From 18d41021d4c7055bc0ade97ab2ae9f17215e200a Mon Sep 17 00:00:00 2001 From: dilpath Date: Wed, 8 Mar 2023 11:22:57 +1100 Subject: [PATCH 01/26] add viz methods; example notebook --- .../calibrated_models/calibrated_models.yaml | 98 ++++++ doc/examples/calibrated_models/conditions.tsv | 2 + .../calibrated_models/measurements.tsv | 7 + doc/examples/calibrated_models/model.xml | 315 ++++++++++++++++++ .../calibrated_models/model_space.tsv | 9 + .../calibrated_models/observables.tsv | 2 + doc/examples/calibrated_models/parameters.tsv | 5 + .../calibrated_models/petab_problem.yaml | 11 + .../petab_select_problem.yaml | 5 + doc/examples/visualization.ipynb | 169 ++++++++++ petab_select/__init__.py | 1 + petab_select/plot.py | 277 +++++++++++++++ petab_select/problem.py | 1 + 13 files changed, 902 insertions(+) create mode 100644 doc/examples/calibrated_models/calibrated_models.yaml create mode 100644 doc/examples/calibrated_models/conditions.tsv create mode 100644 doc/examples/calibrated_models/measurements.tsv create mode 100644 doc/examples/calibrated_models/model.xml create mode 100644 doc/examples/calibrated_models/model_space.tsv create mode 100644 doc/examples/calibrated_models/observables.tsv create mode 100644 doc/examples/calibrated_models/parameters.tsv create mode 100644 doc/examples/calibrated_models/petab_problem.yaml create mode 100644 doc/examples/calibrated_models/petab_select_problem.yaml create mode 100644 doc/examples/visualization.ipynb create mode 100644 petab_select/plot.py diff --git a/doc/examples/calibrated_models/calibrated_models.yaml b/doc/examples/calibrated_models/calibrated_models.yaml new file mode 100644 index 00000000..a3e72834 --- /dev/null +++ b/doc/examples/calibrated_models/calibrated_models.yaml @@ -0,0 +1,98 @@ +- criteria: + AICc: 37.97523003111247 + NLLH: 17.487615015556234 + estimated_parameters: + sigma_x2: 4.462298342504554 + model_hash: 649bcd92d8da15883a6709f3233e277e173aa6ce7ff1eda3f32d342a0f3a0e63ddd6e3764eab0c730dfb96440bab731ee67d412295a48e1e9949b3cc10935f28 + model_id: 649bcd92d8da15883a6709f3233e277e173aa6ce7ff1eda3f32d342a0f3a0e63ddd6e3764eab0c730dfb96440bab731ee67d412295a48e1e9949b3cc10935f28 + model_subspace_id: M1_0 + model_subspace_indices: + - 0 + - 0 + - 0 + parameters: + k1: 0 + k2: 0 + k3: 0 + petab_yaml: petab_problem.yaml + predecessor_model_hash: virtual_initial_model +- criteria: + AICc: -0.17540608110890332 + NLLH: -4.087703040554452 + estimated_parameters: + k3: 0.0 + sigma_x2: 0.12242920045980682 + model_hash: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 + model_id: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 + model_subspace_id: M1_1 + model_subspace_indices: + - 0 + - 0 + - 0 + parameters: + k1: 0.2 + k2: 0.1 + k3: estimate + petab_yaml: petab_problem.yaml + predecessor_model_hash: 649bcd92d8da15883a6709f3233e277e173aa6ce7ff1eda3f32d342a0f3a0e63ddd6e3764eab0c730dfb96440bab731ee67d412295a48e1e9949b3cc10935f28 +- criteria: + AICc: 9.72548629466802 + NLLH: -4.13725685266599 + estimated_parameters: + k2: 0.10147820365454563 + k3: 0.0 + sigma_x2: 0.12142173031067148 + model_hash: 3d668e2ad009b34fe1a46fc87d799f94a946c2b411e18d814d90f935b041c2c31c09a1fa346ac1b2ace69de4cfa1d4319939a8505256bed734e53e276b73fe21 + model_id: 3d668e2ad009b34fe1a46fc87d799f94a946c2b411e18d814d90f935b041c2c31c09a1fa346ac1b2ace69de4cfa1d4319939a8505256bed734e53e276b73fe21 + model_subspace_id: M1_4 + model_subspace_indices: + - 0 + - 0 + - 0 + parameters: + k1: 0.2 + k2: estimate + k3: estimate + petab_yaml: petab_problem.yaml + predecessor_model_hash: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 +- criteria: + AICc: 9.294673635222654 + NLLH: -4.352663182388673 + estimated_parameters: + k1: 0.20160877940548563 + k3: 0.0 + sigma_x2: 0.11714036406481404 + model_hash: d2503eb370a02bfae839b66cc207ed4a04833e609d9cbe70bc956ed1bb79158bbf5f4634a74369704085375deffafc03498754bce2a5ffd1064c7bdb7e549c95 + model_id: d2503eb370a02bfae839b66cc207ed4a04833e609d9cbe70bc956ed1bb79158bbf5f4634a74369704085375deffafc03498754bce2a5ffd1064c7bdb7e549c95 + model_subspace_id: M1_5 + model_subspace_indices: + - 0 + - 0 + - 0 + parameters: + k1: estimate + k2: 0.1 + k3: estimate + petab_yaml: petab_problem.yaml + predecessor_model_hash: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 +- criteria: + AICc: 35.94352922200498 + NLLH: -6.028235388997507 + estimated_parameters: + k1: 0.6223035502886127 + k2: 0.02020974032591268 + k3: 0.0010856659773303976 + sigma_x2: 0.08859827826030148 + model_hash: afa580ef5a7e93e44eaa2901643913761bacd32b35b07d821b91da033bc6dae6ffbfb87d4ba49139d4c68f04921a720063c535cfa9966457d53382d89edc696d + model_id: afa580ef5a7e93e44eaa2901643913761bacd32b35b07d821b91da033bc6dae6ffbfb87d4ba49139d4c68f04921a720063c535cfa9966457d53382d89edc696d + model_subspace_id: M1_7 + model_subspace_indices: + - 0 + - 0 + - 0 + parameters: + k1: estimate + k2: estimate + k3: estimate + petab_yaml: petab_problem.yaml + predecessor_model_hash: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 diff --git a/doc/examples/calibrated_models/conditions.tsv b/doc/examples/calibrated_models/conditions.tsv new file mode 100644 index 00000000..d6a622a6 --- /dev/null +++ b/doc/examples/calibrated_models/conditions.tsv @@ -0,0 +1,2 @@ +conditionId conditionName +model1_data1 condition1 diff --git a/doc/examples/calibrated_models/measurements.tsv b/doc/examples/calibrated_models/measurements.tsv new file mode 100644 index 00000000..4e186f55 --- /dev/null +++ b/doc/examples/calibrated_models/measurements.tsv @@ -0,0 +1,7 @@ +observableId simulationConditionId measurement time noiseParameters +obs_x2 model1_data1 0 0 sigma_x2 +obs_x2 model1_data1 0.19421762 1 sigma_x2 +obs_x2 model1_data1 0.0484032 5 sigma_x2 +obs_x2 model1_data1 0.61288016 10 sigma_x2 +obs_x2 model1_data1 4.07930835 30 sigma_x2 +obs_x2 model1_data1 10.12008893 60 sigma_x2 diff --git a/doc/examples/calibrated_models/model.xml b/doc/examples/calibrated_models/model.xml new file mode 100644 index 00000000..878edbc4 --- /dev/null +++ b/doc/examples/calibrated_models/model.xml @@ -0,0 +1,315 @@ + + + + + + + + + + + 2019-03-27T18:47:48Z + + + + + + + + + + + + + + + 2019-03-27T18:57:38Z + + + + + + + + + + k3 + + + x1 + + + x2 + + + default + + + + + + k3 + x1 + x2 + + default + + + + + + + + + + + + 2019-03-27T18:52:47Z + + + + + + + + + + k1 + + + default + + + + k1 + default + + + + + + + + + + + + 2019-03-27T18:55:12Z + + + + + + + + + + k2 + + + x1 + + + default + + + + + + k2 + x1 + + default + + + + + + + + + + + + + + + + + + + + + 2019-03-27T18:59:36Z + + + + + + + + + + + + + + + 2019-03-27T18:59:35Z + + + + + + + + + + + + + + + 2019-03-27T18:59:34Z + + + + + + + + + + + + + + + 2019-03-27T18:56:09Z + + + + + + + + + + + + + + + 2019-03-27T18:50:50Z + + + + + + + + + + + + + + + + + 2019-03-27T18:53:29Z + + + + + + + + + + + + + + default + + function_for_R1 + k1 + default + + + + + + + + + + + + + 2019-03-27T18:55:51Z + + + + + + + + + + + + + + + + + default + + function_for_R2 + k2 + x1 + default + + + + + + + + + + + + + 2019-03-27T18:49:35Z + + + + + + + + + + + + + + + default + + function_for_R3 + k3 + x1 + x2 + default + + + + + + + + diff --git a/doc/examples/calibrated_models/model_space.tsv b/doc/examples/calibrated_models/model_space.tsv new file mode 100644 index 00000000..5a4b2dea --- /dev/null +++ b/doc/examples/calibrated_models/model_space.tsv @@ -0,0 +1,9 @@ +model_subspace_id petab_yaml k1 k2 k3 +MS_0 petab_problem.yaml 0 0 0 +MS_1 petab_problem.yaml 0.2 0.1 estimate +MS_2 petab_problem.yaml 0.2 estimate 0 +MS_3 petab_problem.yaml estimate 0.1 0 +MS_4 petab_problem.yaml 0.2 estimate estimate +MS_5 petab_problem.yaml estimate 0.1 estimate +MS_6 petab_problem.yaml estimate estimate 0 +MS_7 petab_problem.yaml estimate estimate estimate diff --git a/doc/examples/calibrated_models/observables.tsv b/doc/examples/calibrated_models/observables.tsv new file mode 100644 index 00000000..b3940095 --- /dev/null +++ b/doc/examples/calibrated_models/observables.tsv @@ -0,0 +1,2 @@ +observableId observableFormula noiseFormula +obs_x2 x2 noiseParameter1_obs_x2 diff --git a/doc/examples/calibrated_models/parameters.tsv b/doc/examples/calibrated_models/parameters.tsv new file mode 100644 index 00000000..e75b90f6 --- /dev/null +++ b/doc/examples/calibrated_models/parameters.tsv @@ -0,0 +1,5 @@ +parameterId parameterName parameterScale lowerBound upperBound nominalValue estimate +k1 k_{1} lin 0 1e3 0.2 1 +k2 k_{2} lin 0 1e3 0.1 1 +k3 k_{3} lin 0 1e3 0 1 +sigma_x2 \sigma_{x2} lin 1e-5 1e3 0.15 1 diff --git a/doc/examples/calibrated_models/petab_problem.yaml b/doc/examples/calibrated_models/petab_problem.yaml new file mode 100644 index 00000000..b213a304 --- /dev/null +++ b/doc/examples/calibrated_models/petab_problem.yaml @@ -0,0 +1,11 @@ +format_version: 1 +parameter_file: parameters.tsv +problems: +- condition_files: + - conditions.tsv + measurement_files: + - measurements.tsv + observable_files: + - observables.tsv + sbml_files: + - model.xml diff --git a/doc/examples/calibrated_models/petab_select_problem.yaml b/doc/examples/calibrated_models/petab_select_problem.yaml new file mode 100644 index 00000000..a26354bc --- /dev/null +++ b/doc/examples/calibrated_models/petab_select_problem.yaml @@ -0,0 +1,5 @@ +version: beta_1 +criterion: AICc +method: forward +model_space_files: +- model_space.tsv diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb new file mode 100644 index 00000000..96268400 --- /dev/null +++ b/doc/examples/visualization.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "50d3669e", + "metadata": {}, + "source": [ + "The visualization methods implemented in PEtab Select are demonstrated here. These methods generally visualize the output of a model selection task, so the input is generally a list of already-calibrated models. Here, some calibrated models that were saved to disk with `petab_select.model.models_to_yaml_list` are loaded and used as input." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ca6ce5b4", + "metadata": {}, + "outputs": [], + "source": [ + "import petab_select\n", + "\n", + "models = petab_select.models_from_yaml_list(\n", + " model_list_yaml=\"calibrated_models/calibrated_models.yaml\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "aaeb0606", + "metadata": {}, + "source": [ + "The model IDs were automatically generated, so we set them to something more readable here: a binary string that describes their position in the model space." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "09c9df1d", + "metadata": {}, + "outputs": [], + "source": [ + "for model in models:\n", + " model.model_id = \"M_\" + \"\".join(\n", + " \"1\" if value == petab_select.ESTIMATE else \"0\"\n", + " for value in model.parameters.values()\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "32de6556", + "metadata": {}, + "source": [ + "# Selected models" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "56b4a27b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "petab_select.plot.selected_models(\n", + " models=models,\n", + " criterion=petab_select.Criterion.AICC,\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "8e5243e0", + "metadata": {}, + "source": [ + "# Selection history" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "862a78ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "petab_select.plot.directed_graph(\n", + " models=models,\n", + " criterion=petab_select.Criterion.AICC,\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "d87e75ab", + "metadata": {}, + "source": [ + "# Unordered bar graph" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bce41584", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "petab_select.plot.bar_graph(\n", + " models=models,\n", + " criterion=petab_select.Criterion.AICC,\n", + ");" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/petab_select/__init__.py b/petab_select/__init__.py index bd2994f5..db3171fb 100644 --- a/petab_select/__init__.py +++ b/petab_select/__init__.py @@ -1,5 +1,6 @@ """Model selection extension for PEtab.""" +from . import plot from .candidate_space import * from .constants import * from .criteria import * diff --git a/petab_select/plot.py b/petab_select/plot.py new file mode 100644 index 00000000..2ba0b867 --- /dev/null +++ b/petab_select/plot.py @@ -0,0 +1,277 @@ +"""Visualization routines for model selection with pyPESTO.""" +from typing import Dict, List, Tuple + +import matplotlib.pyplot as plt +import networkx as nx +import numpy as np + +from .constants import VIRTUAL_INITIAL_MODEL, Criterion +from .model import Model + +RELATIVE_LABEL_FONTSIZE = -2 + + +def default_label_maker(model: Model) -> str: + """Create a model label, for plotting.""" + return model.model_hash[:4] + + +def get_model_hashes(models: List[Model]) -> Dict[str, Model]: + model_hashes = {model.get_hash(): model for model in models} + return model_hashes + + +def get_selected_models( + models: List[Model], + criterion: Criterion, +): + criterion_value0 = np.inf + model0 = None + model_hashes = get_model_hashes(models) + for model in models: + criterion_value = model.get_criterion(criterion) + if criterion_value < criterion_value0: + criterion_value0 = criterion_value + model0 = model + + selected_models = [model0] + while True: + model0 = selected_models[-1] + model1 = model_hashes.get(model0.predecessor_model_hash, None) + if model1 is None: + break + selected_models.append(model1) + + return selected_models[::-1] + + +def selected_models( + models: List[Model], + criterion: Criterion, + relative: str = True, + fz: int = 14, + size: Tuple[float, float] = (5, 4), + labels: Dict[str, str] = None, + ax: plt.Axes = None, +) -> plt.Axes: + """Plot criterion for calibrated models. + + Parameters + ---------- + models: + A list of all models. The selected models will be inferred from the + best model and its chain of predecessor model hashes. + criterion: + The criterion by which models are selected. + relative: + If `True`, criterion values are plotted relative to the lowest + criterion value. TODO is the lowest value, always the best? May not + be for different criterion. + fz: + fontsize + size: + Figure size in inches. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + ax: + The axis to use for plotting. + + Returns + ------- + matplotlib.pyplot.Axes + The plot axis. + """ + models = get_selected_models(models=models, criterion=criterion) + + zero = 0 + if relative: + zero = models[-1].get_criterion(criterion) + + if labels is None: + labels = {} + + # FIGURE + if ax is None: + _, ax = plt.subplots(figsize=size) + linewidth = 3 + + models = [model for model in models if model != VIRTUAL_INITIAL_MODEL] + + criterion_values = { + labels.get(model.get_hash(), model.model_id): model.get_criterion( + criterion + ) + - zero + for model in models + } + + ax.plot( + criterion_values.keys(), + criterion_values.values(), + linewidth=linewidth, + color='lightgrey', + # edgecolor='k' + ) + + ax.get_xticks() + ax.set_xticks(list(range(len(criterion_values)))) + ax.set_ylabel( + criterion + ('(relative)' if relative else '(absolute)'), fontsize=fz + ) + # could change to compared_model_ids, if all models are plotted + ax.set_xticklabels( + criterion_values.keys(), + fontsize=fz + RELATIVE_LABEL_FONTSIZE, + ) + for tick in ax.yaxis.get_major_ticks(): + tick.label1.set_fontsize(fz + RELATIVE_LABEL_FONTSIZE) + ytl = ax.get_yticks() + ax.set_ylim([min(ytl), max(ytl)]) + # removing top and right borders + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + return ax + + +def directed_graph( + models: List[Model], + criterion: Criterion = None, + optimal_distance: float = 1, + options: Dict = None, + labels: Dict[str, str] = None, + ax: plt.Axes = None, +) -> plt.Axes: + """Plot all calibrated models in the model space, as a directed graph. + + TODO replace magic numbers with options/constants + + Parameters + ---------- + models: + A list of models. + criterion: + The criterion. + optimal_distance: + See docs for argument `k` in `networkx.spring_layout`. + relative: + If `True`, criterion values are offset by the minimum criterion + value. + options: + Additional keyword arguments for `networkx.draw_networkx`. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + ax: + The axis to use for plotting. + + Returns + ------- + matplotlib.pyplot.Axes + The plot axis. + """ + model_hashes = get_model_hashes(models) + + if labels is None: + labels = { + model_hash: model.model_id + + ( + f"\n{model.get_criterion(criterion):.2f}" + if criterion is not None + else "" + ) + for model_hash, model in model_hashes.items() + } + + G = nx.DiGraph() + edges = [] + for model in models: + predecessor_model_hash = model.predecessor_model_hash + if predecessor_model_hash is not None: + from_ = labels.get(predecessor_model_hash, predecessor_model_hash) + # may only not be the case for + # COMPARED_MODEL_ID == INITIAL_VIRTUAL_MODEL + if predecessor_model_hash in model_hashes: + predecessor_model = model_hashes[predecessor_model_hash] + from_ = labels.get( + predecessor_model.get_hash(), + predecessor_model.model_id, + ) + else: + raise NotImplementedError( + 'Plots for models with `None` as their predecessor model are ' + 'not yet implemented.' + ) + from_ = 'None' + to = labels.get(model.get_hash(), model.model_id) + edges.append((from_, to)) + + G.add_edges_from(edges) + default_options = { + 'node_color': 'lightgrey', + 'arrowstyle': '-|>', + 'node_shape': 's', + 'node_size': 2500, + } + if options is not None: + default_options.update(options) + + if ax is None: + _, ax = plt.subplots(figsize=(12, 12)) + + pos = nx.spring_layout(G, k=optimal_distance, iterations=20) + nx.draw_networkx(G, pos, ax=ax, **default_options) + + return ax + + +def bar_graph( + models: List[Model], + criterion: Criterion = None, + labels: Dict[str, str] = None, + ax: plt.Axes = None, +) -> plt.Axes: + """Plot all calibrated models and their criterion value. + + Parameters + ---------- + models: + A list of models. + criterion: + The criterion. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + ax: + The axis to use for plotting. + + Returns + ------- + matplotlib.pyplot.Axes + The plot axis. + """ + model_hashes = get_model_hashes(models) + + if labels is None: + labels = { + model_hash: model.model_id + for model_hash, model in model_hashes.items() + } + + if ax is None: + _, ax = plt.subplots() + + criterion_values = { + labels.get(model.get_hash(), model.model_id): model.get_criterion( + criterion + ) + for model in models + } + ax.bar(criterion_values.keys(), criterion_values.values()) + ax.set_xlabel("Model labels") + ax.set_ylabel(criterion.value) + + return ax diff --git a/petab_select/problem.py b/petab_select/problem.py index cc7695dd..f69bf1fe 100644 --- a/petab_select/problem.py +++ b/petab_select/problem.py @@ -54,6 +54,7 @@ class Problem(abc.ABC): Reason for not saving: Essentially reproducible from `Problem.method` and `Problem.calibrated_models`. + FIXME now needs to be saved, due to FAMoS changes """ def __init__( From 4aa6e8fb8c14b2ce99a67cc7c386428325347e1a Mon Sep 17 00:00:00 2001 From: dilpath Date: Wed, 8 Mar 2023 11:56:03 +1100 Subject: [PATCH 02/26] plot: scatter_criterion_vs_n_estimated; relative criterion --- doc/examples/visualization.ipynb | 50 +++++++++++--- petab_select/plot.py | 111 ++++++++++++++++++++++++++----- 2 files changed, 138 insertions(+), 23 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index 96268400..a3f1d2fc 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -60,7 +60,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -72,7 +72,7 @@ } ], "source": [ - "petab_select.plot.selected_models(\n", + "petab_select.plot.line_selected(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", ");" @@ -83,7 +83,7 @@ "id": "8e5243e0", "metadata": {}, "source": [ - "# Selection history" + "# Selection history trajectory" ] }, { @@ -94,7 +94,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -104,7 +104,7 @@ } ], "source": [ - "petab_select.plot.directed_graph(\n", + "petab_select.plot.graph_history(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", ");" @@ -115,7 +115,7 @@ "id": "d87e75ab", "metadata": {}, "source": [ - "# Unordered bar graph" + "# Criterion values of each model" ] }, { @@ -126,7 +126,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -138,7 +138,41 @@ } ], "source": [ - "petab_select.plot.bar_graph(\n", + "petab_select.plot.bar_criterion_vs_models(\n", + " models=models,\n", + " criterion=petab_select.Criterion.AICC,\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "d9c2d487", + "metadata": {}, + "source": [ + "# Criterion values vs. number of estimated parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "824e2e6a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "petab_select.plot.scatter_criterion_vs_n_estimated(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", ");" diff --git a/petab_select/plot.py b/petab_select/plot.py index 2ba0b867..148b68d7 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -1,5 +1,5 @@ """Visualization routines for model selection with pyPESTO.""" -from typing import Dict, List, Tuple +from typing import Dict, List, Tuple, Union import matplotlib.pyplot as plt import networkx as nx @@ -45,10 +45,27 @@ def get_selected_models( return selected_models[::-1] -def selected_models( +def get_relative_criterion_values( + criterion_values: Union[Dict[str, float], List[float]], +) -> Union[Dict[str, float], List[float]]: + values = criterion_values + if isinstance(criterion_values, dict): + values = criterion_values.values() + + value0 = np.inf + for value in values: + if value < value0: + value0 = value + + if isinstance(criterion_values, dict): + return {k: v - value0 for k, v in criterion_values.items()} + return [v - value0 for v in criterion_values] + + +def line_selected( models: List[Model], criterion: Criterion, - relative: str = True, + relative: bool = True, fz: int = 14, size: Tuple[float, float] = (5, 4), labels: Dict[str, str] = None, @@ -85,10 +102,6 @@ def selected_models( """ models = get_selected_models(models=models, criterion=criterion) - zero = 0 - if relative: - zero = models[-1].get_criterion(criterion) - if labels is None: labels = {} @@ -103,9 +116,10 @@ def selected_models( labels.get(model.get_hash(), model.model_id): model.get_criterion( criterion ) - - zero for model in models } + if relative: + criterion_values = get_relative_criterion_values(criterion_values) ax.plot( criterion_values.keys(), @@ -118,7 +132,7 @@ def selected_models( ax.get_xticks() ax.set_xticks(list(range(len(criterion_values)))) ax.set_ylabel( - criterion + ('(relative)' if relative else '(absolute)'), fontsize=fz + criterion + (' (relative)' if relative else ' (absolute)'), fontsize=fz ) # could change to compared_model_ids, if all models are plotted ax.set_xticklabels( @@ -135,13 +149,14 @@ def selected_models( return ax -def directed_graph( +def graph_history( models: List[Model], criterion: Criterion = None, + ax: plt.Axes = None, + labels: Dict[str, str] = None, optimal_distance: float = 1, options: Dict = None, - labels: Dict[str, str] = None, - ax: plt.Axes = None, + relative: bool = True, ) -> plt.Axes: """Plot all calibrated models in the model space, as a directed graph. @@ -174,11 +189,18 @@ def directed_graph( """ model_hashes = get_model_hashes(models) + criterion_values = { + model_hash: model.get_criterion(criterion) + for model_hash, model in model_hashes.items() + } + if relative: + criterion_values = get_relative_criterion_values(criterion_values) + if labels is None: labels = { model_hash: model.model_id + ( - f"\n{model.get_criterion(criterion):.2f}" + f"\n{criterion_values[model_hash]:.2f}" if criterion is not None else "" ) @@ -227,10 +249,11 @@ def directed_graph( return ax -def bar_graph( +def bar_criterion_vs_models( models: List[Model], criterion: Criterion = None, labels: Dict[str, str] = None, + relative: bool = True, ax: plt.Axes = None, ) -> plt.Axes: """Plot all calibrated models and their criterion value. @@ -245,6 +268,9 @@ def bar_graph( A dictionary of model labels, where keys are model hashes, and values are model labels, for plotting. If a model label is not provided, it will be generated from its model ID. + relative: + If `True`, criterion values are offset by the minimum criterion + value. ax: The axis to use for plotting. @@ -270,8 +296,63 @@ def bar_graph( ) for model in models } + if relative: + criterion_values = get_relative_criterion_values(criterion_values) ax.bar(criterion_values.keys(), criterion_values.values()) ax.set_xlabel("Model labels") - ax.set_ylabel(criterion.value) + ax.set_ylabel( + criterion.value + (' (relative)' if relative else ' (absolute)') + ) + + return ax + + +def scatter_criterion_vs_n_estimated( + models: List[Model], + criterion: Criterion = None, + relative: bool = True, + ax: plt.Axes = None, +) -> plt.Axes: + """Plot criterion values against number of estimated parameters. + + Parameters + ---------- + models: + A list of models. + criterion: + The criterion. + ax: + The axis to use for plotting. + relative: + If `True`, criterion values are offset by the minimum criterion + value. + + Returns + ------- + matplotlib.pyplot.Axes + The plot axis. + """ + model_hashes = get_model_hashes(models) + + n_estimated = [] + criterion_values = [] + for model in models: + n_estimated.append(len(model.get_estimated_parameter_ids_all())) + criterion_values.append(model.get_criterion(criterion)) + if relative: + criterion_values = get_relative_criterion_values(criterion_values) + + if ax is None: + _, ax = plt.subplots() + + ax.scatter( + n_estimated, + criterion_values, + ) + + ax.set_xlabel("Number of estimated parameters") + ax.set_ylabel( + criterion.value + (' (relative)' if relative else ' (absolute)') + ) return ax From c680ced467776a8e0ef46007894030ed42db73b2 Mon Sep 17 00:00:00 2001 From: dilpath Date: Tue, 14 Mar 2023 12:40:13 +1100 Subject: [PATCH 03/26] graph iterations --- doc/examples/visualization.ipynb | 38 +++++++- petab_select/plot.py | 159 ++++++++++++++++++++++++++++++- setup.py | 5 +- 3 files changed, 198 insertions(+), 4 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index a3f1d2fc..040be3f8 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -94,7 +94,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -177,6 +177,42 @@ " criterion=petab_select.Criterion.AICC,\n", ");" ] + }, + { + "cell_type": "markdown", + "id": "8dc7e142", + "metadata": {}, + "source": [ + "# Iterations as layers in a hierarchical graph" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5ce191fc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yLuk6AAAAAPOrqp6e5PlJrlDSwNJi9AkAAGCAVNWKJG9P8rLW2j90HAc4R0afAAAABkhV3Zbk4tbaS7rOApw7o08AAAADoqq+M8loksu7zgKcHztqAAAABkBVfWWSjyd5YWvtfR3HAc6TogYAAGAAVNW7k/xta21L11mA82f0CQAAYImrqo1Jnpzkqo6jABfIjhoAAIAlrKoel+RjSZ7bWvtwx3GAC+Ty3AAAAEtUVVWS3UnepqSBwaCoAQAAWLpekuRxSX626yDA/DD6BAAAsARV1ROT3JPkO1prh7rOA8wPO2oAAACWmKq6KMkdSd6opIHBoqgBAABYel6V2c9zO7sOAswvo08AAABLSFWtSfL+JNe01j7RdR5gftlRAwAAsERU1cOS3JnkdUoaGEyKGgAAgKXjJ5L8fZLbug4CLAyjTwAAAEtAVV2d5HeTXNla+5uu8wALw44aAACAnquqRyR5Z5JXK2lgsNlRAwAA0HNVdWuS4SQ3NB/iYKBd0nUAAAAAzqyqvj3J85JcoaSBwWf0CQAAoKeqakWSO5K8rLX2D13nARae0ScAAICeqqrbklzUWtvUdRZgcRh9AgAA6KGq+s4k1ya5ousswOKxowYAAKBnquoxSf44yQtba+/rOA6wiBQ1AAAAPVNV70nyqdbalq6zAIvL6BMAAECPVNUNmR13urHrLMDis6MGAACgJ6rqcUk+luS5rbUPdxwH6IDLcwMAAPRAVVWS3UneqqSB5UtRAwAA0A8vSfI1SX626yBAd4w+AQAAdKyqnpjkniTf0Vo71HUeoDt21AAAAHSoqi5K8vYkb1TSAIoaAACAbr06SSXZ2XEOoAeMPgEAAHSkqtYkeX+Sa1prn+g6D9A9O2oAAAA6UFUPS3Jnkp9U0gAnKGoAAAC68ZNJ/i7Jrq6DAP1h9AkAAGCRVdVTkvyPJFe21v6m6zxAf9hRAwAAsIiq6hGZHXl6lZIGeCA7agAAABZRVd2aZKi1dkPXWYD+uaTrAAAAAMtFVX17kuclubzrLEA/GX0CAABYBFW1IskdSV7aWvtM13mAfjL6BAAAsAiqaldmP4Nt6joL0F9GnwAAABZYVT0nybOTXNF1FqDf7KgBAABYQFX1mCQfT/KDrbX3d50H6DdFDQAAwAKqqvck+ZvW2tauswD9Z/QJAABggVTViSs83dh1FmBpsKMGAABgAVTV45Pcl+Q5rbWPdJ0HWBpcnhsAAGCeVVUl2Z3krUoa4FwoagAAAObfpiT/OsnPdh0EWFqMPgEAAMyjqnpiknuSfHtr7XDXeYClxY4aAACAeVJVFyd5R5JfUNIA50NRAwAAMH9effy/b+owA7CEGX0CAACYB1W1Nsn7klzTWvtEx3GAJcqOGgAAgAtUVQ9LcmeSn1DSABdCUQMAAHDhfjLJpzN7SW6A82b0CQAA4AJU1VOS/I8kV7bW/qbrPMDSZkcNAADAeaqqR2R25OlVShpgPthRAwAAcJ6qakeSJ7TWbug6CzAYLuk6AAAAwFJUVd+e5IYkl3edBRgcRp8AAADOUVWtSPL2JJtba5/pOA4wQIw+AQAAnKOq2pUkrbXNXWcBBovRJwAAgHNQVc9J8uwYeQIWgB01AAAAZ6mqHpPk40l+sLX2/q7zAINHUQMAAHCWququJJ9srW3tOgswmIw+AQAAnIWqel6Sy5L8cMdRgAFmRw0AAMBDqKrHJ7kvyXNaax/pOg8wuFyeGwAA4EFUVSXZneQtShpgoSlqAAAAHtzmJF+d5Oe6DgIMPqNPAAAAZ1BVT0ryR0m+vbV2uOs8wOCzowYAAOA0quriJG9P8gYlDbBYFDUAAACn9+okLcmbuo0BLCdGnwAAAB6gqtYmeV+Sb26t/UXHcYBlxI4aAACA+6mqhyW5M8lPKGmAxXZJ1wEAAAB65nVJPp3ZS3KzAA4ePDiTZEXXOebBsUsvvXRl1yEYLIoaAACA46rqqUlenuTJzTkRC2kQSppkcL4PesToEwAAQJKqekRmR55e2Vr7VNd5gOVJUQMAADDr55L8cWvtrq6DAMuX0ScAAGDZq6rvSHJDksu7TQIsd3bUAAAAy1pVrUxyR5LNrbXPdJ0HWN4UNQAAwHK3I8nvtdZ+t+sgAEafAACAZauqnpPkmUmu6DoLQKKoAQAAlqmqekyS25I8v7V2rOs8AInRJwAAYBmqqkryliTvaa19oOs8ACfYUQMAACxHz0tyaZIXdx0E4P4UNQAAwLJSVY9P8qYkz2mtfb7jOAAnMfoEAAAsG8dHnn4jya+31j7SdR6AB1LUAAAAy8nmJI9N8vNdB+HBbdiwIVu3bj3l/h07duSyyy7rIBEsDkUNAACwLFTVk5L8XJIXt9a+0HUeHtr09PQp9x04cCArVqw443MmJiayefPmbNy4MZOTk3P3z8zMZHx8PLfffvtJjz/T/dAVRQ0AADDwquriJO9I8obW2uGu83B2RkZGMjExMXf78OHDWbt27RkfPzMzk927d2fXrl3Zs2dPXv/61899befOnSe91kPdD11R1AAAAMvBliRfyuwhwiwR69evz969e+du7927N6Ojo2d8/L59+zIyMjJ3e2hoKIcPz/Zyt9xyy2mfe6b7oSuKGgAAYKBV1dokr0nyw621L3Wdh7O3Zs2aHDlyZO72/v37s27dujM+/tixYxkeHp67PTQ0dNrxKegzl+cGAAAGVlU9LMmdSV7bWvuLrvNw7q699tpMTExkaGjopN0yMKjsqAEAAAbZ65J8KrOX5GYJGhsby/j4+EOOPSXJihUrMjU1NXd7eno6Q0NDCx0R5pWiBgAAGEhV9dQkL0+yubXWus7D+RkeHs7Ro0dz4MCBBx17SpLrrrsuBw4cmLs9PT2dNWvWLHREmFdGnwAAgIFTVY/I7MjTK1trn+o6DxdmbGwsx44dO+3XLrvsspNu33rrrdm4cWNWrVqVW2+9de7+7du3z5U4Bw8ezI4dOx70fuhKKZYBAIBBU1U7kzyutfa8rrNwqoMHDw7MB9FLL720us7AYLGjBgAAGChV9R1JxpJc0W0SgHPnjBoAAGBgVNXKJHckeWlr7TNd5wE4V4oaAABgkOxIsr+19rtdBwE4H0afAACAgVBV35XkmTHyNPDGx8czMTGRJNmyZcspV3YaHx/P+Pj43O3p6els27Yto6Ojuf322zM5OZmjR49m06ZND3nJb1hsDhMGAACWvKr6qiQfT/K81toHus7Dg7uQw4Snpqby+te/Prt27crMzEw2bdqUPXv2nPHxMzMzufnmm7Nr165MTU3lyJEjGR0dzczMTNavX58PfehD5xslicOEmX921AAAAEtaVVWSX0/yLiXN4Nu/f3/WrVuXJFm5cmVWrVqVqampDA8Pn/bx27Zty5YtW5Ikw8PDc487dOhQhoaGFic0nANFDQAAsNQ9L8naJC/uOggLb8WKFZmampq7ffTo0UxPT5+2qDl8+HCOHTt2ymjUxo0bMz09nb179y54XjhXDhMGAACWrKp6QpI3JXlxa+3zHcdhEYyMjGT//v2ZmZnJ5ORkjhw5csbHjo+PZ2xs7JT79+zZk927d2fTpk0LGRXOi6IGAABYko6PPO1O8uuttY92nYfFMTw8nC1btmTbtm2Znp7O6tWrs3bt2tM+dt++fRkZGTnt107sspmcnFywrHA+FDUAAMBS9dIkj03y810HYXGNjo5mx44dWbt2bYaGhrJy5cozPvb+X5uYmDhpbGp6eto5NfSOM2oAAIAlp6q+PsnPJnl6a+0LXedhcW3evDlJMjQ0lB07dszdv2HDhtx1111ZuXJlpqamTilhRkdHs3379kxPT89dsvtMhxBDV1yeGwAAWFKq6uIk70vyn1trOzuOw3m4kMtz943LczPfjD4BAABLzZYk/zfJm7sOAjDfjD4BAABLRlVdmuQ1Sb65tfalrvMAzDc7agAAgCWhqh6e5M4kr22t/UXXeQAWgqIGAABYKl6X5G+S/EbXQQAWitEnAACg96rqqUleluTJzRVRgAFmRw0AANBrVfWIzI48vaK19qmu8wAsJEUNAADQdz+f5GOttT1dBwFYaEafAACA3qqqZyQZS3J511kAFoMdNQAAQC9V1cokdyR5aWvts13nAVgMihoAAKCvdibZ11r73a6DACwWo08AAEDvVNV3JXlGkiu6zgKwmOyoAQAAeqWqvirJ25L8UGvtWNd5ODdVdXlVvbyqbquqP6mqL1TVz3adC5YKO2oAAIDeqKpK8pYk72qtfbDrPJyXdyf5xiQPO377i5kdYwPOgh01AABAnzw/yZokr+s6COdtU5I6/ut/TvLG1tpnOswDS4qiBgAA6IWqekKSNyV5cWvt8x3H4TxU1eokb01yKMnnk3whyS91GgqWGEUNAADQueMjT7uT/Fpr7aNd5+Hc1KyXJPlAkl9NcmWS30/y0621o6d5yqCcPTQo3wc9Uq21rjMAAADLXFW9LLMjM09rrX2h6zycvapaldnDn9ckeV5r7XDHkWBJs6MGAADoVFV9fZKfzezIk5JmCamqa5Lcl+QzSa5R0sCFc9UnAACgM1V1cZJ3JPn51tqRrvNwdqrqoiQ/mmRrkpe31v5Lx5FgYChqAACALm3N7OWb39x1EM5OVX1NkjuTPCLJU1trf91xJBgoRp8AAIBOVNWlSX4syQ+31r7UdR4eWlVdl+TeJJNJnqGkgflnRw0AALDoqurhmd2V8eOttb/sOA4P4fj/v34uyfOS/GBr7X3dJoLBpagBAAC68Lokf5Pk9q6D8OCOH/b87iR/m+TK1to/dBwJBprRJwAAYFFV1TcneVmSza211nUezqyqnp/kQJLfTPI9ShpYeHbUAAAAi6aqHpHZkadXtNY+1XUeTq+qviLJryT5liSjrbX7Oo4Ey4YdNQAAwGJ6Q5L7Wmt7ug7C6VXVk5N8JEkluVpJA4vLjhoAAGBRVNUzklyf5PKus3Cqqqokr0jyU0le1Vp7V8eRYFlS1AAAAAuuqlYmuSOz59J8tus8nKyqviqzBzs/LslIa+3PO44Ey5bRJwAAYDG8Kcm+1trdXQfhZFX17UnuS/JnSb5FSQPdsqMGAABYUFX1XUm+I8kVHUfhfqrqksyOOW1OclNrbW/HkYAoagAAgAV0fKTmbUluaK0d6zoPs6pqOMm7knw+yVWttb/tOBJwnNEnAABgQRw/nPYtSX6rtfbBrvMwq6q+N7NXdfrvSa5T0kC/2FEDAAAslOcnWZ3kRV0HIamqRyT55SQbknxPa+1Ax5GA01DUAAAA866qnpDZA4TXt9Y+33GcZa+qVie5K8mRzI46fa7bRMCZGH0CAADm1fGRp99I8muttXu7zrOc1axNST6Q5M1JnqekgX6zowYAAJhvL0vymCRv6DrIclZVq5LcluSbkjy9tXak40jAWbCjBgAAmDdV9fVJfibJi1trX+g6z3JVVSNJ7kvy90muUdLA0mFHDQAAMC+q6uIk70jyc4qBblTVRUl+LMmWJC9vrf2XjiMB50hRAwAAzJebk3whya90HWQ5qqqvSfLOJF+e5Kmttb/uOBJwHow+AQAAF6yqLkvyo0lubK19qes8y01Vrc/sqNOHkjxDSQNLlx01AADABamqhye5M8mPt9b+suM4y8rx3/ufT3JDZq/o9P6OIwEXSFEDAABcqJ9K8skkt3cdZDmpqm9I8u4kn0pyZWvtHzqOBMwDo08AAMB5q6pvTrI5yabWWus6z3JRVS9IMpnZnUzfo6SBwWFHDQAAcF6q6pGZLQpe0Vr7267zLAdV9RVJfi3JSJJrW2sf6zYRMN/sqAEAAM7XG5Lc21ob7zrIclBVT07y0SRfSvIUJQ0MJjtqAACAc1ZVz0zyA0ku7zrLoKuqSvKKJK9L8qrW2rs7jgQsIEUNAABwTqpqVWYPDt7cWvts13kGWVV9VWZ/r78mybrW2p93HAlYYEafAACAc7Uzyb7W2t1dBxlkVfUdSe5L8qdJvlVJA8uDHTUAAMBZq6rvTvLtSa7oOsugqqpLkvx0Zq+m9cOttX0dRwIWkaIGAAA4K1X12CRvTXJDa+2fus4ziKrq3yT5rST/O8mVrqYFy4/RJwAA4CEdP9D2LUl+q7X2wa7zDKKq+r4kH07yO0nWK2lgebKjBgAAOBs/mOSbkryw6yCDpqoekeTWJOuTfHdr7Y86jgR0SFEDAAA8qKp6QmYPEF7fWvt813kGSVWtSfKeJIczO+p0tONIQMeMPgEAAGd0fOTp9iS/2lq7t+s8g6JmbU7y/iRvTvJ8JQ2Q2FEDAAA8uJcneXSSN3QdZFBU1aOS3Jbk3yV5emvtSLeJgD6xowYAADitqvqGJD+T5MWttS92nWcQVNW6JPcl+bsk1yhpgAeyowYAADhFVV2c5O1Jfra19qcdx1nyquqiJK9J8uokL2ut/ddOAwG9pagBAABO5+YkX0jyK10HWeqq6nFJ3pnk4Ume0lqb6jgS0GNGnwAAgJNU1WVJfjTJja21L3WdZymrqg1J7k3yB0meqaQBHoodNQAAwJyqeniSO5O8prX2lx3HWbKO/z6+IcnGJM9rrb2/40jAEqGoAQAA7u+nk0wnuaPrIEvV8UOY35Pkk0me3Fr7TMeRgCXE6BMAAJAkqaprkmxKsrm11rrOsxRV1QuTTGb2IObvVdIA58qOGgAAIFX1yMyOPL2itfa3XedZaqrqK5L8WpKRJNe21j7WbSJgqbKjBgAASGbPU/loa2286yBLTVVdmeSjSf5vkquVNMCFsKMGAACWuap6ZpIfSHJ511mWkqqqJK9M8rokr2ytvbvjSMAAUNQAAMAyVlWrMntw8KbW2me7zrNUVNVXZfb37V8nGWmt/XnHkYABYfQJAACWtzclubu1trfrIEtFVT0jyX1JjiT5ViUNMJ/sqAEAgGWqqr47ydOTXNF1lqWgqi5JckuSlyS5sbW2r+NIwABS1AAAwDJUVY9N8tYkN7TW/qnrPH1XVV+b5LeS/K8kV7kyFrBQjD4BAMAyc/wQ3Lcm+c3W2ge7ztN3VfX9ST6c5LeTrFfSAAvJjhoAAFh+XpDk3x3/L2dQVY9IsiPJdUm+q7X2Rx1HApYBO2oAAGAZqaqhzJYPL2qtfb7rPH1VVWuT3JPkUUmuVNIAi0VRAwAAy8TxkaffSPKrrbX7us7TRzXrpUnel2Rnkh9srR3tNhWwnBh9AgCA5ePlSR6d5A1dB+mjqnpUktsyOxb29NbakW4TAcuRHTUAALAMVNU3JPmZJC9urX2x6zx9U1XrktyX5NNJrlHSAF2xowYAAAZcVV2c5B1Jfqa19qdd5+mT4783r0nyqiQvba39t44jAcucogYAAAbff0zyz0l+tesgfVJVj09yZ5KHJ3lKa22q40gARp8AAGCQVdVlmS1qbmytfanrPH1RVd+Z5KNJPpjkmUoaoC/sqAEAgAFVVQ9P8s4kr2mt/VXXefrg+O/JG5KMJbmhtfaBjiMBnERRAwAAg+unk0wluaPrIH1QVd+Y5N1JppNc2Vr7TMeRAE5h9AkAAAZQVY0k2ZRkc2utdZ2na1X1wiQfymxp9X1KGqCv7KgBAIABU1WPzOxVnn6ktfa3XefpUlWtSPJrSa5J8uzW2h93HAngQdlRAwAAg+cXknyktfberoN0qaquyuyBwV9McrWSBlgK7KgBAIABUlXPSvJ9SS7vOktXqqqSvCrJTyZ5RWvtPR1HAjhrihoAABgQVbUqye2ZPZfmH7vO04Wqemxmz6F5bJJrWmuf6DgSwDkx+gQAAIPjTUnubq3t7TpIF6rqGUnuS3IoybcpaYClyI4aAAAYAFX1PUmenuSKrrMstqq6JMm2JDcl+eHW2kS3iQDOn6IGAACWuOPjPm9NMtZa+6eu8yymqvraJO9K8k9JrmytfbrjSAAXxOgTAAAsYccPzn1rkne21v6g6zyLqap+IMmHk/zXJBuUNMAgsKMGAACWthck+XfH/7ssVNUjkuxMcm2S57bW7uk4EsC8saMGAACWqKoaSrIjyYtaa5/vOs9iqKq1md1FszLJVUoaYNAoagAAYAk6PvJ0e5Jfaa3d13WehVazXpbkfUluTfKC1trRblMBzD+jTwAAsDT9+ySrkvxC10EWWlU9OsltSf5tZi+7/acdRwJYMHbUAADAElNV35Dk9Ul+qLX2xa7zLKSqelqS+5J8Ksk1Shpg0NlRAwAAS0hVXZzkHUl+ZpBLi+Pf52uSvCrJS1tr/63jSACLQlEDAABLy39M8s9JfrXrIAulqh6f5J2Z/bxydWttuuNIAIvG6BMAACwRVXV5ZouaG1trX+o6z0KoquckuTfJ+5M8U0kDLDd21AAAwBJQVQ9PcmeSH2ut/VXXeeZbVX1ZkjckuT7JxtbaBzqOBNAJRQ0AACwNtyT56yRv7zjHvKuqb0zyniRTSa5srX2m40gAnTH6BAAAPVdVI0lektlDdVvXeeZTVb0oyYeS3J7k+5Q0wHJnRw0AAPRYVT0ys1d5+pHW2t92nWe+VNWKJP9vkqcmeXZr7Y87jgTQC3bUAABAv/1Cko+01t7bdZD5UlVXJ/lokv+T5ClKGoB/YUcNAAD0VFU9K8n3Jbm86yzzoaoqyauTvDbJK1prd3WbCKB/FDUAANBDVbUqs+e2bGqt/WPXeS5UVT02swchPybJSGvtE90mAugno08AANBPb07yu621fV0HuVBV9cwk9yX5kyTfpqQBODM7agAAoGeq6nuSfGuSJ3cc5YJU1SVJtie5MckPtdb2dxwJoPcUNQAA0CNV9dVJ3ppkrLX2T13nOV9V9XVJ3pVkJsmVrbVPd5sIYGkw+gQAAD1x/LDdtya5s7X2B13nOV9VdX2Se5L85yTfqaQBOHt21AAAQH+8MMk3Jnl+10HOR1U9IsnOJNcmeW5r7Z6OIwEsOXbUAABAD1TVcJJbk7y4tfbPXec5V1V1aZIPJ1mZ2VEnJQ3AeVDUAABAx46PPP1Gkl9prd3XdZ5zUbNeluT3k/xykhe01mY6jgWwZBl9AgCA7v37JKuS/ELXQc5FVT06ya4k35DZy27/aceRAJY8O2oAAKBDVfWNSV6f2ctXf7HrPGerqp6W5L4kn0wyoqQBmB921AAAQEeq6uIk70jy+qVSdBzP/ONJXplkc2vttzuOBDBQFDUAANCdH03y+SS/1nWQs1FVj0/ym5ndmX91a22640gAA8foEwAAdKCqLk9yc5IbW2tf6jrPQ6mq5yS5N8n7kjxLSQOwMOyoAQCARVZVD09yZ5Ifa639Vdd5HkxVfVlmDzn+gSRjrbUPdhwJYKApagAAYPHdkuSvk7y94xwP6vhBx+/JbNYnt9Y+23EkgIFn9AkAABZRVa1L8pLMHsTbus5zJlX1oiQfSvIbSb5fSQOwOOyoAQCARVJV/yqzV3n6D621T3ed53SqakWSX09ydWbPovl4x5EAlhU7agAAYPH8QpJ7Wmv/qesgp1NVV2f2wODPJ3mqkgZg8dlRAwAAi6CqnpXke5Nc3nGUU1TVRUleneTHk7yitXZXt4kAli9FDQAALLCqelSSO5Jsaq39Y8dxTlJVj83socaPSXJNa+0vuk0EsLwZfQIAgIX35iT/o7W2r+sg91dVz0xyX5KPJ/k2JQ1A9+yoAQCABVRV35vkW5I8udsk/6KqHpZkW5Ibk/xQa21/t4kAOEFRAwAAC6SqvjrJW5Jc31r7p67zJElVfV2SdyWZSXJlX68+BbBcGX0CAIAFUFWV5K1J7myt/WHXeZKkqq5Pck+S/5TkO5U0AP1jRw0AACyMFyb5xiTP7zpIVT0yyc4kz0rynNbahzuOBMAZ2FEDAADzrKqGk+xI8uLW2j93nOXSzO6i+YokVylpAPpNUQMAAPOoqi5KcnuSN7XW7uswR1XVy5P8fpJfTvLC1tpMV3kAODtGnwAAYH79+yQrkryxqwBV9egku5J8fZJvba39WVdZADg3dtQAAMA8qapvTLI9s5e8/mJHGb4lyX1JPplkREkDsLTYUQMAAPOgqi5J8o4kr++iHKmqi5O8NsmPJNncWvudxc4AwIVT1AAAwPz40SSfT/Jri/3GVfX4JL+Z2R3zV7fWPrnYGQCYH0afAADgAlXVFUm2JrmxtfalRX7v5ya5N7OHBj9LSQOwtNlRAwAAF6CqvizJnUl+tLX2V4v8vm9M8n1Jrm+t/cFivTcAC0dRAwAAF+aWJH+Z2fNpFkVV/dsk7zn+vle21j67WO8NwMIy+gQAAOepqtYluSnJS1trbZHe88VJ/jCzl9/+ASUNwGCxowYAAM5DVf2rzO6i+Q+ttU8vwvutSPLrSa5O8szW2p8s9HsCsPjsqAEAgPPzxiT3tNb+00K/UVVdndkDg/93kqcoaQAGlx01AABwjqrq2Um+J8nlC/w+FyV5dZIfT/IjrbU9C/l+AHRPUQMAAOegqh6V5PYkL2mt/eMCvs9XJ3l7kkcnuaa19hcL9V4A9IfRJwAAODdvTvLfW2sTC/UGVfWsJPcl+ViSpytpAJYPO2oAAOAsVdX3JfmWJFcs0Os/LMn2JD+U5Idaa7+3EO8DQH8pagAA4CwcH0V6S2Yvif3/LcDrf12Sdyf5xyRXttb+br7fA4D+M/oEAAAPoaoqyduSvKO19ocL8PpjSe5J8t4kz1XSACxfdtQAAMBDe1GSb0jyvPl80ap6ZJI3JXlmku9srX1kPl8fgKXHjhoAAHgQVTWc5JeTvKi19s/z+LqXJvlwkkcmuUpJA0CiqAEAgDOqqouS3JHkza21j83Ta1ZVvTzJ7yf5xcwWQDPz8doALH1GnwAA4Mz+nyRfkeSN8/FiVfXoJLuTPDHJt7bW/mw+XheAwWFHDQAAnEZVPT7JtsxeJvuL8/B635LkviRTSdYpaQA4nWqtdZ0BAAB65/hBv2su9OyYqro4yWuT/EiSTa21/z4f+QAYTIoaAABYIFX1hCS/efzmC1trn+wyDwD9Z/QJAAAWQFU9N8lHk/zPJM9W0gBwNhwmDAAA86iqviyzhw9/b5IfaK39YbeJAFhKFDUsiIMHD84kWdF1jnlw7NJLL13ZdQhYyqwHwAnLYT2oqn+b5D1J/iLJla21f1zUZLBELIf1AM6X0ScWyiAsusngfB/QpUH5ORqU7wO6NCg/R6f9PqrqxUn+MMltSa5X0sCDGuj1AC6EHTUAAHABqmpFkl9PclWSZ7bW/qTjSAAsYXbUAADAeaqqK5Lcm+R/JXmqkgaAC2VHDQAAnL+rkvxEa2286yAADAZFDQAAnKfW2h1dZwBgsBh9AgAAAOgJRQ2d2Lp1a8bHT90hvGPHjkxMTGRqaiobNmxYkPeemZnJxo0bF+S1AQAA4EIoaujE+vXrMzExccr9+/fvz+joaIaHh3P33Xef8flTU1MLGQ8AAAA6oaihE6Ojozl06FBmZmbm7jt8+HBWr159Vs+/+eabFyoaAAAAdEZRQ2euu+667Nu3b+72+Ph41q9fn+Tk8aSZmZls3rw5t99+e7Zu3ZodO3bkyJEjc/c9cJTp/iNTmzdvzubNm7Nx48aTSiEAAADoI0UNnRkdHT3pnJoDBw5kdHT0tI89cOBAktkzbLZu3ZrVq1dn165duemmmx70PXbt2pVdu3ZlbGwsO3funL/wAAAAsAAUNXRm3bp1mZ6ezszMTCYnJzMyMnLGx65YseIhS5nTmZqayvj4eCYnJ3P06NELiQsAAAALTlFDp6677rq8973vzcTExBl30yTJqlWrzvm1Dx8+nJtvvjlr167N2NjYhcQEFtiGDRuydevWU+7fsWNHLrvssg4SAV2xHgAnWA9YrhQ1dGpsbCx79+7NoUOHsm7dujM+bsWKFafcd/8zZ44dOzZ33/T0dJLk0KFDWb9+fdasWTN3H9Bfp/s5PXDgwGl//k+YmJiYO4dqcnLyvO8H+mUx1oOZmZmMj4/n9ttvn9/wwLw6n/XgTD/f1gOWiku6DsDytmbNmhw7duxBx55OZ2RkJJs2bcratWtzyy23ZPXq1dm8eXNWr149d+Wo6667LjfccEMmJyezYsWK89qVAyyekZGRk3bXHT58OGvXrj1j0TozM5Pdu3dnz549SWb/1e3uu+8+5/uB/lno9SBJdu7cmenp6Qf9hyKge+e6HiSn//m2HrCUKGro3Ok+KK1cuXJuEb3/r0944BbIHTt2nPY1Huq1gf5Yv359du/ePfcXsb1792Z0dPSkq8Pd3759+04qeYeGhnL48OEcOnTonO5fs2bNAn1HwPla6PVgzZo1ueWWWzI+Pj63Kxfop3NdD5Kc9uf7TOuE9YA+MvoEQC+sWbMmR44cmbu9f//+B/2XrWPHjmV4eHju9tDQUKanp8/5fqB/Fno9AJaOc10PzsR6wFJiRw0AvXHttddmYmIiQ0ND5zwSCQwW6wFwgvWA5caOGjr3tKc97aSDgR9oamoqGzZsOOevnTAzM5ONGzdeUEZgcYyNjWV8fHxuW/ODWbFiRaampuZuT09PZ2ho6JzvB/ppIdcDYGk5l/XgTKwHLCWKGnpveHj4pLNm7r/APvBrwNI2PDyco0eP5sCBAw+5rfm6667LgQMH5m5PT09nzZo153w/0E8LuR4AS8u5rAdnYj1gKTH6xJJz8803OwwYBtjY2NgZD/O77LLLTrp96623ZuPGjVm1alVuvfXWJLMHhm/atOms7wf6a6HWgyTZvn373Ie2gwcPnvbCBEB/nMt6cP3115/y8209YCmp1lrXGRhABw8ePOv/YT3taU/L3r17k2TuktsnrtKwdevWzMzMZNOmTdmzZ0927NiRO+64IyMjI1m3bl2uv/76ua8lyebNm5MkR48eze7du7Ny5cqTnn8+Lr300jqvJ8KAqKpnJvmz1tonz+f557Ie9J31gOWuqp6a5Fhr7U/P5/nWAxgcVfVNSVa01j58Ps+3HsCZGX2iV44cOZItW7Zkz549ee9733vK17du3ZrVq1dn165duemmm075+q5du7Jr166MjY1l586dixEZloO3J/mLqnpbVT2h6zBAp34pyaGq+s/HP6QBy9crk/xRVb2/qq7pOgwMEqNP9MrQ0FBWrlyZJFm1atU5P39qaioHDhzI5OTkfEc7o6p6WJIbk3z5or0pLK6VSR6W5CVJbqyqySSjrbV/7jZWP1XVC5I8puscsECGMvsPfd+T5LlVdTjJc1trrnF7GlX13CRP6joHLJDLk1SSb0vywar66yTPP98dNsC/UNTQKytWrDjv5x4+fDjbtm3Ltm3bMjQ0lPHx8XlM9qAuTvL1SR6xWG8Ii+zEnxUXJ2lJnpjZ/70rak7vSUke23UIWCAn/qy7KMmXMlvcPDqJoub0hpN8Q9chYIGsPP7fE2M//zrJ13SUBQaKooYlaWZmZm7nzQmHDh3K+vXrs2bNmsUsadJa+3yS1yzaG8Iiq6rvz+yOmgNJfrS1ds9Cv+ftt9+eycnJHD16NJs2bcro6GjGx8dP+tmenp7Otm3bTrlM5/j4eCYmJpIkW7Zsmbuiw+lecyG01n5mQV4YeqCqnpzkq5P8aZL/mGSidXDg4cTERMbHx3P06NFs2bLltFeBmZmZyb59+3Ls2LGTxqUXay1IktbaWxbsxaFjVfWWJGuS/E1m/y68p7X2fxfr/c/05/397dixY+6Q4PuvFWfzXOiSooYlZ2RkZO7Q4S1btszdf9111+WGG27I5ORkVqxYcV6jU8BpvS7J4cUoaJLZEcahoaHs2rUrMzMzWb9+fUZHRzM2NpaxsbEksx/Abr755lM+YE1NTWViYmLuuScOEj98+HAOHjyYXbt2JUk2bNiQkZGRUwpf4CH9cmZ303VS0CSzP/+7d++eu0jAhg0bcvfdd5/yuJ07d2Z6evqkEsdaAPPqN5O8P8n4YhY0yZn/vL+/ycnJDA8PZ+vWrZmamsoNN9yQD33oQ2f1XOiaoobOfehDH5r79f0XyRN/6Vq5cuVJ92/duvWk55/42sqVK0/7F7UHPh84N621ty/m+w0PD2d4eDjJ7E65oaGhUx6zbdu2k4raE/bv3z/3oWzlypVZtWpVpqamThmrXLFihQ9mcB5aa7/ddYZ9+/ZlZGRk7vbQ0FAOHz58yr+I33LLLRkfHz/pcr7WApg/rbU/7Oq9z/Tn/Ym/PySza8OJxwwPD2doaChTU1Nn9VzomqIGgF7auHFjpqens3fv3pPuP3z4cI4dO3babcorVqzI1NTU3O2jR4/O/Yv6+vXrs2HDhiTJrbfeurDhgQVz7NixUz6MTU9Pn9XowvDwsLUABsCZ/ry//9rwwOLlxNpxNs+Frrk8NwC9tGfPnuzevTubNm066f7x8fG5EagHGhkZyf79+zMzM5PJyckcOXIkyeyoxPj4eG677bbceuut2blz54LnB/rHWgCD4Ux/3p/J1q1b54rZc30udEFRQ6c2bNhwyihTMnvw12WXXXbOr3UmMzMz2bhx4znnA7p14l/IJycn5+574NjD/Q0PD2fLli3Ztm1bpqens3r16qxduzb79u3LunXrMjw8nDVr1mTFihU5fPjwonwPwIXZvn17Nm/enM2bN8+NMt7/X8Onp6dPOyJ5OtYCGAxn+vP+dLZv355NmzbN/Z3iXJ4LXVHU0Lnp6VOv6HngwIELulQ3sHRNTEw85IewBztTYnR0NDt27MjatWszNDSUlStXZmho6KSyxxoDS8ctt9ySXbt2ZdeuXRkeHs511103dxWXJGc99pTEWgAD5HR/3j/Q9u3bMzY2dsoacTbPhS45o4bOjYyMZGJiYu7qLYcPH87atWtPW+AAg290dDTbt2/P9PT03CW4T8yNn7gi1ANt2LAhd911V1auXJnNmzcnmf1AtmPHjiTJunXrcuTIkbmddfd/TWBpWblyZTZt2pSNGzdm1apVJ50zc/+1YPv27XOFzsGDB7Njxw5rAQyQ0/15n/zLOrBv377s27cvhw4dmvvatm3bsmbNmjM+F/qiOrqyIgPu4MGDZ/U/rA0bNuTWW2/N7t275xbJE3+Ruvnmm+e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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['text.usetex'] = True\n", + "\n", + "petab_select.plot.graph_iteration_layers(\n", + " models=models,\n", + " criterion=petab_select.Criterion.AICC,\n", + ");" + ] } ], "metadata": { diff --git a/petab_select/plot.py b/petab_select/plot.py index 148b68d7..3906ceaf 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -1,9 +1,12 @@ """Visualization routines for model selection with pyPESTO.""" -from typing import Dict, List, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union import matplotlib.pyplot as plt import networkx as nx import numpy as np +import petab +from more_itertools import one +from toposort import toposort from .constants import VIRTUAL_INITIAL_MODEL, Criterion from .model import Model @@ -356,3 +359,157 @@ def scatter_criterion_vs_n_estimated( ) return ax + + +def graph_iteration_layers( + models: List[Model], + criterion: Optional[Criterion] = None, + labels: Dict[str, str] = None, + relative: bool = True, + ax: plt.Axes = None, + draw_networkx_kwargs: Optional[Dict[str, Any]] = None, +) -> plt.Axes: + """Graph the models of each iteration of model selection. + + Parameters + ---------- + models: + A list of models. + criterion: + The criterion. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + relative: + If `True`, criterion values are offset by the minimum criterion + value. + ax: + The axis to use for plotting. + draw_networkx_kwargs: + Passed to the `networkx.draw_networkx` call. + + Returns + ------- + matplotlib.pyplot.Axes + The plot axis. + """ + if ax is None: + _, ax = plt.subplots(figsize=(20, 10)) + + if labels is None: + labels = { + model.get_hash(): model.model_id + + ( + f'\n{model.get_criterion(criterion):.2f}' + if criterion is not None + else '' + ) + for model in models + } + labels[VIRTUAL_INITIAL_MODEL] = "Virtual\nInitial\nModel" + + if draw_networkx_kwargs is None: + draw_networkx_kwargs = { + 'node_color': 'lightgrey', + 'arrowstyle': '-|>', + 'node_shape': 's', + 'node_size': 2500, + } + + ancestry = { + model.get_hash(): model.predecessor_model_hash for model in models + } + ancestry_as_set = {k: set([v]) for k, v in ancestry.items()} + ordering = [list(hashes) for hashes in toposort(ancestry_as_set)] + + G = nx.DiGraph( + [ + (predecessor, successor) + for successor, predecessor in ancestry.items() + ] + ) + # FIXME change positions so that top edge of topmost node, and bottom edge + # of bottommost nodes, are at 1 and 0, respectively + X = [ + (0 if len(ordering) == 1 else 1 / (len(ordering) - 1)) * i + for i in range(0, len(ordering)) + ] + Y = [ + [ + (0 if len(layer) == 1 else 1 / (len(layer) - 1)) * j + for j in range(0, len(layer)) + ] + for layer in ordering + ] + pos = { + labels[model_hash]: (X[i], Y[i][j]) + for i, layer in enumerate(ordering) + for j, model_hash in enumerate(layer) + } + nx.relabel_nodes(G, mapping=labels, copy=False) + nx.draw_networkx(G, pos, ax=ax, **draw_networkx_kwargs) + + # Add `n=...