Python绘图Matplotlib之坐标轴及刻度总结

学习https://matplotlib.org/gallery/index.html 记录，描述不一定准确，具体请参考官网

Matplotlib使用总结图

1. import matplotlib.pyplot as plt
2. plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签
3. plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号
4.  
5. import pandas as pd
6. import numpy as np

新建隐藏坐标轴

1. from mpl_toolkits.axisartist.axislines import SubplotZero
2. import numpy as np
3.  
4. fig = plt.figure(1, (10, 6))
5.  
6. ax = SubplotZero(fig, 1, 1, 1)
7. fig.add_subplot(ax)
8.  
9. """新建坐标轴"""
10. ax.axis["xzero"].set_visible(True)
11. ax.axis["xzero"].label.set_text("新建y=0坐标")
12. ax.axis["xzero"].label.set_color('green')
13. # ax.axis['yzero'].set_visible(True)
14. # ax.axis["yzero"].label.set_text("新建x=0坐标")
15.  
16. # 新建一条y=2横坐标轴
17. ax.axis["新建1"] = ax.new_floating_axis(nth_coord=0, value=2,axis_direction="bottom")
18. ax.axis["新建1"].toggle(all=True)
19. ax.axis["新建1"].label.set_text("y = 2横坐标")
20. ax.axis["新建1"].label.set_color('blue')
21.  
22. """坐标箭头"""
23. ax.axis["xzero"].set_axisline_style("-|>")
24.  
25. """隐藏坐标轴"""
26. # 方法一：隐藏上边及右边
27. # ax.axis["right"].set_visible(False)
28. # ax.axis["top"].set_visible(False)
29. #方法二：可以一起写
30. ax.axis["top",'right'].set_visible(False)
31. # 方法三：利用 for in
32. # for n in ["bottom", "top", "right"]:
33. # ax.axis[n].set_visible(False)
34.  
35. """设置刻度"""
36. ax.set_ylim(-3, 3)
37. ax.set_yticks([-1,-0.5,0,0.5,1])
38. ax.set_xlim([-5, 8])
39. # ax.set_xticks([-5,5,1])
40.  
41. #设置网格样式
42. ax.grid(True, linestyle='-.')
43.  
44. xx = np.arange(-4, 2*np.pi, 0.01)
45. ax.plot(xx, np.sin(xx))
46.  
47. # 于 offset 处新建一条纵坐标
48. offset = (40, 0)
49. new_axisline = ax.get_grid_helper().new_fixed_axis
50. ax.axis["新建2"] = new_axisline(loc="right", offset=offset, axes=ax)
51. ax.axis["新建2"].label.set_text("新建纵坐标")
52. ax.axis["新建2"].label.set_color('red')
53.  
54. plt.show()
55. # 存为图像
56. # fig.savefig('test.png')

1. from mpl_toolkits.axes_grid1 import host_subplot
2. import mpl_toolkits.axisartist as AA
3. import matplotlib.pyplot as plt
4.  
5. host = host_subplot(111, axes_class=AA.Axes)
6. plt.subplots_adjust(right=0.75)
7.  
8. par1 = host.twinx()
9. par2 = host.twinx()
10.  
11. offset = 100
12. new_fixed_axis = par2.get_grid_helper().new_fixed_axis
13. par2.axis["right"] = new_fixed_axis(loc="right",
14. axes=par2,
15. offset=(offset, 0))
16.  
17. par1.axis["right"].toggle(all=True)
18. par2.axis["right"].toggle(all=True)
19.  
20. host.set_xlim(0, 2)
21. host.set_ylim(0, 2)
22.  
23. host.set_xlabel("Distance")
24. host.set_ylabel("Density")
25. par1.set_ylabel("Temperature")
26. par2.set_ylabel("Velocity")
27.  
28. p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
29. p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature")
30. p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")
31.  
32. par1.set_ylim(0, 4)
33. par2.set_ylim(1, 65)
34.  
35. host.legend()
36.  
37. host.axis["left"].label.set_color(p1.get_color())
38. par1.axis["right"].label.set_color(p2.get_color())
39. par2.axis["right"].label.set_color(p3.get_color())
40.  
41. plt.draw()
42. plt.show()

