2021_07_15.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled3.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPuC3KAr4wsb+caFcZVhj6g",
      "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/2021_07_15.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wX4Bom-lrlOh"
      },
      "source": [
        "#Load data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "260n7Mtjq13T",
        "outputId": "9111c0f0-156f-4276-b15f-e09ee1067137"
      },
      "source": [
        "!python -m pip install --upgrade pip\n",
        "!pip install mindscope_utilities --upgrade"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "      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": "FjFtKoQ2id_6"
      },
      "source": [
        "# Load the session and experiment summary tables\n",
        "\n",
        "The AllenSDK provides functionality for downloading tables that describe all sessions and experiments (individual imaging planes) in the Visual Behavior 2P dataset. We first download the data cache:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXXuN2hqq9uu"
      },
      "source": [
        "# General python packages\n",
        "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",
        "# Allen Institute specific packages\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",
        "# makes notebook display nicer\n",
        "pd.set_option('display.max_columns', 500)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvamvtoUbWTR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c08a09f-3002-43c6-adfa-583e4e83efe9"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ophys_session_table.csv: 100%|██████████| 165k/165k [00:00<00:00, 1.20MMB/s] \n",
            "behavior_session_table.csv: 100%|██████████| 885k/885k [00:00<00:00, 5.95MMB/s] \n",
            "ophys_experiment_table.csv: 100%|██████████| 336k/336k [00:00<00:00, 2.46MMB/s] \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8i-bdFCLQImS"
      },
      "source": [
        "- `Ophys_session_table` 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": "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": null,
      "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "t5JqR9KxjUB_",
        "outputId": "bb90e32c-da67-4aa5-8a6f-e1da2ef3b9b1"
      },
      "source": [
        "session_table.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>VisualBehaviorMultiscope</td>\n",
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              "      <td>[1018028339, 1018028342, 1018028345, 101802835...</td>\n",
              "      <td>VisualBehaviorMultiscope</td>\n",
              "      <td>2019-09-27 08:28:05.415005</td>\n",
              "      <td>OPHYS_4_images_B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 equipment_name  \\\n",
              "ophys_session_id                  \n",
              "951410079                MESO.1   \n",
              "952430817                MESO.1   \n",
              "954954402                MESO.1   \n",
              "955775716                MESO.1   \n",
              "957020350                MESO.1   \n",
              "\n",
              "                                                  full_genotype  mouse_id  \\\n",
              "ophys_session_id                                                            \n",
              "951410079         Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "952430817         Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "954954402         Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "955775716         Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "957020350         Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "\n",
              "                               reporter_line     driver_line sex  age_in_days  \\\n",
              "ophys_session_id                                                                \n",
              "951410079         Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F        206.0   \n",
              "952430817         Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F        209.0   \n",
              "954954402         Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F        210.0   \n",
              "955775716         Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F        212.0   \n",
              "957020350         Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F        213.0   \n",
              "\n",
              "                      cre_line indicator  session_number  \\\n",
              "ophys_session_id                                           \n",
              "951410079         Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "952430817         Sst-IRES-Cre   GCaMP6f             2.0   \n",
              "954954402         Sst-IRES-Cre   GCaMP6f             3.0   \n",
              "955775716         Sst-IRES-Cre   GCaMP6f             3.0   \n",
              "957020350         Sst-IRES-Cre   GCaMP6f             4.0   \n",
              "\n",
              "                  prior_exposures_to_session_type  \\\n",
              "ophys_session_id                                    \n",
              "951410079                                       0   \n",
              "952430817                                       0   \n",
              "954954402                                       0   \n",
              "955775716                                       1   \n",
              "957020350                                       0   \n",
              "\n",
              "                  prior_exposures_to_image_set  prior_exposures_to_omissions  \\\n",
              "ophys_session_id                                                               \n",
              "951410079                                 65.0                             0   \n",
              "952430817                                 66.0                             1   \n",
              "954954402                                 67.0                             2   \n",
              "955775716                                 68.0                             3   \n",
              "957020350                                  0.0                             4   \n",
              "\n",
              "                  behavior_session_id  \\\n",
              "ophys_session_id                        \n",
              "951410079                   951520319   \n",
              "952430817                   952554548   \n",
              "954954402                   953982960   \n",
              "955775716                   956010809   \n",
              "957020350                   957032492   \n",
              "\n",
              "                                                ophys_experiment_id  \\\n",
              "ophys_session_id                                                      \n",
              "951410079         [951980471, 951980473, 951980475, 951980479, 9...   \n",
              "952430817              [953659743, 953659745, 953659749, 953659752]   \n",
              "954954402         [958527464, 958527471, 958527474, 958527479, 9...   \n",
              "955775716                         [956941841, 956941844, 956941846]   \n",
              "957020350         [957759562, 957759564, 957759566, 957759570, 9...   \n",
              "\n",
              "                                                 ophys_container_id  \\\n",
              "ophys_session_id                                                      \n",
              "951410079         [1018028339, 1018028342, 1018028345, 101802835...   \n",
              "952430817          [1018028339, 1018028345, 1018028354, 1018028357]   \n",
              "954954402         [1018028339, 1018028342, 1018028345, 101802835...   \n",
              "955775716                      [1018028339, 1018028342, 1018028345]   \n",
              "957020350         [1018028339, 1018028342, 1018028345, 101802835...   \n",
              "\n",
              "                              project_code         date_of_acquisition  \\\n",
              "ophys_session_id                                                         \n",
              "951410079         VisualBehaviorMultiscope  2019-09-20 09:45:29.897856   \n",
              "952430817         VisualBehaviorMultiscope  2019-09-23 08:13:07.627573   \n",
              "954954402         VisualBehaviorMultiscope  2019-09-24 16:00:00.000000   \n",
              "955775716         VisualBehaviorMultiscope  2019-09-26 08:44:37.199778   \n",
              "957020350         VisualBehaviorMultiscope  2019-09-27 08:28:05.415005   \n",
              "\n",
              "                              session_type  \n",
              "ophys_session_id                            \n",
              "951410079                 OPHYS_1_images_A  \n",
              "952430817         OPHYS_2_images_A_passive  \n",
              "954954402                 OPHYS_3_images_A  \n",
              "955775716                 OPHYS_3_images_A  \n",
              "957020350                 OPHYS_4_images_B  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0BhnucakDQi"
      },
      "source": [
        "The experiment table has one row per experiment. Note that the `ophys_session_id` column links each experiment to its associated session in the session_table."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "VNAmRU0od-RC",
        "outputId": "5762be6a-1647-4f8f-a8bb-88bc330039e5"
      },
      "source": [
        "experiment_table.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>equipment_name</th>\n",
              "      <th>full_genotype</th>\n",
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              "      <th>project_code</th>\n",
              "      <th>imaging_depth</th>\n",
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              "      <th>951980471</th>\n",
              "      <td>MESO.1</td>\n",
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              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>F</td>\n",
              "      <td>206.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>951410079</td>\n",
              "      <td>951520319</td>\n",
              "      <td>1018028342</td>\n",
              "      <td>VisualBehaviorMultiscope</td>\n",
              "      <td>150</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-09-20 09:45:29.897856</td>\n",
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              "      <th>951980484</th>\n",
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              "      <td>457841</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>F</td>\n",
              "      <td>206.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>65.0</td>\n",
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              "      <td>1018028351</td>\n",
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              "      <td>2019-09-20 09:45:29.897856</td>\n",
              "      <td>OPHYS_1_images_A</td>\n",
              "      <td>1085400876</td>\n",
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              "    <tr>\n",
              "      <th>951980473</th>\n",
              "      <td>MESO.1</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>457841</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
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              "      <td>F</td>\n",
              "      <td>206.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>951520319</td>\n",
              "      <td>1018028345</td>\n",
              "      <td>VisualBehaviorMultiscope</td>\n",
              "      <td>225</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-09-20 09:45:29.897856</td>\n",
              "      <td>OPHYS_1_images_A</td>\n",
              "      <td>1085400919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>951980479</th>\n",
              "      <td>MESO.1</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>457841</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
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              "      <td>150</td>\n",
              "      <td>VISl</td>\n",
              "      <td>2019-09-20 09:45:29.897856</td>\n",
              "      <td>OPHYS_1_images_A</td>\n",
              "      <td>1085400977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>951980475</th>\n",
              "      <td>MESO.1</td>\n",
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              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>F</td>\n",
              "      <td>206.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>951410079</td>\n",
              "      <td>951520319</td>\n",
              "      <td>1018028339</td>\n",
              "      <td>VisualBehaviorMultiscope</td>\n",
              "      <td>75</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-09-20 09:45:29.897856</td>\n",
              "      <td>OPHYS_1_images_A</td>\n",
              "      <td>1085400920</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    equipment_name  \\\n",
              "ophys_experiment_id                  \n",
              "951980471                   MESO.1   \n",
              "951980484                   MESO.1   \n",
              "951980473                   MESO.1   \n",
              "951980479                   MESO.1   \n",
              "951980475                   MESO.1   \n",
              "\n",
              "                                                     full_genotype  mouse_id  \\\n",
              "ophys_experiment_id                                                            \n",
              "951980471            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "951980484            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "951980473            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "951980479            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "951980475            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    457841   \n",
              "\n",
              "                                  reporter_line     driver_line sex  \\\n",
              "ophys_experiment_id                                                   \n",
              "951980471            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F   \n",
              "951980484            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F   \n",
              "951980473            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F   \n",
              "951980479            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F   \n",
              "951980475            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F   \n",
              "\n",
              "                     age_in_days      cre_line indicator  session_number  \\\n",
              "ophys_experiment_id                                                        \n",
              "951980471                  206.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "951980484                  206.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "951980473                  206.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "951980479                  206.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "951980475                  206.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "\n",
              "                     prior_exposures_to_session_type  \\\n",
              "ophys_experiment_id                                    \n",
              "951980471                                          0   \n",
              "951980484                                          0   \n",
              "951980473                                          0   \n",
              "951980479                                          0   \n",
              "951980475                                          0   \n",
              "\n",
              "                     prior_exposures_to_image_set  \\\n",
              "ophys_experiment_id                                 \n",
              "951980471                                    65.0   \n",
              "951980484                                    65.0   \n",
              "951980473                                    65.0   \n",
              "951980479                                    65.0   \n",
              "951980475                                    65.0   \n",
              "\n",
              "                     prior_exposures_to_omissions  ophys_session_id  \\\n",
              "ophys_experiment_id                                                   \n",
              "951980471                                       0         951410079   \n",
              "951980484                                       0         951410079   \n",
              "951980473                                       0         951410079   \n",
              "951980479                                       0         951410079   \n",
              "951980475                                       0         951410079   \n",
              "\n",
              "                     behavior_session_id  ophys_container_id  \\\n",
              "ophys_experiment_id                                            \n",
              "951980471                      951520319          1018028342   \n",
              "951980484                      951520319          1018028351   \n",
              "951980473                      951520319          1018028345   \n",
              "951980479                      951520319          1018028354   \n",
              "951980475                      951520319          1018028339   \n",
              "\n",
              "                                 project_code  imaging_depth  \\\n",
              "ophys_experiment_id                                            \n",
              "951980471            VisualBehaviorMultiscope            150   \n",
              "951980484            VisualBehaviorMultiscope             75   \n",
              "951980473            VisualBehaviorMultiscope            225   \n",
              "951980479            VisualBehaviorMultiscope            150   \n",
              "951980475            VisualBehaviorMultiscope             75   \n",
              "\n",
              "                    targeted_structure         date_of_acquisition  \\\n",
              "ophys_experiment_id                                                  \n",
              "951980471                         VISp  2019-09-20 09:45:29.897856   \n",
              "951980484                         VISl  2019-09-20 09:45:29.897856   \n",
              "951980473                         VISp  2019-09-20 09:45:29.897856   \n",
              "951980479                         VISl  2019-09-20 09:45:29.897856   \n",
              "951980475                         VISp  2019-09-20 09:45:29.897856   \n",
              "\n",
              "                         session_type     file_id  \n",
              "ophys_experiment_id                                \n",
              "951980471            OPHYS_1_images_A  1085394308  \n",
              "951980484            OPHYS_1_images_A  1085400876  \n",
              "951980473            OPHYS_1_images_A  1085400919  \n",
              "951980479            OPHYS_1_images_A  1085400977  \n",
              "951980475            OPHYS_1_images_A  1085400920  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHnREoS9r57s"
      },
      "source": [
        "#Get cells\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "aQGfj5oHr-m2",
        "outputId": "40df1764-ef16-4018-ce1d-95e1bcb14678"
      },
      "source": [
        "SP_VT1B_table = experiment_table[experiment_table['project_code'] == 'VisualBehaviorTask1B']\n",
        "SP_VT1B_table.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>993862620</th>\n",
              "      <td>CAM2P.4</td>\n",
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              "      <td>M</td>\n",
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              "    <tr>\n",
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              "      <td>476970</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>195.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
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              "      <td>993984066</td>\n",
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              "      <td>OPHYS_2_images_B_passive</td>\n",
              "      <td>994098960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994278281</th>\n",
              "      <td>CAM2P.4</td>\n",
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              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
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              "      <td>VISp</td>\n",
              "      <td>2019-12-13 20:34:10.000000</td>\n",
              "      <td>OPHYS_3_images_B</td>\n",
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              "    <tr>\n",
              "      <th>994790561</th>\n",
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              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
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              "      <td>42.0</td>\n",
              "      <td>3</td>\n",
              "      <td>994731371</td>\n",
              "      <td>994743822</td>\n",
              "      <td>991913453</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-16 21:20:41.000000</td>\n",
              "      <td>OPHYS_3_images_B</td>\n",
              "      <td>994831840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994955425</th>\n",
              "      <td>CAM2P.4</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>476970</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>200.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>994883056</td>\n",
              "      <td>994894518</td>\n",
              "      <td>991913453</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-17 19:02:07.000000</td>\n",
              "      <td>OPHYS_4_images_A</td>\n",
              "      <td>995003435</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    equipment_name  \\\n",
