Contour plot - how to replicate matplotlib contourf

[Jupiter]

Hi All,
I have been using matplotlib to generate contour plots with the following command:

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.contourf(x, y, z, 50)
cbar = fig.colorbar(cax)

which produces the following plot:

Matplotlib takes into account that y’s is varying across x which intentionally produces blank areas (is what I want ).
I have tried to convert this plotting approach to plotly by:

import plotly.tools as tls
plotly_fig = tls.mpl_to_plotly(fig)

but it fails

I would prefer to build the plot directly with plotly/python e.g. like:

import plotly.graph_objects as go
fig = go.Figure(data =
go.Contour(
x =x,
y=y,
z= z
))
fig.show()

but, it is not producing a plot like with matplotlib.

Any help/guidance on how to get a similar plot via plotly as with matplotlib is greatly appreciated.

thanks

data example arrays are below:

x =
[[864 864 864 864 864 864 864 864 864 864 864 864 864 864 864 864 864 864
864 864]
[865 865 865 865 865 865 865 865 865 865 865 865 865 865 865 865 865 865
865 865]
[866 866 866 866 866 866 866 866 866 866 866 866 866 866 866 866 866 866
866 866]
[867 867 867 867 867 867 867 867 867 867 867 867 867 867 867 867 867 867
867 867]
[868 868 868 868 868 868 868 868 868 868 868 868 868 868 868 868 868 868
868 868]
[869 869 869 869 869 869 869 869 869 869 869 869 869 869 869 869 869 869
869 869]
[870 870 870 870 870 870 870 870 870 870 870 870 870 870 870 870 870 870
870 870]
[871 871 871 871 871 871 871 871 871 871 871 871 871 871 871 871 871 871
871 871]
[872 872 872 872 872 872 872 872 872 872 872 872 872 872 872 872 872 872
872 872]
[873 873 873 873 873 873 873 873 873 873 873 873 873 873 873 873 873 873
873 873]
[874 874 874 874 874 874 874 874 874 874 874 874 874 874 874 874 874 874
874 874]
[875 875 875 875 875 875 875 875 875 875 875 875 875 875 875 875 875 875
875 875]
[876 876 876 876 876 876 876 876 876 876 876 876 876 876 876 876 876 876
876 876]
[877 877 877 877 877 877 877 877 877 877 877 877 877 877 877 877 877 877
877 877]
[878 878 878 878 878 878 878 878 878 878 878 878 878 878 878 878 878 878
878 878]
[879 879 879 879 879 879 879 879 879 879 879 879 879 879 879 879 879 879
879 879]
[880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880
880 880]
[881 881 881 881 881 881 881 881 881 881 881 881 881 881 881 881 881 881
881 881]
[882 882 882 882 882 882 882 882 882 882 882 882 882 882 882 882 882 882
882 882]
[883 883 883 883 883 883 883 883 883 883 883 883 883 883 883 883 883 883
883 883]
[884 884 884 884 884 884 884 884 884 884 884 884 884 884 884 884 884 884
884 884]
[885 885 885 885 885 885 885 885 885 885 885 885 885 885 885 885 885 885
885 885]
[886 886 886 886 886 886 886 886 886 886 886 886 886 886 886 886 886 886
886 886]
[887 887 887 887 887 887 887 887 887 887 887 887 887 887 887 887 887 887
887 887]
[888 888 888 888 888 888 888 888 888 888 888 888 888 888 888 888 888 888
888 888]
[889 889 889 889 889 889 889 889 889 889 889 889 889 889 889 889 889 889
889 889]
[890 890 890 890 890 890 890 890 890 890 890 890 890 890 890 890 890 890
890 890]
[891 891 891 891 891 891 891 891 891 891 891 891 891 891 891 891 891 891
891 891]
[892 892 892 892 892 892 892 892 892 892 892 892 892 892 892 892 892 892
892 892]
[893 893 893 893 893 893 893 893 893 893 893 893 893 893 893 893 893 893
893 893]
