Revision 4dd1adddad54627601f7567354fa7f0af020fc7d authored by Guillaume Viejo on 05 July 2023, 20:05:21 UTC, committed by GitHub on 05 July 2023, 20:05:21 UTC
update link to pynapple documentation
Siegle_dataset.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "1c15e755",
"metadata": {},
"source": [
"# Siegle et al (2021) Dataset Tutorial \n",
"\n",
"*Author: Dhruv Mehrotra*\n",
"\n",
"\n",
"This notebook demonstrates how we use Pynapple on various publicly available datasets in systems neuroscience to streamline analysis. In this notebook, we will examine the dataset from Siegle et al (2021), which was used to generate Figure 4b in the publication. \n",
"\n",
"More information regarding this dataset can be found <a href=\"https://allensdk.readthedocs.io/en/2.16.0/visual_coding_neuropixels.html\" target=\"_blank\">here</a>. We will be using the Allen Software Development Kit (AllenSDK) to download and use the data. Please make sure AllenSDK is installed on your system, a helpful installation guide is available <a href=\"https://allensdk.readthedocs.io/en/latest/install.html\" target=\"_blank\">here</a>.\n",
"\n",
"See the <a href=\"https://pynapple-org.github.io/pynapple/\" target=\"_blank\">documentation</a> of Pynapple for instructions on installing the package.\n",
"\n",
"First, import the necessary libraries: "
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "46324cdd",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import pynapple as nap\n",
"\n",
"from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache"
]
},
{
"cell_type": "markdown",
"id": "2a95b9a5",
"metadata": {},
"source": [
"The __EcephysProjectCache__ object of the AllenSDK delivers the data to you as ready-to-analyze Python objects. It automatically keeps track of which files are stored locally, and will download additional files as needed.\n",
"\n",
"The next bit involves dowloading the data onto your local machine. This section is adapted from <a href=\"https://allensdk.readthedocs.io/en/2.16.0/_static/examples/nb/ecephys_data_access.html\" target=\"_blank\">here</a>, where you can find a more detailed account of how to use data from the Allen Institute.\n",
"\n",
"Before downloading the data, you must decide where the manifest.json file lives. This file serves as the map that guides the EcephysProjectCache object to the file locations.\n",
"\n",
"When you initialize a local cache for the first time, it will create the manifest file at the path that you specify. This file lives in the same directory as the rest of the data, so make sure you put it somewhere that has enough space available.\n",
"\n",
"When you need to access the data in subsequent analysis sessions, you should point the EcephysProjectCache object to an existing manifest.json file; otherwise, it will try to re-download the data in a new location.\n",
"\n",
"Then we can create the cache object, specifying both the local storage directory (the manifest_path) and the remote storage location (the Allen Institute data warehouse).\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "af48d09a",
"metadata": {},
"outputs": [],
"source": [
"manifest_path = os.path.join(os.getcwd(), \"manifest.json\")\n",
"cache = EcephysProjectCache.from_warehouse(manifest = manifest_path)"
]
},
{
"cell_type": "markdown",
"id": "e69adffa",
"metadata": {},
"source": [
"Let's examine the sessions we have:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "a2552fb1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>published_at</th>\n",
" <th>specimen_id</th>\n",
" <th>session_type</th>\n",
" <th>age_in_days</th>\n",
" <th>sex</th>\n",
" <th>full_genotype</th>\n",
" <th>unit_count</th>\n",
" <th>channel_count</th>\n",
" <th>probe_count</th>\n",
" <th>ecephys_structure_acronyms</th>\n",
" </tr>\n",
" <tr>\n",
" <th>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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>715093703</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>699733581</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>118.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>884</td>\n",
" <td>2219</td>\n",
" <td>6</td>\n",
" <td>[CA1, VISrl, nan, PO, LP, LGd, CA3, DG, VISl, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>719161530</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>703279284</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>122.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>755</td>\n",
" <td>2214</td>\n",
" <td>6</td>\n",
" <td>[TH, Eth, APN, POL, LP, DG, CA1, VISpm, nan, N...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>721123822</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>707296982</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>125.0</td>\n",
" <td>M</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>444</td>\n",
" <td>2229</td>\n",
" <td>6</td>\n",
" <td>[MB, SCig, PPT, NOT, DG, CA1, VISam, nan, LP, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>732592105</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>717038288</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>100.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>824</td>\n",
" <td>1847</td>\n",
" <td>5</td>\n",
" <td>[grey, VISpm, nan, VISp, VISl, VISal, VISrl]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>737581020</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>718643567</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>108.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>568</td>\n",
" <td>2218</td>\n",
" <td>6</td>\n",
" <td>[grey, VISmma, nan, VISpm, VISp, VISl, VISrl]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>739448407</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>716813543</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>112.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>625</td>\n",
" <td>2221</td>\n",
" <td>6</td>\n",
" <td>[grey, VISam, nan, VIS, VISp, VISl, VISrl]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>742951821</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>723627604</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>120.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>893</td>\n",
" <td>2219</td>\n",
" <td>6</td>\n",
" <td>[VISal, nan, grey, VISl, VISrl, VISp, VISpm, VIS]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>743475441</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>722882755</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>121.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>553</td>\n",
" <td>2225</td>\n",
" <td>6</td>\n",
" <td>[LP, LGd, HPF, DG, CA3, CA1, VISrl, nan, PP, P...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>744228101</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>719817805</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>122.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>659</td>\n",
" <td>2226</td>\n",
" <td>6</td>\n",
" <td>[Eth, TH, LP, POL, APN, DG, CA1, VIS, nan, CA3...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>746083955</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>726170935</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>98.0</td>\n",
" <td>F</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>582</td>\n",
" <td>2216</td>\n",
" <td>6</td>\n",
" <td>[VPM, TH, LGd, CA3, CA2, CA1, VISal, nan, grey...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>750332458</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>726141251</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>91.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>902</td>\n",
" <td>2216</td>\n",
" <td>6</td>\n",
" <td>[grey, VISrl, nan, VISal, IntG, IGL, LGd, CA3,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>750749662</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>726162197</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>92.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>761</td>\n",
" <td>2223</td>\n",
" <td>6</td>\n",
" <td>[LP, DG, CA1, VISp, nan, LGd, CA3, VISrl, VPM,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>751348571</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>732548380</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>93.0</td>\n",
" <td>F</td>\n",
" <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>859</td>\n",
" <td>2217</td>\n",
" <td>6</td>\n",
" <td>[grey, LGv, CA3, DG, CA1, VISl, nan, APN, POL,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>754312389</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>719828690</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>140.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>502</td>\n",
" <td>2220</td>\n",
" <td>6</td>\n",
" <td>[APN, POL, LP, DG, CA1, VISam, nan, LGd, HPF, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>754829445</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>726298253</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>141.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>832</td>\n",
" <td>1851</td>\n",
" <td>5</td>\n",
" <td>[PoT, LP, LGd, CA3, DG, CA1, VISp, nan, VPM, C...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>755434585</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>730760270</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>100.0</td>\n",
" <td>M</td>\n",
" <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>650</td>\n",
" <td>2220</td>\n",
" <td>6</td>\n",
" <td>[grey, VISrl, nan, MGv, MGd, TH, LGd, CA3, DG,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>756029989</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>734865738</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>96.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>684</td>\n",
" <td>2214</td>\n",
" <td>6</td>\n",
" <td>[TH, DG, CA3, CA1, VISl, nan, PO, Eth, LP, VIS...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>757216464</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>733457989</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>105.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>959</td>\n",
" <td>2225</td>\n",
" <td>6</td>\n",
" <td>[LP, DG, CA3, CA1, VISrl, nan, PO, LGd, HPF, V...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>757970808</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>730756780</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>106.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>773</td>\n",
" <td>1853</td>\n",
" <td>5</td>\n",
