CAVE tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8523156",
   "metadata": {},
   "source": [
    "# Accessing the fly connectome dataset with CAVE\n",
    "\n",
    "This tutorial provides a high-level overview for how to access FlyWire's dataset through CAVE. CAVE is the [connectome annotation versioning engine](https://www.biorxiv.org/content/10.1101/2023.07.26.550598v1.abstract), a service infrastructure for managing connectomics datasets and is hosted in the cloud for broad access. CAVE supports proofreading of datasets and their analysis even while proofreading is ongoing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2297754f",
   "metadata": {},
   "source": [
    "# CAVEclient and setup\n",
    "\n",
    "The CAVEclient is a python library that facilitates communication with a CAVE system. It can be install with \n",
    "\n",
    "`pip install caveclient`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af8cf292-b34f-452d-a8d9-82e2bfc3bee7",
   "metadata": {},
   "source": [
    "To install the caveclient when running this in a colab notebook, run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee9e3b33-7f57-4518-ab7f-e4a417bd576e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install caveclient\n",
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edae0a9c-0ce5-4eaa-98fe-a8035f0ca611",
   "metadata": {},
   "source": [
    "and imported like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97c490d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import caveclient\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05448f0",
   "metadata": {},
   "source": [
    "## CAVE account setup\n",
    "\n",
    "Each and every user needs to create a CAVE account and download a user token to access CAVE's services programmatically. FlyWire's data is publicly available which means that no extra permissions need to be given to a new user account to access the data.\n",
    "\n",
    "A Google account (or Google-enabled account) is required to create a CAVE account.\n",
    "\n",
    "#### Start here if you do not have a CAVE account or are not sure\n",
    "\n",
    "Login to CAVE to setup a new account. To do this go to this [website](https://prod.flywire-daf.com/materialize/views/datastack/flywire_fafb_public).\n",
    "\n",
    "#### Once you have an account: Setup your token\n",
    "\n",
    "Create a new token by running the next cell. Then, copy the token and insert it into the argument of the following cell. These two cells should be redone together to make sure that the correct token is stored on your machine. You can copy your token and store on as many machines as you like. If you think your token has been compromised just reset it but rerunning the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c590dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = caveclient.CAVEclient()\n",
    "client.auth.setup_token(make_new=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efae7b72-0b16-4b00-b06f-25e9f8a152f9",
   "metadata": {},
   "source": [
    "### Set or save your token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e486c9-1c19-4b19-b567-f1c08f49f521",
   "metadata": {},
   "source": [
    "From the website that just opened up, paste your token here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a085e589-1720-482f-8269-63159d12488a",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_token = \"your token goes here\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c7fee6-55c9-4e21-aca9-ae03be91ac64",
   "metadata": {},
   "source": [
    "If you are running this on your local machine or on a server you can (optionally) store the token on your machine. This makes future uses easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1acf6fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This might not work and that is okay\n",
    "client.auth.save_token(token=my_token, overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49610fd6",
   "metadata": {},
   "source": [
    "## Initialize CAVEclient with a datastack\n",
    "\n",
    "Datasets in CAVE are organized as datastacks. These are a combination of an EM dataset, a segmentation and a set of annotations. The datastack for FlyWire's public release is `flywire_fafb_public`. When you instantiate your client with this datastack, it loads all relevant information to access it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32ec45f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "datastack_name = \"flywire_fafb_public\"\n",
    "client = caveclient.CAVEclient(datastack_name)\n",
    "\n",
    "# One can pass the token directly to the client:\n",
    "# client = caveclient.CAVEclient(datastack_name, auth_token=my_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1bea0f",
   "metadata": {},
   "source": [
    "## Materialization versions\n",
    "\n",
    "Data in CAVE is timestamped and periodically versioned - each (materialization) version corresponds to a specific timestamp. Individual versions are made publicly available. The materialization service provides annotation queries to the dataset. It is available under `client.materialize`. \n",
    "\n",
    "Currently the following versions are publicly available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac487681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[630, 783]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.materialize.get_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4940ad95",
   "metadata": {},
   "source": [
    "And these are their associated timestamps (all timestamps are in UTC):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8becbac5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version 630: 2023-03-21 08:10:01.194185+00:00\n",
      "Version 783: 2023-09-30 05:10:01.441140+00:00\n"
     ]
    }
   ],
   "source": [
    "for version in client.materialize.get_versions():\n",
    "    print(f\"Version {version}: {client.materialize.get_timestamp(version)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c776a172",
   "metadata": {},
   "source": [
    "The client will automatically query the latest materialization version. You can specify a `materialization_version` for every query if you want to access a specific version."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ee462a",
   "metadata": {},
   "source": [
    "# Querying the dataset\n",
    "\n",
    "Let's have a look what annotation tables are available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40e1a862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hierarchical_neuron_annotations',\n",
       " 'neuron_information_v2',\n",
       " 'synapses_nt_v1',\n",
       " 'nuclei_v1',\n",
       " 'proofread_neurons',\n",
       " 'fly_synapses_neuropil_v6']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.materialize.get_tables()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15f641c",
   "metadata": {},
   "source": [
    "## Querying neurons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "139ac7a8",
   "metadata": {},
   "source": [
    "The `proofread_neurons` table contains all neurons that were released in a given version. The dataset contains many more segments that were either not proofread because they are small, or belong to non-neuronal cells or other structures such as trachea. Therefore, knowing the list of all segments that represent proofread neurons is useful. It can be queried in full:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eda8ad02",
   "metadata": {},
   "outputs": [
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       "            id                          created  superceded_id valid  \\\n",
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       "\n",
       "         pt_supervoxel_id          pt_root_id               pt_position  \n",
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proofread_neurons_df = client.materialize.query_table(\"proofread_neurons\")\n",
    "proofread_neurons_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e0e7bf6",
   "metadata": {},
   "source": [
    "In the table above each row is a proofread neuron. Segment IDs (aka neuron IDs) are called `root_ids` in CAVE. Each annotation is associated with at least one point with which data is associated; in this case `pt_root_id` and `pt_position` are the most relevant columns.\n",
    "\n",
    "Positions in this table were calculated to be in the backbone of a neuron. This was found to be the most robust location to identify a neuron with as some do not have cell bodies and cell bodies are not central to fly neurons. There is a table that represents all _cell_ nuclei in the brain created by [Shang et al.](https://www.biorxiv.org/content/10.1101/2021.11.04.467197v1.abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d57e393a",
   "metadata": {},
   "outputs": [
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       "      <th>4</th>\n",
       "      <td>7415848</td>\n",
       "      <td>2021-06-23 19:55:35.164060+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>9.280717</td>\n",
       "      <td>83038554439606237</td>\n",
       "      <td>720575940633370649</td>\n",
       "      <td>[721984, 229792, 119560]</td>\n",
       "      <td>[720832, 228320, 118240]</td>\n",
       "      <td>[723232, 231264, 120840]</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143135</th>\n",
       "      <td>4389032</td>\n",
       "      <td>2021-06-23 20:02:31.089586+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>244.330332</td>\n",
       "      <td>79377386675948158</td>\n",
       "      <td>720575940629762043</td>\n",
       "      <td>[511328, 113024, 48560]</td>\n",
       "      <td>[503744, 104128, 44600]</td>\n",
       "      <td>[520704, 122080, 53040]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143136</th>\n",
       "      <td>8558952</td>\n",
       "      <td>2021-06-23 20:02:31.090874+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>253.359391</td>\n",
       "      <td>84870066067479558</td>\n",
       "      <td>720575940621362085</td>\n",
       "      <td>[830720, 344608, 149840]</td>\n",
       "      <td>[821120, 335680, 146960]</td>\n",
       "      <td>[840576, 354912, 152880]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>3076633</td>\n",
       "      <td>2021-06-23 20:02:31.092008+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>261.782528</td>\n",
       "      <td>78042030341189115</td>\n",
       "      <td>720575940650929273</td>\n",
       "      <td>[433152, 210560, 223440]</td>\n",
       "      <td>[428544, 206976, 219120]</td>\n",
       "      <td>[437632, 214048, 227600]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>3125634</td>\n",
       "      <td>2021-06-23 20:02:31.093103+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>274.994586</td>\n",
       "      <td>78113635431454405</td>\n",
       "      <td>720575940621426568</td>\n",
       "      <td>[434208, 282112, 23400]</td>\n",
       "      <td>[429728, 278496, 19040]</td>\n",
       "      <td>[439264, 285568, 27320]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>6655288</td>\n",
       "      <td>2021-06-23 20:02:31.094169+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>322.137088</td>\n",
       "      <td>81769305838115716</td>\n",
       "      <td>720575940629374326</td>\n",
       "      <td>[648704, 72640, 147960]</td>\n",
       "      <td>[644160, 67648, 143560]</td>\n",
       "      <td>[653248, 77024, 152120]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143140 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                          created  superceded_id valid  \\\n",
       "0       7393349 2021-06-23 19:55:35.159040+00:00            NaN     t   \n",
       "1       7416439 2021-06-23 19:55:35.160196+00:00            NaN     t   \n",
       "2       7415038 2021-06-23 19:55:35.161401+00:00            NaN     t   \n",
       "3       7415013 2021-06-23 19:55:35.162579+00:00            NaN     t   \n",
       "4       7415848 2021-06-23 19:55:35.164060+00:00            NaN     t   \n",
       "...         ...                              ...            ...   ...   \n",
       "143135  4389032 2021-06-23 20:02:31.089586+00:00            NaN     t   \n",
       "143136  8558952 2021-06-23 20:02:31.090874+00:00            NaN     t   \n",
       "143137  3076633 2021-06-23 20:02:31.092008+00:00            NaN     t   \n",
       "143138  3125634 2021-06-23 20:02:31.093103+00:00            NaN     t   \n",
       "143139  6655288 2021-06-23 20:02:31.094169+00:00            NaN     t   \n",
       "\n",
       "            volume   pt_supervoxel_id          pt_root_id  \\\n",
       "0        26.141245  82827379285852979  720575940626838909   \n",
       "1        11.523400  82827998029690530  720575940620043899   \n",
       "2        32.895959  83038623024880664  720575940626046919   \n",
       "3        53.711176  83038760463398837  720575940630311383   \n",
       "4         9.280717  83038554439606237  720575940633370649   \n",
       "...            ...                ...                 ...   \n",
       "143135  244.330332  79377386675948158  720575940629762043   \n",
       "143136  253.359391  84870066067479558  720575940621362085   \n",
       "143137  261.782528  78042030341189115  720575940650929273   \n",
       "143138  274.994586  78113635431454405  720575940621426568   \n",
       "143139  322.137088  81769305838115716  720575940629374326   \n",
       "\n",
       "                     pt_position         bb_start_position  \\\n",
       "0        [709888, 227744, 57160]   [708032, 226144, 54760]   \n",
       "1       [710592, 263392, 129800]  [708800, 262112, 128200]   \n",
