{ "cells": [ { "cell_type": "markdown", "id": "md-title", "metadata": {}, "source": [ "# Upload Dataset — Gleason_CNN\n", "\n", "Bu notebook:\n", "1. `polygons.csv` üzerinden tüm dataset'i EDA ile analiz eder (sınıf & TMA dağılımı)\n", "2. 4 sınıf için eşit ve TMA-dengeli **%10** alt küme seçer (`seed=42`)\n", "3. Seçilen görüntüler için sembolik bağlantı tabanlı subset dataset oluşturur\n", "4. `IngestPipeline` ile GCS + Firestore'a yükler\n", "\n", "**Ön Koşullar:** Service account JSON hazır olmalı." ] }, { "cell_type": "markdown", "id": "md-s0", "metadata": {}, "source": [ "## 0. Imports & Konfigürasyon" ] }, { "cell_type": "code", "execution_count": 1, "id": "imports", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Importlar hazır.\n" ] } ], "source": [ "import ast\n", "import os\n", "import shutil\n", "import logging\n", "from pathlib import Path\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as mticker\n", "\n", "logging.basicConfig(level=logging.INFO, format=\"%(levelname)s — %(message)s\")\n", "print(\"Importlar hazır.\")" ] }, { "cell_type": "code", "execution_count": null, "id": "config", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SA Key : /Users/yasin/Projects/histopathai/histopathai-478716-ecc29d5c2715.json\n", "Project : histopathai-478716\n", "Bucket : histopathai-wsi-processed-prod\n", "Dataset : /Users/yasin/Projects/histopathai/datasets/Gleason_CNN/dataset\n", "Hedef oran: %10 | Seed: 42\n" ] } ], "source": [ "# ── Cloud Ayarları ────────────────────────────────────────────────────────────\n", "SA_KEY_PATH = Path(\"\")\n", "PROJECT_ID = \"\"\n", "PROC_BUCKET = \"\"\n", "DB_NAME = \"(default)\"\n", "CREATOR_ID = \"111111111111111111111\"\n", "\n", "# ── Dataset Yolları ───────────────────────────────────────────────────────────\n", "RESOURCE_ROOT = Path(\".\")\n", "DATASET_ROOT = RESOURCE_ROOT / \"dataset\"\n", "POLYGONS_CSV = RESOURCE_ROOT / \"polygons.csv\"\n", "\n", "# ── Örnekleme Ayarları ────────────────────────────────────────────────────────\n", "TARGET_FRACTION = 0.10 # Toplam dataset'in yüklenecek oranı\n", "RANDOM_SEED = 42 # Tekrarlanabilirlik için sabit seed\n", "\n", "# ── Geçici Dizinler ───────────────────────────────────────────────────────────\n", "CACHE_DIR = Path(\"/tmp/histopathai-gleason-cnn-cache\")\n", "SUBSET_ROOT = Path(\"/tmp/histopathai-gleason-cnn-subset\")\n", "\n", "# ── Env Ayarları (admin rolü için) ────────────────────────────────────────────\n", "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = str(SA_KEY_PATH)\n", "os.environ[\"HISTOPATHAI_ROLE\"] = \"admin\"\n", "os.environ[\"PROJECT_ID\"] = PROJECT_ID\n", "os.environ[\"DB\"] = DB_NAME\n", "\n", "CACHE_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "print(f\"SA Key : {SA_KEY_PATH}\")\n", "print(f\"Project : {PROJECT_ID}\")\n", "print(f\"Bucket : {PROC_BUCKET}\")\n", "print(f\"Dataset : {DATASET_ROOT.resolve()}\")\n", "print(f\"Hedef oran: %{TARGET_FRACTION*100:.0f} | Seed: {RANDOM_SEED}\")" ] }, { "cell_type": "markdown", "id": "md-s1", "metadata": {}, "source": [ "## 1. EDA — Dataset Analizi\n", "\n", "`polygons.csv`'den her görüntünün birincil sınıfını türetir: **G5 > G4 > G3 > Benign** \n", "(test_pathologist2 annotasyonları, dataset_creator.ipynb ile tutarlı biçimde dışlanır)" ] }, { "cell_type": "code", "execution_count": 3, "id": "load-polygons", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Polygon satırı: 1,330\n", "\n", "creator\n", "train 968\n", "test_pathologist1 362\n", "\n", "label\n", "G3 528\n", "G4 457\n", "Benign 183\n", "G5 162\n" ] } ], "source": [ "polygons = pd.read_csv(POLYGONS_CSV)\n", "polygons = polygons[polygons[\"creator\"] != \"test_pathologist2\"].copy()\n", "\n", "print(f\"Polygon satırı: {len(polygons):,}\")\n", "print()\n", "print(polygons[\"creator\"].value_counts().to_string())\n", "print()\n", "print(polygons[\"label\"].value_counts().to_string())" ] }, { "cell_type": "code", "execution_count": 4, "id": "load-dataset", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO — DatasetLoader started [multi-workspace mode]: /Users/yasin/Projects/histopathai/datasets/Gleason_CNN/dataset\n", "INFO — images: 886 satır, 23 sütun\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "DatasetLoader görüntü sayısı: 886\n", "Kolonlar: ['entity_type', 'name', 'creator_id', 'is_deleted', 'created_at', 'updated_at', 'parent_id', 'parent_type', 'ws_id', 'format', 'width', 'height', 'processing.status', 'processing.version', 'processing.retry_count', 'processing.last_processed_at', 'id', '_ws_id', '_patient_id', '_local_path', '_has_origin', '_has_processed', '_cache_accessible']\n" ] } ], "source": [ "from histopathai.dataset.loader import DatasetLoader\n", "\n", "loader = DatasetLoader(root=DATASET_ROOT)\n", "img_df = loader.images()\n", "\n", "print(f\"DatasetLoader görüntü sayısı: {len(img_df):,}\")\n", "print(f\"Kolonlar: {list(img_df.columns)}\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "class-assign", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Eşleşen görüntü : 886\n", "\n", "Sınıf dağılımı (tüm dataset):\n", " count pct\n", "primary_class \n", "Benign 115 13.0\n", "G3 272 30.7\n", "G4 350 39.5\n", "G5 149 16.8\n" ] } ], "source": [ "def tma_id(image_name: str) -> str:\n", " \"\"\"ZT111_4_A_1_12 → ZT111_4_A\"\"\"\n", " return \"_\".join(image_name.split(\"_\")[:3])\n", "\n", "def assign_primary_class(labels) -> str:\n", " \"\"\"Görüntüdeki tüm etiketlerden en yüksek Gleason derecesini seç.