imshow在使用自定义刻度时为什么显示不正确？

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英文:

Why isn't imshow displaying correctly when using custom ticks?

问题

I am trying to plot heatmap with average values with x and y axes denote physical parameters.

  1. import csv
  2. import matplotlib.pyplot as plt
  3. from scipy import stats
  4. import numpy as np
  6. #.......................................................................
  7. # URL for temp.csv https://github.com/paragkan/dyanmic-recrystallization/blob/main/temp.csv
  8. #.......................................................................
  9. def read_csv(xfile):
  10.     infile = open(xfile, 'r')
  11.     table = []
  12.     for row in csv.reader(infile):
  13.         table.append(row)
  14.     infile.close()
  15.     for c in range (0, len(table[0])):
  16.         for r in range(0, len(table)):
  17.             try:
  18.                 table[r][c] = float(table[r][c])
  19.             except:
  20.                 pass
  21.     return table
  22. #.......................................................................
  24. xfile = 'temp.csv';
  26. table = read_csv(xfile)
  28. x = []
  29. y = []
  30. z = []
  32. for i in range(0, len(table)):
  33.     x.append(int(1/table[i][0]))
  34.     y.append(table[i][1])
  35.     z.append(table[i][2])
  37. extent = [min(x), max(x), min(y), max(y)]
  38. d = stats.binned_statistic_2d(x, y, z, statistic='mean', bins = [5, 5])
  40. fig, ax = plt.subplots()
  41. extent = [min(x), max(x), min(y), max(y)]
  42. im = ax.imshow(d[0].T, cmap='RdBu_r',  interpolation='nearest', origin='lower')
  44. print(d[0])
  45. print(d[1])
  46. print(d[2])
  48. cbar = ax.figure.colorbar(im, ax = ax, shrink = 1)
  49. plt.show()
  50. plt.close()

Printed Output

  1. [[0.00658065 0.01352    0.03897    0.09360667 0.15375806]
  2.  [       nan        nan        nan        nan        nan]
  3.  [0.00704516 0.02388    0.11518667 0.37206333 0.69959677]
  4.  [       nan        nan        nan        nan        nan]
  5.  [0.00575484 0.01212    0.05937667 0.22448333 0.46173226]]
  6. [ 923.   962.8 1002.6 1042.4 1082.2 1122. ]
  7. [ 2.83236704  4.58410957  6.33585211  8.08759464  9.83933718 11.59107971]

When I use 'extent' parameter or custom axes ticks using set_xticks, the map is not showing correctly. Without 'extent', everything is fine but then the axes ticks are indexed as 0, 1, 2, 3. Kindly help me resolve this issue. It is also OK if someone suggests another python package to plot heatmap with binned stats, I require saved graphs to be used in a manuscript.

Expected Plot

What I need is, the edges of the bins in the graph should show the actual bin's dimension. If xedges are = [900, 925, 975, ..], the ticks on the x-axis should start with 900, and the next tick should be on the next bin edge showing 925. The same for y-axis.

imshow在使用自定义刻度时为什么显示不正确？

temp.csv

  1. 8.90472E-4,2.8323670382,0.0054
  2. 8.90472E-4,2.8903717579,0.0054
  3. 8.90472E-4,2.9483764776,0.0054
  4. 8.90472E-4,3.0063811973,0.0054
  5. ...
英文:

I am trying to plot heatmap with average values with x and y axes denote physical parameters.

