ET_manuscript_Figure_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "falling-syria",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aligned_volume': {'id': 1,\n",
       "  'name': 'minnie65_phase3',\n",
       "  'display_name': 'Minnie65',\n",
       "  'description': \"This is the second alignment of the IARPA 'minnie65' dataset, completed in the spring of 2020 that used the seamless approach.\",\n",
       "  'image_source': 'precomputed://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/em'},\n",
       " 'segmentation_source': 'graphene://https://minnie.microns-daf.com/segmentation/table/minnie3_v1',\n",
       " 'skeleton_source': 'precomputed://middleauth+https://minnie.microns-daf.com/skeletoncache/api/v1/minnie65_phase3_v1/precomputed/skeleton',\n",
       " 'analysis_database': None,\n",
       " 'viewer_site': 'https://neuroglancer.neuvue.io/',\n",
       " 'synapse_table': 'synapses_pni_2',\n",
       " 'soma_table': 'nucleus_neuron_svm',\n",
       " 'local_server': 'https://minnie.microns-daf.com',\n",
       " 'description': 'This is the first version of Minnie that has proofreading enabled. Was first enabled on June 24, 2020. ',\n",
       " 'viewer_resolution_x': 4.0,\n",
       " 'viewer_resolution_y': 4.0,\n",
       " 'viewer_resolution_z': 40.0,\n",
       " 'proofreading_status_table': None,\n",
       " 'cell_identification_table': None,\n",
       " 'proofreading_review_table': None}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#from annotationframeworkclient import imagery\n",
    "#from annotationframeworkclient.jsonservice import JSONService\n",
    "#from annotationframeworkclient import imagery\n",
    "import cloudvolume\n",
    "from caveclient import CAVEclient\n",
    "import nglui\n",
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "client = CAVEclient('minnie65_phase3_v1')\n",
    "#client = CAVEclient('minnie65_public')\n",
    "client.info.get_datastack_info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b940aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAIN\n",
    "#'y' location of cortical surface\n",
    "surface_y_column =[84534, 85689, 86053, 87800, 89421, 90105, 82884, 81677, 86242]\n",
    "average_surface_location = mean(surface_y_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "pressed-standard",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAIN\n",
    "#IMPORT SYNAPSE TABLE\n",
    "\n",
    "pd.options.display.max_rows = None\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "synapse_table = pd.read_feather(\"ET_Column_synapse_table_revision.feather\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf5e4f07-63e2-443f-b66c-b118e5e0702e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate entries:\n",
      "[140819175 137309229 162886969 182778049]\n",
      "original synapse table size\n",
      "(3834, 35)\n",
      " synapse table size without duplicates\n",
      "(3814, 35)\n"
     ]
    }
   ],
   "source": [
    "# check duplicate entries \n",
    "duplicates = synapse_table[synapse_table['synapse_id'].duplicated(keep=False)]\n",
    "\n",
    "print(\"Duplicate entries:\")\n",
    "print(duplicates.synapse_id.unique())\n",
    "\n",
    "# Remove duplicate rows \n",
    "\n",
    "print(\"original synapse table size\")\n",
    "print(synapse_table.shape)\n",
    "\n",
    "synapse_table = synapse_table.drop_duplicates(subset='synapse_id')\n",
    "\n",
    "print(\" synapse table size without duplicates\")\n",
    "print(synapse_table.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "single-fraction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'total_connections': 1431,\n",
       "  'connections_with_class_labels': 1418,\n",
       "  'connections_with_class_labels_manual': 541,\n",
       "  'connections_without_class_labels': 13,\n",
       "  'with_subclass_labels': 1407,\n",
       "  'with_subclass_labels_manual': 236,\n",
       "  'without_subclass_labels': 24},\n",
       " {'total_synapses': 3814,\n",
       "  'synapses_with_class_labels': 3618,\n",
       "  'synapses_with_class_labels_manual': 2465,\n",
       "  'synapses_with_class_labels_automated': 3512,\n",
       "  'synapses_without_class_labels': 196,\n",
       "  'synapses_with_subclass_labels': 3534,\n",
       "  'synapses with_subclass_labels_manual': 1583,\n",
       "  'synapses_without_subclass_labels': 280,\n",
       "  'synapses_with_orphans': 254,\n",
       "  'synapses_with_orphans_with_class_labels': 75,\n",
       "  'synapses_with_orphans_without_class_labels': 179,\n",
       "  'synapses_with_orphan_spines': 130,\n",
       "  'synapses_with_orphan_dendrites_without_class_labels': 47,\n",
       "  'synapses_with_orphans_with_subclass_labels': 4,\n",
       "  'synapses_with_orphans_without_subclass_labels': 250,\n",
       "  'synapses_with_multisoma': 6,\n",
       "  'synapses_with_multisoma_with_class_labels': 2,\n",
       "  'synapses_with_multisoma_without_class_labels': 4})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ANALYSIS - GENERAL\n",
    "\n",
    "#NUMBER OF SYNAPSES\n",
    "\n",
    "synapse_numbers = { 'total_synapses' : len(synapse_table),\n",
    "                    \n",
    "                    'synapses_with_class_labels' : len(synapse_table[pd.notna(synapse_table['consensus_class'])]),\n",
    "\n",
    "                    'synapses_with_class_labels_manual' : len(synapse_table[(pd.notna(synapse_table['manual_class'])) &\n",
    "                                                                            (synapse_table['manual_class'] != 'unclear')]),\n",
    "\n",
    "                    'synapses_with_class_labels_automated' : len(synapse_table[((pd.notna(synapse_table['NEURD_class'])) & (synapse_table['NEURD_class'] != \"None\") |\n",
    "                      (pd.notna(synapse_table['metamodel_class'])) & (synapse_table['metamodel_class'] != \"None\"))]),\n",
    "\n",
    "                    'synapses_without_class_labels' : len(synapse_table[(pd.isna(synapse_table['consensus_class']))]),\n",
    "                   \n",
    "                    'synapses_with_subclass_labels' : len(synapse_table[pd.notna(synapse_table['consensus_subclass'])]),\n",
    "                    \n",
    "                    'synapses with_subclass_labels_manual' : len(synapse_table[(pd.notna(synapse_table['manual_subclass'])) &\n",
    "                                                                            (synapse_table['manual_subclass'] != 'excitatory') & \n",
    "                                                                            (synapse_table['manual_subclass'] != 'inhibitory')]),\n",
    "                    \n",
    "                    'synapses_without_subclass_labels' : len(synapse_table[pd.isna(synapse_table['consensus_subclass'])]),\n",
    "                   \n",
    "                    'synapses_with_orphans' : len(synapse_table[synapse_table['num_soma'] == 0]),\n",
    "                                                 \n",
    "                    'synapses_with_orphans_with_class_labels' : len(synapse_table[(synapse_table['num_soma'] == 0) &\n",
    "                                                                    pd.notna(synapse_table['consensus_class'])]),\n",
    "                   \n",
    "                    'synapses_with_orphans_without_class_labels' : len(synapse_table[(synapse_table['num_soma'] == 0) &\n",
    "                                                                    pd.isna(synapse_table['consensus_class'])]),\n",
    "                   \n",
    "                    'synapses_with_orphan_spines': len(synapse_table[synapse_table['orphan'] == 'single_spine']),\n",
    "                   \n",
    "                    'synapses_with_orphan_dendrites_without_class_labels': len(synapse_table[(synapse_table['num_soma'] == 0) &\n",
    "                                                                    (pd.isna(synapse_table['consensus_class'])) &\n",
    "                                                                    (synapse_table['orphan'] == 'dendrite')]),\n",
    "                   \n",
    "                    'synapses_with_orphans_with_subclass_labels' : len(synapse_table[(synapse_table['num_soma'] == 0) &\n",
    "                                                                    pd.notna(synapse_table['consensus_subclass'])]),\n",
    "                    'synapses_with_orphans_without_subclass_labels' : len(synapse_table[(synapse_table['num_soma'] == 0) &\n",
    "                                                                    pd.isna(synapse_table['consensus_subclass'])]),\n",
    "\n",
    "                                   \n",
    "                    'synapses_with_multisoma' : len(synapse_table[synapse_table['num_soma'] > 1]),\n",
    "                    'synapses_with_multisoma_with_class_labels' : len(synapse_table[(synapse_table['num_soma'] > 1) &\n",
    "                                                                    pd.notna(synapse_table['consensus_class'])]),\n",
    "                    'synapses_with_multisoma_without_class_labels' : len(synapse_table[(synapse_table['num_soma'] > 1) &\n",
    "                                                                    pd.isna(synapse_table['consensus_class'])]),\n",
    "}\n",
    "\n",
    "\n",
    "#NUMBER OF SOMATA\n",
    "\n",
    "connection_numbers = {\n",
    "                    'total_connections' : len(synapse_table[synapse_table['num_soma'] == 1].post_pt_root_id.unique()),\n",
    "                   \n",
    "                    'connections_with_class_labels' : len(synapse_table[pd.notna(synapse_table['consensus_class']) &\n",
    "                                                             (synapse_table['num_soma'] == 1)].post_pt_root_id.unique()),\n",
    "    \n",
    "                    'connections_with_class_labels_manual' : len(synapse_table[(pd.notna(synapse_table['manual_class'])) &\n",
    "                                                             (synapse_table['manual_class'] != 'unclear') &\n",
    "                                                             (synapse_table['num_soma'] == 1)].post_pt_root_id.unique()),\n",
    "                    \n",
    "                    'connections_without_class_labels' : len(synapse_table[(pd.isna(synapse_table['consensus_class'])) &\n",
    "                                                             (synapse_table['num_soma'] == 1)].post_pt_root_id.unique()),\n",
    "                \n",
    "                    'with_subclass_labels' : len(synapse_table[(pd.notna(synapse_table['consensus_subclass'])) &\n",
    "                                                             (synapse_table['num_soma'] == 1)].post_pt_root_id.unique()),\n",
    "                    'with_subclass_labels_manual' : len(synapse_table[(pd.notna(synapse_table['manual_subclass'])) &\n",
    "                                                             (synapse_table['num_soma'] == 1)].post_pt_root_id.unique()),\n",
    "                    'without_subclass_labels' : len(synapse_table[(pd.isna(synapse_table['consensus_subclass'])) &\n",
