axon analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7cb0e015-cf4b-4894-8c7e-9740ff413b96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wanqing.yu/anaconda3/envs/EM/lib/python3.11/site-packages/python_jsonschema_objects/__init__.py:113: UserWarning: Schema id not specified. Defaulting to 'self'\n",
      "  warnings.warn(\"Schema id not specified. Defaulting to 'self'\")\n"
     ]
    }
   ],
   "source": [
    "from meshparty import meshwork\n",
    "from meshparty import trimesh_io, trimesh_vtk, skeletonize, mesh_filters\n",
    "from meshparty import skeletonize\n",
    "from scipy import sparse\n",
    "from scipy import ndimage\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from nglui.statebuilder.helpers import make_neuron_neuroglancer_link\n",
    "import requests\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3512f2b1-1c65-4e47-8c9b-2e8b6a2c8f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# you should pick a location to save meshes, MeshMeta will cache them there so you don't \n",
    "# load them over and over.\n",
    "# this will make a directory in the current directory of this notebook called meshes\n",
    "# mm = trimesh_io.MeshMeta(cv_path = client.info.segmentation_source())\n",
    "\n",
    "mesh_folder = 'meshes'\n",
    "\n",
    "mm = trimesh_io.MeshMeta(cv_path =\"graphene://https://minnie.microns-daf.com/segmentation/table/minnie65_public\",\n",
    "                         disk_cache_path=mesh_folder,\n",
    "                         cache_size=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d08514e1-31f0-46a7-a12a-94ff88a21e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "rid = 864691135122603047 #864691135755817170\t # 864691134884807418 #864691135162983725 864691135755817170 \t\n",
    "sid = 292685 #588983 #518848 #495010 588983\n",
    "cell_type = 'L2a'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f1cd74-2a17-41e7-8537-5685b266f769",
   "metadata": {},
   "source": [
    "# get the axon segment from soma to tip of the axon in the white matter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1c9380f-e2de-4ad9-ad59-22cc89366bfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2/3': 115.1112491335,\n",
       " '4': 333.4658190171,\n",
       " '5': 453.6227158132,\n",
       " '6a': 687.6482650269,\n",
       " '6b': 883.1308910545,\n",
       " 'wm': 922.5861720311}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up path for different skeletons\n",
    "\n",
    "import skeleton_plot as skelplot\n",
    "import skeleton_plot.skel_io as skel_io\n",
    "\n",
    "#raw skeleton files\n",
    "skel_path = 'https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily-pcg-skeletons/minnie_all/v661/skeletons/'\n",
    "\n",
    "skel_dir = str(rid)+'_'+str(sid) #'864691134884807418_518848'\n",
    "skel_filename = str(rid)+'_'+str(sid)+'.swc' #'864691134884807418_518848.swc'\n",
    "\n",
    "# layer aligned\n",
    "layer_aligned_path = 'https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily_skeleton_keys/layer_align/v661/'\n",
    "\n",
    "# polygon files (layer bounds for each cell)\n",
    "# gives 2d shape for each layer \n",
    "layer_bounds_path = 'https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily_skeleton_keys/poly_files/v661_polys/'\n",
    "\n",
    "# average layer bounds from IVSCC (to which layer aligned celled are alligned)\n",
    "# this just gives a single value for each layer (straight line)\n",
    "layer_bounds_average_path = \"https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily_skeleton_keys/avg_layer_depths/\"\n",
    "avg_layerdepths_filename = 'mouse_me_and_met_avg_layer_depths.json'\n",
    "\n",
    "depths = skel_io.read_json(layer_bounds_average_path, avg_layerdepths_filename)\n",
    "depths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f56595a-84d9-4cff-8d6d-1dec97c85125",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2770089/14313382.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_axon_seg['depth'] = df_axon_seg['y']*(-1)\n",
      "/tmp/ipykernel_2770089/14313382.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_axon_seg.loc[index, 'layer'] = assign_layer(row['depth'])\n"
     ]
    }
   ],
   "source": [
    "# using layer aligned skel to get the axon segment in the white matter\n",
    "\n",
    "swc_path = f'{layer_aligned_path}{sid}.swc'\n",
    "swc_file = skel_io.read_swc(swc_path)\n",
    "\n",
    "df_axon_seg = swc_file.loc[swc_file.type == 2,]\n",
    "df_axon_seg['depth'] = df_axon_seg['y']*(-1)\n",
    "df_axon_seg\n",
    "\n",
    "layer_keys = ['1','2/3','4','5','6a','6b','wm']\n",
    "layer_boundaries = np.array(list(depths.values()))\n",
    "# Create a function to assign layers based on depth\n",
    "def assign_layer(depth):\n",
    "    num_layers = len(layer_boundaries)\n",
    "    \n",
    "    # Check if depth is below the first boundary\n",
    "    if depth <= layer_boundaries[0]:\n",
    "        return layer_keys[0]\n",
    "    \n",
    "    # Check if depth is above the last boundary\n",
    "    if depth > layer_boundaries[num_layers - 1]:\n",
    "        return layer_keys[num_layers]\n",
