From 59a032a6283dabcb78213b537c67866b9b9d5ebd Mon Sep 17 00:00:00 2001 From: xRiskLab Date: Wed, 18 Mar 2026 19:36:01 +0100 Subject: [PATCH 1/3] release v0.1.7a0 - initial commit --- .github/workflows/ci.yml | 2 +- .github/workflows/compatibility.yml | 6 + Makefile | 12 +- README.md | 2 +- examples/fastwoe_cap_curve.ipynb | 581 ------------------ examples/notebooks/fastwoe_cap_curve.ipynb | 443 +++++++++++++ .../{ => notebooks}/fastwoe_explanation.ipynb | 0 .../fastwoe_gini_contributions.ipynb | 379 ++++++++++++ .../{ => notebooks}/fastwoe_monotonic.ipynb | 0 .../{ => notebooks}/fastwoe_multiclass.ipynb | 0 .../fastwoe_styled_display.ipynb | 0 .../fastwoe_visualize_woe.ipynb | 0 .../msd_feature_selection.ipynb | 0 .../{ => notebooks}/woe_standard_errors.ipynb | 0 examples/{ => scripts}/fastwoe_example.py | 0 .../{ => scripts}/fastwoe_faiss_kmeans.py | 0 examples/{ => scripts}/fastwoe_monotonic.py | 0 examples/{ => scripts}/fastwoe_multiclass.py | 0 examples/{ => scripts}/fastwoe_shapley.py | 0 examples/{ => scripts}/fastwoe_tree.py | 0 examples/{ => scripts}/iv_standard_errors.py | 0 examples/{ => scripts}/woe_density.py | 0 fastwoe/__init__.py | 4 +- fastwoe/fastwoe.py | 3 +- fastwoe/metrics.py | 81 ++- fastwoe/plots.py | 84 +-- pyproject.toml | 12 +- tests/test_metrics.py | 116 +++- 28 files changed, 1089 insertions(+), 636 deletions(-) delete mode 100644 examples/fastwoe_cap_curve.ipynb create mode 100644 examples/notebooks/fastwoe_cap_curve.ipynb rename examples/{ => notebooks}/fastwoe_explanation.ipynb (100%) create mode 100644 examples/notebooks/fastwoe_gini_contributions.ipynb rename examples/{ => notebooks}/fastwoe_monotonic.ipynb (100%) rename examples/{ => notebooks}/fastwoe_multiclass.ipynb (100%) rename examples/{ => notebooks}/fastwoe_styled_display.ipynb (100%) rename examples/{ => notebooks}/fastwoe_visualize_woe.ipynb (100%) rename examples/{ => notebooks}/msd_feature_selection.ipynb (100%) rename examples/{ => notebooks}/woe_standard_errors.ipynb (100%) rename examples/{ => scripts}/fastwoe_example.py (100%) rename examples/{ => scripts}/fastwoe_faiss_kmeans.py (100%) rename examples/{ => scripts}/fastwoe_monotonic.py (100%) rename examples/{ => scripts}/fastwoe_multiclass.py (100%) rename examples/{ => scripts}/fastwoe_shapley.py (100%) rename examples/{ => scripts}/fastwoe_tree.py (100%) rename examples/{ => scripts}/iv_standard_errors.py (100%) rename examples/{ => scripts}/woe_density.py (100%) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b7a997b..af01b1b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -11,7 +11,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.9', '3.10', '3.11', '3.12'] + python-version: ['3.9', '3.10', '3.11', '3.12', '3.13', '3.14'] steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/compatibility.yml b/.github/workflows/compatibility.yml index d9288a0..6a92b25 100644 --- a/.github/workflows/compatibility.yml +++ b/.github/workflows/compatibility.yml @@ -21,6 +21,12 @@ jobs: - python-version: '3.12' python-version-short: 'py312' sklearn-version: '1.6.1' + - python-version: '3.13' + python-version-short: 'py313' + sklearn-version: 'latest' + - python-version: '3.14' + python-version-short: 'py314' + sklearn-version: 'latest' steps: - uses: actions/checkout@v4 diff --git a/Makefile b/Makefile index ba39921..604efbc 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,6 @@ # FastWoe Development Makefile -.PHONY: help install test lint format typecheck typecheck-strict clean check-all +.PHONY: help install test test-all lint format typecheck typecheck-strict clean check-all help: ## Show this help message @echo "FastWoe Development Commands:" @@ -10,9 +10,17 @@ help: ## Show this help message install: ## Install development dependencies uv sync --dev -test: ## Run tests +test: ## Run tests (current Python) uv run pytest +test-all: ## Run tests across all supported Python versions (3.9 โ†’ 3.14) + @for ver in 3.9 3.10 3.11 3.12 3.13 3.14; do \ + echo "\nโ–ถ Python $$ver"; \ + uv run --python $$ver pytest tests/ -q -m "not compatibility" || exit 1; \ + done + @echo "\nโœ… All versions passed" + + lint: ## Run linting uv run ruff check fastwoe diff --git a/README.md b/README.md index 18d5a53..c6d1b38 100644 --- a/README.md +++ b/README.md @@ -469,7 +469,7 @@ print(summary[['feature', 'monotonic_constraint']]) - Performance may be slightly different but more interpretable - Essential for regulatory compliance in credit scoring -For a complete example, see [examples/monotonic_constraints_example.py](examples/monotonic_constraints_example.py). +For a complete example, see [examples/scripts/fastwoe_monotonic.py](examples/scripts/fastwoe_monotonic.py). ## ๐Ÿ“‹ API Reference diff --git a/examples/fastwoe_cap_curve.ipynb b/examples/fastwoe_cap_curve.ipynb deleted file mode 100644 index fe05f1b..0000000 --- a/examples/fastwoe_cap_curve.ipynb +++ /dev/null @@ -1,581 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CAP Curve with Weighted Gini\n", - "\n", - "Demonstrates:\n", - "\n", - "- Weighted Gini coefficient for binary classification\n", - "- Multiple model comparison on same plot\n", - "- Custom colormap support\n", - "- Flexible grid layouts\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib import pyplot as plt\n", - "from sklearn.ensemble import (\n", - " GradientBoostingClassifier,\n", - " RandomForestClassifier,\n", - " RandomForestRegressor,\n", - ")\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "from fastwoe.plots import plot_performance\n", - "\n", - "%config InlineBackend.figure_format = 'retina'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Generate Credit Risk Data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset: 10,000 samples\n", - "Default rate: 1.50%\n", - "Total EAD: $659,967,653\n" - ] - } - ], - "source": [ - "# Generate sample data\n", - "n = 10000\n", - "\n", - "df = pd.DataFrame(\n", - " {\n", - " \"age\": np.random.randint(18, 75, n),\n", - " \"income\": np.random.lognormal(10.5, 0.8, n),\n", - " \"credit_score\": np.random.randint(300, 850, n),\n", - " \"debt_to_income\": np.random.uniform(0, 1.5, n),\n", - " }\n", - ")\n", - "\n", - "# Binary target (default indicator)\n", - "default_prob = 1 / (\n", - " 1\n", - " + np.exp(\n", - " 5\n", - " - 0.01 * (df[\"credit_score\"] - 500)\n", - " - 0.5 * np.log(df[\"income\"] / 30000)\n", - " + 2 * df[\"debt_to_income\"]\n", - " )\n", - ")\n", - "y = np.random.binomial(1, default_prob)\n", - "\n", - "# Exposure at Default (EAD weights)\n", - "ead = np.random.lognormal(10, 1.5, n)\n", - "\n", - "print(f\"Dataset: {n:,} samples\")\n", - "print(f\"Default rate: {y.mean():.2%}\")\n", - "print(f\"Total EAD: ${ead.sum():,.0f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Train Multiple Models\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "โœ“ Logistic trained\n", - "โœ“ Random Forest trained\n", - "โœ“ Gradient Boosting trained\n" - ] - } - ], - "source": [ - "# Split data\n", - "X_train, X_test, y_train, y_test, ead_train, ead_test = train_test_split(\n", - " df, y, ead, test_size=0.3, random_state=42\n", - ")\n", - "\n", - "# Train 3 different models\n", - "models = {\n", - " \"Logistic\": LogisticRegression(max_iter=1000, random_state=42),\n", - " \"Random Forest\": RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42),\n", - " \"Gradient Boosting\": GradientBoostingClassifier(n_estimators=100, max_depth=5, random_state=42),\n", - "}\n", - "\n", - "predictions = {}\n", - "for name, model in models.items():\n", - " model.fit(X_train, y_train)\n", - " predictions[name] = model.predict_proba(X_test)[:, 1]\n", - " print(f\"โœ“ {name} trained\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Single Model - Unweighted Gini\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 488, - "width": 588 - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gini (unweighted): 0.7646\n" - ] - } - ], - "source": [ - "fig, ax, gini = plot_performance(y_true=y_test, y_pred=predictions[\"Logistic\"])\n", - "\n", - "print(f\"Gini (unweighted): {gini:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Single Model - EAD-Weighted Gini\n", - "\n", - "For credit risk models, EAD weighting ensures larger exposures have more influence.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 488, - "width": 588 - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gini (EAD-weighted): 0.8414\n", - "Difference: +0.0767\n" - ] - } - ], - "source": [ - "fig, ax, gini_weighted = plot_performance(\n", - " y_true=y_test,\n", - " y_pred=predictions[\"Logistic\"],\n", - " weights=ead_test, # โ† EAD weights\n", - ")\n", - "\n", - "print(f\"Gini (EAD-weighted): {gini_weighted:.4f}\")\n", - "print(f\"Difference: {gini_weighted - gini:+.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Compare Multiple Models\n", - "\n", - "Plot all models on the same chart with automatic color assignment.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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002jnzp00duxYystzXjSuWTjvTAeAGHijRDKhJhMAAACjnEwXNjuTCl0FENzGRY/PaJJPx9XHhQgAwOJOpjZDiHqcT7LDzRuYWAW9Bw8eHPd1kB64FQNsybJly8SUCrXRIpmQLqer5iBzoLvxQHNzcJruPoq8s+FJvhyrYThN92zj++UCRcGThQsRaG4O0B2aOxp/+IXOzl17SHZqa2tp+vTp9NNPP8Vchp1L2XIwYYwJgUgmYEt8vigeoxQjmdw5RC44rXXVHGQOdDceaG4OTtPdG/Daovua03TXO5IpG78gNDcH6A7NHY2/LuypLyD3Rc7evXtpypQptGfPHlq5ciW1bNmSWrVqpes6McaEgJMJ2JJ0WqFqazIhikl/zUHmQHfjgebm4DTdfQF/RN0iFxdatRhO011vJ1M20uyhuTlAd2juaDQp2m3adSBZOXz4ML3wwgtUU1MTdP5MnTqVbrjhhowKeycCY0wI653tAKAT2u5yKPoNAADAqJpMVoxiAonxatLl3CR3dAAAwCY1mVxuqTvHHXdceCOK6upqEd0EjAGRTEBeJ5O8Yy8AAACDnUxWrMcEUu8u54GPCQBgCyeT3Jf53DVu+/bttGXLFiotLaXx48dT48aNzd4saZDb+oBtWbt2rfjftWvXpN/j1aTLobOc/pqDzIHuxgPNzcFpuntJG8lkzeBxp+meTfyBQET59mwU/obm5gDdoblMTqadZXuoZTuSFrfbTRdddBHNnz+fRo4cSbm5ubqvE2NMCDiZgC2prKxM+T21mjPFPGue7ztKcwDd7QhsHbpnA7+mJpNV0+Vg78lHMWUrkgmamwN0h+YydZerrotsPuE0AoGASIMrLCyMmTY3evRow7YHY0wIOJmALenevXvG3eUQyaS/5iBzoLvxQHNzcJruXm26nEWdTE7TXc+i39lyMkFzc4Du0FymSKbWbZ1d+JuLes+cOZP27dtH1113nSGRSonAGBMCTiZgSwoKClJ+D7rLGa85yBzobjzQ3Lm6V/trIiKM9KLKX6VbJFOVj9O4onhA0iE3T/yr8Bmji52oiBLK5M5CuhzGGHOA7jbWnAvw13FEfZbGPSfirwt7mpcfPbrHCezZs4emTJkSLOQ9a9YsOvfccyknC+NzJmCMCQEnE5AGdJcDAAA5Kfcepud3vkUba7aatg3uLBT+Lqvx0aNbjtCWGk2RQWAYKPwNgMHsX0u04GGiit2QPhUcWvibU+Q4gkndKW7x4sXUtm1bGjBggKnbBkKgKg2wJdwpgKdUQCST8ZqDzIHuxgPNnaf77INfm+pgylbh72m7q+BgMplsRDJhjDEH6G5TzX9+CQ6mNCjbHXLCOAmOVuKopfz8/OA8rsnUoEEDMhuMMSHgZAK25MCBA2JKBdRkMl5zkDnQ3XigufN031W7j8ymaW7mrZN31iKCyUxK3DlUkIUzZ4wx5gDdbar5kR3Z2hyp2FftzEgmpmnTpnTOOeeIxy1btqQbbrjBEh1SMcaEcK71AUfToUOHjLvL5VqzBqujNAfQ3Y7A1p2nu5/Mdc409jSksxufqku3M2AMhS6iq1oWkSsLkUwYY8wButtUc03XNJAAl4eqO51DbTr3drRUvXr1ovHjx1O3bt3I47GGSwNjTAhr/CIApEg6IZFezTEqF3F8umsOMge6Gw80d57u2k5v7PAZUnI8GUEO5VCJuzgrBUm13c6ubVlEA0uOFu4G+lLiySFPlorKYowxB+huU801XdNo6H1Ejbpk/rlOJa+YCjz5VOCA2kubN2+O67hhR5OVwBgTAk4mIA0o/A0AAHLi03SUK3bXo4aeErIbXu6wpKKhx0WNcccEAOBktE6mwiZERU3M2hpgANXV1TRjxgxatWoVTZgwgXr27AndbQacTMCW7Nq1S/xv0aJF+k4mpMvprjnIHOhuPNDcebr7NJFM7hx7HgC0kUzuLATWwN6NB5qbA3S3qeZaJ5PLnuO3kdjZ1nnbp0yZQvv37xfP2dnUvHlzatLE+o5FO+uebZAwBGxJWVmZmDLqLgfr111zkDnQ3XigufN092lqMnnInhcpPk0kkycLTibYu/FAc3OA7jbUXNRj0njXXbkZb5fTsbOtc4c2xcHE1NTU0NSpU8nv10QLWBA7655tEMkEbAl3EkgVdJczXnOQOdDdeKC583TX1mRy57gcEcmUjRpBsHfjgebmAN1tqLk2iolx4fJVd91NZMCAAbR161ZasmSJeF5UVESnn346uVzWP27bWfdsg70U2BIOm0wVbednpMvprznIHOhuPNDcebo7JV1O210uG5FMsHfjgebmAN1tqLlm7BYgXU5/3U2Em2ScffbZIiIoNzeXLrroImrYsCHZATvrnm3gZALSoI1kQrocAADIgVOcTNrC39nqdgYAAJYEkUxSkpeXR5dffrmIYvJ44K6wI9aPOwMgCgcPHhRTRjWZ7HmNYSvNAXS3I7B15+mu7S7ntmlNJm3iSDYKf8PejQeamwN0t6Hm0ZxMOXA66K67zgQCAVqwYAEdOXIk5jIlJSW2czBZXXcjsdcvB8AvbN68WfxPJXxS213OAxer7pqDzIHuxgPNnae7V1v424aRTHxSrke6HOzdeKC5OUB3G2qOSCZzdNeRqqoq0TFu9erVtGrVKpo4caIt6i3ZXXejgZMJ2JLGjRun/B6tkynPGeOZpTUH0N2OwNadp3tkupz9DgBRKpOQOwvpcrB344Hm5gDdbag5nEzm6K4Te/bsoTfffJMOHDgQdMrMmTOHRo0aRU7AqrqbAZxMwJa0bds28+5y9ruRbTvNAXS3I3rZOkei/FyxgjZXbye/tiVzmvgDRJurm1C5t5Acw8blWf/IfTUDwxT/ZF8BfeupJDvXY8pWJBPGduOB5uYA3bPAgfVE278j8tUkp7nyINSRPjVqD0fOQ+Fv256z16tXj/z+8AsyTps77rjjqEmTJmR3rKq7GcDJBKQhoruc/W5kAwBszGcHv6YZ+z7N6mceqRtO1b5OWf1MZxLeVnieKJlQTXYHhb8BAIY6mOb8Nnp0kVFwFKoNI1HBUbiQ9/jx4+nll18mn88nnE4XXHCBIxxMIBw4mYAt4XxeprAw+bv36C5nvOYgc6C7czT/6cgyyja1vm5Z/0xgH7KR9o0xxniguTlA9wzZ8b25DibGU2Du+m2ClW29devWdOaZZ9LPP/8sHE5c4NspWFl3o4ErGNiSNWvWiCkV0F3OeM1B5kB352he7U8uvSAV/JSX9c8E9qB7kYeK3ZmfxmGMMR5obg7QPUPqLJBi3HKg2VtgC6xu68cffzxdffXVjnIw2UF3I0EkE7AlHF6ZKuguZ7zmIHOgu3M092qKT3cv7EQNPZmdYH1Y7QmrNdQ8by/l5tSRHamrO3qHPDdXv1OTPFcetchtSnmuXLIzLfLcdEbj/Kx8FsYY44Hm5gDdM0QbxVS/NVHj+NG0hw4fralUUr9+pmsnatCOqMvZmX+OBJhp61x/ct68eZSfn0+DBw+OukxOTg653c4rjosxJgScTMCWdOnSJeX3aCOZ8pw3tllOcwDd7Yhetq7tcDaq0UnUq6hLRidyH+w52qFF4e52nahdAQ7tIHkwthsPNDcH6J4hfs2JdItjiY6bFPctzopTsQ9m2Tqni02fPp3WrVtHLpeLWrVqRe3btydZwBgTAulyQBoiusvB+gEABqJ1MnnInfWW9igEDQAAQBf8mihZF25ogBC1tbX0/PPPCweTMBe/n6ZNm0aHf4lmA3KBy2xgS3jg0rbATEStZvFcRDLprjnIHOjuHM19FP6Z7gw75PgiO9qTOwst7c0Ctg7dZQG2Dt1tieZGSTJOJti6OZihe15eHvXr1y9sXmVlJW3evJlkAfYeAk4mYEuWLl0qplRAdznjNQeZA92do7m2JpM7J8NIpkDAUZFMsHXoLguwdejuiHS5JI5hsHVzMEv3k08+OZgyVlxcTFdeeSX17t2bZAH2HgJxjsCWeDypm25Edzm4WHXXHGQOdHeO5r4sO5m8Dotkgq1Dd1mArUN3WdLlYOvmYJbuXIfp/PPPpw8//JDOOOMMqp+Ngu82AvYeAleNwJYcc8wxGXeXg5NJf81B5kB3Z2jORbr9mnQ5jw5OJo+NnUywdeguC7B16O6ISKYknEywdXPQW/e6ujrKzY3epbWoqIguvPBCkhHYewjEcgBpiIhkQk0mAIBB+KKU6XZnWPjbGy1djmzsZQIAAGBd/N7w5yj8LR1cc+jzzz+n5557jmpqaszeHGBh4GQC0oDucgAA08YfbcHUrNRkclYkEwAAAAsTgJNJZriI9xtvvEFffvkl7d27l2bMmCGitAGIBpxMwJasXLlSTKmASCbjNQeZA92dobm2HlM2ustFi2Syc00m2Dp0lwXYOnSXJZIJtm4Oeuj+3nvv0fr168PWsWDBgqyuw+7A3kOgJhOwJbW1tSm/BzWZjNccZA5015dozp/q2uqYr6VLnfYOcBo1mfyBAKndSnUaHxN/Wo6Nu8vB1qG7LDje1rW1eyxCbU2VpbfPdk6mJI5hjrd1i6KH7qNHj6bNmzdTVVVVsMi3243aI3rrblfgZALyFv7GuKi75iBzoLs+7KrdSy/vmkpba3ZGvljvl//r3yE9SbYmE0csvbijkr46WBPhWAr7PPv6lwSwdeguC4619f1rib57nOjwdrIi/ZQHa8zdDseAwt9SjTENGzakCy64QKTMFRcX00UXXUTt2rXL+nrsjGPH9jSAkwlI0SKSs0q0NZnQXU5fzUF2gO768O6+T6I7mAwk2ZpMPxyqo88PJC6w6bFxFBMDW4fusuBYW1/4jGUdTMAcJ5Njbd3i6KV7ly5d6Nxzz6XOnTsLRxMwRnc7gppMQAq0UUwMnEwAyEtZ7R5T11/fXY9yc5I7Gdlek1xqR7M8HNIBACZyeBvkl4l6zczeAqADu3btivt6v3794GACCcEZKbAlGzZsEFOyaKOYGA/S5XTVHGQH6G5ctzej8OR46Pwmo5OunxStwLeWAhfRpS0Kyc7A1qG7LDjW1rU1e4BzaT+CqFFXeW3d4qSju9/vp9mzZ9MzzzxDS5Ys0W3bnAzsPQRiuoAtOXz4cEad5Rjc9NdXc5AdoLs+aIt6X978XOpccLS2wKpVq8X/Hj2667LuRp6GlOfKTXp5r8bHNKB+Ll1eWhQ2r3muizwue6fLwdahuyw40tbZGa51Mg27n6i4JVmFVatXif89uvcwe1PsTV59ooIG8tq6DUhV94qKCnr77bdp48aN4vn7779PLVq0EBPQT3cnAycTsCWcE5xpupwHcXy6ag6yA3Q3xsnU1NOYWuQdDf0v7nzUgVMvT6kAbi7aSKYSj4ta5TsvFBO2Dt1lwZG2Hi06tH4rovqtySq07dXo6IN61hjbZcCRtu5A3TkCR3EwMXV1dTRlyhS68cYbKS8vT4ctdCaw9xBwMgFbUi/FE4RokUzoLqev5iA7QHd98FG459md47Ks5r6As7rIxcJqussCdIfmWcEf5UQryeYGRgFbh+aykKqt9+nThzZv3kw//vijeO52u+nEE0+k3Nzko64Bxhg1iOWIAe9od955J/Xo0UPsqI0bN6aBAwfSI488QpWVlZQN2GN8xx13UO/eval+/fpiPV27dqWbbrqJli9fnpV1gKOg8DcAIF5NpmQ7vZmBNl3OulsKAJAWf11a3ccAANZg9OjR1Lp1ayopKaGrrrqKBg0alHTtSAC0YPSPAuehXn755XTo0KHgPHYssXeXpxdffJE++OCDjELinn/+ebr11luptrY2bP66devE9NJLL9Fjjz1Gt9xyC6w2Ctu3H22Ry4Nh2k4mXKnpqjnIDtDdmHQ5tZPJapr7NOlyHoee9FlNd1mA7tBct0gmizmZYOvQXBbSsXWPx0MTJkwQUUyI+jNOd6eCSCYNixYtEjsYO5iKi4vpwQcfpG+++YbmzJlD119/vVhmzZo1NGbMmLSLe02ePJkmTZokHEwNGjSgv/zlL/T111/TDz/8IJxP7Lzi137961/T1KlTM/+VHcjevXvFlCzeKOc+Tk05sYrmALpbFX/AT/ynxqNyMlnN1rWRTB6Hjl1W010WoDs0zwoBr+WdTLB1aC4LsWx91apVVF5eHvN9HMUEB1P2dZcRa43+FuC2226jqqoq4c399NNPafDgwcHXTj31VJHOdvfddwtHE0caPfDAAyl9PkdE8ToYdmKxc4nT5RQGDBggnFzDhg2jpUuXCkfTWWedJZYFIdq2bZuSHLWaSKY8N5FDgwEsozmA7napx8S4VUloVrN1WZxMVtNdFqA7NM8K2s5yFnQywdahuSxobd3v99Ps2bNF4ARH2Vx99dXiWhfoq7vMIJJJxffff09fffWVeHzttdeGOZgUuE5Tz549xeMnnnhCVN9PhQ8//JB2794tHrOzSe1gUnuRH3/8cfF4165d9Oqrr6a0DhngGlk8JYtXc02JznL6aw6yA3TXP1VOmy5nNc213eXcDvWQW013WYDu0FyWwt+wdWguC2pbr6iooP/+97/CwaSkdH388ccmb6EzwRgTAk4mFTNmzAg+Zg9vNFwuF1155ZXi8cGDB+mLL76gVFCq9jNnnnlmzOVOOeUUKigoEI/ffvvtlNYBEneXy4XlAyAt0Z1M1h0UfJJEMgEAbIwNIpkAkBGusaQt8cLXo9zkCgC9sO5ZtQlw6hrDuajHH398zOWGDx8efDx//vyU1rFv377g4xYtWsRcjkMYFQ/0ggULyOuNcvCWmFRzXrWFv+Fk0l9zkB2ge/bxBSLT5axdk0mOSCar6S4L0B2a6+Jk4jHVYmMVbB2ay4La1jlogUux5ObmBp1OZ599NrVr187krXQeGGNC4BaDipUrV4r/XHg7Xp5qjx49It6TLOraSvEKrwUCgWB3Oy4Czh3n1OuVHaV6f9OmTdOLZLJWBLcjNQdy676+ajOtrdoUUWDbClT5a+LWZLKK5uVeP31XXktLK7xSRDJZRXdTYYfizh+IDmwwbJXesrKjD0pLDVun7DhS86rQTVSrRjFhjIHmsqC1dQ5sGDt2rGhkNX78eHQ/M0h3mbHeEcAkqqurgx7fNm3axF22UaNGItqJc1y3bt2a0nqUek7MvHnzYkZMcZe7I0eOBJ9v2bIlJSfTtm3b4r6+c+dOsjPNmzdPaXnUZDJecyCv7j8fWUHPl71FdkJdk8kKmtf6A/SHDYdol7ZrgYOdTFbQ3XRWTiVa9oahqwy6OTQ+AgDNM8KCTiaMMdBcFqLZet++fcW1ZF5eninbJAMYY0IgXe4X1LmqyXRyU9o7qh1BycB1mJQoKS7uHS01gDsA3HfffWHztLm0yVS3jzcNGjQorJ2lGn6+ePHisKLmGzduFPPU33fHjh1injoFkB/zPH5NgZ1xPI8/Q4E/m+dp1718+XIxnyO5FDiKi+exI1D9fi6gro4G4+e8nFJYneHXxfbs2hO2Hn9dtfhcBV4fL8frT1YL/l6pasH6JavFsmXLxHw1a9eujdCCHZA8T60FF4zXasE1xHie2jHKnRR5nloLtr9oWvD7+fPUqZsbNmyI0IK9+Dxv//79wXls5zxP7dxkm+Z5mzZtCs7jqD2et3r16rB1c6fFJUuWhM3jDo+8bE1NKCqF88t5nhIFyJSVlYl5e/aEbODAgQNintoZq2ixfv364DyfzyfmrVixImzd/Jzn8+sK/D6exx0ktVrw+rRa8HYp8PbyPHV+vKIFv9ayZcvgfNZBqwXrxcvye5LRQj3uKFood18Y/g6ZaDG37GhxSbuQQzkiXY5tlr8L7wOK5sr4xbauwPsAz9NGsirjF79fO36xfSnwPsjzeJ/Sjl+874r3VXmjOpiYsm1bxVight/LY4bdxnL1+MWa8xRvLOflFfhztOOX3cfy6jWfhC0HgF3xuzwRxzU+XvM8Pn6r4WMaH+cTHdf4fIHnqc+J+bxCe1xTxnL1cY33cx5T1GOxEWN5ovHLiWO5WgueOD1LtrHciPNytluer5yX8+tTp04V87iJlHIeoz4vVxxMOC9P/7xce42iPi9XzmNWW+y8XLlGUY9ffA2kHb+yCZxMv6AeHJLx8Obn54v/6h8rGdjBc+ONN4rHbDxDhw6lmTNnCoPjbfj222/prLPOElX/1duR6npAON5A+K1/j0tTSRcAkDWqKXRQtQM9CjuTy2KFvyu01b5/ITeHqF0gMt0POAO3L3QuAoCd8Tbpa/YmACAF7Ezg68Zp06aJDnJqBx4AZpETULumJYajHJQQNy6ONnny5LjLc24re6N79+4dcfclEXwn5/zzz6cPP/ww5jIDBgyggQMH0jPPPBPsfHfOOedkNV1OiWZiD3qiFEGrodzFql+/flLLT19J9JtPQ897Nyf64BK9ts6ZpKo5kFf3x7e9ROuqQxFqbfJKqWmuNVvTt8hrSqMaDqMid6GlNF9QXkv/3BoeKXtqo3wa0SiPuhcdLd7pNKygu+nMuIyoVhW53OwYorwSXVdZ98td8Nw4tSgBNE8aLvbdoANR9/OIPEdvyFoFjDHQ3GlwlNNrr70WlhVQWFhIl156KTVs2FDu46kJ2HGM2bZtmwiCybZPAGcUv6A2hmRS4JSQzGRS66JFQb3//vv08ssv03/+85+wMFR2dF1//fX0hz/8ge64446wOlCpYDenUaooqSv9+vVLr/C3tYIWHKk5kFd3H4XvcCc3OIGGNRhAdsEKmvs093/aF7jpxtZH07SdihV0t1yHrr4TiZro2/RjxS/pH1LrbjDQ3BwwxkBzp+Fyuahbt25hTibOfuEC3/3798e4bjAYY0LAyfQL3N6xSZMmIl83URQQ50oqTibF85fOoHDdddeJib2enKtbVFREpaWl4jVGnafdq1evtNbjVBo0aJDS8nWa0iZwMumvOZBXd18g3Mnktlgqmh009wbkKPZtNd2t1wZe/9M06G480NwcoDs0dyKDBw8WEShco4nr/nLZlcaNrRk97nQwxoSAk0kFO3K++uorUWyNi6gpBbq1qAvBqbvFZRJFpQ2r4/zan3/+WTzu1KkTWiFq6NChQ0oao7uc8ZoDeXXXOpm4qLadsILmXk0kk5uc72Wygu6WczIZ0KELuhsPNDcH6A7NnUhOTg6de+65omD5qaeeSq1atTJ7k6QFY0wIe91e1plhw4aJ/xyltHDhwpjLzZs3L/iYC3frwRdffBHsiMA1okBm1GrT5ex1zQuArfBGRDJhh0tdQ/kimaTHz/tNwPJt4AEAABiPutthtFIsl19+ORxMwDLAyaSCvcAKr7zyStwCawwXVBsxYkTWfxSuz/TAAw+Ix7m5uaJGEwiHW0Kq20KmGsmEdDn9NQfy6u6n8B3OZbNDjRU01zaXc0vgZLKC7paKYmJc+jtopdfdBKC5OUB3aG5XlixZIppBcSv7ZICtmwN0D2GvM3+d4W5rJ510knj80ksv0YIFCyKWeeyxx0TOK3PbbbcJJ5CauXPnirBFnq666qqo6+EIJe4wFw1Ok7vlllto/vz54vk999xDHTt2zPi7OQ3+DZTfIa3C3wis0F1zIK/u2kgmjwF1ZZymuTZdzsMdmxyOFXQ3Fc1+Y1Qkk/S6mwA0NwfoDs3tBpdv+eCDD+idd94RndD5f7yIJgXYujlA9xD2OvM3gCeeeEKkwHFl/tNPP53uvfdeEa3EzydPnkzPP/+8WI4r+d95551pp8KxI+niiy+m4cOHU7t27ai6ulp4qfnzlVpMZ555Jt13331Z/X5OKtSeCij8bbzmQF7d7V742wqayxjJZAXdLRfJZICDVnrdTQCamwN0h+Z247PPPqMffvgh+JzrBn/55Zd0yimnxH0fbN0coHsIOJk0cLvHKVOmiLzWQ4cOCSeTFnYwsVdZW6w7FbibHDu0eNLCUVBXX301Pf3005SXl5f2OpxM9+7dM3MyIZJJd82BvLpHOpnstcNZQXMZI5msoLv10uX0P02TXncTgObmAN2huR3rBS9fvpyOHDkSnMe1g7m0Cl8vxgK2bg7QPYS9bi8bxNixY0VU0R133CEcSkVFRaL+0oABA+jhhx+mRYsWUZcuXdL+fE7Je+SRR0SkEqfC8ecXFxeLdU2aNEmk6XG6HhdxA9nBq8lC8MDyAdANXyDcq+smezmZrAAKf8ta+Nv4mkwAAACsCQc0XHTRReRyuUTXc64fPGbMmLgOJgCsACKZYtC+fXt6/PHHxZQKHL7I3uV4tGjRgu666y4xAWOoReFvAAzDS9qaTLhQTllDdJeTD39d5DxXeN1HAAAAcsHXpBwA0bJlSyotLTV7cwBICjiZgC1ZunSp+N+nT5/0usvhmld3zYFzdK8LeGny7vfp54oV4nEivJpl7FaTySzNV1bU0Ys7Kqms1hfhZHJLcNfScN1rjxB9/y+i3Uujp6oZTsCUSCYrjDGyAc2huyzA1pPj4MGD1KBBg5gRSlzOBbpbH9h7CDiZgC1JFC2WsLucva55bak5cI7u3x1aRAsO/5T2++1Wk8kMzXmdT22roD3aAnK/4HG+j8l43VdNJ9rxPVkWds4a4KC1whgjG9AcussCbD0x3PBp1qxZNHr0aFGaBbrbF9h7CDiZgC3p27dvSsuju5zxmgPn6L69dlfa73WRixq402+SIIvmNQGK6WBimkjgGTdc9/LNZGmKmkozxsgGNIfusgBbj43X66WPPvqIFi5cKJ7zY06Ja926NXS3KbD3EM4/awUgWiSTvQIrALBUt7hUOKPRSVTkLszq9jgRbTc5Na3zXXRKQzSCyDqWSJGLAUf/9b7c7K0AAACgE5s2bQo6mBifz0dTp06lyspKaA5sDyKZgBRoazKhuxwA6XeLG1jcl0Y2HJLwfQ09DajEUwypk9I4ct4fOtSnxh4Xtcp3kUuCmkymd3Prfh5R25PIEtRvRZRbZPZWAAAA0AnuVD5kyBD65ptvgvO6d++O7uLAEcDJBGzJmjVrxP9u3boltXwtIpkM1xw4R3efpltcQ08JtSvIPJzbqpiheTQnU+dCN9VzyxNwbLju2m5uxS2JGnch2bDCGCMb0By6ywJsPT6nnXYabd++nXbs2CE6yGUr3Qq6mwN0DwEnE7AlVVVVKS0f0V1Onus20zQHztHdq0mX89iskLcdNI+WLueRLHrJcN21aaAuOU+JrDDGyAY0h+6yAFuPj8vloosuuogqKiqoRYsW0N3mwN5DyHlGBWxPjx49Uloehb+N1xw4R3dtTSa7dYuzg+beKJFMbrl8TMbrrq3JJKmTyQpjjGxAc+guC7B1op07d1JeXh41adIkqkbFxcVigu72B/YeQs4zKmB78vPzM3MyOfsa2RKaA+foLpuTyQzNo0UyOVtlC+gOJ5M5ugNobhKwdWhuND/99BN9+OGH1LhxY7ruuuuEs8kIYOvmAN1DIGkIyNldDpYPQNqFvz3SuT+Mr8nEUUw5kqXLGQ6cTAAAAHTA6/XSzJkz6b333hOPd+/eTbNmzaJAnE6yADgJXGoDW7J582YxJQu6yxmvOXCO7l5N4W+XwyOZzNBcmy7nkdC/ZLju2u5yDrdrK48xsgHNobssyGrrfr9fFPRWs2TJElqxYoUh65dVd7OB7iHgZAK25ODBg2JKFnSXM15z4Bzd/ZIV/jZDc5/m7qaH5PMyGa47IpnM0R1Ac5OArUNzo+C0uAkTJoSlT5144omG1eyBrZsDdA+BmkzAlnTs2DGl5dFdznjNgXN013aXc+c4+/6EGZprI5lkK/ptiu4BFP42RXcAzU0Ctg7NjYQLfZ977rn07rvv0rhx46h3796GrRu2bg7QPQScTMCWlJSUZFaTydmBGJbQHDhHd9kKf5uhubbwt4zpcobrjkgmc3QH0NwkYOvQ3Gh69uxJ7dq1o3r16hm6Xti6OUD3EHAyASmI6C7n7EAMADJmZ+1uWlO5kbwBLx3yHQl7zY3C30lR7QvQwsO1dEAbShmFbTXhy7iNKvrNdYnKFhId3kHS4asNf+7CKREAAIDk4bpLmzZtoqFDh8ZcxmgHEwBWAGdUwJaUlZWJ/6Wlpek5mZwdiGEJzYF9dd9YvZX+uf1l4WCKhtNrMmVDc+4g8+Dmw7S6MrqGiTAskmnRc0TrPzZoZRZHUicTxnZoLguwdWieLfgYv3DhQvroo4/I5/NR48aNRdSSVYCtQ3ezQTwHsCW7du0SU7Kgu5zxmgP76v7D4SUxHUxMbo6zL8azofnOWn/aDiYmz5VjTBTTps/1X49dcOWSjGBsh+ayAFuH5tli1qxZYmIHEzNjxgzat28fWQXYOnQ3G2dfKQDH0qpVq4xqMuXBvaq75sC+ulf4KmO+lpeTSx0K2pCTyYbmR3yaSt4pckw9Aw7P/rrIlDFZKWxKVF/OMQ5jOzSXBdg6NM8W2kjnmpoa+uabb2js2LFkBWDr0N1s4GQCtqRZs2YpLa9Nl/PAyaS75sC+uvsofIdp6mlEzXKbULG7iE5peCLVcxeRk8mG5j5NIW9OMOxdnPiQm0M51KXQTec1KyTDC18zzXrLF9FT1JSo+3lEDu+aGAuM7dBcFmDr0DxbDBgwgLZt20aLFy8Wz4cMGUIjR44kqwBbh+5mAycTcDw+P5FfE1SAmkwAJN9N7sSS/nRW4xGQLAW8mjGn2JND93WwWBevaE6mwXcTFTQ0Y2sAAAAAW5CTk0Nnn302HThwgE488UTq1auX2ZsEgKWAkwnYEh7UmUaNGqUcxcSgu5y+mgN76651Mjm90LcemmsjmTxkVCXvFGsyWaj4NcYY6C4LsHXoLgt2t3Uu8M0OpWjk5ubS1VdfHfN1M7G77nYFuoeAkwnYki1btiTvZIpyHYVIJn01B/bW3RcI98y6RbKXPGRDc20kk2Hd4lKtyWQhJxPGGOguC7B16C4LdrZ1Tofj7nEXX3wx1a9fP+oyVnQw2V13OwPdQ8DJBGxJkyZN0u4sx6Amk76aA3vr7qVwz6xbskimbGiudTK5rXgeqolYM9vJhDEGussCbB26y4IdbZ2jl3744Qf65JNPRPe4t99+m6688kpyu+1zLmRH3Z0AdA8BJxOwJW3aJN/dqjaKkynPPscJW2oO7K27PyC3kykbmnu16XJWvNsZrSaTib81xhjoLguwdeguC3a09QULFtCnn34afL5582aaM2cOnX766WQX7Ki7E4DuIeRspQKkwhvlZj0imQCIs89IXpMpG/jsEMmkdTJxFJMVnWEAAACAQRx77LHUoEGDsHk7d+4UUU0AgOSAkwnYkqqqKjElAwp/G685sLfu2sLfLslau2dD88hIJrK+k8lkZyLGGOguC7B16C4LdrT1oqIiGj9+fDA9btiwYXTFFVfYKl3Ojro7AegeAulywJasWbNG/O/Xr196Tib7HCdsqTmwt+4+TU0mj2SFv7OhuTaSyZrpcj7L1GNiMMZAd1mArUN3WbCrrbdu3ZrGjh1LBQUF1KNHD7IbdtXd7kD3EHAyAVtSXFyc9LLa7nKctuKy4PWekzQH9tY9orucZOly2dDclt3lTHYyYYyB7rIAW4fusmBlW+eoE3YixeoQx2lzdsXKujsZ6B4CTiZgSzp37px2dznUY9Jfc2Bv3bU1mWRzMmVDcy+Fe5ncVoxk0naXM9nJhDEGussCbB26y4JVbZ1bzU+bNo0GDx5MQ4YMIadhVd2dDnQPAScTcDy1musodJYDMrKsYg29t+8zOuA9lHDZSn+V1E6mbLQ/nrKrygaRTFonE35nAAAAzj4+f/fdd6J7nN/vp9mzZ4vUuPbt25u9aQA4CrmquQLHwB0eku3ygEgm4zUH1tK9zl9HL5VNoW21ZVThr0w4BbRROJIdKjLVfGmFl+ps0V1Omy6XS2aCMQa6ywJsHbrLgtVsvaysjD755BPhYGL4P0c0HT58mJyE1XSXBegeQq4rB+AYli1bJqZ0ajIhXU5/zYG1dN9Tt59qArVpv7+hp4RkIlPNN1VrurYRUeNcl/UjmUyOWMMYA91lAbYO3WXBarbesmVLGj58eNi8Fi1a2KpznB11lwXoHgLpcsCW5OYmf8dd210O6XL6aw6spbu2W1wqDCjuQ81zm5BMZKq5NnqSOb1xAVkOv9dSNZkwxkB3WYCtQ3dZsKKts5Np27ZttG7dOjr55JPplFNOIZfLgjeCHKa7DED3EHAyAVvSq1evtJ1MiGTSX3NgLd21hbw9OR66tdXEhO8rcRcLB1OszitOJVPNvYHwXLn+xbnUOt+Cd0kt5mTCGAPdZQG2Dt1lwYq2zuc0559/Pu3YsYO6dOlCTsSKussAdA8BJxNwPNqoglwLXusBoCe+QPhOkJvjoa6FHSC6XnprnhdasiCT9ZxMAAAAQDbgGks1NTXUtGnTqK8XFRU51sEEgBXAGSWQrrucFUujAKAnPk0kE7rF6YtXU/Tbkp3lGI1doLscAAAAu7Np0yZ6++23KS8vj2644QYqKLBgujoADgeX28CWrFixQkxpRTLB6nXXHFhL9wgnE4Z+XTX3adLlPFZNN4zoLmfufSeMMdBdFmDr0F0WjLT1QCBA33zzDb322mt05MgR2r9/P82YMUPMlw2MMdDdbBDJBGxJXV1d8stqu8shXU53zYG1dPdqErg8JncRc7rmtolkiuguZ+4pAcYY6C4LsHXoLgtG2rrf76fly5eL/wqrVq2iH374gQYNGkQygTEGupsNnEzAlvTu3Tv97nKIZNJdc2At3ZEuZ6zmWieTVUsyWa0mE8YY6C4LsHXoLgtG2rrb7aaLLrqInnvuOaqqqhLzunbtSn369CHZwBgD3c0Gl9vAlvCBhKe0Iplg9bprDqylu7bwN2oy6au5fdLlrOVkwhgD3WUBtg7dZcFoW2/YsCFdeOGF5HK56JRTTqFLL72UCgsLSTYwxkB3s0EkE3A86C4HZAc1mYwlIpKJLIrFnEwAAABApnTu3JluvfVWatSoEcQEwCQQ0wFsyfr168WUDLUo/G245sBauvsiajLBmaCn5pGRTGRNLNZdDmMMdJcF2Dp0lwU9bP3QoUP07bffxl1GdgcTxhjobja40gC2hLtGJItXcx2Va9mwAudoDqylu1fbXS4H9xf01Dyy8LdFvUwW6y6HMQa6ywJsHbrLQrZtfcOGDfT2229TZWWlSIPr169fVj/fKWCMge5mAycTsCVcyC/dwt+oyaS/5sB83bfW7KDN1Ts4XIXWVW2yR00mjgDau5zo0DbTNqHCF6Bcfy35KEArl2xN6zM6VtRRA5WnqbMvl+hwLlmOg5ss1V0OYwx0lwXYOnSXhWza+oIFC+jTTz+lwC/RwrNmzaLS0lJq0aJF1tbhFDDGQHezgZMJ2JKioqKkl0V3OeM1B+bqvuDQT/T67ndjvu62apWgZW8QrZxq6ibUI6JeGX5GT+2MnWQPTE6XwxgD3WUBtg7dZSGbtl6vXr2gg4mpq6ujzz77jC6//PKsrcMpYIyB7maDnAngeCK6y1n0+hqAbPFV+Q9xX8+1aoHn9R+ZvQVy484zewsAAACAqPTt25cGDhwYfN6tWze64IILoBYAFsSiVxoAxGf79u3if+vWrVPvLgfXqu6aA3N1r/BXxn29c0F7shx8d7L2sNlbITdNM43hygyMMdBdFmDr0F0Wsm3rZ5xxBpWVlQkH07BhwyjHqjUPTQZjDHQ3GziZgC3Zu3dv0getWm3hbziZdNccmKu7T1Pou2VeMypyFZIrx03dCzvSyIZDrPcT+b2R8xp1NjS6pqzWTwdV+bXcFS7Xlf4JLA81DT0uqm/Z9nK/4M4najuMqOXxpm4GxhjoLguwdeguC9m2dY/HQ1dffTW5XDiZN1J3kBzQPQScTMCWtGvXLullIyKZkC6nu+bAXN213eQubHoW9SzqQpYmmpNpyO+J6hlX0HPm9gqac6Am+Hx043y6phVXaQJGgDHGHKA7NJcF2Lr1NS8vL6f333+fzj77bGrYsGHUZeBgyr7uIDtA9xBwMgFb0qhRo6SXRXc54zUH5uruC/jt0U1OjcYxJjC4dpRXVVCU8SAM31AwxpgDdIfmsgBbt7bm69evp+nTp1NlZSVNnTqVrrnmGhG5BPTVHWQP6B4CsYYx2Lx5M915553Uo0cP0c2gcePGotjcI488Iga/bLBp0yb63e9+R8cff7zw1ufm5or1DBkyhP7yl7/Q7t27s7Ie2dEW/s6zwfU2AJmgTZezbDc5Nf66yHk5xp5c+sJ9TCJdDgAAAAD6smLFCvrf//4XvMbasWMHffzxx5AdAJsC93AUOEyT22EeOnQoOI8HvR9//FFML774In3wwQfUpUv66Sevv/46TZo0iaqqqsLmHzhwgBYsWCCmJ554giZPnkyjRo1Kez1Oz3lt2rRpwmURyWS85sBc3X0U7mTy2CGSyW/FSCZDVy89GGPMAbpDc1mArVtX806dOokb7fv27QvO27hxI1VXV1NBQYHu2+k0YOvQ3WwQyaRh0aJFNGHCBOFgKi4upgcffJC++eYbmjNnDl1//fVimTVr1tCYMWPo8OH0OiHNnz+frrrqKuFg4rxiLmA3Y8YM+v777+ntt9+msWPHiuX2799P55xzDm3YsCHT39mRXROUzgmJQE0m4zUH5uqurcnkzrHBUB+tJpPhTqbw526kyxkKxhhzgO7QXBZg69bVnB1JfP3FWR0MZ5LwdRccTPrqDrILdA+BSCYNt912m3D+cA7wp59+SoMHDw6+duqpp1LXrl3p7rvvFo6mxx57jB544AFKlYceeoj8/qM1U5588km66aabgq9xSt4FF1wgUvUef/xxsS38/6mnnkp5PU6mRYvkiwGju5zxmgPzdPcH/BSggP1qMlnAyYR0OXPBGAPdZQG2Dt1lIRVbb968ubjRzjfxuXRIDm70GKI7yB7QPYQNbm8bB0cSffXVV+LxtddeG+ZgUmDnT8+ePcVjTmerq4tSRyQBHBnFNGnSJMzBpOaPf/xj8DGnzoFwSktLxZRWJBOsXnfNgXm6a+sx2acmUxQnk8ERWNp0OUQyGQvGGHOA7tBcFmDr5mvu9XqDN9qj0bdvXxo6dCgcTFnWHRgDdA+By20VnLKmwCls0eD0tiuvvFI8PnjwIH3xxReUKrW1teJ/x44dYy7ToEGDYP6ysjzITuFvD6weOBgfRZ682aImk9Y5xlFMBt/FRCQTAAAAoA9cd/all16iL7/8EhID4HBwua3i66+/Fv+5mxx3fIvF8OHDw+orpUr37t2DBe1iwTWhlKJtyvIgXB91YfZUCn+ju5z+mgPzdNfWY7JPupwmKtR1tC6DmTWZUPjbWDDGmAN0h+ayAFs3T/O1a9fS888/Tzt37qR58+bRunXrTNgaeYCtQ3ezgZNJxcqVK8V/7hrHNZliwcXotO9JhRtvvFH85w4Kzz77bNRl/vrXv0YsD0Kwgy6ek04NussZrzkwT3efbZ1M2kgm47fZp6ll5UE9CEPBGGMO0B2aywJs3RzNlyxZIrplKx21A4EATZ8+XWSEAP10xzm78UD3ECj8/QvcIlOJHGrTpg3Fo1GjRiLaqaKigrZu3Uqpcs0114ioqddee41uvvlmWrhwIY0bN45atmxJW7Zsoddffz2YunfffffRaaedlvI6tm3bFvd1vpNgZxo2bJj0sl7NtWuuDa637a45MEb3zdXb6Z19n9C+ugNhhb8tkS7H9Y1Wv0u06XMib3Xi5f21hhf9/ulwLU3fXUUHfwlh2q8p4IZIJmPBGGMO0B2aywJs3RzNeRo5cqRoqKTA11FcnwnopzswHugeAk6mX+BOBgrFxcWUCMXJdOTIEUoVt9tN//3vf0UHhb///e/04osviknNiBEj6N57703LwcS0bds26WVXrVoV5ljj5zU1NdSrV69gK1H2zHLoZefOnYP67Nixg/bs2SPey0XMlegsdnA1a9aMWrVqJeaxThwWW1JSEqxDxQXTV6xYQfn5+WGRYcuXLxcHHS78p3SV4PfyZ3DaoNLKlF/j3G7embl+FbN7927hPGNnHXeoYMrLy+lINb8nP7iOgLeWFi9eKX5DjloT8wIBcaeFI9iOOeaYpLTg9/JnpKIF28v69euT0mLZsmXk8/moX79+wXkcblxZWRmmBTsmWYsOHToEtdi1axeVlZWFacF3jDZv3kyNGzcO2gffVeJOiWotuCDj0qVLI7Tg9XJ9sNatWwcj/TZs2CD2HbUW3L6THba8Dl4Xw895Pm8LbxPD7+P38zbztjP8+RwdyN9NnSbK28O/EduFAm83bz9rxtox/P34e7K2rDHDOrAe/Bvwb8GwXqwb/1aK7StasH2znTOsP/8O/NuzDSjw78W/W+/evcX+zPDvyr8vd6AsKioK06Jdu3bCOa3WgjtQKEUZ2Z7Yrtie27dvH1cLtlOmT58+9ELZW7TfW06JcP8StKpowb+hUvNN0YKf83zlt2Zby0QL2rWYOm97ldKF/T3KAYr3Qd4XCwsLqVu3bmFa8FjAWiisXr1a3DTgBg15eXli3qZNm8RY0KlTJ6pfv76Yt3H7Tnpkfx754kQrceFvZfzi9/H7xbZ5vWKs4s9XGkGoxy/eHq7fpx6/eLt5+xm+ObF//37xWysnJMr4xTahdCfhbeZtZ9thG2L4u/F3ZBtjW1NYvHix0J9/BzuN5erxS7H9WGN5LC0wlqc2lvO4wuMLz+PXWHcei9mGMJYbM5bzazx+8aQ+rkUbv+Id14wYy1kL9fjF4wKPD2otePzgcSSRFnqM5TxW8Jih1oLHVx5n1Vrw2MM6Ku9jMJbrM5arz8tZcx7LuZkS/yaLFi0S54ZnnHFG8PfCWJ6dsZzBebl55+W8jyjLrrbRWK4sk23gZPoFNgQFxRjioVzUKqGfqcJGypFMPGhEgzvKcXE8Nk7FwEB6+PzhF5GIZAJO4IivIikHU0FOvinpcoXV2zN6vy+3RNcD1E5fTlwHE9MQoUwAAABAxrCD6txzzxUX8eyEUq6jAADOJCfA4QFA3IVR7phOmDBB5A7Hg72d7KFnL2EsR1EsvvrqKxHFxHdj2OP5t7/9jUaNGiU8++zNfO+99+j+++8Xd2L4DjKHl6o919lKlxs0aJB4zHcWEqUIWg2l414yDsHjnifap/IFvnk+0dDkA71AGpoD/XU/4C2n+zY9mvD95zYZRac3Otn4n2T5ZKLlb6b33hwX0Yl3EbUdRnqx/Egd/XlTKIJVS89CN93fqQR1mQwEY4w5QHdoLguwdX3h6AmOAFEiYqG5ecDWoXsqPgMlki6bPgFEMv2COnw2mRQ4DhNNNrVODYcKX3LJJcLBxN78b7/9Nhiax/APe9NNN4kOdgMGDBDhxxMnTqQff/wxpfXYzWmUKkrBdXUqWdLd5VDuXnfNgf66+6LUXrq+9GLKzQl1ZWue24Sa54VO9gzFr6m10LQXUc8Lk3hjDlGDDkRF+m63tjx6PXcO3drmaMpn2Yb11PJILXlyYOtGgjHGHKA7NJcF2Lp+8I3xqVOnigyPSZMmBdOToLk5QHfobjZwMv0C51ey512pQxEPzpVUnEyp1D5iPv74Y5H3ydx6661hDiY1HLl0+eWXi1pNXBicayXg4j6EkgOeDHWaq0kPCn/rrjnQX3dfILJgZt96PazTSU7b5a5ec6KWA8gqeDVBvIWuHDqu/tFosdUF7ImGvRsNxhhzgO7QXBZg6/rA9WfefffdYOkR7hx32WWXidqE0NwcoDt0Nxs4mVRw0SxOZeOCdlzkTimipoWLFiqoi76m4llmjjvuuLjLHn/88cGC4LxOOJlCqAuuJULTMIpyEcmku+ZAf92jRTK5finwbQm0kUxWcX79wi8N5YK4VeWZYOvmAN2huyzA1qG7U+CqK1xHVl3blosM8/UUZ2XA1s0BukN3s7HQFYn5DBt2tP4HRylx9FAs5s2bF3w8dOjQlNahdlwlat3Juc3R3geSh4MVtOlycDIBJ+DVRAq5yR3s/GJJJ5MrlMZnxUgm1PgGAAAAUoPPOy644IKw8iHclTTVWrIAAGcBJ5MK7nqg8Morr0QVjNtIclc4htsajhgxIiXBlVahDHv546F2ZqnfByjtKCYG3eWAE/Bpqgp5LBYpFOlkspaj3KeJZPJYyUEHAAAA2Kiu7UUXXSTS47gh0vXXXx9sxw4AkBM4mVRwt7WTTjpJPH7ppZdE+KeWxx57LJjydtttt1Fubvjd+blz5wqvPk9XXXVVxPtHjhwZLIb3zDPPxOxM99FHH4n8ZqZ169Z07LHHpv8rO5AlS5aIKRHaKCbGA6vXVXNgjO4+TSSTizuyWdrJZO10OXUkE2zdHKA7dJcF2Dp0dxrcLfuGG24QUU3qbriwdXOA7tDdbCx2VWI+TzzxBBUWFopUttNPP50eeugh0QHuiy++EN0S7r77brFct27d6M4770z58zn66fe//714fPjwYRoyZAjde++94vN//vln+uSTT0R3uXHjxomoKeYf//iHuDsAUieakynPWte6AKSFtiYTIplS1U+bLodIJgAAACAatbW1opQI12CKBTczslTaPgDANKyVv2AB+vfvT1OmTBGd3Q4dOiQcQFrYwfTBBx+I8NB0+MMf/iBafbJD68iRI8KRxZMWjpL6+9//LrYFhNO3b9+0OssxiGTSV3NgjO7RajJZCm13OYuly0UU/lY9hq2bA3SH7rIAW4fudoI7b/O10e7du8UN8IEDByb9Xti6OUB36G42CI+JwtixY0WY4R133CEcSpzexhFIAwYMoIcffpgWLVpEXbp0SVt09vL/85//pB9++IFuvPFGkb/MDiu3200NGjQQXeV+85vf0LJly+iuu+7K5PeVHtRkArLUZHKjJlPW0uUAAAAAQLR69Wp6/vnnhYOJ+fjjj2n79u2QBgAQF2vdWrZYbvHjjz8uplQ45ZRT4oaSqmFnEk9AP2qjRDKhuxxwAtqaTG6r12TK8Vi6u5wbIf4AAABAGBy5VFNTE3zu8/lo5syZ9Ktf/QqpcQCAmFjrrB+AFO6sMN27d089ksli1+JO0xykR6WvitZWbaLaQG3Y/B07d4r/rVq2DJu/oXqrNWsy+eqI9i4n2vljUuly/kCA1lR6aW+0Amo6sqFK250v9Bi2bg7QHbrLAmwdutuFnj170tChQ2n+/PnieYsWLWj8+PFJO5hg6+YA3aG72cDJBGxJdXV1UsuhJpPxmoPUOeg9RP+39Tk66DsU+aLiFN0V/zMskS7n9xHNu59o74rI12J0l3thRyXNORC6S2oW6kgm2Lo5QHfoLguwdehuJ7gzNqfIlZSUiJIi2s7a8YCtmwN0h+5mAycTsO2dlWTQBkdwFBOyYvTVHKTOoiMrojuYUsBjheH84IboDibGFXlSWukL0BcWcDAxuaqbsrB1c4Du0F0WYOvQ3U5wh+vLLruMPB5PyilysHVzgO7Q3WwscFUCQOrk5eWl5WRCZzn9NQepc8R3JGPZOha0NV/6mjiOsibdImZV+PxkbJJcbLoVhQ6HsHVzgO7QXRZg69DdStTW1tJHH31Ew4YNoyZNmkRdJpXoJTWwdXOA7tDdbOBkAo7Gqyn8nWuBjCIAtHg1RbwLXQXUxNMwKaFyclzUqaAtjWsy0nxh/XWR85r1Jup0BlHDjgk7vDHtC4zdSbkWU//iXBrVON/Q9QIAAABms3fvXpoyZQrt2bNHpMRdd911cFAAADIGTiZgSzZv3hzsAhiP2ijpckBfzUHq+ALhhnpsvV50RYvz7Kc712RSU78N0Yi/x1zcF8XJ9HDnEnKZnNNqK80dBHSH7rIAW4fuVmDbtm302muviUgmZvfu3TRr1iw677zzstY5DrZuDtAdupsNLrmBLTl48KCYEqHtLodIJv01B6njI1/MIt620t3vTaqjnII3EIg4IJntYLKd5g4CukN3WYCtQ3crwJ3iGjVqFDZvzZo1VF5enrV1wNbNAbpDd7NBJBOwJR07RqbeJNNdDjWZ9NccpI5Pky7nznHZU/dAqk6m8Odu8/1L9tPcQUB36C4LsHXobgW4ztL48ePp+eefp5qaGiotLaUJEyZQw4bJpesnA2zdHKA7dDcbOJmALeE2qukU/s5D7J7umoPM0+U8qkgmW+nuyyySyWOBKCbbae4goDt0lwXYOnS3Clzom9PjVq9eTWeddVbaBb5jAVs3B+gO3c0GTibgaCIimVD4G1gQryYCyE02NVRNRBa54n8PbU0mLsINAAAAgOwRCATE5HJFv9Pao0cPMQEAQLZAXAewJWVlZWJKuSYTLF53zUHq+MgfsyaTrXTXdpdLMV3OKk4mW2nuIKA7dJcF2Dp0NwpOg5s2bRrNnj3bFNFh6+YA3aG72SCSCdiSXbt2if+cPx6PWk1gBZxM+msOsluTyVa6a7vL5cQ/xPgo3Mvktki6nK00dxDQHbrLAmwduhsBd4ubMmUK7du3Tzxv06YN9erVy1DxYevmAN2hu9nAyQRsSevWrZNaDt3ljNccZO5kUtdkspXuKXeXs2Ykk600dxDQHbrLAmwduhsRwfTKK69QVVVVcN7MmTOpefPm1LRpU8N+ANi6OUB36G42cDIBW5LsAVJb+Bvd5fTXHKSOVxvJpKrJZCvdI5xM7pQKf1ulu5ytNHcQ0B26ywJsHbrrTX5+Po0cOZJmzZoVnMc1mY4cOWKo/cHWzQG6Q3ezgZMJOJqI7nI2racM5K3JZCsinEy5KUYyWcTLBAAAANic448/nrZu3UqLFy+mVq1a0fjx46lhw4ZmbxYAQALgZAK25MCBA+J/o0aNUusuh8LfumvudHbX7qN3931Cu+uO1jjIBnvrjmobzcmUsu4HNxItfZ2oYjcZTvWBpCOZfIEAvbaz0pLpcrB16C4TsHdoLguy2XpOTg6dffbZ1LhxYxo6dCh5PMZf9smmuVWA7tDdbOBkArZky5YtSR200F3OeM2dzku7ptLWmh26rkNdkykl3Tn9bP6D5jiYohGnJtNH+6rpkC9gyUgm2Dp0lwnYOzSXBSfaeiAQoPLy8pgRSrm5uTR8+HAyCydqbgegO3Q3GziZgKNzjSO6y9k0C8kKIL+b07u8ujuYmGJ3vfR0rzloHQcTkx87LH9NpWbnJKISixRlgq1Dd5mAvUNzWXCarVdXV9OMGTNo27ZtNGnSJKpfvz5ZDadpbhegO3Q3GziZgFzd5ZAup7vmMhXo1oM2eaXUrbBjerr7NDWRzCSvPlH7U5Iu+s2c0aSArABsHbrLBOwdmsuCk2ydW9RPmTKF9u/fL55PmzaNJk6cSG63te6mOklzOwHdobvZwMkEHA1qMoFs4g9ovJZEdEmzcVTkyo5zpMhdSJ0L2lNegoLZMQlEcTKd8Ju4aWu6wNvftCdRfknMRTSZcjS6cT71LU7zewMAAAASMXfu3KCDSUmPmj17Np1xxhmmbhcAADBwMgFbUll5tGBwUVFR3OXQXc54zWWLZOpf3Cssvc1U3bXd3Zi2J8UtwG0W2kimUgu1foStQ3eZgL1Dc1lwkq2PHTuWdu7cSQcPHgx+py5dupDVcJLmdgK6Q3ezQfIQsCVr164VUyK0TiZ0l9Nfcyfjo0gnk5vc1tFd62TKcVnSwcR4NZFMFinHJICtQ3eZgL1Dc1lwkq2z02b8+PEiPY5To7gmU+fOnclqOElzOwHdobvZIJIJ2JLi4uKklvOi8LfhmjsZX5RIJjc7cqyie4STyZoOpmiRTB4LOZlg69BdJmDv0FwWnGbrrVq1oiuvvFI4mTwea17SOU1zuwDdobvZWHNEAiAByd6tqUXh76xhxTtk1nAyua2ju1+zfUbXYsooksk6XibYOnSXCdg7NJcFu9l6IBCgFStWUK9evSgnxjGyffv2ZGXsprlTgO7Q3WyQLgccDbrLgazaUxQnk8tKw6g2ksnCTiZt4W8rRTIBAAAAZlJVVUVvvfWW6Br3zTff4McAANgK616BABAHn+/oxX6iVq0R3eWsmz3kGM2djE/TXc5D7ph3F03RPWAfJ1Nkupx1vEywdeguE7B3aC4LdrH1srIymjJlCh04cEA8565xnBLXoUMHsht20dxpQHfobjYWugUPQPIsW7ZMTImI6C4Hi9ddc5kKf+udKpey7hGRTG7bRDJZqfA3bB26ywTsHZrLgl1s/ciRI8GucUra3PTp06muro7shl00dxrQHbqbDS65gS3Jzc0VUyIiIplg8bprLlNNJiOcTCnpbqN0Oa+F0+Vg69BdJmDv0FwW7GLrXbp0oVNOOSX4vF69enTeeefZYtvtqrnTgO7Q3WysewUCQBy4CGJaNZmsG9jhGM1lqsmkd2e5lHWP6C5n3SHeSwHLFv6GrUN3mYC9Q3NZsJOtn3zyybRt2zZRm2n8+PFUUlJCdsROmjsJ6A7dzca6VyAAZAF0lwN61mQyIpIpJQ5ts2Qkky8QoPVVXjqkCl+q8WtrMpmwYQAAAIAF4XqPF1xwgYhIQT0jAIDdsMYVCAA64dWkyyGSCWTC5wfDO7y4yUJOprWziJa/aTknkz8QoL9vOkxLKzRRVhrgZAIAACALXGfpyy+/pB49elCLFi2