Bivariate colormap in matplotlib

bivariate_matplotlib.py

# bivariate colormaps in matplotlib
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt


## Bivariate colormapping
from scipy.interpolate import RegularGridInterpolator


def bivariate_colormap(img, colormap):
    r = np.linspace(0, 1, colormap.shape[0])
    c = np.linspace(0, 1, colormap.shape[1])
    splines = [RegularGridInterpolator((r,c), colormap[:,:,i]) for i in range(3)]    
    def remap(img, spline):        
        pts = np.dstack([img[:,:,0].ravel(), img[:,:,1].ravel()])
        return spline(pts).reshape(img.shape[0:2])
                                                
    return np.dstack([remap(img, splines[i]) for i in range(3)])
    
    
### Demo colormap
def make_colormap(n, cyclic_x=False, cyclic_y=False):
    import skimage.color
    # simple pretty colormap
    spacing = np.linspace(-100,100,n)
    mx, my = np.meshgrid(spacing, spacing)    
    if cyclic_x:
        mx = np.cos(mx/100*np.pi)*50.0
    if cyclic_y:
        my = np.cos(my/100*np.pi)*50.0
        
    c = np.ones_like(mx)*75 + my*0.15 - mx *0.3
    t = np.dstack([c,mx,my])
    t[:,:,1] = np.tanh(t[:,:,1]/130)*100
    t[:,:,2] = np.tanh(t[:,:,2]/190)*100

    rgb_img = skimage.color.lab2rgb(t)
    return rgb_img

rgb_img = make_colormap(256)
plt.imshow(rgb_img)

    
    
### Demo    
import scipy.special

x = np.linspace(-3, 3, 100)
mx, my = np.meshgrid(x, x)
z = mx + 1j*my

zf = (1+1j)**z

zfc = np.dstack([np.real(zf), np.imag(zf)])
# zfc = np.dstack([np.abs(zf), np.angle(zf)]) (use a cyclic color map) 

def normalize(x):
    return (x-np.min(x)) / (np.max(x)-np.min(x))
zfc[:,:,0] = normalize(zfc[:,:,0])
zfc[:,:,1] = normalize(zfc[:,:,1])
plt.imshow(bivariate_colormap(zfc, rgb_img))    

Hi there!
Thank you for the code, can u please attach some figures so that I can understand what bivariate figure u are intended to make here?

Output of the above code