    "    \n",
    "    # Check if depth is within other boundaries\n",
    "    for i in range(num_layers - 1):\n",
    "        if depth > layer_boundaries[i] and depth <= layer_boundaries[i + 1]:\n",
    "            return layer_keys[i + 1]\n",
    "\n",
    "# Update using .loc for setting the 'layer' column\n",
    "for index, row in df_axon_seg.iterrows():\n",
    "    df_axon_seg.loc[index, 'layer'] = assign_layer(row['depth'])\n",
    "\n",
    "df_axon_wm = df_axon_seg.loc[df_axon_seg['layer'] == 'wm'].id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc5d508e-2730-4b88-9b1a-715babcdab54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8250"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df_axon_wm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2eec2ef1-0979-472a-92d4-ae46cea55f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1285,  1436,  1753,  1765,  2698,  3256,  3333,  3685,  3692,\n",
       "        3999,  4284,  4454,  4544,  4558,  4695,  4754,  4826,  4861,\n",
       "        4868,  4878,  4901,  4933,  4946,  4961,  5048,  5070,  5686,\n",
       "        5714,  5727,  6049,  6066,  6079,  6098,  6300,  6376,  6402,\n",
       "        6773,  6974,  7376,  7384,  7761,  7769,  8029,  8250,  8346,\n",
       "        8385,  8812,  8947,  9150,  9362,  9734,  9772, 10111, 10209,\n",
       "       10359, 10474, 10719, 10943, 11055, 11329, 11628, 11908, 12178,\n",
       "       12455, 12744, 13009, 13113, 13344, 13542, 13783, 13964, 14042,\n",
       "       14054, 14061, 14281, 14474, 14640, 14654, 14663, 14678, 14717,\n",
       "       14730, 14819, 14890, 14913, 14998, 15154, 15294, 15407, 15437,\n",
       "       15475, 15488, 15568, 15614, 15629, 15723, 15729, 15813, 15821,\n",
       "       15898, 15980, 16056, 16138, 16172, 16246, 16307, 16387, 16465,\n",
       "       16498, 16576, 16642, 16707, 16770, 16844, 16915, 16953, 16981,\n",
       "       17036, 17096, 17150, 17209, 17279, 17346, 17400, 17456, 17504,\n",
       "       17535, 17587, 17643, 17672, 17727, 17782, 17821, 17852, 17878,\n",
       "       17931, 17960, 17963, 17970, 18015, 18043, 18046, 18049, 18075,\n",
       "       18077, 18098, 18104, 18110, 18117, 18119])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the vertices of path from endpoint to root in the raw skeleton\n",
    "\n",
    "# raw skel\n",
    "skel = skel_io.read_skeleton(skel_path, skel_filename)\n",
    "end_points = skel.end_points\n",
    "end_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1454f183-8641-4be7-8d87-b8c0210e2e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8250, 8249, 8248, 8247, 8246, 8245, 8244, 8243, 8242, 8241, 8240,\n",
       "       8239, 8238, 8237, 8236, 8235, 8234, 8233, 8232, 8231, 8230, 8229,\n",
       "       8228, 8227, 8226, 8225, 8224, 8223, 8222, 8221, 8220, 8219, 8218,\n",
       "       8217, 8216, 8215, 8214, 8213, 8212, 8211, 8210, 8209, 8208, 8207,\n",
       "       8206, 8205, 8204, 8203, 8202, 8201, 8200, 8199, 8198, 8197, 8196,\n",
       "       8195, 8194, 8193, 8192, 8191, 8190, 8189, 8188, 8187, 8186, 8185,\n",
       "       8184, 8183, 8182, 8181, 8180, 8179, 8178, 8177, 8176, 8175, 8174,\n",
       "       8173, 8172, 8171, 8170, 8169, 8168, 8167, 8166, 8165, 8164, 8163,\n",
       "       8162, 8161, 8160, 8159, 8158, 8157, 8156, 8155, 8154, 8153, 8152,\n",
       "       8151, 8150, 8149, 8148, 8147, 8146, 8145, 8144, 8143, 8142, 8141,\n",
       "       8140, 8139, 8138, 8137, 8136, 8135, 8134, 8133, 8132, 8131, 8130,\n",
       "       8129, 8128, 8127, 8126, 8125, 8124, 8123, 8122, 8121, 8120, 8119,\n",
       "       8118, 8117, 8116, 8115, 8114, 8113, 8112, 8111, 8110, 8109, 8108,\n",
       "       8107, 8106, 8105, 8104, 8103, 8102, 8101, 8100, 8099, 8098, 8097,\n",
       "       8096, 8095, 8094, 8093, 8092, 8091, 8090, 8089, 8088, 8087, 8086,\n",
       "       8085, 8084, 8083, 8082, 8081, 8080, 8079, 8078, 8077, 8076, 8075,\n",
       "       8074, 8073, 8072, 8071, 8070, 8069, 8068, 8067, 8066, 8065, 8064,\n",
       "       8063, 8062, 8061, 8060, 8059, 8058, 8057, 8056, 8055, 8054, 8053,\n",
       "       8052, 8051, 8050, 8049, 8048, 8047, 8046, 8045, 8044, 8043, 8042,\n",
       "       8041, 8040, 8039, 8038, 8037, 8036, 8035, 8034, 8033, 8032, 8031,\n",
       "       8030, 5152, 5151, 5150, 5149, 5148, 5147, 5146, 5145, 5144, 5143,\n",
       "       5142, 5141, 5140, 5139, 5138, 5137, 5136, 5135, 5134, 5133, 5132,\n",
       "       5131, 5130, 5129, 5128, 5127, 5126, 5125, 5124, 5123, 5122, 5121,\n",
       "       5120, 5119, 5118, 5117, 5116, 5115, 5114, 5113, 5112, 5111, 5110,\n",
       "       5109, 5108, 5107, 5106, 5105, 5104, 5103, 5102, 5101, 5100, 5099,\n",
       "       5098, 5097, 5096, 5095, 5094, 5093, 5092, 5091, 5090, 5089, 5088,\n",
       "       5087, 5086, 5085, 5084, 5083, 5082, 5081, 5080, 5079, 5078, 5077,\n",
       "       5076, 5075, 5074, 5073, 5072, 5071, 1782, 1781, 1780, 1779, 1778,\n",
       "       1777, 1776, 1775, 1774, 1773, 1772, 1771, 1770, 1769, 1768, 1767,\n",
       "       1766,  180,  179,  178,  177,  176,  175,  174,  173,  172,  171,\n",