` labels + N = [len(y) for y in Y] + for x, n in zip(X, N): + ax.annotate(f'n={n}', xy=(x, 1.1), fontsize=12) + + # Get selected parameter IDs + # TODO move this logic elsewhere + selected_hashes = set(ancestry.values()) + selected_models = {} + for model in models: + if model.get_hash() in selected_hashes: + selected_models[model.get_hash()] = model + + selected_parameters = { + model_hash: sorted(model.estimated_parameters) + for model_hash, model in selected_models.items() + } + + selected_order = [ + [model_hash for model_hash in layer if model_hash in selected_models] + for layer in ordering + ] + selected_order = [ + None if not model_hash else one(model_hash) + for model_hash in selected_order + ] + + selected_parameter_ids = [] + estimated0 = None + model_hash = None + for model_hash in selected_order: + if model_hash is None: + selected_parameter_ids.append('') + continue + if estimated0 is not None: + new_parameter_id = one( + set(selected_parameters[model_hash]).difference(estimated0) + ) + new_parameter_name = selected_models[ + model_hash + ].petab_problem.parameter_df.get( + 'parameterName', new_parameter_id + )[ + new_parameter_id + ] + new_parameter_name = new_parameter_name.replace( + '\\\\rightarrow ', '->' + ) + new_parameter_name = f'${new_parameter_name}$' + selected_parameter_ids.append(new_parameter_name) + else: + selected_parameter_ids.append('') + estimated0 = selected_parameters[model_hash] + + # Add labels for selected parameters + for x, label in zip(X, selected_parameter_ids): + ax.annotate(label, xy=(x, 1.15), fontsize=12) + + # Set margins for the axes so that nodes aren't clipped + ax.margins(0.15) + ax.axis("off") + + return ax diff --git a/setup.py b/setup.py index 2d91a4c3..4fa1fa7c 100644 --- a/setup.py +++ b/setup.py @@ -71,9 +71,10 @@ def absolute_links(txt): # required for CLI 'click', 'dill', - # plotting - #'matplotlib>=2.2.3', + # plot + 'matplotlib>=2.2.3', #'seaborn', + 'toposort', ], include_package_data=True, python_requires=f'>={minimum_python_version}', From be07a38e7cfe9d319f8a179d008c31ae64d5f4ab Mon Sep 17 00:00:00 2001 From: dilpath Date: Tue, 14 Mar 2023 17:45:37 +1100 Subject: [PATCH 04/26] iterations: fix for arbitrary predecessors --- petab_select/plot.py | 43 +++++++++++++++++++++++++++---------------- 1 file changed, 27 insertions(+), 16 deletions(-) diff --git a/petab_select/plot.py b/petab_select/plot.py index 3906ceaf..025d4170 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -408,6 +408,13 @@ def graph_iteration_layers( for model in models } labels[VIRTUAL_INITIAL_MODEL] = "Virtual\nInitial\nModel" + missing_labels = [ + model.predecessor_model_hash + for model in models + if model.predecessor_model_hash not in labels + ] + for label in missing_labels: + labels[label] = label if draw_networkx_kwargs is None: draw_networkx_kwargs = { @@ -485,28 +492,32 @@ def graph_iteration_layers( selected_parameter_ids.append('') continue if estimated0 is not None: - new_parameter_id = one( - set(selected_parameters[model_hash]).difference(estimated0) - ) - new_parameter_name = selected_models[ - model_hash - ].petab_problem.parameter_df.get( - 'parameterName', new_parameter_id - )[ - new_parameter_id + new_parameter_ids = set( + selected_parameters[model_hash] + ).symmetric_difference(estimated0) + new_parameter_names = [ + selected_models[model_hash].petab_problem.parameter_df.get( + 'parameterName', new_parameter_id + ) + for new_parameter_id in new_parameter_ids ] - new_parameter_name = new_parameter_name.replace( - '\\\\rightarrow ', '->' - ) - new_parameter_name = f'${new_parameter_name}$' - selected_parameter_ids.append(new_parameter_name) + for index, new_parameter_name in enumerate(new_parameter_names): + if not isinstance(new_parameter_name, str): + new_parameter_names[ + index + ] = f"${new_parameter_name[new_parameter_id]}$" + new_parameter_names = [ + new_parameter_name.replace('\\\\rightarrow ', '->') + for new_parameter_name in new_parameter_names + ] + selected_parameter_ids.append(new_parameter_names) else: - selected_parameter_ids.append('') + selected_parameter_ids.append(['']) estimated0 = selected_parameters[model_hash] # Add labels for selected parameters for x, label in zip(X, selected_parameter_ids): - ax.annotate(label, xy=(x, 1.15), fontsize=12) + ax.annotate("\n".join(label), xy=(x, 1.15), fontsize=12) # Set margins for the axes so that nodes aren't clipped ax.margins(0.15) From e0d1178dca785b716ad1998a9ba56d86950227a6 Mon Sep 17 00:00:00 2001 From: dilpath Date: Tue, 14 Mar 2023 17:52:37 +1100 Subject: [PATCH 05/26] remove latex --- petab_select/plot.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/petab_select/plot.py b/petab_select/plot.py index 025d4170..05fbef9b 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -503,9 +503,9 @@ def graph_iteration_layers( ] for index, new_parameter_name in enumerate(new_parameter_names): if not isinstance(new_parameter_name, str): - new_parameter_names[ - index - ] = f"${new_parameter_name[new_parameter_id]}$" + new_parameter_names[index] = new_parameter_name[ + new_parameter_id + ] new_parameter_names = [ new_parameter_name.replace('\\\\rightarrow ', '->') for new_parameter_name in new_parameter_names From ce1b305c08bdd015cd5a3532455d79196b531394 Mon Sep 17 00:00:00 2001 From: dilpath Date: Tue, 14 Mar 2023 18:00:16 +1100 Subject: [PATCH 06/26] typo --- petab_select/plot.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/petab_select/plot.py b/petab_select/plot.py index 05fbef9b..7014a863 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -502,6 +502,8 @@ def graph_iteration_layers( for new_parameter_id in new_parameter_ids ] for index, new_parameter_name in enumerate(new_parameter_names): + if not new_parameter_name: + new_parameter_name = new_parameter_id if not isinstance(new_parameter_name, str): new_parameter_names[index] = new_parameter_name[ new_parameter_id From ddda4c8ba4b82666e5b44314b44236a083303be3 Mon Sep 17 00:00:00 2001 From: dilpath Date: Tue, 14 Mar 2023 22:32:36 +1100 Subject: [PATCH 07/26] fix parameter names --- petab_select/plot.py | 39 ++++++++++++++++++++++++--------------- 1 file changed, 24 insertions(+), 15 deletions(-) diff --git a/petab_select/plot.py b/petab_select/plot.py index 7014a863..41d5fc48 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -4,8 +4,10 @@ import matplotlib.pyplot as plt import networkx as nx import numpy as np +import pandas as pd import petab from more_itertools import one +from petab.C import PARAMETER_NAME from toposort import toposort from .constants import VIRTUAL_INITIAL_MODEL, Criterion @@ -492,27 +494,34 @@ def graph_iteration_layers( selected_parameter_ids.append('') continue if estimated0 is not None: - new_parameter_ids = set( - selected_parameters[model_hash] - ).symmetric_difference(estimated0) - new_parameter_names = [ - selected_models[model_hash].petab_problem.parameter_df.get( - 'parameterName', new_parameter_id + new_parameter_ids = list( + set(selected_parameters[model_hash]).symmetric_difference( + estimated0 ) - for new_parameter_id in new_parameter_ids - ] - for index, new_parameter_name in enumerate(new_parameter_names): - if not new_parameter_name: - new_parameter_name = new_parameter_id - if not isinstance(new_parameter_name, str): - new_parameter_names[index] = new_parameter_name[ - new_parameter_id + ) + new_parameter_names = [] + for new_parameter_id in new_parameter_ids: + # Default to parameter ID, use parameter name if available + new_parameter_name = new_parameter_id + if ( + PARAMETER_NAME + in selected_models[ + model_hash + ].petab_problem.parameter_df.columns + ): + petab_parameter_name = selected_models[ + model_hash + ].petab_problem.parameter_df.loc[ + new_parameter_id, PARAMETER_NAME ] + if not pd.isna(petab_parameter_name): + new_parameter_name = petab_parameter_name + new_parameter_names.append(new_parameter_name) new_parameter_names = [ new_parameter_name.replace('\\\\rightarrow ', '->') for new_parameter_name in new_parameter_names ] - selected_parameter_ids.append(new_parameter_names) + selected_parameter_ids.append(sorted(new_parameter_names)) else: selected_parameter_ids.append(['']) estimated0 = selected_parameters[model_hash] From 6800f1c87824b1cc1a370670842298fd7f55bd1c Mon Sep 17 00:00:00 2001 From: dilpath Date: Tue, 14 Mar 2023 23:31:06 +1100 Subject: [PATCH 08/26] custom colors --- doc/examples/visualization.ipynb | 42 ++++++++-- petab_select/plot.py | 133 ++++++++++++++++++++++++++----- 2 files changed, 145 insertions(+), 30 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index 040be3f8..f2ff37f3 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -27,7 +27,7 @@ "id": "aaeb0606", "metadata": {}, "source": [ - "The model IDs were automatically generated, so we set them to something more readable here: a binary string that describes their position in the model space." + "The model IDs were automatically generated, so we create more readable labels here: a binary string that describes their position in the model space." ] }, { @@ -37,11 +37,22 @@ "metadata": {}, "outputs": [], "source": [ + "# Custom labels\n", + "labels = {}\n", "for model in models:\n", - " model.model_id = \"M_\" + \"\".join(\n", + " labels[model.get_hash()] = \"M_\" + \"\".join(\n", " \"1\" if value == petab_select.ESTIMATE else \"0\"\n", " for value in model.parameters.values()\n", - " )" + " )\n", + "\n", + "# Custom colors for some models\n", + "colors = {\n", + " \"M_000\": \"lightgreen\",\n", + " \"M_001\": \"lightgreen\",\n", + "}\n", + "\n", + "# Change default color\n", + "petab_select.plot.NORMAL_NODE_COLOR = 'darkgray'" ] }, { @@ -60,7 +71,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -75,6 +86,7 @@ "petab_select.plot.line_selected(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", + " labels=labels,\n", ");" ] }, @@ -94,7 +106,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -107,6 +119,8 @@ "petab_select.plot.graph_history(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", + " labels=labels,\n", + " colors=colors,\n", ");" ] }, @@ -126,7 +140,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -141,6 +155,8 @@ "petab_select.plot.bar_criterion_vs_models(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", + " labels=labels,\n", + " colors=colors,\n", ");" ] }, @@ -160,7 +176,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -175,6 +191,8 @@ "petab_select.plot.scatter_criterion_vs_n_estimated(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", + " labels=labels,\n", + " colors=colors,\n", ");" ] }, @@ -194,7 +212,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -211,6 +229,14 @@ "petab_select.plot.graph_iteration_layers(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", + " labels=labels,\n", + " colors=colors,\n", + " draw_networkx_kwargs={\n", + " 'arrowstyle': '-|>',\n", + " 'node_shape': 's',\n", + " 'node_size': 2500,\n", + " 'font_size': 18,\n", + " },\n", ");" ] } diff --git a/petab_select/plot.py b/petab_select/plot.py index 41d5fc48..4b355883 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -14,6 +14,7 @@ from .model import Model RELATIVE_LABEL_FONTSIZE = -2 +NORMAL_NODE_COLOR = 'darkgrey' def default_label_maker(model: Model) -> str: @@ -130,7 +131,7 @@ def line_selected( criterion_values.keys(), criterion_values.values(), linewidth=linewidth, - color='lightgrey', + color=NORMAL_NODE_COLOR, # edgecolor='k' ) @@ -159,6 +160,7 @@ def graph_history( criterion: Criterion = None, ax: plt.Axes = None, labels: Dict[str, str] = None, + colors: Dict[str, str] = None, optimal_distance: float = 1, options: Dict = None, relative: bool = True, @@ -184,6 +186,8 @@ def graph_history( A dictionary of model labels, where keys are model hashes, and values are model labels, for plotting. If a model label is not provided, it will be generated from its model ID. + colors: + Colors for each model, using their labels. ax: The axis to use for plotting. @@ -211,6 +215,8 @@ def graph_history( ) for model_hash, model in model_hashes.items() } + labels = labels.copy() + labels[VIRTUAL_INITIAL_MODEL] = "Virtual\nInitial\nModel" G = nx.DiGraph() edges = [] @@ -237,19 +243,31 @@ def graph_history( G.add_edges_from(edges) default_options = { - 'node_color': 'lightgrey', + 'node_color': NORMAL_NODE_COLOR, 'arrowstyle': '-|>', 'node_shape': 's', 'node_size': 2500, } - if options is not None: - default_options.update(options) + if options is None: + options = default_options + if colors is not None: + if label_diff := set(colors).difference(list(G)): + raise ValueError( + "Colors were provided for the following model labels, but " + "these are not in the graph: {label_diff}" + ) + + node_colors = [ + colors.get(model_label, default_options['node_color']) + for model_label in list(G) + ] + options.update({'node_color': node_colors}) if ax is None: _, ax = plt.subplots(figsize=(12, 12)) pos = nx.spring_layout(G, k=optimal_distance, iterations=20) - nx.draw_networkx(G, pos, ax=ax, **default_options) + nx.draw_networkx(G, pos, ax=ax, **options) return ax @@ -260,6 +278,8 @@ def bar_criterion_vs_models( labels: Dict[str, str] = None, relative: bool = True, ax: plt.Axes = None, + bar_kwargs: Dict[str, Any] = None, + colors: Dict[str, str] = None, ) -> plt.Axes: """Plot all calibrated models and their criterion value. @@ -278,6 +298,8 @@ def bar_criterion_vs_models( value. ax: The axis to use for plotting. + bar_kwargs: + Passed to the matplotlib `ax.bar` call. Returns ------- @@ -286,6 +308,9 @@ def bar_criterion_vs_models( """ model_hashes = get_model_hashes(models) + if bar_kwargs is None: + bar_kwargs = {} + if labels is None: labels = { model_hash: model.model_id @@ -301,9 +326,22 @@ def bar_criterion_vs_models( ) for model in models } + + if colors is not None: + if label_diff := set(colors).difference(criterion_values): + raise ValueError( + "Colors were provided for the following model labels, but " + "these are not in the graph: {label_diff}" + ) + + bar_kwargs['color'] = [ + colors.get(model_label, NORMAL_NODE_COLOR) + for model_label in criterion_values + ] + if relative: criterion_values = get_relative_criterion_values(criterion_values) - ax.bar(criterion_values.keys(), criterion_values.values()) + ax.bar(criterion_values.keys(), criterion_values.values(), **bar_kwargs) ax.set_xlabel("Model labels") ax.set_ylabel( criterion.value + (' (relative)' if relative else ' (absolute)') @@ -317,6 +355,9 @@ def scatter_criterion_vs_n_estimated( criterion: Criterion = None, relative: bool = True, ax: plt.Axes = None, + colors: Dict[str, str] = None, + labels: Dict[str, str] = None, + scatter_kwargs: Dict[str, str] = None, ) -> plt.Axes: """Plot criterion values against number of estimated parameters. @@ -339,6 +380,26 @@ def scatter_criterion_vs_n_estimated( """ model_hashes = get_model_hashes(models) + if labels is None: + labels = { + model_hash: model.model_id + for model_hash, model in model_hashes.items() + } + + if scatter_kwargs is None: + scatter_kwargs = {} + + if colors is not None: + if label_diff := set(colors).difference(labels.values()): + raise ValueError( + "Colors were provided for the following model labels, but " + "these are not in the graph: {label_diff}" + ) + scatter_kwargs['c'] = [ + colors.get(model_label, NORMAL_NODE_COLOR) + for model_label in labels.values() + ] + n_estimated = [] criterion_values = [] for model in models: @@ -353,6 +414,7 @@ def scatter_criterion_vs_n_estimated( ax.scatter( n_estimated, criterion_values, + **scatter_kwargs, ) ax.set_xlabel("Number of estimated parameters") @@ -370,6 +432,7 @@ def graph_iteration_layers( relative: bool = True, ax: plt.Axes = None, draw_networkx_kwargs: Optional[Dict[str, Any]] = None, + colors: Dict[str, str] = None, ) -> plt.Axes: """Graph the models of each iteration of model selection. @@ -409,22 +472,23 @@ def graph_iteration_layers( ) for model in models } - labels[VIRTUAL_INITIAL_MODEL] = "Virtual\nInitial\nModel" - missing_labels = [ - model.predecessor_model_hash - for model in models - if model.predecessor_model_hash not in labels - ] - for label in missing_labels: - labels[label] = label + labels[VIRTUAL_INITIAL_MODEL] = "Virtual\nInitial\nModel" + missing_labels = [ + model.predecessor_model_hash + for model in models + if model.predecessor_model_hash not in labels + ] + for label in missing_labels: + labels[label] = label + default_draw_networkx_kwargs = { + 'node_color': NORMAL_NODE_COLOR, + 'arrowstyle': '-|>', + 'node_shape': 's', + 'node_size': 2500, + } if draw_networkx_kwargs is None: - draw_networkx_kwargs = { - 'node_color': 'lightgrey', - 'arrowstyle': '-|>', - 'node_shape': 's', - 'node_size': 2500, - } + draw_networkx_kwargs = default_draw_networkx_kwargs ancestry = { model.get_hash(): model.predecessor_model_hash for model in models @@ -457,12 +521,33 @@ def graph_iteration_layers( for j, model_hash in enumerate(layer) } nx.relabel_nodes(G, mapping=labels, copy=False) + if colors is not None: + if label_diff := set(colors).difference(list(G)): + raise ValueError( + "Colors were provided for the following model labels, but " + "these are not in the graph: {label_diff}" + ) + + node_colors = [ + colors.get( + model_label, + draw_networkx_kwargs.get( + 'node_color', default_draw_networkx_kwargs['node_color'] + ), + ) + for model_label in list(G) + ] + draw_networkx_kwargs.update({'node_color': node_colors}) nx.draw_networkx(G, pos, ax=ax, **draw_networkx_kwargs) # Add `n=...` labels N = [len(y) for y in Y] for x, n in zip(X, N): - ax.annotate(f'n={n}', xy=(x, 1.1), fontsize=12) + ax.annotate( + f'n={n}', + xy=(x, 1.1), + fontsize=draw_networkx_kwargs.get('font_size', 20), + ) # Get selected parameter IDs # TODO move this logic elsewhere @@ -528,7 +613,11 @@ def graph_iteration_layers( # Add labels for selected parameters for x, label in zip(X, selected_parameter_ids): - ax.annotate("\n".join(label), xy=(x, 1.15), fontsize=12) + ax.annotate( + "\n".join(label), + xy=(x, 1.15), + fontsize=draw_networkx_kwargs.get('font_size', 20), + ) # Set margins for the axes so that nodes aren't clipped ax.margins(0.15) From 228619894d63801efce9cfab4f5b63cc476eac43 Mon Sep 17 00:00:00 2001 From: dilpath Date: Tue, 14 Mar 2023 23:59:41 +1100 Subject: [PATCH 09/26] handle other missing labels --- petab_select/plot.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/petab_select/plot.py b/petab_select/plot.py index 4b355883..0ca2ba10 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -472,8 +472,14 @@ def graph_iteration_layers( ) for model in models } - labels[VIRTUAL_INITIAL_MODEL] = "Virtual\nInitial\nModel" + labels[VIRTUAL_INITIAL_MODEL] = labels.get( + VIRTUAL_INITIAL_MODEL, "Virtual\nInitial\nModel" + ) + missing_labels = [ + model.get_hash() for model in models if model.get_hash() not in labels + ] + missing_labels += [ model.predecessor_model_hash for model in models if model.predecessor_model_hash not in labels From 9fbbbdf9779e38286f5a411cf1265f8914fa28d5 Mon Sep 17 00:00:00 2001 From: dilpath Date: Wed, 15 Mar 2023 00:09:38 +1100 Subject: [PATCH 10/26] model IDs default labels --- petab_select/plot.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/petab_select/plot.py b/petab_select/plot.py index 0ca2ba10..31ae4e0b 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -476,16 +476,20 @@ def graph_iteration_layers( VIRTUAL_INITIAL_MODEL, "Virtual\nInitial\nModel" ) - missing_labels = [ - model.get_hash() for model in models if model.get_hash() not in labels - ] - missing_labels += [ - model.predecessor_model_hash + missing_labels = { + model.get_hash(): model.model_id + for model in models + if model.get_hash() not in labels + } + missing_labels2 = { + model.predecessor_model_hash: model.predecessor_model_hash for model in models if model.predecessor_model_hash not in labels - ] - for label in missing_labels: - labels[label] = label + } + labels.update(missing_labels2) + labels.update(missing_labels) + # for label in missing_labels: + # labels[label] = label default_draw_networkx_kwargs = { 'node_color': NORMAL_NODE_COLOR, From c235a7b6a94e65726716aea02bd8c9652d1bd1fc Mon Sep 17 00:00:00 2001 From: dilpath Date: Tue, 26 Mar 2024 16:35:03 +0100 Subject: [PATCH 11/26] work towards nicer graph_iteration_layers --- petab_select/plot.py | 389 ++++++++++++++++++++++++++++++------------- 1 file changed, 272 insertions(+), 117 deletions(-) diff --git a/petab_select/plot.py b/petab_select/plot.py index 31ae4e0b..0215ed13 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -1,6 +1,8 @@ """Visualization routines for model selection with pyPESTO.""" from typing import Any, Dict, List, Optional, Tuple, Union +import matplotlib.cm +import matplotlib.colors import matplotlib.pyplot as plt import networkx as nx import numpy as np @@ -432,7 +434,10 @@ def graph_iteration_layers( relative: bool = True, ax: plt.Axes = None, draw_networkx_kwargs: Optional[Dict[str, Any]] = None, - colors: Dict[str, str] = None, + # colors: Dict[str, str] = None, + parameter_labels: Dict[str, str] = None, + augment_labels: bool = True, + use_tex: bool = True, ) -> plt.Axes: """Graph the models of each iteration of model selection. @@ -453,49 +458,42 @@ def graph_iteration_layers( The axis to use for plotting. draw_networkx_kwargs: Passed to the `networkx.draw_networkx` call. + parameter_labels: + A dictionary of parameter labels, where keys are parameter IDs, and + values are parameter labels, for plotting. Defaults to parameter IDs. + augment_labels: + If ``True'', provided labels will have estimated parameters and + relative criterion values added to them, for plotting. Returns ------- matplotlib.pyplot.Axes The plot axis. """ + if use_tex: + rcParams0 = dict(plt.rcParams) + rcParams = { + 'text.usetex': True, + #'text.latex': { + # 'preamble': r'\usepackage{color,xcolor}', + # }, + "pgf.rcfonts": False, + "pgf.preamble": r'\usepackage{color}', + } + plt.rcParams.update(rcParams) + # for rcParam, rcParamValues in rcParams.items(): + # plt.rc(rcParam, **rcParamValues) + if ax is None: _, ax = plt.subplots(figsize=(20, 10)) - if labels is None: - labels = { - model.get_hash(): model.model_id - + ( - f'\n{model.get_criterion(criterion):.2f}' - if criterion is not None - else '' - ) - for model in models - } - labels[VIRTUAL_INITIAL_MODEL] = labels.get( - VIRTUAL_INITIAL_MODEL, "Virtual\nInitial\nModel" - ) - - missing_labels = { - model.get_hash(): model.model_id - for model in models - if model.get_hash() not in labels - } - missing_labels2 = { - model.predecessor_model_hash: model.predecessor_model_hash - for model in models - if model.predecessor_model_hash not in labels - } - labels.update(missing_labels2) - labels.update(missing_labels) - # for label in missing_labels: - # labels[label] = label + model_hashes = {model.get_hash(): model for model in models} default_draw_networkx_kwargs = { - 'node_color': NORMAL_NODE_COLOR, + #'node_color': NORMAL_NODE_COLOR, 'arrowstyle': '-|>', 'node_shape': 's', - 'node_size': 2500, + 'node_size': 250, } if draw_networkx_kwargs is None: draw_networkx_kwargs = default_draw_networkx_kwargs @@ -506,6 +504,115 @@ def graph_iteration_layers( ancestry_as_set = {k: set([v]) for k, v in ancestry.items()} ordering = [list(hashes) for hashes in toposort(ancestry_as_set)] + model_estimated_parameters = { + model.get_hash(): set(model.estimated_parameters) for model in models + } + model_criterion_values = None + if criterion is not None: + model_criterion_values = { + model.get_hash(): model.get_criterion(criterion) + for model in models + } + + min_criterion_value = min(model_criterion_values.values()) + model_criterion_values = { + k: v - min_criterion_value + for k, v in model_criterion_values.items() + } + + model_parameter_diffs = { + model.get_hash(): ( + (set(), set()) + if model.predecessor_model_hash not in model_estimated_parameters + else ( + model_estimated_parameters[model.get_hash()].difference( + model_estimated_parameters[model.predecessor_model_hash] + ), + model_estimated_parameters[ + model.predecessor_model_hash + ].difference(model_estimated_parameters[model.get_hash()]), + ) + ) + for model in models + } + + if labels is None: + labels = {model.get_hash(): model.model_id for model in models} + if augment_labels: + + class Identidict(dict): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def __getitem__(self, key): + return key + + if parameter_labels is None: + parameter_labels = Identidict() + + model_added_parameters = { + model_hash: ';'.join( + [ + parameter_labels[parameter_id] + for parameter_id in sorted( + model_parameter_diffs[model_hash][0] + ) + ] + ) + for model_hash in model_estimated_parameters + } + model_removed_parameters = { + model_hash: ';'.join( + [ + parameter_labels[parameter_id] + for parameter_id in sorted( + model_parameter_diffs[model_hash][1] + ) + ] + ) + for model_hash in model_estimated_parameters + } + + labels = { + model_hash: ( + label0 + # + ( + # f'\n{model_criterion_values[model_hash]:.2f}' + # if model_hash in model_criterion_values + # else '' + # ) + + ( + ( + # f'\n' + + r'\textcolor{green}{hi}' + # + ('\\textcolor{green}{' if use_tex else '') + + # f'{model_added_parameters[model_hash]}' + # + ('}' if use_tex else '') + if model_added_parameters.get(model_hash, '') + else '' + ) + + ( + # f'\n' + + r'\textcolor{red}{hi}' + # + ('\\textcolor{red}{' if use_tex else '') + + # f'{model_removed_parameters[model_hash]}' + # + ('}' if use_tex else '') + if model_removed_parameters.get(model_hash, '') + else '' + ) + ) + ) + for model_hash, label0 in labels.items() + } + + norm = matplotlib.colors.Normalize( + vmin=min(model_criterion_values.values()), + vmax=max(model_criterion_values.values()), + ) + cmap = matplotlib.cm.get_cmap('cool') + scalar_mappable = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap) + ax.get_figure().colorbar(scalar_mappable, ax=ax) + G = nx.DiGraph( [ (predecessor, successor) @@ -525,30 +632,76 @@ def graph_iteration_layers( ] for layer in ordering ] + + # Labels + # if labels is None: + # labels = { + # model.get_hash(): model.model_id + # + ( + # f'\n{model.get_criterion(criterion):.2f}' + # if criterion is not None + # else '' + # ) + # for model in models + # } + labels[VIRTUAL_INITIAL_MODEL] = labels.get( + VIRTUAL_INITIAL_MODEL, "Virtual\nInitial\nModel" + ) + + missing_labels = { + model.get_hash(): model.model_id + for model in models + if model.get_hash() not in labels + } + missing_labels2 = { + model.predecessor_model_hash: model.predecessor_model_hash + for model in models + if model.predecessor_model_hash not in labels + } + labels.update(missing_labels2) + labels.update(missing_labels) + # for label in missing_labels: + # labels[label] = label + pos = { labels[model_hash]: (X[i], Y[i][j]) for i, layer in enumerate(ordering) for j, model_hash in enumerate(layer) } nx.relabel_nodes(G, mapping=labels, copy=False) - if colors is not None: - if label_diff := set(colors).difference(list(G)): - raise ValueError( - "Colors were provided for the following model labels, but " - "these are not in the graph: {label_diff}" - ) - node_colors = [ - colors.get( - model_label, - draw_networkx_kwargs.get( - 'node_color', default_draw_networkx_kwargs['node_color'] - ), - ) - for model_label in list(G) - ] - draw_networkx_kwargs.update({'node_color': node_colors}) - nx.draw_networkx(G, pos, ax=ax, **draw_networkx_kwargs) + G_hashes = [ + one([k for k, v in labels.items() if v == label]) for label in G.nodes + ] + node_colors = [ + ( + scalar_mappable.to_rgba(model_criterion_values[model_hash]) + if model_hash in model_criterion_values + else NORMAL_NODE_COLOR + ) + for model_hash in G_hashes + ] + + # if colors is not None: + # if label_diff := set(colors).difference(list(G)): + # raise ValueError( + # "Colors were provided for the following model labels, but " + # "these are not in the graph: {label_diff}" + # ) + + # node_colors = [ + # colors.get( + # model_label, + # draw_networkx_kwargs.get( + # 'node_color', default_draw_networkx_kwargs['node_color'] + # ), + # ) + # for model_label in list(G) + # ] + # draw_networkx_kwargs.update({'node_color': node_colors}) + nx.draw_networkx( + G, pos, ax=ax, node_color=node_colors, **draw_networkx_kwargs + ) # Add `n=...` labels N = [len(y) for y in Y] @@ -559,78 +712,80 @@ def graph_iteration_layers( fontsize=draw_networkx_kwargs.get('font_size', 20), ) - # Get selected parameter IDs - # TODO move this logic elsewhere - selected_hashes = set(ancestry.values()) - selected_models = {} - for model in models: - if model.get_hash() in selected_hashes: - selected_models[model.get_hash()] = model - - selected_parameters = { - model_hash: sorted(model.estimated_parameters) - for model_hash, model in selected_models.items() - } - - selected_order = [ - [model_hash for model_hash in layer if model_hash in selected_models] - for layer in ordering - ] - selected_order = [ - None if not model_hash else one(model_hash) - for model_hash in selected_order - ] - - selected_parameter_ids = [] - estimated0 = None - model_hash = None - for model_hash in selected_order: - if model_hash is None: - selected_parameter_ids.append('') - continue - if estimated0 is not None: - new_parameter_ids = list( - set(selected_parameters[model_hash]).symmetric_difference( - estimated0 - ) - ) - new_parameter_names = [] - for new_parameter_id in new_parameter_ids: - # Default to parameter ID, use parameter name if available - new_parameter_name = new_parameter_id - if ( - PARAMETER_NAME - in selected_models[ - model_hash - ].petab_problem.parameter_df.columns - ): - petab_parameter_name = selected_models[ - model_hash - ].petab_problem.parameter_df.loc[ - new_parameter_id, PARAMETER_NAME - ] - if not pd.isna(petab_parameter_name): - new_parameter_name = petab_parameter_name - new_parameter_names.append(new_parameter_name) - new_parameter_names = [ - new_parameter_name.replace('\\\\rightarrow ', '->') - for new_parameter_name in new_parameter_names - ] - selected_parameter_ids.append(sorted(new_parameter_names)) - else: - selected_parameter_ids.append(['']) - estimated0 = selected_parameters[model_hash] - - # Add labels for selected parameters - for x, label in zip(X, selected_parameter_ids): - ax.annotate( - "\n".join(label), - xy=(x, 1.15), - fontsize=draw_networkx_kwargs.get('font_size', 20), - ) + ## Get selected parameter IDs + ## TODO move this logic elsewhere + # selected_hashes = set(ancestry.values()) + # selected_models = {} + # for model in models: + # if model.get_hash() in selected_hashes: + # selected_models[model.get_hash()] = model + + # selected_parameters = { + # model_hash: sorted(model.estimated_parameters) + # for model_hash, model in selected_models.items() + # } + + # selected_order = [ + # [model_hash for model_hash in layer if model_hash in selected_models] + # for layer in ordering + # ] + # selected_order = [ + # None if not model_hash else one(model_hash) + # for model_hash in selected_order + # ] + + # selected_parameter_ids = [] + # estimated0 = None + # model_hash = None + # for model_hash in selected_order: + # if model_hash is None: + # selected_parameter_ids.append('') + # continue + # if estimated0 is not None: + # new_parameter_ids = list( + # set(selected_parameters[model_hash]).symmetric_difference( + # estimated0 + # ) + # ) + # new_parameter_names = [] + # for new_parameter_id in new_parameter_ids: + # # Default to parameter ID, use parameter name if available + # new_parameter_name = new_parameter_id + # if ( + # PARAMETER_NAME + # in selected_models[ + # model_hash + # ].petab_problem.parameter_df.columns + # ): + # petab_parameter_name = selected_models[ + # model_hash + # ].petab_problem.parameter_df.loc[ + # new_parameter_id, PARAMETER_NAME + # ] + # if not pd.isna(petab_parameter_name): + # new_parameter_name = petab_parameter_name + # new_parameter_names.append(new_parameter_name) + # new_parameter_names = [ + # new_parameter_name.replace('\\\\rightarrow ', '->') + # for new_parameter_name in new_parameter_names + # ] + # selected_parameter_ids.append(sorted(new_parameter_names)) + # else: + # selected_parameter_ids.append(['']) + # estimated0 = selected_parameters[model_hash] + + ## Add labels for selected parameters + # for x, label in zip(X, selected_parameter_ids): + # ax.annotate( + # "\n".join(label), + # xy=(x, 1.15), + # fontsize=draw_networkx_kwargs.get('font_size', 20), + # ) # Set margins for the axes so that nodes aren't clipped ax.margins(0.15) ax.axis("off") + # FIXME + plt.rcParams.update(rcParams0) return ax From aee656b10231ba8a777cd539ab46959a36611d0e Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Tue, 12 Nov 2024 14:53:07 +0100 Subject: [PATCH 12/26] ruff --- doc/examples/visualization.ipynb | 12 +-- petab_select/plot.py | 124 +++++++++++++++---------------- 2 files changed, 67 insertions(+), 69 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index f2ff37f3..bd950d2c 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -52,7 +52,7 @@ "}\n", "\n", "# Change default color\n", - "petab_select.plot.NORMAL_NODE_COLOR = 'darkgray'" + "petab_select.plot.NORMAL_NODE_COLOR = \"darkgray\"" ] }, { @@ -224,7 +224,7 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "plt.rcParams['text.usetex'] = True\n", + "plt.rcParams[\"text.usetex\"] = True\n", "\n", "petab_select.plot.graph_iteration_layers(\n", " models=models,\n", @@ -232,10 +232,10 @@ " labels=labels,\n", " colors=colors,\n", " draw_networkx_kwargs={\n", - " 'arrowstyle': '-|>',\n", - " 'node_shape': 's',\n", - " 'node_size': 2500,\n", - " 'font_size': 18,\n", + " \"arrowstyle\": \"-|>\",\n", + " \"node_shape\": \"s\",\n", + " \"node_size\": 2500,\n", + " \"font_size\": 18,\n", " },\n", ");" ] diff --git a/petab_select/plot.py b/petab_select/plot.py index 0215ed13..3381031a 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -1,22 +1,20 @@ """Visualization routines for model selection with pyPESTO.""" -from typing import Any, Dict, List, Optional, Tuple, Union + +from typing import Any import matplotlib.cm import matplotlib.colors import matplotlib.pyplot as plt import networkx as nx import numpy as np -import pandas as pd -import petab from more_itertools import one -from petab.C import PARAMETER_NAME from toposort import toposort from .constants import VIRTUAL_INITIAL_MODEL, Criterion from .model import Model RELATIVE_LABEL_FONTSIZE = -2 -NORMAL_NODE_COLOR = 'darkgrey' +NORMAL_NODE_COLOR = "darkgrey" def default_label_maker(model: Model) -> str: @@ -24,13 +22,13 @@ def default_label_maker(model: Model) -> str: return model.model_hash[:4] -def get_model_hashes(models: List[Model]) -> Dict[str, Model]: +def get_model_hashes(models: list[Model]) -> dict[str, Model]: model_hashes = {model.get_hash(): model for model in models} return model_hashes def get_selected_models( - models: List[Model], + models: list[Model], criterion: Criterion, ): criterion_value0 = np.inf @@ -54,8 +52,8 @@ def get_selected_models( def get_relative_criterion_values( - criterion_values: Union[Dict[str, float], List[float]], -) -> Union[Dict[str, float], List[float]]: + criterion_values: dict[str, float] | list[float], +) -> dict[str, float] | list[float]: values = criterion_values if isinstance(criterion_values, dict): values = criterion_values.values() @@ -71,12 +69,12 @@ def get_relative_criterion_values( def line_selected( - models: List[Model], + models: list[Model], criterion: Criterion, relative: bool = True, fz: int = 14, - size: Tuple[float, float] = (5, 4), - labels: Dict[str, str] = None, + size: tuple[float, float] = (5, 4), + labels: dict[str, str] = None, ax: plt.Axes = None, ) -> plt.Axes: """Plot criterion for calibrated models. @@ -140,7 +138,7 @@ def line_selected( ax.get_xticks() ax.set_xticks(list(range(len(criterion_values)))) ax.set_ylabel( - criterion + (' (relative)' if relative else ' (absolute)'), fontsize=fz + criterion + (" (relative)" if relative else " (absolute)"), fontsize=fz ) # could change to compared_model_ids, if all models are plotted ax.set_xticklabels( @@ -152,19 +150,19 @@ def line_selected( ytl = ax.get_yticks() ax.set_ylim([min(ytl), max(ytl)]) # removing top and right borders - ax.spines['top'].set_visible(False) - ax.spines['right'].set_visible(False) + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) return ax def graph_history( - models: List[Model], + models: list[Model], criterion: Criterion = None, ax: plt.Axes = None, - labels: Dict[str, str] = None, - colors: Dict[str, str] = None, + labels: dict[str, str] = None, + colors: dict[str, str] = None, optimal_distance: float = 1, - options: Dict = None, + options: dict = None, relative: bool = True, ) -> plt.Axes: """Plot all calibrated models in the model space, as a directed graph. @@ -236,19 +234,19 @@ def graph_history( ) else: raise NotImplementedError( - 'Plots for models with `None` as their predecessor model are ' - 'not yet implemented.' + "Plots for models with `None` as their predecessor model are " + "not yet implemented." ) - from_ = 'None' + from_ = "None" to = labels.get(model.get_hash(), model.model_id) edges.append((from_, to)) G.add_edges_from(edges) default_options = { - 'node_color': NORMAL_NODE_COLOR, - 'arrowstyle': '-|>', - 'node_shape': 's', - 'node_size': 2500, + "node_color": NORMAL_NODE_COLOR, + "arrowstyle": "-|>", + "node_shape": "s", + "node_size": 2500, } if options is None: options = default_options @@ -260,10 +258,10 @@ def graph_history( ) node_colors = [ - colors.get(model_label, default_options['node_color']) + colors.get(model_label, default_options["node_color"]) for model_label in list(G) ] - options.update({'node_color': node_colors}) + options.update({"node_color": node_colors}) if ax is None: _, ax = plt.subplots(figsize=(12, 12)) @@ -275,13 +273,13 @@ def graph_history( def bar_criterion_vs_models( - models: List[Model], + models: list[Model], criterion: Criterion = None, - labels: Dict[str, str] = None, + labels: dict[str, str] = None, relative: bool = True, ax: plt.Axes = None, - bar_kwargs: Dict[str, Any] = None, - colors: Dict[str, str] = None, + bar_kwargs: dict[str, Any] = None, + colors: dict[str, str] = None, ) -> plt.Axes: """Plot all calibrated models and their criterion value. @@ -336,7 +334,7 @@ def bar_criterion_vs_models( "these are not in the graph: {label_diff}" ) - bar_kwargs['color'] = [ + bar_kwargs["color"] = [ colors.get(model_label, NORMAL_NODE_COLOR) for model_label in criterion_values ] @@ -346,20 +344,20 @@ def bar_criterion_vs_models( ax.bar(criterion_values.keys(), criterion_values.values(), **bar_kwargs) ax.set_xlabel("Model labels") ax.set_ylabel( - criterion.value + (' (relative)' if relative else ' (absolute)') + criterion.value + (" (relative)" if relative else " (absolute)") ) return ax def scatter_criterion_vs_n_estimated( - models: List[Model], + models: list[Model], criterion: Criterion = None, relative: bool = True, ax: plt.Axes = None, - colors: Dict[str, str] = None, - labels: Dict[str, str] = None, - scatter_kwargs: Dict[str, str] = None, + colors: dict[str, str] = None, + labels: dict[str, str] = None, + scatter_kwargs: dict[str, str] = None, ) -> plt.Axes: """Plot criterion values against number of estimated parameters. @@ -397,7 +395,7 @@ def scatter_criterion_vs_n_estimated( "Colors were provided for the following model labels, but " "these are not in the graph: {label_diff}" ) - scatter_kwargs['c'] = [ + scatter_kwargs["c"] = [ colors.get(model_label, NORMAL_NODE_COLOR) for model_label in labels.values() ] @@ -421,21 +419,21 @@ def scatter_criterion_vs_n_estimated( ax.set_xlabel("Number of estimated parameters") ax.set_ylabel( - criterion.value + (' (relative)' if relative else ' (absolute)') + criterion.value + (" (relative)" if relative else " (absolute)") ) return ax def graph_iteration_layers( - models: List[Model], - criterion: Optional[Criterion] = None, - labels: Dict[str, str] = None, + models: list[Model], + criterion: Criterion | None = None, + labels: dict[str, str] = None, relative: bool = True, ax: plt.Axes = None, - draw_networkx_kwargs: Optional[Dict[str, Any]] = None, + draw_networkx_kwargs: dict[str, Any] | None = None, # colors: Dict[str, str] = None, - parameter_labels: Dict[str, str] = None, + parameter_labels: dict[str, str] = None, augment_labels: bool = True, use_tex: bool = True, ) -> plt.Axes: @@ -473,12 +471,12 @@ def graph_iteration_layers( if use_tex: rcParams0 = dict(plt.rcParams) rcParams = { - 'text.usetex': True, + "text.usetex": True, #'text.latex': { # 'preamble': r'\usepackage{color,xcolor}', # }, "pgf.rcfonts": False, - "pgf.preamble": r'\usepackage{color}', + "pgf.preamble": r"\usepackage{color}", } plt.rcParams.update(rcParams) # for rcParam, rcParamValues in rcParams.items(): @@ -491,9 +489,9 @@ def graph_iteration_layers( default_draw_networkx_kwargs = { #'node_color': NORMAL_NODE_COLOR, - 'arrowstyle': '-|>', - 'node_shape': 's', - 'node_size': 250, + "arrowstyle": "-|>", + "node_shape": "s", + "node_size": 250, } if draw_networkx_kwargs is None: draw_networkx_kwargs = default_draw_networkx_kwargs @@ -501,7 +499,7 @@ def graph_iteration_layers( ancestry = { model.get_hash(): model.predecessor_model_hash for model in models } - ancestry_as_set = {k: set([v]) for k, v in ancestry.items()} + ancestry_as_set = {k: {v} for k, v in ancestry.items()} ordering = [list(hashes) for hashes in toposort(ancestry_as_set)] model_estimated_parameters = { @@ -551,7 +549,7 @@ def __getitem__(self, key): parameter_labels = Identidict() model_added_parameters = { - model_hash: ';'.join( + model_hash: ";".join( [ parameter_labels[parameter_id] for parameter_id in sorted( @@ -562,7 +560,7 @@ def __getitem__(self, key): for model_hash in model_estimated_parameters } model_removed_parameters = { - model_hash: ';'.join( + model_hash: ";".join( [ parameter_labels[parameter_id] for parameter_id in sorted( @@ -584,21 +582,21 @@ def __getitem__(self, key): + ( ( # f'\n' + - r'\textcolor{green}{hi}' + r"\textcolor{green}{hi}" # + ('\\textcolor{green}{' if use_tex else '') + # f'{model_added_parameters[model_hash]}' # + ('}' if use_tex else '') - if model_added_parameters.get(model_hash, '') - else '' + if model_added_parameters.get(model_hash, "") + else "" ) + ( # f'\n' + - r'\textcolor{red}{hi}' + r"\textcolor{red}{hi}" # + ('\\textcolor{red}{' if use_tex else '') + # f'{model_removed_parameters[model_hash]}' # + ('}' if use_tex else '') - if model_removed_parameters.get(model_hash, '') - else '' + if model_removed_parameters.get(model_hash, "") + else "" ) ) ) @@ -609,7 +607,7 @@ def __getitem__(self, key): vmin=min(model_criterion_values.values()), vmax=max(model_criterion_values.values()), ) - cmap = matplotlib.cm.get_cmap('cool') + cmap = matplotlib.cm.get_cmap("cool") scalar_mappable = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap) ax.get_figure().colorbar(scalar_mappable, ax=ax) @@ -705,11 +703,11 @@ def __getitem__(self, key): # Add `n=...` labels N = [len(y) for y in Y] - for x, n in zip(X, N): + for x, n in zip(X, N, strict=False): ax.annotate( - f'n={n}', + f"n={n}", xy=(x, 1.1), - fontsize=draw_networkx_kwargs.get('font_size', 20), + fontsize=draw_networkx_kwargs.get("font_size", 20), ) ## Get selected parameter IDs From e6f9f7186100ff702b5240fcecf6cc4584807297 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Tue, 12 Nov 2024 14:56:38 +0100 Subject: [PATCH 13/26] add .plot to sphinx --- doc/api.