" <td>[grey, DG, CA3, CA1, VISli, nan, MGd, SGN, LP,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>758798717</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>735109609</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>102.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>593</td>\n",
" <td>1486</td>\n",
" <td>4</td>\n",
" <td>[MGv, MGd, TH, DG, CA1, VISal, nan, SGN, LP, S...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>759883607</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>740268986</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>113.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>483</td>\n",
" <td>1489</td>\n",
" <td>4</td>\n",
" <td>[MB, SCig, PPT, NOT, SUB, ProS, CA1, VISam, na...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>760345702</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>739783171</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>103.0</td>\n",
" <td>M</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>501</td>\n",
" <td>1862</td>\n",
" <td>5</td>\n",
" <td>[MB, TH, PP, PIL, DG, CA3, CA1, VISal, nan, gr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>760693773</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>738651054</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>110.0</td>\n",
" <td>F</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>826</td>\n",
" <td>2233</td>\n",
" <td>6</td>\n",
" <td>[grey, VISrl, nan, VISal, VISp, VISpm, VISam]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>761418226</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>742714475</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>119.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>712</td>\n",
" <td>2230</td>\n",
" <td>6</td>\n",
" <td>[grey, CA3, CA2, CA1, VISal, nan, LGd, HPF, DG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>762120172</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>745276236</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>100.0</td>\n",
" <td>M</td>\n",
" <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>717</td>\n",
" <td>1860</td>\n",
" <td>5</td>\n",
" <td>[grey, VISal, nan, VISl, VISp, VIS, VISam]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>762602078</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>744915204</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>110.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>531</td>\n",
" <td>2233</td>\n",
" <td>6</td>\n",
" <td>[MB, APN, NOT, DG, CA1, VISam, nan, VISrl, LGv...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>763673393</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>742602892</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>126.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>629</td>\n",
" <td>2227</td>\n",
" <td>6</td>\n",
" <td>[CA3, CA2, CA1, VISl, nan, APN, MB, DG, VISam,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766640955</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>744912849</td>\n",
" <td>functional_connectivity</td>\n",
" <td>133.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>842</td>\n",
" <td>2233</td>\n",
" <td>6</td>\n",
" <td>[MB, APN, NOT, DG, CA1, VISam, nan, PF, TH, LP...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>767871931</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>753795610</td>\n",
" <td>functional_connectivity</td>\n",
" <td>135.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>713</td>\n",
" <td>2231</td>\n",
" <td>6</td>\n",
" <td>[MRN, APN, MB, DG, CA1, VISrl, nan, VISam, PO,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768515987</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>754477358</td>\n",
" <td>functional_connectivity</td>\n",
" <td>136.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>802</td>\n",
" <td>2217</td>\n",
" <td>6</td>\n",
" <td>[MB, APN, NOT, DG, CA1, VISam, nan, Eth, LP, P...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>771160300</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>754488979</td>\n",
" <td>functional_connectivity</td>\n",
" <td>142.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>930</td>\n",
" <td>2230</td>\n",
" <td>6</td>\n",
" <td>[SCig, PPT, NOT, SUB, ProS, CA1, VISam, nan, A...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>771990200</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>756578435</td>\n",
" <td>functional_connectivity</td>\n",
" <td>108.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>546</td>\n",
" <td>2229</td>\n",
" <td>6</td>\n",
" <td>[APN, MB, DG, CA1, VISam, nan, PO, LP, VISpm, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>773418906</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>757329624</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>124.0</td>\n",
" <td>F</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>546</td>\n",
" <td>2232</td>\n",
" <td>6</td>\n",
" <td>[PPT, NOT, SUB, ProS, CA1, VISam, nan, APN, DG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>774875821</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>759711152</td>\n",
" <td>functional_connectivity</td>\n",
" <td>114.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>649</td>\n",
" <td>2233</td>\n",
" <td>6</td>\n",
" <td>[APN, LP, DG, CA1, VISrl, nan, MGv, MGd, CA3, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>778240327</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>760938797</td>\n",
" <td>functional_connectivity</td>\n",
" <td>120.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>784</td>\n",
" <td>2234</td>\n",
" <td>6</td>\n",
" <td>[APN, POL, LP, DG, CA1, VISrl, nan, ZI, IntG, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>778998620</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>759674770</td>\n",
" <td>functional_connectivity</td>\n",
" <td>121.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>793</td>\n",
" <td>2229</td>\n",
" <td>6</td>\n",
" <td>[APN, POL, LP, DG, CA3, CA1, VISrl, nan, ZI, M...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>779839471</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>760960653</td>\n",
" <td>functional_connectivity</td>\n",
" <td>122.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>863</td>\n",
" <td>2220</td>\n",
" <td>6</td>\n",
" <td>[APN, LP, MB, DG, CA1, VISrl, nan, TH, LGd, CA...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>781842082</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>760946813</td>\n",
" <td>functional_connectivity</td>\n",
" <td>126.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>833</td>\n",
" <td>2232</td>\n",
" <td>6</td>\n",
" <td>[APN, LP, DG, CA1, VISrl, nan, MGm, TH, MGv, L...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>786091066</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>763884103</td>\n",
" <td>functional_connectivity</td>\n",
" <td>111.0</td>\n",
" <td>F</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>700</td>\n",
" <td>2232</td>\n",
" <td>6</td>\n",
" <td>[APN, MB, DG, CA1, VISrl, nan, MGd, CA3, VISal...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>787025148</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>763236014</td>\n",
" <td>functional_connectivity</td>\n",
" <td>114.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>696</td>\n",
" <td>2231</td>\n",
" <td>6</td>\n",
" <td>[SCiw, SCig, PPT, NOT, DG, SUB, ProS, CA1, VIS...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>789848216</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>763808604</td>\n",
" <td>functional_connectivity</td>\n",
" <td>119.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>415</td>\n",
" <td>2230</td>\n",
" <td>6</td>\n",
" <td>[grey, CA3, CA2, CA1, VISal, nan, LP, LD, VISr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>791319847</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>769360779</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>116.0</td>\n",
" <td>M</td>\n",
" <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>555</td>\n",
" <td>2229</td>\n",
" <td>6</td>\n",
" <td>[APN, DG, CA1, VISam, nan, LP, TH, VISpm, POL,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>793224716</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>769319624</td>\n",
" <td>functional_connectivity</td>\n",
" <td>120.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>781</td>\n",
" <td>2229</td>\n",
" <td>6</td>\n",
" <td>[APN, NOT, MB, DG, CA1, VIS, nan, Eth, LP, SGN...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>794812542</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>774672366</td>\n",
" <td>functional_connectivity</td>\n",
" <td>120.0</td>\n",
" <td>F</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>1005</td>\n",
" <td>2229</td>\n",
" <td>6</td>\n",
" <td>[MB, APN, DG, CA1, VISrl, nan, PIL, MGm, MGv, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>797828357</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>776061251</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>107.0</td>\n",
" <td>M</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>611</td>\n",
" <td>2232</td>\n",
" <td>6</td>\n",
" <td>[PPT, MB, APN, NOT, HPF, ProS, CA1, VISam, nan...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>798911424</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>775876828</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>110.0</td>\n",
" <td>F</td>\n",
" <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>825</td>\n",
" <td>2233</td>\n",
" <td>6</td>\n",
" <td>[APN, TH, Eth, LP, DG, HPF, CA3, CA1, VISrl, n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>799864342</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>772616823</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>129.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>604</td>\n",
" <td>2233</td>\n",
" <td>6</td>\n",
" <td>[APN, POL, LP, DG, CA1, VISrl, nan, LGd, CA3, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>816200189</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>791857608</td>\n",
" <td>functional_connectivity</td>\n",
" <td>128.0</td>\n",
" <td>F</td>\n",
" <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>634</td>\n",
" <td>1857</td>\n",
" <td>5</td>\n",
" <td>[MRN, APN, MB, DG, CA1, VISam, nan, LP, VISpm,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>819186360</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>800249587</td>\n",
" <td>functional_connectivity</td>\n",
" <td>128.0</td>\n",
" <td>F</td>\n",
" <td>wt/wt</td>\n",
" <td>531</td>\n",
" <td>1696</td>\n",
" <td>5</td>\n",
