使用 matplotlib.imshow 将三维数据集绘制为热图

Question

我目前正在尝试将“反射率”、“发射率”和“最大源温度”分别绘制为数据集的 X、Y 和 Z 分量。完整数据文件的一部分如下：

Reflectivity    Emissivity  Max Source Temperature (K)  Evaporation Flux (mg/s) Temperature Difference (K)
0.6 0.0 4093.4386841359888  0.6048990499172073  800.4386841359888
0.6 0.02564102564102564 4025.0643415926234  0.4161884105641208  732.0643415926234
0.6 0.05128205128205128 3944.1723231241936  0.26316934524311886 651.1723231241936
0.6 0.07692307692307693 3846.773147541462   0.1479131145621392  553.7731475414621
0.6 0.10256410256410256 3731.146666110107   0.07188813417550587 438.14666611010716
0.6 0.1282051282051282  3602.90603780689    0.030628121497460244    309.9060378068898
0.6 0.15384615384615385 3474.526898598499   0.012245367701619509    181.52689859849897
0.6 0.1794871794871795  3357.149084679844   0.004979816869050158    64.14908467984378
0.6 0.20512820512820512 3253.90294510584    0.002138016768402961    -39.09705489416001
0.6 0.23076923076923075 3163.857369720834   0.0009771206521967547   -129.14263027916604
0.6 0.2564102564102564  3084.7998933759804  0.00047291824998447216  -208.20010662401955
0.6 0.28205128205128205 3014.2856982857297  0.00023964643450894468  -278.7143017142703
0.6 0.3076923076923077  2951.690011787704   0.00012751152475361122  -341.30998821229605
0.6 0.3333333333333333  2895.2175905656113  7.047686014021697e-05   -397.7824094343887
0.6 0.358974358974359   2843.8854837765575  4.0272208015425235e-05  -449.1145162234425
0.6 0.3846153846153846  2796.4424913052017  2.3569593638895813e-05  -496.55750869479834
0.6 0.41025641025641024 2753.270626656453   1.4242758000337287e-05  -539.7293733435472
0.6 0.4358974358974359  2713.33466879976    8.809740923608618e-06   -579.66533120024
0.6 0.4615384615384615  2676.2228493772222  5.564679207121495e-06   -616.7771506227778
0.6 0.48717948717948717 2641.603940002951   3.5826798039874953e-06  -651.3960599970492
0.6 0.5128205128205128  2609.195548611385   2.34711752860102e-06    -683.8044513886148
0.6 0.5384615384615384  2578.7554243544596  1.5622264672933339e-06  -714.2445756455404
0.6 0.5641025641025641  2549.6240721552927  1.048466420965538e-06   -743.3759278447073
0.6 0.5897435897435898  2522.556370855503   7.178059040682267e-07   -770.4436291444972
0.6 0.6153846153846154  2496.9156076665554  4.97515321727389e-07    -796.0843923334446
0.6 0.641025641025641   2472.5714894952007  3.487920707673146e-07   -820.4285105047993
0.6 0.6666666666666666  2449.411398998468   2.4714438056596686e-07  -843.588601001532
0.6 0.6923076923076923  2427.3352464641503  1.7686817885330674e-07  -865.6647535358497
0.6 0.717948717948718   2406.2550389076464  1.2775786101787292e-07  -886.7449610923536
0.6 0.7435897435897436  2386.092739611005   9.309263749495561e-08   -906.9072603889949
0.6 0.7692307692307692  2366.781957597925   6.839567736867184e-08   -926.2180424020748
0.6 0.7948717948717948  2348.2540550602853  5.0638063345815074e-08  -944.7459449397147
0.6 0.8205128205128205  2330.4566456692905  3.776560782088704e-08   -962.5433543307095
0.6 0.8461538461538461  2312.914089760887   2.8157985384417206e-08  -980.0859102391132
0.6 0.8717948717948718  2296.4544173217364  2.1290457329787035e-08  -996.5455826782636
0.6 0.8974358974358974  2280.586690692045   1.619785866067175e-08   -1012.4133093079549
0.6 0.923076923076923   2265.2732950781196  1.2396055261430032e-08  -1027.7267049218804
0.6 0.9487179487179487  2250.481043980994   9.539935562792993e-09   -1042.518956019006
0.6 0.9743589743589743  2236.1785383496112  7.381194348516508e-09   -1056.8214616503888
0.6 1.0 2222.337352420465   5.740123925349904e-09   -1070.6626475795351
0.6102564102564102  0.0 4089.758485473484   0.5934782530683373  796.7584854734841
0.6102564102564102  0.02564102564102564 4020.015549033518   0.4050337178026037  727.0155490335178
0.6102564102564102  0.05128205128205128 3936.443014897932   0.2519373641307344  643.4430148979318
