post_partners_on_apical.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\clare.gamlin\\AppData\\Local\\anaconda3\\lib\\site-packages\\python_jsonschema_objects\\__init__.py:113: UserWarning: Schema id not specified. Defaulting to 'self'\n",
      "  warnings.warn(\"Schema id not specified. Defaulting to 'self'\")\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import skeleton_plot as skpl\n",
    "from skeleton_plot import skel_io\n",
    "\n",
    "import caveclient\n",
    "import cloudfiles\n",
    "\n",
    "client = caveclient.CAVEclient('minnie65_phase3_v1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that takes a root id and then returns a dataframe will all the input synapses of that cell \n",
    "# with a column that is true/false, is apical\n",
    "\n",
    "# cloud paths \n",
    "minnie_v661_mw_cloudpath = 'https://storage.googleapis.com/allen-minnie-phase3/minniephase3-emily-pcg-skeletons/minnie_all/v661/meshworks'\n",
    "# create a easy dictionary to search through the existing skeletons \n",
    "existing_skel_filenames = list(cloudfiles.CloudFiles(minnie_v661_mw_cloudpath))\n",
    "rid_filename_dict = {int(fn.split('_')[0]):fn for fn in  existing_skel_filenames if '.h5' in fn}\n",
    "\n",
    "\n",
    "# pull out the excitatory neuron table \n",
    "e_neuron_df = client.materialize.query_table('aibs_metamodel_celltypes_v661')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1380, 12)\n",
      "            id                          created  superceded_id valid   size  \\\n",
      "13   259226364 2020-11-04 06:49:46.278849+00:00            NaN     t   2276   \n",
      "18   326936321 2020-11-04 06:51:43.367692+00:00            NaN     t   1500   \n",
      "23   272992975 2020-11-04 06:53:26.207097+00:00            NaN     t    596   \n",
      "26   253390061 2020-11-04 06:57:58.889833+00:00            NaN     t    280   \n",
      "28   253389953 2020-11-04 06:57:58.889833+00:00            NaN     t    384   \n",
      "32   286737398 2020-11-04 06:58:27.474011+00:00            NaN     t   1432   \n",
      "33   348434200 2020-11-04 07:03:28.905689+00:00            NaN     t   2640   \n",
      "36   286945539 2020-11-04 07:03:22.546382+00:00            NaN     t    272   \n",
      "37   296212440 2020-11-04 07:03:46.531441+00:00            NaN     t   7872   \n",
      "45   296212364 2020-11-04 07:03:46.531441+00:00            NaN     t  11416   \n",
      "50   290298824 2020-11-04 07:09:03.516572+00:00            NaN     t   1056   \n",
      "61   275535399 2020-11-04 07:15:06.069283+00:00            NaN     t    876   \n",
      "62   286790607 2020-11-04 07:15:08.074789+00:00            NaN     t   6576   \n",
      "66   307559281 2020-11-04 07:15:06.985893+00:00            NaN     t  12336   \n",
      "68   248147950 2020-11-04 07:15:10.152135+00:00            NaN     t   3008   \n",
      "72   296666002 2020-11-04 07:15:06.985893+00:00            NaN     t  14364   \n",
      "75   320731788 2020-11-04 07:20:03.694071+00:00            NaN     t    616   \n",
      "77   269087421 2020-11-04 07:20:34.391276+00:00            NaN     t   5884   \n",
      "78   297092742 2020-11-04 07:20:41.802332+00:00            NaN     t   4852   \n",
      "83   310852086 2020-11-04 07:20:40.763370+00:00            NaN     t   2076   \n",
      "87   276848531 2020-11-04 07:24:37.723667+00:00            NaN     t    616   \n",
      "88   296219631 2020-11-04 07:26:01.934414+00:00            NaN     t    892   \n",
      "95   297036966 2020-11-04 07:26:06.103330+00:00            NaN     t   2388   \n",
      "97   289521046 2020-11-04 07:26:04.001189+00:00            NaN     t   3568   \n",
      "99   244264625 2020-11-04 07:26:27.893733+00:00            NaN     t   4724   \n",
      "100  259898227 2020-11-04 07:26:12.864075+00:00            NaN     t   3316   \n",
      "106  286738078 2020-11-04 07:31:36.491507+00:00            NaN     t   1484   \n",
      "107  299817032 2020-11-04 07:31:21.361670+00:00            NaN     t    656   \n",
      "110  278764249 2020-11-04 07:31:40.504291+00:00            NaN     t  11904   \n",
      "116  293315402 2020-11-04 07:36:45.176450+00:00            NaN     t   3400   \n",
      "\n",
