semvec/pypi-packages huggingface dataset:
mplscience 0.0.7
Matplotlib style for scientific publications.
License: MIT
Requires Python: >=3.6.1,<4.0

# mplscience
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/11ZA7lq-nFpNpFFWqXnlCphRR6_N9qXP3?usp=sharing)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

Matplotlib style for scientific publications. This style keeps things pretty simple and aims to make moderate improvements to the base matplotlib style. It also sets things like the PDF font type to make it easier to interact with figures in Adobe Illustrator. Open the tutorial in Colab to see examples. `mplscience` is compatible with all Matplotlib-based packages, including Seaborn.

## Usage

To install:

```python
pip install mplscience
```

To use:

```python
import mplscience
import seaborn as sns
mplscience.available_styles()
mplscience.set_style()
df = sns.load_dataset("anscombe")
sns.scatterplot(x="x", y="y", hue="dataset", data=df)
```
<img src="https://github.com/adamgayoso/mplscience/blob/main/images/scatter.png?raw=true" width="300" alt="scatter">

If you're using Seaborn, you may want to run `sns.reset_orig()` first to clear Seaborn-specific styling. You can also use the `reset_current` parameter of `mplscience` functions to reset any custom styling like this:

```python
mplscience.set_style(reset_current=True)
```


The style can also be using in a context like this:

```python
import mplscience
with mplscience.style_context():
    plt.plot(x, y)
```