flywire_spatial_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from connectome_tools import load_flywire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = Path(\"/Users/chase/Documents/science/drosophila/connectomes/flywire\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>root_id</th>\n",
       "      <th>group</th>\n",
       "      <th>nt_type</th>\n",
       "      <th>nt_type_score</th>\n",
       "      <th>da_avg</th>\n",
       "      <th>ser_avg</th>\n",
       "      <th>gaba_avg</th>\n",
       "      <th>glut_avg</th>\n",
       "      <th>ach_avg</th>\n",
       "      <th>oct_avg</th>\n",
       "      <th>...</th>\n",
       "      <th>hemibrain_type</th>\n",
       "      <th>hemilineage</th>\n",
       "      <th>side</th>\n",
       "      <th>nerve</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>J_idx</th>\n",
       "      <th>J_idx_post</th>\n",
       "      <th>J_idx_pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>720575940596125868</td>\n",
       "      <td>LO.LOP</td>\n",
       "      <td>ACH</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.07</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>right</td>\n",
       "      <td>NaN</td>\n",
       "      <td>710944.0</td>\n",
       "      <td>262716.0</td>\n",
       "      <td>205680.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>720575940597856265</td>\n",
       "      <td>ME</td>\n",
       "      <td>ACH</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.07</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>right</td>\n",
       "      <td>NaN</td>\n",
       "      <td>809802.0</td>\n",
       "      <td>350646.0</td>\n",
       "      <td>202660.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>720575940597944841</td>\n",
       "      <td>ME.LO</td>\n",
       "      <td>ACH</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.09</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>right</td>\n",
       "      <td>NaN</td>\n",
       "      <td>754392.0</td>\n",
       "      <td>331248.0</td>\n",
       "      <td>183360.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>720575940598267657</td>\n",
       "      <td>ME</td>\n",
       "      <td>GABA</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>right</td>\n",
       "      <td>NaN</td>\n",
       "      <td>721164.0</td>\n",
       "      <td>298276.0</td>\n",
       "      <td>135720.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>720575940599333574</td>\n",
       "      <td>ME</td>\n",
       "      <td>ACH</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.11</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>right</td>\n",
       "      <td>NaN</td>\n",
       "      <td>694048.0</td>\n",
       "      <td>316512.0</td>\n",
       "      <td>83840.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              root_id   group nt_type  nt_type_score  da_avg  ser_avg  \\\n",
       "0  720575940596125868  LO.LOP     ACH           0.57    0.03     0.00   \n",
       "1  720575940597856265      ME     ACH           0.85    0.01     0.00   \n",
       "2  720575940597944841   ME.LO     ACH           0.82    0.02     0.00   \n",
       "3  720575940598267657      ME    GABA           0.74    0.01     0.01   \n",
       "4  720575940599333574      ME     ACH           0.61    0.02     0.00   \n",
       "\n",
       "   gaba_avg  glut_avg  ach_avg  oct_avg  ... hemibrain_type hemilineage  \\\n",
       "0      0.05      0.28     0.57     0.07  ...            NaN         NaN   \n",
       "1      0.03      0.04     0.85     0.07  ...            NaN         NaN   \n",
       "2      0.01      0.05     0.82     0.09  ...            NaN         NaN   \n",
       "3      0.74      0.19     0.03     0.02  ...            NaN         NaN   \n",
       "4      0.13      0.13     0.61     0.11  ...            NaN         NaN   \n",
       "\n",
       "    side nerve         x         y         z J_idx J_idx_post  J_idx_pre  \n",
       "0  right   NaN  710944.0  262716.0  205680.0     0          0          0  \n",
       "1  right   NaN  809802.0  350646.0  202660.0     1          1          1  \n",
       "2  right   NaN  754392.0  331248.0  183360.0     2          2          2  \n",
       "3  right   NaN  721164.0  298276.0  135720.0     3          3          3  \n",
       "4  right   NaN  694048.0  316512.0   83840.0     4          4          4  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neurons, J = load_flywire(datapath, include_spatial=True)\n",