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/api.rst b/doc/api.rst index 5f239f5b..6f111328 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -16,5 +16,6 @@ petab-select Python API petab_select.model_space petab_select.model_subspace petab_select.petab + petab_select.plot petab_select.problem petab_select.ui From f3ef04e985978c029dc5a2e9afeb986a79cda3bd Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 18 Nov 2024 17:18:55 +0100 Subject: [PATCH 14/26] add upset plot --- .../calibrated_models/calibrated_models.yaml | 101 ++-- .../calibrated_models/model_space.tsv | 16 +- doc/examples/visualization.ipynb | 120 +++-- petab_select/plot.py | 443 ++++++++---------- pyproject.toml | 1 + 5 files changed, 357 insertions(+), 324 deletions(-) diff --git a/doc/examples/calibrated_models/calibrated_models.yaml b/doc/examples/calibrated_models/calibrated_models.yaml index a3e72834..e77c5243 100644 --- a/doc/examples/calibrated_models/calibrated_models.yaml +++ b/doc/examples/calibrated_models/calibrated_models.yaml @@ -1,11 +1,11 @@ - criteria: - AICc: 37.97523003111247 - NLLH: 17.487615015556234 + AICc: 37.97523003111246 + NLLH: 17.48761501555623 estimated_parameters: - sigma_x2: 4.462298342504554 - model_hash: 649bcd92d8da15883a6709f3233e277e173aa6ce7ff1eda3f32d342a0f3a0e63ddd6e3764eab0c730dfb96440bab731ee67d412295a48e1e9949b3cc10935f28 - model_id: 649bcd92d8da15883a6709f3233e277e173aa6ce7ff1eda3f32d342a0f3a0e63ddd6e3764eab0c730dfb96440bab731ee67d412295a48e1e9949b3cc10935f28 - model_subspace_id: M1_0 + sigma_x2: 4.462298385653177 + model_hash: M_0-000 + model_id: M_0-000 + model_subspace_id: M_0 model_subspace_indices: - 0 - 0 @@ -17,14 +17,14 @@ petab_yaml: petab_problem.yaml predecessor_model_hash: virtual_initial_model - criteria: - AICc: -0.17540608110890332 - NLLH: -4.087703040554452 + AICc: -0.1754060811089051 + NLLH: -4.0877030405544525 estimated_parameters: k3: 0.0 - sigma_x2: 0.12242920045980682 - model_hash: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 - model_id: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 - model_subspace_id: M1_1 + sigma_x2: 0.12242920113658744 + model_hash: M_1-000 + model_id: M_1-000 + model_subspace_id: M_1 model_subspace_indices: - 0 - 0 @@ -34,17 +34,16 @@ k2: 0.1 k3: estimate petab_yaml: petab_problem.yaml - predecessor_model_hash: 649bcd92d8da15883a6709f3233e277e173aa6ce7ff1eda3f32d342a0f3a0e63ddd6e3764eab0c730dfb96440bab731ee67d412295a48e1e9949b3cc10935f28 + predecessor_model_hash: M_0-000 - criteria: - AICc: 9.72548629466802 - NLLH: -4.13725685266599 + AICc: -0.27451405630430337 + NLLH: -4.137257028152152 estimated_parameters: - k2: 0.10147820365454563 - k3: 0.0 - sigma_x2: 0.12142173031067148 - model_hash: 3d668e2ad009b34fe1a46fc87d799f94a946c2b411e18d814d90f935b041c2c31c09a1fa346ac1b2ace69de4cfa1d4319939a8505256bed734e53e276b73fe21 - model_id: 3d668e2ad009b34fe1a46fc87d799f94a946c2b411e18d814d90f935b041c2c31c09a1fa346ac1b2ace69de4cfa1d4319939a8505256bed734e53e276b73fe21 - model_subspace_id: M1_4 + k2: 0.10147827639089564 + sigma_x2: 0.12142256779953603 + model_hash: M_2-000 + model_id: M_2-000 + model_subspace_id: M_2 model_subspace_indices: - 0 - 0 @@ -52,19 +51,38 @@ parameters: k1: 0.2 k2: estimate - k3: estimate + k3: 0 + petab_yaml: petab_problem.yaml + predecessor_model_hash: M_0-000 +- criteria: + AICc: -0.7053270517931587 + NLLH: -4.352663525896579 + estimated_parameters: + k1: 0.20160888007873565 + sigma_x2: 0.11713858557052499 + model_hash: M_3-000 + model_id: M_3-000 + model_subspace_id: M_3 + model_subspace_indices: + - 0 + - 0 + - 0 + parameters: + k1: estimate + k2: 0.1 + k3: 0 petab_yaml: petab_problem.yaml - predecessor_model_hash: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 + predecessor_model_hash: M_0-000 - criteria: - AICc: 9.294673635222654 - NLLH: -4.352663182388673 + AICc: 9.294672948206841 + NLLH: -4.352663525896579 estimated_parameters: - k1: 0.20160877940548563 + k1: 0.20160888007873565 k3: 0.0 - sigma_x2: 0.11714036406481404 - model_hash: d2503eb370a02bfae839b66cc207ed4a04833e609d9cbe70bc956ed1bb79158bbf5f4634a74369704085375deffafc03498754bce2a5ffd1064c7bdb7e549c95 - model_id: d2503eb370a02bfae839b66cc207ed4a04833e609d9cbe70bc956ed1bb79158bbf5f4634a74369704085375deffafc03498754bce2a5ffd1064c7bdb7e549c95 - model_subspace_id: M1_5 + sigma_x2: 0.11713858557052499 + model_hash: M_5-000 + model_id: M_5-000 + model_subspace_id: M_5 model_subspace_indices: - 0 - 0 @@ -74,18 +92,17 @@ k2: 0.1 k3: estimate petab_yaml: petab_problem.yaml - predecessor_model_hash: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 + predecessor_model_hash: M_3-000 - criteria: - AICc: 35.94352922200498 - NLLH: -6.028235388997507 + AICc: 7.852170288089528 + NLLH: -5.073914855955236 estimated_parameters: - k1: 0.6223035502886127 - k2: 0.02020974032591268 - k3: 0.0010856659773303976 - sigma_x2: 0.08859827826030148 - model_hash: afa580ef5a7e93e44eaa2901643913761bacd32b35b07d821b91da033bc6dae6ffbfb87d4ba49139d4c68f04921a720063c535cfa9966457d53382d89edc696d - model_id: afa580ef5a7e93e44eaa2901643913761bacd32b35b07d821b91da033bc6dae6ffbfb87d4ba49139d4c68f04921a720063c535cfa9966457d53382d89edc696d - model_subspace_id: M1_7 + k1: 0.20924739987621038 + k2: 0.0859065470362628 + sigma_x2: 0.1038731029818225 + model_hash: M_6-000 + model_id: M_6-000 + model_subspace_id: M_6 model_subspace_indices: - 0 - 0 @@ -93,6 +110,6 @@ parameters: k1: estimate k2: estimate - k3: estimate + k3: 0 petab_yaml: petab_problem.yaml - predecessor_model_hash: 9202aceb387014e83f48a27e577758cd84f682df40dc52b5e79c99b150a27cc997276b7584be893fc59723e1eaa10dff9ba2bc0da53404996fa886aefdc08812 + predecessor_model_hash: M_3-000 diff --git a/doc/examples/calibrated_models/model_space.tsv b/doc/examples/calibrated_models/model_space.tsv index 5a4b2dea..87058e5d 100644 --- a/doc/examples/calibrated_models/model_space.tsv +++ b/doc/examples/calibrated_models/model_space.tsv @@ -1,9 +1,9 @@ model_subspace_id petab_yaml k1 k2 k3 -MS_0 petab_problem.yaml 0 0 0 -MS_1 petab_problem.yaml 0.2 0.1 estimate -MS_2 petab_problem.yaml 0.2 estimate 0 -MS_3 petab_problem.yaml estimate 0.1 0 -MS_4 petab_problem.yaml 0.2 estimate estimate -MS_5 petab_problem.yaml estimate 0.1 estimate -MS_6 petab_problem.yaml estimate estimate 0 -MS_7 petab_problem.yaml estimate estimate estimate +M_0 petab_problem.yaml 0 0 0 +M_1 petab_problem.yaml 0.2 0.1 estimate +M_2 petab_problem.yaml 0.2 estimate 0 +M_3 petab_problem.yaml estimate 0.1 0 +M_4 petab_problem.yaml 0.2 estimate estimate +M_5 petab_problem.yaml estimate 0.1 estimate +M_6 petab_problem.yaml estimate estimate 0 +M_7 petab_problem.yaml estimate estimate estimate diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index bd950d2c..f9486ceb 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -5,7 +5,17 @@ "id": "50d3669e", "metadata": {}, "source": [ - "The visualization methods implemented in PEtab Select are demonstrated here. These methods generally visualize the output of a model selection task, so the input is generally a list of already-calibrated models. Here, some calibrated models that were saved to disk with `petab_select.model.models_to_yaml_list` are loaded and used as input." + "# Visualization gallery" + ] + }, + { + "cell_type": "markdown", + "id": "c01e724e-1d4e-41e7-bb76-742f354fd15f", + "metadata": {}, + "source": [ + "The visualization methods implemented in PEtab Select are demonstrated here. These methods generally visualize the output of a model selection task, so the input is generally a list of already-calibrated models.\n", + "\n", + "Here, some calibrated models that were saved to disk with `petab_select.model.models_to_yaml_list` are loaded and used as input. This is the result of a forward selection with the problem provided in `calibrated_models`." ] }, { @@ -27,7 +37,7 @@ "id": "aaeb0606", "metadata": {}, "source": [ - "The model IDs were automatically generated, so we create more readable labels here: a binary string that describes their position in the model space." + "We customize the labels here to just be the second part of a model hash: a binary string that describes their estimated parameters. We additionally set a custom color for a couple of models, and the default color for the other models. We also set a nicer label for the \"virtual initial model\", which is a hypothetical model that PEtab Select uses by default to initialize a forward search (with no parameters)." ] }, { @@ -44,6 +54,9 @@ " \"1\" if value == petab_select.ESTIMATE else \"0\"\n", " for value in model.parameters.values()\n", " )\n", + "labels[petab_select.ModelHash(petab_select.VIRTUAL_INITIAL_MODEL, \"\")] = (\n", + " \"\\n\".join(petab_select.VIRTUAL_INITIAL_MODEL.split(\"_\")).title()\n", + ")\n", "\n", "# Custom colors for some models\n", "colors = {\n", @@ -57,28 +70,57 @@ }, { "cell_type": "markdown", - "id": "32de6556", + "id": "6c73e0bc-5bf8-4c03-a54b-f19ced731322", "metadata": {}, "source": [ - "# Selected models" + "## UpSet plot\n", + "\n", + "Category membership is whether the parameter is estimated." ] }, { "cell_type": "code", "execution_count": 3, - "id": "56b4a27b", + "id": "96d99572-f74d-4e25-8237-0aa158eb29f6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "petab_select.plot.upset(models=models, criterion=petab_select.Criterion.AICC);" + ] + }, + { + "cell_type": "markdown", + "id": "32de6556", + "metadata": {}, + "source": [ + "## Selected models" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "56b4a27b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] }, + "metadata": {}, "output_type": "display_data" } ], @@ -95,20 +137,20 @@ "id": "8e5243e0", "metadata": {}, "source": [ - "# Selection history trajectory" + "## Selection history trajectory" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "862a78ef", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -129,25 +171,23 @@ "id": "d87e75ab", "metadata": {}, "source": [ - "# Criterion values of each model" + "## Criterion values of each model" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "bce41584", "metadata": {}, "outputs": [ { "data": { - "image/png": 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6JKmLxhL8jddV1SNjPL4kdZJDPZLUMeMK/gK+muS2JMvHVIMkddK4hnoOrqqHkuwC3JDk3qq6eeoGzRvCcoClS5eOo0ZJWpTGcsZfVQ81f9cCVwL7z7DN+VU1WVWTExMToy5RkhatkQd/kl9Lst3658AbgDtHXYckddU4hnp2Ba5Msv74f1lV14+hDknqpJEHf1X9EHj1qI8rSerxck5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOmYswZ/kiCTfT3J/kjPGUYMkddXIgz/JFsBngCOBvYHjk+w96jokqavGcca/P3B/Vf2wqn4BXAa8dQx1SFInjSP4dwf+Ycry6qZNkjQCqarRHjA5Bjiiqt7bLJ8AHFBVp07bbjmwvFl8JfD9kRY6fzsDj4y7iDGx793V5f5vDn1/aVVNTG/ccgyFPATsMWV5SdO2gao6Hzh/VEU9X0lWVNXkuOsYB/vezb5Dt/u/Ofd9HEM93wH2TPKyJC8EjgOuGUMdktRJIz/jr6pnkpwKfAXYAriwqu4adR2S1FXjGOqhqq4DrhvHsVu02QxLtcC+d1eX+7/Z9n3kX+5KksbLKRskqWMMfknqGIO/kaSSfH7K8pZJ1iW5do59kuTcZs6hVUn+5ZR1JyW5r3mcNKV9vyR3NPucmyTt9WpwI+z/x5L8Q5Kn2uvN/LTQ9+uTPD59/ySnNttXkp3b6c38LLDveyX5ZpKnk/zhtHUzzsO1KfYdWun/hUnWJrlzWvvbk9yV5LkkY78E1OD/lZ8Av5lkm2b5cGb4fcE0RwJ7No/lwHkASXYEPgocQG+Kio8m2aHZ5zzg96fsd8QQ+/B8jKr/f920bUqG1vfGx4ETZtjn74HXAw8+r2qHayF9fxQ4DfjE1MY+83Btin2HIfa/cREz/5++E/g94OaFlTlcBv+GrgPe1Dw/Hri0z/ZvBS6pnm8B2yfZDfgd4IaqerSqHgNuAI5o1r24qr5VvW/VLwGOaqMjC9Rq/wGavq9pp/znZVh9p6puBJ6cvkNV3V5VDwyv5KGZV9+ram1VfQf45bRVs87DtQn3HYbXf6rqZnpvDNPb76mqTWb2AYN/Q5cBxyXZGngVcGuf7Webd2iu9tUztG8q2u7/pmxYfd8czbfvs9lc/02G1f/NhsE/RVWtApbRe9dfbL8z6KvL/bfv3ew7dLP/Bv/GrqE3dtfvoz7MPu/QXO1LZmjflLTZ/03dMPq+uZpP32ezOf+bDKP/mw2Df2MXAmdX1R0DbHsNcGJzhceBwI+b8euvAG9IskPzpeYbgK80655IcmBzNc+JwNUt9WOhWut/eyUPzTD6vrmaT99nsznPwzWM/m8+qspH79fLT83Qdghw7Rz7hN5VDD8A7gAmp6x7D3B/83j3lPZJet/w/wD4c5pfT4/7McL+/2d6Y7/PNX/PWoR9/wawDvhZ08ffadpPa5afAf4P8LnNtO+/3vTjCeDx5vmLm3VvBP5X8+/ykSn7bHJ9b6n/lwJr6H3xuxo4uWk/ull+GniY3ong2PrtlA2S1DEO9UhSx4xlds7NTZJ3A6dPa/77qjplHPWMWpf7b9+72XdY3P13qEeSOsahHknqGINfkjrG4NeisJBZFmd5nQf6zR452zYD7juvWUmTnDV9Bkjp+TL4tVgsZJZFqZMMfi0ms86ymGTHJFc1c+d/K8mrmvadkny1mSv9c/R+mLV+n3cl+XaSlUn+WzPt8ECaY93WvO7yaevOadpvTDLRtL08vXn8b0vyjSR7zfCapyW5u+nDZfP5h5GmMvi1mMw1y+LZwO1V9Srgw/SmxIbefQNuqarfAK4ElgIk+efAvwYOqqp9gGeBd86jlvdU1X70fql9WpKdmvZfA1Y0x/u75vjQu3H3HzT7/CHwFzO85hnAvk0f3j+PWqQNeB2/Fo2qWpVkGTPPsngw8LZmu681Z/ovBn6b3g0yqKq/SfJYs/1hwH7Ad3rTKrENsHYe5ZyW5Ojm+R70btjyf+lNVXF50/554MtJtgX+FfDF/OqGbC+a4TVXAV9IchVw1TxqkTZg8GuxWT/L4iHATnNvOqcAF1fVmfPeMTmE3t2mXltVP03ydWDrWTYvep+8H28+WczlTfTeqN4CfCTJv6iqZ+Zbn+RQjxab2WZZ/AbNUE0TzI9U1RP0boX3jqb9SGD9LSJvBI5JskuzbsckLx2whpcAjzWhvxdw4JR1LwCOaZ6/g94w0xPAj5K8vTlWkrx66gsmeQGwR1XdBHyoOca2A9YjbcAzfi0qVbUaOHeGVWcBFyZZBfwUWH8D+LOBS5PcBfxP4H83r3N3kj8BvtqE7i+BUxjsnrHXA+9Pcg/wfeBbU9b9BNi/ee219L5HgN6b0nlN+1b0vq/43pT9tgA+n+Ql9D6NnFtVjw9Qi7QRp2yQpI5xqEeSOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6pj/B1HW/mp+9bmJAAAAAElFTkSuQmCC\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -165,25 +205,23 @@ "id": "d9c2d487", "metadata": {}, "source": [ - "# Criterion values vs. number of estimated parameters" + "## Criterion values vs. number of estimated parameters" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "824e2e6a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -201,20 +239,22 @@ "id": "8dc7e142", "metadata": {}, "source": [ - "# Iterations as layers in a hierarchical graph" + "## History as layers in a hierarchical graph\n", + "\n", + "N.B.: this may give a misleading impression of the models calibrated in each iteration, since it's only based on \"predecessor model\" relationships. In this example, each layer is an iteration." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "5ce191fc", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -222,28 +262,32 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.rcParams[\"text.usetex\"] = True\n", + "# # Customize the colors\n", + "# criterion_values = [model.get_criterion(petab_select.Criterion.AICC) for model in models]\n", + "# norm = matplotlib.colors.Normalize(\n", + "# vmin=min(criterion_values),\n", + "# vmax=max(criterion_values),\n", + "# )\n", + "# cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\"green\",\"lightgreen\"])\n", + "# colorbar_mappable = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)\n", "\n", "petab_select.plot.graph_iteration_layers(\n", " models=models,\n", " criterion=petab_select.Criterion.AICC,\n", " labels=labels,\n", - " colors=colors,\n", " draw_networkx_kwargs={\n", " \"arrowstyle\": \"-|>\",\n", " \"node_shape\": \"s\",\n", " \"node_size\": 2500,\n", - " \"font_size\": 18,\n", " },\n", + " # colorbar_mappable=colorbar_mappable,\n", ");" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -257,7 +301,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.11.1" } }, "nbformat": 4, diff --git a/petab_select/plot.py b/petab_select/plot.py index 3381031a..f64100d2 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -1,17 +1,20 @@ -"""Visualization routines for model selection with pyPESTO.""" +"""Visualization routines for model selection.""" +import warnings from typing import Any +import matplotlib import matplotlib.cm import matplotlib.colors import matplotlib.pyplot as plt import networkx as nx import numpy as np +import upsetplot from more_itertools import one from toposort import toposort from .constants import VIRTUAL_INITIAL_MODEL, Criterion -from .model import Model +from .model import Model, ModelHash RELATIVE_LABEL_FONTSIZE = -2 NORMAL_NODE_COLOR = "darkgrey" @@ -68,6 +71,45 @@ def get_relative_criterion_values( return [v - value0 for v in criterion_values] +def upset( + models: list[Model], criterion: Criterion +) -> dict[str, matplotlib.axes.Axes | None]: + """Plot an UpSet plot of estimated parameters and criterion. + + Args: + models: + The models. + criterion: + The criterion. + + Returns: + The plot axes (see documentation from the `upsetplot` package). + """ + # Get delta criterion values + values = np.array( + [model.get_criterion(Criterion.AICC) for model in models] + ) + values -= values.min() + + # Sort by criterion value + index = np.argsort(values) + values = values[index] + labels = [models[i].get_estimated_parameter_ids_all() for i in index] + + with warnings.catch_warnings(): + # TODO remove warnings context manager when fixed in upsetplot package + warnings.simplefilter(action="ignore", category=FutureWarning) + series = upsetplot.from_memberships( + labels, + data=values, + ) + axes = upsetplot.plot( + series, totals_plot_elements=0, with_lines=False, sort_by="input" + ) + axes["intersections"].set_ylabel(r"$\Delta$" + criterion) + return axes + + def line_selected( models: list[Model], criterion: Criterion, @@ -79,32 +121,29 @@ def line_selected( ) -> plt.Axes: """Plot criterion for calibrated models. - Parameters - ---------- - models: - A list of all models. The selected models will be inferred from the - best model and its chain of predecessor model hashes. - criterion: - The criterion by which models are selected. - relative: - If `True`, criterion values are plotted relative to the lowest - criterion value. TODO is the lowest value, always the best? May not - be for different criterion. - fz: - fontsize - size: - Figure size in inches. - labels: - A dictionary of model labels, where keys are model hashes, and - values are model labels, for plotting. If a model label is not - provided, it will be generated from its model ID. - ax: - The axis to use for plotting. - - Returns - ------- - matplotlib.pyplot.Axes - The plot axis. + Args: + models: + A list of all models. The selected models will be inferred from the + best model and its chain of predecessor model hashes. + criterion: + The criterion by which models are selected. + relative: + If `True`, criterion values are plotted relative to the lowest + criterion value. TODO is the lowest value, always the best? May not + be for different criterion. + fz: + fontsize + size: + Figure size in inches. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + ax: + The axis to use for plotting. + + Returns: + The plot axes. """ models = get_selected_models(models=models, criterion=criterion) @@ -169,32 +208,29 @@ def graph_history( TODO replace magic numbers with options/constants - Parameters - ---------- - models: - A list of models. - criterion: - The criterion. - optimal_distance: - See docs for argument `k` in `networkx.spring_layout`. - relative: - If `True`, criterion values are offset by the minimum criterion - value. - options: - Additional keyword arguments for `networkx.draw_networkx`. - labels: - A dictionary of model labels, where keys are model hashes, and - values are model labels, for plotting. If a model label is not - provided, it will be generated from its model ID. - colors: - Colors for each model, using their labels. - ax: - The axis to use for plotting. - - Returns - ------- - matplotlib.pyplot.Axes - The plot axis. + Args: + models: + A list of models. + criterion: + The criterion. + optimal_distance: + See docs for argument `k` in `networkx.spring_layout`. + relative: + If `True`, criterion values are offset by the minimum criterion + value. + options: + Additional keyword arguments for `networkx.draw_networkx`. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + colors: + Colors for each model, using their labels. + ax: + The axis to use for plotting. + + Returns: + The plot axes. """ model_hashes = get_model_hashes(models) @@ -283,28 +319,25 @@ def bar_criterion_vs_models( ) -> plt.Axes: """Plot all calibrated models and their criterion value. - Parameters - ---------- - models: - A list of models. - criterion: - The criterion. - labels: - A dictionary of model labels, where keys are model hashes, and - values are model labels, for plotting. If a model label is not - provided, it will be generated from its model ID. - relative: - If `True`, criterion values are offset by the minimum criterion - value. - ax: - The axis to use for plotting. - bar_kwargs: - Passed to the matplotlib `ax.bar` call. - - Returns - ------- - matplotlib.pyplot.Axes - The plot axis. + Args: + models: + A list of models. + criterion: + The criterion. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + relative: + If `True`, criterion values are offset by the minimum criterion + value. + ax: + The axis to use for plotting. + bar_kwargs: + Passed to the matplotlib `ax.bar` call. + + Returns: + The plot axes. """ model_hashes = get_model_hashes(models) @@ -361,30 +394,26 @@ def scatter_criterion_vs_n_estimated( ) -> plt.Axes: """Plot criterion values against number of estimated parameters. - Parameters - ---------- - models: - A list of models. - criterion: - The criterion. - ax: - The axis to use for plotting. - relative: - If `True`, criterion values are offset by the minimum criterion - value. - - Returns - ------- - matplotlib.pyplot.Axes - The plot axis. + Args: + models: + A list of models. + criterion: + The criterion. + ax: + The axis to use for plotting. + relative: + If `True`, criterion values are offset by the minimum criterion + value. + + Returns: + The plot axes. """ model_hashes = get_model_hashes(models) - if labels is None: - labels = { - model_hash: model.model_id - for model_hash, model in model_hashes.items() - } + labels = { + model_hash: labels.get(model.model_id, model.model_id) + for model_hash, model in model_hashes.items() + } if scatter_kwargs is None: scatter_kwargs = {} @@ -427,7 +456,7 @@ def scatter_criterion_vs_n_estimated( def graph_iteration_layers( models: list[Model], - criterion: Criterion | None = None, + criterion: Criterion, labels: dict[str, str] = None, relative: bool = True, ax: plt.Axes = None, @@ -435,52 +464,41 @@ def graph_iteration_layers( # colors: Dict[str, str] = None, parameter_labels: dict[str, str] = None, augment_labels: bool = True, - use_tex: bool = True, + colorbar_mappable: matplotlib.cm.ScalarMappable = None, + # use_tex: bool = True, ) -> plt.Axes: """Graph the models of each iteration of model selection. - Parameters - ---------- - models: - A list of models. - criterion: - The criterion. - labels: - A dictionary of model labels, where keys are model hashes, and - values are model labels, for plotting. If a model label is not - provided, it will be generated from its model ID. - relative: - If `True`, criterion values are offset by the minimum criterion - value. - ax: - The axis to use for plotting. - draw_networkx_kwargs: - Passed to the `networkx.draw_networkx` call. - parameter_labels: - A dictionary of parameter labels, where keys are parameter IDs, and - values are parameter labels, for plotting. Defaults to parameter IDs. - augment_labels: - If ``True'', provided labels will have estimated parameters and - relative criterion values added to them, for plotting. - - Returns - ------- - matplotlib.pyplot.Axes - The plot axis. + Args: + models: + A list of models. + criterion: + The criterion. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + relative: + If `True`, criterion values are offset by the minimum criterion + value. + ax: + The axis to use for plotting. + draw_networkx_kwargs: + Passed to the `networkx.draw_networkx` call. + parameter_labels: + A dictionary of parameter labels, where keys are parameter IDs, and + values are parameter labels, for plotting. Defaults to parameter IDs. + augment_labels: + If ``True'', provided labels will be augmented with the relative + change in parameters. + colorbar_mappable: + Customize the colors. + See documentation for the `mappable` argument of + `matplotlib.pyplot.colorbar`. + + Returns: + The plot axes. """ - if use_tex: - rcParams0 = dict(plt.rcParams) - rcParams = { - "text.usetex": True, - #'text.latex': { - # 'preamble': r'\usepackage{color,xcolor}', - # }, - "pgf.rcfonts": False, - "pgf.preamble": r"\usepackage{color}", - } - plt.rcParams.update(rcParams) - # for rcParam, rcParamValues in rcParams.items(): - # plt.rc(rcParam, **rcParamValues) if ax is None: _, ax = plt.subplots(figsize=(20, 10)) @@ -506,17 +524,14 @@ def graph_iteration_layers( model.get_hash(): set(model.estimated_parameters) for model in models } model_criterion_values = None - if criterion is not None: - model_criterion_values = { - model.get_hash(): model.get_criterion(criterion) - for model in models - } + model_criterion_values = { + model.get_hash(): model.get_criterion(criterion) for model in models + } - min_criterion_value = min(model_criterion_values.values()) - model_criterion_values = { - k: v - min_criterion_value - for k, v in model_criterion_values.items() - } + min_criterion_value = min(model_criterion_values.values()) + model_criterion_values = { + k: v - min_criterion_value for k, v in model_criterion_values.items() + } model_parameter_diffs = { model.get_hash(): ( @@ -534,8 +549,22 @@ def graph_iteration_layers( for model in models } - if labels is None: - labels = {model.get_hash(): model.model_id for model in models} + labels = labels or {} + labels = ( + labels + | { + model.get_hash(): model.model_id + for model in models + if model.get_hash() not in labels + } + | { + ModelHash.from_hash( + model.predecessor_model_hash + ): model.predecessor_model_hash + for model in models + if ModelHash.from_hash(model.predecessor_model_hash) not in labels + } + ) if augment_labels: class Identidict(dict): @@ -549,7 +578,7 @@ def __getitem__(self, key): parameter_labels = Identidict() model_added_parameters = { - model_hash: ";".join( + model_hash: ",".join( [ parameter_labels[parameter_id] for parameter_id in sorted( @@ -560,7 +589,7 @@ def __getitem__(self, key): for model_hash in model_estimated_parameters } model_removed_parameters = { - model_hash: ";".join( + model_hash: ",".join( [ parameter_labels[parameter_id] for parameter_id in sorted( @@ -574,42 +603,33 @@ def __getitem__(self, key): labels = { model_hash: ( label0 - # + ( - # f'\n{model_criterion_values[model_hash]:.2f}' - # if model_hash in model_criterion_values - # else '' - # ) - + ( - ( - # f'\n' + - r"\textcolor{green}{hi}" - # + ('\\textcolor{green}{' if use_tex else '') + - # f'{model_added_parameters[model_hash]}' - # + ('}' if use_tex else '') - if model_added_parameters.get(model_hash, "") - else "" - ) - + ( - # f'\n' + - r"\textcolor{red}{hi}" - # + ('\\textcolor{red}{' if use_tex else '') + - # f'{model_removed_parameters[model_hash]}' - # + ('}' if use_tex else '') - if model_removed_parameters.get(model_hash, "") - else "" - ) + if model_hash == ModelHash.from_hash(VIRTUAL_INITIAL_MODEL) + else "\n".join( + [ + label0, + "+ {" + + model_added_parameters.get(model_hash, "") + + "}", + "- {" + + model_removed_parameters.get(model_hash, "") + + "}", + ] ) ) for model_hash, label0 in labels.items() } - norm = matplotlib.colors.Normalize( - vmin=min(model_criterion_values.values()), - vmax=max(model_criterion_values.values()), - ) - cmap = matplotlib.cm.get_cmap("cool") - scalar_mappable = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap) - ax.get_figure().colorbar(scalar_mappable, ax=ax) + if colorbar_mappable is None: + norm = matplotlib.colors.Normalize( + vmin=min(model_criterion_values.values()), + vmax=max(model_criterion_values.values()), + ) + cmap = matplotlib.colors.LinearSegmentedColormap.from_list( + "", ["green", "white"] + ) + colorbar_mappable = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap) + cbar = ax.get_figure().colorbar(colorbar_mappable, ax=ax) + cbar.ax.set_title(r"$\Delta$" + criterion) G = nx.DiGraph( [ @@ -631,36 +651,6 @@ def __getitem__(self, key): for layer in ordering ] - # Labels - # if labels is None: - # labels = { - # model.get_hash(): model.model_id - # + ( - # f'\n{model.get_criterion(criterion):.2f}' - # if criterion is not None - # else '' - # ) - # for model in models - # } - labels[VIRTUAL_INITIAL_MODEL] = labels.get( - VIRTUAL_INITIAL_MODEL, "Virtual\nInitial\nModel" - ) - - missing_labels = { - model.get_hash(): model.model_id - for model in models - if model.get_hash() not in labels - } - missing_labels2 = { - model.predecessor_model_hash: model.predecessor_model_hash - for model in models - if model.predecessor_model_hash not in labels - } - labels.update(missing_labels2) - labels.update(missing_labels) - # for label in missing_labels: - # labels[label] = label - pos = { labels[model_hash]: (X[i], Y[i][j]) for i, layer in enumerate(ordering) @@ -673,30 +663,13 @@ def __getitem__(self, key): ] node_colors = [ ( - scalar_mappable.to_rgba(model_criterion_values[model_hash]) + colorbar_mappable.to_rgba(model_criterion_values[model_hash]) if model_hash in model_criterion_values else NORMAL_NODE_COLOR ) for model_hash in G_hashes ] - # if colors is not None: - # if label_diff := set(colors).difference(list(G)): - # raise ValueError( - # "Colors were provided for the following model labels, but " - # "these are not in the graph: {label_diff}" - # ) - - # node_colors = [ - # colors.get( - # model_label, - # draw_networkx_kwargs.get( - # 'node_color', default_draw_networkx_kwargs['node_color'] - # ), - # ) - # for model_label in list(G) - # ] - # draw_networkx_kwargs.update({'node_color': node_colors}) nx.draw_networkx( G, pos, ax=ax, node_color=node_colors, **draw_networkx_kwargs ) @@ -708,6 +681,7 @@ def __getitem__(self, key): f"n={n}", xy=(x, 1.1), fontsize=draw_networkx_kwargs.get("font_size", 20), + ha="center", ) ## Get selected parameter IDs @@ -783,7 +757,4 @@ def __getitem__(self, key): # Set margins for the axes so that nodes aren't clipped ax.margins(0.15) ax.axis("off") - - # FIXME - plt.rcParams.update(rcParams0) return ax diff --git a/pyproject.toml b/pyproject.toml index c4cf16c5..cac8e043 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,7 @@ dependencies = [ "dill", # TODO: move to [vis]? not required for cli-only usage?! "matplotlib>=2.2.3", + "upsetplot", "toposort", "networkx", ] From 14adba0442610a12450ad65b89b667d187ed3598 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 18 Nov 2024 17:28:40 +0100 Subject: [PATCH 15/26] add viz notebook to rtd --- doc/examples.rst | 1 + petab_select/plot.py | 13 +++++++++++-- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/doc/examples.rst b/doc/examples.rst index 4147a47a..2c6c6aa3 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -7,5 +7,6 @@ Various example notebooks. :maxdepth: 1 examples/example_cli_famos.ipynb + examples/visualization.ipynb examples/workflow_cli.ipynb examples/workflow_python.ipynb diff --git a/petab_select/plot.py b/petab_select/plot.py index f64100d2..2a9115c2 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -20,6 +20,16 @@ NORMAL_NODE_COLOR = "darkgrey" +__all__ = [ + "bar_criterion_vs_models", + "graph_history", + "graph_iteration_layers", + "line_selected", + "scatter_criterion_vs_n_estimated", + "upset", +] + + def default_label_maker(model: Model) -> str: """Create a model label, for plotting.""" return model.model_hash[:4] @@ -86,10 +96,9 @@ def upset( The plot axes (see documentation from the `upsetplot` package). """ # Get delta criterion values - values = np.array( + values = get_relative_criterion_values( [model.get_criterion(Criterion.AICC) for model in models] ) - values -= values.min() # Sort by criterion value index = np.argsort(values) From 874c4fde663c8e6a7b44c3d8dd04785e78a54617 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 18 Nov 2024 17:44:38 +0100 Subject: [PATCH 16/26] unfix doc depedencies --- pyproject.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index cac8e043..834a4c19 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,8 +48,9 @@ doc = [ # pin until ubuntu comes with newer pandoc: # /home/docs/checkouts/readthedocs.org/user_builds/petab-select/envs/63/lib/python3.11/site-packages/nbsphinx/__init__.py:1058: RuntimeWarning: You are using an unsupported version of pandoc (2.9.2.1). # Your version must be at least (2.14.2) but less than (4.0.0). - "nbsphinx==0.9.1", - "nbconvert<7.5.0", + "nbsphinx>=0.9.5", + "pandoc>=2.4", + "nbconvert>=7.16.4", "ipython>=7.21.0", "readthedocs-sphinx-ext>=2.2.5", "sphinx-autodoc-typehints", From 35c53e0d4382b44938f65cf97b4090444966fc1a Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 17:59:48 +0100 Subject: [PATCH 17/26] fix docs --- doc/index.rst | 6 +++--- petab_select/plot.py | 8 ++++---- pyproject.toml | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/doc/index.rst b/doc/index.rst index 8cefeba6..697bcba6 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -33,11 +33,11 @@ Model calibration is performed outside of PEtab Select. For example, PEtab Select is well-integrated with: * `BasiCO `_ - (`example `_) + (`example `__) * `PEtab.jl `_ - (`example `_) + (`example `__) * `pyPESTO `_ - (`example `_) + (`example `__) Other model calibration tools can easily be integrated using the provided Python library or command line interface. diff --git a/petab_select/plot.py b/petab_select/plot.py index 2a9115c2..06f3fb44 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -488,22 +488,22 @@ def graph_iteration_layers( values are model labels, for plotting. If a model label is not provided, it will be generated from its model ID. relative: - If `True`, criterion values are offset by the minimum criterion + If ``True``, criterion values are offset by the minimum criterion value. ax: The axis to use for plotting. draw_networkx_kwargs: - Passed to the `networkx.draw_networkx` call. + Passed to the ``networkx.draw_networkx`` call. parameter_labels: A dictionary of parameter labels, where keys are parameter IDs, and values are parameter labels, for plotting. Defaults to parameter IDs. augment_labels: - If ``True'', provided labels will be augmented with the relative + If ``True``, provided labels will be augmented with the relative change in parameters. colorbar_mappable: Customize the colors. See documentation for the `mappable` argument of - `matplotlib.pyplot.colorbar`. + ``matplotlib.pyplot.colorbar``. Returns: The plot axes. diff --git a/pyproject.toml b/pyproject.toml index 834a4c19..f1b1284b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,7 +40,7 @@ test = [ "tox >= 3.12.4", ] doc = [ - "sphinx>=3.5.3,<7", + "sphinx>=8.1.3", "sphinxcontrib-napoleon>=0.7", "sphinx-markdown-tables>=0.0.15", "sphinx-rtd-theme>=0.5.1", From 4ab2f8287a5e06bdaa6b47674361e36fa575f0b2 Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 18:14:44 +0100 Subject: [PATCH 18/26] closes #104 --- petab_select/ui.py | 18 ++---------------- pyproject.toml | 1 - 2 files changed, 2 insertions(+), 17 deletions(-) diff --git a/petab_select/ui.py b/petab_select/ui.py index 2fb73132..f5ed1f10 100644 --- a/petab_select/ui.py +++ b/petab_select/ui.py @@ -8,7 +8,6 @@ from .candidate_space import CandidateSpace, FamosCandidateSpace from .constants import ( CANDIDATE_SPACE, - INITIAL_MODEL_METHODS, MODELS, PREDECESSOR_MODEL, TERMINATE, @@ -76,12 +75,8 @@ def start_iteration( `dict`, the keys are model hashes. If a model in the candidates has the same hash as a model in `user_calibrated_models`, then the candidate will be replaced with - the calibrated version. This calibrated model will still exist in - the candidate space, so calibration tools should take care to skip - calibration of already-calibrated models. This is so that users - can still see that this model was "visited" during model selection. - If you do not want this, and want the model to be automatically - skipped, supply it in `calibrated_models` instead. + the calibrated version. Calibration tools will only receive uncalibrated + models from this method. Returns: A dictionary, with the following items: @@ -174,15 +169,6 @@ def start_iteration( ): return get_iteration(candidate_space=candidate_space) - if ( - predecessor_model is None - and candidate_space.method in INITIAL_MODEL_METHODS - and calibrated_models - ): - predecessor_model = problem.get_best( - models=calibrated_models.values(), - criterion=criterion, - ) if predecessor_model is not None: candidate_space.reset(predecessor_model) diff --git a/pyproject.toml b/pyproject.toml index f1b1284b..dd9a5636 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -71,7 +71,6 @@ lint.ignore = [ "S101", # Use of assert detected "E501", # Line too long "F403", # star import - "F821", # undefined name "T201", # print statement "S301", # pickle module used "S102", # Use of exec detected From d08cefe71b29c3a98376ed619babad986da48ab8 Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 18:23:40 +0100 Subject: [PATCH 19/26] use actual criterion --- petab_select/plot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/petab_select/plot.py b/petab_select/plot.py index 06f3fb44..414927d6 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -97,7 +97,7 @@ def upset( """ # Get delta criterion values values = get_relative_criterion_values( - [model.get_criterion(Criterion.AICC) for model in models] + [model.get_criterion(criterion) for model in models] ) # Sort by criterion value From 89ab673fe29a861d4d097d64d0ff93ebc698f06c Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 19:50:56 +0100 Subject: [PATCH 20/26] review --- doc/examples/visualization.ipynb | 43 ++++++++++++++++++++++---------- petab_select/plot.py | 24 ++++++++++++------ 2 files changed, 46 insertions(+), 21 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index f9486ceb..a917bf03 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -75,7 +75,9 @@ "source": [ "## UpSet plot\n", "\n", - "Category membership is whether the parameter is estimated." + "This shows models ordered by criterion, with their parameters directly below the bars.\n", + "\n", + "A black dot indicates that the parameter (e.g `k2`) is estimated in the model (e.g. the first bar is a model with `k1` and `sigma_x2` estimated)." ] }, { @@ -86,7 +88,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -104,7 +106,11 @@ "id": "32de6556", "metadata": {}, "source": [ - "## Selected models" + "## Selected models\n", + "\n", + "This shows strict improvements in the criterion, and the corresponding model, across all iterations of model selection.\n", + "\n", + "Since there were no improvements after `M_100`, no other iterations or models are shown." ] }, { @@ -115,7 +121,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -137,7 +143,9 @@ "id": "8e5243e0", "metadata": {}, "source": [ - "## Selection history trajectory" + "## Selection history trajectory\n", + "\n", + "This shows the relationship between models across iterations. For example, `M_000` was the predecessor model to `M_001`, `M_010`, and `M_100`." ] }, { @@ -148,7 +156,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -171,7 +179,9 @@ "id": "d87e75ab", "metadata": {}, "source": [ - "## Criterion values of each model" + "## Criterion values of each model\n", + "\n", + "This shows the criterion value of every calibrated model." ] }, { @@ -182,7 +192,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -205,7 +215,11 @@ "id": "d9c2d487", "metadata": {}, "source": [ - "## Criterion values vs. number of estimated parameters" + "## Criterion values vs. number of estimated parameters\n", + "\n", + "This shows all calibrated models.\n", + "\n", + "In this example, models with 2 estimated parameters tend to perform best. This is also seen in the UpSet plot above." ] }, { @@ -216,7 +230,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -241,7 +255,9 @@ "source": [ "## History as layers in a hierarchical graph\n", "\n", - "N.B.: this may give a misleading impression of the models calibrated in each iteration, since it's only based on \"predecessor model\" relationships. In this example, each layer is an iteration." + "This shows the relative change in parameters of each model, compared to its predecessor model.\n", + "\n", + "N.B.: this may give a misleading impression of the models calibrated in each iteration, since it's only based on \"predecessor model\" relationships. In this example, each layer is indeed an iteration." ] }, { @@ -252,7 +268,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -279,6 +295,7 @@ " \"arrowstyle\": \"-|>\",\n", " \"node_shape\": \"s\",\n", " \"node_size\": 2500,\n", + " \"edgecolors\": \"k\",\n", " },\n", " # colorbar_mappable=colorbar_mappable,\n", ");" @@ -301,7 +318,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.1" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/petab_select/plot.py b/petab_select/plot.py index 414927d6..e2dd25da 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -7,6 +7,7 @@ import matplotlib.cm import matplotlib.colors import matplotlib.pyplot as plt +import matplotlib.ticker import networkx as nx import numpy as np import upsetplot @@ -96,8 +97,10 @@ def upset( The plot axes (see documentation from the `upsetplot` package). """ # Get delta criterion values - values = get_relative_criterion_values( - [model.get_criterion(criterion) for model in models] + values = np.array( + get_relative_criterion_values( + [model.get_criterion(criterion) for model in models] + ) ) # Sort by criterion value @@ -128,7 +131,7 @@ def line_selected( labels: dict[str, str] = None, ax: plt.Axes = None, ) -> plt.Axes: - """Plot criterion for calibrated models. + """Plot the improvement in criterion across iterations. Args: models: @@ -180,14 +183,16 @@ def line_selected( criterion_values.values(), linewidth=linewidth, color=NORMAL_NODE_COLOR, + marker="x", + markersize=10, + markeredgewidth=2, + markeredgecolor="red", # edgecolor='k' ) ax.get_xticks() ax.set_xticks(list(range(len(criterion_values)))) - ax.set_ylabel( - criterion + (" (relative)" if relative else " (absolute)"), fontsize=fz - ) + ax.set_ylabel((r"$\Delta$" if relative else "") + criterion, fontsize=fz) # could change to compared_model_ids, if all models are plotted ax.set_xticklabels( criterion_values.keys(), @@ -386,7 +391,7 @@ def bar_criterion_vs_models( ax.bar(criterion_values.keys(), criterion_values.values(), **bar_kwargs) ax.set_xlabel("Model labels") ax.set_ylabel( - criterion.value + (" (relative)" if relative else " (absolute)") + (r"$\Delta$" if relative else "") + criterion, ) return ax @@ -455,9 +460,11 @@ def scatter_criterion_vs_n_estimated( **scatter_kwargs, ) + ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=True)) + ax.set_xlabel("Number of estimated parameters") ax.set_ylabel( - criterion.value + (" (relative)" if relative else " (absolute)") + (r"$\Delta$" if relative else "") + criterion, ) return ax @@ -519,6 +526,7 @@ def graph_iteration_layers( "arrowstyle": "-|>", "node_shape": "s", "node_size": 250, + "edgecolors": "k", } if draw_networkx_kwargs is None: draw_networkx_kwargs = default_draw_networkx_kwargs From f33f1580377a8b18a2474bfa877a5e4051583839 Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 22:39:03 +0100 Subject: [PATCH 21/26] move vis deps to optional dependencies --- pyproject.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index dd9a5636..fb333fce 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,13 +24,14 @@ dependencies = [ "pyyaml", "click", "dill", - # TODO: move to [vis]? not required for cli-only usage?! +] +[project.optional-dependencies] +plot = [ "matplotlib>=2.2.3", "upsetplot", "toposort", "networkx", ] -[project.optional-dependencies] test = [ "pytest >= 5.4.3", "pytest-cov >= 2.10.0", From cbfa41565210de5bef1e857b0ed0b4948174417f Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 22:40:16 +0100 Subject: [PATCH 22/26] jitter scatter --- doc/examples/visualization.ipynb | 14 ++++++++++---- petab_select/plot.py | 29 +++++++++++++++++++++++++++-- 2 files changed, 37 insertions(+), 6 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index a917bf03..0d6ffd3d 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -15,6 +15,8 @@ "source": [ "The visualization methods implemented in PEtab Select are demonstrated here. These methods generally visualize the output of a model selection task, so the input is generally a list of already-calibrated models.\n", "\n", + "All dependencies for these plots can be installed with `pip install petab_select[plot]`.