pystatplottools

A Python library that simplifies working with and plotting of statistical data and high-dimensional distributions. The library utilizes standard numpy operations in a smart way for an easy processing of more complicated data evaluation methods. Further, the pytorch_data_generation module simplifies the generation and the storage of custom datasets. The pystatsplottools library currently consists of the following main modules:

• distributions - Convenient computation of joint and marginal distributions. In addition, binned statistics can be evaluated.
• expectation_values - Computation of expectation values.
• plotting - Wrapper for plotting 2D contour plots with linear and logarithmic scales.
• pytorch_data_generation - Tools for an easy generation and storage of a custom PyTorch datasets. The data can be pregenerated and stored as a .pt file. Alternatively, data can be generated in real time.
• visualization - Contains a class for visualizing samples and batches from the dataset and a decorator for handling figures
• pdf_env - Adapted tool for an easy saving of plots as pdfs and pngs. The original code can be found on http://bkanuka.com/posts/native-latex-plots/.

Examples

Examples to the different Python modules can be found in the examples/ folder. A more detailed example which covers almost all functionalities of the library can be found here: https://github.com/statphysandml/pystatplottools/blob/master/examples/cheat_sheet.ipynb.

Integration

So far, the library needs to be build locally. This can be done by

cd path_to_pystatplottools/

python setup.py sdist
pip install -e .

For virtual environments, the library needs to be activate beforehand.

After this step, the different modules of the library can be used, for example, by

import pystatplottools

from pystatplottools.distributions.joint_distribution import JointDistribution

Dependencies

• matplotlib
• numpy
• pandas
• scipy
• (pytorch)
• (jupyter lab)

Projects using the pystatplottools library

• MCMCEvalutionLib (https://github.com/statphysandml/MCMCEvaluationLib)

Support and Development

For bug reports/suggestions/complaints please file an issue on GitHub.

Or start a discussion on our mailing list: statphysandml@thphys.uni-heidelberg.de