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mpl-probscale\n\nReal probability scales for matplotlib\n\n![Coverage](https://github.com/matplotlib/mpl-probscale/workflows/Coverage%20via%20codecov/badge.svg)\n![Linter](https://github.com/matplotlib/mpl-probscale/workflows/Lint%20with%20flake8/badge.svg)\n![Tests](https://github.com/matplotlib/mpl-probscale/workflows/Image%20comparison%20tests/badge.svg)\n\n[Sphinx Docs](http://matplotlib.org/mpl-probscale/)\n\n## Installation\n\n### Official releases\n\nOfficial releases are available through the conda-forge channel or pip\n\n`conda install mpl-probscale --channel=conda-forge`\n\n`pip install probscale`\n\n### Development builds\n\nThis is a pure-python package, so building from source is easy on all platforms:\n\n```shell\ngit clone git@github.com:matplotlib/mpl-probscale.git\ncd mpl-probscale\npip install -e .\n```\n\n## Quick start\n\nSimply importing `probscale` lets you use probability scales in your matplotlib figures:\n\n```python\nfrom matplotlib import pyplot\nfrom scipy import stats\nimport probscale  # nothing else needed\n\nbeta = stats.beta(a=3, b=4)\nweibull = stats.weibull_min(c=5)\nscales = [\n    {\"scale\": {\"value\": \"linear\"}, \"label\": \"Linear (built-in)\"},\n    {\"scale\": {\"value\": \"log\", \"base\": 10}, \"label\": \"Log. Base 10 (built-in)\"},\n    {\"scale\": {\"value\": \"log\", \"base\": 2}, \"label\": \"Log. Base 2 (built-in)\"},\n    {\"scale\": {\"value\": \"logit\"}, \"label\": \"Logit (built-in)\"},\n    {\"scale\": {\"value\": \"prob\"}, \"label\": \"Standard Normal Probability (this package)\"},\n    {\n        \"scale\": {\"value\": \"prob\", \"dist\": weibull},\n        \"label\": \"Weibull probability scale, c=5 (this package)\",\n    },\n    {\n        \"scale\": {\"value\": \"prob\", \"dist\": beta},\n        \"label\": \"Beta probability scale, α=3 \u0026 β=4 (this package)\",\n    },\n]\n\nN = len(scales)\nfig, axes = pyplot.subplots(nrows=N, figsize=(9, N - 1), constrained_layout=True)\nfor scale, ax in zip(scales, axes.flat):\n    ax.set_xscale(**scale[\"scale\"])\n    ax.text(0.0, 0.1, scale[\"label\"] + \" →\", transform=ax.transAxes)\n    ax.set_xlim(left=0.5, right=99.5)\n    ax.set_yticks([])\n    ax.spines.left.set_visible(False)\n    ax.spines.right.set_visible(False)\n    ax.spines.top.set_visible(False)\n\noutpath = Path(__file__).parent.joinpath(\"../img/example.png\").resolve()\nfig.savefig(outpath, dpi=300)\n```\n\n![Alt text](docs/img/example.png \"Example axes\")\n\n## Testing\n\nTesting is generally done via ``pytest``.\n\n```shell\npython -m pytest --mpl --doctest-glob=\"probscale/*.py\"\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatplotlib%2Fmpl-probscale","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatplotlib%2Fmpl-probscale","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatplotlib%2Fmpl-probscale/lists"}