I have a set of discrete densities, say n(i,j), and I'd like to plot a 3d bar-type visualization of it. My aim would be some figures like in the following links:
http://qutip.blogspot.de/2012/07/quantum-process-tomography.html
http://qutip.org/docs/2.2.0/guide/guide-visualization.html#visualizing-operators
I'd like to use directly Matlab, matploblib or gnuplot rather than using the qutip toolbox, specially because qutip installation is not very straightforward.
Any help is appropriated.
In matplotlib you could use bar3d. See the example in the gallery, although you'll need to set the color argument appropriately to get different colors for each bar - see the API docs.
Edit
Given the comment, the following code produces output without any reference to histograms:
x = np.array(range(0, 6), float) # I assume that np.loadtxt will give you (from the
y = x.copy() # comment) x,y as a 1d array in the form that this
xpos, ypos = np.meshgrid(x, y) # script would after the xpos.flatten() lines.
z = np.random.rand(6, 6) #
colors = ['b', 'g', 'y', 'r', 'k', 'c']*6 # This colors the bars individually
xpos = xpos.flatten()
ypos = ypos.flatten()
zpos = np.zeros_like(xpos)
dx = 0.5 * np.ones_like(zpos)
dy = dx.copy()
dz = z.flatten() # This is the actual data.
fig = plt.figure()
ax = fig.add_subplot(111,projection = '3d')
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=colors)
In Matlab, you can use bar3:
n = rand(4,5); %// example data
bar3(n)
See also view to change viewing angle of the 3D plot.
x, y and z have?