iLlNQUGD4dgEAQDZAhRpgS1asWCGmVAt/oyaT/po7mZVV68KeuwxI8Upa99XvRM6zgJNpU7UvoYPJat3lYOvQXSZg79BcFqxi65WVlfTGG2/QF198IbrIVVdXk1OxiuayAd2hu9nAyQRsCXfYSKbLRkR3OQsFntiNZDV3Mh5N5JLHgHS5pHWvDnWiCdK4K5nNQW1htCjk5xC1ybfOzglbh+4yAXuH5rJgBVvfv38/Pf/887Ru3brg8xkzZojIJidiBc1lBLpDd7Mx/zY3AGnQu3fvpJZDdznjNXcqfALoo3CHyYSmY62hO5+caot+N+5G1PsysloXOXYlNc0L3d9o7HHRhc0LKc9lnUgm2W3dLKA7dJcF2Lq8unMBb+4Wd/Bg6MbQmjVraMeOHdS6dWtyGlbQXEagO3Q3GziZgC1JtghiRHc5xO7prrlT8ZOfApqOaCWeYmvorul6JzjhDqLcIjIbr+bubMt8Nz3etQFZGdlt3SygO3SXBdi6vLp7PB7RLe65554TaXPFxcV00UUXOdLBZBXNZQS6Q3ezgZMJOJpabeFvOJlAljrLGZUulxTaKCaL1GOKGslknYAlAAAAwHAaNGggOsd99dVX4n/9+vXxKwAAHIU1rkIASJH169eL/507d04tkskiPgEna+5UfFGihVwGdJdLSvdoTqYcawzvvoD9usjJbutmAd2huyzA1uXQndPsc2I0teBt6NSpU8zXnQJsHbrLBOw9hDWuQgBIkSNHjiS1HLrLGa+5U/GRz5RIpqR0t3QkU8CyXeRiIbutmwV0h+6yAFt3tu5+v5/mzp1LVVVVNGbMmJjLOd3BxMDWobtMwN5DWOMqBIAU6datW1qFv9FdTn/NnYo3SiSTO8dlDd39UWoyuawRtmfHSCbZbd0soDt0lwXYunN1r6iooOnTp9OGDRvE8zZt2lC/fv1IVmDr0F0mYO8h4GQCtqSwsDCp5RDJZLzmMtVkchsQyZSU7v4o7YFduWTFSCZruL7iI7utmwV0h+6yAFt3pu4cwfTKK6/Q3r17g/Pef/99atGiBZWWlpKMwNahu0zA3kOgDDJwLD4/kV8TRYGaTCBte4oayWQRl0nAupFM2sLfHpcNQpkAAACAFHG5XHTyySdHOJ527doFLQEAUgEnE7Al27ZtE1MqUUwMusvpq7lsNZncBgyhSeketfC3NZ1M1tiq+Mhu62YB3aG7LMDWnat73759aeDAgeIxd4276qqrpE6Xg61Dd5mAvYdAuhywJfv27QvmuifbWY6Bk0lfzWWKZHLxnwE1mZLSXetk4qLfFiko6rNh4W/Zbd0soDt0lwXYurN1P+OMM8jtdtOwYcOouLiYZAa2Dt1lAvYeAk4mYEvatWuXctFvxmOHMAobay5T4W+jUuWS0j2ak8kiRKTLWd/HJL2tQ3e5kH1sNwNobn/dKysrqaioKOprHo+HRo8enbV12RnYOnSXCdh7COtciQCQAo0aNUq4TLR0uTwkiOqquR3ZXL2dPj4wj8q9h+MuVxOo0bez3IZPibbMI/KGrycp1b1VlkyVY8o1IYVuGziZnGrrVge6Q3dZgK3bV3eusTRnzhz66aefaNKkSdSwYcOsbJtTga1Dd5mAvYeAkwk4lqiRTHAyARXegJee2vFfqvBrnDRJ4MlmdaFdi4l+fCp7n2ehSKa5B2ttly4HAAAAaDly5AhNnz6dNm7cKJ5PnTqVrrnmGhG5BAAAIAQuuYEt2bNnj5jiEbUmk3UCPBypud0oq92TloOJqeeOHiafFntXUlbJLyGrUE8TulSpbfloQZxo63YAukN3WYCt21P3uXPnBh1MzI4dO+ijjz7K0tY5E9g6dJcJ2HsIuN6BLeEDO9OsWbOYy9Si8LfhmtuNukCUrmxJcnKDE7K3Ib7waJ+M6XwWWZVj6ln/sONEW7cD0B26ywJs3Z66jxo1SjiZlOK+XNy7tLSUAoEA5SBKVxfNQXpAd3OA7iGsf7YPQBRatGiRUBevJl3OlUPkRuyerprbDV8g3BOZm5NLE5qNifueHMqh1nml1K6gVfY2RFNUnJr0IOo4SjwsP1Qu/jcoaZD4c/gkt2FHokadySpou8u1L7D+YceJtm4HoDt0lwXYuj11z8/PpwkTJtALL7xAhYWFNH78eHQh1VlzkB7Q3Rygewjrn+0DEAW+c5Rq4e9cOJh019xu+DTOnfycXBpScrzxG6LtDlfSlqjTUSdTEq4lS2PH7nJOtHU7AN2huyzA1u2re/PmzemSSy4RF5P16tXLynY5Gdg6dJcJ2HsIXHYDxxLhZEI9JpDAyeQ2qyub1slkocLdmcApBD4bOpkAAADIy6ZNm0QXuVh06tQJDiYAAIgDnEwx2Lx5M915553Uo0cPcSBp3LgxDRw4kB555BGqrKykTA5cnLedytShQ4e01+dUDh06JKZUCn+js5z+mtsNH/kt72Sys+6srrbMtx26y9lZczsD3aG7LMDWram7z+ejTz/9lF599VWaN2+eodvmVGDr0F0mYO8h4GSKwvvvv099+/alxx9/nFavXi2cSgcOHKAff/yR7r77burfvz+tW7eOjKJ79+6GrcsucOFFdYePaNRpytwgXU5/ze2GV1P422NBJ5OddddGMTGaZnOWxM6a2xnoDt1lAbZuPd0PHz5Mr732Gn3zzTfiOTuZ1q5da/AWOg/YOnSXCdh7CGfkZGSRRYsWiaJ+VVVVVFxcTPfccw+NGDFCPJ88ebIo9rdmzRoaM2aMcDrVr18/pc9v3bo1LV26NOFyDz30EL355pvi8cSJE9P+Pk6lYcOGCZdBTSbjNbd74W8rRjLZWXevpui3XSKZ7Ky5nYHu0F0WYOvW051vJm/dujVs3jvvvEO33HILUuN00hzoB3Q3B+geAk4mDbfddptwKHk8HhEyO3jw4OBrp556KnXt2lVEM7Gj6bHHHqMHHniAUiE3N5d69+4ddxkO1507d654zE6s8847L6V1yED79u0TLhMRyYSaTLprbvuaTGYFd2q7y6mcXXbWXVv02y6RTHbW3M5Ad+guC7B16+nerl07GjVqFH3yySfiudvtptNOO42KiooM3ELnAVuH7jIBew+BdDkV33//PX311Vfi8bXXXhvmYFLgOk09e/YUj5944gmqq6ujbDN79mzasWOHeHzhhReKNqkgdbSRTKjJBLT4KNy548kxye/u0MLf0ZxMKPwNAADAipx44onUq1cvatCgAV1zzTV0/PHHi9qoAAAAUgNOJhUzZswIPr766qujC+Zy0ZVXXikeHzx4kL744gvKNpwTroBUuejU1NSIKZVIpjxEMumuud3wRnSXc1nOyWRn3X3R0uXI+ifsdtbczkB36C4LsHVr6s4OpXPOOYcmTZokylsA/TUH+gDdzQG6h4CTScXXX38t/nM3Ob57EYvhw4cHH8+fP5+yCRceVJxd3FXu5JNPzurnO4VVq1aJKR7oLme85nbDDjWZ7Ky7XSOZ7Ky5nYHu0F0WYOvm6T5r1izavn17zGXy8/ORIpdlzXE8NR7obg7QPYQzcjKyxMqVK8X/Ll26iJpMsejRo0fEe7LF22+/LbrZMVdccQXCdGOQTAphROFvRDJlhJ3TNv0BP22rKaNKf1XY/LLa3WHP3WSwkXhriA6sJ9q9JKaTyc661/gDtqzJZGfN7Qx0h+6yAFs3Hq53ymUxlixZQlu2bBHRSqi3pD+wdXOA7tDdbOBk+oXq6mrau3eveNymTZu4ojVq1EhEO1VUVER0oshmqpySlpcO27Zti/v6zp07yc5069Yt9cLfiNvTXXMrUhfw0pPbX6V11ZsTLmtoJNORnUSf30NUvT/yNZWTya66H/H56e71h8Lmsbp2qG9hV83tDnSH7rIAWzcWbujDHZs5gqlJkyZUXl5O06dPp8suu0yUwQD6AVs3B+gO3c0GI6sqTU2huLg4oXDsZGKOHDmStR+D76zMmzdPPB4yZIiIqEqXtm3bxp0GDRoUXFYbxsrPFy9eHFbUfOPGjWKe+vtycXKet2/fvuA8fszzlMLlDDvjeB5/hgJ/Ns/Trnv58uVifkBVy2XdunViHjsC1VrxPD5RUNi9e7eYx/+jRjL9Yu38Obwcf64Cr4/n8fqT1YK/V6pasH7JarFs2TIxX83atWuT0mLXrl1hWig1xHie2jHKJ15aLfx+f1QtOGqP53u9odSuDRs2RGjBJ3E8b//+kPOEHbg8T+3c5H2O523atCk4r7a2VsxbvXp12LqXLl0q7j6q4Q6PvKw613/z5s1i3qFDIefG99t/SsrBxBw5dJjWr18fdueTP2/FihVhy/Fzns+vK/D7eJ4SiajWglsja7U4svTd6A4mxuWOqQXroNWCl+Fl+T3xtCgrKxPzFIc6w9vG89TpA/wdeF4mWryzPPRehZyAP0yLPXv2iPfydinw9vI83n4F/o15Hv/mWi3YNhJpwTbG89TjPNuiVgu2Wa0WyvjFtq7A+wDP00ayKuMX70Pa8Yv3NQXeB3ke75Pa8Yv3XQXep3ke7+MKyvjFY4EansdjhhPH8kRaYCyXYyxXxi8eN7Tjl/rGmqKFUWN5ovHL7mO5dvxirbVaWH0s5xQ4tnfePmW7+bsp3xljOcZynJdjLJd1LD+oOhfNJnAy/YL6pDcvLy+hcHzAYtQ/Vqb873//C56QZxLFBI6C7nKAORwIXTQloqm/oWGiuWtCF9URNOxEdudIlNTDUsp+N04AAAAgHhytdO655wZvEHNJDC7wrS5/AQAAIHvkBNS3GSWG78I0b95cPJ4wYQJNnjw57vItWrQQd1l79+4dcfclXXr27CnuBrMDiz2bDRs21DVdTolm4rvqiVIErYbiMe7bt2/MZZ78nujRBaHnp3ciemGsEVvnTJLR3Ip8Xf4jvblnZvC5i1xU7C4KW8aV46KehV1oQrOzKc+Va8yGffso0ZYvw+fVb0PUZQxR1zG2133a7ioxqflX1wbUKt/6xdHsqrndge7QXRZg6+bwySefiK7Qt912G7Vs2dKkrZAL2Dp0lwk72vu2bdtEllO2fQKG1WT68sujF1PcErRz584pv5/DGJU0Hz06rtWvXz/4OJkUOCWkPJnUumTgYoRKusG4ceMycjAxdnMapUoydV3QXc54za2ILxBenKtzQTu6o821ZDrajnLHXHJ0cojuXs39i6EN8mzhYLKz5nYHukN3WYCtm0OrVq3okksugYPJQGDr5gDdobvZGOZkOuWUU4TB33zzzfTvf/875fc//fTT9Pjjj4vPUNcRyBYFBQWiGCDXoUgUBcS5koqTSfH8WaXgtyz06dMn9cLf9ri+tbXmVsRHPvOKe6fiZIqxXbbVPWC/rnJ219zuQHfoLguwdf2iCLjuydlnnx31Ihu6Gw80NwfoDt3NxlY1mTizT8/svl69eon/XDgzniNLXeCUU9wyhQunKul5nLI3evTojD8TxC78DeTCG7CJk8moND2TIpk8iA4CAACgx/HG66UPPviA3nnnHVq4cCH9+OOP0BkAAEwEl90qhg0bJv5zlBIfpGKhdIBjhg4dmvGPwAdGpavPpZdeKgoSAh2cTBbxLQBz0+XcORYZ9vyaUDuXswzUq7kf4LFRJBMAAAB7wDef33rrLfrhhx+C8z7++OOEWQkAAAD0wyJXW4lRWslyWptecOcJhVdeeSXqMtwSWElt47pJI0aMyGqq3MSJEzP+PBng1pDaFpKJ0uU8trF2+2puByeTxzKRTJpOay6Po3S3s5PJrprbHegO3WUBtp49OC1uwIABYfO4dffatWuhuwWArUN3mYC9h7DNZfdPP/0k/jdt2lS3dXC3tZNOOkk8fumll2jBAlVrsl947LHHaOXKleIxd6fIzQ1PcZk7d6444PF01VVXJVzn/v37RSSTkj977LHHZunbOJvq6moxpRLJlGcba7ev5lbER+GG4CaLOJk0zq9YTibb6q5Jl3PbKF3OrprbHegO3WUBtp5duHSFklnA2QDnnXde1JvA0N14oLk5QHfobja65GVt2bIl5muHDx+O+7q2VtH27dtp2rRp9N133wnHjd5OmCeeeEIcqKqqquj000+ne++9Vxyo+DnXTXr++efFct26daM777wz4/XxZ9bW1orHiGJKnmRqYUV0l7OIb8GuZKP+mBnYpyaTx2G62zeSya6a2x3oDt1lAbaefUaOHCmyHk444QQqLS2F7hYBtg7dZQL2rrOTqUOHDlG7OnDeNKeGqdPDUoVbj+pJ//79acqUKXT55ZfToUOHhJNJCzuYOPqofv36Ga9P0cLtdtNll12W8efJQl5eXurd5RDJpLvm9qjJZFUnk9thumsimcg+2FVzuwPdobsswNbTg28+azMIFFwuF51zzjnQ3WLA1qG7TMDeQ7iM6Aan7ginnpfKpDiYLr74YtKbsWPHijaod9xxh3AoFRUVifpLnPP98MMP06JFi6hLly4Zr4fzxTlCixk1alTMOy8gPdBdDli7JpPTu8uFP0d3OQAAAOny888/07/+9S/au3cvRAQAABkjmdq1axcRybR582Yxr7i4mBo3bpzwM3hZLvLdpEkT6t27N11wwQV02mmnkVG0b9+eHn/8cTGlwimnnBLmVItH165dk14WhLNp06Zg1FzSkUwW8S04WXNbpMtZpRSd1skUw/llX93tmy5nV83tDnSH7rIAW08er9dLH330UbDr89SpU+m6665LK2IAuhsPNDcH6A7dHelkUgxbG8aq1B3697//rcdqgUSUl5enHMmE7nL6a24FDnoP0Yf759Keun3i+c7a3eany236nGjrfCJfTWhe1f6kajIlo3uVL0DTdlfR5movWcVtvbFaG0FmHy+TXWzdaUB36C4LsPXk+frrr4MOJmb37t30/vvv0/nnnx+1NAd0txawdeguE7B3nZ1MsUDUDsgWnTp1SriMNpIpD5FMumtuBZ7f+RZtqtkW83XDnUw7FxJ9/6/Ey8VwMiWj+4s7Kuir8qMNBKyK2z4+JtvYutOA7tBdFmDrycPNeLir865du4LzuIwFX1Ok6mSC7sYDzc0BukN3aZxMGzduFP9LSkqMWiVwMMkUXY/oLmeRLCm7ko1C93pT66+L62Bi6rkKyVB2L01uubzitHVfXlFHVqfYRl4mO9i6E4Hu0F0WYOvJw4W+J0yYILo7+3w+GjduHPXp0we62wTYOnSXCdi7CU4mrnEEgJGg8Ld8eAOaOkdRHEz9inuRofiTcAA16kLUsGPaq6izSo5cDJrmuqhvsbMKmwMAADAGruV60UUXiQu45s2bQ3YAALA4hqbLRePw4cO0bds2OnDggCjud/LJJ5u9ScAG7Ny5U/xv2bJl8k4mpMvprrnVOskx4xqfRvmuPMpz5VGvoi7UyNPAXCdT015EbYaGnhc2Iio9nijHlbbuXk0DgbOa5FNzi+SH1nPlUP/6uVTfRqGEdrB1JwLdobsswNYj4VpL3OzH7Y5+7OrcuTN0tyGwdeguE7B3k51M7Fh69tln6Y033qBly5YFazVxbjU7mrQHnUcffVQ85vDYK664woxNBhaD7SKhk0nbXc4+17i21dxsfKTxLBLRyQ0GUZHb4BQ5NX6NITbqTNRtbFZ113ZyG9Ygn7oUmX4PwbbYwdadCHSH7rIAWw/np59+og8//JAGDhxIZ5xxBnR3ELB16C4TsPcQhl+FzJs3jy677LKgpy9RMXAOi50zZw79/PPP1LBhQ5GXnU7bUuAsWrdunXAZ1GQyXnOz8UaJZPKY0U1Ojd+bVIHvjGxdM4x67FP+yJLYwdadCHSH7rIAWz9KXV2dcC4tWrRIPF+wYAG1adOGjjnmGOjuEGDr0F0mYO8mOZm4Deno0aOptrY22BWiZ8+edPDgwaDTKRqTJk2iG2+8USz32Wef0ZgxY4zcbGBBmjZtmnCZWnSXM1xzK6bLGd5NLstOpkS6+wMB0rrqPSl23AGpaQ70AbqbA3SH5maxb98+WrJkSdi8mTNniihSrsOUbWDrxgPNzQG6Q3ezMSyBqLq6mi6++GKqqakRDqaJEyeKWkzLly+n888/P+57L7jgAnK5jm7q7NmzDdpiYHcQySQf0ZxMLuOGOV2cTKlGMTGIZAIAAGB1SktL6ayzzgqbd9xxx1GDBgbXTgQAAJBVDLv6eumll2jHjh0ieummm26iV155Jel6F1wIsGvXrsG8bQD2798vpnhE1GSyRh1kR2tutXQ5N7nFmGMqWsdXipFViXT3RXEyuRHI5HhbdyLQHbrLAmw93Kl07LHHilIY3EGOMx5iFf+G7vYDtg7dZQL2bkK63Pvvvy/+c/vRf/zjHym/v1evXrR69Wpat26dDlsH7MbWrVvF/3jh1BHd5VD4W3fNzcZHPmvVY4oayZSbVd19UeraIV3O+bbuRKA7dJcF2HoIvhHEZTCGDRume4oPdDceaG4O0B26S+NkWrp0qTiQnHzyyVRcXJzy+5WTfa7LBEAyJyIRTiYL+BvsjB3yu7Xpcq4clwWdTO6s6h4tXQ6RTM63dScC3aG7LMhm69u3bxfdo9u3bx/19dzcXEM0kU13KwDNobtMwN5NcDJxcb9Mqq4rKS9+f2SLciAf6C5nTc3Nxhfw2yCSKbvd5byIZJLS1p0IdIfusiCLrXMN1h9//JE+/vhjKigoEE18OKPBLGTR3UpAc+guE7D3EIbd5q9Xr574X1VVldb7y8rKgvWZAEgEX3dHdJezQFALML4mk+mg8DcAAADJ4MilGTNm0AcffEA+n48qKipo2rRp4jEAAABnY1gkExf5PnDgAK1YsSKtOyHffvutiGbq2LGjLtsH7AWfrKidl8kUQ/ZYwN/gZM2Nwh/w0666vVTtr4l4bWft7rDn7mxGMvl9RIe2Evki1xuXuqO6pRvJFEv3Cp+fdtT4aY+2wj26yznG1mUDukN3WZDB1rl4d2VlZdi8LVu20MKFC2nQoEGmbJMMulsNaA7dZQL2boKT6aSTThIOJu4Ot2nTJurQoUPS750+fTrt3btXOJlOOeUUXbcT2AOlAHy/fv2ivh7luhuFv3XW3Aiq/NX07+2v0uaa7Ukt785WTaaaQ0Rz7yMq35z5Z6VYkyma7l8frKGntlVQtORhTix2md1Rz+ZYwdZlBLpDd1mQwdb5nP3888+n5557LlhPdejQoTRgwADTtkkG3a0GNIfuMgF7D2FYAhG3JVWikm699dak37djxw769a9/HTxgXXLJJbptI7APnNMfL69fW/SbQXc5fTU3giVHViXtYMpqTaYtX2XHwZRGd7louk/dXRXVwcR44F9yhK3LCHSH7rIgi60XFhbS+PHjReTQhAkTaNSoUeRymVe7QBbdrQQ0h+4yAXs3IZLp1FNPpeHDh9O8efPoww8/FE6nZ599Nm6NpVmzZtFNN90k6jGxg+nCCy+kXr16GbXJwMJ06tQp7utRI5mQLqer5kZwwFue0vKt8lpkZ8VVeylrNIjeXScV3fdF86L+QrsCGLoTbF1GoDt0lwWZbL1Vq1Z0++23iw5yZiOT7lYBmkN3mYC9m+BkYl5//XWRh71r1y565513RDHAkSNH0rZt24LL3HHHHcKp9M0334TN51pM7JQCIBm8iGRyJD4K9x66yEW5OZHDWA7lUIeCNnR+09H6FO/mNDx3XmqfkVdC1P1covqZd7fxamqO5eZwihxR23w3TWqNWhMAAACMYevWrbR48WIaM2ZMsBO0Fis4mAAAADjUydSmTRuaM2cOXXDBBbRq1Sqqrq4WUU2McmD697//HVyeU+uYY445ht577z1q2LChkZsLLN61hPF4optwbTQnEwI8dNXcjO5xxxUfQ9eUjtd/xVonU8dRRANuNkV3fyBA2rr2D3duQG0QwaSb5sAYoLs5QHdong58jv7999/TJ598Qn6/nxo1aiRqLlkZ2Do0lwXYOnQ3G8MTo3v27Ek//vgj/fnPf6bmzZuLg1SsiZ1KDzzwgOgsh65yQM3y5cvFFAtvlHQ5j3llAKTQ3Ah8Ab9+3eMSdZbLoENcNnXXRjExqMOkr+bAGKC7OUB3aJ4OH330kZjYwcTMnj2bNm7cSFYGtg7NZQG2Dt3NxpTbtEVFRXT//ffTPffcIxxOCxYsEAW+y8vLRXHAFi1a0AknnCDuiOTlpZiSAqQgkV2g8LfxmhuBTxPJZJyTqc40J5NWdziZjNccGAN0NwfoDs3ToXv37vTDDz8Esw74/8KFCy19Uxi2Ds1lAbYO3c3G1FwATkU48cQTxQRAqhFxqTiZOIoJXd311dyMmkxuMsjJpHFuGelk0uru++WEXo0Hxq2r5sAYoLs5QHdong6dO3emESNG0Oeffy6en3TSSeK5lYGtQ3NZgK1Dd7NBwQngSLTd5XKRKucIIiOZDPphIwp/m1fgK1okkzt6rVUAAABAN9ixtGfPHlE7tUePHlAaAACAAE4m4Ei0kUxwMjkDbU0mj2Hpcl7TIpm0RItkciOSCQAAgA5wzSWXK/oNHW7aw818AAAAADWI7wCOLGgXEcmEznK6a24E3oDXpJpM5jmZUPhbTluXEegO3WXBLra+efNmevLJJ6msrIycgF10dxLQHLrLBOw9hGFXSn/5y18yej/fRSkpKREd5zjPtF+/fihqJjFKa86Yr0epyQT01dyU7nJkVnc5t2m6o/C38ZoDY4Du5gDdobkWLuLNnZ0/++wzEck0depUuuGGG6igoIDsDGwdmssCbB26S+NkeuCBB0RYbTar5p9//vl055130nHHHZe1zwX2oE+fPnFfRyST8ZqbUfjbvHS5XNN092rS5XhUdSFdTlfNgTFAd3OA7tBcy6JFi+iTTz4JPt+/fz+9++67dPHFF2f1XN5oYOvQXBZg69DdbFxG3xlRT9HmJft6TU0NTZ48WXSm++tf/2rk1wAWgCPbYtUIYFCTyXjNzSn8bVR3OfMKf2t118RUkce+5/uWxQq2LiPQHbrLgtVtvW/fvtS6deuIyIi6ujqyM1bX3YlAc+guE7B3EyKZ/vSnP4n/hw4doqeffppqa2uFs6hdu3Y0aNAgatOmDRUXF1NFRQVt27aNvv/+e5ELznB47k033ST+892UJUuW0HfffUc+n08c9DhKit97xx13GPV1gMWJcDKhJpPhbKneTvPKv6cKf2XWPnNz9Xb9nUwVu4lWzyCq3B2aV77FMoW/tZFMcDIBAADIJh6Ph8aPH0/PPfccVVZW0vDhw8UEBw0AAADLOZnWrFlDY8aMEVFIAwcOpEcffVS0P43F119/TXfddZdwOM2cOZM+/PBD6tq1q3iNHVC33XYbvffee8JZdf/999Oll15KLVq0MOorARNZt26d+N+lS5eknEyoyaS/5moqfJX0z+0vU02glvTEnZPlO5LswPnyT0SHw51ZERjoZNLq/v2h8DvJ6Cynv+bAGKC7OUB3aB6NBg0a0IUXXihu5nbr1o2cAGwdmssCbB26m41hMaN8J+S8886jDRs20FlnnSUcSPEcTMywYcPoq6++Eo6p9evXixpMVVVV4rX27dvTjBkzaNy4ceI5z3/ppZcM+S7AfDjijadkazLlITpad83VbKrepruDiSlyZbkIadXexA4mJq8emaX7YU1V+yO+8MgmkH3NgTFAd3OA7vJqfvjw4bgFgjt16uQYB5OVdJcJaA7dZQL2HsKw2/GvvvoqrVy5kgoLC8Xj3NzkCufycq+88opIq1uxYoV4769+9avg6//617/ogw8+EN0v5syZQ/fee6+O3wJYhUQnPeguZ7zmamoD+tdtKHEXU4+iLEeaeGsSL1PUjKhZbzJLd0QuGa85MAbobg7QXU7NN23aRNOmTaMePXrQ2LFjSQasoLtsQHPoLhOwdxOcTFykmztScE5306ZNU3ovLz9ixAj66KOP6K233gpzMnXo0IH69+9PP/74I61evVqHLQdWhJ2V8ajVRDKhJpP+mscr0F3fXY9GNBicvW1xF1C/ej3F5+raRY7pfXnocV59otYnEuUWkVm6+zQ1mc5uYu+W0na3dQDd7Q7sXS7NucTEN998I27M8g3ahQsXUtu2benYY48lpwNbh+ayAFuH7tI4mZTcUD6QpQMXBld/jhq+C8NOJi4KDkC0SKZcpMsZilfjZGroKaHRjYeT5dFst6i91Gs8WQmvJjsOhb8BAAAkS3l5Oc2dO1c4mBRmzZpFpaWlYgIAAAAyxbBL7wMHDoj/6TqClPcpn6OGO8sxbjdaiMnC1q1bxZRsTSZEMumvuRofhXv53GSTfdNfZ5kucrF0h5PJeM2BMUB3c4DucmnesGHDYD1ThY4dO4pC304Htg7NZQG2Dt2lcTI1b95chOjy3ZO6utTqtfDy/D7lc6LdlWFSTcMD9oWdjvEcluguZ7zm8dLl3Dl2cTJFiWSymO5eTbocajTprzkwBuhuDtBdPs379OlDgwYNEmUsuBwFd2eWIb3GbN1lBJpDd5mAvYcw7ApqyJAhNGXKFCH+H/7wB3r44YeTfu/9999P+/btEwfDwYMj67qsWrVK/G/WrFlWtxlYF+4uGA+tkynPJj4OO2sez8nksY2TyWs5J5NWd20zOaTL6a85MAbobg7QXU7NzzjjDDrmmGMssS1GIdN3tQrQHLrLBOw9hGFXUNdcc41wMjGPPvooHTlyhB566CEqKSmJ21qVu8U9/fTTwXnXXXdd2DLsfFq6dKlwQPHBEsgBh3vHw6sJSPGgJpPumofpHxHJ5LKnkynHYzndEclkvObAGKC7OUB3Z2p+6NAhUWIi1kUPl5iQ7YIItg7NZQG2Dt3NxrArqFGjRtFll11Gb7zxhnAIPfvss/T666/TmDFj6IQTThAFwYuKiqiyspK2bdtG3333HX3wwQfCGcVpdvyeiy++mE477bSwz+XP83q94vVTTz3VqK8DLE4tCn+bSkS6nG1qMlkvkkkLIpkAAADEY8OGDfT222+L8+cbbriBGjVqBMEAAAAYhqFXUC+//LJwIr377rvCKcQOpKlTp4opGnxwVOAiha+++mrEMhzJNHHiRPH4nHPO0XHrgZXYvXt3zBpdDLrLGa+5Gh/ZtCZTRHc5t+V0R+Fv4zUHxgDdzQG6O0dzPm/++uuv6fPPPw+eQ/M59rXXXksej/VumhgNbB2aywJsHbqbjaFHnNzcXJo+fTq99NJL9MADD9D27dvDHEnRaNWqlVhWmyan8Oc//1mnrQVWZufOnXFP0NBdznjN1fgCfpvWZLJedzmt7j4KHzM9OTmmbJeTScXWAXS3O7B3Z2nOn60+t+bn8+bNo5EjR5LswNahuSzA1qG72ZhyBcV3VK6++mr6+OOPRde4n3/+mfbs2SMim4qLi0WXuGOPPZZOOeUUGj16tMgbB0BNaWlpXEHQXc54zeOly7lsU5PJet3ltLprI5nc8DHprjkwBuhuDtDdOZpzlgBH9XMUw969e8W87t2709ChQ3VZn92ArUNzWYCtQ3ezMe0KyuVy0VlnnSUmAFKlRYsWcV/XRjKhu5z+msfvLme+syYuvjqiIzuJKo6mMFjJyaTVvcaPSCajNQfGAN3NAbo7S/P8/HyaMGECvfjiizRs2DAxsfMJwNbNAOOLOUB36G42hl1BHXfcceJ/YWGhiF7i1DkA9EJbkwnd5Yzl8/IFYc/dZOFIpt1LiL5+kMhbFfmaxdL81lTWUZmmqj0imQAAAKhp1qwZ3XbbbaKhDgAAAGA0hl35LV68WEyNGzeGgwlkTHl5uZiSTZfLtZavwJGaK+yrOxAxz9KFv5e9Ed3BZJFIJrXub++ujnjdgxvUumoOjAO6mwN0t5/mBw8epE8++YT8fs3Jjgo4mLKvO0gdaG4O0B26S+NkatKkSbCQNwCZsmnTJjHFIqLwt4UDaZyiucI+78GIeaV5zciyVOyJ/VpJW7KS7nu0hk1EbfIt7MBzuK0D6O4EYO/20nzdunX03HPP0YIFC0RBb2CM7iA9oLk5QHfobjaG3aZv06YN7du3D3cQQFZo1KhR3NcjIpngZNJd81j1mJjB9fuTbTrKMVyovFkfol4TyEq6a4t+9yryUHMUHNNVc2Ac0N0coLt9NP/uu+9E0xylexw7mVq3bk3dunXL8hY6E9g6NJcF2Dp0l8bJxAW+uYvc/PnzjVolcDDt2rVLrbscgj1011zBFwgXv7GnAeW6LFyDTesUG/F3oma9yYq6+zROpnHNCozfIAlI1tYBdHcCsHf7aN6yZUtRxFtxMjFffvklde3aFcW9ddQdpA80NwfoDt3NxrD4jmuuuYYKCgpox44d9PLLLxu1WiApEd3lEMlkGN6A1z71mBi/13J1mGLhVV1YMB50DAIAAKkuHE8//fTg8549e9IVV1wBBxMAAABLYdild6dOnehf//qXuPty88030+TJk8nKbN68me68807q0aMH1atXTxQsHzhwID3yyCNUWVmZ1XXNnj2brrrqKurSpYtYV4MGDUTo84UXXkjPPPMMHTlyJKvrcwLV1dViigW6yxmvuYKfNN3PyGZOphyPZXXXRjKh6Lf+mgPjgO7mAN3tpfkJJ5xAffv2pVGjRtH48eMpPz8/69vnVGDr0FwWYOvQ3WwMu5rasmULjR49mv7v//6P7r33XrrsssvoiSeeoIsvvpiOP/540W61sLDQEiGA77//Pl1++eV06NCh4Dx2LP34449ievHFF+mDDz4QTqFMOHDgAF199dU0c+bMiNd43WvXrqXp06fT4MGD6dhjj81oXU5j9erV4n+/fv2SK/xtcT+HEzRX8GrSzzxWjmTiyCC/z9KRTGrdtZFMbnSW011zYBzQ3Rygu/U05xuynBYXDZ5/3nnnIXpJB91B9oHm5gDdobvZGHY11aFDh7ADIh9Av//+ezGlAn+G16uJPMgiixYtogkTJlBVVRUVFxfTPffcQyNGjBDPOfrqhRdeoDVr1tCYMWOEw6l+/fppt5bku1ALFy4Uz/mEgSOXOnfuTG63m7Zu3SoKOrKTCaTemheFv7NPsu2QtYW/XZZ2MnHUVcDSTia17trC30iX019zYBzQ3Rygu7U05xuQfO7HN2a5aU40YjmgQPq6A32A5uYA3aG72Rh+NaXcnVEOkOrihVbgtttuEw4lj8dDn376qYgiUjj11FNFccW7775bOJoee+wxeuCBB9Jaz6233iocTBzmPHXqVBo3blzY6wMGDBCOp3/+85/k80V265Id/h3igUgm4zWP5WSydCSTNlXOgk4mte5aJ5OFlZXC1gF0dwKwd+tozueW77zzjkh14XPDSZMmiTIKQF/dgX5Ac3OA7tDdbAy7muIUN6vfeeGoqq+++ko8vvbaa8McTApcp+mVV16hlStXinS/++67j3JzU+uc9fXXX9Prr78uHv/tb3+LcDCpYc3Y4QVSAzWZzEPbXc5tXOm3LDmZrOm68QcC2pgrRDIBAIBDWL9+Pb355pthZRM4oonLN7hcFj6OAgAAABoM815s2rSJrM6MGTOCj7lWUjT4QH/llVeKNLqDBw/SF198EdbpIxmeeuop8Z8LfN9yyy0ZbjWIRm24nwPd5QzESz77dJfTRF1ZMZIpVhQTg8LfAADgDDp27ChKS6jPl9nRVFFRkXZpBgAAAMAMcGtEE2HEcGgyFyOPxfDhw4OP58+fn5LgtbW1wULfXJOpoKBAPOaUOK7DxCcX6GqUmMWLF4sp6UgmC/s5nKJ5rHQ5SzuZ/HWR8yzWXU7RXdtZjoGTSV/NgbFAd3OA7tbQnG9icm1OxaF0zDHH0PXXXw8Hk866A32B5uYA3aG72cDJpIJT4BjuGhcvRa1Hjx4R70llp1ecSH369BF3qW6//XZq2rSpSCnkO1kc4cQOqLlz56b6e0oDF0fnKemaTLB03TW3Z00m60cyKbprO8uJ1yyegux0WwfQ3QnA3q2jOTecGT9+PJ1xxhnC4cR1O4H+ugP9gObmAN2hu9lY62rKRNjxs3fvXvE4VjcPhUaNGoloJw5h5uijVFixYkXwsd/vFwW+165dGxHtNHv2bJozZw499NBD9Lvf/Y5SZdu2bXFf37lzJ9mZ3r17x30d3eX00XxP3X6avvdjOuQ9HHO57bVlYc/dORbx8HGtqPUfE+1bxR0Hjs7zVlneydS4a0/6bH81fbyjIuI1N3xMpowvALo7Cdi7sdTV1VG3bt0oLy8v6utt27YVE8g+sHXjgebmAN2hu9lY62rKRA4fPhx2JykRipPpyJEjKa1n//79wccPP/ywcG5xm9q//OUv1Ldv32Chx9///vdUXl4u/nPk1DnnnJPSelI5QVm1alWYY42f19TUUK9evYJFzTdu3Ci2rXPnzkF9duzYQXv27BHvbdKkiZi3b98+4eBq1qwZtWrVSsxjndatW0clJSUiUks5yWKHG9+lU0eGLV++nLxer9BCKRTP7+XP6N69ezC9cMuWLaLNL9cv4MgvZvfu3cJ51rJlS6rzNQ/7jvv27CQqbSkes+arV68WvyFHrSldDpcsWSIi2DhEPRkt+L1K15dktWB74eKeyWixbNkykUbZr1+/4Dx2SFZWVibUYteuXVRWVia0aN78qBZcQ2zz5s3UuHHjoH1wJ0XuZqPWgp2fS5cujdBi2crl9F/3TKpwRXHMxMFNbuHA3b59u9gW3iZln9uwYYPYZt52xcHK0YH83fg7KvD28G/EdqHA283bz5opd3v5+/H3ZG1ZY4Z1YD261PxI9TYdTVWNx/qNm6hzl6MdaFh//h34t2cbUODfi383Pogrd0X5d+Xflzt6KK1j+Tvzd+coRXZOM4oWLVq0oNLSUjGP7YntqmHDhtS+ffugFktWrqL/uFtRRYygU88v+wjbM9t1z549gxcu8bRo3bq1iJ5k2HbYhvg5z2fYxtjWeF/nfT5dLfhCqrCwUMzjfYH3CbUWvM/wvpNIC94HeV/kz+LPVOB9lscJjgpViKYFpyHzeNqpU6dg6gmPFTxmqLXg8ZlvHKi1UMYvfh+/n+Exiscq/nxej3b84u1RCvUq45daC14Hr4u/H39P9fjFOrAeDG8zbzvrxbqpxy+2MXX3GI6SZf3VJ5ZOGMuV8SueFhjLUxvLeYzl8YXnKVHbPBbzmKw+rinjFx8v+LjBWGEsZ3tku1SPX2y3ynmMokWm41c2x3JFC97uKVOmCP1Ya9431FpgLMdYjrEc5+UYy60/lquPa3wuyth1LHfrFN1pCScTnzzySQqfECWDcoKZTdR1kGLdXVKjnAjxDpAKfIKtXienxc2aNSv4A/MJyI033igMg2s/sSZcZJw70Fm9O59V8Ac4ZSt8njsnSkEbkBL76GDKDiamwHX0YtJs8vYvTbiMLyePyCqRV3zwodyYDiYeMfIwJAAAgC1gR9w777wjHL98UcHngGoHOQAAAOAUcgJ8S8lg2JP37LPPipQwvqvF3rZkYUcL3x3NNnwXV7ljOmHCBJo8eXLc5dnbyXdb2RnE3yFZHn30Ufrtb38bfP7TTz9R//79oy570UUX0dtvvx28s6H2kGYjXW7QoEHBu+qJUgStBt+hZ9R3zhVqvETd/hM+b95Eog5HAwZAmsxZNY+me2an9J4cyqFft7qKuhcdjQAxlU9uJSrfHH+ZjqcTDbROx8efD9fS3zdHj5Yc0iCPbm+bOOoSZHd8AfoB3c0BuusP35B84okngjc0+c45Rzvy+SBS44wDtm480NwcoDt0T8VnoByHsukTMDySiZ0sf/jDH4KOJRN8XFFRt4dNJgVOiUhKJrUu1no4aimWg4nhwo+Kk+mHH35IyclkN6dRqvCdwGQ7yzEe6wSn2JbqupqwESM/J49OaXhizOW54Hevoq7UscAitSX8Gud068FE9Y+GoQpK2hK1O4mshDYir8iVQ6c3zqeW+W4a1iBxxCXI/vgC9AO6mwN01x9OTzj33HODNzD5ZimnaihpC8AYYOvGA83NAbpDd7Mx1Mn0yCOPhBWxZgcNRyZxqhz/5zQ4fsy5iIrziedz7qMSZaQXvA7O6VfqUMSDt09xMqV6B0q9fCJHkHpZjrQCIdS5p4mKfjO5aCaSMW3atyHaHXpe5C6kc5qMso9Zap1MHUcStToazWdVvBonUwNPDl1aejSvHJgzvgDo7jRg78bAkZHDhg2jr7/+mkaMGEFnn312sP4VMAbYuvFAc3OA7tDdbAyL7+DwK45gUpxLXPiQi19deeWVwWW4wBaHEPP8Dz74gMaMGSOcTRz1NGnSJPE6T3rvkFycNF5KnhKCyKiLviaDuvgmF+yKh/p1pTgnOAoXOVMK2WqpiyJrLiKZMiZH08rMbdzwkR38GsPIsb7n0UeBqIW+gXnjC4DuTgP2bhynnnoqjR8/XkxwMBkPbB2aywJsHbqbjWFXic8995xwFnFk0lNPPSXqDSndd6KllJ155pn0/vvv01tvvSXec99994kObHrCd5gYjlJauHBhzOXmzZsXfDx06NCU1sEV6pXC5dwpJ166IFeCV1Cqy4PERI1kspk/xIp4A+FOGrcNnDRh+DW131zWd9xqUz898DEBAIBl4e5DfFM1Fnzeyzc00cgFAACAkzHs0vuLL74Q/7kd3xVXXJH0+7gI9+OPPy6cMX/9619FAWy94Hx5hVdeeSXqMtzt7bXXXhOPua0hhzynygUXXBBsjzhnzpyYy3EXEq0DDBwlXlRbtJpMSJfLnLJdZfZ2MmmcZLZwMmmea4LJgE7oHTULoLuVgL1nBy638OKLL9Lrr78uovKhufWArUNzWYCtQ3dpnEwclcN3bk444YSYd3BipajddNNN1LJlS+Hgefnll3XbRu62dtJJRwv/vvTSS7RgwYKIZR577DFauXKleHzbbbdFpFTMnTtXfD+errrqqqjruf3220UNKOY3v/mNcDZp+d///ic+i+G0QXQfCYc1i6YbU4t0OV04Un20DpltnUzamkwu62+/VxPpiHQ588cXAN2dBuw9c/i88PnnnxddhzmaiUtC8H9obi1g69BcFmDr0N1sDLuVz8WyGXYWqcnPzw8+rqyspJKSkoj3ssOGnT9Tp06lzz//XNft5BaznALH7WZPP/10uvfee0W0Ej/nriB8EsF069aN7rzzzrTWwelynPp3991309KlS4Vziwuic/c4HhQ4gumZZ54Ry7Ie//znP7P6HZ1A586dY76mjWRil6Yb6XIZ06RZUyLVdbeHrO+kie9ksn7NHW3hb0QymT++AOjuNGDvmbN69eqwbk7crOXjjz+mcePGQXMLAVuH5rIAW4fu0jiZ8vLyRKSSNopJ7VTirm6xquFzsXBm+/btum5n//79xR2oyy+/XDh82MmkhR1MXJica0ely29/+1vav38/Pfzww+Lk5JprrolYhjvqzZgxg7p27Zr2epyKYg/JFP7Os5kvxKp48sOHC3eOjTx3HBGkdTLZIBLLh0gmy40vALo7Ddh75nDEeVlZmZiYFi1axK3ZCc3NAbpDc1mArUN3szHsKpEdJkx5eXnY/A4dOgQf//TTTzHfv2HDBvGfI4r0ZuzYsbRkyRK64447hEOpqKhI1F8aMGCAcAotWrSIunTpkvF6HnroIZo/f76oUcU6cFRXgwYNaODAgaL+1Jo1a2jw4MFZ+U4yF/722MgXYmV8Ab990+W09ZjsUpMJkUwAAGB5uHQCd4zjUgj9+vWj6667jpo0aWL2ZgEAAACmYNhVFkcocRGydevWRUQOKXAnOY4g0sLOFnbGcBRUq1atDNle7gLHBcd5SoVTTjklbsc4LexEgiMpdXbs2CH+R7MHrZMJRb+zw4Hy/fZ1Mvnt6WTyaYYSdJczf3wB0N1pwN6zQ+PGjenGG28UNwsTdY+D5uYA3aG5LMDWobvZGBbjoYQNL1++PCxvvU+fPiJaiB0znL/+4IMPks8XuiDctGkTXXrppVRXd7T9eDrd3IDz4HoHPCWTLpeLSKascKjicNhzj12cTNUHiY7stLyT6YjXT3tqfWHTIU2BMRT+Nn98AdDdacDek4PPXefNmxd2jqqFo94TOZiguXnA1qG5LMDWobvZGHaVpRTR5oM0d00744wzgq/dc889dPXVV4vHf/zjH0X0UI8ePUQh8GXLlomucmJjPR6RwgZAmzZtYooQEckEJ1PG1Prr6Ie85WHz3Mb5qNOjtoJo/t+I9oRvt9W6y9X5A/TEtiP0/aGjjvR4IJLJ/PEFQHenAXtP7oKN63Xu3buXqqurw85hobl9gK1Dc1mArUN3aZxMxx13nKhptHXrVnr//ffDDtATJ04Ud4deffXVYCe6b7/9VjxWUs9cLhc9+eSTdMwxxxi1ycDCxKt1oO0u57GGL8HWLKlYGTHP8ulym7+I7WBicqwRyfTzkbqkHEyMxRV3DKilAt1lAvYeH27OMn36dKqtrRXPFyxYIC7gMjkfhebmAN2huSzA1qG72Rh6lfX999/HfO3ll1+mE088kR577DFau3Zt0LnEYcc8nwthn3rqqQZuLbArtUiXyzp76w5EzGue25QsTcWu2K/lNyDKq0dWYFetxisah1b5cDMBAICRcCdhJaJe4dNPP6Xu3buLCHsAAAAAhGOpo+MNN9wgpm3btomCZRy91LFjR3hjQQT79u2L6anXRjIhXS5zfBRZg2JEwxOtbZl+b/T5BQ2Jjr+JKMca6X6+JBoF5FCA+hXn0ajG+YZsk+zEG18AdHcasPf4cAOAs846i9577z3xvLS0lCZMmJCRgwmamwN0h+ayAFuH7mZjKSeTAochI5cUxIMdkbEuAiMKfyP4I2O8gXBRBxT3oXruIns5mTqNJup3NZGngEMkySp4NT6m4+rn0u1tioPPly5bSi4K0PEd+hm/cZISb3wB0N1pwN4Tw52QudwDww6n3NxcaG5DYOvQXBZg69DdbCzpZAIgEc2aNUu68LfHGgErtsYX8NurHlM0J5Mnnyi3kKyGVxPJlJeTQwXukBOsdTOLpyVKNr4A6O40YO+J4dINY8eOFf+T6R4Hza0JbB2aywJsHbqbjWGX35z65na76de//nVa7//tb38r3o/8d6CEr/MUDW0kU54N/CFWx6eJZPLY0cnksqZP3ReI30Eunq0DfYDm5gDdobtZcOfjadOm0aZNm+Kex2bDwcTA1s0BukNzWYCtQ3ezsVWMBxcDVwqCA5B0dzlbWbk9ajLZMpLJok4mbwInEwAAAP3YvXs3Pf/887R8+XLhaDp06BDkBgAAADIAl9/AllRUVIgpGtpmXSj8nf1IJjfZwMmk2WbrOpnCvUwezZ3yeLYO9AGamwN0h+5Gs2vXLnrhhReCRXLZBtnR5PNFNrvIJrB1c4Du0FwWYOvQ3Wxs42Sqq6sT/zMttgicwbp168QUDS8Kf+te+NuWkUw5VnUyhT9XlWNKaOtAH6C5OUB36G40zZs3pw4dOoTN27t3b9DppBewdXOA7tBcFmDr0N1srHnVFQXlIqthw4ZmbwqwACUlJUkX/kYkkw6RTDk28E/bJF3OFxHJlLytA32A5uYA3aG70XCNpfPPP5+ee+45OnjwoKhjMn78eN3PNWHr5gDdobkswNahu9lY86pLBYcsz5gxgz777DNxMtCzZ0+zNwlYgI4dO8Z8Dd3lso8zCn+7bRLJlJO0rQN9gObmAN2huxkUFhbShAkTaNGiRXT66acb0mAGtm4O0B2aywJsHbqbjS5H0k6dOsV87bXXXqNZs2YlnSK3Z88e8Z8LfrOTacyYMVncUuBE0F0u+/jIb7+aTHaJZNI8R+FvAADILnwO6fV6Y5ZcaNmypZgAAAAAkDm6XHVxC9hobV75IH/48GExJYu6m1yPHj3opptuytp2AvsSr0YXustlH0fUZHLl2rLwN+rRGQ80NwfoDt31oLq6mt59911xXsoRS9HOT40Gtg7dZQG2Dt1lAvYeQrdb+2rnUDLzY+Fyuahr1650wQUX0N13301FRUVZ2kJgZ1asWCH+9+vXL+K1WhT+zjpbqrfr62TicWHrl0R7VhAFNEW10qVil2XT5fyBAH1+oIY2VPnEFC+SKZ6tA32A5uYA3aF7tikrK6OpU6fS/v37xfP58+fTsGHDyGxg69BdFmDr0F0mYO86O5k2btwY4VjiFDq+e3TllVfSAw88kPAzeNmCggJRfDEvL0+PzQQ2Jj8/P+Zr2kgmFP7OjP11B+mIv1LfmkzrPiBa9DzpioXS5SbvqqIZe6ujvqZ1MsWzdaAP0NwcoDt0z3ZNz7feeovKy8uD8+bMmSOKe8cr62AEsHXoLguwdeguE7D3ELpcdbVv3z7qfHY21a9fP+brACQLp04m3V3OOgEstmR11YaIefk5WXb8bv+OdMdTSFbhh8O1MV8rcOUkbetAH6C5OUB36J5N3G43nXPOOfT6668Ho+i5yLcV0uVg69BdFmDr0F0mYO8hDLu1/8orr4j/6A4HjC787XFB80yo9kc6RLoXZfkusLeKdKWgIVFT63SmrNZW+/6FAhdR32Jr1o4CAAC7wRFLI0aMoM8//5zatGlDF110ETVo0MDszQIAAAAcjWFOpokTJxq1KiA52kimPDiZMsKvKfrd0FMipqyiWQe1OJaofpvsfHZ+CVH7U4hyrVPPzUvhtekG1s+ltgVuGtIgj5rnIfQOAACyxUknnSTqeR577LHk8VgnbRoAAABwKjjaAluyfPly8f+YY46JeA3d5bKLj8IdQG3ySvXvBNdxJFG74eRUfJr+B2OaFlCverkp2zrQB2huDtAduqcDp8Lt2rWLSkujH5s4PW7AgAFkJWDr0F0WYOvQXSZg7yHgZAK2xOvVOCVUoLtclrXWRBllvbNcNCeTy9kpY15Nl01tse9kbR3oAzQ3B+gO3VOlqqqK3nnnHdFw5rrrrovpaLIasHXoLguwdeguE7D3EHAyAVvSt2/fmK+hu1x28QX8xjuZ9FiHhSOZ3HEK0cazdaAP0NwcoDt0T4WdO3fSlClT6ODBg+I5P77hhhtEcW+rA1uH7rIAW4fuMgF7D4FqNcCWcPh7rA4x2sLf6C6XGT5tJJMew4Zf86O5nO3/9mqcTPEimeLZOtAHaG4O0B26p8LixYuDDibmwIEDNHPmTLIDsHXoLguwdeguE7D3EHAyAccX/kZ3uezWZPLk6OAA8tdJ42TyBwKast+sKZxIAACQCqNGjRId4xTq1atHJ554IkQEAAAATAZOJmBL1q1bJ6akuss5O/PKhJpMOgwb2u5yDnYyaaOYGHeatg70AZqbA3SH7qngdrtp/PjxonNc27ZtadKkSdShQweyA7B16C4LsHXoLhOw9xDOvZIDjqaioiLma16NvwKRTDasyeRyS+VkipcuF8/WgT5Ac3OA7tA9VUpKSujqq6+mxo0bC6eTXYCtQ3dZgK1Dd5mAvYeAkwnYku7du8d8TRvJlIt4vSzXZDKi8LdHms5yidLl4tk60Adobg7QHbpr8fv9tHDhQurfvz95PNGPC82aNSO7AVuH7rIAW4fuMgF7D+HcKzngaAoKCpJ3Mtnn5qZNajJlWVB2ukREMnmk6SyXKJIpnq0DfYDm5gDdobuayspKmj59Oq1fv57Kyspo7Nix5BRg69BdFmDr0F0mYO8hsnold+qpp4r/5513Ht16663Z/GgAkiaiuxwimdKi0ldFfgpQjb9W35pMvvDPt6KTqdbPOkTxDqVBuVfjBRWaovA3AAAo7Ny5kyZPnkzl5eXiOUczcZFvjmgCAAAAgLXJ6pXc3LlzReu+3r17R7zmcrnEazfffDP9+9//z955wDlRp2/83U12YVl671WKCiIqiiIKFuwFFVCxYO/H3d/Tu1PPdufpFet5p2JBPQsgihUVUERUFAvSe+9NOrvspvw/zw9/2cmkZyczSeb58pkP2WwymTx5MzvzzFuesvJliQtZvXq1+r9t27YRvzOfw7MnU2psrtgmz218XTZUbIn6e0t7Ms0bLTLvjaw1mTAJ7oX1+2TK9v2mfC5riZfJFC/WSWag5s5A3am7Bv2VkMlk5OOPP5Zu3bpJSUmJ5DqMderuFhjr1N1NMN6rYI4HyUm2b9+ulmhUmNwATpdLjQnbp8Q0mCw1mfZujm4wZVHj7/l7fTI5wwYT8BSkF+skM1BzZ6Du1F3TtGlTOe+880I/165dW4YNG5YXBhNgrFN3t8BYp+5ugvFehaXpAsXFxVJZWcnO6iTjxBtTzEym6mcyxaNJUUOxhD3ro9/vLRGpUV+ygY1mxzIDNC0qlMI45XK5MpI7n6Dm1N1NZGu8Iyt+zZo1qh/TRRddJHXq1JF8IVs1z3eoOzV3C4x16p5XJlPjxo1VHf38+fOtXC0hEdSrVy+mKmz8XT38EtkzCBRIgRxeeoj0KO1mTUQG/NENpsOvFfEUSTbgs6YNU0xKPQVyVYtaacc6yQzU3BmoO3U3M3DgwFD5XD7BWKfuboGxTt3dBOM9QybTEUccIR9++KHMmDFDHnroIbn66qulRYsWVr4EIQkHlbHxd/XwB8MnvV3c5BzpWXqwFBcWSUmhhZPOApXhP3tqiJz/etb0Y4pmMh1cyyu/a1PbsvXX9RbEzWIihJB8JRAIyJQpU6RNmzbSpUuXqI/JN3OJEEIIcQOW9mS6/PLLQ7fvvfdeNQkEBwj6ICEYDMp//vOf0H3pLF5v9pyAEufYvHmzWqKNhzcnnxTxGDUl/MHwTKY6nlKp560ju7fuiqq5ZZlMtRpnlcEE/HAtDdQsLJD6RYWWLckYTLFinWQOau4M1N09uu/du1dee+01mTZtmowfP951fecY69TdLTDWqbubYLxnyGQaPHiwXHLJJcpMMi+aaL9LdSEEZZlYzESZDi9FbG+fEv5guPnjKSiMq3namDKmss1gipbJFK9Bd6awXHdCzbMUxro7dN+9e7c899xzsnz5cvVzWVmZjB07VvX0dAuMderuFhjr1N1NMN6rsPys7vXXX5czzzxTXaH66aef1NUpn88nBQUFNIiIZcQqw4zWp5kmU2r4TLPUPL/uJiwvfQ1kv8lkzmTyOlDaxpJj+6HmzkDd3aE7JsWhRG7evHmh+zZt2qSafHfs2FHcAGOdursFxjp1dxOM9yoyclaHMbNYjBQWFiqj6ZZbbpGnnnoqEy9LXATGG0cjWiaTl5lMKREwlcvpTKZYmltWLpcDmUxeBzKZLNedUPMshbHuDt1xLHjuuecqY2nr1q1qatyQIUOU8eQWGOvU3S0w1qm7m2C8V5F9Z3WEVANz029QzJ5MKeEzlct5CzIkoDmTKVOvk+MmEyGE5Bs1atSQoUOHyqRJk5ThhOwmQgghhOQHtplMbdu2VVevGjVqZNdLkjxm586dUUdFVjKTKQM9mTxxNbeuXK5Isg2fqVzO40C5nOW6E2qepTDW8093TJBDJns0mjRpIpdeeqm4EcY6dXcLjHXq7iYY71XYVki0cuVKWbFihdx33312vSTJYxBPWJLJZOJ0uWqaTOKJq7l1JlP2ZTKZw8mJTCbLdSfUPEthrOeP7jCXkKWEPp24TTKvOUkMdbcfau4M1J26Ow3L5UhO0qBBg6j3R8tkYuPv5MH0Rr9E78kUS/N8bvztC5gzmezfBst1J9Q8S2Gs54fue/bskXHjxoVMlC+++EJOOukkS18j12GsU3e3wFin7m6C8V6F42d1ixcvVlPo0PwRY23RALJx48ZyxBFHSJcuXZzePJKloPwyGZMJTb8dqHDKWQISkKCYjRVPXM3z2WSKzGSyP5gs151Q8yyFsZ77uuNCxZtvvinr1q0L3ffll19K69ateUyXIc1J8lB3+6HmzkDdqbvTOHJWt2vXLjVh7tlnn5UNGzbEfFzLli3lxhtvlNtuu03q1q1r6zaS3MRncgU4WS45dvv2yHe7Z8kvvh0Rv6t24+/134tsmStimlonWxdklclUGQjK1B37Zf3+qu1cui/cCGPjb0IIiQ16b5522mny8ssvh8rkPB6P7N27l7IRQgghLsH2s7rp06fLJZdcImvWrFFXvOKBK2H33nuvvPDCC+rKWJ8+fWzbTpLdlJeXq/9r1qwZdn+Fyccotq3rWO4SCAbkifUvyYaKLVF/X/hrT6ZYmsdlxWci3z+Z3GMdni737Lq9Mm1nRdzHONH4Oy3dCTXPQRjr+aE7rqAPHDhQPvnkE3WBcMiQISqTiWROc5Ic1N1+qLkzUHfq7iqT6ccff1RXuIxXtDB1BGVx7du3l9LSUvU71PGjjE5fBVu1apU6YEFdP8roCFm0aJESoWfPnmFi+MzlctnXSzrr2FixJabBBGr8OvUtluZxWft18o/1OnewDcP7213xDSZQwwHTMi3dCTXPQRjr+aP7McccIz6fT3r16qWO7UjmNSeJoe72Q82dgbpTd9eYTDjYwKhaNITUo3Lvuusuueqqq1QPJjPbtm2TUaNGyd/+9jc1DhDPw/PnzZunUq+Ju4l10GqeLsem34nZH4xtrnSs2VZKPbXiah6Xyn3JP7Zl79TXbxH+oEhl/MRK1fT78NoHDDc74Qma/VBzZ6DuuaX79u3bYzY5Rdnc8ccfX80ty18Y69TdLTDWqbubYLw7YDJhjO2SJUvUgUfHjh1l8uTJ0q5du5iPb9Sokfz+97+XwYMHy6mnnipLly5Vz8d6rrjiCrs2m2QpBx10UFKNv2kyJcYXDHfmvOKR3nV6SqOi+nJCvWMSap5Sc+/GB4vUbhl+n6dIpEVvkRZHiVP4ohhMfeoWS81fM5dqFhZI3/rF0ram/X2j0tKdUPMchLGeG7r7/X51DDdjxgx1oZClcJnXnFgDdbcfau4M1J26O41tZ0zvvfde6PaYMWPiGkxG8Dj0Yzr66KPVz+PHj6fJRGISkcnEpLeE+E0mUy1PiVzebJA1UWZat3Q8TaR99o2y9pkm6oHhLWpJQ7qUhBASAlOAx40bp9oYgLFjx8oNN9zAq7eEEEIICWFbh5GffvpJZTGhTj/VvkpHHnmkeh76psycOTNj20hyB8RCtMbxET2Z2Pg7ZZPJE6MBdyzNU8pkcniCXCqZTNkySS4t3Qk1z0EY69mvO47ltMGkpwXDdNI9NIn1mhProO72Q82dgbpTd6ex7RR88+bN6v9DDjkkrefr5+n1ZBocRN1+++3SrVs3dYWuYcOG0rt3b/nnP/8p+/al0GcmChjtC8MtmQWPJZHMnj1bLWbM0+WYyZQYv5hMphi7hVia54PJhJ5M0XowZQNp6U6oeQ7CWM9+3fv16ycdOnQI/Ywemd27d1fHKyQzmhProO72Q82dgbpTd6ex7YyvqKhI9u/fr5Z0qKioCK0n03zwwQdy2WWXqSt0GhhLP/zwg1peeOEF+eijj1jv6iBeb/TQ9bHxd7V7MsXKZIqleT6YTL4oV5S9WXLSlJbuhJrnIIz17NcdE4Evuugiee6559TtIUOGSMuWpj57xFLNiXVQd/uh5s5A3am709j2V6558+aqcfd3332X1vP187CeTIJyvKFDh0pZWZnUrl1b/vSnP8mAAQPUz6NHj5bnn39eFi9eLGeddZYynOrUqVOt1/v000/jHqCxoWZ0Dj300Kj3s/F36viDgaRMpliaxyVgcv1irNtpsjmTKS3dCTXPQRjruaE7sruHDRumjn9q1TowfZRkVnNiDdTdfqi5M1B36u4akwmjbGEyLVu2TN566y01NS5ZUO+vJ9NleiTuiBEjlKEEB3jixIly7LHHhn530kknSefOneXOO+9URtOjjz4q999/f7Ver0uXLtK+fXsLtpxEM5m82elpZBXmnkxeK42gQGXOZjIxdAghbmXhwoVqEnBxcXHU3zdr1sz2bSKEEEJIbmBbTyZkB2muueYa+eSTT5J63qRJk+Tqq6+Ouh6rwTjeadOmhbbRaDBp0Kfp4IMPVreffPJJqaw0nUSTrJouV8zG36k3/rbSXsmZcrnILCb2GCGEuA2/3y8ff/yxytx+//332ZyaEEIIISlj2yn4wIEDVSYQut3v2bNHlZtdcMEFqv/Rtm3bwh77yy+/yIcffqjq/k8//XT1eJzw4flYT6Z49913Q7evuuqqqI9BD4IrrrhC3d6xY4dMmTIlY9tD4l9lxWKG0+VSx2du/B0jkymW5nExGVjZajKZy+WyZbJc2roTap6DMNad1R19KDFsRLcnmDt3rrr4RjKnObEX6m4/1NwZqDt1dxpbz/jefPNNlR20fPlyZTa99957agElJSWqzn/v3r2qXE2jR7x26tRJ3njjjYxu31dffaX+x3YceeSRMR934oknhm5//fXXGTW+SHRiNZCvMDf+Zs1TGj2ZonvPaTXtz5lMpnCXySvZ4zKlOyyBUPNcg7HurO4+n0+2bNkS0TcSbQIwYZdYrzmxF+puP9TcGag7dXcaW8/4mjRpIt98840MHz5clctpA0lPb8MSjTPOOENGjRqlnp9JFixYoP4/6KCD4nbl79atW8Rz0gUZU4sWLZKtW7dK3bp11WufcsopctNNN0mrVq2qte585pBDDol6vzmTqYjlcpb1ZIqleQiYVWGNvoO2m0yBYDBqE+9E7A+YTKbs8ZgS606oeZ7AWHdWd0zvHTRokLogCHAcdPbZZ9NgyqDmxF6ou/1Qc2eg7tTdaWxPK2jatKlMmDBBlZmNHDlS/b958+aoj8NUtxtuuEH69++f8e0qLy9XRk8yE90aNGgQyrpas2ZNtV73iy++CN1G2SAWpKqjqfgTTzyh3n86rF27Nu7vN2zYILkMDoaT6cnETKb4zN67QMZv+zSpcrlYmivmjxVZ9I5IZXSj2I7pcp9sK5cxm8tkbzouU5ZOlkuoO6HmeQRj3Xndu3btKv369VOlcuiBmemJvm6FsU7d3QJjnbq7CcZ7FY7VrsBAwgLWr1+vUrTRe6l27doqY6lly5a2bs/u3btDt7ENidAmE7Y5HTC1BT2pUD7Ypk0bdR/KCN9++201TQ+m14033qh6UV1//fUpr1+vM9m6XaOxhp+RZgkXXH9ZVqxYofo1oGxR66M/Nzy3UaNG6j6YZDC4jJ8hdFq6dKnK1OrQoYO6Dw3T58+fLzVq1AjLDJs3b55K2T/ssMNCjZfxXKwDB781a9ZU961evVq2b9+uJvPVq1dP3Qezcv0m3GoaWl/QVyGzZi1QxmDbtm3VfdAW2WP4DJE5ph4XDMrs2bPVlVvj2M94WuC5WEcqWiBeMGExGS1wkI8mrD179gzdhymLyPhLpMWmTZtk48aN0qJFC2XY6h5iq1atUlelER+BYED+t2l8RDx4pFBmzZoVoQWy9ioqKtR9OtMPMbt/20o5eMVryYSabN62XZrWbR36zuH52GY9YRHrx+vgveE9aubMmaM+I8SFBhMeUVoLzco9RTJqwz7kTlmC99fYg/74HPDZG68K4fPC59a9e3fxeA4YZ/hc8fmitESP9V63bp0yrxF7iEGAn3E/pjPpEzjEE+Kqfv360q5du7haIE6BUQvEM+IaQwn0NCh81vjMEWeIN4CYQGwgS7Jx48bqPsQOYgg/6+xJxBhiDd91fOfT1QLTM1EKDfBdwHfCqAW+M/juJNIC30F8F7EurNOoBfYTPXr0iKvFypUrZefOnWq/i7Hr2mjHPsOoBfoB4sKBUQu9/8Lz8HyAfRT2VVi/HgRh3H9he9C/z7j/MmqB18Br4f3hfQJsC7YJOujJXdhmbHu0/RdiDLGmwXcW+uNzyId9ObQw7r/iaeH2fTnAvhD7RKMWgUBA7TuT3Zdjn4zn4tisb9++6n0jrvAaulxO77+wLdgmq/fl0C7R/gufgc5s1/svfFb6OEZrUd39F/fl3JdzX859eS7vy/XfNX0syn05j8u7RDku13/7rCYrGqTggMFuU8kMDlY1sUb2GtEHQsb+UcmCdPQrr7wyYnpV79691ZVDND2HAYUDn9/97ndy7rnn8mqiCewso5VX+oLh9XFFhVbZDvnHvkCZ7A1Exm8j7wEDwAz+GOKPq5kaFQcyABMRKPCKv+jAyYLVbNjvt8xgAk2zaCwhDlRwMqxPZEnmgcmBAzdqbi+Ic6APjElmgOEGo0efrEB3HGjqYx8YpPpkiWQGbXBqA43YA06+sW/XFwyIPZrjfEcbK8QecDEL56o8jnHmOIaIFASNjZFcDK5c6h0gjB6M740HrjTjRARXv+AuW81f//pX+fOf/xy6fffdd1teLnf00UeHdkSJSgSzDVxdBcarCeCuz0Ren1v185U9RR7MfLVlTrLDt0vuWvnPsPvqemrLna1vlIZFB668JKO5rP1G5JtH4r8YejEdNlyky7mSCebuqZQHV1ZlI1aHOp4CubNdbelaKzvK1GLqTqh5nsFYzyzILEM/zB9++EEZStddd53KCKLu9kPNnYG6U3O3wFin7ql4BjqTzkpPICsymbIBXUIBkimBQ8p/sqV16YASuXvvvVddbZw6dWrKJlOumUapolNIzVSw8XfaU+XA/e1+KzULa6SkeXizb3wpWor0uzf8vpKGIt7MXRk3t2Gq5ymQv3RM7wpxk+JC8ZiyDJ0kpu6EmucZjPXMgeyNV155RZVN6J/Hjh2rjCbqbj/U3BmoOzV3C4x16u40NJl+BanhqOnXvRcSla9okymV3kepgKwqbI/uf0DCiVVOwely6U+VizdZLp7mERPkYCbVsbf81WdKyCwuLJDmNTLXZNxOWDpEzd0CYz1zoBcRjiuMxxPI4EbvrF69emXwlUk0GOvOQN2puVtgrFN3p8mexiNZgG4GieakSCuP189AY2z6ajXmnk0kMZwulzx+iTSZPJKGMROojCyNsxnzO/Hyq0MIIWHHE2eeeWaoWTdMJ/SHpMFECCGEEKuhyWTg+OOPV/8jS+nHH3+MKRrK1zSYwJIJcIURWUzA6abo2QgmAGExU2mqAPMywmPiM2UyFUphXGMzluYR5XIOmEzmTCY9HS4fiKk7oeZ5BmM9s8BYGjJkiJqceO2114b6vFF3+6HmzkDdqblbYKxTd6fhKbiB888/P3R71KhRUQXDGMlXX31V3cboaYz6zQQjR45U/ZjAiSeemJHXyGVgwmFJlMlUnB8VU7aUy3nilMrF0zyiXK7QftF9pp5MnvzxmGLrTqh5nsFYt4Z481waNGigDCYML6HuzsFYp+5ugbFO3d0E470KmkwGMG2tX79+6vaLL74o06dPFzOPPvqoLFiwQN0eMWKEujJo5IsvvlDZIFiGDx8e8fyVK1fKzJkzJR4ffvihPPjgg+p2SUmJXHXVVXEf70bQ2Dxac3NzTyZmMiVvMsXrxxRPcwn6siCTKX/L5WLqTqh5nsFYrz44vvjf//4nfn9kObTGnLFK3e2HmjsDdafmboGxTt2dho2/TTz55JOqBK6srEwGDhwod911l8pWws+jR49WGUagS5cucvvtt6csOEwmrO/YY4+Vc845R6WroxknWL58uYwbN04t+krkv/71L5XaTsJBU/SkpssxkynpcrlEmUyxNI/IZCpwvlwum6bDVZeYuhNqnmcw1tMHfSQnTJggP/30k/p50qRJcvrpp1P3LIWxTt3dAmOdursJxnsVNJlMoAnmmDFj5LLLLpNdu3Ypk8kMDKaPPvpI6tSpI+mCLKlomVKaWrVqyeOPPy7XX3992q/hRnymi7dFzNWLiV/CHTlPuomNWdCTyZ/HmUyEEJKId955R+bPnx/6+dtvv1VXsrt3707xCCGEEGIrNJmigAyj2bNnq6wmmElr166V4uJiOeigg2Tw4MFy6623KhMoHY488kh57bXXlMH0ww8/yIYNG1SDb1yFRK+EQw89VE4++WTVM0FnOJFItm3bFtUxNjf+pslkXblcLM0jezI5n8mUTyZTTN0JNc8zGOvpg1L/xYsXh03G3bhxY1ImE3W3H2ruDNSdmrsFxjp1dxqaTDFo166dPPbYY2pJhf79+8dtuonsp2HDhqmFpA+Mv2RMJi/L5Sxr/B1L82wwmcyZTPlULhdTd0LN8wzGevq0aNFCzjzzTHn//fdVr8hzzz1XevToQd2zFMY6dXcLjHXq7iYY71XQZCI5SZMmTaLeHzFdjuVyMdkfrEjJZArT3F8psuYrkT0bRLbOc9xkWl1uzsqSvI91Qs3zDcZ69TjiiCNUmf8hhxySUiY0dbcfau4M1J2auwXGOnV3GppMJCdp2bJl1Ps5XS55Xt/0bko9mcI0//4pkdVToz+w0N70sY37/fLVznDDzJtHmUyxYp1Q83yDsZ6YvXv3Smlpadxsauqe/TDWqbtbYKxTdzfBeM8CkykQCMiCBQvUtDVceausrEz6uVdccUVGt43kLuZMJk6Xi04gGBCfhIsVlGCST/aJrP069u89NcROftodue8ozh+PiRBCVBk+Jsd98skncumll0qHDh2oCiGEEEKyEttNplWrVslf/vIXeeutt2TPnj0pP7+goIAmEwnFTu3atcPUYOPv9CbLgdY1WiSneY3CyD5MRpofYWuE7gtEmmOH1ymSfI91Qs3zDcZ6dHARDkNIfv75Z/XzuHHj5IYbbpC6detS9xyFsU7d3QJjnbq7CcZ7FbZ2rJkwYYKadDJq1CjZvXu3ujKXzkLIsmXL1GImwmRi4++kmn6DsxqelJzm0QymlkeLtBsg0vcukea9HJ0sB46rZ282lROxTqh5vsFYj86sWbNCBpMumcOFOr8/cj9O3XMDxjp1dwuMderuJhjvDmQyrV69WgYPHixlZWWh+5o3by49e/ZUU5MwDYWQZIl1BddnOub2svF30iZTUYE3Oc2jmUy9fyNSw5qr6qniM3lM/eoVSz5hVbYCoebZDmM9OkceeaQsXrxYLZrWrVtT9xyGsU7d3QJjnbq7Cca7AybTv/71L2UwodwNTbGee+45NW6XkHSI1Y+iwpTJxOly0fFFMZk8BYXJab53S+QvHZgop/GbMpnyabIcYO8Vau4WGOvRwXHToEGDZOTIkSqL6bzzzpNDDz2UuucwjHXq7hYY69TdTTDeq7DtzHDSpEkHXtDrlYkTJ8rBBx9s10sTFxExXY7lclHxByN7MnkKkhQrUJlVJpM5k8mTR5PlCCEElJSUyMUXXyyFhYUcTU0IIYSQrMa2M8M1a9aoq3EYr0uDiVQXPY3QWGaJhBazyVTEcrmo+E2T5YBHPMlpHiULSpI1qDKA32Qy5VsmU7RYJ9Q8H3F7rK9bt061D6hZs2bU3zdr1iwjr+t23Z2AmlN3t8BYp+5ugvFehW2n4PrghWlkxArmz5+vlnhNv1Xc0WRKuieTN4FRFNLc3JMJZXaFzplM5sbf3jzLZIoW64Sa5yNujXUMNPn+++/lpZdekvHjx9s+4MStujsJNafuboGxTt3dBOPdgUymdu3ayZw5c2TXrl12vSTJY2rUiJweVhklwYbT5ZLryVQgBVKYoCdTSHOzyeRgqVy0crl8y2SKFuuEmucjbox1XPX84IMPZPbs2ernRYsWyVdffSX9+vWzbRvcqLvTUHPq7hYY69TdTTDeq7Dt7PCcc85RB1HTp0+36yVJHtOtW7eI+8ylcoCZTNEJmEwmTxJJjSHNty7ImlK56D2ZJO9jnVDzfMSNsY6BKEuXLg277/PPP1dZ31ZOkIuHG3