1674,19 +2753,15 @@ def _test_inverse_transform_future_without_target(
inverse_transformed_test_ts = transform.inverse_transform(deepcopy(transformed_test_ts))
# check
- expected_columns_to_create = expected_changes.get("create", set())
- expected_columns_to_remove = expected_changes.get("remove", set())
- expected_columns_to_change = expected_changes.get("change", set())
+ assert_column_changes(
+ ts_1=transformed_test_ts, ts_2=inverse_transformed_test_ts, expected_changes=expected_changes
+ )
flat_test_df = test_ts.to_pandas(flatten=True)
flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
flat_inverse_transformed_test_df = inverse_transformed_test_ts.to_pandas(flatten=True)
created_columns, removed_columns, changed_columns = find_columns_diff(
flat_transformed_test_df, flat_inverse_transformed_test_df
)
-
- assert created_columns == expected_columns_to_create
- assert removed_columns == expected_columns_to_remove
- assert changed_columns == expected_columns_to_change
pd.testing.assert_frame_equal(
flat_test_df[list(changed_columns)],
flat_inverse_transformed_test_df[list(changed_columns)],
@@ -1787,14 +2862,14 @@ def _test_inverse_transform_future_without_target(
(AddConstTransform(in_column="positive", value=1, inplace=True), "ts_with_exog", {"change": {"positive"}}),
(
BinaryOperationTransform(
- left_column="positive", right_column="weekday", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
- {"change": {"positive"}},
+ {},
),
(
BinaryOperationTransform(
- left_column="positive", right_column="weekday", operator="+", out_column="new_col"
+ left_column="positive", right_column="target", operator="+", out_column="new_col"
),
"ts_with_exog",
{},
@@ -1804,6 +2879,11 @@ def _test_inverse_transform_future_without_target(
"regular_ts",
{},
),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {},
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
@@ -2020,12 +3100,13 @@ def _test_inverse_transform_future_without_target(
"ts_to_resample",
{},
),
- (
- # this behaviour can be unexpected for someone
- TimeSeriesImputerTransform(in_column="target"),
- "ts_to_fill",
- {},
- ),
+ # this behaviour can be unexpected for someone
+ (TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="running_mean"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"), "ts_to_fill", {}),
# outliers
(DensityOutliersTransform(in_column="target"), "ts_with_outliers", {}),
(MedianOutliersTransform(in_column="target"), "ts_with_outliers", {}),
@@ -2036,20 +3117,60 @@ def _test_inverse_transform_future_without_target(
"regular_ts",
{},
),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{},
),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {},
+ ),
+ (SpecialDaysTransform(), "regular_ts", {}),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{},
),
- (SpecialDaysTransform(), "regular_ts", {}),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {},
+ ),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog", {}),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
diff --git a/tests/test_transforms/test_inference/test_transform.py b/tests/test_transforms/test_inference/test_transform.py
index e7b8a6f8b..96dd9bbf9 100644
--- a/tests/test_transforms/test_inference/test_transform.py
+++ b/tests/test_transforms/test_inference/test_transform.py
@@ -19,6 +19,7 @@
from etna.transforms import DeseasonalityTransform
from etna.transforms import DifferencingTransform
from etna.transforms import EventTransform
+from etna.transforms import ExogShiftTransform
from etna.transforms import FilterFeaturesTransform
from etna.transforms import FourierTransform
from etna.transforms import GaleShapleyFeatureSelectionTransform
@@ -58,10 +59,848 @@
from etna.transforms import TrendTransform
from etna.transforms import YeoJohnsonTransform
from etna.transforms.decomposition import RupturesChangePointsModel
-from tests.test_transforms.test_inference.common import find_columns_diff
+from tests.test_transforms.utils import assert_column_changes
+from tests.utils import convert_ts_to_int_timestamp
from tests.utils import select_segments_subset
-# TODO: figure out what happened to TrendTransform
+
+class TestTransformTrain:
+ """Test transform on train dataset.
+
+ Expected that transformation creates columns, removes columns and changes values.
+ """
+
+ def _test_transform_train(self, ts, transform, expected_changes):
+ # prepare data
+ train_ts = deepcopy(ts)
+ test_ts = deepcopy(ts)
+
+ # fit
+ transform.fit(train_ts)
+
+ # transform
+ transformed_test_ts = transform.transform(deepcopy(test_ts))
+
+ # check
+ assert_column_changes(ts_1=test_ts, ts_2=transformed_test_ts, expected_changes=expected_changes)
+
+ @pytest.mark.parametrize(
+ "transform, dataset_name, expected_changes",
+ [
+ # decomposition
+ (
+ ChangePointsSegmentationTransform(
+ in_column="target",
+ change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
+ out_column="res",
+ ),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ ChangePointsTrendTransform(in_column="target"),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ ChangePointsLevelTransform(in_column="target"),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LinearTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
+ (TheilSenTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
+ (STLTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
+ (DeseasonalityTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
+ (
+ TrendTransform(
+ in_column="target",
+ change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
+ out_column="res",
+ ),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ # encoders
+ (LabelEncoderTransform(in_column="weekday", out_column="res"), "ts_with_exog", {"create": {"res"}}),
+ (
+ OneHotEncoderTransform(in_column="weekday", out_column="res"),
+ "ts_with_exog",
+ {"create": {"res_0", "res_1", "res_2", "res_3", "res_4", "res_5", "res_6"}},
+ ),
+ (MeanSegmentEncoderTransform(), "regular_ts", {"create": {"segment_mean"}}),
+ (SegmentEncoderTransform(), "regular_ts", {"create": {"segment_code"}}),
+ # feature_selection
+ (FilterFeaturesTransform(exclude=["year"]), "ts_with_exog", {"remove": {"year"}}),
+ (
+ GaleShapleyFeatureSelectionTransform(relevance_table=StatisticsRelevanceTable(), top_k=2),
+ "ts_with_exog",
+ {"remove": {"month", "year", "weekday"}},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=True
+ ),
+ "ts_with_exog",
+ {"remove": {"weekday", "monthday", "positive"}},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=False
+ ),
+ "ts_with_exog",
+ {"remove": {"weekday", "monthday", "positive"}},
+ ),
+ (
+ TreeFeatureSelectionTransform(model=DecisionTreeRegressor(random_state=42), top_k=2),
+ "ts_with_exog",
+ {"remove": {"month", "monthday", "year"}},
+ ),
+ # math
+ (
+ AddConstTransform(in_column="target", value=1, inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts", {"change": {"target"}}),
+ (
+ BinaryOperationTransform(
+ left_column="positive", right_column="target", operator="+", out_column="target"
+ ),
+ "ts_with_exog",
+ {"change": {"target"}},
+ ),
+ (
+ BinaryOperationTransform(
+ left_column="positive", right_column="target", operator="+", out_column="new_col"
+ ),
+ "ts_with_exog",
+ {"create": {"new_col"}},
+ ),
+ (
+ LagTransform(in_column="target", lags=[1, 2, 3], out_column="res"),
+ "regular_ts",
+ {"create": {"res_1", "res_2", "res_3"}},
+ ),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {"create": {"feature_1_shift_7", "feature_2_shift_2"}, "remove": {"feature_1", "feature_2"}},
+ ),
+ (
+ LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ LambdaTransform(
+ in_column="target",
+ transform_func=lambda x: x + 1,
+ inverse_transform_func=lambda x: x - 1,
+ inplace=True,
+ ),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LimitTransform(in_column="target"), "regular_ts", {}),
+ (LimitTransform(in_column="target", lower_bound=-50, upper_bound=50), "regular_ts", {"change": {"target"}}),
+ (LogTransform(in_column="target", inplace=False, out_column="res"), "positive_ts", {"create": {"res"}}),
+ (LogTransform(in_column="target", inplace=True), "positive_ts", {"change": {"target"}}),
+ (
+ DifferencingTransform(in_column="target", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (DifferencingTransform(in_column="target", inplace=True), "regular_ts", {"change": {"target"}}),
+ (MADTransform(in_column="target", window=14, out_column="res"), "regular_ts", {"create": {"res"}}),
+ (MaxTransform(in_column="target", window=14, out_column="res"), "regular_ts", {"create": {"res"}}),
+ (MeanTransform(in_column="target", window=14, out_column="res"), "regular_ts", {"create": {"res"}}),
+ (MedianTransform(in_column="target", window=14, out_column="res"), "regular_ts", {"create": {"res"}}),
+ (
+ MinMaxDifferenceTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (MinTransform(in_column="target", window=14, out_column="res"), "regular_ts", {"create": {"res"}}),
+ (
+ QuantileTransform(in_column="target", quantile=0.9, window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (StdTransform(in_column="target", window=14, out_column="res"), "regular_ts", {"create": {"res"}}),
+ (SumTransform(in_column="target", window=14, out_column="res"), "regular_ts", {"create": {"res"}}),
+ (
+ BoxCoxTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "positive_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="per-segment", inplace=True),
+ "positive_ts",
+ {"change": {"target"}},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "positive_ts",
+ {"create": {"res_target"}},
+ ),
+ (BoxCoxTransform(in_column="target", mode="macro", inplace=True), "positive_ts", {"change": {"target"}}),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (YeoJohnsonTransform(in_column="target", mode="macro", inplace=True), "regular_ts", {"change": {"target"}}),
+ # missing_values
+ (
+ ResampleWithDistributionTransform(
+ in_column="regressor_exog", distribution_column="target", inplace=False, out_column="res"
+ ),
+ "ts_to_resample",
+ {"create": {"res"}},
+ ),
+ (
+ ResampleWithDistributionTransform(
+ in_column="regressor_exog", distribution_column="target", inplace=True
+ ),
+ "ts_to_resample",
+ {"change": {"regressor_exog"}},
+ ),
+ (TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {"change": {"target"}}),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ (TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {"change": {"target"}}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {"change": {"target"}}),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="running_mean"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ # outliers
+ (DensityOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
+ (MedianOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
+ (
+ PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel),
+ "ts_with_outliers",
+ {"change": {"target"}},
+ ),
+ # timestamp
+ (
+ DateFlagsTransform(out_column="res"),
+ "regular_ts",
+ {"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
+ ),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res"),
+ "regular_ts",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (HolidayTransform(out_column="res", mode="binary"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
+ (HolidayTransform(out_column="res", mode="category"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
+ (HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {"create": {"res"}},
+ ),
+ (SpecialDaysTransform(), "regular_ts", {"create": {"anomaly_weekdays", "anomaly_monthdays"}}),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"anomaly_weekdays", "anomaly_monthdays"}},
+ ),
+ (
+ TimeFlagsTransform(out_column="res"),
+ "regular_ts",
+ {"create": {"res_minute_in_hour_number", "res_hour_number"}},
+ ),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_minute_in_hour_number", "res_hour_number"}},
+ ),
+ (
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1),
+ "ts_with_binary_exog",
+ {"create": {"holiday_pre", "holiday_post"}},
+ ),
+ (
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
+ "ts_with_binary_exog",
+ {"create": {"holiday_pre", "holiday_post"}},
+ ),
+ ],
+ )
+ def test_transform_train_datetime_timestamp(self, transform, dataset_name, expected_changes, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_transform_train(ts, transform, expected_changes=expected_changes)
+
+ @pytest.mark.parametrize(
+ "transform, dataset_name, expected_changes",
+ [
+ # decomposition
+ (
+ ChangePointsSegmentationTransform(
+ in_column="target",
+ change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
+ out_column="res",
+ ),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ ChangePointsTrendTransform(in_column="target"),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ ChangePointsLevelTransform(in_column="target"),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LinearTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
+ (TheilSenTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
+ (STLTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
+ (DeseasonalityTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
+ (
+ TrendTransform(
+ in_column="target",
+ change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
+ out_column="res",
+ ),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ # encoders
+ (LabelEncoderTransform(in_column="weekday", out_column="res"), "ts_with_exog", {"create": {"res"}}),
+ (
+ OneHotEncoderTransform(in_column="weekday", out_column="res"),
+ "ts_with_exog",
+ {"create": {"res_0", "res_1", "res_2", "res_3", "res_4", "res_5", "res_6"}},
+ ),
+ (MeanSegmentEncoderTransform(), "regular_ts", {"create": {"segment_mean"}}),
+ (SegmentEncoderTransform(), "regular_ts", {"create": {"segment_code"}}),
+ # feature_selection
+ (FilterFeaturesTransform(exclude=["year"]), "ts_with_exog", {"remove": {"year"}}),
+ (
+ GaleShapleyFeatureSelectionTransform(relevance_table=StatisticsRelevanceTable(), top_k=2),
+ "ts_with_exog",
+ {"remove": {"month", "year", "weekday"}},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=True
+ ),
+ "ts_with_exog",
+ {"remove": {"weekday", "monthday", "positive"}},
+ ),
+ (
+ MRMRFeatureSelectionTransform(
+ relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=False
+ ),
+ "ts_with_exog",
+ {"remove": {"weekday", "monthday", "positive"}},
+ ),
+ (
+ TreeFeatureSelectionTransform(model=DecisionTreeRegressor(random_state=42), top_k=2),
+ "ts_with_exog",
+ {"remove": {"month", "monthday", "year"}},
+ ),
+ # math
+ (
+ AddConstTransform(in_column="target", value=1, inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ AddConstTransform(in_column="target", value=1, inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ BinaryOperationTransform(
+ left_column="positive", right_column="target", operator="+", out_column="target"
+ ),
+ "ts_with_exog",
+ {"change": {"target"}},
+ ),
+ (
+ BinaryOperationTransform(
+ left_column="positive", right_column="target", operator="+", out_column="new_col"
+ ),
+ "ts_with_exog",
+ {"create": {"new_col"}},
+ ),
+ (
+ LagTransform(in_column="target", lags=[1, 2, 3], out_column="res"),
+ "regular_ts",
+ {"create": {"res_1", "res_2", "res_3"}},
+ ),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {"create": {"feature_1_shift_7", "feature_2_shift_2"}, "remove": {"feature_1", "feature_2"}},
+ ),
+ (
+ LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ LambdaTransform(
+ in_column="target",
+ transform_func=lambda x: x + 1,
+ inverse_transform_func=lambda x: x - 1,
+ inplace=True,
+ ),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LimitTransform(in_column="target"), "regular_ts", {}),
+ (
+ LimitTransform(in_column="target", lower_bound=-50, upper_bound=50),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (LogTransform(in_column="target", inplace=False, out_column="res"), "positive_ts", {"create": {"res"}}),
+ (LogTransform(in_column="target", inplace=True), "positive_ts", {"change": {"target"}}),
+ (
+ DifferencingTransform(in_column="target", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ DifferencingTransform(in_column="target", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MADTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ MaxTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ MeanTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ MedianTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ MinMaxDifferenceTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ MinTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ QuantileTransform(in_column="target", quantile=0.9, window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ StdTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ SumTransform(in_column="target", window=14, out_column="res"),
+ "regular_ts",
+ {"create": {"res"}},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "positive_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="per-segment", inplace=True),
+ "positive_ts",
+ {"change": {"target"}},
+ ),
+ (
+ BoxCoxTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "positive_ts",
+ {"create": {"res_target"}},
+ ),
+ (BoxCoxTransform(in_column="target", mode="macro", inplace=True), "positive_ts", {"change": {"target"}}),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ MaxAbsScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ MinMaxScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ RobustScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ StandardScalerTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
+ "regular_ts",
+ {"create": {"res_target"}},
+ ),
+ (
+ YeoJohnsonTransform(in_column="target", mode="macro", inplace=True),
+ "regular_ts",
+ {"change": {"target"}},
+ ),
+ # missing_values
+ (TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {"change": {"target"}}),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ (TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {"change": {"target"}}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {"change": {"target"}}),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="running_mean"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ (
+ TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"),
+ "ts_to_fill",
+ {"change": {"target"}},
+ ),
+ # outliers
+ (DensityOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
+ (MedianOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
+ # timestamp
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res"),
+ "regular_ts",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
+ # TODO: fix after discussing conceptual problems
+ # (
+ # HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ # "ts_with_external_timestamp_one_month",
+ # {"create": {"res"}},
+ # ),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"anomaly_weekdays", "anomaly_monthdays"}},
+ ),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_minute_in_hour_number", "res_hour_number"}},
+ ),
+ (
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1),
+ "ts_with_binary_exog",
+ {"create": {"holiday_pre", "holiday_post"}},
+ ),
+ (
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
+ "ts_with_binary_exog",
+ {"create": {"holiday_pre", "holiday_post"}},
+ ),
+ ],
+ )
+ def test_transform_train_int_timestamp(self, transform, dataset_name, expected_changes, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ self._test_transform_train(ts_int_timestamp, transform, expected_changes=expected_changes)
+
+ @pytest.mark.parametrize(
+ "transform, dataset_name, expected_changes",
+ [
+ (
+ ResampleWithDistributionTransform(
+ in_column="regressor_exog", distribution_column="target", inplace=False, out_column="res"