" <td>[SCiw, SCig, NOT, MB, SCop, HPF, SUB, VISam, n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>819701982</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>795770036</td>\n",
" <td>functional_connectivity</td>\n",
" <td>135.0</td>\n",
" <td>F</td>\n",
" <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>585</td>\n",
" <td>1852</td>\n",
" <td>5</td>\n",
" <td>[MB, SCiw, SCig, PPT, NOT, SUB, ProS, CA1, VIS...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>821695405</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>800250057</td>\n",
" <td>functional_connectivity</td>\n",
" <td>134.0</td>\n",
" <td>F</td>\n",
" <td>wt/wt</td>\n",
" <td>474</td>\n",
" <td>1856</td>\n",
" <td>5</td>\n",
" <td>[LGv, TH, DG, CA3, CA1, ProS, VISl, nan, grey,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>829720705</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>811322619</td>\n",
" <td>functional_connectivity</td>\n",
" <td>112.0</td>\n",
" <td>M</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>529</td>\n",
" <td>1841</td>\n",
" <td>5</td>\n",
" <td>[SCig, SCop, SCsg, SCzo, POST, VISp, nan, CA1,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>831882777</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>803390291</td>\n",
" <td>functional_connectivity</td>\n",
" <td>137.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>657</td>\n",
" <td>2227</td>\n",
" <td>6</td>\n",
" <td>[grey, DG, CA1, VISrl, nan, VISal, VISl, MB, S...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>835479236</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>813701562</td>\n",
" <td>functional_connectivity</td>\n",
" <td>121.0</td>\n",
" <td>M</td>\n",
" <td>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>582</td>\n",
" <td>1857</td>\n",
" <td>5</td>\n",
" <td>[LP, DG, CA1, VISmmp, nan, SCiw, SCig, SCop, S...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839068429</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>817060751</td>\n",
" <td>functional_connectivity</td>\n",
" <td>129.0</td>\n",
" <td>F</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>742</td>\n",
" <td>2298</td>\n",
" <td>6</td>\n",
" <td>[APN, LP, MB, DG, CA1, VISam, nan, VISpm, ProS...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839557629</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>821469666</td>\n",
" <td>functional_connectivity</td>\n",
" <td>115.0</td>\n",
" <td>M</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>450</td>\n",
" <td>1853</td>\n",
" <td>5</td>\n",
" <td>[APN, NOT, MB, DG, CA1, VISam, nan, VISpm, LGd...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>840012044</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>820866121</td>\n",
" <td>functional_connectivity</td>\n",
" <td>116.0</td>\n",
" <td>M</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>758</td>\n",
" <td>2298</td>\n",
" <td>6</td>\n",
" <td>[APN, DG, CA1, VISam, nan, LP, VISpm, VISp, LG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>847657808</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>827809884</td>\n",
" <td>functional_connectivity</td>\n",
" <td>126.0</td>\n",
" <td>F</td>\n",
" <td>wt/wt</td>\n",
" <td>874</td>\n",
" <td>2298</td>\n",
" <td>6</td>\n",
" <td>[APN, NOT, DG, HPF, ProS, CA1, VISam, nan, MB,...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" published_at specimen_id session_type \\\n",
"id \n",
"715093703 2019-10-03T00:00:00Z 699733581 brain_observatory_1.1 \n",
"719161530 2019-10-03T00:00:00Z 703279284 brain_observatory_1.1 \n",
"721123822 2019-10-03T00:00:00Z 707296982 brain_observatory_1.1 \n",
"732592105 2019-10-03T00:00:00Z 717038288 brain_observatory_1.1 \n",
"737581020 2019-10-03T00:00:00Z 718643567 brain_observatory_1.1 \n",
"739448407 2019-10-03T00:00:00Z 716813543 brain_observatory_1.1 \n",
"742951821 2019-10-03T00:00:00Z 723627604 brain_observatory_1.1 \n",
"743475441 2019-10-03T00:00:00Z 722882755 brain_observatory_1.1 \n",
"744228101 2019-10-03T00:00:00Z 719817805 brain_observatory_1.1 \n",
"746083955 2019-10-03T00:00:00Z 726170935 brain_observatory_1.1 \n",
"750332458 2019-10-03T00:00:00Z 726141251 brain_observatory_1.1 \n",
"750749662 2019-10-03T00:00:00Z 726162197 brain_observatory_1.1 \n",
"751348571 2019-10-03T00:00:00Z 732548380 brain_observatory_1.1 \n",
"754312389 2019-10-03T00:00:00Z 719828690 brain_observatory_1.1 \n",
"754829445 2019-10-03T00:00:00Z 726298253 brain_observatory_1.1 \n",
"755434585 2019-10-03T00:00:00Z 730760270 brain_observatory_1.1 \n",
"756029989 2019-10-03T00:00:00Z 734865738 brain_observatory_1.1 \n",
"757216464 2019-10-03T00:00:00Z 733457989 brain_observatory_1.1 \n",
"757970808 2019-10-03T00:00:00Z 730756780 brain_observatory_1.1 \n",
"758798717 2019-10-03T00:00:00Z 735109609 brain_observatory_1.1 \n",
"759883607 2019-10-03T00:00:00Z 740268986 brain_observatory_1.1 \n",
"760345702 2019-10-03T00:00:00Z 739783171 brain_observatory_1.1 \n",
"760693773 2019-10-03T00:00:00Z 738651054 brain_observatory_1.1 \n",
"761418226 2019-10-03T00:00:00Z 742714475 brain_observatory_1.1 \n",
"762120172 2019-10-03T00:00:00Z 745276236 brain_observatory_1.1 \n",
"762602078 2019-10-03T00:00:00Z 744915204 brain_observatory_1.1 \n",
"763673393 2019-10-03T00:00:00Z 742602892 brain_observatory_1.1 \n",
"766640955 2019-10-03T00:00:00Z 744912849 functional_connectivity \n",
"767871931 2019-10-03T00:00:00Z 753795610 functional_connectivity \n",
"768515987 2019-10-03T00:00:00Z 754477358 functional_connectivity \n",
"771160300 2019-10-03T00:00:00Z 754488979 functional_connectivity \n",
"771990200 2019-10-03T00:00:00Z 756578435 functional_connectivity \n",
"773418906 2019-10-03T00:00:00Z 757329624 brain_observatory_1.1 \n",
"774875821 2019-10-03T00:00:00Z 759711152 functional_connectivity \n",
"778240327 2019-10-03T00:00:00Z 760938797 functional_connectivity \n",
"778998620 2019-10-03T00:00:00Z 759674770 functional_connectivity \n",
"779839471 2019-10-03T00:00:00Z 760960653 functional_connectivity \n",
"781842082 2019-10-03T00:00:00Z 760946813 functional_connectivity \n",
"786091066 2019-10-03T00:00:00Z 763884103 functional_connectivity \n",
"787025148 2019-10-03T00:00:00Z 763236014 functional_connectivity \n",
"789848216 2019-10-03T00:00:00Z 763808604 functional_connectivity \n",
"791319847 2019-10-03T00:00:00Z 769360779 brain_observatory_1.1 \n",
"793224716 2019-10-03T00:00:00Z 769319624 functional_connectivity \n",
"794812542 2019-10-03T00:00:00Z 774672366 functional_connectivity \n",
"797828357 2019-10-03T00:00:00Z 776061251 brain_observatory_1.1 \n",
"798911424 2019-10-03T00:00:00Z 775876828 brain_observatory_1.1 \n",
"799864342 2019-10-03T00:00:00Z 772616823 brain_observatory_1.1 \n",
"816200189 2019-10-03T00:00:00Z 791857608 functional_connectivity \n",
"819186360 2019-10-03T00:00:00Z 800249587 functional_connectivity \n",
"819701982 2019-10-03T00:00:00Z 795770036 functional_connectivity \n",
"821695405 2019-10-03T00:00:00Z 800250057 functional_connectivity \n",
"829720705 2019-10-03T00:00:00Z 811322619 functional_connectivity \n",
"831882777 2019-10-03T00:00:00Z 803390291 functional_connectivity \n",
"835479236 2019-10-03T00:00:00Z 813701562 functional_connectivity \n",
"839068429 2019-10-03T00:00:00Z 817060751 functional_connectivity \n",
"839557629 2019-10-03T00:00:00Z 821469666 functional_connectivity \n",
"840012044 2019-10-03T00:00:00Z 820866121 functional_connectivity \n",
"847657808 2019-10-03T00:00:00Z 827809884 functional_connectivity \n",
"\n",
" age_in_days sex full_genotype \\\n",
"id \n",
"715093703 118.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"719161530 122.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"721123822 125.0 M Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"732592105 100.0 M wt/wt \n",
"737581020 108.0 M wt/wt \n",
"739448407 112.0 M wt/wt \n",
"742951821 120.0 M wt/wt \n",
"743475441 121.0 M wt/wt \n",
"744228101 122.0 M wt/wt \n",
"746083955 98.0 F Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"750332458 91.0 M wt/wt \n",
"750749662 92.0 M wt/wt \n",
"751348571 93.0 F Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"754312389 140.0 M wt/wt \n",
"754829445 141.0 M wt/wt \n",
"755434585 100.0 M Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"756029989 96.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"757216464 105.0 M wt/wt \n",
"757970808 106.0 M wt/wt \n",
"758798717 102.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"759883607 113.0 M wt/wt \n",
"760345702 103.0 M Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"760693773 110.0 F Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"761418226 119.0 M wt/wt \n",
"762120172 100.0 M Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"762602078 110.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"763673393 126.0 M wt/wt \n",
"766640955 133.0 M wt/wt \n",
"767871931 135.0 M wt/wt \n",
"768515987 136.0 M wt/wt \n",
"771160300 142.0 M wt/wt \n",
"771990200 108.0 M wt/wt \n",
"773418906 124.0 F Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"774875821 114.0 M wt/wt \n",
"778240327 120.0 M wt/wt \n",
"778998620 121.0 M wt/wt \n",
"779839471 122.0 M wt/wt \n",
"781842082 126.0 M wt/wt \n",
"786091066 111.0 F Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"787025148 114.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"789848216 119.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"791319847 116.0 M Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"793224716 120.0 M wt/wt \n",
"794812542 120.0 F Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"797828357 107.0 M Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"798911424 110.0 F Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"799864342 129.0 M wt/wt \n",
"816200189 128.0 F Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"819186360 128.0 F wt/wt \n",
"819701982 135.0 F Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"821695405 134.0 F wt/wt \n",
"829720705 112.0 M Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"831882777 137.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"835479236 121.0 M Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"839068429 129.0 F Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"839557629 115.0 M Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"840012044 116.0 M Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"847657808 126.0 F wt/wt \n",
"\n",
" unit_count channel_count probe_count \\\n",
"id \n",
"715093703 884 2219 6 \n",
"719161530 755 2214 6 \n",
"721123822 444 2229 6 \n",