1. #　第二坐标
2. fig, ax_f = plt.subplots()
3. # 这步是关键
4. ax_c = ax_f.twinx()
5. ax_d = ax_f.twiny()
6.  
7. # automatically update ylim of ax2 when ylim of ax1 changes.
8. # ax_f.callbacks.connect("ylim_changed", convert_ax_c_to_celsius)
9. ax_f.plot(np.linspace(-40, 120, 100))
10. ax_f.set_xlim(0, 100)
11.  
12. # ax_f.set_title('第二坐标', size=14)
13. ax_f.set_ylabel('Y轴',color='r')
14. ax_f.set_xlabel('X轴',color='c')
15.  
16. ax_c.set_ylabel('第二Ｙ轴', color='b')
17. ax_c.set_yticklabels(["$0$", r"$\frac{1}{2}\pi$", r"$\pi$", r"$\frac{3}{2}\pi$", r"$2\pi$"])
18. # ax_c.set_ylim(1,5)
19.  
20. ax_d.set_xlabel('第二X轴', color='g')
21. ax_d.set_xlim(-1,1)
22.  
23. plt.show()

刻度及标记

1. import mpl_toolkits.axisartist.axislines as axislines
2.  
3. fig = plt.figure(1, figsize=(10, 6))
4. fig.subplots_adjust(bottom=0.2)
5.  
6. # 子图1
7. ax1 = axislines.Subplot(fig, 131)
8. fig.add_subplot(ax1)
9. # for axis in ax.axis.values():
10. # axis.major_ticks.set_tick_out(True) # 标签全部在外部
11. ax1.axis[:].major_ticks.set_tick_out(True) # 这句和上面的for循环功能相同
12. ax1.axis["left"].label.set_text("子图1 left标签") # 显示在左边
13. # 设置刻度
14. ax1.set_yticks([2,4,6,8])
15. ax1.set_xticks([0.2,0.4,0.6,0.8])
16.  
17. # 子图2
18. ax2 = axislines.Subplot(fig, 132)
19. fig.add_subplot(ax2)
20. ax2.set_yticks([1,3,5,7])
21. ax2.set_yticklabels(('one','two','three', 'four', 'five')) # 不显示‘five'
22. ax2.set_xlim(5, 0) # X轴刻度
23. ax2.axis["left"].set_axis_direction("right")
24. ax2.axis["left"].label.set_text("子图2 left标签") # 显示在右边
25. ax2.axis["bottom"].set_axis_direction("top")
26. ax2.axis["right"].set_axis_direction("left")
27. ax2.axis["top"].set_axis_direction("bottom")
28.  
29. # 子图3
30. ax3 = axislines.Subplot(fig, 133)
31. fig.add_subplot(ax3)
32. # 前两位表示X轴范围，后两位表示Y轴范围
33. ax3.axis([40, 160, 0, 0.03])
34. ax3.axis["left"].set_axis_direction("right")
35. ax3.axis[:].major_ticks.set_tick_out(True)
36.  
37. ax3.axis["left"].label.set_text("Long Label Left")
38. ax3.axis["bottom"].label.set_text("Label Bottom")
39. ax3.axis["right"].label.set_text("Long Label Right")
40. ax3.axis["right"].label.set_visible(True)
41. ax3.axis["left"].label.set_pad(0)
42. ax3.axis["bottom"].label.set_pad(20)
43.  
44. plt.show()

1. import matplotlib.ticker as ticker
2.  
3. # Fixing random state for reproducibility
4. np.random.seed(19680801)
5.  
6. fig, ax = plt.subplots()
7. ax.plot(100*np.random.rand(20))
8.  
9. # 设置 y坐标轴刻度
10. formatter = ticker.FormatStrFormatter('$%1.2f')
11. ax.yaxis.set_major_formatter(formatter)
12.  
13. # 刻度
14. for tick in ax.yaxis.get_major_ticks():
15. tick.label1On = True # label1On 左边纵坐标
16. tick.label2On = True # label2On 右边纵坐标
17. tick.label1.set_color('red')
18. tick.label2.set_color('green')
19.  
20. # 刻度线
21. for line in ax.yaxis.get_ticklines():
22. # line is a Line2D instance
23. line.set_color('green')
24. line.set_markersize(25)
25. line.set_markeredgewidth(3)
26.  
27. # 刻度 文字
28. for label in ax.xaxis.get_ticklabels():
29. # label is a Text instance
30. label.set_color('red')
31. label.set_rotation(45)
32. label.set_fontsize(16)
33.  
34. plt.show()

