neuprint_synapses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definitive guide to neuprint synapses\n",
    "This notebook serves as a reference for students in my lab who are working with the Hemibrain connectome. It is meant to disambiguate and demistify the various ways that synapses are counted in neuprint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuprint import Client\n",
    "# remove my token before making notebook public\n",
    "c = Client('neuprint.janelia.org', dataset='hemibrain:v1.2.1', token='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJlbWFpbCI6ImdnMjExNEBjb2x1bWJpYS5lZHUiLCJsZXZlbCI6Im5vYXV0aCIsImltYWdlLXVybCI6Imh0dHBzOi8vbGgzLmdvb2dsZXVzZXJjb250ZW50LmNvbS9hLS9BT2gxNEdpb1lJLUVPLWdidGxPRTh6SmQ0eF9ZQ1Y4ZHF0YVFjWGlHeG5CMz1zOTYtYz9zej01MD9zej01MCIsImV4cCI6MTgxMDUyOTYzNH0.jv9eR0SH5RhfBdXrtp4r-dDFOhcsT8GBbE4v69ysCKs') \n",
    "c.fetch_version()\n",
    "\n",
    "# import important stuff here\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will use a clock neuron as my example neuron. Using fetch_neurons, we obtain neuron_df which contains pre and post columns as well as downstream and upstream columns. Notice that the post count matches the upstream count but the downstream count is much higher than the pre count. I think this is because there are many downstream post sites associated with a single pre site and that the downstream count represents the number of post sites that are downstream of the pre sites.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "bodyID = 2068801704"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuprint import fetch_neurons\n",
    "\n",
    "neuron_df, roi_counts_df = fetch_neurons(bodyID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>bodyId</th>\n",
       "      <th>instance</th>\n",
       "      <th>type</th>\n",
       "      <th>pre</th>\n",
       "      <th>post</th>\n",
       "      <th>downstream</th>\n",
       "      <th>upstream</th>\n",
       "      <th>mito</th>\n",
       "      <th>size</th>\n",
       "      <th>status</th>\n",
       "      <th>cropped</th>\n",
       "      <th>statusLabel</th>\n",
       "      <th>cellBodyFiber</th>\n",
       "      <th>somaRadius</th>\n",
       "      <th>somaLocation</th>\n",
       "      <th>roiInfo</th>\n",
       "      <th>notes</th>\n",
       "      <th>inputRois</th>\n",
       "      <th>outputRois</th>\n",
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       "      <th>0</th>\n",
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       "      <td>s-LNv</td>\n",
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       "      <td>133</td>\n",
       "      <td>817</td>\n",
       "      <td>133</td>\n",
       "      <td>133</td>\n",
       "      <td>965744155</td>\n",
       "      <td>Traced</td>\n",
       "      <td>False</td>\n",
       "      <td>Roughly traced</td>\n",
       "      <td>None</td>\n",
       "      <td>581.5</td>\n",
       "      <td>[4069, 27265, 29472]</td>\n",
       "      <td>{'SNP(R)': {'pre': 156, 'post': 60, 'downstrea...</td>\n",
       "      <td>None</td>\n",
       "      <td>[AME(R), OL(R), PLP(R), POC, PVLP(R), SLP(R), ...</td>\n",
       "      <td>[PLP(R), POC, SLP(R), SMP(R), SNP(R), VLNP(R)]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       bodyId instance   type  pre  post  downstream  upstream  mito  \\\n",
       "0  2068801704    s-LNv  s-LNv  173   133         817       133   133   \n",
       "\n",
       "        size  status  cropped     statusLabel cellBodyFiber  somaRadius  \\\n",
       "0  965744155  Traced    False  Roughly traced          None       581.5   \n",
       "\n",
       "           somaLocation                                            roiInfo  \\\n",
       "0  [4069, 27265, 29472]  {'SNP(R)': {'pre': 156, 'post': 60, 'downstrea...   \n",
       "\n",
       "  notes                                          inputRois  \\\n",
       "0  None  [AME(R), OL(R), PLP(R), POC, PVLP(R), SLP(R), ...   \n",
       "\n",
       "                                       outputRois  \n",
       "0  [PLP(R), POC, SLP(R), SMP(R), SNP(R), VLNP(R)]  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neuron_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we use fetch_synapses to count up the number of pre and post sites on the neuron's body, those numbers match up with the pre and post counts returned by fetch_neurons. This leads me to believe that the pre and post columns from fetch_neurons have the raw counts of pre and post sites counted up on the actual body of the neuron. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuprint import fetch_synapses, SynapseCriteria as SC\n",
    "syns = fetch_synapses(bodyID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>roi</th>\n",
