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": 75,
   "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": 63,
   "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, 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]"
      ]
     },
     "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"
     ]
    }
   ],
   "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": [
       "['fly_synapses_neuropil_v2',\n",
       " 'neuron_information_v2',\n",
       " 'proofread_neurons',\n",
       " 'nuclei_v1',\n",
       " 'synapses_nt_v1',\n",
       " 'hierarchical_neuron_annotations']"
      ]
     },
     "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 valid   pt_supervoxel_id          pt_root_id  \\\n",
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       "\n",
       "                     pt_position  \n",
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     "execution_count": 10,
     "metadata": {},
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   ],
   "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",
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   "outputs": [
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       "      <td>[429728, 278496, 19040]</td>\n",
       "      <td>[439264, 285568, 27320]</td>\n",
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       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>6655288</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 × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id valid      volume   pt_supervoxel_id          pt_root_id  \\\n",
       "0       7393349     t   26.141245  82827379285852979  720575940626838909   \n",
       "1       7416439     t   11.523400  82827998029690530  720575940627484553   \n",
       "2       7415038     t   32.895959  83038623024880664  720575940626046919   \n",
       "3       7415013     t   53.711176  83038760463398837  720575940630311383   \n",
       "4       7415848     t    9.280717  83038554439606237  720575940633370649   \n",
       "...         ...   ...         ...                ...                 ...   \n",
       "143135  4389032     t  244.330332  79377386675948158  720575940629762043   \n",
       "143136  8558952     t  253.359391  84870066067479558  720575940636923511   \n",
       "143137  3076633     t  261.782528  78042030341189115  720575940623944072   \n",
       "143138  3125634     t  274.994586  78113635431454405  720575940621426568   \n",
       "143139  6655288     t  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 8 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": [
    {
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       "      <th></th>\n",
       "      <th>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>pt_supervoxel_id</th>\n",
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      "text/plain": [
       "             id valid      volume  pt_position_x  pt_position_y  \\\n",
       "0       7393349     t   26.141245         709888         227744   \n",
       "1       7416439     t   11.523400         710592         263392   \n",
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       "143139  6655288     t  322.137088         648704          72640   \n",
       "\n",
       "        pt_position_z   pt_supervoxel_id          pt_root_id  \\\n",
       "0               57160  82827379285852979  720575940626838909   \n",
       "1              129800  82827998029690530  720575940627484553   \n",
       "2               77000  83038623024880664  720575940626046919   \n",
       "3               65200  83038760463398837  720575940630311383   \n",
       "4              119560  83038554439606237  720575940633370649   \n",
       "...               ...                ...                 ...   \n",
       "143135          48560  79377386675948158  720575940629762043   \n",
       "143136         149840  84870066067479558  720575940636923511   \n",
       "143137         223440  78042030341189115  720575940623944072   \n",
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       "143139         147960  81769305838115716  720575940629374326   \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",
       "[143140 rows x 14 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": [
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       "      <th>2</th>\n",
       "      <td>7415038</td>\n",
       "      <td>t</td>\n",
       "      <td>32.895959</td>\n",
       "      <td>83038623024880664</td>\n",
       "      <td>720575940626046919</td>\n",
       "      <td>[722.528, 234.656, 77.0]</td>\n",
       "      <td>[720.768, 232.896, 74.48]</td>\n",
       "      <td>[724.224, 236.576, 79.36]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7415013</td>\n",
       "      <td>t</td>\n",
       "      <td>53.711176</td>\n",
       "      <td>83038760463398837</td>\n",
       "      <td>720575940630311383</td>\n",
       "      <td>[722.912, 244.032, 65.2]</td>\n",
       "      <td>[720.48, 242.144, 62.48]</td>\n",
       "      <td>[725.6, 246.208, 67.76]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7415848</td>\n",
       "      <td>t</td>\n",
       "      <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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143135</th>\n",
       "      <td>4389032</td>\n",
       "      <td>t</td>\n",
       "      <td>244.330332</td>\n",
       "      <td>79377386675948158</td>\n",
       "      <td>720575940629762043</td>\n",
       "      <td>[511.328, 113.024, 48.56]</td>\n",
       "      <td>[503.744, 104.128, 44.6]</td>\n",
       "      <td>[520.704, 122.08, 53.04]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143136</th>\n",
       "      <td>8558952</td>\n",
       "      <td>t</td>\n",
       "      <td>253.359391</td>\n",
       "      <td>84870066067479558</td>\n",
       "      <td>720575940636923511</td>\n",
       "      <td>[830.72, 344.608, 149.84]</td>\n",
       "      <td>[821.12, 335.68, 146.96]</td>\n",
       "      <td>[840.576, 354.912, 152.88]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>3076633</td>\n",
       "      <td>t</td>\n",
       "      <td>261.782528</td>\n",
       "      <td>78042030341189115</td>\n",
       "      <td>720575940623944072</td>\n",
       "      <td>[433.152, 210.56, 223.44]</td>\n",
       "      <td>[428.544, 206.976, 219.12]</td>\n",
       "      <td>[437.632, 214.048, 227.6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>3125634</td>\n",
       "      <td>t</td>\n",
       "      <td>274.994586</td>\n",
       "      <td>78113635431454405</td>\n",
       "      <td>720575940621426568</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>6655288</td>\n",
       "      <td>t</td>\n",
       "      <td>322.137088</td>\n",
       "      <td>81769305838115716</td>\n",
       "      <td>720575940629374326</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143140 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id valid      volume   pt_supervoxel_id          pt_root_id  \\\n",
       "0       7393349     t   26.141245  82827379285852979  720575940626838909   \n",
       "1       7416439     t   11.523400  82827998029690530  720575940627484553   \n",
       "2       7415038     t   32.895959  83038623024880664  720575940626046919   \n",
       "3       7415013     t   53.711176  83038760463398837  720575940630311383   \n",
       "4       7415848     t    9.280717  83038554439606237  720575940633370649   \n",
