Matplotlib bar3d clipping issues

Question

I am trying to create a 3D histogram with Matplotlib 1.2.0 and Python 2.7.3. I followed the advice at http://www.mail-archive.com/ [e-mail] /msg19740.html and built the bar one by one, but I still get rendering problems (i.e. Bars on top of each Other).

In addition, I get the following when I call my code:

/usr/apps/python/lib/python2.7/site-packages/mpl_toolkits/mplot3d/axes3d.py:1476: RuntimeWarning: division by zero, occurring when dividing by n in normals])

/usr/apps/python/lib/python2.7/site-packages/mpl_toolkits/mplot3d/axes3d.py:1476: RuntimeWarning: invalid value detected by division for n in normals])

My questions:

• Are these serious warnings? Do I need to look into them and try to eliminate them? How to eliminate them?
• What is the difference between zsort = 'max' and zsort = 'average'?
• What else can be done to fix rendering problems?

Thanks in advance!

Here is my code:

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import matplotlib.colors as colors import matplotlib.cm as cmx # my data dat = [2.31778665482167e-310, 0.006232785101850947, 0.0285075971030949, 0.0010248181570355695, 0.0048776795767614825, 0.02877090365176044, 0.002459331469834533, 0.0008594610645495889, 0.002919824084878003, 0.000968081117692596, 0.0, 0.0, 0.0319623949119874, 0.00568752311279771, 0.009994801469036968, 0.03248018520506219, 0.006686905726805326, 0.005987863156039365, 0.0072955095915350045, 0.005568911905473998, 0.0, 0.0, 0.0, 0.028483143996551524, 0.031030793902192794, 0.06125216053962635, 0.02935971973938871, 0.028507530280092265, 0.030112963748812088, 0.028293406731749605, 0.0, 0.0, 0.0, 0.0, 0.004510645022825792, 0.028998119822468988, 0.0013993630391143715, 0.0010726572949244424, 0.002288215944285159, 0.0006513973584945584, 0.0, 1.1625e-320, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.03148966953869102, 0.005215047563268979, 0.004491716298086729, 0.006010166308872446, 0.005186976949223524, 0.0, 0.0, 0.0, 0.0, 0.0, 1.107e-320, 0.02983657915729719, 0.028893006725328373, 0.030526067389954753, 0.028629390713739978, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0015217840289869456, 0.002751587509779179, 0.001413669523724954, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0024680339073824705, 0.0008254364860386303, 0.0, 0.0, 0.0, 9.965e-321, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.0, 0.0, 0.002621588539481613, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.41e-321, 1.15348834e-316, 2.3177866547513e-310] dat = np.reshape(dat,[10,10],order='F') lx = len(dat[0]) ly = len(dat[:,0]) n = lx*ly # generate colors cm = plt.get_cmap('jet') vv = range(len(dat)) cNorm = colors.Normalize(vmin=0, vmax=vv[-1]) scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm) colorVals = [scalarMap.to_rgba(i) for i in range(ly)] # generate plot data xpos = np.arange(0,lx,1) ypos = np.arange(0,ly,1) xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25) xpos = xpos.flatten() ypos = ypos.flatten() zpos = np.zeros(n) dx = 0.5*np.ones_like(zpos) dy = dx.copy() dz = dat.flatten() cc = np.tile(range(lx), (ly,1)) cc = cc.T.flatten() # generate plot fig = plt.figure() ax = fig.add_subplot(111, projection='3d') opacity = 1 for i in range(n): ax.bar3d(xpos[i], ypos[i], zpos[i], dx[i], dy[i], dz[i], color=colorVals[cc[i]], alpha=opacity, zsort='max') plt.autoscale(enable=True, axis='both', tight=True) plt.grid() plt.show(block=False) 

Answer 1

This is not the answer you are looking for, but I think this might be a matplotlib bug. I think the same problem was here. The problem has been described as "unsolvable" according to mplot3d's frequently asked questions .

But for me this does not seem intractable. You just need to find out which object is closer to the viewer and set the z-order accordingly. So I think the problem may just be a mistake.

If I take the matplotlib 3D histogram example and just change “bins = 4” to “bins = 6” or higher, then I get the same “axes3d.py:1476: RuntimeWarning: invalid value found in divide / for n in normals]) ". In addition, I can reproduce the wrong z-order of the bars (look at the tall guy near the front who is jumping in front of his short friend):

Wrong ordering of the bars seems to be related to division by zero error, as the charts look just fine when I use fewer boxes.

