Align_neural_activity_with_stimulus_changes.ipynb

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  "metadata": {
    "colab": {
      "name": "Align neural activity with stimulus changes.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/odmina/misbehaving_armadillos/blob/main/Align_neural_activity_with_stimulus_changes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1CeSqXjhY2k"
      },
      "source": [
        "# Overview\n",
        "The following notebook loads data from single behavioral session (one mouse) and one imaging experiment (just one plane) associated with that session to plot stimulus, licks and reward data and neural activity (one selected neuron). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aT9844s2hkvw"
      },
      "source": [
        "# Set up environment and import packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00Maa0v-hvoD"
      },
      "source": [
        "We have built a package called `mindscope_utilities` which contains some useful convenience functions. The `allenSDK` is a dependency of this package and will be automatically installed when you install `mindscope_utilities` per the instrutions below.\n",
        "\n",
        "We will first install `mindscope_utilities` into our colab environment by running the commands below. When this cell is complete, click on the `RESTART RUNTIME` button that appears at the end of the output. Note that running this cell will produce a long list of outputs and some error messages. Clicking `RESTART RUNTIME` at the end will resolve these issues. \n",
        "\n",
        "You can minimize the cell after you are done to hide the output. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPF6qq5jLmNL"
      },
      "source": [
        "#### Install necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Xm2F5-0aIN2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "937b52d8-2314-4b35-e3a9-a97772f8e9d9"
      },
      "source": [
        "!python -m pip install --upgrade pip\n",
        "!pip install mindscope_utilities --upgrade"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "  Created wheel for argschema: filename=argschema-2.0.2-py2.py3-none-any.whl size=17795 sha256=5bb697428d07025e199f1095a160d1b0b2af08f38893f4af2ac206fe080680ed\n",
            "  Stored in directory: /root/.cache/pip/wheels/ee/97/a8/4dbf0458be0af473d083a6354c0c126490e82447237bb0b907\n",
            "Successfully built mindscope-utilities glymur argschema\n",
            "Installing collected packages: ruamel.yaml.clib, numpy, urllib3, ruamel.yaml, pandas, jmespath, h5py, multidict, hdmf, botocore, yarl, s3transfer, pynwb, marshmallow, distro, async-timeout, xarray, tables, simplejson, simpleitk, scikit-build, requests-toolbelt, pynrrd, pyflakes, pycodestyle, psycopg2-binary, nest-asyncio, ndx-events, mccabe, glymur, boto3, argschema, aiohttp, flake8, allensdk, mindscope-utilities\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.19.5\n",
            "    Uninstalling numpy-1.19.5:\n",
            "      Successfully uninstalled numpy-1.19.5\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.1.5\n",
            "    Uninstalling pandas-1.1.5:\n",
            "      Successfully uninstalled pandas-1.1.5\n",
            "  Attempting uninstall: h5py\n",
            "    Found existing installation: h5py 3.1.0\n",
            "    Uninstalling h5py-3.1.0:\n",
            "      Successfully uninstalled h5py-3.1.0\n",
            "  Attempting uninstall: xarray\n",
            "    Found existing installation: xarray 0.18.2\n",
            "    Uninstalling xarray-0.18.2:\n",
            "      Successfully uninstalled xarray-0.18.2\n",
            "  Attempting uninstall: tables\n",
            "    Found existing installation: tables 3.4.4\n",
            "    Uninstalling tables-3.4.4:\n",
            "      Successfully uninstalled tables-3.4.4\n",
            "  Attempting uninstall: nest-asyncio\n",
            "    Found existing installation: nest-asyncio 1.5.1\n",
            "    Uninstalling nest-asyncio-1.5.1:\n",
            "      Successfully uninstalled nest-asyncio-1.5.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.5.0 requires h5py~=3.1.0, but you have h5py 2.10.0 which is incompatible.\n",
            "tensorflow 2.5.0 requires numpy~=1.19.2, but you have numpy 1.18.5 which is incompatible.\n",
            "nbclient 0.5.3 requires jupyter-client>=6.1.5, but you have jupyter-client 5.3.5 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas~=1.1.0; python_version >= \"3.0\", but you have pandas 0.25.3 which is incompatible.\n",