       "...         ...   ...         ...                ...                 ...   \n",
       "143135  4389032     t  244.330332  79377386675948158  720575940629762043   \n",
       "143136  8558952     t  253.359391  84870066067479558  720575940636923511   \n",
       "143137  3076633     t  261.782528  78042030341189115  720575940623944072   \n",
       "143138  3125634     t  274.994586  78113635431454405  720575940621426568   \n",
       "143139  6655288     t  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 8 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": [
    {
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       "      <th>id</th>\n",
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       "      <td>flow</td>\n",
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       "      <td>391235</td>\n",
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       "      <td>60960</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60960</td>\n",
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       "      <td>[835953, 325029, 132976]</td>\n",
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       "      <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>377694</th>\n",
       "      <td>760321</td>\n",
       "      <td>t</td>\n",
       "      <td>127378</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T4a</td>\n",
       "      <td>127378</td>\n",
       "      <td>t</td>\n",
       "      <td>76001267906418794</td>\n",
       "      <td>720575940640761728</td>\n",
       "      <td>[314129, 204855, 184422]</td>\n",
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       "    <tr>\n",
       "      <th>377695</th>\n",
       "      <td>760322</td>\n",
       "      <td>t</td>\n",
       "      <td>127393</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T5a</td>\n",
       "      <td>127393</td>\n",
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       "      <td>76705299213588578</td>\n",
       "      <td>720575940644327584</td>\n",
       "      <td>[352578, 225656, 253139]</td>\n",
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       "    <tr>\n",
       "      <th>377696</th>\n",
       "      <td>760323</td>\n",
       "      <td>t</td>\n",
       "      <td>127697</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T4a</td>\n",
       "      <td>127697</td>\n",
       "      <td>t</td>\n",
       "      <td>75790161740758808</td>\n",
       "      <td>720575940637229198</td>\n",
       "      <td>[301502, 206108, 197151]</td>\n",
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       "      <th>377697</th>\n",
       "      <td>760324</td>\n",
       "      <td>t</td>\n",
       "      <td>127776</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T4a</td>\n",
       "      <td>127776</td>\n",
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       "      <td>75157049402858357</td>\n",
       "      <td>720575940627302745</td>\n",
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       "      <td>720575940639295558</td>\n",
       "      <td>[367111, 257657, 246857]</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>377699 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id valid  target_id classification_system  cell_type  id_ref  \\\n",
       "0       391232     t      60947                  flow  intrinsic   60947   \n",
       "1       391233     t      60946                  flow  intrinsic   60946   \n",
       "2       391234     t      60945                  flow  intrinsic   60945   \n",
       "3       391235     t      60960                  flow  intrinsic   60960   \n",
       "4       391236     t      60961                  flow  intrinsic   60961   \n",
       "...        ...   ...        ...                   ...        ...     ...   \n",
       "377694  760321     t     127378             cell_type        T4a  127378   \n",
       "377695  760322     t     127393             cell_type        T5a  127393   \n",
       "377696  760323     t     127697             cell_type        T4a  127697   \n",
       "377697  760324     t     127776             cell_type        T4a  127776   \n",
       "377698  760325     t     116748             cell_type        T5a  116748   \n",
       "\n",
       "       valid_ref   pt_supervoxel_id          pt_root_id  \\\n",
       "0              t  83532853635373500  720575940627005443   \n",
       "1              t  84307115980143621  720575940638062141   \n",
       "2              t  85151060007686276  720575940634330991   \n",
       "3              t  85010459891445545  720575940610409946   \n",
       "4              t  83673659843320298  720575940628587324   \n",
       "...          ...                ...                 ...   \n",
       "377694         t  76001267906418794  720575940640761728   \n",
       "377695         t  76705299213588578  720575940644327584   \n",
       "377696         t  75790161740758808  720575940637229198   \n",
       "377697         t  75157049402858357  720575940627302745   \n",
       "377698         t  76916955201745205  720575940639295558   \n",
       "\n",
       "                     pt_position  \n",
       "0       [751197, 330784, 106807]  \n",
       "1       [796396, 343997, 119089]  \n",
       "2       [845576, 316831, 145515]  \n",
       "3       [835953, 325029, 132976]  \n",
       "4       [761422, 334448, 110534]  \n",
       "...                          ...  \n",
       "377694  [314129, 204855, 184422]  \n",
       "377695  [352578, 225656, 253139]  \n",
       "377696  [301502, 206108, 197151]  \n",
       "377697  [263197, 218814, 256978]  \n",
       "377698  [367111, 257657, 246857]  \n",
       "\n",
       "[377699 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hierarchical_annos_df = client.materialize.query_table(\"hierarchical_neuron_annotations\")\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": "code",
   "execution_count": 16,
   "id": "09220402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classification_system\n",
       "flow              127978\n",
       "super_class       127978\n",
       "cell_class         97672\n",
       "cell_type          17404\n",
       "cell_sub_class      6667\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hierarchical_annos_df[\"classification_system\"].value_counts()"
   ]
  },
  {
   "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",
    "While the hierarchical annotation table is small enough to be loaded as a whole, it takes a few seconds to load. If we are only interested in a subset of the data, we can use a filter on any column to reduce the data footprint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5b81513",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>391232</td>\n",
       "      <td>t</td>\n",
       "      <td>60947</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60947</td>\n",
       "      <td>t</td>\n",
       "      <td>83532853635373500</td>\n",
       "      <td>720575940627005443</td>\n",
       "      <td>[751197, 330784, 106807]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>391233</td>\n",
       "      <td>t</td>\n",
       "      <td>60946</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60946</td>\n",
       "      <td>t</td>\n",
       "      <td>84307115980143621</td>\n",
       "      <td>720575940638062141</td>\n",
       "      <td>[796396, 343997, 119089]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>391234</td>\n",
       "      <td>t</td>\n",