       "2        [722528, 234656, 77000]   [720768, 232896, 74480]   \n",
       "3        [722912, 244032, 65200]   [720480, 242144, 62480]   \n",
       "4       [721984, 229792, 119560]  [720832, 228320, 118240]   \n",
       "...                          ...                       ...   \n",
       "143135   [511328, 113024, 48560]   [503744, 104128, 44600]   \n",
       "143136  [830720, 344608, 149840]  [821120, 335680, 146960]   \n",
       "143137  [433152, 210560, 223440]  [428544, 206976, 219120]   \n",
       "143138   [434208, 282112, 23400]   [429728, 278496, 19040]   \n",
       "143139   [648704, 72640, 147960]   [644160, 67648, 143560]   \n",
       "\n",
       "                 bb_end_position  \n",
       "0        [711904, 229440, 59280]  \n",
       "1       [712064, 264832, 131080]  \n",
       "2        [724224, 236576, 79360]  \n",
       "3        [725600, 246208, 67760]  \n",
       "4       [723232, 231264, 120840]  \n",
       "...                          ...  \n",
       "143135   [520704, 122080, 53040]  \n",
       "143136  [840576, 354912, 152880]  \n",
       "143137  [437632, 214048, 227600]  \n",
       "143138   [439264, 285568, 27320]  \n",
       "143139   [653248, 77024, 152120]  \n",
       "\n",
       "[143140 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nuclei_df = client.materialize.query_table(\"nuclei_v1\")\n",
    "nuclei_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13c7711",
   "metadata": {},
   "source": [
    "Not all neurons contains have a cell body in the brain (e.g. sensory, ascending) and for ~6,000 of intrinsic neurons, the segmentation did not reach up to the cell bodies which are sitting at the outer layer of the brain. In those cases, the `pt_root_id` is 0. This table also contains glia cell bodies, as well as a few false positive annotations. \n",
    "\n",
    "Every table has an associated description which provides further context and references to publications. This feature is provided by the annotation service which can be reached at `cave.annotation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c47d15df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "FlyWire nucleus description\n",
      "Nucleus version: 20210322\n",
      "\n",
      "Nuclei in this table consist of center points (in nm), volume (in μm3), and bounding boxes (in nm).\n",
      "\n",
      "The nucleus segmentation was generated by Shang Mu (smu@princeton.edu, Seung Lab at Princeton University) using a 2D convolutional neural network (CNN) and heuristic interpolations. The training data was assembled from annotations by Selden Koolman, Merlin Moore, Sarah Morejohn, Ben Silverman, Kyle Willie, Ryan Willie, Szi-chieh Yu and Shang Mu.\n",
      "\n",
      "As this data was generated using a 2D, rather than 3D, neural network, defects are present in the detected nuclei, particularly where there are large defects in section alignment or a number of consecutive missing sections.\n",
      "\n",
      "False positive fragments, nucleus fragments and partial nuclei are the most common type of defects. A simple, rudimentary method for cleaning up is to disregard small fragments by thresholding by segment size or by the z-dimension of the bounding boxes. A size threshold of 2000 voxels (0.08 μm3) was already applied in producing this segmentation.\n",
      "\n",
      "A minimal number of nucleus merger instances have been observed, caused by section alignment defects or close nuclei appositions in certain non-neuronal cell clusters.\n",
      "\n",
      "When using the nucleus annotations, please acknowledge Shang Mu and let him know about your use case.\n",
      "\n",
      "We encourage making (and using) improvements to this data based on the information provided (for example, utilizing bounding box dimensions and/or the segmentation to find and merge incorrectly split partial nuclei), and such improvements should be discussed with Shang. Data derived solely from this work should be similarly made available on this platform at the time of use in any publication.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(client.annotation.get_table_metadata(\"nuclei_v1\")[\"description\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a987e87c",
   "metadata": {},
   "source": [
    "Positions can be useful for analysis. The CAVEclient provides some convenience functions: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd50486",
   "metadata": {},
   "source": [
    "Splitting of position columns into separate x, y, and z columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ffd470c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>created</th>\n",
       "      <th>superceded_id</th>\n",
       "      <th>valid</th>\n",
       "      <th>volume</th>\n",
       "      <th>pt_position_x</th>\n",
       "      <th>pt_position_y</th>\n",
       "      <th>pt_position_z</th>\n",
       "      <th>bb_start_position_x</th>\n",
       "      <th>bb_start_position_y</th>\n",
       "      <th>bb_start_position_z</th>\n",
       "      <th>bb_end_position_x</th>\n",
       "      <th>bb_end_position_y</th>\n",
       "      <th>bb_end_position_z</th>\n",
       "      <th>pt_supervoxel_id</th>\n",
       "      <th>pt_root_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
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       "      <th>1</th>\n",
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       "      <td>2021-06-23 19:55:35.160196+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2021-06-23 19:55:35.161401+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>32.895959</td>\n",
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       "      <td>77000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7415013</td>\n",
       "      <td>2021-06-23 19:55:35.162579+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>53.711176</td>\n",
       "      <td>722912</td>\n",
       "      <td>244032</td>\n",
       "      <td>65200</td>\n",
       "      <td>720480</td>\n",
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       "      <th>4</th>\n",
       "      <td>7415848</td>\n",
       "      <td>2021-06-23 19:55:35.164060+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9.280717</td>\n",
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       "      <td>119560</td>\n",
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       "      <td>2021-06-23 20:02:31.089586+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>143136</th>\n",
       "      <td>8558952</td>\n",
       "      <td>2021-06-23 20:02:31.090874+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>253.359391</td>\n",
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       "      <th>143137</th>\n",
       "      <td>3076633</td>\n",
       "      <td>2021-06-23 20:02:31.092008+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>261.782528</td>\n",
       "      <td>433152</td>\n",
       "      <td>210560</td>\n",
       "      <td>223440</td>\n",
       "      <td>428544</td>\n",
       "      <td>206976</td>\n",
       "      <td>219120</td>\n",
       "      <td>437632</td>\n",
       "      <td>214048</td>\n",
       "      <td>227600</td>\n",
       "      <td>78042030341189115</td>\n",
       "      <td>720575940650929273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>3125634</td>\n",
       "      <td>2021-06-23 20:02:31.093103+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>274.994586</td>\n",
       "      <td>434208</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>152120</td>\n",
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       "      <td>720575940629374326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143140 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                          created  superceded_id valid  \\\n",
       "0       7393349 2021-06-23 19:55:35.159040+00:00            NaN     t   \n",
       "1       7416439 2021-06-23 19:55:35.160196+00:00            NaN     t   \n",
       "2       7415038 2021-06-23 19:55:35.161401+00:00            NaN     t   \n",
       "3       7415013 2021-06-23 19:55:35.162579+00:00            NaN     t   \n",
       "4       7415848 2021-06-23 19:55:35.164060+00:00            NaN     t   \n",
       "...         ...                              ...            ...   ...   \n",
       "143135  4389032 2021-06-23 20:02:31.089586+00:00            NaN     t   \n",
       "143136  8558952 2021-06-23 20:02:31.090874+00:00            NaN     t   \n",
       "143137  3076633 2021-06-23 20:02:31.092008+00:00            NaN     t   \n",
       "143138  3125634 2021-06-23 20:02:31.093103+00:00            NaN     t   \n",
       "143139  6655288 2021-06-23 20:02:31.094169+00:00            NaN     t   \n",
       "\n",
       "            volume  pt_position_x  pt_position_y  pt_position_z  \\\n",
       "0        26.141245         709888         227744          57160   \n",
       "1        11.523400         710592         263392         129800   \n",
       "2        32.895959         722528         234656          77000   \n",
       "3        53.711176         722912         244032          65200   \n",
       "4         9.280717         721984         229792         119560   \n",
       "...            ...            ...            ...            ...   \n",
       "143135  244.330332         511328         113024          48560   \n",
       "143136  253.359391         830720         344608         149840   \n",
       "143137  261.782528         433152         210560         223440   \n",
       "143138  274.994586         434208         282112          23400   \n",
       "143139  322.137088         648704          72640         147960   \n",
       "\n",
       "        bb_start_position_x  bb_start_position_y  bb_start_position_z  \\\n",
       "0                    708032               226144                54760   \n",
       "1                    708800               262112               128200   \n",
       "2                    720768               232896                74480   \n",
       "3                    720480               242144                62480   \n",
       "4                    720832               228320               118240   \n",
       "...                     ...                  ...                  ...   \n",
       "143135               503744               104128                44600   \n",
       "143136               821120               335680               146960   \n",
       "143137               428544               206976               219120   \n",
       "143138               429728               278496                19040   \n",
       "143139               644160                67648               143560   \n",
       "\n",
       "        bb_end_position_x  bb_end_position_y  bb_end_position_z  \\\n",
       "0                  711904             229440              59280   \n",
       "1                  712064             264832             131080   \n",
       "2                  724224             236576              79360   \n",
       "3                  725600             246208              67760   \n",
       "4                  723232             231264             120840   \n",
       "...                   ...                ...                ...   \n",
       "143135             520704             122080              53040   \n",
       "143136             840576             354912             152880   \n",
       "143137             437632             214048             227600   \n",
       "143138             439264             285568              27320   \n",
       "143139             653248              77024             152120   \n",
       "\n",
       "         pt_supervoxel_id          pt_root_id  \n",
       "0       82827379285852979  720575940626838909  \n",
       "1       82827998029690530  720575940620043899  \n",
       "2       83038623024880664  720575940626046919  \n",
       "3       83038760463398837  720575940630311383  \n",
       "4       83038554439606237  720575940633370649  \n",
       "...                   ...                 ...  \n",
       "143135  79377386675948158  720575940629762043  \n",
       "143136  84870066067479558  720575940621362085  \n",
       "143137  78042030341189115  720575940650929273  \n",
       "143138  78113635431454405  720575940621426568  \n",
       "143139  81769305838115716  720575940629374326  \n",
       "\n",
       "[143140 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nuclei_df = client.materialize.query_table(\"nuclei_v1\", split_positions=True)\n",
    "nuclei_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4b50aa",
   "metadata": {},
   "source": [
    "Defining the position resolution. Resolutions are always in nanometers and this query asks for points to be provided in micrometers (aka 1000 nanometers):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4fbcce94",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>created</th>\n",
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       "      <td>2021-06-23 19:55:35.161401+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[722.528, 234.656, 77.0]</td>\n",
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       "      <td>[724.224, 236.576, 79.36]</td>\n",
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       "      <td>[722.912, 244.032, 65.2]</td>\n",
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       "      <td>[725.6, 246.208, 67.76]</td>\n",
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       "      <td>2021-06-23 19:55:35.164060+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9.280717</td>\n",