    "                                                             (synapse_table['num_soma'] == 1)].post_pt_root_id.unique()),\n",
    "   \n",
    "}\n",
    "\n",
    "connection_numbers, synapse_numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "deda2e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://spelunker.cave-explorer.org/#!middleauth+https://global.daf-apis.com/nglstate/api/v1/5977503257591808'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CREATE NEUROGLANCER LINK\n",
    "\n",
    "#manual_check = synapse_table.iloc[3000:3833]\n",
    "\n",
    "from nglui import statebuilder\n",
    "\n",
    "img, seg = statebuilder.from_client(client)\n",
    "\n",
    "pt_map = statebuilder.PointMapper('post_pt_position', linked_segmentation_column='post_pt_supervoxel_id')\n",
    "anno = statebuilder.AnnotationLayerConfig('post_pt_position', mapping_rules=pt_map, linked_segmentation_layer=seg.name,\n",
    "                                          tags=['spine', 'shaft', 'soma', 'soma_spine', 'orphan', 'other'])\n",
    "sb = statebuilder.StateBuilder([img, seg, anno], client=client)\n",
    "\n",
    "#here is where you add the dataframe\n",
    "sb.render_state(manual_check[['post_pt_supervoxel_id','post_pt_position']], return_as='short', target_site = 'spelunker')\n",
    "\n",
    "#[id, x,y,z]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c05d21cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>synapse_id</th>\n",
       "      <th>size</th>\n",
       "      <th>pre_pt_supervoxel_id</th>\n",
       "      <th>pre_pt_root_id</th>\n",
       "      <th>post_pt_supervoxel_id</th>\n",
       "      <th>post_pt_root_id</th>\n",
       "      <th>pre_pt_position</th>\n",
       "      <th>post_pt_position</th>\n",
       "      <th>ctr_pt_position</th>\n",
       "      <th>pre_pt_level2_id</th>\n",
       "      <th>pre_pt_mesh_ind</th>\n",
       "      <th>pre_pt_mesh_ind_filt</th>\n",
       "      <th>dist_to_root</th>\n",
       "      <th>distance_rank</th>\n",
       "      <th>pre_nucleus_id</th>\n",
       "      <th>post_nucleus_id</th>\n",
       "      <th>post_soma_pt</th>\n",
       "      <th>NEURD_class</th>\n",
       "      <th>metamodel_class</th>\n",
       "      <th>metamodel_cell_type</th>\n",
       "      <th>mtypes_model_cell_type</th>\n",
       "      <th>mtypes_column</th>\n",
       "      <th>motif_group</th>\n",
       "      <th>post_soma_area</th>\n",
       "      <th>pre_soma_pt</th>\n",
       "      <th>pre_soma_area</th>\n",
       "      <th>manual_class_original</th>\n",
       "      <th>manual_subclass_original</th>\n",
       "      <th>num_soma</th>\n",
       "      <th>inhibitory_groups</th>\n",
       "      <th>manual_subclass</th>\n",
       "      <th>manual_class</th>\n",
       "      <th>consensus_class</th>\n",
       "      <th>consensus_subclass</th>\n",
       "      <th>orphan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [synapse_id, size, pre_pt_supervoxel_id, pre_pt_root_id, post_pt_supervoxel_id, post_pt_root_id, pre_pt_position, post_pt_position, ctr_pt_position, pre_pt_level2_id, pre_pt_mesh_ind, pre_pt_mesh_ind_filt, dist_to_root, distance_rank, pre_nucleus_id, post_nucleus_id, post_soma_pt, NEURD_class, metamodel_class, metamodel_cell_type, mtypes_model_cell_type, mtypes_column, motif_group, post_soma_area, pre_soma_pt, pre_soma_area, manual_class_original, manual_subclass_original, num_soma, inhibitory_groups, manual_subclass, manual_class, consensus_class, consensus_subclass, orphan]\n",
       "Index: []"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAIN\n",
    "\n",
    "#Verify that neurons with consensus subclass have somata\n",
    "\n",
    "synapse_table[(synapse_table['num_soma'] == 0) & pd.notna(synapse_table['consensus_subclass'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8e5d1bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAIN\n",
    "#correct_number of soma\n",
    "def correct_soma_number(row):\n",
    "        \n",
    "    if row['orphan'] == 'neuron_with_soma':\n",
    "          return  {'num_soma': 1}    \n",
    "\n",
    "    else:\n",
    "          return row['num_soma']  \n",
    "\n",
    "synapse_table['num_soma'] = synapse_table.apply(correct_soma_number, axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "stupid-bruce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "864691136313000765\n",
      "864691135866820485\n",
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      "864691134941063523\n",
      "864691135576602910\n",
      "864691135503182685\n",
      "864691135489514810\n",
      "864691135498760723\n",
      "864691134940999267\n",
      "864691135214123064\n",
      "864691136335150259\n",
      "864691135911028905\n",
      "864691135948123745\n",
      "864691136437631902\n",
      "864691135782220112\n",
      "864691135716281370\n",
      "864691135865557118\n",
      "864691135777116589\n",
      "864691136024995385\n",
      "864691135591736587\n",
      "864691135214122296\n",
      "864691136991202453\n",
      "864691135851941575\n",
      "864691135585266808\n",
      "864691136330101226\n",
      "864691136362774498\n",
      "864691135565831447\n",
      "864691135276295653\n",
      "864691135688186080\n",
      "864691136228250961\n",
      "864691135778738877\n",
      "864691136419797655\n",
      "864691136287648195\n",
      "864691135517376211\n",
      "864691135275560677\n",
      "864691136039974142\n",
      "864691137020891758\n",
      "864691136444943747\n",
      "864691136137794941\n"
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>pt_soma_ID</th>\n",
       "      <th>number_output_synapses</th>\n",
       "      <th>number_input_synapses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>864691136313000765</td>\n",
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       "      <th>1</th>\n",
       "      <td>864691135866820485</td>\n",
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       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>864691136335150259</td>\n",
       "      <td>98</td>\n",
       "      <td>12166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>864691135911028905</td>\n",
       "      <td>97</td>\n",
       "      <td>6435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>864691135948123745</td>\n",
       "      <td>94</td>\n",
       "      <td>10723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>864691136437631902</td>\n",
       "      <td>97</td>\n",
       "      <td>6769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>864691135782220112</td>\n",
       "      <td>100</td>\n",
       "      <td>5091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>864691135716281370</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>864691135865557118</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>864691135777116589</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>864691136024995385</td>\n",
       "      <td>98</td>\n",
       "      <td>10650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>864691135591736587</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>864691135214122296</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>864691136991202453</td>\n",
       "      <td>97</td>\n",
       "      <td>15326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>864691135851941575</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>864691135585266808</td>\n",
       "      <td>100</td>\n",
       "      <td>7136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>864691136330101226</td>\n",
       "      <td>98</td>\n",
       "      <td>9137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>864691136362774498</td>\n",
       "      <td>97</td>\n",
       "      <td>4944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>864691135565831447</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>864691135276295653</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>864691135688186080</td>\n",
       "      <td>97</td>\n",
       "      <td>10046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>864691136228250961</td>\n",
       "      <td>98</td>\n",
       "      <td>6867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>864691135778738877</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>864691136419797655</td>\n",
       "      <td>99</td>\n",
       "      <td>5131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>864691136287648195</td>\n",
       "      <td>96</td>\n",
       "      <td>4256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>864691135517376211</td>\n",
       "      <td>93</td>\n",
       "      <td>11047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>864691135275560677</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>864691136039974142</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>864691137020891758</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>864691136444943747</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>864691136137794941</td>\n",
       "      <td>98</td>\n",
       "      <td>8815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pt_soma_ID  number_output_synapses  number_input_synapses\n",
       "0   864691136313000765                      95                   5727\n",
       "1   864691135866820485                      98                      0\n",
       "2   864691136663371742                     100                   4503\n",
       "3   864691134941063523                      99                   6338\n",
       "4   864691135576602910                      97                      0\n",
       "5   864691135503182685                      99                  11128\n",
       "6   864691135489514810                      98                  26797\n",
       "7   864691135498760723                      96                   8049\n",
       "8   864691134940999267                      97                      0\n",
       "9   864691135214123064                      98                   4640\n",
       "10  864691136335150259                      98                  12166\n",
       "11  864691135911028905                      97                   6435\n",
       "12  864691135948123745                      94                  10723\n",
       "13  864691136437631902                      97                   6769\n",
       "14  864691135782220112                     100                   5091\n",
       "15  864691135716281370                     100                      0\n",
       "16  864691135865557118                      99                      0\n",
       "17  864691135777116589                     100                      0\n",
       "18  864691136024995385                      98                  10650\n",
       "19  864691135591736587                      99                      0\n",
       "20  864691135214122296                      98                      0\n",
       "21  864691136991202453                      97                  15326\n",
       "22  864691135851941575                      98                      0\n",
       "23  864691135585266808                     100                   7136\n",