+ ),
+ "ts_to_resample_int_timestamp",
+ {"create": {"res"}},
+ ),
+ (
+ ResampleWithDistributionTransform(
+ in_column="regressor_exog", distribution_column="target", inplace=True
+ ),
+ "ts_to_resample_int_timestamp",
+ {"change": {"regressor_exog"}},
+ ),
+ ],
+ )
+ def test_transform_train_int_timestamp_resample(self, transform, dataset_name, expected_changes, request):
+ ts = request.getfixturevalue(dataset_name)
+ self._test_transform_train(ts, transform, expected_changes=expected_changes)
+
+ @pytest.mark.parametrize(
+ "transform, dataset_name, error_match",
+ [
+ # outliers
+ (
+ PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel),
+ "ts_with_external_timestamp",
+ "Invalid timestamp! Only datetime type is supported",
+ ),
+ # timestamp
+ (DateFlagsTransform(out_column="res"), "regular_ts", "Transform can't work with integer index"),
+ (
+ HolidayTransform(out_column="res", mode="binary"),
+ "regular_ts",
+ "Transform can't work with integer index",
+ ),
+ (
+ HolidayTransform(out_column="res", mode="category"),
+ "regular_ts",
+ "Transform can't work with integer index",
+ ),
+ (
+ HolidayTransform(out_column="res", mode="days_count"),
+ "regular_ts_one_month",
+ "Transform can't work with integer index",
+ ),
+ (TimeFlagsTransform(out_column="res"), "regular_ts", "Transform can't work with integer index"),
+ (SpecialDaysTransform(), "regular_ts", "Transform can't work with integer index"),
+ ],
+ )
+ def test_transform_train_int_timestamp_not_supported(self, transform, dataset_name, error_match, request):
+ ts = request.getfixturevalue(dataset_name)
+ ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
+ with pytest.raises(ValueError, match=error_match):
+ self._test_transform_train(ts_int_timestamp, transform, expected_changes={})
class TestTransformTrainSubsetSegments:
@@ -142,17 +981,18 @@ def _test_transform_train_subset_segments(self, ts, transform, segments):
(AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts"),
(
BinaryOperationTransform(
- left_column="weekday", right_column="positive", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
),
(
BinaryOperationTransform(
- left_column="weekday", right_column="positive", operator="+", out_column="new_col"
+ left_column="positive", right_column="target", operator="+", out_column="new_col"
),
"ts_with_exog",
),
(LagTransform(in_column="target", lags=[1, 2, 3]), "regular_ts"),
+ (ExogShiftTransform(lag="auto", horizon=7), "ts_with_exog_to_shift"),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False),
"regular_ts",
@@ -231,15 +1071,44 @@ def _test_transform_train_subset_segments(self, ts, transform, segments):
(PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel), "ts_with_outliers"),
# timestamp
(DateFlagsTransform(), "regular_ts"),
- (FourierTransform(period=7, order=2), "regular_ts"),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
+ (FourierTransform(period=7, order=2, out_column="res"), "regular_ts"),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ ),
(HolidayTransform(mode="binary"), "regular_ts"),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(HolidayTransform(mode="category"), "regular_ts"),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(HolidayTransform(mode="days_count"), "regular_ts_one_month"),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ ),
(SpecialDaysTransform(), "regular_ts"),
+ (SpecialDaysTransform(in_column="external_timestamp"), "ts_with_external_timestamp"),
(TimeFlagsTransform(), "regular_ts"),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog"),
(
- EventTransform(in_column="holiday", out_column="holiday", mode="distance", n_pre=1, n_post=1),
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
"ts_with_binary_exog",
),
],
@@ -353,6 +1222,10 @@ def _test_transform_future_subset_segments(self, ts, transform, segments, horizo
"ts_with_exog",
),
(LagTransform(in_column="target", lags=[1, 2, 3]), "regular_ts"),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False),
"regular_ts",
@@ -455,15 +1328,47 @@ def _test_transform_future_subset_segments(self, ts, transform, segments, horizo
(PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel), "ts_with_outliers"),
# timestamp
(DateFlagsTransform(), "regular_ts"),
- (FourierTransform(period=7, order=2), "regular_ts"),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
+ (FourierTransform(period=7, order=2, out_column="res"), "regular_ts"),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ ),
(HolidayTransform(mode="binary"), "regular_ts"),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(HolidayTransform(mode="category"), "regular_ts"),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(HolidayTransform(mode="days_count"), "regular_ts_one_month"),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ ),
(SpecialDaysTransform(), "regular_ts"),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(TimeFlagsTransform(), "regular_ts"),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog"),
(
- EventTransform(in_column="holiday", out_column="holiday", mode="distance", n_pre=1, n_post=1),
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
"ts_with_binary_exog",
),
],
@@ -493,16 +1398,7 @@ def _test_transform_train_new_segments(self, ts, transform, train_segments, expe
transformed_test_ts = transform.transform(deepcopy(test_ts))
# check
- expected_columns_to_create = expected_changes.get("create", set())
- expected_columns_to_remove = expected_changes.get("remove", set())
- expected_columns_to_change = expected_changes.get("change", set())
- flat_test_df = test_ts.to_pandas(flatten=True)
- flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
- created_columns, removed_columns, changed_columns = find_columns_diff(flat_test_df, flat_transformed_test_df)
-
- assert created_columns == expected_columns_to_create
- assert removed_columns == expected_columns_to_remove
- assert changed_columns == expected_columns_to_change
+ assert_column_changes(ts_1=test_ts, ts_2=transformed_test_ts, expected_changes=expected_changes)
@pytest.mark.parametrize(
"transform, dataset_name, expected_changes",
@@ -549,14 +1445,14 @@ def _test_transform_train_new_segments(self, ts, transform, train_segments, expe
(AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts", {"change": {"target"}}),
(
BinaryOperationTransform(
- left_column="weekday", right_column="positive", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
- {"change": {"positive"}},
+ {"change": {"target"}},
),
(
BinaryOperationTransform(
- left_column="weekday", right_column="positive", operator="+", out_column="new_col"
+ left_column="positive", right_column="target", operator="+", out_column="new_col"
),
"ts_with_exog",
{"create": {"new_col"}},
@@ -566,6 +1462,11 @@ def _test_transform_train_new_segments(self, ts, transform, train_segments, expe
"regular_ts",
{"create": {"res_1", "res_2", "res_3"}},
),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {"create": {"feature_1_shift_7", "feature_2_shift_2"}, "remove": {"feature_1", "feature_2"}},
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
@@ -665,26 +1566,61 @@ def _test_transform_train_new_segments(self, ts, transform, train_segments, expe
"regular_ts",
{"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
+ ),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{"create": {"res_1", "res_2", "res_3", "res_4"}},
),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {"create": {"res"}},
+ ),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{"create": {"res_minute_in_hour_number", "res_hour_number"}},
),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_minute_in_hour_number", "res_hour_number"}},
+ ),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1),
"ts_with_binary_exog",
{"create": {"holiday_pre", "holiday_post"}},
),
(
- EventTransform(in_column="holiday", out_column="holiday", mode="distance", n_pre=1, n_post=1),
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
"ts_with_binary_exog",
{"create": {"holiday_pre", "holiday_post"}},
),
@@ -766,6 +1702,7 @@ def test_transform_train_new_segments(self, transform, dataset_name, expected_ch
(PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel), "ts_with_outliers"),
# timestamp
(SpecialDaysTransform(), "regular_ts"),
+ (SpecialDaysTransform(in_column="external_timestamp"), "ts_with_external_timestamp"),
],
)
def test_transform_train_new_segments_not_implemented(self, transform, dataset_name, request):
@@ -799,16 +1736,7 @@ def _test_transform_future_new_segments(self, ts, transform, train_segments, exp
transformed_test_ts = new_segments_ts.make_future(future_steps=horizon, transforms=[transform])
# check
- expected_columns_to_create = expected_changes.get("create", set())
- expected_columns_to_remove = expected_changes.get("remove", set())
- expected_columns_to_change = expected_changes.get("change", set())
- flat_test_df = test_ts.to_pandas(flatten=True)
- flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
- created_columns, removed_columns, changed_columns = find_columns_diff(flat_test_df, flat_transformed_test_df)
-
- assert created_columns == expected_columns_to_create
- assert removed_columns == expected_columns_to_remove
- assert changed_columns == expected_columns_to_change
+ assert_column_changes(ts_1=test_ts, ts_2=transformed_test_ts, expected_changes=expected_changes)
@pytest.mark.parametrize(
"transform, dataset_name, expected_changes",
@@ -856,10 +1784,10 @@ def _test_transform_future_new_segments(self, ts, transform, train_segments, exp
(AddConstTransform(in_column="positive", value=1, inplace=True), "ts_with_exog", {"change": {"positive"}}),
(
BinaryOperationTransform(
- left_column="positive", right_column="target", operator="+", out_column="positive"
+ left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
- {"change": {"positive"}},
+ {},
),
(
BinaryOperationTransform(
@@ -873,6 +1801,11 @@ def _test_transform_future_new_segments(self, ts, transform, train_segments, exp
"regular_ts",
{"create": {"res_1", "res_2", "res_3"}},
),
+ (
+ ExogShiftTransform(lag="auto", horizon=7),
+ "ts_with_exog_to_shift",
+ {"create": {"feature_1_shift_7", "feature_2_shift_2"}, "remove": {"feature_1", "feature_2"}},
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
@@ -1018,26 +1951,61 @@ def _test_transform_future_new_segments(self, ts, transform, train_segments, exp
"regular_ts",
{"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
+ ),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{"create": {"res_1", "res_2", "res_3", "res_4"}},
),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {"create": {"res"}},
+ ),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{"create": {"res_minute_in_hour_number", "res_hour_number"}},
),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_minute_in_hour_number", "res_hour_number"}},
+ ),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1),
"ts_with_binary_exog",
{"create": {"holiday_pre", "holiday_post"}},
),
(
- EventTransform(in_column="holiday", out_column="holiday", mode="distance", n_pre=1, n_post=1),
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
"ts_with_binary_exog",
{"create": {"holiday_pre", "holiday_post"}},
),
@@ -1126,6 +2094,10 @@ def test_transform_future_new_segments(self, transform, dataset_name, expected_c
(PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel), "ts_with_outliers"),
# timestamp
(SpecialDaysTransform(), "regular_ts"),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ ),
],
)
def test_transform_future_new_segments_not_implemented(self, transform, dataset_name, request):
@@ -1154,16 +2126,7 @@ def _test_transform_future_with_target(self, ts, transform, expected_changes, ga
transformed_test_ts = transform.transform(deepcopy(test_ts))
# check
- expected_columns_to_create = expected_changes.get("create", set())
- expected_columns_to_remove = expected_changes.get("remove", set())
- expected_columns_to_change = expected_changes.get("change", set())
- flat_test_df = test_ts.to_pandas(flatten=True)
- flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
- created_columns, removed_columns, changed_columns = find_columns_diff(flat_test_df, flat_transformed_test_df)
-
- assert created_columns == expected_columns_to_create
- assert removed_columns == expected_columns_to_remove
- assert changed_columns == expected_columns_to_change
+ assert_column_changes(ts_1=test_ts, ts_2=transformed_test_ts, expected_changes=expected_changes)
@pytest.mark.parametrize(
"transform, dataset_name, expected_changes",
@@ -1262,6 +2225,11 @@ def _test_transform_future_with_target(self, ts, transform, expected_changes, ga
"regular_ts",
{"create": {"res_1", "res_2", "res_3"}},
),
+ (
+ ExogShiftTransform(lag="auto", horizon=64),
+ "ts_with_exog_to_shift",
+ {"create": {"feature_1_shift_7", "feature_2_shift_2"}, "remove": {"feature_1", "feature_2"}},
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
@@ -1431,12 +2399,13 @@ def _test_transform_future_with_target(self, ts, transform, expected_changes, ga
"ts_to_resample",
{"change": {"regressor_exog"}},
),
- (
- # this behaviour can be unexpected for someone
- TimeSeriesImputerTransform(in_column="target"),
- "ts_to_fill",
- {},
- ),
+ # this behaviour can be unexpected for someone
+ (TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="running_mean"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"), "ts_to_fill", {}),
# outliers
(DensityOutliersTransform(in_column="target"), "ts_with_outliers", {}),
(MedianOutliersTransform(in_column="target"), "ts_with_outliers", {}),
@@ -1447,27 +2416,67 @@ def _test_transform_future_with_target(self, ts, transform, expected_changes, ga
"regular_ts",
{"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
+ ),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{"create": {"res_1", "res_2", "res_3", "res_4"}},
),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {"create": {"res"}},
+ ),
+ (SpecialDaysTransform(), "regular_ts", {"create": {"anomaly_weekdays", "anomaly_monthdays"}}),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"anomaly_weekdays", "anomaly_monthdays"}},
+ ),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{"create": {"res_minute_in_hour_number", "res_hour_number"}},
),
- (SpecialDaysTransform(), "regular_ts", {"create": {"anomaly_weekdays", "anomaly_monthdays"}}),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_minute_in_hour_number", "res_hour_number"}},
+ ),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1),
"ts_with_binary_exog",
{"create": {"holiday_pre", "holiday_post"}},
),
(
- EventTransform(in_column="holiday", out_column="holiday", mode="distance", n_pre=1, n_post=1),
+ EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
"ts_with_binary_exog",
{"create": {"holiday_pre", "holiday_post"}},
),
@@ -1498,16 +2507,7 @@ def _test_transform_future_without_target(self, ts, transform, expected_changes,
transformed_test_ts = future_ts.make_future(future_steps=transform_size, transforms=[transform])
# check
- expected_columns_to_create = expected_changes.get("create", set())
- expected_columns_to_remove = expected_changes.get("remove", set())
- expected_columns_to_change = expected_changes.get("change", set())
- flat_test_df = test_ts.to_pandas(flatten=True)
- flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
- created_columns, removed_columns, changed_columns = find_columns_diff(flat_test_df, flat_transformed_test_df)
-
- assert created_columns == expected_columns_to_create
- assert removed_columns == expected_columns_to_remove
- assert changed_columns == expected_columns_to_change
+ assert_column_changes(ts_1=test_ts, ts_2=transformed_test_ts, expected_changes=expected_changes)
@pytest.mark.parametrize(
"transform, dataset_name, expected_changes",
@@ -1621,6 +2621,11 @@ def _test_transform_future_without_target(self, ts, transform, expected_changes,
"regular_ts",
{"create": {"res_1", "res_2", "res_3"}},
),
+ (
+ ExogShiftTransform(lag="auto", horizon=35),
+ "ts_with_exog_to_shift",
+ {"create": {"feature_1_shift_7", "feature_2_shift_2"}, "remove": {"feature_1", "feature_2"}},
+ ),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
@@ -1843,12 +2848,19 @@ def _test_transform_future_without_target(self, ts, transform, expected_changes,
"ts_to_resample",
{"change": {"regressor_exog"}},
),
- (
- # this behaviour can be unexpected for someone
- TimeSeriesImputerTransform(in_column="target"),
- "ts_to_fill",
- {},
- ),
+ # (
+ # # this behaviour can be unexpected for someone
+ # TimeSeriesImputerTransform(in_column="target"),
+ # "ts_to_fill",
+ # {},
+ # ),
+ # this behaviour can be unexpected for someone
+ (TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="running_mean"), "ts_to_fill", {}),
+ (TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"), "ts_to_fill", {}),
# outliers
(DensityOutliersTransform(in_column="target"), "ts_with_outliers", {}),
(MedianOutliersTransform(in_column="target"), "ts_with_outliers", {}),
@@ -1859,20 +2871,60 @@ def _test_transform_future_without_target(self, ts, transform, expected_changes,
"regular_ts",
{"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
),
+ (
+ DateFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_day_number_in_week", "res_day_number_in_month", "res_is_weekend"}},
+ ),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{"create": {"res_1", "res_2", "res_3", "res_4"}},
),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
+ (
+ FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
+ "ts_with_external_int_timestamp",
+ {"create": {"res_1", "res_2", "res_3", "res_4"}},
+ ),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res"}},
+ ),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {"create": {"res"}}),
+ (
+ HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
+ "ts_with_external_timestamp_one_month",
+ {"create": {"res"}},
+ ),
+ (SpecialDaysTransform(), "regular_ts", {"create": {"anomaly_weekdays", "anomaly_monthdays"}}),
+ (
+ SpecialDaysTransform(in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"anomaly_weekdays", "anomaly_monthdays"}},
+ ),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{"create": {"res_minute_in_hour_number", "res_hour_number"}},
),
- (SpecialDaysTransform(), "regular_ts", {"create": {"anomaly_weekdays", "anomaly_monthdays"}}),
+ (
+ TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
+ "ts_with_external_timestamp",
+ {"create": {"res_minute_in_hour_number", "res_hour_number"}},
+ ),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1),
"ts_with_binary_exog",
diff --git a/tests/test_transforms/test_math/test_differencing_transform.py b/tests/test_transforms/test_math/test_differencing_transform.py
index 20f6d9e9d..90629f3ee 100644
--- a/tests/test_transforms/test_math/test_differencing_transform.py
+++ b/tests/test_transforms/test_math/test_differencing_transform.py
@@ -17,6 +17,7 @@
from etna.transforms.math.differencing import _SingleDifferencingTransform
from tests.test_transforms.utils import assert_sampling_is_valid
from tests.test_transforms.utils import assert_transformation_equals_loaded_original
+from tests.utils import convert_ts_to_int_timestamp
from tests.utils import select_segments_subset
GeneralDifferencingTransform = Union[_SingleDifferencingTransform, DifferencingTransform]
@@ -63,6 +64,11 @@ def ts_nans(df_nans) -> TSDataset:
return ts
+@pytest.fixture
+def ts_nans_int_timestamp(ts_nans) -> TSDataset:
+ return convert_ts_to_int_timestamp(ts=ts_nans, shift=10)
+
+
@pytest.fixture
def ts_nans_with_regressors(df_nans, df_regressors) -> TSDataset:
"""Create TSDataset with regressors and nans at the beginning of one segment."""