      "     pre_pt_supervoxel_id      pre_pt_root_id  post_pt_supervoxel_id  \\\n",
      "13      97127147353026270  864691136116457636      97127078633528432   \n",
      "18     102687171630646360  864691136116457636     102757540374817126   \n",
      "23      98395022034506129  864691136116457636      98395022034521827   \n",
      "26      96636421770787650  864691136116457636      96636421770779120   \n",
      "28      96636421770774812  864691136116457636      96636421770773250   \n",
      "32      99167085355433298  864691136116457636      99167085355443975   \n",
      "33     104376159063851203  864691136116457636     104376159063856304   \n",
      "36      99448422893098522  864691136116457636      99448422893104712   \n",
      "37     100223097286202287  864691136116457636     100223097286202186   \n",
      "45     100223097286219958  864691136116457636     100223097286227359   \n",
      "50     100082360066309671  864691136116457636     100082291346848055   \n",
      "61      98744941475622662  864691136116457636      98744941475620796   \n",
      "62      99237660123809488  864691136116457636      99237660123804899   \n",
      "66     100855728855841575  864691136116457636     100855728855847871   \n",
      "68      96562960381747111  864691136116457636      96562960381730309   \n",
      "72     100292916341431811  864691136116457636     100292916341428866   \n",
      "75     102053852999642411  864691136116457636     102053852999637744   \n",
      "77      98463809962122914  864691136116457636      98393441217956551   \n",
      "78     100364522170519442  864691136116457636     100364522170504977   \n",
      "83     101349753241397382  864691136116457636     101349753241406618   \n",
      "87      98591009780574298  864691136116457636      98591009780572005   \n",
      "88     100362666945718096  864691136116457636     100362666945705845   \n",
      "95     100575765841738642  864691136116457636     100575834561216237   \n",
      "97      99589778722737445  864691136116457636      99589778722749247   \n",
      "99      95719703615543951  864691136116457636      95719703615538561   \n",
      "100     97396184171188720  864691136116457636      97466552915379246   \n",
      "106     99307822843773332  864691136116457636      99307822843784308   \n",
      "107    100576246743921955  864691136116457636     100576246743909967   \n",
      "110     98882449215726098  864691136116457636      98882449215739311   \n",
      "116    100012060108376768  864691136116457636     100012128827841370   \n",
      "\n",
      "        post_pt_root_id          pre_pt_position         post_pt_position  \\\n",
      "13   864691135013420694  [235366, 196892, 22847]  [235354, 196792, 22848]   \n",
      "18   864691135307684422  [275988, 203582, 24052]  [276092, 203630, 24043]   \n",
      "23   864691135946705505  [244678, 206252, 25580]  [244768, 206304, 25594]   \n",
      "26   864691135864759004  [231664, 211092, 24204]  [231694, 211042, 24201]   \n",
      "28   864691135864759004  [231522, 210908, 24196]  [231522, 210946, 24188]   \n",
      "32   864691136237481999  [250004, 191404, 25364]  [250042, 191362, 25376]   \n",
      "33   864691135518377866  [288238, 204648, 24853]  [288266, 204634, 24862]   \n",
      "36   864691135937969461  [252080, 190558, 25297]  [252176, 190618, 25303]   \n",
      "37   864691136974547228  [257998, 195076, 23492]  [257954, 195010, 23491]   \n",
      "45   864691136974547228  [257890, 195234, 23516]  [257744, 195254, 23516]   \n",
      "50   864691136195279336  [256824, 194816, 25589]  [256800, 194752, 25584]   \n",
      "61   864691136066300312  [246870, 191992, 24366]  [246982, 192134, 24374]   \n",
      "62   864691136066300312  [250674, 192750, 24342]  [250638, 192880, 24337]   \n",
      "66   864691136066300312  [262448, 189716, 23760]  [262430, 189790, 23768]   \n",
      "68   864691135696934298  [231298, 187828, 22218]  [231268, 187870, 22206]   \n",
      "72   864691136066300312  [258562, 191138, 23797]  [258466, 191068, 23796]   \n",
      "75   864691135118549725  [271240, 203704, 24080]  [271312, 203714, 24081]   \n",
      "77   864691136418913175  [244780, 194596, 23392]  [244698, 194638, 23380]   \n",
