Edgelines vanish in mplot3d surf when facecolors are specified

I have produced the following surface plot in matlab:

and I need to create this in .NET instead. I'm hoping to use IronPython to do this. But first I am just trying to create the plot in Python (PyLab). This is what I have so far:

Please have a look at my code and tell me how I can get python to show the black edge lines. It appear that these disappear when I add the facecolors=UWR(heatmap) property to the surf(...). Is this a bug in mplot3d or is it by design? Either way how do I get the lines back?

Here is my code (Apologies for the huge data matrices):

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
import matplotlib
from pylab import *
import numpy as np

fig = plt.figure()
ax = fig.gca(projection='3d')

#Sample colour data
heatmap = np.array([(0.304, 0.288,  0.284,  0.26,   0.248,  0.224,  0.204,  0.184,  0.18,   0.18,   0.156,  0.148,  0.144,  0.136,  0.136,  0.128,  0.124,  0.124,  0.128,  0.124,  0.124),
                    (0.356, 0.348,  0.332,  0.328,  0.308,  0.292,  0.288,  0.272,  0.252,  0.232,  0.216,  0.204,  0.16,   0.148,  0.152,  0.148,  0.132,  0.124,  0.124,  0.132,  0.144),
                    (0.396, 0.384,  0.372,  0.36,   0.34,   0.316,  0.312,  0.312,  0.3,    0.272,  0.244,  0.236,  0.216,  0.192,  0.176,  0.168,  0.148,  0.148,  0.156,  0.156,  0.16),
                    (0.452, 0.444,  0.428,  0.408,  0.388,  0.376,  0.364,  0.348,  0.336,  0.336,  0.3,    0.284,  0.264,  0.256,  0.24,   0.244,  0.212,  0.2,    0.22,   0.224,  0.224),
                    (0.488, 0.476,  0.464,  0.444,  0.424,  0.4,    0.4,    0.384,  0.38,   0.372,  0.356,  0.324,  0.312,  0.312,  0.312,  0.312,  0.308,  0.292,  0.304,  0.332,  0.344),
                    (0.492, 0.492,  0.48,   0.468,  0.452,  0.432,  0.424,  0.412,  0.404,  0.396,  0.396,  0.392,  0.376,  0.356,  0.356,  0.36,   0.368,  0.372,  0.392,  0.404,  0.42),
                    (0.5,   0.5,    0.5,    0.484,  0.46,   0.452,  0.444,  0.436,  0.44,   0.44,   0.44,   0.452,  0.44,   0.436,  0.424,  0.42,   0.404,  0.44,   0.452,  0.468,  0.5),
                    (0.484, 0.48,   0.46,   0.444,  0.44,   0.44,   0.44,   0.44,   0.444,  0.44,   0.456,  0.456,  0.46,   0.448,  0.448,  0.448,  0.436,  0.456,  0.468,  0.492,  0.492),
                    (0.405737704918033, 0.401639344262295,  0.409836065573771,  0.418032786885246,  0.434426229508197,  0.438524590163934,  0.438524590163934,  0.44672131147541,   0.454918032786885,  0.471311475409836,  0.467213114754098,  0.479508196721311,  0.487704918032787,  0.487704918032787,  0.479508196721311,  0.483606557377049,  0.495901639344262,  0.516393442622951,  0.520491803278689,  0.532786885245902,  0.536885245901639),
                    (0.320987654320988, 0.329218106995885,  0.349794238683128,  0.362139917695473,  0.374485596707819,  0.395061728395062,  0.42798353909465,   0.440329218106996,  0.465020576131687,  0.477366255144033,  0.48559670781893,   0.493827160493827,  0.506172839506173,  0.518518518518519,  0.51440329218107,   0.518518518518519,  0.547325102880658,  0.555555555555556,  0.555555555555556,  0.584362139917696,  0.580246913580247),