"732592105 824 1847 5 \n",
"737581020 568 2218 6 \n",
"739448407 625 2221 6 \n",
"742951821 893 2219 6 \n",
"743475441 553 2225 6 \n",
"744228101 659 2226 6 \n",
"746083955 582 2216 6 \n",
"750332458 902 2216 6 \n",
"750749662 761 2223 6 \n",
"751348571 859 2217 6 \n",
"754312389 502 2220 6 \n",
"754829445 832 1851 5 \n",
"755434585 650 2220 6 \n",
"756029989 684 2214 6 \n",
"757216464 959 2225 6 \n",
"757970808 773 1853 5 \n",
"758798717 593 1486 4 \n",
"759883607 483 1489 4 \n",
"760345702 501 1862 5 \n",
"760693773 826 2233 6 \n",
"761418226 712 2230 6 \n",
"762120172 717 1860 5 \n",
"762602078 531 2233 6 \n",
"763673393 629 2227 6 \n",
"766640955 842 2233 6 \n",
"767871931 713 2231 6 \n",
"768515987 802 2217 6 \n",
"771160300 930 2230 6 \n",
"771990200 546 2229 6 \n",
"773418906 546 2232 6 \n",
"774875821 649 2233 6 \n",
"778240327 784 2234 6 \n",
"778998620 793 2229 6 \n",
"779839471 863 2220 6 \n",
"781842082 833 2232 6 \n",
"786091066 700 2232 6 \n",
"787025148 696 2231 6 \n",
"789848216 415 2230 6 \n",
"791319847 555 2229 6 \n",
"793224716 781 2229 6 \n",
"794812542 1005 2229 6 \n",
"797828357 611 2232 6 \n",
"798911424 825 2233 6 \n",
"799864342 604 2233 6 \n",
"816200189 634 1857 5 \n",
"819186360 531 1696 5 \n",
"819701982 585 1852 5 \n",
"821695405 474 1856 5 \n",
"829720705 529 1841 5 \n",
"831882777 657 2227 6 \n",
"835479236 582 1857 5 \n",
"839068429 742 2298 6 \n",
"839557629 450 1853 5 \n",
"840012044 758 2298 6 \n",
"847657808 874 2298 6 \n",
"\n",
" ecephys_structure_acronyms \n",
"id \n",
"715093703 [CA1, VISrl, nan, PO, LP, LGd, CA3, DG, VISl, ... \n",
"719161530 [TH, Eth, APN, POL, LP, DG, CA1, VISpm, nan, N... \n",
"721123822 [MB, SCig, PPT, NOT, DG, CA1, VISam, nan, LP, ... \n",
"732592105 [grey, VISpm, nan, VISp, VISl, VISal, VISrl] \n",
"737581020 [grey, VISmma, nan, VISpm, VISp, VISl, VISrl] \n",
"739448407 [grey, VISam, nan, VIS, VISp, VISl, VISrl] \n",
"742951821 [VISal, nan, grey, VISl, VISrl, VISp, VISpm, VIS] \n",
"743475441 [LP, LGd, HPF, DG, CA3, CA1, VISrl, nan, PP, P... \n",
"744228101 [Eth, TH, LP, POL, APN, DG, CA1, VIS, nan, CA3... \n",
"746083955 [VPM, TH, LGd, CA3, CA2, CA1, VISal, nan, grey... \n",
"750332458 [grey, VISrl, nan, VISal, IntG, IGL, LGd, CA3,... \n",
"750749662 [LP, DG, CA1, VISp, nan, LGd, CA3, VISrl, VPM,... \n",
"751348571 [grey, LGv, CA3, DG, CA1, VISl, nan, APN, POL,... \n",
"754312389 [APN, POL, LP, DG, CA1, VISam, nan, LGd, HPF, ... \n",
"754829445 [PoT, LP, LGd, CA3, DG, CA1, VISp, nan, VPM, C... \n",
"755434585 [grey, VISrl, nan, MGv, MGd, TH, LGd, CA3, DG,... \n",
"756029989 [TH, DG, CA3, CA1, VISl, nan, PO, Eth, LP, VIS... \n",
"757216464 [LP, DG, CA3, CA1, VISrl, nan, PO, LGd, HPF, V... \n",
"757970808 [grey, DG, CA3, CA1, VISli, nan, MGd, SGN, LP,... \n",
"758798717 [MGv, MGd, TH, DG, CA1, VISal, nan, SGN, LP, S... \n",
"759883607 [MB, SCig, PPT, NOT, SUB, ProS, CA1, VISam, na... \n",
"760345702 [MB, TH, PP, PIL, DG, CA3, CA1, VISal, nan, gr... \n",
"760693773 [grey, VISrl, nan, VISal, VISp, VISpm, VISam] \n",
"761418226 [grey, CA3, CA2, CA1, VISal, nan, LGd, HPF, DG... \n",
"762120172 [grey, VISal, nan, VISl, VISp, VIS, VISam] \n",
"762602078 [MB, APN, NOT, DG, CA1, VISam, nan, VISrl, LGv... \n",
"763673393 [CA3, CA2, CA1, VISl, nan, APN, MB, DG, VISam,... \n",
"766640955 [MB, APN, NOT, DG, CA1, VISam, nan, PF, TH, LP... \n",
"767871931 [MRN, APN, MB, DG, CA1, VISrl, nan, VISam, PO,... \n",
"768515987 [MB, APN, NOT, DG, CA1, VISam, nan, Eth, LP, P... \n",
"771160300 [SCig, PPT, NOT, SUB, ProS, CA1, VISam, nan, A... \n",
"771990200 [APN, MB, DG, CA1, VISam, nan, PO, LP, VISpm, ... \n",
"773418906 [PPT, NOT, SUB, ProS, CA1, VISam, nan, APN, DG... \n",
"774875821 [APN, LP, DG, CA1, VISrl, nan, MGv, MGd, CA3, ... \n",
"778240327 [APN, POL, LP, DG, CA1, VISrl, nan, ZI, IntG, ... \n",
"778998620 [APN, POL, LP, DG, CA3, CA1, VISrl, nan, ZI, M... \n",
"779839471 [APN, LP, MB, DG, CA1, VISrl, nan, TH, LGd, CA... \n",
"781842082 [APN, LP, DG, CA1, VISrl, nan, MGm, TH, MGv, L... \n",
"786091066 [APN, MB, DG, CA1, VISrl, nan, MGd, CA3, VISal... \n",
"787025148 [SCiw, SCig, PPT, NOT, DG, SUB, ProS, CA1, VIS... \n",
"789848216 [grey, CA3, CA2, CA1, VISal, nan, LP, LD, VISr... \n",
"791319847 [APN, DG, CA1, VISam, nan, LP, TH, VISpm, POL,... \n",
"793224716 [APN, NOT, MB, DG, CA1, VIS, nan, Eth, LP, SGN... \n",
"794812542 [MB, APN, DG, CA1, VISrl, nan, PIL, MGm, MGv, ... \n",
"797828357 [PPT, MB, APN, NOT, HPF, ProS, CA1, VISam, nan... \n",
"798911424 [APN, TH, Eth, LP, DG, HPF, CA3, CA1, VISrl, n... \n",
"799864342 [APN, POL, LP, DG, CA1, VISrl, nan, LGd, CA3, ... \n",
"816200189 [MRN, APN, MB, DG, CA1, VISam, nan, LP, VISpm,... \n",
"819186360 [SCiw, SCig, NOT, MB, SCop, HPF, SUB, VISam, n... \n",
"819701982 [MB, SCiw, SCig, PPT, NOT, SUB, ProS, CA1, VIS... \n",
"821695405 [LGv, TH, DG, CA3, CA1, ProS, VISl, nan, grey,... \n",
"829720705 [SCig, SCop, SCsg, SCzo, POST, VISp, nan, CA1,... \n",
"831882777 [grey, DG, CA1, VISrl, nan, VISal, VISl, MB, S... \n",
"835479236 [LP, DG, CA1, VISmmp, nan, SCiw, SCig, SCop, S... \n",
"839068429 [APN, LP, MB, DG, CA1, VISam, nan, VISpm, ProS... \n",
"839557629 [APN, NOT, MB, DG, CA1, VISam, nan, VISpm, LGd... \n",
"840012044 [APN, DG, CA1, VISam, nan, LP, VISpm, VISp, LG... \n",
"847657808 [APN, NOT, DG, HPF, ProS, CA1, VISam, nan, MB,... "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sessions = cache.get_session_table()\n",
"\n",
"sessions"
]
},
{
"cell_type": "markdown",
"id": "b2786401",
"metadata": {},
"source": [
"This table gives a comprehensive account of all the session details. Let's see the major types of sessions we have:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "6e9fd318",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['brain_observatory_1.1', 'functional_connectivity']\n"
]
}
],
"source": [
"print(cache.get_all_session_types())"
]
},
{
"cell_type": "markdown",
"id": "830dd1b8",
"metadata": {},
"source": [
"There are 2 types of sessions in this dataset. More information about session types can be found <a href=\"https://allensdk.readthedocs.io/en/2.16.0/visual_coding_neuropixels.html\" target=\"_blank\">here</a>. For our purposes, we will choose the brain observatory sessions, as they have drifting gratings with 8 orientations."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "c8beb209",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>published_at</th>\n",
" <th>specimen_id</th>\n",
" <th>session_type</th>\n",
" <th>age_in_days</th>\n",
" <th>sex</th>\n",
" <th>full_genotype</th>\n",
" <th>unit_count</th>\n",
" <th>channel_count</th>\n",
" <th>probe_count</th>\n",
" <th>ecephys_structure_acronyms</th>\n",
" </tr>\n",
" <tr>\n",
" <th>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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>715093703</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>699733581</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>118.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>884</td>\n",
" <td>2219</td>\n",
" <td>6</td>\n",
" <td>[CA1, VISrl, nan, PO, LP, LGd, CA3, DG, VISl, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>719161530</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>703279284</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>122.0</td>\n",
" <td>M</td>\n",
" <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>755</td>\n",
" <td>2214</td>\n",
" <td>6</td>\n",
" <td>[TH, Eth, APN, POL, LP, DG, CA1, VISpm, nan, N...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>721123822</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>707296982</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>125.0</td>\n",
" <td>M</td>\n",
" <td>Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
" <td>444</td>\n",
" <td>2229</td>\n",
" <td>6</td>\n",
" <td>[MB, SCig, PPT, NOT, DG, CA1, VISam, nan, LP, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>732592105</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>717038288</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>100.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>824</td>\n",
" <td>1847</td>\n",
" <td>5</td>\n",
" <td>[grey, VISpm, nan, VISp, VISl, VISal, VISrl]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>737581020</th>\n",
" <td>2019-10-03T00:00:00Z</td>\n",
" <td>718643567</td>\n",
" <td>brain_observatory_1.1</td>\n",
" <td>108.0</td>\n",
" <td>M</td>\n",
" <td>wt/wt</td>\n",
" <td>568</td>\n",
" <td>2218</td>\n",
" <td>6</td>\n",
" <td>[grey, VISmma, nan, VISpm, VISp, VISl, VISrl]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" published_at specimen_id session_type \\\n",
"id \n",
"715093703 2019-10-03T00:00:00Z 699733581 brain_observatory_1.1 \n",
"719161530 2019-10-03T00:00:00Z 703279284 brain_observatory_1.1 \n",
"721123822 2019-10-03T00:00:00Z 707296982 brain_observatory_1.1 \n",
"732592105 2019-10-03T00:00:00Z 717038288 brain_observatory_1.1 \n",
"737581020 2019-10-03T00:00:00Z 718643567 brain_observatory_1.1 \n",
"\n",
" age_in_days sex full_genotype \\\n",
"id \n",
"715093703 118.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"719161530 122.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"721123822 125.0 M Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n",
"732592105 100.0 M wt/wt \n",
"737581020 108.0 M wt/wt \n",
"\n",
" unit_count channel_count probe_count \\\n",
"id \n",
"715093703 884 2219 6 \n",
"719161530 755 2214 6 \n",
"721123822 444 2229 6 \n",
"732592105 824 1847 5 \n",
"737581020 568 2218 6 \n",
"\n",
" ecephys_structure_acronyms \n",
"id \n",
"715093703 [CA1, VISrl, nan, PO, LP, LGd, CA3, DG, VISl, ... \n",
"719161530 [TH, Eth, APN, POL, LP, DG, CA1, VISpm, nan, N... \n",
"721123822 [MB, SCig, PPT, NOT, DG, CA1, VISam, nan, LP, ... \n",
"732592105 [grey, VISpm, nan, VISp, VISl, VISal, VISrl] \n",
"737581020 [grey, VISmma, nan, VISpm, VISp, VISl, VISrl] "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"brain_observatory_type_sessions = sessions[sessions[\"session_type\"] == \"brain_observatory_1.1\"]\n",
"\n",
"#Show summary of brain observatory sessions\n",
"brain_observatory_type_sessions.head()"
]
},
{
"cell_type": "markdown",
"id": "3ca2bc36",
"metadata": {},
"source": [
"We arbitrarily pick one of the sessions from this list for our analysis."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "653c75ab",
"metadata": {},
"outputs": [],
"source": [
"session_id = 732592105 #We pick this session\n",
"session = cache.get_session_data(session_id) #Download the session "
]
},
{
"cell_type": "markdown",