       "        170,  169,  168,  167,  166,  165,  164,  163,  162,  161,  160,\n",
       "        159,  158,  157,  156,  155,  154,  153,  152,  151,  150,  149,\n",
       "        148,  147,  146,  145,  144,  143,  142,  141,  140,  139,  138,\n",
       "        137,  136,  135,  134,  133,  132,  131,  130,  129,  128,  127,\n",
       "        126,  125,  124,  123,  122,  121,  120,  119,  118,  117,  116,\n",
       "        115,  114,  113,  112,  111,  110,  109,  108,  107,  106,  105,\n",
       "        104,  103,  102,  101,  100,   99,   98,   97,   96,   95,   94,\n",
       "         93,   92,   91,   90,   89,   88,   87,   86,   85,   84,   83,\n",
       "         82,   81,   80,   79,   78,   77,   76,   75,   74,   73,   72,\n",
       "         71,   70,   69,   68,   67,   66,   65,   64,   63,   62,   61,\n",
       "         60,   59,   58,   57,   56,   55,   54,   53,   52,   51,   50,\n",
       "         49,   48,   47,   46,   45,   44,   43,   42,   41,   40,   39,\n",
       "         38,   37,   36,   35,   34,   33,   32,   31,   30,   29,   28,\n",
       "         27,   26,   25,   24,   23,   22,   21,   20,   19,   18,   17,\n",
       "         16,   15,   14,   13,   12,   11,   10,    9,    8,    7,    6,\n",
       "          5,    4,    3,    2,    1,    0])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_to_root = skel.path_to_root(max(df_axon_wm))\n",
    "path_to_root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "587d6979-b598-432f-be49-b081eb3b1db8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 833.276, 1076.983, 1085.431],\n",
       "       [ 833.086, 1075.791, 1084.559],\n",
       "       [ 833.2  , 1074.682, 1083.557],\n",
       "       ...,\n",
       "       [ 770.426,  484.695,  841.052],\n",
       "       [ 770.79 ,  483.23 ,  841.017],\n",
       "       [ 772.608,  475.904,  840.84 ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axon_vertices = skel.vertices[path_to_root]\n",
    "axon_vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a85b864c-de12-48b9-b23d-439f83a654eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[750.4319,\n",
       " 748.9428,\n",
       " 747.44385,\n",
       " 745.9338,\n",
       " 744.42804,\n",
       " 742.91833,\n",
       " 741.4147,\n",
       " 739.9054,\n",
       " 738.41974,\n",
       " 736.915,\n",
       " 735.43805,\n",
       " 734.0336,\n",
       " 732.52405,\n",
       " 731.0359,\n",
       " 729.52576,\n",
       " 728.01636,\n",
       " 726.50616,\n",
       " 725.0717,\n",
       " 723.5669,\n",
       " 722.06055,\n",
       " 720.5512,\n",
       " 719.04205,\n",
       " 717.53314,\n",
       " 716.0565,\n",
       " 714.546,\n",
       " 713.0376,\n",
       " 711.5279,\n",
       " 710.01697,\n",
       " 708.5189,\n",
       " 707.0089,\n",
       " 705.5363,\n",
       " 704.03064,\n",
       " 702.5198,\n",
       " 701.01013,\n",
       " 699.50397,\n",
       " 698.00055,\n",
       " 696.4967,\n",
       " 694.9861,\n",
       " 693.4771,\n",
       " 691.9674,\n",
       " 690.4617,\n",
       " 688.959,\n",
       " 687.452,\n",
       " 685.95593,\n",
       " 684.4457,\n",
       " 683.0132,\n",
       " 681.5036,\n",
       " 680.0016,\n",
       " 678.4918,\n",
       " 676.98206,\n",
       " 675.4822,\n",
       " 673.97217,\n",
       " 672.5702,\n",
       " 671.18494,\n",
       " 669.6753,\n",
       " 668.1697,\n",
       " 666.66003,\n",
       " 665.22437,\n",
       " 663.8484,\n",
       " 662.3496,\n",
       " 660.8666,\n",
       " 659.35645,\n",
       " 658.0841,\n",
       " 656.5747,\n",
       " 655.0653,\n",
       " 653.655,\n",
       " 652.146,\n",
       " 650.6511,\n",
       " 649.1417,\n",
       " 647.6313,\n",
       " 646.13245,\n",
       " 644.62305,\n",
       " 643.11646,\n",
       " 641.60596,\n",
       " 640.09906,\n",
       " 638.6444,\n",
       " 637.1345,\n",
       " 635.6278,\n",
       " 634.13477,\n",
       " 632.6251,\n",
       " 631.12085,\n",
       " 629.6113,\n",
       " 628.1017,\n",
       " 626.5919,\n",
       " 625.10266,\n",
       " 623.5925,\n",
       " 622.1383,\n",
       " 620.6295,\n",
       " 619.1194,\n",
       " 617.6273,\n",
       " 616.12646,\n",
       " 614.6293,\n",
       " 613.1218,\n",
       " 611.6111,\n",
       " 610.105,\n",
       " 608.595,\n",
       " 607.09924,\n",
       " 605.6201,\n",
       " 604.13684,\n",
       " 602.62695,\n",
       " 601.1273,\n",
       " 599.6349,\n",
       " 598.131,\n",
       " 596.62054,\n",
       " 595.1211,\n",
       " 593.61096,\n",
       " 592.10455,\n",
       " 590.60706,\n",
       " 589.11316,\n",
       " 587.60376,\n",
       " 586.1116,\n",
       " 584.60156,\n",
       " 583.0928,\n",
       " 581.58984,\n",
       " 580.07983,\n",
       " 578.6024,\n",
       " 577.1234,\n",
       " 575.6133,\n",
       " 574.1073,\n",
       " 572.6406,\n",
       " 571.1512,\n",
       " 569.676,\n",
       " 568.177,\n",
       " 566.6826,\n",
       " 565.17236,\n",
       " 563.6669,\n",
       " 562.1573,\n",
       " 560.65076,\n",
       " 559.17676,\n",
       " 557.6659,\n",
       " 556.20386,\n",
       " 554.7095,\n",