[894 894 894 894 894 894 894 894 894 894 894 894 894 894 894 894 894 894
894 894]
[895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895
895 895]
[896 896 896 896 896 896 896 896 896 896 896 896 896 896 896 896 896 896
896 896]
[897 897 897 897 897 897 897 897 897 897 897 897 897 897 897 897 897 897
897 897]
[898 898 898 898 898 898 898 898 898 898 898 898 898 898 898 898 898 898
898 898]]
y=
[[-3.9423025 -3.8224788 -3.6936152 -3.5553672 -3.407439 -3.249595
-3.081672 -2.903593 -2.7153785 -2.5171573 -2.3091784 -2.0918162
-1.8655784 -1.631106 -1.3891723 -1.1406771 -0.8866364 -0.6281682
-0.3664745 -0.1028197 ]
[-3.9423907 -3.8227503 -3.6940837 -3.5560474 -3.4083452 -3.2507424
-3.0830765 -2.9052699 -2.717343 -2.5194252 -2.311764 -2.0947344
-1.8688426 -1.6347288 -1.393165 -1.1450499 -0.89139766 -0.6333248
-0.37203127 -0.10877967]
[-3.942479 -3.8230217 -3.6945524 -3.5567274 -3.4092515 -3.25189
-3.0844808 -2.9069464 -2.7193077 -2.5216928 -2.31435 -2.0976527
-1.8721068 -1.6383516 -1.3971578 -1.1494226 -0.89615893 -0.6384813
-0.37758803 -0.11473963]
[-3.942567 -3.8232932 -3.695021 -3.5574074 -3.4101577 -3.2530377
-3.085885 -2.9086232 -2.7212722 -2.5239606 -2.3169358 -2.100571
-1.875371 -1.6419742 -1.4011506 -1.1537954 -0.9009202 -0.6436379
-0.38314477 -0.12069961]
[-3.9426553 -3.8235648 -3.6954894 -3.5580873 -3.411064 -3.2541852
-3.0872896 -2.9103 -2.7232368 -2.5262282 -2.3195214 -2.1034892
-1.8786352 -1.645597 -1.4051433 -1.1581682 -0.9056815 -0.6487944
-0.38870153 -0.12665957]
[-3.9427435 -3.823836 -3.695958 -3.5587673 -3.4119701 -3.255333
-3.0886939 -2.9119768 -2.7252014 -2.528496 -2.3221073 -2.1064072
-1.8818994 -1.6492198 -1.409136 -1.1625409 -0.91044277 -0.653951
-0.3942583 -0.13261954]
[-3.9428318 -3.8241076 -3.6964266 -3.5594473 -3.4128764 -3.2564805
-3.0900984 -2.9136534 -2.727166 -2.5307636 -2.324693 -2.1093254
-1.8851635 -1.6528425 -1.4131287 -1.1669137 -0.91520405 -0.6591075
-0.39981505 -0.1385795 ]
[-3.94292 -3.8243792 -3.6968951 -3.5601273 -3.4137826 -3.257628
-3.0915027 -2.9153302 -2.7291305 -2.5330315 -2.3272789 -2.1122437
-1.8884277 -1.6564653 -1.4171215 -1.1712865 -0.9199653 -0.6642641
-0.40537181 -0.14453948]
[-3.9430084 -3.8246505 -3.6973636 -3.5608072 -3.4146888 -3.2587757
-3.0929072 -2.917007 -2.731095 -2.535299 -2.3298645 -2.115162
-1.8916918 -1.6600881 -1.4211143 -1.1756593 -0.9247266 -0.66942066
-0.41092858 -0.15049945]
[-3.9430966 -3.824922 -3.6978323 -3.5614872 -3.4155948 -3.2599232
-3.0943115 -2.9186838 -2.7330596 -2.537567 -2.3324504 -2.11808
-1.894956 -1.6637108 -1.425107 -1.180032 -0.9294879 -0.6745772
-0.41648534 -0.15645942]
[-3.9431849 -3.8251936 -3.6983008 -3.5621672 -3.416501 -3.2610707
-3.095716 -2.9203603 -2.7350242 -2.5398345 -2.335036 -2.1209981
-1.8982202 -1.6673336 -1.4290998 -1.1844049 -0.93424916 -0.67973375
-0.4220421 -0.16241938]
[-3.943273 -3.8254652 -3.6987693 -3.5628471 -3.4174073 -3.2622185
-3.0971203 -2.9220371 -2.7369888 -2.5421023 -2.337622 -2.1239164
-1.9014844 -1.6709563 -1.4330925 -1.1887776 -0.93901044 -0.6848903
-0.42759886 -0.16837935]
[-3.9433613 -3.8257365 -3.6992378 -3.563527 -3.4183135 -3.263366
-3.0985248 -2.923714 -2.7389534 -2.54437 -2.3402076 -2.1268346
-1.9047486 -1.674579 -1.4370853 -1.1931504 -0.9437717 -0.69004685
-0.43315563 -0.17433932]
[-3.9434516 -3.8260145 -3.6997175 -3.564223 -3.4192412 -3.2645407
-3.0999622 -2.9254303 -2.7409642 -2.5466912 -2.3428545 -2.1298215