\n", + "\n", "Here, some calibrated models that were saved to disk with `petab_select.model.models_to_yaml_list` are loaded and used as input. This is the result of a forward selection with the problem provided in `calibrated_models`." ] }, @@ -156,7 +158,7 @@ "outputs": [ { "data": { - "image/png": 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", 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qVKmiqVOn6ptvvtGqVatMxwEAAADKlc3tdruv9VBmZqYiIyOVkZGhiIiIysgFoJwVFRWpW7duCggI0OrVq2Wz2UxHAgAAAK6oLD2UN7aAj/Dz89Nbb72ltWvX6uuvvzYdBwAAACg3FFvAh/Tr10/x8fF66aWXlJeXZzoOAAAAUC4otoCPmT59uvbt26ePP/7YdBQAAACgXFBsAR/Trl07Pfjgg5o0aZIyMzNNxwEAAABuGMUW8EGTJk3SuXPn9Oabb5qOAgAAANwwii3ggxo2bKjf//73+p//+R8dOXLEdBwAAADghlBsAR/1wgsvKDQ0VBMnTjQdBQAAALghFFvAR0VGRuqVV17RnDlztH37dtNxAAAAgOtGsQV82GOPPaZmzZpp7NixpqMAAAAA141iC/iwoKAgTZs2TQsXLtSyZctMxwEAAACui83tdruv9VBmZqYiIyOVkZGhiIiIysgFoJK43W7FxsaqsLBQ69atk58ff98FAAAA88rSQ/kJFvBxNptNb731ljZu3Ki//e1vpuMAAAAAZUaxBaDevXvrtttu07hx43ThwgXTcQAAAIAyodgCkCS98cYbOnTokD788EPTUQAAAIAyodgCkCS1adNGv/nNb/Taa6/p7NmzpuMAAAAApUaxBVDs1Vdf1YULF/TGG2+YjgIAAACUGsUWQLF69erpv//7v/Xuu+/q0KFDpuMAAAAApUKxBVDCf//3fysyMlITJkwwHQUAAAAoFYotgBLCw8P16quv6i9/+YtSU1NNxwEAAACuiWIL4BK/+c1vFBUVpbFjx5qOAgAAAFwTxRbAJQIDA/XGG28oMTFRiYmJpuMAAAAAV2Vzu93uaz2UmZmpyMhIZWRkKCIiojJyATDM7XarV69eys7O1qZNm+Tnx9+DAQAAoPKUpYfykyqAy7LZbHrrrbe0ZcsW/e///q/pOAAAAMAVUWwBXFH37t115513avz48Tp//rzpOAAAAMBlUWwBXNXUqVN17Ngxvf/++6ajAAAAAJdFsQVwVa1atdJjjz2mKVOm6PTp06bjAAAAAJeg2AK4pldeeUWFhYWaOnWq6SgAAADAJSi2AK6pTp06Gjt2rGbOnKn9+/ebjgMAAACUQLEFUCq///3vVbNmTb388sumowAAAAAlUGwBlEpoaKgmT56szz//XBs3bjQdBwAAAChGsQVQag8++KDatm2r559/Xm6323QcAAAAQBLFFkAZBAQEaPr06Vq+fLkWL15sOg4AAAAgSbK5S/HaJTMzU5GRkcrIyFBERERl5ALgodxut/r166fTp08rNTVV/v7+piMBAADAC5Wlh/LGFkCZ2Gw2vfnmm9q+fbs+++wz03EAAAAAii2AsuvWrZv+67/+S6+88opycnJMxwEAAICPo9gCuC5TpkzRiRMn9O6775qOAgAAAB9HsQVwXVq0aKEnn3xS06ZN08mTJ03HAQAAgA+j2AK4bi+//LJsNptee+0101EAAADgwyi2AK5brVq19NJLL2nWrFnas2eP6TgAAADwURRbADfkmWee0U033aRx48aZjgIAAAAfRbEFcENCQkL02muvaf78+Vq7dq3pOAAAAPBBNrfb7b7WQ2U5GBeA7yksLFSnTp1UrVo1rVy5UjabzXQkAAAAWFxZeihvbAHcMH9/f7355ptatWqV/vnPf5qOAwAAAB9DsQVQLhwOhwYMGKCxY8eqoKDAdBwAAAD4EIotgHJhs9n05ptvateuXfrzn/9sOg4AAAB8CMUWQLmJiYnRr3/9a02cOFHZ2dmm4wAAAMBHUGwBlKvXX39dZ86c0f/8z/+YjgIAAAAfQbEFUK6aNm2qp59+Wm+++aaOHz9uOg4AAAB8AMUWQLkbN26cAgMDNWnSJNNRAAAA4AMotgDKXY0aNTR+/Hh9/PHH+uGHH0zHAQAAgJej2AKoEGPGjFGDBg300ksvmY4CAAAAL0exBVAhgoODNWXKFP3jH/9QSkqK6TgAAADwYja32+2+1kOZmZmKjIxURkaGIiIiKiMXAC9QVFSkLl26KDg4WCkpKbLZbKYjAQAAwCLK0kN5Ywugwvj5+emtt97S6tWr9Y9//MN0HAAAAHgpii2ACjVgwAANHjxYL774ovLz803HAQAAgBei2AKocNOnT9eePXv0pz/9yXQUAAAAeCGKLYAK1759ez3wwAN69dVXlZWVZToOAAAAvAzFFkClmDx5srKysvTWW2+ZjgIAAAAvQ7EFUCkaNWqk3/3ud5oxY4aOHj1qOg4AAAC8CMUWQKV58cUXFRISoldffdV0FAAAAHgRii2AShMZGakJEybok08+0Y4dO0zHAQAAgJeg2AKoVE888YSaNm2qF1980XQUAAAAeAmKLYBKFRQUpKlTp+qf//ynVq5caToOAAAAvIDN7Xa7r/VQZmamIiMjlZGRoYiIiMrIBcCLFRUVKTY2VpK0du1a2Ww2w4kAAADgacrSQ3ljC6DS+fn56c0339T69es1f/5803EAAABgcRRbAEb07dtXw4YN00svvaS8vDzTcQAAAGBhFFsAxrzxxhs6cOCAPvroI9NRAAAAYGEUWwDG3HLLLXrooYc0efJkZWRkmI4DAAAAi6LYAjBq0qRJysnJ0fTp001HAQAAgEVRbAEYVb9+fT333HN65513dPjwYdNxAAAAYEEUWwDGPf/88woPD9crr7xiOgoAAAAsiGILwLiIiAhNnDhRn376qbZt22Y6DgAAACyGYgvAIzz66KNq2bKlXnjhBdNRAAAAYDEUWwAeITAwUNOmTdPixYu1ZMkS03EAAABgITa32+2+1kOZmZmKjIxURkaGIiIiKiMXAB/kdrvVo0cPXbhwQRs2bJCfH3/3BgAA4KvK0kP5qRGAx7DZbHrrrbe0efNmffHFF6bjAAAAwCIotgA8SlxcnIYPH67x48fr/PnzpuMAAADAAii2ADzOtGnTdPjwYX3wwQemowAAAMACKLYAPE7r1q31yCOP6PXXX9eZM2dMxwEAAICHo9gC8EgTJ05Ufn6+pk2bZjoKAAAAPBzFFoBHuummm/T888/rvffe04EDB0zHAQAAgAej2ALwWM8995xq1KihCRMmmI4CAAAAD0axBeCxwsLCNGnSJM2bN0+bN282HQcAAAAeimILwKM99NBDat26tZ5//nm53W7TcQAAAOCBKLYAPFpAQICmT5+upUuXKjEx0XQcAAAAeCCbuxSvQDIzMxUZGamMjAxFRERURi4AKOZ2u9WnTx9lZGRo06ZN8vf3Nx0JAAAAFawsPZQ3tgA8ns1m01tvvaWtW7dq3rx5puMAAADAw1BsAVjCrbfeqrvuuksvv/yycnNzTccBAACAB6HYArCMqVOn6t///rfee+8901EAAADgQSi2ACyjZcuWeuKJJzR16lSdOnXKdBwAAAB4CIotAEuZMGGC3G63pkyZYjoKAAAAPATFFoCl1K5dWy+++KI++OAD7du3z3QcAAAAeACKLQDL+d3vfqfatWtr/PjxpqMAAADAA1BsAVhO1apV9dprr+mvf/2r1q9fbzoOAAAADLO53W73tR4qy8G4AFAZCgsL1aFDB9WqVUvLly+XzWYzHQkAAADlqCw9lDe2ACzJ399fb775plauXKmFCxeajgMAAACDeGMLwLLcbrcGDBig48ePa8uWLQoICDAdCQAAAOWEN7YAfILNZtObb76pHTt26LPPPjMdBwAAAIZQbAFYWpcuXXTPPffolVde0blz50zHAQAAgAEUWwCWN2XKFJ08eVJ/+MMfTEcBAACAARRbAJbXrFkzjRkzRtOnT9eJEydMxwEAAEAlo9gC8Arjx4+Xn5+fXnvtNdNRAAAAUMkotgC8Qs2aNTVu3Dh99NFH2r17t+k4AAAAqEQUWwBe4+mnn1a9evU0btw401EAAABQiSi2ALxGSEiIXn/9dX311Vdas2aN6TgAAACoJDa32+2+1kNlORgXAEwqLCxU586dFR4eru+++042m810JAAAAFyHsvRQ3tgC8Cr+/v568803lZycrG+//dZ0HAAAAFQCii0Ar2O32zVo0CCNHTtWBQUFpuMAAACgglFsAXil6dOnKy0tTbNnzzYdBQAAABWMYgvAK3Xq1En33XefJk6cqKysLNNxAAAAUIEotgC81uuvv66MjAzNmDHDdBQAAABUIIotAK/VuHFjPfPMM3r77bd17Ngx03EAAABQQSi2ALzaSy+9pCpVqmjSpEmmowAAAKCCUGwBeLVq1arp5Zdf1uzZs7Vz507TcQAAAFABKLYAvN6TTz6pRo0a6eOPPzYdBQAAABUgwHQAAKhoVapU0XfffSd/f3/TUQAAAFABKLYAfEKjRo1MRwAAAEAFYSoyAAAAAMDSKLYAAAAAAEuj2AIAAAAALI1iCwAAAACwNIotAAAAAMDS2BUZgKXt3r1bWVlZpmN4lPDwcEVFRZmOAQAAUGkotgAsa/fu3WrVqpXpGB4pLS2NcgsAAHwGxRaAZV18UztmzBg1aNDAcBrPcOTIEc2cOZO32AAAwKdQbAFYXoMGDdS8eXPTMQAAAGAIm0cBAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLCzAdAAAq2ooVKzRr1ixJ0qRJk9S6desS991ut5566imdPn1aMTExGjt2bKnGPXr0qJKSkrRnzx7t379f+fn5ev/991WnTp3LPr9hwwbNnz9fR44cUUREhPr27as777xT/v7+JZ47d+6c/vd//1fr1q1TXl6eWrRooVGjRql58+bX8ekBAAC8H29sAfiMwMBAJScnX3J9x44dOn36tAIDA8s0XlpamhYtWqTc3Fw1aNDgqs9u3rxZb7/9tkJDQzV69Gh17dpVf//73zVnzpwSzxUVFemNN95QcnKyHA6Hfv3rXyszM1OTJ0/WsWPHypQPAADAV/DGFoDP6NSpk9asWaPRo0eXeEuakpKi5s2bKysrq0zjdenSRXPmzFFISIj++c9/6sCBA1d8dt68eWrcuLHGjx9f/L1DQkK0YMECDRkypLgYr127Vmlpafr973+v2NhYSVL37t31u9/9TvPnz9dvf/vbMn5qAAAA78cbWwA+o2fPnsrOztbWrVuLrxUUFGjNmjXq2bNnmccLCwtTSEjINZ87fPiwDh8+rAEDBpQo1Ha7XW63W2vWrCm+tmbNGkVGRqpbt27F1yIiIhQbG6sNGzYoPz+/zDkBAAC8HcUWgM+oXbu2oqKilJKSUnxt8+bNysnJUY8ePSrs++7fv1+S1KJFixLXa9SooZo1a5Z403vgwAE1a9ZMfn4l/3hu2bKlLly4wHRkAACAy6DYAvApcXFx2rBhg/Ly8iRJycnJatu2rWrUqFFh3zM9PV2SVK1atUvuVatWTWfPni3+57Nnz6p69eqXPHfx2pkzZyokIwAAgJVRbAH4lO7duysvL08bN25Ubm6uNm3adF3TkMviYom+3OZUgYGBxfcvPhsQcOn2Bxe/lqnIAAAAl2LzKAA+JSIiQtHR0UpJSVFeXp6KioqKN2mqKEFBQZIuX0rz8/OL7198tqCg4LLPSZcvxwAAAL6ON7YAfE7Pnj2VmpqqpKQkdezYUaGhoRX6/S5OQb44Jfnn0tPTS0w9rl69eompyRddvFaRU6YBAACsimILwOd069ZNNptNu3fvVlxcXIV/v6ZNm0qS9u7dW+L6mTNndPr06eL7ktSkSRPt379fRUVFJZ7ds2ePqlSponr16lV0XAAAAMuh2ALwOcHBwfrNb36jESNGqHPnzhX+/Ro1aqT69etr6dKlJQprUlKSbDabbr311uJrsbGxysjI0Lp164qvZWZmas2aNYqJiWEqMgAAwGWwxhaAT+rTp88Nj5GTk6NFixZJktLS0iRJCQkJqlq1qkJDQzV48ODiZ++77z699dZbmjJlinr06KEff/xRixcvVv/+/dWwYcPi52JjY7Vw4ULNmjVLhw8fVnh4uBITE1VUVKS77777hjMDAAB4I4otAFyn7OxsffnllyWuOZ1OST+dmfvzYtu5c2c999xz+uqrrzRnzhxFRETojjvu0J133lni6/38/PTiiy9q3rx5Wrx4sfLy8tSiRQs9+eSTql+/fsV/KAAAAAui2ALwen379lXfvn2v+dzMmTPLNG6dOnX0t7/9rdTPd+3aVV27dr3mc2FhYXr88cfLlAUAAMCXscYWAAAAAGBpvLEFgF/Izs6+7FmyF/n5+SkiIqISEwEAAOBqKLYA8AszZszQjh07rni/du3aZZ62DAAAgIpDsQWAXxg1apSys7OveD8oKKgS0wAAAOBaKLYA8AvNmzc3HQEAAABlwOZRAAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLY1dkAICysrJ09OjRS3716dNHw4cPNx0PAADgqii2AODjoqOjtX379hLX/P39VVhYqBMnTlBsAQCAx2MqMgD4uAcffPCSa4WFhZKkX//615WcBgAAoOwotgDg45577jl9/PHHl1z38/PT8uXLtWvXLgOpAAAASo9iCwDQI488omnTphX/s7+/vzp27KhPPvlEbdq0Uffu3fXxxx8rIyPDYEoAAIDLo9gCACRJY8eO1bPPPivpp6nIn3/+uY4ePaq//e1vqlatmp544gnVq1dPo0aN0rJly1RUVGQ4MQAAwE/YPAoAIEmy2Wx6++23deHCBR07dkw333yzJOnuu+/W3XffrSNHjmju3Ln685//rHnz5qlp06Z64IEH9OCDD6pp06ZmwwMAAJ/GG1sAQDGbzaaZM2fq66+/vuRegwYN9NJLLyktLU2rVq1S//79NWPGDDVr1kwDBgzQvHnzlJOTYyA1AADwdRRbAECZ2Gw2xcXF6ZNPPtGxY8f06aefqrCwUKNGjVK9evX06KOPavXq1XK73aajAgAAH0GxBQBct7CwMD3wwANasWKF9uzZo9/+9rdavHixevToobZt22r69Ok6duyY6ZgAAMDLUWwBAOWiRYsWeu2117R//34lJiaqU6dOmjhxoho1aqRhw4bp66+/Vl5enumYAADAC1FsAQDlyt/fX4MGDdLnn3+uY8eO6f3339eJEyc0YsQI1a9fX88884xSU1NNxwQAAF6EYgsAqDDVq1fXE088oXXr1mn79u168MEH9de//lWdOnVSTEyM3n//fZ0+fdp0TAAAYHEUWwBApbjlllv09ttv6/Dhw/rmm2/UuHFjPfvss6pfv77uuusuLVy4UAUFBaZjAgAAC6LYAgAqVWBgoG677TYtWLBAR44c0bRp07Rr1y4NHTpUTZo0KT5SCAAAoLQotgAAY+rUqaNnn31WW7du1fr16zV8+HB99NFHuvnmm9WzZ0/Nnj1bmZmZpmMCAAAPR7EFABhns9nUpUsXffDBBzp27Ji++OILhYWF6dFHH1W9evV0//33a8WKFSoqKjIdFQAAeKAA0wEA4EYdOXLEdASP4Q2/F8HBwRo5cqRGjhypH3/8UXPnztWcOXP0l7/8Rc2aNdODDz6oBx54QE2aNDEdFQAAeAib2+12X+uhzMxMRUZGKiMjQxEREZWRCwCuaffu3WrVqpXpGB4pLS1NUVFRpmOUG7fbreTkZP35z3/W/PnzlZOTo/79+2v06NH61a9+pZCQENMRAQBAOStLD6XYArC03bt3Kysry3QMjxIeHu5VpfaXsrOzNX/+fM2ZM0erVq1SZGSkRo4cqdGjR6tbt26y2WymIwIAgHJAsQUA+IQ9e/bo008/1aeffqojR46oTZs2Gj16tEaNGqWbbrrJdDwAAHADKLYAAJ9SWFioJUuWaM6cOVqwYIEKCgoUHx+v0aNHa+jQoQoKCjIdEQAAlFFZeii7IgMALM/f318Oh0N//etfdfToUb377rs6evSofvWrX6lBgwb6/e9/r61bt5qOCQAAKghvbAEAXmvbtm2aM2eO5s2bp5MnTyomJkajR4/Wvffeqxo1apiOBwAAroI3tgAASIqOjtb//M//6PDhw/rHP/6hhg0b6ne/+53q1aun//qv/9LixYtVWFhoOiYAALhBFFsAgNcLCgrS8OHD9c033+jw4cOaMmWKtm/friFDhqhJkyYaN26cdu/ebTomAAC4TkxFBgD4JLfbrfXr12vOnDn64osvlJGRobi4OI0ePVp33XWXwsPDTUcEAMCnsSsyAABlkJubq3/84x+aM2eOli5dqqpVq2rEiBF66KGH1KtXL689G5dzoC/l7edAA4CVUGwBALhOhw4d0meffaZPP/1U+/btU4sWLfTggw/qgQceUKNGjUzHKze7d+9Wq1atTMfwSGlpaZRbAPAAFFsAAG5QUVGRVq1apT//+c/66quvlJubq4EDB2r06NEaPny4QkJCTEe8IZs2bVLnzp113x/vU91WdU3H8QjH045r3mPztHHjRsXExJiOAwA+ryw9NKCSMgGAx8vPz9d//dd/6bXXXtMtt9xiOg4M8/PzU58+fdSnTx/NnDlTX375pebMmaN7771X1apV0z333KPRo0erS5culp6qXLdVXTXq4D1vogEAvoldkQHgP1JSUvSPf/xD58+fNx0FHiY8PFwPP/ywkpOT9cMPP+iJJ57QN998o27duik6OlozZszQ8ePHTccEAMBnUWwB4D+cTqfq1aunTp06mY4CD9aqVStNnTpVhw4d0sKFC9W2bVuNGzdODRs2LD5SKD8/33RMAAB8CsUWAP7D5XIpPj5efn780Yhr8/f315AhQ/Tll1/q6NGjeuedd/Tjjz9q+PDhatiwoZ577jlt377ddEwAAHwCP70BgKS9e/dq165dGjp0qOkosKCaNWtqzJgx2rhxo1JTU3XPPfdo7ty5io6OVteuXTVr1iydPXvWdEwAALwWxRYA9NPb2qCgIA0cONB0FFhchw4d9Ic//EFHjhzR119/rZtuuklPP/206tWrp3vuuUeJiYkqLCw0HRMAAK9CsQUA/VRs+/Tpo/DwcNNR4CWCgoL0q1/9Sv/85z/1448/avLkyUpNTZXD4VDTpk318ssva8+ePaZjAgDgFSi2AHxedna2VqxYoWHDhpmOAi9Vr149vfDCC9qxY4dWr16t+Ph4vf/++4qKilKfPn306aefKjs723RMAAAsi2ILwOctWbJEeXl5rK9FhbPZbIqNjdUf//hHHTt2TPPmzVNAQIBGjx6tm266SQ899JBWrVolt9ttOioAAJZCsQXg85xOp1q3bq0WLVqYjgIfUrVqVf3617/W0qVLtX//fj3//PNavny5evfurZtvvllTp07V4cOHTccEAMASKLYAfJrb7dbChQt5WwujmjZtqokTJ2rv3r1atmyZYmNj9frrr6tJkyYaPHiw/va3v+n8+fOmYwIA4LEotgB82ubNm3Xs2DGKLTyCn5+f+vXrp7lz5+rYsWP66KOPlJmZqZEjR6p+/frFRwoxVRkAgJIotgB8mtPpVEREhOLi4kxHAUqIjIzUI488ou+//147d+7Uo48+qr///e/q0qWLOnTooHfeeUcnT540HRMAAI9AsQXg01wulxwOhwIDA01HAa6odevWeuONN3To0CE5nU61atVKY8eOVf369YuPFCooKDAdEwAAYyi2AHzW8ePHtW7dOo75gWUEBARo6NCh+uqrr3T06FHNmDFD+/fv12233aaGDRvqxRdfVF5enumYAABUOootAJ+1aNEi2Ww2DRkyxHQUoMxq1aql3/72t9q8ebM2bdqku+++W99++60OHTpkOhoAAJUuwHQAADDF5XKpW7duql27tukowA3p1KmTOnXqZDoGAADG8MYWgE/Ky8tTQkIC05ABAAC8AMUWgE9KTk5WVlYWx/wAAAB4AYotAJ/kdDpVv359dezY0XQUAAAA3CCKLQCf5HK5NHToUNlsNtNRAAAAcIMotgB8zu7du5WWlsY0ZAAAAC9BsQXgc1wul6pUqaIBAwaYjgIAAIByQLEF4HNcLpf69u2rsLAw01EAAABQDii2AHxKVlaWVq5cyTE/AAAAXiTAdAAAqExJSUnKz89nfS1QSms/X6svxnwhSfrtwt+qeWzzEvfdbrcmRU9S+tF0tbW31aN/fbTUY6cfTdeC8Qu0a/kuuYvciuoVpeFThqtW01qXPLvmL2u0bOYynTl0RtUaVFPvR3ur96O9b2hMAID34I0tAJ/idDrVpk0bNWvWzHQUwFICgwO18auNl1zfk7JH6UfTFVClbH9XfiH7gj64/QPt+X6PBj07SENeHKLDWw9r5rCZOnfmXIlnUz5N0V+f+avqta6nO9+4U027NtXfX/y7lry75LrHBAB4F4otAJ9RVFSkhQsXMg0ZuA5tBrZR6jepKiwoLHF901eb1KhjI4XXCS/TeMl/TtbJvSf16BePasBvB6jvk331xNdPKPN4ppZ/sLz4ubzcPC18faHa2ttq9Gej1f2B7rpv1n3qfFdnJb6dqJz0nDKPCQDwPhRbAD5j06ZNOn78ONOQgesQc2eMcs7k6IflPxRfK8gr0JZvtyjmzpgyj7fl2y1qHNNYjWMaF1+r26quonpHKXVBavG1Pcl7dO7MOcU9HFfi6+MejlPeuTztSNxR5jEBAN6HYgvAZzidTlWrVk09evQwHQWwnBqNa6hp16ba9PdNxdd2Ltmp3MxcxfyqbMW2qKhIR/91VI06NrrkXpOYJjq1/5TOZ52XJB3eeliSLnm2UcdGsvnZiu+XZUwAgPeh2ALwGS6XSw6HQ4GBgaajAJYUMyJG21zblJebJ0naOH+jWvZsqch6kWUaJ+dsjgouFCiibsQl9yJu+ulaxr8zJEmZxzPl5++n8NolpzoHBAUotEZo8XNlGRMA4H0otgB8wrFjx7RhwwbW1wI3oNPwTso/n68dCTt0Puu8/pX4r+uahpyfmy9Jl91w6uK1/PP5xc/6B/lfdpyAKgElnivtmAAA78NxPwB8wqJFi2Sz2TR48GDTUQDLCqsVplZ9Wmnj1xuVl5unosIidbi9Q5nHCQz5adZEwYWCS+5dvBYYHFj8bGFe4SXPXXz258+VdkwAgPfhjS0An+ByuRQbG6tatTjLErgRnUd01s4lO5UyJ0VtBrZR1ciqZR6javWqCqgSoMzjmZfcy/z3T9cib/ppenNE3QgVFRYp62RWiecK8gp07sy54ufKMiYAwPtQbAF4vQsXLigxMZFpyEA5aD+0vWx+Nh3ccFCd7+x8XWP4+fmpXtt6+jH1x0vuHdx4UDWb1lRweLAkqUF0A0m65NkfN/8od5G7+H5ZxgQAeB+KLQCvt2rVKmVnZ3PMD1AOqoRV0V1v36XBYwfrlsG3XPc4HW7roEObDunQ5kPF147vPq7dq3ar4+0di69F9YpS1epVlfLnlBJfnzInRUFVg9TW3rbMYwIAvI/Xr7HdvXu3srKyrv2gDwkPD1dUVJTpGEClcTqdatiwodq3b286ClAmOTk5OnHixFV/XbhwQV988YXq169fabm63dPthseIeyhOa+au0ccjP1b/p/rLL9BPKz5cofA64er3VL/i54JCghQ/Ll5fPf+V5jw4R637t9a+Nfu04csNGvryUIVWDy3zmAAA7+PVxXb37t1q1aqV6RgeKS0tjXILn+B2u+V0OjV06FDZbDbTceDjCgoKdPr06WuW1RMnTuj48eM6d+7cJWPUqFFDderUKf7VpEkThYWFGfg0NyY4PFhjvh2jf4z/hxJnJMrtdqtlz5YaPmW4wmqV/DxxD8fJP8Bfyz9cru2Lt6t6g+oaPmW4+jze57rHBAB4F68uthff1N73x/tUt1Vdw2k8w/G045r32DzeYsNnpKWlae/evXrnnXdMR4EXcrvdyszMLFVRPXHihE6fPi23211ijJCQENWtW7e4qLZt21Z9+/YtUV4v/qpVq1aln8N867236tZ7b73mcxO3TCzz2NUaVNPoT0eX6tnuD3RX9we6l+uYAADv4dXF9qK6reqqUYdGpmMAMMDlcik4OFgDBgwwHQUWceHCBZ08eVLHjx8vVVnNy8sr8fV+fn6qXbt2cRmtV6+eOnToUKK8/vxXaGjoFZIAAIDS8oliC8B3uVwu9evXT1Wrlv1IEniHoqIinTlzptRvVTMyMi4ZIzIyskQZ7dq162VLap06dVSjRg35+fnu3oznzp674rmzkuTn78e0YABAuaPYAvBaGRkZ+u677/Tuu++ajoJy5Ha7de7cuVIX1ZMnT6qoqKjEGEFBQSXeoLZs2VI9evS4bFGtXbu2qlSpYujTWs+f7/+z9qbsveL96o2qX9e0ZQAAroZiC8BrJSUlqaCggGN+LCA/P1+nTp264iZKv7yWm5tb4uttNptq1qxZopC2adPmskW1bt26Cg8PZzOxCjL8teHKSc+54v3A4MpdIwwA8A0UWwBey+l06pZbblGTJk1MR/E5brdb6enppX6reubMmUvGCAsLK1FIO3TocMXpvzVr1lRAAP+X5gkadWRPCwBA5eOnAABeqaioSIsWLdLo0eyOWl5yc3PLNP03Pz+/xNcHBASodu3axVOAGzdurC5dulxx+i/rogEAQGlRbAF4pQ0bNujEiRNMQ76KwsLCUp+peuLEicseE1a9evUShbRFixZXfKtarVo1n95UCQAAVByKLQCv5HQ6Vb16dXXvfu1zL72F2+1WVlZWqYvqqVOnLjlTNTg4uMSmSq1bt1bv3r2veKZqUFCQoU8LAADwfyi2ALySy+XS4MGDLb/uMi8vTydPnrziJkq//HXhwoUSX+/n56datWqV2DgpOjr6ks2Ufn6mKpsqAQAAq7H2T3wAcBlHjx7Vpk2b9Nxzz5mOcomioiKdPXu21G9V09PTLxkjIiKiRDHt3LnzVc9U9ff3r/wPCgAAUIkotgC8zsKFC+Xn56fBgwdXyvcr65mqhYWFJb4+MDCwxFvT5s2bKzY29oqbKgUHB1fK5wIAALAKii0Ar+NyudS9e3fVqFHjur6+oKDgimeqXm5KcE7OpWd2/vJM1ZtvvvmKb1UjIyOZ/gsAAHADKLYAvMqFCxeUlJSkl19+ufia2+1WRkZGqd+qnj59+pJxQ0NDS5TRX65T/eWmSlZf2wsAAGAl/OQFwJLOnz9/2VK6bt06nTt3Tv/85z/15ZdfFl//5Zmq/v7+ql27dnEZbdCggTp16lRiSvDPp/+GhoYa+qQAAAC4FootAI9QWFioM2fOlPqtamZm5iVjVKtWTZJUpUoV1a1b97Il9eKv6tWrc6YqAACAl6DYAqgQbrdb2dnZZTpTtaioqMQYFwvqxTLaqlUrxcXFXfVM1ZYtW8rhcOjDDz809MkBAABQ2Si2AEotLy/vspsqXel81fPnz5f4epvNVuJM1Tp16uiWW265pKReLLNhYWFl2lRp165d2rdvn4YOHVreHx0AAAAejGIL+LCioiKlp6eX+q3q2bNnLxkjPDy8RCnt1KnTFaf/1qxZs0LPVHU6nQoJCVH//v0r7HsAAADA81BsAS+Tk5NTpjNVCwoKSnx9QEBAiTLatGlTdevW7YpnqoaEhBj6pJdyuVzq37+/R2UCAABAxaPYAh6uoKBAp0+fLlVRPX78uM6dO3fJGDVq1ChRSKOioq74VrVatWqWPFM1PT1dq1at0syZM01HAQAAQCWj2AKVzO12KzMzs0xnqrrd7hJjhISElNhUqW3bturbt+8VN1UKDAw09GkrT2JiogoLCxUfH286CgAAACoZxRYoBxcuXNDJkyevuZnSxV95eXklvt7Pz6/Emar16tVThw4dLtlM6eIvzlS9lNPpVHR0tBo3bmw6CgAvc8899+js2bOaPXu2GjZsaDoOAOAyKLbAZRQVFZXpTNWMjIxLxoiMjCxRRrt27XrF6b81atTgTNUbUFhYqEWLFumRRx4xHQWAF3riiSd07733Kjo6Wh9++KHuuece05EAAL9AsYVPcLvdOnfuXJk2VfrlmapBQUEl3py2bNlSPXr0uOKmSlWqVDH0aX3P+vXrderUKY75AVAhevfurW3btumpp57Svffeq2+++UYffvihatSoYToaAOA/KLa4YQUFBdq5c6eio6Mr9fvm5+df9kzVK52vmpubW+LrbTabatasWaKQtmnT5rJFtW7dugoPD7fkpkq+wOl0qkaNGoqNjTUdBYCXql69uj7//HPddttteuKJJxQdHa05c+bIbrebjgYAEMVWkrT287X6YswXkqTfLvytmsc2L3Hf7XZrUvQkpR9NV1t7Wz3610dLPXb60XQtGL9Au5bvkrvIraheURo+ZbhqNa11ybNr/rJGy2Yu05lDZ1StQTX1frS3ej/au8Qzx3cf1/dzvtfBjQd1eOthFVwo0ITUCarZuOZ1fPIbt3r1aj3yyCP617/+pbS0NEVFRV33WG63u0xnqp45c+aSMcLCwkoU0p+vU73cmaoBAfwr4A1cLpeGDBlSoWfkAoAkjRw5UnFxcXrooYfkcDg0ZswYTZ8+XVWrVjUdDQB8Gj/V/0xgcKA2frXxkmK7J2WP0o+mK6BK2X67LmRf0Ae3f6DczFwNenaQ/AP8tWLWCs0cNlPPf/e8Qmv83wZAKZ+maP6z89Xh/3VQvyf7ae+avfr7i39XXm6eBj4zsPi5A+sP6LuPv9NNN9+kuq3q6si2Izf2oa/T6dOnNXbsWH3yySfFa0N//PHHS4ptbm6uTp48ec3NlC5O/83Pzy/x9QEBASU2VWrcuLG6dOlyxem//GDhew4fPqzU1FS98MILpqMA8BENGzbU4sWL9cEHH+iFF15QUlKS/vKXv6hr166mowGAz6LY/kybgW2U+k2qfvXGr+Qf8H9vfjZ9tUmNOjZS9unsMo2X/Odkndx7Us8ueVaNYxoXf4/pPadr+QfLNWzCMElSXm6eFr6+UG3tbTX6s9GSpO4PdJe7yK3EtxPV44Eeqlrtp8LWbkg7Tds/TcHhwVr2/rJKL7ZFRUV6//33NWHChOLzUi+uRZ0yZYree++9EmU1KyvrkjGqV69eopC2aNHiqmeqsqkSrmbhwoXy9/eXw+EwHQWwpONpx01H8Bhl+b3w8/PT008/rUGDBum+++5T9+7d9corr2jcuHHMBgIAA/iT92di7ozRNtc2/bD8B7Ud1FaSVJBXoC3fbtGg5wbpu4+/K9N4W77dosYxjYtLrSTVbVVXUb2jlLogtbjY7kneo3Nnzinu4bgSXx/3cJw2zt+oHYk71OXuLpKk0Opmj3l56qmn9NFHH1323p49exQdHa3WrVurd+/eVzxTNSgoqJJTw5u5XC716NGDTVyAMgoPD5ckzXtsnuEknufi701ptG7dWqtXr9brr7+uyZMny+Vy6S9/+YtatWpVgQkBAL9Esf2ZGo1rqGnXptr0903FxXbnkp3KzcxVzK9iylRsi4qKdPRfR3Xrr2+95F6TmCb6YfkPOp91XsHhwTq89bAkqVHHRiWea9SxkWx+Nh3eeri42Jo2btw4nTlzRomJiUpPT5e/v78KCwsVEBCgUaNG6fXXXzcdET7k/PnzWrJkiSZOnGg6CmA5UVFRSktLu+zMGl8WHh5e5v0iAgMDNWnSJA0ZMkSjRo1Sx44dNWPGDD3++ONsOggAlYRi+wsxI2LknOxUXm6egkKCtHH+RrXs2VKR9SLLNE7O2RwVXChQRN2IS+5F3PTTtYx/Zyg4PFiZxzPl5++n8Nol/4Y4IChAoTVClfHvS89INaVRo0b629/+poKCArlcLs2aNUuJiYkqKCjQiRMnTMeDj1mxYoVycnI45ge4Tjey4R8uFRsbq9TUVP33f/+3nnzySf3zn//UJ598onr16pmOBgBej8WLv9BpeCfln8/XjoQdOp91Xv9K/Jdi7owp8zj5uT9tgnS5DacuXss/n1/8rH/Q5XdzDagSUPycJwkICNDtt9+uxYsX68CBA5oyZYpGjRplOhZ8jNPpVNOmTdW2bVvTUQBAkhQaGqpZs2bJ5XJp8+bNateunb766ivTsQDA61FsfyGsVpha9WmljV9v1FbnVhUVFqnD7R3KPE5gSKAkqeBCwSX3Ll4LDA4sfrYwr/Cy4xRcKCh+zlM1btxY48aNU69evUxHgQ9xu91yuVwaOnQoU/0AeJz4+Hht27ZN/fr101133aX7779fGRmeMwMLALwNxfYyOo/orJ1LdiplToraDGyjqpFlP0KmavWqCqgSoMzjmZfcy/z3T9cib/ppenNE3QgVFRYp62TJdU4FeQU6d+Zc8XMA/s/OnTt14MABpiED8Fi1atXS/PnzNXfuXH3zzTeKjo7W8uXLTccCAK9Esb2M9kPby+Zn08ENB9X5zs7XNYafn5/qta2nH1N/vOTewY0HVbNpTQWHB0uSGkQ3kKRLnv1x849yF7mL7wP4P06nU1WrVlW/fv1MRwGAK7LZbBo1apS2bt2qFi1aqH///nr22Wd1/vx509EAwKtQbC+jSlgV3fX2XRo8drBuGXzLdY/T4bYOOrTpkA5tPlR87fju49q9arc63t6x+FpUryhVrV5VKX9OKfH1KXNSFFQ1SG3trB8EfsnlcmnAgAEKDg42HQUArqlJkyZaunSpZsyYoQ8++EBdunTR5s2bTccCAK/BrshX0O2ebjc8RtxDcVozd40+Hvmx+j/VX36Bflrx4QqF1wlXv6f+7y1TUEiQ4sfF66vnv9KcB+eodf/W2rdmnzZ8uUFDXx5a4uza3Mxcrfp4lSRp39p9kqTkPyUrJDJEIZEh6vUI61zh/c6ePauUlBR9+OGHpqMAQKn5+fnp2Wefld1u13333adbb71VkyZN0gsvvCB//8tvIgkAKB2KbQUKDg/WmG/H6B/j/6HEGYlyu91q2bOlhk8ZrrBaYSWejXs4Tv4B/lr+4XJtX7xd1RtU1/Apw9Xn8T4lnstJz9HCqQtLXFv+wU/rdao3qk6xhU9ISEhQYWGh4uPjTUcBgDJr166d1q5dq1dffVXjx4+Xy+XS3Llz1bx5c9PRAMCybG63232thzIzMxUZGamMjAxFRFx6Lqun2rRpkzp37qznlj+nRh0amY7jEX7c8qNm9JuhjRs3Kiam7McYAZ7gvvvu0/bt25Wammo6CgDckOTkZN1///06ceKE/vCHP+jhhx9mp3cA+I+y9FDW2AKwlMLCQi1evFjDhg0zHQUAblhcXJy2bNmikSNH6pFHHtHtt9+u48ePm44FAJZDsb0O586eU+bxzCv+yj6VbToi4LXWrl2r06dPc8wPAK8RHh6u2bNn65tvvtGaNWsUHR2tBQsWmI4FAJbCGtvr8Of7/6y9KXuveL96o+qauGViJSYCfIfT6VStWrXUrduNb/AGwLpOnz6twMBASy2RupbbbrtN27dv1yOPPKI77rhDDz30kN555x2v+owAUFEottdh+GvDlZOec8X7gcGBlZgG8C0ul0tDhgxhB1HAx7Vv315ZWVk6ePCgqlevbjpOualTp44WLFigOXPm6JlnntGyZcs0d+5c9erF5pAAcDUU2+vQqCMbUQEmHDp0SFu3btW4ceNMRwFgUFFRkU6ePKn8/HzFx8dr2bJlCgkJMR2r3NhsNj300EPq27evHnjgAfXp00fPP/+8Jk+erCpVqpiOBwAeiTW2ACxj4cKF8vf3l8PhMB0FgEH79u1Tfn6+pJ/W3d9zzz0qLCw0nKr8NW/eXCtWrNC0adP0zjvvqFu3btq2bZvpWADgkSi2ACzD5XIpLi5O1apVMx0FgEEbN24s/s9ut1vffvutnnrqKZXiBEPL8ff319ixY7Vu3ToVFhaqS5cuevvtt72yyAPAjaDYArCE3NxcLV26lGN+AGjTpk0KDPy//Szcbrf++Mc/6u233zaYqmJ17NhRGzZs0JgxY/TCCy9owIABOnjwoOlYAOAxKLYALGH58uXKzc3lmB8AWrduXfFU5IsCAgKUnp5uJlAlCQ4O1owZM7R06VLt27dP0dHR+uyzz7zyTTUAlBXFFoAlOJ1ONWvWTK1btzYdBYBhBw4ckCQ1adJEkvTiiy8qPT1dU6ZMMZiq8vTr10/btm3THXfcoQcffFAjRozQqVOnTMcCAKMotgA8ntvtlsvl0rBhw2Sz2UzHAWBYYmKiDh48qAMHDqhNmzY6efKkQkNDTceqVJGRkfrss880f/58rVixQu3atZPL5TIdCwCModgC8Hj/+te/dOjQIaYhA5AkRUVFqXHjxpIkh8OhhIQEn52OO2LECG3fvl0xMTEaNmyYHnvsMWVnZ5uOBQCVjmILwOM5nU6FhoaqT58+pqMA8DB2u12HDx/Wrl27TEcxpl69enK5XJo1a5bmzZunjh07avXq1aZjAUClotgC8Hgul0sDBw5UcHCw6SgAPEyfPn0UFBSkhIQE01GMstlsevzxx5WamqpatWopLi5OEyZMuGSTLQDwVhRbAB7t9OnT+v777znmB8BlVa1aVb169VJiYqLpKB4hKipKycnJevXVV/XGG28oNjZWO3fuNB0LACocxRaAR0tISFBRUZHi4+NNRwHgoRwOh1asWKHz58+bjuIRAgICNGHCBK1evVo5OTmKiYnRe++9p6KiItPRAKDCUGwBeDSn06lOnTqpfv36pqMA8FB2u125ublKSUkxHcWjdOnSRZs2bdKjjz6qZ555png9MgB4I4otAI9VUFCgxYsXMw0ZwFW1b99edevW9fl1tpcTEhKid999V4mJidq1a5eio6P1xRdfmI4FAOWOYgvAY61Zs0Znz57lmB8AV2Wz2WS321lnexWDBg3Stm3bNGTIEN17770aOXKkzpw5YzoWAJQbii0Aj+V0OlW7dm117drVdBQAHs7hcGjLli3697//bTqKx6pevbo+//xzffHFF0pISFB0dDR/GQDAa1BsAXgsl8ul+Ph4+fnxRxWAqxs0aJAkKSkpyXASzzdy5Eht27ZNt9xyixwOh55++mnl5OSYjgUAN4SfFgF4pIMHD2r79u1MQwZQKnXq1FGnTp1YZ1tKDRs21OLFi/Xee+9p9uzZiomJ0fr1603HAoDrRrEF4JFcLpcCAgJkt9tNRwFgEXa7XUlJSRxrU0p+fn56+umntXnzZoWFhal79+6aPHmyCgoKTEcDgDKj2ALwSC6XS7169VJkZKTpKAAswuFw6MSJE9qyZYvpKJbSunVrrV69WuPGjdOkSZPUs2dPpaWlmY4FAGVCsQXgcXJycrRs2TKO+QFQJj169FDVqlXZEOk6BAYGavLkyUpJSdGZM2fUsWNHzZo1S26323Q0ACgVii0Aj7Ns2TKdP3+e9bUAyqRKlSrq168f62xvQGxsrFJTU/XAAw/oySefVHx8vI4ePWo6FgBcE8UWgMdxOp1q0aKFWrVqZToKAItxOBxKTk7WuXPnTEexrNDQUM2aNUsul0upqamKjo7W/PnzTccCgKui2ALwKG63Wy6XS8OGDZPNZjMdB4DF2O125efna8WKFaajWF58fLy2bdumfv366e6779aoUaOUnp5uOhYAXFaA6QCV4XjacdMRPAa/F/B027Zt0+HDh5mGDOC6tGrVSk2aNFFiYiJ/jpSDWrVqaf78+Zo3b57GjBmjlStX6tNPP1X//v1NRwOAEry62IaHh0uS5j02z3ASz3Px9wbwNE6nU2FhYerdu7fpKAAsyGazyW63s862HNlsNo0aNUq9e/fWgw8+qAEDBuj3v/+9pk6dquDgYNPxAECSZHOXYru7zMxMRUZGKiMjQxEREZWRq9zs3r1bWVlZpmN4lPDwcEVFRZmOAVxWz549VbduXf397383HQWARX399dcaMWKEDhw4oCZNmpiO41WKior0hz/8QS+99JJatmypefPmqVOnTqZjAfBSZemhXl9sAVjHqVOnVKdOHf3pT3/Sww8/bDoOAIs6e/asatWqpY8++kiPPPKI6Theafv27brvvvu0Y8cOTZo0SS+88IL8/f1NxwLgZcrSQ9k8CoDHWLx4sdxut+Lj401HAWBh1atX16233sp5thWoXbt2Wrt2rZ577jmNHz9evXv31t69e03HAuDDKLYAPIbT6VTnzp1Vr14901EAWJzdbteSJUtUUFBgOorXqlKliqZNm6bvvvtOx44dU4cOHfSnP/1JpZgMCADljmILwCMUFBQoISFBw4YNMx0FgBdwOBxKT0/Xhg0bTEfxenFxcdqyZYtGjhypRx99VLfddpuOH+cUBgCVi2ILwCN8//33Sk9P53gOAOWia9euioyMZHfkShIeHq7Zs2frm2++0dq1axUdHa0FCxaYjgXAh1BsAXgEp9OpunXrqnPnzqajAPACAQEBGjhwIOtsK9ltt92m7du3q3v37rrjjjv08MMPKzMz03QsAD6AYgvAI7hcLsXHx8vPjz+WAJQPu92utWvXKj093XQUn1KnTh0tWLBAn3zyib788kt16NBBq1atMh0LgJfjJ0gAxu3fv187duxgGjKAcmW321VYWKhly5aZjuJzbDabHnroIW3ZskUNGjRQnz59NHbsWF24cMF0NABeimILwDiXy6XAwEANGjTIdBQAXqRp06Zq1aoV62wNat68uVauXKmpU6fqnXfeUbdu3bRt2zbTsQB4IYotAONcLpd69+59zYO3AaCsHA6HEhISOILGIH9/f7344otat26dCgsL1aVLF7399tsqLCw0HQ2AF6HYAjDq3LlzWr58Ocf8AKgQdrtdBw8e1O7du01H8XkdO3bUhg0bNGbMGL3wwgsaMGCADh48aDoWAC9BsQVg1NKlS3XhwgXW1wKoEH379lVgYCC7I3uI4OBgzZgxQ0uXLtW+ffsUHR2tzz77jDfqAG4YxRaAUU6nU1FRUYqKijIdBYAXCgsLU8+ePVln62H69eunbdu26Y477tCDDz6oESNG6NSpU6ZjAbAwii0AY9xutxYuXMg0ZAAVyuFwaPny5crLyzMdBT8TGRmpzz77TPPnz9eKFSvUrl07uVwu07EAWBTFFoAxW7Zs0ZEjR5iGDKBC2e12nTt3Tt9//73pKLiMESNGaPv27YqJidGwYcP02GOPKTs723QsABZDsQVgjNPpVHh4uHr16mU6CgAv1rFjR9WuXZt1th6sXr16crlcmjVrlubNm6eOHTtq9erVpmMBsBCKLQBjXC6X7Ha7goKCTEcB4MX8/Pw0aNAg1tl6OJvNpscff1ypqamqVauW4uLi9PLLLzOFHECpUGwBGHHy5EmtXbuWacgAKoXD4dCmTZt08uRJ01FwDVFRUUpOTtarr76q6dOnq3v37tqxY4fpWAA8HMUWgBGLFi2S2+1WfHy86SgAfMCgQYMkSUlJSYaToDQCAgI0YcIErV69Wjk5OYqJidG7776roqIi09EAeCiKLQAjXC6Xunbtqrp165qOAsAH1KtXT+3bt2edrcV06dJFmzZt0mOPPabf/e53stvt+vHHH03HAuCBKLYAKl1+fr4WL17MMT8AKpXdbldiYqLcbrfpKCiDkJAQvfvuu0pMTNSuXbsUHR2tzz//nP8eAZRAsQVQ6VJSUpSZmcn6WgCVyuFw6NixY9q+fbvpKLgOgwYN0rZt2xQfH69f//rXGjlypM6cOWM6FgAPQbEFUOmcTqduuukmderUyXQUAD4kLi5OISEh7I5sYdWrV9fnn3+uzz//XImJiYqOjua/TwCSKLYADHC5XBo6dKj8/PgjCEDlCQ4OVp8+fVhn6wXuuecebdu2TW3bttXgwYM1ZswY5eTkmI4FwCB+qgRQqfbu3atdu3YxDRmAEXa7Xd999x0lyAs0bNhQCQkJeu+99/TJJ5+oU6dOWr9+velYAAyh2AKoVC6XS0FBQRo4cKDpKAB8kMPh0IULF7Rq1SrTUVAO/Pz89PTTT2vTpk0KDw9X9+7dNXnyZBUUFJiOBqCSUWwBVCqXy6U+ffooPDzcdBQAPqhNmzZq0KAB6zK9TJs2bbR69WqNGzdOkyZNUs+ePZWWlmY6FoBKRLEFUGmys7O1YsUKjvkBYIzNZpPD4WCdrRcKDAzU5MmTlZKSojNnzqhjx46aNWsWxwIBPoJiC6DSLFmyRHl5eayvBWCU3W7Xv/71Lx0+fNh0FFSA2NhYpaam6oEHHtCTTz6p+Ph4HT161HQsABWMYgug0jidTt18881q0aKF6SgAfNjAgQNls9l4a+vFQkNDNWvWLLlcLqWmpio6Olrz5883HQtABaLYAqgUbrdbCxcuZBoyAONq1qypLl26UGx9QHx8vLZt26Z+/frp7rvv1qhRo5Senm46FoAKQLEFUCk2b96sY8eOMQ0ZgEdwOBxKSkpSYWGh6SioYLVq1dL8+fM1d+5cffvtt2rfvr2WLVtmOhaAckaxBVApnE6nIiIiFBcXZzoKAMhut+vMmTPatGmT6SioBDabTaNGjdLWrVvVokULDRgwQM8++6zOnz9vOhqAckKxBVApXC6XHA6HAgMDTUcBAMXGxio8PJxjf3xMkyZNtHTpUs2YMUMffPCBOnfurM2bN5uOBaAcUGwBVLjjx49r3bp1TEMG4DECAwPVv39/1tn6ID8/Pz377LPauHGjAgMDdeutt2ratGlMSwcsjmILoMItWrRINptNQ4YMMR0FAIo5HA6tXr1amZmZpqPAgHbt2mnt2rV67rnnNH78ePXu3Vt79+41HQvAdaLYAqhwLpdL3bp1U506dUxHAYBiDodDBQUFWr58uekoMKRKlSqaNm2avvvuOx07dkwdOnTQn/70J7ndbtPRAJQRxRZAhcrLy1NCQgLH/ADwOM2bN1eLFi1YZwvFxcVpy5YtGjlypB599FHddtttOn78uOlYAMqAYgugQiUnJysrK4v1tQA8ksPhYJ0tJEnh4eGaPXu2vvnmG61du1bt2rXTggULTMcCUEoUWwAVyul0qn79+urYsaPpKABwCbvdrr1797K2EsVuu+02bd++XT169NAdd9yhhx56iHXYgAVQbAFUKJfLpaFDh8pms5mOAgCX6NevnwICAnhrixLq1KmjBQsWaPbs2Zo/f746dOigVatWmY4F4CootgAqzO7du5WWlsY0ZAAeKyIiQt27d2edLS5hs9n08MMPa8uWLWrQoIH69OmjsWPH6sKFC6ajAbgMii2ACuNyuVSlShUNGDDAdBQAuCKHw6Fly5YpPz/fdBR4oObNm2vlypWaOnWq3nnnHXXr1k3btm0zHQvAL1BsAVQYl8ulvn37KiwszHQUALgiu92urKwsrVmzxnQUeCh/f3+9+OKLWrdunQoLC9WlSxe9/fbbKiwsNB0NwH9QbAFUiKysLK1cuZJjfgB4vJiYGNWsWZN1trimjh07asOGDRozZoxeeOEFDRgwQAcPHjQdC4AotgAqSFJSkvLz81lfC8Dj+fv7a+DAgayzRakEBwdrxowZWrp0qfbt26fo6Gh99tlncrvdpqMBPo1iC6BCOJ1OtWnTRs2aNTMdBQCuyeFwaMOGDTp9+rTpKLCIfv36adu2bbrjjjv04IMPasSIETp16pTpWIDPotgCKHdFRUVauHAh05ABWMagQYPkdru1ZMkS01FgIZGRkfrss880f/58rVixQu3atZPL5TIdC/BJFFsA5W7Tpk06fvw405ABWEbDhg11yy23sM4W12XEiBHavn27YmJiNGzYMD322GPKzs42HQvwKRRbAOXO6XSqWrVq6tGjh+koAFBqdrtdCQkJrJXEdalXr55cLpdmzZqlefPmqWPHjlq9erXpWIDPoNgCKHcul0sOh0OBgYGmowBAqTkcDh05ckQ7d+40HQUWZbPZ9Pjjjys1NVW1atVSXFycXn75ZeXl5ZmOBng9ii2AcnXs2DFt2LCBacgALKdXr16qUqUKuyPjhkVFRSk5OVmvvvqqpk+fru7du2vHjh2mYwFejWILoFwtWrRINptNQ4YMMR0FAMqkatWq6t27N+tsUS4CAgI0YcIErV69Wjk5OercubPee+89FRUVmY4GeCWKLYBy5XK5FBsbq1q1apmOAgBlZrfbtXLlSp0/f950FHiJLl26aNOmTXr00Uf1zDPPyG636/Dhw6ZjAV6HYgug3Fy4cEGJiYkc8wPAshwOh3Jzc5WcnGw6CrxISEiI3n33XSUmJmrnzp2Kjo7WF198YToW4FUotgDKzapVq5Sdnc36WgCW1a5dO9WrV491tqgQgwYN0rZt2zR48GDde++9GjlypM6cOWM6FuAVKLYAyo3T6VTDhg3Vvn1701EA4LrYbDbZ7XbW2aLC1KhRQ1988YU+//xzJSQkKDo6mv+9AeWAYgugXLjdbjmdTg0dOlQ2m810HAC4bna7XVu3btWxY8dMR4EXu+eee7Rt2za1bdtWDodDTz/9tHJyckzHAiyLYgugXKSlpWnv3r1MQwZgeYMGDZIkJSUlGU4Cb9ewYUMlJCTovffe0+zZsxUTE6P169ebjgVYEsUWQLlwuVwKDg7WgAEDTEcBgBtSu3ZtxcTEsM4WlcLPz09PP/20Nm3apLCwMHXv3l2TJ09WQUGB6WiApVBsAZQLl8ulfv36qWrVqqajAMANczgcSkpK4sxRVJo2bdpo9erVGjdunCZNmqSePXsqLS3NdCzAMii2AG5YRkaGvvvuO6YhA/AadrtdJ0+eVGpqquko8CGBgYGaPHmyUlJSdObMGXXs2FGzZs2S2+02HQ3weBRbADcsKSlJBQUFFFsAXqNHjx4KDQ1lt1oYERsbq9TUVD3wwAN68sknFR8fr6NHj5qOBXg0ii2AG+Z0OnXLLbeoadOmpqMAQLkICgpSv379WGcLY0JDQzVr1iy5XC6lpqYqOjpa8+fPNx0L8FgUWwA3pKioSIsWLdKwYcNMRwGAcuVwOJSSkqLs7GzTUeDD4uPjtW3bNvXr10933323Ro0apfT0dNOxAI9DsQVwQzZs2KATJ04wDRmA17Hb7crPz9eKFStMR4GPq1WrlubPn6+5c+fq22+/Vfv27bV8+XLTsQCPQrEFcEOcTqeqV6+u7t27m44CAOUqKipKTZs2ZZ0tPILNZtOoUaO0detWtWjRQv3799ezzz6r8+fPm44GeASKLYAb4nK5NHjwYAUEBJiOAgDlymazyW63s84WHqVJkyZaunSpZsyYoQ8++EBdunTR5s2bTccCjKPYArhuR48e1aZNm5iGDMBrORwOpaWl6cCBA6ajAMX8/Pz07LPPauPGjQoICNCtt96qadOmqbCw0HQ0wBiKLYDrtnDhQvn5+Wnw4MGmowBAhejfv7/8/f2ZjgyP1K5dO61du1bPPfecxo8frz59+mjfvn2mYwFGUGwBXDeXy6Xu3burZs2apqMAQIWoVq2abr31VootPFaVKlU0bdo0fffddzp69Kjat2+v2bNny+12m44GVCqKLYDrcuHCBSUlJXHMDwCvZ7fbtWTJEhUUFJiOAlxRXFyctmzZopEjR+qRRx7R7bffruPHj5uOBVQaii2A67Jy5UqdO3eO9bUAvJ7D4VBGRobWr19vOgpwVeHh4Zo9e7YWLFigNWvWKDo6WgsWLDAdC6gUFFsA18XpdKpRo0Zq166d6SgAUKG6dOmiatWqsTsyLOP222/X9u3b1b17d91xxx16+OGHlZmZaToWUKEotgDKzO12y+VyadiwYbLZbKbjAECFCggI0MCBA1lnC0upU6eOFixYoNmzZ+vLL79Uhw4dtGrVKtOxgApDsQVQZj/88IP27dvHNGQAPsNut2vt2rU6e/as6ShAqdlsNj388MPasmWLGjRooD59+mjs2LG6cOGC6WhAuaPYAigzp9OpkJAQ9e/f33QUAKgUdrtdRUVFWrZsmekoQJk1b95cK1eu1NSpU/XOO++oW7du2rZtm+lYQLmi2AIoM5fLpf79+yskJMR0FACoFE2aNNHNN9/MOltYlr+/v1588UWtW7dOhYWF6tKli95++20VFhaajgaUC4otgDJJT0/XqlWrmIYMwOc4HA4lJCRwPigsrWPHjtqwYYPGjBmjF154QQMGDNDBgwdNxwJuGMUWQJkkJiaqsLCQYgvA5zgcDh06dEhpaWmmowA3JDg4WDNmzNDSpUu1b98+tW/fXnPnzuUvbWBpFFsAZeJ0OhUdHa3GjRubjgIAlapPnz4KCgpiOjK8Rr9+/bRt2zYNHz5cDzzwgEaMGKFTp06ZjgVcF4otgFIrLCzUokWLNGzYMNNRAKDShYaGKi4ujmN/4FUiIyP12Wefaf78+VqxYoXatWunhQsXmo4FlBnFFkCprV+/XqdOnWIaMgCfZbfbtXz5co5LgdcZMWKEtm/frpiYGA0dOlSPP/64srOzTccCSo1iC6DUnE6natSoodjYWNNRAMAIh8OhnJwcff/996ajAOWuXr16crlcmjVrlv7yl7+oU6dOWrNmjelYQKlQbAGUmsvl0pAhQ+Tv7286CgAY0b59e9WpU4d1tvBaNptNjz/+uFJTU1WzZk317NlTEyZMUH5+vulowFVRbAGUyuHDh5Wamso0ZAA+zc/PT3a7nXW28HpRUVFKTk7Wq6++qjfeeEOxsbHauXOn6VjAFVFsAZTKwoUL5e/vL4fDYToKABhlt9u1efNmHT9+3HQUoEIFBARowoQJWr16tXJychQTE6P33ntPRUVFpqMBl6DYAigVl8ulHj16qEaNGqajAIBRgwYNkiQtWbLEcBKgcnTp0kUbN27UI488omeeeUZ2u12HDx82HQsogWIL4JrOnz+vJUuWcMwPAEi66aab1KFDB9bZwqdUrVpV7733nhITE7Vz505FR0friy++MB0LKEaxBXBNK1asUE5ODutrAeA/HA6HEhMT5Xa7TUcBKtWgQYO0bds2DR48WPfee69GjhypM2fOmI4FUGwBXJvT6VSTJk3Utm1b01EAwCPY7XYdP35cW7duNR0FqHQ1atTQF198oc8//1wJCQmKjo5WUlKS6VjwcRRbAFfldrvlcrk0bNgw2Ww203EAwCPExcUpJCSE3ZHh0+655x5t27ZNbdu2ld1u129/+1vl5OSYjgUfRbEFcFU7d+7UgQMHmIYMAD9TpUoV9e3bl3W28HkNGzZUQkKC3nvvPf3pT39STEyMNmzYYDoWfBDFFsBVOZ1OVa1aVf369TMdBQA8isPh0KpVq3hDBZ/n5+enp59+Wps2bVJYWJi6d++uyZMnq6CgwHQ0+BCKLYCrcrlcGjBggIKDg01HAQCPYrfblZeXp5UrV5qOAniENm3aaPXq1XrppZc0adIkxcXFKS0tzXQs+AiKLYArOnv2rFJSUpiGDACX0bp1azVq1Ih1tsDPBAYGavLkyUpJSdHp06fVqVMnzZo1ix3EUeEotgCuKCEhQYWFhRRbALgMm80mu93OOlvgMmJjY5Wamqr7779fTz75pIYOHapjx46ZjgUvRrEFcEVOp1MdOnRQw4YNTUcBAI/kcDi0c+dO/fjjj6ajAB4nNDRUs2bNksvl0ubNm9WuXTt99dVXpmPBS1FsAVxWYWGhFi9erGHDhpmOAgAea8CAAfLz82M6MnAV8fHx2rZtm/r166e77rpL999/vzIyMkzHgpeh2AK4rLVr1+r06dNMQwaAq6hRo4a6du1KsQWuoVatWpo/f77mzp2rb775RtHR0Vq+fLnpWPAiFFsAl+V0OlWrVi1169bNdBQA8Gh2u11JSUkqLCw0HQXwaDabTaNGjdLWrVvVokUL9e/fX88++6zOnz9vOhq8AMUWwGW5XC4NGTJE/v7+pqMAgEdzOBw6e/asNm7caDoKYAlNmjTR0qVLNWPGDH3wwQfq0qWLNm/ebDoWLI5iC+AShw4d0tatW5mGDACl0K1bN0VERLA7MlAGfn5+evbZZ7Vx40YFBATo1ltv1bRp05j5gOtGsQVwiYULF8rf318Oh8N0FADweIGBgRowYADrbIHr0K5dO61du1bPPfecxo8frz59+mjfvn2mY8GCbO5SnJacmZmpyMhIZWRkKCIiojJyATDo//2//6esrCytWLHCdBQAsISPPvpIY8aM0enTpxUZGWk6DmBJycnJGjVqlE6dOqU//OEPeuihh2Sz2Yrvu93uEv98Nbt371ZWVlZFRbWk8PBwRUVFmY5RJmXpoQGVlAmAReTm5mrp0qWaPHmy6SgAYBkOh0OFhYVavny5hg8fbjoOYElxcXHaunWrfv/73+s3v/mNvvnmG/3pT39S3bp1dfToUfXu3Vuvvfaa7rnnnquOs3v3brVq1aqSUltLWlqa5cptaVFsAZSwfPly5ebmsr4WAMqgWbNmatmypRISEii2wA0IDw/X7Nmz9f/+3//TI488oujoaH388cf64IMPtHfvXv3ud7/TbbfdptDQ0CuOcfFN7ZgxY9SgQYPKiu7Rjhw5opkzZ3r1W2yKLYASnE6nmjVrptatW5uOAgCW4nA4tGjRItMxAK9w++23q3v37nrkkUd0xx13FF+/OE15/Pjx1xyjQYMGat68eUXGhAdh8ygAxdxut1wul4YNG1bqNSwAgJ/Y7Xbt27dPe/bsMR0F8Ap16tTRm2++qYCA/3sXV1RUpKlTp+rEiRMGk8ETUWwBFNu+fbsOHTrENGQAuA79+vVTQEAAuyMD5aSwsFD33nvvJUcA5ebm6pVXXjGUCp6KYgugmMvlUmhoqPr06WM6CgBYTnh4uHr06MF5tkA5yc3N1bFjx/TzQ1z8/Pzkdrv1xz/+UevWrTOYDp6GNbYAirlcLg0cOFDBwcGmowCAJTkcDk2bNk35+fkKDAw0HQewtLCwMB05ckQnTpzQnj17tHv3bu3evVvr16/Xhg0bdOrUKdMR4UEotgAkSadPn9b333+vjz76yHQUALAsu92u8ePHa/Xq1erdu7fpOIDl2Ww21a1bV3Xr1lXPnj1Nx4EHYyoyAElSQkKCioqKFB8fbzoKAFhWTEyMatasyTpbAKhkFFsAkn465qdTp06c9wYAN8DPz0+DBg1inS0AVDKKLQAVFBRo8eLFGjZsmOkoAGB5DodDGzduZP0fAFQiii0ArVmzRmfPnuWYHwAoB4MGDZLb7daSJUtMRwEAn0GxBSCn06natWura9eupqMAgOU1aNBA7dq1Y50tAFQiii0AuVwuxcfHy8+PPxIAoDzY7XYlJCSUOH8TAFBx+CkW8HEHDx7U9u3bmYYMAOXI4XDo6NGj2rFjh+koAOATKLaAj3O5XAoICJDdbjcdBQC8Rq9evRQcHMzuyABQSSi2gI9zuVzq1auXIiMjTUcBAK8REhKi3r17s84WACoJxRbwYTk5OVq2bBnH/ABABXA4HFq5cqVyc3NNRwEAr0exBXzYsmXLdP78edbXAkAFsNvtOn/+vJKTk01HAQCvR7EFfJjT6VSLFi3UqlUr01EAwOvccsstql+/PutsAaASUGwBH+V2u+VyuTRs2DDZbDbTcQDA69hsNtntdtbZAkAloNgCPmrr1q06fPgw05ABoAI5HA5t27ZNR48eNR0FALwaxRbwUS6XS2FhYerdu7fpKADgtQYOHCibzcZbWwCoYBRbwEe5XC4NGjRIVapUMR0FALxWrVq11LlzZ4otAFQwii3gg06dOqXVq1czDRkAKoHdbldSUpKKiopMRwEAr0WxBXzQ4sWL5Xa7FR8fbzoKAHg9h8OhU6dOafPmzaajAIDXotgCPsjpdKpz586qV6+e6SgA4PViY2MVFhbGsT8AUIECTAcAULkKCgqUkJCgZ555xnQUAPAJQUFB6t+/vxITEzVu3DjTcQD8x4oVKzRr1ixJ0qRJk9S6desS991ut5566imdPn1aMTExGjt2bKnHPnPmjD777DNt3bpVbrdbt9xyi+6//37VrVu3xHOJiYnavn279uzZo9OnT6tPnz568sknLzvmuXPn9L//+79at26d8vLy1KJFC40aNUrNmzcv4yf3TryxBXzM999/r/T0dNbXAkAlstvtSklJUVZWlukoAH4hMDBQycnJl1zfsWOHTp8+rcDAwDKNd/78eU2ePFk7d+7U8OHDddddd2n//v2aNGnSJX8GfPvtt/rXv/6lRo0ayd/f/4pjFhUV6Y033lBycrIcDod+/etfKzMzU5MnT9axY8fKlM9bUWwBH+N0OlW3bl117tzZdBQA8BkOh0MFBQVasWKF6SgAfqFTp05as2aNCgsLS1xPSUlR8+bNVa1atTKNl5CQoGPHjumFF17Q7bffrqFDh2r8+PE6e/asnE5niWcnTpyo2bNn66WXXlJAwJUn065du1ZpaWl68sknddddd8nhcGjixIny8/PT/Pnzy5TPW1FsAR/jcrkUHx8vPz/+9QeAytKiRQs1a9aMdbaAB+rZs6eys7O1devW4msFBQVas2aNevbsWebx1q5dqxYtWqhly5bF1xo0aKB27dpp9erVJZ6tXbu2bDbbNcdcs2aNIiMj1a1bt+JrERERio2N1YYNG5Sfn1/mnN6Gn2wBH7J//37t2LGDacgAUMlsNpscDgfFFvBAtWvXVlRUlFJSUoqvbd68WTk5OerRo0eZxioqKtKhQ4cuu+61ZcuWOn78uHJzc8uc8cCBA2rWrNklLyZatmypCxcuMB1ZFFvAp7hcLgUGBmrQoEGmowCAz7Hb7dqzZ4/2799vOgqAX4iLi9OGDRuUl5cnSUpOTlbbtm1Vo0aNMo2TnZ2t/Px8Va9e/ZJ7F6c0nz17tsz5zp49e9kxL147c+ZMmcf0NhRbwIc4nU717t1bERERpqMAgM/p37+//P39lZiYaDoKgF/o3r278vLytHHjRuXm5mrTpk3XNQ35YjG+3HrZoKCgEs+UddzLjXlxYyumIlNsAZ9x7tw5rVixQsOGDTMdBQB8UmRkpGJjY5mODHigiIgIRUdHKyUlRevWrVNRUZFiY2PLPM7F8lpQUHDJvYuF9uIzZR33cmNeLLRl3bnZG1FsAR+xdOlSXbhwgfW1AGCQw+HQ0qVLL/sDKgCzevbsqdTUVCUlJaljx44KDQ0t8xhhYWEKDAy87HTj9PR0SbrslOJrqV69+mXHvHitrFOmvRHFFvARTqdTUVFRioqKMh0FAHyW3W5XZmam1q5dazoKgF/o1q2bbDabdu/erbi4uOsaw8/PT40aNdK+ffsuubdnzx7VrVtXISEhZR63SZMm2r9/v4qKii4Zs0qVKqpXr9515fUmFFvAB7jdbrlcLqYhA4BhXbp0UfXq1VlnC3ig4OBg/eY3v9GIESPUuXPn6x4nNjZWe/fu1d69e4uvHT16VNu3b7+u6c0Xx8zIyNC6deuKr2VmZmrNmjWKiYlhKrKkK58CDMBrpKam6ujRo0xDBgDD/P39NXDgQCUkJGjSpEmm4wD4hT59+tzwGHa7XUuXLtX06dM1bNgw+fv7y+VyKTIy8pKXDBs3btSBAwckSYWFhTp48KC+/vprST/9RViTJk0k/VRsFy5cqFmzZunw4cMKDw9XYmKiioqKdPfdd99wZm9AsQV8gMvlUnh4uHr16mU6CgD4PIfDoUcffVRnzpxhXRzghUJCQjRx4kR99tln+vvf/y632622bdvqgQceuORkirVr12rlypXF/3zgwIHioluzZs3iYuvn56cXX3xR8+bN0+LFi5WXl6cWLVroySefVP369Svts3kym9vtdl/roczMTEVGRiojI4NjQgAL6t69uxo0aKCvvvrKdBQA8Hk//vijGjdurC+//FJ33XWX6TiA19m0aZM6d+6sadOmqXnz5qbjeIR9+/bppZde0saNGxUTE2M6TqmVpYeyxhbwcidPntTatWtZXwsAHqJRo0Zq06YN62wBoBwxFRnwcosWLZLb7daQIUNMRwEA/Ifdbi+eomiz2UzHAVAK2dnZVz2qy8/Pj9mtBlFsAS/ndDrVtWtX1a1b13QUAMB/OBwOvfvuu/rhhx/UunVr03EAlMKMGTO0Y8eOK96vXbu2Zs6cWYmJ8HMUW8CL5efnKyEhQc8995zpKACAn+ndu7eCgoKUkJBAsQUsYtSoUcrOzr7i/aCgoEpMg1+i2AJeLDk5WZmZmRzzAwAeJjQ0VL169VJiYqKeeeYZ03EAlAIbUXk2No8CvJjL5VK9evXUqVMn01EAAL9gt9u1YsUKXbhwwXQUALA8ii3gxVwul+Lj4+Xnx7/qAOBpHA6HcnJylJKSYjoKAFgeP+0CXmrv3r3atWsX05ABwENFR0erbt26SkhIMB0FACyPYgt4KZfLpaCgIA0cONB0FADAZfj5+clut3OeLQCUA4ot4KWcTqf69Omj8PBw01EAAFdgt9uVmpqq48ePm44CAJZGsQW8UHZ2tlauXKlhw4aZjgIAuIpBgwZJkpKSkgwnAQBro9gCXmjJkiXKy8tjfS0AeLi6deuqY8eOrLMFgBtEsQW8kNPpVOvWrdWiRQvTUQAA1+BwOJSUlKSioiLTUQDAsii2gJcpKirSwoULeVsLABZht9t1/Phxbd261XQUALAsii3gZTZv3qxjx45RbAHAInr27KmqVauyOzIA3ACKLeBlXC6XIiIiFBcXZzoKAKAUqlSpon79+rHOFgBuAMUW8DJOp1MOh0OBgYGmowAASslutys5OVnnzp0zHQUALIliC3iR48ePa/369RzzAwAW43A4lJeXp5UrV5qOAgCWRLEFvMiiRYtks9k0ZMgQ01EAAGXQqlUrNW7cmHW2AHCdKLaAF3E6nerWrZtq165tOgoAoAxsNpscDgfrbAHgOlFsAS+Rl5enxMREpiEDgEXZ7Xbt2rVLhw4dMh0FACyHYgt4iVWrVikrK4tjfgDAogYMGCA/Pz+mIwPAdaDYAl7C5XKpfv366tixo+koAIDrUL16dXXr1o1iCwDXgWILeAmXy6WhQ4fKZrOZjgIAuE4Oh0NLlixRYWGh6SgAYCkUW8AL7N69W2lpaayvBQCLs9vtOnv2rNavX286CgBYCsUW8AIul0tVqlTRgAEDTEcBANyAbt26KTIykunIAFBGFFvACzidTvXt21ehoaGmowAAbkBAQIAGDBjAsT8AUEYUW8DiMjMz9d133zENGQC8hMPh0Nq1a5Wenm46CgBYRoDpAABuTFJSkvLz8znmBwC8hN1uV2FhoZYtW6Zf/epXpuMAlnXkyBHTETyGL/xeUGwBi3O5XGrbtq2aNWtmOgoAoBw0bdpUrVq1UmJiIsUWuA7h4eGSpJkzZxpO4nku/t54I4otYGFFRUVauHCh7r//ftNRAADlyG63y+l0yu12c4wbUEZRUVFKS0tTVlaW6SgeJTw8XFFRUaZjVBiKLWBhGzdu1PHjx5mGDABexuFwaObMmdqzZ49X/yAKVBT+vfE9bB4FWJjL5VK1atXUo0cP01EAAOWob9++CgwMZHdkACglii1gYU6nUw6HQ4GBgaajAADKUVhYmHr27Ml5tgBQShRbwKKOHTumjRs3cswPAHgpu92u5cuXKy8vz3QUAPB4FFvAohYtWiSbzabBgwebjgIAqAAOh0PZ2dlavXq16SgA4PEotoBFOZ1OxcbGqlatWqajAAAqQMeOHVW7dm3W2QJAKVBsAQu6cOGCkpKSmIYMAF7Mz89PgwYNYp0tAJQCxRawoO+++07Z2dkc8wMAXs5ut2vTpk06efKk6SgA4NEotoAFuVwuNWzYUO3btzcdBQBQgex2u9xut5YsWWI6CgB4NIotYDFut1tOp1NDhw6VzWYzHQcAUIHq1aun6Oho1tkCwDVQbAGLSUtL0969e1lfCwA+wuFwKDExUW6323QUAPBYFFvAYlwul4KDg9W/f3/TUQAAlcBut+vYsWPavn276SgA4LEotoDFOJ1O9evXT1WrVjUdBQBQCXr16qXg4GB2RwaAq6DYAhaSkZGhVatWMQ0ZAHxIcHCw+vTpwzpbALgKii1gIYmJiSooKOCYHwDwMQ6HQ999951yc3NNRwEAj0SxBSzE5XKpXbt2atKkiekoAIBKZLfbdeHCBX333XemowCAR6LYAhZRVFSkhQsX8rYWAHxQ27Zt1aBBA9bZAsAVUGwBi1i/fr1OnjxJsQUAH2Sz2WS321lnCwBXQLEFLMLlcql69erq3r276SgAAAMcDof+9a9/6ciRI6ajAIDHodgCFuF0OjV48GAFBASYjgIAMGDgwIGy2WxMRwaAy6DYAhZw9OhRbd68mWN+AMCH1axZU126dKHYAsBlUGwBC1i4cKH8/Pw0ePBg01EAAAbZ7XYlJSWpsLDQdBQA8CgUW8ACnE6nevTooRo1apiOAgAwyOFw6PTp09q8ebPpKADgUSi2gIc7f/68lixZwm7IAADFxsYqPDyc3ZEB4BcotoCHW7lypc6dO0exBQAoMDBQ/fv3Z50tAPwCxRbwcC6XS40bN1a7du1MRwEAeACHw6Hvv/9emZmZpqMAgMeg2AIezO12y+l0aujQobLZbKbjAAA8gN1uV0FBgVasWGE6CgB4DIot4MF27dql/fv3c8wPAKBYixYt1KJFC9bZAsDPUGwBD+ZyuRQSEqJ+/fqZjgIA8CB2u511tgDwMxRbwIM5nU71799fISEhpqMAADyIw+HQnj17tG/fPtNRAMAjUGwBD5Wenq7k5GSmIQMALtGvXz8FBATw1hYA/oNiC3iohIQEFRYWKj4+3nQUAICHiYiIUPfu3VlnCwD/QbEFPJTL5VL79u3VuHFj01EAAB7Ibrdr6dKlys/PNx0FAIyj2AIeqLCwUIsWLdLQoUNNRwEAeCiHw6GsrCytXbvWdBQAMI5iC3igdevW6dSpU6yvBQBcUUxMjGrUqMF0ZAAQxRbwSC6XSzVr1tStt95qOgoAwEP5+/tr0KBBbCAFAKLYAh7J6XRq8ODB8vf3Nx0FAODB7Ha71q9fr9OnT5uOAgBGUWwBD3P48GFt2bKFacgAgGuy2+1yu91aunSp6SgAYBTFFvAwLpdL/v7+cjgcpqMAADxcw4YN1bZtW9bZAvB5FFvAw7hcLvXs2VPVq1c3HQUAYAEOh0OJiYlyu92mowCAMRRbwIPk5uZq6dKlHPMDACg1u92uw4cPa+fOnaajAIAxFFvAg6xYsUI5OTkUWwBAqfXu3VtVqlRhd2QAPo1iC3gQl8ulpk2bqm3btqajAAAsomrVqurVqxfrbAH4NIot4CHcbrecTqeGDh0qm81mOg4AwEIcDodWrlyp8+fPm44CAEZQbAEPsWPHDh08eJBjfgAAZWa325Wbm6vk5GTTUQDACIot4CFcLpeqVq2qvn37mo4CALCY6Oho3XTTTayzBeCzKLaAh3A6nRo4cKCCg4NNRwEAWIzNZpPdbmedLQCfRbEFPMCZM2f0/fffsxsyAOC6ORwObd26VceOHTMdBQAqHcUW8AAJCQkqLCxUfHy86SgAAIsaOHCgJCkpKclwEgCofBRbwAO4XC517NhRDRs2NB0FAGBRderUUUxMDOtsAfgkii1gWGFhoRYtWsQ0ZADADbPb7UpMTFRRUZHpKABQqSi2gGFr1qzRmTNnOOYHAHDDHA6HTp48qS1btpiOAgCVimILGOZyuVSrVi117drVdBQAgMX16NFDoaGh7I4MwOdQbAHDnE6nhgwZIn9/f9NRAAAWFxQUpH79+rHOFoDPodgCBh06dEjbtm1jGjIAoNzY7XYlJycrOzvbdBQAqDQUW8Agl8slf39/2e1201EAAF7C4XAoPz9fK1euNB0FACoNxRYwyOVyqVevXqpWrZrpKAAALxEVFaUmTZqwzhaAT6HYAobk5ORo6dKlHPMDAChXNptNDoeDdbYAfArFFjBk+fLlOn/+POtrAQDlzm6364cfftDBgwdNRwGASkGxBQxxuVxq3ry5br75ZtNRAABeZsCAAfLz8+OtLQCfQbEFDHC73XI6nRo6dKhsNpvpOAAAL1OtWjXdeuutrLMF4DMotoAB27dv148//sg0ZABAhXE4HFq6dKkKCgpMRwGACkexBQxwOp0KDQ1Vnz59TEcBAHgpu92u9PR0rV+/3nQUAKhwFFvAAJfLpUGDBqlKlSqmowAAvFTXrl1VrVo11tkC8AkUW6CSnT59WqtXr+aYHwBAhQoICNDAgQNZZwvAJ1BsgUq2ePFiFRUVKT4+3nQUAICXs9vtWrt2rdLT001HAYAKRbEFKpnL5VJMTIzq169vOgoAwMvZ7XYVFRVp6dKlpqMAQIWi2AKVqKCgQIsWLWIaMgCgUjRp0kQ333wz62wBeD2KLVCJVq9erfT0dI75AQBUGofDoYSEBLndbtNRAKDCUGyBSuRyuVSnTh116dLFdBQAgI+w2+06ePCgdu/ebToKAFQYii1QiZxOp+Lj4+Xnx796AIDK0bdvX/3/9u4+qKo68eP456LcIATT5UmwVAIqzEfMDFAh5V58mJ2tNMYSt9pmtjF3ZrfZqXamrZzZdtoedltj7GnaZtdtDbTpjzhR4gMEV0kxVIwKTK1WI/IhAa8Beu/vD3+y3VDk6XLugfdrxnHu93zvuZ97Z5rx0znf8w0ODubpyAAGNf51DQyQw4cP65NPPmF9LQBgQIWFhSkjI4NiC2BQo9gCA8QwDA0fPlzZ2dlmRwEADDFOp1Pbtm1Ta2ur2VEAwC8otsAAMQxDc+bM0ciRI82OAgAYYhwOh9xut7Zv3252FADwC4otMABOnz6trVu3chsyAMAUU6ZMUXR0NNv+ABi0KLbAANi6dataW1vZ5gcAYIqgoCBlZ2ezzhbAoEWxBQaAYRhKTExUcnKy2VEAAEOU0+lUdXW1GhsbzY4CAP2OYgv4mdfrlWEY3IYMADDVhYcXlpSUmJwEAPofxRbws3379um///0vtyEDAEwVGxurKVOmsM4WwKBEsQX8rKioSCNGjNCcOXPMjgIAGOIcDoc2bdokr9drdhQA6FcUW8DPDMOQw+GQ3W43OwoAYIhzOp1qaGhQTU2N2VEAoF9RbAE/OnbsmCorK1lfCwAICOnp6QoNDeXpyAAGHYot4EfFxcXyer1auHCh2VEAAFBISIgyMzNZZwtg0KHYAn5kGIZmzJih2NhYs6MAACDp/Drb8vJyud1us6MAQL+h2AJ+0t7ervfff5/bkAEAAcXpdKq1tVUffvih2VEAoN9QbAE/2b59u06dOsU2PwCAgHL99ddr7NixrLMFMKhQbAE/MQxDMTExmj59utlRAADoYLPZ5HQ6WWcLYFCh2AJ+UlRUpEWLFikoiP/MAACBxeFwqLa2Vl9//bXZUQCgX/AvbsAPDh48qE8//ZT1tQCAgDR//nzZbDaVlJSYHQUA+gXFFvADwzAUHBys7Oxss6MAANDJ6NGjddNNN7HOFsCgQbEF/MAwDM2dO1fh4eFmRwEA4KKcTqc2b96sc+fOmR0FAPqMYgv0s5aWFm3bto3bkAEAAc3hcOjEiRPavXu32VEAoM8otkA/27Jli9ra2tjmBwAQ0G6++WZFRETwdGQAgwLFFuhnhmEoOTlZiYmJZkcBAOCSgoODdeutt7LOFsCgQLEF+pHX65VhGNyGDACwBKfTqR07dqipqcnsKADQJxRboB/t2bNHR48e5TZkAIAlOBwOnTt3Tlu3bjU7CgD0CcUW6EdFRUUKDw9XRkaG2VEAALishIQEJSYmss4WgOVRbIF+ZBiGnE6n7Ha72VEAAOgWh8PBOlsAlkexBfpJY2Ojdu7cyfpaAIClOJ1OHTx4UF988YXZUQCg1yi2QD8pLi6WJC1YsMDkJAAAdF9mZqaGDx/OVVsAlkaxBfqJYRi66aabFBMTY3YUAAC6LSIiQmlpaayzBWBpFFugH7S3t+uDDz7gNmQAgCU5HA5t3bpV7e3tZkcBgF6h2AL9oKKiQk1NTWzzAwCwJKfTqebmZlVWVpodBQB6hWIL9IOioiKNGTNG06ZNMzsKAAA9Nn36dP3sZz9jnS0Ay6LYAv3AMAwtWrRINpvN7CgAAPRYUFCQsrOzWWcLwLIotkAfHThwQJ9//jnrawEAluZ0OlVVVaVjx46ZHQUAeoxiC/SRYRiy2+2aP3++2VEAAOi17Oxseb1ebdmyxewoANBjFFugjwzDUGZmpkaMGGF2FAAAei0+Pl433ngj62wBWBLFFuiD5uZmlZaWchsyAGBQcDgc2rRpk7xer9lRAKBHKLZAH2zevFnt7e0UWwDAoOB0OnXkyBHV1taaHQUAeoRiC/SBYRi6/vrrde2115odBQCAPps9e7ZCQkJ4OjIAy6HYAr3k8XhkGIYWL15sdhQAAPpFaGio5syZwzpbAJZDsQV6qbq6Wg0NDdyGDAAYVBwOh8rKynTmzBmzowBAt1FsgV4qKirSyJEjlZ6ebnYUAAD6jdPp1A8//KCKigqzowBAt1FsgV4yDENOp1PBwcFmRwEAoN9MnDhRcXFx3I4MwFIotkAvNDQ0aNeuXdyGDAAYdGw2W8e2PwBgFRRboBeKi4tls9m0YMECs6MAANDvHA6HampqdPToUbOjAEC3UGyBXjAMQzfffLOioqLMjgIAQL/Lzs6WzWZTSUmJ2VEAoFsotkAPtbW1adOmTdyGDAAYtCIjIzV9+nTW2QKwDIot0EPl5eVqbm5m/1oAwKDmdDpVUlIij8djdhQAuCyKLdBDRUVFio+P15QpU8yOAgCA3zgcDh07dkzV1dVmRwGAy6LYAj1kGIYWLVokm81mdhQAAPzmlltu0YgRI3g6MgBLoNgCPVBXV6f6+nrW1wIABj273a6srCzW2QKwBIot0AOGYeiKK67QvHnzzI4CAIDfOZ1OuVwutba2mh0FALo03OwAgJUYhqGsrCyFhYWZHQUAAL/Ly8tTW1ub7Ha72VEAoEtcsQW6qampSWVlZdyGDAAYMiIiIvS73/2O50oACHgUW6CbSkpKdPbsWYotAAAAEGC4FRnowvbt21VeXq6cnBy9++67SklJ0YQJE8yOBQAAAOBHuGILdGHdunV69NFHNXXqVK1bt07BwcF699135Xa7zY4GAAAA4P9RbIEupKSkdKwr8ng82r9/v37+858rOTlZXq/X5HQAAAAAJIot0KXJkyf7FNhz585JkpYtW8aDNAAAAIAAwRpboAuTJk3yeW2z2XTvvffqmWeeMSkRAAAAgJ/iii3QhdGjRysqKkrS+VL7i1/8Qq+++ipXawEAAIAAQrEFLiMmJkaSlJGRofXr12vYsGEmJwIAAADwY9yKDFxGenq6Tp06pffee09XXHGF2XEAAAAA/ATFFriMl19+2ewIAAAAALpAsQUAABgk6uvr1dzcbHaMgBIeHq6kpCSzYwDwM4otAADAIFBfX6/k5GSzYwSkuro6yi0wyFFsAQAABoELV2pXrVql+Ph4k9MEhiNHjig/P5+r2MAQQLEFAAAYROLj45WQkGB2DAAYUGz3AwAAAACwNIotAAAAfJSWlio3N1eNjY1mR+lk9erVWr16tdkxAAQYii0AAMAQ8MwzzygvL09nzpy55Jw1a9borrvu6vGa1HfeeUe7du3qa0QA6DWKLQAAwBCQkZGhtrY27dy586LHW1tbVVVVpalTp2rRokVat26doqKiunVuii0As1FsAQAAhoAZM2YoNDRULpfroserqqrU2tqqjIwMBQUFyW63y2azXfJ8Xq9XbW1t/ooLAD3CU5EBAACGALvdrpkzZ6qiokKnTp3SyJEjfY5XVFQoNDRUM2bMUGlpqV566SW9+OKLio6OlnR+G6Grr75aTqdTBQUF+vrrr7Vs2TL961//kiSVlZWprKxMkjR37lytXLlSa9euVW1trfLz830+a8OGDdq4caMKCgo6xrZt26by8nJ9/fXXcrvdiomJUU5OjhwOhz9/FgCDBMUWAABgiMjIyFBZWZl27NihnJycjvGWlhbt3btX6enpstvtl3z/0aNHtWbNGs2fP1+33nqr4uLitGrVKr3yyiu69tprNX/+fElSTExMj7OVlJRo7NixSk1N1bBhw7R79269/vrr8nq9cjqdPf+yAIYUii38rr6+no3RLyI8PFxJSUlmxwAADCE33nijRo0aJZfL5VNsd+zYoXPnzikjI6PL9zc0NOgPf/iDpk6d6jP+2muvKSYmRrNnz+51tieffNKnVOfk5OjPf/6zDMOg2AK4LIot/Kq+vl7JyclmxwhYdXV1lFsAwIAJCgpSWlqaDMNQY2Njx23GLpdLI0eO1KRJk7p8f3R0dKdS219+XGrdbrfOnj2rG264QXv37pXb7daVV17pl88FMDhQbOFXF67Urlq1SvHx8SanCRxHjhxRfn4+V7IBAAMuIyNDhmHI5XLptttu0/Hjx/XZZ58pJydHQUFdP1f0QhH2h88++0wbNmxQfX29WltbfY5RbAFcDsUWAyI+Pl4JCQlmxwAAYMhLSEhQXFxcR7F1uVzyer2XvQ1ZUpfrb3vC4/H4vG5oaNCf/vQnxcXFKS8vT5GRkRo2bJj27NkjwzA6zQeAn6LYAgAADDEZGRkqLCzUl19+KZfLpTFjxigxMbHX57vUtkAjRozQ6dOnO41/9913Pq93796t9vZ2Pfzww4qMjOwYr62t7XUmAEML+9gCAAAMMRce8lRYWKjDhw8rPT29T+e74oorLlpgY2Ji5Ha79eWXX3aMnTx5Urt27fKZd+EWaK/X2zHmdrtVWlrap1wAhg6u2AIAAAwx0dHRSk5OVlVVlST16WnG0vnbm2tqalRUVKRRo0YpOjpaSUlJSktL05tvvqnnn39eOTk5am1tVUlJicaMGaNDhw51vH/KlCkaPny4nnnmGc2fP18//PCDtmzZooiICJ08ebJP2QAMDVyxBQAAGIIurKlNTExUbGxsn861YsUKJSQkqKCgQGvWrFFJSYmk81vb/f73v5fdbtebb76pDz/8UMuWLVNqaqrP++Pi4vTQQw9JktatW6eSkhLNmzdPCxYs6FMuAEMHV2wBAACGIKfTecn9YTMzM5WZmekzlp+ff8lzxcXF6cknn7zoscmTJ+u5557rNL506VKf16mpqZ0KryRlZWX5vH7iiScumQPA0MUVWwAAAACApVFsAQAAAACWRrEFAAAAAFgaxRYAAAAAYGkUWwAAAACApVFsAQAAAACWRrEFAABAr+Tm5mrDhg3dmrtq1SqtXbu2x5/R2Nio3NxclZaW9vi9AIYOii0AAMAQVlpaqtzcXH3xxRd9Ptfnn3+uDRs26PTp0/2QDAC6b7jZAQAAAGBN69at07Bhwzpe19XVaePGjZo7d67CwsJ85v7tb3+TzWYb6IgAhgiKLQAAAHrFbrd3e25wcLAfkwAY6ii2AAAA6LB27VpVVlbqhRde0Ouvv66amhrZ7XbNnTtXd999t4KC/reSLTc3V0uWLNHSpUu1YcMGbdy4UZL0m9/8pmPOiy++qOjoaK1atUopKSlauXKlJKmlpUXvvPOO9u7dq8bGRgUFBem6667TsmXLNH78+AH9zgCsj2ILAAAAHx6PR0899ZSSkpKUl5enmpoaFRUVKSYmRg6H46LvmTlzpr755hu5XC6tWLFCERERktTx9099++232rVrl2bNmqXo6GidOnVKmzdv1urVq/X8889r9OjRfvt+AAYfii0AAAB8tLe3Ky0tTXfccYckKTs7W4888oi2bdt2yWI7btw4TZgwQS6XSzfddJOio6O7/IxrrrlGL7zwgs8V4NmzZ+uhhx7Stm3bOj4bALqDpyIDAACgk+zsbJ/XN9xwg7799tt+O39wcHBHqfV4PGpublZISIjGjBmjQ4cO9dvnABgauGILAAAAH8HBwZ1uIQ4LC+vXbXw8Ho+Ki4u1adMmNTY2yuPxdBwLDw/vt88BMDRQbAEAAODjx7cH+8s777yjwsJCZWVl6c4779SIESNks9n0z3/+U16v1++fD2BwodgCAABgwH300UeaOHGiHnjgAZ9xt9t9yQdOAcClsMYWAAAA/SIkJETS+XJ6OUFBQZ2uzO7YsUMnTpzwSzYAgxtXbAEAANAvJkyYIEl66623lJaWpmHDhik1NbWj8P7Y9OnT9fbbb2vt2rW67rrr9NVXX6miokIxMTEDHRvAIECxRUAoLS3VSy+9JElavXq1rr/+ep/jXq9XDz74oI4fP67p06frkUce6dZ5jx49qpKSEh04cECHDh1Se3t7x0bxP7V9+3bt3r1bBw4cUENDg1JSUvTEE09c9Lzt7e0qLCxUeXm5WlpaNG7cOOXm5mry5Mk9/OYAAAweiYmJuvPOO7V582bt2bNHXq9XL7744kWL7W233abW1la5XC7t2LFDEyZM0COPPKL169ebkByA1VFsEVCCg4NVUVHRqdjW1tbq+PHjCg4O7tH56urqVFxcrLFjxyo+Pl6HDx++5NySkhIdPHhQ1157rZqbm7s879q1a/XRRx9p4cKFio2NVVlZmZ5++mk9/vjjnbIDABDIMjMzlZmZ2fF65cqVWrlyZad5S5cu1dKlS33GCgoKOs274447LroHbX5+vs/r4OBg5eXlKS8vz2f8p/9TOTo6+qKfAwA/xhpbBJRp06apsrJS586d8xl3uVxKSEjQVVdd1aPzzZgxQ2+88Yaee+45ZWRkdDn3wQcf1BtvvKHHH39co0aNuuS8AwcOaPv27Vq2bJmWL1+u+fPn649//KMiIyP15ptv9igfAAAAgL6j2CKgpKenq6WlRfv27esYO3v2rCorK5Went7j840YMUKhoaHdmhsZGdmt7Q0qKysVFBSkefPmdYzZ7XZlZWWprq5Ox44d63FOAAAAAL1HsUVAiYqKUlJSklwuV8dYdXW13G630tLSTEz2P4cPH9aYMWN05ZVX+ownJiZKkr788kszYgEAAABDFsUWAScjI0NVVVVqa2uTJFVUVCglJUWjR482Odl5J0+evOityhfG2KYAAAAAGFgUWwScW265RW1tbdq9e7fOnDmjjz/+uFe3IftLe3u7hg/v/Ny1Cw+2ulDIAQAAAAwMnoqMgBMREaFJkybJ5XKpra1NHo9Hs2bNMjtWh+DgYJ09e7bTeHt7u6Tz620BAAAADByu2CIgpaena8+ePSopKdHUqVMVFhZmdqQOo0aN0smTJzuNXxgLlFumAQAIRLm5udqwYUOP39fY2Kjc3FyVlpb2fygAlkexRUCaOXOmbDab6uvrL7tNz0AbP368vvnmG7ndbp/xAwcOSJLGjRtnRiwAAHqktLRUubm5ys3N1WeffdbpuNfr1cqVK5Wbm6u//OUvJiQEgO6j2CIghYSE6P7779eSJUuUmppqdhwfN998szwej7Zs2dIx1t7ertLSUiUmJioyMtLEdAAA9ExwcLAqKio6jdfW1ur48eMdz5AAgEDGGlsErLlz5/b5HG63W8XFxZKkuro6SdIHH3ygK6+8UmFhYcrJyemYW1tbq08//VSS1NzcrNbWVr399tuSpBtuuEEpKSmSpKSkJM2aNUvr16/XqVOnFBsbq7KyMn333Xf69a9/3efMAAAMpGnTpqmyslL33nuvhg0b1jHucrmUkJCg5uZmE9MBQPdQbDGotbS0qLCw0GesqKhI0vk9c39cbD/55BNt3LjRZ+6F9y5ZsqSj2ErSgw8+qMLCQpWXl+v06dO65ppr9PDDD/vMAQDACtLT07Vr1y7t27dP06ZNkySdPXtWlZWVuv322/X+++/7zP/hhx9UWFioyspKnTp1SlFRUZo3b54WL14sm83WMa+9vV3/+c9/VF5ervb2dk2cOFG/+tWvLprhxIkTKigoUHV1tU6fPq3Y2FgtXrxYWVlZ/vviAAYVii0CQmZmpjIzMy87Lz8/v0fnjY6OVkFBQbfmLl26VEuXLu3WXLvdruXLl2v58uU9ygMAQKCJiopSUlKSXC5XR7Gtrq6W2+1WWlqaT7H1er169tln9cknnygrK0vjx4/X3r179e9//1snTpzQL3/5y465r7zyisrLy5Wenq7rrrtO+/fv19NPP93p87///ns99thjkiSn06mIiAjt2bNHL7/8stxutxYtWuTnXwDAYECxBQAAGOIyMjK0fv16tbW1yW63q6KiQikpKZ2e9F9VVaX9+/crNzdXt99+u6TzZfSvf/2riouL5XQ6FRsbq8OHD6u8vFwOh6PjKq3T6dSaNWv01Vdf+Zzzrbfeksfj0bPPPqvw8HBJUnZ2tv7+979r48aNys7OZis9AJfFw6NgSS0tLfr+++8v+aepqcnsiAAAWMYtt9yitrY27d69W2fOnNHHH3+s9PT0TvOqq6sVFBSkBQsW+IwvXrxYXq9Xe/bs6ZgnqdO8hQsX+rz2er3auXOnpk+fLq/Xq6ampo4/U6ZMkdvt1sGDB/vxmwIYrLhiC0t6/vnnVVtbe8njUVFRPb5tGQCAoSoiIkKTJk2Sy+VSW1ubPB6PZs2a1WnesWPHNGrUKIWGhvqMjx07tuP4hb9tNptiYmJ85sXFxfm8bmpq0unTp7Vlyxaf3QZ+OgcALodiC0vKy8tTS0vLJY9zyxIAAD2Tnp6uV199Vd9//72mTp2qsLAwv3+m1+uVJM2ePVtz5sy56Bz2hwfQHRRbWFJCQoLZEQAAGFRmzpyp1157TfX19frtb3970TmRkZGqqanRmTNnfK7aHjlypOP4hb+9Xq++/fZbn6u0R48e9TlfRESEQkND5fF4NHny5H7+RgCGEtbYAgAAQCEhIbr//vu1ZMkSpaamXnTOtGnT5PF4Om0BZBiGbDabpk6d2jFPUsde8he89957Pq+DgoI0c+ZMffTRR50eKiVxGzKA7uOKLQAAACRJc+fO7fJ4amqqJk6cqIKCAn333XcaN26c9u3bp6qqKi1cuFCxsbGSpPHjxys9PV2bNm2S2+1WcnKy9u/fr4aGhk7nvOuuu1RbW6vHHntMt956q8aOHauWlhYdOnRINTU1+sc//uGX7wpgcKHYAgAAoFuCgoL08MMPq7CwUNu3b1dpaamio6O1fPlyLV682GfuAw88oIiICFVUVGjXrl268cYb9eijj2rlypU+86666io99dRTevvtt7Vz505t2rRJ4eHhGjt2rO6+++6B/HoALIxiCwAAMARlZmYqMzPzsvN+ustASEiIVqxYoRUrVnT5PrvdrnvuuUf33HOPz3hBQUGnuSNHjtR9992n++6775Lni46Ovuh7AUBijS0AAAAAwOIotgAAAAAAS6PYAgAAAAAsjWILAAAAALA0ii0AAAAAwNIotgAAAAAAS6PYAgAAAAAsjX1sAQAABpEjR46YHSFg8FsAQwfFFgAAYBAIDw+XJOXn55ucJPBc+G0ADF4UWwAAgEEgKSlJdXV1am5uNjtKQAkPD1dSUpLZMQD4GcUWAABgkKDAARiqeHgUAAAAAMDSKLYAAAAAAEuj2AIAAAAALI1iCwAAAACwNIotAAAAAMDSKLYAAAAAAEuj2AIAAAAALI1iCwAAAACwtOFmB8DQcOTIEbMjBBR+DwAAAKD/UGzhV+Hh4ZKk/Px8k5MEpgu/DwAAAIDeo9jCr5KSklRXV6fm5mazowSc8PBwJSUlmR0DAAAAsDyKLfyO8gYAAADAn3h4FAAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0ii2AAAAAABLo9gCAAAAACyNYgsAAAAAsDSKLQAAAADA0oZ3Z5LX65UkNTU1+TUMAAAAAADS//rnhT7alW4V2+bmZknS1Vdf3YdYAAAAAAD0THNzs0aOHNnlHJu3G/XX4/Ho6NGjCg8Pl81m67eAAAAAAABcjNfrVXNzs+Li4hQU1PUq2m4VWwAAAAAAAhUPjwIAAAAAWBrFFgAAAABgaRRbAAAAAIClUWwBAAAAAJZGsQUAAAAAWBrFFgAAAABgaRRbAAAAAICl/R/qp5jJEWazPAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -219,7 +221,9 @@ "\n", "This shows all calibrated models.\n", "\n", - "In this example, models with 2 estimated parameters tend to perform best. This is also seen in the UpSet plot above." + "In this example, models with 2 estimated parameters tend to perform best. This is also seen in the UpSet plot above.\n", + "\n", + "Jitter is added to distinguish models with the same number of parameters and similar criterion values, according to the optional `max_jitter` argument." ] }, { @@ -230,7 +234,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -245,6 +249,8 @@ " criterion=petab_select.Criterion.AICC,\n", " labels=labels,\n", " colors=colors,\n", + " # Uncomment to turn off jitter.\n", + " # max_jitter=0,\n", ");" ] }, @@ -268,7 +274,7 @@ "outputs": [ { "data": { - "image/png": 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" ] diff --git a/petab_select/plot.py b/petab_select/plot.py index e2dd25da..ae77bfc3 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -297,6 +297,7 @@ def graph_history( "arrowstyle": "-|>", "node_shape": "s", "node_size": 2500, + "edgecolors": "k", } if options is None: options = default_options @@ -349,6 +350,9 @@ def bar_criterion_vs_models( The axis to use for plotting. bar_kwargs: Passed to the matplotlib `ax.bar` call. + colors: + Custom colors. Keys are model hashes or ``labels`` (if provided), + values are matplotlib colors. Returns: The plot axes. @@ -405,6 +409,7 @@ def scatter_criterion_vs_n_estimated( colors: dict[str, str] = None, labels: dict[str, str] = None, scatter_kwargs: dict[str, str] = None, + max_jitter: float = 0.2, ) -> plt.Axes: """Plot criterion values against number of estimated parameters. @@ -413,11 +418,24 @@ def scatter_criterion_vs_n_estimated( A list of models. criterion: The criterion. - ax: - The axis to use for plotting. relative: If `True`, criterion values are offset by the minimum criterion value. + ax: + The axis to use for plotting. + colors: + Custom colors. Keys are model hashes or ``labels`` (if provided), + values are matplotlib colors. + labels: + A dictionary of model labels, where keys are model hashes, and + values are model labels, for plotting. If a model label is not + provided, it will be generated from its model ID. + scatter_kwargs: + Forwarded to ``matplotlib.axes.Axes.scatter``. + max_jitter: + Add noise to distinguish models with the same number of parameters + and similar criterion values. This is a positive value that is the + maximal difference to the original value. Returns: The plot axes. @@ -451,6 +469,13 @@ def scatter_criterion_vs_n_estimated( if relative: criterion_values = get_relative_criterion_values(criterion_values) + if max_jitter: + n_estimated = np.array(n_estimated, dtype=float) + rng = np.random.default_rng() + n_estimated += rng.uniform( + -max_jitter, max_jitter, size=n_estimated.size + ) + if ax is None: _, ax = plt.subplots() From f9aa005fadadb2fd64fa53f6bcc4af601a094b98 Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 23:14:45 +0100 Subject: [PATCH 23/26] review; small refactor of default plot options --- doc/examples/visualization.ipynb | 136 ++++++++++++++++++++++++++++--- petab_select/plot.py | 75 ++++++++++------- 2 files changed, 170 insertions(+), 41 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index 0d6ffd3d..b3bb517f 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -34,6 +34,122 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2574e65a-1f16-4205-8c23-b65ba78f9a1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Model hashAICc criterionPredecessor model hashEstimated parameters
