MandEconnectivity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "38980c68-55a7-4506-8c86-d51690bd1c03",
   "metadata": {},
   "source": [
    "In our paper, we focus on the patterns of connectivity among the morning (M) and evening (E) neurons of the clock network - also collectively known as the lateral neurons (LN). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b61f9e1f-f3b6-41e6-8ba7-1dd7b6eeb293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.1.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from neuprint import Client\n",
    "c = Client('neuprint.janelia.org', dataset='hemibrain:v1.2.1', token='') \n",
    "# insert personal token above. see https://connectome-neuprint.github.io/neuprint-python/docs/quickstart.html#client-and-authorization-token for instructions\n",
    "c.fetch_version()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb732d7-9531-4d23-94ea-34833063b2be",
   "metadata": {},
   "source": [
    "# Network graph of M cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f60927a-2c07-400f-a891-ea944c60f3ed",
   "metadata": {},
   "source": [
    "The interconnectivity of M cells is displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f568e448-9a18-4aea-bb5b-b4e689e03f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuron_criteria import getClock\n",
    "from connection_utils import clock_neuron_connections\n",
    "\n",
    "clock_df = getClock(l_lnv=True)\n",
    "conn_df = clock_neuron_connections(clock_df, 'intra_clock')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af9950ee",
   "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>bodyId_pre</th>\n",
       "      <th>bodyId_post</th>\n",
       "      <th>weight</th>\n",
       "      <th>instance_pre</th>\n",
       "      <th>instance_post</th>\n",
       "      <th>type_pre</th>\n",
       "      <th>phase_pre</th>\n",
       "      <th>type_post</th>\n",
       "      <th>phase_post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1664980698</td>\n",
       "      <td>1975347348</td>\n",
       "      <td>1</td>\n",
       "      <td>sLNv2</td>\n",
       "      <td>sLNv4</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1664980698</td>\n",
       "      <td>2007068523</td>\n",
       "      <td>3</td>\n",
       "      <td>sLNv2</td>\n",
       "      <td>sLNv3</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1664980698</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>6</td>\n",
       "      <td>sLNv2</td>\n",
       "      <td>sLNv1</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1975347348</td>\n",
       "      <td>2007068523</td>\n",
       "      <td>5</td>\n",
       "      <td>sLNv4</td>\n",
       "      <td>sLNv3</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1975347348</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>3</td>\n",
       "      <td>sLNv4</td>\n",
       "      <td>sLNv1</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>1664980698</td>\n",
       "      <td>5</td>\n",
       "      <td>sLNv3</td>\n",
       "      <td>sLNv2</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>1975347348</td>\n",
       "      <td>5</td>\n",
       "      <td>sLNv3</td>\n",
       "      <td>sLNv4</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>3</td>\n",
       "      <td>sLNv3</td>\n",
       "      <td>sLNv1</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>1664980698</td>\n",
       "      <td>4</td>\n",
       "      <td>sLNv1</td>\n",
       "      <td>sLNv2</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>1975347348</td>\n",
       "      <td>9</td>\n",
       "      <td>sLNv1</td>\n",
       "      <td>sLNv4</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>2007068523</td>\n",
       "      <td>5</td>\n",
       "      <td>sLNv1</td>\n",
       "      <td>sLNv3</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "      <td>s-LNv</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post  weight instance_pre instance_post type_pre  \\\n",
       "104  1664980698   1975347348       1        sLNv2         sLNv4    s-LNv   \n",
       "105  1664980698   2007068523       3        sLNv2         sLNv3    s-LNv   \n",
       "106  1664980698   2068801704       6        sLNv2         sLNv1    s-LNv   \n",
       "111  1975347348   2007068523       5        sLNv4         sLNv3    s-LNv   \n",
       "112  1975347348   2068801704       3        sLNv4         sLNv1    s-LNv   \n",
       "113  2007068523   1664980698       5        sLNv3         sLNv2    s-LNv   \n",
       "114  2007068523   1975347348       5        sLNv3         sLNv4    s-LNv   \n",
       "115  2007068523   2068801704       3        sLNv3         sLNv1    s-LNv   \n",
       "122  2068801704   1664980698       4        sLNv1         sLNv2    s-LNv   \n",
       "123  2068801704   1975347348       9        sLNv1         sLNv4    s-LNv   \n",
       "124  2068801704   2007068523       5        sLNv1         sLNv3    s-LNv   \n",
       "\n",
       "    phase_pre type_post phase_post  \n",
       "104   morning     s-LNv    morning  \n",
       "105   morning     s-LNv    morning  \n",
       "106   morning     s-LNv    morning  \n",
       "111   morning     s-LNv    morning  \n",
       "112   morning     s-LNv    morning  \n",
       "113   morning     s-LNv    morning  \n",
       "114   morning     s-LNv    morning  \n",
       "115   morning     s-LNv    morning  \n",
       "122   morning     s-LNv    morning  \n",
       "123   morning     s-LNv    morning  \n",
       "124   morning     s-LNv    morning  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import merge_neuron_properties\n",
    "\n",
    "# merge tables and get connectivity only among M cells\n",
    "conn_df = merge_neuron_properties(clock_df, conn_df, ['type','phase'])\n",
    "conn_df_m = conn_df[(conn_df['phase_pre'] == 'morning') & (conn_df['phase_post'] == 'morning')]\n",
    "conn_df_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d111eaae-378e-4203-aa30-593db27aab2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export to csv table\n",
    "conn_df_m.to_csv('M_intra_connectivity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6a470ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from graph_network import neuron_graph\n",
    "\n",
    "# draw a network graph of the interconnectivity among M cells\n",