@@ -102,6 +108,11 @@ def ts_nans_with_noise(df_nans, random_seed) -> TSDataset:
return ts
+@pytest.fixture
+def ts_nans_with_noise_int_timestamp(ts_nans_with_noise) -> TSDataset:
+ return convert_ts_to_int_timestamp(ts=ts_nans_with_noise, shift=10)
+
+
def check_interface_transform_autogenerate_column_non_regressor(transform: GeneralDifferencingTransform, ts: TSDataset):
"""Check that differencing transform generates non-regressor column in transform according to repr."""
df = ts.to_pandas()
@@ -459,6 +470,7 @@ def test_general_inverse_transform_fail_not_all_test(transform, ts_nans):
transform.inverse_transform(ts_nans)
+@pytest.mark.parametrize("ts_name", ["ts_nans", "ts_nans_int_timestamp"])
@pytest.mark.parametrize(
"transform",
[
@@ -466,9 +478,9 @@ def test_general_inverse_transform_fail_not_all_test(transform, ts_nans):
DifferencingTransform(in_column="target", period=1, order=1, inplace=True),
],
)
-def test_general_inverse_transform_fail_test_not_right_after_train(transform, ts_nans):
+def test_general_inverse_transform_fail_test_not_right_after_train(ts_name, transform, request):
"""Test that differencing transform fails to make inverse_transform on not adjacent test data."""
- ts = ts_nans
+ ts = request.getfixturevalue(ts_name)
ts_train, ts_test = ts.train_test_split(test_size=10)
ts_train.fit_transform(transforms=[transform])
future_ts = ts_train.make_future(10, transforms=[transform])
@@ -570,19 +582,23 @@ def test_full_inverse_transform_inplace_test_quantiles(period, order, ts_nans_wi
check_inverse_transform_inplace_test_quantiles(transform, ts_nans_with_noise)
+@pytest.mark.parametrize("ts_name", ["ts_nans_with_noise", "ts_nans_with_noise_int_timestamp"])
@pytest.mark.parametrize("period", [1, 7])
-def test_single_backtest_sanity(period, ts_nans_with_noise):
+def test_single_backtest_sanity(ts_name, period, request):
"""Test that _SingleDifferencingTransform correctly works in backtest."""
+ ts = request.getfixturevalue(ts_name)
transform = _SingleDifferencingTransform(in_column="target", period=period, inplace=True)
- check_backtest_sanity(transform, ts_nans_with_noise)
+ check_backtest_sanity(transform, ts)
+@pytest.mark.parametrize("ts_name", ["ts_nans_with_noise", "ts_nans_with_noise_int_timestamp"])
@pytest.mark.parametrize("period", [1, 7])
@pytest.mark.parametrize("order", [1, 2])
-def test_full_backtest_sanity(period, order, ts_nans_with_noise):
+def test_full_backtest_sanity(ts_name, period, order, request):
"""Test that DifferencingTransform correctly works in backtest."""
+ ts = request.getfixturevalue(ts_name)
transform = DifferencingTransform(in_column="target", period=period, order=order, inplace=True)
- check_backtest_sanity(transform, ts_nans_with_noise)
+ check_backtest_sanity(transform, ts)
@pytest.mark.parametrize("inplace", [False, True])
diff --git a/tests/test_transforms/test_math/test_exog_shift_transform.py b/tests/test_transforms/test_math/test_exog_shift_transform.py
index 3d13319fe..1b31f7f6c 100644
--- a/tests/test_transforms/test_math/test_exog_shift_transform.py
+++ b/tests/test_transforms/test_math/test_exog_shift_transform.py
@@ -62,10 +62,10 @@ def ts_with_exogs_ms_freq():
},
),
)
-def test_save_exog_last_date(df_exog_with_nans, expected):
+def test_save_exog_last_timestamp(df_exog_with_nans, expected):
t = ExogShiftTransform(lag=1)
- t._save_exog_last_date(df_exog=df_exog_with_nans)
- assert t._exog_last_date == expected
+ t._save_exog_last_timestamp(df_exog=df_exog_with_nans)
+ assert t._exog_last_timestamp == expected
def test_negative_lag():
@@ -150,9 +150,9 @@ def test_estimate_shift(ts_with_exogs, lag, horizon, expected):
@pytest.mark.parametrize("lag", (1, "auto"))
-def test_shift_no_exog(simple_df, lag, expected={"target"}):
+def test_shift_no_exog(simple_tsdf, lag, expected={"target"}):
t = ExogShiftTransform(lag=lag, horizon=1)
- transformed = t.fit_transform(simple_df)
+ transformed = t.fit_transform(simple_tsdf)
assert set(transformed.df.columns.get_level_values("feature")) == expected
@@ -180,6 +180,31 @@ def test_transformed_names(ts_name, lag, horizon, expected, request):
assert set(column_names) == expected
+@pytest.mark.parametrize(
+ "lag,horizon,expected_types",
+ (
+ (1, None, {"feat1_shift_1": "float", "feat2_shift_1": "float", "feat3_shift_1": "float", "target": "float"}),
+ ("auto", 1, {"feat1_shift_1": "float", "feat2_shift_2": "float", "feat3": "int", "target": "float"}),
+ ("auto", 2, {"feat1_shift_2": "float", "feat2_shift_3": "float", "feat3_shift_1": "float", "target": "float"}),
+ ),
+)
+@pytest.mark.parametrize(
+ "ts_name",
+ (
+ "ts_with_exogs",
+ "ts_with_exogs_ms_freq",
+ ),
+)
+def test_transform_type_changes(ts_name, lag, horizon, expected_types, request):
+ ts = request.getfixturevalue(ts_name)
+
+ t = ExogShiftTransform(lag=lag, horizon=horizon)
+ transformed = t.fit_transform(ts=ts).to_pandas(flatten=True)
+ dtypes = transformed.dtypes
+ for column, expected_dtype in expected_types.items():
+ assert dtypes[column] == expected_dtype
+
+
@pytest.mark.parametrize("lag", (3, "auto"))
@pytest.mark.parametrize("horizon", range(1, 3))
@pytest.mark.parametrize(
diff --git a/tests/test_transforms/test_math/test_lag_transform.py b/tests/test_transforms/test_math/test_lag_transform.py
index f10be4ee8..2a4fcabf6 100644
--- a/tests/test_transforms/test_math/test_lag_transform.py
+++ b/tests/test_transforms/test_math/test_lag_transform.py
@@ -111,6 +111,18 @@ def test_lags_values_two_segments(lags: Union[int, Sequence[int]], int_ts_two_se
assert_almost_equal(true_values.values, lags_df[segment, f"regressor_lag_feature_{lag}"].values)
+@pytest.mark.parametrize(
+ "lags, expected_types", [(3, {"lag_1": "float", "lag_2": "float", "lag_3": "float", "target": "int"})]
+)
+def test_transform_type_changes(lags, expected_types, int_ts_two_segments):
+ ts = int_ts_two_segments
+ lf = LagTransform(in_column="target", lags=lags, out_column="lag")
+ transformed = lf.fit_transform(ts=ts).to_pandas(flatten=True)
+ dtypes = transformed.dtypes
+ for column, expected_dtype in expected_types.items():
+ assert dtypes[column] == expected_dtype
+
+
@pytest.mark.parametrize("lags", (0, -1, (10, 15, -2)))
def test_invalid_lags_value_two_segments(lags):
"""Test that LagTransform can't be created with non-positive lags."""
diff --git a/tests/test_transforms/test_timestamp/test_dateflags_transform.py b/tests/test_transforms/test_timestamp/test_dateflags_transform.py
index 9f6d3975c..51313ff73 100644
--- a/tests/test_transforms/test_timestamp/test_dateflags_transform.py
+++ b/tests/test_transforms/test_timestamp/test_dateflags_transform.py
@@ -2,6 +2,7 @@
from datetime import timedelta
from typing import Dict
from typing import List
+from typing import Optional
from typing import Tuple
from typing import Union
@@ -13,6 +14,7 @@
from etna.transforms.timestamp import DateFlagsTransform
from tests.test_transforms.utils import assert_sampling_is_valid
from tests.test_transforms.utils import assert_transformation_equals_loaded_original
+from tests.utils import convert_ts_to_int_timestamp
WEEKEND_DAYS = (5, 6)
SPECIAL_DAYS = [1, 4]
@@ -34,15 +36,8 @@
@pytest.fixture
def dateflags_true_df() -> pd.DataFrame:
- """Generate dataset for TimeFlags feature.
-
- Returns
- -------
- dataset with timestamp column and columns true_minute_in_hour_number, true_fifteen_minutes_in_hour_number,
- true_half_hour_number, true_hour_number, true_half_day_number, true_one_third_day_number that contain
- true answers for corresponding features
- """
- dataframes = [pd.DataFrame({"timestamp": pd.date_range("2010-06-01", "2021-06-01", freq="3h")}) for i in range(5)]
+ """Generate dataset with answers for DateFlagsTransform."""
+ dataframes = [pd.DataFrame({"timestamp": pd.date_range("2020-06-01", "2021-06-01", freq="3H")}) for _ in range(5)]
out_column = "dateflag"
for i in range(len(dataframes)):
@@ -52,7 +47,7 @@ def dateflags_true_df() -> pd.DataFrame:
df[f"{out_column}_day_number_in_year"] = df["timestamp"].apply(
lambda dt: dt.dayofyear + 1 if not dt.is_leap_year and dt.month >= 3 else dt.dayofyear
)
- df[f"{out_column}_week_number_in_year"] = df["timestamp"].dt.isocalendar().week
+ df[f"{out_column}_week_number_in_year"] = df["timestamp"].dt.isocalendar().week.astype(int)
df[f"{out_column}_month_number_in_year"] = df["timestamp"].dt.month
df[f"{out_column}_season_number"] = df["timestamp"].dt.month % 12 // 3 + 1
df[f"{out_column}_year_number"] = df["timestamp"].dt.year
@@ -66,14 +61,15 @@ def dateflags_true_df() -> pd.DataFrame:
df[f"{out_column}_special_days_in_month"] = df[f"{out_column}_day_number_in_month"].apply(
lambda x: x in SPECIAL_DAYS
)
+ features = df.columns.difference(["timestamp"])
+ df[features] = df[features].astype("category")
df["segment"] = f"segment_{i}"
df["target"] = 2
- result = pd.concat(dataframes, ignore_index=True)
- result = result.pivot(index="timestamp", columns="segment")
- result = result.reorder_levels([1, 0], axis=1)
- result = result.sort_index(axis=1)
- result.columns.names = ["segment", "feature"]
+
+ flat_df = pd.concat(dataframes, ignore_index=True)
+ result = TSDataset.to_dataset(flat_df)
+ result.index.freq = "3H"
return result
@@ -81,25 +77,51 @@ def dateflags_true_df() -> pd.DataFrame:
@pytest.fixture
def train_ts() -> TSDataset:
"""Generate dataset without dateflags"""
- dataframes = [pd.DataFrame({"timestamp": pd.date_range("2010-06-01", "2021-06-01", freq="3h")}) for i in range(5)]
+ dataframes = [pd.DataFrame({"timestamp": pd.date_range("2020-06-01", "2021-06-01", freq="3h")}) for i in range(5)]
for i in range(len(dataframes)):
df = dataframes[i]
df["segment"] = f"segment_{i}"
df["target"] = 2
- result = pd.concat(dataframes, ignore_index=True)
- result = result.pivot(index="timestamp", columns="segment")
- result = result.reorder_levels([1, 0], axis=1)
- result = result.sort_index(axis=1)
- result.columns.names = ["segment", "feature"]
- ts = TSDataset(df=result, freq="3H")
+ flat_df = pd.concat(dataframes, ignore_index=True)
+ wide_df = TSDataset.to_dataset(flat_df)
+
+ flat_df["external_timestamp"] = flat_df["timestamp"]
+ flat_df.drop(columns=["target"], inplace=True)
+ df_exog = TSDataset.to_dataset(flat_df)
+
+ ts = TSDataset(df=wide_df, df_exog=df_exog, freq="3H")
+ return ts
+
+
+@pytest.fixture
+def train_ts_int_timestamp(train_ts) -> TSDataset:
+ ts = convert_ts_to_int_timestamp(train_ts)
+ return ts
+
+
+@pytest.fixture
+def train_ts_with_regressor(train_ts) -> TSDataset:
+ df = train_ts.raw_df
+ df_exog = train_ts.df_exog
+ ts = TSDataset(df=df.iloc[:-10], df_exog=df_exog, freq=train_ts.freq, known_future=["external_timestamp"])
+ return ts
+
+
+@pytest.fixture
+def train_ts_with_nans(train_ts) -> TSDataset:
+ ts = train_ts
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[df_exog.index[:3], pd.IndexSlice[:, "external_timestamp"]] = np.NaN
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
return ts
def test_invalid_arguments_configuration():
"""Test that transform can't be created with no features to generate."""
