matplotlib教程学习笔记

这部分给出一些简单的示例。

Line Plot

  1. t = np.arange(0.0, 2.0, 0.01)
  2. s = 1 + np.sin(2 * np.pi * t)
  4. fig, ax = plt.subplots()
  5. ax.plot(t, s)
  7. ax.set(xlabel='time (s)', ylabel='voltage (mV)',
  8.        title='About as simple as it gets, folks') #设置一些属性
  9. ax.grid() #显示网格
  11. #fig.savefig("test.png")   保存图片在当前目录下
  12. plt.show()

matplotlib.axes.Axes.plot

matplotlib.pyplot.plot

matplotlib.pyplot.subplots

matplotlib.figure.Figure.savefig

  2. #First create some toy data:
  3. x = np.linspace(0, 2*np.pi, 400)
  4. y = np.sin(x**2)
  6. #Creates just a figure and only one subplot
  7. fig, ax = plt.subplots()
  8. ax.plot(x, y)
  9. ax.set_title('Simple plot')
  11. #Creates two subplots and unpacks the output array immediately
  12. f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
  13. ax1.plot(x, y)
  14. ax1.set_title('Sharing Y axis')
  15. ax2.scatter(x, y)
  17. #Creates four polar axes, and accesses them through the returned array
  18. fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
  19. axes[0, 0].plot(x, y)
  20. axes[1, 1].scatter(x, y)
  22. #Share a X axis with each column of subplots
  23. plt.subplots(2, 2, sharex='col')
  25. #Share a Y axis with each row of subplots
  26. plt.subplots(2, 2, sharey='row')
  28. #Share both X and Y axes with all subplots
  29. plt.subplots(2, 2, sharex='all', sharey='all')
  31. #Note that this is the same as
  32. plt.subplots(2, 2, sharex=True, sharey=True)
  34. #Creates figure number 10 with a single subplot
  35. #and clears it if it already exists.
  36. fig, ax=plt.subplots(num=10, clear=True)















One figure, a set of subplots

  2. x1 = np.linspace(0.0, 5.0)
  3. x2 = np.linspace(0.0, 2.0)
  5. y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
  6. y2 = np.cos(2 * np.pi * x2)
  8. plt.subplot(2, 1, 1)
  9. plt.plot(x1, y1, 'o-')
  10. plt.title('A tale of 2 subplots')
  11. plt.ylabel('Damped oscillation')
  13. plt.subplot(2, 1, 2)
  14. plt.plot(x2, y2, '.-')
  15. plt.xlabel('time (s)')
  16. plt.ylabel('Undamped')
  18. plt.show()

这样写也可以达到同样的目的：

  1. fig, (ax1, ax2) = plt.subplots(2, 1)   # one figure, 2 * 1 axes
  2. ax1.plot(x1, y1, 'o-')
  3. ax1.set(title='A tale of 2 subplots',
  4.         ylabel='Damped oscillation')
  6. ax2.plot(x2, y2, '.-')
  7. ax2.set(**{'xlabel':'times(s)',
  8.            'ylabel':'Undamped'})

均为下图：

Image 展示图片

imshow()

  1. import numpy as np
  2. import matplotlib.cm as cm
  3. import matplotlib.pyplot as plt
  4. import matplotlib.cbook as cbook
  5. from matplotlib.path import Path
  6. from matplotlib.patches import PathPatch

展示二元正态分布

  1. delta = 0.025
  2. x = y = np.arange(-3.0, 3.0, delta)
  3. X, Y = np.meshgrid(x, y)
  4. Z1 = np.exp(-X**2 - Y**2)
  5. Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
  6. Z = (Z1 - Z2) * 2
  8. fig, ax = plt.subplots()
  9. im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
  10.                origin='lower', extent=[-3, 3, -3, 3],
  11.                vmax=abs(Z).max(), vmin=-abs(Z).max())
  13. plt.show()

np.meshgrid()-千千Sama

A sample image

  1. # A sample image
  2. with cbook.get_sample_data('Lenna.png') as image_file:
  3.     image = plt.imread(image_file)
  5. fig, ax = plt.subplots()
  6. ax.imshow(image)
  7. ax.axis('off')  # clear x-axis and y-axis

Interpolating images 插值图像？

  2. A = np.random.rand(5, 5)
  4. fig, axs = plt.subplots(1, 3, figsize=(10, 3))
  5. for ax, interp in zip(axs, ['nearest', 'bilinear', 'bicubic']):
  6.     ax.imshow(A, interpolation=interp)
  7.     ax.set_title(interp.capitalize())
  8.     ax.grid(True)
  10. plt.show()

  1. x = np.arange(120).reshape((10, 12))
  3. interp = 'bilinear'
  4. fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(3, 5))
  5. axs[0].set_title('blue should be up')
  6. axs[0].imshow(x, origin='upper', interpolation=interp)
  8. axs[1].set_title('blue should be down')
  9. axs[1].imshow(x, origin='lower', interpolation=interp)
  10. plt.show()

Clip path

  2. delta = 0.025
  3. x = y = np.arange(-3.0, 3.0, delta)
  4. X, Y = np.meshgrid(x, y)
  5. Z1 = np.exp(-X**2 - Y**2)
  6. Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
  7. Z = (Z1 - Z2) * 2
  9. path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]])
  10. patch = PathPatch(path, facecolor='none')
  12. fig, ax = plt.subplots()
  13. ax.add_patch(patch)
  15. im = ax.imshow(Z, interpolation='bilinear', cmap=cm.gray,
  16.                origin='lower', extent=[-3, 3, -3, 3],
  17.                clip_path=patch, clip_on=True)
  18. im.set_clip_path(patch)
  20. plt.show()