       "24  864691136330101226                      98                   9137\n",
       "25  864691136362774498                      97                   4944\n",
       "26  864691135565831447                      97                      0\n",
       "27  864691135276295653                      96                      0\n",
       "28  864691135688186080                      97                  10046\n",
       "29  864691136228250961                      98                   6867\n",
       "30  864691135778738877                      99                      0\n",
       "31  864691136419797655                      99                   5131\n",
       "32  864691136287648195                      96                   4256\n",
       "33  864691135517376211                      93                  11047\n",
       "34  864691135275560677                      98                      0\n",
       "35  864691136039974142                      99                      0\n",
       "36  864691137020891758                      99                      0\n",
       "37  864691136444943747                      98                      0\n",
       "38  864691136137794941                      98                   8815"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ANALYSIS - FIGURE 2\n",
    "#Number of synapses per ET neuron\n",
    "\n",
    "client = CAVEclient('minnie65_phase3_v1')\n",
    "client.info.get_datastack_info()\n",
    "\n",
    "# setup an empty list\n",
    "ds=[]\n",
    "\n",
    "#Number of inputs and outputs for each ET neuron\n",
    "pt_IDs = synapse_table.pre_pt_root_id.unique()\n",
    "\n",
    "for pt_id in synapse_table.pre_pt_root_id.unique():\n",
    "\n",
    "        print(pt_id)\n",
    "        \n",
    "        d={\n",
    "                        'pt_soma_ID': pt_id,\n",
    "                        'number_output_synapses': len(synapse_table[synapse_table['pre_pt_root_id'] == pt_id]),\n",
    "                        'number_input_synapses': len(client.materialize.synapse_query(post_ids=pt_id)),\n",
    "        }\n",
    "        \n",
    "        #append entry\n",
    "        ds.append(d)\n",
    "\n",
    "pt_io = pd.DataFrame(ds)\n",
    "pt_io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "critical-instrumentation",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ANALYSIS - FIGURE 2\n",
    "#MAKE STATS DATAFRAME - All synapses and connections\n",
    "\n",
    "np.seterr(divide='ignore', invalid='ignore')\n",
    "\n",
    "percentage = []\n",
    "values = []\n",
    "\n",
    "#IDs of presynaptic PT neurons\n",
    "pre_soma_IDs = synapse_table['pre_nucleus_id'].unique()\n",
    "\n",
    "\n",
    "for ii,pre_soma_ID in enumerate(pre_soma_IDs):        \n",
    "        \n",
    "   \n",
    "    stat_values={\n",
    "\n",
    "                    'ID': pre_soma_ID,\n",
    "                    \n",
    "        #SYNAPSES\n",
    "                    \n",
    "                    'all_syn_total': len(synapse_table[ (synapse_table['pre_nucleus_id'] == pre_soma_ID)]),\n",
    "                    'all_e_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_class'] == 'excitatory')]),\n",
    "                    'all_i_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_class'] == 'inhibitory')]),        \n",
    "                    'all_Undetermined_class_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     ((synapse_table['consensus_class'] == 'Unsure') |\n",
    "                                      pd.isnull(synapse_table['consensus_class']))]),\n",
    "                    \n",
    "        \n",
    "                    'all_23P_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '23P')]),\n",
    "        \n",
    "                    'all_4P_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '4P')]),\n",
    "                    'all_5P-PT_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '5P-PT')]),\n",
    "                    'all_5P-NP_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '5P-NP')]), \n",
    "                    'all_5P-IT_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '5P-IT')]), \n",
    "                \n",
    "                    'all_6P_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '6P')]),\n",
    "        \n",
    "                    'all_6P-CT_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '6P-CT')]),\n",
    "        \n",
    "                    'all_6P-IT_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '6P-IT')]),\n",
    "        \n",
    "                    'all_BC_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == 'BC')]),\n",
    "        \n",
    "                    'all_MC_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == 'MC')]),\n",
    "        \n",
    "                    'all_BPC_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == 'BPC')]),\n",
    "        \n",
    "                    'all_NGC_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == 'NGC')]),\n",
    "        \n",
    "                    'all_Undetermined_subclass_syn#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     ((synapse_table['consensus_subclass'] == 'Unsure') |\n",
    "                                      pd.isnull(synapse_table['consensus_subclass']))]),\n",
    "        \n",
    "        \n",
    "        #CONNECTIONS\n",
    "                    'all_con_total': len(synapse_table[ (synapse_table['pre_nucleus_id'] == pre_soma_ID)]\n",
    "                                         ['post_pt_root_id'].unique()),\n",
    "                    \n",
    "                    'all_e_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_class'] == 'excitatory')]\n",
    "                                      ['post_pt_root_id'].unique()),\n",
    "                    \n",
    "                    'all_i_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_class'] == 'inhibitory')]\n",
    "                                      ['post_pt_root_id'].unique()),\n",
    "        \n",
    "                    'all_Undetermined_class_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     ((synapse_table['consensus_class'] == 'Unsure') |\n",
    "                                      pd.isnull(synapse_table['consensus_class']))]['post_pt_root_id'].unique()),                      \n",
    "        \n",
    "                    'all_23P_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '23P')]['post_pt_root_id'].unique()),\n",
    "        \n",
    "                    'all_4P_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '4P')]['post_pt_root_id'].unique()),\n",
    "                    \n",
    "                    'all_5P-PT_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '5P-PT')]['post_pt_root_id'].unique()),\n",
    "                    \n",
    "                    'all_5P-NP_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '5P-NP')]['post_pt_root_id'].unique()), \n",
    " \n",
    "                    'all_5P-IT_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '5P-IT')]['post_pt_root_id'].unique()),\n",
    "                    \n",
    "                    'all_6P_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '6P')]['post_pt_root_id'].unique()),\n",
    "                \n",
    "                    'all_6P-CT_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '6P-CT')]['post_pt_root_id'].unique()),\n",
    "        \n",
    "                    'all_6P-IT_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == '6P-IT')]['post_pt_root_id'].unique()),\n",
    "                    \n",
    "                    'all_BC_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == 'BC')]['post_pt_root_id'].unique()),\n",
    "                     \n",
    "                    'all_MC_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == 'MC')]['post_pt_root_id'].unique()),\n",
    "       \n",
    "                    'all_BPC_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == 'BPC')]['post_pt_root_id'].unique()),\n",
    "                    \n",
    "                    'all_NGC_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     (synapse_table['consensus_subclass'] == 'NGC')]['post_pt_root_id'].unique()),\n",
    "        \n",
    "                    'all_Undetermined_subclass_con#': len(synapse_table[(synapse_table['pre_nucleus_id'] == pre_soma_ID) &\n",
    "                                     ((synapse_table['consensus_subclass'] == 'Unsure') |\n",
    "                                      pd.isnull(synapse_table['consensus_subclass']))]['post_pt_root_id'].unique()),\n",
    "                        \n",
    "        \n",
    "    }\n",
    "    values.append(stat_values)\n",
    "    \n",
    "    \n",
    "    stat_percentage={\n",
    "\n",
    "                    'ID': pre_soma_ID,\n",
    "                    'Soma_depth':  (client.materialize.query_table('nucleus_detection_v0', \n",
    "                               filter_equal_dict={'id':pre_soma_ID})['pt_position'].apply(lambda x: x[1]).iloc[0]\n",
    "                                                  - average_surface_location)*0.004,\n",
    "                   \n",
    "                      \n",
    "                    'i_syn': stat_values['all_i_syn#'] / stat_values['all_syn_total'],\n",
    "                    'e_syn': stat_values['all_e_syn#'] / stat_values['all_syn_total'],\n",
    "                    'Undetermined_class_syn': stat_values['all_Undetermined_class_syn#'] / stat_values['all_syn_total'],\n",
    "\n",
    "        \n",
    "                    'i_syn_determined': stat_values['all_i_syn#'] / (stat_values['all_i_syn#']+stat_values['all_e_syn#']),\n",
    "                    'e_syn_determined': stat_values['all_e_syn#'] / (stat_values['all_i_syn#']+stat_values['all_e_syn#']),\n",
    "        \n",
    "        \n",
    "                    '23P_syn': stat_values['all_23P_syn#'] / stat_values['all_syn_total'],\n",
    "                    '4P_syn': stat_values['all_4P_syn#'] / stat_values['all_syn_total'],\n",
    "                    '5P-PT_syn': stat_values['all_5P-PT_syn#'] / stat_values['all_syn_total'],\n",
    "                    '5P-IT_syn': stat_values['all_5P-IT_syn#'] / stat_values['all_syn_total'],\n",
    "                    '5P-NP_syn': stat_values['all_5P-NP_syn#'] / stat_values['all_syn_total'],\n",
    "                    '6P_syn': stat_values['all_6P_syn#'] / stat_values['all_syn_total'],\n",
    "                    '6P-IT_syn': stat_values['all_6P-IT_syn#'] / stat_values['all_syn_total'],\n",
    "                    '6P-CT_syn': stat_values['all_6P-CT_syn#'] / stat_values['all_syn_total'],\n",
    "                    'BC_syn': stat_values['all_BC_syn#'] / stat_values['all_syn_total'],\n",
    "                    'MC_syn': stat_values['all_MC_syn#'] / stat_values['all_syn_total'],\n",
    "                    'BPC_syn': stat_values['all_BPC_syn#'] / stat_values['all_syn_total'],\n",
    "                    'NGC_syn': stat_values['all_NGC_syn#'] / stat_values['all_syn_total'],\n",
    "                    'Undetermined_subclass_syn': stat_values['all_Undetermined_subclass_syn#'] / stat_values['all_syn_total'],\n",