      "78   864691136522180753  [258838, 200132, 24942]  [258940, 200202, 24938]   \n",
      "83   864691136444683651  [265888, 200550, 24458]  [265836, 200546, 24462]   \n",
      "87   864691135938238468   [246064, 93506, 23796]   [246082, 93634, 23788]   \n",
      "88   864691136898299246  [258944, 186450, 26217]  [258964, 186434, 26214]   \n",
      "95   864691135156092516  [260600, 201332, 24694]  [260596, 201460, 24692]   \n",
      "97   864691135156092516  [253442, 195224, 24406]  [253416, 195182, 24415]   \n",
      "99   864691135304258471  [224836, 196596, 21645]  [224920, 196584, 21642]   \n",
      "100  864691135256856239  [237566, 104276, 23046]  [237614, 104300, 23053]   \n",
      "106  864691135859619944  [251064, 191396, 25340]  [251038, 191420, 25341]   \n",
      "107  864691135211125952  [260238, 204644, 23701]  [260298, 204706, 23690]   \n",
      "110  864691135780790864  [248096, 167912, 24880]  [248136, 167910, 24895]   \n",
      "116  864691136966087118  [256230, 195756, 25789]  [256310, 195790, 25777]   \n",
      "\n",
      "             ctr_pt_position  \n",
      "13   [235410, 196840, 22849]  \n",
      "18   [276040, 203576, 24043]  \n",
      "23   [244728, 206250, 25578]  \n",
      "26   [231636, 211070, 24204]  \n",
      "28   [231478, 210922, 24190]  \n",
      "32   [249998, 191372, 25370]  \n",
      "33   [288271, 204655, 24859]  \n",
      "36   [252156, 190622, 25301]  \n",
      "37   [257982, 195046, 23489]  \n",
      "45   [257816, 195282, 23515]  \n",
      "50   [256802, 194756, 25585]  \n",
      "61   [246910, 192062, 24371]  \n",
      "62   [250696, 192818, 24337]  \n",
      "66   [262458, 189736, 23764]  \n",
      "68   [231302, 187856, 22210]  \n",
      "72   [258534, 191076, 23802]  \n",
      "75   [271262, 203686, 24077]  \n",
      "77   [244730, 194586, 23385]  \n",
      "78   [258867, 200194, 24941]  \n",
      "83   [265878, 200540, 24460]  \n",
      "87    [246082, 93586, 23790]  \n",
      "88   [258968, 186448, 26218]  \n",
      "95   [260628, 201386, 24689]  \n",
      "97   [253440, 195242, 24409]  \n",
      "99   [224833, 196571, 21642]  \n",
      "100  [237584, 104338, 23047]  \n",
      "106  [251110, 191382, 25338]  \n",
      "107  [260282, 204694, 23694]  \n",
      "110  [248166, 167916, 24888]  \n",
      "116  [256264, 195818, 25783]  \n"
     ]
    }
   ],
   "source": [
    "# start with an example neuron \n",
    "# pre_root_id = 864691135308347974\n",
    "#MET-6 [864691135404765166, 864691135697251738, 864691136238652476, 864691136618564493, 864691136116457636]\n",
    "#MET-8 = [864691135544588584, 864691135572082925, 864691135699487522]\n",
    "#MET-4 didn't work 864691136118647832\n",
    "#MET-4 = [864691135118298333, 864691135577202181, 864691135013417622, 864691135785592004, 864691135969447781, 864691136118647832 ]\n",
    "\n",
    "pre_root_id =  864691136116457636\n",
    "\n",
    "# # check out the excitatory postsynaptic partners of this neuron \n",
    "# postsyn_partners = client.materialize.synapse_query(pre_ids = [pre_root_id])\n",
    "# e_rids = e_neuron_df[e_neuron_df['classification_system'] == 'excitatory_neuron']['pt_root_id'].values\n",
    "# postsyn_e_partners = postsyn_partners[postsyn_partners['post_pt_root_id'].isin(e_rids)]\n",
    "\n",
    "##create synapse table for older ids\n",
    "from datetime import datetime, timezone\n",
    "settime = datetime(2023, 9, 19, 4, 0, 0, 0, tzinfo=timezone.utc)\n",
    "postsyn_partners_old = client.materialize.synapse_query(pre_ids = [pre_root_id], timestamp = settime)\n",
    "print (postsyn_partners_old.shape)\n",
    "\n",
    "# e_rids = e_neuron_df[e_neuron_df['classification_system'] == 'excitatory_neuron']['pt_root_id'].values\n",
    "e_neuron_df = e_neuron_df.loc[(e_neuron_df['cell_type'] == '5P-IT')| (e_neuron_df['cell_type'] == '5P-ET')]\n",
    "e_rids = e_neuron_df['pt_root_id'].values\n",
    "\n",
    "postsyn_e_partners = postsyn_partners_old[postsyn_partners_old['post_pt_root_id'].isin(e_rids)]\n",
    "print (postsyn_e_partners.head(30))\n",
    "\n",
    "\n",
    "\n",
    "# we want to see which of these are excitatory cells and then look at the location \n",
    "# of that synapse on the postsynaptic cell to check if it is on the apical\n",
