Matplotlib学习笔记

import matplotlib.pyplot as plt

import numpy as np

# 一些基本设置

# 设置支持中文字体（黑体）

matplotlib.rcParams['font.family'] = ['SimHei']

matplotlib.rcParams['axes.unicode_minus'] = False

# 设置图片清晰度（200dpi: 200 像素每英寸）

matplotlib.rcParams['figure.dpi'] = 200

# 图画面板调整为白色

matplotlib.rcParams['axes.facecolor']='white'

matplotlib.rcParams['savefig.facecolor']='white'

x = [1, 2, 3, 4]

y1 = [4, 8, 2, 6]

y2 = [10, 12, 5, 3]

plt.plot(x, y1)

plt.plot(x, y2)

plt.show()

plt.plot(x, y1, "ro--", label='abc')

plt.plot(x, y2, "bs", label='def')

plt.title('sample')

plt.xlabel('time')

plt.ylabel('price')

plt.legend()

plt.xticks([1, 2, 3, 4])

plt.yticks(np.arange(2, 13, 1))

plt.grid()

plt.xlim(2.5, 4.5)

plt.ylim(1.5, 6.5)

plt.figure(figsize=(10, 6))

plt.subplot(211)

plt.bar(x, y1)

plt.subplot(212)

plt.plot(x, y2, "b^--")

plt.show()

fig, axes = plt.subplots(2, 1, figsize=(6, 6))

axes[0].bar(x, y1)

axes[1].plot(x, y2, "b^--")

fig, axes = plt.subplots(2, 2, figsize=(6, 6))

axes[0, 0].bar(x, y1)

axes[0, 1].plot(x, y2, "b^--")

ax = axes[1, 0]

ax.scatter(x, np.array(y2) - np.array(y1),

           s=[10, 20, 50, 100], # 大小

           c=["r", "b", "c", "y"], # 颜色

           ) 

axes[0, 0].set_title("Bar")

axes[0, 1].set_title("Plot")

ax.set_ylabel("y2-y1")

fig, axes = plt.subplots(2, 2, figsize=(6, 6),

                         facecolor="lightblue", # 背景色

                         sharex=True, # 共享 x 轴

                         sharey=True, # 共享 y 轴

                         )

axes[1, 1].remove() # 删除子图

axes[0, 0].plot(x, y1, "r+-")

# Figure 对象添加整体大标题 / 注释

fig.suptitle("Four Subplots")

fig.supylabel("y1")

fig.supxlabel("x")

# 重新添加右下角坐标系（改变为三维坐标系）

ax = fig.add_subplot(2, 2, 4, 

                     projection='3d', facecolor="grey")

ax.stem(seasons, stock1, stock2-stock1)

ax.stem(seasons, stock1, stock2-stock1, 

        linefmt='k--', basefmt='k--', 

        bottom=10, orientation='y')

ax.plot_surface(np.array([1,1,4,4]).reshape(2,2),

                np.array([2.5,10,2.5,10]).reshape(2,2),

                np.array([0]*4).reshape(2,2), 

                alpha=0.2, color='red')

ax.plot_surface(np.array([1,1,4,4]).reshape(2,2),

                np.array([10]*4).reshape(2,2),

                np.array([-2.5,8,-2.5,8]).reshape(2,2),

                alpha=0.2, color='black')

ax.set_xlabel("季度(x)")

ax.set_ylabel("股票1(y)")

ax.set_zlabel("差价(z)")

# 改变坐标系的背景颜色（在画图后改变属性，OOP）

axes[1, 0].set_facecolor('grey')

axes[1, 0].patch.set_alpha(0.2)

axes[0, 0].set_facecolor('red')

axes[0, 0].patch.set_alpha(0.2)

plt.tight_layout()

# 步骤 1. 导入包

import matplotlib.pyplot as plt

import matplotlib as mpl  # 整个包

import numpy as np

# 步骤 2. 查询所有画图整体风格

# print(plt.style.available)

plt.style.use('default')  # 默认风格

plt.plot(np.random.randn(50))

plt.title("default style")

# plt.savefig("images/style_1.png", facecolor=plt.gcf().get_facecolor())

plt.show()

plt.style.use('ggplot')

plt.plot(np.random.randn(50))

plt.title("ggplot style")

# plt.savefig("images/style_2.png", facecolor=plt.gcf().get_facecolor())

plt.show()

plt.style.use('seaborn-dark')

plt.plot(np.random.randn(50))

plt.title("seaborn-dark")

# plt.gcf().savefig("images/style_3.png", facecolor=plt.gcf().get_facecolor())

plt.show()

plt.style.use('default')  # 重新设置回默认风格

# 查询当前的一些画图属性设置

print(mpl.rcParams.get('figure.figsize'))  # 当前图画大小（长 * 高）（英寸）

print(mpl.rcParams.get('font.size'))  # 当前字体大小

# 例子：查询当前你的计算机中 matplotlib 的可用字体

import matplotlib.font_manager as fm

fm._load_fontmanager(try_read_cache=False)

fpaths = fm.findSystemFonts(fontpaths=None)

# print(fpaths)

exempt_lst = ["NISC18030.ttf", "Emoji"]

skip=False

for i in fpaths:

    # print(i)

    for ft in exempt_lst:

        if ft in i:

            skip=True

    if skip==True:

        skip=False

        continue

    f = matplotlib.font_manager.get_font(i)

    print(f.family_name)

import matplotlib as mpl

# step 2. 一些常用设定参数

# 设置字体 (主要字体 / 数学公式字符集)

rc = {"font.family" : "Times New Roman",  # 默认字体

      "mathtext.fontset" : "stix",  # 数学字符集

      }

mpl.rcParams.update(rc) # 根据 dict 一次性更新很多参数

# 设置图片清晰度（300dpi）

mpl.rcParams['figure.dpi'] = 300

# 图画面板调整为白色

mpl.rcParams['axes.facecolor'] = 'white'

mpl.rcParams['savefig.facecolor'] = 'white'

