{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00a28b50",
   "metadata": {},
   "source": [
    "# Create genetic perturabation data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836badb3",
   "metadata": {},
   "source": [
    "## 1. import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a086ed9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xpert as xp\n",
    "from xpert.data.utils import get_info_txt\n",
    "import scanpy as sc\n",
    "import os\n",
    "import pickle\n",
    "import warnings\n",
    "import logging\n",
    "warnings.filterwarnings('ignore')\n",
    "sc.settings.verbosity = 0\n",
    "logging.getLogger('scanpy').setLevel(logging.ERROR)\n",
    "logging.getLogger('anndata').setLevel(logging.ERROR)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f2c9e6",
   "metadata": {},
   "source": [
    "## 2. load original adata file of perturbation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81e8b788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 111445 × 33694\n",
       "    obs: 'guide_id', 'read_count', 'UMI_count', 'coverage', 'gemgroup', 'good_coverage', 'number_of_cells', 'tissue_type', 'cell_line', 'cancer', 'disease', 'perturbation_type', 'celltype', 'organism', 'perturbation', 'nperts', 'ngenes', 'ncounts', 'percent_mito', 'percent_ribo'\n",
       "    var: 'ensemble_id', 'ncounts', 'ncells'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = sc.read('../../data/Norman2019/NormanWeissman2019_filtered.h5ad')\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d19fe9",
   "metadata": {},
   "source": [
    "add obs.column with 'perturbation_new' and 'cell_type_new', which is used to perform data proprecessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2441a6fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perturbation(x):\n",
    "    return '; '.join(x['perturbation'].split('_'))\n",
    "\n",
    "adata.obs['perturbation_new'] = adata.obs.apply(get_perturbation, axis=1)\n",
    "adata.obs['celltype_new'] = adata.obs['celltype']\n",
    "\n",
    "def get_perturbation_group(x):\n",
    "    return ' | '.join([x['perturbation_new'], x['celltype_new']])\n",
    "perturbation_group = adata.obs.apply(get_perturbation_group, axis=1)\n",
    "adata.obs['perturbation_group'] = perturbation_group\n",
    "\n",
    "adata.obs['sgRNA_new'] = 'control'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b741f37",
   "metadata": {},
   "source": [
    "## 3. generate Pert_Data object\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "189334c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "\n",
    "pert_cell_filter = 100 # this is used to filter perts, cell number less than this will be filtered\n",
    "seed = 2024 # this is the random seed\n",
    "split_type = 1 # 1 for unseen perts; 0 for unseen celltypes\n",
    "split_ratio = [0.7, 0.2, 0.1] # train:test:val; val is used to choose data, test is for final validation\n",
    "var_num = 5000 # selecting hvg number\n",
    "num_de_genes = 20 # number of de genes\n",
    "bs_train = 32 # batch size of trainloader\n",
    "bs_test = 32 # batch size of testloader\n",
    "data_dir = '../../data/Norman2019/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237fa71a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1: Reading files...\n",
      "========== read file finished!\n",
      "Step 2: Filtering perturbations...\n",
      "retain_pert_num is: 225\n",
      "filtered pert num is:  12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 236/236 [00:03<00:00, 73.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== filter perturbation finished!\n",
      "Step 3: Preprocessing adata and selecting HVGs...\n",
      "len of exclude_var_list is 4\n",
      "len of pert_gene_list is 343\n",
      "len of final var_names is 5040\n",
      "========== get var genes finished!\n",
      "this is new version\n",
      "Step 4: Setting control barcodes...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 224/224 [00:03<00:00, 69.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== set control barcodes finished!\n",
      "Step 5: Calculating E-distances...\n",
      "Step 6: Filtering sgRNAs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 7: Data splitting...\n",
      "========== data split finished!\n",
      "Step 8: Getting differential genes...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 224/224 [01:04<00:00,  3.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== get de genes finished!\n",
      "Step 9: Saving processed data...\n",
      "Pert_Data object generation completed!\n",
      "Final dataset shape: (110554, 5040)\n",
      "Number of perturbation groups: 224\n"
     ]
    }
   ],
   "source": [
    "# Create Pert_Data object\n",
    "pert_data = xp.data.Byte_Pert_Data(\n",
    "    prefix='Norman2019',\n",
    "    pert_cell_filter=pert_cell_filter,\n",
    "    seed=seed,\n",
    "    split_ratio=split_ratio,\n",
    "    split_type=split_type,\n",
    "    var_num=var_num,\n",
    "    num_de_genes=num_de_genes,\n",
    "    bs_train=bs_train,\n",
    "    bs_test=bs_test\n",
    ")\n",
    "\n",
    "# Complete data processing pipeline\n",
    "print(\"Step 1: Reading files...\")\n",
    "pert_data.read_files(adata)\n",
    "\n",
    "print(\"Step 2: Filtering perturbations...\")\n",
    "pert_data.filter_perturbation()\n",
    "\n",
    "print(\"Step 3: Preprocessing adata and selecting HVGs...\")\n",
    "pert_data.get_and_process_adata(var_num=pert_data.var_num)\n",
    "\n",
    "print(\"Step 4: Setting control barcodes...\")\n",
    "pert_data.set_control_barcode()\n",
    "\n",
    "print(\"Step 5: Calculating E-distances...\")\n",
    "pert_data.get_edis_2()\n",
    "\n",
    "print(\"Step 6: Filtering sgRNAs...\")\n",
    "pert_data.adata_split.obs['sgRNA_new'] = 'control'\n",
    "pert_data.filter_sgRNA()\n",
    "\n",
    "print(\"Step 7: Data splitting...\")\n",
    "pert_data.data_split(split_type=1, test_perts=None)\n",
    "\n",
    "print(\"Step 8: Getting differential genes...\")\n",
    "pert_data.get_de_genes()\n",
    "\n",
    "print(\"Step 9: Saving processed data...\")\n",
    "# Save the processed data\n",
    "pickle.dump(pert_data, open(os.path.join(data_dir, 'pert_data.pkl'), 'wb'))\n",
    "\n",
    "print(\"Pert_Data object generation completed!\")\n",
    "print(f\"Final dataset shape: {pert_data.adata_split.shape}\")\n",
    "print(f\"Number of perturbation groups: {len(pert_data.filter_perturbation_list)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26875c7c",
   "metadata": {},
   "source": [
    "## 4. Pert_Data Information Analysis and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6655965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Analyzing cell count distribution...\n"
     ]
    },
    {
     "data": {