                    (0.282700421940928, 0.29535864978903,   0.30379746835443,   0.320675105485232,  0.337552742616034,  0.354430379746835,  0.383966244725738,  0.434599156118144,  0.464135021097046,  0.485232067510549,  0.493670886075949,  0.514767932489452,  0.527426160337553,  0.535864978902954,  0.544303797468354,  0.561181434599156,  0.594936708860759,  0.59915611814346,   0.590717299578059,  0.60337552742616,   0.607594936708861),
                    (0.230434782608696, 0.256521739130435,  0.273913043478261,  0.304347826086957,  0.334782608695652,  0.360869565217391,  0.373913043478261,  0.408695652173913,  0.469565217391304,  0.504347826086957,  0.521739130434783,  0.539130434782609,  0.552173913043478,  0.560869565217391,  0.578260869565217,  0.6,    0.617391304347826,  0.61304347826087,   0.61304347826087,   0.617391304347826,  0.643478260869565),
                    (0.161137440758294, 0.175355450236967,  0.218009478672986,  0.28436018957346,   0.327014218009479,  0.341232227488152,  0.388625592417062,  0.436018957345972,  0.488151658767773,  0.516587677725119,  0.549763033175356,  0.573459715639811,  0.578199052132701,  0.592417061611374,  0.611374407582938,  0.649289099526066,  0.658767772511848,  0.658767772511848,  0.677725118483412,  0.66824644549763,   0.691943127962085),
                    (0.224719101123596, 0.269662921348315,  0.303370786516854,  0.365168539325843,  0.382022471910112,  0.404494382022472,  0.443820224719101,  0.48876404494382,   0.5,    0.556179775280899,  0.567415730337079,  0.612359550561798,  0.612359550561798,  0.629213483146067,  0.634831460674157,  0.646067415730337,  0.662921348314607,  0.685393258426966,  0.707865168539326,  0.707865168539326,  0.724719101123596),
                    (0.333333333333333, 0.363636363636364,  0.401515151515152,  0.431818181818182,  0.446969696969697,  0.46969696969697,   0.515151515151515,  0.53030303030303,   0.553030303030303,  0.583333333333333,  0.613636363636364,  0.621212121212121,  0.636363636363636,  0.643939393939394,  0.651515151515152,  0.651515151515152,  0.666666666666667,  0.666666666666667,  0.674242424242424,  0.681818181818182,  0.696969696969697),
                    (0.373626373626374, 0.406593406593407,  0.483516483516484,  0.505494505494506,  0.527472527472528,  0.54945054945055,   0.571428571428571,  0.582417582417583,  0.593406593406593,  0.637362637362637,  0.659340659340659,  0.681318681318681,  0.692307692307692,  0.692307692307692,  0.703296703296703,  0.692307692307692,  0.703296703296703,  0.736263736263736,  0.736263736263736,  0.703296703296703,  0.67032967032967),
                    (0.484375,  0.5625, 0.578125,   0.578125,   0.578125,   0.625,  0.625,  0.640625,   0.65625,    0.671875,   0.703125,   0.734375,   0.75,   0.734375,   0.734375,   0.75,   0.734375,   0.640625,   0.65625,    0.625,  0.609375),
                    (0.617647058823529, 0.617647058823529,  0.617647058823529,  0.617647058823529,  0.617647058823529,  0.588235294117647,  0.588235294117647,  0.588235294117647,  0.617647058823529,  0.647058823529412,  0.676470588235294,  0.705882352941177,  0.676470588235294,  0.705882352941177,  0.705882352941177,  0.735294117647059,  0.705882352941177,  0.705882352941177,  0.735294117647059,  0.705882352941177,  0.647058823529412),
                    (0.6,   0.6,    0.6,    0.6,    0.6,    0.6,    0.6,    0.5,    0.5,    0.5,    0.5,    0.5,    0.4,    0.4,    0.4,    0.4,    0.3,    0.3,    0.3,    0.4,    0.4)
]);

