SWC_from_Graph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "43061b65-37d5-458f-b856-08672ee110c9",
   "metadata": {},
   "source": [
    "# Context"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0263d2df-ed74-486d-bae9-73d376b3d568",
   "metadata": {},
   "source": [
    "**Input**:\n",
    "\n",
    "NetworkX Directed Graph data\n",
    "\n",
    "**Output**:\n",
    "\n",
    "Standardized swc files (www.neuromorpho.org)\n",
    "\n",
    "[SWC specification](http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html)\n",
    "\n",
    "# To Do:\n",
    "[ ] Filtering the data out of redudant nodes\n",
    "\n",
    "[ ] Add soma section* from graph[soma_shell]\n",
    "\n",
    "*see [soma docs](https://docs.arbor-sim.org/en/stable/fileformat/swc.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d7e86d0b-00c0-4f7e-ba27-9783cae3822b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import visualization as vis\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from astropy.io import ascii\n",
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8bc8224d-972d-4495-93d8-568cbdc02762",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filename = '3wk-both1-grn-raw.pic-sequential_graph.pickle'\n",
    "# data = pd.read_pickle(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d74fdc32-fda1-460a-864d-ea6dd87a5483",
   "metadata": {},
   "outputs": [],
   "source": [
    "# graph stuff\n",
    "def get_tips(g):\n",
    "    return {n for n in g.nodes if len(list(g.successors(n))) == 0}\n",
    "            \n",
    "def get_roots(g):\n",
    "    return {n for n in g.nodes if len(list(g.predecessors(n))) < 1}\n",
    "\n",
    "def get_branch_points(g):\n",
    "    return {n for n in g.nodes if len(list(g.successors(n))) > 1}       \n",
    "\n",
    "# Note: neighbors() == successors() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f19e46aa-4c6d-4d39-9528-68bb589eb34f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m r \u001b[38;5;241m=\u001b[39m get_roots(\u001b[43mdata\u001b[49m)\n\u001b[1;32m      2\u001b[0m t \u001b[38;5;241m=\u001b[39m get_tips(data)\n\u001b[1;32m      3\u001b[0m b \u001b[38;5;241m=\u001b[39m get_branch_points(data)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "r = get_roots(data)\n",
    "t = get_tips(data)\n",
    "b = get_branch_points(data)\n",
    "\n",
    "data_paths = vis.graph_to_paths(data)\n",
    "print(f'Keys:{data_paths.keys()}', '\\n',  f'\\nRoots: {r}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2780af8b-115f-480c-b869-6426a1cbd92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "root = data_paths[(38, 202, 203)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "id": "99390c75-dda6-4be1-a741-37ece35ffae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 6911)"
      ]
     },
     "execution_count": 671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We've got 7 times increased detalization than usual SWC contain\n",
    "range(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "id": "e28c51f7-98af-43cf-9ced-966c9e9bfaf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((39, 145, 130),\n",
       " {'root': (37, 206, 180),\n",
       "  'occurence': 6.860663671448287,\n",
       "  'sigma_mask': 5.656854249492381,\n",
       "  'sigma_opt': 4.0})"
      ]
     },
     "execution_count": 672,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.nodes.data())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "id": "50fbb07a-267b-4a4b-8a18-f20fedd6f430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((39, 145, 130), (39, 144, 129), {'weight': 1})"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.edges.data())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "id": "c3456bd6-075f-4a4a-b2ca-4cb4ff42c02e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
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       " ((30, 202, 84), (36, 211, 176)),\n",
       " ((30, 293, 202), (35, 246, 203)),\n",
       " ((29, 288, 180), (35, 246, 203)),\n",
       " ((37, 185, 163), (37, 206, 180)),\n",
       " ((38, 180, 196), (39, 198, 198)),\n",
       " ((41, 189, 125), (37, 208, 178)),\n",
       " ((36, 267, 171), (36, 248, 178)),\n",
       " ((36, 193, 222), (38, 202, 203)),\n",
       " ((30, 286, 199), (35, 246, 203)),\n",
       " ((36, 202, 227), (38, 202, 203)),\n",
       " ((43, 116, 89), (37, 206, 180)),\n",
       " ((45, 128, 56), (37, 206, 180)),\n",
       " ((40, 123, 104), (37, 206, 180)),\n",
       " ((30, 177, 148), (36, 211, 176)),\n",
       " ((38, 61, 73), (37, 206, 180)),\n",
       " ((36, 255, 174), (36, 248, 178)),\n",
       " ((42, 63, 87), (37, 206, 180)),\n",
       " ((34, 191, 134), (36, 211, 176)),\n",
       " ((33, 257, 259), (35, 216, 202)),\n",
       " ((43, 172, 226), (39, 198, 198)),\n",
       " ((38, 177, 140), (37, 208, 178)),\n",
       " ((39, 193, 115), (37, 208, 178)),\n",
       " ((33, 296, 187), (36, 248, 178)),\n",
       " ((29, 289, 243), (35, 246, 203)),\n",
       " ((38, 195, 194), (39, 199, 196)),\n",
       " ((47, 111, 157), (39, 198, 198)),\n",
       " ((36, 185, 236), (38, 202, 203)),\n",
       " ((40, 167, 123), (36, 208, 179)),\n",
       " ((35, 231, 234), (38, 202, 203)),\n",
       " ((34, 195, 145), (36, 212, 176)),\n",
       " ((38, 172, 146), (37, 206, 180)),\n",
       " ((47, 137, 65), (37, 206, 180)),\n",
       " ((38, 181, 151), (37, 208, 178)),\n",
       " ((44, 212, 184), (40, 204, 199)),\n",
       " ((38, 93, 38), (37, 206, 180)),\n",
       " ((31, 272, 223), (35, 246, 203)),\n",
       " ((42, 176, 206), (39, 198, 198)),\n",
       " ((34, 172, 97), (36, 211, 176)),\n",
       " ((39, 79, 70), (37, 206, 180)),\n",
       " ((35, 195, 173), (36, 206, 181)),\n",
       " ((39, 139, 121), (37, 206, 180)),\n",
       " ((44, 132, 191), (39, 198, 198)),\n",
       " ((42, 148, 144), (37, 206, 180)),\n",
       " ((25, 267, 240), (35, 246, 203)),\n",
       " ((34, 47, 102), (37, 206, 180)),\n",
       " ((37, 115, 93), (37, 206, 180)),\n",
       " ((46, 154, 152), (37, 206, 180)),\n",
       " ((23, 289, 234), (35, 246, 203)),\n",
       " ((41, 121, 101), (37, 206, 180)),\n",
       " ((31, 258, 180), (36, 248, 178)),\n",
       " ((42, 162, 163), (37, 206, 180)),\n",
       " ((31, 200, 175), (36, 206, 181)),\n",
       " ((38, 67, 106), (37, 206, 180)),\n",
       " ((29, 256, 200), (34, 246, 199)),\n",
       " ((42, 176, 219), (39, 198, 198)),\n",
       " ((36, 226, 142), (36, 212, 176)),\n",
       " ((46, 120, 167), (39, 198, 198)),\n",
       " ((44, 132, 204), (39, 198, 198)),\n",
       " ((41, 150, 142), (37, 206, 180)),\n",
       " ((39, 141, 180), (39, 198, 198)),\n",
       " ((41, 190, 214), (39, 199, 199)),\n",
       " ((38, 252, 165), (36, 248, 178)),\n",
       " ((38, 136, 219), (39, 198, 198)),\n",
       " ((39, 83, 94), (37, 206, 180)),\n",
       " ((33, 203, 106), (36, 211, 176)),\n",
       " ((28, 324, 199), (35, 246, 203)),\n",
       " ((28, 257, 209), (34, 246, 199)),\n",
       " ((38, 85, 109), (37, 206, 180)),\n",
       " ((36, 105, 94), (37, 206, 180)),\n",
       " ((33, 154, 106), (36, 211, 176)),\n",
       " ((39, 138, 151), (37, 206, 180)),\n",
       " ((28, 317, 196), (35, 246, 203)),\n",
       " ((26, 263, 232), (35, 246, 203)),\n",
       " ((52, 125, 197), (39, 198, 198)),\n",
       " ((34, 247, 122), (36, 212, 176)),\n",
       " ((34, 213, 245), (38, 202, 203)),\n",
       " ((46, 188, 176), (39, 198, 198)),\n",
       " ((42, 85, 171), (39, 198, 198)),\n",
       " ((27, 308, 207), (35, 246, 203)),\n",
       " ((37, 195, 169), (37, 206, 180)),\n",
       " ((32, 221, 112), (36, 212, 176)),\n",
       " ((38, 110, 179), (39, 198, 198)),\n",
       " ((45, 150, 180), (39, 198, 198)),\n",
       " ((40, 193, 205), (39, 199, 199)),\n",
       " ((28, 305, 199), (35, 246, 203)),\n",
       " ((33, 172, 217), (38, 202, 203)),\n",
       " ((42, 96, 185), (39, 198, 198)),\n",
       " ((22, 298, 261), (35, 246, 203)),\n",
       " ((37, 107, 177), (39, 198, 198)),\n",
       " ((35, 194, 152), (36, 212, 176)),\n",
       " ((36, 285, 159), (36, 248, 178)),\n",
       " ((40, 112, 172), (39, 198, 198)),\n",
       " ((45, 138, 183), (39, 198, 198)),\n",
       " ((39, 89, 100), (37, 206, 180)),\n",
       " ((26, 271, 190), (35, 246, 203)),\n",
       " ((33, 183, 123), (36, 211, 176)),\n",
       " ((38, 196, 195), (39, 199, 196)),\n",
       " ((35, 106, 160), (39, 198, 198)),\n",
       " ((32, 232, 220), (35, 216, 202)),\n",
       " ((34, 258, 122), (36, 212, 176)),\n",
       " ((29, 281, 232), (35, 246, 203)),\n",
       " ((43, 107, 95), (37, 206, 180)),\n",
       " ((34, 193, 141), (36, 211, 176)),\n",
       " ((38, 189, 192), (39, 198, 198)),\n",
       " ((43, 175, 229), (39, 198, 198)),\n",
       " ((33, 178, 129), (36, 211, 176)),\n",
       " ((29, 292, 246), (35, 246, 203)),\n",
       " ((41, 191, 118), (37, 208, 178)),\n",
       " ((41, 196, 233), (39, 199, 199)),\n",
       " ((37, 205, 173), (37, 209, 177)),\n",
       " ((38, 181, 193), (39, 198, 198)),\n",
       " ((38, 200, 206), (38, 202, 203)),\n",
       " ((41, 214, 250), (39, 199, 199)),\n",
       " ((38, 175, 149), (37, 206, 180)),\n",
       " ((47, 102, 42), (37, 206, 180)),\n",
       " ((39, 117, 175), (39, 198, 198)),\n",
       " ((42, 181, 134), (37, 208, 178)),\n",
       " ((40, 139, 224), (39, 198, 198)),\n",
       " ((37, 201, 175), (37, 206, 180)),\n",
       " ((31, 256, 213), (35, 246, 203)),\n",
       " ((36, 183, 245), (38, 202, 203)),\n",
       " ((42, 179, 209), (39, 198, 198)),\n",
       " ((39, 142, 124), (37, 206, 180)),\n",
       " ((32, 261, 116), (36, 212, 176)),\n",
       " ((47, 143, 164), (39, 198, 198)),\n",
       " ((35, 214, 157), (37, 214, 176)),\n",
       " ((39, 163, 183), (39, 198, 198)),\n",
       " ((36, 179, 247), (38, 202, 203)),\n",
       " ((29, 245, 147), (36, 212, 176)),\n",
       " ((23, 273, 224), (35, 246, 203)),\n",
       " ((45, 183, 181), (39, 198, 198)),\n",
       " ((33, 212, 140), (36, 212, 176)),\n",