    "        \n",
    "        \n",
    "                    'i_con': stat_values['all_i_con#'] / stat_values['all_con_total'],\n",
    "                    'e_con': stat_values['all_e_con#'] / stat_values['all_con_total'],\n",
    "                    'Undetermined_class_con': stat_values['all_Undetermined_class_con#'] / stat_values['all_con_total'],\n",
    "                   \n",
    "        \n",
    "                    '23P_con': stat_values['all_23P_con#'] / stat_values['all_con_total'],\n",
    "                    '4P_con': stat_values['all_4P_con#'] / stat_values['all_con_total'],\n",
    "                    '5P-PT_con': stat_values['all_5P-PT_con#'] / stat_values['all_con_total'],\n",
    "                    '5P-IT_con': stat_values['all_5P-IT_con#'] / stat_values['all_con_total'],\n",
    "                    '5P-NP_con': stat_values['all_5P-NP_con#'] / stat_values['all_con_total'],\n",
    "                    '6P_con': stat_values['all_6P_con#'] / stat_values['all_con_total'],\n",
    "                    '6P-IT_con': stat_values['all_6P-IT_con#'] / stat_values['all_con_total'],\n",
    "                    '6P-CT_con': stat_values['all_6P-CT_con#'] / stat_values['all_con_total'],\n",
    "                    'BC_con': stat_values['all_BC_con#'] / stat_values['all_con_total'],\n",
    "                    'MC_con': stat_values['all_MC_con#'] / stat_values['all_con_total'],\n",
    "                    'BPC_con': stat_values['all_BPC_con#'] / stat_values['all_syn_total'],\n",
    "                    'NGC_con': stat_values['all_NGC_con#'] / stat_values['all_con_total'],\n",
    "                    'Undetermined_subclass_con': stat_values['all_Undetermined_subclass_con#'] / stat_values['all_con_total'],\n",
    "                   \n",
    "    }\n",
    "    percentage.append(stat_percentage) \n",
    "    \n",
    "\n",
    "synapse_table_values = pd.DataFrame(values)\n",
    "synapse_table_percentage = pd.DataFrame(percentage)\n",
    "\n",
    "\n",
    "#stats_type.append(stat)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "objective-dealer",
   "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>ID</th>\n",
       "      <th>all_syn_total</th>\n",
       "      <th>all_e_syn#</th>\n",
       "      <th>all_i_syn#</th>\n",
       "      <th>all_Undetermined_class_syn#</th>\n",
       "      <th>all_23P_syn#</th>\n",
       "      <th>all_4P_syn#</th>\n",
       "      <th>all_5P-PT_syn#</th>\n",
       "      <th>all_5P-NP_syn#</th>\n",
       "      <th>all_5P-IT_syn#</th>\n",
       "      <th>all_6P_syn#</th>\n",
       "      <th>all_6P-CT_syn#</th>\n",
       "      <th>all_6P-IT_syn#</th>\n",
       "      <th>all_BC_syn#</th>\n",
       "      <th>all_MC_syn#</th>\n",
       "      <th>all_BPC_syn#</th>\n",
       "      <th>all_NGC_syn#</th>\n",
       "      <th>all_Undetermined_subclass_syn#</th>\n",
       "      <th>all_con_total</th>\n",
       "      <th>all_e_con#</th>\n",
       "      <th>all_i_con#</th>\n",
       "      <th>all_Undetermined_class_con#</th>\n",
       "      <th>all_23P_con#</th>\n",
       "      <th>all_4P_con#</th>\n",
       "      <th>all_5P-PT_con#</th>\n",
       "      <th>all_5P-NP_con#</th>\n",
       "      <th>all_5P-IT_con#</th>\n",
       "      <th>all_6P_con#</th>\n",
       "      <th>all_6P-CT_con#</th>\n",
       "      <th>all_6P-IT_con#</th>\n",
       "      <th>all_BC_con#</th>\n",
       "      <th>all_MC_con#</th>\n",
       "      <th>all_BPC_con#</th>\n",
       "      <th>all_NGC_con#</th>\n",
       "      <th>all_Undetermined_subclass_con#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>267008</td>\n",
       "      <td>95</td>\n",
       "      <td>24</td>\n",
       "      <td>63</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>267260</td>\n",
       "      <td>98</td>\n",
       "      <td>39</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>81</td>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>266848</td>\n",
       "      <td>100</td>\n",
       "      <td>27</td>\n",
       "      <td>69</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>267183</td>\n",
       "      <td>99</td>\n",
       "      <td>38</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>267185</td>\n",
       "      <td>97</td>\n",
       "      <td>55</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>87</td>\n",
       "      <td>54</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>267029</td>\n",
       "      <td>99</td>\n",
       "      <td>26</td>\n",
       "      <td>71</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>267033</td>\n",
       "      <td>98</td>\n",
       "      <td>20</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>64</td>\n",
       "      <td>19</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>267038</td>\n",
       "      <td>96</td>\n",
       "      <td>29</td>\n",
       "      <td>59</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>73</td>\n",
       "      <td>28</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>267194</td>\n",
       "      <td>97</td>\n",
       "      <td>23</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>267295</td>\n",
       "      <td>98</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>82</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>267294</td>\n",
       "      <td>98</td>\n",
       "      <td>21</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>19</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>267299</td>\n",
       "      <td>97</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>267307</td>\n",
       "      <td>94</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>59</td>\n",
       "      <td>16</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>265045</td>\n",
       "      <td>97</td>\n",
       "      <td>38</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>77</td>\n",
       "      <td>35</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>269350</td>\n",
       "      <td>100</td>\n",
       "      <td>42</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>75</td>\n",
       "      <td>38</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>267319</td>\n",
       "      <td>100</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>300897</td>\n",
       "      <td>99</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>83</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>303135</td>\n",
       "      <td>100</td>\n",
       "      <td>8</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>303032</td>\n",
       "      <td>98</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>54</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>304956</td>\n",
       "      <td>99</td>\n",
       "      <td>42</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>303035</td>\n",
       "      <td>98</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>84</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>302951</td>\n",
       "      <td>97</td>\n",
       "      <td>22</td>\n",
       "      <td>71</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>302955</td>\n",
       "      <td>98</td>\n",
       "      <td>36</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>79</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>302957</td>\n",
       "      <td>100</td>\n",
       "      <td>29</td>\n",
       "      <td>64</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>63</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>303139</td>\n",
       "      <td>98</td>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>73</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>303046</td>\n",
       "      <td>97</td>\n",
       "      <td>39</td>\n",
       "      <td>49</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>79</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>303048</td>\n",
       "      <td>97</td>\n",
       "      <td>31</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>303144</td>\n",
       "      <td>96</td>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>303145</td>\n",
       "      <td>97</td>\n",
       "      <td>22</td>\n",
       "      <td>65</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>304973</td>\n",
       "      <td>98</td>\n",
       "      <td>27</td>\n",
       "      <td>61</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>69</td>\n",
       "      <td>26</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>302972</td>\n",
       "      <td>99</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>72</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>303066</td>\n",
       "      <td>99</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "      <td>45</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>305197</td>\n",
       "      <td>96</td>\n",
       "      <td>44</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>73</td>\n",
       "      <td>42</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>303216</td>\n",
       "      <td>93</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>61</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>302861</td>\n",
       "      <td>98</td>\n",
       "      <td>46</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>74</td>\n",
       "      <td>43</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>301225</td>\n",
       "      <td>99</td>\n",
       "      <td>64</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>303095</td>\n",
       "      <td>99</td>\n",
       "      <td>42</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>303174</td>\n",
       "      <td>98</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>32</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>303100</td>\n",
       "      <td>98</td>\n",
       "      <td>28</td>\n",
       "      <td>66</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  all_syn_total  all_e_syn#  all_i_syn#  \\\n",
       "0   267008             95          24          63   \n",
       "1   267260             98          39          56   \n",
       "2   266848            100          27          69   \n",
       "3   267183             99          38          59   \n",
       "4   267185             97          55          38   \n",
       "5   267029             99          26          71   \n",
       "6   267033             98          20          76   \n",
       "7   267038             96          29          59   \n",
       "8   267194             97          23          71   \n",
       "9   267295             98          44          46   \n",
       "10  267294             98          21          76   \n",
       "11  267299             97          28          64   \n",
       "12  267307             94          17          72   \n",
       "13  265045             97          38          56   \n",
       "14  269350            100          42          54   \n",
       "15  267319            100          35          60   \n",
       "16  300897             99          45          49   \n",
       "17  303135            100           8          90   \n",
