3D Histograms and Python Contour Stories

Question

I have a problem with the matplotlib contour function. I have a txt data file from which I am importing my data. I have data columns (pm1 and pm2) and I am executing a 2D histogram. I want to build this data as a three-dimensional histogram and as a contour graph to see where the maximum values ​​are located.

This is my code:

fig = plt.figure() ax = fig.add_subplot(111, projection='3d') rows = np.arange(200,1300,10) hist, xedges, yedges = np.histogram2d (pm1_n, pm2_n, bins = (rows, rows) ) elements = (len(xedges) - 1) * (len(yedges) - 1) xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1]) xpos = xpos.flatten() ypos = ypos.flatten() zpos = np.zeros(elements) dx = 0.1 * np.ones_like(zpos) dy = dx.copy() dz = hist.flatten() #####The problem is here##### #ax.contourf(xpos,ypos,hist) #ax.bar3d(xpos, ypos, zpos, dx, dy, dz, zsort='average') plt.show() 

I can plot a 3D chart, but I can’t build a contour, if I put hist in a contour function, I get an error: Length of x must be number of columns in z , and if I put dz , I get Input z must be a 2D array I also tried using xedges and yexges, but this does not solve the problem.

I think the problem is with the histogram2D function return form. But I do not know how to solve it.

I would also like to execute a 3D plot with changing the color code from minimum to maximum. Is there any way to do this?

thanks

Answer 1

Perhaps I don’t understand what exactly you are trying to do, since I don’t know what your data looks like, but it seems wrong that your contourf chart contourf the same axis as your bar3d chart. If you add an axis without 3D projection to a new shape, you can make the contourf graph just thin using hist . An example of using data from a random, normal distribution:

 import numpy as np import matplotlib.pyplot as plt n_points = 1000 x = np.random.normal(0, 2, n_points) y = np.random.normal(0, 2, n_points) hist, xedges, yedges = np.histogram2d(x, y, bins=np.sqrt(n_points)) fig2D = plt.figure() ax2D = fig2D.add_subplot(111) ax2D.contourf(hist, interpolation='nearest', extent=(xedges[0], xedges[-1], yedges[0], yedges[-1])) plt.show() 

returns an image of type this .

As for your second question regarding 3D color coding, how about this (using the same data as above but with 1/10 size):

 import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.colors as colors n_points = 100 x = np.random.normal(0, 2, n_points) y = np.random.normal(0, 2, n_points) hist, xedges, yedges = np.histogram2d(x, y, bins=np.sqrt(n_points)) # Following your data reduction process xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1]) length, width = 0.4, 0.4 xpos = xpos.flatten() ypos = ypos.flatten() zpos = np.zeros(n_points) dx = np.ones(n_points) * length dy = np.ones(n_points) * width dz = hist.flatten() # This is where the colorbar customization comes in dz_normed = dz / dz.max() normed_cbar = colors.Normalize(dz_normed.min(), dz_normed.max()) # Using jet, but should work with any colorbar color = cm.jet(normed_cbar(dz_normed)) fig3D = plt.figure() ax3D = fig3D.add_subplot(111, projection='3d') ax3D.bar3d(xpos, ypos, zpos, dx, dy, dz, color=color) plt.show() 

I get this image .