matplotlib.axes.Axes.imshow

matplotlib.pyplot.imshow

matplotlib.artist.Artist.set_clip_path

matplotlib.patches.PathPatch

Contouring and Pseudocolor 轮廓与伪彩色

  1. # make these smaller to increase the resolution
  2. dx, dy = 0.05, 0.05
  4. # generate 2 2d grids for the x & y bounds
  5. y, x = np.mgrid[slice(1, 5 + dy, dy),
  6.                 slice(1, 5 + dx, dx)]
  8. z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)
  10. # x and y are bounds, so z should be the value *inside* those bounds.
  11. # Therefore, remove the last value from the z array.
  12. z = z[:-1, :-1]
  13. levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())
  15. # pick the desired colormap, sensible levels, and define a normalization
  16. # instance which takes data values and translates those into levels.
  17. cmap = plt.get_cmap('PiYG')
  18. norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) #压缩到[0-1]，因为颜色的要求？
  20. fig, (ax0, ax1) = plt.subplots(nrows=2)
  22. im = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm)
  23. fig.colorbar(im, ax=ax0)  # 添加右边的颜色的标度
  24. ax0.set_title('pcolormesh with levels')
  26. # contours are *point* based plots, so convert our bound into point
  27. # centers
  28. cf = ax1.contourf(x[:-1, :-1] + dx/2.,  #在轮廓里面填充颜色？
  29.                   y[:-1, :-1] + dy/2., z, levels=levels,
  30.                   cmap=cmap)
  31. fig.colorbar(cf, ax=ax1)
  32. ax1.set_title('contourf with levels')
  34. # adjust spacing between subplots so `ax1` title and `ax0` tick labels
  35. # don't overlap
  36. fig.tight_layout()
  38. plt.show()

numpy.mgrid j表示末端数字也包括

matplotlib.axes.Axes.pcolormesh

matplotlib.pyplot.pcolormesh

matplotlib.axes.Axes.contourf

matplotlib.pyplot.contourf

matplotlib.figure.Figure.colorbar

matplotlib.pyplot.colorbar

matplotlib.colors.BoundaryNorm

matplotlib.ticker.MaxNLocator

Histograms hist()

  2. np.random.seed(19680801)
  4. # example data
  5. mu = 100  # mean of distribution
  6. sigma = 15  # standard deviation of distribution
  7. x = mu + sigma * np.random.randn(437)#x ~ N(mu, sigma^2)
  9. num_bins = 50 #柱状图数目
  11. fig, ax = plt.subplots()
  13. # the histogram of the data
  14. n, bins, patches = ax.hist(x, num_bins, density=1)
  15. #n: 每个bin在图上对应的y轴，这里就是概率密度
  16. #bins:如果没猜错，是bin所对应的x轴（中间的那个值）
  17. #patches：应该是画bin所用的一些信息？
  18. #density:一个可选参数，如果为True，那么bins下的面积和会被调整(scale)为1,这样#就类似一个密度函数了
  20. # add a 'best fit' line
  21. y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
  22.      np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
  23. ax.plot(bins, y, '--')
  24. ax.set_xlabel('Smarts')
  25. ax.set_ylabel('Probability density')
  26. ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
  28. # Tweak spacing to prevent clipping of ylabel
  29. fig.tight_layout()
  30. plt.show()

matplotlib.axes.Axes.hist

matplotlib.axes.Axes.set_title

matplotlib.axes.Axes.set_xlabel

matplotlib.axes.Axes.set_ylabel

Paths

你可以使用matplotlib.path模块添加任意路径。

  1. fig, ax = plt.subplots()
  3. Path = mpath.Path  #import matplotlib.path as mpath
  4. path_data = [
  5.     (Path.MOVETO, (1.58, -2.57)),
  6.     (Path.CURVE4, (0.35, -1.1)),
  7.     (Path.CURVE4, (-1.75, 2.0)),
  8.     (Path.CURVE4, (0.375, 2.0)),
  9.     (Path.LINETO, (0.85, 1.15)),
  10.     (Path.CURVE4, (2.2, 3.2)),
  11.     (Path.CURVE4, (3, 0.05)),
  12.     (Path.CURVE4, (2.0, -0.5)),
  13.     (Path.CLOSEPOLY, (1.58, -2.57)),
  14.     ]
  15. codes, verts = zip(*path_data)
  16. #codes:(1, 4, 4, 4, 2, 4, 4, 4, 79) 大概是对应的path的方法，曲线啊，直线啊啥的
  17. #verts:((1.58, -2.57), (0.35, -1.1), (-1.75, 2.0), (0.375, 2.0), (0.85, 1.15),
  18. #(2.2, 3.2), (3, 0.05), (2.0, -0.5), (1.58, -2.57))
  19. path = mpath.Path(verts, codes)
  20. patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)
  21. ax.add_patch(patch)
  23. # plot control points and connecting lines
  24. x, y = zip(*path.vertices)
  25. #x:(1.58, 0.35, -1.75, 0.375, 0.85, 2.2, 3.0, 2.0, 1.58)
  26.  #y:(-2.57, -1.1, 2.0, 2.0, 1.15, 3.2, 0.05, -0.5, -2.57)
  27. line, = ax.plot(x, y, 'go-')
  29. ax.grid()
  30. ax.axis('equal')
  31. for i, (item1, item2) in enumerate(path_data, 1):
  32.     plt.text(item2[0], item2[1], str(i), size=20)
  34. plt.show()

zip() ，传入迭代子，讲不同迭代子相同位置的元素“捏”在一起返回一个新的迭代子。注意，视情况，zip不仅可以解压，而且可以反解。

matplotlib.path

matplotlib.path.Path

matplotlib.patches

matplotlib.patches.PathPatch

matplotlib.axes.Axes.add_patch

Three-dimensional plotting

mplot3d 工具包支持3d作图，比如曲面，线框，散点等。

  2. # This import registers the 3D projection, but is otherwise unused.
  3. from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
  5. import matplotlib.pyplot as plt
  6. from matplotlib import cm
  7. from matplotlib.ticker import LinearLocator, FormatStrFormatter
  8. import numpy as np
  10. fig = plt.figure()
  11. ax = fig.gca(projection='3d') #.gca() 获取当前的axes，如果有必要（大概是没有？），就创建一个
  12. #projection 设置工程为3d？
  14. # Make data.
  15. X = np.arange(-5, 5, 0.25)
  16. Y = np.arange(-5, 5, 0.25)
  17. X, Y = np.meshgrid(X, Y) #生成二维坐标点
  18. R = np.sqrt(X**2 + Y**2)
  19. Z = np.sin(R)
  21. # Plot the surface.
  22. surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
  23.                        linewidth=0, antialiased=False)
  25. # Customize the z axis.
  26. ax.set_zlim(-1.01, 1.01) #设置z轴标度的表示范围
  27. ax.zaxis.set_major_locator(LinearLocator(10)) #大概就是将z轴分成10格
  28. ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))#2位小数
  30. # Add a color bar which maps values to colors.
  31. fig.colorbar(surf, shrink=0.5, aspect=5) #添加颜色标度
  33. plt.show()

