From c8f02bc352fc0bac940b9d4c48cbfe13327d48d0 Mon Sep 17 00:00:00 2001
From: Jeff VanOss <jeff.vanoss@bamfhealth.com>
Date: Wed, 7 Aug 2024 00:10:51 -0400
Subject: [PATCH 1/6] add poetry

---
 .pre-commit-config.yaml       |   31 +-
 model_performance.ipynb       |   24 +-
 model_performance_aimi2.ipynb | 1103 ++++++++++++++++-----------------
 poetry.lock                   |  113 +---
 pyproject.toml                |   13 +-
 5 files changed, 611 insertions(+), 673 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3cb4568..6712457 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,27 +1,26 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
-        exclude_types: [jupyter, csv]
       - id: check-yaml
       - id: check-merge-conflict
       - id: debug-statements
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-        args: ["--profile", "black", "--filter-files"]
-        additional_dependencies: [toml]
-  - repo: https://github.com/psf/black
-    rev: 23.9.1
-    hooks:
-      - id: black-jupyter
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.5.1
+    rev: v1.11.1
     hooks:
       - id: mypy
-        args: ["--implicit-optional"]
-        additional_dependencies:
-          [numpy, pydicom, SimpleITK, types-requests, segmentationmetrics]
+        args: [--implicit-optional]
+        additional_dependencies: [pydicom, SimpleITK, types-requests]
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.5.6
+    hooks:
+      # Run the linter.
+      - id: ruff
+        types_or: [python, pyi, jupyter]
+        args: [--select, I, --fix]
+      # Run the formatter.
+      - id: ruff-format
+        types_or: [python, pyi, jupyter]
diff --git a/model_performance.ipynb b/model_performance.ipynb
index 7a3b303..7489aa0 100644
--- a/model_performance.ipynb
+++ b/model_performance.ipynb
@@ -38,22 +38,24 @@
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
-    "from pathlib import Path\n",
-    "import requests\n",
-    "import zipfile\n",
     "import io\n",
+    "import zipfile\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import skimage.measure\n",
-    "import scipy.stats as stats\n",
     "import pydicom\n",
     "import pydicom_seg\n",
-    "import SimpleITK as sitk\n",
+    "import requests\n",
+    "import scipy.stats as stats\n",
+    "import seaborn as sns\n",
     "import segmentationmetrics as sm\n",
+    "import SimpleITK as sitk\n",
+    "import skimage.measure\n",
     "from segmentationmetrics.surface_distance import compute_surface_dice_at_tolerance\n",
+    "\n",
     "from kits_metrics import compute_metrics_for_case\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
     "\n",
     "sns.set_theme(style=\"whitegrid\")\n",
     "%matplotlib inline"
@@ -101,9 +103,9 @@
    "outputs": [],
    "source": [
     "def download_inference(\n",
-    "    extract_dir=\"qa-results\",record='13244892',filename = 'kidney-ct.zip'\n",
+    "    extract_dir=\"qa-results\", record=\"13244892\", filename=\"kidney-ct.zip\"\n",
     "):\n",
-    "    url=f\"https://zenodo.org/record/{record}/files/{filename}\"\n",
+    "    url = f\"https://zenodo.org/record/{record}/files/{filename}\"\n",
     "    # download the zip file and extract it\n",
     "    r = requests.get(url)\n",
     "    z = zipfile.ZipFile(io.BytesIO(r.content))\n",
@@ -1160,7 +1162,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/model_performance_aimi2.ipynb b/model_performance_aimi2.ipynb
index c55fa3c..e82a46c 100644
--- a/model_performance_aimi2.ipynb
+++ b/model_performance_aimi2.ipynb
@@ -1,556 +1,553 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Model Performance\n",
-                "\n",
-                "\n",
-                "## Prerequisites\n",
-                "Install python packages"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "%%capture\n",
-                "%pip install pandas pydicom pydicom-seg scikit-learn seaborn segmentationmetrics requests"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Import required python packages"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 24,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import warnings\n",
-                "\n",
-                "warnings.filterwarnings(\"ignore\")\n",
-                "\n",
-                "from pathlib import Path\n",
-                "import requests\n",
-                "import zipfile\n",
-                "import io\n",
-                "import numpy as np\n",
-                "import pandas as pd\n",
-                "import scipy.stats as stats\n",
-                "import pydicom\n",
-                "import pydicom_seg\n",
-                "import SimpleITK as sitk\n",
-                "import matplotlib.pyplot as plt\n",
-                "import segmentationmetrics as sm\n",
-                "from segmentationmetrics.surface_distance import compute_surface_dice_at_tolerance\n",
-                "import seaborn as sns\n",
-                "from tqdm.auto import tqdm\n",
-                "\n",
-                "sns.set_theme(style=\"whitegrid\")\n",
-                "%matplotlib inline"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Download the segmentation results from zenodo"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 25,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "qa_dir = Path(\"qa-results\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 26,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "def download_inference(\n",
-                "    extract_dir=\"qa-results\", record=\"13244892\", filename=\"kidney-ct.zip\"\n",
-                "):\n",
-                "    url = f\"https://zenodo.org/record/{record}/files/{filename}\"\n",
-                "    # download the zip file and extract it\n",
-                "    r = requests.get(url)\n",
-                "    z = zipfile.ZipFile(io.BytesIO(r.content))\n",
-                "    z.extractall(extract_dir)\n",
-                "\n",
-                "\n",
-                "if not qa_dir.exists() or not (qa_dir / \"ai-segmentations-dcm\").exists():\n",
-                "    download_inference(qa_dir)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 27,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "plt_colors = {\n",
-                "    \"ne2\": \"#5eceb0\",\n",
-                "    \"rad1\": \"#9e70e1\",\n",
-                "    \"tp\": \"k\",\n",
-                "    \"fp\": \"b\",\n",
-                "    \"fn\": \"r\",\n",
-                "}\n",
-                "\n",
-                "revewer_cmap = sns.color_palette([plt_colors[\"ne2\"], plt_colors[\"rad1\"]])\n",
-                "vol_cmap = sns.color_palette([plt_colors[\"fp\"], plt_colors[\"fn\"]])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 34,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "def load_dcm_seg(seg_path: Path, label=1):\n",
-                "    dcm = pydicom.dcmread(str(seg_path))\n",
-                "    reader = pydicom_seg.SegmentReader()\n",
-                "    result = reader.read(dcm)\n",
-                "    if label in result.available_segments:\n",
-                "        image = result.segment_image(label)\n",
-                "    else:\n",
-                "        # create an empty image\n",
-                "        tmp_img = result.segment_image(list(result.available_segments)[0])\n",
-                "        tmp_arr = sitk.GetArrayFromImage(tmp_img)\n",
-                "        image = sitk.GetImageFromArray(np.zeros_like(tmp_arr))\n",
-                "        image.CopyInformation(tmp_img)\n",
-                "    image = sitk.Cast(image, sitk.sitkUInt8)\n",
-                "    return image\n",
-                "\n",
-                "\n",
-                "def load_nii_seg(seg_path: Path, label=1):\n",
-                "    img = sitk.ReadImage(str(seg_path))\n",
-                "    if label == 1:\n",
-                "        img = sitk.Cast(img >= label, sitk.sitkUInt8)\n",
-                "    else:\n",
-                "        img = sitk.Cast(img == label, sitk.sitkUInt8)\n",
-                "    return img\n",
-                "\n",
-                "\n",
-                "def load_seg(seg_path: Path, label=1):\n",
-                "    if seg_path.suffix == \".dcm\":\n",
-                "        return load_dcm_seg(seg_path, label)\n",
-                "    else:\n",
-                "        return load_nii_seg(seg_path, label)\n",
-                "\n",
-                "\n",
-                "def resize_label(img: sitk.Image, ref_img: sitk.Image, interp=sitk.sitkNearestNeighbor):\n",
-                "    resampler = sitk.ResampleImageFilter()\n",
-                "    resampler.SetReferenceImage(ref_img)\n",
-                "    resampler.SetInterpolator(interp)\n",
-                "    resampler.SetDefaultPixelValue(0)\n",
-                "    resampled_img = resampler.Execute(img)\n",
-                "    return resampled_img\n",
-                "\n",
-                "\n",
-                "def calc_metrics_for_label(\n",
-                "    ai_seg_file, qa_seg_file, fname, label_value=1, version=\"aimiv2\", label_suffix=\"\"\n",
-                "):\n",
-                "    ai_img = load_seg(ai_seg_file, label_value)\n",
-                "    qa_img = load_seg(qa_seg_file, label_value)\n",
-                "    qa_img = resize_label(qa_img, ai_img)  # match the size of the ai_img\n",
-                "    ai_arr = sitk.GetArrayFromImage(ai_img)\n",
-                "    qa_arr = sitk.GetArrayFromImage(qa_img)\n",
-                "    spacing = ai_img.GetSpacing()[::-1]  # numpy is reversed dimensions from sitk\n",
-                "\n",
-                "    qa_img = resize_label(qa_img, ai_img)  # match the size of the ai_img\n",
-                "    m = sm.SegmentationMetrics(ai_arr, qa_arr, spacing)\n",
-                "    metrics = {\n",
-                "        \"dice\": m.dice,\n",
-                "        \"hausdorff_distance_95\": m.hausdorff_distance,\n",
-                "        \"mean_surface_distance\": m.mean_surface_distance,\n",
-                "        \"mean_surface_distance_tol_7\": compute_surface_dice_at_tolerance(\n",
-                "            m._surface_dist, 7\n",
-                "        ),\n",
-                "        \"SeriesUID\": fname,  # from medical segmentation decathlon\n",
-                "        \"label\": label_value,\n",
-                "        \"version\": version,\n",
-                "    }\n",
-                "\n",
-                "    if label_suffix:\n",
-                "        metrics = {f\"{k}_{label_suffix}\": v for k, v in metrics.items()}\n",
-                "    return metrics"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 35,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "100%|██████████| 85/85 [02:31<00:00,  1.78s/it]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# Load the CSV file\n",
-                "df = pd.read_csv(Path(\"qa-results/qa-results.csv\"))\n",
-                "qa_df = df[df.Validation]\n",
-                "\n",
-                "# Paths for aimiv1, aimiv2, and qa\n",
-                "ai_path = Path(\"qa-results/ai-segmentations-dcm\")\n",
-                "qa_path = Path(\"qa-results/qa-segmentations-dcm\")\n",
-                "\n",
-                "# Initialize lists for metrics\n",
-                "metrics = []\n",
-                "\n",
-                "# Iterate over all QA cases\n",
-                "for _, row in tqdm(qa_df.iterrows(), total=len(qa_df)):\n",
-                "    ai_file = ai_path / row.AISegmentation\n",
-                "    if not pd.isna(row.CorrectedSegmentation):\n",
-                "        qa_file = qa_path / row.CorrectedSegmentation\n",
-                "    else:\n",
-                "        # reviewer had no corrections, use AI segmentation as QA\n",
-                "        qa_file = ai_file\n",
-                "\n",
-                "    assert ai_file.exists(), f\"AI segmentation not found for {row.AISegmentation}\"\n",
-                "    assert qa_file.exists(), f\"QA segmentation not found for {row.QASegmentation}\"\n",
-                "\n",
-                "    version = \"aimiv1\" if row.AimiProjectYear == 2023 else \"aimiv2\"\n",
-                "    metrics.append(\n",
-                "        calc_metrics_for_label(\n",
-                "            ai_file, qa_file, row.SeriesInstanceUID, version=version, label_value=1\n",
-                "        )\n",
-                "    )\n",
-                "    metrics.append(\n",
-                "        calc_metrics_for_label(\n",
-                "            ai_file, qa_file, row.SeriesInstanceUID, version=version, label_value=2\n",
-                "        )\n",
-                "    )\n",
-                "    metrics.append(\n",
-                "        calc_metrics_for_label(\n",
-                "            ai_file, qa_file, row.SeriesInstanceUID, version=version, label_value=3\n",
-                "        )\n",
-                "    )\n",
-                "\n",
-                "metrics_df = pd.DataFrame(metrics)\n",
-                "\n",
-                "# Replace infinite values with NaN and drop NaN values\n",
-                "metrics_df = metrics_df.replace([np.inf, -np.inf], np.nan).dropna()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 42,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# add label names\n",
-                "ds = pydicom.dcmread(ai_path / df.AISegmentation.iloc[0])\n",
-                "result = pydicom_seg.SegmentReader().read(ds)\n",
-                "label_names = {\n",
-                "    k: v[\"SegmentDescription\"].value for k, v in result.segment_infos.items()\n",
-                "}\n",
-                "\n",
-                "# update metrics_df with label names column\n",
-                "metrics_df[\"label_name\"] = metrics_df[\"label\"].map(label_names)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 44,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead tr th {\n",
-                            "        text-align: left;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead tr:last-of-type th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th colspan=\"2\" halign=\"left\">dice</th>\n",
-                            "      <th colspan=\"2\" halign=\"left\">hausdorff_distance_95</th>\n",
-                            "      <th colspan=\"2\" halign=\"left\">mean_surface_distance</th>\n",
-                            "      <th colspan=\"2\" halign=\"left\">mean_surface_distance_tol_7</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>std</th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>std</th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>std</th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>std</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>label_name</th>\n",
-                            "      <th>version</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"2\" valign=\"top\">Cyst</th>\n",
-                            "      <th>aimiv1</th>\n",
-                            "      <td>0.91</td>\n",
-                            "      <td>0.23</td>\n",
-                            "      <td>7.36</td>\n",
-                            "      <td>21.80</td>\n",
-                            "      <td>1.57</td>\n",
-                            "      <td>4.76</td>\n",
-                            "      <td>0.94</td>\n",
-                            "      <td>0.17</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>aimiv2</th>\n",
-                            "      <td>1.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>1.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"2\" valign=\"top\">Kidney</th>\n",
-                            "      <th>aimiv1</th>\n",
-                            "      <td>0.97</td>\n",
-                            "      <td>0.11</td>\n",
-                            "      <td>8.08</td>\n",
-                            "      <td>29.10</td>\n",
-                            "      <td>1.22</td>\n",
-                            "      <td>4.44</td>\n",
-                            "      <td>0.97</td>\n",
-                            "      <td>0.09</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>aimiv2</th>\n",
-                            "      <td>1.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.01</td>\n",
-                            "      <td>1.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"2\" valign=\"top\">Tumor</th>\n",
-                            "      <th>aimiv1</th>\n",
-                            "      <td>0.95</td>\n",
-                            "      <td>0.19</td>\n",
-                            "      <td>4.52</td>\n",
-                            "      <td>18.31</td>\n",
-                            "      <td>1.38</td>\n",
-                            "      <td>7.13</td>\n",
-                            "      <td>0.95</td>\n",
-                            "      <td>0.18</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>aimiv2</th>\n",
-                            "      <td>1.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "      <td>0.15</td>\n",
-                            "      <td>0.38</td>\n",
-                            "      <td>1.00</td>\n",
-                            "      <td>0.00</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                    dice       hausdorff_distance_95         \\\n",
-                            "                    mean   std                  mean    std   \n",
-                            "label_name version                                            \n",
-                            "Cyst       aimiv1   0.91  0.23                  7.36  21.80   \n",
-                            "           aimiv2   1.00  0.00                  0.00   0.00   \n",
-                            "Kidney     aimiv1   0.97  0.11                  8.08  29.10   \n",
-                            "           aimiv2   1.00  0.00                  0.00   0.00   \n",
-                            "Tumor      aimiv1   0.95  0.19                  4.52  18.31   \n",
-                            "           aimiv2   1.00  0.00                  0.00   0.00   \n",
-                            "\n",
-                            "                   mean_surface_distance       mean_surface_distance_tol_7  \\\n",
-                            "                                    mean   std                        mean   \n",
-                            "label_name version                                                           \n",
-                            "Cyst       aimiv1                   1.57  4.76                        0.94   \n",
-                            "           aimiv2                   0.00  0.00                        1.00   \n",
-                            "Kidney     aimiv1                   1.22  4.44                        0.97   \n",
-                            "           aimiv2                   0.00  0.01                        1.00   \n",
-                            "Tumor      aimiv1                   1.38  7.13                        0.95   \n",
-                            "           aimiv2                   0.15  0.38                        1.00   \n",
-                            "\n",
-                            "                          \n",
-                            "                     std  \n",
-                            "label_name version        \n",
-                            "Cyst       aimiv1   0.17  \n",
-                            "           aimiv2   0.00  \n",
-                            "Kidney     aimiv1   0.09  \n",
-                            "           aimiv2   0.00  \n",
-                            "Tumor      aimiv1   0.18  \n",
-                            "           aimiv2   0.00  "
-                        ]
-                    },
-                    "execution_count": 44,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "aggregated_df = (\n",
-                "    metrics_df.groupby([\"label_name\", \"version\"])\n",
-                "    .agg(\n",
-                "        {\n",
-                "            \"dice\": [\"mean\", \"std\"],\n",
-                "            \"hausdorff_distance_95\": [\"mean\", \"std\"],\n",
-                "            \"mean_surface_distance\": [\"mean\", \"std\"],\n",
-                "            \"mean_surface_distance_tol_7\": [\"mean\", \"std\"],\n",
-                "        }\n",
-                "    )\n",
-                "    .round(2)\n",
-                ")\n",
-                "\n",
-                "aggregated_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 47,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1200x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1200x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1200x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1200x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# create seaborn categorical boxplot for each metric, by label and version\n",
-                "for metric in [\n",
-                "    \"dice\",\n",
-                "    \"hausdorff_distance_95\",\n",
-                "    \"mean_surface_distance\",\n",
-                "    \"mean_surface_distance_tol_7\",\n",
-                "]:\n",
-                "    fig, ax = plt.subplots(figsize=(12, 6))\n",
-                "    sns.boxplot(\n",
-                "        x=\"label_name\",\n",
-                "        y=metric,\n",
-                "        hue=\"version\",\n",
-                "        data=metrics_df,\n",
-                "        palette=revewer_cmap,\n",
-                "        ax=ax,\n",
-                "    )\n",
-                "    ax.set_title(f\"{metric} by label and version\")\n",
-                "    ax.set_ylabel(metric)\n",
-                "    ax.set_xlabel(\"Label\")\n",
-                "    plt.xticks(rotation=45)\n",
-                "    plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3 (ipykernel)",
-            "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": "3.11.9"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Performance\n",
+    "\n",
+    "\n",
+    "## Prerequisites\n",
+    "Install python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "%pip install pandas pydicom pydicom-seg scikit-learn seaborn segmentationmetrics requests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import required python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "import io\n",
+    "import zipfile\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pydicom\n",
+    "import pydicom_seg\n",
+    "import requests\n",
+    "import scipy.stats as stats\n",
+    "import seaborn as sns\n",
+    "import segmentationmetrics as sm\n",
+    "import SimpleITK as sitk\n",
+    "from segmentationmetrics.surface_distance import compute_surface_dice_at_tolerance\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download the segmentation results from zenodo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qa_dir = Path(\"qa-results\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def download_inference(\n",
+    "    extract_dir=\"qa-results\", record=\"13244892\", filename=\"kidney-ct.zip\"\n",
+    "):\n",
+    "    url = f\"https://zenodo.org/record/{record}/files/{filename}\"\n",
+    "    # download the zip file and extract it\n",
+    "    r = requests.get(url)\n",
+    "    z = zipfile.ZipFile(io.BytesIO(r.content))\n",
+    "    z.extractall(extract_dir)\n",
+    "\n",
+    "\n",
+    "if not qa_dir.exists() or not (qa_dir / \"ai-segmentations-dcm\").exists():\n",
+    "    download_inference(qa_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt_colors = {\n",
+    "    \"ne2\": \"#5eceb0\",\n",
+    "    \"rad1\": \"#9e70e1\",\n",
+    "    \"tp\": \"k\",\n",
+    "    \"fp\": \"b\",\n",
+    "    \"fn\": \"r\",\n",
+    "}\n",
+    "\n",
+    "revewer_cmap = sns.color_palette([plt_colors[\"ne2\"], plt_colors[\"rad1\"]])\n",
+    "vol_cmap = sns.color_palette([plt_colors[\"fp\"], plt_colors[\"fn\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_dcm_seg(seg_path: Path, label=1):\n",
+    "    dcm = pydicom.dcmread(str(seg_path))\n",
+    "    reader = pydicom_seg.SegmentReader()\n",
+    "    result = reader.read(dcm)\n",
+    "    if label in result.available_segments:\n",
+    "        image = result.segment_image(label)\n",
+    "    else:\n",
+    "        # create an empty image\n",
+    "        tmp_img = result.segment_image(list(result.available_segments)[0])\n",
+    "        tmp_arr = sitk.GetArrayFromImage(tmp_img)\n",
+    "        image = sitk.GetImageFromArray(np.zeros_like(tmp_arr))\n",
+    "        image.CopyInformation(tmp_img)\n",
+    "    image = sitk.Cast(image, sitk.sitkUInt8)\n",
+    "    return image\n",
+    "\n",
+    "\n",
+    "def load_nii_seg(seg_path: Path, label=1):\n",
+    "    img = sitk.ReadImage(str(seg_path))\n",
+    "    if label == 1:\n",
+    "        img = sitk.Cast(img >= label, sitk.sitkUInt8)\n",
+    "    else:\n",
+    "        img = sitk.Cast(img == label, sitk.sitkUInt8)\n",
+    "    return img\n",
+    "\n",
+    "\n",
+    "def load_seg(seg_path: Path, label=1):\n",
+    "    if seg_path.suffix == \".dcm\":\n",
+    "        return load_dcm_seg(seg_path, label)\n",
+    "    else:\n",
+    "        return load_nii_seg(seg_path, label)\n",
+    "\n",
+    "\n",
+    "def resize_label(img: sitk.Image, ref_img: sitk.Image, interp=sitk.sitkNearestNeighbor):\n",
+    "    resampler = sitk.ResampleImageFilter()\n",
+    "    resampler.SetReferenceImage(ref_img)\n",
+    "    resampler.SetInterpolator(interp)\n",
+    "    resampler.SetDefaultPixelValue(0)\n",
+    "    resampled_img = resampler.Execute(img)\n",
+    "    return resampled_img\n",
+    "\n",
+    "\n",
+    "def calc_metrics_for_label(\n",
+    "    ai_seg_file, qa_seg_file, fname, label_value=1, version=\"aimiv2\", label_suffix=\"\"\n",
+    "):\n",
+    "    ai_img = load_seg(ai_seg_file, label_value)\n",
+    "    qa_img = load_seg(qa_seg_file, label_value)\n",
+    "    qa_img = resize_label(qa_img, ai_img)  # match the size of the ai_img\n",
+    "    ai_arr = sitk.GetArrayFromImage(ai_img)\n",
+    "    qa_arr = sitk.GetArrayFromImage(qa_img)\n",
+    "    spacing = ai_img.GetSpacing()[::-1]  # numpy is reversed dimensions from sitk\n",
+    "\n",
+    "    qa_img = resize_label(qa_img, ai_img)  # match the size of the ai_img\n",
+    "    m = sm.SegmentationMetrics(ai_arr, qa_arr, spacing)\n",
+    "    metrics = {\n",
+    "        \"dice\": m.dice,\n",
+    "        \"hausdorff_distance_95\": m.hausdorff_distance,\n",
+    "        \"mean_surface_distance\": m.mean_surface_distance,\n",
+    "        \"mean_surface_distance_tol_7\": compute_surface_dice_at_tolerance(\n",
+    "            m._surface_dist, 7\n",
+    "        ),\n",
+    "        \"SeriesUID\": fname,  # from medical segmentation decathlon\n",
+    "        \"label\": label_value,\n",
+    "        \"version\": version,\n",
+    "    }\n",
+    "\n",
+    "    if label_suffix:\n",
+    "        metrics = {f\"{k}_{label_suffix}\": v for k, v in metrics.items()}\n",
+    "    return metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 85/85 [02:39<00:00,  1.88s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the CSV file\n",
+    "df = pd.read_csv(Path(\"qa-results/qa-results.csv\"))\n",
+    "qa_df = df[df.Validation]\n",
+    "\n",
+    "# Paths for aimiv1, aimiv2, and qa\n",
+    "ai_path = Path(\"qa-results/ai-segmentations-dcm\")\n",
+    "qa_path = Path(\"qa-results/qa-segmentations-dcm\")\n",
+    "\n",
+    "# Initialize lists for metrics\n",
+    "metrics = []\n",
+    "\n",
+    "# Iterate over all QA cases\n",
+    "for _, row in tqdm(qa_df.iterrows(), total=len(qa_df)):\n",
+    "    ai_file = ai_path / row.AISegmentation\n",
+    "    if not pd.isna(row.CorrectedSegmentation):\n",
+    "        qa_file = qa_path / row.CorrectedSegmentation\n",
+    "    else:\n",
+    "        # reviewer had no corrections, use AI segmentation as QA\n",
+    "        qa_file = ai_file\n",
+    "\n",
+    "    assert ai_file.exists(), f\"AI segmentation not found for {row.AISegmentation}\"\n",
+    "    assert qa_file.exists(), f\"QA segmentation not found for {row.QASegmentation}\"\n",
+    "\n",
+    "    version = \"aimiv1\" if row.AimiProjectYear == 2023 else \"aimiv2\"\n",
+    "    for label_value in [1, 2, 3]:\n",
+    "        metrics.append(\n",
+    "            calc_metrics_for_label(\n",
+    "                ai_file,\n",
+    "                qa_file,\n",
+    "                row.SeriesInstanceUID,\n",
+    "                version=version,\n",
+    "                label_value=label_value,\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "metrics_df = pd.DataFrame(metrics)\n",
+    "\n",
+    "# Replace infinite values with NaN and drop NaN values\n",
+    "metrics_df = metrics_df.replace([np.inf, -np.inf], np.nan).dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add label names\n",
+    "ds = pydicom.dcmread(ai_path / df.AISegmentation.iloc[0])\n",
+    "result = pydicom_seg.SegmentReader().read(ds)\n",
+    "label_names = {\n",
+    "    k: v[\"SegmentDescription\"].value for k, v in result.segment_infos.items()\n",
+    "}\n",
+    "assert len(label_names) == 3, \"Expected 3 labels\"\n",
+    "\n",
+    "# update metrics_df with label names column\n",
+    "metrics_df[\"label_name\"] = metrics_df[\"label\"].map(label_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">dice</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">hausdorff_distance_95</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">mean_surface_distance</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">mean_surface_distance_tol_7</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>label_name</th>\n",
+       "      <th>version</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Cyst</th>\n",
+       "      <th>aimiv1</th>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>21.80</td>\n",
+       "      <td>1.57</td>\n",
+       "      <td>4.76</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aimiv2</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Kidney</th>\n",
+       "      <th>aimiv1</th>\n",
+       "      <td>0.97</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>8.08</td>\n",
+       "      <td>29.10</td>\n",
+       "      <td>1.22</td>\n",
+       "      <td>4.44</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aimiv2</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Tumor</th>\n",
+       "      <th>aimiv1</th>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>4.52</td>\n",
+       "      <td>18.31</td>\n",
+       "      <td>1.38</td>\n",
+       "      <td>7.13</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aimiv2</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    dice       hausdorff_distance_95         \\\n",
+       "                    mean   std                  mean    std   \n",
+       "label_name version                                            \n",
+       "Cyst       aimiv1   0.91  0.23                  7.36  21.80   \n",
+       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
+       "Kidney     aimiv1   0.97  0.11                  8.08  29.10   \n",
+       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
+       "Tumor      aimiv1   0.95  0.19                  4.52  18.31   \n",
+       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
+       "\n",
+       "                   mean_surface_distance       mean_surface_distance_tol_7  \\\n",
+       "                                    mean   std                        mean   \n",
+       "label_name version                                                           \n",
+       "Cyst       aimiv1                   1.57  4.76                        0.94   \n",
+       "           aimiv2                   0.00  0.00                        1.00   \n",
+       "Kidney     aimiv1                   1.22  4.44                        0.97   \n",
+       "           aimiv2                   0.00  0.01                        1.00   \n",
+       "Tumor      aimiv1                   1.38  7.13                        0.95   \n",
+       "           aimiv2                   0.15  0.38                        1.00   \n",
+       "\n",
+       "                          \n",
+       "                     std  \n",
+       "label_name version        \n",
+       "Cyst       aimiv1   0.17  \n",
+       "           aimiv2   0.00  \n",
+       "Kidney     aimiv1   0.09  \n",
+       "           aimiv2   0.00  \n",
+       "Tumor      aimiv1   0.18  \n",
+       "           aimiv2   0.00  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aggregated_df = (\n",
+    "    metrics_df.groupby([\"label_name\", \"version\"])\n",
+    "    .agg(\n",
+    "        {\n",
+    "            \"dice\": [\"mean\", \"std\"],\n",
+    "            \"hausdorff_distance_95\": [\"mean\", \"std\"],\n",
+    "            \"mean_surface_distance\": [\"mean\", \"std\"],\n",
+    "            \"mean_surface_distance_tol_7\": [\"mean\", \"std\"],\n",
+    "        }\n",
+    "    )\n",
+    "    .round(2)\n",
+    ")\n",
+    "\n",
+    "aggregated_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A+A9G2gEAsNiqVauUmZmpRYsWqVevXgR2AADgwUJ0AABYbM+ePXrjjTfUokULjRkzxupyAACAjTA9HgAAAAAAm2KkHQAAAAAAmyK0AwAAAABgU36/EN33338v0zTLdN9ZAAAAAADOVWFhoQzDUOvWrf+yr9+HdtM0xWX9AAAAAABfOZsM6vehvXiEPTY21uJKAAAAAAD+IDMzs8x9uaYdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsClCOwAAAAAANkVoBwAAAADApgjtAAAAAADYFKEdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsClCOwAAAAAANkVoBwDgHGVkZGjEiBHKyMiwuhQAAFDB2Cq0v/LKK+rXr9+f9jl8+LBGjx6ttm3bql27dpo4caLy8vJ8VCGAs0WYQUWXn5+vF198UQcOHNCLL76o/Px8q0vCBcbnGvwB73PAPmwT2hcsWKAXX3zxL/sNHz5cu3btUmpqqmbMmKG1a9dqwoQJF7w+AGcvPz9fKSkpOnDggFJSUggzqJAWLFigoqIiSVJRUZEWLlxocUW4kPhcgz/gfQ7YS6DVBezbt09PPvmkNmzYoHr16v1p3++//14bN27UihUr1LBhQ0nSU089pYEDB2rUqFGqU6eODyo+f0zTVH5+vmUfhKZpqqCgwOfH/d///qclS5aoV69eatGihc+PHxwcLMMwfH5cp9Mpp9Opo0eP+vzYkuR2u3X8+HGfHvPTTz/V4cOHJZ2eJfP222/rhhtu8GkNVapUkcPh+/OT1atX9/kx7eTIkSOWHNfX7/MDBw5o9erVXm2rVq1Sq1atVLNmTZ/VYfX73F9eb4nPNX/G+9y/3uf+9HrbgdWvt90ZpmmaVhbw+eef68MPP9TYsWM1Z84c/fbbb3rrrbdK7fvqq6/qjTfe0L///W9PW0FBgVq2bKlp06bp5ptvPuvjZ2ZmyjRNNWrUqNzPobzy8/M1bNgwnx8X1pk9ezavuZ949dVXrS7BUoMGDbK6BPhA8fuc19s/8LnG+9wf8LnmX6z8XNu+fbsMw1BsbOxf9rV8pL1Lly7q0qVLmfru27dPUVFRXm3BwcEKCwvT3r17y11DYWGhtm7dWu79z+W48C8//vij1SXAR6z4TAF8jfe5f+H1hj/gfe5frH69g4ODy9TP8tB+NvLy8kp9Yk6n85ymmAcFBVk20g7/0qRJE02bNs2SY7vdbp04ccInx9q/f7/mzJlzxu1Dhw5VrVq1fFJLaGioJdOtqlWr5vNj2ok/vM8///xzffnll2fcfvXVV5f5pPS5svp97g+vN59rfK7xPvev97k/vN52YvXrbYXt27eXue9FFdpDQkJKvQY7Pz9flStXLvfjGoZxTvuXV6VKlTR//nyfH1c6fT37c889d1ZvloqiUaNGevTRRy27rt2K4/pao0aN9OWXX2rz5s1yu92edofDoRYtWqhDhw5+8XvwZ1Z8pvraZZdd9qehPSkpqcxn0C92/vB687kG3uf+9T73h9cb1jqb/5cuqtAeGRmpVatWebUVFBTI5XKpdu3aFlVVfoZhKCQkxLLjT5gwwWej/VlZWXr88cfPuP3pp59WdHS0T2rxl+BsJcMwNGDAAI0dO7bUdn7/qAgyMzP/cnubNm18VA0uND7X4A94nwP2dFGF9rZt22rq1KnatWuXLrvsMknSxo0bJYkvRuXgy5MG9erVU0hIiE6ePFliW0hIiOrVq2fJlBhcOJGRkUpISNDSpUtlmqYMw1BiYuJFd5cH4Ez+6mTxxXgyGX+OzzX4A97ngP3YOiUVFRVp//79nqDXsmVLxcfHa+TIkfrhhx+0fv16PfHEE+rZsycfJDb322+/lRrYJenkyZP67bfffFwRfCExMVFhYWGSpPDwcCUkJFhbEHAe1a1b94wnPkNCQlS3bl0fVwRf4HMN/oD3OWAvtg7te/fu1VVXXaUVK1ZIOj0yPHv2bF1yySXq37+/Hn74YV199dWaMGGCtYXiL+Xk5JzTdlycnE6nkpOTVbNmTSUlJcnpdFpdEnDe7N27909PRp7LXU1gX3yuwR/wPgfsxfL7tFut+JrEstwfD+Xndrt1//33Kzc3t8S20NBQvfzyy0yPB3BRMU1TkydPLvXa9ri4OI0dO5brPwEAQKnOJoeSkuATDodD/fr1K3Vbv379COwALjpnWpiJBZsAAMD5RFKCT5imqa+//rrUbevWrZOfT/gAUMHwmQYAAM4XQjt8Iisr64y3R8rMzFRWVpaPKwKAc2OaplJTU0sE9DO1AwAAlAehHT4RHR2t2NjYUqeRxsXF+ewe7QBwvnAyEgAA+AKhHT5RfI1naSNPXPsJ4GIUHR2txo0bl7qtSZMmnIwEAADnBaEdljJNkymkAC5anHAEAAAXGqEdPmGapubNm1fqtnnz5hHcAVx0srKy9OOPP5a67ccff2R6PAAAOC8I7fCJ33777U+/3P72228+rggAzg1rdQAAAF8gtAMAUA6GYah79+6lrh5/yy23MHUeAACcF4R2+ETdunVVr169UrfVr19fdevW9W1BAHCOTNPUsmXLSt22bNkyLvsBAADnBaEdPuN0OkttDw4O9nElAHDuuOUbAADwBUI7fIIFmwBUNMXXtDsc3n9KHQ4H17QDAIDzhtAOn+DLLYCKxjAMDRgwoNSF6EprBwAAKA9CO3yCL7cAKqLIyEglJCR4PsMMw1BiYqLq1KljcWUAAKCiILTDZ/hyC6AiSkxMVFhYmCQpPDxcCQkJ1hYEAAAqFEI7fCoxMVGhoaGSpNDQUL7cArjoOZ1OJScnq2bNmkpKSjrjopsAAADlEWh1AfA/xbdB4nZIACqK+Ph4xcfHW10GAACogBhph0+lpaUpNzdXkpSbm6v09HSLKwIAAAAA+yK0w2eys7OVnp7uNdKelpam7OxsiysDAADA72VkZGjEiBHKyMiwuhTA7xHa4ROmaSo1NbXElPgztQMAAMAa+fn5SklJ0YEDB5SSkqL8/HyrSwL8GqEdPpGVlaXMzEy53W6vdrfbrczMTGVlZVlUGQAAAH4vLS1NLpdLkuRyubicEbAYoR0+ER0drdjY2FLv0x4XF6fo6GiLKgMAAEAxLmcE7IfQDp8wDEMDBgwodduAAQNKhHkAAAD4FpczAvZEaIfl+AMAAABgPS5nBOyJ0A6fKD5DW9r0eM7cAgAAWI/LGQF7IrTDJzhzCwAAYG9czgjYE6EdPlF85tbh8H7LORwOztwCAADYHLMiAesQ2uETxWduS5tuxZlbAAAA63E5I2BPhHb4TGRkpBISEjx/CAzDUGJiourUqWNxZQAAAOByRsCeCO3wqcTERIWFhUmSwsPDlZCQYG1BAAAAkMTljIBdEdrhU06nU8nJyapZs6aSkpLkdDqtLgkAAADickbArgKtLgD+Jz4+XvHx8VaXAQAAgD8ovpxx6dKlMk2TyxkBG2CkHQAAAIAHlzMC9kJoBwAAAODB5YyAvTA9HgAAAIAXLmcE7IORdgAAAAAAbIrQDgAAAACATRHaAQAAAACwKUI7AAAAAAA2RWgHAAAAAMCmCO0AAAAAANgUoR0AAAAAAJsitAMAAAAAYFOEdgAAAAAAbIrQDgAAAACATRHaAQAAAACwKUI7AAAAAAA2RWgHAAAAAMCmCO0AAAAAANgUoR0AAAAAAJsitAMAAAAAYFOEdvhcRkaGRowYoYyMDKtLAQAAAABbI7TDp/Lz85WSkqIDBw4oJSVF+fn5VpcEAAAAALZFaIdPpaWlyeVySZJcLpfS09OtLQgAAAAAbIzQDp/Jzs5Wenq6TNOUJJmmqbS0NGVnZ1tcGQAAAADYE6EdPmGaplJTUz2B/a/aAQAAAACEdvhIVlaWMjMz5Xa7vdrdbrcyMzOVlZVlUWUAAAAAYF+EdvhEdHS0mjRpUuq2Jk2aKDo62scVAQAAAID9EdrhM2eaAs/UeAAAAAAoHaEdPpGVlaWffvqp1G0//fQT0+MBAAAAoBSEdvhEdHS0GjduXOo2pscDAAAAQOkI7fAZwzCsLgEAAAAALiqEdvhEVlaWfvzxx1K3/fjjj0yPBwAAAIBSENrhE9HR0YqNjZXD4f2WczgciouLY3o8AAAAAJSC0A6fMAxDAwYMKDFF/kztAAAAAABCO3woMjJSCQkJnoBuGIYSExNVp04diysDAAAAAHsitMOnEhMTFRYWJkkKDw9XQkKCtQUBAAAAgI0R2uFTTqdTycnJqlmzppKSkuR0Oq0uCQAAAABsK9DqAuB/4uPjFR8fb3UZAAAAAGB7jLQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE1ZHtrdbrdmzpypTp06qVWrVho0aJB27959xv4HDx7U6NGjdcUVV6h9+/YaOXKk9u3b58OKAQAAAADwDctD+9y5c7Vw4UI9/fTTWrRokdxutwYOHKiCgoJS+z/88MPKysrS66+/rtdff11ZWVkaOnSoj6sGAAAAAODCszS0FxQUKCUlRcOHD1fnzp0VExOj6dOnKzs7W59++mmJ/kePHtXGjRs1aNAgNW3aVM2aNdPgwYOVmZkpl8vl+ycAAAAAAMAFZOkt37Zt26YTJ06oQ4cOnrZq1aqpWbNm+vbbb9W9e3ev/iEhIQoNDdVHH32kdu3aSZKWLl2q+vXrq1q1auWuwzRN5ebmlnt/AAAAAADKyjRNGYZRpr6Whvbs7GxJUlRUlFd77dq1Pdt+Lzg4WM8995yeeOIJXX755TIMQ7Vr19bbb78th6P8kwYKCwu1devWcu8PAAAAAMDZCA4OLlM/S0N7Xl6epJLFOp1OHTlypER/0zS1detWtW7dWgMHDlRRUZGmT5+uIUOG6J133lGVKlXKVUdQUJAaNWpUrn0BAAAAADgb27dvL3NfS0N7SEiIpNPXthf/LEn5+fmqVKlSif4rV67U22+/rS+++MIT0F9++WVde+21Wrx4sQYMGFCuOgzDUOXKlcu1LwAAAAAAZ6OsU+MlixeiK54Wn5OT49Wek5OjOnXqlOi/adMm1a9f32tEvXr16qpfv7527dp1YYsFAAAAAMDHLA3tMTExqlKlijZs2OBpO3r0qLZs2aK2bduW6B8ZGaldu3YpPz/f05abm6s9e/aoXr16vigZAAAAqPAyMjI0YsQIZWRkWF0K4PcsDe3BwcHq27evpk6dqtWrV2vbtm0aOXKkIiMjdcMNN6ioqEj79+/XyZMnJUk9e/aUdPpe7du2bdO2bds0atQoOZ1O9erVy8JnAgAAAFQM+fn5SklJ0YEDB5SSkuI1YAbA9ywN7ZI0fPhw9e7dW+PHj9ddd92lgIAAzZ8/X0FBQdq7d6+uuuoqrVixQtLpVeUXLlwo0zTVv39/JSUlKSgoSAsXLlTVqlUtfiYAAADAxS8tLU0ul0uS5HK5lJ6ebm1BgJ8zTNM0rS7CSpmZmZKk2NhYiysBAAAArJWdna2xY8eqqKjI0xYQEKDnn39ekZGRFlYGVCxnk0MtH2kHAAAAYD3TNJWamqo/jumdqR2AbxDaAQAAACgrK0uZmZlyu91e7W63W5mZmcrKyrKoMsC/EdoBAAAAKDo6WrGxsXI4vCOCw+FQXFycoqOjLaoM8G+EdgAAAAAyDEMDBgyQYRhlagfgG4R2AAAAAJKkyMhIJSQkeAK6YRhKTExUnTp1LK4M8F+EdgAAAAAeiYmJCgsLkySFh4crISHB2oIAP0doBwAAAODhdDqVnJysmjVrKikpSU6n0+qSAL8WaHUBAABc7DIyMvTGG2+of//+io+Pt7ocADhn8fHxfJ4BNsFIOwAA5yA/P18pKSk6cOCAUlJSlJ+fb3VJAACgAiG0AwBwDtLS0uRyuSRJLpdL6enp1hYEAAAqFEI7AADllJ2drfT0dJmmKUkyTVNpaWnKzs62uDIAAFBRENoBACgH0zSVmprqCex/1Q4AAFAehHYAAMohKytLmZmZcrvdXu1ut1uZmZnKysqyqDIAAFCRENoBACiH6OhoxcbGyuHw/lPqcDgUFxen6OhoiyoDAAAVCaEdAIByMAxDAwYMkGEYZWoHAAAoD0I7fC4jI0MjRoxQRkaG1aUAwDmJjIxUQkKCJ6AbhqHExETVqVPH4soAAEBFQWiHT3E/YwAVTWJiosLCwiRJ4eHhSkhIsLYgAABQoRDa4VPczxhAReN0OpWcnKyaNWsqKSlJTqfT6pIAAEAFEmh1AfAfZ7qf8VVXXaXIyEiLqwOA8ouPj1d8fLzVZQAAgAqIkXb4BPczBgAAAICzR2iHT3A/YwAAAAA4e4R2+AT3MwYAAACAs0doh09wP2MAAAAAOHuEdvgM9zMGAAAAgLNDaIdPcT9jAAAAACg7Qjt8ivsZAwAAAEDZcZ92+Bz3MwYAAACAsmGkHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApy0O72+3WzJkz1alTJ7Vq1UqDBg3S7t27z9i/sLBQ06ZN8/Tv27evtm7d6sOKAQAAAADwDctD+9y5c7Vw4UI9/fTTWrRokdxutwYOHKiCgoJS+0+YMEFLlizRv/71L33wwQeKiIjQoEGDdOzYMR9XDgAAAADAhWVpaC8oKFBKSoqGDx+uzp07KyYmRtOnT1d2drY+/fTTEv13796tDz74QJMmTVKnTp3UsGFDPfPMMwoODtb//vc/C54BAAAAAAAXTqCVB9+2bZtOnDihDh06eNqqVaumZs2a6dtvv1X37t29+q9bt05Vq1bV1Vdf7dX/888/P6c6TNNUbm7uOT0GAAAAAABlYZqmDMMoU19LQ3t2drYkKSoqyqu9du3anm2/t3PnTl166aX69NNPNW/ePO3bt0/NmjXTo48+qoYNG5a7jsLCQq6LBwAAAP5/v/zyiz7//HN16dLlnL5nAziz4ODgMvWzNLTn5eVJKlms0+nUkSNHSvQ/fvy4du3apblz52rs2LGqVq2aXnrpJd19991asWKFatSoUa46goKC1KhRo3LtCwAAAFQk+fn5SklJ0bFjx7R27Vpdf/31cjqdVpcFVCjbt28vc19LQ3tISIik09e2F/8snf6gqFSpUon+gYGBOn78uKZPn+454zd9+nRdc801+vDDDzVw4MBy1WEYhipXrlyufQEAAICKZPny5Z4BtCNHjmj16tXq3bu3xVUBFUtZp8ZLFi9EVzwtPicnx6s9JydHderUKdE/MjJSgYGBXlN0QkJCdOmll2rPnj0XtlgAAACggsvOzlZ6erpM05R0+rrbtLS0Ui9dBeAblob2mJgYValSRRs2bPC0HT16VFu2bFHbtm1L9G/btq1OnTqlzMxMT9vJkye1e/duXXbZZT6pGQAAAKiITNNUamqqJ7D/VTsA37A0tAcHB6tv376aOnWqVq9erW3btmnkyJGKjIzUDTfcoKKiIu3fv18nT56UJF1++eW68sorNW7cOG3atEnbt2/X2LFjFRAQoB49elj5VAAAAICLWlZWljIzM+V2u73a3W63MjMzlZWVZVFlgH+zNLRL0vDhw9W7d2+NHz9ed911lwICAjR//nwFBQVp7969uuqqq7RixQpP/1mzZqldu3YaNmyYevfurePHj+vNN99URESEhc8CAAAAuLhFR0crNjZWDod3RHA4HIqLi1N0dLRFlQH+zTD9fJ5L8VT72NhYiysBAAAArJWdna2xY8eqqKjI0xYQEKApU6aUuuYUgPI5mxxq+Ug7AAAAAHuIjIxUQkKCZ2VrwzCUmJhIYAcsRGgHAAAA4JGYmKiwsDBJUnh4uBISEqwtCPBzhHYAAAAAHk6nU8nJyapZs6aSkpLkdDqtLgnwa4FWFwAAAADAXuLj4xUfH291GQDESDsAAAAAALZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAADgJSMjQyNGjFBGRobVpQB+j9AOAAAAwCM/P18pKSk6cOCAUlJSlJ+fb3VJgF8rd2g/dOiQpkyZoltvvVVXXXWVtm3bptmzZ2vVqlXnsz4AAAAAPpSWliaXyyVJcrlcSk9Pt7YgwM+VK7Tv3r1biYmJeu+991SnTh0dPHhQRUVF2rlzp4YPH641a9ac5zIBAAAAXGjZ2dlKT0+XaZqSJNM0lZaWpuzsbIsrA/xXuUL75MmTVaNGDa1evVqzZ8/2/E89bdo0denSRS+//PJ5LRIAAADAhWWaplJTUz3f7f+qHYBvlCu0f/PNNxoyZIiqVasmwzC8tt155536+eefz0txAAAAAHwjKytLmZmZcrvdXu1ut1uZmZnKysqyqDLAv5X7mvbAwMBS2wsKCkoEeQAAAAD2Fh0drdjYWDkc3hHB4XAoLi5O0dHRFlUG+LdyhfbLL79cr7zyinJzcz1thmHI7XbrnXfeUXx8/HkrEAAAAMCFZxiGBgwYUGIA7kztAHyj9OHyvzB69GjddddduuGGG9S+fXsZhqH58+frl19+0a5du7Rw4cLzXScAAACACywyMlIJCQlaunSpTNOUYRhKTExUnTp1rC4N8FvlGmlv3LixFi9erPbt22vDhg0KCAjQ119/rb/97W9atGiRmjZter7rBAAAAOADiYmJCgsLkySFh4crISHB2oIAP1eukXZJql+/vp5//nkFBARIkvLy8nTq1ClVrVr1vBUHAAAAwLecTqeSk5P1xhtvqH///nI6nVaXBPi1co20FxYW6sknn9Qdd9zhafv+++/VoUMHTZ48ucSKkwAAAAAuHvHx8ZoxYwZrVQE2UK7QPmvWLKWlpemWW27xtDVr1kyPPPKI3nvvPb322mvnrUAAAAAAAPxVuabHp6ena9y4cerTp4+nLSwsTAMGDFBgYKDefPNNDR48+LwVCQAAAACAPyrXSPvhw4d16aWXlrqtQYMGys7OPqeiAAAAAABAOUN7gwYN9Mknn5S67fPPP9dll112TkUBAAAAAIByTo+/99579eijj8rlcqlr166qUaOGDh06pC+++EIrV67Us88+e77rBAAAAADA75QrtPfs2VMnTpzQ3Llz9emnn3raw8PD9fjjj6tnz57nqz4AAAAAAPxWue/Tfs899+juu+/Wzp075XK5VK1aNTVo0EAOR7lm3AMAAAAAgD8od2iXJMMw1KBBg/NVCwAAAAAA+J0yh/amTZvq3XffVVxcnGJiYmQYxhn7GoahLVu2nJcCAQAAAADwV2UO7UOHDlWdOnU8P/9ZaAcAAAAAAOeuzKG9V69eKioqUlZWlm677bYLWRMAAAAAANBZhPYuXbqc1ej61q1by1UQAAAAAAA4rcyh/V//+pcntB85ckRTp05Vhw4ddNNNN6lWrVpyuVz6/PPPtWbNGj366KMXrGAAAAAAAPyFYZqmebY7DR06VOHh4XrmmWdKbJs0aZJ+/vlnpaamno/6LrjMzExJUmxsrMWVAAAAAAD8wdnk0HLdVH3dunW66aabSt3WuXNnff/99+V5WAAAAAAA8DvlCu3h4eH64YcfSt22fv16zyrzAAAAAACg/Mp8Tfvv3X777ZozZ45Onjypzp07Kzw8XAcOHNDHH3+sd955R4899tj5rhMAAAAAAL9TrtD+4IMP6tixY5o/f77mzZsnSTJNUyEhIRoxYoTuueee81okAAAAAAD+qFwL0RU7duyY/vOf/+jIkSMKDw9X69atVbly5fNZ3wXHQnQAAAAAAF86mxxarpH2YlWrVlWnTp3O5SEAAAAAAMAZlGshOgAAAAAAcOER2gEAAAAAsClCOwAAAAAANkVoBwAAAADApgjtAAAAAADYFKEdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsClCOwAAAAAANkVoBwAAAADApgjtAAAAAADYFKEdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsClCOwAAAAAvGRkZGjFihDIyMqwuBfB7lod2t9utmTNnqlOnTmrVqpUGDRqk3bt3l2nftLQ0NWnSRHv27LnAVQIAAAD+IT8/XykpKTpw4IBSUlKUn59vdUmAX7M8tM+dO1cLFy7U008/rUWLFsntdmvgwIEqKCj40/1+++03PfXUUz6qEgAAAPAPaWlpcrlckiSXy6X09HRrCwL8nKWhvaCgQCkpKRo+fLg6d+6smJgYTZ8+XdnZ2fr000/PuJ/b7daYMWPUvHlzH1YLAAAAVGzZ2dlKT0+XaZqSJNM0lZaWpuzsbIsrA/xXoJUH37Ztm06cOKEOHTp42qpVq6ZmzZrp22+/Vffu3Uvd7+WXX1ZhYaGGDRum9evXn3MdpmkqNzf3nB8HAAAAuFiZpqmUlBRPYP9j+4gRI2QYhkXVARWLaZpl/v/J0tBefMYuKirKq7127dpnPJv3ww8/KCUlRYsXL9a+ffvOSx2FhYXaunXreXksAAAA4GJ08OBBbd68uUS72+3W5s2btW7dOtWoUcOCyoCKKTg4uEz9LA3teXl5kkoW63Q6deTIkRL9c3Nz9cgjj+iRRx5RvXr1zltoDwoKUqNGjc7LYwEAAAAXI9M0tWnTJm3dulVut9vT7nA41LRpU3Xs2JGRduA82b59e5n7WhraQ0JCJJ2+tr34Z+n0ipWVKlUq0f+ZZ55R/fr11adPn/Nah2EYqly58nl9TAAAAOBik5ycrLFjx3q1GYah++67T6GhoRZVBVQ8Z3MCzNKF6Iqnxefk5Hi15+TkqE6dOiX6f/DBB/r666/VunVrtW7dWoMGDZIkde/eXS+//PKFLxgAAACowCIjI5WQkOAJFIZhKDExsdTv5gB8w9KR9piYGFWpUkUbNmzQ3/72N0nS0aNHtWXLFvXt27dE/z+uKP/f//5XY8aM0bx589S4cWOf1AwAAABUZImJiVq7dq0OHz6s8PBwJSQkWF0S4NcsDe3BwcHq27evpk6dqoiICNWtW1dTpkxRZGSkbrjhBhUVFenQoUOqWrWqQkJCdNlll3ntX7xYXXR0tMLCwix4BgAAAEDF4nQ6lZycrDfeeEP9+/eX0+m0uiTAr1ka2iVp+PDhOnXqlMaPH6+TJ0+qbdu2mj9/voKCgrRnzx5dd911evbZZ9WrVy+rSwUAAAD8Qnx8vOLj460uA4Akw/zjjRj9TGZmpiQpNjbW4koAAAAAAP7gbHKopQvRAQAAAACAMyO0AwBwjjIyMjRixAhlZGRYXQoAAKhgCO0AAJyD/Px8paSk6MCBA0pJSVF+fr7VJQEAgAqE0A4AwDlIS0uTy+WSJLlcLqWnp1tbEAAAqFAI7QAAlFN2drbS09NVvKaraZpKS0vz3JIUAADgXBHaAQAoB9M0lZqaqj/ehOVM7QAAAOVBaAcAoByysrKUmZkpt9vt1e52u5WZmamsrCyLKgMAABUJoR0AgHKIjo5WbGysHA7vP6UOh0NxcXGKjo62qDIAAFCRENoBACgHwzA0YMAAGYZRpnYAAIDyILQDAFBOkZGRSkhI8AR0wzCUmJioOnXqWFwZAACoKAjtAACcg8TERIWFhUmSwsPDlZCQYG1BAACgQiG0AwBwDpxOp6655ho5HA5dffXVcjqdVpcEAAAqEEI7AADnID8/X2vXrpXb7dbatWuVn59vdUkAAKACIbQDAHAO0tLS5HK5JEkul0vp6enWFgQAACoUQjsAAOWUnZ2t9PR0maYpSTJNU2lpacrOzra4MgAAUFEQ2gEAKAfTNJWamuoJ7H/VDgAAUB6EdgAAyiErK0uZmZlyu91e7W63W5mZmcrKyrKoMgAAUJEQ2gEAKIfo6GjFxsbK4fD+U+pwOBQXF6fo6GiLKgMAABUJoR0AgHIwDEMDBgyQYRhlagcAACgPQjsAAOUUGRmphIQET0A3DEOJiYmqU6eOxZUBAICKgtAOAMA5SExMVFhYmCQpPDxcCQkJ1hYEAAAqFEI7AADnwOl0Kjk5WTVr1lRSUpKcTqfVJQEAgAok0OoCAAC42MXHxys+Pt7qMgAAQAXESDsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAALxkZGRoxYoQyMjKsLgXwe5aHdrfbrZkzZ6pTp05q1aqVBg0apN27d5+x/88//6zBgwerffv26tChg4YPH66srCwfVgwAAABUXPn5+UpJSdGBAweUkpKi/Px8q0sC/JrloX3u3LlauHChnn76aS1atEhut1sDBw5UQUFBib6HDx9WUlKSQkJC9NZbb+nVV1/VoUOHNHDgQD5MAAAAgPMgLS1NLpdLkuRyuZSenm5tQYCfszS0FxQUKCUlRcOHD1fnzp0VExOj6dOnKzs7W59++mmJ/qtWrVJubq6ef/55NW7cWC1atNCUKVP0yy+/MHUHAAAAOEfZ2dlKT0+XaZqSJNM0lZaWpuzsbIsrA/xXoJUH37Ztm06cOKEOHTp42qpVq6ZmzZrp22+/Vffu3b36d+jQQXPnzlVISIinzeE4fd7h6NGj5a7DNE3l5uaWe38AAADgYmeaplJSUjyB/Y/tI0aMkGEYFlUHVCymaZb5/ydLQ3vxGbuoqCiv9tq1a5d6Nu+SSy7RJZdc4tU2b948hYSEqG3btuWuo7CwUFu3bi33/gAAAMDF7uDBg9q8eXOJdrfbrc2bN2vdunWqUaOGBZUBFVNwcHCZ+lka2vPy8iSVLNbpdOrIkSN/uf9bb72lt99+W+PHj1dERES56wgKClKjRo3KvT8AAABwsTNNU5s2bdLWrVvldrs97Q6HQ02bNlXHjh0ZaQfOk+3bt5e5r6WhvXiae0FBgdeU9/z8fFWqVOmM+5mmqRkzZuill17Sgw8+qH79+p1THYZhqHLlyuf0GAAAAMDFLjk5WY888ohXm2mauu+++xQaGmpRVUDFczYnwCxdiK54WnxOTo5Xe05OjurUqVPqPoWFhRozZoxefvll/eMf/9DDDz98ocsEAAAA/Nofr3MH4DuWhvaYmBhVqVJFGzZs8LQdPXpUW7ZsOeM16mPHjtXHH3+sadOmacCAAT6qFAAAAKjYTNNUampqqdtSU1MJ7oBFLJ0eHxwcrL59+2rq1KmKiIhQ3bp1NWXKFEVGRuqGG25QUVGRDh06pKpVqyokJERLlizRihUrNHbsWLVr10779+/3PFZxHwAAAABnLysrS5mZmSXaTdNUZmamsrKyVLduXQsqA/ybpSPtkjR8+HD17t1b48eP11133aWAgADNnz9fQUFB2rt3r6666iqtWLFCkrRs2TJJ0vPPP6+rrrrK67/iPgAAAADOXnR0tGJjYz23VC7mcDgUFxen6OhoiyoD/Jth+vk8l+KzibGxsRZXAgAAAFgrOztbY8eOVVFRkactICBAU6ZMOeOaUwDO3tnkUMtH2gEAAADYQ2RkpBISEjwrWxuGocTERAI7YCFCOwAAAACPxMREhYWFSZLCw8OVkJBgbUGAnyO0AwAAAPBwOp1KTk5WzZo1lZSUJKfTaXVJgF+zdPV4AAAAAPYTHx+v+Ph4q8sAIEbaAQAAAPxBRkaGRowYoYyMDKtLAfweoR0AAACAR35