    "neuron_graph(conn_df_m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "386a9f86-8efc-478d-bd7b-c7ea1b3faa3e",
   "metadata": {},
   "source": [
    "# Strong connections to and from M cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c08fb0b-3dff-45be-9571-3531c5d12348",
   "metadata": {},
   "source": [
    "The strong connections **to** M cells were retrieved from the hemibrain. These were inputs to each M cell with a weight of 10 or greater. Only strong connections were displayed in the Sankey diagrams of the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a799282-5942-4c93-8509-c060f0589d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuron_criteria import getClock, bodyIds_by_type\n",
    "\n",
    "# get bodyIds organized by neuron type\n",
    "bodyIds_types = bodyIds_by_type(clock_df)\n",
    "\n",
    "# get s-LNv bodyIds\n",
    "sLNv_IDs = bodyIds_types['s-LNv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b513734-828b-4258-b81f-42bb5a1d882c",
   "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>bodyId_pre</th>\n",
       "      <th>bodyId_post</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5813022753</td>\n",
       "      <td>1664980698</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5813022753</td>\n",
       "      <td>1975347348</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5813022753</td>\n",
       "      <td>2007068523</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5813022753</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5813040744</td>\n",
       "      <td>1664980698</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5813054835</td>\n",
       "      <td>1664980698</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5813054835</td>\n",
       "      <td>1975347348</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5813054835</td>\n",
       "      <td>2007068523</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5813054835</td>\n",
       "      <td>2068801704</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bodyId_pre  bodyId_post  weight\n",
       "0  5813022753   1664980698      26\n",
       "1  5813022753   1975347348      22\n",
       "2  5813022753   2007068523      23\n",
       "3  5813022753   2068801704      18\n",
       "4  5813040744   1664980698      12\n",
       "5  5813054835   1664980698      13\n",
       "6  5813054835   1975347348      22\n",
       "7  5813054835   2007068523      19\n",
       "8  5813054835   2068801704      13"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import fetch_adjacencies\n",
    "\n",
    "# get strong inputs to M cells\n",
    "neuron_df, strong_M_inputs = fetch_adjacencies(None, sLNv_IDs, min_total_weight=10)\n",
    "\n",
    "#consolidate since we don't care about separating connections between 2 neurons that happen in different ROIs.\n",
    "strong_M_inputs = strong_M_inputs.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum()\n",
    "strong_M_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33c53949-7584-4572-95b1-a00c6e394b65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export table\n",
    "strong_M_inputs.to_csv('strong_M_inputs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445ae8f0-b8fa-43a8-ae43-b19f91a938a8",
   "metadata": {},
   "source": [
    "The strong connections **from** M cells were retrieved from the hemibrain. These were outputs from each M cell with a weight of 10 or greater. Only strong connections were displayed in the Sankey diagrams of the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9108acc1-e44d-4719-a148-e7333f40bebb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
       "      <td>1664980698</td>\n",
       "      <td>5812980250</td>\n",
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       "      <th>6</th>\n",
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       "      <td>1975347348</td>\n",
       "      <td>5813055759</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>294783216</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>325122525</td>\n",
       "      <td>71</td>\n",
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       "      <th>17</th>\n",
       "      <td>2007068523</td>\n",
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       "      <th>18</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>355453590</td>\n",
       "      <td>97</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>358286442</td>\n",
       "      <td>11</td>\n",
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       "      <th>20</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>540998882</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>571372889</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2007068523</td>\n",
       "      <td>5813047586</td>\n",
       "      <td>69</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
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       "      <td>294783216</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325122525</td>\n",
       "      <td>64</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>325455002</td>\n",
       "      <td>20</td>\n",
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       "      <td>75</td>\n",
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       "      <th>27</th>\n",
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       "      <td>540998882</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2068801704</td>\n",
       "      <td>5813047586</td>\n",
       "      <td>57</td>\n",
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      ],
      "text/plain": [
       "    bodyId_pre  bodyId_post  weight\n",
       "0   1664980698    294783216      24\n",
       "1   1664980698    325122525      60\n",
       "2   1664980698    325455002      31\n",
       "3   1664980698    355453590      61\n",
       "4   1664980698    540998882      18\n",
       "5   1664980698   5812980250      10\n",
       "6   1664980698   5813009378      12\n",
       "7   1664980698   5813047586      84\n",
       "8   1975347348    294783216      17\n",
       "9   1975347348    325122525      56\n",
       "10  1975347348    325455002      22\n",
       "11  1975347348    355453590      72\n",
       "12  1975347348    540998882      13\n",
       "13  1975347348   5813047586      64\n",
       "14  1975347348   5813055759      10\n",
