matplotlib

阅读量：6914 次

发布时间：2019-06-27

本文共 19802 字，大约阅读时间需要 66 分钟。

五、数据可视化

1. 基本绘图

mp.plot(水平坐标, 垂直坐标)

# plt1.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000) # arange可以生成类似的整数print(x.shape)cos_y = np.cos(x)sin_y = np.sin(x)mp.plot(x, cos_y)mp.plot(x, sin_y)mp.show()

(1000,)

png

2. 线型、线宽和颜色

mp.plot(..., linestyle=线型, linewidth=线宽, color=颜色, ...)

# plt2.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000) # arange可以生成类似的整数print(x.shape)cos_y = np.cos(x)sin_y = np.sin(x)mp.plot(x, cos_y, linestyle='--', linewidth=1, color='red')mp.plot(x, sin_y, linestyle=':', linewidth=3, color='green')mp.show()

(1000,)

png

3. 设置坐标范围

mp.xlim(水平坐标最小值, 水平坐标最大值)

mp.ylim(垂直坐标最小值, 垂直坐标最大值)

# plt3.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000) # arange可以生成类似的整数mp.xlim(x.min() * 1.2, x.max() * 1.2)mp.ylim(sin_y.min() * 1.2, sin_y.max() * 5)cos_y = np.cos(x)sin_y = np.sin(x)mp.plot(x, cos_y)mp.plot(x, sin_y)mp.show()

png

4. 设置坐标刻度

mp.xticks(位置序列[, 标签序列])

mp.yticks(位置序列[, 标签序列])

# plt4.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000) # arange可以生成类似的整数cos_y = np.cos(x)sin_y = np.sin(x)mp.yticks([-1, -0.5, 0, 0.5, 1])mp.xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi],           [r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'])mp.plot(x, cos_y)mp.plot(x, sin_y)mp.show()

png

5. 设置坐标轴

坐标轴名：left/right/bottom/top

ax = mp.gca() # 获取当前坐标轴

ax.spines['坐标轴名'].set_position((坐标系, 坐标))

ax.spines['坐标轴名'].set_color(颜色)

# plt5.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000) # arange可以生成类似的整数cos_y = np.cos(x)sin_y = np.sin(x)ax = mp.gca()ax.spines['left'].set_position(('data', 0))ax.spines['bottom'].set_position(('data', 0))ax.spines['right'].set_color(None)ax.spines['top'].set_color(None)mp.plot(x, cos_y)mp.plot(x, sin_y)mp.show()

png

6. 图例

mp.plot(..., label=图例标签, ...)

mp.legend([loc=位置])

# plt6.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000) # arange可以生成类似的整数cos_y = np.cos(x)sin_y = np.sin(x)mp.plot(x, cos_y, label=r'$cos(x)$')mp.plot(x, sin_y, label=r'$sin(x)$')# mp.legend(loc='lower right')mp.legend()mp.show()

png

7. 特殊点

mp.scatter(水平坐标, 垂直坐标, marker=点型, s=大小,           color=颜色, edgecolor=边缘色, facecolor=填充色,           zorder=图层序号)

# plt7.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000) # arange可以生成类似的整数cos_y = np.cos(x)sin_y = np.sin(x)xo = np.pi * 3 / 4yo_cos = np.cos(xo)yo_sin = np.sin(xo)mp.plot(x, cos_y, label=r'$cos(x)$')mp.plot(x, sin_y, label=r'$sin(x)$')mp.legend()# zorder表示图层上下mp.scatter([xo, xo], [yo_cos, yo_sin], marker='*', s=100, edgecolor='red', facecolor='green', zorder=3) mp.plot([xo, xo], [yo_cos, yo_sin], linewidth=1, color='dodgerblue')mp.show()

png

8. 备注

mp.annotate(备注文本,            xy=目标坐标,            xycoords=目标坐标系,            xytext=文本坐标,            textcoords=文本坐标系,            fontsize=字体大小,            arrowprops=箭头线属性)