0M_0-00037.975230virtual_initial_model-sigma_x2
1M_1-000-0.175406M_0-000k3, sigma_x2
2M_2-000-0.274514M_0-000k2, sigma_x2
3M_3-000-0.705327M_0-000k1, sigma_x2
4M_5-0009.294673M_3-000k1, k3, sigma_x2
5M_6-0007.852170M_3-000k1, k2, sigma_x2
\n", + "
" + ], + "text/plain": [ + " Model hash AICc criterion Predecessor model hash Estimated parameters\n", + "0 M_0-000 37.975230 virtual_initial_model- sigma_x2\n", + "1 M_1-000 -0.175406 M_0-000 k3, sigma_x2\n", + "2 M_2-000 -0.274514 M_0-000 k2, sigma_x2\n", + "3 M_3-000 -0.705327 M_0-000 k1, sigma_x2\n", + "4 M_5-000 9.294673 M_3-000 k1, k3, sigma_x2\n", + "5 M_6-000 7.852170 M_3-000 k1, k2, sigma_x2" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "pd.DataFrame(\n", + " {\n", + " \"Model hash\": [model.model_id for model in models],\n", + " \"AICc criterion\": [\n", + " model.get_criterion(petab_select.Criterion.AICC)\n", + " for model in models\n", + " ],\n", + " \"Predecessor model hash\": [\n", + " model.predecessor_model_hash for model in models\n", + " ],\n", + " \"Estimated parameters\": [\n", + " \", \".join(model.get_estimated_parameter_ids_all())\n", + " for model in models\n", + " ],\n", + " }\n", + ")" + ] + }, { "cell_type": "markdown", "id": "aaeb0606", @@ -44,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "09c9df1d", "metadata": {}, "outputs": [], @@ -84,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "96d99572-f74d-4e25-8237-0aa158eb29f6", "metadata": {}, "outputs": [ @@ -117,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "56b4a27b", "metadata": {}, "outputs": [ @@ -152,13 +268,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "862a78ef", "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -188,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "bce41584", "metadata": {}, "outputs": [ @@ -228,13 +344,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "824e2e6a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -268,13 +384,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "5ce191fc", "metadata": {}, "outputs": [ { "data": { - "image/png": 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dHa1XX31Vy5cvtx0HAADAa1H2AgAAAC4zfvx4nXrqqZo7d26J7YGBgZYSHb/+/furYcOGiomJsR0FAAB4IGOM61Y3ouwFAAAAXGTbtm26++67tXfvXuXl5ZXYFxQUZCnV8QsKClJ8fLw++OAD/fHHH7bjAAAAeCXKXgAAAMBF6tSpo6+++krt2rU7aJ8nj+yVpL59+6pt27YaNGiQa0fDAAAAeDLKXgAAAMBlevbsqZdeekmSVLt27aLtnjyyV5J8fX2VnJys6dOna9q0abbjAAAAD2NctLgVZS8AAADgMsYYRUVF6YwzztC6dev0wgsvqE+fPqpTp47taMftqquu0vnnn6/IyEjl5+fbjgMAAOBVKHsBAAAAl/nkk0/0yy+/KDU1VUFBQerfv78mTZokPz8/29GOm+M4SklJ0bx58/TBBx/YjgMAAOBVHMNkWQAAAIBr5OXl6bTTTlODBg307bff2o5TYa6++motXrxYf/31lwICAmzHAQAALpWenq7w8HB9u+RbVQ+tbjtOkcyMTHVv211paWkKCwuzHacII3sBAAAAF3n77be1ZMkSpaSk2I5SoZKSkrRq1SqNGzfOdhQAAIAKN3bsWJ122mkKCwtTWFiYOnXqpC+//LJo/759+/TQQw+pdu3aCgkJUe/evbV58+YyX4eyFwAAAHCJvXv3KjY2VjfeeKPOPvts23EqVLt27XTHHXdo2LBh2r17t+04AAAAFapx48ZKSUnRnDlz9Mcff+iiiy7SNddco8WLF0uSBg4cqE8//VSTJ0/WjBkztGHDBl133XVlvg7TOAAAAAAu8cwzzygqKkpLlixRy5YtbcepcGvWrFGrVq0UHR2t2NhY23EAAIAL7Z/GYdqSaa6bxqFH2x5au3ZtiWkcAgMDFRgYWKpz1KpVS08//bSuv/561a1bVxMnTtT1118vSfr777/Vpk0bzZo1Sx07dix1Lkb2AgAAAC6wa9cuJSUlqV+/flWi6JWkpk2bqn///nr66ae1ZcsW23EAAADKrEmTJgoPDy9ak5OTj/qavLw8TZo0SZmZmerUqZPmzJmjnJwcde/eveiY1q1bq2nTppo1a1aZ8lD2AgAAAC6QmpqqrKysKjfCNSoqSj4+PkpMTLQdBQAAoMzWrl2rtLS0ojUqKuqwxy5cuFAhISEKDAzU/fffr48++kht27bVpk2bFBAQoBo1apQ4vl69etq0aVOZ8lD2AgAAAJatX79eo0eP1mOPPab69evbjlOpateurcjISI0dO1arVq2yHQcAALiUMcZ1q6SiB67tX480hcPJJ5+s+fPn67ffftMDDzygO+64Q0uWLCnXvyfKXgAAAMCy+Ph4BQcH68knn7QdxYpHHnlEderU0ZAhQ2xHAQAAqDABAQFq0aKFzjrrLCUnJ+v000/X6NGjVb9+fWVnZ2vXrl0ljt+8eXOZBwJQ9gIAAAAW/f3333rjjTc0ePDgEg/3qEqCg4M1dOhQTZw4UfPnz7cdBwAAoFLk5+crKytLZ511lvz9/fXdd98V7Vu6dKnWrFmjTp06lemcjtk/5hgAAABApevdu7fmzJmjpUuXlvrJzd4oJydH7dq100knnaQvv/zSdhwAAOAS6enpCg8P19eLv1b10Oq24xTJzMjUJadcorS0tFL9wj4qKkqXXXaZmjZtqoyMDE2cOFGpqan6+uuv1aNHDz3wwAP64osvNH78eIWFhWnAgAGSpF9++aVMufyO6asBAAAAcNx+/fVXTZkyRW+99VaVLnolyd/fX4mJibrhhhv0ww8/qGvXrrYjAQAAlJstW7aob9++2rhxo8LDw3XaaacVFb2S9Nxzz8nHx0e9e/dWVlaWLrnkEo0ZM6bM12FkLwAAAGCBMUbdunXTjh07NG/ePPn6+tqOZJ0xRh07dpRUUIQ7jmM5EQAAsM1bRvZWFubsBQAAACz46quvNGPGDCUnJ1P0FnIcRykpKZo9e7amTJliOw4AAHARY4zrVjdiZC8AAABQyfLz83XGGWeoRo0a+uGHHxjBeoBLL71Uq1at0uLFi+Xnx8xzAABUZftH9n616CvXjey9tN2ljOwFAAAAqrqJEydqwYIFSk1Npeg9hJSUFC1btkxvvPGG7SgAAAAehbIXAAAAqERZWVkaMmSIevXqVTQ/LUpq3769brnlFsXFxWnPnj224wAAABcwLlzciLIXAAAAqEQvv/yy1qxZo6SkJNtRXG348OHatm2bRo8ebTsKAACAx6DsBQAAACpJenq6EhISdNddd6lNmza247jaSSedpPvvv1+pqanasWOH7TgAAAAegbIXAAAAqCTPPPOMdu/erbi4ONtRPEJMTIzy8vKUnJxsOwoAALDMGOO61Y0oewEAAIBKsHnzZj377LN6+OGH1bhxY9txPEJERISeeOIJvfDCC1qzZo3tOAAAAK5H2QsAAABUguHDh8vf31+RkZG2o3iUxx57TGFhYYyGBgAAKAXKXgAAAKCCrVixQq+88oqioqJUs2ZN23E8SmhoqGJjY/XWW29p8eLFtuMAAABLjAsXN6LsBQAAACrYkCFDVK9ePQ0YMMB2FI9077336sQTT1R0dLTtKAAAAK5G2QsAAABUoDlz5mjSpEmKj49XtWrVbMfxSAEBAUpISNAnn3yimTNn2o4DAADgWo5x66PjAAAAAC/Qs2dPrV27VgsXLpSfn5/tOB4rPz9fZ599toKDg/XTTz/JcRzbkQAAQCVIT09XeHi4PlvwmaqHVrcdp0hmRqauPO1KpaWlKSwszHacIozsBQAAACrIt99+q2nTpikpKYmi9zj5+PgoJSVFP//8sz777DPbcQAAAFyJkb0AAABABcjPz9c555wjf39//fLLL4xELQfGGHXv3l2bN2/Wn3/+KV9fX9uRAABABWNkb9kwshcAAACoAB9++KHmzJmj1NRUit5y4jiOUlJStHjxYr3zzju24wAAgEpkXLi4EWUvAAAAUM5ycnI0ePBgXXHFFerSpYvtOF6lQ4cOuuGGGxQbG6t9+/bZjgMAAOAqlL0AAABAOXvttde0cuVKJScn247ilRISErRhwwa99NJLtqMAAAC4CmUvAAAAUI52796t+Ph43X777Tr11FNtx/FKrVq1Ur9+/ZSUlKRdu3bZjgMAACqBMcZ1qxtR9gIAAADlaNSoUdq5c6eGDRtmO4pX2z+Nw4gRI2xHAQAAcA3KXgAAAKCcbNu2TSNGjNCDDz6oE044wXYcr9agQQM9+uijGjVqlDZs2GA7DgAAgCtQ9gIAAADlJDExUZI0ePBgy0mqhqeeekrVqlVTfHy87SgAAKCCGRcubkTZCwAAAJSD1atXa8yYMXrqqadUp04d23GqhPDwcA0ePFivv/66li5dajsOAACAdZS9AAAAQDmIjY1VzZo1NXDgQNtRqpQHH3xQjRo1YjQ1AACAKHsBAACA47ZgwQK9++67Gjp0qKpXr247TpUSFBSkYcOG6f/+7/80e/Zs23EAAEAFMca4bnUjyl4AAADgOEVFRal58+b63//+ZztKlXTbbbepXbt2GjRokGt/8AIAAKgMlL0AAADAcfjxxx/1xRdfKDExUf7+/rbjVEm+vr5KTk7WDz/8oK+//tp2HAAAAGscw6++AQAAgGNijNF5552nnJwczZ49Wz4+jKWwxRijLl26KCMjQ3PnzuWfBQAAXiI9PV3h4eH6aP5Hqh7qnumyMjMydW37a5WWlqawsDDbcYrwHRAAAABwjKZOnapff/1VqamplIuWOY6j1NRU/fnnn3rvvfdsxwEAALCC70gBAACAY5Cbm6vo6Gj16NFDF198se04kHTeeefpmmuuUUxMjLKysmzHAQAAqHSUvQAAAMAxGD9+vP7++2+lpKTYjoJikpKStGbNGr3yyiu2owAAgHJkjHHd6kaUvQAAAEAZ7dmzR3Fxcbrpppt05pln2o6DYtq2bas777xTCQkJysjIsB0HAACgUlH2AgAAAGX0wgsvaPPmzRo+fLjtKDiEuLg4ZWRkaOTIkbajAAAAVCrKXgAAAKAMdu7cqZSUFN13331q0aKF7Tg4hCZNmmjAgAF65plntHnzZttxAABAOTAuXNyIshcAAAAog5SUFOXk5GjIkCG2o+AIIiMj5e/vr4SEBNtRAAAAKg1lLwAAAFBK69at0/PPP6/HH39c9erVsx0HR1CrVi1FRkbq5Zdf1sqVK23HAQAAqBSUvQAAAEApxcXFKSQkRI8//rjtKCiFAQMGKCIiglHYAAB4AWOM61Y3ouwFAAAASmHJkiV68803FRMTo7CwMNtxUArBwcGKj4/Xe++9p7lz59qOAwAAUOEoewEAAIBSGDx4sJo2bar777/fdhSUwZ133qnWrVsrKirKdhQAAIAKR9kLAAAAHMWsWbM0depUDR8+XIGBgbbjoAz8/PyUmJiob775Rt99953tOAAA4DgYFy1u5Ri3TjABAAAAuIAxRhdeeKHS09M1d+5c+fgwXsLTGGPUqVMn5eXlafbs2XIcx3YkAABQSunp6QoPD9eHcz9UcGiw7ThF9mTs0fVnXq+0tDRXTfHFd6oAAADAEXzxxRf66aeflJycTNHroRzHUWpqqv744w99+OGHtuMAAABUGEb2AgAAAIeRl5en9u3bq06dOpo+fTojQj3cFVdcoWXLlmnJkiXy9/e3HQcAAJTC/pG9k+dOVnCIi0b27t6jG868gZG9AAAAgKeYMGGCFi1apJSUFIpeL5CcnKyVK1fq9ddftx0FAACgQlD2AgAAAIewb98+DRkyRNddd53OPfdc23FQDk477TTddtttio+PV2Zmpu04AAAA5Y6yFwAAADiEsWPHav369UpKSrIdBeVo2LBh2rFjh0aNGmU7CgAAKAPjwsWNKHsBAACAA6SlpSkxMVF33323Tj75ZNtxUI5OPPFEPfDAAxoxYoS2bdtmOw4AAEC5ouwFAAAADvD0009rz549Gjp0qO0oqACDBw+WMYZR2wAAwOtQ9gIAAADFbNy4Uc8995weeeQRNWrUyHYcVIC6devqySef1EsvvaR///3XdhwAAFAKxhjXrW5E2QsAAAAUM2zYMAUGBmrQoEG2o6ACDRw4UDVr1lRsbKztKAAAAOWGshcAAAAotGzZMo0bN07R0dGqUaOG7TioQCEhIYqNjdU777yjhQsX2o4DAABQLih7AQAAgEIxMTFq0KCB+vfvbzsKKkG/fv3UvHlzRUdH244CAACOwrhwcSPKXgAAAEDSH3/8ocmTJ2vYsGEKCgqyHQeVwN/fXwkJCfrss8/0008/2Y4DAABw3Bzj1tmEAQAAgEpijFH37t21adMmLViwQL6+vrYjoZLk5+erQ4cOCgwM1M8//yzHcWxHAgAAxaSnpys8PFyT5kxScEiw7ThF9uzeo5vOuklpaWkKCwuzHacII3sBAABQ5U2bNk3Tp09XcnIyRW8V4+Pjo9TUVM2aNUsff/yx7TgAAOAwjDGuW92Ikb0AAACo0vLz83X22WcrODhYP/30EyM7q6gePXpo/fr1WrBggfz8/GzHAQAAhfaP7H3vj/dcN7L35rNvZmQvAAAA4Cbvv/++5s2bp5SUFIreKiwlJUV//fWX3nrrLdtRAAAAjhllLwAAAKqs7OxsxcTE6KqrrtIFF1xgOw4sOuuss9SnTx8NHTpUe/futR0HAAAcwLhwcSPKXgAAAFRZr776qlatWqWkpCTbUeACCQkJ2rx5s1588UXbUQAAAI4JZS8AAACqpN27d2v48OG644471K5dO9tx4AItWrTQvffeq6SkJO3cudN2HAAAgDKj7AUAAECV9OyzzyotLU3x8fG2o8BFhgwZouzsbKWmptqOAgAAijHGuG51I8peAAAAVDlbtmzR008/rYceekhNmza1HQcuUr9+fT3++OMaPXq01q1bZzsOAABAmVD2AgAAoMpJTEyUj4+PoqOjbUeBCz3xxBMKCQlh1DcAAPA4lL0AAACoUv755x+NHTtWgwYNUu3atW3HgQuFhYUpJiZGb7zxhv766y/bcQAAgCTjwsWNKHsBAABQpcTGxqpOnTp65JFHbEeBi91///1q2rSpBg8ebDsKAABAqVH2AgAAoMqYP3++Jk6cqKFDh6p69eq248DFAgMDNWzYMH300Uf69ddfbccBAAAoFce49dFxAAAAQDm77LLL9M8//2jRokXy9/e3HQcul5eXpzPPPFM1atTQDz/8IMdxbEcCAKDKSU9PV3h4uN6Z/Y6CQ4JtxymyZ/ce3X7O7UpLS1NYWJjtOEUY2QsAAIAq4fvvv9dXX32lxMREil6Uiq+vr5KTk/Xjjz/qyy+/tB0HAADgqBjZCwAAAK9njFHHjh1ljNFvv/3GCE2UmjFG3bp1044dOzRv3jz5+vrajgQAQJXCyN6yYWQvAAAAvN6UKVM0e/ZspaamUvSiTBzHUUpKihYuXKiJEyfajgMAQJVlXLi4EWUvAAAAvFpubq6io6N1ySWXqFu3brbjwAN17NhR1157rYYMGaKsrCzbcQAAAA6LshcAAABe7Y033tCyZcuUkpJiOwo8WFJSktauXauxY8fajgIAAHBYlL0AAADwWnv27FFcXJxuueUWtW/f3nYceLDWrVvr7rvvVkJCgtLS0mzHAQCgyjHGuG51I8peAAAAeK3Ro0dr27ZtGj58uO0o8AJxcXHKzMzUM888YzsKAADAIVH2AgAAwCtt375dqampuv/++3XSSSfZjgMv0KhRIz3yyCN69tlntWnTJttxAAAADkLZCwAAAK+UnJysvLw8xcTE2I4CLzJo0CAFBgZq2LBhtqMAAFClGBcubkTZCwAAAK+zZs0avfjii3riiScUERFhOw68SM2aNRUVFaVx48ZpxYoVtuMAAACUQNkLAAAArzN06FCFhYXpsccesx0FXqh///6qX78+o8YBAIDrUPYCAADAqyxevFhvv/22YmNjFRoaajsOvFC1atUUHx+v999/X3PmzLEdBwCAKsEY47rVjSh7AQAA4FWio6N14okn6t5777UdBV6sb9++atOmjSIjI21HAQAAKELZCwAAAK8xc+ZMffLJJxo+fLgCAgJsx4EX8/PzU3Jysr799ltNmzbNdhwAAABJkmPcOuYYAAAAKANjjDp37qzMzEzNmTNHPj6Ma0DFMsboggsu0L59+/T777/z7xwAABUgPT1d4eHheuPXNxQcEmw7TpE9u/fo7o53Ky0tTWFhYbbjFOG7EQAAAHiFTz/9VD///LNSUlIo3VApHMdRSkqK5s6dqw8++MB2HAAAAMpeAAAAeL68vDxFR0froosuUs+ePW3HQRXSuXNnXXnllYqJiVF2drbtOAAAoIqj7AUAAIDHe+edd7R48WKlpKTIcRzbcVDFJCUl6Z9//tFrr71mOwoAAF7LGOO61Y0oewEAAODR9u3bp9jYWF1//fXq0KGD7Tiogk499VT17dtX8fHx2r17t+04AACgCqPsBQAAgEd76aWXtGHDBiUmJtqOgiosPj5eu3bt0nPPPWc7CgAAqMIoewEAAOCxdu3apcTERP3vf/9Tq1atbMdBFXbCCSeof//+GjFihLZu3Wo7DgAAXse4cHEjyl4AAAB4rBEjRigrK0tDhw61HQVQdHS0fHx8GGUOAAAOkpycrA4dOig0NFQRERHq1auXli5dWuKYrl27ynGcEuv9999fputQ9gIAAMAjbdiwQaNGjdKjjz6qBg0a2I4DqHbt2nrqqac0ZswYrVq1ynYcAADgIjNmzNBDDz2kX3/9VdOmTVNOTo569uypzMzMEsf169dPGzduLFpHjBhRpuv4lWdoAAAAoLLEx8erWrVqeuqpp2xHAYo8+uijevHFFxUbG6t33nnHdhwAALyGMUbGuGfqhLJm+eqrr0r8efz48YqIiNCcOXPUpUuXou3BwcGqX7/+MediZC8AAAA8ztKlS/X6669r8ODBCg8Ptx0HKFK9enUNHTpUEyZM0J9//mk7DgAAqGDp6ekl1qysrFK9Li0tTZJUq1atEtsnTJigOnXqqF27doqKitKePXvKlMcxbqrEAQAAgFK4/vrrNXv2bC1btkxBQUG24wAl5OTk6JRTTlGLFi30xRdf2I4DAIBHS09PV3h4uMb9Mk7BIcG24xTZs3uP+p3X76DtQ4cOVVxc3BFfm5+fr6uvvlq7du3SzJkzi7a/+uqrOuGEE9SwYUMtWLBAgwYN0jnnnKMpU6aUOhfTOAAAAMCj/Pbbb/q///s/jR8/nqIXruTv76/ExETdeOON+uGHH9S1a1fbkQAA8HimcHGL/VnWrl2rsLCwou2BgYFHfe1DDz2kRYsWlSh6Jenee+8t+vzUU09VgwYNdPHFF2vlypVq3rx5qXIxjQMAAAA8hjFGkZGRateunW677TbbcYDDuv7669WhQwcNGjTIVfMLAgCA8hUWFlZiPVrZ279/f3322Wf6/vvv1bhx4yMee+6550qSVqxYUeo8lL0AAADwGF9//bV++OEHJScny9fX13Yc4LAcx1FKSopmz56tjz76yHYcAABgmTFG/fv310cffaTp06erWbNmR33N/PnzJUkNGjQo9XWYsxcAAAAeIT8/X2eeeaZCQ0P1448/ynEc25GAo7rkkkv077//atGiRfLzYxY9AADKav+cva/+/KqqhVSzHafI3t17de/59yotLa3ENA6H8+CDD2rixIn6+OOPdfLJJxdtDw8PV7Vq1bRy5UpNnDhRl19+uWrXrq0FCxZo4MCBaty4sWbMmFHqXIzsBQAAgEd477339Oeffyo1NZWiFx4jJSVFS5cu1Ztvvmk7CgAAsGjs2LFKS0tT165d1aBBg6L1/ffflyQFBATo22+/Vc+ePdW6dWs9/vjj6t27tz799NMyXYeRvQAAAHC9rKwstW7dWqeffrqmTp1qOw5QJrfccotmzJih5cuXKzjYPU8RBwDAE3jLyN7KwsheAAAAuN4rr7yiNWvWKCkpyXYUoMwSEhK0detWPf/887ajAADgsYwLFzei7AUAAICrpaena/jw4brzzjvVtm1b23GAMjvppJN03333KSUlRTt27LAdBwAAeDHKXgAAALjayJEjlZGRobi4ONtRgGM2ZMgQ5eXlKTk52XYUAADgxSh7AQAA4FqbN2/WyJEjNWDAADVp0sR2HOCYRURE6PHHH9cLL7ygtWvX2o4DAIDHMca4bnUjyl4AAAC41vDhw+Xn56eoqCjbUYDj9vjjjyssLIxR6gAAoMJQ9gIAAMCVVq5cqVdeeUWRkZGqVauW7TjAcQsNDdWQIUM0fvx4LVmyxHYcAADghSh7AQAA4EpDhgxRRESEHn74YdtRgHJz33336YQTTlB0dLTtKAAAeBTjwsWNKHsBAADgOnPnztV7772nuLg4BQcH244DlJuAgAAlJCTo448/1s8//2w7DgAA8DKUvQAAAHCdqKgonXzyybrrrrtsRwHK3U033aT27dsrMjLStQ93AQAAnomyFwAAAK7y3Xff6ZtvvlFSUpL8/PxsxwHKnY+Pj5KTkzVz5kx99tlntuMAAOARjDGuW92IshcAAACuYYxRZGSkzj33XF177bW24wAV5pJLLlG3bt0UFRWlvLw823EAAICXoOwFAACAa3z44Yf6448/lJqaKsdxbMcBKozjOEpJSdHixYv17rvv2o4DAAC8BGUvAAAAXCEnJ0fR0dG67LLLdOGFF9qOA1S4c845R9dff72GDBmiffv22Y4DAICrGRcubkTZCwAAAFd4/fXXtXLlSiUnJ9uOAlSaxMREbdiwQWPGjLEdBQAAeAHKXgAAAFiXmZmp+Ph43XrrrTr99NNtxwEqTatWrfS///1PiYmJSktLsx0HAAB4OMpeAAAAWDdq1Cht375dw4YNsx0FqHSxsbHau3evRowYYTsKAACuZYxx3epGlL0AAACwatu2bUpNTdWDDz6oZs2a2Y4DVLqGDRvq0Ucf1XPPPaeNGzfajgMAADwYZS8AAACsSkpKkiQNHjzYchLAnqeeekrVqlVTfHy87SgAAMCDUfYCAADAmn///VcvvfSSnnzySdWtW9d2HMCaGjVqKDo6Wq+99pqWLVtmOw4AAK5jXLi4EWUvAAAArImNjVXNmjU1cOBA21EA6x566CE1bNiQUe4AAOCYUfYCAADAioULF+qdd95RbGysQkJCbMcBrAsKCtKwYcP04Ycfavbs2bbjAAAAD0TZCwAAACuioqJ00kknqV+/frajAK5x++2365RTTlFkZKRrn/INAIANxhjXrW5E2QsAAIBK9+OPP+rzzz9XQkKC/P39bccBXMPX11fJycn6/vvv9c0339iOAwAAPAxlLwAAACqVMUaDBg3SmWeeqRtvvNF2HMB1rrzySp1//vmKjIxUfn6+7TgAAMCDUPYCAACgUn388cf69ddflZKSIh8fvh0FDuQ4jlJTUzV//nxNmjTJdhwAAFzBuHBxI767BgAAQKXJzc1VdHS0unfvrh49etiOA7jW+eefr6uvvloxMTHKzs62HQcAAHgIyl4AAABUmrfeekt//fWXUlJSbEcBXC8pKUn//vuvXnnlFdtRAACAh6DsBQAAQKXYu3evhg4dqhtvvFFnnXWW7TiA651yyim64447NHz4cGVkZNiOAwCAVcYY161uRNkLAACASvHCCy9o8+bNSkhIsB0F8Bjx8fFKT0/XyJEjbUcBAAAegLIXAAAAFW7nzp1KTk5Wv3791LJlS9txAI/RpEkTDRgwQCNHjtSWLVtsxwEAAC5H2QsAAIAKl5qaquzsbMXGxtqOAnicqKgo+fr6MioeAFClGRcubkTZCwAAgAq1bt06jR49Wo899pjq169vOw7gcWrVqqXIyEi9/PLL+ueff2zHAQAALkbZCwAAgAoVHx+v6tWr68knn7QdBfBYDz/8sOrWrashQ4bYjgIAAFyMshcAAAAV5q+//tIbb7yhmJgYhYWF2Y4DeKzg4GDFxcVp4sSJmjdvnu04AABUOmOM61Y3ouwFAABAhRk8eLCaNGmiBx54wHYUwOPdddddOvnkkxUVFWU7CgAAcCnKXgAAAFSIWbNm6aOPPtLw4cMVGBhoOw7g8fz8/JSYmKivv/5a06dPtx0HAAC4kGPcOuYYAAAAHssYo65du2rnzp2aN2+efH19bUcCvIIxRh07dpQxRr/99pscx7EdCQCACpWenq7w8HCN/H6kqoVUsx2nyN7de/V4t8eVlpbmqunKGNkLAACAcvfll1/qxx9/VEpKCkUvUI4cx1Fqaqp+//13/d///Z/tOAAAwGUoewEAAFCu8vLyFBkZqS5duuiyyy6zHQfwOl27dtVll12m6Oho5eTk2I4DAABchLIXAAAA5WrixIlauHChUlNTeYs5UEGSk5O1YsUKvfHGG7ajAABQKYwxrlvdiLIXAAAA5SYrK0tDhgzRtddeq44dO9qOA3it008/Xbfccovi4uKUmZlpOw4AAHAJyl4AAACUm7Fjx2rt2rVKTEy0HQXwesOHD9f27ds1evRo21EAAIBLUPYCAACgXKSlpSkhIUF333232rRpYzsO4PWaNWumBx54QKmpqdq+fbvtOAAAVCjjwsWNKHsBAABQLp555hllZmYqLi7OdhSgyoiJiZExRklJSbajAAAAF6DsBQAAwHHbtGmTnn32WT3yyCNq1KiR7ThAlVG3bl098cQTevHFF/Xvv//ajgMAACyj7AUAAMBxGzZsmAICAjRo0CDbUYAq57HHHlONGjU0dOhQ21EAAKgwxhjXrW5E2QsAAIDjsnz5co0bN05RUVGqWbOm7ThAlRMSEqLY2Fi9/fbbWrhwoe04AADAIspeAAAAHJeYmBjVq1dPAwYMsB0FqLL69eunZs2aKTo62nYUAABgEWUvAAAAjtkff/yhDz74QPHx8apWrZrtOECVFRAQoMTERH322WeaOXOm7TgAAJQ748LFjSh7AQAAcMwiIyPVpk0b3XHHHbajAFXejTfeqDPOOEODBg1y7TyCAACgYlH2AgAA4JhMmzZN3333nZKSkuTn52c7DlDl+fj4KDU1Vb/88os++eQT23EAAIAFjuFXvgAAACij/Px8dejQQYGBgfr555/lOI7tSAAKde/eXRs3btSff/7JL2IAAB4vPT1d4eHhSv0uVUHVg2zHKbIvc58GXTxIaWlpCgsLsx2nCCN7AQAAUGYffPCB5s6dq5SUFIpewGVSUlK0ZMkSvf3227ajAACASkbZCwAAgDLJzs5WTEyMrrjiCnXp0sV2HAAHOPvss3XjjTdq6NCh2rt3r+04AACgElH2AgAAoEzGjRunf/75R8nJybajADiMhIQEbdq0SS+++KLtKAAAlAvjwsWNKHsBAABQart379awYcN0++2369RTT7UdB8BhtGzZUv369VNycrJ27dplOw4AAKgklL0AAAAoteeee067du3SsGHDbEcBcBSxsbHKyspSamqq7SgAAKCSUPYCAACgVLZu3aoRI0booYce0gknnGA7DoCjqF+/vh577DGNGjVK69evtx0HAIDjYoxx3epGlL0AAAAolcTERPn4+Cg6Otp2FACl9OSTT6p69eqKj4+3HQUAAFQCyl4AAAAc1apVqzRmzBg99dRTqlOnju04AEopLCxMgwcP1uuvv66///7bdhwAAFDBKHsBAABwVLGxsapdu7YeffRR21EAlNGDDz6oJk2aaPDgwbajAABwzIwLFzei7AUAAMAR/fnnn5owYYKGDh2q6tWr244DoIwCAwM1bNgwTZkyRb/++qvtOAAAoAJR9gIAAOCIoqKi1KJFC91zzz22owA4RrfeeqtOPfVURUZGuvaBMgAA4PhR9gIAAOCwfvjhB3355ZdKSEiQv7+/7TgAjpGvr6+Sk5M1Y8YMffXVV7bjAABQZsYY161uRNkLAACAQzLGaNCgQTr77LN1/fXX244D4Dhdfvnl6tKliyIjI5Wfn287DgAAqACUvQAAADikjz76SLNnz1ZKSop8fPi2EfB0juMoNTVVCxYs0MSJE23HAQAAFYDv2gEAAHCQ3NxcRUdHq2fPnrr44ottxwFQTjp27KhevXopJiZGWVlZtuMAAFBqxoWLG1H2AgAA4CBvvvmmli5dqpSUFNtRAJSzpKQkrV27Vi+//LLtKAAAoJxR9gIAAKCEPXv2aOjQobr55pt1xhln2I4DoJy1adNGd911lxISEpSenm47DgAAKEeUvQAAACjh+eef19atWzV8+HDbUQBUkLi4OO3evVvPPPOM7SgAAJSKMcZ1qxv52Q4AAAAA99ixY4dSUlJ03333qXnz5rbjVAnLly9XRkaG7RiuEhoaqpYtW9qO4dUaN26shx9+WM8++6wefPBB1a9f33YkAABQDih7AQAAUCQ5OVm5ubkaMmSI7ShVwvLly9WqVSvbMVxp2bJlFL4VLDIyUq+++qqGDx+ul156yXYcAABQDih7AQAAIElau3atXnjhBQ0aNEj16tWzHadK2D+i1+ntSHUsh3GLbZL5P8No50pQs2ZNRUVFafDgwRo4cKBatGhhOxIAAIdlChe3cFOW4ih7AQAAIEkaOnSowsLC9Pjjj9uOUvXUkZyGju0UruDWH5y81YABA/T8888rJiZGkyZNsh0HAAAcJx7QBgAAAC1evFhvvfWWYmJiFBYWZjsOgEpSrVo1xcfH6/3339ecOXNsxwEAAMeJshcAAACKjo5W06ZNdd9999mOAqCS3XHHHWrdurWioqJsRwEA4LCMMa5b3YiyFwAAoIr7+eef9cknnyghIUGBgYG24wCoZH5+fkpOTta0adP07bff2o4DAACOA2UvAABAFWaMUWRkpE4//XTdfPPNtuMAsOSaa65Rp06dFBkZqfz8fNtxAADAMeIBbQAAAFXYZ599ppkzZ+rLL7+Ujw/jANzKzDMyUwveKujc7cg5oeTD3IwxMs8aKV1SK8nn1tL/szTpRuYrI62UZCSdKDmXOnJqHXCN2UZmlZHWS0qT1F7yufbQ1zF7jcw0I/0lKUdSI8m5xOEhdC7mOI5SUlJ04YUXavLkyerTp4/tSAAAlGAKF7dwU5bi+I4eAACgisrLy1NUVJS6du2qSy65xHYclIafZBYe4geL1Sooess4lMNkGZnxRlotOZ0dOV0daZNk3jQye0pex/xspFWS6uqIP0WYfCMzwUgLJeccR04PR8qUzHgjs92dPxShQJcuXXTFFVdo8ODBysnJsR0HAAAcA8peAACAKuqdd97R4sWLlZKSIsdhxKVHaClpsWTyDihiFxqpoaSQMp7vd0nbJedWR84FjpzzHDm3O9JuyfxS8hrOXY6cQY58bvc5cqm8RNJayenlyOnmyDnXkXOXIzmS+Z6y1+2Sk5P1zz//aNy4cbajAACAY0DZCwAAUAXt27dPsbGx6t27t84991zbcVBKzqmOtFfSP/9tM7lGWlK4r4zMElMwxUKj/17r1HWkZpIWH3DtGk6pfilglpiC0rlNsddWd6RTJP1dmBeudeqpp+r222/XsGHDtHv3bttxAAAoYoxx3epGlL0AAABV0JgxY7RhwwYlJibajoKyqCGp8QFTOayQtE9Su7KdyuQbabMKRgQfqJGkHQXTPJTZRkkNJMenZDHsNHIK5u/dXvZTonINGzZMO3fu1KhRo2xHAQAAZUTZCwAAUMWkpaUpMTFR99xzj04++WTbcVBGzqlOwQjZnIIi1iwwBQ9VCyvjyN69knIlJ+Tg1zmhhdsyjiHgbh16OolQHfs5UalOOOEEPfTQQxoxYoS2bt1qOw4AACgDyl4AAIAqZsSIEdq7d6+GDh1qOwqORTsVjJBdVjjydtmxTeGg3MKPh5p/d/+2Y3lGV04FnBOVLjo6Wo7jKCkpyXYUAAAkScaFixtR9gIAAFQhGzZs0HPPPadHH31UDRse6v37cDunuiOdVDii9y9J+ZLaHsOJ9pevuYfYt3+b/zGc178CzolKV6dOHT355JMaM2aMVq9ebTsOAAAoJcpeAACAKmTYsGEKCgrSU089ZTsKjoNzmiOtkMzvRmopOdWOYWRvNUl+ktl98KgUk1G4LfSgXUcXooKpHA60f/qGYzknrBg4cKBq1qyp2NhY21EAAEApUfYCAABUEUuXLtVrr72m6Oho1ahRw3YcHI/WkhxJ645xCgcVPkAtQtKGQ+xcL6mm5AQew7nrS9pY+AC4Ysx6UzCqt3bZTwk7qlevrqFDh+rdd9/VggULbMcBAFRxxhjXrW5E2QsAAFBFxMTEqGHDhurfv7/tKDhOTqAj50pHTldHOo5n7DltHWl9YRFbyGwz0ipJpxzjOU9xCkb2/vXfNpNppMWSTpYcv2Mrp2HH//73PzVv3lxRUVG2owAAgFKg7AUAAKgCZs+erQ8//FDx8fEKCgqyHQflwGnvyOnmyPE/jvL0HEm1JDPByMw0MrOMzNtGqi4555U8r1lqZGYUrMqTtFlFfzabio1saSupsWSmGpkfjMxsIzPeSEZyulH0ehp/f38lJibqiy++0IwZM2zHAQDAYyUnJ6tDhw4KDQ1VRESEevXqpaVLl5Y4Zt++fXrooYdUu3ZthYSEqHfv3tq8eXOZrkPZCwAA4OWMMYqMjNQpp5yivn372o4DF3ECHTl3OtIJkvnRyEw3Uj3JucspeBBcMWZJwX4zvbDs3aj//ryx2Dl9HDm3OVI7yfxmZL4xUrDk3OHIqUPZ64muv/56nX322Ro0aJBr37IKAPB+xoVLWcyYMUMPPfSQfv31V02bNk05OTnq2bOnMjMzi44ZOHCgPv30U02ePFkzZszQhg0bdN1115XpOn5HPwQAAACe7JtvvtH333+vjz/+WL6+vrbj4Bg4Zzhyzjh6UeozsOxjOZxwR06fUpz7Wh/p2lKes5oj5xpHuqbMceBCPj4+SklJUffu3TV16lRde20p/0UAAKAKSE9PL/HnwMBABQYGHnTcV199VeLP48ePV0REhObMmaMuXbooLS1Nr7/+uiZOnKiLLrpIkvTmm2+qTZs2+vXXX9WxY8dS5WFkLwAAgBfLz8/XoEGDdP755+uqq66yHQeAh7r44ovVo0cPRUVFKTc313YcAABco0mTJgoPDy9ak5OTS/W6tLQ0SVKtWrUkSXPmzFFOTo66d+9edEzr1q3VtGlTzZo1q9R5GNkLAADgxSZNmqQ///xTP/30kxyHt9BXFWZP4VQLh+Ojg6ZpAI4mJSVFZ511lsaPH6///e9/tuMAAKoYY4yrphPan2Xt2rUKCwsr2n6oUb0Hys/P16OPPqrzzz9f7dq1kyRt2rRJAQEBqlGjRolj69Wrp02bNpU6F2UvAACAl8rOzlZMTIyuuuoqXXDBBbbjoBKZ9420+ggH1JCcgZS9KJszzzxTN910k+Li4nTLLbcoODjYdiQAAKwLCwsrUfaWxkMPPaRFixZp5syZ5Z6HshcAAMBLvfLKK/r333/1ySef2I6CSuZc4kh7j3CAf6VFgZdJSEhQ69at9cILL2jQoEG24wAA4HH69++vzz77TD/++KMaN25ctL1+/frKzs7Wrl27Sozu3bx5s+rXr1/q8zNnLwAAgBfKyMjQ8OHD1bdv36K3hqHqcBo6cpofYW3KqF4cm+bNm+u+++5TSkqKduzYYTsOAKAKMS5cypTfGPXv318fffSRpk+frmbNmpXYf9ZZZ8nf31/fffdd0balS5dqzZo16tSpU6mvQ9kLAADghUaOHKn09HTFx8fbjgLAywwZMkQ5OTlKSUmxHQUAAI/x0EMP6d1339XEiRMVGhqqTZs2adOmTdq7t+DtWOHh4brnnnv02GOP6fvvv9ecOXN01113qVOnTurYsWOpr0PZCwAA4GU2b96skSNHqn///mratKntOHAZs8gof0y+8ofnK/+j/BL78t/MV/5L+Yd55bHJfzNf+Qn5yn81X2aVex6qgmNXr149Pf7443r++ee1du1a23EAAPAIY8eOVVpamrp27aoGDRoUre+//37RMc8995yuvPJK9e7dW126dFH9+vU1ZcqUMl2HOXsBAAC8TEJCgnx8fBQVFWU7ClzGZBuZqUaqLjndHanx0V9zqHNonmSWGmmzpGxJtSTnLEc6W3J8Sk4R4XRxpE2SmWVkPjJyHmMKCW/w+OOPa8yYMYqLi9Prr79uOw4AoAowxsgY9/ziuKxZSnN8UFCQXnrpJb300kvHGouRvQAAAN7kn3/+0SuvvKLIyEjVrl3bdhy4zVZJOZJzniOnkyOnyTEUrzsl86WRjArO0dORakrm88Ii+QBOc0fO+Y6ccxwpTTJ73PNDGo5dWFiYhgwZovHjx2vJkiW24wAAgEKUvQAAAF5kyJAhqlOnjh555BHbUVDO8t88eNqFMssp/BhyHOcIkZwHHfnc4SPnAkdOB0c+N/lIZ0j6UzLbD1Pm7r9m9nFcG65y3333qWnTpho8eLDtKAAAoBBlLwAAgJeYN2+eJk6cqLi4OAUHB9uOAzc6hkG1ZoUpmHN3cr5MnpFT3ZETcfCIYKdN4bZthznR/pcwsNdrBAYGKiEhQVOnTtUvv/xiOw4AwMsZFy5uRNkLAADgJaKiotSqVSvdfffdtqPArfb/TFLK2RvMUiPznpHaSk5vR47vEV6YUfjxcL9noOz1SjfffLNOP/10RUZGumoeRQAAqirKXgAAAC8wffp0ff3110pMTJSfH8/gxWHsL2SDjn6oWWJk3jfS6ZLTyznowWsljs01Mr8aqaakhoc5KLDw4+4y5IXr+fj4KDk5WT/99JM+//xz23EAAKjyKHsBAAA8nDFGkZGROuecc9S7d2/bcVAOTJ6RySy5Kk9Srg7abvKPPprS7DUyG4zMLFNQujY6yvELjcxkI50lOVcdueiVJPOFkbZKzuVHGP3bWJKfZH40MluMTBajQL3FpZdeqq5duyoqKkp5eXm24wAAUKUx7AMAAMDDffjhh/r99981ffp0OU4p358Pd1sjmfGHKEPXSmZRye3Oo07BiNojMJOMtFpSoOTc5MgJPMK/Jzsl839GOkXyueLoY0PMTCPNkZyLHDmtDn9eJ9SRrpPMFCPzkpHaS861/PvqDRzHUUpKijp27Kh3331Xd9xxh+1IAABvZOSuKYNcFKU4yl4AAAAPlpOTo8GDB+vSSy9Vt27dbMdBeakvOX1LFqHmayOFSM75BxSkIUc/ndPTkbZI5gcjM8VID0tOwGGK1tDCcy6XzHojp9ERpm+YZ2S+NdLZknPhUUb/ZhqZT41Ut/BrqHf03PAc5557rnr37q3Y2Fj16dNHQUGlmCsEAACUO6ZxAAAA8GBvvPGGli9fruTkZNtRUI6cao6c5iVXVZMUqoO2O/5HHx3rNHLknOHI6eoUzNu77ggH+0nOrY5USzLvFky5cCjmbyPziZHaSM4VpRihu0bSXsm51JFzqiMnglG93iYxMVHr16/X2LFjbUcBAKDKouwFAADwUJmZmYqLi9Ott96q9u3b244DTxBe+HHfkQ9zghw5tztSdcm8bWR2lCx8zerCOX1PkJzeR5/TV5KUVfgxrMyp4SFOPvlk3X333UpISFBaWprtOAAAL2NcuLgRZS8AAICHGj16tLZv367hw4fbjgJPsb+TLcXPJk51p2AqCV/JvGVk0gteZHYZmfeM5EhOW0daLJk/zX/rpsOcfP9mBvR6taFDh2rv3r16+umnbUcBAKBKouwFAADwQNu3b1dqaqruv/9+NWvWzHYceIr93/3nlu5wJ8yRc4cj5RWO8M000k4VjAzOkcznBXMAl1j/OkzZm3NABnilRo0a6ZFHHtGzzz6rjRs32o4DAECVwwPaAAAAPFBSUpLy8/MVExNjOwoqic9d5dCShhZ8MIuN1FgFD3wL/G+o7aGu4dRy5DxRbDhuM8mJL/3wXLPPSLtVUAL7Sqp+jNnhMQYNGqRXXnlFw4YNY/5eAEC5McbIGPdMneCmLMXxe3UAAAAP8++//+rFF1/UE088oYiICNtx4EGcWo50iqSlknneyHxR8T+kmPeMzAtG+kdSR8nxYx4Hb1ejRg1FR0dr3LhxWrZsme04AABUKZS9AAAAHmbo0KGqUaOGHnvsMdtR4IF8bvSR85gj525HzvkVX7w6lzhy7nHkPOnIpyc/flQV/fv3V8OGDXn3AQAAlYxpHAAAADzIwoUL9fbbb+v5559XaGio7TjwUE64I4VX0rUaMpK3KgoKClJ8fLzuvvtu/f777+rQoYPtSAAAD2cKF7dwU5bi+NU6AACAB4mOjlazZs1077332o4CAEfUt29ftW3bVpGRka6d1xAAAG9D2QsAAOAhfvrpJ3322WdKSEhQQECA7TgAcES+vr5KTk7W9OnTNW3aNNtxAACoEpjGAQAAwAMYYzRo0CCdccYZ6tOnj+048EJmkZH50UjbJbWTfK79b1yI2WlkRhupmqSTJOdKR041pmfA0V111VU6//zzNWjQIHXv3l0+Pow3AgAcG2OMq94p4qYsxfF/WgAAAA/wySefaNasWUpJSaEsQbkz2UZmqpGyJKe7I+fsA4rc6pJzjSO1k7RIMrPc+cMN3MdxHKWkpGj+/Pl6//33bccBAMDr8ZMCAACAy+Xm5io6OloXXXSRevToYTsOvNFWSTmSc54jp5Mjp0nJstcJcOSc4cjnCh+ptqRNVlLCQ11wwQW66qqrFBMTo+zsbNtxAADwapS9AAAALvf2229ryZIlSklJkePw1nlUgJzCjyGlODZEUlYFZoFXSkpK0urVq/Xqq6/ajgIA8FDGhYsbUfYCAAC42N69ezV06FDdcMMN6tChg+048FZl+VmF3zfgGLRr1059+/bV8OHDlZGRYTsOAABei7IXAADAxV588UVt3LhRCQkJtqPAm+0ve0tb5LpzIAtcLj4+XmlpaXr22WdtRwEAwGtR9gIAALjUrl27lJycrH79+qlVq1a248Cb7R9oGVSKY4OKHQ+UQdOmTdW/f38988wz2rJli+04AAAPY4xx3epGlL0AAAAulZqaqqysLMXGxtqOAi9l9hqZDUZmlpECJTU6+mucExxph2R+MzJpRibPnT/owJ2ioqLk4+OjxMRE21EAAPBKlL0AAAAutH79eo0aNUoDBw5UgwYNbMeBBzC5RibjgDX/yEWsmWRkXjHSDsm5yZETWIp5HDpKOkUyXxiZZ420pnzyo2qoXbu2IiMjNXbsWP3zzz+24wAA4HUoewEAAFwoPj5e1atX15NPPmk7CjzFWsk8Y0qsSjvyS5yejpxejlRNMlOMTHYpRunOk7RY0jmSc4sj1S+P8KhKHnnkEdWpU4d3LQAAysS4cHEjyl4AAACX+fvvv/X6669r8ODBCg8Ptx0HnqK+5PR1SqwKOfJLnEaOnDMcOV2dgnl41x39MmapkWpKPlf4yDnZkVOttE91AwoEBwdr6NChmjBhgubPn287DgAAXoWyFwAAwGUGDx6sxo0b64EHHrAdBR7EqebIaX7A6l/KInb/7xT2leLYLElhxxgSKHT33XerVatWioqKsh0FAACvQtkLAADgIr/++qumTJmiYcOGKSgoyHYcVBX7O+HSvhuRwbw4Tv7+/kpMTNRXX32l77//3nYcAIAHMMa4bnUjyl4AAACXMMYoMjJS7dq102233WY7DqqS/T8V5Jbi2BzxUwTKRe/evXXOOecoMjLStT8wAwDgafg2DQAAwCW++uorzZgxQ8nJyfL19bUdB1VJaMEHs9jIbDcyWSWLN5NvZHYbmVVG2vLf8cDxcBxHKSkpmj17tqZMmWI7DgAAXoGyFwAAwAXy8/MVGRmpzp0764orrrAdB1WMU8uRTpG0VDLPG5kvDhhlmSaZp43MeCP5Sk4H5nFA+ejWrZsuvfRSRUdHKze3NEPLAQBVlXHh4kZ+tgMAAABAmjhxohYsWKBffvlFjkORhsrnc6OPTJqRdkmqdsDOEMm5w5GCJNVV6R/8BpRCcnKyzjjjDL3xxhu69957bccBAMCjMbIXAADAsqysLMXExKhXr17q1KmT7TiowpxwR84JjpyIkmWu4+/IOcmR09Ch6EW5a9++vW655RbFxcVpz549tuMAAODRKHsBAAAse/nll7V27VolJSXZjgIAVgwfPlzbtm3T6NGjbUcBALiUMcZ1qxtR9gIAAFiUnp6uhIQE3XnnnWrTpo3tOABgxUknnaT7779fqamp2r59u+04AAB4LMpeAAAAi5555hnt3r1bcXFxtqMAgFUxMTHKy8tTcnKy7SgAAHgsHtAGAABgyaZNm/Tss89qwIABatKkie04qASrVq3S+PHj9eWXX+rHH3/8b8c2ufaJzpVum+0AsCUiIkJPPPGEkpOT9fDDD6tp06a2IwEAXMQULm7hpizFUfYCAABYMnz4cPn7+ysyMtJ2FFSQ3Nxc/fLLL/rss8/08ccfa9myZZIkx3GUnZ2t0NBQSZL5P3f+sGDT/r8bVC2PPfaYXnrpJQ0dOlRvvvmm7TgAAHgcyl4AAAALVqxYoVdffVUJCQmqVauW7TioADt27FCbNm20ZcsW+fn5KTc3t2hfr169FBYWprCwMC1btkwZGRkWk7pPaGioWrZsaTsGLAgNDVVsbKweeeQRPfHEEzrllFNsRwIAwKNQ9gIAAFgQExOjevXq6eGHH7YdBRUkPDxcLVu21NatW0sUvZJ0//33F31OqQmUdO+99+q5555TdHS0Pv74Y9txAAAuYYyRMe55N5SbshTHA9oAAAAq2Zw5c/T+++8rLi5O1apVsx0HFcTX11dffPHFQfOO1qtXTxdffLGlVID7BQQEKCEhQZ988olmzpxpOw4AAB6FshcAAKCSRUVFqXXr1rrzzjttR0EF+/DDD7VmzRqFhITI19dXvr6+uuuuu+Tr62s7GuBqffr00RlnnKHIyEjXjpwCAMCNKHsBAAAq0bfffqtp06YpKSlJfn7MqOXNRo4cqXvuuUf333+/Fi1apIiICOXl5emOO+6wHQ1wPR8fH6WkpOjnn3/Wp59+ajsOAMAFjAsXN6LsBQAAqCT5+fmKjIxUx44d1atXL9txUEGMMRo8eLCeeOIJRUdH66WXXtIJJ5ygn3/+We+++65at25tOyLgEXr06KGLLrpIUVFRysvLsx0HAACPwHASAACASjJ58mTNmTNHP/zwgxzHsR0HFSA/P18DBgzQmDFj9PTTT+uJJ54o2tesWTM1a9bMYjrAsziOo5SUFJ1zzjl6++23ddddd9mOBACA6zGyFwAAoBLk5OQoJiZGl19+uS688ELbcVABcnJydPvtt+vll1/Wa6+9VqLoBXBsOnTooBtuuEFDhw7Vvn37bMcBAFhkjHHd6kaUvQAAAJXgtdde08qVK5WcnGw7CirAnj17dO2112ry5Ml6//33dc8999iOBHiNhIQEbdiwQS+99JLtKAAAuB5lLwAAQAXbvXu34uPjddttt+m0006zHQflLC0tTZdeeqm+//57ff7557r++uttRwK8SqtWrdSvXz8lJiZq165dtuMAAOBqlL0AAAAVbNSoUdq5c6eGDRtmOwrK2ZYtW9StWzctXLhQ3377rXr06GE7EuCVYmNjlZWVpREjRtiOAgCwxLhwcSPKXgAAgAq0detWjRgxQg8++KBOPPFE23FQjtasWaPOnTtrw4YNmjFjhjp16mQ7EuC1GjRooEcffVSjRo3Shg0bbMcBAMC1KHsBAAAqUFJSkiRp8ODBlpOgPC1dulQXXHCBsrOzNXPmTKbnACrBU089pWrVqik+Pt52FAAAXIuyFwAAoIKsXr1aY8aM0VNPPaU6derYjoNyMnfuXHXu3FmhoaGaOXOmWrRoYTsSUCWEh4dr8ODBev3117V06VLbcQAAlcwY47rVjSh7AQAAKkhsbKxq1qypgQMH2o6CcvLjjz+qW7duatasmX788Uc1atTIdiSgSnnwwQfVqFEj3i0BAMBhUPYCAABUgAULFujdd99VbGysqlevbjsOysHnn3+uSy65RGeffba+/fZb1a5d23YkoMoJCgrSsGHD9H//93/67bffbMcBAMB1KHsBAAAqQFRUlJo3b65+/frZjoJy8N5776lXr1669NJL9fnnnys0NNR2JKDKuu2229SuXTtFRka69i20AIDyZ1y4uBFlLwAAQDmbMWOGvvjiCyUkJMjf3992HBynsWPH6tZbb9Wtt96qyZMnKygoyHYkoErz9fVVcnKyfvjhB3399de24wAA4CqUvQAAAOXIGKNBgwbprLPO0g033GA7Do6DMUZJSUl68MEH9fDDD+uNN96Qn5+f7VgAJF1xxRW64IILFBkZqfz8fNtxAABwDcpeAACAcjR16lT99ttvSklJkY8P32p5KmOMnnrqKQ0ePFjDhg3Tc889xz9PwEUcx1Fqaqr+/PNPvffee7bjAAAqiTHGNatb8R0rAABAOcnNzVV0dLR69Oih7t27246DY5SXl6d+/frpmWee0fPPP68hQ4bIcRzbsQAc4LzzztM111yjmJgYZWVl2Y4DAIArUPYCAACUk/Hjx+vvv/9WSkqK7Sg4RllZWerTp4/Gjx+vd955RwMGDLAdCcARJCUlac2aNXrllVdsRwEAwBUoewEAAMrBnj17FBcXpz59+ujMM8+0HQfHYPfu3brqqqv02WefacqUKbrttttsRwJwFG3bttWdd96p4cOHKz093XYcAEAFMi5c3IiyFwAAoBy88MIL2rx5sxISEmxHwTHYsWOHevTooVmzZunLL7/U1VdfbTsSgFKKi4tTRkaGRo4caTsKAADWUfYCAAAcpx07diglJUX33nuvWrRoYTsOymjjxo268MILtXz5cn3//ffq1q2b7UgAyqBJkyZ6+OGHNXLkSG3evNl2HAAArKLsBQAAOE4pKSnKycnRkCFDbEdBGa1atUqdO3fWzp079dNPP+nss8+2HQnAMYiMjJS/vz/vrgAAL2aMcd3qRpS9AAAAx2HdunV64YUX9Nhjj6l+/fq246AMFi9erPPPP1+O4+jnn39WmzZtbEcCcIxq1aqlyMhIvfzyy1q5cqXtOAAAWEPZCwAAcBzi4uIUEhKiJ554wnYUlMFvv/2mLl26KCIiQjNnztQJJ5xgOxKA4zRgwABFRETwLgsAQJVG2QsAAHCMlixZojfffFMxMTEKCwuzHQel9N133+niiy9WmzZt9MMPP6hevXq2IwEoB8HBwYqPj9d7772nuXPn2o4DAChnxoWLG1H2AgAAHKPBgweradOmuv/++21HQSl99NFHuvzyy9W5c2d9/fXXqlGjhu1IAMrRnXfeqdatWysqKsp2FAAArKDsBQAAOAa//PKLpk6dquHDhyswMNB2HJTC+PHjdf3116tXr176+OOPVb16dduRAJQzPz8/JSYm6ptvvtF3331nOw4AAJXOMW59dBwAAIBLGWN04YUXKi0tTfPmzZOPD78/d7tRo0Zp4MCBuvfeezVmzBj5+vrajgSgghhj1KlTJ+Xl5Wn27NlyHMd2JADAcUhPT1d4eLju+eAeBQQH2I5TJHtPtl6/8XWlpaW5ako3fjIBAAAooy+++EI//fSTUlJSKHpdzhijoUOHauDAgRo0aJBefvllil7AyzmOo9TUVP3xxx/68MMPbccBAKBS8dMJAABAGeTl5SkyMlIXXnihLr30UttxcAT5+fl65JFHNGzYMKWkpCglJYURfkAVceGFF+ryyy9XdHS0cnJybMcBAKDSUPYCAACUwYQJE7Ro0SKKQ5fLycnRHXfcoRdffFGvvPKKBg0aZDsSgEqWnJyslStX6vXXX7cdBQBQDowLFzei7AUAACilffv2aciQIbruuuvUsWNH23FwGHv37lXv3r31/vvv67333tO9995rOxIAC0477TTdeuutio+PV2Zmpu04AABUCspeAACAUho7dqzWrVunxMRE21FwGOnp6brsssv07bff6pNPPlGfPn1sRwJg0fDhw7Vjxw6NGjXKdhQAACoFZS8AAEAppKWlKSEhQffcc49at25tOw4OYdu2bbrooos0f/58TZs2jTmVAejEE0/UAw88oBEjRmjbtm224wAAjoMxxnWrG1H2AgAAlMLTTz+tPXv2aOjQobaj4BDWrVunzp07a+3atZoxY4bOP/9825EAuMTgwYNljFFSUpLtKAAAVDjKXgAAgKPYuHGjnnvuOT366KNq1KiR7Tg4wPLly3X++edr7969mjlzpk4//XTbkQC4SN26dfXkk0/qpZde0r///ms7DgAAFYqyFwAA4CiGDRumwMBADRo0yHYUHGD+/Pm64IILFBwcrJkzZ6ply5a2IwFwoYEDB6pmzZqKjY21HQUAcIyMCxc3ouwFAAA4gmXLlmncuHGKiopSjRo1bMdBMTNnzlTXrl3VpEkT/fTTT2rcuLHtSABcKiQkRLGxsXrnnXe0cOFC23EAAKgwlL0AAABHEBMTowYNGqh///62o6CYL7/8Uj179lT79u01ffp01alTx3YkAC7Xr18/nXTSSYqKirIdBQCACkPZCwAAcBh//PGHJk+erPj4eFWrVs12HBR6//33dfXVV6t79+768ssvFRYWZjsSAA/g7++vxMREff755/rpp59sxwEAlJExxnWrG1H2AgAAHIIxRoMGDVLbtm3/v737Do+ibLg4fGbTISR0QpMiRUQ6okKQIggIvoIoCEoVPgXxtaBSpZeAWEFQFMUC0nyxoQgCAYKAgCJFijRBek0ICWn7fH9AIjG0QJKZbH73XnvFzMzOnMQrw+7ZZ59R586d7Y6Di6ZOnaoOHTroscce05dffkkJDyBdHn30UdWsWVP9+vVz7It0AABuBmUvAADAZSxevFhLly7VmDFj5O3tbXccSBo3bpyeeuop9enTR5988ol8fHzsjgQgm3G5XBo3bpxWr16tr7/+2u44AABkOMvwdiYAAEAqbrdbtWvXVkBAgCIiImRZlt2RcjRjjPr376/x48dryJAhGjZsGP9PANyUpk2b6uDBg9q0aRNv6AGAw0VFRSk4OFidZ3WWby5fu+OkiI+J16ePfarIyEhHTSvGyF4AAIB/mT17tn777TeNGzeOUtFmSUlJeuqppzR+/Hi9+eabGj58OP9PANy0sLAwbdu2TZ988ondUQAAOcSKFSv04IMPqlixYrIsS1999VWq9V27dpVlWanuzZs3T/dxKHsBAAAuER8fr8GDB6tVq1YKDQ21O06OFh8frw4dOmjatGn6+OOP9fzzz9sdCYCHqFWrltq3b6+hQ4cqNjbW7jgAgBzg3Llzqlatmt59990rbtO8eXMdPnw45f7FF1+k+zh8XgUAAOASU6dO1d69e5nL0Wbnzp1T27ZttWzZMn355Zdq3bq13ZEAeJhRo0apUqVKmjRpkl5++WW74wAArsEY46iLa6Y3S4sWLdSiRYurbuPn56eQkJCbicXIXgAAgGTR0dEaOXKkOnfurDvuuMPuODnWmTNndP/99ysiIkLff/89RS+ATFGuXDn93//9n8aMGaPTp0/bHQcAkE1FRUWlusfFxd3wvsLDw1W4cGFVrFhRvXr10smTJ9O9D8peAACAi9544w1FRkZqxIgRdkfJsY4ePaqGDRtq+/btWrp0qe677z67IwHwYK+++qri4+M1btw4u6MAALKpkiVLKjg4OOU+duzYG9pP8+bN9emnn2rJkiUaN26cli9frhYtWigpKSld+2EaBwAAAEnHjh3Ta6+9pmeeeUa33HKL3XFypH379qlp06Y6d+6cVqxYocqVK9sdCYCHCwkJUd++ffXaa6+pT58+KlGihN2RAABXYC7enCI5y4EDBxQUFJSy3M/P74b299hjj6X8d5UqVVS1alXdeuutCg8PT9cACEb2AgAASBo9erRcLpcGDhxod5Qc6Y8//lBoaKjcbrdWrVpF0Qsgy7z00ksKDAzU8OHD7Y4CAMiGgoKCUt1vtOz9t7Jly6pgwYLatWtXuh5H2QsAAHK8PXv2aMqUKerXr58KFChgd5wcZ926dbr33nuVP39+RUREqEyZMnZHApCDBAUFadCgQfroo4+0bds2u+MAACBJ+vvvv3Xy5EkVLVo0XY+j7AUAADnekCFDVKBAAT333HN2R8lxli1bpsaNG6t8+fIKDw9P95NZAMgIvXr1UsmSJTVo0CC7owAArsAY47h7ekRHR2vjxo3auHGjJGnv3r3auHGj9u/fr+joaL388stas2aN9u3bpyVLluihhx5SuXLl1KxZs3Qdh7IXAADkaBs3btTMmTM1bNgw5c6d2+44Oco333yjFi1a6J577tFPP/2k/Pnz2x0JQA7l5+enkSNHav78+VqzZo3dcQAAHmj9+vWqUaOGatSoIUl68cUXVaNGDQ0ZMkReXl7atGmT/vOf/6hChQp68sknVatWLa1cuTLd00JYJr01NAAAgAdp0aKFdu/era1bt8rHx8fuODnGZ599pm7duqlNmzb6/PPPM2xuMwC4UUlJSapZs6by5s2r8PBwWZZldyQAgKSoqCgFBwfr8ZmPyzeXr91xUsTHxGtGxxmKjIxMdYE2uzGyFwAA5FjLli3TwoULNWbMGIreLDRx4kR17txZXbt21axZsyh6ATiCl5eXxo4dqxUrVuiHH36wOw4A4F+MA29ORNkLAAByJGOM+vfvrzvvvFNt27a1O06OYIzRiBEj9N///ld9+/bVBx98IC8vL7tjAUCKFi1aqEGDBurfv7+SkpLsjgMAQLpR9gIAgBzpf//7n3755ReFhYXxUd0s4Ha79cILL2jo0KEaPXq0XnvtNX7vABzHsiyFhYVp8+bNmjlzpt1xAABIN+bsBQAAOU5iYqIqV66sMmXKaOHChXbH8XiJiYnq0aOHPv30U7377rvq1auX3ZEA4Koefvhh/frrr9qxYwdTzQCAzZLn7O0wo4Pj5uz94vEvmLMXAADAbh999JF27typsWPH2h3F450/f16PPPKIZsyYoRkzZlD0AsgWxowZowMHDmjKlCl2RwEAIF0oewEAQI4SExOjYcOGqWPHjqpRo4bdcTza2bNn1bJlS/3444/66quv1KFDB7sjAcB1ue2229S9e3eNGjVKkZGRdscBAOC6UfYCAIAc5e2339aJEyc0cuRIu6N4tJMnT+q+++7T+vXrtWjRIrVs2dLuSACQLsOGDdO5c+c0YcIEu6MAACQZB96ciLIXAADkGCdPntS4ceP09NNPq2zZsnbH8VgHDx7Uvffeq3379mnZsmWqX7++3ZEAIN2KFy+u5557Tm+88YaOHDlidxwAAK4LZS8AAMgxxo4dq6SkJA0ePNjuKB5r165dCg0N1dmzZ7Vy5UrVrFnT7kgAcMP69esnX19fjRgxwu4oAABcF8peAACQI+zfv1+TJk1S3759VbhwYbvjeKRNmzYpNDRUvr6+ioiIUMWKFe2OBAA3JV++fBo4cKA++OAD7dq1y+44AJCjGWMcd3ciyl4AAJAjDBs2TEFBQerbt6/dUTzSzz//rAYNGqhYsWJauXKlbrnlFrsjAUCG6NOnj0JCQvhUCAAgW6DsBQAAHm/r1q365JNP9OqrrypPnjx2x/E4ixYtUtOmTVW1alUtW7aMkdMAPEpAQICGDx+u2bNna8OGDXbHAQDgqih7AQCAxxs4cKBKlSqlp556yu4oHmfevHlq1aqVGjVqpIULFyo4ONjuSACQ4Tp37qxKlSqpf//+dkcBgBzLOPDmRJS9AADAo0VEROibb77RqFGj5Ovra3ccjzJt2jS1b99ejzzyiObPn6+AgAC7IwFApvD29tbYsWP1008/afHixXbHAQDgiih7AQCAxzLGqH///qpevboee+wxu+N4lAkTJqhHjx566qmn9Pnnn8vHx8fuSACQqf7zn/+obt266t+/v9xut91xAAC4LMpeAADgsb799lutWrVKYWFhcrl42pMRjDEaOHCgXn75ZQ0aNEjvvvsuv1sAOYJlWQoLC9Ovv/6quXPn2h0HAHIec+G5qFPuDp3FgbIXAAB4pqSkJA0cOFCNGjXS/fffb3ccj5CUlKTevXtr7NixmjBhgkaNGiXLsuyOBQBZpn79+mrVqpUGDRqk+Ph4u+MAAJAGZS8AAPBIn332mbZu3apx48ZRSGaA+Ph4PfHEE5o6daqmTZumvn372h0JAGwxZswY7dmzRx9++KHdUQAASIOyFwAAeJzz589ryJAheuSRR3TnnXfaHSfbi4mJUevWrfXll19qzpw56t69u92RAMA2VapUUefOnTV8+HBFR0fbHQcAcgzjwJsTUfYCAACP8+677+rQoUMaPXq03VGyvcjISDVv3lzLly/XggUL1LZtW7sjAYDthg8frjNnzujNN9+0OwoAAKlQ9gIAAI9y5swZjR49Wj169FCFChXsjpOtHTt2TA0bNtTmzZv1008/qWnTpnZHAgBHKFWqlJ555hm99tprOn78uN1xAABIQdkLAAA8yvjx41OmccCN279/v+rXr68jR45oxYoVuueee+yOBACOMnDgQFmWxadIACCLGGMcd3ciyl4AAOAxDh06pLfeeksvvPCCihUrZnecbGv79u2qV6+eEhISFBERoSpVqtgdCQAcp2DBgnrllVc0efJk7d271+44AABIouwFAAAeZPjw4QoICNArr7xid5Rsa8OGDapfv76Cg4MVERGhW2+91e5IAOBYzz//vAoUKMCnSQAAjkHZCwAAPMKOHTs0bdo0DRo0SMHBwXbHyZZWrFihRo0aqWzZslq+fDmjowHgGnLnzq2hQ4dqxowZ+v333+2OAwAezTjw5kSUvQAAwCMMGjRIxYoVU+/eve2Oki0tWLBAzZo105133qmffvpJBQoUsDsSAGQLTz75pMqVK6cBAwbYHQUAAMpeAACQ/a1du1ZffvmlRo4cKX9/f7vjZDszZ85U69at1bx5cy1YsEB58uSxOxIAZBs+Pj4aPXq0fvjhBy1fvtzuOACAHM4yTr10HAAAwHUwxqhx48Y6ceKENm7cKC8vL7sjZSuTJ09Wnz591KVLF33wwQfy9va2OxIAZDvGGNWpU0deXl5avXq1LMuyOxIAeIyoqCgFBwfr4U8elk8uH7vjpEiISdD/uvxPkZGRCgoKsjtOCkb2AgCAbO3HH39UeHi4xowZQ9GbDsYYjRo1Ss8884yee+45TZs2jaIXAG6QZVkaN26c1q5dq/nz59sdBwCQg1H2AgCAbMvtdqt///4KDQ1Vq1at7I6TbRhj9NJLL+nVV1/VyJEj9cYbb8jl4mkhANyMxo0b6/7779fAgQOVmJhodxwAQA7F8A0AAJBtffHFF/r999+1atUqPjJ7nRITE/V///d/+vjjjzVx4kT16dPH7kgA4DHCwsJUs2ZNffzxx+rZs6fdcQDAo5iLN6dwUpZLMYQDAABkS3FxcRo8eLAeeugh1a1b1+442UJcXJzat2+vTz/9VJ999hlFLwBksBo1aqhDhw4aNmyYYmJi7I4DAMiBKHsBAEC29P7772v//v0aM2aM3VGyhejoaLVq1UoLFizQ/Pnz9cQTT9gdCQA80siRI3Xs2DFNnDjR7igAgByIshcAAGQ7Z8+e1ahRo9S1a1fdfvvtdsdxvFOnTqlJkyZau3atFi5cqAcffNDuSADgsW699VY9/fTTGjt2rE6dOmV3HADwGMYYx92diLIXAABkO6+//rqioqI0bNgwu6M43uHDh9WgQQPt2rVLS5cuVcOGDe2OBAAeb/DgwUpMTFRYWJjdUQAAOQxlLwAAyFaOHj2qCRMm6Nlnn1XJkiXtjuNoe/bsUWhoqM6cOaOVK1eqdu3adkcCgByhSJEieumll/TOO+/owIEDdscBAOQglL0AACBbGTVqlLy9vTVgwAC7ozjali1bFBoaKpfLpYiICFWqVMnuSACQo/Tt21dBQUF8CgUAMohx4M2JKHsBAEC2sXv3br333nvq37+/8ufPb3ccx1q7dq3uvfdeFS5cWBERESpVqpTdkQAgx8mTJ49effVVTZ8+XX/88YfdcQAAOQRlLwAAyDZeffVVFS5cWP/973/tjuJYP/30k+677z7dfvvtCg8PV5EiReyOBAA51lNPPaVSpUpp4MCBdkcBAOQQlL0AACBb+O233/TFF19o2LBhypUrl91xHOl///ufWrZsqfr162vRokXKmzev3ZEAIEfz9fXVyJEj9fXXX+vnn3+2Ow4AZGvGGMfdnYiyFwAAZAv9+/dXxYoV1a1bN7ujONLHH3+sRx99VG3atNHXX39NIQ4ADtGhQwdVq1ZN/fr1c2wxAADwHJS9AADA8ZYsWaJFixZpzJgx8vb2tjuO47z55pvq3r27evTooRkzZsjX19fuSACAi1wul8LCwhQREaEFCxbYHQcA4OEsw1uLAADAwYwxqlOnjry8vLR69WpZlmV3JMcwxmjIkCEaNWqU+vfvrzFjxvD7AQAHMsbovvvu07Fjx/T777/Ly8vL7kgAkG1ERUUpODhYD378oHxy+dgdJ0VCTIK+7fatIiMjFRQUZHecFIzsBQAAjjZv3jytX79e48aNo8i8hNvt1rPPPqtRo0YpLCxMY8eO5fcDAA5lWZbCwsK0detWff7553bHAQB4MMpeAADgWAkJCRo4cKBatGihBg0a2B3HMRISEtS5c2dNnjxZU6dOVb9+/eyOBAC4hjp16uiRRx7RkCFDdP78ebvjAAA8FGUvAABwrGnTpmn37t0aO3as3VEcIzY2Vg8//LDmzJmjWbNmqWfPnnZHAgBcp9GjR+vgwYOaPHmy3VEAINsxxjju7kSUvQAAwJHOnTun4cOH6/HHH1e1atXsjuMIUVFRatGihZYsWaJvvvlG7dq1szsSACAdKlSooCeffFKjR49WZGSk3XEAAB6IshcAADjSW2+9pZMnT2rEiBF2R3GE48ePq1GjRtq4caMWL16s5s2b2x0JAHADhg4dqtjYWI0fP97uKAAAD0TZCwAAHOfEiRMaP368evfurTJlytgdx3YHDhxQ/fr19ffff2v58uWqV6+e3ZEAADeoWLFiev755/Xmm2/q8OHDdscBgGzDOPDmRJS9AADAccaMGSNjjAYNGmR3FNvt3LlT9erV0/nz5xUREcGUFgDgAV555RUFBARo+PDhdkcBAHgYyl4AAOAof/31l9599129/PLLKlSokN1xbPXbb78pNDRUgYGBioiIUPny5e2OBADIAHnz5tXAgQP14YcfaufOnXbHAQB4EMpeAADgKEOHDlW+fPn0wgsv2B3FVhEREWrYsKFKlSqlFStWqESJEnZHAgBkoGeeeUbFihXT4MGD7Y4CANmCMcZxdyei7AUAAI6xefNmffrppxoyZIgCAwPtjmOb77//Xvfff79q1qypJUuWqGDBgnZHAgBkMH9/f40YMUJz587VunXr7I4DAPAQlL0AAMAxBg4cqLJly6pnz552R7HNrFmz9NBDD6lp06b64YcfFBQUZHckAEAm6dSpkypXrqx+/fo5doQYACB7oewFAACOsHLlSn333XcaNWqUfHx87I5ji/fee08dO3ZUhw4d9OWXX8rf39/uSACATOTl5aWxY8dq2bJlWrRokd1xAMDRjANvTkTZCwAAbGeMUb9+/VSzZk21a9fO7jhZzhijsWPHqlevXurTp4+mT58ub29vu2MBALJAq1atVK9ePfXv319ut9vuOACAbI6yFwAA2O7rr7/W6tWrFRYWJpcrZz09SS66Bw4cqKFDh+rtt9/Ocb8DAMjJLMvSuHHjtHHjRs2ePdvuOACAbM4yTAwEAABslJiYqKpVq6p48eJavHix3XGyVFJSkp5++ml9+OGHeuutt/Tcc8/ZHQkAYJOHHnpIW7Zs0bZt2+Tr62t3HABwjKioKAUHB6v5h83lk8s5070lxCRoYY+FioyMdNR1Nhg2AgAAbPXpp59q27ZtCgsLsztKloqLi1OHDh308ccfa/r06RS9AJDDjRkzRvv27dPUqVPtjgIAyMYoewEAgG1iY2M1ZMgQtW/fXrVq1bI7TpY5d+6c/vOf/+jrr7/WvHnz1KVLF7sjAQBsVrlyZXXp0kUjRozQ2bNn7Y4DAMimKHsBAIBtJk2apKNHj2rUqFF2R8kyp0+fVtOmTbVq1Sr98MMPat26td2RAAAOMXz4cEVFRemNN96wOwoAOI5x4M2JKHsBAIAtTp8+rTFjxqhnz54qV66c3XGyxJEjR9SgQQPt2LFDS5cuVePGje2OBABwkJIlS+rZZ5/VhAkTdOzYMbvjAACyIcpeAABgi3Hjxik+Pl5DhgyxO0qW2Ldvn0JDQ3Xy5EmtWLFCderUsTsSAMCBBgwYIC8vrxz1qRcAQMah7AUAAFnu4MGDevvtt/Xiiy8qJCTE7jiZ7o8//lC9evUkSREREapcubLNiQAATpU/f37169dP7733nvbs2WN3HABwDGOM4+5ORNkLAACy3LBhw5Q7d269/PLLdkfJdOvWrVP9+vVVoEABrVy5UmXKlLE7EgDA4Z577jkVLFhQr776qt1RAADZDGUvAADIUtu3b9dHH32kwYMHKygoyO44mWrZsmVq3LixKlasqOXLl6to0aJ2RwIAZAO5cuXSsGHDNHPmTP322292xwEAZCOUvQAAIEsNHDhQJUuWVK9eveyOkqm+/vprtWjRQnXr1tXixYuVL18+uyMBALKR7t27q0KFChowYIDdUQDAEYwDb05E2QsAALLMmjVrNH/+fI0cOVJ+fn52x8k0n376qdq2basHH3xQ33zzjXLnzm13JABANuPt7a0xY8boxx9/1LJly+yOAwDIJih7AQBAljDGqF+/fqpSpYo6duxod5xM8/bbb6tLly7q2rWrZs2a5dGlNgAgcz388MOqU6eO+vXr59gLAQEAnIWyFwAAZIkffvhBK1asUFhYmLy8vOyOk+GMMRo2bJief/55vfTSS/rggw888ucEAGQdy7I0btw4rVu3Tl9++aXdcQDAVsYYx92dyDJOTQYAADxGUlKSatSooXz58ik8PFyWZdkdKUO53W698MILeueddzRmzBj179/f435GAIB9WrRooT179mjLli3y8fGxOw4AZKmoqCgFBwerydQm8snlnHNgQkyCfvq/nxQZGemoC08zshcAAGS6mTNnavPmzRo3bpzHlaCJiYnq1q2bJk6cqClTpmjAgAEe9zMCAOwVFhamP//8Ux999JHdUQAADkfZCwAAMlVcXJxeffVVtWnTRnfffbfdcTLU+fPn1bZtW82cOVMzZszQ008/bXckAIAHqlatmjp27Kjhw4fr3LlzdscBANsYB92cirIXAABkqilTpujAgQMaPXq03VEy1NmzZ/XAAw9o0aJF+uqrr9ShQwe7IwEAPNjIkSN14sQJvf3223ZHAQA4GGUvAADINFFRURo9erS6d++uSpUq2R0nw5w4cUKNGzfWhg0btGjRIrVs2dLuSAAAD1emTBn16tVL48aN08mTJ+2OAwBwKMpeAACQaSZMmKDo6GgNGzbM7igZ5uDBg7r33nv1119/KTw8XPXr17c7EgAghxg0aJDcbrfGjh1rdxQAyHJGRsY46O7QqRwoewEAQKY4cuSIXn/9dT333HMqXry43XEyxK5du1SvXj1FR0crIiJCNWrUsDsSACAHKVy4sF5++WVNnDhR+/fvtzsOAMCBKHsBAECmGDlypHx9fdWvXz+7o2SI33//XaGhofL399eqVatUoUIFuyMBAHKgF198UXnz5tXQoUPtjgIAcCDKXgAAkOF27dqlqVOnauDAgcqXL5/dcW7azz//rIYNG6p48eJauXKlSpYsaXckAEAOFRgYqCFDhuiTTz7Rli1b7I4DAFnGOPDmRJS9AAAgww0ePFghISHq06eP3VFu2o8//qgmTZqoatWqWrp0qQoVKmR3JABADtezZ0+VKVNGAwcOtDsKAMBhKHsBAECG2rBhg2bPnq1hw4YpICDA7jg3Zc6cOXrwwQfVuHFjLVy4UMHBwXZHAgBAvr6+Gj16tL799ltFRETYHQcA4CCWMcaZY44BAEC21LRpUx08eFCbNm2St7e33XFu2AcffKCnnnpKHTp00PTp0+Xj42N3JAAAUrjdbtWuXVsBAQGKiIiQZVl2RwKATBEVFaXg4GA1er+RvAOc8/oiMTZRy55apsjISAUFBdkdJwUjewEAQIZZvHixfvrpJ40ZMyZbF73jx4/X//3f/6lXr1767LPPKHoBAI7jcrkUFhamn3/+Wd9++63dcQAADsHIXgAAkCHcbrfuvPNO+fn5adWqVdlyhJExRgMHDlRYWJgGDx6sESNGZMufAwCQMxhj1LRpUx0+fFibNm2Sl5eX3ZEAIMMxsjd9GNkLAAAyxNy5c/Xrr79q3Lhx2bIgTUpKUq9evRQWFqbXX39dI0eOzJY/BwAg57AsS2FhYfrjjz/06aef2h0HADKVceDNiSh7AQDATYuPj9egQYPUqlUr1a9f3+446RYfH6/HH39cH3zwgaZNm6YXX3zR7kgAAFyX2rVrq127dhoyZIhiY2PtjgMAsBllLwAAuGkffvih9uzZozFjxtgdJd1iYmLUunVrzZ8/X3PmzFH37t3tjgQAQLqMGjVKR44c0bvvvmt3FACAzSh7AQDATYmOjtaIESPUqVMnValSxe446XLmzBndf//9WrFihb777ju1bdvW7kgAAKRb+fLl1aNHD40ZM0ZnzpyxOw4AZApjjOPuTkTZCwAAbsqbb76p06dPa8SIEXZHSZejR4+qUaNG+uOPP/TTTz+padOmdkcCAOCGDRkyRHFxcRo3bpzdUQAANqLsBQAAN+z48eN67bXX9Mwzz6hUqVJ2x7luf/31l+rXr68jR45o+fLluvvuu+2OBADATSlatKheeOEFvf322zp48KDdcQAA/7JixQo9+OCDKlasmCzL0ldffZVqvTFGQ4YMUdGiRRUQEKAmTZrozz//TPdxKHsBAMANGz16tCzL0sCBA+2Oct22b9+u0NBQJSYmKiIiIttNPQEAwJW8/PLLypUrl4YPH253FADIcMaBt/Q4d+6cqlWrdsX51cePH6933nlH7733ntauXavcuXOrWbNmOn/+fLqOQ9kLAABuyN69ezV58mS98sorKliwoN1xrsuGDRtUv359BQcHKyIiQrfeeqvdkQAAyDDBwcEaNGiQPvroI+3YscPuOACQI0RFRaW6x8XFXXa7Fi1aaNSoUWrTpk2adcYYvfXWWxo8eLAeeughVa1aVZ9++qkOHTqUZgTwtVD2AgCAGzJkyBAVKFBAzz//vN1Rrkt4eLgaNWqksmXLavny5SpWrJjdkQAAyHC9e/dWiRIlNGjQILujAECOULJkSQUHB6fcx44dm+597N27V0eOHFGTJk1SlgUHB+uuu+7S6tWr07Uv73QfHQAA5HibNm3SjBkzNHnyZOXOndvuONf07bff6tFHH1VoaKi++uorBQYG2h0JAIBM4efnpxEjRqhLly5au3at7rrrLrsjAUCGMMbImPRNnZCZkrMcOHBAQUFBKcv9/PzSva8jR45IkooUKZJqeZEiRVLWXS9G9gIAgHQbMGCAypUrpyeffNLuKNf0+eefq02bNnrggQe0YMECil4AgMd7/PHHdccdd6hfv36OKkYAwBMFBQWlut9I2ZuRKHsBAEC6LF++XN9//71Gjx4tHx8fu+Nc1bvvvqtOnTqpU6dOmjNnju1PvAAAyApeXl4KCwvT8uXLtXDhQrvjAACuISQkRJJ09OjRVMuPHj2asu56UfYCAIDrZoxRv379VLt2bT3yyCN2x7kiY4xGjRqlPn366IUXXtC0adPk7c3sVQCAnOOBBx5Q/fr11b9/f7ndbrvjAMBNMw68ZZQyZcooJCRES5YsSVkWFRWltWvX6p577knXvnjVAwAArtv8+fO1du1aLVmyRJZl2R3nstxut1566SW9+eabGjlypAYNGuTYrAAAZBbLsjRu3DjVrVtXM2fO1BNPPGF3JADI0aKjo7Vr166U7/fu3auNGzcqf/78uuWWW/T8889r1KhRKl++vMqUKaNXX31VxYoVU+vWrdN1HMswgQ8AALgOiYmJuuOOO1SqVCn9+OOPdse5rMTERPXs2VPTp0/XxIkT1adPH7sjAQBgqzZt2mjjxo3avn070xkByJaioqIUHBys0Cmh8g5wzrjVxNhERfSKUGRkZKoLtF1JeHi4GjVqlGZ5ly5dNH36dBljNHToUE2dOlVnzpxRaGioJk+erAoVKqQrF2UvAAC4Lh9++KF69uypX3/9VTVq1LA7Thrnz59Xx44d9c033+iTTz7R448/bnckAABst23bNt1xxx1688039d///tfuOACQbsllb73J9RxX9q7qveq6y96swpy9AADgmmJiYjR06FB16NDBkUXv2bNn1apVK/3www+aP38+RS8AABdVqlRJ3bp108iRIxUVFWV3HABAJqPsBQAA1zRx4kQdO3ZMI0eOtDtKGqdOnVKTJk30yy+/aOHChXrwwQftjgQAgKMMGzZM0dHRev311+2OAgDIZJS9AADgqk6dOqWxY8fq6aef1q233mp3nFQOHTqke++9V7t379ayZcvUoEEDuyMBAOA4JUqU0H//+1+9/vrrOnr0qN1xAOCGGAfenIiyFwAAXFVYWJgSExM1ePBgu6Oksnv3boWGhioyMlIrV65UrVq17I4EAIBj9e/fXz4+Po78lA4AIONQ9gIAgCs6cOCA3nnnHb300ksqUqSI3XFSbN68WaGhofL29lZERIQqVapkdyQAABwtX758GjBggN5//33t3r3b7jgAgExC2QsAAK5o2LBhCgoKUt++fe2OkmLNmjVq0KCBQkJCtHLlSpUqVcruSAAAZAvPPvusihQp4rhP6wDA9TDGOO7uRJS9AADgsv744w9Nnz5dgwcPVp48eeyOI0lavHix7rvvPlWuXFnLli1z1GhjAACcLiAgQMOGDdOsWbP066+/2h0HAJAJKHsBAMBlDRw4UKVKldJTTz1ldxRJ0pdffqmWLVuqQYMG+vHHH5U3b167IwEAkO107dpVt912m/r37293FABAJqDsBQAAafz888/6+uuvNXLkSPn5+dkdRx999JHatWunhx9+WF999ZVy5cpldyQAALIlb29vjRkzRosXL9aSJUvsjgMA18048OZElnHqBBMAAMAWxhjde++9Onv2rH799Ve5XPa+N/zGG2+ob9++euqpp/Tuu+/Ky8vL1jwAAGR3xhjVrVtXCQkJ+uWXX2z/tx4AriYqKkrBwcG6+9275R3gbXecFImxiVrzzBpFRkYqKCjI7jgpOKMDAIBUFixYoIiICIWFhdn64s8Yo8GDB6tv377q37+/pkyZQtELAEAGsCxL48aN04YNGzRv3jy74wAAMhAjewEAQIqkpCRVr15dhQoV0pIlS2RZli053G63nn32WU2ePFnjxo3TK6+8YksOAAA8WatWrbRjxw798ccf8vHxsTsOAFxW8sjeuybd5biRvWv7rGVkLwAAcK7PP/9cW7ZsUVhYmG1Fb0JCgjp16qQpU6Zo6tSpFL0AAGSSsWPHavfu3frwww/tjgIAyCCUvQAAQJJ0/vx5DRkyRG3btlWdOnVsyRATE6M2bdpo7ty5mj17tnr27GlLDgAAcoIqVaroiSee0PDhwxUdHW13HABABqDsBQAAkqTJkyfr4MGDGj16tC3Hj4yMVPPmzbV06VJ9++23evTRR23JAQBATjJixAidPn1ab731lt1RAOCqjANvTkTZCwAAFBkZqdGjR+vJJ59UxYoVs/z4x48fV+PGjbVp0yYtXrxYzZo1y/IMAADkRKVLl1bv3r01fvx4nThxwu44AICbRNkLAAA0fvx4xcbGaujQoVl+7P3796t+/fo6ePCgli9frnr16mV5BgAAcrJBgwZJksaMGWNzEgDAzaLsBQAghzt8+LDefPNNPf/88ypWrFiWHnvHjh0KDQ3V+fPntXLlSlWrVi1Ljw8AAKSCBQvqlVde0bvvvqu//vrL7jgAcFnGGMfdnYiyFwCAHG7EiBHy9/fXK6+8kqXH/fXXX1W/fn0FBgZq1apVKl++fJYeHwAA/OOFF15Qvnz5NGTIELujAABuAmUvAAA52M6dO/XBBx9o0KBByps3b5Ydd8WKFWrUqJFKly6tFStWqHjx4ll2bAAAkFbu3Lk1dOhQffbZZ9q0aZPdcQAAN4iyFwCAHGzw4MEqVqyYnnnmmSw75vfff69mzZqpVq1aWrJkiQoWLJhlxwYAAFfWo0cP3XrrrRo4cKDdUQAgDePAmxNR9gIAkEOtW7dOc+fOTZnGISt88cUXeuihh3T//ffr+++/V548ebLkuAAA4Np8fHw0evRoLViwQCtWrLA7DgDgBljGqbMJAwCATGOMUZMmTXT06FH9/vvv8vLyyvRjTpkyRc8884yeeOIJffTRR/L29s70YwIAgPRxu92qU6eOfHx89PPPP8uyLLsjAcjhoqKiFBwcrNoTa8s7wDmvIRJjE7X+2fWKjIxUUFCQ3XFSMLIXAIAcaPHixVq6dKnGjh2b6UWvMUZjxoxR79691adPH02fPp2iFwAAh3K5XBo3bpzWrFmjr7/+2u44AJDCGOO4uxNR9gIAkMO43W7169dP9erVU6tWrTL1WMYYvfLKKxo0aJCGDRumt99+Wy4XTz8AAHCy++67T02bNtWAAQMUGxur999/X23bttX58+ftjgYAuAaG1QAAkMPMnj1bGzduVERERKZ+NDMpKUlPPfWUpk2bprfeekvPPfdcph0LAABkrLFjx6p27dq65ZZbdOLECUnSwYMHdeutt9qcDABwNZS9AADkIPHx8Ro8eLD+85//qF69epl2nLi4OD3++OP66quv9Mknn6hz586ZdiwAAJCxwsPD9eKLL0pSStEriZG9AGxn5MypE5yEshcAgBxk6tSp2rdvn7755ptMO8a5c+f08MMPKzw8XPPmzVPr1q0z7VgAACBjnT17Vvfff78SEhLSrKPsBQDnY9I8AAByiLNnz2rEiBHq0qWLKleunCnHOH36tJo2baqff/5ZP/zwA0UvAADZTJ48efTpp58qMDAwzUVc4+LibEoFALhelL0AAOQQb7zxhqKiojR8+PBM2f/hw4fVoEED7dixQ0uWLFHjxo0z5TgAACBzPfbYY9q6davuueeeVMsZ2QvATsYYx92diLIXAIAc4NixY5owYYKeffZZlSxZMsP3v3fvXtWvX18nT57UypUrVadOnQw/BgAAyDq33HKLwsPDFRYWlnJB18OHD9ucCgBwLZS9AADkAKNGjZKXl5cGDBiQ4fveunVrysXeVq1apdtvvz3DjwEAALKel5eX+vXrp/DwcJUsWVIVKlSwOxIA4Bq4QBsAADnAt99+q1dffVX58+fP0P2uXbtWDzzwgIoXL65FixYpJCQkQ/cPAADsd++992r//v12xwCQw5mLN6dwUpZLUfYCAJADbN68WYGBgRm6zyVLluihhx5S1apVtWDBAuXLly9D9w8AAAAASB+mcQAAIAfI6KL3q6++0gMPPKB69epp8eLFFL0AAAAA4ACM7AUAAOnyySefqHv37mrbtq0+++wz+fn52R0JAAAAgIczxsgY50yd4KQsl2JkLwAAuG5vv/22unbtqu7du+uLL76g6AUAAAAAB6HsBQAA12SM0dChQ/X888/r5Zdf1tSpU+Xl5WV3LAAAAADAJZjGAQAAXJXb7dbzzz+viRMnauzYserfv7/dkQAAAADkMObizSmclOVSlL0AAOCKEhIS1L17d82YMUNTpkzR008/bXckAAAAAMAVUPbCEf7880+dPXvW7hiOkidPHpUvX97uGECOwDkorTx58qhkyZJq3769vv/+e82cOVOPPfaY3bEAj8Q5KC2eBwFZh3NQWpyDAGRnlL2w3Z9//qkKFSrYHcORdu7cyZMMIJNxDrqyOnXqaNOmTfr666/1wAMP2B0H8Eicg66M50FA5uMcdGWcgwDnMcbIGOdMneCkLJei7IXtkt9F7tOnj4oXL25zGmc4ePCgJk2axDvsQBZI/jv7/PPPValSJZvTOMO2bdv0xBNP6Pjx41q0aJHq169vdyTAYyWfg6y2lqyCls1pnMGcMDJfGp4HAVkg+e+sXI9yCigaYHMaZ4g9HKtdH+7iHAQg26LshWMUL15cZcuWtTsGgByqUqVKqlmzpt0xHGXevHn8ToAsYhW0ZBWj7E3m1AueAJ4qoGiAcpfKbXcMAEAGoOwFAAAAAAAA4Gjm4s0pnJTlUi67AwAAAAAAAAAAbh5lLwAAAAAAAAB4AMpeeJTw8HC1b99ex44dsztKGsOHD9fw4cPtjgEAAAAAAJDtGGMcd3ciyl443vjx49WpUyfFxsZecZt33nlHHTt2TPcVU+fPn69169bdbEQAOcD06dNlWZYsy1JERESa9cYYlSxZUpZlqVWrVuna98GDB9WuXTvlzZtXQUFBeuihh7Rnz57Lbjtt2jRVqlRJ/v7+Kl++vCZOnHjT+wTgfO7f3EoamqSkoUkyf6V9YWGMUdLrF9YnzUhK175NlJF7jltJY5OUNCZJSTOTZE5d/sWLe4NbSROTlDQySUlvJ8m9xp12fyeM3D+4lfThxe2GJsmcduaLIQDX59iqY1rTY43W9FijqD+j0qw3xujXl3/Vmh5rtP2d7de939gjsdo3a5+2jN2itU+v1Zoea3T+xPnLbnvilxPa9cEubRy4UWt6rNHW8VuvuF93glt/zftLG/pu0Npea7V59Gad2XrmunMBQHZG2QvHCw0NVXx8vH755ZfLro+Li9P69etVvXp1tWzZUp999pkKFSp0Xfum7AWQXv7+/po5c2aa5cuXL9fff/8tPz+/dO0vOjpajRo10vLlyzVw4EANHz5cv/32mxo0aKCTJ0+m2vb9999Xjx49VLlyZU2cOFH33HOP/vvf/2rcuHE3vE8A2Yy3ZDZfpjjdJylK6b78sokzck93y+wzsupbshpa0hHJ/bFbJib1cdzr3DLfGKmwZLWwZJW0ZH4wcq9MXfiaA0ZmrZHiJBVMXx4Azmb5WDq5Nu1ziagdUYo/HS/L20rX/s7uPqsjS44o6XySAooGXHXbo+FHdWrjKfnm95VXLq+rbrv74906sviICt5dUKUfKy3LZWnHOzsuW1QDgKdJ59NBIOvVrl1bAQEBWrVqlRo0aJBm/fr16xUXF6fQ0FC5XC75+vpedX/GGCUkJFxzOwC4nAceeEBz587VO++8I2/vf/4ZnTlzpmrVqqUTJ06ka3+TJ0/Wn3/+qV9++UV33nmnJKlFixa644479Prrr2vMmDGSpNjYWA0aNEgtW7bUvHnzJEk9e/aU2+3WyJEj9X//93/Kly9fuvYJIBsqL5mtRqaFkeX1T6liNhupmKSY9O3OrDPSScn1fy5ZxS/sz5Q3ck92y/xsZDW5uCzByCw1UgXJq/3FkqW25DZumRVGpraRFXBhW6uiJWuAJcvPknuVW+YIo3oBT5GvSj6dXH9SpTuUTnUOOvnLSeUulVsJ0Qnp2l/+avlVYGIBefl76dCPh7T/wP4rbluuRzn55vWV5bL0+5Dfr7hd9J5onfzlpG559BYVa1ZMklSobiH9PuR37Z+3X3cMuCNdGQE4h7l4cwonZbkUI3vheL6+vqpTp462bNmiyMjINOsjIiIUEBCg2rVrX3bO3j59+mjcuHHauHGjBgwYoE6dOmnx4sVq37694uLitHz5crVv317t27fX5MmTJV0oSvr06ZPmWHPnzlX79u1TLVu2bJlGjBihnj176vHHH9eLL76oRYsWZfBvAYBTdOjQQSdPntTixYtTlsXHx2vevHnq2LFjuvc3b9483XnnnSmlrCTddtttuu+++zRnzpyUZcuWLdPJkyfVu3fvVI9/5plndO7cOS1YsCDd+wSQ/biquKRYSZfMymISjcwfRlaV9I2okyTzh5GKK6XolSSrkCWVuVAqp9grKUZy3Zn65YNVx5LiJbPzn22tXBeKXgCep0CdAko8l6gzf5xJWeZOdOvk+pMqeFf6h/J7B3rLy//qo3ST+eX3k+W69rnl5IaTkksqfG/hlGUuH5cK1y+s6N3RijsVl+6cAJCdUPYiWwgNDVVSUpJWr16danl0dLR+//133XnnnVcdqXvo0CG98847qlKlirp06aLSpUurT58+8vHx0W233aY+ffqoT58+atKkSbqzLV68WAULFlTr1q3VqVMnFShQQNOmTdOPP/6Y7n0BcL7SpUvrnnvu0RdffJGy7IcfflBkZKQee+yxdO3L7XZr06ZNql27dpp1derU0e7du1PmIv/tt98kKc22tWrVksvlSlmfnn0CyIbySirxr6kcdkk6L1l3pK9gNW4jHZWsYmkfZxW3pFMXpnmQJHP44vGK/WvDopIsSYfTdWgA2ZRfQT8Flg3UyV/+mcrhzOYzSopNUoE6BWxM9o+Y/TEKKBIg74DUH2QOLBOYsh4APBnTOCBbuOOOO5QvXz6tWrVKzZs3T1m+evVqJSUlKTQ09KqPP3LkiAYMGKDq1aunWv7BBx+oSJEiql+//g1nGzZsWKqiuXnz5hozZowWLFigZs2a3fB+AThXx44dNWDAAMXGxiogIEAzZsxQgwYNVKzYv1uQqzt16pTi4uJUtGjRNOuSlx06dEgVK1bU4cOH5eXlpcKFC6faztfXVwUKFNChQ4fSvU8A2ZNVxZJZYmQSjCwfS2aTkUpLVlA6R9PGSkqUFHiZdXkufj0ryU9StCSXZAWmPoblbUm5Lm4HIEcoeFdBHfjfAbnj3XL5unRi7QkFVQySb15nTJMXHxkvn2CfNMuTl8VHxmd1JAAZxBgjY5wzdYKTslyKkb3IFlwul+rWraudO3emmqJh1apVCg4OVpUqVa76+MKFC6cpejPKpUVvTEyMoqKiVKlSJR09elQxMbxrDHiidu3aKTY2Vt99953Onj2r77777oamcIiNjZWky17Uzd/fP9U2sbGxV/wEg7+/f6rtrnefALIn6w5LSpC088LIW7PzxqZwUOLFr5cb/pG8LOGSr1f6pLX3hakkAOQMBe4sIHeCW6c3nVbS+SSd2XRGBes452qM7gS3XN5pqw6Xz4Vl7nh3mnUA4EkY2YtsIzQ0VAsWLNCqVavUpk0bnTx5Utu3b1fz5s3lcl39fYt/j4TLSNu3b9fcuXP1559/Ki4u9fxPMTExypUrV6YdG4A9ChUqpCZNmmjmzJmKiYlRUlKSHnnkkXTvJyDgwlWn/33ukKTz58+n2iYgIEDx8ZcfiXL+/PlU213vPgFkT1ZuSyoruTe5ZSVYkluybr+Bsjf5lUDiZdYlL/O55GvSFfaTeHGEL4AcwSePj4IqBenE2hNyx7ll3Eb5a+e3O1YKl49L7sS0ha474cIyly9j3gB4NspeZBtly5ZVsWLFUsreVatWyRhzzSkcJF11Pt/0cLtTP2k4cuSIRo0apWLFiqlTp04qWLCgvLy8tHHjRi1YsCDN9gA8R8eOHdWzZ08dOXJELVq0UN68edO9j/z588vPz0+HD6ed7DJ5WfLUEEWLFlVSUpKOHTuW6g2s+Ph4nTx5MmW79OwTQPZlVbVkvjEy0UYqL1kBN1C2BujCq4Hoy6xLnpYheTqHQEluyUSbVFM5mEQjxVyyHYAcoeBdBbXnkz1KiExQ3jvyyjuXc6oF32BfxZ9J+wZ5QmRCynoA2ZO5eHMKJ2W5FG9pIVsJDQ3VgQMH9Ndff2nVqlUqWrSoypUrd8P7s6zLvzAKDAzUuXPn0iw/fvx4qu83bNighIQEvfLKK2ratKlq1KihqlWrZli5DMC52rRpI5fLpTVr1tzQFA7ShSlqqlSpovXr16dZt3btWpUtW1Z58lxoUJKnovn3tuvXr5fb7U5Zn559Asi+rNusCxdG+1s3NoWDdOGq9oUlcyjtCxVz0Ej5JMvvwr6tohePcehfGx6SZHThQm0Acoz8NfLLclmK3hOtgnc5ZwoHScp1Sy7FHo1VYmzqjy1E74lOWQ8AnoyyF9lK8oXU5syZo3379qlevXo3tT8/P7/LlrpFihRRTEyM/vrrr5Rlp0+f1rp161Jtlzx9xKWTcsfExCg8PPymcgFwvsDAQE2ZMkXDhg3Tgw8+eMP7eeSRR7Ru3bpU5eyOHTu0dOlSPfrooynLGjdurPz582vKlCmpHj9lyhTlypVLLVu2TPc+AWRflp8lq5Ulq6Elq+KNT6Fg3W5JBy+WuxeZE0baK1mVL9lvGUkBkntd6k8tmXVG8pGs8kzjAOQkXv5eKvN4GZX4Twnlq5bP7jip5K+VX3JLx1b8c60Xd4Jbx1cdV2DZQPnlT3tdAwDwJM75rAVwHQoXLqwKFSqkFBjJ5e+NKlu2rDZv3qzvvvtO+fLlU+HChVW+fHnVrVtXM2bM0Ouvv67mzZsrLi5OixcvVtGiRbV3796Ux1erVk3e3t4aP368mjRpovPnz2vJkiUKCgrS6dOnbyobAOfr0qXLTe+jd+/e+uCDD9SyZUu99NJL8vHx0RtvvKEiRYqob9++KdsFBARo5MiReuaZZ/Too4+qWbNmWrlypT7//HONHj1a+fPnT/c+AWRvruo3P27DqmPJ/GrknuGWVdeSvCSz2ki5deH75O18LFmNLZkFRkmzk2SVs6S/JLPJyLrPkpXrkqkdzhuZtRfKY7P/4tdfjIy/kfwl112MNwE8QaF6hW56H4kxiTqy9Igk6eyuC/PHHF16VF65vOSdy1shjUNSto3aGaWonVEXHnc2Ue54t/7+7m9JUlCFIAVVCJIk5SmbR/lr59eB/x1QQlSC/Av76/jPxxV3Mk5lu5a96cwA7GOMSTXYzm5OynIpyl5kO6Ghodq5c6fKlSunkJCQaz/gKjp37qypU6dq9uzZio+PV4MGDVS+fHnlyZNHL730kj799FPNmDFDhQsXVocOHXTkyJFUZW+xYsX04osvatasWfrss8+UN29eNW3aVEFBQXrvvfdu9kcFkAPkyZNH4eHheuGFFzRq1Ci53W41bNhQb775pgoVSv0iqnfv3vLx8dHrr7+ub775RiVLltSbb76p55577ob3CSBns/wsubq65F7olllhLkzJUFpyNXdduBDcJVx1XHK73DKrjcwOIwVLVnNL1t3/GtUbK5mlqV/8mJ8vfp9X0l2Z9dMAyG4SYxL191d/p1p2eNGFawz4FvBNVfZGbovUwW8P/rPhWaU8tviDxVPKXkkq92Q5HfjqgE6sOaHEc4nKVSKXKj5bMdU2AOCpLOPUGho5xq+//qpatWpp7NixKluWd1olac+ePRowYIA2bNigmjVr2h0H8GjJ5yD+3v7B7wTIOsl/b66nXLKKMRWCdGEOYff7bs5BQBZIPgdVebWKcpfKbXccRzj31zltHrmZcxDgIFFRUQoODtZtE26TV4CX3XFSJMUmaftL2xUZGamgIOe8mcTIXgAAAAAAAACOZi7enMJJWS5F2QsAQCY4deqU4uPjr7jey8uLKRUAZBoTY6Skq2zgUpppGgAgoyRGJ8qd5L7iestlySePTxYmAoCcg7IXAIBM8PDDD2v58uVXXF+qVCnt27cv6wIByFHcs93SvqtskFfyesE5H4ME4Fl2TN6hszvPXnG9bwFf1RzHFAkAkBkoewEAyASvv/66Tp8+fcX1AQEBWZgGQE7jauaSYq+yAQPqAGSiUu1KKSnmyh8vcPm4sjANAE9hjJGTLj3mpCyXouwFACAT1KpVy+4IAHIwLvYGwE6BpQPtjgAAORZvpwEAAAAAAACAB6DsRY7Uvn17zZ0797q27dOnjyZPnpzuYxw7dkzt27dXeHh4uh8LAJI0Z84cVa1aVQEBAerWrVuqdfv27Uu5yNtjjz2mM2fO2BMSQLbj3uJW0uQkJY1Mknt+6gsoJX2cpKR3r3Zlt/RL+jhJSaOSlDQ1SWavMz/uCCDrnFx3UpuGbtLaXmu1+6PdqdZtHb9Vvw/5PUOP98drf+iX3r9o86jNitwemaH7BpC1jANvTsQ0Dsi2wsPDNWXKFI0ZM0a33nrrTe1rx44d2rRpkx544AHlzp07gxICwI07d+6cunbtqiJFiigsLEx16tRJtb5QoUKaNm2aNmzYoEmTJqlChQoaMWKETWkBZBcm3sh8ZaTcktXEklXixqZ7cK9wy+ww0ilJ8ZKCJKuCJeteS1bu1Pt03euSOWJkVhu557vl9SIXhgNyqqS4JO3+eLd8gnx0S9tbFFgm/dM9JMUl6fiq4zq98bRiDsYo6XyS/Av7q/C9hVWkQRFZrtTnoGIPFFPM3zE6vOiwdn+0WzXHc2E4AJ6Nshc50meffSYvr39eaOzcuVPz5s1TgwYN0pS9b775piyLee8AZK1t27YpNjZWffv2VZ8+fdKsz507t7p27aquXbvqxx9/1MaNG7M+JIDs57ikBMmqa8l1141/yM8cMrJCLOkOSb6STkhmg5HZaeTq5ZLl+89zJ+tWS9atltxJbpklRibGyMrFcysgJ4o9HCt3vFtF7y+qkMYhN7SPuBNx2vfFPgXfFqyiTYvKK8BLZ7ac0b4Z+xS9J1rlniyXavu8lfMqb+W8MolGB+YfUGJ0orwDqUIAeC7OcMiRfH19r3tbHx8uVw0gfRo2bKjSpUtr+vTpN7yPc+fOSZKKFClyzW1DQkJ09uzZGz4WgOwh6eMkWXktudrcxExsCRe+WIE3V7Z6PZZ2dK4paeSefWHEr1XlMvtPHsAXLynXTR0egA22jt8qv4J+Kte93LU3vgJ33IWpY3yCbvw1lk+Qj6oOq6pcxf85kRRpUES7P96t46uOq0SrEvIv4p/2ccEXjpkUl0TZC2RTxhgZ45ypE5yU5VLM2QuPMXnyZHXu3FmnTp3Sa6+9ps6dO6tHjx767LPP5Hanno/u0jl7586dq88//1yS9Oyzz6p9+/Zq3769jh07JintnL3R0dH67LPP9NJLL6lz587q2rWrxo4dq3379mXNDwrkUL///ru6deumdevW2R0lSyQ/cbieTxa4XC7HPtEAPMWOHTvUtWtXrVq1yu4oN+cGThVml1HSqCS557plkq6yg7wXv56/wvrk0xmnKyDHupHnK2e2ntEvvX/Rn1P/lEky8snjk6roTZa/Rn5JF0YPX5Z14xkAIDuh7IVHcbvdGj16tPLkyaNOnTrp9ttv13fffaeffvrpio+pU6eO6tWrJ0nq3Lmz+vTpoz59+igoKOiy2x89elTr1q1TzZo11blzZz344IPav3+/hg8frlOnTmXKzwXgwjzd06dPV506ddSiRQuPL32T36Ryua79T7VlWWne1AKQsVauXKlPPvlEoaGhuu+++7Jv6ZvccVznwF6zw8j9hVvW7ZastpYsr38eaIyROWdkzhqZv4zc37sll2SVvsLOKXsBXPz7v95p8k7/flo7Ju5Q/lr5Va5HuVTnoH+Lj4qXJHnnufyoXSul7b3+uACQHVH2wqMkJCSobt26evrpp9W0aVO9+OKLKl26tJYtW3bFx5QqVUplypSRJN15552qX7++6tevL3//tB/9kaRbbrlFb731ljp27KgmTZqobdu2Gj58uBISEq56HAA3L7n4XLx4sceXvocOHZIk5c2b95rbBgcH6/Dhw5mcCECy5cuXZ9vS15y92HJc/mlO6m3/uDAtg1XNktXaSnPRI0VL7vFuuSe45f7ILUXqQiFc6PJljOVnpTwOQM4Uf+ZCIeuV69oXajy14ZR2Tt6pQnUL6dZut6Y9B13CnejWkZ+OyK+gnwJLX/6ib8nHTDiTcAPJATiBceDNiZioBh6nadOmqb6vVKmSVqxYkWH7v3QOX7fbrXPnzsnf319FixbV3r17M+w4N+LcuXOqVKmSjh8/bmsOIDMkJiamjF5NSkqSJC1cuFALFy5Uv379FBYWZkuuhIQERUZGplkWFxenEydOpFqeP3/+a47UPXPmjHbv3q033nhDQUFBuvPOO6+ZoUGDBvr66681adIkPfTQQwoJCbFlvvHExESVL19eR44cyfJjA5ktMTEx5b+Tz0FLly7V0qVL1atXr1RTPmUEk2TSToeQJClRMuf+9cIiQFctQSTJxBrptGRWG8lPUvGrH9+92S3zPyOrtiXrAevyo/ACJFdn14VMh43MNnNhPt4rKSHJW3KvcMt1v0sKvqQABuAo7kS3kmKTUi0zSUYm0SjhbOqy1Du39zXPQYkxiTp/7LwOLz4srwAvBZa5fCGb7MTaE9r90W4VblBYpTuUvuZI4H0z9yn2UKwq/rfiFUf/BpYJlOVt6eCCg7ql3S3yy+8nL/9rl84AkN1Q9sKj+Pj4pJl+IXfu3CkXOsoIbrdbP/zwgxYtWqRjx46l+uh0njx5Muw4NyIgIEDjx4+n7IVHCg8P1/z581PmWXO5XHK73apevbqeeOIJ23KtWrVKjRo1SrP8559/1qxZs1It27t3r0qXLn3V/bVu3VrLly9XUFCQ5s+ff13nleeee06rV6/Ws88+q2effVbLli1Tw4YN0/NjZAgvLy+NHz+eshce6d9/08nnoDvuuENPPvlkxh9wv+SennZ6FnPAyGxJXfa6nndJ+a6+O/cst7RPkp/kesx19ZL1tGS+NLIqW3K1vPIbVJa3Jd168b8rWjJljdzT3DK5jayKafdv5bHketgl9//ccr/rllXdktWGshdworO7zmrbhG1plkfvjtbJX06mWlY9rLr8C1794wI7Ju3Q2Z1n5RXgpQq9K1y1ZI07EaddH+5SgdoFVKZjmWtmPbTwkI6tOKYSrUsoX9Urnwx98/qqXI9y2vXhLm0askkF6xa8qYvNAYBTUfbCo1zP3JY3a/78+ZozZ44aNWqkdu3aKTAwUJZl6ZNPPrF9sn+Xy6XHHnvM1gxAZnG73Zo/f768vb2VmJioNm3aaNiwYbrjjjtszVWtWjUtXrw41bK+ffsqJCREL7/8cqrlISEh19zfhAkTtHnzZo0YMUKdOnXSzp07lTt37qs+5uOPP9bcuXPVp08f3X///apWrVr6f5AMYFmWHn30UVuODWS2gIAAzZo1K+Uc1LJlSw0fPlw1atTInAOGXBw1ewn3j24pUHLV+9f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qGjVqpNl37bXX6p577rGUCACunHHh5kaUvQAAAAAAeJjExEQ9//zzWrt2rW6++WZ5eXnJy8tL3bt3l+M4tuMBALIIZS8AAAAAAB4kLi5OXbt21fvvv6//+7//0y+//KKGDRvKcRw9+uijtuMBALIQa/YCAAAAAOAhzpw5ow4dOmj+/PmaMmWKOnToIEn69ttvtX37dpUrV85yQgC4MsaYlJtNuoGbsqRG2QsAAAAAgAeIiopSq1at9NNPP2nOnDlq1qxZynN+fn6qXLmyxXQAgOxA2QsAAAAAQA53+PBhNWvWTJs2bdLixYtVu3Zt25EAABawZi8AAADgcosWLVKZMmX0999/244CwIV2796tunXr6t9//9Xy5cspegF4JOPCzY0oewEAAACXio+P15AhQ9SsWTPt3r1bW7dutR0JgMts3rxZtWvX1smTJxUREaFbb73VdiQAgEUs4wAAAAC40NatW9WxY0f9/PPPKfuKFi1qMREAt/njjz/UpEkTFSxYUIsXL1aZMmVsRwIAWMbMXgAAAMBlJk2apJtvvlm//vprmv1+fn6WEgFwm1WrVqlevXoqXbq0VqxYQdELwOMZY1z3cCPKXgAAAMBFDh8+rG7duun06dNKSEhI81zevHktpQLgJgsXLlRISIiqV6+uZcuW6ZprrrEdCQDgEpS9AAAAgIsUKVJECxcuVNWqVc97jpm9AKZNm6b7779fDRs21IIFCxQYGGg7EgDARSh7AQAAAJdp3LixPvjgA0lS4cKFU/YzsxfI3T7++GN17NhR7du314wZM5QvXz7bkQAgWxkXbW5F2QsAAAC4jDFGffv21a233qrdu3dr7Nix6tChg4oUKWI7GgBL3njjDT355JN65pln9Pnnn8vHx8d2JACAC+WxHQAAAABAWnPmzNEPP/ygxYsXK2/evOrZs6d69uxpOxYAC87+48/rr7+ugQMHaujQoXIcx3YsAIBLUfYCAAAALpKQkKB+/fqpYcOGCgkJsR0HgEUJCQl65pln9NFHH+ntt9/WCy+8YDsSAFhjjJEx7lk+wU1ZUmMZBwAAAMBFPv/8c23YsEGjRo2yHQWARbGxserUqZMmTJigTz/9lKIXAHK48ePHq1q1agoMDFRgYKBq1qypBQsWpDx/5swZ9ejRQ4ULF5a/v7/atm2rAwcOZPg8lL0AAACAS5w+fVqDBg1S+/btdfvtt9uOA8CSU6dOqVWrVpo1a5amT5+uxx57zHYkAMBVKl26tEaNGqW1a9fql19+0b333qtWrVpp/fr1kqQXXnhB3377raZPn67w8HDt3btXDzzwQIbP4xi3zjkGAAAAcpm33npLffv21YYNG1SxYkXbcQBYcPz4cd133336/fffNWvWLDVq1Mh2JACwKioqSkFBQVqyYYkKBBSwHSfFyeiTCrkxRLt27VJgYGDKfj8/P/n5+aXrGIUKFdKbb76pdu3a6ZprrtHkyZPVrl07SdI///yjKlWqaPXq1br77rvTnYuZvQAAAIALHD9+XK+++qqeeOIJil4glzpw4IDq16+vDRs26LvvvqPoBYAcoEyZMgoKCkp5vPbaa5d9TUJCgqZOnaqTJ0+qZs2aWrt2reLi4tJc9ytXrqyyZctq9erVGcrDDdoAAAAAF3j99dcVExOjQYMG2Y4CwIJ///1XjRo10smTJ7VixQpVrVrVdiQAQDpcaGbvxfz555+qWbOmzpw5I39/f82cOVM33nijfv/9d/n6+io4ODjN+GLFimn//v0ZykPZCwAAAFi2Z88ejRkzRi+++KKKFy9uOw6AbPb3338rJCREfn5+ioiI0HXXXWc7EgC4jjFGblqN9myWszdcS48bbrhBv//+uyIjI/X111+rS5cuCg8Pz9RclL0AAACAZUOHDlX+/Pn18ssv244CIJv98ssvatq0qUqUKKHFixerRIkStiMBALKIr6+vKlSoIEmqUaOGfv75Z40ZM0YdOnRQbGysjh8/nmZ274EDBzI8EYA1ewEAAACL/vnnH3366afq379/umeFAPAMy5cvV4MGDVSxYkWFh4dT9AJALpOYmKiYmBjVqFFDPj4++u6771Ke27hxo3bu3KmaNWtm6JjM7AUAAAAs6t+/v0qXLq1nnnnGdhQA2WjOnDlq37696tSpo5kzZ8rf3992JABwNZO8uUVGs/Tt21fNmjVT2bJlFR0drcmTJ2v58uVatGiRgoKC1L17d/Xu3VuFChVSYGCgevXqpZo1a+ruu+/O0HkoewEAAABLfvzxR82YMUOfffbZJW/mAcCzfPnll+ratatatWqlyZMn8/cfAHKBgwcPqnPnztq3b5+CgoJUrVo1LVq0SCEhIZKkd955R15eXmrbtq1iYmLUpEkTjRs3LsPncYybVjYGAAAAcgljjBo0aKCjR4/qt99+k7e3t+1IALLB+++/r169eumxxx7TRx99pDx5mIMFAJcSFRWloKAgLVq/SAUCCtiOk+Jk9Ek1uamJIiMjXbUUF/9XAQAAACxYuHChwsPDNXfuXIpeIBcwxmjEiBEaNGiQevfurbfeekuO49iOBQA5hjFGbpqz6qYsqVH2AgAAANksMTFRoaGhqlu3rpo3b247DoAslpiYqBdffFHvvvuuRowYoX79+lH0AgCyBGUvAAAAkM0mT56sdevWafXq1RQ+gIeLj4/XE088oc8++0wffPABN2MEAGQpyl4AAAAgG8XExGjgwIFq3bp1hu+uDCBnOXPmjDp27Khvv/1WX375pTp16mQ7EgDkWCZ5cws3ZUmNshcAAADIRv/3f/+nnTt3av78+bajAMhC0dHRat26tX744QfNmjVL9913n+1IAIBcgLIXAAAAyCZRUVEaMWKEHnvsMVWpUsV2HABZ5MiRI2revLn+/vtvLVy4UPXq1bMdCQCQS1D2AgAAANnkrbfe0okTJzRkyBDbUQBkkb1796px48Y6cOCAli9frttuu812JADwCMYYGeOepRPclCU1yl4AAAAgGxw4cEBvv/22nn32WZUuXdp2HABZYOvWrWrUqJESEhK0cuVKVa5c2XYkAEAu42U7AAAAAJAbDB8+XD4+PgoNDbUdBUAWWLdunWrXri1fX19FRERQ9AIArKDsBQAAALLYli1b9OGHH6pv374qWLCg7TgAMtnq1atVr149FS9eXCtXrlTZsmVtRwIAj2NcuLkRZS8AAACQxQYOHKhixYqpV69etqMAyGRLlixRo0aNVLVqVS1fvlxFixa1HQkAkItR9gIAAABZaO3atZo6daqGDh2qfPny2Y4DIBN98803atGiherVq6dFixYpKCjIdiQAQC7HDdoAAACALNS3b19VrlxZXbp0sR0FQCb69NNP9cQTT6h9+/b67LPP5OvrazsSAHg0Y4yMcc/SCW7KkhplLwAAAJBFli5dqiVLlmjGjBnKk4cfvQFPMXr0aL300kt6+umn9f7778vb29t2JAAAJLGMAwAAAJAlEhMTFRoaqrvvvlutW7e2HQdAJjDGqH///nrppZfUr18/jRs3jqIXAOAqTC8AAAAAssDXX3+ttWvXKjw8XI7j2I4D4ColJiaqZ8+eGj9+vN5880299NJLtiMBQK5ikje3cFOW1Ch7AQAAgEwWFxen/v37q0WLFqpbt67tOACuUlxcnLp06aJp06ZpwoQJ6t69u+1IAABcEGUvAAAAkMkmTJigrVu3asaMGbajALhKp06d0oMPPqglS5Zo2rRpateune1IAABcFGUvAAAAkIlOnDihoUOH6tFHH9XNN99sOw6AqxAZGamWLVtq7dq1mjt3rho3bmw7EgDkWsYYGeOepRPclCU1yl4AAAAgE7377rs6duyYhg0bZjsKgKtw8OBBNW3aVNu3b9fSpUtVs2ZN25EAALgsyl4AAAAgkxw+fFhvvPGGnnnmGZUrV852HABXaOfOnQoJCVFkZKTCw8NVrVo125EAAEgXyl4AAAAgk4wcOVKS1L9/f8tJAFypjRs3KiQkRN7e3oqIiFCFChVsRwIASDLJm1u4KUtqXrYDAAAAAJ5gx44dGjdunF555RUVKVLEdhwAV+DXX39V7dq1FRAQQNELAMiRKHsBAACATDBo0CAVLFhQL7zwgu0oAK7AihUr1KBBA5UvX14rVqxQqVKlbEcCACDDWMYBAAAAuErr1q3Tl19+qQ8++EAFChSwHQdABs2bN0/t2rXTPffco1mzZikgIMB2JADAOYwxMsY9Sye4KUtqzOwFAAAArlLfvn11/fXX6/HHH7cdBUAGTZkyRa1bt1bTpk01b948il4AQI7GzF4AAADgKqxYsULz58/XtGnT5OPjYzsOgAwYP368evTooc6dO2vChAnKk4dfkQEAORszewEAAIArZIxRnz59VKNGDbVr1852HADpZIzRyJEj9cwzz+jZZ5/Vp59+StELAC5nXLi5Ef83AwAAAK7QrFmz9OOPP2rp0qXy8mIeBZATGGP08ssva/To0Ro6dKgGDhwox3FsxwIAIFNQ9gIAAABXID4+Xv369VNISIgaNmxoOw6AdIiPj9dTTz2lTz/9VGPGjNGzzz5rOxIAAJmKshcAAAC4ApMmTdI///yjsLAw21EApENMTIw6deqk2bNn6/PPP9ejjz5qOxIAIAOMMTLGPUsnuClLapS9AAAAQAadOnVKQ4YM0UMPPaTbbrvNdhwAl3HixAk98MADWrFihWbMmKH777/fdiQAALIEZS8AAACQQWPHjtWBAwc0fPhw21EAXMbRo0fVokUL/fXXX1qwYIEaNGhgOxIAAFmGshcAAADIgGPHjmnUqFF66qmnVKFCBdtxAFzCvn371LhxY+3bt0/ff/+9br/9dtuRAABXyCRvbuGmLKlR9gIAAAAZMGrUKMXFxWngwIG2owC4hG3btikkJEQxMTFauXKlqlSpYjsSAABZzst2AAAAACCn2L17t9577z29+OKLKlasmO04AC7ir7/+Uu3ateXl5aWIiAiKXgBArsHMXgAAACCdhgwZIn9/f7344ou2owC4iJ9++knNmjVTmTJltGjRIhUvXtx2JABAJjDGyBj3LJ3gpiypMbMXAAAASIcNGzZo4sSJGjBggAIDA23HAXAB3333nRo2bKgqVaooPDycohcAkOtQ9gIAAADp0L9/f5UtW1ZPP/207SgALmDmzJlq3ry56tSpo8WLFys4ONh2JAAAsh3LOAAAAACXsXr1as2aNUtffPGF/Pz8bMcBcI5Jkyape/fuateunb744gv5+vrajgQAyAJG7lw6wU2Y2QsAAABcgjFGffr00S233KJOnTrZjgPgHO+++64ee+wxPf7445o8eTJFLwAgV2NmLwAAAHAJ8+fP18qVKzV//nx5eTFXAnALY4wGDx6s4cOHq0+fPnrttdfkOI7tWAAAWEXZCwAAAFxEQkKCQkNDVb9+fTVt2tR2HADJEhMT9dxzz+n999/XqFGj1KdPH9uRAABZzMjIGPcs4+DWJSUoewEAAICLCAsL019//aUff/yRGYOAS8TFxalbt24KCwvThx9+qCeffNJ2JAAAXIOyFwAAALiAM2fOaODAgXrggQd011132Y4DQNLp06fVoUMHLVy4UFOmTFGHDh1sRwIAwFUoewEAAIALGD9+vPbs2aPFixfbjgJAUlRUlO6//36tWbNGc+bMYWkVAMhlTPLmFm7KkhplLwAAAHCOyMhIjRw5Ut26ddMNN9xgOw6Q6x06dEjNmjXTli1btGTJEtWqVct2JAAAXImyFwAAADjHm2++qVOnTmnw4MG2owC53q5du9S4cWMdPXpU4eHhuuWWW2xHAgDAtSh7AQAAgFT27dund955R88995xKlSplOw6Qq23atEkhISGSpIiICFWsWNFyIgCALcYYGeOepRPclCU1L9sBAAAAADcZNmyY/Pz81KdPH9tRgFzt999/V506dZQ/f36tWrWKohcAgHSg7AUAAACSbdq0SR9//LH69eun4OBg23GAXCsiIkL169dXmTJltHLlSpUuXdp2JAAAcgTKXgAAACDZgAEDVKJECfXs2dN2FCDXWrBggRo3bqzq1atr2bJlKlKkiO1IAAAXMC7c3IiyFwAAAJD0yy+/aPr06Ro2bJjy5s1rOw6QK02bNk3333+/GjVqpAULFigwMNB2JAAAchTKXgAAAOR6xhj16dNHN954ozp37mw7DpArffjhh+rYsaMeeughffPNN8qXL5/tSAAA5Dh5bAcAAAAAbFuyZImWLVum2bNny9vb23YcINcZNWqU+vbtq549e2rMmDHy8mJeEgAgLWOMjHHP0gluypIa/wcFAABArpaYmKjQ0FDVqlVLLVu2tB0HyFXOzqrv27evBg0apPfee4+iFwCAq8DMXgAAAORq06ZN02+//aaVK1fKcRzbcYBcIyEhQf/73//08ccf65133tHzzz9vOxIAADkeZS8AAAByrdjYWA0YMEAtW7ZU7dq1bccBco3Y2Fg98sgj+uabbzRx4kR17drVdiQAgMuZ5M0t3JQlNcpeAAAA5FofffSRtm/frtmzZ9uOAuQaJ0+eVNu2bfX999/rm2++UevWrW1HAgDAY1D2AgAAIFc6ceKEhg8fri5duqhq1aq24wC5wrFjx3Tffffpjz/+0Pz589WwYUPbkQAA8CiUvQAAAMiV3n77bUVGRmro0KG2owC5wv79+9WkSRPt3r1by5Yt05133mk7EgAgBzHGyBj3LJ3gpiypUfYCAAAg1zl48KDefPNN9ejRQ2XLlrUdB/B4O3bsUEhIiE6ePKnw8HBm0wMAkEW8bAcAAAAAstvIkSPl5eWlfv362Y4CeLwNGzaoVq1aSkxM1KpVqyh6AQDIQpS9AAAAyFW2bdum8ePHq0+fPipcuLDtOIBH+/nnn1W3bl0VLlxYERERKl++vO1IAIAcyrhwcyPKXgAAAOQqgwYNUpEiRfTcc8/ZjgJ4tO+//1733nuvKlasqOXLl6tEiRK2IwEA4PEoewEAAJBr/P7775o8ebIGDx6sAgUK2I4DeKzZs2erWbNmqlmzppYuXapChQrZjgQAQK7ADdoAAACQa/Tt21cVK1ZUt27dbEcBPNbnn3+ubt26qU2bNvryyy/l5+dnOxIAwAMYY2SMe5ZOcFOW1Ch7AQAAkCt8//33WrhwoaZPny4fHx/bcQCP9N577+m5555T9+7d9eGHH8rb29t2JAAAchWWcQAAAIDHM8YoNDRUd9xxh9q2bWs7DuBxjDEaOnSonnvuOb344ov6+OOPKXoBALCAmb0AAADweDNmzNCaNWu0bNkyOY5jOw7gURITE9W7d2+NGTNGI0eOVN++ffl7BgDIdCZ5cws3ZUmNshcAAAAeLT4+Xv369VOTJk3UoEED23EAjxIfH6/HH39cn3/+ucaNG6f//e9/tiMBAJCrUfYCAADAo3366afatGmTpk2bZjsK4FHOnDmjhx56SPPmzVNYWJg6duxoOxIAALkeZS8AAAA81qlTpzRkyBB16tRJ1atXtx0H8BjR0dFq1aqVVq9erVmzZqlFixa2IwEAPJwxRsa4Z+kEN2VJjbIXAAAAHmvMmDE6fPiwhg8fbjsK4DGOHDmiZs2aaePGjVq8eLHq1KljOxIAAEhG2QsAAACPdOTIEb3++ut6+umndd1119mOA3iEPXv2qHHjxjp06JC+//573XbbbbYjAQCAVCh7AQAA4JFee+01JSQkaMCAAbajAB5hy5YtCgkJUUJCglauXKkbbrjBdiQAQC5ikje3cFOW1LxsBwAAAAAy286dO/X+++/rpZdeUtGiRW3HAXK8devWqXbt2vL19VVERARFLwAALkXZCwAAAI8zePBgBQYGqnfv3rajADneDz/8oHr16qlkyZJauXKlypYtazsSAAC4CJZxAAAAgEdZv369Pv/8c40ZM0YBAQG24wA52qJFi/TAAw/o9ttv15w5cxQUFGQ7EgAglzLGyBj3LJ3gpiypMbMXAAAAHqVfv3669tpr9eSTT9qOAuRo06dPV8uWLdWgQQMtXLiQohcAgByAmb0AAADwGBEREZozZ47CwsLk6+trOw6QY02YMEFPPfWUOnTooM8++0w+Pj62IwEAgHRgZi8AAAA8gjFGoaGhql69uh566CHbcYAc66233tITTzyhp556Sl9++SVFLwDAFYwLNzdiZi8AAAA8wrfffqtVq1Zp4cKF8vJiTgOQUcYY9e/fX6+99pr69++v4cOHy3Ec27EAAEAGUPYCAAAgx0tISFC/fv107733qnHjxrbjADlOQkKCevbsqf/7v//TW2+9pRdffNF2JAAAcAUoewEAAJDjffHFF1q/fr0mTpzITEQgg2JjY9WlSxd99dVX+uSTT9StWzfbkQAAOI8xRsa4Z+kEN2VJjbIXAAAAOdqZM2c0aNAgtWvXTnfccYftOECOcurUKbVr105Lly7VV199pbZt29qOBAAArgJlLwAAAHK0Dz74QHv37tXIkSNtRwFylMjISN1333369ddfNW/ePIWEhNiOBAAArhJlLwAAAHKs48ePa+TIkXr88cdVqVIl23GAHOPgwYNq0qSJduzYoaVLl6pmzZq2IwEAcEkmeXMLN2VJjdsUAwAAIMd64403FBMTo8GDB9uOAuQYO3fuVJ06dbR//36tWLGCohcAgGzw2muv6Y477lBAQICKFi2q1q1ba+PGjWnG1K9fX47jpHk8/fTTGToPZS8AAABypL179+rdd9/V888/rxIlStiOA+QI//zzj2rVqqW4uDhFRETo5ptvth0JAIBcITw8XD169NCPP/6oJUuWKC4uTo0bN9bJkyfTjHviiSe0b9++lMcbb7yRofOwjAMAAABypKFDhypfvnx65ZVXbEcBcoS1a9eqadOmKlasmBYvXqySJUvajgQAQLoZY2SMe5ZOyGiWhQsXpvl60qRJKlq0qNauXau6deum7M+fP7+KFy9+xbmY2QsAAIAcZ+PGjfrkk0/Uv39/BQUF2Y4DuF54eLgaNGig6667TuHh4RS9AABkkqioqDSPmJiYdL0uMjJSklSoUKE0+8PCwlSkSBFVrVpVffv21alTpzKUh5m9AAAAyHH69++vkiVL6plnnrEdBXC9uXPn6sEHH9Q999yjWbNmKSAgwHYkAAA8RpkyZdJ8PXjwYA0ZMuSSr0lMTNTzzz+vWrVqqWrVqin7O3XqpHLlyqlkyZJat26d+vTpo40bN2rGjBnpzkPZCwAAgBzlp59+0jfffKNJkyYpb968tuMArjZ58mR16dJF9913n6ZMmcLfGQBAjmWSN7c4m2XXrl0KDAxM2e/n53fZ1/bo0UN//fWXIiIi0ux/8sknUz6/+eabVaJECTVs2FBbt27V9ddfn65cLOMAAACAHMMYo9DQUFWtWlWPPPKI7TiAq40bN06PPPKIHnnkEU2fPp2iFwCALBAYGJjmcbmyt2fPnpo7d66+//57lS5d+pJj77rrLknSli1b0p2Hmb0AAADIMRYtWqTly5fr22+/lbe3t+04gCsZYzRy5EgNHDhQzz//vEaPHi0vL+b5AABgkzFGvXr10syZM7V8+XKVL1/+sq/5/fffJUklSpRI93koewEAAJAjJCYmKjQ0VLVr11aLFi1sxwFcyRijl156SW+//baGDx+u/v37y3Ec27EAALh6Jun/c66RwSg9evTQ5MmTNXv2bAUEBGj//v2SpKCgIOXLl09bt27V5MmT1bx5cxUuXFjr1q3TCy+8oLp166patWrpPg9lLwAAAHKEKVOm6I8//tCqVasor4ALiI+P15NPPqmJEydq7Nix6tmzp+1IAAAg2fjx4yVJ9evXT7N/4sSJ6tq1q3x9fbV06VK9++67OnnypMqUKaO2bdtqwIABGToPZS8AAABcLyYmRgMGDFCrVq10zz332I4DuE5MTIw6deqk2bNn64svvmBNawAAXOZys5LLlCmj8PDwqz4PZS8AAABc78MPP9TOnTs1b94821EA1zlx4oTatGmjlStXaubMmWrZsqXtSAAAZDqTvLmFm7KkRtkLAAAAV4uKitLw4cPVtWtX3XjjjbbjAK5y9OhRNW/eXBs2bNDChQvPe2soAADIXSh7AQAA4GqjR49WdHS0hgwZYjsK4Cr79u1T48aNtW/fPi1btky333677UgAAMAyyl4AAAC41oEDBzR69Gj16tVLZcqUsR0HcI1t27YpJCREsbGxWrlypapUqWI7EgAAWcoYc9l1b7OTm7Kk5mU7AAAAAHAxw4cPV548edS3b1/bUQDX+Ouvv1S7dm15eXkpIiKCohcAAKSg7AUAAIArbd26VR9++KFCQ0NVqFAh23EAV/jpp59Ut25dFS1aVBERESpXrpztSAAAwEUoewEAAOBKAwcOVNGiRfXss8/ajgK4wtKlS9WwYUPdeOONWr58uYoVK2Y7EgAA2ca4cHMjyl4AAAC4zq+//qopU6ZoyJAhyp8/v+04gHUzZsxQixYtVKdOHS1evFjBwcG2IwEAABei7AUAAIDr9O3bVzfccIMee+wx21EA6yZOnKgHH3xQbdq00ezZs/kHEAAAcFF5bAcAAAAAUvvuu++0ePFiffPNN8qThx9Xkbu988476t27t5566il98MEH8vb2th0JAAArjDEyxj1LJ7gpS2rM7AUAAIBrGGMUGhqqu+66S23atLEdB7DGGKOBAweqd+/eCg0N1fjx4yl6AQDAZTFVAgAAAK7x9ddf65dfftHy5cvlOI7tOIAViYmJevbZZ/XBBx9o1KhR6tOnj+1IAAAgh6DsBQAAgCvExcWpX79+atasmerVq2c7DmBFXFycHnvsMU2ePFkfffSRnnjiCduRAABwBZO8uYWbsqRG2QsAAABX+OSTT7R161Z9/fXXtqMAVpw+fVrt27fXokWLNHXqVLVv3952JAAAkMNQ9gIAAMC6kydPaujQoXr44Yd1yy232I4DZLuoqCjdf//9WrNmjebMmaOmTZvajgQAAHIgyl4AAABY9+677+rIkSMaNmyY7ShAtjt06JCaNm2qrVu3asmSJapVq5btSAAAuI4xRsa4Z+kEN2VJjbIXAAAAVh0+fFivv/66nnnmGZUvX952HCBb7dq1SyEhITp27JjCw8OZ2Q4AAK4KZS8AAACsevXVVyVJ/fv3t5wEyF6bNm1SSEiIHMdRRESEKlasaDsSAADI4bxsBwAAAEDu9e+//+qDDz7Qyy+/rGuuucZ2HCDb/P7776pTp44KFChA0QsAQDoYF25uRNkLAAAAawYNGqSCBQvqhRdesB0FyDYRERGqV6+eypYtqxUrVqh06dK2IwEAAA9B2QsAAAAr/vzzT33xxRcaNGiQ/P39bccBssX8+fPVuHFj3Xbbbfruu+9UpEgR25EAAIAHYc1eAAAAWNG3b19dd911euKJJ2xHAbLF1KlT9eijj6p58+aaNm2a8ubNazsSAAA5hjFGxrhn6QQ3ZUmNshcAAADZbsWKFZo3b56mTJkiHx8f23GALPd///d/euaZZ/TII4/o008/VZ48/CoGAAAyH8s4AAAAIFsZY9SnTx/ddtttat++ve04QJYbNWqU/ve//6lnz56aNGkSRS8AAMgy/JQBAACAbDV79mz9+OOPWrx4sby8mHsAz2WMUWhoqN544w0NHjxYgwcPluM4tmMBAJAjmeTNLdyUJTXKXgAAAGSb+Ph49evXT40aNVJISIjtOECWSUhI0NNPP60JEybo3Xff1XPPPWc7EgAAyAUoewEAAJBtPvvsM/3999/64osvbEcBskxMTIweffRRzZgxQ5MmTVKXLl1sRwIAALkEZS8AAACyxenTpzV48GC1b99eNWrUsB0HyBInT57UAw88oOXLl+vrr79W69atbUcCAMAjGGNkjHuWTnBTltQoewEAAJAtxo4dqwMHDmjEiBG2owBZ4tixY2rRooXWrVunBQsW6N5777UdCQAA5DKUvQAAAMhyx44d02uvvaYnnnhCFStWtB0HyHT79+9XkyZNtHv3bi1btkx33nmn7UgAACAXouwFAABAlnv99dcVGxurQYMG2Y4CZLodO3YoJCREp06d0ooVK3TTTTfZjgQAgMcxyZtbuClLal62AwAAAMCz7d69W2PGjFHv3r1VvHhx23GATLVhwwbVqlVLxhhFRERQ9AIAAKsoewEAAJClhg4dqgIFCujll1+2HQXIVD///LPq1KmjwoULa+XKlSpfvrztSAAAIJdjGQcAAABkmb///luffvqpRo8ercDAQNtxgEzz/fff6/7779fNN9+sefPmqWDBgrYjAQDg0YwxMsY9Sye4KUtqzOwFAABAlunfv7/KlCmj//3vf7ajAJlm9uzZatasme655x4tWbKEohcAALgGM3sBAACQJVavXq2ZM2fq888/l5+fn+04QKb4/PPP1a1bN7Vp00Zffvkl/20DAABXYWYvAAAAMp0xRqGhobr55pvVqVMn23GATPHee++pS5cu6tq1q6ZOnUrRCwBANjIu3NyImb0AAADIdAsWLNCKFSs0b948eXt7244DXBVjjIYNG6YhQ4bopZde0htvvCHHcWzHAgAAOA9lLwAAADJVQkKCQkNDVbduXTVr1sx2HOCqJCYm6oUXXtB7772nV199VaGhoRS9AADAtSh7AQAAkKkmT56sP//8U6tXr6YUQ44WHx+v7t2764svvtD48eP19NNP244EAECuZYyRMe5ZOsFNWVKj7AUAAECmiYmJ0cCBA9WmTRvdfffdtuMAV+zMmTPq0KGD5s+fr7CwMHXs2NF2JAAAgMui7AUAAECmGT9+vHbt2qUFCxbYjgJcsejoaLVq1UqrV6/WrFmz1KJFC9uRAAAA0oWyFwAAAJkiMjJSI0aMULdu3VSlShXbcYArcuTIETVr1kwbN27U4sWLVadOHduRAACAJJO8uYWbsqRG2QsAAIBM8dZbb+nkyZMaMmSI7SjAFdmzZ48aN26sQ4cOafny5br11lttRwIAAMgQyl4AAABctf379+vtt9/Wc889p1KlStmOA2TYli1b1KhRIyUmJioiIkKVKlWyHQkAACDDKHsBAABw1YYNGyZfX1/16dPHdhQgw/744w81adJEwcHBWrJkicqUKWM7EgAAOIcxRsa4Z+kEN2VJzct2AAAAAORsmzdv1scff6y+ffuqYMGCtuMAGfLDDz+ofv36KlWqlFauXEnRCwAAcjTKXgAAAFyVAQMGqFixYurVq5ftKECGLFq0SI0aNVK1atW0bNkyXXPNNbYjAQAAXBWWcQAAAMAV++WXX/TVV19pwoQJypcvn+04QLpNnz5dDz/8sBo3bqzp06fz3y8AAC5nkje3cFOW1JjZCwAAgCsWGhqqKlWqqEuXLrajAOk2YcIEPfTQQ3rwwQc1c+ZMil4AAOAxmNkLAACAK7JkyRJ99913mjlzpvLk4cdK5AxvvvmmXnnlFT3zzDMaO3asvLyY/wIAADwHP5UDAAAgwxITExUaGqqaNWuqVatWtuMAl2WMUb9+/TRq1CgNGDBAw4YNk+M4tmMBAIAMMMadSye4CWUvAAAAMuyrr77Sr7/+qvDwcAozuF5CQoJ69OihDz/8UKNHj1bv3r1tRwIAAMgSlL0AAADIkNjYWA0YMEAtWrRQ3bp1bccBLik2NladO3fW9OnT9cknn6hbt262IwEAAGQZyl4AAABkyMcff6xt27Zp5syZtqMAl3Tq1Cm1a9dO3333nb766iu1bdvWdiQAAHCFTPLmFm7KkhplLwAAANLtxIkTGjZsmB599FHdfPPNtuMAF3X8+HG1bNlSv/32m+bOnauQkBDbkQAAALIcZS8AAADS7Z133tHx48c1bNgw21GAizpw4ICaNm2qf//9V0uXLtXdd99tOxIAAEC2oOwFAABAuhw6dEhvvPGGevTooXLlytmOA1zQv//+q5CQEEVHRys8PJwZ6AAAeAhjjIxxz9IJbsqSmpftAAAAAMgZRo4cKS8vL/Xr1892FOCC/vnnH9WuXVvx8fGKiIig6AUAALkOZS8AAAAua/v27Ro3bpxeeeUVFSlSxHYc4Dxr165VnTp1FBQUpIiICF1//fW2IwEAAGQ7yl4AAABc1qBBg1S4cGE9//zztqMA5wkPD1eDBg103XXXKTw8XCVLlrQdCQAAZDLjws2NKHsBAABwSX/88YfCwsI0ePBgFShQwHYcII25c+eqadOmuvPOO/Xdd9+pcOHCtiMBAABYQ9kLAACAS+rbt68qVKig7t27244CpBEWFqbWrVurWbNmmjdvnvz9/W1HAgAAsCqP7QAAAABwr+XLl2vBggWaNm2afHx8bMcBUnzwwQfq2bOnunbtqo8//lh58vCrDQAAnswYI2Pcs3SCm7KkxsxeAAAAXJAxRn369NHtt9+udu3a2Y4DSEr673LEiBHq2bOnXnjhBX3yyScUvQAAAMn4qQgAAAAXNHPmTK1Zs0ZLly6VlxdzBGCfMUYvvvii3nnnHQ0fPlz9+/eX4zi2YwEAALgGZS8AAADOEx8fr379+qlx48Zq2LCh7TiA4uPj9eSTT2rixIkaO3asevbsaTsSAADIRiZ5cws3ZUmNshcAAADnmThxojZu3KgpU6bYjgIoJiZGnTp10uzZs/Xll1/q4Ycfth0JAADAlSh7AQAAkMapU6c0ePBgdezYUbfeeqvtOMjlTpw4odatW2vVqlWaOXOmWrZsaTsSAACAa1H2AgAAII333ntPhw4d0vDhw21HQS539OhRNW/eXBs2bNDChQtVr14925EAAIAlxhgZ456lE9yUJTXKXgAAAKQ4evSoRo0apaeeekrXX3+97Ti5wubNmxUdHW07hqsEBASoQIECaty4sfbv36/vv/9eNWrUsB0LAADA9Sh7AQAAkOK1115TfHy8Bg4caDtKrrB582ZVqlTJdgxXKl26tCRp5cqVqlKliuU0AAAAOQNlLwAAACRJu3bt0tixY9WnTx8VK1bMdpxcIWVG7wOSiliN4h6HJc2Q7rzzTr399tsqV66c7UQAAMAFTPLmFm7KkhplLwAAACRJgwcPVmBgoF588UXbUXKfIpJK2g7hLv3796foBQAAyCDKXgAAAGj9+vX67LPP9M477ygwMNB2HAAAAABXgLIXAAAA6tevn8qWLaunnnrKdhQAAADgPMYYGeOepRPclCU1yl4AAIBcbtWqVZozZ46+/PJL+fn52Y4DAAAA4Ap52Q4AAAAAe4wxCg0N1S233KKOHTvajgMAAADgKjCzFwAAIBebO3euIiIitGDBAnl5MQ/AtX6TNDv588cknXvfMiPpHUlRkipKejidxz0s6RdJuyXtk5Qg6TlJBS8y/h9JyyUdklRA0q2S6kryPmfcaUlLksfHSSolqbG4CR0AALhiJnlzCzdlSY2f6AEAAHKphIQE9e3bV/Xr11eTJk1sx0F65JH05wX271BS0Xtu6Xo5uyT9JClW0jWXGbtZ0lRJeSU1l1RZ0gpJC84ZlyhpcnLOOyWFSDopaZKkIxnMBwAAgAxhZi8AAEAu9cUXX2j9+vX68ccf5TiO7ThIj4qSNkhqprTF7p+SSkg6lcHj3SApVJKfpFWS9l9i7GJJxSQ9murcfpJWSrpL/5XFG5RUIj8o6abkfTdJGivpe0ntMpgRAAAA6cbMXgAAgFzozJkzGjRokNq2bau77rrLdhykV1UlFbrbUu2LV1LBevMVHC+/kgrbyzmopKUbaihtyXxH8scNqfZtUNISD1VS7SugpMJ3Y3JeAACADDLGuO7hRpS9AAAAudC4ceO0d+9ejRw50nYUZESwpDJKu5TDFkkxSiqCs8rZGb/nrrkbmPzYf87YEjr/N41SSlq/l6UcAAAAsgxlLwAAQC4TGRmpkSNHqnv37rrhhhtsx0FG3az/bnwmSeuUdMO2wCw8Z3Tyx4ALPOef6vmzYy82TueMBQAAQKai7AUAAMhl3njjDZ0+fVqDBw+2HQVX4iYlLYWwSUkzejfpypZwyIizSy9c6AZwefRf8Xx27MXG6ZyxAAAA6WRcuLkRN2gDAADIRfbu3at33nlHzz//vEqWPPc9+cgRCki6TklLOcRJMpJuzOJznv2tIeECz8VL8jln7MXG6ZyxAAAAyFTM7AUAAMhFhg0bprx58+qVV16xHQVX42ZJmyX9IqmCpHxZfL6zyzJcaAmGE0q7bEPAJcZJF17iAQAAAJmCshcAACCX2LhxoyZMmKB+/fopODjYdhxcjcqSHEm7lfVLOEhS8eSPe8/ZH5X8KH7O2H2SEs8Zu1tJs3oLZ0VAAADg6Ywxrnu4EWUvAABALjFgwACVLFlSPXv2tB0FV8tP0n2S6kvKjnvsFZVURNJapS1xf0n+mHoZiRslnZT0d6p9JyVtkFRJLCQHAACQhSh7AQAAcoE1a9bo66+/1tChQ5U3b17bcZAZqiup7L2aNXDPSApPfmxP3rcm+eufzhkbIumApC+UVPoukLRS0m2Srkk17kZJpSXNlrQ8+XiTlFQSN7iKrAAAADnYa6+9pjvuuEMBAQEqWrSoWrdurY0bN6YZc+bMGfXo0UOFCxeWv7+/2rZtqwMHDmToPJS9AAAAHs4Yo9DQUN10003q3Lmz7Thwk9OSvk9+bEnetzr56x/OGXuDpA7Jr5mvpJm7dSS1OGecl6SHJd2kpMJ4iaT8krooaXYwAADAFTAu3DIiPDxcPXr00I8//qglS5YoLi5OjRs31smTJ1PGvPDCC/r22281ffp0hYeHa+/evXrggQcydB7eRAUAAODhFi9erO+//16zZ8+Wt7e37Ti4ErcmPy7nhQwet6CkIRkYXyX5cTn5JLVKfgAAAHiwqKioNF/7+fnJz8/vvHELFy5M8/WkSZNUtGhRrV27VnXr1lVkZKQ++eQTTZ48Wffee68kaeLEiapSpYp+/PFH3X333enKw8xeAAAAD5aYmKg+ffqoVq1aatmype04AAAAgEcpU6aMgoKCUh6vvfZaul4XGRkpSSpUqJAkae3atYqLi1OjRo1SxlSuXFlly5bV6tWr052Hmb0AAAAebOrUqfrjjz+0cuVKOY5jOw6yyylJCZd43ktSgWzKAgAAkAmMMTImY0snZKWzWXbt2qXAwMCU/Rea1XuuxMREPf/886pVq5aqVq0qSdq/f798fX0VHBycZmyxYsW0f//+dOei7AUAAPBQsbGxGjBggFq2bKnatWvbjoPsNE3Sv5d4PkgZX/IBAAAA5wkMDExT9qZHjx499NdffykiIiLT81D2AgAAeKgPP/xQ//77r+bMmWM7CrJbEyXdSO1ifLIrCAAAAFLr2bOn5s6dqxUrVqh06dIp+4sXL67Y2FgdP348zezeAwcOqHjx4uk+PmUvAACAB4qOjtbw4cPVuXPnlLeGIRcpaTsAAABA5jLJm1tkNIsxRr169dLMmTO1fPlylS9fPs3zNWrUkI+Pj7777ju1bdtWkrRx40bt3LlTNWvWTPd5KHsBAAA80OjRoxUVFaWhQ4fajgIAAADkej169NDkyZM1e/ZsBQQEpKzDGxQUpHz58ikoKEjdu3dX7969VahQIQUGBqpXr16qWbOm7r777nSfh7IXAADAwxw4cECjR49Wz549VbZsWdtx4DZ/SVoh6aikqpJap3puopJu7tYjE883SdIeSddICpFU/pKjAQAAPNL48eMlSfXr10+zf+LEieratask6Z133pGXl5fatm2rmJgYNWnSROPGjcvQeSh7AQAAPMyIESPk5eWlvn372o4Ct4mVNEuSv6RGkkpd4XG2SFovabekw5ICdfEbvtWRdEDSD5JmSup9hecEAAC5mjFGxrhoGYcMZknP+Lx58+qDDz7QBx98cKWxKHsBAAA8ybZt2/Thhx9q6NChKly4sO04cJtDkuIl1ZR011Uc508llb0lJAVcZuz1yY94ScuUNHM4/1WcGwAAABdF2QsAAOBBBg4cqCJFiui5556zHQWZbaKkYEltruIYcckf/a8yS0NJ90vylhQm6WA6XnP2nLGi7AUAAMgilL0AAAAe4rffftPkyZP14YcfKn9+2jRcwJW883GLpKmSKiupaPZW0rINGeVcRQYAAJDrmeTNLdyUJTXKXgAAAA/Rt29fVapUSd26dbMdBW519ncS55Kj/rNR0ldKupFbK0leV3Fuyl4AAIAsR9kLAADgAZYtW6ZFixZp+vTpypOHH/FwEdHJH/OmY+wGSV9Lqi7pPl1d0StJfskfT0gqdJXHAgAAwAXxmwAAAEAOZ4xRaGio7rzzTrVt29Z2HGSGBElnztmXmLz/5Dn78+nyRexpScckrVZS6VrqMuP/lDRT0u2Smin9M4EvpbSSloBYIamxpCD9VwADAAAgU1D2AgAA5HBff/21fv75Zy1btkyOkxmtHKzbKemzC+zfJemvc/Y9J6ngZY43VdK/SipXO+jSJetxSTMk3SipeTqypleApAeSjz1O0i26upvNAQCA3MUkTXJwDRdFSY2yFwAAIAeLi4tT//791bRpUzVo0MB2HGSW4pIePWffYkn+ku45Z79/Oo7XWNJBSeFKmrHbS5LvRcb6K6mY3Sxpjy4/Czi9Tkr6VlJRJX0PxTLpuAAAAEhB2QsAAJCDffrpp9q8ebO++uor21GQmfJJuv6cfXmVVMSeuz89Sum/0na2pN2SrrvI2DySOilpZvGXkh5TUkF7tXYqaWmKJpKuzYTjAQAA4DxXe5sFAAAAWHLy5EkNGTJEDz/8sKpXr247DnKCoOSP564HfK68kh6RVEDS55KOZsK5Y5I/BmbCsQAAQK5jXLi5EWUvAABADjVmzBgdOXJEw4cPtx0FOcXZJZ3T87tJAUmdlTTT93NJUame26+kJSHClVQEx6T6euNFjnf2nCwrDQAAkGUoewEAAHKgI0eO6PXXX9fTTz+t8uXL246DnOLsT//x6RwfqKTCN15Jhe/J5P37JH2f/DiipJnCZ7/ecJFjnT0nv4EAAABkGdbsBQAAyIFeffVVJSYmasCAAbajILs8lgnHCEj+uF5SaSWtAex3mXMUkvTSOftuTX6kxxlJJyT9raSit0B6wwIAAPzHGCNj3LN0gpuypEbZCwAAkMP8+++/ev/999WvXz8VLZoZd85CrlFI0k1KKns3SbpFUpssPucUSf8mf36P+A0EAAAgC/GjFgAAQA4zePBgBQcHq3fv3rajICd6UFJjSccl5cuG8zVR0hIOhZQ0kxgAAABZhrIXAAAgB/nzzz/1+eef67333lNAQMDlXwBcSFDyIzuUzKbzAAAAj2aSN7dwU5bUuD0CAABADtKvXz+VL19eTz75pO0oAAAAAFyGmb0AAAA5xMqVKzV37lxNnjxZvr6+tuMAAAAAcBnKXgAAgBzAGKM+ffro1ltvVYcOHWzHgSf6S9IKSUclVZXUOtVzxyS9p6Q1fstLuk/Zs94vAABAMmOMjHHP0gluypIaZS8AAEAOMGfOHK1evVqLFi2SlxcrcSGTxUqapaQbqDWSVOqc5wtIul/SPklrJBWWdG825gMAAEC6UPYCAAC4XHx8vPr166d7771XISEhtuPAEx2SFC+ppqS7LvC8r6Rbkx9bJO3PvmgAAABIP8peAAAAl/v888+1YcMGTZo0SY7j2I4DTxSX/NE/HWP9lTQTGAAAIBuZ5M0t3JQlNd4DCAAA4GKnT5/W4MGD9eCDD+qOO+6wHQeeKiO/qzgZHA8AAIBsQ9kLAADgYu+//7727dunESNG2I4CT3a2vE3PxHHKXgAAANdiGQcAAACXOn78uF577TU98cQTqlSpku048GTRyR/zpmOsn5LW+AUAAMhGxhgZ455/cXZTltSY2QsAAOBSr7/+umJiYjRo0CDbUeCpTkvaK2m1kkrcUul4zbWSjkr6SVKkpISsCgcAAICMYmYvAACAC+3Zs0fvvvuuXnzxRZUoUcJ2HOQE8Uoqb1MroEtP75gq6V8lFb0dkj9ezl2SdklakPzoIql8RsMCAAAgK1D2AgAAuNDQoUNVoEABvfzyy7ajIKfYJemzc/Y9J6ngJV7TWNJBSeGSZkrqJcn3Muf5XdIGSXdKul5S8SvICgAAkEEmeXMLN2VJjbIXAADAZf755x998skneuuttxQUFGQ7DnKK4pIePWef/2VeU0r/Ld0wW9JuSddd5jUbJQVLap7BfAAAAMhylL0AAAAu079/f5UuXVr/+9//bEdBTpJPSTNtr8TZf1M4k46xMZICr/A8AAAAyFKUvQAAAC7y448/asaMGZo0aZLy5s1rOw5yCyf5Y3rejWhSjQcAAMgmxhgZ456lE9yUJbVL3a4BAAAA2cgYo9DQUFWtWlWPPPKI7TjITc7+VhCfjrHx4rcIAAAAl2JmLwAAgEssXLhQ4eHh+vbbb+Xt7W07DnKTgOSP6yWVVtJav36pnk+UdErSIUkHJN2UrekAAACQTpS9AAAALpCYmKjQ0FDVqVNHLVq0sB0HuU0hJRW46yVtknSLpDapno+UNCb5cz9Jt2drOgAAAJnkzS3clCU1yl4AAAAXmDx5statW6cffvhBjsOCqLDgQUmNJR1X0s3eUvOX1FlSXknXSPLJ1mQAAABIJ8peAAAAy2JiYjRgwAC1bt1aNWvWtB0HuVlQ8uNcPpKuy+YsAAAAyDDKXgAAAMv+7//+T7t27dKCBQtsRwEAAABcyRgjY9yzdIKbsqTGfXQBAAAsioqK0ogRI9S1a1dVqVLFdhwAAAAAORhlLwAAgEVvvfWWTpw4oSFDhtiOArjKyJEj9e+//9qOAQAAkKOwjAMAAIAl+/fv19tvv61evXqpTJkytuMgG2zfvl2TJk3SggULtGLFiv+eOGwvk+sk/1n89NNPqlWrlpYsWcKsdwAAIJO8uYWbsqRG2QsAAGDJ8OHD5ePjo9DQUNtRkEXi4+P1ww8/aO7cuZo9e7Y2bdokSXIcR7GxsQoICEgaOMNiSJeaOnWq/ve//6lOnTpauHChbr/9dtuRAAAAXI+yFwAAwIItW7boo48+0ogRI1SoUCHbcZAFjh49qipVqujgwYPKkyeP4uPjU55r3bq1AgMDFRgYqE2bNik6OtpiUvcJCAhQxYoVFR4erubNm+vee+/VnDlzVL9+fdvRAAAAXI2yFwAAwIIBAwaoWLFievbZZ21HQRYJCgpSxYoVdejQoTRFryQ9/fTTKZ9XrFgxu6PlGIUKFdLSpUvVpk0bNW3aVNOnT1fLli1txwIAABYYY2SMe5ZOcFOW1LhBGwAAQDZbu3atpk2bpiFDhihfvny24yCLeHt7a/78+Spbtmya/cWKFVPDhg0tpcp5/P39NXfuXLVo0UJt2rTRl19+aTsSAACAa1H2AgAAZLO+ffuqcuXK6tq1q+0oyGJff/21du7cKX9/f3l7e8vb21uPPfaYvL29bUfLUfz8/DRt2jR17txZjz76qN5//33bkQAAAFyJshcAACAbLV26VEuWLNGrr76qPHlYUcuTjR49Wt27d9fTTz+tv/76S0WLFlVCQoK6dOliO1qOlCdPHn3yySfq3bu3evXqpeHDh7v27ZMAACDzGRdubsRvGAAAANkkMTFRoaGhuvvuu9W6dWvbcZBFjDEaMGCAXn31VfXr108jRoyQ4zhatWqVfvjhB1WuXNl2xBzLcRy99dZbKlSokAYMGKCjR49q9OjR8vJiDgsAAIBE2QsAAJBtpk+frrVr12r58uVyHMd2HGSBxMRE9erVS+PGjdObb76pl156KeW58uXLq3z58hbTeQbHcdS/f38FBwerZ8+eOn78uD7++GNmygMAAIiyFwAAIFvExcVpwIABat68uerVq2c7DrJAXFycunbtqqlTp2rChAnq3r277UgerUePHgoODlaXLl0UGRmpyZMnK2/evLZjAQCALGKMcdUSTm7KkhplLwAAQDaYMGGCtm7dqm+++cZ2FGSBU6dOqX379lq8eLGmTZumdu3a2Y6UKzz88MMKCgrSgw8+qPvuu08zZ85UQECA7VgAAADWsLgVAABAFjtx4oSGDh2qRx55RNWqVbMdB5ksMjJSTZs21ffff6958+ZR9Gaz++67TwsXLtSaNWvUqFEjHTlyxHYkAAAAayh7AQAAsti7776rY8eOadiwYbajIJMdPHhQDRo00J9//qmlS5cqJCTEdqRcqV69evr++++1bds21atXT3v37rUdCQAAZDLjws2NKHsBAACy0KFDh/TGG2/omWee0bXXXms7DjLRzp07VadOHe3du1fh4eGqWbOm7Ui5Wo0aNbRy5UpFRkaqdu3a2rp1q+1IAAAA2Y6yFwAAIAu9+uqrkqT+/ftbToLMtHHjRtWuXVuxsbGKiIhgeQ6XqFy5siIiIpQnTx7Vrl1bf/75p+1IAAAA2YqyFwAAIIvs2LFD48aN0yuvvKIiRYrYjoNM8uuvv6pOnToKCAhQRESEKlSoYDsSUilXrpwiIiJUvHhx1atXTz/++KPtSAAAIBMYY1z3cCPKXgAAgCwyaNAgFSxYUC+88ILtKMgkK1asUIMGDVS+fHmtWLFCpUqVsh0JF1C0aFEtX75cN910kxo2bKglS5bYjgQAAJAtKHsBAACywLp16/Tll19q0KBBKlCggO04yATz5s1TkyZNdPvtt2vp0qUqXLiw7Ui4hKCgIC1atEj16tVTixYt9M0339iOBAAAkOUoewEAALJA3759df311+uJJ56wHQWZYMqUKWrdurWaNm2qefPmKSAgwHYkpEP+/Pk1a9YstW3bVu3bt9enn35qOxIAALhCxoWbG+WxHQAAAMDThIeHa/78+Zo6dap8fHxsx8FVGj9+vHr06KHOnTtrwoQJypOHH6FzEl9fX3355ZcKDg5W9+7ddezYMb344ou2YwEAAGQJflIFAADIRMYY9enTRzVq1NCDDz5oOw6ugjFGr732mvr376/nnntOb7/9try8eGNcTuTt7a1x48apYMGCeumll3Ts2DENHz5cjuPYjgYAAJCpKHsBAAAy0axZs/TTTz9pyZIlFIM5mDFGr7zyit566y0NGzZMAwYMoBjM4RzH0auvvqqCBQvqlVde0bFjxzR27Fj+ngIAkIMY486lE9yEshcAACCTxMfHq1+/fgoJCVGjRo1sx8EVSkhI0FNPPaVPPvlE7733nnr16mU7EjLRyy+/rIIFC+qpp57S8ePHNWnSJJZbAQAAHoOyFwAAIJNMmjRJ//zzj8LCwmxHwRWKiYnRww8/rFmzZumLL77QI488YjsSssDjjz+u4OBgderUSZGRkfrqq6+UP39+27EAAACuGmUvAABAJjh16pSGDBmiDh066LbbbrMdB1fgxIkTeuCBB7RixQrNmDFD999/v+1IyELt2rVTQECAHnjgATVt2lTffvutgoKCbMcCAAAXYZI3t3BTltRYoAoAACATjB07VgcOHNCIESNsR8EVOHr0qEJCQrR69WotWLCAojeXaNKkiZYsWaI///xTDRo00KFDh2xHAgAAuCqUvQAAAFfp6NGjGjVqlJ588klVqFDBdhxk0L59+1SvXj1t3rxZ33//vRo0aGA7ErLRPffco/DwcO3du1d16tTRzp07bUcCAAC4YpS9AAAAV2nUqFGKi4vTwIEDbUdBBm3fvl116tTRsWPHtHLlSt1+++22I8GCatWqKSIiQjExMapdu7Y2btxoOxIAADiHMcZ1Dzei7AUAALgKu3fv1tixY9W7d28VL17cdhxkwPr161WrVi05jqNVq1apSpUqtiPBogoVKigiIkIBAQGqU6eOfv31V9uRAAAAMoyyFwAA4CoMGTJE/v7+eumll2xHQQb89NNPqlu3rooWLaqIiAiVK1fOdiS4QKlSpbRixQpde+21atCggVasWGE7EgAAQIZQ9gIAAFyhDRs2aOLEiRowYIACAwNtx0E6fffdd2rYsKGqVKmi5cuXq1ixYrYjwUUKFy6s7777TjVq1FCTJk00b94825EAAIAk48LNjSh7AQAArlD//v1VtmxZPf3007ajIJ1mzpyp5s2bq06dOlq0aJGCg4NtR4ILBQQEaP78+WrSpIlat26tKVOm2I4EAACQLpS9AAAAV+CHH37QrFmzNHz4cPn5+dmOg3SYNGmS2rVrp9atW2v27NkqUKCA7Uhwsbx58+rrr79Wp06d9PDDD2v8+PG2IwEAAFxWHtsBAAAAchpjjEJDQ1WtWjV16tTJdhykw7vvvqsXXnhBTz75pMaNGydvb2/bkZAD5MmTRxMnTlRwcLCeeeYZHTt2TH379pXjOLajAQCQ6xhjZIx7lk5wU5bUKHsBAAAyaP78+Vq5cqXmz58vLy/eKOVmxhgNGTJEw4YNU58+ffTaa69R1CFDvLy89O6776pw4cLq37+/jh07pjfeeIP/jgAAgCtR9gIAAGRAQkKCQkNDVa9ePTVt2tR2HFxCYmKinn/+eY0dO1ajRo1Snz59bEdCDuU4jgYNGqTg4GA999xzOnbsmD788ENmiAMAANeh7AUAAMiAsLAw/fXXX1q9ejUz+1wsLi5O3bp1U1hYmD788EM9+eSTtiPBAzz77LMKDg5Wt27ddPz4cYWFhbFmNwAA2cQkb27hpiypUfYCAACk05kzZzRw4EA98MADuvvuu23HwUWcPn1aHTp00MKFCzVlyhR16NDBdiR4kM6dOysoKEgdOnRQy5YtNXPmTG72BwAAXINF5gAAANJp/Pjx2r17t0aOHGk7Ci4iKipKzZo109KlSzVnzhyKXmSJVq1aacGCBVq9erVCQkJ07Ngx25EAAAAkUfYCAACkS2RkpEaMGKHu3burcuXKtuPgAg4fPqx7771Xv//+u5YsWcKayshSDRo00LJly7Rp0ybVq1dP+/btsx0JAACPZoxx3cONKHsBAADS4c0339SpU6c0ePBg21FwAbt371adOnW0a9cuhYeHq1atWrYjIRe44447tGLFCh09elR16tTR9u3bbUcCAAC5HGUvAADAZezbt0/vvPOOnn/+eZUqVcp2HJxj8+bNqlWrlk6fPq2IiAjdcssttiMhF7nxxhsVEREhx3FUq1YtrV+/3nYkAACQi1H2AgAAXMawYcPk5+enPn362I6Cc/z++++qXbu28ufPr4iICFWsWNF2JORC1157rVauXKlrrrlGdevW1U8//WQ7EgAAHse4cHMjyl4AAIBL2LRpkz7++GP17dtXwcHBtuMglYiICNWvX19lypTRypUrVbp0aduRkIsVL15cy5cvV+XKldWwYUN99913tiMBAIBciLIXAADgEgYMGKASJUqoZ8+etqMglQULFqhx48aqXr26li1bpiJFitiOBKhgwYJavHixateurebNm2vWrFm2IwEAgFyGshcAAOAifvnlF02fPl1Dhw5Vvnz5bMdBsmnTpun+++9Xo0aNtGDBAgUGBtqOhP9v777Do6jaMA4/s+kQ0mgBRIoCUpQqfkhCrwICojSlqVgQe6FKx4JYQawoioUqglIEAoEkIgKCgIiIgiBITwiEEpI93x9AJIYWSDKTze/ea6+YmdlznsQrw+67Z99Bmvz582vOnDlq27atOnTooE8++cTuSAAAeARjjOPuTkSxFwAA4DyMMerXr58qVaqk7t272x0HZ7z//vvq0qWLOnfurJkzZ1KEhyP5+vrqyy+/1H333aeePXvqzTfftDsSAADII7ztDgAAAOBEixYt0pIlS/T111/L25unTE7w8ssvq3///nr00Uf1xhtvyOVi3QKcy8vLS++9955CQ0P1xBNP6NChQxo2bJgsy7I7GgAA8GC8cgEAAPgPt9ut/v3769Zbb9Xtt99ud5w8zxij/v37a8yYMRoyZAgFM+QalmXp5ZdfVmhoqAYMGKD4+HjeqAAA4AqZMzencFKWc/EsAwAA4D+mTp2qtWvX6uWXX6aoaLPU1FQ9+OCDGjNmjF5//XUNHz6c/yfIdfr37693331X48ePV48ePXTq1Cm7IwEAgBy2fPlytWnTRsWLF5dlWRku5NqzZ09ZlpXu3qJFi0zPw8peAACAcyQnJ2vw4MFq3bq1IiIi7I6TpyUnJ+uee+7RzJkz9fHHH6tnz552RwKu2IMPPqiQkBDdc889SkxM1NSpU+Xv7293LAAAkEOSkpJUtWpV3XvvvbrjjjvOe0yLFi308ccfp33v5+eX6Xko9gIAAJzj/fff17Zt2zR79my7o+RpSUlJ6tChg5YuXaqZM2eqXbt2dkcCrlqnTp0UFBSkDh06qGXLlpo9e7aCgoLsjgUAQK5gjJExzmmdkNksLVu2VMuWLS96jJ+fn8LDw68mFm0cAAAAzjp69KhGjhyp7t27q0qVKnbHybMSEhLUrFkzxcbGat68eRR64VFatmyphQsXau3atWrcuLEOHDhgdyQAAHAVEhMT091Pnjx5xWNFR0erSJEiqlChgh5++GEdPHgw02NQ7AUAADjjtdde0+HDhzVixAi7o+RZe/fuVYMGDbR582YtWbJEjRs3tjsSkOUiIiIUHR2tHTt2KDIyUn///bfdkQAAwBUqWbKkgoOD0+4vvvjiFY3TokULffrpp4qKitLLL7+sZcuWqWXLlkpNTc3UOLRxAAAAkLRv3z698soreuSRR3TttdfaHSdP2r59u5o2baqkpCQtX75clStXtjsSkG2qVaummJgYNW3aVHXr1tXixYtVrlw5u2MBAOBY5szNKc5m2blzZ7q2TFfSZ1eSOnfunPbfN954o2666SZdd911io6OztQCCFb2AgAASBo9erRcLpcGDhxod5Q8adOmTYqIiJDb7VZcXByFXuQJ5cuXV2xsrPLly6eIiAj9/PPPdkcCAACZFBQUlO5+pcXe/ypbtqwKFSqkrVu3ZupxFHsBAECe9+eff+qdd95Rv379VLBgQbvj5DmrVq1SvXr1FBYWptjYWJUpU8buSECOKVmypJYvX66SJUuqfv36iouLszsSAABwgL///lsHDx5UsWLFMvU4ir0AACDPGzJkiAoWLKjHH3/c7ih5ztKlS9WoUSOVK1dO0dHRmX4yC3iCwoULa8mSJapWrZqaNm2qBQsW2B0JAADHMcY47p4ZR48e1bp167Ru3TpJ0rZt27Ru3Trt2LFDR48e1bPPPqsffvhB27dvV1RUlNq2bavrr79ezZs3z9Q8FHsBAECetm7dOn3xxRcaNmyY8ufPb3ecPGXOnDlq2bKl6tSpo8WLFyssLMzuSIBtgoKCNH/+fDVp0kS33367pk6danckAACQhVavXq3q1aurevXqkqSnnnpK1atX15AhQ+Tl5aX169fr9ttvV/ny5XXfffepZs2aiomJyXRbCC7QBgAA8rQBAwbo+uuv17333mt3lDxl8uTJ6tWrl9q3b6/PPvssy3qbAblZQECAZs6cqXvvvVddunTR4cOH9cADD9gdCwAAZIEGDRpcdDXwd999lyXzUOwFAAB51tKlS7VgwQJNnz5dPj4+dsfJM8aNG6fHHntM9913n9577z15eXnZHQlwDB8fH33yyScKDg7Wgw8+qPj4ePXr18/uWAAA2M6cuTmFk7Kci2IvAADIk4wx6t+/v26++WZ16NDB7jh5gjFGI0eO1NChQ/X000/rlVdekWVZdscCHMflcmncuHEKCwtT//79FR8frxdffJG/FwAAcEkUewEAQJ701Vdf6ccff1RUVBQFlBzgdrv11FNP6c0339To0aM1YMAAfu/ARViWpREjRig0NFRPPfWU4uPjNWHCBFbCAwCAi6LYCwAA8pyUlBQNHDhQzZs3V6NGjeyO4/FSUlJ0//3369NPP9WECRP08MMP2x0JyDWefPJJhYSE6P7771dCQoImT54sX19fu2MBAJDjjDEX7Xmb05yU5VwUewEAQJ7z0UcfacuWLZoyZYrdUTzeiRMn1LlzZ82dO1eff/65unTpYnckINfp1auXQkJC1LlzZ7Vt21YzZ85Uvnz57I4FAAAcyGV3AAAAgJx07NgxDRs2TF27dlX16tXtjuPRjhw5olatWum7777T119/TaEXuArt27fXvHnzFBMTo2bNmikhIcHuSAAAwIEo9gIAgDzlzTff1IEDBzRy5Ei7o3i0gwcPqnHjxlq9erUWLlyoVq1a2R0JyPUaN26sqKgobdq0SQ0aNNDevXvtjgQAQI4xDrw5EcVeAACQZxw8eFAvv/yyHnroIZUtW9buOB5r165dqlevnrZv366lS5cqMjLS7kiAx7jlllu0fPly7du3TxEREfrrr7/sjgQAAByEYi8AAMgzXnzxRaWmpmrw4MF2R/FYW7duVUREhI4cOaKYmBjVqFHD7kiAx6lSpYpiY2PldrtVt25d/frrr3ZHAgAADkGxFwAA5Ak7duzQ+PHj9fTTT6tIkSJ2x/FI69evV0REhHx9fRUbG6sKFSrYHQnwWGXLllVsbKxCQ0MVGRmp1atX2x0JAIBsZYxx3N2JKPYCAIA8YdiwYQoKCtLTTz9tdxSP9P3336t+/foqXry4YmJidO2119odCfB4xYoV07Jly1SuXDk1bNhQ0dHRdkcCAAA2o9gLAAA83i+//KJPPvlEzz//vAoUKGB3HI+zcOFCNW3aVDfddJOWLl3KymkgB4WFhWnRokWqU6eOWrRooTlz5tgdCQAA2IhiLwAA8HgDBw5UqVKl9OCDD9odxePMmDFDrVu3VsOGDbVgwQIFBwfbHQnIcwIDA/XNN9+oVatWuuOOOzR58mS7IwEAkOWMA29ORLEXAAB4tNjYWM2ZM0ejRo2Sr6+v3XE8ysSJE9WpUyfdeeedmjVrlgICAuyOBORZfn5+mjp1qrp3767u3btr3LhxdkcCAAA28LY7AAAAQHYxxqh///6qVq2aOnfubHccjzJ27Fg9++yzevjhhzV+/Hi5XKwhAOzm7e2tiRMnKjQ0VI899pgSEhI0ePBgWZZldzQAAJBDKPYCAACP9c033yguLk4LFiygGJlFjDEaNGiQXnzxRQ0aNEgjR46kkAQ4iGVZGjt2rMLCwjR48GAdOnRIr776KudAAEDuZ04/F3UMB0U5F8VeAADgkVJTUzVw4EA1bNhQzZo1szuOR0hNTVXfvn317rvvauzYsXr66aftjgTgPCzL0qBBgxQaGqq+ffsqISFBH3zwgby9efkHAICn4197AADgkSZPnqxffvlFH3/8MStPs0BycrJ69OihadOmaeLEibr33nvtjgTgEvr06aOQkBD16NFDCQkJ+vLLL+Xv7293LAAAkI0o9gIAAI9z4sQJDRkyRHfeeaduvvlmu+PkeseOHdOdd96pxYsXa9q0aerQoYPdkQBcpq5duyo4OFh33nmnWrVqpa+//loFChSwOxYAAJlmztycwklZzkXjJgAA4HHefvtt7d69W6NHj7Y7Sq53+PBhtWjRQsuWLdPcuXMp9AK5UKtWrbRgwQKtWrVKTZo00cGDB+2OBAAAsgnFXgAA4FESEhI0evRo3X///SpfvrzdcXK1ffv2qUGDBtqwYYMWL16spk2b2h0JwBWqX7++oqOj9eeff6p+/fravXu33ZEAAEA2oNgLAAA8ypgxY9LaOODK7dixQ5GRkdqzZ4+WL1+uOnXq2B0JwFWqUaOGYmJilJiYqLp16+qPP/6wOxIAAJfNGOO4uxNR7AUAAB5j9+7deuONN/Tkk0+qePHidsfJtTZv3qy6devq1KlTio2N1Y033mh3JABZ5IYbblBsbKx8fX0VERGh9evX2x0JAABkIYq9AADAYwwfPlwBAQF67rnn7I6Sa61Zs0aRkZEKDg5WbGysrrvuOrsjAchi1157rWJiYlSsWDHVr19fK1assDsSAADIIhR7AQCAR/jtt980ceJEDRo0SMHBwXbHyZWWL1+uhg0bqmzZslq2bBmrowEPVqRIES1dulRVqlRRkyZNtHDhQrsjAQBwUcaBNyei2AsAADzCoEGDVLx4cfXp08fuKLnS3Llz1bx5c918881avHixChYsaHckANksODhY3333nerXr6/WrVtrxowZdkcCAABXiWIvAADI9VauXKmZM2dq5MiR8vf3tztOrvPFF1+oXbt2atGihebOnasCBQrYHQlADsmXL5++/vprdejQQZ06ddJHH31kdyQAAHAVvO0OAAAAcDWMMerfv7+qVKmie+65x+44uc6ECRPUt29f9ejRQx988IG8vXl6COQ1vr6++uyzzxQSEqL77rtP8fHxevrpp+2OBQBAOsYYGeOc1glOynIuns0DAIBc7bvvvlN0dLTmzJkjLy8vu+PkGsYYjR49Ws8//7yeeOIJvfrqq3K5+NAXkFd5eXlpwoQJCgsL0zPPPKNDhw5p1KhRsizL7mgAACATKPYCAIBcy+12q3///oqIiFDr1q3tjpNrGGP0zDPP6LXXXtPIkSM1aNAgCjoAZFmWRo8erdDQUD377LOKj4/X+PHjeSMIAIBchGIvAADItb788kv9/PPPiouLo1h5mVJSUvTAAw/o448/1rhx49S3b1+7IwFwmGeeeUahoaF64IEHlJCQoE8++UQ+Pj52xwIA5HHmzM0pnJTlXBR7AQBArnTy5EkNHjxYbdu21a233mp3nFzh5MmT6tq1q2bPnq3JkyfT4xjABd13330KDg5W165ddfjwYU2fPl358uWzOxYAALgEir0AACBXeu+997Rjxw7NnTvX7ii5wtGjR9W+fXvFxMRo1qxZatOmjd2RADjcnXfeqaCgILVv314tWrTQN998o+DgYLtjAQCAi6D5EgAAyHWOHDmiUaNGqWfPnqpUqZLdcRzv0KFDatKkiVauXKkFCxZQ6AVw2Zo1a6bFixdrw4YNatiwofbt22d3JABAHmWMcdzdiSj2AgCAXOfVV19VYmKihg0bZncUx/vnn39Uv359bd26VUuWLFGDBg3sjgQgl6lTp46WLVum3bt3KzIyUjt27LA7EgAAuACKvQAAIFfZu3evxo4dq0cffVQlS5a0O46j/fnnn4qIiFBCQoJiYmJUq1YtuyMByKVuuukmxcbGKjk5WREREfrtt9/sjgQAAM6DYi8AAMhVRo0aJW9vbw0YMMDuKI62ceNGRUREyOVyKTY2VhUrVrQ7EoBc7vrrr1dsbKwKFCigiIgI/fTTT3ZHAgDkIcaBNyei2AsAAHKNP/74Q++++6769++vsLAwu+M41sqVK1WvXj0VKVJEsbGxKlWqlN2RAHiIEiVKaPny5SpTpowaNmyo5cuX2x0JAACcg2IvAADINZ5//nkVKVJEjz32mN1RHGvx4sVq3LixKlWqpOjoaBUtWtTuSAA8TMGCBRUVFaVatWqpefPmmjt3rt2RAADAGRR7AQBArrB27Vp9+eWXGjZsmPLly2d3HEf66quv1KpVK0VGRmrhwoUKCQmxOxIAD1WgQAHNnTtXLVq0ULt27fTll1/aHQkA4OGMMY67OxHFXgAAkCv0799fFSpUUK9eveyO4kgff/