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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: about 2 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 (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-06-01T14:40:40.000Z (4 months ago)
- Last Synced: 2024-07-04T00:58:44.458Z (3 months ago)
- Topics: contour-plots, custom-datasets, distributions, expectation-values, high-dimensional-data
- Language: Python
- Homepage:
- Size: 497 KB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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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 environments, the library needs to be activate beforehand.
After this step, the different modules of the library can be used, for example, by
```python
import pystatplottoolsfrom 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]