    "\n",
    "# many cells in the dataset have skeletons that have apical labels \n",
    "# we will pull out the ones that do and then check if those synapses are on apicals\n",
    "# the skeleton might not exist if there have been edits to the root id since dataset 661\n",
    "# in that case, you can create that skeleton yourself and then run this analysis \n",
    "# that is covered in another notebook \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's take a look at the first postsynaptic excitatory cell\n",
    "# we will also note the 'id' of the synapse so we can visualize it \n",
    "\n",
    "post_root_id = 864691135780790864\n",
    "post_syn_id = 278764249\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# some functions to help us along \n",
    "def check_if_syn_on_apical(mw, synapse_id):\n",
    "    '''\n",
    "    checks if a specific synapse on a given meshwork (mw) is on the apical node\n",
    "    returns True or False\n",
    "    \n",
    "    '''\n",
    "    # get the mesh index of the nrn at that synapse id\n",
    "    syn_df = mw.anno.post_syn.df\n",
    "    syn_mw_ind = syn_df[syn_df['id'] == synapse_id]['post_pt_mesh_ind_filt'].values\n",
    "    \n",
    "    return syn_mw_ind in mw.anno.apical_mesh_labels.df['mesh_index_filt'].values\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def post_df_on_apical(pre_root_id, e_neuron_df = e_neuron_df, rid_filename_dict = rid_filename_dict,\n",
    "                     mw_folder_path = minnie_v661_mw_cloudpath):\n",
    "    \n",
    "    '''\n",
    "    takes a presynaptic neuron and gets all postsynaptic partners that are classified as excitatory \n",
    "    from those excitatory neurons, find the skeletons that already exist and print those numbers \n",
    "    \n",
    "    '''\n",
    "    # first get all postsynaptic partners \n",
    "    postsyn_partners = client.materialize.synapse_query(pre_ids = [pre_root_id])\n",
    "    ##CG edit\n",
    "    if postsyn_partners.empty:\n",
    "        postsyn_partners = client.materialize.synapse_query(pre_ids = [pre_root_id], timestamp = settime)\n",
    "\n",
    "\n",
    "    # can only classify apical on excitatory, so reduce this table to just \n",
    "    # the post partners belonging to excitatory cells\n",
    "    e_neuron_df = e_neuron_df.loc[(e_neuron_df['cell_type'] == '5P-IT')| (e_neuron_df['cell_type'] == '5P-ET')]\n",
    "    e_rids = L5_e_neuron_df['pt_root_id'].values\n",
    "\n",
    "    #     e_rids = e_neuron_df[e_neuron_df['classification_system'] == 'excitatory_neuron']['pt_root_id'].values\n",
    "    postsyn_e_partners = postsyn_partners[postsyn_partners['post_pt_root_id'].isin(e_rids)]\n",
    "    \n",
    "    \n",
    "    # now check how many of these already have a skeleton stored in the cloud\n",
    "    already_have = set(postsyn_e_partners['post_pt_root_id'])&set(rid_filename_dict.keys())\n",
    "    to_create = already_have ^ set(postsyn_e_partners['post_pt_root_id'])\n",
    "    \n",
    "    print(f'{len(already_have)} postsynaptic excitatory skeletons already exist, {len(to_create)} must be created.')\n",
    "        \n",
    "    # create the dictionary from which to store if the synapse id is onto an apical dendrite \n",
    "    synapse_id_apical_dict = {}\n",
    "    \n",
    "    syn_ids = postsyn_e_partners[postsyn_e_partners['post_pt_root_id'].isin(already_have)]['id']\n",
    "    syn_ids_rid_dict = postsyn_e_partners[['id', 'post_pt_root_id']].set_index('id').to_dict()['post_pt_root_id']\n",
    "    \n",
    "    for syn_id in syn_ids:\n",
    "        rid = syn_ids_rid_dict[syn_id]\n",
    "        print(f'working on postsynaptic neuron {rid} on synapse id {syn_id}')\n",
    "        fn = rid_filename_dict[rid]\n",
    "        mw = skel_io.load_mw(mw_folder_path, fn)\n",
    "        \n",
    "        # figure out if this synapse is on an apical\n",
    "        is_on_apical = check_if_syn_on_apical(mw, syn_id)\n",
    "        \n",
    "        synapse_id_apical_dict[syn_id] = is_on_apical\n",
    "        \n",
    "    \n",
    "    return synapse_id_apical_dict\n",
    "    \n",
    "    \n",
    "\n",
    "# function to double check if a synapse is on the apical of a neuron (apical is pink)\n",
    "def plot_synapse_id_on_mw(mw, syn_id, synapse_plot_size = 100, figsize=(7,10), pull_compartment_colors=True, pull_radius=True, line_width=5, \n",
    "                          plot_soma=True, invert_y=True):\n",
    "    f , ax = plt.subplots(figsize=figsize)\n",
    "    skpl.plot_tools.plot_mw_skel(mw, pull_compartment_colors=pull_compartment_colors, pull_radius=pull_radius, \n",
    "                                 line_width=line_width, plot_soma=plot_soma, invert_y=invert_y)\n",
    "    \n",
    "    syn_df = mw.anno.post_syn.df\n",
    "    syn_mw_ind = syn_df[syn_df['id'] == syn_id]['post_pt_mesh_ind_filt'].values[0]\n",
    "    syn_loc = mw.vertices[syn_mw_ind]\n",
    "\n",
    "    plt.scatter(syn_loc[0], syn_loc[1], s = synapse_plot_size)\n",
    "    \n",
    "\n",
    "\n",
    "def load_cloud_mw_from_rid(rid, cloudpath = minnie_v661_mw_cloudpath):\n",
    "    try:\n",
    "        fn = rid_filename_dict[rid]\n",
    "    except:\n",
    "        print('a skeleton meshwork does not exist for this root id in the cloud. try another one')\n",
    "    \n",
    "    # load mw \n",
    "    mw = skel_io.load_mw(cloudpath, fn)\n",
    "    return mw\n",
    "    \n",
    "    \n",
    "# let's visualize that first postsynaptic excitatory cell synapse now \n",
    "# first, load the meshwork \n",
    "# post_root_id = 864691136039810558\n",
    "# post_syn_id = 67080509\n",
    "post_mw = load_cloud_mw_from_rid(post_root_id)\n",
    "plot_synapse_id_on_mw(post_mw, post_syn_id)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can see this synapse is on the apical\n",
    "# check that the function I wrote works, should return True \n",
    "check_if_syn_on_apical(post_mw, post_syn_id)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "177 postsynaptic excitatory skeletons already exist, 4 must be created.\n",
      "working on postsynaptic neuron 864691135013420694 on synapse id 259226364\n",
      "working on postsynaptic neuron 864691135307684422 on synapse id 326936321\n",
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      "working on postsynaptic neuron 864691135969520997 on synapse id 268411814\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on postsynaptic neuron 864691135404562670 on synapse id 262178327\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on postsynaptic neuron 864691135182508802 on synapse id 290298851\n",
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      "working on postsynaptic neuron 864691135837705875 on synapse id 301813606\n",
      "working on postsynaptic neuron 864691135510015497 on synapse id 280430760\n",
      "working on postsynaptic neuron 864691136108718776 on synapse id 273236106\n",
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      "working on postsynaptic neuron 864691135940595110 on synapse id 273369057\n",
      "working on postsynaptic neuron 864691135940595110 on synapse id 257535112\n",
      "working on postsynaptic neuron 864691135510015497 on synapse id 255455915\n",
      "working on postsynaptic neuron 864691135510015497 on synapse id 280648456\n",
      "working on postsynaptic neuron 864691135781376848 on synapse id 307826672\n",
      "working on postsynaptic neuron 864691135059606683 on synapse id 258817729\n",
      "working on postsynaptic neuron 864691134918393098 on synapse id 286659723\n",
      "working on postsynaptic neuron 864691135938222084 on synapse id 279861497\n",
      "working on postsynaptic neuron 864691135487611890 on synapse id 273304736\n",
      "working on postsynaptic neuron 864691135740557972 on synapse id 293355690\n",
      "working on postsynaptic neuron 864691135082235383 on synapse id 296219457\n",
      "working on postsynaptic neuron 864691135564985943 on synapse id 303051761\n",
      "working on postsynaptic neuron 864691135697860373 on synapse id 277152194\n",
      "working on postsynaptic neuron 864691135938238468 on synapse id 266053020\n",
      "working on postsynaptic neuron 864691135568760556 on synapse id 272956260\n",
      "working on postsynaptic neuron 864691135837705875 on synapse id 280034061\n",
      "working on postsynaptic neuron 864691136991018901 on synapse id 286850168\n",