-1.9080898 -1.6782873 -1.4411721 -1.1976262 -0.9486453 -0.695325
-0.43884346 -0.18043986]
[-3.9435565 -3.826337 -3.7002745 -3.5650313 -3.4203184 -3.265905
-3.1016316 -2.9274232 -2.7432995 -2.5493867 -2.3459282 -2.1332905
-1.9119699 -1.6825936 -1.4459183 -1.2028241 -0.954305 -0.7014546
-0.44544876 -0.18752448]
[-3.9436615 -3.82666 -3.7008314 -3.5658398 -3.4213955 -3.267269
-3.103301 -2.9294164 -2.7456348 -2.5520825 -2.349002 -2.1367593
-1.91585 -1.6869 -1.4506645 -1.2080221 -0.95996475 -0.7075842
-0.45205408 -0.19460909]
[-3.9437664 -3.8269825 -3.7013884 -3.566648 -3.4224727 -3.2686331
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-1.9197301 -1.6912063 -1.4554107 -1.21322 -0.96562445 -0.71371377
-0.4586594 -0.2016937 ]
[-3.943871 -3.8273053 -3.7019453 -3.5674562 -3.42355 -3.2699974
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-1.9236102 -1.6955128 -1.4601569 -1.2184179 -0.9712842 -0.7198434
-0.46526474 -0.2087783 ]
[-3.943976 -3.8276281 -3.7025023 -3.5682645 -3.424627 -3.2713614
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-1.9274904 -1.6998191 -1.464903 -1.2236158 -0.9769439 -0.72597295
-0.47187003 -0.21586291]
[-3.9440808 -3.8279507 -3.7030594 -3.5690727 -3.4257042 -3.2727256
-3.1099787 -2.937389 -2.754976 -2.562865 -2.3612967 -2.1506348
-1.9313705 -1.7041255 -1.4696492 -1.2288138 -0.98260367 -0.7321026
-0.47847536 -0.22294754]
[-3.9441857 -3.8282735 -3.7036164 -3.5698812 -3.4267817 -3.2740898
-3.111648 -2.939382 -2.7573113 -2.5655606 -2.3643703 -2.1541035
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-0.4850807 -0.23003215]
[-3.9442906 -3.828596 -3.7041733 -3.5706894 -3.4278588 -3.2754538
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-1.9391308 -1.7127383 -1.4791416 -1.2392095 -0.99392307 -0.74436176
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[-3.9445004 -3.8292415 -3.7052872 -3.572306 -3.4300132 -3.2781823
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[-3.9447103 -3.829887 -3.706401 -3.5739226 -3.4321675 -3.2809105
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-0.5181073 -0.2654552 ]
[-3.9448152 -3.8302097 -3.706958 -3.5747309 -3.4332447 -3.2822747
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-0.52471256 -0.2725398 ]
[-3.94492 -3.8305323 -3.707515 -3.575539 -3.434322 -3.2836387
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-0.5313179 -0.27962443]
[-3.9450207 -3.8308418 -3.7080493 -3.5763142 -3.435355 -3.284947
-3.124935 -2.9552453 -2.7758975 -2.587015 -2.3888338 -2.181712
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-0.5376518 -0.28641787]
[-3.9450855 -3.8310413 -3.7083936 -3.576814 -3.4360209 -3.2857902
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[-3.9451504 -3.831241 -3.7087379 -3.5773137 -3.4366868 -3.2866335
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[-3.9452152 -3.8314404 -3.7090821 -3.5778134 -3.4373527 -3.2874768
-3.128031 -2.958942 -2.7802286 -2.5920143 -2.3945343 -2.1881452
-1.9733284 -1.7506925 -1.5209721 -1.2850215 -1.0438052 -0.7983851
-0.5499022 -0.29955724]
[-3.9452798 -3.83164 -3.7094264 -3.5783129 -3.4380188 -3.28832
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-0.55806917 -0.3083168 ]
[-3.9454095 -3.8320389 -3.7101152 -3.5793123 -3.4393506 -3.2900069
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-1.9805245 -1.7586793 -1.5297745 -1.2946616 -1.054302 -0.80975324
-0.5621526 -0.31269658]]

