{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.3.0 Codex 4-Tile 5K-Cell Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> clustergrammer2 backend version 0.5.1\n"
     ]
    }
   ],
   "source": [
    "from clustergrammer2 import net\n",
    "\n",
    "import ipywidgets as widgets\n",
    "import numpy as np\n",
    "from bqplot import pyplot as plt\n",
    "import bqplot\n",
    "\n",
    "from copy import deepcopy\n",
    "from glob import glob\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "from scipy.spatial import Voronoi\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Expression Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 5018)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.load_file('../data/codex_data/4-tile_5k-cells_expression-cat.txt')\n",
    "df['tile-neighbor'] = net.export_df()\n",
    "df['tile-neighbor'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Location Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5146, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.load_file('../data/codex_data/4-tile_5k-cells_location-cat.txt')\n",
    "df['tile-loc'] = net.export_df()\n",
    "df['tile-loc'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_colors = net.load_json_to_dict('../data/codex_data/cat_colors.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.load_df(df['tile-neighbor'])\n",
    "net.set_cat_colors(axis='col', cat_colors=cat_colors, cat_index=1, cat_title='Cell Type')\n",
    "net.set_cat_colors(axis='col', cat_colors=cat_colors, cat_index=2, cat_title='Neighbor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expression Levels of All Cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_highlight(inst_value):\n",
    "\n",
    "    \n",
    "    if inst_value != 'reset_cats':\n",
    "        \n",
    "        inst_cat = inst_value.split(': ')[1]\n",
    "\n",
    "        list_opacities = []\n",
    "\n",
    "        for inst_label in region_labels:\n",
    "            inst_opacity = 0.25\n",
    "            if inst_label == inst_cat:\n",
    "                inst_opacity = 1\n",
    "            list_opacities.append(inst_opacity)\n",
    "\n",
    "    else:\n",
    "        list_opacities = [1 for x in region_labels]\n",
    "        patch.opacities = list_opacities\n",
    "\n",
    "    patch.opacities = list_opacities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_value_change(change):\n",
    "    \n",
    "    if change['new'] == 'null':\n",
    "        cat_highlight('reset_cats')            \n",
    "    else: \n",
    "        # mousing over category\n",
    "        if 'cat-' in change['new']:\n",
    "            inst_cat = change['new'].split(' -> ')[1]\n",
    "            cat_highlight(inst_cat)       \n",
    "            \n",
    "        # mousing over marker\n",
    "        else:\n",
    "            inst_marker = change['new'].split(' -> ')[1]\n",
    "            ser_opacity = df['tile-neighbor'].loc[inst_marker]\n",
    "            list_opacities = [float(x/ser_opacity.max()) for x in list(ser_opacity.get_values())]\n",
    "            patch.opacities = list_opacities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.load_df(df['tile-neighbor'])\n",
    "net.normalize(axis='row', norm_type='zscore')\n",
    "net.clip(-5,5)\n",
    "net.widget()\n",
    "net.widget_instance.observe(on_value_change, names='value')\n",
    "# tmp = net.widget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Voronoi Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vor = Voronoi(df['tile-loc'])\n",
    "\n",
    "point_list = df['tile-loc'].index.tolist()\n",
    "point_names = [x[0] for x in point_list]\n",
    "cat_names   = [x[1].split(': ')[1] for x in point_list]\n",
    "\n",
    "patch_data = {}\n",
    "patch_data['x'] = []\n",
    "patch_data['y'] = []\n",
    "patch_data['colors'] = []\n",
    "region_labels = []\n",
    "\n",
    "region_point_dict = {}\n",
    "for point_index in range(vor.point_region.shape[0]):\n",
    "    region_index = vor.point_region[point_index]\n",
    "    region_point_dict[region_index] = point_index\n",
    "\n",
    "for region_index in range(len(vor.regions)):\n",
    "    \n",
    "    inst_region = vor.regions[region_index]\n",
    "\n",
    "    if -1 not in inst_region and len(inst_region) > 0:\n",
    "\n",
    "        point_index = region_point_dict[region_index]\n",
    "        point_cat = cat_names[point_index]\n",
    "        region_labels.append(point_cat)\n",
    "        \n",
    "        # save cat_colors\n",
    "        inst_color = cat_colors[point_cat]\n",
    "        patch_data['colors'].append(inst_color)\n",