+vlJSUnTgwAGlpKQoPz/f6pIAv0ZoBwAAAOCRlpYml8slSXK5XEpPT7e2IMDPEdoBAAAASDp9n/b09HSZpilJMk1TaWlpys7OtrgywH8R2gEAAADINE2lpqZ6AvtftQPwDUI7AAAAAGVlZSkzM1Nut9ur3e12KzMzU1lZWRZVBvg3QjsAAAAARUdHKzY2Vg6Hd0RwOByKi4tTdHS0RZUB/o3QDgAAAECGYWjAgAEyDKNM7QB8g9AOAAAAQJIUGRmphIQET0A3DEOJiYmqU6eOxZUB/ovQDgAAAMAjMTFRYWFhkqTw8HAlJCRYWxDg5wjt8LmMjAyNGDFCGRkZVpcCAACAP3A6nUpOTlbNmjWVlJQkp9NpdUmAXwu0ugD4l/z8fKWkpOjw4cNKSUlR8+bN+UMAAABgM/Hx8YqPj7e6DABipB0+lpaWJpfLJUlyuVxKT0+3tiAAAAAAsDFCO3wmOztb6enpMk1TkmSaptLS0pSdnW1xZQAAAABgT0yPPwtFRUUqLCy0uoyLkmmaeu+99xQWFia32+1pdzgceu+99zRo0KA/vY1IUFCQAgICfFEqAAAAANgGob0MTNNUdna2Z1o3zt6pU6fUunVrtW7dutTt27dvV2Dgn78dw8LCFBkZyT1CAQAAAPgNQnsZFAf22rVrq3LlyoTGcjBNU/v371d+fn6JbU6nU7Vq1Trj79U0TeXm5ionJ0eSFBUVdUFrBQAAAAC7ILT/haKiIk9gr1GjhtXlXNSioqK0Z88ezzXtkmQYhqKiohQUFPSn+1aqVEmSlJOTo9q1azNVHgAAAIBfYCG6v1B8DXvlypUtruTiFxQUpOrVq3u1Va9e/S8De7Hi14B1BQAAAAD4C0J7GTEl/vwICwvzXLseGBiosLCwMu/LawAAAADA3xDa4VMOh0M1atRQYGCgatSoIYeDtyAAAAAAnAnXtMPnQkNDFRoaanUZAAAAAGB7DHP6sSVLlqhJkybas2eP1aUAAAAAAEpBaPdjnTt31rvvvqvatWtbXQoAAAAAoBRMj/djERERioiIsLoMAAAAAMAZMNJusccff1wdO3ZUUVGRV/ukSZPUvn17FRYW6qefftL999+v+Ph4xcfHa+jQodq9e7en74YNG9SkSRMtWrRI1157reLj47Vu3TodOnRIo0ePVseOHRUbG6sePXroo48+8uxX2vT4devW6e6771abNm3Uvn17jR49Wnv37vXap1mzZvrvf/+rO++8U7Gxsbr22ms1f/78C/dLAgAAAAA/RWi3WI8ePXTgwAFt2LDB0+Z2u7Vy5Urdcsst2rNnj/r06aODBw9q8uTJmjRpknbv3q277rpLBw8e9Hqs2bNna9y4cXriiSfUunVrjRkzRr/88osmTpyoV199Vc2aNdO4ceO0fv36Umv56KOPlJycrKioKL3wwgv6xz/+oe+//1533nmn17Hcbrcefvhh3XzzzZo3b57i4+P1/PPP66uvvrowvyQAAAAA8FNMj7dYmzZtVLduXS1btkxXXnmlpNMj5/v371ePHj00e/ZsVapUSampqapSpYokqUOHDuratatee+01jRs3zvNYd999t2688UbPvzdu3KihQ4eqa9eukqR27dopLCxMwcHBJepwu92aOnWqrrrqKk2bNs3THh8fr5tvvlnz58/X2LFjJUmmaWrIkCG6/fbbPc/hs88+05o1a9SpU6fz/BsCAAAAAP/FSLvFDMNQYmKiVq1apYKCAknS8uXLVa9ePbVs2VLr169Xu3btFBISolOnTunUqVOqUqWKLr/8cn399ddej9W0aVOvf7dv316zZs3S8OHD9f777+vAgQMaN26c4uPjS9Sxc+dO7d+/X927d/dq/9vf/qbWrVtr48aNXu2tW7f2/BwcHKyIiAjl5uae0+8CAAAAAOCN0G4DPXr00JEjR/TVV1+poKBAn376qRITEyVJLpdLK1asUPPmzb3+++KLL5STk+P1OJUrV/b69/Tp0zVgwAD973//0/jx43XNNdfovvvu02+//VaiBpfLJUmqWbNmiW01a9bUsWPHvNpCQkK8/u1wOGSa5lk/dwAAAADAmTE93gbq16+vuLg4rVy5Ug6HQ0ePHvWE9qpVq+rKK69UUlJSif0CA//85atatarGjBmjMWPGaMeOHVq9erXmzp2riRMnat68eV59w8LCJEkHDhwo8Tj79+9XeHh4OZ8dAAAAAKC8GGm3iR49euirr77S8uXLFR8fr0svvVTS6evQt2/frqZNmyo2NlaxsbFq0aKFUlNT9dlnn53x8X777Tddc801+vjjjyVJDRo00KBBg3TllVcqKyurRP/69eurVq1aWrZsmVf77t279Z///KfUKfUAAAAAgAuL0G4TN998s06cOKEVK1aoR48envYhQ4bo119/1f33369Vq1bpq6++0kMPPaTly5crJibmjI9Xt25dRUZG6plnntHixYu1ceNGpaSkaO3aterWrVuJ/g6HQ6NGjdK///1vjR49WmvXrtVHH32kpKQkVa9evdSRfgAAAADAhcX0eJuIiIjQVVddpXXr1nmtAB8TE6MFCxZo+vTpGjt2rEzTVOPGjTVnzhxdd911f/qYs2fP1gsvvKAZM2bo8OHDioqK0rBhwzR48OBS+/fq1UuhoaF65ZVXNHToUFWpUkWdOnXSqFGjVKtWrfP6fAEAAAAAf80w/Xz1sMzMTElSbGxsqdtPnjypnTt3qn79+iUWX4Nv8VoAAAAAqAj+Kof+HtPjAQAAAACwKUI7AAAAAAA2RWgHAAAAAMCmCO0AAAAAANgUoR0AAAAAAJsitAMAAAAAYFOEdgAAAAAAbIrQDgAAAACATQVaXcDFzO12y+12+/y4DodDDgfnWwAAAACgoiO0l5Pb7dbQhx7SUZfL58euFhamObNmEdwBAAAAoIIjtJeT2+3WUZdL4cndJV+GZ7dbh1OWye12n9fQPmvWLH344Yf6/PPPfbr/4cOHlZCQoGnTpql9+/blOjYAAAAAVFSE9nPlcMjwYWg3L9DjJicn65577vHp/vv27dODDz6o/fv3l/u4AAAAAFCRMb8akqTQ0FBFRET4bP/FixcrMTGx3McDAAAAAH9AaPcjP/30k+6//361bdtWLVq00HXXXaeUlBRJp6e3d+nSRZK0Z88eNWnSRMuXL1fPnj0VGxurXr166ZdfftGcOXN05ZVXql27dpo4caJM0yyxf79+/fTwww97Hfvbb79VkyZNtGvXLknSZ599ppEjR2rGjBk+evYAAAAAcPEhtPuJvLw8JScnKywsTIsWLdKyZct04403avLkydq6dWup+0yfPl2PPfaY3n//fR09elR33XWX/u///k9vvfWWRo4cqYULF+qLL74osV+vXr30xRdf6Pjx4562tLQ0xcfH67LLLpMkvfLKK+rTp48Mw7gwTxgAAAAAKgBCu5/Iy8vTvffeqyeeeEINGzZUvXr1NHz4cEnSjz/+WOo+ycnJateunWJiYnT99dcrNzdXTz31lBo2bKi77rpLNWrU0M8//1xiv27dusnhcGjVqlWSpIKCAn3yySfq1avXhXuCAAAAAFABsRCdn4iIiNDdd9+tZcuWacuWLfr111+1bds2STrjveaLR8UlqXLlyqpZs6YqVarkaQsJCVFBQUGJ/SpXrqwbb7xR6enp6tmzp9auXauCggLddNNN5/lZAQAAAEDFxki7n9i/f78SExP1/vvvq06dOrr77rv14Ycf/uk+gYHe53TO5hZzvXr10jfffKMDBw4oPT1dXbt2VZUqVcpVOwAAAAD4K0ba/cSyZcvkcrn0ySefKCgoSNL/mxZfvJjc+XT55Zerbt26Wrp0qdasWaOXX375vB8DAAAAACo6Qvu5crsv2L3Tz3S88oiMjFReXp4+/vhjtWnTRjt27NCzzz4rSaVOcT9XhmGoZ8+emjNnjiIiInTFFVec92MAAAAAQEVHaC8nh8OhamFhOpyyzOfHrhYWdlZT1SXpxhtv1ObNm/Xcc8/p+PHjqlu3rm6//XatXr1amZmZioqKOu913nrrrZo9e7b69et31vUCAAAAACTDvBBzoy8imZmZkqTY2NhSt588eVI7d+5U/fr1FRIS4rXN7XafcRG3C8nhcPhlCP6z1wIAAAAALhZ/lUN/j5H2c+Cv4RkAAAAA4BskTgAAAAAAbIrQDgAAAACATRHaAQAAAACwKUI7AAAAAAA2RWgHAAAAAMCmCO0AAJyjjIwMjRgxQhkZGVaXAgAAKhhCOwAA5yA/P18pKSk6cOCAUlJSlJ+fb3VJAACgAuE+7efA7XbL7Xb7/LjcHx4A7CMtLU0ul0uS5HK5lJ6ert69e1tbFAAAqDAI7eXkdrs1bOhwHTl62OfHrl4tXLPnzCS4A4DFsrOzlZ6eLtM0JUmmaSotLU1XXXWVIiMjLa4OAABUBIT2cnK73Tpy9LC6NnhEhuG78Gyabq3aMVVut/u8hvZZs2bpww8/1Oeff+6T/T///HPNmTNHO3bsUHh4uLp166YRI0YoJCSkXMcHAF8zTVOpqamewP7H9nHjxskwDIuqAwAAFYXlQ7Vut1szZ85Up06d1KpVKw0aNEi7d+8+Y//Dhw9r9OjRatu2rdq1a6eJEycqLy/PhxV7MwyHHEaAz/67UCcIkpOTtXjxYp/sv2nTJg0bNkzXX3+9PvzwQz355JNasWKFJk6cWO7jA4CvZWVlKTMzs8RlUm63W5mZmcrKyrKoMgAAUJFYHtrnzp2rhQsX6umnn9aiRYvkdrs1cOBAFRQUlNp/+PDh2rVrl1JTUzVjxgytXbtWEyZM8G3RFVBoaKgiIiJ8sv+iRYvUvn17PfDAA6pXr56uueYajRw5Uunp6Wd83QHAbqKjoxUbG1tiNN0wDMXFxSk6OtqiygAAQEViaWgvKChQSkqKhg8frs6dOysmJkbTp09Xdna2Pv300xL9v//+e23cuFGTJ09W8+bN1aFDBz311FNaunSp9u3bZ8EzuLj89NNPuv/++9W2bVu1aNFC1113nVJSUiSdnt7epUsXSdKePXvUpEkTLV++XD179lRsbKx69eqlX375RXPmzNGVV17pmeVQPC309/v369dPDz/8sNexv/32WzVp0kS7du1ScnKyxo0b57Xd4XCosLBQx48fv8C/BQA4PwzDUPfu3UudHn/LLbcwNR4AAJwXll7Tvm3bNp04cUIdOnTwtFWrVk3NmjXTt99+q+7du3v137Rpk2rVqqWGDRt62tq1ayfDMPTdd9/p5ptvLlcdpmkqNze31G35+flyu90qKipSUVGRp/33P1uhqKjorL4Q5uXlKSkpSR07dtSCBQsUEBCgDz74QJMnT1a7du3kdrtlmqaKioo8Uz2nT5+uZ555RtWqVdPw4cPVp08fXXPNNXrjjTe0ceNGPfXUU+rYsaOuvfZar/179uypp556SkePHlVoaKgkaenSpYqPj9cll1zi9RwkqbCwUK+//rpatGih6tWrn/F3W1xbXl6eJav2A8DvFS86V5q0tDTVr1+f4A4AAEplmmaZvydYGtqzs7MlSVFRUV7ttWvX9mz7vX379pXoGxwcrLCwMO3du7fcdRQWFmrr1q1n3B4YGFjivrunTp0q9/HOh5MnTyowsOwvn8vl0t1336077rhDlStXliQNHDhQ8+fP1+bNm3Xq1CmZpqmTJ096nmvfvn0VGxsrSercubPeeecdPfroo6pUqZJ69uyp2bNna+vWrerQoYPX/tdcc40cDodWrlyp7t27q7CwUJ988olGjBihkydPetV16tQpjR8/Xtu3b9f8+fNLbP+9/Px8nTp1Sjt27DjbXxcAnHcHDx7U5s2bS922efNmrVu3TjVq1PBxVQAA4GIRHBxcpn6WhvbiBeT+WKzT6dSRI0dK7V/aE3M6nSVC9dkICgpSo0aNSt2Wn5+vrKwsOZ1Or5XNrQ7tISEhZxXao6Ki1K9fPy1fvlxbt27Vr7/+qh9//FGSFBAQoMDAQBmGoZCQEDmdTklSw4YNPc+5atWqqlmzpsLDw71qcLvdnlqK9w8JCVG3bt30ySefqHfv3vr3v/+twsJCJSQkeP0OT5w4oVGjRunbb7/VjBkz1KZNm798HoGBgfrb3/7mqREArGKapjZt2qStW7d6zf5xOBxq2rSpOnbsyEg7AAAo1fbt28vc19LQXhzgCgoKvMJcfn6+KlWqVGr/0hYqy8/P94wel4dhGGfc3+FwyOFwKCAgQAEBAZ72P17D6Gt/rOev7N+/X3feeaciIiLUpUsXderUSbGxsZ5RcYfDIcMwFBAQ4LmVXHBwsOcYpf0ODMPwtP9+f0m67bbb1L9/fx0+fFjLly9X165dVb16dc++OTk5GjRokH777TfNnz9fbdu2LdNzdjgcqlSpEreGA2ALycnJGjt2rFebYRi67777PJcHAQAA/NHZnNi3dCG64qnuOTk5Xu05OTmqU6dOif6RkZEl+hYUFMjlcql27doXrtAKYNmyZXK5XHrnnXc0ZMgQXX/99Z7ZDBfiBMTll1+uunXraunSpVqzZo169erl2XbkyBH1799fhw4d0oIFC8oU2AHAjiIjI5WQkOD5w2sYhhITE0v9GwYAAFAelob2mJgYValSRRs2bPC0HT16VFu2bCk1yLVt21bZ2dnatWuXp23jxo2SVKap1ReCabrlNot89p9plm8BtsjISOXl5enjjz9WVlaW/v3vf2vUqFGSdEFus2YYhnr27Kk5c+YoIiJCV1xxhWfbs88+q927d2vKlCmKiIjQ/v37Pf9ZvcAfAJytxMREhYWFSZLCw8OVkJBgbUEAAKBCsXR6fHBwsPr27aupU6cqIiJCdevW1ZQpUxQZGakbbrhBRUVFOnTokKpWraqQkBC1bNlS8fHxGjlypCZMmKDc3Fw98cQT6tmzp89HNRwOh6pXC9eqHVN9elxJql4t3DOFvaxuvPFGbd68Wc8995yOHz+uunXr6vbbb9fq1auVmZlZYoG/8+HWW2/V7Nmz1a9fP0+9RUVFWrFihQoLC9W/f/8S+6xevdprhXkAsDun06nk5GS98cYb6t+/P2tuAACA88owLb44u6ioSC+88IKWLFmikydPqm3btnriiSd0ySWXaM+ePbruuuv07LPPeqZXHzx4UBMnTtRXX30lp9OpG2+8Uf/4xz/K/SUpMzNTkjyrpP/RyZMntXPnTtWvX7/EddRut9uSW48VX4Pub/7stQAAAACAi8Vf5dDfszy0W+1cQjt8i9cCAAAAQEVwNqHd/4ZrAQAAAAC4SBDaAQAAAACwKUI7AAAAAAA2RWgvIz+/9N8WeA0AAAAA+BtC+18ICgqSJOXm5lpcCYpfg+LXBAAAAAAqOkvv034xCAgIUFhYmHJyciRJlStXlmEYFlflX0zTVG5urnJychQWFqaAgACrSwIAAAAAnyC0l0FkZKQkeYI7rBEWFuZ5LQAAAADAHxDay8AwDEVFRal27doqLCy0uhy/FBQUxAg7AAAAAL9DaD8LAQEBBEcAAAAAgM+wEB0AAAAAADZFaAcAAAAAwKYI7QAAAAAA2JTfX9NeWFgo0zSVmZlpdSkAAAAAAD9QUFBQ5luJ+31o557rAAAAAABfMgyjzFnUME3TvMD1AAAAAACAcuCadgAAAAAAbIrQDgAAAACATRHaAQAAAACwKUI7AAAAAAA2RWgHAAAAAMCmCO0AAAAAANgUoR0AAAAAAJsitAMAAAAAYFOEdgAAAAAAbIrQDgAAAACATRHaAQAAAACwKUI7AAAAAAA2RWgHAAAAAMCmCO0AAAAAANgUoR0AAAAAzuDEiROSJLfbbXEl8FeEdsCGTNP0/MwfCAAXu+LPtN27d+vIkSMWVwMAZXfo0CE9/vjj2rZtmxwOhw4dOmR1SfBDhHbAhgzDkCS99dZb+v777yVJRUVFVpYEAOVmGIZWr16tgQMHau/evVaXAwBlFhoaqiNHjqh379566KGHNH36dJ08edLqsuBnCO2AjS1ZskTz5s2TJAUEBFhcDQCUz6+//qqFCxfqjjvuUExMjNXlAECZOZ1OTZo0STVr1tRnn32mdu3aKSQkhMEU+BShHbCJ30+JL/5DMGzYMB08eFA//PCDVWUBwDn55ZdfNG7cOG3ZskWNGzeWxGU/AC4Oxd/NDh06pIiICLVq1UpPPPGE/ve//ykgIIDgDp8htAM2YJqmZ0q89P9G1Vu0aKHjx49r9erVVpUGAOekYcOGatWqlfLy8rRgwQIdOnRIDofD60QlANhJ8YnF4u9mjRo10ptvvqmpU6fq8ssvV9++fT3BnZOQ8AVCO2ADxX8U3nzzTY0ZM0a7d+/W8ePHVadOHT3wwANaunSpNm/ebHGVAPDXfh/GCwsLJUnjxo3ToEGDtH37dr300ks6dOiQDMMguAOwHbfbLYfjdER69913NXbsWD3++ONyu9265JJLNGbMGLVt21b9+vXTli1b5HA4tGPHDj7PcEEFWl0AgNN/II4dO6acnBxt3LhR9913n5o3b67BgwcrLi5OMTEx2rp1q5o3b66ioiKubwdgS8Wzhr755hutXLlSv/zyi+rXr69bb71VQ4cOlWmaWr58uQzD0AMPPKCIiIgSM40AwErFgX3y5Mn68MMPddNNN3l972rcuLGeeOIJTZgwQXfeeaciIyN15ZVXauLEiVaVDD9gmJwWAizx+zO5f2x/7bXXtH79em3cuFEDBgzQ2rVrFRQUpIULFyokJMSCagGgbFavXq3Ro0fr+uuvV6VKlbRu3TqFhIRo4MCBuvXWW/Xiiy/q888/V8uWLTVq1CiFh4dbXTIAePnss880efJkzZ49WzExMTp8+LC2b9+ur776SpdddpluvfVW7du3T0uXLtWuXbv01FNPKSgoyOqyUYEx0g5Y4PeBvXjqe1ZWluLi4jRo0CANHjxYgwcP1tKlS7V+/XqZpqktW7YoPT1dt99+OyNTAGxp3759mjlzpsaMGaN77rlHknTgwAGNHz9eKSkpaty4sR5++GEdOXJEW7duZREnALbwx4GUo0ePKjIyUjExMVq/fr1SU1M9n1nBwcEKCAhQz5499cADD3j2OXXqlAIDiVa4MLimHbBA8R+GKVOm6MUXX9SJEyfUsGFDvfDCC/rnP/+pY8eOSZJ69OihRx99VDNmzFDbtm312WefSRKBHYAtFRQUKDc3V61bt5Z0+pr2mjVratKkSTpy5Ig+/PBDSdKTTz6pOXPmqGbNmlaWCwCS/t/3svfff1+7du1SjRo1tHv3bt12221KTk5W5cqV9Y9//ENr1qxRaGiocnJySjwGgR0XEu8uwCJfffWVli1bpunTpys+Pl5ffvmlAgMDdd111yknJ0dVq1aVJIWGhqp69eqaMmWKEhIS9O2336pt27YWVw8AJeXm5mr37t3au3evmjVrpsDAQBUWFqpGjRpq1aqV1xfdGjVqWFgpAPw/pmnq1KlTmjp1qm677TaNHTtWDz30kLZu3aohQ4boyiuvVKVKlSRJ1apVU2hoqMUVw98Q2gGL7N27V40aNVJ8fLw+/fRTjRs3TuPHj1e7du30wAMPqG/fvrrpppsUGBgot9utnJwchYeHszopAFsovkwnJydHlStXliQ1adJEHTt2VGpqqsLDwxUfH++5zjM/P18xMTFWlgwAHr+/1NDtdisoKEijRo3SBx98oL1796p3796SpMOHD2vdunWKjIzUjBkzdOzYMd15551Wlg4/xPR4wAdKC9qGYejUqVNavHixxo0bp7Fjx6pPnz7Ky8tTVlaW14hUQUGB1q9fr19//VVRUVG+LB0ASmUYhlatWqV77rlHd9xxh5577jkdPnxYgwcP1tGjR/X8889r2bJlWr9+vSZPnqyMjAz16NHD6rIBQJL3pYbFq8PHxcUpJydH33zzjSTp5MmTSktL03PPPacxY8bI7Xbrgw8+UGBgIGtywKdYPR64wH6/uMmhQ4cUEREhSfruu+80dOhQHT16VKNHj9Z9990nSTpy5Ij69eunBx54QDfffLPncY4dOyaXy6VLL73U908CAP7g559/Vt++fTVgwADt3btX//vf/xQZGanJkydr27Ztmj9/vtasWaP69eurUqVKmjRpkpo2bWp12QD82MGDB7V//34FBATo73//uxYtWqQDBw6ob9++qlKligIDA/Xiiy9q5cqVSk1NVVRUlI4dO6asrCwFBASoQYMGcjgcLDoHnyO0AxfQ76dezZs3T2vWrJHD4dA999yjm266SW+99ZYmTZqkpKQkdejQQdWqVdOsWbPkcrn03nvvec78slo8ADso/iwqKCjQd999py+++EKPPfaYJOm9997TokWLFBkZqaefflo1atTQjh07FBoaqkqVKqlatWoWVw/Anz322GP69ddftWnTJjkcDiUnJ+vgwYNauXKlGjdurLi4OA0bNkxZWVmaNm2a7rrrLnXt2rXE45zplr3AhURoBy6Q33+oz5o1S2+88YbuvvturVu3TtnZ2XrwwQfVt29fvf7661q8eLF++eUXNW3aVNWqVdNrr72moKAgFRUVeYI7AFipOLCvWbNG6enp2rlzp6pXr65XX33VM+K0aNEivffee4qKitKYMWNUr149a4sGAElJSUnKy8vT4MGDVbVqVblcLnXo0MGzEvxrr72mjRs3at++fUpKStKSJUv097//XbNnz7a6dEASoR244LKzszVp0iQNGDBAbdq0kSSNHTtW3377rZKTk9WvXz8dOnRIe/bsUdWqVXXZZZcx9QqALX3zzTd68MEH1a5dOx0+fFg///yz5syZo44dO3r6vP/++3rttdfUrFkzTZkyRQEBAcwUAmCZWbNmad26dZo7d67nEsXi71iFhYUKCgrSqVOn5Ha7NW/ePO3cuVMbNmzQgQMHNGfOHF133XUWPwOAheiA8+qf//yn3nvvPc+/33nnHSUkJOinn37ymhr6/PPPq23btnr99df15ptvKiAgQHFxcapfv74cDofcbjeBHYCt7Nu3T1999ZVGjx6tefPmaebMmbruuus0bNgwff31155+t99+ux544AGNGjVKgYGBBHYAltqyZYu6du3qCezS/7unelBQkNxut9555x0VFBRo2LBhGjNmjKZMmaJ69erpyy+/tKpswAuhHThPsrKydPjwYc2ePVvp6emSpLvuukt///vftWvXLm3evFmFhYWe/s8//7zat2+vadOmad26dV6PxbVSAOzC7XZr9+7duvPOO7V48WLl5+dLkqKiojRu3Dh16dJFQ4cO9ay2LEm33nori2YCsJTb7da+ffuUkZGhFi1aeNr+6MiRI0pNTdWMGTMkSbVr11aHDh309NNPa/ny5frll198WjdQGpIBcJ5ER0dr5MiR6tixo6ZMmaIPPvhAkrRw4UI1b95c06dP1zfffKNTp0559nn22Wd1//33q1u3blaVDQClKr56zuFw6NJLL1X//v1VWFio7777Tjt27JB0+svto48+qq5duyopKUkbN260smQA8HA4HKpZs6aqVKniOan4x0ER0zQVHh6uv//97zp06JCnT/HnX82aNb2+twFWIbQD50HxB/rf//53XX311YqJidGzzz6r5cuXS5I++OADRUREaMKECSWC+5AhQxQQEMD9PgHYimEYWr9+ve69915JpxdyGjFihH744QctWLBAO3fulCTVqlVLjzzyiHr37q0aNWpYWTIAeJimqcLCQjVo0EDr16/3fGb9XvHlO263W06n09Oel5enzz//XL/++quqV6/us5qBMyG0A+dB8bVR06ZNU2pqqvLy8mSapiZNmqTFixdLkj788EOFhYVp4sSJWrNmTYmQzirxAKz2+7VpTdOUy+VSRkaG7rvvPknSgAEDdN999+mTTz7R22+/rf/7v/+TJNWpU0cTJ05Uw4YNrSgbAEowDEMhISEaNmyYtmzZotdee0379+8v0e/QoUM6evSomjVr5mmrXLmy+vTpo2XLlikyMtKXZQOlIrQD5ZSXl+f17xUrVui9997T2LFjNW/ePL355pvq1q2bZs6c6Zkqv2TJEhUWFiotLY2QDsA2Jk2apG+++cZr0TjDMHTttddq+vTp2rx5swYMGCBJSk5OVnJyslavXq1XXnlFv/76qyROPAKwp5YtW+qJJ57Q0qVL9fTTT3umyp86dUpZWVl67LHHZJqm7rrrLs8+pmnqsssuU4MGDawqG/DC8tRAOYwePVrXXnutunfv7rl38Y4dOxQbG+u5rVvz5s1VpUoVnTx5UlOnTlXlypV10003ae3atUyFB2Abzz33nN566y3ddtttkqRt27YpJiZGkuR0OnXNNddo4sSJevLJJ5WUlKTXX39dycnJysvLU3p6uipXrmxl+QDwl3r37q3q1avrySefVEZGhurUqSPDMBQYGCiHw6G3337bc6kit6mEHXGfduAsrV27Vl9++aXGjh0rp9OpgoICBQcH66WXXtJHH32kN998U3Xq1PH0X7VqlYYNGyZJXvf7LP7DAABWmTJlit599129++67atiwofbt26du3bqpffv2euWVVzz9Tp48qVWrVnkWnXvxxRclSS6XS2FhYdYUDwBnKSsrS19//bV++OEHhYeHq0mTJurWrZsCAgI8924H7IjQDpyFUaNG6fjx45o3b54kacGCBcrNzVW/fv303//+V6NGjdK9997rtSDTDz/8oBkzZuj666/X7bffTlAHYAtz5szRrFmzNGHCBPXp00eSdOLECa1cuVJTpkzRFVdc4bkFkiQdPXpU/fr1048//qirr75a8+bN88w0AoCLGQMpsDtOJwFn4dChQ4qKivL8e82aNfr5559VtWpV9enTR3369NHs2bPlcrl09dVXq1atWpo5c6ZCQ0N15513yjAM/jAAsNykSZP04YcfKjo6Wm+//bYuvfRStW/fXqGhobrpppsUFBSkiRMnavjw4Zo5c6YkqVq1amrZsqVGjhzpWXCOwA7gYvP7k43FP/O9DHZHaAfKoLCwUEFBQQoICFClSpU87a+++qpGjx6tefPmyTAMPfTQQwoJCdGyZcuUmpqqJk2ayOl06qWXXpJhGHK73fxhAGCp5557Tu+//75WrlypqKgoJSQk6KmnntKTTz6pdu3aKTQ0VF27dpUkTZw4UUOGDNHdd9+tf//73/r66681bNgw1a5d2+JnAQDl88cFN4GLAdPjgTIoPhN777336tJLL9WkSZM8QV6SRo4cqf/+978aPHiw+vTpI5fLpV9//VWVK1dWgwYN5HA4uFYKgOUOHTqkxx57TA8//LBnsbmCggLddtttKigo0IQJE9S2bVsFBgbqxIkTWrt2rSZPnqyioiJVqlRJM2bM8LotEgAAuPAI7cCfWLJkidq0aaPLLrtMknTvvffqkksu0b/+9a8SfUeMGKHMzEwNHDhQCQkJqlq1qmeb2+2Ww8EdFgFYr3jxTNM0VVRUpMDAwDMGd0k6fvy4du3apaioKEVERFhcPQAA/ocUAZzBL7/8oscee0yLFi3Szp07JUmVK1fWoUOH9NFHH2nz5s06ePCgDh06JEmaMWOGOnbsqNmzZ+vf//6312MR2AHYRXBwsCR5bnd06tQpBQcH64MPPlBwcLAmTJigb7/91nNryipVqqh58+YEdgAALMJIO/AnvvvuO40cOVLdunXTwIED9eyzz+rLL7+UaZrKy8tTcHCwoqOjddlll6lx48YKCAjQrl27NHXqVK5dB3DRKL58p3jE3e12a9y4cbrqqqs46QgAgMUI7cBf+O677zRixAjddttt+uKLL9SwYUM9/vjj2rJli7Zv364dO3boP//5j3bv3q2mTZvqrbfeUkBAAKvEA7io/D64d+3aVTVr1tSCBQu8Ft8EAAC+R2gHymDTpk166KGH5HK5dP/99+vhhx/22m6apvLz8xUcHCyHw8E17AAuSr8P7vv27dOll15qdUkAAPg9UgVQBpdffrleeeUVSdKOHTu0b98+z7ZTp07JMAyFhIQQ2AFc1H5/jTuBHQAAeyBZAGUUFxenBQsW6Pvvv1dKSop2794tSSVu40ZgB3Ax49aUAADYC9PjgbNUvDhdp06dNGLECNWuXdvqkgAAAABUUAwJAmepTZs2mjx5sg4ePKhatWpZXQ4AAACACoyRdqCcTNOUYRhcww4AAADggiG0A+egOLgDAAAAwIXA8CBwDgjsAAAAAC4kQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAD8TL9+/dSvX79zeowlS5aoSZMm2rNnzznX06RJE82aNeucHwcAgIqI0A4AAAAAgE0R2gEAAAAAsClCOwAAKOH9999Xr1691KpVK8XFxalHjx5auXJliX4ZGRnq2bOnWrRooe7du2vFihVe2/Pz8/X888/rmmuuUYsWLZSQkFCiDwAAOLNAqwsAAAD2smDBAj3zzDN66KGH1KZNGx05ckSvvvqqHnnkEbVu3VqRkZGevk888YQefPBBNW3aVB9++KFGjhyp4OBgde3aVaZpaujQocrIyNDw4cPVsGFDffbZZxo5cqQKCgrUs2dP654kAAAXCUI7AADwsnv3bt13330aMmSIp61u3brq1auXvvvuO91yyy2e9oceekj33XefJOnqq6/W//3f/2nu3Lnq2rWrvv76a3311VeaPn26br75ZklSp06dlJeXp6lTp6p79+4KDOSrCAAAf4a/lAAAwMujjz4qSTp69Kh27NihXbt2acOGDZKkgoICr77FYbxY165dNWvWLJ04cULffPONDMPQNddco1OnTnn6dOnSRWlpafr555/VtGnTC/xsAAC4uBHaAQCAl19//VVPPPGEvvnmGwUFBalBgwaKiYmRJJmm6dW3Zs2aXv+uUaOGTNPU8ePH5XK5ZJqm4uPjSz1OTk4OoR0AgL9AaAcAAB5ut1uDBw9WUFCQFi9erKZNmyowMFDbt2/X0qVLS/Q/cuSIV3A/cOCAAgICVL16dVWtWlWVK1fWm2++WeqxLrvssgv2PAAAqChYPR4AAHgcPnxYO3fuVO/evRUbG+u55vzLL7+UdDrU/96aNWs8P7vdbn388cdq2bKlQkJC1K5dO+Xm5so0TcXGxnr+++mnnzRnzhyvKfMAAKB0jLQDAOCHsrOzlZqaWqK9cePGqlu3rhYsWKDIyEhVq1ZNX331lWe0PC8vz6v/iy++qKKiIkVFRemdd97Rzp079frrr0uSrrnmGrVt21ZDhgzRkCFD1LBhQ/3www+aOXOmOnXqpIiIiAv+PAEAuNgR2gEA8EO//vqrnn322RLtvXv31ty5czVp0iQ9+uijCg4OVqNGjfTSSy/pX//6lzZt2qR+/fp5+j/77LN67rnntGvXLjVu3Fivvvqq2rVrJ0lyOByaN2+eZsyYoVdeeUUHDx5UnTp1lJSUpKFDh/rsuQIAcDEzzD+uKAMAAAAAAGyBa9oBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKb+P2B87aBTJfABAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
-    "nbformat": 4,
-    "nbformat_minor": 4
+    {
+     "data": {
+      "image/png": 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9wsLCLB8SpKWl6eTJk6pbt26B2588edLy3v/757Jfv34qU6aMVq1apaioKE2ZMkW1atXS2LFj1bRp03yPdebMmSv+G2A2my3zTEhX/7kAgFsdJR4ASiCTyaT+/fvLzc1Nn3zyierUqSNXV1cdOHDA6ppjg8EgScrNzbUcBbz8aFieFi1aqEWLFsrIyNCOHTu0ZMkSvfnmm6pfv76lWPzzzz+68847LdvkFVBJKleunAwGg06dOpXvsU+ePCkvLy97PO1r8vb2LjDD5VkL0rFjR3Xs2FFnz57Vd999p/fff1+jRo3SPffcU+Dt7SZOnKiNGzdq+vTpat68uaX8NGvW7Lry5t2y7fTp01avbVJSko4cOaJ69erJYDCod+/eBV4e8e8idDWpqanasmWLWrZsafWc9u7dK4PBoDp16ujs2bPauHGjQkJCVLt2bcs6JpNJ2dnZV/ywIc+/X+eTJ09K+l+ZNxgM6ty5s5YuXaonn3xShw8f1uTJkwv9HOLj4zVp0iSNGjVKXbp0seQZNmyYEhMTr5olNzdXqampqlixosqUKWOX1/V68lyNl5eXjEZjvu/da33fSpfKb3h4uNatW6eBAwdq27ZteuONNyzLy5Urp+rVqysqKqrA7Qv60Ovyx+7evbu6d++uf/75R1u3btXcuXP13HPPFXi2QoUKFa74b4B06eezMHMrAMCtjtPpAaAESk1N1eHDh/X4448rODhYrq6XPtP95ptvJP3vaH3eaa2XT3R3+UzRkjR58mQ99thjMpvNKl26tFq3bm25VjUpKUkNGzZUqVKl9Pnnn1tt9/XXX1v+7unpqXr16mnDhg2WicOkS0fot2zZku9+4zdL06ZN9eOPP1pNZHfgwAHLpG0Fef755y3XD5crV04dOnTQoEGDlJOTYykcl58iLl16DZs0aWKZWEySfv31V50+fdry2hdG3iUTl7+WkhQTE6Phw4fL09NTd999tw4dOqTg4GDLn1q1amnWrFn6/vvvC70vs9msl156SV988YVl7Pz58/r444/VuHFjVahQQe7u7powYYLmzZtnte3mzZt18eLFa97uL+/7L8/69etlMBgUFhZmGevSpYvS09M1efJk1axZ03KddmH88MMPKl++vPr162cpzOfPn9cPP/yQ73Xftm2bcnJyLF9v3LhROTk5atKkicqWLWuX1/V68lyNh4eHGjZsqC+++MLqaPTmzZsLtX2nTp2UnJys2bNny8XFxeryjLCwMB0/flwVK1a0eq7btm3TggULrE7x/7du3brpzTfflHTpg5guXbqoe/fuSk9Ptzqqnqdx48b6+uuvrZbl5uZq/fr1Cg4Otsy5AQAlHUfiAaAEqlixoqpWraply5bJ399f5cuX17fffqslS5ZIkuW2Xi1bttTbb7+t1157TX379tXx48c1e/ZslSlTxvJYTZs21aJFi/Tiiy8qIiJC2dnZWrBggby8vNS0aVOVKVNGgwYN0vTp01W6dGk1bdpUW7duzVc8R4wYob59+6p///566qmnlJ2drfnz5ysrK8tSkm+2Xr166ZNPPlHfvn313HPPKTc3V9OmTbOaW+DfmjZtqnHjxmny5Mm6//77lZ6erujoaFWvXt1yeUL58uW1d+9e7dy5UyEhIQoJCdGGDRv04YcfqmbNmtq/f7/mzJkjg8Fgee0Lw8fHR08//bRiY2Pl7u6usLAw/fzzz/rwww81evRoGY1GDR8+XP3799eIESMUERGh3NxcxcTE6Oeff7aa1LAw+3rkkUc0ffp0lSpVSj4+Ppo3b55SUlIsk6d5eHjomWee0axZs+Tr66uWLVvq999/16xZs/TAAw9c80yDxMREvfLKK+rYsaMSExM1c+ZMPf7446pevbplnYCAADVv3lzfffedZeb6wgoJCdGHH36oSZMmqXXr1kpJSdHChQt16tQpyynheU6ePKnnnntOPXv21J9//ql3331X9957r+U52ON1vZ481zJ8+HD16tVLQ4YM0RNPPKHDhw9r7ty5hdq2du3aluvzO3ToYHVNepcuXfTBBx+oT58+GjhwoKpUqaL//ve/ev/999WjR4+r/mw0btxYMTEx8vX1VcOGDXXixAktWrRIYWFh8vHxyXcN+5AhQ/TNN9/o6aeftpwp9MEHH+jo0aNasGDBdb0eAHAro8QDQAn13nvvaeLEiXrxxRfl7u6uu+66S3PmzNFbb72l3bt3q2fPnqpRo4YmT56sOXPmqH///qpZs6YmTJhguX2adKnoR0VFKSYmxjKZ3T333KMlS5ZYToMfMGCAPD09tXjxYi1evFgNGzbUmDFj9Prrr1sep1mzZlq0aJFmzpyp4cOHy93dXY0aNdLkyZNVq1atInlNvL299eGHH1pelzJlyqhfv3767LPPrrhNt27dlJ2drRUrVmj58uUqVaqUmjVrplGjRlkKTmRkpN566y317dvX8oFHdna2pk+frqysLFWrVk3PPvusDhw4oM2bN1udjXAto0aNUsWKFbVixQotWLBA1apV06uvvqpu3bpJujTD/8KFCxUdHa2hQ4fKzc1NdevW1aJFi65r5n5JGj9+vKKiohQVFaWMjAw1aNBAixcvVr169SzrDBo0SD4+Plq+fLk+/PBDeXl5qVu3bnruueeu+fiDBw/Wr7/+qoEDB6pcuXLq16+f5Y4Gl2vVqpW2b99+xQnSrqRz5876+++/tWrVKi1fvlx+fn5q2bKlnnrqKb366qs6ePCgatasKenSHRDOnj2rwYMHy93dXeHh4Ro1apTlEhN7vK7Xk+daGjVqpPfff1/vvvuuhgwZomrVqumtt97SwIEDC7V9p06dNGnSJMuEdnk8PT21bNkyTZ06VVOmTNHZs2dVtWpVjRgxQpGRkVd9zGHDhsnd3V2rVq3S7NmzVa5cObVp0+aKM8jXqlVLy5cv17vvvquXXnpJBoNBISEhWrJkiRo1alSo5wEAJYHBzCwgAADAifTr108eHh6aPXu2o6MAAFDkOBIPALjlXX5t85UYjcZ8166XBLm5udec1dtgMFz12ueiMnv2bB0+fFjfffedli9f7ug4AAA4BCUeAHDLu9LtsS7XuXNnTZo0qQjSFC+9e/fWzp07r7pO1apVCz1J2s20efNmHTlyRKNHj1ZoaKij4wAA4BCcTg8AuOUV5nZd3t7eV71d1q3q0KFDBd428HLu7u4KDAwsokQAAOBqKPEAAAAAADiJknfxHwAAAAAATooSDwAAAACAk2BiuwL8+OOPMpvNlvv7AgAAAABwM2VnZ8tgMKhhw4ZXXY8SXwCz2XzN2+0AAAAAAGAvhe2glPgC5B2BDw4OdnASAAAAAEBJUJi76UhcEw8AAAAAgNOgxAMAAAAA4CQo8QAAAAAAOAlKPAAAAAAAToISDwAAAACAk2B2egAAAAAoAXJzc5Wdne3oGCWSm5ubXFxc7PJYlHgAAAAAuIWZzWYlJycrLS3N0VFKNC8vL/n7+8tgMNzQ41DiAQAAAOAWllfgK1euLE9Pzxsukbg+ZrNZFy5cUEpKiiSpSpUqN/R4lHgAAAAAuEXl5uZaCnzFihUdHafEKl26tCQpJSVFlStXvqFT65nYDgAAAABuUXnXwHt6ejo4CfLegxudl4ASDwAAAAC3OE6hdzx7vQeUeAAAAAAAnAQlHgAAAADgdD799FMFBgbq77//dnSUIkWJBwAAAAA4nVatWumjjz5S5cqVHR2lSFHiUSwkJCRo2LBhSkhIcHQUAAAAAE7Ax8dHDRo0kLu7u6OjFClKPBwuMzNTMTExOnXqlGJiYpSZmenoSAAAAAAK4dVXX9W9996r3Nxcq/GJEyeqSZMmys7O1u+//64BAwYoNDRUoaGhGjx4sI4ePWpZ9/vvv1dgYKBWrFih1q1bKzQ0VNu2bdPp06c1YsQI3XvvvQoODlanTp20Zs0ay3YFnU6/bds2PfXUU7rnnnvUpEkTjRgxQsePH7fa5u6779bPP/+sJ554QsHBwWrdurUWLlx4814kO6PEw+Hi4uKUlpYmSUpLS1N8fLxjAwEAAAAolE6dOunUqVP6/vvvLWMmk0kbNmzQI488or///lvdunXTP//8o8mTJ2vixIk6evSonnzySf3zzz9WjxUdHa0xY8botddeU8OGDTVq1CgdPHhQ48eP1/vvv6+7775bY8aM0Y4dOwrMsmbNGkVGRqpKlSp699139dJLL+nHH3/UE088YbUvk8mk559/Xg8//LDmz5+v0NBQvfPOO/r2229vzotkZ66ODoCSLTk5WfHx8TKbzZIks9msuLg43XffffL393dwOgAAAABXc88996hq1apat26dmjdvLunSkfWTJ0+qU6dOio6OVunSpRUbG6uyZctKkpo1a6a2bdtqwYIFGjNmjOWxnnrqKbVv397y9c6dOzV48GC1bdtWkhQWFiYvL68CT583mUyKiorSfffdp6lTp1rGQ0ND9fDDD2vhwoUaPXq0pEudY9CgQeratavlOXz55ZfasmWLWrRoYedXyP44Eg+HMZvNio2NtRT4a40DAAAAKF4MBoMiIiL01VdfKSsrS5K0fv16Va9eXfXr19eOHTsUFhamUqVKKScnRzk5OSpbtqwaNWqk//73v1aPVadOHauvmzRpolmzZmno0KFauXKlTp06pTFjxig0NDRfjsOHD+vkyZPq2LGj1fjtt9+uhg0baufOnVbjDRs2tPzd3d1dPj4+unDhwg29FkWFEg+HSUpKUmJiokwmk9W4yWRSYmKikpKSHJQMAAAAQGF16tRJZ86c0bfffqusrCx98cUXioiIkHTpctnPPvtMdevWtfrz9ddfKyUlxepxPD09rb6eNm2aevfurV9//VVjx45Vy5Yt1bdvXx07dixfhrzLc319ffMt8/X11dmzZ63GSpUqZfW10Wh0moOInE4PhwkICFBwcLD27NljVeSNRqPq1aungIAAB6YDAAAAUBg1atRQSEiINmzYIKPRqPT0dEuJL1eunJo3b64+ffrk287V9ep1tFy5cho1apRGjRqlQ4cOadOmTXrvvfc0fvx4zZ8/32pdLy8vSdKpU6fyPc7Jkyfl7e1t47MrfjgSD4cxGAzq3bu3DAZDocYBAAAAFE+dOnXSt99+q/Xr1ys0NFS33XabpEvXsR84cEB16tRRcHCwgoODVa9ePcXGxurLL7+84uMdO3ZMLVu21Oeffy5JuvPOO/XMM8+oefPmBZ6xW6NGDVWqVEnr1q2zGj969Kh++umnAk/Bd1aUeDiUv7+/wsPDLYU975oaPz8/BycDAAAAUFgPP/ywzp8/r88++0ydOnWyjA8aNEhHjhzRgAED9NVXX+nbb7/Vc889p/Xr1ysoKOiKj1e1alX5+/vrzTff1CeffKKdO3cqJiZGW7du1UMPPZRvfaPRqOHDh+u7777TiBEjtHXrVq1Zs0Z9+vRRhQoVCjwTwFlxOj0cLiIiQlu3blVqaqq8vb0VHh7u6EgAAAAAroOPj4/uu+8+bdu2zWqG+aCgIC1btkzTpk3T6NGjZTabVbt2bc2ePVsPPPDAVR8zOjpa7777rmbMmKHU1FRVqVJFQ4YMUf/+/Qtcv0uXLipTpozmzZunwYMHq2zZsmrRooWGDx+uSpUq2fX5OpLB7CxX7xehxMRESVJwcLCDk5QcCQkJWrx4sXr16nVLneoCAAAAONLFixd1+PBh1ahRI99kbiha13ovCttDORKPYiE0NJTyDgAAAADXwDXxAAAAAAA4CUo8AAAAAABOghIPAAAAAICToMQDAAAAAOAkKPEoFhISEjRs2DAlJCQ4OgoAAAAAFFuUeDhcZmamYmJidOrUKcXExCgzM9PRkQAAAACgWKLEw+Hi4uKUlpYmSUpLS1N8fLxjAwEAAABAMUWJh0MlJycrPj5eZrNZkmQ2mxUXF6fk5GQHJwMAAACA4ocSD4cxm82KjY21FPhrjQMAAACwH5PJpJycHIf8MZlMjn76TsvV0QFQciUlJSkxMTHfuMlkUmJiopKSklS1alUHJAMAAABubSaTSYOfe07p/39Za1Er7+Wl2bNmyWi0z3HlWbNmafXq1dq8eXORbp+amqrw8HBNnTpVTZo0sWnf14sSD4cJCAhQcHCw9uzZY/VJnNFoVL169RQQEODAdAAAAMCty2QyKT0tTd6RHSU7Fenr2LlSY9bJZDLZrcRHRkaqe/fuRbr9iRMn9Oyzz+rkyZM279cWnE4PhzEYDOrdu7cMBkOhxgEAAADYmdEoQxH/uRkfGpQpU0Y+Pj5Ftv0nn3yiiIgIm/d3IyjxcCh/f3+Fh4dbCrvBYFBERIT8/PwcnAwAAABAcfL7779rwIABaty4serVq6cHHnhAMTExki6dDt+mTRtJ0t9//63AwECtX79ejz76qIKDg9WlSxcdPHhQs2fPVvPmzRUWFqbx48db5uG6fPuePXvq+eeft9r3rl27FBgYqL/++kuS9OWXX+qFF17QjBkziujZ/w8lHg4XEREhLy8vSZK3t7fCw8MdGwgAAABAsZKRkaHIyEh5eXlpxYoVWrdundq3b6/Jkydr3759BW4zbdo0vfzyy1q5cqXS09P15JNP6s8//9TSpUv1wgsvaPny5fr666/zbdelSxd9/fXXOnfunGUsLi5OoaGhuuOOOyRJ8+bNU7du3Rxy9jAlHg7n4eGhyMhI+fr6qk+fPvLw8HB0JAAAAADFSEZGhp5++mm99tprqlmzpqpXr66hQ4dKkn777bcCt4mMjFRYWJiCgoLUrl07XbhwQW+88YZq1qypJ598UhUrVtQff/yRb7uHHnpIRqNRX331lSQpKytLGzduVJcuXW7eE7wOTGyHYiE0NFShoaGOjgEAAACgGPLx8dFTTz2ldevWae/evTpy5Ij2798vSVe8XV3eUXNJ8vT0lK+vr0qXLm0ZK1WqlLKysvJt5+npqfbt2ys+Pl6PPvqotm7dqqysLHXo0MHOz8o2HIkHAAAAABRrJ0+eVEREhFauXCk/Pz899dRTWr169VW3cXW1PmZ9PTPhd+nSRdu3b9epU6cUHx+vtm3bqmzZsjZltzeOxAMAAAAAirV169YpLS1NGzdulJubm6T/nUafNzmdPTVq1EhVq1bV2rVrtWXLFs2dO9fu+7AVR+IBAAAAAMWav7+/MjIy9PnnnyspKUnfffedhg8fLkkFnhJ/owwGgx599FHNnj1bPj4+atq0qd33YSuOxAMAAABASWUyyf7Hsa+9z+vVvn177dmzR5MmTdK5c+dUtWpVde3aVZs2bVJiYqKqVKli95idO3dWdHS0evbseV2n4t9sBvPNOPfAySUmJkqSgoODHZwEAAAAAGx38eJFHT58WDVq1FCpUqUs4yaTSYOfe07paWkOyVXey0uzZ80qVuX4ZrvSe5GnsD2UI/EAAAAAUMIYjUbNnjXrijO7F8X+S1KBt6di96rNmzdPPXv2vOLysWPHqk2bNlZjJpNJM2fOVIsWLdSgQQM988wzOnr06M2OCgAAAABOy2g0ytXV1SF/KPC2K1av3LJlyzR9+vQrLv/qq6+0cuXKfOPvvfeeli9frgkTJmjFihUymUzq16/fTZngAAAAAAAARykWJf7EiRMaOHCgoqKiVL169QLXSUlJ0auvvqqwsDCr8aysLMXExGjo0KFq1aqVgoKCNG3aNCUnJ+uLL74ogvQAAAAAABSNYlHi9+zZIzc3N8XFxal+/fr5lpvNZr344ovq1KlTvhK/f/9+nT9/Xs2aNbOMlS9fXnfffbd27dp107MDAAAAAFBUisXEdm3atMl3nfvlYmNjdfLkSc2dO1fz5s2zWpacnCxJ+W4pULlyZcsyW5jNZl24cMHm7QEAAADA0TIzM2UymZSbm6vc3FxHxynRcnNzZTKZlJGRUeCEgmazWQaD4ZqPUyxK/NXs379f0dHRWrZsmdzd3fMtz8jIkKR8yzw8PHTmzBmb95udna19+/bZvD0AAAAAFAeurq7KzMx0dIwSLzMzUzk5OTp06NAV1ymo8/5bsS7xmZmZGjlypJ599lkFBQUVuE7e/fWysrKs7rWXmZmp0qVL27xvNzc33XXXXTZvDwAAAACOlpmZqaSkJHl4eBR4b3IULVdXV91+++3y8PDIt+zAgQOFewx7h7Knn3/+WX/88Yeio6M1e/ZsSZeOkOfk5Khhw4Z6//33LafRp6Sk6Pbbb7dsm5KSosDAQJv3bTAY5OnpeWNPAAAAAAAcKO9+7C4uLnJxcXF0nBLNxcVFRqNRpUuXLvADlcKcSi8V8xIfEhKSb4b5pUuX6osvvtDSpUvl5+cno9GosmXL6vvvv7eU+PT0dO3du1c9evRwRGwAAAAAKPZMJlOB12YXhbwPF+xl1qxZWr16tTZv3lwk22/evFmzZ8/WoUOH5O3trYceekjDhg0rkrMdinWJL1WqlO644w6rsQoVKsjV1dVqvEePHoqKipKPj4+qVq2qKVOmyN/fXw8++GBRRwYAAACAYs9kMmnI4KE6k57qkP1XKO+t6Nkz7VbkIyMj1b179yLZfvfu3RoyZIiGDh2q9u3b66+//tJrr72mtLQ0vf322zZnKKxiXeILa+jQocrJydHYsWN18eJFNW7cWAsXLpSbm5ujowEASqiEhAQtXrxYvXr1UmhoqKPjAABgxWQy6Ux6qtreOVIGQ9HeedxsNumrQ1EymUx2K/FlypRRmTJlimT7FStWqEmTJho4cKAkqXr16nrhhRc0duxYjR8/vlCT092IYnGf+MtNmjRJS5cuveLy5557Lt8pDi4uLho1apS2b9+uH3/8UfPnz1e1atVudlQAAAqUmZmpmJgYnTp1SjExMcwIDAAotgwGo4wGlyL9Y+uHBr///rsGDBigxo0bq169enrggQcUExMj6dLp8Hm3Lf/7778VGBio9evX69FHH1VwcLC6dOmigwcPavbs2WrevLnCwsI0fvx4mc3mfNv37NlTzz//vNW+d+3apcDAQP3111+KjIzUmDFjrJYbjUZlZ2fr3LlzNj2361HsSjwAAM4uLi5OaWlpkqS0tDTFx8c7NhAAAE4uIyNDkZGR8vLy0ooVK7Ru3Tq1b99ekydPvuKtwadNm6aXX35ZK1euVHp6up588kn9+eefWrp0qV544QUtX75cX3/9db7tunTpoq+//tqqkMfFxSk0NFR33HGH7r77bqu7p2VnZys2Nlb16tWTj4+P/Z/8v1DiAQCwo+TkZMXHx1s+2TebzYqLi1NycrKDkwEA4LwyMjL09NNP67XXXlPNmjVVvXp1DR06VJL022+/FbhNZGSkwsLCFBQUpHbt2unChQt64403VLNmTT355JOqWLGi/vjjj3zbPfTQQzIajfrqq68kXbqd+caNG9WlS5d86+bk5Gj06NH6448/NG7cODs+4yujxAMAYCdms1mxsbGWAn+tcQAAUDg+Pj566qmntG7dOo0bN059+vRRq1atJOmKM+xfPhm6p6enfH19Vbp0actYqVKllJWVlW87T09PtW/f3nIm3datW5WVlaUOHTpYrXfu3DkNHDhQmzZtUnR0tEJCQm70aRYKJR4AADtJSkpSYmJivv9MmEwmJSYmKikpyUHJAABwbidPnlRERIRWrlwpPz8/PfXUU1q9evVVt3F1tZ7H/Xom0evSpYu2b9+uU6dOKT4+Xm3btlXZsmUty1NSUtS9e3f99NNPWrhwoVq2bHl9T+gG3BKz0wMAUBwEBAQoODhYe/bssSryRqNR9erVU0BAgAPTAQDgvNatW6e0tDRt3LjRcheyvNPob8aZbo0aNVLVqlW1du1abdmyRXPnzrUsO3PmjHr16qVz585p2bJlCgwMtPv+r4Yj8QAA2InBYFDv3r1lMBgKNQ4AAArH399fGRkZ+vzzz5WUlKTvvvtOw4cPl6QCT4m/UQaDQY8++qhmz54tHx8fNW3a1LLs7bff1tGjRzVlyhT5+Pjo5MmTlj+5ubl2z/JvHIkHAMCO/P39FR4errVr18psNstgMCgiIkJ+fn6OjgYAQD5ms0kFX1F+c/d5vdq3b689e/Zo0qRJOnfunKpWraquXbtq06ZNSkxMVJUqVeyes3PnzoqOjlbPnj0tp+Ln5ubqs88+U3Z2tnr16pVvm02bNt30250bzMyyk09iYqIkKTg42MFJAADOKDMzUyNGjFBqaqp8fHwUFRUlDw8PR8cCAJRAFy9e1OHDh1WjRg2VKlXKMm4ymTRk8FCdSU91SK4K5b0VPXvmdV2n7uyu9F7kKWwP5Ug8AAB25uHhocjISC1evFi9evWiwAMAih2j0ajo2TOvOLN7Uey/JBV4e6LEAwBwE4SGhio0NNTRMQAAuCKKtHPiHQMAAAAAwElQ4gEAAAAAcBKUeAAAAAAAnAQlHgAAAABucdyUzPHs9R5Q4gEAAADgFuXqemku85ycHAcnQd57kPee2IoSDwAAAAC3KBcXF7m4uCg9Pd3RUUq89PR0y/txI7jFHAAAAADcogwGgypXrqzjx4/Lw8NDZcqUkcFgcHSsEsVsNuv8+fNKT09XlSpVbvj1p8QDAAAAwC2sQoUKysjI0KlTp3Ty5ElHxymRDAaDvLy8VKFChRt+LEo8AAAAANzCDAaDqlSposqVKys7O9vRcUokNze3Gz6NPg8lHgAAAABKAHtcjw3HY2I7AAAAAACcBCUeAAAAAAAnQYkHAAAAcE0JCQkaNmyYEhISHB0FKNEo8QAAAACuKjMzUzExMTp16pRiYmKUmZnp6EhAiUWJBwAAAHBVcXFxSktLkySlpaUpPj7esYGAEowSDwAAAOCKkpOTFR8fL7PZLEkym82Ki4tTcnKyg5MBJRMlHgAAAECBzGazYmNjLQX+WuMAbj5KPAAAAIACJSUlKTExUSaTyWrcZDIpMTFRSUlJDkoGlFyUeAAAAAAFCggIUHBwsIxG69pgNBoVEhKigIAAByUDSi5KPAAAAIACGQwG9e7dWwaDoVDjAG4+SjwAAACAK/L391d4eLilsBsMBkVERMjPz8/ByYCSiRIPAAAA4KoiIiLk5eUlSfL29lZ4eLhjAwElGCUeAAAAwFV5eHgoMjJSvr6+6tOnjzw8PBwdCSixXB0dAAAAAEDxFxoaqtDQUEfHAEo8jsQDAAAAAOAkKPEAAAAAADgJSjwAAAAAAE6CEg8AAAAAgJOgxAMAAAAA4CQo8QAAAAAAOAlKPAAAAAAAToISDwAAAACAk6DEAwAAAADgJIpdiZ83b5569uxpNbZ582Y99thjatiwodq0aaPJkyfr4sWLluWZmZkaP368mjVrpoYNG2rEiBE6ffp0UUcHAAAAAOCmKlYlftmyZZo+fbrV2O7duzVkyBC1a9dOq1ev1rhx4/TZZ59p/PjxlnVef/11fffdd5o1a5YWL16sQ4cOaejQoUWcHgAAAACAm6tYlPgTJ05o4MCBioqKUvXq1a2WrVixQk2aNNHAgQNVvXp1tWzZUi+88ILi4+OVlZWlEydOaM2aNRo7dqwaNWqkkJAQvfvuu9q1a5d+/PFHxzwhAAAAAABugmJR4vfs2SM3NzfFxcWpfv36VssiIyM1ZswYqzGj0ajs7GydO3dOP/zwgySpadOmluU1atSQn5+fdu3adfPDAwAAAABQRFwdHUCS2rRpozZt2hS47O6777b6Ojs7W7GxsapXr558fHx04sQJeXt7y8PDw2q9ypUrKzk52eZMZrNZFy5csHl7AAAAAAAKy2w2y2AwXHO9YlHiCysnJ0ejR4/WH3/8oWXLlkmSMjIy5O7unm9dDw8PZWZm2ryv7Oxs7du3z+btAQAAAAC4HgV1239zmhJ/7tw5Pf/889q5c6eio6MVEhIiSSpVqpSysrLyrZ+ZmanSpUvbvD83NzfdddddNm8PAAAAAEBhHThwoFDrOUWJT0lJ0TPPPKNjx45p4cKFaty4sWWZv7+/0tLSlJWVZfWpRUpKivz8/Gzep8FgkKen5w3lBgAAAACgMApzKr1UTCa2u5ozZ86oV69eOn36tJYtW2ZV4CXpnnvukclkskxwJ0mHDx/WiRMn8q0LAAAAAIAzK/ZH4t9++20dPXpUCxYskI+Pj06ePGlZ5uPjIz8/Pz3yyCMaO3as3nrrLZUuXVrjxo1TWFiYGjRo4LjgAAAAAADYWbEu8bm5ufrss8+UnZ2tXr165Vu+adMmVatWTRMmTNBbb72lIUOGSJLuv/9+jR07tqjjAgAAAABwUxnMZrPZ0SGKm8TERElScHCwg5MAAAAAAEqCwvbQYn9NPAAAAAAAuIQSDwAAAACAk6DEAwAAAADgJCjxAAAAAAA4CUo8AAAAAABOghIPAAAAAICToMQDAAAAAOAkKPEAAAAAADgJSjwAAAAAAE6CEg8AAAAAgJOgxAMAAAAA4CQo8QAAAAAAOAlKPAAAAAAAToISDwAAAACAk6DEAwAAAADgJCjxAAAAAAA4CUo8AAAAAABOghIPAAAAAICToMQDAAAAAOAkKPEAAAAAADgJSjwAAAAAAE6CEg8AAAAAgJOgxKNYSEhI0LBhw5SQkODoKAAAAABQbFHi4XCZmZmKiYnRqVOnFBMTo8zMTEdHAgAAAIBiiRIPh4uLi1NaWpokKS0tTfHx8Y4NBAAAAADFFCUeDpWcnKz4+HiZzWZJktlsVlxcnJKTkx2cDAAAAACKH0o8HMZsNis2NtZS4K81DgAAAAAlHSUeDpOUlKTExESZTCarcZPJpMTERCUlJTkoGQAAAAAUT5R4OExAQICCg4NlMBisxg0Gg0JCQhQQEOCgZAAAAABQPFHi4TAGg0EdO3Ys8HT6Rx55JF+5BwAAAICSjhIPhzGbzVq3bl2By9atW8c18QAAAADwL5R4OEzeNfEF4Zp4AAAAAMiPEg+Hybsm3mi0/jY0Go1cEw8AAAAABaDEw2EMBoN69+5d4MR2BY0DAAAAQEnnWtgV09LSVL58eaujphcvXtSaNWu0f/9++fj4qGnTpgoLC7spQXFr8vf3V3h4uNauXSuz2SyDwaCIiAj5+fk5OhoAAAAAFDuFLvHNmjXTRx99pJCQEEnSyZMn1bNnT/35558qV66csrOzNWfOHLVt21ZTp06Vu7v7TQuNW0tERIS2bt2q1NRUeXt7Kzw83NGRAAAAAKBYKvTp9P+eKfytt97SuXPntHz5cu3atUs//vijZs2ape3btys6OtruQXHr8vDwUGRkpHx9fdWnTx95eHg4OhIAAAAAFEs2XxO/ZcsWDRs2TKGhoZIuXcfctm1bDRkyRHFxcXYLiJIhNDRUM2bMsHw/AQAAAADys7nEu7i46M4778w3HhgYqLS0tBvJBAAAAAAACnBdJT4hIUF///23JKl58+b64Ycf8q2zdetW3XbbbfZJBwAAAAAALAo9sV3lypU1adIkTZ48WWXKlFH58uW1efNmNW/eXPXq1dMff/yh+fPna926dRo3btzNzAwAAAAAQIlU6BL/zTffKDU1Vfv379e+ffu0b98+7d+/X7m5uZKkX375RV9++aWGDBmibt263bTAAAAAAACUVAbzv6edt9G5c+fk7u5+S9xaLjExUZIUHBzs4CQAAAAAgJKgsD3Upont9u/fr6+++kpxcXH68ssvtXPnTpnNZrsU+Hnz5qlnz55WY/v27VOPHj3UoEEDtWnTRkuWLLFabjKZNHPmTLVo0UINGjTQM888o6NHj95wFgAAAAAAipNCn04vSZs2bdKbb76p5ORky1jegXyj0aiwsDCNGTNGderUsSnMsmXLNH36dDVq1Mgylpqaqj59+qhNmzYaP368fvrpJ40fP15lypTRY489Jkl67733tHz5ck2aNEn+/v6aMmWK+vXrp/j4+FvizAAAAAAAAKTrKPH//e9/9fzzz6tHjx5q2rSp/vrrL8XExOiFF16Qn5+f9uzZo5UrV6p79+5aunSp6tatW+gQJ06c0Lhx4/T999+revXqVss+/vhjubm56Y033pCrq6tq1qypv/76S/Pnz9djjz2mrKwsxcTEaOTIkWrVqpUkadq0aWrRooW++OILdezYsdA5AAAAAAAozgp9On10dLQiIyM1ZswYtWzZUk8//bRef/11vffee2ratKn69u2rVatWqU6dOpo+ffp1hdizZ4/c3NwUFxen+vXrWy3bvXu3wsLC5Or6v88bmjZtqj///FOnTp3S/v37df78eTVr1syyvHz58rr77ru1a9eu68oBAAAAAEBxVugj8fv27dNzzz1nNdaoUSP99ddfOnLkiG6//XaVKVNGAwYM0IgRI64rRJs2bdSmTZsClyUnJ6t27dpWY5UrV5YkHT9+3HJqf5UqVfKtc/lp/9fLbDbrwoULNm8PAAAAAEBhmc1mGQyGa65X6BLv6empvXv3Wh3xPnTokAwGg0qVKmUZO336tNXXN+rixYv5rmv38PCQJGVmZiojI0OSClznzJkzNu83Oztb+/bts3l7AAAAAACuR2HmdCt0iW/btq1mz54tf39/tWrVSklJSXr99ddVq1YtVa5cWWlpafriiy80ffp0tW/f/oaCX65UqVLKysqyGsvMzJR06YOFvA8MsrKyrD48yMzMVOnSpW3er5ubm+666y6btwcAAAAAoLAOHDhQqPUKXeJHjRqlX3/9VSNGjLAc4i9XrpwWLVokSdqyZYvGjRunRx55RCNHjrQhcsH8/f2VkpJiNZb3tZ+fn3Jycixjt99+u9U6gYGBNu/XYDDI09PT5u0BAAAAACiswpxKL11HiS9btqw+/vhjbd68WYcOHZK3t7fatWsnb29vSVKrVq20bds2+fj42Jb4Cho3bqwVK1YoNzdXLi4ukqQdO3aoRo0aqlixosqVK6eyZcvq+++/t5T49PR07d27Vz169LBrFgAAAAAAHOm67hPv4uKidu3aFbjMy8sr35jJZFK7du00d+5c1apVy6aAjz32mBYsWKBXXnlF/fr10y+//KLY2FiNHz9e0qVrBnr06KGoqCj5+PioatWqmjJlivz9/fXggw/atE8AAAAAAIqj6yrx18tsNuvYsWP5rmm/HhUrVtSCBQs0ceJEde7cWZUqVdLo0aPVuXNnyzpDhw5VTk6Oxo4dq4sXL6px48ZauHCh3Nzc7PE0AAAAAAAoFgxms9l8sx48NzdXdevW1apVq1S3bt2btRu7S0xMlCQFBwc7OAkAAAAAoCQobA81FkUYAAAAAABw4yjxAAAAAAA4CUo8AAAAAABOghIPAAAAAICToMQDAAAAAOAkKPEAAAAAADiJm1riDQaDGjdurDJlytzM3QAAAAAAUCK42rphVlaWPvnkE/33v//VyZMn9dZbb2nnzp2qW7euQkJCJElGo1FLly61W1gAAAAAAEoym47Enz59Wo899pgmTpyov/76S7/88osuXryoLVu2qGfPnvrxxx/tnRMAAAAAgBLPphL/zjvv6Pz58/rss8+0evVqmc1mSdLMmTMVHBysmTNn2jUkAAAAAACwscR//fXXGjZsmO644w4ZDAbLuIeHhyIjI7Vnzx67BQQAAAAAAJfYVOIzMzPl5eVV4DIXFxdlZ2ffSCYAAAAAAFAAm0p8cHCwli9fXuCy+Ph41atX74ZCAQAAAACA/GyanX7YsGHq3bu3OnXqpJYtW8pgMGjdunWaNWuWvvvuOy1YsMDeOQEAAAAAKPFsOhLfqFEjLVq0SKVLl9aCBQtkNpsVGxurkydPat68eWratKm9cwIAAAAAUOLZfJ/4xo0ba8WKFbp48aLOnDmjsmXLqkyZMvbMBgAAAAAALmPTkXhJmj9/vvr3769SpUrJz89Pv/76q+677z598MEH9swHAAAAAAD+n00lPiYmRtOnT1f16tUtY7fffrvat2+vSZMmaeXKlfbKBwAAAAAA/p9Np9OvWLFCzz//vPr3728Zq1KlisaOHStfX1/Fxsaqa9eudgsJAAAAAABsPBJ/4sQJBQcHF7isfv36+vvvv28oFAAAAAAAyM+mEl+1alVt3769wGW7du2Sv7//DYUCAAAAAAD52XQ6/X/+8x9NmTJF2dnZatu2rSpWrKjTp0/r66+/1qJFizRixAh75wQAAAAAoMSzqcT37t1bJ06c0NKlSxUbG2sZd3FxUa9evdSnTx975QMAAAAAAP/PYDabzbZufPbsWf344486c+aMypcvr5CQEHl7e9szn0MkJiZK0hWv+wcAAAAAwJ4K20NtOhKfp1y5crr//vtv5CEAAAAAAEAh2VTiL168qDlz5ujrr79WRkaGTCaT1XKDwaCvvvrKLgEBAAAAAMAlNpX4iRMn6pNPPlFYWJjq1Kkjo9GmSe4BAAAAAMB1sKnEf/HFF3rhhRfUv39/e+cBAAAAAABXYNMh9OzsbIWEhNg7CwAAAAAAuAqbSvx9992nb775xt5ZAAAAAADAVdh0Ov3DDz+scePG6fTp06pfv75Kly6db51HH330RrMBAAAAAIDL2HSf+KCgoKs/qMGgffv22RzK0bhPPAAAAACgKN3U+8Rv2rTJls0AAAAAAMANsKnEV61a9arLbTi4DwAAAAAArsGmEi9Jn332mXbu3KmsrCxLaTebzbpw4YJ++uknJr4DAAAAAMDObCrx0dHRio6OVrly5ZSTkyM3Nze5urrq9OnTMhqN6tq1q71zAgAAAABQ4tl0i7nVq1fr0Ucf1c6dO9W7d2+1bt1a//3vf/XJJ5/Iy8tLtWrVsndOAAAAAABKPJtK/IkTJxQeHi6DwaA6deroxx9/lCTVq1dPAwcO1MqVK+0aEgAAAAAA2FjiPT09ZTAYJEl33HGH/v77b128eFGSVKdOHf3999/2SwgAAAAAACTZWOKDg4O1Zs0aSVKNGjXk4uKi7du3S5IOHjwod3d3uwUEAAAAAACX2DSx3cCBA9WnTx+lp6dr7ty5ioiI0JgxY9SkSRN99913atu2rb1zAgAAAABQ4tlU4hs3bqxPPvlEv/32myTptddek9FoVEJCgtq3b6+XXnrJriEBAAAAAICNJT4pKUk1a9ZUUFCQJMnDw0MTJkyQJGVmZmrPnj0KDQ21X0oAAAAAAGDbNfEPPPCA9u3bV+CyX375RX369LmhUP+Wk5OjGTNmqHXr1mrYsKG6d++un376ybJ837596tGjhxo0aKA2bdpoyZIldt0/AAAAAADFQaGPxE+ePFlpaWmSJLPZrPfee0/e3t751tu3b5/KlStnt4CSNGfOHK1cuVKTJk3Sbbfdpvfff1/9+vXTZ599Jjc3N/Xp00dt2rTR+PHj9dNPP2n8+PEqU6aMHnvsMbvmAAAAAADAkQpd4u+8807NmTNHkmQwGPTrr7/mm4XexcVF5cqVs/s18V999ZU6duyo++67T5L04osvauXKlfrpp590+PBhubm56Y033pCrq6tq1qypv/76S/Pnz6fEAwAAAABuKYUu8V27dlXXrl0lSW3atNHs2bNVp06dmxbschUrVtTXX3+tHj16qEqVKvroo4/k7u6uoKAgrVy5UmFhYXJ1/d9Tadq0qebNm6dTp07J19e3SDICAAAAAHCz2TSx3ebNm/ONnTx5UikpKQoKCpKLi8sNB7vcK6+8omHDhumBBx6Qi4uLjEajZs2apdtvv13JycmqXbu21fqVK1eWJB0/fpwSDwAAAAC4ZdhU4s+fP68333xT9erVU/fu3bVhwwaNGjVKubm5ql69umJiYlSlShW7hTxw4IDKlSun2bNny8/PTytXrtTIkSP1wQcf6OLFi/lO6/fw8JB0aaZ8W5nNZl24cOGGcgMAAAAAUBhms1kGg+Ga69lU4qOiorRx40bde++9lq+DgoL07LPPavr06YqKitLUqVNteeh8jh8/rhEjRig2NlaNGjWSJAUHB+vAgQOaNWuWSpUqpaysLKtt8sq7p6enzfvNzs6+4gz8AAAAAADY278PUBfEphK/adMmvfjii+rYsaN+/fVXHTt2TKNHj9YDDzygnJwcjRs3zpaHLdDPP/+s7OxsBQcHW43Xr19f33zzjQICApSSkmK1LO9rPz8/m/fr5uamu+66y+btAQAAAAAorAMHDhRqPZtKfFpamu68805J0tatW+Xq6mo5Kl+hQoUbOo393/z9/SVJv/32m0JCQizjv//+u6pXr6769etrxYoVys3NtVyLv2PHDtWoUUMVK1a0eb8Gg+GGjuQDAAAAAFBYhTmVXpKMtjx41apV9dtvv0m6dPu3Bg0aqGzZspIulfpq1arZ8rAFCgkJ0T333KMxY8Zox44d+vPPPzV9+nRt375d/fv312OPPaZz587plVde0YEDB/Tpp58qNjZWAwYMsFsGAAAAAACKA5uOxHfr1k2TJk3SsmXLdOjQIb377ruSpCFDhmjTpk0aO3as3QIajUbNmTNH06dP10svvaQzZ86odu3aio2NVf369SVJCxYs0MSJE9W5c2dVqlRJo0ePVufOne2WAQAAAACA4sBgNpvNtmy4bt067dq1S02aNNHDDz8sSXrhhRfUtGlTPfHEE3YNWdQSExMlKd91+AAAAAAA3AyF7aE2l/hbGSUeAAAAAFCUCttDC306fXR0tLp27So/Pz9FR0dfdV2DwaDBgwcX9qEBAAAAAEAhXFeJv//++ynxAAAAAAA4SKFL/P79+wv8OwAAAAAAKBo23WIOAAAAAAAUvUIfiX/ppZeu64Hffvvt6w4DAAAAAACurNAl/vvvv7f6OiUlRTk5OQoICFClSpWUlpamo0ePyt3dXUFBQXYPCgAAAABASVfoEr9582bL3+Pj4xUVFaVZs2YpJCTEMn7gwAENGjRIHTp0sG9KAAAAAABg2zXx06ZN0/Dhw60KvCTdddddev7557VgwQK7hAMAAAAAAP9jU4lPTU1V+fLlC1zm6uqqCxcu3FAoAAAAAACQn00lvkGDBpozZ47OnDljNZ6SkqJZs2apSZMmdgkHAAAAAAD+p9DXxF9uzJgx6tmzp1q3bq2GDRvKy8tL//zzj3788UdVqFBBc+bMsXdOAAAAAABKPJuOxAcFBWndunV64okndO7cOf3666+6ePGiIiMjFRcXp2rVqtk7JwAAAAAAJZ5NR+Ilyc/PT2PGjLnqOmazWS+//LKee+45BQQE2LorAAAAAAAgG4/EF5bJZNKaNWuUmpp6M3cDAAAAAECJcFNLvHTpaDwAAAAAALhxN73EAwAAAAAA+6DEAwAAAADgJCjxAAAAAAA4CUo8AAAAAABOghIPAAAAAICTuOkl3mAw3OxdAAAAAABQIhS6xI8bN05HjhyRJCUlJSk7O7tQ23GLOQAAAAAA7KPQJf7TTz9VSkqKJOmBBx7Qvn37rrmNi4uL9u/fr7p169qeEAAAAAAASJJcC7tipUqVFBUVpfvuu09ms1krV67UN998U+C6BoNBgwcPtltIAAAAAAAgGcyFPN99/fr1mjBhgtLS0mQwGK56mrzBYCjUkfriKjExUZIUHBzs4CQAAAAAgJKgsD30uo7Eb968WZ6engoKCtLHH3+skJCQG0sJAAAAAAAKrdDXxA8aNMhydL1x48YqVarUTQsFAAAAAADyK/SReJPJpO3bt8vf31+7d+/W0aNHVbZs2SuuHxAQYJeAAAAAAADgkkJfE//iiy9qzZo1luvhr3X/d66JBwAAAACgcOx+TfzEiRPVvn17paam6qWXXtLAgQN1++2331hKAAAAAABQaIUu8S4uLmrVqpUkadq0aerYsaNq1qx5s3IBAAAAAIB/KfTEdpc7f/689uzZY+8sAAAAAADgKmwq8RUqVGB2egAAAAAAilihT6e/3IABA/Tmm2/q8OHDCgoKkqenZ751GjdufMPhAAAAAADA/xR6dvrLBQUFWT/IZTPV581cz+z0AAAAAAAUjt1np7/ckiVLbNkMAAAAAADcAJtKfFhYmL1zAAAAAACAa7CpxEvS4cOHNXPmTO3cuVPp6eny9vZWo0aNNHjwYG49BwAAAADATWBTiT9w4IC6desmFxcXtWnTRr6+vjp58qS+/vprbdmyRStXrqTIAwAAAABgZzaV+KioKFWrVk1Lly5VuXLlLONnz55Vr169NG3aNEVHR9stJAAAAAAAsPE+8bt27dLAgQOtCrwklStXTv3799euXbvsEg4AAAAAAPyPTSXe1dVVHh4eBS5zd3dXVlbWDYUCAAAAAAD52VTig4ODtXz5cv37FvNms1nLli1TvXr17BLucmvWrNHDDz+s4OBgPfLII9qwYYNl2d9//60BAwYoNDRU9913n6ZPn67c3Fy7ZwAAAAAAwJFsuiZ+2LBhevLJJxUREaH27durUqVKOnnypD7//HMdPnxYixYtsmvItWvX6pVXXtHLL7+sFi1aaP369Ro+fLj8/f1Vr1499e3bV9WrV9eKFSt05MgRvfLKKzIajRo6dKhdcwAAAAAA4Eg2lfjg4GAtWLBAU6dOVXR0tMxmswwGg+rVq6f3339fjRs3tltAs9msGTNm6Omnn1b37t0lSc8++6x2796tnTt36tixY0pKStLHH3+sChUqqHbt2vrnn3/0zjvvaODAgXJ3d7dbFgAAAAAAHMnm+8Q3bdpUK1euVEZGhtLT01W+fHmVLl3antkkXbof/bFjxxQeHm41vnDhQknS66+/rrp166pChQpW2c6dO6d9+/apfv36ds8EAAAAAIAjFLrEJyUlXXV5amqqUlNTLV8HBATYnuoyhw8fliRduHBBffv21d69e1WtWjU9++yzatOmjZKTk+Xv72+1TeXKlSVJx48fp8QDAAAAAG4ZhS7xbdq0kcFgKPQD79u3z6ZA/3bu3DlJ0pgxYzRkyBCNHDlSGzdu1KBBg7Ro0SJdvHhR5cuXt9omb+b8zMxMm/drNpt14cIF24MDAAAAAFBIeZepX0uhS/xbb71lecAzZ84oKipKzZo1U4cOHVSpUiWlpaVp8+bN2rJli1588UXbk/+Lm5ubJKlv377q3LmzJKlOnTrau3evFi1apFKlSuW7pV1eeff09LR5v9nZ2Xb7IAIAAAAAgGspzJxuhS7xXbp0sfx98ODBevTRR/Xmm29arRMeHq6JEydqw4YNeuKJJ64j6pX5+flJkmrXrm01ftddd2nLli0KCwvT77//brUsJSXFaltbuLm56a677rJ5ewAAAAAACuvAgQOFWs+mie22bdum2bNnF7isVatW+vjjj2152ALVrVtXZcqU0c8//6xGjRpZxn///Xfdfvvtaty4sdasWaNz586pbNmykqQdO3aoTJkyCgoKsnm/BoPhho7kAwAAAABQWIW9fN1oy4N7e3vrl19+KXDZjh07bugI+L+VKlVK/fr10+zZs7Vu3TodOXJEc+bM0bZt29SnTx+1bdtWlSpV0vPPP6/9+/frq6++0rvvvqvIyEhuLwcAAAAAuKXYdCS+a9eumj17ti5evKhWrVrJ29tbp06d0ueff64PP/xQL7/8sl1DDho0SKVLl9a0adN04sQJ1axZU7NmzVKTJk0kSQsWLND48eP1n//8RxUqVNBTTz2lQYMG2TUDAADXIyEhQYsXL1avXr0UGhrq6DgAAOAWYTCbzebr3chsNuudd97R0qVLlZubaxkrVaqUBg0apP79+9s9aFFKTEyUJAUHBzs4CQDAGWVmZmrEiBFKTU2Vt7e3pk6darlzCgAAQEEK20NtOhJvMBg0ZswYDRo0SD/99JPOnDkjb29vNWzYkOvIAQAlXlxcnNLS0iRJaWlpio+P1+OPP+7YUAAA4JZg0zXxecqVK6cWLVqoY8eOqlChgr777julp6fbKxsAAE4nOTlZ8fHxyjvRzWw2Ky4uTsnJyQ5OBgAAbgU2lfiUlBT17NlT7733niTpgw8+UNeuXTV06FA9+OCD+uOPP+waEgAAZ2A2mxUbG6t/X6l2pXEAAIDrZVOJnzJlig4fPqzg4GCZTCbNnTtXzZs315o1a3TXXXdp6tSp9s4JAECxl5SUpMTERJlMJqtxk8mkxMREJSUlOSgZAAC4VdhU4r/77juNGTNGLVq0UEJCgk6dOqWnn35aQUFB6tevn3bv3m3vnAAAFHsBAQEKDg6W0Wj969VoNCokJEQBAQEOSgYAAG4VNpX4CxcuyN/fX5L0zTffyN3dXU2bNpUkubu7c7ogAKBEMhgM6t27twwGQ6HGAQAArpdNJb569eravXu3srOztXHjRoWFhVlunRMXF6fq1avbMyMAAE7D399f4eHhlsJuMBgUEREhPz8/BycDAAC3AptK/DPPPKPo6Gg1a9ZMR48eVZ8+fSRJjz/+uOLi4tS3b1+7hgQAwJlERETIy8tLkuTt7a3w8HDHBgIAALcMm+4T37FjR1WpUkU//PCDwsLC1KBBA0lS48aNNXToUN1///32zAgAgFPx8PBQZGSkFi9erF69elnOVgMAALhRBjMXsOeTmJgoSQoODnZwEgAAAABASVDYHmrTkfjo6OhrrjNkyBBbHhoAAAAAAFyB3Ut82bJlVblyZUo8AAAAAAB2ZlOJ379/f76xCxcuaPfu3Xr99df16quv3nAwAAAAAABgzabZ6Qvi6emp+++/X4MHD9Y777xjr4cFAAAAAAD/z24lPk9AQIAOHjxo74cFAAAAAKDEs+l0+oKYzWYlJydrwYIFqlq1qr0eFgAAAAAA/D+bSnxQUJAMBkOBy8xmM6fTAwAAAABwE9hU4gcPHlxgiS9btqxatWql6tWr32guAAAAAADwLzaV+Oeee87eOQAAAAAAwDXYfE38iRMn9MMPPygrK8syZjKZlJGRod27d2vatGl2CQgAAAAAAC6xqcR//vnnGjlypHJyciyn1ZvNZsvf77zzTvslBAAAAAAAkmy8xdzcuXNVt25dffrpp+rSpYs6deqk9evXa9SoUXJxcdHLL79s75wAAAAAAJR4NpX4w4cP65lnntHdd9+tJk2aaP/+/apZs6YiIyP19NNPa+7cufbOCQAAAMCBEhISNGzYMCUkJDg6ClCi2VTijUajKlSoIEm64447dOjQIZlMJknS/fffrwMHDtgvIQAAAACHyszMVExMjE6dOqWYmBhlZmY6OhJQYtlU4u+8807LJ3B33nmnsrKytH//fklSenq61WR3AAAAAJxbXFyc0tLSJElpaWmKj493bCCgBLOpxHfr1k0zZszQtGnTVK5cOTVt2lQvvfSSli5dqqlTp6pu3br2zgkAAADAAZKTkxUfHy+z2Szp0oTWcXFxSk5OdnAyoGSyqcR37dpVr7zyiuWI+xtvvKHMzExNnDhROTk5euWVV+waEgAAAEDRM5vNio2NtRT4a40DuPlsvk989+7ddf78eSUnJ8vV1VULFizQ6dOn5evra898AAAAABwkKSlJiYmJ+cZNJpMSExOVlJSkqlWrOiAZUHLZVOKPHDmi4cOHa8+ePVdcZ9++fTaHAgAAAOB4AQEBCg4O1p49eywTWUuXJrquV6+eAgICHJgOKJlsKvHjx4/X0aNHNXDgQFWrVk1Go01n5QMAAAAoxgwGg3r37q3Ro0cXOG4wGByUDCi5bCrxCQkJGjdunB599FE7xwEAAABQnPj7+ys8PFxr166V2WyWwWBQRESE/Pz8HB0NKJFsOoRepkwZVapUyd5ZAAAAABRDERER8vLykiR5e3srPDzcsYGAEsymEt+pUyctWbJEubm59s4DAAAAoJjx8PBQZGSkfH191adPH3l4eDg6ElBiFfp0+pdeesny95ycHH377bdq166dQkJCVLp0aat1DQaD3nrrLfulBAAAAOBQoaGhCg0NdXQMoMQrdIn//vvvrb729/eXJP3yyy/51mWCCwAAAAAA7K/QJX7z5s03MwcAAACAYiwhIUGLFy9Wr169OCIPOBD3hgMAAABwVZmZmZo7d65OnTqluXPnKjMz09GRgBKLEg8AAADgqj799FOdP39eknT+/HmtXr3awYmAkosSDwAAAOCKkpOTtX79equxdevWKTk52UGJgJKNEg8AAACgQGazWfPmzZPZbC7UOICbjxIPAAAAoEDHjh3T77//XuCy33//XceOHSviRAAo8QAAAAAAOAlKPAAAAIACVa1aVYGBgQUuCwoKUtWqVYs4EQBKPAAAAIACGQwG9e/fXwaDoVDjAG4+pyvxhw8fVsOGDfXpp59axvbt26cePXqoQYMGatOmjZYsWeLAhAAAAMCtw9/fX4888ojVWMeOHeXn5+egREDJ5lQlPjs7WyNHjtSFCxcsY6mpqerTp49uv/12rVq1SoMHD1ZUVJRWrVrlwKQAAADAraNLly4qW7asJKls2bLq3LmzgxMBJZdTlfhZs2ZZ/vHI8/HHH8vNzU1vvPGGatasqccee0y9e/fW/PnzHZQSAAAAuLV4eHhowIAB8vX11YABA+Th4eHoSECJ5TQlfteuXfroo480adIkq/Hdu3crLCxMrq6ulrGmTZvqzz//1KlTp4o6JgAAAHBLCg0N1YwZMxQaGuroKECJ5nrtVRwvPT1do0eP1tixY1WlShWrZcnJyapdu7bVWOXKlSVJx48fl6+vr037NJvNVqftAwAAAABws5jN5kJNFukUJf71119Xw4YNFR4enm/ZxYsX5e7ubjWWd3pPZmamzfvMzs7Wvn37bN4eAAAAuJUcPHhQmzdvVps2bVSzZk1HxwFuSf/utgUp9iV+zZo12r17t+Lj4wtcXqpUKWVlZVmN5ZV3T09Pm/fr5uamu+66y+btAQAAgFtFZmamYmJidPbsWW3dulXt2rXjunjAzg4cOFCo9Yp9iV+1apX++ecftWrVymp83Lhx+uyzz+Tv76+UlBSrZXlf38htLwwGww19CAAAAADcKtavX68zZ85Iks6cOaNNmzbp8ccfd3Aq4NZSmFPpJSco8VFRUbp48aLV2IMPPqihQ4cqIiJCa9eu1YoVK5SbmysXFxdJ0o4dO1SjRg1VrFjREZEBAACAW0ZycrLi4+NlNpslXbpuNy4uTvfdd5/8/f0dnA4oeYr97PR+fn664447rP5IUsWKFeXn56fHHntM586d0yuvvKIDBw7o008/VWxsrAYMGODg5AAAAIBzM5vNio2NtRT4a40DuPmKfYm/looVK2rBggU6fPiwOnfurOjoaI0ePVqdO3d2dDQAAADAqSUlJSkxMVEmk8lq3GQyKTExUUlJSQ5KBpRcxf50+oL89ttvVl+HhIToo48+clAaAAAA4NYUEBCg4OBg7dmzx6rIG41G1atXTwEBAQ5MB5RMTn8kHgAAAMDNYTAY1Lt373wTbl1pHMDNR4kHAAAAcEX+/v4KDw+3FHaDwaCIiIgbuhMUANtR4gEAAABcVUREhLy8vCRJ3t7eCg8Pd2wgoASjxAMAAAC4Kg8PD0VGRsrX11d9+vSRh4eHoyMBJZZTTmwHAEBxl5CQoMWLF6tXr14KDQ11dBwAuGGhoaH8ewYUAxyJBwDAzjIzMzVv3jydOnVK8+bNU2ZmpqMjAQCAWwQlHgAAO/v000917tw5SdK5c+e0evVqBycCAAC3Cko8AAB2lJycrPXr11uNrVu3TsnJyQ5KBAAAbiWUeAAA7MRsNmv+/Pkym82FGgcAALhelHgAAOzk2LFj+u233wpc9ttvv+nYsWNFnAgAANxqKPEAAAAAADgJSjwAAHZStWpV1a5du8BlgYGBqlq1ahEnAgAAtxpKPAAAdmIwGDRgwIAClw0YMEAGg6GIEwEAgFsNJR4AADvy9/dXrVq1rMZq1aolPz8/ByUCAAC3Eko8AAB2lJycrIMHD1qNHTp0iFvMAQAAu6DEAwBgJ2azWbGxsVcc5xZzAADgRlHiAQCwk6SkJCUmJspkMlmNm0wmJSYmKikpyUHJAADArYISDwCAnQQEBCg4OFhGo/WvV6PRqJCQEAUEBDgoGQAAuFVQ4lEsJCQkaNiwYUpISHB0FACwmcFgUO/evfPNQn+lcQAAgOtFiYfDZWZmKiYmRqdOnVJMTIwyMzMdHQkAbObv76/w8HBLYTcYDIqIiGB2egAAYBeUeDhcXFyc0tLSJElpaWmKj493bCAAuEERERHy8vKSJHl7eys8PNyxgQAAwC2DEg+HSk5OVnx8vGXGZrPZrLi4OG7FBMCpeXh4KDIyUr6+vurTp488PDwcHQkAANwiKPFwmCvdcolbMQG4FYSGhmrGjBkKDQ11dBQAAHALocTDYbgVEwAAAABcH0o8HIZbMQEAAADA9aHEw2G4FRMAAAAAXB9KPByKWzEBAAAAQOFR4uFw3IoJAAAAAAqHEg+H41ZMAAAAAFA4ro4OAEiXbsXEbZgAAAAA4Oo4Eg8AAAAAgJOgxAMAAAAA4CQo8QAAAAAAOAlKPAAAAAAAToISDwAAAACAk6DEAwAAAADgJCjxAAAAAAA4CUo8AAAAAABOghIPAAAAAICToMQDAAAAAOAkKPEAAAAAADgJSjwAAAAAAE6CEo9iISEhQcOGDVNCQoKjowAAAABAseUUJT4tLU2vvfaa7r//foWGhurJJ5/U7t27Lcu3b9+uLl26qH79+mrfvr3Wr1/vwLS4XpmZmYqJidGpU6cUExOjzMxMR0cCAAAAgGLJKUr88OHD9eOPP+rdd9/VqlWrVKdOHfXt21eHDh3SwYMHNWDAALVo0UKffvqpunbtqtGjR2v79u2Ojo1CiouLU1pamqRLH9jEx8c7NhAAAAAAFFOujg5wLX/99Ze2bdum5cuX65577pEkvfrqq/r2228VHx+vf/75R4GBgXrhhRckSTVr1tTevXu1YMECNWvWzJHRUQjJycmKj4+X2WyWJJnNZsXFxem+++6Tv7+/g9MBgO0SEhK0ePFi9erVS6GhoY6OAwAAbhHF/ki8t7e35s+fr+DgYMuYwWCQwWBQenq6du/ena+sN23aVD/88IOlGKJ4MpvNio2Nzfc+XWkcAJwFlwkBAICbpdgfiS9fvrxatmxpNbZx40b99ddfevnll7V69ep8R2wrV66sjIwMpaamysfHx6b9ms1mXbhwwebcuLbjx48rMTEx37jJZFJiYqIOHTqkKlWqOCAZANyYNWvWWF0m9Omnn6pTp06ODQUAAIo1s9ksg8FwzfWKfYn/t4SEBL300kt68MEH1apVK128eFHu7u5W6+R9nZWVZfN+srOztW/fvhvKiqszm8264447dOTIEauj7gaDQXfccYdSU1Mt/wkGAGeRmpqqDRs2WF0m9Nlnn6lSpUry9vZ2cDoAAFCc/bvbFsSpSvxXX32lkSNHKjQ0VFFRUZIkDw+PfGU97+vSpUvbvC83NzfdddddtodFofj6+urVV1+1KvFGo1HPPPOMKleu7MBkAHD9zGazZsyYUeCynTt3atiwYYX6hB0AiqOff/5Zy5cv11NPPaX69es7Og5wyzlw4ECh1nOaEv/BBx9o4sSJat++vSZPnmz5hKJKlSpKSUmxWjclJUWenp4qV66czfszGAzy9PS8ocy4turVqysiIkJr1qyxjEVERKh69eoOywQAtjp27Jj27NmTb9xkMmnPnj1KS0tT1apVHZAMAG5MZmamli1bptTUVC1btkyhoaHy8PBwdCzgllLYD/qL/cR2krR8+XJNmDBB3bt317vvvmt1ikGjRo20c+dOq/V37Nih0NBQGY1O8fRKvIceesjyDWswGPTggw86OBEA2CYgIEDBwcH5fv8YjUaFhIQoICDAQckA4MZwS2Cg+Cj2Lffw4cN666231K5dOw0YMECnTp3SyZMndfLkSZ09e1Y9e/bUL7/8oqioKB08eFAxMTH6/PPP1a9fP0dHRyFt3LjR6trRL774wsGJAMA2BoNBvXv3zvdJ+pXGAcAZXOmWwMnJyQ5OBpRMxb7Eb9y4UdnZ2fryyy913333Wf2ZOHGiatWqpffee09bt27Vo48+qpUrV2rKlCncI95J5P1SuBy/FAA4M39/f4WHh1udYRQRESE/Pz8HJwOA68ctgYHip9hfEz9w4EANHDjwquvcf//9uv/++4soEezlWr8UxowZw1ErAE4pIiJCW7duVWpqqry9vRUeHu7oSABgk6SkpKveEjgpKYm5PoAiVuyPxOPWlfdLwWQyWY1f/ksBAJyRh4eHIiMj5evrqz59+jD5EwCnxVwfQPFDiYfD8EsBwK0sNDRUM2bMUGhoqKOjAIDNmOsDKH4o8XAYfikAAAAUf8z1ARQvlHg4FL8UAAAAir+IiAh5eXlJEnN9AA5GiYfD8UsBAACgeGOuD6D4KPaz0+PWl/dLYfHixerVqxe/FAAAAIqh0NBQ5vkAigFKPIoFfikAAAAAwLVxOj0AAAAAAE6CEg8AAAAAgJOgxAMAAAAA4CQo8QAAAACuKSEhQcOGDVNCQoKjowAlGiUeAAAAwFVlZmYqJiZGp06dUkxMjDIzMx0dCSixKPEAAAAAriouLk5paWmSpLS0NMXHxzs2EFCCUeIBAAAAXFFycrLi4+NlNpslSWazWXFxcUpOTnZwMqBkosQDAAAAKJDZbFZsbKylwF9rHMDNR4kHAAAAUKCkpCQlJibKZDJZjZtMJiUmJiopKclByYCSixKPYoHZTgEAAIqfgIAABQcHy2i0rg1Go1EhISEKCAhwUDKg5KLEw+GY7RQAAKB4MhgM6t27twwGQ6HGAdx8lHg4XFxcnFJTUyVJqampzHYKAABQjPj7+6tDhw5WYw8//LD8/PwclAgo2SjxcKjk5GTFxcVZjTHbKQAAAAAUjBIPh7nSrKYmk4nZTgEAAIqJ5ORkbdiwwWrss88+46AL4CCUeDhM3mynBd2yhNlOAQAAHI9bzAHFDyUeDlOlShWVLVu2wGVly5ZVlSpVijgRAAAALsct5oDihxIPhzl+/LjOnTtX4LJz587p+PHjRZwIAAAAl+MWc0DxQ4mHwwQEBKh27doFLgsMDOSXAgAAgINxizmg+KHEw6Gys7MLHM/KyiriJAAAACiIv7+/wsPDLYXdYDAoIiKCW8wBDkKJh8McO3ZMhw8fLnDZ4cOHdezYsSJOBAD2k5CQoGHDhikhIcHRUQDghkVERMjLy0uS5O3trfDwcMcGAkowSjwAAHaWmZmpmJgYnTp1SjExMcrMzHR0JAC4IR4eHmrZsqWMRqPuv/9+eXh4ODoSUGJR4uEwAQEB8vT0LHCZp6cn18QDcFpxcXFKS0uTJKWlpSk+Pt6xgQDgBmVmZmrr1q0ymUzaunUrH04CDkSJh8McP35cFy5cKHDZhQsXmJ0egFNKTk5WfHy85d7JZrNZcXFxSk5OdnAyALAdH04CxQclHg6Td8uSgnDLEgDOyGw2KzY21lLgrzUOAM6ADyeB4oUSD4fJuzVJQfcd5ZYlAJxRUlKSEhMTZTKZrMZNJpMSExOVlJTkoGQAYBs+nASKH0o8HMrf318RERFWY506deKWJQCcUt4ZRgXdT5kzjAA4Iz6cBIofSjwcLiIiQt7e3pIkHx8fblkCwGkZDAZ17NixwCNWjzzyCGcYAXA6eR9OFnTmJB9OAo5BiYfDeXh4KDIyUr6+vurTpw+3LAHgtMxms1atWlXgslWrVnHaKQCnk3f5Y0FnGHH5I+AYro4OAEhSaGioQkNDHR0DAG7IsWPH9Pvvvxe47Pfff9exY8dUrVq1Ik4FADfG399f4eHhWrt2rcxmswwGgyIiIrj8EXAQjsQDAAAAuKqIiAh5eXlJkry9vbn8EXAgSjwAAHZStWpVBQYGFrgsKChIVatWLeJEAGAfXP4IFB+UeAAA7MRgMKh///4FLuvfvz/XjgJwaqGhoZoxYwaXQAIORokHAOAmMxgMTGoHAADsghIPAICdmM1mxcbGFnjEPTY2liIPAABuGCUeAAA7SUpKUmJiYoH3iU9MTFRSUpKDkgEAgFsFJR7FQkJCgoYNG6aEhARHRwEAmwUEBCg4OFhGo/WvV6PRqJCQEAUEBDgoGQAAuFXcEiXeZDJp5syZatGihRo0aKBnnnlGR48edXQsFFJmZqZiYmJ06tQpxcTEKDMz09GRAMAmBoNBvXv3znc6/ZXGAQAArtctUeLfe+89LV++XBMmTNCKFStkMpnUr18/ZWVlOToaCiEuLk5paWmSpLS0NMXHxzs2EADcAH9/f4WHh1sKu8FgUEREhPz8/BycDAAA3AqcvsRnZWUpJiZGQ4cOVatWrRQUFKRp06YpOTlZX3zxhaPj4RqSk5MVHx9vuX7UbDYrLi5OycnJDk4GALaLiIiQl5eXJMnb21vh4eGODQQAAG4Zro4OcKP279+v8+fPq1mzZpax8uXL6+6779auXbvUsWNHB6az3ZkzZ4p8nyaTSefOnSuy/ZnNZsXExMhkMuXLMXfuXEVGRhbJqadly5bNd/1qUahQoYLl7yXh/S4uisP7XVKVtO/z8PBwxcXFqWPHjkpJSSnSfReH7/OS9n47UnF4v0sqvs+LTnH4Puf9LjrF4f0uzpy+xOcdsa1SpYrVeOXKlW/oaK7ZbNaFCxduKNuNGDRokMP27Whms1l//PGHXnrpJUdHuanef/99y99L8vtdUlz+fpdUJfX7fMmSJY6OUGT4d61k4d81vs9LAv5dK1kc/e+a2Wwu1EFMpy/xGRkZkiR3d3ercQ8Pjxv6tCw7O1v79u27oWzA1fD9VbLwfqMk4Pu8ZOH9RknA93nJUhze73/32oIYzP++ma2T2bhxo4YOHaqff/5ZpUqVsowPGzZMWVlZmjNnznU/Zt49fu+66y57Rr0u6enpRb5Pk8mk8+fPF+k+T58+rVmzZlndU9lgMOi5556Tj49PkWQoU6aMQ07XKV++vOXvJeX9Lg6Kw/tdUvF9XnSKw/c573fRKQ7vd0nF93nRKQ7f57zfRac4vN+OcODAARkMBgUHB191Pac/Ep93Gn1KSopuv/12y3hKSooCAwNtflyDwSBPT88bzmcrR+67qB05ckRr1661nD7y6KOPqkmTJo6OVaRK0vuNkovv85KF9xslAd/nJQvvN262ws4H5vSz0wcFBals2bL6/vvvLWPp6enau3evGjdu7MBkKCxmcQYAAACAwnH6Eu/u7q4ePXooKipKmzZt0v79+/XCCy/I399fDz74oKPjoRA8PDwUGRkpX19f9enTRx4eHo6OBAAAAADFktOfTi9JQ4cOVU5OjsaOHauLFy+qcePGWrhwodzc3BwdDYUUGhqq0NBQR8cAAAAAgGLN6Se2uxkSExMl6ZoTCgAAAAAAYA+F7aFOfzo9AAAAAAAlBSUeAAAAAAAnQYkHAAAAAMBJUOIBAAAAAHASlHgAAAAAAJwEJR4AAAAAACdBiQcAAAAAwElQ4gEAAAAAcBKUeAAAAAAAnAQlHgAAAAAAJ0GJBwAAAADASVDiAQAAAABwEpR4AAAAAACchKujAxRH2dnZMpvNSkxMdHQUAAAAAEAJkJWVJYPBcM31KPEFKMwLBwAAAACAvRgMhkJ1UYPZbDYXQR4AAAAAAHCDuCYeAAAAAAAnQYkHAAAAAMBJUOIBAAAAAHASlHgAAAAAAJwEJR4AAAAAACdBiQcAAAAAwElQ4gEAAAAAcBKUeAAAAAAAnAQlHgAAAAAAJ0GJBwAAAADASVDiAQAAAABwEpR4AAAAAACcBCUeAAAAAAAnQYkHAAAAgEI6f/68JMlkMjk4CUoqSjzgBMxms+Xv/MIA4Ozy/k07evSozpw54+A0AFB4p0+f1quvvqr9+/fLaDTq9OnTjo6EEogSDzgBg8EgSVq6dKl+/PFHSVJubq4jIwGAzQwGgzZt2qR+/frp+PHjjo4DAIVWpkwZnTlzRo8//riee+45TZs2TRcvXnR0LJQwlHjAiXz66aeaP3++JMnFxcXBaQDANkeOHNHy5cv1n//8R0FBQY6OAwCF5uHhoYkTJ8rX11dffvmlwsLCVKpUKQ6uoEhR4oFi6vJT6PN+MQwZMkT//POPfvnlF0fFAoAbcvDgQY0ZM0Z79+5V7dq1JXGZEADnkPd/s9OnT8vHx0cNGjTQa6+9pl9//VUuLi4UeRQZSjxQDJnNZssp9NL/jrrXq1dP586d06ZNmxwVDQBuSM2aNdWgQQNlZGRo2bJlOn36tIxGo9UHlwBQnOR90Jj3f7O77rpLS5YsUVRUlBo1aqQePXpYijwfSqIoUOKBYijvl8SSJUs0atQoHT16VOfOnZOfn58GDhyotWvXas+ePQ5OCQDXdnk5z87OliSNGTNGzzzzjA4cOKA5c+bo9OnTMhgMFHkAxY7JZJLReKkyffTRRxo9erReffVVmUwmVatWTaNGjVLjxo3Vs2dP7d27V0ajUYcOHeLfM9xUro4OACA/k8mks2fPKiUlRTt37lTfvn1Vt25d9e/fXyEhIQoKCtK+fftUt25d5ebmcn08gGIp76yi7du3a8OGDTp48KBq1Kihzp07a/DgwTKbzVq/fr0MBoMGDhwoHx+ffGciAYAj5RX4yZMna/Xq1erQoYPV/7tq166t1157Ta+//rqeeOIJ+fv7q3nz5ho/fryjIqMEMJj5mAgoFi7/pPff4wsWLNCOHTu0c+dO9e7dW1u3bpWbm5uWL1+uUqVKOSAtABTOpk2bNGLECLVr106lS5fWtm3bVKpUKfXr10+dO3fW9OnTtXnzZtWvX1/Dhw+Xt7e3oyMDgJUvv/xSkydPVnR0tIKCgpSamqoDBw7o22+/1R133KHOnTvrxIkTWrt2rf766y+98cYbcnNzc3Rs3MI4Eg8UA5cX+LxT5ZOSkhQSEqJnnnlG/fv3V//+/bV27Vrt2LFDZrNZe/fuVXx8vLp27cqRKwDF0okTJzRz5kyNGjVK3bt3lySdOnVKY8eOVUxMjGrXrq3nn39eZ86c0b59+5gUCkCx8O8DK+np6fL391dQUJB27Nih2NhYy79Z7u7ucnFx0aOPPqqBAwdatsnJyZGrK1ULNwfXxAPFQN4viilTpmj69Ok6f/68atasqXfffVevvPKKzp49K0nq1KmTXnzxRc2YMUONGzfWl19+KUkUeADFUlZWli5cuKCGDRtKunRNvK+vryZOnKgzZ85o9erVkqRx48Zp9uzZ8vX1dWRcAJD0v/+XrVy5Un/99ZcqVqyoo0eP6rHHHlNkZKQ8PT310ksvacuWLSpTpoxSUlLyPQYFHjcT311AMfHtt99q3bp1mjZtmkJDQ/XNN9/I1dVVDzzwgFJSUlSuXDlJUpkyZVShQgVNmTJF4eHh2rVrlxo3buzg9ACQ34ULF3T06FEdP35cd999t1xdXZWdna2KFSuqQYMGVv/xrVixogOTAsD/mM1m5eTkKCoqSo899phGjx6t5557Tvv27dOgQYPUvHlzlS5dWpJUvnx5lSlTxsGJUdJQ4oFi4vjx47rrrrsUGhqqL774QmPGjNHYsWMVFhamgQMHqkePHurQoYNcXV1lMpmUkpIib29vZj8FUCzkXdaTkpIiT09PSVJgYKDuvfdexcbGytvbW6GhoZbrRDMzMxUUFOTIyABgcfmliSaTSW5ubho+fLhWrVql48eP6/HHH5ckpaamatu2bfL399eMGTN09uxZPfHEE46MjhKI0+kBByioeBsMBuXk5OiTTz7RmDFjNHr0aHXr1k0ZGRlKSkqyOmKVlZWlHTt26MiRI6pSpUpRRgeAAhkMBn311Vfq3r27/vOf/2jSpElKTU1V//79lZ6ernfeeUfr1q3Tjh07NHnyZCUkJKhTp06Ojg0AkqwvTcybfT4kJEQpKSnavn27JOnixYuKi4vTpEmTNGrUKJlMJq1atUqurq7M6YEixez0QBG7fLKU06dPy8fHR5L0ww8/aPDgwUpPT9eIESPUt29fSdKZM2fUs2dPDRw4UA8//LDlcc6ePau0tDTddtttRf8kAOBf/vjjD/Xo0UO9e/fW8ePH9euvv8rf31+TJ0/W/v37tXDhQm3ZskU1atRQ6dKlNXHiRNWpU8fRsQGUYP/8849OnjwpFxcX1apVSytWrNCpU6fUo0cPlS1bVq6urpo+fbo2bNig2NhYValSRWfPnlVSUpJcXFx05513ymg0MokdihwlHihCl5+qNX/+fG3ZskVGo1Hdu3dXhw4dtHTpUk2cOFF9+vRRs2bNVL58ec2aNUtpaWn6+OOPLZ8MMxs9gOIg79+irKws/fDDD/r666/18ssvS5I+/vhjrVixQv7+/powYYIqVqyoQ4cOqUyZMipdurTKly/v4PQASrKXX35ZR44c0e7du2U0GhUZGal//vlHGzZsUO3atRUSEqIhQ4YoKSlJU6dO1ZNPPqm2bdvme5wr3SIYuJko8UARufwf+VmzZmnx4sV66qmntG3bNiUnJ+vZZ59Vjx49tGjRIn3yySc6ePCg6tSpo/Lly2vBggVyc3NTbm6upcgDgCPlFfgtW7YoPj5ehw8fVoUKFfT+++9bjkitWLFCH3/8sapUqaJRo0apevXqjg0NAJL69OmjjIwM9e/fX+XKlVNaWpqaNWtmmWl+wYIF2rlzp06cOKE+ffro008/Va1atRQdHe3o6IAkSjxQ5JKTkzVx4kT17t1b99xzjyRp9OjR2rVrlyIjI9WzZ0+dPn1af//9t8qVK6c77riDU7UAFEvbt2/Xs88+q7CwMKWmpuqPP/7Q7Nmzde+991rWWblypRYsWKC7775bU6ZMkYuLC2cSAXCYWbNmadu2bXrvvfcslzTm/R8rOztbbm5uysnJkclk0vz583X48GF9//33OnXqlGbPnq0HHnjAwc8AYGI74KZ65ZVX9PHHH1u+/vDDDxUeHq7ff//d6lTSd955R40bN9aiRYu0ZMkSubi4KCQkRDVq1JDRaJTJZKLAAyhWTpw4oW+//VYjRozQ/PnzNXPmTD3wwAMaMmSI/vvf/1rW69q1qwYOHKjhw4fL1dWVAg/Aofbu3au2bdtaCrz0v3u6u7m5yWQy6cMPP1RWVpaGDBmiUaNGacqUKapevbq++eYbR8UGrFDigZskKSlJqampio6OVnx8vCTpySefVK1atfTXX39pz549ys7Otqz/zjvvqEmTJpo6daq2bdtm9VhcawWguDCZTDp69KieeOIJffLJJ8rMzJQkValSRWPGjFGbNm00ePBgy2zOktS5c2cm4QTgUCaTSSdOnFBCQoLq1atnGfu3M2fOKDY2VjNmzJAkVa5cWc2aNdOECRO0fv16HTx4sEhzAwWhGQA3SUBAgF544QXde++9mjJlilatWiVJWr58uerWratp06Zp+/btysnJsWzz9ttva8CAAXrooYccFRsACpR39Z3RaNRtt92mXr16KTs7Wz/88IMOHTok6dJ/dl988UW1bdtWffr00c6dOx0ZGQAsjEajfH19VbZsWcuHjP8+SGI2m+Xt7a1atWrp9OnTlnXy/v3z9fW1+n8b4CiUeOAmyPsHvlatWrr//vsVFBSkt99+W+vXr5ckrVq1Sj4+Pnr99dfzFflBgwbJxcWF+40CKFYMBoN27Nihp59+WtKliaGGDRumX375RcuWLdPhw4clSZUqVdLIkSP1+OOPq2LFio6MDAAWZrNZ2dnZuvPOO7Vjxw7Lv1mXy7vcx2QyycPDwzKekZGhzZs368iRI6pQoUKRZQauhBIP3AR511ZNnTpVsbGxysjIkNls1sSJE/XJJ59IklavXi0vLy+NHz9eW7ZsyVfamYUegKNdPvet2WxWWlqaEhIS1LdvX0lS79691bdvX23cuFEffPCB/vzzT0mSn5+fxo8fr5o1azoiNgDkYzAYVKpUKQ0ZMkR79+7VggULdPLkyXzrnT59Wunp6br77rstY56enurWrZvWrVsnf3//oowNFIgSD9hJRkaG1defffaZPv74Y40ePVrz58/XkiVL9NBDD2nmzJmWU+s//fRTZWdnKy4ujtIOoNiYOHGitm/fbjUJncFgUOvWrTVt2jTt2bNHvXv3liRFRkYqMjJSmzZt0rx583TkyBFJfBAJoHiqX7++XnvtNa1du1YTJkywnFqfk5OjpKQkvfzyyzKbzXryySct25jNZt1xxx268847HRUbsMJ014AdjBgxQq1bt1bHjh0t904+dOiQgoODLbeRq1u3rsqWLauLFy8qKipKnp6e6tChg7Zu3cqp8wCKjUmTJmnp0qV67LHHJEn79+9XUFCQJMnDw0MtW7bU+PHjNW7cOPXp00eLFi1SZGSkMjIyFB8fL09PT0fGB4Brevzxx1WhQgWNGzdOCQkJ8vPzk8FgkKurq4xGoz744APLpY3cFhPFEfeJB27Q1q1b9c0332j06NHy8PBQVlaW3N3dNWfOHK1Zs0ZLliyRn5+fZf2vvvpKQ4YMkSSr+43m/aIAAEeZMmWKPvroI3300UeqWbOmTpw4oYceekhNmjTRvHnzLOtdvHhRX331lWUSu+nTp0uS0tLS5OXl5ZjwAHCdkpKS9N///le//PKLvL29FRgYqIceekguLi6We8cDxRElHrgBw4cP17lz5zR//nxJ0rJly3ThwgX17NlTP//8s4YPH66nn37aaoKnX375RTNmzFC7du3UtWtXijuAYmH27NmaNWuWXn/9dXXr1k2SdP78eW3YsEFTpkxR06ZNLbdckqT09HT17NlTv/32m+6//37Nnz/fciYSADgzDqyguOPjJeAGnD59WlWqVLF8vWXLFv3xxx8qV66cunXrpm7duik6OlppaWm6//77ValSJc2cOVNlypTRE088IYPBwC8KAA43ceJErV69WgEBAfrggw902223qUmTJipTpow6dOggNzc3jR8/XkOHDtXMmTMlSeXLl1f9+vX1wgsvWCawo8ADcDaXf/iY93f+X4bijhIP2CA7O1tubm5ycXFR6dKlLePvv/++RowYofnz58tgMOi5555TqVKltG7dOsXGxiowMFAeHh6aM2eODAaDTCYTvygAONSkSZO0cuVKbdiwQVWqVFF4eLjeeOMNjRs3TmFhYSpTpozatm0rSRo//v/au7eQqNYGDuN/T4NJFpSZZiJoaENqB9ObMiEGglIS9SIChVCMxkwHgkRI/CA1QyizDBMqCimo7HDRDrqoVJRI7URdlGlqFxlpGpoo6vouPhxyW+2vg9tWPT8QdK2XmbWu9PGd913/kd1u1/bt29XQ0KDGxkbt3r1bvr6+s3wXAPB9/r6BJ2AGfJwe+A6T/6lNS0tTYGCgioqKnGEvSQ6HQ48ePVJmZqa2bdum/v5+dXV1ycvLS8HBwXJ1dWWtFYBZ19fXp/z8fOXm5jo3rxsdHVVycrJGR0dVWFio6Ohoubu7a2hoSHfv3lVpaanGx8c1Z84clZeXT3kMEwAAmHlEPPANamtrFRUVpaCgIElSWlqali5dquLi4mljc3Jy9OTJE2VkZCghIUHe3t7OcxMTE3J15QmPAGbf5GachmFofHxc7u7uXwx5SRocHFRnZ6f8/f21YMGCWb56AAD+PFQE8H96+fKl8vPzdeHCBXV0dEiSvLy81NfXp6tXr+rp06fq7e1VX1+fJKm8vFzr1q3TsWPH1NDQMOW1CHgAvwqLxSJJzscrjY2NyWKx6PLly7JYLCosLNT9+/edj8KcO3euVqxYQcADADBLmIkHvkFLS4scDoc2bdqkjIwMlZSUqK6uToZhaHh4WBaLRUuWLFFQUJBCQ0Pl5uamzs5OlZWVsfYdgGlMLveZnJGfmJjQvn37tH79ev4JCQDALCPigW/U0tKinJwcJScn6/bt2woJCdH+/fv17NkztbW1qb29XQ8fPlR3d7esVqvOnTsnNzc3dqEHYCqfhrzNZpOPj49qamqmbOYJAAD+fUQ88B2am5uVnZ2t/v5+7dy5U7m5uVPOG4ahkZERWSwWubq6sgYegCl9GvI9PT0KDAyc7UsCAOCPR1UA32Ht2rWqqqqSJLW3t6unp8d5bmxsTC4uLvL09CTgAZjap2vkCXgAAH4NlAXwnSIjI1VTU6MHDx7o1KlT6u7ulqRpj40j4AGYGY/CBADg18LH6YEfNLnZXWxsrHJycuTr6zvblwQAAADgN8UUIfCDoqKiVFpaqt7eXi1atGi2LwcAAADAb4yZeOAnMQxDLi4urIEHAAAAMGOIeOAnmgx5AAAAAJgJTBcCPxEBDwAAAGAmEfEAAAAAAJgEEQ8AAAAAgEkQ8QAAAAAAmAQRDwAAAACASRDxAAAAAACYBBEPAMAfLjU1VampqT/0GrW1tQoLC9Pr169/+HrCwsJUUVHxw68DAMDviIgHAAAAAMAkiHgAAAAAAEyCiAcAAP/o4sWLSkpK0qpVqxQZGamtW7fqr7/+mjautbVViYmJCg8PV3x8vG7cuDHl/MjIiA4dOqS4uDiFh4crISFh2hgAAPBl7rN9AQAA4NdWU1OjAwcOKDs7W1FRURoYGFB1dbX27t2r1atXy8/Pzzm2oKBAu3btktVq1ZUrV+RwOGSxWGSz2WQYhrKystTa2qo9e/YoJCREt27dksPh0OjoqBITE2fvJgEAMAkiHgAAfFV3d7fS09Nlt9udxwICApSUlKSWlhZt2bLFeTw7O1vp6emSpA0bNujVq1eqrKyUzWZTY2Oj6uvrdfjwYW3evFmSFBsbq+HhYZWVlSk+Pl7u7vxpAgDA1/CbEgAAfFVeXp4k6cOHD2pvb1dnZ6fu3bsnSRodHZ0ydjLOJ9lsNlVUVGhoaEhNTU1ycXFRXFycxsbGnGM2btyo69ev68WLF7JarTN8NwAAmBsRDwAAvqqrq0sFBQVqamqSh4eHgoODtXz5ckmSYRhTxvr4+Ez5eeHChTIMQ4ODg+rv75dhGFqzZs1n3+ft27dEPAAA/4CIBwAAXzQxMaHMzEx5eHjo0qVLslqtcnd3V1tbm65duzZt/MDAwJSQf/fundzc3DR//nx5e3vLy8tLZ8+e/ex7BQUFzdh9AADwu2B3egAA8EXv379XR0eHUlJSFBER4VyzXldXJ+l/kf+pO3fuOL+fmJjQzZs3tXLlSnl6eiomJkYfP36UYRiKiIhwfj1//lzHjx+f8hF7AADweczEAwAAvXnzRmfOnJl2PDQ0VAEBAaqpqZGfn5/mzZun+vp652z68PDwlPFHjhzR+Pi4/P39df78eXV0dOj06dOSpLi4OEVHR8tut8tutyskJESPHz/W0aNHFRsbqwULFsz4fQIAYHZEPAAAUFdXl0pKSqYdT0lJUWVlpYqKipSXlyeLxaJly5bpxIkTKi4uVnNzs1JTU53jS0pKdPDgQXV2dio0NFTV1dWKiYmRJLm6uurkyZMqLy9XVVWVent7tXjxYu3YsUNZWVn/2r0CAGBmLsbfd6QBAAAAAAC/JNbEAwAAAABgEkQ8AAAAAAAmQcQDAAAAAGASRDwAAAAAACZBxAMAAAAAYBJEPAAAAAAAJkHEAwAAAABgEkQ8AAAAAAAmQcQDAAAAAGASRDwAAAAAACZBxAMAAAAAYBJEPAAAAAAAJvFfSCzLYRIkir0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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5c+dkGIZN0ExJSbH5wKRTp056+eWX9fnnn2v37t1q1aqVAgMDC1V/Ua/v9PR0m6CekpIiNzc3VahQQeXKlZOPj4+WLFlS4LFq1KhR7PX8E3uvJUm6//779fLLL2vjxo06dOiQqlevrttvv13S/30/TJgwQc2aNbti2woVKvzjvgvz/f33fZ0+fVrVq1e3tufm5io1NVV+fn7/ei4AcKNi4jgAuAE1atRIHh4eOnnypEJDQ63/3N3d9frrr+v48eNKTU3V4cOH1bNnT+sySdZZk4u7l+rzzz9XixYtdPr0abm5ualJkyZ64YUXVL58eSUlJUn6XzD8+4Ree/bsset4e/bsUfny5fXoo49ag9/58+e1Z88e67mFhYXJw8PDOsQ6X0xMjMaOHSsfHx/Vr19fhw4dsvk63nbbbZo3b551qHJhtGzZUpcuXbJORib9b4jz9u3br7pNbGys2rVrp5ycHHl6eqply5Z66aWXJMn6Nft7b3RhzrswfH19Va9evSu+Nl9//bUGDx6sU6dOqVmzZjp8+LBq1qxp8/VZt26dVq5caRMg/y4rK0s7d+60vj5//ry2bNmi5s2bW9t8fHzUuXNnrV+/Xtu3by/0hHGSinx9b9myxfp/i8Wizz//XI0aNZK3t7eaNWumCxcuyDAMm/Pcv3+/FixYcMVtBMVRzz+x51rKV758ebVr106bNm3SF198ocjISOsHJbVq1VKlSpV0/Phxm/MMDAzUa6+99o+z9hfm+/ty+R8CbNiwwaZ9w4YNysvLs35wAACuiJ50ALgB+fn56dFHH9Ubb7yhzMxMNW/eXCdPntQbb7whk8mkunXrqly5cqpevbqWLl2qoKAglS9fXtu2bbP2Fhb3/Z7h4eGyWCwaPny4Bg8eLF9fX3322Wc6d+6c9d7kdu3a6auvvtK0adPUvn177d69u0izZ18uLCxMH330kaZPn6527drp1KlTevfdd5WSkmLtxfP391f//v0VGxsrT09PNWvWTD/99JM++ugjTZgwQWazWWPHjtXgwYM1btw4RUZGWmeW/+mnnzRs2LBC19OyZUu1bt1azz77rM6cOaPq1atryZIlOnv27FWHlLdo0UKzZs3S8OHD1bdvX7m5uWn58uXy9PRUu3btJP0vdKWkpGjr1q2qV69eoc67sEaNGqXHH39cY8eOVdeuXZWSkqLXX39dHTp0UEhIiAYOHKh169Zp4MCBio6Olp+fnz799FN98sknevrpp/9x3x4eHpo0aZLGjh2rsmXLatGiRbp48eIVX9OePXuqV69eqlChgjp06FDo2itVqlSk63vOnDnKy8tT1apV9dFHH+nw4cN67733JElt27ZV06ZNNWzYMA0bNkzBwcH6+eefNXfuXN11111XvV3iWur5J/ZcS5eLjIzUqFGjlJeXZ53VXZLc3Nw0ZswYTZ48WW5ubmrXrp0yMjL05ptv6uTJk1e9rUEq3Pf35WrXrq1u3bpp7ty5ysrKUtOmTbVv3z7Nnz9fzZs3t7nfHgBcDSEdAG5QTzzxhCpXrqxly5Zp8eLFqlChglq2bKmxY8daJ5x78803NXXqVD311FPy9PRU7dq1tXDhQr3yyivavXv3Fc8svxZVqlTR4sWL9cYbb+iZZ55RVlaWtUe6RYsWkqQePXro6NGjWrNmjZYvX66mTZtq7ty5evjhh4t8vG7duun48eNatWqVli1bpsDAQLVt21aPPPKInnvuOR08eFDBwcEaP368KlWqpOXLl2vx4sW66aab9Nxzz6l3796SpNatW+vdd9/V/PnzNWrUKHl4eKhBgwZ677331Lhx4yLVNH/+fM2aNUtz585Vdna2OnfurIceekibNm0qcP26devqrbfe0oIFCzR27Fjl5eWpYcOGiomJsQ7P7969u7Zu3arhw4dr1KhReuyxxwp13oXRrl07vfXWW5o/f76GDx8uf39/RUREaOTIkZKkwMBALV++XK+99ppeeOEFZWdn69Zbb9XUqVOtj8S7Gn9/f40bN06vv/66Tp8+rUaNGunDDz+0ue1Akho3bqyKFSuqc+fORZ63oCjX97Rp0zR9+nQdOXJEISEheuedd6y9vWazWYsWLdIbb7yht99+W2fOnFFgYKCioqI0fPjwEqnn3xT1Wrpc27ZtVa5cOd18882qWbOmzbIHH3xQvr6+Wrx4sT7++GP5+PgoPDxcs2bN0s0333zVfRbm+/vvpk6dqho1amjVqlV65513VKVKFfXv31/Dhg27YoQIALgSk1GUWX8AAABK0U8//aSHHnpI69ats06yBgDAjYyedADANbFYLIW6n/byR8jdSPLy8v51lnuTyfSP92fjSt99952+++47rV27Vq1btyagAwBcxo35FxMAoNQsWLBA8+fP/9f1Nm3apJtuuqkUKipdAwcOtD7z+WqqV6+ur776qpQqujGkpqbqvffe02233aaXX37Z0eUAAFBqGO4OALgmJ0+evOL5yQWpU6dOke8pvh4cOnRI58+f/8d1PD09VadOnVKqCAAAXM+cIqSvXbtWixYt0rFjx3TLLbdoxIgR1me/Hj9+XC+99JK+//57+fj4qGfPnho5ciTDBgEAAAAANxyHT525bt06PfPMM+rTp482bNigBx54QGPHjtUPP/yg3NxcDRo0SJK0fPlyvfDCC/roo4+0YMECB1cNAAAAAEDxc2hPumEYuueee9SxY0dNnDjR2j5o0CA1a9ZM1atX19NPP61vvvnG+mzXjz/+WDNnztSOHTtuyGGTAAAAAADX5dCJ4w4fPqy//vpLERERNu3vvvuuJOmFF15QgwYNrAFdklq0aKHMzEzt27dPjRo1KvIxf/jhBxmGIQ8Pj2srHgAAAACAQsjNzZXJZFKTJk3+dV2Hh3RJunDhggYNGqS9e/fqpptu0uOPP6727dsrOTlZQUFBNttUqVJFknTixAm7QrphGNZ/AAAAAACUtKLkT4eG9MzMTEnSxIkTNWLECD355JP64osvNGzYML333nu6ePGiypcvb7ONl5eXJCk7O9uuY3p4eCgnJ0e5ubnXVjwAAAAAAIVU2NHcDg3p+UUOGjRI3bp1kyTVq1dPe/fu1XvvvSdvb2/l5OTYbJMfzn18fK7puLVr17Z7ewAAAAAACuvAgQOFXtehIT0wMFCSFBISYtNeu3ZtbdmyRc2aNdP+/fttluU/izd/W3uYTKZrCvkAAAAAABSWyWQq9LoOfQRbgwYN5Ovrq59++smmff/+/brlllvUtGlT7d271zosXpJ27twpX19f1a1bt7TLBQAAAACgRDk0pHt7e+vRRx/VggULtH79eh09elQLFy7U9u3bFRUVpQ4dOqhy5cp64okn9Ntvv2njxo16/fXXFR0dzePXAAAAAAA3HIcOd5ekYcOGqUyZMpo9e7ZOnjyp4OBgzZs3T82bN5ckLV68WFOmTNFDDz2kChUq6JFHHtGwYcMcXDUAAAAAAMXPZLjYs8gSExMlSaGhoQ6uBAAAAACKT15eHk+xcgB3d3e5ubn9433nRcmhDu9JBwAAAADYzzAMJScnKy0tzdGluCw3NzdVqVJFFSpUKNIkcQUhpAMAAADAdSw/oFepUkU+Pj7XHBJReIZh6NKlS8rIyNCJEyeUlZWlqlWrXtM+CekAAAAAcJ3Ky8uzBvRKlSo5uhyXVa5cOXl5eSklJUVVqlSRm5ub3fty6OzuAAAAAAD75d+D7uPj4+BK4OvrK8MwrnleAEI6AAAAAFznGOLueMX1HhDSAQAAAABwEoR0AAAAAIBTWb16terUqaPjx487upRSR0gHAAAAADiVu+++Wx9//LGqVKni6FJKHSEdAIAiSkhI0OjRo5WQkODoUgAAuCH5+/urcePG8vT0dHQppY6QDgBAEWRnZysmJkYpKSmKiYlRdna2o0sCAKBEPPfcc2rVqpXy8vJs2qdOnarmzZsrNzdX+/fv15AhQxQeHq7w8HANHz5cx44ds6773XffqU6dOlq+fLnatWun8PBwbd++XWfPntW4cePUqlUrhYaGqkuXLlq7dq11u4KGu2/fvl2PPPKIbr/9djVv3lzjxo3TiRMnbLapX7++fvrpJ/Xq1UuhoaFq166d3n333ZL7IpUAQjoAAEUQFxentLQ0SVJaWpri4+MdWxAAACWkS5cuSklJ0XfffWdts1gs+uyzz/Sf//xHx48fV+/evXXmzBnNmDFDU6dO1bFjx/Twww/rzJkzNvuaP3++Jk6cqMmTJ6tJkyYaP368Dh48qClTpuidd95R/fr1NXHiRO3cubPAWtauXavo6GhVrVpVr7/+up5++mn98MMP6tWrl82xLBaLnnjiCXXu3FmLFi1SeHi4Zs6cqW3btpXMF6kEuDu6AAAArhfJycmKj4+XYRiSJMMwFBcXp9atWysoKMjB1QEAULxuv/12Va9eXevXr9edd94p6X8946dPn1aXLl00f/58lSlTRrGxsSpbtqwkqWXLlurQoYMWL16siRMnWvf1yCOPqFOnTtbXu3bt0vDhw9WhQwdJUrNmzVSxYsUCh7dbLBbNmjVLrVu31muvvWZtDw8PV+fOnfXuu+9qwoQJkv73u3nYsGF68MEHrefw5ZdfasuWLbrrrruK+StUMuhJBwCgEAzDUGxsrDWg/1s7AADXO5PJpMjISG3cuFE5OTmSpA0bNujWW29Vo0aNtHPnTjVr1kze3t66dOmSLl26pLJly+qOO+7Qt99+a7OvevXq2bxu3ry55s2bp1GjRmnFihVKSUnRxIkTFR4efkUdhw8f1unTp/XAAw/YtN9yyy1q0qSJdu3aZdPepEkT6/89PT3l7++vCxcuXNPXojQR0gEAKISkpCQlJibKYrHYtFssFiUmJiopKclBlQEAUHK6dOmi9PR0bdu2TTk5Ofrvf/+ryMhISf+77evTTz9VgwYNbP5t3rxZp06dstmPj4+PzevZs2dr4MCB+uWXX/Tss8+qbdu2GjRokP76668rasi/zSwgIOCKZQEBATp37pxNm7e3t81rs9l8XX2YznB3AAAKoVq1agoNDdWvv/5qE9TNZrMaNmyoatWqObA6AABKRs2aNRUWFqbPPvtMZrNZGRkZ1pBerlw53XnnnYqKirpiO3f3f46a5cqV0/jx4zV+/HgdOnRImzZt0ptvvqkpU6Zo0aJFNutWrFhRkpSSknLFfk6fPi0/Pz87z8450ZMOAEAhmEwmDRw4UCaTqVDtAADcKLp06aJt27Zpw4YNCg8P18033yzpf/eRHzhwQPXq1VNoaKhCQ0PVsGFDxcbG6ssvv7zq/v766y+1bdtWn3/+uSSpVq1aeuyxx3TnnXcWODKtZs2aqly5stavX2/TfuzYMf34448FDpG/nhHSAQAopKCgIEVERFgDef69eoGBgQ6uDACAktO5c2edP39en376qbp06WJtHzZsmI4ePaohQ4Zo48aN2rZtm0aOHKkNGzaobt26V91f9erVFRQUpJdfflkrV67Url27FBMTo61bt6pjx45XrG82mzV27Fh98803GjdunLZu3aq1a9cqKipKFSpUKLAn/3rGcHcAAIogMjJSW7duVWpqqvz8/BQREeHokgAAKFH+/v5q3bq1tm/fbjNDe926dbV06VLNnj1bEyZMkGEYCgkJ0YIFC3TPPff84z7nz5+v119/XW+88YZSU1NVtWpVjRgxQoMHDy5w/e7du8vX11dvv/22hg8frrJly+quu+7S2LFjVbly5WI9X0czGdfTHfTFIDExUZIUGhrq4EoAANerhIQEvf/++xowYMANN8QOAHB9uXjxog4fPqyaNWteMWEaStc/vRdFyaH0pAMAUETh4eGEcwAAUCK4Jx0AAAAAACdBSAcAAAAAwEkQ0gEAAAAAcBKEdAAAAAAAnAQhHQAAAAAAJ0FIBwAAAADASRDSAQAAAABwEoR0AAAAAACchLujCwAAAAAAFD+LxSKLxVLqxzWbzTKb6Q+2FyEdAAAAAG4wFotFw0eOVEZaWqkfu3zFilowb16xBvV58+ZpzZo1+uqrr0p1+9TUVEVEROi1115T8+bN7Tp2URHSAQAAAOAGY7FYlJGWJr/oB6TS7NW2WJQas14Wi6VYQ3p0dLT69OlTqtufPHlSjz/+uE6fPm33ce3BGAQAAAAAuFGZzTKV4r+S+kDA19dX/v7+pbb9ypUrFRkZaffxrgUhHQAAAADgcPv379eQIUPUtGlTNWzYUPfcc49iYmIk/W+4evv27SVJx48fV506dbRhwwZ17dpVoaGh6t69uw4ePKgFCxbozjvvVLNmzTRlyhQZhnHF9v369dMTTzxhc+zvv/9ederU0ZEjRyRJX375pcaMGaM33nijlM7+/xDSAQAAAAAOlZWVpejoaFWsWFHLly/X+vXr1alTJ82YMUP79u0rcJvZs2dr0qRJWrFihTIyMvTwww/rzz//1AcffKAxY8Zo2bJl2rx58xXbde/eXZs3b1ZmZqa1LS4uTuHh4apRo4Yk6e2331bv3r1lMplK5oT/ASEdAAAAAOBQWVlZ6t+/vyZPnqzg4GDdeuutGjVqlCTp999/L3Cb6OhoNWvWTHXr1tW9996rCxcu6MUXX1RwcLAefvhhVapUSX/88ccV23Xs2FFms1kbN26UJOXk5OiLL75Q9+7dS+4Ei4CJ4wAAAAAADuXv769HHnlE69ev1969e3X06FH99ttvknTVx8jl93pLko+PjwICAlSmTBlrm7e3t3Jycq7YzsfHR506dVJ8fLy6du2qrVu3KicnR/fff38xn5V96EkHAAAAADjU6dOnFRkZqRUrVigwMFCPPPKI1qxZ84/buLvb9jkXZTb57t27a8eOHUpJSVF8fLw6dOigsmXL2lV7caMnHQAAAADgUOvXr1daWpq++OILeXh4SPq/Ye75k78VpzvuuEPVq1fXunXrtGXLFr311lvFfgx70ZMOAAAAAHCooKAgZWVl6fPPP1dSUpK++eYbjR07VpIKHLJ+rUwmk7p27aoFCxbI399fLVq0KPZj2IuedAAAAAC4UVksKv5+6H8+nj06deqkX3/9VdOnT1dmZqaqV6+uBx98UJs2bVJiYqKqVq1azIVK3bp10/z589WvX78iDZUvaSajJMYOOLHExERJUmhoqIMrAQAAAIBrc/HiRR0+fFg1a9aUt7e3td1isWj4yJHKSEsr9ZrKV6yoBfPmOVXwLQ1Xey+kouVQetIBAAAA4AZjNpu1YN68q86MXtLHdrWAXpwI6QAAAABwAyIsX594xwAAAAAAcBKEdAAAAAAAnAQhHQAAAAAAJ0FIBwAAAADASRDSAQAAAABwEoR0AAAAAACcBCEdAAAAAAAnQUgHAAAAgBuQxWLRpUuXSv2fxWIp9nOZN2+e2rdvX2rbf/XVV+rRo4eaNGmi9u3ba8aMGbp48aLdxy8K91I5CgAAAACg1FgsFo0YPkrpGamlfuwK5f00f8Fcmc3F1yccHR2tPn36lMr2u3fv1ogRIzRq1Ch16tRJR44c0eTJk5WWlqZp06bZXUNhEdIBAAAA4AZjsViUnpGqDrWelMlUegOoDcOijYdmyWKxFGtI9/X1la+vb6lsv3z5cjVv3lxDhw6VJN16660aM2aMnn32WU2ZMkWenp5211EYDHcHAAAAgBuUyWSW2eRWav+u5QOB/fv3a8iQIWratKkaNmyoe+65RzExMZJsh6sfP35cderU0YYNG9S1a1eFhoaqe/fuOnjwoBYsWKA777xTzZo105QpU2QYxhXb9+vXT0888YTNsb///nvVqVNHR44cUXR0tCZOnGiz3Gw2Kzc3V5mZmXafX2ER0gEAAAAADpWVlaXo6GhVrFhRy5cv1/r169WpUyfNmDFD+/btK3Cb2bNna9KkSVqxYoUyMjL08MMP688//9QHH3ygMWPGaNmyZdq8efMV23Xv3l2bN2+2CdxxcXEKDw9XjRo1VL9+fdWtW9e6LDc3V7GxsWrYsKH8/f2L/+T/hpAOAAAAAHCorKws9e/fX5MnT1ZwcLBuvfVWjRo1SpL0+++/F7hNdHS0mjVrprp16+ree+/VhQsX9OKLLyo4OFgPP/ywKlWqpD/++OOK7Tp27Ciz2ayNGzdKknJycvTFF1+oe/fuV6x76dIlTZgwQX/88Yeef/75Yjzjq+OedAAAAACAQ/n7++uRRx7R+vXrtXfvXh09elS//fabJF11tvgaNWpY/+/j46OAgACVKVPG2ubt7a2cnJwrtvPx8VGnTp0UHx+vrl27auvWrcrJydH9999vs15mZqaeeOIJ7dq1S/Pnz1dYWFhxnOq/IqQDAAAAABzq9OnT6tWrl/z9/dW+fXu1bt1aoaGhatu27VW3cXe3jbNFmaiue/fuGjBggFJSUhQfH68OHTqobNmy1uWnTp3SY489pr/++kvvvvuumjZtWvSTshMhHQAAAADgUOvXr1daWpq++OILeXh4SPq/Ye75k78VpzvuuEPVq1fXunXrtGXLFr311lvWZenp6RowYIAyMzO1dOlS1alTp9iP/08I6QAAAAAAhwoKClJWVpY+//xz3X777Tp06JD1meQFDVm/ViaTSV27dtWCBQvk7++vFi1aWJdNmzZNx44d0+LFi+Xv76/Tp09bl/n7+8vNza3Y67kcIR0AAAAAblCGYVHBd3SX3PHs0alTJ/3666+aPn26MjMzVb16dT344IPatGmTEhMTVbVq1WKuVOrWrZvmz5+vfv36WYfK5+Xl6dNPP1Vubq4GDBhwxTabNm3STTfdVOy1XM5klMTYgSI6efKk2rRpc0X7tGnT1L17d+3bt09Tp07VL7/8In9/fw0cOFD9+/e361iJiYmSpNDQ0GuqGQAAAAAc7eLFizp8+LBq1qwpb29va7vFYtGI4aOUnpFa6jVVKO+n+QvmFuke8RvB1d4LqWg51Cl60n/77Td5eXlp48aNMplM1vZy5copNTVVUVFRat++vaZMmaIff/xRU6ZMka+vr3r06OHAqgEAAADAOZnNZs1fMPeqM6OX9LFdLaAXJ6cI6fv379ett96qKlWqXLHs/fffl4eHh1588UW5u7srODhYR44c0aJFiwjpAAAAAHAVhOXrk1O8Y7///ruCg4MLXLZ79241a9bMZnr9Fi1a6M8//1RKSkpplQgAAAAAQIlzipC+f/9+nT17Vn369NGdd96phx9+WF9//bUkKTk5WUFBQTbr5/e4nzhxotRrBQAAAACgpDh8uPulS5d06NAh1a5dW0899ZTKli2rDRs2aPDgwXrvvfd08eJFeXp62mzj5eUlScrOzrbrmIZh6MKFC9dcOwAAAAA4UnZ2tiwWi/Ly8pSXl+foclxaXl6eLBaLsrKyrpgLwDAMm/nX/onDQ7q7u7u+++47ubm5WWfAa9iwof744w+9++678vb2vuK5ePnh3MfHx65j5ubmat++fddWOAAAAAA4ATc3N7s7MFF8srOzlZubq0OHDhW4/O+dz1fj8JAuSb6+vle03Xbbbfrmm28UFBSkU6dO2SzLfx0YGGjX8Tw8PFS7dm27tgUAAAAAZ3Hp0iUdO3bMptMTjpGbmysPDw/VqlXLZk41STpw4ECh9+PwkP7HH3+oV69eWrhwoZo3b25t/+WXX1S7dm3Vq1dPy5cvV15entzc3CRJO3fuVM2aNVWpUiW7jmkymezuhQcAAAAAZ2EYhjw8PJSZmakKFSo4uhyXZRiGzp07pzJlyqh8+fJXLC/sUHfJCUJ6cHCwatWqpRdffFFTpkyRn5+fPvnkE/34449atWqVKlWqpMWLF+uZZ57Ro48+qp9//lmxsbGaMmWKo0sHAAAAAIcymUyqUqWKTpw4IS8vL/n6+hYpEOLaGIah3NxcpaenKzMzU9WrV7/mfZoMwzCKobZrkpKSotdee03btm1TRkaG6tevryeffFJ33HGHJOnnn3/W1KlTtXfvXlWuXFnR0dHq27evXcdKTEyUJIWGhhZb/QAAAADgKIZhKDk5Wenp6XKCeOeSvLy8FBAQUGAvulS0HOoUIb00EdIBAAAA3Ijy8vKUm5vr6DJcjpubmzw8PP5xnaLkUIcPdwcAAAAAXDs3NzfrPF64fpkdXQAAAAAAAPgfQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6QAAAAAAOAlCOgAAAAAAToKQDgAAAACAkyCkAwAAAADgJAjpAAAAAAA4CUI6AAAAAABOgpAOAAAAAICTIKQDAAAAAOAkCOkAAAAAADgJQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6QAAAAAAOAlCOgAAAAAAToKQDgAAAACAkyCkAwAAAADgJAjpAAAAAAA4CUI6AAAAAABOgpAOAAAAAICTIKQDAAAAAOAkCOkAAAAAADgJQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6QAAAAAAOAlCOgAAAAAAToKQDgAAAACAkyCkAwAAAADgJAjpAAAAAAA4CUI6AAAAAABOgpAOAAAAAICTIKQDAAAAAOAkCOkAAAAAADgJQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6QAAAAAAOAlCOgAAAAAAToKQDgAAAACAkyCkAwAAAADgJAjpAAAAAAA4CUI6AAAAAABOgpAOAAAAAICTIKQDAAAAAOAkCOkAAAAAADgJQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6QAAAAAAOAlCOgAAAAAAToKQDgAAAACAkyCkAwAAAADgJAjpAAAAAAA4CUI6AAAAAABOgpAOAAAAAICTIKQDAAAAAOAkCOkAAAAAADgJpwrphw8fVpMmTbR69Wpr2759+9S3b181btxY7du315IlSxxYIQAAAAAAJcdpQnpubq6efPJJXbhwwdqWmpqqqKgo3XLLLVq1apWGDx+uWbNmadWqVQ6sFAAAAACAkuHu6ALyzZs3T2XLlrVp++STT+Th4aEXX3xR7u7uCg4O1pEjR7Ro0SL16NHDQZUCAAAAAFAyiqUnPTs7W4Zh2L39999/r48//ljTp0+3ad+9e7eaNWsmd/f/+yyhRYsW+vPPP5WSkmL38QAAAAAAcEZ296QfOnRIc+fO1bfffqvMzEytWLFCK1euVK1atdSvX79C7ycjI0MTJkzQs88+q6pVq9osS05OVkhIiE1blSpVJEknTpxQQECAXbUbhmEzrB4AAAAAgJJiGIZMJlOh1rUrpO/bt099+vRRpUqVFBERoWXLlkmS3Nzc9Morr6hs2bLq1q1bofb1wgsvqEmTJoqIiLhi2cWLF+Xp6WnT5uXlJel/vff2ys3N1b59++zeHgAAAACAovh7tr0au0L6jBkz1LBhQ8XExEiSli5dKkl69tlnlZ2drSVLlhQqpK9du1a7d+9WfHx8gcu9vb2Vk5Nj05Yfzn18fOwpXZLk4eGh2rVr2709AAAAAACFdeDAgUKva1dI//HHH/X666/L3d1deXl5Nss6d+6s9evXF2o/q1at0pkzZ3T33XfbtD///PP69NNPFRQUpFOnTtksy38dGBhoT+mSJJPJdE0hHwAAAACAwirsUHfJzpDu5eWlixcvFrgsLS2t0N34s2bNumI/9913n0aNGqXIyEitW7dOy5cvV15entzc3CRJO3fuVM2aNVWpUiV7SgcAAAAAwGnZNbt7q1atNHfuXCUnJ1vbTCaTzp8/r5iYGN15552F2k9gYKBq1Khh80+SKlWqpMDAQPXo0UOZmZl65plndODAAa1evVqxsbEaMmSIPWUDAAAAAODU7OpJHz9+vHr16qVOnTqpbt26MplMmj59ug4fPizDMPT6668XS3GVKlXS4sWLNXXqVHXr1k2VK1fWhAkTCj0pHQAAAAAA1xOTYecDzlNTUxUbG6udO3cqLS1N5cqVU9OmTRUVFWV9TJozSkxMlCSFhoY6uBIAAAAAgCsoSg61+znpfn5+GjBggMaMGSNJSk9P1+nTp506oAMAAAAA4Mzsuif93LlzevTRR9WnTx9r208//aQHHnhAo0aNuuqkcgAAAAAA4OrsCumzZs3Svn37NHLkSGtbixYtNG/ePCUkJGjevHnFViAAAAAAAK7CrpD+1VdfaeLEiercubO1zdPTU/fee6/Gjh2rTz/9tNgKBAAAAADAVdgV0jMzM1WhQoUCl1WuXFlnz569pqIAAAAAAHBFdoX0unXratWqVQUuW7t2rerUqXNNRQEAAAAA4Irsmt196NChGjp0qLp37657771XlSpV0tmzZ7V582YlJiZq4cKFxV0nAAAAAAA3PLtCetu2bfXmm29q3rx5mjt3rgzDkMlkUr169fTmm2+qbdu2xV0nAAAAAAA3PLufk96uXTu1a9dO2dnZSktLU7ly5eTj41OctQEAAAAA4FLsDumSlJ6erqysLFksFqWlpSktLc26rFq1atdaGwAAAAAALsWukH7kyBFNnDhRP/3001XX2bdvn91FAQAAAADgiuwK6S+99JL+/PNPjRgxQkFBQTKb7ZokHgAAAAAAXMaukP79999r6tSpeuCBB4q7HgAAAAAAXJZdXeBly5ZVhQoVirsWAAAAAABcml0hvUuXLlq6dKkMwyjuegAAAAAAcFl2DXcvU6aM9uzZo3vvvVehoaHy9va2WW4ymfTKK68US4EAAAAAALgKu0L6mjVrVK5cOVkslgJneDeZTNdcGAAAAAAArsaukP7VV18Vdx0AAAAAALi8Enl22qFDh0pitwAAAAAA3NDs6klPS0vTnDlztGvXLuXk5FgnkDMMQxcuXFB6err27dtXrIUCAAAAAHCjs6snfdq0aVq5cqVq1KghNzc3lStXTqGhocrNzVVGRoZefPHF4q4TAAAAAIAbnl0hfdu2bRo5cqQWLlyoXr16KSgoSHPmzNHnn3+uOnXq6MCBA8VdJwAAAAAANzy7QnpGRoaaNGkiSQoODtYvv/wiSfL19VV0dLS2bNlSbAUCAAAAAOAq7Arpfn5+OnfunCTp1ltv1ZkzZ5SWliZJCgwM1MmTJ4utQAAAAAAAXIVdIb1ly5Z666239Ndff+mWW25RhQoVtGbNGknS5s2b5efnV6xFAgAAAADgCuwK6aNHj9aZM2c0ceJEmUwmDRkyRDNmzFDz5s0VGxurHj16FHedAAAAAADc8Ox6BFv16tX16aef6s8//5QkRUVFKSAgQAkJCQoLC1O3bt2Ks0YAAAAAAFyCXT3pa9euVVZWlurWrWtti4iI0PPPP6/WrVvrnXfeKbYCAQAAAABwFXaF9KefflrHjh0rcNm+ffs0d+7cayoKAAAAAABXVOjh7oMHD9bBgwclSYZhaPjw4fL09LxivTNnzuiWW24pvgoBAAAAAHARhQ7pQ4cO1YoVKyRJa9asUf369eXv72+zjtlsVvny5dW9e/firRIAAAAAABdQ6JAeHh6u8PBw6+thw4bp5ptvLpGiAAAAAABwRXbN7j5t2jRJ0oULF+Tj4yNJ+uKLL5SUlKT27durRo0axVchAAAAAAAuwq6J4w4dOqR7771XixYtkiTNmTNHo0eP1owZMxQZGak9e/YUa5EAAAAAALgCu0L6rFmz5O7urnvuuUc5OTlatmyZOnfurN27d+uuu+7SnDlzirlMAAAAAABufHaF9N27d2vcuHEKDQ3Vrl27dO7cOfXq1Utly5ZV79699csvvxR3nQAAAAAA3PDsCum5ubkqX768JOnrr79WmTJldPvtt0uS8vLy5O5u163uAAAAAAC4NLtCekhIiP773//q9OnT+vzzz9W6dWu5u7srNzdXS5cuVUhISHHXCQAAAADADc+ukD5q1CitXLlSbdq0UXp6uh577DFJUseOHbVz504NHz68WIsEAAAAAMAV2DUuvVWrVoqPj1diYqIaNWqk6tWrS5IGDBigFi1aqE6dOsVaJAAAAAAArsDum8dvvvlm3XzzzTZtAwYMuOaCAAAAAABwVYUO6f3799fzzz+v4OBg9e/f/x/XNZlMev/996+5OAAAAAAAXEmhQ7phGAX+/9/WBQAAAAAAhWMyXCxRJyYmSpJCQ0MdXAkAAAAAwBUUJYfaNbs7AAAAAAAofoUe7t6+fXuZTKZC73jTpk12FQQAAAAAgKsqdEhv1qyZNaRbLBZt2LBB5cqVU9u2bVW5cmWlpaVp+/btOnv2rHr16lViBQMAAAAAcKMqdEifPn269f+zZs1SWFiY3n33XZUpU8banpubq8cff1wXLlwo3ioBAAAAAHABdt2TvmLFCj322GM2AV2SPDw81K9fP3366afFUhwAAAAAAK7E7onj0tPTC2xPSkqSl5eX3QUBAAAAAOCq7Arp7du316xZs7R9+3Zrm2EY+vLLLzVnzhx17ty52AoEAAAAAMBVFPqe9Ms9/fTTOnDggAYNGiRPT09VqFBBqampysvLU6tWrTR+/PjirhMAAAAAgBueXSG9fPny+uSTT7R161bt2bNH6enp8vPzU4sWLdSyZcvirhEAAAAAAJdgV0iXJJPJpLvvvlt33333VdexWCy699579dZbb+m2226z91AAAAAAALgEuyeOKwzDMPTXX38pJyenJA8DAAAAAMANoURDOgAAAAAAKDxCOgAAAAAAToKQDgAAAACAkyCkAwAAAADgJAjpAAAAAAA4CUI6AAAAAABOwilC+pkzZzR+/Hi1aNFCTZo00eDBg3Xw4EHr8n379qlv375q3Lix2rdvryVLljiwWgAAAAAASkaJhnSTyaSmTZvK19f3H9cbPny4jhw5okWLFmnlypXy9vbWwIEDlZWVpdTUVEVFRemWW27RqlWrNHz4cM2aNUurVq0qydIBAAAAACh17vZumJOTo5UrV+rbb7/V6dOn9corr2jXrl1q0KCBwsLCJElms1kffPDBP+4nPT1d1atX15AhQxQSEiJJGjZsmLp06aI//vhDO3bskIeHh1588UW5u7srODjYGuh79Ohhb/kAAAAAADgdu3rSz549qx49emjq1Kk6cuSIfv75Z128eFFbtmxRv3799MMPPxR6XxUqVNBrr71mDehnz55VbGysgoKCVLt2be3evVvNmjWTu/v/fZ7QokUL/fnnn0pJSbGnfAAAAAAAnJJdPekzZ87U+fPn9emnn6p69epq2LChJGnu3LkaNGiQ5s6dq/fee6/I+33uuef0ySefyNPTUwsXLpSPj4+Sk5OtAT5flSpVJEknTpxQQEBAkY9jGIYuXLhQ5O0AAAAAACgqwzBkMpkKta5dIX3z5s2aNGmSatSooby8PGu7l5eXoqOj9dRTT9mzWw0YMEC9evXS0qVLNXz4cC1btkwXL16Up6enzXpeXl6SpOzsbLuOk5ubq3379tm1LQAAAAAARfX3XHs1doX07OxsVaxYscBlbm5uys3NtWe3ql27tiRp6tSp+umnn/Thhx/K29tbOTk5Vxxfknx8fOw6joeHh/VYAAAAAACUpAMHDhR6XbtCemhoqJYtW6a2bdtesSw+Pt46/L0wzp49qx07dqhjx47W+87NZrNq166tU6dOKSgoSKdOnbLZJv91YGCgPeXLZDLZHfABAAAAACiKwg51l+ycOG706NHavn27unTpojfeeEMmk0nr16/X0KFD9fnnn2v48OGF3ldKSorGjh2rHTt2WNtyc3O1d+9eBQcHq2nTptqzZ4/NsPqdO3eqZs2aqlSpkj3lAwAAAADglOwK6XfccYfee+89lSlTRosXL5ZhGIqNjdXp06f19ttvq0WLFoXeV0hIiNq0aaOXX35Z33//vfbv36+nnnpKGRkZGjhwoHr06KHMzEw988wzOnDggFavXq3Y2FgNGTLEntIBAAAAAHBaJsMwjGvZwcWLF5Wenq6yZcvK19fXrn2cO3dOr732mjZu3Khz587pjjvu0FNPPaXbbrtNkvTzzz9r6tSp2rt3rypXrqzo6Gj17dvXrmMlJiZK+t+QfQAAAAAASlpRcqjdIX3RokXavXu3Fi1aJEn67rvvNG7cOA0dOtTuAF0aCOkAAAAAgNJUlBxq13D3mJgYzZkzR7feequ17ZZbblGnTp00ffp0rVixwp7dAgAAAADg0uya3X358uV64oknNHjwYGtb1apV9eyzzyogIECxsbF68MEHi61IAAAAAABcgV096SdPnrxqN32jRo10/PjxayoKN5aEhASNHj1aCQkJji4FAAAAAJyaXSG9evXqNo9Mu9z333+voKCgayoKN47s7GzFxMQoJSVFMTExys7OdnRJAAAAAOC07Bru/tBDD+nVV19Vbm6uOnTooEqVKuns2bPavHmz3nvvPY0bN66468R1Ki4uTmlpaZKktLQ0xcfHq2fPno4tCgAAAACclF0hfeDAgTp58qQ++OADxcbGWtvd3Nw0YMAARUVFFVd9uI4lJycrPj5e+Q8QMAxDcXFxat26NaMtAAAAAKAA1/Sc9HPnzumHH35Qenq6ypcvr7CwMPn5+RVnfcWOR7CVDsMwNGPGDP3666+yWCzWdrPZrAYNGmjixIkymUwOrBAAAAAASkdRcqhdPen5ypUrpzZt2lzRfujQIdWqVetado3rXFJSkvVCvJzFYlFiYqKSkpJUvXp1B1QGAAAAAM7LrpCenp6u2bNna9euXcrJybEZznzhwgWlp6dr3759xVoori/VqlVTaGhogT3pDRs2VLVq1RxYHQAAAAA4J7tmd3/llVe0cuVK1ahRQ25ubipXrpxCQ0OVm5urjIwMvfjii8VdJ64zJpNJAwcOvGJI+9XaAQAAAAB2hvRt27Zp5MiRWrhwoXr16qWgoCDNmTNHn3/+uerUqaMDBw4Ud524DgUFBSkiIsIayE0mkyIjIxUYGOjgygAAAADAOdkV0jMyMtSkSRNJUnBwsH755RdJkq+vr6Kjo7Vly5ZiKxDXt8jISFWsWFGS5Ofnp4iICMcWBAAAAABOzK6Q7ufnp3PnzkmSbr31Vp05c8b6LOzAwECdPHmy2ArE9c3Ly0vR0dEKCAhQVFSUvLy8HF0SAAAAADgtuyaOa9mypd566y3VrVtXt9xyiypUqKA1a9YoKipKmzdvdvrHsKF0hYeHKzw83NFlAAAAAIDTs6snffTo0Tpz5oz1WddDhgzRjBkz1Lx5c8XGxqpHjx7FXScAAAAAADe8QvekHz16VDfddJPMZrOqV6+uTz/9VH/++ackKSoqSgEBAUpISFBYWJi6detWUvUCAAAAAHDDKnRP+oMPPqiEhARJ0tNPP63Tp0+rbt261uURERF6/vnnCegAAAAAANip0CE9Ozvb+mi1NWvWKDU1tcSKAgAAAADAFZkMwzAKs+LQoUO1ZcsWmUwmGYZhffZ1gTs1mbR3795iK7I4JSYmSpJCQ0MdXAkAAAAAwBUUJYcW+p70WbNmad26dUpNTdX8+fPVo0cPBQUF2V8lAAAAAACwUeiQXrZsWfXp00eStGzZMkVFRSk4OLjECgMAAAAAwNXY9Qi2vLw8a3c9AAAAAAAoHnaFdA8PD/n5+RV3LQAAAAAAuLRCD3e/3OjRozVz5kydO3dOdevWlY+PzxXrVKtW7ZqLAwAAAADAlRR6dvfLNWjQQHl5ef84w/u+ffuuqbCSwuzuAAAAAIDSVCKzu1/u5ZdftmczAAAAAADwD+wK6d26dSvuOgAAAAAAcHl2hfTvv//+X9dp2rSpPbsGAAAAAMBl2RXS+/XrJ5PJpMtvZ//7/enOek86AAAAAADOyq6QvmTJkivaLly4oN27d2vdunWaN2/eNRcGAAAAAICrsSukN2vWrMD2u+++Wz4+Plq4cKHefvvtayoMAAAAAABXYy7uHd5xxx3atWtXce8WAAAAAIAbXrGH9K+++kq+vr7FvVsAAAAAAG54dg1379+//xVtFotFycnJ+uuvv/TYY49dc2EAAAAAALgau0L65bO65zObzQoJCdGQIUPUo0ePay4MAAAAAABXY1dI/+CDD4q7DgAAAAAAXJ5dIV2SMjMzdf78eQUGBio3N1cffPCBkpKS1LFjRzVt2rQ4awQAAAAAwCXYNXHcTz/9pHbt2unDDz+UJL388suaOXOm4uLiNGDAAG3atKlYiwQAAAAAwBXYFdLnzJmj4OBgPfTQQ8rKytK6dev0yCOPaNeuXerZs6feeuut4q4TAAAAAIAbnt096Y8//rhuvvlmbd++XdnZ2erSpYskqXPnzvrjjz+KtUgAAAAAAFyBXSHdbDbLy8tLkrRt2zaVL19eYWFhkv53r7q3t3fxVQgAAAAAgIuwa+K4hg0basWKFfL29tbnn3+uu+++WyaTSWfOnNE777yjhg0bFnedAAAAAADc8OzqSR8/fry+/fZb9e7dW25ubnr88cclSQ888ID+/PNPPfHEE8VZIwAAAAAALsGunvQGDRroyy+/1MGDB3XbbbfJx8dHkvTCCy8oPDxclStXtq6blJSkKlWqyN3d7qe9AQAAAChBCQkJev/99zVgwACFh4c7uhzApdnVky5JZcuWVaNGjawBXZI6duxoE9Dz8vJ0zz336Pfff7+2KgEAAACUiOzsbMXExCglJUUxMTHKzs52dEmAS7M7pBeWYRglfQgAAAAAdoqLi1NaWpokKS0tTfHx8Y4tCHBxJR7SAQAAADin5ORkxcfHWzvWDMNQXFyckpOTHVwZ4LoI6QAAAIALMgxDsbGxV4x8vVo7gNJBSAcAAABcUFJSkhITE2WxWGzaLRaLEhMTlZSU5KDKANdGSAcAAABcULVq1RQaGiqz2TYSmM1mhYWFqVq1ag6qDHBthHQAAADABZlMJg0cOFAmk6lQ7QBKByEdAAAAcFFBQUGKiIiwBnKTyaTIyEgFBgY6uDLAdRHSAQAAABcWGRmpihUrSpL8/PwUERHh2IIAF1eiId1sNmvEiBGqUqVKSR4GAAAAgJ28vLwUHR2tgIAARUVFycvLy9ElAS7NZNj5bIXt27dr8+bNysrKumJGSJPJpFdeeaVYCixuiYmJkqTQ0FAHVwIAAAAAcAVFyaHu9hwgJiZGM2fOlJeXl/z9/QucbAIAAAAAABSNXSH9ww8/VEREhKZOnSpPT8/irgkAAAAAAJdk1z3pKSkp6tmzJwEdAAAAAIBiZFdIr1+/vv7444/irgUAAAAAAJdm13D3SZMm6YknnpCPj48aNWqkMmXKXLFOtWrVrrk4AAAAAABciV0h/eGHH5bFYtGkSZOuOkncvn37rqkwAAAAAABcjV0h/eWXXy7WItLS0vT6669ry5YtyszMVJ06dTRu3DjdcccdkqQdO3bo1Vdf1cGDB1W1alWNHDlS//nPf4q1BgAAAAAAHM2ukN6tW7diLWLs2LE6ffq0Xn/9dVWqVEkffPCBBg0apDVr1sgwDA0ZMkRRUVF69dVXtWXLFk2YMEH+/v5q2bJlsdYBAAAAAIAj2RXSJenkyZPas2ePcnJyrG0Wi0VZWVnavXu3Zs+eXaj9HDlyRNu3b9eyZct0++23S5Kee+45bdu2TfHx8Tpz5ozq1KmjMWPGSJKCg4O1d+9eLV68mJAOAAAAALih2BXSP//8cz355JO6dOmS9Z50wzCs/69Vq1ah9+Xn56dFixYpNDTU2mYymWQymZSRkaHdu3erQ4cONtu0aNFCU6dOtTkmAAAAAADXO7sewfbWW2+pQYMGWr16tbp3764uXbpow4YNGj9+vNzc3DRp0qRC76t8+fJq27atzTPXv/jiCx05ckR33XWXkpOTFRQUZLNNlSpVlJWVpdTUVHvKBwAAAADAKdnVk3748GG99tprql+/vpo3b66YmBgFBwcrODhYKSkpeuutt9SqVSu7CkpISNDTTz+t++67T3fffbcuXrxoE+AlWV9fPtS+KAzD0IULF+zaFgAAAACAoijKKHC7QrrZbFaFChUkSTVq1NChQ4dksVhkNpvVpk0brVmzxp7dauPGjXryyScVHh6uWbNmSZK8vLyuCOP5rwt6Pnth5Obm8og4AIDdDh48qK+++krt27dXcHCwo8sBAADXgb93Pl+NXSG9Vq1aSkhIUNOmTVWrVi3l5OTot99+U/369ZWRkWFXD/eHH36oqVOnqlOnTpoxY4b1BKpWrapTp07ZrHvq1Cn5+PioXLly9pQvDw8P1a5d265tAQCuLTs7WzExMTp37py2bt2qe++9V15eXo4uCwAAOLEDBw4Uel27Qnrv3r31/PPP68KFCxozZoxatGihp59+Wj179tSHH36oBg0aFGl/y5Yt00svvaR+/frpmWeesRkGcMcdd2jXrl026+/cuVPh4eEym+26pV4mk0k+Pj52bQsAcG0bNmxQenq6JCk9PV2bNm1Sz549HVwVAABwZkWZ8NyulPvggw/qmWeesfaYv/jii8rOztbUqVN16dIlPfPMM4Xe1+HDh/XKK6/o3nvv1ZAhQ5SSkqLTp0/r9OnTOnfunPr166eff/5Zs2bN0sGDBxUTE6PPP/9cjz76qD2lAwBgt+TkZMXHx8swDEn/u78sLi5OycnJDq4MAK5NQkKCRo8erYSEBEeXArg8k5H/l8Y1MgxDqamp8vf3L9J2b7311lWfqd6tWzdNnz5dX3/9tV599VX9+eefuummmzRy5Eh17tzZrjoTExMlyeaRbwAA/BvDMDRjxgz9+uuvslgs1naz2awGDRpo4sSJPBYUwHUpOztb48aNU2pqqvz8/PTaa69xGw9QzIqSQ+0a7p7v4MGD2r59u06dOqV+/frp2LFj8vT0VNmyZQu9j6FDh2ro0KH/uE6bNm3Upk2baykVAIBrkpSUZP0FezmLxaLExEQlJSWpevXqDqgMAK5NXFyc0tLSJElpaWmKj4/nNh7AgewK6RaLRZMnT9aqVausU8nff//9evPNN3XkyBEtXbr0imebAwBwPatWrZpCQ0ML7Elv2LChqlWr5sDqAMA+V7uNp3Xr1vw9DziIXfekv/nmm4qPj9fLL7+s7du3W7+px48fL8Mwrjp8HQCA65XJZNLAgQOvGNJ+tXYAcHaGYSg2NlZ/v/v1au0ASoddIX3VqlUaNWqUevTooYoVK1rb69Wrp1GjRmn79u3FVR8AAE4jKChIERER1kBuMpkUGRmpwMBAB1cGAEWXfxvP5aODJNvbeACUPrtCekpKiurVq1fgssDAQGVkZFxTUQAAOKvIyEjrB9R+fn6KiIhwbEEAYKf823j+/lhjs9mssLAwbuMBHMSukF6jRg1t3bq1wGW7du1SjRo1rqkoAACclZeXl6KjoxUQEKCoqChmQAZw3eI2HsA52TVx3IABAzR58mTl5uaqXbt2MplMOnLkiL777jvFxMToqaeeKu46AQBwGuHh4QoPD3d0GQBwzfJv41m3bp11Qmhu4wEcy66Q/uCDD+rs2bNauHChli1bJkkaO3asPDw89Oijj+rhhx8u1iIBAAAAlIzIyEht3brV+px0buMBHMtkXMO0jZmZmfrhhx+Ulpam8uXLq3HjxqpQoUJx1lfsivIQeQAAAMAVJCQk6P3339eAAQMYKQSUgKLkULt60tPT0zV37lwlJCQUOEmcyWTSxo0b7dk1AAAAgFLGbTyA87ArpD/33HPatGmT7rrrLtWtW7e4awIAAAAAwCXZFdK//fZbPfvss9x7DgAAAABAMbLrEWy+vr666aabirsWAAAAAABcml0hvU+fPnr33Xd1/vz54q4HN6CEhASNHj1aCQkJji4FAAAAAJyaXcPd+/btqzVr1qht27aqWbOmypQpY7PcZDLp/fffL5YCcX3Lzs5WTEyMUlNTFRMTowYNGsjLy8vRZQEAAACAU7KrJ33y5Mk6fPiwqlSpIm9vbxmGYfPPYrEUd524TsXFxSktLU2SlJaWpvj4eMcWBAAAAABOzK6e9K+++krjxo3TY489Vtz14AaSnJys+Ph4GYYhSTIMQ3FxcWrdurWCgoIcXB0AAAAAOB+7etI9PT3VsGHD4q4FNxDDMBQbG2sN6P/WDgAAAACwM6R36dJFH330EcPacVVJSUlKTEy84hqxWCxKTExUUlKSgyoDAAAAAOdl13D3cuXKaeXKlWrfvr3CwsLk6+trs9xkMumVV14plgJxfapWrZpCQ0P166+/2gR1s9mshg0bqlq1ag6sDgAAAACck10hffXq1apQoYIk6ZdffrliuclkuraqcN0zmUwaOHCgxo8fX2A71wgAAAAAXMnuieOAfxMUFKTatWtr//791rbatWsrMDDQgVUBAAAAgPOy6550oDCSk5N14MABm7Y//vhDycnJDqoIAAAAAJwbIR0lIn8W94IwuzsAAAAAFIyQjhLB7O4AAAAAUHSEdJSI/NndzWbbS8xsNissLIzZ3QEAAACgAIR0lIirzeLO7O4AAAAAcHWEdJSYoKAgRUREWAO5yWRSZGQks7sDAAAAwFUQ0lGiIiMjVbFiRUmSn5+fIiIiHFsQAAAAADgxQjpKlJeXl6KjoxUQEKCoqCh5eXk5uiQAAAD8TUJCgkaPHq2EhARHlwK4PHdHF4AbX3h4uMLDwx1dBgAAAAqQnZ2tmJgYpaamKiYmRg0aNKBjBXAgetIBAAAAFxYXF6e0tDRJUlpamuLj4x1bEODiCOkAAACAi0pOTlZ8fLwMw5AkGYahuLg4JScnO7gywHUR0gEAAAAXZBiGYmNjrQH939oBlA5COgAAAOCCkpKSlJiYKIvFYtNusViUmJiopKQkB1UGuDZCOgAAAOCCqlWrptDQUJnNtpHAbDYrLCxM1apVc1BlgGsjpAMAAAAuyGQyaeDAgTKZTIVqB1A6COkAAACAiwoKClJERIQ1kJtMJkVGRiowMNDBlQGui5AOAAAAuLDIyEhVrFhRkuTn56eIiAjHFgS4OEI6AAAA4MK8vLwUHR2tgIAARUVFycvLy9ElAS7N3dEFAAAAAHCs8PBwhYeHO7oMAKInHQAAAAAAp0FIBwAAAADASRDSUeISEhI0evRoJSQkOLoUAAAAAHBqhHSUqOzsbMXExCglJUUxMTHKzs52dEkAAAAA4LQI6ShRcXFxSktLkySlpaUpPj7esQUBAAAAgBMjpKPEJCcnKz4+XoZhSJIMw1BcXJySk5MdXBkAAAAAOCdCOkqEYRiKjY21BvR/awcAAAAAENJRQpKSkpSYmCiLxWLTbrFYlJiYqKSkJAdVBgAAAADOi5COElGtWjWFhobKbLa9xMxms8LCwlStWjUHVQYAAAAAzouQjhJhMpk0cOBAmUymQrUDAAAAAAjpKEFBQUGKiIiwBnKTyaTIyEgFBgY6uDIAAAAAcE6EdJSoyMhIVaxYUZLk5+eniIgIxxYEAAAAAE6MkI4S5eXlpejoaAUEBCgqKkpeXl6OLgkAAAAAnJa7owvAjS88PFzh4eGOLgMAAAAAnB496QAAAAAAOAlCOgAAAAAAToKQDgAAAACAkyCkAwAAAADgJAjpAAAAAAA4CUI6SlxCQoJGjx6thIQER5cCAAAAAE6NkI4SlZ2drZiYGKWkpCgmJkbZ2dmOLgkAAAAAnBYhHSUqLi5OaWlpkqS0tDTFx8c7tiAAAAAAcGJOF9Lffvtt9evXz6Zt37596tu3rxo3bqz27dtryZIlDqoORZGcnKz4+HgZhiFJMgxDcXFxSk5OdnBlAAAAAOCcnCqkL126VHPmzLFpS01NVVRUlG655RatWrVKw4cP16xZs7Rq1SrHFIlCMQxDsbGx1oD+b+0AAAAAAMnd0QVI0smTJ/X888/ru+++06233mqz7JNPPpGHh4defPFFubu7Kzg4WEeOHNGiRYvUo0cPxxSMf5WUlKTExMQr2i0WixITE5WUlKTq1as7oDIAAAAAcF5O0ZP+66+/ysPDQ3FxcWrUqJHNst27d6tZs2Zyd/+/zxNatGihP//8UykpKaVdKgqpWrVqCg0Nldlse4mZzWaFhYWpWrVqDqoMAAAAAJyXU/Skt2/fXu3bty9wWXJyskJCQmzaqlSpIkk6ceKEAgICinw8wzB04cKFoheKIundu7eee+45mzaTyaRevXopKyvLQVUBAAAAQOkyDEMmk6lQ6zpFSP8nFy9elKenp02bl5eXJNn9OK/c3Fzt27fvmmvDv2vatKm+++476+s77rhDZ86c0ZkzZxxYFQAAAACUrr/n2qtx+pDu7e2tnJwcm7b8cO7j42PXPj08PFS7du1rrg3/rlatWvr999+VlpYmPz8/9evXz/ohCwAAAAC4ggMHDhR6XacP6UFBQTp16pRNW/7rwMBAu/ZpMpnsDvgoGh8fHw0aNEjvv/++BgwYID8/P0eXBAAAAAClqrBD3aXrIKQ3bdpUy5cvV15entzc3CRJO3fuVM2aNVWpUiUHV4fCCA8PV3h4uKPLAAAAAACn5xSzu/+THj16KDMzU88884wOHDig1atXKzY2VkOGDHF0aQAAAAAAFCunD+mVKlXS4sWLdfjwYXXr1k3z58/XhAkT1K1bN0eXBgAAAABAsTIZhmE4uojSlJiYKEkKDQ11cCUAAAAAAFdQlBzq9D3pAAAAAAC4CkI6AABFlJCQoNGjRyshIcHRpQAAgBsMIR0AgCLIzs5WTEyMUlJSFBMTo+zsbEeXBAAAbiCEdAAAiiAuLk5paWmSpLS0NMXHxzu2IAAAcEMhpAMAUEjJycmKj49X/pyrhmEoLi5OycnJDq4MAADcKAjpAAAUgmEYio2N1d8finK1dgAAAHsQ0gEAKISkpCQlJibKYrHYtFssFiUmJiopKclBlQEAgBsJIR0AgEKoVq2a6tSpU+CyOnXqqFq1aqVcEQAAuBER0gEAKKSrDWlnqDsAACguhHQAAAohKSlJ+/fvL3DZ/v37Ge4OAACKBSEdAIBCqFatmkJDQ2UymWzaTSaTwsLCGO4OAACKBSEdAIBCMJlMGjhwoMxm21+dZrNZAwcOvCK8AwAA2IOQDgBAIQUFBSkiIsIayE0mkyIjIxUYGOjgygAAwI2CkA4AQBFERkaqYsWKkiQ/Pz9FREQ4tiAAAHBDIaQDAFAEXl5eio6OVkBAgKKiouTl5eXokgAAwA3E3dEFAABwvQkPD1d4eLijywAAADcgetIBAAAAAHAShHQAAAAAAJwEIR0AAAAAACdBSAcAAABcXEJCgkaPHq2EhARHlwK4PEI6AAAA4MKys7MVExOjlJQUxcTEKDs729ElAS6NkA4AAAC4sLi4OKWlpUmS0tLSFB8f79iCABdHSAcAAABcVHJysuLj42UYhiTJMAzFxcUpOTnZwZUBrouQDgAAALggwzAUGxtrDej/1g6gdBDSAQAAABeUlJSkxMREWSwWm3aLxaLExEQlJSU5qDLAtRHSAQAAABdUrVo1hYaGymy2jQRms1lhYWGqVq2agyoDXBshHQAAAHBBJpNJAwcOlMlkKlQ7gNJBSAcAAABcVFBQkCIiImzaIiMjFRgY6KCKABDSAQAAABfWsWNHa6+5yWTSfffd5+CKANdGSAcAAABc2BdffGHzCLb//ve/Dq4IcG2EdAAAAMBF5T8n/XI8Jx1wLEI6AAAA4IJ4TjrgnAjpAAAAgAviOemAcyKkAwAAAC4o/znpBeE56YDjENIBAAAAF2QymfTAAw8UuOw///kPz0kHHISQDgAAALggwzC0fv36K8K4yWTS+vXruScdcBBCOgAAAOCC8u9JL2jiOO5JBxyHkA4AQBElJCRo9OjRSkhIcHQpAGC3/HvSzWbbSGA2m7knHXAgQjoAAEWQnZ2tmJgYpaSkKCYmRtnZ2Y4uCQDsYjKZNHDgwAKHuxfUDqB0ENIBACiCuLg4paWlSZLS0tIUHx/v2IIA4BoEBQUpIiLCGshNJpMiIyMVGBjo4MoA10VIBwCgkJKTkxUfH2+9f9MwDMXFxSk5OdnBlQGA/SIjI1WxYkVJkp+fnyIiIhxbEODiCOkAABSCYRiKjY0tcIKlgtoB4Hrh5eWl6OhoBQQEKCoqSl5eXo4uCXBp7o4uAACA60H+LMh/Z7FYrLMgV69e3QGVAcC1Cw8PV3h4uKPLACB60gEAKBRmQQYAAKWBkA4AQCHkz3Zc0HB3ZkEGAADFhZAOAMA14n50AABQXAjpAAAUQv4EcQVh4jgAAFBcCOkAABRC/sRxBQ13z584DgCuVwkJCRo9erQSEhIcXQrg8gjpKHErVqxQv379tGLFCkeXAgB2Y+I4ADeq7OxsxcTEKCUlRTExMcrOznZ0SYBLI6SjRGVkZGjdunWyWCxat26dMjIyHF0SANglf+K4v08Qd7V2ALhexMXFKS0tTZKUlpam+Ph4xxYEuDhCOkrU7NmzrUNDDcPQnDlzHFsQAFyDoKAgRUREWAO5yWRSZGSkAgMDHVwZANgnOTlZ8fHxNn+vxcXFKTk52cGVAa6LkI4S88svv2j//v02bb///rt++eUXB1UEANcuMjJSFStWlCT5+fkpIiLCsQUBgJ3yJ8QsaK4NJsQEHIeQjhJhsVg0b968ApfNmzdPFoullCsCgOLh5eWl6OhoBQQEKCoqSl5eXo4uCQDskj8h5t//LrNYLEyICTiQu6MLwI3pxx9/VGZmZoHLMjMz9eOPPyo8PLyUqwKA4hEeHs7PMADXvfwJMX/99VeboG42m9WwYUMmxAQchJ50lIjGjRurbNmyBS4rW7asGjduXLoFAQBgJx5NhRsVE2ICzomQjhJhNps1cuTIApeNGjXqikcYAQDgjHg0FW50TIgJOB+SEkpMw4YNVbNmTZu2mjVrqkGDBg6qCACKBz2rroNHU8EVMCEm4FwI6ShRqamp//gaAK439Ky6Dh5NBVfh5eWltm3bymw2q02bNkyICTgYIR0l5uuvv7b2PuRLS0vT119/7ZiCAKAYxMXFWT9wTE1NpWf1BsWjqeBKsrOztXHjRlksFm3cuJEPHwEHI6SjROTl5emdd94pcNk777yjvLy8Uq4IAK5dcnKy1q1bZ9O2du1aelZvQDyaCq5k9erV1qfyZGZmas2aNQ6uCHBthHSUiE2bNl31WegWi0WbNm0q5YoA4NrQs+pa8h9N9feJTs1ms8LCwng0FW4YycnJ2rBhg03b+vXr+fARcKDrIqRbLBbNnTtXd911lxo3bqzHHntMx44dc3RZ+AchISHXtBzXLybUwo0qv2e1IPSs3nh4NBVcgWEYWrRoUYEfPhbUDqB0XBch/c0339SyZcv00ksvafny5bJYLHr00UeVk5Pj6NJwFWfOnLmm5bg+MaEWbmRBQUFXfXyk2WxWUFBQKVeEksajqXCj++uvv/T7778XuOz333/XX3/9VcoVAZCug5Cek5OjmJgYjRo1Snfffbfq1q2r2bNnKzk5Wf/9738dXR6uokqVKte0HNcnHlWEG9mPP/74j7fx/Pjjj6VbEEoFj6YCAJQ2d0cX8G9+++03nT9/Xi1btrS2lS9fXvXr19f333+vBx54wIHV2Sc9Pd0hx7VYLNZJQUrav00Ml5eXV2q3LJQtW/aqvV8lqUKFCpJc4/2WpJSUFK1bt87mUUXr1q1TcHCwAgICSq0OR7/frsoVrvPTp0//63J+rpWs0v65li8iIkJxcXF64IEHdOrUqVI/vqPfb1flCte5YRiqXr16gT3m1atXl8Vi4edaCXPUzzVHc/T77exMhpPfbPLf//5XI0eO1E8//SRvb29r++jRo3Xx4kW9/fbbRdpfYmKiDMNQ7dq1i7vUQnvsscccdmyUnvzZ7Xm/XcPVnmbgKrjOXQM/11wLP9e4zl0BP9dciyN/rh04cEAmk0mhoaH/uq7T96RnZWVJkjw9PW3avby87P7EKzc3V/v27bvm2oB/wjXmWni/4Qq4zl0L7zdcAde5a3H0+/33THs1Tt+T/sUXX2jUqFEF9qTn5ORo4cKFRdqfM/SkZ2RkOOS4FotF58+fL5VjGYaht99+u8DHdwQFBWnIkCGlNjOur6+vQ4bTlC9fXpLrvN8ffvihDh06ZDMTrMlkUq1atdS3b1+Xeb9dlStc5zk5OXrllVeuunzSpEmF/uV7rRx9nbvC++1MHP1+uypXuM7zf38fPHjwimXBwcEu9fvbFd5vZ+Lo99sRbqie9KpVq0qSTp06pVtuucXafurUKdWpU8eufZpMJvn4+BRLffZw5LFL0/jx4zVu3Lgr2idMmOBSM+O6yvs9bNgwTZgwwWY+ArPZrOHDh7vU++2qXOU6v/vuu7Vly5Yr2tu3b68GDRqUfkEO4irvN1ybq1znBf3+dnNzc7nf367yfsNxivKBl9PP7l63bl2VLVtW3333nbUtIyNDe/fuVdOmTR1YGf5NUFCQ7r//fpu2+++/36V+4LsSHlUEV/DYY48V+NzsQYMGOagiALg2/P4GnI/Th3RPT0/17dtXs2bN0qZNm/Tbb79pzJgxCgoK0n333efo8vAvHnzwQXl5eUmSvL299eCDDzq4IpQkHlUEVzB58uR/fA0A1xt+fwPOxelDuiSNGjVKPXv21LPPPquHH35Ybm5uevfdd+Xh4eHo0vAvvLy8NGLECAUEBGj48OHWwI4bk5eXl6KjoxUQEKCoqCjeb9yQQkJC5O/vL0ny9/dXSEiIgysCgGvD72/AuTj9xHHFLTExUZIKdcM+AAAAAADXqig59LroSQcAAAAAwBUQ0gEAAAAAcBKEdAAAAAAAnAQhHQAAAAAAJ0FIBwAAAADASRDSAQAAAABwEoR0AAAAAACcBCEdAAAAAAAnQUgHAAAAAMBJENIBAAAAAHAShHQAAAAAAJwEIR0AAAAAACdBSAcAAAAAwEm4O7qA0pabmyvDMJSYmOjoUgAAAAAALiAnJ0cmk6lQ67pcSC/sFwYAAAAAgOJgMpkKnUVNhmEYJVwPAAAAAAAoBO5JBwAAAADASRDSAQAAAABwEoR0AAAAAACcBCEdAAAAAAAnQUgHAAAAAMBJENIBAAAAAHAShHQAAAAAAJwEIR0AAAAAACdBSAcAAAAAwEkQ0gEAAAAAcBKEdAAAAAAAnAQhHQAAAAAAJ0FIBwAAAADASRDSAQAAAOD/O3/+vCTJYrE4uBK4KkI64AQMw7D+n18IAK53+T/Tjh07pvT0dAdXAwCFd/bsWT333HP67bffZDabdfbsWUeXBBdESAecgMlkkiR98MEH+uGHHyRJeXl5jiwJAOxmMpm0adMmPfroozpx4oSjywGAQvP19VV6erp69uypkSNHavbs2bp48aKjy4KLIaQDTmT16tVatGiRJMnNzc3B1QCAfY4ePaply5bpoYceUt26dR1dDgAUmpeXl6ZOnaqAgAB9+eWXatasmby9vek8QakipAMOcvkQ9/wf/CNGjNCZM2f0888/O6osALgmBw8e1MSJE7V3716FhIRI4jYeANeH/L/Nzp49K39/fzVu3FiTJ0/WL7/8Ijc3N4I6Sg0hHXAAwzCsQ9yl/+s1b9iwoTIzM7Vp0yZHlQYA1yQ4OFiNGzdWVlaWli5dqrNnz8psNtt8MAkAziT/g8T8v81q166tJUuWaNasWbrjjjvUt29fa1DnQ0eUBkI64AD5vwSWLFmi8ePH69ixY8rMzFRgYKCGDh2qdevW6ddff3VwlQDw7y4P37m5uZKkiRMn6rHHHtOBAwe0cOFCnT17ViaTiaAOwOlYLBaZzf+LRB9//LEmTJig5557ThaLRTfddJPGjx+vpk2bql+/ftq7d6/MZrMOHTrEzzOUKHdHFwC4IovFonPnzunUqVPatWuXBg0apAYNGmjw4MEKCwtT3bp1tW/fPjVo0EB5eXncnw7AKeWPCtqxY4c+++wzHTx4UDVr1lS3bt00fPhwGYahDRs2yGQyaejQofL3979iJBEAOFJ+QJ8xY4bWrFmj+++/3+bvrpCQEE2ePFkvvPCCevXqpaCgIN15552aMmWKo0qGCzAZfAwElIrLP6n9e/vixYu1c+dO7dq1SwMHDtTWrVvl4eGhZcuWydvb2wHVAkDhbNq0SePGjdO9996rMmXKaPv27fL29tajjz6qbt26ac6cOfrqq6/UqFEjjR07Vn5+fo4uGQBsfPnll5oxY4bmz5+vunXrKjU1VQcOHNC2bdtUo0YNdevWTSdPntS6det05MgRvfjii/Lw8HB02biB0ZMOlILLA3r+UPakpCSFhYXpscce0+DBgzV48GCtW7dOO3fulGEY2rt3r+Lj4/Xggw/S8wTAKZ08eVJz587V+PHj1adPH0lSSkqKnn32WcXExCgkJERPPPGE0tPTtW/fPiZdAuAU/t5xkpGRoaCgINWtW1c7d+5UbGys9WeWp6en3Nzc1LVrVw0dOtS6zaVLl+TuTpRCyeCedKAU5P8iePXVVzVnzhydP39ewcHBev311/XMM8/o3LlzkqQuXbroqaee0htvvKGmTZvqyy+/lCQCOgCnlJOTowsXLqhJkyaS/ndPekBAgKZOnar09HStWbNGkvT8889rwYIFCggIcGS5ACDp//4uW7FihY4cOaJKlSrp2LFj6tGjh6Kjo+Xj46Onn35aW7Zska+vr06dOnXFPgjoKElcXUAp2bZtm9avX6/Zs2crPDxcX3/9tdzd3XXPPffo1KlTKleunCTJ19dXFSpU0KuvvqqIiAh9//33atq0qYOrB4ArXbhwQceOHdOJEydUv359ubu7Kzc3V5UqVVLjxo1t/rCtVKmSAysFgP9jGIYuXbqkWbNmqUePHpowYYJGjhypffv2adiwYbrzzjtVpkwZSVL58uXl6+vr4IrhagjpQCk5ceKEateurfDwcP33v//VxIkT9eyzz6pZs2YaOnSo+vbtq/vvv1/u7u6yWCw6deqU/Pz8mD0UgFPIv+3m1KlT8vHxkSTVqVNHrVq1UmxsrPz8/BQeHm69TzM7O1t169Z1ZMkAYHX5rYMWi0UeHh4aO3asVq1apRMnTqhnz56SpNTUVG3fvl1BQUF64403dO7cOfXq1cuRpcMFMdwdKAEFBWuTyaRLly5p5cqVmjhxoiZMmKDevXsrKytLSUlJNj1OOTk52rlzp44ePaqqVauWZukAUCCTyaSNGzeqT58+euihhzR9+nSlpqZq8ODBysjI0MyZM7V+/Xrt3LlTM2bMUEJCgrp06eLosgFAku2tg/mzt4eFhenUqVPasWOHJOnixYuKi4vT9OnTNX78eFksFq1atUru7u7MqYFSxezuQDG7fDKSs2fPyt/fX5K0Z88eDR8+XBkZGRo3bpwGDRokSUpPT1e/fv00dOhQde7c2bqfc+fOKS0tTTfffHPpnwQA/M0ff/yhvn37auDAgTpx4oR++eUXBQUFacaMGfrtt9/07rvvasuWLapZs6bKlCmjqVOnql69eo4uG4ALO3PmjE6fPi03NzfddtttWr58uVJSUtS3b1+VLVtW7u7umjNnjj777DPFxsaqatWqOnfunJKSkuTm5qZatWrJbDYzSRxKHSEdKEaXD6VatGiRtmzZIrPZrD59+uj+++/XBx98oKlTpyoqKkotW7ZU+fLlNW/ePKWlpemTTz6xfrLLbO4AnEH+z6KcnBzt2bNHmzdv1qRJkyRJn3zyiZYvX66goCC99NJLqlSpkg4dOiRfX1+VKVNG5cuXd3D1AFzZpEmTdPToUe3evVtms1nR0dE6c+aMPvvsM4WEhCgsLEwjRoxQUlKSXnvtNT388MPq0KHDFfu52iN0gZJESAeKyeU/xOfNm6f3339fjzzyiLZv367k5GQ9/vjj6tu3r9577z2tXLlSBw8eVL169VS+fHktXrxYHh4eysvLswZ1AHCk/IC+ZcsWxcfH6/Dhw6pQoYLeeecda4/S8uXL9cknn6hq1aoaP368br31VscWDQCSoqKilJWVpcGDB6tcuXJKS0tTy5YtrTO1L168WLt27dLJkycVFRWl1atX67bbbtP8+fMdXTogiZAOFLvk5GRNnTpVAwcO1O233y5JmjBhgr7//ntFR0erX79+Onv2rI4fP65y5cqpRo0aDKUC4JR27Nihxx9/XM2aNVNqaqr++OMPLViwQK1atbKus2LFCi1evFj169fXq6++Kjc3N0YCAXCYefPmafv27XrzzTettxzm/42Vm5srDw8PXbp0SRaLRYsWLdLhw4f13XffKSUlRQsWLNA999zj4DMAmDgOuCbPPPOMPvnkE+vrjz76SBEREdq/f7/NUM+ZM2eqadOmeu+997RkyRK5ubkpLCxMNWvWlNlslsViIaADcConT57Utm3bNG7cOC1atEhz587VPffcoxEjRujbb7+1rvfggw9q6NChGjt2rNzd3QnoABxq79696tChgzWgS//3THMPDw9ZLBZ99NFHysnJ0YgRIzR+/Hi9+uqruvXWW/X11187qmzABiEdsFNSUpJSU1M1f/58xcfHS5Iefvhh3XbbbTpy5Ih+/fVX5ebmWtefOXOmmjdvrtdee03bt2+32Rf3OgFwFhaLRceOHVOvXr20cuVKZWdnS5KqVq2qiRMnqn379ho+fLh1NmRJ6tatG5NcAnAoi8WikydPKiEhQQ0bNrS2/V16erpiY2P1xhtvSJKqVKmili1b6qWXXtKGDRt08ODBUq0bKAjJALBTtWrVNGbMGLVq1UqvvvqqVq1aJUlatmyZGjRooNmzZ2vHjh26dOmSdZtp06ZpyJAh6tixo6PKBoAC5d/9ZjabdfPNN2vAgAHKzc3Vnj17dOjQIUn/+2P2qaeeUocOHRQVFaVdu3Y5smQAsDKbzQoICFDZsmWtHyL+vRPEMAz5+fnptttu09mzZ63r5P/8CwgIsPm7DXAUQjpgh/wf4LfddpvatGmjunXratq0adqwYYMkadWqVfL399cLL7xwRVAfNmyY3NzceN4mAKdiMpm0c+dO9e/fX9L/Jl4aPXq0fv75Zy1dulSHDx+WJFWuXFlPPvmkevbsqUqVKjmyZACwMgxDubm5qlWrlnbu3Gn9mXW5/NtxLBaLvLy8rO1ZWVn66quvdPToUVWoUKHUagauhpAO2CH/3qbXXntNsbGxysrKkmEYmjp1qlauXClJWrNmjSpWrKgpU6Zoy5YtV4RyZnEH4GiXzx1rGIbS0tKUkJCgQYMGSZIGDhyoQYMG6YsvvtCHH36oP//8U5IUGBioKVOmKDg42BFlA8AVTCaTvL29NWLECO3du1eLFy/W6dOnr1jv7NmzysjIUP369a1tPj4+6t27t9avX6+goKDSLBsoECEdKKSsrCyb159++qk++eQTTZgwQYsWLdKSJUvUsWNHzZ071zr0ffXq1crNzVVcXByhHIDTmDp1qnbs2GEzyZvJZFK7du00e/Zs/frrrxo4cKAkKTo6WtHR0dq0aZPefvttHT16VBIfNAJwTo0aNdLkyZO1bt06vfTSS9ah75cuXVJSUpImTZokwzD08MMPW7cxDEM1atRQrVq1HFU2YIPppIFCGDdunNq1a6cHHnjA+uzgQ4cOKTQ01PqYtQYNGqhs2bK6ePGiZs2aJR8fH91///3aunUrQ9sBOI3p06frgw8+UI8ePSRJv/32m+rWrStJ8vLyUtu2bTVlyhQ9//zzioqK0nvvvafo6GhlZWUpPj5ePj4+jiwfAP5Vz549VaFCBT3//PNKSEhQYGCgTCaT3N3dZTab9eGHH1pvPeSxkXBGPCcd+Bdbt27V119/rQkTJsjLy0s5OTny9PTUwoULtXbtWi1ZskSBgYHW9Tdu3KgRI0ZIks3zNvN/EQCAo7z66qv6+OOP9fHHHys4OFgnT55Ux44d1bx5c7399tvW9S5evKiNGzdaJ4mbM2eOJCktLU0VK1Z0TPEAUERJSUn69ttv9fPPP8vPz0916tRRx44d5ebmZn12OuCMCOnAPxg7dqwyMzO1aNEiSdLSpUt14cIF9evXTz/99JPGjh2r/v3720yg9PPPP+uNN97QvffeqwcffJBgDsApLFiwQPPmzdMLL7yg3r17S5LOnz+vzz77TK+++qpatGhhfSSRJGVkZKhfv376/fff1aZNGy1atMg6kggArmd0nMDZ8fER8A/Onj2rqlWrWl9v2bJFf/zxh8qVK6fevXurd+/emj9/vtLS0tSmTRtVrlxZc+fOla+vr3r16iWTycQvAgAON3XqVK1Zs0bVqlXThx9+qJtvvlnNmzeXr6+v7r//fnl4eGjKlCkaNWqU5s6dK0kqX768GjVqpDFjxlgniCOgA7jeXP7hYv7/+bsMzo6QDhQgNzdXHh4ecnNzU5kyZazt77zzjsaNG6dFixbJZDJp5MiR8vb21vr16xUbG6s6derIy8tLCxculMlkksVi4RcBAIeaPn26VqxYoc8++0xVq1ZVRESEXnzxRT3//PNq1qyZfH191aFDB0nSlClTNGzYMD3yyCP65ptv9O2332rEiBGqUqWKg88CAOzz9wkygesBw92BAuR/0tq/f3/dfPPNmjp1qjW4S9KYMWP0008/afDgwerdu7fS0tJ09OhR+fj4qFatWjKbzdzrBMDhzp49q0mTJumJJ56wTg6Xk5OjHj16KCcnRy+88IKaNm0qd3d3nT9/Xlu3btWMGTOUl5enMmXK6I033rB5TBEAACh5hHTgMqtXr9btt9+uGjVqSJL69++vm266Sa+88soV644ePVqJiYl69NFHFRERoXLlylmXWSwWmc084RCA4+VPdmkYhvLy8uTu7n7VoC5JmZmZOnLkiKpWrSp/f38HVw8AgOshRQD/38GDBzVp0iQtX75chw8fliT5+Pjo7NmzWrt2rX799VedOXNGZ8+elSS98cYbatWqlebPn69vvvnGZl8EdADOwtPTU5Ksjx+6dOmSPD09tWrVKnl6euqFF17Q999/b31UZNmyZdWgQQMCOgAADkJPOnCZPXv2aMyYMerYsaMeffRRTZs2TV9//bUMw1BWVpY8PT1VrVo11ahRQyEhIXJzc9ORI0c0a9Ys7j0HcN3Ivx0nv0fdYrFo4sSJat26NR8yAgDgYIR04G/27Nmj0aNHq0ePHtq8ebOCg4P13HPPae/evTpw4IAOHTqkH3/8UceOHVO9evX0wQcfyM3NjVncAVxXLg/qHTp0UEBAgJYuXWozWSYAACh9hHSgALt379bIkSOVlpamIUOG6IknnrBZbhiGsrOz5enpKbPZzD3oAK5Llwf1kydP6uabb3Z0SQAAuDxSBVCAO+64Q2+//bYk6dChQzp58qR12aVLl2QymeTt7U1AB3Bdu/wedQI6AADOgWQBXEVYWJiWLl2qH374QTExMTp27JgkXfFYNQI6gOsZj4oEAMC5MNwd+Bf5k8ndddddGj16tKpUqeLokgAAAADcoOgCBP7F7bffrhkzZujMmTOqXLmyo8sBAAAAcAOjJx0oJMMwZDKZuAcdAAAAQIkhpANFkB/UAQAAAKAk0B0IFAEBHQAAAEBJIqQDAAAAAOAkCOkAAAAAADgJQjoAAAAAAE6CkA4AAAAAgJMgpAMAAAAA4CQI6QAA3OD69eunfv36XdM+Vq9erTp16uj48ePXXE+dOnU0b968a94PAAA3IkI6AAAAAABOgpAOAAAAAICTIKQDAACtWLFC3bt3V+PGjRUWFqYuXbros88+u2K9hIQEde3aVQ0bNtQDDzygTz/91GZ5dna2Zs6cqbZt26phw4aKiIi4Yh0AAHB17o4uAAAAONbSpUv18ssva+TIkbr99tuVnp6ud955R08++aSaNGmioKAg67qTJ0/W448/rnr16mnNmjUaM2aMPD091aFDBxmGoeHDhyshIUGjRo1ScHCwvvzyS40ZM0Y5OTnq2rWr404SAIDrBCEdAAAXd+zYMQ0aNEjDhg2ztlWvXl3du3fXnj179J///MfaPnLkSA0aNEiS1KZNG/35559688031aFDB3377bfatm2bZs+erc6dO0uS7rrrLmVlZWnWrFl64IEH5O7Onx4AAPwTflMCAODinnrqKUlSRkaGDh06pCNHjui7776TJOXk5Nismx++83Xo0EHz5s3T+fPntWPHDplMJrVt21aXLl2yrtO+fXvFxcXpjz/+UL169Ur4bAAAuL4R0gEAcHFHjx7V5MmTtWPHDnl4eKhWrVqqW7euJMkwDJt1AwICbF5XqlRJhmEoMzNTaWlpMgxD4eHhBR7n1KlThHQAAP4FIR0AABdmsVg0ePBgeXh4aOXKlapXr57c3d114MABrVu37or109PTbYJ6SkqK3NzcVKFCBZUrV04+Pj5asmRJgceqUaNGiZ0HAAA3CmZ3BwDAhaWmpurw4cPq2bOnQkNDrfeMf/3115L+F+Ivt2XLFuv/LRaLPv/8czVq1Eje3t5q1qyZLly4IMMwFBoaav23f/9+LViwwGYIPAAAKBg96QAAuIDk5GTFxsZe0R4SEqLq1atr6dKlCgoKUvny5bVt2zZrb3hWVpbN+nPmzFFeXp6qVq2qjz76SIcPH9Z7770nSWrbtq2aNm2qYcOGadiwYQoODtbPP/+suXPn6q677pK/v3+JnycAANc7QjoAAC7g6NGjmjZt2hXtPXv21JtvvqmpU6fqqaeekqenp2rXrq2FCxfqlVde0e7du9WvXz/r+tOmTdP06dN15MgRhYSE6J133lGzZs0kSWazWYsWLdIbb7yht99+W2fOnFFgYKCioqI0fPjwUjtXAACuZybj7zPCAAAAAAAAh+CedAAAAAAAnAQhHQAAAAAAJ0FI/3/t17EAAAAAwCB/62nsKIsAAABgQtIBAABgQtIBAABgQtIBAABgQtIBAABgQtIBAABgQtIBAABgQtIBAABgQtIBAABgQtIBAABgInihoEpl8hZbAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create seaborn categorical boxplot for each metric, by label and version\n",
+    "for metric in [\n",
+    "    \"dice\",\n",
+    "    \"hausdorff_distance_95\",\n",
+    "    \"mean_surface_distance\",\n",
+    "    \"mean_surface_distance_tol_7\",\n",
+    "]:\n",
+    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "    sns.boxplot(\n",
+    "        x=\"label_name\",\n",
+    "        y=metric,\n",
+    "        hue=\"version\",\n",
+    "        data=metrics_df,\n",
+    "        palette=revewer_cmap,\n",
+    "        ax=ax,\n",
+    "    )\n",
+    "    ax.set_title(f\"{metric} by label and version\")\n",
+    "    ax.set_ylabel(metric)\n",
+    "    ax.set_xlabel(\"Label\")\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/poetry.lock b/poetry.lock
index bb35fe7..b272b41 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -59,52 +59,6 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
-[[package]]
-name = "black"
-version = "23.12.1"
-description = "The uncompromising code formatter."
-optional = false
-python-versions = ">=3.8"
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-    {file = "black-23.12.1-cp38-cp38-win_amd64.whl", hash = "sha256:c04b6d9d20e9c13f43eee8ea87d44156b8505ca8a3c878773f68b4e4812a421e"},
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-    {file = "black-23.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:dd15245c8b68fe2b6bd0f32c1556509d11bb33aec9b5d0866dd8e2ed3dba09c2"},
-    {file = "black-23.12.1-py3-none-any.whl", hash = "sha256:78baad24af0f033958cad29731e27363183e140962595def56423e626f4bee3e"},
-    {file = "black-23.12.1.tar.gz", hash = "sha256:4ce3ef14ebe8d9509188014d96af1c456a910d5b5cbf434a09fef7e024b3d0d5"},
-]
-
-[package.dependencies]
-click = ">=8.0.0"
-mypy-extensions = ">=0.4.3"
-packaging = ">=22.0"
-pathspec = ">=0.9.0"
-platformdirs = ">=2"
-tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
-
-[package.extras]
-colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
-jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
-uvloop = ["uvloop (>=0.15.2)"]
-
 [[package]]
 name = "certifi"
 version = "2024.7.4"
@@ -305,20 +259,6 @@ files = [
     {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
 ]
 