z =
[[14.313813 14.315964 14.318469 14.321613 14.32581 14.33176
14.336363 14.340804 14.3467865 14.353908 14.362883 14.375611
14.396833 14.425026 14.465987 14.513266 14.562815 14.606856
14.643214 14.66736 ]
[15.887756 15.889947 15.892176 15.894657 15.897562 15.901045
15.903842 15.906494 15.9097185 15.91339 15.917785 15.923505
15.931823 15.942587 15.957335 15.975217 15.995712 16.016893
16.037842 16.055864 ]
[17.049894 17.050686 17.051254 17.051804 17.052402 17.053082
17.053614 17.054104 17.054684 17.055332 17.056093 17.057064
17.058443 17.06021 17.062592 17.065508 17.06892 17.072506
17.075472 17.073118 ]
[16.214447 16.216217 16.220085 16.225588 16.234282 16.247406
16.257439 16.271952 16.294804 16.321278 16.347477 16.389015
16.466505 16.561855 16.763414 17.039776 17.349918 17.527622
17.599398 17.604877 ]
[16.245964 16.247759 16.252758 16.262054 16.276989 16.29979
16.324738 16.356382 16.40259 16.464748 16.558044 16.786062
17.0495 17.200289 17.409216 17.999342 18.59744 18.943869
19.15316 19.170692 ]
[16.28217 16.284122 16.290758 16.303991 16.325975 16.360651
16.399502 16.44853 16.52168 16.627068 16.79588 17.173845
18.92067 19.113386 19.228382 19.299023 19.344383 19.369473
19.382442 19.383179 ]
[16.323608 16.32608 16.334759 16.352392 16.382248 16.430138
16.485277 16.565353 16.780577 17.677654 18.424421 18.532057
18.67504 19.089836 19.535816 19.812393 20.038866 20.185343
20.258522 20.274668 ]
[16.371004 16.373983 16.384985 16.407871 16.44792 16.516808
16.610392 16.779013 17.167183 17.883453 18.552233 19.228518
19.691988 19.881737 20.241898 20.834574 21.327736 21.648172
21.783743 21.800867 ]
[16.425554 16.429245 16.443497 16.474134 16.530468 16.6336
16.779413 17.02106 17.46875 18.117199 18.80158 19.484497
20.205254 21.034807 21.375565 22.387415 22.567026 22.65478
22.695255 22.702171 ]
[16.48819 16.493292 16.512716 16.554754 16.6334 16.777487
16.973152 17.264404 17.739105 18.376041 19.410532 22.322193
22.354116 22.378033 22.403164 22.425953 22.445843 22.461252
22.47197 22.473854 ]
[16.55854 16.56718 16.594595 16.652258 16.767515 17.132513
20.59621 21.945702 21.953814 21.957834 21.960566 21.963057
21.96607 21.969473 21.973572 21.978086 21.982708 21.98679
21.989103 21.983799 ]
[18.184958 21.26905 21.460497 21.465714 21.469286 21.472916
21.47512 21.476795 21.478771 21.48093 21.483362 21.486286
21.490614 21.495874 21.502464 21.50954 21.516348 21.521946
21.52526 21.520382 ]
[20.908823 20.90946 20.909956 20.910435 20.910942 20.911503
20.911966 20.912382 20.912865 20.913403 20.914026 20.914787
20.915852 20.917206 20.918976 20.921116 20.92352 20.925808
20.926884 20.921223 ]
[20.589975 20.591402 20.592667 20.594002 20.595535 20.597366
20.598888 20.600245 20.601871 20.603739 20.605932 20.60865
20.61267 20.61786 20.62473 20.632906 20.641865 20.65061
20.657906 20.659029 ]
[20.877579 20.878698 20.879612 20.880522 20.881517 20.882668
20.88362 20.884457 20.88545 20.886587 20.887917 20.889555
20.89199 20.895142 20.899307 20.904282 20.909763 20.915195
20.919708 20.919119 ]
[20.821203 20.82272 20.824125 20.825636 20.827387 20.829498
20.83127 20.832825 20.83469 20.836838 20.839352 20.842438
20.847073 20.85307 20.860958 20.870249 20.880203 20.889683
20.897478 20.899462 ]
[20.579361 20.587263 20.606749 20.645985 20.809416 20.972391
21.029253 21.057379 21.08351 21.105028 21.12341 21.140663
21.164244 21.188866 21.216295 21.24285 21.26572 21.282871
21.294615 21.298609 ]
[20.872255 20.8746 20.879995 20.891062 20.91357 20.964853
21.093004 21.401453 21.573086 21.650467 21.691422 21.718254
21.752502 21.782753 21.815449 21.845903 21.870775 21.887964
21.89876 21.900192 ]
[21.914911 21.915985 21.91685 21.917727 21.918709 21.91987
21.920858 21.921711 21.922726 21.9239 21.925274 21.926956
21.92948 21.932789 21.937172 21.942497 21.948465 21.95458
21.95997 21.9604 ]
[21.686197 21.686632 21.687008 21.687384 21.68778 21.688217
21.688595 21.688923 21.689297 21.689714 21.690193 21.690763
21.691551 21.692558 21.693865 21.695482 21.697365 21.699314
21.700686 21.698395 ]
[21.242893 21.243637 21.244297 21.244959 21.245663 21.24644
21.247116 21.247705 21.248377 21.249134 21.250002 21.251041
21.252481 21.254337 21.256767 21.259836 21.26354 21.267702
21.271664 21.272038 ]
[21.932615 21.93318 21.933693 21.934216 21.93478 21.935411
21.935955 21.936417 21.936949 21.93755 21.938238 21.939064
21.940254 21.941797 21.943804 21.946234 21.948948 21.951662
21.953585 21.951048 ]
[21.513063 21.514042 21.514896 21.515774 21.516747 21.517862
21.51885 21.5197 21.520687 21.521824 21.523136 21.524725
21.526989 21.529934 21.533794 21.538603 21.544203 21.550251
21.556028 21.558725 ]
[21.267338 21.268145 21.268858 21.269602 21.270437 21.27141
21.27227 21.272997 21.27384 21.274805 21.27591 21.277227
21.279089 21.281462 21.284462 21.28799 21.291725 21.295128
21.297138 21.293688 ]
[20.545183 20.545486 20.545723 20.545944 20.546175 20.546423
20.54664 20.546824 20.54703 20.54726 20.54752 20.547823
20.54823 20.548733 20.54935 20.550053 20.550735 20.55113
20.550392 20.544556 ]
[20.325447 20.32622 20.326859 20.32749 20.328161 20.328909
20.329573 20.330133 20.330772 20.331503 20.332335 20.333334
20.334726 20.336538 20.338902 20.341883 20.345427 20.349342
20.352962 20.352972 ]
[20.606873 20.60824 20.60949 20.610853 20.61246 20.61441
20.616177 20.617619 20.619314 20.621286 20.623545 20.62626
20.630175 20.635263 20.64176 20.649546 20.657953 20.66615
20.672838 20.67394 ]
[20.509209 20.510353 20.51137 20.512415 20.513567 20.514881
20.516077 20.517073 20.518219 20.519539 20.521046 20.522852
20.525372 20.528625 20.53277 20.537804 20.543379 20.548887
20.552881 20.549585 ]
[20.699625 20.700956 20.702156 20.70342 20.704845 20.7065
20.70803 20.709314 20.7108 20.712524 20.714504 20.716887
20.720217 20.72455 20.730122 20.737005 20.744852 20.753046
20.760065 20.760195 ]
[20.468256 20.469505 20.472555 20.478392 20.488081 20.502594
20.520546 20.537676 20.561481 20.592623 20.644712 20.710976
20.837364 21.067465 21.384876 21.706043 21.925213 22.046432
22.084719 22.084715 ]
[20.502464 20.50474 20.51082 20.521942 20.53975 20.566988
20.600801 20.63903 20.693974 20.773703 20.89891 21.164757
21.488718 21.645157 21.942432 22.566725 22.879276 23.226435
23.37105 23.36105 ]
[20.539028 20.541437 20.548954 20.563591 20.587805 20.625774
20.67341 20.727478 20.810947 20.993093 22.149906 22.266224
22.291168 22.307337 22.320976 22.333176 22.343012 22.35017
22.353893 22.349796 ]
[20.577251 20.582644 20.615046 21.010063 22.057549 22.06511
22.067507 22.068762 22.069984 22.071285 22.07269 22.074326
22.076607 22.079578 22.083368 22.088037 22.093332 22.098978
22.104258 22.10609 ]
[21.959843 21.960651 21.961306 21.961935 21.962595 21.963314
21.96398 21.964529 21.965145 21.965847 21.966635 21.967566
21.968807 21.970387 21.972368 21.974792 21.977526 21.980207
21.981503 21.975012 ]
[20.765831 20.767197 20.771086 20.776735 20.786068 20.801369
20.817577 20.829584 20.846834 20.879152 20.917301 20.971231
21.070385 21.257074 21.50744 21.682371 21.754982 21.780502
21.785727 21.779581 ]]