       "      <td>60945</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60945</td>\n",
       "      <td>t</td>\n",
       "      <td>85151060007686276</td>\n",
       "      <td>720575940634330991</td>\n",
       "      <td>[845576, 316831, 145515]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>391235</td>\n",
       "      <td>t</td>\n",
       "      <td>60960</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60960</td>\n",
       "      <td>t</td>\n",
       "      <td>85010459891445545</td>\n",
       "      <td>720575940610409946</td>\n",
       "      <td>[835953, 325029, 132976]</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>391236</td>\n",
       "      <td>t</td>\n",
       "      <td>60961</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60961</td>\n",
       "      <td>t</td>\n",
       "      <td>83673659843320298</td>\n",
       "      <td>720575940628587324</td>\n",
       "      <td>[761422, 334448, 110534]</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>127973</th>\n",
       "      <td>503938</td>\n",
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       "      <td>109940</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>109940</td>\n",
       "      <td>t</td>\n",
       "      <td>74664055675374018</td>\n",
       "      <td>720575940627905383</td>\n",
       "      <td>[234729, 191865, 193858]</td>\n",
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       "      <th>127974</th>\n",
       "      <td>503939</td>\n",
       "      <td>t</td>\n",
       "      <td>110216</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>110216</td>\n",
       "      <td>t</td>\n",
       "      <td>82689047382048903</td>\n",
       "      <td>720575940628019985</td>\n",
       "      <td>[702449, 370626, 173721]</td>\n",
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       "    <tr>\n",
       "      <th>127975</th>\n",
       "      <td>503940</td>\n",
       "      <td>t</td>\n",
       "      <td>111137</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>111137</td>\n",
       "      <td>t</td>\n",
       "      <td>81984535373702682</td>\n",
       "      <td>720575940639551797</td>\n",
       "      <td>[663170, 321144, 196507]</td>\n",
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       "    <tr>\n",
       "      <th>127976</th>\n",
       "      <td>503941</td>\n",
       "      <td>t</td>\n",
       "      <td>112845</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>112845</td>\n",
       "      <td>t</td>\n",
       "      <td>74171543118689555</td>\n",
       "      <td>720575940611095160</td>\n",
       "      <td>[205765, 196206, 178359]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127977</th>\n",
       "      <td>503942</td>\n",
       "      <td>t</td>\n",
       "      <td>127977</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>127977</td>\n",
       "      <td>t</td>\n",
       "      <td>75297580732428963</td>\n",
       "      <td>720575940641724661</td>\n",
       "      <td>[273840, 204492, 246800]</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>127978 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id valid  target_id classification_system  cell_type  id_ref  \\\n",
       "0       391232     t      60947                  flow  intrinsic   60947   \n",
       "1       391233     t      60946                  flow  intrinsic   60946   \n",
       "2       391234     t      60945                  flow  intrinsic   60945   \n",
       "3       391235     t      60960                  flow  intrinsic   60960   \n",
       "4       391236     t      60961                  flow  intrinsic   60961   \n",
       "...        ...   ...        ...                   ...        ...     ...   \n",
       "127973  503938     t     109940                  flow  intrinsic  109940   \n",
       "127974  503939     t     110216                  flow  intrinsic  110216   \n",
       "127975  503940     t     111137                  flow  intrinsic  111137   \n",
       "127976  503941     t     112845                  flow  intrinsic  112845   \n",
       "127977  503942     t     127977                  flow  intrinsic  127977   \n",
       "\n",
       "       valid_ref   pt_supervoxel_id          pt_root_id  \\\n",
       "0              t  83532853635373500  720575940627005443   \n",
       "1              t  84307115980143621  720575940638062141   \n",
       "2              t  85151060007686276  720575940634330991   \n",
       "3              t  85010459891445545  720575940610409946   \n",
       "4              t  83673659843320298  720575940628587324   \n",
       "...          ...                ...                 ...   \n",
       "127973         t  74664055675374018  720575940627905383   \n",
       "127974         t  82689047382048903  720575940628019985   \n",
       "127975         t  81984535373702682  720575940639551797   \n",
       "127976         t  74171543118689555  720575940611095160   \n",
       "127977         t  75297580732428963  720575940641724661   \n",
       "\n",
       "                     pt_position  \n",
       "0       [751197, 330784, 106807]  \n",
       "1       [796396, 343997, 119089]  \n",
       "2       [845576, 316831, 145515]  \n",
       "3       [835953, 325029, 132976]  \n",
       "4       [761422, 334448, 110534]  \n",
       "...                          ...  \n",
       "127973  [234729, 191865, 193858]  \n",
       "127974  [702449, 370626, 173721]  \n",
       "127975  [663170, 321144, 196507]  \n",
       "127976  [205765, 196206, 178359]  \n",
       "127977  [273840, 204492, 246800]  \n",
       "\n",
       "[127978 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "id": "16e73a4a",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "            id valid  target_id classification_system cell_type  id_ref  \\\n",
       "0       631921     t      24809            cell_class      ALIN   24809   \n",
       "1       631922     t       3439            cell_class      ALIN    3439   \n",
       "2       631923     t      42371            cell_class      ALIN   42371   \n",
       "3       631924     t      15670            cell_class      ALIN   15670   \n",
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       "...        ...   ...        ...                   ...       ...     ...   \n",
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       "115074  760324     t     127776             cell_type       T4a  127776   \n",
       "115075  760325     t     116748             cell_type       T5a  116748   \n",
       "\n",
       "       valid_ref   pt_supervoxel_id          pt_root_id  \\\n",
       "0              t  81350666718746524  720575940618149686   \n",
       "1              t  80717416606245362  720575940628675433   \n",
       "2              t  77337311972566683  720575940630166583   \n",
       "3              t  80787785350455829  720575940630738683   \n",
       "4              t  78676860598498552  720575940624706062   \n",
       "...          ...                ...                 ...   \n",
       "115071         t  76001267906418794  720575940640761728   \n",
       "115072         t  76705299213588578  720575940644327584   \n",
       "115073         t  75790161740758808  720575940637229198   \n",
       "115074         t  75157049402858357  720575940627302745   \n",
       "115075         t  76916955201745205  720575940639295558   \n",
       "\n",
       "                     pt_position  \n",