              "ophys_experiment_id                  \n",
              "993862620                  CAM2P.4   \n",
              "994053903                  CAM2P.4   \n",
              "994278281                  CAM2P.4   \n",
              "994790561                  CAM2P.4   \n",
              "994955425                  CAM2P.4   \n",
              "\n",
              "                                                     full_genotype  mouse_id  \\\n",
              "ophys_experiment_id                                                            \n",
              "993862620            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "994053903            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "994278281            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "994790561            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "994955425            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "\n",
              "                                  reporter_line     driver_line sex  \\\n",
              "ophys_experiment_id                                                   \n",
              "993862620            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "994053903            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "994278281            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "994790561            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "994955425            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "\n",
              "                     age_in_days      cre_line indicator  session_number  \\\n",
              "ophys_experiment_id                                                        \n",
              "993862620                  194.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "994053903                  195.0  Sst-IRES-Cre   GCaMP6f             2.0   \n",
              "994278281                  196.0  Sst-IRES-Cre   GCaMP6f             3.0   \n",
              "994790561                  199.0  Sst-IRES-Cre   GCaMP6f             3.0   \n",
              "994955425                  200.0  Sst-IRES-Cre   GCaMP6f             4.0   \n",
              "\n",
              "                     prior_exposures_to_session_type  \\\n",
              "ophys_experiment_id                                    \n",
              "993862620                                          0   \n",
              "994053903                                          0   \n",
              "994278281                                          0   \n",
              "994790561                                          1   \n",
              "994955425                                          0   \n",
              "\n",
              "                     prior_exposures_to_image_set  \\\n",
              "ophys_experiment_id                                 \n",
              "993862620                                    39.0   \n",
              "994053903                                    40.0   \n",
              "994278281                                    41.0   \n",
              "994790561                                    42.0   \n",
              "994955425                                     0.0   \n",
              "\n",
              "                     prior_exposures_to_omissions  ophys_session_id  \\\n",
              "ophys_experiment_id                                                   \n",
              "993862620                                       0         993727065   \n",
              "994053903                                       1         993984066   \n",
              "994278281                                       2         994199725   \n",
              "994790561                                       3         994731371   \n",
              "994955425                                       4         994883056   \n",
              "\n",
              "                     behavior_session_id  ophys_container_id  \\\n",
              "ophys_experiment_id                                            \n",
              "993862620                      993766500           991913453   \n",
              "994053903                      993995204           991913453   \n",
              "994278281                      994210659           991913453   \n",
              "994790561                      994743822           991913453   \n",
              "994955425                      994894518           991913453   \n",
              "\n",
              "                             project_code  imaging_depth targeted_structure  \\\n",
              "ophys_experiment_id                                                           \n",
              "993862620            VisualBehaviorTask1B            275               VISp   \n",
              "994053903            VisualBehaviorTask1B            275               VISp   \n",
              "994278281            VisualBehaviorTask1B            275               VISp   \n",
              "994790561            VisualBehaviorTask1B            275               VISp   \n",
              "994955425            VisualBehaviorTask1B            275               VISp   \n",
              "\n",
              "                            date_of_acquisition              session_type  \\\n",
              "ophys_experiment_id                                                         \n",
              "993862620            2019-12-11 19:01:03.000000          OPHYS_1_images_B   \n",
              "994053903            2019-12-12 20:47:45.000000  OPHYS_2_images_B_passive   \n",
              "994278281            2019-12-13 20:34:10.000000          OPHYS_3_images_B   \n",
              "994790561            2019-12-16 21:20:41.000000          OPHYS_3_images_B   \n",
              "994955425            2019-12-17 19:02:07.000000          OPHYS_4_images_A   \n",
              "\n",
              "                       file_id  \n",
              "ophys_experiment_id             \n",
              "993862620            993906653  \n",
              "994053903            994098960  \n",
              "994278281            994320564  \n",
              "994790561            994831840  \n",
              "994955425            995003435  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYm6ghk5sIy_",
        "outputId": "2e519cba-8770-49d1-e5df-73721d5d20be"
      },
      "source": [
        "SP_VT1b_SST = SP_VT1B_table[SP_VT1B_table['cre_line'] == 'Sst-IRES-Cre']\n",
        "SP_VT1b_SST.imaging_depth.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([275])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "j1wWvtBasS_g",
        "outputId": "128feca8-39fd-40b0-9386-3c59402d0905"
      },
      "source": [
        "single_plane_sst = SP_VT1B_table[SP_VT1B_table['cre_line'] == 'Sst-IRES-Cre']\n",
        "single_plane_sst.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>equipment_name</th>\n",
              "      <th>full_genotype</th>\n",
              "      <th>mouse_id</th>\n",
              "      <th>reporter_line</th>\n",
              "      <th>driver_line</th>\n",
              "      <th>sex</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>cre_line</th>\n",
              "      <th>indicator</th>\n",
              "      <th>session_number</th>\n",
              "      <th>prior_exposures_to_session_type</th>\n",
              "      <th>prior_exposures_to_image_set</th>\n",
              "      <th>prior_exposures_to_omissions</th>\n",
              "      <th>ophys_session_id</th>\n",
              "      <th>behavior_session_id</th>\n",
              "      <th>ophys_container_id</th>\n",
              "      <th>project_code</th>\n",
              "      <th>imaging_depth</th>\n",
              "      <th>targeted_structure</th>\n",
              "      <th>date_of_acquisition</th>\n",
              "      <th>session_type</th>\n",
              "      <th>file_id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ophys_experiment_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>993862620</th>\n",
              "      <td>CAM2P.4</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>476970</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>194.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>993727065</td>\n",
              "      <td>993766500</td>\n",
              "      <td>991913453</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-11 19:01:03.000000</td>\n",
              "      <td>OPHYS_1_images_B</td>\n",
              "      <td>993906653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994053903</th>\n",
              "      <td>CAM2P.4</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>476970</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>195.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "      <td>993984066</td>\n",
              "      <td>993995204</td>\n",
              "      <td>991913453</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-12 20:47:45.000000</td>\n",
              "      <td>OPHYS_2_images_B_passive</td>\n",
              "      <td>994098960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994278281</th>\n",
              "      <td>CAM2P.4</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>476970</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>196.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>2</td>\n",
              "      <td>994199725</td>\n",
              "      <td>994210659</td>\n",
              "      <td>991913453</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-13 20:34:10.000000</td>\n",
              "      <td>OPHYS_3_images_B</td>\n",
              "      <td>994320564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994790561</th>\n",
              "      <td>CAM2P.4</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>476970</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>199.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>42.0</td>\n",
              "      <td>3</td>\n",
              "      <td>994731371</td>\n",
              "      <td>994743822</td>\n",
              "      <td>991913453</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-16 21:20:41.000000</td>\n",
              "      <td>OPHYS_3_images_B</td>\n",
              "      <td>994831840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994955425</th>\n",
              "      <td>CAM2P.4</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>476970</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>200.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>994883056</td>\n",
              "      <td>994894518</td>\n",
              "      <td>991913453</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-17 19:02:07.000000</td>\n",
              "      <td>OPHYS_4_images_A</td>\n",
              "      <td>995003435</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    equipment_name  \\\n",
              "ophys_experiment_id                  \n",
              "993862620                  CAM2P.4   \n",
              "994053903                  CAM2P.4   \n",
              "994278281                  CAM2P.4   \n",
              "994790561                  CAM2P.4   \n",
              "994955425                  CAM2P.4   \n",
              "\n",
              "                                                     full_genotype  mouse_id  \\\n",
              "ophys_experiment_id                                                            \n",
              "993862620            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "994053903            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "994278281            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "994790561            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "994955425            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "\n",
              "                                  reporter_line     driver_line sex  \\\n",
              "ophys_experiment_id                                                   \n",
              "993862620            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "994053903            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "994278281            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "994790561            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "994955425            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "\n",
              "                     age_in_days      cre_line indicator  session_number  \\\n",
              "ophys_experiment_id                                                        \n",
              "993862620                  194.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "994053903                  195.0  Sst-IRES-Cre   GCaMP6f             2.0   \n",
              "994278281                  196.0  Sst-IRES-Cre   GCaMP6f             3.0   \n",
              "994790561                  199.0  Sst-IRES-Cre   GCaMP6f             3.0   \n",
              "994955425                  200.0  Sst-IRES-Cre   GCaMP6f             4.0   \n",
              "\n",
              "                     prior_exposures_to_session_type  \\\n",
              "ophys_experiment_id                                    \n",
              "993862620                                          0   \n",
              "994053903                                          0   \n",
              "994278281                                          0   \n",
              "994790561                                          1   \n",
              "994955425                                          0   \n",
              "\n",
              "                     prior_exposures_to_image_set  \\\n",
              "ophys_experiment_id                                 \n",
              "993862620                                    39.0   \n",
              "994053903                                    40.0   \n",
              "994278281                                    41.0   \n",
              "994790561                                    42.0   \n",
              "994955425                                     0.0   \n",
              "\n",
              "                     prior_exposures_to_omissions  ophys_session_id  \\\n",
              "ophys_experiment_id                                                   \n",
              "993862620                                       0         993727065   \n",
              "994053903                                       1         993984066   \n",
              "994278281                                       2         994199725   \n",
              "994790561                                       3         994731371   \n",
              "994955425                                       4         994883056   \n",
              "\n",
              "                     behavior_session_id  ophys_container_id  \\\n",
              "ophys_experiment_id                                            \n",
              "993862620                      993766500           991913453   \n",
              "994053903                      993995204           991913453   \n",
              "994278281                      994210659           991913453   \n",
              "994790561                      994743822           991913453   \n",
              "994955425                      994894518           991913453   \n",
              "\n",
              "                             project_code  imaging_depth targeted_structure  \\\n",
              "ophys_experiment_id                                                           \n",
              "993862620            VisualBehaviorTask1B            275               VISp   \n",
              "994053903            VisualBehaviorTask1B            275               VISp   \n",
              "994278281            VisualBehaviorTask1B            275               VISp   \n",
              "994790561            VisualBehaviorTask1B            275               VISp   \n",
              "994955425            VisualBehaviorTask1B            275               VISp   \n",
              "\n",
              "                            date_of_acquisition              session_type  \\\n",
              "ophys_experiment_id                                                         \n",
              "993862620            2019-12-11 19:01:03.000000          OPHYS_1_images_B   \n",
              "994053903            2019-12-12 20:47:45.000000  OPHYS_2_images_B_passive   \n",
              "994278281            2019-12-13 20:34:10.000000          OPHYS_3_images_B   \n",
              "994790561            2019-12-16 21:20:41.000000          OPHYS_3_images_B   \n",
              "994955425            2019-12-17 19:02:07.000000          OPHYS_4_images_A   \n",
              "\n",
              "                       file_id  \n",
              "ophys_experiment_id             \n",
              "993862620            993906653  \n",
              "994053903            994098960  \n",
              "994278281            994320564  \n",
              "994790561            994831840  \n",
              "994955425            995003435  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "aY9KSU2tsZgV",
        "outputId": "58ebf2ab-786a-4544-98de-1b43576c8b78"
      },
      "source": [
        "#load sessions for SST-mice-Visual Behaviour 1b (n=7 mice from session 1), this is to check the overlapping cell ids\n",
        "ophys_experiment_id = [993862620,955276580, 992296756, 977935606, 1010256573, 1000743008, 982862443]\n",
        "experiment_table.loc[ophys_experiment_id]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>equipment_name</th>\n",
              "      <th>full_genotype</th>\n",
              "      <th>mouse_id</th>\n",
              "      <th>reporter_line</th>\n",
              "      <th>driver_line</th>\n",
              "      <th>sex</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>cre_line</th>\n",
              "      <th>indicator</th>\n",
              "      <th>session_number</th>\n",
              "      <th>prior_exposures_to_session_type</th>\n",
              "      <th>prior_exposures_to_image_set</th>\n",
              "      <th>prior_exposures_to_omissions</th>\n",
              "      <th>ophys_session_id</th>\n",
              "      <th>behavior_session_id</th>\n",
              "      <th>ophys_container_id</th>\n",
              "      <th>project_code</th>\n",
              "      <th>imaging_depth</th>\n",
              "      <th>targeted_structure</th>\n",
              "      <th>date_of_acquisition</th>\n",
              "      <th>session_type</th>\n",
              "      <th>file_id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ophys_experiment_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>993862620</th>\n",
              "      <td>CAM2P.4</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>476970</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>194.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>993727065</td>\n",
              "      <td>993766500</td>\n",
              "      <td>991913453</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-11 19:01:03.000000</td>\n",
              "      <td>OPHYS_1_images_B</td>\n",
              "      <td>993906653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>955276580</th>\n",
              "      <td>CAM2P.5</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>470784</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>F</td>\n",
              "      <td>149.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>954981981</td>\n",
              "      <td>955110047</td>\n",
              "      <td>941373529</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-09-25 20:06:45.000000</td>\n",
              "      <td>OPHYS_1_images_B</td>\n",
              "      <td>957317925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>992296756</th>\n",
              "      <td>CAM2P.3</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>485688</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>143.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1</td>\n",
              "      <td>992232718</td>\n",
              "      <td>992251261</td>\n",
              "      <td>989461845</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-12-04 20:10:14.000000</td>\n",
              "      <td>OPHYS_1_images_B</td>\n",
              "      <td>992343096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>977935606</th>\n",
              "      <td>CAM2P.3</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>477202</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>157.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0</td>\n",
              "      <td>977726123</td>\n",
              "      <td>977838176</td>\n",
              "      <td>975371413</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-11-06 21:24:49.000000</td>\n",
              "      <td>OPHYS_1_images_B</td>\n",
              "      <td>978036824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1010256573</th>\n",
              "      <td>CAM2P.3</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>498972</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>145.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1010159622</td>\n",
              "      <td>1010177949</td>\n",
              "      <td>1006386845</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2020-02-25 18:35:33.000000</td>\n",
              "      <td>OPHYS_1_images_B</td>\n",
              "      <td>1011074390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1000743008</th>\n",
              "      <td>CAM2P.3</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>489056</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>165.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1000580083</td>\n",
              "      <td>1000600602</td>\n",
              "      <td>999191979</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2020-01-16 18:49:41.000000</td>\n",
              "      <td>OPHYS_1_images_B</td>\n",
              "      <td>1003077167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>982862443</th>\n",
