[python] Getting individual colors from a color map in matplotlib

If you have a Colormap cmap, for example:

cmap = matplotlib.cm.get_cmap('Spectral')

How can you get a particular colour out of it between 0 and 1, where 0 is the first colour in the map and 1 is the last colour in the map?

Ideally, I would be able to get the middle colour in the map by doing:

>>> do_some_magic(cmap, 0.5) # Return an RGBA tuple
(0.1, 0.2, 0.3, 1.0)

To build on the solutions from Ffisegydd and amaliammr, here's an example where we make CSV representation for a custom colormap:

#! /usr/bin/env python3
import matplotlib
import numpy as np 

vmin = 0.1
vmax = 1000

norm = matplotlib.colors.Normalize(np.log10(vmin), np.log10(vmax))
lognum = norm(np.log10([.5, 2., 10, 40, 150,1000]))

cdict = {
    'red':
    (
        (0., 0, 0),
        (lognum[0], 0, 0),
        (lognum[1], 0, 0),
        (lognum[2], 1, 1),
        (lognum[3], 0.8, 0.8),
        (lognum[4], .7, .7),
    (lognum[5], .7, .7)
    ),
    'green':
    (
        (0., .6, .6),
        (lognum[0], 0.8, 0.8),
        (lognum[1], 1, 1),
        (lognum[2], 1, 1),
        (lognum[3], 0, 0),
        (lognum[4], 0, 0),
    (lognum[5], 0, 0)
    ),
    'blue':
    (
        (0., 0, 0),
        (lognum[0], 0, 0),
        (lognum[1], 0, 0),
        (lognum[2], 0, 0),
        (lognum[3], 0, 0),
        (lognum[4], 0, 0),
    (lognum[5], 1, 1)
    )
}


mycmap = matplotlib.colors.LinearSegmentedColormap('my_colormap', cdict, 256)   
norm = matplotlib.colors.LogNorm(vmin, vmax)
colors = {}
count = 0
step_size = 0.001
for value in np.arange(vmin, vmax+step_size, step_size):
    count += 1
    print("%d/%d %f%%" % (count, vmax*(1./step_size), 100.*count/(vmax*(1./step_size))))
    rgba = mycmap(norm(value), bytes=True)
    color = (rgba[0], rgba[1], rgba[2])
    if color not in colors.values():
        colors[value] = color

print ("value, red, green, blue")
for value in sorted(colors.keys()):
    rgb = colors[value]
    print("%s, %s, %s, %s" % (value, rgb[0], rgb[1], rgb[2]))

For completeness these are the cmap choices I encountered so far:

Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r

In order to get rgba integer value instead of float value, we can do

rgba = cmap(0.5,bytes=True)

So to simplify the code based on answer from Ffisegydd, the code would be like this:

#import colormap
from matplotlib import cm

#normalize item number values to colormap
norm = matplotlib.colors.Normalize(vmin=0, vmax=1000)

#colormap possible values = viridis, jet, spectral
rgba_color = cm.jet(norm(400),bytes=True) 

#400 is one of value between 0 and 1000

I had precisely this problem, but I needed sequential plots to have highly contrasting color. I was also doing plots with a common sub-plot containing reference data, so I wanted the color sequence to be consistently repeatable.

I initially tried simply generating colors randomly, reseeding the RNG before each plot. This worked OK (commented-out in code below), but could generate nearly indistinguishable colors. I wanted highly contrasting colors, ideally sampled from a colormap containing all colors.

I could have as many as 31 data series in a single plot, so I chopped the colormap into that many steps. Then I walked the steps in an order that ensured I wouldn't return to the neighborhood of a given color very soon.

My data is in a highly irregular time series, so I wanted to see the points and the lines, with the point having the 'opposite' color of the line.

Given all the above, it was easiest to generate a dictionary with the relevant parameters for plotting the individual series, then expand it as part of the call.

Here's my code. Perhaps not pretty, but functional.

from matplotlib import cm
cmap = cm.get_cmap('gist_rainbow')  #('hsv') #('nipy_spectral')

max_colors = 31   # Constant, max mumber of series in any plot.  Ideally prime.
color_number = 0  # Variable, incremented for each series.

def restart_colors():
    global color_number
    color_number = 0
    #np.random.seed(1)

def next_color():
    global color_number
    color_number += 1
    #color = tuple(np.random.uniform(0.0, 0.5, 3))
    color = cmap( ((5 * color_number) % max_colors) / max_colors )
    return color

def plot_args():  # Invoked for each plot in a series as: '**(plot_args())'
    mkr = next_color()
    clr = (1 - mkr[0], 1 - mkr[1], 1 - mkr[2], mkr[3])  # Give line inverse of marker color
    return {
        "marker": "o",
        "color": clr,
        "mfc": mkr,
        "mec": mkr,
        "markersize": 0.5,
        "linewidth": 1,
    }

My context is JupyterLab and Pandas, so here's sample plot code:

restart_colors()  # Repeatable color sequence for every plot

fig, axs = plt.subplots(figsize=(15, 8))
plt.title("%s + T-meter"%name)

# Plot reference temperatures:
axs.set_ylabel("°C", rotation=0)
for s in ["T1", "T2", "T3", "T4"]:
    df_tmeter.plot(ax=axs, x="Timestamp", y=s, label="T-meter:%s" % s, **(plot_args()))

# Other series gets their own axis labels
ax2 = axs.twinx()
ax2.set_ylabel(units)

for c in df_uptime_sensors:
    df_uptime[df_uptime["UUID"] == c].plot(
        ax=ax2, x="Timestamp", y=units, label="%s - %s" % (units, c), **(plot_args())
    )

fig.tight_layout()
plt.show()

The resulting plot may not be the best example, but it becomes more relevant when interactively zoomed in.