       "0       [624510, 288189, 127797]  \n",
       "1        [586065, 292954, 82718]  \n",
       "2       [389107, 148134, 167223]  \n",
       "3        [592115, 291905, 83257]  \n",
       "4       [468615, 298327, 130296]  \n",
       "...                          ...  \n",
       "115071  [314129, 204855, 184422]  \n",
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       "115074  [263197, 218814, 256978]  \n",
       "115075  [367111, 257657, 246857]  \n",
       "\n",
       "[115076 rows x 10 columns]"
      ]
     },
     "execution_count": 18,
     "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": "code",
   "execution_count": 19,
   "id": "6806c4a4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>t</td>\n",
       "      <td>60945</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60945</td>\n",
       "      <td>t</td>\n",
       "      <td>85151060007686276</td>\n",
       "      <td>720575940634330991</td>\n",
       "      <td>[845576, 316831, 145515]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>391235</td>\n",
       "      <td>t</td>\n",
       "      <td>60960</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60960</td>\n",
       "      <td>t</td>\n",
       "      <td>85010459891445545</td>\n",
       "      <td>720575940610409946</td>\n",
       "      <td>[835953, 325029, 132976]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>391236</td>\n",
       "      <td>t</td>\n",
       "      <td>60961</td>\n",
       "      <td>flow</td>\n",
       "      <td>intrinsic</td>\n",
       "      <td>60961</td>\n",
       "      <td>t</td>\n",
       "      <td>83673659843320298</td>\n",
       "      <td>720575940628587324</td>\n",
       "      <td>[761422, 334448, 110534]</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>377694</th>\n",
       "      <td>760321</td>\n",
       "      <td>t</td>\n",
       "      <td>127378</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T4a</td>\n",
       "      <td>127378</td>\n",
       "      <td>t</td>\n",
       "      <td>76001267906418794</td>\n",
       "      <td>720575940640761728</td>\n",
       "      <td>[314129, 204855, 184422]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377695</th>\n",
       "      <td>760322</td>\n",
       "      <td>t</td>\n",
       "      <td>127393</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T5a</td>\n",
       "      <td>127393</td>\n",
       "      <td>t</td>\n",
       "      <td>76705299213588578</td>\n",
       "      <td>720575940644327584</td>\n",
       "      <td>[352578, 225656, 253139]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377696</th>\n",
       "      <td>760323</td>\n",
       "      <td>t</td>\n",
       "      <td>127697</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T4a</td>\n",
       "      <td>127697</td>\n",
       "      <td>t</td>\n",
       "      <td>75790161740758808</td>\n",
       "      <td>720575940637229198</td>\n",
       "      <td>[301502, 206108, 197151]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377697</th>\n",
       "      <td>760324</td>\n",
       "      <td>t</td>\n",
       "      <td>127776</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T4a</td>\n",
       "      <td>127776</td>\n",
       "      <td>t</td>\n",
       "      <td>75157049402858357</td>\n",
       "      <td>720575940627302745</td>\n",
       "      <td>[263197, 218814, 256978]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377698</th>\n",
       "      <td>760325</td>\n",
       "      <td>t</td>\n",
       "      <td>116748</td>\n",
       "      <td>cell_type</td>\n",
       "      <td>T5a</td>\n",
       "      <td>116748</td>\n",
       "      <td>t</td>\n",
       "      <td>76916955201745205</td>\n",
       "      <td>720575940639295558</td>\n",
       "      <td>[367111, 257657, 246857]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>377699 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id valid  target_id classification_system  cell_type  id_ref  \\\n",
       "0       391232     t      60947                  flow  intrinsic   60947   \n",
       "1       391233     t      60946                  flow  intrinsic   60946   \n",
       "2       391234     t      60945                  flow  intrinsic   60945   \n",
       "3       391235     t      60960                  flow  intrinsic   60960   \n",
       "4       391236     t      60961                  flow  intrinsic   60961   \n",
       "...        ...   ...        ...                   ...        ...     ...   \n",
       "377694  760321     t     127378             cell_type        T4a  127378   \n",
       "377695  760322     t     127393             cell_type        T5a  127393   \n",
       "377696  760323     t     127697             cell_type        T4a  127697   \n",
       "377697  760324     t     127776             cell_type        T4a  127776   \n",
       "377698  760325     t     116748             cell_type        T5a  116748   \n",
       "\n",
       "       valid_ref   pt_supervoxel_id          pt_root_id  \\\n",
       "0              t  83532853635373500  720575940627005443   \n",
       "1              t  84307115980143621  720575940638062141   \n",
       "2              t  85151060007686276  720575940634330991   \n",
       "3              t  85010459891445545  720575940610409946   \n",
       "4              t  83673659843320298  720575940628587324   \n",
       "...          ...                ...                 ...   \n",
       "377694         t  76001267906418794  720575940640761728   \n",
       "377695         t  76705299213588578  720575940644327584   \n",
       "377696         t  75790161740758808  720575940637229198   \n",
       "377697         t  75157049402858357  720575940627302745   \n",
       "377698         t  76916955201745205  720575940639295558   \n",
       "\n",
       "                     pt_position  \n",
       "0       [751197, 330784, 106807]  \n",
       "1       [796396, 343997, 119089]  \n",
       "2       [845576, 316831, 145515]  \n",
       "3       [835953, 325029, 132976]  \n",
       "4       [761422, 334448, 110534]  \n",
       "...                          ...  \n",
       "377694  [314129, 204855, 184422]  \n",
       "377695  [352578, 225656, 253139]  \n",
       "377696  [301502, 206108, 197151]  \n",
       "377697  [263197, 218814, 256978]  \n",
       "377698  [367111, 257657, 246857]  \n",
       "\n",
       "[377699 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_flow_annos_df = client.materialize.query_table(\"hierarchical_neuron_annotations\", filter_out_dict={\"classification_system\": [\"flow\"]})\n",
    "no_flow_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": 22,
   "id": "b6e0cf9c-ed15-4bd2-90fc-5b4e87498de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "community_anno_df = client.materialize.query_table(\"neuron_information_v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "30c558bc-c736-461f-b896-60fb3b0cd0f2",
   "metadata": {},
   "outputs": [
    {
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       "      <td>LPLC2_L, lobula plate-lobula columnar neuron 2...</td>\n",
       "      <td>392</td>\n",
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       "      <th>1</th>\n",
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       "      <td>VLPl&amp;d1_lateral; right</td>\n",
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       "      <th>3</th>\n",