[Jupiter]

Does anybody have an idea on how to code the above?
thank you very much for any hint

[empet]

@Jupiter

To run your code we need data uploaded somewhere, not pasted here with no comma, that should be inserted after each inner list.

[Jupiter]

Im sorry about that I just realized what you are pointing out.

[Jupiter]

Here is a link to the data:
data (json)

to parse data (once downloaded):

with open("datalink", 'r') as f:
        data_dict = json.load(f)

and then:

x = data_dict["x"]
y = data_dict["y"]
z = data_dict["z"]

Is that usable?
thanks

[empet]

@Jupiter

If we define a go.Heatmap trace from your data, we get almost the same plot as with contourf , but the white space cannot be displayed because Plotly works only with uniform grids i.e.
with grids defined in the following way:

X = np.linspace(a, b, N)
Y = np.linspace(c, d, M)  # In the y direction your data are NOT uniform
X, Y = np.meshgrid(X, Y)
Z  of the shape as X and Y

y from the your json file gives the y-coordinates of a non-uniform grid (I analized both x and y).

import json
import numpy as np
import plotly.graph_objects as go

with open("data_for_contour.json", 'r') as f:
        data_dict = json.load(f)

x = data_dict["x"]
y = data_dict["y"]
z = data_dict["z"]
z = z=np.array(z).T
fig=go.Figure(go.Heatmap(x=np.array(x)[:, 0], y=np.array(y)[0], z=z, connectgaps=False,
                         zsmooth='best',
                         colorscale='Viridis', colorbar_thickness=25))
fig.update_layout(width=500, height=500)

Unfortunately the conturf cannot be reproduced, because Plotly cannot generate a Heatmap or a Contour trace from irregularly spaced data.

[Jupiter]

Thank you very much for your input, and for clearing up that Plotly does not support the irregular data on the y-axis. Hence the problem of replicating contourf

I was playing around with coloring the scatter type of Plotly to achieve what I’m looking for:

import plotly.graph_objs as go
import plotly.offline as py_off

data = []
data.append(go.Scatter(
            x = x.flatten(),
            y = y.flatten(),
            mode='markers',
            connectgaps = True,
            marker=dict(color= z.flatten(), colorscale='Viridis', size=14, 
                        colorbar=dict(title= "unit",
                                      thickness=20,
                                      titleside ='top',
                        ),
                        )
                        )
            )
            
fig = go.Figure(data=data)
py_off.plot(fig, filename = "test.html")

The result yields the support of the irregular data, however that approach is then missing the fill or smooth between dots vertically. Is that somehow possible?

thanks again

[Jupiter]

Hi empet, will there be any possibility of supporting an implementation of the functionality that mirrors matplotlib contourf into ploty (fixing the current constrains as described in this issue)?