# plt8.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000) # arange可以生成类似的整数cos_y = np.cos(x)sin_y = np.sin(x)ax = mp.gca()ax.spines['left'].set_position(('data', 0))ax.spines['bottom'].set_position(('data', 0))ax.spines['right'].set_color(None)ax.spines['top'].set_color(None)xo = np.pi * 3 / 4yo_cos = np.cos(xo)yo_sin = np.sin(xo)mp.scatter([xo, xo], [yo_cos, yo_sin], marker='*', s=100, edgecolor='red', facecolor='green', zorder=3) mp.plot([xo, xo], [yo_cos, yo_sin], linewidth=1, color='dodgerblue')mp.plot(x, cos_y, label=r'$cos(x)$')mp.plot(x, sin_y, label=r'$sin(x)$')mp.legend()mp.annotate(r'$cos(\frac{3\pi}{4})=-\frac{\sqrt{2}}{4}$', xy=(xo, yo_cos), xycoords='data', xytext=(-90, -40),             textcoords='offset points',             fontsize=14,             arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=.2'))mp.annotate(r'$sin(\frac{3\pi}{4})=\frac{\sqrt{2}}{2}$', xy=(xo, yo_sin), xycoords='data', xytext=(20, 40),             textcoords='offset points',             fontsize=14,             arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=.2'))mp.show()

png

9. 图形对象(图形窗口)

如果"窗口名称"是第一次出现，那么就创建一个新窗口，其标题栏显示该名称，如果"窗口名称"已经出现过，那么不再创建新窗口，而只是将与该名称相对应的窗口设置为当前窗口。所谓当前窗口，就是接受后续绘图操作的窗口。
mp.figure(窗口名称, figsize=窗口大小, dpi=分辨率, facecolor=颜色)
mp.title(标题文本, fontsize=字体大小)
mp.xlabel(水平轴标签, fontsize=字体大小)
mp.ylabel(垂直轴标签, fontsize=字体大小)
mp.tick_params(..., labelsize=刻度标签字体大小, ...)
mp.grid(linestyle=网格线型)
mp.tight_layout() # 紧凑布局

# figimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-np.pi, np.pi, 1000)cos_y = np.cos(x)sin_y = np.sin(x)mp.figure('Figure Object 1', figsize=(4, 3), dpi=120, facecolor='lightgray')mp.title('Figure Object 1', fontsize=12)mp.xlabel('x', fontsize=10)mp.ylabel('y', fontsize=10)mp.tight_layout()mp.tick_params(labelsize=6)mp.grid(linestyle=':')mp.figure('Figure Object 2', figsize=(4, 3), dpi=120, facecolor='lightgray')mp.title('Figure Object 2', fontsize=12)mp.xlabel('x', fontsize=10)mp.ylabel('y', fontsize=10)mp.tight_layout()mp.tick_params(labelsize=6)mp.grid(linestyle=':')mp.figure('Figure Object 1')mp.plot(x, cos_y, c='dodgerblue', label=r'$y=cos(x)$')mp.legend()mp.figure('Figure Object 2')mp.plot(x, sin_y, c='orange', label=r'$y=sin(x)$')mp.legend()mp.show()

png

10. 子图

矩阵式布局

mp.subplot(行数, 列数, 序号)1 2 34 5 67 8 9mp.subplot(3, 3, 5)

# sub1.pyimport matplotlib.pyplot as mpmp.figure('Subplot', facecolor='lightgray')mp.subplot(221)mp.xticks(())  # 不显示刻度mp.yticks(())mp.text(0.5, 0.5, '1', ha='center', va='center', size=36, alpha=0.5)mp.subplot(222)mp.xticks(())  # 不显示刻度mp.yticks(())mp.text(0.5, 0.5, '2', ha='center', va='center', size=36, alpha=0.5)mp.subplot(223)mp.xticks(())  # 不显示刻度mp.yticks(())mp.text(0.5, 0.5, '3', ha='center', va='center', size=36, alpha=0.5)mp.subplot(224)mp.xticks(())  # 不显示刻度mp.yticks(())mp.text(0.5, 0.5, '4', ha='center', va='center', size=36, alpha=0.5)mp.tight_layout()mp.show()

png

网格式布局

import matplotlib.gridspec as mggs = mg.GridSpec(行数, 列数)mp.subplot(gs[占行, 占列])