       "18  303032             98          18          76   \n",
       "19  304956             99          42          52   \n",
       "20  303035             98          45          46   \n",
       "21  302951             97          22          71   \n",
       "22  302955             98          36          54   \n",
       "23  302957            100          29          64   \n",
       "24  303139             98          31          63   \n",
       "25  303046             97          39          49   \n",
       "26  303048             97          31          60   \n",
       "27  303144             96          10          80   \n",
       "28  303145             97          22          65   \n",
       "29  304973             98          27          61   \n",
       "30  302972             99          28          64   \n",
       "31  303066             99          45          49   \n",
       "32  305197             96          44          49   \n",
       "33  303216             93          31          57   \n",
       "34  302861             98          46          48   \n",
       "35  301225             99          64          30   \n",
       "36  303095             99          42          50   \n",
       "37  303174             98          32          64   \n",
       "38  303100             98          28          66   \n",
       "\n",
       "    all_Undetermined_class_syn#  all_23P_syn#  all_4P_syn#  all_5P-PT_syn#  \\\n",
       "0                             8             0            0               0   \n",
       "1                             3             0            0               0   \n",
       "2                             4             1            0               0   \n",
       "3                             2             0            0               0   \n",
       "4                             4             0            1               0   \n",
       "5                             2             0            0               0   \n",
       "6                             2             0            0               0   \n",
       "7                             8             0            0               0   \n",
       "8                             3             0            0               0   \n",
       "9                             8             0            0               0   \n",
       "10                            1             1            0               0   \n",
       "11                            5             0            0               0   \n",
       "12                            5             0            0               0   \n",
       "13                            3             0            0               0   \n",
       "14                            4             0            0               0   \n",
       "15                            5             0            0               0   \n",
       "16                            5             0            1               0   \n",
       "17                            2             0            0               0   \n",
       "18                            4             0            0               0   \n",
       "19                            5             0            0               0   \n",
       "20                            7             0            7               0   \n",
       "21                            4             0            0               0   \n",
       "22                            8             0            1               0   \n",
       "23                            7             0            0               0   \n",
       "24                            4             0            0               0   \n",
       "25                            9             0            0               0   \n",
       "26                            6             0            0               0   \n",
       "27                            6             0            0               0   \n",
       "28                           10             0            0               0   \n",
       "29                           10             0            0               0   \n",
       "30                            7             0            0               0   \n",
       "31                            5             0            0               0   \n",
       "32                            3             0            0               0   \n",
       "33                            5             0            0               0   \n",
       "34                            4             0            1               0   \n",
       "35                            5             0            2               0   \n",
       "36                            7             0            1               0   \n",
       "37                            2             0            0               0   \n",
       "38                            4             0            0               0   \n",
       "\n",
       "    all_5P-NP_syn#  all_5P-IT_syn#  all_6P_syn#  all_6P-CT_syn#  \\\n",
       "0                0               6           11               0   \n",
       "1                1               5           25               0   \n",
       "2                0               6           14               0   \n",
       "3                5              14           14               0   \n",
       "4                3              15           22               0   \n",
       "5                5               8           10               0   \n",
       "6                5               5           10               0   \n",
       "7                5               6           13               0   \n",
       "8                7               7            5               0   \n",
       "9                1               3           34               0   \n",
       "10               4               6            7               0   \n",
       "11               1               2           21               0   \n",
       "12               5               3            7               0   \n",
       "13               4               5           21               0   \n",
       "14               0               4           31               0   \n",
       "15               1               3           24               0   \n",
       "16               2               7           24               0   \n",
       "17               0               3            4               0   \n",
       "18               2               6            6               0   \n",
       "19               0               5           29               0   \n",
       "20               4               8           18               0   \n",
       "21               6               4           12               0   \n",
       "22               7               1           24               0   \n",
       "23               1               7           16               0   \n",
       "24               3               1           17               0   \n",
       "25               0               1           31               0   \n",
       "26               0               4           20               0   \n",
       "27               0               4            3               0   \n",
       "28               3               1           14               0   \n",
       "29               1               2           24               0   \n",
       "30               2               7            9               0   \n",
       "31               2               4           34               0   \n",
       "32               1               5           27               0   \n",
       "33               6               5           17               0   \n",
       "34               2               4           31               0   \n",
       "35               0              25           26               0   \n",
       "36               1               8           21               0   \n",
       "37               1               1           23               0   \n",
       "38               1               2           23               0   \n",
       "\n",
       "    all_6P-IT_syn#  all_BC_syn#  all_MC_syn#  all_BPC_syn#  all_NGC_syn#  \\\n",
       "0                0           25           37             1             0   \n",
       "1                0           27           28             0             0   \n",
       "2                0           33           36             0             0   \n",
       "3                0           37           19             0             1   \n",
       "4                0           16           17             2             2   \n",
       "5                0           48           21             2             0   \n",
       "6                0           52           18             0             2   \n",
       "7                0           27           26             2             0   \n",
       "8                0           46           24             1             0   \n",
       "9                0           16           23             5             0   \n",
       "10               0           31           40             4             0   \n",
       "11               0           29           32             0             0   \n",
       "12               0           38           34             0             0   \n",
       "13               0           27           20             2             0   \n",
       "14               0           26           22             4             0   \n",
       "15               0           35           24             1             0   \n",
       "16               0           16           25             3             2   \n",
       "17               0           51           35             1             0   \n",
       "18               0           50           24             1             0   \n",
       "19               0           17           31             2             0   \n",
       "20               0           18           22             0             0   \n",
       "21               0           42           29             0             0   \n",
       "22               0           20           31             2             0   \n",
       "23               0           21           43             0             0   \n",
       "24               0           27           27             9             0   \n",
       "25               0           21           25             2             0   \n",
       "26               0           28           32             0             0   \n",
       "27               0           46           33             1             0   \n",
       "28               0           34           28             1             0   \n",
       "29               0           31           21             8             0   \n",
       "30               0           38           25             0             0   \n",
       "31               0           25           20             2             0   \n",
       "32               0           22           18             5             0   \n",