\"\"\"\n", " s = set(labels)\n", " if \"G5\" in s: return \"G5\"\n", " if \"G4\" in s: return \"G4\"\n", " if \"G3\" in s: return \"G3\"\n", " return \"Benign\"\n", "\n", "# Görüntü başına birincil sınıf\n", "img_class_df = (\n", " polygons.groupby(\"image_name\")[\"label\"]\n", " .apply(assign_primary_class)\n", " .reset_index()\n", " .rename(columns={\"label\": \"primary_class\"})\n", ")\n", "img_class_df[\"tma\"] = img_class_df[\"image_name\"].apply(tma_id)\n", "\n", "# DatasetLoader ile birleştir (image_id ve patient_id için)\n", "df = img_class_df.merge(\n", " img_df[[\"id\", \"name\", \"_patient_id\"]],\n", " left_on=\"image_name\",\n", " right_on=\"name\",\n", " how=\"inner\",\n", ").rename(columns={\"id\": \"image_id\", \"_patient_id\": \"patient_id\"})\n", "\n", "classes = [\"Benign\", \"G3\", \"G4\", \"G5\"]\n", "class_counts = df[\"primary_class\"].value_counts().reindex(classes)\n", "\n", "print(f\"Eşleşen görüntü : {len(df):,}\")\n", "print()\n", "print(\"Sınıf dağılımı (tüm dataset):\")\n", "print(\n", " class_counts.to_frame(\"count\")\n", " .assign(pct=lambda x: (x[\"count\"] / x[\"count\"].sum() * 100).round(1))\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "id": "eda-plots", "metadata": {}, "outputs": [ { "data": { "image/png": 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sySBbScr4XNQ2qRw8TFm239SuFSX1M9M5V7prq5DMseeeey42+SOdN6TgbrLrrrsW+6bpPKmQaJCC+N8UtC1k6aZAfcqeTVnQKeO5EHBPSTbpXKUQtF2a88N0HnT22WfnoG76XekzCts5/bZC5vOipH2dspxTgkfh/C+9b9ttt41jjjmmyl2L8G0pjwD1WOWrj0m64r04lTtVlTskhUDuggq3jy+rCRMm5IzHgkKHL434WpD+6KZbYNJV6HSVvKAQYPwm1113XTFg+8Mf/jB3CNJnVR4ltfBZqWOXsixThmzqQKQOVrqKm0of7LfffsWOyJJIHY0FMznLTQpGp9u9UqZx2g+FTlGS5i2pBTMxGjduvNyOkWWRskQWPKbSCLSFgG0q8ZGu3FcuY7Gkx9SSHnffRk3/OwSgbtIP1A+s6/3AFEhLffaUoHHWWWfl2/STdEdSIUiXSigUAraphFYqd5D6gCnDdMH+XbqrKfUj02346U69dG6QgohjxozJmagpiLss26W6bZKCy4U2tmrVKmeXptINaVqwXTVRImFRrxdU+XwoZa+mxJ40VQ6o3nfffTFr1qwl+v60jVMJsnS+lRJnUomGQrJQOi4/+uijpT4/7NKlS/zgBz/Iz1OmbUq4SXdcJikIW3l/VGfjjTfO54SpXenfSeqbT506NZ9PpGOsUE4DlgdBW6iHCkGf9IevELBMNWi/bfbn8pQ6LOk2o0LHpXv37vkWoQUzJVOdp/THOF0hrzy/ssqBrQU7N5XfM3z48HzVN3XuCleIF2xTasevfvWr3AlIQe50e1hBoeZpoRxCIav1/x/0scqU6oGlW4a+qX2Lcu6551b7uUsyLS7LNkmlESrfhpR07Ngx3wq1qBO92iJ1xCt3dtNtTAseB6lOVgrCp+Ogug5loQ5Wkm6/qlxOo3JAGADKkX7g/9EPrF/9wMpB4k8//XShYyBl5aY761IfsHIJrcrvTzVxUwA4lTOYPHlyzthNQdXK5wLLQ+V2pUDg0UcfnbNzC+cPNaVv3765jmyhjFvKnl2UVE5tSbZBCpKnRJnFSeemb7zxRpV5hdIM6a6zBY/FpT0/LNTlTUk06Y7JwjnYN5VGKBwHG220Ufz2t7/Nd9ilDN5CSYj0Oel8A5YX5RGgHkp/eNPVwXSVOdXo3H777fPV21J3wF577bV85TT9kU6Dd6XAaEEa+KBw1TMFTgslCNKtLakjk2p5LviHvbor2X/4wx9y7dFUzyrVk02fVTlomzoCDz/8cLWDqKVbeVJm7oABA3JN23TSU6jrmxQyZ9M2TX/Ek5NPPjl3ClP9pPQHPd1Slq78pu9NJQ4WbF+6ipz2R6r91KtXrxovHZCkGlHpqnO60pw6h+lqdApiV74N7rvf/W7UBumqd7odLu2DVD8r/baCVGOrcPW98nGQ9staa62Va4ql+mYLSrfApTpmqa5t6qynwcDSPk/HTOUgPgCUI/1A/cD61g9MF9hT9mPqCxYUkiwq9wFTPdSmTZvmrMt0LrKg9BkpuzJljaYyCakc1auvvlqsNbs876Kr3K50vpHOQ1IJs8oDGNeEdG6WgqApOJnOnRZX/iGdQxVKEKT+cqpHW1ka7CudSyXp91TO3l5Q6oenAHEqVZDuhtxwww1zsDQNHpbudEzSsVkYtHppzw/TZ6ZM7HSMFJIuUlA6BXq/ScqoTkkgqU5vek/Lli2rnDuW+92U1DIVQJ3UvXv3dCk5T6NHj66y7He/+11xWWFq1apVRdeuXYuvJ0yYkNdNj4V5O+ywQ/Ez0mcW5g8ePLg4/4YbbijOP+ecc5aqndVNTZo0qbjuuuuqvOeuu+5aaL2VVlqpYvPNN6/2N1955ZULrZ++N3nuuecqGjRoUGVZer311lsXX6fflNx8882Lbevtt99e/M6zzjprsetW3mYff/xxRbNmzRZaZ8H9VlMGDRq02LZ36tSpYvLkycX102+prs0LbuuCdBwteJwt6nha1PG3OOm4W1z707THHntUTJ8+vfie9Lxz584LrbfttttW26677757oeMmTb17917ouFnwOC9Y1G9emu25tP8+F/XZANQt+oH6gcuqPvQDv/Od71R8/fXXef333nuvokWLFovtAxbOaZ588snFfu7w4cMX+/sXdZ60qN+Z+quLa9eC2/abVP6cwravrPJ+6NOnz2I/q/J+fPjhh/O8/fffvzgvnX8t6JNPPqlo1KhRXt68efOKL774YpGfP2rUqG/cj5X72kt7fpiceuqpVdY//fTTF/s7C9vs/PPPX2SbGjZsWPHUU08t9riGpaE8AtRDqRbR0KFD89XFlHGaajKlrNbKt32XSrrtJd12lK7knnLKKfHmm2/GkUceuVBR+t/97nf5CnfKSE1X+1OGaqovVJ30/lSHNl0JXfDq8JZbbpnrD6Ws1vRZ6VaXNPhWdbf+pDpXJ554Yr5ynK6sp6vdKRM2DUqQaqCmjMuCVOvqwQcfzPWSUu3b9LvSleB0u1Uqr1B5ELH0Wek2mjTiadofpZYGaUhXkNM2SMdEunqcsg7S83RrVhqpNtWXqi3SPk+DIKSMipQ1kuprpakwMEKSnqfsi/RvId3ilvZV2odpqk7KDEiDIvTs2TNvm3T1Pw3SkN5fkOqeAUC50Q/8P/qBdb8fWJD62OlcIQ0qPHr06OJAsun8IN0Fl46FtE76nekOxOpqt6a+ZDqnSCXbUsmIgnQucvXVV+dly1PKFE13AaZzhVQS4KCDDsp92HKUSr9VHjQtDVhW3cBmhUzWdMdnqm27KGkbp0HC0jgjab+l86l012XaFun8KmX1Vi75trTnh5VLJBQsSWmEJN21mc4v02enOybTOWH6benfTMq4TQOSwfLSIEVul9unAUA9kP50FgZBqCwNRvD8888XB+lIgXgAAOqeFHQ87rjj8q38NV1rluUjlURLg1+nBIxFlVKAUpJpCwBLKWVoHHjggflq+nvvvRfjxo2Lo446qhiwXX/99WOTTTaxXQEA6qg0gHGqiVp5HA7KX6pxnAZDS4OHpYBtkjJ6oRwZiAwAllIaGTbdspWmBaVSCyNGjFjsQA0AANRO6YL922+/nW/xnzNnTn6k9rjllluqDJKWSgam5AsoR4K2ALAMI28PGjQonnnmmfjoo49i3rx50a1bt9hll13itNNOix49etimAAB10Mcff5zrn6Y6rqm+anVjYVD+Uu3bNI7Kb3/721wzGMqRmrYAAAAAAGXEvZsAAAAAAGVE0BYAAAAAoIyoafv/Dyjz4Ycf5sFjGjRoUOp9AgDAEqqoqIgvvvgiunTpUq8GANR/BQCo2/1XQduIHLBNA8gAAFA7TZo0Kbp27Rr1hf4rAEDd7r8K2kbkDNvCxmrTpk3N7R0AAL6V6dOn54vvhf5cfaH/CgBQt/uvgrYRxZIIKWAraAsAUPvUtxJX+q8AAHW7/1p/Cn8BAAAAANQCgrYA1AvHH398vgUl3VGx+uqrx0knnRRff/11Xta3b99o1qxZtGrVqjilepGVb1854IAD8ns7duwY559/fgl/CQAA9c1VV10VW2yxRe6zDhgwoDh/6tSpMWjQoFwXM/VVN9tss7j//vtL2lZg+RC0BaBeOOaYY+Ktt97KAdhXXnklT5dccklx+cUXXxwzZswoTmkkz8oB308//TQmTpwYTz75ZPzhD3+Im266qUS/BACA+ib1Tc8888wYMmRIlfmp35oCtc8++2xMmzYtzjvvvPjJT34Sb7zxRsnaCiwfatoCUC9suOGGxecVFRXRsGHDeOedd77xfV9++WXccccd8c9//jPatWuXpxTEvf766+Pggw9ewa0GAICIvffeO2+GcePGxfvvv1/cJGuttVaceuqpxdf9+/eP9ddfPwdxe/bsadNBLSbTFoB641e/+lUufdChQ4ecaZuCrwUXXHBBtG/fPmcqVM6iffvtt3MZhU033bQ4Lz1/9dVXa7z9AACwOKlcwptvvhm9e/e2oaCWE7QFoN74+c9/nm8hS7eLHXXUUdGpU6c8f/jw4fHuu+/GlClTcmA3BXPvueeevCyt37Jly2jc+P9uTknZtl988UXJfgcAACwoJRrsv//+sd9+++X6t0DtJmgLQL0slbDJJpvEIYcckl9vvfXW0bZt22jSpEn069cvjjzyyLjzzjvzspSZm0okzJ07t/j+zz//PFq3bl2y9gMAwIIB23322SdatGiRx18Aaj9BWwDqpTlz5iyypm2qd1uQaoKlYG4qp1CQaon16tWrRtoJAADfFLDdd9998+Pdd98dTZs2tcGgDhC0BaDOSyUObrjhhjyibhqE7LXXXss1bFNWbZr30EMP5WzaefPmxaOPPhrXXXddDBw4ML83ZSv8+Mc/jrPOOitn2KZA75VXXhk//elPS/2zAACoJ9JdX7NmzcqP8+fPz89TkDYlIqRyCDNnzox77703mjVrVuqmAsuJoC0AdV6DBg3itttui7XXXjuXNdhrr71ijz32iMsvvzx3dIcNG5br26688spx8sknx29+85ucrVBw1VVX5fIJXbt2jW233TYOP/zwOPjgg0v6mwAAqD9SwkHz5s3jwgsvjAceeCA/33XXXePpp5+O++67L/75z3/Gqquumkt7pemiiy4qdZOBb6lBRUo5quemT5+eT8ZTBlWbNm1K3RwAAJZQfe3H1dffDQBQX/pxMm0BAAAAAMqIoC0AAAAAQBkpadD22muvjd69e+dU4DRtvfXW8fDDDxeX9+3bN9chrDwdddRRVT5j4sSJuS5hGiimQ4cOcdppp+XC3AAAUApPPPFE9O/fP7p06ZL7r2lgmIJUR/uMM86IXr16RcuWLfM6qUb2hx9+aGcBAFAeQds0oMuvfvWrePHFF+OFF16I73//+3lwmH/961/FdYYMGRKTJ08uTpdccklxWRrlOwVs04iJqfj2jTfeGCNGjIizzz67RL8IAID6Lo3gvckmm8TVV1+90LIvv/wyXnrppTjrrLPy41//+td4++23Y8899yxJWwEAKE9lNxBZ+/bt49JLL80jc6dM20033TSP7l2dlJX7wx/+MGcmdOzYMc+77rrrcvbCxx9/HE2bNl2i7zSQAwBA7VTu/biUaXvPPffEgAEDFrnO2LFjY8stt4z33nsv1lhjjTrxuwEAqCMDkaWs2TvuuCNnJqQyCQW33nprrLrqqrHxxhvH0KFDc3ZCwTPPPJNvLSsEbJN+/frlH185WxcAAMpV6rCn4G67du1K3RQAAMpE41I34LXXXstB2lmzZkWrVq1yJkLPnj3zsgMOOCC6d++ea329+uqrOYM23T6WbiNLPvrooyoB26TwOi1blNmzZ+epIAV5AQCgpqU+cOrj/uQnP1lspoX+KwBA/VLyoO36668f48aNyxkGf/nLX2Lw4MExZsyYHLg94ogjiuuljNrOnTvHTjvtFO+++26svfbay/ydw4cPj2HDhi2nXwBQx82bHdGoWalbwYpi/0LJpEHJ9ttvv0jVytIAvYuj/wqwFPRv6jb7l3qi5EHbVHd2nXXWyc8333zzXNPrt7/9bfzud79baN0+ffrkx/Hjx+egbadOneL555+vss6UKVPyY1q2KKnMwimnnFIl07Zbt27L7TcB1CkpYHtL14iv3ZVQ5zRtE3Hg+6VuBdTrgG2qY/vYY499Y11a/VeApaD/Wnfpv1KPlDxou6D58+dXKV1QWcrITVLGbZLKKlx44YUxderU6NChQ543atSo3OktlFioTrNmzfIEwBJKAds5X9hcAMsxYPvOO+/E6NGjY5VVVvnG9+i/Aiwl/Veglitp0DZlDOy22255lNwvvvgibrvttnj88cfj73//ey6BkF7vvvvuuSObatqefPLJsf3220fv3r3z+3fdddccnD3ooIPikksuyXVszzzzzDj22GMFZQEAKIkZM2bkO8MKJkyYkJMP2rdvn5MP9tlnn3jppZfiwQcfzIPxFsZiSMvTXWgAAFDSoG3KkD344INj8uTJ0bZt2xyMTQHbXXbZJSZNmhSPPPJIXH755TFz5sxcvmDgwIE5KFvQqFGj3Nk9+uijc9Zty5Ytc03c8847z54FAKAkXnjhhdhxxx2LrwtluVI/9dxzz437778/v950002rvC9l3fbt27eGWwsAQDkqadD2+uuvX+SyFKRNA5J9k+7du8dDDz20nFsGAADLJgVe0+Bii7K4ZQAAkDS0GQAAAAAAyoegLQAAAABAGRG0BQAAAAAoI4K2AAAAAABlRNAWAAAAAKCMCNoCAAAAAJQRQVsAAAAAgDIiaAsAAAAAUEYEbQEAAAAAyoigLQAAAABAGRG0BQAAAAAoI4K2AAAAsJxcddVVscUWW0SzZs1iwIABVZadddZZ0atXr2jcuHGcdNJJtjkAiyRoCwAAAMtJly5d4swzz4whQ4YstGydddaJSy65JPbcc0/bG4DFarz4xQAAAMCS2nvvvfPjuHHj4v3336+ybPDgwfnxzjvvtEEBWCyZtgAAAAAAZUTQFgAAAACgjAjaAgAAAACUEUFbAAAAAIAyYiAyAAAAWE7mzp1bnObPnx+zZs2Khg0bRtOmTWPOnDkxb9684pSWNWrUKJo0aWL7A1CFTFsAAABYTi644IJo3rx5XHjhhfHAAw/k57vuumteNmTIkPz6lltuiauuuio/T/MAYEGCtgAAALCcnHvuuVFRUVFlevzxx/OyESNGLLQszQOABQnaAgAAAACUEUFbAAAAAIAyImgLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtAUAAAAAKCOCtgAAAJTE1/MqbPk6zP4FWHaNv8V7AQAAYJk1bdQgtrlpZsz4WvC2rmnVtEE8fXDLUjcDoNYStAUAAKBkUsB2xhw7oO4RiAf4NpRHAAAAAAAoI4K2AAAAAABlRNAWAAAAAKCMCNoCAAAAAJQRQVsAAAAAgDIiaAsAAAAAUEYEbQEAAAAAyoigLQAAAABAGRG0BQAAAAAoI4K2AAAAAABlRNAWAAAAAKCMCNoCAAAAAJQRQVsAAAAAgDIiaAsAAAAAUEYEbQEAAAAAyoigLQAAAADUM1dddVVsscUW0axZsxgwYECVZdOnT48DDjgg2rRpEx07dozzzz+/ZO2srxqXugEAAAAAQM3q0qVLnHnmmfHII4/E+++/X2XZ8ccfH59++mlMnDgxpk6dGjvvvHN07949Dj74YLupPmTaXnvttdG7d+8ctU/T1ltvHQ8//HBx+axZs+LYY4+NVVZZJVq1ahUDBw6MKVOmVPmMdPDsscce0aJFi+jQoUOcdtppMXfu3BL8GgAAAACoHfbee++cYbvqqqtWmf/ll1/GHXfcERdccEG0a9cu1ltvvRzEvf7660vW1vqopEHbrl27xq9+9at48cUX44UXXojvf//7sddee8W//vWvvPzkk0+OBx54IO66664YM2ZMfPjhh/mAKpg3b14O2H799dfx9NNPx4033hgjRoyIs88+u4S/CgAAAABqp7fffjvH2jbddNPivPT81VdfLWm76puSlkfo379/ldcXXnhhzr599tlnc0A3RfBvu+22HMxNbrjhhthwww3z8q222ir+8Y9/xBtvvJHTuFN9jXQApRobZ5xxRpx77rnRtGnTEv0yAAAAAKh9ZsyYES1btozGjf8vbJgybr/44ouStqu+KZuByFLWbEq9njlzZi6TkLJv58yZk2tmFGywwQaxxhprxDPPPJNfp8devXrlgG1Bv379crHkQrZudWbPnp3XqTwBAAAAQH2XSpSmEgmVy49+/vnn0bp165K2q74pedD2tddeywdDGqnuqKOOinvuuSd69uwZH330Uc6UTZH8ylKANi1L0mPlgG1heWHZogwfPjzatm1bnLp167ZCfhsAAAAA1Cbrr79+NGnSJF555ZXivHHjxuXESepR0DYdCGnHP/fcc3H00UfH4MGDc8mDFWno0KH5CkFhmjRp0gr9PgAAAAAoJymTdtasWflx/vz5+XmqZduiRYv48Y9/HGeddVaOm73zzjtx5ZVXxk9/+tNSN7leKWlN2yRl066zzjr5+eabbx5jx46N3/72t/ngSAfKtGnTqmTbTpkyJTp16pSfp8fnn3++yuel5YVli5KyetMEAAAAAPXRBRdcEMOGDSu+bt68eeywww7x+OOPx1VXXRVHHnlkHnMqzT/uuOPi4IMPLml765uSZ9ouKEX2U83ZFMBNqdiPPvpoldHrJk6cmGveJukxlVeYOnVqcZ1Ro0ZFmzZtcokFAAAAAGBh5557blRUVFSZUsA2SbG122+/PQ8+luJuZ599tk1YnzJtU5mC3XbbLQ8ulg6C2267LR8cf//733Ot2cMPPzxOOeWUaN++fT5Yjj/++Byo3WqrrfL7d9111xycPeigg+KSSy7JdWzPPPPMOPbYY2XSAgAAAAC1UkkzbVOkPqVWp7q2O+20Uy6NkAK2u+yyS15+2WWXxQ9/+MMYOHBgbL/99rnkwV//+tfi+xs1ahQPPvhgfkzB3AMPPDB/3nnnnVfCXwUAQH32xBNPRP/+/aNLly7RoEGDuPfee6ssT1ksKVulc+fO+XbDnXfeOdeKAwCAssi0vf766xe7fKWVVoqrr746T4vSvXv3eOihh1ZA6wAAYOnNnDkzNtlkkzjssMNi7733Xmh5ukPsiiuuiBtvvDF69OiRB/no169fHow39X8BAKDkA5EBAEBdksp/pak6Kcv28ssvzyW99tprrzzvpptuio4dO+aM3P3337+GWwsAQDkqu4HIAACgrpowYUIehyGVRChIYzn06dMnnnnmmZK2DQCA8iHTFgAAakgK2CYps7ay9LqwrDqzZ8/OU8H06dNXYCsBACg1mbYAAFDmhg8fnjNyC1O3bt1K3SQAqFcq5swpdROoZ/tXpi0AANSQTp065ccpU6ZE586di/PT60033XSR7xs6dGiccsopVTJtBW4BoOY0aNIkvty/f1R8OdNmr2MatGgZLe54IMqNoC0AANSQHj165MDto48+WgzSpgDsc889F0cfffQi39esWbM8AQClkwO2grZ1TkWUJ0FbAABYjmbMmBHjx4+vMvjYuHHjon379rHGGmvESSedFBdccEGsu+66OYh71llnRZcuXWLAgAH2AwAAmaAtAAAsRy+88ELsuOOOxdeFsgaDBw+OESNGxOmnnx4zZ86MI444IqZNmxbbbbddjBw5MlZaaSX7AQCATNAWAACWo759+0ZFxaJvtGvQoEGcd955eQIAgOo0rHYuQInMnj07hgwZkm8Xbd26dWywwQbxpz/9KS+bOHFitGrVqsrUuHHj2HPPPfPyqVOnxqBBg6Jr167Rpk2b2GyzzeL++++3LwEAAIBaRaYtUFbmzp2bR9N+5JFHYq211soDs+y22245ELvrrrvmOoEFX3/9da4BuP/+++fXaVkK1F588cV5/t/+9re8bOzYsdGzZ88S/ioAAACAJSfTFigrLVu2zLeLrr322vn20a222irXBXzqqacWWvfee++N+fPnx957751fpyDvqaeemgO8DRs2jP79+8f6668fzz77bAl+CQAAAMCyEbQFytqsWbPi+eefj969ey+07Prrr8/lEBY1cEsql/Dmm29W+14AAACAciVoC5StNIjLT3/601h33XWL2bQF7733Xi6hkJZXJ5VOSKUR9ttvv9hiiy1qqMUAAAAA356atkDZBmyPOeaYePvtt3NwNpU7qOyGG27I9Ws32WSTagO2++yzT7Ro0SL+8Ic/1GCrAQAAAL49mbZAWQZsjz322DwI2T/+8Y9o27ZtleWpjm0K2laXZZsCtvvuu29+vPvuu6Np06Y12HIAAACAb0+mLVB2jjvuuPjnP/8Zjz32WKy88soLLR81alT897//jZ/85CdV5s+ZMyeXQ5g5c2Y8+OCD0axZsxpsNQAAAMDyIdMWKCupVu0111yTyyJ07949WrVqlaejjjqqygBkqfzBghm4Tz/9dNx333054LvqqqsW33vRRReV4JcAAAAALBuZtkBZSYHaVB5hcf785z9XO3+HHXb4xvcCAAAAlDuZtgAAAAAAZUTQFgAAAACgjAjaAgAAAACUEUFbAAAAAIAyImgLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtIU65Ot5FaVuAiuQ/QsAAAD1Q+NSNwBYfpo2ahDb3DQzZnwteFvXtGraIJ4+uGWpmwEAAADUAEFbqGNSwHbGnFK3guVPIB4AAADqC+URAAAAAADKiKAtAAAAAEAZEbQFAAAAACgjgrYAAAAAAGVE0BYAAAAAoIwI2gIAAAAAlBFBWwAAAACAMiJoCwAAAABQRgRtAQAAAADKiKAtAAAAAEAZEbQFAAAAACgjgrYAAAAAAGVE0BYAAAAAoIwI2gIAAAAAlBFBWwAAAACAMiJoCwAAAABQRgRtAQAAAADKSEmDtsOHD4/vfve70bp16+jQoUMMGDAg3n777Srr9O3bNxo0aFBlOuqoo6qsM3HixNhjjz2iRYsW+XNOO+20mDt3bg3/GgAAAACAb69xlNCYMWPi2GOPzYHbFGT9xS9+Ebvuumu88cYb0bJly+J6Q4YMifPOO6/4OgVnC+bNm5cDtp06dYqnn346Jk+eHAcffHA0adIkLrroohr/TQAAAAAAtTZoO3LkyCqvR4wYkTNlX3zxxdh+++2rBGlTULY6//jHP3KQ95FHHomOHTvGpptuGueff36cccYZce6550bTpk1X+O8AAAAAAKiTNW0///zz/Ni+ffsq82+99dZYddVVY+ONN46hQ4fGl19+WVz2zDPPRK9evXLAtqBfv34xffr0+Ne//lWDrQcAAAAAqOWZtpXNnz8/TjrppNh2221zcLbggAMOiO7du0eXLl3i1VdfzRm0qe7tX//617z8o48+qhKwTQqv07LqzJ49O08FKcALAAAAAFAOyiZom2rbvv766/HUU09VmX/EEUcUn6eM2s6dO8dOO+0U7777bqy99trLPADasGHDvnWbAQAAAADqZHmE4447Lh588MEYPXp0dO3adbHr9unTJz+OHz8+P6Zat1OmTKmyTuH1ourgphILqRRDYZo0adJy+iUAAAAAALU4aFtRUZEDtvfcc0889thj0aNHj298z7hx4/JjyrhNtt5663jttddi6tSpxXVGjRoVbdq0iZ49e1b7Gc2aNcvLK08AAAAAAFHfyyOkkgi33XZb3HfffdG6detiDdq2bdtG8+bNcwmEtHz33XePVVZZJde0Pfnkk2P77beP3r1753V33XXXHJw96KCD4pJLLsmfceaZZ+bPTsFZAAAAAIDapKSZttdee20uT9C3b9+cOVuY7rzzzry8adOm8cgjj+TA7AYbbBA/+9nPYuDAgfHAAw8UP6NRo0a5tEJ6TFm3Bx54YBx88MFx3nnnlfCXAQAAAADUwkzbVB5hcbp16xZjxoz5xs/p3r17PPTQQ8uxZQAAAAAA9XggMgAAAAAA/h9BWwAAAACAMiJoCwAAAABQRgRtAQAAAADKiKAtAAAAAEAZEbQFAAAAACgjgrYAAAAAAGVE0BYAAAAAoIwI2gIAAAAAlBFBWwAAqEHz5s2Ls846K3r06BHNmzePtddeO84///yoqKiwHwAAyBr/vwcAAKAmXHzxxXHttdfGjTfeGBtttFG88MILceihh0bbtm3jhBNOsBMAABC0BQCAmvT000/HXnvtFXvssUd+veaaa8btt98ezz//vB0BAECmPAIAANSgbbbZJh599NH497//nV+/8sor8dRTT8Vuu+22yPfMnj07pk+fXmUCAKDuUh4BAABq0M9//vMcdN1ggw2iUaNGucbthRdeGIMGDVrke4YPHx7Dhg2znwAA6gmZtgAAUIP+/Oc/x6233hq33XZbvPTSS7m27a9//ev8uChDhw6Nzz//vDhNmjTJPgMAqMNk2gIAQA067bTTcrbt/vvvn1/36tUr3nvvvZxNO3jw4Grf06xZszwBAFA/yLQFAIAa9OWXX0bDhlW74alMwvz58+0HAAAymbYAAFCD+vfvn2vYrrHGGrHRRhvFyy+/HL/5zW/isMMOsx8AAMgEbQEAoAZdeeWVcdZZZ8UxxxwTU6dOjS5dusSRRx4ZZ599tv0AAEAmaAsAADWodevWcfnll+cJAACqo6YtAAAAAEBtzrS9//77F7t8zz33/DbtAQAAAACo15Y6aDtgwIBFLmvQoEHMmzfv27YJAAAAAKDeWuqg7fz581dMSwAAAAAAWD41badNm2ZTAgAAAACUImh78cUXx5133ll8ve+++0b79u1j9dVXj1deeWV5tAkAAAAAoN5a6qDtddddF926dcvPR40aFY888kiMHDkydttttzjttNNWRBsBAAAAAOqNpa5p+9FHHxWDtg8++GDst99+seuuu8aaa64Zffr0WRFtBAAAAACoN5Y603bllVeOSZMm5ecpw3bnnXfOzysqKmLevHnLv4UAAAAAAPXIUmfa7r333nHAAQfEuuuuG5988kkui5C8/PLLsc4666yINgIAAAAA1BtLHbS97LLLcimElG17ySWXRKtWrfL8yZMnxzHHHLMi2ggAAAAAUG8sddC2SZMmceqppy40/+STT15ebQIAAAAAqLeWOmh7//33L3b5nnvu+W3aAwAAAABQry110HbAgAGLXNagQQODkQEAAAAA1GTQdv78+d/m+wAAAAAAWJ5B2//85z+x1lprLe3bAACgbJ1yyimLXf6b3/ymxtoCAABLHbRdZ511YocddojDDz889tlnn1hppZVsRQAAarWXX355sSXAAACgrIO2L730Utxwww05G+G4446LH//4xzmAu+WWW66YFgIAwAo2evRo2xgAgLLRcGnfsOmmm8Zvf/vb+PDDD+NPf/pTTJ48ObbbbrvYeOON821jH3/88YppKQAA1JDp06fHvffeG2+99ZZtDgBA+QdtCxo3bhx777133HXXXXHxxRfH+PHj49RTT41u3brFwQcfnIO5AABQG+y3335x1VVX5edfffVVbLHFFnler1694u677y518wAAqGeWOWj7wgsvxDHHHBOdO3fOGbYpYPvuu+/GqFGjchbuXnvttXxbCgAAK8gTTzwR3/ve9/Lze+65JyoqKmLatGlxxRVXxAUXXGC7AwBQ3kHbFKBNGQfbbLNNDs7edNNN8d577+XObI8ePXJnd8SIEbn2LQAA1Aaff/55tG/fPj8fOXJkDBw4MFq0aBF77LFHvPPOO6VuHgAA9cxSD0R27bXXxmGHHRaHHHJIzrKtTocOHeL6669fHu0DAIAVLpX4euaZZ3LgNgVt77jjjjz/s88+i5VWWskeAACgvIO2S5Jp0LRp0xg8ePCytgkAAGrUSSedFIMGDYpWrVpF9+7do2/fvsWyCekuMwAAKOugbcGXX34ZEydOjK+//rrK/N69ey+PdgEAQI1JYzVsueWWMWnSpNhll12iYcP/V0VsrbXWUtMWAIDyD9p+/PHHuTRCum2sOvPmzVse7QIAgBq1xRZb5KmyVNMWAADKPmibbh1LAzU899xz+baxNLrulClTcgbC//zP/6yYVgIAwAp0yimnfONgvAAAUFP+331fS+Gxxx7LndaUhZBuG0s1vw488MC45JJLYvjw4Uv1WWn97373u9G6des8eNmAAQPi7bffrrLOrFmz4thjj41VVlkl1xhLI/mmIHFlqUxDyoJII/ymzznttNNi7ty5S/vTAACop15++eVFTuPGjSt18wAAqGeWOtN25syZOTCarLzyyrlcwnrrrZcHaHjppZeW6rPGjBmTA7IpcJuCrL/4xS9i1113jTfeeCNatmyZ1zn55JPjb3/7W9x1113Rtm3bOO6442LvvfeOf/7zn8VyDClg26lTp3j66adj8uTJcfDBB0eTJk3ioosuWtqfBwBAPTR69OhSNwEAAJY903b99dcvZsNusskm8bvf/S4++OCDuO6666Jz585L9VmpLm6qj7vRRhvlzxoxYkTOmn3xxRfz8lSG4frrr8+Zvd///vdj8803jxtuuCEHZ5999tm8zj/+8Y8c5L3lllti0003jd122y3OP//8uPrqqxcaJA0AAKqT+phfffWVjQMAQO0M2p544ok5mzU555xz4uGHH4411lgjrrjiim+d2ZqCtEn79u3zYwrezpkzJ3beeefiOhtssEH+vmeeeSa/To8py7djx47Fdfr16xfTp0+Pf/3rX9+qPQAA1A8///nPc3/y8MMPzwkCAABQq8ojpPq1BSnz9b333ou33norB1JXXXXVZW7I/Pnz8yBn2267bWy88cZ53kcffRRNmzaNdu3aVVk3dajTssI6lQO2heWFZdWZPXt2ngpSgBcAgPor3Tn2wAMP5Du/0mC7a621Vhx66KExePDgXIYLAADKOtN2QSmommrafpuAbZJq277++utxxx13xIqWBkBL9XELU7du3Vb4dwIAUL4aN24cP/rRj+K+++6LSZMmxZAhQ+LWW2/NiQl77rlnnp+SDAAAoKyCtoXMg8ouvPDCaNWqVc6ETQOIffbZZ8vUiDS42IMPPpgHgOjatWtxfspqSHVpp02bVmX9KVOmFDMe0mN6veDywrLqDB06NJdiKEypYw4AAIW7trbbbrvYeuuto2HDhvHaa6/ljNu11147Hn/8cRsJAIDyCdqmwcBmzpxZfJ1qfZ199tlx1llnxZ///Occ+EwDgC2NioqKHLC955574rHHHosePXpUWZ7KLzRp0iQeffTR4rw0CFoarCx1opP0mDrSU6dOLa4zatSoaNOmTfTs2bPa723WrFleXnkCAKB+Sxf+f/3rX+dBclOJhFRCKyUWTJgwIZdP2G+//XLwFgAAyqambRrUKwVuC/7yl7/ELrvsEr/85S/z65VWWikPUlZ5nSUpiXDbbbfl281at25drEGbShY0b948P6bBIE455ZQ8OFkKrh5//PE5ULvVVlvldVOGbwrOHnTQQXHJJZfkzzjzzDPzZ6fgLAAAfJP+/fvH3//+91z2K5VGOPjgg4uD4yYtW7aMn/3sZ3HppZfamAAAlE/Q9osvvohVVlml+Pqpp56Kfffdt/g6ZSR8+OGHS/Xl1157bX5MmQyV3XDDDXHIIYfk55dddlm+LW3gwIF58LB+/frFNddcU1y3UaNGOQPi6KOPzsHc1KFOGRDnnXfeUrUFAID6q0OHDjFmzJji3VzVWW211XLWLQAAlE3QdvXVV48333wzD8YwY8aMeOWVV3JAteCTTz6JFi1aLHV5hG+SMnivvvrqPC1K9+7d46GHHlqq7wYAgILrr7/+GzdGgwYNcr8TAADKJmibsmpPOumk+MUvfpEDpGmQr0KJguSFF16I9ddff0W1EwAAVqg0fkPKtk3jJ6TBcCs74YQTbH0AAMovaJsGHUsDMKQOawrY3nLLLbk0QcHtt9+ea4EBAEBt8/LLL8fuu+8eX375ZQ7epnq2//3vf/OdZKl0gqAtAABlGbRNA4PddNNNi1w+evTo5dUmAACoUSeffHJOQLjuuuvyYLjPPvtsNGnSJA488MA82C4AANSkhjX6bQAAUIbGjRsXP/vZz/IAuOlusjQAbrdu3eKSSy7J5cEAAKAmCdoCAFDvpazaFLBNUjmEVNc2SVm3kyZNqvfbBwCAMi2PAAAAddVmm20WY8eOjXXXXTd22GGHPJ5Dqml78803x8Ybb1zq5gEAUM/ItAUAoN676KKLonPnznk7XHjhhbHyyivH0UcfHR9//HH8/ve/r/fbp765//77Y9NNN42WLVtGly5dcq1jAICaJNMWAIB6b4sttihug1QeYeTIkfV+m9RXad8fc8wxccstt8T3vve9mD59ekyZMqXUzQIA6pmlDtqed955i12ebiUDAIDabMyYMfHll1/GVlttlbNuqT/OOuusfE7Tt2/f/Drtf8cAAFCWQdsnnngittlmm2jcuHHcc889VZbNmTMnJkyYkJetvfbagrYAANQaF198ccyYMSPOP//8/LqioiJ22223+Mc//lHMun300Udjo402KnFLqQkzZ86MF198MXbfffdYb731cpZtyra94ooriuUzAADKpqZtusr86aef5ucvv/xylen111+PyZMnx0477RQnn3zyim4vAAAsN3feeWeVgcb+8pe/5ISFJ598Mg9ElsomDBs2zBavJz777LMcuL/33ntj1KhRMX78+GjWrFkceOCBpW4aAFDPNFzSzkvKMliUNm3a5M5supUIAABqi3THWO/evYuvH3roodhnn31i2223jfbt28eZZ54ZzzzzTEnbSM1p1apVfjzhhBOie/fu+XU6zxk9enTOwgUAKKugbeqofFMn5fPPP88TAADUFnPnzs2ZlAUpQJvKghV06dIlZ9xSP7Rr1y7WWGONapelDFwAgLKqaZvKIKTatUmq57Rg5yWVR7j55ptz/S8AAKgt0pgMqRzCWmutFRMnTox///vfsf322xeXv//++7HKKquUtI3UrCOOOCKuvPLK+MEPfpCzrdNAzKkUXCELFwCgbIK26Xaggssuu6zKsoYNG8Zqq60WgwcPjqFDhy7/FgIAwApy7LHHxnHHHZdr2D777LOx9dZbR8+ePYvLH3vssdhss81s/3rk5z//eR7PY5NNNsmvd9xxx5ygAgBQdkHbBet+AQBAXTBkyJBo1KhRPPDAAznD9pxzzqmy/MMPP4zDDjusZO2j5qXj4X/+53/yBABQ1jVtK0ud1i+++GKh+anmrQ4tAAC1TerD3nPPPXHttddGp06dqiy75ppr4kc/+lHJ2gYAQP201EHbG2+8Mb766quF5qd5N9100/JqFwAAAABAvbTEQdvp06fH559/ngceS5m26XVh+uyzz+Khhx6KDh06rNjWAgBAHfDBBx/EgQcemAc5a968efTq1SteeOGFUjcLAIDaVtO2Xbt20aBBgzytt956Cy1P84cNG7a82wcAAHVKSnjYdttt8wBXDz/8cB7U95133omVV1651E0DAKC2BW1Hjx6ds2y///3vx9133x3t27cvLmvatGl07949unTpsqLaCQAAdcLFF18c3bp1ixtuuKE4r0ePHiVtEwAAtTRou8MOO+THCRMm5E5mw4ZLXQ4XAABqjZSwMHLkyLj++uvjL3/5y3L73Pvvvz/69esX++67b4wZMyZWX331OOaYY2LIkCGLfM/s2bPzVJBKlAEAUHctcdC2IGXUTps2LZ5