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match="DateFlagsTransform feature does nothing with given init args configuration"):
_ = DateFlagsTransform(
day_number_in_month=False,
day_number_in_week=False,
@@ -135,11 +157,12 @@ def test_repr():
true_repr = (
f"{transform_class_repr}(day_number_in_week = True, day_number_in_month = True, day_number_in_year = False, "
f"week_number_in_month = False, week_number_in_year = False, month_number_in_year = True, season_number = True, year_number = True, "
- f"is_weekend = True, special_days_in_week = (1, 2), special_days_in_month = (12,), out_column = None, )"
+ f"is_weekend = True, special_days_in_week = (1, 2), special_days_in_month = (12,), out_column = None, in_column = None, )"
)
assert transform_repr == true_repr
+@pytest.mark.parametrize("in_column", [None, "external_timestamp"])
@pytest.mark.parametrize(
"true_params",
(
@@ -165,27 +188,31 @@ def test_repr():
],
),
)
-def test_interface_correct_args_out_column(true_params: List[str], train_ts: TSDataset):
+def test_interface_correct_args_out_column(in_column: Optional[str], true_params: List[str], train_ts: TSDataset):
"""Test that transform generates correct column names using out_column parameter."""
init_params = deepcopy(INIT_PARAMS_TEMPLATE)
segments = train_ts.columns.get_level_values("segment").unique()
out_column = "dateflags"
for key in true_params:
init_params[key] = True
- transform = DateFlagsTransform(**init_params, out_column=out_column)
+ transform = DateFlagsTransform(**init_params, out_column=out_column, in_column=in_column)
+ initial_columns = train_ts.columns.get_level_values("feature").unique()
+
result = transform.fit_transform(train_ts).to_pandas()
assert sorted(result.columns.names) == ["feature", "segment"]
assert sorted(segments) == sorted(result.columns.get_level_values("segment").unique())
true_params = [f"{out_column}_{param}" for param in true_params]
- for seg in result.columns.get_level_values(0).unique():
+ for seg in result.columns.get_level_values("segment").unique():
tmp_df = result[seg]
- assert sorted(tmp_df.columns) == sorted(true_params + ["target"])
+ new_columns = tmp_df.columns.difference(initial_columns)
+ assert sorted(new_columns) == sorted(true_params)
for param in true_params:
assert tmp_df[param].dtype == "category"
+@pytest.mark.parametrize("in_column", [None, "external_timestamp"])
@pytest.mark.parametrize(
"true_params",
(
@@ -214,7 +241,7 @@ def test_interface_correct_args_out_column(true_params: List[str], train_ts: TSD
["special_days_in_week", "special_days_in_month"],
),
)
-def test_interface_correct_args_repr(true_params: List[str], train_ts: TSDataset):
+def test_interface_correct_args_repr(in_column: Optional[str], true_params: List[str], train_ts: TSDataset):
"""Test that transform generates correct column names without setting out_column parameter."""
init_params = deepcopy(INIT_PARAMS_TEMPLATE)
segments = train_ts.columns.get_level_values("segment").unique()
@@ -223,30 +250,40 @@ def test_interface_correct_args_repr(true_params: List[str], train_ts: TSDataset
init_params[key] = SPECIAL_DAYS
else:
init_params[key] = True
- transform = DateFlagsTransform(**init_params)
+ transform = DateFlagsTransform(**init_params, in_column=in_column)
+ initial_columns = train_ts.columns.get_level_values("feature").unique()
+
result = transform.fit_transform(deepcopy(train_ts)).to_pandas()
assert sorted(result.columns.names) == ["feature", "segment"]
assert sorted(segments) == sorted(result.columns.get_level_values("segment").unique())
- columns = result.columns.get_level_values("feature").unique().drop("target")
- assert len(columns) == len(true_params)
- for column in columns:
+ new_columns = result.columns.get_level_values("feature").unique().difference(initial_columns)
+ assert len(new_columns) == len(true_params)
+ for column in new_columns:
# check category dtype
assert np.all(result.loc[:, pd.IndexSlice[segments, column]].dtypes == "category")
# check that a transform can be created from column name and it generates the same results
transform_temp = eval(column)
df_temp = transform_temp.fit_transform(deepcopy(train_ts)).to_pandas()
- columns_temp = df_temp.columns.get_level_values("feature").unique().drop("target")
- assert len(columns_temp) == 1
- generated_column = columns_temp[0]
+ new_columns_temp = df_temp.columns.get_level_values("feature").unique().difference(initial_columns)
+ assert len(new_columns_temp) == 1
+ generated_column = new_columns_temp[0]
assert generated_column == column
- assert np.all(
- df_temp.loc[:, pd.IndexSlice[segments, generated_column]] == result.loc[:, pd.IndexSlice[segments, column]]
+ pd.testing.assert_frame_equal(
+ df_temp.loc[:, pd.IndexSlice[segments, generated_column]], result.loc[:, pd.IndexSlice[segments, column]]
)
+@pytest.mark.parametrize(
+ "in_column, ts_name",
+ [
+ (None, "train_ts"),
+ ("external_timestamp", "train_ts"),
+ ("external_timestamp", "train_ts_int_timestamp"),
+ ],
+)
@pytest.mark.parametrize(
"true_params",
(
@@ -263,15 +300,20 @@ def test_interface_correct_args_repr(true_params: List[str], train_ts: TSDataset
{"special_days_in_month": SPECIAL_DAYS},
),
)
-def test_feature_values(
- true_params: Dict[str, Union[bool, Tuple[int, int]]], train_ts: TSDataset, dateflags_true_df: pd.DataFrame
+def test_transform_values(
+ in_column: Optional[str],
+ ts_name: str,
+ true_params: Dict[str, Union[bool, Tuple[int, int]]],
+ dateflags_true_df: pd.DataFrame,
+ request,
):
"""Test that transform generates correct values."""
+ ts = request.getfixturevalue(ts_name)
out_column = "dateflag"
init_params = deepcopy(INIT_PARAMS_TEMPLATE)
init_params.update(true_params)
- transform = DateFlagsTransform(**init_params, out_column=out_column)
- result = transform.fit_transform(train_ts).to_pandas()
+ transform = DateFlagsTransform(**init_params, out_column=out_column, in_column=in_column)
+ result = transform.fit_transform(ts).to_pandas()
segments_true = dateflags_true_df.columns.get_level_values("segment").unique()
segment_result = result.columns.get_level_values("segment").unique()
@@ -281,9 +323,187 @@ def test_feature_values(
true_params = [f"{out_column}_{param}" for param in true_params.keys()]
for seg in segment_result:
segment_true = dateflags_true_df[seg]
- true_df = segment_true[true_params + ["target"]].sort_index(axis=1)
- result_df = result[seg].sort_index(axis=1)
- assert (true_df == result_df).all().all()
+ columns = true_params + ["target"]
+ true_df = segment_true[columns].sort_index(axis=1).reset_index(drop=True)
+ result_df = result[seg][columns].sort_index(axis=1).reset_index(drop=True)
+ pd.testing.assert_frame_equal(result_df, true_df)
+
+
+@pytest.mark.parametrize(
+ "true_params",
+ (
+ {"day_number_in_week": True},
+ {"day_number_in_month": True},
+ {"day_number_in_year": True},
+ {"week_number_in_year": True},
+ {"week_number_in_month": True},
+ {"month_number_in_year": True},
+ {"season_number": True},
+ {"year_number": True},
+ {"is_weekend": True},
+ {"special_days_in_week": SPECIAL_DAYS},
+ {"special_days_in_month": SPECIAL_DAYS},
+ ),
+)
+def test_transform_values_with_nans(true_params: Dict[str, Union[bool, Tuple[int, int]]], train_ts_with_nans):
+ out_column = "dateflag"
+ init_params = deepcopy(INIT_PARAMS_TEMPLATE)
+ init_params.update(true_params)
+ transform = DateFlagsTransform(**init_params, out_column=out_column, in_column="external_timestamp")
+ result = transform.fit_transform(train_ts_with_nans).to_pandas()
+
+ segment_result = result.columns.get_level_values("segment").unique()
+
+ true_params = [f"{out_column}_{param}" for param in true_params.keys()]
+ for seg in segment_result:
+ result_df = result[seg][true_params].sort_index(axis=1).reset_index(drop=True)
+ assert np.all(result_df.isna().sum() == 3)
+
+
+def test_transform_index_fail_int_timestamp(train_ts_int_timestamp: TSDataset):
+ transform = DateFlagsTransform(out_column="dateflag", in_column=None)
+ transform.fit(train_ts_int_timestamp)
+ with pytest.raises(ValueError, match="Transform can't work with integer index, parameter in_column should be set"):
+ _ = transform.transform(train_ts_int_timestamp)
+
+
+@pytest.mark.parametrize(
+ "true_params",
+ (
+ ["day_number_in_week"],
+ ["day_number_in_month"],
+ ["day_number_in_year"],
+ ["week_number_in_year"],
+ ["week_number_in_month"],
+ ["month_number_in_year"],
+ ["season_number"],
+ ["year_number"],
+ ["is_weekend"],
+ [
+ "day_number_in_week",
+ "day_number_in_month",
+ "day_number_in_year",
+ "week_number_in_year",
+ "week_number_in_month",
+ "month_number_in_year",
+ "season_number",
+ "year_number",
+ "is_weekend",
+ ],
+ ["special_days_in_week"],
+ ["special_days_in_month"],
+ ["special_days_in_week", "special_days_in_month"],
+ ),
+)
+def test_get_regressors_info_index(true_params):
+ out_column = "dateflag"
+ init_params = deepcopy(INIT_PARAMS_TEMPLATE)
+ for key in true_params:
+ if key in SPECIAL_DAYS_PARAMS:
+ init_params[key] = SPECIAL_DAYS
+ else:
+ init_params[key] = True
+ transform = DateFlagsTransform(**init_params, out_column=out_column)
+
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = [f"{out_column}_{param}" for param in true_params]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
+
+
+def test_get_regressors_info_in_column_fail_not_fitted(train_ts):
+ transform = DateFlagsTransform(out_column="dateflag", in_column="external_timestamp")
+ with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+ _ = transform.get_regressors_info()
+
+
+@pytest.mark.parametrize(
+ "true_params",
+ (
+ ["day_number_in_week"],
+ ["day_number_in_month"],
+ ["day_number_in_year"],
+ ["week_number_in_year"],
+ ["week_number_in_month"],
+ ["month_number_in_year"],
+ ["season_number"],
+ ["year_number"],
+ ["is_weekend"],
+ [
+ "day_number_in_week",
+ "day_number_in_month",
+ "day_number_in_year",
+ "week_number_in_year",
+ "week_number_in_month",
+ "month_number_in_year",
+ "season_number",
+ "year_number",
+ "is_weekend",
+ ],
+ ["special_days_in_week"],
+ ["special_days_in_month"],
+ ["special_days_in_week", "special_days_in_month"],
+ ),
+)
+def test_get_regressors_info_in_column_fitted_exog(true_params, train_ts):
+ out_column = "dateflag"
+ init_params = deepcopy(INIT_PARAMS_TEMPLATE)
+ for key in true_params:
+ if key in SPECIAL_DAYS_PARAMS:
+ init_params[key] = SPECIAL_DAYS
+ else:
+ init_params[key] = True
+ transform = DateFlagsTransform(**init_params, out_column=out_column, in_column="external_timestamp")
+
+ transform.fit(train_ts)
+ regressors_info = transform.get_regressors_info()
+
+ assert regressors_info == []
+
+
+@pytest.mark.parametrize(
+ "true_params",
+ (
+ ["day_number_in_week"],
+ ["day_number_in_month"],
+ ["day_number_in_year"],
+ ["week_number_in_year"],
+ ["week_number_in_month"],
+ ["month_number_in_year"],
+ ["season_number"],
+ ["year_number"],
+ ["is_weekend"],
+ [
+ "day_number_in_week",
+ "day_number_in_month",
+ "day_number_in_year",
+ "week_number_in_year",
+ "week_number_in_month",
+ "month_number_in_year",
+ "season_number",
+ "year_number",
+ "is_weekend",
+ ],
+ ["special_days_in_week"],
+ ["special_days_in_month"],
+ ["special_days_in_week", "special_days_in_month"],
+ ),
+)
+def test_get_regressors_info_in_column_fitted_regressor(true_params, train_ts_with_regressor):
+ out_column = "dateflag"
+ init_params = deepcopy(INIT_PARAMS_TEMPLATE)
+ for key in true_params:
+ if key in SPECIAL_DAYS_PARAMS:
+ init_params[key] = SPECIAL_DAYS
+ else:
+ init_params[key] = True
+ transform = DateFlagsTransform(**init_params, out_column=out_column, in_column="external_timestamp")
+
+ transform.fit(train_ts_with_regressor)
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = [f"{out_column}_{param}" for param in true_params]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
def test_save_load(train_ts):
diff --git a/tests/test_transforms/test_timestamp/test_fourier_transform.py b/tests/test_transforms/test_timestamp/test_fourier_transform.py
index d5f6f4a1f..f3b44d856 100644
--- a/tests/test_transforms/test_timestamp/test_fourier_transform.py
+++ b/tests/test_transforms/test_timestamp/test_fourier_transform.py
@@ -5,11 +5,13 @@
import pytest
from etna.datasets import TSDataset
+from etna.datasets import generate_ar_df
from etna.metrics import R2
from etna.models import LinearPerSegmentModel
from etna.transforms.timestamp import FourierTransform
from tests.test_transforms.utils import assert_sampling_is_valid
from tests.test_transforms.utils import assert_transformation_equals_loaded_original
+from tests.utils import convert_ts_to_int_timestamp
def add_seasonality(series: pd.Series, period: int, magnitude: float) -> pd.Series:
@@ -33,10 +35,106 @@ def get_one_df(period_1, period_2, magnitude_1, magnitude_2):
return df
+@pytest.fixture
+def example_df():
+ return generate_ar_df(periods=10, start_time="2020-01-01", n_segments=2, freq="H")
+
+
@pytest.fixture
def example_ts(example_df):
- df = TSDataset.to_dataset(example_df)
- ts = TSDataset(df=df, freq="H")
+ df = example_df
+ df_wide = TSDataset.to_dataset(df)
+ df["external_timestamp"] = df["timestamp"]
+ df.drop(columns=["target"], inplace=True)
+ df_exog_wide = TSDataset.to_dataset(df)
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq="H")
+ return ts
+
+
+@pytest.fixture
+def example_ts_int_timestamp(example_df):
+ df = example_df
+ df["timestamp"] = (df["timestamp"] - df["timestamp"].min()) // pd.Timedelta("1H")
+ df_wide = TSDataset.to_dataset(example_df)
+ ts = TSDataset(df=df_wide, freq=None)
+ return ts
+
+
+@pytest.fixture
+def example_ts_external_datetime_timestamp(example_df):
+ df = example_df
+ df_wide = TSDataset.to_dataset(df)
+ df_exog = df.copy()
+ df_exog["external_timestamp"] = df_exog["timestamp"]
+ df_exog.drop(columns=["target"], inplace=True)
+ df_exog.loc[df_exog["segment"] == "segment_1", "external_timestamp"] += pd.Timedelta("6H")
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq="H")
+ ts_int_index = convert_ts_to_int_timestamp(ts=ts, shift=10)
+ return ts_int_index
+
+
+@pytest.fixture
+def example_ts_external_irregular_datetime_timestamp(example_ts_external_datetime_timestamp) -> TSDataset:
+ ts = example_ts_external_datetime_timestamp
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[df_exog.index[3], pd.IndexSlice["segment_1", "external_timestamp"]] += pd.Timedelta("3H")
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
+ return ts
+
+
+@pytest.fixture
+def example_ts_external_datetime_timestamp_different_freq(example_ts_external_datetime_timestamp) -> TSDataset:
+ ts = example_ts_external_datetime_timestamp
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[:, pd.IndexSlice["segment_1", "external_timestamp"]] = pd.date_range(
+ start="2020-01-01", periods=len(df_exog), freq="D"
+ )
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
+ return ts
+
+
+@pytest.fixture
+def example_ts_external_datetime_timestamp_with_nans(example_ts_external_datetime_timestamp) -> TSDataset:
+ ts = example_ts_external_datetime_timestamp
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[df_exog.index[:3], pd.IndexSlice["segment_0", "external_timestamp"]] = np.NaN
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
+ return ts
+
+
+@pytest.fixture
+def example_ts_external_int_timestamp(example_df):
+ df_wide = TSDataset.to_dataset(example_df.copy())
+ df_exog = example_df.copy()
+ df_exog["external_timestamp"] = (example_df["timestamp"] - example_df["timestamp"].min()) // pd.Timedelta("1H")
+ df_exog.drop(columns=["target"], inplace=True)
+ df_exog.loc[df_exog["segment"] == "segment_1", "external_timestamp"] += 6
+ df_exog_wide = TSDataset.to_dataset(df_exog)
+ ts = TSDataset(df=df_wide, df_exog=df_exog_wide, freq="H")
+ ts_int_index = convert_ts_to_int_timestamp(ts=ts, shift=10)
+ return ts_int_index
+
+
+@pytest.fixture
+def example_ts_external_int_timestamp_with_nans(example_ts_external_int_timestamp) -> TSDataset:
+ ts = example_ts_external_int_timestamp
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[df_exog.index[:3], pd.IndexSlice["segment_0", "external_timestamp"]] = np.NaN
+ df_exog.loc[df_exog.index[3:6], pd.IndexSlice["segment_1", "external_timestamp"]] = np.NaN
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
+ return ts
+
+
+@pytest.fixture
+def example_ts_with_regressor(example_ts):
+ df = example_ts.raw_df
+ df_exog = example_ts.df_exog
+ ts = TSDataset(df=df.iloc[:-1], df_exog=df_exog, freq=example_ts.freq, known_future=["external_timestamp"])
return ts
@@ -58,7 +156,7 @@ def test_repr(order, mods):
mods=mods,
)
transform_repr = transform.__repr__()
- true_repr = f"FourierTransform(period = 10, order = {order}, mods = {mods}, out_column = None, )"
+ true_repr = f"FourierTransform(period = 10, order = {order}, mods = {mods}, out_column = None, in_column = None, )"
assert transform_repr == true_repr
@@ -101,14 +199,18 @@ def test_fail_set_both():
def test_column_names(example_ts, period, order, num_columns):
"""Test that transform creates expected number of columns and they can be recreated by its name."""