            "fbprophet 0.7.1 requires pandas>=1.0.4, but you have pandas 0.25.3 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "arviz 0.11.2 requires xarray>=0.16.1, but you have xarray 0.15.1 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed aiohttp-3.7.4 allensdk-2.11.2 argschema-2.0.2 async-timeout-3.0.1 boto3-1.17.21 botocore-1.20.112 distro-1.5.0 flake8-3.9.2 glymur-0.8.19 h5py-2.10.0 hdmf-2.4.0 jmespath-0.10.0 marshmallow-3.0.0rc6 mccabe-0.6.1 mindscope-utilities-0.1.7 multidict-5.1.0 ndx-events-0.2.0 nest-asyncio-1.2.0 numpy-1.18.5 pandas-0.25.3 psycopg2-binary-2.9.1 pycodestyle-2.7.0 pyflakes-2.3.1 pynrrd-0.4.2 pynwb-1.4.0 requests-toolbelt-0.9.1 ruamel.yaml-0.17.10 ruamel.yaml.clib-0.2.6 s3transfer-0.3.7 scikit-build-0.11.1 simpleitk-2.0.2 simplejson-3.17.3 tables-3.6.1 urllib3-1.25.11 xarray-0.15.1 yarl-1.6.3\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy",
                  "pandas"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iT7O4WaiYAH"
      },
      "source": [
        "#### Next we will import packages we need later in the notebook"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgFHDxOcaJut"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import mindscope_utilities\n",
        "import mindscope_utilities.visual_behavior_ophys as ophys\n",
        "\n",
        "from allensdk.brain_observatory.behavior.behavior_project_cache import VisualBehaviorOphysProjectCache\n",
        "\n",
        "pd.set_option('display.max_columns', 500)"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjFtKoQ2id_6"
      },
      "source": [
        "# Load the session and experiment summary tables\n",
        "\n",
        "We download the data cache:\n",
        "\n",
        "- `ophys_session_table` contains data describing imaging sessions - one session can contain more than one imaging experiment (more than one plane), and not all behavioral session are accompanied by imaging. contains metadata describing imaging sessions. If more than one plane was imaged during a session, one ophys session id will be associated multiple *ophys experiment ids*. Each ophys session id will also have a unique *behavior session id*\n",
        "- `behavior_session_table` contains metadata describing behavioral sessions, which may or may not be during imaging. Behavior session ids that do not have *ophys session ids* were training sessions\n",
        "- `ophys_experiment_table` contains metadata describing imaging experiments (aka imaging planes). When mesoscope is used, one ophys session may contain up to 8 unique experiments (two visual areas by four imaging depths). Some imaging planes may not be released due to quality control issues, thus each ophys session id is associated with anywhere from one to eight unique *experiment ids*. Ophys experiment ids are unique and do not repeat across sessions. To find the same imaging plane that was matched across multiple sessions, use the *ophys_container_id* column that can be found in both ophys_session_table and ophys_experiment_table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvamvtoUbWTR"
      },
      "source": [
        "data_storage_directory = \"/temp\" # Note: this path must exist on your local drive\n",
        "cache = VisualBehaviorOphysProjectCache.from_s3_cache(cache_dir=data_storage_directory)"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XL5LV5fti5tc"
      },
      "source": [
        "Then we can access the session and experiment tables directly.\n",
        "\n",
        "Note that a 'session' is a single behavioral session. Sessions that are performed on the mesoscope will have multiple (up to 8) 'experiments' associated with them, where an experiment is a distinct imaging plane. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOCbdeNbd7w8"
      },
      "source": [
        "session_table = cache.get_ophys_session_table()\n",
        "experiment_table = cache.get_ophys_experiment_table()\n",
        "behavior_session_table = cache.get_behavior_session_table()"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jUguZ4QGjQF7"
      },
      "source": [
        "We can then view the contents of the session table. Note that this contains a lot of useful metadata about each session. One of the columns, `ophys_experiment_id` provides a list of the experiments (aka imaging planes) that are associated with each session."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ai6Fuk4RbH8Z"
      },
      "source": [
        "# EXTRACT AND AVERAGE ACTIVITY OF ALL THE CELLS IN THE SESSION\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nu0ZivXfrzXT"
      },
      "source": [
        "### Make a dictionary of experiments in one session"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWiK9667ryhq"
      },
      "source": [