Streamplot 流线图？

streamplot()函数，可以画向量场的流线，还可以。。。

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. import matplotlib.gridspec as gridspec
  5. #并没有采用Demo里面的模型，而是选择了:
  6. #F=(-yi+xj)/(x^2+y^2)^(1/2)
  7. #缺点是，线宽啥的都体现不出来了（因为速度大小均为1）
  8. w = 3
  9. Y, X = np.mgrid[-w:w:100j, -w:w:100j] # 生成10000个[-3, 3] * [-3, 3]的均匀网格坐标
  10. U = -Y / np.sqrt(X ** 2 + Y ** 2)
  11. V = X / np.sqrt(X ** 2 + Y ** 2)
  12. speed = np.sqrt(U*U + V*V)
  13. print(np.where(speed == np.sqrt(2)))
  15. fig = plt.figure(figsize=(7, 9)) #7行9列
  16. #应该是把分好的块再重新分了, height_ratios, 从下面的图片中可以看到
  17. #3行图片的高度比例是1：1：2
  18. #不出意外，len(height_ratios) == nrows
  19. gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])
  21. #  Varying density along a streamline
  22. ax0 = fig.add_subplot(gs[0, 0])
  23. ax0.streamplot(X, Y, U, V, density=[0.5, 1])
  24. ax0.set_title('Varying Density')
  26. #x,y: 位置坐标
  27. #U, V： x and y-velocities(x轴分速度，y-轴分速度)
  29. # Varying color along a streamline
  30. ax1 = fig.add_subplot(gs[0, 1])
  31. strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn') #颜色的区别
  32. fig.colorbar(strm.lines)
  33. ax1.set_title('Varying Color')
  35. #  Varying line width along a streamline
  36. ax2 = fig.add_subplot(gs[1, 0])
  37. lw = 5*speed / speed.max()
  38. ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw) #不同密度线宽不同
  39. ax2.set_title('Varying Line Width')
  41. # Controlling the starting points of the streamlines
  42. seed_points = np.array([[-2, -1, 0, 1, 2, -1], [-2, -1,  0, 1, 2, 2]])
  43. #与图中蓝色点对应，好像是只有经过上述某点的流线才被保留
  45. ax3 = fig.add_subplot(gs[1, 1])
  46. strm = ax3.streamplot(X, Y, U, V, color=U, linewidth=2,
  47.                      cmap='autumn', start_points=seed_points.T)
  48. fig.colorbar(strm.lines)
  49. ax3.set_title('Controlling Starting Points')
  51. # Displaying the starting points with blue symbols.
  52. ax3.plot(seed_points[0], seed_points[1], 'bo')
  53. ax3.axis((-w, w, -w, w))
  55. # Create a mask
  56. mask = np.zeros(U.shape, dtype=bool)
  57. mask[40:60, 40:60] = True
  58. U[:20, :20] = np.nan
  59. U = np.ma.array(U, mask=mask) #掩码数组
  61. ax4 = fig.add_subplot(gs[2:, :])
  62. ax4.streamplot(X, Y, U, V, color='r')
  63. ax4.set_title('Streamplot with Masking')
  65. ax4.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
  66.           interpolation='nearest', cmap='gray', aspect='auto')
  67. ax4.set_aspect('equal')
  69. plt.tight_layout()
  70. plt.show()

matplotlib.axes.Axes.streamplot

matplotlib.pyplot.streamplot

matplotlib.gridspec

matplotlib.gridspec.GridSpec

Ellipses 椭圆

  2. #这个例子画了很多椭圆
  3. import matplotlib.pyplot as plt
  4. import numpy as np
  5. from matplotlib.patches import Ellipse
  7. NUM = 250 #250个
  9. ells = [Ellipse(xy=np.random.rand(2) * 10, #（x, y） [0,10)
  10.                 width=np.random.rand(), height=np.random.rand(),
  11.                 angle=np.random.rand() * 360)  #看来angle的表示是用度
  12.         for i in range(NUM)]
  14. fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'})
  15. for e in ells:
  16.     ax.add_artist(e)
  17.     e.set_clip_box(ax.bbox)#
  18.     e.set_alpha(np.random.rand())
  19.     e.set_facecolor(np.random.rand(3)) #rgb?
  21. ax.set_xlim(0, 10)
  22. ax.set_ylim(0, 10)
  24. plt.show()

  2. import matplotlib.pyplot as plt
  3. import numpy as np
  4. from matplotlib.patches import Ellipse
  6. delta = 45.0  # degrees 每次偏转的角度
  8. angles = np.arange(0, 360 + delta, delta)
  9. ells = [Ellipse((1, 1), 4, 2, a) for a in angles]
  11. a = plt.subplot(111, aspect='equal')
  13. for e in ells:
  14.     e.set_clip_box(a.bbox)
  15.     e.set_alpha(0.1)
  16.     a.add_artist(e) #和上面的例子进行比较，可以发现，这行代码是绑定
  18. plt.xlim(-2, 4)
  19. plt.ylim(-1, 3)
  21. plt.show()