       "15  2007068523    294783216      12\n",
       "16  2007068523    325122525      71\n",
       "17  2007068523    325455002      23\n",
       "18  2007068523    355453590      97\n",
       "19  2007068523    358286442      11\n",
       "20  2007068523    540998882      13\n",
       "21  2007068523    571372889      10\n",
       "22  2007068523   5813047586      69\n",
       "23  2068801704    294783216      19\n",
       "24  2068801704    325122525      64\n",
       "25  2068801704    325455002      20\n",
       "26  2068801704    355453590      75\n",
       "27  2068801704    540998882      13\n",
       "28  2068801704   5813047586      57"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import fetch_adjacencies\n",
    "\n",
    "# get strong connections from M cells to anything else\n",
    "neuron_df, strong_M_outputs = fetch_adjacencies(sLNv_IDs, None, min_total_weight=10)\n",
    "\n",
    "# consolidate since we don't care about separating connections between 2 neurons that happen in different ROIs.\n",
    "strong_M_outputs = strong_M_outputs.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum()\n",
    "strong_M_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ca1b82a-7737-4b76-b9d8-36db3983281a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export table\n",
    "strong_M_outputs.to_csv('strong_M_outputs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ee2630-1b64-40bb-887a-6001a8a41641",
   "metadata": {},
   "source": [
    "# Shared strong connections to and from M cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e3dccd-56c7-4749-95c1-a2e7b5c58474",
   "metadata": {},
   "source": [
    "We retrieved the strong connections made by M cells that are shared by all of the M cells. \n",
    "\n",
    "There were only 2 neurons that sent strong inputs to all 4 M cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "142372b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from connection_utils import strong_shared_connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09f7dea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dbing\\Documents\\Research\\Code\\clock-connectome\\connection_utils.py:213: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  conns_df = conns_df.groupby(['bodyId_pre', 'instance_pre'], as_index=False)['weight', 'shared'].sum()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bodyId_pre</th>\n",
       "      <th>instance_pre</th>\n",
       "      <th>weight</th>\n",
       "      <th>shared</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5813022753</td>\n",
       "      <td>aMe</td>\n",
       "      <td>89</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5813054835</td>\n",
       "      <td>None</td>\n",
       "      <td>67</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   bodyId_pre instance_pre  weight  shared\n",
       "0  5813022753          aMe      89       4\n",
       "2  5813054835         None      67       4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get strong input connections shared by all 4 M cells\n",
    "num_shared_by = 4\n",
    "strong_shared_M_inputs = strong_shared_connections(sLNv_IDs,'in',num_shared_by)\n",
    "strong_shared_M_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baefbf5a-78ab-4ae1-86d2-80a7057e4c68",
   "metadata": {},
   "source": [
    "There were 6 neurons that received strong synaptic connections from all 4 M cells. They came from the SLP and SMP neuropils. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "040c373a-aac2-4f55-9712-e410319391f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dbing\\Documents\\Research\\Code\\clock-connectome\\connection_utils.py:208: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  conns_df = conns_df.groupby(['bodyId_post', 'instance_post'], as_index=False)['weight', 'shared'].sum()\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bodyId_post</th>\n",
       "      <th>instance_post</th>\n",
       "      <th>weight</th>\n",
       "      <th>shared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>355453590</td>\n",
       "      <td>SLP316_R</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5813047586</td>\n",
       "      <td>SLP316_R</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325122525</td>\n",
       "      <td>SLP316_R</td>\n",
       "      <td>251</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>325455002</td>\n",
       "      <td>SLP403_R</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>294783216</td>\n",
       "      <td>SLP403_R</td>\n",
       "      <td>72</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>540998882</td>\n",
       "      <td>SMP232_R</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bodyId_post instance_post  weight  shared\n",
       "3    355453590      SLP316_R     305       4\n",
       "9   5813047586      SLP316_R     274       4\n",
       "1    325122525      SLP316_R     251       4\n",
       "2    325455002      SLP403_R      96       4\n",
       "0    294783216      SLP403_R      72       4\n",
       "5    540998882      SMP232_R      57       4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get strong shared output connections from M cells\n",
    "strong_shared_M_outputs = strong_shared_connections(sLNv_IDs,'out',num_shared_by)\n",
    "strong_shared_M_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6da0a31b-228d-4770-9d48-ee1cadeab088",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save tables\n",
    "strong_shared_M_inputs.to_csv('strong_shared_M_inputs.csv')\n",
    "strong_shared_M_outputs.to_csv('strong_shared_M_outputs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4771799-a288-4702-a6fa-f116df6bc4e5",
   "metadata": {},
   "source": [
    "# Network graph of E cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c950927-db18-4cc4-bc51-b18ddbe991cc",
   "metadata": {},
   "source": [
    "Among the E cells, the network graph shows that there are 2 groupings. \n",
    "The interconnectivity of E cells is displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae4c2f3f-4bea-4647-abe0-b4b4df731fb7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>7</th>\n",
       "      <td>296544364</td>\n",
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       "      <td>6</td>\n",