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       "      <th>85970</th>\n",
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       "      <td>720575940615348130</td>\n",
       "      <td>Retinula axon; R1-6</td>\n",
       "      <td>1931</td>\n",
       "      <td>[874136, 285596, 136360]</td>\n",
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       "      <th>85971</th>\n",
       "      <td>87465</td>\n",
       "      <td>t</td>\n",
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       "      <td>Retinula axon: R7</td>\n",
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       "      <td>87468</td>\n",
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      "text/plain": [
       "          id valid   pt_supervoxel_id          pt_root_id  \\\n",
       "0      84493     t  82615517542161729  720575940638857048   \n",
       "1      30981     t  77972211084042129  720575940610698052   \n",
       "2      63470     t  79731085956690501  720575940626976522   \n",
       "3      63471     t  79730948451135673  720575940605328230   \n",
       "4      63472     t  79730948451038265  720575940606001074   \n",
       "...      ...   ...                ...                 ...   \n",
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       "85970  87458     t  85643160113858648  720575940615348130   \n",
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       "85973  87468     t  75229479396070775  720575940620611157   \n",
       "\n",
       "                                                     tag  user_id  \\\n",
       "0      LPLC2_L, lobula plate-lobula columnar neuron 2...      392   \n",
       "1                                 VLPl&d1_lateral; right     1063   \n",
       "2                                        DM1 / Or42b ORN     1019   \n",
       "3                                        DM1 / Or42b ORN     1019   \n",
       "4                                        DM1 / Or42b ORN     1019   \n",
       "...                                                  ...      ...   \n",
       "85969                                  Retinula axon; R8     1931   \n",
       "85970                                Retinula axon; R1-6     1931   \n",
       "85971                                  Retinula axon: R7     1927   \n",
       "85972                                  Retinula axon; R7     1929   \n",
       "85973                                  Retinula axon; R8     1929   \n",
       "\n",
       "                    pt_position  \n",
       "0      [699936, 181456, 181080]  \n",
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       "2       [529436, 219840, 83680]  \n",
       "3       [530340, 213700, 77240]  \n",
       "4       [531008, 211772, 74760]  \n",
       "...                         ...  \n",
       "85969  [837284, 285120, 133440]  \n",
       "85970  [874136, 285596, 136360]  \n",
       "85971  [290160, 353496, 162840]  \n",
       "85972  [268964, 338588, 161280]  \n",
       "85973  [268184, 338800, 161280]  \n",
       "\n",
       "[85974 rows x 7 columns]"
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     },
     "execution_count": 24,
     "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. 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"
     ]
    },
    {
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       "      <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\", 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": 30,
   "id": "f1f0c92e-7f12-452c-9297-1f83d02f8869",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_id = 720575940631693610\n",
    "\n",
    "presyn_df = client.materialize.query_view(\"valid_synapses_nt_np\", filter_in_dict={\"pre_pt_root_id\": [root_id]})\n",
    "postsyn_df = client.materialize.query_view(\"valid_synapses_nt_np\", filter_in_dict={\"post_pt_root_id\": [root_id]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ebf3575c-0f78-4ece-8e02-a870c184dbbb",
   "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>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>221436213</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940644968302</td>\n",
       "      <td>98.712677</td>\n",
       "      <td>118</td>\n",
       "      <td>0.509936</td>\n",
       "      <td>0.272994</td>\n",
       "      <td>0.172750</td>\n",
       "      <td>0.015273</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>0.027859</td>\n",
       "      <td>t</td>\n",
       "      <td>81561566725804451</td>\n",
       "      <td>81561566725806887</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[637856, 276884, 109120]</td>\n",
       "      <td>[637972, 276884, 109120]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>223434587</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940597669941</td>\n",
       "      <td>54.514755</td>\n",
       "      <td>57</td>\n",
       "      <td>0.765357</td>\n",
       "      <td>0.115891</td>\n",
       "      <td>0.107662</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.001443</td>\n",
       "      <td>0.007497</td>\n",
       "      <td>t</td>\n",
       "      <td>81561429287069071</td>\n",
       "      <td>81561429287053390</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[636804, 264976, 114760]</td>\n",
       "      <td>[636904, 264872, 114760]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>214034291</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940603637438</td>\n",
       "      <td>67.696281</td>\n",
       "      <td>65</td>\n",
       "      <td>0.132610</td>\n",
       "      <td>0.377170</td>\n",
       "      <td>0.244310</td>\n",
       "      <td>0.009690</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>0.189155</td>\n",
       "      <td>t</td>\n",
       "      <td>81561223128872977</td>\n",
       "      <td>81561223128885641</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[635924, 253824, 123200]</td>\n",
       "      <td>[636032, 253820, 123200]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75607825</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940485618968</td>\n",
       "      <td>60.265942</td>\n",
       "      <td>118</td>\n",
       "      <td>0.030305</td>\n",
       "      <td>0.076801</td>\n",
       "      <td>0.026933</td>\n",
       "      <td>0.007474</td>\n",
       "      <td>0.428630</td>\n",
       "      <td>0.429857</td>\n",
       "      <td>t</td>\n",
       "      <td>81349842084972100</td>\n",
       "      <td>81349842084967416</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[624612, 237832, 125600]</td>\n",
       "      <td>[624584, 237896, 125640]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>145360801</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940608212548</td>\n",
       "      <td>199.030396</td>\n",
       "      <td>79</td>\n",
       "      <td>0.063221</td>\n",
       "      <td>0.774963</td>\n",
       "      <td>0.125500</td>\n",
       "      <td>0.002389</td>\n",
       "      <td>0.011018</td>\n",
       "      <td>0.022909</td>\n",
       "      <td>t</td>\n",
       "      <td>81279473274125723</td>\n",
       "      <td>81279473274113577</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[620300, 236112, 119080]</td>\n",
       "      <td>[620348, 236128, 119040]</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>383</th>\n",
       "      <td>104889412</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940623693942</td>\n",
       "      <td>7.629858</td>\n",