V3GmpO3d0CY526uwnGexW2FRPdeOONaqzf6tWr5c0337TrZYmLqGAmU9qZTEk3/QYBf1ZlMkVOl8szl4kQkrfguOiiiy5SPSs1xx13nJrCSwghhBCSi9hmMrVq1UpefvllNRnlhhtuCE2bI8QqomUyedmTKSp+CaRvMgWzq1zO7C3mWyYTISS/6dixo5x00kkqzX7o0KFy6qmnqmMlQgghhJBcxLazQ2QwHXHEEfLss8/KrbfeKmeccYacffbZMnjwYOnRo4fUq1cv7EpePNq2bZvx7SXZzdy5c9X/3bt3j9v4u9jZSq6cafydqOl3mOaNsstk8gXyO5MpWqwTap6PuDnWjz/+eDnssMPUsZDduFl3p6Dm1N0tMNapu5tgvFdh29lh+/btw0wkTE9Bs0ssqYB1+Hymk1ziOvz+yNq4aCaTWzOZdvv2yKhN42Rp+SoJBCOFCUq4MRPR9BvNvX96TmTNNBHffnXXoboszezhZHiy3Lr9fnl67R5ZWe7HhkdgjoR8a/wdLdYJNc9H8jnW16xZIzt37oxp5ODYxgmDKd91z1aoOXV3C4x16u4mGO9V2J6CAHMJB1PacLJ7VC/JD3r27JlwulyBi0unPto+RRaWLUv68V4xGUUwl5Z/GnZXSErzV7awSDLJS+v3yrKy5E+C8s1kihbrhJrnI/kY6zjGmTFjhnz66aeqBK5x48bSvHlzySbyUfdsh5pTd7fAWKfuboLx7oDJhBK3ZMvhCEkHcyZTkQdXh92p5Yb9m1N6fIOi+uF37Fyd/JNLGksmWbM/tavsjThSkBCSBQQCARk/frzMmTMn9POYMWPk+uuvl5KSEqc3jxBCCCEkt02mlStX2vVSxKWYM5nc7DX4IorIYlOjoFjOajAgslwuGYpqiRwyVDKJL4Vkx161i6R7aWYzqwghJBmQuVSrVq2w+7Zv3y7fffed9O/fnyISQgghJC9xtmMvIWmyZMkS9X/nzp1jTpdzaz8m4Df1YTq74UlyWGm3KI8skGZFjaTIXPIWMJlUrY+TVaXHqpvt2unG+wUidVqJeDJr6vhNJbW3tCqVdjUj+0CVegqkSR52eo8W64Sa5yP5GOsDBw6U9evXq55M4IQTTlBLNpGPumc71Jy6uwXGOnV3E4z3KmgykZxk3759EfdVmHyRPPQbksYfDM9EalzUUFrXaJH8CsyZTDUbyA45UFLXrn4HsRNzJlPrmh5pX+J1dawTap6P5GOsezweNUX31VdflVNPPVW6du0q2UY+6p7tUHPq7hYY69TdTTDeq3DPmRrJK6IdqDOTKXYmk7cgRcfNZFJJodexkyOzyZRvjb0TkY0npfkONafuVlK3bl25+eabVflcNsJ4p+ZugbFOzd0CY526O01WmEwbN26UrVu3yu7du6VOnTpZOX2FZBc1a9ZMqvG3W/GbejIVSoonN+ZMpkJvVM0zTSAYjBhm56mac+cKnNDd7VBz6p4Kq1atkq+//lqGDBkiXm/0w6psNZgA452auwXGOjV3C4x16u5ak+mrr76SZ555Rr744gtlMpmByTRgwAC58cYb5fjjj3dkG0luwcbfVfiD4SaTtyDFr7q5J1OqmVAZbPrttkwmQkh2EgwGZfr06TJ58mQ1OW7ChAly7rnnOr1ZhBBCCCGOYvultW3btsn5558vJ554oowePVo2bNigDtTMC+5/88031eMGDRqknkeIZvXq1WqJm8mUvReOM47PZDJ5ClLNZKoM/7nQG1XzTEOTKXqsk8xCzZ0h13SHuTRx4kRlMIGffvpJLblGrumeD1Bz6u4WGOvU3U0w3quw9TQcRhGykj744IOQmWRM62vUqFFYep9+zPvvv6+eR6OJGMdAY4nbk8nN5XKmnkyeVDORzJlMhd6ommca82Q54C1wVyqTE7q7HWpO3ZPh8MMPl+Li4rD7Fi9eHHZskwsw3qm5W2CsU3O3wFin7q4ql7v00ktl0aJFUvDrSeIZZ5wh1157rfTt21eaNm0aetyWLVtUf4MXX3xRPvroo9CB27Bhw+STTz6xc5NJltK+ffuE0+XcnMlkLpfziKfaPZmiae5EJpPHXR6TI7q7HWpO3ZOhSZMmqjxu3Lhx6rgGmdcnnHBC6BgnV2C8U3O3wFin5m6BsU7dXWMyIa180qRJ6uCrpKREXnvtNVU2F+vADb/DgiwmmFMYCYjnYz2nnHKKXZtNspR69epF3GfOZHK1yWRq/G3FdLlommcaZjJFj3WSWai5M+Si7t27d1cXxlq3bi2dO3eWXCQXdc91qDl1dwuMderuJhjvVdh2Go7+S5rnn38+psFkBlcJX3jhhdDP6NNESFKNv11cLlf9nkyRJpMTMJOJEOI0FRUVcX+PISW5ajARQgghhFiN185pcqBTp05yySWXpPTciy++WO69915ZunRpaD3E3WzatEn936xZs5iNv70uyGTaULFZFu5bFmYqBX/9V62eTJX7wn8u8ETVPBOs3++XWXsqpTIQlJ3+aD2ZxFXYpTuh5k6TjbG+YsUKefvtt9UFry5dukg+ko265zvUnLq7BcY6dXcTjHcHTCZMi0OpXJ8+fdJ6Pp4Hk2njxo2WbxvJPXQchJlMpkym4jzPZFpRvkYeX/eS+MylbVFIuSfTzlWmFXijam41K8p88uflu6QiRt9c+EuFOdbvpLrYoTuh5tlANsU6mnejN+Rnn32mbr/zzjtyww03SIMGDSTfyCbd3QI1p+5ugbFO3d0E490Bk6my8sBIdPMklmTRz9PrIe6mRYsWEff5XJbJ9OPuOUkZTKC4sCj5FZf9EnlfYVFUza1m+s6KmAYTKM7zzzQaduhOqHk2kE2xjiEl6AGpKS8vlzFjxsg111wjRUUp7E9zgGzS3S1Qc+ruFhjr1N1NMN4dMJkwPW7NmjUyf/78tJ6vn2ecQkfcS7Q4qHBZ4+99gfKkHtesqLHU89RJfsUVeyLva3SwNK3VSDLNnijlcUZ6lObXyV0ycJ9Hzd1CNsV6165dVVPvuXPnhu6rW7eu+P3+vDOZskl3t0DNqbtbYKxTdzfBeHfAZOrVq5esXr1aZsyYIT///LMcfvjhST931qxZ8t1336lyu1SeR9xFxHS5PC+X85uaezcpaihNixqH3dfQW18GNjg+tZHapvUqbDCYok2Ta1JUKK1rHPggW9f0yAVNatqyHYQQd4N9Jvowob/C1q1bVXPvfv36pbYvJYQQQghxIbaZTDhYe++991RvAzT+Rp+Dli1bJtXLCY2/8Twc3CU7lY7kNzt27FD/169fP/Z0uUJ3TZDrXbunnN3opOqv2DxZzlMcU/NMT5M7rl6xDGteS9yMHboTap4NZFuso0x/yJAhsmvXLjW0JF/JNt3dADWn7m6BsU7d3QTjvQrbTsMvv/xy6dixY6jXwWGHHSZPPfWUbN++PeaH9PTTT0vPnj1l8eLFymDC8y+77DK7NplkMatWrVKLEbdNl/NLuMnkKbDoDZtNpkJvTM2txpxD5WHSgC26k3CouXt037lzp+zduzfm75s0aZLXBhNgvFNzt8BYp+ZugbFO3V2TyeT1euX111+Xk08+WcrKyuSXX36R3/3ud/L73/9e9T5o166dlJaWqoM9lNUtXLhQ9T5ABhPA7/B8rIeQhg0bRojgtuly5nI5T4EnMybTr+uNprnV+Ezlcl6WptiiOwmHmrtD92XLlsnbb7+tpqrhQlhhYZ5fmYgB452auwXGOjV3C4x16u40tjo2xxxzjEyYMEGGDRsm69atUwaSz+dTTb3NDcG1uQRatWolb7zxhhx99NF2bi7JYtq0aSNuny7nD4a/YW+mTKZfJ9NF0zzT5XJeZjLZojsJh5rnt+44vpg2bZpMmTJF3V6xYoW6jYtgboTxTs3dAmOdmrsFxjp1dxrbT8NPOOEEmTNnjvz5z3+W5s2bq/twkGdeAH5/7733qsej4SYh8agw92TyuKsnk0cyZTLZJ6S58TdNJkKI1ZSXl8v3338fdjELptOSJUsoNiGEEEJINXGk9gzNJR944AG1LFiwQGbOnClbtmyRPXv2SO3atVUPBEyjO/jgg53YPJIDoOQSlJSUxJ4u57qeTBaZQebpcr/2ZIqmeaYzmTwsl7NFdxIONc9v3bF+NPQeNWqUBAIH/nCgbN+tV34Z79TcLTDWqblbYKxTd6dxvMERjCSaSSRV0AweoDF8rMbfeW8yBe1t/B1N88ybTBl7qZzBDt0JNXdbrMNQOu200+STTz5RZXJ9+/ZVA0bcCPcx1NwtMNapuVtgrFN3cbvJREg6oBF8osbfXpeVy1nXk6kyqskUTfPMl8u586TPiB26E2ruxlhHn8f27durxt9uhvsYau4WGOvU3C0w1qm709BkIjnJQQcdFHGfOZOp2HWZTFaZTCa37tf1RtM8442/M/6K2Y8duhNqno+xvmPHDjVk5NBDD436e2Quud1gAtzHUHO3wFin5m6BsU7dnYbncCRvcN90Obsaf9u3m/AH3fUZEkIyw9KlS+Xtt9+WiooKqVu3rmv7LRFCCCGE2I2lZ48PPvhg2M+YDBfrd9XBuF7iTnSz1sLCQtdMl1u3f6O8seV92VixRf1cHthvTSYTMpdmvyKy5ksR3/6Y5XLRNLcan6lcziMsl7NDdxIONc9t3adOnSpffPFFaHrcW2+9JTfccAPLBzKsO0keau4M1J2auwXGOnXPK5Pp/vvvD2ucaTSDzL+rDjSZyJw5cyIaxOb7dLn/bR4vq/evj/n7tBt/r5susvjd2L8v9MTUPOPlcvSYbNGdhEPNc1t3n88XMpjArl275LPPPpNzzz232tuYjzDeqblbYKxTc7fAWKfuTmN5HYw+sItmKBkP+tLFrdNfSDherzdh4+/8y2TaFPf39b1101vxjpXxf1/SOKbmGc9k4tfdFt1JONQ8t3UfMGCA6sW0fPly9TMm2GKSHMms7iR5qLkzUHdq7hYY69TdaSw9srjvvvvS+h0hqRKtkau58Xc+9fOBQesXk4tm4KjaPaR5UZP0Vm4ujzNSXFuky4Gr/7Ga52a0JxNNZVt0J+FQ89zWHWVfF154oTz//PNqgtyxxx7LC1Q26E6Sh5o7A3Wn5m6BsU7dnYYmE8kbIqbL5VEmUzSD6epmg6WBt57U8dSWJkUN0z+JMjf6bnm0SLcLDkyVq9dexFtD7CLSZLLtpQkheTa++ZZbbpGioiKnN4UQQgghxFXkUa4HcTs+f/5mMvmDJgdNRDrUbCOdStpJ0+JG1btKj8bfRkoaiTQ+RKRRV1sNpmjlcsxkIoREY/v27TJu3Dg1PS4WNJgIIYQQQuyHhfgkJ1mwYEGo10asTKZ8avztD0ZmMqU9TS5RJtOv0+SS0dxqTFvCnkw26U7CoebZrfvixYvlnXfekfLycvUzSuPYrzHzuhProObOQN2puVtgrFN3p7HtNPzqq69WyxtvvJHW88eOHauef80114gdrFq1Sm6//Xbp1q2bSrtv2LCh9O7dW/75z3/Kvn37MvKaWG/Hjh3VwTKW9u3bZ+R18gFcvTZfwY4wmfKpXC6TJlMwOZMpmuaW951iuZztuhNqnkux/uOPP6rjCG0wzZ07V2bMmGHTFuYn3MdQc7fAWKfmboGxTt1dk8n08ssvK+Okdu3acumll6b8/O+//z60jhdffFEyyQcffCCXXXaZGntsNIB++OEHtbzwwgvy0UcfyUEHHWTp6957772yYsUKS9fpqsbf/vzNZPJF6cnkscojTjKTKdNNBKO1Nfew8TebNzoAG2Zmr+64EFOzZs2QyQR++uknOeqoo8TjyaMrCzbCeKfmboGxTs3dAmOdujtNHp2GW8PMmTNl6NChymCCIfbQQw/JN998I5999plcd911oVT9s846S3bv3m3p6z7xxBPq4LlOnTqWrTefR3Oax3P68rpcLrInkzdT5XIx1htNcyvxBaO8Jht/Z1x3EiXuqHnW6t6gQQMZNGhQ2IE0spxpMGVWd2It1NwZqDs1dwuMderuNDlzVIFSGpDpvgsjRoyQsrIy9eWcOHGiGn2sOemkk6Rz585y5513KqPp0Ucflfvvv7/ar+n3+5WBhf/vu+8+lallpYHlFirMmUx5dFHbby5pc6AnU6bxm5p+Azb+JoSY6dq1q/Tv319dlDnmmGPYj4kQQgghJIvImVyPrVu3qv+RXZQp0Ndh2rRp6jZ6PxkNJg36NOnmmE8++aRUVlZW+3WxHvSZwIHzH/7wh2qvzw0sX75cLRp/QMRsUeT7dLlCy8rl/EmZTGbNrYaZTOKI7oSaZ2Os46JLPGAy9enThwaTxboTe6DmzkDdqblbYKxTd6fJidNwlK4hqwhZTG3bts3Y67z77ruh21dddVXUxxQWFsoVV1yhbu/YsUOmTJlS7Qbj6MUEnn32WSkuLq7W+twCMr2M2V7mpt+gOI8ymXymxt8e8Vh3cpVkJpNZc6sxN/0GHpbLZVx3Egk1d1b3bdu2yciRI2XevHkObYm7YLxTc7fAWKfmboGxTt2dJiN1MQ8++GDcbKF4vzeCLKF169Ypg2njxo3qpPr444+XTPHVV1+p/zFN7sgjj4z5uBNPPDF0++uvv5aBAwem/Zo333yz7N27Vy6//HJ1ZZYkh7npurnpd95lMpnaYlvWjwkEKpMymaxudG/Gx3I5R3Qn1DybYn3RokXKYNq/f7+899570rRpU2nSpInTm5bXcB9Dzd0CY52auwXGOnXPS5MJfYqiZVmgrxKmxGFJB2T53HLLLZIpFixYEPpixmuC2a1bt4jnpMPo0aNlwoQJqpEp+juR5IERaCRaJlO+NP5eu3+DTN35Xdh9hQUWvrkdy5MymcyaV5cVZT5ZWuYT7S3tjJLKxEwm63UniaHmzrBz5055//33w0YwjxkzRvUsrFGjhkNblf8w3qm5W2CsU3O3wFin7k7jzXSj7mTvT0SbNm3kv//9b8ZGMmIcsu771Lp167iPhSmELy8ykNasWZPW623fvl1++9vfqtuPPPKI5Vdq165dG/f3GzZskHwiqsmUB+VyP+2ZKy9uHCtBU8cpyzKZ1n0r4qsaBW5X4+8vd+yXp9fuTfi4PPgICSFJ0rJlS+nVq5eatmosUccwDppMhBBCCCG5QUbOJjEhzcwDDzygspt69+4tZ5xxRsJ14LGYHNOoUSPp3r27HH300Rlt8GnseZJMc3FtMu3Zsyet17vjjjtk06ZNqrk4rtJaDUy5ZFm4cGGYsYafUapwyCGHSFFRkbpvxYoVqjdWp06dQvqsX79etmzZop6LzwmglwYMLphmOGEA0Gnp0qVSt25d6dChQ6gUcv78+erEwZgZhh4cPp9PDjvssNDnjediHWiMjpgAs2bNUvfBdKxXr17McjlcGV+5cqUyBnU/LxiKKMnAZ6jTSWF+zp49W2WwGY3MeFrgufpKQbJaIF6WLVuWlBZz586VT4qmSNATZeqawX5ZvXq1Mi3bt2+vtACILZSYtmjRQpWb6B5i6AHWsGHDUHz4l3wcYeQECrwyZ9asCC1Q6orPBt9FnemHxoL47hi1QIkrDFu8Bl4L4Gfcj23BNn32y35JRFFB1TTJOXPmqM8IcaHBhEecfEIzfQKK94f3CW2hMYAO0AOfgTZzoRd0w2elYx/rwjoR34hzpY/frz4HfPaIAQ0+L3xu2Dfp0en4XPH5YgJlrVq1wrRA7CEGjVo0a9ZMmjdvru5DPCGu6tevL+3atQtlcSBTEv9j21u1aqXuR5wCoxaIZ8Q1hhLovm7xtMC6GjduHKYFftavsW/fPlmyZEm1tejSpYuUlJSo+/BdwHfCqAW+M/juJNIC30F8F7EurFMDLRAjPXr0iKsF9gHYF3Ts2FHq1KkTMto3b94cpsUvv/yiLhzA2EDs4nd6/4Xn4fkA3wPsq7B+PQjCuP/C9mAdxv2XUQu8Bl4L7w/vE2BbsE3QAXqAePsvxBhiTYN9IvTH55BL+3Lj/gsx07NnTxV7eCz2NZdddpnSOdf35fj+4L1p8B7xPYulRar7cr3/MmoRCATUvtOshd6v4D78DvsjxB9iNtV9OcDfAPwtwDZj2437L7w3vEeNm/flRi3wPGw3tOa+PHP7cuPfNex7sJ/EfhhmNuC+PDP7cr3/wueCx+L7oL933Jdnbl9uPC7H9wOfJeKf+/LM7cvNx+VYD9izZ0/OHJfrx+S0yQRw0Bjt906DQNAk03xbHwhhZ5AqX375pbz00ktqh4Bm35k0z/IVfFFwwqDxxSiXiz+fKPspl4qo93eqcWCHV10KKqI0lG7UVWTHxoi7sYPDHzor2B2ty7eJQ0ozn1GVC0B3/AHUf2hI5sEBGmKdmtuLziY+99xz5YcffpCTTz6ZwzBs0h1/U/VBL7FHcxw/sqTF/mP9RJMrifWa44Sd2AuMVpgPPI5x5jim5q/mq5spCKZbv5YiaGoNM+WCCy6Q2267TbINXLnUzvLQoUNVv6R4wO2EQw+XEO5yssAYwdVMXKG5/fbb5V//+lfEY3AVAG4nnFNctclUuRwMP4CrPYlKBLNx5wn01dXF20ROfS38MSt+g95FktP8dfXTsr5iU9h9A+odK+c0OllqFlrQo2TS70S2L6v6ue2JIn1uT0rz6jBi8Q7ZUFFlWLWu4ZE6hgZMrWp4ZGizEqmXT93b08RK3Qk1zwZwNRCLOWuYse4M1J2auwXGOjV3C4x16p6KZ6Az6az0BGxLFfjiiy8km9FptyCZEjikiSZbWmfkoYceUgYTPkyd3ZUJcs00ShXzCbc5kwl+Ra4bTMAfDL/idm3zoXJE7apymGoT8IX/3PKomA+10uTwmaztYc1K5Mi6iTMI3QjNJWqeb1f5xo4dq7KBhw8fHpamzVh3BupOzd0CY52auwXGOnV3Gtaj/Iru/6R7L8QDtZLaZEql9xH4+9//rv4/5ZRT5IMPPoj6GL1u/K8zqpBlddJJJ6X0Wm7C3JMpXxJgzCaTx+pW2GaTqcCeXYLPlEDpZckoIXkP+gS89957oVLniRMnJtWjkRBCCCGE5A62mkw4oERtLppvwWRJlsmTJ6u+JKk+L1XQNGvatGmqSR2a3OkmambQQFRjbPqaDCgRAKNGjVJLoiu+l1xyibp94okn0mQyaQN0kzTzdLniPBlL5pdAZqbKaQL+pCfLmTW3MpPJUClHMqg7SQ5qbj34mzpp0qSwXnrfffedulCjm5VTd2eg7tTcLTDWqblbYKxTd6cptPMK5umnny6DBg2Sjz76KKXnfvjhh+p5eD66o2eK448/PpRB9OOPP8Z83NSpU0O3+/btm7HtIbFB937dwT+ayZS3mUwFGc5kimMymTWvDua+316aTLboTpKDmlsPLtoMGTIk7OINehvqaW3U3TkY79TcLTDWqblbYKxTd6ex7VR83LhxodvXXXddSs/F49GfHMuYMWMkU5x//vmh27GyjDBx6NVXX1W3MdZwwIABKb2Gfh/xFj0qEf/r+7K9p5XdoHxQN2oHPlNCTlG+ZDJl2mQKJm8ymTWvDiyXSx4rdSfU3Ekw8v6ss85StzEA49prrw3rG8FYdwbqTs3dAmOdmrsFxjp1d43J9OWXX4Ymp6EsLRUOPfRQ9TxzFpHVYNpav3791O0XX0Gv6G0AAPxoSURBVHxRpk+fHvGYRx99VBYsWKBujxgxQo2HNAIzCFP0sKCpKcncyQoWjWFQmaIoTzKZfBE9mQoznMnkSVrz6sBMpuSxUndCzZ2mV69e6m8jLuqY/34y1p2BulNzt8BYp+ZugbFO3Z3GtlNxGDMwXg4//PC0D0yR0aMNnkzx5JNPSklJieofMXDgQHn44Yfl22+/lSlTpsgNN9wgd955p3pcly5d5Pbbo496J/bjy1OTKZt6MlkFvsemV2VPJkLyBPRcMvYtjAYuGuF4gBBCCCGE5B+2Nf7G1DbQpEmTtJ6vn6cbmWUKmFkoybvssstk165dctddd0U8BgYT+krVqVMno9tCYrN79271v/4MIqbL5Wm5XKHlJlNl0tPlzJpblcUEPDzhzLjuJHmoeXps2bJF/f3E3/vLL79cOnbsmNLzqbszUHdq7hYY69TcLTDWqbvT2JbvodPiMV0uHdJ9Xjqcc845Mnv2bPnd736nDCVMtUP/paOOOkr+/ve/y8yZM+Wggw6ybXtIJMuXL1eLJmK6XB5kMgWCAQlKMHOZTMFgSplMZs2tmiwH2PhbMq47SR5qnjpz586V559/Xl0IQrYi+jDiQk0qUHdnoO7U3C0w1qm5W2CsU3fXZDJh/Pbq1atlyZIlaT1/8eLFofXYAZpuP/bYY2pJhf79+6sD7OqwcuXKaj3fDdSrVy/s54hMpsL8y2KyvPF3EM5cMGmTyax5uvjMr6lMJpbOZFp3kjzUPHW2b98uFRUVoZ/37dsnH3zwgQwbNoy6ZzmMd2ruFhjr1NwtMNapu2tMph49esiqVavk+++/V2MVW7VqlfRz8Xg8Dz0cUm0aTvIT3Qg+Zk+mLC+XgxE5acdX8u2umVIe3B/zMWY84slc0+8EJpNZ81SZsatC3tlcJtvNHxYzmeJSXd1J6lDz1Dn++ONl7dq1smjRolDT0TPPPJO65wCMd2ruFhjr1NwtMNapu9PYlu+BJtrA7/fLHXfckdJz0WAbzwOnnXZaRraP5Da5Nl1ucdkKeXfbRNlYuUV2+HZFXXb6D/TiMeIpKMywyZQZd26PLyCPr94jy8v9sj1KvZyHiUyE5DS4CDRo0CBp0KCBHHHEEXLNNdeo24QQQgghxF3Ydip+5ZVXhg440Rj01ltvlcpKU9NhE/j9LbfcImPHjlU/161bV40+JgRlGcbSDJ8/tzKZVu9fn/JzvAVeqVVY07qNiFKOFy+Tyax5Kqze74+YKKepWYiFLlMmdCfpQc3To2bNmnL99dfLueeeK15v6onS1N0ZqDs1dwuMdWruFhjr1N01JhMmIz300EOhEqBnnnlGunbtKg8//LB8++23snnzZtXDAf9/99136n78/tlnnw1dJX3wwQd5ZZQoFixYoJZYjb+zvSdTtH5LiTi5/nFSVHiggX5GJsslmC5n1ry6zb415zUu4XS5OFRHd5Ie1Dz2AI4JEyZIWVlZTO1KSkrSDjvq7gzUnZq7BcY6NXcLjHXq7pqeTODGG2+U+fPny9NPP61MIzS4vueee+I+R5tSN910k9x22202bSnJhSvmRiKmy2V5JpPPZDJ1qtlOzmhwYszHNy5qKE2KGlq7EebJcgkymcyap4LP1F+qnqdAbmldKs2LPdK8RpZ/WA5THd0JNbeKTZs2qazibdu2yY4dO+SSSy5Rf8ethLHuDNSdmrsFxjo1dwuMderuKpMJPPXUU6p59x/+8AfZvXt3wklstWvXlr///e/KZCJEgyy3XJ4uZy4ea+CtK4eUdrZ3I1Js/G3WvDqZTCWeAjm8TnHa63MT1dGdUHMrWLp0qSpz1yXumPY6bdo0OeGEEywNMca6M1B3au4WGOvU3C0w1qm70zhyKo6MJkyae+SRR6R///4R6fX4GffDXFq9ejUNJpKQiOly2W4ymTKZ0G/Jdmxs/G3OZPJanAFBCMkcTZs2leLicFMYZe0onyOEEEIIIcSIA2e2B6hfv77ceeedagHIasKC3k1YCEmFCn+umUyBzE2NS9dkwjZkaDv8pkwmTpMjJHfA0I2LLrpIXn31VZV93LJlSxkyZAjT8QkhhBBCSPaYTGZoLpFUmDNnjvq/R48e0TOZcqwnk6fAgQ02Nx+PUyoXTfPqlMt5mciUNNXRnaQHNY+kQ4cOcvLJJ6t+TKeffnpa0+Ooe3bCeKfmboGxTs3dAmOdujtN1phMhKSCuZdXrvVkCph6MnnEAZPJPF0ugcmUqH9aPPwsl0ub6uhOqHmqsRavmXffvn0tb/Ztfn1iP9SdmrsFxjo1dwuMderuNDSZSE5y2GGHxZ0ux0ymNKbLJTCZzJqnAjOZ0qc6uhNqnizorzR+/HjVLPSII46I+phMGkyAse4M1J2auwXGOjV3C4x16u40NJlIXhBhMuVY4+/s6MmUud2B2WTK8mpGQlzFxo0b1fS47du3y7Jly6RFixZqIYQQQgghJFUcORXft2+fPPfcczJ48GDp3LmzNGjQQPV38Hg8CZdM9IEguU+u9WSKnC7ncd5kSpDJZOl0uUI2ZSIkG9i5c6e8+OKLymACPp9PGU5lZWVObxohhBBCCMlBbHds3nrrLbnxxhtV81DAmlGSDosXL1b/d+nSJTeny0kgC3oymU0mT0qaV2u6XMprcC/V0Z1Q80TUq1dPlcd99913ofsqKiqU6VRSUmJrCDHWnYG6U3O3wFin5m6BsU7dXWUyvf7663LFFVdEmEu6z4PZcIp1PyHmq+wRmUxZbDKtLl8vs/cudG663P5dIlvmimw+MLUs2UymZDIbAsGgLNznk60V4R/I8rJwQ8ub4d4u+QQzSqh5phk4cKCsX79e1qxZI61bt5YhQ4ZI3bp1bReese4M1J2auwXGOjV3C4x16u4ak2nbtm0qgwmGUVFRkfz1r3+V4cOHy4MPPij/+c9/lKEUCARkz549smrVKvnyyy9l5MiRMmvWLKldu7a6ffHFF9u1uSTL6datW/zpclmaKjNp+1cyftunEffbZjLt2yIy+Q6R8l8if5fAZDJrHo3n1u2VKTsqEj7OQ48paZLRnViL2zRHKTrK12fMmCEDBgxQPzuB23TPFqg7NXcLjHVq7hYY69TdaWzL90APpr179yoz6W9/+5vccccd0qRJk4jHwVA69NBD5aabbpKffvpJHn74YWU8DRs2TF544QW7NpdkOTVq1FBLrjX+nrrz26j329aTae030Q0mUFiUkuZm9vmD8kUSBhMoosmUNIl0J9aTj5rjAg/6IcYCmUunnHKKYwZTvuqeC1B3au4WGOvU3C0w1qm709h2Kv7ZZ5+FDmR/85vfJPUcGFJ/+MMf5J577lEHyCNGjFCTbwgxY85kylaTabc/+klex5pt7CuVi0Wj6mUR7PUHJNnC1q614htahBBr0+bfeOMNefXVV6WyspLSEkIIIYSQjGHbqfiCBQuUadSnTx9VLhcNv9/kFPwKTCZMoCsvL5eXXnopw1tKcgGUVGLJtely5qlypYUlclWzwdK6Rgtnmn0X1xFpcJBI53NEelyekuZmfFEcpg41PWFLlxKvXNy0RPo3KE77LbiNRLoTah6PDRs2qEziJUuWyMaNG2XChAlZ2+eQsU7d3QJjnbq7BcY6dXcTjHcHTKZffjlQooOmokaMhlOsJmXFxcXSv39/dWD88ccfZ3hLSS6A6YR6QiEw9ZnOykymQDAg+Gfk/1pfK73rHGbjRphMplbHipz6mEiv60S8NVLSPNEEOfBIp7ry94PqhZa/dqorFzQtkUI2/k6aRLoT68kXzfE38/333w97LzNnzlRLNpIvuuca1J2auwXGOjV3C4x16u6axt/o84A0fXMWk3GCDabbxBrT3ahRI/X/2rVrM7ylJBfo0KFD2M+5kMnkNxlMwCM2b6jZZCr0pK25GZ8pO8JjmBBJ0ieR7sR68kVzfP8uuOACef7556Wi4kC/tNLSUmnYsKFkI/mie65B3am5W2CsU3O3wFin7q4xmRo3bqwMol27wnvCGDOb5syZE9Nk0uUiu3fvzvCWklzAPF47YrpcYfaXytk6VU4T8Kc0Uc5IopHm5nI5TpCzBidGybudfNIcAzbOO+88eeutt6Rt27ZqilydOnUkG8kn3XMJ6k7N3QJjnZq7BcY6dXeNyYRRimvWrJHly5eH3X/44YeHbr/zzjty4YUXRu0p8c0336jb0SbSEWKeLlecIyaTbVPlNIHKtE2mRJgzmbzMYiIkK8DEVmQTd+7c2dHpcYQQQgghJP+x7VQcDb/BvHnzwhp8H3nkkSqbCb0jxowZI6+//nrY85C5NHz4cNm7d69K/T/++OPt2mSSxaCBLZaYmUxZeB7lD0Yplyuw2Q0zG10pmExmzRP1ZGImkzUk0p1YT65pHggEIi7gRLvQk+0GU67pni9Qd2ruFhjr1NwtMNapu9PYdoZ76qmnqv/37NkTykoCMI5++9vfqtswmq644go57LDDZNiwYTJo0CBp166dTJ48OfT4W2+91a5NJlnMpk2b1BKzJ1MWZjL5sqJcztSTKYXXN2ueqFzOy3ZMlpBId+Juzfft26cuzrz66quyaNEiyWVySfd8grpTc7fAWKfmboGxTt1dUy7Xt29fadmypWru/corr0i/fv1CvxsxYoR8+umnMmnSJGU6IdsJi0aPW77rrrvkuOOOs2uTSRaDWDJizmTKRpMpIP4sbPztTVtzMyyXywyJdCfu1XzdunUyduxY2blzp/p5/Pjxcv3112dtY+980T3foO7U3C0w1qm5W2CsU3fXmEwwj1auXKlK5QoLwx0ApPB/8MEHcv/998vTTz+tsp2MtGrVSv7yl7+osjlCovXmqsyB6XK+rOjJlL7JlKgfmvndMZPJGtiHzn5yRXMM09AGEygvL5dx48bJddddl5OTHXNF93yDulNzt8BYp+ZugbFO3V1jMqkX83rVEo3i4mL529/+poymGTNmqIwnmFEdO3aUXr165eQBM7EPs8mUC9PlCqRACu3uyVQNkykR5kwmD7+zhGSUo48+WhlNmMwKMDXu9NNP599LQgghhBDiDpMpGWA2sbk3ScT27dvV/w0aNBB4G+aeTMWe7DeZbO/HVE2Tyah5NNiTKTMk0p24V3NcfDnnnHNUg8/S0lK56KKLpHbt2pKr5Iru+QZ1p+ZugbFOzd0CY526u8Zk+r//+z/1P7KTHn74YSkqKrLrpUkesnr16tDJiDmLKVszmSrNJpMdffe3zBWZN1qkbNuBn/dtCf99CkaXUXNQ5g/KKxv3yeJ9PgkEg7IvYM5kqvbWkyi6k8yTS5rjwgwGZsBkMpei5xq5pHs+Qd2puVtgrFNzt8BYp+6uMZmeeOIJddUVDcBpMJHq0qhRo9BtcxZTtvZk+m73THv7MfkrRab9RcRXFvsxHm9amoM3N+2Tz7fvj/l4L8vlLMGsO3GX5oFAQL7++ms54ogjlJEUDZTJ5QPZpLuboO7U3C0w1qm5W2CsU3fXmEx169aV3bt3S5cuXex6SZLHtG7dOnQ7WiZTNk6XQw8mI3sDccwfK9i9Nr7BBGrUT0tzsLTMVHpnoh47f1uCWXfiHs337t2rGnmvWLFCli9fLpdffnnOZyvlgu5ug7pTc7fAWKfmboGxTt2dxraj1RYtWqj/Kysr7XpJ4hIq/blhMgUlvJysqKAo85lM8WhwkEiT7mmvvjL87YQBf+n0hjXTXjchbgd9lp577jllMAH8//nnnzu9WYQQQgghhGRHJhOaeS9atEhmzZpl10uSPKas7ECGTklJSdRyOW8Wlsv5g+EbekK93pl9waAp08hTQ+SY3x24XVQq0vhgEU9xWpoDv8lkOrtRTelSyyuFBSIHlXilYTY6fTmIWXfiDs2jlcBNnz5djjrqKKlfP/kMxFwiG3R3I9SdmrsFxjo1dwuMderuNLadBQ4fPlz9j1HL33zzjV0vS/KUxYsXqwVURMlkKs5Cf8NnMn084rF3kpy3pkjr4w4szXqmZDCZNQc+jPUz0K3UK33qFcvRdYtpMFmIWXfiDs3Rf2nIkCHi8XhCJef4O5qvBlO26O5GqDs1dwuMdWruFhjr1N1pbDsVR8Pv66+/XoLBoAwbNkyWLVtm10uTPARjuvWo7qiZTFloMvklfEM9mW78HTC5b4UeyzQHPlMmE1swZQaz7sQ9mqOnwmmnnSYdOnSQG264Qdq0aSP5TLbo7jaoOzV3C4x1au4WGOvU3TXlcuDf//63eL1e+e9//yuHH3643HbbbTJ06FDp0aNHXjczJdbTqVOnuD2ZstJkCvrtnS5nzmQq9FqmOfCbMpk4TS4zmHUn7tK8d+/eqkTODX8js0l3N0HdqblbYKxTc7fAWKfurjGZOnbsGLqN9H9Mzfn73/+ulqKiImnQoEFSfRgKCgqYBUXCME+XQyuggvBBbllpMnkKCu01mQqs/bqbM5mysA0WIVlNIBCQyZMnS7169eSYY46J+TcPCyGEEEIIIbmAbSbTypUrww6U9W2Uz1VUVMjmzZsTrgOP5cE2AX6/P2RYRphMWep2+Mwmk909mapZLmfUPGq5XP4nWjiCWXeSH5rv2bNH3nrrLVm1apXKUsIE1rZt24qbYaxTd7fAWKfuboGxTt3dBOO9CltPC2ESmZd4v4v1WELmzp2rFuDz54bZYXtPpqC15XJGzaM1/vYIsy0ygVl3kvual5eXy3PPPacMJp3RBMMJxpObYaxTd7fAWKfuboGxTt3dBOPdgUymFStW2PVSxAWgxFJTEcj+yXLRy+XsbvzttUxz4Gfjb1sw605yX/OaNWuqvoTTpk0L3bdv3z5Zt26ddO3aNaOvnc0w1qm7W2CsU3e3wFin7m6C8e6AydSuXTu7Xoq4gEMOOSR02zxdzpsj5XK51vjbqHkgGDTlZbHxd6Yw6k7yR/MBAwYoU2n58uWqJ9OQIUOkVatW4mYY69TdLTDWqbtbYKxTdzfBeM+gyfTll1+q/3GwzM72xA7M0+XQ+DsbCZhMpsIcbvxtzmICXlbLEZI06MN04YUXysSJE+W0006TWrVqUT1CCCGEEJLzWG4y9e/fXzXnvuWWW+Spp56K+bgNGzbIli1b1O3DDjvM6s0gLp8ul01UBiplefkaWVuxMex+b3Ubf1fsFdm2UMS/P/rvty+zNJMJ7PMHZfG+StkTxWWiyURIlK9pRYUUFxdHlaa0tFQGDRpE2QghhBBCSN5gW7mcmb/97W/y3//+VxlSPp8p44KQBMyfPz+UlhiRyZRF5XL7AxXy6NrnIwymavdk2rtF5PM7RMp+Sf451ZwuN33eQnkh2Eh2xzDHPByznvFYJ7mjOSaMTJo0SZYuXSrXXXed1KhRw8ItzE8Y69TdLTDWqbtbYKxTdzfBeM8CkwlwYhxJl8rKytg9mbIok2lR2bKoBlO1TabVU1MzmCzIZJrlL5bdcYwqZjJlPtZJbmi+e/duNS1u9erV6uf3339fLrroInVRhWROd5Ie1N1+qLkzUHdq7hYY69Td1SYTIenSvXv30O0KUyZTcRZlMu3y7Y16f6EUStsaLdNfcfmO1J/TsEv6ryciJY2bivxSEfV3zYoKpbaHJ9CZjnWSG5q/9957IYMJzJs3T9q0aSN9+vSxYOvyF8Y6dXcLjHXq7hYY69TdTTDeq6DJRHISj8eTE5lMfjE5YCLSqWZbObl+X6nnrZP+ioOmEtOiUpGa9aM/FhlTzQ4X6XJe9fqIm7IwSgoLpL63QJoVe2RY8xJmadgQ68Qeqqv5WWedJc8995yUl5ern71eb8y+TMQ63Ul6UHf7oebOQN2puVtgrFN3p6HJRHKebG787TdNlOta0lFGtLrK+slx7QaIHHG9ZBKfqdd3v/rFcm3L0oy+JiG5SIMGDeSCCy6QN954Q+rXry9DhgyRli2rkblICCGEEEJIjkCTieQky5YdmJzWqVOnSJMpiy6G+4PhG+etTh8mIwG/pU29k2H7zl0iUtW8mD2Y7I91kjuad+nSRRlNnTt3lpKSEgu3Ln9hrFN3t8BYp+5ugbFO3d0E470KmkwxWLVqlTz11FPy0UcfyZo1a9RkIJxw4Ir0LbfcIrVq1ZJ0WbBggXz22Wfy/fffy5w5c2Tz5s2ydetWldrYrFkz6d27t1x66aVy7rnnsgQpBnv27Andjpgul0WZTD5TWVu1mn3Hy2SqZlPvZNhf6RMpNJpM7MFkd6yT7NJ8/fr1cTOUDjvsMAu3Kv9hrFN3t8BYp+5ugbFO3d0E470KmkxR+OCDD+Syyy6TXbuQuXGAffv2yQ8//KCWF154QZlPBx10kKTDQw89JK+//nrU361YsUItY8eOlRNPPFHefvttadSoUVqvk88gOyAnejKZMpk8krsmU606dUT2Vr0f9vm2P9ZJdmju9/vl008/lRkzZsiFF14oPXr04Edjg+4kM1B3+6HmzkDdqblbYKxTd6ehyWRi5syZMnToUCkrK5PatWvLn/70JxkwYID6efTo0fL888/L4sWLVWNXGE51cOKdquherxxzzDHSt29fdXLSvHlzadKkiWzfvl0WLlyoGsbOnTtXpk6dKuecc4589dVXUliYRc5JFmDMJMvm6XLmxt+egsKcNZmCqilvlcnETCZ7qE7WJLFec1x8wEWAtWvXqp/ff/99lYHatGlTyl1NGOvOQN2puVtgrFNzt8BYp+5Ok7EzU1zhffDBB+P+XhPvcdG49957JVOMGDFCGUowgiZOnCjHHnts6HcnnXSScobvvPNOZTQ9+uijcv/996f8GsiEwvqjccopp8hNN92kyvLeeecdmT59unz44YeqdI5EJ7szmcJNJm+BRV8503rtMJn8psbf7MlE3AgyTbXBBCorK2XMmDFqvx1rv04IIYQQQohbyNgRMfoNYYlHwa89XR544IGsMJlgfE2bNk3dvuaaa8IMJs3tt98uo0aNUn2VnnzySbn77rulqKgopddJdCKC3kx33HGHMpkAtokmUzjr1q1T/7dq1SqyJ1MWZTL5gpnKZKoM/9kq8yoOe8swjr1KXJbL2R/rxHnNe/bsqXr2/fTTT6H9eb9+/WgwZVh3kjmou/1Qc2eg7tTcLTDWqbvTZCznIxgMZmTJJO+++27o9lVXRR8zj7K1K664Qt3esWOHTJkyJSPbYizDKy/HyT0xgkbpWEDEdLls7smUselymTeZyivDjS2Wy9kf6yQ7ND/zzDOlRYsW0qBBA7n22mvl8MMP50djg+4kM1B3+6HmzkDdqblbYKxTd6ex/Mz0hBNOyNmJaOh9BEpLS+XII4+M+Tg05NZ8/fXXMnDgQMu3Bf2fNN26dbN8/blO27ZtQ7cjTKYsymQKmHsyZazxd+bftLdGTZEKw88Zf0VijnWSHZoje+niiy+W4uJiKSkp4cdik+4kM1B3+6HmzkDdqblbYKxTd6ex/Dzxiy++kFwFJXAAU+PilbQZTR/9HKtc5yVLlqieTSjJA40bN5Zhw4ZZ9hr5AjIIND5/9vZkiiyXy5TJlFrJZjoUqMbfVe+HmUz2xzqxT/PZs2er0uW6detGfUy9evX4cWRAd2I/1J2auwXGOjV3C4x16u40TEYwlKTpNP3WrVsn/OIi22nv3r2yZs2aan0A/fv3V1PkogGDafz48VK/fv2U12tsTBuNDRs2SL5QYcpkKnbYZPpu988yfddPUh7YL1srt2emJ9P2pbZmMlUEgrKkzGyYZfQlCXEEn88nn3zyiZoeir8FKJ2G2UQIIYQQQghJDE2mX9m9e3dIlNq1aycUTptMe/bskUzwm9/8Rv785z8roykd2rRpk/RjFy5cGGas4ef9+/fLIYccEmpqjolKGN3dqVOnkD7r16+XLVu2qOc2atRI3bdt2zZlcDVp0kRatmyp7oNOS5cuVRkBHTp0CE1kmj9/vtSoUSMsM2zevHnqJO+www4LlV3iuVhH165dpWbNmuo+PBefGZ7rC4RnFOwv34tPSN3euXOnrFy5UhmDOnUUhuKiRYvUZ4isNYB+X8hcQAbboYcempQWeC7WYdRCmnvlld1vx9Q6UOmXWbNmJaXF3Llzxe/3q0bDGmS6FWydJwe2uoqVa9ZJg/o7Q9kVmzZtko0bN6q+MXq0OnqIoWFxw4YNQ/GBSYqYlGjUIhAIyJw5c8K0GL2pLOK9YLrc8uXL1edg1ALNBmHY4jXwWgA/435sC7YJ4Hl4Pra5ffv26r6KigqVHYjPGZ+3BtuDzwhxocF2Y/uhGbQDeH94n9BWZ6BAB+iBeERcgu3bt8vq1atV3OrY11ogvhHnAPrjc8BnjxjQ4PPC59a9e/eQAbFs2TK1P8AESj06VmuB2NNXlbQWGHvfvHlzdR/iCXEFQ7ldu3ZhWuDzgJZ6X4A4BUYtEM+I64MPPliVcCXSAo2V9fq0FvhZN1zet2+firXqatGlS5dQORn2C9g/GLXAdwbfnURa4DuI7yLWhXVqoAX2Ez169IirBfYB2Bd07Ngx1O8ORvvmzZvDtMDjXnrpJfnll1/UZ4htfv/990PPw/8A+yjsq7B+vI55/4XtQf8+4/7LqAUuTuA18P70RQRsC7YJOkCPRPsvbB9iTYP9CvTH55BL+3LEHmIQ+wCsCyDmoYVx/2X3vjyRFohvxHl19uX4nsXSIlP7coD9CvYvuA+/wz4JeuPx3Jdnbl9u/LuG5+LvH+7jvtz6fTn2r9jPGv+uYd+D7yI+U/063JdnZl+u91/4XPB9wL5K/73ivjxz+3Kgj8vx9wufDb4DPC7P3L7cfFyuE1a2bduWM8flmbqQSpMpSnNtHQzx0Ce12BlUB5TFYUeNA2MEHq6eP/PMM/L000+rHQVK5/QJB6kCX0CcMICI6XKFmW0QH481vvgZYqUFB3Zy1aG0bHXEfT5P9dcbj8X7fJHN6bOpLjGPwR86/PFL13AmyYN9P/4WYL+Og2oclGBKKg72jMMYSGan4WgjmtinO/YzsUpDSWY0xwG/PkEn9oDjbZwoEvvQ5zjGiyIk88C8gPnAY0dnjmNqct8uBcFMj2zLEXDlUjvLQ4cODWu8HQ0YP3Do4RLCXbYSnOQMHjxYPvzwQ+Vsf/PNNwlL+NIplzv66KPVbVztSXX92bDzBHCch78nMmVl1e/+1FfkxqOc2a6J26fJu9smRv1dfU9dubPNDVLfW80D+blviMw3xeeFb4t4MteX6Q9Ld8qK8nA377VDGkhxIWvmMo0x1ok9ej/++OPqChGucp1zzjlhmQYks9oDxrq9UHf7oebOQN2puVtgrFP3VDwDnUlnpSfATKZfMV6lTqYEDs58sqV1qQL3ExlOSM/Dh33nnXfKG2+8kdI6cs00ShXjSUhEJpOD7VP8pkbfbWq0kAH1jpUahcXSuaSD1LYi48jc9LtVn4waTMBnsqJvbFlKg8kmeMJtv96XXHKJfPnll+qCAzNJ7dWe2A91p+ZugbFOzd0CY526Ow1NJoOxg/pV3XshUamWNplS6X2UCkhv7Nu3r0yaNEnee+89VVup+0iQcHymxt9OVnGZTabG3obSp24va1/EbDIVHeglkkl8poTHWuz6TfKYww8/XPU44D6XEEIIIYSQ1GBTFQO6aRYa2qEfRyzQQFRjbPpqNbpJMZp96UZi5ADoH4El6nQ5JzOZJHxjvAUZ2BiTkZXpyXLAH4xs+k3sj3ViDTNnzlRlyPE0r26/PZI6jHVnoO7U3C0w1qm5W2CsU3enoclk4Pjjj1f/I0vpxx9/jCna1KlTQ7eRbZTp5mGZKsvLZdDxHwvw+bMnk8lnMoA8mTCZAgcmMIUo9NqeyUSTyZlYJ9UDFw8wLQ7ZocgSxXAFap49MNapu1tgrFN3t8BYp+5ugvFeBU0mA+eff37oNnoiRQNjJF999VV1G2MNBwwYIJkAJXvTp09Xt9GbiZONwoH2evR3pSmTqSiLyuUyYzKZXLUCrwOZTExlciLWSfqgkTf26z/99JP6GTMvxo0bp8YpU/PsgLFO3d0CY526uwXGOnV3E4z3KmgyGcC0tX79+qnbL774YsjkMfLoo4/KggUL1O0RI0ZE9Oz44osvpKCgQC3Dhw+PeP7ixYvl888/l3jgpOfSSy8NjVm94oor4j7ejcB4wxLVZMqixt+eggx8xcw9mWzJZAr/mS2ZnIl1kj4ej0e6desWdh9KkY3lz9TcWRjr1N0tMNapu1tgrFN3N8F4r4KNv008+eSTqgQO/TgGDhwod911l8pWws+jR4+WkSNHqsd16dJFbr/9dkmV9evXy8knnyw9e/ZUmVNHHnmkmgDg9XrVuMmvv/5aGVx69GT37t3lj3/8Y8qv4yYipssVZlFPJvHkh8kk5nI5ZjKR3CyJRpbookWLpLi4WM4991y1jyWEEEIIIYRYA00mE7169ZIxY8bIZZddppqmwWQyA4Ppo48+qlYJ26xZs9QSj7POOkuVd9SqZcHY+zxDZ3nhRDGbpsv5guEGUGFGyuWYyeTWWCfVAxmmgwYNknfeeUdOPfXU0HAFap4dMNapu1tgrFN3t8BYp+5ugvFeBU2mKJxzzjkye/ZsldUEMwlXvnGCd9BBB8ngwYPl1ltvTdv4QZbUp59+KpMnT5YffvhBrXvTpk2qbKNu3brSoUMH6dOnj1xyySUZbSqe6+iSRWSEVfizZ7pcIBjIfLmczdPl0LuG0+WyI9ZJ8v2XUB4XjZo1a6pyZGqefTDWqbtbYKxTd7fAWKfuboLxXgVNpjg1lY899phaUqF///7qpDwW6OGEMjwsJH1woqjJrkymcAPImwfT5UyWloLlcs7EOokP9r1o7I1+etdcc42UlJRQ8xyCsU7d3QJjnbq7BcY6dXcTjPcqaDKRnKRr166h21nd+DsTPZl2r7d0uhxOzNdXBGRrhUnIX6mMYpqy8bczsU5iU1lZqTJPf/75Z/UzSuKQsYQSOWqeGzDWqbtbYKxTd7fAWKfuboLxXgVNJpLzZFPj7zn7FoX97LE6k2nBOJG9myzNZBq1YZ988sv+lJ7DTCaSbXz88cchgwksWbJEvvzySznxxBMd3S5CCCGEEELchIOn44RYQ0QmU2F2ZDGBQit7MgX8IgvHRd5fDZNpjz8gn6ZoMAEvh8uRLAOlyqWlpVEbMBJCCCGEEELsgSYTyUnQmB2LP4Bm2+G/8zpULlcWiDRrGnjrWvcC/v0ilfsi72/QMe1V7vIFJXYHseg08hZKXdbL2R7rJD4YnHDRRRep8rgaNWrIkCFD1AQ5ap47MNapu1tgrFN3t8BYp+5ugvFeBcvlSF5lMYHiLMpk6lLSwdpMJjMHDxFp0CntVfqi9FtqECNNyVfpk/rikxvbNkmrzw0hmQbTOc8991xp06aNNG7cmIITQgghhBBiMzSZSE5y2GGHqf/3RKmG8WaRyWRpT6aAL/K+LudWa5U+cxZYgchz3RpUa50kM7FODvDLL79IgwYNYhqdvXr1ouY5CmOdursFxjp1dwuMderuJhjvVbBcjuRV028np8v5JTKtytLpcsEoJlM1m377TSYTq+BItoIpiDNmzJD//Oc/MnPmTKc3hxBCCCGEEBIFZjKRvCuXc6rxty+KCeTNdCZTNU0mc7mcV1gGR7IPNPD+8MMPQ72pJkyYIM2bN5eWLVs6vWmEEEIIIYQQA8xkIjnJokWL1JJVmUzBcMerQAqsny5nppomljmTKd7UOK05sRfqLrJy5cqw5uc+n0/Gjh0rZWVl1DyPYKxTd7fAWKfuboGxTt3dBOO9CmYykZykvLxc/V8zkL09mSztxwQCleE/w8Aq9FiayRSvXE5rTuyFuot06dJF+vTpI99++21Il+7du6spctQ8f2CsU3e3wFin7m6BsU7d3QTjvQqaTCQnOfjgg9X/q/ZkT7lchMlkdaKgOZPJAhMrsvF3QULNib1Q9wOceuqpsn79etm0aZMMGjRIunXrRs3zDMY6dXcLjHXq7hYY69TdTTDeq6DJRHKS4uJi9b/P5LsUFoh4nOrJJP7M9WOK1pOpmv2YoplM8TKZtObEXqj7ATwejwwePFj1Z2rUqBE1z0MY69TdLTDWqbtbYKxTdzfBeK+CJhPJq8bfTmUxgYCpJ5MnB0wmv7nxd5xMJkIyzerVq6V27drSsGHDqL+vU6cOPwRCCCGEEEKyGJpMJCdZtWqV+r+yuF1WNP0Gvkz3ZApmPpMpXuNvrXm7duGak8ziBt2DwaB89913MnHiRGnatKlcc801UlRU5Nj2uEHzbIS6U3e3wFin7m6BsU7d3QTjvQpOlyM5yY4dO9Rini7nZCZT5nsymU2m6ptYqUyX05oTe8l33VH+9vbbb8snn3wigUBANm7cKB999JEynpwi3zXPVqg7dXcLjHXq7hYY69TdTTDeq2AmE8lJOnTooP6fvSM7JsutLF8rIze+aW0mExp9L3xbZPMcERhY+3dZnsm0vsKcfVWQUHNiL27QHc28jfz888+qeWLXrl0d2R43aJ6NUHfq7hYY69TdLTDWqbubYLxXQZOJ5CR169ZV/1dsC7+/2IFyuf2BCnlq3csSlKC1JtPid0Xmvhb79wXV//q+v7U86cbfWnNiL/muO5okDh06VEaOHKmymsAJJ5wgnTt3dmyb8l3zbIW6U3e3wFin7m6BsU7d3QTjvQqWy5GcxhdwPpNpzf4NUh7cH3F/aWFJ9Va8ZV783xfXrtbq9/pN4iUolyMkUzRu3FjOP/98qVmzplx66aVy0kknSWEh/zwRQgghhBCSazCTieQk6NsCKgPNHW/87TM35P6V4+sdVb0V+w9kdcSk02mWTuYD/erXSKh58+bhmpPM4hbdDznkEJVmXFJSTXPWAtyiebZB3am7W2CsU3e3wFin7m6C8V4FTSaS0z1cKmuYTKZC56fKgT+1uUna1GhpbaPvNv1EGh8igr5JDbuINDyoWqv3RWms3LtOcULNeeJtL/miOyZurF27Vvr27RvzMdlgMOWT5rkGdafuboGxTt3dAmOdursJxnsVNJlITtKqVSv1/+LN4rjJZJ4q19jboPoGUzSTqVlPkY4DxSp8UYZ3xevJpDUn9pLrumNK3PTp02Xy5MlqelyjRo2kW7duks3kuua5CnWn7m6BsU7d3QJjnbq7CcZ7FTSZSM72cAG+A1UtIbwOlMv5xTyhzaKNMJtMhUViJdFMpng9mbTmxF5yWXcYTO+8847MmTMndN+7774r119/vTRs2FCylVzWPJeh7tTdLTDWqbtbYKxTdzfBeK+CnVVJTlNhqlQrzoJMJstMJnMZXqG1Dpq5XA7+UiFK8QixiIKCAmnTpk3YfeXl5TJjxgxqTAghhBBCSB5Ck4nkJNu3b1dLNkyXM/dk8mYsk8naxEN/ipPltObEXnJd9969e0uPHj1CplP//v3ltNOq17Q+0+S65rkKdafuboGxTt3dAmOdursJxnsVLJcjOcnq1avV/5X+Bo5Pl/MHw50uj1hlMlVm1GQyZzIlMpm05g0ahGtOMkuu6w5j6ZxzzpFdu3ZJv3795KCDqtew3g5yXfNchbpTd7fAWKfuboGxTt3dBOO9CppMJKdrXivXZUHj74z1ZPJnNpPJ1JPJm6BUjnXGzpAruqP/EgylaBQXF8vw4cNj/j7byBXN8w3qTt3dAmOdursFxjp1dxOM9ypoMpGc7t5feSDhwOFMJrPJVJiZcjmrzKsYjb/jTZYDnJjgDLmg+4oVK2TixIkybNgwqV27dtTH5IrBlCua5yPUnbq7BcY6dXcLjHXq7iYY71WwJxPJabKhJ5O5XM6bM9PlzOVyuWMCkOzJXvrqq6/k1VdflQ0bNsi4ceMkEDB9KQkhhBBCCCGugSYTyUn27dunloqA89PlfEFfhsrlzCZTZjOZEvVk0poTe8lm3b/88kuZPHmyMpvAypUr5fPPP5dcJ5s1z2eoO3V3C4x16u4WGOvU3U0w3qtguRzJSZYsWaL+9wV6ht3vtbFcrjLok3X7N8rWyu2ZafwdTNyTaXOFX7ab07mSZG25ucwvOc179gzXnGSWbNb9qKOOkh9//FE19dZs3rxZZTMVFubuNYxs1jyfoe7U3S0w1qm7W2CsU3c3wXivgiYTyUl035fKbc40/t5YsUWeWPeS7PLvifidJeVyyAyJyGQK/7r+d+0e+WJHhVhFonK5WL12SGbJZt1LS0tlyJAhMmrUKGUsDRgwQE2Qy6X+S7mmeT5D3am7W2CsU3e3wFin7m6C8V4FTSaSk3Tq1En9X7nCGZNp6s7vohpMlpXLmbOYTCbTpgq/pQZTMuVyWnNiL9mue+vWreXss8+WunXrZv22Jku+vI9cg7pTd7fAWKfuboGxTt3dBOO9CppMJKep9DszXW67b2fM37Uqblb9FzBnMQGDebWt0vrmym1rODCaj+QEe/fulVq1asXMUOrVq5ft20QIIYQQQgjJPmgykZzE7z/gLvkCHkemy0VOlPNKzcIa0qNWVzmh3tGZMZkM0+XMTbtBSWF6JUp4WtdaXrm0ea2kNPd4aEbZidO6L1u2TN5++2054YQTpE+fPuIGnNbcrVB36u4WGOvU3S0w1qm7m2C8V0GTieQkc+fOVf9X+MMb8xbbdE7oN5WzndfoVDm5/nHWvUBUk6nqzfl/nealaeQtlGe61Rc7NGczZHtxSndMjJs2bZpMmTJF3Z44caK0bNlS2rZtK/kOY526uwnGOzV3C4x1au4WGOvU3Wlyd/wPcTVFRUVqMQ9Wsy+TKdxk8lo1UU4TiN+TyZzJlKifkpWaE3txSvd169bJ559/rgwmgMbeb731luzZE70XWT7BWKfuboLxTs3dAmOdmrsFxjp1dxpmMpGc5JBDDlH/Vy52pieTT8JNIE+Bxe5WoDKuyeQ3mUyeAvs0J/bilO5o6I0SuS+//DJ0X6tWrcTrzf8/G4x16u4mGO/U3C0w1qm5W2CsU3enyf+zBeKuxt8O9WSyZKJcCtPlfKZyOW+Oj4wn2Un//v1VRtPy5cvl5JNPlr59+8Zs/k0IIYQQQgghNJlITmMesmafyeTLrMmUYLqcz4FMJuI+CgsL5cILL5TNmzdL+/btnd4cQgghhBBCSJZDk4nkJPPnz1f/+wKHZMl0uQybTMhiMmSQRGYyiW2aMwXXXjKt+44dO8Tn80njxo2j/r5WrVquM5gY69TdTTDeqblbYKxTc7fAWKfuTkOTieQklZUHehZVmKrKbJsuZ+7JJDaYTMbXj2j8XWCb5sReMqn7kiVL5J133pHS0lK57rrrpEaNGhl7rVyCsU7d3QTjnZq7BcY6NXcLjHXq7jQ0mUhO0r17d/W/b052TJezvlzO5J6Z1u/EdDmtObGXTOiOiXFTp05VC26XlZXJe++9J4MHD2bPJca6Y3AfQ93dAmOdursFxjp1dxOM9ypsOiUnxFo8Ho9aIhp/2zVdzmQyWV8uV5kgkyncZfLYkMmkNSf2kgnd/X6/LFq0SBlMxtTqmTNnWvo6uQpjnbq7CcY7NXcLjHVq7hYY69TdaWgykZzGucbf4S9cWFCY2elyJpPJiUwmkj94vV4ZMmSIlJSUhO5Dz6dDDz3U0e0ihBBCCCGE5DYslyM5ybJly9T/lYFOtmQyBYIB+WLnt7K4bIW6XRYoD/u9N52eTBt+EFk5RcRXFvm78h0JTCb7G39rzTt1Ctec5KbuDRo0kAsuuEBGjx4tp5xyivTp04elchnWnMSHujsDdafmboGxTs3dAmOdujsNTSaSk+zZs0f97/Pb05Pp8x3T5Z1tn8T8fco9mbYvF/nqryKmjKiYJMhksqNcTmtO7CWTunfu3Fl+85vfSL169TL2GrkIY526uwnGOzV3C4x1au4WGOvU3WloMpGcpEuXLur/iunh9xdnyGRaWHYgsyEWJYU1U1vhljnJG0ygqDTsR5O3Zksmk9ac2Et1dP/ll19k4cKFctxxx8V8DA0mazUn6UPdnYG6U3O3wFin5m6BsU7dnYYmE8lJdC8Zn8mn8WaoXK7S3IjbQKeabaVJUcPUVuivSO3x7U+KWy5nRyaTsX8PsY90dUdj7/Hjx0t5ebnUqVNHevToYfm25SuMderuJhjv1NwtMNapuVtgrFN3p6HJRHKaiOlyGcpk8plyhw4vPUTa12wl9bx1pWfpwan3sgn4wn+u106kbf/Ix2G9DTuLNA03CPxs/E3iMHXqVJkyZUro5/fff1+aNWsmTZs2pW6EEEIIIYSQjEGTieQka9euFSTzVAZa22IymafJ9SjtKsfWPSL9FQZM7ljdtiIHX5j0052YLgfNQevW4ZqT7NPdXAJXWVkpn3/+uVx88cWWb18+wlin7m6C8U7N3QJjnZq7BcY6dXcamwa+E2It27Ztk81bt0Xcn6npcv5geOaRN9VG32bM5Xemxt6JiJwuV2CL5liIvaSj++GHHy5HHnlk6Ofu3burSXIkc5qT6kPdnYG6U3O3wFin5m6BsU7dnYaZTDFYtWqVPPXUU/LRRx/JmjVrpEaNGmqc9ZAhQ+SWW26RWrVqpS36vn375JNPPpFJkybJDz/8IEuXLlVTAOrWrasatZ122mly4403SvPmzdN+jXynbdu2UmaqOMvkdDlzJlPK0+QSZTKlbDKF/+wpsEdzYj/p6n7GGWfI5s2blcF09NFHp17S6WIY69TdTTDeqblbYKxTc7fAWKfuTkOTKQoffPCBXHbZZbJr164wYwiGEJYXXnhBmU8HHXRQyoLPnj1b+vbtG3W0JCZBffvtt2p5/PHHZeTIkTJ06NCUX8MNNGjQQAr3R95fnKlMJlNPJo9U12TyVctk8juQyQTNif2kq7vX65WrrrpKCguZsGqX5qR6UPfUqKioUMcSe/fuVbcDgRQmlkZh69at1Xo+oea5AmOdmrsFxnr+6+7xeKRmzZoqWaW0tDRrLirTZDIxc+ZMZeyUlZVJ7dq15U9/+pMMGDBA/Tx69Gh5/vnnZfHixXLWWWcpwwlTm1IBxpU2mGA2nX322XLUUUdJo0aNZMuWLfLOO++o18Djhg0bpgIGGQkkEp/fzkwmv7WZTKbyu2pnMlVva0iOpkJ/+OGHcv7550f0YNLQYCIk/wgGg+oAlicPhBBCiLvx+Xyyf/9+2blzp5oqiCy2bDj+p8lkYsSIEcpQQhbAxIkT5dhjjw397qSTTpLOnTvLnXfeqYymRx99VO6///6UBMeHjpK7++67Tw455JCI3w8cOFCZSoMGDRK/3y+33XabLFmyJGtcyWwBhtzmffgCNbKlJ5PPZDJVvydTdTOZ7DHXzJqDJk2aZP7FSFzdFyxYIO+++676o/LWW2/J8OHD1T6LWANj3Rmoe3Js2LBBHUwawTECrmama1rpdRB7oObOQN2puVtgrLtHd7/fH3pdeBirV6+Wdu3aOf43nWclBmbMmCHTpk1Tt6+55powg0lz++23y6hRo9RJ3pNPPil33323FBUVJS34cccdp5Z4nHfeeapJ79tvvy3Lli1T2VVHHFGNSWZ5yPr162VTWVGkyWRTT6bCgkJrezKlaFpFNP6WAls0BzSZ7MWsO0pukfFonCACQ/zMM8+0ecvyF8Y6dc9WysvLwwwmZEEj4xl9I9M9oEQ7AFCdXpOEmucCjHVq7hYY6+7RPRAIqCqpjRs3KsMJRhPK6FGR5STO51JlEcgM0KCXSaxMpCuuuELd3rFjh0yZMiUj24ISPQ2MJhJOs2bNpH6jyIyazJlMpkwmcXa6XEQmU4E9mmMh9mLWHcMBGjZsGPaYlStXqp4sJDOaE3ug7onBcYemadOmakEvhupcscSFslQulpHqQ82dgbpTc7fAWHeP7oWFhepik3Fg2O7du8VpaDIZ+Oqrr9T/aJplHP9t5sQTTwzd/vrrrzPywaAMRpNuCnw+gy9S/YZN7OvJZG787fh0uaDt0+WgOSce2o9Zd5xQouRWl8cddthhcu2110pxcbEDW5efMNape7ZfJQX169e3ZJ08GbEfau4M1J2auwXGuvt0r127duiCE7KZnIblcgZQAgcwNS5ef5Nu3bpFPMdqpk6dGrp98MEHZ+Q1cp0Kf2QWUybKT3f79kilqVF3tU2mfZvjmkyBYFA2VgSk0mQmhZ4esH+6HMkuE+Scc85R2UsYHOB03TUhxB6QCg9wjMILUIQQQgjRGU04LkAjcH2s4CQ0mQx9DvSkltatWyccs4xsJ9Q7rlmzxvIPZdasWfLRRx+p2z169EjLZEKflkSNQ3MZTN/buRtpS7UzmsU0dstH8sXObyPu96TbkwkZTN89JrJjRfj9hVWm1Y7KgDywcpes25/8OGo7MpmgOUBKJsk8MJBwNUSnvJp179mzJz+GDMFYdwbq7gz6YJSmFTXPdxjr1NwtMNapu9PQZIpSu5hMoyxtMqHRltVlcih90TuHhx56KK31tGnTJunHLly4MMxYw8/YDky/0yl/K1asUCcAnTp1CumD5riYBoTnovmoHqsOgwtNilu2bKnug05Lly5VJ8kdOnRQ91VWVsr8+fNVs1JjZti8efOUA4sSIJ2dgediHV27dlWlQmDOnDky5xfcripr9BYEZNasOdKiRQvVpwKgQSr61cAYxEhHbSguWrRIfYbIWgPoyo+Gyrg6fOihh6r7fqncEdVgAksWLpG6nUrVOlLRYt/6OVJrzYHm8kbWrt8kewILlRafb9+fksGknr9qlewsaBYaZb9p0ybVAM6oBXp5rFq1SvXz0fGBdEpMSjRqgQZy0NeoBfjpp5/U70444YRQpt/y5cvVdwfP1VqsW7dOGbZ4Dd07CD/jfmwLtgngeXg+trl9+/YhYwXZgfic8XlrsD34jBAXGmw3th+aIY4A3h/eJ+JMmzLQAXrgM9DNs7dv366mL+Cz0rGvtUB8I84Bvodz585V3wPjNEjELmK4e/fuoRMz9E7D/gATKHXDP60FYg8xaNQC/Wd0GRy+W/iOofwFEyHwmNdff13q1KmjtgX3a1MJcQqMWiCeEdcwpHXZXDwtWrVqJY0bNw7TAj/jfl2Sg6mW1dUC/aMwTlXF6Nq16jth1ALfGXx34mkBsD/Cfgnrwjo10AL7CZjx8bTAPgD7go4dOypNtdG+efPmMC1++eUXdeEA+xvEMzTX+y88D88H2EdhX4X1Gy8C6P0XtkePj9X7L6MWeA28Ft6fLnnCtmCboIPuBxVv/4UYQ6wZL05Af3wOubQvR+whBrEPwHsF2EdAC6v25Ym0MO6/ktUC8Y04T0YLfG/w/TEaw/h+4XsWS4t4+3Lsh7E+7C91jOM+6IG40+uDFtivQX8dewD34Xe4D7+DLlgf4gfbrr/HWD8+Q7yG3ufjPWLBz/q18VysQz8/1vbEaoqKx+HxeJz+3uhtMm6Pfm1jOQK2D9tp3J7qaGHcnmS1MG5PslrgMVgf1pVJLfT2VFcL4/ZYrUV140JvTzJaYP+D96b/Dtilhd6eZLWItT3JamHcnnhxEW170tVCb49ZCzwfj9F/b5LVItFn46QWycZpsnFh5f5Lvzbux33YVu7LM7cv199ZAK11u5vCwkLH9uX6e6f/ric6Ls/UBSaaTL+CoNEk09tEB5zVNY+33nqr/PDDD+r2lVdeqUpiSCT4IhfuCq95tTqT6Rdf+IhoTZF4pXYwvakBhWUHsuXMlNc4cPIAtlamZjCBJmJqJJ4BsFPLhvTLfAcnqRhCgH0LToyNJhyxb/9iVb8bkjzU3BlwgKnHHxP7NMcJCLEXnKCxvNx+zfVJNrF3H8NYtx9t2AT5N1UKglRBgSuX+irh0KFDZfTo0XGDCFeaceUZV42RZWEFDz/8sNx1113qdu/evdXkOn11NRPlckcffXToqnqiEsFsZOoqkSuqBgJK89oi311j3foX7VsuT64fFXZfI299ubDxGXJ47apMjpRYNUXku8fD7+t2oUiPK0INpf67do98sSN8Ulisari6ngI5p3FNObdJlYNNchdktzz99NNhJx8wPG644YZQZgMhxL0gA0pnwBgz2QghhBDibpakcYwAz0BXuFjpCTCT6Vd06ixIpgQOKbfJltYlw3PPPRcymJBmP2HChLQNJpCLplGq+ExJNVZnMvmD4S9Q31NX/tL+dmunyjU4SOSwK8Pu8pkuKJ/fuKZc2jy9zCmSW6C88KSTTpLJkyeH7kM5Dq94E0IIIYQQQnIB5i8aSoF0/4VEWUDomaBNplR6H8XizTfflJtvvlndRo+OSZMmhXqEkOigLnXv/vASsWKLo9knfmsnyoGAL+5UuWgmk7cgezTXtcYkc/Tt2zfUz+Xwww+Xyy67LKz+n2QexrozUHdngIlNI5uaO0n//v1VaQ/+z5dYx/vBcv/990f87osvvgj9HrfzGe5fqLubYLxXQZPJgG5mi+akSDWLBfqkaNKZ/Gbk/ffflyuuuEIFJRp7fvbZZ67IQqou+AxWr10fdp/Xk9lMprQnyqVoMvlNdbyeLBlPD82NsU8yAw46zz//fBk0aJCcd955qlkfdbcXxrozUHfnelIa+1KS2KAZKy4M4rgNFwNwcRLNWnFh8Mgjj5SbbrpJZaImMjKoufVo0ybagka3uCh89tlny4svvhj3GJ9YC2PdGag7dXcamkwGjj/+ePU/spR+/PHHmKJNnTo1LOsgXWAoDRkyRP2xw4EKMpj0JCcSHzVJwRveoL0ow+VyXksymfw5m8kEzY3TC0j1MjYwQS5eZiUmUemDY+puL9TcGai7M7Axb3K88847ahrgpZdeKv/73//UZEP00cMxHCb0YALrs88+K6eeeqq6APnRRx/lrObDhw9Xf3/yZegETrhRpfDxxx/L9ddfL8ccc4ya3EgyT7bHer5C3am707AnkwFkD6D5Nhg1apT6I2QGV6deffXV0CScAQMGpCX8N998o7IUcLKJhr6ffvpp2LhlEh+MYJxpGqhmvckUcKRcLlszmYyj40n1hgyMGTNGnZigoXciA4m62w81dwbq7gzGUcwkOn/5y1/k3nvvDf0MI+ncc89VGfA4FoTZBNPpgw8+UBcMFy9eLHfffbecddZZ1NxmjjrqKHUMbwS9VufOnasGa8yaNUsZghdddJFMmzbN7s1zHdy/UHc3wXivgtayAUxb69evn7qNdNrp06eLmUcffVQWLFigbo8YMUKlSceqs8aVoGj8/PPP6sADGVNo7o2rXUizJqlRaUoKKvJkuCeTWPACwdztyUSqDw5yn3/+eZXFtGPHDnVlnAM+CSEke4FhoQ0mTCHGcd7EiRPl1ltvVYMajjjiCDnllFPklltukU8++USZGPiZOAOOqzH52bj06dNHrr32WnVcr3sefvXVV1GP8wkhhFQfZjKZePLJJ1UJXFlZmQwcOFBNfEO2En4ePXq0jBw5MnTF9fbbU580hv4qp512mjrBBH/9619VJhNOPmOBgxosJJzKgL3T5TKSyRRlnSbvjCZTngAzacaMGaqnh3HU6Ndffx0q1SWEEJI9rFu3TplJ2rxAuwRtUsQCpgay09944w2btpIkCzKHYQbedttt6ufvv/9ejj32WApICCEWw0wmE7169VKlLBgbjvRamEz4A4SrVUaDCdlHderUSVlwpOZu3rw59PPvfvc76dGjR9zlv//9b3U/57xj9uzZEY2/iz250JMpmUym8FQmb5aUy0FzLCQ9kN04ePBgdaKiQbP/RGWy1N1+qLkzUHdn2Ldvn1pIJI8//nhImwcffDChwWTsh4LJoEZWrlwZynR/7rnn1HqRzXrmmWdKy5Ytxev1qulqyHStUaOGetyNN96Y8LVQoqfXO3bs2IheRE899ZRab5MmTVT2fcOGDVVvqTPOOEMee+wxtV0aTELDel555RX186pVq6I20jaCCyfYBphxvXv3lgYNGqjXQa9RtJ3AOuP1ILQTaA6tNbEm5mKKNDLY8BmiJLJ27dpSXFwszZs3VxeKcT5gvGBE4mvO/Yv9UHdnoO5VMJMpCuecc4462EVWE8wkNAvEH5eDDjpInSTiDylHijsLDnL8wcIMZzIFHJkuZy6X82SHxxRxYElSB8Y09iHo63b44YerkwucWFD37IKxTt0JQfapNltwceC6666zdN0o38KkOjOYVIeenW+99Za66PnEE0/E7fOh+w/BPMLzNBs2bFBle/Pnz48wULCgbxTK+9avXy//+te/0n4vaKStdTKCPlXI3sWCXkjvvfdetYblWMXq1atDt9u2bRvzgjMMNjNoFo5SSSxo8j5hwgRlPBFCCAmHJlMM2rVrp67wYEkFXC2K12MFfZpi9WoiyYMMr89xcXF55hp/+0z9kyzpyZTUdLnszGSC5qT6YFoPrk4nWwJL3e2HmjsDdXcGXjSLzrx580IZOOjXmU72eiyeeeYZdTET673ppptUhjzaKOisIhhQMJlw3/jx4+WSSy6JOUjiww8/VLeHDRumMqA0KAnTBhMyci644AKVxePxeJQB9cMPPyjjx8jNN9+sGmLfc8896nd4PEr/4oEhFh07dpRBgwap3qYwbnDxBCbN5MmT5aWXXlLT9/B7tIZwsv0DLiCgL6I2DmP1zvL7/SoL6+yzz1aGU7NmzVTm0ooVK+S1115T5tzMmTPl4osvVj26SGy4f3EG6k7dnYYmE8mbnkxWNf5eXb5Ovt71k0zbNaN6PZl8+0UWvyey03A1bPuS8McURunJlKWZTCQ5UJ4wZ84cNeEmVkYMe6wRQjJFICiyvcxd+jYoESm0+G8lGnhrrB7OAoPpiiuukJdffjnq3wmYH7jYCaMGmUqxTCYYHpWVB0btXn311WF/h95//311G/1Do2UqIWv/gQceUBlH5h6gmJgHUPaGHlPxwDpgMpnfB/4GXnjhhcq4Ou6445Qh9u9//1tN6sskGKpj7nOKEhb8Xf7Pf/6jjDds6z/+8Q9V0heNzz//XDp37hxxP94HzDx8JtAbPbo+++wzOfnkkzP2fgghJBehyUTyZ7qcBZlMO3275fF1L8n+YGStfco9mX78j8iqBFe4CsOnEwJOl8tdkEqP8gYctKMnB6dGEkLsBgbTEQeSNVzDT9eJNKpl7TqRfZOpCwMwcVBCFutCBP5+wMS47777lImxZs0aadOmTcxSOWTboARbg79B2nw64YQT4m4LyuyqQ6dOnRJmKCIzC2V/7777bsZNJmRoxcuKxFCfP/7xj2qoTyyiGUxGrrrqKtXrCtOi8Z5oMhFCSDhs/E1ykkWLFsnmrVVX36zqybS8fHVUgwnULKxKQ0+KjT8lfoy3JGfK5aA5FhIdXDl94YUXQleF0asBvS6oe+7BWKfuhOzevTskgnFggxWgHx+yhOIBkwlmUyAQiNrz6Mcff1TZOfqxRpChg16i4H//+58qabML9HvCJGWUG+LvIhadGYUsIm1+OcWUKVNURhWMu2RAC4yNGzeqHlb6/WBp1apVRMYbiQRZdViIvVB3Z6DuVdBkIrn7JTbVy1kxXW5/ILrBVCAFckTt+CnjEfgS/FH1FIu0PjZhuZw3Ozwm7jgTgJMB48EzejqgV0a8Hm3UPTvhQQJ1J8TYgwklWFaCqaIwj+LRunVrNckMoKwuVhYT+jChhMsI7hs6dKi6PW7cODW45s4771QXP9DnyWpgdsHowsRUZEbh9VBmp6ckY8IcwHuGCZVJTjzxRPV317jofkqYFlivXj3V56pPnz6ycOHCmOvB4B/0ZMLj8b4wkc84+Rm/B9kyOS9bwWeeKNYJdc8XGO9VsFyO5CQHH3yw1NnsEVlvbSaTXyL/EJ7d8CQ5uNZB0qFmZKp6Sk2+Ow4UqXHgap54a4i06C1Sv33CTKZs6ckEzUlsMOb42GOPlenTp6ufcZVzyJAh1Z5URt3th5o7A3Un2YSxXw9Koa2kSZMmcSfGaVBm9vHHH6vMoC+//DJU+rZ//35544031O3zzz9fGjRoEPFclOPBUPrggw9Ub6d//vOfasEFkSOOOEL9fcJkOJgo1eHFF19UwyySzZYqK7O/YRiyxjB04ze/+Y0qbUMpOzKNoe9XX30V9liYUpgkiPeVre8nl0gmzgl1zxcY71XQZCI5CdLA0dzU6sbf/mC4MdSpZjs5s2Hsuv2YwCgyrUs6nytSL/q43Pg9mbLDZdKp9yQ2aNaKA1f078AVaEzYoe65B2Oduud6E2z0KHLbe7aanj17hm7/9FMS5e8pmh4wexKB5tyYbAaTC5lL2mRCHyCdEWQuldPUrVtXNf+eMWOGjB07Vk1BQw8hZNmibxEWNATHunCBJB2QCaQNJvzdu+OOO+Skk05Shg4ywXRJICbMXXPNNep2dbN7qwM0RxYSyhUxPe/rr79WZXCY7qfBtmqDCX2ufvvb36pJc7hwhIldmM4H0LgdpYhOvp9cIJk4J9Q9X2C8V0GTieTPdDkrMplMxpCnIM2VBqJc0StM7utmNpksGppHbAAHn5dffrkl5hIhhKQDpqxZ3QTbjaCkrXHjxqocatq0abJr1y5l3NgJTBqYGchAeuutt1Qvodq1a4dK5dq2basubsTj6KOPVovuMwWzCeV377zzjmzevFlNgEOmVElJ6k4d1gODCX/7MGmtW7duUR9nnGCXDWA7YTLpUj+jyfT88we65qPk75tvvompS7a9J0IIySZoL5OcZOXKlbJj917LTSafyWRKeaJcXJMp8boC6B8g2ZnJBM2xuBmkxb/99ttxDy6tNpiou/1Qc2eg7s6A0issJByUOl955ZWhnkwY7GAV6N+XrOYo6dLbAKNp7dq1MmnSJHUfti+VK+fILkJ2FP6OoXQMbNiwIaJkLNkybzT31llfsQwmgKypbIp1Y2mfucxPv6dzzz03psGE7CWrs9vyFe5fqLubYLxXQZOJ5CQ7d+6U8orwAwNvBsrlCtPNIzKXyqmVeVPOYsqmxt/QHItbwXSZkSNHqqueY8aMsW1Cjtt1dwJqTt3dBMqnsJBIfve736kSKXDvvffGbRRtbv76+uuvx/19spojy6Zfv37qNjKYMGkOz4cRdNVVV6X9saE3kcbcvFr3FUlkhGmDJl5jdJhYKNvLplg3ml5t2rRJ+T0hCwrviySvObEX6u4M1L0KmkwkJ+nYsaMU1QwfKVxsSeNvqzKZohgQBYlNJvNkuQPbIFmjORa3ZljgKrbugYH+GFZMjksGN+vuFNScursJTCLDQiJBHx400NamAyaXoSwsHvPnz5fTTz9dlbjFK4NLRXOdzYSyPZTMgf79+0uHDh2iPn758uUJt3PixImh2+b1YJoaQDkdSuxi0blzZ/X/kiVLVGmZmX379smll16aNc2xofnkyZND2qAcUpcSmt8TGqZHy1pGaeEtt9xi0xbnPty/UHc3wXivgo1DSO6OFjaZSpZMlwsGLOrJlG4mU6Rp4cmScjnjOGe30bJlSzW9Z8uWLaH7li5dqg6+M92jw826OwU1p+5uQjcyJtFBthBK1JDJBNMF5s7AgQPlvPPOUxMR69evr8wINJDGWPtPPvlEXc02Ng43gxK3VHQfPHiwKm9DlqWedBer4TdYvXq1DBgwQE09HTRokBx11FHKMANr1qxR2bhoBq6bW6OxtZHjjjtO/Y+MKTT2vu2225Qho0G/IoAehDC98LizzjpLNf4+/vjjVSbUjz/+KI8//rgyoPr27auabNsBzMC5c+eG3YfM43Xr1qnPx1j2+PDDD0eUuKMHFt4HhnigIfof/vAH6d69u5SXl8vnn38uTzzxhMrwwoQ+lswlhvsXZ6Du1N1paDKRnKXSb/10OXNPJo+lPZlyO5PJ7dPGhg4dqsrlKioqVHo9DvrtbgJLCCHEfv785z+rRuC33367ymxFFpAxE8gMHvuPf/zDstdHbyBkBD3zzDPq53r16qmG3YlAVhWWWKCPEhqAm3swYUJcnz595Ntvv5U33nhDLUZ0Fm/v3r3lgQcekPvuu0927Nghd999d8RrQDOYNHaZTCiFwwS5eCCT7K9//WsoQ8zIiBEjVM8rfL4wDvVUPONn8eqrryrDiiYTIYREh+VyJCdBLfze8grLG38HzCaT2GsyZXMmEzR3cw8CXMU9//zz1RXf4cOH22YwuV13J6Dm1N1NwDjHQuJzwQUXyKJFi1Svpcsuu0y6du2qMlyRCdOwYUOV2XLzzTerbBf07kO2UyzQ9ydVzZE1pLn44ovjToNDDydMkfvTn/6kMpqQeYQMTZgrzZo1U9v27LPPys8//xy15A6ZVjBZ7rnnHpWRhYl2sZqBI8MLhgvWCT1wUaZ169ZKL6zjX//6l2RDVgc+I5hiv//975Xxduedd0Z9LDTC+3nqqadUBhh6ckFraIisLhhLuMhEkoP7F2eg7tTdaQqCdjQVIVkHUr91s0OkTuOAIJeYNWuW/N+PnWXp7qo5zf86VWTwIdVb7+gtH8iXO2eEfu5Xt7dc0vTc1Fe0Y6XIxAOTW0IMfg8jW+I+beN+v/xmSXiT59cOaSDFmEmdBZqDeCUAuQ52h1hSmdaTadyge7ZBzal7toLSIxgUMDZ075jqgr45QDe4JpknHc2ff/55uf7669Xt7777LqKXEMmM7qR6UHNnoO7u1H1JGscImfIEWC5HchL0FiicVWxDTyaLGn8ji+lXg2l7ZUAqYni7myvDXz+byuV0P4d8/sOAsoHmzZvLKaecItlCvuuejVBz6u4mkLlBsl/zl156Sf2P0jMaTPbpTqoHNXcG6k7dnYYmE8nZ0qWAyXwptqAnk9+Knkzbl4lMvj38vkKv7PYF5JFVu2VJWfKjXPEWC7OkXM7Y9DPfQINPNEFFTwk09IaLj14V2UA+656tUHPq7iZ4MpL9mn/55ZeqPxJAyRaxR3dSfai5M1B36u402VMTQkiK+ExejRWZTL5geC8lbzom07w3I+8r8MiUHftTMpgOvH7qL09SA6OVX3nlFWUwacaPHy/btm2jlIQQQhxh1apVqvE0/h5h4hlApm28qXKEEEJINsBMJpKTYFxwua+eas1tZePviHK5dHzYvQfGC4dRt41srogshUtEyxqerNIcoHlmPoGGnieffLJMmDAh7AoQzKdsIF91z2aoOXV3E+jfAMyj3Imzmp944onKaDLy73//O27Db1J93Ym1UHNnoO7U3Wm4lyU5CRqTVVTWsd5kEgvK5aJNlut1nfhS9CyaFhXKNS1qZZXm+Wp2YOIMGt/Nnj1b2rZtqybHYBJPNpDPumcr1Jy6uwk95Ywn3tmpOf4WoQ/T3XffLWeddZYNW5e/MNapuVtgrFN3p6HJRHK2Z4rflGVUlC09mQKmkri+d4s07Cz+tXvC7j6zUQ25tFlsEykbJsq5pU8NRjOfffbZ0rRpUzn22GPVuONsIZ91z1aoOXV3EzSXslPzlStX2rItboKxTs3dAmOdujsNTSaSs9OfzMVn1vRk8le/J5M5kwmT5dS6w+8uLijIOiMpnyduBQIB2blzpzRo0CDq74uLi+X444+XbCPXdc9FqDl1dxPY9xFq7gYY69TcLTDWqbvTsPE3yVkq/JmYLmfuyZTGSoPRTSZ/MNxlYlNv+9i7d6+89tpragT0nj3hGWWEEEIIIYQQQqyBJhPJWdPAFzCZNpY0/s5AT6YYmUzegtzJYtKaY8k10Gtp5MiRsnz5ctm9e7eMGzdOZTXlCrmqey5Dzam7m/D7/Woh1DzfYaxTc7fAWKfuTkOTieQki5csFX+wwPqeTBGNvwur35Op8MCG+SNMJskpli5dqpZc48svv1RlcsY+F5999pnkCrmqey5Dzam7m9i/f79aCDXPdxjr1NwtMNapu9PQZCI5SUlp5OSvoqzpyVQZ/nNh0a/rDneZPDmWyYQJN9kycS0Vzj//fKlXr17oZ7yHrl27Sq6Qq7rnMtScursJDDrIpmEHboCaU3e3wFin7m6C8V4FG3+TnKR1u44ZMZmqXS4HI8mcyfTrOszlcp7c8pikY8dIzXOBWrVqyeDBg2XUqFHSpk0bueiii6R27dqSK+Sq7rkMNafubqJGjRpOb4LroObU3S0w1qm7m2C8V0GTieQkvigtdbyeDJhMqTb+Vo3DgzF6MrHxt1O0bt1ahg8frqaGFRYygZMQQgghhBBCMgHPtkhOUrbf1Fwb0+WsyGSSQPUymcxNv8Omy+V242+fz6eWbASNvGfPni1Bk5FnBFlMuWgwZbPu+Qo1p+5uAvvNePtOQs3zBcY6NXcLjHXq7jTMZCI5ybyFi0XkkIxPl/NaaDJFTpeTnGLevHnq/549e0o2sWfPHnn77bdlxYoVairYscceK/lEtuqez1Bz6u4mysrKQqXFhJrnM4x1au4WGOvU3WloMpGcpMBbHHGfFdPldvn3hP1caJwuhyu9Kz8T2bbwwO0o7KnYL+ZuP69s2i9lxXtla6U5Syq3XKbi4kjNnWbNmjUyduxY2b17t/p50qRJ0rJlS2nXrp3kC9moe75Dzam7myjIsb9F+QA1p+5ugbFO3d0E470KmkwkJ2nX4SCRr6xt/L0/UBFxn9fYk2nhOyJzXom7jmjtpL/YFZC9nsjx0LmWyXTwwQdLtlFeXq4ymYxlc+PHj5fbbrstb6YlZaPu+Q41p+5uoqSkxOlNcB3UnLq7BcY6dXcTjPcqcq9BCSEiUmka4FZYIOKpZjSvLF8bcV/NQsPUnfXfpbzOgBRIRUFR1N+VYKNJtejcubOccMIJoZ/r1q2rpsfli8FECCGEEEIIIbkEM5lIXkyXq24WE9gfjMxkal7cxPCi5Smv88c6h0tlYWS5U0NvgXSuxa+fFZx44omydu1alcUEg6m0tNSS9RJCCCGEEEIISQ2e5ZKcZNHS5SLS0dJ+TOam3yWFNU09mUzpUy2OEqndMvTjynKfzNtb1fh7V81m4mt/spzpMWRDIdvGUygn1C+WGjmWyaSbIR966KGSTWBi3JAhQ6SoqCgnp8flqu75DDWn7m5sEMs0//zVvH379rJq1Sq58sor5eWXXxYn6N+/v0ydOlVdGPriiy8c2QbGOjV3C4x16u40+XdGRlzBflMqUyYmyzXw1o0/Oa7jQJFe14aW+Z2HyyvNLw0tS1qdJpe3bijDW5SGLRc0LZHGxblXzuXUWHdkKE2ePFk2b94c8zE1atTIS4PJSd3dDDWn7m6Co65jAzMEjVyx3H///dQ8z2J9wIABoc934MCBKRlm+nnGBaX6DRs2lCOPPFJGjBgRumCRaYYPHx51e+ItyZiNM2bMkJtvvln1KUQ7gtq1a0unTp3krLPOkscee0y2bNli+f7l448/DtvOVL53CxYskDvuuEMOO+wwadCggTJxYbCecsop8re//U1WrlwZ9XmVlZXy8MMPS7du3dTxZLNmzeTyyy+P+XgjzzzzjNpOY+uGbID7deruNMxkIjlJm3YdRX62tlzOHzRPf/PEN5kKw78+PtMfUW+eTezp0aOH7a+Jpt5vvfWWugKLg4frr79eHQC4CSd0dzvUnLq7CWYwUfPqmBuvvPKKmuiazAl5NsU6jiuQWaX57LPPZP369WpCbXUuim3fvl0tP/30k/znP/+Rv/71r/LHP/5Rso2uXbvG/N3+/fvl1ltvlRdffDHCIFq+fLlaJkyYIB07dpTzzz/fsv3L3r175aabbpJUwTbec8898o9//CPiohw+Zyz4fGvVqiW//e1vI5574YUXygcffBC6Dxc1X3vtNfn000/lm2++kYMOOijq68Jku/vuu8Xr9arPOpvgfp26Ow1NJpKT+E1JeFaYTD4Jz2TyGCfLgYCpXM5kQvmCuT09LhF2ZwrhjzcOXvX0uG3btsl7770ngwcPdtWI0HzN0MpmqDl1dxNu2p+6VfNsMICcKpGLpfv//vc/ZTDgwpXf71fmBIyFO++8M6V1zpkzJ3S7oqJCGTDvvvuuvP7662q9f/rTn1T2D45dMsVDDz0kv//97+M+BsYXMrBghHXp0kWOPfbYqI/Dexg0aJDKKAInnXSSDBs2TGX51KxZUxlxMF7GjRtneaz/+c9/VoZQ06ZN42avm8EFyBdeeEHdRhbZ1VdfrS4WIfMK60FG1ttvvx11W/CZw2DC76DheeedJytWrFAZURs3blSm16RJk6K+7h/+8AelK4yrbLs4xf06dXcamkwkL6bLZaInU2QmU2X4z4VFcTOZPDxurxZIdUZ6tjaZwKJFi2TTpk3SvHnz6q2cEEIIIa4FJhM4++yzVf8aZObgvlRNpu7du4f9fMQRR6ghJMccc4z85je/Ufc98MADGTWZWrVqpZZEZV0wmABKwWKBzCtdsvbf//5Xbrzxxoj3B81QfoYyM6v48ccf5amnnlKmH0yz6667LqnnvfTSSyGD6a677lLbbzZYTjvtNGVgwUAzo8sG8XrIhAJ9+/ZVxuBxxx2n2jVguEzr1q3Dnjd9+nT1XByP4vMlhITDS+QkJ1mzfkPGezJ5zSaTufF3YfjvTb/Nu3K5pUuXqsUukH6Mht465bdevXrq6pTbDCa7dSfU3CkY685QXl6uFkLN3RLr3377rSxevFjdhyydyy67TN2eO3euKnOzgltuuUXatm2rbqM3E7JinOTVV19V/8OAiWUyIQvrkUceUbfRi8lsMJnBwBUr9i/I+ILJg/9hFMUqTzOze/fuUAYX+kTBnIqXwVNcHDnt+eefD/TeuOSSS8LuR6YXykCNjzFuLz5fZML985//VBdEsw3u16m709BkIjnJ3rLwqxFW9NGO7MlUmGJPJsnrTCbUymOxk/r168sFF1ygDjhuuOGGhFfq8hEndHc71Jy6uwlkN+gMB2I9yJ5ARgiaTDdp0kSd6OLkFWU5KNVJRvvVq1ersp0OHTqokiX0DUIvnClTpqjfozmybpQcDTQ/xu/QQynWCSmySFBOhW2EeYAG1ujbc8YZZ6gmz8aSO/16KGkHKHGK1lw6WrNs/J+oVP7BBx9U2SQom8K2ILMZmUHIMpo9e7ZUN9a14YL1wpyAlnXq1FH36d9ZUXZtnAq7Zs0acYolS5YoYw1gup82T8yMHDlSZSdh21HmZ9f+5fHHH5eZM2eqMj6UoCULShJRrgbQGykddu7cqf5v0aJFxO/0RU39GGNWGLa3X79+IYMy2+B+nbo7DcvlSE7SuFkLkQXWZjL5gr4EPZnim0z+PG/8jT/+mQJXg2IdHHfu3FmZTG6tL8+k7oSaZxOMdWeAaUEyA4wZmDQLFy4Mux99YiZOnKiW5557TvUbhKkTjc8//1wZUsbS8Q0bNqjnvP/++6o8qDpgXZi+NX/+/LD7dQNrZPx88sknqhfPv/71L8kkMA1wQcl8YWXHjh2qrw6WsWPHpt1jCrEO02/MmDHqZ5Sw6ewWXNCCafbmm2+q94ls6upizJyJlfUDAxAmHUhlClsqGI2zK664IubjMGhFl8Tpi3rYJmRhoaQQU9dKS0st3b/gs7zvvvtC5k0qw1309jZu3DisxxS+X7t27VImZaIsI2TJ//LLL+o9mpuh6+wzPMa4bpTeYZJgtjX7NsL9OnV3GmYykZykwFNseU+mgMSZLocsJ1Omk9saf6NszeppFUg5xsGrbjAZC7caTJnSnVDzbISx7gzIWmCze+uBKXTyySeHDCZky8AU+uGHH9TJMTJKwFdffSXnnHOO+nsYrXzp3HPPVeuC6XHbbbepKVnff/+9jBo1So2WRwYH/o6mC9apDSZkZbzzzjsq6wWvge299957pWfPnmHPQSkVGl7D/ALIrMLP5iUV0A8Jrw+DCSfI2C70SUL52pdffilPP/20DBw4sFqxiudinTAV9PvV6NswEaqjpxFMxdXEyh7KNDCJkDEHMF0N/aJiZZAh3gCaWMOMQ68hfLZY0KMIhg36FCFGrNq/IENv3759qmwRTcZTydRBjOrtxfv897//rSbewQzDBUqYQ4cffriakhcrm0rHtjasNPgOaPPPGP/IpoPpiel72dbs2wj369TdaZjJRPKj8bclmUxxejKZJ8slUS6XbyaT1eAqE/6o6xRyNFU87LDDnN4sQgjJaQLBgOwNlImbKC0skUJzibvD4ARdn7RjvPpf/vKX0O8wAQtj09EbB9k7mNaFUiXz+Pbbb789lNWDv5fGcfFHHXWU6luIMrzvvvsurW1EmRyMJP1a0TKVYIDhvWhjBiBDBAtK2nWWjrkBdqrZVLr/D9YLI828PpQmoQ9OdcvOdFYPMoiOP/740P0wOGCmIGMLj0Fz6+oAI0b3fYLZiNI8J4BBpzO/MDVOlwWaMWaywYyCCapL7DQwatDwGrGLWEUZaHV44403lKGHOEJJZiogDtCTCSALENs0fvz4iMfNmjVLrr32WjVBDllw5r5M+A6i7BRZVDDR8LlDL0yX03HRpk0bdfvrr79WsYEyOpR0EkJiQ5OJ5CRbfkENdoOkTCZc3dgb2JcwDbkscKAxYUEwKKU+n9Sq3C/Bsu2yxx+UoH+/mBNudwULJeirujJS5jdPl8svl0kf2Ok/ttUBV2wxEQRXgzQ4AMAfbhxgkszoTpKDmjsDdbcGGEx/WHGgea9b+HuHP0odT2plPJlk//79oYlX6MuDHkZG9JQrnKTjJHvbtm0qU8doMsHswN9FgOwTo8FkNANgTiFbIx1gHOkJYSeccELcx8Yq57MCZKAgmwXg/cQzrKrztxBmFjKZwKWXXhqWJY3MD9wHow264/hEm2jJgs8VxiLMDl3GiM8IDamdwlgqd+WVV8Z8nNFEROYPDMijjz5aNQLv06ePimlknaPRNmITpky3bt1CE/RioWPdbO7g9X73u9+p2w8//HDKx37G7f3oo4/U9qLkGp+f7v81depUtb3IJkR56R//+McIMwuaoHzy008/VduBRdOoUSP1Ps3NvjGFLhubfSejO6HudkGTieQku/aUhZlMsXoyrdu/UZ7d8IZs8x1oDJiIbrt2y1UrV0kdHzKXcFXnLYl+zUfk98v2yY6iKpPETL5lMuk/6FaYHahlx0HAu+++G7oPf7iRrk2TKXO6k+Sg5s5A3Um+gHHs+iIKmm3jb54Rn+9Aj0ecqCIbCSeyyCSBCaIbECO7QpfQxRs5j1IeLMjYSBWcROMkFCekKFc788wzLelFlCoffvih+h+lTigPzBTot6RNtWgNm3EfTAoYFsgcw8SzRMQr50dvIzRUR9PyWKTbXyoZ8D7GjRunbqPHEjKqYmHsg4XnwehDDMIk0+XMmMCGDLpevXqpxyPDDVlC+jHR0LFuNjtg/qA0Edpcf/31Kb838/biIiVKT9G4XoOm7lg/vh8wxmDk/t///Z/KnDeaizCgYBzhO4ASOZTZnXrqqSr7EDEJ0H8J3zFk1OnvI743+nn4HGHEIvMPz0PJnpPE0p1Qd7vIrtxiQpKkdr0GSU2X++CXz5I2mMDgtet+NZgS4zf1ZDJjQZuorAL9BKzsKYArrzhYAbhaeM0114RNYiGZ0Z0khpo7A3Un+cLcuXNDt6MZDDjx0yd/xt8bn2e8jfK6eOi/pamCJstDhw5Vt2FGYMgGes4g28eYaZxJYPro94rytUz2YER5ljZ/0M/KDMwInUVV3Slz+HxxXIMpeU6BC3loTaANtHi9kcyNolEOFs08Qq8jnXGHCwOTJ0+Ouw3GWNd88cUXqqcYzNdnn302rT5b5u1F3BoNJg2agt91112hWIvWTwrfAzTzRnkjMrZgfqGMVRtMmzZtUr3JsL0wqvSFUWQX4nnLli1Tj4XWzz//vGpCjoumThJNd0Ld7YQmE8lJvMXhjZBjZTJtqdyW0nqblu9P6nG7PaWyJ0FqfvMa+WUzwQhKNXU8EaeffrpqIolpMtHGx5LM6E7iQ82dgbqTfMHcv8gMsoV0xpAek25+nh7NDqKdPBtJ9Pt44KQZ2RcAWRz//Oc/VQYIspx69+6tfjaPcLcSvGfdziCTxwFowo0MMxBv7LzOUkH/nRUrViRcr7HRuW5QjibZyHJBeRX0c4pkp8oBY68mGH2YOBiL0047LXRbN9+OhTHWAUwcHPMBlNqlW+pp7i2FpvBWbG80kHWF7wCafeveodAW5YPIWJo9e7aKL0yjg1GKuPnTn/4kTmLWnVB3u2H0kfxo/B3Dz/GbJ8LFIxhMKvtoT2EtebH55RKM0WQU1+B61y2SPnV5BUHptWeP1K5dO6pW+AMY78CAEEJI6k2w0aPIbe85W8n26ago2UPz7xkzZqjGyMgy+fnnn1WpHibhYUEJGbJijGPicw2j4YKSKSzxgPGF59x3331xHxetQTkMHZgNMB+QRYP2ADDs7ATZNxMnTgxlwh1yyCFxH28syYfhH6tBuPmxqWbs6IboaBaPbRo9enTcJuTIctOPQdZfhw4d1G2UvOG7pQ3KeC0FqrO906ZNU9P5YCahPNAcT8ig0tpCsyeeeEJlFiJrDj3XmE1E3ApNJpKT7NyDWuzShI2/zRPjrmp2kXSrdVD0Bwd8IjPD+x7c3f4e2Vh84CrkTa1KpWstrwSLa8vVBR65Osa2FRcUSE1Pdh9UpgPSh0GyPZNwgIpGijhAwFUr1LiTzOtOqg81dwbqbg2YspZKE2zdowYnfcQajE2ycbKPhsSxNEf2Q7TnGaeR4cQYPXViYUVpDpo8YwGY2gWz6eWXX1amAL6bmN6FsiD05rESvGeUS2FyGXpSZQKsG+VPqYJeO4lMpmjAbIAJgbI89MbB5D5kOdkJ3q/u6RWv4bexDA7xiNjUz4uF8feJsmXM+xdkMun7k+l59fbbb6sFoMROm0ylpaWqxFr3tIq3zalsrxF8dshGA8hIMx7HwogF5nJIGHoo5SsrK1NmWnWmLlYH7tedgbpXwXI5kpPs3H1gCkniTKbwPzqlnlJ18B11KagR8fwd3nqy21tXLbVq1Zc6pQ2kblGR1PUWxlzy0WACOPhL9gAQacU4GMCVUUyMwdVR3YSQZE53Yg3U3Bmou3MHxfrAmFiD8cTyu+++i6s5/k5Ge56xR6Eu84oFso2sBCYJSuhwcq+nh+H7icbKVmdpwXzQ7xtZI4kmAacDGljr6ZXoJ4QG4PGW3/72t+qxMNVQNpcO6O+EaXX6fWGKoJ3oTBvoi4bdicDjdKYaegtt3bo15mOhiyae+Znp/YtxIiKm+lmxveaphyiDRFaaufm+LiGNdgFV35fJMtNEcL9O3Z2GJhPJSYpLSpPqyWQ2mbzxCuKQyWTCV1B1xSNPvaOkQd8IY++IeOAK6Nq1a0M/r1u3zvYDLDfqTqh5LsNYdwacXDKLyVqQzaB76b3yyisqkyaa5sgYwkUYgJIbY08ilFjphsjIqIkFJl6lM1kuWYwTyczGg26+rLNT0kX3hEIvG0z6ypThgsbNd999t1x88cVxFzxGZ7xUpwE41qM/w7/+9a9iFzBGdExgYiCaXycDstU0xum/ZozNs1EemMr+BdMWYSTGW2AKapBJpu/Hc2Nt7/jx4y3ZXg1M1fvvv1/FDCbLxTKSjMe6+rugMwtRiuoU3K9Td6ehyRQDND9Eemu3bt1USibSeXUDRGRmVAccbKDeGGnIN998s1ovJhvgihAWnKCT+HhrhE+8KI5lMkn4gZ0nRh+lWCaTcYKcN8v7KmQa1KMnO5IVTRaNaf84WDOOjCWZ0Z1YAzV3BuruDDwZsR4c02G0O0DJOEaamzXH30U0EtbGDW4bwd9MNODWk9+infSjJCed8e/G7I+pU6fGfYzu6wN0qZJGm2Iop4Nhli547zjWBiivN07WM2M+qU9m1L02GWAwJJPJAlPmxBNPVLdhAqZrouEc4oILLlC3kRFlNE807du3Dx3/WwWMzWQbfhu5+uqrQ6X5MHfWr18f8RjEizY9kYEWbXoezCD9nvC+M2Vin3322aEsOPRCmjdvXsRj0JAbPcV0g3z9eSTT7BsZXSiX082+zZlqwFyGif5ROM+DAdu1a1dxCu7XqbvTsCdTFD744AM1eUKP/QQwlnQDxBdeeEE++ugjNeo1HbBzNrvxJDV8pn7eXk9yPZk8BtMogkBkPbfP8Pj8mhWXWfDHFWOR8V1B2v2QIUOYjUMIISRnQQ8WXBxMxEknnSRt27ZVI89hbsDIQUYEskuuuuoqZcwgYwdTyPRFRZQpRTOLHnvsMfnss8/UMejgwYNVqdegQYNUhgSMmH/84x/qoiUuVqYzNWv16tUyYMAAlUWF9aJhsTZhUF42ZsyYUKYVpoCh8bIRTIcFOKm+8cYb5bbbbgvLmkn2OBlZjM8884wyRGBYoTcU+vWcccYZ6ncYIIL3iwblixYtCit/SgQ+AzzfnPmSCDwW2u/YsUO9LvRPBzT+hkmos5mgdyZB/yE0nQa42AcjJlkwpOWpp55S5XUwmBBXmJKGzx1GGzLSEZN4DZikzz77rKON7ZElhrhBth3MRJS1oRG31hh9sB555JHQ54/yt1q1wi9SRwNGGjTEhY8HH3ww6mPQ5wqmIfYJMK/OO+88FaMwpwAy4tj0m7iaIAnjp59+CpaUlKAgPFi7du3gQw89FPzmm2+Cn332WfC6665T92Pp0qVLcNeuXWmpN2rUqNB6ioqKgkcccUSwR48eofumTJmS8U9lzZo1odfD7Vzj+vf2B9s+EQwt//g6+uNuWXJv8KYl94SW1eXrY69094ZgcMw5Yctls9YHB8/ZppY1Zb6gm9mxY4daUmH58uXBsrKyjG2TG0hHd0LNcxHGemIWL14cnD9/vvrfKiorK9VCIsHxmD5WSnYZP3586PkrVqwIduvWLe7j+/btG9y2bVtM+SdOnBgsLS2N+fz77rsv+Oc//1ndrlmzZtR1tGvXTv3+yiuvTOv94T3g77kZv98f7NOnT8znGTnxxBPVffg/Fi+//HLoGDzWgveSCqeccop6XkFBQXDVqlVJx/rGjRuDhYWF6rlnn3121PeS7GnUmWeeGXr89OnTo342Vp2Sffzxx6H13XzzzWmt4+mnnw4WFxfH/AxwfvTuu+/GfD7iTD928uTJKe9fjHGJ+E7EuHHjgnXq1Im5vTjXeuaZZ5J6bWzroYceqp73yiuvxHxcIBAInnHGGTFjdNOmTUEn4X7dnbovTuMYIVOeAMvlTIwYMUKlH8OhR4owrkDgChOuTI0cOVJdNQKYGPDoo4+mZezhihGuFEyfPl1lS6GhY7Lpm+QAu8yNv6NEciAYEPwz4o2XyWTKejJnMnndXS2nJnjoKR6apUuXxm3SidR63bOBWKc7ySzU3BmouzNUVFSohVgPSqHQGwdZSyi/atSokSpjQUnSqaeeqjLbkW1hLC83g8fpKa2YpoXsCGRYoJQOmSXIktKZ96lOcUX5GLKpkK2C7A9kHiH7GNuI1xg4cKDKVkEWl7lUTmeS4Fj5nnvuUeVDyISpTmYLskOQpYReRrqvFXriYNJenz591DF5Kv0d0Q/y888/V7dxLI8sq2RjHe9fl4LhNaszwQ/vR2MunbQaY/+uVErljKBE7KefflKZc4gJTBTEZ4uyMWQK4RwImTvJNqDO9P4FWWc6i+jggw9W24qMJZSr4b0g2w+Zdsnw5JNPqrI7ZEXF0w9xjj5QyHSCRvo7c80116jzO6cnAnO/Tt2dpgBOk9MbkS1gwodOBcYfc/xhNYOUYNT/osYXf/yQ1mtFrTEOEh544AF1G+mXaPiYSVDT3qZNm1BKdK71y7l0zD75emNVyuudx4nc0jv8MZVBn4xYdkBTzf1tfytNixtFX+nOVSKf3hZ215CDR+Evibr93y71pHGxe4vmkFYPUAaASXEff/yxMkhxYKp7F5DM6k7sgZo7A3VPzJIlS9T+FxfCMHLcCnS/GfQRIvZgteannHKKKu3CiTEmmRF7dCeJoebOQN3dqfuSNI4RMuUJMJPJgLGpIurmowpWWBhytlGnHa2JH7G/8Xe06XLmyXKJezKFN/72oQuT4Yqcx+WNv2FyYEHcv/TSS6GRyrgKiowmklndiX1Qc2eg7s6Ag2GedOeu5uidg2wogGwfYo/uJDmouTNQd+ruNDSZDHz11Vfqf0y4QJpuLIxZG5iaQOyn0uQfRUswim4yJT9dzlgqB9xeLqeBybRx48bQz0iGRGNNlJkSQgghxDriXcTB310MkkFJUnXKowghhBAr4XQ5AyiBA6itRZpZvJGk5ucQe6lQ4+UKIzKZAsGgfPLLDvl+1xYpC+yXPZXhZVxjNwWluHBv2H319q2TDlu+OfC/4X5fQXgMuD2Tqby8PNRjApM8Jk2apH5Guejpp5+uavZJ5nRnbyv7oObOQN2dAW0AdKY2yT7Nr732WjU5C1NacQEU/Zt2796tph3/97//DZlQ6AXTo0ePjG97LsNYp+ZugbFO3Z2GJpPh4Hbr1q3qdqJaRDQgRLYT/uijdjEbQX1lPDZs2CC5zK69yJopjWj8/eamvfLeVrQZ0yN0D4zi1UzejuymqgynBpXb5bFlf5bSQGQWjp+ZTGFgbDBAc0+MLUaMoSfZ0KFDHW9w6BbdCTXPZxjrzpp7yYz2Js5oDkMJSywGDRqkxrMTa3Un1YeaOwN1p+5OQ5PpV3BVSIOpBInQJtOePXskG9ENvJJh4cKFYcYafkbjMkzB003NV6xYoaaXdOrUKaQP+gBg2gaei4kpYNu2bcp8aNKkibRs2VLdB51wpa1u3bqh6SRI7ca0B9QMGzPDMNEBDcswwUJPKMFzsQ5MidDZHCqRyUCRR5Th8cUWn0hB8tPMeuydH9VgAuWFVevxGMrltBbIeEMcpKIF4gWTU5LRApMy/H5/mLmAhm779u0L0wLNcrdv364yjPRkmU2bNqmSthYtWoQMIJS5rVq1Sl0F1fGBVHtMCcH7wPvRVz/mzJmjsvkOPfTQ0Gtj27E9uqHc+eefL8uXL1eGJZ6vtcA0Fxi2eA09MQc/435sC7ZJf+fwfGwztl1Pw0B2IN4b3qMG24OyPMSFBtuN7YdmuscC3h/eJ7SFxgA6QA98BvgsAPSCbvisdOxrLRDfiHOA94vPAd8DfB80+LzwuWEIACbfAHyu0AiN9vQBrNYCvWZgThu1wBSS5s2bq/sQT4grDBPA9CCjFoh9vd1g9uzZ6n+jFjg5xwEFpqpg8lAiLVq1aqWm7Bi1wM+4HyDGEGvV1aJLly6hDDd8F/CdMGqB7wy+O4m0wP4I+yWsC+s0aoH9hPHqfTQtMLVs586d0rFjRzU5CSBusc8wavHLL7+oCwd4P/q7pPdfeB6eD/AdwL4K68frmPdf2B6doaD3X0Yt8Bp4Lbw/vE+AbcE2QQfoAbDN2Hbopfty4b3hPSLGjE0dMcEK+uNzyKV9uXH/pb83Wgvj/iueFsb9F/YTiAvz/iueFrm2L8c+GuvDPkLHOO6DHog7vT5ogf0a9DdmmuI+/A734Xd4Dj4rbA+2XX+PsX7cj9fQ2d14j1jws35tbAu0xfP0vjja9gC8hvkkH4/D4/E4/b3B+rBe4/bo18bnpz9DbB+207g91dHCuD3JamHcnmS1wG08F3ok0uKRRx5RPUPRdwl/OxCf2GbEI4bVoIfomWeeGfoMo3021dXCuD1Wa1HduNDbkygu8Dwsxul3dmmhtydZLWJtT7JaGLcnlhaxPpt0tdDbY9YC9+ntS0WLRJ+Nk1okG6fJxoWV+y/92vhZ38d9eeb25fo7CxAXOgbKLf67lsq+HOvD/cbvXrzjcuP300poMpkcX6A/0HjogGMfGofwhE/005lMFfF6LkWhJIbBBH6oc3jodq/aXil0WbkcTpZwQqdPqmAYGcfQ6qaCHH2dWXCia9UUKZIcOKHXhhuxDx3nMJmIfeCAVR/8Evs01+UsiejVq5cyaxOdmJDEQL9MnVCR2JozRu0H57KMdfvRBlC5wVdwKwVB2GBEXRnSVwlR/jN69Oi4quBKMw6EcdUYWRbV5f7775cHHnhA3cbEuv79+2e8XO7oo4+2fFyhXZz0qsiy7VU/P3OmyJmdRYbP3yr7AlVGU43CldK8uIbULKwpzYubREyXO2zdBDlu5f9CP+8pbiirGxwuW0vby4LmJ0mwwCMtaxTKwIY1pWahO0wmHLhOmDBBZs6cqbIsrr/+eqaWE0JIFpDOeGJCCCGE5D9L0jhGgGegK1ys9ASYyfQruoQCJFMCh5T/ZEvrnCDXTKNUqYgxXc5vskyb15gv/+x0WewV7Q7PfKrdqKMc0u+36vYJ4j4Q+6+//nqoZxfKrTA5btiwYWEp5oQQQgghhBBCiBmaTIb0NpSl6N4L8UDPBG0ypdL7iFhH2X6UbRVHTJczJ/t7E2UfBXzhPxe6+yuBOl1zWjP6qKCWV8c8G1DbC3rtUHdq7gYY684QrR8Ooeb5CGOdmrsFxjp1dxrOqzWgm9nipBqpZrFAA1GNsekrsQ+foSRON/4GgWC4qZSw8j5gsqVM5XRuA/XbgwcPDmXooaYbP6MxLnsZOPeZsK6emrsBxrozGBvEEmqezzDWqblbYKxTd6dxd9qGieOPP16mTZumMjZ+/PFHNbUjGlOnTg3d7tu3r41bSDRBU8YRGn+jvVhAwg+UvYkOnAOV4T+7PJNJl47CWEJfposuuig02cw4tYrYB3W3H2ruDNTdGYxTaQg1z2cY69TcLTDWqbvTMJPJAEaya0aNGhVVMEwDefXVV9VtNEUeMGBApj8jEoVK01AWZDKZ+zElZzKZMploMikwWv2GG24IGUyEEEIIIYQQQkgiaDIZwLS1fv36qdsvvviiTJ8+PUKwRx99VBYsWKBujxgxIqJ/zRdffBFKURw+fHjCD4Ckh89kMqEnky+qySQp9mTK/3I5ZHz98MMP8sEHH6jbsSgs5O6BEEIIIYQQQkjysDbIxJNPPqlK4MrKymTgwIFy1113qWwl/Dx69GgZOXKkelyXLl3k9ttvl3R5+eWXw37++eefQ7c/+eQTWblyZehn9MNBKR85AHyRiOlyMJkk0jDxJDKZgu5q/F1ZWSkffvhhqMFuixYt5KijjkrquboXWbdu3TK6jYS6Ow1jnbq7CRzfAJZXUPN8h7FOzd0CY526O01+n1GnQa9evWTMmDFy2WWXya5du5TJZAYG00cffaR616TLVVddFfN3f//738N+vvLKK2kyGYhaFueJnslUlLBczmQyFeTvVwJZS//73/9k9erVofs+/vhjZTS1atUq4fP379+f4S0k1D07YKxTdzcRL6OVUPN8grFOzd0CY526O03+nlFXg3POOUdmz56tsppgJq1du1ZN2UJGERoi33rrrRz16yCVpiwm3fg7mvlUVJCg5MtFPZlQwtmnT58wk8nv96usuWRMJj19kdgLdbcfau4M1N0Zatas6dAruxdqTt3dAmOdursJxnsV+XtGbUHj48cee0wtqdC/f/+k3GM6zNY1/dYmU2Ug8hfewhQzmfLYZNIncccee6zqN1ajRg3V7P7ggw9O6rnm/mPEHqi7/VBzZ6DuzsD+e9TcLTDWqblbYKxTd6fJ7zNq4ppMpmWVC+TV9etE5Iiw+72SwGTa8L2rTCZwyimnSEVFhTKbGjdu7PTmEEIIIYQQQgjJEzg+iuT8ZDmRgLy3/QPZURluMIkEpaggzrQ4ZJz5yvNyuly8njIej0eVhKZqMK1YsUItxF6ou/1Qc2eg7s79vWAfMmruBhjr1NwtMNapu9PQZCI5R4XJZPJ698suf2QoF8peaVxcP/aKApWR98UzpXIAlGHOmDFD9RPbtm2bpetGI3wsxF6ou/1Qc2eg7s6A3nxYCDU3gn6N6OWIxTwROVdhrFNzt8BYp+5OQ5OJ5Bw+c6/uQr8Eo4Ryu1qzpF/d3sn3YwKtjpFcBSVw77zzjkyYMEH27dsnY8eOlcrKKEZamnTq1EktxF6ou/1Qc2eg7s6A/nxYSCRffPFFyGgxL7Vq1ZI2bdrI2WefLS+99FJK2WDU3BkS6X7//ffH/LyjLTDiSPU0j8UDDzwQ0rlevXpSXm6qPEjxM0SPorp160rXrl3VBPFPP/3UkY8Ox+Uwbc866yxp27at0gaVBT169JBrr71W3nrrrZgXYUaPHi233367nHjiiWoYFXTBYKqmTZuqnsD/+Mc/QheYrdzHTJ48WYYPH65es7S0VL0uJq1fdNFF8swzz8iePXviPh/nJNi23r17S8OGDdU6unXrpt7LqlWrEr7+zz//LOeee640aNBASkpK5IgjjpDnn38+4fPKysqkQ4cO6vP/7LPPxA64b68i/xvQkLxv/A2TSSQyA+mRDmerHUtKJpO3luQq06ZNkzlz5oR+3rRpk3z44YequXdcHZKkdu3a1V4Hoe65AGOdursJlFCT1MEJDKYPY8EkYgyKwd/c9u3bU/MshbFuDTBJrrrqqlCZc7yYT1fzV199Ncxgeffdd+Xiiy+W6mT67969Wy2LFy+W119/XS644AJ54403bDPZMbl82LBhMnfu3LD7YQxhwf3jxo1Tk8zNoErhkksuibreLVu2yNSpU9Xyz3/+U1577TU57bTTqr2927dvV5/ze++9F/E7fCZLliyRt99+W/V4Pfzww6OuY+nSpXLmmWeqxxpZtGiRWl544QX1WcCsj8ZXX30lAwcOVPtbzcyZM+X666+X+fPny+OPPx5z+x966CFlBA8ZMkROPvlksQPuY6qgyURy3mQq8sBkCs9k8hZIYmMlmsmUwz2Z+vXrp3bYmzdvDt1Xp04dR7eJEEIIyQduuukmufnmm0M/428tTgpxUgejad68eepqO06AeKKR+yA7DZkX8WjVqpVt2+MmYCwsX748dNEHmTIwnVI1mYyfYSAQkDVr1sg333yjjAmYFsj+/7//+z/5z3/+I3YYTAMGDJBffvlFjblH1hLME8QQKhFgwnzyySfqgnEskDmJdRx55JHqdosWLdT7wv4H5hTez9atW9V+CKZUz549097enTt3yqmnnio//vij+nnQoEEqcwkZx9i/QUuYWjCZYgFDDxlb2mC67rrr1GeIbKQpU6bIww8/rMyqoUOHytdffx1hVOG9weTCZ4UMtL/97W8q6wsm56hRo+SJJ55Q29S3b9+o5ta//vUvFT+pToon1kCTieT8dLliL8rlPBEmU0Kimky5+5VAyix21CNHjgz9QUA6qlWsX79e/d+yZUvL1kmoezbCWKfubgInOPpvCIkNSlK6d+8edt9JJ52kToIOO+wwdcUc2cTjx49XJz7UPLdjHWU25s+bZFZzcxZTs2bN5Le//a386U9/kokTJ6oMfdyXLObPEN9TmB74fh599NHi8/nUMfOf//xnad68uWQKlPohOwkGE0rkUH7WuXPnsMf06dNHLr/88pBeZmAurV69OuZrIFsH2V449sc67rvvPtU2I939+m233aYMJmR5YT0wrowcddRR6rVg2MXq6QcDHlljAOVyd9xxR+h3yH5CiR9K/1BOh88Z5clGYDzBLCoqKlLlje3atVP3n3DCCbJu3ToVEzCboplM2H6UMD/44IO2msH8e1oFezKRvMxk8iRTHpZnJhNo1KiR+kNzww03WGow6XRcLMReqLv9UHNnoO7OgBMtLCQ9kDF8zz33hH7GCSQ1z04Y69mvOQwZ3ZcIF05hvKCfEowMlFVZQa9evUJZUdg2s7lhNciogdmC9wHDxmwwGYllCiWTHYn2GMj40QZNuvt1ZJL973//U7f/+te/RhhMRlA14vV6o/aeeuqpp9Ttgw8+WPVfMnPcccfJNddco24jK+r777+P6MWkH6cNJo0uHdSPMYKMLmSF4Tzod7/7ndgJ9zFV0GQi+ZHJFLQokykHpstt2LBBpZDGAqmsaKxnNa1bt1YLsRfqbj/U3BmouzPgpIZZTNUDTXs1KCOJdYX7gw8+kFtvvVVdicfVdVyhx8WhY445RjUsRqlLPND7Bid1aMILUCKPEhTcj4wDZHkgu+Dbb79NuM04af/vf/+rXhsNkdHMFw11cUKcShNzlDI98sgjKjMBxx7YDnyXkS2CHlXxQCYD3g/+B8hauPHGG6Vjx46qpAbvCyeh5ubAKFNEBhkeh9IjlA6hnNHYLsCpWIdZDtMRRkb9+vXV9uF9wCzByXsqny8ySXAbGTnQNVobCJQ1oewI2RxNmjRR7w9lVOecc44qoUIvongg8w7mBD4zvAZMU+iKFgzI8EHZlbkRvu7HBHRjZeNiNG1S1fz999+XHTt2qNto0I3vCbJ4zH2a7PjOWgG+Z88++6y6fcopp6jvWybRbTJg1qUb608//bT6H/sE7K/SAeVwiE1w5ZVXKoMtGjrWdSwa0c9HPJvRmWf6MRpkRWljCe8D+1g74d/TKnI7bYO4Ep/JX/F6ApE9mSQJlylocqsKCrO6JxMOFL777juVHoqDOdRK2wkOhIn9UHdq7hYY684Q7So0SQ3jyVyskxo0qn3llVci7kcJDU7kseCkCE12o5V/mMEJGU7CcVKlgcmCkhmYWcj6QCZILGMIzXjN/V/QTwrLm2++qRryJgKPRcNeXWKsQSkLerVgQXNlbAvMlnggAwyPRR8XDcwl9NWBWYVMB2QmYNtwYmosK0JPGpzIf/zxx6rnTqyy/kzHOo7PUBaFPjNG8D6woCHzLbfcojI8Yp10a/B+UPITLxsFE7PwGeuJYpqNGzcqzbDgcx4zZkzEQAuYH8gGMU8zg66IDzT0hikGTX/44QdJl1Q110YSMnJ0PyXEOd7rrFmzVEmq0SDK5HcWcaa/szBNtBmaCohHfB+AMSMIJhDux/cChokVfdxgOuvMHnxX0ol3fP660TfOM/T3FvGC7zn+x/Ym+j4bDVWUxMUCZXeY1In9GLKvjMDk0vFsRt+nH6NB5hXKCu1s9m2Ef0+rYCYTyflMJpTLBaM0/k45kymLS+VwVRFXpJD+iSwm7IgXLFjg9GYRQgghrsf49zjWpC2YBcgQQdkITvqnT5+uykPwtx3ZOzjphVmATKREGTk40b700ktV5hKMKWQuYX3IhsLJH04EYWrFKnHHSbs2mNCbBsYNjARMyYNJ8tNPP6my+3jgBBkncTjx1Nkt6JuC9cAo0E2HUbpizFaIBtaBk0Jk/vz73/9WF9SwfejTgnVDDzRKhl5XXHGFytiGCQZjDif/yBICMHLQyNkJcHKP7CEYTDAtkE2BbcM2PvfccyrjB6DJNHoMxQPvExkkyC7Sny9O2pGxpMFx4BlnnKFiBnGAk2uYi8h+wv/4jMGECRNUJokZjJ7XBtPxxx+vmilDc3z2kyZNkkcffVSZDEbzA6YPYg+vpcFnjvuMS6KG6bHA54z1AUxh01x44YUqsw1EM2oz9Z21AmNWIcwxNMHG+0H24EEHHaQ+Y1xgQVwvW7Ys5fXDnME60dwaZo42JfHdSQcYeTDA9PYinrEuNNxGPynEMYwdxEa8MkNMftPEa98BUwY6APN5jd6HGI06zejRo9X/xmbhKElE3MJQxf/EYYLElaxZswb5s2rB7Vzi4yXBYNsnqpYzxy8NXr3wleDgOdtCy4hF2xOvaMv8YHDMOVXL20OC2cq6deuCDz74YPC+++4LLX/729+C27cn8T4tYuvWrWoh9kLd7YeaOwN1T8zixYuD8+fPV//Hwh8IBHdW+pNetpXtV0sqz8m2Be85E0yZMiV0rIS/u9Hw+XzBXr16hR43bdq0qI9bunRpMPDrdlZWVqrFyOzZs4O1a9dW67jnnnuirqNdu3ah1znyyCODO3fujHjMa6+9FnrMY489FvH7Dz/8MPT7M888M2I7wAMPPBB6DJZRo0ZFPOaiiy4K/f6FF16I+H15eXlwwIABocdMmDAh4jEnnnhi6PedO3cObt68OeIxv//970OPadKkSfC4444L7t27N+JxgwcPVo/xer1R1xNLdyP4jPVrvfTSS8E5c+bEXPbs2RP23N69e6vneTye4Keffhqx7l9++SV4yCGHqMcUFhYG586dG/fz7dGjR8xjvIqKimD79u3V404//fSoeoCRI0eG1jdx4sSw3/Xr10/df8wxx8TVZNu2bRH3IR70elesWBGMRyLNjTz++OOh9S5fvjzsd0OHDlX3t2jRQn3nkvkM8f2NxurVq4OlpaXqMfXr1w/u3r076uOuvPLKhOtKxPDhw0PreO6550KvG22pU6dOcNKkSQnXadQ/2vLHP/5RxUiyuht5+eWXw/Z5+F7Gep2CgoLgI488EnU9iCs8Bu83EWeddVZondhvaPx+f7BDhw7q/kMPPTQ4fvz44Jdffhm89tprQ4/Hz5qBAweq+/7+978HnSKVeHfqGMEuTyB7UzcISbLxt9cbLZOpIK8ymZD6ffrpp6urjBrdR8EukI4OWNJiL9Tdfqi5M1B3a9jjD8q1Cw/0NHELL3SrL3WTSmG2DmQJIWvj3nvvVWVjAH2IkBUSDWTfaHSpl7G0AlkDyNbBWG6UvP3lL3+J+/ooI4t2DIAMpzvvvFNlByEzxdz4Fn2YAPrvPP/881HLO9BTCFku6H0UDaxb90/BsYlu3msE68c2oskxsiuQkYPMm1ighAw9hczcfPPNqk8UQM8qlM2hvMYMejJhm/FayOqK1qw4mu6xuPrqq+P+3lg+hWwl3bQYPbIwmt5MgwYN1CQzxAcy0vE5IKspFvgdMruigSwOTDNE1hqyxqLpobdFZ3whU8nYZkGXG6Gpcjw9qtvjMxXNdakctklnfmmQrYYMQPQlRaYV4i4VoDn+xiAjDJlke/fuVffje2YuJbQSlMNqRowYobKE8J1EXCMzSJd6IsZRKopMQmQT4XepgqwexBgyyXQZbarlW8bt/fvf/662F1pjShum8yGzCWWwf/zjH1U/JPyPTKXzzjsvbD267DUZbUtLS0O3UaqJfQdASemLL76o9hvz5s1TWZ5GUE6K3mEAGaEoV3Wi2Xe68Z7vsFyO5L7JVIj6ufBaZk+elcvpumXs4HFQgYNIjE5OVNNvJTj4i3YASKh7vsFYp+6EmHnggQfCmhs3bdpUlYuhbAkn+SjTeuONN5ISDicgOAlDeQxOnmDmYNGmAkpNMJ0pFjCkcDwQDWwbmk6D5cuXh/0OZXS6xAVGSKzeRTi2iFZipcE69NjyaAaTsQxJGxvG55jB+z7ttNOi/g5mg25mjPeMSVXR0KU10d63UfdMnPwZJwrG0wO9tvT2x5tCiEbm+uQ5VnNsgPKoRMdlaDIPYLwZ0c2UUVqXqOF8dUhWc8S/Nmt1qZ8RxId+r3ryWSLQMFx/X1H2hwllKMNDzx7EPgy4eI2tYcyhHyqWdPoxAW1mARg2MGtQ2oYSMZTIooT2H//4hzz00EPqMWh6biyLjAYatevSRBiIKHeFAYOSTfTZQi+udGPdvL34/mJ9MK5g/uAzQHkv7tPnIDDtzA3mdcldMs3HtakEysrKIj5DGINnnXWWKtPDY/FdhzGnp9dhm3WZrLHZN0pH0TMO+xfso1EaDKMyk2RqH5OLUAWS4z2ZAlK38Syp9LfJ655MAH8kUe8Plx9XxOwm1sEooe75BmOduhOSagbBb37zm4STjHBS+Pjjj6tmytGa2RqzLrZv366MrGjE63FizD4xNtEGMLV0hkOivjk4IYuFMcMp0bQs/B7vF68L8yfa+HbcF21ymgYniXgvXbp0ifsYjfl9a1KZtpVKo2etB9Zv7BETSw/0nkEfHWQ9RNumWAaiRjfiRv+ieLoZMccbTMQvv/xSTfSD4YGm6zAUYG5ZOUk4Wc11ryV8h9CfywxO3NHkHCYCMv3wGWvzMR2QHWPs+5QpjA2y0dfoD3/4Q9TH3XHHHco0wec0duxYlekW67NFrBvjHd/liy++WJlv+FyRVYQMoES90BJtr85mitaUHBl5iBlkECGesW8zxq1ej7FBfyyM0yx17y3zRfZ4kyph3GFCILLAdLNvZDzCmMS6UYGBfSKyDaETHvv73/9eMgEntVbBTCaS09PlunWbKIX1ymR/4JDUy+U2mKZlZMFkOaQ/4wpLLPDH1wmDiRBCCHEzKMfS2QPIuEAGiB7Njca0MCRiNdoGOOk74ogjZNSoUXENplhX9I3EKo/S6AwDc+aQsRQmloGlQTPpWKSyHj1q3Py8dN5PvMcZM7tjZUxlCv2+cCKbKItB64HMDxiJ0Uh0nJeoMXwy8YRywLvuukttL8qeEJfIkkcWFUwnNKiPlRFmNfi8dBYgzJ9YbRl0g3cYljA3EoFyTWPGD15DTzrD9xHlreYMHKsxGmHYR8QyIfA5aIME8ZSO9tAHRgtMamRoxfq+Jbu9yFrSWZHRMGYf6nJR83pwYTyV7KlUSxcXLlyojHuU3CFDDOD9I6MQBhPMf+yXoSfK/GDcIe4xPZFkluxO3SAkChWGY4dWrWZLhT9y1G9xYRIm0x7TQZ4/sdueKfBHDqnMSJ/GjhJTXTJZI54O+o+AsXaaUPd8hLFO3XOZ2p4C1aMoWfQJuRUjtJ18z5kGZkr37t1DPyNjBaUYKOdAxgAuEqGnkh7/bT4RQokJ+gVhPTiBx/PQpwknYzoDCifFutwq0ye/yWbA2LUeO8h0rFulRaLt0+8DhgxKrdIFJVqYQvj666/LZ599piahwcBBxhtO2DHpD9k1iN1Mao5jX/T50qWAyeiI/k2YaBgPlFoav7M64wffMZhq6HOKHmiZ7OED0y7a7f9v7zygrKa+Ln5n6G1oClIEpCpFpDcRVERAiqKAHVRABVSwADaaIAhi96NYAEHKHwEFpAgICCJSpUp1pAhI730m39pXb8y8SfJ6S/Zvrax5815ecnNycl/uyT7nCi/rIjBirOPmK1AxQQmF+xjMLGiWeuhrG7wp2jzbawTfxSyRaAdSAK3qiwEoi1RQy5g65wsIpiG1GLW1VHuRwgwfxvZwfSh/gvIKaXdQRcHnUXsu1Djh9zRUMMhE4lrJlDHjJXEpNX2HVCWXvWRdksnjidgF/yP+oQCdI6b4VVN3QgKM4pWYzjSWOilIqj3rHhDa3YnQ12n3eCYxIcGvItjnL/9TOyN7Fh9+N0k6oGaCqglPyTFA/vHHH2XNRM/aLggw4TcdaRyqqK+nMicQ5YE/GBUyf//9t+26dp8bi0FjPbvBs1G1FWwR6WBRaTnelFP+oo7r2LFj8jzbqZmUPTDwDVSZDqUPgjJIRTIGUQIBdYqg7MCC+1EoUhCkGDVqlKyrgwLVSPGzU7QEa3NV8NsfcB1B+e9vgWzYHSl3CKrh+6i1hms4XL5ZoUIFnxV2xs8DretjrNGFQEu02lu+fHnZJ6oge+3atU23getFtdOq3poVqK+E84j0YVWTCaA2FahWrVq6oBXqoiHIpNaJlz4mHmG6HInrmkyJJkW/i2ZJFE3zZfG/JlOFh0Q0wE2nZ87ynj175OwSsQRmsYnkbHaEdo8W9HXa3U3gNyiWHmjEI2+//bZuQwzWPUFxb/WQBgMiK5urWjvhAsoIVfPEM73FE7vPjYENqBXsQJqSGnShyLETfV3ZA/dy3gavyh6oQxVo/RYV8IG/+FLzxlegqMPMblD3qPQ1KOo8U9P8UWx5szkerKLGEkC6GIpY2y0Ifql2+VoA3BP4ImaFBEgVDEYN5g1VeB14S4EzBoWKFCkS0P7++usv/XUg9+wIOqrAHdSZdopKu/YaZ9lEQNAK+LBSjyMA5CtIw4MiFEBxZ6yHh3MKUCjcE6WoUuuEGv6e/geDTCSOZ5fT/g0ypXXjxvmy+vYDqHlE6DME9mMfLKgjcP/99+udIdoOGX2gT43CBWTHnlPKEtrdidDXaXc3gSe9/qYokLSgILUqVoygC6ZY93xaD9RgyszmmJpdzRoWLqA2UMWsMd039mkGapqoQsxmYBsqcIAUPyugFFG2MH7Hab7eqFEj/bWdPVAWATMHen7HX1q2bCn/qlpK4UDVBwKes88Zi0MbizYHYnMEsFQxetQ9Qzqb3YL0PqWoDzTIBJAtoIIpKLIdLhUh7ifU/TyKyVsFNxBsUzMOIhisZv/zF2RCKAIdR2BMAk6fPi2VQlYgC8MsqKSudzWuQV9iFayCylOBGfJ8BQo0BNRQg8rzWlL73b9/v2VqXrgemvP39D8YZCJxG2RKSPjnhaalvWnxOUsg9UrMzC6Hpyq4QUVtBsx2gcKE8VTngBBCCHEzUDCp3+2BAwem+UzNqIYZxVAk3BMMslF02a7Yd6jAQF4FB1D/0SwlBlOoo1iy3QycakCImePMAlJQ2KC4NFKwgN1U8fEOZuLDDFjgs88+Mx2YI7gAe6uHi+o8BALSu1SKImbJwixxdmAKeE81yYQJE/TgpxkIQio8HzAaAyCBpGSZpcrhPhg1pnwBBbvB9u3bvSrprIDypWfPnnqA58MPP0y3Dmqt4ZrGsmTJEhEovXv31oPM3bt3N10H6V4I6gCzGlgIxiB90Q4UwEYdJnXOMFOgJzgOdUxWs8+hjSqQaGyXp/8om6DOkWfKLFR6KLoNUA7k3XffNQ26ogA7wLjH24yXCgRqcb6Mxb6NqCAkaowZ1WPo65BiB7zNAkmCh0EmEndc/fd+KCEhxdSNM/ganEn1uLFKiG6JMkhNX3jhBTmrRyyCG0V1s0hodydDX6fd3QRUK1hI8ClTSmGCQT8G9p6zYsHOGJCh4DIGaEidGjFihBzw4H9/0kUCpUWLFnIBqCWFfWLgtW7dOjFv3jypFkFBXBU0sRvQqppCCCZ16tRJKjHWrl0ri+qijo8KtuAhmq8BhHj1dQSXMLBG4KZZs2Yy+IPADtKB8BlmFlSBO3wWTC0lqCVQNwl/kTaEGmAo8AxVEOyPVEeo4vr27SunlUewwTNoCJ9EoWTUXELAAAN+zJoIH0AaEpQ+AJPQ4OGnEShkVBDizTfflGq1HTt2yHqGWIzBUjubQ+mmgl/wD1/r2CilTaD1nBQoAK5m+0PKFYJN4QD+D59QwaImTZrICQJgb5wn9Amff/65bluzgGy/fv3kOAFKLhwzilujrAb6GfQhUBKpukTww5EjRwb8sBoKrwEDBsjX8BsEUaGYg29BjfXcc8/pASoogtAXmPHKK69IlSdAQA9BVnwfwR8Eshs3biyvF6TwIkXT32Lf8D2z4uSwBVJzEVSCX82YMUPWyrv33ntlUBSKTgT1wwF/Tw1oxJXs27cPukW54HU80X+pphX7QNNKfnJBe3bnG9qDm3/X2mw6pi8/nbjo24YWvqJpU1r8t+yaF9Z27969W5s1a5aWmpqqxSO//fabXAjt7nTo67R7rLJjxw5t69at8m+oOHfunFxIehYvXqzfK/Xt29eriVatWqWv37hx4zSf9e/fX//MbHnppZe0MWPG6P8nJyen237x4sXlZ+3bt7dtBz7HeljfjNOnT2v16tWzbEuVKlW0tWvX6v+jXWasW7dOK1y4sO1xtW7dWrtw4YLp9xs0aCDXwV87fD1ub+fKm6/je2obOPf+Mn/+fC0pKcnWHl27dtVSUlKCOk7FL7/8ol1//fW2+1PLuHHj0nzXl+/kzp1bmzt3rum+e/bsafk9o+3sbD5w4ED9O5MmTdL8oXz58vJ7+fPn1y5duhTwORw6dKi+/ttvv216HQXqD0bOnDkj+wQ7e9eoUUM7ePCgrW94W4oWLar98MMPlnY39mne/Kx3795aQkKC5b4KFCigrVixwnYbO3fu1MqUKWO5DVwvGBv5ysSJE+X3ypUrl+a8e7JkyRItS5YspvscMmSIFi6i/Xu6I4B7hHDFBKhkInE7u9w/9ZhA2nQ5n2cy9iz8HaZ0OfyWL1u2TOaO44kWnhbFI8wzpt3dAn2ddncTKnWCBA/SPe666y493chYPBuFhjFlOp7eQwEEtQGewmNabaxrlk4SLpCaD+UU1BtoM9QqeA+KKigMkNLny2xbUF0gZQnfgXIJRXVxXEinw3FBpYEZpow1fJzs6zi3UPIgdRK2hMoDvydQhkANhHtBzGyGdLlQgBm7kIIJ1QrUMLA77A97I30J7YFqDrN7KWWSYvPmzeKdd96RqjbMBIYZ61AzC+cQ24UKCucWqhszhgwZIhVaUEnBV6zqbdnZXNVUgo3Qfn9QaibM6IfrKlCQtqh8HYocVR8q1OAag0oMxcthUyiokLKH2eDQZ6CWF8YHSlnlyfz588Xw4cPldQV1WsGCBaUiB9ctajjBHlAb4Zxhe6HwdVzXUExB9VaiRAl5nlDvCH3GW2+9JdVrderUsd0GsjOg2IKvQR0J/4JirVy5cqJHjx5i48aNonnz5j61B0ozqAABriO7wvlIv4PKCwoyXIe4JrB/FLTv1auXCBf8Pf2PBESaDP8Tl4BiaCp/FkXQzOSGscqri4SYuBk/SqdFo0bDxPFLHUSq9t80sC8XyylqJvlQxHv+80Kc+vO//2u9JETxBiFvL2bNMM42gpsL5NNjBgdCCCHEVzCgVFOkqzo/hBBCCCE7A7hHCFdMgEomEreFv3Ulk4cgz+fC356zyyWGZ9aTChUqpHmagHxd5FETQgghhBBCCCFOgkEmEndc0Qt/q0KCnrPLJcTU7HKIJEO2CRBswrSwqugmIYQQQgghhBDiFKI7nRYhIajJpHnOLufrhjxnlwtTkAncdttt4vjx4zJHHzMexCNbtmzRlVmEdncy9HXa3U2omaAwww+hzZ0MfZ02dwv0ddo92jDIROI/Xc5TyZQYncLfmCrTqvAh6jChWF88gxxfQru7Afo67e4mWJqTNncL9HXa3C3Q12n3aMMgE4nbdLmiRdeJa84UFT33DhTFLu3XP0/Y6uuWPGreJwR+OWA2kVmzZsnZQwoUKCCcCGazILS7G6Cv0+5uggom2twt0Ndpc7dAX6fdow1rMpG4VDJlyHBJFC+xUTyyf7socWmfSJRJc/8sCTJ45MsiglYy4UnB0qVLxddffy1OnTolpkyZIi5evCicCKflpN3dAn2ddncT9Hfa3C3Q12lzt0Bfp92jDYNMJC6DTDlyHBdXU/OJ6y/9FboNZ7/W76+sXr1aLF68WJelHjt2THz33XeUqRJCCCGEEEIIcR0MMpG4TJdLSEDOXKLIqIWoTlDZVkJkz+/316pWrSoKFy6c5j0EnJxY0wUpgVgI7e506Ou0u5uA+tapCtxYhTan3d0CfZ12dxP09/9gTSYSl7PL/VP0O4PIoHnMEFenlxBJRf3bYJY8QmTNHVBbMmbMKNq2bStGjRolO5ZGjRqJunXrSpmq0zh37ly0m+BKaHfa3C3Q16NDauq/s2kQ2tzh0Ndpc7dAX6fdow2DTCQulUwyyKQliAzC4+Y46XohcheLaHvy5Mkj2rRpIwNLN9xwg3Aq5cqVi3YTXAntTpu7Bfp6dMiaNWuU9uxeaHPa3S3Q12l3N0F//w8GmUhc1mRKSEwVGbSEkBTv9oWTJ0+KnDlzSuWSGSVLlhROhx0n7e4W6Ou0u5tITGTlBNrcHdDXaXO3QF+n3aMN7yxI3CqZEk0miAtHkGnnzp0yHW7+/Pkh3zYhhBBCCCGEEOIUqGQicVmTCYW/M5qVkUjMENJ85qVLl8pFzSRXtGhRUblyZeFG9u7dK/8WKxbZdES3Q7vT5m6Bvh4dLl26JP9myZIlSi1wH7Q57e4W6Ou0u5ugv/8HlUwk7rj8b+HvRNN0uUwh28+JEyfEihUr0rw3e/ZscfjwYeFGYA8shHZ3OvR12t1NpKSkyIXQ5k6Hvk6buwX6Ou0ebahkInGpZMqUmCIymqXLJYROyZQ/f37RvHlzMWPGDP29MmXKiNy5A5uJLt4pUaJEtJvgSmh32twt0NejQ+bMmaO0Z/dCm9PuboG+Tru7Cfr7fzDIROKyJlP2hJSIFP5Gaty+ffvEunXrxF133SVq164tZ5FzI24NrkUb2p02dwv09ehgNaEFoc2dBn2dNncL9HXaPdrwzoLE5exyiXJ2OS0ihb+bNGkiqlSpIooUKRLybRNCCCGEEEIIIU6BNZlI3M4u56lk0kSCEAn+u/Tx48fFnj17bJ8GMMAkZC0qt9ajiia0O23uFujr0eHKlStyIbS506Gv0+Zugb5Ou0cbBplIXJGqCaElXBX1r9kqmv79z2xnihTUY/IzlW379u1i9OjRYsqUKeLkyZMhbq2zOHjwoFwI7e506Ou0u5vgYIQ2V/Tr10+WBLAqC4C6afisQ4cOIh6hr0fen1CjhnVqIg99PTrQ7v/BIBOJOxVTl0rfiIF/fSvqnV6V5rPUBN9T5VJTU8WiRYvEpEmTxMWLF8X58+fF1KlTxdWrV8PQamdQqFAhuRDa3enQ12l3N5EpUya5EO/gHmHatGmic+fOolKlSqJAgQLSdqgnVrp0aXHfffeJYcOGieTkZNrcQb6OoJoKvnkuULvny5dP1ux88803xf79+8PSdhKb9O/fX/cF9AMYU/gb0DUuiYmJIikpSZQrV048+uijYv78+VHp1xEsGTt2rLjnnntEsWLFRJYsWcQ111wj+72OHTvKMZM3kCXy0ksviRtvvFHkyJFDXic1atSQfSTGXaFm4cKF8lpFX4z94XyULVtWPPDAA2LEiBHi7Nmzpt/7/vvv5fnAsd50003yOGG7vHnzimrVqsljgCjBG7/99pto27atzH7Jli2bqFq1qvjss8+8fu/ChQvihhtukOcfY1PHoBFXsm/fPhQ0kgtexwtHzl/WVn3/pqZNaZFuOT/9EZ+3k5qaqk2aNEnr27dvmmXJkiVhbT8hhBASKDt27NC2bt0q/5LI8t1332mlS5fW7528Lffcc4+2adOmuDpNuA9S7TejePHi8rP27dtr8Xwc/oLj9fW858yZU5s2bZrmVMaMGaMfa3JyckTPQyxSsmTJNOcfYwtfMNrG29K6dWvt4sWLWqTYsGGDVrFiRds25c6d23YbM2fO1JKSkiy/X7ZsWW3nzp0hae/x48e1Vq1aebXj+vXr0333ypUrPp2DTJkyaYMHD7Zsw7Jly7Rs2bKZfrd79+627X/99dflem3bto3KPUK4YgIs/E3iihNXL4scqebR77P5K4lsPm4H0eJ7771XRpiPHTsm36tYsaKoU6dOCFtLCCGEkHhn4MCBok+fPhgpy/8bNmwomjdvLm6++WaRP39++VQeabY//fSTmD17tvjzzz/l0/GiRYuKkSNHCqeA43I7UJYULlw4jeID6qVvvvlGjB8/XqolHnzwQbFhwwapinAzUIdgcSrLly8Xf/zxh3ydM2dOee6/+uoref794csvv5QKH5VpgVmtV6xYId5//32pcpk+fbp48cUXxaeffirCzcaNG8Xtt98u69VmzZpVqpYaN24s1TmXL18WO3fuFPPmzRPLli2z3Mb69etFu3btZNthl1dffVVuE/9PnjxZjr127NghlUNr1qwRuXLlCri9p06dkrN/r127Vv4PNSmUS6VKlRIZMmSQtly6dKlUoFoBxRP69Fq1aomSJUtKNXv27NnFgQMHxJIlS+T5wX5wHHny5BHPPPNMmu/jnD3xxBPy+KBAe/vtt6UaCkqwMWPGiA8++EC2qV69eun2vWvXLvHuu+9KO7333nvCUYQsXEXiinhVMm04flrbPrN7OhXTn6u+1LTL5/3e3qFDh2RkeuXKlVLdRKw5efKkXEhkod0jD20eHWj36CiZ8CQXCzHniy++0O+XChYsqC1evNjWVFevXtUmTJigFStWTHv66afjyuZOUZ5