      "image/png": 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1C1evXsXEiRPL7a6Sm5uLLVu2AIB8m7BPP/3UYJ1vvvkGeXl5BrcRa9SoUZn3ftu2bcjNzTW7rcYcJ3Xr1sXQoUMxfvx4XLlypcxdMMrzxRdfGLz2tLQ0pKSkyH841tR7UZF7770XdevWxYkTJyrcv1KprDJORd8DAQEBcHV1LfN+fffdd2a197PPPjN4/vnnnwO4eQ/q0uK3tD1AyR9B5d0uzN6+G4gqwzOwVOv8+eefcr+2rKws7Ny5E6tXr4aTkxM2btxY5ifbW61YsQLbtm1Dv3790LBhQxQUFGDVqlUAIA+A4OnpibCwMHz33Xfo2bMnfHx84OvrW+ktnyoTHByMgQMHIiEhAUFBQfj000+xdetWLFiwQD7b1alTJzRv3hzTp0+HVqtFvXr1sHHjRuzatatMvFatWmHDhg1Yvnw5OnToAIVCUeHZqNmzZ+OHH35ATEwMXnvtNfj4+OCzzz7D5s2bsXDhQnh7e5v1mm6XmJiIXr16ISYmBtOnT4dSqcSyZcvw559/4osvvjD5DBsAeHt747vvvkP//v3Rrl07g4EMzpw5g08//RRHjx6Vz/KtXLkScXFx6N27N+Lj49GgQQNcuXIFJ0+exKFDh/D1119X+3U+/PDDmDVrFt544w389ddfGDt2rDyQwd69e+UryGNjY9GrVy/07t0bL730EtRqNe6991788ccfmD17Ntq1a4eRI0fKcUeOHIlZs2bhtddeQ7du3XDixAksXbq0Wu9Pq1at8OWXX2LdunVo3LgxXF1d0apVKwwYMEC+l7Kfnx/S0tKwePFihIWFoVmzZlXGzcrKwuDBg/HUU08hJycHs2fPhqurK2bMmCGvUxPvRUXq1KmD9957D6NHj8aVK1cwdOhQ+Pv7Izs7G0ePHkV2djaWL19eZZxWrVoBABYsWIC4uDg4OTmhdevWUCqVePzxx7Fq1So0adIEbdq0wb59++TC0xRKpRJvv/02cnNz0alTJ6SkpGDu3LmIi4tD165dAQC9evWCUqnEY489hhdffBEFBQVYvnx5uQN42ON3A1GFbHgBGVGNKr1iufShVCqFv7+/6Natm5g3b57Iysoqs83tV5fv3r1bDB48WISFhQmVSiXq168vunXrJjZt2mSw3S+//CLatWsnVCqVACBfCV4aLzs7u8p9CVFyFXm/fv3E+vXrRWRkpFAqlaJRo0Zi0aJFZbY/ffq0iI2NFV5eXsLPz088//zzYvPmzWWumL9y5YoYOnSoqFu3rpAkyWCfKOdq9mPHjokBAwYIb29voVQqRZs2bcpcQV16pfHXX39tML+yK65vt3PnTtGjRw/h4eEh3NzcRJcuXcT3339fbjxj7kJQKjMzU7z00ksiMjJSuLu7C5VKJZo2bSqeeeYZcezYMYN1jx49Kh555BHh7+8vXFxcRGBgoOjRo4dYsWJFmddq6l0IbrVjxw4xdOhQERQUJFxcXISXl5eIiooSb775plCr1fJ6169fFy+99JIICwsTLi4uIigoSDz33HPi6tWrBvEKCwvFiy++KEJDQ4Wbm5vo1q2bOHLkSIV3Ibj9ThzlvaZz586J2NhY4enpKQDIV6e//fbbIjo6Wvj6+gqlUikaNmwoxo4dK86dO1fpay7dxyeffCImTpwo/Pz8hEqlEvfdd584cOBAmfWNeS+MubPIrUw5fnbs2CH69esnfHx8hIuLi2jQoIHo16+fwTFe2ee5sLBQPPnkk8LPz0/+nJXeJSQnJ0c8+eSTIiAgQHh4eIgBAwaIc+fOVXgXgvLijx49Wnh4eIg//vhDdO/eXbi5uQkfHx/x3HPPidzcXIN1v//+e9GmTRvh6uoqGjRoIF544QXx008/Ocx3A1F5JCGquOyaiIiompKTkxETE4Ovv/66zAASRESmYh9YIiIiInIoLGCJiIiIyKGwCwERERERORSegSUiIiIih8ICloiIiIgcCgtYIiIiInIod/xABnq9HhcuXICnp6dZN0MnIiIiIusRQkCj0SA4OBgKhXHnVu/4AvbChQsIDQ21dTOIiIiIqBLp6ekICQkxat07voAtHTM7PT0dXl5eNm6N/dLr9cjOzoafn5/Rf/1YtgE64NqRkum6bQGFk2Xj63TAkRvx27YFnMyPb/NcORjmyzTMl/GYK9MwX6ZhvoxX3Vyp1WqEhobKNZsx7vgCtrTbgJeXFwvYSuj1ehQUFMDLy8s2H1RtHvBjj5LpR3IBZw/Lxs/LA3rciJ+bC3iYH9/muXIwzJdpmC/jMVemYb5Mw3wZz1K5MqWrJ98RIiIiInIoLGCJiIiIyKHYTQGbmJgISZIwefJkeZ4QAgkJCQgODoabmxu6d++O48eP266RRERERGRzdtEHdv/+/fjggw/QunVrg/kLFy7EokWLsGbNGkRERGDu3Lno1asXTp06ZVJHXyIiIjKPTqdDcXGxrZtR4/R6PYqLi1FQUMA+sFUwJldKpdKiebR5AZubm4sRI0bgf//7H+bOnSvPF0Jg8eLFmDlzJoYMGQIAWLt2LQICAvD555/jmWeesVWTiYiI7nhCCGRmZuLatWu2bopNCCGg1+uh0Wh4H/kqGJMrhUKB8PBwKJVKi+zT5gXs+PHj0a9fPzzwwAMGBWxqaioyMzMRGxsrz1OpVOjWrRtSUlJYwBIREVlRafHq7+8Pd3f3WlfECSGg1Wrh7Oxc6167qarKVemgUhkZGWjYsKFF8mnTAvbLL7/EoUOHsH///jLLMjMzAQABAQEG8wMCApCWllZhzMLCQhQWFsrP1Wo1gJLk6fV6SzT7jqTX6+W/oGxCOEGKfK1kUjgBlm6HkxOk127Ed6pefJvnysEwX6ZhvozHXJnGlHzpdDpcvXoV/v7+8PHxqYHW2afi4mK4uLjYuhkOoapc+fn54b///kNRUVGZ9cz5DNusgE1PT8ekSZOwZcsWuLq6Vrje7VW6EKLSyj0xMRFz5swpMz87OxsFBQXmN/gOp9frkZOTAyGE7fr6BDxX8u/la9aJ/9yN+NX8OcwucuVAmC/TMF/GY65MY0q+iouLodfroVQqodVqa6iF9kUIAZ1OB8C0+5PWRsbkSqFQQK/XIysrq0wBq9FoTN6nzQrYgwcPIisrCx06dJDn6XQ6/Pbbb1i6dClOnToFoORMbFBQkLxOVlZWmbOyt5oxYwamTp0qPy8d3cHPz48DGVRCr9dDkiSOOGIE5so0zJdpmC/jMVemMSVfBQUF0Gg0cHFxgbOzzXsb2hTPwBqvslw5OztDoVCgfv36ZU5cVnYis8J4Jm9hIT179sSxY8cM5j3xxBO466678NJLL6Fx48YIDAzE1q1b0a5dOwBAUVERduzYgQULFlQYV6VSQaVSlZmvUCj4BVcFSZJslyehB3JOlkx7twAkC7dBrwdO3ojfogVQzddo01w5IObLNMyX8Zgr0xibL4VCAUmS5EdtdOsvvrU1B8YyJlelx1J5x585n1+bFbCenp5o2bKlwTwPDw/Ur19fnj958mTMmzcPzZo1Q7NmzTBv3jy4u7tj+PDhtmgyWZPuOvDjjePBGkPJXr8OlB5v1RxKloiIiGzLrv9kffHFFzF58mSMGzcOHTt2xH///YctW7bwHrBERERURnx8PCRJwrPPPltm2bhx4yBJEuLj42u+YSZ65plnIEkSFi9eXO5yIQTi4uIgSRK+/fZbg2VXr17FyJEj4e3tDW9vb4wcOdKoW6GdPHkSAwcOhLe3Nzw9PdGlSxecP3+++i/GSuyqgE1OTjZ4syRJQkJCAjIyMlBQUIAdO3aUOWtLREREVCo0NBRffvklrl+/Ls8rKCjAF198gYYNG9qwZcb59ttvsXfvXgQHB1e4zuLFiyv8qX748OE4cuQIkpKSkJSUhCNHjmDkyJGV7vPs2bPo2rUr7rrrLiQnJ+Po0aOYNWuWWX1Ta4pdFbBERERE1dG+fXs0bNgQGzZskOdt2LABoaGh8jU1pYQQWLhwIRo3bgw3Nze0adMG69evl5frdDqMHTsW4eHhcHNzQ/PmzbFkyRKDGPHx8XjwwQfx1ltvISgoCPXr18f48ePNGr3sv//+w4QJE/DZZ59VeEHU0aNHsWjRIqxatarMspMnTyIpKQkffvghoqKiEBUVhf/973/44Ycf5IvjyzNz5kz07dsXCxcuRLt27dC4cWP069cP/v7+Jr+GmsICloiIiIyTl1fx4/ZbVVa27i1nRytd10xPPPEEVq9eLT9ftWoVxowZU2a9V199FatXr8by5ctx/PhxTJkyBY8//jh27NgBoOTODSEhIfjqq69w4sQJvPbaa3jllVfw1VdfGcTZvn07zp49i+3bt2Pt2rVYs2YN1qxZIy9PSEhAo0aNKm2zXq/HyJEj8cILLyAyMrLcdfLz8/HYY49h6dKlCAwMLLN89+7d8Pb2RufOneV5Xbp0gbe3N1JSUirc7+bNmxEREYHevXvD398fnTt3LtM1wd6wgCUiIiLj1KlT8eOhhwzX9feveN24OMN1GzUqfz0zjRw5Ert27cK5c+eQlpaG33//HY8//rjBOnl5efKZzN69e6Nx48aIj4/H448/jpUrVwIouS3UnDlz0KlTJ4SHh2PEiBGIj48vU8DWq1cPS5cuxV133YX+/fujX79++PXXX+Xlvr6+aNKkSaVtXrBgAZydnTFx4sQK15kyZQqio6MxaNCgcpdnZmaWe9bU399fHiDqdllZWcjNzcX8+fPRp08fbNmyBYMHD8aQIUPkQt4e1e6bu5FZsrOz5RHOqsvLywt+fn4WiUVERASUFIz9+vXD2rVrIYRAv3794Ovra7DOiRMnUFBQgF69ehnMLyoqMuhqsGLFCnz00UdIS0vD9evXUVRUhLZt2xpsExkZCScnJ/l5UFCQwa1CJ0yYgAkTJlTY3oMHD2LJkiU4dOhQhX1bN23ahG3btuHw4cOVvvbytq9sEKjSUbAGDRqEKVOmAADatm2LlJQUrFixAt26dat0f7bCApZMkp2djWfjx6DQjFEzyqPy9MSKNavg5+MNtJheMlOywk2jXVyA6dNvThMRkelycytedksBBwDIyqp43dvv+3nunNlNqsiYMWPkovH9998vs7y0cNu8eTMaNGhgsKz0fvJff/01pk6dirfffhtRUVHw9PTEm2++ib179xqsf3t/VUmSTBoedefOncjKyjK4yEyn02HatGlYvHgxzp07h23btuHs2bOoW7euwbYPPfQQ7rvvPiQnJyMwMBAXL14sEz87O7vCQaB8fX3h7OyMu+++22B+ixYtsGvXLqNfQ01jAUsmUavVKNRoMKlzDELq+Va9QSX+vXoJS/Zuh1qtLjkL2+5NC7WyHEol8KYV4xMR1Qam3EPbWusaqU+fPigqKgIA9O7du8zyu+++GyqVCufPny/3LKMQAr///juio6Mxbtw4ef7Zs2ct3taRI0figQceMJjXu3dvjBw5Ek888QQA4OWXX8aTTz5psE6rVq3wzjvvYMCAAQCAqKgo5OTkYN++fbjnnnsAAHv37kVOTg6io6PL3bdSqUSnTp3KXOR1+vRphIWFWeT1WQMLWDJLSD1fNPEv24GciIjIHjg5OeHkjREYnW4/O4ySAZWmT5+OKVOmQK/Xo2vXrlCr1UhJSUGdOnUwatQoNGnSBJ9++il+/vlnhIeH45NPPsH+/fsRHh5uUluWLl2KjRs3GvSLvVX9+vVRv359g3kuLi4IDAxE8+bNAQCBgYHlXrjVsGFDuT0tWrRAnz598NRTT8n9eJ9++mn0799fjgMAd911FxITEzF48GAAwAsvvIBhw4bh/vvvR0xMDJKSkvD9998jOTnZpNdZk1jAkn0QeiDvxg2TPRpaZyjZ0hsyN2xY7aFkiYjI/nl5eVW6/I033oC/vz8SExPxzz//oG7dumjfvj1eeeUVACXF37FjxzBs2DBIkoTHHnsM48aNw08//WRSOy5dumSVM7fl+eyzzzBx4kTExsYCAAYOHIilS5carHPq1Cnk5OTIzwcPHowVK1YgMTEREydORPPmzfHNN9+ga9euNdJmc0hCCGHrRliTWq2Gt7c3cnJyqjyQazO9Xo+srCz4+/tXOibx2bNnMemJsXizz8PVPgN7NisTLyR9jSWrP0KTsEDgqxtXnFpjKNm8vJtXtFZzKFljc0UlmC/TMF/GY65MY0q+CgoKkJqaivDwcLu+mb01CSGg1Wrh7Oxc4QVQVMKYXFV2TJlTq/ETT0REREQOhQUsERERETkUFrBERERE5FBYwBIRERGRQ2EBS0REREQOhQUsERERETkU3geW7IPkDDQbd3Pa0pydgdKRVJx52BMRETky/k9O9sFJBXQqO1a1xahUQDljYRMREZHjYRcCIiIiInIoLGDJPggBFGSXPKwxOJwQQHZ2yePOHnyOiIisKDk5GZIk4dq1awCANWvWoG7dujZtU23EApbsgy4f2OBf8tDlWz5+fj7g71/yyLdCfCIisrn4+HhIkoRnn322zLJx48ZBkiTEx8dbdJ/Dhg3D6dOnLRrTWAMHDkTDhg3h6uqKoKAgjBw5EhcuXCiz3po1a9C6dWu4uroiMDAQEyZMkJclJydj0KBBCAoKgoeHB9q2bYvPPvusyn1fvXoVI0eOhLe3N+rWrYv4+Hi5qK8JLGCJiIjojhEaGoovv/wS169fl+cVFBTgiy++QMOGDS2+Pzc3N/j7+1s8rjFiYmLw1Vdf4dSpU/jmm29w9uxZDB061GCdRYsWYebMmXj55Zdx/Phx/Prrr+jdu7e8PCUlBa1bt8Y333yDP/74A2PGjMGoUaPw/fffV7rv4cOH48iRI0hKSsJPP/2Eo0ePYtSoUVZ5neVhAUtERER3jPbt26Nhw4bYsGGDPG/Dhg0IDQ1Fu3btDNYVQmDhwoVo3Lgx3Nzc0KZNG6xfv95gnR9//BERERFwc3NDTEwMzp07Z7D89i4EZ8+exaBBgxAQEIA6deqgU6dO+OWXXwy2adSoEebNm4cxY8bA09MTDRs2xAcffGDya50yZQq6dOmCsLAwREdH4+WXX8aePXtQXFwMoOQs6auvvoqPP/4Yw4cPR5MmTRAZGYkBAwbIMV555RW88cYbiI6ORpMmTTBx4kT06dMHGzdurHC/J0+eRFJSEj788ENERUUhKioKK1aswA8//IBTp06Z/DrMwQKWiIiIjKPNq/ihKzB+Xe1149Y10xNPPIHVq1fLz1etWoUxY8aUWe/VV1/F6tWrsXz5chw/fhxTpkzB448/jh07dgAA0tPT8dBDD6Fv3744cuQInnzySbz88suV7js3Nxd9+/bFL7/8gsOHD6N3794YMGAAzp8/b7De22+/jY4dO+Lw4cMYN24cnnvuOfz111/y8u7du5vU3eHKlSv47LPPEB0dDRcXFwDA1q1bodfr8d9//6FFixYICQnBI488gvT09Epj5eTkwMfHp8Llu3fvhre3Nzp37izP69y5M7y9vZGSkmJ0m6uDt9EiIiIi43xVp+JlwX2B7ptvPv+mkmsa/LsBDyTffP5dI6DwUtn1hpt30e3IkSMxY8YMnDt3DpIk4ffff8eXX36J5OSb+8zLy8OiRYuwbds2REVFAQAaN26MXbt2YeXKlbj//vuxcuVKNG7cGO+88w4kSULz5s1x7NgxLFiwoMJ9t2nTBm3atJGfz507Fxs3bsSmTZsM+p727dsX427cn/yll17CO++8g+TkZNx1110AgIYNGyIoKKjK1/rSSy9h6dKlyM/PR5cuXfDDDz/Iy/755x/o9XrMmzcPS5Ysgbe3N1599VX06tULf/zxB5RKZZl469evx/79+7Fy5coK95mZmVlutwl/f39kZmZW2WZLYAFLREREdxRfX1/069cPa9euhRAC/fr1g6+vr8E6J06cQEFBAXr16mUwv6ioSO5q8Ndff6Fz586QJEleXlrsViQvLw9z5szBDz/8gAsXLkCr1eL69etlzsC2bt1anpYkCYGBgcjKypLnffzxx0a91