        "def session_experiments(ophys_session_id):\n",
        "  experiments = {}\n",
        "  ophys_experiment_ids = session_table.loc[ophys_session_id]['ophys_experiment_id']\n",
        "  for ophys_experiment_id in ophys_experiment_ids:\n",
        "    experiments[ophys_experiment_id] = cache.get_behavior_ophys_experiment(ophys_experiment_id)\n",
        "  return experiments"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1spAdQsKsnNJ"
      },
      "source": [
        "### Make a neural data dataset for one session"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nnZxXzVq2yP"
      },
      "source": [
        "def session_neural_data(this_session_experiments):\n",
        "  neural_data = []\n",
        "  for ophys_experiment_id in tqdm(this_session_experiments.keys()): #tqdm is a package that shows progress bars for items that are iterated over\n",
        "    this_experiment = this_session_experiments[ophys_experiment_id]\n",
        "    this_experiment_neural_data = ophys.build_tidy_cell_df(this_experiment)\n",
        "    \n",
        "    # add some columns with metadata for the experiment\n",
        "    metadata_keys = [\n",
        "        'ophys_experiment_id',\n",
        "        'ophys_session_id',\n",
        "        'targeted_structure',\n",
        "        'imaging_depth',\n",
        "        'equipment_name',\n",
        "        'cre_line',\n",
        "        'mouse_id',\n",
        "        'sex',\n",
        "    ]\n",
        "    for metadata_key in metadata_keys:\n",
        "        this_experiment_neural_data[metadata_key] = this_experiment.metadata[metadata_key]\n",
        "        \n",
        "    # append the data for this experiment to a list\n",
        "    neural_data.append(this_experiment_neural_data)\n",
        "    \n",
        "  # concatate the list of dataframes into a single dataframe\n",
        "  neural_data = pd.concat(neural_data)\n",
        "  \n",
        "  return neural_data"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOoVNIqKt9N4"
      },
      "source": [
        "### Make a session dictionary including a list of experiments and a dataframe with neural data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl6Ciw0duD6m"
      },
      "source": [
        "def make_session_dictionary_entry(ophys_session_id):\n",
        "  this_session_experiments = session_experiments(ophys_session_id) #creates a dictionary of experiments in the session (experiment_id as a key)\n",
        "  this_session_neural_data = session_neural_data(this_session_experiments)\n",
        "  return [this_session_experiments, this_session_neural_data]"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAsVdGFLY5mk",
        "outputId": "34b8ef18-19e7-4b20-cb4d-2f180282c396"
      },
      "source": [
        "ophys_session_ids = [993727065, 954981981, 992232718, 977726123, 1010159622, 1000580083, 982431228]\n",
        "session_dictionary = {}\n",
        "for ophys_session_id in ophys_session_ids:\n",
        "  session_dictionary[ophys_session_id] = make_session_dictionary_entry(ophys_session_id)\n",
        "\n"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1/1 [00:09<00:00,  9.21s/it]\n",
            "100%|██████████| 1/1 [00:09<00:00,  9.14s/it]\n",
            "100%|██████████| 1/1 [00:09<00:00,  9.80s/it]\n",
            "100%|██████████| 1/1 [00:06<00:00,  6.32s/it]\n",
            "100%|██████████| 1/1 [00:07<00:00,  7.43s/it]\n",
            "100%|██████████| 1/1 [00:07<00:00,  7.05s/it]\n",
            "100%|██████████| 1/1 [00:05<00:00,  5.95s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTHZyukxroxX"
      },
      "source": [
        "### Extract data for one cell and align it with event times"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnxbUx1lbcZc"
      },
      "source": [
        "def align_cell_with_event(ophys_session_id, ophys_experiment_id, cell_specimen_id, window_width = 3, session_dictionary = session_dictionary):\n",
        "  #the function requires neural data for all the experiments in the session and a dictionary with experiments ids\n",
        "\n",
        "  #identify experiment & corresponding neural data\n",
        "  this_experiment = session_dictionary[ophys_session_id][0][ophys_experiment_id]\n",
        "  neural_data = session_dictionary[ophys_session_id][1]\n",
        "\n",
        "  # extract hits\n",
        "  etr_hit = mindscope_utilities.event_triggered_response(\n",
        "    data = neural_data.query('ophys_experiment_id == @ophys_experiment_id and cell_specimen_id == @cell_specimen_id'),\n",
        "    t = 'timestamps',\n",
        "    y = 'dff',\n",
