| Name | cmyt JSON |
| Version |
2.0.1
JSON |
| download |
| home_page | None |
| Summary | A collection of Matplotlib colormaps from the yt project |
| upload_time | 2024-11-26 14:05:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | BSD 3-Clause |
| keywords |
visualization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# cmyt
[](https://pypi.org/project/cmyt)
[](https://anaconda.org/conda-forge/cmyt)
[](https://github.com/yt-project/cmyt/actions/workflows/ci.yml)
[](https://github.com/yt-project/cmyt/actions/workflows/bleeding-edge.yaml)
[](https://results.pre-commit.ci/latest/github/yt-project/cmyt/main)
[![yt-project](https://img.shields.io/static/v1?label="works%20with"&message="yt"&color="blueviolet")](https://yt-project.org)
[](https://github.com/charliermarsh/ruff)
<a href="http://yt-project.org"><img src="https://raw.githubusercontent.com/yt-project/yt/main/doc/source/_static/yt_logo.png" width="150"></a>
Matplotlib colormaps from the yt project !
## Colormaps overview
The following colormaps, as well as their respective reversed (`*_r`) versions are available
### Perceptually uniform sequential colormaps
<p align="center">
<img src="https://raw.githubusercontent.com/yt-project/cmyt/main/doc/overview_perceptually_uniform.png" width="800"></a>
</p>
### Monochromatic sequential colormaps
<p align="center">
<img src="https://raw.githubusercontent.com/yt-project/cmyt/main/doc/overview_pixel.png" width="800"></a>
</p>
### Miscellaneous
<p align="center">
<img src="https://raw.githubusercontent.com/yt-project/cmyt/main/doc/overview_misc.png" width="800"></a>
</p>
## Installation
with `pip`
```shell
python -m pip install cmyt
```
or with `conda`
```shell
conda install -c conda-forge cmyt
```
## Usage
cmyt integrates with matplotlib in a similar fashion to
[cmocean](https://matplotlib.org/cmocean/) or
[cmasher](https://cmasher.readthedocs.io)
```python
import numpy as np
import matplotlib.pyplot as plt
import cmyt # that's it !
# generate example data
prng = np.random.RandomState(0x4D3D3D3)
noise = prng.random_sample((100, 100))
x, y = np.mgrid[-50:50, -50:50]
z = 5 * np.exp(-(x**2 + y**2) / 1000)
# setup the figure
fig, ax = plt.subplots()
ax.set(aspect="equal")
# now we can refer to cmyt colormaps as strings
im = ax.pcolormesh(x, y, z + noise, cmap="cmyt.arbre", shading="flat")
fig.colorbar(im, ax=ax)
```
<p align="center">
<img src="https://raw.githubusercontent.com/yt-project/cmyt/main/doc/demo.png" width="400"></a>
</p>
```python
# alternatively, cmyt maps can also be imported as objects
from cmyt import pastel
fig, ax = plt.subplots()
ax.set(aspect="equal")
im = ax.contourf(x, y, z + noise, cmap=pastel)
fig.colorbar(im, ax=ax)
```
<p align="center">
<img src="https://raw.githubusercontent.com/yt-project/cmyt/main/doc/demo_alt.png" width="400"></a>
</p>
A gallery of comparable examples using all colormaps from cmyt is available [in the test directory](https://github.com/yt-project/cmyt/tree/main/tests/baseline).
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