       " ((35, 193, 155), (36, 211, 176)),\n",
       " ((42, 60, 86), (37, 206, 180)),\n",
       " ((41, 189, 206), (39, 198, 198)),\n",
       " ((36, 55, 73), (37, 206, 180)),\n",
       " ((32, 150, 140), (36, 211, 176)),\n",
       " ((41, 188, 210), (39, 199, 199)),\n",
       " ((21, 274, 249), (35, 246, 203)),\n",
       " ((32, 194, 183), (36, 206, 181)),\n",
       " ((41, 86, 199), (39, 198, 198)),\n",
       " ((45, 230, 148), (40, 223, 178)),\n",
       " ((32, 257, 239), (35, 216, 202)),\n",
       " ((25, 315, 191), (35, 246, 203)),\n",
       " ((38, 132, 142), (37, 206, 180)),\n",
       " ((36, 224, 232), (38, 202, 203)),\n",
       " ((35, 200, 235), (38, 202, 203)),\n",
       " ((30, 238, 145), (36, 212, 176)),\n",
       " ((43, 181, 235), (39, 198, 198)),\n",
       " ((35, 184, 227), (38, 202, 203)),\n",
       " ((35, 299, 177), (36, 248, 178)),\n",
       " ((38, 144, 152), (37, 206, 180)),\n",
       " ((29, 298, 252), (35, 246, 203)),\n",
       " ((44, 153, 197), (39, 198, 198)),\n",
       " ((24, 271, 245), (35, 246, 203)),\n",
       " ((35, 202, 244), (38, 202, 203)),\n",
       " ((38, 185, 153), (37, 209, 177)),\n",
       " ((28, 179, 183), (36, 206, 181)),\n",
       " ((35, 172, 109), (36, 211, 176)),\n",
       " ((30, 236, 220), (35, 216, 202)),\n",
       " ((37, 78, 94), (37, 206, 180)),\n",
       " ((34, 264, 168), (36, 248, 178)),\n",
       " ((33, 233, 212), (35, 216, 202)),\n",
       " ((38, 199, 209), (38, 202, 203)),\n",
       " ((35, 164, 110), (36, 211, 176)),\n",
       " ((53, 134, 207), (39, 198, 198)),\n",
       " ((27, 285, 181), (35, 246, 203)),\n",
       " ((43, 224, 174), (40, 223, 178)),\n",
       " ((37, 112, 92), (37, 206, 180)),\n",
       " ((27, 307, 199), (35, 246, 203)),\n",
       " ((48, 183, 195), (39, 198, 198)),\n",
       " ((36, 180, 202), (39, 198, 198)),\n",
       " ((39, 148, 130), (37, 206, 180)),\n",
       " ((46, 148, 176), (39, 198, 198)),\n",
       " ((44, 214, 190), (40, 204, 199)),\n",
       " ((36, 94, 135), (37, 206, 180)),\n",
       " ((37, 186, 162), (37, 206, 180)),\n",
       " ((35, 195, 254), (38, 202, 203)),\n",
       " ((39, 152, 141), (37, 206, 180)),\n",
       " ((41, 150, 210), (39, 198, 198)),\n",
       " ((37, 182, 164), (37, 206, 180)),\n",
       " ((41, 102, 169), (39, 198, 198)),\n",
       " ((32, 108, 105), (37, 206, 180)),\n",
       " ((45, 129, 176), (39, 198, 198)),\n",
       " ((38, 249, 164), (36, 248, 178)),\n",
       " ((36, 112, 165), (39, 198, 198)),\n",
       " ((44, 87, 98), (37, 206, 180)),\n",
       " ((34, 192, 133), (36, 211, 176)),\n",
       " ((43, 136, 195), (39, 198, 198)),\n",
       " ((20, 303, 218), (35, 246, 203)),\n",
       " ((37, 285, 154), (36, 248, 178)),\n",
       " ((30, 290, 186), (35, 246, 203)),\n",
       " ((39, 161, 203), (39, 198, 198)),\n",
       " ((32, 214, 164), (37, 214, 176)),\n",
       " ((39, 92, 103), (37, 206, 180)),\n",
       " ((40, 202, 265), (39, 199, 199)),\n",
       " ((30, 210, 193), (36, 206, 181)),\n",
       " ((30, 223, 132), (36, 212, 176)),\n",
       " ((29, 186, 121), (36, 211, 176)),\n",
       " ((30, 228, 247), (38, 202, 203)),\n",
       " ((22, 289, 267), (35, 246, 203)),\n",
       " ((30, 231, 252), (38, 202, 203)),\n",
       " ((24, 282, 221), (35, 246, 203)),\n",
       " ((32, 177, 218), (38, 202, 203)),\n",
       " ((36, 221, 229), (38, 202, 203)),\n",
       " ((31, 178, 214), (38, 202, 203)),\n",
       " ((36, 205, 221), (38, 202, 203)),\n",
       " ((34, 166, 238), (38, 202, 203)),\n",
       " ((46, 169, 194), (39, 198, 198)),\n",
       " ((33, 264, 167), (36, 248, 178)),\n",
       " ((47, 149, 196), (39, 198, 198)),\n",
       " ((36, 135, 162), (37, 206, 180)),\n",
       " ((38, 147, 129), (37, 206, 180)),\n",
       " ((45, 228, 168), (40, 223, 178)),\n",
       " ((41, 134, 70), (37, 206, 180)),\n",
       " ((34, 195, 121), (36, 211, 176)),\n",
       " ((40, 163, 165), (37, 206, 180)),\n",
       " ((31, 257, 214), (35, 246, 203)),\n",
       " ((47, 142, 156), (39, 198, 198)),\n",
       " ((36, 271, 171), (36, 248, 178)),\n",
       " ((33, 259, 173), (36, 248, 178)),\n",
       " ((30, 202, 86), (36, 211, 176)),\n",
       " ((40, 63, 163), (39, 198, 198)),\n",
       " ((35, 215, 158), (37, 214, 176)),\n",
       " ((38, 82, 121), (37, 206, 180)),\n",
       " ((37, 204, 178), (37, 206, 180)),\n",
       " ((40, 72, 168), (39, 198, 198)),\n",
       " ((40, 116, 174), (39, 198, 198)),\n",
       " ((45, 222, 245), (39, 199, 199)),\n",
       " ((43, 116, 91), (37, 206, 180)),\n",
       " ((37, 104, 176), (39, 198, 198)),\n",
       " ((34, 202, 137), (36, 212, 176)),\n",
       " ((33, 130, 180), (37, 206, 180)),\n",
       " ((36, 259, 174), (36, 248, 178)),\n",
       " ((24, 277, 240), (35, 246, 203)),\n",
       " ((34, 300, 150), (36, 248, 178)),\n",
       " ((42, 54, 84), (37, 206, 180)),\n",
       " ((36, 255, 176), (36, 248, 178)),\n",
       " ((31, 272, 212), (35, 246, 203)),\n",
       " ((39, 95, 104), (37, 206, 180)),\n",
       " ((35, 256, 172), (36, 248, 178)),\n",
       " ((31, 129, 163), (37, 206, 180)),\n",
       " ((29, 296, 248), (35, 246, 203)),\n",
       " ((35, 112, 164), (39, 198, 198)),\n",
       " ((25, 267, 229), (35, 246, 203)),\n",
       " ((48, 140, 69), (37, 206, 180)),\n",
       " ((39, 91, 106), (37, 206, 180)),\n",
       " ((38, 176, 146), (37, 208, 178)),\n",
       " ((35, 105, 161), (39, 198, 198)),\n",
       " ((36, 282, 171), (36, 248, 178)),\n",
       " ((36, 227, 235), (38, 202, 203)),\n",
       " ((47, 106, 44), (37, 206, 180)),\n",
       " ((45, 158, 179), (39, 198, 198)),\n",
       " ((36, 185, 238), (38, 202, 203)),\n",
       " ((39, 130, 182), (39, 198, 198)),\n",
       " ((36, 74, 141), (37, 206, 180)),\n",
       " ((38, 153, 135), (37, 206, 180)),\n",
       " ((38, 172, 148), (37, 206, 180)),\n",
       " ((41, 72, 90), (37, 206, 180)),\n",
       " ((44, 212, 186), (40, 204, 199)),\n",
       " ((49, 184, 257), (39, 198, 198)),\n",
       " ((39, 162, 107), (36, 211, 176)),\n",
       " ((38, 165, 145), (37, 206, 180)),\n",
       " ((44, 129, 188), (39, 198, 198)),\n",
       " ((42, 176, 208), (39, 198, 198)),\n",
       " ((35, 195, 175), (36, 206, 181)),\n",
       " ((33, 217, 138), (36, 212, 176)),\n",
       " ((32, 257, 186), (36, 248, 178)),\n",
       " ((23, 289, 236), (35, 246, 203)),\n",
       " ((34, 246, 169), (36, 248, 178)),\n",
       " ((35, 162, 119), (36, 211, 176)),\n",
       " ((31, 200, 177), (36, 206, 181)),\n",
       " ((42, 164, 211), (39, 198, 198)),\n",
       " ((36, 52, 72), (37, 206, 180)),\n",
       " ((39, 145, 180), (39, 198, 198)),\n",
       " ((45, 81, 93), (37, 206, 180)),\n",
       " ((35, 259, 173), (36, 248, 178)),\n",
       " ((45, 153, 198), (39, 198, 198)),\n",
       " ((50, 154, 216), (39, 198, 198)),\n",
       " ((42, 176, 221), (39, 198, 198)),\n",
       " ((36, 226, 144), (36, 212, 176)),\n",
       " ((44, 154, 194), (39, 198, 198)),\n",
       " ((39, 180, 137), (37, 208, 178)),\n",
       " ((45, 121, 165), (39, 198, 198)),\n",
       " ((34, 176, 231), (38, 202, 203)),\n",
       " ((38, 78, 95), (37, 206, 180)),\n",
       " ((35, 274, 188), (36, 248, 178)),\n",
       " ((40, 201, 257), (39, 199, 199)),\n",
       " ((34, 183, 126), (36, 211, 176)),\n",
       " ((35, 308, 186), (36, 248, 178)),\n",
       " ((34, 259, 121), (36, 212, 176)),\n",
       " ((30, 284, 221), (35, 246, 203)),\n",
       " ((33, 188, 214), (38, 202, 203)),\n",
       " ((35, 139, 121), (37, 206, 180)),\n",
       " ((38, 215, 221), (37, 217, 202)),\n",
       " ((32, 229, 221), (35, 216, 202)),\n",
       " ((28, 324, 201), (35, 246, 203)),\n",
       " ((42, 152, 106), (36, 208, 179)),\n",
       " ((46, 189, 245), (39, 198, 198)),\n",
       " ((34, 213, 126), (36, 212, 176)),\n",
       " ((35, 294, 143), (36, 248, 178)),\n",
       " ((36, 129, 160), (37, 206, 180)),\n",
       " ((42, 92, 176), (39, 198, 198)),\n",
       " ((30, 100, 117), (37, 206, 180)),\n",
       " ((38, 150, 132), (37, 206, 180)),\n",
       " ((29, 197, 106), (36, 211, 176)),\n",
       " ((37, 204, 220), (38, 202, 203)),\n",
       " ((27, 308, 209), (35, 246, 203)),\n",
       " ((32, 202, 101), (36, 211, 176)),\n",
       " ((41, 106, 171), (39, 198, 198)),\n",
       " ((43, 117, 92), (37, 206, 180)),\n",
       " ((45, 150, 182), (39, 198, 198)),\n",
       " ((40, 66, 166), (39, 198, 198)),\n",
       " ((34, 263, 189), (36, 248, 178)),\n",
       " ((39, 263, 161), (36, 248, 178)),\n",
       " ((34, 216, 114), (36, 212, 176)),\n",
       " ((40, 128, 226), (39, 198, 198)),\n",
       " ((31, 268, 199), (34, 246, 202)),\n",
       " ((26, 272, 259), (35, 246, 203)),\n",
       " ((34, 168, 73), (36, 211, 176)),\n",
       " ((52, 166, 213), (39, 198, 198)),\n",
       " ((38, 138, 135), (37, 206, 180)),\n",
       " ((35, 194, 154), (36, 212, 176)),\n",
       " ((34, 189, 132), (36, 211, 176)),\n",
       " ((39, 96, 105), (37, 206, 180)),\n",
       " ((46, 156, 202), (39, 198, 198)),\n",
       " ((29, 285, 232), (35, 246, 203)),\n",
       " ((46, 125, 71), (37, 206, 180)),\n",
       " ((29, 301, 176), (35, 246, 203)),\n",
       " ((38, 133, 141), (37, 206, 180)),\n",
       " ((38, 193, 192), (39, 199, 196)),\n",
       " ((26, 271, 192), (35, 246, 203)),\n",
       " ((33, 183, 125), (36, 211, 176)),\n",
       " ((35, 161, 98), (36, 211, 176)),\n",
       " ((34, 258, 124), (36, 212, 176)),\n",
       " ((41, 196, 114), (37, 208, 178)),\n",
       " ((30, 216, 197), (36, 206, 181)),\n",
       " ((38, 186, 189), (39, 198, 198)),\n",
       " ((34, 193, 143), (36, 211, 176)),\n",
       " ((45, 196, 247), (39, 198, 198)),\n",
       " ...]"
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.nodes.data('root'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "449e36ef-3315-4462-b73e-eb543cb824c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 68, 194)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "node = random.choice(list(data.nodes()))\n",