        "    event_times = this_experiment.trials.query('hit')['change_time'],\n",
        "    t_before=window_width,\n",
        "    t_after=window_width,\n",
        "    output_sampling_rate = 50,\n",
        "  )\n",
        "  # add hit indicator\n",
        "  etr_hit['hit_miss'] = 'hit'\n",
        "\n",
        "  # extract misses\n",
        "  etr_miss = mindscope_utilities.event_triggered_response(\n",
        "    data = neural_data.query('ophys_experiment_id == @ophys_experiment_id and cell_specimen_id == @cell_specimen_id'),\n",
        "    t = 'timestamps',\n",
        "    y = 'dff',\n",
        "    event_times = this_experiment.trials.query('miss')['change_time'],\n",
        "    t_before=window_width,\n",
        "    t_after=window_width,\n",
        "    output_sampling_rate = 50,\n",
        "  )\n",
        "  # add miss indicator\n",
        "  etr_miss['hit_miss'] = 'miss'\n",
        "\n",
        "  #concatenate\n",
        "  etr = pd.concat([etr_hit, etr_miss])\n",
        "\n",
        "  #add session and cell specimen id, metadata\n",
        "  etr['ophys_session_id'] = ophys_session_id\n",
        "  etr['ophys_experiment_id'] = ophys_experiment_id  \n",
        "  etr['cell_specimen_id'] = cell_specimen_id\n",
        "  etr['cell_roi_id'] = this_experiment.dff_traces.loc[cell_specimen_id]['cell_roi_id']\n",
        "  etr['cre_line'] = this_experiment.metadata['cre_line']\n",
        "  etr['imaging_depth'] = this_experiment.metadata['imaging_depth']\n",
        "  etr['mouse_id'] = this_experiment.metadata['mouse_id']\n",
        "  etr['sex'] = this_experiment.metadata['sex']\n",
        "\n",
        "  return etr"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lYZNXfizWiYK"
      },
      "source": [
        "### Get cells from one session"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0S-40uLPWgTr"
      },
      "source": [
        "def get_cells_from_session(ophys_session_id, session_dictionary = session_dictionary):\n",
        "  ophys_experiment_ids = list(session_dictionary[ophys_session_id][0].keys())\n",
        "\n",
        "  aligned_cells = []\n",
        "  for ophys_experiment_id in ophys_experiment_ids:\n",
        "    cells_specimen_ids = session_dictionary[ophys_session_id][0][ophys_experiment_id].dff_traces.index\n",
        "    for cell_specimen_id in cells_specimen_ids:\n",
        "      print('Session:', ophys_session_id, 'Experiment:', ophys_experiment_id, 'Cell:', cell_specimen_id)\n",
        "      this_cell = align_cell_with_event(ophys_session_id, ophys_experiment_id, cell_specimen_id)\n",
        "      aligned_cells.append(this_cell)\n",
        "  \n",
        "  aligned_cells = pd.concat(aligned_cells)\n",
        "\n",
        "  return aligned_cells"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bdttWqUlWSt",
        "outputId": "13ae0767-66a5-44a7-fed2-20ccf63f6769"
      },
      "source": [
        "my_cells = []\n",
        "for ophys_session_id in ophys_session_ids:\n",
        "  this_cells = get_cells_from_session(ophys_session_id)\n",
        "  my_cells.append(this_cells)\n",
        "  print(type(my_cells))\n",
        "  #print(len(this_cells))\n",
        "  #print(this_cells.keys())\n",
        "\n",
        "my_cells = pd.concat(my_cells)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Session: 993727065 Experiment: 993862620 Cell: 1086496500\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086496689\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086497129\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086497550\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086500812\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086497908\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086498071\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086498205\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086498334\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086498461\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086498820\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086498977\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086499259\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086499316\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086499479\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086499910\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086500121\n",
            "Session: 993727065 Experiment: 993862620 Cell: 1086500281\n",