matplotlib.patches

matplotlib.patches.Ellipse

matplotlib.axes.Axes.add_artist

matplotlib.artist.Artist.set_clip_box

matplotlib.artist.Artist.set_alpha

matplotlib.patches.Patch.set_facecolor

Bar charts 条形图

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. from matplotlib.ticker import MaxNLocator
  4. from collections import namedtuple
  6. n_groups = 5
  8. means_men = (20, 35, 30, 35, 27)
  9. std_men = (2, 3, 4, 1, 2)
  11. means_women = (25, 32, 34, 20, 25)
  12. std_women = (3, 5, 2, 3, 3)
  14. fig, ax = plt.subplots()
  16. index = np.arange(n_groups)
  17. bar_width = 0.35 #条宽
  19. opacity = 0.4 #不透明度
  20. error_config = {'ecolor': '#0000FF'} #设置误差的一些属性
  21. error_config2 = {'ecolor': '#00FF00'}
  23. rects1 = ax.bar(index, means_men, bar_width,
  24.                 alpha=opacity, color='b',
  25.                 yerr=std_men, error_kw=error_config, #有yerr才会有那个一竖
  26.                 label='Men')
  28. rects2 = ax.bar(index + bar_width, means_women, bar_width,
  29.                 alpha=opacity, color='r',
  30.                 yerr=std_women, error_kw=error_config2,
  31.                 label='Women')
  33. ax.set_xlabel('Group')
  34. ax.set_ylabel('Scores')
  35. ax.set_title('Scores by group and gender')
  36. ax.set_xticks(index + bar_width / 2)
  37. ax.set_xticklabels(('A', 'B', 'C', 'D', 'E')) #设置x轴标签
  38. ax.legend()
  40. fig.tight_layout()
  41. plt.show()

  3. # Credit: Josh Hemann
  5. import numpy as np
  6. import matplotlib.pyplot as plt
  7. from matplotlib.ticker import MaxNLocator
  8. from collections import namedtuple
  10. Student = namedtuple('Student', ['name', 'grade', 'gender']) #利用name来存取的tuple
  11. Score = namedtuple('Score', ['score', 'percentile'])
  13. # GLOBAL CONSTANTS
  14. testNames = ['Pacer Test', 'Flexed Arm\n Hang', 'Mile Run', 'Agility',
  15.              'Push Ups']  #测试项目的名称
  16. testMeta = dict(zip(testNames, ['laps', 'sec', 'min:sec', 'sec', ''])) #项目与单位匹配
  18. def attach_ordinal(num):
  19.     """helper function to add ordinal string to integers
  21.     1 -> 1st
  22.     56 -> 56th
  23.     """
  24.     suffixes = {str(i): v
  25.                 for i, v in enumerate(['th', 'st', 'nd', 'rd', 'th',
  26.                                        'th', 'th', 'th', 'th', 'th'])} #名次缩写的匹配
  28.     v = str(num)
  29.     # special case early teens
  30.     if v in {'11', '12', '13'}:
  31.         return v + 'th' #特殊情况
  32.     return v + suffixes[v[-1]] #普通情况依照最后一位数字分配缩写
  34. def format_score(scr, test):
  35.     """
  36.     Build up the score labels for the right Y-axis by first
  37.     appending a carriage return to each string and then tacking on
  38.     the appropriate meta information (i.e., 'laps' vs 'seconds'). We
  39.     want the labels centered on the ticks, so if there is no meta
  40.     info (like for pushups) then don't add the carriage return to
  41.     the string
  42.     """
  43.     md = testMeta[test] #获取对应的单位
  44.     if md:
  45.         return '{0}\n{1}'.format(scr, md)
  46.     else:
  47.         return scr
  49. def format_ycursor(y):
  50.     y = int(y)
  51.     if y < 0 or y >= len(testNames):
  52.         return ''
  53.     else:
  54.         return testNames[y]
  56. def plot_student_results(student, scores, cohort_size):
  57.     #  create the figure
  58.     fig, ax1 = plt.subplots(figsize=(9, 7))
  59.     fig.subplots_adjust(left=0.115, right=0.88)
  60.     fig.canvas.set_window_title('Eldorado K-8 Fitness Chart')
  62.     pos = np.arange(len(testNames))
  64.     rects = ax1.barh(pos, [scores[k].percentile for k in testNames],
  65.                      align='center',
  66.                      height=0.5, color='m', #height 就是bar的高（用宽可能更加贴切）占的百分比
  67.                      tick_label=testNames)
  69.     ax1.set_title(student.name)
  71.     ax1.set_xlim([0, 100]) #设置x轴的范围
  72.     ax1.xaxis.set_major_locator(MaxNLocator(11)) #MaxNLocator：就是x轴最多的标度数目为11
  73.     ax1.xaxis.grid(True, linestyle='--', which='major', #which???
  74.                    color='grey', alpha=.25)
  76.     # Plot a solid vertical gridline to highlight the median position
  77.     ax1.axvline(50, color='grey', alpha=0.25) #画一条实线，在50坐标处
  78.     # set X-axis tick marks at the deciles
  79.     cohort_label = ax1.text(.5, -.07, 'Cohort Size: {0}'.format(cohort_size),
  80.                             horizontalalignment='center', size='small',
  81.                             transform=ax1.transAxes)
  83.     # Set the right-hand Y-axis ticks and labels
  84.     ax2 = ax1.twinx() #twinx, 拥有同一个x？
  86.     scoreLabels = [format_score(scores[k].score, k) for k in testNames]
  88.     # set the tick locations
  89.     ax2.set_yticks(pos)
  90.     # make sure that the limits are set equally on both yaxis so the
  91.     # ticks line up
  92.     ax2.set_ylim(ax1.get_ylim()) #ax2设置为同ax1一样的y的范围
  94.     # set the tick labels
  95.     ax2.set_yticklabels(scoreLabels)
  97.     ax2.set_ylabel('Test Scores')
  99.     ax2.set_xlabel(('Percentile Ranking Across '
  100.                     '{grade} Grade {gender}s').format(
  101.                         grade=attach_ordinal(student.grade),
  102.                         gender=student.gender.title()))
  104.     rect_labels = []
  105.     # Lastly, write in the ranking inside each bar to aid in interpretation
  106.     for rect in rects: #rect 条 每个bar
  107.         # Rectangle widths are already integer-valued but are floating
  108.         # type, so it helps to remove the trailing decimal point and 0 by
  109.         # converting width to int type
  110.         width = int(rect.get_width())
  112.         rankStr = attach_ordinal(width)
  113.         # The bars aren't wide enough to print the ranking inside
  114.         if width < 5: #如果bar的宽度(可能叫长度更为贴切)不够容纳排名，那么就写在右边
  115.             # Shift the text to the right side of the right edge
  116.             xloc = width + 1
  117.             # Black against white background
  118.             clr = 'black'
  119.             align = 'left'#应该是指，xloc是文本的左贴边
  120.         else:
  121.             # Shift the text to the left side of the right edge
  122.             xloc = 0.98*width
  123.             # White on magenta
  124.             clr = 'white'
  125.             align = 'right'
  127.         # Center the text vertically in the bar
  128.         yloc = rect.get_y() + rect.get_height()/2.0 #居中显示
  129.         label = ax1.text(xloc, yloc, rankStr, horizontalalignment=align,
  130.                          verticalalignment='center', color=clr, weight='bold',
  131.                          clip_on=True)
  132.         rect_labels.append(label)
  134.     # make the interactive mouse over give the bar title
  135.     ax2.fmt_ydata = format_ycursor #这个交互信息，从图片没法直接体现
  136.     # return all of the artists created
  137.     return {'fig': fig,
  138.             'ax': ax1,
  139.             'ax_right': ax2,
  140.             'bars': rects,
  141.             'perc_labels': rect_labels,
  142.             'cohort_label': cohort_label}
  144. student = Student('Johnny Doe', 2, 'boy')
  145. scores = dict(zip(testNames,
  146.                   (Score(v, p) for v, p in
  147.                    zip(['7', '48', '12:52', '17', '14'],
  148.                        np.round(np.random.uniform(0, 1,
  149.                                                   len(testNames))*100, 0)))))
  150. cohort_size = 62  # The number of other 2nd grade boys
  152. arts = plot_student_results(student, scores, cohort_size)
  153. plt.show()