    "node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d7d64e6b-816e-4279-80c4-7e854cb44b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_root(node, graph, steps=0):\n",
    "    parent = list(graph.predecessors(node))\n",
    "    \n",
    "    if not parent:\n",
    "            return list(node), f'Steps = {steps}'\n",
    "    \n",
    "    else: \n",
    "        steps += 1\n",
    "        for i in parent:\n",
    "            return find_root(i, graph, steps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "id": "3429d390-4cc1-46aa-b912-912bffa64d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[35, 246, 203], 'Steps = 30']"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(find_root((27, 272, 204), data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "id": "067374b2-0b35-467c-bd04-1a7d824c84c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 246, 203)"
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root = nx.get_node_attributes(data, \"root\")\n",
    "root[(27, 272, 204)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "id": "ef258807-8192-4da8-8951-bbf986a869c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = [i[0] for i in data.nodes.data()]\n",
    "all_roots = [i[1][\"root\"] for i in data.nodes.data()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "id": "2bda2add-3067-4309-9078-75184558e367",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (np.array(all_roots)[:,0]==40) & (np.array(all_roots)[:,1]==223) & (np.array(all_roots)[:,2]==178)\n",
    "new_root = np.array(coords)[mask]\n",
    "new_root = [tuple(i) for i in new_root]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "id": "f966883b-0984-4297-9525-0b3e367e6bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "id": "ec4d3808-3ac0-4817-a0a1-abc430440a28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7510/3306571992.py:1: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.\n",
      "  adj = nx.adjacency_matrix(data).todense()\n"
     ]
    }
   ],
   "source": [
    "adj = nx.adjacency_matrix(data).todense()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e809cc76-abbd-4b32-86fc-4c8415057505",
   "metadata": {},
   "source": [
    "# Actual code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2c1c226-3608-4b4c-bd8f-2374fc4c1867",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def related_tips(graph, root):\n",
    "    \n",
    "    # collect tree nodes\n",
    "    coords = [i[0] for i in graph.nodes.data()]\n",
    "    all_roots = [i[1][\"root\"] for i in graph.nodes.data()]\n",
    "    \n",
    "    #create root-specialized mask\n",
    "    x, y, z = root\n",
    "    root_mask = (np.array(all_roots)[:,0]==x) & (np.array(all_roots)[:,1]==y) & (np.array(all_roots)[:,2]==z)\n",
    "    root_nodes = [tuple(i) for i in np.array(coords)[root_mask]]\n",
    "    \n",
    "    #get all tips\n",
    "    my_tips = np.array(list(get_tips(graph)))\n",
    "    \n",
    "    #filter tips\n",
    "    root_tips = []\n",
    "\n",
    "    for tip in my_tips:\n",
    "        tip = tuple(tip)\n",
    "        \n",
    "        if tip in root_nodes:\n",
    "            root_tips.append(tip)\n",
    "            \n",
    "    return root_tips\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7305f0a2-d719-4c0e-88fc-41d54facd232",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mlen\u001b[39m(related_tips(\u001b[43mdata\u001b[49m, (\u001b[38;5;241m35\u001b[39m, \u001b[38;5;241m246\u001b[39m, \u001b[38;5;241m203\u001b[39m)))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "len(related_tips(data, (35, 246, 203)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "id": "ddce9769-2ed5-4a21-8e4d-b126f34c2617",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "D = {}\n",
    "count = 1\n",
    "r = (35, 246, 203)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "id": "e60028d8-a595-4452-88ef-c3ba876318b9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "D[r] = (0, -1)\n",
    "for t in related_tips(data, r):\n",
    "    for n in list(nx.shortest_path(data, source=r, target=t))[1:]:\n",
    "        if n in D:\n",
    "            continue\n",
    "        else:\n",
    "            num = count\n",
    "             #parent name\n",
    "            p_name = nx.predecessor(data, r, n)\n",
    "            parent = D[p_name[0]][0]\n",
    "\n",
    "            D[n] = (num, parent)\n",
    "            count+=1\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "id": "38d3fac0-cbbb-4407-b87d-9cda55442098",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " (35, 248, 205): (2, 1),\n",
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       " (28, 268, 222): (22, 21),\n",
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       " (29, 288, 180): (774, 773),\n",
       " (28, 287, 179): (775, 774),\n",
       " (28, 286, 178): (776, 775),\n",
       " (28, 285, 177): (777, 776),\n",
       " (28, 285, 176): (778, 777),\n",
       " (28, 285, 175): (779, 778),\n",
       " (27, 284, 174): (780, 779),\n",
       " (27, 283, 173): (781, 780),\n",
       " (27, 283, 172): (782, 781),\n",
       " (27, 283, 171): (783, 782),\n",
       " (27, 283, 170): (784, 783),\n",
       " (27, 283, 169): (785, 784),\n",
       " (27, 283, 168): (786, 785),\n",
       " (27, 283, 167): (787, 786),\n",
       " (27, 282, 166): (788, 787)}"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ce3e254-f7dc-41f9-9e3e-ec3365626ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def root_travel(graph, root):\n",
    "    \n",
    "    root_path = {}\n",
    "    root_path[root] = (1, -1)\n",
    "    count = 2\n",
    "    \n",
    "    tips = related_tips(graph, root)\n",
    "    \n",
    "    for tip in tips:\n",
    "        for n in list(nx.shortest_path(graph, source=root, target=tip))[1:]:\n",
    "            if n in root_path:\n",
    "                continue\n",
    "            \n",
    "            else:\n",
    "                num = count\n",
    "                #parent name\n",
    "                #return list with name of parent node\n",
    "                p_name = nx.predecessor(graph, root, n)\n",
    "                parent = root_path[p_name[0]][0]\n",
    "                root_path[n] = (num, parent)\n",
    "                count+=1\n",
    "                \n",
    "    return root_path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1a851a38-22b5-4389-9264-29f1a31c395d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [66]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m test_travel \u001b[38;5;241m=\u001b[39m \u001b[43mroot_travel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [65]\u001b[0m, in \u001b[0;36mroot_travel\u001b[0;34m(graph, root)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mroot_travel\u001b[39m(graph, root):\n\u001b[1;32m      3\u001b[0m     root_path \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m----> 4\u001b[0m     root_path[root] \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      5\u001b[0m     count \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m      7\u001b[0m     tips \u001b[38;5;241m=\u001b[39m related_tips(graph, root)\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "test_travel = root_travel(data, r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "id": "e464dfb4-fc84-4635-b808-f136169534c7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 641,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r not in test_travel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "id": "7b4513bf-d365-4134-858e-5cdfde775613",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(789, 788)"
      ]
     },
     "execution_count": 644,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(test_travel.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "id": "9a37eeef-f72c-4de2-8acc-81a6c3fff5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "roots = get_roots(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "id": "70fa7a19-a54a-4eba-9c9c-473bb15d06eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "roots = [r for r in roots]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "id": "c293b919-5dc6-4879-8eb4-c0b65fc6f78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rx = list((map(lambda x: x[0], roots)))\n",
    "ry = list((map(lambda x: x[1], roots)))\n",
    "rz = list((map(lambda x: x[2], roots)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "id": "f8838d60-dcbe-46fe-8fcc-a2fba491e130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<mpl_toolkits.mplot3d.art3d.Line3D at 0x7f8af304fcd0>]"
      ]
     },
     "execution_count": 677,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot(rx, ry, rz, 'o')\n",
    "ax.plot(np.average(rx), np.average(ry), np.average(rz), 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bd049c8-7128-416b-a730-5de71b252563",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def roots_convergence(roots):\n",
    "    roots = [r for r in roots]\n",
    "    x = list((map(lambda x: x[0], roots)))\n",
    "    y = list((map(lambda x: x[1], roots)))\n",
    "    z = list((map(lambda x: x[2], roots)))\n",
    "\n",
    "    return (np.average(x), np.average(y), np.average(z))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "31c9373f-7d94-44ff-8f0e-eca02f845fb0",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [68]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mroots_convergence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroots\u001b[49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [67]\u001b[0m, in \u001b[0;36mroots_convergence\u001b[0;34m(roots)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mroots_convergence\u001b[39m(roots):\n\u001b[0;32m----> 2\u001b[0m     roots \u001b[38;5;241m=\u001b[39m [r \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m roots]\n\u001b[1;32m      3\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m((\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;241m0\u001b[39m], roots)))\n\u001b[1;32m      4\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m((\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;241m1\u001b[39m], roots)))\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "roots_convergence(roots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b6c608e6-4e53-489c-9b61-2b1a0cd510f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Merge_astrocyte(graph, convergence = False):\n",