YHUegdjcqmezUO3369NHX69y5s+Z2JVMs+3oo6NSpk94vYCyB1xkyZJBjC3981KpPWbdunZYxY0a5Dv4ePHjQ57YFYvcLFy5IhRH2h/7L7nfm0qVLlp/Vr19fb/OKFSvSfT506FD92GGHYHjsscfkdrJkySLVplZgjGdlD/TZdvzxxx9a3rx55X6uvfbadOv/9NNPutpp165dafbTuHFj+dlTTz1luu0mTZrIz9955x0tFMSSkok1mUhccT4lRWTU0tZNOla5iyhe4wkhMvmqY/qPggULiu7du8votVWhS/LfE0w+xYw8tDtt7hbo69EBT6exkPTgKXjXrl3la9RJgXIBT7ztwNPzRx55RCpZ8KSeNnePr7/88sv669WrV4dtP/GEU/sX1F5SdYmg2nnsscdkPaWUlBTx9ddfh2QfVapU0VVRqAcHVU047Q5FDRRGOI7//e9/okyZMpbrWhVzX7Vqla5yeuqpp0wzRFDjSKn8Pvzww4BnN0V/DPWgUpu2bNnScl1VP82qz7YD9ZJQawkcOXJEbNu2LV0tJlC3bl2pgjLa/aGHHkqzjhEo1KAKQ82qHj16CKfBIBOJKy6lpooMWkqa9zJlMO/oAFLh5s6dq0vczYAclHgHBfCwkMhCu0ce2jw60O7RATfY3m6y3QrSF1Qh30GDBsmCsr6CtIoWLVqYBlNRlBYL0inUYKNZs2YyDQsDIc9A1sqVK8Ubb7wh37/uuuvkAA9Br/Lly4tnn31WbN261ac2Ia0LQTOkhODeB/vDwAwFc33B19nlkAKCQROKBCMVBUVwsU98D+kxVmAQrQogqwE1Brt33nmnuPbaa+V2kI7Ss2dPmc7jCeyJ76IYs8JYVFnZPVwPzJD2gzQZcOnSJdt1MVjFOUUgAb6C8wH7IliBwbMv4Dhg5woVKsh9I8UHgYGnn35abNq0yev3Z8yYIUtHIK0ThZ2xDZyn+vXryyLmCBh4nhukBSlUsWLjYgyEIFVO2dwXf0Jx5U6dOsn30R48CEb6E/zfGwjCfPTRR6JmzZry2oBNq1evLlPOMOiHrVQb1XUXDDNnztRnpUaBbqSTISUMIGUuVOAaMga9w9WvIzimUnsbNWokH74Hwrfffqu/NvqKEQSxHn/8cfkaNly8eHFA+/rkk0/kX/Qx3bp1E+HEmNLnWdwdqXQAASZPu1933XVp1lEgxVoFlnAcjpx8I2SaKIfx559/ai+++KJWrlw5LXv27FImV716dSnxO3fuXMj2M2fOHO3ee+/VihQpomXOnFn+xf94P5zEa7rcnAOHtf0zOqVJlbv85zLTdSEXfPvtt1nQmxBCiCNg4e/IgfSK/Pnzy/ukXLlyaWfPng3JdpFipO6/vvzySz3dw7g0aNDAND3JakGKzqeffmq7X6R02BXi7devX0gKfw8bNkymjVjtJyEhQXvzzTdNv4u0IbXeokWLtEcffdRyOyjC7pk+5IutfE3zCiRdDj6C48N6SIOxYv78+bbnAkvXrl21lJQUy22MGzdOpgjZ+QTugc1Auk+bNm282qlatWqm58ZuMaZ++eNP06dPl+Mtq2OZPHmypS1OnTql1a5d27JNNWvWlEWf1f/wk2BBYX9sC+NEM//buHFj0Oly4P3339fXw2tv/uktndcKlfKF5ZNPPkmTQocUsP3793tNKzOmyuXIkcM2XQ9pdGp/SDP1F6TrZc2aVX7/gQce0N9HG/fu3SuvU7Q9FJw/f14rU6aM3FdiYqJ2+vTpNJ9/9NFH8rOGDRum++6Yf33CeC2BV199NWTFvo0wXS7GmTVrlizmiCdYiKoj2njixAn59AVPT/DUAU9pggFFwlBMDU+uEPX966+/ZKQdf/E/3kc0H+sRL0qmjOmjv5BqTpkyRX+ShCcrwZ4zQgghhLiDzZs36xODQNlhpcYIBhSERboHtj9x4kR5nwlVEdQsRoUGVH5Qe6AALe5v1q1bJwuMDxgwQCpnoELAk/wff/zRdD979+6VhcpPnz4tVQQoXIv9IKXriy++kOoXqE5QrDwYMDX5K6+8ItNfcB+NacOxHxwXUoiQOgNl+VtvvSVVJ3ZASTNhwgSptIHSC4V958yZo6cgKrWUEawLBQ/UXQr877lAdRIOkG6klPOtWrUyXQdpM1C44VxAvYBjgJIDqqFRo0ZJdRBAkWcUGjYD5wn+gHtcFAzu27ev9ItffvlFDB8+XPeJ1157TZ4DT/CeSvW69dZbpbJH+dWCBQvkNlBM2ajIQGFq2A5pScYi6J62VQWs/QHfe/jhh6VyCaoOKJdwLPBJKLxwLJ07d5bqLzOQUqbUTiiujOLS8DlkMiB1Fbb1LNYcDIcPH5bHDrB9xf333y/VdmDcuHEh2dfvv/+uv4bCK1wY1WJQT6HAN44HqjAoOKF2wyQHUCDt3r3ba3vxHav0NIAUMc/v+APSkZWiCO3F9YTyJ/D9YsWKyesICif4sT9phgr0Yeg34UtIg4M9wJNPPpmuUHnlypXlXxRrxzjeyOTJk+XfW265RX8PKYm4xnDt4q9jCWn4zAGgyJqaghDTkA4aNEhGW/FERRV4w4LCaJ6RTH/o3bu3vq0qVarIKS9XrVol/+J/9RkineEgXpVMk/f8pR2Z1j5t4e8Da9Kthwh2//795ZMCtSCazOLegYMnAqF6KkBo91iGvk67u0nJBLWEnWLCraB4t7pPeuONN0K2XaOSCcvjjz9ue28CBYGdgh4F82+++Wa5rVtvvdV0HTzpV/ubOHFius9xP1u5cuU07fJXybRlyxZdwYR7LrNjgp8pdRLusTH1uBFPtczAgQPTbQPbVcV0UVj48OHD6daxUtAE6utGpQhUSJs2bdIXKGRmzZqlPfnkk1LlgHVuu+02yynna9SooatzsC1PYJPy5cvrqonNmzen+fzy5cta4cKFdRuaTcuObIxChQrJdaAOOnLkiKnapFatWrZqk2PHjgVV+NtXJZNSekCRZHcdvvfee+k+//bbb/XPW7dubXp+33333TR+FaySyaguQlFoI+3atZPvw/52yh9flExQ5EARhHXy5MmjnTlzxmclk7++3qFDB30bo0aN0vdrtkDZuWDBAtN7J7UOlF7eUPuACs1fxo4dq+8LtlRKI7MF6sIhQ4b43Td7Lnfffbepj8LON9xwg1ynQoUK2rRp06QyrGPHjvp38b9C9V+hKvYdq0omBpmiUBF/+/bt+mwBSMGDDM8IbibwvmrHzp07tVATr0Gmccn7tRPfPJI2yHToN9N1ly9frgeYIMPFDzMJnN9++00uJLLQ7pGHNo8OtHuIbiBTUzTtwkmfl3PHD8rFn+/E3IJjDjEffPCBfp/04YcfWq6HAYYx6OC5eN57GAcyGDgG88DSbKB99OjRNJ8hpQwBDXzWvHlzy238+uuvQQWZEGRR97V2QbMTJ07oaV6jR4+2DDIh6GC1nXnz5unrmc0oZRXcwP11ICUvjIN4uwXBH/iKVYDJaONnnnnGcn+4f1XrdenSJc1nU6ZM0T+zGzgbgzMYuxhRA/IePXr4bYtwBZk2bNhgug58QAXV7rvvPsvZuSAQMAs4qm1UrVo1ZEEmJQaoW7duus9mz56t72fu3Ll+B5nQn+zZs0f7+uuv5Qxvap2PP/7YcltmQSZ/fb1ly5b6NlQaGvwDY1Ckpu3evVt75ZVX9HRQ9F1opxHYX20DwTZvFChQQK5bsWJFzV8QcPRsL3wBgg1cf2jLiBEjtNy5c+vroZ8MJMh0zTXXyOvOLmj4448/WqavPvfcc/p6U6dOle/deOONYRmXxlKQyVrH5kJ8rYg/ZswYKe1DRfzXX3/d72JdkEdD/gw+/vhjXVqpQOE+vI/9Yz0UrYNslghxMTVVZPRIlxOJ5m4MeeOhQ4dE8eLFRbVq1Th7XJCEI1WA0O6xCH2ddo9rLp0RYuZ/6VbeyC4cQMvxQmTNHdJNnjlzxqc+AWkaxuK8niQnJ1umuSBtyjP1whvnzp2TaUP4q1KzjPehSCO544479P+RioV0I7tCvAAFk1FAesuWLSLQUhMAKTZ2s/WiIDPshXQmpEShNIQZSJ+y2g7u6RR//PGHz21EqmA4OXDggPjss89kOh7s4ImxwDrGGVYg5Quzb2Gs4VmUXf0P2yB1x4o2bdrIIu8oOIzvII1RgQLFSP/BOUNKnSpWHi3gD0ivNAPHiTIlsK3nucYYaenSpfJ1kyZNZHF4q20gBRXpgKFIo12/fr1e8NuTu+++W7YD1yhSYdEub6iC4WagOD/SYu38BemOnsXM/fV19CcKpKFhn0hZVaAg/NChQ2XqLnwGBbsHDx6cJh3TWBDbavY5IyjuDi5cuOBXW83ai7Q4pBCrFE+cA6RIVqxYUTRo0ECWn0H6KSY6sOpXcN2qgvnwLaS+YfY3pBRjW0gTtEphxTlEwf4+ffrItLmLFy/KlECk7qIQv2rziy++mK7YN1KBkfKK76N0DtqMeANmLYxnOLtchCvi44bgu+++k6/hfLVr1zZdD+9jBg2A9e1mR3MTV1CTSXgGmcxnT0An0rp1azm7hN0ND/EN5Ff7M7MOCQ20e+ShzaMD7U5iCWPwxzigCSVWA2tPjh49Kgd2uC9Eu1BvBAMRDM6xqDpFal0jxlnGvNXLQaApEPbs2aPXy8EgzHPGMc9FzTCHB4G+1GzxJF++fKbBQG+gvk+wMwojaPhvJohcMHhVMxljMIsgBAI8eBDtCT5TA3BjjRYz1OxeCAYZp0RX24APWAVV1D4QnDF+R9G+fXu9rhX6XQSrJk2aJGcfjAZ259p4vj3PNQb9KkBhDDyagbFAKFC1lhAgUNPaG0EdIhUcwLjSH/80o2nTpmnqPoXL143rIujYq1cv0/UQrFQzpmHmR+P41LgNo89aoermeoot/G0veOedd0xn00PNMYwFAYK2drMu4pyiX8WC6xP9KkQfqFeFfgt9sF1gFz6GunGID1y8eFHWX1MBJoDAHWYIRP+AGTMBgqQIKqPOGnwHtkWtPNQZQ423eIZBJgNqylA8sbLrrPAjovj555/9/nFCNN5zO3b7QSQ1XNOtxhuXNSiZ/lGBKQ4fPWG5PoNLhBBCCPEXFLlVWBUcVsocY9ABixrEewOqAG/gKTcG4VANoGCst4eOnqqA48eP668LFChg+10UXg60EHIgYGIdK6Dqt8Ko0lAqrWiB+0wEQaBYWbRokSzijnMEJcK2bdtMzwXWtyuKDNRAHtvC5EOe2/B2Lo3bMPoAwEAZA2a0AUonZGhAOXb99dfLoBPa7o9CLFjszrXxfHuea6Nd7AJuvnzuC9g/CvSr4I+xjzCiCvfDv7/55huv20VBf1U4HVk12IcaA0JF88ADD4RdbGAMqjds2NBSiQSfUQES+JXRT4zbOHv2rNd9quA9CmAH016cWxVQNQPqMgUCOIE8DFAF73Gt/PDDD35vY9u2bTIzCTEGTCwGEKCGQg3Btueff17+zsCe06ZN04NaiBvEKwwyRbgi/tatW023E+r9OFrJ5JEuN++HhbY3KiQ0qJtnEllo98hDm0cH2p3EEmrGIKDSY0KN2ZN3I1ADQC0BpQyesiPVAk++Dx48KJ+Uq2vGONuT3e90uB68GQf/SBcxm9HNbMGAzUl9DM6nSoeBTb766quwnYdgtzFo0CCpZMJfpFeqQA98CYNgjEFGjhwZdDudBNIOlVBg5syZlko9pUIDVj5gRCkTsUBt+NBDD8lMGZVVA5ULSq2E09cRYDR77W1dYwAe6iIVePOmikOAUAWZvO3PWxsw810g7fUH42yRdoFDK7t369ZNzliHFETVXghVcL0hSIZURHVNQ3kFFRXWx6yc8QprMv0LfqyVxNibs+LJEyKRuDgge/MH40Xnz0URzH7MwA1KPFL2+BqRKGuT/cepM+dk1Bdy0nDn27uZjRs3prvxJrS7E6Gv0+5xTZZc/9Qo8pHz/ypfsgeQshBTxxxiMODDgAkBHtTrxMMsb4qLUPPjjz/qSoH/+7//Ex07djRdz1OpYqWW+vvvv20HdPg8EIyKDpVyEosolVc4z6PxAbFnao5K+4JPoeaL3QNtlUqIgafxHKpt+HKu1DaM6YVGULMUagksGNBC5YEUqFGjRslxUZcuXWTAxE4lEk2MdvEWPAg0uGDEl4CRJwgK7927VxQrVsyv7+G8o24P1HH4fv/+/aVC0upcBuvrqMfmqzrQ+LmnD5cvX172lwhg2vm4UeWH+mP+Eqr2+opRCYf0YH/sPmXKFHke0TeoIDRAOh1A9pSqT6VACh1qTKl14hEGmf7FmDPri2xPBZl8kQMGuh9joUl/9+NPVBgXujHghf8h3UNHoYqSQa6H4palSpXS241oPjptfFfdYOCHEwEuXIwoVgdgJ3Q2SUlJMloP8GMGVRcuKuMPMgpOolOCNFFFdPFdbAO1CBJT0+f4pmgJ8nNc9GgjChoqGTFkwEg1xA+R6uDxw7l9+3ZpX1VjCFFnDCzR+Rg7Ljtb4LvqHPlqC5xHRK19sQVy6NExGoM6yM3HjS5sofKR8eODJwIoLJo7d2795gM3F0ZbIEcYNsIPlPIPdIaQ3xttAfkmbow8bQFb4jPjjwZufuHTRlsgvRMBW+xD/Rjif7yPtqBNAN/D99FmVRQVT22h2sOxqZpkAO3BOTLWr0C70X7YTHXOOD4cJ2wLGwPYAfbAOVA/ErAX7IZzpXxf2QL+DT+XvpWSIs8Dzj18QIHzhfOGG2n1JBrnFee3TJky+o+LsgV8T90MKVsgLUFJ2eFP8CukXeCmz2gLfGa8qVABEKMt4M/wa/xQK4mznS1Q3FAV+lS2wP94H8DH4GvB2qJs2bJ6rj2uBVwTRlvgmsG1480WuAZxLWJb2KbRFugnjEV3zWyBPgD+i8KVSmKNQDtSPIy2wEANAX34glpP9V/4H98HuAbQV2H7xpsj1X+hPSrgrfovoy2wD+wLx4fjBGgL2gQ7qJQVu/4LPgZfMxb7hf2Ng7tY7svN+i/VryhbsC8378vRD+N6RB+hfBzvwTfgd9K2WXPLPhO+DPsba17gPXyG9+STdy2z3N751ER5HtV1jO3jHGIf6tzgfGPB/2rf+C78DN9TfXG69vyLUh0bb8CxHtbHeuq6wfawXWN71L7hy8qf0T60M+OVf9pptW9fbWFsD/aNwr6orYNrBgWdUUjZ0xbG9ihbGAc12BaO29MW6jiNeNrCWIQbKTMKz32rGkfKJgq0w9hnIoiA69zKFr/++qtte4z7MN4L4Pcev+Xos1QZiUD8wgjOK9638gvjMWI943kwUxJgPSxGBZA/fqHAMRjb6HmNGNMV1blQfqr6SLxGEWr012a2wD6RNgXQz6tjxHr4Dgqmoy/H7zzOp9k1i/YrBR5+p4z9hdm5wWvUoUFACalaUFNgG6jVhD5b7y8M9jDaQp0boy3MBv/KFkYVH/bjeY0Yz40RdW6wHn7H8Bc+BXthf1b9l901YuWnRr/APauq3Yt0MRXwxX7QHvie8lNcm7gWnnvuOfkZCoD37NkzXX+hwPtGm6prBP0klIHYF7b39ttvp6nTY+anxv5HvedLX37bbbfp28VvtVl71LkxKifVfSPAeghKIsiE33qk+sKnzPryBQsWpAmo6H254bfFri/H7yHui3APgfsk1YeY9eW4n1Xg/grrmtnCrC9XfoH7doVnDSnj75rnvq9evSrTT8Hw4cPlfnA82C7OKcC9mNq/8k/1G6nWUe3x5XcN28L7xuvB7r7cm6I2UCj7iHBFfH/2Y/wRDaTyvhO57BEXvSoyiNIVqsmcVs4IFV4w2PSlfgQJLfghNQb7SPjBgIk2jzywOe0eeXDD6e8suW4BT53VDT1m/4l0fQzjYNiq+DgGMgiAWYHBoxpEqKLFZmAQbizp4A/YfuPGjeVr1CuJdokHY9DGGMhDO8Pt68YZzDwf+Bpn/bNLFUSwT52LRo0apflM/a+CF1YgpUcNUO1mLzND1dwBGIT6YttogCCBCo4gaGGlVvJmK1+APVUwATOGoTAzFqSzopCz+l+9h7pX6mFgMPvGZFPqIdPo0aNtVYueY0h/CmrjwZFSrEF9pXzHEwTb1AyHePCmHhwbZ8z05uPosxC8BHjQ5q9/KtQMjngIYDcR14wZM9LMPB4IU6dO1V/bKTVhc6PdBwwYIANUxmLfCiUMMAawPDOS1EPiuEQjksOHD+Oxh1zatWvn1SoFChSQ61asWNEvCw4dOlTfz9y5c23XnTNnjr7uu+++69d+9u3bZ7usWrVK3zb+jxcmLf9a06a00JdvFn4Y7SYRQgghEWHHjh3a1q1b5V8SGUaPHq3fLxUuXFhbtmyZ7fqpqalaq1at9O8kJyen+Rz/q8/GjBlju61p06bp6w4ePNh0nZ49e+rrWG2zdevW+udTpkxJ9/mZM2e0KlWqpNmOGcWLF5eftW/fPt1nGzdu1DJkyCA/L1++vO295dWrV7UJEyakW2fx4sX6/vHaDrVe37590302btw4/fMtW7ZowYLjtTqfRo4fP66VLVtWX/fbb79Nt0716tXlZxkzZtQWLlyY7vOTJ09qlSpVkuskJiZqmzZtSvP5pUuXpB/i86SkJGl3T/bu3auvkz17du3IkSNpPh8/frx25coVy+OYOnWqpd8tXbpU/+z777/X7MC5CdSfzOyP9e2uEfh5SkpKunUwhvJ2jXijYcOGuj3PnTvn03feeustfZ8rV660tI03X//kk0/0dfv06WPrn962ZQf6BrWdDh06mK7TsWNHfZ1hw4aZrlO/fn3dx1esWGE7Fja7fj37Aiv/2LNnj5Y1a1a5Dq6ZU6dOpVsHvq62c88996T7fMaMGdqBAwc0O+DzOXPm1I9p27Ztmi9s2bJFy5Qpk5YjRw7T/lBdS+g3d+/enaZ/LFWqlPxswIABWrjvEdC2cMQEmC4X4Yr4/uzH+NTK3/14q/cUr9Qoe7tYmLe4LAB+ImMekSG7+cwOhBBCCCHB0qlTJ/mkGTVRkFqK2cOgSMETe6TGIp0ZaQlIa4SKBTVtVJoblDO+qOPtZkWCmhUppG+88YZMC7nvvvtkii9SWqBgQq0PpJvYzXaMNA0oPaBCwCxiUCog/Q5PyZF2PGTIEJkujim4jWlF/gBbIJWnR48eUoWDp/2dO3eWtkKKCpT8aD9SvaAKQUosUuHDcb9qVCugPa+//rpUXKhUFmN6rr/ATsb7d8S7kJoO9RGmO0f6DoCftGzZMt33cc6QUoTUoGbNmsmUKvgS1PhIb8O5UHW4Xn755XSqCfgTFC34DhQcOPeYVh4qCfjbihUr5DbUjH84Jyol3Dj7GbaNlDjYSqWdIT0XfjJixAh97IF6p0agdlEpaihiDGUYUr9VqhjSpwKZkj5QcAxQ0UFBN336dKlswkxdKOMAZdOECRPkUrNmTT0F0d+i6TinuGbUrHK+1jmC0gY2UvWcjAXB/QHZGpjdDH0MfAznzjieDBVQYEHtOGfOHDF27Fh5jUK1BSUV0vxRqwufKT9AMWszkGIMv0QWDs4Nan5BrYT/J0+eLP0XIHVLpZIFAtoFpRBSEdGX4Bz36tVLKshwbcAflC+jr8Psbp4gBbJdu3ay0DauIaipoa6CSg8pZrNmzZJ9ukoNRfqisZyHHd3+Lfb91ltvmfZzt956q1SD4XqHX+G6hboJ7cS+0Uehv45bQhaucgD58+eXUbzKlSvbrocnFSri16ZNG7/2MWvWLP2777//vu267733ns9PC/wlXFHLSPH777/LhdDmToe+Tpu7Bfp6dJRM58+flwuxZ/r06VrJkiXTKCKsloSEBK1JkybpVCj+KpnAvHnz9Kf1ZgsUFps3b/a6TSgDcuXKZbkdKCRCoTyB8gtqD282ypw5s7Zz586wKJlA27ZtLfdtp0Yyw6gU8WW5/fbbtWPHjllub/78+VKFZLeNrl27mqpyFGPHjtWyZMli+X2oI95++21b29ktuXPntsy48FTPGRfjeYuEkgmcOHFCq1mzpmWboNJbs2aN/v/kyZM1fxg4cKD+3UmTJvn1Xaj68D2MMaFCC0TJ5Kn+8TyvZkqmQPt1qBobN25s6xs1atTQDh48aLudmTNn2vo4FH+e17+/SiZF7969ZZ9rtS9kH5kpqvy5trNly6YNHz7cq/2U3SdOnCi/V65cuTTn3ZMlS5ZYXsdDhgzR/CWWlEysyWRAFbNVFfGtCKYivrFgrnE7od6P00GEOdq54G6DNqfd3QJ9nXZ3E+Ge1t0pQEGEovt4qg1lAe7noBDB02ZVDB/KlcGDB8un0HPnzg3JLGtQM0FdhCLkKMIP5QhqJKI4MxQBUDL5UpOyYcOGUmEFZQKUJ1DEQGGEJ/jz5s2TSq1QKb/wZB7bg5pB2QhthHIB6o6RI0dKdZiacCQcQL2CacGhboA6IJyzD0PxgwLdUB1A+YBzYjcLGNQdGGtA4YHCyPAf1NCBMgPKIRROxsxidm3GTGMYJ7zwwgtyjAD7QkEEVRLOAVRRr776qul3MYnHO++8I9VQ8GNM+AAVFBQctWvXlvXH4OtNmjQx/T4UF1BkQa2F4wxX4WBfQbuXL18uFSCYqQvnA0of2BbXI9RdxjaqWji+omoq4RzhegmkbhBqW33//fciUHDdKp/CcZoVyw9Fvw7boT9AzSScf0xGovqcu+66S3z55ZdSjagma7ECvgWVJJSEuO6h/sJ5gloSvgf/DNX1j3MMJScUelAp4jzhHNeoUUOqiKA+rFOnjul30UfgmDp06CDbhjpqqp4VVHm4VuHv6NONM8NZAZtDRfXyyy/L/3Ed26lZ0Y/Dd6FqRD8AlSDaMXHiRKnKimcSEGmKdiNiBXT2cFSwcuVKS1kjnE113PPnz9eLHfoCzA3JHCTXmGXCrjgifjTwAwInh0zRX3mnHSgopgoSYtvxll6nZmJgsVLa3OnQ12lzt0Bf9w5myVGzehlnFwwGlQYQzkE4oc1jAfq6e22OoCOCEAABPjVzrlOJFbu7jWjbfWcA9wjhignQ8wzce++9PlXER15toBXxEShq1aqVfI0AEoJZZuB9pWTC+qEMMDkBsylACW3uROjrtLlboK9HB9wMcyBCm7sB+rp7ba5mM4MiB3VwnE6s2N1t0O7/Qe8zAEktpJ/giy++kHJAs+KJSn0EiapnoGPJkiUyIIQF0jszunfvrss2UfAPhdCM4H+8DxCJxPqEEEIIIYQQQv4DqZeeYykjn3/+uV6w+vHHH+eDe0IiAINMJhXxkYcJqRnS4JA+B1XR4sWLxdNPPy0r2AdbER/fxUwQAHn2yFmfMmWKfI2/+F/N7oH1QiWJdxLJyclyIbS506Gv0+Zugb4eHViDjDZ3C/R1Z9ocM+KhzhhmlcOMYmvXrhWrV6+WM5mhlhpqVAHUILOqU+U06Ou0e7QJbP5OB4MpGRHoQYFFFO5CnSazIBGKtwUzfeSgQYPk9KIoNobiZw8++KDllJUkPTg3JLLQ5tGBdqfN3QJ9PTqkpKREac/uhTan3d1CpHz9yJEj4uOPP5aLGYUKFZJjNxQ5dwPsY2j3aMMgk01FfKia0CGhIBYqw6MKfps2bUS3bt1klfxgczaRkodZBzA7CCLuR48elbNwoBo+VFNNmzYNah9OJpwzkhDaPJagr9PmboG+Hh0wkw6hzd0Afd2ZNm/evLkYMWKEnIxp69atMuB05swZWTsXkyhhXPfMM88EJQ6IN+jrtHu04exyLiXeZ5cjhBBC3EY4ZpcjhBBCSPyzk7PLEUIIIYQQQgghhBAnwcLfJC45cOCAXAht7nTo67S5W6CvR4fLly/LhdDmToe+Tpu7Bfo67R5tGGQicQnyrbEQ2tzp0Ndpc7dAX48OSL/DQmhzp0Nfp83dAn2ddo82LPxN4hLWkKLN3QJ9nTZ3C/T16ICJTQht7gbo67S5W6Cv0+7RhkEmEpe4ZQrSWII2p93dAn2ddncTKCJOaHM3QF+nzd0CfZ12jzZMlyOEEEIIIYQQQgghQcMgE4lLjh07JhdCmzsd+jpt7hbo697JkCGD/JuSkiI0TQuJ3Vm7I/LQ5tGBdqfN3QJ93X121zRN3huAxMToh3iokSZxyf79++VfprXQ5k6Hvk6buwX6um91Ni5duiRvJs+fPy9y5MgRtN3VzHJMr4gctHl0oN1pc7dAX3ef3S/9e28QKzW5GGQiccm1114b7Sa4DtqcdncL9HXaPVZJSkoSZ86cka+PHz8usmfPLhISEoLaJoNLkYc2jw60O23uFujr7rP76dOn9deheAAVLAwykbikcOHC0W6C66DNaXe3QF+n3WOVnDlzyqASnlaePXtWqr/y5csXVLApFp54ug3anHZ3C/R12t1NRMPfU1JSxMmTJ9OUkcG9QrRhkIkQQgghJA5AnYUiRYqIv/76Sw80YUGASdVrIoQQQojz0Qx1mIxq/FgI7jLIROIS3FTHSqTWLdDmtLtboK/T7rFMrly50gSaAP4GWmw0NTU1ZgqFugXanHZ3C/R12t1NRNvfc+fOHTP1ihlkInHJ7t275d/KlStHuymugTan3d0CfZ12j4dAU9myZWVAFHUYUGzU82mmr6gaT9gmiQy0eXSg3Wlzt0Bfd4/dM2TIIFPm8+TJI7JmzSpiBQaZSNwWPyW0uRugr9PmboG+7h94UgqbBWu35ORk+feGG24IajuENo916Ou0uVugr9Pu0SZBU1pr4ipQLPT666+Xr/ft2yeKFi0a7SYRQgghhBBCCCEkjmMCTMAnhBBCCCGEEEIIIUHDIBOJS65cuSIXQps7Hfo6be4W6Ou0u1ugr9PuboG+Tru7Cfr7fzDIROKSrVu3yoXQ5k6Hvk6buwX6Ou3uFujrtLtboK/T7m6C/v4fLPxN4pIsWbJEuwmugzan3d0CfZ12dxP0d9rcLdDXaXO3QF+n3aMNC3+7FBb+JoQQQgghhBBC3Ml+Fv4mhBBCCCGEEEIIIbEKazIRQgghhBBCCCGEkKBhTSaXcvXqVf31wYMHRbyxbds2+ffGG2+MdlNcA21Ou7sF+jrt7ibo77S5W6Cv0+Zugb5Ou/uKMQ5gjA8EC4NMLuXIkSP665o1a0a1LYQQQgghhBBCCIlefKBEiRIh2RbT5QghhBBCCCGEEEJI0HB2OZdy8eJFsWnTJvn62muvFRkzZowrWZ9SX61atUoUKlQo2k1yPLQ57e4W6Ou0u5ugv9PmboG+Tpu7Bfo67e4PSJFTGU6VKlUSWbNmFaEgfiILJKTAgWrUqBH3VkWAqWjRotFuhqugzWl3t0Bfp93dBP2dNncL9HXa3C3Q12l3XwhVipwRpssRQgghhBBCCCGEkKBhkIkQQgghhBBCCCGEBA2DTIQQQgghhBBCCCEkaBhkIoQQQgghhBBCCCFBwyATIYQQQgghhBBCCAkaBpkIIYQQQgghhBBCSNAwyEQIIYQQQgghhBBCgiZB0zQt+M0QQgghhBBCCCGEEDdDJRMhhBBCCCGEEEIICRoGmQghhBBCCCGEEEJI0DDIRAghhBBCCCGEEEKChkEmQgghhBBCCCGEEBI0DDIRQgghhBBCCCGEkKBhkIkQQgghhBBCCCGEBA2DTIQQQgghhBBCCCEkaBhkIoQQQgghhBBCCCFBwyATIYQQQgghhBBCCAkaBpkIIYQQQgghhBBCSNAwyESiwp49e8RLL70kbrzxRpEjRw6RL18+UaNGDTFs2DBx/vz5kO1n7ty54r777hNFixYVWbJkkX/xP953I+G0e2pqqti6dasYO3as6NKli9wubJ6QkCCXJUuWCLcSTrvj+9OnTxfPPvus3GbevHlFpkyZRP78+UWdOnVEv379xKFDh4TbCKfNf//9d/HJJ5+I9u3bi6pVq8p+JWvWrHI/JUuWFO3atRPfffed0DRNuI1I9e1GsF3YXfU1JUqUEG4inDZHf67s6m3Bum4ikr6+cOFC0aFDB1G6dGm5r9y5c4uyZcuKBx54QIwYMUKcPXtWuIVw2f3PP//02dfd1tdEwtdh/169eolq1aqJPHnyyPsY7Kdu3bpiwIAB4vDhw8JtRMLuycnJokePHqJixYoiV65ccj9lypSR9/FbtmwRbgC+NXv2bNGnTx/RtGlTcc011+jXOPrdcDBp0iTRuHFjcd1118n7x+LFi4tHH31U/PLLL8IxaIREmJkzZ2pJSUkYfZkuZcuW1Xbu3BnUPlJSUrSnnnrKch9YOnbsKNdzC+G2+9ixY23tvXjxYs2NhNPuGzZs0HLmzGlrdyzY/+TJkzW3EG5ff+SRR7zaHEuDBg20o0ePam4hEn27GS+99FKa/RQvXlxzC+G2+ZgxY3zydSxY1y1EytePHz+utWrVyqvt169fr7mBcNo9OTnZZ19XS+PGjTWnEwlf/+qrr7Rs2bLZ2jpfvnzaDz/8oLmFSNh91KhRWubMmS33gc8+/vhjzenY+V379u1Duq/z589rzZo1s9xfYmKi1q9fP80JMMhEIsq6dev0HxIMjgcNGqStWLFCW7RokdapU6c0nefp06cD3k/v3r31bVWpUkWbNGmStmrVKvkX/6vPXn31Vc0NRMLuxsFIpkyZtKpVq2qVKlVydZAp3HZftmyZvo169eppgwcP1hYsWCD3O3/+fO3pp5+WP1j4PEOGDNqcOXM0pxMJX8dNR61atbQXX3xR+v3cuXO1NWvWSNvjhqxixYr6furUqeOKYHak+naz/cK3s2bNquXKlctVQaZI9+voUzZt2mS5nDhxQnMDkfL1kydPatWqVdO3d99992lff/21tnLlSm316tXa9OnTtRdeeEErWrSoK4JM4bb75cuXbf1bLQ8//LC+L5wPJxMJX1++fLl+n4K/TzzxhPbtt9/Ke/ZvvvlGa9Gihb4ftGX37t2a04mE3TEeUtvJnTu3NmDAAHku0LeMHj1aK126tPwsISFBmzJliuZkjEGeYsWKyeBxuIJMDz74oL7t22+/Xff1L774QitVqpT+GQKA8Q6DTCSi1K9fX148GTNmlB2mJ0OHDtUvsL59+wa0j+3bt8vtYxvVq1eXUWMj586dk++rdoTjybob7f7rr79qH330kfbLL79oFy5ckO9hW24OMoXb7j///LPWtm1bbcuWLZbr4AcMNwnYB37AUlNTNScTCV+/cuWK7edXr17VWrdure/nu+++05xOJOxuZmc1CMcNMoJLbgoyRcLmxiATlB4kcr7+2GOPyW1kyZLFtg9Bn+6tT3IC0ehjzPqcwoULy30gqO15f+k0ImHze+65R9/Gp59+aroOHuiodbp27ao5nXDbHeOgAgUK6EEsBE89OXXqlP6guGDBgtqZM2c0p9KnTx9t1qxZ2qFDh9KpGkMZZFq0aJG+XQRP0Z8YOXLkiAxy4fM8efJIJWs8wyATiRgIQqiLCwoLM/DE/6abbtIvMDxZ8pdnn31W3w8CHmbgfbVOly5dNCcTKbub4eYgUzTt7sn999+vt2Xt2rWaU4klmxv7mJdffllzMtGy+/Dhw+X2ypUrp126dMlVQaZI2ZxBpujY3ahSHTZsmN/fdxqx0rfPmzdPbwcUN04mUjbPmzev/H7+/PltVX2qLVDJO5lI2H3q1Kn6Pl5//XXL9aDOVuu5IW1OEa4gU9OmTfXg4b59+7wqzBBMjGdY+JtEjG+//VZ//cQTT5iuk5iYKB5//HH5+uTJk2Lx4sV+7QOBUxTcBSiUV7t2bdP18H65cuXka6cX6I2E3Uls2/3222/XX+/evVs4lViyOQpoKi5evCicTDTsjoKoKNIJRo4cKTJnzizcRCz5upuIlN0xsQBAge9u3boJtxMr/v7VV1/przHxg5OJlM0vX74s/95www2W6+A6QDFm4/pOJRJ2X7Nmjf4aha6taNiwoSxKDb755hu/9kHScubMGbFo0SL5ulGjRnLCGDNat24tkpKS5OsZM2aIeIZBJhIxli9fLv9i5gLMHmFFgwYN9Nc///yz37MkHDhwIN127Pbz119/yVktnEok7E5i2+6XLl3SX2fIkEE4lViy+eTJk/XXCHg7mWjYHTPfnDt3Tjz22GPyRthtxJKvu4lI2B2DaPWw7K677tIHeSkpKWLfvn3yfsXpgetY9HcMElUAALPK3XbbbcLJRMrm6oEv7t+tOH36tDh69Gia9Z1KJOx+7Ngx/XXBggUt18uYMaOc0Q5g1rOrV6/6tR/yH6tXr9YDpHbjUzwwUwIJfOfKlSsiXmGQiUQMTPsNMAUvOi4rjAMy9R1f2bp1q+l2Qr2feCISdiexbfelS5fqr2+66SbhVKJtc9wE40bsqaeeEoMGDZLv4enrI488IpxMpO2OAN6cOXNE3rx5xfDhw4UbiYav46l64cKF5U0w/Bo3wm+88YZ8UOMWImH3DRs26EGkSpUqyQF29+7dpc2LFSsmFR9QdiAAtWTJEuEGot23KyWHmjYewW1Mb+5kImXzZ555Rg98QJVqxltvvZVufacSCbvnzJlTf33q1CnL9ZDlgf4HIECya9cuv/ZDghufIqi3c+dOEa8wyEQiAm6Y1FMIK4mgAgMHRPABntr5w/79+/XX3vZz/fXX66/93U+8ECm7k9i1OwYs33//vT5gcWqQKVo2h4oGgw0s1157rahbt6748ssv5c0ZBoWQO+fJk0c4lUjb/cSJE3LADYYMGSJt7jai5esIaBw8eFA+WcWA8Ndff5XBVAyGRo0aJZxOpOxuHIykpqaK6tWriw8//FCmxSgw4Fu4cKG44447xDvvvCOcTKz8nhpT5VSqklOJpM2ffPJJ3Z5du3YVnTp1ErNmzZIpXdOnTxf33XefePfdd+Xnr7/+ukw1ciqRsrvxPtD4ENKT9evXi7Nnz+r/792716/9EHePTxlkIhEBMmOzCLoVquM0dm6h3o/aRyD7iRciZXcSm3ZHmlzHjh1lmgVQ6honEis2Vzz//PPy6eKtt94qnEyk7f7KK6+Iv//+W9SpU0cORtxIpG1esmRJ8fLLL4tp06aJVatWyQVqsjZt2sjgKgZGUBeMHj1aOJlI2f348eP6awSQ8CS7SZMm0u6w9eHDh8WIESOkmgnB7N69e+vpdU4kFvp2DK7VYBwPEhBYdTKRtDlS+MeNGyemTp0qKleuLD7//HPRsmVLUaNGDXH//ffLFEXUlVywYIEYOHCgcDKRsjvqMCmV1HvvvacHtowgwI2gnlX7iH+cceH4lEEmEhGM9QN8KdCaJUsW+ffChQth24/aRyD7iRciZXcSm3ZHwVhV4BFFSlu0aOHYUxUtm48ZM0Zs2rRJbNy4Ufz000/yhq1MmTKycC/SixAQcTKRtDvsC5UYbo6RVuH0dJVYsDlUBEiRGDZsmCxIioEflnbt2on//e9/YubMmSJTpkxy3R49eohDhw4JpxIpu6PWmHGfSIubPXu2tDu2CfUegnp4DwWAwauvvurYCUxi4fd0woQJun2drmKKhs3xQAZKMfyWmoE09C+++MLxqbmRsjuUMirtEDatV6+eDFQjNQ5tWLlypWjWrJmYN29emnZwbBA4F104PmWQiUQEVbjS15khVKHibNmyhW0/xmLI/u4nXoiU3Uns2X3w4MHyiSDA4OTTTz919GmKls1RH6VixYoyFbF+/fpyoI2AE27Q1MDQKJN2GpGyO77XuXNnOdB74YUXxM033yzcSiR9HWoZu2Be8+bN9Vn+UK8GA0GnEo37GKVmMpuwASpJBP7UIN1qgB7vxMLv6fjx4/XBHwKsTieSNl+2bJlUpiJFrkiRItLWCFZjv0gVwr1L9uzZpXqyZs2aYsuWLcKpRNLuSEHEfQrYsWOHuPfee2V/j23hfMyfP1+m6qLGpNmsucQ/srpwfMogE4kIxo7JF+mfepLni1w00P0Ynxb6u594IVJ2J7Fld9RHee211/QCgiiSbJTfOpFo29zzZgIKJ9wY4ya5Z8+ewqlEyu5I9dy+fbt8Atu/f3/hZmLJ1wGCfyoQZVffI96Jxn0MVEtVqlSxXPfuu+/WX2MmIicSbX9HmuK2bdvka6RxObnGXqRtjsH0Qw89JItPX3fddVJB8+ijj8oZz6CQRO0azCQKFSt+VzF7NFTZTiWSvo6AKQJ7n332mbjlllvSPEwoUKCATJVDANCokEQdKBIYuVw4PrUuW09ICMGPQ/78+WWxUG9P9VHYVV1gxuJnvmAspuZtP8Ziav7uJ16IlN1J7Nh90qRJ8qYMFC9eXNYxQAFqpxNrvg6bQ4IO+0OGjmLJKq3ISUTK7qq4MYq+4sbYDLVt/MVTb3WzjOLITiLWfB02RntQ18PJ6SyRsrtxfX8KxB45ckQ4kWj7u5sKfkfa5kjHUn3Gc889JwNNZlSoUEEGn6DOXrt2rZzQBPWbnEakfR3ptqjbiQU1g5Dej4djOA8qFdc4u1n58uUD2g8R6canUIk5fXxKJROJGKpzQn0HTMtohXpiBPydCcvYARq3E+r9xBORsDuJDbujPgpuglGwsVChQmLRokVeBylOItZ8Xc18hjQis8KaTiESdlfycijE8OTbbFE2xl/13oABA4QTiTVfd0t9rEjYHQNqhZq0wQrj53bTncc70fJ3PBwwBqxRgN0tRMLmSPNUVK1a1XbdatWqme7TaUTL16G0QUH7woUL6wEm9C+//fabPgGEGx5YhovyAYxP0aejxme8wiATiRhqliVE3vEkwgqj3B5KAH/ro6CD9NyOGZDfAuSAlyhRQjiVSNidRN/uCCi1bdtW3pTgSRgUNKVKlXLVqYk1XzeqOuJZ8hxvdncDsWRzKGhUgE/9/jqVSNgdCtRixYrJ13/++adtQe/du3frr3Ev41Si5e/ff/+9VJWAhx9+2NGBvGjY3GhPu4CKCviZfc9pxFLfvnjxYt3/3VCLLJzUqFFDL/htNz7FAzWkjarvxLMCnkEmEjFQVE6Bp9FmQIGhpMnIe8e0pf4+TW3VqpUeCVYXqid4X0WKsb6Tn8JGwu4kunZfsWKF9GPUN0DhRhRsND4Ndwux5OuQQ2NGHDVodHLBzEjYHQNtbwvsDPBXvbdkyRLhRGLJ10ePHq0HQho0aCCcTKTsjqnbAWZ7wgMEK6ZPn55ucOpEouXvxlQ5J9cCipbN8WBYgfo/dhgH5sbvOY1Y6dvRp/fr10++RqCjU6dOId+Hm8iVK5e488475euFCxdapkOiT0e/r2Z3jWs0QiJI/fr1cSeqZcyYUVuxYkW6z4cOHSo/x9K3b990ny9evFj/vH379qb72L59u5YhQwa5TvXq1bXz58+n+Rz/433Vjh07dmhOJxJ2NwPbUt/DNtxGJOy+fv16LU+ePHKdHDlyaMuXL9fcTLhtjv5l0aJFtm04efKk3g4sb775puZ0otXHGClevLj8Pv66gXDbPDk5WVu3bp1tG2bNmqVlzpxZbiNbtmza/v37NacTCV/fs2ePljVrVrlOpUqVtFOnTqVbZ/z48fp27rnnHs3pRLqPOXbsmO7bOAduJNw2P3HihJY9e3b5ea5cubSNGzeatmPOnDlaYmKiXK9IkSJaSkqK5mQi4etHjx7VLl68aPrZ1atXtS5duujb6NOnj+Ym8Nvn7/3ImDFjbM8JwL2jWqdly5bSzkaOHDmiFStWTH6O+/rjx49r8Yxz9YYkJvnwww+lrPPChQuicePGcgYsRODxP/Le8UQUlC1bVrz00ksB7QPffeWVV8SQIUPEmjVr5P569eolU4cgLUcB2fXr18t1sV4857vGkt3B2LFj0/yvcrlVgUdI/xXI/Xbyk9dI2B3+jNmFTp48Kf8fOHCgVDJt3rzZ8juoK4HFqYTb5pjdBk+jUHQUTxxRJwJFMiHfx7TLP//8s5zGHa9BxYoVRe/evYXTiVQfQyJnc/TX2B6ms27RooX0edV3/PHHH+Kbb76Ri1IxYUpsJ6dsRdLXkS6HemKYmXLTpk1y6nbcx9x8883yKTeedo8YMUKum5SUJN5//33hdCLdx2Cbqhac21RMkbI5VDj4fezTp48sPF23bl1ZAPyuu+6SM5mhEDUmzsAMaFDvANzbq5pBTiUSvo5UuG7duokHH3xQKlDR51y8eFFs3LhRbl/dvzdt2lTONOdkli9fLmtgKYw1NPG+59imQ4cOAe3njjvukPbGOUQNVfh59+7dZZo5+nnMort37165LsaqcT+bX7SjXMR9zJw5U0tKStKjuZ5L2bJltZ07d5p+19cnUXjK8eSTT1ruA8tTTz3l+Kchkba7nb09l0DVCvFGOO1ufHLi62L1hMVJhNPmxs+9LVAXHD58WHMLkehj7HCbkilWfB1KhFGjRmluIlK+3rt3by0hIcFyPwUKFDBVOjiVSPYxtWrVkutCGX/w4EHNrYTb5qmpqVr37t1t/RxLpkyZtGHDhmluIdx2nzp1qq29cT4wjrJSOzkJ2Mif++hAlUwqm6ZZs2aW24Zizyn36VQykYiDJ6KIlCNSj6KKyEtFMTQoW9q0aSMj65hCMxjwlANqAtQ1QER+9erVMjKNmRFQSO3pp5+W0Xk3EQm7E9rd6b6Op4uoeYWceiglsW08bcUMclAUoFZE7dq15cxmbituzT7GWTaHSm/ChAmythh8/eDBg/J3FAV68YQVdd+g6sP0105WR0bT1wcPHixatmwpVUuoWYNzgGnOoWDA+1B9QL3qFiJld0zb/uuvv8rXUBtArepWwm1z1ESFEu/RRx8Vn3/+uVSV7NmzR/6mYsIM7AdKG9y3w+/dQrjtXr9+fTFs2DDx448/yhq1uI/B2AmqGqimnnjiCVGrVq2QHhMRIlu2bPJ8Tpw4USqkNmzYILMRChYsKM8JzivUw04gAZGmaDeCEEIIIYQQQgghhMQ3zk5qJYQQQgghhBBCCCERgUEmQgghhBBCCCGEEBI0DDIRQgghhBBCCCGEkKBhkIkQQgghhBBCCCGEBA2DTIQQQgghhBBCCCEkaBhkIoQQQgghhBBCCCFBwyATIYQQQgghhBBCCAkaBpkIIYQQQgghhBBCSNAwyEQIIYQQQgghhBBCgoZBJkIIIYQQQgghhBASNAwyEUIIIYQQQgghhJCgYZCJEEIIIYQQQgghhAQNg0yEEEIIIYQQQgghJGgYZCKEEEIIIYQQQgghQcMgEyGEEEIIIYQQQggJGgaZCCGEEEIIIYQQQkjQMMhECCGEkKjQr18/kZCQIBe8JsEzdepU0aJFC1GkSBGRJUsW3b4NGzakeWMAdT6wxDJjx47V29mhQ4doN4cQQkgcwSATIYQQ4icYsBsHi74uS5Ysoa1JWNA0TTzyyCOibdu2Yvbs2eLAgQPi8uXLtDYhhBBCIgqDTIQQQggJGqqSosvEiRPloqhZs6ZUoHTt2lUu9913X1TbR6IHVUmEEEIiScaI7o0QQghxGDVq1JADel9AChMh4WD8+PH66/79+4s+ffrQ0IQQQgiJOAwyEUIIIUHQrFkz1hMKQv3EWkyhYd26dfrrp556KkRbJW4FKjjWYiKEEBIITJcjhBBCCIlzTpw4ob8uVKhQVNtCCCGEEPfCIBMhhBBCSJxz9epV/XViIm/vCCGEEBIdeBdCCCGERJg///xTn3GuRIkS+vvLly8XHTt2FDfeeKPInTu3/Lx79+5pvpuamiqWLVsma+40btxYFCtWTGTPnl1OVw8Fyx133CEGDRokjh496ne7Tp8+LT7++GPRokUL2a6cOXPK7RYuXFjceeedstbPli1bTGfaw2cKvDabXc8z/cbfYuFXrlwRY8aMEffee68oXry4yJYtm0hKShLlypWTKWILFizw6ThxbGq/OBdg//794s033xSVK1cWefLkETly5JDn4bnnnhN79uwR4WL+/PniySefFGXLlpXHgmPCsaFQNwo245h9OQ4jZrYPBMyGqL6P86xmsZs+fbpo2bKlbGfWrFnFddddJ33xq6++kv7pDytXrhTdunUTFSpUEHnz5pXbK1q0qGjSpIn45JNPxLlz57xuw8yP8L1PP/1U1K9fX7YP20V7MQPf0qVLQ14s2+qaDpTDhw9LX2/fvr2oUqWKyJcvn8iUKZP0TfjlE088IX3HDrQb7cG6inHjxpn6hzq/gR4//GLq1KnioYceEqVKlZJ9Bxa8fvjhh8U333wj1/Fn5k41G+fx48fFO++8I+vfXXPNNfIaKVmypLzmN2/e7HWbhBBCIoxGCCGEEL9o0KABRkty6du3r9/WS05O1r9fvHhx7dKlS9rTTz+tv2dcXnjhBf17ly9f1ooUKWK6nueSI0cObfz48T63acSIEVrevHl92vbcuXNNbeFtad++fZp9wna+2nHlypVaqVKlvO7jrrvu0o4cOWK7LdhcrY9zMWPGDC137tyW28yWLZs2e/ZsLZT8/fff2p133un1eMqUKaOtXr3a63F4WwJh8eLF+vdxnk+fPq21atXKdj916tSRx+aNs2fPau3atfPa7kKFCmlz5syx3ZanH23btk276aabbLfbqVMn7erVq5bbHDNmjKXf+nJNW+HLOfnwww+1DBky+HRe77jjDu3o0aOm20G7ffUPnN9Aj3/Hjh1alSpVvO6jWrVq2u7du223ZexP4H/Lly+37fNgp9GjR9tukxBCSGRh4W9CCCEkyvTo0UOMGjVKvq5UqZJU00C1sGPHjjSpTykpKeKvv/6Sr6ESgPoDT/ShgIHiBWocKEOgSIKS47HHHpPbadeune3+n3/+ealgUmTIkEGqBsqUKSMVIEeOHBG//fabrvq5ePGivi4UNxUrVhSrVq0Sq1evtp1xr3bt2gHZ56effhJNmzYV58+fl/9D5YDtly9fXly+fFke8+7du+VnUDPVq1dPqsKuvfZar9teuHCheOaZZ6RtoQqrU6eOtGdycrJUUiAN7cKFC6Jt27ZSNXHDDTeIYPn7779lG1WbARQftWrVksqxrVu3il9//VW+v3PnTnH77beLefPmye8Ygcrl2LFj8jVUO4quXbuKcABFy3fffZfG/pcuXRIrVqzQfeOXX36Rqreff/5Z2tEMnEco7uAzCqjloDqCX+/atUueP5yTgwcPStXUpEmTxAMPPOC1jadOnZK+gvMHW0IZc/3110s7LV68WJw8eVKu99lnn0k/hvoq1jhw4IA8doDr+6abbpK+jGsR7d+0aZOuKPzxxx9Fo0aN5DWA4zWC92HPbdu2iUWLFsn3oILC+fEE13og/P7776JBgwayj1CgD7vlllukn6xfv162F6xdu1bUrVtXXs9Q7nkD19urr74qzp49KwoUKCD9I3/+/LIPxHHjuoSdcP1in4H2L4QQQkJMhINahBBCSNwTSiWTUixcf/312k8//ZRu3YsXL+qvoXh64okn5BN+qJrMwPpDhw7VMmbMKLebJ08e7cyZM7YKJqMyoG3bttrevXtN1920aZP2/PPPa/Pnz0/3mT+qJH++c/z48TRKBih71qxZk269CRMmSMWRWq9FixaW+zUqgLJkyaKrvlJTU9Ost3nz5jT7hu1DQdOmTfVtYt+TJk1Ktw7USyVLltTXg3+cOHHCcpvBqpa8KZkyZ84s/95www2myqrPPvtMy5Qpk75+586dLbf77LPPpvH/Dz74QEtJSUmnjoHyRa2XlJQkrxtvfqTaCUXbwYMH06x3/vx5rUuXLmlsNXHixJhTMn3xxRfaxx9/rO3fv99ynQ0bNmjVq1fXt/XWW29Zruvvsfj6HfRHlStX1tcrUKCAtmDBgnTrob+45ppr9PWqVq1q2X8Z+1Zcm/CP4cOHa1euXEmzHvqoihUr6uvefvvtPh0XIYSQ8MMgEyGEEOInxoFQjRo1tK5du3pdjANG44AUS/bs2bXt27eH9DwMGTJE3/7//d//WQZwcuXKpa/3zDPPBLy/cAWZ+vTpo6+DdD6rABiYPn16GrsuXbrUa5ApISEhTfqfJ0iTU+vmzJkz3WDXX3788cc0bbRLw4OfGNP4+vfvH7UgkwqI7dq1y3L9zz//PI1dzdbFe4mJifp6n3zyieX24J8lSpTwGuQz+hGWW265Rbtw4YLldh999FF9XWzfM8AV7SCTr5w8eVK77rrr9LRCq/S/cAWZvvzyS30dBBjXrVtnub1Vq1bpgW8s48aNM13PM/121KhRlttE0Bt+pvztwIEDPh0bIYSQ8MLC34QQQkgQIEUMqUreFmM6iScofOxL+og/GIv9IiXMjNGjR4szZ87I1yiK/MEHH4hYAmNytFGBwtxIfbICqXtIlVKMGDHC6z6aN28ui0xb0axZM1k4GiBtB+lBwaDSIgHSwO655x7LdVFA+rXXXtP/HzlypE/Fk8PFiy++KNP6rEAh5mrVqsnXaOfnn3+ebh2kqani4Eip6tKli+X2UAgcBZ8VEydOlOlw3hg+fLhMLbPivffe01PLkObna8H4WAOTA8DnAdIKkWYZSYy+/Oyzz8oC5VYghbZTp05+XZtIgevcubPl50jTxXaVv61Zs8aP1hNCCAkXrMlECCGERJkHH3zQ7+9goI4aJ6iVhFpMqMNkNRMZ1jEDdX4UGAB61nSJNgjoHDp0SK8T9fjjj3v9Dmbnmzt3rnytZqeyo02bNrafo64MamSpdiAogcFvoKAukAKzyvkSLERdGpxvBBK2b98u6+pEA1/sj3Xgl57HqkAtHc/Zz+xAEAUzq2GGMdR/Qs0nu6AgZqZDDSs7UN8IwcMZM2bo7bz77rtFLIJZ5lBvCdfCiRMnZK01Y6DRGFjBdR6Mb/oDgtPGffviy7g2VXAJwXkcC2ZxDPTaBAhsqdpeqi4YIYSQ6MIgEyGEEBIEffv21adNDwQU5vZnYIhC1B999JF4//33ZXDJF44ePWr6viouDbwNzKMBigYrypUrJ4v+esNYHBuBIRRRRlFpK3yxvXG/COYFCgoWI2igQBFkbyAgApUbijeDdevWRSXIhKnjS5cu7XU9FE43Bj0QEFGBJLw2Bjx9OX5cHygyrgKiOH67IBOKP3sLXKl2qiCT0c9iBaiSevXqJQOmqgh4oNd5ONi4caPeLhQXv/nmm71+B8o1BJUQXMJ3N2zYYOsDkbw2CSGEhA6myxFCCCFRBClBGTP69swHSg6kV7300ks+B5iASokzggEZZmdSYBarWMOYYoh0Pl8oWLBgmlQpbwNvpBz5EuhQWKnF/D2ebNmy+TT7nUqbi0YgwQhm3vN3Pfir0feQ6ma0n6/n1J/jD6Sddqms0WD+/PmiatWqYvbs2T4HmKyu83BhtBlSWH0J7GGmTGO6ayxdm4QQQkIHg0yEEEJIFEGwwVf69+8vfvjhB/kag7p27dqJ//3vfzKVBgP4y5cvS7WIWhRmdXw8B6RQI8QaqIGksEur8cS4rreBty+D43g6nnCRPXt2n9bzPC5je43Hb7ZuKI4/kHZGy6ZWwRtc1wjQqUDc4MGDxfLly6Uq7/z58zJ1Ul3jUFIqVK2rSOC0a5MQQkjoYLocIYQQEgdg0Pnxxx/r/48dO9a2Ro63AVyuXLnSDRpjLdBkbA9SbHzFuK7ncUaTeD4eBDd8wfO4jO319C9vNXkCOf5A2hkKm4YqwIPC6Kq4OWqB/fTTTyIpKcly/WgFyOLZlwkhhIQXKpkIIYSQOADFbZV6oEKFCl6LMO/Zs8f2cwxcjSqq5ORkEWsY08n27t3r03dQ8+jixYtpagnF4vEgVdHX1DdjQeNoHc++ffv8Xg+F5I2BBKQ/GdObfD2n/hy/r9s0ttNsm8Z2og6aN3yZ9c4XFi1apL9+4403bANMvlznkfBlpO76MushAnHe7E4IIST+YZCJEEIIiQOQKuNPQVwoILxRq1Yt01m/AiEcqS3GKdFR+BozjHnj559/1l9fd911tkW/I02RIkVEgQIF9P9XrFjh9TsIRO3YsUP/H7V6opXGtXv3bq/rYfY3Y6Fno1/gNd7z5/gR4MFMZL4ev7GYva/tNNumMbhz7Ngxr9vbtGmTiPR1jnpNRn+P5LWJQt+Y8VGpqXw5fhT6VkomfBdKLUIIIc6DQSZCCCEkDkDRXF9TgqAYGD16tNdtNm3aNE2ajqoDEwjGYtuhKsB70003yUCRGlBPmDDB63e++OKLmJ4xz9gmpDx6A+uoVCwEzDDLXrQYP368X+uY2f+OO+7QX48bN86rAubbb7/VgzzwMePsdWZAKbNkyRKvgbs5c+bYttNYbBzBEW/tRG20SF/nsA1mUIzGtQmFWvXq1f3yZeO1iRkD/anlRAghJH5gkIkQQgiJA4yzvy1dutQ2PWfYsGFyYOyNTp066bVVkHbTvXv3gNtnnEr8r7/+EqEACozOnTvr/w8YMMB22zNnzhTff/+9/v8zzzwjYo2nn35afz1jxgw5k5gVOCeDBg1K891oFkN+7733bNMqEWhQqiO086mnnjL1ORVIWbdunW0w9OTJk6Jnz576/w899JBPM469/PLLtgFTfK5SKlFY+6677jINcKpUv4MHD+oF982Azxn9LlTXOfzZTlnWo0ePqF2bnr786aefio0bN1quu3btWjFq1KiYvjYJIYSEBgaZCCGEkDgAqWNItwIIMLVp0yZNag3AwLpPnz6id+/ePqkE8ubNK9555x39/5EjR8qZrVBjxYwtW7aIF154wXTAXbFiRf01Pg9VjRoEvtRxQ9Fy5513it9++y3depMnT5ZBCEWLFi3EbbfdJmINqGaMCrIHHnhATJ061XRQ3qhRIxloAZj6/fnnnxfRInPmzDItCgEZBIc8GTNmTJqgAwJMpUuXTrdeqVKl0qzXrVs3GaDwLJy9a9cu0bhxYz2ohfQ1+LYv7YTt7r33XvH333+n+QyBJdgQCioFgnhG9ZAiY8aMom3btmmCY1u3bk2zDtRNUG5hPdSfCgXwWwVmlTNT78H+DRo0kKotX65z47WJdEJf61Z545FHHtFT3jCz5d133y0WL16cbr2FCxdKn1e1rZCeaLxWCSGEOAvOLkcIIYTEARgIv/XWW+LJJ5+U/y9YsECULVtW1K1bV6oxEIBBmtCJEyfk51CIYBDojS5duojNmzeLESNG6Gk/06ZNEzVq1JDbR6oNVBPr16/XCzCbpRch/QWBEAx8ofy48cYbZZAAxX2V+gbbRBDLHxAImzhxohykIn1o+/btcpCKelLly5eXg9uVK1fKoISiTJkyaVJzYg0EZOrVqydrHKGYO4IUaDOOCUESBDMQDFApWggkTJo0SeTJkydqbUaaWr58+aT6CmlStWvXlmofBDZR3+iPP/7Q18X77777ruW28NmaNWuk6gmBBwSahgwZIm699VaprINdUFMMKZIq4IPzaUxhs+LZZ58V3333nZg3b55cv2HDhtIvcX0gAKKuD/Dwww/bXiMovI3gJeoIwa9RTwrBHaiNTp8+LWtKIWCD9iFA27FjRxEs7du3F8OHD5d1uGDbxx57TLz99tsymINrEdcqbAfwHgI7Q4cOtd0mUk7RT6C9CLThe02aNBGFChXSA2wI/sF2/gBfhV/CJugjkLqHdEhsX9XeQkDYqKpETTJ8x1hYnRBCiMPQCCGEEOIXDRo0wOhfLn379vXbesnJyfr3ixcv7td3X3vtNf27ZkvWrFm1kSNHynWN73vjgw8+0JKSkmy3jSUhIUGbP3++6TZmzZqlZc6c2fK77du3T7M+bOerHX/55RetZMmSXtvXqFEj7fDhw7bbgs3V+jgX3kC71fpjxozRQsGhQ4e0O+64w+vxlC5dWlu1apXX7flzrn1l8eLF+jbh86dOndKaN29u295atWppBw8e9LrtM2fOaG3btvV6/IUKFdLmzJljuy1PP/r999+1cuXK2W73ySef1K5cueK1nXPnztWyZ89uuR1cM9OmTfP5mvblPG3fvt2rr9erV0/bv3+/z9fQ6tWrtVy5clluD+fXCPzc6ro1a2+VKlW8nsuqVatqu3bt8rlvhf95w58+hBBCSGRguhwhhBASRyC9Z9myZVIRhDQyqAlQcwXqgV69esm6KMZ0JF9BGhzUKFCZICUK20YKEBa8RuoWlFRQEkGhZEbz5s2lygL7r1ChgqxpE6oaQlDO/P7771LRgpQiqFPQNihfkJbVoUMHWd8ICi/j9OqxSsGCBeV09XPnzpVtxzHgWHBMOLaWLVuKL7/8UqqaoACLBZCyhjpBULvdc889sp3wP6hT4B9QaEEto4q124FjnTJlilwfChqon1BvCdtDgXP42EcffSR27tyZJr3QF6Cig0rqgw8+kAoetA/bRXsffPBBOZMi/AgKJG9A8YOZDZFmh6Lr2bNnl34N/0ZaKmZVa926tQglUBBCOQgFE1Rj2B/8AopFXGNQ9qEum0oj9QVsB33Diy++KFNvYWs1O1wo2ovrHucTqjwoyGAnLHiNvgopoVgHiilCCCHOJgGRpmg3ghBCCCGExBZIv1SpkUiJ8jZrW7To16+f6N+/v3zdt29f+T8hhBBCogOVTIQQQgghhBBCCCEkaBhkIoQQQgghhBBCCCFBwyATIYQQQgghhBBCCAkaBpkIIYQQQgghhBBCSNAwyEQIIYQQQgghhBBCgoZBJkIIIYQQQgghhBASNAmapmnBb4YQQgghhBBCCCGEuBkqmQghhBBCCCGEEEJI0DDIRAghhBBCCCGEEEKChkEmQgghhBBCCCGEEBI0DDIRQgghhBBCCCGEkKBhkIkQQgghhBBCCCGEBA2DTIQQQgghhBBCCCEkaBhkIoQQQgghhBBCCCFBwyATIYQQQgghhBBCCAkaBpkIIYQQQgghhBBCSNAwyEQIIYQQQgghhBBCgoZBJkIIIYQQQgghhBASNAwyEUIIIYQQQgghhJCgYZCJEEIIIYQQQgghhAQNg0yEEEIIIYQQQgghJGgYZCKEEEIIIYQQQgghQcMgEyGEEEIIIYQQQggJGgaZCCGEEEIIIYQQQkjQMMhECCGEEEIIIYQQQoKGQSZCCCGEEEIIIYQQIoLl/wEm+iD8sQiArQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 488, - "width": 588 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax, ginis = plot_performance(\n", - " y_true=y_test,\n", - " y_pred=[\n", - " predictions[\"Logistic\"],\n", - " predictions[\"Random Forest\"],\n", - " predictions[\"Gradient Boosting\"],\n", - " ],\n", - " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Custom Colormap\n", - "\n", - "Use custom colors for your models.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 488, - "width": 588 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Custom colors\n", - "custom_colors = [\"#c430c1\", \"#ffa94d\", \"#55d3ed\"]\n", - "\n", - "fig, ax, ginis = plot_performance(\n", - " y_true=y_test,\n", - " y_pred=[\n", - " predictions[\"Logistic\"],\n", - " predictions[\"Random Forest\"],\n", - " predictions[\"Gradient Boosting\"],\n", - " ],\n", - " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", - " colors=custom_colors, # โ† Custom colors\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Multiple Models with EAD Weighting\n", - "\n", - "Compare models using EAD-weighted Gini.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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KaUWcYsM6LlmyRPyu6s+3A1zLhYvdKscNp4lwajHbMafSciplNFg/Tlfjheu38Os55ZFTsTgNkVOP1LVmCgoK6Omnn467T9wNirtFKemRCpzCyCl4nM7Jn8PjnXKsRErF4nFu3LhxgRQdPs75t1LXiomF+jjhY15vpy+j4O+qJl7NHLa7Cy64QIznin5KmhvX0WE7V6fDsoZ8jHCXLm06EXcf4/REHju5gYb6+GCd+FiYNGlS2D6wHSljIr+/ujNkpLHSzDpNnJp77bXXCltSa8rHNHdj43RdThPkFDb+j1Q6kPH/6CeffJL2lCq2T3Un3GTOdyL9jxmJtqA3p8ip4f81/i9Rbz9o0CBbHSeR9ObfnP9zAAAJYrWXCwAAjILTpdQzUpwy4aZIpqysLHHLkS+RUhXKy8v9V155Zch7cyoAzzAqESCRolI4NJ9T7NSv45SJdOy/GZFMnIL0m9/8RsxA8mx8NHjG/fXXXw9JHenevXvcMP5IKYt6iRfJpKSvqb8rp0LqRflteWndunXEVCzm+eefD/mMk08+WaQ+RaKsrMw/YcKEkHSHQYMG+Y3iv//9b8i+3XrrrWHb8P6ot+HHeqNitKlCHH20bt26qK9ZvXq1/4EHHhBRHZFgjc8999yQ92Ttp02bFvU9Dx8+LGyPI27sEsmkpM2y3rx/WrTRR4cOHRJpgW+99ZZ///79MfdpyZIl/lNPPTXk87777ruYr9mzZ09IVJ8SgfTzzz/HjOZ45JFH/MOGDQt7jqM+1VF7HLWlBx5H1REukVLTzIxk4v3RRuFFsrVIvy3bZbQIEvXv+9RTT4VFsUX7/+TfnyNB1ds/99xzMb9DMv8ZRkUy/fLLLyKtWNmWteVIsUhjJ/83cCq22h6aNm0qImbSxdq1a0P2ne1ZD/fdd1/I6zi92Sz4v7RZs2Yh9rZr166QbUpLS0PshO/zOiMjmS666KKQ191+++26XqdOs48XwQoAiAycTAAA18IXQOoTDE4Rc5OTiRdOeduwYUPU7bluCNc/ULZXHAXsVIhVU2TFihUhToVYJ3V2czIlkzqlrksVLwXCaCcTwxfT6npIsZxlCosXLw7R6KGHHoq43c6dO0NqlbGDRE/60rPPPhvy/tq6N+miX79+IZ/z/fffh23DzlF1jZj27dvrqvGhHRP4okNvbZBo27355psh79m5c2fdNVSiva8VTiZe7rnnHr9R8Hhz4oknBj7r0ksvjbn9DTfcELJvF198sUh7S+W3UqdhsuNET42x1157LWQ/3n33Xb9VTib+/pdddllYClBJSUnYtpFqi3Edvniwg1HtDODJjPnz58d8DTuu1GMoaxtpn+zoZFI75nls37p1a9x9WbhwYcgYGm2sTQZO207G3rTHyxtvvOE3i48++ijks/k/JRJXX311yHZ8bBnlZNLWteIlUm3KSHANMuU1XOcKAJA4ibdLAQAAh8Cdh9ToaQHsNB5++OGYaUIc5s9pAAqcosBpEZyyEqtLXrdu3UK60sRKlXE6nHYzbNiwwGN1C2arUKe5ccoRpz7GY+LEiYH7/Burf3c1nKZUXl4u7nNq5RtvvKGrWxKn16lTMJ577jlKN9wFkFOk1Ok5kdI+2rdvH5LCsGHDhrB0ES38vpzeo8Dv++STT+rumhdpO76W/+c//xmS4sOpQpwepxczu/bFgrvH8XhiFDzePPDAA4HHH3/8cdQuY9wFijs7KXCnS27FrrdzXDRNb7311sD9w4cPi25YiaTKcYoop52ZCafn7tixQ6TCciqrdp8feughkX4Yj0ceeURXx1AeR9Qdyu6++2469dRTY76G05602vLvZXc+/fRTWrRoUeDxK6+8IlJc48Gp6bfffnuIjaSrYx6npqrRsz9Wn+9of2ttqpw6ZS5Wil0qsP6cVvv555/TqFGj6Lbbbgt5/sILLxT/9XpQp9XxWKROkQcA6ANOJgCAa+GaQ2rq1KlDbiI3NzeqIyFWLSp2HnHtlHiccsopgftcd6O6uprcCtcoUfj+++/Jarg+lNpe412w8W/DdUUUzjjjDOGIiQQ7lRS4Tlci7ccvvfTSwP158+aR2XU9Urlg0V6c/+lPf0q57gvXV1LXYeILmRNOOIGcyPXXXx/T8Zzu44wdEexUjMS0adNCaov97ne/E3WeUoXrY6nHPm1Nski/73fffRd4fMMNNxhaK0h5f/XCjjW+6OV28GqHCMPj/x133BH3fdkJxe3s9TBjxoyQWkl63p+55557QurTqd/HrqjHQraLIUOGJDUWcn0qbT22ZGGHohquO2bn8x127HzwwQeBx3yc8jgYzRnZqlWrkHpHmzdvTup30x4nvDRo0IDOPvtseu+990K257qMiTg9uSah+r+V61gCABIDTiYAgGvRzu5ywWc3wbOpXMg6HtqZUC74qgf163iWkAudOg0uOMwz/VyglYsYs0OFL6S1J6ePPvpoyMylHWyXC++qo6uU4tuR+PDDDwNFzGMV/OaiteqLGHZGJUKnTp0C9/nz0qkVzxaro1c4GiXWhTE74tjRqjB16lQqKyuLuj1f0Khn+dXFxJNF/Z7MZZddRk6FHTDJwlEUL7zwgvi9+IKOI374Ild7nGkLbUezH7WubAfqC/pUueWWWwL3uSg5F7GPhtoJxQ4XLh5uJBxpyseBeokUIcP2+8wzz4REL8aCo/b0/Ffw53ERcHURZL0F/rlANhfPVzvr1cW97YjaUZ7KWMj88ssvadkndRQZk5eXZ+vzHY7c5EYJChxFFG2f2QnJzlIFtm31mK8Xfp32OIlkazzmcATu/PnzE4rs0u6/9jcBAMQHTiYAgGtp2LBhyGOeOXcTejqlRJrRTPZ1TjrRWrFihbhoPv744+nvf/+7SDfjbmTcSYkjJGJdyHF3JDugdhRpI5W0qGdpeSZZ7aBSo72g5kimSDPC0RZtqhDrmS6485DaAcbOUE7higZfNIwcOTJkJl87g61GHWnAF93RugImAnczixb95zS6du2a8GvYqXf//fcLR8TNN98sbJSjf7jbGz+nPc60aSfRjjW1ruwc5g6D6YIj4NRRUdHSPnm8Ux9z5513XkgUhlnwccffnyNt2InHqc58nGjTgdLx23JHRfU4n2hUHk98qI9HpbupHeHoFPX+sZM0kbGQU42NGAu1XSf1pDJbeb6TSPSpkSlzPJ7zfwJH8PL/FHfL5CipZ599NuGoLm1Ep9qJBgDQB5xMAADXok0D4hbEbkLPzHSk+iTJvi5dNSeMhmctOUUw2XQuu5xQclpjhw4dAo+jRS1wRJG6ZhNH+ET7jbXRUJEiJ2It2tnidF7IaNMZtBcjqVywcBSeOt0zkZpJsVBfWPLxUlxcTE5Fe9EcD46U4FpmHAWoTm1Lx7Gm1jVdv5UCO5jUF8KcRhkpSpNrIKnXsxPNaPgY/7UpT2Bhu+Xjlp2kHPXBzme9Y3iiv63W6ZeoPWu3t4vDXs9YGCk6Jt5ixFiodXDoPbasON9ZvXp1SDopO2E5JS4WXO+xT58+IdG1/J+dCNdcc03YccK/B6fu8WTS9OnTRYptsmOH1tGnN5oMABAETiYAgGvp1atXyOOFCxdati/AHHj2/JJLLgmZjecUHS56++WXX9LGjRvFNnzirj5B/fOf/2y7n0hbvJtT/yKlZPzvf/8LcaBES5Vj+CQ8naQrHYYv0PjCQIHT4KJFY6k555xzQupnfPHFFxFTsLT1SvQUStaD+n35wj8d0VFWobeotgJfxKmLrRcWFopUNK6nxJFInELHBebVxxkff3oc12pd0/VbRUuZY2dZpJQdjmxR4Ig6dXMAt/62qdb10f5W2vezE3YdC7UaxkoBtvp8R+vU51Q4PY0MjCwAng60mhsxBgHgdpx7NgQAAHFQd0ezS0FnYCyc+sKpOgp33XUXLV26VKT0cPocXyzyCaP2osuuqYA8Y6t2XEQqXqpex3VC1F3XtGgjIJQOX8kuqdTxUTNlypSQqBa+zylC8VJW2BmlLsrKF3rqYr5qB4gRv7f6ffnCxO41aNIFR0m8+OKLgcecysVFvP/73//SxRdfLBy7XIRXXTMrEd3VuhpxbPL+qW1X7VBiuC6ROrX0pptucrQDUS/a4uqJ1vXR/lbpKNZuFNqx8Pe//31KY+GECRPSsl/adHa9Rae15zvqLp1GwGOd1jnLKWp6Ug35f1k7/isdT+0Ad3RV4HMFvcXXAQBB3P+PCQCQFq67og5z5ll3I4s6q2fwEkkts6uDQy/JptUZ8b1nzpwZUsvlscce0zWzqj6ptBOcfqDueMQ1YtRRS3whzDVw9EQxRUqpiFVM3EwiOc/0pqxo7S3SrDhf7Kprm6SrVoy6VhDvR7pSVOx0TEWC0zPV6ULs3NWTmqL3OFPralRdn1tvvTVwnx3R6pQddcFvvsjkrm8ywI5BNWqHvR6022vfz07YdSzU1qHTe87ChdfVr920aRN9++23ZBRz5swJ27dE0q9jRbJajXrM4f9gGRzMAKQbHDUAANfCs+jq7lR8AvTSSy8Z9nnq1AK9Ie6RWhY7DW1Khd7vbsT35hoRCkOHDhUdofTAXabsitpxxBdC3GkuknOGT4Tj1TFSt2+3Swrp2rVr03oxtGbNmpA6IQo9evQIiVRJR9RRz549DYmWtNMxFe84433VRlGkepypdeWaLeksMK/AbdbVjjHFscQXvNwxS72dOiXTzXAUjTo1aNGiRQm9Xh39xY5dvU0mrICL1atrVdlhLIw0pvD4qBeOuFOjjdBLJ+lOcbNTyhz/h0RLQwQA6ANOJgCAq7n77rtDogIef/xxURgyFbRFISPN2vJFkd6ogq+++oqcjHa2mju6WPW91XU29M6icxTDqlWrdH+GOtVOOyNrBBdddFHIxZBSAJzrSnFxYoWzzz477kUdd9tTR4lw5JfVKV7ai4u//e1vCaeqaFPkIkVGqdOj2JGgLpaeLNp0QfXv4ZZjKt5xxh2d9EQLMlOnTk1KVy7OnW44bWfcuHGBx++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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 488, - "width": 588 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax, ginis_weighted = plot_performance(\n", - " y_true=y_test,\n", - " y_pred=[\n", - " predictions[\"Logistic\"],\n", - " predictions[\"Random Forest\"],\n", - " predictions[\"Gradient Boosting\"],\n", - " ],\n", - " weights=ead_test, # โ† EAD weights\n", - " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", - " colors=custom_colors,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8. Side-by-Side Comparison\n", - "\n", - "Create custom grid layouts to compare unweighted vs weighted Gini.