hdeeAFjx45FWloa5syZg1GjRuGHH36AJEnQ6/UoLi7Gu+++i9jYWADAF198gcDAQGzfvt2gLyxQckFXfHw8/ve//yEyMrLS/d6ak1JCiHLnWwMLWCIiIjLOI7kVL5OcDJ8/lFX+egDK9GAcdM7cFlVozJgx8hnP98sZyEav1wMANm/ejAYNGhgsU6lUAEoKMlO98MIL+Pnnn/HWW2+hadOmcHNzw9ChQ1FUVGSwXunP/KVKC05T+fr6wtfXFxEREWjRogVCQ0OxZ88eREVFyWdw7777bnl9Pz8/+Pr6limod+zYgQEDBmDRokVVXowVGBiIixcvlpmfnZ2NgIAAk1+DOVjAkn2QnIHw0TenLc3ZGRg9+uY0ERGZztnD9usaqU+fPnLRePuZRqCkqFOpVDh//jy6detWZrkQAi1atChzNf6ePXsq3e/OnTsRHx+PwYMHAyjpE3v7hV/WUlpwFxYWAgDuvfdeAMCpU6cQEhICoKSv7KVLlxAWFiZvl5ycjP79+2PBggV4+umnq9xPVFQUcnJysG/fPtxzzz0AgH379iEnJwfR0dEWfU0V4f/kZB+cVEDUGuvFV6mANVaMT0REdsXJyQknT56Up2/n6emJ6dOnY8qUKdDr9ejatSvUajVSUlJQp04djBo1Ck8//TQWL16MqVOn4plnnsHBgwexpor/S5o2bYoNGzZgwIABkCQJs2bNMuvM6qhRo9CgQQMkJiaWu3zfvn3Yt28funbtinr16uGff/7Ba6+9hiZNmsjdHCIiIjBo0CBMmjQJH3zwAby8vDBjxgzcddddiImJAVBSvPbr1w+TJk3CQw89JPdhVSqV8oVc+/btw6hRo/Drr7+iQYMGaNGiBfr06YOnnnoKK1euhBACzz77LPr374/mzZub/FrNwbsQEBER0R3Jy8sLXl5eFS5/44038NprryExMREtWrRA79698f333yM8PBxAyYVU69evx/fff482bdpgxYoVmDdvXqX7fOedd1CvXj1ER0djwIAB6N27N9q3b29y28+fP4+MjIwKl7u5uWHDhg3o2bMnmjdvjjFjxqBly5bYsWOH3AUCKOlL27lzZ/Tr1w/dunWDi4sLkpKS5C4Ma9asQX5+PhITExEUFCQ/hgwZIsfIz8/HqVOn5NtzAcBnn32GVq1aITY2Fr1790arVq2M7rdrCZIwp4OHA1Gr1fD29kZOTk6lB3Ftp9frkZWVBX9/fygUFf9dc/bsWUx6Yize7PMwmvgHVmufZ7My8ULS11iy+iM0adz45tWqTu6ApTuBC3FzBC736sU3NldUgvkyDfNlPObKNKbkq6CgAKmpqQgPD4erq2sNtdC+CCGg1Wrh7OxcYxcmOSpjclXZMWVOrcZPPNkHXX7J7Vm+qmO9oWTr1Cl5cChZIiIih8YCloiIiIgcCgtYIiIiInIoLGCJiIiIyKGwgCUiIiIih8ICloiIiMplzv1Licpj6ZtecSADIiIiMqBUKqFQKHDhwgX4+flBqVTWultJ8TZaxqsqV0IIZGdnQ5KkMkPomosFLNkHyQkIHXpz2tKcnIChQ29OExFRhRQKBcLDw5GRkYELFy7Yujk2IYSAXq+HQqFgAVsFY3IlSRJCQkLKHRXNHCxgyT44uQL3fW29+K6uwNdWjE9EdIdRKpVo2LAhtFotdDqdrZtT4/R6PS5fvoz69etzoIwqGJMrFxcXixWvgI0L2OXLl2P58uU4d+4cACAyMhKvvfYa4uLiAADx8fFYu3atwTadO3fGnj17arqpREREtU7pT76W+tnXkej1eri4uMDV1ZUFbBVskSubFrAhISGYP38+mjZtCgBYu3YtBg0ahMOHDyMyMhIA0KdPH6xevVreRqlU2qStRERERGQfbFrADhgwwOD5//3f/2H58uXYs2ePXMCqVCoEBgbaonlUk7R5JcPIAsAjuYCzh2Xj5+WVDCMLALm5gIeF4xMREVGNsZs+sDqdDl9//TXy8vIQFRUlz09OToa/vz/q1q2Lbt264f/+7//g7+9fYZzCwkIUFhbKz9VqNYCS09u8HUjF9Hq93Am7MkIISJIEAaC62RQo+XlK7vx9S1tg6ffKgvGNzRWVYL5Mw3wZj7kyDfNlGubLeNXNlTnb2byAPXbsGKKiolBQUIA6depg48aNuPvuuwEAcXFxePjhhxEWFobU1FTMmjULPXr0wMGDB6FSqcqNl5iYiDlz5pSZn52djYKCAqu+Fkem1+uRk5MDIUSl/Vc0Gg1Cw8ORV8cV2crqXZWZV8cVoeHh0Gg0yM5WIODG/OzsbAinvGrFvp2Un28YP8/8+MbmikowX6ZhvozHXJmG+TIN82W86uZKo9GYvI3NC9jmzZvjyJEjuHbtGr755huMHj0aO3bswN13341hw4bJ67Vs2RIdO3ZEWFgYNm/ejCFDhpQbb8aMGZg6dar8XK1WIzQ0FH5+fvDy8rL663FUer0ekiTBz8+v0oMvNzcX6amp8IjoAD/36t2UWJNbgPTUVHh6esLPz0+e7+fnZ50uBLfGr0YXAmNzRSWYL9MwX8ZjrkzDfJmG+TJedXPl6upq8jY2L2CVSqV8EVfHjh2xf/9+LFmyBCtXriyzblBQEMLCwnDmzJkK46lUqnLPzioUCh6AVZAkqco8lf7kL6H6w7hJuNkl4dZ9KhQKwNLvlYXjG5Mruon5Mg3zZTzmyjTMl2mYL+NVJ1dmbWPyFlYmhDDow3qry5cvIz09HUFBQTXcKiIiIiKyFzY9A/vKK68gLi4OoaGh0Gg0+PLLL5GcnIykpCTk5uYiISEBDz30EIKCgnDu3Dm88sor8PX1xeDBg23ZbCIiIiKyIZsWsBcvXsTIkSORkZEBb29vtG7dGklJSejVqxeuX7+OY8eO4eOPP8a1a9cQFBSEmJgYrFu3Dp6enrZsNlmD5AQE9705bWlOTkDfvjeniYiIyGHZtID96KOPKlzm5uaGn3/+uQZbQzbl5Ap032y9+K6uwGYrxiciIqIaY3d9YImIiIiIKsMCloiIiIgcCgtYsg/aPGCdR8lDa9lBDACU3AfWw6PkUY1BDIiIiMj2bH4fWCKZLt+68fOtHJ+IiIhqBM/AEhEREZFDYQFLRERERA6FBSwRERERORQWsERERETkUFjAEhEREZFD4V0IyE4oAP9uN6ctHl4BdOt2c5qIiIgcFgtYsg/ObsADydaL7+YGJFsxPhEREdUYnooiIiIiIofCApaIiIiIHAoLWLIP2jzgG7+Sh7WGkvXzK3lwKFkiIiKHxj6wZD8KL1k3/iUrxyciIqIawTOwRERERORQWMASERERkUNhAUtEREREDoUFLBERERE5FBawRERERORQeBcCshMKwKfjzWmLh1cAHTvenCYiIiKHxQKW7IOzG9Bnv/Xiu7kB+60Yn4iIiGoMT0URERERkUNhAUtEREREDoUFLNkHbT7wXaOShzbf8vHz84FGjUoe+VaIT0RERDWGfWDJTgggL+3mtMXDCyAt7eY0EREROSyegSUiIiIih8ICloiIiIgcCgtYIiIiInIoLGCJiIiIyKGwgCUiIiIih8K7EJCdkADvu29OWzy8BNx9981pIiIiclgsYMk+OLsD/Y5bL767O3DcivGJiIioxrALARERERE5FBawRERERORQWMCSfdDmA5sjSx7WGko2MrLkwaFkiYiIHBr7wJKdEEDOiZvTFg8vgBMnbk4TERGRw+IZWCIiIiJyKCxgiYiIiMih2LSAXb58OVq3bg0vLy94eXkhKioKP/30k7xcCIGEhAQEBwfDzc0N3bt3x3HeComIiIioVrNpARsSEoL58+fjwIEDOHDgAHr06IFBgwbJRerChQuxaNEiLF26FPv370dgYCB69eoFjUZjy2YTERERkQ3ZtIAdMGAA+vbti4iICEREROD//u//UKdOHezZswdCCCxevBgzZ87EkCFD0LJlS6xduxb5+fn4/PPPbdlsIiIiIrIhu7kLgU6nw9dff428vDxERUUhNTUVmZmZiI2NlddRqVTo1q0bUlJS8Mwzz5Qbp7CwEIWFhfJztVoNANDr9dDr9dZ9EQ5Mr9dDCFFljoQQkCQJAkB1symAklhCQK8XkNzDSubrBWDp90oISGE34ovqxTc2V1SC+TIN82U85so0zJdpmC/jVTdX5mxn8wL22LFjiIqKQkFBAerUqYONGzfi7rvvRkpKCgAgICDAYP2AgACkpaVVGC8xMRFz5swpMz87OxsFBQWWbfwdRK/XIycnB0IIKBQVn5jXaDQIDQ9HXh1XZCulau0zr44rQsPDodFokHWlDtBlT8mCK7kAcqsVu1x7bsTPzS15mMnYXFEJ5ss0zJfxmCvTMF+mYb6MV91cmdM11OYFbPPmzXHkyBFcu3YN33zzDUaPHo0dO3bIyyXJsEgqPQNYkRkzZmDq1Knyc7VajdDQUPj5+cHLy8vyL+AOodfrIUkS/Pz8Kj34cnNzkZ6aCo+IDvBzr979VDW5BUhPTYWnpyf8/f2rFasmGZsrKsF8mYb5Mh5zZRrmyzTMl/GqmytXV1eTt7F5AatUKtG0aVMAQMeOHbF//34sWbIEL730EgAgMzMTQUFB8vpZWVllzsreSqVSQaVSlZmvUCh4AFZBkqQq81T6k7+E6neglnDzDxJHe2+MyRXdxHyZhvkyHnNlGubLNMyX8aqTK7O2MXkLKxNCoLCwEOHh4QgMDMTWrVvlZUVFRdixYweio6Nt2EKyCu11IKlTyUN73fLxr18HOnUqeVy3QnwiIiKqMTY9A/vKK68gLi4OoaGh0Gg0+PLLL5GcnIykpCRIkoTJkydj3rx5aNasGZo1a4Z58+bB3d0dw4cPt2WzySr0wJUDN6ctHl4PHDhwc5qIiIgclk0L2IsXL2LkyJHIyMiAt7c3WrdujaSkJPTq1QsA8OKLL+L69esYN24crl69is6dO2PLli3w9PS0ZbOJiIiIyIZsWsB+9NFHlS6XJAkJCQlISEiomQYRERERkd2zuz6wRERERESVYQFLRERERA7F5rfRopqTnZ0tj0x2OyEENBoNcnNzK73PblpaGrRarbWaSERERFQlFrC1RHZ2Np6NH4PCCka7kCQJoeHhSE9NLRlqtQJ516/j4oUMFBcXW76RKl/Lx7yVr5XjExERUY1gAVtLqNVqFGo0mNQ5BiH1yhZyAiVDu3pEdEBlA8TuSz2N+enrodVZuIB19gAeyrZszFt5eADZVoxPRERENYYFbC0TUs8XTfwDy8zXA8hWSvBzF5V2jD5/mUUgERER2RYv4iIiIiIih8ICluyD9jrwS/eSh7WGku3eveTBoWSJiIgcGrsQkJ3QA1k7bk5bPLwe2LHj5jQRERE5LJ6BJSIiIiKHwgKWiIiIiBwKC1giIiIicigsYImIiIjIobCAJSIiIiKHwrsQkP1wcrdufHcrxyciIqIawQKW7IOzBzAsz3rxPTyAPCvGJyIiohrDLgRERERE5FBYwBIRERGRQ2EBS/ZBVwAk9yt56AosH7+gAOjXr+RRYIX4REREVGPYB5bsg9ABF368OW1pOh3w4483p4mIiMhh8QwsERERETkUFrBERERE5FBYwBIRERGRQ2EBS0REREQOhQUsERERETkUFrBERERE5FB4Gy2yD84ewHBhvfgeHoCwYnwiIiKqMTwDS0REREQOhQUsERERETkUFrBkH3QFwM6HSx7WGkr24YdLHhxKloiIyKGxDyzZB6ED0tffmF5j+fg6HbD+Rvw1VohPRERENYZnYImIiIjIobCAJSIiIiKHwgKWiIiIiBwKC1giIiIicigsYImIiIjIobCAJSIiIiKHwttokX1wcgceyb05bWnu7kBu7s1pIiIiclg2PQObmJiITp06wdPTE/7+/njwwQdx6tQpg3Xi4+MhSZLBo0uXLjZqMVmNJAHOHiUPSbJOfA+Pkoc14hMREVGNsWkBu2PHDowfPx579uzB1q1bodVqERsbi7y8PIP1+vTpg4yMDPnx448/2qjFRERERGRrNu1CkJSUZPB89erV8Pf3x8GDB3H//ffL81UqFQIDA2u6eVSTdIXAvmdKpu9ZCTipLBu/sBB45kb8lSsBlYXjExERUY2xq4u4cnJyAAA+Pj4G85OTk+Hv74+IiAg89dRTyMrKskXzyJqEFkhdW/IQWsvH12qBtWtLHlorxCciIqIaYzcXcQkhMHXqVHTt2hUtW7aU58fFxeHhhx9GWFgYUlNTMWvWLPTo0QMHDx6EqpyzaIWFhSgsLJSfq9VqAIBer4der7f+C7FTQghIkgQBoLws6IEKlxnEAaBQKIxat8o2ASVtEgJ6vV7+a0qv1wOWfq8sGF+v18ttpqoxX6ZhvozHXJmG+TIN82W86ubKnO3spoCdMGEC/vjjD+zatctg/rBhw+Tpli1bomPHjggLC8PmzZsxZMiQMnESExMxZ86cMvOzs7NRUFBg+YY7CI1Gg9DwcOTVcUW2suxFTAJAjjMASKjsEie9jxeat4xEgbdHuXFMkVfHFaHh4dBoNMjOViDgxvzs7GwIp7xKtzWVlJ9vGD/P/Ph6vR45OTkQQkChsKsfMewS82Ua5st4zJVpmC/TMF/Gq26uNBqNydvYRQH7/PPPY9OmTfjtt98QEhJS6bpBQUEICwvDmTNnyl0+Y8YMTJ06VX6uVqsRGhoKPz8/eHl5WbTdjiQ3NxfpqanwiOgAP3dRZnnJ3z4SfItEpf1KFFfUOPXncbh26Qk/77JxTKHJLUB6aio8PT3h5+cnz/fz8yu5G4El3VKw+vn5ldyNwEx6vR6SJMHPz49fakZgvkzDfBmPuTIN82Ua5st41c2Vq6urydvYtIAVQuD555/Hxo0bkZycjPDw8Cq3uXz5MtLT0xEUFFTucpVKVW7XAoVCUasPwNKf6iVU3PG5dFllWZJw40CtYj2j2oSbXRtufW8UCgVg6ffKwvFL21ybjylTMF+mYb6Mx1yZhvkyDfNlvOrkyqxtTN7CgsaPH49PP/0Un3/+OTw9PZGZmYnMzExcv34dQMlZw+nTp2P37t04d+4ckpOTMWDAAPj6+mLw4MG2bDoRERER2YhNz8AuX74cANC9e3eD+atXr0Z8fDycnJxw7NgxfPzxx7h27RqCgoIQExODdevWwdPT0wYtJiIiIiJbs3kXgsq4ubnh559/rqHWkE05uQNDsm5OW5q7O1B6+zUOJUtEROTQ7OIiLiJIEuDqV/V61YnvZ8X4REREVGPYK5mIiIiIHAoLWLIPukJg//iSh66w6vVNVVgIjB9f8ii0QnwiIiKqMexCQPZBaIEzy0qm2y0EUPZWaFXJzs6WR167nZSfj8bLSuL/8+yzEFX0g/Xy8jK4Ny0RERHZDxawdEfIzs7Gs/FjUFjBaB4qnQ7f3Jh+4dlxKHRyqjSeytMTK9asYhFLRERkh8wqYFNTU40adICopqjVahRqNJjUOQYh9XzLLJeKCoGUkmGK/6/XYAhlxWd4/716CUv2bodarWYBS0REZIfMKmCbNm2K+++/H2PHjsXQoUPNGgKMyBpC6vmiiX9g2QWFBfJkY78AQMVjloiIyFGZdRHX0aNH0a5dO0ybNg2BgYF45plnsG/fPku3jYiIiIioDLMK2JYtW2LRokX477//sHr1amRmZqJr166IjIzEokWLkJ2dbel2EhEREREBqOZttJydnTF48GB89dVXWLBgAc6ePYvp06cjJCQEo0aNQkZGhqXaSUREREQEoJoF7IEDBzBu3DgEBQVh0aJFmD59Os6ePYtt27bhv//+w6BBgyzVTrrTObkBA1NLHk5ulo/vokRR4jIUJS4DXJSWj09EREQ1xqyLuBYtWoTVq1fj1KlT6Nu3Lz7++GP07dsXCkVJPRweHo6VK1firrvusmhj6Q4mKYA6jawXX6EAfP2tF5+IiIhqjFkF7PLlyzFmzBg88cQTCAws54pvAA0bNsRHH31UrcbRna+wqAhpaWnVjpOWlgatVmuBFhEREZG9M6uAPXPmTJXrKJVKjB492pzwVEtcydPgn9RUJM6YCQ83F4y8JxUA8Mm+cGj1pvVuybt+HRcvZKC4uLj8FbTFcNr4BQBAN/gxwNmlWm0nIiIi2zGrgF29ejXq1KmDhx9+2GD+119/jfz8fBauZJTcggIoJQUm3tMdzYL9cFfYNABAZL0pEMK0oWT3pZ7G/PT10OoqKGB1Ojht2VQyOfARFrBEREQOzKwCdv78+VixYkWZ+f7+/nj66adZwJJJGtTzKRlc4IaSadMGGjh/mbduIyIiqi3MugtBWlpauUPJhoWF4fz589VuFBERERFRRcwqYP39/fHHH3+UmX/06FHUr1+/2o0iIiIiIqqIWQXso48+iokTJ2L79u3Q6XTQ6XTYtm0bJk2ahEcffdTSbSQiIiIikpnVB3bu3LlIS0tDz5494excEkKv12PUqFGYN2+eRRtIRERERHQrswpYpVKJdevW4Y033sDRo0fh5uaGVq1aISwszNLtIyIiIiIyYFYBWyoiIgIRERGWagvVakoUX39HnrY4FyWKE96Rp4mIiMhxmVXA6nQ6rFmzBr/++iuysrKg1+sNlm/bts0ijaPaRAEhQq0YXgHRwIrxiYiIqMaYVcBOmjQJa9asQb9+/dCyZUtIkmTpdhERERERlcusAvbLL7/EV199hb59+1q6PVRrFcPJZQMAQFc8BICFR8rSFsPpxxvx+w7hSFxEREQOzOyLuJo2bWrptlCtpoOTy9clU8WDYPECVqeD0/c34vcexAKWiIjIgZl1H9hp06ZhyZIlEEJYuj1ERERERJUy6wzsrl27sH37dvz000+IjIyEi4vh2awNGzZYpHFERERERLczq4CtW7cuBg8ebOm2EBERERFVyawCdvXq1ZZuBxERERGRUczqAwsAWq0Wv/zyC1auXAmNRgMAuHDhAnJzcy3WOCIiIiKi25l1BjYtLQ19+vTB+fPnUVhYiF69esHT0xMLFy5EQUEBVqxYYel2EhEREREBqMZABh07dsTRo0dRv359ef7gwYPx5JNPWqxxBGRnZ0OtVlc7TlpaGrRarQVaZC0uKC6YL09bPrwLil+ZL08TERGR4zL7LgS///47lErDMeXDwsLw33//WaRhVFK8Phs/BoU3umhUR97167h4IQPFxcUWaJk1OEHorXhvYYUTRDjvXUxERHQnMKuA1ev10Ol0Zeb/+++/8PT0rHajqIRarUahRoNJnWMQUs+3WrH2pZ7G/PT10OrstYAlIiIiMo5ZBWyvXr2wePFifPDBBwAASZKQm5uL2bNnc3hZKwip54sm/oHVinH+craFWmMtxVA4/wgA0Gv7whpDySp+vRG/Z1+OxEVEROTAzCpg33nnHcTExODuu+9GQUEBhg8fjjNnzsDX1xdffPGFpdtItYIOzspPAABF2t6wxlCyzutvxO/emwUsERGRAzOrgA0ODsaRI0fwxRdf4NChQ9Dr9Rg7dixGjBgBNzc3S7eRiIiIiEhmVgELAG5ubhgzZgzGjBljyfYQEREREVXKrAL2448/rnT5qFGjjIqTmJiIDRs24K+//oKbmxuio6OxYMECNG/eXF5HCIE5c+bggw8+wNWrV9G5c2e8//77iIyMNKfpREREROTgzL4P7K2Ki4uRn58PpVIJd3d3owvYHTt2YPz48ejUqRO0Wi1mzpyJ2NhYnDhxAh4eHgCAhQsXYtGiRVizZg0iIiIwd+5c9OrVC6dOneIdD4iIiIhqIbMK2KtXr5aZd+bMGTz33HN44YUXjI6TlJRk8Hz16tXw9/fHwYMHcf/990MIgcWLF2PmzJkYMmQIAGDt2rUICAjA559/jmeeecac5hMRERGRAzO7D+ztmjVrhvnz5+Pxxx/HX3/9ZVaMnJwcAICPjw8AIDU1FZmZmYiNjZXXUalU6NatG1JSUsotYAsLC1FYWCg/Lx3FSq/XQ6/Xm9UuWxFCQJIkCADVbbkAoFAoKoylv7FOVfupKo4l2mROXFPaZcxrlCQJQohyjxm9Xl/hMiqL+TIN82U85so0zJdpmC/jVTdX5mxnsQIWAJycnHDhwgWzthVCYOrUqejatStatmwJAMjMzAQABAQEGKwbEBCAtLS0cuMkJiZizpw5ZeZnZ2ejoKDArLbZikajQWh4OPLquCJbKVUrlt7HC81bRqLA26PcWAJAjjMASKhsT1XFMb9NSqh0Je9boVIJVNoKM9rlrITq5RvxPZSAouL4eXVcERoeDo1Gg6ysrLL70uuRk5MDIQQUCoVJ7ayNmC/TMF/GY65Mw3yZhvkyXnVzpTFjxFGzCthNmzYZPBdCICMjA0uXLsW9995rTkhMmDABf/zxB3bt2lVmmSQZFhulZybLM2PGDEydOlV+rlarERoaCj8/P3h5eZnVNlvJzc1FemoqPCI6wM9dVCuW4ooap/48DtcuPeHnXTZWyd8+EnyLBCo79KqKY36bAgDcemGeabGrbpcCaHIjvrby+JrcAqSnpsLT0xP+/v5lluv1ekiSBD8/P36pGYH5Mg3zZTzmyjTMl2mYL+NVN1eurq4mb2NWAfvggw8aPC9tdI8ePfD222+bHO/555/Hpk2b8NtvvyEkJESeHxhYMvpUZmYmgoKC5PlZWVllzsqWUqlUUKlUZeYrFAqHOwBLf8aWgEqLSqNi4cYBVkms0mWV7cuYOJZsk61ilf6RVNExU7rM0Y4pW2G+TMN8GY+5Mg3zZRrmy3jVyZU525hVwFqqP4gQAs8//zw2btyI5ORkhIeHGywPDw9HYGAgtm7dinbt2gEAioqKsGPHDixYsMAibSB7oYXCeSsAQK/tBQv3bgG0Wih23oh/Xy/A2cLxiYiIqMbY9H/x8ePH4/PPP8d3330HT09Puc+rt7c33NzcIEkSJk+ejHnz5qFZs2Zo1qwZ5s2bB3d3dwwfPtyWTSeL08JZ+REAoEgbA4sfmjotnD+/ET86hgUsERGRAzPrf/Fb+5hWZdGiRRUuW758OQCge/fuBvNXr16N+Ph4AMCLL76I69evY9y4cfJABlu2bOE9YImIiIhqKbMK2MOHD+PQoUPQarXyqFmnT5+Gk5MT2rdvL69X0YVWpYSo+kIdSZKQkJCAhIQEc5pKRERERHcYswrYAQMGwNPTE2vXrkW9evUAlAxu8MQTT+C+++7DtGnTLNpIIiIiIqJSZl1W9/bbbyMxMVEuXgGgXr16mDt3rll3ISAiIiIiMpZZBaxarcbFixfLzM/KyjLrZrRERERERMYyq4AdPHgwnnjiCaxfvx7//vsv/v33X6xfvx5jx47FkCFDLN1GIiIiIiKZWX1gV6xYgenTp+Pxxx9HcXFxSSBnZ4wdOxZvvvmmRRtItYULigtmyNMW5+yC4udnyNNERETkuMwqYN3d3bFs2TK8+eabOHv2LIQQaNq0KTw8PCzdPqo1nCD0HawY3gmitRXjExERUY2p1thoGRkZyMjIQEREBDw8PIy6LRYRERERUXWYVcBevnwZPXv2REREBPr27YuMjAwAwJNPPslbaJGZtFA4bYfCaTsArRXCa6H4fTsUv28HtFaIT0RERDXGrAJ2ypQpcHFxwfnz5+Hu7i7PHzZsGJKSkizWOKpNtHBWvQ9n1fuwSgGr08J5zftwXvM+oGMBS0RE5MjM6gO7ZcsW/PzzzwgJCTGY36xZM6SlpVmkYURERERE5THrDGxeXp7BmddSly5dgkqlqnajiIiIiIgqYlYBe//99+Pjjz+Wn0uSBL1ejzfffBMxMTEWaxwRERER0e3M6kLw5ptvonv37jhw4ACKiorw4osv4vjx47hy5Qp+//13S7eRiIiIiEhm1hnYu+++G3/88Qfuuece9OrVC3l5eRgyZAgOHz6MJk2aWLqNREREREQyk8/AFhcXIzY2FitXrsScOXOs0SYiIiIiogqZXMC6uLjgzz//hCRJ1mgP1VouKC6cKk9bnLMLip+ZKk8TERGR4zKrC8GoUaPw0UcfWbotVKs5QeiiIXTRAJysEN4JomM0RMdowMkK8YmIiKjGmHURV1FRET788ENs3boVHTt2hIeHh8HyRYsWWaRxRERERES3M6mA/eeff9CoUSP8+eefaN++PQDg9OnTBuuwawGZRwfJaS8AQOg6w+JnYXU6SIdvxG/XmWdhiYiIHJhJBWyzZs2QkZGB7du3AygZOvbdd99FQECAVRpHtUkxXFQlZ+6L8j+FxQtYbTFcVt6Iv/RTFrBEREQOzKQ+sEIIg+c//fQT8vLyLNogIiIiIqLKmHURV6nbC1oiIiIiImszqYCVJKlMH1f2eSUiIiKimmRSH1ghBOLj46FSqQAABQUFePbZZ8vchWDDhg2WayERERER0S1MKmBHjx5t8Pzxxx+3aGOIiIiIiKpiUgG7evVqa7WDiIiIiMgoZg1kQGR5ztAWjpenLc7JGdr48fI0EREROS7+T052whl6XYwVwztDf68V4xMREVGNqdZttIiIiIiIahrPwJKd0EFSHAEACH1bWGUo2eM34ke25UhcREREDowFLNmJYri4JgKw4lCy792Iz6FkiYiIHBq7EBARERGRQ2EBS0REREQOhQUsERERETkUFrBERERE5FBYwBIRERGRQ2EBS0REREQOhbfRIjvhDG3RWHna4pycoR0+Vp4mIiIix2XTM7C//fYbBgwYgODgYEiShG+//dZgeXx8PCRJMnh06dLFNo0lK3OGXhsHvTYOVilgnZ2hj4mDPiYOcGYBS0RE5MhsWsDm5eWhTZs2WLp0aYXr9OnTBxkZGfLjxx9/rMEWEhEREZG9sempqLi4OMTFxVW6jkqlQmBgYA21iGxHB0lxEgAg9C1g8ZG49DpIZ27Eb9YCUHAkLiIiIkdl9xdxJScnw9/fHxEREXjqqaeQlZVl6yaRVRTDxTUBLq4JAIqtEL4YLm8lwOWtBKDYCvGJiIioxth1Z8C4uDg8/PDDCAsLQ2pqKmbNmoUePXrg4MGDUKlU5W5TWFiIwsJC+blarQYA6PV66PX6Gmm3pQghIEkSBIDqtlwAUCgUFcbS31inqv1UFccSbTInrintMuY1SpIEIUS5x4xer69wGZXFfJmG+TIec2Ua5ss0zJfxqpsrc7az6wJ22LBh8nTLli3RsWNHhIWFYfPmzRgyZEi52yQmJmLOnDll5mdnZ6OgoMBqbbUGjUaD0PBw5NVxRbZSqlYsvY8XmreMRIG3R7mxBIAcZwCQUNmeqopjbpsuKSWE3Jh/SSlBVNoK09slidviV9L2vDquCA0Ph0ajKfeMv16vR05ODoQQUCjs/kcMm2O+TMN8GY+5Mg3zZRrmy3jVzZVGozF5G7suYG8XFBSEsLAwnDlzpsJ1ZsyYgalTp8rP1Wo1QkND4efnBy8vr5popsXk5uYiPTUVHhEd4OcuqhVLcUWNU38eh2uXnvDzLhur5G8fCb5FotJ+JVXFMbdNvt7e8tHoWyRQUlKbF6vcdhXdnOdbJACp4via3AKkp6bC09MT/v7+ZZbr9XpIkgQ/Pz9+qRmB+TIN82U85so0zJdpmC/jVTdXrq6uJm/jUAXs5cuXkZ6ejqCgoArXUalU5XYvUCgUDncAlv6MLaH6nZUl3DjAKolVuqyyfRkTp7ptMieuKe2qarmEm903KjpmSpc52jFlK8yXaZgv4zFXpmG+TMN8Ga86uTJnG5sWsLm5ufj777/l56mpqThy5Ah8fHzg4+ODhIQEPPTQQwgKCsK5c+fwyiuvwNfXF4MHD7Zhq4mIiIjIlmxawB44cAAxMTHy89Kf/kePHo3ly5fj2LFj+Pjjj3Ht2jUEBQUhJiYG69atg6enp62aTEREREQ2ZtMCtnv37hCi4r6IP//8cw22hmzLCdqikfK05cM7QTt0pDxNREREjsuh+sDSncwFeu0g64V3doG+txXjExERUY1hr2QiIiIicig8A0t2QgdJkQoAEPpwWGUo2bQb8cPCOZQsERGRA2MBawXZ2dnyCGDVkZaWBq1Wa4EWOYJiuLi+DAAoyv8UFi9gi4vhMu9G/KWfAioWsERERI6KBayFZWdn49n4MSg0Y1SJ2+Vdv46LFzJQXFxsgZYRERER3RlYwFqYWq1GoUaDSZ1jEFLPt1qx9qWexvz09dDqWMASERERlWIBayUh9XzRxD+wWjHOX862UGuIiIiI7hy8CwERERERORQWsERERETkUFjAEhEREZFDYR9YshNO0BU/LE9bPrwTdAMelqeJiIjIcbGAJTvhAl3xMOuFd3aBbqAV4xMREVGNYRcCIiIiInIoPANLdkIPSfoPACBEA1j8byu9HlLGjfhBDQAF/3YjIiJyVCxgyU4UwcVtSslU/qcAXC0bvrgILgk34i/9FFBZOD4RERHVGJ6GIiIiIiKHwgKWiIiIiBwKC1giIiIicigsYImIiIjIobCAJSIiIiKHwgKWiIiIiBwKb6NFdsIJuuKB8rTlwztBFztQniYiIiLHxQKW7IQLdMWjrBfe2QW6h60Yn4iIiGoMC1iichQWFSEtLa3cZUIIaDQa5ObmQpKkKmN5eXnBz8+v2m3Kzs6GWq2udhwAKCoqglKptEgsS70+IiIiY7GAJTuhB6RLJZPCF9YYShZXbsT38a10KNkreRr8k5qKxBkzoVKpyiyXJAmh4eFIT02FEKLKXas8PbFizapqFXnZ2dl4Nn4MCjUas2OUKiwqwrn0dDQJC4Ozc/W/Aizx+oiIiEzBApbsRBGUbuNKpqw0lKxyxo34VQwlm1tQAKWkwMR7uqNpUEiZ5QJAXh1XeER0QFXnX/+9eglL9m6HWq2uVoGnVqtRqNFgUucYhNTzNTsOAOxLPY3559IwvsN95b4+U1jq9REREZmCBSxRBRrU80ET/8Ay8/UAspUS/NxFjd/GI6Seb7ltMsX5y9kAKn59RERE9o630