Normalization and Scaling

S3Dlib object defaults to a domain in the range [-1,1]. In this example, first the domain is transformed to [0,1]. The surface x and y coordinates are then scaled and then plotted in a scaled coordinate view.

This example is based on the function used in the Matplotlib example.

import numpy as np
import matplotlib.pyplot as plt
import s3dlib.surface as s3d

#.. Normalization and Scaling

# 1. Define functions to examine ....................................

def sincos(xyz) :
    x,y,z = xyz
    X, Y = 6*np.pi*x, 4*np.pi*y 
    Z = np.sqrt(np.abs(np.cos(X) + np.cos(Y)))
    return x,y,Z

# 2. Setup and map surfaces .........................................
rez = 6

surface = s3d.PlanarSurface(rez, cmap='autumn')
# change normalization from [-1,1] to [0,1] .......... 
surface.transform(scale=.5,translate=[0.5,0.5,0.5])    
surface.map_geom_from_op(sincos)
surface.map_cmap_from_op( lambda xyz : xyz[2] )
surface.shade(.5).hilite(.5)
# scale the x and y coordinate directions ............
surface.transform(scale=[1e5,1e5,1])

# 3. Construct figure, add surfaces, and plot ......................

fig = plt.figure()
ax = plt.axes(projection='3d')
ax.view_init(25)
# scale the x, y and z axes ..........................
ax.set(xlim=(0,1e5), ylim=(0,1e5), zlim=(0,2) )

ax.add_collection3d(surface)

plt.show()