如何检查对象是否为“matplotlib.collectives.PolyCollection”类型

gt0wga4j  于 2023-08-06  发布在  其他
关注(0)|答案(1)|浏览(87)

对于一个特定的任务（Link），我想检查一个对象是否是：

  1. matplotlib.collections.PolyCollection

字符串
或a：

  1. matplotlib.lines.Line2D

型
对象。
我把它累成这样：

  1. if isinstance(handle, matplotlib.collections.PolyCollection):

型
但这并不奏效。如果要测试两个变量h和句柄是否属于同一类型，我如何检查它们是否都是 matplotlib.collections.PolyCollection 或 matplotlib.lines.Line2D 对象？

编辑1

下面是有问题的代码，它适应了上面链接中的解决方案：

  1. import matplotlib.pyplot as plt
  2. import numpy as np
  3. import matplotlib.mlab as mlab
  4. import math
  6. def is_inlist(handle, handles):
  7.     for h in handles:
  8.         if h.get_color() == handle.get_color() and \
  9.             h.get_linestyle() == handle.get_linestyle() and \
  10.             h.get_marker() == handle.get_marker():
  11.             return True
  12.     return False
  14. lines=[]
  15. labels=[]
  16. legend_properties = {'weight':'bold','size':10}  
  17. # Example data
  19. mu = 0
  20. mu2 = 5
  21. variance = 1
  22. variance2 = 2
  23. sigma = math.sqrt(variance)
  24. sigma2 = math.sqrt(variance2)
  25. x = np.linspace(mu-3*variance,mu+3*variance, 100)
  26. x2 = np.linspace(mu2-3*variance2,mu2+3*variance2, 100)
  28. nrows = 4
  29. # Plot
  31. fig, axis = plt.subplots(nrows, sharex=True, sharey=False, figsize=(5, 8))
  32. fig.subplots_adjust(hspace=0.0001)
  33. #fig.suptitle("Stacked Plots with global Legend which contains to little elements",fontsize=14,weight='bold')
  35. axis[0].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  36. axis[0].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
  37. axis[0].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  38. axis[0].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  39. axis[0].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  41. axis[1].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  42. axis[1].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
  43. axis[1].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  44. axis[1].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  45. axis[1].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='yellow',alpha=0.5,label="PEAK5", interpolate=True)
  46. axis[1].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  48. axis[2].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  49. axis[2].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
  50. axis[2].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK3", interpolate=True)
  51. axis[2].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  52. axis[2].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  54. axis[3].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  55. axis[3].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
  56. axis[3].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  57. axis[3].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  58. axis[3].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK5", interpolate=True)
  59. axis[3].fill_between(x+5.5,0,mlab.normpdf(x, mu, sigma), color='violet',alpha=0.5,label="PEAK6", interpolate=True)
  60. axis[3].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  66. for i in range(nrows):
  67.     h, l = axis[i].get_legend_handles_labels()
  68.     for hi, li in zip(h,l):
  69.         if not is_inlist(hi, lines):
  70.             lines.append(hi)
  71.             labels.append(li)
  72.            
  76. #x for x in item if x not in Z
  84.             
  85. # only 3 Legend entrys Label1 , Label2 and Label3 are visible .. Differences in cloors and markers are ignored
  86. plt.legend(handles=lines, labels=labels,bbox_to_anchor=(0., nrows+.02, 1., .102), loc=3,ncol=3, prop=legend_properties,mode="expand", borderaxespad=0.,frameon=False,framealpha=0.0) 
  88. plt.show()

型
不幸的是，它给了我错误：

  1. Traceback (most recent call last):
  2.       File "PATH..../.py", line 76, in <module>
  3.         if not is_inlist(hi, lines):
  4.       File "PATH..../.py", line 9, in is_inlist
  5.         if h.get_color() == handle.get_color() and \
  6.     AttributeError: 'PolyCollection' object has no attribute 'get_color'