  1. import csv
  2. import matplotlib.pyplot as plt
  3. from scipy import stats
  4. import numpy as np
  6. #.......................................................................
  7. # URL for temp.csv https://github.com/paragkan/dyanmic-recrystallization/blob/main/temp.csv
  8. #.......................................................................
  9. def read_csv(xfile):
  10.     infile = open(xfile, 'r')
  11.     table = []
  12.     for row in csv.reader(infile):
  13.         table.append(row)
  14.     infile.close() # better leave that file alone.
  15.     for c in range (0, len(table[0])):
  16.         for r in range(0, len(table)):
  17.             try:
  18.                 table[r][c] = float(table[r][c])
  19.             except:
  20.                 pass
  21.     return table
  22. #.......................................................................
  24. xfile = 'temp.csv'
  26. table = read_csv(xfile)
  28. x = []
  29. y = []
  30. z = []
  32. for i in range(0, len(table)):
  33.     x.append(int(1/table[i][0]))
  34.     y.append(table[i][1])
  35.     z.append(table[i][2])
  37. extent = [min(x), max(x), min(y), max(y)]
  38. d = stats.binned_statistic_2d(x, y, z, statistic='mean', bins = [5, 5])
  40. fig, ax = plt.subplots()
  41. extent = [min(x), max(x), min(y), max(y)]
  42. # im = ax.imshow(d[0].T, cmap='RdBu_r',  interpolation='nearest', origin='lower', extent=extent)
  43. im = ax.imshow(d[0].T, cmap='RdBu_r',  interpolation='nearest', origin='lower')
  45. print(d[0])
  46. print(d[1])
  47. print(d[2])
  49. # ax.set_xticks(d[1])
  50. # ax.set_yticks(d[2])
  52. cbar = ax.figure.colorbar(im, ax = ax, shrink = 1)
  53. # average = d[0].T
  54. # xdist = d[1]
  55. # ydist = d[2]
  56. # for i in range(0, len(d[1])-1):
  57. #     for j in range(len(d[2])-1):
  58. #         xcord = (xdist[j] + xdist[j+1])/2
  59. #         ycord = (ydist[i] + ydist[i+1])/2
  60. #         txtext = plt.text(xcord, ycord, round(average[i][j], 2),  fontsize = 12, ha="center", va="center", color="w")
  61. plt.show()
  62. plt.close()

Printed Output

  1. [[0.00658065 0.01352    0.03897    0.09360667 0.15375806]
  2.  [       nan        nan        nan        nan        nan]
  3.  [0.00704516 0.02388    0.11518667 0.37206333 0.69959677]
  4.  [       nan        nan        nan        nan        nan]
  5.  [0.00575484 0.01212    0.05937667 0.22448333 0.46173226]]
  6. [ 923.   962.8 1002.6 1042.4 1082.2 1122. ]
  7. [ 2.83236704  4.58410957  6.33585211  8.08759464  9.83933718 11.59107971]

Without extent

imshow在使用自定义刻度时为什么显示不正确？

With extent=extent

imshow在使用自定义刻度时为什么显示不正确？

When I use 'extent' parameter or custom axes ticks using set_xticks, the map is not showing correctly. Without 'extent', everything is fine but then the axes ticks are indexed as 0, 1, 2, 3. Kindly help me resolve this issue. It is also OK if someone suggests another python package to plot heatmap with binned stats, I require saved graphs to be used in a manuscript.

Expected Plot

What I need is, the edges of the bins in the graph should show the actual bin's dimension. If xedges are = [900, 925, 975, ..], the ticks on the x-axis should start with 900, and the next tick should be on the next bin edge showing 925. The same for y-axis.