-[[package]]
-name = "click"
-version = "8.1.7"
-description = "Composable command line interface toolkit"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
-    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "platform_system == \"Windows\""}
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -726,20 +666,6 @@ qtconsole = ["qtconsole"]
 test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
-[[package]]
-name = "isort"
-version = "5.13.2"
-description = "A Python utility / library to sort Python imports."
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"},
-    {file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"},
-]
-
-[package.extras]
-colors = ["colorama (>=0.4.6)"]
-
 [[package]]
 name = "jedi"
 version = "0.19.1"
@@ -1250,17 +1176,6 @@ files = [
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["docopt", "pytest"]
 
-[[package]]
-name = "pathspec"
-version = "0.12.1"
-description = "Utility library for gitignore style pattern matching of file paths."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"},
-    {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
-]
-
 [[package]]
 name = "pexpect"
 version = "4.9.0"
@@ -1919,6 +1834,32 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "ruff"
+version = "0.3.7"
+description = "An extremely fast Python linter and code formatter, written in Rust."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ruff-0.3.7-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:0e8377cccb2f07abd25e84fc5b2cbe48eeb0fea9f1719cad7caedb061d70e5ce"},
+    {file = "ruff-0.3.7-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:15a4d1cc1e64e556fa0d67bfd388fed416b7f3b26d5d1c3e7d192c897e39ba4b"},
+    {file = "ruff-0.3.7-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d28bdf3d7dc71dd46929fafeec98ba89b7c3550c3f0978e36389b5631b793663"},
+    {file = "ruff-0.3.7-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:379b67d4f49774ba679593b232dcd90d9e10f04d96e3c8ce4a28037ae473f7bb"},
+    {file = "ruff-0.3.7-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c060aea8ad5ef21cdfbbe05475ab5104ce7827b639a78dd55383a6e9895b7c51"},
+    {file = "ruff-0.3.7-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:ebf8f615dde968272d70502c083ebf963b6781aacd3079081e03b32adfe4d58a"},
+    {file = "ruff-0.3.7-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d48098bd8f5c38897b03604f5428901b65e3c97d40b3952e38637b5404b739a2"},
+    {file = "ruff-0.3.7-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:da8a4fda219bf9024692b1bc68c9cff4b80507879ada8769dc7e985755d662ea"},
+    {file = "ruff-0.3.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c44e0149f1d8b48c4d5c33d88c677a4aa22fd09b1683d6a7ff55b816b5d074f"},
+    {file = "ruff-0.3.7-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:3050ec0af72b709a62ecc2aca941b9cd479a7bf2b36cc4562f0033d688e44fa1"},
+    {file = "ruff-0.3.7-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:a29cc38e4c1ab00da18a3f6777f8b50099d73326981bb7d182e54a9a21bb4ff7"},
+    {file = "ruff-0.3.7-py3-none-musllinux_1_2_i686.whl", hash = "sha256:5b15cc59c19edca917f51b1956637db47e200b0fc5e6e1878233d3a938384b0b"},
+    {file = "ruff-0.3.7-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e491045781b1e38b72c91247cf4634f040f8d0cb3e6d3d64d38dcf43616650b4"},
+    {file = "ruff-0.3.7-py3-none-win32.whl", hash = "sha256:bc931de87593d64fad3a22e201e55ad76271f1d5bfc44e1a1887edd0903c7d9f"},
+    {file = "ruff-0.3.7-py3-none-win_amd64.whl", hash = "sha256:5ef0e501e1e39f35e03c2acb1d1238c595b8bb36cf7a170e7c1df1b73da00e74"},
+    {file = "ruff-0.3.7-py3-none-win_arm64.whl", hash = "sha256:789e144f6dc7019d1f92a812891c645274ed08af6037d11fc65fcbc183b7d59f"},
+    {file = "ruff-0.3.7.tar.gz", hash = "sha256:d5c1aebee5162c2226784800ae031f660c350e7a3402c4d1f8ea4e97e232e3ba"},
+]
+
 [[package]]
 name = "scikit-image"
 version = "0.21.0"
@@ -2347,4 +2288,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "5fd1376a72bbdaee14e2f6d062edf1ec026ad388445350b46171aa5cd5a632ad"
+content-hash = "6794d413eeb940b749563b34fc3520234965ab1d6df2c4acfa4813160c104144"
diff --git a/pyproject.toml b/pyproject.toml
index 7173969..2231967 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,16 +21,15 @@ tqdm = "^4.66.1"
 
