#!/usr/bin/env python
# coding: utf-8

# # 借助 [SciencePlots ](https://github.com/garrettj403/SciencePlots) 进行优美的科研绘图
# 
# ## 安装
# 

# In[ ]:


get_ipython().system(' pip install SciencePlots')


# ## 使用示例
# 
# 构建数据，导入相关包
# 
# From version v1.1.0 on, import scienceplots is needed on top of your scripts so Matplotlib can make use of the styles.
# 
# 包 scienceplots 在 v1.1.0 之后，需要先导入才能让 Matplotlib 使用该包提供的样式。

# In[2]:


import numpy as np
import matplotlib.pyplot as plt
import scienceplots
from mplfonts import use_font


def model(x, p):
    return x ** (2 * p + 1) / (1 + x ** (2 * p))


x = np.linspace(0.75, 1.25, 201)


# 未使用SciencePlots，绘制结果如下：

# In[3]:


fig, ax = plt.subplots(dpi=150, figsize=(4.5, 3))
for p in [10, 15, 20, 30, 50, 100]:
    ax.plot(x, model(x, p), label=p)
ax.legend(title="Order")
ax.set(xlabel="Voltage (mV)")
ax.set(ylabel="Current ($\mu$A)")
ax.autoscale(tight=True)
plt.show()


# 若使用 SciencePlots 中 Science 的提交格式，绘制结果如下：

# In[4]:


with plt.style.context(["science", "no-latex"]):
    fig, ax = plt.subplots(dpi=150, figsize=(4.5, 3))
    for p in [10, 15, 20, 30, 50, 100]:
        ax.plot(x, model(x, p), label=p)
    ax.legend(title="Order")
    ax.set(xlabel="Voltage (mV)")
    ax.set(ylabel="Current ($\mu$A)")
    ax.autoscale(tight=True)
    plt.show()


# IEEE要求黑白印刷出来的文章也需要能够区分颜色，使用 SciencePlots 中 ieee 的提交格式，绘制结果如下：

# In[5]:


with plt.style.context(["science", "ieee", "no-latex"]):
    fig, ax = plt.subplots(dpi=150, figsize=(4.5, 3))
    for p in [10, 20, 50]:
        ax.plot(x, model(x, p), label=p)
    ax.legend(title="Order")
    ax.set(xlabel="Voltage (mV)")
    ax.set(ylabel="Current ($\mu$A)")
    ax.autoscale(tight=True)
    plt.show()


# 莫名的 `-` 显示不正常

# In[6]:


use_font("Source Han Serif SC")
with plt.style.context(["science", "scatter", "no-latex"]):
    fig, ax = plt.subplots(figsize=(4, 4), dpi=150)
    ax.plot([-2, 2], [-2, 2], "k--")
    ax.fill_between(
        [-2, 2], [-2.2, 1.8], [-1.8, 2.2], color="dodgerblue", alpha=0.2, lw=0
    )
    for i in range(7):
        x1 = np.random.normal(0, 0.5, 10)
        y1 = x1 + np.random.normal(0, 0.2, 10)
        ax.plot(x1, y1, label=r"$^\#${}".format(i + 1))
        ax.legend(title="Sample", loc=2)
        ax.set_xlabel(
            r"中文 $\log_{10}\left(\frac{L_\mathrm{IR}}{\mathrm{L}_\odot}\right)$"
        )
        ax.set_ylabel(
            r"$\log_{10}\left(\frac{L_\mathrm{6.2}}{\mathrm{L}_\odot}\right)$"
        )
        ax.set_xlim([-2, 2])
        ax.set_ylim([-2, 2])