       "33               0           38           19             0             0   \n",
       "34               0           24           20             4             0   \n",
       "35               0           18           11             1             0   \n",
       "36               0           26           22             2             0   \n",
       "37               0           31           30             3             0   \n",
       "38               0           30           35             0             0   \n",
       "\n",
       "    all_Undetermined_subclass_syn#  all_con_total  all_e_con#  all_i_con#  \\\n",
       "0                                8             60          24          28   \n",
       "1                                5             81          38          40   \n",
       "2                                4             74          25          45   \n",
       "3                                4             71          35          34   \n",
       "4                                9             87          54          29   \n",
       "5                                2             65          24          39   \n",
       "6                                6             64          19          43   \n",
       "7                               13             73          28          37   \n",
       "8                                3             62          20          39   \n",
       "9                               13             82          42          33   \n",
       "10                               2             53          19          33   \n",
       "11                               9             69          27          37   \n",
       "12                               5             59          16          38   \n",
       "13                              13             77          35          39   \n",
       "14                               6             75          38          33   \n",
       "15                               5             72          32          35   \n",
       "16                               9             83          44          34   \n",
       "17                               5             51           8          41   \n",
       "18                               5             54          17          33   \n",
       "19                               8             84          40          39   \n",
       "20                              14             84          45          32   \n",
       "21                               4             61          22          35   \n",
       "22                               9             79          34          37   \n",
       "23                               7             63          27          29   \n",
       "24                               4             73          30          39   \n",
       "25                              12             79          36          34   \n",
       "26                               6             70          31          33   \n",
       "27                               6             54          10          38   \n",
       "28                              14             70          22          38   \n",
       "29                              11             69          26          33   \n",
       "30                               8             72          27          39   \n",
       "31                               8             84          45          34   \n",
       "32                               9             73          42          28   \n",
       "33                               5             61          30          26   \n",
       "34                               5             74          43          27   \n",
       "35                               8             90          60          25   \n",
       "36                               8             80          41          32   \n",
       "37                               3             86          32          52   \n",
       "38                               5             60          26          30   \n",
       "\n",
       "    all_Undetermined_class_con#  all_23P_con#  all_4P_con#  all_5P-PT_con#  \\\n",
       "0                             8             0            0               0   \n",
       "1                             3             0            0               0   \n",
       "2                             4             1            0               0   \n",
       "3                             2             0            0               0   \n",
       "4                             4             0            1               0   \n",
       "5                             2             0            0               0   \n",
       "6                             2             0            0               0   \n",
       "7                             8             0            0               0   \n",
       "8                             3             0            0               0   \n",
       "9                             7             0            0               0   \n",
       "10                            1             1            0               0   \n",
       "11                            5             0            0               0   \n",
       "12                            5             0            0               0   \n",
       "13                            3             0            0               0   \n",
       "14                            4             0            0               0   \n",
       "15                            5             0            0               0   \n",
       "16                            5             0            1               0   \n",
       "17                            2             0            0               0   \n",
       "18                            4             0            0               0   \n",
       "19                            5             0            0               0   \n",
       "20                            7             0            7               0   \n",
       "21                            4             0            0               0   \n",
       "22                            8             0            1               0   \n",
       "23                            7             0            0               0   \n",
       "24                            4             0            0               0   \n",
       "25                            9             0            0               0   \n",
       "26                            6             0            0               0   \n",
       "27                            6             0            0               0   \n",
       "28                           10             0            0               0   \n",
       "29                           10             0            0               0   \n",
       "30                            6             0            0               0   \n",
       "31                            5             0            0               0   \n",
       "32                            3             0            0               0   \n",
       "33                            5             0            0               0   \n",
       "34                            4             0            1               0   \n",
       "35                            5             0            2               0   \n",
       "36                            7             0            1               0   \n",
       "37                            2             0            0               0   \n",
       "38                            4             0            0               0   \n",
       "\n",
       "    all_5P-NP_con#  all_5P-IT_con#  all_6P_con#  all_6P-CT_con#  \\\n",
       "0                0               6           11               0   \n",
       "1                1               4           25               0   \n",
       "2                0               6           14               0   \n",
       "3                4              12           14               0   \n",
       "4                3              14           22               0   \n",
       "5                5               6           10               0   \n",
       "6                4               5           10               0   \n",
       "7                5               6           12               0   \n",
       "8                4               7            5               0   \n",
       "9                1               3           33               0   \n",
       "10               3               5            7               0   \n",
       "11               1               2           20               0   \n",
       "12               4               3            7               0   \n",
       "13               4               4           19               0   \n",
       "14               0               4           28               0   \n",
       "15               1               3           21               0   \n",
       "16               2               7           23               0   \n",
       "17               0               3            4               0   \n",
       "18               2               5            6               0   \n",
       "19               0               5           28               0   \n",
       "20               4               8           18               0   \n",
       "21               6               4           12               0   \n",
       "22               6               1           23               0   \n",
       "23               1               6           15               0   \n",
       "24               3               1           16               0   \n",
       "25               0               1           29               0   \n",
       "26               0               4           20               0   \n",
       "27               0               4            3               0   \n",
       "28               3               1           14               0   \n",
       "29               1               2           23               0   \n",
       "30               2               6            9               0   \n",
       "31               2               4           34               0   \n",
       "32               1               5           26               0   \n",
       "33               5               5           17               0   \n",
       "34               2               4           29               0   \n",
       "35               0              23           25               0   \n",
       "36               1               8           20               0   \n",
       "37               1               1           23               0   \n",
       "38               1               2           21               0   \n",
       "\n",
       "    all_6P-IT_con#  all_BC_con#  all_MC_con#  all_BPC_con#  all_NGC_con#  \\\n",
       "0                0           12           15             1             0   \n",
       "1                0           19           20             0             0   \n",
       "2                0           19           26             0             0   \n",