//vmYOnVqzJ8/v8rygw8+eHm2DwAAalRKUvjTn/4UI0aMiI8//jh23nnn5fr5//nPf+Laa6+NU045JX7xi1/E2LFj44QTTsh3rw0ePLja9wwfPrzkpcjmzJsTTRo1KWkbWHHsXwCo5UHbBx54IAYNGhQzZsyINm3a5Fq2Bem5oC0AALVNymJN2bQpq/app56KefPmxa9//es4/PDDc593eUpJD1tssUVcdNFF+fVmm20Wr7/+elx33XWLDNoOHTo0B3krZ9qmu99qUgrYHjLyp/Hl3C9r9HtZ8Vo0bhEjfvBHmxoAanPQ9mc/+1kcdthhuZPZokWLFdMqAACoAS+++GIO1N5+++2xzjrrxEEHHZSfd+3aNZcwWN4B26Rz587Rs2fPKvM23HDDPG7EojRr1ixPpZYCtl/N/arUzQAAqPOWOmj7wQcf5Nu3BGwBAKjt+vTpE8cff3w8++yzsf7669fId2677bbx9ttvV5n373//O5chAwCAZKlHE0sZBy+88IKtBwBArbfTTjvlTNvzzjsvDzqWBh9b0U4++eQcJE53ro0fPz5uu+22+P3vfx/HHnvsCv9uAADqaKbtHnvsEaeddlq88cYb0atXr2jSpOpgBHvuuefybB8AAKwwf//732PSpElxww03xNFHHx1fffVV/PjHP87LKo/dsDx997vfjXvuuSfXqU3B4h49esTll1+ex40AAIBlCtoOGTIkP6YO5oJSxzYN2gAAALVFGtDr7LPPztOoUaNyALdx48ax1157xT777JOn73znO8v1O3/4wx/mCQAAlkt5hDTa7aImAVsAAGqzXXbZJZcr+PDDD3Ot24cffjhnxgIAQFkHbQEAoK5beeWVc9D25ZdfjrFjx5a6OQAA1DNLXR6hurIIlaXbygAAoDaYOHHiN66z6qqr1khbAABgqYK2TzzxRGyzzTa5tlcaNKGyOXPmxIQJE/KytddeW9AWAIBaIw0CVlBRUbHQAGRpnnEbAAAoy6Bt375946OPPooOHTrkW8QWNH369DjkkEPiRz/60YpoIwAArBApINu1a9fcl+3fv39ORAAAgFpR0/azzz7LAdtFadOmTQwbNizOOuus5dk2AABYod5///04+uij44477og99tgjbr755mjatGlssskmVSYAACi7oG0KyM6cOXOx63z++ed5AgCA2qJTp05xxhlnxFtvvRV/+ctfcrJCnz59Yquttoo//OEPMX/+/FI3EQCAemiJgrapJEKqXZtcccUVVabf/va38fOf/zx+/OMfx2677bai2wsAUBauuuqq2GKLLaJZs2YxYMCAateZMmVKtG/fPjbddNMq80eNGhXf+c53onXr1tGzZ88YOXJkDbWaxdluu+3i+uuvj3feeSdatGgRRx11VEybNs1GAwCgxi1R0a7Ro0cXn1922WVVljVs2DBWW221GDx4cAwdOnT5txAAoAx16dIlzjzzzHjkkUfyLfbVOe6442KzzTaLTz75pDjvP//5Tx4HIN2Ov/vuu8dDDz0UAwcOjNdeey3WWmutGvwFLOjpp5+OP/3pT3HXXXfF+uuvH1dffXW0a9fOhgIAoMYt9UgLEyZMWDEtAQCoRfbee+/8OG7cuGqDtvfdd198+umncdBBB8Xll19enJ+yalOW7Q9/+MP8Oj1uueWWcdNNN8W5555bg7+AZPLkyXnb33DDDbk0wqBBg+Kf//xnbLzxxjYQAAC1I2ibSiQ0b948n5zoyAIAVC/V+T/llFNygDYFACtLNVIrKioWmvfqq6/anCWwxhprxOqrr57vGttzzz2jSZMm1e6P3r172z8AAJRXTduC1IlNHdt58+atuBYBANRyp59+ehxyyCGx7rrrLrRsl112ibFjx8a9994bc+fOzY8psDt9+vSStLW+S/3aiRMnxvnnn58znlM5i1SDuPKU5gEAQFmXR/jlL38Zv/jFL+Lmm2/OA2sAAPB/nnzyyRyEfemll6rdLKlW6p133plLIRx22GGx7bbbxv77718c9JWapfQXAAB1ImibRkoeP358Hnyje/fu0bJlyyrLF3WCAgBQHzz66KN5sLHUV0pmz54dX331Vay66qp5sLHOnTvHXnvtlaeCPn365NvzqXk33nhjnHrqqdGiRQubHwCA2hu0HTBgwIppCQBALZJKGxSmVAN11qxZ0bBhw1zL9qc//Wlxvbvuuiv++Mc/xt///vfo0KFDnvfCCy/k2+5TMPeyyy7LA5YJ2pbGsGHD4qijjhK0BQCgdgdtzznnnBXTEgCAWuSCCy7IAb+CNFjrDjvsEI8//ni0adOmOH/llVfO4wJ07dq1OG/o0KHx3HPPRYMGDXKN29GjRy909xI1Y8FB4QAAoFYGbQtefPHFePPNN/PzjTbayAANAEC9kmrSpumbpAHJ0lTZqFGjVmDLWFopeA4AALU6aDt16tQ8WEbKImnXrl2eN23atNhxxx3jjjvuiNVWW21FtBMAAFaI9dZb7xsDt6mEBQAAlFXQtn379vHvf/87D6Bx/PHHxxdffBH/+te/YsMNN8zL33jjjVyH7YQTTojbb799ib/8iSeeiEsvvTRn7U6ePDnuueeeKjVzU1ZKGhyisn79+sXIkSOrdKBTmx544IFcR27gwIHx29/+Nlq1arXE7QAAoP5KZS7atm1b6mYAAMDSBW3TABmtW7fOz1PA9JFHHikGbJOePXvG1VdfHbvuumssjZkzZ8Ymm2wShx12WOy9997VrvODH/wgbrjhhuLrZs2aVVk+aNCgHPBNtxnOmTMnDj300DjiiCPitttuW6q2AABQP6W7yAqDxAEAQK0J2qYBNFIWa5JGR06DaSwozUvLlsZuu+2Wp8VJQdpOnTpVuyzV1E1B5LFjx8YWW2yR51155ZWx++67x69//evo0qXLUrUHAID6RT1bAADK0f+LxH6DVLLgs88+y8+///3vx4knnhgffvhhcfkHH3wQJ598cuy0007LvYGpdm7KfFh//fXj6KOPjk8++aS47Jlnnsl1dQsB22TnnXfOAeY0IvOizJ49O6ZPn15lAgCg/qmoqCh1EwAAYNmCtimDtnDL2FVXXZWDnGuuuWasvfbaeerRo0eel7Jcl6dUGuGmm26KRx99NC6++OIYM2ZMzsydN29eXv7RRx8tdCtb48aNcw3etGxRhg8fnuuWFaZu3bot13YDAFA7VO7nAgBArSqPUFkKcL700ku5ru1bb72V56X6tinDdUXUFyvo1atX9O7dOweJU/btt8nqHTp0aJxyyinF1yngLHALADWnYs6caFBNuSVqP/sWAABKELQt1P7aZZdd8lST1lprrVh11VVj/PjxOWibat1OnTq1yjpz586NTz/9dJF1cAt1chcc0AwAqDkpYPvl/v2j4suZNnsd0qBFy2hxxwOlbgYAANSfoO1XX32VyxT88Ic/LGarptqwBY0aNYrzzz8/VlpppRXT0oh4//33c03bzp0759dbb711TJs2LV588cXYfPPN87zHHnss3+bWp0+fFdYOAODbywFbQds6RXVYAACo4aDtjTfeGH/729+KQdtU23ajjTaK5s2b59epVEKXLl3ygGRLasaMGTlrtmDChAkxbty4XJM2TcOGDYuBAwfmrNl33303Tj/99FhnnXWiX79+xbIMqe7tkCFD4rrrros5c+bEcccdl8sqpLYAAAAAANTJgciSW2+9NY444ogq82677bYYPXp0ni699NL485//vFRf/sILL8Rmm22WpyTVmU3Pzz777Jy5++qrr8aee+4Z6623Xhx++OE5m/bJJ5+sUtogtWuDDTbI5RJ233332G677eL3v//9UrUDAAAAAKDWZdqmjNg0GFhBKoPQsOH/xXy33HLLOPbYY5fqy/v27RsVFYu+ke7vf//7N35GyshNwWMAAAAAgHoVtE21YyvXsP3444+rLE91ZCsvBwAAAABgBZZH6Nq1a7z++uuLXJ5KGaR1AAAAAACogaBtqhebas3OmjVroWVfffVVHjRsjz32+BZNAQAAAABgicsj/OIXv8gDja2//vpx3HHH5cHBkrfffjuuuuqqmDt3bl4HAAAAAIAaCNp27Ngxnn766Tj66KPj5z//eXEAsQYNGsQuu+wS11xzTV4HAAAAAIAaCNomPXr0iJEjR8ann34a48ePz/PWWWedaN++/bdoAgAAAAAAyxS0LUhB2i233HJZ3goAAAAAwPIYiAwAAAAAgBVP0BYAAAAAoIwI2gIAAAAAlBFBWwAAAACAMiJoCwAAAABQRgRtAQAAAADKiKAtAAAAAEAZEbQFAAAAACgjgrYAAAAAAGVE0BYAAAAAoIwI2rLMrrrqqthiiy2iWbNmMWDAgCrLzjrrrOjVq1c0btw4TjrppIXeu+aaa0bz5s2jVatWeWrXrp09AQAAAACCtnwbXbp0iTPPPDOGDBmy0LJ11lknLrnkkthzzz0X+f7bb789ZsyYkadp06bZGQAAAAAQEY1tBZbV3nvvnR/HjRsX77//fpVlgwcPzo933nmnDQwAAAAAS0F5BErmyCOPjFVXXTW23nrreOihh+wJAAAAABC0pVRuvvnmmDBhQnzwwQdx/PHHx8CBA2Ps2LF2CAAAAAD1nkxbSuJ73/tetGjRIg9idsABB0T//v3j7rvvtjcAAAAAqPcEbSkLDRs6FAEAAAAgMRAZy2zu3LnFaf78+TFr1qwcfG3atGnMmTMn5s2bV5zSskaNGkWTJk1i4sSJ8b//+7/Rp0+fvP4999wT9913X4wePdreAAAAAKDeE7RlmV1wwQUxbNiw4uvmzZvHDjvsEI8//ngMGTIkbrzxxuKyq666KgYPHhwjRoyIGTNmxAknnBDjx4+Pxo0bx3rrrRd//vOfY6uttrI3AAAAAKj3BG1ZZueee26eqpOCs2mqTs+ePWPcuHG2PAAAAABUQyFRAAAooV/96lfRoEGDOOmkk+wHAAAyQVsAACiRsWPHxu9+97vo3bu3fQAAQJGgLQAAlECq8z9o0KD4wx/+ECuvvLJ9AABAkaAtAACUwLHHHht77LFH7LzzzrY/AABVGIgMAABq2B133BEvvfRSLo+wJGbPnp2ngunTp6/A1gEAUGoybQEAoAZNmjQpTjzxxLj11ltjpZVWWqL3DB8+PNq2bVucunXrtsLbCQBA6QjaAgBADXrxxRdj6tSp8Z3vfCcaN26cpzFjxsQVV1yRn8+bN2+h9wwdOjQ+//zz4pQCvwAA1F3KI5TInHlzokmjJqX6elYw+xcAWJSddtopXnvttSrzDj300Nhggw3ijDPOiEaNGi30nmbNmuUJAID6QdC2RFLA9pCRP40v535ZqiawgrRo3CJG/OCPti8AUK3WrVvHxhtvXGVey5YtY5VVVlloPgAA9ZOgbQmlgO1Xc78qZRMAAAAAgDIjaAsAACX2+OOPl7oJAACUEQORAQAAAACUEUFbAAAAAIAyImgLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtAUAAAAAKCOCtgAAAAAAZUTQFgAAAACgjJQ0aPvEE09E//79o0uXLtGgQYO49957qyyvqKiIs88+Ozp37hzNmzePnXfeOd55550q63z66acxaNCgaNOmTbRr1y4OP/zwmDFjRg3/EgAAAACAOhC0nTlzZmyyySZx9dVXV7v8kksuiSuuuCKuu+66eO6556Jly5bRr1+/mDVrVnGdFLD917/+FaNGjYoHH3wwB4KPOOKIGvwVAAAAAADLT+Mood122y1P1UlZtpdffnmceeaZsddee+V5N910U3Ts2DFn5O6///7x5ptvxsiRI2Ps2LGxxRZb5HWuvPLK2H333ePXv/51zuAFAAAAAKhNyram7YQJE+Kjjz7KJREK2rZtG3369Ilnnnkmv06PqSRCIWCbpPUbNmyYM3MBAAAAAGqbkmbaLk4K2CYps7ay9LqwLD126NChyvLGjRtH+/bti+tUZ/bs2XkqmD59+nJuPQAAAABAHcu0XZGGDx+es3YLU7du3UrdJAAAAACA8g7adurUKT9OmTKlyvz0urAsPU6dOrXK8rlz58ann35aXKc6Q4cOjc8//7w4TZo0aYX8BgAAAACAOhO07dGjRw68Pvroo1XKGKRatVtvvXV+nR6nTZsWL774YnGdxx57LObPn59r3y5Ks2bNok2bNlUmAAAAAICo7zVtZ8yYEePHj68y+Ni4ceNyTdo11lgjTjrppLjgggti3XXXzUHcs846K7p06RIDBgzI62+44Ybxgx/8IIYMGRLXXXddzJkzJ4477rjYf//983oAAAAAALVNSYO2L7zwQuy4447F16ecckp+HDx4cIwYMSJOP/30mDlzZhxxxBE5o3a77baLkSNHxkorrVR8z6233poDtTvttFM0bNgwBg4cGFdccUVJfg8AAAAAQK0O2vbt2zcqKioWubxBgwZx3nnn5WlRUlbubbfdtoJaCAAAAABQs8q2pi0AAAAAQH0kaAsAAAAAUEYEbQEAAAAAyoigLQAAAABAGRG0BQAAAAAoI4K2AAAAAABlRNAWAAAAAKCMCNoCAAAAAJQRQVsAAAAAgDIiaAsAAAAAUEYEbQEAAAAAyoigLQAAAABAGRG0BQAAAAAoI4K2AAAAAABlRNAWAAAAAKCMCNoCAAAAAJQRQVsAAAAAgDIiaAsAAAAAUEYEbQEAAAAAyoigLQAAAABAGRG0BQAAAAAoI4K2AAAAAABlRNAWAAAAAKCMCNoCAAAAAJQRQVsAAAAAgDIiaAsAAAAAUEYEbQEAAAAAyoigLQAAAABAGRG0BQAAAAAoI4K2AAAAAABlRNAWAAAAAKCMCNoCAAAAAJQRQVsAAAAAgDIiaAsAAAAAUEYEbQEAAAAAyoigLQAAAABAGRG0BQAAAAAoI4K2AABQg4YPHx7f/e53o3Xr1tGhQ4cYMGBAvP322/YBAABFgrYAAFCDxowZE8cee2w8++yzMWrUqJgzZ07suuuuMXPmTPsBAICs8f97AAAAasLIkSOrvB4xYkTOuH3xxRdj++23txMAAJBpCwAApfT555/nx/bt29sRAABkMm0BAKBE5s+fHyeddFJsu+22sfHGGy9yvdmzZ+epYPr06TXUQgAASkFNWwAAKJFU2/b111+PO+644xsHL2vbtm1x6tatW421EQCAmidoCwAAJXDcccfFgw8+GKNHj46uXbsudt2hQ4fmMgqFadKkSTXWTgAAap7yCAAAUIMqKiri+OOPj3vuuScef/zx6NGjxze+p1mzZnkCAKB+ELQFAIAaLolw2223xX333RetW7eOjz76KM9PZQ+aN29uXwAAoDwCAADUpGuvvTaXOOjbt2907ty5ON155512BAAA5V/T9txzz40GDRpUmTbYYIPi8lmzZuVMhVVWWSVatWoVAwcOjClTppS0zQAA8E3lEaqbDjnkEBsOAIDyD9omG220UUyePLk4PfXUU8VlJ598cjzwwANx1113xZgxY+LDDz+Mvffeu6TtBQAAAACo0zVtGzduHJ06dVpofrql7Prrr8/1wL7//e/neTfccENsuOGG8eyzz8ZWW21VgtYCAAAAANTxTNt33nknunTpEmuttVYMGjQoJk6cmOe/+OKLMWfOnNh5552L66bSCWussUY888wzJWwxAAAAAEAdzbTt06dPjBgxItZff/1cGmHYsGHxve99L15//fU8ym7Tpk2jXbt2Vd7TsWPH4gi8izJ79uw8FUyfPn2F/QYAAAAAgDoTtN1tt92Kz3v37p2DuN27d48///nP0bx582X+3OHDh+cAMAAAAABAuSn78giVpaza9dZbL8aPH5/r3H799dcxbdq0KutMmTKl2hq4lQ0dOjTXxC1MkyZNWsEtBwAAAACog0HbGTNmxLvvvhudO3eOzTffPJo0aRKPPvpocfnbb7+da95uvfXWi/2cZs2aRZs2bapMAAAAAADloKzLI5x66qnRv3//XBLhww8/jHPOOScaNWoUP/nJT6Jt27Zx+OGHxymnnBLt27fPgdfjjz8+B2y32mqrUjcdAAAAAKDuBW3ff//9HKD95JNPYrXVVovtttsunn322fw8ueyyy6Jhw4YxcODAPLBYv3794pprril1swEAAAAA6mbQ9o477ljs8pVWWimuvvrqPAEAAAAA1AW1qqYtAAAAAEBdJ2gLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtAUAAAAAKCOCtgAAAAAAZUTQFgAAAACgjAjaAgAAAACUEUFbAAAAAIAyImgLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtAUAAAAAKCOCtgAAAAAAZUTQFgAAAACgjAjaAgAAAACUEUFbAAAAAIAyImgLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtAUAAAAAKCOCtgAAAAAAZUTQFgAAAACgjAjaAgAAAACUEUFbAAAAAIAyImgLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtAUAAAAAKCOCtgAAAAAAZUTQFgAAAACgjAjaAgAAAACUEUFbAAAAAIAyImgLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtAUAAAAAKCOCtgAAAAAAZUTQFgAAAACgjAjaAgAAAACUEUFbAAAAAIAyImgLAAAAAFBGBG0BAAAAAMqIoC0AAAAAQBkRtAUAAAAAKCOCtgAAAAAAZUTQFgAAAACgjNSZoO3VV18da665Zqy00krRp0+feP7550vdJAAAWCT9VwAA6nTQ9s4774xTTjklzjnnnHjppZdik002iX79+sXUqVNL3TQAAFiI/isAAItTJ4K2v/nNb2LIkCFx6KGHRs+ePeO6666LFi1axJ/+9KdSNw0AABai/woAwOI0jlru66+/jhdffDGGDh1anNewYcPYeeed45lnnqn2PbNnz85Tweeff54fp0+fHjWp8ezG0Xherd8FLCDt05o+lipbad7MmDe3omTfz4qxUsMGMX36/NJt3nmtIhxXdU/DVumPX8m+/ssmTaOiydySfT/LX4MmTWNeDR9Thb+5FRW152+f/ivlRv+VFUH/lRVC/5U60Idd0v5rrY8Y/ve//4158+ZFx44dq8xPr996661q3zN8+PAYNmzYQvO7deu2wtpJ/XJ73FzqJlAHtT2q1C2g7pkRcUTbUjeCuuavpTmmvvjii2jbtnYcz/qvlCP9V1YE/VeWP/1X6k4f9pv6r7U+aLssUlZuqoFbMH/+/Pj0009jlVVWiQYNGpS0bXVRuoKQAuKTJk2KNm3alLo51BGOKxxX1Ab+r1rxUoZC6vB26dIl6jL915rl3y6OK2oL/1/huKq7/ddaH7RdddVVo1GjRjFlypQq89PrTp06VfueZs2a5amydu3ardB2EjlgK2jL8ua4YkVwXOGYql1qS4Ztgf5r7eHvAY4ragv/X+G4qnv911o/EFnTpk1j8803j0cffbRK5mx6vfXWW5e0bQAAsCD9VwAAvkmtz7RNUqmDwYMHxxZbbBFbbrllXH755TFz5sw49NBDS900AABYiP4rAAB1Pmj74x//OD7++OM4++yz46OPPopNN900Ro4cudDgZJRGKkVxzjnnLFSSAhxXlBv/X+GYoqbov5Y3fw9wXFFb+P8Kx1Xd1aAiVb8FAAAAAKAs1PqatgAAAAAAdYmgLQAAAABAGRG0BQAAAAAoI4K21Lg111wzLr/8clseAIBaQf8VAKhpgrYUHXLIIdGgQYPitMoqq8QPfvCDePXVV5frVho7dmwcccQRtjxL5KOPPooTTzwx1llnnVhppZWiY8eOse2228a1114bX375ZV7nyCOPjLXXXjuaN28eq622Wuy1117x1ltv2cJ8q+OqII3Xudtuu+X/F++9915blWU+pvr27Vvl72yajjrqKFsUvgX9V8qR/iulOq4K9F9ZnseVPmzpNC7hd1OGUpD2hhtuKP7jPfPMM+OHP/xhTJw4cbl9RwqqwZL4z3/+k/9gtGvXLi666KLo1atXNGvWLF577bX4/e9/H6uvvnrsueeesfnmm8egQYNijTXWiE8//TTOPffc2HXXXWPChAnRqFEjG5tlOq4K0p0BKbgG3/b/qmTIkCFx3nnnFd/bokULGxa+Jf1Xyon+K6U8rgr0X1nex5U+bIlUwP9v8ODBFXvttVeV7fHkk09WpMNk6tSp+fXEiRMr9t1334q2bdtWrLzyyhV77rlnxYQJExb6jEsvvbSiU6dOFe3bt6845phjKr7++uviOt27d6+47LLLiq/ffPPNim233baiWbNmFRtuuGHFqFGj8nfec889eXn6/PT67rvvrujbt29F8+bNK3r37l3x9NNP23d1XL9+/Sq6du1aMWPGjGqXz58/v9r5r7zySj5mxo8fv4JbSF0/rl5++eWK1VdfvWLy5MlV/l+CZTmmdthhh4oTTzzRxoPlSP+VcqP/SqmPK/1XlvdxpQ9bOsojsEgzZsyIW265JafJp1IJc+bMiX79+kXr1q3jySefjH/+85/RqlWrnN3w9ddfF983evToePfdd/PjjTfeGCNGjMhTdebNmxcDBgzImUbPPfdcvprzy1/+stp10/xTTz01xo0bF+utt1785Cc/iblz59qDddQnn3wS//jHP+LYY4+Nli1bVrtOddmPM2fOzNniPXr0iG7dutVAS6mrx1W6HeiAAw6Iq6++Ojp16lTDLaWu/l916623xqqrrhobb7xxDB06dKHbGYFvR/+VUtJ/pdTHlf4rK+K4SvRhS0PQlioefPDBHIhNUwrO3n///XHnnXdGw4YN8+P8+fPjj3/8Y06b33DDDXNwLJVOePzxx4ufsfLKK8dVV10VG2ywQS6tsMcee8Sjjz5a7ZYeNWpUDvDedNNNsckmm8R2220XF154YbXrpoBt+qwUsB02bFi89957MX78eHuwjkr7NtViWn/99avMT8GOwjF6xhlnFOdfc801xfkPP/xwPraaNm1agpZTV46rk08+ObbZZptcIxmWxzGVLgKki6HpomYK2N58881x4IEH2rjwLem/Ui70Xyn1caX/yoo4rvRhS0dNW6rYcccdc8Hp5LPPPsuBsDQAz/PPPx+vvPJK/oedgrmVzZo1KwdeCzbaaKMqdUQ7d+6ca6JU5+23387ZkJWz2Lbccstq1+3du3eVz0ymTp2ag8PUH+lYTBcPUg3b2bNnF+en17vssktMnjw5fv3rX8d+++2Xs8FTMXVY2uMqXbB67LHH4uWXX7bxWG7/V1UehDNd/Ex/y3baaaf8NzQNpggsG/1Xyp3+KzVxXOm/siKOq0QftnQEbakipcWncggFKau2bdu28Yc//CHfbpYGfEpp8YsbXKxJkyYLpdSnf/TfVuXPLaTpL4/PpTyl4zDt5xTYr2yttdbKj82bN68yPx2naVp33XVjq622yhnf99xzTy6jAUt7XKWAbQqkpaL8lQ0cODC+973vVbm7gPptaf+vqqxPnz75MV0QFbSFZaf/SrnQf6WUx5X+KyviuKqOPmzNUR6BxUr/iFNphK+++iq+853vxDvvvBMdOnTI/8ArTylYtixSKv6kSZNiypQpxXljx461V8h1lFPmbCq1kerULo10m0eaKmfiwtIcVz//+c/j1VdfzTW0C1Ny2WWX5bIwsDz+ryocV4W7R4DlQ/+VUtF/pZTHlf4rK+K4qo4+bM0RtKWKFOT66KOP8vTmm2/G8ccfnzNs+/fvn9PjU32TVN8xDUQ2YcKEnG12wgknxPvvv79MWzL9J5GyiwYPHpwDJOl29jPPPHORg0xRv6TyHGmwuS222CLXVE7HZLoSmGpCvvXWW7kMx3/+858YPnx4vPjii7m+8tNPPx377rtvvjK4++67l/onUEuPq1SyJQ0UVXlK1lhjjTzIHSztMZUyt88///z8f9X//u//5lsYDz744Nh+++2rlP8Blp7+K+VE/5VSHVf6r6yI40oftrSUR6CKkSNHFjN+Uu3aVC/2rrvuir59++Z5TzzxRC5Gvffee8cXX3wRq6++eq7H16ZNm2Xakuk/gXvvvTd++tOfxne/+92cin/ppZfmILFapKSAfqopetFFF+VBe9LFgWbNmkXPnj3zwHTHHHNMTJs2LV9EuPzyy3Md5o4dO+YgSArepqxwWJbjCpb3MZVG6H3kkUfy/1UpmyHVc0/lNgoXKoFlp/9KOdF/pVTHFayI40oftrQaVKR7iKGMpGzb7bbbTo0/AABqBf1XAGB5E7Sl5NJgUa1atcoDSKXBWE488cQ8iNRTTz1V6qYBAMBC9F8BgBVNeQRKLpVZSCUXUj3SVDN35513jv/5n/8pdbMAAKBa+q8AwIom0xYAAAAAoIw0LHUDAAAAAAD4P4K2AAAAAABlRNAWAAAAAKCMCNoCAAAAAJQRQVsAAAAAgDIiaAsAAAAAUEYEbQEAAAAAyoigLQAAAABAGRG0BQAAAACI8vH/AUwuGu6vxjYzAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Toplam