型
有人建议我为每种类型的matplotlib对象做一个案例分析。这就是我奋斗的地方。我想改变“is_inlist”函数，以适应不同的情况。但案例分析本身还不起作用：

  1. import matplotlib.pyplot as plt
  2. import numpy as np
  3. import matplotlib.mlab as mlab
  4. import math
  6. def is_inlist(handle, handles):
  7.     for h in handles:
  8.         if isinstance(handle, matplotlib.collections.PolyCollection) and isinstance(h, matplotlib.collections.PolyCollection):
  9.             if h.get_color() == handle.get_color() and \
  10.                 h.get_linestyle() == handle.get_linestyle() and \
  11.                 h.get_marker() == handle.get_marker():
  12.                 return True
  13.         if isinstance(handle, matplotlib.lines.Line2D) and isinstance(h, matplotlib.lines.Line2D):
  14.             if h.get_color() == handle.get_color() and \
  15.                 h.get_linestyle() == handle.get_linestyle() and \
  16.                 h.get_marker() == handle.get_marker():
  17.                 return True        
  18.                 
  19.                 
  20.     return False
  22. lines=[]
  23. labels=[]
  24. legend_properties = {'weight':'bold','size':10}  
  25. # Example data
  27. mu = 0
  28. mu2 = 5
  29. variance = 1
  30. variance2 = 2
  31. sigma = math.sqrt(variance)
  32. sigma2 = math.sqrt(variance2)
  33. x = np.linspace(mu-3*variance,mu+3*variance, 100)
  34. x2 = np.linspace(mu2-3*variance2,mu2+3*variance2, 100)
  36. nrows = 4
  37. # Plot
  39. fig, axis = plt.subplots(nrows, sharex=True, sharey=False, figsize=(5, 8))
  40. fig.subplots_adjust(hspace=0.0001)
  41. #fig.suptitle("Stacked Plots with global Legend which contains to little elements",fontsize=14,weight='bold')
  43. axis[0].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  44. axis[0].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
  45. axis[0].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  46. axis[0].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  47. axis[0].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  49. axis[1].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  50. axis[1].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
  51. axis[1].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  52. axis[1].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  53. axis[1].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='yellow',alpha=0.5,label="PEAK5", interpolate=True)
  54. axis[1].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  56. axis[2].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  57. axis[2].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
  58. axis[2].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK3", interpolate=True)
  59. axis[2].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  60. axis[2].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  62. axis[3].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  63. axis[3].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
  64. axis[3].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  65. axis[3].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  66. axis[3].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK5", interpolate=True)
  67. axis[3].fill_between(x+5.5,0,mlab.normpdf(x, mu, sigma), color='violet',alpha=0.5,label="PEAK6", interpolate=True)
  68. axis[3].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  71. for i in range(nrows):
  72.     h, l = axis[i].get_legend_handles_labels()
  73.     for hi, li in zip(h,l):
  74.         if not is_inlist(hi, lines):
  75.             lines.append(hi)
  76.             labels.append(li)
  77.            
  81. # only 3 Legend entrys Label1 , Label2 and Label3 are visible .. Differences in cloors and markers are ignored
  82. plt.legend(handles=lines, labels=labels,bbox_to_anchor=(0., nrows+.02, 1., .102), loc=3,ncol=3, prop=legend_properties,mode="expand", borderaxespad=0.,frameon=False,framealpha=0.0) 
  84. plt.show()

型
我得到的错误是：

  1. Traceback (most recent call last):
  2.       File "Path/.. .py", line 84, in <module>
  3.         if not is_inlist(hi, lines):
  4.       File "Path/.. .py", line 9, in is_inlist
  5.         if isinstance(handle, matplotlib.collections.PolyCollection) and isinstance(handle, matplotlib.collections.PolyCollection):
  6.     NameError: global name 'matplotlib' is not defined

