Matplotlib 代码模版

记录一下自己用 Matplot 画图使用的一些模版代码

全局配置

# 字体与字号
font = {'family': 'serif', 'serif': 'Times New Roman', 'weight': 'normal', 'size': 10}
plt.rc('font', **font)
## 解决某些情况下图中的字体会不正确，见：https://github.com/matplotlib/matplotlib/issues/8017
plt.rc('mathtext', default='regular')

# 保存分辨率
plt.rcParams['savefig.dpi'] = 240
plt.rcParams['figure.dpi'] = 240

# 字体与字号

font = {'family': 'serif', 'serif': 'Times New Roman', 'weight': 'normal', 'size': 10}

plt.rc('font', **font)

## 解决某些情况下图中的字体会不正确，见：https://github.com/matplotlib/matplotlib/issues/8017

plt.rc('mathtext', default='regular')

# 保存分辨率

plt.rcParams['savefig.dpi'] = 240

plt.rcParams['figure.dpi'] = 240

brewer2mpl 配对色

一共12种，共6组颜色（奇数颜色与偶数颜色组成深色与浅色的对比）

# bmap = brewer2mpl.get_map('Paired', 'qualitative', 12)
# colors = bmap.mpl_colors 

colors = [(0.6509803921568628, 0.807843137254902, 0.8901960784313725), (0.12156862745098039, 0.47058823529411764, 0.7058823529411765), (0.6980392156862745, 0.8745098039215686, 0.5411764705882353), (0.2, 0.6274509803921569, 0.17254901960784313), (0.984313725490196, 0.6039215686274509, 0.6), (0.8901960784313725, 0.10196078431372549, 0.10980392156862745), (0.9921568627450981, 0.7490196078431373, 0.43529411764705883), (1.0, 0.4980392156862745, 0.0), (0.792156862745098, 0.6980392156862745, 0.8392156862745098), (0.41568627450980394, 0.23921568627450981, 0.6039215686274509), (1.0, 1.0, 0.6), (0.6941176470588235, 0.34901960784313724, 0.1568627450980392)]

# bmap = brewer2mpl.get_map('Paired', 'qualitative', 12)

# colors = bmap.mpl_colors

colors = [(0.6509803921568628, 0.807843137254902, 0.8901960784313725), (0.12156862745098039, 0.47058823529411764, 0.7058823529411765), (0.6980392156862745, 0.8745098039215686, 0.5411764705882353), (0.2, 0.6274509803921569, 0.17254901960784313), (0.984313725490196, 0.6039215686274509, 0.6), (0.8901960784313725, 0.10196078431372549, 0.10980392156862745), (0.9921568627450981, 0.7490196078431373, 0.43529411764705883), (1.0, 0.4980392156862745, 0.0), (0.792156862745098, 0.6980392156862745, 0.8392156862745098), (0.41568627450980394, 0.23921568627450981, 0.6039215686274509), (1.0, 1.0, 0.6), (0.6941176470588235, 0.34901960784313724, 0.1568627450980392)]

brweer2mpl 单色

# colors = brewer2mpl.get_map('Set1', 'qualitative', 9).mpl_colors
colors = [(0.8941176470588236, 0.10196078431372549, 0.10980392156862745), (0.21568627450980393, 0.49411764705882355, 0.7215686274509804), (0.30196078431372547, 0.6862745098039216, 0.2901960784313726), (0.596078431372549, 0.3058823529411765, 0.6392156862745098), (1.0, 0.4980392156862745, 0.0), (1.0, 1.0, 0.2), (0.6509803921568628, 0.33725490196078434, 0.1568627450980392), (0.9686274509803922, 0.5058823529411764, 0.7490196078431373), (0.6, 0.6, 0.6)]

# colors = brewer2mpl.get_map('Set1', 'qualitative', 9).mpl_colors

colors = [(0.8941176470588236, 0.10196078431372549, 0.10980392156862745), (0.21568627450980393, 0.49411764705882355, 0.7215686274509804), (0.30196078431372547, 0.6862745098039216, 0.2901960784313726), (0.596078431372549, 0.3058823529411765, 0.6392156862745098), (1.0, 0.4980392156862745, 0.0), (1.0, 1.0, 0.2), (0.6509803921568628, 0.33725490196078434, 0.1568627450980392), (0.9686274509803922, 0.5058823529411764, 0.7490196078431373), (0.6, 0.6, 0.6)]

# colors = brewer2mpl.get_map('Set3', 'qualitative', 12).mpl_colors
colors = [(0.5529411764705883, 0.8274509803921568, 0.7803921568627451), (1.0, 1.0, 0.7019607843137254), (0.7450980392156863, 0.7294117647058823, 0.8549019607843137), (0.984313725490196, 0.5019607843137255, 0.4470588235294118), (0.5019607843137255, 0.6941176470588235, 0.8274509803921568), (0.9921568627450981, 0.7058823529411765, 0.3843137254901961),(0.7019607843137254, 0.8705882352941177, 0.4117647058823529), (0.9882352941176471, 0.803921568627451, 0.8980392156862745), (0.8509803921568627, 0.8509803921568627, 0.8509803921568627), (0.7372549019607844, 0.5019607843137255, 0.7411764705882353), (0.8, 0.9215686274509803, 0.7725490196078432), (1.0, 0.9294117647058824, 0.43529411764705883)]