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      "working on postsynaptic neuron 864691136109108664 on synapse id 279984168\n",
      "working on postsynaptic neuron 864691135118559453 on synapse id 258967042\n",
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      "working on postsynaptic neuron 864691136209976508 on synapse id 259799845\n",
      "working on postsynaptic neuron 864691137197239617 on synapse id 257742388\n",
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      "working on postsynaptic neuron 864691135256856239 on synapse id 255961091\n",
      "working on postsynaptic neuron 864691135256856239 on synapse id 259897844\n",
      "working on postsynaptic neuron 864691136522180753 on synapse id 263404207\n",
      "working on postsynaptic neuron 864691135856335662 on synapse id 264367493\n",
      "working on postsynaptic neuron 864691135256856239 on synapse id 263224818\n",
      "working on postsynaptic neuron 864691135367645689 on synapse id 293012468\n",
      "working on postsynaptic neuron 864691134940504419 on synapse id 300677625\n",
      "working on postsynaptic neuron 864691136008962990 on synapse id 310942986\n",
      "working on postsynaptic neuron 864691135725001387 on synapse id 260028749\n",
      "working on postsynaptic neuron 864691136105950169 on synapse id 280449628\n",
      "working on postsynaptic neuron 864691135572471589 on synapse id 300301766\n",
      "working on postsynaptic neuron 864691136812708467 on synapse id 266116319\n",
      "working on postsynaptic neuron 864691134885139194 on synapse id 234678223\n",
      "working on postsynaptic neuron 864691135572471589 on synapse id 300301627\n",
      "working on postsynaptic neuron 864691135755362514 on synapse id 275660395\n",
      "working on postsynaptic neuron 864691135645366255 on synapse id 268654570\n",
      "working on postsynaptic neuron 864691135919229104 on synapse id 268872084\n",
      "working on postsynaptic neuron 864691135725001387 on synapse id 253390102\n",
      "working on postsynaptic neuron 864691135211125952 on synapse id 299816411\n",
      "working on postsynaptic neuron 864691135585875836 on synapse id 286792766\n",
      "working on postsynaptic neuron 864691135082573559 on synapse id 334631522\n",
      "working on postsynaptic neuron 864691135927147988 on synapse id 296170489\n",
      "working on postsynaptic neuron 864691135616687465 on synapse id 272381624\n",
      "working on postsynaptic neuron 864691136362517730 on synapse id 277152072\n",
      "working on postsynaptic neuron 864691136237449532 on synapse id 267193462\n",
      "working on postsynaptic neuron 864691135740557972 on synapse id 275536754\n",
      "working on postsynaptic neuron 864691135383309803 on synapse id 299748234\n",
      "working on postsynaptic neuron 864691135163324717 on synapse id 310944183\n",
      "working on postsynaptic neuron 864691135256856239 on synapse id 257741653\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on postsynaptic neuron 864691135256856239 on synapse id 259898089\n",
      "working on postsynaptic neuron 864691135256856239 on synapse id 257744095\n"
     ]
    }
   ],
   "source": [
    "# now run this on all downstream cells\n",
    "post_partner_is_apical = post_df_on_apical(pre_root_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# now run this on all downstream cells \n",
    "# post_partner_is_apical = post_df_on_apical(pre_root_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here is a dictionary that tells you which post neurons are on apicals vs not \n",
    "post_partner_is_apical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>created</th>\n",
       "      <th>superceded_id</th>\n",
       "      <th>valid</th>\n",
       "      <th>size</th>\n",
       "      <th>pre_pt_supervoxel_id</th>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>2020-11-04 06:49:46.278849+00:00</td>\n",
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       "      <td>[235410, 196840, 22849]</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <td>2020-11-04 06:51:43.367692+00:00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
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       "      <td>2020-11-04 06:53:26.207097+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
