hdestimator --- history dependence estimator

The history dependence estimator tool provides a set of routines that facilitate the estimation of history dependence in neural spiking data, using estimators based on information-theoretical measures, as presented in (Rudelt et al, in prep.).

A guide for how to use the tool can be found under docs/howto.pdf.

Dependencies

• Python (>=3.2)
• h5py
• pyyaml
• numpy
• scipy
• mpmath
• matplotlib

Optional Dependencies

• cython, for significantly faster running times

Installation

Python packages can be installed either via your operating system's package manager or using eg pip or conda.

• ubuntu: sudo apt install python3-h5py python3-yaml python3-numpy python3-scipy python3-mpmath python3-matplotlib cython3

• fedora: sudo dnf install python3-h5py python3-pyyaml python3-numpy python3-scipy python3-mpmath python3-matplotlib python3-Cython

• using pip: pip install h5py pyyaml numpy scipy mpmath matplotlib cython3

• using conda: conda install h5py pyyaml numpy scipy mpmath matplotlib cython

The repository can be cloned via ssh

git clone git@github.com:Priesemann-Group/hdestimator.git

or https

git clone https://github.com/Priesemann-Group/hdestimator.git

Once downloaded, you can change directory into the the repository's folder:

cd hdestimator

and you're ready to go!

Recommended: Compile the Cython modules

From within the repository's base folder, change into the src directory:

cd src

There, compile the Cython modules:

python3 setup.py build_ext --inplace

If no errors occured (warnings are OK), you are all set.

Windows users

Under Windows, you can use the tool eg through miniconda.

Install miniconda for python3, 32bit.

To meet the dependencies to compile the Cython modules, download and install Visual Studio. There, select Desktop development with C++ and install

• MSVC v140 - VS 2015 C++ build tools (v14.00) (more recent versions probably work, too)
• Windows 10 SDK (10.0.18362.0)

Then compile the modules by running the commands as above.