       "      <td>70</td>\n",
       "      <td>0.238973</td>\n",
       "      <td>0.099027</td>\n",
       "      <td>0.106075</td>\n",
       "      <td>0.059795</td>\n",
       "      <td>0.160853</td>\n",
       "      <td>0.335278</td>\n",
       "      <td>t</td>\n",
       "      <td>81561635445196401</td>\n",
       "      <td>81561635445182582</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[637168, 278376, 105960]</td>\n",
       "      <td>[637320, 278288, 105960]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>82517660</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940432638195</td>\n",
       "      <td>162.705566</td>\n",
       "      <td>57</td>\n",
       "      <td>0.059498</td>\n",
       "      <td>0.768504</td>\n",
       "      <td>0.044680</td>\n",
       "      <td>0.007790</td>\n",
       "      <td>0.020414</td>\n",
       "      <td>0.099115</td>\n",
       "      <td>t</td>\n",
       "      <td>81772741610773993</td>\n",
       "      <td>81772741610760058</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[648424, 278604, 91040]</td>\n",
       "      <td>[648528, 278560, 91040]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>82133371</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940586498593</td>\n",
       "      <td>208.810974</td>\n",
       "      <td>73</td>\n",
       "      <td>0.665327</td>\n",
       "      <td>0.143955</td>\n",
       "      <td>0.071342</td>\n",
       "      <td>0.003440</td>\n",
       "      <td>0.052067</td>\n",
       "      <td>0.063869</td>\n",
       "      <td>t</td>\n",
       "      <td>81491541511996488</td>\n",
       "      <td>81561910256159610</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[634528, 295036, 91120]</td>\n",
       "      <td>[634588, 294992, 91080]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>104889407</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940620208293</td>\n",
       "      <td>37.372658</td>\n",
       "      <td>123</td>\n",
       "      <td>0.292735</td>\n",
       "      <td>0.159893</td>\n",
       "      <td>0.205434</td>\n",
       "      <td>0.035986</td>\n",
       "      <td>0.055993</td>\n",
       "      <td>0.249959</td>\n",
       "      <td>t</td>\n",
       "      <td>81561635445190517</td>\n",
       "      <td>81561635445174170</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[637132, 278416, 105920]</td>\n",
       "      <td>[637296, 278336, 105920]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>194338531</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>720575940585608809</td>\n",
       "      <td>15.259972</td>\n",
       "      <td>146</td>\n",
       "      <td>0.112456</td>\n",
       "      <td>0.159680</td>\n",
       "      <td>0.014500</td>\n",
       "      <td>0.045115</td>\n",
       "      <td>0.406888</td>\n",
       "      <td>0.261361</td>\n",
       "      <td>t</td>\n",
       "      <td>78042304950719299</td>\n",
       "      <td>78042304950716343</td>\n",
       "      <td>PVLP_L</td>\n",
       "      <td>[431992, 225436, 140960]</td>\n",
       "      <td>[431908, 225472, 140920]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>388 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id      pre_pt_root_id     post_pt_root_id  connection_score  \\\n",
       "0    221436213  720575940631693610  720575940644968302         98.712677   \n",
       "1    223434587  720575940631693610  720575940597669941         54.514755   \n",
       "2    214034291  720575940631693610  720575940603637438         67.696281   \n",
       "3     75607825  720575940631693610  720575940485618968         60.265942   \n",
       "4    145360801  720575940631693610  720575940608212548        199.030396   \n",
       "..         ...                 ...                 ...               ...   \n",
       "383  104889412  720575940631693610  720575940623693942          7.629858   \n",
       "384   82517660  720575940631693610  720575940432638195        162.705566   \n",
       "385   82133371  720575940631693610  720575940586498593        208.810974   \n",
       "386  104889407  720575940631693610  720575940620208293         37.372658   \n",
       "387  194338531  720575940631693610  720575940585608809         15.259972   \n",
       "\n",
       "     cleft_score      gaba       ach      glut       oct       ser        da  \\\n",
       "0            118  0.509936  0.272994  0.172750  0.015273  0.001187  0.027859   \n",
       "1             57  0.765357  0.115891  0.107662  0.002150  0.001443  0.007497   \n",
       "2             65  0.132610  0.377170  0.244310  0.009690  0.047065  0.189155   \n",
       "3            118  0.030305  0.076801  0.026933  0.007474  0.428630  0.429857   \n",
       "4             79  0.063221  0.774963  0.125500  0.002389  0.011018  0.022909   \n",
       "..           ...       ...       ...       ...       ...       ...       ...   \n",
       "383           70  0.238973  0.099027  0.106075  0.059795  0.160853  0.335278   \n",
       "384           57  0.059498  0.768504  0.044680  0.007790  0.020414  0.099115   \n",
       "385           73  0.665327  0.143955  0.071342  0.003440  0.052067  0.063869   \n",
       "386          123  0.292735  0.159893  0.205434  0.035986  0.055993  0.249959   \n",
       "387          146  0.112456  0.159680  0.014500  0.045115  0.406888  0.261361   \n",
       "\n",
       "    valid_nt  pre_pt_supervoxel_id  post_pt_supervoxel_id neuropil  \\\n",
       "0          t     81561566725804451      81561566725806887   AVLP_R   \n",
       "1          t     81561429287069071      81561429287053390   AVLP_R   \n",
       "2          t     81561223128872977      81561223128885641   PVLP_R   \n",
       "3          t     81349842084972100      81349842084967416   PVLP_R   \n",
       "4          t     81279473274125723      81279473274113577   AVLP_R   \n",
       "..       ...                   ...                    ...      ...   \n",
       "383        t     81561635445196401      81561635445182582   AVLP_R   \n",
       "384        t     81772741610773993      81772741610760058   AVLP_R   \n",
       "385        t     81491541511996488      81561910256159610   AMMC_R   \n",
       "386        t     81561635445190517      81561635445174170   AVLP_R   \n",
       "387        t     78042304950719299      78042304950716343   PVLP_L   \n",
       "\n",
       "              pre_pt_position          post_pt_position  \n",
       "0    [637856, 276884, 109120]  [637972, 276884, 109120]  \n",
       "1    [636804, 264976, 114760]  [636904, 264872, 114760]  \n",
       "2    [635924, 253824, 123200]  [636032, 253820, 123200]  \n",
       "3    [624612, 237832, 125600]  [624584, 237896, 125640]  \n",
       "4    [620300, 236112, 119080]  [620348, 236128, 119040]  \n",
       "..                        ...                       ...  \n",
       "383  [637168, 278376, 105960]  [637320, 278288, 105960]  \n",
       "384   [648424, 278604, 91040]   [648528, 278560, 91040]  \n",
       "385   [634528, 295036, 91120]   [634588, 294992, 91080]  \n",
       "386  [637132, 278416, 105920]  [637296, 278336, 105920]  \n",
       "387  [431992, 225436, 140960]  [431908, 225472, 140920]  \n",
       "\n",
       "[388 rows x 17 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "presyn_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35e0ee3d-ddaf-4983-8904-daa5dc007c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>94679747</td>\n",