SIiIiIih8ICloiIiIgcCgtYIiIiInIoLGCJiIiIyKGwgCUiIiIih8K7EJCdcIKuuLc8bXEKJ+i695aniYiIyHGxgCU74QJd8VNWDO8C3Qgrxq9EZaN6GSstLQ1ardZCLSIiInJsLGCJrKiqUb2MlXf9Oi5eyEBxcbEFW0dEROSYWMCSnRAA1DemvYAqx7gyNbwAcm/Er+MFSBaOX4GqRvUy1r7U05ifvh5aHQtYIiIiFrBkJwqhdB8LwEpDyRYVQjn1RvwqhpK1huqOelU6ehYRERHxLgRERERE5GBYwBIRERGRQ7FpAfvbb79hwIABCA4OhiRJ+Pbbbw2WCyGQkJCA4OBguLm5oXv37jh+/LhtGktEREREdsGmBWxeXh7atGmDpUuXlrt84cKFWLRoEZYuXYr9+/cjMDAQvXr1gkajqeGWEhEREZG9sOlFXHFxcYiLiyt3mRACixcvxsyZMzFkyBAAwNq1axEQEIDPP/8czzzzTE02lYiIiIjshN3ehSA1NRWZmZmIjY2V56lUKnTr1g0pKSkVFrCFhYUoLCyUn6vVJbdO0uv10Ov11m00SgpvSZIgAFR3bwKAQqGokVj6G+tUtZ+aaJM5cU1pV3Vfo7G5MrVdNRHHGrEkSYIQosLPl16vr3Q5GWK+jMdcmYb5Mg3zZbzq5sqc7ey2gM3MzAQABAQEGMwPCAiodFSjxMREzJkzp8z87OxsFBQUWLaR5dBoNAgND0deHVdkK6t3r1G9jxeat4xEgbeH1WMJADnOACBVegdW67XJGT767gCAK0pnmHof2CrbJTnDp+uN+K7OgEvF8S2VK6PaZaSaPBZMkVfHFaHh4dBoNMjKyip/f3o9cnJyIISAQsHrRqvCfBmPuTIN82Ua5st41c2VOV1D7baALSXddsP50jOcFZkxYwamTp0qP1er1QgNDYWfnx+8vLys1s5Subm5SE9NhUdEB/i5i2rFUlxR49Sfx+HapSf8vK0bq+RvHwm+RaLSjtHWa1MAgAkAAD8AJWWiubHK29YZGH0jvgBQVHF8S+XKuHYZpyaPBVNocguQnpoKT09P+Pv7l7uOXq+HJEnw8/PjfwJGYL6Mx1yZhvkyDfNlvOrmytXV9Huz220BGxhYctP3zMxMBAUFyfOzsrLKnJW9lUqlKnfIToVCUSMHYOnPqRKqf4WchBsHRQ3FKl1W2b5quk32GsuYXFmyXfacq9I/Kiv7fJUu538CxmG+jMdcmYb5Mg3zZbzq5MqsbUzeooaEh4cjMDAQW7dulecVFRVhx44diI6OtmHLyDoEgIIbj+qdFSw/vAAKC0oewgrxiYiIqMbY9Axsbm4u/v77b/l5amoqjhw5Ah8fHzRs2BCTJ0/GvHnz0KxZMzRr1gzz5s2Du7s7hg8fbsNWk3UUQun+OAArDiU74UZ8GwwlS0RERJZj0wL2wIEDiImJkZ+X9l0dPXo01qxZgxdffBHXr1/HuHHjcPXqVXTu3BlbtmyBp6enrZpMRERERDZm0wK2e/fuEJX8nCtJEhISEpCQkFBzjSIiIiIiu2a3fWCJiIiIiMrDApaIiIiIHAoLWCIiIiJyKCxgiYiIiMih2O1ABlTbKKDXdpGnLR9eAX2HLvI0EREROS4WsGQnlNAWTbdeeBcltM9aMT4RERHVGJ6KIiIiIiKHwgKWiIiIiBwKC1iyEwVQug+F0n0ogALLhy8sgPKpoVA+NRQotEJ8IiIiqjEsYImIiIjIobCAJSIiIiKHwgKWiIiIiBwKb6NFRNVSWFSEtLS0CpcLIaDRaJCbmwtJkiqN5eXlBT8/P0s3kYiI7jAsYInIbFfyNPgnNRWJM2ZCpVKVu44kSQgND0d6aiqEEJXGU3l6YsWaVSxiiYioUixgichsuQUFUEoKTLynO5oGhZS7jgCQV8cVHhEdUNn513+vXsKSvduhVqtZwBIRUaVYwJKdUECvay9PWz68AvpW7eVpsqwG9XzQxD+w3GV6ANlKCX7ugp3uiYjIIljAkp1QQlv4ivXCuyihnWjF+ERERFRjeEKEiIiIiBwKC1giIiIicigsYMlOFMDFbQRc3EbAWkPJuowfAZfxIziULBERkYNjH1iyG5JUaN34RdaNT0RERDWDZ2CJiIiIyKGwgCUiIiIih8ICloiIiIgcCgtYIiIiInIoLGCJiIiIyKHwLgRkJyTodXfL05YPL0Efcbc8TURERI6LBSzZCRW0ha9bL7xSBe0LVoxPRERENYZdCIiIiIjIobCAJSIiIiKHwgKW7EQBXNzGwMVtDKw2lOyUMXCZMoZDyRIRETk49oEluyFJauvGz7VufCIiIqoZPANLRERERA6FBSwRERERORQWsERERETkUFjAEhEREZFDYQFLRERERA6FdyEgOyFBr2siT1s+vAR9WBN5moiIiBwXC1iyEypoCxdYL7xSBe2rVoxPRERENcauuxAkJCRAkiSDR2BgoK2bRUREREQ2ZPdnYCMjI/HLL7/Iz52cnGzYGiIiIiKyNbsvYJ2dnXnWtVYohIvrZABAccFiACoLhy+Ey+wb8ecsBlQWjk9EREQ1xu4L2DNnziA4OBgqlQqdO3fGvHnz0Lhx4wrXLywsRGFhofxcrS4ZPlSv10Ov11u9vUIISJIEAaC6exMAFApFjcTS31inqv1Yr00CkiL7RltENWOVv4Z02bj4lsqVce0yTk0eC5aOZcqxJUkShBDV/qxeunRJ/uxXl5eXF3x9fS0Syxh6vd4iOagNmCvTMF+mYb6MV91cmbOdXRewnTt3xscff4yIiAhcvHgRc+fORXR0NI4fP4769euXu01iYiLmzJlTZn52djYKCgqs3WRoNBqEhocjr44rspXVu9pd7+OF5i0jUeDtYfVYAkCOMwBIld4DwFptuqSUEHJj/iWlBGHinQiqapckbotfSdstlStjYhmrJo8FS8cyNl95dVwRGh4OjUaDrKwss9uUk5ODZYvfRfH162bHuJWLmxvGTZ4Ib29vi8Sril6vR05ODoQQUCjs+jIFm2OuTMN8mYb5Ml51c6XRaEzexq4L2Li4OHm6VatWiIqKQpMmTbB27VpMnTq13G1mzJhhsEytViM0NBR+fn7w8vKyeptzc3ORnpoKj4gO8HM3/UzirRRX1Dj153G4dukJP2/rxir520eCb5Go9Mo+a7XJ19tbPhp9iwRg4lnYKttVdHOeb5EApIrjWypXRrXLSDV5LFg6lrH50uQWID01FZ6envD39ze7Tbm5ufjnxAk8f093hNSr3pnTf69ewnv7kuHk5FStNplCr9dDkiT4+fnxP80qMFemYb5Mw3wZr7q5cnV1NXkbuy5gb+fh4YFWrVrhzJkzFa6jUqmgKqd/o0KhqJEDsPQnUAnVv8WDhBsHRQ3FKl1W2b5qok3mxDWlXVUtt1SuTG1XTcSxVSxjj63SLjjV+ayWfgZD6/miiX/1+s9bqk0m7/fG/vifZtWYK9MwX6ZhvoxXnVyZtY3JW9hQYWEhTp48iaCgIFs3hYiIiIhsxK4L2OnTp2PHjh1ITU3F3r17MXToUKjVaowePdrWTSMiIiIiG7HrLgT//vsvHnvsMVy6dAl+fn7o0qUL9uzZg7CwMFs3jSxOgl4fIk9bJX6QNeMTERFRTbHrAvbLL7+0dROoxqigLVhsxfAqaF+3YnwiIiKqMXbdhYCIiIiI6HYsYImIiIjIobCAJTtRCGfXyXB2nQygsKqVzQhfCOfXJsP5tclAoRXiExERUY2x6z6wVJsIKBT/ytNWiZ9hzfhERERUU3gGloiIiIgcCgtYIiIiInIoLGCJiIiIyKGwgCUiIiIih8ICloiIiIgcCu9CQHZCgtD7ydNWiV/fmvGJiIioprCAJTuhQnHBciuGV6F4vhXjExERUY1hFwIiIiIicig8A0tEdqOwqAhpaWnVipGWlgatVmuhFllWdnY21Gp1pesIIaDRaJCbmwtJqri7i5eXF/z8/CpcbgvGvD5j2ePrIyL7wQKW7EQhnFWvAQC0ha8DUFk2fFEhnBfeiP/i64DSwvGp2q7kafBPaioSZ8yESmX++5N3/TouXshAcXGxBVtXfdnZ2Xg2fgwKNZpK15MkCaHh4UhPTYUQFY8ap/L0xIo1q+ymyDP29RnL3l4fEdkXFrBkJwQUTmflacuHF1CknZWnyf7kFhRAKSkw8Z7uaBoUYnacfamnMT99PbQ6+ypg1Wo1CjUaTOocg5B6vhWuJwDk1XGFR0SHCi83/PfqJSzZux1qtdpuCjxjX58x7PH1EZF9YQFLRHalQT0fNPEPNHv785ezLdgaywup51vp69MDyFZK8HMXDnmRQlWvj4jIEhzx+5GIiIiIajEWsERERETkUFjAEhEREZFDYQFLRERERA6FF3GR3RDCy7rx61g3PhEREdUMFrBkJ1xRfH2V9cKrXFH8jhXjExERUY1hAUtERHbHmFHZHHnUMiKqHhawRERkV4wdlc1RRy0joupjAUt2ohDOqv8DAGgLZ8IqQ8kuuRF/0kwOJUtkx4wdlc1RRy0joupjAUt2QkDhdEKetnx4AcXpE/I0Edm/qkZlc/RRy4jIfPzMExEREZFDYQFLRERERA6FBSwRERERORQWsERERETkUFjAEhEREZFD4V0IyG4IYd1bWwneOouIiOiOwAKW7IQriq9/Zr3wKlcUv2/F+HTHMmZEKGOkpaVBq9VaoEV0J8jOzoZarbZILI40RqVq03HFApaIqALGjghljLzr13HxQgaKi4st1DpyVNnZ2Xg2fgwKNRqLxONIYwTUvuOKBSwRUQWMHRHKGPtST2N++npodSxgazu1Wo1CjQaTOscgpJ5vtWJxpDEqVduOKxawZCeK4Kx6CwCgLZwOQGnZ8MVFcF5+I/5z0wEXC8enO1pVI0IZ4/zlbAu1hu4UIfV8q31cEd2uthxXLGDJTuihcDokT1s+vB6KY4fkaSIiInJcvI0WERERETkUhyhgly1bhvDwcLi6uqJDhw7YuXOnrZtERERERDZi9wXsunXrMHnyZMycOROHDx/Gfffdh7i4OJw/f97WTSMiIiIiG7D7AnbRokUYO3YsnnzySbRo0QKLFy9GaGgoli9fbuumEREREZEN2HUBW1RUhIMHDyI2NtZgfmxsLFJSUmzUKiIiIiKyJbu+C8GlS5eg0+kQEBBgMD8gIACZmZnlblNYWIjCwkL5eU5ODgDg2rVr0NfA1edqtRpanQ5/Zf4LTcH1asVKvXwRAsDprAxoIVk1lgCQV8cVF3ILKt2Ttdqkk4rRPLRk/qn0NAhh2m2uqmqXVFyE5jemT/2bBlHJbbQslStjYhmrJo8FS8eq6WPLkXMFGJev/65dxvWCAhw/ftxio+5UV3p6OgoLi2r0u4+5Mv31aTQaZGRkVGuftYkj5cvSx5VWp4Narca1a9eqXF+v10OtVkOpVEKhMP3caOmxK4QwfiNhx/777z8BQKSkpBjMnzt3rmjevHm528yePVug5HuNDz744IMPPvjggw8HeaSnpxtdI9r1GVhfX184OTmVOdualZVV5qxsqRkzZmDq1Knyc71ejytXrqB+/fqQpOqdQbmTqdVqhIaGIj09HV5eXrZujl1jrkzDfJmG+TIec2Ua5ss0zJfxqpsrIQQ0Gg2Cg4ON3sauC1ilUokOHTpg69atGDx4sDx/69atGDRoULnbqFSqMmOW161b15rNvKN4eXnxg2ok5so0zJdpmC/jMVemYb5Mw3wZrzq58vb2Nml9uy5gAWDq1KkYOXIkOnbsiKioKHzwwQc4f/48nn32WVs3jYiIiIhswO4L2GHDhuHy5ct4/fXXkZGRgZYtW+LHH39EWFiYrZtGRERERDZg9wUsAIwbNw7jxo2zdTPuaCqVCrNnzy7T/YLKYq5Mw3yZhvkyHnNlGubLNMyX8WyRK0kIU+5ZQERERERkW3Y9kAERERER0e1YwBIRERGRQ2EBS0REREQOhQXsHSoxMRGdOnWCp6cn/P398eCDD+LUqVMG68THx0OSJINHly5dDNYpLCzE888/D19fX3h4eGDgwIH4999/a/Kl1IiEhIQyuQgMDJSXCyGQkJCA4OBguLm5oXv37jh+/LhBjNqSKwBo1KhRmXxJkoTx48cDqN3H1m+//YYBAwYgODgYkiTh22+/NVhuqWPp6tWrGDlyJLy9veHt7Y2RI0caNeSjvaksX8XFxXjppZfQqlUreHh4IDg4GKNGjcKFCxcMYnTv3r3M8fboo48arFMb8gVY7rN3J+SrqlyV9x0mSRLefPNNeZ3adGwZUzfY0/cXC9g71I4dOzB+/Hjs2bMHW7duhVarRWxsLPLy8gzW69OnDzIyMuTHjz/+aLB88uTJ2LhxI7788kvs2rULubm56N+/P3Q6XU2+nBoRGRlpkItjx47JyxYuXIhFixZh6dKl2L9/PwIDA9GrVy9oNBp5ndqUq/379xvkauvWrQCAhx9+WF6nth5beXl5aNOmDZYuXVrucksdS8OHD8eRI0eQlJSEpKQkHDlyBCNHjrT667O0yvKVn5+PQ4cOYdasWTh06BA2bNiA06dPY+DAgWXWfeqppwyOt5UrVxosrw35KmWJz96dkK+qcnVrjjIyMrBq1SpIkoSHHnrIYL3acmwZUzfY1feX0YPOkkPLysoSAMSOHTvkeaNHjxaDBg2qcJtr164JFxcX8eWXX8rz/vvvP6FQKERSUpI1m1vjZs+eLdq0aVPuMr1eLwIDA8X8+fPleQUFBcLb21usWLFCCFG7clWeSZMmiSZNmgi9Xi+E4LFVCoDYuHGj/NxSx9KJEycEALFnzx55nd27dwsA4q+//rLyq7Ke2/NVnn379gkAIi0tTZ7XrVs3MWnSpAq3qU35ssRn707MlzHH1qBBg0SPHj0M5tXWY0uIsnWDvX1/8QxsLZGTkwMA8PHxMZifnJwMf39/RERE4KmnnkJWVpa87ODBgyguLkZsbKw8Lzg4GC1btkRKSkrNNLwGnTlzBsHBwQgPD8ejjz6Kf/75BwCQmpqKzMxMgzyoVCp069ZNzkNty9WtioqK8Omnn2LMmDGQJEmez2OrLEsdS7t374a3tzc6d+4sr9OlSxd4e3vf0fkDSr7LJEkqM0T4Z599Bl9fX0RGRmL69OkGZ4RqW76q+9mrbfkCgIsXL2Lz5s0YO3ZsmWW19di6vW6wt+8vhxjIgKpHCIGpU6eia9euaNmypTw/Li4ODz/8MMLCwpCamopZs2ahR48eOHjwIFQqFTIzM6FUKlGvXj2DeAEBAcjMzKzpl2FVnTt3xscff4yIiAhcvHgRc+fORXR0NI4fPy6/1oCAAINtAgICkJaWBgC1Kle3+/bbb3Ht2jXEx8fL83hslc9Sx1JmZib8/f3LxPf397+j81dQUICXX34Zw4cPNxhvfcSIEQgPD0dgYCD+/PNPzJgxA0ePHpW7ttSmfFnis1eb8lVq7dq