    "N = len(neurons)\n",
    "\n",
    "neurons.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create adjacency matrix\n",
    "Jadj = J.astype(bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Structure\n",
    "We calculate below:\n",
    "- The spatial coordinates of the neuron (a proxy for the soma location),\n",
    "- The spatial coordinates of the center of mass and average radius of the ingoing (post) and outgoing (pre) synapses of each neuron.\n",
    "\n",
    "We then plot:\n",
    "- the histogram for the radii of input and output synapses over neurons\n",
    "- the histogram of the location of all neurons (soma) and the center of mass of their outgoing synapses. The soma are closer to the exterior of the brain, visible along the x axis (left vs right hemisphere).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load synapse coordinates\n",
    "coordinates = pd.read_csv(datapath / 'coordinates.csv')\n",
    "syn_coordinates = pd.read_csv(datapath / 'synapse_coordinates.csv')\n",
    "\n",
    "# Get average coordinates for presynaptic synapses, postsynaptic synapses and 'neuron'\n",
    "syn_coordinates = syn_coordinates.ffill() # forward fill nan values\n",
    "\n",
    "presyn_coor = syn_coordinates.groupby('pre_root_id').mean().reset_index().drop(['post_root_id'], axis=1)\n",
    "presyn_coorVar = syn_coordinates.groupby('pre_root_id').var().reset_index().drop(['post_root_id'], axis=1)\n",
    "presyn_coor.insert(4, \"rho\", presyn_coorVar[['x','y','z']].T.sum().pow(0.5))\n",
    "\n",
    "postsyn_coor = syn_coordinates.groupby('post_root_id').mean().reset_index().drop(['pre_root_id'], axis=1)\n",
    "postsyn_coorVar = syn_coordinates.groupby('post_root_id').var().reset_index().drop(['pre_root_id'], axis=1)\n",
    "postsyn_coor.insert(4, \"rho\", postsyn_coorVar[['x','y','z']].T.sum().pow(0.5))\n",
    "\n",
    "neur_coor = neurons[[\"root_id\", \"x\", \"y\", \"z\"]].set_index(\"root_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig  = plt.figure(figsize=[4,3])\n",
    "ax = fig.add_subplot(111)\n",
    "postsyn_coor['rho'].mul(1/1000).hist(bins=1000, alpha=0.8, label='ingoing')\n",
    "presyn_coor['rho'].mul(1/1000).hist(bins=1000, alpha=0.5, label='outgoing')\n",
    "plt.grid(False)\n",
    "plt.xlabel(r'spatial dispersion of synapses per neuron ($\\mu$m)')\n",
    "plt.ylabel(r'counts')\n",
    "plt.legend()\n",
    "plt.xscale('log')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 300\n",
    "factor = 1./1000. # turn units into um\n",
    "\n",
    "fig  = plt.figure(figsize=[8,3])\n",
    "ax = fig.add_subplot(131)\n",
    "ax.set_title(r'x ($\\mu$m)')\n",
    "neur_coor[\"x\"].mul(factor).hist(bins=bins, label='soma', alpha=0.8)\n",
    "presyn_coor[\"x\"].mul(factor).hist(bins=bins, alpha=0.5, label='synapse')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(False)\n",
    "presyn_coor\n",
    "ax = fig.add_subplot(132)\n",
    "ax.set_title(r'y ($\\mu$m)')\n",
    "neur_coor[\"y\"].mul(factor).hist(bins=bins, label='soma', alpha=0.8)\n",
    "presyn_coor[\"y\"].mul(factor).hist(bins=bins, alpha=0.5, label='synapse')\n",
    "plt.grid(False)\n",
    "ax = fig.add_subplot(133)\n",
    "neur_coor[\"z\"].mul(factor).hist(bins=bins, label='soma', alpha=0.8)\n",
    "presyn_coor[\"z\"].mul(factor).hist(bins=bins, alpha=0.5, label='synapse')\n",
    "plt.grid(False)\n",
    "ax.set_title(r'z ($\\mu$m)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network properties: in-degree and out-degree\n",
    "We show below:\n",
    "- the distribution of indegree and outdegree across neurons. For that, we use the adjacency matrix (no information about the synapse count between to neurons, only whether there is a connection or not). We find that the variability in indegree is slightly larger than in outdegree.\n",
    "- the correlation between indegree and outdegree per neuron. On average, neurons that project more to other neurons are those that also receive more connections.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SD of indegree: 49.383734153749344\n",
      "SD of outdegree: 45.634198672568104\n"
     ]
    }
   ],
   "source": [
    "nbins = 1000\n",
    "plt.figure(figsize=[4,3])\n",
    "plt.hist(Jadj.sum(1), nbins, alpha=0.8, label='in-degree')\n",
    "plt.hist(Jadj.T.sum(1), nbins, alpha=0.5, label='out-degree')\n",
    "plt.yscale('log')\n",
    "#plt.xscale('log')\n",
    "plt.xlabel('number of connections')\n",