       "      <td>83038554439606237</td>\n",
       "      <td>720575940633370649</td>\n",
       "      <td>[721.984, 229.792, 119.56]</td>\n",
       "      <td>[720.832, 228.32, 118.24]</td>\n",
       "      <td>[723.232, 231.264, 120.84]</td>\n",
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       "      <th>...</th>\n",
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       "      <th>143135</th>\n",
       "      <td>4389032</td>\n",
       "      <td>2021-06-23 20:02:31.089586+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>79377386675948158</td>\n",
       "      <td>720575940629762043</td>\n",
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       "      <td>[503.744, 104.128, 44.6]</td>\n",
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       "      <th>143136</th>\n",
       "      <td>8558952</td>\n",
       "      <td>2021-06-23 20:02:31.090874+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[840.576, 354.912, 152.88]</td>\n",
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       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>3076633</td>\n",
       "      <td>2021-06-23 20:02:31.092008+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>261.782528</td>\n",
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       "      <td>3125634</td>\n",
       "      <td>2021-06-23 20:02:31.093103+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[434.208, 282.112, 23.4]</td>\n",
       "      <td>[429.728, 278.496, 19.04]</td>\n",
       "      <td>[439.264, 285.568, 27.32]</td>\n",
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       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>6655288</td>\n",
       "      <td>2021-06-23 20:02:31.094169+00:00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[648.704, 72.64, 147.96]</td>\n",
       "      <td>[644.16, 67.648, 143.56]</td>\n",
       "      <td>[653.248, 77.024, 152.12]</td>\n",
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       "<p>143140 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "             id                          created  superceded_id valid  \\\n",
       "0       7393349 2021-06-23 19:55:35.159040+00:00            NaN     t   \n",
       "1       7416439 2021-06-23 19:55:35.160196+00:00            NaN     t   \n",
       "2       7415038 2021-06-23 19:55:35.161401+00:00            NaN     t   \n",
       "3       7415013 2021-06-23 19:55:35.162579+00:00            NaN     t   \n",
       "4       7415848 2021-06-23 19:55:35.164060+00:00            NaN     t   \n",
       "...         ...                              ...            ...   ...   \n",
       "143135  4389032 2021-06-23 20:02:31.089586+00:00            NaN     t   \n",
       "143136  8558952 2021-06-23 20:02:31.090874+00:00            NaN     t   \n",
       "143137  3076633 2021-06-23 20:02:31.092008+00:00            NaN     t   \n",
       "143138  3125634 2021-06-23 20:02:31.093103+00:00            NaN     t   \n",
       "143139  6655288 2021-06-23 20:02:31.094169+00:00            NaN     t   \n",
       "\n",
       "            volume   pt_supervoxel_id          pt_root_id  \\\n",
       "0        26.141245  82827379285852979  720575940626838909   \n",
       "1        11.523400  82827998029690530  720575940620043899   \n",
       "2        32.895959  83038623024880664  720575940626046919   \n",
       "3        53.711176  83038760463398837  720575940630311383   \n",
       "4         9.280717  83038554439606237  720575940633370649   \n",
       "...            ...                ...                 ...   \n",
       "143135  244.330332  79377386675948158  720575940629762043   \n",
       "143136  253.359391  84870066067479558  720575940621362085   \n",
       "143137  261.782528  78042030341189115  720575940650929273   \n",
       "143138  274.994586  78113635431454405  720575940621426568   \n",
       "143139  322.137088  81769305838115716  720575940629374326   \n",
       "\n",
       "                       pt_position           bb_start_position  \\\n",
       "0        [709.888, 227.744, 57.16]   [708.032, 226.144, 54.76]   \n",
       "1        [710.592, 263.392, 129.8]     [708.8, 262.112, 128.2]   \n",
       "2         [722.528, 234.656, 77.0]   [720.768, 232.896, 74.48]   \n",
       "3         [722.912, 244.032, 65.2]    [720.48, 242.144, 62.48]   \n",
       "4       [721.984, 229.792, 119.56]   [720.832, 228.32, 118.24]   \n",
       "...                            ...                         ...   \n",
       "143135   [511.328, 113.024, 48.56]    [503.744, 104.128, 44.6]   \n",
       "143136   [830.72, 344.608, 149.84]    [821.12, 335.68, 146.96]   \n",
       "143137   [433.152, 210.56, 223.44]  [428.544, 206.976, 219.12]   \n",
       "143138    [434.208, 282.112, 23.4]   [429.728, 278.496, 19.04]   \n",
       "143139    [648.704, 72.64, 147.96]    [644.16, 67.648, 143.56]   \n",
       "\n",
       "                   bb_end_position  \n",
       "0         [711.904, 229.44, 59.28]  \n",
       "1       [712.064, 264.832, 131.08]  \n",
       "2        [724.224, 236.576, 79.36]  \n",
       "3          [725.6, 246.208, 67.76]  \n",
       "4       [723.232, 231.264, 120.84]  \n",
       "...                            ...  \n",
       "143135    [520.704, 122.08, 53.04]  \n",
       "143136  [840.576, 354.912, 152.88]  \n",
       "143137   [437.632, 214.048, 227.6]  \n",
       "143138   [439.264, 285.568, 27.32]  \n",
       "143139   [653.248, 77.024, 152.12]  \n",
       "\n",
       "[143140 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nuclei_df = client.materialize.query_table(\"nuclei_v1\", desired_resolution=[1000, 1000, 1000])\n",
    "nuclei_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e871f26",
   "metadata": {},
   "source": [
    "## Querying annotations - hierarchical annotations from Schlegel et al., 2023\n",
    "\n",
    "[Schlegel et al](https://www.biorxiv.org/content/10.1101/2023.06.27.546055v2.abstract) introduced hierarchical annotations for all proofread neurons in the dataset. Figure 1 from their paper (shown below) outlines the hierarchy and renders individual groups of neuurons.\n",
    "\n",
    "\n",
    "\n",
    "![](https://www.biorxiv.org/content/biorxiv/early/2023/07/15/2023.06.27.546055/F1.large.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ef0def",
   "metadata": {},
   "source": [
    "To load all annotations (this will take ~20s):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bcf6b038",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "201 - \"Limited query to 10 rows\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_ref</th>\n",
       "      <th>created_ref</th>\n",
       "      <th>valid_ref</th>\n",
       "      <th>pt_supervoxel_id</th>\n",
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       "      <th>pt_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-06-19 06:43:32.562416+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>78253067652813181</td>\n",
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       "      <td>2023-06-19 23:31:33.449997+00:00</td>\n",
       "      <td>t</td>\n",
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       "      <td>super_class</td>\n",
       "      <td>central</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-06-19 06:43:32.562416+00:00</td>\n",
       "      <td>t</td>\n",
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       "      <td>2023-06-19 23:26:01.973320+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[443342, 203965, 157450]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-06-19 06:43:32.563301+00:00</td>\n",
       "      <td>t</td>\n",
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       "      <td>720575940626838909</td>\n",
       "      <td>879934</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>CB0924</td>\n",
       "      <td>[664417, 227538, 77011]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-06-19 06:43:32.563301+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>82053323167374089</td>\n",
       "      <td>720575940626838909</td>\n",
       "      <td>601616</td>\n",
       "      <td>2023-06-19 23:31:33.451271+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>super_class</td>\n",
       "      <td>central</td>\n",
       "      <td>[664417, 227538, 77011]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-06-19 06:43:32.563301+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>82053323167374089</td>\n",
       "      <td>720575940626838909</td>\n",
       "      <td>473638</td>\n",
       "      <td>2023-06-19 23:26:01.974049+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[664417, 227538, 77011]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2023-06-19 06:43:32.564159+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>81842766824648491</td>\n",
       "      <td>720575940626046919</td>\n",
       "      <td>601617</td>\n",
       "      <td>2023-06-19 23:31:33.452516+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>3</td>\n",
       "      <td>super_class</td>\n",
       "      <td>central</td>\n",
       "      <td>[653666, 259618, 110467]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2023-06-19 06:43:32.564159+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>81842766824648491</td>\n",
       "      <td>720575940626046919</td>\n",
       "      <td>473639</td>\n",
       "      <td>2023-06-19 23:26:01.974760+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>3</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[653666, 259618, 110467]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>2023-06-19 06:43:32.565063+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>82405647924682371</td>\n",
       "      <td>720575940630311383</td>\n",
       "      <td>601618</td>\n",
       "      <td>2023-06-19 23:31:33.453745+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>4</td>\n",
       "      <td>super_class</td>\n",
       "      <td>central</td>\n",
       "      <td>[687457, 254763, 80194]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>2023-06-19 06:43:32.565063+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>82405647924682371</td>\n",
       "      <td>720575940630311383</td>\n",
       "      <td>473640</td>\n",
       "      <td>2023-06-19 23:26:01.975449+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>4</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[687457, 254763, 80194]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>2023-06-19 06:43:32.566002+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>82756942157675987</td>\n",
       "      <td>720575940633370649</td>\n",
       "      <td>601619</td>\n",
       "      <td>2023-06-19 23:31:33.455445+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>super_class</td>\n",
       "      <td>visual_projection</td>\n",
       "      <td>[707653, 222503, 145366]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_ref                      created_ref valid_ref   pt_supervoxel_id  \\\n",
       "0       1 2023-06-19 06:43:32.562416+00:00         t  78253067652813181   \n",
       "1       1 2023-06-19 06:43:32.562416+00:00         t  78253067652813181   \n",
       "2       2 2023-06-19 06:43:32.563301+00:00         t  82053323167374089   \n",
       "3       2 2023-06-19 06:43:32.563301+00:00         t  82053323167374089   \n",
       "4       2 2023-06-19 06:43:32.563301+00:00         t  82053323167374089   \n",
       "5       3 2023-06-19 06:43:32.564159+00:00         t  81842766824648491   \n",
       "6       3 2023-06-19 06:43:32.564159+00:00         t  81842766824648491   \n",
       "7       4 2023-06-19 06:43:32.565063+00:00         t  82405647924682371   \n",
       "8       4 2023-06-19 06:43:32.565063+00:00         t  82405647924682371   \n",
       "9       5 2023-06-19 06:43:32.566002+00:00         t  82756942157675987   \n",
       "\n",
       "           pt_root_id      id                          created valid  \\\n",
       "0  720575940628857210  601615 2023-06-19 23:31:33.449997+00:00     t   \n",
       "1  720575940628857210  473637 2023-06-19 23:26:01.973320+00:00     t   \n",
       "2  720575940626838909  879934 2023-09-30 05:09:51.441140+00:00     t   \n",
       "3  720575940626838909  601616 2023-06-19 23:31:33.451271+00:00     t   \n",
       "4  720575940626838909  473638 2023-06-19 23:26:01.974049+00:00     t   \n",
       "5  720575940626046919  601617 2023-06-19 23:31:33.452516+00:00     t   \n",
       "6  720575940626046919  473639 2023-06-19 23:26:01.974760+00:00     t   \n",
       "7  720575940630311383  601618 2023-06-19 23:31:33.453745+00:00     t   \n",
       "8  720575940630311383  473640 2023-06-19 23:26:01.975449+00:00     t   \n",
       "9  720575940633370649  601619 2023-06-19 23:31:33.455445+00:00     t   \n",
       "\n",
       "   target_id classification_system          cell_type  \\\n",
       "0          1           super_class            central   \n",
       "1          1                  flow          intrinsic   \n",
       "2          2             cell_type             CB0924   \n",
       "3          2           super_class            central   \n",
       "4          2                  flow          intrinsic   \n",
       "5          3           super_class            central   \n",
       "6          3                  flow          intrinsic   \n",
       "7          4           super_class            central   \n",
       "8          4                  flow          intrinsic   \n",
       "9          5           super_class  visual_projection   \n",
       "\n",
       "                pt_position  \n",