18PT0xJAhQwzm19Zjq7y6wd6+v1jA1gITJkzAH3/8gV27dhnMHzZsmDzdsmVLdOzYEWFhYdi8eXOZD/GthBAGZ9ruBHFxcfJ0q1atEBUVhSZNmmDt2rXyBRC3v2Zj8nAn5up2H330EeLi4hAcHCzP47FVOUscS+Wtfyfnr7i4GI8++ij0ej2WLVtmsOypp56Sp1u2bIlmzZqhY8eOOHToENq3bw+g9uTLUp+92pKvUqtWrcKIESPg6upqML+2HlsV1Q2A/Xx/sQvBHe7555/Hpk2bsH37doSEhFS6blBQEMLCwnDmzBkAQGBgIIqKinD16lWD9bKyssr8BXan8fDwQKtWrXDmzBn5bgS3/2V4ax5qa67S0tLwyy+/4Mknn6x0PR5bJSx1LAUGBuLixYtl4mdnZ9+R+SsuLsYjjzyC1NRUbN261eDsa3nat28PFxcXg+OtNuXrVuZ89mpbvnbu3IlTp05V+T0G1I5jq6K6wd6+v1jA3qGEEJgwYQI2bNiAbdu2ITw8vMptLl++jPT0dAQFBQEAOnToABcXF/mnEqDkqs0///wT0dHRVmu7PSgsLMTJkycRFBQk/3x0ax6KioqwY8cOOQ+1NVerV6+Gv78/+vXrV+l6PLZKWOpYioqKQk5ODvbt2yevs3fvXuTk5Nxx+SstXs+cOYNffvkF9evXr3Kb48ePo7i4WD7ealO+bmfOZ6+25eujjz5Chw4d0KZNmyrXvZOPrarqBrv7/jLhgjRyIM8995zw9vYWycnJIiMjQ37k5+cLIYTQaDRi2rRpIiUlRaSmport27eLqKgo0aBBA6FWq+U4zz77rAgJCRG//PKLOHTokOjRo4do06aN0Gq1tnppVjFt2jSRnJws/vnnH7Fnzx7Rv39/4enpKc6dOyeEEGL+/PnC29tbbNiwQRw7dkw89thjIigoqFbmqpROpxMNGzYUL730ksH82n5saTQacfjwYXH48GEBQCxatEgcPnxYvmreUsdSnz59ROvWrcXu3bvF7t27RatWrUT//v1r/PVWV2X5Ki4uFgMHDhQhISHiyJEjBt9lhYWFQggh/v77bzFnzhyxf/9+kZqaKjZv3izuuusu0a5du1qXL0t+9u6EfFX1WRRCiJycHOHu7i6WL19eZvvadmxVVTcIYV/fXyxg71AAyn2sXr1aCCFEfn6+iI2NFX5+fsLFxUU0bNhQjB49Wpw/f94gzvXr18WECROEj4+PcHNzE/379y+zzp1g2LBhIigoSLi4uIjg4GAxZMgQcfz4cXm5Xq8Xs2fPFoGBgUKlUon7779fHDt2zCBGbclVqZ9//lkAEKdOnTKYX9uPre3bt5f72Rs9erQQwnLH0uXLl8WIESOEp6en8PT0FCNGjBBXr16toVdpOZXlKzU1tcLvsu3btwshhDh//ry4//77hY+Pj1AqlaJJkyZi4sSJ4vLlywb7qQ35suRn707IV1WfRSGEWLlypXBzcxPXrl0rs31tO7aqqhuEsK/vL+lGo4mIiIiIHAL7wBIRERGRQ2EBS0REREQOhQUsERERETkUFrBERERE5FBYwBIRERGRQ2EBS0REREQOhQUsERERETkUFrBERERE5FBYwBJRjTp37hwkScKRI0ds3RTZX3/9hS5dusDV1RVt27a16r4SEhIM9hEfH48HH3zQqvusbW7PsaPEJiLjsYAlqmXi4+MhSRLmz59vMP/bb7+FJEk2apVtzZ49Gx4eHjh16hR+/fXXCtfLzMzE888/j8aNG0OlUiE0NBQDBgyodJs7SemxI0kSXFxc0LhxY0yfPh15eXnVimuvRaEkSfj2228N5k2fPr3WvN9E9owFLFEt5OrqigULFuDq1au2borFFBUVmb3t2bNn0bVrV4SFhaF+/frlrnPu3Dl06NAB27Ztw8KFC3Hs2DEkJSUhJiYG48ePN3vf9qiyXPbp0wcZGRn4559/MHfuXCxbtgzTp083az9CCGi1WnObafV45alTp06FxwgR1RwWsES10AMPPIDAwEAkJiZWuE55Z8UWL16MRo0ayc9Lf/6eN28eAgICULduXcyZMwdarRYvvPACfHx8EBISglWrVpWJ/9dffyE6Ohqurq6IjIxEcnKywfITJ06gb9++qFOnDgICAjBy5EhcunRJXt69e3dMmDABU6dOha+vL3r16lXu69Dr9Xj99dcREhIClUqFtm3bIikpSV4uSRIOHjyI119/HZIkISEhodw448aNgyRJ2LdvH4YOHYqIiAhERkZi6tSp2LNnj7xeTk4Onn76afj7+8PLyws9evTA0aNHK8hyWevXr0erVq3g5uaG+vXr44EHHqjwDGdycjIkScLmzZvRpk0buLq6onPnzjh27JjBeikpKbj//vvh5uaG0NBQTJw40SBmo0aNMHfuXMTHx8Pb2xtPPfVUhe1TqVQIDAxEaGgohg8fjhEjRshnKYUQWLhwIRo3bgw3Nze0adMG69evL9Pen3/+GR07doRKpcInn3yCOXPm4OjRo/LZ3TVr1pTb1eTatWuQJEk+VsqLt3PnTnn9lStXIjQ0FO7u7nj44Ydx7do1edn+/fvRq1cv+Pr6wtvbG926dcOhQ4cMcgIAgwcPhiRJ8vPbPxdVHV+lr2PDhg2IiYmBu7s72rRpg927d1eYYyKqGgtYolrIyckJ8+bNw3vvvYd///23WrG2bduGCxcu4LfffsOiRYuQkJCA/v37o169eti7dy+effZZPPvss0hPTzfY7oUXXsC0adNw+PBhREdHY+DAgbh8+TIAICMjA926dUPbtm1x4MABJCUl4eLFi3jkkUcMYqxduxbOzs74/fffsXLlynLbt2TJErz99tt466238Mcff6B3794YOHAgzpw5I+8rMjIS06ZNQ0ZGRrlnE69cuYKkpCSMHz8eHh4eZZbXrVsXQEkB169fP2RmZuLHH3/EwYMH0b59e/Ts2RNXrlypMpcZGRl47LHHMGbMGJw8eRLJyckYMmQIhBCVbvfCCy/grbfewv79++Hv74+BAweiuLgYAHDs2DH07t0bQ4YMwR9//IF169Zh165dmDBhgkGMN998Ey1btsTBgwcxa9asKttays3NTd7Xq6++itWrV2P58uU4fvw4pkyZgscffxw7duww2ObFF19EYmIiTp48idjYWEybNg2RkZHIyMhARkYGhg0bZvT+b4/XunVrAMDff/+Nr776Ct9//z2SkpJw5MgRgzPlGo0Go0ePxs6dO7Fnzx40a9YMffv2hUajAVBS4ALA6tWrkZGRIT+/XVXHV6mZM2di+vTpOHLkCCIiIvDYY49Z/Wwx0R1NEFGtMnr0aDFo0CAhhBBdunQRY8aMEUIIsXHjRnHrV8Ls2bNFmzZtDLZ95513RFhYmEGssLAwodPp5HnNmzcX9913n/xcq9UKDw8P8cUXXwghhEhNTRUAxPz58+V1iouLRUhIiFiwYIEQQohZs2aJ2NhYg32np6cLAOLUqVNCCCG6desm2rZtW+XrDQ4OFv/3f/9nMK9Tp05i3Lhx8vM2bdqI2bNnVxhj7969AoDYsGFDpfv69ddfhZeXlygoKDCY36RJE7Fy5UohRNm83vp+HDx4UAAQ586dq/J1CSHE9u3bBQDx5ZdfyvMuX74s3NzcxLp164QQQowcOVI8/fTTBtvt3LlTKBQKcf36dSGEEGFhYeLBBx+scn+3tlWIkrzUr19fPPLIIyI3N1e4urqKlJQUg23Gjh0rHnvsMYP2fvvttwbrlHeslR4nhw8fluddvXpVABDbt2+vMp6Tk5NIT0+X5/30009CoVCIjIyMcl+bVqsVnp6e4vvvv5fnARAbN26stK1VHV+lr+PDDz+Ulx8/flwAECdPniy3LURUNWebVM1EZBcWLFiAHj16YNq0aWbHiIyMhEJx88ecgIAAtGzZUn7u5OSE+vXrIysry2C7qKgoedrZ2RkdO3bEyZMnAQAHDx7E9u3bUadOnTL7O3v2LCIiIgAAHTt2rLRtarUaFy5cwL333msw/9577zXpZ31x4wxoVRe5HTx4ELm5uWX6SF6/fh1nz56tcj9t2rRBz5490apVK/Tu3RuxsbEYOnQo6tWrV+l2t+bSx8cHzZs3N8jl33//jc8++8zg9ej1eqSmpqJFixYAqs5lqR9++AF16tSBVqtFcXExBg0ahPfeew8nTpxAQUFBma4cRUVFaNeuncE8Y/dlrPLiNWzYECEhIfLzqKgo6PV6nDp1CoGBgcjKysJrr72Gbdu24eLFi9DpdMjPz8f58+eN3q8px1fpmWEACAoKAgBkZWXhrrvuMnp/RHQTC1iiWuz+++9H79698corryA+Pt5gmUKhKPPTdelPxbdycXExeF56hfrt8/R6fZXtKS0Q9Xo9BgwYgAULFpRZp/Q/fwDl/pxfWdxSQgiT7rjQrFkzSJKEkydPVnrLK71ej6CgoDL9eYGb3Qwq4+TkhK1btyIlJQVbtmzBe++9h5kzZ2Lv3r0IDw83ur2AYS6feeYZTJw4scw6DRs2lKeNzWVMTAyWL18OFxcXBAcHy+91amoqAGDz5s1o0KCBwTYqlcrguTH7Kv2j6NZjsLzjz9h4pfko/Tc+Ph7Z2dlYvHgxwsLCoFKpEBUVZdbFgMYcX7d+Jm59b4jIPOwDS1TLzZ8/H99//z1SUlIM5vv5+SEzM9OggLDkvVtvvfBJq9Xi4MGD8tmo9u3b4/jx42jUqBGaNm1q8DC20AIALy8vBAcHY9euXQbzU1JS5DOPxvDx8UHv3r3x/vvvl3tBVenFQe3bt0dmZiacnZ3LtNvX19eofUmShHvvvRdz5szB4cOHoVQqsXHjxkq3uTWXV69exenTp8vk8vb2NG3aFEql0sgM3OTh4YGmTZsiLCzMoCi7++67oVKpcP78+TL7CQ0NrTSmUqmETqczmOfn5wegpF9wKVOOv/Pnz+PChQvy8927d0OhUMhn73fu3ImJEyeib9++iIyMhEqlMrhIECgpOm9v160sdXwRkelYwBLVcq1atcKIESPw3nvvGczv3r07srOzsXDhQpw9exbvv/8+fvrpJ4vt9/3338fGjRvx119/Yfz48bh69SrGjBkDABg/fjyuXLmCxx57DPv27cM///yDLVu2YMyYMZUWFOV54YUXsGDBAqxbtw6nTp3Cyy+/jCNHjmDSpEkmxVm2bBl0Oh3uuecefPPNNzhz5gxOnjyJd999V/4J/4EHHkBUVBQefPBB/Pzzzzh37hxSUlLw6quv4sCBA1XuY+/evZg3bx4OHDiA8+fPY8OGDcjOzq6yGHr99dfx66+/4s8//0R8fDx8fX3lM8UvvfQSdu/ejfHjx+PIkSM4c+YMNm3ahOeff96k118VT09PTJ8+HVOmTMHatWtx9uxZHD58GO+//z7Wrl1b6baNGjVCamoqjhw5gkuXLqGwsBBubm7o0qUL5s+fjxMnTuC3337Dq6++anR7XF1dMXr0aBw9elQuVh955BEEBgYCAJo2bYpPPvkEJ0+exN69ezFixAi4ubmVadevv/6KzMzMCm85Z6nji4hMwwKWiPDGG2+U6S7QokULLFu2DO+//z7atGmDffv2mX2/z/LMnz8fCxYsQJs2bbBz505899138lnK4OBg/P7779DpdOjduzdatmyJSZMmwdvb26C/rTEmTpyIadOmYdq0aWjVqhWSkpKwadMmNGvWzKQ44eHhOHToEGJiYjBt2jS0bNkSvXr1wq+//orly5cDKDl7+uOPP+L+++/HmDFjEBERgUcffRTnzp1DQEBAlfvw8vLCb7/9hr59+yIiIgKvvvoq3n77bcTFxVW63fz58zFp0iR06NABGRkZ2LRpk3x2tXXr1tixYwfOnDmD++67D+3atcOsWbMMumJYyhtvvIHXXnsNiYmJaNGiBXr37o3vv/++yu4PDz30EPr06YOYmBj4+fnhiy++AACsWrUKxcXF6NixIyZNmoS5c+ca3ZamTZtiyJAh6Nu3L2JjY9GyZUssW7ZMXr5q1SpcvXoV7dq1w8iRIzFx4kT4+/sbxHj77bexdetWhIaGlunHW8pSxxcRmUYSt/+vRUREDiE5ORkxMTG4evWqUX1siYjuFDwDS0REREQOhQUsERERETkUdiEgIiIiIofCM7BERERE5FBYwBIRERGRQ2EBS0REREQOhQUsERERETkUFrBERERE5FBYwBIRERGRQ2EBS0REREQOhQUsERERETkUFrBERERE5FD+H/4qpXwlosRZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   - Mean cells per perturbation: 440.6\n",
      "   - Median cells per perturbation: 362.0\n",
      "   - Total perturbations: 224\n",
      "\n",
      "2. Analyzing E-distance distribution...\n",
      "   - Mean E-distance: 38.914\n",
      "   - Median E-distance: 28.915\n",
      "   - Min E-distance: 0.285\n",
      "   - Max E-distance: 203.848\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3. Generating UMAP plot for specific perturbation...\n",
      "   - Selected perturbation: AHR | lymphoblasts\n",
      "   - Cell count: 558\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfs/public/lichen/software/miniconda3/envs/scGPT_2/lib/python3.10/site-packages/anndata/_core/anndata.py:1818: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"obs\")\n",
      "/nfs/public/lichen/software/miniconda3/envs/scGPT_2/lib/python3.10/site-packages/anndata/_core/anndata.py:1818: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"obs\")\n",
      "/nfs/public/lichen/software/miniconda3/envs/scGPT_2/lib/python3.10/site-packages/scanpy/preprocessing/_simple.py:842: UserWarning: Received a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\n",