       " 553.20374,\n",
       " 551.7422,\n",
       " 550.2421,\n",
       " 548.7327,\n",
       " 547.2285,\n",
       " 545.718,\n",
       " 544.20953,\n",
       " 542.7155,\n",
       " 541.2412,\n",
       " 539.73193,\n",
       " 538.33984,\n",
       " 536.83044,\n",
       " 535.3384,\n",
       " 533.8282,\n",
       " 532.3335,\n",
       " 530.8259,\n",
       " 529.31696,\n",
       " 527.81946,\n",
       " 526.3097,\n",
       " 524.80615,\n",
       " 523.3255,\n",
       " 521.81744,\n",
       " 520.31055,\n",
       " 518.9075,\n",
       " 517.4822,\n",
       " 516.05414,\n",
       " 514.5436,\n",
       " 513.0408,\n",
       " 511.5455,\n",
       " 510.03534,\n",
       " 508.52594,\n",
       " 507.02545,\n",
       " 505.5251,\n",
       " 504.01544,\n",
       " 502.5078,\n",
       " 501.00067,\n",
       " 499.4909,\n",
       " 497.98444,\n",
       " 496.47394,\n",
       " 494.97742,\n",
       " 493.49017,\n",
       " 491.9803,\n",
       " 490.49213,\n",
       " 488.9843,\n",
       " 487.47388,\n",
       " 485.97253,\n",
       " 484.4624,\n",
       " 482.95367,\n",
       " 481.4433,\n",
       " 479.9358,\n",
       " 478.43524,\n",
       " 476.97113,\n",
       " 475.5119,\n",
       " 474.07877,\n",
       " 472.5694,\n",
       " 471.06018,\n",
       " 469.56458,\n",
       " 468.05963,\n",
       " 466.54968,\n",
       " 465.04,\n",
       " 463.54553,\n",
       " 462.04944,\n",
       " 460.5395,\n",
       " 459.03693,\n",
       " 457.54498,\n",
       " 456.04172,\n",
       " 454.5335,\n",
       " 453.03333,\n",
       " 451.59402,\n",
       " 450.10352,\n",
       " 448.5941,\n",
       " 447.0847,\n",
       " 445.57617,\n",
       " 444.0698,\n",
       " 442.55975,\n",
       " 441.05463,\n",
       " 439.57187,\n",
       " 438.0705,\n",
       " 436.56796,\n",
       " 435.0705,\n",
       " 433.5608,\n",
       " 432.05078,\n",
       " 430.5471,\n",
       " 429.03745,\n",
       " 427.52963,\n",
       " 426.01974,\n",
       " 424.51508,\n",
       " 423.05835,\n",
       " 421.5484,\n",
       " 421.12085,\n",
       " 419.94934,\n",
       " 418.46692,\n",
       " 416.95663,\n",
       " 415.49643,\n",
       " 414.02637,\n",
       " 412.51733,\n",
       " 411.0137,\n",
       " 409.5454,\n",
       " 408.03497,\n",
       " 406.5284,\n",
       " 405.023,\n",
       " 403.51395,\n",
       " 402.0042,\n",
       " 400.4954,\n",
       " 398.9951,\n",
       " 397.4851,\n",
       " 395.98148,\n",
       " 394.50595,\n",
       " 392.99628,\n",
       " 391.4864,\n",
       " 389.99323,\n",
       " 388.49582,\n",
       " 386.9889,\n",
       " 385.4784,\n",
       " 383.9715,\n",
       " 382.4748,\n",
       " 380.9726,\n",
       " 379.46277,\n",
       " 377.98047,\n",
       " 376.50226,\n",
       " 374.99323,\n",
       " 373.49908,\n",
       " 371.98883,\n",
       " 370.4801,\n",
       " 368.99384,\n",
       " 367.48618,\n",
       " 365.98157,\n",
       " 364.47696,\n",
       " 363.0938,\n",
       " 361.6117,\n",
       " 360.13043,\n",
       " 358.63885,\n",
       " 357.12952,\n",
       " 355.6344,\n",
       " 354.12366,\n",
       " 352.63486,\n",
       " 351.12637,\n",
       " 349.61646,\n",
       " 348.1339,\n",
       " 346.68274,\n",
       " 345.17273,\n",
       " 343.66296,\n",
       " 342.15533,\n",
       " 340.67017,\n",
       " 339.16013,\n",
       " 337.68518,\n",
       " 336.17514,\n",
       " 334.69366,\n",
       " 333.19226,\n",
       " 331.69104,\n",
       " 330.1807,\n",
       " 328.69696,\n",
       " 327.19873,\n",
       " 325.68942,\n",
       " 324.17914,\n",
       " 322.67548,\n",
       " 321.17822,\n",
       " 319.66876,\n",
       " 318.17688,\n",
       " 316.68134,\n",
       " 315.17154,\n",
       " 313.66983,\n",
       " 312.22482,\n",
       " 310.7141,\n",
       " 309.22446,\n",
       " 307.73102,\n",
       " 306.22467,\n",
       " 304.7323,\n",
       " 303.2234,\n",
       " 301.71378,\n",
       " 300.45172,\n",
       " 300.15973,\n",
       " 298.74097,\n",
       " 297.2528,\n",
       " 295.78552,\n",
       " 294.30835,\n",
       " 292.79907,\n",
       " 291.30515,\n",
       " 289.81277,\n",
       " 288.3019,\n",
       " 286.85403,\n",
       " 285.35687,\n",
       " 283.8591,\n",
       " 282.3774,\n",
       " 280.8894,\n",
       " 279.39264,\n",
       " 277.8899,\n",
       " 276.38028,\n",
       " 275.89227,\n",
       " 274.3844,\n",
       " 272.89395,\n",
       " 271.38672,\n",
       " 269.8863,\n",
       " 268.38464,\n",
       " 266.88904,\n",
       " 265.42194,\n",
       " 263.9198,\n",
       " 262.4182,\n",
       " 260.93716,\n",
       " 259.43225,\n",
       " 257.9225,\n",
       " 256.42218,\n",
       " 254.91911,\n",
       " 253.40868,\n",
       " 251.91852,\n",
       " 250.41928,\n",
       " 248.9224,\n",
       " 247.42249,\n",
       " 245.92236,\n",
       " 244.41304,\n",
       " 242.93372,\n",
       " 241.44695,\n",
       " 239.93674,\n",
       " 238.46614,\n",
       " 236.96194,\n",
       " 235.46057,\n",
       " 233.95087,\n",
       " 232.44785,\n",
       " 230.9531,\n",