# sub2.pyimport matplotlib.pyplot as mpimport matplotlib.gridspec as mgmp.figure('Grid', facecolor='lightgray')gs = mg.GridSpec(3, 3)mp.subplot(gs[0, :2])mp.xticks(())mp.yticks(())mp.text(0.5, 0.5, '1', ha='center', va='center', size=36, alpha=0.5)mp.subplot(gs[1:, :1])mp.xticks(())mp.yticks(())mp.text(0.5, 0.5, '2', ha='center', va='center', size=36, alpha=0.5)mp.subplot(gs[2, 1:])mp.xticks(())mp.yticks(())mp.text(0.5, 0.5, '3', ha='center', va='center', size=36, alpha=0.5)mp.subplot(gs[:2, 2])mp.xticks(())mp.yticks(())mp.text(0.5, 0.5, '4', ha='center', va='center', size=36, alpha=0.5)mp.subplot(gs[1, 1])mp.xticks(())mp.yticks(())mp.text(0.5, 0.5, '5', ha='center', va='center', size=36, alpha=0.5)mp.tight_layout()mp.show()

png

自由式布局

mp.axes([左, 底, 宽, 高])

# sub3.pyimport matplotlib.pyplot as mpmp.figure('Axes', facecolor='lightgray')mp.xticks(())mp.yticks(())mp.text(0.5, 0.5, '1', ha='center', va='center', size=56, alpha=0.5)mp.axes([0.58, 0.15, 0.3, 0.3])mp.xticks(())mp.yticks(())mp.text(0.5, 0.5, '2', ha='center', va='center', size=25, alpha=0.5)mp.show()

png

11. 刻度定位器

刻度定位器对象 = mp.xxxLocator(...)ax = mp.gca()ax.xaxis.set_major_locator(刻度定位器对象)ax.xaxis.set_minor_locator(刻度定位器对象)ax.yaxis.set_major_locator(刻度定位器对象)ax.yaxis.set_minor_locator(刻度定位器对象)

# tick.pyimport numpy as npimport matplotlib.pyplot as mpmp.figure('Locator', dpi=150)# 刻度定位器列表locators = [# 空定位器：不绘制刻度            'mp.NullLocator()',            # 最大定位器：最多绘制nbins个刻度，每两个刻度之间间隔葱steps列表中选择            'mp.MaxNLocator(nbins=3, steps=[1, 3, 5, 7, 9])',            # 定点定位器：根据locs参数中的位置绘制刻度            'mp.FixedLocator(locs=[0, 2.5, 5, 7.5, 10])',            # 自动定位器：由系统自动选择刻度绘制位置            'mp.AutoLocator()',            # 索引定位器：由offset确定起始刻度，由base确定相邻间隔            'mp.IndexLocator(offset=0.5, base=1.5)',            # 多点定位器：从0开始，按照参数指定的间隔（缺省1）绘制刻度            'mp.MultipleLocator()',            # 线性定位器：线性等分成numticks-1份，绘制numticks个刻度            'mp.LinearLocator(numticks=21)',            # 对数定位器：以base为底，用subs中的元素作为指数的增量            'mp.LogLocator(base=2, subs=[1.0])']    n_locators = len(locators)# 遍历刻度定位器列表for i, locator in enumerate(locators):    mp.subplot(n_locators, 1, i + 1)    # 设置坐标范围    mp.xlim(0, 10)    mp.ylim(-1, 1)    # 关闭垂直坐标轴刻度    mp.yticks(())    # 获取当前坐标轴    ax = mp.gca()    # 隐藏除了底轴外所有的坐标轴    ax.spines['left'].set_color(None)    ax.spines['top'].set_color(None)    ax.spines['right'].set_color(None)    # 将底轴调整到子图中心位置    ax.spines['bottom'].set_position(('data', 0))        # 设置水平坐标轴的主刻度定位器    ax.xaxis.set_major_locator(eval(locator))    # 设置水平坐标轴的此刻度定位器为多点定位，间隔0.1    ax.xaxis.set_minor_locator(mp.MultipleLocator(0.1))        # 绘制一条与水平坐标轴重合的直线，无色透明    mp.plot(np.arange(11), np.zeros(11), c=None)    # 标记所用的刻度定位器类名    mp.text(5, 0.3, locator[3:], ha='center', size=12)mp.tight_layout()mp.show()

png

12. 刻度网格线

ax = mp.gca()ax.grid(which='major'/'minor', axis='x'/'y'/'both',    linewidth=线宽, linestyle=线型, color=颜色,    alpha=透明度)

