借助 SciencePlots 进行优美的科研绘图¶
安装¶
In [ ]:
! 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])
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