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       "      <th>2</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>14962</td>\n",
       "      <td>16835</td>\n",
       "      <td>8866</td>\n",
       "      <td>0.551020</td>\n",
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       "      <th>3</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>9437</td>\n",
       "      <td>15306</td>\n",
       "      <td>10147</td>\n",
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       "      <th>27</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>5316</td>\n",
       "      <td>23630</td>\n",
       "      <td>29696</td>\n",
       "      <td>0.991409</td>\n",
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       "      <th>28</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>5090</td>\n",
       "      <td>23740</td>\n",
       "      <td>29599</td>\n",
       "      <td>0.580829</td>\n",
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       "      <th>29</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>5382</td>\n",
       "      <td>23563</td>\n",
       "      <td>29719</td>\n",
       "      <td>0.851347</td>\n",
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       "      <th>...</th>\n",
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       "      <th>297</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>12940</td>\n",
       "      <td>16251</td>\n",
       "      <td>8017</td>\n",
       "      <td>0.949778</td>\n",
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       "      <th>298</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>17964</td>\n",
       "      <td>18028</td>\n",
       "      <td>7954</td>\n",
       "      <td>0.433000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>10986</td>\n",
       "      <td>15873</td>\n",
       "      <td>8655</td>\n",
       "      <td>0.814511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>10978</td>\n",
       "      <td>15603</td>\n",
       "      <td>8813</td>\n",
       "      <td>0.693000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>post</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>8497</td>\n",
       "      <td>14765</td>\n",
       "      <td>12144</td>\n",
       "      <td>0.617000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         bodyId  type      roi      x      y      z  confidence\n",
       "2    2068801704  post   SLP(R)  14962  16835   8866    0.551020\n",
       "3    2068801704  post   SLP(R)   9437  15306  10147    0.533048\n",
       "27   2068801704  post  PVLP(R)   5316  23630  29696    0.991409\n",
       "28   2068801704  post  PVLP(R)   5090  23740  29599    0.580829\n",
       "29   2068801704  post  PVLP(R)   5382  23563  29719    0.851347\n",
       "..          ...   ...      ...    ...    ...    ...         ...\n",
       "297  2068801704  post   SLP(R)  12940  16251   8017    0.949778\n",
       "298  2068801704  post   SMP(R)  17964  18028   7954    0.433000\n",
       "299  2068801704  post   SLP(R)  10986  15873   8655    0.814511\n",
       "302  2068801704  post   SLP(R)  10978  15603   8813    0.693000\n",
       "303  2068801704  post   SLP(R)   8497  14765  12144    0.617000\n",
       "\n",
       "[133 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syns[syns['type'] == 'post']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
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       "      <td>14879</td>\n",
       "      <td>16823</td>\n",
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       "      <td>0.991</td>\n",
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       "      <th>1</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>15683</td>\n",
       "      <td>17207</td>\n",
       "      <td>8593</td>\n",
       "      <td>0.995</td>\n",
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       "      <th>4</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>16795</td>\n",
       "      <td>17746</td>\n",
       "      <td>8560</td>\n",
       "      <td>0.975</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>15707</td>\n",
       "      <td>17608</td>\n",
       "      <td>8442</td>\n",
       "      <td>0.993</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>16585</td>\n",
       "      <td>17707</td>\n",
       "      <td>8476</td>\n",
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       "      <th>276</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>12225</td>\n",
       "      <td>15956</td>\n",
       "      <td>8259</td>\n",
       "      <td>0.911</td>\n",
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       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>15576</td>\n",
       "      <td>17061</td>\n",
       "      <td>8642</td>\n",