Pie charts 饼图

本节的例子同时对下面的功能进行了展式：

  • 切片标签
  • 自动标记百分比
  • 用“爆炸”来偏移切片
  • 下拉阴影
  • 自定义起始角度
  2. import matplotlib.pyplot as plt
  4. # Pie chart, where the slices will be ordered and plotted counter-clockwise:
  5. labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
  6. sizes = [15, 30, 43, 10] #sum == 98 not 100
  7. explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs') hogs被孤立了
  9. fig1, ax1 = plt.subplots()
  10. ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
  11.         shadow=True, startangle=90) #startangle :起始角度
  12. ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
  14. plt.show()

matplotlib.axes.Axes.pie

matplotlib.pyplot.pie

Tables

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. data = [[ 66386, 174296,  75131, 577908,  32015],
  5.         [ 58230, 381139,  78045,  99308, 160454],
  6.         [ 89135,  80552, 152558, 497981, 603535],
  7.         [ 78415,  81858, 150656, 193263,  69638],
  8.         [139361, 331509, 343164, 781380,  52269]]
  10. columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
  11. rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]
  13. values = np.arange(0, 2500, 500)
  14. value_increment = 1000
  16. # Get some pastel shades for the colors
  17. colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))#colormap?
  18. n_rows = len(data)
  20. index = np.arange(len(columns)) + 0.3
  21. bar_width = 0.4
  23. # Initialize the vertical-offset for the stacked bar chart.
  24. y_offset = np.zeros(len(columns))
  26. # Plot bars and create text labels for the table
  27. cell_text = []
  28. for row in range(n_rows):
  29.     plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row]) #bottom!!!!!!!!!!!!!
  30.     y_offset = y_offset + data[row]
  31.     cell_text.append(['%1.1f' % (x / 1000.0) for x in y_offset]) #记录下每层的数据
  32. # Reverse colors and text labels to display the last value at the top.
  33. colors = colors[::-1]
  34. cell_text.reverse()
  36. # Add a table at the bottom of the axes
  37. the_table = plt.table(cellText=cell_text, #在图片下方加一个表格
  38.                       rowLabels=rows,
  39.                       rowColours=colors,
  40.                       colLabels=columns,
  41.                       loc='bottom')#loc location 位置
  43. # Adjust layout to make room for the table:
  44. plt.subplots_adjust(left=0.2, bottom=0.2)
  46. plt.ylabel("Loss in ${0}'s".format(value_increment))
  47. plt.yticks(values * value_increment, ['%d' % val for val in values])
  48. plt.xticks([])
  49. plt.title('Loss by Disaster')
  51. plt.show()

Scatter plots 散点图

下面代码我并没有执行，图片是直接照搬的。

  2. import numpy as np
  3. import matplotlib.pyplot as plt
  4. import matplotlib.cbook as cbook
  6. # Load a numpy record array from yahoo csv data with fields date, open, close,
  7. # volume, adj_close from the mpl-data/example directory. The record array
  8. # stores the date as an np.datetime64 with a day unit ('D') in the date column.
  9. with cbook.get_sample_data('goog.npz') as datafile:
  10.     price_data = np.load(datafile)['price_data'].view(np.recarray)
  11. price_data = price_data[-250:]  # get the most recent 250 trading days
  13. delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]
  15. # Marker size in units of points^2
  16. volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
  17. close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]
  19. fig, ax = plt.subplots()
  20. ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)
  22. ax.set_xlabel(r'$\Delta_i$', fontsize=15)
  23. ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=15)
  24. ax.set_title('Volume and percent change')
  26. ax.grid(True)
  27. fig.tight_layout()
  29. plt.show()