型

编辑2

我补充道：

  1. import matplotlib.collections

型
正如我所建议的

  1. import matplotlib.pyplot as plt
  2. import numpy as np
  3. import matplotlib.mlab as mlab
  4. import math
  5. import matplotlib.collections
  7. def is_inlist(handle, handles):
  8.     for h in handles:
  9.         if isinstance(handle, matplotlib.collections.PolyCollection) and isinstance(h, matplotlib.collections.PolyCollection):
  10.             if h.get_facecolor() == handle.get_facecolor() and \
  11.                 h.get_linestyle() == handle.get_linestyle() and \
  12.                 h.get_alpha() == handle.get_alpha():
  13.                 return True
  14.         if isinstance(handle, matplotlib.lines.Line2D) and isinstance(h, matplotlib.lines.Line2D):
  15.             if h.get_color() == handle.get_color() and \
  16.                 h.get_linestyle() == handle.get_linestyle() and \
  17.                 h.get_marker() == handle.get_marker():
  18.                 return True        
  19.                 
  20.                 
  21.     return False
  23. lines=[]
  24. labels=[]
  25. legend_properties = {'weight':'bold','size':10}  
  26. # Example data
  28. mu = 0
  29. mu2 = 5
  30. variance = 1
  31. variance2 = 2
  32. sigma = math.sqrt(variance)
  33. sigma2 = math.sqrt(variance2)
  34. x = np.linspace(mu-3*variance,mu+3*variance, 100)
  35. x2 = np.linspace(mu2-3*variance2,mu2+3*variance2, 100)
  37. nrows = 4
  38. # Plot
  40. fig, axis = plt.subplots(nrows, sharex=True, sharey=False, figsize=(5, 8))
  41. fig.subplots_adjust(hspace=0.0001)
  42. #fig.suptitle("Stacked Plots with global Legend which contains to little elements",fontsize=14,weight='bold')
  44. axis[0].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  45. axis[0].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
  46. axis[0].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  47. axis[0].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  48. axis[0].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  50. axis[1].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  51. axis[1].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
  52. axis[1].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  53. axis[1].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  54. axis[1].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='yellow',alpha=0.5,label="PEAK5", interpolate=True)
  55. axis[1].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  57. axis[2].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  58. axis[2].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
  59. axis[2].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK3", interpolate=True)
  60. axis[2].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  61. axis[2].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  63. axis[3].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  64. axis[3].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
  65. axis[3].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  66. axis[3].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  67. axis[3].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK5", interpolate=True)
  68. axis[3].fill_between(x+5.5,0,mlab.normpdf(x, mu, sigma), color='violet',alpha=0.5,label="PEAK6", interpolate=True)
  69. axis[3].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  72. for i in range(nrows):
  73.     h, l = axis[i].get_legend_handles_labels()
  74.     for hi, li in zip(h,l):
  75.         if not is_inlist(hi, lines):
  76.             lines.append(hi)
  77.             labels.append(li)
  78.            
  82. # only 3 Legend entrys Label1 , Label2 and Label3 are visible .. Differences in cloors and markers are ignored
  83. plt.legend(handles=lines, labels=labels,bbox_to_anchor=(0., nrows-1+.02, 1., .102), loc=3,ncol=3, prop=legend_properties,mode="expand", borderaxespad=0.,frameon=False,framealpha=0.0) 
  85. plt.show()

型
我现在得到的错误是：

  1. Traceback (most recent call last):
  2.       File "Path/.. .py", line 80, in <module>
  3.         if not is_inlist(hi, lines):
  4.       File "Dath/.. .py", line 10, in is_inlist
  5.         if h.get_facecolor() == handle.get_facecolor() and \
  6.     ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