    "plt.ylabel('counts')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=[4,3])\n",
    "plt.scatter(np.array(Jadj.sum(1)), np.array(Jadj.T.sum(1)), s=2, alpha=0.3)#nbins, alpha=0.8, label='in-degree')\n",
    "#plt.hist(, nbins, alpha=0.5, label='out-degree')\n",
    "plt.plot([1, 6000], [1,6000], c='k', lw=0.5)\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('in-degree')\n",
    "plt.ylabel('out-degree')\n",
    "plt.show()\n",
    "print('SD of indegree: '+str(np.std(Jadj.sum(1))))\n",
    "print('SD of outdegree: '+str(np.std(Jadj.T.sum(1))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependence of connectivity with distance\n",
    "We calculate:\n",
    "- how the probability of two neurons being connected decays with distance. The probability decays by an order of magnitude within 100 microns.\n",
    "- how the average synaptic count between connected neurons barely changes as a function of distance.\n",
    "For both analyses, we subsample the number of pairs of neurons we use.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate distance between neurons\n",
    "Nselneur = 10000000#00000000000\n",
    "rnd_neur1 = np.random.randint(0, N, Nselneur)\n",
    "rnd_neur2 = np.random.randint(0, N, Nselneur)\n",
    "\n",
    "\n",
    "p_conns = Jadj[rnd_neur1, rnd_neur2]#Jadj[:,rnd_neur2][rnd_neur1, :]>0.\n",
    "loc1 = neur_coor.iloc[rnd_neur1].values\n",
    "loc2 = neur_coor.iloc[rnd_neur2].values\n",
    "dist_conns = np.sqrt(np.sum((loc1-loc2)**2,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate statistics. \n",
    "# We are grouping sampled pairs of neurons into 'Nfold' groups of equal size based on their sorted distance.\n",
    "# We then take the average within group\n",
    "\n",
    "ix_ = np.argsort(dist_conns)\n",
    "\n",
    "Nfold = 20\n",
    "dist_avg = np.zeros(Nfold)\n",
    "p_conns_avg = np.zeros(Nfold)\n",
    "\n",
    "sdist_conns = dist_conns[ix_]\n",
    "sp_conns = np.array(p_conns)[0][ix_]\n",
    "\n",
    "Nums = len(ix_)//Nfold\n",
    "for iN in range(Nfold):\n",
    "    dist_avg[iN] = np.mean(sdist_conns[iN*Nums:(iN+1)*Nums])\n",
    "    p_conns_avg[iN] = np.mean(sp_conns[iN*Nums:(iN+1)*Nums])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[4,3])\n",
    "plt.plot(dist_avg/1000, p_conns_avg)\n",
    "plt.xlabel(r'distance between neurons ($\\mu$m)')\n",
    "plt.ylabel(r'probability of connection')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>pre_root_id</th>\n",
       "      <th>post_root_id</th>\n",
       "      <th>neuropil</th>\n",
       "      <th>syn_count</th>\n",
       "      <th>nt_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>720575940629970489</td>\n",
       "      <td>720575940631267655</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>7</td>\n",
       "      <td>GABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>720575940605876866</td>\n",
       "      <td>720575940606514878</td>\n",
       "      <td>LAL_R</td>\n",
       "      <td>15</td>\n",
       "      <td>GABA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>720575940627737365</td>\n",
       "      <td>720575940628914436</td>\n",
       "      <td>AL_L</td>\n",
       "      <td>32</td>\n",
       "      <td>ACH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>720575940633587552</td>\n",
       "      <td>720575940626452879</td>\n",
       "      <td>SMP_R</td>\n",
       "      <td>15</td>\n",
       "      <td>ACH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>720575940616871878</td>\n",
       "      <td>720575940621203973</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>13</td>\n",
       "      <td>GABA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pre_root_id        post_root_id neuropil  syn_count nt_type\n",
       "0  720575940629970489  720575940631267655   AVLP_R          7    GABA\n",
       "1  720575940605876866  720575940606514878    LAL_R         15    GABA\n",
       "2  720575940627737365  720575940628914436     AL_L         32     ACH\n",
       "3  720575940633587552  720575940626452879    SMP_R         15     ACH\n",
       "4  720575940616871878  720575940621203973   AVLP_L         13    GABA"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conns = pd.read_csv(datapath / 'connections.csv')\n",
    "conns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>root_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>720575940596125868</th>\n",
       "      <td>710944.0</td>\n",
       "      <td>262716.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940661335681</th>\n",