       "0  [443342, 203965, 157450]  \n",
       "1  [443342, 203965, 157450]  \n",
       "2   [664417, 227538, 77011]  \n",
       "3   [664417, 227538, 77011]  \n",
       "4   [664417, 227538, 77011]  \n",
       "5  [653666, 259618, 110467]  \n",
       "6  [653666, 259618, 110467]  \n",
       "7   [687457, 254763, 80194]  \n",
       "8   [687457, 254763, 80194]  \n",
       "9  [707653, 222503, 145366]  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hierarchical_annos_df = client.materialize.query_table(\"hierarchical_neuron_annotations\", limit=10)\n",
    "hierarchical_annos_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a04eb1a4",
   "metadata": {},
   "source": [
    "The `classification_system` column encodes the level of the hierarchy a given annotation belongs to. Only annotations for `flow` and `super class` are available for all neurons but finer annotations are only available for subsets of neurons. We can see that in the annotation counts for each hierarchy level:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc17707",
   "metadata": {},
   "source": [
    "## Subqueries - hierarchical annotations from Schlegel et al., 2023 continued\n",
    "\n",
    "CAVE poses a limit on the size of a table that can be loaded at once. The current limit is `500,000` rows. This is to ensure the system is working for everyone and prevents accidentaly large queries to the server - that is particularly relevant for synapse queries. \n",
    "\n",
    "The hierarchical annotation table is too large to be loaded as a whole. To load a subset of the data, we can use a filter on any column to reduce the data footprint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5b81513",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_ref</th>\n",
       "      <th>created_ref</th>\n",
       "      <th>valid_ref</th>\n",
       "      <th>pt_supervoxel_id</th>\n",
       "      <th>pt_root_id</th>\n",
       "      <th>id</th>\n",
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       "      <th>target_id</th>\n",
       "      <th>classification_system</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>pt_position</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>2023-06-19 06:43:32.694340+00:00</td>\n",
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       "      <td>78536398189730110</td>\n",
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       "      <td>t</td>\n",
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       "      <td>flow</td>\n",
       "      <td>efferent</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131206</td>\n",
       "      <td>2024-01-08 19:46:33.536372+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>79097217839414238</td>\n",
       "      <td>720575940620156283</td>\n",
       "      <td>760327</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>131206</td>\n",
       "      <td>flow</td>\n",
       "      <td>efferent</td>\n",
       "      <td>[492890, 187909, 204117]</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>138571</td>\n",
       "      <td>2024-01-08 19:46:56.609400+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>81277136946061019</td>\n",
       "      <td>720575940640372277</td>\n",
       "      <td>760328</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>138571</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[620140, 99296, 158026]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>137463</td>\n",
       "      <td>2024-01-08 19:46:56.002999+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>80434498856324372</td>\n",
       "      <td>720575940634601954</td>\n",
       "      <td>760329</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>137463</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[570818, 205376, 191260]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>133024</td>\n",
       "      <td>2024-01-08 19:46:44.939864+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>81840568002630413</td>\n",
       "      <td>720575940624155416</td>\n",
       "      <td>760330</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>133024</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[652270, 128644, 169218]</td>\n",
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       "      <th>...</th>\n",
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       "      <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>139250</th>\n",
       "      <td>104828</td>\n",
       "      <td>2023-06-19 06:48:15.001237+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>83952592400822837</td>\n",
       "      <td>720575940626100238</td>\n",
       "      <td>397994</td>\n",
       "      <td>2023-06-19 23:22:47.293270+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>104828</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[776911, 183792, 174548]</td>\n",
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       "    <tr>\n",
       "      <th>139251</th>\n",
       "      <td>104809</td>\n",
       "      <td>2023-06-19 06:48:14.983796+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>84798048190170122</td>\n",
       "      <td>720575940626915216</td>\n",
       "      <td>436766</td>\n",
       "      <td>2023-06-19 23:24:21.179830+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>104809</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[824625, 246096, 194229]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139252</th>\n",
       "      <td>83362</td>\n",
       "      <td>2023-06-19 06:47:20.709997+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>75298954786869572</td>\n",
       "      <td>720575940645731620</td>\n",
       "      <td>402701</td>\n",
       "      <td>2023-06-19 23:23:00.124836+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>83362</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[272447, 288010, 156593]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139253</th>\n",
       "      <td>104819</td>\n",
       "      <td>2023-06-19 06:48:14.992064+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>84658822530652061</td>\n",
       "      <td>720575940629075755</td>\n",
       "      <td>436758</td>\n",
       "      <td>2023-06-19 23:24:21.174050+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>104819</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[815896, 335911, 204215]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139254</th>\n",
       "      <td>104808</td>\n",
       "      <td>2023-06-19 06:48:14.982973+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>83883117009526705</td>\n",
       "      <td>720575940624159751</td>\n",
       "      <td>436767</td>\n",
       "      <td>2023-06-19 23:24:21.180525+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>104808</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>[772781, 238450, 165215]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139255 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id_ref                      created_ref valid_ref   pt_supervoxel_id  \\\n",
       "0          135 2023-06-19 06:43:32.694340+00:00         t  78536398189730110   \n",
       "1       131206 2024-01-08 19:46:33.536372+00:00         t  79097217839414238   \n",
       "2       138571 2024-01-08 19:46:56.609400+00:00         t  81277136946061019   \n",
       "3       137463 2024-01-08 19:46:56.002999+00:00         t  80434498856324372   \n",
       "4       133024 2024-01-08 19:46:44.939864+00:00         t  81840568002630413   \n",
       "...        ...                              ...       ...                ...   \n",
       "139250  104828 2023-06-19 06:48:15.001237+00:00         t  83952592400822837   \n",
       "139251  104809 2023-06-19 06:48:14.983796+00:00         t  84798048190170122   \n",
       "139252   83362 2023-06-19 06:47:20.709997+00:00         t  75298954786869572   \n",
       "139253  104819 2023-06-19 06:48:14.992064+00:00         t  84658822530652061   \n",
       "139254  104808 2023-06-19 06:48:14.982973+00:00         t  83883117009526705   \n",
       "\n",
       "                pt_root_id      id                          created valid  \\\n",
       "0       720575940628372613  760326 2023-09-30 05:09:51.441140+00:00     t   \n",
       "1       720575940620156283  760327 2023-09-30 05:09:51.441140+00:00     t   \n",
       "2       720575940640372277  760328 2023-09-30 05:09:51.441140+00:00     t   \n",
       "3       720575940634601954  760329 2023-09-30 05:09:51.441140+00:00     t   \n",
       "4       720575940624155416  760330 2023-09-30 05:09:51.441140+00:00     t   \n",
       "...                    ...     ...                              ...   ...   \n",
       "139250  720575940626100238  397994 2023-06-19 23:22:47.293270+00:00     t   \n",
       "139251  720575940626915216  436766 2023-06-19 23:24:21.179830+00:00     t   \n",
       "139252  720575940645731620  402701 2023-06-19 23:23:00.124836+00:00     t   \n",
       "139253  720575940629075755  436758 2023-06-19 23:24:21.174050+00:00     t   \n",
       "139254  720575940624159751  436767 2023-06-19 23:24:21.180525+00:00     t   \n",
       "\n",
       "        target_id classification_system  cell_type               pt_position  \n",
       "0             135                  flow   efferent  [462134, 314944, 202595]  \n",
       "1          131206                  flow   efferent  [492890, 187909, 204117]  \n",
       "2          138571                  flow  intrinsic   [620140, 99296, 158026]  \n",
       "3          137463                  flow  intrinsic  [570818, 205376, 191260]  \n",
       "4          133024                  flow  intrinsic  [652270, 128644, 169218]  \n",
       "...           ...                   ...        ...                       ...  \n",
       "139250     104828                  flow  intrinsic  [776911, 183792, 174548]  \n",
       "139251     104809                  flow  intrinsic  [824625, 246096, 194229]  \n",
       "139252      83362                  flow  intrinsic  [272447, 288010, 156593]  \n",
       "139253     104819                  flow  intrinsic  [815896, 335911, 204215]  \n",
       "139254     104808                  flow  intrinsic  [772781, 238450, 165215]  \n",
       "\n",
       "[139255 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow_annos_df = client.materialize.query_table(\"hierarchical_neuron_annotations\", filter_equal_dict={\"classification_system\": \"flow\"})\n",
    "flow_annos_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4f012f",
   "metadata": {},
   "source": [
    "Besides `filter_equal_dict`, the CAVEclient provides `filter_in_dict` and `filter_out_dict` as options to restrict what data is loaded. Examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16e73a4a",
   "metadata": {},
   "outputs": [
    {
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       "        id_ref                      created_ref valid_ref   pt_supervoxel_id  \\\n",
       "0           30 2023-06-19 06:43:32.592246+00:00         t  79588355603633144   \n",
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       "...        ...                              ...       ...                ...   \n",
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       "216661  104828 2023-06-19 06:48:15.001237+00:00         t  83952592400822837   \n",
       "\n",
       "                pt_root_id      id                          created valid  \\\n",
       "0       720575940614623455  784021 2023-09-30 05:09:51.441140+00:00     t   \n",
       "1       720575940603765280  784022 2023-09-30 05:09:51.441140+00:00     t   \n",
       "2       720575940616153371  784023 2023-09-30 05:09:51.441140+00:00     t   \n",
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       "...                    ...     ...                              ...   ...   \n",
       "216657  720575940632125521  753717 2023-06-22 03:47:42.513711+00:00     t   \n",
       "216658  720575940619054238  753718 2023-06-22 03:47:42.514694+00:00     t   \n",
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       "216661  720575940626100238  754847 2023-06-22 03:47:43.418214+00:00     t   \n",
       "\n",
       "        target_id classification_system             cell_type  \\\n",
       "0              30            cell_class  pars_intercerebralis   \n",
       "1              32            cell_class  pars_intercerebralis   \n",
       "2             106            cell_class  pars_intercerebralis   \n",
       "3             322            cell_class                   TPN   \n",
       "4          116554            cell_class        pars_lateralis   \n",
       "...           ...                   ...                   ...   \n",
       "216657      83345             cell_type                   T4a   \n",
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       "216659     104840             cell_type                   Mi1   \n",
       "216660      83326             cell_type                   Mi1   \n",
       "216661     104828             cell_type                   Mi1   \n",
       "\n",
       "                     pt_position  \n",
       "0        [521186, 102405, 87485]  \n",
       "1        [522523, 97588, 102599]  \n",
       "2        [518966, 183364, 54017]  \n",
       "3        [503664, 265302, 85460]  \n",
       "4       [578618, 142964, 148739]  \n",
       "...                          ...  \n",
       "216657  [316848, 199076, 184279]  \n",
       "216658  [317912, 208064, 180147]  \n",
       "216659  [232864, 289213, 251319]  \n",
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       "\n",