    "    \n",
    "    roots  = get_roots(graph)\n",
    "    collection = []\n",
    "    \n",
    "    if convergence == True:\n",
    "        #connect all roots for continuous structure\n",
    "        convergence = {roots_convergence(roots): (1, -1)}\n",
    "        collection.append(convergence)\n",
    "\n",
    "    \n",
    "    for r in tqdm(roots):\n",
    "        visit = root_travel(graph, r)\n",
    "        \n",
    "        #write first root \n",
    "        if not collection:\n",
    "            collection.append(visit)\n",
    "        \n",
    "        #write subsequent roots with updated vals\n",
    "        else:\n",
    "            value = max(collection[-1].values())[0]\n",
    "            \n",
    "            for i in visit.items():\n",
    "                \n",
    "                #check if current node is root\n",
    "                if i[0] is not r:\n",
    "                    new_pos = i[1][0] + value\n",
    "                    new_par = i[1][1] + value\n",
    "                    visit[i[0]] = (new_pos, new_par)\n",
    "                    \n",
    "                else:\n",
    "                    new_pos = i[1][0] + value\n",
    "                    # new_par = i[1][1]\n",
    "                    new_par = 1\n",
    "                    visit[i[0]] = (new_pos, new_par)\n",
    "                    \n",
    "            collection.append(visit)\n",
    "        \n",
    "    return collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "id": "ce40b136-a16f-4502-a68f-630735c6e2ca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 21/21 [00:09<00:00,  2.24it/s]\n"
     ]
    }
   ],
   "source": [
    "M = Merge_astrocyte(data, convergence=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "id": "bb76cb33-e34f-4bd2-a904-883289cbfda0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6912, 6911)"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(M[-1].values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "id": "49cfc5a7-7240-4597-b4c9-a27e712ecf05",
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = Table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "05b0c618-30ab-4523-b89f-17cd299ae623",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "Y = []\n",
    "Z = []\n",
    "POS = []\n",
    "PAR = []\n",
    "\n",
    "for r in M:\n",
    "    for n in r.items():\n",
    "        x, y, z = n[0]\n",
    "        X.append(x)\n",
    "        Y.append(y)\n",
    "        Z.append(z)\n",
    "        pos, par = n[1]\n",
    "        POS.append(pos)\n",
    "        PAR.append(par)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "f8a203ff-5250-43f3-b8af-609cb998f9ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "dd['X'] = np.array(X)\n",
    "dd['Y'] = np.array(Y)\n",
    "dd['Z'] = np.array(Z)\n",
    "dd['index'] = np.array(POS)\n",
    "dd['parent'] = np.array(PAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "d3661c84-009d-4d78-882a-a12e78dff1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=6912</i>\n",
       "<table id=\"table140234997866960\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>X</th><th>Y</th><th>Z</th><th>index</th><th>parent</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>37.0</td><td>215.1904761904762</td><td>189.66666666666666</td><td>1</td><td>-1</td></tr>\n",
       "<tr><td>38.0</td><td>202.0</td><td>203.0</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>38.0</td><td>201.0</td><td>204.0</td><td>3</td><td>2</td></tr>\n",
       "<tr><td>38.0</td><td>200.0</td><td>205.0</td><td>4</td><td>3</td></tr>\n",
       "<tr><td>38.0</td><td>199.0</td><td>206.0</td><td>5</td><td>4</td></tr>\n",
       "<tr><td>38.0</td><td>199.0</td><td>207.0</td><td>6</td><td>5</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>40.0</td><td>202.0</td><td>267.0</td><td>6906</td><td>6905</td></tr>\n",
       "<tr><td>40.0</td><td>203.0</td><td>268.0</td><td>6907</td><td>6906</td></tr>\n",
       "<tr><td>40.0</td><td>203.0</td><td>269.0</td><td>6908</td><td>6907</td></tr>\n",
       "<tr><td>40.0</td><td>204.0</td><td>270.0</td><td>6909</td><td>6908</td></tr>\n",
       "<tr><td>41.0</td><td>198.0</td><td>234.0</td><td>6910</td><td>6874</td></tr>\n",
       "<tr><td>41.0</td><td>199.0</td><td>235.0</td><td>6911</td><td>6910</td></tr>\n",
       "<tr><td>41.0</td><td>200.0</td><td>236.0</td><td>6912</td><td>6911</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=6912>\n",
       "   X            Y                 Z          index parent\n",
       "float64      float64           float64       int64 int64 \n",
       "------- ----------------- ------------------ ----- ------\n",
       "   37.0 215.1904761904762 189.66666666666666     1     -1\n",
       "   38.0             202.0              203.0     2      1\n",
       "   38.0             201.0              204.0     3      2\n",
       "   38.0             200.0              205.0     4      3\n",
       "   38.0             199.0              206.0     5      4\n",
       "   38.0             199.0              207.0     6      5\n",
       "    ...               ...                ...   ...    ...\n",
       "   40.0             202.0              267.0  6906   6905\n",
       "   40.0             203.0              268.0  6907   6906\n",
       "   40.0             203.0              269.0  6908   6907\n",
       "   40.0             204.0              270.0  6909   6908\n",
       "   41.0             198.0              234.0  6910   6874\n",
       "   41.0             199.0              235.0  6911   6910\n",
       "   41.0             200.0              236.0  6912   6911"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57c0c826-e342-4467-b0a8-3daebdf61b99",
   "metadata": {},
   "source": [
    "*Woohoo!!!*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "46e9731a-b2bc-494c-849b-a4377b03807d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SWC_from_Graph(graph, cell_type, filename):\n",
    "    \n",
    "    astro = Merge_astrocyte(graph, convergence=True)\n",
    "    lines = []\n",
    "    # credits = '# Created by Anya :))\\n'\n",
    "    keys = ['# index ', 'type ', 'X ', 'Y ', 'Z ', 'radius ', 'parent', '\\n']\n",
    "    ntype = cell_type\n",
    "    soma = 1\n",
    "    radius = 0.125\n",
    "    \n",
    "#     #txt version\n",
    "#     with open(f'{filename}.swc', 'w') as f:\n",
    "#         # f.write(credits)\n",
    "#         # f.writelines(keys)\n",
    "        \n",
    "#         for r in astro:\n",
    "#             for n in r.items():\n",
    "#                 x, y, z = n[0]\n",
    "#                 index, par = n[1]\n",
    "\n",
    "#                 f.writelines([f'{index} ', f'{ntype} ',\n",
    "#                               f'{x} ', f'{y} ', f'{z} ', \n",
    "#                              f'{radius} ', f'{par}', '\\n'])\n",
    "    \n",
    "    #ascii version\n",
    "    data = Table()\n",
    "\n",
    "    X = []\n",
    "    Y = []\n",
    "    Z = []\n",
    "    POS = []\n",
    "    PAR = []\n",
    "\n",
    "    for r in astro:\n",
    "        for n in r.items():\n",
    "            x, y, z = n[0]\n",
    "            X.append(x)\n",
    "            Y.append(y)\n",
    "            Z.append(z)\n",
    "            pos, par = n[1]\n",
    "            POS.append(pos)\n",
    "            PAR.append(par)\n",
    "\n",
    "    data['# index'] = np.array(POS)\n",
    "    data['type'] = ntype\n",
    "    data['X'] = np.array(X)\n",
    "    data['Y'] = np.array(Y)\n",
    "    data['Z'] = np.array(Z)\n",
    "    data['radius'] = radius\n",
    "    data['parent'] = np.array(PAR)\n",
    "\n",
    "    data.write(filename, format='ascii', overwrite=True) \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "id": "abdb713d-de34-4308-92d5-620ad0d42945",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 21/21 [00:08<00:00,  2.39it/s]\n"
     ]
    }
   ],
   "source": [
    "SWC_from_Graph(data, 7, 'Astro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e877acf3-8981-4a98-8a1c-0c7ae40b6877",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6f0a76bc-7ca7-4039-98e9-987f202b6f5a",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "169b5e69-c15d-4e7c-b16b-213ae940334a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tmd \n",
    "from tmd.view import view, plot\n",
    "import morphio\n",
    "import sys\n",
    "import morphio \n",
    "import neurom as nm\n",
    "from neurom.view import matplotlib_impl, matplotlib_utils\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8cea0230-5ef9-445d-9a34-8181abb345b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "neuromorpho = '/home/anna/Documents/jupy_projects/TMD_test/Astro-10_2.CNG.swc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55ce5010-c014-4387-8b99-209105f3cc29",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e4c2fad6-0749-4ada-b91d-a9e6cfc081dc",
   "metadata": {},
   "outputs": [
    {
     "ename": "RawDataError",
     "evalue": "File: 3wk-both1-grn-raw.swc does not exist.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRawDataError\u001b[0m                              Traceback (most recent call last)",
      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m morphology \u001b[38;5;241m=\u001b[39m \u001b[43mmorphio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmut\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMorphology\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m3wk-both1-grn-raw.swc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mRawDataError\u001b[0m: File: 3wk-both1-grn-raw.swc does not exist."
