https://github.com/statphysandml/pystatplottools

Easy evaluation and plotting of statistical data and high-dimensional distributions in python - Fast generation, loading and storing of custom datasets.
https://github.com/statphysandml/pystatplottools

contour-plots custom-datasets distributions expectation-values high-dimensional-data

Last synced: 4 months ago
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Easy evaluation and plotting of statistical data and high-dimensional distributions in python - Fast generation, loading and storing of custom datasets.

• Host: GitHub
• URL: https://github.com/statphysandml/pystatplottools
• Owner: statphysandml
• License: mit
• Created: 2020-12-10T10:16:41.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-04-07T19:16:11.000Z (about 2 years ago)
• Last Synced: 2024-01-16T10:45:59.433Z (5 months ago)
• Topics: contour-plots, custom-datasets, distributions, expectation-values, high-dimensional-data
• Language: Python
• Homepage:
• Size: 496 KB
• Stars: 7
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-stars - statphysandml/pystatplottools - Easy evaluation and plotting of statistical data and high-dimensional distributions in python - Fast generation, loading and storing of custom datasets. (Python)

README

        
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

```bash

cd path_to_pystatplottools/

python setup.py sdist

pip install -e .

```

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

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

```python

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: [email protected]