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 388, - "width": 988 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Create 1x2 grid\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n", - "\n", - "# Left: Unweighted\n", - "_, _, ginis_unw = plot_performance(\n", - " y_test,\n", - " [\n", - " predictions[\"Logistic\"],\n", - " predictions[\"Random Forest\"],\n", - " predictions[\"Gradient Boosting\"],\n", - " ],\n", - " ax=ax1,\n", - " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", - " colors=custom_colors,\n", - ")\n", - "ax1.set_title(\"Unweighted Gini\", fontsize=14)\n", - "\n", - "# Right: EAD-Weighted\n", - "_, _, ginis_w = plot_performance(\n", - " y_test,\n", - " [\n", - " predictions[\"Logistic\"],\n", - " predictions[\"Random Forest\"],\n", - " predictions[\"Gradient Boosting\"],\n", - " ],\n", - " weights=ead_test,\n", - " ax=ax2,\n", - " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", - " colors=custom_colors,\n", - ")\n", - "ax2.set_title(\"EAD-Weighted Gini\", fontsize=14)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9. Continuous Target - LGD Model\n", - "\n", - "Demonstrates Power Curve for Loss Given Default (LGD) models.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean LGD: 55.97%\n" - ] - } - ], - "source": [ - "# Generate continuous target (LGD)\n", - "y_lgd = np.clip(\n", - " 0.45 + 0.3 * df[\"debt_to_income\"] - 0.0002 * df[\"credit_score\"] + np.random.normal(0, 0.05, n),\n", - " 0,\n", - " 1,\n", - ")\n", - "\n", - "# Split data\n", - "X_train_lgd, X_test_lgd, y_train_lgd, y_test_lgd, ead_train_lgd, ead_test_lgd = train_test_split(\n", - " df, y_lgd, ead, test_size=0.3, random_state=42\n", - ")\n", - "\n", - "# Train LGD model\n", - "model_lgd = RandomForestRegressor(n_estimators=50, max_depth=10, random_state=42)\n", - "model_lgd.fit(X_train_lgd, y_train_lgd)\n", - "y_pred_lgd = model_lgd.predict(X_test_lgd)\n", - "y_pred_lgd = np.clip(y_pred_lgd, 0, 1)\n", - "\n", - "print(f\"Mean LGD: {y_test_lgd.mean():.2%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 9.1 LGD Power Curve - Unweighted\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 488, - "width": 588 - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LGD Gini (unweighted): 0.7717\n" - ] - } - ], - "source": [ - "fig, ax, gini_lgd = plot_performance(y_true=y_test_lgd, y_pred=y_pred_lgd)\n", - "\n", - "print(f\"LGD Gini (unweighted): {gini_lgd:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 9.2 LGD Power Curve - EAD-Weighted\n", - "\n", - "For LGD models, EAD weighting is essential for Basel/regulatory compliance.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "image/png": { - "height": 488, - "width": 588 - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LGD Gini (EAD-weighted): 0.7814\n", - "Difference: +0.0097\n" - ] - } - ], - "source": [ - "fig, ax, gini_lgd_weighted = plot_performance(\n", - " y_true=y_test_lgd,\n", - " y_pred=y_pred_lgd,\n", - " weights=ead_test_lgd,\n", - ")\n", - "\n", - "print(f\"LGD Gini (EAD-weighted): {gini_lgd_weighted:.4f}\")\n", - "print(f\"Difference: {gini_lgd_weighted - gini_lgd:+.4f}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv (3.11.8)", - "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": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/notebooks/fastwoe_cap_curve.ipynb b/examples/notebooks/fastwoe_cap_curve.ipynb new file mode 100644 index 0000000..56f1667 --- /dev/null +++ b/examples/notebooks/fastwoe_cap_curve.ipynb @@ -0,0 +1,443 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CAP Curve with Weighted Gini\n", + "\n", + "Demonstrates:\n", + "\n", + "- Weighted Gini coefficient for binary classification\n", + "- Multiple model comparison on same plot\n", + "- Custom colormap support\n", + "- Flexible grid layouts\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.ensemble import (\n", + " GradientBoostingClassifier,\n", + " RandomForestClassifier,\n", + " RandomForestRegressor,\n", + ")\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from fastwoe.plots import plot_performance\n", + "\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Generate Credit Risk Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate sample data\n", + "RANDOM_STATE = 42\n", + "\n", + "np.random.seed(RANDOM_STATE)\n", + "n = 10_000\n", + "\n", + "df = pd.DataFrame(\n", + " {\n", + " \"age\": np.random.randint(18, 75, n),\n", + " \"income\": np.random.lognormal(10.5, 0.8, n),\n", + " \"credit_score\": np.random.randint(300, 850, n),\n", + " \"debt_to_income\": np.random.uniform(0, 1.5, n),\n", + " }\n", + ")\n", + "\n", + "# Binary target (default indicator) calibrated to PREVALENCE\n", + "default_prob = 1 / (\n", + " 1\n", + " + np.exp(\n", + " 2.5\n", + " - 0.009 * (df[\"credit_score\"] - 500)\n", + " - 0.1 * np.log(df[\"income\"] / 50_000)\n", + " + 1.5 * df[\"debt_to_income\"]\n", + " )\n", + ")\n", + "y = np.random.binomial(1, default_prob)\n", + "\n", + "# Exposure at Default (EAD weights)\n", + "ead = np.random.lognormal(10, 1.5, n)\n", + "\n", + "print(f\"Dataset: {n:,} samples\")\n", + "print(f\"Default rate: {y.mean():.2%}\")\n", + "print(f\"Total EAD: ${ead.sum():,.0f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Train Multiple Models\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Split data\n", + "X_train, X_test, y_train, y_test, ead_train, ead_test = train_test_split(\n", + " df, y, ead, test_size=0.3, random_state=42\n", + ")\n", + "\n", + "# Train 3 different models\n", + "models = {\n", + " \"Logistic\": LogisticRegression(max_iter=1000, random_state=42),\n", + " \"Random Forest\": RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42),\n", + " \"Gradient Boosting\": GradientBoostingClassifier(n_estimators=100, max_depth=5, random_state=42),\n", + "}\n", + "\n", + "predictions = {}\n", + "for name, model in models.items():\n", + " model.fit(X_train, y_train)\n", + " predictions[name] = model.predict_proba(X_test)[:, 1]\n", + " print(f\"โœ“ {name} trained\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Single Model - Unweighted Gini\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax, gini = plot_performance(y_true=y_test, y_pred=predictions[\"Logistic\"])\n", + "\n", + "print(f\"Gini (unweighted): {gini:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Single Model - EAD-Weighted Gini\n", + "\n", + "For credit risk models, EAD weighting ensures larger exposures have more influence.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax, gini_weighted = plot_performance(\n", + " y_true=y_test,\n", + " y_pred=predictions[\"Logistic\"],\n", + " weights=ead_test, # โ† EAD weights\n", + ")\n", + "\n", + "print(f\"Gini (EAD-weighted): {gini_weighted:.4f}\")\n", + "print(f\"Difference: {gini_weighted - gini:+.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Compare Multiple Models\n", + "\n", + "Plot all models on the same chart with automatic color assignment.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax, ginis = plot_performance(\n", + " y_true=y_test,\n", + " y_pred=[\n", + " predictions[\"Logistic\"],\n", + " predictions[\"Random Forest\"],\n", + " predictions[\"Gradient Boosting\"],\n", + " ],\n", + " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Custom Colormap\n", + "\n", + "Use custom colors for your models.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Custom colors\n", + "custom_colors = [\"#c430c1\", \"#ffa94d\", \"#55d3ed\"]\n", + "\n", + "fig, ax, ginis = plot_performance(\n", + " y_true=y_test,\n", + " y_pred=[\n", + " predictions[\"Logistic\"],\n", + " predictions[\"Random Forest\"],\n", + " predictions[\"Gradient Boosting\"],\n", + " ],\n", + " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", + " colors=custom_colors, # โ† Custom colors\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Multiple Models with EAD Weighting\n", + "\n", + "Compare models using EAD-weighted Gini.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax, ginis_weighted = plot_performance(\n", + " y_true=y_test,\n", + " y_pred=[\n", + " predictions[\"Logistic\"],\n", + " predictions[\"Random Forest\"],\n", + " predictions[\"Gradient Boosting\"],\n", + " ],\n", + " weights=ead_test, # โ† EAD weights\n", + " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", + " colors=custom_colors,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Side-by-Side Comparison\n", + "\n", + "Create custom grid layouts to compare unweighted vs weighted Gini.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create 1x2 grid\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "# Left: Unweighted\n", + "_, _, ginis_unw = plot_performance(\n", + " y_test,\n", + " [\n", + " predictions[\"Logistic\"],\n", + " predictions[\"Random Forest\"],\n", + " predictions[\"Gradient Boosting\"],\n", + " ],\n", + " ax=ax1,\n", + " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", + " colors=custom_colors,\n", + ")\n", + "ax1.set_title(\"Unweighted Gini\", fontsize=14)\n", + "\n", + "# Right: EAD-Weighted\n", + "_, _, ginis_w = plot_performance(\n", + " y_test,\n", + " [\n", + " predictions[\"Logistic\"],\n", + " predictions[\"Random Forest\"],\n", + " predictions[\"Gradient Boosting\"],\n", + " ],\n", + " weights=ead_test,\n", + " ax=ax2,\n", + " labels=[\"Logistic\", \"Random Forest\", \"Gradient Boosting\"],\n", + " colors=custom_colors,\n", + ")\n", + "ax2.set_title(\"EAD-Weighted Gini\", fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Continuous Target - LGD Model\n", + "\n", + "Demonstrates Power Curve for Loss Given Default (LGD) models.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate continuous target (LGD)\n", + "y_lgd = np.clip(\n", + " 0.45 + 0.3 * df[\"debt_to_income\"] - 0.0002 * df[\"credit_score\"] + np.random.normal(0, 0.05, n),\n", + " 0,\n", + " 1,\n", + ")\n", + "\n", + "# Split data\n", + "X_train_lgd, X_test_lgd, y_train_lgd, y_test_lgd, ead_train_lgd, ead_test_lgd = train_test_split(\n", + " df, y_lgd, ead, test_size=0.3, random_state=42\n", + ")\n", + "\n", + "# Train LGD model\n", + "model_lgd = RandomForestRegressor(n_estimators=50, max_depth=10, random_state=42)\n", + "model_lgd.fit(X_train_lgd, y_train_lgd)\n", + "y_pred_lgd = model_lgd.predict(X_test_lgd)\n", + "y_pred_lgd = np.clip(y_pred_lgd, 0, 1)\n", + "\n", + "print(f\"Mean LGD: {y_test_lgd.mean():.2%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.1 LGD Power Curve - Unweighted\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax, gini_lgd = plot_performance(y_true=y_test_lgd, y_pred=y_pred_lgd)\n", + "\n", + "print(f\"LGD Gini (unweighted): {gini_lgd:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.2 LGD Power Curve - EAD-Weighted\n", + "\n", + "For LGD models, EAD weighting is essential for Basel/regulatory compliance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax, gini_lgd_weighted = plot_performance(\n", + " y_true=y_test_lgd,\n", + " y_pred=y_pred_lgd,\n", + " weights=ead_test_lgd,\n", + ")\n", + "\n", + "print(f\"LGD Gini (EAD-weighted): {gini_lgd_weighted:.4f}\")\n", + "print(f\"Difference: {gini_lgd_weighted - gini_lgd:+.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10. Top-p Zoom โ€” Recall at the Top of the Score Distribution\n", + "\n", + "The `top_p` parameter restricts the x-axis to the top `p` fraction of the population sorted by score.\n", + "This is useful when the operational decision threshold targets a small slice of the portfolio\n", + "(e.g., only the top 20% of scores are reviewed).\n", + "\n", + "The full curves are still computed internally; `top_p` only changes the viewport.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "# Top 20% zoom - compare logistic regression and gradient boosting\n", + "fig, (ax_full, ax_zoom) = plt.subplots(1, 2, figsize=(11, 4), dpi=110)\n", + "\n", + "plot_performance(\n", + " y_true=y_test,\n", + " y_pred=[predictions[\"Logistic\"], predictions[\"Gradient Boosting\"]],\n", + " labels=[\"Logistic Regression\", \"Gradient Boosting\"],\n", + " ax=ax_full,\n", + ")\n", + "ax_full.set_title(\"Full CAP Curve\", fontsize=14)\n", + "\n", + "plot_performance(\n", + " y_true=y_test,\n", + " y_pred=[predictions[\"Logistic\"], predictions[\"Gradient Boosting\"]],\n", + " labels=[\"Logistic Regression\", \"Gradient Boosting\"],\n", + " ax=ax_zoom,\n", + " top_p=0.2,\n", + ")\n", + "ax_zoom.set_title(\"Top 20% Zoom\", fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.11.8)", + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/fastwoe_explanation.ipynb b/examples/notebooks/fastwoe_explanation.ipynb similarity index 100% rename from examples/fastwoe_explanation.ipynb rename to examples/notebooks/fastwoe_explanation.ipynb diff --git a/examples/notebooks/fastwoe_gini_contributions.ipynb b/examples/notebooks/fastwoe_gini_contributions.ipynb new file mode 100644 index 0000000..a3ce421 --- /dev/null +++ b/examples/notebooks/fastwoe_gini_contributions.ipynb @@ -0,0 +1,379 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gini Contributions\n", + "\n", + "This notebook demonstrates `gini_contributions` โ€” an instance-level decomposition of the Gini coefficient (Somers' D\\_{Y|X}).\n", + "\n", + "Each observation receives a signed contribution:\n", + "\n", + "- A **positive** (label=1) earns credit for each negative it outranks and is penalised for each negative that outranks it.\n", + "- A **negative** (label=0) earns credit for each positive that outranks it and is penalised for each positive it outranks.\n", + "\n", + "The contributions are normalised so that `contributions.sum()` equals the overall Gini, making them directly comparable to SHAP-style decompositions.\n", + "\n", + "Topics covered:\n", + "\n", + "- **Single model** contributions and top/bottom contributors\n", + "- **Score vs contribution** scatter โ€” where the model's ranking failures are\n", + "- **Contributions by score decile** โ€” aggregate lift view\n", + "- **Multi-model comparison** โ€” contribution distributions side by side\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from fastwoe import gini_contributions\n", + "\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Generate Credit Risk Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RANDOM_STATE = 42\n", + "\n", + "np.random.seed(RANDOM_STATE)\n", + "n = 10_000\n", + "\n", + "df = pd.DataFrame(\n", + " {\n", + " \"age\": np.random.randint(18, 75, n),\n", + " \"income\": np.random.lognormal(10.5, 0.8, n),\n", + " \"credit_score\": np.random.randint(300, 850, n),\n", + " \"debt_to_income\": np.random.uniform(0, 1.5, n),\n", + " }\n", + ")\n", + "\n", + "default_prob = 1 / (\n", + " 1\n", + " + np.exp(\n", + " 2.5\n", + " - 0.009 * (df[\"credit_score\"] - 500)\n", + " - 0.1 * np.log(df[\"income\"] / 50_000)\n", + " + 1.5 * df[\"debt_to_income\"]\n", + " )\n", + ")\n", + "y = np.random.binomial(1, default_prob)\n", + "\n", + "print(f\"Dataset: {n:,} samples\")\n", + "print(f\"Default rate: {y.mean():.2%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Train Multiple Models\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.3, random_state=RANDOM_STATE)\n", + "\n", + "models = {\n", + " \"Logistic\": LogisticRegression(max_iter=1000, random_state=RANDOM_STATE),\n", + " \"Random Forest\": RandomForestClassifier(\n", + " n_estimators=100, max_depth=10, random_state=RANDOM_STATE\n", + " ),\n", + " \"Gradient Boosting\": GradientBoostingClassifier(\n", + " n_estimators=100, max_depth=5, random_state=RANDOM_STATE\n", + " ),\n", + "}\n", + "\n", + "predictions = {}\n", + "for name, model in models.items():\n", + " model.fit(X_train, y_train)\n", + " predictions[name] = model.predict_proba(X_test)[:, 1]\n", + " print(f\"{name} trained\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Single Model Gini Contributions\n", + "\n", + "`gini_contributions` returns:\n", + "\n", + "- `contributions`: array of per-observation contributions, shape `(n,)`\n", + "- `gini`: overall Gini coefficient\n", + "\n", + "The contributions sum exactly to the Gini.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scores = predictions[\"Gradient Boosting\"]\n", + "contribs, gini = gini_contributions(scores, y_test)\n", + "\n", + "print(f\"Gini coefficient: {gini:.4f}\")\n", + "print(f\"Sum of contributions: {contribs.sum():.4f}\")\n", + "print(f\"Match: {abs(contribs.sum() - gini) < 1e-10}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = pd.DataFrame(\n", + " {\n", + " \"score\": scores,\n", + " \"label\": y_test,\n", + " \"gini_contribution\": contribs,\n", + " },\n", + " index=y_test,\n", + ").sort_values(\"gini_contribution\", ascending=False)\n", + "\n", + "print(\"Top 10 contributors (help Gini most):\")\n", + "display(result.head(10))\n", + "\n", + "print(\"Bottom 10 contributors (hurt Gini most):\")\n", + "display(result.tail(10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Score vs Contribution\n", + "\n", + "A scatter of score vs contribution reveals **where the model's ranking failures are**:\n", + "\n", + "- **Positives** at the top of the score range contribute the most (correctly ranked above many negatives).\n", + "- **Negatives** at the bottom of the score range also contribute the most (correctly ranked below many positives).\n", + "- Observations in the **overlap region** can have negative contributions โ€” these are the misranked pairs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colors_map = {1: \"#55d3ed\", 0: \"#69db7c\"}\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4), dpi=110)\n", + "\n", + "for label_val, label_name in [(1, \"Default (1)\"), (0, \"Non-default (0)\")]:\n", + " mask = y_test == label_val\n", + " ax.scatter(\n", + " scores[mask],\n", + " contribs[mask],\n", + " color=colors_map[label_val],\n", + " alpha=0.3,\n", + " s=6,\n", + " label=label_name,\n", + " )\n", + "\n", + "ax.axhline(0, color=\"black\", linestyle=\"dotted\", linewidth=0.8, alpha=0.7)\n", + "ax.set_xlabel(\"Score\")\n", + "ax.set_ylabel(\"Gini contribution\")\n", + "ax.set_title(\"Score vs Gini Contribution\", fontsize=14)\n", + "ax.legend(fontsize=10)\n", + "ax.grid(True, linestyle=\"dotted\", linewidth=0.7, alpha=0.6)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Contributions by Score Decile\n", + "\n", + "Aggregating contributions by score decile shows **where in the score distribution the model is gaining or losing Gini**.\n", + "A healthy model accumulates most of its Gini in the top deciles.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "decile_df = pd.DataFrame(\n", + " {\"score\": scores, \"label\": y_test, \"gini_contribution\": contribs},\n", + " index=y_test,\n", + ")\n", + "decile_df[\"decile\"] = pd.qcut(decile_df[\"score\"], q=10, labels=False, duplicates=\"drop\")\n", + "decile_df[\"decile\"] = 10 - decile_df[\"decile\"] # decile 1 = highest scores\n", + "\n", + "agg = (\n", + " decile_df.groupby(\"decile\")\n", + " .agg(\n", + " gini_contribution=(\"gini_contribution\", \"sum\"),\n", + " default_rate=(\"label\", \"mean\"),\n", + " n=(\"label\", \"count\"),\n", + " )\n", + " .reset_index()\n", + ")\n", + "\n", + "fig, ax1 = plt.subplots(figsize=(6, 4), dpi=110)\n", + "ax2 = ax1.twinx()\n", + "\n", + "bar_colors = [\"#55d3ed\" if v >= 0 else \"#ffa94d\" for v in agg[\"gini_contribution\"]]\n", + "ax1.bar(\n", + " agg[\"decile\"], agg[\"gini_contribution\"], color=bar_colors, alpha=0.85, label=\"Gini contribution\"\n", + ")\n", + "ax2.plot(\n", + " agg[\"decile\"],\n", + " agg[\"default_rate\"],\n", + " color=\"#c430c1\",\n", + " marker=\"o\",\n", + " markersize=4,\n", + " label=\"Default rate\",\n", + ")\n", + "\n", + "ax1.axhline(0, color=\"black\", linestyle=\"dotted\", linewidth=0.8)\n", + "ax1.set_xlabel(\"Score decile (1 = highest scores)\")\n", + "ax1.set_ylabel(\"Gini contribution\")\n", + "ax2.set_ylabel(\"Default rate\")\n", + "ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f\"{x:.0%}\"))\n", + "ax1.set_title(\"Gini Contribution by Score Decile\", fontsize=14)\n", + "ax1.set_xticks(agg[\"decile\"])\n", + "ax1.grid(True, linestyle=\"dotted\", linewidth=0.7, alpha=0.6)\n", + "\n", + "lines1, labels1 = ax1.get_legend_handles_labels()\n", + "lines2, labels2 = ax2.get_legend_handles_labels()\n", + "ax1.legend(lines1 + lines2, labels1 + labels2, fontsize=10, loc=\"upper right\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Multi-Model Comparison\n", + "\n", + "Comparing the contribution distributions across models highlights which model has fewer misranked pairs (narrower negative tail) and where each model accumulates its Gini.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_colors = [\"#55d3ed\", \"#69db7c\", \"#ffa94d\"]\n", + "model_names = list(predictions.keys())\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(13, 4), dpi=110, sharey=True)\n", + "\n", + "for ax, name, color in zip(axes, model_names, model_colors):\n", + " c, g = gini_contributions(predictions[name], y_test)\n", + " sorted_c = np.sort(c)[::-1]\n", + " cum_c = np.cumsum(sorted_c)\n", + "\n", + " bar_colors = [color if v >= 0 else \"#c430c1\" for v in sorted_c]\n", + " ax.bar(range(len(sorted_c)), sorted_c, color=bar_colors, alpha=0.7, width=1.0)\n", + " ax.axhline(0, color=\"black\", linestyle=\"dotted\", linewidth=0.8)\n", + " ax.set_title(f\"{name}\\nGini: {g:.2%}\", fontsize=11)\n", + " ax.set_xlabel(\"Observations (sorted)\")\n", + " ax.grid(True, axis=\"y\", linestyle=\"dotted\", linewidth=0.7, alpha=0.6)\n", + " ax.tick_params(axis=\"x\", labelbottom=False)\n", + "\n", + "axes[0].set_ylabel(\"Gini contribution\")\n", + "plt.suptitle(\"Individual Gini Contributions by Model\", fontsize=14, y=1.01)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Cumulative Contribution Curve\n", + "\n", + "Sorting observations by contribution and plotting the cumulative sum shows how quickly each model accumulates its Gini. A steeper initial rise means the model concentrates its discriminatory power in fewer observations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 4), dpi=110)\n", + "\n", + "for name, color in zip(model_names, model_colors):\n", + " c, g = gini_contributions(predictions[name], y_test)\n", + " sorted_c = np.sort(c)[::-1]\n", + " cum_c = np.cumsum(sorted_c)\n", + " n_obs = len(cum_c)\n", + " ax.plot(\n", + " np.arange(1, n_obs + 1) / n_obs,\n", + " cum_c,\n", + " color=color,\n", + " label=f\"{name} Gini: {g:.2%}\",\n", + " )\n", + "\n", + "ax.axhline(0, color=\"black\", linestyle=\"dotted\", linewidth=0.8, alpha=0.7)\n", + "ax.set_xlabel(\"Fraction of observations (sorted by contribution)\")\n", + "ax.set_ylabel(\"Cumulative Gini contribution\")\n", + "ax.set_title(\"Cumulative Gini Contribution Curve\", fontsize=14)\n", + "ax.set_xticks(np.arange(0, 1.1, 0.1))\n", + "ax.legend(loc=\"lower right\", fontsize=10)\n", + "ax.grid(True, linestyle=\"dotted\", linewidth=0.7, alpha=0.6)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.11.8)", + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/fastwoe_monotonic.ipynb b/examples/notebooks/fastwoe_monotonic.ipynb similarity index 100% rename from examples/fastwoe_monotonic.ipynb rename to examples/notebooks/fastwoe_monotonic.ipynb diff --git a/examples/fastwoe_multiclass.ipynb b/examples/notebooks/fastwoe_multiclass.ipynb similarity index 100% rename from examples/fastwoe_multiclass.ipynb rename to examples/notebooks/fastwoe_multiclass.ipynb diff --git a/examples/fastwoe_styled_display.ipynb b/examples/notebooks/fastwoe_styled_display.ipynb similarity index 100% rename from examples/fastwoe_styled_display.ipynb rename to examples/notebooks/fastwoe_styled_display.ipynb diff --git a/examples/fastwoe_visualize_woe.ipynb b/examples/notebooks/fastwoe_visualize_woe.ipynb similarity index 100% rename from examples/fastwoe_visualize_woe.ipynb rename to examples/notebooks/fastwoe_visualize_woe.ipynb diff --git a/examples/msd_feature_selection.ipynb b/examples/notebooks/msd_feature_selection.ipynb similarity index 100% rename from examples/msd_feature_selection.ipynb rename to examples/notebooks/msd_feature_selection.ipynb diff --git a/examples/woe_standard_errors.ipynb b/examples/notebooks/woe_standard_errors.ipynb similarity index 100% rename from examples/woe_standard_errors.ipynb rename to examples/notebooks/woe_standard_errors.ipynb diff --git a/examples/fastwoe_example.py b/examples/scripts/fastwoe_example.py similarity index 100% rename from examples/fastwoe_example.py rename to examples/scripts/fastwoe_example.py diff --git a/examples/fastwoe_faiss_kmeans.py b/examples/scripts/fastwoe_faiss_kmeans.py similarity index 100% rename from examples/fastwoe_faiss_kmeans.py rename to examples/scripts/fastwoe_faiss_kmeans.py diff --git a/examples/fastwoe_monotonic.py b/examples/scripts/fastwoe_monotonic.py similarity index 100% rename from examples/fastwoe_monotonic.py rename to examples/scripts/fastwoe_monotonic.py diff --git a/examples/fastwoe_multiclass.py b/examples/scripts/fastwoe_multiclass.py similarity index 100% rename from examples/fastwoe_multiclass.py rename to examples/scripts/fastwoe_multiclass.py diff --git a/examples/fastwoe_shapley.py b/examples/scripts/fastwoe_shapley.py similarity index 100% rename from examples/fastwoe_shapley.py rename to examples/scripts/fastwoe_shapley.py diff --git a/examples/fastwoe_tree.py b/examples/scripts/fastwoe_tree.py similarity index 100% rename from examples/fastwoe_tree.py rename to examples/scripts/fastwoe_tree.py diff --git a/examples/iv_standard_errors.py b/examples/scripts/iv_standard_errors.py similarity index 100% rename from examples/iv_standard_errors.py rename to examples/scripts/iv_standard_errors.py diff --git a/examples/woe_density.py b/examples/scripts/woe_density.py similarity index 100% rename from examples/woe_density.py rename to examples/scripts/woe_density.py diff --git a/fastwoe/__init__.py b/fastwoe/__init__.py index 2ce0081..4843eaf 100644 --- a/fastwoe/__init__.py +++ b/fastwoe/__init__.py @@ -16,6 +16,7 @@ from .display import StyledDataFrame, iv_styled, style_iv_analysis, style_woe_mapping, styled from .fastwoe import FastWoe, WoePreprocessor from .interpret_fastwoe import WeightOfEvidence +from .metrics import gini_contributions # Optional plotting functionality try: @@ -35,7 +36,7 @@ def _plotting_not_available(*args, **kwargs): plot_performance = _plotting_not_available visualize_woe = _plotting_not_available -__version__ = "0.1.6" +__version__ = "0.1.7a0" __author__ = "xRiskLab" __email__ = "contact@xrisklab.ai" @@ -50,4 +51,5 @@ def _plotting_not_available(*args, **kwargs): "style_woe_mapping", "styled", "iv_styled", + "gini_contributions", ] diff --git a/fastwoe/fastwoe.py b/fastwoe/fastwoe.py index e4e21e2..dc9825d 100644 --- a/fastwoe/fastwoe.py +++ b/fastwoe/fastwoe.py @@ -62,6 +62,7 @@ def fit(self, X: pd.DataFrame, y=None, cat_features: Union[list[str], None] = No else: self.cat_features_ = [] + assert self.cat_features_ is not None for col in self.cat_features_: vc = X[col].astype(str).value_counts(dropna=False).sort_values(ascending=False) original_cats = len(vc) @@ -1734,7 +1735,7 @@ def _bin_with_tree( else: col_values = np.array(col_data) # Custom binning logic to handle right-inclusive intervals correctly - binned_values = np.zeros(len(col_values), dtype=int) + binned_values: np.ndarray = np.zeros(len(col_values), dtype=int) for i, val in enumerate(col_values): for bin_idx in range(len(bin_edges) - 1): if bin_edges[bin_idx] < val <= bin_edges[bin_idx + 1]: diff --git a/fastwoe/metrics.py b/fastwoe/metrics.py index 6731d33..7e0a9f6 100644 --- a/fastwoe/metrics.py +++ b/fastwoe/metrics.py @@ -132,7 +132,7 @@ def _somers_yx_core(y: np.ndarray, x: np.ndarray) -> tuple[float, int, int, int, m = len(uniq) # ranks array - y_rank = np.empty(n, dtype=np.int64) + y_rank: np.ndarray = np.empty(n, dtype=np.int64) for i in range(n): lo, hi = 0, m v = y[i] @@ -145,7 +145,7 @@ def _somers_yx_core(y: np.ndarray, x: np.ndarray) -> tuple[float, int, int, int, y_rank[i] = lo + 1 idx = np.argsort(x, kind="mergesort") - bit = np.zeros(m, dtype=np.int64) + bit: np.ndarray = np.zeros(m, dtype=np.int64) processed = 0 S = 0 @@ -208,7 +208,7 @@ def _somers_xy_core(y: np.ndarray, x: np.ndarray) -> tuple[float, int, int, int, m = len(uniq) # Rank Y values - y_rank = np.empty(n, dtype=np.int64) + y_rank: np.ndarray = np.empty(n, dtype=np.int64) for i in range(n): lo, hi = 0, m v = y[i] @@ -222,7 +222,7 @@ def _somers_xy_core(y: np.ndarray, x: np.ndarray) -> tuple[float, int, int, int, # Sort by X values and process with Fenwick tree idx = np.argsort(x, kind="mergesort") - bit = np.zeros(m, dtype=np.int64) + bit: np.ndarray = np.zeros(m, dtype=np.int64) processed = 0 S = 0 @@ -375,6 +375,79 @@ def somersd_pairwise( return None if np.isnan(statistic) else float(statistic) +def gini_contributions( + scores: np.ndarray, + labels: np.ndarray, +) -> tuple[np.ndarray, float]: + """Calculate each observation's contribution to the Gini coefficient. + + Assigns a signed contribution to every observation based on how well it is + ranked relative to observations of the opposite class (Somers' D definition, + so ties receive zero credit). + + - A **positive** (label=1) earns credit for each negative it outranks and + gets penalised for each negative that outranks it. + - A **negative** (label=0) earns credit for each positive that outranks it + and gets penalised for each positive it outranks. + + The contributions are normalised so that their sum equals the overall Gini + coefficient (Somers' D_{Y|X}), making the array directly comparable to + SHAP-style decompositions. + + The algorithm is O(n log n) via ``np.searchsorted`` on sorted sub-arrays, + avoiding the O(nยฒ) loop of a naive pairwise implementation. + + Args: + scores: Model scores, shape (n,). + labels: Binary labels (0/1), shape (n,). + + Returns: + contributions: Per-observation contributions, shape (n,). + ``contributions.sum()`` equals ``gini``. + gini: Overall Gini coefficient (Somers' D_{Y|X}). + + Examples: + >>> import numpy as np + >>> scores = np.array([0.9, 0.8, 0.4, 0.3]) + >>> labels = np.array([1, 1, 0, 0]) + >>> contribs, gini = gini_contributions(scores, labels) + >>> np.isclose(contribs.sum(), gini) + True + """ + scores = np.asarray(scores, dtype=np.float64) + labels = np.asarray(labels, dtype=np.int32) + + n_pos: int = int(np.sum(labels)) + n_neg: int = len(labels) - n_pos + n_pairs: int = n_pos * n_neg + + if n_pairs == 0: + return np.zeros(len(scores), dtype=np.float64), 0.0 + + pos_mask = labels == 1 + neg_mask = ~pos_mask + + pos_scores_sorted = np.sort(scores[pos_mask]) + neg_scores_sorted = np.sort(scores[neg_mask]) + + contributions: np.ndarray = np.empty(len(scores), dtype=np.float64) + + # Positives: credit for each negative they outrank, penalty for each that outranks them + pos_scores = scores[pos_mask] + concordant_pos = np.searchsorted(neg_scores_sorted, pos_scores, side="left") + discordant_pos = n_neg - np.searchsorted(neg_scores_sorted, pos_scores, side="right") + contributions[pos_mask] = (concordant_pos - discordant_pos) / (2 * n_pairs) + + # Negatives: credit for each positive that outranks them, penalty for each they outrank + neg_scores = scores[neg_mask] + concordant_neg = n_pos - np.searchsorted(pos_scores_sorted, neg_scores, side="right") + discordant_neg = np.searchsorted(pos_scores_sorted, neg_scores, side="left") + contributions[neg_mask] = (concordant_neg - discordant_neg) / (2 * n_pairs) + + gini = float(contributions.sum()) + return contributions, gini + + def somersd_clustered_matrix( df: pd.DataFrame, score_col: str, diff --git a/fastwoe/plots.py b/fastwoe/plots.py index 295182c..8359d06 100644 --- a/fastwoe/plots.py +++ b/fastwoe/plots.py @@ -20,13 +20,13 @@ else: # Runtime import - needed for actual plotting try: - from matplotlib import pyplot as plt # noqa: F401 - from matplotlib.axes import Axes # noqa: F401 - from matplotlib.figure import Figure # noqa: F401 + from matplotlib import pyplot as plt + from matplotlib.axes import Axes + from matplotlib.figure import Figure except ImportError: - plt = None # type: ignore[assignment] - Axes = Any # type: ignore[assignment, misc] - Figure = Any # type: ignore[assignment, misc] + plt = None + Axes = Any + Figure = Any from .metrics import somersd_yx @@ -36,11 +36,12 @@ def plot_performance( y_pred: Union[np.ndarray, pd.Series, list], weights: Optional[Union[np.ndarray, pd.Series]] = None, ax: Optional[Axes] = None, - figsize: tuple = (6, 5), - dpi: int = 100, + figsize: tuple = (6, 4), + dpi: int = 110, show_plot: bool = True, labels: Optional[list[str]] = None, colors: Optional[list[str]] = None, + top_p: Optional[float] = None, ) -> tuple: """ Plot model performance curve (CAP for binary, Power curve for continuous). @@ -55,11 +56,15 @@ def plot_performance( - List of arrays: multiple models for comparison weights: Optional weights (e.g., EAD for LGD models) ax: Optional matplotlib axes to plot on. If None, creates new figure. - figsize: Figure size as (width, height). Only used if ax is None. - dpi: Figure resolution. Only used if ax is None. + figsize: Figure size as (width, height). Only used if ax is None. Defaults to (6, 4). + dpi: Figure resolution. Only used if ax is None. Defaults to 110. show_plot: Whether to display the plot. Only used if ax is None. labels: Optional labels for multiple predictions. If None, uses "Model 1", "Model 2", etc. colors: Optional colors for multiple predictions. If None, uses default colormap. + top_p: Optional float in (0, 1] to zoom the x-axis to the top p fraction of the + population (e.g., top_p=0.2 shows the top 20%). When set, the x-axis ticks + are adjusted to the zoomed range. Useful for inspecting model lift at the top + of the score distribution. Returns: Tuple of (figure, axes, gini_coefficient(s)) @@ -105,8 +110,8 @@ def plot_performance( if colors is None: # Default colormap default_colors = [ - "#69db7c", "#55d3ed", + "#69db7c", "#ffa94d", "#c430c1", "#ff6b6b", @@ -140,46 +145,36 @@ def plot_performance( fig_raw = ax1.get_figure() if fig_raw is None: raise ValueError("Could not get figure from axes") - # Type narrowing for matplotlib figure - fig = fig_raw # type: ignore[assignment, no-redef] + if not isinstance(fig_raw, Figure): + raise ValueError("Axes must belong to a Figure, not a SubFigure") + fig = fig_raw # CAP curve for binary and continuous targets n = len(y_true) n_events = y_true.sum() - # Plot random line + # Plot random line (below everything) ax1.plot( [0, 1], [0, 1], linestyle="dotted", color="black", alpha=0.5, + zorder=1, ) - # Plot perfect/ideal line + # Pre-compute ideal curve data (plotted after models so it renders on top) if is_binary: - # Binary: perfect line is step function at bad_rate bad_rate = n_events / n - ax1.plot( - [0, bad_rate, 1], - [0, 1, 1], - color="dodgerblue", - label="Crystal Ball", - ) + ideal_x = [0, bad_rate, 1] + ideal_y = [0, 1, 1] else: - # Continuous: perfect line is sorting by TRUE target values perfect_idx = np.argsort(y_true)[::-1] y_perfect = y_true[perfect_idx] - cum_pop_perfect = np.concatenate([[0], np.arange(1, n + 1) / n]) - cum_events_perfect = np.concatenate([[0], np.cumsum(y_perfect) / n_events]) - ax1.plot( - cum_pop_perfect, - cum_events_perfect, - color="dodgerblue", - label="Crystal Ball", - ) + ideal_x = list(np.concatenate([[0], np.arange(1, n + 1) / n])) + ideal_y = list(np.concatenate([[0], np.cumsum(y_perfect) / n_events])) - # Plot each model (same for both binary and continuous) + # Plot each model first (zorder=2), ideal line drawn on top (zorder=3) for yp, label, color, g in zip(y_preds, labels, colors, ginis): sort_idx = np.argsort(yp)[::-1] y_sorted = y_true[sort_idx] @@ -191,15 +186,30 @@ def plot_performance( cum_pop, cum_events, color=color, - label=f"{label} AR: {g:.2%}", + label=f"{label} Gini: {g:.2%}", + zorder=2, ) - # Styling (same for both) - ax1.set_xlabel("Fraction of population", fontsize=12) - ax1.set_ylabel("Fraction of target", fontsize=12) + # Plot ideal line on top of model curves + ax1.plot( + ideal_x, + ideal_y, + color="dodgerblue", + label="Ideal Model", + zorder=3, + ) + + # Styling + ax1.set_xlabel("Fraction of population sorted by score") + ax1.set_ylabel("Recall") ax1.set_title("Cumulative Accuracy Profile (CAP)", fontsize=14) - ax1.set_xticks(np.arange(0, 1.1, 0.1)) ax1.set_yticks(np.arange(0, 1.1, 0.1)) + if top_p is not None: + x_step = round(top_p / 10, 10) + ax1.set_xlim(-x_step * 0.5, top_p + x_step * 0.5) + ax1.set_xticks(np.arange(0, top_p + x_step / 2, x_step)) + else: + ax1.set_xticks(np.arange(0, 1.1, 0.1)) ax1.legend(loc="lower right", fontsize=10) ax1.grid(True, which="both", linestyle="dotted", linewidth=0.7, alpha=0.6) # Only apply tight_layout and show if we created the figure diff --git a/pyproject.toml b/pyproject.toml index c05e0a3..bb92a3e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,7 @@ name = "fastwoe" dynamic = ["version"] description = "Fast Weight of Evidence (WOE) encoding and inference" readme = "README.md" -requires-python = ">=3.9,<3.13" +requires-python = ">=3.9" authors = [ {name = "xRiskLab", email = "contact@xrisklab.ai"} ] @@ -37,6 +37,8 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ] @@ -150,7 +152,7 @@ ignore = [ "N801", # class name should use CapWords convention "N802", # function name should be lowercase ] -"examples/*" = [ +"examples/**/*" = [ "D", # ignore docstring rules in examples "N803", # argument name should be lowercase "N806", # variable in function should be lowercase @@ -258,6 +260,12 @@ module = [ ] ignore_missing_imports = true +[[tool.mypy.overrides]] +# pandas operations return Any; suppressing warn_return_any for files +# that return DataFrames constructed from pandas internals +module = ["fastwoe.fastwoe", "fastwoe.fastwoe_multiclass"] +warn_return_any = false + # Sourcery configuration [tool.sourcery] [tool.sourcery.rules] diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 6b89c5c..c2d845d 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -121,7 +121,7 @@ def test_weighted_somersd(): auc_unweighted = roc_auc_score(y_true, y_pred) gini_unweighted = 2 * auc_unweighted - 1 - logger.info(f"Somers' D (unweighted): {somers_unweighted:.8f}") + logger.info(f"Somers' D (unweighted): {somers_unweighted:.8f}") logger.info(f"Gini from AUC (unweighted): {gini_unweighted:.8f}") assert np.isclose(somers_unweighted, gini_unweighted, atol=1e-10), ( @@ -134,8 +134,8 @@ def test_weighted_somersd(): auc_weighted = roc_auc_score(y_true, y_pred, sample_weight=weights) gini_weighted = 2 * auc_weighted - 1 - logger.info(f"Somers' D (weighted): {somers_weighted:.8f}") - logger.info(f"Gini from AUC (weighted): {gini_weighted:.8f}") + logger.info(f"Somers' D (weighted): {somers_weighted:.8f}") + logger.info(f"Gini from AUC (weighted): {gini_weighted:.8f}") assert np.isclose(somers_weighted, gini_weighted, atol=1e-10), ( "Weighted Somers' D should match weighted Gini" @@ -144,8 +144,8 @@ def test_weighted_somersd(): # Test 3: Verify weights have effect logger.info("=== Test 3: Weights Effect ===") logger.info(f"Unweighted Gini: {gini_unweighted:.6f}") - logger.info(f"Weighted Gini: {gini_weighted:.6f}") - logger.info(f"Difference: {abs(gini_weighted - gini_unweighted):.6f}") + logger.info(f"Weighted Gini: {gini_weighted:.6f}") + logger.info(f"Difference: {abs(gini_weighted - gini_unweighted):.6f}") assert not np.isclose(gini_weighted, gini_unweighted, atol=1e-3), ( "Weights should change the result" @@ -297,7 +297,7 @@ def test_somersd_clustered_matrix(): gini_from_auc = 2 * auc - 1 logger.info(f"Global Somers' D (from matrix): {global_somersd:.8f}") - logger.info(f"Gini from AUC: {gini_from_auc:.8f}") + logger.info(f"Gini from AUC: {gini_from_auc:.8f}") assert np.isclose(global_somersd, gini_from_auc, atol=1e-10), ( "Global Somers' D should match Gini from AUC" @@ -608,6 +608,110 @@ def test_somersd_pairwise_vs_scipy(): logger.info("โœ“ All scipy comparison tests passed!") +class TestGiniContributions: + """Tests for gini_contributions() instance-level decomposition.""" + + def test_contributions_sum_equals_gini(self): + """contributions.sum() must equal the overall Gini coefficient.""" + from fastwoe.metrics import gini_contributions + + rng = np.random.default_rng(42) + n = 200 + scores = rng.uniform(0, 1, n) + labels = rng.binomial(1, 0.1 + 0.6 * scores, n).astype(int) + + contribs, gini = gini_contributions(scores, labels) + + assert np.isclose(contribs.sum(), gini, atol=1e-10), ( + f"contributions.sum()={contribs.sum():.6f} != gini={gini:.6f}" + ) + + def test_matches_somersd_yx(self): + """Gini from contributions must match somersd_yx.""" + from fastwoe.metrics import gini_contributions, somersd_yx + + rng = np.random.default_rng(42) + n = 200 + scores = rng.uniform(0, 1, n) + labels = rng.binomial(1, 0.1 + 0.6 * scores, n).astype(int) + + _, gini = gini_contributions(scores, labels) + gini_ref = somersd_yx(labels.astype(float), scores).statistic + + assert np.isclose(gini, gini_ref, atol=1e-10), ( + f"gini_contributions={gini:.6f} != somersd_yx={gini_ref:.6f}" + ) + + def test_known_values(self): + """Verify contributions on a small known dataset.""" + from fastwoe.metrics import gini_contributions + + labels = np.array([0] * 10 + [1] * 10) + scores = np.concatenate( + [ + np.array([0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65]), + np.array([0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85]), + ] + ) + + contribs, gini = gini_contributions(scores, labels) + + assert np.isclose(gini, 0.64, atol=1e-6), f"Expected Gini=0.64, got {gini:.6f}" + assert np.isclose(contribs.sum(), gini, atol=1e-10) + assert len(contribs) == len(scores) + + def test_perfect_separation(self): + """Perfect model: all positives score above all negatives โ†’ Gini = 1.""" + from fastwoe.metrics import gini_contributions + + labels = np.array([0, 0, 0, 1, 1, 1]) + scores = np.array([0.1, 0.2, 0.3, 0.7, 0.8, 0.9]) + + contribs, gini = gini_contributions(scores, labels) + + assert np.isclose(gini, 1.0, atol=1e-10) + assert np.isclose(contribs.sum(), 1.0, atol=1e-10) + assert np.all(contribs >= 0), "Perfect model should have no negative contributions" + + def test_worst_separation(self): + """Worst model: all positives score below all negatives โ†’ Gini = -1.""" + from fastwoe.metrics import gini_contributions + + labels = np.array([0, 0, 0, 1, 1, 1]) + scores = np.array([0.7, 0.8, 0.9, 0.1, 0.2, 0.3]) + + contribs, gini = gini_contributions(scores, labels) + + assert np.isclose(gini, -1.0, atol=1e-10) + assert np.isclose(contribs.sum(), -1.0, atol=1e-10) + assert np.all(contribs <= 0), "Worst model should have no positive contributions" + + def test_no_pairs_returns_zeros(self): + """All-positive or all-negative labels โ†’ zero contributions and zero Gini.""" + from fastwoe.metrics import gini_contributions + + scores = np.array([0.1, 0.5, 0.9]) + labels_all_pos = np.array([1, 1, 1]) + labels_all_neg = np.array([0, 0, 0]) + + for labels in (labels_all_pos, labels_all_neg): + contribs, gini = gini_contributions(scores, labels) + assert gini == 0.0 + assert np.all(contribs == 0.0) + + def test_output_shape(self): + """contributions array must have the same length as the input.""" + from fastwoe.metrics import gini_contributions + + rng = np.random.default_rng(0) + n = 500 + scores = rng.uniform(0, 1, n) + labels = rng.integers(0, 2, n) + + contribs, _ = gini_contributions(scores, labels) + assert contribs.shape == (n,) + + if __name__ == "__main__": # Allow running this file directly for development print("๐Ÿงช Running FastSomersD tests...") From de59596d8104a3a058e5d1d4add389758e36cc9d Mon Sep 17 00:00:00 2001 From: xRiskLab Date: Sat, 11 Apr 2026 14:03:11 +0200 Subject: [PATCH 2/3] prepare release for fastwoe 0.1.7 --- CHANGELOG.md | 35 + docs/somersd_ase.md | 248 +++++++ examples/notebooks/fastwoe_finetuning.ipynb | 778 ++++++++++++++++++++ fastwoe/__init__.py | 2 +- fastwoe/fast_somersd.py | 14 +- fastwoe/fastwoe.py | 401 +++++++--- fastwoe/metrics.py | 107 +++ tests/test_fastwoe.py | 269 +++++++ 8 files changed, 1748 insertions(+), 106 deletions(-) create mode 100644 docs/somersd_ase.md create mode 100644 examples/notebooks/fastwoe_finetuning.ipynb diff --git a/CHANGELOG.md b/CHANGELOG.md index a5ab67a..4498bb4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,40 @@ # Changelog +## Version 0.1.7 (2026-04-11) + +**WOE Recalibration & Somers' D Standard Errors** + +### New Features + +- **`finetune(X_new, y_new)`**: WOE recalibration without redevelopment + - Updates WOE values using new data while preserving bin structure (edges, categories) + - Pass a full DataFrame to update all features, or a single-column DataFrame for one + - Bins absent from new data retain their original WOE (with warning) + - Unknown columns are skipped (with warning) + - `update_prior=True` shifts the global prior and warns about stale features + - Returns `self` for method chaining + +- **Asymptotic Standard Errors of Somers' D**: Uncertainty quantification via Goktas & Oznur (2011) + - Contingency-table ASE using per-cell concordant/discordant counts and the delta method + - Works for binary, ordinal, and continuous targets (for binary targets Somers' D = Gini) + - New `somersd_se()` function in `fastwoe.metrics` + - New fields in `feature_stats_` and `get_feature_stats()`: `somersd_se`, `somersd_ci_lower`, `somersd_ci_upper` + - Returns NaN gracefully for degenerate inputs + +### Internal + +- Extracted `_apply_binning_to_column()` helper from `transform()` for reuse by `finetune()` +- Added `_recalibrate_feature()` private method encapsulating per-feature recalibration logic + +### Testing + +- Added `TestFinetune` class with 13 test cases covering single-feature, full-DataFrame, numerical/categorical, error handling, prior update, missing bins, transform-after-finetune, and method chaining +- All 132 tests passing + +### Examples + +- Added `examples/notebooks/fastwoe_finetuning.ipynb` demonstrating recalibration workflow + ## Version 0.1.6 (2026-01-07) **Stable Release: Type Safety, Robustness & Code Quality** ๐ŸŽฏ diff --git a/docs/somersd_ase.md b/docs/somersd_ase.md new file mode 100644 index 0000000..32800bc --- /dev/null +++ b/docs/somersd_ase.md @@ -0,0 +1,248 @@ +# Asymptotic Standard Error of Somers' D + +Reference: Goktas, A., Oznur, I., 2011. *A Comparison of the Most Commonly Used Measures of Association for Doubly Ordered Square Contingency Tables via Simulation.* Metodoloski zvezki 8 (1), 17-37. + +## 1. Setup + +Given $n$ paired observations $(y_i, x_i)$, build the contingency table $\mathbf{F}$ where rows correspond to sorted unique values of $Y$ and columns to sorted unique values of $X$. + +| Symbol | Definition | +|--------|-----------| +| $f_{ij}$ | Cell count: number of observations in row $i$, column $j$ | +| $a$ | Number of rows (unique $Y$ values) | +| $b$ | Number of columns (unique $X$ values) | +| $r_i$ | Row sum: $r_i = \sum_j f_{ij}$ | +| $W$ | Sample size: $W = \sum_{ij} f_{ij} = n$ | + +## 2. Concordant and Discordant Counts + +For each cell $(i, j)$ of the contingency table, define: + +**$C_{ij}$** = number of observations that would form a **concordant** pair with any observation in cell $(i, j)$. These are observations in cells strictly above-left or strictly below-right: + +$$C_{ij} = \sum_{\substack{r > i \\ s > j}} f_{rs} \;+\; \sum_{\substack{r < i \\ s < j}} f_{rs}$$ + +**$D_{ij}$** = number of observations that would form a **discordant** pair: + +$$D_{ij} = \sum_{\substack{r > i \\ s < j}} f_{rs} \;+\; \sum_{\substack{r < i \\ s > j}} f_{rs}$$ + +These are computed efficiently using 2D prefix sums. + +## 3. Somers' D Statistic + +Total concordant and discordant pairs: + +$$P = \sum_{ij} f_{ij} \cdot C_{ij} \qquad Q = \sum_{ij} f_{ij} \cdot D_{ij}$$ + +Denominator - number of ordered pairs untied on $Y$: + +$$D_r = W^2 - \sum_i r_i^2$$ + +For binary $Y$ with $m$ positives and $W - m$ negatives: $D_r = 2m(W - m)$. + +**Somers' D:** + +$$D = \frac{P - Q}{D_r}$$ + +For binary targets, this equals the Gini coefficient $= 2 \cdot \text{AUC} - 1$. + +## 4. Row Midranks + +Define the midrank of row $i$ as the average position of observations in that row: + +$$R_i = \sum_{k=1}^{i} r_k + \frac{1 - r_i}{2}$$ + +This places each row at the center of its range in the marginal ranking. + +For example, if $r = [3, 3, 3]$: + +- $R_1 = 3 + (1-3)/2 = 2$ +- $R_2 = 6 + (1-3)/2 = 5$ +- $R_3 = 9 + (1-3)/2 = 8$ + +## 5. ASE via the Delta Method + +Somers' D is a **ratio of two U-statistics**: $D = (P - Q) / D_r$. + +To get the variance of a ratio $f/g$, the delta method gives: + +$$\text{Var}\!\left(\frac{f}{g}\right) \approx \frac{1}{g^2} \cdot \text{Var}(f - D \cdot g)$$ + +For each cell $(i, j)$, the **linearized residual** has two parts: + +### Part 1: Numerator contribution (scaled) + +$$D_r \cdot (C_{ij} - D_{ij})$$ + +This is how much cell $(i, j)$ contributes to the net concordance, scaled by the denominator. + +### Part 2: Denominator correction + +$$(P - Q) \cdot (W - R_i)$$ + +Here $(W - R_i)$ captures how much row $i$ contributes to $D_r$ (pairs untied on $Y$). The factor $(P - Q) = D \cdot D_r$ weights this by the current value of $D$. + +### Combined residual per cell + +$$\varepsilon_{ij} = D_r \cdot (C_{ij} - D_{ij}) \;-\; (P - Q) \cdot (W - R_i)$$ + +Note that $R_i$ depends only on the row, not the column - the denominator $D_r$ is determined entirely by the $Y$ marginal distribution. + +### ASE formula + +The weighted sum of squared residuals, normalized: + +$$\boxed{\text{ASE}_1 = \frac{2}{D_r^2} \sqrt{\sum_{ij} f_{ij} \cdot \varepsilon_{ij}^2}}$$ + +Expanding: + +$$\text{ASE}_1 = \frac{2}{D_r^2} \sqrt{\sum_{ij} f_{ij} \left\{ D_r(C_{ij} - D_{ij}) - (P - Q)(W - R_i) \right\}^2}$$ + +The factor of 2 accounts for the symmetry of ordered pairs (each unordered pair is counted twice). + +## 6. ASE Under the Null ($\text{ASE}_0$) + +Under $H_0\!: D = 0$, the denominator correction term vanishes (since $P - Q = 0$), giving a simpler formula: + +$$\boxed{\text{ASE}_0 = \frac{2}{D_r} \sqrt{\sum_{ij} f_{ij} (C_{ij} - D_{ij})^2 \;-\; \frac{(P - Q)^2}{W}}}$$ + +This is used for hypothesis testing (is $D$ significantly different from zero?). + +## 7. Summary + +| Quantity | Formula | Use | +|----------|---------|-----| +| $D$ | $(P - Q) \;/\; D_r$ | Point estimate | +| $\text{ASE}_1$ | $\frac{2}{D_r^2} \sqrt{\sum f_{ij} \cdot \varepsilon_{ij}^2}$ | Confidence intervals | +| $\text{ASE}_0$ | $\frac{2}{D_r} \sqrt{\sum f_{ij}(C_{ij}-D_{ij})^2 - (P-Q)^2/W}$ | Hypothesis testing | + +For a 95% confidence interval: + +$$D \;\pm\; z_{0.975} \cdot \text{ASE}_1$$ + +For a two-sided test of $H_0\!: D = 0$: + +$$z = \frac{D}{\text{ASE}_0}$$ + +## 8. Worked Example + +A small binary credit risk table: 10 applicants, target $Y \in \{0, 1\}$, WOE-encoded feature $X \in \{1, 2, 3\}$ (three bins). + +```python +import numpy as np + +#----------------------------------------------------------------------------------------------- +# Data +#----------------------------------------------------------------------------------------------- +y = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype=float) +x = np.array([1, 1, 2, 2, 3, 1, 2, 3, 3, 3], dtype=float) + +#----------------------------------------------------------------------------------------------- +# Step 1: Contingency table +#----------------------------------------------------------------------------------------------- +# x=1 x=2 x=3 +# y=0 [ 2 2 1 ] +# y=1 [ 1 1 3 ] +CT = np.array([[2, 2, 1], + [1, 1, 3]], dtype=float) + +a, b = CT.shape # 2 rows, 3 columns +W = CT.sum() # 10 +r = CT.sum(axis=1) # [5, 5] + +print(f"CT:\n{CT}") +print(f"W={W}, r={r}") + +#----------------------------------------------------------------------------------------------- +# Step 2: Concordant (C) and discordant (D) matrices +#----------------------------------------------------------------------------------------------- +# C[i,j] = sum of cells strictly below-right + strictly above-left +# D[i,j] = sum of cells strictly below-left + strictly above-right +C = np.zeros_like(CT) +D = np.zeros_like(CT) +for i in range(a): + for j in range(b): + C[i, j] = CT[i+1:, j+1:].sum() + CT[:i, :j].sum() + D[i, j] = CT[i+1:, :j].sum() + CT[:i, j+1:].sum() + +print(f"\nC (concordant counts):\n{C}") +print(f"D (discordant counts):\n{D}") + +#----------------------------------------------------------------------------------------------- +# Step 3: Somers' D +#----------------------------------------------------------------------------------------------- +P = (CT * C).sum() # total concordant +Q = (CT * D).sum() # total discordant +Dr = W**2 - (r**2).sum() # pairs untied on Y + +D_val = (P - Q) / Dr + +print(f"\nP={P}, Q={Q}, Dr={Dr}") +print(f"Somers' D = (P-Q)/Dr = {D_val:.6f}") + +#----------------------------------------------------------------------------------------------- +# Step 4: Row midranks +#----------------------------------------------------------------------------------------------- +RR = np.cumsum(r) + (1.0 - r) / 2.0 +print(f"\nRow midranks R = {RR}") + +#----------------------------------------------------------------------------------------------- +# Step 5: ASE (delta method) +#----------------------------------------------------------------------------------------------- +RR_mat = np.repeat(RR[:, np.newaxis], b, axis=1) + +# Linearized residual per cell +eps = Dr * (C - D) - (P - Q) * (W - RR_mat) +print(f"\nResiduals eps:\n{eps}") + +ASE = 2.0 / Dr**2 * np.sqrt((CT * eps**2).sum()) +print(f"\nASE = {ASE:.6f}") + +#----------------------------------------------------------------------------------------------- +# Step 6: ASE under H0 +#----------------------------------------------------------------------------------------------- +ASE0 = 2.0 / Dr * np.sqrt((CT * (C - D)**2).sum() - (P - Q)**2 / W) +print(f"ASE0 = {ASE0:.6f}") + +#----------------------------------------------------------------------------------------------- +# Step 7: 95% confidence interval +#----------------------------------------------------------------------------------------------- +print(f"\n95% CI: [{D_val - 1.96*ASE:.4f}, {D_val + 1.96*ASE:.4f}]") +print(f"z-test (H0: D=0): z = {D_val / ASE0:.4f}") +``` + +Output: + +``` +CT: +[[2. 2. 1.] + [1. 1. 3.]] +W=10.0, r=[5. 5.] + +C (concordant counts): +[[4. 3. 0.] + [0. 2. 4.]] +D (discordant counts): +[[0. 1. 2.] + [3. 1. 0.]] + +P=28.0, Q=8.0, Dr=50.0 + +Somers' D = (P-Q)/Dr = 0.400000 + +Row midranks R = [3. 8.] + +Residuals eps: +[[ 60. -40. -240.] + [-190. 10. 160.]] + +ASE = 0.340353 +ASE0 = 0.314960 + +95% CI: [-0.2671, 1.0671] +z-test (H0: D=0): z = 1.2700 +``` + +## 9. Production Implementation + +See `fastwoe.metrics.somersd_se()` - validated against the VUROCS R package with exact numerical agreement across binary, ordinal, and continuous targets. diff --git a/examples/notebooks/fastwoe_finetuning.ipynb b/examples/notebooks/fastwoe_finetuning.ipynb new file mode 100644 index 0000000..ddf59ce --- /dev/null +++ b/examples/notebooks/fastwoe_finetuning.ipynb @@ -0,0 +1,778 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1b2c3d4", + "metadata": {}, + "source": "# FastWoe Finetuning (WOE Recalibration)\n\nAuthor: https://www.github.com/xRiskLab\n\nWOE recalibration is a middle ground in credit risk between full redevelopment\n(rebuild everything) and simple recalibration (adjust intercept/scaling).\nThe bins stay fixed, the likelihood ratios inside them shift.\n\nThe `finetune(X_new, y_new)` method updates WOE values using new data\nwhile preserving the original bin structure. Pass a full DataFrame to\nupdate all features, or a single-column DataFrame to update just one." + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b1c2d3e4", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from fastwoe import FastWoe" + ] + }, + { + "cell_type": "markdown", + "id": "c1d2e3f4", + "metadata": {}, + "source": [ + "## 1. Fit on Original Data\n", + "\n", + "Simulate a credit scoring dataset with two features:\n", + "\n", + "- `grade` โ€” categorical (loan grade)\n", + "- `income` โ€” numerical (applicant income, will be binned automatically)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1e2f3a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set: 1000 obs, event rate = 0.120\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " grade income\n", + "0 A 39689.839442\n", + "1 D 18626.262397\n", + "2 C 43918.851477\n", + "3 B 49280.949853\n", + "4 A 48045.090001" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(42)\n", + "n = 1000\n", + "\n", + "grade = np.random.choice([\"A\", \"B\", \"C\", \"D\"], size=n, p=[0.4, 0.3, 0.2, 0.1])\n", + "income = np.random.lognormal(mean=10.5, sigma=0.5, size=n)\n", + "\n", + "# Event rates vary by grade\n", + "default_prob = np.where(\n", + " grade == \"A\", 0.05, np.where(grade == \"B\", 0.10, np.where(grade == \"C\", 0.20, 0.35))\n", + ")\n", + "y_train = np.random.binomial(1, default_prob)\n", + "\n", + "X_train = pd.DataFrame({\"grade\": grade, \"income\": income})\n", + "y_train = pd.Series(y_train)\n", + "\n", + "print(f\"Training set: {len(X_train)} obs, event rate = {y_train.mean():.3f}\")\n", + "X_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e1f2a3b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original WOE mapping for 'grade':\n" + ] + }, + { + "data": { + "text/html": [ + "
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categorycountcount_pctgood_countbad_countevent_ratewoewoe_sewoe_ci_lowerwoe_ci_upper
0A42142.1401200.047506-1.0057990.229115-1.454856-0.556741
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" + ], + "text/plain": [ + " category count count_pct good_count bad_count event_rate woe \\\n", + "0 A 421 42.1 401 20 0.047506 -1.005799 \n", + "1 B 291 29.1 263 28 0.096220 -0.247519 \n", + "2 C 188 18.8 146 42 0.223404 0.746493 \n", + "3 D 100 10.0 70 30 0.300000 1.145132 \n", + "\n", + " woe_se woe_ci_lower woe_ci_upper \n", + "0 0.229115 -1.454856 -0.556741 \n", + "1 0.198788 -0.637136 0.142097 \n", + "2 0.175097 0.403310 1.089676 \n", + "3 0.218218 0.717433 1.572831 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "woe = FastWoe(\n", + " binning_method=\"tree\",\n", + " tree_kwargs={\"max_depth\": 2},\n", + " numerical_threshold=10,\n", + " random_state=42,\n", + ")\n", + "woe.fit(X_train, y_train)\n", + "\n", + "print(\"Original WOE mapping for 'grade':\")\n", + "woe.get_mapping(\"grade\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f1a2b3c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original WOE mapping for 'income':\n" + ] + }, + { + "data": { + "text/html": [ + "
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categorycountcount_pctgood_countbad_countevent_ratewoewoe_sewoe_ci_lowerwoe_ci_upper
0(-โˆž, 19682.5]898.98270.078652-0.4683790.393767-1.2401480.303390
1(19682.5, 21198.9]222.22200.000000-14.7318020.621261-15.949450-13.514153
2(21198.9, 21280.1]20.2020.99999614.3263371.00000012.36637316.286301
3(21280.1, โˆž)88788.77761110.1251410.0478080.101477-0.1510840.246700
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" + ], + "text/plain": [ + " category count count_pct good_count bad_count event_rate \\\n", + "0 (-โˆž, 19682.5] 89 8.9 82 7 0.078652 \n", + "1 (19682.5, 21198.9] 22 2.2 22 0 0.000000 \n", + "2 (21198.9, 21280.1] 2 0.2 0 2 0.999996 \n", + "3 (21280.1, โˆž) 887 88.7 776 111 0.125141 \n", + "\n", + " woe woe_se woe_ci_lower woe_ci_upper \n", + "0 -0.468379 0.393767 -1.240148 0.303390 \n", + "1 -14.731802 0.621261 -15.949450 -13.514153 \n", + "2 14.326337 1.000000 12.366373 16.286301 \n", + "3 0.047808 0.101477 -0.151084 0.246700 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"Original WOE mapping for 'income':\")\n", + "woe.get_mapping(\"income\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a2b3c4d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original feature stats:\n" + ] + }, + { + "data": { + "text/html": [ + "
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featuren_categoriestotal_observationsmissing_countmissing_rateginiiviv_seiv_ci_loweriv_ci_uppermin_woemax_woe
0grade4100000.00.4246970.6395260.1088170.4262490.852803-1.0057991.145132
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" + ], + "text/plain": [ + " feature n_categories total_observations missing_count missing_rate \\\n", + "0 grade 4 1000 0 0.0 \n", + "1 income 4 1000 0 0.0 \n", + "\n", + " gini iv iv_se iv_ci_lower iv_ci_upper min_woe \\\n", + "0 0.424697 0.639526 0.108817 0.426249 0.852803 -1.005799 \n", + "1 0.076004 0.257158 0.169943 0.000000 0.590239 -14.731802 \n", + "\n", + " max_woe \n", + "0 1.145132 \n", + "1 14.326337 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"Original feature stats:\")\n", + "woe.get_feature_stats()" + ] + }, + { + "cell_type": "markdown", + "id": "b2c3d4e5", + "metadata": {}, + "source": [ + "## 2. Simulate Population Shift\n", + "\n", + "A year later, the population has shifted: event rates for each grade have increased (economic downturn).\n", + "\n", + "The bins (grades) are still the same, but the risk within each bin has changed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2d3e4f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New data: 800 obs, event rate = 0.194 (was 0.120)\n" + ] + } + ], + "source": [ + "np.random.seed(123)\n", + "n_new = 800\n", + "\n", + "grade_new = np.random.choice([\"A\", \"B\", \"C\", \"D\"], size=n_new, p=[0.35, 0.30, 0.25, 0.10])\n", + "income_new = np.random.lognormal(mean=10.4, sigma=0.5, size=n_new)\n", + "\n", + "# Shifted event rates โ€” higher defaults across the board\n", + "default_prob_new = np.where(\n", + " grade_new == \"A\", 0.10, np.where(grade_new == \"B\", 0.18, np.where(grade_new == \"C\", 0.30, 0.45))\n", + ")\n", + "y_new = pd.Series(np.random.binomial(1, default_prob_new))\n", + "\n", + "X_new = pd.DataFrame({\"grade\": grade_new, \"income\": income_new})\n", + "\n", + "print(f\"New data: {len(X_new)} obs, event rate = {y_new.mean():.3f} (was {y_train.mean():.3f})\")" + ] + }, + { + "cell_type": "markdown", + "id": "d2e3f4a5", + "metadata": {}, + "source": "## 3. Finetune a Single Feature\n\nPass a single-column DataFrame to update just one feature.\nThe bin structure (A, B, C, D) stays the same โ€” only the evidence shifts." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2f3a4b5", + "metadata": {}, + "outputs": [], + "source": "# Save original values for comparison\noriginal_grade_woe = woe.get_mapping(\"grade\").set_index(\"category\")[\"woe\"].copy()\n\n# Finetune just 'grade' โ€” pass single-column DataFrame\nwoe.finetune(X_new[[\"grade\"]], y_new)\n\nprint(\"Updated WOE mapping for 'grade':\")\nwoe.get_mapping(\"grade\")" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2a3b4c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WOE shift after finetuning 'grade':\n" + ] + }, + { + "data": { + "text/html": [ + "
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original_woefinetuned_woedelta
category
A-1.0058-0.73120.2746
B-0.24750.46540.7129
C0.74651.18150.4350
D1.14511.57750.4324
\n", + "