GUI widgets

  2. import numpy as np
  3. import matplotlib.pyplot as plt
  4. from matplotlib.widgets import Slider, Button, RadioButtons
  6. fig, ax = plt.subplots()
  7. plt.subplots_adjust(left=0.25, bottom=0.25) #左边右边都留点空
  8. t = np.arange(0.0, 1.0, 0.001)
  9. a0 = 5 #幅度
  10. f0 = 3 #频率相关
  11. delta_f = 5.0 #频率的幅度吧
  12. s = a0*np.sin(2*np.pi*f0*t)
  13. l, = plt.plot(t, s, lw=2, color='red')
  14. plt.axis([0, 1, -10, 10])   # 设置axis的范围  x:[0, 1], y:[-10, 10]
  16. axcolor = 'lightgoldenrodyellow'  #调整框的颜色
  17. axfreq = plt.axes([0.25, 0.3, 0.65, 0.03], facecolor=axcolor)  #[left, bottom, length, height]
  18. axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
  20. sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=delta_f) #valstep?
  21. samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
  23. def update(val):
  24.     amp = samp.val #获取调节后的amp  幅度
  25.     freq = sfreq.val  #获取调节后的 freq  频率
  26.     l.set_ydata(amp*np.sin(2*np.pi*freq*t))  #画新的图
  27.     fig.canvas.draw_idle()   #？？？
  28. sfreq.on_changed(update)   #绑定更新函数
  29. samp.on_changed(update)
  31. resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
  32. #设定button reset的一些属性
  33. button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
  35. def reset(event):
  36.     sfreq.reset()
  37.     samp.reset()
  38. button.on_clicked(reset) #button reset点击动作绑定reset
  40. rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)
  41. radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)  # 倒是有点像网页 选择按钮
  43. def colorfunc(label):   #改变曲线颜色
  44.     l.set_color(label)
  45.     fig.canvas.draw_idle()
  46. radio.on_clicked(colorfunc)  #绑定colorfunc到选择按钮
  48. plt.show()

matplotlib.pyplot.axes

matplotlib.widgets

Filled curves 填充曲线？

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. x = [0, 1, 2, 1]
  5. y = [1, 2, 1, 0]
  7. fig, ax = plt.subplots()
  8. ax.fill(x, y)
  9. plt.show()

下面的例子要稍微复杂一些，涉及多条曲线，设置填充颜色，设置透明度等。

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. x = np.linspace(0, 1.5 * np.pi, 500)
  5. y1 = np.sin(x)
  6. y2 = np.sin(3 * x)
  8. fig, ax = plt.subplots()
  10. ax.fill(x, y1, 'b', x, y2, 'r', alpha=0.3)
  12. # Outline of the region we've filled in
  13. ax.plot(x, y1, c='b', alpha=0.8)
  14. ax.plot(x, y2, c='r', alpha=0.8)
  15. ax.plot([x[0], x[-1]], [y1[0], y1[-1]], c='b', alpha=0.8)
  16. ax.plot([x[0], x[-1]], [y2[0], y2[-1]], c='r', alpha=0.8)
  18. plt.show()

matplotlib.pyplot.fill

Date handling 处理时间（序列?）

下面代码并没有跑，只是照搬

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. import matplotlib.dates as mdates
  4. import matplotlib.cbook as cbook
  6. years = mdates.YearLocator()   # every year
  7. months = mdates.MonthLocator()  # every month
  8. yearsFmt = mdates.DateFormatter('%Y')
  10. # Load a numpy record array from yahoo csv data with fields date, open, close,
  11. # volume, adj_close from the mpl-data/example directory. The record array
  12. # stores the date as an np.datetime64 with a day unit ('D') in the date column.
  13. with cbook.get_sample_data('goog.npz') as datafile:
  14.     r = np.load(datafile)['price_data'].view(np.recarray)
  16. fig, ax = plt.subplots()
  17. ax.plot(r.date, r.adj_close)
  19. # format the ticks
  20. ax.xaxis.set_major_locator(years)
  21. ax.xaxis.set_major_formatter(yearsFmt)
  22. ax.xaxis.set_minor_locator(months)
  24. # round to nearest years...
  25. datemin = np.datetime64(r.date[0], 'Y')
  26. datemax = np.datetime64(r.date[-1], 'Y') + np.timedelta64(1, 'Y')
  27. ax.set_xlim(datemin, datemax)
  29. # format the coords message box
  30. def price(x):
  31.     return '$%1.2f' % x
  32. ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
  33. ax.format_ydata = price
  34. ax.grid(True)
  36. # rotates and right aligns the x labels, and moves the bottom of the
  37. # axes up to make room for them
  38. fig.autofmt_xdate()
  40. plt.show()

Log plots

  3. import numpy as np
  4. import matplotlib.pyplot as plt
  6. # Data for plotting
  7. t = np.arange(0.01, 20.0, 0.01)
  9. # Create figure
  10. fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
  12. # log y axis
  13. ax1.semilogy(t, np.exp(-t / 5.0))
  14. ax1.set(title='semilogy')
  15. ax1.grid()
  17. # log x axis
  18. ax2.semilogx(t, np.sin(2 * np.pi * t))
  19. ax2.set(title='semilogx')
  20. ax2.grid()
  22. # log x and y axis
  23. ax3.loglog(t, 20 * np.exp(-t / 10.0), basex=2)
  24. ax3.set(title='loglog base 2 on x')
  25. ax3.grid()
  27. # With errorbars: clip non-positive values
  28. # Use new data for plotting
  29. x = 10.0**np.linspace(0.0, 2.0, 20)
  30. y = x**2.0
  32. ax4.set_xscale("log", nonposx='clip')
  33. ax4.set_yscale("log", nonposy='clip')
  34. ax4.set(title='Errorbars go negative')
  35. ax4.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)
  36. # ylim must be set after errorbar to allow errorbar to autoscale limits
  37. ax4.set_ylim(bottom=0.1)
  39. fig.tight_layout()
  40. plt.show()