# grid.pyimport numpy as npimport matplotlib.pyplot as mpx = np.linspace(-5, 5, 1000)y = 8 * np.sinc(x)mp.figure('Grid', dpi=150, facecolor='lightgray')mp.title('Grid', fontsize=20)mp.xlabel('x', fontsize=14)mp.ylabel('y', fontsize=14)mp.tick_params(labelsize=10)mp.plot(x, y, c='dodgerblue', label=r'$y=8sinc(x)$')ax = mp.gca()ax.xaxis.set_major_locator(mp.MultipleLocator(1))ax.xaxis.set_minor_locator(mp.MultipleLocator(0.1))ax.yaxis.set_major_locator(mp.MultipleLocator(1))ax.yaxis.set_minor_locator(mp.MultipleLocator(0.1))ax.grid(which='major', axis='both', linewidth=0.75, linestyle='-', color='orange')ax.grid(which='minor', axis='both', linewidth=0.25, linestyle='-', color='orange')# mp.grid(linestyle=':')mp.legend()mp.tight_layout()mp.show()

png

13. 半对数坐标

mp.semilogy(参数同plot函数)

# grid.pyimport numpy as npimport matplotlib.pyplot as mpy = np.array([1, 10, 100, 1000, 100, 10, 1])mp.figure('Normal & Log', dpi=150, facecolor='lightgray')mp.subplot(211)mp.title('Normal', fontsize=20)mp.xlabel('x', fontsize=14)mp.ylabel('y', fontsize=14)mp.tick_params(labelsize=10)mp.plot(y, 'o-', c='dodgerblue', label=r'plot')ax = mp.gca()ax.xaxis.set_major_locator(mp.MultipleLocator(1))ax.xaxis.set_minor_locator(mp.MultipleLocator(0.1))ax.yaxis.set_major_locator(mp.MultipleLocator(250))ax.yaxis.set_minor_locator(mp.MultipleLocator(50))mp.tick_params(labelsize=10)ax.grid(which='major', axis='both', linewidth=0.75, linestyle='-', color='orange')ax.grid(which='minor', axis='both', linewidth=0.25, linestyle='-', color='orange')# ----------------------------------------mp.subplot(212)mp.title('Log', fontsize=20)mp.xlabel('x', fontsize=14)mp.ylabel('y', fontsize=14)mp.tick_params(labelsize=10)ax2 = mp.gca()ax2.xaxis.set_major_locator(mp.MultipleLocator(1))ax2.xaxis.set_minor_locator(mp.MultipleLocator(0.1))ax2.yaxis.set_major_locator(mp.AutoLocator())ax2.yaxis.set_minor_locator(mp.AutoLocator())mp.tick_params(labelsize=10)ax2.grid(which='major', axis='both', linewidth=0.75, linestyle='-', color='orange')ax2.grid(which='minor', axis='both', linewidth=0.25, linestyle='-', color='orange')mp.semilogy(y, 'o-', c='dodgerblue', label=r'semilogy')mp.legend()mp.tight_layout()mp.show()

png

14. 散点图

可以通过每个点的坐标、颜色、大小和形状表示不同的特征值。

身高	性别	体重	年龄段	种族
1.8	80	男	中间	亚洲
1.6	50	女	青少	美洲

x、y、颜色、大小、形状

# scatter.pyimport numpy as npimport matplotlib.pyplot as mpn = 1000x = np.random.normal(0, 1, n)  # 标准正态分布y = np.random.normal(0, 1, n)  # 标准正态分布d = np.sqrt(x ** 2 + y ** 2)mp.figure('Scatter', facecolor='lightgray')mp.title('Scatter', fontsize=14)mp.xlabel('x', fontsize=14)mp.ylabel('y', fontsize=14)mp.tick_params(labelsize=10)mp.grid(linestyle=':')mp.scatter(x, y, c=d, cmap='jet_r', s=60, alpha=0.5)  #  'jet_r'从深红到深蓝的渐变颜色表，根据d中的从小到大元素来选取mp.tight_layout()mp.show()