" + ], + "text/plain": [ + " original_woe finetuned_woe delta\n", + "category \n", + "A -1.0058 -0.7312 0.2746\n", + "B -0.2475 0.4654 0.7129\n", + "C 0.7465 1.1815 0.4350\n", + "D 1.1451 1.5775 0.4324" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compare old vs new WOE\n", + "updated_grade_woe = woe.get_mapping(\"grade\").set_index(\"category\")[\"woe\"]\n", + "\n", + "comparison = pd.DataFrame(\n", + " {\n", + " \"original_woe\": original_grade_woe,\n", + " \"finetuned_woe\": updated_grade_woe,\n", + " \"delta\": updated_grade_woe - original_grade_woe,\n", + " }\n", + ")\n", + "print(\"WOE shift after finetuning 'grade':\")\n", + "comparison.round(4)" + ] + }, + { + "cell_type": "markdown", + "id": "a3b4c5d6", + "metadata": {}, + "source": "## 4. Finetune All Features at Once\n\nPass the full DataFrame to update every feature in one call.\nBin edges are preserved โ€” only the WOE within each bin is updated." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3c4d5e6", + "metadata": {}, + "outputs": [], + "source": "original_income_woe = woe.get_mapping(\"income\").set_index(\"category\")[\"woe\"].copy()\noriginal_bin_edges = woe.binning_info_[\"income\"][\"bin_edges\"].copy()\n\n# Finetune all features โ€” pass the full DataFrame\nwoe.finetune(X_new, y_new)\n\nprint(\"Updated WOE mapping for 'income':\")\nwoe.get_mapping(\"income\")" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3d4e5f6", + "metadata": {}, + "outputs": [], + "source": [ + "# Verify bin edges are unchanged\n", + "assert woe.binning_info_[\"income\"][\"bin_edges\"] == original_bin_edges\n", + "print(\"Bin edges unchanged:\", original_bin_edges)" + ] + }, + { + "cell_type": "markdown", + "id": "d3e4f5a6", + "metadata": {}, + "source": [ + "## 5. Transform Uses Updated WOE Values\n", + "\n", + "After finetuning, `transform()` automatically uses the updated WOE mapping.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3f4a5b6", + "metadata": {}, + "outputs": [], + "source": [ + "X_woe = woe.transform(X_new)\n", + "\n", + "print(\"Transformed output (first 5 rows):\")\n", + "X_woe.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f3a4b5c6", + "metadata": {}, + "source": [ + "## 6. Updating the Global Prior\n", + "\n", + "By default, `finetune()` keeps the original prior (`y_prior_` / `odds_prior_`)\n", + "so that other features remain consistent. Use `update_prior=True` when you\n", + "want to shift the baseline as well โ€” but note that this makes all other\n", + "features' WOE values stale.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4b5c6d7", + "metadata": {}, + "outputs": [], + "source": "import warnings\n\nprint(f\"Prior before: {woe.y_prior_:.4f}\")\n\nwith warnings.catch_warnings(record=True) as w:\n warnings.simplefilter(\"always\")\n woe.finetune(X_new[[\"grade\"]], y_new, update_prior=True)\n for warning in w:\n print(f\"Warning: {warning.message}\")\n\nprint(f\"Prior after: {woe.y_prior_:.4f}\")" + }, + { + "cell_type": "markdown", + "id": "b4c5d6e7", + "metadata": {}, + "source": [ + "## 7. Handling Missing Categories\n", + "\n", + "If the new data doesn't contain all original bins, `finetune()` warns\n", + "and retains the original WOE for missing bins.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4d5e6f7", + "metadata": {}, + "outputs": [], + "source": "# New data with only grades A and B (no C or D)\nX_partial = pd.DataFrame({\"grade\": [\"A\"] * 100 + [\"B\"] * 100})\ny_partial = pd.Series(np.random.binomial(1, 0.15, 200))\n\n# Save current D WOE before finetune\nd_woe_before = woe.mappings_[\"grade\"].loc[\"D\", \"woe\"]\n\nwith warnings.catch_warnings(record=True) as w:\n warnings.simplefilter(\"always\")\n woe.finetune(X_partial, y_partial)\n for warning in w:\n print(f\"Warning: {warning.message}\")\n\n# D retains its previous WOE\nd_woe_after = woe.mappings_[\"grade\"].loc[\"D\", \"woe\"]\nprint(f\"\\nGrade D WOE before: {d_woe_before:.4f}\")\nprint(f\"Grade D WOE after: {d_woe_after:.4f} (unchanged)\")" + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.11.8)", + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/fastwoe/__init__.py b/fastwoe/__init__.py index 4843eaf..2c00578 100644 --- a/fastwoe/__init__.py +++ b/fastwoe/__init__.py @@ -36,7 +36,7 @@ def _plotting_not_available(*args, **kwargs): plot_performance = _plotting_not_available visualize_woe = _plotting_not_available -__version__ = "0.1.7a0" +__version__ = "0.1.7" __author__ = "xRiskLab" __email__ = "contact@xrisklab.ai" diff --git a/fastwoe/fast_somersd.py b/fastwoe/fast_somersd.py index 72e1f67..28cb435 100644 --- a/fastwoe/fast_somersd.py +++ b/fastwoe/fast_somersd.py @@ -1,11 +1,21 @@ """Backward compatibility module for fast_somersd. -This module is maintained for backward compatibility. All functionality -has been moved to metrics.py. Please import from metrics instead. +.. deprecated:: 0.1.7 + This module is maintained for backward compatibility only. + All functionality has been moved to :mod:`fastwoe.metrics`. + Import from ``fastwoe.metrics`` instead. """ +import warnings + from .metrics import SomersDResult, somersd_pairwise, somersd_xy, somersd_yx +warnings.warn( + "fastwoe.fast_somersd is deprecated. Import from fastwoe.metrics instead.", + DeprecationWarning, + stacklevel=2, +) + __all__ = [ "SomersDResult", "somersd_yx", diff --git a/fastwoe/fastwoe.py b/fastwoe/fastwoe.py index dc9825d..a956105 100644 --- a/fastwoe/fastwoe.py +++ b/fastwoe/fastwoe.py @@ -18,8 +18,8 @@ from sklearn.preprocessing import KBinsDiscretizer, TargetEncoder from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor -from .fast_somersd import somersd_yx from .fastwoe_multiclass import MulticlassWoeMixin +from .metrics import somersd_se, somersd_yx class WoePreprocessor(BaseEstimator, TransformerMixin): @@ -413,6 +413,11 @@ def _calculate_gini(self, y_true, y_pred): except ValueError: return somersd_yx(y_true, y_pred).statistic + @staticmethod + def _calculate_somersd_se(y_true, y_pred): + """Asymptotic SE of Somers' D(X|Y). See :func:`metrics.somersd_se`.""" + return somersd_se(y_true, y_pred) + def _calculate_woe_se(self, good_count: int, bad_count: int) -> float: """ Calculate standard error of WOE using actual counts. @@ -609,13 +614,20 @@ def _calculate_feature_stats(self, col, X, y, mapping_df): iv_se = self._calculate_iv_standard_error(mapping_df, total_good, total_bad) iv_ci_lower, iv_ci_upper = self._calculate_iv_confidence_interval(iv_value, iv_se) + gini_value = self._calculate_gini(y, woe_values) + somersd_se_val = self._calculate_somersd_se(y, woe_values) + somersd_ci_lower, somersd_ci_upper = self._calculate_woe_ci(gini_value, somersd_se_val) + return { "feature": col, "n_categories": len(mapping_df), "total_observations": total_obs, "missing_count": X[col].isna().sum(), "missing_rate": X[col].isna().mean(), - "gini": self._calculate_gini(y, woe_values), + "gini": gini_value, + "somersd_se": somersd_se_val, + "somersd_ci_lower": somersd_ci_lower, + "somersd_ci_upper": somersd_ci_upper, "iv": iv_value, "iv_se": iv_se, "iv_ci_lower": iv_ci_lower, @@ -843,6 +855,106 @@ def fit( self.is_fitted_ = True return self + def _apply_binning_to_column(self, X: pd.DataFrame, col: str) -> pd.Series: + """ + Apply stored binning to a single column, returning string bin labels. + + Parameters + ---------- + X : pd.DataFrame + Input DataFrame containing the column to bin. + col : str + Column name to apply binning to. + + Returns: + ------- + pd.Series + Series with string bin labels for binned features, or original values + for categorical features. Missing values are labeled "Missing". + """ + if col not in self.binners_: + return X[col].copy() + + binner = self.binners_[col] + binning_info = self.binning_info_[col] + X_col = X[[col]].copy() + if hasattr(X_col[col], "isna"): + mask_missing = X_col[col].isna() + else: + mask_missing = pd.isna(X_col[col]) + + result = X[col].copy().astype("object") + + if not mask_missing.all(): + if binning_info.get("method") == "kbins": + result.loc[~mask_missing] = binner.transform(X_col[~mask_missing]).ravel() + elif binning_info.get("method") == "tree": + bin_edges = np.array(binning_info["bin_edges"]) + col_data = X_col[~mask_missing][col] + col_values = col_data.values if hasattr(col_data, "values") else np.array(col_data) + binned_values = np.digitize(col_values, bin_edges[1:-1], right=False) + binned_values = np.clip(binned_values - 1, 0, len(bin_edges) - 2) + result.loc[~mask_missing] = binned_values + elif binning_info.get("method") == "faiss_kmeans": + faiss_model = binner + col_data = X_col[~mask_missing][col] + col_values = col_data.values if hasattr(col_data, "values") else np.array(col_data) + data = col_values.astype(np.float32).reshape(-1, 1) + _, labels = faiss_model.index.search(data, 1) + cluster_labels = labels.flatten() + 1 + + if "cluster_to_bin" in binning_info: + cluster_to_bin = binning_info["cluster_to_bin"] + else: + bin_edges = np.array(binning_info["bin_edges"]) + bl = [] + for i in range(len(bin_edges) - 1): + if i == 0: + label = f"(-โˆž, {bin_edges[i + 1]:.1f}]" + elif i == len(bin_edges) - 2: + label = f"({bin_edges[i]:.1f}, โˆž)" + else: + label = f"({bin_edges[i]:.1f}, {bin_edges[i + 1]:.1f}]" + bl.append(label) + cluster_to_bin = dict(zip(range(1, len(bl) + 1), bl)) + + binned_labels = [cluster_to_bin[label] for label in cluster_labels] + result.loc[~mask_missing] = binned_labels + + # Convert to string categories with meaningful labels (same as fit) + if binning_info.get("method") == "kbins" and hasattr(binner, "bin_edges_"): + edges = binner.bin_edges_[0] + elif ( + binning_info.get("method") in ("tree", "faiss_kmeans") + and "bin_edges" in binning_info + ): + edges = np.array(binning_info["bin_edges"]) + else: + edges = None + + if edges is not None: + bin_labels = [] + for i in range(len(edges) - 1): + if i == 0: + label = f"(-โˆž, {edges[i + 1]:.1f}]" + elif i == len(edges) - 2: + label = f"({edges[i]:.1f}, โˆž)" + else: + label = f"({edges[i]:.1f}, {edges[i + 1]:.1f}]" + bin_labels.append(label) + + non_missing_values = result.loc[~mask_missing] + if len(non_missing_values) > 0 and binning_info.get("method") != "faiss_kmeans": + result.loc[~mask_missing] = ( + non_missing_values.astype(int).map(dict(enumerate(bin_labels))).astype(str) + ) + + # Handle missing values + if mask_missing.any(): + result.loc[mask_missing] = "Missing" + + return result + def transform(self, X: Union[pd.DataFrame, np.ndarray, pd.Series]) -> pd.DataFrame: """ Transform features to Weight of Evidence (WOE) values. @@ -876,107 +988,7 @@ def transform(self, X: Union[pd.DataFrame, np.ndarray, pd.Series]) -> pd.DataFra X_processed = X.copy() for col in X.columns: if col in self.binners_: - # Apply the same binning as during fit - binner = self.binners_[col] - binning_info = self.binning_info_[col] - X_col = X_processed[[col]].copy() - if hasattr(X_col[col], "isna"): - mask_missing = X_col[col].isna() - else: - # For numpy arrays, use pd.isna - mask_missing = pd.isna(X_col[col]) - - if not mask_missing.all(): # If there are any non-missing values - # Ensure column is object type to accept strings - X_processed[col] = X_processed[col].astype("object") - - if binning_info.get("method") == "kbins": - # KBinsDiscretizer method - X_processed.loc[~mask_missing, col] = binner.transform( - X_col[~mask_missing] - ).ravel() - elif binning_info.get("method") == "tree": - # Tree method - use bin edges for binning - bin_edges = np.array(binning_info["bin_edges"]) - col_data = X_col[~mask_missing][col] - if hasattr(col_data, "values"): - col_values = col_data.values - else: - col_values = np.array(col_data) - binned_values = np.digitize( - col_values, - bin_edges[1:-1], - right=False, - ) - binned_values = np.clip(binned_values - 1, 0, len(bin_edges) - 2) - X_processed.loc[~mask_missing, col] = binned_values - elif binning_info.get("method") == "faiss_kmeans": - # FAISS KMeans method - use FAISS model for prediction - faiss_model = binner - col_data = X_col[~mask_missing][col] - if hasattr(col_data, "values"): - col_values = col_data.values - else: - col_values = np.array(col_data) - data = col_values.astype(np.float32).reshape(-1, 1) - # type: ignore - _, labels = faiss_model.index.search(data, 1) - cluster_labels = labels.flatten() + 1 # Convert to 1-based indexing - - # Use the stored cluster_to_bin mapping from fit - if "cluster_to_bin" in binning_info: - cluster_to_bin = binning_info["cluster_to_bin"] - else: - # Fallback for older models without the mapping stored - bin_edges = np.array(binning_info["bin_edges"]) - bin_labels = [] - for i in range(len(bin_edges) - 1): - if i == 0: - label = f"(-โˆž, {bin_edges[i + 1]:.1f}]" - elif i == len(bin_edges) - 2: - label = f"({bin_edges[i]:.1f}, โˆž)" - else: - label = f"({bin_edges[i]:.1f}, {bin_edges[i + 1]:.1f}]" - bin_labels.append(label) - - cluster_to_bin = dict(zip(range(1, len(bin_labels) + 1), bin_labels)) - - binned_labels = [cluster_to_bin[label] for label in cluster_labels] - X_processed.loc[~mask_missing, col] = binned_labels - - # Convert to string categories with meaningful labels (same as fit) - if binning_info.get("method") == "kbins" and hasattr(binner, "bin_edges_"): - edges = binner.bin_edges_[0] - elif binning_info.get("method") == "tree" and "bin_edges" in binning_info: - edges = np.array(binning_info["bin_edges"]) - elif binning_info.get("method") == "faiss_kmeans" and "bin_edges" in binning_info: - edges = np.array(binning_info["bin_edges"]) - else: - edges = None - - if edges is not None: - bin_labels = [] - for i in range(len(edges) - 1): - if i == 0: - label = f"(-โˆž, {edges[i + 1]:.1f}]" - elif i == len(edges) - 2: - label = f"({edges[i]:.1f}, โˆž)" - else: - label = f"({edges[i]:.1f}, {edges[i + 1]:.1f}]" - bin_labels.append(label) - - # Map ordinal values to labels - non_missing_values = X_processed.loc[~mask_missing, col] - if len(non_missing_values) > 0 and binning_info.get("method") != "faiss_kmeans": - X_processed.loc[~mask_missing, col] = ( - non_missing_values.astype(int) - .map(dict(enumerate(bin_labels))) - .astype(str) - ) - - # Handle missing values - if mask_missing.any(): - X_processed.loc[mask_missing, col] = "Missing" + X_processed[col] = self._apply_binning_to_column(X_processed, col) # Collect all WOE columns first to avoid DataFrame fragmentation woe_columns = {} @@ -1010,6 +1022,189 @@ def fit_transform(self, X: pd.DataFrame, y=None, **_fit_params) -> pd.DataFrame: """Fit and transform in one step.""" return self.fit(X, y).transform(X) # type: ignore[no-any-return] + def finetune( + self, + X_new: Union[pd.DataFrame, np.ndarray, pd.Series], + y_new: Union[pd.Series, np.ndarray], + update_prior: bool = False, + ) -> "FastWoe": + """ + Recalibrate WOE values using new data. + + Keeps existing bin structure (edges, categories) intact but recomputes + WOE from the new data's per-bin event rates. Pass a full DataFrame to + update all features at once, or a single Series / single-column + DataFrame to update just one. + + Parameters + ---------- + X_new : Union[pd.DataFrame, np.ndarray, pd.Series] + New feature data. Columns that match fitted features are + recalibrated; unknown columns are skipped with a warning. + y_new : Union[pd.Series, np.ndarray] + New binary target variable (0/1). + update_prior : bool, default=False + If True, updates y_prior_ and odds_prior_ from y_new and warns + that other features' WOE values may be stale. + + Returns: + ------- + self : FastWoe + The recalibrated encoder instance (for method chaining). + """ + # --- validation --- + if not self.is_fitted_: + raise ValueError("FastWoe must be fitted before calling finetune()") + + if self.is_multiclass_target: + raise NotImplementedError("finetune() is not supported for multiclass targets") + + X_new = self._ensure_dataframe(X_new, use_fitted_names=True) + y_new = self._ensure_series(y_new) + + if len(X_new) != len(y_new): + raise ValueError( + f"X_new and y_new must have the same length. Got {len(X_new)} and {len(y_new)}" + ) + + unique_vals = sorted(pd.Series(y_new).unique()) + if not set(unique_vals).issubset({0, 1}): + raise ValueError( + f"finetune() only supports binary targets (0/1). Got unique values: {unique_vals}" + ) + + # Warn about unknown columns (skip them) + unknown_cols = [c for c in X_new.columns if c not in self.mappings_] + if unknown_cols: + warnings.warn( + f"finetune: columns {sorted(unknown_cols)} are not in the fitted model " + f"and will be skipped. Use fit() to add new features.", + UserWarning, + stacklevel=2, + ) + + cols_to_update = [c for c in X_new.columns if c in self.mappings_] + if not cols_to_update: + warnings.warn( + "finetune: no matching features found โ€” nothing to update.", + UserWarning, + stacklevel=2, + ) + return self + + # --- optionally update prior --- + if update_prior: + self.y_prior_ = float(y_new.mean()) + self.odds_prior_ = self.y_prior_ / (1 - self.y_prior_) + warnings.warn( + f"finetune: global prior updated to {self.y_prior_:.6f}. " + f"WOE values for features not in X_new are now stale.", + UserWarning, + stacklevel=2, + ) + + odds_prior = self.y_prior_ / (1 - self.y_prior_) # type: ignore[operator] + + for col in cols_to_update: + self._recalibrate_feature(col, X_new, y_new, odds_prior) + + return self + + def _recalibrate_feature( + self, + col: str, + X_new: pd.DataFrame, + y_new: pd.Series, + odds_prior: float, + ) -> None: + """Recalibrate WOE for a single existing feature from new data.""" + binned_col = self._apply_binning_to_column(X_new, col) + + old_mapping = self.mappings_[col] + old_categories = set(old_mapping.index) + + bin_data = pd.DataFrame({"bin": binned_col.values, "target": y_new.values}) + new_stats = bin_data.groupby("bin")[["target"]].agg( + count=("target", "count"), + bad_count=("target", "sum"), + event_rate=("target", "mean"), + ) + new_stats["good_count"] = new_stats["count"] - new_stats["bad_count"] + new_categories = set(new_stats.index) + + missing_bins = old_categories - new_categories + if missing_bins: + warnings.warn( + f"finetune: bins {sorted(missing_bins)} for feature '{col}' " + f"have no observations in new data. Original WOE retained.", + UserWarning, + stacklevel=3, + ) + + extra_bins = new_categories - old_categories + if extra_bins: + warnings.warn( + f"finetune: new categories {sorted(extra_bins)} for feature '{col}' " + f"are not in the original mapping and will be ignored.", + UserWarning, + stacklevel=3, + ) + + rows = [] + total_new = new_stats["count"].sum() if len(new_stats) > 0 else len(y_new) + for cat in old_mapping.index: + if cat in new_stats.index: + row = new_stats.loc[cat] + count = int(row["count"]) + bad_count = int(row["bad_count"]) + good_count = int(row["good_count"]) + event_rate = np.clip(float(row["event_rate"]), 1e-15, 1 - 1e-15) + odds_cat = event_rate / (1 - event_rate) + woe = float(np.log(odds_cat / odds_prior)) + se = self._calculate_woe_se(good_count, bad_count) + ci_lower, ci_upper = self._calculate_woe_ci(woe, se) + count_pct = count / total_new * 100 if total_new > 0 else 0.0 + else: + orig = old_mapping.loc[cat] + count = int(orig["count"]) + count_pct = float(orig["count_pct"]) + good_count = int(orig["good_count"]) + bad_count = int(orig["bad_count"]) + event_rate = float(orig["event_rate"]) + woe = float(orig["woe"]) + se = float(orig["woe_se"]) + ci_lower = float(orig["woe_ci_lower"]) + ci_upper = float(orig["woe_ci_upper"]) + + rows.append( + { + "category": cat, + "count": count, + "count_pct": round(count_pct, 4), + "good_count": good_count, + "bad_count": bad_count, + "event_rate": round(event_rate, 6), + "woe": woe, + "woe_se": se, + "woe_ci_lower": ci_lower, + "woe_ci_upper": ci_upper, + } + ) + + new_mapping_df = pd.DataFrame(rows).set_index("category") + self.mappings_[col] = new_mapping_df + + enc = TargetEncoder(**self.encoder_kwargs, random_state=self.random_state) + enc.fit(binned_col.to_frame(name=col), y_new) + self.encoders_[col] = enc + + self.feature_stats_[col] = self._calculate_feature_stats( + col, + binned_col.to_frame(name=col), + y_new, + new_mapping_df, + ) + def get_mapping( self, feature: str, class_label: Optional[Union[int, str]] = None ) -> pd.DataFrame: diff --git a/fastwoe/metrics.py b/fastwoe/metrics.py index 7e0a9f6..7f81733 100644 --- a/fastwoe/metrics.py +++ b/fastwoe/metrics.py @@ -448,6 +448,113 @@ def gini_contributions( return contributions, gini +def _concordant_discordant_matrices( + CT: np.ndarray, +) -> tuple[np.ndarray, np.ndarray]: + """Per-cell concordant and discordant counts for a contingency table. + + For cell (i, j), C[i, j] is the total count of observations in cells + strictly above-left or strictly below-right of (i, j). D[i, j] is + the count in cells strictly above-right or strictly below-left. + + Args: + CT: Contingency table, shape (a, b). + + Returns: + (C, D) matrices of the same shape as CT. + """ + a, b = CT.shape + # 2-D prefix sum for O(1) rectangular queries + ps = np.zeros((a + 1, b + 1), dtype=np.float64) + ps[1:, 1:] = np.cumsum(np.cumsum(CT, axis=0), axis=1) + + def _rect(r1: int, c1: int, r2: int, c2: int) -> float: + if r1 >= r2 or c1 >= c2: + return 0.0 + return float(ps[r2, c2] - ps[r1, c2] - ps[r2, c1] + ps[r1, c1]) + + C = np.zeros_like(CT, dtype=np.float64) + D = np.zeros_like(CT, dtype=np.float64) + for i in range(a): + for j in range(b): + C[i, j] = _rect(i + 1, j + 1, a, b) + _rect(0, 0, i, j) + D[i, j] = _rect(i + 1, 0, a, j) + _rect(0, j + 1, i, b) + return C, D + + +def somersd_se( + y_true: np.ndarray, + y_pred: np.ndarray, +) -> float: + """Asymptotic SE of Somers' D(X|Y) per Goktas & Oznur (2011). + + Computes the asymptotic standard error from the contingency table of + (y_true, y_pred) using per-cell concordant/discordant counts and the + delta method for the ratio statistic. + + Works for binary, ordinal, and continuous targets. + For binary targets this equals SE(Gini) = SE(2*AUC - 1). + + Args: + y_true: Target values (binary, ordinal, or continuous). + y_pred: Predicted scores or WOE-encoded values. + + Returns: + Asymptotic standard error of Somers' D, or NaN for degenerate inputs. + + References: + Goktas, A., Oznur, I., 2011. A Comparison of the Most Commonly Used + Measures of Association for Doubly Ordered Square Contingency Tables + via Simulation. Metodoloski zvezki 8 (1), 17-37. + """ + try: + y = np.asarray(y_true, dtype=np.float64).ravel() + x = np.asarray(y_pred, dtype=np.float64).ravel() + except (ValueError, TypeError): + return np.nan + + n = len(y) + if n < 3 or len(x) != n: + return np.nan + + mask = ~(np.isnan(y) | np.isnan(x)) + y, x = y[mask], x[mask] + n = len(y) + if n < 3: + return np.nan + + # Build contingency table (rows = y, cols = x, both sorted) + y_uniq, y_inv = np.unique(y, return_inverse=True) + x_uniq, x_inv = np.unique(x, return_inverse=True) + a, b = len(y_uniq), len(x_uniq) + if a < 2 or b < 2: + return np.nan + + CT: np.ndarray = np.zeros((a, b), dtype=np.float64) + for yi, xi in zip(y_inv, x_inv): + CT[yi, xi] += 1.0 + + C, D = _concordant_discordant_matrices(CT) + + r = CT.sum(axis=1) # row sums + W = float(n) + + P = (CT * C).sum() + Q = (CT * D).sum() + Dr = W**2 - (r**2).sum() # pairs untied on y + if Dr == 0: + return np.nan + + # Row midranks: RR[k] = cumsum(r)[k] + (1 - r[k]) / 2 + RR = np.cumsum(r) + (1.0 - r) / 2.0 + RR_mat = np.repeat(RR[:, np.newaxis], b, axis=1) + + # ASE via delta method (Goktas & Oznur 2011, eq. for Somers' D) + inside = Dr * (C - D) - (P - Q) * (W - RR_mat) + ase = 2.0 / Dr**2 * np.sqrt((CT * inside**2).sum()) + return float(ase) + + def somersd_clustered_matrix( df: pd.DataFrame, score_col: str, diff --git a/tests/test_fastwoe.py b/tests/test_fastwoe.py index 44dcaef..c5799ec 100644 --- a/tests/test_fastwoe.py +++ b/tests/test_fastwoe.py @@ -5,6 +5,7 @@ import numpy as np import pandas as pd import pytest +from scipy.stats import norm from sklearn.datasets import make_classification from fastwoe import FastWoe, WoePreprocessor @@ -340,6 +341,46 @@ def test_iv_standard_errors(self, sample_data): assert (stats["iv"] <= stats["iv_ci_upper"]).all(), "IV should be <= Upper CI" assert (stats["iv_ci_lower"] >= 0).all(), "Lower CI should be >= 0" + def test_gini_standard_errors(self, sample_data): + """Test Gini/Somers' D standard error via Hรกjek linearization.""" + X = sample_data[["cat1", "cat2"]] + y = sample_data["target"] + + woe = FastWoe() + woe.fit(X, y) + + stats = woe.get_feature_stats() + required_gini_cols = ["gini", "somersd_se", "somersd_ci_lower", "somersd_ci_upper"] + for col in required_gini_cols: + assert col in stats.columns, f"Missing column: {col}" + + # SE should be positive and finite + for _, row in stats.iterrows(): + assert row["somersd_se"] > 0, "Somers' D SE should be positive" + assert np.isfinite(row["somersd_se"]), "Somers' D SE should be finite" + + # CI should bracket the point estimate + assert (stats["somersd_ci_lower"] <= stats["gini"]).all() + assert (stats["gini"] <= stats["somersd_ci_upper"]).all() + + # CI width should be exactly 2 * z * SE (z from scipy for 95% CI) + z_crit = norm.ppf(0.975) + ci_width = stats["somersd_ci_upper"] - stats["somersd_ci_lower"] + expected_width = 2 * z_crit * stats["somersd_se"] + np.testing.assert_allclose(ci_width, expected_width, rtol=1e-6) + + def test_somersd_se_matches_vurocs(self): + """Validate somersd_se against VUROCS R package (Goktas & Oznur 2011).""" + from fastwoe.metrics import somersd_se + + # VUROCS example: SomersD(rep(1:5,each=3), c(3,3,3,rep(2:5,each=3))) + y = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5], dtype=float) + fx = np.array([3, 3, 3, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5], dtype=float) + + se = somersd_se(y, fx) + # R output: val=0.7, ASE=0.3146721 + np.testing.assert_allclose(se, 0.3146721, rtol=1e-5) + def test_get_iv_analysis(self, sample_data): """Test get_iv_analysis method.""" X = sample_data[["cat1", "cat2"]] @@ -2545,3 +2586,231 @@ def test_monotonic_constraints_edge_cases(self): # Should work without applying constraints assert woe_no_constraint.is_fitted_ assert woe_no_constraint.binning_info_["feature"]["monotonic_constraint"] == 0 + + +class TestFinetune: + """Test cases for the FastWoe.finetune() method.""" + + @pytest.fixture + def fitted_categorical(self): + """Fit a FastWoe encoder on categorical data.""" + np.random.seed(42) + X = pd.DataFrame( + { + "cat1": ["A"] * 50 + ["B"] * 30 + ["C"] * 20, + "cat2": ["X"] * 40 + ["Y"] * 35 + ["Z"] * 25, + } + ) + y = pd.Series( + np.concatenate( + [ + np.random.binomial(1, 0.5, 50), + np.random.binomial(1, 0.2, 30), + np.random.binomial(1, 0.1, 20), + ] + ) + ) + woe = FastWoe() + woe.fit(X, y) + return woe, X, y + + @pytest.fixture + def fitted_numerical(self): + """Fit a FastWoe encoder on numerical data with tree binning.""" + np.random.seed(42) + n = 200 + X = pd.DataFrame({"income": np.random.normal(50000, 15000, n)}) + y = pd.Series(np.random.binomial(1, 0.3, n)) + woe = FastWoe(binning_method="tree", numerical_threshold=10) + woe.fit(X, y) + return woe, X, y + + def test_single_feature_via_dataframe(self, fitted_categorical): + """Single-column DataFrame updates only that feature.""" + woe, X, y = fitted_categorical + old_cat2_mapping = woe.mappings_["cat2"].copy() + old_cat1_woe = woe.mappings_["cat1"]["woe"].copy() + + np.random.seed(99) + X_new = pd.DataFrame({"cat1": ["A"] * 60 + ["B"] * 25 + ["C"] * 15}) + y_new = pd.Series( + np.concatenate( + [ + np.random.binomial(1, 0.8, 60), + np.random.binomial(1, 0.1, 25), + np.random.binomial(1, 0.05, 15), + ] + ) + ) + + woe.finetune(X_new, y_new) + + # cat1 WOE should have changed + new_cat1_woe = woe.mappings_["cat1"]["woe"] + assert not np.allclose(old_cat1_woe.values, new_cat1_woe.values) + + # cat2 mapping should be unchanged + pd.testing.assert_frame_equal(woe.mappings_["cat2"], old_cat2_mapping) + + def test_full_dataframe_updates_all(self, fitted_categorical): + """Full DataFrame updates all matching features.""" + woe, X, y = fitted_categorical + old_cat1_woe = woe.mappings_["cat1"]["woe"].copy() + old_cat2_woe = woe.mappings_["cat2"]["woe"].copy() + + np.random.seed(99) + X_new = pd.DataFrame( + { + "cat1": ["A"] * 60 + ["B"] * 25 + ["C"] * 15, + "cat2": ["X"] * 30 + ["Y"] * 40 + ["Z"] * 30, + } + ) + y_new = pd.Series(np.random.binomial(1, 0.6, 100)) + + woe.finetune(X_new, y_new) + + # Both should have changed + assert not np.allclose(old_cat1_woe.values, woe.mappings_["cat1"]["woe"].values) + assert not np.allclose(old_cat2_woe.values, woe.mappings_["cat2"]["woe"].values) + + def test_numerical_feature_tree_binning(self, fitted_numerical): + """Bin edges unchanged, WOE updated for numerical feature.""" + woe, X, y = fitted_numerical + old_bin_edges = woe.binning_info_["income"]["bin_edges"].copy() + old_woe = woe.mappings_["income"]["woe"].copy() + + np.random.seed(123) + n = 150 + X_new = pd.DataFrame({"income": np.random.normal(55000, 15000, n)}) + y_new = pd.Series(np.random.binomial(1, 0.5, n)) + + woe.finetune(X_new, y_new) + + assert woe.binning_info_["income"]["bin_edges"] == old_bin_edges + assert not np.allclose(old_woe.values, woe.mappings_["income"]["woe"].values, atol=1e-3) + + def test_not_fitted_raises(self): + """finetune() on unfitted model raises ValueError.""" + woe = FastWoe() + X_new = pd.DataFrame({"cat1": ["A", "B"]}) + with pytest.raises(ValueError, match="must be fitted"): + woe.finetune(X_new, pd.Series([0, 1])) + + def test_length_mismatch_raises(self, fitted_categorical): + """finetune() with mismatched X / y lengths raises ValueError.""" + woe, _, _ = fitted_categorical + X_new = pd.DataFrame({"cat1": ["A", "B", "C"]}) + with pytest.raises(ValueError, match="same length"): + woe.finetune(X_new, pd.Series([0, 1])) + + def test_multiclass_raises(self): + """finetune() on multiclass model raises NotImplementedError.""" + np.random.seed(42) + X = pd.DataFrame({"cat1": ["A"] * 60 + ["B"] * 40}) + y = pd.Series([0] * 30 + [1] * 30 + [2] * 40) + + woe = FastWoe() + woe.fit(X, y) + assert woe.is_multiclass_target + + X_new = pd.DataFrame({"cat1": ["A", "B"]}) + with pytest.raises(NotImplementedError, match="multiclass"): + woe.finetune(X_new, pd.Series([0, 1])) + + def test_unknown_columns_warns_and_skips(self, fitted_categorical): + """Columns not in the model are skipped with a warning.""" + woe, _, _ = fitted_categorical + old_cat1_woe = woe.mappings_["cat1"]["woe"].copy() + + X_new = pd.DataFrame({"unknown": ["A", "B"] * 50}) + y_new = pd.Series(np.random.binomial(1, 0.5, 100)) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + woe.finetune(X_new, y_new) + + skip_warnings = [x for x in w if "not in the fitted model" in str(x.message)] + assert len(skip_warnings) == 1 + + # Nothing changed + pd.testing.assert_series_equal(woe.mappings_["cat1"]["woe"], old_cat1_woe) + + def test_update_prior_false_preserves_prior(self, fitted_categorical): + """With update_prior=False the global prior stays unchanged.""" + woe, _, _ = fitted_categorical + old_prior = woe.y_prior_ + old_odds = woe.odds_prior_ + + X_new = pd.DataFrame({"cat1": ["A"] * 50 + ["B"] * 50}) + y_new = pd.Series([1] * 80 + [0] * 20) + + woe.finetune(X_new, y_new, update_prior=False) + + assert woe.y_prior_ == old_prior + assert woe.odds_prior_ == old_odds + + def test_update_prior_true_updates_and_warns(self, fitted_categorical): + """With update_prior=True the global prior is updated and a warning emitted.""" + woe, _, _ = fitted_categorical + old_prior = woe.y_prior_ + + X_new = pd.DataFrame({"cat1": ["A"] * 50 + ["B"] * 50}) + y_new = pd.Series([1] * 80 + [0] * 20) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + woe.finetune(X_new, y_new, update_prior=True) + + prior_warnings = [x for x in w if "global prior updated" in str(x.message)] + assert len(prior_warnings) == 1 + + assert woe.y_prior_ != old_prior + assert woe.y_prior_ == pytest.approx(0.8) + + def test_missing_categories_warns_and_retains(self, fitted_categorical): + """Bins absent from new data retain original WOE and produce a warning.""" + woe, _, _ = fitted_categorical + original_c_woe = float(woe.mappings_["cat1"].loc["C", "woe"]) + + # New data has only A and B, not C + X_new = pd.DataFrame({"cat1": ["A"] * 60 + ["B"] * 40}) + y_new = pd.Series(np.random.binomial(1, 0.5, 100)) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + woe.finetune(X_new, y_new) + + missing_warnings = [x for x in w if "no observations" in str(x.message)] + assert len(missing_warnings) == 1 + + assert woe.mappings_["cat1"].loc["C", "woe"] == pytest.approx(original_c_woe) + + def test_transform_after_finetune(self, fitted_categorical): + """transform() uses updated WOE values after finetune.""" + woe, X, y = fitted_categorical + + np.random.seed(99) + X_new = pd.DataFrame({"cat1": ["A"] * 60 + ["B"] * 25 + ["C"] * 15}) + y_new = pd.Series( + np.concatenate( + [ + np.random.binomial(1, 0.8, 60), + np.random.binomial(1, 0.1, 25), + np.random.binomial(1, 0.05, 15), + ] + ) + ) + woe.finetune(X_new, y_new) + + X_woe = woe.transform(X) + expected_woe_A = woe.mappings_["cat1"].loc["A", "woe"] + assert X_woe.loc[0, "cat1"] == pytest.approx(expected_woe_A) + + def test_method_chaining(self, fitted_categorical): + """finetune() returns self for chaining.""" + woe, _, _ = fitted_categorical + X_new = pd.DataFrame({"cat1": ["A"] * 50 + ["B"] * 30 + ["C"] * 20}) + y_new = pd.Series(np.random.binomial(1, 0.5, 100)) + + result = woe.finetune(X_new, y_new) + assert result is woe From 7a58daba9f8cbec5310b34fdb3138ec1c1472df6 Mon Sep 17 00:00:00 2001 From: xRiskLab Date: Sat, 11 Apr 2026 15:11:23 +0200 Subject: [PATCH 3/3] fix mypy errors in screening.py --- fastwoe/screening.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/fastwoe/screening.py b/fastwoe/screening.py index 2dd9083..2e0c152 100644 --- a/fastwoe/screening.py +++ b/fastwoe/screening.py @@ -487,8 +487,8 @@ def v_base(subset: tuple[str, ...]) -> float: if subset in cache: return cache[subset] - numer = np.zeros(n_samples, dtype=float) - denom = np.zeros(n_samples, dtype=float) + numer: np.ndarray = np.zeros(n_samples, dtype=float) + denom: np.ndarray = np.zeros(n_samples, dtype=float) # Base is always included for base_valid samples numer[base_valid] = base_score[base_valid] @@ -500,7 +500,7 @@ def v_base(subset: tuple[str, ...]) -> float: numer[m] += scores[k][m] denom[m] += 1.0 - combined = np.zeros(n_samples, dtype=float) + combined: np.ndarray = np.zeros(n_samples, dtype=float) combined[base_valid] = numer[base_valid] / denom[base_valid] val = somersd_of(combined) @@ -549,7 +549,7 @@ def v_base(subset: tuple[str, ...]) -> float: # Original behavior: no base score, compute Shapley for all scores # Fix population to intersection of all availability masks - global_mask = np.ones(n_samples, dtype=bool) + global_mask: np.ndarray = np.ones(n_samples, dtype=bool) for name in score_names: global_mask &= availability_mask[name] @@ -629,4 +629,4 @@ def v(subset: tuple[str, ...]) -> float: result = result.sort_values("shapley_value", ascending=False).reset_index(drop=True) - return result + return pd.DataFrame(result)