Matplotlib colormaps in Nilearn

Visualize HCP connectome workbench color maps shipped with Nilearn which can be used for plotting brain images on surface.

See Surface plotting for surface plotting details.

Plot color maps

import numpy as np

from nilearn.plotting import show
from nilearn.plotting.cm import _cmap_d as nilearn_cmaps

nmaps = len(nilearn_cmaps)
a = np.outer(np.arange(0, 1, 0.01), np.ones(10))

# Initialize the figure
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 4.2))
plt.subplots_adjust(top=0.4, bottom=0.05, left=0.01, right=0.99)

for index, cmap in enumerate(nilearn_cmaps):
    plt.subplot(1, nmaps + 1, index + 1)
    plt.imshow(a, cmap=nilearn_cmaps[cmap])
    plt.axis("off")
    plt.title(cmap, fontsize=10, va="bottom", rotation=90)

Plot matplotlib color maps

plt.figure(figsize=(10, 5))
plt.subplots_adjust(top=0.8, bottom=0.05, left=0.01, right=0.99)
deprecated_cmaps = ["Vega10", "Vega20", "Vega20b", "Vega20c", "spectral"]
m_cmaps = [
    m
    for m in plt.cm.datad
    if not m.endswith("_r") and m not in deprecated_cmaps
]
m_cmaps.sort()

for index, cmap in enumerate(m_cmaps):
    plt.subplot(1, len(m_cmaps) + 1, index + 1)
    plt.imshow(a, cmap=plt.get_cmap(cmap), aspect="auto")
    plt.axis("off")
    plt.title(cmap, fontsize=10, va="bottom", rotation=90)

show()

Estimated memory usage: 115 MB