       " 229.44267,\n",
       " 227.93457,\n",
       " 226.45467,\n",
       " 224.9454,\n",
       " 223.43875,\n",
       " 221.98825,\n",
       " 220.48135,\n",
       " 218.9711,\n",
       " 217.47897,\n",
       " 215.97775,\n",
       " 214.46698,\n",
       " 212.96129,\n",
       " 211.52855,\n",
       " 210.02255,\n",
       " 208.51245,\n",
       " 207.01721,\n",
       " 205.50981,\n",
       " 203.99951,\n",
       " 202.5007,\n",
       " 201.01378,\n",
       " 199.51193,\n",
       " 198.00337,\n",
       " 196.50504,\n",
       " 195.10785,\n",
       " 193.61127,\n",
       " 192.10774,\n",
       " 190.61563,\n",
       " 189.11389,\n",
       " 187.60672,\n",
       " 186.09702,\n",
       " 184.58739,\n",
       " 183.0851,\n",
       " 181.58601,\n",
       " 180.07607,\n",
       " 178.5736,\n",
       " 177.07173,\n",
       " 175.5893,\n",
       " 174.08429,\n",
       " 172.57489,\n",
       " 171.06761,\n",
       " 169.5575,\n",
       " 168.05331,\n",
       " 166.55945,\n",
       " 165.05331,\n",
       " 163.54332,\n",
       " 162.04117,\n",
       " 160.53665,\n",
       " 159.0266,\n",
       " 157.54398,\n",
       " 156.03954,\n",
       " 154.53314,\n",
       " 153.02887,\n",
       " 151.51848,\n",
       " 150.04366,\n",
       " 148.534,\n",
       " 147.02371,\n",
       " 145.5198,\n",
       " 144.05501,\n",
       " 142.54762,\n",
       " 141.0381,\n",
       " 139.56313,\n",
       " 138.0592,\n",
       " 136.54933,\n",
       " 135.07101,\n",
       " 133.56558,\n",
       " 132.05528,\n",
       " 130.54608,\n",
       " 129.07259,\n",
       " 127.58083,\n",
       " 126.070786,\n",
       " 124.56741,\n",
       " 123.07588,\n",
       " 121.565575,\n",
       " 120.05787,\n",
       " 118.55459,\n",
       " 117.04434,\n",
       " 115.535164,\n",
       " 114.0258,\n",
       " 112.52399,\n",
       " 111.01353,\n",
       " 109.51642,\n",
       " 108.02398,\n",
       " 106.51644,\n",
       " 105.006966,\n",
       " 103.52284,\n",
       " 102.03236,\n",
       " 100.5276,\n",
       " 99.025345,\n",
       " 97.51579,\n",
       " 96.0061,\n",
       " 94.496544,\n",
       " 93.00954,\n",
       " 91.50067,\n",
       " 89.99026,\n",
       " 88.4875,\n",
       " 86.97884,\n",
       " 85.46826,\n",
       " 83.95957,\n",
       " 82.45451,\n",
       " 80.94882,\n",
       " 79.43901,\n",
       " 77.94462,\n",
       " 76.43926,\n",
       " 74.92998,\n",
       " 73.41989,\n",
       " 71.92019,\n",
       " 70.41358,\n",
       " 68.903725,\n",
       " 67.44099,\n",
       " 66.09051,\n",
       " 64.591415,\n",
       " 63.12675,\n",
       " 61.618805,\n",
       " 60.109417,\n",
       " 58.61588,\n",
       " 57.107727,\n",
       " 55.59774,\n",
       " 54.093937,\n",
       " 52.58818,\n",
       " 51.07917,\n",
       " 49.569168,\n",
       " 48.06834,\n",
       " 46.596607,\n",
       " 45.09749,\n",
       " 43.58846,\n",
       " 42.081963,\n",
       " 40.574924,\n",
       " 39.064693,\n",
       " 37.621086,\n",
       " 36.125534,\n",
       " 34.61538,\n",
       " 33.108234,\n",
       " 31.62893,\n",
       " 30.12139,\n",
       " 28.611568,\n",
       " 27.119373,\n",
       " 25.614943,\n",
       " 24.104828,\n",
       " 22.594824,\n",
       " 21.096947,\n",
       " 19.601913,\n",
       " 18.091623,\n",
       " 16.595575,\n",
       " 15.096766,\n",
       " 13.589883,\n",
       " 12.07991,\n",
       " 10.569962,\n",
       " 9.060229,\n",
       " 7.55028,\n",
       " 0.0]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_path_length = []\n",
    "\n",
    "for node in path_to_root:\n",
    "    node_path_length.append(skel.path_length(skel.path_to_root(node)))\n",
    "\n",
    "node_path_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe48238f-2a7e-4be2-a551-6b4e00024577",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6f9c1ac-7c24-46ec-8e70-bd3424c6c56f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ba981ad6-4c07-42fc-be8c-0cd5bfc5f58f",
   "metadata": {},
   "source": [
    "# estimate radius along the axonal segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8be8c990-0945-4370-8b64-089051119bce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading Meshes: 100%|█████████████████████████████████████████████████| 1/1 [01:55<00:00, 115.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: deduplication not currently supported for this layer's variable layered draco meshes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "/home/wanqing.yu/anaconda3/envs/EM/lib/python3.11/site-packages/meshparty/skeletonize.py:622: RuntimeWarning: invalid value encountered in multiply\n",
      "  target = np.nanargmax(root_ds * valid)\n"
     ]
    }
   ],
   "source": [
    "post_mesh = mm.mesh(seg_id = rid)\n",
    "average_radius = []\n",
    "\n",
    "for vertices in axon_vertices:\n",
    "    pt_ctr = vertices * 1000\n",
    "    filt = mesh_filters.filter_spatial_distance_from_points(\n",
    "        post_mesh,\n",
    "        pt_ctr,\n",
    "        d_max=3_000,\n",
    "    )\n",
    "    filt_mesh = post_mesh.apply_mask(filt)\n",
    "\n",