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       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>5813064789</td>\n",
       "      <td>3</td>\n",
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       "      <th>64</th>\n",
       "      <td>448260940</td>\n",
       "      <td>296544364</td>\n",
       "      <td>3</td>\n",
       "      <td>LNd2</td>\n",
       "      <td>LNd1</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
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       "    <tr>\n",
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       "      <td>3</td>\n",
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       "      <td>LNd3</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>511051477</td>\n",
       "      <td>5813021192</td>\n",
       "      <td>25</td>\n",
       "      <td>5th sLNv</td>\n",
       "      <td>LNd5</td>\n",
       "      <td>5th s-LNv</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>511051477</td>\n",
       "      <td>5813056917</td>\n",
       "      <td>18</td>\n",
       "      <td>5th sLNv</td>\n",
       "      <td>LNd4</td>\n",
       "      <td>5th s-LNv</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>511051477</td>\n",
       "      <td>5813064789</td>\n",
       "      <td>1</td>\n",
       "      <td>5th sLNv</td>\n",
       "      <td>LNd3</td>\n",
       "      <td>5th s-LNv</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>511051477</td>\n",
       "      <td>5813069648</td>\n",
       "      <td>40</td>\n",
       "      <td>5th sLNv</td>\n",
       "      <td>LNd6</td>\n",
       "      <td>5th s-LNv</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5813021192</td>\n",
       "      <td>5813056917</td>\n",
       "      <td>5</td>\n",
       "      <td>LNd5</td>\n",
       "      <td>LNd4</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5813021192</td>\n",
       "      <td>5813069648</td>\n",
       "      <td>1</td>\n",
       "      <td>LNd5</td>\n",
       "      <td>LNd6</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>5813056917</td>\n",
       "      <td>5813021192</td>\n",
       "      <td>1</td>\n",
       "      <td>LNd4</td>\n",
       "      <td>LNd5</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>5813056917</td>\n",
       "      <td>5813064789</td>\n",
       "      <td>2</td>\n",
       "      <td>LNd4</td>\n",
       "      <td>LNd3</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>5813064789</td>\n",
       "      <td>296544364</td>\n",
       "      <td>6</td>\n",
       "      <td>LNd3</td>\n",
       "      <td>LNd1</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>5813064789</td>\n",
       "      <td>448260940</td>\n",
       "      <td>3</td>\n",
       "      <td>LNd3</td>\n",
       "      <td>LNd2</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>5813069648</td>\n",
       "      <td>511051477</td>\n",
       "      <td>37</td>\n",
       "      <td>LNd6</td>\n",
       "      <td>5th sLNv</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>5th s-LNv</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>5813069648</td>\n",
       "      <td>5813021192</td>\n",
       "      <td>21</td>\n",
       "      <td>LNd6</td>\n",
       "      <td>LNd5</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>5813069648</td>\n",
       "      <td>5813056917</td>\n",
       "      <td>31</td>\n",
       "      <td>LNd6</td>\n",
       "      <td>LNd4</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "      <td>LNd</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post  weight instance_pre instance_post   type_pre  \\\n",
       "7     296544364    448260940       6         LNd1          LNd2        LNd   \n",
       "8     296544364   5813064789       3         LNd1          LNd3        LNd   \n",
       "64    448260940    296544364       3         LNd2          LNd1        LNd   \n",
       "66    448260940   5813064789       3         LNd2          LNd3        LNd   \n",
       "98    511051477   5813021192      25     5th sLNv          LNd5  5th s-LNv   \n",
       "100   511051477   5813056917      18     5th sLNv          LNd4  5th s-LNv   \n",
       "101   511051477   5813064789       1     5th sLNv          LNd3  5th s-LNv   \n",
       "102   511051477   5813069648      40     5th sLNv          LNd6  5th s-LNv   \n",
       "142  5813021192   5813056917       5         LNd5          LNd4        LNd   \n",
       "143  5813021192   5813069648       1         LNd5          LNd6        LNd   \n",
       "154  5813056917   5813021192       1         LNd4          LNd5        LNd   \n",
       "155  5813056917   5813064789       2         LNd4          LNd3        LNd   \n",
       "156  5813064789    296544364       6         LNd3          LNd1        LNd   \n",
       "157  5813064789    448260940       3         LNd3          LNd2        LNd   \n",
       "165  5813069648    511051477      37         LNd6      5th sLNv        LNd   \n",
       "170  5813069648   5813021192      21         LNd6          LNd5        LNd   \n",
       "173  5813069648   5813056917      31         LNd6          LNd4        LNd   \n",
       "\n",
       "    phase_pre  type_post phase_post  \n",
       "7     evening        LNd    evening  \n",
       "8     evening        LNd    evening  \n",
       "64    evening        LNd    evening  \n",
       "66    evening        LNd    evening  \n",
       "98    evening        LNd    evening  \n",
       "100   evening        LNd    evening  \n",
       "101   evening        LNd    evening  \n",
       "102   evening        LNd    evening  \n",
       "142   evening        LNd    evening  \n",
       "143   evening        LNd    evening  \n",
       "154   evening        LNd    evening  \n",
       "155   evening        LNd    evening  \n",
       "156   evening        LNd    evening  \n",
       "157   evening        LNd    evening  \n",
       "165   evening  5th s-LNv    evening  \n",
       "170   evening        LNd    evening  \n",
       "173   evening        LNd    evening  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn_df_e = conn_df[(conn_df['phase_pre'] == 'evening') & (conn_df['phase_post'] == 'evening')]\n",
    "conn_df_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf0bb2b2-e038-4b20-8650-c9440021a66b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export to csv table\n",