0.6102564102564102  0.07692307692307693 3835.9622135353943  0.13869108144140924 542.9622135353943
0.6102564102564102  0.10256410256410256 3716.001862593243   0.0652915198863712  423.001862593243
0.6102564102564102  0.1282051282051282  3583.276474169524   0.026780034204686637    290.27647416952414
0.6102564102564102  0.15384615384615385 3453.29301036468    0.010472592821755186    160.29301036467996
0.6102564102564102  0.1794871794871795  3335.2397797631425  0.004188552710560718    42.23977976314245
0.6102564102564102  0.20512820512820512 3231.5556367220734  0.0017714440394004838   -61.44436327792664
0.6102564102564102  0.23076923076923075 3141.8876107515835  0.0008034258071031709   -151.11238924841655
0.6102564102564102  0.2564102564102564  3063.258409345691   0.00038644023383260236  -229.74159065430922
0.6102564102564102  0.28205128205128205 2993.1018777930335  0.000194629404414767    -299.8981222069665
0.6102564102564102  0.3076923076923077  2930.9086182941815  0.00010305772351264343  -362.09138170581855
0.6102564102564102  0.3333333333333333  2874.80656912216    5.670575345634133e-05   -418.19343087784
0.6102564102564102  0.358974358974359   2823.8140078427205  3.226677086573632e-05   -469.18599215727954
0.6102564102564102  0.3846153846153846  2777.157327888055   1.8911940542269504e-05  -515.8426721119449
0.6102564102564102  0.41025641025641024 2733.7431368477382  1.1314809916125549e-05  -559.2568631522618
0.6102564102564102  0.4358974358974359  2694.076716673613   6.973617299774141e-06   -598.9232833263868
0.6102564102564102  0.4615384615384615  2657.221399283425   4.39012489175898e-06    -635.778600716575
0.6102564102564102  0.48717948717948717 2622.845931424028   2.8175420527631197e-06  -670.1540685759719
0.6102564102564102  0.5128205128205128  2590.6620810029694  1.8401214780453332e-06  -702.3379189970306
0.6102564102564102  0.5384615384615384  2560.4262157605667  1.2210244244245512e-06  -732.5737842394333
0.6102564102564102  0.5641025641025641  2531.9333666347898  8.221161478996223e-07   -761.0666333652102
0.6102564102564102  0.5897435897435898  2504.5473829105094  5.57302928823424e-07    -788.4526170894906
0.6102564102564102  0.6153846153846154  2479.081058589309   3.8521127824390946e-07  -813.9189414106909
0.6102564102564102  0.641025641025641   2454.902487883553   2.6934155696808167e-07  -838.097512116447
0.6102564102564102  0.6666666666666666  2431.8990679894373  1.9035323732555907e-07  -861.1009320105627
0.6102564102564102  0.6923076923076923  2409.97188068404    1.358817937526897e-07   -883.0281193159599
0.6102564102564102  0.717948717948718   2389.0332008649307  9.790978932177212e-08   -903.9667991350693
0.6102564102564102  0.7435897435897436  2369.006372028793   7.11720515957582e-08    -923.9936279712069
0.6102564102564102  0.7692307692307692  2349.8220009613638  5.216525355877487e-08   -943.1779990386362
0.6102564102564102  0.7948717948717948  2331.4217741692014  3.853533801029536e-08   -961.5782258307986
0.6102564102564102  0.8205128205128205  2313.743281127155   2.8675037620767172e-08  -979.2567188728449
0.6102564102564102  0.8461538461538461  2296.7403227753507  2.1486672067335364e-08  -996.2596772246493
0.6102564102564102  0.8717948717948718  2280.367822611922   1.620675650179381e-08   -1012.6321773880782
0.6102564102564102  0.8974358974358974  2264.1567397077774  1.2208653876788755e-08  -1028.8432602922226
0.6102564102564102  0.923076923076923   2248.9478344236195  9.324249727524238e-09   -1044.0521655763805
0.6102564102564102  0.9487179487179487  2234.255974742707   7.161596332134007e-09   -1058.7440252572928
0.6102564102564102  0.9743589743589743  2220.0503665890365  5.530200799471455e-09   -1072.9496334109635
0.6102564102564102  1.0 2206.3024238889902  4.292374196845114e-09   -1086.6975761110098
0.6205128205128205  0.0 4085.9562609151026  0.5818806524606029  792.9562609151026
0.6205128205128205  0.02564102564102564 4014.543968068865   0.393232872375126   721.5439680688651