    "        \n",
    "        x_list = []\n",
    "        y_list = []\n",
    "        for inst_vertex in inst_region:\n",
    "            inst_pos = vor.vertices[inst_vertex]\n",
    "            x_list.append(inst_pos[0])\n",
    "            y_list.append(inst_pos[1])\n",
    "            \n",
    "        patch_data['x'].append(x_list)\n",
    "        patch_data['y'].append(y_list)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearScale(max=2000.0, min=0.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dim = 1200\n",
    "y_dim = 1000\n",
    "\n",
    "def_tt = bqplot.Tooltip(fields=['name'], formats=[''])\n",
    "\n",
    "fig = plt.figure(animation_duration=1000)\n",
    "patch = plt.plot([], [], \n",
    "                 fill='inside',\n",
    "                 fill_colors=patch_data['colors'],\n",
    "                 stroke_width=1,\n",
    "                 close_path=True,\n",
    "                 labels=region_labels,\n",
    "                 tooltip=def_tt,\n",
    "                 axes_options={'x': {'visible': False}, 'y': {'visible': False}},\n",
    "                )\n",
    "\n",
    "scatter = plt.scatter(df['tile-loc']['X.X'], \n",
    "                      df['tile-loc']['Y.Y'],\n",
    "#                       tooltip=def_tt, \n",
    "                      names=point_names,\n",
    "                      display_names=False, default_size=2)\n",
    "\n",
    "\n",
    "inst_width = 950\n",
    "fig.layout.min_height = str(inst_width/(1.15)) + 'px'\n",
    "fig.layout.min_width  = str(inst_width) + 'px'\n",
    "\n",
    "patch.x = patch_data['x']\n",
    "patch.y = patch_data['y']\n",
    "\n",
    "plt.xlim(0, 2.0*x_dim)\n",
    "plt.ylim(0, 2.0*y_dim)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a6a7e03f6c64db1bd62323ae8e174c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(ExampleWidget(network='{\"row_nodes\": [{\"name\": \"CD45\", \"ini\": 29, \"clust\": 6, \"rank\": 13, \"rank…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import HBox\n",
    "HBox([net.widget_instance, fig])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# net.widget_instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def mouseover_highlight(self, target):\n",
    "#     # print('cat name', target['data']['name'])\n",
    "#     list_opacities = []\n",
    "#     for inst_label in region_labels:\n",
    "#         inst_opacity = 0.25\n",
    "#         if inst_label == target['data']['name']:\n",
    "#             inst_opacity = 1\n",
    "#         list_opacities.append(inst_opacity)\n",
    "\n",
    "#     self.opacities = list_opacities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def reset_highlight(self, target):\n",
    "# #     print('CLICKING')\n",
    "#     list_opacities = [1 for x in region_labels]\n",
    "#     self.opacities = list_opacities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# patch.on_hover(mouseover_highlight)\n",
    "# patch.on_element_click(reset_highlight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap and Voronoi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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  "widgets": {
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    "state": {
     "07ee0144373e4fd3b060234f823e16cf": {
      "model_module": "bqplot",
      "model_module_version": "^0.4.5",
      "model_name": "LinearScaleModel",
      "state": {
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       "min": 0,
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     "3b0d63fcfa3c415cb455e2b39579b75f": {
      "model_module": "clustergrammer2",
      "model_module_version": "^0.4.0",
      "model_name": "ExampleModel",
      "state": {
       "_model_module_version": "^0.4.0",
       "_view_module_version": "^0.4.0",
       "layout": "IPY_MODEL_4226f45252974e728856e580950694f2",
       "mat_string": "ini_mat_string",
       "network": "{\"row_nodes\": [{\"name\": \"CD45\", \"ini\": 29, \"clust\": 19, \"rank\": 26, \"rankvar\": 20, \"group\": [19.0, 18.0, 17.0, 15.0, 10.0, 9.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"Ly6C\", \"ini\": 28, \"clust\": 2, \"rank\": 1, \"rankvar\": 1, \"group\": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0]}, {\"name\": \"TCR\", \"ini\": 27, \"clust\": 4, \"rank\": 7, \"rankvar\": 7, \"group\": [5.0, 5.0, 5.0, 5.0, 4.0, 4.0, 4.0, 2.0, 1.0, 1.0, 1.0]}, {\"name\": \"Ly6G\", \"ini\": 26, \"clust\": 10, \"rank\": 0, \"rankvar\": 0, \"group\": [11.0, 11.0, 11.0, 10.0, 7.0, 6.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD19\", \"ini\": 25, \"clust\": 20, \"rank\": 28, \"rankvar\": 27, \"group\": [20.0, 19.0, 18.0, 15.0, 10.0, 9.