From 890b85208d041acf0d22d3226dd0fc9f9288ed60 Mon Sep 17 00:00:00 2001
From: Timothe Jost <timothe.jost@wanadoo.fr>
Date: Wed, 19 Apr 2023 20:47:24 +0200
Subject: [PATCH] Update tutorial.ipynb
---
my_first_analysis/tutorial.ipynb | 279 ++++++++++++++++++++++++++++++-
1 file changed, 275 insertions(+), 4 deletions(-)
diff --git a/my_first_analysis/tutorial.ipynb b/my_first_analysis/tutorial.ipynb
index b606adc..732fc74 100644
--- a/my_first_analysis/tutorial.ipynb
+++ b/my_first_analysis/tutorial.ipynb
@@ -1,8 +1,17 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "91dea70d-852a-4874-a2d6-3942647ddea0",
+ "metadata": {},
+ "source": [
+ "# Perform imports\n",
+ "This only needs to be done once, each time you open the jupyter lab document"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 10,
"id": "d9f870bc-db29-4c14-bacb-d8e5d03e92d0",
"metadata": {},
"outputs": [],
@@ -13,9 +22,37 @@
"from ResearchProjects.adaptation import aliases as alias"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "3fd56db3-da3e-49b0-a691-81cbf9069f34",
+ "metadata": {},
+ "source": [
+ "## Enable logging messages and files\n",
+ "This only needs to be done once, each time you open the jupyter lab document"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 15,
+ "id": "2512ee5c-3309-4c8f-a170-008ea1e9e592",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Inflow.logging.enable_logging()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a41c05b-1d8e-40b0-ad3e-80b68e95327d",
+ "metadata": {},
+ "source": [
+ "# Manage data selection\n",
+ "We use one-api to connect to alyx database service (hosted here in the lab)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
"id": "47b1ad86-ac3d-42cc-a46b-f33e3ae8a251",
"metadata": {},
"outputs": [],
@@ -23,15 +60,249 @@
"connector = one.ONE()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "3c23ffa5-896e-45cd-a4d4-358ccfbad80f",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Search a set of sessions or a specific session\n",
+ "Using the one connector object creaed before, and the search method.\n",
+ "The available options for `search` are : \n",
+ "- dataset : str, list\n",
+ " List of dataset names. Returns sessions containing all these datasets.\n",
+ " A dataset matches if it contains the search string e.g. 'wheel.position' matches\n",
+ " '_ibl_wheel.position.npy'\n",
+ "- date_range : str, list, datetime.datetime, datetime.date, pandas.timestamp\n",
+ " A single date to search or a list of 2 dates that define the range (inclusive). To\n",
+ " define only the upper or lower date bound, set the other element to None.\n",
+ "- lab : str, list\n",
+ " A str or list of lab names, returns sessions from any of these labs\n",
+ "- number : str, int\n",
+ " Number of session to be returned, i.e. number in sequence for a given date\n",
+ "- subject : str, list\n",
+ " A list of subject nicknames, returns sessions for any of these subjects\n",
+ "- task_protocol : str, list\n",
+ " The task protocol name (can be partial, i.e. any task protocol containing that str\n",
+ " will be found)\n",
+ "- project(s) : str, list\n",
+ " The project name (can be partial, i.e. any task protocol containing that str\n",
+ " will be found)\n",
+ "- performance_lte / performance_gte : float\n",
+ " Search only for sessions whose performance is less equal or greater equal than a\n",
+ " pre-defined threshold as a percentage (0-100)\n",
+ "- users : str, list\n",
+ " A list of users\n",
+ "- location : str, list\n",
+ " A str or list of lab location (as per Alyx definition) name\n",
+ " Note: this corresponds to the specific rig, not the lab geographical location per se\n",
+ "- dataset_types : str, list\n",
+ " One or more of dataset_types\n",
+ "- details : bool\n",
+ " If true also returns a dict of dataset details\n",
+ "- query_type : str, None\n",
+ " Query cache ('local') or Alyx database ('remote')\n",
+ "- limit : int\n",
+ " The number of results to fetch in one go (if pagination enabled on server)"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "661207a3-8a5a-4125-b276-e9ba86c195dd",
"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>subject</th>\n",
+ " <th>start_time</th>\n",
+ " <th>number</th>\n",
+ " <th>lab</th>\n",
+ " <th>projects</th>\n",