       "[216662 rows x 12 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_class_type_annos_df = client.materialize.query_table(\"hierarchical_neuron_annotations\", filter_in_dict={\"classification_system\": [\"cell_class\", \"cell_type\"]})\n",
    "cell_class_type_annos_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eb2f414",
   "metadata": {},
   "source": [
    "## Community annotations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e6308b-6822-40f9-a805-96b48bf85a66",
   "metadata": {},
   "source": [
    "The FlyWire community created additional annotations for ~85k cells in the dataset by the time of publication. These are free-form annotation tags that can be used for exploration and are not following any nomenclature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6e0cf9c-ed15-4bd2-90fc-5b4e87498de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "community_anno_df = client.materialize.query_table(\"neuron_information_v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30c558bc-c736-461f-b896-60fb3b0cd0f2",
   "metadata": {},
   "outputs": [
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       "      <td>96</td>\n",
       "      <td>83602741276248008</td>\n",
       "      <td>720575940632709945</td>\n",
       "      <td>[754976, 302952, 90120]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156158</th>\n",
       "      <td>158474</td>\n",
       "      <td>2023-09-30 03:56:57.900205+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>Retinula axon; R1-6</td>\n",
       "      <td>96</td>\n",
       "      <td>74665086266541236</td>\n",
       "      <td>720575940624979431</td>\n",
       "      <td>[234464, 253764, 141120]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156159 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                          created  superceded_id valid  \\\n",
       "0            1 2022-02-07 04:55:09.705964+00:00            NaN     t   \n",
       "1            2 2022-02-07 04:55:09.709199+00:00            NaN     t   \n",
       "2            3 2022-02-07 04:55:09.711648+00:00            NaN     t   \n",
       "3            4 2022-02-07 04:55:09.714147+00:00            NaN     t   \n",
       "4            5 2022-02-07 04:55:09.716362+00:00            NaN     t   \n",
       "...        ...                              ...            ...   ...   \n",
       "156154  158470 2023-09-29 18:34:01.919332+00:00            NaN     t   \n",
       "156155  158471 2023-09-29 22:29:22.074318+00:00            NaN     t   \n",
       "156156  158472 2023-09-30 00:20:57.515962+00:00            NaN     t   \n",
       "156157  158473 2023-09-30 00:24:42.774939+00:00            NaN     t   \n",
       "156158  158474 2023-09-30 03:56:57.900205+00:00            NaN     t   \n",
       "\n",
       "                                                      tag  user_id  \\\n",
       "0                                            putative fru       22   \n",
       "1                                            putative fru     2356   \n",
       "2                                                   aIP1c       22   \n",
       "3                                                   aIP1c     2356   \n",
       "4                                            putative fru       22   \n",
       "...                                                   ...      ...   \n",
       "156154  protocerebral interneuron Delta7; PB interneur...      392   \n",
       "156155  Putative Translobula-plate 1; Tlp1 (with LOP a...        8   \n",
       "156156                                  Retinula axon; R7       96   \n",
       "156157                                  Retinula axon; R7       96   \n",
       "156158                                Retinula axon; R1-6       96   \n",
       "\n",
       "         pt_supervoxel_id          pt_root_id               pt_position  \n",
       "0       76774705348089545  720575940620306785  [356596, 169884, 102760]  \n",
       "1       76774705348089545  720575940620306785  [356596, 169884, 102760]  \n",
       "2       76774705348089545  720575940620306785  [356596, 169884, 102760]  \n",
       "3       76774705348089545  720575940620306785  [356596, 169884, 102760]  \n",
       "4       77197123904568079  720575940607848203   [381456, 181024, 80800]  \n",
       "...                   ...                 ...                       ...  \n",
       "156154  81278923786668088  720575940628493828  [620616, 204680, 197880]  \n",
       "156155  82546385882696610  720575940611942130  [695628, 255712, 219440]  \n",
       "156156  84165210193334222  720575940630042011  [788080, 276144, 101680]  \n",
       "156157  83602741276248008  720575940632709945   [754976, 302952, 90120]  \n",
       "156158  74665086266541236  720575940624979431  [234464, 253764, 141120]  \n",
       "\n",
       "[156159 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_anno_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401fc7e0",
   "metadata": {},
   "source": [
    "## Synapse queries\n",
    "\n",
    "### Details about FlyWire's synapses\n",
    "\n",
    "FlyWire uses automatically annotated synapses that were produced by [Buhmann et al.](https://www.nature.com/articles/s41592-021-01183-7). Automation of synapse annotation is critical for circuit analysis but one should keep in mind that the classifier may contain biases that lead to better or worse results in different brain regions. For instance, this classifier was trained on data acquired from neuropils in the central brain and might perform worse in the optic lobe or for sensory neurons. In total, Buhmann et al. identified ~244 million _putative_ synapses."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be2a64d",
   "metadata": {},
   "source": [
    "### Details about removal of false positive synapses\n",
    "\n",
    "Each synapse is a link from a pre- to a posynaptic site. As presynapses in the fly are usually polysynaptic, there are usually multiple synapses for each presynaptic site. They assigned a `connection_score` to every synapse which can be used to filter out false positives. Buhmann et al. suggest to correlate their predictions with those from [Heinrich et al.](https://link.springer.com/chapter/10.1007/978-3-030-00934-2_36) who segmented synaptic cleft on the same dataset. This is implemented through a `cleft_score` that is associated with each synapse. We found that filtering synapses with a `cleft_score > 50` works well for removing false positives without using the `connection_score`.\n",
    "\n",
    "Additionally, some synapses were annotated multiple times. We implemented a distance based filtering to remove redundant synapses.\n",
    "\n",
    "## Synapse assignment to neuropils\n",
    "\n",
    "We assigned every synapse to a neuropil based on its presynaptic location. Synapses that were outside a neuropil were assigned to the closest neuropil if that was within 10 µm. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7b92162",
   "metadata": {},
   "source": [
    "### TLDR: Give me the data!\n",
    "\n",
    "We created a filtered view of the synapse table that automatically applies all established filters to remove as many false positive synapses as possible. It also adds neuropil information. The view published with version 630 is `valid_synapses_nt_np`. For version 783 and later, we created `valid_synapses_nt_np_v6`, an updated version which only has minor differences to the previous version (`valid_synapses_nt_np` will remain functional for consistency). The main differences are that neuropil assignments in the optic lobes were improved. \n",
    "\n",
    "For technical reasons, a different function has to be called to query this view: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3e4c0a53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "201 - \"Limited query to 10 rows\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>pre_pt_root_id</th>\n",
       "      <th>post_pt_root_id</th>\n",
       "      <th>connection_score</th>\n",
       "      <th>cleft_score</th>\n",
       "      <th>gaba</th>\n",
       "      <th>ach</th>\n",
       "      <th>glut</th>\n",
       "      <th>oct</th>\n",
       "      <th>ser</th>\n",
       "      <th>da</th>\n",
       "      <th>valid_nt</th>\n",
       "      <th>pre_pt_supervoxel_id</th>\n",
       "      <th>post_pt_supervoxel_id</th>\n",
       "      <th>neuropil</th>\n",
       "      <th>pre_pt_position</th>\n",
       "      <th>post_pt_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>720575940630786105</td>\n",
       "      <td>720575940610739662</td>\n",
       "      <td>389.255432</td>\n",
       "      <td>79</td>\n",
       "      <td>0.011380</td>\n",
       "      <td>0.786194</td>\n",
       "      <td>0.003691</td>\n",
       "      <td>0.004627</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>0.192179</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439646533</td>\n",
       "      <td>76916748439640439</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[366592, 246144, 74360]</td>\n",
       "      <td>[366584, 246216, 74320]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>720575940630786105</td>\n",
       "      <td>720575940545913232</td>\n",
       "      <td>469.296295</td>\n",
       "      <td>147</td>\n",
       "      <td>0.004232</td>\n",
       "      <td>0.967565</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000342</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.027592</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439639250</td>\n",
       "      <td>76916748439645298</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[366560, 246144, 74440]</td>\n",
       "      <td>[366508, 246232, 74440]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>720575940630786105</td>\n",
       "      <td>720575940545912208</td>\n",
       "      <td>431.531738</td>\n",
       "      <td>130</td>\n",
       "      <td>0.000786</td>\n",
       "      <td>0.993792</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.005412</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439651358</td>\n",
       "      <td>76916748439646534</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[366428, 246060, 74560]</td>\n",
       "      <td>[366356, 246136, 74520]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>720575940630906435</td>\n",
       "      <td>720575940627911895</td>\n",
       "      <td>475.069672</td>\n",
       "      <td>140</td>\n",
       "      <td>0.376253</td>\n",
       "      <td>0.278226</td>\n",
       "      <td>0.332757</td>\n",
       "      <td>0.001309</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.010693</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439657631</td>\n",
       "      <td>76916748439647565</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[365584, 245064, 74680]</td>\n",
       "      <td>[365672, 245036, 74640]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>720575940630906435</td>\n",
       "      <td>720575940545916816</td>\n",
       "      <td>407.200562</td>\n",
       "      <td>151</td>\n",
       "      <td>0.341289</td>\n",
       "      <td>0.149622</td>\n",
       "      <td>0.499909</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>0.005916</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439651251</td>\n",
       "      <td>76916748439648883</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[365624, 245136, 74680]</td>\n",
       "      <td>[365712, 245200, 74720]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>720575940630786105</td>\n",
       "      <td>720575940545920912</td>\n",
       "      <td>217.466217</td>\n",
       "      <td>122</td>\n",
       "      <td>0.007093</td>\n",
       "      <td>0.939359</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.051474</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439663007</td>\n",
       "      <td>76916748439651348</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[366252, 245948, 74640]</td>\n",
       "      <td>[366168, 245976, 74640]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21</td>\n",
       "      <td>720575940630786105</td>\n",
       "      <td>720575940624184764</td>\n",
       "      <td>56.978813</td>\n",
       "      <td>146</td>\n",
       "      <td>0.006275</td>\n",
       "      <td>0.971317</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000983</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.020363</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439651358</td>\n",
       "      <td>76916748439650144</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[366232, 246012, 74680]</td>\n",
       "      <td>[366136, 246084, 74640]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22</td>\n",
       "      <td>720575940630906435</td>\n",
       "      <td>720575940545901968</td>\n",
       "      <td>478.272552</td>\n",
       "      <td>146</td>\n",
       "      <td>0.334301</td>\n",
       "      <td>0.341355</td>\n",
       "      <td>0.311997</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.004572</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439651251</td>\n",
       "      <td>76916748439653785</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[365624, 245112, 74720]</td>\n",
       "      <td>[365732, 245096, 74720]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>720575940619149904</td>\n",
       "      <td>720575940627911895</td>\n",
       "      <td>58.005123</td>\n",
       "      <td>145</td>\n",
       "      <td>0.310759</td>\n",
       "      <td>0.131895</td>\n",
       "      <td>0.522028</td>\n",
       "      <td>0.017178</td>\n",
       "      <td>0.004899</td>\n",