     ]
    }
   ],
   "source": [
    "morphology = morphio.mut.Morphology('3wk-both1-grn-raw.swc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "id": "fbdbffa5-2ee5-4593-89f6-9ae907e7454f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell = tmd.io.load_neuron('3wk-both1-grn-raw.swc', remove_duplicates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "id": "545af31e-5433-44d6-906d-e149850c6ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "view.neuron(cell, nosoma=True, diameter=False)\n",
    "# plt.savefig('cell.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "id": "3baadb60-5fa8-4e13-b79f-24be6b3e2d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "ph_astro = tmd.methods.get_ph_neuron(cell)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "id": "728d8ffb-1586-440a-bea6-3aa6bbcfca62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.diagram(ph_astro, subplot=True)\n",
    "plt.savefig('diagram.png', dpi=300)\n",
    "\n",
    "plot.barcode(ph_astro, subplot=True)\n",
    "plt.savefig('barcode.png', dpi=300)\n",
    "\n",
    "plot.persistence_image(ph_astro, subplot=True)\n",
    "plt.savefig('persistence_image.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "id": "8f02c034-bd77-4ddd-bc36-246adae467b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Morphology <soma: SomaSinglePoint(array([[3.80e+01, 2.02e+02, 2.03e+02, 1.25e-01]], dtype=float32)) <center: [ 38. 202. 203.], radius: 0.125>, n_neurites: 22>"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = nm.io.utils.load_morphology('3wk-both1-grn-raw.swc')\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "id": "74ba21b0-0b34-48c6-937f-c196af285f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "matplotlib_impl.plot_morph3d(m, color='blue')\n",
    "plt.savefig('cell3d.png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "id": "f6de4c9b-8fd2-4922-97db-dfa19f146311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BY0bBG5XDrYClmpOtj48klnkLFizgnnvuYfr06TRr1oz169dz883BflBHjRrFoEGDuOuuuygtLWX79u0cd9xxPPbYY8ydOxeA2bNn8+yzzzJjxgxUlSOOOIIBAwbQuHFjFi9ezCuvvMI///lP+vbty6hRo5g2bRqvvfYaf/rTnxg3bty+e23fvp0JEybw2GOPZaQaKsy6SVVXquocZ3sLsAgTxOgsTM8EZ322s30W8JKq7lLV7zAqrX4VVV6Lh2R/wqDjDQ/OTlkslkrC5MmTOe+882jWrBlAQtVO3759efbZZxkxYgTz58+nQYMGZfJMmzaNIUOGUK9ePerXr88555zDhx+aecodOnSge/fu5OXl0bVrVwYOHIiI0L17d5YsWRJ3nddff51jjjkmI6omiDaZLmOISDHQG5gBtFTVlWAEiYi0cLK1AT72nLbcSfNf6xrgGoD27dtnsdTVhGzNXdi2LPPXtFhSIUdzcVS1TECggoIC9u7du+/47t27Aejfvz8ffPABb775Jpdeeim33norl112WZnrheF3Le51O15SUhKX96WXXsqYqglyME9CROoD/wVuUtXNibIGpJWpRVX9p6r2UdU+zZs3z1Qxqwf+L/xsjgmMPwCGesYhdqyMbY9uaBaLpRoxcOBARo8ezbp16wBYv349xcXFzJ49G4Dx48ezZ88eAJYuXUqLFi24+uqrufLKK5kzZw4AhYWF+/L079+fcePGsX37drZt28bYsWM57rhoswo2bdrE+++/z1lnnZWpx6zYnoSIFGIExEhVfdVJXi0irZxeRCtMbAowPYd2ntPbAisqrrSWlDnpA5jYHwrrxdLWTIttl2yp+DJZLFmma9eu3HXXXQwYMID8/Hx69+7N/fffz1lnnUW/fv0YOHAg9eqZ/8TUqVN54IEHKCwspH79+rzwwgsAXHPNNfTo0YPDDjuMkSNHMnz4cPr1M1r1q666it69e5dRJyVi7NixnHLKKfvumwnK5SpcRApVdU+KeQUz5rBeVW/ypD8ArPMMXDdR1dtEpCswCjMO0RqYBHRKNHBtXYU7/PAWfHgu7N0Z3xV3exJ+a6NE3fVkeUYJDF4Eb3aBg2+EL/9i8v74oREcfqybDksGsK7C0ydrrsJF5AYROdez/y9gh2Oemsoo5THApcCJIjLXWU4H7gNOFpGvgZOdfVR1ATAaWAhMAK6zlk0pUrLFCAgvjp40K+zeYNZf/iWW1sLTTZZa8fmtKazFUmWIMiZxA2byHCLSHzgfuAiYCzyU7GRVnaaqoqo9VLWXs7ylqutUdaCqdnLW6z3n3KOqB6nqwar6dqQnq8m0Pq1smrcBj8rZ3yc+3vBgOG6s2Q7qKZzxVfr3tlgsOSWKkGgDLHG2zwBeUdXRwAjgyMwWy1IuCgMGifd6tIJRv+Trtk18vHYTaHd2+PH6B0S7n8ViqTREERKbAdd86GTMGAHAHqAok4WyZIGDf5l63vKog949Gpa8GJ9WsiMz17ZYPJQ39HJNJJ06iyIk3gWecsYiOgKu+qcr8F3kO1sqloK6Zh2kDspkw732o3jzV4BSR0i0Oz9z97HUaIqKili3bp0VFBFQVdatW0dRUbRv+igmsNcBfwQOAM7zjB0cBrwYepalZnHydKhXHJ+W77yU34+u8OJYqidt27Zl+fLlrFmT0GWcxUdRURFt2yZRH/tISUiISAFwCXCfqsbNVVDV30e6o6VykC21T/Ojy6YV1DU9GHe2t/feNnqdJQ0KCwvp0KFDrotRI0hJ3aSqJcADQGF2i2MpN1sWw2udwo+n2j23YwcWi4VoYxIfA4dnqyCWDLG3JDYGkA7pCAcrUCyWaksUIfEU8KCI3CQix4nIYd4lWwW0RKTBQWZcIIyV75i1X8VjG3qLxRJAlIHrUc764YBjCuSXvziWcpNXmHhewuwbwo9lS1Csfh9q2ci2FktVJIqQsKNEVYU1np6Ef2B4y9cVX57pF1av+BJ2sN1Sg0hZ3aSqSxMt2SxktSYbX+/blwenuw1bRTVweUUg+bBzJTTuWTH3zDZWLWepYUSKJyEip4nIGyKyUETaOWlXicjA7BTPkhbNjsaE4/BoAIMat0Y9sluOYTvgQicgyuGP2q9vi6UKEsUL7MUYr6xfY1RPrjlsPnBb5otmSZt67TDDRKWJG+ZjRpp1v6dg0Cdljyf7at4TEDNq89dlF4vFUmWJMiZxG3C1qr4kIld50j8G7s5ssSwVwn7dzHrm1dD6J2a7z99h1nXx+WbfDAX1oOcf4tPXBcTueKOzWTc9EtZ9XPa4pfy4Uf7OTxTY0WLJDFHUTZ2AjwLStwI2NmVl5Y0uMG9E8nxNnCkwbQabdVOPY98vHwF14lF4exeFnmDu7zkBhvKLIL8ubP3OrGs7PiEX/9Ms348rex1LNEq2VEy0P/sbWYgmJFYAnQPS+wPfZKY4lozhBvrZlcS3jauO6jHCbNdzzGd3rTXrg2+MHffTtC/UamK213xo1qU7oXA/c9/S7bH7z/yZWaZfkMbDWCyWXBFF3fRP4K8eVVM7ETkO+DMmpoSlMnHuWhjTEAYvhKIW8Pn/S/3c3g/Bp7eY7dVTzTrP55HFNQM9YULZGd4718PMK2H3esqwd3fq5bBYLDknZSGhqn8WkUbAe5j4EVOAXcCDqvr3LJWv+hBkW5/N7nye00lcNQmKWkY7t9lR0H0EzB8Bna41SxhN+wanNz8axraEs5aVPVZQzwQqslgslZ4oPQlU9S4RuQc4FKOqWqiqW7NSsurE1hxMI9m1zqz/dxG0jGih3Pwos7hCIh3qtDDreu3SO9+SPuWd7GcnC1o8pCwkROQZ4EZV3QLM8qTXA/6mqldkoXzVg/cHl03b9EXmrh/0p853ggzVOwD2OIOcHwwx621Ly+YLvbYz1yKvABp1K39Z913XNkSVmlWTc10CSyUhSk/icuB2wG9WUQe4DLBCIoxNCwMSs9xAFjWD+h1h62LYsdqkLR9n1uOLY/naD01yIceqae9uMyidKu78iC//Fp4n0+q2TAqeyirEdq6toBtVwme35ISkQkJEmmCm7wrQWERKPIfzgcHA6uwUr7rg/OHmjYhZCbm+jPLrGLt31+Y9k41Tvydhxwr47PdQstmYo275Auq2j+Wp3Sy27be/H7IKxu4Pg2Ya9xr1DoDaTVO798a5Zp3IoWAmeO8EaNwd+vw189dWhRfzKpewWDWxYu7T8oT0zquswtWSNqn0JNZiWjkFwj6JbXS6REgtUJ9VjzgDy6U7oKBB2XOg/H+4/Z2xiA6Xxl/zbN8YSd/Hzdpve1/HGfAOG5xORP2OZt35l8HHv3os8fluLyPZ8+/eAEtfzpKQKEmep6JxhXTYO1MeRnumO71onTpbDKkIiRMwvYjJwLmA165xN7DUH9LU4mPoRhhdt+xcg/6vwwdnlJ05m62vsQ1zzfrVVrE0yYtNlHNZ+ED579Wkt1n3CVE39flbZtRN3X8L084r/3WSsXmxUeHlmimnmHU2ZltnaoKe7U1UK5IKCVV9H0BEOgDLVFONf2kJdZ/g/ona/iQ+feOC7JanZLtZ71yVON/cCnLF5QrJdBndMCbgppyamTJ58c4NmXEl1D8w8/eo7GxfDnXb5roUlhwSZZ7EUhHpLiI/Aw4CrlDVlSJyNqY38Wm2ClllifpltsPTIUv1KzvK13jjnsbdhje2w3fPQ4fLzXZ+XSisF3/