              "      <td>CAM2P.3</td>\n",
              "      <td>Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt</td>\n",
              "      <td>480753</td>\n",
              "      <td>Ai148(TIT2L-GC6f-ICL-tTA2)</td>\n",
              "      <td>[Sst-IRES-Cre]</td>\n",
              "      <td>M</td>\n",
              "      <td>148.0</td>\n",
              "      <td>Sst-IRES-Cre</td>\n",
              "      <td>GCaMP6f</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>982431228</td>\n",
              "      <td>982558306</td>\n",
              "      <td>978206330</td>\n",
              "      <td>VisualBehaviorTask1B</td>\n",
              "      <td>275</td>\n",
              "      <td>VISp</td>\n",
              "      <td>2019-11-14 17:08:08.000000</td>\n",
              "      <td>OPHYS_1_images_B</td>\n",
              "      <td>982976617</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    equipment_name  \\\n",
              "ophys_experiment_id                  \n",
              "993862620                  CAM2P.4   \n",
              "955276580                  CAM2P.5   \n",
              "992296756                  CAM2P.3   \n",
              "977935606                  CAM2P.3   \n",
              "1010256573                 CAM2P.3   \n",
              "1000743008                 CAM2P.3   \n",
              "982862443                  CAM2P.3   \n",
              "\n",
              "                                                     full_genotype  mouse_id  \\\n",
              "ophys_experiment_id                                                            \n",
              "993862620            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    476970   \n",
              "955276580            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    470784   \n",
              "992296756            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    485688   \n",
              "977935606            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    477202   \n",
              "1010256573           Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    498972   \n",
              "1000743008           Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    489056   \n",
              "982862443            Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt    480753   \n",
              "\n",
              "                                  reporter_line     driver_line sex  \\\n",
              "ophys_experiment_id                                                   \n",
              "993862620            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "955276580            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   F   \n",
              "992296756            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "977935606            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "1010256573           Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "1000743008           Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "982862443            Ai148(TIT2L-GC6f-ICL-tTA2)  [Sst-IRES-Cre]   M   \n",
              "\n",
              "                     age_in_days      cre_line indicator  session_number  \\\n",
              "ophys_experiment_id                                                        \n",
              "993862620                  194.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "955276580                  149.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "992296756                  143.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "977935606                  157.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "1010256573                 145.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "1000743008                 165.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "982862443                  148.0  Sst-IRES-Cre   GCaMP6f             1.0   \n",
              "\n",
              "                     prior_exposures_to_session_type  \\\n",
              "ophys_experiment_id                                    \n",
              "993862620                                          0   \n",
              "955276580                                          0   \n",
              "992296756                                          1   \n",
              "977935606                                          0   \n",
              "1010256573                                         1   \n",
              "1000743008                                         0   \n",
              "982862443                                          0   \n",
              "\n",
              "                     prior_exposures_to_image_set  \\\n",
              "ophys_experiment_id                                 \n",
              "993862620                                    39.0   \n",
              "955276580                                    20.0   \n",
              "992296756                                    19.0   \n",
              "977935606                                    45.0   \n",
              "1010256573                                   14.0   \n",
              "1000743008                                   32.0   \n",
              "982862443                                    30.0   \n",
              "\n",
              "                     prior_exposures_to_omissions  ophys_session_id  \\\n",
              "ophys_experiment_id                                                   \n",
              "993862620                                       0         993727065   \n",
              "955276580                                       0         954981981   \n",
              "992296756                                       1         992232718   \n",
              "977935606                                       0         977726123   \n",
              "1010256573                                      4        1010159622   \n",
              "1000743008                                      1        1000580083   \n",
              "982862443                                       0         982431228   \n",
              "\n",
              "                     behavior_session_id  ophys_container_id  \\\n",
              "ophys_experiment_id                                            \n",
              "993862620                      993766500           991913453   \n",
              "955276580                      955110047           941373529   \n",
              "992296756                      992251261           989461845   \n",
              "977935606                      977838176           975371413   \n",
              "1010256573                    1010177949          1006386845   \n",
              "1000743008                    1000600602           999191979   \n",
              "982862443                      982558306           978206330   \n",
              "\n",
              "                             project_code  imaging_depth targeted_structure  \\\n",
              "ophys_experiment_id                                                           \n",
              "993862620            VisualBehaviorTask1B            275               VISp   \n",
              "955276580            VisualBehaviorTask1B            275               VISp   \n",
              "992296756            VisualBehaviorTask1B            275               VISp   \n",
              "977935606            VisualBehaviorTask1B            275               VISp   \n",
              "1010256573           VisualBehaviorTask1B            275               VISp   \n",
              "1000743008           VisualBehaviorTask1B            275               VISp   \n",
              "982862443            VisualBehaviorTask1B            275               VISp   \n",
              "\n",
              "                            date_of_acquisition      session_type     file_id  \n",
              "ophys_experiment_id                                                            \n",
              "993862620            2019-12-11 19:01:03.000000  OPHYS_1_images_B   993906653  \n",
              "955276580            2019-09-25 20:06:45.000000  OPHYS_1_images_B   957317925  \n",
              "992296756            2019-12-04 20:10:14.000000  OPHYS_1_images_B   992343096  \n",
              "977935606            2019-11-06 21:24:49.000000  OPHYS_1_images_B   978036824  \n",
              "1010256573           2020-02-25 18:35:33.000000  OPHYS_1_images_B  1011074390  \n",
              "1000743008           2020-01-16 18:49:41.000000  OPHYS_1_images_B  1003077167  \n",
              "982862443            2019-11-14 17:08:08.000000  OPHYS_1_images_B   982976617  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zypLJs_JsfEs",
        "outputId": "4f89e525-2af0-4234-f28f-68d6a3f6aa1b"
      },
      "source": [
        "experiments = {}\n",
        "ophys_experiment_ids = [993862620,955276580, 992296756, 977935606, 1010256573, 1000743008,982862443]\n",
        "for ophys_experiment_id in ophys_experiment_ids:\n",
        "    experiments[ophys_experiment_id] = cache.get_behavior_ophys_experiment(ophys_experiment_id)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "behavior_ophys_experiment_993862620.nwb: 100%|██████████| 357M/357M [00:12<00:00, 27.7MMB/s]\n",
            "behavior_ophys_experiment_955276580.nwb: 100%|██████████| 280M/280M [00:09<00:00, 29.7MMB/s]\n",
            "behavior_ophys_experiment_992296756.nwb: 100%|██████████| 327M/327M [00:11<00:00, 28.4MMB/s]\n",
            "behavior_ophys_experiment_977935606.nwb: 100%|██████████| 255M/255M [00:08<00:00, 29.3MMB/s]\n",
            "behavior_ophys_experiment_1010256573.nwb: 100%|██████████| 258M/258M [00:08<00:00, 29.9MMB/s]\n",
            "behavior_ophys_experiment_1000743008.nwb: 100%|██████████| 246M/246M [00:08<00:00, 30.5MMB/s]\n",
            "behavior_ophys_experiment_982862443.nwb: 100%|██████████| 248M/248M [00:08<00:00, 30.7MMB/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBv3i6o-sybE",
        "outputId": "57c226b4-4fa3-4731-970e-aa668bd08646"
      },
      "source": [
        "neural_data = []\n",
        "for ophys_experiment_id in tqdm(experiments.keys()): #tqdm is a package that shows progress bars for items that are iterated over\n",
        "    this_experiment = 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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 7/7 [00:44<00:00,  6.36s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6Wj37vUs_Pn",
        "outputId": "b6bd7ca3-5e12-4107-b59d-e496cb1f8823"
      },
      "source": [
        "cell_ids = neural_data['cell_specimen_id'].unique()\n",
        "print('there are {} unique cells'.format(len(cell_ids)))\n",
        "print('cell ids are: {}'.format(cell_ids))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "there are 78 unique cells\n",
            "cell ids are: [1086496500 1086496689 1086497129 1086497550 1086500812 1086497908\n",
            " 1086498071 1086498205 1086498334 1086498461 1086498820 1086498977\n",
            " 1086499259 1086499316 1086499479 1086499910 1086500121 1086500281\n",
            " 1086490519 1086490561 1086490598 1086490636 1086490697 1086490748\n",
            " 1086490784 1086490867 1086490995 1086491035 1086491079 1086491172\n",
            " 1086491215 1086491256 1086491328 1086491397 1086491469 1086490391\n",
            " 1086491368 1086490482 1086490526 1086490572 1086490648 1086490689\n",
            " 1086490777 1086490794 1086490839 1086490936 1086490994 1086491066\n",
            " 1086491194 1086491263 1086491339 1086491760 1086499499 1086499628\n",
            " 1086499783 1086499911 1086500092 1086500194 1086500392 1086500633\n",
            " 1086500583 1086500807 1086501072 1086501262 1086501360 1086501675\n",
            " 1086502269 1086668247 1086668144 1086668169 1086668188 1086668228\n",
            " 1086490603 1086490729 1086490846 1086490807 1086490741 1086490751]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "xb-QbU5ht1Sn",
        "outputId": "6468b84c-6bcc-429a-c3f9-8efd94f74e8e"
      },
      "source": [
        "stimulus_table = experiments[ophys_experiment_ids[0]].stimulus_presentations.drop(columns = ['image_set']) # dropping the 'image_set' column to avoid confusion. Image_set column contains a unique string for set of images presented in a session.\n",
        "stimulus_table.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>duration</th>\n",
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            "text/plain": [
              "                           duration  end_frame  image_index image_name  index  \\\n",
              "stimulus_presentations_id                                                       \n",
              "0                           0.25019    18000.0            0      im000      0   \n",
              "1                           0.25025    18045.0            0      im000      1   \n",
              "2                           0.25022    18090.0            0      im000      2   \n",
              "3                           0.25026    18135.0            0      im000      3   \n",
              "4                           0.25014    18180.0            0      im000      4   \n",
              "5                           0.25012    18225.0            0      im000      5   \n",
              "6                           0.25020    18270.0            5      im054      6   \n",
              "7                           0.25017    18315.0            5      im054      7   \n",
              "8                           0.25023    18360.0            5      im054      8   \n",
              "9                           0.25015    18405.0            5      im054      9   \n",
              "\n",
              "                           omitted  start_frame  start_time  stop_time  \\\n",
              "stimulus_presentations_id                                                \n",
              "0                            False        17985   307.34715  307.59734   \n",
              "1                            False        18030   308.09772  308.34797   \n",
              "2                            False        18075   308.84836  309.09858   \n",
              "3                            False        18120   309.59899  309.84925   \n",
              "4                            False        18165   310.34966  310.59980   \n",
              "5                            False        18210   311.10029  311.35041   \n",
              "6                            False        18255   311.85081  312.10101   \n",
              "7                            False        18300   312.60143  312.85160   \n",
              "8                            False        18345   313.35201  313.60224   \n",
              "9                            False        18390   314.10266  314.35281   \n",
              "\n",
              "                           is_change  \n",
              "stimulus_presentations_id             \n",
              "0                              False  \n",
              "1                              False  \n",
              "2                              False  \n",
              "3                              False  \n",
              "4                              False  \n",
              "5                              False  \n",
              "6                               True  \n",
              "7                              False  \n",
              "8                              False  \n",
              "9                              False  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6_pzLTLtyHp",
        "outputId": "31e671bf-0ce4-483e-a19d-35fbaf6fa27e"
      },
      "source": [
        "full_etr = []\n",
        "# iterate over each unique cell\n",
        "for cell_specimen_id in tqdm(neural_data['cell_specimen_id'].unique()):\n",
        "    # calculate the event triggered response for this cell to every stimulus\n",
        "    full_etr_this_cell = mindscope_utilities.event_triggered_response(\n",
        "        neural_data.query('cell_specimen_id == @cell_specimen_id'),\n",
        "        t = 'timestamps',\n",
        "        y = 'dff',\n",
        "        event_times = stimulus_table['start_time'],\n",
        "        t_before = 0,\n",
        "        t_after = 0.75,\n",
        "        output_sampling_rate = 30 \n",
        "    )\n",
        "    # add a column identifying the cell_specimen_id\n",
        "    full_etr_this_cell['cell_specimen_id'] = cell_specimen_id\n",
        "    # append to our list\n",
        "    full_etr.append(full_etr_this_cell)\n",
        "\n",
        "# concatenate our list of dataframes into a single dataframe\n",
        "full_etr = pd.concat(full_etr)\n",
        "\n",
        "# cast these numeric columns to int and float, respectively \n",
        "full_etr['event_number'] = full_etr['event_number'].astype(int)\n",
        "full_etr['event_time'] = full_etr['event_number'].astype(float)\n",
        "# rename 'event_number' as \n",
        "full_etr.rename(columns = {'event_number': 'stimulus_presentations_id'}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 78/78 [04:51<00:00,  3.74s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vGTAdILusJE"
      },
      "source": [
        "trials x cells matrix\n",
        "Each row would be one trial, where a trial is defined as a unique image presentation\n",
        "Each column would represent the average response of a given cell on that image presentation.\n",
        "To do so, let's construct another intermediate dataframe called average_responses that contains the average response of each cell (in the 750 ms window we've selected above) to each image presentation. We'll do this using a Pandas groupby to group by cell_specimen_id and stimulus_presentations_id (aka trial)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "xflyY2Nbuhm-",
        "outputId": "c0b38875-bb14-4e22-9404-75be55149454"
      },
      "source": [
        "average_responses = full_etr.groupby(['cell_specimen_id','stimulus_presentations_id'])[['dff']].mean().reset_index().merge(\n",
        "    stimulus_table,\n",
        "    on = 'stimulus_presentations_id',\n",
        "    how = 'left'\n",
        ")\n",
        "average_responses"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>False</td>\n",
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              "      <th>3</th>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>374710</th>\n",
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              "      <td>im035</td>\n",
              "      <td>4617</td>\n",
              "      <td>False</td>\n",
              "      <td>234068</td>\n",
              "      <td>3911.80384</td>\n",
              "      <td>3912.05399</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>3912.55443</td>\n",
              "      <td>3912.80458</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>374712 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        cell_specimen_id  stimulus_presentations_id       dff  duration  \\\n",
              "0             1086490391                          0 -0.026495   0.25019   \n",
              "1             1086490391                          1 -0.008747   0.25025   \n",
              "2             1086490391                          2  0.001102   0.25022   \n",
              "3             1086490391                          3  0.016059   0.25026   \n",
              "4             1086490391                          4  0.004637   0.25014   \n",
              "...                  ...                        ...       ...       ...   \n",
              "374707        1086668247                       4799  0.000302   0.25020   \n",
              "374708        1086668247                       4800 -0.002106   0.25029   \n",
              "374709        1086668247                       4801  0.003465   0.25022   \n",
              "374710        1086668247                       4802  0.000460   0.25015   \n",
              "374711        1086668247                       4803 -0.003460   0.25015   \n",
              "\n",
              "        end_frame  image_index image_name  index  omitted  start_frame  \\\n",
              "0         18000.0            0      im000      0    False        17985   \n",
              "1         18045.0            0      im000      1    False        18030   \n",
              "2         18090.0            0      im000      2    False        18075   \n",
              "3         18135.0            0      im000      3    False        18120   \n",
              "4         18180.0            0      im000      4    False        18165   \n",
              "...           ...          ...        ...    ...      ...          ...   \n",
              "374707   233948.0            7      im035   4614    False       233933   \n",
              "374708   233993.0            7      im035   4615    False       233978   \n",
              "374709   234038.0            7      im035   4616    False       234023   \n",
              "374710   234083.0            7      im035   4617    False       234068   \n",
              "374711   234128.0            7      im035   4618    False       234113   \n",
              "\n",
              "        start_time   stop_time  is_change  \n",
              "0        307.34715   307.59734      False  \n",
              "1        308.09772   308.34797      False  \n",
              "2        308.84836   309.09858      False  \n",
              "3        309.59899   309.84925      False  \n",
              "4        310.34966   310.59980      False  \n",
              "...            ...         ...        ...  \n",
              "374707  3909.55193  3909.80213      False  \n",
              "374708  3910.30252  3910.55281      False  \n",
              "374709  3911.05312  3911.30334      False  \n",
              "374710  3911.80384  3912.05399      False  \n",
              "374711  3912.55443  3912.80458      False  \n",
              "\n",
              "[374712 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "oLEGPO90u4r2",
        "outputId": "f5bd0c32-c16b-41ac-823a-d17532b7fb3f"