"id": "29499475",
"metadata": {},
"source": [
"Let's examine the types of stimuli presented in this session."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "b26f78b5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>color</th>\n",
" <th>contrast</th>\n",
" <th>frame</th>\n",
" <th>orientation</th>\n",
" <th>phase</th>\n",
" <th>size</th>\n",
" <th>spatial_frequency</th>\n",
" <th>start_time</th>\n",
" <th>stimulus_block</th>\n",
" <th>stimulus_name</th>\n",
" <th>stop_time</th>\n",
" <th>temporal_frequency</th>\n",
" <th>x_position</th>\n",
" <th>y_position</th>\n",
" <th>duration</th>\n",
" <th>stimulus_condition_id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>stimulus_presentation_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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>21.579074</td>\n",
" <td>null</td>\n",
" <td>spontaneous</td>\n",
" <td>81.645874</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>60.066800</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>45.0</td>\n",
" <td>[3644.93333333, 3644.93333333]</td>\n",
" <td>[20.0, 20.0]</td>\n",
" <td>0.08</td>\n",
" <td>81.645874</td>\n",
" <td>0.0</td>\n",
" <td>gabors</td>\n",
" <td>81.879397</td>\n",
" <td>4.0</td>\n",
" <td>-30.0</td>\n",
" <td>-20.0</td>\n",
" <td>0.233523</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>90.0</td>\n",
" <td>[3644.93333333, 3644.93333333]</td>\n",
" <td>[20.0, 20.0]</td>\n",
" <td>0.08</td>\n",
" <td>81.879397</td>\n",
" <td>0.0</td>\n",
" <td>gabors</td>\n",
" <td>82.129600</td>\n",
" <td>4.0</td>\n",
" <td>20.0</td>\n",
" <td>-10.0</td>\n",
" <td>0.250203</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>90.0</td>\n",
" <td>[3644.93333333, 3644.93333333]</td>\n",
" <td>[20.0, 20.0]</td>\n",
" <td>0.08</td>\n",
" <td>82.129600</td>\n",
" <td>0.0</td>\n",
" <td>gabors</td>\n",
" <td>82.379803</td>\n",
" <td>4.0</td>\n",
" <td>40.0</td>\n",
" <td>30.0</td>\n",
" <td>0.250203</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>45.0</td>\n",
" <td>[3644.93333333, 3644.93333333]</td>\n",
" <td>[20.0, 20.0]</td>\n",
" <td>0.08</td>\n",
" <td>82.379803</td>\n",
" <td>0.0</td>\n",
" <td>gabors</td>\n",
" <td>82.630006</td>\n",
" <td>4.0</td>\n",
" <td>40.0</td>\n",
" <td>-40.0</td>\n",
" <td>0.250203</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70383</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>60.0</td>\n",
" <td>0.75</td>\n",
" <td>[250.0, 250.0]</td>\n",
" <td>0.04</td>\n",
" <td>9142.164805</td>\n",
" <td>14.0</td>\n",
" <td>static_gratings</td>\n",
" <td>9142.415016</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>0.250210</td>\n",
" <td>4806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70384</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>[250.0, 250.0]</td>\n",
" <td>0.08</td>\n",
" <td>9142.415016</td>\n",
" <td>14.0</td>\n",
" <td>static_gratings</td>\n",
" <td>9142.665223</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>0.250207</td>\n",
" <td>4857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70385</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>60.0</td>\n",
" <td>0.75</td>\n",
" <td>[250.0, 250.0]</td>\n",
" <td>0.32</td>\n",
" <td>9142.665223</td>\n",
" <td>14.0</td>\n",
" <td>static_gratings</td>\n",
" <td>9142.915430</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>0.250207</td>\n",
" <td>4876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70386</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>90.0</td>\n",
" <td>0.5</td>\n",
" <td>[250.0, 250.0]</td>\n",
" <td>0.16</td>\n",
" <td>9142.915430</td>\n",
" <td>14.0</td>\n",
" <td>static_gratings</td>\n",
" <td>9143.165637</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>0.250207</td>\n",
" <td>4790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70387</th>\n",
" <td>null</td>\n",
" <td>0.8</td>\n",
" <td>null</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>[250.0, 250.0]</td>\n",
" <td>0.08</td>\n",
" <td>9143.165637</td>\n",
" <td>14.0</td>\n",
" <td>static_gratings</td>\n",
" <td>9143.415844</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>null</td>\n",
" <td>0.250207</td>\n",
" <td>4905</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>70388 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" color contrast frame orientation \\\n",
"stimulus_presentation_id \n",
"0 null null null null \n",
"1 null 0.8 null 45.0 \n",
"2 null 0.8 null 90.0 \n",
"3 null 0.8 null 90.0 \n",
"4 null 0.8 null 45.0 \n",
"... ... ... ... ... \n",
"70383 null 0.8 null 60.0 \n",
"70384 null 0.8 null 30.0 \n",
"70385 null 0.8 null 60.0 \n",
"70386 null 0.8 null 90.0 \n",
"70387 null 0.8 null 0.0 \n",
"\n",
" phase size \\\n",
"stimulus_presentation_id \n",
"0 null null \n",
"1 [3644.93333333, 3644.93333333] [20.0, 20.0] \n",
"2 [3644.93333333, 3644.93333333] [20.0, 20.0] \n",
"3 [3644.93333333, 3644.93333333] [20.0, 20.0] \n",
"4 [3644.93333333, 3644.93333333] [20.0, 20.0] \n",
"... ... ... \n",
"70383 0.75 [250.0, 250.0] \n",
"70384 0.0 [250.0, 250.0] \n",
"70385 0.75 [250.0, 250.0] \n",
"70386 0.5 [250.0, 250.0] \n",
"70387 0.5 [250.0, 250.0] \n",
"\n",
" spatial_frequency start_time stimulus_block \\\n",
"stimulus_presentation_id \n",
"0 null 21.579074 null \n",
"1 0.08 81.645874 0.0 \n",
"2 0.08 81.879397 0.0 \n",
"3 0.08 82.129600 0.0 \n",
"4 0.08 82.379803 0.0 \n",
"... ... ... ... \n",
"70383 0.04 9142.164805 14.0 \n",
"70384 0.08 9142.415016 14.0 \n",
"70385 0.32 9142.665223 14.0 \n",
"70386 0.16 9142.915430 14.0 \n",
"70387 0.08 9143.165637 14.0 \n",
"\n",
" stimulus_name stop_time temporal_frequency \\\n",
"stimulus_presentation_id \n",
"0 spontaneous 81.645874 null \n",
"1 gabors 81.879397 4.0 \n",
"2 gabors 82.129600 4.0 \n",
"3 gabors 82.379803 4.0 \n",
"4 gabors 82.630006 4.0 \n",
"... ... ... ... \n",
"70383 static_gratings 9142.415016 null \n",
"70384 static_gratings 9142.665223 null \n",
"70385 static_gratings 9142.915430 null \n",
"70386 static_gratings 9143.165637 null \n",
"70387 static_gratings 9143.415844 null \n",
"\n",
" x_position y_position duration \\\n",
"stimulus_presentation_id \n",
"0 null null 60.066800 \n",
"1 -30.0 -20.0 0.233523 \n",
"2 20.0 -10.0 0.250203 \n",
"3 40.0 30.0 0.250203 \n",
"4 40.0 -40.0 0.250203 \n",
"... ... ... ... \n",
"70383 null null 0.250210 \n",
"70384 null null 0.250207 \n",
"70385 null null 0.250207 \n",
"70386 null null 0.250207 \n",
"70387 null null 0.250207 \n",
"\n",
" stimulus_condition_id \n",
"stimulus_presentation_id \n",
"0 0 \n",
"1 1 \n",
"2 2 \n",
"3 3 \n",
"4 4 \n",
"... ... \n",
"70383 4806 \n",
"70384 4857 \n",
"70385 4876 \n",
"70386 4790 \n",
"70387 4905 \n",
"\n",
"[70388 rows x 16 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"session.get_stimulus_table()"
]
},
{
"cell_type": "markdown",
"id": "9fe725a5",
"metadata": {},
"source": [
"This table has a comprehensive list of all the stimuli and its characteristics. For our purposes, we will only examine the drifting gratings. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "ebf467f2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>start</th>\n",
" <th>end</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1582.883624</td>\n",
" <td>1584.885254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1585.886106</td>\n",
" <td>1587.887776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1588.888594</td>\n",
" <td>1590.890294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1591.891116</td>\n",
" <td>1593.892796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1594.893664</td>\n",
" <td>1596.895314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5375.251646</td>\n",
" <td>5377.253316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5378.254184</td>\n",
" <td>5380.255834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5381.256656</td>\n",
" <td>5383.258336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5384.259204</td>\n",
" <td>5386.260854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>5387.261686</td>\n",
" <td>5389.263356</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>628 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" start end\n",
"0 1582.883624 1584.885254\n",
"1 1585.886106 1587.887776\n",
"2 1588.888594 1590.890294\n",
"3 1591.891116 1593.892796\n",
"4 1594.893664 1596.895314\n",
".. ... ...\n",
"623 5375.251646 5377.253316\n",
"624 5378.254184 5380.255834\n",
"625 5381.256656 5383.258336\n",
"626 5384.259204 5386.260854\n",
"627 5387.261686 5389.263356\n",
"\n",
"[628 rows x 2 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"driftstim = session.get_stimulus_table(['drifting_gratings']) #Get drifting grating stimuli \n",
"\n",
"#Time to use Pynapple! \n",
"#Create an IntervalSet with the start and stop times of drifting gratings\n",
"epoch = nap.IntervalSet(start = driftstim['start_time'], end = driftstim['stop_time'], time_units = 's')\n",
"\n",
"#Visualize the epoch\n",
"epoch"
]
},
{
"cell_type": "markdown",
"id": "8e9cbe4b",
"metadata": {},
"source": [
"We can now conveniently access all the drifting grating trials. \n",
"\n",
"***\n",
"\n",
"## Step-by-step tutorial\n",
"\n",
"### Orientation Tuning Curves\n",
"\n",
"Now, let's find some units and plot their tuning curves! \n",
"\n",
"The primary visual cortex (V1) is known to have orientation tuning. Therefore, we will find units recorded in V1 and look at their responses to drifting gratings. To do so, we must first select units recorded in V1."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "eafcd4de",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>waveform_PT_ratio</th>\n",
" <th>waveform_amplitude</th>\n",
" <th>amplitude_cutoff</th>\n",
" <th>cluster_id</th>\n",
" <th>cumulative_drift</th>\n",
" <th>d_prime</th>\n",
" <th>firing_rate</th>\n",
" <th>isi_violations</th>\n",
" <th>isolation_distance</th>\n",
" <th>L_ratio</th>\n",
" <th>...</th>\n",
" <th>ecephys_structure_id</th>\n",
" <th>ecephys_structure_acronym</th>\n",
" <th>anterior_posterior_ccf_coordinate</th>\n",
" <th>dorsal_ventral_ccf_coordinate</th>\n",
" <th>left_right_ccf_coordinate</th>\n",
" <th>probe_description</th>\n",
" <th>location</th>\n",
" <th>probe_sampling_rate</th>\n",
" <th>probe_lfp_sampling_rate</th>\n",
" <th>probe_has_lfp_data</th>\n",
" </tr>\n",
" <tr>\n",
" <th>unit_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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>915960317</th>\n",
" <td>0.500665</td>\n",
" <td>145.342665</td>\n",
" <td>0.000005</td>\n",
" <td>340</td>\n",