       "      <td>0.935</td>\n",
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       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>None</td>\n",
       "      <td>7283</td>\n",
       "      <td>13979</td>\n",
       "      <td>15207</td>\n",
       "      <td>0.998</td>\n",
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       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>10502</td>\n",
       "      <td>15641</td>\n",
       "      <td>8909</td>\n",
       "      <td>0.844</td>\n",
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       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>pre</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>16244</td>\n",
       "      <td>17234</td>\n",
       "      <td>9140</td>\n",
       "      <td>0.941</td>\n",
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       "<p>173 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "         bodyId type     roi      x      y      z  confidence\n",
       "0    2068801704  pre  SLP(R)  14879  16823   8380       0.991\n",
       "1    2068801704  pre  SMP(R)  15683  17207   8593       0.995\n",
       "4    2068801704  pre  SMP(R)  16795  17746   8560       0.975\n",
       "5    2068801704  pre  SMP(R)  15707  17608   8442       0.993\n",
       "6    2068801704  pre  SMP(R)  16585  17707   8476       0.962\n",
       "..          ...  ...     ...    ...    ...    ...         ...\n",
       "276  2068801704  pre  SLP(R)  12225  15956   8259       0.911\n",
       "300  2068801704  pre  SLP(R)  15576  17061   8642       0.935\n",
       "301  2068801704  pre    None   7283  13979  15207       0.998\n",
       "304  2068801704  pre  SLP(R)  10502  15641   8909       0.844\n",
       "305  2068801704  pre  SMP(R)  16244  17234   9140       0.941\n",
       "\n",
       "[173 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syns[syns['type'] == 'pre']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even if we specify \"primary_only\" the counts are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuprint import fetch_synapses, SynapseCriteria as SC\n",
    "syns = fetch_synapses(bodyID, SC(primary_only=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(syns[syns['type'] == 'post'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(syns[syns['type'] == 'pre'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, the number of pre rows from fetch_synapses equals the number of pre sites from fetch_neurons. Likewise for posts. Therefore, pre and post from fetch_neurons must be the raw synapse site counts on the neuron body.\n",
    "\n",
    "If we turn to the functions that return synaptic weights, I start to find discrepancies. I find that for inputs to the neuron, fetch_simple_connections and fetch_adjacencies both return a weight of 113 which is 20 less than the post count (and upstream count) from fetch_neurons (133). It is possible that this discrepancy is because fetch_neurons returns a count of all post sites on the body of the neuron regardless of whether or not there is anything on the other side of that synapse. In other words, perhaps the connectivity weights are strictly functional synapses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inputs to neuron\n",
    "from neuprint import fetch_simple_connections\n",
    "inputs = fetch_simple_connections(None,bodyID)\n",
    "inputs['weight'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import fetch_adjacencies\n",
    "n_df, conn_in_df = fetch_adjacencies(None,bodyID,include_nonprimary=False)\n",
    "conn_in_df['weight'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fetch_synapse_connections also returns 113 rows where each one is presumably a functional synapse between 2 neurons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "677e629252ae47aeb797e652043086a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/113 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bodyId_pre</th>\n",
       "      <th>bodyId_post</th>\n",
       "      <th>roi_pre</th>\n",
       "      <th>roi_post</th>\n",
       "      <th>x_pre</th>\n",
       "      <th>y_pre</th>\n",
       "      <th>z_pre</th>\n",
       "      <th>x_post</th>\n",
       "      <th>y_post</th>\n",
       "      <th>z_post</th>\n",
       "      <th>confidence_pre</th>\n",
       "      <th>confidence_post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>297541369</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>14984</td>\n",
       "      <td>16842</td>\n",
       "      <td>8863</td>\n",
       "      <td>14962</td>\n",
       "      <td>16835</td>\n",
       "      <td>8866</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.551020</td>\n",
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       "      <td>297541369</td>\n",