segments = example_ts.columns.get_level_values("segment").unique()
+ initial_columns = example_ts.columns.get_level_values("feature").unique()
transform = FourierTransform(period=period, order=order)
+
transformed_df = transform.fit_transform(deepcopy(example_ts)).to_pandas()
- columns = transformed_df.columns.get_level_values("feature").unique().drop("target")
- assert len(columns) == num_columns
- for column in columns:
+ new_columns = transformed_df.columns.get_level_values("feature").unique().difference(initial_columns)
+
+ assert len(new_columns) == num_columns
+ for column in new_columns:
transform_temp = eval(column)
df_temp = transform_temp.fit_transform(deepcopy(example_ts)).to_pandas()
- columns_temp = df_temp.columns.get_level_values("feature").unique().drop("target")
+ columns_temp = df_temp.columns.get_level_values("feature").unique().difference(initial_columns)
+
assert len(columns_temp) == 1
generated_column = columns_temp[0]
assert generated_column == column
@@ -120,31 +222,172 @@ def test_column_names(example_ts, period, order, num_columns):
def test_column_names_out_column(example_ts):
"""Test that transform creates expected columns if `out_column` is set"""
+ initial_columns = example_ts.columns.get_level_values("feature").unique()
transform = FourierTransform(period=10, order=3, out_column="regressor_fourier")
transformed_df = transform.fit_transform(example_ts).to_pandas()
- columns = transformed_df.columns.get_level_values("feature").unique().drop("target")
+ columns = transformed_df.columns.get_level_values("feature").unique().difference(initial_columns)
expected_columns = {f"regressor_fourier_{i}" for i in range(1, 7)}
assert set(columns) == expected_columns
+def test_fit_irregular_datetime_fail(example_ts_external_irregular_datetime_timestamp):
+ ts = example_ts_external_irregular_datetime_timestamp
+ transform = FourierTransform(period=10, order=3, out_column="regressor_fourier", in_column="external_timestamp")
+ with pytest.raises(
+ ValueError, match="Invalid in_column values! Datetime values should be regular timestamps with some frequency."
+ ):
+ transform.fit(ts)
+
+
+def test_fit_different_freq_fail(example_ts_external_datetime_timestamp_different_freq):
+ ts = example_ts_external_datetime_timestamp_different_freq
+ transform = FourierTransform(period=10, order=3, out_column="regressor_fourier", in_column="external_timestamp")
+ with pytest.raises(
+ ValueError, match="Invalid in_column values! Datetime values should have the same frequency for every segment."
+ ):
+ transform.fit(ts)
+
+
+def test_transform_irregular_datetime_fail(
+ example_ts_external_datetime_timestamp, example_ts_external_irregular_datetime_timestamp
+):
+ ts_fit = example_ts_external_datetime_timestamp
+ ts_transform = example_ts_external_irregular_datetime_timestamp
+ transform = FourierTransform(period=10, order=3, out_column="regressor_fourier", in_column="external_timestamp")
+ transform.fit(ts_fit)
+ with pytest.raises(
+ ValueError, match="Invalid in_column values! Datetime values should be regular timestamps with some frequency."
+ ):
+ _ = transform.transform(ts_transform)
+
+
+def test_transform_different_freq_fail(
+ example_ts_external_datetime_timestamp, example_ts_external_datetime_timestamp_different_freq
+):
+ ts_fit = example_ts_external_datetime_timestamp
+ ts_transform = example_ts_external_datetime_timestamp_different_freq
+ transform = FourierTransform(period=10, order=3, out_column="regressor_fourier", in_column="external_timestamp")
+ transform.fit(ts_fit)
+ with pytest.raises(
+ ValueError, match="Invalid in_column values! Datetime values should have the same frequency for every segment."
+ ):
+ transform.fit(ts_transform)
+
+
+def test_transform_fail_not_fitted(example_ts):
+ transform = FourierTransform(period=10, order=3, out_column="regressor_fourier")
+ with pytest.raises(ValueError, match="The transform isn't fitted"):
+ _ = transform.transform(example_ts)
+
+
+@pytest.mark.parametrize(
+ "in_column, ts_name, expected_timestamp",
+ [
+ (None, "example_ts", list(range(10)) + list(range(10))),
+ (None, "example_ts_int_timestamp", list(range(10)) + list(range(10))),
+ ("external_timestamp", "example_ts_external_int_timestamp", list(range(10)) + list(range(6, 16))),
+ ("external_timestamp", "example_ts_external_datetime_timestamp", list(range(10)) + list(range(6, 16))),
+ (
+ "external_timestamp",
+ "example_ts_external_int_timestamp_with_nans",
+ [None, None, None] + list(range(3, 10)) + [6, 7, 8, None, None, None, 12, 13, 14, 15],
+ ),
+ (
+ "external_timestamp",
+ "example_ts_external_datetime_timestamp_with_nans",
+ list(range(-3, 7)) + list(range(3, 13)),
+ ),
+ ],
+)
@pytest.mark.parametrize("period, mod", [(24, 1), (24, 2), (24, 9), (24, 20), (24, 23), (7.5, 3), (7.5, 4)])
-def test_column_values(example_ts, period, mod):
+def test_transform_values(in_column, ts_name, expected_timestamp, period, mod, request):
"""Test that transform generates correct values."""
- transform = FourierTransform(period=period, mods=[mod], out_column="regressor_fourier")
- transformed_df = transform.fit_transform(example_ts).to_pandas()
- for segment in example_ts.segments:
- transform_values = transformed_df.loc[:, pd.IndexSlice[segment, f"regressor_fourier_{mod}"]]
-
- timestamp = example_ts.index
- freq = pd.Timedelta("1H")
- elapsed = (timestamp - timestamp[0]) / (period * freq)
- order = (mod + 1) // 2
- if mod % 2 == 0:
- expected_values = np.cos(2 * np.pi * order * elapsed).values
- else:
- expected_values = np.sin(2 * np.pi * order * elapsed).values
-
- assert np.allclose(transform_values, expected_values, atol=1e-12)
+ ts = request.getfixturevalue(ts_name)
+ transform = FourierTransform(period=period, mods=[mod], out_column="regressor_fourier", in_column=in_column)
+ transformed_df = transform.fit_transform(ts).to_pandas(flatten=True)
+ transform_values = transformed_df[f"regressor_fourier_{mod}"]
+
+ elapsed = np.array(expected_timestamp, dtype=float) / period
+ order = (mod + 1) // 2
+ if mod % 2 == 0:
+ expected_values = np.cos(2 * np.pi * order * elapsed)
+ else:
+ expected_values = np.sin(2 * np.pi * order * elapsed)
+
+ np.testing.assert_allclose(transform_values, expected_values, atol=1e-12)
+
+
+@pytest.mark.parametrize(
+ "shift",
+ [
+ 0,
+ 3,
+ ],
+)
+@pytest.mark.parametrize(
+ "in_column, ts_name",
+ [
+ (None, "example_ts"),
+ (None, "example_ts_int_timestamp"),
+ ("external_timestamp", "example_ts_external_int_timestamp"),
+ ("external_timestamp", "example_ts_external_datetime_timestamp"),
+ ("external_timestamp", "example_ts_external_int_timestamp_with_nans"),
+ (
+ "external_timestamp",
+ "example_ts_external_datetime_timestamp_with_nans",
+ ),
+ ],
+)
+@pytest.mark.parametrize("period, mod", [(24, 1), (24, 2), (24, 9), (24, 20), (24, 23), (7.5, 3), (7.5, 4)])
+def test_transform_values_with_shift(shift, in_column, ts_name, period, mod, request):
+ ts = request.getfixturevalue(ts_name)
+
+ ts_1 = ts
+ ts_2 = TSDataset(df=ts.raw_df.iloc[shift:], df_exog=ts.df_exog, freq=ts.freq, known_future=ts.known_future)
+ transform = FourierTransform(period=period, mods=[mod], out_column="regressor_fourier", in_column=in_column)
+
+ transform.fit(ts)
+ transformed_df_1 = transform.transform(ts_1).to_pandas()
+ transformed_df_2 = transform.transform(ts_2).to_pandas()
+
+ for segment in ts.segments:
+ transform_values_1 = transformed_df_1.loc[:, pd.IndexSlice[segment, f"regressor_fourier_{mod}"]].iloc[shift:]
+ transform_values_2 = transformed_df_2.loc[:, pd.IndexSlice[segment, f"regressor_fourier_{mod}"]]
+ pd.testing.assert_series_equal(transform_values_1, transform_values_2)
+
+
+def test_get_regressors_info_index(example_ts):
+ transform = FourierTransform(period=10, order=3, out_column="fourier")
+
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = [f"{transform.out_column}_{mod}" for mod in range(1, 7)]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
+
+
+def test_get_regressors_info_in_column_fail_not_fitted(example_ts):
+ transform = FourierTransform(period=10, order=3, out_column="fourier", in_column="external_timestamp")
+ with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+ _ = transform.get_regressors_info()
+
+
+def test_get_regressors_info_in_column_fitted_exog(example_ts):
+ transform = FourierTransform(period=10, order=3, out_column="fourier", in_column="external_timestamp")
+
+ transform.fit(example_ts)
+ regressors_info = transform.get_regressors_info()
+
+ assert regressors_info == []
+
+
+def test_get_regressors_info_in_column_fitted_regressor(example_ts_with_regressor):
+ transform = FourierTransform(period=10, order=3, out_column="fourier", in_column="external_timestamp")
+
+ transform.fit(example_ts_with_regressor)
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = [f"{transform.out_column}_{mod}" for mod in range(1, 7)]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
def test_forecast(ts_trend_seasonal):
diff --git a/tests/test_transforms/test_timestamp/test_holiday_transform.py b/tests/test_transforms/test_timestamp/test_holiday_transform.py
index 828963659..9b2d30be5 100644
--- a/tests/test_transforms/test_timestamp/test_holiday_transform.py
+++ b/tests/test_transforms/test_timestamp/test_holiday_transform.py
@@ -1,3 +1,5 @@
+from typing import Optional
+
import numpy as np
import pandas as pd
import pytest
@@ -7,6 +9,7 @@
from etna.transforms.timestamp import HolidayTransform
from etna.transforms.timestamp.holiday import define_period
from tests.test_transforms.utils import assert_transformation_equals_loaded_original
+from tests.utils import convert_ts_to_int_timestamp
@pytest.fixture()
@@ -39,14 +42,44 @@ def simple_constant_df_day_15_min():
def two_segments_simple_ts_daily(simple_constant_df_daily: pd.DataFrame):
df_1 = simple_constant_df_daily.reset_index()
df_2 = simple_constant_df_daily.reset_index()
- df_1 = df_1[3:]
df_1["segment"] = "segment_1"
df_2["segment"] = "segment_2"
classic_df = pd.concat([df_1, df_2], ignore_index=True)
df = TSDataset.to_dataset(classic_df)
- ts = TSDataset(df, freq="D")
+ df.iloc[:3, 0] = np.NaN
+
+ classic_df["external_timestamp"] = classic_df["timestamp"]
+ classic_df.drop(columns=["target"], inplace=True)
+ df_exog = TSDataset.to_dataset(classic_df)
+
+ ts = TSDataset(df=df, df_exog=df_exog, freq="D")
+ return ts
+
+
+@pytest.fixture()
+def two_segments_simple_ts_daily_int_timestamp(two_segments_simple_ts_daily: TSDataset):
+ ts = convert_ts_to_int_timestamp(ts=two_segments_simple_ts_daily)
+ return ts
+
+
+@pytest.fixture
+def two_segments_simple_ts_daily_with_regressor(two_segments_simple_ts_daily: TSDataset) -> TSDataset:
+ ts = two_segments_simple_ts_daily
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ ts = TSDataset(df=df.iloc[:-3], df_exog=df_exog, freq=ts.freq, known_future=["external_timestamp"])
+ return ts
+
+
+@pytest.fixture()
+def two_segments_simple_ts_daily_with_nans(two_segments_simple_ts_daily: TSDataset):
+ ts = two_segments_simple_ts_daily
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[df_exog.index[:3], pd.IndexSlice[:, "external_timestamp"]] = np.NaN
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
return ts
@@ -54,14 +87,19 @@ def two_segments_simple_ts_daily(simple_constant_df_daily: pd.DataFrame):
def two_segments_simple_ts_day_15min(simple_constant_df_day_15_min: pd.DataFrame):
df_1 = simple_constant_df_day_15_min.reset_index()
df_2 = simple_constant_df_day_15_min.reset_index()
- df_1 = df_1[3:]
df_1["segment"] = "segment_1"
df_2["segment"] = "segment_2"
classic_df = pd.concat([df_1, df_2], ignore_index=True)
df = TSDataset.to_dataset(classic_df)
- ts = TSDataset(df, freq="1D 15MIN")
+ df.iloc[:3, 0] = np.NaN
+
+ classic_df["external_timestamp"] = classic_df["timestamp"]
+ classic_df.drop(columns=["target"], inplace=True)
+ df_exog = TSDataset.to_dataset(classic_df)
+
+ ts = TSDataset(df=df, df_exog=df_exog, freq="1D 15MIN")
return ts
@@ -85,29 +123,44 @@ def simple_week_mon_df():
def two_segments_w_mon(simple_week_mon_df: pd.DataFrame):
df_1 = simple_week_mon_df.reset_index()
df_2 = simple_week_mon_df.reset_index()
- df_1 = df_1[3:]
df_1["segment"] = "segment_1"
df_2["segment"] = "segment_2"
classic_df = pd.concat([df_1, df_2], ignore_index=True)
df = TSDataset.to_dataset(classic_df)
- ts = TSDataset(df, freq="W-MON")
+ df.iloc[:3, 0] = np.NaN
+
+ classic_df["external_timestamp"] = classic_df["timestamp"]
+ classic_df.drop(columns=["target"], inplace=True)
+ df_exog = TSDataset.to_dataset(classic_df)
+
+ ts = TSDataset(df=df, df_exog=df_exog, freq="W-MON")
return ts
@pytest.fixture()
-def two_segments_simple_ts_hour(simple_constant_df_hour: pd.DataFrame):
- df_1 = simple_constant_df_hour.reset_index()
- df_2 = simple_constant_df_hour.reset_index()
- df_1 = df_1[3:]
+def two_segments_w_mon_int_timestamp(two_segments_w_mon: TSDataset):
+ ts = convert_ts_to_int_timestamp(ts=two_segments_w_mon)
+ return ts
- df_1["segment"] = "segment_1"
- df_2["segment"] = "segment_2"
- classic_df = pd.concat([df_1, df_2], ignore_index=True)
- df = TSDataset.to_dataset(classic_df)
- ts = TSDataset(df, freq="H")
+@pytest.fixture()