# Figure 自动调整格式

plt.rcParams['figure.constrained_layout.use'] = True

# 例子。中英文 / 数学符号混排

fig, ax = plt.subplots()

# 单独添加字体

st_font = fm.FontProperties(fname="/System/Library/Fonts/Supplemental/Songti.ttc")

# 注 (理论上): SC: 简体中文；TC: 繁体中文；

# 实际上一般应该是使用 Heiti TC 即可（所见即所得）

ax.set_xlabel(r'乌龟烏龜/密度$\mathrm{kg/m}^3$',

              fontname = 'Heiti TC', fontsize=20)  # 单独指定字体大小与格式

ax.text(0.2, 0.8, r'乌龟烏龜/黑体 $\mathrm{Times New Roman}$',

        fontname='Heiti TC', fontsize=20)

# 目前其他字体支持也比较有限。。。如这里宋体没有繁体

ax.text(0.2, 0.6, r'乌龟烏龜/宋体 $Times New Roman$', 

        fontproperties=st_font, fontsize=30)

plt.ylim(0.5, 0.9)

# plt.savefig("images/pic3_1.png", facecolor=fig.get_facecolor())

plt.show()

# 三个 “三角函数”

x = np.linspace(0, 10, 100)

y1 = np.cos(x)

y2 = np.sin(x)

y3 = np.tanh(x) # tanh 函数

# 画时间序列曲线

# 输入 / 输出都包含坐标轴变量

def plot_time_series(x, y, fmt, lab="", ax=None):

    if ax is None:

        fig, ax = plt.subplot()

    ax.plot(x, y, fmt, label=lab)

    # x 轴固有格式

    ax.set_xlabel("time")

    ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))

    ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))

    labs = ax.xaxis.get_ticklabels()

    ax.xaxis.set_ticklabels([r"{:.2f}$\pi$".format(i/2) for i, l in enumerate(labs)])

    return ax

# 两个坐标周

fig, axes = plt.subplots(2, 1, figsize=(6, 3),

                         sharex=True, facecolor="white")

# 在第一个坐标周画两条线

plot_time_series(x, y1, 'b-', r'$y=sin(x)$', ax=axes[0])

plot_time_series(x, y2, 'r:', r'$y=cos(x)$', ax=axes[0])

# 在第二个坐标周画一条线

plot_time_series(x, y3, 'g--', ax=axes[1])

# plt.savefig("images/pic3_2.png", facecolor=fig.get_facecolor())

plt.show()

# 拓展：很多格式可以后期再函数外处理

def plot_time_series(x, y, fmt, lab="", ax=None):

    if ax is None:

        fig, ax = plt.subplot()

    ax.plot(x, y, fmt, label=lab)

    # x 轴固有格式

    ax.set_xlabel("time")

    ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))

    ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))

    labs = ax.xaxis.get_ticklabels()

    ax.xaxis.set_ticklabels([r"{:.2f}$\pi$".format(i/2) for i, l in enumerate(labs)])

    return ax

fig, axes = plt.subplots(2, 1, figsize=(6, 3), 

                         sharex=True, facecolor="white")

plot_time_series(x, y1, 'b-', r'$y=sin(x)$', axes[0])

plot_time_series(x, y2, 'r:', r'$y=cos(x)$', axes[0])

plot_time_series(x, y3, 'g--', ax=axes[1])

# 后期调整格式 / 添加内容 / 美化...

axes[0].set_xlabel("")

axes[0].legend(loc="upper right")

axes[1].legend(loc="upper right")

axes[0].grid()

axes[1].grid()

# plt.savefig("images/pic3_3.png", facecolor=fig.get_facecolor())

plt.show()

# 后期调整

fig, axes = plt.subplots(2, 1, figsize=(6, 3),

                         sharex=True, facecolor="white")

plot_time_series(x, y1, 'b-', r'$y=sin(x)$', axes[0])

plot_time_series(x, y2, 'r:', r'$y=cos(x)$', axes[0])

plot_time_series(x, y3, 'g--', ax=axes[1])

# 调整格式 / 添加内容 / 美化...

axes[0].set_xlabel("")

# 补充图示

axes[1].lines[0].set_label(r"$y=tanh(x)$")

axes[0].legend(loc="upper right")

axes[1].legend(loc="upper right")

axes[0].grid()

axes[1].grid()

plt.savefig("images/pic3_4.png", facecolor=fig.get_facecolor())

plt.show()

import matplotlib.pyplot as plt

from matplotlib import cm

from matplotlib.ticker import LinearLocator

import numpy as np

%matplotlib widget

# 交互式画图

fig, ax = plt.subplots(subplot_kw={"projection": "3d"})

# Make data.

X = np.arange(-5, 5, 0.25)

Y = np.arange(-5, 5, 0.25)

X, Y = np.meshgrid(X, Y)

R = np.sqrt(X**2 + Y**2)

Z = np.sin(R)

# Plot the surface.

surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,

                       linewidth=0, antialiased=False)

# Customize the z axis.

ax.set_zlim(-1.01, 1.01)

ax.zaxis.set_major_locator(LinearLocator(10))

# A StrMethodFormatter is used automatically

ax.zaxis.set_major_formatter('{x:.02f}')

# Add a color bar which maps values to colors.

fig.colorbar(surf, shrink=0.5, aspect=5)

# plt.savefig("images/pic3_5.png", facecolor=fig.get_facecolor())

plt.show()