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       "      <td>SLP(R)</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>9460</td>\n",
       "      <td>15294</td>\n",
       "      <td>10154</td>\n",
       "      <td>9437</td>\n",
       "      <td>15306</td>\n",
       "      <td>10147</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.533048</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2129848864</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>5295</td>\n",
       "      <td>23631</td>\n",
       "      <td>29690</td>\n",
       "      <td>5316</td>\n",
       "      <td>23630</td>\n",
       "      <td>29696</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.991409</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2129848864</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>5089</td>\n",
       "      <td>23736</td>\n",
       "      <td>29626</td>\n",
       "      <td>5090</td>\n",
       "      <td>23740</td>\n",
       "      <td>29599</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.580829</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2129848864</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>PVLP(R)</td>\n",
       "      <td>5366</td>\n",
       "      <td>23547</td>\n",
       "      <td>29721</td>\n",
       "      <td>5382</td>\n",
       "      <td>23563</td>\n",
       "      <td>29719</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.851347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>386834269</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>12914</td>\n",
       "      <td>16257</td>\n",
       "      <td>8015</td>\n",
       "      <td>12940</td>\n",
       "      <td>16251</td>\n",
       "      <td>8017</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.949778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>386833850</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>17968</td>\n",
       "      <td>18047</td>\n",
       "      <td>7988</td>\n",
       "      <td>17964</td>\n",
       "      <td>18028</td>\n",
       "      <td>7954</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.433000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>355816896</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>10966</td>\n",
       "      <td>15869</td>\n",
       "      <td>8651</td>\n",
       "      <td>10986</td>\n",
       "      <td>15873</td>\n",
       "      <td>8655</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.814511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>355453590</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>10969</td>\n",
       "      <td>15607</td>\n",
       "      <td>8809</td>\n",
       "      <td>10978</td>\n",
       "      <td>15603</td>\n",
       "      <td>8813</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.693000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>355453590</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>SLP(R)</td>\n",
       "      <td>8506</td>\n",
       "      <td>14761</td>\n",
       "      <td>12148</td>\n",
       "      <td>8497</td>\n",
       "      <td>14765</td>\n",
       "      <td>12144</td>\n",
       "      <td>0.773</td>\n",
       "      <td>0.617000</td>\n",
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       "<p>113 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post  roi_pre roi_post  x_pre  y_pre  z_pre  x_post  \\\n",
       "0     297541369   2068801704   SLP(R)   SLP(R)  14984  16842   8863   14962   \n",
       "1     297541369   2068801704   SLP(R)   SLP(R)   9460  15294  10154    9437   \n",
       "2    2129848864   2068801704  PVLP(R)  PVLP(R)   5295  23631  29690    5316   \n",
       "3    2129848864   2068801704  PVLP(R)  PVLP(R)   5089  23736  29626    5090   \n",
       "4    2129848864   2068801704  PVLP(R)  PVLP(R)   5366  23547  29721    5382   \n",
       "..          ...          ...      ...      ...    ...    ...    ...     ...   \n",
       "108   386834269   2068801704   SLP(R)   SLP(R)  12914  16257   8015   12940   \n",
       "109   386833850   2068801704   SMP(R)   SMP(R)  17968  18047   7988   17964   \n",
       "110   355816896   2068801704   SLP(R)   SLP(R)  10966  15869   8651   10986   \n",
       "111   355453590   2068801704   SLP(R)   SLP(R)  10969  15607   8809   10978   \n",
       "112   355453590   2068801704   SLP(R)   SLP(R)   8506  14761  12148    8497   \n",
       "\n",
       "     y_post  z_post  confidence_pre  confidence_post  \n",
       "0     16835    8866           0.989         0.551020  \n",
       "1     15306   10147           0.986         0.533048  \n",
       "2     23630   29696           0.989         0.991409  \n",
       "3     23740   29599           0.993         0.580829  \n",