      "/nfs/public/lichen/software/miniconda3/envs/scGPT_2/lib/python3.10/site-packages/anndata/_core/anndata.py:1818: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"obs\")\n",
      "/nfs/public/lichen/software/miniconda3/envs/scGPT_2/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:391: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "DETAILED PERTURBATION ANALYSIS\n",
      "============================================================\n",
      "\n",
      "Top 10 perturbations by cell count:\n",
      "  KLF1 | lymphoblasts: 1960 cells\n",
      "  BAK1 | lymphoblasts: 1457 cells\n",
      "  CEBPE | lymphoblasts: 1233 cells\n",
      "  CEBPE_RUNX1T1 | lymphoblasts: 1219 cells\n",
      "  UBASH3B | lymphoblasts: 1202 cells\n",
      "  ETS2 | lymphoblasts: 1201 cells\n",
      "  TBX3_TBX2 | lymphoblasts: 1167 cells\n",
      "  OSR2 | lymphoblasts: 1003 cells\n",
      "  SLC4A1 | lymphoblasts: 1000 cells\n",
      "  SET | lymphoblasts: 986 cells\n",
      "\n",
      "Perturbation type distribution:\n",
      "  control: 11855 cells\n",
      "  KLF1: 1960 cells\n",
      "  BAK1: 1457 cells\n",
      "  CEBPE: 1233 cells\n",
      "  CEBPE_RUNX1T1: 1219 cells\n",
      "  UBASH3B: 1202 cells\n",
      "  ETS2: 1201 cells\n",
      "  TBX3_TBX2: 1167 cells\n",
      "  OSR2: 1003 cells\n",
      "  SLC4A1: 1000 cells\n",
      "  SET: 986 cells\n",
      "  ELMSAN1: 937 cells\n",
      "  ETS2_CNN1: 905 cells\n",
      "  MAP2K6: 878 cells\n",
      "  FOXF1: 874 cells\n",
      "  C19orf26: 872 cells\n",
      "  FOXA1: 851 cells\n",
      "  UBASH3A: 819 cells\n",
      "  UBASH3B_OSR2: 796 cells\n",
      "  DUSP9_ETS2: 787 cells\n",
      "  RUNX1T1: 779 cells\n",
      "  MEIS1: 776 cells\n",
      "  MAPK1: 765 cells\n",
      "  CNN1: 765 cells\n",
      "  MAP7D1: 751 cells\n",
      "  ZBTB25: 740 cells\n",
      "  DLX2: 732 cells\n",
      "  DUSP9: 731 cells\n",
      "  FEV: 704 cells\n",
      "  HOXB9: 701 cells\n",
      "  ZBTB1: 693 cells\n",
      "  TBX3: 691 cells\n",
      "  TBX2: 690 cells\n",
      "  PRTG: 685 cells\n",
      "  IKZF3: 681 cells\n",
      "  CLDN6: 670 cells\n",
      "  SET_KLF1: 667 cells\n",
      "  CBL: 663 cells\n",
      "  CBFA2T3: 658 cells\n",
      "  SET_CEBPE: 658 cells\n",
      "  FOXL2: 652 cells\n",
      "  ZNF318: 650 cells\n",
      "  LHX1_ELMSAN1: 647 cells\n",
      "  TGFBR2: 646 cells\n",
      "  KIF2C: 634 cells\n",
      "  FOSB: 630 cells\n",
      "  MAML2: 626 cells\n",
      "  LYL1_IER5L: 623 cells\n",
      "  ZC3HAV1_HOXC13: 618 cells\n",
      "  SPI1: 613 cells\n",
      "  IGDCC3: 613 cells\n",
      "  COL2A1: 598 cells\n",
      "  BCL2L11: 582 cells\n",
      "  ISL2: 582 cells\n",
      "  MAP2K3_ELMSAN1: 581 cells\n",
      "  MAP2K3: 579 cells\n",
      "  MAP2K3_MAP2K6: 571 cells\n",
      "  SLC6A9: 558 cells\n",
      "  AHR: 558 cells\n",
      "  UBASH3B_UBASH3A: 554 cells\n",
      "  CDKN1B: 550 cells\n",
      "  TMSB4X: 543 cells\n",
      "  S1PR2: 541 cells\n",
      "  BCORL1: 533 cells\n",
      "  ZC3HAV1: 516 cells\n",
      "  MAPK1_PRTG: 500 cells\n",
      "  SAMD1: 498 cells\n",
      "  MAPK1_TGFBR2: 497 cells\n",
      "  ARRDC3: 495 cells\n",
      "  FOXA3: 494 cells\n",
      "  CEBPB: 488 cells\n",
      "  SGK1: 483 cells\n",
      "  AHR_KLF1: 481 cells\n",
      "  CSRNP1: 478 cells\n",
      "  CELF2: 475 cells\n",
      "  CNNM4: 470 cells\n",
      "  CEBPE_KLF1: 468 cells\n",
      "  SNAI1: 467 cells\n",
      "  BPGM: 464 cells\n",
      "  GLB1L2: 463 cells\n",
      "  BCL2L11_TGFBR2: 463 cells\n",
      "  CEBPA: 460 cells\n",
      "  ETS2_MAPK1: 459 cells\n",
      "  MAP2K6_ELMSAN1: 451 cells\n",
      "  ETS2_IKZF3: 445 cells\n",
      "  PTPN12: 445 cells\n",
      "  ETS2_PRTG: 445 cells\n",
      "  PTPN9: 445 cells\n",
      "  IRF1_SET: 444 cells\n",
      "  ETS2_IGDCC3: 441 cells\n",
      "  TSC22D1: 441 cells\n",
      "  LYL1: 432 cells\n",
      "  IRF1: 427 cells\n",
      "  FOSB_UBASH3B: 426 cells\n",
      "  PTPN1: 419 cells\n",
      "  CEBPE_PTPN12: 415 cells\n",
      "  CEBPB_MAPK1: 412 cells\n",
      "  CBL_UBASH3B: 410 cells\n",
      "  ZC3HAV1_CEBPE: 410 cells\n",
      "  IER5L: 405 cells\n",
      "  UBASH3B_CNN1: 402 cells\n",
      "  CNN1_MAPK1: 398 cells\n",
      "  KLF1_BAK1: 395 cells\n",
      "  HOXC13: 385 cells\n",
      "  FOSB_OSR2: 382 cells\n",
      "  DUSP9_IGDCC3: 382 cells\n",
      "  ATL1: 379 cells\n",
      "  LHX1: 375 cells\n",
      "  IGDCC3_MAPK1: 368 cells\n",
      "  DUSP9_KLF1: 366 cells\n",
      "  MAP4K5: 366 cells\n",
      "  PTPN12_PTPN9: 364 cells\n",
      "  MAP4K3: 363 cells\n",
      "  KLF1_FOXA1: 361 cells\n",
      "  CDKN1A: 360 cells\n",
      "  POU3F2: 359 cells\n",
      "  BPGM_ZBTB1: 358 cells\n",
      "  SGK1_S1PR2: 358 cells\n",
      "  ETS2_CEBPE: 354 cells\n",
      "  SAMD1_PTPN12: 351 cells\n",
      "  SGK1_TBX2: 349 cells\n",
      "  CBL_CNN1: 348 cells\n",
      "  FOSB_PTPN12: 345 cells\n",
      "  PTPN12_OSR2: 339 cells\n",
      "  MAP2K3_SLC38A2: 338 cells\n",
      "  KLF1_TGFBR2: 337 cells\n",
      "  CBL_PTPN12: 335 cells\n",
      "  SLC38A2: 332 cells\n",
      "  CEBPB_PTPN12: 331 cells\n",
      "  RREB1: 331 cells\n",
      "  FOXA3_HOXB9: 329 cells\n",
      "  TMSB4X_BAK1: 329 cells\n",
      "  COL1A1: 328 cells\n",
      "  RHOXF2: 328 cells\n",
      "  TGFBR2_C19orf26: 325 cells\n",
      "  UBASH3B_PTPN9: 325 cells\n",
      "  ETS2_MAP7D1: 324 cells\n",
      "  EGR1: 322 cells\n",
      "  RHOXF2_SET: 321 cells\n",
      "  TGFBR2_ETS2: 318 cells\n",
      "  PTPN12_UBASH3A: 311 cells\n",
      "  KLF1_CEBPA: 311 cells\n",
      "  STIL: 309 cells\n",
      "  POU3F2_FOXL2: 309 cells\n",
      "  CBL_PTPN9: 306 cells\n",
      "  PTPN12_ZBTB25: 303 cells\n",
      "  HK2: 303 cells\n",
      "  MAPK1_IKZF3: 302 cells\n",
      "  MAP2K6_SPI1: 302 cells\n",
      "  BPGM_SAMD1: 301 cells\n",
      "  TGFBR2_IGDCC3: 301 cells\n",
      "  MAP2K6_IKZF3: 300 cells\n",
      "  SGK1_TBX3: 300 cells\n",
      "  SNAI1_UBASH3B: 299 cells\n",
      "  KLF1_COL2A1: 298 cells\n",
      "  UBASH3B_PTPN12: 298 cells\n",
      "  MIDN: 298 cells\n",
      "  PRDM1: 297 cells\n",
      "  DUSP9_PRTG: 297 cells\n",
      "  FOXA1_HOXB9: 295 cells\n",
      "  DUSP9_MAPK1: 290 cells\n",
      "  FEV_ISL2: 284 cells\n",
      "  PTPN12_SNAI1: 283 cells\n",
      "  KIAA1804: 278 cells\n",
      "  FEV_MAP7D1: 277 cells\n",
      "  AHR_FEV: 276 cells\n",
      "  HOXA13: 274 cells\n",
      "  FOXA1_FOXF1: 272 cells\n",
      "  PTPN13: 270 cells\n",
      "  JUN: 266 cells\n",
      "  ZBTB10_PTPN12: 265 cells\n",
      "  TGFBR2_PRTG: 265 cells\n",
      "  C3orf72: 264 cells\n",
      "  KLF1_MAP2K6: 263 cells\n",
      "  RHOXF2_ZBTB25: 263 cells\n",
      "  MAP2K3_IKZF3: 261 cells\n",
      "  UBASH3B_ZBTB25: 255 cells\n",
      "  KIF18B: 254 cells\n",
      "  TP73: 248 cells\n",
      "  FOSB_IKZF3: 247 cells\n",
      "  ZNF318_FOXL2: 246 cells\n",
      "  CKS1B: 241 cells\n",
      "  KMT2A: 241 cells\n",
      "  SAMD1_UBASH3B: 240 cells\n",
      "  NCL: 237 cells\n",
      "  CEBPE_CNN1: 237 cells\n",
      "  HNF4A: 237 cells\n",
      "  KLF1_CLDN6: 237 cells\n",
      "  ARID1A: 232 cells\n",
      "  CDKN1C: 230 cells\n",
      "  FOXA3_FOXA1: 216 cells\n",
      "  POU3F2_CBFA2T3: 216 cells\n",
      "  CITED1: 215 cells\n",