            "<class 'list'>\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490519\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490561\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490598\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490636\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490697\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490748\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490784\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490867\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086490995\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086491035\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086491079\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086491172\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086491215\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086491256\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086491328\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086491397\n",
            "Session: 954981981 Experiment: 955276580 Cell: 1086491469\n",
            "<class 'list'>\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490391\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086491368\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490482\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490526\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490572\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490648\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490689\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490777\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490794\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490839\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490936\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086490994\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086491066\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086491194\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086491263\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086491339\n",
            "Session: 992232718 Experiment: 992296756 Cell: 1086491760\n",
            "<class 'list'>\n",
            "Session: 977726123 Experiment: 977935606 Cell: 1086499499\n",
            "Session: 977726123 Experiment: 977935606 Cell: 1086499628\n",
            "Session: 977726123 Experiment: 977935606 Cell: 1086499783\n",
            "Session: 977726123 Experiment: 977935606 Cell: 1086499911\n",
            "Session: 977726123 Experiment: 977935606 Cell: 1086500092\n",
            "Session: 977726123 Experiment: 977935606 Cell: 1086500194\n",
            "Session: 977726123 Experiment: 977935606 Cell: 1086500392\n",
            "Session: 977726123 Experiment: 977935606 Cell: 1086500633\n",
            "<class 'list'>\n",
            "Session: 1010159622 Experiment: 1010256573 Cell: 1086500583\n",
            "Session: 1010159622 Experiment: 1010256573 Cell: 1086500807\n",
            "Session: 1010159622 Experiment: 1010256573 Cell: 1086501072\n",
            "Session: 1010159622 Experiment: 1010256573 Cell: 1086501262\n",
            "Session: 1010159622 Experiment: 1010256573 Cell: 1086501360\n",
            "Session: 1010159622 Experiment: 1010256573 Cell: 1086501675\n",
            "Session: 1010159622 Experiment: 1010256573 Cell: 1086502269\n",
            "<class 'list'>\n",
            "Session: 1000580083 Experiment: 1000743008 Cell: 1086668247\n",
            "Session: 1000580083 Experiment: 1000743008 Cell: 1086668144\n",
            "Session: 1000580083 Experiment: 1000743008 Cell: 1086668169\n",
            "Session: 1000580083 Experiment: 1000743008 Cell: 1086668188\n",
            "Session: 1000580083 Experiment: 1000743008 Cell: 1086668228\n",
            "<class 'list'>\n",
            "Session: 982431228 Experiment: 982862443 Cell: 1086490603\n",
            "Session: 982431228 Experiment: 982862443 Cell: 1086490729\n",
            "Session: 982431228 Experiment: 982862443 Cell: 1086490846\n",
            "Session: 982431228 Experiment: 982862443 Cell: 1086490807\n",
            "Session: 982431228 Experiment: 982862443 Cell: 1086490741\n",
            "Session: 982431228 Experiment: 982862443 Cell: 1086490751\n",
            "<class 'list'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "li-DLpGU3EdV",
        "outputId": "3ba766c6-70a2-49ee-cdc2-108e738a3f4c"
      },
      "source": [
        "len(my_cells)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5420107"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "tW9ZCRlFncZL",
        "outputId": "f3c1529f-7947-46b7-a036-a5d96c3c8359"
      },
      "source": [
        "my_cells.head()"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>dff</th>\n",
              "      <th>event_number</th>\n",
              "      <th>event_time</th>\n",
              "      <th>hit_miss</th>\n",
              "      <th>ophys_session_id</th>\n",