      },
      "source": [
        "features_and_labels = average_responses.pivot(\n",
        "    index = 'stimulus_presentations_id',\n",
        "    columns = 'cell_specimen_id',\n",
        "    values = 'dff'\n",
        ").merge(\n",
        "    stimulus_table[['image_index','image_name']],\n",
        "    on = 'stimulus_presentations_id',\n",
        "    how = 'left'\n",
        ")\n",
        "features_and_labels.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.006736</td>\n",
              "      <td>0.008553</td>\n",
              "      <td>0.074277</td>\n",
              "      <td>0.021142</td>\n",
              "      <td>0.018715</td>\n",
              "      <td>0.376158</td>\n",
              "      <td>0.053071</td>\n",
              "      <td>0.019182</td>\n",
              "      <td>-0.003581</td>\n",
              "      <td>0.011362</td>\n",
              "      <td>0.008205</td>\n",
              "      <td>0.019201</td>\n",
              "      <td>0.025111</td>\n",
              "      <td>0.077685</td>\n",
              "      <td>0.021665</td>\n",
              "      <td>0.029861</td>\n",
              "      <td>0.034027</td>\n",
              "      <td>0.055778</td>\n",
              "      <td>0.033293</td>\n",
              "      <td>0.019995</td>\n",
              "      <td>-0.019132</td>\n",
              "      <td>-0.003996</td>\n",
              "      <td>0.005535</td>\n",
              "      <td>0.046272</td>\n",
              "      <td>0.008471</td>\n",
              "      <td>-0.001670</td>\n",
              "      <td>-0.000422</td>\n",
              "      <td>-0.000225</td>\n",
              "      <td>0.008764</td>\n",
              "      <td>0.023327</td>\n",
              "      <td>-0.000061</td>\n",
              "      <td>0.010631</td>\n",
              "      <td>-0.004477</td>\n",
              "      <td>0.004531</td>\n",
              "      <td>3</td>\n",
              "      <td>im073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4571</th>\n",
              "      <td>0.002222</td>\n",
              "      <td>0.001747</td>\n",
              "      <td>-0.003950</td>\n",
              "      <td>-0.006423</td>\n",
              "      <td>-0.001865</td>\n",
              "      <td>-0.005080</td>\n",
              "      <td>-0.004171</td>\n",
              "      <td>-0.000133</td>\n",
              "      <td>0.005905</td>\n",
              "      <td>0.001885</td>\n",
              "      <td>0.017521</td>\n",
              "      <td>0.023397</td>\n",
              "      <td>-0.003024</td>\n",
              "      <td>0.003037</td>\n",
              "      <td>-0.034136</td>\n",
              "      <td>-0.007811</td>\n",
              "      <td>0.011996</td>\n",
              "      <td>-0.028177</td>\n",
              "      <td>0.001608</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.020268</td>\n",
              "      <td>0.007071</td>\n",
              "      <td>0.850092</td>\n",
              "      <td>0.012337</td>\n",
              "      <td>0.018162</td>\n",
              "      <td>0.012706</td>\n",
              "      <td>-0.002904</td>\n",
              "      <td>0.026949</td>\n",
              "      <td>0.003621</td>\n",
              "      <td>0.202994</td>\n",
              "      <td>0.016229</td>\n",
              "      <td>-0.027711</td>\n",
              "      <td>0.012698</td>\n",
              "      <td>0.025311</td>\n",
              "      <td>0.005050</td>\n",
              "      <td>-0.003169</td>\n",
              "      <td>-0.009263</td>\n",
              "      <td>-0.006478</td>\n",
              "      <td>0.008216</td>\n",
              "      <td>0.013158</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>0.027633</td>\n",
              "      <td>0.003372</td>\n",
              "      <td>0.004112</td>\n",
              "      <td>-0.004595</td>\n",
              "      <td>-0.002298</td>\n",
              "      <td>-0.024687</td>\n",
              "      <td>-0.000604</td>\n",
              "      <td>0.026839</td>\n",
              "      <td>0.047212</td>\n",
              "      <td>0.036752</td>\n",
              "      <td>-0.006999</td>\n",
              "      <td>-0.006784</td>\n",
              "      <td>0.006196</td>\n",
              "      <td>0.177035</td>\n",
              "      <td>0.048094</td>\n",
              "      <td>0.014279</td>\n",
              "      <td>-0.034531</td>\n",
              "      <td>0.020558</td>\n",
              "      <td>0.036701</td>\n",
              "      <td>0.011926</td>\n",
              "      <td>0.334332</td>\n",
              "      <td>0.003963</td>\n",
              "      <td>0.010244</td>\n",
              "      <td>0.014058</td>\n",
              "      <td>0.015209</td>\n",
              "      <td>0.003823</td>\n",
              "      <td>-0.022468</td>\n",
              "      <td>0.008617</td>\n",
              "      <td>0.001481</td>\n",
              "      <td>-0.007659</td>\n",
              "      <td>0.036333</td>\n",
              "      <td>0.003408</td>\n",
              "      <td>0.010141</td>\n",
              "      <td>0.012862</td>\n",
              "      <td>0.006593</td>\n",
              "      <td>0.174516</td>\n",
              "      <td>-0.000617</td>\n",
              "      <td>4</td>\n",
              "      <td>im045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1624</th>\n",
              "      <td>-0.006231</td>\n",
              "      <td>0.011771</td>\n",
              "      <td>-0.034586</td>\n",
              "      <td>-0.019165</td>\n",
              "      <td>-0.001013</td>\n",
              "      <td>-0.033054</td>\n",
              "      <td>0.003279</td>\n",
              "      <td>0.010746</td>\n",
              "      <td>-0.001501</td>\n",
              "      <td>0.002346</td>\n",
              "      <td>-0.022430</td>\n",
              "      <td>0.002937</td>\n",
              "      <td>-0.010811</td>\n",
              "      <td>-0.008319</td>\n",
              "      <td>0.006561</td>\n",
              "      <td>0.009582</td>\n",
              "      <td>0.018773</td>\n",
              "      <td>-0.023075</td>\n",
              "      <td>-0.006774</td>\n",
              "      <td>0.005361</td>\n",
              "      <td>-0.023407</td>\n",
              "      <td>0.002230</td>\n",
              "      <td>-0.036032</td>\n",
              "      <td>-0.001076</td>\n",
              "      <td>0.009338</td>\n",
              "      <td>-0.047546</td>\n",
              "      <td>-0.055792</td>\n",
              "      <td>0.006758</td>\n",
              "      <td>0.021335</td>\n",
              "      <td>-0.027532</td>\n",
              "      <td>-0.006914</td>\n",
              "      <td>-0.028757</td>\n",
              "      <td>0.000123</td>\n",
              "      <td>0.002736</td>\n",
              "      <td>-0.064557</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>-0.011574</td>\n",
              "      <td>0.008244</td>\n",
              "      <td>-0.087856</td>\n",
              "      <td>0.001596</td>\n",
              "      <td>0.008997</td>\n",
              "      <td>-0.036614</td>\n",
              "      <td>-0.000874</td>\n",
              "      <td>-0.010894</td>\n",
              "      <td>0.000592</td>\n",
              "      <td>0.011995</td>\n",
              "      <td>0.000933</td>\n",
              "      <td>-0.011968</td>\n",
              "      <td>0.057242</td>\n",
              "      <td>0.208111</td>\n",
              "      <td>0.000643</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.007701</td>\n",
              "      <td>0.010099</td>\n",
              "      <td>0.013160</td>\n",
              "      <td>-0.012054</td>\n",
              "      <td>0.017769</td>\n",
              "      <td>0.045422</td>\n",
              "      <td>-0.018842</td>\n",
              "      <td>-0.033843</td>\n",
              "      <td>-0.001822</td>\n",
              "      <td>-0.029414</td>\n",
              "      <td>0.002017</td>\n",
              "      <td>0.007867</td>\n",
              "      <td>-0.002474</td>\n",
              "      <td>-0.020486</td>\n",
              "      <td>-0.000749</td>\n",
              "      <td>-0.028870</td>\n",
              "      <td>0.052410</td>\n",
              "      <td>0.053775</td>\n",
              "      <td>0.002032</td>\n",
              "      <td>0.027753</td>\n",
              "      <td>0.018273</td>\n",
              "      <td>0.018346</td>\n",
              "      <td>0.001280</td>\n",
              "      <td>0.017200</td>\n",
              "      <td>0.067932</td>\n",
              "      <td>0.001591</td>\n",
              "      <td>4</td>\n",
              "      <td>im045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4687</th>\n",
              "      <td>0.019718</td>\n",
              "      <td>0.008133</td>\n",
              "      <td>-0.017385</td>\n",
              "      <td>0.013665</td>\n",
              "      <td>-0.022226</td>\n",
              "      <td>-0.001213</td>\n",
              "      <td>-0.014796</td>\n",
              "      <td>0.000597</td>\n",
              "      <td>0.031954</td>\n",
              "      <td>0.002371</td>\n",
              "      <td>-0.018126</td>\n",
              "      <td>-0.011571</td>\n",
              "      <td>0.002922</td>\n",
              "      <td>0.004012</td>\n",
              "      <td>0.008368</td>\n",
              "      <td>-0.002230</td>\n",
              "      <td>-0.012939</td>\n",
              "      <td>-0.006869</td>\n",
              "      <td>-0.021223</td>\n",
              "      <td>-0.002178</td>\n",
              "      <td>0.005808</td>\n",
              "      <td>0.002520</td>\n",
              "      <td>-0.026954</td>\n",
              "      <td>0.003855</td>\n",
              "      <td>0.002117</td>\n",
              "      <td>0.005957</td>\n",
              "      <td>-0.012892</td>\n",
              "      <td>-0.008533</td>\n",
              "      <td>0.011652</td>\n",
              "      <td>-0.012311</td>\n",
              "      <td>-0.000960</td>\n",
              "      <td>-0.005654</td>\n",
              "      <td>0.021616</td>\n",
              "      <td>-0.011808</td>\n",
              "      <td>-0.066263</td>\n",
              "      <td>0.002470</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>-0.008318</td>\n",
              "      <td>0.007703</td>\n",
              "      <td>-0.005285</td>\n",
              "      <td>-0.013480</td>\n",
              "      <td>-0.052450</td>\n",
              "      <td>-0.046610</td>\n",
              "      <td>-0.036897</td>\n",
              "      <td>0.020530</td>\n",
              "      <td>-0.009000</td>\n",
              "      <td>-0.026606</td>\n",
              "      <td>0.003601</td>\n",
              "      <td>-0.013548</td>\n",
              "      <td>-0.018531</td>\n",
              "      <td>0.004632</td>\n",
              "      <td>-0.017898</td>\n",
              "      <td>0.017612</td>\n",
              "      <td>-0.027145</td>\n",
              "      <td>0.525006</td>\n",
              "      <td>0.394318</td>\n",
              "      <td>0.067022</td>\n",
              "      <td>-0.021211</td>\n",
              "      <td>0.034297</td>\n",
              "      <td>0.125743</td>\n",
              "      <td>-0.028627</td>\n",
              "      <td>0.078087</td>\n",
              "      <td>0.002796</td>\n",
              "      <td>0.038130</td>\n",
              "      <td>0.034203</td>\n",
              "      <td>0.033169</td>\n",
              "      <td>0.003903</td>\n",
              "      <td>-0.004002</td>\n",
              "      <td>0.007976</td>\n",
              "      <td>-0.009168</td>\n",
              "      <td>0.019215</td>\n",
              "      <td>-0.001041</td>\n",
              "      <td>0.321892</td>\n",
              "      <td>0.022100</td>\n",
              "      <td>0.018772</td>\n",
              "      <td>0.014255</td>\n",
              "      <td>0.044508</td>\n",
              "      <td>0.002772</td>\n",
              "      <td>8</td>\n",
              "      <td>omitted</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1951</th>\n",
              "      <td>0.020862</td>\n",
              "      <td>-0.024411</td>\n",
              "      <td>-0.006535</td>\n",
              "      <td>0.005498</td>\n",
              "      <td>-0.006209</td>\n",
              "      <td>0.015611</td>\n",
              "      <td>0.026013</td>\n",
              "      <td>-0.003311</td>\n",
              "      <td>-0.007190</td>\n",
              "      <td>-0.003858</td>\n",
              "      <td>-0.000148</td>\n",
              "      <td>-0.022776</td>\n",
              "      <td>0.010924</td>\n",
              "      <td>0.010120</td>\n",
              "      <td>0.010180</td>\n",
              "      <td>0.129604</td>\n",
              "      <td>0.000756</td>\n",
              "      <td>0.019753</td>\n",
              "      <td>-0.025121</td>\n",
              "      <td>-0.002600</td>\n",
              "      <td>-0.017511</td>\n",
              "      <td>0.003229</td>\n",
              "      <td>-0.014147</td>\n",
              "      <td>0.016452</td>\n",
              "      <td>-0.049420</td>\n",
              "      <td>-0.034030</td>\n",
              "      <td>-0.023918</td>\n",
              "      <td>-0.002992</td>\n",
              "      <td>-0.007140</td>\n",
              "      <td>-0.009957</td>\n",
              "      <td>-0.016373</td>\n",
              "      <td>-0.002582</td>\n",
              "      <td>-0.025189</td>\n",
              "      <td>-0.023707</td>\n",
              "      <td>-0.020591</td>\n",
              "      <td>0.015726</td>\n",
              "      <td>0.015913</td>\n",
              "      <td>-0.046138</td>\n",
              "      <td>-0.010070</td>\n",
              "      <td>0.023317</td>\n",
              "      <td>-0.008125</td>\n",
              "      <td>-0.028159</td>\n",
              "      <td>-0.016092</td>\n",
              "      <td>-0.029086</td>\n",
              "      <td>-0.020557</td>\n",
              "      <td>0.155071</td>\n",
              "      <td>-0.008732</td>\n",
              "      <td>-0.006033</td>\n",
              "      <td>-0.004172</td>\n",
              "      <td>-0.000518</td>\n",
              "      <td>0.028062</td>\n",
              "      <td>0.020624</td>\n",
              "      <td>-0.004244</td>\n",
              "      <td>-0.059679</td>\n",
              "      <td>-0.010481</td>\n",
              "      <td>0.017218</td>\n",
              "      <td>-0.022454</td>\n",
              "      <td>0.124863</td>\n",
              "      <td>0.003615</td>\n",
              "      <td>-0.022955</td>\n",
              "      <td>0.009598</td>\n",
              "      <td>-0.025902</td>\n",
              "      <td>-0.015819</td>\n",
              "      <td>-0.012421</td>\n",
              "      <td>-0.018052</td>\n",
              "      <td>0.002249</td>\n",
              "      <td>0.004764</td>\n",
              "      <td>-0.028140</td>\n",
              "      <td>0.022568</td>\n",
              "      <td>-0.006246</td>\n",
              "      <td>-0.016524</td>\n",
              "      <td>0.023752</td>\n",
              "      <td>0.006976</td>\n",
              "      <td>0.002367</td>\n",
              "      <td>0.000476</td>\n",
              "      <td>-0.014663</td>\n",
              "      <td>-0.009239</td>\n",
              "      <td>0.004520</td>\n",
              "      <td>1</td>\n",
              "      <td>im106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4795</th>\n",
              "      <td>-0.004018</td>\n",
              "      <td>-0.010699</td>\n",
              "      <td>-0.010558</td>\n",
              "      <td>-0.002970</td>\n",
              "      <td>0.001230</td>\n",
              "      <td>0.011598</td>\n",
              "      <td>-0.024772</td>\n",
              "      <td>0.000927</td>\n",
              "      <td>-0.011435</td>\n",
              "      <td>-0.007435</td>\n",
              "      <td>0.006330</td>\n",
              "      <td>0.043721</td>\n",
              "      <td>-0.002293</td>\n",
              "      <td>0.008678</td>\n",
              "      <td>0.015054</td>\n",
              "      <td>0.007053</td>\n",
              "      <td>0.009966</td>\n",
              "      <td>-0.044215</td>\n",
              "      <td>-0.018651</td>\n",
              "      <td>-0.004008</td>\n",
              "      <td>0.011974</td>\n",
              "      <td>-0.000979</td>\n",
              "      <td>0.496665</td>\n",
              "      <td>-0.013701</td>\n",
              "      <td>-0.010687</td>\n",
              "      <td>0.262385</td>\n",
              "      <td>0.511310</td>\n",
              "      <td>0.004374</td>\n",
              "      <td>0.006034</td>\n",
              "      <td>-0.023656</td>\n",
              "      <td>0.010205</td>\n",
              "      <td>0.307939</td>\n",
              "      <td>-0.011910</td>\n",
              "      <td>-0.012468</td>\n",
              "      <td>0.292581</td>\n",
              "      <td>0.013156</td>\n",
              "      <td>0.009403</td>\n",
              "      <td>0.051161</td>\n",
              "      <td>0.279429</td>\n",
              "      <td>-0.023309</td>\n",
              "      <td>-0.021843</td>\n",
              "      <td>-0.037454</td>\n",
              "      <td>-0.174267</td>\n",
              "      <td>-0.008288</td>\n",
              "      <td>-0.016841</td>\n",
              "      <td>-0.026460</td>\n",
              "      <td>-0.029047</td>\n",
              "      <td>-0.014737</td>\n",
              "      <td>-0.013607</td>\n",
              "      <td>0.039883</td>\n",
              "      <td>-0.024266</td>\n",
              "      <td>-0.021642</td>\n",
              "      <td>0.008978</td>\n",
              "      <td>-0.001001</td>\n",
              "      <td>0.015110</td>\n",
              "      <td>0.031136</td>\n",
              "      <td>0.013263</td>\n",
              "      <td>0.018523</td>\n",
              "      <td>0.230145</td>\n",
              "      <td>-0.009973</td>\n",
              "      <td>-0.000395</td>\n",
              "      <td>0.437084</td>\n",
              "      <td>-0.006190</td>\n",
              "      <td>0.020911</td>\n",
              "      <td>-0.028377</td>\n",
              "      <td>-0.008593</td>\n",
              "      <td>0.028485</td>\n",
              "      <td>0.002773</td>\n",
              "      <td>0.016401</td>\n",
              "      <td>0.070914</td>\n",
              "      <td>0.014084</td>\n",
              "      <td>0.056521</td>\n",
              "      <td>0.013397</td>\n",
              "      <td>0.009639</td>\n",
              "      <td>-0.001811</td>\n",
              "      <td>0.006845</td>\n",
              "      <td>0.003536</td>\n",
              "      <td>0.002561</td>\n",
              "      <td>8</td>\n",
              "      <td>omitted</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2939</th>\n",
              "      <td>0.000584</td>\n",
              "      <td>-0.032034</td>\n",
              "      <td>0.033356</td>\n",
              "      <td>0.117433</td>\n",
              "      <td>-0.007544</td>\n",
              "      <td>-0.018926</td>\n",
              "      <td>0.020358</td>\n",
              "      <td>0.003481</td>\n",
              "      <td>-0.005499</td>\n",
              "      <td>-0.062128</td>\n",
              "      <td>-0.010154</td>\n",
              "      <td>-0.018568</td>\n",
              "      <td>-0.002396</td>\n",
              "      <td>0.001180</td>\n",
              "      <td>-0.047672</td>\n",
              "      <td>-0.000569</td>\n",
              "      <td>-0.014984</td>\n",
              "      <td>0.002116</td>\n",
              "      <td>-0.045513</td>\n",
              "      <td>0.000607</td>\n",
              "      <td>0.001385</td>\n",
              "      <td>-0.001374</td>\n",
              "      <td>-0.027366</td>\n",
              "      <td>-0.004391</td>\n",
              "      <td>-0.020753</td>\n",
              "      <td>0.002289</td>\n",
              "      <td>-0.026196</td>\n",
              "      <td>-0.043145</td>\n",
              "      <td>-0.002935</td>\n",
              "      <td>-0.009109</td>\n",
              "      <td>-0.020080</td>\n",
              "      <td>0.034446</td>\n",
              "      <td>-0.000889</td>\n",
              "      <td>-0.016045</td>\n",
              "      <td>-0.004724</td>\n",
              "      <td>-0.036808</td>\n",
              "      <td>-0.000415</td>\n",
              "      <td>-0.017693</td>\n",
              "      <td>-0.008982</td>\n",
              "      <td>-0.009844</td>\n",
              "      <td>0.008086</td>\n",
              "      <td>-0.003848</td>\n",
              "      <td>-0.007421</td>\n",
              "      <td>-0.012869</td>\n",
              "      <td>0.014550</td>\n",
              "      <td>0.026676</td>\n",
              "      <td>-0.015301</td>\n",
              "      <td>0.063289</td>\n",
              "      <td>0.016748</td>\n",
              "      <td>0.033366</td>\n",
              "      <td>0.002421</td>\n",
              "      <td>-0.002816</td>\n",
              "      <td>-0.006658</td>\n",
              "      <td>-0.009473</td>\n",
              "      <td>-0.062474</td>\n",
              "      <td>-0.027158</td>\n",
              "      <td>-0.002374</td>\n",
              "      <td>0.008066</td>\n",
              "      <td>-0.037934</td>\n",
              "      <td>0.060955</td>\n",
              "      <td>-0.033916</td>\n",
              "      <td>-0.065639</td>\n",
              "      <td>0.002184</td>\n",
              "      <td>-0.018161</td>\n",
