aind-dynamic-foraging-basic-analysis 0.3.7

Generated from aind-library-template

aind-dynamic-foraging-basic-analysis

Usage

• To use this template, click the green Use this template button and Create new repository.
• After github initially creates the new repository, please wait an extra minute for the initialization scripts to finish organizing the repo.
• To enable the automatic semantic version increments: in the repository go to Settings and Collaborators and teams. Click the green Add people button. Add svc-aindscicomp as an admin. Modify the file in .github/workflows/tag_and_publish.yml and remove the if statement in line 10. The semantic version will now be incremented every time a code is committed into the main branch.
• To publish to PyPI, enable semantic versioning and uncomment the publish block in .github/workflows/tag_and_publish.yml. The code will now be published to PyPI every time the code is committed into the main branch.
• The .github/workflows/test_and_lint.yml file will run automated tests and style checks every time a Pull Request is opened. If the checks are undesired, the test_and_lint.yml can be deleted. The strictness of the code coverage level, etc., can be modified by altering the configurations in the pyproject.toml file and the .flake8 file.

Installation

To use the software, in the root directory, run

pip install -e .

To develop the code, run

pip install -e .[dev]

Usage

Annotate licks

To create a dataframe of licks that has been annotated with licking bout starts/stops, cue responsive licks, reward triggered licks, and intertrial choices.

import aind_dynamic_foraging_basic_analysis.licks.annotation as annotation
df_licks = annotation.annotate_licks(nwb)

You can then plot interlick interval analyses with:

import aind_dynamic_foraging_basic_analysis.licks.plot_interlick_interval as pii

#Plot interlick interval of all licks
pii.plot_interlick_interval(df_licks)

#plot interlick interval for left and right licks separately
pii.plot_interlick_interval(df_licks, categories='event')

Create lick analysis report

To create a figure with several licking pattern analyses:

import aind_dynamic_foraging_basic_analysis.licks.lick_analysis as lick_analysis
lick_analysis.plot_lick_analysis(nwb)

Compute trial by trial metrics

To annotate the trials dataframe with trial by trial metrics:

import aind_dynamic_foraging_basic_analysis.metrics.trial_metrics as tm
df_trials = tm.compute_all_trial_metrics(nwb)

Plot interactive session scroller

import aind_dynamic_foraging_basic_analysis.plot.plot_session_scroller as pss
pss.plot_session_scroller(nwb)

To disable lick bout and other annotations:

pss.plot_session_scroller(nwb,plot_bouts=False)

This function will automatically plot FIP data if available. To change the processing method plotted use:

pss.plot_session_scroller(nwb, processing="bright")

To change which trial by trial metrics plotted:

pss.plot_session_scroller(nwb, metrics=['response_rate'])

Plot FIP PSTH

You can use the plot_fip module to compute and plot PSTHs for the FIP data.

To compare one channel to multiple event types

from aind_dynamic_foraging_basic_analysis.plot import plot_fip as pf
channel = 'G_1_dff-poly'
rewarded_go_cues = nwb.df_trials.query('earned_reward == 1')['goCue_start_time_in_session'].values
unrewarded_go_cues = nwb.df_trials.query('earned_reward == 0')['goCue_start_time_in_session'].values
pf.plot_fip_psth_compare_alignments(
    nwb, 
    {'rewarded goCue':rewarded_go_cues,'unrewarded goCue':unrewarded_go_cues}, 
    channel, 
    censor=True
    )

To compare multiple channels to the same event type:

pf.plot_fip_psth(nwb, 'goCue_start_time')

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.