       "      <td>0.013241</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439651360</td>\n",
       "      <td>76916748439648983</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[365704, 246068, 74720]</td>\n",
       "      <td>[365656, 245948, 74720]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>720575940619149904</td>\n",
       "      <td>720575940545911952</td>\n",
       "      <td>17.302750</td>\n",
       "      <td>143</td>\n",
       "      <td>0.262804</td>\n",
       "      <td>0.049032</td>\n",
       "      <td>0.643314</td>\n",
       "      <td>0.018544</td>\n",
       "      <td>0.008741</td>\n",
       "      <td>0.017565</td>\n",
       "      <td>t</td>\n",
       "      <td>76916748439651360</td>\n",
       "      <td>76916748439651344</td>\n",
       "      <td>AVLP_L</td>\n",
       "      <td>[365756, 246080, 74720]</td>\n",
       "      <td>[365800, 245980, 74720]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id      pre_pt_root_id     post_pt_root_id  connection_score  cleft_score  \\\n",
       "0   6  720575940630786105  720575940610739662        389.255432           79   \n",
       "1  11  720575940630786105  720575940545913232        469.296295          147   \n",
       "2  14  720575940630786105  720575940545912208        431.531738          130   \n",
       "3  15  720575940630906435  720575940627911895        475.069672          140   \n",
       "4  16  720575940630906435  720575940545916816        407.200562          151   \n",
       "5  18  720575940630786105  720575940545920912        217.466217          122   \n",
       "6  21  720575940630786105  720575940624184764         56.978813          146   \n",
       "7  22  720575940630906435  720575940545901968        478.272552          146   \n",
       "8  26  720575940619149904  720575940627911895         58.005123          145   \n",
       "9  27  720575940619149904  720575940545911952         17.302750          143   \n",
       "\n",
       "       gaba       ach      glut       oct       ser        da valid_nt  \\\n",
       "0  0.011380  0.786194  0.003691  0.004627  0.001929  0.192179        t   \n",
       "1  0.004232  0.967565  0.000087  0.000342  0.000182  0.027592        t   \n",
       "2  0.000786  0.993792  0.000002  0.000006  0.000004  0.005412        t   \n",
       "3  0.376253  0.278226  0.332757  0.001309  0.000762  0.010693        t   \n",
       "4  0.341289  0.149622  0.499909  0.001995  0.001269  0.005916        t   \n",
       "5  0.007093  0.939359  0.001130  0.000747  0.000196  0.051474        t   \n",
       "6  0.006275  0.971317  0.000977  0.000983  0.000085  0.020363        t   \n",
       "7  0.334301  0.341355  0.311997  0.007425  0.000349  0.004572        t   \n",
       "8  0.310759  0.131895  0.522028  0.017178  0.004899  0.013241        t   \n",
       "9  0.262804  0.049032  0.643314  0.018544  0.008741  0.017565        t   \n",
       "\n",
       "   pre_pt_supervoxel_id  post_pt_supervoxel_id neuropil  \\\n",
       "0     76916748439646533      76916748439640439   AVLP_L   \n",
       "1     76916748439639250      76916748439645298   AVLP_L   \n",
       "2     76916748439651358      76916748439646534   AVLP_L   \n",
       "3     76916748439657631      76916748439647565   AVLP_L   \n",
       "4     76916748439651251      76916748439648883   AVLP_L   \n",
       "5     76916748439663007      76916748439651348   AVLP_L   \n",
       "6     76916748439651358      76916748439650144   AVLP_L   \n",
       "7     76916748439651251      76916748439653785   AVLP_L   \n",
       "8     76916748439651360      76916748439648983   AVLP_L   \n",
       "9     76916748439651360      76916748439651344   AVLP_L   \n",
       "\n",
       "           pre_pt_position         post_pt_position  \n",
       "0  [366592, 246144, 74360]  [366584, 246216, 74320]  \n",
       "1  [366560, 246144, 74440]  [366508, 246232, 74440]  \n",
       "2  [366428, 246060, 74560]  [366356, 246136, 74520]  \n",
       "3  [365584, 245064, 74680]  [365672, 245036, 74640]  \n",
       "4  [365624, 245136, 74680]  [365712, 245200, 74720]  \n",
       "5  [366252, 245948, 74640]  [366168, 245976, 74640]  \n",
       "6  [366232, 246012, 74680]  [366136, 246084, 74640]  \n",
       "7  [365624, 245112, 74720]  [365732, 245096, 74720]  \n",
       "8  [365704, 246068, 74720]  [365656, 245948, 74720]  \n",
       "9  [365756, 246080, 74720]  [365800, 245980, 74720]  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.materialize.query_view(\"valid_synapses_nt_np_v6\", limit=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20bb803",
   "metadata": {},
   "source": [
    "This query limits the query to 10 randomly chosen rows. The maximum per query is 500,000. There are approximately 110 million synapses available in this view. Queries need to be resticted to a given set of neurons."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc74fc3c-7a63-4585-99ab-82cfba81e640",
   "metadata": {},
   "source": [
    "### Querying synapses for specific neurons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce0c4e6-5df3-4a1c-9edf-b14d2aae977b",
   "metadata": {},
   "source": [
    "Let's query synapses for this AMMC-A1:\n",
    "https://ngl.flywire.ai/?json_url=https://globalv1.flywire-daf.com/nglstate/5319936417202176"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f1f0c92e-7f12-452c-9297-1f83d02f8869",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_id = 720575940631693610\n",
    "\n",
    "presyn_df = client.materialize.query_view(\"valid_synapses_nt_np_v6\", filter_in_dict={\"pre_pt_root_id\": [root_id]})\n",
    "postsyn_df = client.materialize.query_view(\"valid_synapses_nt_np_v6\", filter_in_dict={\"post_pt_root_id\": [root_id]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ebf3575c-0f78-4ece-8e02-a870c184dbbb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>pre_pt_root_id</th>\n",
       "      <th>post_pt_root_id</th>\n",
       "      <th>connection_score</th>\n",
       "      <th>cleft_score</th>\n",
       "      <th>gaba</th>\n",
       "      <th>ach</th>\n",
       "      <th>glut</th>\n",
       "      <th>oct</th>\n",
       "      <th>ser</th>\n",
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       "      <th>valid_nt</th>\n",
       "      <th>pre_pt_supervoxel_id</th>\n",
       "      <th>post_pt_supervoxel_id</th>\n",
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       "      <th>post_pt_position</th>\n",
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       "      <td>0.130120</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>0.007914</td>\n",
       "      <td>0.079971</td>\n",
       "      <td>t</td>\n",
       "      <td>81560742159585205</td>\n",
       "      <td>81560742159589162</td>\n",
       "      <td>PLP_R</td>\n",
       "      <td>[634620, 226424, 141680]</td>\n",
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       "      <td>75679622</td>\n",
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       "      <td>720575940635047672</td>\n",
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       "      <td>0.032243</td>\n",
       "      <td>0.883559</td>\n",
       "      <td>0.036865</td>\n",
       "      <td>0.009253</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.032856</td>\n",
       "      <td>t</td>\n",
       "      <td>81420279548633717</td>\n",
       "      <td>81420279548622891</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[628280, 243384, 125360]</td>\n",
       "      <td>[628188, 243296, 125360]</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10626687</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940623646963</td>\n",
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       "      <td>0.018603</td>\n",
       "      <td>0.923716</td>\n",
       "      <td>0.005724</td>\n",
       "      <td>0.001676</td>\n",
       "      <td>0.002917</td>\n",
       "      <td>0.047364</td>\n",
       "      <td>t</td>\n",
       "      <td>81702166708387316</td>\n",
       "      <td>81702166708369049</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[643152, 267728, 97200]</td>\n",
       "      <td>[643112, 267824, 97240]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10626689</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940621104628</td>\n",
       "      <td>131.851089</td>\n",
       "      <td>141</td>\n",
       "      <td>0.010995</td>\n",
       "      <td>0.971356</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>t</td>\n",
       "      <td>81702166708387316</td>\n",
       "      <td>81702166708384466</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[643104, 267720, 97280]</td>\n",
       "      <td>[643144, 267800, 97320]</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10293850</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940624425344</td>\n",
       "      <td>185.360291</td>\n",
       "      <td>126</td>\n",
       "      <td>0.087320</td>\n",
       "      <td>0.796282</td>\n",
       "      <td>0.013954</td>\n",
       "      <td>0.007258</td>\n",
       "      <td>0.025200</td>\n",
       "      <td>0.069985</td>\n",
       "      <td>t</td>\n",
       "      <td>81632004122605454</td>\n",
       "      <td>81632004122600098</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[638932, 279244, 96280]</td>\n",
       "      <td>[638836, 279144, 96240]</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>383</th>\n",
       "      <td>151947837</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940621547689</td>\n",
       "      <td>8.619675</td>\n",
       "      <td>140</td>\n",
       "      <td>0.048986</td>\n",
       "      <td>0.500239</td>\n",
       "      <td>0.012006</td>\n",
       "      <td>0.017138</td>\n",
       "      <td>0.400640</td>\n",
       "      <td>0.020991</td>\n",
       "      <td>t</td>\n",
       "      <td>81491541511938809</td>\n",
       "      <td>81491541511940403</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[634416, 295480, 89520]</td>\n",
       "      <td>[634512, 295492, 89520]</td>\n",
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       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>126950546</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940632499757</td>\n",
       "      <td>102.689369</td>\n",
       "      <td>71</td>\n",
       "      <td>0.125350</td>\n",
       "      <td>0.505188</td>\n",
       "      <td>0.085491</td>\n",
       "      <td>0.002560</td>\n",
       "      <td>0.071755</td>\n",
       "      <td>0.209656</td>\n",
       "      <td>t</td>\n",
       "      <td>81491197914884088</td>\n",
       "      <td>81491197914895913</td>\n",
       "      <td>SAD</td>\n",
       "      <td>[631112, 275452, 100200]</td>\n",
       "      <td>[631228, 275412, 100160]</td>\n",
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       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>218790405</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940611602552</td>\n",
       "      <td>6.678510</td>\n",
       "      <td>92</td>\n",
       "      <td>0.044414</td>\n",
       "      <td>0.142395</td>\n",
       "      <td>0.312924</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>0.251710</td>\n",
       "      <td>0.246964</td>\n",
       "      <td>t</td>\n",
       "      <td>81560879531460479</td>\n",
       "      <td>81560879531454661</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[635924, 232912, 121880]</td>\n",
       "      <td>[635960, 232988, 121880]</td>\n",
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       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>218047340</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940628579066</td>\n",
       "      <td>30.840708</td>\n",
       "      <td>142</td>\n",
       "      <td>0.032452</td>\n",
       "      <td>0.721918</td>\n",
       "      <td>0.149166</td>\n",
       "      <td>0.031120</td>\n",
       "      <td>0.007521</td>\n",
       "      <td>0.057823</td>\n",
       "      <td>t</td>\n",
       "      <td>81349773298958087</td>\n",
       "      <td>81349773298949222</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[625480, 235308, 122640]</td>\n",
       "      <td>[625472, 235412, 122640]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>74175051</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940624377224</td>\n",
       "      <td>79.806732</td>\n",
       "      <td>152</td>\n",
       "      <td>0.284517</td>\n",
       "      <td>0.038989</td>\n",
       "      <td>0.586527</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.032435</td>\n",
       "      <td>0.054883</td>\n",
       "      <td>t</td>\n",
       "      <td>81561360567639535</td>\n",
       "      <td>81561360567636681</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[635936, 262376, 116480]</td>\n",
       "      <td>[635912, 262316, 116480]</td>\n",
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       "<p>388 rows × 17 columns</p>\n",
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      ],
      "text/plain": [
       "            id      pre_pt_root_id     post_pt_root_id  connection_score  \\\n",
       "0    117830668  720575940631693610  720575940620443162        370.392456   \n",
       "1     75679622  720575940631693610  720575940635047672        139.755951   \n",
       "2     10626687  720575940631693610  720575940623646963         72.089195   \n",
       "3     10626689  720575940631693610  720575940621104628        131.851089   \n",
       "4     10293850  720575940631693610  720575940624425344        185.360291   \n",
       "..         ...                 ...                 ...               ...   \n",
