This is an implementation of ideas from Clayton V. Deutsch and Michael Pyrcz's book Geostatistical Reservoir Modeling in order to better understand geostatistics. I am working through things first in Python in order to prototype things quickly and make sure I understand them.

There is also an R package for geostatistics named gstat, but I have not used it much.

This software is licensed under the MIT License.

This package depends on:

• numpy, the fundamental package for scientific computing with Python.
• matplotlib, a Python 2D plotting library which produces publication quality figures.
• scipy, a Python library which provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.
• pandas, a Python library which implements excellent tools for data analysis and modeling.

The best/easiest way to install these packages is to use one of the Python distributions described here. Anaconda has been successfully tested with geostatsmodels.

Most of those distributions should include pip or conda, which are command line tools for installing and managing Python packages. You can use pip to install geostatsmodels itself.

You may need to open a new terminal window to ensure that the newly installed versions of python and pip are in your path.

To install geostatsmodels:

pip install git+git://github.com/bhavesh-20/geostatsmodels.git

To uninstall:

pip uninstall geostatsmodels

To update:

pip install -U git+git://github.com/cjohnson318/geostatsmodels.git

Some notebooks exploring the functionality of geostatsmodels are included below.

Variogram Analysis

Kriging Example

More to come!