pycaan 0.1.2

Python calcium imaging analysis (PyCaAn)

PyCaAn stands for Python Calcium imaging Analysis This repository contains tools to analyze large datasets of calcium imaging data, plot, and extract statistics.

Features:

• Tuning curves
• Unbiased information theoretic metrics
• Low-dimensional embeddings (manifolds)
• Data simulator
• Dataframe support for quick plotting/stats
• and more!

Installation

To use internal functions in other projects, you need to install PyCaAn in your preferred environment. Currently, this is done with a developer install: pip install -e .

Run analyses

PyCaAn provides both high- and low-level access to analytical functions.

Low level

Any function can be called using: import pycaan

Example: to binarize calcium transients, use: from pycaan import binarize_ca_traces binarized_traces, neural_data = binarize_ca_traces(ca_traces, z_threshold, sampling_frequency)

High level

First define your paths (input dataset, output result folders) and other parameters in params.yaml, you can analyze a single session from the terminal using: % python3 pycaan//analysis/extract_tuning_data.py --session_path ../../example_dataset/example_region/example_subject/example_mouse

you can also perform a specific type of analysis on selected sessions (batch process) after running run_dataset_curation.py: % python3 run_analysis.py --extract_tuning

Finally, for a fully automatized dataset analysis, you can run: sh runAll.sh This function will curate your dataset with desired threshold (e.g. minimum number of neurons per recording) and run all analyses.

Dataset naming convention

Dataset path has to be specified in params.yaml The naming convention should follow these principles: region/subject/subject_task_condition1_condition2_..._date For example: amygdala/F173/F173_OF_darkness_20230804