#Sample Z data
volatility = np.array([(0.2964396,  0.28628612, 0.27630128, 0.26648508, 0.25683752, 0.2473586,  0.23804832, 0.22890668, 0.21993368, 0.21112932, 0.2024936,  0.19402652, 0.18572808, 0.17759828, 0.16963712, 0.1618446,  0.15422072, 0.14676548, 0.13947888, 0.13236092, 0.1254116),
                       (0.2979793,  0.287974509333333,  0.278154444,    0.268519104,    0.259068489333333,  0.2498026,  0.240721436,    0.231824997333333,  0.223113284,    0.214586296,    0.206244033333333,  0.198086496,    0.190113684,    0.182325597333333,  0.174722236,    0.1673036,  0.160069689333333,  0.153020504,    0.146156044,    0.139476309333333,  0.1329813),
                       (0.299519,   0.289662898666667,  0.280007608,    0.270553128,    0.261299458666667,  0.2522466,  0.243394552,    0.234743314666667,  0.226292888,    0.218043272,    0.209994466666667,  0.202146472,    0.194499288,    0.187052914666667,  0.179807352,    0.1727626,  0.165918658666667,  0.159275528,    0.152833208,    0.146591698666667,  0.140551),
                       (0.3010587,  0.291351288,    0.281860772,    0.272587152,    0.263530428,    0.2546906,  0.246067668,    0.237661632,    0.229472492,    0.221500248,    0.2137449,  0.206206448,    0.198884892,    0.191780232,    0.184892468,    0.1782216,  0.171767628,    0.165530552,    0.159510372,    0.153707088,    0.1481207),
                       (0.3025984,  0.293039677333333,  0.283713936,    0.274621176,    0.265761397333333,  0.2571346,  0.248740784,    0.240579949333333,  0.232652096,    0.224957224,    0.217495333333333,  0.210266424,    0.203270496,    0.196507549333333,  0.189977584,    0.1836806,  0.177616597333333,  0.171785576,    0.166187536,    0.160822477333333,  0.1556904),
                       (0.3041381,  0.294728066666667,  0.2855671,  0.2766552,  0.267992366666667,  0.2595786,  0.2514139,  0.243498266666667,  0.2358317,  0.2284142,  0.221245766666667,  0.2143264,  0.2076561,  0.201234866666667,  0.1950627,  0.1891396,  0.183465566666667,  0.1780406,  0.1728647,  0.167937866666667,  0.1632601),
                       (0.3056778,  0.296416456,    0.287420264,    0.278689224,    0.270223336,    0.2620226,  0.254087016,    0.246416584,    0.239011304,    0.231871176,    0.2249962,  0.218386376,    0.212041704,    0.205962184,    0.200147816,    0.1945986,  0.189314536,    0.184295624,    0.179541864,    0.175053256,    0.1708298),
                       (0.3008828768,   0.292424567021333,  0.284187283338667,  0.276171025752, 0.268375794261333,  0.260801588866667,  0.253448409568, 0.246316256365333,  0.239405129258667,  0.232715028248, 0.226245953333333,  0.219997904514667,  0.213970881792, 0.208164885165333,  0.202579914634667,  0.1972159702,   0.192073051861333,  0.187151159618667,  0.182450293472, 0.177970453421333,  0.173711639466667),
                       (0.2960879536,   0.288432678042667,  0.280954302677333,  0.273652827504, 0.266528252522667,  0.259580577733333,  0.252809803136, 0.246215928730667,  0.239798954517333,  0.233558880496, 0.227495706666667,  0.221609433029333,  0.215900059584, 0.210367586330667,  0.205012013269333,  0.1998333404,   0.194831567722667,  0.190006695237333,  0.185358722944, 0.180887650842667,  0.176593478933333),
                       (0.2912930304,   0.284440789064, 0.277721322016, 0.271134629256, 0.264680710784, 0.2583595666,   0.252171196704, 0.246115601096, 0.240192779776, 0.234402732744, 0.22874546, 0.223220961544, 0.217829237376, 0.212570287496, 0.207444111904, 0.2024507106,   0.197590083584, 0.192862230856, 0.188267152416, 0.183804848264, 0.1794753184),