png

15. 填充

mp.fill_between(    边界曲线上点的水平坐标,    下边界曲线上点的垂直坐标,    上边界曲线上点的垂直坐标,    填充条件,     color=颜色, alpha=透明度)

# fill.pyimport numpy as npimport matplotlib.pyplot as mpn = 1000x = np.linspace(0, 8 * np.pi, n)  # 标准正态分布sin_y = np.sin(x)cos_y = np.cos(x / 2) / 2d = np.sqrt(x ** 2 + y ** 2)mp.figure('Fill', facecolor='lightgray')mp.title('Fill', fontsize=20)mp.xlabel('x', fontsize=14)mp.ylabel('y', fontsize=14)mp.tick_params(labelsize=10)mp.grid(linestyle=':')mp.plot(x, sin_y, c='dodgerblue', label=r'$y=sin(x)$')mp.plot(x, cos_y, c='orange', label=r'$y=\frac{1}{2}cos(\frac{x}{2}$')mp.fill_between(x, cos_y, sin_y, cos_y < sin_y,                color='dodgerblue', alpha=0.5)mp.fill_between(x, cos_y, sin_y, cos_y > sin_y,                color='orange', alpha=0.5)mp.legend()mp.tight_layout()mp.show()

png

16. 条形图

mp.bar(水平坐标, 高度, 宽度[, 底坐标], color=颜色, alpha=透明度, label=图例标签)

# bar.py# fill.pyimport numpy as npimport matplotlib.pyplot as mpapples = np.array([30, 25, 22, 36, 21, 29, 20, 24, 33, 19, 27, 15])oranges = np.array([24, 33, 19, 27, 35, 20, 15, 27, 20, 32, 20, 22])mp.figure('Bar', facecolor='lightgray')mp.title('Bar', fontsize=20)mp.xlabel('Month', fontsize=14)mp.ylabel('Number', fontsize=14)mp.tick_params(labelsize=10)mp.grid(axis='y', linestyle=':')mp.ylim((0, 40))x = np.arange(len(apples))mp.bar(x, apples, 0.6, color='dodgerblue', label='Apple')mp.bar(x + 0.3, oranges, 0.6, color='orange', label='Orange', alpha=0.75)mp.xticks(x, ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])mp.legend()mp.tight_layout()mp.show()

png

17. 饼图

mp.pie(值, 间隙, 标签, 颜色, 格式,    shadow=是否带阴影, startangel=起始角度)

# pie.pyvalues = [26, 17, 21,29, 11]spaces = [0.01, 0.01, 0.01, 0.01, 0.01]lables = ['Python', 'JavaScript', 'C++', 'Java', 'PHP']colors = ['dodgerblue', 'orange', 'limegreen', 'violet', 'gold']mp.figure('Pie', facecolor='lightgray')mp.title('Pie', fontsize=20)mp.pie(values, spaces, lables, colors, '%d%%', shadow=True, startangle=45)mp.legend()mp.tight_layout()# 等轴比例mp.axis('equal')mp.show()

png

18. 等高线图

mp.contourf(x, y, z, 阶数(密集成都), cmap=颜色映射)mp.contour(x, y, z, 阶数, linewidths=线宽)

# cntr.pyimport numpy as npimport matplotlib.pyplot as mpn = 1000# 网格化x, y = np.meshgrid(np.linspace(-3, 3, n), np.linspace(-3, 3, n))z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)mp.figure('Contour', facecolor='lightgray')mp.title('Contour', fontsize=14)mp.xlabel('x', fontsize=14)mp.ylabel('y', fontsize=14)mp.tick_params(labelsize=10)mp.grid(linestyle=':')# 绘制等高线图cnrt = mp.contour(x, y, z, 10, cmap='jet', linewidth=0.5)# 为等高线图添加高度标签mp.clabel(cnrt, inline_spacing=1, fmt='%.1f', fontsize=10)mp.tight_layout()mp.show()

png

19. 热成像图

用图形的方式显示矩阵1 2 34 5 67 8 9mp.imshow(矩阵, cmap=颜色映射, origin=纵轴方向    hight: 缺省，原点在左上角    low: 原点在左下角