+def two_segments_w_mon_with_nans(two_segments_w_mon: TSDataset):
+ ts = two_segments_w_mon
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[df_exog.index[:3], pd.IndexSlice[:, "external_timestamp"]] = np.NaN
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
+ return ts
+
+
+@pytest.fixture
+def two_segments_w_mon_with_regressor(two_segments_w_mon: TSDataset) -> TSDataset:
+ ts = two_segments_w_mon
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ ts = TSDataset(df=df.iloc[:-3], df_exog=df_exog, freq=ts.freq, known_future=["external_timestamp"])
return ts
@@ -127,25 +180,30 @@ def two_segments_simple_ts_hour(simple_constant_df_hour: pd.DataFrame):
@pytest.fixture()
-def simple_constant_df_min():
- df = pd.DataFrame({"timestamp": pd.date_range(start="2020-11-25 22:30", end="2020-11-26 02:15", freq="15MIN")})
+def simple_constant_df_minute():
+ df = pd.DataFrame({"timestamp": pd.date_range(start="2020-11-25 22:30", end="2020-11-26 02:15", freq="15T")})
df["target"] = 42
df.set_index("timestamp", inplace=True)
return df
@pytest.fixture()
-def two_segments_simple_ts_min(simple_constant_df_min: pd.DataFrame):
- df_1 = simple_constant_df_min.reset_index()
- df_2 = simple_constant_df_min.reset_index()
- df_1 = df_1[3:]
+def two_segments_simple_ts_minute(simple_constant_df_minute):
+ df_1 = simple_constant_df_minute.reset_index()
+ df_2 = simple_constant_df_minute.reset_index()
df_1["segment"] = "segment_1"
df_2["segment"] = "segment_2"
classic_df = pd.concat([df_1, df_2], ignore_index=True)
df = TSDataset.to_dataset(classic_df)
- ts = TSDataset(df, freq="15MIN")
+ df.iloc[:3, 0] = np.NaN
+
+ classic_df["external_timestamp"] = classic_df["timestamp"]
+ classic_df.drop(columns=["target"], inplace=True)
+ df_exog = TSDataset.to_dataset(classic_df)
+
+ ts = TSDataset(df=df, df_exog=df_exog, freq="15MIN")
return ts
@@ -161,6 +219,28 @@ def us_holiday_names_daily():
return np.array(values)
+@pytest.mark.parametrize(
+ "freq, timestamp, expected_result",
+ (
+ ("Y", pd.Timestamp("2000-12-31"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-12-31")]),
+ ("YS", pd.Timestamp("2000-01-01"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-12-31")]),
+ ("A-OCT", pd.Timestamp("2000-10-31"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-12-31")]),
+ ("AS-OCT", pd.Timestamp("2000-10-01"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-12-31")]),
+ ("Q", pd.Timestamp("2000-12-31"), [pd.Timestamp("2000-10-01"), pd.Timestamp("2000-12-31")]),
+ ("QS", pd.Timestamp("2000-01-01"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-03-31")]),
+ ("Q-NOV", pd.Timestamp("2000-11-30"), [pd.Timestamp("2000-09-01"), pd.Timestamp("2000-11-30")]),
+ ("QS-NOV", pd.Timestamp("2000-11-01"), [pd.Timestamp("2000-11-01"), pd.Timestamp("2001-01-31")]),
+ ("M", pd.Timestamp("2000-01-31"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-01-31")]),
+ ("MS", pd.Timestamp("2000-01-01"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-01-31")]),
+ ("W", pd.Timestamp("2000-12-03"), [pd.Timestamp("2000-11-27"), pd.Timestamp("2000-12-03")]),
+ ("W-THU", pd.Timestamp("2000-11-30"), [pd.Timestamp("2000-11-27"), pd.Timestamp("2000-12-03")]),
+ ),
+)
+def test_define_period_end(freq, timestamp, expected_result):
+ assert (define_period(pd.tseries.frequencies.to_offset(freq), timestamp, freq))[0] == expected_result[0]
+ assert (define_period(pd.tseries.frequencies.to_offset(freq), timestamp, freq))[1] == expected_result[1]
+
+
def test_holiday_with_regressors(simple_ts_with_regressors: TSDataset):
holiday = HolidayTransform(out_column="holiday")
new = holiday.fit_transform(simple_ts_with_regressors)
@@ -168,105 +248,159 @@ def test_holiday_with_regressors(simple_ts_with_regressors: TSDataset):
assert len_holiday == len(np.unique(new.columns.get_level_values("segment")))
-def test_interface_two_segments_daily(two_segments_simple_ts_daily: TSDataset):
- holidays_finder = HolidayTransform(out_column="regressor_holidays")
- ts = holidays_finder.fit_transform(two_segments_simple_ts_daily)
- df = ts.to_pandas()
- for segment in df.columns.get_level_values("segment").unique():
- assert "regressor_holidays" in df[segment].columns
- assert df[segment]["regressor_holidays"].dtype == "category"
-
-
-def test_interface_two_segments_hour(two_segments_simple_ts_hour: TSDataset):
- holidays_finder = HolidayTransform(out_column="regressor_holidays")
- ts = holidays_finder.fit_transform(two_segments_simple_ts_hour)
+@pytest.mark.parametrize(
+ "in_column, ts_name",
+ [
+ (None, "two_segments_simple_ts_daily"),
+ ("external_timestamp", "two_segments_simple_ts_daily"),
+ ("external_timestamp", "two_segments_simple_ts_daily_int_timestamp"),
+ ],
+)
+@pytest.mark.parametrize(
+ "iso_code,answer",
+ (
+ ("RUS", np.array([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])),
+ ("US", np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
+ ),
+)
+def test_holidays_binary_day(in_column: Optional[str], ts_name, iso_code: str, answer: np.array, request):
+ ts = request.getfixturevalue(ts_name)
+ holidays_finder = HolidayTransform(iso_code=iso_code, mode="binary", out_column="holiday", in_column=in_column)
+ ts = holidays_finder.fit_transform(ts)
df = ts.to_pandas()
for segment in df.columns.get_level_values("segment").unique():
- assert "regressor_holidays" in df[segment].columns
- assert df[segment]["regressor_holidays"].dtype == "category"
+ assert np.array_equal(df[segment]["holiday"].values, answer)
+ assert df[segment]["holiday"].dtype == "category"
-def test_interface_two_segments_min(two_segments_simple_ts_min: TSDataset):
- holidays_finder = HolidayTransform(out_column="regressor_holidays")
- ts = holidays_finder.fit_transform(two_segments_simple_ts_min)
- df = ts.to_pandas()
+@pytest.mark.parametrize(
+ "iso_code,answer",
+ (
+ ("RUS", np.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
+ ("US", np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
+ ),
+)
+def test_holidays_binary_hour(iso_code: str, answer: np.array, two_segments_simple_ts_hour: TSDataset):
+ holidays_finder = HolidayTransform(iso_code=iso_code, mode="binary", out_column="holiday")
+ df = holidays_finder.fit_transform(two_segments_simple_ts_hour).to_pandas()
for segment in df.columns.get_level_values("segment").unique():
- assert "regressor_holidays" in df[segment].columns
- assert df[segment]["regressor_holidays"].dtype == "category"
+ assert np.array_equal(df[segment]["holiday"].values, answer)
+ assert df[segment]["holiday"].dtype == "category"
@pytest.mark.parametrize(
"iso_code,answer",
(
- ("RUS", np.array([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])),
- ("US", np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
+ ("RUS", np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
+ ("US", np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])),
),
)
-def test_holidays_day(iso_code: str, answer: np.array, two_segments_simple_ts_daily: TSDataset):
- holidays_finder = HolidayTransform(iso_code=iso_code, out_column="regressor_holidays")
- ts = holidays_finder.fit_transform(two_segments_simple_ts_daily)
- df = ts.to_pandas()
+def test_holidays_binary_minute(iso_code: str, answer: np.array, two_segments_simple_ts_minute):
+ holidays_finder = HolidayTransform(iso_code=iso_code, mode="binary", out_column="holiday")
+ df = holidays_finder.fit_transform(two_segments_simple_ts_minute).to_pandas()
for segment in df.columns.get_level_values("segment").unique():
- assert np.array_equal(df[segment]["regressor_holidays"].values, answer)
+ assert np.array_equal(df[segment]["holiday"].values, answer)
+ assert df[segment]["holiday"].dtype == "category"
-def test_uk_holidays_day_category(uk_holiday_names_daily: np.array, two_segments_simple_ts_daily: TSDataset):
- holidays_finder = HolidayTransform(iso_code="UK", mode="category", out_column="regressor_holidays")
- ts = holidays_finder.fit_transform(two_segments_simple_ts_daily)
+def test_holidays_binary_day_with_nans(two_segments_simple_ts_daily_with_nans):
+ ts = two_segments_simple_ts_daily_with_nans
+ holidays_finder = HolidayTransform(
+ iso_code="RUS", mode="binary", out_column="holiday", in_column="external_timestamp"
+ )
+ ts = holidays_finder.fit_transform(ts)
df = ts.to_pandas()
for segment in df.columns.get_level_values("segment").unique():
- assert np.array_equal(df[segment]["regressor_holidays"].values, uk_holiday_names_daily)
+ assert df[segment]["holiday"].isna().sum() == 3
+ assert df[segment]["holiday"].dtype == "category"
-def test_us_holidays_day_category(us_holiday_names_daily: np.array, two_segments_simple_ts_daily: TSDataset):
- holidays_finder = HolidayTransform(iso_code="US", mode="category", out_column="regressor_holidays")
- ts = holidays_finder.fit_transform(two_segments_simple_ts_daily)
+@pytest.mark.parametrize(
+ "in_column, ts_name",
+ [
+ (None, "two_segments_simple_ts_daily"),
+ ("external_timestamp", "two_segments_simple_ts_daily"),
+ ("external_timestamp", "two_segments_simple_ts_daily_int_timestamp"),
+ ],
+)
+@pytest.mark.parametrize(
+ "iso_code, answer_name",
+ [
+ ("UK", "uk_holiday_names_daily"),
+ ("US", "us_holiday_names_daily"),
+ ],
+)
+def test_holidays_category_day(in_column, ts_name, iso_code, answer_name, request):
+ ts = request.getfixturevalue(ts_name)
+ answer = request.getfixturevalue(answer_name)
+ holidays_finder = HolidayTransform(iso_code=iso_code, mode="category", out_column="holiday", in_column=in_column)
+ df = holidays_finder.fit_transform(ts).to_pandas()
+ for segment in df.columns.get_level_values("segment").unique():
+ assert np.array_equal(df[segment]["holiday"].values, answer)
+ assert df[segment]["holiday"].dtype == "category"
+
+
+def test_holidays_category_day_with_nans(two_segments_simple_ts_daily_with_nans):
+ ts = two_segments_simple_ts_daily_with_nans
+ holidays_finder = HolidayTransform(
+ iso_code="RUS", mode="category", out_column="holiday", in_column="external_timestamp"
+ )
+ ts = holidays_finder.fit_transform(ts)
df = ts.to_pandas()
for segment in df.columns.get_level_values("segment").unique():
- assert np.array_equal(df[segment]["regressor_holidays"].values, us_holiday_names_daily)
+ assert df[segment]["holiday"].isna().sum() == 3
+ assert df[segment]["holiday"].dtype == "category"
+# TODO: fix after discussing conceptual problems
+@pytest.mark.xfail()
+@pytest.mark.parametrize(
+ "in_column, ts_name",
+ [
+ (None, "two_segments_w_mon"),
+ ("external_timestamp", "two_segments_w_mon"),
+ ("external_timestamp", "two_segments_w_mon_int_timestamp"),
+ ],
+)
@pytest.mark.parametrize(
"iso_code,answer",
(
- ("RUS", np.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
- ("US", np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
+ ("RUS", np.array([0, 0, 0, 0, 0, 1 / 7, 0, 1 / 7, 0, 0, 0, 0, 0, 0, 0, 1 / 7, 1 / 7, 0])),
+ ("US", np.array([0, 1 / 7, 0, 0, 0, 1 / 7] + 12 * [0])),
),
)
-def test_holidays_hour(iso_code: str, answer: np.array, two_segments_simple_ts_hour: TSDataset):
- holidays_finder = HolidayTransform(iso_code=iso_code, out_column="regressor_holidays")
- ts = holidays_finder.fit_transform(two_segments_simple_ts_hour)
+def test_holidays_days_count_w_mon(in_column, ts_name, iso_code, answer, request):
+ ts = request.getfixturevalue(ts_name)
+ holidays_finder = HolidayTransform(iso_code=iso_code, mode="days_count", out_column="holiday", in_column=in_column)
+ ts = holidays_finder.fit_transform(ts)
df = ts.to_pandas()
for segment in df.columns.get_level_values("segment").unique():
- assert np.array_equal(df[segment]["regressor_holidays"].values, answer)
+ assert np.array_equal(df[segment]["holiday"].values, answer)
-@pytest.mark.parametrize(
- "iso_code,answer",
- (
- ("RUS", np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])),
- ("US", np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])),
- ),
-)
-def test_holidays_min(iso_code: str, answer: np.array, two_segments_simple_ts_min: TSDataset):
- holidays_finder = HolidayTransform(iso_code=iso_code, out_column="regressor_holidays")
- ts = holidays_finder.fit_transform(two_segments_simple_ts_min)
+def test_holidays_days_count_w_mon_with_nans(two_segments_w_mon_with_nans):
+ ts = two_segments_w_mon_with_nans
+ holidays_finder = HolidayTransform(
+ iso_code="RUS", mode="days_count", out_column="holiday", in_column="external_timestamp"
+ )
+ ts = holidays_finder.fit_transform(ts)
df = ts.to_pandas()
for segment in df.columns.get_level_values("segment").unique():
- assert np.array_equal(df[segment]["regressor_holidays"].values, answer)
+ assert df[segment]["holiday"].isna().sum() == 3
@pytest.mark.parametrize("ts_name", ("two_segments_w_mon", "two_segments_simple_ts_day_15min"))
-def test_holidays_failed(ts_name, request):
+@pytest.mark.parametrize("mode", ("binary", "category"))
+def test_holidays_binary_category_failed_wrong_freq(ts_name, mode, request):
ts = request.getfixturevalue(ts_name)
- holidays_finder = HolidayTransform(out_column="holiday")
+ holidays_finder = HolidayTransform(out_column="holiday", mode=mode)
with pytest.raises(
ValueError, match="For binary and category modes frequency of data should be no more than daily."