                       (0.2864981072,   0.280448900085333,  0.274488341354667,  0.268616431008, 0.262833169045333,  0.257138555466667,  0.251532590272, 0.246015273461333,  0.240586605034667,  0.235246584992, 0.229995213333333,  0.224832490058667,  0.219758415168, 0.214772988661333,  0.209876210538667,  0.2050680808,   0.200348599445333,  0.195717766474667,  0.191175581888, 0.186722045685333,  0.182357157866667),
                       (0.281703184,    0.276457011106667,  0.271255360693333,  0.26609823276,  0.260985627306667,  0.255917544333333,  0.25089398384,  0.245914945826667,  0.240980430293333,  0.23609043724,  0.231244966666667,  0.226444018573333,  0.22168759296,  0.216975689826667,  0.212308309173333,  0.207685451,    0.203107115306667,  0.198573302093333,  0.19408401136,  0.189639243106667,  0.185238997333333),
                       (0.2769082608,   0.272465122128, 0.268022380032, 0.263580034512, 0.259138085568, 0.2546965332,   0.250255377408, 0.245814618192, 0.241374255552, 0.236934289488, 0.23249472, 0.228055547088, 0.223616770752, 0.219178390992, 0.214740407808, 0.2103028212,   0.205865631168, 0.201428837712, 0.196992440832, 0.192556440528, 0.1881208368),
                       (0.279132175333333,  0.27446485122,  0.26979833968,  0.265132640713333,  0.26046775432,  0.2558036805,   0.251140419253333,  0.24647797058,  0.24181633448,  0.237155510953333,  0.2324955,  0.22783630162,  0.223177915813333,  0.21852034258,  0.21386358192,  0.209207633833333,  0.20455249832,  0.19989817538,  0.195244665013333,  0.19059196722,  0.185940082),
                       (0.281356089866667,  0.276464580312, 0.271574299328, 0.266685246914667,  0.261797423072, 0.2569108278,   0.252025461098667,  0.247141322968, 0.242258413408, 0.237376732418667,  0.23249628, 0.227617056152, 0.222739060874667,  0.217862294168, 0.212986756032, 0.208112446466667,  0.203239365472, 0.198367513048, 0.193496889194667,  0.188627493912, 0.1837593272),
                       (0.2835800044,   0.278464309404, 0.273350258976, 0.268237853116, 0.263127091824, 0.2580179751,   0.252910502944, 0.247804675356, 0.242700492336, 0.237597953884, 0.23249706, 0.227397810684, 0.222300205936, 0.217204245756, 0.212109930144, 0.2070172591,   0.201926232624, 0.196836850716, 0.191749113376, 0.186663020604, 0.1815785724),
                       (0.285803918933333,  0.280464038496, 0.275126218624, 0.269790459317333,  0.264456760576, 0.2591251224,   0.253795544789333,  0.248468027744, 0.243142571264, 0.237819175349333,  0.23249784, 0.227178565216, 0.221861350997333,  0.216546197344, 0.211233104256, 0.205922071733333,  0.200613099776, 0.195306188384, 0.190001337557333,  0.184698547296, 0.1793978176),
                       (0.288027833466667,  0.282463767588, 0.276902178272, 0.271343065518667,  0.265786429328, 0.2602322697,   0.254680586634667,  0.249131380132, 0.243584650192, 0.238040396814667,  0.23249862, 0.226959319748, 0.221422496058667,  0.215888148932, 0.210356278368, 0.204826884366667,  0.199299966928, 0.193775526052, 0.188253561738667,  0.182734073988, 0.1772170628),
                       (0.290251748,    0.28446349668,  0.27867813792,  0.27289567172,  0.26711609808,  0.261339417,    0.25556562848,  0.24979473252,  0.24402672912,  0.23826161828,  0.2324994,  0.22674007428,  0.22098364112,  0.21523010052,  0.20947945248,  0.203731697,    0.19798683408,  0.19224486372,  0.18650578592,  0.18076960068,  0.175036308)
]);