1. import mpl_toolkits.axisartist as axisartist
2. def setup_axes(fig, rect):
3. ax = axisartist.Subplot(fig, rect)
4. fig.add_subplot(ax)
5.  
6. ax.set_yticks([0.2, 0.8])
7. # 设置刻度标记
8. ax.set_yticklabels(["short", "loooong"])
9. ax.set_xticks([0.2, 0.8])
10. ax.set_xticklabels([r"$\frac{1}{2}\pi$", r"$\pi$"])
11.  
12. return ax
13.  
14. fig = plt.figure(1, figsize=(3, 5))
15. fig.subplots_adjust(left=0.5, hspace=0.7)
16.  
17. ax = setup_axes(fig, 311)
18. ax.set_ylabel("ha=right")
19. ax.set_xlabel("va=baseline")
20.  
21. ax = setup_axes(fig, 312)
22. # 刻度标签对齐方式
23. ax.axis["left"].major_ticklabels.set_ha("center") # 居中
24. ax.axis["bottom"].major_ticklabels.set_va("top") # 项部
25. ax.set_ylabel("ha=center")
26. ax.set_xlabel("va=top")
27.  
28. ax = setup_axes(fig, 313)
29. ax.axis["left"].major_ticklabels.set_ha("left") # 左边
30. ax.axis["bottom"].major_ticklabels.set_va("bottom") # 底部
31. ax.set_ylabel("ha=left")
32. ax.set_xlabel("va=bottom")
33.  
34. plt.show()

共享坐标轴

1. # 共享坐标轴 方法一
2. t = np.arange(0.01, 5.0, 0.01)
3. s1 = np.sin(2 * np.pi * t)
4. s2 = np.exp(-t)
5. s3 = np.sin(4 * np.pi * t)
6.  
7. plt.subplots_adjust(top=2) #位置调整
8.  
9. ax1 = plt.subplot(311)
10. plt.plot(t, s1)
11. plt.setp(ax1.get_xticklabels(), fontsize=6)
12. plt.title('我是原坐标')
13.  
14. # 只共享X轴 sharex
15. ax2 = plt.subplot(312, sharex=ax1)
16. plt.plot(t, s2)
17. # make these tick labels invisible
18. plt.setp(ax2.get_xticklabels(), visible=False)
19. plt.title('我共享了X轴')
20.  
21. # 共享X轴和Y轴 sharex、sharey
22. ax3 = plt.subplot(313, sharex=ax1, sharey=ax1)
23. plt.plot(t, s3)
24. plt.xlim(0.01, 5.0) #不起作用
25. plt.title('我共享了X轴和Y轴')
26. plt.show()

1. # 共享坐标轴 方法二
2. x = np.linspace(0, 2 * np.pi, 400)
3. y = np.sin(x ** 2)
4.  
5. f, axarr = plt.subplots(2, sharex=True)
6. f.suptitle('共享X轴')
7. axarr[0].plot(x, y)
8. axarr[1].scatter(x, y, color='r')
9.  
10. f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
11. f.suptitle('共享Y轴')
12. ax1.plot(x, y)
13. ax2.scatter(x, y)
14.  
15. f, axarr = plt.subplots(3, sharex=True, sharey=True)
16. f.suptitle('同时共享X轴和Ｙ轴')
17. axarr[0].plot(x, y)
18. axarr[1].scatter(x, y)
19. axarr[2].scatter(x, 2 * y ** 2 - 1, color='g')
20. # 间距调整为０
21. f.subplots_adjust(hspace=0)
22. # 设置全部标签在外部
23. for ax in axarr:
24. ax.label_outer()

放大缩小

1. def f(t):
2. return np.exp(-t) * np.cos(2*np.pi*t)
3.  
4. t1 = np.arange(0.0, 3.0, 0.01)
5.  
6. ax1 = plt.subplot(212)
7. ax1.margins(0.05) # Default margin is 0.05, value 0 means fit
8. ax1.plot(t1, f(t1), 'k')
9.  
10. ax2 = plt.subplot(221)
11. ax2.margins(2, 2) # Values >0.0 zoom out
12. ax2.plot(t1, f(t1), 'r')
13. ax2.set_title('Zoomed out')
14.  
15. ax3 = plt.subplot(222)
16. ax3.margins(x=0, y=-0.25) # Values in (-0.5, 0.0) zooms in to center
17. ax3.plot(t1, f(t1), 'g')
18. ax3.set_title('Zoomed in')
19.  
20. plt.show()