              "      <td>-0.026967</td>\n",
              "      <td>0.017577</td>\n",
              "      <td>0.014474</td>\n",
              "      <td>-0.006078</td>\n",
              "      <td>-0.014891</td>\n",
              "      <td>0.004310</td>\n",
              "      <td>0.027397</td>\n",
              "      <td>-0.004364</td>\n",
              "      <td>-0.005335</td>\n",
              "      <td>0.021580</td>\n",
              "      <td>-0.000696</td>\n",
              "      <td>0.009514</td>\n",
              "      <td>-0.000142</td>\n",
              "      <td>-0.002868</td>\n",
              "      <td>2</td>\n",
              "      <td>im075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>0.005611</td>\n",
              "      <td>0.036011</td>\n",
              "      <td>0.032803</td>\n",
              "      <td>0.085339</td>\n",
              "      <td>0.011128</td>\n",
              "      <td>0.031344</td>\n",
              "      <td>0.013295</td>\n",
              "      <td>0.066061</td>\n",
              "      <td>-0.009786</td>\n",
              "      <td>0.574271</td>\n",
              "      <td>0.038591</td>\n",
              "      <td>0.013612</td>\n",
              "      <td>0.008121</td>\n",
              "      <td>-0.000976</td>\n",
              "      <td>0.043867</td>\n",
              "      <td>0.022482</td>\n",
              "      <td>0.020378</td>\n",
              "      <td>0.051062</td>\n",
              "      <td>0.105484</td>\n",
              "      <td>0.051751</td>\n",
              "      <td>-0.013672</td>\n",
              "      <td>-0.001899</td>\n",
              "      <td>1.061762</td>\n",
              "      <td>0.026626</td>\n",
              "      <td>0.049377</td>\n",
              "      <td>0.012607</td>\n",
              "      <td>0.229743</td>\n",
              "      <td>0.180867</td>\n",
              "      <td>-0.003705</td>\n",
              "      <td>0.395150</td>\n",
              "      <td>0.123852</td>\n",
              "      <td>0.045980</td>\n",
              "      <td>0.012738</td>\n",
              "      <td>0.174331</td>\n",
              "      <td>0.119136</td>\n",
              "      <td>0.059674</td>\n",
              "      <td>0.042590</td>\n",
              "      <td>0.029918</td>\n",
              "      <td>0.020335</td>\n",
              "      <td>0.021361</td>\n",
              "      <td>-0.017704</td>\n",
              "      <td>0.002535</td>\n",
              "      <td>-0.047495</td>\n",
              "      <td>-0.029728</td>\n",
              "      <td>-0.051463</td>\n",
              "      <td>0.010552</td>\n",
              "      <td>0.006442</td>\n",
              "      <td>-0.040326</td>\n",
              "      <td>-0.037353</td>\n",
              "      <td>0.001816</td>\n",
              "      <td>-0.033189</td>\n",
              "      <td>-0.002803</td>\n",
              "      <td>-0.014467</td>\n",
              "      <td>-0.026643</td>\n",
              "      <td>-0.073581</td>\n",
              "      <td>-0.007308</td>\n",
              "      <td>-0.021261</td>\n",
              "      <td>-0.045519</td>\n",
              "      <td>-0.025112</td>\n",
              "      <td>0.065284</td>\n",
              "      <td>0.000967</td>\n",
              "      <td>-0.044286</td>\n",
              "      <td>-0.003312</td>\n",
              "      <td>-0.022304</td>\n",
              "      <td>0.001160</td>\n",
              "      <td>0.007161</td>\n",
              "      <td>0.003305</td>\n",
              "      <td>0.013153</td>\n",
              "      <td>0.005550</td>\n",
              "      <td>-0.004700</td>\n",
              "      <td>-0.019711</td>\n",
              "      <td>0.017759</td>\n",
              "      <td>-0.005907</td>\n",
              "      <td>0.003797</td>\n",
              "      <td>-0.004592</td>\n",
              "      <td>0.008971</td>\n",
              "      <td>0.004573</td>\n",
              "      <td>0.003226</td>\n",
              "      <td>7</td>\n",
              "      <td>im035</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           1086490391  1086490482  1086490519  1086490526  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                         0.059679    0.043337    0.010550    0.005097   \n",
              "1466                        -0.014051    0.049902   -0.025210    0.080978   \n",
              "1261                         0.014956   -0.005141    0.044761   -0.020085   \n",
              "4571                         0.002222    0.001747   -0.003950   -0.006423   \n",
              "1624                        -0.006231    0.011771   -0.034586   -0.019165   \n",
              "4687                         0.019718    0.008133   -0.017385    0.013665   \n",
              "1951                         0.020862   -0.024411   -0.006535    0.005498   \n",
              "4795                        -0.004018   -0.010699   -0.010558   -0.002970   \n",
              "2939                         0.000584   -0.032034    0.033356    0.117433   \n",
              "1999                         0.005611    0.036011    0.032803    0.085339   \n",
              "\n",
              "                           1086490561  1086490572  1086490598  1086490603  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.010583    0.013195    0.011689    0.004849   \n",
              "1466                        -0.003823   -0.000884   -0.020765   -0.004035   \n",
              "1261                         0.008341    0.008279    0.010381    0.022521   \n",
              "4571                        -0.001865   -0.005080   -0.004171   -0.000133   \n",
              "1624                        -0.001013   -0.033054    0.003279    0.010746   \n",
              "4687                        -0.022226   -0.001213   -0.014796    0.000597   \n",
              "1951                        -0.006209    0.015611    0.026013   -0.003311   \n",
              "4795                         0.001230    0.011598   -0.024772    0.000927   \n",
              "2939                        -0.007544   -0.018926    0.020358    0.003481   \n",
              "1999                         0.011128    0.031344    0.013295    0.066061   \n",
              "\n",
              "                           1086490636  1086490648  1086490689  1086490697  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.021314    0.051991    0.020344    0.013736   \n",
              "1466                        -0.027170   -0.022013    0.010836   -0.042728   \n",
              "1261                         0.020920    0.005022    0.030691    0.007705   \n",
              "4571                         0.005905    0.001885    0.017521    0.023397   \n",
              "1624                        -0.001501    0.002346   -0.022430    0.002937   \n",
              "4687                         0.031954    0.002371   -0.018126   -0.011571   \n",
              "1951                        -0.007190   -0.003858   -0.000148   -0.022776   \n",
              "4795                        -0.011435   -0.007435    0.006330    0.043721   \n",
              "2939                        -0.005499   -0.062128   -0.010154   -0.018568   \n",
              "1999                        -0.009786    0.574271    0.038591    0.013612   \n",
              "\n",
              "                           1086490729  1086490741  1086490748  1086490751  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                         0.020446    0.010156    0.027473    0.040449   \n",
              "1466                        -0.002450   -0.000330   -0.023225    0.002154   \n",
              "1261                        -0.005435    0.005462   -0.002526    0.003691   \n",
              "4571                        -0.003024    0.003037   -0.034136   -0.007811   \n",
              "1624                        -0.010811   -0.008319    0.006561    0.009582   \n",
              "4687                         0.002922    0.004012    0.008368   -0.002230   \n",
              "1951                         0.010924    0.010120    0.010180    0.129604   \n",
              "4795                        -0.002293    0.008678    0.015054    0.007053   \n",
              "2939                        -0.002396    0.001180   -0.047672   -0.000569   \n",
              "1999                         0.008121   -0.000976    0.043867    0.022482   \n",
              "\n",
              "                           1086490777  1086490784  1086490794  1086490807  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                         0.039498   -0.025774    0.083231    0.066705   \n",
              "1466                         0.014300   -0.006471    0.078409    0.006155   \n",
              "1261                        -0.003567    0.007953   -0.024317    0.016451   \n",
              "4571                         0.011996   -0.028177    0.001608    0.004444   \n",
              "1624                         0.018773   -0.023075   -0.006774    0.005361   \n",
              "4687                        -0.012939   -0.006869   -0.021223   -0.002178   \n",
              "1951                         0.000756    0.019753   -0.025121   -0.002600   \n",
              "4795                         0.009966   -0.044215   -0.018651   -0.004008   \n",
              "2939                        -0.014984    0.002116   -0.045513    0.000607   \n",
              "1999                         0.020378    0.051062    0.105484    0.051751   \n",
              "\n",
              "                           1086490839  1086490846  1086490867  1086490936  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                         0.030379    0.114269    1.008788   -0.001022   \n",
              "1466                        -0.000623    0.005512    0.151472    0.008930   \n",
              "1261                         0.012167    0.001125   -0.009095    0.003377   \n",
              "4571                         0.020268    0.007071    0.850092    0.012337   \n",
              "1624                        -0.023407    0.002230   -0.036032   -0.001076   \n",
              "4687                         0.005808    0.002520   -0.026954    0.003855   \n",
              "1951                        -0.017511    0.003229   -0.014147    0.016452   \n",
              "4795                         0.011974   -0.000979    0.496665   -0.013701   \n",
              "2939                         0.001385   -0.001374   -0.027366   -0.004391   \n",
              "1999                        -0.013672   -0.001899    1.061762    0.026626   \n",
              "\n",
              "                           1086490994  1086490995  1086491035  1086491066  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                         0.020511   -0.019711    1.986979    0.030024   \n",
              "1466                        -0.007366   -0.022816    0.000592   -0.012933   \n",
              "1261                         0.013782    0.010260   -0.017700   -0.016183   \n",
              "4571                         0.018162    0.012706   -0.002904    0.026949   \n",
              "1624                         0.009338   -0.047546   -0.055792    0.006758   \n",
              "4687                         0.002117    0.005957   -0.012892   -0.008533   \n",
              "1951                        -0.049420   -0.034030   -0.023918   -0.002992   \n",
              "4795                        -0.010687    0.262385    0.511310    0.004374   \n",
              "2939                        -0.020753    0.002289   -0.026196   -0.043145   \n",
              "1999                         0.049377    0.012607    0.229743    0.180867   \n",
              "\n",
              "                           1086491079  1086491172  1086491194  1086491215  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.003607   -0.042412    0.018767    0.119028   \n",
              "1466                        -0.004043   -0.017916    0.020863    0.004779   \n",
              "1261                         0.017778    0.022868    0.007281   -0.031303   \n",
              "4571                         0.003621    0.202994    0.016229   -0.027711   \n",
              "1624                         0.021335   -0.027532   -0.006914   -0.028757   \n",
              "4687                         0.011652   -0.012311   -0.000960   -0.005654   \n",
              "1951                        -0.007140   -0.009957   -0.016373   -0.002582   \n",
              "4795                         0.006034   -0.023656    0.010205    0.307939   \n",
              "2939                        -0.002935   -0.009109   -0.020080    0.034446   \n",
              "1999                        -0.003705    0.395150    0.123852    0.045980   \n",
              "\n",
              "                           1086491256  1086491263  1086491328  1086491339  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.020212   -0.006489    0.342231    0.040511   \n",
              "1466                         0.033397   -0.026691   -0.016375    0.007522   \n",
              "1261                        -0.020858   -0.013026   -0.034772    0.026255   \n",
              "4571                         0.012698    0.025311    0.005050   -0.003169   \n",
              "1624                         0.000123    0.002736   -0.064557    0.001800   \n",
              "4687                         0.021616   -0.011808   -0.066263    0.002470   \n",
              "1951                        -0.025189   -0.023707   -0.020591    0.015726   \n",
              "4795                        -0.011910   -0.012468    0.292581    0.013156   \n",
              "2939                        -0.000889   -0.016045   -0.004724   -0.036808   \n",
              "1999                         0.012738    0.174331    0.119136    0.059674   \n",
              "\n",
              "                           1086491368  1086491397  1086491469  1086491760  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                         0.006472    0.001659   -0.026861    0.033629   \n",
              "1466                         0.017713   -0.001705    0.015504   -0.012627   \n",
              "1261                         0.002560    0.027788    0.024539    0.014195   \n",
              "4571                        -0.009263   -0.006478    0.008216    0.013158   \n",
              "1624                        -0.011574    0.008244   -0.087856    0.001596   \n",
              "4687                         0.010623   -0.008318    0.007703   -0.005285   \n",
              "1951                         0.015913   -0.046138   -0.010070    0.023317   \n",
              "4795                         0.009403    0.051161    0.279429   -0.023309   \n",
              "2939                        -0.000415   -0.017693   -0.008982   -0.009844   \n",
              "1999                         0.042590    0.029918    0.020335    0.021361   \n",
              "\n",
              "                           1086496500  1086496689  1086497129  1086497550  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.015228    0.087171    0.643712    0.000942   \n",
              "1466                         0.015895    0.009344   -0.010002   -0.016746   \n",
              "1261                         0.037427    0.223072    0.724278    0.145090   \n",
              "4571                         0.000061    0.027633    0.003372    0.004112   \n",
              "1624                         0.008997   -0.036614   -0.000874   -0.010894   \n",
              "4687                        -0.013480   -0.052450   -0.046610   -0.036897   \n",
              "1951                        -0.008125   -0.028159   -0.016092   -0.029086   \n",
              "4795                        -0.021843   -0.037454   -0.174267   -0.008288   \n",
              "2939                         0.008086   -0.003848   -0.007421   -0.012869   \n",
              "1999                        -0.017704    0.002535   -0.047495   -0.029728   \n",
              "\n",
              "                           1086497908  1086498071  1086498205  1086498334  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                         0.008901    0.012378    0.027601   -0.019955   \n",
              "1466                         0.001230   -0.031729    0.022029   -0.016372   \n",
              "1261                         0.006736    0.008553    0.074277    0.021142   \n",
              "4571                        -0.004595   -0.002298   -0.024687   -0.000604   \n",
              "1624                         0.000592    0.011995    0.000933   -0.011968   \n",
              "4687                         0.020530   -0.009000   -0.026606    0.003601   \n",
              "1951                        -0.020557    0.155071   -0.008732   -0.006033   \n",
              "4795                        -0.016841   -0.026460   -0.029047   -0.014737   \n",
              "2939                         0.014550    0.026676   -0.015301    0.063289   \n",
              "1999                        -0.051463    0.010552    0.006442   -0.040326   \n",
              "\n",
              "                           1086498461  1086498820  1086498977  1086499259  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.041626    0.043978   -0.016098    0.031179   \n",
              "1466                         0.042122   -0.005413    0.011188    0.016532   \n",
              "1261                         0.018715    0.376158    0.053071    0.019182   \n",
              "4571                         0.026839    0.047212    0.036752   -0.006999   \n",
              "1624                         0.057242    0.208111    0.000643    0.000025   \n",
              "4687                        -0.013548   -0.018531    0.004632   -0.017898   \n",
              "1951                        -0.004172   -0.000518    0.028062    0.020624   \n",
              "4795                        -0.013607    0.039883   -0.024266   -0.021642   \n",
              "2939                         0.016748    0.033366    0.002421   -0.002816   \n",
              "1999                        -0.037353    0.001816   -0.033189   -0.002803   \n",
              "\n",
              "                           1086499316  1086499479  1086499499  1086499628  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.022157   -0.021880   -0.046552    0.008994   \n",
              "1466                        -0.012043    0.001385    0.127224    0.025265   \n",
              "1261                        -0.003581    0.011362    0.008205    0.019201   \n",
              "4571                        -0.006784    0.006196    0.177035    0.048094   \n",
              "1624                         0.007701    0.010099    0.013160   -0.012054   \n",
              "4687                         0.017612   -0.027145    0.525006    0.394318   \n",
              "1951                        -0.004244   -0.059679   -0.010481    0.017218   \n",
              "4795                         0.008978   -0.001001    0.015110    0.031136   \n",
              "2939                        -0.006658   -0.009473   -0.062474   -0.027158   \n",
              "1999                        -0.014467   -0.026643   -0.073581   -0.007308   \n",
              "\n",
              "                           1086499783  1086499910  1086499911  1086500092  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.027552    0.221980   -0.016193   -0.011782   \n",
              "1466                         0.007885   -0.016411    0.012639    0.015712   \n",
              "1261                         0.025111    0.077685    0.021665    0.029861   \n",
              "4571                         0.014279   -0.034531    0.020558    0.036701   \n",
              "1624                         0.017769    0.045422   -0.018842   -0.033843   \n",
              "4687                         0.067022   -0.021211    0.034297    0.125743   \n",
              "1951                        -0.022454    0.124863    0.003615   -0.022955   \n",
              "4795                         0.013263    0.018523    0.230145   -0.009973   \n",
              "2939                        -0.002374    0.008066   -0.037934    0.060955   \n",
              "1999                        -0.021261   -0.045519   -0.025112    0.065284   \n",
              "\n",
              "                           1086500121  1086500194  1086500281  1086500392  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.011227   -0.031365   -0.016415   -0.021852   \n",
              "1466                        -0.007000    0.088204   -0.021429   -0.006089   \n",
              "1261                         0.034027    0.055778    0.033293    0.019995   \n",
              "4571                         0.011926    0.334332    0.003963    0.010244   \n",
              "1624                        -0.001822   -0.029414    0.002017    0.007867   \n",
              "4687                        -0.028627    0.078087    0.002796    0.038130   \n",
              "1951                         0.009598   -0.025902   -0.015819   -0.012421   \n",
              "4795                        -0.000395    0.437084   -0.006190    0.020911   \n",
              "2939                        -0.033916   -0.065639    0.002184   -0.018161   \n",
              "1999                         0.000967   -0.044286   -0.003312   -0.022304   \n",
              "\n",
              "                           1086500583  1086500633  1086500807  1086500812  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.023881   -0.014958   -0.001866    0.003135   \n",
              "1466                         0.003243   -0.003940   -0.000534    0.027581   \n",
              "1261                        -0.019132   -0.003996    0.005535    0.046272   \n",
              "4571                         0.014058    0.015209    0.003823   -0.022468   \n",
              "1624                        -0.002474   -0.020486   -0.000749   -0.028870   \n",
              "4687                         0.034203    0.033169    0.003903   -0.004002   \n",
              "1951                        -0.018052    0.002249    0.004764   -0.028140   \n",
