Companion notebook to my article about population density and social distancing

cmap_map.py

import matplotlib
import numpy as np
import matplotlib.pyplot as plt

#https://scipy-cookbook.readthedocs.io/items/Matplotlib_ColormapTransformations.html

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()))
        colorvector.sort()
        cdict[key] = colorvector

    return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)

new_york_distancing_space.ipynb