       "      <td>720575940652611745</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>25.088167</td>\n",
       "      <td>73</td>\n",
       "      <td>0.019709</td>\n",
       "      <td>0.444505</td>\n",
       "      <td>0.213508</td>\n",
       "      <td>1.926297e-02</td>\n",
       "      <td>0.028767</td>\n",
       "      <td>0.274249</td>\n",
       "      <td>t</td>\n",
       "      <td>81561017037206984</td>\n",
       "      <td>81561017037209403</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[637800, 240848, 133320]</td>\n",
       "      <td>[637944, 240844, 133360]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97560735</td>\n",
       "      <td>720575940622337635</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>276.129608</td>\n",
       "      <td>155</td>\n",
       "      <td>0.964871</td>\n",
       "      <td>0.006899</td>\n",
       "      <td>0.025409</td>\n",
       "      <td>9.002436e-05</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.002266</td>\n",
       "      <td>t</td>\n",
       "      <td>81702304147021774</td>\n",
       "      <td>81702304147017103</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[645416, 276700, 87760]</td>\n",
       "      <td>[645352, 276776, 87760]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>119758975</td>\n",
       "      <td>720575940623084170</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>389.490631</td>\n",
       "      <td>146</td>\n",
       "      <td>0.764292</td>\n",
       "      <td>0.032513</td>\n",
       "      <td>0.200262</td>\n",
       "      <td>8.977578e-06</td>\n",
       "      <td>0.001071</td>\n",
       "      <td>0.001853</td>\n",
       "      <td>t</td>\n",
       "      <td>81561841537024413</td>\n",
       "      <td>81561841537021981</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[635064, 292780, 101040]</td>\n",
       "      <td>[635012, 292660, 101040]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>126094786</td>\n",
       "      <td>720575940637197710</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>211.466309</td>\n",
       "      <td>143</td>\n",
       "      <td>0.009558</td>\n",
       "      <td>0.976294</td>\n",
       "      <td>0.002785</td>\n",
       "      <td>2.238585e-03</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.008869</td>\n",
       "      <td>t</td>\n",
       "      <td>81491129195437387</td>\n",
       "      <td>81491129195426050</td>\n",
       "      <td>AVLP_R</td>\n",
       "      <td>[631072, 270828, 100640]</td>\n",
       "      <td>[631000, 270900, 100600]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>124774233</td>\n",
       "      <td>720575940603533246</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>538.636658</td>\n",
       "      <td>144</td>\n",
       "      <td>0.919018</td>\n",
       "      <td>0.028537</td>\n",
       "      <td>0.015342</td>\n",
       "      <td>5.329636e-07</td>\n",
       "      <td>0.036865</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>t</td>\n",
       "      <td>81421310206520252</td>\n",
       "      <td>81421310206522745</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[628412, 305036, 84200]</td>\n",
       "      <td>[628464, 304936, 84200]</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>225949607</td>\n",
       "      <td>720575940615528678</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>258.338348</td>\n",
       "      <td>147</td>\n",
       "      <td>0.468469</td>\n",
       "      <td>0.006129</td>\n",
       "      <td>0.482845</td>\n",
       "      <td>3.717917e-02</td>\n",
       "      <td>0.002674</td>\n",
       "      <td>0.002704</td>\n",
       "      <td>t</td>\n",
       "      <td>81349842085339332</td>\n",
       "      <td>81349842085329400</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[625272, 236448, 137120]</td>\n",
       "      <td>[625252, 236560, 137080]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>72010224</td>\n",
       "      <td>720575940619325424</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>775.096008</td>\n",
       "      <td>142</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.005188</td>\n",
       "      <td>0.232250</td>\n",
       "      <td>9.976415e-03</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>t</td>\n",
       "      <td>81349979457153703</td>\n",
       "      <td>81349979457163985</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[624580, 246316, 116160]</td>\n",
       "      <td>[624572, 246192, 116160]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>133761713</td>\n",
       "      <td>720575940631842360</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>321.287170</td>\n",
       "      <td>140</td>\n",
       "      <td>0.503800</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.493103</td>\n",
       "      <td>1.171101e-03</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>t</td>\n",
       "      <td>81491404139912789</td>\n",
       "      <td>81491404139909039</td>\n",
       "      <td>SAD</td>\n",
       "      <td>[633664, 285820, 104600]</td>\n",
       "      <td>[633608, 285696, 104600]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>179794968</td>\n",
       "      <td>720575940622559512</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>11.471473</td>\n",
       "      <td>146</td>\n",
       "      <td>0.020073</td>\n",
       "      <td>0.549246</td>\n",
       "      <td>0.068511</td>\n",
       "      <td>2.865043e-03</td>\n",
       "      <td>0.015128</td>\n",
       "      <td>0.344178</td>\n",
       "      <td>t</td>\n",
       "      <td>81491404006288815</td>\n",
       "      <td>81491404006292828</td>\n",
       "      <td>AMMC_R</td>\n",
       "      <td>[631136, 286916, 81520]</td>\n",
       "      <td>[631136, 286792, 81520]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>90958005</td>\n",
       "      <td>720575940622330582</td>\n",
       "      <td>720575940631693610</td>\n",
       "      <td>949.162354</td>\n",
       "      <td>91</td>\n",
       "      <td>0.564561</td>\n",
       "      <td>0.075302</td>\n",
       "      <td>0.233075</td>\n",
       "      <td>1.126638e-02</td>\n",
       "      <td>0.021780</td>\n",
       "      <td>0.094016</td>\n",
       "      <td>t</td>\n",
       "      <td>81209104596624702</td>\n",
       "      <td>81209104596626048</td>\n",
       "      <td>PVLP_R</td>\n",
       "      <td>[614928, 236116, 126440]</td>\n",
       "      <td>[614848, 236048, 126440]</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      94679747  720575940652611745  720575940631693610         25.088167   \n",
       "1      97560735  720575940622337635  720575940631693610        276.129608   \n",
       "2     119758975  720575940623084170  720575940631693610        389.490631   \n",
       "3     126094786  720575940637197710  720575940631693610        211.466309   \n",
       "4     124774233  720575940603533246  720575940631693610        538.636658   \n",
       "...         ...                 ...                 ...               ...   \n",
       "1917  225949607  720575940615528678  720575940631693610        258.338348   \n",
       "1918   72010224  720575940619325424  720575940631693610        775.096008   \n",
       "1919  133761713  720575940631842360  720575940631693610        321.287170   \n",
       "1920  179794968  720575940622559512  720575940631693610         11.471473   \n",