    "    # skeletonize the mesh using a 12 um invalidation radius\n",
    "    # assuming units of mesh.vertices is in nm\n",
    "    sk = skeletonize.skeletonize_mesh(filt_mesh,\n",
    "                          invalidation_d=12000)\n",
    "    average_radius.append(np.mean(sk.radius))\n",
    "    average_radius\n",
    "\n",
    "average_radius\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eac68b7-d400-49d9-b86e-bf98477bf2e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "\n",
    "#plt.scatter(node_path_length, average_radius)\n",
    "plt.plot(node_path_length, average_radius, marker='o', linestyle='-', linewidth=2, markersize=4, label='Line Plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ce51868-cb98-4b70-959c-4b4f16275daa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d939b96f-2202-49ec-995c-3afb5f0aea7c",
   "metadata": {},
   "source": [
    "# detect myelin wrapping around the axonal segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b061edf-edb6-4d66-b137-8645819f317e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from caveclient import CAVEclient\n",
    "import imageryclient as ic\n",
    "\n",
    "datastack_name = 'minnie65_public_v661'\n",
    "client = CAVEclient(datastack_name)\n",
    "\n",
    "img_client = ic.ImageryClient(client=client)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3182172c-0501-4d85-9b97-44d2181eebe8",
   "metadata": {},
   "source": [
    "### currently only work with 2D xy-section "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96a6fb30-2571-4f7f-84bb-cceedd484feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import ndimage\n",
    "\n",
    "def compute_mylin_wrap(img_client, center, root_id, mip=2, img_width=1024, depth=1, dilation_iterations=3):\n",
    "    # Calculate bounding box\n",
    "    bounds = ic.bounds_from_center(center, width=img_width, height=img_width, depth=depth)\n",
    "    \n",
    "    # cut out segmentation data with only the neuron interested in\n",
    "    seg_rid = img_client.segmentation_cutout(bounds, mip=mip, root_ids=root_id)\n",
    "    # calculate binary masks\n",
    "    bseg = seg_rid > 0\n",
    "    # dilating the segmentation data\n",
    "    seg_new = ndimage.binary_dilation(bseg, iterations=dilation_iterations, border_value=0)\n",
    "    # get the dilated outline of the segmentation\n",
    "    seg_new[bseg] = 0\n",
    "\n",
    "    # cut out all segmentation data (seg_all == 0 indicates myelin and others unsegmented stuff)\n",
    "    seg_all = img_client.segmentation_cutout(bounds, mip=mip, root_ids=None)\n",
    "    \n",
    "    # calculate binary masks\n",
    "    seg_0 = seg_all == 0\n",
    "    # dilated outline falls on seg_all is 0\n",
    "    seg_mylin = seg_0 & seg_new\n",
    "    \n",
    "    # Calculate the metric (percentage of pixels where seg_all is 0)\n",
    "    if seg_new is not None:\n",
    "        metric = np.sum(seg_all[seg_new] == 0) / len(seg_new[seg_new == True]) * 100\n",
    "    else:\n",
    "        metric = None\n",
    "    \n",
    "    return metric \n",
    "\n",
    "# Example usage:\n",
    "# result = compute_mylin_wrap(img_client, center=[304176, 263880, 23199.275], root_id=rid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dde4e0f8-5ff4-4398-bb2d-5ba5cfb979de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "mylin_wrap = []\n",
    "\n",
    "mip=0\n",
    "# Loop through each set of axon vertices\n",
    "for asid, as_vertices in enumerate(axon_vertices):\n",
    "    # get center point\n",
    "    ctr = as_vertices * 1000 / np.array([4, 4, 40])\n",
    "    \n",
    "    # calculate mylin metric\n",
    "    result = compute_mylin_wrap(img_client, center=ctr, root_id=rid, mip=mip)\n",
    "    \n",
    "    mylin_wrap.append(result)\n",
    "    end_time = time.time()\n",
    "    processing_time = end_time - start_time\n",
    "    print(f\"Total processing time: {processing_time} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19c4eda3-9727-4a0e-b09e-73f6730e3922",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "\n",
    "#plt.scatter(node_path_length, average_radius)\n",
    "plt.plot(node_path_length, mylin_wrap, marker='o', linestyle='-', linewidth=2, markersize=4, label='Line Plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c661460-5fe9-4752-a1e3-158645528eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "axon_coordinates = axon_vertices * 1000 / np.array([4, 4, 40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "191994b7-4551-4a29-aa0b-ff2a1ecd2d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d26a69-afa2-4496-86c2-1bfe7ab41190",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving data to file\n",
    "df = pd.DataFrame({'x':axon_coordinates[:,0],'y':axon_coordinates[:,1],'z':axon_coordinates[:,2],'Axon_to_Soma Length': node_path_length, 'Mylin_Metric': mylin_wrap, 'Axon_Radius': average_radius})\n",
    "\n",
    "file_path = f'/home/wanqing.yu/EM Connectome/axon_analysis/data/{rid}_{cell_type}_myelin.csv'\n",
