aind-dynamic-foraging-data-utils 0.1.42

Functions to help with postprocessing dynamic foraging data.

aind-dynamic-foraging-data-utils

Scope

Purpose: Ingests NWB and spits out dataframes with the relevant information. Focused on dynamic foraging. Other tasks can branch and build task-specific utils. Inputs are nwbs, outputs are dataframes (tidy and not) Dependencies: xarray (includes numpy and pandas), scikit-learn (includes scipy), matplotlib

Installation

To use the software, in the root directory, run

pip install -e .

To develop the code, run

pip install -e .[dev]

Usage

Accessing data from an NWB file

To load an NWB file

import aind_dynamic_foraging_data_utils.nwb_utils as nwb_utils
nwb = nwb_utils.load_nwb_from_filename(<filepath>)

To extract a pandas dataframe of trials

df_trials = nwb_utils.create_df_trials(nwb)

To extract a pandas dataframe of events

df_events = nwb_utils.create_df_events(nwb)

To extract a pandas dataframe of photometry data

df_fip = nwb_utils.create_df_fip(nwb)

By default, all of these functions adjust timestamps such that t(0) is the time of the first go cue. If you wish to disable this feature, use adjust_time=False

Time alignment tools

To align a data variable to a set of timepoints and create an event triggered response use the alignment module. For example to align FIP data to each go cue:

import aind_dynamic_foraging_data_utils.alignment as alignment

etr = alignment.event_triggered_response(
    df_fip.query('event == "<FIP channel>"'),
    "timestamps",
    "data",
    df_trials['goCue_start_time_in_session'].values,
    t_start = 0,
    t_end = 1,
    output_sampling_rate=40
    )

Code ocean utility code

To attach data, you'll want to create a token on code ocean with all read/write permissions. Make sure to attach your token on your capsule.

Then, you should be able to access the token via os.getenv(token_name).

Get list of assets

To get a list of code ocean assets for a subject

import aind_dynamic_foraging_data_utils.code_ocean_utils as cou
results = cou.get_subject_assets(my_id)

Users can give a list of required data modalities

import aind_dynamic_foraging_data_utils.code_ocean_utils as co
# FIP data
results = co.get_subject_assets(<subject_id>, modality=['fib'])

# FIP and behavior-videos
results = co.get_subject_assets(<subject_id>, modality=['fib','behavior-videos'])

# any modalities (default)
results = co.get_subject_assets(<subject_id>, modality=[])

Or supply an additional filter string

results = co.get_subject_assets(<subject_id>, extra_filter = <my docdb query string>)

Or filter by a task type:

results = co.get_subject_assets(<subject_id>, task=['Uncoupled Baiting', 'Coupled Baiting'])

Attach data

The 'code_ocean_asset_id' column gives you the data asset ID's on Code Ocean. the 'id' column is the docDB id.

To attach a long list of data, simply call

cou.attach_data(results['code_ocean_asset_id'].values)
results = co.add_data_asset_path(results)

with results as the dataframe from 'get_subject_assets', and 'code_ocean_asset_id' the 16 digit data asset ID from code ocean.

Load data

To get the dataframes from the NWBs, you can call function

filename_sessions = glob.glob(f"../data/**/nwb/behavior**")
SAVED_LOC = '../scratch/dfs'
interested_channels = ['G_1_dff-poly', 'R_1_dff-poly', 'R_2_dff-poly']

get_all_df_for_nwb(filename_sessions, loc = SAVED_LOC, interested_channels = interested_channels)

where filename_sessions are the folder locations for the nwbs, loc is a folder location where the dataframes will be saved, interested channels are the channels you want to save for df_fip.

All dataframes are saved per session, other than df_trials (this is because some df_trials have 2 y coordinates for the lick tube, some have 1).

To load the dataframes, use:

df_sess = pd.read_csv(SAVED_LOC + 'df_sess.csv', index_col = False)
df_events = pd.read_csv(SAVED_LOC + 'df_events.csv', index_col = False)
df_trials = pd.read_csv(SAVED_LOC + 'df_trials.csv', index_col = 0)
df_fip = pd.read_csv(SAVED_LOC + 'df_fip.csv', index_col = False)

To check what available fitted models we already have for each session, you can check with:

check_avail_model_by_nwb_name('746345_2024-11-22_09-55-54.nwb')

where you input the name of the session (formatted as <subject_ID>_<collection_date>_<collection_time>.nwb; sometimes a prefix of behavior_ is needed). Currently the models that are fitted on all sessions should include:

['QLearning_L2F1_softmax', 'QLearning_L1F1_CK1_softmax', 'WSLS', 'QLearning_L1F0_epsi', 'QLearning_L2F1_CK1_softmax']

You can find out more about these models by going here.

To enrich df_sessions and df_trials with the model information, you can use

nwb_name_for_models = [filename.split('/')[-1].replace('behavior_', '') for filename in filename_sessions]
SAVED_LOC = '../scratch/dfs'
get_foraging_model_info(df_trials, df_sess, nwb_name_for_models, loc = SAVED_LOC)

df_trials and df_sess are dataframes created from get_all_df_for_nwb and nwb_name_for_models formatted the same way for check_avail_model_by_nwb_name.

Contributing

Linters and testing

There are several libraries used to run linters, check documentation, and run tests.

• Please test your changes using the coverage library, which will run the tests and log a coverage report:

coverage run -m unittest discover && coverage report

• Use interrogate to check that modules, methods, etc. have been documented thoroughly:

interrogate .

• Use flake8 to check that code is up to standards (no unused imports, etc.):

flake8 .

• Use black to automatically format the code into PEP standards:

black .

• Use isort to automatically sort import statements:

isort .

Pull requests

For internal members, please create a branch. For external members, please fork the repository and open a pull request from the fork. We'll primarily use Angular style for commit messages. Roughly, they should follow the pattern:

<type>(<scope>): <short summary>

where scope (optional) describes the packages affected by the code changes and type (mandatory) is one of:

• build: Changes that affect build tools or external dependencies (example scopes: pyproject.toml, setup.py)
• ci: Changes to our CI configuration files and scripts (examples: .github/workflows/ci.yml)
• docs: Documentation only changes
• feat: A new feature
• fix: A bugfix
• perf: A code change that improves performance
• refactor: A code change that neither fixes a bug nor adds a feature
• test: Adding missing tests or correcting existing tests

Semantic Release

The table below, from semantic release, shows which commit message gets you which release type when semantic-release runs (using the default configuration):

Commit message	Release type
fix(pencil): stop graphite breaking when too much pressure applied	Patch Fix Release, Default release
feat(pencil): add 'graphiteWidth' option	Minor Feature Release
perf(pencil): remove graphiteWidth option BREAKING CHANGE: The graphiteWidth option has been removed. The default graphite width of 10mm is always used for performance reasons.	Major Breaking Release (Note that the BREAKING CHANGE: token must be in the footer of the commit)

Documentation

To generate the rst files source files for documentation, run

sphinx-apidoc -o doc_template/source/ src 

Then to create the documentation HTML files, run

sphinx-build -b html doc_template/source/ doc_template/build/html

More info on sphinx installation can be found here.