matplotlib.axes. Axes.semilogx

matplotlib.axes. Axes.semilogy

Polar plots 极坐标

  2. import numpy as np
  3. import matplotlib.pyplot as plt
  5. r = np.arange(0, 2, 0.01)
  6. theta = 2 * np.pi * r #看来采用的是弧度制
  8. ax = plt.subplot(111, projection='polar')
  9. ax.plot(theta, r)
  10. ax.set_rmax(2) #数字大了 整体会缩小？明白了，rmax就是极坐标最大半径，最大半径设置得越大，相应得原本的内容就显得越小。
  11. ax.set_rticks([0.5, 1, 1.5, 2])  # Less radial ticks
  12. ax.set_rlabel_position(-12.5)  # 0.5 1 1.5 2位于-12.5度处
  13. ax.grid(True)
  15. ax.set_title("A line plot on a polar axis", va='bottom')
  16. plt.show()

matplotlib.axes.Axes.plot

matplotlib.projections.polar

matplotlib.projections.polar.PolarAxes

matplotlib.projections.polar.PolarAxes.set_rticks

matplotlib.projections.polar.PolarAxes.set_rmax

matplotlib.projections.polar.PolarAxes.set_rlabel_position

Legends 图例

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. # Make some fake data.
  5. a = b = np.arange(0, 3, .02)
  6. c = np.exp(a)
  7. d = c[::-1]
  9. # Create plots with pre-defined labels.
  10. fig, ax = plt.subplots()
  11. ax.plot(a, c, 'k--', label='Model length')
  12. ax.plot(a, d, 'k:', label='Data length')
  13. ax.plot(a, c + d, 'k', label='Total message length')
  15. legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')
  16. #loc: location best:0, upper right:1 upper left:2 ...
  17. #shadow 是否添加阴影
  18. #fontsize  字体大小
  19. # xx-small, x-small, small, medium, large, x-large, xx-large, larger, smaller, None
  20. #或者，数字 fontsize=15
  22. # Put a nicer background color on the legend.
  23. legend.get_frame().set_facecolor('C0')
  25. plt.show()

matplotlib.axes.Axes.plot

matplotlib.pyplot.plot

matplotlib.axes.Axes.legend

matplotlib.pyplot.legend

TeX-notation for text objects

  3. import matplotlib.pyplot as plt
  4. import subprocess
  5. import sys
  6. import re
  8. # Selection of features following "Writing mathematical expressions" tutorial
  9. mathtext_titles = {
  10.     0: "Header demo",
  11.     1: "Subscripts and superscripts",
  12.     2: "Fractions, binomials and stacked numbers",
  13.     3: "Radicals",
  14.     4: "Fonts",
  15.     5: "Accents",
  16.     6: "Greek, Hebrew",
  17.     7: "Delimiters, functions and Symbols"}
  18. n_lines = len(mathtext_titles)
  20. # Randomly picked examples
  21. mathext_demos = {
  22.     0: r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = "
  23.     r"U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} "
  24.     r"\int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ "
  25.     r"U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_"
  26.     r"{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$",
  28.     1: r"$\alpha_i > \beta_i,\ "
  29.     r"\alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ "
  30.     r"\ldots$",
  32.     2: r"$\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ "
  33.     r"\left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$",
  35.     3: r"$\sqrt{2},\ \sqrt[3]{x},\ \ldots$",
  37.     4: r"$\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ "
  38.     r"\mathrm{or}\ \mathcal{CALLIGRAPHY}$",
  40.     5: r"$\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ "
  41.     r"\hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ "
  42.     r"\ldots$",
  44.     6: r"$\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ "
  45.     r"\Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ "
  46.     r"\aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$",
  48.     7: r"$\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ "
  49.     r"\log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ "
  50.     r"\infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$"}
  52. def doall():
  53.     # Colors used in mpl online documentation.
  54.     mpl_blue_rvb = (191. / 255., 209. / 256., 212. / 255.)
  55.     mpl_orange_rvb = (202. / 255., 121. / 256., 0. / 255.)
  56.     mpl_grey_rvb = (51. / 255., 51. / 255., 51. / 255.)
  58.     # Creating figure and axis.
  59.     plt.figure(figsize=(6, 7))
  60.     plt.axes([0.01, 0.01, 0.98, 0.90], facecolor="white", frameon=True)
  61.     plt.gca().set_xlim(0., 1.)
  62.     plt.gca().set_ylim(0., 1.)
  63.     plt.gca().set_title("Matplotlib's math rendering engine",
  64.                         color=mpl_grey_rvb, fontsize=14, weight='bold')
  65.     plt.gca().set_xticklabels("", visible=False)
  66.     plt.gca().set_yticklabels("", visible=False)
  68.     # Gap between lines in axes coords
  69.     line_axesfrac = (1. / (n_lines))
  71.     # Plotting header demonstration formula
  72.     full_demo = mathext_demos[0]
  73.     plt.annotate(full_demo,
  74.                  xy=(0.5, 1. - 0.59 * line_axesfrac),
  75.                  xycoords='data', color=mpl_orange_rvb, ha='center',
  76.                  fontsize=20)
  78.     # Plotting features demonstration formulae
  79.     for i_line in range(1, n_lines):
  80.         baseline = 1 - (i_line) * line_axesfrac
  81.         baseline_next = baseline - line_axesfrac
  82.         title = mathtext_titles[i_line] + ":"
  83.         fill_color = ['white', mpl_blue_rvb][i_line % 2]
  84.         plt.fill_between([0., 1.], [baseline, baseline],
  85.                          [baseline_next, baseline_next],
  86.                          color=fill_color, alpha=0.5)
  87.         plt.annotate(title,
  88.                      xy=(0.07, baseline - 0.3 * line_axesfrac),
  89.                      xycoords='data', color=mpl_grey_rvb, weight='bold')
  90.         demo = mathext_demos[i_line]
  91.         plt.annotate(demo,
  92.                      xy=(0.05, baseline - 0.75 * line_axesfrac),
  93.                      xycoords='data', color=mpl_grey_rvb,
  94.                      fontsize=16)
  96.     for i in range(n_lines):
  97.         s = mathext_demos[i]
  98.         print(i, s)
  99.     plt.show()
  101. if '--latex' in sys.argv:
  102.     # Run: python mathtext_examples.py --latex
  103.     # Need amsmath and amssymb packages.
  104.     fd = open("mathtext_examples.ltx", "w")
  105.     fd.write("\\documentclass{article}\n")
  106.     fd.write("\\usepackage{amsmath, amssymb}\n")
  107.     fd.write("\\begin{document}\n")
  108.     fd.write("\\begin{enumerate}\n")
  110.     for i in range(n_lines):
  111.         s = mathext_demos[i]
  112.         s = re.sub(r"(?<!\\)\$", "$$", s)
  113.         fd.write("\\item %s\n" % s)
  115.     fd.write("\\end{enumerate}\n")
  116.     fd.write("\\end{document}\n")
  117.     fd.close()
  119.     subprocess.call(["pdflatex", "mathtext_examples.ltx"])
  120. else:
  121.     doall()