imshow在使用自定义刻度时为什么显示不正确？

temp.csv

  1. 8.90472E-4,2.8323670382,0.0054
  2. 8.90472E-4,2.8903717579,0.0054
  3. 8.90472E-4,2.9483764776,0.0054
  4. 8.90472E-4,3.0063811973,0.0054
  5. 8.90472E-4,3.064385917,0.0054
  6. 8.90472E-4,3.1223906367,0.0054
  7. 8.90472E-4,3.1803953564,0.0054
  8. 8.90472E-4,3.2384000762,0.0055
  9. 8.90472E-4,3.2964047959,0.0055
  10. 8.90472E-4,3.3544095156,0.0055
  11. 8.90472E-4,3.4124142353,0.0055
  12. 8.90472E-4,3.470418955,0.0055
  13. 8.90472E-4,3.5284236747,0.0055
  14. 8.90472E-4,3.5864283944,0.0056
  15. 8.90472E-4,3.6444331141,0.0056
  16. 8.90472E-4,3.7024378338,0.0056
  17. 8.90472E-4,3.7604425536,0.0057
  18. 8.90472E-4,3.8184472733,0.0057
  19. 8.90472E-4,3.876451993,0.0057
  20. 8.90472E-4,3.9344567127,0.0058
  21. 8.90472E-4,3.9924614324,0.0058
  22. 8.90472E-4,4.0504661521,0.0059
  23. 8.90472E-4,4.1084708718,0.0059
  24. 8.90472E-4,4.1664755915,0.006
  25. 8.90472E-4,4.2244803112,0.0061
  26. 8.90472E-4,4.282485031,0.0061
  27. 8.90472E-4,4.3404897507,0.0062
  28. 8.90472E-4,4.3984944704,0.0063
  29. 8.90472E-4,4.4564991901,0.0064
  30. 8.90472E-4,4.5145039098,0.0065
  31. 8.90472E-4,4.5725086295,0.0067
  32. 8.90472E-4,4.6305133492,0.0068
  33. 8.90472E-4,4.6885180689,0.0069
  34. 8.90472E-4,4.7465227886,0.0071
  35. 8.90472E-4,4.8045275084,0.0073
  36. 8.90472E-4,4.8625322281,0.0075
  37. 8.90472E-4,4.9205369478,0.0077
  38. 8.90472E-4,4.9785416675,0.0079
  39. 8.90472E-4,5.0365463872,0.0082
  40. 8.90472E-4,5.0945511069,0.0084
  41. 8.90472E-4,5.1525558266,0.0087
  42. 8.90472E-4,5.2105605463,0.009
  43. 8.90472E-4,5.268565266,0.0094
  44. 8.90472E-4,5.3265699857,0.0097
  45. 8.90472E-4,5.3845747055,0.0101
  46. 8.90472E-4,5.4425794252,0.0105
  47. 8.90472E-4,5.5005841449,0.011
  48. 8.90472E-4,5.5585888646,0.0115
  49. 8.90472E-4,5.6165935843,0.012
  50. 8.90472E-4,5.674598304,0.0126
  51. 8.90472E-4,5.7326030237,0.0132
  52. 8.90472E-4,5.7906077434,0.0139
  53. 8.90472E-4,5.8486124631,0.0146
  54. 8.90472E-4,5.9066171829,0.0154
  55. 8.90472E-4,5.9646219026,0.0162
  56. 8.90472E-4,6.0226266223,0.0171
  57. 8.90472E-4,6.080631342,0.018
  58. 8.90472E-4,6.1386360617,0.019
  59. 8.90472E-4,6.1966407814,0.0201
  60. 8.90472E-4,6.2546455011,0.0213
  61. 8.90472E-4,6.3126502208,0.0225
  62. 8.90472E-4,6.3706549405,0.0238
  63. 8.90472E-4,6.4286596603,0.0252
  64. 8.90472E-4,6.48666438,0.0266
  65. 8.90472E-4,6.5446690997,0.0282
  66. 8.90472E-4,6.6026738194,0.0298
  67. 8.90472E-4,6.6606785391,0.0316
  68. 8.90472E-4,6.7186832588,0.0334
  69. 8.90472E-4,6.7766879785,0.0354
  70. 8.90472E-4,6.8346926982,0.0375
  71. 8.90472E-4,6.8926974179,0.0397
  72. 8.90472E-4,6.9507021376,0.042
  73. 8.90472E-4,7.0087068574,0.0444
  74. 8.90472E-4,7.0667115771,0.047
  75. 8.90472E-4,7.1247162968,0.0497
  76. 8.90472E-4,7.1827210165,0.0525
  77. 8.90472E-4,7.2407257362,0.0555
  78. 8.90472E-4,7.2987304559,0.0586
  79. 8.90472E-4,7.3567351756,0.0619
  80. 8.90472E-4,7.4147398953,0.0654
  81. 8.90472E-4,7.472744615,0.069
  82. 8.90472E-4,7.5307493348,0.0727
  83. 8.90472E-4,7.5887540545,0.0766
  84. 8.90472E-4,7.6467587742,0.0807