    "conn_df_e.to_csv('E_intra_connectivity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "46266144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neuron_graph(conn_df_e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e153663b-f222-4dd0-b332-acc5a4ce218d",
   "metadata": {},
   "source": [
    "# Strong connections to and from LNds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a466e92-7840-4c08-997d-7f3ed11376ba",
   "metadata": {},
   "source": [
    "The strong connections **to** LNds were retrieved from the hemibrain. These were inputs to each LNd with a weight of 10 or greater. Only strong connections were displayed in the Sankey diagrams of the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2733f6d9-f0ce-4dd9-a095-5ee62f431619",
   "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>bodyId_pre</th>\n",
       "      <th>bodyId_post</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>264083994</td>\n",
       "      <td>5813069648</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>267214250</td>\n",
       "      <td>5813064789</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>294445804</td>\n",
       "      <td>5813064789</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>295443136</td>\n",
       "      <td>5813056917</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>297243542</td>\n",
       "      <td>296544364</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>5813069859</td>\n",
       "      <td>5813056917</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>5813071319</td>\n",
       "      <td>5813069648</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>5813077562</td>\n",
       "      <td>296544364</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>5813077562</td>\n",
       "      <td>448260940</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>5813077562</td>\n",
       "      <td>5813064789</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post  weight\n",
       "0     264083994   5813069648      81\n",
       "1     267214250   5813064789      11\n",
       "2     294445804   5813064789      10\n",
       "3     295443136   5813056917      10\n",
       "4     297243542    296544364      53\n",
       "..          ...          ...     ...\n",
       "131  5813069859   5813056917      16\n",
       "132  5813071319   5813069648      11\n",
       "133  5813077562    296544364      21\n",
       "134  5813077562    448260940      31\n",
       "135  5813077562   5813064789      33\n",
       "\n",
       "[136 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import fetch_adjacencies\n",
    "\n",
    "# gets LNd bodyIds\n",
    "LNd_IDs = bodyIds_types['LNd']\n",
    "\n",
    "# get connections to LNd cells from anything else\n",
    "neuron_df, strong_LNd_inputs = fetch_adjacencies(None, LNd_IDs, min_total_weight=10)\n",
    "\n",
    "#consolidate since we don't care about separating connections between 2 neurons that happen in different ROIs.\n",
    "strong_LNd_inputs = strong_LNd_inputs.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum()\n",
    "strong_LNd_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "51a35ce3-8c3d-49ce-b110-f8b5bde3e62a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export table\n",
    "strong_LNd_inputs.to_csv('strong_LNd_inputs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9f103f-d509-4947-85fa-9b112dc60200",
   "metadata": {},
   "source": [
    "The strong connections **from** LNd cells were retrieved from the hemibrain. These were outputs from each LNd with a weight of 10 or greater. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "320d2589-2820-499d-9dc0-7b455a6fa74a",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post  weight\n",
       "0     296544364    267214250      15\n",
       "1     296544364    296885362      13\n",
       "2     296544364    297230760      10\n",
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       "4     296544364    297580512      10\n",
       "..          ...          ...     ...\n",
       "229  5813069648   5813040744      18\n",
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       "231  5813069648   5813057153      19\n",
       "232  5813069648   5813087531      18\n",
       "233  5813069648   5813097957      10\n",
       "\n",
       "[234 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuprint import fetch_adjacencies\n",
    "\n",
    "# get strong connections from LNd cells to anything else\n",
    "neuron_df, strong_LNd_outputs = fetch_adjacencies(LNd_IDs, None, min_total_weight=10)\n",
    "\n",
    "# consolidate since we don't care about separating connections between 2 neurons that happen in different ROIs.\n",
    "strong_LNd_outputs = strong_LNd_outputs.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum()\n",
    "strong_LNd_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3520d559-21c7-456f-bdf0-b3f657418605",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export table\n",
    "strong_LNd_outputs.to_csv('strong_LNd_outputs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88dd4eed",
   "metadata": {},
   "source": [
    "# 5th s-LNv Strong Inputs and Outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea65deaf",
   "metadata": {},
   "source": [
    "The 5th s-LNv is not included in either LNds or M cells, but its information is used for comparison to both, so here its connections are generated separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bb74f94c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fetch id\n",
    "sLNv_5th_id = bodyIds_types['5th s-LNv']\n",
    "\n",
    "# fetch inputs\n",
    "neuron_df, strong_5thslnv_inputs = fetch_adjacencies(None, sLNv_5th_id, min_total_weight=10)\n",
    "strong_5thslnv_inputs = strong_5thslnv_inputs.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum()\n",
    "\n",
    "#export table\n",
    "strong_5thslnv_inputs.to_csv('strong_5thslnv_inputs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "896a8d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fetch id\n",
    "sLNv_5th_id = bodyIds_types['5th s-LNv']\n",
    "\n",
    "# fetch outputs\n",
    "neuron_df, strong_5thslnv_outputs = fetch_adjacencies(sLNv_5th_id, None, min_total_weight=10)\n",
    "strong_5thslnv_outputs = strong_5thslnv_outputs.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum()\n",