):
- ts = holidays_finder.fit_transform(ts)
+ _ = holidays_finder.fit_transform(ts)
-@pytest.mark.parametrize("ts_name", ("two_segments_simple_ts_daily", "two_segments_simple_ts_min"))
+@pytest.mark.parametrize("ts_name", ("two_segments_simple_ts_daily", "two_segments_simple_ts_minute"))
def test_holidays_days_count_mode_failed(ts_name, request):
ts = request.getfixturevalue(ts_name)
holidays_finder = HolidayTransform(out_column="holiday", mode="days_count")
@@ -274,12 +408,74 @@ def test_holidays_days_count_mode_failed(ts_name, request):
ValueError,
match=f"Days_count mode works only with weekly, monthly, quarterly or yearly data. You have freq={ts.freq}",
):
- ts = holidays_finder.fit_transform(ts)
+ _ = holidays_finder.fit_transform(ts)
+
+
+def test_transform_index_fail_int_timestamp(two_segments_simple_ts_daily_int_timestamp):
+ transform = HolidayTransform(out_column="holiday", in_column=None)
+ transform.fit(two_segments_simple_ts_daily_int_timestamp)
+ with pytest.raises(ValueError, match="Transform can't work with integer index, parameter in_column should be set"):
+ _ = transform.transform(two_segments_simple_ts_daily_int_timestamp)
+
+
+@pytest.mark.parametrize("mode", ["binary", "category", "days_count"])
+def test_get_regressors_info_index(mode):
+ transform = HolidayTransform(mode=mode, out_column="holiday")
+
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = ["holiday"]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
-@pytest.mark.parametrize("expected_regressors", ([["regressor_holidays"]]))
+@pytest.mark.parametrize("mode", ["binary", "category", "days_count"])
+def test_get_regressors_info_in_column_fail_not_fitted(mode):
+ transform = HolidayTransform(mode=mode, out_column="holiday", in_column="external_timestamp")
+ with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+ _ = transform.get_regressors_info()
+
+
+@pytest.mark.parametrize(
+ "ts_name, mode",
+ [
+ ("two_segments_simple_ts_daily", "binary"),
+ ("two_segments_simple_ts_daily", "category"),
+ ("two_segments_w_mon", "days_count"),
+ ],
+)
+def test_get_regressors_info_in_column_fitted_exog(ts_name, mode, request):
+ ts = request.getfixturevalue(ts_name)
+ transform = HolidayTransform(mode=mode, out_column="holiday", in_column="external_timestamp")
+
+ transform.fit(ts)
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = []
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
+
+
+@pytest.mark.parametrize(
+ "ts_name, mode",
+ [
+ ("two_segments_simple_ts_daily_with_regressor", "binary"),
+ ("two_segments_simple_ts_daily_with_regressor", "category"),
+ ("two_segments_w_mon_with_regressor", "days_count"),
+ ],
+)
+def test_get_regressors_info_in_column_fitted_regressor(ts_name, mode, request):
+ ts = request.getfixturevalue(ts_name)
+ transform = HolidayTransform(mode=mode, out_column="holiday", in_column="external_timestamp")
+
+ transform.fit(ts)
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = ["holiday"]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
+
+
+@pytest.mark.parametrize("expected_regressors", ([["holiday"]]))
def test_holidays_out_column_added_to_regressors(example_tsds, expected_regressors):
- holidays_finder = HolidayTransform(out_column="regressor_holidays")
+ holidays_finder = HolidayTransform(out_column="holiday")
example_tsds = holidays_finder.fit_transform(example_tsds)
assert sorted(example_tsds.regressors) == sorted(expected_regressors)
@@ -292,25 +488,3 @@ def test_save_load(example_tsds):
def test_params_to_tune():
transform = HolidayTransform()
assert len(transform.params_to_tune()) == 0
-
-
-@pytest.mark.parametrize(
- "freq, timestamp, expected_result",
- (
- ("Y", pd.Timestamp("2000-12-31"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-12-31")]),
- ("YS", pd.Timestamp("2000-01-01"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-12-31")]),
- ("A-OCT", pd.Timestamp("2000-10-31"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-12-31")]),
- ("AS-OCT", pd.Timestamp("2000-10-01"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-12-31")]),
- ("Q", pd.Timestamp("2000-12-31"), [pd.Timestamp("2000-10-01"), pd.Timestamp("2000-12-31")]),
- ("QS", pd.Timestamp("2000-01-01"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-03-31")]),
- ("Q-NOV", pd.Timestamp("2000-11-30"), [pd.Timestamp("2000-09-01"), pd.Timestamp("2000-11-30")]),
- ("QS-NOV", pd.Timestamp("2000-11-01"), [pd.Timestamp("2000-11-01"), pd.Timestamp("2001-01-31")]),
- ("M", pd.Timestamp("2000-01-31"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-01-31")]),
- ("MS", pd.Timestamp("2000-01-01"), [pd.Timestamp("2000-01-01"), pd.Timestamp("2000-01-31")]),
- ("W", pd.Timestamp("2000-12-03"), [pd.Timestamp("2000-11-27"), pd.Timestamp("2000-12-03")]),
- ("W-THU", pd.Timestamp("2000-11-30"), [pd.Timestamp("2000-11-27"), pd.Timestamp("2000-12-03")]),
- ),
-)
-def test_define_period_end(freq, timestamp, expected_result):
- assert (define_period(pd.tseries.frequencies.to_offset(freq), timestamp, freq))[0] == expected_result[0]
- assert (define_period(pd.tseries.frequencies.to_offset(freq), timestamp, freq))[1] == expected_result[1]
diff --git a/tests/test_transforms/test_timestamp/test_special_days_transform.py b/tests/test_transforms/test_timestamp/test_special_days_transform.py
index e10565aa4..4476473c8 100644
--- a/tests/test_transforms/test_timestamp/test_special_days_transform.py
+++ b/tests/test_transforms/test_timestamp/test_special_days_transform.py
@@ -1,5 +1,7 @@
from datetime import datetime
+from typing import Optional
+import numpy as np
import pandas as pd
import pytest
@@ -8,6 +10,7 @@
from etna.transforms.timestamp.special_days import _OneSegmentSpecialDaysTransform
from tests.test_transforms.utils import assert_sampling_is_valid
from tests.test_transforms.utils import assert_transformation_equals_loaded_original
+from tests.utils import convert_ts_to_int_timestamp
@pytest.fixture()
@@ -42,8 +45,9 @@ def df_with_specials():
special_weekdays = (2,)
special_monthdays = (7, 10)
- df["week_true"] = df["timestamp"].apply(lambda x: x.weekday() in special_weekdays)
- df["month_true"] = df["timestamp"].apply(lambda x: x.day in special_monthdays)
+ df["week_true"] = df["timestamp"].apply(lambda x: x.weekday() in special_weekdays).astype(int).astype("category")
+ df["month_true"] = df["timestamp"].apply(lambda x: x.day in special_monthdays).astype(int).astype("category")
+ df["external_timestamp"] = df["timestamp"]
df.set_index("timestamp", inplace=True)
return df
@@ -51,10 +55,35 @@ def df_with_specials():
@pytest.fixture()
def ts_with_specials(df_with_specials):
"""Create dataset with special weekdays and monthdays."""
- df = df_with_specials.reset_index()
- df["segment"] = "1"
- df = df[["timestamp", "segment", "target"]]
- ts = TSDataset(df=TSDataset.to_dataset(df), freq="D")
+ flat_df = df_with_specials.reset_index()
+ flat_df["segment"] = "1"
+ flat_df = flat_df[["timestamp", "segment", "target"]]
+
+ wide_df = TSDataset.to_dataset(flat_df)
+
+ flat_df["external_timestamp"] = flat_df["timestamp"]
+ flat_df.drop(columns=["target"], inplace=True)
+ df_exog = TSDataset.to_dataset(flat_df)
+
+ ts = TSDataset(df=wide_df, df_exog=df_exog, freq="D")
+ return ts
+
+
+@pytest.fixture()
+def ts_with_specials_and_regressor(ts_with_specials) -> TSDataset:
+ df = ts_with_specials.raw_df
+ df_exog = ts_with_specials.df_exog
+ ts = TSDataset(df=df.iloc[:-10], df_exog=df_exog, freq=ts_with_specials.freq, known_future=["external_timestamp"])
+ return ts
+
+
+@pytest.fixture()
+def ts_with_specials_and_nans_in_timestamp(ts_with_specials) -> TSDataset:
+ ts = ts_with_specials
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[df_exog.index[:3], pd.IndexSlice[:, "external_timestamp"]] = np.NaN
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
return ts
@@ -164,18 +193,24 @@ def test_interface_two_segments_noweek_nomonth():
_ = SpecialDaysTransform(find_special_weekday=False, find_special_month_day=False)
-def test_week_feature(df_with_specials: pd.DataFrame):
+@pytest.mark.parametrize("in_column", [None, "external_timestamp"])
+def test_week_feature(in_column: Optional[str], df_with_specials: pd.DataFrame):
"""This test checks that _OneSegmentSpecialDaysTransform computes weekday feature correctly."""
- special_days_finder = _OneSegmentSpecialDaysTransform(find_special_weekday=True, find_special_month_day=False)
+ special_days_finder = _OneSegmentSpecialDaysTransform(
+ find_special_weekday=True, find_special_month_day=False, in_column=in_column
+ )
df = special_days_finder.fit_transform(df_with_specials)
- assert (df_with_specials["week_true"] == df["anomaly_weekdays"]).all()
+ pd.testing.assert_series_equal(df_with_specials["week_true"], df["anomaly_weekdays"], check_names=False)
-def test_month_feature(df_with_specials: pd.DataFrame):
+@pytest.mark.parametrize("in_column", [None, "external_timestamp"])
+def test_month_feature(in_column: Optional[str], df_with_specials: pd.DataFrame):
"""This test checks that _OneSegmentSpecialDaysTransform computes monthday feature correctly."""
- special_days_finder = _OneSegmentSpecialDaysTransform(find_special_weekday=False, find_special_month_day=True)
+ special_days_finder = _OneSegmentSpecialDaysTransform(
+ find_special_weekday=False, find_special_month_day=True, in_column=in_column
+ )
df = special_days_finder.fit_transform(df_with_specials)
- assert (df_with_specials["month_true"] == df["anomaly_monthdays"]).all()
+ pd.testing.assert_series_equal(df_with_specials["month_true"], df["anomaly_monthdays"], check_names=False)
def test_no_false_positive_week(constant_days_df: pd.DataFrame):
@@ -199,11 +234,64 @@ def test_transform_raise_error_if_not_fitted(constant_days_df: pd.DataFrame):
_ = transform.transform(df=constant_days_df)
-def test_fit_transform_with_nans(ts_diff_endings):
+def test_fit_transform_with_nans_in_target(ts_diff_endings):
transform = SpecialDaysTransform(find_special_weekday=True, find_special_month_day=True)
transform.fit_transform(ts_diff_endings)
+def test_fit_transform_with_nans_in_timestamp(ts_with_specials_and_nans_in_timestamp):
+ ts = ts_with_specials_and_nans_in_timestamp
+ transform = SpecialDaysTransform(
+ find_special_weekday=True, find_special_month_day=True, in_column="external_timestamp"
+ )
+ result = transform.fit_transform(ts)
+ columns = ["anomaly_weekdays", "anomaly_monthdays"]
+ for segment in ts.segments:
+ result_df = result.loc[:, pd.IndexSlice[segment, columns]]
+ assert np.all(result_df.isna().sum() == 3)
+
+
+def test_transform_index_fail_int_timestamp(ts_with_specials):
+ ts = convert_ts_to_int_timestamp(ts=ts_with_specials)
+ transform = SpecialDaysTransform(in_column=None)
+ with pytest.raises(ValueError, match="Transform can't work with integer index, parameter in_column should be set"):
+ _ = transform.fit(ts)
+
+
+def test_get_regressors_info_index(ts_with_specials):
+ transform = SpecialDaysTransform(in_column=None)
+
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = ["anomaly_weekdays", "anomaly_monthdays"]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
+
+
+def test_get_regressors_info_in_column_fail_not_fitted(ts_with_specials):
+ transform = SpecialDaysTransform(in_column="external_timestamp")
+ with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+ _ = transform.get_regressors_info()
+
+
+def test_get_regressors_info_in_column_fitted_exog(ts_with_specials):
+ transform = SpecialDaysTransform(in_column="external_timestamp")
+
+ transform.fit(ts_with_specials)
+ regressors_info = transform.get_regressors_info()
+
+ assert regressors_info == []
+
+
+def test_get_regressors_info_in_column_fitted_regressor(ts_with_specials_and_regressor):
+ transform = SpecialDaysTransform(in_column="external_timestamp")
+
+ transform.fit(ts_with_specials_and_regressor)
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = ["anomaly_weekdays", "anomaly_monthdays"]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
+
+
def test_save_load(ts_with_specials):
ts = ts_with_specials
transform = SpecialDaysTransform()
diff --git a/tests/test_transforms/test_timestamp/test_timeflags_transform.py b/tests/test_transforms/test_timestamp/test_timeflags_transform.py
index 02caace68..2a5593746 100644
--- a/tests/test_transforms/test_timestamp/test_timeflags_transform.py
+++ b/tests/test_transforms/test_timestamp/test_timeflags_transform.py
@@ -1,6 +1,7 @@
from copy import deepcopy
from typing import Dict
from typing import List
+from typing import Optional
from typing import Tuple
from typing import Union
@@ -12,6 +13,7 @@
from etna.transforms.timestamp import TimeFlagsTransform
from tests.test_transforms.utils import assert_sampling_is_valid
from tests.test_transforms.utils import assert_transformation_equals_loaded_original
+from tests.utils import convert_ts_to_int_timestamp
INIT_PARAMS_TEMPLATE = {
"minute_in_hour_number": False,
@@ -24,18 +26,9 @@
@pytest.fixture
-def dateflags_true_df() -> pd.DataFrame:
- """Generate dataset for TimeFlags feature.
-
- Returns
- -------
- dataset with timestamp column and columns true_minute_in_hour_number, true_fifteen_minutes_in_hour_number,
- true_half_hour_number, true_hour_number, true_half_day_number, true_one_third_day_number that contain
- true answers for corresponding features
- """
- dataframes = [
- pd.DataFrame({"timestamp": pd.date_range("2020-06-01", "2021-06-01", freq="5 min")}) for i in range(5)
- ]
+def timeflags_true_df() -> pd.DataFrame:
+ """Generate dataset with answers for TimeFlagsTransform."""
+ dataframes = [pd.DataFrame({"timestamp": pd.date_range("2020-06-01", "2021-06-01", freq="5T")}) for _ in range(5)]
out_column = "timeflag"
for i in range(len(dataframes)):
@@ -48,45 +41,67 @@ def dateflags_true_df() -> pd.DataFrame:
df[f"{out_column}_half_day_number"] = df[f"{out_column}_hour_number"] // 12
df[f"{out_column}_one_third_day_number"] = df[f"{out_column}_hour_number"] // 8
+ features = df.columns.difference(["timestamp"])
+ df[features] = df[features].astype("category")
+
df["segment"] = f"segment_{i}"
df["target"] = 2
- result = pd.concat(dataframes, ignore_index=True)
- result = result.pivot(index="timestamp", columns="segment")
- result = result.reorder_levels([1, 0], axis=1)
- result = result.sort_index(axis=1)
- result.columns.names = ["segment", "feature"]
+ flat_df = pd.concat(dataframes, ignore_index=True)
+ result = TSDataset.to_dataset(flat_df)
+ result.index.freq = "5T"
return result
@pytest.fixture
def train_ts() -> TSDataset:
- """
- Generate dataset without dateflags
- """
- dataframes = [
- pd.DataFrame({"timestamp": pd.date_range("2020-06-01", "2021-06-01", freq="5 min")}) for i in range(5)
- ]
+ """Generate dataset without timeflags"""
+ dataframes = [pd.DataFrame({"timestamp": pd.date_range("2020-06-01", "2021-06-01", freq="5T")}) for _ in range(5)]
for i in range(len(dataframes)):
df = dataframes[i]
df["segment"] = f"segment_{i}"
df["target"] = 2
- result = pd.concat(dataframes, ignore_index=True)
- result = result.pivot(index="timestamp", columns="segment")
- result = result.reorder_levels([1, 0], axis=1)
- result = result.sort_index(axis=1)
- result.columns.names = ["segment", "feature"]
- ts = TSDataset(df=result, freq="5 min")
+ flat_df = pd.concat(dataframes, ignore_index=True)
+ wide_df = TSDataset.to_dataset(flat_df)
+
+ flat_df["external_timestamp"] = flat_df["timestamp"]
+ flat_df.drop(columns=["target"], inplace=True)
+ df_exog = TSDataset.to_dataset(flat_df)
+
+ ts = TSDataset(df=wide_df, df_exog=df_exog, freq="5T")
+ return ts
+
+
+@pytest.fixture
+def train_ts_int_timestamp(train_ts) -> TSDataset:
+ ts = convert_ts_to_int_timestamp(train_ts)
+ return ts
+
+
+@pytest.fixture
+def train_ts_with_regressor(train_ts) -> TSDataset:
+ df = train_ts.raw_df
+ df_exog = train_ts.df_exog
+ ts = TSDataset(df=df.iloc[:-10], df_exog=df_exog, freq=train_ts.freq, known_future=["external_timestamp"])
+ return ts
+
+@pytest.fixture
+def train_ts_with_nans(train_ts) -> TSDataset:
+ ts = train_ts
+ df = ts.raw_df
+ df_exog = ts.df_exog
+ df_exog.loc[df_exog.index[:3], pd.IndexSlice[:, "external_timestamp"]] = np.NaN
+ ts = TSDataset(df=df, df_exog=df_exog, freq=ts.freq)
return ts
-def test_interface_incorrect_args():
+def test_invalid_arguments_configuration():
"""Test that transform can't be created with no features to generate."""