Thanks

[Jupiter]

Hi empet, do you know if things have evolved since summer regarding the issue of mirroring matplotlib contourf into plotly regarding the shortcommings highlighted above. Is it possible to support the development?

thanks

[empet]

Hi @Jupiter,

You can plot your data as a Heatmap or Contour with no lines (to imitate your Matplotlib example), following this recent answer to a similar question:

Contour plot with non-uniform points

Hi @yootis, Welcome to Plotly forum! You can plot a contour over irregular data, by defining a regular grid that covers the initial data, and interpolating the so called z-values at the regular grid points: import plotly.graph_objects as go import numpy as np from scipy.interpolate import griddata np.random.seed(1273) #generate irregular data x= -1+2*np.random.rand(100) y= np.random.rand(100) xm, xM = x.min(), x.max() ym, yM = y.min(), y.max() #z-values at the irregular data z = np.sin(np.…

but replace the line:

Z = griddata((x, y), z, (xr, yr) , method='cubic')

with:

Z = griddata((x, y), z, (xr, yr) , method='linear')

[Jupiter]

Hi empet, You have already assisted much in pointing to solutions for this topic, but I’m reaching out again to see if Plotly has evolved around the issue with contour plots. Basically it is to mirror the matplotlib contourf functionality (fixing the current constrains as described in this issue). Alternatively is it possible to support the development?

Thanks

[Jupiter]

Hi @empet (or any others),

@empet have made some really nice suggestions to the issue above I created some time ago (Contour plot - how to replicate matplotlib contourf - #9 by Jupiter).

I’m reaching out to see any updates to plotly has been made to mimic the Matplotlib contourf, and especially that Matplotlib takes into account that y’s are varying across x which intentionally produces blank areas (what is what I want).

Alternatively, would it be possible to support its development?

Thanks

[Jupiter]

Hi all,

Sorry for drawing attention to my earlier question again, but just wanted to check and hear from you experts if any new updates to plotly have solved my challenge of mimicking the matplotlib contour in plotly and allowing plotting of irregularly spaced data; or if any new workarounds have surfaced.

Thanks

[Jupiter]

@Jupiter

Hi all,

I’m reaching out again to check and hear from you experts if any new updates to plotly have solved my challenge of mimicking the matplotlib contour in plotly and allowing plotting of irregularly spaced data; or if any new workarounds have surfaced. We will be happy to support the development.

Thanks

Hi all,

I’m reaching out again to check and hear from you experts if any new updates to plotly have solved my challenge of mimicking the matplotlib contour in plotly and allowing plotting of irregularly spaced data; or if any new workarounds have surfaced. We will be happy to support the development.

Thanks

[fingerpartyy]

i think you might achieve it with connectgaps = False

fig = go.Figure()
trace_surf = go.Contour(x=x,y=y,z=z, colorscale=‘Viridis’, connectgaps=False)
fig.add_trace(trace_surf)

[Jupiter]

Hi fingerpartyy,
Sorry for missing out on your reply and getting back with delay. I did look at using “connectgaps=False” and just tried again with your suggestions. Unfortunately, the plot is not rendering as intended:

Any other suggestions?
thanks

[fingerpartyy]

it worked for me.
Do you have data to reproduce your code?

ps:
Why on the y axis you have two values? Like a tuple?
Same for the x axis? It seems weird

[Jupiter]

Hi,
Thanks for the response. Data can be found here: (data). After download it can be parsed like:

import json
import numpy as np

with open("data_for_contour.json", 'r') as f:
        data_dict = json.load(f)

x = np.asarray(data_dict["x"])
y = np.asarray(data_dict["y"])
z = np.asarray(data_dict["z"])

x represents the time dimension. To support the matplotlib contourf function, x records is repeated to match the dimensions of y and x. y is varying across x which intentionally produces blank areas (which is what I want):

# This is the plot I wish to mimic:

fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.contourf(x, y, z, 50)
cbar = fig.colorbar(cax)

I have tried the suggestions to replicate the matplotlib plot via plotly, but it does not work out.

#-----------------------------------------------------------------------------#
# Trying with plotly:
#-----------------------------------------------------------------------------#

fig         = go.Figure()
trace_surf  = go.Contour(x=x,y=y,z=z, colorscale="Viridis", connectgaps=False)
fig.add_trace(trace_surf)
plotly.offline.plot(fig, filename="Experimental.html")

Produced plot:

As you point out it looks like plotly create pairs at the axes - it seems to be intervals. Since the x dimension is repeated to have same dimensions as y and z (requirements in matplotlib) it is okay to slice this dimension (eg. like: x = x[:,1]), but this cannot be done for y. So I’m stuck here:

Let me know if you need further details.

Thanks

[vrinkstay]

Hi empet, will there be any possibility of supporting an implementation of the functionality that mirrors matplotlib contourf into ploty (fixing the current constrains as described in this issue)?

[vrinkstay]

Do you have data to reproduce your code?.
.