" <td>188.64</td>\n",
" <td>6.324283</td>\n",
" <td>5.734863</td>\n",
" <td>0.003632</td>\n",
" <td>86.304506</td>\n",
" <td>2.468253e-04</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960312</th>\n",
" <td>0.474360</td>\n",
" <td>186.312360</td>\n",
" <td>0.033885</td>\n",
" <td>339</td>\n",
" <td>90.45</td>\n",
" <td>4.099412</td>\n",
" <td>0.226459</td>\n",
" <td>0.000000</td>\n",
" <td>45.657709</td>\n",
" <td>1.395749e-03</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960330</th>\n",
" <td>0.537582</td>\n",
" <td>172.424850</td>\n",
" <td>0.001525</td>\n",
" <td>343</td>\n",
" <td>98.15</td>\n",
" <td>5.087503</td>\n",
" <td>1.066119</td>\n",
" <td>0.490380</td>\n",
" <td>50.376811</td>\n",
" <td>1.805190e-03</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960359</th>\n",
" <td>0.561683</td>\n",
" <td>169.444080</td>\n",
" <td>0.004483</td>\n",
" <td>352</td>\n",
" <td>138.72</td>\n",
" <td>5.225291</td>\n",
" <td>1.435973</td>\n",
" <td>0.077230</td>\n",
" <td>55.298175</td>\n",
" <td>2.098185e-03</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960460</th>\n",
" <td>0.697996</td>\n",
" <td>452.087805</td>\n",
" <td>0.000004</td>\n",
" <td>382</td>\n",
" <td>117.94</td>\n",
" <td>8.128883</td>\n",
" <td>15.016905</td>\n",
" <td>0.000530</td>\n",
" <td>134.322146</td>\n",
" <td>7.551944e-08</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960562</th>\n",
" <td>0.532659</td>\n",
" <td>364.425945</td>\n",
" <td>0.000330</td>\n",
" <td>408</td>\n",
" <td>252.29</td>\n",
" <td>5.355198</td>\n",
" <td>10.825190</td>\n",
" <td>0.014609</td>\n",
" <td>73.237770</td>\n",
" <td>8.179673e-04</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960624</th>\n",
" <td>0.465205</td>\n",
" <td>321.817860</td>\n",
" <td>0.000061</td>\n",
" <td>428</td>\n",
" <td>210.55</td>\n",
" <td>6.058147</td>\n",
" <td>13.999222</td>\n",
" <td>0.004672</td>\n",
" <td>106.041157</td>\n",
" <td>4.566157e-05</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960848</th>\n",
" <td>0.358100</td>\n",
" <td>431.861625</td>\n",
" <td>0.001946</td>\n",
" <td>497</td>\n",
" <td>97.11</td>\n",
" <td>6.777499</td>\n",
" <td>0.849963</td>\n",
" <td>0.495973</td>\n",
" <td>63.549831</td>\n",
" <td>6.243700e-04</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960876</th>\n",
" <td>0.412983</td>\n",
" <td>348.758475</td>\n",
" <td>0.000957</td>\n",
" <td>507</td>\n",
" <td>223.83</td>\n",
" <td>5.740318</td>\n",
" <td>1.441071</td>\n",
" <td>0.038342</td>\n",
" <td>79.911522</td>\n",
" <td>9.804225e-05</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960921</th>\n",
" <td>0.441819</td>\n",
" <td>236.340585</td>\n",
" <td>0.004872</td>\n",
" <td>521</td>\n",
" <td>182.83</td>\n",
" <td>7.684920</td>\n",
" <td>0.831163</td>\n",
" <td>0.000000</td>\n",
" <td>95.561650</td>\n",
" <td>3.811258e-06</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960947</th>\n",
" <td>0.385906</td>\n",
" <td>291.149040</td>\n",
" <td>0.001179</td>\n",
" <td>531</td>\n",
" <td>286.48</td>\n",
" <td>6.216708</td>\n",
" <td>1.196556</td>\n",
" <td>0.027807</td>\n",
" <td>107.133720</td>\n",
" <td>8.772260e-07</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960962</th>\n",
" <td>0.356183</td>\n",
" <td>355.492800</td>\n",
" <td>0.003936</td>\n",
" <td>537</td>\n",
" <td>92.61</td>\n",
" <td>4.711792</td>\n",
" <td>0.978382</td>\n",
" <td>0.291137</td>\n",
" <td>43.952967</td>\n",
" <td>9.788836e-03</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960975</th>\n",
" <td>0.374368</td>\n",
" <td>262.344420</td>\n",
" <td>0.002301</td>\n",
" <td>542</td>\n",
" <td>85.86</td>\n",
" <td>5.000994</td>\n",
" <td>0.543841</td>\n",
" <td>0.269217</td>\n",
" <td>59.157642</td>\n",
" <td>9.685702e-04</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960968</th>\n",
" <td>0.372298</td>\n",
" <td>345.604740</td>\n",
" <td>0.001666</td>\n",
" <td>539</td>\n",
" <td>176.93</td>\n",
" <td>8.156430</td>\n",
" <td>0.634339</td>\n",
" <td>0.000000</td>\n",
" <td>95.795791</td>\n",
" <td>1.019108e-05</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>915960996</th>\n",
" <td>0.401461</td>\n",
" <td>227.728995</td>\n",
" <td>0.004779</td>\n",
" <td>550</td>\n",
" <td>191.13</td>\n",
" <td>6.107058</td>\n",
" <td>0.511019</td>\n",
" <td>0.000000</td>\n",
" <td>66.139560</td>\n",
" <td>1.610780e-04</td>\n",
" <td>...</td>\n",
" <td>385.0</td>\n",
" <td>VISp</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>probeC</td>\n",
" <td>See electrode locations</td>\n",
" <td>29999.991665</td>\n",
" <td>1249.999653</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15 rows × 40 columns</p>\n",
"</div>"
],
"text/plain": [
" waveform_PT_ratio waveform_amplitude amplitude_cutoff \\\n",
"unit_id \n",
"915960317 0.500665 145.342665 0.000005 \n",
"915960312 0.474360 186.312360 0.033885 \n",
"915960330 0.537582 172.424850 0.001525 \n",
"915960359 0.561683 169.444080 0.004483 \n",
"915960460 0.697996 452.087805 0.000004 \n",
"915960562 0.532659 364.425945 0.000330 \n",
"915960624 0.465205 321.817860 0.000061 \n",
"915960848 0.358100 431.861625 0.001946 \n",
"915960876 0.412983 348.758475 0.000957 \n",
"915960921 0.441819 236.340585 0.004872 \n",
"915960947 0.385906 291.149040 0.001179 \n",
"915960962 0.356183 355.492800 0.003936 \n",
"915960975 0.374368 262.344420 0.002301 \n",
"915960968 0.372298 345.604740 0.001666 \n",
"915960996 0.401461 227.728995 0.004779 \n",
"\n",
" cluster_id cumulative_drift d_prime firing_rate \\\n",
"unit_id \n",
"915960317 340 188.64 6.324283 5.734863 \n",
"915960312 339 90.45 4.099412 0.226459 \n",
"915960330 343 98.15 5.087503 1.066119 \n",
"915960359 352 138.72 5.225291 1.435973 \n",
"915960460 382 117.94 8.128883 15.016905 \n",
"915960562 408 252.29 5.355198 10.825190 \n",
"915960624 428 210.55 6.058147 13.999222 \n",
"915960848 497 97.11 6.777499 0.849963 \n",
"915960876 507 223.83 5.740318 1.441071 \n",
"915960921 521 182.83 7.684920 0.831163 \n",
"915960947 531 286.48 6.216708 1.196556 \n",
"915960962 537 92.61 4.711792 0.978382 \n",
"915960975 542 85.86 5.000994 0.543841 \n",
"915960968 539 176.93 8.156430 0.634339 \n",
"915960996 550 191.13 6.107058 0.511019 \n",
"\n",
" isi_violations isolation_distance L_ratio ... \\\n",
"unit_id ... \n",
"915960317 0.003632 86.304506 2.468253e-04 ... \n",
"915960312 0.000000 45.657709 1.395749e-03 ... \n",
"915960330 0.490380 50.376811 1.805190e-03 ... \n",
"915960359 0.077230 55.298175 2.098185e-03 ... \n",
"915960460 0.000530 134.322146 7.551944e-08 ... \n",
"915960562 0.014609 73.237770 8.179673e-04 ... \n",
"915960624 0.004672 106.041157 4.566157e-05 ... \n",
"915960848 0.495973 63.549831 6.243700e-04 ... \n",
"915960876 0.038342 79.911522 9.804225e-05 ... \n",
"915960921 0.000000 95.561650 3.811258e-06 ... \n",
"915960947 0.027807 107.133720 8.772260e-07 ... \n",
"915960962 0.291137 43.952967 9.788836e-03 ... \n",
"915960975 0.269217 59.157642 9.685702e-04 ... \n",
"915960968 0.000000 95.795791 1.019108e-05 ... \n",
"915960996 0.000000 66.139560 1.610780e-04 ... \n",
"\n",
" ecephys_structure_id ecephys_structure_acronym \\\n",
"unit_id \n",
"915960317 385.0 VISp \n",
"915960312 385.0 VISp \n",
"915960330 385.0 VISp \n",
"915960359 385.0 VISp \n",
"915960460 385.0 VISp \n",
"915960562 385.0 VISp \n",
"915960624 385.0 VISp \n",
"915960848 385.0 VISp \n",
"915960876 385.0 VISp \n",
"915960921 385.0 VISp \n",
"915960947 385.0 VISp \n",
"915960962 385.0 VISp \n",
"915960975 385.0 VISp \n",
"915960968 385.0 VISp \n",
"915960996 385.0 VISp \n",
"\n",
" anterior_posterior_ccf_coordinate dorsal_ventral_ccf_coordinate \\\n",
"unit_id \n",
"915960317 NaN NaN \n",
"915960312 NaN NaN \n",
"915960330 NaN NaN \n",
"915960359 NaN NaN \n",
"915960460 NaN NaN \n",
"915960562 NaN NaN \n",
"915960624 NaN NaN \n",
"915960848 NaN NaN \n",
"915960876 NaN NaN \n",
"915960921 NaN NaN \n",
"915960947 NaN NaN \n",
"915960962 NaN NaN \n",
"915960975 NaN NaN \n",
"915960968 NaN NaN \n",
"915960996 NaN NaN \n",
"\n",
" left_right_ccf_coordinate probe_description \\\n",
"unit_id \n",
"915960317 NaN probeC \n",
"915960312 NaN probeC \n",
"915960330 NaN probeC \n",
"915960359 NaN probeC \n",
"915960460 NaN probeC \n",
"915960562 NaN probeC \n",
"915960624 NaN probeC \n",
"915960848 NaN probeC \n",
"915960876 NaN probeC \n",
"915960921 NaN probeC \n",
"915960947 NaN probeC \n",
"915960962 NaN probeC \n",
"915960975 NaN probeC \n",
"915960968 NaN probeC \n",
"915960996 NaN probeC \n",
"\n",
" location probe_sampling_rate \\\n",
"unit_id \n",
"915960317 See electrode locations 29999.991665 \n",
"915960312 See electrode locations 29999.991665 \n",
"915960330 See electrode locations 29999.991665 \n",
"915960359 See electrode locations 29999.991665 \n",
"915960460 See electrode locations 29999.991665 \n",
"915960562 See electrode locations 29999.991665 \n",
"915960624 See electrode locations 29999.991665 \n",
"915960848 See electrode locations 29999.991665 \n",
"915960876 See electrode locations 29999.991665 \n",
"915960921 See electrode locations 29999.991665 \n",
"915960947 See electrode locations 29999.991665 \n",
"915960962 See electrode locations 29999.991665 \n",
"915960975 See electrode locations 29999.991665 \n",
"915960968 See electrode locations 29999.991665 \n",
"915960996 See electrode locations 29999.991665 \n",
"\n",
" probe_lfp_sampling_rate probe_has_lfp_data \n",
"unit_id \n",
"915960317 1249.999653 True \n",
"915960312 1249.999653 True \n",
"915960330 1249.999653 True \n",
"915960359 1249.999653 True \n",
"915960460 1249.999653 True \n",
"915960562 1249.999653 True \n",
"915960624 1249.999653 True \n",
"915960848 1249.999653 True \n",
"915960876 1249.999653 True \n",
"915960921 1249.999653 True \n",
"915960947 1249.999653 True \n",
"915960962 1249.999653 True \n",
"915960975 1249.999653 True \n",
"915960968 1249.999653 True \n",
"915960996 1249.999653 True \n",
"\n",
"[15 rows x 40 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"units = session.units[session.units[\"ecephys_structure_acronym\"] == 'VISp'] #Select units in V1 \n",
"units_with_high_snr = units[units['snr'] > 4] #Select units with high signal-to-noise (SnR) ratio\n",
"high_snr_unit_ids = units_with_high_snr.index.values #Get unit IDs of high SnR\n",
"\n",
"#Let's see how many units we have\n",
"units_with_high_snr"
]
},
{
"cell_type": "markdown",
"id": "1b1a2dd5",
"metadata": {},
"source": [
"We have 15 high SnR units in V1, and this table lists all their characteristics.\n",
"\n",
"Let's get the spike times of these units, and put them in a Pynapple TsGroup. "