    "\n",
    "#export table\n",
    "strong_5thslnv_outputs.to_csv('strong_5thslnv_outputs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316e1670-fea6-43f6-b561-29cc718afbb8",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Shared strong connections to and from E cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5597967e-7feb-4870-bd1e-64f4f03c3d0f",
   "metadata": {
    "tags": []
   },
   "source": [
    "Strong inputs and outputs that are shared by at least 2 E cells were retrieved. This allowed us to see if there were natural pairings among E cells. The E cells include the LNds and the 5th sLNv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9eca5ddb-6e76-4238-acab-1b1b4ca42b82",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabriellegutierrez/Documents/GitHub/clock-connectome/connection_utils.py:213: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  conns_df = conns_df.groupby(['bodyId_pre', 'instance_pre'], as_index=False)['weight', 'shared'].sum()\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>75</th>\n",
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       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>74</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
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       "      <td>SMP161_R</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5813056887</td>\n",
       "      <td>SMP001_R</td>\n",
       "      <td>68</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>328273806</td>\n",
       "      <td>SMP226_R</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>419000001</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>5813026589</td>\n",
       "      <td>SMP082_R</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>5813040712</td>\n",
       "      <td>SMP512(PDM22)_L</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1501332510</td>\n",
       "      <td>None</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>574511458</td>\n",
       "      <td>SMP514(PDM22)_L</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>357607932</td>\n",
       "      <td>None</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>607929192</td>\n",
       "      <td>SMP514_R</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>327588074</td>\n",
       "      <td>(PDM09)_L</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5813010153</td>\n",
       "      <td>DN1pA_R</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>668340913</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>358639742</td>\n",
       "      <td>SMP512_R</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>330415332</td>\n",
       "      <td>SMP513(PDM09)_L</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>453527730</td>\n",
       "      <td>SMP161(AVL23)_L</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>453130054</td>\n",
       "      <td>SMP516_R</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>328265389</td>\n",
       "      <td>SMP082_R</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>481070027</td>\n",
       "      <td>SMP513_R</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>699687305</td>\n",
       "      <td>SMP512(PDM09)_L</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>5812980662</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "      <td>SLP460(SCB019)_R</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>362473525</td>\n",
       "      <td>AstA1_L</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5812980006</td>\n",
       "      <td>SLP368_R</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>418650351</td>\n",
       "      <td>None</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>329310230</td>\n",
       "      <td>SMP082(ADM04)_L</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5813067337</td>\n",
       "      <td>SLP346_R</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>974843957</td>\n",
       "      <td>None</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>298254517</td>\n",
       "      <td>SMP082(ADM04)_L</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>5813069859</td>\n",
       "      <td>AstA1_R</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>695124651</td>\n",
       "      <td>aMe3</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>674108632</td>\n",
       "      <td>AVLP075_R</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>358285424</td>\n",
       "      <td>SMP226_R</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5813047739</td>\n",
       "      <td>SMP367_R</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>326817510</td>\n",
       "      <td>SLP207_R</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5813047713</td>\n",
       "      <td>SLP280_R</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>702152113</td>\n",
       "      <td>AVLP075(ADL25)_L</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bodyId_pre      instance_pre  weight  shared\n",
       "66  5812981460            aMe8_R     280       4\n",
       "55  1066511487            aMe8_R     254       4\n",
       "42   540674783              None     184       4\n",
       "74  5813021666           aMe22_R     152       2\n",
       "75  5813022274            DN1a_R     138       2\n",
       "0    264083994            DN1a_R     136       2\n",
       "4    297243542          SMP335_R      98       3\n",
       "52   911332304   aMe12(lVLPT9)_R      92       2\n",
       "86  5813069648             LNd_R      89       3\n",
       "83  5813058431           aMe12_R      86       2\n",
       "89  5813077562         LHPV5i1_R      85       3\n",
       "41   511051477         5th s-LNv      83       3\n",
       "6    324846570           DN1pA_R      75       3\n",
       "33   450583886  SLP460(SCB019)_R      74       4\n",
       "28   388638672          SMP161_R      70       2\n",
       "82  5813056887          SMP001_R      68       2\n",
       "38   482183384              None      65       3\n",
       "73  5813021615             aMe23      64       2\n",
       "68  5813011683              aMe2      63       2\n",
       "7    325529237           DN1pA_R      63       2\n",
       "27   387944118           DN1pA_R      56       2\n",
       "26   387166379           DN1pA_R      55       2\n",
       "79  5813047284              None      53       3\n",