+ " <th>url</th>\n",
+ " <th>task_protocol</th>\n",
+ " <th>date</th>\n",
+ " <th>json</th>\n",
+ " <th>extended_qc</th>\n",
+ " <th>rel_path</th>\n",
+ " <th>alias_name</th>\n",
+ " <th>short_path</th>\n",
+ " <th>path</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",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>04f92e4a-da64-4018-aa6c-d9a79a91c831</th>\n",
+ " <td>wm24</td>\n",
+ " <td>2022-08-22T15:27:00</td>\n",
+ " <td>1</td>\n",
+ " <td>HaissLab</td>\n",
+ " <td>[Adaptation]</td>\n",
+ " <td>http://157.99.138.172/sessions/04f92e4a-da64-4...</td>\n",
+ " <td></td>\n",
+ " <td>2022-08-22</td>\n",
+ " <td>{'channels': ['R', 'G'], 'whisker_stims': {'St...</td>\n",
+ " <td>{'exclude_whisker': ['C1']}</td>\n",
+ " <td>wm24\\2022-08-22\\001</td>\n",
+ " <td>wm24_2022_08_22_001</td>\n",
+ " <td>wm24\\2022-08-22\\001</td>\n",
+ " <td>D:\\LOCAL_DATA\\wm24\\2022-08-22\\001</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " subject start_time number \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 wm24 2022-08-22T15:27:00 1 \n",
+ "\n",
+ " lab projects \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 HaissLab [Adaptation] \n",
+ "\n",
+ " url \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 http://157.99.138.172/sessions/04f92e4a-da64-4... \n",
+ "\n",
+ " task_protocol date \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 2022-08-22 \n",
+ "\n",
+ " json \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 {'channels': ['R', 'G'], 'whisker_stims': {'St... \n",
+ "\n",
+ " extended_qc \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 {'exclude_whisker': ['C1']} \n",
+ "\n",
+ " rel_path \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 wm24\\2022-08-22\\001 \n",
+ "\n",
+ " alias_name \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 wm24_2022_08_22_001 \n",
+ "\n",
+ " short_path \\\n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 wm24\\2022-08-22\\001 \n",
+ "\n",
+ " path \n",
+ "id \n",
+ "04f92e4a-da64-4018-aa6c-d9a79a91c831 D:\\LOCAL_DATA\\wm24\\2022-08-22\\001 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "subject wm24\n",
+ "start_time 2022-08-22T15:27:00\n",
+ "number 1\n",
+ "lab HaissLab\n",
+ "projects [Adaptation]\n",
+ "url http://157.99.138.172/sessions/04f92e4a-da64-4...\n",
+ "task_protocol \n",
+ "date 2022-08-22\n",
+ "json {'channels': ['R', 'G'], 'whisker_stims': {'St...\n",
+ "extended_qc {'exclude_whisker': ['C1']}\n",
+ "rel_path wm24\\2022-08-22\\001\n",
+ "alias_name wm24_2022_08_22_001\n",
+ "short_path wm24\\2022-08-22\\001\n",
+ "path D:\\LOCAL_DATA\\wm24\\2022-08-22\\001\n",
+ "Name: 04f92e4a-da64-4018-aa6c-d9a79a91c831, dtype: object"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sessions = connector.search(subject = \"wm24\", date_range = \"2022-08-22\", number = 1, details = True)\n",
+ "my_session = sessions.iloc[0]\n",
+ "display(sessions)\n",
+ "display(my_session)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62e9a6ec-17ff-4d73-b265-c7f0aad6e810",
+ "metadata": {},
+ "source": [
+ "## Using adaptation pipelines\n",
+ "### for pipelines : \n",
+ "Either :\n",
+ "- `generate_afunction` which tryes o load previously savec results for that function, and if it doesnt find it, generates the results and saves them. You can force regenerating by using ` refresh = True` in the arguments to the function.\n",
+ "- `get_afunction` which returns already processed results of that function (no make sure to not reprocess and overwrite previously analysed data)\n",
+ "- `afunction` which only creates and returns processed results for that function (does not check if there is processed data, and does not save data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e205f79-ef58-48ea-b523-8f86a48ea6a5",
+ "metadata": {},
+ "source": [
+ "# Getting structured data for analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "c7a5d315-2c7a-4534-b3af-3cdc855efc89",
+ "metadata": {},
"outputs": [],
"source": [
- "connector.search()"
+ "trials_df = adaptation.pipelines.get_trials_df(my_session)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aadc5365-d4e8-4f91-a9c7-6bd0ac8934b6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
--
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