       "4     23563   29719           0.991         0.851347  \n",
       "..      ...     ...             ...              ...  \n",
       "108   16251    8017           0.998         0.949778  \n",
       "109   18028    7954           0.721         0.433000  \n",
       "110   15873    8655           0.988         0.814511  \n",
       "111   15603    8813           0.724         0.693000  \n",
       "112   14765   12144           0.773         0.617000  \n",
       "\n",
       "[113 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import fetch_synapse_connections, SynapseCriteria as SC\n",
    "pre_syn_conns = fetch_synapse_connections(None, bodyID)\n",
    "pre_syn_conns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could check to make sure that every row that fetch_synapse_connections returns is accounted for in fetch_synapses..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the outputs from this neuron, fetch_simple_connections and fetch_adjacencies both return 411 but neither of those match the pre count nor the downstream count from fetch_neurons. It is possible that the pre count is simply the number of pre-synaptic sites on the neuron's body and that the output weights and downstream counts are some kind of count of the post-synaptic sites on the other side of those pre sites. That would explain why the weights and downstream counts are larger than the pre count, but it is really unclear to me why those counts would be different from each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# outputs to neuron\n",
    "from neuprint import fetch_simple_connections\n",
    "outputs = fetch_simple_connections(bodyID,None)\n",
    "outputs['weight'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import fetch_adjacencies\n",
    "n_df, conn_out_df = fetch_adjacencies(bodyID,None,include_nonprimary=False)\n",
    "conn_out_df['weight'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do fetch_synapse_connections for the outputs to the neuron, the returned dataframe has the same number of rows as weights from fetch_simple_connections and fetch_adjacencies. Therefore, fetch_simple_connections and fetch_adjacencies return the number of post sites even when the neuron of interest is pre-synaptic. I like that neuprint uses this convention and we will aim to use it everywhere we have a choice. This reduces any asymmetry that is introduced by the fact that these synapses are polyadic.\n",
    "\n",
    "__Whenever presenting synapse counts or weights, always default to the count of post-synaptic sites within the synapse regardless of whether the analysis is focused on a pre-synaptic entity.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc01222de9474867ae862027e9ff4499",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/411 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <td>SLP(R)</td>\n",
       "      <td>9944</td>\n",
       "      <td>15478</td>\n",
       "      <td>9490</td>\n",
       "      <td>9950</td>\n",
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       "      <td>SLP(R)</td>\n",
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       "      <th>3</th>\n",
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       "      <td>SLP(R)</td>\n",
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       "      <td>0.981</td>\n",
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       "      <th>4</th>\n",
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       "      <td>SLP(R)</td>\n",
       "      <td>12227</td>\n",
       "      <td>16085</td>\n",
       "      <td>8284</td>\n",
       "      <td>12228</td>\n",
       "      <td>16083</td>\n",
       "      <td>8299</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.995000</td>\n",
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       "      <td>SLP(R)</td>\n",
       "      <td>9300</td>\n",
       "      <td>15404</td>\n",
       "      <td>10213</td>\n",
       "      <td>9316</td>\n",
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       "      <th>407</th>\n",
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       "      <td>16379</td>\n",
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       "      <td>0.992</td>\n",
       "      <td>0.522653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325122525</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>SMP(R)</td>\n",
       "      <td>16696</td>\n",
       "      <td>17741</td>\n",
       "      <td>8518</td>\n",
       "      <td>16688</td>\n",
       "      <td>17728</td>\n",
       "      <td>8518</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.634028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>411 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post roi_pre roi_post  x_pre  y_pre  z_pre  x_post  \\\n",
