Skip to content

Instantly share code, notes, and snippets.

Created October 17, 2020 13:49
What would you like to do?
Companion notebook to my article about population density and social distancing
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
def cmap_map(function, cmap):
""" Applies function (which should operate on vectors of shape 3: [r, g, b]), on colormap cmap.
This routine will break any discontinuous points in a colormap.
cdict = cmap._segmentdata
step_dict = {}
# Firt get the list of points where the segments start or end
for key in ('red', 'green', 'blue'):
step_dict[key] = list(map(lambda x: x[0], cdict[key]))
step_list = sum(step_dict.values(), [])
step_list = np.array(list(set(step_list)))
# Then compute the LUT, and apply the function to the LUT
reduced_cmap = lambda step : np.array(cmap(step)[0:3])
old_LUT = np.array(list(map(reduced_cmap, step_list)))
new_LUT = np.array(list(map(function, old_LUT)))
# Now try to make a minimal segment definition of the new LUT
cdict = {}
for i, key in enumerate(['red','green','blue']):
this_cdict = {}
for j, step in enumerate(step_list):
if step in step_dict[key]:
this_cdict[step] = new_LUT[j, i]
elif new_LUT[j,i] != old_LUT[j, i]:
this_cdict[step] = new_LUT[j, i]
colorvector = list(map(lambda x: x + (x[1], ), this_cdict.items()))
cdict[key] = colorvector
return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment