/mi-uniform

Primary LanguageC++

TDMI-Utilities

To calculate time-delayed mutual information (TDMI) for neurophysiology data.

Repository hierarchy

  • doc: Necessary documents and configuration files;
  • include: C/C++ header files;
  • src: C/C++ source files;
  • external: third-party modules;
  • test: test codes;
  • utils: utilities for data processing, developed in Python;

Get Started

Installation

  1. Build external cnpy library

    mkdir build
cd build
cmake ../external/cnpy -DCMAKE_INSTALL_PREFIX=../
make
make install

  2. Build main modules

    cd ..
make

How to use

Currnetly, there are five distinct modules here, including

# basic modules
bin/cal-mi			# calculate tdmi between types of data
# extended module
bin/cal-cc			# cross-correlation between continuous variables
bin/cal-sta			# spike triggered average

Sample usage:

# calculate tdmi between spike trains and LFP (-t BD),
# stored in [input_filename_1] and [input_filename2], 
# with range of time delay [-40,40] steps,
# 0.001 bin size for calculating PDF of LFP
bin/cal-mi --prefix [/path/of/data] -t BD --drange 40 40 --dx 0.001 [input_filename_1] [input_filename2] [output_filename]

Use -h for more detailed help

Python Utilities

  • Python TDMI calculator

For quick test and small-sized calculation.

10 times slower than c++ version

>>> python utils/info.py -h
usage: info.py [-h] [-v] prefix spike_fname lfp_fname tdmi_fname bins ndelays

Calculate TDMI script.

positional arguments:
  prefix         working directory of source data and output data
  spike_fname    filename of spike train data, *.npy
  lfp_fname      filename of LFP data, *.npy
  tdmi_fname     filename of output TDMI data, *.csv
  bins           number of bins in LFP histogram
  ndelays        number of delayed bins in TDMI calculation

optional arguments:
  -h, --help     show this help message and exit
  -v, --verbose  enable verbose of running time