              "4795                        -0.028377   -0.008593    0.028485    0.002773   \n",
              "2939                        -0.026967    0.017577    0.014474   -0.006078   \n",
              "1999                         0.001160    0.007161    0.003305    0.013153   \n",
              "\n",
              "                           1086501072  1086501262  1086501360  1086501675  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.020806    0.003486   -0.015576    0.028093   \n",
              "1466                         0.004541    0.007604    0.017881    0.006511   \n",
              "1261                         0.008471   -0.001670   -0.000422   -0.000225   \n",
              "4571                         0.008617    0.001481   -0.007659    0.036333   \n",
              "1624                         0.052410    0.053775    0.002032    0.027753   \n",
              "4687                         0.007976   -0.009168    0.019215   -0.001041   \n",
              "1951                         0.022568   -0.006246   -0.016524    0.023752   \n",
              "4795                         0.016401    0.070914    0.014084    0.056521   \n",
              "2939                        -0.014891    0.004310    0.027397   -0.004364   \n",
              "1999                         0.005550   -0.004700   -0.019711    0.017759   \n",
              "\n",
              "                           1086502269  1086668144  1086668169  1086668188  \\\n",
              "stimulus_presentations_id                                                   \n",
              "3321                        -0.010096   -0.003811   -0.005539   -0.004697   \n",
              "1466                        -0.004519    0.009093    0.005929    0.020128   \n",
              "1261                         0.008764    0.023327   -0.000061    0.010631   \n",
              "4571                         0.003408    0.010141    0.012862    0.006593   \n",
              "1624                         0.018273    0.018346    0.001280    0.017200   \n",
              "4687                         0.321892    0.022100    0.018772    0.014255   \n",
              "1951                         0.006976    0.002367    0.000476   -0.014663   \n",
              "4795                         0.013397    0.009639   -0.001811    0.006845   \n",
              "2939                        -0.005335    0.021580   -0.000696    0.009514   \n",
              "1999                        -0.005907    0.003797   -0.004592    0.008971   \n",
              "\n",
              "                           1086668228  1086668247  image_index image_name  \n",
              "stimulus_presentations_id                                                  \n",
              "3321                        -0.000514    0.007265            0      im000  \n",
              "1466                         0.034869    0.002533            6      im031  \n",
              "1261                        -0.004477    0.004531            3      im073  \n",
              "4571                         0.174516   -0.000617            4      im045  \n",
              "1624                         0.067932    0.001591            4      im045  \n",
              "4687                         0.044508    0.002772            8    omitted  \n",
              "1951                        -0.009239    0.004520            1      im106  \n",
              "4795                         0.003536    0.002561            8    omitted  \n",
              "2939                        -0.000142   -0.002868            2      im075  \n",
              "1999                         0.004573    0.003226            7      im035  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "MkeUUhIHu8Pw",
        "outputId": "bbfad99e-cf90-438c-d546-a070fc345379"
      },
      "source": [
        "X = features_and_labels[cell_ids]\n",
        "X.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1086490751</th>\n",
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              "    <tr>\n",
              "      <th>stimulus_presentations_id</th>\n",
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              "      <th>2086</th>\n",
              "      <td>-0.016676</td>\n",
              "      <td>0.003152</td>\n",
              "      <td>0.007965</td>\n",
              "      <td>-0.007311</td>\n",
              "      <td>-0.017549</td>\n",
              "      <td>-0.010242</td>\n",
              "      <td>0.022443</td>\n",
              "      <td>0.005003</td>\n",
              "      <td>0.015786</td>\n",
              "      <td>0.034340</td>\n",
              "      <td>0.069028</td>\n",
              "      <td>-0.010218</td>\n",
              "      <td>0.022155</td>\n",
              "      <td>-0.020663</td>\n",
              "      <td>0.011431</td>\n",
              "      <td>-0.003285</td>\n",
              "      <td>0.001192</td>\n",
              "      <td>0.010370</td>\n",
              "      <td>-0.026390</td>\n",
              "      <td>-0.017587</td>\n",
              "      <td>0.015905</td>\n",
              "      <td>0.069328</td>\n",
              "      <td>-0.053884</td>\n",
              "      <td>0.026336</td>\n",
              "      <td>0.210128</td>\n",
              "      <td>1.082757</td>\n",
              "      <td>-0.055008</td>\n",
              "      <td>0.858020</td>\n",
              "      <td>0.014891</td>\n",
              "      <td>0.506860</td>\n",
              "      <td>0.001264</td>\n",
              "      <td>0.004638</td>\n",
              "      <td>-0.075995</td>\n",
              "      <td>0.012149</td>\n",
              "      <td>-0.015850</td>\n",
              "      <td>0.015646</td>\n",
              "      <td>0.013684</td>\n",
              "      <td>-0.017270</td>\n",
              "      <td>0.034951</td>\n",
              "      <td>0.017226</td>\n",
              "      <td>0.016621</td>\n",
              "      <td>0.014940</td>\n",
              "      <td>0.020485</td>\n",
              "      <td>0.012219</td>\n",
              "      <td>0.001260</td>\n",
              "      <td>-0.031259</td>\n",
              "      <td>-0.006062</td>\n",
              "      <td>0.069423</td>\n",
              "      <td>0.023960</td>\n",
              "      <td>0.003989</td>\n",
              "      <td>0.002064</td>\n",
              "      <td>0.007475</td>\n",
              "      <td>0.094570</td>\n",
              "      <td>0.007431</td>\n",
              "      <td>0.016076</td>\n",
              "      <td>0.010792</td>\n",
              "      <td>-0.020193</td>\n",
              "      <td>0.041131</td>\n",
              "      <td>0.001499</td>\n",
              "      <td>0.008277</td>\n",
              "      <td>-0.017234</td>\n",
              "      <td>-0.025371</td>\n",
              "      <td>0.013944</td>\n",
              "      <td>0.033143</td>\n",
              "      <td>0.009155</td>\n",
              "      <td>-0.031895</td>\n",
              "      <td>-0.034395</td>\n",
              "      <td>-0.000581</td>\n",
              "      <td>0.019191</td>\n",
              "      <td>0.014165</td>\n",
              "      <td>0.057749</td>\n",
              "      <td>0.051829</td>\n",
              "      <td>0.038485</td>\n",
              "      <td>-0.002511</td>\n",
              "      <td>0.001547</td>\n",
              "      <td>0.019550</td>\n",
              "      <td>-0.012476</td>\n",
              "      <td>0.020143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3151</th>\n",
              "      <td>-0.007288</td>\n",
              "      <td>-0.007911</td>\n",
              "      <td>-0.031992</td>\n",
              "      <td>-0.053970</td>\n",
              "      <td>-0.034117</td>\n",
              "      <td>-0.012911</td>\n",
              "      <td>0.203908</td>\n",
              "      <td>0.023255</td>\n",
              "      <td>-0.029756</td>\n",
              "      <td>-0.035819</td>\n",
              "      <td>-0.034289</td>\n",
              "      <td>-0.005932</td>\n",
              "      <td>0.018209</td>\n",
              "      <td>-0.016066</td>\n",
              "      <td>0.006082</td>\n",
              "      <td>0.027375</td>\n",
              "      <td>0.006957</td>\n",
              "      <td>0.002787</td>\n",
              "      <td>0.021735</td>\n",
              "      <td>0.011021</td>\n",
              "      <td>0.014360</td>\n",
              "      <td>-0.018976</td>\n",
              "      <td>0.023359</td>\n",
              "      <td>-0.015684</td>\n",
              "      <td>0.021852</td>\n",
              "      <td>0.000639</td>\n",
              "      <td>0.017995</td>\n",
              "      <td>0.010634</td>\n",
              "      <td>0.007064</td>\n",
              "      <td>0.015827</td>\n",
              "      <td>0.039930</td>\n",
              "      <td>0.013039</td>\n",
              "      <td>0.014293</td>\n",
              "      <td>0.015226</td>\n",
              "      <td>0.004205</td>\n",
              "      <td>-0.011211</td>\n",
              "      <td>0.020537</td>\n",
              "      <td>0.023322</td>\n",
              "      <td>0.010325</td>\n",
              "      <td>-0.002651</td>\n",
              "      <td>-0.025229</td>\n",
              "      <td>-0.004411</td>\n",
              "      <td>0.009221</td>\n",
              "      <td>0.014824</td>\n",
              "      <td>0.016327</td>\n",
              "      <td>0.008798</td>\n",
              "      <td>0.019344</td>\n",
              "      <td>0.011081</td>\n",
              "      <td>-0.035440</td>\n",
              "      <td>-0.009055</td>\n",
              "      <td>-0.016829</td>\n",
              "      <td>0.001136</td>\n",
              "      <td>-0.032314</td>\n",
              "      <td>-0.053997</td>\n",
              "      <td>-0.020065</td>\n",
              "      <td>-0.013386</td>\n",
              "      <td>0.094204</td>\n",
              "      <td>-0.132432</td>\n",
              "      <td>-0.023502</td>\n",
              "      <td>-0.028785</td>\n",
              "      <td>-0.015884</td>\n",
              "      <td>-0.004431</td>\n",
              "      <td>-0.002365</td>\n",
              "      <td>0.001965</td>\n",
              "      <td>0.014245</td>\n",
              "      <td>0.033495</td>\n",
              "      <td>-0.019494</td>\n",
              "      <td>-0.002713</td>\n",
              "      <td>0.002721</td>\n",
              "      <td>-0.000552</td>\n",
              "      <td>-0.001499</td>\n",
              "      <td>-0.001481</td>\n",
              "      <td>0.004546</td>\n",
              "      <td>0.005228</td>\n",
              "      <td>0.001939</td>\n",
              "      <td>-0.005877</td>\n",
              "      <td>-0.000269</td>\n",
              "      <td>0.001033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3106</th>\n",
              "      <td>-0.006872</td>\n",
              "      <td>0.024483</td>\n",
              "      <td>0.028600</td>\n",
              "      <td>-0.005271</td>\n",
              "      <td>-0.010932</td>\n",
              "      <td>0.007454</td>\n",
              "      <td>-0.028295</td>\n",
              "      <td>-0.047562</td>\n",
              "      <td>-0.022598</td>\n",
              "      <td>0.004952</td>\n",
              "      <td>0.187260</td>\n",
              "      <td>-0.006879</td>\n",
              "      <td>-0.001461</td>\n",
              "      <td>-0.006901</td>\n",
              "      <td>-0.005558</td>\n",
              "      <td>-0.051291</td>\n",
              "      <td>0.000156</td>\n",
              "      <td>-0.022834</td>\n",
              "      <td>-0.012719</td>\n",
              "      <td>0.007431</td>\n",
              "      <td>-0.000149</td>\n",
              "      <td>0.001787</td>\n",
              "      <td>-0.028228</td>\n",
              "      <td>-0.021848</td>\n",
              "      <td>-0.043193</td>\n",
              "      <td>-0.037089</td>\n",
              "      <td>0.021162</td>\n",
              "      <td>0.157478</td>\n",
              "      <td>0.001330</td>\n",
              "      <td>0.096675</td>\n",
              "      <td>0.093280</td>\n",
              "      <td>-0.019469</td>\n",
              "      <td>0.837632</td>\n",
              "      <td>0.002785</td>\n",
              "      <td>-0.029305</td>\n",
              "      <td>0.002976</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.082955</td>\n",
              "      <td>0.302268</td>\n",
              "      <td>-0.019194</td>\n",
              "      <td>0.011447</td>\n",
              "      <td>0.013328</td>\n",
              "      <td>0.030896</td>\n",
              "      <td>0.016335</td>\n",
              "      <td>-0.010312</td>\n",
              "      <td>0.422657</td>\n",
              "      <td>0.061380</td>\n",
              "      <td>0.341228</td>\n",
              "      <td>0.019147</td>\n",
              "      <td>0.034812</td>\n",
              "      <td>0.033227</td>\n",
              "      <td>-0.009207</td>\n",
              "      <td>-0.002212</td>\n",
              "      <td>0.025097</td>\n",
              "      <td>-0.005204</td>\n",
              "      <td>-0.009248</td>\n",
              "      <td>-0.026604</td>\n",
              "      <td>-0.040010</td>\n",
              "      <td>-0.000112</td>\n",
              "      <td>-0.016652</td>\n",
              "      <td>-0.035313</td>\n",
              "      <td>0.014830</td>\n",
              "      <td>-0.008225</td>\n",
              "      <td>-0.014175</td>\n",
              "      <td>0.002714</td>\n",
              "      <td>0.003478</td>\n",
              "      <td>0.015411</td>\n",
              "      <td>0.031555</td>\n",
              "      <td>0.099141</td>\n",
              "      <td>-0.007398</td>\n",
              "      <td>0.000404</td>\n",
              "      <td>0.007699</td>\n",
              "      <td>-0.000778</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>-0.001365</td>\n",
              "      <td>-0.005182</td>\n",
              "      <td>0.000611</td>\n",
              "      <td>-0.002719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4641</th>\n",
              "      <td>0.010684</td>\n",
              "      <td>0.030719</td>\n",
              "      <td>-0.011732</td>\n",
              "      <td>0.004865</td>\n",
              "      <td>0.002924</td>\n",
              "      <td>0.025359</td>\n",
              "      <td>-0.029253</td>\n",
              "      <td>0.029568</td>\n",
              "      <td>0.002550</td>\n",
              "      <td>0.065843</td>\n",
              "      <td>-0.031164</td>\n",
              "      <td>0.026223</td>\n",
              "      <td>0.021709</td>\n",
              "      <td>0.007289</td>\n",
              "      <td>0.042450</td>\n",
              "      <td>0.014095</td>\n",
              "      <td>-0.005164</td>\n",
              "      <td>-0.008686</td>\n",
              "      <td>-0.013496</td>\n",
              "      <td>0.000526</td>\n",
              "      <td>-0.000006</td>\n",
              "      <td>-0.002029</td>\n",
              "      <td>-0.006164</td>\n",
              "      <td>0.007536</td>\n",
              "      <td>0.007457</td>\n",
              "      <td>-0.016023</td>\n",
              "      <td>0.005791</td>\n",
              "      <td>0.003662</td>\n",
              "      <td>0.008868</td>\n",
              "      <td>0.019604</td>\n",
              "      <td>0.011552</td>\n",
              "      <td>-0.009868</td>\n",
              "      <td>0.022434</td>\n",
              "      <td>-0.007237</td>\n",
              "      <td>0.016475</td>\n",
              "      <td>-0.017331</td>\n",
              "      <td>0.000674</td>\n",
              "      <td>-0.031671</td>\n",
              "      <td>0.011337</td>\n",
              "      <td>0.004897</td>\n",
              "      <td>-0.014116</td>\n",
              "      <td>-0.014532</td>\n",
              "      <td>0.003132</td>\n",
              "      <td>-0.006204</td>\n",
              "      <td>-0.004854</td>\n",
              "      <td>-0.003050</td>\n",
              "      <td>-0.004778</td>\n",
              "      <td>0.038117</td>\n",
              "      <td>0.018954</td>\n",
              "      <td>0.001495</td>\n",
              "      <td>-0.011962</td>\n",
              "      <td>0.019454</td>\n",
              "      <td>-0.026809</td>\n",
              "      <td>0.009171</td>\n",
              "      <td>-0.005366</td>\n",
              "      <td>0.009939</td>\n",
              "      <td>0.018281</td>\n",
              "      <td>0.010433</td>\n",
              "      <td>-0.010189</td>\n",
              "      <td>0.000846</td>\n",
              "      <td>0.008383</td>\n",
              "      <td>-0.004488</td>\n",
              "      <td>-0.010176</td>\n",
              "      <td>0.004589</td>\n",
              "      <td>0.018169</td>\n",
              "      <td>0.001906</td>\n",
              "      <td>0.014994</td>\n",
              "      <td>-0.000932</td>\n",
              "      <td>0.027714</td>\n",
              "      <td>0.029048</td>\n",
              "      <td>0.005997</td>\n",
              "      <td>0.003226</td>\n",
              "      <td>-0.004636</td>\n",
              "      <td>-0.004816</td>\n",
              "      <td>0.006182</td>\n",
              "      <td>0.002853</td>\n",
              "      <td>-0.001734</td>\n",
              "      <td>0.009685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3153</th>\n",
              "      <td>0.007864</td>\n",
              "      <td>0.073909</td>\n",
              "      <td>0.087301</td>\n",
              "      <td>0.118123</td>\n",
              "      <td>0.006653</td>\n",
              "      <td>0.003324</td>\n",
              "      <td>-0.026303</td>\n",
              "      <td>0.019397</td>\n",
              "      <td>0.007707</td>\n",
              "      <td>0.006138</td>\n",
              "      <td>-0.000353</td>\n",
              "      <td>-0.008738</td>\n",
              "      <td>-0.024516</td>\n",
              "      <td>-0.003631</td>\n",
              "      <td>0.014467</td>\n",
              "      <td>-0.015046</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>0.009330</td>\n",
              "      <td>-0.018235</td>\n",
              "      <td>-0.027788</td>\n",
              "      <td>0.003782</td>\n",
              "      <td>-0.001411</td>\n",
              "      <td>-0.037130</td>\n",
              "      <td>0.013623</td>\n",
              "      <td>-0.018449</td>\n",
              "      <td>-0.003272</td>\n",
              "      <td>-0.012516</td>\n",
              "      <td>-0.016137</td>\n",
              "      <td>0.019327</td>\n",
              "      <td>0.003708</td>\n",
              "      <td>-0.005300</td>\n",
              "      <td>0.004059</td>\n",
              "      <td>0.013768</td>\n",
              "      <td>0.002073</td>\n",
              "      <td>-0.003395</td>\n",
              "      <td>0.025104</td>\n",
              "      <td>-0.000957</td>\n",
              "      <td>0.016523</td>\n",
              "      <td>0.001785</td>\n",
              "      <td>-0.014949</td>\n",
              "      <td>-0.010964</td>\n",
              "      <td>0.006751</td>\n",
              "      <td>0.001156</td>\n",
              "      <td>-0.005912</td>\n",
              "      <td>0.019860</td>\n",
              "      <td>-0.000202</td>\n",
              "      <td>-0.000447</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.029499</td>\n",
              "      <td>0.012401</td>\n",
              "      <td>0.012486</td>\n",
              "      <td>-0.020079</td>\n",
              "      <td>-0.000880</td>\n",
              "      <td>0.008131</td>\n",
              "      <td>-0.000960</td>\n",
              "      <td>0.003209</td>\n",
              "      <td>-0.041401</td>\n",
              "      <td>0.025646</td>\n",
              "      <td>-0.032099</td>\n",
              "      <td>0.037308</td>\n",
              "      <td>-0.000745</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.019161</td>\n",
              "      <td>-0.008068</td>\n",
              "      <td>0.013891</td>\n",
              "      <td>0.017839</td>\n",
              "      <td>0.013458</td>\n",
              "      <td>0.001645</td>\n",
              "      <td>-0.000719</td>\n",
              "      <td>-0.006571</td>\n",
              "      <td>-0.012654</td>\n",
              "      <td>0.011422</td>\n",
              "      <td>0.001850</td>\n",
              "      <td>-0.008610</td>\n",
              "      <td>0.004886</td>\n",
              "      <td>-0.003894</td>\n",
              "      <td>-0.004720</td>\n",
              "      <td>0.003850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2107</th>\n",
              "      <td>-0.033691</td>\n",
              "      <td>-0.014605</td>\n",
              "      <td>-0.025983</td>\n",
              "      <td>-0.000663</td>\n",
              "      <td>0.009163</td>\n",
              "      <td>0.013956</td>\n",
              "      <td>-0.010383</td>\n",
              "      <td>0.009317</td>\n",
              "      <td>0.018615</td>\n",
              "      <td>-0.032451</td>\n",
              "      <td>-0.007530</td>\n",
              "      <td>0.026028</td>\n",
              "      <td>0.002158</td>\n",
              "      <td>-0.011429</td>\n",
              "      <td>-0.006207</td>\n",
              "      <td>0.009697</td>\n",
              "      <td>0.001758</td>\n",
              "      <td>-0.006241</td>\n",
              "      <td>0.213405</td>\n",
              "      <td>-0.000591</td>\n",
              "      <td>-0.014160</td>\n",
              "      <td>0.003793</td>\n",
              "      <td>0.024234</td>\n",
              "      <td>-0.007712</td>\n",
