pyIncore-incubator is a component of IN-CORE that allows users to extend the core set of analyses provided by pyincore. New analyses in pyincore-incubator extend the pyincore BaseAnalysis class and can be chained with existing pyincore analyses to add new functionality. The incubator gives the community around the IN-CORE project a forum and set of resources for innovation and investigation of new ideas and alternative ideas.
Installing pyincore-incubator with Conda is officially supported by IN-CORE development team.
To add conda-forge channel to your environment, run
conda config –-add channels conda-forgeTo install pyincore-incubator package, run
conda install -c in-core pyincore-incubatorTo update pyIncore-incubator, run
conda update -c in-core pyincore-incubatorYou can find detail information at the Installation section at IN-CORE manual.
Installing pyincore-incubator with pip is NOT supported by IN-CORE development team. Please use pip for installing pyincore-incubator at your discretion.
Installing pyincore-incubator with pip is only tested on the linux environment.
Prerequisite
- GDAL C library must be installed to install pyincore-incubator. (for Ubuntu, gdal-bin and libgdal-dev)
To install pyincore-incubator package, run
pip install pyincore-incubatorPlease read the Testing and Running section at IN-CORE manual.
For reference, the documentation of pyincore can be found below since pyincore-incubator adds new analyses to the core set provided by pyincore.
pyIncore documentation can be found at https://incore.ncsa.illinois.edu/doc/incore/pyincore.html
pyIncore technical reference (API) can be found at https://incore.ncsa.illinois.edu/doc/pyincore/.
This work herein was supported by the National Institute of Standards and Technology (NIST) (Award No. 70NANB15H044). This support is gratefully acknowledged. The views expressed in this work are those of the authors and do not necessarily reflect the views of NIST.