scikit-chem
is easy to install and configure. Detailed instructions are
listed below. The quickest way to get everything installed is by
using conda.
scikit-chem
is tested on Python 2.7 and 3.5. It depends on rdkit, most of
the core Scientific Python Stack, as well as several smaller pure Python
libraries.
The full list of dependencies is:
The package and these dependencies are available through two different Python package managers, conda and pip. It is recommended to use conda.
conda is a cross-platform, Python-agnostic package and environment manager developed by Continuum Analytics. It provides packages as prebuilt binary files, allowing for straightforward installation of Python packages, even those with complex C/C++ extensions. It is installed as part of the Anaconda Scientific Python distribution, or as the lightweight miniconda.
The package and all dependencies for scikit-chem
are available through the
defaults or richlewis conda channel. To install:
conda install -c richlewis scikit-chem
This will install scikit-chem
with all its dependencies from the
author’s anaconda repository as conda
packages.
Currently, scikit-chem cannot be configured in a config file. This feature is planned to be added in future releases. To request this feature as a priority, please mention it in the appropriate github issue
To use the data functionality, you will need to set up fuel. This involves configuring the .fuelrc. An example .fuelrc might be as follows:
data_path: ~/datasets
extra_downloaders:
- skchem.data.downloaders
extra_converters:
- skchem.data.converters
This adds the location for fuel datasets, and adds the scikit-chem
data
downloaders and converters to the fuel command line tools.