 
 [tool.poetry.group.dev.dependencies]
-black = "^23.7.0"
+ruff = "^0.3.5"
 mypy = "^1.5.1"
 pre-commit = "^3.3.3"
-isort = "^5.12.0"
 
-[tool.isort]
-profile = "black"
-
-[tool.black]
-target-version = ['py310']
+[tool.ruff]
+target-version = "py39"
+lint.select = ["I", "UP", "SIM"]
+lint.ignore = ["UP007"]
+lint.fixable = ["ALL"]
 
 [tool.mypy]
 implicit_optional = true

From f12f4587cef42e07835bb7b085f6ee1ac53477c8 Mon Sep 17 00:00:00 2001
From: Jeff VanOss <jeff.vanoss@bamfhealth.com>
Date: Wed, 7 Aug 2024 00:19:31 -0400
Subject: [PATCH 2/6] clean up notebook

---
 model_performance_aimi2.ipynb | 54 +++++++++++++++++------------------
 1 file changed, 27 insertions(+), 27 deletions(-)

diff --git a/model_performance_aimi2.ipynb b/model_performance_aimi2.ipynb
index e82a46c..dd26481 100644
--- a/model_performance_aimi2.ipynb
+++ b/model_performance_aimi2.ipynb
@@ -18,7 +18,8 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "%pip install pandas pydicom pydicom-seg scikit-learn seaborn segmentationmetrics requests"
+    "%pip install pandas pydicom pydicom-seg scikit-learn seaborn requests\n",
+    "%pip install segmentationmetrics"
    ]
   },
   {
@@ -38,27 +39,44 @@
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
-    "import io\n",
-    "import zipfile\n",
     "from pathlib import Path\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "import zipfile\n",
+    "import io\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import scipy.stats as stats\n",
     "import pydicom\n",
     "import pydicom_seg\n",
-    "import requests\n",
-    "import scipy.stats as stats\n",
-    "import seaborn as sns\n",
-    "import segmentationmetrics as sm\n",
     "import SimpleITK as sitk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import segmentationmetrics as sm\n",
     "from segmentationmetrics.surface_distance import compute_surface_dice_at_tolerance\n",
+    "import seaborn as sns\n",
     "from tqdm.auto import tqdm\n",
     "\n",
     "sns.set_theme(style=\"whitegrid\")\n",
     "%matplotlib inline"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt_colors = {\n",
+    "    \"ne2\": \"#5eceb0\",\n",
+    "    \"rad1\": \"#9e70e1\",\n",
+    "    \"tp\": \"k\",\n",
+    "    \"fp\": \"b\",\n",
+    "    \"fn\": \"r\",\n",
+    "}\n",
+    "\n",
+    "revewer_cmap = sns.color_palette([plt_colors[\"ne2\"], plt_colors[\"rad1\"]])\n",
+    "vol_cmap = sns.color_palette([plt_colors[\"fp\"], plt_colors[\"fn\"]])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -95,24 +113,6 @@
     "    download_inference(qa_dir)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt_colors = {\n",
-    "    \"ne2\": \"#5eceb0\",\n",
-    "    \"rad1\": \"#9e70e1\",\n",
-    "    \"tp\": \"k\",\n",
-    "    \"fp\": \"b\",\n",
-    "    \"fn\": \"r\",\n",
-    "}\n",
-    "\n",
-    "revewer_cmap = sns.color_palette([plt_colors[\"ne2\"], plt_colors[\"rad1\"]])\n",
-    "vol_cmap = sns.color_palette([plt_colors[\"fp\"], plt_colors[\"fn\"]])"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,

From 09660bba83fb2f801ab38221623ece896923b40d Mon Sep 17 00:00:00 2001
From: Jeff VanOss <jeff.vanoss@bamfhealth.com>
Date: Thu, 8 Aug 2024 13:13:16 -0400
Subject: [PATCH 3/6] add NSD tolerances

---
 model_performance_aimi2.ipynb | 166 ++++++++++++++++++++++------------
 1 file changed, 107 insertions(+), 59 deletions(-)

diff --git a/model_performance_aimi2.ipynb b/model_performance_aimi2.ipynb
index dd26481..03ef20f 100644
--- a/model_performance_aimi2.ipynb
+++ b/model_performance_aimi2.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,7 +161,13 @@
     "\n",
     "\n",
     "def calc_metrics_for_label(\n",
-    "    ai_seg_file, qa_seg_file, fname, label_value=1, version=\"aimiv2\", label_suffix=\"\"\n",
+    "    ai_seg_file,\n",
+    "    qa_seg_file,\n",
+    "    fname,\n",
+    "    label_value=1,\n",
+    "    version=\"aimiv2\",\n",
+    "    label_suffix=\"\",\n",
+    "    nsd_tol=7,\n",
     "):\n",
     "    ai_img = load_seg(ai_seg_file, label_value)\n",
     "    qa_img = load_seg(qa_seg_file, label_value)\n",
@@ -176,12 +182,13 @@
     "        \"dice\": m.dice,\n",
     "        \"hausdorff_distance_95\": m.hausdorff_distance,\n",
     "        \"mean_surface_distance\": m.mean_surface_distance,\n",
-    "        \"mean_surface_distance_tol_7\": compute_surface_dice_at_tolerance(\n",
-    "            m._surface_dist, 7\n",
+    "        \"normalized_surface_distance\": compute_surface_dice_at_tolerance(\n",
+    "            m._surface_dist, nsd_tol\n",
     "        ),\n",
     "        \"SeriesUID\": fname,  # from medical segmentation decathlon\n",
     "        \"label\": label_value,\n",
     "        \"version\": version,\n",
+    "        \"normalized_surface_distance_tolerance\": nsd_tol,\n",
     "    }\n",
     "\n",
     "    if label_suffix:\n",
@@ -191,26 +198,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the CSV file\n",
+    "df = pd.read_csv(Path(\"qa-results/qa-results.csv\"))\n",
+    "qa_df = df[df.Validation]\n",
+    "\n",
+    "# Paths for aimiv1, aimiv2, and qa\n",
+    "ai_path = Path(\"qa-results/ai-segmentations-dcm\")\n",
+    "qa_path = Path(\"qa-results/qa-segmentations-dcm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# labels and tolerances\n",
+    "label_names = {1: \"Kidney\", 2: \"Tumor\", 3: \"Cyst\"}\n",
+    "nsd_tolerances = {1: 7, 2: 2, 3: 2}  # per label tolerances in mm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# verify that the labels in the dcm file match the expected labels\n",
+    "ds = pydicom.dcmread(ai_path / df.AISegmentation.iloc[0])\n",
+    "result = pydicom_seg.SegmentReader().read(ds)\n",
+    "label_names_from_dcm = {\n",
+    "    k: v[\"SegmentDescription\"].value for k, v in result.segment_infos.items()\n",
+    "}\n",
+    "assert (\n",
+    "    label_names_from_dcm == label_names\n",
+    "), \"labels in the dcm file do not match expected labels\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 85/85 [02:39<00:00,  1.88s/it]\n"
+      "100%|██████████| 85/85 [02:34<00:00,  1.82s/it]\n"
      ]
     }
    ],
    "source": [
-    "# Load the CSV file\n",
-    "df = pd.read_csv(Path(\"qa-results/qa-results.csv\"))\n",
-    "qa_df = df[df.Validation]\n",
-    "\n",
-    "# Paths for aimiv1, aimiv2, and qa\n",
-    "ai_path = Path(\"qa-results/ai-segmentations-dcm\")\n",
-    "qa_path = Path(\"qa-results/qa-segmentations-dcm\")\n",
-    "\n",
     "# Initialize lists for metrics\n",
     "metrics = []\n",
     "\n",
@@ -235,28 +277,14 @@
     "                row.SeriesInstanceUID,\n",
     "                version=version,\n",
     "                label_value=label_value,\n",
+    "                nsd_tol=nsd_tolerances[label_value],\n",
     "            )\n",
     "        )\n",
     "\n",
     "metrics_df = pd.DataFrame(metrics)\n",
     "\n",
     "# Replace infinite values with NaN and drop NaN values\n",
-    "metrics_df = metrics_df.replace([np.inf, -np.inf], np.nan).dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# add label names\n",
-    "ds = pydicom.dcmread(ai_path / df.AISegmentation.iloc[0])\n",
-    "result = pydicom_seg.SegmentReader().read(ds)\n",
-    "label_names = {\n",
-    "    k: v[\"SegmentDescription\"].value for k, v in result.segment_infos.items()\n",
-    "}\n",
-    "assert len(label_names) == 3, \"Expected 3 labels\"\n",
+    "metrics_df = metrics_df.replace([np.inf, -np.inf], np.nan).dropna()\n",
     "\n",
     "# update metrics_df with label names column\n",
     "metrics_df[\"label_name\"] = metrics_df[\"label\"].map(label_names)"
@@ -264,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -296,7 +324,8 @@
        "      <th colspan=\"2\" halign=\"left\">dice</th>\n",
        "      <th colspan=\"2\" halign=\"left\">hausdorff_distance_95</th>\n",
        "      <th colspan=\"2\" halign=\"left\">mean_surface_distance</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">mean_surface_distance_tol_7</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">normalized_surface_distance</th>\n",
+       "      <th>normalized_surface_distance_tolerance</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
@@ -309,6 +338,7 @@
        "      <th>std</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
+       "      <th>first</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>label_name</th>\n",
@@ -321,6 +351,7 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -333,8 +364,9 @@
        "      <td>21.80</td>\n",
        "      <td>1.57</td>\n",
        "      <td>4.76</td>\n",
-       "      <td>0.94</td>\n",
-       "      <td>0.17</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aimiv2</th>\n",
@@ -346,6 +378,7 @@
        "      <td>0.00</td>\n",
        "      <td>1.00</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Kidney</th>\n",
@@ -358,6 +391,7 @@
        "      <td>4.44</td>\n",
        "      <td>0.97</td>\n",
        "      <td>0.09</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aimiv2</th>\n",
@@ -369,6 +403,7 @@
        "      <td>0.01</td>\n",
        "      <td>1.00</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Tumor</th>\n",
@@ -379,8 +414,9 @@
        "      <td>18.31</td>\n",
        "      <td>1.38</td>\n",
        "      <td>7.13</td>\n",
-       "      <td>0.95</td>\n",
-       "      <td>0.18</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aimiv2</th>\n",
@@ -392,6 +428,7 @@
        "      <td>0.38</td>\n",
        "      <td>1.00</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -408,28 +445,28 @@
        "Tumor      aimiv1   0.95  0.19                  4.52  18.31   \n",
        "           aimiv2   1.00  0.00                  0.00   0.00   \n",
        "\n",
-       "                   mean_surface_distance       mean_surface_distance_tol_7  \\\n",
+       "                   mean_surface_distance       normalized_surface_distance  \\\n",
        "                                    mean   std                        mean   \n",
        "label_name version                                                           \n",
-       "Cyst       aimiv1                   1.57  4.76                        0.94   \n",
+       "Cyst       aimiv1                   1.57  4.76                        0.92   \n",
        "           aimiv2                   0.00  0.00                        1.00   \n",
        "Kidney     aimiv1                   1.22  4.44                        0.97   \n",
        "           aimiv2                   0.00  0.01                        1.00   \n",
-       "Tumor      aimiv1                   1.38  7.13                        0.95   \n",
+       "Tumor      aimiv1                   1.38  7.13                        0.94   \n",
        "           aimiv2                   0.15  0.38                        1.00   \n",
        "\n",
-       "                          \n",
-       "                     std  \n",
-       "label_name version        \n",
-       "Cyst       aimiv1   0.17  \n",
-       "           aimiv2   0.00  \n",
-       "Kidney     aimiv1   0.09  \n",
-       "           aimiv2   0.00  \n",
-       "Tumor      aimiv1   0.18  \n",
-       "           aimiv2   0.00  "
+       "                         normalized_surface_distance_tolerance  \n",
+       "                     std                                 first  \n",
+       "label_name version                                              \n",
+       "Cyst       aimiv1   0.19                                     2  \n",
+       "           aimiv2   0.00                                     2  \n",
+       "Kidney     aimiv1   0.09                                     7  \n",
+       "           aimiv2   0.00                                     7  \n",
+       "Tumor      aimiv1   0.20                                     2  \n",
+       "           aimiv2   0.00                                     2  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -442,7 +479,8 @@
     "            \"dice\": [\"mean\", \"std\"],\n",
     "            \"hausdorff_distance_95\": [\"mean\", \"std\"],\n",
     "            \"mean_surface_distance\": [\"mean\", \"std\"],\n",
-    "            \"mean_surface_distance_tol_7\": [\"mean\", \"std\"],\n",
+    "            \"normalized_surface_distance\": [\"mean\", \"std\"],\n",
+    "            \"normalized_surface_distance_tolerance\": \"first\",\n",
     "        }\n",
     "    )\n",
     "    .round(2)\n",
@@ -453,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -488,7 +526,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -503,7 +541,7 @@
     "    \"dice\",\n",
     "    \"hausdorff_distance_95\",\n",
     "    \"mean_surface_distance\",\n",
-    "    \"mean_surface_distance_tol_7\",\n",
+    "    \"normalized_surface_distance\",\n",
     "]:\n",
     "    fig, ax = plt.subplots(figsize=(12, 6))\n",
     "    sns.boxplot(\n",
@@ -514,9 +552,19 @@
     "        palette=revewer_cmap,\n",
     "        ax=ax,\n",
     "    )\n",
+    "    if metric == \"normalized_surface_distance\":\n",
+    "        # set x axis labels to be 'label name (tolerance)'\n",
+    "        labels = [\n",
+    "            f\"{label_name}\\n({nsd_tolerances[label_value]}mm)\"\n",
+    "            for label_value, label_name in label_names.items()\n",
+    "        ]\n",
+    "        ax.set_xticklabels(labels)\n",
+    "        ax.set_xlabel(\"Label (Tolerance)\")\n",
+    "    else:\n",
+    "        ax.set_xlabel(\"Label\")\n",
+    "\n",
     "    ax.set_title(f\"{metric} by label and version\")\n",
     "    ax.set_ylabel(metric)\n",
-    "    ax.set_xlabel(\"Label\")\n",
     "    plt.xticks(rotation=45)\n",
     "    plt.show()"
    ]

From b4100ad6a1f906cc605c945e588b9ed3fbe0d15f Mon Sep 17 00:00:00 2001
From: Jeff VanOss <jeff.vanoss@bamfhealth.com>
Date: Thu, 8 Aug 2024 13:32:34 -0400
Subject: [PATCH 4/6] fix up labels and nsd tolerance

---
 model_performance_aimi2.ipynb | 190 ++--------------------------------
 1 file changed, 6 insertions(+), 184 deletions(-)

diff --git a/model_performance_aimi2.ipynb b/model_performance_aimi2.ipynb
index 03ef20f..d7532fb 100644
--- a/model_performance_aimi2.ipynb
+++ b/model_performance_aimi2.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,185 +292,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr:last-of-type th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">dice</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">hausdorff_distance_95</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">mean_surface_distance</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">normalized_surface_distance</th>\n",
-       "      <th>normalized_surface_distance_tolerance</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>first</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>label_name</th>\n",
-       "      <th>version</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Cyst</th>\n",
-       "      <th>aimiv1</th>\n",
-       "      <td>0.91</td>\n",
-       "      <td>0.23</td>\n",
-       "      <td>7.36</td>\n",
-       "      <td>21.80</td>\n",
-       "      <td>1.57</td>\n",
-       "      <td>4.76</td>\n",
-       "      <td>0.92</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>aimiv2</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Kidney</th>\n",
-       "      <th>aimiv1</th>\n",
-       "      <td>0.97</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>8.08</td>\n",
-       "      <td>29.10</td>\n",
-       "      <td>1.22</td>\n",
-       "      <td>4.44</td>\n",
-       "      <td>0.97</td>\n",
-       "      <td>0.09</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>aimiv2</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Tumor</th>\n",
-       "      <th>aimiv1</th>\n",
-       "      <td>0.95</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>4.52</td>\n",
-       "      <td>18.31</td>\n",
-       "      <td>1.38</td>\n",
-       "      <td>7.13</td>\n",
-       "      <td>0.94</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>aimiv2</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    dice       hausdorff_distance_95         \\\n",
-       "                    mean   std                  mean    std   \n",
-       "label_name version                                            \n",
-       "Cyst       aimiv1   0.91  0.23                  7.36  21.80   \n",
-       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
-       "Kidney     aimiv1   0.97  0.11                  8.08  29.10   \n",
-       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
-       "Tumor      aimiv1   0.95  0.19                  4.52  18.31   \n",
-       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
-       "\n",
-       "                   mean_surface_distance       normalized_surface_distance  \\\n",
-       "                                    mean   std                        mean   \n",
-       "label_name version                                                           \n",
-       "Cyst       aimiv1                   1.57  4.76                        0.92   \n",
-       "           aimiv2                   0.00  0.00                        1.00   \n",
-       "Kidney     aimiv1                   1.22  4.44                        0.97   \n",
-       "           aimiv2                   0.00  0.01                        1.00   \n",
-       "Tumor      aimiv1                   1.38  7.13                        0.94   \n",
-       "           aimiv2                   0.15  0.38                        1.00   \n",
-       "\n",
-       "                         normalized_surface_distance_tolerance  \n",
-       "                     std                                 first  \n",
-       "label_name version                                              \n",
-       "Cyst       aimiv1   0.19                                     2  \n",
-       "           aimiv2   0.00                                     2  \n",
-       "Kidney     aimiv1   0.09                                     7  \n",
-       "           aimiv2   0.00                                     7  \n",
-       "Tumor      aimiv1   0.20                                     2  \n",
-       "           aimiv2   0.00                                     2  "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "aggregated_df = (\n",
     "    metrics_df.groupby([\"label_name\", \"version\"])\n",
@@ -484,9 +308,7 @@
     "        }\n",
     "    )\n",
     "    .round(2)\n",
-    ")\n",
-    "\n",
-    "aggregated_df"
+    ")"
    ]
   },
   {

From ac5a7cc4583afa0a3bfbded3f31a75a858a36c46 Mon Sep 17 00:00:00 2001
From: Jeff VanOss <jeff.vanoss@bamfhealth.com>
Date: Mon, 12 Aug 2024 15:58:42 -0400
Subject: [PATCH 5/6] add detection stats

---
 model_performance_aimi2.ipynb | 373 ++++++++++++++++++++++++++++++----
 1 file changed, 334 insertions(+), 39 deletions(-)