  85. 8.90472E-4,7.7047634939,0.085
  86. 8.90472E-4,7.7627682136,0.0894
  87. 8.90472E-4,7.8207729333,0.094
  88. 8.90472E-4,7.878777653,0.0988
  89. 8.90472E-4,7.9367823727,0.1038
  90. 8.90472E-4,7.9947870924,0.1089
  91. 8.90472E-4,8.0527918122,0.1142
  92. 8.90472E-4,8.1107965319,0.1197
  93. 8.90472E-4,8.1688012516,0.1255
  94. 8.90472E-4,8.2268059713,0.1313
  95. 8.90472E-4,8.284810691,0.1374
  96. 8.90472E-4,8.3428154107,0.1437
  97. 8.90472E-4,8.4008201304,0.1501
  98. 8.90472E-4,8.4588248501,0.1567
  99. 8.90472E-4,8.5168295698,0.1635
  100. 8.90472E-4,8.5748342895,0.1705
  101. 8.90472E-4,8.6328390093,0.1776
  102. 8.90472E-4,8.690843729,0.1849
  103. 8.90472E-4,8.7488484487,0.1924
  104. 8.90472E-4,8.8068531684,0.2
  105. 8.90472E-4,8.8648578881,0.2078
  106. 8.90472E-4,8.9228626078,0.2157
  107. 8.90472E-4,8.9808673275,0.2238
  108. 8.90472E-4,9.0388720472,0.232
  109. 8.90472E-4,9.0968767669,0.2403
  110. 8.90472E-4,9.1548814867,0.2487
  111. 8.90472E-4,9.2128862064,0.2572
  112. 8.90472E-4,9.2708909261,0.2658
  113. 8.90472E-4,9.3288956458,0.2745
  114. 8.90472E-4,9.3869003655,0.2833
  115. 8.90472E-4,9.4449050852,0.2921
  116. 8.90472E-4,9.5029098049,0.301
  117. 8.90472E-4,9.5609145246,0.3099
  118. 8.90472E-4,9.6189192443,0.3189
  119. 8.90472E-4,9.6769239641,0.3278
  120. 8.90472E-4,9.7349286838,0.3367
  121. 8.90472E-4,9.7929334035,0.3457
  122. 8.90472E-4,9.8509381232,0.3545
  123. 8.90472E-4,9.9089428429,0.3634
  124. 8.90472E-4,9.9669475626,0.3721
  125. 8.90472E-4,10.0249522823,0.3808
  126. 8.90472E-4,10.082957002,0.3894
  127. 8.90472E-4,10.1409617217,0.3979
  128. 8.90472E-4,10.1989664414,0.4062
  129. 8.90472E-4,10.2569711612,0.4144
  130. 8.90472E-4,10.3149758809,0.4224
  131. 8.90472E-4,10.3729806006,0.4303
  132. 8.90472E-4,10.4309853203,0.438
  133. 8.90472E-4,10.48899004,0.4454
  134. 8.90472E-4,10.5469947597,0.4526
  135. 8.90472E-4,10.6049994794,0.4596
  136. 8.90472E-4,10.6630041991,0.4664
  137. 8.90472E-4,10.7210089188,0.4728
  138. 8.90472E-4,10.7790136386,0.479
  139. 8.90472E-4,10.8370183583,0.4849
  140. 8.90472E-4,10.895023078,0.4905
  141. 8.90472E-4,10.9530277977,0.4957
  142. 8.90472E-4,11.0110325174,0.5007
  143. 8.90472E-4,11.0690372371,0.5052
  144. 8.90472E-4,11.1270419568,0.5095
  145. 8.90472E-4,11.1850466765,0.5133
  146. 8.90472E-4,11.2430513962,0.5168
  147. 8.90472E-4,11.301056116,0.5199
  148. 8.90472E-4,11.3590608357,0.5226
  149. 8.90472E-4,11.4170655554,0.5249
  150. 8.90472E-4,11.4750702751,0.5268
  151. 8.90472E-4,11.5330749948,0.5283
  152. 8.90472E-4,11.5910797145,0.5294
  153. 9.775171E-4,2.8323670382,0.0056
  154. 9.775171E-4,2.8903717579,0.0057
  155. 9.775171E-4,2.9483764776,0.0057
  156. 9.775171E-4,3.0063811973,0.0057
  157. 9.775171E-4,3.064385917,0.0058
  158. 9.775171E-4,3.1223906367,0.0058
  159. 9.775171E-4,3.1803953564,0.0059
  160. 9.775171E-4,3.2384000762,0.0059
  161. 9.775171E-4,3.2964047959,0.006
  162. 9.775171E-4,3.3544095156,0.0061
  163. 9.775171E-4,3.4124142353,0.0061
  164. 9.775171E-4,3.470418955,0.0062
  165. 9.775171E-4,3.5284236747,0.0063
  166. 9.775171E-4,3.5864283944,0.0064