Native TeX rendering

不晓得，为啥使用原始的TeX,我的程序会挂，不过，matplotlib提供的已经足够了吧。下面的直接照搬了。

  1. import numpy as np
  2. import matplotlib
  3. matplotlib.rcParams['text.usetex'] = True
  4. import matplotlib.pyplot as plt
  6. t = np.linspace(0.0, 1.0, 100)
  7. s = np.cos(4 * np.pi * t) + 2
  9. fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)
  10. ax.plot(t, s)
  12. ax.set_xlabel(r'\textbf{time (s)}')
  13. ax.set_ylabel('\\textit{Velocity (\N{DEGREE SIGN}/sec)}', fontsize=16)
  14. ax.set_title(r'\TeX\ is Number $\displaystyle\sum_{n=1}^\infty'
  15.              r'\frac{-e^{i\pi}}{2^n}$!', fontsize=16, color='r')
  16. plt.show()

EEG GUI

你可以把matplotlib嵌入到pygtk,wx,Tk或者Qt应用中。

这部分姑且先跳过吧。

  1. import tkinter
  3. from matplotlib.backends.backend_tkagg import (
  4.     FigureCanvasTkAgg, NavigationToolbar2Tk)
  5. # Implement the default Matplotlib key bindings.
  6. from matplotlib.backend_bases import key_press_handler
  7. from matplotlib.figure import Figure
  9. import numpy as np
  11. root = tkinter.Tk()
  12. root.wm_title("Embedding in Tk")
  14. fig = Figure(figsize=(5, 4), dpi=100)
  15. t = np.arange(0, 3, .01)
  16. fig.add_subplot(111).plot(t, 2 * np.sin(2 * np.pi * t))
  18. canvas = FigureCanvasTkAgg(fig, master=root)  # A tk.DrawingArea.
  19. canvas.draw()
  20. canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
  22. toolbar = NavigationToolbar2Tk(canvas, root)
  23. toolbar.update()
  24. canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
  26. def on_key_press(event):
  27.     print("you pressed {}".format(event.key))
  28.     key_press_handler(event, canvas, toolbar)
  30. canvas.mpl_connect("key_press_event", on_key_press)
  32. def _quit():
  33.     root.quit()     # stops mainloop
  34.     root.destroy()  # this is necessary on Windows to prevent
  35.                     # Fatal Python Error: PyEval_RestoreThread: NULL tstate
  37. button = tkinter.Button(master=root, text="Quit", command=_quit)
  38. button.pack(side=tkinter.BOTTOM)
  40. tkinter.mainloop()
  41. # If you put root.destroy() here, it will cause an error if the window is
  42. # closed with the window manager.

XKCD-style sketch plots

单纯为了好玩，matplotlib支持xkcd-style.

  1. import matplotlib.pyplot as plt
  2. import numpy as np
  4. with plt.xkcd():
  5.     # Based on "Stove Ownership" from XKCD by Randall Munroe
  6.     # http://xkcd.com/418/
  8.     fig = plt.figure()
  9.     ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))
  10.     ax.spines['right'].set_color('none')
  11.     ax.spines['top'].set_color('none')
  12.     plt.xticks([])
  13.     plt.yticks([])
  14.     ax.set_ylim([-30, 10])
  16.     data = np.ones(100)
  17.     data[70:] -= np.arange(30)
  19.     plt.annotate(
  20.         'THE DAY I REALIZED\nI COULD COOK BACON\nWHENEVER I WANTED',
  21.         xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))
  23.     plt.plot(data)
  25.     plt.xlabel('time')
  26.     plt.ylabel('my overall health')
  27.     fig.text(
  28.         0.5, 0.05,
  29.         '"Stove Ownership" from xkcd by Randall Munroe',
  30.         ha='center')
  31. with plt.xkcd():
  32.     # Based on "The Data So Far" from XKCD by Randall Munroe
  33.     # http://xkcd.com/373/
  35.     fig = plt.figure()
  36.     ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))
  37.     ax.bar([0, 1], [0, 100], 0.25)
  38.     ax.spines['right'].set_color('none')
  39.     ax.spines['top'].set_color('none')
  40.     ax.xaxis.set_ticks_position('bottom')
  41.     ax.set_xticks([0, 1])
  42.     ax.set_xlim([-0.5, 1.5])
  43.     ax.set_ylim([0, 110])
  44.     ax.set_xticklabels(['CONFIRMED BY\nEXPERIMENT', 'REFUTED BY\nEXPERIMENT'])
  45.     plt.yticks([])
  47.     plt.title("CLAIMS OF SUPERNATURAL POWERS")
  49.     fig.text(
  50.         0.5, 0.05,
  51.         '"The Data So Far" from xkcd by Randall Munroe',
  52.         ha='center')
  54. plt.show()

Subplot examples

  1. import matplotlib.pyplot as plt
  2. import numpy as np
  4. np.random.seed(19680801)
  5. data = np.random.randn(2, 100)
  7. fig, axs = plt.subplots(2, 2, figsize=(5, 5))
  8. axs[0, 0].hist(data[0])
  9. axs[1, 0].scatter(data[0], data[1])
  10. axs[0, 1].plot(data[0], data[1])
  11. axs[1, 1].hist2d(data[0], data[1])
  13. plt.show()

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