aind-disrnn-utils 0.0.8

Generated from aind-library-template

aind_disrnn_utils

Usage

Creating a dataset

• Obtain a list of NWB files you wish to fit the model to

import aind_dynamic_foraging_multisession_analysis.multisession_load as ms_load
import aind_disrnn_utils as dl

nwbs, df_trials = ms_load.make_multisession_trials_df(nwb_files)
dataset = dl.create_disrnn_dataset(df_trials)

• You don't need to use make_multisession_trials_df, but the trials data frame does need to have a column "ses_idx" that splits trials into sessions.

Predefined datasets

This Code Ocean Capsule can be used for loading a list of sessions and saving the result as a dataframe: Code Ocean Capsule

The resulting data assets can be used like:

import pandas as pd
import aind_disrnn_utils.data_loader as dl
df = pd.read('/data/disrnn_dataset_774212/disrnn_dataset.csv')
dataset = dl.create_disrrn_dataset(df)

Dataset name	mouse id	# trials	# sessions	data asset ID	Task
disrnn_dataset_774212	774212	16184	31	ad5ec889-f4e0-45a2-802c-f843266d3cce	Uncoupled Without Baiting
disrnn_dataset_779531	779531	7272	12	64fa1cb4-8af8-4d96-a965-3454d59439f6	Uncoupled Without Baiting
disrnn_dataset_781173	781173	8132	15	9788eb8d-ea88-4c60-bacc-1a23efd2f5e1	Uncoupled Without Baiting
disrnn_dataset_781162	781162	6417	12	8eaa487e-e78c-4635-b24b-eabe680a55ae	Uncoupled Without Baiting
disrnn_dataset_778077	778077	8336	15	76fc65d3-eec4-4578-a20d-499193fc920e	Uncoupled Without Baiting

The datasets can be combined to fit easily:

import pandas as pd
import aind_disrnn_utils.data_loader as dl

mice = [77412, 779531, 781173, 781162, 778077]
dfs = []
for mouse in mice:
    dfs.append(pd.read_csv('/data/disrnn_dataset_{}/disrnn_dataset.csv'.format(mouse)))
df = pd.concat(dfs)
dataset = dl.create_disrrn_dataset(df)

Installation

To install the software from PyPi

pip install aind-disrnn-utils

To use the software, in the root directory, run

pip install -e .

To develop the code, run

pip install -e . --group dev

Note: --group flag is available only in pip versions >=25.1

Alternatively, if using uv, run

uv sync

Required dependency

[!IMPORTANT]
PyPi does not allow "direct dependencies" where you install straight from github, therefore you must manually install the disentangled_rnn package

pip install git+https://github.com/google-deepmind/disentangled_rnns.git

Level of Support

• Occasional updates: We are planning on occasional updating this tool with no fixed schedule. Community involvement is encouraged through both issues and pull requests.