       "3                0           21           10             0             1   \n",
       "4                0           12           13             2             1   \n",
       "5                0           23           15             1             0   \n",
       "6                0           25           14             0             1   \n",
       "7                0           15           17             2             0   \n",
       "8                0           22           16             1             0   \n",
       "9                0           11           17             3             0   \n",
       "10               0           11           20             1             0   \n",
       "11               0           19           16             0             0   \n",
       "12               0           21           17             0             0   \n",
       "13               0           19           11             2             0   \n",
       "14               0           14           14             3             0   \n",
       "15               0           22           12             1             0   \n",
       "16               0           13           15             2             1   \n",
       "17               0           24           14             1             0   \n",
       "18               0           15           16             1             0   \n",
       "19               0           13           22             2             0   \n",
       "20               0           17           10             0             0   \n",
       "21               0           19           16             0             0   \n",
       "22               0           13           21             2             0   \n",
       "23               0           13           16             0             0   \n",
       "24               0           16           20             3             0   \n",
       "25               0           17           14             2             0   \n",
       "26               0           15           18             0             0   \n",
       "27               0           25           12             1             0   \n",
       "28               0           22           14             1             0   \n",
       "29               0           17           12             3             0   \n",
       "30               0           25           13             0             0   \n",
       "31               0           17           13             2             0   \n",
       "32               0           11           10             4             0   \n",
       "33               0           14           12             0             0   \n",
       "34               0           12           13             2             0   \n",
       "35               0           14           10             1             0   \n",
       "36               0           16           14             2             0   \n",
       "37               0           25           24             3             0   \n",
       "38               0           17           12             0             0   \n",
       "\n",
       "    all_Undetermined_subclass_con#  \n",
       "0                                8  \n",
       "1                                5  \n",
       "2                                4  \n",
       "3                                4  \n",
       "4                                9  \n",
       "5                                2  \n",
       "6                                5  \n",
       "7                               12  \n",
       "8                                3  \n",
       "9                               11  \n",
       "10                               2  \n",
       "11                               8  \n",
       "12                               5  \n",
       "13                              13  \n",
       "14                               6  \n",
       "15                               5  \n",
       "16                               9  \n",
       "17                               4  \n",
       "18                               5  \n",
       "19                               8  \n",
       "20                              13  \n",
       "21                               4  \n",
       "22                               9  \n",
       "23                               7  \n",
       "24                               4  \n",
       "25                              12  \n",
       "26                               6  \n",
       "27                               6  \n",
       "28                              13  \n",
       "29                              11  \n",
       "30                               7  \n",
       "31                               8  \n",
       "32                               7  \n",
       "33                               5  \n",
       "34                               5  \n",
       "35                               7  \n",
       "36                               8  \n",
       "37                               3  \n",
       "38                               5  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#synapse_table_percentage\n",
    "synapse_table_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "charged-default",
   "metadata": {},
   "outputs": [],
   "source": [
    "#synapses versus connections\n",
    "\n",
    "\n",
    "print('total number of synapses with I targets = ', synapse_table_values['all_i_syn#'].sum())\n",
    "\n",
    "print('total number of connections with I targets = ', synapse_table_values['all_i_con#'].sum())\n",
    "\n",
    "print('synapses per connection with I targets = ', synapse_table_values['all_i_syn#'].sum()/\n",
    "      synapse_table_values['all_i_con#'].sum())\n",
    "\n",
    "print('total number of synapses with E targets = ', synapse_table_values['all_e_syn#'].sum())\n",
    "\n",
    "print('total number of connections with E targets = ', synapse_table_values['all_e_con#'].sum())\n",
    "\n",
    "print('synapses per connection with E targets = ', synapse_table_values['all_e_syn#'].sum()/\n",
    "      synapse_table_values['all_e_con#'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7dabbf00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ANALYSIS - FIGURE 2.c1\n",
    "#percentage of inhibitory targets\n",
    "\n",
    "# size and color:\n",
    "sizes = 40\n",
    "\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(synapse_table_percentage['i_syn_determined'].values, synapse_table_percentage['Soma_depth'].values, s=sizes) \n",
    "ax.invert_yaxis()\n",
    "plt.tight_layout()\n",
    "\n",
    "fig.savefig('Fig2c1_E_I_revision.eps', bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "31c55397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ANALYSIS - FIGURE 2.c2\n",
    "#boxplot\n",
    "\n",
    "\n",
    "plt.style.use('_mpl-gallery')\n",
    "\n",
    "# make data:\n",
    "np.random.seed(10)\n",
    "#D = np.random.normal((3, 5, 4))\n",
    "D = synapse_table_percentage.i_syn_determined.values\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots()\n",
    "VP = ax.boxplot(D,  widths=1.5, patch_artist=True,\n",
    "                showmeans=True, showfliers=False,\n",
    "                medianprops={\"color\": \"white\", \"linewidth\": 0.5},\n",
    "                boxprops={\"facecolor\": \"C0\", \"edgecolor\": \"white\",\n",
    "                          \"linewidth\": 0.5},\n",
    "                whiskerprops={\"color\": \"C0\", \"linewidth\": 1.5},\n",
    "                capprops={\"color\": \"C0\", \"linewidth\": 1.5})\n",
    "\n",
    "\n",
    "#ax.set_yticks()\n",
    "ax.set(xlim=(0, 2), xticks=np.arange(0, 2),\n",
    "       ylim=(0, 1), yticks=np.arange(0, 1.1 , 0.1))\n",
    "\n",
    "fig.savefig('Fig2c2_E_I_box_revision.eps', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9b32d89a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "dlopen(/Users/nunod/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/DracoPy.cpython-310-darwin.so, 0x0002): tried: '/Users/nunod/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/DracoPy.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/System/Volumes/Preboot/Cryptexes/OS/Users/nunod/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/DracoPy.cpython-310-darwin.so' (no such file), '/Users/nunod/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/DracoPy.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'))",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[49], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mload_ext\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mautoreload\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mautoreload\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mskeleton_plot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mskelplot\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mskeleton_plot\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mskel_io\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/skeleton_plot/__init__.py:2\u001b[0m\n\u001b[1;32m      1\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0.0.8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_tools\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/skeleton_plot/plot_tools.py:5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LineCollection\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmeshparty\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m meshwork, skeleton\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m utils\n\u001b[1;32m      9\u001b[0m axis_dict \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m}\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/meshparty/meshwork/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmeshwork\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Meshwork, load_meshwork\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m algorithms\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/meshparty/meshwork/meshwork.py:9\u001b[0m\n\u001b[1;32m      6\u001b[0m     _vtk_loaded \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mskeleton_io\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m swc_node_labels\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrimesh_io\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Mesh\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mskeleton\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Skeleton, resample\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcontextlib\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/meshparty/trimesh_io.py:37\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymeshfix\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _meshfix\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m trange\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mDracoPy\u001b[39;00m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmeshparty\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m utils, trimesh_repair\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "\u001b[0;31mImportError\u001b[0m: dlopen(/Users/nunod/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/DracoPy.cpython-310-darwin.so, 0x0002): tried: '/Users/nunod/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/DracoPy.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/System/Volumes/Preboot/Cryptexes/OS/Users/nunod/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/DracoPy.cpython-310-darwin.so' (no such file), '/Users/nunod/opt/anaconda3/envs/EM_analysis_20241205/lib/python3.10/site-packages/DracoPy.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'))"