              "      <td>0.017761</td>\n",
              "      <td>0.022902</td>\n",
              "      <td>0.002801</td>\n",
              "      <td>0.076495</td>\n",
              "      <td>2.729562</td>\n",
              "      <td>0.327880</td>\n",
              "      <td>-0.001461</td>\n",
              "      <td>0.013795</td>\n",
              "      <td>-0.024544</td>\n",
              "      <td>-0.014013</td>\n",
              "      <td>0.041989</td>\n",
              "      <td>0.010233</td>\n",
              "      <td>0.029507</td>\n",
              "      <td>0.034215</td>\n",
              "      <td>0.266187</td>\n",
              "      <td>0.021205</td>\n",
              "      <td>0.054197</td>\n",
              "      <td>-0.003162</td>\n",
              "      <td>0.022482</td>\n",
              "      <td>0.042735</td>\n",
              "      <td>0.001865</td>\n",
              "      <td>0.018941</td>\n",
              "      <td>-0.006587</td>\n",
              "      <td>0.022851</td>\n",
              "      <td>0.008129</td>\n",
              "      <td>0.026954</td>\n",
              "      <td>0.003491</td>\n",
              "      <td>-0.016154</td>\n",
              "      <td>-0.039798</td>\n",
              "      <td>-0.010167</td>\n",
              "      <td>-0.024031</td>\n",
              "      <td>-0.011853</td>\n",
              "      <td>-0.042279</td>\n",
              "      <td>-0.038919</td>\n",
              "      <td>-0.012486</td>\n",
              "      <td>0.010516</td>\n",
              "      <td>0.027424</td>\n",
              "      <td>0.007002</td>\n",
              "      <td>-0.020616</td>\n",
              "      <td>0.002779</td>\n",
              "      <td>0.014949</td>\n",
              "      <td>0.023204</td>\n",
              "      <td>-0.009977</td>\n",
              "      <td>0.001624</td>\n",
              "      <td>0.017504</td>\n",
              "      <td>0.006444</td>\n",
              "      <td>0.003633</td>\n",
              "      <td>0.011756</td>\n",
              "      <td>0.014624</td>\n",
              "      <td>0.000240</td>\n",
              "      <td>0.010610</td>\n",
              "      <td>-0.003037</td>\n",
              "      <td>-0.011039</td>\n",
              "      <td>0.003652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1426</th>\n",
              "      <td>-0.018804</td>\n",
              "      <td>0.045204</td>\n",
              "      <td>0.064579</td>\n",
              "      <td>0.492089</td>\n",
              "      <td>-0.009721</td>\n",
              "      <td>-0.002021</td>\n",
              "      <td>-0.066295</td>\n",
              "      <td>-0.002672</td>\n",
              "      <td>-0.021655</td>\n",
              "      <td>-0.017069</td>\n",
              "      <td>-0.009297</td>\n",
              "      <td>-0.025307</td>\n",
              "      <td>-0.044073</td>\n",
              "      <td>-0.004907</td>\n",
              "      <td>-0.022564</td>\n",
              "      <td>-0.018114</td>\n",
              "      <td>-0.024833</td>\n",
              "      <td>-0.009231</td>\n",
              "      <td>-0.018048</td>\n",
              "      <td>0.011876</td>\n",
              "      <td>-0.016183</td>\n",
              "      <td>-0.021415</td>\n",
              "      <td>0.008997</td>\n",
              "      <td>-0.054801</td>\n",
              "      <td>-0.010375</td>\n",
              "      <td>-0.051559</td>\n",
              "      <td>0.033964</td>\n",
              "      <td>-0.013366</td>\n",
              "      <td>0.006523</td>\n",
              "      <td>-0.084666</td>\n",
              "      <td>-0.004994</td>\n",
              "      <td>-0.009824</td>\n",
              "      <td>-0.019759</td>\n",
              "      <td>-0.006969</td>\n",
              "      <td>-0.028398</td>\n",
              "      <td>0.003752</td>\n",
              "      <td>0.012362</td>\n",
              "      <td>0.015029</td>\n",
              "      <td>-0.049110</td>\n",
              "      <td>-0.011750</td>\n",
              "      <td>0.011734</td>\n",
              "      <td>0.009944</td>\n",
              "      <td>-0.001116</td>\n",
              "      <td>0.037157</td>\n",
              "      <td>0.026710</td>\n",
              "      <td>0.008739</td>\n",
              "      <td>0.095875</td>\n",
              "      <td>0.102101</td>\n",
              "      <td>0.037890</td>\n",
              "      <td>0.039717</td>\n",
              "      <td>0.004295</td>\n",
              "      <td>0.017503</td>\n",
              "      <td>-0.011457</td>\n",
              "      <td>0.015333</td>\n",
              "      <td>-0.022434</td>\n",
              "      <td>-0.007645</td>\n",
              "      <td>-0.029114</td>\n",
              "      <td>-0.000537</td>\n",
              "      <td>-0.018540</td>\n",
              "      <td>-0.001683</td>\n",
              "      <td>-0.011079</td>\n",
              "      <td>-0.003834</td>\n",
              "      <td>-0.003409</td>\n",
              "      <td>0.001091</td>\n",
              "      <td>-0.066838</td>\n",
              "      <td>-0.022395</td>\n",
              "      <td>0.472822</td>\n",
              "      <td>0.001765</td>\n",
              "      <td>-0.001809</td>\n",
              "      <td>0.003084</td>\n",
              "      <td>-0.000350</td>\n",
              "      <td>0.005902</td>\n",
              "      <td>0.002137</td>\n",
              "      <td>0.003498</td>\n",
              "      <td>-0.001817</td>\n",
              "      <td>0.007916</td>\n",
              "      <td>-0.007581</td>\n",
              "      <td>0.000077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3052</th>\n",
              "      <td>-0.012900</td>\n",
              "      <td>0.269689</td>\n",
              "      <td>0.396795</td>\n",
              "      <td>-0.013227</td>\n",
              "      <td>0.009058</td>\n",
              "      <td>0.020709</td>\n",
              "      <td>-0.023677</td>\n",
              "      <td>0.116921</td>\n",
              "      <td>-0.018403</td>\n",
              "      <td>0.055411</td>\n",
              "      <td>0.227053</td>\n",
              "      <td>0.058900</td>\n",
              "      <td>-0.014737</td>\n",
              "      <td>-0.000186</td>\n",
              "      <td>-0.008809</td>\n",
              "      <td>-0.006006</td>\n",
              "      <td>0.001668</td>\n",
              "      <td>0.006387</td>\n",
              "      <td>-0.005617</td>\n",
              "      <td>0.014816</td>\n",
              "      <td>0.028891</td>\n",
              "      <td>0.032147</td>\n",
              "      <td>0.067323</td>\n",
              "      <td>0.027495</td>\n",
              "      <td>0.021209</td>\n",
              "      <td>0.241272</td>\n",
              "      <td>-0.004513</td>\n",
              "      <td>0.012207</td>\n",
              "      <td>0.024252</td>\n",
              "      <td>-0.004446</td>\n",
              "      <td>-0.000502</td>\n",
              "      <td>-0.007713</td>\n",
              "      <td>0.000192</td>\n",
              "      <td>0.032198</td>\n",
              "      <td>0.011241</td>\n",
              "      <td>-0.021868</td>\n",
              "      <td>-0.031080</td>\n",
              "      <td>-0.043320</td>\n",
              "      <td>-0.032360</td>\n",
              "      <td>-0.001637</td>\n",
              "      <td>0.011090</td>\n",
              "      <td>-0.023415</td>\n",
              "      <td>-0.021621</td>\n",
              "      <td>-0.030608</td>\n",
              "      <td>-0.019523</td>\n",
              "      <td>-0.021118</td>\n",
              "      <td>-0.014643</td>\n",
              "      <td>-0.047363</td>\n",
              "      <td>-0.004217</td>\n",
              "      <td>-0.004016</td>\n",
              "      <td>-0.007669</td>\n",
              "      <td>-0.007681</td>\n",
              "      <td>0.014402</td>\n",
              "      <td>-0.015032</td>\n",
              "      <td>0.010874</td>\n",
              "      <td>-0.003644</td>\n",
              "      <td>-0.004326</td>\n",
              "      <td>0.063447</td>\n",
              "      <td>0.019159</td>\n",
              "      <td>0.050346</td>\n",
              "      <td>-0.057570</td>\n",
              "      <td>0.000898</td>\n",
              "      <td>0.005018</td>\n",
              "      <td>0.012740</td>\n",
              "      <td>-0.005704</td>\n",
              "      <td>0.031695</td>\n",
              "      <td>-0.005482</td>\n",
              "      <td>0.002003</td>\n",
              "      <td>0.004629</td>\n",
              "      <td>-0.005553</td>\n",
              "      <td>-0.001590</td>\n",
              "      <td>0.005981</td>\n",
              "      <td>0.001882</td>\n",
              "      <td>0.145329</td>\n",
              "      <td>0.004950</td>\n",
              "      <td>0.006707</td>\n",
              "      <td>0.055229</td>\n",
              "      <td>0.059874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3212</th>\n",
              "      <td>-0.013294</td>\n",
              "      <td>0.179976</td>\n",
              "      <td>0.478441</td>\n",
              "      <td>0.071275</td>\n",
              "      <td>-0.005463</td>\n",
              "      <td>-0.022127</td>\n",
              "      <td>0.178264</td>\n",
              "      <td>0.025634</td>\n",
              "      <td>-0.021243</td>\n",
              "      <td>-0.012833</td>\n",
              "      <td>-0.105518</td>\n",
              "      <td>-0.002179</td>\n",
              "      <td>-0.023138</td>\n",
              "      <td>-0.004023</td>\n",
              "      <td>-0.017832</td>\n",
              "      <td>-0.061320</td>\n",
              "      <td>0.114141</td>\n",
              "      <td>0.004000</td>\n",
              "      <td>-0.015148</td>\n",
              "      <td>-0.004375</td>\n",
              "      <td>0.019928</td>\n",
              "      <td>0.014156</td>\n",
              "      <td>-0.011990</td>\n",
              "      <td>0.015530</td>\n",
              "      <td>0.005397</td>\n",
              "      <td>-0.069819</td>\n",
              "      <td>0.018970</td>\n",
              "      <td>-0.001470</td>\n",
              "      <td>0.007757</td>\n",
              "      <td>-0.007201</td>\n",
              "      <td>0.006631</td>\n",
              "      <td>0.003692</td>\n",
              "      <td>-0.021812</td>\n",
              "      <td>0.021634</td>\n",
              "      <td>0.018084</td>\n",
              "      <td>0.013808</td>\n",
              "      <td>0.006696</td>\n",
              "      <td>0.073552</td>\n",
              "      <td>0.248612</td>\n",
              "      <td>0.065985</td>\n",
              "      <td>-0.023318</td>\n",
              "      <td>0.008256</td>\n",
              "      <td>0.008229</td>\n",
              "      <td>0.001456</td>\n",
              "      <td>-0.003792</td>\n",
              "      <td>0.005234</td>\n",
              "      <td>0.223808</td>\n",
              "      <td>0.021776</td>\n",
              "      <td>0.005779</td>\n",
              "      <td>0.003369</td>\n",
              "      <td>0.044208</td>\n",
              "      <td>-0.010425</td>\n",
              "      <td>0.015175</td>\n",
              "      <td>-0.016374</td>\n",
              "      <td>0.028646</td>\n",
              "      <td>-0.001192</td>\n",
              "      <td>0.001334</td>\n",
              "      <td>0.025348</td>\n",
              "      <td>0.019771</td>\n",
              "      <td>0.031323</td>\n",
              "      <td>-0.002562</td>\n",
              "      <td>0.018554</td>\n",
              "      <td>-0.004633</td>\n",
              "      <td>-0.031004</td>\n",
              "      <td>-0.004802</td>\n",
              "      <td>0.001269</td>\n",
              "      <td>0.001901</td>\n",
              "      <td>0.002817</td>\n",
              "      <td>-0.011427</td>\n",
              "      <td>0.022219</td>\n",
              "      <td>0.002941</td>\n",
              "      <td>0.007230</td>\n",
              "      <td>-0.007004</td>\n",
              "      <td>0.004085</td>\n",
              "      <td>0.059550</td>\n",
              "      <td>-0.003496</td>\n",
              "      <td>0.000872</td>\n",
              "      <td>-0.002479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3890</th>\n",
              "      <td>-0.011373</td>\n",
              "      <td>-0.024437</td>\n",
              "      <td>0.016878</td>\n",
              "      <td>0.002650</td>\n",
              "      <td>-0.015834</td>\n",
              "      <td>0.005686</td>\n",
              "      <td>0.035236</td>\n",
              "      <td>0.016039</td>\n",
              "      <td>0.001852</td>\n",
              "      <td>-0.021134</td>\n",
              "      <td>-0.015045</td>\n",
              "      <td>-0.022077</td>\n",
              "      <td>0.018751</td>\n",
              "      <td>0.017116</td>\n",
              "      <td>-0.015212</td>\n",
              "      <td>0.033863</td>\n",
              "      <td>0.021224</td>\n",
              "      <td>-0.009433</td>\n",
              "      <td>-0.030704</td>\n",
              "      <td>0.032191</td>\n",
              "      <td>0.019621</td>\n",
              "      <td>0.020549</td>\n",
              "      <td>0.000183</td>\n",
              "      <td>-0.035650</td>\n",
              "      <td>-0.011130</td>\n",
              "      <td>0.497094</td>\n",
              "      <td>0.014550</td>\n",
              "      <td>0.006690</td>\n",
              "      <td>0.005373</td>\n",
              "      <td>0.015421</td>\n",
              "      <td>-0.017167</td>\n",
              "      <td>-0.002896</td>\n",
              "      <td>-0.009703</td>\n",
              "      <td>-0.020015</td>\n",
              "      <td>0.016001</td>\n",
              "      <td>0.023177</td>\n",
              "      <td>-0.005525</td>\n",
              "      <td>-0.005168</td>\n",
              "      <td>-0.050240</td>\n",
              "      <td>0.001912</td>\n",
              "      <td>-0.024277</td>\n",
              "      <td>-0.000561</td>\n",
              "      <td>-0.007090</td>\n",
              "      <td>-0.008911</td>\n",
              "      <td>0.031974</td>\n",
              "      <td>-0.011883</td>\n",
              "      <td>0.004270</td>\n",
              "      <td>-0.021761</td>\n",
              "      <td>0.018986</td>\n",
              "      <td>-0.008757</td>\n",
              "      <td>-0.004967</td>\n",
              "      <td>0.000988</td>\n",
              "      <td>-0.026466</td>\n",
              "      <td>-0.001222</td>\n",
              "      <td>0.013734</td>\n",
              "      <td>0.025120</td>\n",
              "      <td>-0.013303</td>\n",
              "      <td>-0.010520</td>\n",
              "      <td>0.008540</td>\n",
              "      <td>-0.009093</td>\n",
              "      <td>0.021101</td>\n",
              "      <td>0.010815</td>\n",
              "      <td>0.026437</td>\n",
              "      <td>-0.003939</td>\n",
              "      <td>0.018161</td>\n",
              "      <td>0.012439</td>\n",
              "      <td>0.079201</td>\n",
              "      <td>0.003665</td>\n",
              "      <td>0.008989</td>\n",
              "      <td>-0.005470</td>\n",
              "      <td>0.004617</td>\n",
              "      <td>-0.003447</td>\n",
              "      <td>-0.002864</td>\n",
              "      <td>-0.013841</td>\n",
              "      <td>-0.000329</td>\n",
              "      <td>0.010624</td>\n",
              "      <td>0.014438</td>\n",
              "      <td>-0.016253</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           1086496500  1086496689  1086497129  1086497550  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                        -0.016676    0.003152    0.007965   -0.007311   \n",
              "3151                        -0.007288   -0.007911   -0.031992   -0.053970   \n",
              "3106                        -0.006872    0.024483    0.028600   -0.005271   \n",
              "4641                         0.010684    0.030719   -0.011732    0.004865   \n",
              "3153                         0.007864    0.073909    0.087301    0.118123   \n",
              "2107                        -0.033691   -0.014605   -0.025983   -0.000663   \n",
              "1426                        -0.018804    0.045204    0.064579    0.492089   \n",
              "3052                        -0.012900    0.269689    0.396795   -0.013227   \n",
              "3212                        -0.013294    0.179976    0.478441    0.071275   \n",
              "3890                        -0.011373   -0.024437    0.016878    0.002650   \n",
              "\n",
              "                           1086500812  1086497908  1086498071  1086498205  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                        -0.017549   -0.010242    0.022443    0.005003   \n",
              "3151                        -0.034117   -0.012911    0.203908    0.023255   \n",
              "3106                        -0.010932    0.007454   -0.028295   -0.047562   \n",
              "4641                         0.002924    0.025359   -0.029253    0.029568   \n",
              "3153                         0.006653    0.003324   -0.026303    0.019397   \n",
              "2107                         0.009163    0.013956   -0.010383    0.009317   \n",
              "1426                        -0.009721   -0.002021   -0.066295   -0.002672   \n",
              "3052                         0.009058    0.020709   -0.023677    0.116921   \n",
              "3212                        -0.005463   -0.022127    0.178264    0.025634   \n",
              "3890                        -0.015834    0.005686    0.035236    0.016039   \n",
              "\n",
              "                           1086498334  1086498461  1086498820  1086498977  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.015786    0.034340    0.069028   -0.010218   \n",
              "3151                        -0.029756   -0.035819   -0.034289   -0.005932   \n",
              "3106                        -0.022598    0.004952    0.187260   -0.006879   \n",
              "4641                         0.002550    0.065843   -0.031164    0.026223   \n",
              "3153                         0.007707    0.006138   -0.000353   -0.008738   \n",
              "2107                         0.018615   -0.032451   -0.007530    0.026028   \n",
              "1426                        -0.021655   -0.017069   -0.009297   -0.025307   \n",
              "3052                        -0.018403    0.055411    0.227053    0.058900   \n",
              "3212                        -0.021243   -0.012833   -0.105518   -0.002179   \n",
              "3890                         0.001852   -0.021134   -0.015045   -0.022077   \n",
              "\n",
              "                           1086499259  1086499316  1086499479  1086499910  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.022155   -0.020663    0.011431   -0.003285   \n",
              "3151                         0.018209   -0.016066    0.006082    0.027375   \n",
              "3106                        -0.001461   -0.006901   -0.005558   -0.051291   \n",
              "4641                         0.021709    0.007289    0.042450    0.014095   \n",
              "3153                        -0.024516   -0.003631    0.014467   -0.015046   \n",
              "2107                         0.002158   -0.011429   -0.006207    0.009697   \n",
              "1426                        -0.044073   -0.004907   -0.022564   -0.018114   \n",
              "3052                        -0.014737   -0.000186   -0.008809   -0.006006   \n",
              "3212                        -0.023138   -0.004023   -0.017832   -0.061320   \n",
              "3890                         0.018751    0.017116   -0.015212    0.033863   \n",
              "\n",
              "                           1086500121  1086500281  1086490519  1086490561  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.001192    0.010370   -0.026390   -0.017587   \n",
              "3151                         0.006957    0.002787    0.021735    0.011021   \n",
              "3106                         0.000156   -0.022834   -0.012719    0.007431   \n",
              "4641                        -0.005164   -0.008686   -0.013496    0.000526   \n",
              "3153                         0.000059    0.009330   -0.018235   -0.027788   \n",
              "2107                         0.001758   -0.006241    0.213405   -0.000591   \n",
              "1426                        -0.024833   -0.009231   -0.018048    0.011876   \n",
              "3052                         0.001668    0.006387   -0.005617    0.014816   \n",
              "3212                         0.114141    0.004000   -0.015148   -0.004375   \n",
              "3890                         0.021224   -0.009433   -0.030704    0.032191   \n",
              "\n",
              "                           1086490598  1086490636  1086490697  1086490748  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.015905    0.069328   -0.053884    0.026336   \n",
              "3151                         0.014360   -0.018976    0.023359   -0.015684   \n",
              "3106                        -0.000149    0.001787   -0.028228   -0.021848   \n",
              "4641                        -0.000006   -0.002029   -0.006164    0.007536   \n",
              "3153                         0.003782   -0.001411   -0.037130    0.013623   \n",
              "2107                        -0.014160    0.003793    0.024234   -0.007712   \n",
              "1426                        -0.016183   -0.021415    0.008997   -0.054801   \n",