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "9b683902",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" Index rate\n",
"--------- ------\n",
"915960312 0.23\n",
"915960317 5.73\n",
"915960330 1.07\n",
"915960359 1.44\n",
"915960460 15.02\n",
"915960562 10.83\n",
"915960624 14\n",
"915960848 0.85\n",
"915960876 1.44\n",
"915960921 0.83\n",
"915960947 1.2\n",
"915960962 0.98\n",
"915960968 0.63\n",
"915960975 0.54\n",
"915960996 0.51"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spikes = session.spike_times #Get spike times for all units in the session\n",
"\n",
"#Create Pynapple TsGroup, using unit IDs as keys\n",
"spike_times = nap.TsGroup(\n",
" {\n",
" unit_id:nap.Ts(spikes[unit_id], time_units = 's') for unit_id in spikes\n",
" })\n",
"\n",
"spike_times = spike_times[high_snr_unit_ids] #Spike times for high SnR units\n",
"\n",
"#What does this look like? \n",
"spike_times"
]
},
{
"cell_type": "markdown",
"id": "49187cde",
"metadata": {},
"source": [
"The spike times TsGroup has 2 columns. The unit ID, and the firing rate of the units in Hz.\n",
"\n",
"To plot orientation tuning curves, we first need to get the range of orientations displayed, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "7c6a4b27",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.0, 45.0, 225.0, 0.0, 0.0, 270.0, 180.0, 225.0, 180.0, 315.0,\n",
" 180.0, 0.0, 180.0, 90.0, 135.0, 0.0, 'null', 225.0, 'null', 135.0,\n",
" 0.0, 45.0, 270.0, 225.0, 270.0, 45.0, 90.0, 90.0, 315.0, 135.0,\n",
" 315.0, 0.0, 90.0, 135.0, 180.0, 90.0, 270.0, 135.0, 135.0, 135.0,\n",
" 180.0, 180.0, 135.0, 270.0, 225.0, 135.0, 90.0, 225.0, 225.0,\n",
" 135.0, 90.0, 270.0, 90.0, 270.0, 0.0, 90.0, 0.0, 225.0, 225.0,\n",
" 90.0, 'null', 270.0, 315.0, 180.0, 270.0, 90.0, 180.0, 135.0,\n",
" 225.0, 315.0, 0.0, 135.0, 90.0, 135.0, 0.0, 0.0, 135.0, 45.0,\n",
" 270.0, 270.0, 45.0, 315.0, 315.0, 315.0, 'null', 135.0, 270.0,\n",
" 90.0, 270.0, 0.0, 180.0, 270.0, 'null', 90.0, 270.0, 135.0, 225.0,\n",
" 315.0, 0.0, 180.0, 0.0, 45.0, 270.0, 180.0, 225.0, 270.0, 45.0,\n",
" 45.0, 270.0, 180.0, 225.0, 0.0, 45.0, 180.0, 180.0, 'null', 180.0,\n",
" 135.0, 315.0, 180.0, 270.0, 'null', 45.0, 90.0, 225.0, 270.0,\n",
" 225.0, 225.0, 45.0, 315.0, 225.0, 0.0, 270.0, 135.0, 45.0, 90.0,\n",
" 135.0, 45.0, 'null', 'null', 225.0, 0.0, 45.0, 315.0, 225.0, 315.0,\n",
" 45.0, 135.0, 90.0, 180.0, 270.0, 45.0, 0.0, 135.0, 270.0, 0.0,\n",
" 135.0, 180.0, 315.0, 45.0, 135.0, 225.0, 0.0, 45.0, 0.0, 'null',\n",
" 0.0, 135.0, 0.0, 'null', 0.0, 'null', 45.0, 315.0, 180.0, 315.0,\n",
" 180.0, 90.0, 225.0, 180.0, 270.0, 180.0, 45.0, 225.0, 90.0, 90.0,\n",
" 135.0, 315.0, 0.0, 180.0, 135.0, 315.0, 225.0, 90.0, 180.0, 45.0,\n",
" 225.0, 'null', 225.0, 90.0, 270.0, 90.0, 0.0, 315.0, 90.0, 225.0,\n",
" 45.0, 315.0, 270.0, 315.0, 225.0, 0.0, 225.0, 45.0, 90.0, 180.0,\n",
" 45.0, 45.0, 225.0, 270.0, 90.0, 180.0, 270.0, 315.0, 45.0, 180.0,\n",
" 135.0, 135.0, 225.0, 0.0, 135.0, 315.0, 180.0, 225.0, 0.0, 0.0,\n",
" 180.0, 225.0, 270.0, 270.0, 180.0, 0.0, 135.0, 90.0, 90.0, 45.0,\n",
" 315.0, 45.0, 45.0, 180.0, 135.0, 0.0, 135.0, 270.0, 0.0, 'null',\n",
" 270.0, 90.0, 90.0, 315.0, 90.0, 135.0, 90.0, 225.0, 135.0, 315.0,\n",
" 135.0, 'null', 180.0, 90.0, 45.0, 90.0, 45.0, 135.0, 180.0, 315.0,\n",
" 225.0, 90.0, 135.0, 180.0, 135.0, 270.0, 45.0, 180.0, 270.0, 0.0,\n",
" 315.0, 180.0, 45.0, 315.0, 180.0, 90.0, 90.0, 270.0, 225.0, 225.0,\n",
" 225.0, 180.0, 180.0, 225.0, 135.0, 315.0, 135.0, 225.0, 270.0,\n",
" 315.0, 'null', 90.0, 90.0, 270.0, 90.0, 0.0, 45.0, 0.0, 270.0,\n",
" 225.0, 225.0, 135.0, 180.0, 45.0, 225.0, 315.0, 0.0, 270.0, 180.0,\n",
" 90.0, 180.0, 180.0, 180.0, 45.0, 135.0, 45.0, 45.0, 0.0, 180.0,\n",
" 0.0, 135.0, 180.0, 315.0, 45.0, 90.0, 135.0, 270.0, 45.0, 180.0,\n",
" 0.0, 270.0, 225.0, 135.0, 'null', 225.0, 270.0, 90.0, 90.0, 315.0,\n",
" 270.0, 0.0, 90.0, 225.0, 315.0, 225.0, 315.0, 225.0, 270.0, 135.0,\n",
" 225.0, 45.0, 225.0, 90.0, 180.0, 135.0, 180.0, 225.0, 225.0, 270.0,\n",
" 0.0, 315.0, 0.0, 270.0, 225.0, 315.0, 0.0, 225.0, 90.0, 270.0,\n",
" 270.0, 135.0, 225.0, 90.0, 0.0, 90.0, 270.0, 225.0, 0.0, 225.0,\n",
" 135.0, 90.0, 270.0, 90.0, 45.0, 225.0, 315.0, 45.0, 180.0, 225.0,\n",
" 135.0, 315.0, 45.0, 180.0, 45.0, 'null', 90.0, 45.0, 45.0, 225.0,\n",
" 0.0, 45.0, 270.0, 0.0, 270.0, 45.0, 90.0, 135.0, 45.0, 270.0,\n",
" 135.0, 315.0, 315.0, 135.0, 315.0, 135.0, 45.0, 225.0, 135.0,\n",
" 315.0, 180.0, 315.0, 270.0, 45.0, 180.0, 135.0, 45.0, 315.0, 225.0,\n",
" 180.0, 180.0, 315.0, 270.0, 180.0, 135.0, 0.0, 180.0, 90.0, 270.0,\n",
" 315.0, 225.0, 180.0, 'null', 'null', 135.0, 315.0, 180.0, 225.0,\n",
" 225.0, 90.0, 45.0, 315.0, 225.0, 135.0, 90.0, 90.0, 270.0, 'null',\n",
" 0.0, 315.0, 90.0, 135.0, 90.0, 315.0, 225.0, 0.0, 315.0, 225.0,\n",
" 0.0, 45.0, 90.0, 0.0, 225.0, 180.0, 315.0, 180.0, 'null', 90.0,\n",
" 315.0, 0.0, 'null', 270.0, 180.0, 'null', 315.0, 'null', 'null',\n",
" 225.0, 90.0, 180.0, 180.0, 315.0, 45.0, 315.0, 315.0, 45.0, 45.0,\n",
" 45.0, 0.0, 270.0, 180.0, 315.0, 0.0, 45.0, 135.0, 270.0, 270.0,\n",
" 45.0, 45.0, 135.0, 270.0, 'null', 0.0, 90.0, 45.0, 270.0, 90.0,\n",
" 315.0, 90.0, 135.0, 135.0, 0.0, 270.0, 90.0, 0.0, 135.0, 315.0,\n",
" 225.0, 225.0, 270.0, 270.0, 45.0, 315.0, 225.0, 45.0, 45.0, 45.0,\n",
" 0.0, 135.0, 45.0, 45.0, 90.0, 180.0, 45.0, 90.0, 90.0, 0.0, 180.0,\n",
" 315.0, 45.0, 180.0, 315.0, 180.0, 'null', 135.0, 45.0, 315.0,\n",
" 135.0, 270.0, 135.0, 315.0, 0.0, 180.0, 90.0, 0.0, 315.0, 270.0,\n",
" 0.0, 225.0, 45.0, 90.0, 'null', 90.0, 135.0, 'null', 0.0, 225.0,\n",
" 0.0, 315.0, 135.0, 270.0, 315.0, 0.0, 270.0, 0.0, 135.0, 45.0,\n",
" 225.0, 0.0, 315.0, 270.0, 270.0, 0.0, 315.0, 180.0, 270.0, 90.0,\n",
" 90.0, 135.0, 0.0, 0.0, 315.0, 315.0, 135.0, 270.0, 315.0, 270.0,\n",
" 135.0, 180.0, 180.0, 180.0, 0.0, 45.0], dtype=object)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spkindex = spike_times.keys() #Index of all units\n",
"ori = driftstim['orientation'].values #Get the orientations of the drifting gratings\n",
"\n",
"#What does this look like? \n",
"ori"
]
},
{
"cell_type": "markdown",
"id": "c9628b01",
"metadata": {},
"source": [
"The orientation data is organized as an object array, with some trials having null values. We will convert these orientations into floats for easier handling, as shown below: "
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "a00eb9fa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 45., 90., 135., 180., 225., 270., 315.])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ori[ori == 'null'] = np.nan #Replace all null values with NaN\n",
"ori = ori.astype(float) #Convert into a float array\n",
"ang_range = np.unique(ori)[0:-1] #Find the unique values in this array and exclude the NaNs, to get the list of orientations\n",
"\n",
"#What does this look like? \n",
"ang_range"
]
},
{
"cell_type": "markdown",
"id": "9cd4b23e",
"metadata": {},
"source": [
"These are the 8 orientations of the drifting gratings, sampling 360 degrees at 45 degree intervals. \n",
"\n",
"Now, we need to create a dictionary of IntervalSets for each orientation. This will tell us all the trial timings for a given orientation. We do so as follows: "
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "33f605fc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0.0: start end\n",
" 0 1582.883624 1584.885254\n",
" 1 1591.891116 1593.892796\n",
" 2 1594.893664 1596.895314\n",
" 3 1615.911186 1617.912876\n",
" 4 1627.921236 1629.922916\n",
" .. ... ...\n",
" 69 5315.201496 5317.203176\n",
" 70 5327.211526 5329.213206\n",
" 71 5348.229094 5350.230754\n",
" 72 5351.231596 5353.233256\n",
" 73 5384.259204 5386.260854\n",
" \n",
" [74 rows x 2 columns],\n",
" 45.0: start end\n",
" 0 1585.886106 1587.887776\n",
" 1 1645.936286 1647.937966\n",
" 2 1657.946336 1659.947996\n",
" 3 1814.076806 1816.078496\n",
" 4 1823.084294 1825.086004\n",
" .. ... ...\n",
" 70 5198.103674 5200.105344\n",
" 71 5216.118734 5218.120404\n",
" 72 5258.153854 5260.155514\n",
" 73 5309.196486 5311.198166\n",
" 74 5387.261686 5389.263356\n",
" \n",
" [75 rows x 2 columns],\n",
" 90.0: start end\n",
" 0 1621.916206 1623.917896\n",
" 1 1660.948864 1662.950504\n",
" 2 1663.951346 1665.953026\n",
" 3 1678.963904 1680.965624\n",
" 4 1687.971426 1689.973096\n",
" .. ... ...\n",
" 69 5240.138804 5242.140464\n",
" 70 5261.156356 5263.158036\n",
" 71 5267.161376 5269.163056\n",
" 72 5339.221566 5341.223226\n",
" 73 5342.224074 5344.225764\n",
" \n",
" [74 rows x 2 columns],\n",
" 135.0: start end\n",
" 0 1624.918724 1626.920384\n",
" 1 1639.931266 1641.932946\n",
" 2 1669.956366 1671.958046\n",
" 3 1681.966416 1683.968086\n",
" 4 1693.976446 1695.978106\n",
" .. ... ...\n",
" 70 5288.178914 5290.180604\n",
" 71 5306.193984 5308.195664\n",
" 72 5345.226576 5347.228246\n",
" 73 5360.239104 5362.240814\n",
" 74 5372.249114 5374.250814\n",
" \n",
" [75 rows x 2 columns],\n",
" 180.0: start end\n",
" 0 1600.898654 1602.900304\n",
" 1 1606.903684 1608.905344\n",
" 2 1612.908704 1614.910364\n",
" 3 1618.913744 1620.915414\n",
" 4 1684.968964 1686.970584\n",
" .. ... ...\n",
" 70 5237.136306 5239.137956\n",
" 71 5333.216546 5335.218216\n",
" 72 5375.251646 5377.253316\n",
" 73 5378.254184 5380.255834\n",
" 74 5381.256656 5383.258336\n",
" \n",
" [75 rows x 2 columns],\n",
" 225.0: start end\n",
" 0 1588.888594 1590.890294\n",
" 1 1603.901156 1605.902826\n",
" 2 1633.926236 1635.927916\n",
" 3 1651.941306 1653.942996\n",
" 4 1714.993994 1716.995684\n",
" .. ... ...\n",
" 70 5135.051046 5137.052726\n",
" 71 5150.063554 5152.065244\n",
" 72 5255.151346 5257.153026\n",
" 73 5279.171416 5281.173106\n",
" 74 5312.199004 5314.200704\n",
" \n",
" [75 rows x 2 columns],\n",
" 270.0: start end\n",
" 0 1597.896146 1599.897816\n",
" 1 1648.938784 1650.940494\n",
" 2 1654.943824 1656.945514\n",
" 3 1690.973944 1692.975584\n",
" 4 1711.991496 1713.993176\n",
" .. ... ...\n",
" 70 5321.206526 5323.208186\n",
" 71 5324.209004 5326.210684\n",
" 72 5336.219044 5338.220724\n",
" 73 5363.241626 5365.243296\n",
" 74 5369.246636 5371.248316\n",
" \n",
" [75 rows x 2 columns],\n",
" 315.0: start end\n",
" 0 1609.906186 1611.907846\n",
" 1 1666.953844 1668.955544\n",
" 2 1672.958874 1674.960544\n",
" 3 1769.039174 1771.040864\n",
" 4 1790.056756 1792.058426\n",
" .. ... ...\n",
" 70 5318.204014 5320.205684\n",
" 71 5330.214034 5332.215694\n",
" 72 5354.234104 5356.235754\n",
" 73 5357.236606 5359.238286\n",
" 74 5366.244134 5368.245794\n",
" \n",