  167. 9.775171E-4,3.6444331141,0.0065
  168. 9.775171E-4,3.7024378338,0.0066
  169. 9.775171E-4,3.7604425536,0.0067
  170. 9.775171E-4,3.8184472733,0.0069
  171. 9.775171E-4,3.876451993,0.007
  172. 9.775171E-4,3.9344567127,0.0072
  173. 9.775171E-4,3.9924614324,0.0074
  174. 9.775171E-4,4.0504661521,0.0075
  175. 9.775171E-4,4.1084708718,0.0078
  176. 9.775171E-4,4.1664755915,0.008
  177. 9.775171E-4,4.2244803112,0.0082
  178. 9.775171E-4,4.282485031,0.0085
  179. 9.775171E-4,4.3404897507,0.0087
  180. 9.775171E-4,4.3984944704,0.009
  181. 9.775171E-4,4.4564991901,0.0094
  182. 9.775171E-4,4.5145039098,0.0097
  183. 9.775171E-4,4.5725086295,0.0101
  184. 9.775171E-4,4.6305133492,0.0105
  185. 9.775171E-4,4.6885180689,0.011
  186. 9.775171E-4,4.7465227886,0.0114
  187. 9.775171E-4,4.8045275084,0.0119
  188. 9.775171E-4,4.8625322281,0.0125
  189. 9.775171E-4,4.9205369478,0.0131
  190. 9.775171E-4,4.9785416675,0.0137
  191. 9.775171E-4,5.0365463872,0.0144
  192. 9.775171E-4,5.0945511069,0.0151
  193. 9.775171E-4,5.1525558266,0.0159
  194. 9.775171E-4,5.2105605463,0.0167
  195. 9.775171E-4,5.268565266,0.0176
  196. 9.775171E-4,5.3265699857,0.0185
  197. 9.775171E-4,5.3845747055,0.0195
  198. 9.775171E-4,5.4425794252,0.0206
  199. 9.775171E-4,5.5005841449,0.0217
  200. 9.775171E-4,5.5585888646,0.0229
  201. 9.775171E-4,5.6165935843,0.0242
  202. 9.775171E-4,5.674598304,0.0256
  203. 9.775171E-4,5.7326030237,0.0271
  204. 9.775171E-4,5.7906077434,0.0286
  205. 9.775171E-4,5.8486124631,0.0302
  206. 9.775171E-4,5.9066171829,0.032
  207. 9.775171E-4,5.9646219026,0.0338
  208. 9.775171E-4,6.0226266223,0.0358
  209. 9.775171E-4,6.080631342,0.0378
  210. 9.775171E-4,6.1386360617,0.04
  211. 9.775171E-4,6.1966407814,0.0423
  212. 9.775171E-4,6.2546455011,0.0447
  213. 9.775171E-4,6.3126502208,0.0473
  214. 9.775171E-4,6.3706549405,0.05
  215. 9.775171E-4,6.4286596603,0.0528
  216. 9.775171E-4,6.48666438,0.0558
  217. 9.775171E-4,6.5446690997,0.0589
  218. 9.775171E-4,6.6026738194,0.0622
  219. 9.775171E-4,6.6606785391,0.0657
  220. 9.775171E-4,6.7186832588,0.0693
  221. 9.775171E-4,6.7766879785,0.0731
  222. 9.775171E-4,6.8346926982,0.077
  223. 9.775171E-4,6.8926974179,0.0812
  224. 9.775171E-4,6.9507021376,0.0855
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答案1

得分: 1

你可以在imshow中使用"aspect"选项来强制图像返回正确的纵横比。要获得正方形图像，纵横比需要设置为两边长度的倒数。参见：https://stackoverflow.com/questions/13384653/imshow-extent-and-aspect

在你的情况下，你的x长度约为200，y长度约为10，所以你需要设置一个约为20的纵横比。

英文:

You can use the "aspect" option in imshow to force the image to return to the correct aspect ratio. To get a square image, the aspect ratio would need to be the inverse of the ratio of the two side lengths. See also: https://stackoverflow.com/questions/13384653/imshow-extent-and-aspect

In your case your x length is about 200 and your y length is about 10, so you'd want to set an aspect of about 20.

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  • 本文由 发表于 2023年6月9日 01:18:23
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  • axis
  • imshow
  • matplotlib
  • python
  • xticks
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