1. from matplotlib.transforms import Bbox, TransformedBbox, \
2. blended_transform_factory
3.  
4. from mpl_toolkits.axes_grid1.inset_locator import BboxPatch, BboxConnector,\
5. BboxConnectorPatch
6.  
7. def connect_bbox(bbox1, bbox2,
8. loc1a, loc2a, loc1b, loc2b,
9. prop_lines, prop_patches=None):
10. if prop_patches is None:
11. prop_patches = prop_lines.copy()
12. prop_patches["alpha"] = prop_patches.get("alpha", 1) * 0.2
13.  
14. c1 = BboxConnector(bbox1, bbox2, loc1=loc1a, loc2=loc2a, **prop_lines)
15. c1.set_clip_on(False)
16. c2 = BboxConnector(bbox1, bbox2, loc1=loc1b, loc2=loc2b, **prop_lines)
17. c2.set_clip_on(False)
18.  
19. bbox_patch1 = BboxPatch(bbox1, **prop_patches)
20. bbox_patch2 = BboxPatch(bbox2, **prop_patches)
21.  
22. p = BboxConnectorPatch(bbox1, bbox2,
23. # loc1a=3, loc2a=2, loc1b=4, loc2b=1,
24. loc1a=loc1a, loc2a=loc2a, loc1b=loc1b, loc2b=loc2b,
25. **prop_patches)
26. p.set_clip_on(False)
27.  
28. return c1, c2, bbox_patch1, bbox_patch2, p
29.  
30. def zoom_effect01(ax1, ax2, xmin, xmax, **kwargs):
31. """
32. ax1 : the main axes
33. ax1 : the zoomed axes
34. (xmin,xmax) : the limits of the colored area in both plot axes.
35.  
36. connect ax1 & ax2. The x-range of (xmin, xmax) in both axes will
37. be marked. The keywords parameters will be used ti create
38. patches.
39.  
40. """
41.  
42. trans1 = blended_transform_factory(ax1.transData, ax1.transAxes)
43. trans2 = blended_transform_factory(ax2.transData, ax2.transAxes)
44.  
45. bbox = Bbox.from_extents(xmin, 0, xmax, 1)
46.  
47. mybbox1 = TransformedBbox(bbox, trans1)
48. mybbox2 = TransformedBbox(bbox, trans2)
49.  
50. prop_patches = kwargs.copy()
51. prop_patches["ec"] = "none"
52. prop_patches["alpha"] = 0.2
53.  
54. c1, c2, bbox_patch1, bbox_patch2, p = \
55. connect_bbox(mybbox1, mybbox2,
56. loc1a=3, loc2a=2, loc1b=4, loc2b=1,
57. prop_lines=kwargs, prop_patches=prop_patches)
58.  
59. ax1.add_patch(bbox_patch1)
60. ax2.add_patch(bbox_patch2)
61. ax2.add_patch(c1)
62. ax2.add_patch(c2)
63. ax2.add_patch(p)
64.  
65. return c1, c2, bbox_patch1, bbox_patch2, p
66.  
67. def zoom_effect02(ax1, ax2, **kwargs):
68. """
69. ax1 : the main axes
70. ax1 : the zoomed axes
71.  
72. Similar to zoom_effect01. The xmin & xmax will be taken from the
73. ax1.viewLim.
74. """
75.  
76. tt = ax1.transScale + (ax1.transLimits + ax2.transAxes)
77. trans = blended_transform_factory(ax2.transData, tt)
78.  
79. mybbox1 = ax1.bbox
80. mybbox2 = TransformedBbox(ax1.viewLim, trans)
81.  
82. prop_patches = kwargs.copy()
83. prop_patches["ec"] = "none"
84. prop_patches["alpha"] = 0.2
85.  
86. c1, c2, bbox_patch1, bbox_patch2, p = \
87. connect_bbox(mybbox1, mybbox2,
88. loc1a=3, loc2a=2, loc1b=4, loc2b=1,
89. prop_lines=kwargs, prop_patches=prop_patches)
90.  
91. ax1.add_patch(bbox_patch1)
92. ax2.add_patch(bbox_patch2)
93. ax2.add_patch(c1)
94. ax2.add_patch(c2)
95. ax2.add_patch(p)
96.  
97. return c1, c2, bbox_patch1, bbox_patch2, p
98.  
99. import matplotlib.pyplot as plt
100.  
101. plt.figure(1, figsize=(5, 5))
102. ax1 = plt.subplot(221)
103. ax2 = plt.subplot(212)
104. ax2.set_xlim(0, 1)
105. ax2.set_xlim(0, 5)
106. zoom_effect01(ax1, ax2, 0.2, 0.8)
107.  
108. ax1 = plt.subplot(222)
109. ax1.set_xlim(2, 3)
110. ax2.set_xlim(0, 5)
111. zoom_effect02(ax1, ax2)
112.  
113. plt.show()