       "0    2068801704    386834269  SLP(R)   SLP(R)  12811  16275   8141   12807   \n",
       "1    2068801704   5813032924  SLP(R)   SLP(R)   9944  15478   9490    9950   \n",
       "2    2068801704    325113795  SLP(R)   SLP(R)  14879  16823   8380   14889   \n",
       "3    2068801704    357944930  SLP(R)   SLP(R)  12897  16287   8175   12928   \n",
       "4    2068801704    357945095  SLP(R)   SLP(R)  12227  16085   8284   12228   \n",
       "..          ...          ...     ...      ...    ...    ...    ...     ...   \n",
       "406  2068801704    325122525  SLP(R)   SLP(R)   9300  15404  10213    9316   \n",
       "407  2068801704    325122525  SMP(R)   SMP(R)  16379  17621   8663   16331   \n",
       "408  2068801704    325122525  SLP(R)   SLP(R)  12641  15900   8503   12635   \n",
       "409  2068801704    325122525  SLP(R)   SLP(R)  11625  15760   8594   11620   \n",
       "410  2068801704    325122525  SMP(R)   SMP(R)  16696  17741   8518   16688   \n",
       "\n",
       "     y_post  z_post  confidence_pre  confidence_post  \n",
       "0     16292    8125           0.996         0.428457  \n",
       "1     15495    9489           0.925         0.997977  \n",
       "2     16813    8394           0.991         0.796530  \n",
       "3     16297    8165           0.981         0.416790  \n",
       "4     16083    8299           0.824         0.995000  \n",
       "..      ...     ...             ...              ...  \n",
       "406   15422   10243           0.936         0.567212  \n",
       "407   17603    8652           0.971         0.460560  \n",
       "408   15921    8513           0.996         0.996365  \n",
       "409   15758    8570           0.992         0.522653  \n",
       "410   17728    8518           0.951         0.634028  \n",
       "\n",
       "[411 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from neuprint import fetch_synapse_connections, SynapseCriteria as SC\n",
    "post_syn_conns = fetch_synapse_connections(bodyID, None)\n",
    "post_syn_conns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what explains the discrepancy between the pre counts, downstream counts, and weights??? I can accept that either the weights or the downstream counts represent the number of functional synapses that are on the post side of the pre sites, but which one represents that number? \n",
    "\n",
    "Since the downstream count isn't explained by including non-primary entries (see below), then I'm inclined to think that the downstream count is BS for now. I will default to the connection weights when seeking to get a count of output synapses.\n",
    "\n",
    "__Henceforth, connections weights are to be used for output synapse counts - never downstream counts which are likely BS until proven otherwise.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f83abb5dc7e24a238d6ce7fc9ac5b630",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/411 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\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>bodyId_pre</th>\n",
       "      <th>bodyId_post</th>\n",
       "      <th>roi_pre</th>\n",
       "      <th>roi_post</th>\n",
       "      <th>x_pre</th>\n",
       "      <th>y_pre</th>\n",
       "      <th>z_pre</th>\n",
       "      <th>x_post</th>\n",
       "      <th>y_post</th>\n",
       "      <th>z_post</th>\n",
       "      <th>confidence_pre</th>\n",
       "      <th>confidence_post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>386834269</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>12811</td>\n",
       "      <td>16275</td>\n",
       "      <td>8141</td>\n",
       "      <td>12807</td>\n",
       "      <td>16292</td>\n",
       "      <td>8125</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.428457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>5813032924</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>9944</td>\n",
       "      <td>15478</td>\n",
       "      <td>9490</td>\n",
       "      <td>9950</td>\n",
       "      <td>15495</td>\n",
       "      <td>9489</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.997977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325113795</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>14879</td>\n",
       "      <td>16823</td>\n",
       "      <td>8380</td>\n",
       "      <td>14889</td>\n",
       "      <td>16813</td>\n",
       "      <td>8394</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.796530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>357944930</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>12897</td>\n",
       "      <td>16287</td>\n",
       "      <td>8175</td>\n",
       "      <td>12928</td>\n",
       "      <td>16297</td>\n",
