Metadata-Version: 2.1
Name: startorch
Version: 0.0.4
Summary: synthetic time-series generator in PyTorch
Home-page: https://github.com/durandtibo/startorch
License: BSD-3-Clause
Keywords: synthetic,time-series,pytorch
Author: Thibaut Durand
Author-email: durand.tibo+gh@gmail.com
Requires-Python: >=3.9,<3.12
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Provides-Extra: all
Requires-Dist: coola (>=0.0.20,<0.0.24)
Requires-Dist: matplotlib (>=3.6,<3.9) ; extra == "all"
Requires-Dist: objectory (>=0.0.7,<0.0.8)
Requires-Dist: redcat (>=0.0.13,<0.0.14)
Requires-Dist: torch (>=2.0,<2.1)
Project-URL: Repository, https://github.com/durandtibo/startorch
Description-Content-Type: text/markdown

# startorch

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---

## Overview

Collecting datasets to train Machine Learning models can be time consuming.
Another alternative is to use synthetic datasets.
`startorch` is a Python library to generate synthetic time-series.
As the name suggest, `startorch` relies mostly on PyTorch to generate the time series and to control
the randomness.
`startorch` is built to be modular, flexible and extensible.
Below show some generated sequences by `startorch` where the values are sampled from different
distribution.

<table align="center">
  <tr>
    <td><img src="https://durandtibo.github.io/startorch/assets/figures/uniform.png" width="400" align="center"></td>
    <td><img src="https://durandtibo.github.io/startorch/assets/figures/log-uniform.png" width="400" align="center"></td>
  </tr>
  <tr>
    <td align="center">uniform</td>
    <td align="center">log-uniform</td>
  </tr>
  <tr>
    <td><img src="https://durandtibo.github.io/startorch/assets/figures/sinewave.png" width="400" align="center"></td>
    <td><img src="https://durandtibo.github.io/startorch/assets/figures/wiener.png" width="400" align="center"></td>
  </tr>
  <tr>
    <td align="center">sine wave</td>
    <td align="center">Wiener process</td>
  </tr>
</table>

- [Documentation](https://durandtibo.github.io/startorch/)
- [Installation](https://durandtibo.github.io/startorch/get_started/)
- [Contributing](#contributing)
- [API stability](#api-stability)
- [License](#license)

## Dependencies

| `startorch` | `coola`            | `objectory`      | `redcat`           | `torch`      | `matplotlib`<sup>*</sup> | `python`      |
|-------------|--------------------|------------------|--------------------|--------------|--------------------------|---------------|
| `0.0.4`     | `>=0.0.20,<0.0.24` | `>=0.0.7,<0.0.8` | `>=0.0.13,<0.0.14` | `>=2.0,<2.1` | `>=3.6,<3.9`             | `>=3.9,<3.12` |
| `0.0.3`     | `>=0.0.20,<0.0.24` | `>=0.0.7,<0.0.8` | `>=0.0.9,<0.0.10`  | `>=2.0,<2.1` | `>=3.6,<3.9`             | `>=3.9,<3.12` |

<sup>*</sup> indicates an optional dependency

## Contributing

Please check the instructions in [CONTRIBUTING.md](.github/CONTRIBUTING.md).

## API stability

:warning: While `startorch` is in development stage, no API is guaranteed to be stable from one
release to the next.
In fact, it is very likely that the API will change multiple times before a stable 1.0.0 release.
In practice, this means that upgrading `startorch` to a new version will possibly break any code
that was using the old version of `startorch`.

## License

`startorch` is licensed under BSD 3-Clause "New" or "Revised" license available
in [LICENSE](LICENSE) file.