      "  FOXO4: 215 cells\n",
      "  CEBPB_OSR2: 211 cells\n",
      "  FOXF1_HOXB9: 200 cells\n",
      "  FOXL2_MEIS1: 193 cells\n",
      "  NIT1: 192 cells\n",
      "  LYL1_CEBPB: 192 cells\n",
      "  FOXA1_FOXL2: 192 cells\n",
      "  FOXF1_FOXL2: 192 cells\n",
      "  SAMD1_ZBTB1: 191 cells\n",
      "  CBL_TGFBR2: 188 cells\n",
      "  DUSP9_SNAI1: 183 cells\n",
      "  ZBTB10_ELMSAN1: 182 cells\n",
      "  CEBPE_CEBPA: 180 cells\n",
      "  CEBPE_SPI1: 179 cells\n",
      "  CNN1_UBASH3A: 178 cells\n",
      "  BCL2L11_BAK1: 175 cells\n",
      "  FOXA3_FOXF1: 175 cells\n",
      "  ZBTB10: 162 cells\n",
      "  FEV_CBFA2T3: 159 cells\n",
      "  FOSB_CEBPE: 151 cells\n",
      "  IGDCC3_ZBTB25: 141 cells\n",
      "  IGDCC3_PRTG: 140 cells\n",
      "  CDKN1B_CDKN1A: 134 cells\n",
      "  SNAI1_DLX2: 131 cells\n",
      "  FOXA3_FOXL2: 130 cells\n",
      "  HES7: 126 cells\n",
      "  CDKN1C_CDKN1A: 126 cells\n",
      "  CEBPE_CEBPB: 114 cells\n",
      "  PLK4: 113 cells\n",
      "  CDKN1C_CDKN1B: 107 cells\n",
      "  PRDM1_CBFA2T3: 106 cells\n",
      "  ZBTB10_SNAI1: 100 cells\n",
      "\n",
      "Cell type distribution:\n",
      "  lymphoblasts: 110554 cells\n",
      "\n",
      "Analysis completed!\n"
     ]
    }
   ],
   "source": [
    "# =============================================================================\n",
    "# Pert_Data Information Analysis and Visualization\n",
    "# =============================================================================\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Set plotting style\n",
    "plt.style.use('default')\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "# --------- 1. 分布相关的两个统计图分开画 ---------\n",
    "print(\"1. Analyzing cell count distribution...\")\n",
    "pert_cell_counts = []\n",
    "pert_names = []\n",
    "for pert in pert_data.filter_perturbation_list:\n",
    "    cell_count = len(pert_data.adata_split[pert_data.adata_split.obs['perturbation_group'] == pert])\n",
    "    pert_cell_counts.append(cell_count)\n",
    "    pert_names.append(pert.split(' | ')[0])  # 只取gene name\n",
    "\n",
    "mean_cells = np.mean(pert_cell_counts)\n",
    "median_cells = np.median(pert_cell_counts)\n",
    "\n",
    "# 单独画：每个扰动的细胞数量分布（直方图）\n",
    "fig1, ax1 = plt.subplots(figsize=(7, 5))\n",
    "ax1.hist(pert_cell_counts, bins=30, alpha=0.7, edgecolor='black')\n",
    "ax1.set_xlabel('Number of Cells per Perturbation')\n",
    "ax1.set_ylabel('Frequency')\n",
    "ax1.set_title('Distribution of Cell Counts per Perturbation')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "ax1.axvline(mean_cells, color='red', linestyle='--', label=f'Mean: {mean_cells:.1f}')\n",
    "ax1.axvline(median_cells, color='orange', linestyle='--', label=f'Median: {median_cells:.1f}')\n",
    "ax1.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"   - Mean cells per perturbation: {mean_cells:.1f}\")\n",
    "print(f\"   - Median cells per perturbation: {median_cells:.1f}\")\n",
    "print(f\"   - Total perturbations: {len(pert_cell_counts)}\")\n",
    "\n",
    "# --------- 2. E-distance分布（单独画） ---------\n",
    "print(\"\\n2. Analyzing E-distance distribution...\")\n",
    "fig2, ax2 = plt.subplots(figsize=(7, 5))\n",
    "if hasattr(pert_data, 'filter_pert_edis') and pert_data.filter_pert_edis:\n",
    "    edis_values = list(pert_data.filter_pert_edis.values())\n",
    "\n",
    "    ax2.hist(edis_values, bins=30, alpha=0.7, edgecolor='black', color='green')\n",
    "    ax2.set_xlabel('E-distance')\n",
    "    ax2.set_ylabel('Frequency')\n",
    "    ax2.set_title('Distribution of E-distances')\n",
    "    ax2.grid(True, alpha=0.3)\n",
    "\n",
    "    mean_edis = np.mean(edis_values)\n",
    "    median_edis = np.median(edis_values)\n",
    "    ax2.axvline(mean_edis, color='red', linestyle='--', label=f'Mean: {mean_edis:.3f}')\n",
    "    ax2.axvline(median_edis, color='orange', linestyle='--', label=f'Median: {median_edis:.3f}')\n",
    "    ax2.legend()\n",
    "\n",
    "    print(f\"   - Mean E-distance: {mean_edis:.3f}\")\n",
    "    print(f\"   - Median E-distance: {median_edis:.3f}\")\n",
    "    print(f\"   - Min E-distance: {np.min(edis_values):.3f}\")\n",
    "    print(f\"   - Max E-distance: {np.max(edis_values):.3f}\")\n",
    "\n",
    "else:\n",
    "    ax2.text(0.5, 0.5, 'E-distance data not available',\n",
    "             ha='center', va='center', transform=ax2.transAxes)\n",
    "    ax2.set_title('E-distance Distribution (Not Available)')\n",
    "    print(\"   - E-distance data not available\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# --------- 3. UMAP单独画 ---------\n",
    "print(\"\\n3. Generating UMAP plot for specific perturbation...\")\n",
    "if pert_cell_counts:\n",
    "    # Get perturbations sorted by cell count\n",
    "    pert_cell_df = pd.DataFrame({\n",
    "        'perturbation': pert_data.filter_perturbation_list,\n",
    "        'cell_count': pert_cell_counts\n",
    "    }).sort_values('cell_count', ascending=False)\n",
    "    # 选一个cell丰度前25%里的扰动\n",
    "    top_25_percent = int(len(pert_cell_df) * 0.25)\n",
    "    selected_pert = pert_cell_df.iloc[top_25_percent]['perturbation']\n",
    "    selected_cell_count = pert_cell_df.iloc[top_25_percent]['cell_count']\n",
    "\n",
    "    print(f\"   - Selected perturbation: {selected_pert}\")\n",
    "    print(f\"   - Cell count: {selected_cell_count}\")\n",
    "\n",
    "    try:\n",
    "        pert_data.plot_umap(\n",
    "            pert=selected_pert,\n",
    "            point_size=20,\n",
    "            color=['perturbation_group'],\n",
    "            return_adata=False\n",
    "        )\n",
    "    except Exception as e:\n",
    "        print(f\"   - UMAP generation failed: {e}\")\n",
    "else:\n",
    "    print(\"   - No perturbation data available for UMAP plot.\")\n",
    "\n",
    "# ======= 4. 不画图，只保留详细统计信息 =======\n",
    "# 汇报数据信息\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"DETAILED PERTURBATION ANALYSIS\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Top 10 perturbations by cell count\n",
    "print(\"\\nTop 10 perturbations by cell count:\")\n",
    "pert_cell_df = pd.DataFrame({\n",
    "    'perturbation': pert_data.filter_perturbation_list,\n",
    "    'cell_count': pert_cell_counts\n",
    "}).sort_values('cell_count', ascending=False)\n",
    "top_10_perts = pert_cell_df.head(10)\n",
    "for idx, row in top_10_perts.iterrows():\n",
    "    print(f\"  {row['perturbation']}: {row['cell_count']} cells\")\n",
    "\n",
    "# Perturbation type distribution\n",
    "print(f\"\\nPerturbation type distribution:\")\n",
    "pert_type_counts = pert_data.adata_split.obs['perturbation_new'].value_counts()\n",
    "for pert_type, count in pert_type_counts.items():\n",
    "    print(f\"  {pert_type}: {count} cells\")\n",
    "\n",
    "# Cell type distribution\n",
    "print(f\"\\nCell type distribution:\")\n",
    "celltype_counts = pert_data.adata_split.obs['celltype_new'].value_counts()\n",
    "for celltype, count in celltype_counts.items():\n",
    "    print(f\"  {celltype}: {count} cells\")\n",
    "\n",
    "print(\"\\nAnalysis completed!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "449e2fe8",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1010889",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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