- with pytest.raises(ValueError):
+ with pytest.raises(ValueError, match="TimeFlagsTransform feature does nothing with given init args configuration"):
_ = TimeFlagsTransform(
minute_in_hour_number=False,
fifteen_minutes_in_hour_number=False,
@@ -97,6 +112,7 @@ def test_interface_incorrect_args():
)
+@pytest.mark.parametrize("in_column", [None, "external_timestamp"])
@pytest.mark.parametrize(
"true_params",
(
@@ -116,27 +132,31 @@ def test_interface_incorrect_args():
],
),
)
-def test_interface_out_column(true_params: List[str], train_ts: TSDataset):
+def test_interface_out_column(in_column: Optional[str], true_params: List[str], train_ts: TSDataset):
"""Test that transform generates correct column names using out_column parameter."""
init_params = deepcopy(INIT_PARAMS_TEMPLATE)
segments = train_ts.columns.get_level_values("segment").unique()
out_column = "timeflag"
for key in true_params:
init_params[key] = True
- transform = TimeFlagsTransform(**init_params, out_column=out_column)
+ transform = TimeFlagsTransform(**init_params, out_column=out_column, in_column=in_column)
+ initial_columns = train_ts.columns.get_level_values("feature").unique()
+
result = transform.fit_transform(train_ts).to_pandas()
assert sorted(result.columns.names) == ["feature", "segment"]
assert sorted(segments) == sorted(result.columns.get_level_values("segment").unique())
true_params = [f"{out_column}_{param}" for param in true_params]
- for seg in result.columns.get_level_values(0).unique():
+ for seg in result.columns.get_level_values("segment").unique():
tmp_df = result[seg]
- assert sorted(tmp_df.columns) == sorted(true_params + ["target"])
+ new_columns = tmp_df.columns.difference(initial_columns)
+ assert sorted(new_columns) == sorted(true_params)
for param in true_params:
assert tmp_df[param].dtype == "category"
+@pytest.mark.parametrize("in_column", [None, "external_timestamp"])
@pytest.mark.parametrize(
"true_params",
(
@@ -156,36 +176,46 @@ def test_interface_out_column(true_params: List[str], train_ts: TSDataset):
],
),
)
-def test_interface_correct_args_repr(true_params: List[str], train_ts: TSDataset):
+def test_interface_correct_args_repr(in_column: Optional[str], true_params: List[str], train_ts: TSDataset):
"""Test that transform generates correct column names without setting out_column parameter."""
init_params = deepcopy(INIT_PARAMS_TEMPLATE)
segments = train_ts.columns.get_level_values("segment").unique()
for key in true_params:
init_params[key] = True
- transform = TimeFlagsTransform(**init_params)
+ transform = TimeFlagsTransform(**init_params, in_column=in_column)
+ initial_columns = train_ts.columns.get_level_values("feature").unique()
+
result = transform.fit_transform(deepcopy(train_ts)).to_pandas()
assert sorted(result.columns.names) == ["feature", "segment"]
assert sorted(segments) == sorted(result.columns.get_level_values("segment").unique())
- columns = result.columns.get_level_values("feature").unique().drop("target")
- assert len(columns) == len(true_params)
- for column in columns:
+ new_columns = result.columns.get_level_values("feature").unique().difference(initial_columns)
+ assert len(new_columns) == len(true_params)
+ for column in new_columns:
# check category dtype
assert np.all(result.loc[:, pd.IndexSlice[segments, column]].dtypes == "category")
# check that a transform can be created from column name and it generates the same results
transform_temp = eval(column)
df_temp = transform_temp.fit_transform(deepcopy(train_ts)).to_pandas()
- columns_temp = df_temp.columns.get_level_values("feature").unique().drop("target")
- assert len(columns_temp) == 1
- generated_column = columns_temp[0]
+ new_columns_temp = df_temp.columns.get_level_values("feature").unique().difference(initial_columns)
+ assert len(new_columns_temp) == 1
+ generated_column = new_columns_temp[0]
assert generated_column == column
- assert np.all(
- df_temp.loc[:, pd.IndexSlice[segments, generated_column]] == result.loc[:, pd.IndexSlice[segments, column]]
+ pd.testing.assert_frame_equal(
+ df_temp.loc[:, pd.IndexSlice[segments, generated_column]], result.loc[:, pd.IndexSlice[segments, column]]
)
+@pytest.mark.parametrize(
+ "in_column, ts_name",
+ [
+ (None, "train_ts"),
+ ("external_timestamp", "train_ts"),
+ ("external_timestamp", "train_ts_int_timestamp"),
+ ],
+)
@pytest.mark.parametrize(
"true_params",
(
@@ -197,27 +227,169 @@ def test_interface_correct_args_repr(true_params: List[str], train_ts: TSDataset
{"one_third_day_number": True},
),
)
-def test_feature_values(
- true_params: Dict[str, Union[bool, Tuple[int, int]]], train_ts: TSDataset, dateflags_true_df: pd.DataFrame
+def test_transform_values(
+ in_column: Optional[str],
+ ts_name: str,
+ true_params: Dict[str, Union[bool, Tuple[int, int]]],
+ timeflags_true_df: pd.DataFrame,
+ request,
):
"""Test that transform generates correct values."""
+ ts = request.getfixturevalue(ts_name)
init_params = deepcopy(INIT_PARAMS_TEMPLATE)
init_params.update(true_params)
out_column = "timeflag"
- transform = TimeFlagsTransform(**init_params, out_column=out_column)
- result = transform.fit_transform(train_ts).to_pandas()
+ transform = TimeFlagsTransform(**init_params, out_column=out_column, in_column=in_column)
+ result = transform.fit_transform(ts).to_pandas()
- segments_true = dateflags_true_df.columns.get_level_values("segment").unique()
+ segments_true = timeflags_true_df.columns.get_level_values("segment").unique()
segment_result = result.columns.get_level_values("segment").unique()
assert sorted(segment_result) == sorted(segments_true)
true_params = [f"{out_column}_{param}" for param in true_params.keys()]
for seg in segment_result:
- segment_true = dateflags_true_df[seg]
- true_df = segment_true[true_params + ["target"]].sort_index(axis=1)
- result_df = result[seg].sort_index(axis=1)
- assert (true_df == result_df).all().all()
+ segment_true = timeflags_true_df[seg]
+ columns = true_params + ["target"]
+ true_df = segment_true[columns].sort_index(axis=1).reset_index(drop=True)
+ result_df = result[seg][columns].sort_index(axis=1).reset_index(drop=True)
+ pd.testing.assert_frame_equal(true_df, result_df)
+
+
+@pytest.mark.parametrize(
+ "true_params",
+ (
+ {"minute_in_hour_number": True},
+ {"fifteen_minutes_in_hour_number": True},
+ {"hour_number": True},
+ {"half_hour_number": True},
+ {"half_day_number": True},
+ {"one_third_day_number": True},
+ ),
+)
+def test_transform_values_with_nans(true_params: Dict[str, Union[bool, Tuple[int, int]]], train_ts_with_nans):
+ out_column = "timeflag"
+ init_params = deepcopy(INIT_PARAMS_TEMPLATE)
+ init_params.update(true_params)
+ transform = TimeFlagsTransform(**init_params, out_column=out_column, in_column="external_timestamp")
+ result = transform.fit_transform(train_ts_with_nans).to_pandas()
+
+ segment_result = result.columns.get_level_values("segment").unique()
+
+ true_params = [f"{out_column}_{param}" for param in true_params.keys()]
+ for seg in segment_result:
+ result_df = result[seg][true_params].sort_index(axis=1).reset_index(drop=True)
+ assert np.all(result_df.isna().sum() == 3)
+
+
+def test_transform_index_fail_int_timestamp(train_ts_int_timestamp: TSDataset):
+ transform = TimeFlagsTransform(out_column="timeflag", in_column=None)
+ transform.fit(train_ts_int_timestamp)
+ with pytest.raises(ValueError, match="Transform can't work with integer index, parameter in_column should be set"):
+ _ = transform.transform(train_ts_int_timestamp)
+
+
+@pytest.mark.parametrize(
+ "true_params",
+ (
+ ["minute_in_hour_number"],
+ ["fifteen_minutes_in_hour_number"],
+ ["hour_number"],
+ ["half_hour_number"],
+ ["half_day_number"],
+ ["one_third_day_number"],
+ [
+ "minute_in_hour_number",
+ "fifteen_minutes_in_hour_number",
+ "hour_number",
+ "half_hour_number",
+ "half_day_number",
+ "one_third_day_number",
+ ],
+ ),
+)
+def test_get_regressors_info_index(true_params):
+ out_column = "timeflag"
+ init_params = deepcopy(INIT_PARAMS_TEMPLATE)
+ for key in true_params:
+ init_params[key] = True
+ transform = TimeFlagsTransform(**init_params, out_column=out_column)
+
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = [f"{out_column}_{param}" for param in true_params]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
+
+
+def test_get_regressors_info_in_column_fail_not_fitted(train_ts):
+ transform = TimeFlagsTransform(out_column="timeflag", in_column="external_timestamp")
+ with pytest.raises(ValueError, match="Fit the transform to get the correct regressors info!"):
+ _ = transform.get_regressors_info()
+
+
+@pytest.mark.parametrize(
+ "true_params",
+ (
+ ["minute_in_hour_number"],
+ ["fifteen_minutes_in_hour_number"],
+ ["hour_number"],
+ ["half_hour_number"],
+ ["half_day_number"],
+ ["one_third_day_number"],
+ [
+ "minute_in_hour_number",
+ "fifteen_minutes_in_hour_number",
+ "hour_number",
+ "half_hour_number",
+ "half_day_number",
+ "one_third_day_number",
+ ],
+ ),
+)
+def test_get_regressors_info_in_column_fitted_exog(true_params, train_ts):
+ out_column = "timeflag"
+ init_params = deepcopy(INIT_PARAMS_TEMPLATE)
+ for key in true_params:
+ init_params[key] = True
+ transform = TimeFlagsTransform(**init_params, out_column=out_column, in_column="external_timestamp")
+
+ transform.fit(train_ts)
+ regressors_info = transform.get_regressors_info()
+
+ assert regressors_info == []
+
+
+@pytest.mark.parametrize(
+ "true_params",
+ (
+ ["minute_in_hour_number"],
+ ["fifteen_minutes_in_hour_number"],
+ ["hour_number"],
+ ["half_hour_number"],
+ ["half_day_number"],
+ ["one_third_day_number"],
+ [
+ "minute_in_hour_number",
+ "fifteen_minutes_in_hour_number",
+ "hour_number",
+ "half_hour_number",
+ "half_day_number",
+ "one_third_day_number",
+ ],
+ ),
+)
+def test_get_regressors_info_in_column_fitted_regressor(true_params, train_ts_with_regressor):
+ out_column = "dateflag"
+ init_params = deepcopy(INIT_PARAMS_TEMPLATE)
+ for key in true_params:
+ init_params[key] = True
+ transform = TimeFlagsTransform(**init_params, out_column=out_column, in_column="external_timestamp")
+
+ transform.fit(train_ts_with_regressor)
+ regressors_info = transform.get_regressors_info()
+
+ expected_regressor_info = [f"{out_column}_{param}" for param in true_params]
+ assert sorted(regressors_info) == sorted(expected_regressor_info)
def test_save_load(train_ts):
diff --git a/tests/test_transforms/utils.py b/tests/test_transforms/utils.py
index a7f63fbe2..4f8e1df3a 100644
--- a/tests/test_transforms/utils.py
+++ b/tests/test_transforms/utils.py
@@ -2,7 +2,9 @@
import tempfile
from copy import deepcopy
from typing import Callable
+from typing import Dict
from typing import Optional
+from typing import Set
from typing import Tuple
import optuna
@@ -50,3 +52,31 @@ def _objective(trial: optuna.Trial) -> float:
study = optuna.create_study(sampler=optuna.samplers.RandomSampler(seed=seed))
study.optimize(_objective, n_trials=n_trials)
+
+
+def find_columns_diff(df_before: pd.DataFrame, df_after: pd.DataFrame) -> Tuple[Set[str], Set[str], Set[str]]:
+ columns_before_transform = set(df_before.columns)
+ columns_after_transform = set(df_after.columns)
+ created_columns = columns_after_transform - columns_before_transform
+ removed_columns = columns_before_transform - columns_after_transform
+
+ columns_to_check_changes = columns_after_transform.intersection(columns_before_transform)
+ changed_columns = set()
+ for column in columns_to_check_changes:
+ if not df_before[column].equals(df_after[column]):
+ changed_columns.add(column)
+
+ return created_columns, removed_columns, changed_columns
+
+
+def assert_column_changes(ts_1: TSDataset, ts_2: TSDataset, expected_changes: Dict[str, Set[str]]):
+ expected_columns_to_create = expected_changes.get("create", set())
+ expected_columns_to_remove = expected_changes.get("remove", set())
+ expected_columns_to_change = expected_changes.get("change", set())
+ flat_df_1 = ts_1.to_pandas(flatten=True)
+ flat_df_2 = ts_2.to_pandas(flatten=True)
+ created_columns, removed_columns, changed_columns = find_columns_diff(flat_df_1, flat_df_2)
+
+ assert created_columns == expected_columns_to_create
+ assert removed_columns == expected_columns_to_remove
+ assert changed_columns == expected_columns_to_change
diff --git a/tests/utils.py b/tests/utils.py
index 602f25699..9ae8b5ba9 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -6,6 +6,7 @@
import pytest
from etna.datasets import TSDataset
+from etna.datasets.utils import determine_num_steps
from etna.metrics.base import Metric
from etna.metrics.base import MetricAggregationMode
@@ -34,6 +35,26 @@ def select_segments_subset(ts: TSDataset, segments: List[str]) -> TSDataset:
return subset_ts
+def convert_ts_to_int_timestamp(ts: TSDataset, shift=0):
+ df = ts.to_pandas(features=["target"])
+ df_exog = ts.df_exog
+
+ if df_exog is not None:
+ exog_shift = determine_num_steps(start_timestamp=df_exog.index[0], end_timestamp=df.index[0], freq=ts.freq)
+ df_exog.index = pd.Index(np.arange(len(df_exog)) + shift - exog_shift, name=df.index.name)
+
+ df.index = pd.Index(np.arange(len(df)) + shift, name=df.index.name)
+
+ ts = TSDataset(
+ df=df,
+ df_exog=df_exog,
+ known_future=ts.known_future,
+ freq=None,
+ hierarchical_structure=ts.hierarchical_structure,
+ )
+ return ts
+
+
def create_dummy_functional_metric(alpha: float = 1.0):
def dummy_functional_metric(y_true: np.ndarray, y_pred: np.ndarray) -> float:
return alpha