Line 1476 in axes.py:

 shade = np.array([np.dot(n / proj3d.mod(n), [-1, -1, 0.5]) for n in normals]) 

Basically, I think he is trying to understand shading using ordinary vectors for each face. But one or more normal vectors are zero, which should not be. So, I think this is just a bug in matplotlib, which can probably be fixed by someone with more programming skills than me.

The mplot3d FAQs are true that MayaVI can be used if you need a better 3D engine. I used

 from mayavi import mlab mlab.barchart(xpos,ypos,dz*100) 

To create a graph of your data:

I hope this is clarified soon. I would like to make some similar 3D cards in the near future.

Answer 2

This answer is a quick fix, allowing you to create certain types of 3D diagrams in matplotlib with the correct rendering. The trick is to A) apply a separate stroke, B) crack the zsort algorithm to force wrt sort bars to “distance” from the camera. This can be done by overwriting the _sort_zpos attribute of the _sort_zpos instance returned by ax.bar3d . The following code demonstrates the solution using data obtained from 2D Gaussian.

 import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy.stats import multivariate_normal def sph2cart(r, theta, phi): '''spherical to cartesian transformation.''' x = r * np.sin(theta) * np.cos(phi) y = r * np.sin(theta) * np.sin(phi) z = r * np.cos(theta) return x, y, z def sphview(ax): '''returns the camera position for 3D axes in spherical coordinates''' r = np.square(np.max([ax.get_xlim(), ax.get_ylim()], 1)).sum() theta, phi = np.radians((90-ax.elev, ax.azim)) return r, theta, phi def ravzip(*itr): '''flatten and zip arrays''' return zip(*map(np.ravel, itr)) #Generate data res = 15 sl = slice(-3, 3, complex(res)) Y, X = np.mgrid[sl, sl] grid = np.array([X, Y]) (dx,), (dy,) = 0.8*np.diff(X[0,:2]), 0.8*np.diff(Y[:2,0]) #2D Gaussian mu = (0, 0) covm = np.array([[ 0.8, 0.3], [ 0.3, 0.5]]) rv = multivariate_normal(mu, covm) Zg = rv.pdf(grid.transpose(1,2,0)).T #generate the figure fig, (ax1, ax2) = plt.subplots(1,2, subplot_kw=dict(projection='3d')) #standard bar3d ax1.set_title('Standard') ax1.bar3d(X.ravel(), Y.ravel(), np.zeros(X.size), dx, dy, Zg.ravel(), '0.85') #Fixed bar3d ax2.set_title('Fixed') xyz = np.array(sph2cart(*sphview(ax2)), ndmin=3).T #camera position in xyz zo = np.multiply([X, Y, np.zeros_like(Zg)], xyz).sum(0) #"distance" of bars from camera bars = np.empty(X.shape, dtype=object) for i, (x,y,dz,o) in enumerate(ravzip(X, Y, Zg, zo)): j, k = divmod(i, res) bars[j, k] = pl = ax2.bar3d(x, y, 0, dx, dy, dz, '0.85') pl._sort_zpos = o plt.show() 

Which gives the following figure:

Note: This will only work for the original viewing angle. If you rotate the axes, you will have to set _sort_zpos again for all the bars and redraw the canvas to fix the rendering.

Answer 3

I took the apodemus code , which apparently works, unpacked it and applied it to the original question to give it a direct answer. My code could certainly be cleared, in particular the loop in getDistances() , but it solves the presented problem and should be much easier to follow. To guess, the distance to the viewer, that is, the distance to the camera, must be determined by calling sphview() and sph2cart() . Then, the distances of all bars from the camera should be calculated by calling getDistances() . After that, the bars must be drawn once and once, and, to a decisive extent, each z-order must be explicitly set based on predetermined distances.

If the resulting graph is rotated in the graph window, it may not be updated correctly. However, pre-setting the location of the camera allows you to arbitrarily create arbitrary initial views without errors. (Perhaps there is a callback mechanism that can be called to force the z-orders of the bars to be explicitly recounted, but I don’t know such an API.) The camera position can be pre-set by going azim and elev to fig.add_subplot() . You can change its distance by setting the dist field of the Axes instance returned by fig.add_subplot() .