# hot.pyimport numpy as npimport matplotlib.pyplot as mpn = 1000# 网格化x, y = np.meshgrid(np.linspace(-3, 3, n), np.linspace(-3, 3, n))z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)mp.figure('Hot', facecolor='lightgray')mp.title('Hot', fontsize=14)mp.xlabel('x', fontsize=14)mp.ylabel('y', fontsize=14)mp.tick_params(labelsize=10)mp.grid(linestyle=':')# 绘制热成像图mp.imshow(z, cmap='jet', origin='low')mp.colorbar().set_label('z', fontsize=14)mp.tight_layout()mp.show()

png

20. 极坐标系

mp.gca(projection='polar')水平坐标 -> 极角垂直坐标 -> 极径mp.plot(水平坐标, 垂直坐标, ...)mp.plot(极角, 极径, ...)

# polar.pyimport numpy as npimport matplotlib.pyplot as mpt = np.linspace(0, 4 * np.pi, 1001) # 720度r_spiral = 0.8 * tr_rose = 5 * np.sin(6 * t)mp.figure('Polar', facecolor='lightgray')mp.title('Polar', fontsize=20)mp.xlabel(r'$\theta$', fontsize=14)mp.ylabel(r'$\rho$', fontsize=14)mp.tick_params(labelsize=10)mp.grid(linestyle=':')mp.plot(t, r_spiral, c='dodgerblue', label=r'$\rho=0.8\theta$')mp.plot(t, r_rose, c='orange', label=r'$5sin(6\theta)$')mp.legend()# 函数值   = f (自变量)# 垂直坐标 = f (水平坐标)# 极径     = f (极角)mp.figure('Flower', facecolor='lightgray')mp.title('Flower', fontsize=20)mp.xlabel(r'$\theta$', fontsize=14)mp.ylabel(r'$\rho$', fontsize=14)mp.tick_params(labelsize=10)mp.grid(linestyle=':')mp.gca(projection='polar')mp.plot(t, r_spiral, c='dodgerblue', label=r'$\rho=0.8\theta$')mp.plot(t, r_rose, c='orange', label=r'$5sin(6\theta)$')mp.legend()mp.show()

png

21. 三维曲面

from mpl_toolkits.mplot3d import axes3dax = mp.gca(projection='3d') # class axes3dax.set_xlabel(...)...ax.plot_surface(x, y, z, rstride=行跨距, cstride=列跨距,    cmap=颜色映射)ax.plot_wireframe(x, y, z, rstride=行跨距, cstride=列跨距,    linewidth=线宽, color=颜色)

# 3d.pyimport numpy as npimport matplotlib.pyplot as mpfrom mpl_toolkits.mplot3d import axes3dn = 1000# 网格化x, y = np.meshgrid(np.linspace(-3, 3, n), np.linspace(-3, 3, n))z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)mp.figure('3D Surface', facecolor='lightgray')# 创建三维坐标系ax = mp.gca(projection='3d')mp.title('3D Surface', fontsize=14)ax.set_xlabel('x', fontsize=14)ax.set_ylabel('y', fontsize=14)ax.set_zlabel('z', fontsize=14)mp.tick_params(labelsize=10)# 绘制三维表面图ax.plot_surface(x, y, z, rstride=10, cstride=10, cmap='jet')mp.figure('3D Wireframe', facecolor='lightgray')# 创建三维坐标系ax = mp.gca(projection='3d')mp.title('3D Wireframe', fontsize=14)ax.set_xlabel('x', fontsize=14)ax.set_ylabel('y', fontsize=14)ax.set_zlabel('z', fontsize=14)mp.tick_params(labelsize=10)# 绘制三维表面图ax.plot_wireframe(x, y, z, rstride=10, cstride=10, linewidth=1, color='limegreen')mp.tight_layout()mp.show()

png

22. 简单动画

import matplotlib.animation as madef 更新回调函数(序列号):    更新画面显示    ...ma.FuncAnimation(图像窗口, 更新回调函数, interval=时间间隔(毫秒))