       "383  151947837  720575940631693610  720575940621547689          8.619675   \n",
       "384  126950546  720575940631693610  720575940632499757        102.689369   \n",
       "385  218790405  720575940631693610  720575940611602552          6.678510   \n",
       "386  218047340  720575940631693610  720575940628579066         30.840708   \n",
       "387   74175051  720575940631693610  720575940624377224         79.806732   \n",
       "\n",
       "     cleft_score      gaba       ach      glut       oct       ser        da  \\\n",
       "0             60  0.047310  0.718833  0.130120  0.015852  0.007914  0.079971   \n",
       "1            142  0.032243  0.883559  0.036865  0.009253  0.005225  0.032856   \n",
       "2            149  0.018603  0.923716  0.005724  0.001676  0.002917  0.047364   \n",
       "3            141  0.010995  0.971356  0.000803  0.000471  0.001374  0.015000   \n",
       "4            126  0.087320  0.796282  0.013954  0.007258  0.025200  0.069985   \n",
       "..           ...       ...       ...       ...       ...       ...       ...   \n",
       "383          140  0.048986  0.500239  0.012006  0.017138  0.400640  0.020991   \n",
       "384           71  0.125350  0.505188  0.085491  0.002560  0.071755  0.209656   \n",
       "385           92  0.044414  0.142395  0.312924  0.001593  0.251710  0.246964   \n",
       "386          142  0.032452  0.721918  0.149166  0.031120  0.007521  0.057823   \n",
       "387          152  0.284517  0.038989  0.586527  0.002648  0.032435  0.054883   \n",
       "\n",
       "    valid_nt  pre_pt_supervoxel_id  post_pt_supervoxel_id neuropil  \\\n",
       "0          t     81560742159585205      81560742159589162    PLP_R   \n",
       "1          t     81420279548633717      81420279548622891   PVLP_R   \n",
       "2          t     81702166708387316      81702166708369049   AVLP_R   \n",
       "3          t     81702166708387316      81702166708384466   AVLP_R   \n",
       "4          t     81632004122605454      81632004122600098   AVLP_R   \n",
       "..       ...                   ...                    ...      ...   \n",
       "383        t     81491541511938809      81491541511940403   AMMC_R   \n",
       "384        t     81491197914884088      81491197914895913      SAD   \n",
       "385        t     81560879531460479      81560879531454661   PVLP_R   \n",
       "386        t     81349773298958087      81349773298949222   AVLP_R   \n",
       "387        t     81561360567639535      81561360567636681   AVLP_R   \n",
       "\n",
       "              pre_pt_position          post_pt_position  \n",
       "0    [634620, 226424, 141680]  [634608, 226444, 141720]  \n",
       "1    [628280, 243384, 125360]  [628188, 243296, 125360]  \n",
       "2     [643152, 267728, 97200]   [643112, 267824, 97240]  \n",
       "3     [643104, 267720, 97280]   [643144, 267800, 97320]  \n",
       "4     [638932, 279244, 96280]   [638836, 279144, 96240]  \n",
       "..                        ...                       ...  \n",
       "383   [634416, 295480, 89520]   [634512, 295492, 89520]  \n",
       "384  [631112, 275452, 100200]  [631228, 275412, 100160]  \n",
       "385  [635924, 232912, 121880]  [635960, 232988, 121880]  \n",
       "386  [625480, 235308, 122640]  [625472, 235412, 122640]  \n",
       "387  [635936, 262376, 116480]  [635912, 262316, 116480]  \n",
       "\n",
       "[388 rows x 17 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "presyn_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "35e0ee3d-ddaf-4983-8904-daa5dc007c88",
   "metadata": {},
   "outputs": [
    {
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       "      <th>pre_pt_root_id</th>\n",
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       "      <td>7.517577e-10</td>\n",
       "      <td>8.469752e-09</td>\n",
       "      <td>6.843862e-07</td>\n",
       "      <td>t</td>\n",
       "      <td>81632485225862123</td>\n",
       "      <td>81632485225856040</td>\n",
       "      <td>SAD</td>\n",
       "      <td>[642020, 307040, 110920]</td>\n",
       "      <td>[641824, 307016, 110920]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>65</td>\n",
       "      <td>0.870307</td>\n",
       "      <td>0.108823</td>\n",
       "      <td>8.390854e-03</td>\n",
       "      <td>3.947024e-05</td>\n",
       "      <td>7.658201e-04</td>\n",
       "      <td>1.167465e-02</td>\n",
       "      <td>t</td>\n",
       "      <td>81561635445229950</td>\n",
       "      <td>81561635445221449</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[637820, 280568, 107320]</td>\n",
       "      <td>[637960, 280444, 107280]</td>\n",
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       "      <th>2</th>\n",
       "      <td>824004</td>\n",
       "      <td>720575940625887119</td>\n",
       "      <td>720575940631693610</td>\n",
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       "      <td>0.047908</td>\n",
       "      <td>3.154862e-04</td>\n",
       "      <td>1.214293e-05</td>\n",
       "      <td>9.513536e-01</td>\n",
       "      <td>6.087947e-05</td>\n",
       "      <td>t</td>\n",
       "      <td>81561978908660621</td>\n",
       "      <td>81561978908674707</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[638592, 299008, 74440]</td>\n",
       "      <td>[638576, 299132, 74440]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>352072</td>\n",
       "      <td>720575940613380914</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>378.164032</td>\n",
       "      <td>143</td>\n",
       "      <td>0.026012</td>\n",
       "      <td>0.919231</td>\n",
       "      <td>8.127017e-04</td>\n",
       "      <td>1.418136e-05</td>\n",
       "      <td>1.580350e-02</td>\n",
       "      <td>3.812663e-02</td>\n",
       "      <td>t</td>\n",
       "      <td>81491472725507849</td>\n",
       "      <td>81491472725512561</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[632944, 292452, 74200]</td>\n",
       "      <td>[632848, 292488, 74160]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>352078</td>\n",
       "      <td>720575940613380914</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>56.065281</td>\n",
       "      <td>130</td>\n",
       "      <td>0.005345</td>\n",
       "      <td>0.981488</td>\n",
       "      <td>3.448543e-05</td>\n",
       "      <td>4.510183e-07</td>\n",
       "      <td>8.544329e-03</td>\n",
       "      <td>4.587861e-03</td>\n",
       "      <td>t</td>\n",
       "      <td>81491472725513991</td>\n",
       "      <td>81491472725512561</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[633000, 292556, 74320]</td>\n",
       "      <td>[632956, 292672, 74320]</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>129449505</td>\n",
       "      <td>720575940631624785</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>334.592163</td>\n",
       "      <td>139</td>\n",
       "      <td>0.025598</td>\n",
       "      <td>0.849592</td>\n",
       "      <td>1.021429e-02</td>\n",
       "      <td>5.777769e-04</td>\n",
       "      <td>1.153867e-02</td>\n",
       "      <td>1.024786e-01</td>\n",
       "      <td>t</td>\n",
       "      <td>81490854384391854</td>\n",
       "      <td>81490854384366188</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[631952, 253672, 112400]</td>\n",
       "      <td>[631864, 253828, 112400]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>91118513</td>\n",
       "      <td>720575940626404337</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>530.672913</td>\n",
       "      <td>124</td>\n",
       "      <td>0.004727</td>\n",
       "      <td>0.957765</td>\n",
       "      <td>1.230918e-02</td>\n",
       "      <td>8.542001e-03</td>\n",
       "      <td>1.444312e-04</td>\n",
       "      <td>1.651207e-02</td>\n",
       "      <td>t</td>\n",
       "      <td>81138804571932051</td>\n",
       "      <td>81138804571933468</td>\n",
       "      <td>SPS_R</td>\n",
       "      <td>[610936, 242004, 126720]</td>\n",
       "      <td>[610832, 242064, 126720]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>235515151</td>\n",
       "      <td>720575940612612094</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>78.667389</td>\n",
       "      <td>158</td>\n",
       "      <td>0.049604</td>\n",
       "      <td>0.184570</td>\n",
       "      <td>7.333407e-01</td>\n",
       "      <td>8.835521e-03</td>\n",
       "      <td>4.156235e-03</td>\n",
       "      <td>1.949376e-02</td>\n",
       "      <td>t</td>\n",
       "      <td>81138667066289445</td>\n",
       "      <td>81138667066307148</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[613988, 235216, 119680]</td>\n",
       "      <td>[614064, 235148, 119720]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>10093480</td>\n",
       "      <td>720575940625738073</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>25.832794</td>\n",
       "      <td>149</td>\n",
       "      <td>0.033539</td>\n",
       "      <td>0.704020</td>\n",
       "      <td>5.440734e-02</td>\n",
       "      <td>3.434047e-03</td>\n",
       "      <td>4.135636e-02</td>\n",
       "      <td>1.632432e-01</td>\n",
       "      <td>t</td>\n",
       "      <td>81772535452571229</td>\n",
       "      <td>81772535452576339</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[648748, 266056, 97240]</td>\n",
       "      <td>[648704, 266164, 97280]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>145444410</td>\n",
       "      <td>720575940612612094</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>120.278595</td>\n",
       "      <td>66</td>\n",
       "      <td>0.033292</td>\n",
       "      <td>0.642177</td>\n",
       "      <td>2.248493e-01</td>\n",
       "      <td>1.534073e-02</td>\n",
       "      <td>8.802129e-03</td>\n",
       "      <td>7.553937e-02</td>\n",
       "      <td>t</td>\n",
       "      <td>81279473274073176</td>\n",
       "      <td>81279473274095477</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[620836, 236940, 118080]</td>\n",
       "      <td>[620860, 237008, 118160]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1922 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id      pre_pt_root_id     post_pt_root_id  connection_score  \\\n",
       "0     128738026  720575940639232309  720575940631693610        166.925522   \n",
       "1     105342698  720575940639232309  720575940631693610        296.410767   \n",
       "2        824004  720575940625887119  720575940631693610         18.143377   \n",
       "3        352072  720575940613380914  720575940631693610        378.164032   \n",
       "4        352078  720575940613380914  720575940631693610         56.065281   \n",
       "...         ...                 ...                 ...               ...   \n",
       "1917  129449505  720575940631624785  720575940631693610        334.592163   \n",
       "1918   91118513  720575940626404337  720575940631693610        530.672913   \n",
       "1919  235515151  720575940612612094  720575940631693610         78.667389   \n",
       "1920   10093480  720575940625738073  720575940631693610         25.832794   \n",
       "1921  145444410  720575940612612094  720575940631693610        120.278595   \n",
       "\n",
       "      cleft_score      gaba       ach          glut           oct  \\\n",
       "0             140  0.293312  0.706687  3.033506e-08  7.517577e-10   \n",
       "1              65  0.870307  0.108823  8.390854e-03  3.947024e-05   \n",
       "2              87  0.000350  0.047908  3.154862e-04  1.214293e-05   \n",
       "3             143  0.026012  0.919231  8.127017e-04  1.418136e-05   \n",
       "4             130  0.005345  0.981488  3.448543e-05  4.510183e-07   \n",
       "...           ...       ...       ...           ...           ...   \n",
       "1917          139  0.025598  0.849592  1.021429e-02  5.777769e-04   \n",
       "1918          124  0.004727  0.957765  1.230918e-02  8.542001e-03   \n",
       "1919          158  0.049604  0.184570  7.333407e-01  8.835521e-03   \n",
       "1920          149  0.033539  0.704020  5.440734e-02  3.434047e-03   \n",
       "1921           66  0.033292  0.642177  2.248493e-01  1.534073e-02   \n",
       "\n",
       "               ser            da valid_nt  pre_pt_supervoxel_id  \\\n",
       "0     8.469752e-09  6.843862e-07        t     81632485225862123   \n",
       "1     7.658201e-04  1.167465e-02        t     81561635445229950   \n",
       "2     9.513536e-01  6.087947e-05        t     81561978908660621   \n",
       "3     1.580350e-02  3.812663e-02        t     81491472725507849   \n",
       "4     8.544329e-03  4.587861e-03        t     81491472725513991   \n",
       "...            ...           ...      ...                   ...   \n",
       "1917  1.153867e-02  1.024786e-01        t     81490854384391854   \n",
       "1918  1.444312e-04  1.651207e-02        t     81138804571932051   \n",
       "1919  4.156235e-03  1.949376e-02        t     81138667066289445   \n",
       "1920  4.135636e-02  1.632432e-01        t     81772535452571229   \n",
       "1921  8.802129e-03  7.553937e-02        t     81279473274073176   \n",
       "\n",
       "      post_pt_supervoxel_id neuropil           pre_pt_position  \\\n",
       "0         81632485225856040      SAD  [642020, 307040, 110920]   \n",
       "1         81561635445221449   AMMC_R  [637820, 280568, 107320]   \n",
       "2         81561978908674707   AMMC_R   [638592, 299008, 74440]   \n",
       "3         81491472725512561   AMMC_R   [632944, 292452, 74200]   \n",
       "4         81491472725512561   AMMC_R   [633000, 292556, 74320]   \n",