0.6205128205128205  0.05128205128205128 3928.747576760244   0.24119897097118417 635.7475767602441
0.6205128205128205  0.07692307692307693 3824.169681232023   0.12922372944568014 531.1696812320229
0.6205128205128205  0.10256410256410256 3699.5150077169374  0.05873931928116341 406.51500771693736
0.6205128205128205  0.1282051282051282  3563.3409430738075  0.023331322606508734    270.34094307380747
0.6205128205128205  0.15384615384615385 3430.8070703284325  0.008854793881484713    137.80707032843247
0.6205128205128205  0.1794871794871795  3312.2578300362356  0.0034844161781834534   19.257830036235646
0.6205128205128205  0.20512820512820512 3209.275238718651   0.0014647335114379933   -83.72476128134895
0.6205128205128205  0.23076923076923075 3119.463656552239   0.0006560437426282356   -173.53634344776083
0.6205128205128205  0.2564102564102564  3041.2911680923103  0.00031357887902450115  -251.70883190768973
0.6205128205128205  0.28205128205128205 2972.0905951815694  0.00015787708885446115  -320.90940481843063
0.6205128205128205  0.3076923076923077  2909.727838603062   8.269049672567261e-05   -383.2721613969379
0.6205128205128205  0.3333333333333333  2854.008281739071   4.529049251803833e-05   -438.9917182609288
0.6205128205128205  0.358974358974359   2803.3653782260967  2.566016338292907e-05   -489.6346217739033
0.6205128205128205  0.3846153846153846  2757.0291906427346  1.4978320372571313e-05  -535.9708093572654
0.6205128205128205  0.41025641025641024 2714.3856746558727  8.97770262924924e-06    -578.6143253441273
0.6205128205128205  0.4358974358974359  2674.462733998719   5.477129079602506e-06   -618.5372660012808
0.6205128205128205  0.4615384615384615  2637.871119671546   3.436195865074016e-06   -655.1288803284542
0.6205128205128205  0.48717948717948717 2603.7392985295196  2.1979808002760715e-06  -689.2607014704804
0.6205128205128205  0.5128205128205128  2571.785490301246   1.4309077493310205e-06  -721.2145096987538
0.6205128205128205  0.5384615384615384  2541.7584979869102  9.464990586631703e-07   -751.2415020130898
0.6205128205128205  0.5641025641025641  2513.461946858778   6.353368050516752e-07   -779.538053141222
0.6205128205128205  0.5897435897435898  2486.7243094681153  4.322796565427778e-07   -806.2756905318847
0.6205128205128205  0.6153846153846154  2461.3975723077706  2.978260300355784e-07   -831.6024276922294
0.6205128205128205  0.641025641025641   2436.9052578882215  2.061855877907098e-07   -856.0947421117785
0.6205128205128205  0.6666666666666666  2414.062538764044   1.4533045520413267e-07  -878.9374612359561
0.6205128205128205  0.6923076923076923  2392.2881091188488  1.0347344997907934e-07  -900.7118908811512
0.6205128205128205  0.717948717948718   2371.495322913007   7.436968001354505e-08   -921.5046770869931
0.6205128205128205  0.7435897435897436  2351.6073277996775  5.392687676981694e-08   -941.3926722003225
0.6205128205128205  0.7692307692307692  2332.5556665786467  3.9430130799373704e-08  -960.4443334213533
0.6205128205128205  0.7948717948717948  2314.2791172529955  2.9057391641594306e-08  -978.7208827470045
0.6205128205128205  0.8205128205128205  2296.726049063505   2.1573729166044833e-08  -996.2739509364951
0.6205128205128205  0.8461538461538461  2279.8400358172066  1.6129061122026443e-08  -1013.1599641827934
0.6205128205128205  0.8717948717948718  2263.5799751899604  1.2138784673483903e-08  -1029.4200248100396
0.6205128205128205  0.8974358974358974  2247.905318866411   9.193412972071419e-09   -1045.094681133589
0.6205128205128205  0.923076923076923   2232.779198717875   7.004518656110249e-09   -1060.2208012821252
0.6205128205128205  0.9487179487179487  2217.733362577675   5.324671619961591e-09   -1075.266637422325
0.6205128205128205  0.9743589743589743  2203.6270946852055  4.103456454256474e-09   -1089.3729053147945
0.6205128205128205  1.0 2189.975488110135   3.178710518899966e-09   -1103.0245118898652
0.6307692307692307  0.0 4082.014910142676   0.5700710054063685  789.014910142676
