https://github.com/matplotlib/mpl-probscale
Real probability scales for matplotlib
https://github.com/matplotlib/mpl-probscale
matplotlib probability-scales visualization
Last synced: 2 months ago
JSON representation
Real probability scales for matplotlib
- Host: GitHub
- URL: https://github.com/matplotlib/mpl-probscale
- Owner: matplotlib
- License: bsd-3-clause
- Created: 2015-11-20T02:10:03.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2023-11-03T18:04:12.000Z (about 2 years ago)
- Last Synced: 2025-09-25T16:53:24.873Z (3 months ago)
- Topics: matplotlib, probability-scales, visualization
- Language: Python
- Homepage: http://matplotlib.org/mpl-probscale
- Size: 2.17 MB
- Stars: 40
- Watchers: 3
- Forks: 11
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Authors: AUTHORS.rst
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README
# mpl-probscale
Real probability scales for matplotlib
[Sphinx Docs](http://matplotlib.org/mpl-probscale/)
## Installation
### Official releases
Official releases are available through the conda-forge channel or pip
`conda install mpl-probscale --channel=conda-forge`
`pip install probscale`
### Development builds
This is a pure-python package, so building from source is easy on all platforms:
```shell
git clone git@github.com:matplotlib/mpl-probscale.git
cd mpl-probscale
pip install -e .
```
## Quick start
Simply importing `probscale` lets you use probability scales in your matplotlib figures:
```python
from matplotlib import pyplot
from scipy import stats
import probscale # nothing else needed
beta = stats.beta(a=3, b=4)
weibull = stats.weibull_min(c=5)
scales = [
{"scale": {"value": "linear"}, "label": "Linear (built-in)"},
{"scale": {"value": "log", "base": 10}, "label": "Log. Base 10 (built-in)"},
{"scale": {"value": "log", "base": 2}, "label": "Log. Base 2 (built-in)"},
{"scale": {"value": "logit"}, "label": "Logit (built-in)"},
{"scale": {"value": "prob"}, "label": "Standard Normal Probability (this package)"},
{
"scale": {"value": "prob", "dist": weibull},
"label": "Weibull probability scale, c=5 (this package)",
},
{
"scale": {"value": "prob", "dist": beta},
"label": "Beta probability scale, α=3 & β=4 (this package)",
},
]
N = len(scales)
fig, axes = pyplot.subplots(nrows=N, figsize=(9, N - 1), constrained_layout=True)
for scale, ax in zip(scales, axes.flat):
ax.set_xscale(**scale["scale"])
ax.text(0.0, 0.1, scale["label"] + " →", transform=ax.transAxes)
ax.set_xlim(left=0.5, right=99.5)
ax.set_yticks([])
ax.spines.left.set_visible(False)
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
outpath = Path(__file__).parent.joinpath("../img/example.png").resolve()
fig.savefig(outpath, dpi=300)
```
## Testing
Testing is generally done via ``pytest``.
```shell
python -m pytest --mpl --doctest-glob="probscale/*.py"
```