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       "      <td>2020-11-04 06:57:58.889833+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>96636421770779120</td>\n",
       "      <td>864691135864759004</td>\n",
       "      <td>[231664, 211092, 24204]</td>\n",
       "      <td>[231694, 211042, 24201]</td>\n",
       "      <td>[231636, 211070, 24204]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>253389953</td>\n",
       "      <td>2020-11-04 06:57:58.889833+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>384</td>\n",
       "      <td>96636421770774812</td>\n",
       "      <td>864691136116457636</td>\n",
       "      <td>96636421770773250</td>\n",
       "      <td>864691135864759004</td>\n",
       "      <td>[231522, 210908, 24196]</td>\n",
       "      <td>[231522, 210946, 24188]</td>\n",
       "      <td>[231478, 210922, 24190]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                          created  superceded_id valid  size  \\\n",
       "13  259226364 2020-11-04 06:49:46.278849+00:00            NaN     t  2276   \n",
       "18  326936321 2020-11-04 06:51:43.367692+00:00            NaN     t  1500   \n",
       "23  272992975 2020-11-04 06:53:26.207097+00:00            NaN     t   596   \n",
       "26  253390061 2020-11-04 06:57:58.889833+00:00            NaN     t   280   \n",
       "28  253389953 2020-11-04 06:57:58.889833+00:00            NaN     t   384   \n",
       "\n",
       "    pre_pt_supervoxel_id      pre_pt_root_id  post_pt_supervoxel_id  \\\n",
       "13     97127147353026270  864691136116457636      97127078633528432   \n",
       "18    102687171630646360  864691136116457636     102757540374817126   \n",
       "23     98395022034506129  864691136116457636      98395022034521827   \n",
       "26     96636421770787650  864691136116457636      96636421770779120   \n",
       "28     96636421770774812  864691136116457636      96636421770773250   \n",
       "\n",
       "       post_pt_root_id          pre_pt_position         post_pt_position  \\\n",
       "13  864691135013420694  [235366, 196892, 22847]  [235354, 196792, 22848]   \n",
       "18  864691135307684422  [275988, 203582, 24052]  [276092, 203630, 24043]   \n",
       "23  864691135946705505  [244678, 206252, 25580]  [244768, 206304, 25594]   \n",
       "26  864691135864759004  [231664, 211092, 24204]  [231694, 211042, 24201]   \n",
       "28  864691135864759004  [231522, 210908, 24196]  [231522, 210946, 24188]   \n",
       "\n",
       "            ctr_pt_position  \n",
       "13  [235410, 196840, 22849]  \n",
       "18  [276040, 203576, 24043]  \n",
       "23  [244728, 206250, 25578]  \n",
       "26  [231636, 211070, 24204]  \n",
       "28  [231478, 210922, 24190]  "
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now you could apply that to the original df that lists all postsynaptic excitatory partners\n",
    "\n",
    "postsyn_e_partners.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\clare.gamlin\\AppData\\Local\\Temp\\ipykernel_7028\\6627022.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  postsyn_e_partners['on_apical'] = postsyn_e_partners['id'].map(post_partner_is_apical)\n"
     ]
    }
   ],
   "source": [
    "postsyn_e_partners['on_apical'] = postsyn_e_partners['id'].map(post_partner_is_apical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "postsyn_e_partners\n",
    "postsyn_e_partners.to_csv('{}_postsyn_e_L5_partners.csv'.format(pre_root_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    0.631737\n",
      "True     0.368263\n",
      "Name: on_apical, dtype: float64\n",
      "False    211\n",
      "True     123\n",
      "NaN        9\n",
      "Name: on_apical, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(postsyn_e_partners['on_apical'].value_counts(normalize = True)) \n",
    "print(postsyn_e_partners['on_apical'].value_counts(dropna = False)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         post_pt_root_id  on_apical  Unnamed: 0   id  created  superceded_id  \\\n",
      "syn_loc                                                                        \n",
      "False                109        109         109  109      109            109   \n",