# colors = brewer2mpl.get_map('Set3', 'qualitative', 12).mpl_colors

colors = [(0.5529411764705883, 0.8274509803921568, 0.7803921568627451), (1.0, 1.0, 0.7019607843137254), (0.7450980392156863, 0.7294117647058823, 0.8549019607843137), (0.984313725490196, 0.5019607843137255, 0.4470588235294118), (0.5019607843137255, 0.6941176470588235, 0.8274509803921568), (0.9921568627450981, 0.7058823529411765, 0.3843137254901961),(0.7019607843137254, 0.8705882352941177, 0.4117647058823529), (0.9882352941176471, 0.803921568627451, 0.8980392156862745), (0.8509803921568627, 0.8509803921568627, 0.8509803921568627), (0.7372549019607844, 0.5019607843137255, 0.7411764705882353), (0.8, 0.9215686274509803, 0.7725490196078432), (1.0, 0.9294117647058824, 0.43529411764705883)]

折线样式

lines = ["-^", "-D", "-s", "-p", "-x", "-v", "-+", "-h", "-o", "-<", "->", "-p", "-x", "-v", "-"]
dash_lines = [":", "--", "-."]

lines = ["-^", "-D", "-s", "-p", "-x", "-v", "-+", "-h", "-o", "-<", "->", "-p", "-x", "-v", "-"]

dash_lines = [":", "--", "-."]

模版代码

单图模版

plt.title("Title", size=18)

# 绘制带 Stdev 区间的图
plt.plot(X, mean, "-D", label="Line", color="r")
plt.fill_between(X, mean - stdev, mean + stdev, color="r", alpha=0.5)

# X、Y 坐标轴标题
plt.xlabel("Epoches", size=18)
plt.ylabel("AUROC", size=18)

# X、Y 坐标轴刻度
plt.xticks(X, X_labels, fontsize=12, rotation=0)
plt.yticks(Y, Y_labels, fontsize=12)

plt.legend(fontsize=12, loc="lower left")

plt.tight_layout()
plt.grid()
plt.show()

plt.title("Title", size=18)

# 绘制带 Stdev 区间的图

plt.plot(X, mean, "-D", label="Line", color="r")

plt.fill_between(X, mean - stdev, mean + stdev, color="r", alpha=0.5)

# X、Y 坐标轴标题

plt.xlabel("Epoches", size=18)

plt.ylabel("AUROC", size=18)

# X、Y 坐标轴刻度

plt.xticks(X, X_labels, fontsize=12, rotation=0)

plt.yticks(Y, Y_labels, fontsize=12)

plt.legend(fontsize=12, loc="lower left")

plt.tight_layout()

plt.grid()

plt.show()

多图模版

# 设置子图情况 行、列、图像大小、限制布局、共享 X 轴、共享 Y 轴
fig, axs = plt.subplots(1, 2, figsize=(7, 3), constrained_layout=True, sharex=True, sharey=True)

# 绘制 axs[0]
axs[0].plot(X, mean, "-D", label="Line", color="r")
axs[0].set_title(title, fontsize=12) # 标题
axs[0].set_xticks(X, X_ticks) # X轴刻度
axs[0].set_yticks(Y, Y_ticks) # Y轴刻度
axs[0].tick_params(axis="both", labelsize=12) # X、Y轴参数
axs[0].grid() # 网格
axs[0].legend(loc="lower right", fontsize=12) # 图注

# …… 绘制 axs[1]

# 设置图标超级标题与超级X、Y轴标题
fig.suptitle("Influence of hyper-parameter $s$", size=18)
fig.supxlabel("The value of $s$", size=18)
fig.supylabel("G-Mean", size=18)

plt.show()

# 设置子图情况行、列、图像大小、限制布局、共享 X 轴、共享 Y 轴

fig, axs = plt.subplots(1, 2, figsize=(7, 3), constrained_layout=True, sharex=True, sharey=True)

# 绘制 axs[0]

axs[0].plot(X, mean, "-D", label="Line", color="r")

axs[0].set_title(title, fontsize=12) # 标题

axs[0].set_xticks(X, X_ticks) # X轴刻度

axs[0].set_yticks(Y, Y_ticks) # Y轴刻度

axs[0].tick_params(axis="both", labelsize=12) # X、Y轴参数

axs[0].grid() # 网格

axs[0].legend(loc="lower right", fontsize=12) # 图注

# …… 绘制 axs[1]

# 设置图标超级标题与超级X、Y轴标题

fig.suptitle("Influence of hyper-parameter $s$", size=18)

fig.supxlabel("The value of $s$", size=18)

fig.supylabel("G-Mean", size=18)

plt.show()

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