The following is the graph created by the updated code applied to the original question:

 import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import matplotlib.colors as colors import matplotlib.cm as cmx # from apodemus Stackoverflow answer, # https://stackoverflow.com/questions/18602660/matplotlib-bar3d-clipping-problems def sph2cart(r, theta, phi): '''spherical to Cartesian transformation.''' x = r * np.sin(theta) * np.cos(phi) y = r * np.sin(theta) * np.sin(phi) z = r * np.cos(theta) return x, y, z def sphview(ax): '''returns the camera position for 3D axes in spherical coordinates''' r = np.square(np.max([ax.get_xlim(), ax.get_ylim()], 1)).sum() theta, phi = np.radians((90-ax.elev, ax.azim)) return r, theta, phi # # end of apodemus code def getDistances(view): distances = [] a = np.array((xpos, ypos, dz)) for i in range(len(xpos)): distance = (a[0, i] - view[0])**2 + (a[1, i] - view[1])**2 + (a[2, i] - view[2])**2 distances.append(np.sqrt(distance)) return distances # ================================================================ # my data dat = [2.31778665482167e-310, 0.006232785101850947, 0.0285075971030949, 0.0010248181570355695, 0.0048776795767614825, 0.02877090365176044, 0.002459331469834533, 0.0008594610645495889, 0.002919824084878003, 0.000968081117692596, 0.0, 0.0, 0.0319623949119874, 0.00568752311279771, 0.009994801469036968, 0.03248018520506219, 0.006686905726805326, 0.005987863156039365, 0.0072955095915350045, 0.005568911905473998, 0.0, 0.0, 0.0, 0.028483143996551524, 0.031030793902192794, 0.06125216053962635, 0.02935971973938871, 0.028507530280092265, 0.030112963748812088, 0.028293406731749605, 0.0, 0.0, 0.0, 0.0, 0.004510645022825792, 0.028998119822468988, 0.0013993630391143715, 0.0010726572949244424, 0.002288215944285159, 0.0006513973584945584, 0.0, 1.1625e-320, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.03148966953869102, 0.005215047563268979, 0.004491716298086729, 0.006010166308872446, 0.005186976949223524, 0.0, 0.0, 0.0, 0.0, 0.0, 1.107e-320, 0.02983657915729719, 0.028893006725328373, 0.030526067389954753, 0.028629390713739978, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0015217840289869456, 0.002751587509779179, 0.001413669523724954, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0024680339073824705, 0.0008254364860386303, 0.0, 0.0, 0.0, 9.965e-321, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.0, 0.0, 0.002621588539481613, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.41e-321, 1.15348834e-316, 2.3177866547513e-310] dat = np.reshape(dat,[10,10],order='F') lx = len(dat[0]) ly = len(dat[:,0]) n = lx*ly # generate colors cm = plt.get_cmap('jet') vv = range(len(dat)) cNorm = colors.Normalize(vmin=0, vmax=vv[-1]) scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm) colorVals = [scalarMap.to_rgba(i) for i in range(ly)] # generate plot data xpos = np.arange(0,lx,1) ypos = np.arange(0,ly,1) xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25) xpos = xpos.flatten() ypos = ypos.flatten() zpos = np.zeros(n) dx = 0.5*np.ones_like(zpos) dy = dx.copy() dz = dat.flatten() cc = np.tile(range(lx), (ly,1)) cc = cc.T.flatten() # generate plot fig = plt.figure() ax = fig.add_subplot(111, projection='3d') opacity = 1 # Get the camera location in Cartesian coordinates. x1, y1, z1 = sph2cart(*sphview(ax)) camera = np.array((x1,y1,0)) # Calculate the distance of each bar from the camera. z_order = getDistances(camera) max = max(z_order) for i in range(n): pl = ax.bar3d(xpos[i], ypos[i], zpos[i], dx[i], dy[i], dz[i], color=colorVals[cc[i]], alpha=opacity, zsort='max') # The z-order must be set explicitly. # # z-order values are somewhat backwards in magnitude, in that the largest # value is closest to the camera - unlike, in say, a coordinate system. # Therefore, subtracting the maximum distance from the calculated distance # inverts the z-order to the proper form. pl._sort_zpos = max - z_order[i] plt.autoscale(enable=True, axis='both', tight=True) plt.grid() plt.show() 

This approach (in contrast to using Mayavi to process a 3D drawing, for example) allows you to save the appearance of matplotlib in the graphic itself, as well as its decorations, such as axis numbers, labels and legends.

Answer 4

I think the answer of marisano has a rendering problem with different heights because it uses the Euclidean distance from the top of the bar to the camera position and subtracts this value from the maximum z_order , I don't think this is the right way. Finally, I take the same z_order dimension from apodemus and update it below:

z_order = np.multiply([xpos,ypos, np.zeros_like(xpos)],camera).sum(0)

and

pl._sort_zpos = z_order[i]

Now it works in my case.