嵌入式标轴轴

1. #　相同随机数
2. np.random.seed(19680801)
3.  
4. # create some data to use for the plot
5. dt = 0.001
6. t = np.arange(0.0, 10.0, dt)
7. r = np.exp(-t[:1000] / 0.05) # impulse response
8. x = np.random.randn(len(t))
9. s = np.convolve(x, r)[:len(x)] * dt # colored noise
10.  
11. # the main axes is subplot(111) by default
12. plt.plot(t, s)
13. #坐标轴
14. plt.axis([0, 1, 1.1 * np.min(s), 2 * np.max(s)])
15. plt.xlabel('time (s)')
16. plt.ylabel('current (nA)')
17. plt.title('Gaussian colored noise')
18.  
19. # this is an inset axes over the main axes
20. a = plt.axes([.65, .6, .2, .2], facecolor='k')
21. n, bins, patches = plt.hist(s, 400, density=True, orientation='horizontal')
22. plt.title('Probability')
23. plt.xticks([])
24. plt.yticks([])
25.  
26. # # this is another inset axes over the main axes
27. a = plt.axes([0.2, 0.6, .2, .2], facecolor='k')
28. plt.plot(t[:len(r)], r)
29. plt.title('Impulse response')
30. plt.xlim(0, 0.2)
31. plt.xticks([])
32. plt.yticks([])
33.  
34. plt.show()

非常规坐标轴

1. # 30 points between [0, 0.2) originally made using np.random.rand(30)*.2
2. pts = np.array([
3. 0.015, 0.166, 0.133, 0.159, 0.041, 0.024, 0.195, 0.039, 0.161, 0.018,
4. 0.143, 0.056, 0.125, 0.096, 0.094, 0.051, 0.043, 0.021, 0.138, 0.075,
5. 0.109, 0.195, 0.050, 0.074, 0.079, 0.155, 0.020, 0.010, 0.061, 0.008])
6.  
7. # Now let's make two outlier points which are far away from everything.
8. pts[[3, 14]] += .8
9.  
10. # If we were to simply plot pts, we'd lose most of the interesting
11. # details due to the outliers. So let's 'break' or 'cut-out' the y-axis
12. # into two portions - use the top (ax) for the outliers, and the bottom
13. # (ax2) for the details of the majority of our data
14. f, (ax, ax2) = plt.subplots(2, 1, sharex=True)
15.  
16. # plot the same data on both axes
17. ax.plot(pts)
18. ax2.plot(pts)
19.  
20. # zoom-in / limit the view to different portions of the data
21. ax.set_ylim(.78, 1.) # outliers only
22. ax2.set_ylim(0, .22) # most of the data
23.  
24. # hide the spines between ax and ax2
25. ax.spines['bottom'].set_visible(False)
26. ax2.spines['top'].set_visible(False)
27. ax.xaxis.tick_top()
28. ax.tick_params(labeltop=False) # don't put tick labels at the top
29. ax2.xaxis.tick_bottom()
30.  
31. # This looks pretty good, and was fairly painless, but you can get that
32. # cut-out diagonal lines look with just a bit more work. The important
33. # thing to know here is that in axes coordinates, which are always
34. # between 0-1, spine endpoints are at these locations (0,0), (0,1),
35. # (1,0), and (1,1). Thus, we just need to put the diagonals in the
36. # appropriate corners of each of our axes, and so long as we use the
37. # right transform and disable clipping.
38.  
39. d = .015 # how big to make the diagonal lines in axes coordinates
40. # arguments to pass to plot, just so we don't keep repeating them
41. kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
42. ax.plot((-d, +d), (-d, +d), **kwargs) # top-left diagonal
43. ax.plot((1 - d, 1 + d), (-d, +d), **kwargs) # top-right diagonal
44.  
45. kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
46. ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
47. ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs) # bottom-right diagonal
48.  
49. # What's cool about this is that now if we vary the distance between
50. # ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
51. # the diagonal lines will move accordingly, and stay right at the tips
52. # of the spines they are 'breaking'
53.  
54. plt.show()