0.6307692307692307  0.02564102564102564 4008.8408676948898  0.3812613450275986  715.8408676948898
0.6307692307692307  0.05128205128205128 3920.3047954046897  0.22988272700323523 627.3047954046897
0.6307692307692307  0.07692307692307693 3811.64966881932    0.11981751553993202 518.6496688193201
0.6307692307692307  0.10256410256410256 3682.5364750557437  0.052626268696471724    389.5364750557437
0.6307692307692307  0.1282051282051282  3541.918478375224   0.02008224969220397 248.9184783752239
0.6307692307692307  0.15384615384615385 3407.551202855716   0.007426357423113649    114.55120285571593
0.6307692307692307  0.1794871794871795  3288.67250906648    0.002876842800189789    -4.327490933520039
0.6307692307692307  0.20512820512820512 3186.00189758527    0.0011973438638229169   -106.99810241473006
0.6307692307692307  0.23076923076923075 3096.563994323525   0.0005317621070387426   -196.4360056764749
0.6307692307692307  0.2564102564102564  3018.8741331428364  0.00025256426670229945  -274.1258668571636
0.6307692307692307  0.28205128205128205 2950.1369732772646  0.00012645142700852004  -342.8630267227354
0.6307692307692307  0.3076923076923077  2888.6965556435757  6.624104803235243e-05   -404.3034443564243
0.6307692307692307  0.3333333333333333  2832.8023786778244  3.5890270343479926e-05  -460.19762132217556
0.6307692307692307  0.358974358974359   2782.519806360227   2.024441934775926e-05   -510.480193639773
0.6307692307692307  0.3846153846153846  2736.5132743993577  1.1767503561003353e-05  -556.4867256006423
0.6307692307692307  0.41025641025641024 2694.1740300173683  7.0251357388225254e-06  -598.8259699826317
0.6307692307692307  0.4358974358974359  2655.0166762734484  4.2956651737299366e-06  -637.9833237265516
0.6307692307692307  0.4615384615384615  2618.150572161647   2.666872852328474e-06   -674.8494278383532
0.6307692307692307  0.48717948717948717 2584.2711770530696  1.7000567174821655e-06  -708.7288229469304
0.6307692307692307  0.5128205128205128  2552.5431531883733  1.1029973810244411e-06  -740.4568468116267
0.6307692307692307  0.5384615384615384  2522.734599736544   7.272753305975323e-07   -770.2654002634558
0.6307692307692307  0.5641025641025641  2494.639434233172   4.866528913745321e-07   -798.3605657668281
0.6307692307692307  0.5897435897435898  2468.0915357584845  3.301098963460793e-07   -824.9084642415155
0.6307692307692307  0.6153846153846154  2442.944062781507   2.2676385592208542e-07  -850.055937218493
0.6307692307692307  0.641025641025641   2419.0694840188216  1.5760583132198434e-07  -873.9305159811784
0.6307692307692307  0.6666666666666666  2395.8850430225925  1.0992408706016279e-07  -897.1149569774075
0.6307692307692307  0.6923076923076923  2374.2674477037312  7.8055682485317e-08 -918.7325522962688
0.6307692307692307  0.717948717948718   2353.62399967357    5.595508300553129e-08   -939.3760003264301
0.6307692307692307  0.7435897435897436  2333.8784719361324  4.047100831731566e-08   -959.1215280638676
0.6307692307692307  0.7692307692307692  2314.9629595827046  2.9518054724771524e-08  -978.0370404172954
0.6307692307692307  0.7948717948717948  2296.8167308651996  2.1700073206423222e-08  -996.1832691348004
0.6307692307692307  0.8205128205128205  2279.385269710868   1.607212331527882e-08   -1013.6147302891318
0.6307692307692307  0.8461538461538461  2262.619474140465   1.1988061719036208e-08  -1030.3805258595348
0.6307692307692307  0.8717948717948718  2246.478710053116   9.00236895744079e-09    -1046.5212899468838
0.6307692307692307  0.8974358974358974  2230.914966434676   6.802820115727801e-09   -1062.085033565324
0.6307692307692307  0.923076923076923   2215.8953734468187  5.171748535762425e-09   -1077.1046265531813
0.6307692307692307  0.9487179487179487  2201.387282363486   3.954408080858642e-09   -1091.612717636514
0.6307692307692307  0.9743589743589743  2187.359868716557   3.0401965632714095e-09  -1105.6401312834432
0.6307692307692307  1.0 2173.340503564733   2.329704282785878e-09   -1119.659496435267

