I am trying to plot some HDF data in matplotlib. After importing them using h5py, the data is stored in a form of array, like this:
array([[151, 176, 178], [121, 137, 130], [120, 125, 126])
In this case, x and y values are just the indexes of the array's fields, while z value is the value of specific field. In the (x,y,z) form it would look like:
(1,1,151) (2,1,176) (3,1,178) (1,2,121) ...
and so on.
Is there an easy way to do a surface plot from this kind of data? I know I can change this to (x,y,z) tuples by iterating all over the array, but maybe it is not needed?
If you want a 3-d surface plot, you have to create the
meshgrid first. You can try:
from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np X = np.arange(1, 10) Y = np.arange(1, 10) X, Y = np.meshgrid(X, Y) R = np.sqrt(X**2 + Y**2) Z = np.sin(R) fig = plt.figure() ax = fig.gca(projection='3d') surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot', linewidth=0, antialiased=False) ax.set_zlim(-1.01, 1.01) fig.colorbar(surf, shrink=0.5, aspect=5) plt.show()
which will generate,
However, if the only relevant information is in the z-values, you can simply use
imshow. Here, z-values are represented by their color. You can achieve this by:
im = plt.imshow(Z, cmap='hot') plt.colorbar(im, orientation='horizontal') plt.show()
Which will give,