Construct a contour plot from a random landscape.

random_contour.py

import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
import random as rdm

def add_mountain(V, idx, idy, height):
    w,h = V.shape
    X = np.broadcast_to(range(w), (h,w))
    Y = np.transpose(np.broadcast_to(range(h), (w,h)))
    R = np.sqrt((X-idx)**2.0+(Y-idy)**2.0)
    V += stats.norm.pdf(np.transpose(R), 0, height)
    return V
        
def add_random_mountain(V, margin=[0,0]):
    w, h = V.shape
    idx = rdm.randint(margin[1],w-margin[1])
    idy = rdm.randint(margin[0],h-margin[0])
    height = (rdm.random()+0.5)*12
    V = add_mountain(V, idx, idy, height)
    return V

def get_cmap():
    import matplotlib as mpl 
    C = ['white', '#31a354']
    cm = mpl.colors.LinearSegmentedColormap.from_list("test", C)
    return cm

#============================
# Parameters
#============================

figscale      = 8.0 # Scaling of the figure
scaling       = 200  # Number of pixels
aspect_ratio  = 3 # Ratio width/height
margin        = [0,20] # x,y margin. Mountains are spanned inside the margins.
n_mountains   = 90 # Number of mountains
n_contours    = 8 # Controls the number of contours
contour_color = "#3182bd"
output_name   = "./gist_test.png"
with_cmap     = False

#============================
# Routine
#============================

# Init potential
h,w = int(scaling*aspect_ratio),int(scaling)
V = np.zeros((w,h))

# Add mountains
for k in range(n_mountains):
    V = add_random_mountain(V, margin=margin)


X, Y = np.meshgrid(range(h), range(w))

fig = plt.figure(figsize=(aspect_ratio*figscale,figscale))
ax = plt.gca()
CS = plt.contour(X, Y, V, n_contours, colors=contour_color)

if with_cmap: plt.imshow(V, interpolation='bilinear', origin='lower', cmap=get_cmap(), alpha=0.3)

ax.axis("off")

plt.savefig(output_name, dpi=100)