Matplotilb - How to set colorbar for line plot with log scale

Question

I'm having a problem adding a colorbar to a plot of many lines corresponding to a power-law.

To create the color-bar for a non-image plot, I added a dummy plot (from answers here: Matplotlib - add colorbar to a sequence of line plots).

To colorbar ticks do not correspond to the colors of the plot.

I have tried changing the norm of the colorbar, and I can fine-tune it to be semy accurate for a particular case, but I can't do that generally.

def plot_loglog_gauss():
    from matplotlib import cm as color_map
    import matplotlib as mpl

    """Creating the data"""
    time_vector = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256]
    amplitudes = [t ** 2 * np.exp(-t * np.power(np.linspace(-0.5, 0.5, 100), 2)) for t in time_vector]

    """Getting the non-zero minimum of the data"""
    data = np.concatenate(amplitudes).ravel()
    data_min = np.min(data[np.nonzero(data)])

    """Creating K-space data"""
    k_vector = np.linspace(0,1,100)

    """Plotting"""
    number_of_plots = len(time_vector)
    color_map_name = 'jet'
    my_map = color_map.get_cmap(color_map_name)
    colors = my_map(np.linspace(0, 1, number_of_plots, endpoint=True))

    # plt.figure()
    # dummy_plot = plt.contourf([[0, 0], [0, 0]], time_vector, cmap=my_map)
    # plt.clf()

    norm = mpl.colors.Normalize(vmin=time_vector[0], vmax=time_vector[-1])
    cmap = mpl.cm.ScalarMappable(norm=norm, cmap=color_map_name)
    cmap.set_array([])


    for i in range(number_of_plots):
        plt.plot(k_vector, amplitudes[i], color=colors[i], label=time_vector[i])

    c = np.arange(1, number_of_plots + 1)
    plt.xlabel('Frequency')
    plt.ylabel('Amplitude')
    plt.yscale('symlog', linthreshy=data_min)
    plt.xscale('log')
    plt.legend(loc=3)

    ticks = time_vector
    plt.colorbar(cmap, ticks=ticks, shrink=1.0, fraction=0.1, pad=0)

    plt.show()

By comparing with the legend you see the ticks values don't match the actual colors. For example, 128 is shown in green in the colormap while red in the legend.

The actual result should be a linear-color colorbar. with ticks at regular intervals on the colorbar (corresponding to irregular time intervals...). And of course correct color for value of tick.

(Eventually the plot contains many plots (len(time_vector) ~ 100), I lowered the number of plots to illustrate and to be able to show the legend.)

To clarify, this is what I want the result to look like.

The accepted answer from the linked question is not useful. Did you consider any of the other answers? — ImportanceOfBeingErnest
@ImportanceOfBeingErnest: While you were writing this comment, I was preparing a solution. Indeed, I was about to say the same thing — Sheldore
Yeah I tried all those answers, couldn't get any of them to work. — AsaridBeck91

ImportanceOfBeingErnest ImportanceOfBeingErnest · Accepted Answer · 2019-02-04T11:59:19

The most important principle is to keep the colors from the line plots and the ScalarMappable in sync. This means, the color of the line should not be taken from an independent list of colors, but rather from the same colormap and using the same normalization as the colorbar to be shown.

One major problem is then to decide what to do with 0 which cannot be part of a loagrithmic normalization. The following is a workaround assuming a linear scale between 0 and 2, and a log scale above, using a SymLogNorm.

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

"""Creating the data"""
time_vector = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256]
amplitudes = [t ** 2 * np.exp(-t * np.power(np.linspace(-0.5, 0.5, 100), 2)) for t in time_vector]

"""Getting the non-zero minimum of the data"""
data = np.concatenate(amplitudes).ravel()
data_min = np.min(data[np.nonzero(data)])

"""Creating K-space data"""
k_vector = np.linspace(0,1,100)

"""Plotting"""
cmap = plt.cm.get_cmap("jet")
norm = mpl.colors.SymLogNorm(2, vmin=time_vector[0], vmax=time_vector[-1])

sm = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([])

for i in range(len(time_vector)):
    plt.plot(k_vector, amplitudes[i], color=cmap(norm(time_vector[i])), label=time_vector[i])

#c = np.arange(1, number_of_plots + 1)
plt.xlabel('Frequency')
plt.ylabel('Amplitude')
plt.yscale('symlog', linthreshy=data_min)
plt.xscale('log')
plt.legend(loc=3)

cbar = plt.colorbar(sm, ticks=time_vector, format=mpl.ticker.ScalarFormatter(), 
                    shrink=1.0, fraction=0.1, pad=0)

plt.show()

Matplotilb - How to set colorbar for line plot with log scale

1 Answers