       "      <td>8165</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.416790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>357945095</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>12227</td>\n",
       "      <td>16085</td>\n",
       "      <td>8284</td>\n",
       "      <td>12228</td>\n",
       "      <td>16083</td>\n",
       "      <td>8299</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325122525</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>9300</td>\n",
       "      <td>15404</td>\n",
       "      <td>10213</td>\n",
       "      <td>9316</td>\n",
       "      <td>15422</td>\n",
       "      <td>10243</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.567212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325122525</td>\n",
       "      <td>[SMP(R), SNP(R)]</td>\n",
       "      <td>[SMP(R), SNP(R)]</td>\n",
       "      <td>16379</td>\n",
       "      <td>17621</td>\n",
       "      <td>8663</td>\n",
       "      <td>16331</td>\n",
       "      <td>17603</td>\n",
       "      <td>8652</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.460560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325122525</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>12641</td>\n",
       "      <td>15900</td>\n",
       "      <td>8503</td>\n",
       "      <td>12635</td>\n",
       "      <td>15921</td>\n",
       "      <td>8513</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.996365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325122525</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>[SLP(R), SNP(R)]</td>\n",
       "      <td>11625</td>\n",
       "      <td>15760</td>\n",
       "      <td>8594</td>\n",
       "      <td>11620</td>\n",
       "      <td>15758</td>\n",
       "      <td>8570</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.522653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325122525</td>\n",
       "      <td>[SMP(R), SNP(R)]</td>\n",
       "      <td>[SMP(R), SNP(R)]</td>\n",
       "      <td>16696</td>\n",
       "      <td>17741</td>\n",
       "      <td>8518</td>\n",
       "      <td>16688</td>\n",
       "      <td>17728</td>\n",
       "      <td>8518</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.634028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>411 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post           roi_pre          roi_post  x_pre  \\\n",
       "0    2068801704    386834269  [SLP(R), SNP(R)]  [SLP(R), SNP(R)]  12811   \n",
       "1    2068801704   5813032924  [SLP(R), SNP(R)]  [SLP(R), SNP(R)]   9944   \n",
       "2    2068801704    325113795  [SLP(R), SNP(R)]  [SLP(R), SNP(R)]  14879   \n",
       "3    2068801704    357944930  [SLP(R), SNP(R)]  [SLP(R), SNP(R)]  12897   \n",
       "4    2068801704    357945095  [SLP(R), SNP(R)]  [SLP(R), SNP(R)]  12227   \n",
       "..          ...          ...               ...               ...    ...   \n",
       "406  2068801704    325122525  [SLP(R), SNP(R)]  [SLP(R), SNP(R)]   9300   \n",
       "407  2068801704    325122525  [SMP(R), SNP(R)]  [SMP(R), SNP(R)]  16379   \n",
       "408  2068801704    325122525  [SLP(R), SNP(R)]  [SLP(R), SNP(R)]  12641   \n",
       "409  2068801704    325122525  [SLP(R), SNP(R)]  [SLP(R), SNP(R)]  11625   \n",
       "410  2068801704    325122525  [SMP(R), SNP(R)]  [SMP(R), SNP(R)]  16696   \n",
       "\n",
       "     y_pre  z_pre  x_post  y_post  z_post  confidence_pre  confidence_post  \n",
       "0    16275   8141   12807   16292    8125           0.996         0.428457  \n",
       "1    15478   9490    9950   15495    9489           0.925         0.997977  \n",
       "2    16823   8380   14889   16813    8394           0.991         0.796530  \n",
       "3    16287   8175   12928   16297    8165           0.981         0.416790  \n",
       "4    16085   8284   12228   16083    8299           0.824         0.995000  \n",
       "..     ...    ...     ...     ...     ...             ...              ...  \n",
       "406  15404  10213    9316   15422   10243           0.936         0.567212  \n",
       "407  17621   8663   16331   17603    8652           0.971         0.460560  \n",
       "408  15900   8503   12635   15921    8513           0.996         0.996365  \n",
       "409  15760   8594   11620   15758    8570           0.992         0.522653  \n",
       "410  17741   8518   16688   17728    8518           0.951         0.634028  \n",
       "\n",
       "[411 rows x 12 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import fetch_synapse_connections, SynapseCriteria as SC\n",
    "post_syn_conns = fetch_synapse_connections(bodyID, None, SC(primary_only=False))\n",
    "post_syn_conns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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