diff --git a/model_performance_aimi2.ipynb b/model_performance_aimi2.ipynb
index d7532fb..53881c8 100644
--- a/model_performance_aimi2.ipynb
+++ b/model_performance_aimi2.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,20 +39,22 @@
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
-    "from pathlib import Path\n",
-    "import requests\n",
-    "import zipfile\n",
     "import io\n",
+    "import zipfile\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import scipy.stats as stats\n",
     "import pydicom\n",
     "import pydicom_seg\n",
-    "import SimpleITK as sitk\n",
-    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "import scipy.stats as stats\n",
+    "import seaborn as sns\n",
     "import segmentationmetrics as sm\n",
+    "import SimpleITK as sitk\n",
+    "import skimage.measure\n",
     "from segmentationmetrics.surface_distance import compute_surface_dice_at_tolerance\n",
-    "import seaborn as sns\n",
     "from tqdm.auto import tqdm\n",
     "\n",
     "sns.set_theme(style=\"whitegrid\")\n",
@@ -61,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,12 +195,72 @@
     "\n",
     "    if label_suffix:\n",
     "        metrics = {f\"{k}_{label_suffix}\": v for k, v in metrics.items()}\n",
-    "    return metrics"
+    "    return metrics\n",
+    "\n",
+    "\n",
+    "def get_false_discovery(\n",
+    "    img_file_pred: str, img_file_gt: str, label_value: int = 1, prefix=\"\"\n",
+    "):\n",
+    "    \"\"\"Get the false discovery rate for two images, predicted (image_file_pred) and ground truth (image_file_gt).\"\"\"\n",
+    "    img_pred = load_seg(img_file_pred, label_value)\n",
+    "    img_gt = load_seg(img_file_gt, label_value)\n",
+    "    img_gt = resize_label(img_gt, img_pred)  # match the size of the qa_img\n",
+    "\n",
+    "    vox_ml = np.prod(img_pred.GetSpacing()) / 1000\n",
+    "\n",
+    "    pd_cc = sitk.GetArrayViewFromImage(sitk.ConnectedComponent(img_pred))\n",
+    "    gt_cc = sitk.GetArrayViewFromImage(sitk.ConnectedComponent(img_gt))\n",
+    "\n",
+    "    # pd_arr = sitk.GetArrayFromImage(img_pred)\n",
+    "    # gt_arr = sitk.GetArrayFromImage(img_gt)\n",
+    "    # pd_cc = skimage.measure.label(pd_arr)\n",
+    "    # gt_cc = skimage.measure.label(gt_arr)\n",
+    "    true_pd_cc = pd_cc.copy()\n",
+    "    true_pd_cc[gt_cc == 0] = 0\n",
+    "    overlap_gt_cc = gt_cc.copy()  # gt labels that overlap with predicted\n",
+    "    overlap_gt_cc[pd_cc == 0] = 0\n",
+    "\n",
+    "    pd_labels = np.dstack(np.unique(pd_cc, return_counts=True))[0, 1:]\n",
+    "    gt_labels = np.dstack(np.unique(gt_cc, return_counts=True))[0, 1:]\n",
+    "    true_pd_labels = np.dstack(np.unique(true_pd_cc, return_counts=True))[0, 1:]\n",
+    "    overlap_gt_labels = np.dstack(np.unique(overlap_gt_cc, return_counts=True))[0, 1:]\n",
+    "\n",
+    "    true_vol_overlap_ml = true_pd_labels[..., 1] * vox_ml\n",
+    "\n",
+    "    # get false positives\n",
+    "    false_pos_vols_ml = []\n",
+    "    for pd_label, pd_size in pd_labels:\n",
+    "        if pd_label not in true_pd_labels[..., 0]:\n",
+    "            false_pos_vols_ml.append(pd_size * vox_ml)\n",
+    "\n",
+    "    # get false negatives\n",
+    "    false_neg_vols_ml = []\n",
+    "    for gt_label, gt_size in gt_labels:\n",
+    "        if gt_label not in overlap_gt_labels[..., 0]:\n",
+    "            false_neg_vols_ml.append(gt_size * vox_ml)\n",
+    "\n",
+    "    stats = {\n",
+    "        \"true_vol_overlap_ml\": true_vol_overlap_ml,\n",
+    "        \"false_pos_vols_ml\": false_pos_vols_ml,\n",
+    "        \"false_neg_vols_ml\": false_neg_vols_ml,\n",
+    "        \"true_vol_overlap_ml_total\": sum(true_vol_overlap_ml),\n",
+    "        \"false_pos_vol_ml_total\": sum(false_pos_vols_ml),\n",
+    "        \"false_neg_vol_ml_total\": sum(false_neg_vols_ml),\n",
+    "        \"true_pos_cnt\": len(true_vol_overlap_ml),\n",
+    "        \"false_pos_cnt\": len(false_pos_vols_ml),\n",
+    "        \"false_neg_cnt\": len(false_neg_vols_ml),\n",
+    "    }\n",
+    "    stats[\"pos_cnt\"] = stats[\"true_pos_cnt\"] + stats[\"false_neg_cnt\"]\n",
+    "\n",
+    "    if prefix:\n",
+    "        stats = {f\"{prefix}-{k}\": v for k, v in stats.items()}\n",
+    "\n",
+    "    return stats"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,14 +303,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 85/85 [02:34<00:00,  1.82s/it]\n"
+      "100%|██████████| 85/85 [04:49<00:00,  3.41s/it]\n"
      ]
     }
    ],
@@ -270,31 +332,259 @@
     "\n",
     "    version = \"aimiv1\" if row.AimiProjectYear == 2023 else \"aimiv2\"\n",
     "    for label_value in [1, 2, 3]:\n",
-    "        metrics.append(\n",
-    "            calc_metrics_for_label(\n",
-    "                ai_file,\n",
-    "                qa_file,\n",
-    "                row.SeriesInstanceUID,\n",
-    "                version=version,\n",
-    "                label_value=label_value,\n",
-    "                nsd_tol=nsd_tolerances[label_value],\n",
-    "            )\n",
+    "        stats = calc_metrics_for_label(\n",
+    "            ai_file,\n",
+    "            qa_file,\n",
+    "            row.SeriesInstanceUID,\n",
+    "            version=version,\n",
+    "            label_value=label_value,\n",
+    "            nsd_tol=nsd_tolerances[label_value],\n",
     "        )\n",
+    "        stats[\"label_name\"] = label_names[label_value]\n",
+    "        if label_value in (2, 3):\n",
+    "            detection_metrics = get_false_discovery(\n",
+    "                ai_file, qa_file, label_value=label_value\n",
+    "            )\n",
+    "            # ignore array values\n",
+    "            for x in (\"true_vol_overlap_ml\", \"false_neg_vols_ml\", \"false_pos_vols_ml\"):\n",
+    "                detection_metrics.pop(x)\n",
+    "            stats.update(detection_metrics)\n",
+    "        metrics.append(stats)\n",
+    "\n",
     "\n",
     "metrics_df = pd.DataFrame(metrics)\n",
     "\n",
     "# Replace infinite values with NaN and drop NaN values\n",
-    "metrics_df = metrics_df.replace([np.inf, -np.inf], np.nan).dropna()\n",
-    "\n",
-    "# update metrics_df with label names column\n",
-    "metrics_df[\"label_name\"] = metrics_df[\"label\"].map(label_names)"
+    "# metrics_df = metrics_df.replace([np.inf, -np.inf], np.nan).dropna()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">dice</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">hausdorff_distance_95</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">mean_surface_distance</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">normalized_surface_distance</th>\n",
+       "      <th>normalized_surface_distance_tolerance</th>\n",
+       "      <th>pos_cnt</th>\n",
+       "      <th>true_pos_cnt</th>\n",
+       "      <th>false_pos_cnt</th>\n",
+       "      <th>false_neg_cnt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>first</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>sum</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>label_name</th>\n",
+       "      <th>version</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Cyst</th>\n",
+       "      <th>aimiv1</th>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.57</td>\n",
+       "      <td>4.76</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>2</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aimiv2</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Kidney</th>\n",
+       "      <th>aimiv1</th>\n",
+       "      <td>0.96</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.22</td>\n",
+       "      <td>4.44</td>\n",
+       "      <td>0.96</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aimiv2</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Tumor</th>\n",
+       "      <th>aimiv1</th>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.38</td>\n",
+       "      <td>7.13</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>2</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aimiv2</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    dice       hausdorff_distance_95       \\\n",
+       "                    mean   std                  mean  std   \n",
+       "label_name version                                          \n",
+       "Cyst       aimiv1   0.87  0.29                   inf  NaN   \n",
+       "           aimiv2   1.00  0.00                   inf  NaN   \n",
+       "Kidney     aimiv1   0.96  0.15                   inf  NaN   \n",
+       "           aimiv2   1.00  0.00                   0.0  0.0   \n",
+       "Tumor      aimiv1   0.91  0.27                   inf  NaN   \n",
+       "           aimiv2   1.00  0.00                   0.0  0.0   \n",
+       "\n",
+       "                   mean_surface_distance       normalized_surface_distance  \\\n",
+       "                                    mean   std                        mean   \n",
+       "label_name version                                                           \n",
+       "Cyst       aimiv1                   1.57  4.76                        0.89   \n",
+       "           aimiv2                   0.00  0.00                        1.00   \n",
+       "Kidney     aimiv1                   1.22  4.44                        0.96   \n",
+       "           aimiv2                   0.00  0.01                        1.00   \n",
+       "Tumor      aimiv1                   1.38  7.13                        0.90   \n",
+       "           aimiv2                   0.15  0.38                        1.00   \n",
+       "\n",
+       "                         normalized_surface_distance_tolerance pos_cnt  \\\n",
+       "                     std                                 first     sum   \n",
+       "label_name version                                                       \n",
+       "Cyst       aimiv1   0.26                                     2   163.0   \n",
+       "           aimiv2   0.00                                     2    25.0   \n",
+       "Kidney     aimiv1   0.14                                     7     0.0   \n",
+       "           aimiv2   0.00                                     7     0.0   \n",
+       "Tumor      aimiv1   0.27                                     2   180.0   \n",
+       "           aimiv2   0.00                                     2    13.0   \n",
+       "\n",
+       "                   true_pos_cnt false_pos_cnt false_neg_cnt  \n",
+       "                            sum           sum           sum  \n",
+       "label_name version                                           \n",
+       "Cyst       aimiv1         163.0           0.0           0.0  \n",
+       "           aimiv2          25.0           0.0           0.0  \n",
+       "Kidney     aimiv1           0.0           0.0           0.0  \n",
+       "           aimiv2           0.0           0.0           0.0  \n",
+       "Tumor      aimiv1         177.0           0.0           3.0  \n",
+       "           aimiv2          13.0           0.0           0.0  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "aggregated_df = (\n",
     "    metrics_df.groupby([\"label_name\", \"version\"])\n",
@@ -305,20 +595,25 @@
     "            \"mean_surface_distance\": [\"mean\", \"std\"],\n",
     "            \"normalized_surface_distance\": [\"mean\", \"std\"],\n",
     "            \"normalized_surface_distance_tolerance\": \"first\",\n",
+    "            \"pos_cnt\": [\"sum\"],\n",
+    "            \"true_pos_cnt\": [\"sum\"],\n",
+    "            \"false_pos_cnt\": [\"sum\"],\n",
+    "            \"false_neg_cnt\": [\"sum\"],\n",
     "        }\n",
     "    )\n",
     "    .round(2)\n",
-    ")"
+    ")\n",
+    "aggregated_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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AZCtXrlR6eroWLVqknj17EtgBAIAbC9EBAGCyvXv36q233lKLFi00atQos8sBAAAWwvB4AAAAAAAsip52AAAAAAAsitAOAAAAAIBFVfqF6H766ScZhnFW950FAAAAAOB8FRQUyGazqVWrVmfct9KHdsMwxLR+AAAAAIC3nEsGrfShvbiHPSoqyuRKAAAAAACVQXp6+lnvy5x2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAi7JUaH/ttdfUt2/fv93n8OHDGjlypNq0aaO2bdtqwoQJys3N9VKFAM5VWlqaEhMTlZaWZnYpwAUzYcIEPfDAA5owYYLZpcAL+FxDZcD7HLAOy4T2BQsW6OWXXz7jfsOGDdOuXbuUnJysGTNmaM2aNRo/fvwFrw/AucvLy1NSUpIOHjyopKQk5eXlmV0SUO4yMjK0bds2SdK2bduUkZFhckW4kPhcQ2XA+xywFl+zC9i/f7+eeeYZrV+/XldcccXf7vvTTz9pw4YNWr58uRo2bChJmjhxogYMGKARI0YoLCzMCxWXH8MwlJeXZ9oHoWEYys/PN+W5zeTv7y+bzeb153U4HHI4HDp69KjXn1uSXC6Xjh8/7tXn/OKLL3T48GFJp0bJvPvuu7rlllu8WkO1atVkt3v/+mSNGjW8/pxWcuTIEVOe14z3+bhx4zx+/uc//6mJEyd6tQaz3+eV6fXmc63y4n1eud7nlen1tgKzX2+rMz20b9q0SX5+fkpJSdGcOXP0xx9/nHbfjRs3qnbt2u7ALklt27aVzWbTjz/+qNtvv71MNRiGoZycnDIdez7y8vI0dOhQrz8vzDN79uxK/Zp/9dVX+uqrr8wuwytef/11s0sw1eDBg80uwTT5+fl68sknzS7DK4rf55X59eZzrfLgfV653ueV+fWuTMz8XDMM46w7Ek0P7Z07d1bnzp3Pat/9+/erbt26Hm3+/v4KDg7Wvn37ylxDQUGBtmzZUubjz+d5Ubn8+uuvZpcALzHjMwXwNt7nlQuvNyoD3ueVi9mvt7+//1ntZ3poPxe5ubmlnpjD4TivIeZ+fn5q1KjR+ZRWJswPqnyaNGmiadOmmfLcLpdLJ06c8MpzHThwQHPmzDnt9iFDhqh27dpeqSUwMNCU4VZBQUFef04rqQzv86+++krffPPNabdff/31Z31R+nyZ/T6vDK83n2t8rvE+r1zv88rweluJ2a+3GbZv337W+15UoT0gIKDUOdh5eXmqWrVqmR/XZrOd1/FlVaVKFc2fP9/rzyudGo7xwgsvnNObpaJo1KiRnnzySdPmtZvxvN7WqFEjffPNN9q0aZNcLpe73W63q0WLFmrfvn2l+D1UZmZ8pnrb5Zdf/rehPT4+/qyvoF/sKsPrzecaeJ9Xrvd5ZXi9Ya5z+X/pogrt4eHhWrlypUdbfn6+nE6n6tSpY1JVZWez2RQQEGDKcxuGYcrVLCuw2+0KCAioNH90zGCz2dS/f3+NHj261HZ+96gI0tPTz7i9devWXqoGFxqfa6gMeJ8D1nRRhfY2bdpo6tSp2rVrly6//HJJ0oYNGySJL0bnyGazady4cV4bom8YhiZOnKjdu3eX2HbZZZdp3LhxXvtDUFl6u80WHh6u2NhYLVmyxL3QRlxc3EV3lwfgdM50sfhivJiMv8fnGioD3ueA9Vg6tBcVFSk7O1vVq1dXQECAWrZsqZiYGA0fPlzjx49XTk6Oxo0bpx49evBBUgbe7Onfs2dPqYFdknbv3q2DBw/q0ksv9Uot8J64uDitWbNGhw8fVkhIiGJjY80uCSg39erVU0BAgE6ePFliW0BAgOrVq2dCVbjQ+FxDZcD7HLAWS4+P3rdvn6677jotX75c0qmQOXv2bF1yySXq16+fHn/8cV1//fUaP368uYXijLKyss5rOy5ODodDCQkJqlWrluLj4+VwOMwuCSg3+/btKzWwS9LJkyfP664msC4+11AZ8D4HrMVmGIZhdhFmKp6TGBUVZXIlFZvL5dLDDz+snJycEtsCAwP16quvVto59gAuToZhaPLkyaXObY+Ojtbo0aOZigMAAEp1LjmUlASvsNvt6tu3b6nb+vbtS2AHcNE53cJMLNgEAADKE0kJXmEYhr777rtSt61du1aVfMAHgAqGzzQAAFBeCO3wioyMjNPeHik9PV0ZGRlerggAzo9hGEpOTi4R0E/XDgAAUBaEdnhFRESEoqKiSh1GGh0drYiICJMqA4Cy4WIkAADwBkI7vKJ4jmdpPU/M/QRwMYqIiFDjxo1L3dakSRMuRgIAgHJBaIepDMNgCCmAixYXHAEAwIVGaIdXGIahefPmlbpt3rx5BHcAF52MjAz9+uuvpW779ddfGR4PAADKBaEdXvHHH3/87ZfbP/74w8sVAcD5Ya0OAADgDYR2AADKwGazqVu3bqWuHn/HHXcwdB4AAJQLQju8ol69erriiitK3Va/fn3Vq1fPuwUBwHkyDENLly4tddvSpUuZ9gMAAMoFoR1e43A4Sm339/f3ciUAcP645RsAAPAGQju8ggWbAFQ0xXPa7XbPP6V2u5057QAAoNwQ2uEVfLkFUNHYbDb179+/1IXoSmsHAAAoC0I7vIIvtwAqovDwcMXGxro/w2w2m+Li4hQWFmZyZQAAoKIgtMNr+HILoCKKi4tTcHCwJCkkJESxsbHmFgQAACoUQju8Ki4uToGBgZKkwMBAvtwCuOg5HA4lJCSoVq1aio+PP+2imwAAAGXha3YBqHyKb4PE7ZAAVBQxMTGKiYkxuwwAAFAB0dMOr0pJSVFOTo4kKScnR6mpqSZXBAAAAADWRWiH12RmZio1NdWjpz0lJUWZmZkmVwYAAIA/S0tLU2JiotLS0swuBaj0CO3wCsMwlJycXGJI/OnaAQAAYI68vDwlJSXp4MGDSkpKUl5entklAZUaoR1ekZGRofT0dLlcLo92l8ul9PR0ZWRkmFQZAAAA/iwlJUVOp1OS5HQ6mc4ImIzQDq+IiIhQVFRUqfdpj46OVkREhEmVAQAAoBjTGQHrIbTDK2w2m/r371/qtv79+5cI8wAAAPAupjMC1kRoh+n4AwAAAGA+pjMC1kRoh1cUX6EtbXg8V24BAADMx3RGwJoI7fAKrtwCAABYG9MZAWsitMMriq/c2u2ebzm73c6VWwAAAItjVCRgHkI7vKL4ym1pw624cgsAAGA+pjMC1kRoh9eEh4crNjbW/YfAZrMpLi5OYWFhJlcGAAAApjMC1kRoh1fFxcUpODhYkhQSEqLY2FhzCwIAAIAkpjMCVkVoh1c5HA4lJCSoVq1aio+Pl8PhMLskAAAAiOmMgFX5ml0AKp+YmBjFxMSYXQYAAAD+ong645IlS2QYBtMZAQugpx0AAACAG9MZAWshtAMAAABwYzojYC0MjwcAAADggemMgHXQ0w4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A6vS0tLU2JiotLS0swuBQAAAAAsjdAOr8rLy1NSUpIOHjyopKQk5eXlmV0SAAAAAFgWoR1elZKSIqfTKUlyOp1KTU01tyAAAAAAsDBCO7wmMzNTqampMgxDkmQYhlJSUpSZmWlyZQAAAABgTYR2eIVhGEpOTnYH9jO1AwAAAAAI7fCSjIwMpaeny+VyebS7XC6lp6crIyPDpMoAAAAAwLoI7fCKiIgINWnSpNRtTZo0UUREhJcrAgAAAADrI7TDa043BJ6h8QAAAABQOkI7vCIjI0Pbtm0rddu2bdsYHg8AAAAApSC0wysiIiLUuHHjUrcxPB4AAAAASkdoh9fYbDazSwAAAACAiwqhHV6RkZGhX3/9tdRtv/76K8PjAQAAAKAUhHZ4RUREhKKiomS3e77l7Ha7oqOjGR4PAAAAAKUgtMMrbDab+vfvX2KI/OnaAQAAAACEdnhReHi4YmNj3QHdZrMpLi5OYWFhJlcGAAAAANZEaIdXxcXFKTg4WJIUEhKi2NhYcwsCAAAAAAsjtMOrHA6HEhISVKtWLcXHx8vhcJhdEgAAAABYlq/ZBaDyiYmJUUxMjNllAAAAAIDl0dMOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZlemh3uVyaOXOmOnbsqKuuukoDBw7Unj17Trv/oUOHNHLkSF1zzTVq166dhg8frv3793uxYgAAAAAAvMP00D537lwtXLhQzz77rBYtWiSXy6UBAwYoPz+/1P0ff/xxZWRk6M0339Sbb76pjIwMDRkyxMtVAwAAAABw4Zka2vPz85WUlKRhw4apU6dOioyM1PTp05WZmakvvviixP5Hjx7Vhg0bNHDgQDVt2lTNmjXToEGDlJ6eLqfT6f0TAAAAAADgAjL1lm9bt27ViRMn1L59e3dbUFCQmjVrph9++EHdunXz2D8gIECBgYH69NNP1bZtW0nSkiVLVL9+fQUFBZW5DsMwlJOTU+bjAQAAAAA4W4ZhyGazndW+pob2zMxMSVLdunU92uvUqePe9mf+/v564YUXNG7cOF199dWy2WyqU6eO3n33XdntZR80UFBQoC1btpT5eAAAAAAAzoW/v/9Z7WdqaM/NzZVUsliHw6EjR46U2N8wDG3ZskWtWrXSgAEDVFRUpOnTp2vw4MF67733VK1atTLV4efnp0aNGpXpWAAAAAAAzsX27dvPel9TQ3tAQICkU3Pbi/8tSXl5eapSpUqJ/VesWKF3331XX3/9tTugv/rqq7rxxhu1ePFi9e/fv0x12Gw2Va1atUzHAgAAAABwLs52aLxk8kJ0xcPis7KyPNqzsrIUFhZWYv+NGzeqfv36Hj3qNWrUUP369bVr164LWywAAAAAAF5mamiPjIxUtWrVtH79enfb0aNHtXnzZrVp06bE/uHh4dq1a5fy8vLcbTk5Odq7d6+uuOIKb5QMAAAAVHhpaWlKTExUWlqa2aUAlZ6pod3f3199+vTR1KlTtWrVKm3dulXDhw9XeHi4brnlFhUVFenAgQM6efKkJKlHjx6STt2rfevWrdq6datGjBghh8Ohnj17mngmAAAAQMWQl5enpKQkHTx4UElJSR4dZgC8z9TQLknDhg1Tr169NHbsWN13333y8fHR/Pnz5efnp3379um6667T8uXLJZ1aVX7hwoUyDEP9+vVTfHy8/Pz8tHDhQlWvXt3kMwEAAAAufikpKXI6nZIkp9Op1NRUcwsCKjmbYRiG2UWYKT09XZIUFRVlciUAAACAuTIzMzV69GgVFRW523x8fPTiiy8qPDzcxMqAiuVccqjpPe0AAAAAzGcYhpKTk/XXPr3TtQPwDkI7AAAAAGVkZCg9PV0ul8uj3eVyKT09XRkZGSZVBlRuhHYAAAAAioiIUFRUlOx2z4hgt9sVHR2tiIgIkyoDKjdCOwAAAADZbDb1799fNpvtrNoBeAehHQAAAIAkKTw8XLGxse6AbrPZFBcXp7CwMJMrAyovQjsAAAAAt7i4OAUHB0uSQkJCFBsba25BQCVHaAcAAADg5nA4lJCQoFq1aik+Pl4Oh8PskoBKzdfsAgAAuNilpaXprbfeUr9+/RQTE2N2OQBw3mJiYvg8AyyCnnYAAM5DXl6ekpKSdPDgQSUlJSkvL8/skgAAQAVCaAcA4DykpKTI6XRKkpxOp1JTU80tCAAAVCiEdgAAyigzM1OpqakyDEOSZBiGUlJSlJmZaXJlAACgoiC0AwBQBoZhKDk52R3Yz9QOAABQFoR2AADKICMjQ+np6XK5XB7tLpdL6enpysjIMKkyAABQkRDaAQAog4iICEVFRclu9/xTarfbFR0drYiICJMqAwAAFQmhHV6XlpamxMREpaWlmV0KAJSZzWZT//79ZbPZzqodAACgLAjt8CpujQSgIgkPD1dsbKw7oNtsNsXFxSksLMzkygAAQEVBaIdXcWskABVNXFycgoODJUkhISGKjY01tyAAAFChENrhNdwaCUBF5HA4lJCQoFq1aik+Pl4Oh8PskgAAQAVCaIdXcGskABVZTEyMZsyYoZiYGLNLAQAAFQyhHV7BrZEAAAAA4NwR2uEV3BoJAAAAAM4doR1ewa2RAAAAAODcEdrhNdwaCQAAAADODaEdXsWtkQAAAADg7BHa4VXcGgkAAAAAzp6v2QWg8omJieG2SAAAAABwFuhpBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEWZHtpdLpdmzpypjh076qqrrtLAgQO1Z8+e0+5fUFCgadOmuffv06ePtmzZ4sWKAQAAAADwDtND+9y5c7Vw4UI9++yzWrRokVwulwYMGKD8/PxS9x8/frw+/vhj/etf/9JHH32k0NBQDRw4UMeOHfNy5QAAAAAAXFimhvb8/HwlJSVp2LBh6tSpkyIjIzV9+nRlZmbqiy++KLH/nj179NFHH2nSpEnq2LGjGjZsqOeee07+/v765ZdfTDgDAAAAAAAuHF8zn3zr1q06ceKE2rdv724LCgpSs2bN9MMPP6hbt24e+69du1bVq1fX9ddf77H/V199dV51GIahnJyc83oMAAAAAADOhmEYstlsZ7WvqaE9MzNTklS3bl2P9jp16ri3/dnOnTt16aWX6osvvtC8efO0f/9+NWvWTE8++aQaNmxY5joKCgqYFw8AAAD8fzt27NBXX32lzp07n9f3bACn5+/vf1b7mRrac3NzJZUs1uFw6MiRIyX2P378uHbt2qW5c+dq9OjRCgoK0iuvvKL7779fy5cvV82aNctUh5+fnxo1alSmYwEAAICKJC8vT0lJSTp27JjWrFmjm2++WQ6Hw+yygApl+/btZ72vqaE9ICBA0qm57cX/lk59UFSpUqXE/r6+vjp+/LimT5/uvuI3ffp03XDDDfrkk080YMCAMtVhs9lUtWrVMh0LAAAAVCTLli1zd6AdOXJEq1atUq9evUyuCqhYznZovGTyQnTFw+KzsrI82rOyshQWFlZi//DwcPn6+noM0QkICNCll16qvXv3XthiAQAAgAouMzNTqampMgxD0ql5tykpKaVOXQXgHaaG9sjISFWrVk3r1693tx09elSbN29WmzZtSuzfpk0bFRYWKj093d128uRJ7dmzR5dffrlXagYAAAAqIsMwlJyc7A7sZ2oH4B2mhnZ/f3/16dNHU6dO1apVq7R161YNHz5c4eHhuuWWW1RUVKQDBw7o5MmTkqSrr75a1157rcaMGaONGzdq+/btGj16tHx8fNS9e3czTwUAAAC4qGVkZCg9PV0ul8uj3eVyKT09XRkZGSZVBlRupoZ2SRo2bJh69eqlsWPH6r777pOPj4/mz58vPz8/7du3T9ddd52WL1/u3n/WrFlq27athg4dql69eun48eN6++23FRoaauJZAAAAABe3iIgIRUVFyW73jAh2u13R0dGKiIgwqTKgcrMZlXycS/FQ+6ioKJMrAQAAAMyVmZmp0aNHq6ioyN3m4+OjKVOmlLrmFICyOZccanpPOwAAAABrCA8PV2xsrHtla5vNpri4OAI7YCJCOwAAAAC3uLg4BQcHS5JCQkIUGxtrbkFAJUdoBwAAAODmcDiUkJCgWrVqKT4+Xg6Hw+ySgErN1+wCAAAAAFhLTEyMYmJizC4DgOhpBwAAAADAsgjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAADykpaUpMTFRaWlpZpcCVHqEdgAAAABueXl5SkpK0sGDB5WUlKS8vDyzSwIqtTKH9uzsbE2ZMkV33nmnrrvuOm3dulWzZ8/WypUry7M+AAAAAF6UkpIip9MpSXI6nUpNTTW3IKCSK1No37Nnj+Li4vTBBx8oLCxMhw4dUlFRkXbu3Klhw4Zp9erV5VwmAAAAgAstMzNTqampMgxDkmQYhlJSUpSZmWlyZUDlVabQPnnyZNWsWVOrVq3S7Nmz3f9TT5s2TZ07d9arr75arkUCAAAAuLAMw1BycrL7u/2Z2gF4R5lC+/fff6/BgwcrKChINpvNY9u9996r3377rVyKAwAAAOAdGRkZSk9Pl8vl8mh3uVxKT09XRkaGSZUBlVuZ57T7+vqW2p6fn18iyAMAAACwtoiICEVFRclu94wIdrtd0dHRioiIMKkyoHIrU2i/+uqr9dprryknJ8fdZrPZ5HK59N577ykmJqbcCgQAAABw4dlsNvXv379EB9zp2gF4R+nd5WcwcuRI3XfffbrlllvUrl072Ww2zZ8/Xzt27NCuXbu0cOHC8q4TAAAAwAUWHh6u2NhYLVmyRIZhyGazKS4uTmFhYWaXBlRaZeppb9y4sRYvXqx27dpp/fr18vHx0XfffafLLrtMixYtUtOmTcu7TgAAAABeEBcXp+DgYElSSEiIYmNjzS0IqOTK1NMuSfXr19eLL74oHx8fSVJubq4KCwtVvXr1cisOAAAAgHc5HA4lJCTorbfeUr9+/eRwOMwuCajUytTTXlBQoGeeeUb33HOPu+2nn35S+/btNXny5BIrTgIAAAC4eMTExGjGjBmsVQVYQJlC+6xZs5SSkqI77rjD3dasWTM98cQT+uCDD/TGG2+UW4EAAAAAAFRWZRoen5qaqjFjxqh3797utuDgYPXv31++vr56++23NWjQoHIrEgAAAACAyqhMPe2HDx/WpZdeWuq2Bg0aKDMz87yKAgAAAAAAZQztDRo00Oeff17qtq+++kqXX375eRUFAAAAAADKODz+wQcf1JNPPimn06kuXbqoZs2ays7O1tdff60VK1bo+eefL+86AQAAAACodMoU2nv06KETJ05o7ty5+uKLL9ztISEhevrpp9WjR4/yqg8AAAAAgEqrzPdpf+CBB3T//fdr586dcjqdCgoKUoMGDWS3l2nEPQAAAAAA+Isyh3ZJstlsatCgQXnVAgAAAAAA/uSsQ3vTpk31/vvvKzo6WpGRkbLZbKfd12azafPmzeVSIAAAAAAAldVZh/YhQ4YoLCzM/e+/C+0AAAAAAOD8nXVo79mzp4qKipSRkaG77rrrQtYEAAAAAAB0DqG9c+fO59S7vmXLljIVBAAAAAAATjnr0P6vf/3LHdqPHDmiqVOnqn379rrttttUu3ZtOZ1OffXVV1q9erWefPLJC1YwAAAAAACVhc0wDONcDxoyZIhCQkL03HPPldg2adIk/fbbb0pOTi6P+i649PR0SVJUVJTJlQAAAAAAKoNzyaFluqn62rVrddttt5W6rVOnTvrpp5/K8rAAAAAAAOBPyhTaQ0JC9PPPP5e6bd26de5V5gEAAAAAQNmd9Zz2P7v77rs1Z84cnTx5Up06dVJISIgOHjyozz77TO+9956eeuqp8q4TAAAAAIBKp0yh/dFHH9WxY8c0f/58zZs3T5JkGIYCAgKUmJioBx54oFyLBAAAAACgMirTQnTFjh07pv/85z86cuSIQkJC1KpVK1WtWrU867vgWIgOAAAAAOBN55JDy9TTXqx69erq2LHj+TwEAAAAAAA4jTItRAcAAAAAAC48QjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAADgIS0tTYmJiUpLSzO7FKDSMz20u1wuzZw5Ux07dtRVV12lgQMHas+ePWd1bEpKipo0aaK9e/de4CoBAACAyiEvL09JSUk6ePCgkpKSlJeXZ3ZJQKVmemifO3euFi5cqGeffVaLFi2Sy+XSgAEDlJ+f/7fH/fHHH5o4caKXqgQAAAAqh5SUFDmdTkmS0+lUamqquQUBlZypoT0/P19JSUkaNmyYOnXqpMjISE2fPl2ZmZn64osvTnucy+XSqFGj1Lx5cy9WCwAAAFRsmZmZSk1NlWEYkiTDMJSSkqLMzEyTKwMqL18zn3zr1q06ceKE2rdv724LCgpSs2bN9MMPP6hbt26lHvfqq6+qoKBAQ4cO1bp16867DsMwlJOTc96PAwAAAFysDMNQUlKSO7D/tT0xMVE2m82k6oCKxTCMs/7/ydTQXnzFrm7duh7tderUOe3VvJ9//llJSUlavHix9u/fXy51FBQUaMuWLeXyWAAAAMDF6NChQ9q0aVOJdpfLpU2bNmnt2rWqWbOmCZUBFZO/v/9Z7WdqaM/NzZVUsliHw6EjR46U2D8nJ0dPPPGEnnjiCV1xxRXlFtr9/PzUqFGjcnksAAAA4GJkGIY2btyoLVu2yOVyudvtdruaNm2qDh060NMOlJPt27ef9b6mhvaAgABJp+a2F/9bOrViZZUqVUrs/9xzz6l+/frq3bt3udZhs9lUtWrVcn1MAAAA4GKTkJCg0aNHe7TZbDY99NBDCgwMNKkqoOI5lwtgpi5EVzwsPisry6M9KytLYWFhJfb/6KOP9N1336lVq1Zq1aqVBg4cKEnq1q2bXn311QtfMAAAAFCBhYeHKzY21h0obDab4uLiSv1uDsA7TO1pj4yMVLVq1bR+/XpddtllkqSjR49q8+bN6tOnT4n9/7qi/H//+1+NGjVK8+bNU+PGjb1SMwAAAFCRxcXFac2aNTp8+LBCQkIUGxtrdklApWZqaPf391efPn00depUhYaGql69epoyZYrCw8N1yy23qKioSNnZ2apevboCAgJ0+eWXexxfvFhdRESEgoODTTgDAAAAoGJxOBxKSEjQW2+9pX79+snhcJhdElCpmRraJWnYsGEqLCzU2LFjdfLkSbVp00bz58+Xn5+f9u7dq5tuuknPP/+8evbsaXapAAAAQKUQExOjmJgYs8sAIMlm/PVGjJVMenq6JCkqKsrkSgAAAAAAlcG55FBTF6IDAAAAAACnR2gHAOA8paWlKTExUWlpaWaXAgAAKhhCOwAA5yEvL09JSUk6ePCgkpKSlJeXZ3ZJAACgAiG0AwBwHlJSUuR0OiVJTqdTqamp5hYEAAAqFEI7AABllJmZqdTUVBWv6WoYhlJSUty3JAUAADhfhHYAAMrAMAwlJyfrrzdhOV07AABAWRDaAQAog4yMDKWnp8vlcnm0u1wupaenKyMjw6TKAABARUJoBwCgDCIiIhQVFSW73fNPqd1uV3R0tCIiIkyqDAAAVCSEdgAAysBms6l///6y2Wxn1Q4AAFAWhHYAAMooPDxcsbGx7oBus9kUFxensLAwkysDAAAVBaEdAIDzEBcXp+DgYElSSEiIYmNjzS0IAABUKIR2AADOg8Ph0A033CC73a7rr79eDofD7JIAAEAFQmgHAOA85OXlac2aNXK5XFqzZo3y8vLMLgkAAFQghHYAAM5DSkqKnE6nJMnpdCo1NdXcggAAQIVCaAcAoIwyMzOVmpoqwzAkSYZhKCUlRZmZmSZXBgAAKgpCOwAAZWAYhpKTk92B/UztAAAAZUFoBwCgDDIyMpSeni6Xy+XR7nK5lJ6eroyMDJMqAwAAFQmhHQCAMoiIiFBUVJTsds8/pXa7XdHR0YqIiDCpMgAAUJEQ2gEAKAObzab+/fvLZrOdVTsAAEBZENoBACij8PBwxcbGugO6zWZTXFycwsLCTK4MAABUFIR2AADOQ1xcnIKDgyVJISEhio2NNbcgAABQoRDaAQA4Dw6HQwkJCapVq5bi4+PlcDjMLgkAAFQgvmYXAADAxS4mJkYxMTFmlwEAACogetoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAeEhLS1NiYqLS0tLMLgWo9EwP7S6XSzNnzlTHjh111VVXaeDAgdqzZ89p9//tt980aNAgtWvXTu3bt9ewYcOUkZHhxYoBAACAiisvL09JSUk6ePCgkpKSlJeXZ3ZJQKVmemifO3euFi5cqGeffVaLFi2Sy+XSgAEDlJ+fX2Lfw4cPKz4+XgEBAXrnnXf0+uuvKzs7WwMGDODDBAAAACgHKSkpcjqdkiSn06nU1FRzCwIqOVNDe35+vpKSkjRs2DB16tRJkZGRmj59ujIzM/XFF1+U2H/lypXKycnRiy++qMaNG6tFixaaMmWKduzYwdAdAAAA4DxlZmYqNTVVhmFIkgzDUEpKijIzM02uDKi8fM188q1bt+rEiRNq3769uy0oKEjNmjXTDz/8oG7dunns3759e82dO1cBAQHuNrv91HWHo0ePlrkOwzCUk5NT5uMBAACAi51hGEpKSnIH9r+2JyYmymazmVQdULEYhnHW/z+ZGtqLr9jVrVvXo71OnTqlXs275JJLdMkll3i0zZs3TwEBAWrTpk2Z6ygoKNCWLVvKfDwAAABwsTt06JA2bdpUot3lcmnTpk1au3atatasaUJlQMXk7+9/VvuZGtpzc3MllSzW4XDoyJEjZzz+nXfe0bvvvquxY8cqNDS0zHX4+fmpUaNGZT4eAAAAuNgZhqGNGzdqy5Ytcrlc7na73a6mTZuqQ4cO9LQD5WT79u1nva+pob14mHt+fr7HkPe8vDxVqVLltMcZhqEZM2bolVde0aOPPqq+ffueVx02m01Vq1Y9r8cAAAAALnYJCQl64oknPNoMw9BDDz2kwMBAk6oCKp5zuQBm6kJ0xcPis7KyPNqzsrIUFhZW6jEFBQUaNWqUXn31Vf3jH//Q448/fqHLBAAAACq1v85zB+A9pob2yMhIVatWTevXr3e3HT16VJs3bz7tHPXRo0frs88+07Rp09S/f38vVQoAAABUbIZhKDk5udRtycnJBHfAJKYOj/f391efPn00depUhYaGql69epoyZYrCw8N1yy23qKioSNnZ2apevboCAgL08ccfa/ny5Ro9erTatm2rAwcOuB+reB8AAAAA5y4jI0Pp6ekl2g3DUHp6ujIyMlSvXj0TKgMqN1N72iVp2LBh6tWrl8aOHav77rtPPj4+mj9/vvz8/LRv3z5dd911Wr58uSRp6dKlkqQXX3xR1113ncd/xfsAAAAAOHcRERGKiopy31K5mN1uV3R0tCIiIkyqDKjcbEYlH+dSfDUxKirK5EoAAAAAc2VmZmr06NEqKipyt/n4+GjKlCmnXXMKwLk7lxxqek87AAAAAGsIDw9XbGyse2Vrm82muLg4AjtgIkI7AAAAALe4uDgFBwdLkkJCQhQbG2tuQUAlR2gHAAAA4OZwOJSQkKBatWopPj5eDofD7JKASs3U1eMBAAAAWE9MTIxiYmLMLgOA6GkHAAAA8BdpaWlKTExUWlqa2aUAlR6hHQAAAIBbXl6ekpKSdPDgQSUlJSkvL8/skoBKjdAOAAAAwC0lJUVOp1OS5HQ6lZqaam5BQCVHaAcAAAAg6dR92lNTU2UYhiTJMAylpKQoMzPT5MqAyovQDgAAAECGYSg5Odkd2M/UDsA7CO0AAAAAlJGRofT0dLlcLo92l8ul9PR0ZWRkmFQZULkR2gEAAAAoIiJCUVFRsts9I4Ldbld0dLQiIiJMqgyo3AjtAAAAAGSz2dS/f3/ZbLazagfgHYR2AAAAAJKk8PBwxcbGugO6zWZTXFycwsLCTK4MqLwI7QAAAADc4uLiFBwcLEkKCQlRbGysuQUBlRyhHV6XlpamxMREpaWlmV0KAAAA/sLhcCghIUG1atVSfHy8HA6H2SUBlZqv2QWgcsnLy1NSUpIOHz6spKQkNW/enD8EAAAAFhMTE6OYmBizywAgetrhZSkpKXI6nZIkp9Op1NRUcwsCAAAAAAsjtMNrMjMzlZqaKsMwJEmGYSglJUWZmZkmVwYAAAAA1sTweHiFYRhKTk52B/a/to8ZM4bbiAAAAADlxDAMFRUVqbCw0OxSKh0/Pz/5+PiU2+MR2uEVGRkZSk9PL9HucrmUnp6ujIwM1atXz4TKAAAAgIrDMAw5nU4dOHBARUVFZpdTaQUHBys8PLxcOiYJ7fCKiIgIRUVFadOmTXK5XO52u92uFi1aKCIiwsTqAAAAgIohMzNTTqdTQUFBCgoKkq+vLyNavcgwDOXk5CgrK0uSVLdu3fN+TEI7vMJms6l///4aPXp0qe18kAAAAADnp6ioSEeOHFHt2rVVq1Yts8uptKpUqSJJysrKUp06dc57qDwL0cFrwsPDFRsb6w7oNptNcXFxCgsLM7kyAAAA4OJXUFAgwzAUGBhodimVXtWqVSWdek3OF6EdXhUXF6fg4GBJUkhIiGJjY80tCAAAAKhgGMVqvvJ8DQjt8CqHw6GEhATVqlVL8fHxcjgcZpcEAAAAAJbFnHZ4XUxMjGJiYswuAwAAAAAsj552AAAAAIClffzxx2rSpIn27t1rdileR2gHAAAAAFhap06d9P7776tOnTpml+J1DI8HAAAAAFhaaGioQkNDzS7DFPS0AwAAAADO6Omnn1aHDh1UVFTk0T5p0iS1a9dOBQUF2rZtmx5++GH3OlZDhgzRnj173PuuX79eTZo00aJFi3TjjTcqJiZGa9euVXZ2tkaOHKkOHTooKipK3bt316effuo+rrTh8WvXrtX999+v1q1bq127dho5cqT27dvncUyzZs303//+V/fee6+ioqJ04403av78+Rful3QBENoBAAAAAGfUvXt3HTx4UOvXr3e3uVwurVixQnfccYf27t2r3r1769ChQ5o8ebImTZqkPXv26L777tOhQ4c8Hmv27NkaM2aMxo0bp1atWmnUqFHasWOHJkyYoNdff13NmjXTmDFjtG7dulJr+fTTT5WQkKC6devqpZde0j/+8Q/99NNPuvfeez2ey+Vy6fHHH9ftt9+uefPmKSYmRi+++KK+/fbbC/NLugAYHg8AAAAAOKPWrVurXr16Wrp0qa699lpJp3rODxw4oO7du2v27NmqUqWKkpOTVa1aNUlS+/bt1aVLF73xxhsaM2aM+7Huv/9+3Xrrre6fN2zYoCFDhqhLly6SpLZt2yo4OFj+/v4l6nC5XJo6daquu+46TZs2zd0eExOj22+/XfPnz9fo0aMlSYZhaPDgwbr77rvd5/Dll19q9erV6tixYzn/hi4MetoBAAAAAGdks9kUFxenlStXKj8/X5K0bNkyXXHFFWrZsqXWrVuntm3bKiAgQIWFhSosLFS1atV09dVX67vvvvN4rKZNm3r83K5dO82aNUvDhg3Thx9+qIMHD2rMmDGl3ip6586dOnDggLp16+bRftlll6lVq1basGGDR3urVq3c//b391doaKhycnLO63fhTYR2AAAAAMBZ6d69u44cOaJvv/1W+fn5+uKLLxQXFydJcjqdWr58uZo3b+7x39dff62srCyPx6latarHz9OnT1f//v31yy+/aOzYsbrhhhv00EMP6Y8//ihRg9PplCTVqlWrxLZatWrp2LFjHm0BAQEeP9vtdhmGcc7nbhaGxwMAAAAAzkr9+vUVHR2tFStWyG636+jRo+7QXr16dV177bWKj48vcZyv799Hz+rVq2vUqFEaNWqUfv/9d61atUpz587VhAkTNG/ePI99g4ODJUkHDx4s8TgHDhxQSEhIGc/OmuhpBwAAAACcte7du+vbb7/VsmXLFBMTo0svvVTSqXno27dvV9OmTRUVFaWoqCi1aNFCycnJ+vLLL0/7eH/88YduuOEGffbZZ5KkBg0aaODAgbr22muVkZFRYv/69eurdu3aWrp0qUf7nj179J///KfUIfUXM0I7AAAAAOCs3X777Tpx4oSWL1+u7t27u9sHDx6s3bt36+GHH9bKlSv17bff6rHHHtOyZcsUGRl52serV6+ewsPD9dxzz2nx4sXasGGDkpKStGbNGnXt2rXE/na7XSNGjNC///1vjRw5UmvWrNGnn36q+Ph41ahRo9Se/osZw+MBAAAAAGctNDRU1113ndauXeuxAnxkZKQWLFig6dOna/To0TIMQ40bN9acOXN00003/e1jzp49Wy+99JJmzJihw4cPq27duho6dKgGDRpU6v49e/ZUYGCgXnvtNQ0ZMkTVqlVTx44dNWLECNWuXbtcz9dsNuNimoF/AaSnp0uSoqKiTK4EAAAAAMru5MmT2rlzp+rXr19i8TV415lei3PJoQyPBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFuVrdgEAAAAAgAvP5XLJ5XJ5/XntdrvsdvqLy4rQDgAAAAAVnMvl0pDHHtNRp9Przx0UHKw5s2YR3MuI0A4AAAAAFZzL5dJRp1MhCd0kb4Znl0uHk5bK5XKVa2ifNWuWPvnkE3311VdePf7w4cOKjY3VtGnT1K5duzI997kitAMAAABAZWG3y+bF0G5coMdNSEjQAw884NXj9+/fr0cffVQHDhwo8/OWBeMTAAAAAAAXlcDAQIWGhnrt+MWLFysuLq7Mz3c+CO0AAAAAAMvZtm2bHn74YbVp00YtWrTQTTfdpKSkJEmnhrd37txZkrR37141adJEy5YtU48ePRQVFaWePXtqx44dmjNnjq699lq1bdtWEyZMkGEYJY7v27evHn/8cY/n/uGHH9SkSRPt2rVLkvTll19q+PDhmjFjhpfO/n8I7QAAAAAAS8nNzVVCQoKCg4O1aNEiLV26VLfeeqsmT56sLVu2lHrM9OnT9dRTT+nDDz/U0aNHdd999+n//u//9M4772j48OFauHChvv766xLH9ezZU19//bWOHz/ubktJSVFMTIwuv/xySdJrr72m3r17y2azXZgT/huEdgAAAACApeTm5urBBx/UuHHj1LBhQ11xxRUaNmyYJOnXX38t9ZiEhAS1bdtWkZGRuvnmm5WTk6OJEyeqYcOGuu+++1SzZk399ttvJY7r2rWr7Ha7Vq5cKUnKz8/X559/rp49e164EzwHLEQHAAAAALCU0NBQ3X///Vq6dKk2b96s3bt3a+vWrZJ02nvNF/eKS1LVqlVVq1YtValSxd0WEBCg/Pz8EsdVrVpVt956q1JTU9WjRw+tWbNG+fn5uu2228r5rMqGnnYAAAAAgKUcOHBAcXFx+vDDDxUWFqb7779fn3zyyd8e4+vr2Sd9LreY69mzp77//nsdPHhQqamp6tKli6pVq1am2ssbPe0AAAAAAEtZunSpnE6nPv/8c/n5+Un637D44sXkytPVV1+tevXqacmSJVq9erVeffXVcn+OsiK0AwAAAEBl4XJdsHunn+75yiI8PFy5ubn67LPP1Lp1a/3+++96/vnnJanUIe7ny2azqUePHpozZ45CQ0N1zTXXlPtzlBWhHQAAAAAqOLvdrqDgYB1OWur15w4KDj6noeqSdOutt2rTpk164YUXdPz4cdWrV0933323Vq1apfT0dNWtW7fc67zzzjs1e/Zs9e3b95zrvZBsxoUYW3ARSU9PlyRFRUWZXAkAAAAAlN3Jkye1c+dO1a9fXwEBASW2u1yu0y7idiHZ7XZLhWBvONNrcS45lJ52AAAAAKgEKmN4rgh4xQAAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAHCe0tLSlJiYqLS0NLNLAQAAFQyhHQCA85CXl6ekpCQdPHhQSUlJysvLM7skAABQgXCfdgAAzkNKSoqcTqckyel0KjU1Vb169TK3KAAASuFyueRyubz+vNwf/vwQ2gEAKKPMzEylpqbKMAxJkmEYSklJ0XXXXafw8HCTqwMA4H9cLpeGDhmmI0cPe/25awSFaPacmQT3MiK0w+vS0tL01ltvqV+/foqJiTG7HAAoE8MwlJyc7A7sf20fM2aMbDabSdUBwPn58MMPlZKSori4ON19991ml4Ny4HK5dOToYXVp8IRsNu+FZ8NwaeXvU+Vyuco1tM+aNUuffPKJvvrqK68c/9VXX2nOnDn6/fffFRISoq5duyoxMVEBAQFlev5zYfqlDpfLpZkzZ6pjx4666qqrNHDgQO3Zs+e0+x8+fFgjR45UmzZt1LZtW02YMEG5ublerBjng7mfACqKjIwMpaenlxhm6HK5lJ6eroyMDJMqA4Dzc/ToUS1ZskQul0tLlizR0aNHzS4J5chms8tu8/HafxfqAkFCQoIWL17sleM3btyooUOH6uabb9Ynn3yiZ555RsuXL9eECRPK/PznwvTQPnfuXC1cuFDPPvusFi1aJJfLpQEDBig/P7/U/YcNG6Zdu3YpOTlZM2bM0Jo1azR+/HjvFo0yK23uJwBcjCIiIhQVFVXqtujoaEVERHi5IgAoH9OnT/eY9vPyyy+bWxBQisDAQIWGhnrl+EWLFqldu3Z65JFHdMUVV+iGG27Q8OHDlZqaetrcWp5MDe35+flKSkrSsGHD1KlTJ0VGRmr69OnKzMzUF198UWL/n376SRs2bNDkyZPVvHlztW/fXhMnTtSSJUu0f/9+E84A5+J0cz8zMzNNrgwAzp3NZtNll11W6rbLLruMofEALkq//PKLtm3b5tH266+/6pdffjGpIlRm27Zt08MPP6w2bdqoRYsWuummm5SUlCTp1PD2zp07S5L27t2rJk2aaNmyZerRo4eioqLUs2dP7dixQ3PmzNG1117rHqVdnEX+fHzfvn31+OOPezz3Dz/8oCZNmmjXrl1KSEjQmDFjPLbb7XYVFBTo+PHjF/i3YPKc9q1bt+rEiRNq3769uy0oKEjNmjXTDz/8oG7dunnsv3HjRtWuXVsNGzZ0t7Vt21Y2m00//vijbr/99jLVYRiGcnJyynYSOCuGYSgpKanUuZ9JSUlKTEzkCy6Ai0phYaGWLVtW6ralS5fqjjvukK8vS8cAuHgUT1stzcyZM/XSSy+xkJjF5eXlyeVyqaioSEVFRR7b/vqztxUVFZ3T9/3c3FzFx8erQ4cOWrBggXx8fPTRRx9p8uTJatu2rVwulwzDUFFRkXuq2vTp0/Xcc88pKChIw4YNU+/evXXDDTforbfe0oYNGzRx4kR16NBBN954o8fxPXr00MSJE3X06FEFBgZKkpYsWaKYmBhdcsklHucgSQUFBXrzzTfVokUL1ahRo9TfbXFdubm5pa7YbxjGWf8+TP02UdzDWrduXY/2OnXqlNr7un///hL7+vv7Kzg4WPv27StzHQUFBdqyZUuZj8eZHTp0SJs2bSrR7nK5tGnTJq1du1Y1a9Y0oTIAKJu1a9f+7fbk5GR16NDBS9UAwPnbsWOHTpw4Ueq2EydOaPny5R6dZ7AmX1/fUteNKiwsNKGa/zl58uQ5Xcx2Op26//77dc8996hq1aqSpAEDBmj+/PnatGmTCgsLZRiGTp486T7fPn36uKeuderUSe+9956efPJJValSRT169NDs2bO1ZcsWtW/f3uP4G264QXa7XStWrFC3bt1UUFCgzz//XImJiTp58qRHXYWFhRo7dqy2b9+u+fPnl9heLC8vT4WFhfr9999Pe47+/v5n9bswNbQXLyD312IdDoeOHDlS6v6lnZjD4TivBc38/PzUqFGjMh+PMzMMQxs3btSWLVs8rjTZ7XY1bdpUHTp0oKcdwEXlyiuv1Pr160+7vX///vS0A7ioNGnSRCtXriw1uAcGBur222+np93i8vLylJGRIYfDUWJVc7NDe0BAwDn9Xaxbt6769u2rZcuWacuWLdq9e7d+/fVXSZKPj498fX1ls9kUEBAgh8MhSWrYsKH7vKtXr65atWopJCTEowaXy+Wupfj4gIAAde3aVZ9//rl69eqlf//73yooKFBsbKzH7/HEiRMaMWKEfvjhB82YMUOtW7f+23Pw9fXVZZdd5q7vz7Zv337WvwtTv00U/wLy8/M9fhl5eXmqUqVKqfuXNtE/Ly/PffWlLGw223kdj7OTkJCg0aNHe7TZbDY99NBD7mEoAHAx6d27txYtWlSi/f7771dQUJAJFQHA+Rk2bJief/75Eu2JiYmqVq2aCRXhXNjtdtntdvn4+MjHx8dj21+nqXpbaTX9nQMHDujee+9VaGioOnfurI4dOyoqKsrdK26322Wz2eTj4+O+mOTv7+9+jtJ+Dzabzd3+5+Ml6a677lK/fv10+PBhLVu2TF26dFGNGjXcx2ZlZWngwIH6448/NH/+fLVp0+aM52u321WlSpVSbwt3Lh2Wpl4qKx7qnpWV5dGelZWlsLCwEvuHh4eX2Dc/P19Op1N16tS5cIWiXISHhys2Ntb9BrXZbIqLiyv1tQaAi8Ffr8BLUpUqVXTHHXeYVBEAnJ8WLVqocePGHm1NmjRR8+bNTaoIldXSpUvldDr13nvvafDgwbr55pvdo7EvxAWIq6++WvXq1dOSJUu0evVq9ezZ073tyJEj6tevn7Kzs7VgwYIzBvbyZmpoj4yMVLVq1TyGFx49elSbN28u9RfRpk0bZWZmateuXe62DRs2SNIZhybAGuLi4hQcHCxJCgkJUWxsrLkFAcB5evbZZz1+njhxokmVAED5GD58uEcny19X1cbFzTBcchlFXvvPMEouwnY2wsPDlZubq88++0wZGRn697//rREjRkjSBbnNms1mU48ePTRnzhyFhobqmmuucW97/vnntWfPHk2ZMkWhoaE6cOCA+z9vLPBn6vB4f39/9enTR1OnTlVoaKjq1aunKVOmKDw8XLfccouKioqUnZ2t6tWrKyAgQC1btlRMTIyGDx+u8ePHKycnR+PGjVOPHj3orb1IOBwOJSQk6K233lK/fv1Knd8BABeTiIgINW7cWNu2bVPjxo25PzuAi15QUJC6d++ulJQUxcXFMd2ngrDb7aoRFKKVv0/1+nPXCAo55/UQbr31Vm3atEkvvPCCjh8/rnr16unuu+/WqlWrlJ6eXmKB8vJw5513avbs2erbt6+73qKiIi1fvlwFBQXq169fiWNWrVrlscL8hWAzTJ7cUFRUpJdeekkff/yxTp48qTZt2mjcuHG65JJLtHfvXt100016/vnn3cMTDh06pAkTJujbb7+Vw+HQrbfeqn/84x9lDn/p6emS5F5lEAAAAAAuRidPntTOnTtVv379UudRu1yuUm8/dqEVz0GvTM70WpxLDjU9tJuN0A4AAACgIjhTUIT3lGdor1yXOwAAAAAAuIgQ2gEAAAAAsChCOwAAAAAAFkVoBwAAAIAKpJIvW2YJ5fkaENoBAAAAoALw8/OTzWbTiRMnzC6l0svJyZF06jU5X6bepx0AAAAAUD58fHxUo0YNHThwQHl5eQoKCpKvr69sNpvZpVUahmEoJydHWVlZCg4Olo+Pz3k/JqEdAAAAACqI8PBwValSRVlZWTp69KjZ5VRawcHBCg8PL5fHIrQDAAAAQAVhs9kUHBysGjVqqKioSIWFhWaXVOn4+fmVSw97MUI7AAAAAFQwNptNvr6+8vUl8l3sWIgOAAAAAACLIrQDAAAAAGBRhHYAAAAAACyq0k9wKCgokGEYSk9PN7sUAAAAAEAlkJ+ff9a34qv0oZ17FgIAAAAAvMlms511FrUZhmFc4HoAAAAAAEAZMKcdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAIDTOHHihCTJ5XKZXAkqK0I7YEGGYbj/zR8IABe74s+0PXv26MiRIyZXAwBnLzs7W08//bS2bt0qu92u7Oxss0tCJURoByzIZrNJkt555x399NNPkqSioiIzSwKAMrPZbFq1apUGDBigffv2mV0OAJy1wMBAHTlyRL169dJjjz2m6dOn6+TJk2aXhUqG0A5Y2Mcff6x58+ZJknx8fEyuBgDKZvfu3Vq4cKHuueceRUZGml0OAJw1h8OhSZMmqVatWvryyy/Vtm1bBQQE0JkCryK0Axbx5yHxxX8Ihg4dqkOHDunnn382qywAOC87duzQmDFjtHnzZjVu3FgS034AXByKv5tlZ2crNDRUV111lcaNG6dffvlFPj4+BHd4DaEdsADDMNxD4qX/9aq3aNFCx48f16pVq8wqDQDOS8OGDXXVVVcpNzdXCxYsUHZ2tux2u8eFSgCwkuILi8XfzRo1aqS3335bU6dO1dVXX60+ffq4gzsXIeENhHbAAor/KLz99tsaNWqU9uzZo+PHjyssLEyPPPKIlixZok2bNplcJQCc2Z/DeEFBgSRpzJgxGjhwoLZv365XXnlF2dnZstlsBHcAluNyuWS3n4pI77//vkaPHq2nn35aLpdLl1xyiUaNGqU2bdqob9++2rx5s+x2u37//Xc+z3BB+ZpdAIBTfyCOHTumrKwsbdiwQQ899JCaN2+uQYMGKTo6WpGRkdqyZYuaN2+uoqIi5rcDsKTiUUPff/+9VqxYoR07dqh+/fq68847NWTIEBmGoWXLlslms+mRRx5RaGhoiZFGAGCm4sA+efJkffLJJ7rttts8vnc1btxY48aN0/jx43XvvfcqPDxc1157rSZMmGBWyagEbAaXhQBT/PlK7l/b33jjDa1bt04bNmxQ//79tWbNGvn5+WnhwoUKCAgwoVoAODurVq3SyJEjdfPNN6tKlSpau3atAgICNGDAAN155516+eWX9dVXX6lly5YaMWKEQkJCzC4ZADx8+eWXmjx5smbPnq3IyEgdPnxY27dv17fffqvLL79cd955p/bv368lS5Zo165dmjhxovz8/MwuGxUYPe2ACf4c2IuHvmdkZCg6OloDBw7UoEGDNGjQIC1ZskTr1q2TYRjavHmzUlNTdffdd9MzBcCS9u/fr5kzZ2rUqFF64IEHJEkHDx7U2LFjlZSUpMaNG+vxxx/XkSNHtGXLFhZxAmAJf+1IOXr0qMLDwxUZGal169YpOTnZ/Znl7+8vHx8f9ejRQ4888oj7mMLCQvn6Eq1wYTCnHTBB8R+GKVOm6OWXX9aJEyfUsGFDvfTSS/rnP/+pY8eOSZK6d++uJ598UjNmzFCbNm305ZdfShKBHYAl5efnKycnR61atZJ0ak57rVq1NGnSJB05ckSffPKJJOmZZ57RnDlzVKtWLTPLBQBJ//te9uGHH2rXrl2qWbOm9uzZo7vuuksJCQmqWrWq/vGPf2j16tUKDAxUVlZWiccgsONC4t0FmOTbb7/V0qVLNX36dMXExOibb76Rr6+vbrrpJmVlZal69eqSpMDAQNWoUUNTpkxRbGysfvjhB7Vp08bk6gGgpJycHO3Zs0f79u1Ts2bN5Ovrq4KCAtWsWVNXXXWVxxfdmjVrmlgpAPyPYRgqLCzU1KlTddddd2n06NF67LHHtGXLFg0ePFjXXnutqlSpIkkKCgpSYGCgyRWjsiG0AybZt2+fGjVqpJiYGH3xxRcaM2aMxo4dq7Zt2+qRRx5Rnz59dNttt8nX11cul0tZWVkKCQlhdVIAllA8TScrK0tVq1aVJDVp0kQdOnRQcnKyQkJCFBMT457nmZeXp8jISDNLBgC3P081dLlc8vPz04gRI/TRRx9p37596tWrlyTp8OHDWrt2rcLDwzVjxgwdO3ZM9957r5mloxJieDzgBaUFbZvNpsLCQi1evFhjxozR6NGj1bt3b+Xm5iojI8OjRyo/P1/r1q3T7t27VbduXW+WDgClstlsWrlypR544AHdc889euGFF3T48GENGjRIR48e1YsvvqilS5dq3bp1mjx5stLS0tS9e3ezywYASZ5TDYtXh4+OjlZWVpa+//57SdLJkyeVkpKiF154QaNGjZLL5dJHH30kX19f1uSAV7F6PHCB/Xlxk+zsbIWGhkqSfvzxRw0ZMkRHjx7VyJEj9dBDD0mSjhw5or59++qRRx7R7bff7n6cY8eOyel06tJLL/X+SQDAX/z222/q06eP+vfvr3379umXX35ReHi4Jk+erK1bt2r+/PlavXq16tevrypVqmjSpElq2rSp2WUDqMQOHTqkAwcOyMfHR1deeaUWLVqkgwcPqk+fPqpWrZp8fX318ssva8WKFUpOTlbdunV17NgxZWRkyMfHRw0aNJDdbmfROXgdoR24gP489GrevHlavXq17Ha7HnjgAd1222165513NGnSJMXHx6t9+/YKCgrSrFmz5HQ69cEHH7iv/LJaPAArKP4sys/P148//qivv/5aTz31lCTpgw8+0KJFixQeHq5nn31WNWvW1O+//67AwEBVqVJFQUFBJlcPoDJ76qmntHv3bm3cuFF2u10JCQk6dOiQVqxYocaNGys6OlpDhw5VRkaGpk2bpvvuu09dunQp8Tinu2UvcCER2oEL5M8f6rNmzdJbb72l+++/X2vXrlVmZqYeffRR9enTR2+++aYWL16sHTt2qGnTpgoKCtIbb7whPz8/FRUVuYM7AJipOLCvXr1aqamp2rlzp2rUqKHXX3/d3eO0aNEiffDBB6pbt65GjRqlK664wtyiAUBSfHy8cnNzNWjQIFWvXl1Op1Pt27d3rwT/xhtvaMOGDdq/f7/i4+P18ccf68orr9Ts2bPNLh2QRGgHLrjMzExNmjRJ/fv3V+vWrSVJo0eP1g8//KCEhAT17dtX2dnZ2rt3r6pXr67LL7+coVcALOn777/Xo48+qrZt2+rw4cP67bffNGfOHHXo0MG9z4cffqg33nhDzZo105QpU+Tj48NIIQCmmTVrltauXau5c+e6pygWf8cqKCiQn5+fCgsL5XK5NG/ePO3cuVPr16/XwYMHNWfOHN10000mnwHAQnRAufrnP/+pDz74wP3ze++9p9jYWG3bts1jaOiLL76oNm3a6M0339Tbb78tHx8fRUdHq379+rLb7XK5XAR2AJayf/9+ffvttxo5cqTmzZunmTNn6qabbtLQoUP13Xffufe7++679cgjj2jEiBHy9fUlsAMw1ebNm9WlSxd3YJf+d091Pz8/uVwuvffee8rPz9fQoUM1atQoTZkyRVdccYW++eYbs8oGPBDagXKSkZGhw4cPa/bs2UpNTZUk3Xfffbryyiu1a9cubdq0SQUFBe79X3zxRbVr107Tpk3T2rVrPR6LuVIArMLlcmnPnj269957tXjxYuXl5UmS6tatqzFjxqhz584aMmSIe7VlSbrzzjtZNBOAqVwul/bv36+0tDS1aNHC3fZXR44cUXJysmbMmCFJqlOnjtq3b69nn31Wy5Yt044dO7xaN1AakgFQTiIiIjR8+HB16NBBU6ZM0UcffSRJWrhwoZo3b67p06fr+++/V2FhofuY559/Xg8//LC6du1qVtkAUKri2XN2u12XXnqp+vXrp4KCAv3444/6/fffJZ36cvvkk0+qS5cuio+P14YNG8wsGQDc7Ha7atWqpWrVqrkvKv61U8QwDIWEhOjKK69Udna2e5/iz79atWp5fG8DzEJoB8pB8Qf6lVdeqeuvv16RkZF6/vnntWzZMknSRx99pNDQUI0fP75EcB88eLB8fHy43ycAS7HZbFq3bp0efPBBSacWckpMTNTPP/+sBQsWaOfOnZKk2rVr64knnlCvXr1Us2ZNM0sGADfDMFRQUKAGDRpo3bp17s+sPyuevuNyueRwONztubm5+uqrr7R7927VqFHDazUDp0NoB8pB8dyoadOmKTk5Wbm5uTIMQ5MmTdLixYslSZ988omCg4M1YcIErV69ukRIZ5V4AGb789q0hmHI6XQqLS1NDz30kCSpf//+euihh/T555/r3Xff1f/93/9JksLCwjRhwgQ1bNjQjLIBoASbzaaAgAANHTpUmzdv1htvvKEDBw6U2C87O1tHjx5Vs2bN3G1Vq1ZV7969tXTpUoWHh3uzbKBUhHagjHJzcz1+Xr58uT744AONHj1a8+bN09tvv62uXbtq5syZ7qHyH3/8sQoKCpSSkkJIB2AZkyZN0vfff++xaJzNZtONN96o6dOna9OmTerfv78kKSEhQQkJCVq1apVee+017d69WxIXHgFYU8uWLTVu3DgtWbJEzz77rHuofGFhoTIyMvTUU0/JMAzdd9997mMMw9Dll1+uBg0amFU24IHlqYEyGDlypG688UZ169bNfe/i33//XVFRUe7bujVv3lzVqlXTyZMnNXXqVFWtWlW33Xab1qxZw1B4AJbxwgsv6J133tFdd90lSdq6dasiIyMlSQ6HQzfccIMmTJigZ555RvHx8XrzzTeVkJCg3NxcpaamqmrVqmaWDwBn1KtXL9WoUUPPPPOM0tLSFBYWJpvNJl9fX9ntdr377rvuqYrcphJWxH3agXO0Zs0affPNNxo9erQcDofy8/Pl7++vV155RZ9++qnefvtthYWFufdfuXKlhg4dKkke9/ss/sMAAGaZMmWK3n//fb3//vtq2LCh9u/fr65du6pdu3Z67bXX3PudPHlSK1eudC869/LLL0uSnE6ngoODzSkeAM5RRkaGvvvuO/38888KCQlRkyZN1LVrV/n4+Ljv3Q5YEaEdOAcjRozQ8ePHNW/ePEnSggULlJOTo759++q///2vRowYoQcffNBjQaaff/5ZM2bM0M0336y7776boA7AEubMmaNZs2Zp/Pjx6t27tyTpxIkTWrFihaZMmaJrrrnGfQskSTp69Kj69u2rX3/9Vddff73mzZvnHmkEABczOlJgdVxOAs5Bdna26tat6/559erV+u2331S9enX17t1bvXv31uzZs+V0OnX99derdu3amjlzpgIDA3XvvffKZrPxhwGA6SZNmqRPPvlEERERevfdd3XppZeqXbt2CgwM1G233SY/Pz9NmDBBw4YN08yZMyVJQUFBatmypYYPH+5ecI7ADuBi8+eLjcX/5nsZrI7QDpyFgoIC+fn5ycfHR1WqVHG3v/766xo5cqTmzZsnm82mxx57TAEBAVq6dKmSk5PVpEkTORwOvfLKK7LZbHK5XPxhAGCqF154QR9++KFWrFihunXrKjY2VhMnTtQzzzyjtm3bKjAwUF26dJEkTZgwQYMHD9b999+vf//73/ruu+80dOhQ1alTx+SzAICy+euCm8DFgOHxwFkovhL74IMP6tJLL9WkSZPcQV6Shg8frv/+978aNGiQevfuLafTqd27d6tq1apq0KCB7HY7c6UAmC47O1tPPfWUHn/8cfdic/n5+brrrruUn5+v8ePHq02bNvL19dWJEye0Zs0aTZ48WUVFRapSpYpmzJjhcVskAABw4RHagb/x8ccfq3Xr1rr88sslSQ8++KAuueQS/etf/yqxb2JiotLT0zVgwADFxsaqevXq7m0ul0t2O3dYBGC+4sUzDcNQUVGRfH19TxvcJen48ePatWuX6tatq9DQUJOrBwCg8iFFAKexY8cOPfXUU1q0aJF27twpSapataqys7P16aefatOmTTp06JCys7MlSTNmzFCHDh00e/Zs/fvf//Z4LAI7AKvw9/eXJPftjgoLC+Xv76+PPvpI/v7+Gj9+vH744Qf3rSmrVaum5s2bE9gBADAJPe3A3/jxxx81fPhwde3aVQMGDNDzzz+vb775RoZhKDc3V/7+/oqIiNDll1+uxo0by8fHR7t27dLUqVOZuw7golE8fae4x93lcmnMmDG67rrruOgIAIDJCO3AGfz4449KTEzUXXfdpa+//loNGzbU008/rc2bN2v79u36/fff9Z///Ed79uxR06ZN9c4778jHx4dV4gFcVP4c3Lt06aJatWppwYIFHotvAgAA7yO0A2dh48aNeuyxx+R0OvXwww/r8ccf99huGIby8vLk7+8vu93OHHYAF6U/B/f9+/fr0ksvNbskAAAqPVIFcBauvvpqvfbaa5Kk33//Xfv373dvKywslM1mU0BAAIEdwEXtz3PcCewAAFgDyQI4S9HR0VqwYIF++uknJSUlac+ePZJU4jZuBHYAFzNuTQkAgLUwPB44R8WL03Xs2FGJiYmqU6eO2SUBAAAAqKDoEgTOUevWrTV58mQdOnRItWvXNrscAAAAABUYPe1AGRmGIZvNxhx2AAAAABcMoR04D8XBHQAAAAAuBLoHgfNAYAcAAABwIRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAUMn07dtXffv2Pa/H+Pjjj9WkSRPt3bv3vOtp0qSJZs2add6PAwBARURoBwAAAADAogjtAAAAAABYFKEdAACU8OGHH6pnz5666qqrFB0dre7du2vFihUl9ktLS1OPHj3UokULdevWTcuXL/fYnpeXpxdffFE33HCDWrRoodjY2BL7AACA0/M1uwAAAGAtCxYs0HPPPafHHntMrVu31pEjR/T666/riSeeUKtWrRQeHu7ed9y4cXr00UfVtGlTffLJJxo+fLj8/f3VpUsXGYahIUOGKC0tTcOGDVPDhg315Zdfavjw4crPz1ePHj3MO0kAAC4ShHYAAOBhz549euihhzR48GB3W7169dSzZ0/9+OOPuuOOO9ztjz32mB566CFJ0vXXX6//+7//09y5c9WlSxd99913+vbbbzV9+nTdfvvtkqSOHTsqNzdXU6dOVbdu3eTry1cRAAD+Dn8pAQCAhyeffFKSdPToUf3+++/atWuX1q9fL0nKz8/32Lc4jBfr0qWLZs2apRMnTuj777+XzWbTDTfcoMLCQvc+nTt3VkpKin777Tc1bdr0Ap8NAAAXN0I7AADwsHv3bo0bN07ff/+9/Pz81KBBA0VGRkqSDMPw2LdWrVoeP9esWVOGYej48eNyOp0yDEMxMTGlPk9WVhahHQCAMyC0AwAAN5fLpUGDBsnPz0+LFy9W06ZN5evrq+3bt2vJkiUl9j9y5IhHcD948KB8fHxUo0YNVa9eXVWrVtXbb79d6nNdfvnlF+w8AACoKFg9HgAAuB0+fFg7d+5Ur169FBUV5Z5z/s0330g6Fer/bPXq1e5/u1wuffbZZ2rZsqUCAgLUtm1b5eTkyDAMRUVFuf/btm2b5syZ4zFkHgAAlI6edgAAKqHMzEwlJyeXaG/cuLHq1aunBQsWKDw8XEFBQfr222/dveW5ubke+7/88ssqKipS3bp19d5772nnzp168803JUk33HCD2rRpo8GDB2vw4MFq2LChfv75Z82cOVMdO3ZUaGjoBT9PAAAudoR2AAAqod27d+v5558v0d6rVy/NnTtXkyZN0pNPPil/f381atRIr7zyiv71r39p48aN6tu3r3v/559/Xi+88IJ27dqlxo0b6/XXX1fbtm0lSXa7XfPmzdOMGTP02muv6dChQwoLC1N8fLyGDBnitXMFAOBiZjP+uqIMAAAAAACwBOa0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBF/T/Ox6eUHXencQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -328,7 +623,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -348,7 +643,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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ZhmG99KMw6enpOnjwoJo3b17o8rzwXxhfX1/r4+WpXr16vuvl+/fvr3/96186evSodu/erXPnzhV5KLok/fXXX5o8ebK+/fZbubu7q1GjRmratKmkgr9DV/9+V69eXYZhKDMzU+np6TIMQ2FhYYU+zvHjx4sU2otTz9WXXZjNZus6tryX8vTu3Vsvv/yy0tLSdPjwYR08eFCvvvqqpKJ/JuSpXLlyvu+L8/udkZFRaM15ny1Xvlevfg/lHfffXcMPAI5AaAcAF+Dj4yNJOnnyZIFlJ06cKHZ4LYq867+Dg4O1YcMGNWrUSGazWdu2bdNnn31W5P1kZGRoxIgR8vf316xZs/L9k1+1alVJ0oQJE9SmTZsC2+aNDPDx8Slw7IZhWP/pLo5GjRpp5syZys3N1X//+1/Fx8frgw8+UL169TRkyBBJl69Xv9LVk+IVVWJioqZNm6bx48erX79+1mA+evRoJScnS/q/5+D06dNq1KiRdduUlBT99ddfCgkJkclkUmRkZIEeQalgmLgeX19fpaWlKTc3N1/vdl5QupYWLVpowYIFys7O1u7du/Xhhx9q8eLFatq0qf7xj3/YdNxF4e3tLZPJVGDCvaysLH333Xdq2bKlqlSpojZt2mjChAmF7uN6J2LOnj0rwzDyvSdPnjyZ7wRKjx499Morr+jTTz/Vrl271L59e9WsWbNI9VssFg0bNkzu7u5as2aNbrnlFlWoUEG///674uPjC6yfkZGRL7ifPHlSbm5uqlatmqpUqaJKlSrpvffeK/Sx6tevb/d6rsfW95Ik/eMf/9Arr7yizZs3a//+/QoMDNRtt90mqeifCddSlN/vq/d14sQJBQYGWttzcnKUlpZWKp+rAFAamD0eAFxAw4YNddNNN2n9+vX52g8dOqQff/zxmr1vJbF//36lp6frscceU5MmTaw9knkzJhelh+nSpUt6+umndfLkSS1cuLBAb3+jRo1UvXp1HT58WKGhodavmjVr6vXXX7eOIGjXrp2++uqrfENRv/76a+Xk5BTrmD799FPdcccdOnHihNzc3NSqVSu9+OKLqlq1qlJSUiRdDoqpqan5ttu9e3exHufK7apWraohQ4ZYg+C5c+e0e/du6/PXokULubu7W4dk54mNjdXYsWNVqVIlNWvWTPv378/3HN18882aP3++dWhzUbRr106XLl3S5s2brW3Z2dnavn37NbeJi4tTly5dlJ2dLQ8PD7Vr104vv/yyJFmfs6t7q4ty3EVRuXJl3XLLLQWem6+++krDhg3T8ePH1aZNGx04cEANGzbM9/zEx8drzZo1172E4sKFC/nuhHDu3Dl9+eWXatu2rbWtUqVK6tmzp9avX6/t27erX79+Ra4/LS1NBw4cUP/+/RUaGqoKFSpY65cK/g59+eWX1u8tFos+/fRTtWzZUl5eXmrTpo3Onz8vwzDyHef//vc/LVy4sMBlB/ao53pseS/lqVq1qrp06aIvvvhCn332mSIiIqwnTor6mVCYovx+XynvpMCGDRvytW/YsEG5ubnWEwkA4OroaQcAF2A2mzV27FhNnDhR48aNU0REhNLS0rRgwQJVq1ZNgwcPtvtjNmzYUN7e3lq8eLEqVKigChUq6LPPPtOaNWskFe1azmnTpumbb77Rs88+q3PnzuW79ZK3t7eaNGmiMWPGaPLkyXJzc1OXLl105swZLVq0SMeOHbMOeR41apQ2b96sxx9/XEOGDNHp06c1Z86cfNe4F0VYWJgsFotGjRqlYcOGqXLlyvrkk0909uxZ67XNXbp00ZYtW/Taa6/p7rvv1q5du7Ru3bpiPU6eFi1a6IMPPtC0adPUpUsXHT9+XO+8845Onjxp7eXz8/PTY489pri4OHl4eKhNmzb66aef9MEHH2jChAnW137YsGHW1z5v5vqffvpJI0eOLHI97dq1U4cOHTRp0iSdOnVKgYGBeu+993T69OlrDkG/4447NGvWLI0aNUoDBgyQm5ubVq1aJQ8PD3Xp0kXS5RB28uRJbdu2TbfcckuRjruooqOjNWLECI0dO1Z9+vTRyZMn9cYbb6hr164KCgpSZGSk4uPjFRkZqaioKPn6+mrjxo3697//rYkTJ1533+7u7vrXv/6lsWPHytvbW0uWLNHFixcLPKf9+/fXgw8+qGrVqlnnYyiK6tWrKzAwUO+//74CAgJUtWpVff3119be8qt/h+bMmaPc3FzVqlVLH3zwgQ4cOKB3331XktSpUye1bt1aI0eO1MiRI9W4cWP997//1bx589SxY8drXl5Rknqux5b30pUiIiIUHR2t3Nxc9e7d29ru5uZWpM+EwhTl9/tKTZo0Ud++fTVv3jxduHBBrVu31r59+7RgwQK1bdu2wJ0vAMBVEdoBwEX069dPlStX1ltvvaVRo0bJ29tbHTt21NixY4t0HWlxValSRYsWLdKMGTM0evRoa6/nihUrNHToUO3atSvf/Y4Ls2XLFkkq9PZYbdq00fLly/XAAw+ocuXKWrp0qT788ENVqlRJYWFhmjVrlnWSvAYNGmjFihWaNm2axowZo+rVqysmJqbIt93KU6NGDS1dulRz587Vc889pwsXLlh7rO+44w5J0v3336+//vpLa9eu1apVq9S6dWvNmzdPDz/8cLEeS5L69u2rw4cP66OPPtLKlStVs2ZNderUSY888oief/55/fHHH2rcuLHGjx+v6tWra9WqVVq6dKnq1Kmj559/Xg899JAkqUOHDnrnnXe0YMECRUdHy93dXc2bN9e7776rW2+9tVg1LViwQLNmzdK8efOUlZWlnj176p///Ke++OKLQtdv2rSpFi9erIULF2rs2LHKzc1VSEiIYmNjrcP5+/Xrp23btmnUqFGKjo7W0KFDi3TcRdGlSxctXrxYCxYs0KhRo+Tn56fw8HDr3Ag1a9bUqlWr9Prrr+vFF19UVlaWGjRooKlTp6p///7X3befn5/GjRunN954QydOnFDLli21YsWKfJcpSNKtt94qHx8f9ezZs9jzHixatEhTp07Vs88+Kw8PDzVp0kRvvvmmXn31Ve3atUsDBw60rvvaa69p2rRpOnjwoIKCgvT2229be4PNZrOWLFmiuXPn6q233tKpU6dUs2ZNDR48WKNGjSqVev5Ocd9LV+rUqZOqVKmiunXrqmHDhvmWFeUzoTBF+f2+2tSpU1W/fn199NFHevvtt1WjRg099thjGjlyZIERJADgqkxGcWYaAgAAKGN++ukn/fOf/1R8fLx10jYAAFwFPe0AAJdnsViKdD1u3jW8ZU1ubu7fzuZvMpmKfYu88u7777/X999/r3Xr1qlDhw4EdgCASyqb/90AAMqUhQsXasGCBX+73hdffKE6deo4oCLHioyMtN5z+loCAwOtlyugaNLS0vTuu+/q5ptv1iuvvOLscgAAKBTD4wEALu/YsWMF7t9cmODg4GJfk3wj2L9/v86dO3fddTw8PBQcHOygigAAgKMQ2gEAAAAAcFFMmwkAAAAAgIsitAMAAAAA4KLK/UR0P/zwgwzDkLu7u7NLAQAAAACUAzk5OTKZTGrVqtXfrlvuQ7thGH97Gx0AAAAAAOylOBm03If2vB720NBQJ1cCAAAAACgPkpOTi7wu17QDAAAAAOCiCO0AAAAAALgoQjsAAAAAAC6K0A4AAAAAgIsitAMAAAAA4KII7QAAAAAAuChCOwAAAAAALorQDgAAAACAiyK0AwAAAADgogjtAAAAAAC4KEI7AAAAAAAuitAOAAAAAICLIrQDAAAAAOCiCO0AAAAAALgolwvtb731lgYOHHjdddLS0jRu3Di1bt1abdq00UsvvaQLFy44qEIAxZGUlKTRo0crKSnJ2aUAAAAANxyXCu3vv/++5syZ87frRUdH6+DBg4qLi9PcuXO1bds2vfjii6VeH4DiycrKUmxsrE6ePKnY2FhlZWU5uySgVHByCgAAlBaXCO3Hjh3TE088oVmzZqlBgwbXXfeHH37Qjh07NH36dDVv3lzt2rXTlClTFB8fr2PHjjmmYJQI/9yWHwkJCUpPT5ckpaenKzEx0bkFAaWAk1MAAKA0VXB2AZK0Z88eubu7KyEhQQsXLtSRI0euue6uXbt00003qXHjxta2Nm3ayGQyaffu3erZs6cjSra7jIwMhz+mYRjKzs526GNmZ2dr6dKlysjI0NKlS/Wvf/1LHh4eDq3Bw8NDJpPJoY8pSdWqVbN+74zX22KxKDMz02GPd/LkScXHx8swDEmX32/x8fFq3Lix/P39HVaHt7e3zGbHn5+88vUur8rD+1ySNm3apLS0NEmXL99asWKFunXr5tAaXOF9Xl5eb0nau3evEhISFBERoWbNmjn88V3h9S6vytP73Nlc4X3O6+04rvB6uzKXCO1333237r777iKte+zYMdWqVStfm4eHh3x8fHT06FGbHt8wDJ0/f96mbe1l5MiRTn18Z8jIyFBMTIyzy3CYt99+2/p9eXy9pct/iGbNmuXsMhziyte7vCqv7/MtW7Zoy5Ytzi7DIcr759p7773n7BIcis+18vk+L2/K++daeePMzzXDMIrckegSob04Lly4UGjPrKenp81DEnNycrRv376SlgZcF++x8oXXG+UB7/Pyhdcb5QHv8/LF2a93UUcc33Ch3cvLq9Ah3VlZWapUqZJN+3R3d1eTJk1KWlqJvP766w59PMMwNH/+fB08eNChj+sK6tevr6eeesrhQ+SrVq1q/d7Rr7d0uZf73LlzDnkswzC0YsUK7d+/3zo8XpJMJpMaNWqkAQMGOOz5r1y5slOGW135epdXZf19fuLECS1cuPCay0eNGqWbbrrJIbW4wvu8rL/eknT69GnNnz+/wOfaU089JT8/P4fV4Qqvd3lVHt7nrsIV3ue83o7jCq+3o/3+++9FXveGC+0BAQHavHlzvrbs7Gylp6erRo0aNu3TZDLZHPjtxRmPP3XqVIdNmJSSkqLnn3/+mstffvll1a5d2yG1eHp6OuWa9is5+/3mCCNHjtSECROUm5trbTObzRo1apRq1qzpxMrgKGX9fd6oUSO9+eabslgsBZaZzWa1bdtWbm5uTqjMOcr6620YhqZPny6TyVQgtG/dulUxMTFO/9uC0lfW3+fIj9cbpak4fzNuuNDeunVrzZo1SwcPHlT9+vUlSTt27JAk3Xbbbc4s7YZjMpnk5eXlkMdq0KCBKlWqVOjcAZUqVVKDBg2ccnYNpScgIEDh4eHWyehMJpMiIiII7Cgzfvzxx0IDu3S5p+THH3/k71IZkpKSouTk5ALtFotFycnJSklJUWBgoBMqAwCUdS6fknJzc3XixAldvHhRktSyZUuFhYVpzJgx+u9//6vvvvtOkydPVp8+fQgDLiwlJeWak/2dP39eKSkpDq4IjhARESEfHx9Jkq+vr8LDw51bEGBHfze6y9bRX3BNtWvXVmhoaIETzGazWS1atHDYaDEAQPnj8qH96NGj6tChgzZu3Cjpcu/wggULVKdOHQ0aNEhPP/207rrrLr344ovOLRTXda3eqKIux43J09NTUVFR8vf31+DBg+Xp6enskgC7qVOnjurUqVPosrp1615zGW5MJpNJkZGRBYYzXqsdAAB7cbnh8dOmTcv3c506dfTrr7/ma6tevbrmzZvnyLJQQidOnPjb5fXq1XNQNXCksLAwhYWFObsMoFRUqFD4n9HydC17ecJlPwAAZ3D5nnaUDa1atbrmZB6VKlVSq1atHFwRAJTMkSNH9Oeffxa67M8//9SRI0ccWxAcgst+AACORmiHQ5jNZo0ePbrQZU8//TST0AEAbghc9gMAcDSXGx6Pssvf37/Q9urVqzu4EgAoucDAQAUHBxe4hEuSmjZtykziZRiX/QAAHInuTTiEYRhasGBBocsWLFiQ7563AHAjMJlMGjZsWKETkxXWDgAAYAtCOxzi8OHDOnDgQKHLDhw4oMOHDzu4IgAouYCAAN1333352nr16sXEZAAAwG4I7XCI48ePl2g5ALiqfv36ydvbW5Lk7e2tvn37OrkiAABQlhDa4RDMHg+grPL09NTw4cPl7++v4cOHMzEZAACwKyaig0OYzWYNHDhQb731VoFljz32GLPHA7ihMTEZAAAoLSQlOIRhGPrmm28KXbZ9+3YmogMAAACAQhDa4RApKSlKTk4udFlycrJSUlIcXBEAAAAAuD5COxyiVq1a1omarubt7a1atWo5uCIAsJ+kpCSNHj1aSUlJzi4FAACUMYR2OMTRo0eVmZlZ6LLMzEwdPXrUwRUBgH1kZWUpNjZWJ0+eVGxsrLKyspxdEgAAKEMI7XCI2rVrKzQ0VCaTKV+7yWRSixYtVLt2bSdVBgAlk5CQoPT0dElSenq6EhMTnVsQAAAoUwjtcAiTyaTIyMgCod1sNhfaDgA3gtTUVCUmJlon0zQMQwkJCUpNTXVyZQAAoKwgtMNhAgICFBERka8tIiJCNWvWdFJFAGA7wzAUFxdX4O4X12oHAACwBaEdDhURESFfX19Jkp+fn8LDw51cEQDYJu+uGBaLJV+7xWLhrhgAbnhMsAm4DkI7HMrT01NRUVHy9/fX4MGD5enp6eySAMAmzNUBoKxigk3AtRDa4XBhYWGaO3euwsLCnF0KANjMZDKpV69ehQ6Pv++++5irA8ANiwk2AddCaAcAwAaGYWj9+vWFLlu/fj3XtAO4ITHBJuB6CO0AANgg75r2wnBNO4AbERNsAq6J0A6HY2ITAGVB7dq1FRQUVOiy4OBgrmkHcMNhgk3ANRHa4VBMbAKgLMnJySm0PTs728GVAEDJ5U2waTbnjwhms5kJNgEnIrTDoZjYBEBZceTIER04cKDQZQcOHNCRI0ccXBEAlIzJZFJkZGShd8UorB2AYxDa4TBMbAIAAODaAgICFB4ebg3oJpNJERERqlmzppMrA8ovQjscgolNAJQ1gYGB172mPTAw0MEVAYB9REREyMfHR5Lk6+ur8PBw5xYElHOEdjgEE5sAKGtMJpOGDx9e6DDSwtoB4Ebh6empqKgo+fv7a/DgwfL09HR2SUC5RmiHQzCxCYCyKCAgQPfdd1++tl69ejGMFMANLywsTHPnzlVYWJizSwHKPUI7HIKJTQCUVf369VPlypUlSd7e3urbt6+TKwIAAGUJoR0Ow8QmAMoiT09PPfHEE/L399fw4cMZRgoAAOyqgrMLQPkSERGhbdu2KS0tjYlNAJQZYWFhDCEFAAClgp52OBQTmwAAAABA0RHaAQAAAABwUYR2OFRWVpZiY2N18uRJxcbGKisry9klAQAAAIDLIrTDoRISEpSeni5JSk9PV2JionMLAgA7SEpK0ujRo5WUlOTsUgAAQBlDaIfDpKamKjExUYZhSJIMw1BCQoJSU1OdXBkA2I4RRAAAoDQR2uEQhmEoLi7OGtj/rh0AbhSMIAIAAKWJ0A6HSElJUXJysiwWS752i8Wi5ORkpaSkOKkyALAdI4gAAEBpI7TDIWrXrq3Q0FCZzfnfcmazWS1atFDt2rWdVBkA2IYRRAAAwBEI7XAIk8mkyMhImUymIrUDgKtjBBEAAHAEQjscJiAgQP/4xz/ytfXs2VM1a9Z0UkUAYDtGEAEoy7grBuA6CO0AANiAEUQAyiruigG4FkI7HCY1NVWffPJJvraNGzcyYROAG1ZAQIDCw8OtAd1kMikiIoIRRABuaNwVA3AthHY4BBM2ASirIiIi5OPjI0ny9fVVeHi4cwsCgBLgrhiA6yG0wyGYsAlAWeXp6amoqCj5+/tr8ODB8vT0dHZJAGATOlkA11TB2QWgfMibsGnPnj35grvZbFZISAgTNgG4oYWFhSksLMzZZQBAieR1slztyk6WwMBAJ1QGlG/0tMMhmLAJAADAtXFXDMA1EdrhMEzYBAAA4LroZAFcE6EdDsWETQAAAK6LThbA9RDa4VBM2AQAAODa6GQBXAsT0cHhmLAJAADAdeV1sixbtkyDBg2ikwVwMkI7AAAAgHzoZAFcB8PjAQAAAABwUYR2AAAAAABcFKEdAAAAAAAXRWgHAAAAAMBFEdoBAAAAAHBRhHYAAAAAAFwUoR0AAAAAABdFaAcAAAAAwEUR2gEAAAAAcFGEdgAAAAAAXBShHQAAAAAAF0VoBwAAAADARRHaAQAAAABwUYR2AAAAAABcFKEdAAAAAAAXRWgHAAAAAMBFEdoBAAAAAHBRhHYAAAAAAFwUoR0AAAAAABdFaAcAAAAAwEUR2gEAAAAAcFEuEdotFovmzZunjh076tZbb9XQoUN16NCha65/6tQpjRs3TnfccYfatm2rMWPG6NixYw6sGAAAAACA0ucSoX3RokVauXKlXn75Za1atUoWi0VDhgxRdnZ2oes//fTTSklJ0bvvvqt3331XKSkpGjVqlIOrBgAAAACgdDk9tGdnZys2NlbR0dHq3LmzmjZtqtmzZys1NVWbNm0qsP6ZM2e0Y8cODR06VLfccouaNWumYcOGKTk5Wenp6Y4/AAAAAAAASonTQ/svv/yic+fOqV27dta2qlWrqlmzZtq5c2eB9b28vFS5cmWtW7dOmZmZyszMVHx8vBo2bKiqVas6snQAAAAAAEpVBWcXkJqaKkmqVatWvvYaNWpYl13Jw8ND06ZN0+TJk3X77bfLZDKpRo0aWrFihcxm285BGIah8+fP27QtAAAAAADFYRiGTCZTkdZ1emi/cOGCpMth/Eqenp7KyMgosL5hGNq3b59atWqlIUOGKDc3V7Nnz9bIkSP1wQcfyNvbu9g15OTkaN++fbYdAAAAAAAAxXR1Br4Wp4d2Ly8vSZevbc/7XpKysrJUsWLFAut/8sknWrFihbZu3WoN6IsXL1aXLl20Zs0aRUZGFrsGd3d3NWnSxLYDAAAAAACgGH7//fcir+v00J43LP748eOqV6+etf348eMKDg4usP6uXbvUsGHDfD3q1apVU8OGDXXw4EGbajCZTKpUqZJN2wIAAABlTVJSkpYtW6ZBgwYpLCzM2eUAZU5Rh8ZLLjARXdOmTeXt7a3vv//e2nbmzBnt3btXrVu3LrB+QECADh48qKysLGvb+fPndfjwYTVo0MARJaOEkpKSNHr0aCUlJTm7FAAAAFwlKytLsbGxOnnypGJjY/P93w3A8Zwe2j08PDRgwADNmjVLX3zxhX755ReNGTNGAQEB6tatm3Jzc3XixAldvHhRktSnTx9Jl+/V/ssvv+iXX37R2LFj5enpqX79+jnxSFAU/BEAAABwbQkJCdZbKaenpysxMdG5BQHlnNNDuyRFR0erf//+mjRpkh5++GG5ubnpnXfekbu7u44ePaoOHTpo48aNki7PKr9y5UoZhqFBgwZp8ODBcnd318qVK1WlShUnHwn+Dn8EAAAAXFdqaqoSExNlGIaky5NAJyQkFHpXJwCOYTLyfiPLqeTkZElSaGiokysp+1JTUzVhwgTl5uZa29zc3DRjxgwFBAQ4sTIAAAAYhqHp06drz549slgs1naz2azmzZsrJiamWNfhAri24uRQl+hpR9lnGIbi4uJ09Tmia7UDAADAsVJSUpScnJwvsEuSxWJRcnKyUlJSnFQZUL4R2uEQ/BEAAABwbbVr11ZoaKjM5vwRwWw2q0WLFqpdu7aTKgPKN0I7HCLvj0Bh+CMAAADgfCaTSZGRkQWGwF+rHYBjENrhECaTSb169Sp02X333ccfAQAAABcQEBCg8PBw6/9mJpNJERERqlmzppMrA8ovQjscwjAMrV+/vtAzt+vXr+eadgAAABcREREhHx8fSZKvr6/Cw8OdWxBQzhHa4RB517QXNhEd17QDAAC4Dk9PT0VFRcnf31+DBw+Wp6ens0sCyrUKzi4A5UPeNe2F3UIkJCSEa9oBAABcSFhYmMLCwpxdBgDR0w4HYWITAAAAACg+QjscholNAAAAAKB4CO1wKCY2AQAAAICiI7TDoZjYBAAAAACKjono4HBMbAIAAAAARUNPOwAAAAAALorQDgAAAACAi7JLaM/KypJhGPbYFQAAAAAA+P9svqZ9//79mjdvnr755htlZmZq9erVWrNmjRo1aqSBAwfas0YAAAAAAMolm3ra9+3bp/79+2vPnj0KDw+39rK7ubnp1Vdf1dq1a+1aJAAAAAAA5ZFNPe3Tp09XSEiIYmNjJUnvv/++JGnSpEnKysrSe++9p759+9qvSgAAAAAAyiGbetp//PFHRUZGqkKFCjKZTPmW9ezZU3/++ac9agMAAAAAoFyzKbR7enrq4sWLhS5LT0+Xh4dHiYpC2ZaUlKTRo0crKSnJ2aUAAAAAgEuzKbS3b99e8+bNU2pqqrXNZDLp3Llzio2N1Z133mm3AlG2ZGVlKTY2VidPnlRsbKyysrKcXRIAAAAAuCybQvv48eN1/vx59ejRQ48++qhMJpOmTZumHj166OjRoxo7dqy960QZkZCQoPT0dEmXR2UkJiY6tyAAAAAAcGE2hfZatWopPj5egwYNkmEYqlevns6fP69evXrp448/Vt26de1dJ8qA1NRUJSYmWu82YBiGEhIS8o3YAAAAAAD8H5vv0+7r66tBgwZpzJgxkqSMjAydOHFCNWrUsFtxKDsMw1BcXJw1sF/dHhMTU2BSQwAAAAAo72zqaT979qyGDBmiRx991Nr2008/qVevXoqOjr7mJHUov1JSUpScnCyLxZKv3WKxKDk5WSkpKU6qDAAAAABcl02hfdasWdq3b5+eeuopa9sdd9yh+fPnKykpSfPnz7dbgSgbateurdDQUJnN+d9yZrNZLVq0UO3atZ1UGQAAAK7G3X4A12FTaN+yZYtiYmLUs2dPa5uHh4fuvfdejR07Vhs3brRbgSgbTCaTIiMjCwyBv1Y7AAAAnIO7/QCuxabQnpmZqWrVqhW67KabbtLp06dLVBTKpoCAAIWHh1sDuslkUkREhGrWrOnkygAAAJCHu/0ArsWm0N60aVN99NFHhS5bt26dgoODS1QUyq6IiAj5+PhIujyZYXh4uHMLAgAAgBV3+wFcj02zxz/xxBN64okn1K9fP917772qXr26Tp8+ra1btyo5OVlvvvmmvetEGeHp6amoqCgtW7ZMgwYNkqenp7NLAgAAgLjbD+CqbArtnTp10qJFizR//nzNmzdPhmHIZDLplltu0aJFi9SpUyd714kyJCwsTGFhYc4uAwAAAFfIu9vP1a68209gYKATKgPKN5vv096lSxd16dJFWVlZSk9PV5UqVVSpUiV71gYAAADAQfLu9rNnz558t+k1m80KCQnhbj+Ak9h0TXuejIwMpaWlKTc3V+np6UpJSbF+AQAAALhxcLcfwDXZ1NN+8OBBxcTE6KeffrrmOvv27bO5KAAAAACOl3e3n/j4eOslsNztB3Aum0L7yy+/rD///FNPPvmkAgICZDaXqMMeAAAAgIuIiIjQtm3blJaWxt1+ABdgU2jfuXOnpk6dql69etm7HgAAAABOxN1+ANdiU2j39vZWtWrV7F0LAAAAABfA3X4A12HTuPbevXvr/fffL3APRwAAAAAAYD829bRXrFhRu3fv1r333qvQ0FB5eXnlW24ymfTqq6/apUAAAAAAAMorm0L72rVrVaVKFVkslkJnkOd2EAAAAAAAlJxNoX3Lli32rgMAAAAAAFylVO7Vtn///tLYLQAAAAAA5YpNPe3p6emaM2eOduzYoezsbOuEdIZh6Pz588rIyNC+ffvsWigAAAAAAOWNTT3tr732mtasWaP69evLzc1NVapUUWhoqHJycnTmzBlNmTLF3nUCAAAAAFDu2BTav/76az311FN688039eCDDyogIEBz5szRp59+quDgYP3+++/2rhMAAAAAgHLHptB+5swZtWrVSpLUuHFj/fzzz5KkypUrKyoqSl9++aXdCgQAAAAAoLyyKbT7+vrq7NmzkqQGDRro1KlTSk9PlyTVrFlTx44ds1uBAAAAAACUVzaF9nbt2mnx4sU6cuSI6tWrp2rVqmnt2rWSpK1bt8rX19euRQIAAAAAUB7ZFNpHjx6tU6dOKSYmRiaTScOHD9f06dPVtm1bxcXF6f7777d3nQAAAAAAlDs23fItMDBQGzdu1J9//ilJGjx4sPz9/ZWUlKQWLVqob9++9qwRAAAAAIByyaae9nXr1unChQtq2rSptS08PFwvvPCCOnTooLfffttuBQIAAAAAUF7ZFNonTpyoQ4cOFbps3759mjdvXomKAgAAAAAAxRgeP2zYMP3xxx+SJMMwNGrUKHl4eBRY79SpU6pXr579KgQAAAAAoJwqcmh/4okntHr1aknS2rVr1axZM/n5+eVbx2w2q2rVqurXr599qwQAAAAAoBwqcmgPCwtTWFiY9eeRI0eqbt26pVIUAAAAAACwcfb41157TZJ0/vx5VapUSZL02WefKSUlRXfffbfq169vvwoBAAAAACinbJqIbv/+/br33nu1ZMkSSdKcOXM0evRoTZ8+XREREdq9e7ddiwQAAAAAoDyyKbTPmjVLFSpU0D333KPs7GytXLlSPXv21K5du9SxY0fNmTPHzmUCAAAAcJSkpCSNHj1aSUlJzi4FKPdsCu27du3SuHHjFBoaqh07dujs2bN68MEH5e3trYceekg///yzvesEAAAA4ABZWVmKjY3VyZMnFRsbq6ysLGeXBJRrNoX2nJwcVa1aVZL01VdfqWLFirrtttskSbm5uapQwaZL5QEAAAA4WUJCgtLT0yVJ6enpSkxMdG5BQDlnU2gPCgrSpk2bdOLECX366afq0KGDKlSooJycHL3//vsKCgqyd50AAAAASllqaqoSExNlGIYkyTAMJSQkKDU11cmVAeWXTaE9Ojpaa9as0V133aWMjAwNHTpUktS9e3d99913GjVqlF2LBAAAAFC6DMNQXFycNbD/XTsAx7BpHHv79u2VmJio5ORktWzZUoGBgZKkQYMG6Y477lBwcLBdiwQAAABQulJSUpScnFyg3WKxKDk5WSkpKdb/+wE4js0Xn9etW1d169bN1zZo0KASFwQAAADA8WrXrq3Q0FDt2bNHFovF2m42mxUSEqLatWs7sTqg/CpyaH/sscf0wgsvqHHjxnrssceuu67JZNKyZctKXBwAAAAAxzCZTIqMjNSECRMKbTeZTE6qDCjfinxN+5XXsBiGcd2vK8/MAQAAALgxBAQEKDw83BrQTSaTIiIiVLNmTSdXBpRfRe5pX758eaHfAwAAACg7IiIitG3bNqWlpcnX11fh4eHOLgko12yaPR4AAABA2eTp6amoqCj5+/tr8ODB8vT0dHZJQLlW5J72u+++u1jXsXzxxRc2FQQAAADAucLCwhQWFubsMgCoGKG9TZs21tBusVi0YcMGValSRZ06ddJNN92k9PR0bd++XadPn9aDDz5YagUDAAAAAFBeFDm0T5s2zfr9rFmz1KJFC73zzjuqWLGitT0nJ0cjRozQ+fPn7VslAAAuLCkpScuWLdOgQYPomQIAAHZl0zXtq1ev1tChQ/MFdklyd3fXwIEDtXHjRrsUBwCAq8vKylJsbKxOnjyp2NhYZWVlObskAABQhtg8EV1GRkah7SkpKcWarMJisWjevHnq2LGjbr31Vg0dOlSHDh265vo5OTl6/fXXresPGDBA+/btK3b9AADYQ0JCgtLT0yVJ6enpSkxMdG5BAACgTLEptN99992aNWuWtm/fbm0zDEOff/655syZo549exZ5X4sWLdLKlSv18ssva9WqVbJYLBoyZIiys7MLXf/FF1/Uxx9/rFdffVUfffSR/Pz8NHToUJ09e9aWQwEAwGapqalKTEyUYRiSLv8tTEhIUGpqqpMrAwAAZYVNoX3ixImqVauWHn/8cbVo0UIdO3ZUaGiooqOjFRoaqvHjxxdpP9nZ2YqNjVV0dLQ6d+6spk2bavbs2UpNTdWmTZsKrH/o0CF99NFHmjp1qjp27KjGjRvrlVdekYeHh37++WdbDgUAAJsYhqG4uDhrYP+7dgAAAFsUeSK6K1WtWlX//ve/tW3bNu3evVsZGRny9fXVHXfcoXbt2hV5P7/88ovOnTuXb5uqVauqWbNm2rlzp3r16pVv/e3bt6tKlSq666678q2/ZcsWWw4DAACbpaSkKDk5uUC7xWJRcnKyUlJSFBgY6ITKAABAWWJTaJckk8mkzp07q3Pnztdcx2Kx6N5779XixYt18803F1ieN3ywVq1a+dpr1KhR6NDCAwcOqG7dutq0aZOWLFmiY8eOqVmzZnr22WfVuHFjWw9FhmEw4z0AoFh8fHzUvHlz7du3TxaLxdpuNpt1yy23yMfHh78tAACgUIZhWG+p/ndsDu1FLeTIkSPXvD79woULkiQPD4987Z6enoVOdJeZmamDBw9q0aJFmjBhgqpWrao333xTjzzyiDZu3Kjq1avbVGdOTg6T2QEAiq1NmzaF/v1o27atfvnlFydUBAAAbhRX5+BrKdXQ/ne8vLwkXb62Pe976fLtc66+nZwkVahQQZmZmZo9e7a1Z3327Nnq1KmT1q5dqyFDhthUh7u7u5o0aWLTtgCA8u348ePauHGj9Yx5z549i3WpGAAAKH9+//33Iq/r1NCeNyz++PHjqlevnrX9+PHjCg4OLrB+QECAKlSokG8ovJeXl+rWravDhw/bXIfJZFKlSpVs3h4AUH7df//9+uabb5SWliZfX1/169evWLc+BQAA5U9Rh8ZLJbhPuz00bdpU3t7e+v77761tZ86c0d69e9W6desC67du3VqXLl3KN/HPxYsXdejQIdWvX98hNQMAcCVPT09FRUXJ399fgwcPJrADAAC7cmpPu4eHhwYMGKBZs2bJz89PgYGBmjlzpgICAtStWzfl5ubq9OnTqlKliry8vHT77bfrzjvvVExMjKZMmSIfHx/NmzdPbm5u6t27tzMPBQBQjoWFhSksLMzZZQAAgDLIqT3tkhQdHa3+/ftr0qRJevjhh+Xm5qZ33nlH7u7uOnr0qDp06KCNGzda158/f77atGmjJ598Uv3791dmZqbee+89+fn5OfEoAAAAAACwP5NhGEZp7Tw3N1fNmzfXRx99pObNm5fWw5RI3lD70NBQJ1cCAAAAACgPipNDnd7TDgAAAAAACleqod1kMql169aqXLlyaT4MAAAAAABlks0T0WVnZ2vNmjX65ptvdOLECb366qvasWOHmjdvrhYtWkiSzGazli9fbrdiAQAAAAAoT2zqaT99+rTuv/9+TZ06VQcPHtR///tfXbx4UV9++aUGDhyoH374wd51AgAAAABQ7tgU2mfMmKFz585p48aNWrt2rfLmsps3b55CQ0M1b948uxYJAAAAAEB5ZFNo37p1q0aPHq369evLZDJZ2z09PRUVFaU9e/bYrUAAAAAAAMorm0J7VlaWfHx8Cl3m5uamnJycktQEAAAAAABkY2gPDQ3VypUrC12WmJiokJCQEhUFAAAAAABsnD1+9OjRioyMVO/evdWpUyeZTCatX79e8+fP13/+8x8tXbrU3nUCAAAAAFDu2NTTfvvtt+vdd99VxYoVtXTpUhmGobi4OJ04cUJvvfWW7rjjDnvXCQAAAABAuWPzfdpbt26tVatW6eLFi8rIyJC3t7cqV65sz9oAAAAAACjXbOppl6QlS5Zo2LBh8vLyUs2aNfXzzz+rQ4cOWrFihT3rAwAAAACg3LIptMfGxmrOnDlq0KCBta1evXrq0aOHpk2bptWrV9urPgAAAAAAyi2bhsevWrVKTz/9tIYNG2Ztq1WrliZNmiR/f3/FxcXpgQcesFuRAAAAAACURzb1tB87dkyhoaGFLmvZsqUOHz5coqIAAAAAAICNoT0wMFDffvttoct27typgICAEhUFAAAAAABsHB7/z3/+UzNnzlROTo66du2q6tWr6/Tp09q6daveffddjRs3zt51AgAAAABQ7tgU2iMjI3Xs2DEtX75ccXFx1nY3NzcNGjRIgwcPtld9AAAAAACUWybDMAxbNz579qx++OEHZWRkqGrVqmrRooV8fX3tWV+pS05OlqRrXqMPAAAAAIA9FSeH2tTTnqdKlSq66667CrTv379fjRo1KsmuAQAAAAAo92wK7RkZGZo9e7Z27Nih7Oxs5XXWG4ah8+fPKyMjQ/v27bNroQAAAAAAlDc2zR7/6quvas2aNapfv77c3NxUpUoVhYaGKicnR2fOnNGUKVPsXScAAAAAAOWOTaH966+/1lNPPaU333xTDz74oAICAjRnzhx9+umnCg4O1u+//27vOgEAAAAAKHdsCu1nzpxRq1atJEmNGzfWzz//LEmqXLmyoqKi9OWXX9qtQAAAAAAAyiubQruvr6/Onj0rSWrQoIFOnTql9PR0SVLNmjV17NgxuxUIAAAAAEB5ZVNob9eunRYvXqwjR46oXr16qlatmtauXStJ2rp16w132zcAAAAAAFyRTaF99OjROnXqlGJiYmQymTR8+HBNnz5dbdu2VVxcnO6//3571wkAAAAAQLlT5Fu+/fXXX6pTp47MZrMCAwO1ceNG/fnnn5KkwYMHy9/fX0lJSWrRooX69u1bWvUCAAAAAFBuFLmn/YEHHlBSUpIkaeLEiTpx4oSaNm1qXR4eHq4XXniBwA4AAAAAgJ0UObRnZWVZb+W2du1apaWllVpRAAAAAABAMhmGYRRlxSeeeEJffvmlTCaTDMOQyWS69k5NJu3du9duRZam5ORkSVJoaKiTKwEAAAAAlAfFyaFFvqZ91qxZio+PV1pamhYsWKD7779fAQEBtlcJAAAAAACuq8ih3dvbW48++qgkaeXKlRo8eLAaN25caoUBAAAAAFDe2XTLt9zcXGt3PgAAAAAAKB02hXZ3d3f5+vrauxYAAAAAAHCFIg+Pv9Lo0aM1Y8YMnT17Vk2bNlWlSpUKrFO7du0SFwcAAAAAQHlW5Nnjr9S8eXPl5uZedwb5ffv2lagwR2H2eAAAAACAI5XK7PFXeuWVV2zZDAAAAMANICkpScuWLdOgQYMUFhbm7HKAcs2m0N63b1971wEAAADABWRlZSk2NlZpaWmKjY1V8+bN5enp6eyygHLLptC+c+fOv12ndevWtuwaAAAAgBMlJCQoPT1dkpSenq7ExET179/fuUUB5ZhNoX3gwIEymUy68nL4q69vv1GuaQcAAABwWWpqqhITE63/5xuGoYSEBHXo0EEBAQFOrg4on2wK7e+9916BtvPnz2vXrl2Kj4/X/PnzS1wYAAAAAMcxDENxcXG6ep7qvPaYmJjrTkQNoHTYFNrbtGlTaHvnzp1VqVIlvfnmm3rrrbdKVBgAAAAAx0lJSbHOaH0li8Wi5ORkpaSkKDAw0AmVAeWb2d47vP3227Vjxw577xYAAABAKapdu7ZCQ0NlNuePCGazWS1atFDt2rWdVBlQvtk9tG/ZskWVK1e2924BAAAAlCKTyaTIyMgCQ+Cv1Q7AMWwaHv/YY48VaLNYLEpNTdWRI0c0dOjQEhcGAAAAwLECAgIUHh6u+Ph4GYYhk8mkiIgI1axZ09mlAeWWTT3thmEU+DKbzQoKCtKUKVP09NNP27lMAAAAAI4QEREhHx8fSZKvr6/Cw8OdWxBQztnU0758+XJ71wEAAADABXh6eioqKkrLli3ToEGD5Onp6eySgHLNptAuSZmZmTp37pxq1qypnJwcLV++XCkpKerevbtat25tzxoBAAAAOFBYWJjCwsKcXQYA2Tg8/qefflKXLl20YsUKSdIrr7yiGTNmKCEhQYMGDdIXX3xh1yIBAAAAACiPbArtc+bMUePGjfXPf/5TFy5cUHx8vB555BHt2LFD/fv31+LFi+1dJwAAAAAA5Y7NPe0jRoxQ3bp1tX37dmVlZal3796SpJ49e+q3336za5EAAAAAAJRHNoV2s9lsnZDi66+/VtWqVdWiRQtJl6919/Lysl+FAAAAAACUUzZNRBcSEqLVq1fLy8tLn376qTp37iyTyaRTp07p7bffVkhIiL3rBAAAAACg3LGpp338+PH65ptv9NBDD8nNzU0jRoyQJPXq1Ut//vkn92kHAAAAAMAObOppb968uT7//HP98ccfuvnmm1WpUiVJ0osvvqiwsDDddNNN1nVTUlJUo0YNVahg893lAAAAAAAol2zqaZckb29vtWzZ0hrYJal79+75Antubq7uuece/frrryWrEgAAAACAcsjm0F5UhmGU9kMAAAAAAFAmlXpoBwAAAAAAtiG0AwAAAADgogjtAAAAAAC4KEI7AAAAAAAuitAOAAAAAICLIrQDAAAAAOCiCO0AAAAAALioUg3tZrNZTz75pGrUqFGaDwMAAAAAQJlUoagrLliwoFg7fvLJJ2UymfTkk08WuygAAAAAAFCC0G4ymWQYhtzc3OTr66uMjAzl5OTI3d1d1apVI6wDAAAAAFBCRQ7tv/zyi/X7b7/9VmPHjtXzzz+v7t27y83NTZL01Vdf6bnnntOzzz5r/0oBAAAAAChnbLqmfcqUKYqOjlbPnj2tgV2S7rrrLo0ePVqzZ8+2W4EAAAAAAJRXNoX2o0ePKjAwsNBl1atX16lTp0pUFAAAAAAAsDG0N23aVO+//75yc3PztWdlZWnp0qVq0aKFXYoDAAAAAKA8K/I17VcaO3asHn/8cXXt2lUdO3aUr6+vTp48qW3btunChQtasWJFsfZnsVi0YMECrV69WmfPnlXr1q01efJk1a1b92+3TUhI0Pjx4/XFF1+oTp06thwOAAAAAAAuyaae9jZt2mjVqlUKDQ3Vli1b9M477+irr77SnXfeqY8//li33HJLsfa3aNEirVy5Ui+//LJWrVoli8WiIUOGKDs7+7rbHTlyRFOmTLHlEAAAAAAAcHkmwzAMZxaQnZ2tO+64Q88884weeeQRSdKZM2fUsWNHTZ06Vb169Sp0O4vFogEDBsjd3V3fffedzT3tycnJkqTQ0FDbDwIAAAAAgCIqTg61qac9z7Zt2/Taa69p7NixOnTokDZt2qQjR44Uax+//PKLzp07p3bt2lnbqlatqmbNmmnnzp3X3G7x4sXKycnR8OHDba4fAAAAAABXZtM17RcuXNCoUaP0zTffyNvbW+fOndPjjz+uDz74QHv37tWKFSt08803F2lfqampkqRatWrla69Ro4Z12dX++9//KjY2VmvWrNGxY8dsOYR8DMPQ+fPnS7wfAAAAAAD+jmEYMplMRVrXptD+xhtvaM+ePYqLi9Ptt9+ukJAQSdL06dM1ZMgQzZ07VwsWLCjSvi5cuCBJ8vDwyNfu6empjIyMAuufP39ezzzzjJ555hk1aNDALqE9JydH+/btK/F+AAAAAAAoiqsz8LXYFNo/+eQTjR07VnfccUe+277VqFFDI0aMKNbkcF5eXpIuX9ue9710+fZxFStWLLD+K6+8ooYNG+qhhx6ypfRCubu7q0mTJnbbHwAAAAAA1/L7778XeV2bQvuZM2cUGBhY6LJq1aoVa6h53rD448ePq169etb248ePKzg4uMD6H330kTw8PNSqVStJsp406NWrl5544gk98cQTRX7sPCaTSZUqVSr2dgAAAAAAFFdRh8ZLNob2m2++WYmJierQoUOBZVu2bCny9eyS1LRpU3l7e+v777+3hvYzZ85o7969GjBgQIH1N23alO/nn376SePHj9eSJUsUFBRUzCMBAAAAAMB12RTaR4wYoSeffFLp6enq0qWLTCaTdu7cqY8//lirVq3S66+/XuR9eXh4aMCAAZo1a5b8/PwUGBiomTNnKiAgQN26dVNubq5Onz6tKlWqyMvLS/Xr18+3fd5kdbVr15aPj48thwMAAAAAgEuyKbR37dpVM2fO1Ouvv65t27ZJkqZNm6bq1avrxRdfVI8ePYq1v+joaF26dEmTJk3SxYsX1bp1a73zzjtyd3fX4cOHdc899+i1115Tv379bCkXAAAAAIAbkskwDKMkO9i/f7/S09NVtWpVNWrUSGZziW797nDFuak9AAAAAAAlVZwcalPCXrBggQ4cOCBJatSokcLCwtSkSROZzWb98ccfeuyxx2zZLQAAAAAAuILNof2BBx7Qli1bCizLzMzUzp07S1wYAAAAAADlnc1j2YODg/Xkk09qwYIF9qwHAAAAAAD8fzZNRCdJMTExSkpK0syZM7Vv3z7NmDFDlStXtmdtAAAAAACUayWaNS4yMlKLFy/Wzp079eCDD+rgwYNyc3OzV20AAAAAAJRrJZ7qvWPHjlq1apVycnLUv39/7dq1yx51AQAAAABQ7tnl/myNGjXS6tWrFRISomnTptljlwAAAAAAlHs2hfYnn3xSNWvWzNdWtWpVLV26VAMGDFCtWrXsUhwAAAAAAOWZyTAMw9lFOFNxbmoPAAAAAEBJFSeHFnn2+IkTJ2rkyJGqW7euJk6ceN11TSaTXn311aLuGgAAAIALSUpK0rJlyzRo0CCFhYU5uxygXCtyaP/+++81aNAg6/fXYzKZSlYVAAAAAKfIyspSbGys0tLSFBsbq+bNm8vT09PZZQHlVpFD+5YtWwr9HgAAAEDZkZCQoPT0dElSenq6EhMT1b9/f+cWBZRjdpk9HgAAAMCNLzU1VYmJicqb9sowDCUkJCg1NdXJlQHlV5F72u++++4iD3s3mUzavHmzzUUBAAAAcCzDMBQXF6er56nOa4+JieEyWMAJihza27Rpwy8pAAAAUEalpKRYZ7S+ksViUXJyslJSUhQYGOiEyoDyrcihfdq0aaVZBwAAAAAnql27tkJDQ7Vnzx5ZLBZru9lsVkhIiGrXru3E6oDyq8ih/WpZWVn69ddflZ2dbR1CY7FYdOHCBe3atUvPPPOM3YoEAAAAULpMJpMiIyM1YcKEQtsZdQs4h02h/fvvv9fo0aOVkZFR6PLKlSsT2gEAAIAbTEBAgMLDw7Vu3TprW0REhGrWrOm8ooByzqbZ42fPni1fX1/NmzdPXbt2Vbdu3bR48WI98sgjMplMevvtt+1dJwAAAAAH6N69u7VX3WQyqVu3bk6uCCjfbArtv/76q5588knde++96tKli44ePapOnTrp+eefV//+/fXmm2/au04AAAAADvDZZ5/lu+Xbpk2bnFwRUL7ZFNotFot1iEz9+vX122+/WZd1795de/futU91AAAAABwm7z7tV+I+7YBz2RTa69Wrp19//VWS1LBhQ124cEH79++XJF26dEnnzp2zX4UAAAAASt3f3af96nYAjmFTaA8PD9esWbO0YsUK+fn5KSQkRC+//LK2bNmihQsXqkmTJvauEwAAAEApyrtP+5W3e5Py36cdgOPZFNqHDBmihx56SD/99JMk6YUXXtC+ffs0cuRI7d+/v8BtIgAAAAC4trz7tJvN+SOC2WxWixYtuE874CQmw07jXDIzM7V//341atRI3t7e9tilQyQnJ0uSQkNDnVwJAAAA4FypqamaMGGCcnNzrW1ubm6aOXMmt30D7Kg4OdSmnvbCeHt7q0WLFjdUYAcAAADwf/Lu037lLd+4TzvgXBVs2SglJUVTpkxRUlKSzp49W2C5yWRiBnkAAADgBhQREaFt27YpLS1Nvr6+Cg8Pd3ZJQLlmU2h/7rnn9OOPP+r++++Xj4+PnUsCAAAA4Cyenp6KiorSsmXLNGjQIHl6ejq7JKBcsym0//jjj3rllVd033332bseAAAAAE4WFhamsLAwZ5cBQDZe037TTTepYsWK9q4FAAAAAABcwabQPnz4cM2fP19Hjhyxdz0oB5KSkjR69GglJSU5uxQAAAAAcGk2DY/v3Lmzli5dqq5du8rX17dAr7vJZNLmzZvtUiDKlqysLMXGxiotLU2xsbFq3rw510kBAAAAwDXYFNonTpyoQ4cOqUOHDvL397d3TSjDEhISlJ6eLklKT09XYmKi+vfv79yiAKCEkpKSrBM2cQ0oAACwJ5tC+44dO/TCCy/ogQcesHc9KMNSU1OVmJgowzAkSYZhKCEhQR06dFBAQICTqwMA2zCCCEBZxMlIwHXYdE171apVVatWLXvXgjLMMAzFxcVZA/vftQPAjaKwEUQAcCPLOxl58uRJxcbGKisry9klAeWaTaH94Ycf1pIlS5SZmWnvelBGpaSkKDk5WRaLJV+7xWJRcnKyUlJSnFQZANjuWiOIUlNTnVwZANiOk5GAa7FpePzRo0e1Z88edejQQY0aNZK3t3e+5SaTScuWLbNLgSgbateurdDQUO3ZsydfcDebzQoJCVHt2rWdWB0AFN/fjSCKiYmRyWRyUnUAYBsuZwRcj0097QcOHFCzZs0UGhqqypUryzCMfF9X96YCJpNJkZGRBf6BvVY7ALg6RhABKGu4nBFwTTb1tL/44otq3LixvWtBGRcQEKDw8HDFx8fLMAyZTCZFRESoZs2azi4NAIqNEUQAypq8k5FXu/JkZGBgoBMqA8o3m3raH3nkEa1bt87OpaA8iIiIkI+PjyTJ19dX4eHhzi0IAGzECCIAZU3eyUizOX9EMJvNatGiBScjASexKbS7u7vL19fX3rWgHPD09FRUVJT8/f01ePBgbosE4IaWN4IoL6AzggjAjYyTkYBrsml4/OjRozVjxgydPXtWTZs2VaVKlQqsw5k4XEtYWBj3+wRQZkRERGjbtm1KS0tjBBGAGx6XMwKux2TYMKNE8+bNlZube92zbfv27StRYY6Sd91OaGiokysBANyokpKStGzZMg0aNIiTkgBueFlZWRo3bpzS0tLk5+enWbNmMToSsLPi5FCbetpfeeUVWzYDAKBMYgQRgLIk73LGvJORBHbAuWwK7X379rV3HQAAAABcBCcjAddhU2iXpNOnTys2NlY7duzQmTNn5Ovrq9tvv12RkZGqXr26PWsEAAAAAKBcsmn2+NTUVPXt21fLli2Tp6enmjVrpgoVKujdd99Vnz59dOzYMXvXCQAAAABAuWNTT/vMmTNVoUIFbdy4UXXr1rW2Hzp0SFFRUZo9e7amTZtmtyIBAAAAACiPbOpp/89//qPo6Oh8gV2S6tatq1GjRumrr76yS3EAAAAAAJRnNoX23Nxc+fr6FrrMz89PmZmZJSoKAAAAAADYGNqDg4OVmJhY6LL4+HgFBQWVqCgAAAAAAGDjNe0jR47U448/royMDPXs2VM33XSTTpw4oQ0bNug///mP5s2bZ+86AQAAAAAod2wK7e3bt9e0adM0a9asfNev+/v769VXX9W9995rtwIBAAAAACivbL5Pe58+fdS5c2f99ttvqlChgqpVqyYPDw+ZzWalpKSodu3a9qwTAACXlZSUpGXLlmnQoEEKCwtzdjkAAKAMsSm0Hzx4UDExMfrpp5+uuc6+fftsLgoAgBtFVlaWYmNjlZaWptjYWDVv3lyenp7OLgsAAJQRNoX2l19+WX/++aeefPJJBQQEyGy2aT47AABueAkJCUpPT5ckpaenKzExUf3793duUQAAoMywKbTv3LlTU6dOVa9evexdDwAAN4zU1FQlJibKMAxJkmEYSkhIUIcOHRQQEODk6gAAQFlgUxe5t7e3qlWrZu9aAAC4YRiGobi4OGtg/7t2AAAAW9gU2nv37q3333+ff0gAAOVWSkqKkpOTZbFY8rVbLBYlJycrJSXFSZUBAICyxKbh8RUrVtTu3bt17733KjQ0VF5eXvmWm0wmvfrqq3YpEAAAV1S7dm2FhoZqz549+YK72WxWSEgId1EBAAB2YVNoX7t2rapUqSKLxVLoDPImk6nEhQEA4MpMJpMiIyM1YcKEQtv5WwgAAOzBptC+ZcsWe9cBAMANJyAgQOHh4YqPj5dhGDKZTIqIiFDNmjWdXRoAACgjuFcbAAAlEBERIR8fH0mSr6+vwsPDnVsQAAAoUwjtAACUgKenp6KiouTv76/BgwfL09PT2SUBAIAyxKbh8QAA4P+EhYUpLCzM2WUAAIAyiJ52AAAAAABcFKEdAAAAAAAXRWgHAAAAAMBFEdoBAAAAAHBRhHYAAAAAAFwUoR0AAAAAABdFaAcAAAAAwEW5RGi3WCyaN2+eOnbsqFtvvVVDhw7VoUOHrrn+b7/9pmHDhqlt27Zq166doqOjlZKS4sCKAQAAAAAofS4R2hctWqSVK1fq5Zdf1qpVq2SxWDRkyBBlZ2cXWDctLU2DBw+Wl5eXli9frrffflunT5/WkCFDlJWV5YTqAQAAAAAoHU4P7dnZ2YqNjVV0dLQ6d+6spk2bavbs2UpNTdWmTZsKrL9582adP39eM2bMUFBQkEJCQjRz5kz98ccfSkpKcsIRAAAAAABQOpwe2n/55RedO3dO7dq1s7ZVrVpVzZo1086dOwus365dOy1atEheXl7WNrP58mGcOXOm9AsGAAAAAMBBKji7gNTUVElSrVq18rXXqFHDuuxKderUUZ06dfK1LVmyRF5eXmrdurVNNRiGofPnz9u0LQAAAAAAxWEYhkwmU5HWdXpov3DhgiTJw8MjX7unp6cyMjL+dvvly5drxYoVmjRpkvz8/GyqIScnR/v27bNpWwAAAAAAiuvqDHwtTg/tecPcs7Oz8w15z8rKUsWKFa+5nWEYmjt3rt58802NGDFCAwcOtLkGd3d3NWnSxObtAQAAAAAoqt9//73I6zo9tOcNiz9+/Ljq1atnbT9+/LiCg4ML3SYnJ0cTJ07U+vXrNXHiREVGRpaoBpPJpEqVKpVoHwAAAAAAFEVRh8ZLLjARXdOmTeXt7a3vv//e2nbmzBnt3bv3mteoT5gwQZ9++qlef/31Egd2AAAAAABcldN72j08PDRgwADNmjVLfn5+CgwM1MyZMxUQEKBu3bopNzdXp0+fVpUqVeTl5aWPP/5YGzdu1IQJE9SmTRudOHHCuq+8dQAAAAAAKAuc3tMuSdHR0erfv78mTZqkhx9+WG5ubnrnnXfk7u6uo0ePqkOHDtq4caMkaf369ZKkGTNmqEOHDvm+8tYBAAAAAKAsMBmGYTi7CGdKTk6WJIWGhjq5EgAAAABAeVCcHOoSPe0AAAAAAKAgQjsAAAAAAC6K0A4AAAAAgIsitAMAAAAA4KII7QAAAAAAuChCOwAAAAAALorQDgAAAACAiyK0AwAAAADgogjtAAAAAAC4KEI7AAAAAAAuitAOAAAAAICLIrQDAAAAAOCiCO0AAAAAALgoQjsAAAAAAC6K0A4AAAAAgIsitAMAAAAA4KII7QAAAAAAuChCOwAAAAAALorQDgAAAACAi6rg7AIAAAAAAPaXm5urnJwcZ5dRLrm7u8vNzc0u+yK0AwAAAEAZYhiGUlNTlZ6e7uxSyjUfHx8FBATIZDKVaD+EdgAAAAAoQ/ICe40aNVSpUqUSh0YUj2EYOn/+vI4fPy5JqlWrVon2R2gHAAAAgDIiNzfXGtirV6/u7HLKrYoVK0qSjh8/rho1apRoqDwT0QEAAABAGZF3DXulSpWcXAnyXoOSzitAaAcAAACAMoYh8c5nr9eA0A4AAAAAgIsitAMAAAAAXN7HH3+s4OBgHT582NmlOBShHQAAAADg8jp37qwPP/xQNWrUcHYpDsXs8QAAAAAAl+fn5yc/Pz9nl+Fw9LQDAAAAAIrk+eefV/v27ZWbm5uvferUqWrbtq1ycnL0v//9T8OHD1dYWJjCwsI0atQoHTp0yLru999/r+DgYK1atUpdunRRWFiYtm/frtOnT2vcuHFq3769QkND1bt3b61bt866XWHD47dv365HHnlEt912m9q2batx48bp6NGj+bZp1qyZfvrpJz344IMKDQ1Vly5d9M4775Tek2RnhHYAAAAAQJH07t1bJ0+e1Pfff29ts1gs+uSTT3Tffffp8OHDeuihh3Tq1ClNnz5dU6dO1aFDh/Twww/r1KlT+fa1YMECxcTEaPLkyWrVqpXGjx+vP/74Qy+99JLefvttNWvWTDExMfruu+8KrWXdunWKiopSrVq19MYbb2jixIn64Ycf9OCDD+Z7LIvFoqefflo9e/bUkiVLFBYWphkzZujrr78unSfJzhgeDwAAAAAokttuu02BgYFav3697rzzTkmXe85PnDih3r17a8GCBapYsaLi4uLk7e0tSWrXrp26du2qpUuXKiYmxrqvRx55RD169LD+vGPHDo0aNUpdu3aVJLVp00Y+Pj7y8PAoUIfFYtGsWbPUoUMHvf7669b2sLAw9ezZU++8844mTJggSTIMQyNHjtQDDzxgPYbPP/9cX375pTp27GjnZ8j+6GkHAAAAABSJyWRSRESENm/erOzsbEnShg0b1KBBA7Vs2VLfffed2rRpIy8vL126dEmXLl2St7e3br/9dn3zzTf59nXLLbfk+7lt27aaP3++oqOjtXr1ap08eVIxMTEKCwsrUMeBAwd04sQJ9erVK197vXr11KpVK+3YsSNfe6tWrazfe3h4yM/PT+fPny/Rc+EohHYAAAAAQJH17t1bGRkZ+vrrr5Wdna1NmzYpIiJCkpSenq6NGzeqefPm+b62bt2q48eP59tPpUqV8v08e/ZsRUZG6ueff9akSZPUqVMnPf744zpy5EiBGtLT0yVJ/v7+BZb5+/vr7Nmz+dq8vLzy/Ww2m2UYRrGP3RkYHg8AAAAAKLKGDRuqRYsW+uSTT2Q2m3XmzBlraK9SpYruvPNODR48uMB2FSpcP35WqVJF48eP1/jx47V//3598cUXWrRokV566SUtWbIk37o+Pj6SpJMnTxbYz4kTJ+Tr62vj0bkeetoBAAAAAMXSu3dvff3119qwYYPCwsJUt25dSZevQ//99991yy23KDQ0VKGhoQoJCVFcXJw+//zza+7vyJEj6tSpkz799FNJUqNGjTR06FDdeeedSklJKbB+w4YNddNNN2n9+vX52g8dOqQff/yx0CH1NypCOwAAAACgWHr27Klz585p48aN6t27t7V95MiR+uuvvzR8+HBt3rxZX3/9tZ566ilt2LBBTZs2veb+AgMDFRAQoFdeeUVr1qzRjh07FBsbq23btql79+4F1jebzRo7dqz+85//aNy4cdq2bZvWrVunwYMHq1q1aoX29N+oGB4PAAAAACgWPz8/dejQQdu3b883A3zTpk31/vvva/bs2ZowYYIMw1BQUJAWLlyoe+6557r7XLBggd544w3NnTtXaWlpqlWrlp588kkNGzas0PX79eunypUr66233tKoUaPk7e2tjh07auzYsbrpppvserzOZDJulKvvS0lycrIkKTQ01MmVAAAAAEDJXLx4UQcOHFDDhg0LTL4Gx7rea1GcHMrweAAAAAAAXBShHQAAAAAAF0VoBwAAAADARRHaAQAAAABwUYR2AAAAAABcFKEdAAAAAAAXRWgHAAAAAMBFEdoBAAAAAHBRhHYAAAAAAFxUBWcXAAAAAAAofRaLRRaLxeGPazabZTbTX2wrQjsAAAAAlHEWi0WjnnpKZ9LTHf7YVX18tHD+fLsG9/nz52vt2rXasmWLQ7dPS0tTeHi4Xn/9dbVt29amxy4uQjsAAAAAlHEWi0Vn0tPlG9VLcmSvt8WitNj1slgsdg3tUVFRevTRRx26/bFjxzRixAidOHHC5se1BWMUAAAAAKC8MJtlcuBXaZ0gqFy5svz8/By2/Zo1axQREWHz45UEoR0AAAAA4HL+97//afjw4WrdurVCQkJ0zz33KDY2VtLl4e133323JOnw4cMKDg7Whg0b1KdPH4WGhqpfv376448/tHDhQt15551q06aNXnrpJRmGUWD7gQMH6umnn8732Dt37lRwcLAOHjwoSfr88881ZswYzZ0710FH/38I7QAAAAAAl3LhwgVFRUXJx8dHq1at0vr169WjRw9Nnz5d+/btK3Sb2bNn61//+pdWr16tM2fO6OGHH9aff/6p5cuXa8yYMVq5cqW2bt1aYLt+/fpp69atyszMtLYlJCQoLCxM9evXlyS99dZbeuihh2QymUrngK+D0A4AAAAAcCkXLlzQY489psmTJ6tx48Zq0KCBoqOjJUm//vprodtERUWpTZs2atq0qe69916dP39eU6ZMUePGjfXwww+revXq+u233wps1717d5nNZm3evFmSlJ2drc8++0z9+vUrvQMsBiaiAwAAAAC4FD8/Pz3yyCNav3699u7dq7/++ku//PKLJF3ztnV5veKSVKlSJfn7+6tixYrWNi8vL2VnZxfYrlKlSurRo4cSExPVp08fbdu2TdnZ2frHP/5h56OyDT3tAACUUFJSkkaPHq2kpCRnlwIAQJlw4sQJRUREaPXq1apZs6YeeeQRrV279rrbVKiQv0+6OLPV9+vXT99++61OnjypxMREde3aVd7e3jbVbm/0tAMAUAJZWVmKjY1VWlqaYmNj1bx5c3l6ejq7LAAAbmjr169Xenq6PvvsM7m7u0v6v2HxeZPJ2dPtt9+uwMBAxcfH68svv9TixYvt/hi2oqcdAIASSEhIUHp6uiQpPT1diYmJzi0IAIAyICAgQBcuXNCnn36qlJQU/ec//9HYsWMlqdAh7iVlMpnUp08fLVy4UH5+frrjjjvs/hi2oqcdAAAbpaamKjEx0XrG3zAMJSQkqEOHDgoICHBydQAAFMJikf37qa//eLbo0aOH9uzZo2nTpikzM1OBgYF64IEH9MUXXyg5OVm1atWyc6FS3759tWDBAg0cOLBYQ+tLm8kojbEFN5Dk5GRJUmhoqJMrAQDcSAzD0PTp07Vnz558E+KYzWY1b95cMTExTrktDACgfLt48aIOHDighg0bysvLy9pusVg06qmndOb/jw5zpKo+Plo4f75LBWFHuNZrIRUvh9LTDgCADVJSUqx/cK9ksViUnJyslJQUBQYGOqEyAAAKMpvNWjh//jVnXi/txy5vgd2eCO0AANigdu3aCg0NLbSnPSQkRLVr13ZidQAAFER4vjHxigEAYAOTyaTIyMgCQ+Cv1Q4AAGALQjsAADYKCAhQeHi4NaCbTCZFRESoZs2aTq4MAACUFYR2AABKICIiQj4+PpIkX19fhYeHO7cgAABQphDaAQAoAU9PT0VFRcnf31+DBw+Wp6ens0sCAABlCBPRAQBQQmFhYQoLC3N2GQAAoAyipx0AAAAAABdFTzsAAAAAlAMWi4X7tN+ACO0AAAAAUMZZLBY9OSpaGWfSHP7Y1ar6asHCeQR3GxHaAQAooaSkJC1btkyDBg3i2nYAgEuyWCzKOJOmro2ekcnkuPBsGBZt3j9LFovFrqF9/vz5Wrt2rbZs2eKQ7bds2aKFCxdq//798vX1Vffu3TV69Gh5eXnZ9PjF4RKnOiwWi+bNm6eOHTvq1ltv1dChQ3Xo0KFrrp+WlqZx48apdevWatOmjV566SVduHDBgRWjJJKSkjR69GglJSU5uxQAKLGsrCzNnTtXJ0+e1Ny5c5WVleXskgCgxFavXq2BAwdq9erVzi4FdmYymWU2uTnsq7ROEERFRWnNmjUO2X7Xrl168sknde+992rt2rV64YUXtHHjRr300ks2P35xuERoX7RokVauXKmXX35Zq1atksVi0ZAhQ5SdnV3o+tHR0Tp48KDi4uI0d+5cbdu2TS+++KJji4ZNsrKyFBsbq5MnTyo2NpZ/bgHc8FatWqVLly5Jki5duqRVq1Y5uSIAKJkzZ84oPj5eFotF8fHxOnPmjLNLAgqoXLmy/Pz8HLL9qlWr1LZtWz3xxBNq0KCBOnXqpDFjxigxMfGamdWenB7as7OzFRsbq+joaHXu3FlNmzbV7NmzlZqaqk2bNhVY/4cfftCOHTs0ffp0NW/eXO3atdOUKVMUHx+vY8eOOeEIUBwJCQlKT0+XJKWnpysxMdG5BQFACRT2t2rTpk1KTU11UkUAUHKzZ8+WYRiSJMMwNGfOHOcWhHLrf//7n4YPH67WrVsrJCRE99xzj2JjYyVdHt5+9913S5IOHz6s4OBgbdiwQX369FFoaKj69eunP/74QwsXLtSdd95pHaGd996+cvuBAwfq6aefzvfYO3fuVHBwsA4ePKioqCjFxMTkW242m5WTk6PMzMxSfhZcILT/8ssvOnfunNq1a2dtq1q1qpo1a6adO3cWWH/Xrl266aab1LhxY2tbmzZtZDKZtHv3bofUDNukpqYqMTEx3x+BhIQE/rkFcEMyDENvvPFGocveeOMN62cdANxIfv75Z/3vf//L1/brr7/q559/dlJFKK8uXLigqKgo+fj4aNWqVVq/fr169Oih6dOna9++fYVuM3v2bP3rX//S6tWrdebMGT388MP6888/tXz5co0ZM0YrV67U1q1bC2zXr18/bd26NV8AT0hIUFhYmOrXr69mzZqpadOm1mU5OTmKi4tTSEhIiXr7i8rpE9HlBbZatWrla69Ro0ahYe7YsWMF1vXw8JCPj4+OHj1qUw2GYej8+fM2bYuiMQxDsbGxBf6JzWsfPXq0TCaTk6oDgOI7fPiwjhw5UuiyI0eO6LffflOdOnUcXBUA2C5vnqnCzJs3T2+88Qazf98AsrKyZLFYlJubq9zcXGv7ld87Q25ubrH+38/MzNTAgQP18MMPq3LlypKkUaNGaenSpfrll19ksVhkGIZyc3Ott7EbPHiwbrvtNklS165dtXz5cr3wwguqWLGiGjRooPnz5+vXX39Vp06d8m3ftWtXTZkyRZs2bVLv3r2VnZ2tTz/9VM8880yB5+3SpUuKiYnRb7/9puXLl1/3ec2r7cKFCwVutWcYRpGfD6eH9rwJ5Dw8PPK1e3p6KiMjo9D1r143b31br4/Oycm55tka2MepU6e0Z8+eAu0Wi0V79uzR9u3bVb16dSdUBgC2+eGHH667/KuvvlKrVq0cVA0AlNwff/yhc+fOFbrs3Llz2rhxY77RrnBdFSpUKJCN8uZfcZaLFy+qQoWix89KlSqpb9++WrdunX799VcdOnTIOgrk4sWLunTpkgzD0MWLF63HGhAQoIsXL0qS3N3d5e/vL5PJZG3z8PDQ+fPnC2xvNpvVtWtXxcfHq3v37tqyZYuys7PVuXNn67bS5d+DmJgY7d69W7NmzdLNN9+cb/nVsrKydOnSJe3fv7/Q5YXl2sI4PbTnTZGfnZ2db7r8rKwsVaxYsdD1C7vYPysrS5UqVbKpBnd3dzVp0sSmbVE0hmFo165d2rdvX76zTGazWbfccovat29PTzuAG0pQUJC2bdtW4My5dPmz7cEHH5Sbm5sTKgMA2wQHB2vz5s2FBvfKlSurZ8+e9LTfALKyspSSkiJPT898+crZod3Ly6tYof3EiRN6+OGHVb16dXXp0kV33XWXQkJCdPfdd8vd3V0VKlSQyWSSl5eXPD09JV0O+nnHXKFCBZnN5nzPgclkUoUKFay15G0vSf3799fgwYOVmZmpTZs2qWvXrvL3989Xz7Bhw5SSkqK3335bt99+e5GOo0KFCqpXr561xjy///57kZ8Lp4f2vKHux48fV7169aztx48fV3BwcIH1AwICtHnz5nxt2dnZSk9PV40aNWyqwWQy2Rz4UXRRUVGaMGFCvjaTyaTHH3/cOuQFAG4kQ4cO1VtvvVWgffjw4apSpYoTKgKAkomOjtZrr71WoH306NHy9vZ2QkUoLrPZLLPZLDc3t3wnj50918rV9fydTz75RBkZGdq0aZPc3d0lXZ5fQbqcIcxms0wmk9zc3Kwnk/KOO+/7vOV58rbL2+bK5W3atFFgYKDWr1+vbdu2afHixdZlGRkZ1kD//vvvF5pTr3XMZrNZFStWLHA/9+J0WDr9VFnTpk3l7e2t77//3tp25swZ7d27V61bty6wfuvWrZWamqqDBw9a23bs2CFJ1usX4JoCAgIUHh5ufYOaTCZFRESoZs2aTq4MAGxz1113FZiAxs/PTx06dHBSRQBQMiEhIQoKCsrXFhwcrObNmzupItibYVhkMXId9mUYBUekFUVAQIAuXLigTz/9VCkpKfrPf/6jsWPHSlKp3GbNZDKpT58+Wrhwofz8/HTHHXdYl7322ms6dOiQZs6cKT8/P504ccL65Yi5Apze0+7h4aEBAwZo1qxZ8vPzU2BgoGbOnKmAgAB169ZNubm5On36tKpUqSIvLy+1bNlSYWFhGjNmjF588UWdP39ekydPVp8+fQh/N4CIiAht27ZNaWlp8vX1VXh4uLNLAoASeeGFFzR69Oh8PwPAjWzMmDEaOXKkdaKsq2+FhRuT2WxWtaq+2rx/lsMfu1pV32JfWtGjRw/t2bNH06ZNU2ZmpgIDA/XAAw/oiy++UHJycoHJye2hb9++WrBggQYOHGitNzc3Vxs3blROTo4GDRpUYJsvvvii1CeeNRnOHiehy0/EG2+8oY8//lgXL15U69atNXnyZNWpU0eHDx/WPffco9dee039+vWTdHlSs5deeklff/21PD091aNHD02cOLHAdQJFkZycLEkKDQ216zHh2pKSkrRs2TINGjRIYWFhzi4HAEps3rx5+v7779W2bVtFR0c7uxwAKLHVq1crISFBEREReuCBB5xdDorh4sWLOnDggBo2bFhgSLbFYil0LpbSljdkv7y53mtRnBzqEqHdmQjtAAAAAMqK6wVFOJa9Qnv5O90BAAAAAMANgtAOAAAAAICLIrQDAAAAAOCiCO0AAAAAUMaU86nLXIK9XgNCOwAAAACUEe7u7pKk8+fPO7kS5L0Gea+JrZx+n3YAAAAAgH24ubnJx8dHx48flyRVqlRJJpPJyVWVL4Zh6Pz58zp+/Lh8fHzk5uZWov0R2gEAAACgDAkICJAka3CHc/j4+Fhfi5IgtAMAAABAGWIymVSrVi3VqFFDOTk5zi6nXHJ3dy9xD3seQjsAAAAAlEFubm52C45wHiaiAwAAAADARRHaAQAAAABwUYR2AAAAAABcFKEdAAAAAAAXVe4nosvJyZFhGEpOTnZ2KQAAAACAciA7O1smk6lI65b70F7UJwoAAAAAAHswmUxFzqImwzCMUq4HAAAAAADYgGvaAQAAAABwUYR2AAAAAABcFKEdAAAAAAAXRWgHAAAAAMBFEdoBAAAAAHBRhHYAAAAAAFwUoR0AAAAAABdFaAcAAAAAwEUR2gEAAAAAcFGEdgAAAAAAXBShHQAAAAAAF0VoBwAAAADARRHaAQAAAABwUYR2AAAAAABcFKEdcCLDMKzfWywWJ1YCAPaR97l26NAhZWRkOLkaAABufIR2wIlMJpMkafny5frhhx8kSbm5uc4sCQBKxGQy6YsvvtCQIUN09OhRZ5cDAMANj9AOuICPP/5YS5YskSS5ubk5uRoAsN1ff/2llStX6p///KeaNm3q7HIAALjhEdoBB7tySHxer/qTTz6pU6dO6b///a+zygKAEvvjjz8UExOjvXv3KigoSBKX/gAAUFKEdsCBDMOwDomX/q9XPSQkRJmZmfriiy+cVRoAlFjjxo1166236sKFC3r//fd1+vRpmc3mfCcrAcCVnTt3ThInHOFaCO2AA+UF9vfee0/jx4/XoUOHlJmZqZo1a+qJJ55QfHy89uzZ4+QqAaBorgzjOTk5kqSYmBgNHTpUv//+u958802dPn1aJpOJ4A7A5Z0+fVrPP/+8fvnlF5nNZp0+fdrZJQGSpArOLgAoTywWi86ePavjx49rx44devzxx9W8eXMNGzZMLVq0UNOmTbVv3z41b95cubm5XN8OwGXljRz69ttv9cknn+iPP/5Qw4YN1bdvX40aNUqGYWjDhg0ymUx64okn5OfnV2C0EQC4ksqVKysjI0P9+/dXly5d5OPjo+eee05eXl7OLg3lnMng1DdQqiwWi8zmgoNaLBaLli5dqu+++047duxQZGSktm3bJnd3d61cuZI/EABc3hdffKFx48bp3nvvVcWKFbV9+3Z5eXlpyJAh6tu3r+bMmaMtW7aoZcuWGjt2rHx9fZ1dMgBcV2pqqh566CGlpqZq5syZCg8PpyMFTkdPO1CKrgzseUPfU1JS1KJFCw0dOlTDhg3TsGHDFB8fr++++06GYWjv3r1KTEzUAw88QK8UAJd17NgxzZs3T+PHj9ejjz4qSTp58qQmTZqk2NhYBQUF6emnn1ZGRob27dvH7SwBuLS8/7lOnz4tPz8/BQQEaPLkyWrYsKFCQkII7nAqetoBB5g5c6Y2btyoO++8U/7+/nrrrbfUr18/TZw4UVWqVJEkZWRk6OTJk3rxxRdVsWJF6y3gAMAVHTp0SFFRUZo7d66aNWumnJwcubu769SpU+rbt6+6deumSZMmSZJOnTql6tWrO7liACjo6hGR2dnZys7OVnp6ul566SXt3LlTK1asUEhIyDVHTwKljXcdUMq+/vprrV+/Xq+//rqmTp2q2267TRUqVNA999yj48ePW9erXLmyGjdurJkzZ+qHH37Qzp07nVg1AFzf+fPndejQIR09elSSVKFCBeXk5Kh69eq69dZb832+EdgBuKIrQ/iHH36oCRMm6Pnnn5fFYlGdOnU0fvx4tW7dWgMHDtTevXtlNpu1f/9+JtaEwxHagVJ29OhRNWnSRGFhYdq0aZNGjx6tSZMmqU2bNpo8ebI++eQTSZf/4bVYLDp+/Lh8fX35gwDAZeR9Hh0/flyZmZnKzMxUcHCw2rdvr7i4OCUlJclkMsnd3V2SlJWVpYYNGxbrMbi9EgBHywvs06dP1+zZs1W5cmXrCEhJCgoK0uTJkxUWFqYHH3xQ9957r5YtW1bkSxf5XIO9cE07YEeFXYNuMpl06dIlrVmzRlOnTtWECRP00EMP6fjx40pJScnXG5Wdna3vvvtOf/31l2rVqlWsxwGA0mIymbR582ZNnz5d7u7uCgsL07hx4zRs2DC9+uqrmjFjhgYMGCB/f39t27ZNSUlJiomJue4+Z8+erdtuu01VqlRRq1atrP888/kGwJE+//xzff7554qLi1PTpk2VlpamX3/9VV9//bXq16+vvn376pVXXlF8fLwOHjxoveynMHyuobQQ2gE7uXKIVd4kJpLUqFEj/frrr9q5c6fGjRunhx9+WJLk6empKlWq6KabbrLuw8vLSw8//LD+8Y9/qG7dupKkF154QQ0aNFDFihXVq1cvVaxYUW5ublxXBcBhfvvtNz333HOKjIzU0aNH9fPPP+u5557T9OnTNWnSJL3zzjt65pln1LBhQ1WsWFHvvfeeGjVqdM39ZWZmqmLFivrkk0+0c+dOBQcHq3PnzoqIiJCnpyefbwBKzdWfL2fOnFFAQICaNm2q7777TnFxcdbJMz08POTm5qY+ffroiSeesG5z6dIlVaiQP0bxuYbSxER0gB1ceQZ1yZIl+vLLL2U2m/Xoo4/qH//4h5YvX66pU6dq8ODBateunapWrar58+crPT1d//73v62zkRZ2Jnb9+vW6cOGC3nnnHdWoUUM33XSTJk2axK2TAJSqvM+j7Oxs7d69W1u3btW//vUvSdK///1vrVq1SgEBAXr55ZdVvXp17d+/X5UrV1bFihVVtWrVIj3GpUuXlJqaqjfeeEMpKSmSpEWLFnFPdwClbvXq1WrTpo0OHDigF154Qf7+/tq3b5969Oihbt26qWvXrurbt6/Cw8M1bNiwIu+XzzWUBkI7UEJXnjmdP3++li1bpkceeUTbt29XamqqRowYoQEDBujdd9/VmjVr9Mcff+iWW25R1apVtXTpUrm7uxfpNiIXL15UYmKi1q5dq/3792vy5Mnq3LmzKlWq5IjDBFCO5P1j+eWXXyoxMVEHDhxQtWrV9Pbbb1t7l1atWqV///vfqlWrlsaPH68GDRoUe/95vVXZ2dn69ttvNXfuXJ0+fVrLli1T/fr1ucUSALszDEOXLl1Shw4ddP/992vChAlas2aN9u3bpzvvvFN33nmnKlasKEl69NFH1bNnT+ttLf9uv3yuobQQ2gE7SU1N1dSpUxUZGanbbrtNkjRhwgTt3LlTUVFRGjhwoE6fPq3Dhw+rSpUqql+/vsxmc6FDrK525TqZmZmaOnWq1q9fr8mTJ6t3797y8PAo9eMDUL58++23GjFihNq0aaO0tDT99ttvWrhwodq3b29dZ/Xq1Vq6dKmaNWummTNnys3N7bq9SH83PHT//v16/vnndfToUa1fv16VKlWiZwpAiV35OZIXmj/88EN99NFHmjt3rnUeobS0NO3evVsBAQGaO3eujh07po8//vi6/6fxuQZHILQDNnjuuefUsmVL/fOf/5QkffDBB3rjjTfk5+enBQsW6Oabb7auO2HCBO3atUuRkZHq3bu3qlWrZl12rQ/6a7VfeXb2xRdfVHx8vBYvXqy2bdtyrRQAuzl27JiWLVumWrVqaeDAgTp69KhmzZqlLVu2aOHChbrzzjut665du1a33367dR6Oa7nyM2rTpk06f/68GjRooFtvvTXfevv27dOzzz6rRo0aafr06ZyUBFAq9u3bpxEjRig6Olr9+vXTxYsX9eGHH2r58uVyd3dX7dq1tXjx4uuOiORzDY5CaAeKKSUlRa+88op+/vlnjR8/XuHh4ZKkRx55RElJSZo2bZruu+8+662PJGnixInauHGjXnvtNfXs2fO6+7/y7Ovq1at15MgR+fr66rHHHrNeX5r3YR8dHa29e/cqPj5elStXLqUjBlBeWCwWHTlyRAMHDtT58+c1bNgwDRkyRNLl271Nnz5dW7Zs0aJFi9SuXTubHmP69Olas2aNcnNzFRgYqP79+2vQoEHW5bm5ufrwww8VHx+v5557Ti1atKBXCkCxnTp1SidOnJCbm5tuvvlmrVq1SidPntSAAQPk7e2tChUqaM6cOfrkk08UFxenWrVq6ezZs0pJSZGbm5saNWpU5BGRfK6htNEtBxRT7dq1NWbMGLVv314zZ87URx99JElauXKlmjdvrtmzZ+vbb7/VpUuXrNu89tprGj58uLp37/63+8/7AH/99dc1ffp0bdq0SUuXLlV0dLQsFos8PDyUnZ0t6XJo9/f316pVqySJe7sDsEneZ4fZbFbdunU1aNAg5eTkaPfu3dq/f78kqUaNGnr22WfVtWtXDR48WDt27CjSvq+8T/H+/fv1ww8/KDY2Vv/+97/VuHFjrVu3TnFxcdZ13Nzc1K9fP+Xm5iohIUGS+McWQLH861//0ujRo9WnTx/17t1bs2bN0k8//aSlS5dq2LBhmjZtmtLT09WtWzfVqVNHe/bskSRVqVJFwcHBatKkicxmsywWS6GBnc81OBqhHSiGvCB+880366677lLTpk312muvacOGDZKkjz76SH5+fnrxxRcLBPeRI0fKzc1Nubm5he77yj8A586d059//qlly5bpgw8+0FNPPaX//e9/euqpp6zBXZLq16+v0NBQ7dy5UxJ/AADYxmQy6bvvvtNjjz0mSRo8eLBGjx6t//73v3r//fd14MABSdJNN92kZ555Rv3791f16tX/dr9XDh09cOCAdYb5Ro0aqUmTJnr++edVr149xcfH5/sH18vLS2PGjNGBAwd05syZfJ+PAHA9gwcP1v79+xUVFaXly5dr7ty5euKJJ/Tqq6/qs88+U8uWLbVz50716NFDX3/9tQ4fPqx169YVuq+/u4SRzzU4CqEdKIa8s62vv/664uLidOHCBRmGoalTp2rNmjWSLl/f6ePjo5deeklffvllgZD+d9dEffvtt1q/fr2OHTumypUrq1q1aurVq5ciIyP1+++/W4O7JLm7u2vYsGE6cOCAfv7559I8dABlzJUjcwzDUHp6upKSkvT4449LkiIjI/X444/rs88+04oVK/Tnn39KkmrWrKmXXnpJjRs3/tvHyPtcmzlzph544AG98MIL+vXXX+Xp6SlJql69uiZPnqz69esrMTFRCxcutG5bt25dnT59WhkZGczXAaBI5s+frwsXLvy/9u48uqZ7///4M3NiSIUQ4SoVRAwxB60xDWlNV9VtUZQaQ81jVYheaRLVkpipMSKG0KaKmqpoUUSVW1pFBO0VQ6Yv0hyS8/vDL/vmNEG0hmhfj7WyVrL3Z+/9OWfx3vu9PxNz587F19eXBg0a0LJlS4oUKcLt27dxc3Nj3LhxrFu3ju7du3Pq1Clu3LjBjh072LlzZ76uobgmT4L+tYjcR3p6usXfmzdvZu3atYwdO5aFCxeyYsUK/P39iYiIMLrKb9iwgVu3bvHZZ5/dd1kPs9lsBO6wsDACAgKYP38+x44d4+LFi2RmZlKoUCE6duxI7969iY+Pp0ePHpjNZrKysnB1daVixYr89ttvj+YLEJG/lODgYPbv32/RM8fKyoqWLVsyY8YMfvjhB3r16gXAW2+9xVtvvcXOnTtZsGAB58+fB/J++ZhTzhak7du3s3XrVqZOncqbb76JjY2NcX743wOus7MzFy9eNF4mPPvss7Ru3Vo9iEQk306cOIGfnx/Fixc3tmU3uNjZ2ZGVlUV0dDQmk4m3336bMWPG8MEHH1ChQgX27Nlzz3MrrsmTpInoRO5h1KhRtGzZknbt2hkThsyePZujR4/y8ccfG+USEhKYP38+X331FZMmTeLll18GuO9anDknIdm3bx8zZ85kxIgRODo6MnPmTC5cuMCHH36It7c3NjY2pKens3r1ao4fP8706dONZH/dunXUr1+f55577hF+GyLytAsNDWXZsmV8+umnVK1alR9//JGqVasa+00mE7t27WLy5Ml4eXmxdOlSAObMmcPGjRtZuXIlrq6u+b5eTEwM//3vf3F0dKRfv35kZGSwd+9egoKCqFChAitXrjTKpqWlUaRIEWMcqbW1tdYzFpF8ycrK4sqVK3To0IHw8HAaNWqU56o6ycnJdO7cGV9fX959912jzKFDhwgICGDNmjX37UWkuCZPglraRe5i9+7dFCtWjFatWgFw69Yt4E4L04ULF0hMTDTKli9fnhdffJHk5GRGjBhhdLG61xh2+N8Y9M2bN7NhwwYqVapE48aNqVOnDhEREbi7uzNq1CiOHTtGZmYmTk5OdOvWjQ8//NCY0RTgX//6lxJ2EbmnDz74gJiYGDZt2kTVqlVJTEykS5cuDBgwwChjb29P8+bNmThxIocOHWL48OEADB48mNWrVz9Qwg4QGRnJnDlzOH78OCaTCQcHB5o2bUpQUBAJCQnGGHoAZ2dniwdbuH+LvogI3Omy7urqSpEiRdi/f7+xLSez2YyLiwuVK1cmKSnJKJPdfunq6moxF9HdKK7Jk6CkXSQPI0eOJCoqisDAQBwcHIiKimL58uX89ttv1K1bl+vXr/Ppp59y7do145hSpUrxwgsvEBQURIsWLYzt+QnOhw4dYteuXcTFxZGcnAzAM888w9y5cylTpgxjxowhLi6OrKwsHBwcsLKywmw233cJEhERuNNSvnjxYkaPHm20IhUpUoSJEydy9OhRhg0bZpR1dHSkWbNmeHh48MUXX9C/f3/gTky6l7wmVYqNjaVFixbs27ePnTt38ttvv+Hg4ECzZs2YMmUKBw8e5L333rM4RuM8ReRBmc1mbt26RcWKFTlw4IAxeWZO2Q0l2c9S2dLT0/nyyy85f/58rjinuCYFhbrHi+ShV69euLu7ExISAkC/fv34+eefGThwIF26dGHWrFksXLiQ7t2706xZM0qWLEloaCiFChUiPDwcKyuru3Z/yvm2Neea6wsWLGD16tU0a9aMwYMHU6pUKeBO16ouXbpQqVIlIiIiHtM3ICJ/FcHBwXzyySc4OztTqFAh3nnnHRo2bIitra0xAdOUKVNo0qSJRYyZNGkSvr6+eHh4UK5cuXteI2dc27x5M6mpqRQrVswYKtS9e3fi4+OZMmUKTZs2xcHBgYyMDI4fP06dOnXU8iQiD8X3339P9+7d6dChA8OHD6dkyZIW+5OSkhg0aBDt2rWje/fuxvaEhAQyMzOpWLGisU1xTQoSJe0iOdy6dQs7Ozv69OlD+fLlmTRpkrFv1KhRfPfddwwYMIDXX3+dRYsW8fnnn3Pq1Ck8PT1xcHBg5cqVxkQneb1VzTmGPTo6mh9//BE3NzcGDRoEQHh4ONu2baNx48YMGDDAuNncuHEDR0dH3QBE5IGEhoayevVqtmzZgru7O+3bt8dkMjF58mR8fHxyJe6NGjWiW7dufP311+zYsYNVq1YZLxDzY/r06URGRlKiRAkSExMZMGAAQ4cOBeCNN94gISGBoKAgmjRpgqOjo3GcxniKyMOybt06pkyZgq+vL127dqVx48bcvn2by5cv895775GcnMyqVauMmJPz2SwvimtSEChpF8khO3D37NmTcuXKERwcbCTyACNGjOD777+nf//+dOnShZSUFM6fP0+hQoWoWLGiMc78ft3WZ82axccff0ytWrU4ePAgvr6+hIWFUbRoUcLDw9m+fTuNGzemb9++uLm5GcfpBiAi+ZWUlMSECRMYPny4MdmcyWTi1VdfxWQyERQURIMGDYzEfffu3YSFhRnzZ4SHh1OtWrV7XiPnC8qkpCTefvttJkyYgJOTEzt37uSjjz5i4MCBxtj4nj17cvDgQSIjI2nQoMEj/fwi8vdkNpvZvn07kydPxsbGBjc3N6ysrLC1tcXa2prly5djZ2eXrx6RimtSUGhArAh3lmirV68e5cuXN7Zlv8/KTtgBZsyYwbBhw1i4cCFZWVm0b98eb29vY39WVlaeCXvOG0BmZiY//fQTixcvplatWpw4cYK+ffsyZswYpk2bxrBhw7CxsSEyMpKyZctaLCGihF1E8qt48eJERERgb2+P2WwmMzMTe3t71q9fz6uvvkpQUJCRuBcuXJg2bdrQrFkzEhIScHd3t1gyKS8549qRI0e4dOkSxYoVo0KFChQpUgQXFxcyMzMJDw8HYPjw4axYsYIpU6ZQt27dR/75ReTvycrKitatW1OjRg327dvHsWPHcHFxwdPTE39/f2xsbO7awKK4JgWVWtrlb+/MmTO0bduW3r1789prr/Hcc88xcOBAAF566SUqV65M6dKlsbKyMh5iAwMD2blzJ4GBgcbYprvJ2e1q69atpKWlsWHDBqZNm2aMEz18+DABAQHUq1ePadOm4ezszLp16+jUqZMSdRF5aLIfVH/f4u7j4/NAsSZnXAsNDSU2NhaTyYTJZGLZsmXUq1cPuNNKtXbtWmbNmkXXrl2ZOHGicQ71HBKRJ+FusUdxTQoyJe0iQFxcHCNGjMDf35++ffsSEhLCnj17MJvNpKenY29vT5kyZShfvjxVqlTBxsaGhIQEpk+fftfgnP1fK/sGEBISQkxMDDY2NqSlpTFy5EhjVubsOgwePJgKFSqwePFiChcuDOgGICIP1+8T96ysLMaNG0eTJk3yNcNxzgfbb775htDQUEaMGEFWVhbvvvsuVatWZfz48Xh5eQF3HnCXLFnC4cOHiY6OBrjn+FERkYclZ7y619h1xTUp6JS0i/x/cXFxDBs2jFdffZVdu3bh4eFBYGAgJ06c4PTp05w9e5ajR49y4cIFvLy8iIyMNNZhv19SHR8fT1BQEGPHjsXR0ZEZM2bw66+/0qlTJ4vZSw8cOMCiRYtYtGiRlgcRkUcmZ+Lu5+eHq6srUVFRODk55fscO3bsIDY2Fnd3dyZMmADc6bn0+uuvU716dYsH3LS0NIoWLWosV6mHWxEpiBTXpKBS0i6Sw+HDhxkyZAgpKSkMGDDAmGQkm9lsJiMjA3t7e6ytrfOcJX7w4MG8/PLLtGvXDoBPPvmEJUuW4O7uzowZMyhcuDCJiYn8+9//zjNxz3a3GehFRB6GnIl7YmLiPZd127x5M5cuXaJcuXJUrlyZ8uXLM27cOHbt2oWnpycrV640yp45c4auXbtSo0YNhg8fbjHvhx5sRaSgUFyTp4kmohPJoX79+ixYsIDXX3+ds2fPkpiYaMzenv2Am72cR15Jdd++ffn666+NYG4ymYwyp06dwsHBAQA3NzcCAwOZOnUqsbGx3Lx506KrPKCEXUQeKVtbW27fvo29vf09E/Y+ffpw6dIlbGxsSExMpFatWgQGBjJ+/HicnZ3ZtGkTM2fONF5yenh4EB0dTdu2balYsaLFw60ebEWkIFBck6eNsgKR3/H29iYqKorvvvuOJUuWcOHCBYBcs4z+Pqnu2rUraWlptG/fnpMnTwJgb29P69atGTJkCCaTiX79+hnl3dzcmDhxIo6Ojpw7dw51ehGRx+1+y1NOnTqV9PR0li1bxmeffcbMmTM5fvw4GzdupHjx4vTt25eXXnqJHTt2MGvWLOM4Dw8Pdu7cyfjx4x/1RxAReSCKa/I0UtIukoe6desyc+ZMtmzZwvz587l8+fI9y7/xxhtkZGSwYsUKKlSowE8//WTsc3JyokWLFkyePJmTJ0/Sp08fY5+bmxvh4eFMnTrVGBMlIlIQZGZmcvbsWVq1akXJkiUBaNy4MS1atGDbtm2YTCZKly7NwIEDadCgAdu2bWP27NnG8WXLljVa80VECgLFNXlaKWkXuYt69eoRFhbGtWvXjMCel06dOnHjxg0iIyNxdHSkUaNGpKamcuTIEeBON3p7e3tatmzJlClTOHHihEWLe/HixY3x8epiJSIFwVdffcXNmze5ePEiJpMJuBPLAFxdXbl+/bqx/rubmxsDBw7Ex8eHyMhIYmJiLM51v9Z8EZHHQXFNnmZK2kXuoXHjxsybNw8rKysjsOe0fv16WrVqxcqVK40l2kqVKsXt27f5z3/+A9zpRm82my0S97179xISEmJxLo1hF5GC4PTp04SFhWFtbU2zZs348ccfycjIMF4qFitWjNu3b2Mymbh16xZwp9fQ4MGDCQgI4JVXXnmS1RcRyUVxTZ52ek0kch/Z3dZ/n1SfOXOGRYsWsXjxYooUKWJ0bS9XrhydOnViw4YN+Pv74+bmZtwU7O3tad68OcuXL6d+/fqP/bOIiNxPpUqVqF69Oj///DMTJ07k/PnzODg4GDEuLS2NQoUKYWtra8TF6dOn06FDB3r16gWQr6UwRUQeF8U1edqpaU8kH/Lqtu7h4UGtWrWM8e45u7fXqVOHa9eucebMGQCLsU8ODg40bNgQGxsbjYkSkQLJ2tqajz76CIBnn33WYt+lS5ews7MzHmz79OnDl19+iYeHh1FGD7YiUtAorsnTTEm7yJ80Y8YM4E4wz+5C7+/vT7Vq1QgNDQXujH3Kq3u9xkSJSEGS3erUt29f0tLS2LhxY64yJpMJZ2dn4M6D7a+//kpsbCw2NjZkZmY+1vqKiNyP4pr8FShpF/kDsm8Affr0sbgBWFtbG63nQ4cOJTMzk/fffz/P7vUiIgVNdm+hUqVKUbp0aT7//HNj2cvsfUWLFuXWrVv07t2bhIQEPvvsM+zs7Lh9+7ZaokSkwFFck78CZREif8C9bgDZreeVKlXi5ZdfZuvWrXm+1RURKaiKFSvGyJEj2b9/P3PmzOHKlSvGPnd3d44ePUpqaipbtmwxHmzVc0hECjLFNXmaKWkX+RPudQNwcHCga9euvPbaa7Rt2/YJ1lJE5MFVqVKFuXPnsnnzZoKDg/nhhx8AaNGiBZ07d2bNmjV6sBWRp4rimjytrMzZ/XxF5A/7+uuvGTRoEL6+vgwYMAAvL69cZTTrqIg8jQ4fPsyQIUOoXLkyTZs2pW/fvkZvo1u3bmFnZ/eEaygi8mAU1+Rpo6Rd5CHJvgFUqVKFRo0aERAQ8KSrJCLyUCQkJBAdHc0333yDlZUV7dq1w9PTk4YNG+Lo6Pikqyci8sAU1+RpoqRd5CHKvgEcOHCArKws2rVrh5eXFw0aNNANQESeaiaTCYBVq1aRkpJCXFwcs2bNolixYk+2YiIif5DimjwtlLSLPGTZN4CoqChSU1N1AxCRvwSz2Wx0HwXIyMjAwcHhCdZIROTPUVyTp4WSdpGHTDcAEfk7+H2sExF52imuSUGlpF3kEdMNQERERERE/igt+SbyiClhFxERERGRP0pJu4iIiIiIiEgBpaRdREREREREpIBS0i4iIiIiIiJSQClpFxERERERESmglLSLiIiIiIiIFFBK2kVERP6kHj160KNHjz99ng0bNuDp6cnFixf/9Lk8PT2ZNWvWfcsdO3YMf39/TCYTvr6+eHp63vMnP+d8WN9HQZOcnEyLFi24cOHCk66KiIj8jdg+6QqIiIjIk5GRkcG4ceMYM2YM9vb2zJ49G5PJZOx/++23qVatGoMGDTK2lS5d+klUtUBwcXGhV69eTJgwgRUrVmhJTxEReSyUtIuIiPxNrVq1CltbW/z8/ACoVq2axX57e3uKFy9O7dq1n0DtCqZu3boxb948tm/fTuvWrZ90dURE5G9A3eNFREQek3Xr1tGpUydq166Nt7c3//znP9myZUuuckeOHKFjx47UqFGDdu3asXnzZov9GRkZTJs2jebNm1OjRg3at2+fq8z9mEwmli5dSrt27R7ouMzMTKKiomjfvj3e3t60aNGC6dOnk5GRcddjsrKyWLhwIa1ataJGjRr4+/sTGRlpUaZHjx6MHj2aoUOHUrt2bXr37g3AxYsXGTt2LE2aNKF69eo0btyYsWPHkpycbBzr6+tLREQEYWFhPP/883h7e9OnTx/OnTtncY3du3fTpUsXateuTZMmTZg0aRJpaWnG/l9//ZWRI0fi4+NDrVq1ePPNNzlx4oTFOezt7fH392fBggUP9L2JiIj8UWppFxEReQyioqKYOnUqQ4YMoV69eqSmprJo0SJGjx5NnTp1LLqdT5o0iYCAALy8vPjkk08YMWIE9vb2+Pn5YTabGTx4MEeOHGHo0KF4eHiwfft2RowYgclkomPHjvmqz7fffktiYuIDtxZPmjSJ2NhY+vXrR/369Tlx4gRz5szh5MmTfPzxx3l2GQ8KCmLDhg0MGDCAOnXqcOjQId5//33S0tIYPHiwUW7Lli106NCBefPmkZWVRXp6Oj179sTFxYXJkydTtGhRvvvuO2bPno2joyPvvfeeceyKFSuoV68eISEhpKamEhwczLhx41izZg0Au3btIiAggBdffJGZM2eSkpLCtGnT+OWXX1i8eDFJSUl06dIFJycnAgMDcXJyYvny5bzxxhvExMTg4eFhXOull15izZo1xMfH89xzzz3Q9yciIvKglLSLiIg8BhcuXKBPnz4W48PLli1Lp06diIuLo23btsb2IUOG0KdPHwCaNWvGuXPnmDt3Ln5+fuzbt4+9e/cyY8YM2rRpA0DTpk1JT09n+vTptGvXDlvb+9/eDxw4gLOz8wMlnadPnyYmJoZRo0bRv39/AF544QVKlSrF2LFj2bNnD82bN7c4Jj4+nrVr1zJy5EjjmCZNmmBlZcWCBQvo1q0bLi4uANjZ2TFlyhTs7e0BOHnyJKVLlyYsLIxy5coB0KhRI77//nsOHjxocR1nZ2fmzp2LjY0NAOfPn2fWrFkkJyfj4uLCrFmz8PLyYvbs2caLBXt7e8LDw7l69SqRkZGkpKQQHR1N2bJlje++TZs2hIeHExERYVyrZs2aAOzfv19Ju4iIPHLqHi8iIvIYjB8/ntGjR5OWlsbRo0eJjY0lKioKwGLyN8BIxrP5+flx4sQJbty4wf79+7GysqJ58+bcvn3b+PH19eXKlSv8/PPP+arPhQsXjOQ0v7IT5ZwvGLL/trGx4dtvv811zIEDBzCbzfj6+uaqb0ZGBnFxcUbZihUrGgk7gJeXF6tWraJs2bKcO3eO3bt3s3jxYs6ePZvrO6tZs6aRsMP/JsxLT0/nt99+48SJE/j5+Vn0BGjTpg1bt27F1dWV/fv34+XlhZubm1FHa2trmjVrxr59+yyuVbRoUZydnR/KLP8iIiL3o5Z2ERGRx+D8+fNMmjSJ/fv3Y2dnR8WKFalatSoAZrPZoqyrq6vF3yVKlMBsNnP9+nVSUlIwm83UrVs3z+tcvnwZLy+v+9bn+vXrODk5PdBnSE1NBaBkyZIW221tbXFxceH//u//ch2TkpIC5E70syUmJhq/Fy5cONf+pUuXMn/+fFJSUnB1daVGjRo4OTnlutbvP4u19Z12iaysLFJTUzGbzZQoUeKuny0lJYWEhASqV6+e5/709HSLazg5OXH9+vW7nk9ERORhUdIuIiLyiGVlZdG/f3/s7OyIiYnBy8sLW1tbTp8+TWxsbK7yqampFon71atXsbGx4ZlnnqFo0aIUKlSIFStW5Hmt8uXL56tOLi4uXL58+YE+xzPPPAPAlStXLFrpb926ZXRD/z1nZ2cAli9fnmdSXqZMmbteb+PGjYSGhjJmzBg6depE8eLFARg2bBjHjx/Pd72LFCmClZUVSUlJFtszMjI4cOAAtWrVomjRovj4+DB27Ng8z5GzBwBAWlpanp9XRETkYVP3eBERkUcsOTmZ+Ph4OnfuTM2aNY0x53v27AHuJPU5ffXVV8bvWVlZfPHFF9SqVQtHR0d8fHy4efMmZrOZmjVrGj+nTp1izpw53L59O191KlOmDJcuXcrVyn8vPj4+AGzatMli+6ZNm8jMzKRevXq5jqlfvz5w5zvIWd+kpCTCw8ONlvi8xMXF4ezsTN++fY2E/caNG8TFxeX6zu6lcOHCeHl5sWvXLovte/bsoX///ly+fBkfHx9jYrmc9YyNjSUmJsai631qairp6en3fOEgIiLysKilXURE5CG4dOkSy5Yty7W9SpUqPP/885QtW5aoqChKly6Ns7Mze/fuNVrL09PTLY6ZOXMmmZmZuLu7Ex0dTXx8PEuXLgWgefPmNGjQgEGDBjFo0CA8PDw4duwYERERNG3a1Ehu7+eFF15g4cKFnDp1Ck9Pz3wdU6lSJV555RUiIiJIT0+nQYMGnDx5ktmzZ9OwYUOaNm2a6xhPT086dOhAYGAgv/zyCzVq1CA+Pp4ZM2bwj3/8gwoVKtz1et7e3kRHRxMaGkrLli25fPkyixcv5urVq0arf34NHTqUgIAARo4cSceOHbl69SofffQRfn5+VKlShV69ehEbG0uvXr146623cHFxYfPmzaxdu5Z33nnH4lzZ4/CbNGnyQHUQERH5I5S0i4iIPATnz58nJCQk1/bOnTvz/PPPM3fuXIKDgxk/fjz29vZUqlSJefPm8f7773P48GF69OhhHBMSEkJoaCgJCQlUqVKFRYsWGa3c1tbWLFy4kPDwcBYsWMC1a9dwc3Ojd+/eFsun3U/9+vUpUaIEu3fvznfSDhAcHEz58uVZv349ixYtolSpUvTs2ZNBgwYZ48h/LyQkhAULFrB69WouXbpEiRIlaNOmDcOHD7dowf69V155hYsXL7J+/XpWrVqFm5sbzZs3p1u3bgQGBnLmzBmLpdjupWXLlsyfP5/Zs2czePBgihcvTvv27RkyZAgAbm5urF69mg8//JCgoCAyMjKoUKECwcHBdO7c2eJce/bswdvb+4En8hMREfkjrMwP0i9ORERE/jKWLFlCdHQ027Zty3N9dcnt5s2bNG3alLCwMPz8/J50dURE5G9AY9pFRET+prp162aMmZf8Wb16NZUrV+bFF1980lUREZG/CbW0i4iI/I0dOXKE8ePH8/nnn+eaIV0sJSUl0bFjRyIjI/M9S7+IiMifpaRdREREREREpIBS93gRERERERGRAkpJu4iIiIiIiEgBpaRdREREREREpIBS0i4iIiIiIiJSQClpFxERERERESmglLSLiIiIiIiIFFBK2kVEREREREQKKCXtIiIiIiIiIgWUknYRERERERGRAur/AaOjQOo/4pBWAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]