görüntü : 886\n", "Toplam TMA : 12\n", "Hedef subset (%10) : 88 görüntü\n" ] } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "colors = [\"#4CAF50\", \"#2196F3\", \"#FF9800\", \"#F44336\"]\n", "\n", "# Sol: Sınıf bazında görüntü sayısı\n", "ax = axes[0]\n", "counts = [int(class_counts.get(c, 0)) for c in classes]\n", "bars = ax.bar(classes, counts, color=colors, edgecolor=\"white\", linewidth=0.8)\n", "ax.bar_label(bars, fmt=\"%d\", padding=3, fontsize=9)\n", "ax.set_title(\"Tüm Dataset — Sınıf Dağılımı\", fontweight=\"bold\")\n", "ax.set_ylabel(\"Görüntü Sayısı\")\n", "ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"{int(x):,}\"))\n", "\n", "# Sağ: Sınıf başına TMA (hasta) sayısı\n", "ax = axes[1]\n", "pt_per_class = df.groupby(\"primary_class\")[\"tma\"].nunique().reindex(classes)\n", "bars = ax.bar(classes, pt_per_class.values, color=colors, edgecolor=\"white\", linewidth=0.8)\n", "ax.bar_label(bars, fmt=\"%d\", padding=3, fontsize=9)\n", "ax.set_title(\"Tüm Dataset — Sınıf Başına TMA Sayısı\", fontweight=\"bold\")\n", "ax.set_ylabel(\"TMA Sayısı\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"\\nToplam görüntü : {len(df):,}\")\n", "print(f\"Toplam TMA : {df['tma'].nunique()}\")\n", "print(f\"Hedef subset (%{TARGET_FRACTION*100:.0f}) : {int(len(df) * TARGET_FRACTION):,} görüntü\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "eda-patient-detail", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sınıf başına TMA ve görüntü istatistikleri:\n" ] }, { "data": { "text/html": [ "
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n_tmaimg_per_tma_meanimg_per_tma_stdimg_per_tma_minimg_per_tma_max
primary_class
Benign619.217.4242
G31124.710.9140
G41229.212.91250
G51014.913.1440
\n", "
" ], "text/plain": [ " n_tma img_per_tma_mean img_per_tma_std img_per_tma_min \\\n", "primary_class \n", "Benign 6 19.2 17.4 2 \n", "G3 11 24.7 10.9 1 \n", "G4 12 29.2 12.9 12 \n", "G5 10 14.9 13.1 4 \n", "\n", " img_per_tma_max \n", "primary_class \n", "Benign 42 \n", "G3 40 \n", "G4 50 \n", "G5 40 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sınıf başına TMA & görüntü istatistikleri\n", "tma_stats = (\n", " df.groupby([\"primary_class\", \"tma\"])\n", " .size()\n", " .reset_index(name=\"n_images\")\n", " .groupby(\"primary_class\")[\"n_images\"]\n", " .agg([\"count\", \"mean\", \"std\", \"min\", \"max\"])\n", " .rename(columns={\n", " \"count\": \"n_tma\",\n", " \"mean\": \"img_per_tma_mean\",\n", " \"std\": \"img_per_tma_std\",\n", " \"min\": \"img_per_tma_min\",\n", " \"max\": \"img_per_tma_max\",\n", " })\n", " .reindex(classes)\n", " .round(1)\n", ")\n", "print(\"Sınıf başına TMA ve görüntü istatistikleri:\")\n", "tma_stats" ] }, { "cell_type": "markdown", "id": "md-s2", "metadata": {}, "source": [ "## 2. Subset Seçimi\n", "\n", "**Strateji:**\n", "- Her 4 sınıftan eşit sayıda görüntü (`n_per_class = total_target / 4`)\n", "- Her sınıf içinde TMA katkısı, o TMA'nın sınıftaki görüntü oranıyla orantılıdır\n", "- Seed `42` sabit — her çalıştırmada aynı görüntüler seçilir" ] }, { "cell_type": "code", "execution_count": 8, "id": "sampling-logic", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Toplam hedef : 88\n", "Sınıf başına hedef : 22\n", "Gerçek kota (sınırlı) : {'Benign': 22, 'G3': 22, 'G4': 22, 'G5': 22}\n" ] } ], "source": [ "def balanced_tma_sample(class_df, n_target, tma_col=\"tma\", seed=42):\n", " \"\"\"TMA oranına göre dengeli örnekleme — her TMA'dan orantılı katkı.\"\"\"\n", " if len(class_df) <= n_target:\n", " return class_df.copy()\n", "\n", " total = len(class_df)\n", " allocated = []\n", "\n", " for tma, grp in class_df.groupby(tma_col):\n", " share = max(1, round(len(grp) / total * n_target))\n", " share = min(share, len(grp))\n", " allocated.append(grp.sample(n=share, random_state=seed))\n", "\n", " selected = pd.concat(allocated)\n", "\n", " # Miktar düzeltmesi\n", " if len(selected) > n_target:\n", " selected = selected.sample(n=n_target, random_state=seed)\n", " elif len(selected) < n_target:\n", " deficit = n_target - len(selected)\n", " pool = class_df[~class_df.index.isin(selected.index)]\n", " deficit = min(deficit, len(pool))\n", " if deficit > 0:\n", " selected = pd.concat([selected, pool.sample(n=deficit, random_state=seed)])\n", "\n", " return selected.sort_index()\n", "\n", "\n", "n_total_target = int(len(df) * TARGET_FRACTION)\n", "n_per_class_raw = n_total_target // len(classes)\n", "n_per_class = {c: min(n_per_class_raw, int(class_counts.get(c, 0))) for c in classes}\n", "\n", "print(f\"Toplam hedef : {n_total_target:,}\")\n", "print(f\"Sınıf başına hedef : {n_per_class_raw:,}\")\n", "print(f\"Gerçek kota (sınırlı) : {n_per_class}\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "run-sampling", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Benign: 22 görüntü / 6 TMA\n", " G3 : 22 görüntü / 11 TMA\n", " G4 : 22 görüntü / 12 TMA\n", " G5 : 22 görüntü / 10 TMA\n", "\n", "Toplam seçilen: 88 görüntü\n", "Toplam TMA : 12\n", "Seçilen oran : 9.9%\n" ] } ], "source": [ "parts = []\n", "for cls in classes:\n", " cls_df = df[df[\"primary_class\"] == cls]\n", " sampled = balanced_tma_sample(cls_df, n_per_class[cls], seed=RANDOM_SEED)\n", " parts.append(sampled)\n", " print(f\" {cls:6s}: {len(sampled):,} görüntü / {sampled['tma'].nunique()} TMA\")\n", "\n", "selected_df = pd.concat(parts).reset_index(drop=True)\n", "\n", "print(f\"\\nToplam seçilen: {len(selected_df):,} görüntü\")\n", "print(f\"Toplam TMA : {selected_df['tma'].nunique()}\")\n", "print(f\"Seçilen oran : {len(selected_df)/len(df)*100:.1f}%\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "selection-stats", "metadata": {}, "outputs": [ { "data": { 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G5221025.0
