fear-data 0.1.1

Python package for data analysis collected from Med-Associates VideoFreeze software.

fear_data

Python package used to analyze files generated from Med-Associates VideoFreeze software. An example notebook is provided in docs/.

Installation

The easiest way to install fear_data is with pip. First, clone the repository.

git clone https://github.com/kpuhger/fear_data.git

Next, navigate to the cloned repo and type the following into your terminal:

pip install .

Note: The installation method currently is not likely to work. For the time being it is recommended to add a .pth file to your site-packages folder to add the repo to your system's path.

1. Use the terminal to navigate to your site-packages folder (e.g., cd opt/miniconda3/lib/python3.10/site-packages)

2. Add .pth file pointing to your repo path

   > touch `fear_data.pth` # create pth file
   > open `fear_data.pth` # add path to repo in this file
   

Features

Experiment configuration files

The recomended way to set up an experiment is to use a expt_config.yaml file (see here for an overview of YAML). This allows you to use a template notebook to analyze data from different experiments by simply providing the path to the expt_config.yaml file. An example configuration file can be found in docs/expt_config.yaml.

The function fd.create_expt_config(...) can be used to automatically generate an expt_config.yaml file from template.

The function fd.update_expt_config(update_dict, ...) allows you to update an expt_config with information provided in update_dict.

NOTE: The keys in update_dict should be identical to expt_config.

Loading data

To load Video Freeze data:

1. Define config_path variable.
2. Load data using fd.load_tfc_data(...)
3. Group labels can be added via fd.add_group_labels(...)

Visualizing data

• Plot aesthetics are applied via @style_plot decorator.

  • Can pass arguments to modify axes info (e.g., labels, labelsize, title, fig_size, ranges (xlim/ylim) -- check docs for more info.
  • Set save_fig=True to apply @savefig decorator and save figure, can set fig_path if desired (default set to Desktop).

• plot_fc_bins : pointplot across time for every 'Component'

  • session sets plot aes (label tone bins for train/tone, label shock for train)

• plot_fc_phase : use kind for two ways to plot data by phase (baseline, tone, trace, iti)

  1. kind='point' : pointplot by phase.
  2. kind='bar' : barplot by phase.
     • adds swarmplot of subject data by default set pts=False to remove.

Analyzing data

Use the pingouin python package for statistcal analysis. An example analysis can be found in docs/stats-eample_analysis.ipynb.