       "...                     ...      ...                       ...   \n",
       "1917      81490854384366188   PVLP_R  [631952, 253672, 112400]   \n",
       "1918      81138804571933468    SPS_R  [610936, 242004, 126720]   \n",
       "1919      81138667066307148   PVLP_R  [613988, 235216, 119680]   \n",
       "1920      81772535452576339   AVLP_R   [648748, 266056, 97240]   \n",
       "1921      81279473274095477   AVLP_R  [620836, 236940, 118080]   \n",
       "\n",
       "              post_pt_position  \n",
       "0     [641824, 307016, 110920]  \n",
       "1     [637960, 280444, 107280]  \n",
       "2      [638576, 299132, 74440]  \n",
       "3      [632848, 292488, 74160]  \n",
       "4      [632956, 292672, 74320]  \n",
       "...                        ...  \n",
       "1917  [631864, 253828, 112400]  \n",
       "1918  [610832, 242064, 126720]  \n",
       "1919  [614064, 235148, 119720]  \n",
       "1920   [648704, 266164, 97280]  \n",
       "1921  [620860, 237008, 118160]  \n",
       "\n",
       "[1922 rows x 17 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postsyn_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2179aab2-63dd-4609-b4c0-5f933e1ad06c",
   "metadata": {},
   "source": [
    "## Example Analysis: Super class distribution of partners\n",
    "\n",
    "For a single neuron, we want to analyze the distribution of pre- and postsynaptic partner types (here: super classes).\n",
    "\n",
    "First, we load the super classes from the hierarchical annotation table and simplify the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "55b9a3f3-4bd2-4f6a-9504-c2db2692f847",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>created</th>\n",
       "      <th>valid</th>\n",
       "      <th>target_id</th>\n",
       "      <th>classification_system</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>id_ref</th>\n",
       "      <th>created_ref</th>\n",
       "      <th>valid_ref</th>\n",
       "      <th>pt_supervoxel_id</th>\n",
       "      <th>pt_root_id</th>\n",
       "      <th>pt_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>771969</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>135</td>\n",
       "      <td>super_class</td>\n",
       "      <td>motor</td>\n",
       "      <td>135</td>\n",
       "      <td>2023-06-19 06:43:32.694340+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>78536398189730110</td>\n",
       "      <td>720575940628372613</td>\n",
       "      <td>[462134, 314944, 202595]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>771970</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>131206</td>\n",
       "      <td>super_class</td>\n",
       "      <td>descending</td>\n",
       "      <td>131206</td>\n",
       "      <td>2024-01-08 19:46:33.536372+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>79097217839414238</td>\n",
       "      <td>720575940620156283</td>\n",
       "      <td>[492890, 187909, 204117]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>771971</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>138571</td>\n",
       "      <td>super_class</td>\n",
       "      <td>central</td>\n",
       "      <td>138571</td>\n",
       "      <td>2024-01-08 19:46:56.609400+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>81277136946061019</td>\n",
       "      <td>720575940640372277</td>\n",
       "      <td>[620140, 99296, 158026]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>771972</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>137463</td>\n",
       "      <td>super_class</td>\n",
       "      <td>central</td>\n",
       "      <td>137463</td>\n",
       "      <td>2024-01-08 19:46:56.002999+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>80434498856324372</td>\n",
       "      <td>720575940634601954</td>\n",
       "      <td>[570818, 205376, 191260]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>771973</td>\n",
       "      <td>2023-09-30 05:09:51.441140+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>133024</td>\n",
       "      <td>super_class</td>\n",
       "      <td>central</td>\n",
       "      <td>133024</td>\n",
       "      <td>2024-01-08 19:46:44.939864+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>81840568002630413</td>\n",
       "      <td>720575940624155416</td>\n",
       "      <td>[652270, 128644, 169218]</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>139250</th>\n",
       "      <td>525972</td>\n",
       "      <td>2023-06-19 23:28:18.685794+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>104828</td>\n",
       "      <td>super_class</td>\n",
       "      <td>optic</td>\n",
       "      <td>104828</td>\n",
       "      <td>2023-06-19 06:48:15.001237+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>83952592400822837</td>\n",
       "      <td>720575940626100238</td>\n",
       "      <td>[776911, 183792, 174548]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139251</th>\n",
       "      <td>564744</td>\n",
       "      <td>2023-06-19 23:29:51.384628+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>104809</td>\n",
       "      <td>super_class</td>\n",
       "      <td>optic</td>\n",
       "      <td>104809</td>\n",
       "      <td>2023-06-19 06:48:14.983796+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>84798048190170122</td>\n",
       "      <td>720575940626915216</td>\n",
       "      <td>[824625, 246096, 194229]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139252</th>\n",
       "      <td>530679</td>\n",
       "      <td>2023-06-19 23:28:31.069595+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>83362</td>\n",
       "      <td>super_class</td>\n",
       "      <td>optic</td>\n",
       "      <td>83362</td>\n",
       "      <td>2023-06-19 06:47:20.709997+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>75298954786869572</td>\n",
       "      <td>720575940645731620</td>\n",
       "      <td>[272447, 288010, 156593]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139253</th>\n",
       "      <td>564736</td>\n",
       "      <td>2023-06-19 23:29:51.379168+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>104819</td>\n",
       "      <td>super_class</td>\n",
       "      <td>optic</td>\n",
       "      <td>104819</td>\n",
       "      <td>2023-06-19 06:48:14.992064+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>84658822530652061</td>\n",
       "      <td>720575940629075755</td>\n",
       "      <td>[815896, 335911, 204215]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139254</th>\n",
       "      <td>564745</td>\n",
       "      <td>2023-06-19 23:29:51.385612+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>104808</td>\n",
       "      <td>super_class</td>\n",
       "      <td>optic</td>\n",
       "      <td>104808</td>\n",
       "      <td>2023-06-19 06:48:14.982973+00:00</td>\n",
       "      <td>t</td>\n",
       "      <td>83883117009526705</td>\n",
       "      <td>720575940624159751</td>\n",
       "      <td>[772781, 238450, 165215]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139255 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                          created valid  target_id  \\\n",
       "0       771969 2023-09-30 05:09:51.441140+00:00     t        135   \n",
       "1       771970 2023-09-30 05:09:51.441140+00:00     t     131206   \n",
       "2       771971 2023-09-30 05:09:51.441140+00:00     t     138571   \n",
       "3       771972 2023-09-30 05:09:51.441140+00:00     t     137463   \n",
       "4       771973 2023-09-30 05:09:51.441140+00:00     t     133024   \n",
       "...        ...                              ...   ...        ...   \n",
       "139250  525972 2023-06-19 23:28:18.685794+00:00     t     104828   \n",
       "139251  564744 2023-06-19 23:29:51.384628+00:00     t     104809   \n",
       "139252  530679 2023-06-19 23:28:31.069595+00:00     t      83362   \n",
       "139253  564736 2023-06-19 23:29:51.379168+00:00     t     104819   \n",
       "139254  564745 2023-06-19 23:29:51.385612+00:00     t     104808   \n",
       "\n",
       "       classification_system   cell_type  id_ref  \\\n",
       "0                super_class       motor     135   \n",
       "1                super_class  descending  131206   \n",
       "2                super_class     central  138571   \n",
       "3                super_class     central  137463   \n",
       "4                super_class     central  133024   \n",
       "...                      ...         ...     ...   \n",
       "139250           super_class       optic  104828   \n",
       "139251           super_class       optic  104809   \n",
       "139252           super_class       optic   83362   \n",
       "139253           super_class       optic  104819   \n",
       "139254           super_class       optic  104808   \n",
       "\n",
       "                            created_ref valid_ref   pt_supervoxel_id  \\\n",
       "0      2023-06-19 06:43:32.694340+00:00         t  78536398189730110   \n",
       "1      2024-01-08 19:46:33.536372+00:00         t  79097217839414238   \n",
       "2      2024-01-08 19:46:56.609400+00:00         t  81277136946061019   \n",
       "3      2024-01-08 19:46:56.002999+00:00         t  80434498856324372   \n",
       "4      2024-01-08 19:46:44.939864+00:00         t  81840568002630413   \n",
       "...                                 ...       ...                ...   \n",
       "139250 2023-06-19 06:48:15.001237+00:00         t  83952592400822837   \n",
       "139251 2023-06-19 06:48:14.983796+00:00         t  84798048190170122   \n",
       "139252 2023-06-19 06:47:20.709997+00:00         t  75298954786869572   \n",
       "139253 2023-06-19 06:48:14.992064+00:00         t  84658822530652061   \n",
       "139254 2023-06-19 06:48:14.982973+00:00         t  83883117009526705   \n",
       "\n",
       "                pt_root_id               pt_position  \n",
       "0       720575940628372613  [462134, 314944, 202595]  \n",
       "1       720575940620156283  [492890, 187909, 204117]  \n",
       "2       720575940640372277   [620140, 99296, 158026]  \n",
       "3       720575940634601954  [570818, 205376, 191260]  \n",
       "4       720575940624155416  [652270, 128644, 169218]  \n",
       "...                    ...                       ...  \n",
       "139250  720575940626100238  [776911, 183792, 174548]  \n",
       "139251  720575940626915216  [824625, 246096, 194229]  \n",
       "139252  720575940645731620  [272447, 288010, 156593]  \n",
       "139253  720575940629075755  [815896, 335911, 204215]  \n",
       "139254  720575940624159751  [772781, 238450, 165215]  \n",
       "\n",
       "[139255 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "super_class_annos_df = client.materialize.query_table(\"hierarchical_neuron_annotations\", filter_equal_dict={\"classification_system\": \"super_class\"})\n",
    "unique_super_class = super_class_annos_df[\"cell_type\"].unique()\n",
    "\n",
    "super_class_annos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d5e28e0-36fc-4cde-b3ee-bd21354d8da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_super_class_annos_df = super_class_annos_df[[\"pt_root_id\", \"cell_type\"]].rename(\n",
    "    columns={\"pt_root_id\": \"pre_pt_root_id\", \"cell_type\": \"pre_super_class\"})\n",
    "post_super_class_annos_df = super_class_annos_df[[\"pt_root_id\", \"cell_type\"]].rename(\n",
    "    columns={\"pt_root_id\": \"post_pt_root_id\", \"cell_type\": \"post_super_class\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd609542-b619-4f9c-a161-8918538c6570",
   "metadata": {},
   "source": [
    "Next, we load the synapses for a specific neuron (this could also be a set of neurons):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca7829ce-65df-4892-8c64-eb49cf0b46ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_id = 720575940631693610\n",
    "\n",
    "presyn_df = client.materialize.query_view(\"valid_synapses_nt_np_v6\", filter_in_dict={\"pre_pt_root_id\": [root_id]})\n",
    "postsyn_df = client.materialize.query_view(\"valid_synapses_nt_np_v6\", filter_in_dict={\"post_pt_root_id\": [root_id]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aed7047-31b4-45fe-adf0-ac9fc75272c5",
   "metadata": {},
   "source": [
    "Merging the synapse and the annotation tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "654adf36-0c44-43cf-9982-a83378ea5e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "post_syn_super_df = pd.merge(postsyn_df, pre_super_class_annos_df, on=\"pre_pt_root_id\")\n",
    "pre_syn_super_df = pd.merge(presyn_df, post_super_class_annos_df, on=\"post_pt_root_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91240954-dfc6-40ab-9f85-4fbb745b8d26",
   "metadata": {},
   "source": [
    "And finally plotting the distribution of synapses by partner super class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c834a5ea-8fa8-4d88-ac5a-6eb79e1f82c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='pre_super_class', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12, 4))\n",
    "sns.countplot(post_syn_super_df, x=\"pre_super_class\", order=unique_super_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9e577dda-f5b8-4bdb-ac46-4cd3680562a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='post_super_class', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12, 4))\n",
    "sns.countplot(pre_syn_super_df, x=\"post_super_class\", order=unique_super_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b5f46f-13f5-4097-b3e2-cfff0d75baea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}