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import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage
from matplotlib.transforms import Bbox, TransformedBbox
if __name__ == "__main__":
fig = plt.figure(1)
ax = plt.subplot(121)
txt = ax.text(0.5, 0.5, "test", size=30, ha="center", color="w")
kwargs = dict()
bbox_image = BboxImage(txt.get_window_extent,
norm=None,
origin=None,
clip_on=False,
**kwargs
)
a = np.arange(256).reshape(1, 256)/256.
bbox_image.set_data(a)
ax.add_artist(bbox_image)
ax = plt.subplot(122)
a = np.linspace(0, 1, 256).reshape(1, -1)
a = np.vstack((a, a))
maps = sorted(m for m in plt.cm.cmap_d if not m.endswith("_r"))
maps.remove('spectral') # Deprecated.
#nmaps = len(maps) + 1
#fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
ncol = 2
nrow = len(maps)//ncol + 1
xpad_fraction = 0.3
dx = 1./(ncol + xpad_fraction*(ncol - 1))
ypad_fraction = 0.3
dy = 1./(nrow + ypad_fraction*(nrow - 1))
for i, m in enumerate(maps):
ix, iy = divmod(i, nrow)
#plt.figimage(a, 10, i*10, cmap=plt.get_cmap(m), origin='lower')
bbox0 = Bbox.from_bounds(ix*dx*(1 + xpad_fraction),
1. - iy*dy*(1 + ypad_fraction) - dy,
dx, dy)
bbox = TransformedBbox(bbox0, ax.transAxes)
bbox_image = BboxImage(bbox,
cmap=plt.get_cmap(m),
norm=None,
origin=None,
**kwargs
)
bbox_image.set_data(a)
ax.add_artist(bbox_image)
plt.draw()
plt.show()
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