    "df.to_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dee7e0f-4b2f-4d7e-b4b2-eb32c6c83dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "192cf7d3-2aaa-4b54-be2f-6151993d715e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Seaborn style\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Plot Axon_to_Soma Length vs Mylin_Metric using Seaborn\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "#sns.scatterplot(data=df, x='Axon_to_Soma Length', y='Mylin_Metric')\n",
    "sns.lineplot(data=df, x='Axon_to_Soma Length', y='Mylin_Metric', marker='.')\n",
    "plt.title('Myelination')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('% myelinated')\n",
    "\n",
    "\n",
    "# Plot Axon_to_Soma Length vs Axon_Radius using Seaborn\n",
    "plt.subplot(2, 1, 2)\n",
    "#sns.scatterplot(data=df, x='Axon_to_Soma Length', y='Axon_Radius')\n",
    "sns.lineplot(data=df, x='Axon_to_Soma Length', y='Axon_Radius', marker='.', color='orange')\n",
    "plt.title('Axon Radius')\n",
    "plt.xlabel('Path length to Soma (microns)')\n",
    "plt.ylabel('axon radius (nm)')\n",
    "\n",
    "# Adjust layout and show plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d2d1160-1734-4238-8c86-705e19a6ab06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1d46555f-a63c-4de6-a3e9-0f04f18c45e5",
   "metadata": {},
   "source": [
    "# loading data from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "525a7768-c78e-4ebd-bf5c-9ed46818786c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rid = 864691135162983725"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aca9ac8e-8848-4c86-97ed-1fa627205ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Axon_to_Soma Length</th>\n",
       "      <th>Mylin_Metric</th>\n",
       "      <th>Axon_Radius</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>308309.25</td>\n",
       "      <td>264298.00</td>\n",
       "      <td>23265.950</td>\n",
       "      <td>442.640600</td>\n",
       "      <td>96.471774</td>\n",
       "      <td>599.349105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>307934.50</td>\n",
       "      <td>264255.50</td>\n",
       "      <td>23264.950</td>\n",
       "      <td>441.131470</td>\n",
       "      <td>76.345561</td>\n",
       "      <td>635.839939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>307557.50</td>\n",
       "      <td>264240.75</td>\n",
       "      <td>23263.475</td>\n",
       "      <td>439.621150</td>\n",
       "      <td>69.018024</td>\n",
       "      <td>617.378951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>307185.25</td>\n",
       "      <td>264190.25</td>\n",
       "      <td>23261.050</td>\n",
       "      <td>438.115400</td>\n",
       "      <td>62.026940</td>\n",
       "      <td>654.513216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>306814.00</td>\n",
       "      <td>264129.50</td>\n",
       "      <td>23258.075</td>\n",
       "      <td>436.605960</td>\n",
       "      <td>62.962963</td>\n",
       "      <td>696.277346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>288</td>\n",
       "      <td>288537.50</td>\n",
       "      <td>195825.00</td>\n",
       "      <td>19549.000</td>\n",
       "      <td>12.079796</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1323.728833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>289</td>\n",
       "      <td>288424.25</td>\n",
       "      <td>195465.00</td>\n",
       "      <td>19549.000</td>\n",
       "      <td>10.570224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1181.564279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>290</td>\n",
       "      <td>288311.00</td>\n",
       "      <td>195104.75</td>\n",
       "      <td>19549.000</td>\n",
       "      <td>9.059697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>421.405374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>291</td>\n",
       "      <td>288198.00</td>\n",
       "      <td>194744.75</td>\n",
       "      <td>19549.000</td>\n",
       "      <td>7.550424</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>269.557065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>292</td>\n",
       "      <td>287632.00</td>\n",
       "      <td>192944.00</td>\n",
       "      <td>19549.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.800257</td>\n",
       "      <td>13.094521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>293 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0          x          y          z  Axon_to_Soma Length  \\\n",
       "0             0  308309.25  264298.00  23265.950           442.640600   \n",
       "1             1  307934.50  264255.50  23264.950           441.131470   \n",
       "2             2  307557.50  264240.75  23263.475           439.621150   \n",
       "3             3  307185.25  264190.25  23261.050           438.115400   \n",
       "4             4  306814.00  264129.50  23258.075           436.605960   \n",
       "..          ...        ...        ...        ...                  ...   \n",
       "288         288  288537.50  195825.00  19549.000            12.079796   \n",
       "289         289  288424.25  195465.00  19549.000            10.570224   \n",
       "290         290  288311.00  195104.75  19549.000             9.059697   \n",