可以在这里找到完整的数据集：https://fz-juelich.sciebo.de/s/SjwZyAPB4oEerZE

理想情况下，我想使用

imshow

来表示这一点，使用

meshgrid

将 X 和 Y 分量网格化在一起，并让每个点的 Z 值由颜色条表示。整个数据集总共有 1600 个点，这可能会非常费力。我已经制作了以下代码来绘制这个：

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import scipy.interpolate

# Load the 3D data file
data = np.genfromtxt("Ta_parameterspace_2mm.txt", skip_header=14, delimiter="\t", dtype = float)
#print(data)
reflect = data[:,0]
emiss = data[:,1]
temp = data[:,2]

X,Y = np.meshgrid(reflect,emiss)

rbf = scipy.interpolate.Rbf(reflect,emiss,temp,function="linear")
Z = rbf(X,Y)

plt.imshow(Z, vmin = temp.min(), vmax = temp.max(), origin = 'lower', extent = [reflect.min(), reflect.max(), emiss.min(), emiss.max()], cmap = 'seismic')
plt.colorbar()

#im = ax.imshow(array, extent=[x1.min(), x1.max(), y1.min(), y1.max()], origin='lower', cmap='seismic')
plt.set_xlabel('Reflectivity')
plt.set_ylabel('Emissivity')
plt.set_zlabel('Temperature (K)')
plt.show()
plt.savefig('Ta_parameterplot.pdf', bbox_inches='tight', format='pdf')
plt.savefig('Ta_parameterplot.png', dpi=300, bbox_inches='tight', format='png')

但目前代码无法正常运行。代码读取数据集为 1600 x 1600 x 1600 个数据点，然后导致内存溢出，如下所示：

Traceback (most recent call last):
  File "C:\Users\t.smart\Documents\Data\PhD_Work\Ta_simulations_Fenics\ParameterSpace\parameterplot.py", line 16, in <module>
    Z = rbf(X,Y)
  File "C:\Users\t.smart\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\scipy\interpolate\_rbf.py", line 288, in __call__
    r = self._call_norm(xa, self.xi)
  File "C:\Users\t.smart\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\scipy\interpolate\_rbf.py", line 277, in _call_norm
    return cdist(x1.T, x2.T, self.norm)
  File "C:\Users\t.smart\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\scipy\spatial\distance.py", line 2939, in cdist
    return cdist_fn(XA, XB, out=out, **kwargs)
numpy.core._exceptions._ArrayMemoryError: Unable to allocate 30.5 GiB for an array with shape (2560000, 1600) and data type float64

而这个数据集实际上是 40 x 40 点，每个点都有单独的 z 值。

如何解决这个问题并获取使用 imshow 绘制数据集的代码？

Answer 1

tricontourf

适合你吗？

使用这个你不必制作网格（我认为这是失败的，因为你没有制作只有

reflect

和

emiss

的唯一值的网格）

import matplotlib.pyplot as plt 

fig, ax = plt.subplots()
cb = ax.tricontourf(data[:, 0], data[:, 1], data[:, 2])
fig.colorbar(cb)
plt.show()

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