       "      <td>479150.0</td>\n",
       "      <td>351822.0</td>\n",
       "      <td>175460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940661336193</th>\n",
       "      <td>264160.0</td>\n",
       "      <td>156468.0</td>\n",
       "      <td>192540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940661337217</th>\n",
       "      <td>407744.0</td>\n",
       "      <td>106944.0</td>\n",
       "      <td>190120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940661338497</th>\n",
       "      <td>587074.0</td>\n",
       "      <td>329344.0</td>\n",
       "      <td>45120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720575940661339777</th>\n",
       "      <td>862404.0</td>\n",
       "      <td>221528.0</td>\n",
       "      <td>146160.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139255 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           x         y         z\n",
       "root_id                                         \n",
       "720575940596125868  710944.0  262716.0  205680.0\n",
       "720575940597856265  809802.0  350646.0  202660.0\n",
       "720575940597944841  754392.0  331248.0  183360.0\n",
       "720575940598267657  721164.0  298276.0  135720.0\n",
       "720575940599333574  694048.0  316512.0   83840.0\n",
       "...                      ...       ...       ...\n",
       "720575940661335681  479150.0  351822.0  175460.0\n",
       "720575940661336193  264160.0  156468.0  192540.0\n",
       "720575940661337217  407744.0  106944.0  190120.0\n",
       "720575940661338497  587074.0  329344.0   45120.0\n",
       "720575940661339777  862404.0  221528.0  146160.0\n",
       "\n",
       "[139255 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neur_coor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate distance between pairs of connected neurons\n",
    "Nsyns = 10000\n",
    "rnd_syns = np.random.choice(len(conns), Nsyns, replace=False)\n",
    "dists = np.zeros(Nsyns)\n",
    "counts = np.zeros(Nsyns)\n",
    "for isyn, syn in enumerate(rnd_syns):\n",
    "    preI = conns.pre_root_id.iloc[syn]\n",
    "    postI = conns.post_root_id.iloc[syn]\n",
    "    loc_pre = neur_coor.loc[preI].values\n",
    "    loc_post = neur_coor.loc[postI].values\n",
    "    \n",
    "    counts[isyn] = conns.syn_count[syn]\n",
    "    dists[isyn] = np.sqrt(np.sum((loc_pre-loc_post)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# same averaging procedure as for the plot above\n",
    "ix_ = np.argsort(dists)\n",
    "Nfold = 20\n",
    "dist_avg = np.zeros(Nfold)\n",
    "dist_std = np.zeros(Nfold)\n",
    "\n",
    "syncount_avg = np.zeros(Nfold)\n",
    "syncount_std = np.zeros(Nfold)\n",
    "\n",
    "\n",
    "sdist = dists[ix_]\n",
    "s_syncount = counts[ix_]\n",
    "\n",
    "Nums = len(ix_)//Nfold\n",
    "for iN in range(Nfold):\n",
    "    dist_avg[iN] = np.mean(sdist[iN*Nums:(iN+1)*Nums])\n",
    "    dist_std[iN] = np.std(sdist[iN*Nums:(iN+1)*Nums])/np.sqrt(Nums)\n",
    "    \n",
    "    syncount_avg[iN] = np.mean(s_syncount[iN*Nums:(iN+1)*Nums])\n",
    "    syncount_std[iN] = np.std(s_syncount[iN*Nums:(iN+1)*Nums])/np.sqrt(Nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'synaptic count')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[4,3])\n",
    "plt.scatter(dists/1000., counts, s=4, alpha=0.5)\n",
    "plt.plot(dist_avg/1000., syncount_avg, c='k')\n",
    "plt.fill_between(dist_avg/1000., syncount_avg-syncount_std, syncount_avg+syncount_std, color='k', alpha=0.5)\n",
    "#plt.fill_betweenx(syncount_avg, dist_avg/1000.-dist_std/1000., dist_avg/1000.+dist_std/1000., color='k', alpha=0.5)\n",
    "#plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel(r'distance between connected neurons ($\\mu$m)')\n",
    "plt.ylabel(r'synaptic count')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ideas to explore\n",
    "- Could we build connectomes that have the same spatial structure using a directed configuration model?\n",
    "- How different are the statistics of such a generated connectome? For instance, compare the statistics of bidirectional connections between the FlyWire data and the generated connectome\n",
    "- What if we don't take the spatial structure into account?\n",
    "\n",
    "To generate directed graphs, take a look at the package `networkx`, in particular the class `directed_configuration_model`\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "connectome",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}