# 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` | `batchtensor` | `coola` | `objectory` | `numpy` | `torch` | `iden`<sup>*</sup> | `matplotlib`<sup>*</sup> | `plotly`<sup>*</sup> | `python` |
|-------------|----------------|--------------|--------------|---------------|--------------|--------------------|--------------------------|----------------------|---------------|
| `main` | `>=0.0.1,<0.1` | `>=0.2,<1.0` | `>=0.1,<1.0` | `>=1.22,<2.0` | `>=2.0,<3.0` | `>=0.0.2,<0.1` | `>=3.6,<4.0` | `>=5.0,<6.0` | `>=3.9,<3.12` |
| `0.1.0` | `>=0.0.1,<0.1` | `>=0.2,<1.0` | `>=0.1,<1.0` | `>=1.22,<2.0` | `>=2.0,<3.0` | `>=0.0.2,<0.1` | `>=3.6,<4.0` | `>=5.0,<6.0` | `>=3.9,<3.12` |
<sup>*</sup> indicates an optional dependency
<details>
<summary>older versions</summary>
| `startorch` | `coola` | `objectory` | `redcat` | `torch` | `matplotlib`<sup>*</sup> | `plotly`<sup>*</sup> | `python` |
|-------------|--------------------|------------------|--------------------|--------------|--------------------------|----------------------|---------------|
| `0.0.8` | `>=0.0.20,<0.2` | `>=0.0.7,<0.2` | `>=0.0.16,<0.1` | `>=2.0,<3.0` | `>=3.6,<4.0` | `>=5.12,<6.0` | `>=3.9,<3.12` |
| `0.0.7` | `>=0.0.20,<0.0.25` | `>=0.0.7,<0.0.9` | `>=0.0.16,<0.0.18` | `>=2.0,<2.2` | `>=3.6,<3.9` | `>=5.12,<5.18` | `>=3.9,<3.12` |
| `0.0.6` | `>=0.0.20,<0.0.25` | `>=0.0.7,<0.0.9` | `>=0.0.16,<0.0.18` | `>=2.0,<2.2` | `>=3.6,<3.9` | | `>=3.9,<3.12` |
| `0.0.5` | `>=0.0.20,<0.0.24` | `>=0.0.7,<0.0.8` | `>=0.0.16,<0.0.17` | `>=2.0,<2.1` | `>=3.6,<3.9` | | `>=3.9,<3.12` |
| `0.0.4` | `>=0.0.20,<0.0.24` | `>=0.0.7,<0.0.8` | `>=0.0.16,<0.0.17` | `>=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` |
</details>
## 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.
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synthetic datasets.\n`startorch` is a Python library to generate synthetic time-series.\nAs the name suggest, `startorch` relies mostly on PyTorch to generate the time series and to control\nthe randomness.\n`startorch` is built to be modular, flexible and extensible.\nBelow show some generated sequences by `startorch` where the values are sampled from different\ndistribution.\n\n<table align=\"center\">\n <tr>\n <td><img src=\"https://durandtibo.github.io/startorch/assets/figures/uniform.png\" width=\"400\" align=\"center\"></td>\n <td><img src=\"https://durandtibo.github.io/startorch/assets/figures/log-uniform.png\" width=\"400\" align=\"center\"></td>\n </tr>\n <tr>\n <td align=\"center\">uniform</td>\n <td align=\"center\">log-uniform</td>\n </tr>\n <tr>\n <td><img src=\"https://durandtibo.github.io/startorch/assets/figures/sinewave.png\" width=\"400\" align=\"center\"></td>\n <td><img src=\"https://durandtibo.github.io/startorch/assets/figures/wiener.png\" width=\"400\" align=\"center\"></td>\n </tr>\n <tr>\n <td align=\"center\">sine wave</td>\n <td align=\"center\">Wiener process</td>\n </tr>\n</table>\n\n- [Documentation](https://durandtibo.github.io/startorch/)\n- [Installation](https://durandtibo.github.io/startorch/get_started/)\n- [Contributing](#contributing)\n- [API stability](#api-stability)\n- [License](#license)\n\n## Dependencies\n\n| `startorch` | `batchtensor` | `coola` | `objectory` | `numpy` | `torch` | `iden`<sup>*</sup> | `matplotlib`<sup>*</sup> | `plotly`<sup>*</sup> | `python` |\n|-------------|----------------|--------------|--------------|---------------|--------------|--------------------|--------------------------|----------------------|---------------|\n| `main` | `>=0.0.1,<0.1` | `>=0.2,<1.0` | `>=0.1,<1.0` | `>=1.22,<2.0` | `>=2.0,<3.0` | `>=0.0.2,<0.1` | `>=3.6,<4.0` | `>=5.0,<6.0` | `>=3.9,<3.12` |\n| `0.1.0` | `>=0.0.1,<0.1` | `>=0.2,<1.0` | `>=0.1,<1.0` | `>=1.22,<2.0` | `>=2.0,<3.0` | `>=0.0.2,<0.1` | `>=3.6,<4.0` | 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