              "3052                         0.028891    0.032147    0.067323    0.027495   \n",
              "3212                         0.019928    0.014156   -0.011990    0.015530   \n",
              "3890                         0.019621    0.020549    0.000183   -0.035650   \n",
              "\n",
              "                           1086490784  1086490867  1086490995  1086491035  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.210128    1.082757   -0.055008    0.858020   \n",
              "3151                         0.021852    0.000639    0.017995    0.010634   \n",
              "3106                        -0.043193   -0.037089    0.021162    0.157478   \n",
              "4641                         0.007457   -0.016023    0.005791    0.003662   \n",
              "3153                        -0.018449   -0.003272   -0.012516   -0.016137   \n",
              "2107                         0.017761    0.022902    0.002801    0.076495   \n",
              "1426                        -0.010375   -0.051559    0.033964   -0.013366   \n",
              "3052                         0.021209    0.241272   -0.004513    0.012207   \n",
              "3212                         0.005397   -0.069819    0.018970   -0.001470   \n",
              "3890                        -0.011130    0.497094    0.014550    0.006690   \n",
              "\n",
              "                           1086491079  1086491172  1086491215  1086491256  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.014891    0.506860    0.001264    0.004638   \n",
              "3151                         0.007064    0.015827    0.039930    0.013039   \n",
              "3106                         0.001330    0.096675    0.093280   -0.019469   \n",
              "4641                         0.008868    0.019604    0.011552   -0.009868   \n",
              "3153                         0.019327    0.003708   -0.005300    0.004059   \n",
              "2107                         2.729562    0.327880   -0.001461    0.013795   \n",
              "1426                         0.006523   -0.084666   -0.004994   -0.009824   \n",
              "3052                         0.024252   -0.004446   -0.000502   -0.007713   \n",
              "3212                         0.007757   -0.007201    0.006631    0.003692   \n",
              "3890                         0.005373    0.015421   -0.017167   -0.002896   \n",
              "\n",
              "                           1086491328  1086491397  1086491469  1086490391  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                        -0.075995    0.012149   -0.015850    0.015646   \n",
              "3151                         0.014293    0.015226    0.004205   -0.011211   \n",
              "3106                         0.837632    0.002785   -0.029305    0.002976   \n",
              "4641                         0.022434   -0.007237    0.016475   -0.017331   \n",
              "3153                         0.013768    0.002073   -0.003395    0.025104   \n",
              "2107                        -0.024544   -0.014013    0.041989    0.010233   \n",
              "1426                        -0.019759   -0.006969   -0.028398    0.003752   \n",
              "3052                         0.000192    0.032198    0.011241   -0.021868   \n",
              "3212                        -0.021812    0.021634    0.018084    0.013808   \n",
              "3890                        -0.009703   -0.020015    0.016001    0.023177   \n",
              "\n",
              "                           1086491368  1086490482  1086490526  1086490572  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.013684   -0.017270    0.034951    0.017226   \n",
              "3151                         0.020537    0.023322    0.010325   -0.002651   \n",
              "3106                         0.000049    0.082955    0.302268   -0.019194   \n",
              "4641                         0.000674   -0.031671    0.011337    0.004897   \n",
              "3153                        -0.000957    0.016523    0.001785   -0.014949   \n",
              "2107                         0.029507    0.034215    0.266187    0.021205   \n",
              "1426                         0.012362    0.015029   -0.049110   -0.011750   \n",
              "3052                        -0.031080   -0.043320   -0.032360   -0.001637   \n",
              "3212                         0.006696    0.073552    0.248612    0.065985   \n",
              "3890                        -0.005525   -0.005168   -0.050240    0.001912   \n",
              "\n",
              "                           1086490648  1086490689  1086490777  1086490794  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.016621    0.014940    0.020485    0.012219   \n",
              "3151                        -0.025229   -0.004411    0.009221    0.014824   \n",
              "3106                         0.011447    0.013328    0.030896    0.016335   \n",
              "4641                        -0.014116   -0.014532    0.003132   -0.006204   \n",
              "3153                        -0.010964    0.006751    0.001156   -0.005912   \n",
              "2107                         0.054197   -0.003162    0.022482    0.042735   \n",
              "1426                         0.011734    0.009944   -0.001116    0.037157   \n",
              "3052                         0.011090   -0.023415   -0.021621   -0.030608   \n",
              "3212                        -0.023318    0.008256    0.008229    0.001456   \n",
              "3890                        -0.024277   -0.000561   -0.007090   -0.008911   \n",
              "\n",
              "                           1086490839  1086490936  1086490994  1086491066  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.001260   -0.031259   -0.006062    0.069423   \n",
              "3151                         0.016327    0.008798    0.019344    0.011081   \n",
              "3106                        -0.010312    0.422657    0.061380    0.341228   \n",
              "4641                        -0.004854   -0.003050   -0.004778    0.038117   \n",
              "3153                         0.019860   -0.000202   -0.000447    0.011490   \n",
              "2107                         0.001865    0.018941   -0.006587    0.022851   \n",
              "1426                         0.026710    0.008739    0.095875    0.102101   \n",
              "3052                        -0.019523   -0.021118   -0.014643   -0.047363   \n",
              "3212                        -0.003792    0.005234    0.223808    0.021776   \n",
              "3890                         0.031974   -0.011883    0.004270   -0.021761   \n",
              "\n",
              "                           1086491194  1086491263  1086491339  1086491760  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.023960    0.003989    0.002064    0.007475   \n",
              "3151                        -0.035440   -0.009055   -0.016829    0.001136   \n",
              "3106                         0.019147    0.034812    0.033227   -0.009207   \n",
              "4641                         0.018954    0.001495   -0.011962    0.019454   \n",
              "3153                         0.029499    0.012401    0.012486   -0.020079   \n",
              "2107                         0.008129    0.026954    0.003491   -0.016154   \n",
              "1426                         0.037890    0.039717    0.004295    0.017503   \n",
              "3052                        -0.004217   -0.004016   -0.007669   -0.007681   \n",
              "3212                         0.005779    0.003369    0.044208   -0.010425   \n",
              "3890                         0.018986   -0.008757   -0.004967    0.000988   \n",
              "\n",
              "                           1086499499  1086499628  1086499783  1086499911  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.094570    0.007431    0.016076    0.010792   \n",
              "3151                        -0.032314   -0.053997   -0.020065   -0.013386   \n",
              "3106                        -0.002212    0.025097   -0.005204   -0.009248   \n",
              "4641                        -0.026809    0.009171   -0.005366    0.009939   \n",
              "3153                        -0.000880    0.008131   -0.000960    0.003209   \n",
              "2107                        -0.039798   -0.010167   -0.024031   -0.011853   \n",
              "1426                        -0.011457    0.015333   -0.022434   -0.007645   \n",
              "3052                         0.014402   -0.015032    0.010874   -0.003644   \n",
              "3212                         0.015175   -0.016374    0.028646   -0.001192   \n",
              "3890                        -0.026466   -0.001222    0.013734    0.025120   \n",
              "\n",
              "                           1086500092  1086500194  1086500392  1086500633  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                        -0.020193    0.041131    0.001499    0.008277   \n",
              "3151                         0.094204   -0.132432   -0.023502   -0.028785   \n",
              "3106                        -0.026604   -0.040010   -0.000112   -0.016652   \n",
              "4641                         0.018281    0.010433   -0.010189    0.000846   \n",
              "3153                        -0.041401    0.025646   -0.032099    0.037308   \n",
              "2107                        -0.042279   -0.038919   -0.012486    0.010516   \n",
              "1426                        -0.029114   -0.000537   -0.018540   -0.001683   \n",
              "3052                        -0.004326    0.063447    0.019159    0.050346   \n",
              "3212                         0.001334    0.025348    0.019771    0.031323   \n",
              "3890                        -0.013303   -0.010520    0.008540   -0.009093   \n",
              "\n",
              "                           1086500583  1086500807  1086501072  1086501262  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                        -0.017234   -0.025371    0.013944    0.033143   \n",
              "3151                        -0.015884   -0.004431   -0.002365    0.001965   \n",
              "3106                        -0.035313    0.014830   -0.008225   -0.014175   \n",
              "4641                         0.008383   -0.004488   -0.010176    0.004589   \n",
              "3153                        -0.000745    0.000112    0.019161   -0.008068   \n",
              "2107                         0.027424    0.007002   -0.020616    0.002779   \n",
              "1426                        -0.011079   -0.003834   -0.003409    0.001091   \n",
              "3052                        -0.057570    0.000898    0.005018    0.012740   \n",
              "3212                        -0.002562    0.018554   -0.004633   -0.031004   \n",
              "3890                         0.021101    0.010815    0.026437   -0.003939   \n",
              "\n",
              "                           1086501360  1086501675  1086502269  1086668247  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.009155   -0.031895   -0.034395   -0.000581   \n",
              "3151                         0.014245    0.033495   -0.019494   -0.002713   \n",
              "3106                         0.002714    0.003478    0.015411    0.031555   \n",
              "4641                         0.018169    0.001906    0.014994   -0.000932   \n",
              "3153                         0.013891    0.017839    0.013458    0.001645   \n",
              "2107                         0.014949    0.023204   -0.009977    0.001624   \n",
              "1426                        -0.066838   -0.022395    0.472822    0.001765   \n",
              "3052                        -0.005704    0.031695   -0.005482    0.002003   \n",
              "3212                        -0.004802    0.001269    0.001901    0.002817   \n",
              "3890                         0.018161    0.012439    0.079201    0.003665   \n",
              "\n",
              "                           1086668144  1086668169  1086668188  1086668228  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.019191    0.014165    0.057749    0.051829   \n",
              "3151                         0.002721   -0.000552   -0.001499   -0.001481   \n",
              "3106                         0.099141   -0.007398    0.000404    0.007699   \n",
              "4641                         0.027714    0.029048    0.005997    0.003226   \n",
              "3153                        -0.000719   -0.006571   -0.012654    0.011422   \n",
              "2107                         0.017504    0.006444    0.003633    0.011756   \n",
              "1426                        -0.001809    0.003084   -0.000350    0.005902   \n",
              "3052                         0.004629   -0.005553   -0.001590    0.005981   \n",
              "3212                        -0.011427    0.022219    0.002941    0.007230   \n",
              "3890                         0.008989   -0.005470    0.004617   -0.003447   \n",
              "\n",
              "                           1086490603  1086490729  1086490846  1086490807  \\\n",
              "stimulus_presentations_id                                                   \n",
              "2086                         0.038485   -0.002511    0.001547    0.019550   \n",
              "3151                         0.004546    0.005228    0.001939   -0.005877   \n",
              "3106                        -0.000778    0.000284   -0.001365   -0.005182   \n",
              "4641                        -0.004636   -0.004816    0.006182    0.002853   \n",
              "3153                         0.001850   -0.008610    0.004886   -0.003894   \n",
              "2107                         0.014624    0.000240    0.010610   -0.003037   \n",
              "1426                         0.002137    0.003498   -0.001817    0.007916   \n",
              "3052                         0.001882    0.145329    0.004950    0.006707   \n",
              "3212                        -0.007004    0.004085    0.059550   -0.003496   \n",
              "3890                        -0.002864   -0.013841   -0.000329    0.010624   \n",
              "\n",
              "                           1086490741  1086490751  \n",
              "stimulus_presentations_id                          \n",
              "2086                        -0.012476    0.020143  \n",
              "3151                        -0.000269    0.001033  \n",
              "3106                         0.000611   -0.002719  \n",
              "4641                        -0.001734    0.009685  \n",
              "3153                        -0.004720    0.003850  \n",
              "2107                        -0.011039    0.003652  \n",
              "1426                        -0.007581    0.000077  \n",
              "3052                         0.055229    0.059874  \n",
              "3212                         0.000872   -0.002479  \n",
              "3890                         0.014438   -0.016253  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wGv06-3u-OY",
        "outputId": "8b5de821-b2fc-4017-ce41-afa7c5ca0269"
      },
      "source": [
        "y = features_and_labels['image_name']\n",
        "y.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "stimulus_presentations_id\n",
              "0    im000\n",
              "1    im000\n",
              "2    im000\n",
              "3    im000\n",
              "4    im000\n",
              "5    im000\n",
              "6    im054\n",
              "7    im054\n",
              "8    im054\n",
              "9    im054\n",
              "Name: image_name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwgHhluPvCDU"
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "X_embedded = TSNE(n_components=2, perplexity= 50).fit_transform(X.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "i4urTacevGCw",
        "outputId": "35b67b39-c434-48c8-c187-1e13b2ebb827"
      },
      "source": [
        "features_and_labels['tsne-2d-one'] = X_embedded[:,0]\n",
        "features_and_labels['tsne-2d-two'] = X_embedded[:,1]\n",
        "plt.figure(figsize=(16,10))\n",
        "ax = sns.scatterplot(\n",
        "    data=features_and_labels,\n",
        "    x=\"tsne-2d-one\", \n",
        "    y=\"tsne-2d-two\",\n",
        "    hue=\"image_name\",\n",
        "    hue_order = np.sort(features_and_labels['image_name'].unique()),\n",
        "    palette=sns.color_palette()[:9],\n",
        "    legend=\"full\",\n",
        "    alpha=0.3\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEjtcyCrwNj8"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjr0AmL4wQ-h",
        "outputId": "aebf5908-cd17-4b5f-e98a-7ff3a9ae95c6"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
        "model = svm.SVC(probability=True)\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAM8Gqnbwe6l"
      },
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJIh0LfrwiOH",
        "outputId": "6740c30b-2f92-44a1-f8cc-a06e9b2fe656"
      },
      "source": [
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5390920554854981"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "vfnrtuoLwkod",
        "outputId": "ede3faaf-9d96-4fb8-fcb5-345722fad7ef"
      },
      "source": [
        "pd.DataFrame(\n",
        "    confusion_matrix(y_test, y_pred), \n",
        "    columns = ['predicted_{}'.format(im) for im in model.classes_],\n",
        "    index = ['actual_{}'.format(im) for im in model.classes_]\n",
        ")"
      ],
      "execution_count": null,
      "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>predicted_im000</th>\n",
              "      <th>predicted_im031</th>\n",
              "      <th>predicted_im035</th>\n",
              "      <th>predicted_im045</th>\n",
              "      <th>predicted_im054</th>\n",
              "      <th>predicted_im073</th>\n",
              "      <th>predicted_im075</th>\n",
              "      <th>predicted_im106</th>\n",
              "      <th>predicted_omitted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>actual_im000</th>\n",
              "      <td>125</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_im031</th>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_im035</th>\n",
              "      <td>1</td>\n",
              "      <td>62</td>\n",
              "      <td>77</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_im045</th>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>3</td>\n",
              "      <td>134</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_im054</th>\n",
              "      <td>21</td>\n",
              "      <td>57</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>77</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_im073</th>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>114</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_im075</th>\n",
              "      <td>2</td>\n",
              "      <td>91</td>\n",
              "      <td>7</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>66</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_im106</th>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>59</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>actual_omitted</th>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                predicted_im000  predicted_im031  predicted_im035  \\\n",
              "actual_im000                125               24                3   \n",
              "actual_im031                  0              174                0   \n",
              "actual_im035                  1               62               77   \n",
              "actual_im045                  0               69                3   \n",
              "actual_im054                 21               57                5   \n",
              "actual_im073                  2               26                6   \n",
              "actual_im075                  2               91                7   \n",
              "actual_im106                  0               60                1   \n",
              "actual_omitted                0               26                3   \n",
              "\n",
              "                predicted_im045  predicted_im054  predicted_im073  \\\n",
              "actual_im000                  8               15               15   \n",
              "actual_im031                 15                1                1   \n",
              "actual_im035                  9               11               15   \n",
              "actual_im045                134                0               12   \n",
              "actual_im054                  8               77                1   \n",
              "actual_im073                 11                2              114   \n",
              "actual_im075                 11                8                2   \n",
              "actual_im106                 17                1               10   \n",
              "actual_omitted                3                0                1   \n",
              "\n",
              "                predicted_im075  predicted_im106  predicted_omitted  \n",
              "actual_im000                  4                3                  3  \n",
              "actual_im031                 15                1                  0  \n",
              "actual_im035                  3                3                  2  \n",
              "actual_im045                  2                3                  6  \n",
              "actual_im054                 12                0                  2  \n",
              "actual_im073                  4                0                  1  \n",
              "actual_im075                 66                2                  3  \n",
              "actual_im106                  8               59                  3  \n",
              "actual_omitted                5                0                 29  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    }
  ]
}