OogdTv35U/EISYO+eWI8nGd76dSPuZZL3BsS2CxvCd89l/h5R2LEys9dL5Yv/k+thwNjUrpdJiz1LpemRRTGBPQV4DXgbOBFj1QRGYAwHzs5w2WoejQ6Nlj+quqagHgzyud/67nk46rnwc3pnQPWUDO+foWQrfBhRfXTOGni1ublGJv9YXqHd/1V4qVZmrpsuuzfGtku2md8zU2z8PDj9h3GpX+PNLhkpiqVyEcV30x+Am1V1CGYswmUq0C/ZySLyjIj8KCKfe9KaiMh7IvK1s27sOXaHiCwWkS9FZFCEclY+9joDoBvnJ27Y67apmPJUJLs3hC8l28rml4KYWiwR3npc8aZZf3ZXZsoMcMbXcLhnMHzzV5m7drrU2R8O+pnZnnFNcJ5UPxw2fenbd1Sd3gFxV9hG/RipBF+/lswRRUh0Bd4KSF8PpOJj4TnArzi+HZikqp2ASc4+InIoMMy556nA4yJSdc0tXN9GX/8j+Hgqf8Kq6pFzTJPwZebPY/nc51v+GrQ8Mdo93LgWO5M4M4xCg44xddaCP8HXT8SOFTQw4yGjG1bs71KrcUxF5/elBdHKsnpS/H6jrmbtVeEtfjr16+31WIK55Vj4UGoeiCsDVfX/VQFEERIbgKBP3cOAkHiZMVT1A+ItowDOAp53tp8nprI6C3hJVXep6nfAYlLorVRaXPm2X5IZy0Ev6iip+i9w4X7BS0HAbO9PfgFbnK/cVL9Iix0T355/LHuO25inw84fzXrrt2Y+iXvt8zdXnLtuP20cIZFIRZgKDTrF73vfzbxaQJ6JM5IqQY4b5/7arKv6+1vVy19OogiJUcADItIWMzeiQEQGAA8CL6R5/5aquhLAWTsOf2gDfO/Jt5xgAVU1cBtDrx+kj6+Jd4/xRsRQolXhxXUb1aEbgpd+T8Y36qME9usOrZJoF/3PXuqorYpalM1bnsa83blmfegdwa7Ss0HYh0IYpbtgdxrmsK1ODj92+udwvKPCS1VQh3X0D7kxWrm8VMQ7nuwee0PmylTU/68S/M+jCIn/A74DlgL1MRPrJgPTgHsyXK6gmgl8W0XkGhGZJSKz1qzJoLrBS3l/qM9+Wzbt2+fgveNi+5vnp16WoPLMG5FGwdLg5QZmyRanTIPe94cfD3r2if2zU5a6znySBgdl5nrl6dWE8eMH8MFZmb1mw07QOqJJcX5tM0Pfjzt2Miof5txS/rKli/+98e4n+n+vmW7W80ZkukRVhpSFhKruUdWLMYGHzgcuAg5R1UtVtTTN+68WkVYAztrp37Mc8LoPbYsJehRUrn+qah9V7dO8efM0i5El3MA/C/4Un97jT1DUHLZ/X/acKAxLt9rLQelWs1QWpMCog7LB3j1mPaGfaUgmOq4qNsyFvKJo1xol4b2a8nyE5NWCH6fCu8emf40gSneZdRSz26Df4fsxzsZe+OLhcherwsnVUKj77kHmPywiEqUnAYCqfqOqY1R1tKqWN8r9axjHgTjr8Z70YSJS25nE1wmYWc57VTznrDYxo/HNaO52B5yxCI4dDcc6f6L+403XPlH3fsIR8ft5np+votQhkkMz0Cmnx7Y7X2+Wbk549fJa1CRqqHdvMOsfp5r1pJNiEfxyTUtnLke632nuc/uff800sx7buuw5a2dC6e6y53jVfaMELvAFo0qXbI3LpXLNFo7wjRK0KxPs8gzf5mLsy0MkISEiQ0TkryLykoiM9i4pnPsiJkb2wSKyXESuBO4DThaRr4GTnX1UdQEwGqPSmgBcV47eSu6ovR90/kXwscKGxgNre0fv3fbM+OOnB9itr08gJytKd3nhroq5j59REh8B76u/mWXb4vJfO+xLLa/QCJ8+fyVuStHudbAjqa1GjD0p9LyS/X7Jjg+cknp5wDgvDOPzP8J3/zHbRfuXPf7JtTDv/8qmu2o5V2BvCwg4FZXy1kuqedIhq0HDAqawhd0vG2pMD1Em0z0EXA9Mx3h9jdRoq+qFIYcCI+Ko6j1kfqwjOq5r5oqe/bh2evx+/9fggzNjE8YAvnm24sqTDq510LvHhOeJ4vLh6FEwzvmy7eSYz3pNU9Ml2Zda69PgpKkw8Vg46QOTllcIzY5MraEImg+SiLB3zZ/u3S/wqL/Czi1oEOzz6TNfg//Fw7He0zkedZN7P9XE5rHvHm3W+bXNOq/I1Fe2voh3rjHq24oiijeEdNuMVL0tQ9Z7GlHjSQxV1fFJc1YndubIC/rMn8Xvtw3wcTSjkofwcM0i1/4vPI/rLTaMz34b85jrCgjvH+/rJypGeLdwBF2L4xLnC8I7M7qgQWIhkC6r30+ex9+YFLWGnSuMEQUYt/VaCofcDPN8xhZetdQxL8P0C8x+kH+ytc6sflclN2xH/DUyzastor8DJ38YbzhSHio6NHEyMvxBG0XdtB2oec5ZwiR6Ol28GVellm+wM/vV+0O/fohZv+OMS5w+v2yeSmAuF0dt5+vu5Gnhy7EvwbGvxJ+3xmlkXiyERfcnDlxUFSis73E7ksZX36mzk+f57M74fe+78OOHZfOLGBNkiAmxC7bDsF3QzelZhDU0ruXTRVp2MP60z8x6aASz3FTe27CynDwt8TVGCZzycdn05ikO9Puvuz2Jw+tc/gcnn5YVK6woPYn7gNtE5GeqldHRfpao4+hk/S9plD+7G0/im3+llj/Ih5M7wWydMy7hnfzU5+8w67rUy1NRuOqG5gnUTQA7fKaTe50IeFpiDJ8LA2YXZ4rKJli9uGW7cG/8OohEvTVXdeSPQeE29idPj3eD7vqImhAyf7XQ+TjyGhK4NHbm+xR67rVnO+RlyUpoTwrCqNkRyfOk+uXt1qVL0PuTyhhUeQjrKax6B1ZnPuxslJ7EU0Ar4AcR+VBEJnuXjJcs22TSYiLZdQbNMOu+T8Lp84x5W+lus/aauoVxysdw/Dux7Qt8g8cS2UitctHGFwO86ZHwE0conjIDTp2VHZVSptx4ZEPQeAeWReLXYNyXuLh1E1ZH7lezfzzCde3hj5PhTiBb/0lsCWLl285GfmJT2fFt4cNzyqavmhQ8gB5Un988W/YreXRD+ODc8PtmgzqtgtO9moXNWVS4lLFC845datngZhkgSk/iSeA4jLXRamwQ3NRxrT4+8cy4Jg/Ya9xzA0y7IPx875dQQT3Y4nM21+la+OTn1cexWkGdWOwGtyHaL+KM9FTIxXiT1/DA5fxdMLp2fFpJkq9RdwJdKvrn2h7XaqlE/KvVyKxPmRFLa9YvvtxHvQAfXQbnrIX3BxtT2bBr7t7guPrwMfsmaHdO/HN47+Hdn3EFdPMFwPT25qO++8vfiJYfjCFG0Kx+f1neSSOSYxBer8b+wGBgBOx7rtosn4i2RCkTRUicD5yjqu9lpSSVhbA/XVgAoSiIp7pdjV2p4/F0WVIrYsPHP4X1s+LT3Bdo3u+NPjnI+VtVw62f+SPMer9emb9H7WbJ87i4sRL+m2ErmkwMMpY66rnpYQaEEXHfn2UvxzzyfuuzpOtwqRESRU3hsEfgvaODr+U2dP0DYlIUtYTP7w4+zzved/L/zPUzNRdolMR7+AWYfbP572xbBk16xR/rcjssus9YgR3xz9Tvk0nX9UE9ru0eE2MRICBMcgaIIiTWAj9kvARVhfKYmRU2KPuiLHsFpp0Px7wUfE6Y/6L1s2IeO133zi6f3w1d7wAqmZD48q+p6Y69uDNdazU18xI2zs14sfaNNzXqBptC4im4uNN0dq3NfDmCcHX6QQ1MXh3YuwMOvR0W3hdTDy0NeJfK00h9+TfQFNShzY+KbX/zjFm/0hjyi2ICLIiBE8NVdd7/m/f6XvKKnHGrNIZIW58Gs4k15F8+AgdebqwKW58WL5BaDTJCwmulllcb9lbgnCHx9ACD6iy/jjFweKNzxjUKUYTE74G7RWS4qlYivww5wh2MfufI9M5vP9SswyY1rZhgXEN7cV+Qwf4GzXlpuo+I762kymudoCTFRrwoRCebiGWvwpoUTDS95BVCu6GwemrMSqqwIWz9Bt7KsOopmYCA+Gh+XgoapPYBsf5Ts557R9ljS19Ofr6XYdvNe9DrXrPs3QP9noaZV5kv5HW+MYQwFU4ijp8AH10OuwJUcjtWEjqc6Zo979kIKciXchHVtNYrMOu2N+spp0GjHrBpHhQ2MhNWW59mju1xftfFj5v14Y/ErjVkBfw3wlyGZOVJRtC4Y70DYr9nNmKeO0RpUW4FijH+lpbhewVUNQtK4wrAqwuNIoGHbobRRdEnSvn5XwIVQePeqV3D/cro/vvE+cLYGmHWcmka3VnXxUOYS486+8e6zn6zYm8D7FrdbEzRGWIqpPrbe2fAth0S286vA0tHJb/PLmeQfKEn0p/buHn1zXm1gt1uJyxbITRyTKRn3wAHDo92/q51ZdOa9oGOV8GCe6DJEbDeMz4xtnX4JMgDfwoHOONrY5rAeetN45sKQaa62aZkqxEQAPWLzdrtRbi/S5CBw56Nse2CBuY327sb0Ng7lUmDhhxa4UUREmOSZ6kmJGs09mwxAeLB+F5685DUzgujfcigdf1i2PBp9OtF4dx10b6IvH+OVJ73IoWXasPe0nB9aZ1WMSGR6Ku8qKWJp+DOtgao1z7x/cWnpy2P+uWCXfBybTjSp5/v94/k123cy9koLZuv/fmQVx+mnQktTjCmjFGp5fyG7c6FJn1iE+RS4b8hYzPuRMedK6GgvllcareI/Qe85NeOmT5D2d6wl/W++R+zy+FW3MsoMebCLyaw+ssrhCErTU9+XMige0F9GLwIkNh/3KVWY+j9EHx6i/mK3/SFsWr6cEjZ66Ra5rD3Z1y74PR0rxeRlIWEqlawh6sck6iSX2kI9Rzrm0YhaohUOPYVmDYUVjqNQqOuZccZCvcz+t1s4Vq+SAEUX5T6ed9FCCEydHNi/a0qjNkPhm5KciGJt78Pwv+7nToD3k6xR5YM16PpmP3i05sfF3xvL36rGG841B+nxNR96QgIiPUkjnPKOOuXqZ9bKySwpOQD4ghwcQaxnefbudqkjcqL9XhXTQq+jhdvHa3zGWDU9tRRMp1/skYwSK0HsZ6qSGxMKizP+Ztj9eqnVmPocrMREmDie6fbKCfrJQQJ4woksgJbRE4EDsW8LQtUdWqmC1XhzBth1lG6dNu+K/993TkS7tf5nk3xX+phLI5gYZEqWgINQ/4QQRz2MMy5ObW8/q/LMAobJs8TlX1f8CmSqPFxrX68A5gQP660tzT5xLG3epngSi6TT46N9Rw9ysxryIR6ISiGgv/ZEjVsix4yja3uwfzdQ/JumGPWk08qe6w0oKF3y9HksPj0Pn+BN5x30BUQ6bovWRQSkySV8SN/ns3OnJ0lLyVWD0ch6u/b4w9lXaWEsXVJ5OIkIoqDvzbAWOBwYrEdWovILGCIqiaZr17JGLbbqEHykjReE/rCUSPho4vN/j79bQa6cu3ONTpbl5WTYfp50PBQ2Lww/Dy/X6dM4XftkAov+0KQNjwk+mSitmdHv28isuWM0TU28I9DrZ0GnX8JXz1m5quEmUm6qq+Nn5nFpeVAIyCXj4XiBI3Qns1GIFXU5MlDfgXL34wPASaFsGZq6uMmL3scD/pVT037xo8JLcuQRvuUj+Fdj0HJhrll86x1xlhScQy4YoJZewXEqsmJTahdVVQQ6fQMXAEx89rE+QBWvBX9+gmI0pP4K2a2Rkcn7jQiciDwH+fYeRktWbbJKwTUuIAIig3gNjLrZ8cEBMTPkPa7OYhKfi2zuOzXxazbDDZC4qAIMYa/eSr9cuQVxVxhRMXrvhvSG0OJOlCbLVL+usuDhp3jk4pamvU3T5klSEj9ZIHxiLvrR9PA7FobzffWK42gSd9YI7NhPil9rBw7BqY5f8+SCDEeJA9O9jlT2Py1MbMMmhyXDO85QYK8ga9OXVyhnIrwHyVmjAjMIPq3z8KWb8rmm34RbPsWDkrBn5o7sdPL5IGJP25cVVRQeRNNnPWTX9cxI3YG0VP5r7hGBRn6WIoiJE4GjncFBICqfisiNwApKCMrIe6XXSK1Sb8nzZe7+8UztpVxFz2xPzQ53MwB8JPuj9Po0Nh5ix6INnEnSl4/F2xLbM8ehdH14PyIFl+SD/kVbBdRrj/QXvjJorLJHX9mPJKG0aAjnLva3PvcNFyCFDQwDeZOxwXG9GFle5wfDi17nhuzBILjQEShYSez7hEyCS4ZddvD/0LGvhqHGEhuTMFE2UtTx+dUj7uNkGjcEw7/Gyy411ip1WpkLOSa9oP6HZJfr/Xp0PN+WPRnM2en2THm426/7rB8XLSyQeI4G/738oJtULITRtcx/yvJhyOfSVLewYmPRyQNo/oyJPA6Vsk58R2YdELicYCO1xgh8bkntIXrLvrHqfGO9qoqkgcFdZPnS5VMXitTRBEK80akN7t329L07hdEkIsKgBVvw0fDoU5LxzGizx3D9x5h27ArbPYZQuQXZaZ8c29P77yWJyQ+nl+nrCVc1PfJHYdy5zA16AgH/9IsLqPECIl1s4xJ8wce31KSH3OXA2aMqettZhklJg67SyrC0l/Xp3wE49vF0s78Dl5LIKzcWCE5+l9FERKTgL+KyIWq+j2AiLQH/kJV7Uk0dXwinTjR+AZK9BXo/wI7aTpMPMaE0OxTxV1Z1zSSqXUW3msC70SdoBTFzYcf17vovN8alWaYaqb1aaY3ArBpIWz6EqadY0Lh+mkxIGZV5QqdnveYJRMD411ui35Og4Ng9RSz/cVfoE6b+OMXOG5AvM/sDvKnOhHQZYfjIOJt3wB5fac3tOQl2O2bQd/8ePPh1zdL/+nVU8rOS3HnZ5TnN5l1Q2wekavyrWgTWOAGTAzqb0VkBUYZ2gaY5xyrehTUMev9A4PjJaaF46vG1U1/+3xmymTJPfsmRiVg8inhx/yTqVL5s+52zH8//6NZe+ckhNHo0Jhb+fYBaqZs4T5f7xALomR0vMacP+cmaOw04FNONyapO1aZAe2o7C2Nd89x0jQTSRDKjpO5+/UPhK7/iD9Wp7WJOJgtZv48fm7PKDGz28vL4qdi72ymHAw6RBES64B+wAnAIRibh4WqOjGjJapMjBIT3Of0+fBW9/g/uzuYNuVUaNAJti6NnWOpHKSrUsmrHT4wm1fL+EpalWE/l7UaQ8974bM7zDqvlhn8rM50vgHWO+azK9+OzTZf6TSaL6bQPLmOF/93CWz3qPuaHBYerGnC4eZY3fZl3aRnC/ddrFdcNuZDlHjp/usB9HvKjLdsWwLkQZPeZScqloOUhISI5AObgJ6OF9jq7QkWYo39W92hWYCDMdfh28p3jJVKq9NgxesVV77KzKYE5rsVRSrC2vtH27U+5v56WIJB/NPnw/bvzZyAEwImvrkqzLD7hFGrEXS93QiJrmnq+xPhGiYsfTla/ORs4dbHxs9jA7kf/9QICXeMUEsDT407f+u3Zr3sJWjiCZJUUKfsPAwviY5lkxMnxN7NJY5Dxv2dXmmq6rRvfFqLjleZxT331FllzykHKQkJVS0VkaVAGnZvVYB1nxBvDO6hUddgp3buwNbxbxsb9+ZHw4I/xo5ny1a/KvBm11yXIDpTT4tF/UtEw84xFWOrBCqnyobrrG76sOCPnlyxX7eY8ceQFcZf0su1YJjPO6DrFdjfkHodL57wVriLkcqAv01w513U87ndGN0w8XjYtm8zX7YERFE3/QG4T0QuUdUK8pdcQUweBHs2lE1PZMPuOnxzQ0C6nP0DjGsTO6emCoqqRn4EyxF3kNnvVmLftYqC03OJO+O9/flmPsLaj3JbniDy8jHWWsQ7VEyEG9ALMttDytZ/d3TD2LykesWxXoSXZLPC258fHocjC0QREr8GOmDCly4H4ozhq6wXWIh9ZWWCH9/P3LWqImd+C2/2hNIM1mm2cRuDVBuG1VPNOmyAsGGX8HM3f23W7sxZqQ07lsbPifOGpCwK8S8UFdflybGOW/Kef8jMdTONqxZb8S45D34Z9j7MuCZ+352Mm4o7jJItsd7QEc8ApbAq4rDufhXbU7deYC9SGNs2Zi5XXsJi4NYU6neAgZPh3b5lvaVWJAUNzCSybBgS1NrPrJscHny8XgfYHDDZDmDnKrNe7FjV1G1b1k3DvpCUQKdfpF3MKolrHjrjp7AjiaefXPXUw7wbvOWY6r4d8l74mf97WJOGe/Qdzjs09y7odU/ivBnAeoEF8/X7cm0YODWW5nW74E6SWvpycn//LY8HxHhVTSWqV3WkWR+zjhrXINv4Y1UAzLjaqJrCggoF4U4ISzZAGCSgGnSEvk/E9ndvNIPV7rhXrUbEBfQpdd6hlSG2It741dUB15fajhXBqhiAVe+W/z7zRqR33hHPwixHcPtd0rhxyV2Hh37yiuJDCzc82AiJlgNhtW+q2Zxb4bAHCMRVdyYa2M8gURz8DQBQ1fcD0lVVP8hw2SoO139SywHBx93u//Rh0HxAClHW1AgIOx5RuQjS9X7zNNRpm54ZYjrUaQWdfE7aPrsj5mrDXbu4PZIpIQ1m4X5m/V7/jBWxQgjrBRQ1i6ljTkzTbXoqfJ7mN+9Bw82yfblxlz6hT/jHgjsHZMhK485nmEeoXKQm5sc3TxuHmHXawCfXxY5/8WC4kHD9hPWomO/2KOqmR4Cg0ZKGwAiMd9iqzUeXByRKTEVQ7wBYO930EtqdA8sCZrl+EeDLyVL5GPSpCVUpAuTDzCszK9S3fR8t//4BbrbBqOVXOzrrIEeMrrloOmqLys6EfuHhfV3XOFFjpwP0+GPqPqy2h6mhJTZeFKZ2dAmLW+HOtXJV3bvXB+fz4/YeU3G/nwGiCImDgc8C0uc7x6o+yQLpbFtq1E15tZ0BwIDYxMsCgtFbKh9Ne5nFZeaV0a/xzlGwcV7wsb2e2b+pRO8LQ9UjAMQ4ljwpTeOIRAPqFYkbDnRDSN25rP8k/NgG5+v9lUbR79/lluRColE3E/t8XEiY1kxQfJkJDnW280GRXweWVr72I4qQ2AG0BvzRdtoCWfP1LCKnYvxD5QNPq+p9WbnRiZPMJCk/Hw+HI5+L7TfumTiQTZuzjHlhed2IWyo/e3dD6fbs3kMEWvhUSf79qsaS/5j12z1TyJxn5kh4dfng9ACdsZu9u5LHhfHimijXaR2ep6ilERKJ8kDywfVE1HLaiLB44ZUE0bDunD+jyEigPXCmqm5w0poA44AfVDVDIZvi7pkPfIVxU74c+AS4UFUDp/T26dNHZ83K7GxDRgkUNmRD/sHU3T4/afbFu3vStWgGf137l7RvuWnTJn570O8497/nJM9cCdm9ezevX/gGPe/PrVX0rl27mH3bl9QLmLaQd0lsUFlV0ZEgF5fNF0adWqad2vYM1LsiOI8q7KgkoTJqCiIQOjE20nUEYW9sEl85KcgrDRRktfN2xUVkrJ+3mVqF0LCIfa7Ti4qKaLt3JrSPtQcD6r1qIvl5uKHZjWYjDbWpiMxW1T6BxyIIiVbAB0ALjFM/gB7Aj8CAbESmE5GjgBGqOsjZvwNAVe8Nyp81IQGUqpAvyevqxf/BhUdHa3CCiNpoVTYqS/lXPAatGpdN95ct3fJWlue0WHSks5FhIRHFBHaliPQELgZ6YcT188AoVc1Wn7sN4NUBLQfinOOIyDXANQDt27cn45y3EYC9Y1qyp7SU380J+Wx0KC3I46OPC7n++vRvuWnTJuAFevSomvMTS0pKgIU0bZpbH0ElJSXUrW28q252DEsa1slhgSw1FtPBKdvDyRON660U5pVSkAe1C9k3g7x27do0K1hhwhk4DNh/Hhwc38js4WkKCzIRIshX9lR7ErlARIYCg1T1Kmf/UqCfqgY2wVnpSVgsFks1J+2ehIicA7yuqnuc7VBU9dVylDGM5YDX+1VbIONqLYvFYrEEk6xvMgbYHzPukMgth7LPM1dG+QToJCIdgB+AYUBIgFyLxWKxZJqEQkJV84K2KwpVLRGRXwLvYITQM6q6IMlpFovFYskQmR/lyDCq+hbwVq7LYbFYLDWRSEJCRPYHjsaYwcb1LFT18QyWy2KxWCyVgCgO/i4BnsZYc20g3tm7AlZIWCwWSzUjSk/iHuDPwN2qWpIss8VisViqPlEGoxsCz1kBYbFYLDWHKEJiJDA4WwWxWCwWS+Ujiu+mWhhnfrsx7sHjwq6pasVF5g5BRNYAS1PM3gxYm8XiVAdsHSXG1k9ybB0lprLUzwGq2jzoQBQhcT3GZfdazOS6uIFrVa1SjoZEZFbYNHSLwdZRYmz9JMfWUWKqQv1EGbj+LXCLqj6SrcJYLBaLpXIRZUwiH3gtWwWxWCwWS+UjipB4FuMmvLrwz1wXoApg6ygxtn6SY+soMZW+fqKMSTyOca63ABN0yD9wfUPGS2exWCyWnBJlTKIL8KmzfYjvWOUNSmGxWCyWtElZ3aSqJyRYTsxmITONiJwqIl+KyGIRuT3X5ckFItJORKaIyCIRWSAiNzrpTUTkPRH52lk39pxzh1NnX4rIoNyVvuIQkXwR+VRE3nD2bf14EJH9RGSMiHzhvEtH2TqKISK/cv5fn4vIiyJSVNXqp8Ldf+caEckH/g6cBhwKXCgih+a2VDmhBGOt1gU4ErjOqYfbgUmq2gmY5OzjHBsGdAVOBR536rK6cyOwyLNv6yeevwATVPUQoCemrmwdASLSBrgB6KOq3TDGP8OoYvVT44QE0A9YrKrfqupu4CXgrByXqcJR1ZWqOsfZ3oL5c7fB1MXzTrbngbOd7bOAl1R1l6p+ByzG1GW1RUTaYrwMPO1JtvXjICINgf7AvwBUdbeqbsTWkZcCoI6IFAB1MZE1q1T91EQh0Qb43rO/3EmrsYhIMdAbmAG0VNWVYAQJxi081Mx6exS4DdjrSbP1E+NAYA3wrKOSe1pE6mHrCABV/QF4EFgGrAQ2qeq7VLH6qYlCQgLSauzAu4jUB/4L3KSqmxNlDUirtvUmIj8BflTV2ameEpBWbevHoQA4DHhCVXsD23BUJyHUqDpyxhrOAjoArYF6TsiF0FMC0nJePzVRSCwH2nn222K6gDUOESnECIiRqvqqk7xaRFo5x1thXLBAzau3Y4AzRWQJRiV5ooj8B1s/XpYDy1V1hrM/BiM0bB0ZTgK+U9U1qroHeBUTtK1K1U9NFBKfAJ1EpIPjtHAYNXAmuYgIRpe8SFUf9hx6Dbjc2b4cGO9JHyYitUWkA9AJmFlR5a1oVPUOVW2rqsWYd2Syql6CrZ99qOoq4HsROdhJGggsxNaRyzLgSBGp6/zfBmLG/qpU/VT6GNeZRlVLROSXwDsYa4NnVHVBjouVC44BLgXmi8hcJ+1O4D5gtIhciXnJhwKo6gIRGY1pBEqA61S1tMJLnXts/cRzPTDS+eD6Fvgp5uOzxteRqs4QkTHAHMzzfoqZYV2fKlQ/Kc+4tlgsFkvNoyaqmywWi8WSIlZIWCwWiyUUKyQsFovFEooVEhaLxWIJxQoJi8VisYRihYTFYrFYQrFCwlIlEBEVkfNyXY6qjOMa/rIKuM9PRGSuiNj2pRpgf0RLVaEV8HquC5EOInK8I+Sa5bAMgzEuH0Zm+16q+gZQSvUKd1xjsULCUiVQ1VWquiudc0WkwHGLUOVx/G2lw43AcxU4g/dZTCwFSxXHCglLhSIiU0XkCRF5SETWi8gaEbnR8VfzdxHZKCLLRORS33lx6iYRaS0iI0VknYhsd9QbJzjHRjiRwIaLyDfALowHzvYiMlZEtjjLq07MCHznXS4iS0Rkq4g8KyK1ROQXIvK9c7+HvaoU5/j9IrJcRLaJyCduVDHHDfsUJ+sa5zmec46JiNwmIt+IyA4Rme/1EioixU7+C0VksojsAH4mIo1E5N8i8qOI7BSRb0XkpgR13hzjbO41X3oZFZ7z3L/25fm5iIx36vkrETlBRNqKyDvO884VkcN8t30N6CMiHcPKZakaWCFhyQUXA1uAIzC+kB4FxgFfAX0wgVieFpHWQSeLiVnwPlAMDAG6A3f7snUALsL4xemJERTjgJbAicAJGPfN43y9jGKMe+efAOc6548H+gKnAFdh/BUN8ZzzLDDAuV93p/yvi0hPTHyAc518XTFqsxud/T8CVwLXYaIk3gv8w1ENebkXeNzJM845r7tTxkOAK4AfgurK4Vjn+dP1UfZ/GE+4PYFZwIsY55CPY+KQrACe856gqsuA1Zh6sVRlVNUudqmwBZgKfOTZF0zgmtc8aYXAbuA8T5q6+8DVGCHTLOQeI4A9mOAubtrJGD15sSftQExAoZM85+0AGnnyjHHKV8v3DI852wc512jvK8M44HFn+3in/M08x+s59zrOd96jwFvOdrFz3i2+PK8Bz0ao85uApQHp++rUk7YE+LUvz72e/W5O2s2etDLP56TPAf6Q63fOLuVbapwXWEulYJ67oaoqIj8C8z1pe0RkA7GIXX56A/NUdW2CeyxX1dWe/S7AClVd4rnPtyKyAvOFPtFJXqaqmzznrQa+UhPq1pvmlu0wjKBb6Bv2qA1MTlC+Q4EiYIKIeL1sFmIaai+zfPtPAGMcFc97wOuq+n6Ce9UBdiY4nox5nm23TucHpLUAvL/JDufeliqMFRKWXLDHt68haWHq0FQGobcFnBPm8tibnmrZ3AD1ec5+34B8OxKUz322MzDuor34rxP3LKr6togcAJyGiVHwpoi8oqo/DbnXWqBxQLpSti6DBsa95dEEaf7fqwmmF2apwlghYamKzAEuEZFmSXoTXhYCbUSk2O1NiMiBmHGJheUoy6eYhnZ/VZ0SksftheR70hZixgkOUNVEPY5AnOf+N/BvEXkbeFFErtVgC7BPgeYB9bUGM0YCgIi09O6XBxEpwqji5mTiepbcYQeuLVWRUZiQj+NE5DgxUQbPdK2bQpgIfIYJkHO4iPTBzBmYQ2K1UEJU9SvnOs+JyHkicqCI9BGRX4vIOU62pZiv7cEi0lxE6qvqFuBB4EERuUJEOopILxG5VkSuSXRPEblbRM4WkU4i0gU4B/g2RECAERI/YgawvUwGrnPK2xsz+FwetZSXIzFCcHqGrmfJEVZIWKocqroNYzXzA2aC3QLg/5EgaLyqKnA25ut5KsYsdRVwtnOsPPwUY+H0Z+AL4A2gP0Y4oKo/AL8H7sHo7x9zzvstZrD8184zvIexhPouyf12Odf6DNMIN8CorQJRMzfiGcpObrsFE01uKmaA/mli8ZbLy4WY2OnbM3Q9S46wkekslhqAiLTAqLj6qeq3Wb5Xc4yw7KOqyQSepZJjexIWSw1AVX/EzKdoVwG36wD8wgqI6oHtSVgsFoslFNuTsFgsFksoVkhYLBaLJRQrJCwWi8USihUSFovFYgnFCgmLxWKxhGKFhMVisVhC+f8COZK1JeLEzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib_utils.get_figure()\n",
    "matplotlib_impl.plot_dendrogram(m, ax)\n",
    "matplotlib_utils.plot_style(fig=fig, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b55c60e-4f12-4169-8aec-1210f88c4e71",
   "metadata": {},
   "source": [
    "# Convert them all!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75d87d80-d41f-4e26-bb8f-3346f04c0ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "convert = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c890b81-e55f-49f7-a8a2-d9493aa3129e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "filepath = '/home/anna/Documents/astrocyte-graphs/*.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f769e12-0b3b-4612-9fbb-78843a36fbc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def dat_inf(paths):\n",
    "    \n",
    "#     graphs = {}\n",
    "#     t = []\n",
    "#     r = []\n",
    "#     n = []\n",
    "#     p = []\n",
    "\n",
    "#     for path in glob.glob(filepath):\n",
    "#         name = path.split('/')[-1].split('.')[0]\n",
    "#         dat = pd.read_pickle(path)\n",
    "#         tips = len(get_tips(dat))\n",
    "#         roots = len(get_roots(dat))\n",
    "#         nodes = len(get_branch_points(dat))\n",
    "#         t.append(tips)\n",
    "#         r.append(roots)\n",
    "#         n.append(nodes)\n",
    "#         p.append(path)\n",
    "#         graphs[name] = [path, dat]\n",
    "    \n",
    "#     k = list(graphs.keys())\n",
    "#     table = Table()\n",
    "#     table['name'] = k\n",
    "#     table['tips'] = t\n",
    "#     table['roots'] = r\n",
    "#     table['nodes'] = n\n",
    "#     table['path'] = p\n",
    "#     return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e45fb305-a2f4-414c-98e0-14e81a7e2d67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DF = dat_inf(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6fcd4a52-4ab1-4941-9b41-f0edc1438263",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = {}\n",
    "\n",
    "\n",
    "for path in glob.glob(filepath):\n",
    "    name = path.split('/')[-1].split('.')[0]\n",
    "    d = pd.read_pickle(path)\n",
    "    DATA[name] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "655bc707-9e6d-439c-9985-ba452c2348cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = list(DATA.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f3ecccd-9048-4348-844f-68c6b60a4fae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'convert' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mconvert\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m k:\n\u001b[1;32m      3\u001b[0m         filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/home/anna/Documents/jupy_projects/Skeletonization/astro_swc/\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.swc\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(key)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'convert' is not defined"
     ]
    }
   ],
   "source": [
    "if convert == True:\n",
    "    for key in k:\n",
    "        filename = '/home/anna/Documents/jupy_projects/Skeletonization/astro_swc/{}.swc'.format(key)\n",
    "        SWC_from_Graph(DATA[key], 7, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42ad1bcc-1e48-4738-8e2d-36a47d2b0a31",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ba910f7-8942-46b0-816c-bec5cea54482",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30e661e8-11c7-4fbc-a3b8-41b24d26d8df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de562e73-e98d-4d8f-bcf9-487da1d45ab2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}