\n", "
" ], "text/plain": [ " n_images n_tma img_pct\n", "primary_class \n", "Benign 22 6 25.0\n", "G3 22 11 25.0\n", "G4 22 12 25.0\n", "G5 22 10 25.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", "ax = axes[0]\n", "sel_counts = selected_df[\"primary_class\"].value_counts().reindex(classes)\n", "bars = ax.bar(classes, sel_counts.values, color=colors, edgecolor=\"white\", linewidth=0.8)\n", "ax.bar_label(bars, fmt=\"%d\", padding=3, fontsize=9)\n", "ax.set_title(f\"Seçilen Subset — Sınıf Dağılımı ({len(selected_df):,} görüntü)\", fontweight=\"bold\")\n", "ax.set_ylabel(\"Görüntü Sayısı\")\n", "\n", "ax = axes[1]\n", "sel_pt = selected_df.groupby(\"primary_class\")[\"tma\"].nunique().reindex(classes)\n", "bars = ax.bar(classes, sel_pt.values, color=colors, edgecolor=\"white\", linewidth=0.8)\n", "ax.bar_label(bars, fmt=\"%d\", padding=3, fontsize=9)\n", "ax.set_title(\n", " f\"Seçilen Subset — Sınıf Başına TMA ({selected_df['tma'].nunique()} toplam)\",\n", " fontweight=\"bold\",\n", ")\n", "ax.set_ylabel(\"TMA Sayısı\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "summary_sel = (\n", " selected_df.groupby(\"primary_class\")\n", " .agg(n_images=(\"image_id\", \"count\"), n_tma=(\"tma\", \"nunique\"))\n", " .reindex(classes)\n", ")\n", "summary_sel[\"img_pct\"] = (summary_sel[\"n_images\"] / len(selected_df) * 100).round(1)\n", "summary_sel" ] }, { "cell_type": "markdown", "id": "md-s3", "metadata": {}, "source": [ "## 3. Subset Dataset Oluştur\n", "\n", "Seçilen görüntü dizinlerine sembolik bağlantı içeren hafif bir dataset oluşturur. \n", "Orijinal dosyalar kopyalanmaz; `SUBSET_ROOT` silinip yeniden oluşturulur." ] }, { "cell_type": "code", "execution_count": 11, "id": "create-subset", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO — DatasetLoader started [multi-workspace mode]: /private/tmp/histopathai-gleason-cnn-subset\n", "INFO — images: 88 satır, 23 sütun\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Subset oluşturuldu : /tmp/histopathai-gleason-cnn-subset\n", " Görüntü (loader) : 88\n", " Beklenen : 88\n", " OK — sayı doğrulandı.\n" ] } ], "source": [ "ws_src = DATASET_ROOT / \"Gleason_CNN\"\n", "ws_dst = SUBSET_ROOT / \"Gleason_CNN\"\n", "\n", "if SUBSET_ROOT.exists():\n", " shutil.rmtree(SUBSET_ROOT)\n", "ws_dst.mkdir(parents=True)\n", "\n", "# workspace.json\n", "ws_json_src = ws_src / \"workspace.json\"\n", "if ws_json_src.exists():\n", " (ws_dst / \"workspace.json\").symlink_to(ws_json_src.resolve())\n", "\n", "# Hasta (TMA) & görüntü dizinleri\n", "for patient_id, grp in selected_df.groupby(\"patient_id\"):\n", " pt_src = ws_src / patient_id\n", " pt_dst = ws_dst / patient_id\n", " pt_dst.mkdir(exist_ok=True)\n", "\n", " pj_src = pt_src / \"patient.json\"\n", " pj_dst = pt_dst / \"patient.json\"\n", " if pj_src.exists() and not pj_dst.exists():\n", " pj_dst.symlink_to(pj_src.resolve())\n", "\n", " for img_id in grp[\"image_id\"]:\n", " img_src = pt_src / img_id\n", " img_dst = pt_dst / img_id\n", " if img_src.exists() and not img_dst.exists():\n", " img_dst.symlink_to(img_src.resolve())\n", "\n", "# Doğrulama\n", "subset_loader = DatasetLoader(root=SUBSET_ROOT)\n", "subset_img_df = subset_loader.images()\n", "\n", "print(f\"Subset oluşturuldu : {SUBSET_ROOT}\")\n", "print(f\" Görüntü (loader) : {len(subset_img_df):,}\")\n", "print(f\" Beklenen : {len(selected_df):,}\")\n", "assert len(subset_img_df) == len(selected_df), \"Sayı eşleşmedi!\"\n", "print(\" OK — sayı doğrulandı.\")" ] }, { "cell_type": "markdown", "id": "md-s4", "metadata": {}, "source": [ "## 4. Auth & Cloud Bağlantısı" ] }, { "cell_type": "code", "execution_count": 12, "id": "auth", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Firestore DB'leri : ['(default)', 'default-clone-migration-test', 'dev-database']\n", "Bağlantı başarılı.\n" ] } ], "source": [ "from histopathai.cloud.adapter.db.firestore import FirestoreAdapter\n", "from histopathai.cloud.adapter.storage.gcs import GCSStorageAdapter\n", "from histopathai.cloud.ingest.pipeline import IngestPipeline\n", "\n", "db = FirestoreAdapter(\n", " project_id = PROJECT_ID,\n", " db_name = DB_NAME,\n", " credentials_path = str(SA_KEY_PATH),\n", ")\n", "storage = GCSStorageAdapter(\n", " bucket_name = PROC_BUCKET,\n", " credentials_path = str(SA_KEY_PATH),\n", " project_id = PROJECT_ID,\n", ")\n", "\n", "print(f\"Firestore DB'leri : {db.list_dbs()}\")\n", "print(\"Bağlantı başarılı.\")" ] }, { "cell_type": "markdown", "id": "md-s5", "metadata": {}, "source": [ "## 5. IngestPipeline" ] }, { "cell_type": "code", "execution_count": 13, "id": "pipeline-init", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO — workspaces: 1 satır, 17 sütun\n", "INFO — images: 88 satır, 23 sütun\n", "INFO — patients: 12 satır, 11 sütun\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Hedef workspace ID: n48XxS5gZIFcwEOu7POf\n", "\n", "Subset özeti:\n", " ws_id n_images n_processed n_pending n_failed n_has_origin n_has_processed n_cache_accessible n_patients\n", "n48XxS5gZIFcwEOu7POf 88 0 88 0 88 4 4 12\n" ] } ], "source": [ "pipeline = IngestPipeline(\n", " firestore = db,\n", " storage = storage,\n", " creator_id = CREATOR_ID,\n", " cache_dir = CACHE_DIR,\n", ")\n", "\n", "ws_df = subset_loader.workspaces()\n", "TARGET_WS_ID = ws_df.iloc[0][\"id\"]\n", "\n", "print(f\"Hedef workspace ID: {TARGET_WS_ID}\")\n", "print(\"\\nSubset özeti:\")\n", "print(subset_loader.summary().to_string(index=False))" ] }, { "cell_type": "code", "execution_count": 14, "id": "run-ingest", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4a8db6789b114aedb7c13a74cf54821a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Yükleniyor: 0%| | 0/88 [00:00