              "      <th>ophys_experiment_id</th>\n",
              "      <th>cell_specimen_id</th>\n",
              "      <th>cell_roi_id</th>\n",
              "      <th>cre_line</th>\n",
              "      <th>imaging_depth</th>\n",
              "      <th>mouse_id</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-3.00</td>\n",
              "      <td>-0.020865</td>\n",
              "      <td>0</td>\n",
              "      <td>389.91903</td>\n",
              "      <td>hit</td>\n",
              "      <td>993727065</td>\n",
              "      <td>993862620</td>\n",
              "      <td>1086496500</td>\n",
              "      <td>1080706889</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>275</td>\n",
              "      <td>476970</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.98</td>\n",
              "      <td>-0.042798</td>\n",
              "      <td>0</td>\n",
              "      <td>389.91903</td>\n",
              "      <td>hit</td>\n",
              "      <td>993727065</td>\n",
              "      <td>993862620</td>\n",
              "      <td>1086496500</td>\n",
              "      <td>1080706889</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>275</td>\n",
              "      <td>476970</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.96</td>\n",
              "      <td>-0.095776</td>\n",
              "      <td>0</td>\n",
              "      <td>389.91903</td>\n",
              "      <td>hit</td>\n",
              "      <td>993727065</td>\n",
              "      <td>993862620</td>\n",
              "      <td>1086496500</td>\n",
              "      <td>1080706889</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>275</td>\n",
              "      <td>476970</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-2.94</td>\n",
              "      <td>-0.106680</td>\n",
              "      <td>0</td>\n",
              "      <td>389.91903</td>\n",
              "      <td>hit</td>\n",
              "      <td>993727065</td>\n",
              "      <td>993862620</td>\n",
              "      <td>1086496500</td>\n",
              "      <td>1080706889</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>275</td>\n",
              "      <td>476970</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-2.92</td>\n",
              "      <td>-0.099691</td>\n",
              "      <td>0</td>\n",
              "      <td>389.91903</td>\n",
              "      <td>hit</td>\n",
              "      <td>993727065</td>\n",
              "      <td>993862620</td>\n",
              "      <td>1086496500</td>\n",
              "      <td>1080706889</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>275</td>\n",
              "      <td>476970</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   time       dff  event_number  event_time hit_miss  ophys_session_id  \\\n",
              "0 -3.00 -0.020865             0   389.91903      hit         993727065   \n",
              "1 -2.98 -0.042798             0   389.91903      hit         993727065   \n",
              "2 -2.96 -0.095776             0   389.91903      hit         993727065   \n",
              "3 -2.94 -0.106680             0   389.91903      hit         993727065   \n",
              "4 -2.92 -0.099691             0   389.91903      hit         993727065   \n",
              "\n",
              "   ophys_experiment_id  cell_specimen_id  cell_roi_id      cre_line  \\\n",
              "0            993862620        1086496500   1080706889  Sst-IRES-Cre   \n",
              "1            993862620        1086496500   1080706889  Sst-IRES-Cre   \n",
              "2            993862620        1086496500   1080706889  Sst-IRES-Cre   \n",
              "3            993862620        1086496500   1080706889  Sst-IRES-Cre   \n",
              "4            993862620        1086496500   1080706889  Sst-IRES-Cre   \n",
              "\n",
              "   imaging_depth  mouse_id sex  \n",
              "0            275    476970   M  \n",
              "1            275    476970   M  \n",
              "2            275    476970   M  \n",
              "3            275    476970   M  \n",
              "4            275    476970   M  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "qXWFC_xtjIuQ",
        "outputId": "6092fdb0-0d5a-4cce-ed97-8216e9ec852c"
      },
      "source": [
        "sns.lineplot(\n",
        "    data=my_cells,\n",
        "    x='time',\n",
        "    y='dff',\n",
        "    hue='hit_miss',\n",
        "    n_boot=500\n",
        ")"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b072659d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}