" [75 rows x 2 columns]}"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dict_ep = {} #Initialize empty dictionary\n",
"\n",
"for j in ang_range:\n",
" \n",
" tokeep = [] #Create a list of trials to keep\n",
" \n",
" for i in range(len(epoch)): #Loop over all grating trials\n",
" if float(ori[i] ) == j: #Find those trials with a given orientation \n",
" tokeep.append(i) \n",
"\n",
" dict_ep[j] = epoch.loc[tokeep] #Make the dictionary of IntervalSets\n",
"\n",
"#What does this look like? \n",
"dict_ep"
]
},
{
"cell_type": "markdown",
"id": "193c5b47",
"metadata": {},
"source": [
"This dictionary shows all the trials, now ordered by orientation. We can use this to compute the orientation tuning curves. Since the stimuli are presented in discrete orientations (i.e. not spannning all angular values from 0 to 360 degrees), we will be using Pynapple's *compute_discrete_tuning_curves*.\n",
"\n",
"Computing the tuning curves looks so easy in Pynapple, just a single line of code! "
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "d2d5d3ae",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>915960312</th>\n",
" <th>915960317</th>\n",
" <th>915960330</th>\n",
" <th>915960359</th>\n",
" <th>915960460</th>\n",
" <th>915960562</th>\n",
" <th>915960624</th>\n",
" <th>915960848</th>\n",
" <th>915960876</th>\n",
" <th>915960921</th>\n",
" <th>915960947</th>\n",
" <th>915960962</th>\n",
" <th>915960968</th>\n",
" <th>915960975</th>\n",
" <th>915960996</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.0</th>\n",
" <td>0.512971</td>\n",
" <td>2.065385</td>\n",
" <td>0.526471</td>\n",
" <td>0.492723</td>\n",
" <td>19.499663</td>\n",
" <td>9.003998</td>\n",
" <td>7.519081</td>\n",
" <td>0.546720</td>\n",
" <td>0.661463</td>\n",
" <td>0.472474</td>\n",
" <td>0.573718</td>\n",
" <td>0.593967</td>\n",
" <td>0.485973</td>\n",
" <td>0.492723</td>\n",
" <td>0.465724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45.0</th>\n",
" <td>0.492919</td>\n",
" <td>1.225637</td>\n",
" <td>0.506241</td>\n",
" <td>1.585334</td>\n",
" <td>24.352867</td>\n",
" <td>8.666050</td>\n",
" <td>8.959137</td>\n",
" <td>0.519563</td>\n",
" <td>0.812650</td>\n",
" <td>0.499580</td>\n",
" <td>0.512902</td>\n",
" <td>0.639463</td>\n",
" <td>0.512902</td>\n",
" <td>0.492919</td>\n",
" <td>0.479597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90.0</th>\n",
" <td>0.533277</td>\n",
" <td>1.424323</td>\n",
" <td>0.533277</td>\n",
" <td>0.560279</td>\n",
" <td>25.455556</td>\n",
" <td>12.710902</td>\n",
" <td>9.295227</td>\n",
" <td>0.816792</td>\n",
" <td>0.695286</td>\n",
" <td>0.492775</td>\n",
" <td>0.702036</td>\n",
" <td>0.546778</td>\n",
" <td>0.634533</td>\n",
" <td>0.492775</td>\n",
" <td>0.479275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135.0</th>\n",
" <td>0.492921</td>\n",
" <td>0.972519</td>\n",
" <td>0.832636</td>\n",
" <td>0.506243</td>\n",
" <td>18.564462</td>\n",
" <td>7.553678</td>\n",
" <td>8.892557</td>\n",
" <td>0.519565</td>\n",
" <td>1.045791</td>\n",
" <td>0.579515</td>\n",
" <td>0.519565</td>\n",
" <td>1.831800</td>\n",
" <td>0.526226</td>\n",
" <td>0.506243</td>\n",
" <td>0.479599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180.0</th>\n",
" <td>0.486262</td>\n",
" <td>2.258121</td>\n",
" <td>0.512906</td>\n",
" <td>0.472940</td>\n",
" <td>16.852642</td>\n",
" <td>8.672783</td>\n",
" <td>7.880109</td>\n",
" <td>0.539551</td>\n",
" <td>0.639468</td>\n",
" <td>0.492923</td>\n",
" <td>1.651959</td>\n",
" <td>0.546212</td>\n",
" <td>0.472940</td>\n",
" <td>0.486262</td>\n",
" <td>0.472940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>225.0</th>\n",
" <td>0.499580</td>\n",
" <td>1.498740</td>\n",
" <td>0.492919</td>\n",
" <td>1.492079</td>\n",
" <td>18.171391</td>\n",
" <td>9.045729</td>\n",
" <td>7.540328</td>\n",
" <td>0.499580</td>\n",
" <td>0.812650</td>\n",
" <td>0.519563</td>\n",
" <td>0.832633</td>\n",
" <td>0.805989</td>\n",
" <td>0.479597</td>\n",
" <td>0.499580</td>\n",
" <td>0.472936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270.0</th>\n",
" <td>0.526167</td>\n",
" <td>1.844916</td>\n",
" <td>0.492866</td>\n",
" <td>0.652714</td>\n",
" <td>23.677527</td>\n",
" <td>10.709835</td>\n",
" <td>10.123725</td>\n",
" <td>0.672695</td>\n",
" <td>0.546148</td>\n",
" <td>0.539488</td>\n",
" <td>0.879166</td>\n",
" <td>0.619412</td>\n",
" <td>0.506186</td>\n",
" <td>0.492866</td>\n",
" <td>0.479545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>315.0</th>\n",
" <td>0.499581</td>\n",
" <td>1.039129</td>\n",
" <td>0.546209</td>\n",
" <td>0.519565</td>\n",
" <td>20.496160</td>\n",
" <td>8.059914</td>\n",
" <td>8.213119</td>\n",
" <td>0.559531</td>\n",
" <td>0.786008</td>\n",
" <td>0.492920</td>\n",
" <td>0.512904</td>\n",
" <td>2.717723</td>\n",
" <td>0.532887</td>\n",
" <td>0.492920</td>\n",
" <td>0.486259</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 915960312 915960317 915960330 915960359 915960460 915960562 \\\n",
"0.0 0.512971 2.065385 0.526471 0.492723 19.499663 9.003998 \n",
"45.0 0.492919 1.225637 0.506241 1.585334 24.352867 8.666050 \n",
"90.0 0.533277 1.424323 0.533277 0.560279 25.455556 12.710902 \n",
"135.0 0.492921 0.972519 0.832636 0.506243 18.564462 7.553678 \n",
"180.0 0.486262 2.258121 0.512906 0.472940 16.852642 8.672783 \n",
"225.0 0.499580 1.498740 0.492919 1.492079 18.171391 9.045729 \n",
"270.0 0.526167 1.844916 0.492866 0.652714 23.677527 10.709835 \n",
"315.0 0.499581 1.039129 0.546209 0.519565 20.496160 8.059914 \n",
"\n",
" 915960624 915960848 915960876 915960921 915960947 915960962 \\\n",
"0.0 7.519081 0.546720 0.661463 0.472474 0.573718 0.593967 \n",
"45.0 8.959137 0.519563 0.812650 0.499580 0.512902 0.639463 \n",
"90.0 9.295227 0.816792 0.695286 0.492775 0.702036 0.546778 \n",
"135.0 8.892557 0.519565 1.045791 0.579515 0.519565 1.831800 \n",
"180.0 7.880109 0.539551 0.639468 0.492923 1.651959 0.546212 \n",
"225.0 7.540328 0.499580 0.812650 0.519563 0.832633 0.805989 \n",
"270.0 10.123725 0.672695 0.546148 0.539488 0.879166 0.619412 \n",
"315.0 8.213119 0.559531 0.786008 0.492920 0.512904 2.717723 \n",
"\n",
" 915960968 915960975 915960996 \n",
"0.0 0.485973 0.492723 0.465724 \n",
"45.0 0.512902 0.492919 0.479597 \n",
"90.0 0.634533 0.492775 0.479275 \n",
"135.0 0.526226 0.506243 0.479599 \n",
"180.0 0.472940 0.486262 0.472940 \n",
"225.0 0.479597 0.499580 0.472936 \n",
"270.0 0.506186 0.492866 0.479545 \n",
"315.0 0.532887 0.492920 0.486259 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Plot firing rate of V1 units as a function of orientation, i.e. an orientation tuning curve \n",
"discrete_tuning_curves = nap.compute_discrete_tuning_curves(spike_times, dict_ep) \n",
"\n",
"#What does this look like? \n",
"discrete_tuning_curves"
]
},
{
"cell_type": "markdown",
"id": "349b8a35",
"metadata": {},
"source": [
"Each column is a single unit, and each row is the orientation of the stimulus (in degrees). The values in the table indicate the firing rate of the unit (in Hz). Let's plot them!"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "2020f771",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1200x900 with 15 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (12,9)) \n",
"for i in range(len(spkindex)): #Loop over all unit IDs\n",
" plt.subplot(5,3,i+1) #Plot tuning curves in 5 rows and 3 columns\n",
" plt.plot(discrete_tuning_curves[spkindex[i]],'o-', color = 'k', linewidth = 2) \n",
" plt.xlabel('Orientation (deg)') #Orientation in degrees, on the X-axis\n",
" plt.ylabel('Firing rate (Hz)') #Firing rate in Hz, on the Y-axis\n",
" plt.subplots_adjust(wspace = 0.5, hspace = 1, top = 0.85) \n",
" "
]
},
{
"cell_type": "markdown",
"id": "d8d6a58b",
"metadata": {},
"source": [
"Awesome!\n",
"\n",
"***\n",
"\n",
"### Cross-correlogram\n",
"\n",
"Now that we have orientation tuning curves, we can go one step further. We will take 2 units and compute their cross-correlation. This will tell us the temporal relationship between the firing of the 2 units with respect to each other.\n",
"\n",
"For the purposes of demonstration, we will pick 2 units arbitrarily. We will pick the units whose orientation tuning curves are shown below: "
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "3176429e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in [3,5]:\n",
" if i == 3:\n",
" c = 'r' #Set the colour to red\n",
" elif i == 5:\n",
" c ='c' #Set the colour to cyan\n",
"\n",
" plt.figure()\n",
" plt.plot(discrete_tuning_curves[spkindex[i]],'o-', color = c, linewidth = 2)\n",
" plt.xlabel('Orientation (deg)') #Orientation in degrees, on the X-axis\n",
" plt.ylabel('Firing rate (Hz)') #Firing rate in Hz, on the Y-axis\n",
" "
]
},
{
"cell_type": "markdown",
"id": "d41e535f",
"metadata": {},
"source": [
"We will now compute the cross-correlogram for these 2 units. How many lines of code would this take? \\\n",
"With Pynapple, just one line is enough! "
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "8460b869",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Norm. Firing rate')"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Compute cross-correlation for all unit pairs\n",
"cc = nap.compute_crosscorrelogram(spike_times, binsize = 0.01, windowsize = 0.3 , ep = epoch, norm = True, time_units = 's', reverse = False) \n",
"\n",
"#Get the cross-correlogram for our example units \n",
"cc_35 = cc[spkindex[3]][spkindex[5]]\n",
"\n",
"#Let's smoothen the cross-correlogram a bit\n",
"cc_35_smooth = cc_35.rolling(4, win_type = 'gaussian', min_periods = 1, center = True, axis = 0).mean(std = 2) \n",
"\n",
"#Now let's plot it!\n",
"plt.figure()\n",
"plt.rc('font', size = 12) \n",
"plt.bar(cc_35_smooth.index.values, cc_35_smooth.values, width = 0.01, color = 'indigo', edgecolor = 'k') #Plot as bars\n",
"plt.xlabel('Lag (s)') #Time lag in seconds, on the X-axis\n",
"plt.ylabel('Norm. Firing rate') #Firing rate normalized to the mean firing rate of the reference unit, on the Y-axis"
]
},
{
"cell_type": "markdown",
"id": "bb932cfe",
"metadata": {},
"source": [
"We observe peaks in the cross-correlogram. What do they mean? \\\n",
"The peak at zero lag indicates that the units fire synchronously. This makes sense, as their preferred orientations overlap.\\\n",
"The peak at 0.2s suggests an oscillatory co-modulation at about 5Hz during visual stimulation.\n",
"\n",
"I hope this tutorial was helpful. If you have any questions, comments or suggestions, please feel free to reach out to the Pynapple Team!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Computing file changes ...