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD169\", \"ini\": 24, \"clust\": 27, \"rank\": 24, \"rankvar\": 28, \"group\": [27.0, 26.0, 25.0, 21.0, 14.0, 12.0, 8.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD106\", \"ini\": 23, \"clust\": 24, \"rank\": 16, \"rankvar\": 16, \"group\": [24.0, 23.0, 22.0, 18.0, 12.0, 10.0, 7.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD3\", \"ini\": 22, \"clust\": 3, \"rank\": 5, \"rankvar\": 6, \"group\": [4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD1632\", \"ini\": 21, \"clust\": 17, \"rank\": 27, \"rankvar\": 26, \"group\": [17.0, 16.0, 15.0, 14.0, 10.0, 9.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD8a\", \"ini\": 20, \"clust\": 16, \"rank\": 14, \"rankvar\": 14, \"group\": [23.0, 22.0, 21.0, 17.0, 11.0, 9.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD90\", \"ini\": 19, \"clust\": 21, \"rank\": 21, \"rankvar\": 21, \"group\": [21.0, 20.0, 19.0, 16.0, 10.0, 9.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"F480\", \"ini\": 18, \"clust\": 25, \"rank\": 12, \"rankvar\": 10, \"group\": [25.0, 24.0, 23.0, 19.0, 12.0, 10.0, 7.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD11c\", \"ini\": 17, \"clust\": 22, \"rank\": 19, \"rankvar\": 24, \"group\": [22.0, 21.0, 20.0, 16.0, 10.0, 9.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"Ter119\", \"ini\": 16, \"clust\": 0, \"rank\": 9, \"rankvar\": 9, \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD11b\", \"ini\": 15, \"clust\": 11, \"rank\": 3, \"rankvar\": 2, \"group\": [12.0, 12.0, 12.0, 11.0, 7.0, 6.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"IgD\", \"ini\": 14, \"clust\": 8, \"rank\": 18, \"rankvar\": 17, \"group\": [9.0, 9.0, 9.0, 8.0, 6.0, 5.0, 5.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD27\", \"ini\": 13, \"clust\": 5, \"rank\": 15, \"rankvar\": 15, \"group\": [6.0, 6.0, 6.0, 5.0, 4.0, 4.0, 4.0, 2.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD5\", \"ini\": 12, \"clust\": 6, \"rank\": 2, \"rankvar\": 3, \"group\": [7.0, 7.0, 7.0, 6.0, 5.0, 4.0, 4.0, 2.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD79b\", \"ini\": 11, \"clust\": 12, \"rank\": 23, \"rankvar\": 22, \"group\": [13.0, 13.0, 13.0, 12.0, 8.0, 7.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD71\", \"ini\": 10, \"clust\": 1, \"rank\": 4, \"rankvar\": 4, \"group\": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD31\", \"ini\": 9, \"clust\": 18, \"rank\": 20, \"rankvar\": 23, \"group\": [18.0, 17.0, 16.0, 14.0, 10.0, 9.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD4\", \"ini\": 8, \"clust\": 7, \"rank\": 11, \"rankvar\": 12, \"group\": [8.0, 8.0, 8.0, 7.0, 5.0, 4.0, 4.0, 2.0, 1.0, 1.0, 1.0]}, {\"name\": \"IgM\", \"ini\": 7, \"clust\": 13, \"rank\": 8, \"rankvar\": 8, \"group\": [14.0, 14.0, 13.0, 12.0, 8.0, 7.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"B220\", \"ini\": 6, \"clust\": 9, \"rank\": 17, \"rankvar\": 19, \"group\": [10.0, 10.0, 10.0, 9.0, 6.0, 5.0, 5.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"ERTR7\", \"ini\": 5, \"clust\": 23, \"rank\": 10, \"rankvar\": 11, \"group\": [26.0, 25.0, 24.0, 20.0, 13.0, 11.0, 7.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD35\", \"ini\": 4, \"clust\": 14, \"rank\": 25, \"rankvar\": 18, \"group\": [15.0, 15.0, 14.0, 13.0, 9.0, 8.0, 6.0, 3.0, 1.0, 1.0, 1.0]}, {\"name\": \"CD2135\", 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         1027,
         1027,
         1028,
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         1109,
         111,
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         1262,
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         1302,
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         131,
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         140,
         140,
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         143,
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         145,
         146,
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         147,
         148,
         149,
         152,
         156,
         156,
         158,
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         161,
         162,
         162,
         163,
         163,
         164,
         166,
         166,
         166,
         167,
         169,
         177,
         179,
         181,
         182,
         182,
         183,
         184,
         184,
         185,
         190,
         193,
         194,
         194,
         196,
         201,