1. from matplotlib.transforms import Affine2D
2. import mpl_toolkits.axisartist.floating_axes as floating_axes
3. import numpy as np
4. import mpl_toolkits.axisartist.angle_helper as angle_helper
5. from matplotlib.projections import PolarAxes
6. from mpl_toolkits.axisartist.grid_finder import (FixedLocator, MaxNLocator,
7. DictFormatter)
8. import matplotlib.pyplot as plt
9.  
10. # Fixing random state for reproducibility
11. np.random.seed(19680801)
12.  
13. def setup_axes1(fig, rect):
14. """
15. A simple one.
16. """
17. tr = Affine2D().scale(2, 1).rotate_deg(30)
18.  
19. grid_helper = floating_axes.GridHelperCurveLinear(
20. tr, extremes=(-0.5, 3.5, 0, 4))
21.  
22. ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
23. fig.add_subplot(ax1)
24.  
25. aux_ax = ax1.get_aux_axes(tr)
26.  
27. grid_helper.grid_finder.grid_locator1._nbins = 4
28. grid_helper.grid_finder.grid_locator2._nbins = 4
29.  
30. return ax1, aux_ax
31.  
32. def setup_axes2(fig, rect):
33. """
34. With custom locator and formatter.
35. Note that the extreme values are swapped.
36. """
37. tr = PolarAxes.PolarTransform()
38.  
39. pi = np.pi
40. angle_ticks = [(0, r"$0$"),
41. (.25*pi, r"$\frac{1}{4}\pi$"),
42. (.5*pi, r"$\frac{1}{2}\pi$")]
43. grid_locator1 = FixedLocator([v for v, s in angle_ticks])
44. tick_formatter1 = DictFormatter(dict(angle_ticks))
45.  
46. grid_locator2 = MaxNLocator(2)
47.  
48. grid_helper = floating_axes.GridHelperCurveLinear(
49. tr, extremes=(.5*pi, 0, 2, 1),
50. grid_locator1=grid_locator1,
51. grid_locator2=grid_locator2,
52. tick_formatter1=tick_formatter1,
53. tick_formatter2=None)
54.  
55. ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
56. fig.add_subplot(ax1)
57.  
58. # create a parasite axes whose transData in RA, cz
59. aux_ax = ax1.get_aux_axes(tr)
60.  
61. aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax
62. ax1.patch.zorder = 0.9 # but this has a side effect that the patch is
63. # drawn twice, and possibly over some other
64. # artists. So, we decrease the zorder a bit to
65. # prevent this.
66.  
67. return ax1, aux_ax
68.  
69. def setup_axes3(fig, rect):
70. """
71. Sometimes, things like axis_direction need to be adjusted.
72. """
73.  
74. # rotate a bit for better orientation
75. tr_rotate = Affine2D().translate(-95, 0)
76.  
77. # scale degree to radians
78. tr_scale = Affine2D().scale(np.pi/180., 1.)
79.  
80. tr = tr_rotate + tr_scale + PolarAxes.PolarTransform()
81.  
82. grid_locator1 = angle_helper.LocatorHMS(4)
83. tick_formatter1 = angle_helper.FormatterHMS()
84.  
85. grid_locator2 = MaxNLocator(3)
86.  
87. # Specify theta limits in degrees
88. ra0, ra1 = 8.*15, 14.*15
89. # Specify radial limits
90. cz0, cz1 = 0, 14000
91. grid_helper = floating_axes.GridHelperCurveLinear(
92. tr, extremes=(ra0, ra1, cz0, cz1),
93. grid_locator1=grid_locator1,
94. grid_locator2=grid_locator2,
95. tick_formatter1=tick_formatter1,
96. tick_formatter2=None)
97.  
98. ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
99. fig.add_subplot(ax1)
100.  
101. # adjust axis
102. ax1.axis["left"].set_axis_direction("bottom")
103. ax1.axis["right"].set_axis_direction("top")
104.  
105. ax1.axis["bottom"].set_visible(False)
106. ax1.axis["top"].set_axis_direction("bottom")
107. ax1.axis["top"].toggle(ticklabels=True, label=True)
108. ax1.axis["top"].major_ticklabels.set_axis_direction("top")
109. ax1.axis["top"].label.set_axis_direction("top")
110.  
111. ax1.axis["left"].label.set_text(r"cz [km$^{-1}$]")
112. ax1.axis["top"].label.set_text(r"$\alpha_{1950}$")
113.  
114. # create a parasite axes whose transData in RA, cz
115. aux_ax = ax1.get_aux_axes(tr)
116.  
117. aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax
118. ax1.patch.zorder = 0.9 # but this has a side effect that the patch is
119. # drawn twice, and possibly over some other
120. # artists. So, we decrease the zorder a bit to
121. # prevent this.
122.  
123. return ax1, aux_ax
124.  
125. fig = plt.figure(1, figsize=(8, 4))
126. fig.subplots_adjust(wspace=0.3, left=0.05, right=0.95)
127.  
128. ax1, aux_ax1 = setup_axes1(fig, 131)
129. aux_ax1.bar([0, 1, 2, 3], [3, 2, 1, 3])
130.  
131. ax2, aux_ax2 = setup_axes2(fig, 132)
132. theta = np.random.rand(10)*.5*np.pi
133. radius = np.random.rand(10) + 1.
134. aux_ax2.scatter(theta, radius)
135.  
136. ax3, aux_ax3 = setup_axes3(fig, 133)
137.  
138. theta = (8 + np.random.rand(10)*(14 - 8))*15. # in degrees
139. radius = np.random.rand(10)*14000.
140. aux_ax3.scatter(theta, radius)
141.  
142. plt.show()

