This is a module I wrote that enables the analysis of behavioral data alongside neural electrophysiology data.
Some key features include:
spikes.reader.py
T files (e.g. TT8_08.T) are binary encoded timestamp files that hold information about the times when a neuron spiked. This module reads them out as a list of timestamps in seconds.
To convert the timestamp data to timeseries data with firing rates per unit time, use:
analyzer.decompress_timestamp_data(timestamp_data, significant_digits_to_include)
read_t_files(t_files): reads a list of T files
positions.positions.py
Position matrices (e.g. HSpos_080602_ps17_160704.mat) are matrix files that contain at least 3 columns: time, x, and y. The module reads these columns into a PositionMatrix object that contains the data and various analysis functions.
These functions include:
- Smoothing
smooth_values() - Finding velocities
find_velocities() - Finding angles
find_angles() - Finding angular velocity
find_angular_velocity() - Finding rotational velocity
find_rotational_velocity()
analysis.analyzer.py
The analysis module provides many functions for analyzing the data in various ways.
These functions include:
- Applying a convolution on the timeseries data
- Converting two parallel timeseries into a mutual information matrix
- For example:
