xbpch is written in pure Python (version >= 3.5), and leans on two important libraries:

1. xarray (version >= 0.9): a pandas-like toolkit for working with labeled, n-dimensional data

2. dask (version >= 0.14): a library for performing out-of-core, parallel computations on both tabular and array-like datasets

The easiest way to install these libraries is to use the conda package manager:

$ conda install -c conda-forge xarray dask

conda can be obtained as part of the Anaconda Python distribution from Continuum IO, although you do not need all of the packages it provides in order to use xbpch. Note that we recommend installing the latest versions from community-maintained conda-forge collection, since these usually contain bug-fixes and additional features.


Basic support for Python 2.7 is available in xbpch but it has not been tested, since the evolutionary GCPy package will only support Python 3. If, for some reason, you must use Python 2.7 and encounter problems, please reach out to us and we may be able to fix them.

Installation via conda

The preferred way to install xbpch is also via conda:

$ conda install -c conda-forge xbpch

Installation via pip

xbpch is available on PyPI, and can be installed using setuptools:

$ pip install xbpch

Installation from source

If you’re developing or contributing to xbpch, you may wish instead to install directly from a local copy of the source code. To do so, you must first clone the the master repository (or a fork) and install locally via pip:

$ git clone
$ cd xbpch
$ python install

You will need to substitute in the path to your preferred repository/mirror of the source code.

Note that you can also install directly from the source using setuptools:

$ pip install git+