       "1921   90958005  720575940622330582  720575940631693610        949.162354   \n",
       "\n",
       "      cleft_score      gaba       ach      glut           oct       ser  \\\n",
       "0              73  0.019709  0.444505  0.213508  1.926297e-02  0.028767   \n",
       "1             155  0.964871  0.006899  0.025409  9.002436e-05  0.000464   \n",
       "2             146  0.764292  0.032513  0.200262  8.977578e-06  0.001071   \n",
       "3             143  0.009558  0.976294  0.002785  2.238585e-03  0.000254   \n",
       "4             144  0.919018  0.028537  0.015342  5.329636e-07  0.036865   \n",
       "...           ...       ...       ...       ...           ...       ...   \n",
       "1917          147  0.468469  0.006129  0.482845  3.717917e-02  0.002674   \n",
       "1918          142  0.750177  0.005188  0.232250  9.976415e-03  0.000996   \n",
       "1919          140  0.503800  0.001675  0.493103  1.171101e-03  0.000034   \n",
       "1920          146  0.020073  0.549246  0.068511  2.865043e-03  0.015128   \n",
       "1921           91  0.564561  0.075302  0.233075  1.126638e-02  0.021780   \n",
       "\n",
       "            da valid_nt  pre_pt_supervoxel_id  post_pt_supervoxel_id neuropil  \\\n",
       "0     0.274249        t     81561017037206984      81561017037209403   PVLP_R   \n",
       "1     0.002266        t     81702304147021774      81702304147017103   AVLP_R   \n",
       "2     0.001853        t     81561841537024413      81561841537021981   AMMC_R   \n",
       "3     0.008869        t     81491129195437387      81491129195426050   AVLP_R   \n",
       "4     0.000237        t     81421310206520252      81421310206522745   AMMC_R   \n",
       "...        ...      ...                   ...                    ...      ...   \n",
       "1917  0.002704        t     81349842085339332      81349842085329400   PVLP_R   \n",
       "1918  0.001413        t     81349979457153703      81349979457163985   PVLP_R   \n",
       "1919  0.000217        t     81491404139912789      81491404139909039      SAD   \n",
       "1920  0.344178        t     81491404006288815      81491404006292828   AMMC_R   \n",
       "1921  0.094016        t     81209104596624702      81209104596626048   PVLP_R   \n",
       "\n",
       "               pre_pt_position          post_pt_position  \n",
       "0     [637800, 240848, 133320]  [637944, 240844, 133360]  \n",
       "1      [645416, 276700, 87760]   [645352, 276776, 87760]  \n",
       "2     [635064, 292780, 101040]  [635012, 292660, 101040]  \n",
       "3     [631072, 270828, 100640]  [631000, 270900, 100600]  \n",
       "4      [628412, 305036, 84200]   [628464, 304936, 84200]  \n",
       "...                        ...                       ...  \n",
       "1917  [625272, 236448, 137120]  [625252, 236560, 137080]  \n",
       "1918  [624580, 246316, 116160]  [624572, 246192, 116160]  \n",
       "1919  [633664, 285820, 104600]  [633608, 285696, 104600]  \n",
       "1920   [631136, 286916, 81520]   [631136, 286792, 81520]  \n",
       "1921  [614928, 236116, 126440]  [614848, 236048, 126440]  \n",
       "\n",
       "[1922 rows x 17 columns]"
      ]
     },
     "execution_count": 29,
     "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": 70,
   "id": "55b9a3f3-4bd2-4f6a-9504-c2db2692f847",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>valid</th>\n",
       "      <th>target_id</th>\n",
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       "      <th>cell_type</th>\n",
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       "      <td>109940</td>\n",
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       "      <td>720575940627905383</td>\n",
       "      <td>[234729, 191865, 193858]</td>\n",
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       "<p>127978 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id valid  target_id classification_system cell_type  id_ref  \\\n",
       "0       503943     t      24809           super_class   central   24809   \n",
       "1       503944     t       3439           super_class   central    3439   \n",
       "2       503945     t      42371           super_class   central   42371   \n",
       "3       503946     t      15670           super_class   central   15670   \n",
       "4       503947     t      34597           super_class   central   34597   \n",
       "...        ...   ...        ...                   ...       ...     ...   \n",
       "127973  631916     t     109940           super_class     optic  109940   \n",
       "127974  631917     t     110216           super_class     optic  110216   \n",
       "127975  631918     t     111137           super_class     optic  111137   \n",
       "127976  631919     t     112845           super_class     optic  112845   \n",
       "127977  631920     t     127977           super_class     optic  127977   \n",
       "\n",
       "       valid_ref   pt_supervoxel_id          pt_root_id  \\\n",
       "0              t  81350666718746524  720575940618149686   \n",
       "1              t  80717416606245362  720575940628675433   \n",
       "2              t  77337311972566683  720575940630166583   \n",
       "3              t  80787785350455829  720575940630738683   \n",
       "4              t  78676860598498552  720575940624706062   \n",
       "...          ...                ...                 ...   \n",
       "127973         t  74664055675374018  720575940627905383   \n",
       "127974         t  82689047382048903  720575940628019985   \n",
       "127975         t  81984535373702682  720575940639551797   \n",
       "127976         t  74171543118689555  720575940611095160   \n",
       "127977         t  75297580732428963  720575940641724661   \n",
       "\n",
       "                     pt_position  \n",
       "0       [624510, 288189, 127797]  \n",
       "1        [586065, 292954, 82718]  \n",
       "2       [389107, 148134, 167223]  \n",
       "3        [592115, 291905, 83257]  \n",
       "4       [468615, 298327, 130296]  \n",
       "...                          ...  \n",
       "127973  [234729, 191865, 193858]  \n",
       "127974  [702449, 370626, 173721]  \n",
       "127975  [663170, 321144, 196507]  \n",
       "127976  [205765, 196206, 178359]  \n",
       "127977  [273840, 204492, 246800]  \n",
       "\n",
       "[127978 rows x 10 columns]"
      ]
     },
     "execution_count": 70,
     "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": 52,
   "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": 53,
   "id": "ca7829ce-65df-4892-8c64-eb49cf0b46ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_id = 720575940631693610\n",
    "\n",
    "presyn_df = client.materialize.query_view(\"valid_synapses_nt_np\", filter_in_dict={\"pre_pt_root_id\": [root_id]})\n",
    "postsyn_df = client.materialize.query_view(\"valid_synapses_nt_np\", 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": 67,
   "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": 72,
   "id": "c834a5ea-8fa8-4d88-ac5a-6eb79e1f82c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='pre_super_class', ylabel='count'>"
      ]
     },
     "execution_count": 72,
     "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": 74,
   "id": "9e577dda-f5b8-4bdb-ac46-4cd3680562a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='post_super_class', ylabel='count'>"
      ]
     },
     "execution_count": 74,
     "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
}