From f7ca2a388161057cb855c60cd9a5f6f2f24f514d Mon Sep 17 00:00:00 2001
From: Jeff VanOss <jeff.vanoss@bamfhealth.com>
Date: Tue, 13 Aug 2024 20:13:44 -0400
Subject: [PATCH 6/6] add detection rate

---
 model_performance_aimi2.ipynb | 309 ++++++++++++++++++++--------------
 poetry.lock                   |  49 +++++-
 pyproject.toml                |   1 +
 3 files changed, 234 insertions(+), 125 deletions(-)

diff --git a/model_performance_aimi2.ipynb b/model_performance_aimi2.ipynb
index 53881c8..4415e9d 100644
--- a/model_performance_aimi2.ipynb
+++ b/model_performance_aimi2.ipynb
@@ -18,7 +18,7 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "%pip install pandas pydicom pydicom-seg scikit-learn seaborn requests\n",
+    "%pip install pandas pydicom pydicom-seg scikit-learn seaborn requests connected-components-3d\n",
     "%pip install segmentationmetrics"
    ]
   },
@@ -54,6 +54,7 @@
     "import segmentationmetrics as sm\n",
     "import SimpleITK as sitk\n",
     "import skimage.measure\n",
+    "import cc3d\n",
     "from segmentationmetrics.surface_distance import compute_surface_dice_at_tolerance\n",
     "from tqdm.auto import tqdm\n",
     "\n",
@@ -198,59 +199,101 @@
     "    return metrics\n",
     "\n",
     "\n",
-    "def get_false_discovery(\n",
+    "def con_comp(seg_array):\n",
+    "    # input: a binary segmentation array output: an array with seperated (indexed) connected components of the segmentation array\n",
+    "    connectivity = 18\n",
+    "    conn_comp = cc3d.connected_components(seg_array, connectivity=connectivity)\n",
+    "    return conn_comp\n",
+    "\n",
+    "\n",
+    "def get_discovery_rates(\n",
     "    img_file_pred: str, img_file_gt: str, label_value: int = 1, prefix=\"\"\n",
     "):\n",
-    "    \"\"\"Get the false discovery rate for two images, predicted (image_file_pred) and ground truth (image_file_gt).\"\"\"\n",
     "    img_pred = load_seg(img_file_pred, label_value)\n",
     "    img_gt = load_seg(img_file_gt, label_value)\n",
     "    img_gt = resize_label(img_gt, img_pred)  # match the size of the qa_img\n",
+    "    gt_arr = sitk.GetArrayFromImage(img_gt)\n",
+    "    pred_arr = sitk.GetArrayFromImage(img_pred)\n",
+    "    voxel_vol = np.prod(img_pred.GetSpacing()) / 1000\n",
     "\n",
-    "    vox_ml = np.prod(img_pred.GetSpacing()) / 1000\n",
-    "\n",
-    "    pd_cc = sitk.GetArrayViewFromImage(sitk.ConnectedComponent(img_pred))\n",
-    "    gt_cc = sitk.GetArrayViewFromImage(sitk.ConnectedComponent(img_gt))\n",
+    "    assert pred_arr.shape == gt_arr.shape, \"Shape mismatch\"\n",
+    "    # check that pred_array is binary\n",
+    "    assert set(np.unique(pred_arr)) in ({0}, {0, 1}), \"pred_array is not binary\"\n",
+    "    # check that gt_array is binary\n",
+    "    assert set(np.unique(gt_arr)) in ({0}, {0, 1}), \"gt_array is not binary\"\n",
     "\n",
-    "    # pd_arr = sitk.GetArrayFromImage(img_pred)\n",
-    "    # gt_arr = sitk.GetArrayFromImage(img_gt)\n",
-    "    # pd_cc = skimage.measure.label(pd_arr)\n",
-    "    # gt_cc = skimage.measure.label(gt_arr)\n",
-    "    true_pd_cc = pd_cc.copy()\n",
-    "    true_pd_cc[gt_cc == 0] = 0\n",
-    "    overlap_gt_cc = gt_cc.copy()  # gt labels that overlap with predicted\n",
-    "    overlap_gt_cc[pd_cc == 0] = 0\n",
+    "    pred_offset = pred_arr.copy()\n",
+    "    pred_offset[pred_offset == 1] = 2  # reset pred_array to 0,2\n",
     "\n",
-    "    pd_labels = np.dstack(np.unique(pd_cc, return_counts=True))[0, 1:]\n",
-    "    gt_labels = np.dstack(np.unique(gt_cc, return_counts=True))[0, 1:]\n",
-    "    true_pd_labels = np.dstack(np.unique(true_pd_cc, return_counts=True))[0, 1:]\n",
-    "    overlap_gt_labels = np.dstack(np.unique(overlap_gt_cc, return_counts=True))[0, 1:]\n",
+    "    combined = pred_offset + gt_arr\n",
+    "    tp = (combined == 3).sum() * voxel_vol\n",
+    "    fp = (combined == 2).sum() * voxel_vol\n",
+    "    fn = (combined == 1).sum() * voxel_vol\n",
     "\n",
-    "    true_vol_overlap_ml = true_pd_labels[..., 1] * vox_ml\n",
-    "\n",
-    "    # get false positives\n",
+    "    tp_s = []\n",
     "    false_pos_vols_ml = []\n",
-    "    for pd_label, pd_size in pd_labels:\n",
-    "        if pd_label not in true_pd_labels[..., 0]:\n",
-    "            false_pos_vols_ml.append(pd_size * vox_ml)\n",
-    "\n",
-    "    # get false negatives\n",
     "    false_neg_vols_ml = []\n",
-    "    for gt_label, gt_size in gt_labels:\n",
-    "        if gt_label not in overlap_gt_labels[..., 0]:\n",
-    "            false_neg_vols_ml.append(gt_size * vox_ml)\n",
+    "\n",
+    "    pred_cc = con_comp(pred_arr)\n",
+    "    for idx in range(1, pred_cc.max() + 1):\n",
+    "        comp_mask = np.isin(pred_cc, idx)\n",
+    "        vals, cnts = np.unique(combined[comp_mask], return_counts=True)\n",
+    "        dect_arr = {v: c for v, c in zip(vals.tolist(), cnts.tolist())}\n",
+    "\n",
+    "        if 0 in dect_arr:\n",
+    "            raise ValueError(\"should be no 0 values in the label\")\n",
+    "        if 1 in dect_arr:\n",
+    "            raise ValueError(\"should be no 1 values in the label\")\n",
+    "        if 3 in dect_arr:\n",
+    "            tp_s.append(\n",
+    "                (dect_arr[3], dect_arr.get(2, 0))\n",
+    "            )  # (true positive, false positive)\n",
+    "            pass  # true positive, overlaps with gt\n",
+    "        elif 2 in dect_arr and 1 in dect_arr:\n",
+    "            raise ValueError(\"should not be 2 and 1 values in the label\")\n",
+    "        elif 2 in dect_arr:\n",
+    "            false_pos_vols_ml.append(\n",
+    "                dect_arr[2] * voxel_vol\n",
+    "            )  # false positive, does not overlap with gt\n",
+    "        # elif 1 in dect_arr:\n",
+    "        #     false_neg_vols_ml.append(\n",
+    "        #         dect_arr[1] * voxel_vol\n",
+    "        #     )  # false negative, does not overlap with pred\n",
+    "\n",
+    "    gt_cc = con_comp(gt_arr)\n",
+    "    for idx in range(1, gt_cc.max() + 1):\n",
+    "        comp_mask = np.isin(gt_cc, idx)\n",
+    "        vals, cnts = np.unique(combined[comp_mask], return_counts=True)\n",
+    "        dect_arr = {v: c for v, c in zip(vals.tolist(), cnts.tolist())}\n",
+    "\n",
+    "        if 0 in dect_arr:\n",
+    "            raise ValueError(\"should be no 0 values in the label\")\n",
+    "        if 2 in dect_arr:\n",
+    "            raise ValueError(\"should be no 2 values in the label\")\n",
+    "        elif 1 in dect_arr:\n",
+    "            false_neg_vols_ml.append(\n",
+    "                dect_arr[1] * voxel_vol\n",
+    "            )  # false negative, does not overlap with pred\n",
+    "        if 3 in dect_arr:\n",
+    "            continue  # true positive, overlaps with pred\n",
+    "        elif 2 in dect_arr and 1 in dect_arr:\n",
+    "            raise ValueError(\"should not be 2 and 1 values in the label\")\n",
     "\n",
     "    stats = {\n",
-    "        \"true_vol_overlap_ml\": true_vol_overlap_ml,\n",
+    "        # \"true_vol_overlap_ml\": true_vol_overlap_ml,\n",
     "        \"false_pos_vols_ml\": false_pos_vols_ml,\n",
     "        \"false_neg_vols_ml\": false_neg_vols_ml,\n",
-    "        \"true_vol_overlap_ml_total\": sum(true_vol_overlap_ml),\n",
-    "        \"false_pos_vol_ml_total\": sum(false_pos_vols_ml),\n",
-    "        \"false_neg_vol_ml_total\": sum(false_neg_vols_ml),\n",
-    "        \"true_pos_cnt\": len(true_vol_overlap_ml),\n",
-    "        \"false_pos_cnt\": len(false_pos_vols_ml),\n",
-    "        \"false_neg_cnt\": len(false_neg_vols_ml),\n",
+    "        # \"true_vol_overlap_ml_total\": sum(true_vol_overlap_ml),\n",
+    "        \"true_pos_pix_vol_ml_total\": tp,\n",
+    "        \"false_pos_pix_vol_ml_total\": fp,\n",
+    "        \"false_neg_pix_vol_ml_total\": fn,\n",
+    "        \"false_pos_roi_vol_ml_total\": sum(false_pos_vols_ml),\n",
+    "        \"false_neg_roi_vol_ml_total\": sum(false_neg_vols_ml),\n",
+    "        \"true_pos_roi_cnt\": len(tp_s),\n",
+    "        \"false_pos_roi_cnt\": len(false_pos_vols_ml),\n",
+    "        \"false_neg_roi_cnt\": len(false_neg_vols_ml),\n",
     "    }\n",
-    "    stats[\"pos_cnt\"] = stats[\"true_pos_cnt\"] + stats[\"false_neg_cnt\"]\n",
+    "    stats[\"pos_roi_cnt\"] = stats[\"true_pos_roi_cnt\"] + stats[\"false_neg_roi_cnt\"]\n",
     "\n",
     "    if prefix:\n",
     "        stats = {f\"{prefix}-{k}\": v for k, v in stats.items()}\n",
@@ -310,7 +353,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 85/85 [04:49<00:00,  3.41s/it]\n"
+      "100%|██████████| 85/85 [04:47<00:00,  3.38s/it]\n"
      ]
     }
    ],
@@ -331,7 +374,7 @@
     "    assert qa_file.exists(), f\"QA segmentation not found for {row.QASegmentation}\"\n",
     "\n",
     "    version = \"aimiv1\" if row.AimiProjectYear == 2023 else \"aimiv2\"\n",
-    "    for label_value in [1, 2, 3]:\n",
+    "    for label_value, label_name in label_names.items():\n",
     "        stats = calc_metrics_for_label(\n",
     "            ai_file,\n",
     "            qa_file,\n",
@@ -340,27 +383,45 @@
     "            label_value=label_value,\n",
     "            nsd_tol=nsd_tolerances[label_value],\n",
     "        )\n",
-    "        stats[\"label_name\"] = label_names[label_value]\n",
+    "        stats[\"label_name\"] = label_name\n",
     "        if label_value in (2, 3):\n",
-    "            detection_metrics = get_false_discovery(\n",
+    "            detection_metrics = get_discovery_rates(\n",
     "                ai_file, qa_file, label_value=label_value\n",
     "            )\n",
     "            # ignore array values\n",
     "            for x in (\"true_vol_overlap_ml\", \"false_neg_vols_ml\", \"false_pos_vols_ml\"):\n",
-    "                detection_metrics.pop(x)\n",
+    "                if x in detection_metrics:\n",
+    "                    detection_metrics.pop(x)\n",
     "            stats.update(detection_metrics)\n",
     "        metrics.append(stats)\n",
     "\n",
-    "\n",
-    "metrics_df = pd.DataFrame(metrics)\n",
-    "\n",
-    "# Replace infinite values with NaN and drop NaN values\n",
-    "# metrics_df = metrics_df.replace([np.inf, -np.inf], np.nan).dropna()"
+    "metrics_df = pd.DataFrame(metrics)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics_df.to_csv(\"qa-metrics.csv\", index=False)\n",
+    "metrics_df = pd.read_csv(\"qa-metrics.csv\")\n",
+    "\n",
+    "metrics_df = metrics_df.replace([np.inf, -np.inf], np.nan)\n",
+    "# drop na sample for volume/surface metrics\n",
+    "metrics_df = metrics_df.dropna(\n",
+    "    subset=[\n",
+    "        \"dice\",\n",
+    "        \"hausdorff_distance_95\",\n",
+    "        \"mean_surface_distance\",\n",
+    "        \"normalized_surface_distance\",\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -394,10 +455,10 @@
        "      <th colspan=\"2\" halign=\"left\">mean_surface_distance</th>\n",
        "      <th colspan=\"2\" halign=\"left\">normalized_surface_distance</th>\n",
        "      <th>normalized_surface_distance_tolerance</th>\n",
-       "      <th>pos_cnt</th>\n",
-       "      <th>true_pos_cnt</th>\n",
-       "      <th>false_pos_cnt</th>\n",
-       "      <th>false_neg_cnt</th>\n",
+       "      <th>pos_roi_cnt</th>\n",
+       "      <th>true_pos_roi_cnt</th>\n",
+       "      <th>false_pos_roi_cnt</th>\n",
+       "      <th>false_neg_roi_cnt</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
@@ -438,26 +499,26 @@
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Cyst</th>\n",
        "      <th>aimiv1</th>\n",
-       "      <td>0.87</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>inf</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>21.80</td>\n",
        "      <td>1.57</td>\n",
        "      <td>4.76</td>\n",
-       "      <td>0.89</td>\n",
-       "      <td>0.26</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.19</td>\n",
        "      <td>2</td>\n",
-       "      <td>163.0</td>\n",
-       "      <td>163.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>164.0</td>\n",
+       "      <td>154.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aimiv2</th>\n",
        "      <td>1.00</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>inf</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
        "      <td>0.00</td>\n",
        "      <td>0.00</td>\n",
        "      <td>1.00</td>\n",
@@ -471,14 +532,14 @@
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Kidney</th>\n",
        "      <th>aimiv1</th>\n",
-       "      <td>0.96</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>inf</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>8.08</td>\n",
+       "      <td>29.10</td>\n",
        "      <td>1.22</td>\n",
        "      <td>4.44</td>\n",
-       "      <td>0.96</td>\n",
-       "      <td>0.14</td>\n",
+       "      <td>0.97</td>\n",
+       "      <td>0.09</td>\n",
        "      <td>7</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -489,8 +550,8 @@
        "      <th>aimiv2</th>\n",
        "      <td>1.00</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
        "      <td>0.00</td>\n",
        "      <td>0.01</td>\n",
        "      <td>1.00</td>\n",
@@ -504,26 +565,26 @@
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Tumor</th>\n",
        "      <th>aimiv1</th>\n",
-       "      <td>0.91</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>inf</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>4.52</td>\n",
+       "      <td>18.31</td>\n",
        "      <td>1.38</td>\n",
        "      <td>7.13</td>\n",
-       "      <td>0.90</td>\n",
-       "      <td>0.27</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0.20</td>\n",
        "      <td>2</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>177.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>118.0</td>\n",
        "      <td>3.0</td>\n",
+       "      <td>13.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aimiv2</th>\n",
        "      <td>1.00</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
        "      <td>0.15</td>\n",
        "      <td>0.38</td>\n",
        "      <td>1.00</td>\n",
@@ -531,7 +592,7 @@
        "      <td>2</td>\n",
        "      <td>13.0</td>\n",
        "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -539,48 +600,48 @@
        "</div>"
       ],
       "text/plain": [
-       "                    dice       hausdorff_distance_95       \\\n",
-       "                    mean   std                  mean  std   \n",
-       "label_name version                                          \n",
-       "Cyst       aimiv1   0.87  0.29                   inf  NaN   \n",
-       "           aimiv2   1.00  0.00                   inf  NaN   \n",
-       "Kidney     aimiv1   0.96  0.15                   inf  NaN   \n",
-       "           aimiv2   1.00  0.00                   0.0  0.0   \n",
-       "Tumor      aimiv1   0.91  0.27                   inf  NaN   \n",
-       "           aimiv2   1.00  0.00                   0.0  0.0   \n",
+       "                    dice       hausdorff_distance_95         \\\n",
+       "                    mean   std                  mean    std   \n",
+       "label_name version                                            \n",
+       "Cyst       aimiv1   0.91  0.23                  7.36  21.80   \n",
+       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
+       "Kidney     aimiv1   0.97  0.11                  8.08  29.10   \n",
+       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
+       "Tumor      aimiv1   0.95  0.19                  4.52  18.31   \n",
+       "           aimiv2   1.00  0.00                  0.00   0.00   \n",
        "\n",
        "                   mean_surface_distance       normalized_surface_distance  \\\n",
        "                                    mean   std                        mean   \n",
        "label_name version                                                           \n",
-       "Cyst       aimiv1                   1.57  4.76                        0.89   \n",
+       "Cyst       aimiv1                   1.57  4.76                        0.92   \n",
        "           aimiv2                   0.00  0.00                        1.00   \n",
-       "Kidney     aimiv1                   1.22  4.44                        0.96   \n",
+       "Kidney     aimiv1                   1.22  4.44                        0.97   \n",
        "           aimiv2                   0.00  0.01                        1.00   \n",
-       "Tumor      aimiv1                   1.38  7.13                        0.90   \n",
+       "Tumor      aimiv1                   1.38  7.13                        0.94   \n",
        "           aimiv2                   0.15  0.38                        1.00   \n",
        "\n",
-       "                         normalized_surface_distance_tolerance pos_cnt  \\\n",
-       "                     std                                 first     sum   \n",
-       "label_name version                                                       \n",
-       "Cyst       aimiv1   0.26                                     2   163.0   \n",
-       "           aimiv2   0.00                                     2    25.0   \n",
-       "Kidney     aimiv1   0.14                                     7     0.0   \n",
-       "           aimiv2   0.00                                     7     0.0   \n",
-       "Tumor      aimiv1   0.27                                     2   180.0   \n",
-       "           aimiv2   0.00                                     2    13.0   \n",
+       "                         normalized_surface_distance_tolerance pos_roi_cnt  \\\n",
+       "                     std                                 first         sum   \n",
+       "label_name version                                                           \n",
+       "Cyst       aimiv1   0.19                                     2       164.0   \n",
+       "           aimiv2   0.00                                     2        25.0   \n",
+       "Kidney     aimiv1   0.09                                     7         0.0   \n",
+       "           aimiv2   0.00                                     7         0.0   \n",
+       "Tumor      aimiv1   0.20                                     2       131.0   \n",
+       "           aimiv2   0.00                                     2        13.0   \n",
        "\n",
-       "                   true_pos_cnt false_pos_cnt false_neg_cnt  \n",
-       "                            sum           sum           sum  \n",
-       "label_name version                                           \n",
-       "Cyst       aimiv1         163.0           0.0           0.0  \n",
-       "           aimiv2          25.0           0.0           0.0  \n",
-       "Kidney     aimiv1           0.0           0.0           0.0  \n",
-       "           aimiv2           0.0           0.0           0.0  \n",
-       "Tumor      aimiv1         177.0           0.0           3.0  \n",
-       "           aimiv2          13.0           0.0           0.0  "
+       "                   true_pos_roi_cnt false_pos_roi_cnt false_neg_roi_cnt  \n",
+       "                                sum               sum               sum  \n",
+       "label_name version                                                       \n",
+       "Cyst       aimiv1             154.0               3.0              10.0  \n",
+       "           aimiv2              25.0               0.0               0.0  \n",
+       "Kidney     aimiv1               0.0               0.0               0.0  \n",
+       "           aimiv2               0.0               0.0               0.0  \n",
+       "Tumor      aimiv1             118.0               3.0              13.0  \n",
+       "           aimiv2              13.0               1.0               0.0  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -595,10 +656,10 @@
     "            \"mean_surface_distance\": [\"mean\", \"std\"],\n",
     "            \"normalized_surface_distance\": [\"mean\", \"std\"],\n",
     "            \"normalized_surface_distance_tolerance\": \"first\",\n",
-    "            \"pos_cnt\": [\"sum\"],\n",
-    "            \"true_pos_cnt\": [\"sum\"],\n",
-    "            \"false_pos_cnt\": [\"sum\"],\n",
-    "            \"false_neg_cnt\": [\"sum\"],\n",
+    "            \"pos_roi_cnt\": [\"sum\"],\n",
+    "            \"true_pos_roi_cnt\": [\"sum\"],\n",
+    "            \"false_pos_roi_cnt\": [\"sum\"],\n",
+    "            \"false_neg_roi_cnt\": [\"sum\"],\n",
     "        }\n",
     "    )\n",
     "    .round(2)\n",
@@ -608,12 +669,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -623,7 +684,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -643,7 +704,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
diff --git a/poetry.lock b/poetry.lock
index b272b41..1e70e88 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -287,6 +287,53 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
+name = "connected-components-3d"
+version = "3.18.0"
+description = "Connected components on discrete and continuous multilabel 3D and 2D images. Handles 26, 18, and 6 connected variants; periodic boundaries (4, 8, & 6)."
+optional = false
+python-versions = "<4.0,>=3.8"
+files = [
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+    {file = "connected_components_3d-3.18.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1f9e635c2cf30aa8e3328521c40d8029611972ba21f11ecfe206f564584fd3c"},
+    {file = "connected_components_3d-3.18.0-cp39-cp39-win32.whl", hash = "sha256:f24f9a960937fb0efbf15d27ea3f967e49003918977b99f21185136ce72afe17"},
+    {file = "connected_components_3d-3.18.0-cp39-cp39-win_amd64.whl", hash = "sha256:8ea14f9bb82def2d97661c0cf633548029e88d53163a237478b7fe47cc5b051f"},
+    {file = "connected_components_3d-3.18.0.tar.gz", hash = "sha256:e4f187eeed4202f5fd97dca7fa63bea0dd71826b4d2240bc14e4b53920e7988c"},
+]
+
+[package.dependencies]
+numpy = "*"
+
 [[package]]
 name = "contourpy"
 version = "1.2.1"
@@ -2288,4 +2335,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "6794d413eeb940b749563b34fc3520234965ab1d6df2c4acfa4813160c104144"
+content-hash = "1842bc83a724f4d13d3b8291ceea102e7b3891b94c9317ade3346cebd8abeb7d"
diff --git a/pyproject.toml b/pyproject.toml
index 2231967..e2dc669 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ pydicom-seg = "^0.4.1"
 scikit-learn = "^1.3.1"
 segmentationmetrics = "^1.0.1"
 tqdm = "^4.66.1"
+connected-components-3d = "^3.18.0"
 
 
 [tool.poetry.group.dev.dependencies]