每经过一个"时间间隔"，"更新回调函数"会被matplotlib调用一次。

位置         大小     生长                颜色  float float | float | float | float float float float

# bud.pyimport numpy as npimport matplotlib.pyplot as mpimport matplotlib.animation as man_bubbles = 100# 气泡数组bubbles = np.zeros(n_bubbles, dtype=[    ('position', float, 2), # 位置    ('size', float, 1), # 大小    ('growth', float, 1), # 生长速度    ('color', float, 4)]) # 颜色（红绿蓝透明度）bubbles['position'] = np.random.uniform(0, 1, (n_bubbles, 2))bubbles['size'] = np.random.uniform(50, 750, n_bubbles)bubbles['growth'] = np.random.uniform(30, 150, n_bubbles)bubbles['color'] = np.random.uniform(0, 1, (n_bubbles, 4))mp.figure('Bubbles', facecolor='lightgray')mp.title('Bubbles', fontsize=20)mp.xticks(())mp.yticks(())sc = mp.scatter(bubbles['position'][:, 0],                bubbles['position'][:, 1], s=bubbles['size'], c=bubbles['color'])def update(number):    # 更新气泡的大小    bubbles['size'] += bubbles['growth']    # 确定哪个气泡破裂    burst = number % n_bubbles    # 更新破裂气泡的位置    bubbles['position'][burst] = np.random.uniform(0, 1, 2)    # 更新破裂气泡的大小    bubbles['size'][burst] = 0    # 更新破裂气泡的生长速度    bubbles['growth'][burst] = np.random.uniform(30, 150)    # 更新破裂气泡的颜色    bubbles['color'][burst] = np.random.uniform(0, 1, 4)        # 设置气泡的位置    sc.set_offsets(bubbles['position'])    # 设置气泡的大小    sc.set_sizes(bubbles['size'])    # 设置气泡的颜色    sc.set_facecolors(bubbles['color'])        anim = ma.FuncAnimation(mp.gcf(), update, interval=10)mp.show()

png

每经过一个"时间间隔"，matplotlib先调用"生成器函数"，用"生成器函数"产生(yeild)的值作为参数调用"更新回调函数"。
matplotlib:
...
while True:
延时interval毫秒
生成值 = 生成器函数()
更新回调函数(生成值)
...

# bud.pyimport numpy as npimport matplotlib.pyplot as mpimport matplotlib.animation as mamp.figure('Signal', facecolor='lightgray')mp.title('Signal', fontsize=20)mp.xlabel('Time', fontsize=14)mp.ylabel('Signal', fontsize=14)ax = mp.gca()ax.set_ylim(-3, 3)ax.set_xlim(0, 10)mp.tick_params(labelsize=10)mp.grid(linestyle=':')# 创建一个曲线对象pl = mp.plot([], [], c='orange')[0]# 在曲线对象内部创建缓冲区，以容纳曲线上点的横纵坐标pl.set_data([], [])# 更新回调函数def update(data):    t, v = data    # 获取曲线上当前所有的点    x, y = pl.get_data()    # 追加新采集到的点    x.append(t)    y.append(v)    # 获取当前水平坐标范围    x_min, x_max = ax.get_xlim()    # 如果新点的水平坐标超过水平坐标范围    if t > x_max:        # 重新设置水平坐标范围        ax.set_xlim(t - (x_max - x_min), t)        # 重新绘制坐标轴        ax.figure.canvas.draw()    # 设置曲线上的点    pl.set_data(x, y)# 生成器函数def generator():    t = 0    while True:        v = np.sin(2 * np.pi * t) * np.exp(np.sin(0.2 * np.pi * t))        yield t, v        t += 0.05    anim = ma.FuncAnimation(mp.gcf(), update, generator, interval=5)mp.show()

png