型

解决方案基于ImportanceOfBeingErnest的解释：

  1. import matplotlib.pyplot as plt
  2. import numpy as np
  3. import matplotlib.mlab as mlab
  4. import math
  5. import matplotlib.collections
  7. def is_inlist(handle, handles):
  8.     for h in handles:
  9.         if isinstance(handle, matplotlib.collections.PolyCollection) and isinstance(h, matplotlib.collections.PolyCollection): 
  10.             if np.all(h.get_facecolor() == handle.get_facecolor()) and \
  11.                 np.all(h.get_linestyle() == handle.get_linestyle()) and \
  12.                 np.all(h.get_alpha() == handle.get_alpha()):
  13.                 return True
  14.         elif isinstance(handle, matplotlib.lines.Line2D) and isinstance(h, matplotlib.lines.Line2D):
  15.             if h.get_color() == handle.get_color() and \
  16.                 h.get_linestyle() == handle.get_linestyle() and \
  17.                 h.get_marker() == handle.get_marker():
  18.                 return True        
  19.                 
  20.                 
  21.     return False
  23. lines=[]
  24. labels=[]
  25. legend_properties = {'weight':'bold','size':10}  
  26. # Example data
  28. mu = 0
  29. mu2 = 5
  30. variance = 1
  31. variance2 = 2
  32. sigma = math.sqrt(variance)
  33. sigma2 = math.sqrt(variance2)
  34. x = np.linspace(mu-3*variance,mu+3*variance, 100)
  35. x2 = np.linspace(mu2-3*variance2,mu2+3*variance2, 100)
  37. nrows = 4
  38. # Plot
  40. fig, axis = plt.subplots(nrows, sharex=True, sharey=False, figsize=(5, 8))
  41. fig.subplots_adjust(hspace=0.0001)
  42. #fig.suptitle("Stacked Plots with global Legend which contains to little elements",fontsize=14,weight='bold')
  44. axis[0].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  45. axis[0].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
  46. axis[0].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  47. axis[0].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  48. axis[0].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  50. axis[1].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  51. axis[1].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
  52. axis[1].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  53. axis[1].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  54. axis[1].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='yellow',alpha=0.5,label="PEAK5", interpolate=True)
  55. axis[1].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  57. axis[2].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  58. axis[2].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
  59. axis[2].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK3", interpolate=True)
  60. axis[2].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  61. axis[2].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  63. axis[3].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
  64. axis[3].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
  65. axis[3].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
  66. axis[3].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
  67. axis[3].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK5", interpolate=True)
  68. axis[3].fill_between(x+5.5,0,mlab.normpdf(x, mu, sigma), color='violet',alpha=0.5,label="PEAK6", interpolate=True)
  69. axis[3].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)
  72. for i in range(nrows):
  73.     h, l = axis[i].get_legend_handles_labels()
  74.     for hi, li in zip(h,l):
  75.         if not is_inlist(hi, lines):
  76.             lines.append(hi)
  77.             labels.append(li)
  78.            
  82. # only 3 Legend entrys Label1 , Label2 and Label3 are visible .. Differences in cloors and markers are ignored
  83. plt.legend(handles=lines, labels=labels,bbox_to_anchor=(0., nrows-1+.02, 1., .102), loc=3,ncol=3, prop=legend_properties,mode="expand", borderaxespad=0.,frameon=False,framealpha=0.0) 
  85. plt.show()

型

matplotlib

1条答案

按热度按时间
wgx48brx

wgx48brx1#

最初问题的解决方案是实际导入为比较提供类的模块。
你只是缺少import matplotlib.collections。
下一个错误其实是不言自明的。它说不可能比较两个数组。
所以我们假设
h的面颜色为[[ 0., 0.50196078, 0., 0.5]]，并且
handle的面色为[[ 1., 0.64705882, 0., 0.5]]，则
h.get_facecolor() == handle.get_facecolor()的结果为[[False False True True]]
现在，两次假和两次真是真还是假？谁也不知道。因此，您需要使用any()或all()来决定是要知道是否有任何元素为True，还是所有元素都为True。
在这里，您可能希望检查是否有相同的颜色，因此使用all：

  1. np.all(h.get_facecolor() == handle.get_facecolor())

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