       "61  1378212488            aMe1_R      53       2\n",
       "12   328273806          SMP226_R      48       2\n",
       "31   419000001              None      48       3\n",
       "60  1283406432              aMe2      47       2\n",
       "76  5813026589          SMP082_R      46       2\n",
       "77  5813040712   SMP512(PDM22)_L      45       2\n",
       "62  1501332510              None      43       2\n",
       "43   574511458   SMP514(PDM22)_L      43       2\n",
       "17   357607932              None      43       2\n",
       "45   607929192          SMP514_R      41       2\n",
       "10   327588074         (PDM09)_L      41       2\n",
       "67  5813010153           DN1pA_R      40       2\n",
       "46   668340913              None      40       2\n",
       "21   358639742          SMP512_R      40       2\n",
       "15   330415332   SMP513(PDM09)_L      39       2\n",
       "35   453527730   SMP161(AVL23)_L      37       2\n",
       "34   453130054          SMP516_R      37       2\n",
       "11   328265389          SMP082_R      37       2\n",
       "37   481070027          SMP513_R      36       2\n",
       "50   699687305   SMP512(PDM09)_L      35       2\n",
       "65  5812980662              None      33       2\n",
       "16   357482735  SLP460(SCB019)_R      32       2\n",
       "25   362473525           AstA1_L      31       2\n",
       "64  5812980006          SLP368_R      30       2\n",
       "30   418650351              None      29       2\n",
       "14   329310230   SMP082(ADM04)_L      29       2\n",
       "84  5813067337          SLP346_R      29       2\n",
       "54   974843957              None      28       2\n",
       "5    298254517   SMP082(ADM04)_L      28       2\n",
       "87  5813069859           AstA1_R      28       2\n",
       "48   695124651              aMe3      28       2\n",
       "47   674108632         AVLP075_R      27       2\n",
       "18   358285424          SMP226_R      27       2\n",
       "81  5813047739          SMP367_R      26       2\n",
       "8    326817510          SLP207_R      23       2\n",
       "80  5813047713          SLP280_R      21       2\n",
       "51   702152113  AVLP075(ADL25)_L      21       2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get strong connections to E cells that are shared by any 2 E cells\n",
    "\n",
    "E_Ids = clock_df[clock_df['phase']=='evening']['bodyId']\n",
    "\n",
    "num_shared_by = 2\n",
    "strong_shared_E_inputs = strong_shared_connections(E_Ids,'in',num_shared_by)\n",
    "strong_shared_E_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "665e8755-3583-47dc-8dcb-5f00b8f5fc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save tables\n",
    "strong_shared_E_inputs.to_csv('strong_shared_E_inputs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573931af-478c-4c0a-a3ee-f553766a3fce",
   "metadata": {},
   "source": [
    "In our paper, we took the strong inputs shared by any 2 E cells and displayed the medium and strong connections from those neurons to E cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d3d97cd1-1acb-4906-8260-3c6bc2a08aa2",
   "metadata": {},
   "outputs": [
    {
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       "      <td>17</td>\n",
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       "      <th>165</th>\n",
       "      <td>5813077562</td>\n",
       "      <td>296544364</td>\n",
       "      <td>21</td>\n",
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       "      <th>169</th>\n",
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       "      <td>33</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post  weight\n",
       "0     264083994    511051477      55\n",
       "1     264083994   5813069648      81\n",
       "2     297243542    296544364      53\n",
       "3     297243542    448260940      17\n",
       "4     297243542   5813056917       3\n",
       "..          ...          ...     ...\n",
       "165  5813077562    296544364      21\n",
       "166  5813077562    448260940      31\n",
       "167  5813077562   5813021192       3\n",
       "168  5813077562   5813056917       4\n",
       "169  5813077562   5813064789      33\n",
       "\n",
       "[170 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get medium and strong connections to E cells from the strong shared inputs found above\n",
    "neuron_df, strong_shared_E_inputs_med_strong = fetch_adjacencies(strong_shared_E_inputs['bodyId_pre'], E_Ids, min_total_weight=3)\n",
    "\n",
    "# consolidate since we don't care about separating connections between 2 neurons that happen in different ROIs.\n",
    "strong_shared_E_inputs_med_strong = strong_shared_E_inputs_med_strong.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum()\n",
    "strong_shared_E_inputs_med_strong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cef56a8c-571d-4714-a06a-d2d230748f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export table\n",
    "strong_shared_E_inputs_med_strong.to_csv('strong_shared_E_inputs_med_strong.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "431322dc-289e-465b-b9cb-6a586feb5fa1",
   "metadata": {},
   "source": [
    "The same was done for the outputs of E cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "73d9348d-55c6-44e0-b0e3-7671a62c0ebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabriellegutierrez/Documents/GitHub/clock-connectome/connection_utils.py:208: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  conns_df = conns_df.groupby(['bodyId_post', 'instance_post'], as_index=False)['weight', 'shared'].sum()\n"
     ]
    },
    {
     "data": {
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       "      <th>instance_post</th>\n",
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       "  <tbody>\n",
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       "      <th>56</th>\n",
       "      <td>390331583</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>329732855</td>\n",
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       "      <td>166</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>5813021666</td>\n",
       "      <td>aMe22_R</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
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       "      <th>25</th>\n",