1. import numpy as np
2. import matplotlib.pyplot as plt
3. import mpl_toolkits.axisartist.angle_helper as angle_helper
4. from matplotlib.projections import PolarAxes
5. from matplotlib.transforms import Affine2D
6. from mpl_toolkits.axisartist import SubplotHost
7. from mpl_toolkits.axisartist import GridHelperCurveLinear
8.  
9. def curvelinear_test2(fig):
10. """Polar projection, but in a rectangular box.
11. """
12. # see demo_curvelinear_grid.py for details
13. tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
14.  
15. extreme_finder = angle_helper.ExtremeFinderCycle(20,
16. 20,
17. lon_cycle=360,
18. lat_cycle=None,
19. lon_minmax=None,
20. lat_minmax=(0,
21. np.inf),
22. )
23.  
24. grid_locator1 = angle_helper.LocatorDMS(12)
25.  
26. tick_formatter1 = angle_helper.FormatterDMS()
27.  
28. grid_helper = GridHelperCurveLinear(tr,
29. extreme_finder=extreme_finder,
30. grid_locator1=grid_locator1,
31. tick_formatter1=tick_formatter1
32. )
33.  
34. ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
35.  
36. fig.add_subplot(ax1)
37.  
38. # Now creates floating axis
39.  
40. # floating axis whose first coordinate (theta) is fixed at 60
41. ax1.axis["lat"] = axis = ax1.new_floating_axis(0, 60)
42. axis.label.set_text(r"$\theta = 60^{\circ}$")
43. axis.label.set_visible(True)
44.  
45. # floating axis whose second coordinate (r) is fixed at 6
46. ax1.axis["lon"] = axis = ax1.new_floating_axis(1, 6)
47. axis.label.set_text(r"$r = 6$")
48.  
49. ax1.set_aspect(1.)
50. ax1.set_xlim(-5, 12)
51. ax1.set_ylim(-5, 10)
52.  
53. ax1.grid(True)
54.  
55. fig = plt.figure(1, figsize=(5, 5))
56. fig.clf()
57.  
58. curvelinear_test2(fig)
59.  
60. plt.show()

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。

原文链接：https://blog.csdn.net/wuzlun/article/details/80053277