         202,
         203,
         205,
         207,
         208,
         208,
         208,
         21,
         210,
         215,
         217,
         218,
         219,
         223,
         226,
         226,
         233,
         233,
         234,
         237,
         237,
         239,
         240,
         242,
         242,
         243,
         244,
         246,
         254,
         254,
         255,
         257,
         258,
         259,
         26,
         260,
         260,
         261,
         262,
         262,
         263,
         264,
         265,
         265,
         266,
         266,
         267,
         268,
         268,
         269,
         27,
         270,
         272,
         273,
         275,
         275,
         278,
         280,
         280,
         281,
         283,
         284,
         286,
         29,
         290,
         291,
         292,
         292,
         293,
         296,
         296,
         297,
         297,
         298,
         300,
         301,
         307,
         307,
         307,
         308,
         31,
         313,
         313,
         315,
         315,
         315,
         316,
         316,
         317,
         318,
         32,
         321,
         321,
         324,
         324,
         328,
         329,
         329,
         33,
         331,
         331,
         331,
         335,
         335,
         335,
         336,
         338,
         338,
         339,
         340,
         341,
         341,
         344,
         345,
         345,
         345,
         347,
         349,
         349,
         349,
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         350,
         352,
         352,
         353,
         353,
         356,
         356,
         358,
         358,
         36,
         361,
         362,
         364,
         364,
         365,
         366,
         366,
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         371,
         372,
         373,
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         375,
         375,
         376,
         378,
         378,
         379,
         38,
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         383,
         383,
         385,
         386,
         386,
         387,
         39,
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         397,
         397,
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         411,
         415,
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         422,
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         43,
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         435,
         436,
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         438,
         44,
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         441,
         443,
         445,
         445,
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         448,
         448,
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         449,
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         459,
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         49,
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         5,
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         539,
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         548,
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         56,
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         565,
         566,
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         569,
         570,
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         571,
         573,
         573,
         573,
         575,
         576,
         579,
         581,
         583,
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         586,
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         589,
         59,
         590,
         590,
         590,
         593,
         594,
         595,
         598,
         600,
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         1240,
         1402,
         1272,
         1671,
         1431,
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         1919,
         1058,
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         1979,
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         1348,
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         1199,
         1813,
         1538,
         1540,
         1637,
         1344,
         1501,
         1371,
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