#Create X and Y data
x = np.arange(80, 121, 2)
y = np.arange(3, 12.01, 0.5)
X, Y = np.meshgrid(x, y)

#Create a color map that goes from blue to white to red
cdict = {'red':   ((0,    0, 0),  #i.e. at value 0, red component is 0. First parameter is the value, second is the color component. Ignore the third parameter, it is for discontinuities.
                   (0.5,  1, 1),  #     at value 0.5, red component is 1.
                   (1,    1, 1)), #     at value 1, red component is 1
         'green': ((0,    0, 0),
                   (0.5,  1, 1),
                   (1,    0, 0)),
         'blue':  ((0,    1, 1),
                   (0.5,  1, 1),
                   (1,    0, 0))}
#Make the color map and register it                
cmap1 = matplotlib.colors.LinearSegmentedColormap('UWR',cdict,256)
cm.register_cmap(name='UWR', cmap=cmap1)
UWR = cm.get_cmap('UWR')
#Create a variable for the colorbar
m = cm.ScalarMappable(cmap=UWR)
m.set_array(heatmap)

#Create the surface, multiply vol by 100 so axis label can be in units of %.
surf = ax.plot_surface(X, Y, volatility*100, rstride=1, cstride=1, facecolors=UWR(heatmap), linewidth=1, shade=False, edgecolors='#000000', antialiased=True)

#Axis limits
ax.set_xlim3d(80, 120)
ax.set_ylim3d(0, 12)

#Tick locations. 7 ticks for Y axis, 5 ticks for X. For Z axis maximum 6 ticks, only allow integers and only in steps of either 2, 5 or 10.
ax.yaxis.set_major_locator(LinearLocator(7))
ax.xaxis.set_major_locator(LinearLocator(5))
ax.zaxis.set_major_locator(MaxNLocator(6, interger = True, steps=[2, 5, 10]))

#Format X and Z axis tick labels as percentages and as integers
ax.xaxis.set_major_formatter(FormatStrFormatter('%d%%'))
ax.zaxis.set_major_formatter(FormatStrFormatter('%d%%'))

#Create a color bar with 11 ticks
cbar = plt.colorbar(m, ticks=LinearLocator(11), shrink=0.85)
#Make the tick label go from 0 to 1 in steps of 0.1
cbar.ax.set_yticklabels(arange(0,1.01,0.1))

ax.xaxis.set_label_text("Moneyness (Strike / Future)")
ax.yaxis.set_label_text("Term (Months)")
ax.zaxis.set_label_text("Implied Volatility")

cbar.ax.yaxis.set_label_text("Percentile of current volatility compared with historical levels")

#Set view angle
ax.view_init(20, -40) 

#Show the plot
plt.show()

Answers


You can put the lines in the surface plot adding the keyword argument edgecolors

# Add black lines in the edges
surf = ax.plot_surface(X, Y, volatility, rstride=1, cstride=1, facecolors= UWR(heatmap), linewidth=1, shade=False, edgecolors='#000000')

Directions over how to format the axis tick labels and locations here: http://matplotlib.sourceforge.net/api/ticker_api.html#matplotlib.ticker.FormatStrFormatter


Solved - Moved up from comments:

Adding surf.set_edgecolor('k') after plot_surface overrides the edge color. I think that may be related to the fact that facecolors is an option of plot_surface, but the edgecolors is an option of Poly3DCollection. More details here.


Need Your Help

In JavaScript, is there a way to retrieve the content-type of a URL without retrieving the entire file?

javascript ajax http-headers content-type

I would like to determine if a given URL is an image without relying on the file's extension. In JavaScript, is there a way to get a URL's HTTP response headers without retrieving the entire conten...

About UNIX Resources Network

Original, collect and organize Developers related documents, information and materials, contains jQuery, Html, CSS, MySQL, .NET, ASP.NET, SQL, objective-c, iPhone, Ruby on Rails, C, SQL Server, Ruby, Arrays, Regex, ASP.NET MVC, WPF, XML, Ajax, DataBase, and so on.