       "291         291  288198.00  194744.75  19549.000             7.550424   \n",
       "292         292  287632.00  192944.00  19549.000             0.000000   \n",
       "\n",
       "     Mylin_Metric  Axon_Radius  \n",
       "0       96.471774   599.349105  \n",
       "1       76.345561   635.839939  \n",
       "2       69.018024   617.378951  \n",
       "3       62.026940   654.513216  \n",
       "4       62.962963   696.277346  \n",
       "..            ...          ...  \n",
       "288      0.000000  1323.728833  \n",
       "289      0.000000  1181.564279  \n",
       "290      0.000000   421.405374  \n",
       "291      0.000000   269.557065  \n",
       "292      1.800257    13.094521  \n",
       "\n",
       "[293 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path_tmp = f'/Users/wanqing.yu/Documents/GitHub/EM-connectome/data/{rid}_myelin.csv'\n",
    "\n",
    "df_tmp = pd.read_csv(file_path_tmp)\n",
    "df_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "140505df-9fac-4f3e-aef7-1ecc82c6742a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wanqing.yu/anaconda3/envs/EM/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"orientation\" which is no longer supported as of 3.3 and will become an error two minor releases later\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/wanqing.yu/anaconda3/envs/EM/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"facecolor\" which is no longer supported as of 3.3 and will become an error two minor releases later\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/wanqing.yu/anaconda3/envs/EM/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"edgecolor\" which is no longer supported as of 3.3 and will become an error two minor releases later\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/wanqing.yu/anaconda3/envs/EM/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"bbox_inches_restore\" which is no longer supported as of 3.3 and will become an error two minor releases later\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set Seaborn style\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Plot Axon_to_Soma Length vs Mylin_Metric using Seaborn\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "#sns.scatterplot(data=df, x='Axon_to_Soma Length', y='Mylin_Metric')\n",
    "sns.lineplot(data=df_tmp, x='Axon_to_Soma Length', y='Mylin_Metric', marker='.')\n",
    "plt.title('Myelination')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('% myelinated')\n",
    "\n",
    "\n",
    "# Plot Axon_to_Soma Length vs Axon_Radius using Seaborn\n",
    "plt.subplot(2, 1, 2)\n",
    "#sns.scatterplot(data=df, x='Axon_to_Soma Length', y='Axon_Radius')\n",
    "sns.lineplot(data=df_tmp, x='Axon_to_Soma Length', y='Axon_Radius', marker='.', color='orange')\n",
    "plt.title('Axon Radius')\n",
    "plt.xlabel('Path length to Soma (microns)')\n",
    "plt.ylabel('axon radius (nm)')\n",
    "\n",
    "# Adjust layout and show plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "033c1dd1-0ea4-47b2-adb7-1af7cee234c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07a19d30-71a5-4126-97e9-78cd26378159",
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive plot\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from urllib.parse import quote\n",
    "\n",
    "rid = [864691135162983725]\n",
    "#df_tmp\n",
    "\n",
    "# Function to generate links based on data\n",
    "def generate_link(rid, client, x, y, z):\n",
    "    from nglui.statebuilder import helpers\n",
    "    pt_position = [x, y, z]\n",
    "    view_options = {'layout': 'xy-3d',\n",
    "                  'zoom_3d': 6000,\n",
    "                  'zoom_image':12,\n",
    "                  'position': pt_position}\n",
    "    \n",
    "    url_link = helpers.make_neuron_neuroglancer_link(client, rid, show_outputs=False, return_as='url', view_kws=view_options)\n",
    "\n",
    "    return url_link\n",
    "\n",
    "\n",
    "# Add a new column with dynamically generated links\n",
    "df_tmp['Link'] = df_tmp.apply(lambda row: generate_link(rid, client, row['x'], row['y'], row['z']), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4fa3a5d-e78e-4827-bcec-5c2a1129c3cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Sample data\n",
    "\n",
    "df = df_tmp\n",
    "\n",
    "# Create a line+marker plot with clickable links\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=df['Axon_to_Soma Length'],\n",
    "    y=df['Mylin_Metric'],\n",
    "    mode='lines+markers',\n",
    "    hovertext=[f'<a href=\"{link}\">{link}</a>' for link in df['Link']],\n",
    "    hoverinfo='text',\n",
    "    marker=dict(size=12, opacity=0.8),\n",
    "    line=dict(shape='linear')\n",
    "))\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(\n",
    "    hoverlabel=dict(\n",
    "        bgcolor=\"white\",\n",
    "        font_size=16,\n",
    "        font_family=\"Rockwell\"\n",
    "    )\n",
    ")\n",
    "\n",
    "# Show the interactive plot\n",
    "fig.show()\n",
    "plotly.offline.plot(fig, filename= f'/Users/wanqing.yu/Documents/GitHub/EM-connectome/data/{rid}_{cell_type}_myelin.html')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}