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       "      <td>SMP162_R</td>\n",
       "      <td>82</td>\n",
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       "      <th>50</th>\n",
       "      <td>360677632</td>\n",
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       "      <td>22</td>\n",
       "      <td>2</td>\n",
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       "      <th>6</th>\n",
       "      <td>297230760</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>5812981517</td>\n",
       "      <td>SMP317_R</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>512787296</td>\n",
       "      <td>SMP317_R</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_post    instance_post  weight  shared\n",
       "56     390331583         SMP368_R     200       4\n",
       "37     329732855  SMP368(PDM08)_L     166       4\n",
       "169   5813021666          aMe22_R      95       2\n",
       "25     327588446         SMP162_R      82       2\n",
       "7      297243542         SMP335_R      75       3\n",
       "..           ...              ...     ...     ...\n",
       "166   5813020696         SMP337_R      23       2\n",
       "50     360677632         SMP251_R      22       2\n",
       "6      297230760     5-HTPMPD01_R      22       2\n",
       "157   5812981517         SMP317_R      21       2\n",
       "90     512787296         SMP317_R      20       2\n",
       "\n",
       "[80 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get strong shared connections from any 2 E cells\n",
    "num_shared_by = 2\n",
    "strong_shared_E_outputs = strong_shared_connections(E_Ids,'out',num_shared_by)\n",
    "strong_shared_E_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "740740e2-e43d-4463-9f0d-a47c1afaea3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save tables\n",
    "strong_shared_E_outputs.to_csv('strong_shared_E_outputs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ba05a0ae-ce45-458f-b71e-24b499273b75",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>296544364</td>\n",
       "      <td>298279629</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>5813069648</td>\n",
       "      <td>5813022753</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>5813069648</td>\n",
       "      <td>5813040744</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>5813069648</td>\n",
       "      <td>5813056917</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>5813069648</td>\n",
       "      <td>5813057153</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>5813069648</td>\n",
       "      <td>5813087531</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bodyId_pre  bodyId_post  weight\n",
       "0     296544364    267214250      15\n",
       "1     296544364    297230760      10\n",
       "2     296544364    297243542      49\n",
       "3     296544364    297588882       4\n",
       "4     296544364    298279629      17\n",
       "..          ...          ...     ...\n",
       "200  5813069648   5813022753      20\n",
       "201  5813069648   5813040744      18\n",
       "202  5813069648   5813056917      31\n",
       "203  5813069648   5813057153      19\n",
       "204  5813069648   5813087531      18\n",
       "\n",
       "[205 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get medium and strong connections from E cells to the strong shared outputs found above\n",
    "neuron_df, strong_shared_E_outputs_med_strong = fetch_adjacencies(E_Ids, strong_shared_E_outputs['bodyId_post'], min_total_weight=3)\n",
    "\n",
    "# consolidate since we don't care about separating connections between 2 neurons that happen in different ROIs.\n",
    "strong_shared_E_outputs_med_strong = strong_shared_E_outputs_med_strong.groupby(['bodyId_pre', 'bodyId_post'], as_index=False)['weight'].sum()\n",
    "strong_shared_E_outputs_med_strong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "95cdda27-c9ff-4158-8a9a-89b0f632cbfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export table\n",
    "strong_shared_E_outputs_med_strong.to_csv('strong_shared_E_outputs_med_strong.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed6ab39f-7452-4a12-92db-d37ca71221e8",
   "metadata": {},
   "source": [
    "# Similarity among LN cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b737b0-bb0d-42b0-bdaf-8d2a76ad87f4",
   "metadata": {},
   "source": [
    "To assess the similarity among the connectivity patterns of the LNs, the jaccard index was computed for pairs of LNs and displayed as a heatmap. Only neurons that had medium or strong connections to or from LNs were included in the analysis. Thus only connections of strength greater than or equal to 3 were included.\n",
    "\n",
    "The jaccard indices for the strong and medium outputs from LNs conveyed groupings of similar connectivity patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "92a384c0-0712-4476-ac9f-9f900b8b82b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x880 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from visualization_utils import jaccard_vis\n",
    "from connection_utils import clock_neuron_connections\n",
    "\n",
    "LNIDs = clock_df[clock_df['phase']!='']['bodyId']\n",
    "\n",
    "#Jaccard out\n",
    "conn_df_out = clock_neuron_connections(clock_df, 'out', min_weight=3)\n",
    "matrix, fig = jaccard_vis(conn_df_out, clock_df, LNIDs, 'out', diag_mask=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4971cb9d-6329-4e76-9688-e35f1eb666ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('jaccard_LN_targets.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c612ea-4cbf-40cc-8038-169727cfbb95",
   "metadata": {},
   "source": [
    "The jaccard indices for the strong and medium inputs to LNs also conveyed clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "877dba00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x880 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Jaccard in\n",
    "conn_df_in = clock_neuron_connections(clock_df, 'in', min_weight=3)\n",
    "matrix, fig = jaccard_vis(conn_df_in, clock_df, LNIDs, 'in', diag_mask=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "52f81915-737d-471d-aa92-049f5f50628c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save figure\n",
    "fig.savefig('jaccard_LN_inputs.svg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}