      "True                  22         22          22   22       22             22   \n",
      "Both                 109        109         109  109      109            109   \n",
      "\n",
      "         valid  size  pre_pt_supervoxel_id  pre_pt_root_id  \\\n",
      "syn_loc                                                      \n",
      "False      109   109                   109             109   \n",
      "True        22    22                    22              22   \n",
      "Both       109   109                   109             109   \n",
      "\n",
      "         post_pt_supervoxel_id  pre_pt_position  post_pt_position  \\\n",
      "syn_loc                                                             \n",
      "False                      109              109               109   \n",
      "True                        22               22                22   \n",
      "Both                       109              109               109   \n",
      "\n",
      "         ctr_pt_position  duplicated  \n",
      "syn_loc                               \n",
      "False                109         109  \n",
      "True                  22          22  \n",
      "Both                 109         109  \n",
      "           Unnamed: 0   id  created  superceded_id  valid  size  \\\n",
      "on_apical                                                         \n",
      "False             235  235      235              0    235   235   \n",
      "True               88   88       88              0     88    88   \n",
      "\n",
      "           pre_pt_supervoxel_id  pre_pt_root_id  post_pt_supervoxel_id  \\\n",
      "on_apical                                                                \n",
      "False                       235             235                    235   \n",
      "True                         88              88                     88   \n",
      "\n",
      "           post_pt_root_id  pre_pt_position  post_pt_position  ctr_pt_position  \n",
      "on_apical                                                                       \n",
      "False                  235              235               235              235  \n",
      "True                    88               88                88               88  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\clare.gamlin\\AppData\\Local\\Temp\\ipykernel_7028\\918958510.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mgrouped_not_dup['syn_loc'] = mgrouped_not_dup['on_apical'].copy()\n"
     ]
    }
   ],
   "source": [
    "fname = '864691136118647832_postsyn_e_L5_partners.csv'\n",
    "postsyn_e_targets = pd.read_csv(fname)\n",
    "grouped = postsyn_e_targets.groupby(['post_pt_root_id']).count().reset_index()\n",
    "# print (grouped.head())\n",
    "\n",
    "multi_cxn = grouped[grouped[\"on_apical\"] > 1]\n",
    "multcxn_id = multi_cxn['post_pt_root_id'].values\n",
    "# print (multi_cxn.head())\n",
    "multcxn_id_partners = postsyn_e_targets[postsyn_e_targets['post_pt_root_id'].isin(multcxn_id)]\n",
    "# print (multcxn_id_partners.head())\n",
    "mgrouped = multcxn_id_partners.groupby([\"post_pt_root_id\", \"on_apical\"]).count().reset_index()\n",
    "# print (mgrouped.head())\n",
    "\n",
    "mgrouped['duplicated'] = mgrouped.duplicated(subset=['post_pt_root_id'], keep = False)\n",
    "mgrouped_not_dup = mgrouped.loc[mgrouped['duplicated'] == False]\n",
    "mgrouped_not_dup['syn_loc'] = mgrouped_not_dup['on_apical'].copy()\n",
    "\n",
    "mgrouped_dup = mgrouped.loc[mgrouped['duplicated'] == True].drop_duplicates(subset = ['post_pt_root_id'] )\n",
    "mgrouped_dup['syn_loc'] = 'Both'\n",
    "mgrouped_append = mgrouped_dup.append(mgrouped_not_dup)\n",
    "\n",
    "print (mgrouped_append.groupby([\"syn_loc\"]).count())\n",
    "\n",
    "single_cxn = grouped[grouped[\"on_apical\"] == 1]\n",
    "single_cxn = single_cxn['post_pt_root_id'].values\n",
    "# print (multi_cxn.head())\n",
    "single_cxn_id_partners = postsyn_e_targets[postsyn_e_targets['post_pt_root_id'].isin(single_cxn)]\n",
    "print (single_cxn_id_partners.groupby([\"on_apical\"]).count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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