Note
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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¶
try:
import matplotlib.pyplot as plt
except ImportError:
raise RuntimeError("This script needs the matplotlib library")
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
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()
Total running time of the script: (0 minutes 3.468 seconds)
Estimated memory usage: 19 MB