# cmplstyle
A Python package providing matplotlib style for scientific plotting with traditional Chinese color palette.
## Installation
You can install the package using pip:
```bash
pip install cmplstyle
```
## Features
- A collection of 365 traditional Chinese colors.
- Optmised matplotlib style for scientific plotting.
- Small handy functions for helping scientific plotting.
## Traditional Chinese Colors (TCC)
Our color collection is sourced from the reference [《中国传统色：国民版色卡》](https://www.douban.com/doubanapp/dispatch/book/35951952?dt_dapp=1) by 郭浩, featuring hues widely used in traditional Chinese art and design. See [here](https://jinyiliu.github.io/2025/08/13/cmplstyle/TCC_ncols_5.pdf) for a complete color reference.
The color names are in Chinese and their HEX color values are stored in the `cmplstyle.TCC` dictionary. Once the package is imported, the colors can be accessed easily by their names. For example,
```python
import cmplstyle
import seaborn
seaborn.palplot(["群青", "西子", "胭脂", "桂黄", "苍苍", "青骊", "官绿", "米汤娇", "沧浪", "梅子青", "石榴裙"])
```
will plot a color palette with the specified colors in the list:
For readers unfamiliar with Chinese characters, the colors can also be accessed by the numbered indices (`TCC_1` through `TCC_365`). See [here](https://jinyiliu.github.io/2025/08/13/cmplstyle/TCC_indexed_ncols_5.pdf) for a complete indexed color reference.
The package provides conversions between Chinese color names and their `TCC_` indexed names: use `cmplstyle.color_index_to_name` to retrieve the Chinese name for a given `TCC_` index, and `cmplstyle.index_name_to_color` to find the corresponding `TCC_` index for a Chinese color name.
## Built-in Matplotlib style
The package includes a built-in Matplotlib style. Activate it with:
```python
import cmplstyle
cmplstyle.use_builtin_mplstyle()
```
### Example: Linear regression plot
<details>
<summary>View Plotting Code</summary>
```python
import numpy as np
import matplotlib.pyplot as plt
import cmplstyle
from cmplstyle import onecol_wth, cm2inch
cmplstyle.use_builtin_mplstyle()
np.random.seed(20)
F = lambda x: 2.0 * x + 3.0
X = np.linspace(0, 10, 100)
N = 15 # sample size
Y_NOISE_STD_MIN = 5.0
Y_NOISE_STD_MAX = 15.0
X_sample = np.random.normal(loc=5, scale=2, size=N)
Y_sample = F(X_sample)
NOISE_STD = np.random.uniform(Y_NOISE_STD_MIN, Y_NOISE_STD_MAX, size=N)
Y_sample += np.random.normal(size=N) * NOISE_STD
# Fit a linear model with numpy
a, b = np.polyfit(X_sample, Y_sample, deg=1)
F_fit = lambda x: a * x + b
residuals = Y_sample - F_fit(X_sample)
RSE = np.sqrt(np.sum(residuals ** 2) / (N - 2)) # Residual Standard Error
SE = RSE * np.sqrt(1 / N + (X - X.mean())**2 / np.sum((X_sample - X_sample.mean())**2))
fig, ax = plt.subplots(figsize=cm2inch(onecol_wth, onecol_wth * 0.7))
ax.errorbar(x=X_sample, y=Y_sample, yerr=NOISE_STD, color="茶色", fmt=".", elinewidth=0.6, markersize=4, zorder=0)
ax.fill_between(X, F_fit(X) - SE, F_fit(X) + SE, facecolor="天缥", edgecolor=None, alpha=0.7, zorder=1)
ax.plot(X, F_fit(X), color="空青", label="Fitted line", zorder=3)
ax.plot(X, F(X), color="娇红", ls="dotted", label="True line", zorder=4)
ax.set_xlabel("$x$")
ax.set_ylabel("$y$")
ax.set_ylim(bottom=-25, top=40)
ax.set_xlim(left=0, right=10)
ax.legend(loc="lower right", prop={"family": "DejaVu Serif"})
```
</details>
## LICENSE
This package is licensed under the [MIT License](https://github.com/jinyiliu/cmplstyle/blob/main/LICENSE).
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