How to use correct cmap colors in nested pie chart in matplotlib

Question

How do I use the correct np.array of cmap that the inside colors correspond to shades of the outside colors in a nested pie chart in matplotlib?

I tried using different arrays of cmap, but I don't understand how the arrays get transformed into cmap colors.

import numpy as np
import matplotlib.pyplot as plt

y =np.array([17, 16, 10, 8 ,6, 5, 5, 4, 3,  17 ,2 ,1, 1, 3, 2 ])
x = np.array([74 ,21 ,5])

fig, ax = plt.subplots()

size = 0.3

cmap = plt.get_cmap("tab20c")

outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))

ax.pie(x, radius=1, colors=outer_colors,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(y, radius=1-size, colors=inner_colors,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.set(aspect="equal", title='Pie plot with `ax.pie`')
plt.show()

I want the inside colors to be shades of the outside colors (greenish, blueish and orangish), but I have no idea how to change them accordingly.

Thanks!

Answer 1

The tab20c colormap has 4 shades per hue. So it will not be possible to use that for 9 subcategories.

A. Extend the number of shades per hue

Taking the categorical_cmap from matplotlib generic colormap from tab10 one get get more shades per hue.

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

def categorical_cmap(nc, nsc, cmap="tab10", continuous=False):
    if nc > plt.get_cmap(cmap).N:
        raise ValueError("Too many categories for colormap.")
    if continuous:
        ccolors = plt.get_cmap(cmap)(np.linspace(0,1,nc))
    else:
        ccolors = plt.get_cmap(cmap)(np.arange(nc, dtype=int))
    cols = np.zeros((nc*nsc, 3))
    for i, c in enumerate(ccolors):
        chsv = matplotlib.colors.rgb_to_hsv(c[:3])
        arhsv = np.tile(chsv,nsc).reshape(nsc,3)
        arhsv[:,1] = np.linspace(chsv[1],0.25,nsc)
        arhsv[:,2] = np.linspace(chsv[2],1,nsc)
        rgb = matplotlib.colors.hsv_to_rgb(arhsv)
        cols[i*nsc:(i+1)*nsc,:] = rgb       
    cmap = matplotlib.colors.ListedColormap(cols)
    return cmap

y =np.array([17, 16, 10, 8 ,6, 5, 5, 4, 3,  17 ,2 ,1, 1, 3, 2 ])
x = np.array([74 ,21 ,5])

fig, ax = plt.subplots()

size = 0.3

cmap = categorical_cmap(3, 10)

outer_colors = cmap(np.array([0, 10, 20]))
ar = np.concatenate((np.arange(1,10), [13,15,17,19], [25,30]))
inner_colors = cmap(ar)

ax.pie(x, radius=1, colors=outer_colors,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(y, radius=1-size, colors=inner_colors,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.set(aspect="equal", title='Pie plot with `ax.pie`')
plt.show()

B. Use three different colormaps

Alternatively, one could use three different continuous colormaps, and take some of those colors.

import numpy as np
import matplotlib.pyplot as plt

y =np.array([17, 16, 10, 8 ,6, 5, 5, 4, 3, 17 ,2 ,1, 1, 3, 2 ])
x = np.array([74 ,21 ,5])

fig, ax = plt.subplots()

size = 0.3

cmap1 = plt.cm.Reds
cmap2 = plt.cm.Purples
cmap3 = plt.cm.Greens

outer_colors = [cmap1(.8), cmap2(.8), cmap3(.8)]
inner_colors = [*cmap1(np.linspace(.6, .1, 9)),
                *cmap2(np.linspace(.6, .2, 4)),
                *cmap3(np.linspace(.6, .2, 2))]

ax.pie(x, radius=1, colors=outer_colors,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(y, radius=1-size, colors=inner_colors,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.set(aspect="equal", title='Pie plot with `ax.pie`')
plt.show()