yx7rrrLrVv316zZ8+mIA4g2/n7+2v69Om6++67dffdd2vChAl2RwIAIM/ztjsAAADApURFRWnhwoWaOXOmvL15+vJfr7/+up566ik98MADmjBhgry8vOyOBCCP8Pb21kcffaSQkBA98sgjio+P18CBA2VZlt3RAADIk3i1BAAAHM0Yo/79++uWW25R+/bt7Y7jKMYYDRkyRKNGjVL//v31wgsvUGABkONcLpdef/11hYWFafDgwYqPj9crr7zC+QgAkKXMmZtTOCnLuSj2AgAAR5sxY4ZWr16t6OhoCgfncLvdeuyxx/T222/rpZdeUr9+/eyOBCAPsyxLQ4YMUUhIiB5//HHFx8fr/fff55MGAADkMIq9AADAsU6dOqWBAweqZcuWql+/vt1xHOPUqVPq1auXvvjiC73//vvq3bu33ZEAQJL02GOPKTQ0VL169dLhw4f1+eefy8/Pz+5YAADkGRR7AQCAY02cOFF//PGHZsyYYXcUxzh+/Lg6duyo7777TlOmTFHHjh3tjgQA6XTr1k3BwcHq2LGj2rRpo6+++kqBgYF2xwIA5HLGGBnjnNYJTspyLpfdAQAAAM4nKSlJw4cP1913362qVavaHccREhMT1bJlS0VFRWnOnDkUegE41u2336758+drxYoVatq0qQ4dOmR3JAAA8gSKvQAAwJHeeOMNHTx4UCNGjLA7iiPs379fDRs21Lp167Ro0SK1aNHC7kgAcFENGzbU0qVL9fvvv6t+/fr6559/7I4EAIDHo9gLAAAc58CBAxozZoz69OmjMmXK2B3Hdjt37lRkZKT+/vtvLVu2THXr1rU7EgBcllq1amn58uWKj49XRESE/vzzT7sjAQByKePAmxNR7AUAAI7zwgsvyBijQYMG2R3Fdlu2bFHdunV14sQJxcbG0tICQK5TqVIlxcbGyuVyKSIiQhs3brQ7EgAAHotiLwAAcJS//vpLb7/9tp599lkVLlzY7ji2Wrt2rSIiIhQYGKjY2FiVK1fO7kgAcEVKly6tmJgYFS5cWPXq1dPKlSvtjgQAgEei2AsAABxl6NChCg0N1ZNPPml3FFvFxsaqQYMGKlWqlJYvX65rrrnG7kgAcFXCw8O1bNkyVaxYUY0bN1ZUVJTdkQAAuYgxxnF3J6LYCwAAHGPDhg369NNPNWTIEAUGBtodxzbz5s1Ts2bNVKNGDUVFRalQoUJ2RwKALBESEqKFCxcqMjJSt912m2bNmmV3JAAAPArFXgAA4BgDBw5U2bJl1bt3b7uj2GbKlClq27atmjZtqvnz5ysoKMjuSACQpfLnz6/Zs2erXbt2uvPOOzVp0iS7IwEA4DG87Q4AAAAgSTExMfr222/15ZdfysfHx+44tnj33XfVp08f3XPPPfroo4/k7c1TNQCeydfXV1988YVCQkLUq1cvJSQk6IknnrA7FgDAwcyZm1M4Kcu5eAUBAABsZ4xRv379VKNGDXXs2NHuODnOGKOXXnpJAwcO1KOPPqo33nhDLhcfwALg2by8vPTuu++m9Wk/dOiQhg8fLsuy7I4GAECuRbEXAADYbvbs2VqxYoUWLlyY54qcZwvdr7zyioYOHaqhQ4dS6ACQZ1iWpZdeekmhoaHq37+/4uPj9eabb+a5fwsAAMgqFHsBAICtUlJSNHDgQDVp0kRNmza1O06OSk1N1UMPPaQPP/xQb7zxhh5//HG7IwGALfr166fQ0FA99NBDSkhI0EcffZRnW/oAAM7PGCNjnNM6wUlZzkWxFwAA2OrTTz/Vr7/+qsmTJ9sdJUedPHlS3bp101dffaVJkyapR48edkcCAFs98MADCg4OVrdu3XT48GFNnTpVAQEBdscCACBX4bMxAADANsePH9eQIUPUqVMn1axZ0+44OSYpKUm33367Zs+erRkzZlDoBYAzOnXqpDlz5mjx4sVq2bKlEhMT7Y4EAECuQrEXAADYZvz48dq7d69GjRpld5QcEx8fr6ZNmyouLk7z589Xu3bt7I4EAI7SokULLVq0SOvWrVOjRo20f/9+uyMBABzAOPDmRBR7AQCALeLj4/XCCy+od+/euv766+2OkyP27Nmj+vXr67ffftOSJUvUqFEjuyMBgCPVrVtX0dHR2rlzp+rVq6edO3faHQkAgFyBYi8AALDFyy+/rOTkZA0ZMsTuKDli+/btioiI0MGDB7V8+XLVrl3b7kgA4GjVqlVTTEyMjh07poiICP3+++92RwIAwPEo9gIAgBy3a9cuvfnmm3rqqacUHh5ud5xst2nTJtWtW1eSFBsbq8qVK9ucCAByh/LlyysuLk758uVTRESE1q1bZ3ckAIBNjDGOuzsRxV4AAJDjhg0bpvz58+vZZ5+1O0q2W7VqlSIjI1WwYEHFxMSoTJkydkcCgFzlmmuuUUxMjEqWLKkGDRooNjbW7kgAADgWxV4AAJCjNm/erI8++kiDBw9WUFCQ3XGy1dKlS9WoUSNVqFBBy5YtU7FixeyOBAC5UqFChbRkyRJVq1ZNzZo10/z58+2OBACAI1HsBQAAOWrgwIEqWbKkHn74YbujZKvZs2erZcuWuvXWW7Vo0SKFhobaHQkAcrWgoCDNnz9fTZo00e23366pU6faHQkAkIOMA29ORLEXAADkmB9++EGzZs3SyJEj5efnZ3ecbPPpp5+qQ4cOatOmjebMmaP8+fPbHQkAPEJAQIBmzpypzp07q0uXLnrvvffsjgQAgKN42x0AAADkDcYY9evXTzfeeKO6du1qd5xs8+abb+qJJ57Qfffdp/fee09eXl52RwIAj+Lj46NPPvlEISEheuihhxQfH6/+/fvbHQsAAEeg2AsAAHLE/PnztXz5cs2dO9cjC6DGGA0fPlzDhw/XM888ozFjxsiyLLtjAYBHcrlceuuttxQWFqYBAwYoPj5eL730EuddAPBgxhgZ45zWCU7Kci6KvQAAINulpqaqf//+qlevnlq2bGl3nCzndrv15JNP6q233tILL7yg/v37U3AAgGxmWZaGDx+u0NBQPfnkk4qPj9c777zjkW8oAgBwuSj2AgCAbPfFF19ow4YNWrFihccVQVNSUnTfffdp8uTJeuedd/TQQw/ZHQkA8pQnnnhCISEhuu+++5SQkKDPPvtMvr6+dscCAMAWFHsBAEC2OnnypJ5//nm1b99e//vf/+yOk6VOnDihTp06ad68efr888/VpUsXuyMBQJ7Us2dPhYSEqFOnTrr99ts1c+ZMLo4JAB7IyJmtE5zEZXcAAADg2d555x3t3LlTo0ePtjtKljpy5Ihuu+02LVy4UF9//TWFXgCwWbt27TRv3jzFxsaqWbNmio+PtzsSAAA5jmIvAADINomJiRo9erTuvfdeVaxY0e44WebAgQNq1KiR1qxZo4ULF6pVq1Z2RwIASGrcuLGWLFmizZs3q0GDBtqzZ4/dkQAAyFEUewEAQLYZO3asjh49qmHDhtkdJcvs2rVL9erV019//aXo6GhFRkbaHQkAcI7atWtr2bJl2r9/vyIjI7V9+3a7IwEAsoCRkTEOuju0pQTFXgAAkC327NmjV199VY8//rhKlChhd5wssXXrVtWtW1dHjx5VbGysqlevbnckAMB5VKlSRXFxcXK73YqIiNCmTZvsjgQAQI6g2AsAALLFyJEj5evrq379+tkdJUv8/PPPioiIkL+/v+Li4lS+fHm7IwEALqJMmTKKjY1VWFiY6tWrp1WrVtkdCQCAbEexFwAAZLmtW7fq/fff18CBAxUaGmp3nKv2/fffq0GDBipRooRiYmJUsmRJuyMBAC5DsWLFFB0drXLlyqlRo0ZaunSp3ZEAAFfIOPDmRBR7AQBAlhs8eLDCw8PVt29fu6Ncte+++05NmjTRTTfdpCVLlqhw4cJ2RwIAZEJYWJgWL16sOnXqqGXLlpo9e7bdkQAAyDYUewEAQJZas2aNpk6dqmHDhikgIMDuOFdl2rRpatOmjRo1aqQFCxYoODjY7kgAgCuQP39+ffPNN2rdurU6dOigTz/91O5IAABkC4q9AAAgS/Xv318VK1ZUjx497I5yVT744AN17txZd911l2bNmpXrC9cAkNf5+flpypQp6tGjh3r06KG33nrL7kgAgEwwxjju7kTedgcAAACeY9GiRVq8eLFmzZolb+/c+zRjzJgx6tevn/r06aNx48bJ5eL9cQDwBN7e3vrwww8VGhqqxx9/XPHx8RoyZIgsy7I7GgAAWSL3vgoDAACO4na71b9/f9WpU0dt27a1O84VMcZo4MCBeumllzR48GCNGDGCAgAAeBjLsvTKK68oLCxMgwYNUnx8vF577TXe2AMAeASKvQAAIEtMnz5dP/30k5YvX54rC6Spqal65JFH9N577+nVV1/VU089ZXckAEA2sSxLAwcOVGhoqB555BElJCToww8/zNWfSgEAT2fO3JzCSVnOxb9kAADgqiUnJ2vQoEFq3bq1IiMj7Y6TacnJyerevbumT5+uiRMn6t5777U7EgAgBzz88MMKCQlR9+7dlZCQoClTpsjf39/uWAAAXDGKvQAA4Kp9+OGH+vPPPzVr1iy7o2TasWPHdOeddyoqKkrTpk1Thw4d7I4EAMhBXbp0UVBQkO68807ddtttmj17tgoUKGB3LAAArghNiQAAwFU5evSoRowYoW7duunGG2+0O06mJCQkqFmzZlq+fLm+/fZbCr0AkEe1atVKCxcu1Jo1a9S4cWMdPHjQ7kgAgP8wxjju7kQUewEAwFV5/fXXFR8frxEjRtgdJVP27t2rhg0batOmTVq8eLGaNm1qdyQAgI0iIyO1dOlSbd++XfXq1dOuXbvsjgQAQKZR7AUAAFds//79euWVV/TII4+oVKlSdse5bH/99ZciIyO1Z88eLVu2TP/73//sjgQAcIAaNWooJiZGR44cUUREhLZu3Wp3JACAh1i+fLnatGmj4sWLy7Isff311+n2G2M0ZMgQFStWTAEBAWrSpIl+//33TM9DsRcAAFyx0aNHp13RPLfYvHmzIiIilJKSotjY2FzXegIAkL0qVKig2NhY+fr6KiIiQuvXr7c7EgBAknHgLTOSkpJUtWpVvf322+fdP2bMGL311lt69913tXLlSuXPn1/NmzfXiRMnMjUPxV4AAHBFtm3bpgkTJui5555ToUKF7I5zWdasWaPIyEgFBwcrNjZW1113nd2RAAAOdO211yomJkbFixdX/fr19f3339sdCQDgUImJienuJ0+ePO9xLVu21KhRo9S+ffsM+4wxeuONNzR48GC1bdtWN910kz799FPt3r07wwrgS6HYCwAArsiQIUNUsGBBPfHEE3ZHuSzR0dFq2LChypYtq2XLlql48eJ2RwIAOFiRIkW0dOlS3XTTTWratKm+++47uyMBAByoZMmSCg4OTru/+OKLmR5j27Zt2rNnj5o0aZK2LTg4WLfccotWrFiRqbG8Mz07AADI89avX6/PP/9cEyZMUP78+e2Oc0nffPON7rrrLkVEROjrr79WYGCg3ZEAALlAcHCwFixYoLvuuktt2rTR559/rrvuusvuWACQJxljZEzmWidkp7NZdu7cqaCgoLTtfn5+mR5rz549kqSiRYum2160aNG0fZeLlb0AACDTBgwYoOuvv1733Xef3VEu6bPPPlP79u112223ae7cuRR6AQCZEhAQoFmzZunOO+9U586d9eGHH9odCQDgIEFBQenuV1LszUqs7AUAAJmybNkyzZs3T9OmTZOPj4/dcS7q7bffVt++fdWzZ0998MEH8vbmqQ8AIPN8fHz02WefKSQkRL1791ZCQoKeeeYZu2MBADxEeHi4JGnv3r0qVqxY2va9e/eqWrVqmRqLlb0AAOCyGWPUr18/1apVS3feeafdcS7IGKNRo0apb9++evLJJzVx4kQKvQCAq+JyufT2229r0KBBevbZZzVw4EBHfZwYADydceAtq5QpU0bh4eGKiopK25aYmKiVK1eqTp06mRqLVz0AAOCyzZo1SytXrlRUVJQsy7I7znm53W4988wzev311zVy5EgNGjTIsVkBALmLZVkaNWqUQkND9cwzzyg+Pl7jx4+Xl5eX3dEAAA539OhRbd26Ne37bdu2ad26dQoLC9O1116rJ554QqNGjVK5cuVUpkwZPf/88ypevLjatWuXqXko9gIAgMuSkpKigQMHqlmzZmrUqJHdcc4rJSVFvXv31qRJkzRu3Dj17dvX7kgAAA/09NNPKzQ0NK2lwyeffCJfX1+7YwEAHGz16tVq2LBh2vdPPfWUJKlHjx6aNGmSnnvuOSUlJemBBx5QQkKCIiIitGDBAvn7+2dqHsvwuRMAAHAZPvzwQ/Xu3Vs//fSTqlevbnecDE6cOKGuXbtqzpw5+uSTT3T33XfbHQkA4OFmzpyprl27qnHjxpoxY4by5ctndyQA8DiJiYkKDg5W3Ql15R3gnHWrKcdTFNcnTocPH1ZQUJDdcdLQsxcAAFzSsWPHNHToUHXp0sWRhd4jR46odevWmj9/vmbNmkWhFwCQIzp06KBvv/1Wy5YtU/PmzZWQkGB3JABAHkexFwAAXNK4ceO0b98+jRw50u4oGRw6dEhNmjTRjz/+qAULFqhNmzZ2RwIA5CFNmzbV4sWLtXHjRjVs2FD79u2zOxIAIA+j2AsAAC7q0KFDevHFF/XQQw/puuuusztOOrt371a9evX0xx9/aOnSpapfv77dkQAAeVCdOnW0fPly7dmzR5GRkdqxY4fdkQDA4xgH3pyIYi8AALiol156SSkpKRo8eLDdUdL5448/FBERocOHDysmJkY1a9a0OxIAIA+78cYbFRsbq1OnTqlu3bravHmz3ZEAAHkQxV4AAHBBO3fu1FtvvaVnnnlGRYsWtTtOmg0bNigiIkLe3t6KjY1VxYoV7Y4EAICuu+46xcbGKjg4WJGRkVqzZo3dkQAAeQzFXgAAcEHDhg1TUFCQnn76abujpPnhhx9Uv359hYeHKyYmRqVKlbI7EgAAaYoXL65ly5apbNmyatiwoZYtW2Z3JADwCMYYx92diGIvAAA4r02bNmnSpEkaPHiwChQoYHccSdKiRYvUuHFjVa5cWUuXLnXUamMAAM4qWLCgoqKiVLt2bbVo0ULffvut3ZEAAHkExV4AAHBeAwcOVKlSpfTggw/aHUWSNHPmTLVq1Ur169fXd999p5CQELsjAQBwQYGBgfr222/VokULtWvXTp9//rndkQAAeYC33QEAAIDzfP/995o9e7Y+++wz+fn52R1HH330kXr37q277rpLn376qXx9fe2OBADAJfn7+2v69Onq3bu3unXrpsOHD6tPnz52xwKAXMmcuTmFk7Kci2IvAABIxxijfv36qWrVqurSpYvdcfTaa6/p6aef1oMPPqi3335bXl5edkcCAOCyeXt7a+LEiQoJCdEjjzyiQ4cOadCgQbIsy+5oAAAPRLEXAACkM3fuXMXGxmr+/Plyuezr+GSM0fPPP6/Ro0erf//+euGFF3hhDADIlVwul1577TUVLFhQzz//vOLj4zV27Fj+XQMAZDmKvQAAIE1qaqoGDBighg0bqnnz5rblcLvdevTRRzVhwgS9/PLLeu6552zLAgBAVrAsS4MHD1ZISIgeffRRxcfH6/3335e3Ny/LAeByGGNkjHNaJzgpy7n4VwUAAKT57LPPtHHjRq1cudK21UanTp1Sz5499eWXX+r9999X7969bckBAEB26Nu3r0JCQtSzZ08dPnxYX3zxhSP64wMAPAPFXgAAIEk6ceKEhgwZog4dOqh27dq2ZDh27Jg6duyohQsXaurUqbrrrrtsyQEAQHa65557FBwcrLvuukutW7fWrFmzFBgYaHcsAIAHoNgLAAAkSRMmTNCuXbu0cOFCW+Y/fPiw2rRpo9WrV+ubb76xtY0EAADZrU2bNlqwYIFuv/12NWnSRPPmzVNYWJjdsQDAscyZm1M4Kcu57LvqCgAAcIzDhw9r9OjRuu+++1ShQoUcn3///v1q1KiR1q9fr0WLFlHoBQDkCQ0aNNCSJUu0detW1a9fX//884/dkQAAuRzFXgAAoDFjxuj48eMaOnRojs+9Y8cORUZGateuXVq2bJnq1q2b4xkAALBLrVq1FBMTo4SEBEVEROjPP/+0OxIAIBej2AsAQB73zz//6PXXX9cTTzyh4sWL5+jcv/32myIiInTixAnFxMSoatWqOTo/AABOULFiRcXGxsrLy0sRERHauHGj3ZEAwHGMMY67OxHFXgAA8rgRI0bI399fzz33XI7O+9NPPykyMlKBgYGKi4tTuXLlcnR+AACcpFSpUoqJiVHRokVVr149/fDDD3ZHAgDkQhR7AQDIw7Zs2aIPPvhAgwYNUkhISI7Nu3z5cjVs2FClS5fW8uXLVaJEiRybGwAApypatKiWLl2qSpUqqUmTJlq8eLHdkQAAuQzFXgAA8rDBgwerePHieuSRR3Jsznnz5ql58+aqWbOmoqKiVKhQoRybGwAApwsJCdHChQsVGRmpVq1a6auvvrI7EgA4gnHgzYko9gIAkEetWrVK06dPT2vjkBO+/PJLtW3bVs2aNdO8efNUoECBHJkXAIDcJF++fJo9e7bat2+vu+66Sx9//LHdkQAAuYS33QEAAEDOM8aof//+qly5srp165Yjc77zzjt65JFHdM899+ijjz6StzdPQwAAuBBfX199/vnnCgkJ0b333quEhAQ9+eSTdscCADgcr7IAAMiDFi1apCVLlmjOnDny8vLK1rmMMXrxxRc1aNAgPfroo3rjjTfkcvHhIgAALsXLy0vvvPOOQkND9dRTT+nQoUMaMWKELMuyOxoA5DhjjIxxTusEJ2U5F8VeAADyGLfbrX79+qlu3bpq3bp1ts5ljNFzzz2nsWPHatiwYRoyZAgvUAEAyATLsvTiiy8qNDRU/fr1U3x8vN566y3eOAUAnBfFXgAA8pipU6dq3bp1io2NzdbCa2pqqh588EFNnDhRb7zxhh5//PFsmwsAAE/33HPPKTQ0VA8++KDi4+M1adIk+fj42B0LAOAwFHsBAMhDkpOTNXjwYN1+++2qW7duts1z8uRJ3X333fr666/1ySefqHv37tk2FwAAeUXv3r0VHByse+65R4mJiZo2bZoCAgLsjgUAOcbIma0TnIRiLwAAecj777+v7du3a86cOdk2R1JSku644w5FR0drxowZateuXbbNBQBAXtOxY0cFBQXpjjvuUMuWLTVnzhwFBQXZHQsA4BA0+QEAII84cuSIRowYoR49eqhy5crZMkd8fLyaNm2q77//XvPnz6fQCwBANmjRooUWLVqkdevWqWHDhtq/f7/dkQAADkGxFwCAPOK1115TYmKihg8fni3j//PPP6pfv75+++03RUVFqVGjRtkyDwAAkOrWratly5Zp165dioyM1M6dO+2OBADZyhjjuLsTUewFACAP2Ldvn8aOHatHH31UJUuWzPLxt23bpsjISB08eFAxMTGqXbt2ls8BAADSq1q1qmJiYnTixAnVrVtXW7ZssTsSAMBmFHsBAMgDRo0aJS8vLw0YMCDLx/7ll1/SLvYWFxenSpUqZfkcAADg/MqVK6e4uDgFBgYqIiJCa9eutTsSAMBGFHsBAMgDvvnmGz3//PMKCwvL0nFXrlypevXqqVChQoqNjVXp0qWzdHwAAHBpJUqU0PLly1WqVCk1aNBAMTExdkcCgCxnHHhzIoq9AADkARs2bNDTTz+dpWNGRUWpcePGqlChgpYtW6bw8PAsHR8AAFy+QoUKKSoqSjVq1FDz5s01b948uyMBAGxAsRcAgDwgMDAwS8f7+uuvddttt6lu3bpatGiRQkNDs3R8AACQeUFBQZo/f76aNm2qtm3basqUKWn7Tp48qc2bN9uYDgCQEyj2AgCATPnkk0/UoUMHtW3bVnPmzFH+/PntjgQAAM7w9/fXzJkz1aVLF3Xt2lXvvvuukpOT1aZNG1WpUkV//fWX3REB4IoYYxx3dyKKvQAA4LK9+eab6tmzp+699159+eWX8vPzszsSAAD4D29vb02aNEl9+/bVww8/rJo1ayoqKkrGGE2ePNnueACAbESxFwAAXJIxRkOHDtUTTzyhZ599Vu+//768vLzsjgUAAC7A5XLpjTfeUI0aNbRx40a53W653W5NnDjRsavRAABXj2IvAAC4KLfbrccff1wjRozQiy++qDFjxsiyLLtjAQCASxg4cKB++umndNu2b9+u77//3qZEAHDljANvTkSxFwAAXNCpU6fUo0cPjR8/Xu+884769+9vdyQAAHAZjh49qtdee02WZaV7k9ayLH300Uc2JgMAZCdvuwMAkvT777/ryJEjdsdwlAIFCqhcuXJ2xwDyBM5BGRUoUEAlS5ZUp06dNG/ePH3xxRfq3Lmz3bEAj8Q5KCOeBwFXLzAwUBs3btTs2bM1Z84cff/993K73TLG6JNPPtGbb76pwMBAzkHnwTkIQG5mGZr1wGa///67ypcvb3cMR9qyZQtPMoBsxjnowmrXrq3169dr5syZuu222+yOA3gkzkEXxvMgIGvFx8dr4cKF+uCDD/TDDz/o119/1YkTJzgHXQDnIMA5EhMTFRwcrJtev0leAc65bkjq8VStf3K9Dh8+rKCgILvjpGFlL2x39l3kvn37qkSJEjancYZdu3Zp/PjxvMMO5ICzf2efffaZKlasaHMaZ/j11191zz33aP/+/Vq4cKEiIyPtjgR4rLPnIKuDJasQvbAlyRwwMjMNz4OALBYaGqpOnTqpU6dOadvO9vOt9GAl5S+e365ojpK0O0mb3tvEOQhArkWxF45RokQJlS1b1u4YAPKoihUrqkaNGnbHcJQZM2bwOwFyiFXIklWcYu9ZTr3gCeCp8hfPrwKlC9gdAwCQBSj2AgAAAAAAAHA0c+bmFE7Kci6X3QEAAAAAAAAAAFePYi8AAAAAAAAAeACKvfAo0dHR6tSpk/bt22d3lAyGDx+u4cOH2x0DAAAAAAAg1zHGOO7uRBR74XhjxoxRt27ddPz48Qse89Zbb6lr166ZvmLqrFmztGrVqquNCCAPmDRpkizLkmVZio2NzbDfGKOSJUvKsiy1bt06U2Pv2rVLHTt2VEhIiIKCgtS2bVv9+eef5z124sSJqlixovz9/VWuXDmNGzfuqscE4HzutW6lDk1V6tBUmb8yvrAwxij11dP7Uz9PzdTYJtHIPc2t1BdTlfpCqlK/SJU5dP4XL+41bqWOS1XqyFSlvpkq9w/ujOMdMHLPdyv1wzPHDU2ViXfmiyEAl+efmH+0pMcSLemxRAlbEjLsN8Yo7sk4LemxRD+/9vNlj5v0T5J+//x3rR65WtH3R2tJjyU6vv/Cr/v2/7RfPw75UdH3RyvuyTj9+dWfcqemPw+dTDiprdO26qcXf9KyB5dpSY8liv81/rIzAUBuR7EXjhcREaHk5GT9+OOP591/8uRJrV69WtWqVVOrVq00efJkFS5c+LLGptgLILP8/f31xRdfZNi+bNky/f333/Lz88vUeEePHlXDhg21bNkyDRw4UMOHD9fatWtVv359HTx4MN2x7733nu6//35VrlxZ48aNU506dfTYY4/p5ZdfvuIxAeQy3pLZcJ7C6XZJicr05ZfNSSP3JLfMdiMr0pLVwJL2SO6P3TLH0s/jXuWWmWOkIpLV0pJV0pKZb+SOSV9oMTuNzEojnZRUKHN5ADiby8elvSv2ZtiesDlBJw+dlMsncyWGxK2J2rlop1JPpCpfsXwXPfbgzwe14a0N8s7nrXL3lFPhmoW1fc52/T7593THHfvnmHbM3aGT8SeV/5r8mcoDAJ4gk08HgZxXq1YtBQQEKC4uTvXr18+wf/Xq1Tp58qQiIiLkcrnk6+t70fGMMTp16tQljwOA87nttts0ffp0vfXWW/L2/vef0S+++EI1a9bUgQMHMjXehAkT9Pvvv+vHH3/UzTffLElq2bKlqlSpoldffVUvvPCCJOn48eMaNGiQWrVqpRkzZkiSevfuLbfbrZEjR+qBBx5QaGhopsYEkAuVk8wvRqalkeVlpW02G4xUXNKxzA1nVhnpoOR6wCWrxOnxTDkj9wS3zPdGVpMz204ZmSVGKi95dfI6/eBaktu4ZZYbmVpGVsDpY60KlqwBliw/S+44t8weVvUCnqJg1YLat2qfyt1TTi6vfwu7e1fsVYHSBXTq6KlMjVeoeiHVe6eevAO8tWPeDm3dsfWCx26dslWBJQNV7dlqaXN7+Xvpr2//0jXNrlH+4qcLuwVKF1Dk25HyCfTRvlX7tHH8xiv4SQE4kTlzcwonZTkXK3vheL6+vqpdu7Y2btyow4cPZ9gfGxurgIAA1apV67w9e/v27auXX35Z69at04ABA9StWzctWrRInTp10smTJ7Vs2TJ16tRJnTp10oQJEySdLpT07ds3w1zTp09Xp06d0m1bunSpRowYod69e+vuu+/WU089pYULF2bxbwGAU3Tp0kUHDx7UokWL0rYlJydrxowZ6tq1a6bHmzFjhm6++ea0oqwk3XDDDWrcuLGmTZuWtm3p0qU6ePCg+vTpk+7xjzzyiJKSkjR37txMjwkg93Hd6JKOSzqnK4tJMTKbjKwbrQs+7kLMJiOVUFqhV5KswpZU5nRROc02Scck183pXz5YtS0pWTJb/j3Wyne60AvA8xT9X1GdOnpK8Rv/bYvgTnFr3+p9KlqnaKbH8wn0kXfApdegJe1KUtLuJBVvUDxdkfmaxtdIRtq36t/Xf94B3vIJ9Ml0FgDwFBR7kStEREQoNTVVK1asSLf96NGj+vnnn3XzzTdfdKXu7t279dZbb+nGG29Ujx49VLp0afXt21c+Pj664YYb1LdvX/Xt21dNmjTJdLZFixapUKFCateunbp166aCBQtq4sSJ+u677zI9FgDnK126tOrUqaMvv/wybdv8+fN1+PBhde7cOVNjud1urV+/XrVq1cqwr3bt2vrjjz/SepGvXbtWkjIcW7NmTblcrrT9mRkTQC4UIuma/7Ry2CrphGRVyVyB1biNtFeyimd8nFXCkg6dbvMgSeafM/MV/8+BxSRZkv7J1NQAcin/Qv4Kvi5Ye3/4t5XDwfUHlXIsRUVvyXyx93Id+ev0c5eg0kHptvuF+skvzE9HdxzNtrkBILehjQNyhSpVqig0NFRxcXFq0aJF2vYVK1YoNTVVERERF338nj17NGDAAFWrVi3d9g8++EBFixZVZGTkFWcbNmxYukJzixYt9MILL2ju3Llq3rz5FY8LwLm6du2qAQMG6Pjx4woICNDnn3+u+vXrq3jx/1ZBLu7QoUM6efKkihUrlmHf2W27d+9WhQoV9M8//8jLy0tFihRJd5yvr68KFiyo3bt3Z3pMALmTdaMlE2VkThlZPpbMeiOVlqygTK6mPS4pRVLgefYVOPP1iCQ/SUcluSQrMP0clrcl5TtzHIA8oWidovpj+h9KTU6Vl6+X9n6/VyE3hMgvNHPXLciM5IRkSZJvSMYFPr7BvjoZfzLb5gbgHMYYGeOc1glOynIuVvYiV3C5XLr11lu1ZcuWdC0a4uLiFBwcrBtvvPGijy9SpEiGQm9WObfQe+zYMSUmJqpixYrau3evjh3LZOM8ALlCx44ddfz4cX377bc6cuSIvv322ytq4XD8+OmrTZ/vom7+/v7pjjl+/PgFP8Hg7++f7rjLHRNA7mRVsaRTkracXnlrtlxZCwelnPl6vuUfZ7edOuer1wXG8T7dSgJA3lCkdhG5T7l1cN1BpRxP0YGfDyj8f+HZOmfqqVRJOu8F4Fw+LrlPuTNsB4C8ipW9yDUiIiI0d+5cxcXFqX379jp48KA2b96sFi1ayOW6+PsW/10Jl5U2b96s6dOn6/fff9fJk+nfUT527Jjy5bv4VWUB5D6FCxdWkyZN9MUXX+jYsWNKTU3VnXfemelxAgICJCnDuUOSTpw4ke6YgIAAJScnn3ecEydOpDvucscEkDtZ+S2prORe75Z1ypLcklXpCoq9Z18JpJxn39ltPud8Tb3AOClnVvgCyBN8g3wVWilUe1bsUWpyqozbqPDNhbN1Ti+f0+82na+o6z7lPm8RGADyKoq9yDXKli2r4sWLpxV74+LiZIy5ZAsHSRft55sZbnf6Jxd79uzRqFGjVLx4cXXr1k2FChWSl5eX1q1bp7lz52Y4HoDn6Nq1q3r37q09e/aoZcuWCgkJyfQYYWFh8vPz0z//ZGx2eXbb2dYQxYoVU2pqqvbt25fuDazk5GQdPHgw7bjMjAkg97JusmTmGJmjRionWQFXUGwN0OlXA+drdXm2LcPZdg6BktySOWrStXIwKUY6ds5xAPKEonWK6rePf1Py4WQVvKmgfPJn7wXRzrZvSE5Iln9B/3T7kg8nK6hs0PkeBsDDmDM3p3BSlnPx9hdylYiICO3cuVN//fWX4uLiVKxYMV1//fVXPJ5lnf+FUWBgoJKSkjJs379/f7rv16xZo1OnTum5555T06ZNVb16dd10001ZVlwG4Fzt27eXy+XSDz/8cEUtHKTTLWpuvPFGrV69OsO+lStXqmzZsipQ4HQF5Wwrmv8eu3r1arnd7rT9mRkTQO5l3WCdvjDa37qyFg6SLJclFZHM7owvVMwuI4VKlt/psa1iZ+bY/Z8Dd0syOn2hNgB5RuGahSVLSvwjUUX/l30XZjsr8NrTzcUTtyem234y/qROHjqZth8AQLEXuczZC6lNmzZN27dvV926da9qPD8/v/MWdYsWLapjx47pr7/+StsWHx+vVatWpTvubPuIc5tyHzt2TNHR0VeVC4DzBQYG6p133tGwYcPUpk2bKx7nzjvv1KpVq9IVZ3/77TctWbJEd911V9q2Ro0aKSwsTO+88066x7/zzjvKly+fWrVqlekxAeRelp8lq7Ulq4Elq8KVt1CwKlnSrjPF3TPMASNtk6zK54xbRlKA5F6V/lNLZpWRfCSrHG0cgLzE299bFXpUUJl2ZVSoeqFsny/wmkDlK5ZPu6N3y7j/PV/tWrJLsqQiN2df2z4AyG1o44BcpUiRIipfvnxaAeNs8fdKlS1bVhs2bNC3336r0NBQFSlSROXKldOtt96qzz//XK+++qpatGihkydPatGiRSpWrJi2bduW9viqVavK29tbY8aMUZMmTXTixAlFRUUpKChI8fHxV5UNgPP16NHjqsfo06ePPvjgA7Vq1UrPPPOMfHx89Nprr6lo0aJ6+umn044LCAjQyJEj9cgjj+iuu+5S8+bNFRMTo88++0yjR49WWFhYpscEkLu5ql39ug2rtiXzk5H7c7esWy3JSzIrjJRfp78/e5yPJauRJTPXKHVqqqzrLekvyaw3shpbsvKd09rhhJFZeboYY3ac+fqjkfE3kr/kuoX1JoAnKBZx9Uv6U46laOeinZKkw78fliT9vfhveefzlk8+H13T9Jq0Y6/vfL3Wv7Fe615ZpyK3FFHS30n6e/HfKl6vuPIXz59u3G2zT79mS9p1emHPnu/3KGFLgiSpTNsyV50bgD2MMekW29nNSVnORbEXuU5ERIS2bNmi66+/XuHhV3fV1+7du+v999/X1KlTlZycrPr166tcuXIqUKCAnnnmGX366af6/PPPVaRIEXXp0kV79uxJV+wtXry4nnrqKU2ZMkWTJ09WSEiImjZtqqCgIL377rtX+6MCyAMKFCig6OhoPfnkkxo1apTcbrcaNGig119/XYULp7/YSZ8+feTj46NXX31Vc+bMUcmSJfX666/r8ccfv+IxAeRtlp8lV0+X3AvcMsvN6ZYMpSVXC9fpC8Gdw1XbJbfLLbPCyPxmpGDJamHJ+t9/VvUel8yS9C9+zPdnvg+RdEt2/TQAcptTSae07att6bbtXHC6+OtfyD9dsbdQtUK68dEbte3rbfr9s9/lU8BHpduUVum2pTOM+98x/1n+77UMKPYC8HSWcWoZGnnGTz/9pJo1a+rFF19U2bJl7Y7jCH/++acGDBigNWvWqEaNGnbHATza2XMQf2//4ncC5Jyzf2+uB12yitMKQTrdQ9j9nptzEJADzp6Dbh5+swqUpqe/JB3ZfkSrhq7iHAQ4SGJiooKDg3XD2BvkFeBld5w0qcdTtfmZzTp8+LCCgpxzoUhW9gIAAAAAAABwNHPm5hROynIuir0AAGSDQ4cOKTk5+YL7vby8aKkAINuYY0ZKvcgBLmVo0wAAWeXU0VNyp7gvuN9yWfIN8s3BRACQd1DsBQAgG9xxxx1atmzZBfeXKlVK27dvz7lAAPIU91S3tP0iB4RIXk8652OQADzLhnEblLA54YL7/Qv569ZXb825QACQh1DsBQAgG7z66quKj4+/4P6AgIAcTAMgr3E1d0nHL3KAT45FAZAHXd/5eqUcS7ngfpePKwfTAPAUxhg56dJjTspyLoq9AABkg5o1a9odAUAexsXeANgpqIxzLlQEAHkNb6cBAAAAAAAAgAeg2Is8qVOnTpo+ffplHdu3b19NmDAh03Ps27dPnTp1UnR0dKYfCwCSNG3aNN10000KCAhQr1690u3bvn172kXeOnfurISEBHtCAsh13BvdSp2QqtSRqXLPSn8BpdSPU5X69sWu7JZ5qR+nKnVUqlLfT5XZ5syPOwLIOXtX7tXKQSsVfX+0Nn2wKd2+n178SSsHrszS+X568SdF947WqmGrFP/rhVtsAXA+48CbE9HGAblWdHS03nnnHb3wwgu67rrrrmqs3377TevXr9dtt92m/PnzZ1FCALhySUlJ6tmzp4oWLaqXXnpJtWvXTre/cOHCmjhxotasWaPx48erfPnyGjFihE1pAeQWJtnIfG2k/JLVxJJ1zZW1e3Avd8v8ZqRDkpIlBUlWeUtWPUtW/vRjuuq5ZPYYmRVG7llueT3FheGAvCr1ZKp+/fBX+Qb56rqO1ymo7JW1e9j+zXYdWHtAx/cdV+qJVPmF+alg1YIq3aa0fIN80x1buk1pHd15VDsW7NCm9zep7ut1s+JHAQDHotiLPGny5Mny8vr3hcaWLVs0Y8YM1a9fP0Ox9/XXX5dl0fcOQM769ddfdfz4cT399NPq27dvhv358+dXz5491bNnT3333Xdat25dzocEkPvsl3RKsm615Lrlyj/kZ3YbWeGWVEWSr6QDklljZLYYuR52yfL997mTdZ0l6zpL7lS3TJSROWZk5eO5FZAXJe1OkjvZrWtbXKtrml5zxeMc2X5EgdcGqugtReXl76Wk3UnavWy3Dv58ULVH1paX37+v9cKqhCmsSpjcKW79OeNPnTp6Sj6BXKUSgOei2Is8ydfX99IHneHjwxMBAJnToEEDlS5dWpMmTbriMZKSkiRJRYsWveSx4eHhOnLkyBXPBSB3SP04VVaIJVf7q+jEdur0Fyvw6oqtXp0zrs41JY3cU0+v+LVuPM/4gWe+JkvKd1XTA7DBTy/+JP9C/qrUu9IVj5F68nSbGN/gy389dj43Pnpjhm3B1wdr4/iNOrD2gIr+L+Pzp7NzppxIodgL5FLGGBnjnNYJTspyLnr2wmNMmDBB3bt316FDh/TKK6+oe/fuuv/++zV58mS53en70Z3bs3f69On67LPPJEmPPvqoOnXqpE6dOmnfvn2SMvbsPXr0qCZPnqxnnnlG3bt3V8+ePfXiiy9q+/btOfODAnnUzz//rF69emnVqlV2R8kRZ584XM4nC1wul2OfaACe4rffflPPnj0VFxdnd5SrcwWnCrPVKHVUqtzT3TKpFxkg5MzXExfYf/Z0xukKyLvO/v1n4v2mgxsOKrp3tDZO2Ch3qvuCx/kX8pckpRxLOe/+tOdUnIMAeDiKvfAobrdbo0ePVoECBdStWzdVqlRJ3377rRYvXnzBx9SuXVt1657u29S9e3f17dtXffv2VVDQ+ftH7d27V6tWrVKNGjXUvXt3tWnTRjt27NDw4cN16NChbPm5AJzu0z1p0iTVrl1bLVu29Pii79k3qVyuS/9TbVlWhje1AGStmJgYffLJJ4qIiFDjxo1zb9E3k4UW85uR+0u3rEqWrA6WLK9/H2iMkUkyMkeMzF9G7nluySVZpS8wOMVeIM9Le3P6Ms9BB9Yd0IY3N6jIzUVU+aHKcnn9+7zIGKPkI8k6mXBSCb8l6PfPfpflshRyQ8j5B+McBCCPoI0DPMqpU6d06623qkOHDpKkpk2bql+/flq6dKmaNWt23seUKlVKZcqUUVxcnG6++WYVKVLkonNce+21euONN9IVYCIjI/XUU09p6dKlaXMDyHoul0tut1uLFi3SggUL1KJFC40YMUI333yz3dGy3O7duyVJISEhlzw2ODhYv/zySzYnAnDWsmXLFBERoUaNGmnEiBFpbxrnBubImSqH/2Ucu8nIPcMtq5olq7Uly/Wf6sxRyT32nDeagnS6IFz4/FUcy886fdXqo5LCriw/gNwtOT5ZkuSd79KliH2r9+mXCb+oWEQxVehZIcM5KPlwsuIe//eNN78wP1V6qJLyFz//Bbe9A07PeTLhpAKKBFzpjwDARubMzSmclOVcFHvhcZo2bZru+4oVK2r58uVZNv65PXzdbreSkpLk7++vYsWKadu2bVk2z5VISkpSxYoVtX//fltzANkhJSUlbfVqaurpfm8LFizQggUL1K9fP7300ku25Dp16pQOHz6cYdvJkyd14MCBdNvDwsIuuVI3ISFBf/zxh1577TUFBQVdViG7fv36mj17tsaPH6+2bdsqPDzcln7jKSkpKleunPbs2ZPjcwPZLSXl348Fnz0HLVmyREuWLNHDDz+cruVTVjCpJmM7hFRJKZJJ+s8LiwBlLMT+d7zjRoqXzAoj+UkqcfH53RvcMl8ZWbUsWbdZ528pEyC5urtOZ/rHyPxqTvfjvZBrJHlL7uVuuZq5pODTBWAAzuNOcSvlePp2CCbFyH3KreQj6f/QffL7XPIcdCrplI7vO64d3+2QV4CXgsqc/1OUZ+1ZsUe/fvCrSjQsoXL3lDvvOcgn0EfVnqsm9ym3jvx1RPtX70/rCXw+QdcFyeXj0l/f/CWfzj7yK+gnb39KIgA8D2c2eBQfH58M7Rfy58+fdqGjrOB2uzV//nwtXLhQ+/btS/fR6QIFCmTZPFciICBAY8aModgLjxQdHa1Zs2alffzv7CrfatWq6Z577rEtV1xcnBo2bJhh+/fff68pU6ak27Zt2zaVLl36ouO1a9dOy5YtU1BQkGbNmnVZ55XHH39cK1as0KOPPqpHH31US5cuVYMGDTLzY2QJLy8vjRkzhmIvPNJ//6bPnoOqVKmi++67L+sn3CG5J2Vsz2J2GpmN6Yu9ridcUujFh3NPcUvbJflJrs6uixdZ4yUz08iqbMnV6sJvUFnelnTdmf+uYMmUNXJPdMvkN7IqZBzfKmDJdYdL7q/ccr99ZsVwe4q9gBMd/v2w1r60NuOOrdK+lfvSbaozto4CCl98peyGtzYoYXOCvAK8dONjN6atsj2f4/uPa9N7m1SkdhGV71b+gse5vF0Kq3z6YwKFqhVSWKUwrRm1Rr5BvipUrVCG4/1C/FTpwUra9N4mrRy4UuER4Vd1sTkAcCqKvfAol9Pb8mrNmjVL06ZNU8OGDdWxY0cFBgbKsix98skntl8gyeVyqXPnzrZmALKL2+3WrFmz5O3trZSUFLVv317Dhg1TlSpVbM1VtWpVLVq0KN22p59+WuHh4Xr22WfTbQ8PD7/keGPHjtWGDRs0YsQIdevWTVu2bFH+/Of/OOJZH3/8saZPn66+ffuqWbNmqlq1auZ/kCxgWZbuuusuW+YGsltAQICmTJmSdg5q1aqVhg8frurVq2fPhOFnVs2ew/2dWwqUXHX/83wn8NLDuZq5ZPYZmWgj91duuR5zyfK9QKG1wOkxze9GZpeRVeLyCrLWtZZUQDLrz1/sNUlG7m/cUmHJqmvJKkqhF3CqwGsDVe25aum2bf1yq3yDfXXtbdem2+4b7HvJ8a7vfL2O7jyq7bO3a9N7m1RnTB15+Xmd91i/ED/5hvjq4M8Hlbgt8ZKrgM8KLhcs3xBf7fl+z3mLvcmJydr88WblL55fJVuWVIFr7V2oAyDzjDG2113O5aQs56LYC2TSypUrVblyZT300EPpth87duyCF3UDcPVCQ0NljFHbtm0dUeQ9KzQ0VE2aNMmwrVixYhm2X45atWqpVq1asixLvXr10g8//KDGjRtf9DFz5sxRmTJlNG7cuEzPB+DyhIaeXjrbsmXL7C3ynmEF/LtqNk3A6dWx1nWZL5JaJSxZJSy55Zb52kh/Syp7gYO9JdfdLrknueX+zC1XL5esIpc5Z4pkTl7ghc8OScfPrCy+0EXcADiCT36ftFWzZ3nn95ZviG+G7ZcjqEyQgsoEybIs/frhrzq89fAFx3H5uFT1yapa+9Ja/Tz2Z1UfUF2B11zGu1qS3KfcSj1+/lYOh7ccVkpSiq5/7HqF3nCJj0MAQC6W/csggVzA3//0VUqOHTt2yWNdLleGd29WrFihQ4cOZUs2AKd17dpVe/fu1YwZMxxT6M1O1157etVMQkLCJY9NTEzUNddck82JgLytXbt22rNnj+bMmZPthd7sZAWfKbL+tx/wf4/zt+Tq5pLyS+5P3TKH/n3uY5KNTHLGgq7ZZKTjklX8/IXctCIw740DeZZfQT9JUsqxlIse553PW1WfrSqfIB+te2Wdju3993Va6snU8/bm3bdqn1KSUlSgzPlX7KacOD2nf9hlXKESAHIxVvYCksqUKSNJmjJlim699VZ5eXmpZs2aaUXgc9WoUUMzZ87UhAkTVKFCBe3YsUOxsbEqWrRoTscG8hRvb28VKVLE7hg55mxbmsv5aJAxJkfa2AB5mZeXl2f8W3+2DnsZnzq08ltydXfJPdEt9yduue5zyQqypIOnC8BWZUsqdGbM3afbNyhEsv53gVW7Z+dkUS+QZ533Yo8X4FvAV9Wfra41o9do3Zh1qjmopvzC/HRszzGtG7NORW4ponzF8smyLCVuS9TeFXvlX8hfJZuVPP+AnIMAj2Au50lMHkexF5B0/fXXq2PHjlq8eLHWrVsnY4zGjRt33mJv+/btdfLkScXFxWnFihUqU6aM+vXrpy+//NKG5AA8lZfX6T52J05cYvmdpOPHj9t+gUgAucSZ94VMipF1GRUPK8iSq4dL7o/ccn96uqWDgiSroiWzzUg/S0rV6SJvbUtWPUtWvguMeyp9BgB5j+U6fX5wJ2e8AOX5+IX5qdpz1fTTCz9p7StrVWNgDfmF+alwrcKK3xSvPbF75E51y7+gv65pfI1K3V5KPoE+5x0rNfn0amDLi2ovAM9GsRe5VoMGDdJdbb5Pnz7q06dPhuPuuuuuDBcMmjp1aobjOnTooA4dOmTYPn78+HTf+/j4qFu3burWrVu67UOHDk33fZEiRc47DwDPFx0dfdVjlChRQpI0bdo0/e9//1N4eLgCA//tV+d2u7V//379+uuv2rBhAxdGA/IAr17nv5hRppx5X8j8YmSuMVKgZPn9W/g43xxWmCWvZ9Jvt26//GKJOWGko5L51Uheki5+zUkADlVjQI2rHsMv9HQbh30/7lPQ9UHyDfaVt/+/ZYnzzZGvaD5FvBmRbtsNvW647DlTjqUoOTFZ+9fsl+VlybfApS8oBwC5GcVeAAAcqGzZsurYsaOmTZumb775Rj169NCkSZPS9u/YsSOtBU1wcLAefvhhm5ICyE2sMEtWZUvmFyP3b25Z1SxZ7bN3lZv7S7e0/cz8dS1Z3qyqA/KqgCIBKlK7iPb9uE8H1h1QeES4KvWulK1zrn9zvRI2J0iSrr3tWrl8+HgBkFsZmctqc5dTnNpSgmIvAAAONXXqVI0dO1bbt29XwYIF0+0LDw9XVFSUgoODVbly5fO2nQGA83F1dMkcNlKCpIAcmK+5S0qRFCZZgRR6gbyuyiNVdKLzCZ04cOKCLReyUrku5ZSanKp8RfPJN5hVvQA8H8VeAAAcrGTJkipZMuOFRvz9/dWoUSMbEgHwBFawJQXn0FzFKfACSM+/oL/8C+bMG9UFSnNdAwB5C8VeAAAAAAAAAI5mztycwklZzkWzGgAAAAAAAADwABR7gcvUqVMnTZ8+PdOP27dvnzp16qTo6OisDwUAAAAAAADHGzZsmCzLSne/4YYbsnweir3IdaKjo9WpUyd16tRJmzdvzrDfGKM+ffqoU6dOevnll21ICABXZtq0abrpppsUEBCgXr16pdu3fft2eXl5qXDhwurcubMSEhLsCQnAY7k3upU6IVWpI1PlnuVOt8/EG6UOS1Xqy6lyT3fLHHfmxxYB5F57V+7VykErFX1/tDZ9sCndvuP7j2tJzyWK6RujjRM26lTSKZtSArCTMcZx98yqXLmy/vnnn7R7bGxslv+e6NmLXMvHx0exsbEZ3gXZtGmTDh48KB+f7L+yKwBklaSkJPXs2VNFixbVSy+9pNq1a6fbX7hwYU2cOFFr1qzR+PHjVb58eY0YMcKmtAA8jUk2Ml8bKb9kNbFkXfOfi6rll6y2lrRbMj8aqaBkNeLCawCyRurJVP364a/yDfLVdR2vU1DZoHT7fYN8VfG+ijqy/Yj+Xvy38oXnU9k7ytqUFgCunLe3t8LDw7N1Dlb2IteqXr26fvjhB6WmpqbbHhcXp7JlyyokJMSeYABwBX799VcdP35cTz/9tB5//HHVqVMn3f78+fOrZ8+eGjdunMqVK6d169bZExSAZ9ov6ZRk3WrJVcclq2T6Qq7la8lV3SVXK5dUUDJ7WNkLIOsk7U6SO9mta1tcq5LNSir4+uB0+738vFQsspjKdyuvgKIBOvrXUZuSAkBGiYmJ6e4nT5684LG///67ihcvrrJly+ruu+/Wjh07sjwPK3uRa9WtW1erVq3S+vXrVb16dUlSSkqKfvjhB91xxx1asGBBuuNPnDihadOm6YcfftDhw4dVuHBhNW7cWK1bt5Zl/fuC5tSpU/riiy8UExOjU6dOqXLlyrrvvvvOm+HQoUOaOnWq1q5dq6SkJIWHh6t169Zq2LBh9v3gADxSUlKSJKlo0aKXPDY8PFxHjhzJ7kgA8pIzn4i2Ai9jtW6gpAu/hgGATEs9eXoBj2+w7yWP9Q32VcqJlOyOBMCBzJmbU5zNUrJkyXTbhw4dqmHDhmU4/pZbbtGkSZNUoUIF/fPPPxo+fLgiIyO1ceNGFShQIMtyUexFrlW4cGGVK1dOcXFxacXetWvX6tixY7r11lvTFXuNMXrllVf0yy+/qGHDhipdurR+/vlnffbZZzp06JB69OiRdux7772nmJgY1a1bVxUqVNDGjRv10ksvZZg/ISFBgwcPliQ1b95cQUFBWrdund59910dO3ZMrVq1yubfAABPcrbf07lvPl2Iy+W6ov5QAHBBmTml0L0BQFY7ew66jPOL5eIkBMBZdu7cqaCgf9vP+Pn5nfe4li1bpv33TTfdpFtuuUWlSpXStGnTLrjI8EpQ7EWuFhERoS+//FLJycny9fVVbGysKlWqpLCwsHTHrV69Whs3blSnTp10xx13SDpdoH3ttdc0f/58NW/eXOHh4dq+fbtiYmLUrFmztD+05s2b66233sqwtH7KlClyu9165ZVX0t6Badq0qd58803NmDFDTZs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" ] diff --git a/petab_select/plot.py b/petab_select/plot.py index ae77bfc3..2642de48 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -31,12 +31,16 @@ ] -def default_label_maker(model: Model) -> str: - """Create a model label, for plotting.""" - return model.model_hash[:4] +def get_model_hashes(models: list[Model]) -> dict[str, Model]: + """Get the model hash to model mapping. + Args: + models: + The models. -def get_model_hashes(models: list[Model]) -> dict[str, Model]: + Returns: + The mapping. + """ model_hashes = {model.get_hash(): model for model in models} return model_hashes @@ -44,7 +48,18 @@ def get_model_hashes(models: list[Model]) -> dict[str, Model]: def get_selected_models( models: list[Model], criterion: Criterion, -): +) -> list[Model]: + """Get the models that strictly improved on their predecessors. + + Args: + models: + The models. + criterion: + The criterion + + Returns: + The strictly improving models. + """ criterion_value0 = np.inf model0 = None model_hashes = get_model_hashes(models) @@ -127,7 +142,6 @@ def line_selected( criterion: Criterion, relative: bool = True, fz: int = 14, - size: tuple[float, float] = (5, 4), labels: dict[str, str] = None, ax: plt.Axes = None, ) -> plt.Axes: @@ -140,13 +154,10 @@ def line_selected( criterion: The criterion by which models are selected. relative: - If `True`, criterion values are plotted relative to the lowest - criterion value. TODO is the lowest value, always the best? May not - be for different criterion. + If ``True``, criterion values are plotted relative to the lowest + criterion value. fz: fontsize - size: - Figure size in inches. labels: A dictionary of model labels, where keys are model hashes, and values are model labels, for plotting. If a model label is not @@ -164,7 +175,7 @@ def line_selected( # FIGURE if ax is None: - _, ax = plt.subplots(figsize=size) + _, ax = plt.subplots(figsize=(5, 4)) linewidth = 3 models = [model for model in models if model != VIRTUAL_INITIAL_MODEL] @@ -211,41 +222,43 @@ def line_selected( def graph_history( models: list[Model], criterion: Criterion = None, - ax: plt.Axes = None, labels: dict[str, str] = None, colors: dict[str, str] = None, - optimal_distance: float = 1, - options: dict = None, + draw_networkx_kwargs: dict[str, Any] = None, relative: bool = True, + spring_layout_kwargs: dict[str, Any] = None, + ax: plt.Axes = None, ) -> plt.Axes: """Plot all calibrated models in the model space, as a directed graph. - TODO replace magic numbers with options/constants - Args: models: A list of models. criterion: The criterion. - optimal_distance: - See docs for argument `k` in `networkx.spring_layout`. - relative: - If `True`, criterion values are offset by the minimum criterion - value. - options: - Additional keyword arguments for `networkx.draw_networkx`. labels: A dictionary of model labels, where keys are model hashes, and values are model labels, for plotting. If a model label is not provided, it will be generated from its model ID. colors: Colors for each model, using their labels. + draw_networkx_kwargs: + Forwarded to ``networkx.draw_networkx``. + relative: + If ``True``, criterion values are offset by the minimum criterion + value. + spring_layout_kwargs: + Forwarded to ``networkx.spring_layout``. ax: The axis to use for plotting. + Returns: The plot axes. """ + default_spring_layout_kwargs = {"k": 1, "iterations": 20} + if spring_layout_kwargs is None: + spring_layout_kwargs = default_spring_layout_kwargs model_hashes = get_model_hashes(models) criterion_values = { @@ -292,15 +305,15 @@ def graph_history( edges.append((from_, to)) G.add_edges_from(edges) - default_options = { + default_draw_networkx_kwargs = { "node_color": NORMAL_NODE_COLOR, "arrowstyle": "-|>", "node_shape": "s", "node_size": 2500, "edgecolors": "k", } - if options is None: - options = default_options + if draw_networkx_kwargs is None: + draw_networkx_kwargs = default_draw_networkx_kwargs if colors is not None: if label_diff := set(colors).difference(list(G)): raise ValueError( @@ -309,16 +322,16 @@ def graph_history( ) node_colors = [ - colors.get(model_label, default_options["node_color"]) + colors.get(model_label, default_draw_networkx_kwargs["node_color"]) for model_label in list(G) ] - options.update({"node_color": node_colors}) + draw_networkx_kwargs.update({"node_color": node_colors}) if ax is None: _, ax = plt.subplots(figsize=(12, 12)) - pos = nx.spring_layout(G, k=optimal_distance, iterations=20) - nx.draw_networkx(G, pos, ax=ax, **options) + pos = nx.spring_layout(G, **spring_layout_kwargs) + nx.draw_networkx(G, pos, ax=ax, **draw_networkx_kwargs) return ax From 7fc4b9ca3d3b134a15e6869173ac5cf6e83cca36 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana <59329744+dilpath@users.noreply.github.com> Date: Mon, 18 Nov 2024 23:25:31 +0100 Subject: [PATCH 24/26] Apply suggestions from code review Co-authored-by: Daniel Weindl --- petab_select/plot.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/petab_select/plot.py b/petab_select/plot.py index 2642de48..08137c82 100644 --- a/petab_select/plot.py +++ b/petab_select/plot.py @@ -109,7 +109,7 @@ def upset( The criterion. Returns: - The plot axes (see documentation from the `upsetplot` package). + The plot axes (see documentation from the `upsetplot `__ package). """ # Get delta criterion values values = np.array( @@ -318,7 +318,7 @@ def graph_history( if label_diff := set(colors).difference(list(G)): raise ValueError( "Colors were provided for the following model labels, but " - "these are not in the graph: {label_diff}" + f"these are not in the graph: {label_diff}" ) node_colors = [ @@ -395,7 +395,7 @@ def bar_criterion_vs_models( if label_diff := set(colors).difference(criterion_values): raise ValueError( "Colors were provided for the following model labels, but " - "these are not in the graph: {label_diff}" + f"these are not in the graph: {label_diff}" ) bar_kwargs["color"] = [ @@ -467,7 +467,7 @@ def scatter_criterion_vs_n_estimated( if label_diff := set(colors).difference(labels.values()): raise ValueError( "Colors were provided for the following model labels, but " - "these are not in the graph: {label_diff}" + f"these are not in the graph: {label_diff}" ) scatter_kwargs["c"] = [ colors.get(model_label, NORMAL_NODE_COLOR) From 85248c025f8e6887170f258ee0a5c4af1eea3e90 Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 23:28:29 +0100 Subject: [PATCH 25/26] leave plot out of main module --- doc/examples/visualization.ipynb | 7 ++++--- petab_select/__init__.py | 1 - 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index b3bb517f..9e3e7fa6 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -28,6 +28,7 @@ "outputs": [], "source": [ "import petab_select\n", + "import petab_select.plot\n", "\n", "models = petab_select.models_from_yaml_list(\n", " model_list_yaml=\"calibrated_models/calibrated_models.yaml\"\n", @@ -274,7 +275,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -350,7 +351,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -390,7 +391,7 @@ "outputs": [ { "data": { - "image/png": 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qGjVqpNl37bXX6p577rGUCACunHHh5kaUvQAAAAAAeJjExEQ9//zzWrt2rW6++WZ5eXnJy8tL3bt3l+M4tuMBALIIZS8AAAAAAB4kLi5OXbt21fvvv6//+7//0y+//KKGDRvKcRw9+uijtuMBALIQa/YCAAAAAOAhzpw5ow4dOmj+/PmaMmWKOnToIEn69ttvtX37dpUrV85yQgC4MsaYlJtNuoGbsqRG2QsAAAAAgAeIiopSq1at9NNPP2nOnDlq1qxZynN+fn6qXLmyxXQAgOxA2QsAAAAAQA53+PBhNWvWTJs2bdLixYtVu3Zt25EAABawZi8AAADgcosWLVKZMmX0999/244CwIV2796tunXr6t9//9Xy5cspegF4JOPCzY0oewEAAACXio+P15AhQ9SsWTPt3r1bW7dutR0JgMts3rxZtWvX1smTJxUREaFbb73VdiQAgEUs4wAAAAC40NatW9WxY0f9/PPPKfuKFi1qMREAt/njjz/UpEkTFSxYUIsXL1aZMmVsRwIAWMbMXgAAAMBlJk2apJtvvlm//vprmv1+fn6WEgFwm1WrVqlevXoqXbq0VqxYQdELwOMZY1z3cCPKXgAAAMBFDh8+rG7duun06dNKSEhI81zevHktpQLgJgsXLlRISIiqV6+uZcuW6ZprrrEdCQDgEpS9AAAAgIsUKVJECxcuVNWqVc97jpm9AKZNm6b7779fDRs21IIFCxQYGGg7EgDARSh7AQAAAJdp3LixPvjgA0lS4cKFU/YzsxfI3T7++GN17NhR7du314wZM5QvXz7bkQAgWxkXbW5F2QsAAAC4jDFGffv21a233qrdu3dr7Nix6tChg4oUKWI7GgBL3njjDT355JN65pln9Pnnn8vHx8d2JACAC+WxHQAAAABAWnPmzNEPP/ygxYsXK2/evOrZs6d69uxpOxYAC87+48/rr7+ugQMHaujQoXIcx3YsAIBLUfYCAAAALpKQkKB+/fqpYcOGCgkJsR0HgEUJCQl65pln9NFHH+ntt9/WCy+8YDsSAFhjjJEx7lk+wU1ZUmMZBwAAAMBFPv/8c23YsEGjRo2yHQWARbGxserUqZMmTJigTz/9lKIXAHK48ePHq1q1agoMDFRgYKBq1qypBQsWpDx/5swZ9ejRQ4ULF5a/v7/atm2rAwcOZPg8lL0AAACAS5w+fVqDBg1S+/btdfvtt9uOA8CSU6dOqVWrVpo1a5amT5+uxx57zHYkAMBVKl26tEaNGqW1a9fql19+0b333qtWrVpp/fr1kqQXXnhB3377raZPn67w8HDt3btXDzzwQIbP4xi3zjkGAAAAcpm33npLffv21YYNG1SxYkXbcQBYcPz4cd133336/fffNWvWLDVq1Mh2JACwKioqSkFBQVqyYYkKBBSwHSfFyeiTCrkxRLt27VJgYGDKfj8/P/n5+aXrGIUKFdKbb76pdu3a6ZprrtHkyZPVrl07SdI///yjKlWqaPXq1br77rvTnYuZvQAAAIALHD9+XK+++qqeeOIJil4glzpw4IDq16+vDRs26LvvvqPoBYAcoEyZMgoKCkp5vPbaa5d9TUJCgqZOnaqTJ0+qZs2aWrt2reLi4tJc9ytXrqyyZctq9erVGcrDDdoAAAAAF3j99dcVExOjQYMG2Y4CwIJ///1XjRo10smTJ7VixQpVrVrVdiQAQDpcaGbvxfz555+qWbOmzpw5I39/f82cOVM33nijfv/9d/n6+io4ODjN+GLFimn//v0ZykPZCwAAAFi2Z88ejRkzRi+++KKKFy9uOw6AbPb3338rJCREfn5+ioiI0HXXXWc7EgC4jjFGblqN9myWszdcS48bbrhBv//+uyIjI/X111+rS5cuCg8Pz9RclL0AAACAZUOHDlX+/Pn18ssv244CIJv98ssvatq0qUqUKKHFixerRIkStiMBALKIr6+vKlSoIEmqUaOGfv75Z40ZM0YdOnRQbGysjh8/nmZ274EDBzI8EYA1ewEAAACL/vnnH3366afq379/umeFAPAMy5cvV4MGDVSxYkWFh4dT9AJALpOYmKiYmBjVqFFDPj4++u6771Ke27hxo3bu3KmaNWtm6JjM7AUAAAAs6t+/v0qXLq1nnnnGdhQA2WjOnDlq37696tSpo5kzZ8rf3992JABwNZO8uUVGs/Tt21fNmjVT2bJlFR0drcmTJ2v58uVatGiRgoKC1L17d/Xu3VuFChVSYGCgevXqpZo1a+ruu+/O0HkoewEAAABLfvzxR82YMUOfffbZJW/mAcCzfPnll+ratatatWqlyZMn8/cfAHKBgwcPqnPnztq3b5+CgoJUrVo1LVq0SCEhIZKkd955R15eXmrbtq1iYmLUpEkTjRs3LsPncYybVjYGAAAAcgljjBo0aKCjR4/qt99+k7e3t+1IALLB+++/r169eumxxx7TRx99pDx5mIMFAJcSFRWloKAgLVq/SAUCCtiOk+Jk9Ek1uamJIiMjXbUUF/9XAQAAACxYuHChwsPDNXfuXIpeIBcwxmjEiBEaNGiQevfurbfeekuO49iOBQA5hjFGbpqz6qYsqVH2AgAAANksMTFRoaGhqlu3rpo3b247DoAslpiYqBdffFHvvvuuRowYoX79+lH0AgCyBGUvAAAAkM0mT56sdevWafXq1RQ+gIeLj4/XE088oc8++0wffPABN2MEAGQpyl4AAAAgG8XExGjgwIFq3bp1hu+uDCBnOXPmjDp27Khvv/1WX375pTp16mQ7EgDkWCZ5cws3ZUmNshcAAADIRv/3f/+nnTt3av78+bajAMhC0dHRat26tX744QfNmjVL9913n+1IAIBcgLIXAAAAyCZRUVEaMWKEHnvsMVWpUsV2HABZ5MiRI2revLn+/vtvLVy4UPXq1bMdCQCQS1D2AgAAANnkrbfe0okTJzRkyBDbUQBkkb1796px48Y6cOCAli9frttuu812JADwCMYYGeOepRPclCU1yl4AAAAgGxw4cEBvv/22nn32WZUuXdp2HABZYOvWrWrUqJESEhK0cuVKVa5c2XYkAEAu42U7AAAAAJAbDB8+XD4+PgoNDbUdBUAWWLdunWrXri1fX19FRERQ9AIArKDsBQAAALLYli1b9OGHH6pv374qWLCg7TgAMtnq1atVr149FS9eXCtXrlTZsmVtRwIAj2NcuLkRZS8AAACQxQYOHKhixYqpV69etqMAyGRLlixRo0aNVLVqVS1fvlxFixa1HQkAkItR9gIAAABZaO3atZo6daqGDh2qfPny2Y4DIBN98803atGiherVq6dFixYpKCjIdiQAQC7HDdoAAACALNS3b19VrlxZXbp0sR0FQCb69NNP9cQTT6h9+/b67LPP5OvrazsSAHg0Y4yMcc/SCW7KkhplLwAAAJBFli5dqiVLlmjGjBnKk4cfvQFPMXr0aL300kt6+umn9f7778vb29t2JAAAJLGMAwAAAJAlEhMTFRoaqrvvvlutW7e2HQdAJjDGqH///nrppZfUr18/jRs3jqIXAOAqTC8AAAAAssDXX3+ttWvXKjw8XI7j2I4D4ColJiaqZ8+eGj9+vN5880299NJLtiMBQK5ikje3cFOW1Ch7AQAAgEwWFxen/v37q0WLFqpbt67tOACuUlxcnLp06aJp06ZpwoQJ6t69u+1IAABcEGUvAAAAkMkmTJigrVu3asaMGbajALhKp06d0oMPPqglS5Zo2rRpateune1IAABcFGUvAAAAkIlOnDihoUOH6tFHH9XNN99sOw6AqxAZGamWLVtq7dq1mjt3rho3bmw7EgDkWsYYGeOepRPclCU1yl4AAAAgE7377rs6duyYhg0bZjsKgKtw8OBBNW3aVNu3b9fSpUtVs2ZN25EAALgsyl4AAAAgkxw+fFhvvPGGnnnmGZUrV852HABXaOfOnQoJCVFkZKTCw8NVrVo125EAAEgXyl4AAAAgk4wcOVKS1L9/f8tJAFypjRs3KiQkRN7e3oqIiFCFChVsRwIASDLJm1u4KUtqXrYDAAAAAJ5gx44dGjdunF555RUVKVLEdhwAV+DXX39V7dq1FRAQQNELAMiRKHsBAACATDBo0CAVLFhQL7zwgu0oAK7AihUr1KBBA5UvX14rVqxQqVKlbEcCACDDWMYBAAAAuErr1q3Tl19+qQ8++EAFChSwHQdABs2bN0/t2rXTPffco1mzZikgIMB2JADAOYwxMsY9Sye4KUtqzOwFAAAArlLfvn11/fXX6/HHH7cdBUAGTZkyRa1bt1bTpk01b948il4AQI7GzF4AAADgKqxYsULz58/XtGnT5OPjYzsOgAwYP368evTooc6dO2vChAnKk4dfkQEAORszewEAAIArZIxRnz59VKNGDbVr1852HADpZIzRyJEj9cwzz+jZZ5/Vp59+StELAC5nXLi5Ef83AwAAAK7QrFmz9OOPP2rp0qXy8mIeBZATGGP08ssva/To0Ro6dKgGDhwox3FsxwIAIFNQ9gIAAABXID4+Xv369VNISIgaNmxoOw6AdIiPj9dTTz2lTz/9VGPGjNGzzz5rOxIAAJmKshcAAAC4ApMmTdI///yjsLAw21EApENMTIw6deqk2bNn6/PPP9ejjz5qOxIAIAOMMTLGPUsnuClLapS9AAAAQAadOnVKQ4YM0UMPPaTbbrvNdhwAl3HixAk98MADWrFihWbMmKH777/fdiQAALIEZS8AAACQQWPHjtWBAwc0fPhw21EAXMbRo0fVokUL/fXXX1qwYIEaNGhgOxIAAFmGshcAAADIgGPHjmnUqFF66qmnVKFCBdtxAFzCvn371LhxY+3bt0/ff/+9br/9dtuRAABXyCRvbuGmLKlR9gIAAAAZMGrUKMXFxWngwIG2owC4hG3btikkJEQxMTFauXKlqlSpYjsSAABZzst2AAAAACCn2L17t9577z29+OKLKlasmO04AC7ir7/+Uu3ateXl5aWIiAiKXgBArsHMXgAAACCdhgwZIn9/f7344ou2owC4iJ9++knNmjVTmTJltGjRIhUvXtx2JABAJjDGyBj3LJ3gpiypMbMXAAAASIcNGzZo4sSJGjBggAIDA23HAXAB3333nRo2bKgqVaooPDycohcAkOtQ9gIAAADp0L9/f5UtW1ZPP/207SgALmDmzJlq3ry56tSpo8WLFys4ONh2JAAAsh3LOAAAAACXsXr1as2aNUtffPGF/Pz8bMcBcI5Jkyape/fuateunb744gv5+vrajgQAyAJG7lw6wU2Y2QsAAABcgjFGffr00S233KJOnTrZjgPgHO+++64ee+wxPf7445o8eTJFLwAgV2NmLwAAAHAJ8+fP18qVKzV//nx5eTFXAnALY4wGDx6s4cOHq0+fPnrttdfkOI7tWAAAWEXZCwAAAFxEQkKCQkNDVb9+fTVt2tR2HADJEhMT9dxzz+n999/XqFGj1KdPH9uRAABZzMjIGPcs4+DWJSUoewEAAICLCAsL019//aUff/yRGYOAS8TFxalbt24KCwvThx9+qCeffNJ2JAAAXIOyFwAAALiAM2fOaODAgXrggQd011132Y4DQNLp06fVoUMHLVy4UFOmTFGHDh1sRwIAwFUoewEAAIALGD9+vPbs2aPFixfbjgJAUlRUlO6//36tWbNGc+bMYWkVAMhlTPLmFm7KkhplLwAAAHCOyMhIjRw5Ut26ddMNN9xgOw6Q6x06dEjNmjXTli1btGTJEtWqVct2JAAAXImyFwAAADjHm2++qVOnTmnw4MG2owC53q5du9S4cWMdPXpU4eHhuuWWW2xHAgDAtSh7AQAAgFT27dund955R88995xKlSplOw6Qq23atEkhISGSpIiICFWsWNFyIgCALcYYGeOepRPclCU1L9sBAAAAADcZNmyY/Pz81KdPH9tRgFzt999/V506dZQ/f36tWrWKohcAgHSg7AUAAACSbdq0SR9//LH69eun4OBg23GAXCsiIkL169dXmTJltHLlSpUuXdp2JAAAcgTKXgAAACDZgAEDVKJECfXs2dN2FCDXWrBggRo3bqzq1atr2bJlKlKkiO1IAAAXMC7c3IiyFwAAAJD0yy+/aPr06Ro2bJjy5s1rOw6QK02bNk3333+/GjVqpAULFigwMNB2JAAAchTKXgAAAOR6xhj16dNHN954ozp37mw7DpArffjhh+rYsaMeeughffPNN8qXL5/tSAAA5Dh5bAcAAAAAbFuyZImWLVum2bNny9vb23YcINcZNWqU+vbtq549e2rMmDHy8mJeEgAgLWOMjHHP0gluypIa/wcFAABArpaYmKjQ0FDVqlVLLVu2tB0HyFXOzqrv27evBg0apPfee4+iFwCAq8DMXgAAAORq06ZN02+//aaVK1fKcRzbcYBcIyEhQf/73//08ccf65133tHzzz9vOxIAADkeZS8AAAByrdjYWA0YMEAtW7ZU7dq1bccBco3Y2Fg98sgj+uabbzRx4kR17drVdiQAgMuZ5M0t3JQlNcpeAAAA5FofffSRtm/frtmzZ9uOAuQaJ0+eVNu2bfX999/rm2++UevWrW1HAgDAY1D2AgAAIFc6ceKEhg8fri5duqhq1aq24wC5wrFjx3Tffffpjz/+0Pz589WwYUPbkQAA8CiUvQAAAMiV3n77bUVGRmro0KG2owC5wv79+9WkSRPt3r1by5Yt05133mk7EgAgBzHGyBj3LJ3gpiypUfYCAAAg1zl48KDefPNN9ejRQ2XLlrUdB/B4O3bsUEhIiE6ePKnw8HBm0wMAkEW8bAcAAAAAstvIkSPl5eWlfv362Y4CeLwNGzaoVq1aSkxM1KpVqyh6AQDIQpS9AAAAyFW2bdum8ePHq0+fPipcuLDtOIBH+/nnn1W3bl0VLlxYERERKl++vO1IAIAcyrhwcyPKXgAAAOQqgwYNUpEiRfTcc8/ZjgJ4tO+//1733nuvKlasqOXLl6tEiRK2IwEA4PEoewEAAJBr/P7775o8ebIGDx6sAgUK2I4DeKzZs2erWbNmqlmzppYuXapChQrZjgQAQK7ADdoAAACQa/Tt21cVK1ZUt27dbEcBPNbnn3+ubt26qU2bNvryyy/l5+dnOxIAwAMYY2SMe5ZOcFOW1Ch7AQAAkCt8//33WrhwoaZPny4fHx/bcQCP9N577+m5555T9+7d9eGHH8rb29t2JAAAchWWcQAAAIDHM8YoNDRUd9xxh9q2bWs7DuBxjDEaOnSonnvuOb344ov6+OOPKXoBALCAmb0AAADweDNmzNCaNWu0bNkyOY5jOw7gURITE9W7d2+NGTNGI0eOVN++ffl7BgDIdCZ5cws3ZUmNshcAAAAeLT4+Xv369VOTJk3UoEED23EAjxIfH6/HH39cn3/+ucaNG6f//e9/tiMBAJCrUfYCAADAo3366afatGmTpk2bZjsK4FHOnDmjhx56SPPmzVNYWJg6duxoOxIAALkeZS8AAAA81qlTpzRkyBB16tRJ1atXtx0H8BjR0dFq1aqVVq9erVmzZqlFixa2IwEAPJwxRsa4Z+kEN2VJjbIXAAAAHmvMmDE6fPiwhg8fbjsK4DGOHDmiZs2aaePGjVq8eLHq1KljOxIAAEhG2QsAAACPdOTIEb3++ut6+umndd1119mOA3iEPXv2qHHjxjp06JC+//573XbbbbYjAQCAVCh7AQAA4JFee+01JSQkaMCAAbajAB5hy5YtCgkJUUJCglauXKkbbrjBdiQAQC5ikje3cFOW1LxsBwAAAAAy286dO/X+++/rpZdeUtGiRW3HAXK8devWqXbt2vL19VVERARFLwAALkXZCwAAAI8zePBgBQYGqnfv3rajADneDz/8oHr16qlkyZJauXKlypYtazsSAAC4CJZxAAAAgEdZv369Pv/8c40ZM0YBAQG24wA52qJFi/TAAw/o9ttv15w5cxQUFGQ7EgAglzLGyBj3LJ3gpiypMbMXAAAAHqVfv3669tpr9eSTT9qOAuRo06dPV8uWLdWgQQMtXLiQohcAgByAmb0AAADwGBEREZozZ47CwsLk6+trOw6QY02YMEFPPfWUOnTooM8++0w+Pj62IwEAgHRgZi8AAAA8gjFGoaGhql69uh566CHbcYAc66233tITTzyhp556Sl9++SVFLwDAFYwLNzdiZi8AAAA8wrfffqtVq1Zp4cKF8vJiTgOQUcYY9e/fX6+99pr69++v4cOHy3Ec27EAAEAGUPYCAAAgx0tISFC/fv107733qnHjxrbjADlOQkKCevbsqf/7v//TW2+9pRdffNF2JAAAcAUoewEAAJDjffHFF1q/fr0mTpzITEQgg2JjY9WlSxd99dVX+uSTT9StWzfbkQAAOI8xRsa4Z+kEN2VJjbIXAAAAOdqZM2c0aNAgtWvXTnfccYftOECOcurUKbVr105Lly7VV199pbZt29qOBAAArgJlLwAAAHK0Dz74QHv37tXIkSNtRwFylMjISN1333369ddfNW/ePIWEhNiOBAAArhJlLwAAAHKs48ePa+TIkXr88cdVqVIl23GAHOPgwYNq0qSJduzYoaVLl6pmzZq2IwEAcEkmeXMLN2VJjdsUAwAAIMd64403FBMTo8GDB9uOAuQYO3fuVJ06dbR//36tWLGCohcAgGzw2muv6Y477lBAQICKFi2q1q1ba+PGjWnG1K9fX47jpHk8/fTTGToPZS8AAABypL179+rdd9/V888/rxIlStiOA+QI//zzj2rVqqW4uDhFRETo5ptvth0JAIBcITw8XD169NCPP/6oJUuWKC4uTo0bN9bJkyfTjHviiSe0b9++lMcbb7yRofOwjAMAAABypKFDhypfvnx65ZVXbEcBcoS1a9eqadOmKlasmBYvXqySJUvajgQAQLoZY2SMe5ZOyGiWhQsXpvl60qRJKlq0qNauXau6deum7M+fP7+KFy9+xbmY2QsAAIAcZ+PGjfrkk0/Uv39/BQUF2Y4DuF54eLgaNGig6667TuHh4RS9AABkkqioqDSPmJiYdL0uMjJSklSoUKE0+8PCwlSkSBFVrVpVffv21alTpzKUh5m9AAAAyHH69++vkiVL6plnnrEdBXC9uXPn6sEHH9Q999yjWbNmKSAgwHYkAAA8RpkyZdJ8PXjwYA0ZMuSSr0lMTNTzzz+vWrVqqWrVqin7O3XqpHLlyqlkyZJat26d+vTpo40bN2rGjBnpzkPZCwAAgBzlp59+0jfffKNJkyYpb968tuMArjZ58mR16dJF9913n6ZMmcLfGQBAjmWSN7c4m2XXrl0KDAxM2e/n53fZ1/bo0UN//fWXIiIi0ux/8sknUz6/+eabVaJECTVs2FBbt27V9ddfn65cLOMAAACAHMMYo9DQUFWtWlWPPPKI7TiAq40bN06PPPKIHnnkEU2fPp2iFwCALBAYGJjmcbmyt2fPnpo7d66+//57lS5d+pJj77rrLknSli1b0p2Hmb0AAADIMRYtWqTly5fr22+/lbe3t+04gCsZYzRy5EgNHDhQzz//vEaPHi0vL+b5AABgkzFGvXr10syZM7V8+XKVL1/+sq/5/fffJUklSpRI93koewEAAJAjJCYmKjQ0VLVr11aLFi1sxwFcyRijl156SW+//baGDx+u/v37y3Ec27EAALh6Jun/c66RwSg9evTQ5MmTNXv2bAUEBGj//v2SpKCgIOXLl09bt27V5MmT1bx5cxUuXFjr1q3TCy+8oLp166patWrpPg9lLwAAAHKEKVOm6I8//tCqVasor4ALiI+P15NPPqmJEydq7Nix6tmzp+1IAAAg2fjx4yVJ9evXT7N/4sSJ6tq1q3x9fbV06VK9++67OnnypMqUKaO2bdtqwIABGToPZS8AAABcLyYmRgMGDFCrVq10zz332I4DuE5MTIw6deqk2bNn64svvmBNawAAXOZys5LLlCmj8PDwqz4PZS8AAABc78MPP9TOnTs1b94821EA1zlx4oTatGmjlStXaubMmWrZsqXtSAAAZDqTvLmFm7KkRtkLAAAAV4uKitLw4cPVtWtX3XjjjbbjAK5y9OhRNW/eXBs2bNDChQvPe2soAADIXSh7AQAA4GqjR49WdHS0hgwZYjsK4Cr79u1T48aNtW/fPi1btky333677UgAAMAyyl4AAAC41oEDBzR69Gj16tVLZcqUsR0HcI1t27YpJCREsbGxWrlypapUqWI7EgAAWcoYc9l1b7OTm7Kk5mU7AAAAAHAxw4cPV548edS3b1/bUQDX+Ouvv1S7dm15eXkpIiKCohcAAKSg7AUAAIArbd26VR9++KFCQ0NVqFAh23EAV/jpp59Ut25dFS1aVBERESpXrpztSAAAwEUoewEAAOBKAwcOVNGiRfXss8/ajgK4wtKlS9WwYUPdeOONWr58uYoVK2Y7EgAA2ca4cHMjyl4AAAC4zq+//qopU6ZoyJAhyp8/v+04gHUzZsxQixYtVKdOHS1evFjBwcG2IwEAABei7AUAAIDr9O3bVzfccIMee+wx21EA6yZOnKgHH3xQbdq00ezZs/kHEAAAcFF5bAcAAAAAUvvuu++0ePFiffPNN8qThx9Xkbu988476t27t5566il98MEH8vb2th0JAAArjDEyxj1LJ7gpS2rM7AUAAIBrGGMUGhqqu+66S23atLEdB7DGGKOBAweqd+/eCg0N1fjx4yl6AQDAZTFVAgAAAK7x9ddf65dfftHy5cvlOI7tOIAViYmJevbZZ/XBBx9o1KhR6tOnj+1IAAAgh6DsBQAAgCvExcWpX79+atasmerVq2c7DmBFXFycHnvsMU2ePFkfffSRnnjiCduRAABwBZO8uYWbsqRG2QsAAABX+OSTT7R161Z9/fXXtqMAVpw+fVrt27fXokWLNHXqVLVv3952JAAAkMNQ9gIAAMC6kydPaujQoXr44Yd1yy232I4DZLuoqCjdf//9WrNmjebMmaOmTZvajgQAAHIgyl4AAABY9+677+rIkSMaNmyY7ShAtjt06JCaNm2qrVu3asmSJapVq5btSAAAuI4xRsa4Z+kEN2VJjbIXAAAAVh0+fFivv/66nnnmGZUvX952HCBb7dq1SyEhITp27JjCw8OZ2Q4AAK4KZS8AAACsevXVVyVJ/fv3t5wEyF6bNm1SSEiIHMdRRESEKlasaDsSAADI4bxsBwAAAEDu9e+//+qDDz7Qyy+/rGuuucZ2HCDb/P7776pTp44KFChA0QsAQDoYF25uRNkLAAAAawYNGqSCBQvqhRdesB0FyDYRERGqV6+eypYtqxUrVqh06dK2IwEAAA9B2QsAAAAr/vzzT33xxRcaNGiQ/P39bccBssX8+fPVuHFj3Xbbbfruu+9UpEgR25EAAIAHYc1eAAAAWNG3b19dd911euKJJ2xHAbLF1KlT9eijj6p58+aaNm2a8ubNazsSAAA5hjFGxrhn6QQ3ZUmNshcAAADZbsWKFZo3b56mTJkiHx8f23GALPd///d/euaZZ/TII4/o008/VZ48/CoGAAAyH8s4AAAAIFsZY9SnTx/ddtttat++ve04QJYbNWqU/ve//6lnz56aNGkSRS8AAMgy/JQBAACAbDV79mz9+OOPWrx4sby8mHsAz2WMUWhoqN544w0NHjxYgwcPluM4tmMBAJAjmeTNLdyUJTXKXgAAAGSb+Ph49evXT40aNVJISIjtOECWSUhI0NNPP60JEybo3Xff1XPPPWc7EgAAyAUoewEAAJBtPvvsM/3999/64osvbEcBskxMTIweffRRzZgxQ5MmTVKXLl1sRwIAALkEZS8AAACyxenTpzV48GC1b99eNWrUsB0HyBInT57UAw88oOXLl+vrr79W69atbUcCAMAjGGNkjHuWTnBTltQoewEAAJAtxo4dqwMHDmjEiBG2owBZ4tixY2rRooXWrVunBQsW6N5777UdCQAA5DKUvQAAAMhyx44d02uvvaYnnnhCFStWtB0HyHT79+9XkyZNtHv3bi1btkx33nmn7UgAACAXouwFAABAlnv99dcVGxurQYMG2Y4CZLodO3YoJCREp06d0ooVK3TTTTfZjgQAgMcxyZtbuClLal62AwAAAMCz7d69W2PGjFHv3r1VvHhx23GATLVhwwbVqlVLxhhFRERQ9AIAAKsoewEAAJClhg4dqgIFCujll1+2HQXIVD///LPq1KmjwoULa+XKlSpfvrztSAAAIJdjGQcAAABkmb///luffvqpRo8ercDAQNtxgEzz/fff6/7779fNN9+sefPmqWDBgrYjAQDg0YwxMsY9Sye4KUtqzOwFAABAlunfv7/KlCmj//3vf7ajAJlm9uzZatasme655x4tWbKEohcAALgGM3sBAACQJVavXq2ZM2fq888/l5+fn+04QKb4/PPP1a1bN7Vp00Zffvkl/20DAABXYWYvAAAAMp0xRqGhobr55pvVqVMn23GATPHee++pS5cu6tq1q6ZOnUrRCwBANjIu3NyImb0AAADIdAsWLNCKFSs0b948eXt7244DXBVjjIYNG6YhQ4bopZde0htvvCHHcWzHAgAAOA9lLwAAADJVQkKCQkNDVbduXTVr1sx2HOCqJCYm6oUXXtB7772nV199VaGhoRS9AADAtSh7AQAAkKkmT56sP//8U6tXr6YUQ44WHx+v7t2764svvtD48eP19NNP244EAECuZYyRMe5ZOsFNWVKj7AUAAECmiYmJ0cCBA9WmTRvdfffdtuMAV+zMmTPq0KGD5s+fr7CwMHXs2NF2JAAAgMui7AUAAECmGT9+vHbt2qUFCxbYjgJcsejoaLVq1UqrV6/WrFmz1KJFC9uRAAAA0oWyFwAAAJkiMjJSI0aMULdu3VSlShXbcYArcuTIETVr1kwbN27U4sWLVadOHduRAACAJJO8uYWbsqRG2QsAAIBM8dZbb+nkyZMaMmSI7SjAFdmzZ48aN26sQ4cOafny5br11lttRwIAAMgQyl4AAABctf379+vtt9/Wc889p1KlStmOA2TYli1b1KhRIyUmJioiIkKVKlWyHQkAACDDKHsBAABw1YYNGyZfX1/16dPHdhQgw/744w81adJEwcHBWrJkicqUKWM7EgAAOIcxRsa4Z+kEN2VJzct2AAAAAORsmzdv1scff6y+ffuqYMGCtuMAGfLDDz+ofv36KlWqlFauXEnRCwAAcjTKXgAAAFyVAQMGqFixYurVq5ftKECGLFq0SI0aNVK1atW0bNkyXXPNNbYjAQAAXBWWcQAAAMAV++WXX/TVV19pwoQJypcvn+04QLpNnz5dDz/8sBo3bqzp06fz3y8AAC5nkje3cFOW1JjZCwAAgCsWGhqqKlWqqEuXLrajAOk2YcIEPfTQQ3rwwQc1c+ZMil4AAOAxmNkLAACAK7JkyRJ99913mjlzpvLk4cdK5AxvvvmmXnnlFT3zzDMaO3asvLyY/wIAADwHP5UDAAAgwxITExUaGqqaNWuqVatWtuMAl2WMUb9+/TRq1CgNGDBAw4YNk+M4tmMBAIAMMMadSye4CWUvAAAAMuyrr77Sr7/+qvDwcAozuF5CQoJ69OihDz/8UKNHj1bv3r1tRwIAAMgSlL0AAADIkNjYWA0YMEAtWrRQ3bp1bccBLik2NladO3fW9OnT9cknn6hbt262IwEAAGQZyl4AAABkyMcff6xt27Zp5syZtqMAl3Tq1Cm1a9dO3333nb766iu1bdvWdiQAAHCFTPLmFm7KkhplLwAAANLtxIkTGjZsmB599FHdfPPNtuMAF3X8+HG1bNlSv/32m+bOnauQkBDbkQAAALIcZS8AAADS7Z133tHx48c1bNgw21GAizpw4ICaNm2qf//9V0uXLtXdd99tOxIAAEC2oOwFAABAuhw6dEhvvPGGevTooXLlytmOA1zQv//+q5CQEEVHRys8PJwZ6AAAeAhjjIxxz9IJbsqSmpftAAAAAMgZRo4cKS8vL/Xr1892FOCC/vnnH9WuXVvx8fGKiIig6AUAALkOZS8AAAAua/v27Ro3bpxeeeUVFSlSxHYc4Dxr165VnTp1FBQUpIiICF1//fW2IwEAAGQ7yl4AAABc1qBBg1S4cGE9//zztqMA5wkPD1eDBg103XXXKTw8XCVLlrQdCQAAZDLjws2NKHsBAABwSX/88YfCwsI0ePBgFShQwHYcII25c+eqadOmuvPOO/Xdd9+pcOHCtiMBAABYQ9kLAACAS+rbt68qVKig7t27244CpBEWFqbWrVurWbNmmjdvnvz9/W1HAgAAsCqP7QAAAABwr+XLl2vBggWaNm2afHx8bMcBUnzwwQfq2bOnunbtqo8//lh58vCrDQAAnswYI2Pcs3SCm7KkxsxeAAAAXJAxRn369NHtt9+udu3a2Y4DSEr673LEiBHq2bOnXnjhBX3yyScUvQAAAMn4qQgAAAAXNHPmTK1Zs0ZLly6VlxdzBGCfMUYvvvii3nnnHQ0fPlz9+/eX4zi2YwEAALgGZS8AAADOEx8fr379+qlx48Zq2LCh7TiA4uPj9eSTT2rixIkaO3asevbsaTsSAADIRiZ5cws3ZUmNshcAAADnmThxojZu3KgpU6bYjgIoJiZGnTp10uzZs/Xll1/q4Ycfth0JAADAlSh7AQAAkMapU6c0ePBgdezYUbfeeqvtOMjlTpw4odatW2vVqlWaOXOmWrZsaTsSAACAa1H2AgAAII333ntPhw4d0vDhw21HQS539OhRNW/eXBs2bNDChQtVr14925EAAIAlxhgZ456lE9yUJTXKXgAAAKQ4evSoRo0apaeeekrXX3+97Ti5wubNmxUdHW07hqsEBASoQIECaty4sfbv36/vv/9eNWrUsB0LAADA9Sh7AQAAkOK1115TfHy8Bg4caDtKrrB582ZVqlTJdgxXKl26tCRp5cqVqlKliuU0AAAAOQNlLwAAACRJu3bt0tixY9WnTx8VK1bMdpxcIWVG7wOSiliN4h6HJc2Q7rzzTr399tsqV66c7UQAAMAFTPLmFm7KkhplLwAAACRJgwcPVmBgoF588UXbUXKfIpJK2g7hLv3796foBQAAyCDKXgAAAGj9+vX67LPP9M477ygwMNB2HAAAAABXgLIXAAAA6tevn8qWLaunnnrKdhQAAADgPMYYGeOepRPclCU1yl4AAIBcbtWqVZozZ46+/PJL+fn52Y4DAAAA4Ap52Q4AAAAAe4wxCg0N1S233KKOHTvajgMAAADgKjCzFwAAIBebO3euIiIitGDBAnl5MQ/AtX6TNDv588cknXvfMiPpHUlRkipKejidxz0s6RdJuyXtk5Qg6TlJBS8y/h9JyyUdklRA0q2S6kryPmfcaUlLksfHSSolqbG4CR0AALhiJnlzCzdlSY2f6AEAAHKphIQE9e3bV/Xr11eTJk1sx0F65JH05wX271BS0Xtu6Xo5uyT9JClW0jWXGbtZ0lRJeSU1l1RZ0gpJC84ZlyhpcnLOOyWFSDopaZKkIxnMBwAAgAxhZi8AAEAu9cUXX2j9+vX68ccf5TiO7ThIj4qSNkhqprTF7p+SSkg6lcHj3SApVJKfpFWS9l9i7GJJxSQ9murcfpJWSrpL/5XFG5RUIj8o6abkfTdJGivpe0ntMpgRAAAA6cbMXgAAgFzozJkzGjRokNq2bau77rrLdhykV1UlFbrbUu2LV1LBevMVHC+/kgrbyzmopKUbaihtyXxH8scNqfZtUNISD1VS7SugpMJ3Y3JeAACADDLGuO7hRpS9AAAAudC4ceO0d+9ejRw50nYUZESwpDJKu5TDFkkxSiqCs8rZGb/nrrkbmPzYf87YEjr/N41SSlq/l6UcAAAAsgxlLwAAQC4TGRmpkSNHqnv37rrhhhtsx0FG3az/bnwmSeuUdMO2wCw8Z3Tyx4ALPOef6vmzYy82TueMBQAAQKai7AUAAMhl3njjDZ0+fVqDBw+2HQVX4iYlLYWwSUkzejfpypZwyIizSy9c6AZwefRf8Xx27MXG6ZyxAAAA6WRcuLkRN2gDAADIRfbu3at33nlHzz//vEqWPPc9+cgRCki6TklLOcRJMpJuzOJznv2tIeECz8VL8jln7MXG6ZyxAAAAyFTM7AUAAMhFhg0bprx58+qVV16xHQVX42ZJmyX9IqmCpHxZfL6zyzJcaAmGE0q7bEPAJcZJF17iAQAAAJmCshcAACCX2LhxoyZMmKB+/fopODjYdhxcjcqSHEm7lfVLOEhS8eSPe8/ZH5X8KH7O2H2SEs8Zu1tJs3oLZ0VAAADg6Ywxrnu4EWUvAABALjFgwACVLFlSPXv2tB0FV8tP0n2S6kvKjnvsFZVURNJapS1xf0n+mHoZiRslnZT0d6p9JyVtkFRJLCQHAACQhSh7AQAAcoE1a9bo66+/1tChQ5U3b17bcZAZqiup7L2aNXDPSApPfmxP3rcm+eufzhkbIumApC+UVPoukLRS0m2Srkk17kZJpSXNlrQ8+XiTlFQSN7iKrAAAADnYa6+9pjvuuEMBAQEqWrSoWrdurY0bN6YZc+bMGfXo0UOFCxeWv7+/2rZtqwMHDmToPJS9AAAAHs4Yo9DQUN10003q3Lmz7Thwk9OSvk9+bEnetzr56x/OGXuDpA7Jr5mvpJm7dSS1OGecl6SHJd2kpMJ4iaT8krooaXYwAADAFTAu3DIiPDxcPXr00I8//qglS5YoLi5OjRs31smTJ1PGvPDCC/r22281ffp0hYeHa+/evXrggQcydB7eRAUAAODhFi9erO+//16zZ8+Wt7e37Ti4ErcmPy7nhQwet6CkIRkYXyX5cTn5JLVKfgAAAHiwqKioNF/7+fnJz8/vvHELFy5M8/WkSZNUtGhRrV27VnXr1lVkZKQ++eQTTZ48Wffee68kaeLEiapSpYp+/PFH3X333enKw8xeAAAAD5aYmKg+ffqoVq1aatmype04AAAAgEcpU6aMgoKCUh6vvfZaul4XGRkpSSpUqJAkae3atYqLi1OjRo1SxlSuXFlly5bV6tWr052Hmb0AAAAebOrUqfrjjz+0cuVKOY5jOw6yyylJCZd43ktSgWzKAgAAkAmMMTImY0snZKWzWXbt2qXAwMCU/Rea1XuuxMREPf/886pVq5aqVq0qSdq/f798fX0VHBycZmyxYsW0f//+dOei7AUAAPBQsbGxGjBggFq2bKnatWvbjoPsNE3Sv5d4PkgZX/IBAAAA5wkMDExT9qZHjx499NdffykiIiLT81D2AgAAeKgPP/xQ//77r+bMmWM7CrJbEyXdSO1ifLIrCAAAAFLr2bOn5s6dqxUrVqh06dIp+4sXL67Y2FgdP348zezeAwcOqHjx4uk+PmUvAACAB4qOjtbw4cPVuXPnlLeGIRcpaTsAAABA5jLJm1tkNIsxRr169dLMmTO1fPlylS9fPs3zNWrUkI+Pj7777ju1bdtWkrRx40bt3LlTNWvWTPd5KHsBAAA80OjRoxUVFaWhQ4fajgIAAADkej169NDkyZM1e/ZsBQQEpKzDGxQUpHz58ikoKEjdu3dX7969VahQIQUGBqpXr16qWbOm7r777nSfh7IXAADAwxw4cECjR49Wz549VbZsWdtx4DZ/SVoh6aikqpJap3puopJu7tYjE883SdIeSddICpFU/pKjAQAAPNL48eMlSfXr10+zf+LEieratask6Z133pGXl5fatm2rmJgYNWnSROPGjcvQeSh7AQAAPMyIESPk5eWlvn372o4Ct4mVNEuSv6RGkkpd4XG2SFovabekw5ICdfEbvtWRdEDSD5JmSup9hecEAAC5mjFGxrhoGYcMZknP+Lx58+qDDz7QBx98cKWxKHsBAAA8ybZt2/Thhx9q6NChKly4sO04cJtDkuIl1ZR011Uc508llb0lJAVcZuz1yY94ScuUNHM4/1WcGwAAABdF2QsAAOBBBg4cqCJFiui5556zHQWZbaKkYEltruIYcckf/a8yS0NJ90vylhQm6WA6XnP2nLGi7AUAAMgilL0AAAAe4rffftPkyZP14YcfKn9+2jRcwJW883GLpKmSKiupaPZW0rINGeVcRQYAAJDrmeTNLdyUJTXKXgAAAA/Rt29fVapUSd26dbMdBW519ncS55Kj/rNR0ldKupFbK0leV3Fuyl4AAIAsR9kLAADgAZYtW6ZFixZp+vTpypOHH/FwEdHJH/OmY+wGSV9Lqi7pPl1d0StJfskfT0gqdJXHAgAAwAXxmwAAAEAOZ4xRaGio7rzzTrVt29Z2HGSGBElnztmXmLz/5Dn78+nyRexpScckrVZS6VrqMuP/lDRT0u2Smin9M4EvpbSSloBYIamxpCD9VwADAAAgU1D2AgAA5HBff/21fv75Zy1btkyOkxmtHKzbKemzC+zfJemvc/Y9J6ngZY43VdK/SipXO+jSJetxSTMk3SipeTqypleApAeSjz1O0i26upvNAQCA3MUkTXJwDRdFSY2yFwAAIAeLi4tT//791bRpUzVo0MB2HGSW4pIePWffYkn+ku45Z79/Oo7XWNJBSeFKmrHbS5LvRcb6K6mY3Sxpjy4/Czi9Tkr6VlJRJX0PxTLpuAAAAEhB2QsAAJCDffrpp9q8ebO++uor21GQmfJJuv6cfXmVVMSeuz89Sum/0na2pN2SrrvI2DySOilpZvGXkh5TUkF7tXYqaWmKJpKuzYTjAQAA4DxXe5sFAAAAWHLy5EkNGTJEDz/8sKpXr247DnKCoOSP564HfK68kh6RVEDS55KOZsK5Y5I/BmbCsQAAQK5jXLi5EWUvAABADjVmzBgdOXJEw4cPtx0FOcXZJZ3T87tJAUmdlTTT93NJUame26+kJSHClVQEx6T6euNFjnf2nCwrDQAAkGUoewEAAHKgI0eO6PXXX9fTTz+t8uXL246DnOLsT//x6RwfqKTCN15Jhe/J5P37JH2f/DiipJnCZ7/ecJFjnT0nv4EAAABkGdbsBQAAyIFeffVVJSYmasCAAbajILs8lgnHCEj+uF5SaSWtAex3mXMUkvTSOftuTX6kxxlJJyT9raSit0B6wwIAAPzHGCNj3LN0gpuypEbZCwAAkMP8+++/ev/999WvXz8VLZoZd85CrlFI0k1KKns3SbpFUpssPucUSf8mf36P+A0EAAAgC/GjFgAAQA4zePBgBQcHq3fv3rajICd6UFJjSccl5cuG8zVR0hIOhZQ0kxgAAABZhrIXAAAgB/nzzz/1+eef67333lNAQMDlXwBcSFDyIzuUzKbzAAAAj2aSN7dwU5bUuD0CAABADtKvXz+VL19eTz75pO0oAAAAAFyGmb0AAAA5xMqVKzV37lxNnjxZvr6+tuMAAAAAcBnKXgAAgBzAGKM+ffro1ltvVYcOHWzHgSf6S9IKSUclVZXUOtVzxyS9p6Q1fstLuk/Zs94vAABAMmOMjHHP0gluypIaZS8AAEAOMGfOHK1evVqLFi2SlxcrcSGTxUqapaQbqDWSVOqc5wtIul/SPklrJBWWdG825gMAAEC6UPYCAAC4XHx8vPr166d7771XISEhtuPAEx2SFC+ppqS7LvC8r6Rbkx9bJO3PvmgAAABIP8peAAAAl/v888+1YcMGTZo0SY7j2I4DTxSX/NE/HWP9lTQTGAAAIBuZ5M0t3JQlNd4DCAAA4GKnT5/W4MGD9eCDD+qOO+6wHQeeKiO/qzgZHA8AAIBsQ9kLAADgYu+//7727dunESNG2I4CT3a2vE3PxHHKXgAAANdiGQcAAACXOn78uF577TU98cQTqlSpku048GTRyR/zpmOsn5LW+AUAAMhGxhgZ455/cXZTltSY2QsAAOBSr7/+umJiYjRo0CDbUeCpTkvaK2m1kkrcUul4zbWSjkr6SVKkpISsCgcAAICMYmYvAACAC+3Zs0fvvvuuXnzxRZUoUcJ2HOQE8Uoqb1MroEtP75gq6V8lFb0dkj9ezl2SdklakPzoIql8RsMCAAAgK1D2AgAAuNDQoUNVoEABvfzyy7ajIKfYJemzc/Y9J6ngJV7TWNJBSeGSZkrqJcn3Muf5XdIGSXdKul5S8SvICgAAkEEmeXMLN2VJjbIXAADAZf755x998skneuuttxQUFGQ7DnKK4pIePWef/2VeU0r/Ld0wW9JuSddd5jUbJQVLap7BfAAAAMhylL0AAAAu079/f5UuXVr/+9//bEdBTpJPSTNtr8TZf1M4k46xMZICr/A8AAAAyFKUvQAAAC7y448/asaMGZo0aZLy5s1rOw5yCyf5Y3rejWhSjQcAAMgmxhgZ456lE9yUJbVL3a4BAAAA2cgYo9DQUFWtWlWPPPKI7TjITc7+VhCfjrHx4rcIAAAAl2JmLwAAgEssXLhQ4eHh+vbbb+Xt7W07DnKTgOSP6yWVVtJav36pnk+UdErSIUkHJN2UrekAAACQTpS9AAAALpCYmKjQ0FDVqVNHLVq0sB0HuU0hJRW46yVtknSLpDapno+UNCb5cz9Jt2drOgAAAJnkzS3clCU1yl4AAAAXmDx5statW6cffvhBjsOCqLDgQUmNJR1X0s3eUvOX1FlSXknXSPLJ1mQAAABIJ8peAAAAy2JiYjRgwAC1bt1aNWvWtB0HuVlQ8uNcPpKuy+YsAAAAyDDKXgAAAMv+7//+T7t27dKCBQtsRwEAAABcyRgjY9yzdIKbsqTGfXQBAAAsioqK0ogRI9S1a1dVqVLFdhwAAAAAORhlLwAAgEVvvfWWTpw4oSFDhtiOArjKyJEj9e+//9qOAQAAkKOwjAMAAIAl+/fv19tvv61evXqpTJkytuMgG2zfvl2TJk3SggULtGLFiv+eOGwvk+sk/1n89NNPqlWrlpYsWcKsdwAAIJO8uYWbsqRG2QsAAGDJ8OHD5ePjo9DQUNtRkEXi4+P1ww8/aO7cuZo9e7Y2bdokSXIcR7GxsQoICEgaOMNiSJeaOnWq/ve//6lOnTpauHChbr/9dtuRAAAAXI+yFwAAwIItW7boo48+0ogRI1SoUCHbcZAFjh49qipVqujgwYPKkyeP4uPjU55r3bq1AgMDFRgYqE2bNik6OtpiUvcJCAhQxYoVFR4erubNm+vee+/VnDlzVL9+fdvRAAAAXI2yFwAAwIIBAwaoWLFievbZZ21HQRYJCgpSxYoVdejQoTRFryQ9/fTTKZ9XrFgxu6PlGIUKFdLSpUvVpk0bNW3aVNOnT1fLli1txwIAABYYY2SMe5ZOcFOW1LhBGwAAQDZbu3atpk2bpiFDhihfvny24yCLeHt7a/78+Spbtmya/cWKFVPDhg0tpcp5/P39NXfuXLVo0UJt2rTRl19+aTsSAACAa1H2AgAAZLO+ffuqcuXK6tq1q+0oyGJff/21du7cKX9/f3l7e8vb21uPPfaYvL29bUfLUfz8/DRt2jR17txZjz76qN5//33bkQAAAFyJshcAACAbLV26VEuWLNGrr76qPHlYUcuTjR49Wt27d9fTTz+tv/76S0WLFlVCQoK6dOliO1qOlCdPHn3yySfq3bu3evXqpeHDh7v27ZMAACDzGRdubsRvGAAAANkkMTFRoaGhuvvuu9W6dWvbcZBFjDEaMGCAXn31VfXr108jRoyQ4zhatWqVfvjhB1WuXNl2xBzLcRy99dZbKlSokAYMGKCjR49q9OjR8vJiDgsAAIBE2QsAAJBtpk+frrVr12r58uVyHMd2HGSBxMRE9erVS+PGjdObb76pl156KeW58uXLq3z58hbTeQbHcdS/f38FBwerZ8+eOn78uD7++GNmygMAAIiyFwAAIFvExcVpwIABat68uerVq2c7DrJAXFycunbtqqlTp2rChAnq3r277UgerUePHgoODlaXLl0UGRmpyZMnK2/evLZjAQCALGKMcdUSTm7KkhplLwAAQDaYMGGCtm7dqm+++cZ2FGSBU6dOqX379lq8eLGmTZumdu3a2Y6UKzz88MMKCgrSgw8+qPvuu08zZ85UQECA7VgAAADWsLgVAABAFjtx4oSGDh2qRx55RNWqVbMdB5ksMjJSTZs21ffff6958+ZR9Gaz++67TwsXLtSaNWvUqFEjHTlyxHYkAAAAayh7AQAAsti7776rY8eOadiwYbajIJMdPHhQDRo00J9//qmlS5cqJCTEdqRcqV69evr++++1bds21atXT3v37rUdCQAAZDLjws2NKHsBAACy0KFDh/TGG2/omWee0bXXXms7DjLRzp07VadOHe3du1fh4eGqWbOm7Ui5Wo0aNbRy5UpFRkaqdu3a2rp1q+1IAAAA2Y6yFwAAIAu9+uqrkqT+/ftbToLMtHHjRtWuXVuxsbGKiIhgeQ6XqFy5siIiIpQnTx7Vrl1bf/75p+1IAAAA2YqyFwAAIIvs2LFD48aN0yuvvKIiRYrYjoNM8uuvv6pOnToKCAhQRESEKlSoYDsSUilXrpwiIiJUvHhx1atXTz/++KPtSAAAIBMYY1z3cCPKXgAAgCwyaNAgFSxYUC+88ILtKMgkK1asUIMGDVS+fHmtWLFCpUqVsh0JF1C0aFEtX75cN910kxo2bKglS5bYjgQAAJAtKHsBAACywLp16/Tll19q0KBBKlCggO04yATz5s1TkyZNdPvtt2vp0qUqXLiw7Ui4hKCgIC1atEj16tVTixYt9M0339iOBAAAkOUoewEAALJA3759df311+uJJ56wHQWZYMqUKWrdurWaNm2qefPmKSAgwHYkpEP+/Pk1a9YstW3bVu3bt9enn35qOxIAALhCxoWbG+WxHQAAAMDThIeHa/78+Zo6dap8fHxsx8FVGj9+vHr06KHOnTtrwoQJypOHH6FzEl9fX3355ZcKDg5W9+7ddezYMb344ou2YwEAAGQJflIFAADIRMYY9enTRzVq1NCDDz5oOw6ugjFGr732mvr376/nnntOb7/9try8eGNcTuTt7a1x48apYMGCeumll3Ts2DENHz5cjuPYjgYAAJCpKHsBAAAy0axZs/TTTz9pyZIlFIM5mDFGr7zyit566y0NGzZMAwYMoBjM4RzH0auvvqqCBQvqlVde0bFjxzR27Fj+ngIAkIMY486lE9yEshcAACCTxMfHq1+/fgoJCVGjRo1sx8EVSkhI0FNPPaVPPvlE7733nnr16mU7EjLRyy+/rIIFC+qpp57S8ePHNWnSJJZbAQAAHoOyFwAAIJNMmjRJ//zzj8LCwmxHwRWKiYnRww8/rFmzZumLL77QI488YjsSssDjjz+u4OBgderUSZGRkfrqq6+UP39+27EAAACuGmUvAABAJjh16pSGDBmiDh066LbbbrMdB1fgxIkTeuCBB7RixQrNmDFD999/v+1IyELt2rVTQECAHnjgATVt2lTffvutgoKCbMcCAAAXYZI3t3BTltRYoAoAACATjB07VgcOHNCIESNsR8EVOHr0qEJCQrR69WotWLCAojeXaNKkiZYsWaI///xTDRo00KFDh2xHAgAAuCqUvQAAAFfp6NGjGjVqlJ588klVqFDBdhxk0L59+1SvXj1t3rxZ33//vRo0aGA7ErLRPffco/DwcO3du1d16tTRzp07bUcCAAC4YpS9AAAAV2nUqFGKi4vTwIEDbUdBBm3fvl116tTRsWPHtHLlSt1+++22I8GCatWqKSIiQjExMapdu7Y2btxoOxIAADiHMcZ1Dzei7AUAALgKu3fv1tixY9W7d28VL17cdhxkwPr161WrVi05jqNVq1apSpUqtiPBogoVKigiIkIBAQGqU6eOfv31V9uRAAAAMoyyFwAA4CoMGTJE/v7+eumll2xHQQb89NNPqlu3rooWLaqIiAiVK1fOdiS4QKlSpbRixQpde+21atCggVasWGE7EgAAQIZQ9gIAAFyhDRs2aOLEiRowYIACAwNtx0E6fffdd2rYsKGqVKmi5cuXq1ixYrYjwUUKFy6s7777TjVq1FCTJk00b94825EAAIAk48LNjSh7AQAArlD//v1VtmxZPf3007ajIJ1mzpyp5s2bq06dOlq0aJGCg4NtR4ILBQQEaP78+WrSpIlat26tKVOm2I4EAACQLpS9AAAAV+CHH37QrFmzNHz4cPn5+dmOg3SYNGmS2rVrp9atW2v27NkqUKCA7Uhwsbx58+rrr79Wp06d9PDDD2v8+PG2IwEAAFxWHtsBAAAAchpjjEJDQ1WtWjV16tTJdhykw7vvvqsXXnhBTz75pMaNGydvb2/bkZAD5MmTRxMnTlRwcLCeeeYZHTt2TH379pXjOLajAQCQ6xhjZIx7lk5wU5bUKHsBAAAyaP78+Vq5cqXmz58vLy/eKOVmxhgNGTJEw4YNU58+ffTaa69R1CFDvLy89O6776pw4cLq37+/jh07pjfeeIP/jgAAgCtR9gIAAGRAQkKCQkNDVa9ePTVt2tR2HFxCYmKinn/+eY0dO1ajRo1Snz59bEdCDuU4jgYNGqTg4GA999xzOnbsmD788ENmiAMAANeh7AUAAMiAsLAw/fXXX1q9ejUz+1wsLi5O3bp1U1hYmD788EM9+eSTtiPBAzz77LMKDg5Wt27ddPz4cYWFhbFmNwAA2cQkb27hpiypUfYCAACk05kzZzRw4EA98MADuvvuu23HwUWcPn1aHTp00MKFCzVlyhR16NDBdiR4kM6dOysoKEgdOnRQy5YtNXPmTG72BwAAXINF5gAAANJp/Pjx2r17t0aOHGk7Ci4iKipKzZo109KlSzVnzhyKXmSJVq1aacGCBVq9erVCQkJ07Ngx25EAAAAkUfYCAACkS2RkpEaMGKHu3burcuXKtuPgAg4fPqx7771Xv//+u5YsWcKayshSDRo00LJly7Rp0ybVq1dP+/btsx0JAACPZoxx3cONKHsBAADS4c0339SpU6c0ePBg21FwAbt371adOnW0a9cuhYeHq1atWrYjIRe44447tGLFCh09elR16tTR9u3bbUcCAAC5HGUvAADAZezbt0/vvPOOnn/+eZUqVcp2HJxj8+bNqlWrlk6fPq2IiAjdcssttiMhF7nxxhsVEREhx3FUq1YtrV+/3nYkAACQi1H2AgAAXMawYcPk5+enPn362I6Cc/z++++qXbu28ufPr4iICFWsWNF2JORC1157rVauXKlrrrlGdevW1U8//WQ7EgAAHse4cHMjyl4AAIBL2LRpkz7++GP17dtXwcHBtuMglYiICNWvX19lypTRypUrVbp0aduRkIsVL15cy5cvV+XKldWwYUN99913tiMBAIBciLIXAADgEgYMGKASJUqoZ8+etqMglQULFqhx48aqXr26li1bpiJFitiOBKhgwYJavHixateurebNm2vWrFm2IwEAgFyGshcAAOAifvnlF02fPl1Dhw5Vvnz5bMdBsmnTpun+++9Xo0aNtGDBAgUGBtqOhP9v777Do6jaMA4/s+kQ0mgBRIoCUpQqfkhCrwICojSlqVgQe6FKx4JYQawoioUqglIEAoEkIgKCgIiIgiBITwiEEpI93x9AJIYWSDKTze/ea6+YmdlznsQrw+67Z99Bmvz582vOnDlq27atOnTooE8++cTuSAAAeARjjOPuTkSxFwAA4DyMMerXr58qVaqk7t272x0HZ7z//vvq0qWLOnfurJkzZ1KEhyP5+vrqyy+/1H333aeePXvqzTfftDsSAADII7ztDgAAAOBEixYt0pIlS/T111/L25unTE7w8ssvq3///nr00Uf1xhtvyOVi3QKcy8vLS++9955CQ0P1xBNP6NChQxo2bJgsy7I7GgAA8GC8cgEAAPgPt9ut/v3769Zbb9Xtt99ud5w8zxij/v37a8yYMRoyZAgFM+QalmXp5ZdfVmhoqAYMGKD4+HjeqAAA4AqZMzencFKWc/EsAwAA4D+mTp2qtWvX6uWXX6aoaLPU1FQ9+OCDGjNmjF5//XUNHz6c/yfIdfr37693331X48ePV48ePXTq1Cm7IwEAgBy2fPlytWnTRsWLF5dlWRku5NqzZ09ZlpXu3qJFi0zPw8peAACAcyQnJ2vw4MFq3bq1IiIi7I6TpyUnJ+uee+7RzJkz9fHHH6tnz552RwKu2IMPPqiQkBDdc889SkxM1NSpU+Xv7293LAAAkEOSkpJUtWpV3XvvvbrjjjvOe0yLFi308ccfp33v5+eX6Xko9gIAAJzj/fff17Zt2zR79my7o+RpSUlJ6tChg5YuXaqZM2eqXbt2dkcCrlqnTp0UFBSkDh06qGXLlpo9e7aCgoLsjgUAQK5gjJExzmmdkNksLVu2VMuWLS96jJ+fn8LDw68mFm0cAAAAzjp69KhGjhyp7t27q0qVKnbHybMSEhLUrFkzxcbGat68eRR64VFatmyphQsXau3atWrcuLEOHDhgdyQAAHAVEhMT091Pnjx5xWNFR0erSJEiqlChgh5++GEdPHgw02NQ7AUAADjjtdde0+HDhzVixAi7o+RZe/fuVYMGDbR582YtWbJEjRs3tjsSkOUiIiIUHR2tHTt2KDIyUn///bfdkQAAwBUqWbKkgoOD0+4vvvjiFY3TokULffrpp4qKitLLL7+sZcuWqWXLlkpNTc3UOLRxAAAAkLRv3z698soreuSRR3TttdfaHSdP2r59u5o2baqkpCQtX75clStXtjsSkG2qVaummJgYNW3aVHXr1tXixYtVrlw5u2MBAOBY5szNKc5m2blzZ7q2TFfSZ1eSOnfunPbfN954o2666SZdd911io6OztQCCFb2AgAASBo9erRcLpcGDhxod5Q8adOmTYqIiJDb7VZcXByFXuQJ5cuXV2xsrPLly6eIiAj9/PPPdkcCAACZFBQUlO5+pcXe/ypbtqwKFSqkrVu3ZupxFHsBAECe9+eff+qdd95Rv379VLBgQbvj5DmrVq1SvXr1FBYWptjYWJUpU8buSECOKVmypJYvX66SJUuqfv36iouLszsSAABwgL///lsHDx5UsWLFMvU4ir0AACDPGzJkiAoWLKjHH3/c7ih5ztKlS9WoUSOVK1dO0dHRmX4yC3iCwoULa8mSJapWrZqaNm2qBQsW2B0JAADHMcY47p4ZR48e1bp167Ru3TpJ0rZt27Ru3Trt2LFDR48e1bPPPqsffvhB27dvV1RUlNq2bavrr79ezZs3z9Q8FHsBAECetm7dOn3xxRcaNmyY8ufPb3ecPGXOnDlq2bKl6tSpo8WLFyssLMzuSIBtgoKCNH/+fDVp0kS33367pk6danckAACQhVavXq3q1aurevXqkqSnnnpK1atX15AhQ+Tl5aX169fr9ttvV/ny5XXfffepZs2aiomJyXRbCC7QBgAA8rQBAwbo+uuv17333mt3lDxl8uTJ6tWrl9q3b6/PPvssy3qbAblZQECAZs6cqXvvvVddunTR4cOH9cADD9gdCwAAZIEGDRpcdDXwd999lyXzUOwFAAB51tKlS7VgwQJNnz5dPj4+dsfJM8aNG6fHHntM9913n9577z15eXnZHQlwDB8fH33yyScKDg7Wgw8+qPj4ePXr18/uWAAA2M6cuTmFk7Kci2IvAADIk4wx6t+/v26++WZ16NDB7jh5gjFGI0eO1NChQ/X000/rlVdekWVZdscCHMflcmncuHEKCwtT//79FR8frxdffJG/FwAAcEkUewEAQJ701Vdf6ccff1RUVBQFlBzgdrv11FNP6c0339To0aM1YMAAfu/ARViWpREjRig0NFRPPfWU4uPjNWHCBFbCAwCAi6LYCwAA8pyUlBQNHDhQzZs3V6NGjeyO4/FSUlJ0//3369NPP9WECRP08MMP2x0JyDWefPJJhYSE6P7771dCQoImT54sX19fu2MBAJDjjDEX7Xmb05yU5VwUewEAQJ7z0UcfacuWLZoyZYrdUTzeiRMn1LlzZ82dO1eff/65unTpYnckINfp1auXQkJC1LlzZ7Vt21YzZ85Uvnz57I4FAAAcyGV3AAAAgJx07NgxDRs2TF27dlX16tXtjuPRjhw5olatWum7777T119/TaEXuArt27fXvHnzFBMTo2bNmikhIcHuSAAAwIEo9gIAgDzlzTff1IEDBzRy5Ei7o3i0gwcPqnHjxlq9erUWLlyoVq1a2R0JyPUaN26sqKgobdq0SQ0aNNDevXvtjgQAQI4xDrw5EcVeAACQZxw8eFAvv/yyHnroIZUtW9buOB5r165dqlevnrZv366lS5cqMjLS7kiAx7jlllu0fPly7du3TxEREfrrr7/sjgQAAByEYi8AAMgzXnzxRaWmpmrw4MF2R/FYW7duVUREhI4cOaKYmBjVqFHD7kiAx6lSpYpiY2PldrtVt25d/frrr3ZHAgAADkGxFwAA5Ak7duzQ+PHj9fTTT6tIkSJ2x/FI69evV0REhHx9fRUbG6sKFSrYHQnwWGXLllVsbKxCQ0MVGRmp1atX2x0JAIBsZYxx3N2JKPYCAIA8YdiwYQoKCtLTTz9tdxSP9P3336t+/foqXry4YmJidO2119odCfB4xYoV07Jly1SuXDk1bNhQ0dHRdkcCAAA2o9gLAAA83i+//KJPPvlEzz//vAoUKGB3HI+zcOFCNW3aVDfddJOWLl3KymkgB4WFhWnRokWqU6eOWrRooTlz5tgdCQAA2IhiLwAA8HgDBw5UqVKl9OCDD9odxePMmDFDrVu3VsOGDbVgwQIFBwfbHQnIcwIDA/XNN9+oVatWuuOOOzR58mS7IwEAkOWMA29ORLEXAAB4tNjYWM2ZM0ejRo2Sr6+v3XE8ysSJE9WpUyfdeeedmjVrlgICAuyOBORZfn5+mjp1qrp3767u3btr3LhxdkcCAAA28LY7AAAAQHYxxqh///6qVq2aOnfubHccjzJ27Fg9++yzevjhhzV+/Hi5XKwhAOzm7e2tiRMnKjQ0VI899pgSEhI0ePBgWZZldzQAAJBDKPYCAACP9c033yguLk4LFiygGJlFjDEaNGiQXnzxRQ0aNEgjR46kkAQ4iGVZGjt2rMLCwjR48GAdOnRIr776KudAAEDuZ04/F3UMB0U5F8VeAADgkVJTUzVw4EA1bNhQzZo1szuOR0hNTVXfvn317rvvauzYsXr66aftjgTgPCzL0qBBgxQaGqq+ffsqISFBH3zwgby9efkHAICn4197AADgkSZPnqxffvlFH3/8MStPs0BycrJ69OihadOmaeLEibr33nvtjgTgEvr06aOQkBD16NFDCQkJ+vLLL+Xv7293LAAAkI0o9gIAAI9z4sQJDRkyRHfeeaduvvlmu+PkeseOHdOdd96pxYsXa9q0aerQoYPdkQBcpq5duyo4OFh33nmnWrVqpa+//loFChSwOxYAAJlmztycwklZzkXjJgAA4HHefvtt7d69W6NHj7Y7Sq53+PBhtWjRQsuWLdPcuXMp9AK5UKtWrbRgwQKtWrVKTZo00cGDB+2OBAAAsgnFXgAA4FESEhI0evRo3X///SpfvrzdcXK1ffv2qUGDBtqwYYMWL16spk2b2h0JwBWqX7++oqOj9eeff6p+/fravXu33ZEAAEA2oNgLAAA8ypgxY9LaOODK7dixQ5GRkdqzZ4+WL1+uOnXq2B0JwFWqUaOGYmJilJiYqLp16+qPP/6wOxIAAJfNGOO4uxNR7AUAAB5j9+7deuONN/Tkk0+qePHidsfJtTZv3qy6devq1KlTio2N1Y033mh3JABZ5IYbblBsbKx8fX0VERGh9evX2x0JAABkIYq9AADAYwwfPlwBAQF67rnn7I6Sa61Zs0aRkZEKDg5WbGysrrvuOrsjAchi1157rWJiYlSsWDHVr19fK1assDsSAADIIhR7AQCAR/jtt980ceJEDRo0SMHBwXbHyZWWL1+uhg0bqmzZslq2bBmrowEPVqRIES1dulRVqlRRkyZNtHDhQrsjAQBwUcaBNyei2AsAADzCoEGDVLx4cfXp08fuKLnS3Llz1bx5c918881avHixChYsaHckANksODhY3333nerXr6/WrVtrxowZdkcCAABXiWIvAADI9VauXKmZM2dq5MiR8vf3tztOrvPFF1+oXbt2atGihebOnasCBQrYHQlADsmXL5++/vprdejQQZ06ddJHH31kdyQAAHAVvO0OAAAAcDWMMerfv7+qVKmie+65x+44uc6ECRPUt29f9ejRQx988IG8vXl6COQ1vr6++uyzzxQSEqL77rtP8fHxevrpp+2OBQBAOsYYGeOc1glOynIuns0DAIBc7bvvvlN0dLTmzJkjLy8vu+PkGsYYjR49Ws8//7yeeOIJvfrqq3K5+NAXkFd5eXlpwoQJCgsL0zPPPKNDhw5p1KhRsizL7mgAACATKPYCAIBcy+12q3///oqIiFDr1q3tjpNrGGP0zDPP6LXXXtPIkSM1aNAgCjoAZFmWRo8erdDQUD377LOKj4/X+PHjeSMIAIBchGIvAADItb788kv9/PPPiouLo1h5mVJSUvTAAw/o448/1rhx49S3b1+7IwFwmGeeeUahoaF64IEHlJCQoE8++UQ+Pj52xwIA5HHmzM0pnJTlXBR7AQBArnTy5EkNHjxYbdu21a233mp3nFzh5MmT6tq1q2bPnq3JkyfT4xjABd13330KDg5W165ddfjwYU2fPl358uWzOxYAALgEir0AACBXeu+997Rjxw7NnTvX7ii5wtGjR9W+fXvFxMRo1qxZatOmjd2RADjcnXfeqaCgILVv314tWrTQN998o+DgYLtjAQCAi6D5EgAAyHWOHDmiUaNGqWfPnqpUqZLdcRzv0KFDatKkiVauXKkFCxZQ6AVw2Zo1a6bFixdrw4YNatiwofbt22d3JABAHmWMcdzdiSj2AgCAXOfVV19VYmKihg0bZncUx/vnn39Uv359bd26VUuWLFGDBg3sjgQgl6lTp46WLVum3bt3KzIyUjt27LA7EgAAuACKvQAAIFfZu3evxo4dq0cffVQlS5a0O46j/fnnn4qIiFBCQoJiYmJUq1YtuyMByKVuuukmxcbGKjk5WREREfrtt9/sjgQAAM6DYi8AAMhVRo0aJW9vbw0YMMDuKI62ceNGRUREyOVyKTY2VhUrVrQ7EoBc7vrrr1dsbKwKFCigiIgI/fTTT3ZHAgDkIcaBNyei2AsAAHKNP/74Q++++6769++vsLAwu+M41sqVK1WvXj0VKVJEsbGxKlWqlN2RAHiIEiVKaPny5SpTpowaNmyo5cuX2x0JAACcg2IvAADINZ5//nkVKVJEjz32mN1RHGvx4sVq3LixKlWqpOjoaBUtWtTuSAA8TMGCBRUVFaVatWqpefPmmjt3rt2RAADAGRR7AQBArrB27Vp9+eWXGjZsmPLly2d3HEf66quv1KpVK0VGRmrhwoUKCQmxOxIAD1WgQAHNnTtXLVq0ULt27fTll1/aHQkA4OGMMY67OxHFXgAAkCv0799fFSpUUK9eveyO4kgff/yx7rrrLrVv316zZ8+mIA4g2/n7+2v69Om6++67dffdd2vChAl2RwIAIM/ztjsAAADApURFRWnhwoWaOXOmvL15+vJfr7/+up566ik98MADmjBhgry8vOyOBCCP8Pb21kcffaSQkBA98sgjio+P18CBA2VZlt3RAADIk3i1BAAAHM0Yo/79++uWW25R+/bt7Y7jKMYYDRkyRKNGjVL//v31wgsvUGABkONcLpdef/11hYWFafDgwYqPj9crr7zC+QgAkKXMmZtTOCnLuSj2AgAAR5sxY4ZWr16t6OhoCgfncLvdeuyxx/T222/rpZdeUr9+/eyOBCAPsyxLQ4YMUUhIiB5//HHFx8fr/fff55MGAADkMIq9AADAsU6dOqWBAweqZcuWql+/vt1xHOPUqVPq1auXvvjiC73//vvq3bu33ZEAQJL02GOPKTQ0VL169dLhw4f1+eefy8/Pz+5YAADkGRR7AQCAY02cOFF//PGHZsyYYXcUxzh+/Lg6duyo7777TlOmTFHHjh3tjgQA6XTr1k3BwcHq2LGj2rRpo6+++kqBgYF2xwIA5HLGGBnjnNYJTspyLpfdAQAAAM4nKSlJw4cP1913362qVavaHccREhMT1bJlS0VFRWnOnDkUegE41u2336758+drxYoVatq0qQ4dOmR3JAAA8gSKvQAAwJHeeOMNHTx4UCNGjLA7iiPs379fDRs21Lp167Ro0SK1aNHC7kgAcFENGzbU0qVL9fvvv6t+/fr6559/7I4EAIDHo9gLAAAc58CBAxozZoz69OmjMmXK2B3Hdjt37lRkZKT+/vtvLVu2THXr1rU7EgBcllq1amn58uWKj49XRESE/vzzT7sjAQByKePAmxNR7AUAAI7zwgsvyBijQYMG2R3Fdlu2bFHdunV14sQJxcbG0tICQK5TqVIlxcbGyuVyKSIiQhs3brQ7EgAAHotiLwAAcJS//vpLb7/9tp599lkVLlzY7ji2Wrt2rSIiIhQYGKjY2FiVK1fO7kgAcEVKly6tmJgYFS5cWPXq1dPKlSvtjgQAgEei2AsAABxl6NChCg0N1ZNPPml3FFvFxsaqQYMGKlWqlJYvX65rrrnG7kgAcFXCw8O1bNkyVaxYUY0bN1ZUVJTdkQAAuYgxxnF3J6LYCwAAHGPDhg369NNPNWTIEAUGBtodxzbz5s1Ts2bNVKNGDUVFRalQoUJ2RwKALBESEqKFCxcqMjJSt912m2bNmmV3JAAAPArFXgAA4BgDBw5U2bJl1bt3b7uj2GbKlClq27atmjZtqvnz5ysoKMjuSACQpfLnz6/Zs2erXbt2uvPOOzVp0iS7IwEA4DG87Q4AAAAgSTExMfr222/15ZdfysfHx+44tnj33XfVp08f3XPPPfroo4/k7c1TNQCeydfXV1988YVCQkLUq1cvJSQk6IknnrA7FgDAwcyZm1M4Kcu5eAUBAABsZ4xRv379VKNGDXXs2NHuODnOGKOXXnpJAwcO1KOPPqo33nhDLhcfwALg2by8vPTuu++m9Wk/dOiQhg8fLsuy7I4GAECuRbEXAADYbvbs2VqxYoUWLlyY54qcZwvdr7zyioYOHaqhQ4dS6ACQZ1iWpZdeekmhoaHq37+/4uPj9eabb+a5fwsAAMgqFHsBAICtUlJSNHDgQDVp0kRNmza1O06OSk1N1UMPPaQPP/xQb7zxhh5//HG7IwGALfr166fQ0FA99NBDSkhI0EcffZRnW/oAAM7PGCNjnNM6wUlZzkWxFwAA2OrTTz/Vr7/+qsmTJ9sdJUedPHlS3bp101dffaVJkyapR48edkcCAFs98MADCg4OVrdu3XT48GFNnTpVAQEBdscCACBX4bMxAADANsePH9eQIUPUqVMn1axZ0+44OSYpKUm33367Zs+erRkzZlDoBYAzOnXqpDlz5mjx4sVq2bKlEhMT7Y4EAECuQrEXAADYZvz48dq7d69GjRpld5QcEx8fr6ZNmyouLk7z589Xu3bt7I4EAI7SokULLVq0SOvWrVOjRo20f/9+uyMBABzAOPDmRBR7AQCALeLj4/XCCy+od+/euv766+2OkyP27Nmj+vXr67ffftOSJUvUqFEjuyMBgCPVrVtX0dHR2rlzp+rVq6edO3faHQkAgFyBYi8AALDFyy+/rOTkZA0ZMsTuKDli+/btioiI0MGDB7V8+XLVrl3b7kgA4GjVqlVTTEyMjh07poiICP3+++92RwIAwPEo9gIAgBy3a9cuvfnmm3rqqacUHh5ud5xst2nTJtWtW1eSFBsbq8qVK9ucCAByh/LlyysuLk758uVTRESE1q1bZ3ckAIBNjDGOuzsRxV4AAJDjhg0bpvz58+vZZ5+1O0q2W7VqlSIjI1WwYEHFxMSoTJkydkcCgFzlmmuuUUxMjEqWLKkGDRooNjbW7kgAADgWxV4AAJCjNm/erI8++kiDBw9WUFCQ3XGy1dKlS9WoUSNVqFBBy5YtU7FixeyOBAC5UqFChbRkyRJVq1ZNzZo10/z58+2OBACAI1HsBQAAOWrgwIEqWbKkHn74YbujZKvZs2erZcuWuvXWW7Vo0SKFhobaHQkAcrWgoCDNnz9fTZo00e23366pU6faHQkAkIOMA29ORLEXAADkmB9++EGzZs3SyJEj5efnZ3ecbPPpp5+qQ4cOatOmjebMmaP8+fPbHQkAPEJAQIBmzpypzp07q0uXLnrvvffsjgQAgKN42x0AAADkDcYY9evXTzfeeKO6du1qd5xs8+abb+qJJ57Qfffdp/fee09eXl52RwIAj+Lj46NPPvlEISEheuihhxQfH6/+/fvbHQsAAEeg2AsAAHLE/PnztXz5cs2dO9cjC6DGGA0fPlzDhw/XM888ozFjxsiyLLtjAYBHcrlceuuttxQWFqYBAwYoPj5eL730EuddAPBgxhgZ45zWCU7Kci6KvQAAINulpqaqf//+qlevnlq2bGl3nCzndrv15JNP6q233tILL7yg/v37U3AAgGxmWZaGDx+u0NBQPfnkk4qPj9c777zjkW8oAgBwuSj2AgCAbPfFF19ow4YNWrFihccVQVNSUnTfffdp8uTJeuedd/TQQw/ZHQkA8pQnnnhCISEhuu+++5SQkKDPPvtMvr6+dscCAMAWFHsBAEC2OnnypJ5//nm1b99e//vf/+yOk6VOnDihTp06ad68efr888/VpUsXuyMBQJ7Us2dPhYSEqFOnTrr99ts1c+ZMLo4JAB7IyJmtE5zEZXcAAADg2d555x3t3LlTo0ePtjtKljpy5Ihuu+02LVy4UF9//TWFXgCwWbt27TRv3jzFxsaqWbNmio+PtzsSAAA5jmIvAADINomJiRo9erTuvfdeVaxY0e44WebAgQNq1KiR1qxZo4ULF6pVq1Z2RwIASGrcuLGWLFmizZs3q0GDBtqzZ4/dkQAAyFEUewEAQLYZO3asjh49qmHDhtkdJcvs2rVL9erV019//aXo6GhFRkbaHQkAcI7atWtr2bJl2r9/vyIjI7V9+3a7IwEAsoCRkTEOuju0pQTFXgAAkC327NmjV199VY8//rhKlChhd5wssXXrVtWtW1dHjx5VbGysqlevbnckAMB5VKlSRXFxcXK73YqIiNCmTZvsjgQAQI6g2AsAALLFyJEj5evrq379+tkdJUv8/PPPioiIkL+/v+Li4lS+fHm7IwEALqJMmTKKjY1VWFiY6tWrp1WrVtkdCQCAbEexFwAAZLmtW7fq/fff18CBAxUaGmp3nKv2/fffq0GDBipRooRiYmJUsmRJuyMBAC5DsWLFFB0drXLlyqlRo0ZaunSp3ZEAAFfIOPDmRBR7AQBAlhs8eLDCw8PVt29fu6Ncte+++05NmjTRTTfdpCVLlqhw4cJ2RwIAZEJYWJgWL16sOnXqqGXLlpo9e7bdkQAAyDYUewEAQJZas2aNpk6dqmHDhikgIMDuOFdl2rRpatOmjRo1aqQFCxYoODjY7kgAgCuQP39+ffPNN2rdurU6dOigTz/91O5IAABkC4q9AAAgS/Xv318VK1ZUjx497I5yVT744AN17txZd911l2bNmpXrC9cAkNf5+flpypQp6tGjh3r06KG33nrL7kgAgEwwxjju7kTedgcAAACeY9GiRVq8eLFmzZolb+/c+zRjzJgx6tevn/r06aNx48bJ5eL9cQDwBN7e3vrwww8VGhqqxx9/XPHx8RoyZIgsy7I7GgAAWSL3vgoDAACO4na71b9/f9WpU0dt27a1O84VMcZo4MCBeumllzR48GCNGDGCAgAAeBjLsvTKK68oLCxMgwYNUnx8vF577TXe2AMAeASKvQAAIEtMnz5dP/30k5YvX54rC6Spqal65JFH9N577+nVV1/VU089ZXckAEA2sSxLAwcOVGhoqB555BElJCToww8/zNWfSgEAT2fO3JzCSVnOxb9kAADgqiUnJ2vQoEFq3bq1IiMj7Y6TacnJyerevbumT5+uiRMn6t5777U7EgAgBzz88MMKCQlR9+7dlZCQoClTpsjf39/uWAAAXDGKvQAA4Kp9+OGH+vPPPzVr1iy7o2TasWPHdOeddyoqKkrTpk1Thw4d7I4EAMhBXbp0UVBQkO68807ddtttmj17tgoUKGB3LAAArghNiQAAwFU5evSoRowYoW7duunGG2+0O06mJCQkqFmzZlq+fLm+/fZbCr0AkEe1atVKCxcu1Jo1a9S4cWMdPHjQ7kgAgP8wxjju7kQUewEAwFV5/fXXFR8frxEjRtgdJVP27t2rhg0batOmTVq8eLGaNm1qdyQAgI0iIyO1dOlSbd++XfXq1dOuXbvsjgQAQKZR7AUAAFds//79euWVV/TII4+oVKlSdse5bH/99ZciIyO1Z88eLVu2TP/73//sjgQAcIAaNWooJiZGR44cUUREhLZu3Wp3JACAh1i+fLnatGmj4sWLy7Isff311+n2G2M0ZMgQFStWTAEBAWrSpIl+//33TM9DsRcAAFyx0aNHp13RPLfYvHmzIiIilJKSotjY2FzXegIAkL0qVKig2NhY+fr6KiIiQuvXr7c7EgBAknHgLTOSkpJUtWpVvf322+fdP2bMGL311lt69913tXLlSuXPn1/NmzfXiRMnMjUPxV4AAHBFtm3bpgkTJui5555ToUKF7I5zWdasWaPIyEgFBwcrNjZW1113nd2RAAAOdO211yomJkbFixdX/fr19f3339sdCQDgUImJienuJ0+ePO9xLVu21KhRo9S+ffsM+4wxeuONNzR48GC1bdtWN910kz799FPt3r07wwrgS6HYCwAArsiQIUNUsGBBPfHEE3ZHuSzR0dFq2LChypYtq2XLlql48eJ2RwIAOFiRIkW0dOlS3XTTTWratKm+++47uyMBAByoZMmSCg4OTru/+OKLmR5j27Zt2rNnj5o0aZK2LTg4WLfccotWrFiRqbG8Mz07AADI89avX6/PP/9cEyZMUP78+e2Oc0nffPON7rrrLkVEROjrr79WYGCg3ZEAALlAcHCwFixYoLvuuktt2rTR559/rrvuusvuWACQJxljZEzmWidkp7NZdu7cqaCgoLTtfn5+mR5rz549kqSiRYum2160aNG0fZeLlb0AACDTBgwYoOuvv1733Xef3VEu6bPPPlP79u112223ae7cuRR6AQCZEhAQoFmzZunOO+9U586d9eGHH9odCQDgIEFBQenuV1LszUqs7AUAAJmybNkyzZs3T9OmTZOPj4/dcS7q7bffVt++fdWzZ0998MEH8vbmqQ8AIPN8fHz02WefKSQkRL1791ZCQoKeeeYZu2MBADxEeHi4JGnv3r0qVqxY2va9e/eqWrVqmRqLlb0AAOCyGWPUr18/1apVS3feeafdcS7IGKNRo0apb9++evLJJzVx4kQKvQCAq+JyufT2229r0KBBevbZZzVw4EBHfZwYADydceAtq5QpU0bh4eGKiopK25aYmKiVK1eqTp06mRqLVz0AAOCyzZo1SytXrlRUVJQsy7I7znm53W4988wzev311zVy5EgNGjTIsVkBALmLZVkaNWqUQkND9cwzzyg+Pl7jx4+Xl5eX3dEAAA539OhRbd26Ne37bdu2ad26dQoLC9O1116rJ554QqNGjVK5cuVUpkwZPf/88ypevLjatWuXqXko9gIAgMuSkpKigQMHqlmzZmrUqJHdcc4rJSVFvXv31qRJkzRu3Dj17dvX7kgAAA/09NNPKzQ0NK2lwyeffCJfX1+7YwEAHGz16tVq2LBh2vdPPfWUJKlHjx6aNGmSnnvuOSUlJemBBx5QQkKCIiIitGDBAvn7+2dqHsvwuRMAAHAZPvzwQ/Xu3Vs//fSTqlevbnecDE6cOKGuXbtqzpw5+uSTT3T33XfbHQkA4OFmzpyprl27qnHjxpoxY4by5ctndyQA8DiJiYkKDg5W3Ql15R3gnHWrKcdTFNcnTocPH1ZQUJDdcdLQsxcAAFzSsWPHNHToUHXp0sWRhd4jR46odevWmj9/vmbNmkWhFwCQIzp06KBvv/1Wy5YtU/PmzZWQkGB3JABAHkexFwAAXNK4ceO0b98+jRw50u4oGRw6dEhNmjTRjz/+qAULFqhNmzZ2RwIA5CFNmzbV4sWLtXHjRjVs2FD79u2zOxIAIA+j2AsAAC7q0KFDevHFF/XQQw/puuuusztOOrt371a9evX0xx9/aOnSpapfv77dkQAAeVCdOnW0fPly7dmzR5GRkdqxY4fdkQDA4xgH3pyIYi8AALiol156SSkpKRo8eLDdUdL5448/FBERocOHDysmJkY1a9a0OxIAIA+78cYbFRsbq1OnTqlu3bravHmz3ZEAAHkQxV4AAHBBO3fu1FtvvaVnnnlGRYsWtTtOmg0bNigiIkLe3t6KjY1VxYoV7Y4EAICuu+46xcbGKjg4WJGRkVqzZo3dkQAAeQzFXgAAcEHDhg1TUFCQnn76abujpPnhhx9Uv359hYeHKyYmRqVKlbI7EgAAaYoXL65ly5apbNmyatiwoZYtW2Z3JADwCMYYx92diGIvAAA4r02bNmnSpEkaPHiwChQoYHccSdKiRYvUuHFjVa5cWUuXLnXUamMAAM4qWLCgoqKiVLt2bbVo0ULffvut3ZEAAHkExV4AAHBeAwcOVKlSpfTggw/aHUWSNHPmTLVq1Ur169fXd999p5CQELsjAQBwQYGBgfr222/VokULtWvXTp9//rndkQAAeYC33QEAAIDzfP/995o9e7Y+++wz+fn52R1HH330kXr37q277rpLn376qXx9fe2OBADAJfn7+2v69Onq3bu3unXrpsOHD6tPnz52xwKAXMmcuTmFk7Kci2IvAABIxxijfv36qWrVqurSpYvdcfTaa6/p6aef1oMPPqi3335bXl5edkcCAOCyeXt7a+LEiQoJCdEjjzyiQ4cOadCgQbIsy+5oAAAPRLEXAACkM3fuXMXGxmr+/Plyuezr+GSM0fPPP6/Ro0erf//+euGFF3hhDADIlVwul1577TUVLFhQzz//vOLj4zV27Fj+XQMAZDmKvQAAIE1qaqoGDBighg0bqnnz5rblcLvdevTRRzVhwgS9/PLLeu6552zLAgBAVrAsS4MHD1ZISIgeffRRxcfH6/3335e3Ny/LAeByGGNkjHNaJzgpy7n4VwUAAKT57LPPtHHjRq1cudK21UanTp1Sz5499eWXX+r9999X7969bckBAEB26Nu3r0JCQtSzZ08dPnxYX3zxhSP64wMAPAPFXgAAIEk6ceKEhgwZog4dOqh27dq2ZDh27Jg6duyohQsXaurUqbrrrrtsyQEAQHa65557FBwcrLvuukutW7fWrFmzFBgYaHcsAIAHoNgLAAAkSRMmTNCuXbu0cOFCW+Y/fPiw2rRpo9WrV+ubb76xtY0EAADZrU2bNlqwYIFuv/12NWnSRPPmzVNYWJjdsQDAscyZm1M4Kcu57LvqCgAAcIzDhw9r9OjRuu+++1ShQoUcn3///v1q1KiR1q9fr0WLFlHoBQDkCQ0aNNCSJUu0detW1a9fX//884/dkQAAuRzFXgAAoDFjxuj48eMaOnRojs+9Y8cORUZGateuXVq2bJnq1q2b4xkAALBLrVq1FBMTo4SEBEVEROjPP/+0OxIAIBej2AsAQB73zz//6PXXX9cTTzyh4sWL5+jcv/32myIiInTixAnFxMSoatWqOTo/AABOULFiRcXGxsrLy0sRERHauHGj3ZEAwHGMMY67OxHFXgAA8rgRI0bI399fzz33XI7O+9NPPykyMlKBgYGKi4tTuXLlcnR+AACcpFSpUoqJiVHRokVVr149/fDDD3ZHAgDkQhR7AQDIw7Zs2aIPPvhAgwYNUkhISI7Nu3z5cjVs2FClS5fW8uXLVaJEiRybGwAApypatKiWLl2qSpUqqUmTJlq8eLHdkQAAuQzFXgAA8rDBgwerePHieuSRR3Jsznnz5ql58+aqWbOmoqKiVKhQoRybGwAApwsJCdHChQsVGRmpVq1a6auvvrI7EgA4gnHgzYko9gIAkEetWrVK06dPT2vjkBO+/PJLtW3bVs2aNdO8efNUoECBHJkXAIDcJF++fJo9e7bat2+vu+66Sx9//LHdkQAAuYS33QEAAEDOM8aof//+qly5srp165Yjc77zzjt65JFHdM899+ijjz6StzdPQwAAuBBfX199/vnnCgkJ0b333quEhAQ9+eSTdscCADgcr7IAAMiDFi1apCVLlmjOnDny8vLK1rmMMXrxxRc1aNAgPfroo3rjjTfkcvHhIgAALsXLy0vvvPOOQkND9dRTT+nQoUMaMWKELMuyOxoA5DhjjIxxTusEJ2U5F8VeAADyGLfbrX79+qlu3bpq3bp1ts5ljNFzzz2nsWPHatiwYRoyZAgvUAEAyATLsvTiiy8qNDRU/fr1U3x8vN566y3eOAUAnBfFXgAA8pipU6dq3bp1io2NzdbCa2pqqh588EFNnDhRb7zxhh5//PFsmwsAAE/33HPPKTQ0VA8++KDi4+M1adIk+fj42B0LAOAwFHsBAMhDkpOTNXjwYN1+++2qW7duts1z8uRJ3X333fr666/1ySefqHv37tk2FwAAeUXv3r0VHByse+65R4mJiZo2bZoCAgLsjgUAOcbIma0TnIRiLwAAecj777+v7du3a86cOdk2R1JSku644w5FR0drxowZateuXbbNBQBAXtOxY0cFBQXpjjvuUMuWLTVnzhwFBQXZHQsA4BA0+QEAII84cuSIRowYoR49eqhy5crZMkd8fLyaNm2q77//XvPnz6fQCwBANmjRooUWLVqkdevWqWHDhtq/f7/dkQAADkGxFwCAPOK1115TYmKihg8fni3j//PPP6pfv75+++03RUVFqVGjRtkyDwAAkOrWratly5Zp165dioyM1M6dO+2OBADZyhjjuLsTUewFACAP2Ldvn8aOHatHH31UJUuWzPLxt23bpsjISB08eFAxMTGqXbt2ls8BAADSq1q1qmJiYnTixAnVrVtXW7ZssTsSAMBmFHsBAMgDRo0aJS8vLw0YMCDLx/7ll1/SLvYWFxenSpUqZfkcAADg/MqVK6e4uDgFBgYqIiJCa9eutTsSAMBGFHsBAMgDvvnmGz3//PMKCwvL0nFXrlypevXqqVChQoqNjVXp0qWzdHwAAHBpJUqU0PLly1WqVCk1aNBAMTExdkcCgCxnHHhzIoq9AADkARs2bNDTTz+dpWNGRUWpcePGqlChgpYtW6bw8PAsHR8AAFy+QoUKKSoqSjVq1FDz5s01b948uyMBAGxAsRcAgDwgMDAwS8f7+uuvddttt6lu3bpatGiRQkNDs3R8AACQeUFBQZo/f76aNm2qtm3basqUKWn7Tp48qc2bN9uYDgCQEyj2AgCATPnkk0/UoUMHtW3bVnPmzFH+/PntjgQAAM7w9/fXzJkz1aVLF3Xt2lXvvvuukpOT1aZNG1WpUkV//fWX3REB4IoYYxx3dyKKvQAA4LK9+eab6tmzp+699159+eWX8vPzszsSAAD4D29vb02aNEl9+/bVww8/rJo1ayoqKkrGGE2ePNnueACAbESxFwAAXJIxRkOHDtUTTzyhZ599Vu+//768vLzsjgUAAC7A5XLpjTfeUI0aNbRx40a53W653W5NnDjRsavRAABXj2IvAAC4KLfbrccff1wjRozQiy++qDFjxsiyLLtjAQCASxg4cKB++umndNu2b9+u77//3qZEAHDljANvTkSxFwAAXNCpU6fUo0cPjR8/Xu+884769+9vdyQAAHAZjh49qtdee02WZaV7k9ayLH300Uc2JgMAZCdvuwMAkvT777/ryJEjdsdwlAIFCqhcuXJ2xwDyBM5BGRUoUEAlS5ZUp06dNG/ePH3xxRfq3Lmz3bEAj8Q5KCOeBwFXLzAwUBs3btTs2bM1Z84cff/993K73TLG6JNPPtGbb76pwMBAzkHnwTkIQG5mGZr1wGa///67ypcvb3cMR9qyZQtPMoBsxjnowmrXrq3169dr5syZuu222+yOA3gkzkEXxvMgIGvFx8dr4cKF+uCDD/TDDz/o119/1YkTJzgHXQDnIMA5EhMTFRwcrJtev0leAc65bkjq8VStf3K9Dh8+rKCgILvjpGFlL2x39l3kvn37qkSJEjancYZdu3Zp/PjxvMMO5ICzf2efffaZKlasaHMaZ/j11191zz33aP/+/Vq4cKEiIyPtjgR4rLPnIKuDJasQvbAlyRwwMjMNz4OALBYaGqpOnTqpU6dOadvO9vOt9GAl5S+e365ojpK0O0mb3tvEOQhArkWxF45RokQJlS1b1u4YAPKoihUrqkaNGnbHcJQZM2bwOwFyiFXIklWcYu9ZTr3gCeCp8hfPrwKlC9gdAwCQBSj2AgAAAAAAAHA0c+bmFE7Kci6X3QEAAAAAAAAAAFePYi8AAAAAAAAAeACKvfAo0dHR6tSpk/bt22d3lAyGDx+u4cOH2x0DAAAAAAAg1zHGOO7uRBR74XhjxoxRt27ddPz48Qse89Zbb6lr166ZvmLqrFmztGrVqquNCCAPmDRpkizLkmVZio2NzbDfGKOSJUvKsiy1bt06U2Pv2rVLHTt2VEhIiIKCgtS2bVv9+eef5z124sSJqlixovz9/VWuXDmNGzfuqscE4HzutW6lDk1V6tBUmb8yvrAwxij11dP7Uz9PzdTYJtHIPc2t1BdTlfpCqlK/SJU5dP4XL+41bqWOS1XqyFSlvpkq9w/ujOMdMHLPdyv1wzPHDU2ViXfmiyEAl+efmH+0pMcSLemxRAlbEjLsN8Yo7sk4LemxRD+/9vNlj5v0T5J+//x3rR65WtH3R2tJjyU6vv/Cr/v2/7RfPw75UdH3RyvuyTj9+dWfcqemPw+dTDiprdO26qcXf9KyB5dpSY8liv81/rIzAUBuR7EXjhcREaHk5GT9+OOP591/8uRJrV69WtWqVVOrVq00efJkFS5c+LLGptgLILP8/f31xRdfZNi+bNky/f333/Lz88vUeEePHlXDhg21bNkyDRw4UMOHD9fatWtVv359HTx4MN2x7733nu6//35VrlxZ48aNU506dfTYY4/p5ZdfvuIxAeQy3pLZcJ7C6XZJicr05ZfNSSP3JLfMdiMr0pLVwJL2SO6P3TLH0s/jXuWWmWOkIpLV0pJV0pKZb+SOSV9oMTuNzEojnZRUKHN5ADiby8elvSv2ZtiesDlBJw+dlMsncyWGxK2J2rlop1JPpCpfsXwXPfbgzwe14a0N8s7nrXL3lFPhmoW1fc52/T7593THHfvnmHbM3aGT8SeV/5r8mcoDAJ4gk08HgZxXq1YtBQQEKC4uTvXr18+wf/Xq1Tp58qQiIiLkcrnk6+t70fGMMTp16tQljwOA87nttts0ffp0vfXWW/L2/vef0S+++EI1a9bUgQMHMjXehAkT9Pvvv+vHH3/UzTffLElq2bKlqlSpoldffVUvvPCCJOn48eMaNGiQWrVqpRkzZkiSevfuLbfbrZEjR+qBBx5QaGhopsYEkAuVk8wvRqalkeVlpW02G4xUXNKxzA1nVhnpoOR6wCWrxOnxTDkj9wS3zPdGVpMz204ZmSVGKi95dfI6/eBaktu4ZZYbmVpGVsDpY60KlqwBliw/S+44t8weVvUCnqJg1YLat2qfyt1TTi6vfwu7e1fsVYHSBXTq6KlMjVeoeiHVe6eevAO8tWPeDm3dsfWCx26dslWBJQNV7dlqaXN7+Xvpr2//0jXNrlH+4qcLuwVKF1Dk25HyCfTRvlX7tHH8xiv4SQE4kTlzcwonZTkXK3vheL6+vqpdu7Y2btyow4cPZ9gfGxurgIAA1apV67w9e/v27auXX35Z69at04ABA9StWzctWrRInTp10smTJ7Vs2TJ16tRJnTp10oQJEySdLpT07ds3w1zTp09Xp06d0m1bunSpRowYod69e+vuu+/WU089pYULF2bxbwGAU3Tp0kUHDx7UokWL0rYlJydrxowZ6tq1a6bHmzFjhm6++ea0oqwk3XDDDWrcuLGmTZuWtm3p0qU6ePCg+vTpk+7xjzzyiJKSkjR37txMjwkg93Hd6JKOSzqnK4tJMTKbjKwbrQs+7kLMJiOVUFqhV5KswpZU5nRROc02Scck183pXz5YtS0pWTJb/j3Wyne60AvA8xT9X1GdOnpK8Rv/bYvgTnFr3+p9KlqnaKbH8wn0kXfApdegJe1KUtLuJBVvUDxdkfmaxtdIRtq36t/Xf94B3vIJ9Ml0FgDwFBR7kStEREQoNTVVK1asSLf96NGj+vnnn3XzzTdfdKXu7t279dZbb+nGG29Ujx49VLp0afXt21c+Pj664YYb1LdvX/Xt21dNmjTJdLZFixapUKFCateunbp166aCBQtq4sSJ+u677zI9FgDnK126tOrUqaMvv/wybdv8+fN1+PBhde7cOVNjud1urV+/XrVq1cqwr3bt2vrjjz/SepGvXbtWkjIcW7NmTblcrrT9mRkTQC4UIuma/7Ry2CrphGRVyVyB1biNtFeyimd8nFXCkg6dbvMgSeafM/MV/8+BxSRZkv7J1NQAcin/Qv4Kvi5Ye3/4t5XDwfUHlXIsRUVvyXyx93Id+ev0c5eg0kHptvuF+skvzE9HdxzNtrkBILehjQNyhSpVqig0NFRxcXFq0aJF2vYVK1YoNTVVERERF338nj17NGDAAFWrVi3d9g8++EBFixZVZGTkFWcbNmxYukJzixYt9MILL2ju3Llq3rz5FY8LwLm6du2qAQMG6Pjx4woICNDnn3+u+vXrq3jx/1ZBLu7QoUM6efKkihUrlmHf2W27d+9WhQoV9M8//8jLy0tFihRJd5yvr68KFiyo3bt3Z3pMALmTdaMlE2VkThlZPpbMeiOVlqygTK6mPS4pRVLgefYVOPP1iCQ/SUcluSQrMP0clrcl5TtzHIA8oWidovpj+h9KTU6Vl6+X9n6/VyE3hMgvNHPXLciM5IRkSZJvSMYFPr7BvjoZfzLb5gbgHMYYGeOc1glOynIuVvYiV3C5XLr11lu1ZcuWdC0a4uLiFBwcrBtvvPGijy9SpEiGQm9WObfQe+zYMSUmJqpixYrau3evjh3LZOM8ALlCx44ddfz4cX377bc6cuSIvv322ytq4XD8+OmrTZ/vom7+/v7pjjl+/PgFP8Hg7++f7rjLHRNA7mRVsaRTkracXnlrtlxZCwelnPl6vuUfZ7edOuer1wXG8T7dSgJA3lCkdhG5T7l1cN1BpRxP0YGfDyj8f+HZOmfqqVRJOu8F4Fw+LrlPuTNsB4C8ipW9yDUiIiI0d+5cxcXFqX379jp48KA2b96sFi1ayOW6+PsW/10Jl5U2b96s6dOn6/fff9fJk+nfUT527Jjy5bv4VWUB5D6FCxdWkyZN9MUXX+jYsWNKTU3VnXfemelxAgICJCnDuUOSTpw4ke6YgIAAJScnn3ecEydOpDvucscEkDtZ+S2prORe75Z1ypLcklXpCoq9Z18JpJxn39ltPud8Tb3AOClnVvgCyBN8g3wVWilUe1bsUWpyqozbqPDNhbN1Ti+f0+82na+o6z7lPm8RGADyKoq9yDXKli2r4sWLpxV74+LiZIy5ZAsHSRft55sZbnf6Jxd79uzRqFGjVLx4cXXr1k2FChWSl5eX1q1bp7lz52Y4HoDn6Nq1q3r37q09e/aoZcuWCgkJyfQYYWFh8vPz0z//ZGx2eXbb2dYQxYoVU2pqqvbt25fuDazk5GQdPHgw7bjMjAkg97JusmTmGJmjRionWQFXUGwN0OlXA+drdXm2LcPZdg6BktySOWrStXIwKUY6ds5xAPKEonWK6rePf1Py4WQVvKmgfPJn7wXRzrZvSE5Iln9B/3T7kg8nK6hs0PkeBsDDmDM3p3BSlnPx9hdylYiICO3cuVN//fWX4uLiVKxYMV1//fVXPJ5lnf+FUWBgoJKSkjJs379/f7rv16xZo1OnTum5555T06ZNVb16dd10001ZVlwG4Fzt27eXy+XSDz/8cEUtHKTTLWpuvPFGrV69OsO+lStXqmzZsipQ4HQF5Wwrmv8eu3r1arnd7rT9mRkTQO5l3WCdvjDa37qyFg6SLJclFZHM7owvVMwuI4VKlt/psa1iZ+bY/Z8Dd0syOn2hNgB5RuGahSVLSvwjUUX/l30XZjsr8NrTzcUTtyem234y/qROHjqZth8AQLEXuczZC6lNmzZN27dvV926da9qPD8/v/MWdYsWLapjx47pr7/+StsWHx+vVatWpTvubPuIc5tyHzt2TNHR0VeVC4DzBQYG6p133tGwYcPUpk2bKx7nzjvv1KpVq9IVZ3/77TctWbJEd911V9q2Ro0aKSwsTO+88066x7/zzjvKly+fWrVqlekxAeRelp8lq7Ulq4Elq8KVt1CwKlnSrjPF3TPMASNtk6zK54xbRlKA5F6V/lNLZpWRfCSrHG0cgLzE299bFXpUUJl2ZVSoeqFsny/wmkDlK5ZPu6N3y7j/PV/tWrJLsqQiN2df2z4AyG1o44BcpUiRIipfvnxaAeNs8fdKlS1bVhs2bNC3336r0NBQFSlSROXKldOtt96qzz//XK+++qpatGihkydPatGiRSpWrJi2bduW9viqVavK29tbY8aMUZMmTXTixAlFRUUpKChI8fHxV5UNgPP16NHjqsfo06ePPvjgA7Vq1UrPPPOMfHx89Nprr6lo0aJ6+umn044LCAjQyJEj9cgjj+iuu+5S8+bNFRMTo88++0yjR49WWFhYpscEkLu5ql39ug2rtiXzk5H7c7esWy3JSzIrjJRfp78/e5yPJauRJTPXKHVqqqzrLekvyaw3shpbsvKd09rhhJFZeboYY3ac+fqjkfE3kr/kuoX1JoAnKBZx9Uv6U46laOeinZKkw78fliT9vfhveefzlk8+H13T9Jq0Y6/vfL3Wv7Fe615ZpyK3FFHS30n6e/HfKl6vuPIXz59u3G2zT79mS9p1emHPnu/3KGFLgiSpTNsyV50bgD2MMekW29nNSVnORbEXuU5ERIS2bNmi66+/XuHhV3fV1+7du+v999/X1KlTlZycrPr166tcuXIqUKCAnnnmGX366af6/PPPVaRIEXXp0kV79uxJV+wtXry4nnrqKU2ZMkWTJ09WSEiImjZtqqCgIL377rtX+6MCyAMKFCig6OhoPfnkkxo1apTcbrcaNGig119/XYULp7/YSZ8+feTj46NXX31Vc+bMUcmSJfX666/r8ccfv+IxAeRtlp8lV0+X3AvcMsvN6ZYMpSVXC9fpC8Gdw1XbJbfLLbPCyPxmpGDJamHJ+t9/VvUel8yS9C9+zPdnvg+RdEt2/TQAcptTSae07att6bbtXHC6+OtfyD9dsbdQtUK68dEbte3rbfr9s9/lU8BHpduUVum2pTOM+98x/1n+77UMKPYC8HSWcWoZGnnGTz/9pJo1a+rFF19U2bJl7Y7jCH/++acGDBigNWvWqEaNGnbHATza2XMQf2//4ncC5Jyzf2+uB12yitMKQTrdQ9j9nptzEJADzp6Dbh5+swqUpqe/JB3ZfkSrhq7iHAQ4SGJiooKDg3XD2BvkFeBld5w0qcdTtfmZzTp8+LCCgpxzoUhW9gIAAAAAAABwNHPm5hROynIuir0AAGSDQ4cOKTk5+YL7vby8aKkAINuYY0ZKvcgBLmVo0wAAWeXU0VNyp7gvuN9yWfIN8s3BRACQd1DsBQAgG9xxxx1atmzZBfeXKlVK27dvz7lAAPIU91S3tP0iB4RIXk8652OQADzLhnEblLA54YL7/Qv569ZXb825QACQh1DsBQAgG7z66quKj4+/4P6AgIAcTAMgr3E1d0nHL3KAT45FAZAHXd/5eqUcS7ngfpePKwfTAPAUxhg56dJjTspyLoq9AABkg5o1a9odAUAexsXeANgpqIxzLlQEAHkNb6cBAAAAAAAAgAeg2Is8qVOnTpo+ffplHdu3b19NmDAh03Ps27dPnTp1UnR0dKYfCwCSNG3aNN10000KCAhQr1690u3bvn172kXeOnfurISEBHtCAsh13BvdSp2QqtSRqXLPSn8BpdSPU5X69sWu7JZ5qR+nKnVUqlLfT5XZ5syPOwLIOXtX7tXKQSsVfX+0Nn2wKd2+n178SSsHrszS+X568SdF947WqmGrFP/rhVtsAXA+48CbE9HGAblWdHS03nnnHb3wwgu67rrrrmqs3377TevXr9dtt92m/PnzZ1FCALhySUlJ6tmzp4oWLaqXXnpJtWvXTre/cOHCmjhxotasWaPx48erfPnyGjFihE1pAeQWJtnIfG2k/JLVxJJ1zZW1e3Avd8v8ZqRDkpIlBUlWeUtWPUtW/vRjuuq5ZPYYmRVG7llueT3FheGAvCr1ZKp+/fBX+Qb56rqO1ymo7JW1e9j+zXYdWHtAx/cdV+qJVPmF+alg1YIq3aa0fIN80x1buk1pHd15VDsW7NCm9zep7ut1s+JHAQDHotiLPGny5Mny8vr3hcaWLVs0Y8YM1a9fP0Ox9/XXX5dl0fcOQM769ddfdfz4cT399NPq27dvhv358+dXz5491bNnT3333Xdat25dzocEkPvsl3RKsm615Lrlyj/kZ3YbWeGWVEWSr6QDklljZLYYuR52yfL997mTdZ0l6zpL7lS3TJSROWZk5eO5FZAXJe1OkjvZrWtbXKtrml5zxeMc2X5EgdcGqugtReXl76Wk3UnavWy3Dv58ULVH1paX37+v9cKqhCmsSpjcKW79OeNPnTp6Sj6BXKUSgOei2Is8ydfX99IHneHjwxMBAJnToEEDlS5dWpMmTbriMZKSkiRJRYsWveSx4eHhOnLkyBXPBSB3SP04VVaIJVf7q+jEdur0Fyvw6oqtXp0zrs41JY3cU0+v+LVuPM/4gWe+JkvKd1XTA7DBTy/+JP9C/qrUu9IVj5F68nSbGN/gy389dj43Pnpjhm3B1wdr4/iNOrD2gIr+L+Pzp7NzppxIodgL5FLGGBnjnNYJTspyLnr2wmNMmDBB3bt316FDh/TKK6+oe/fuuv/++zV58mS53en70Z3bs3f69On67LPPJEmPPvqoOnXqpE6dOmnfvn2SMvbsPXr0qCZPnqxnnnlG3bt3V8+ePfXiiy9q+/btOfODAnnUzz//rF69emnVqlV2R8kRZ584XM4nC1wul2OfaACe4rffflPPnj0VFxdnd5SrcwWnCrPVKHVUqtzT3TKpFxkg5MzXExfYf/Z0xukKyLvO/v1n4v2mgxsOKrp3tDZO2Ch3qvuCx/kX8pckpRxLOe/+tOdUnIMAeDiKvfAobrdbo0ePVoECBdStWzdVqlRJ3377rRYvXnzBx9SuXVt1657u29S9e3f17dtXffv2VVDQ+ftH7d27V6tWrVKNGjXUvXt3tWnTRjt27NDw4cN16NChbPm5AJzu0z1p0iTVrl1bLVu29Pii79k3qVyuS/9TbVlWhje1AGStmJgYffLJJ4qIiFDjxo1zb9E3k4UW85uR+0u3rEqWrA6WLK9/H2iMkUkyMkeMzF9G7nluySVZpS8wOMVeIM9Le3P6Ms9BB9Yd0IY3N6jIzUVU+aHKcnn9+7zIGKPkI8k6mXBSCb8l6PfPfpflshRyQ8j5B+McBCCPoI0DPMqpU6d06623qkOHDpKkpk2bql+/flq6dKmaNWt23seUKlVKZcqUUVxcnG6++WYVKVLkonNce+21euONN9IVYCIjI/XUU09p6dKlaXMDyHoul0tut1uLFi3SggUL1KJFC40YMUI333yz3dGy3O7duyVJISEhlzw2ODhYv/zySzYnAnDWsmXLFBERoUaNGmnEiBFpbxrnBubImSqH/2Ucu8nIPcMtq5olq7Uly/Wf6sxRyT32nDeagnS6IFz4/FUcy886fdXqo5LCriw/gNwtOT5ZkuSd79KliH2r9+mXCb+oWEQxVehZIcM5KPlwsuIe//eNN78wP1V6qJLyFz//Bbe9A07PeTLhpAKKBFzpjwDARubMzSmclOVcFHvhcZo2bZru+4oVK2r58uVZNv65PXzdbreSkpLk7++vYsWKadu2bVk2z5VISkpSxYoVtX//fltzANkhJSUlbfVqaurpfm8LFizQggUL1K9fP7300ku25Dp16pQOHz6cYdvJkyd14MCBdNvDwsIuuVI3ISFBf/zxh1577TUFBQVdViG7fv36mj17tsaPH6+2bdsqPDzcln7jKSkpKleunPbs2ZPjcwPZLSXl348Fnz0HLVmyREuWLNHDDz+cruVTVjCpJmM7hFRJKZJJ+s8LiwBlLMT+d7zjRoqXzAoj+UkqcfH53RvcMl8ZWbUsWbdZ528pEyC5urtOZ/rHyPxqTvfjvZBrJHlL7uVuuZq5pODTBWAAzuNOcSvlePp2CCbFyH3KreQj6f/QffL7XPIcdCrplI7vO64d3+2QV4CXgsqc/1OUZ+1ZsUe/fvCrSjQsoXL3lDvvOcgn0EfVnqsm9ym3jvx1RPtX70/rCXw+QdcFyeXj0l/f/CWfzj7yK+gnb39KIgA8D2c2eBQfH58M7Rfy58+fdqGjrOB2uzV//nwtXLhQ+/btS/fR6QIFCmTZPFciICBAY8aModgLjxQdHa1Zs2alffzv7CrfatWq6Z577rEtV1xcnBo2bJhh+/fff68pU6ak27Zt2zaVLl36ouO1a9dOy5YtU1BQkGbNmnVZ55XHH39cK1as0KOPPqpHH31US5cuVYMGDTLzY2QJLy8vjRkzhmIvPNJ//6bPnoOqVKmi++67L+sn3CG5J2Vsz2J2GpmN6Yu9ridcUujFh3NPcUvbJflJrs6uixdZ4yUz08iqbMnV6sJvUFnelnTdmf+uYMmUNXJPdMvkN7IqZBzfKmDJdYdL7q/ccr99ZsVwe4q9gBMd/v2w1r60NuOOrdK+lfvSbaozto4CCl98peyGtzYoYXOCvAK8dONjN6atsj2f4/uPa9N7m1SkdhGV71b+gse5vF0Kq3z6YwKFqhVSWKUwrRm1Rr5BvipUrVCG4/1C/FTpwUra9N4mrRy4UuER4Vd1sTkAcCqKvfAol9Pb8mrNmjVL06ZNU8OGDdWxY0cFBgbKsix98skntl8gyeVyqXPnzrZmALKL2+3WrFmz5O3trZSUFLVv317Dhg1TlSpVbM1VtWpVLVq0KN22p59+WuHh4Xr22WfTbQ8PD7/keGPHjtWGDRs0YsQIdevWTVu2bFH+/Of/OOJZH3/8saZPn66+ffuqWbNmqlq1auZ/kCxgWZbuuusuW+YGsltAQICmTJmSdg5q1aqVhg8frurVq2fPhOFnVs2ew/2dWwqUXHX/83wn8NLDuZq5ZPYZmWgj91duuR5zyfK9QKG1wOkxze9GZpeRVeLyCrLWtZZUQDLrz1/sNUlG7m/cUmHJqmvJKkqhF3CqwGsDVe25aum2bf1yq3yDfXXtbdem2+4b7HvJ8a7vfL2O7jyq7bO3a9N7m1RnTB15+Xmd91i/ED/5hvjq4M8Hlbgt8ZKrgM8KLhcs3xBf7fl+z3mLvcmJydr88WblL55fJVuWVIFr7V2oAyDzjDG2113O5aQs56LYC2TSypUrVblyZT300EPpth87duyCF3UDcPVCQ0NljFHbtm0dUeQ9KzQ0VE2aNMmwrVixYhm2X45atWqpVq1asixLvXr10g8//KDGjRtf9DFz5sxRmTJlNG7cuEzPB+DyhIaeXjrbsmXL7C3ynmEF/LtqNk3A6dWx1nWZL5JaJSxZJSy55Zb52kh/Syp7gYO9JdfdLrknueX+zC1XL5esIpc5Z4pkTl7ghc8OScfPrCy+0EXcADiCT36ftFWzZ3nn95ZviG+G7ZcjqEyQgsoEybIs/frhrzq89fAFx3H5uFT1yapa+9Ja/Tz2Z1UfUF2B11zGu1qS3KfcSj1+/lYOh7ccVkpSiq5/7HqF3nCJj0MAQC6W/csggVzA3//0VUqOHTt2yWNdLleGd29WrFihQ4cOZUs2AKd17dpVe/fu1YwZMxxT6M1O1157etVMQkLCJY9NTEzUNddck82JgLytXbt22rNnj+bMmZPthd7sZAWfKbL+tx/wf4/zt+Tq5pLyS+5P3TKH/n3uY5KNTHLGgq7ZZKTjklX8/IXctCIw740DeZZfQT9JUsqxlIse553PW1WfrSqfIB+te2Wdju3993Va6snU8/bm3bdqn1KSUlSgzPlX7KacOD2nf9hlXKESAHIxVvYCksqUKSNJmjJlim699VZ5eXmpZs2aaUXgc9WoUUMzZ87UhAkTVKFCBe3YsUOxsbEqWrRoTscG8hRvb28VKVLE7hg55mxbmsv5aJAxJkfa2AB5mZeXl2f8W3+2DnsZnzq08ltydXfJPdEt9yduue5zyQqypIOnC8BWZUsqdGbM3afbNyhEsv53gVW7Z+dkUS+QZ533Yo8X4FvAV9Wfra41o9do3Zh1qjmopvzC/HRszzGtG7NORW4ponzF8smyLCVuS9TeFXvlX8hfJZuVPP+AnIMAj2Au50lMHkexF5B0/fXXq2PHjlq8eLHWrVsnY4zGjRt33mJv+/btdfLkScXFxWnFihUqU6aM+vXrpy+//NKG5AA8lZfX6T52J05cYvmdpOPHj9t+gUgAucSZ94VMipF1GRUPK8iSq4dL7o/ccn96uqWDgiSroiWzzUg/S0rV6SJvbUtWPUtWvguMeyp9BgB5j+U6fX5wJ2e8AOX5+IX5qdpz1fTTCz9p7StrVWNgDfmF+alwrcKK3xSvPbF75E51y7+gv65pfI1K3V5KPoE+5x0rNfn0amDLi2ovAM9GsRe5VoMGDdJdbb5Pnz7q06dPhuPuuuuuDBcMmjp1aobjOnTooA4dOmTYPn78+HTf+/j4qFu3burWrVu67UOHDk33fZEiRc47DwDPFx0dfdVjlChRQpI0bdo0/e9//1N4eLgCA//tV+d2u7V//379+uuv2rBhAxdGA/IAr17nv5hRppx5X8j8YmSuMVKgZPn9W/g43xxWmCWvZ9Jvt26//GKJOWGko5L51Uheki5+zUkADlVjQI2rHsMv9HQbh30/7lPQ9UHyDfaVt/+/ZYnzzZGvaD5FvBmRbtsNvW647DlTjqUoOTFZ+9fsl+VlybfApS8oBwC5GcVeAAAcqGzZsurYsaOmTZumb775Rj169NCkSZPS9u/YsSOtBU1wcLAefvhhm5ICyE2sMEtWZUvmFyP3b25Z1SxZ7bN3lZv7S7e0/cz8dS1Z3qyqA/KqgCIBKlK7iPb9uE8H1h1QeES4KvWulK1zrn9zvRI2J0iSrr3tWrl8+HgBkFsZmctqc5dTnNpSgmIvAAAONXXqVI0dO1bbt29XwYIF0+0LDw9XVFSUgoODVbly5fO2nQGA83F1dMkcNlKCpIAcmK+5S0qRFCZZgRR6gbyuyiNVdKLzCZ04cOKCLReyUrku5ZSanKp8RfPJN5hVvQA8H8VeAAAcrGTJkipZMuOFRvz9/dWoUSMbEgHwBFawJQXn0FzFKfACSM+/oL/8C+bMG9UFSnNdAwB5C8VeAAAAAAAAAI5mztycwklZzkWzGgAAAAAAAADwABR7gcvUqVMnTZ8+PdOP27dvnzp16qTo6OisDwUAAAAAAADHGzZsmCzLSne/4YYbsnweir3IdaKjo9WpUyd16tRJmzdvzrDfGKM+ffqoU6dOevnll21ICABXZtq0abrpppsUEBCgXr16pdu3fft2eXl5qXDhwurcubMSEhLsCQnAY7k3upU6IVWpI1PlnuVOt8/EG6UOS1Xqy6lyT3fLHHfmxxYB5F57V+7VykErFX1/tDZ9sCndvuP7j2tJzyWK6RujjRM26lTSKZtSArCTMcZx98yqXLmy/vnnn7R7bGxslv+e6NmLXMvHx0exsbEZ3gXZtGmTDh48KB+f7L+yKwBklaSkJPXs2VNFixbVSy+9pNq1a6fbX7hwYU2cOFFr1qzR+PHjVb58eY0YMcKmtAA8jUk2Ml8bKb9kNbFkXfOfi6rll6y2lrRbMj8aqaBkNeLCawCyRurJVP364a/yDfLVdR2vU1DZoHT7fYN8VfG+ijqy/Yj+Xvy38oXnU9k7ytqUFgCunLe3t8LDw7N1Dlb2IteqXr26fvjhB6WmpqbbHhcXp7JlyyokJMSeYABwBX799VcdP35cTz/9tB5//HHVqVMn3f78+fOrZ8+eGjdunMqVK6d169bZExSAZ9ov6ZRk3WrJVcclq2T6Qq7la8lV3SVXK5dUUDJ7WNkLIOsk7U6SO9mta1tcq5LNSir4+uB0+738vFQsspjKdyuvgKIBOvrXUZuSAkBGiYmJ6e4nT5684LG///67ihcvrrJly+ruu+/Wjh07sjwPK3uRa9WtW1erVq3S+vXrVb16dUlSSkqKfvjhB91xxx1asGBBuuNPnDihadOm6YcfftDhw4dVuHBhNW7cWK1bt5Zl/fuC5tSpU/riiy8UExOjU6dOqXLlyrrvvvvOm+HQoUOaOnWq1q5dq6SkJIWHh6t169Zq2LBh9v3gADxSUlKSJKlo0aKXPDY8PFxHjhzJ7kgA8pIzn4i2Ai9jtW6gpAu/hgGATEs9eXoBj2+w7yWP9Q32VcqJlOyOBMCBzJmbU5zNUrJkyXTbhw4dqmHDhmU4/pZbbtGkSZNUoUIF/fPPPxo+fLgiIyO1ceNGFShQIMtyUexFrlW4cGGVK1dOcXFxacXetWvX6tixY7r11lvTFXuNMXrllVf0yy+/qGHDhipdurR+/vlnffbZZzp06JB69OiRdux7772nmJgY1a1bVxUqVNDGjRv10ksvZZg/ISFBgwcPliQ1b95cQUFBWrdund59910dO3ZMrVq1yubfAABPcrbf07lvPl2Iy+W6ov5QAHBBmTml0L0BQFY7ew66jPOL5eIkBMBZdu7cqaCgf9vP+Pn5nfe4li1bpv33TTfdpFtuuUWlSpXStGnTLrjI8EpQ7EWuFhERoS+//FLJycny9fVVbGysKlWqpLCwsHTHrV69Whs3blSnTp10xx13SDpdoH3ttdc0f/58NW/eXOHh4dq+fbtiYmLUrFmztD+05s2b66233sqwtH7KlClyu9165ZVX0t6Badq0qd58803NmDFDTZs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" ] diff --git a/petab_select/__init__.py b/petab_select/__init__.py index d4395bab..233d233b 100644 --- a/petab_select/__init__.py +++ b/petab_select/__init__.py @@ -2,7 +2,6 @@ import sys -from . import plot # noqa: F401 from .candidate_space import * from .constants import * from .criteria import * From 8738f8cc27727a503a91f0c7bc64fbb780d2400d Mon Sep 17 00:00:00 2001 From: dilpath Date: Mon, 18 Nov 2024 23:44:41 +0100 Subject: [PATCH 26/26] color table --- doc/examples/visualization.ipynb | 133 +++++++++++++++++-------------- 1 file changed, 71 insertions(+), 62 deletions(-) diff --git a/doc/examples/visualization.ipynb b/doc/examples/visualization.ipynb index 9e3e7fa6..8b86ad63 100644 --- a/doc/examples/visualization.ipynb +++ b/doc/examples/visualization.ipynb @@ -44,85 +44,90 @@ { "data": { "text/html": [ - "
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Model hashAICc criterionPredecessor model hashEstimated parameters
 Model hashAICc criterionPredecessor model hashEstimated parameters
0M_0-00037.975230virtual_initial_model-sigma_x20M_0-00037.975230virtual_initial_model-sigma_x2
1M_1-000-0.175406M_0-000k3, sigma_x21M_1-000-0.175406M_0-000k3, sigma_x2
2M_2-000-0.274514M_0-000k2, sigma_x22M_2-000-0.274514M_0-000k2, sigma_x2
3M_3-000-0.705327M_0-000k1, sigma_x23M_3-000-0.705327M_0-000k1, sigma_x2
4M_5-0009.294673M_3-000k1, k3, sigma_x24M_5-0009.294673M_3-000k1, k3, sigma_x2
5M_6-0007.852170M_3-000k1, k2, sigma_x25M_6-0007.852170M_3-000k1, k2, sigma_x2
\n", - "
" + "\n" ], "text/plain": [ - " Model hash AICc criterion Predecessor model hash Estimated parameters\n", - "0 M_0-000 37.975230 virtual_initial_model- sigma_x2\n", - "1 M_1-000 -0.175406 M_0-000 k3, sigma_x2\n", - "2 M_2-000 -0.274514 M_0-000 k2, sigma_x2\n", - "3 M_3-000 -0.705327 M_0-000 k1, sigma_x2\n", - "4 M_5-000 9.294673 M_3-000 k1, k3, sigma_x2\n", - "5 M_6-000 7.852170 M_3-000 k1, k2, sigma_x2" + "" ] }, "execution_count": 2, @@ -131,6 +136,7 @@ } ], "source": [ + "import matplotlib\n", "import pandas as pd\n", "\n", "pd.DataFrame(\n", @@ -148,6 +154,9 @@ " for model in models\n", " ],\n", " }\n", + ").style.background_gradient(\n", + " cmap=matplotlib.colormaps.get_cmap(\"summer\"),\n", + " subset=[\"AICc criterion\"],\n", ")" ] }, @@ -275,7 +284,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -351,7 +360,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -391,7 +400,7 @@ "outputs": [ { "data": { - "image/png": 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