     ]
    }
   ],
   "source": [
    "#ANALYSIS - FIGURE 2.d\n",
    "# skeleton of inhibitory neuron\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import skeleton_plot as skelplot\n",
    "from skeleton_plot.skel_io import *\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import meshparty\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#raw skeleton files\n",
    "skel_path = 'https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily-pcg-skeletons/minnie_all/BIL_neurons/file_groups/'\n",
    "\n",
    "# upright\n",
    "upright_path = 'https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily_skeleton_keys/upright/BIL_excitatory/'\n",
    "\n",
    "# layer aligned\n",
    "layer_aligned_path = 'https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily_skeleton_keys/layer_align/BIL_excitatory/'\n",
    "\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/BIL_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 = \"gs://allen-minnie-phase3/minniephase3-emily_skeleton_keys/avg_layer_depths/\"\n",
    "avg_layerdepths_filename = 'mouse_me_and_met_avg_layer_depths.json'\n",
    "\n",
    "\n",
    "# mesh path with gs://\n",
    "#mw_path = 'gs://allen-minnie-phase3/minniephase3-emily-pcg-skeletons/minnie_all/BIL_neurons/file_groups/'\n",
    "\n",
    "# neuron IDs\n",
    "rid = 864691135856066862\n",
    "sid = 305046\n",
    "\n",
    "# now https version \n",
    "depths = read_json('https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily_skeleton_keys/avg_layer_depths/',\n",
    "                                         avg_layerdepths_filename)\n",
    "# plot the radius from the swc \n",
    "\n",
    "sk = read_skeleton(skel_path+f'{rid}_{sid}', f'{rid}_{sid}.swc')\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,10))\n",
    "skelplot.plot_tools.plot_skel(sk,  ax=ax, pull_radius = True, pull_compartment_colors = True,\n",
    "                              invert_y=True, plot_soma = True, \n",
    "                             line_width = 3, color = 'darkslategray') \n",
    "\n",
    "\n",
    "fig.savefig('basket_skel.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e08d1dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ANALYSIS - FIGURE 2.e\n",
    "# connectivity plot\n",
    "import tqdm\n",
    "import pcg_skel\n",
    "client = CAVEclient('minnie65_phase3_v1')\n",
    "skeldir = 'skeletons'\n",
    "now = client.materialize.get_timestamp()\n",
    "\n",
    "neuron_df = client.materialize.query_table(\n",
    "    \"nucleus_detection_v0\", filter_equal_dict={\"id\": 305046})\n",
    "\n",
    "# Build the skeletons\n",
    "nrns = {}\n",
    "\n",
    "for _, row in tqdm.tqdm(neuron_df.iterrows()):\n",
    "\n",
    "    #print(row)\n",
    "    if os.path.exists(f\"{skeldir}/{row['pt_root_id']}.h5\"):\n",
    "        nrns[row[\"pt_root_id\"]] = meshwork.load_meshwork(f\"{skeldir}/{row['pt_root_id']}.h5\")\n",
    "   \n",
    "    else:\n",
    "    \n",
    "        nrns[row[\"pt_root_id\"]] = pcg_skel.coord_space_meshwork(\n",
    "            row[\"pt_root_id\"],\n",
    "            client=client,\n",
    "            root_point=row[\"pt_position\"],\n",
    "            root_point_resolution=[4, 4, 40],\n",
    "            collapse_soma=True,\n",
    "            synapses=\"all\",\n",
    "            synapse_table=client.info.get_datastack_info().get(\"synapse_table\"),\n",
    "            timestamp = now,\n",
    "        )\n",
    "\n",
    "        nrns[row[\"pt_root_id\"]].save_meshwork(f\"{skeldir}/{row['pt_root_id']}.h5\")        \n",
    "        \n",
    "# Get the axons\n",
    "for rid, nrn in nrns.items():\n",
    "    is_axon = meshwork.algorithms.split_axon_by_annotation(\n",
    "        nrn,\n",
    "        'pre_syn',\n",
    "        'post_syn',\n",
    "        return_quality=False\n",
    "    )\n",
    "    nrn.anno.add_annotations('is_axon', is_axon, mask=True)\n",
    "    \n",
    "pre_df = []\n",
    "for rid in neuron_df[\"pt_root_id\"]:\n",
    "    syn_filt = nrns[rid].anno.pre_syn.filter_query(\n",
    "            nrns[rid].anno.is_axon.mesh_mask\n",
    "    )\n",
    "    df = syn_filt.df\n",
    "    df['dist_to_root'] = nrns[rid].distance_to_root(syn_filt.mesh_index)\n",
    "    df['distance_rank'] = df['dist_to_root'].rank()\n",
    "    df.attrs = {}\n",
    "    pre_df.append(df)\n",
    "    \n",
    "neuron_pre_df= pd.concat(pre_df, ignore_index=True)\n",
    "neuron_pre_df['pre_pt_root_id'] = client.chunkedgraph.get_roots(neuron_pre_df['pre_pt_supervoxel_id'], timestamp=now)\n",
    "neuron_pre_df['post_pt_root_id'] = client.chunkedgraph.get_roots(neuron_pre_df['post_pt_supervoxel_id'], timestamp=now).astype('int')\n",
    "        \n",
    "\n",
    "# Get single soma root ids and add cell types\n",
    "\n",
    "soma_df = client.materialize.query_table(\n",
    "    \"nucleus_neuron_svm\", filter_equal_dict={\"cell_type\": \"neuron\"}\n",
    ")\n",
    "\n",
    "soma_df = soma_df.drop_duplicates(subset=\"pt_root_id\", keep='first')\n",
    "\n",
    "mtypes_model_df = client.materialize.query_table(\n",
    "    \"aibs_metamodel_mtypes_v661_v2\",\n",
    ").drop_duplicates('pt_root_id', keep=False)\n",
    "\n",
    "# Enrich soma_df with all this info\n",
    "soma_df = (\n",
    "    soma_df.merge(        \n",
    "        mtypes_model_df[[\"pt_root_id\", \"cell_type\"]].rename(\n",
    "            columns={\"cell_type\": \"model_cell_type\"}\n",
    "        ),\n",
    "        on=\"pt_root_id\",\n",
    "        how=\"left\",\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "#Add subclass labels to soma_df\n",
    "def standardize_subclass(row):\n",
    "        \n",
    "        \n",
    "    if row['model_cell_type'] == 'L3b':\n",
    "          return 'L23P'\n",
    "\n",
    "    if row['model_cell_type'] == 'MC':\n",
    "          return 'DTC'\n",
    "    \n",
    "    if row['model_cell_type'] == 'BC':\n",
    "          return 'PTC'\n",
    "    \n",
    "    if row['model_cell_type'] == 'BPC':\n",
    "          return 'ITC'\n",
    "    \n",
    "    if row['model_cell_type'] == 'NGC':\n",
    "          return 'STC'\n",
    "            \n",
    "    if row['model_cell_type'] == '23P':\n",
    "          return 'L23P'\n",
    "            \n",
    "    if row['model_cell_type'] == 'L2b':\n",
    "          return 'L23P'\n",
    "          \n",
    "    if row['model_cell_type'] == 'L6tall-c':\n",
    "          return 'L6P'\n",
    "          \n",
    "    if row['model_cell_type'] == 'L6short-b':\n",
    "          return 'L6P'\n",
    "          \n",
    "    if row['model_cell_type'] == 'L4b':\n",
    "          return 'L4P'\n",
    "          \n",
    "    if row['model_cell_type'] == 'L2a':\n",
    "          return 'L23P'\n",
    "   \n",
    "    if row['model_cell_type'] == 'L3a':\n",
    "          return 'L23P'\n",
    "          \n",
    "    if row['model_cell_type'] == 'L6tall-b':\n",
    "          return 'L6P'\n",
    "          \n",
    "    if row['model_cell_type'] == 'L2c':\n",
    "          return 'L23P'\n",
    "          \n",
    "    if row['model_cell_type'] == 'L4a':\n",
    "          return 'L4P'\n",
    "\n",
    "    if row['model_cell_type'] == '4P':\n",
    "          return 'L4P'\n",
    "        \n",
    "    if row['model_cell_type'] == 'L6wm':\n",
    "          return 'L6P'\n",
    "        \n",
    "    if row['model_cell_type'] == 'L6tall-a':\n",
    "          return 'L6P'\n",
    " \n",
    "    if row['model_cell_type'] == 'L6short-a':\n",
    "          return 'L6P'\n",
    "          \n",
    "    if row['model_cell_type'] == 'L5a':\n",
    "          return 'L5IT'\n",
    "        \n",
    "    if row['model_cell_type'] == 'L5b':\n",
    "          return 'L5IT'\n",
    "        \n",
    "    if row['model_cell_type'] == 'L4c':\n",
    "          return 'L4P'\n",
    "  \n",
    "    if row['model_cell_type'] == 'none':\n",
    "          return None\n",
    "               \n",
    "    if pd.isna(row['model_cell_type']) == True:\n",
    "          return None\n",
    "        \n",
    "    else:\n",
    "          return row['model_cell_type'] \n",
    "\n",
    "soma_df['model_subclass'] = soma_df.apply(standardize_subclass, axis=1)\n",
    "\n",
    "\n",
    "#Merge all this info from cell types into the synapse dataframe, as well as add area locations.\n",
    "\n",
    "#merge presynaptic nucleous ID\n",
    "synapse_table_305046 = neuron_pre_df.merge(\n",
    "    soma_df[\n",
    "        [\"id\", \"pt_root_id\", \"pt_position\", \"model_subclass\"]\n",
    "    ].rename(columns={\"pt_position\": \"post_soma_pt\"}).rename(columns={\"id\": \"post_nucleus_id\"}),\n",
    "    left_on=\"post_pt_root_id\",\n",
    "    right_on=\"pt_root_id\",\n",
    "    how=\"left\",\n",
    ").drop(columns=\"pt_root_id\")\n",
    "\n",
    "synapse_table_305046 = synapse_table_305046.rename(columns={\"id\": \"synapse_id\"})\n",
    "    \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "ax = sns.histplot(data=synapse_table_305046, y=\"model_subclass\",shrink=.8,)\n",
    "\n",
    "plt.savefig('fig2_7_hist.eps')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1694d86c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ANALYSIS - FIGURE 2.g\n",
    "#percentage of Inhibitory group 2 targets\n",
    "\n",
    "\n",
    "#number of synapses with Inhibitory group 1 targets\n",
    "group1 = len(synapse_table[(synapse_table['inhibitory_groups'] ==  1)])\n",
    "\n",
    "#number of synapses with Inhibitory group 2 targets\n",
    "group2 = len(synapse_table[(synapse_table['inhibitory_groups'] ==  2)])\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.style.use('_mpl-gallery')\n",
    "\n",
    "# make data:\n",
    "np.random.seed(3)\n",
    "x = ['Group 1', 'Group 2']\n",
    "y = [group1, group2]\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize = (3,4))\n",
    "\n",
    "ax.bar(x, y, width=0.4, edgecolor=\"white\", linewidth=0.5, align='center')\n",
    "\n",
    "# Add empty space on the edges\n",
    "ax.set_xlim(-0.7, 1.7)\n",
    "\n",
    "# Set y-axis ticks at increments of 100\n",
    "max_y = max(y)\n",
    "ax.set_yticks(np.arange(0, max_y + 100, 100))\n",
    "\n",
    "\n",
    "#ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n",
    "#       ylim=(0, 8), yticks=np.arange(1, 8))\n",
    "\n",
    "fig.savefig('Fig2g_inhibitory_groups_input_revision.eps', bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c06f657",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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