def test_colormap_hess():
transition = 0.5
cmap = colormap_hess(transition=transition)
vals = [
(0, (0.0, 0.0, 0.0)),
(0.25, (0.0, 0.0, 0.50196078)),
(0.5, (1.0, 0.0058823529411764722, 0.0)),
(0.75, (1.0, 0.75882352941176501, 0.0)),
(1, (1.0, 1.0, 1.0)),
]
_check_cmap_rgb_vals(vals, cmap)
"""
import numpy as np
import matplotlib.pyplot as plt
from astropy.visualization.mpl_normalize import ImageNormalize
from astropy.visualization import LinearStretch
from gammapy.image import colormap_hess, colormap_milagro
from gammapy.maps import Map
filename = '$GAMMAPY_EXTRA/test_datasets/unbundled/poisson_stats_image/expected_ts_0.000.fits.gz'
image = Map.read(filename, hdu='SQRT_TS')
# Plot with the HESS and Milagro colormap
vmin, vmax, vtransition = -5, 15, 5
fig = plt.figure(figsize=(15.5, 6))
normalize = ImageNormalize(vmin=vmin, vmax=vmax, stretch=LinearStretch())
transition = normalize(vtransition)
ax = fig.add_subplot(121, projection=image.geom.wcs)
cmap = colormap_hess(transition=transition)
image.plot(ax=ax, cmap=cmap, norm=normalize, add_cbar=True)
plt.title('HESS-style colormap')
ax = fig.add_subplot(122, projection=image.geom.wcs)
cmap = colormap_milagro(transition=transition)
image.plot(ax=ax, cmap=cmap, norm=normalize, add_cbar=True)
plt.title('MILAGRO-style colormap')
plt.tight_layout()
plt.show()
from astropy.visualization import LinearStretch
# Compute an example significance image
counts = load_poisson_stats_image()
counts = disk_correlate(counts, radius=5, mode='reflect')
background = np.median(counts) * np.ones_like(counts)
image = significance(counts, background)
# Plot with the HESS and Milagro colormap
vmin, vmax, vtransition = -5, 15, 5
plt.figure(figsize=(8, 4))
normalize = ImageNormalize(vmin=vmin, vmax=vmax, stretch=LinearStretch())
transition = normalize(vtransition)
plt.subplot(121)
cmap = colormap_hess(transition=transition)
plt.imshow(image, cmap=cmap, norm=normalize)
plt.axis('off')
plt.colorbar()
plt.title('HESS-style colormap')
plt.subplot(122)
cmap = colormap_milagro(transition=transition)
plt.imshow(image, cmap=cmap, norm=normalize)
plt.axis('off')
plt.colorbar()
plt.title('MILAGRO-style colormap')
plt.show()
from gammapy.datasets import load_poisson_stats_image
from gammapy.image import disk_correlate
from gammapy.stats import significance
from gammapy.image import colormap_hess, colormap_milagro
# Compute an example significance image
counts = load_poisson_stats_image()
counts = disk_correlate(counts, radius=5, mode='reflect')
background = np.median(counts) * np.ones_like(counts)
image = significance(counts, background)
# Plot with the HESS and Milagro colormap
vmin, vmax, vtransition = -5, 15, 5
plt.figure(figsize=(8, 4))
plt.subplot(121)
cmap = colormap_hess(vtransition=vtransition, vmin=vmin, vmax=vmax)
plt.imshow(image, cmap=cmap, vmin=vmin, vmax=vmax)
plt.axis('off')
plt.colorbar()
plt.title('HESS-style colormap')
plt.subplot(122)
cmap = colormap_milagro(vtransition=vtransition, vmin=vmin, vmax=vmax)
plt.imshow(image, cmap=cmap, vmin=vmin, vmax=vmax)
plt.axis('off')
plt.colorbar()
plt.title('MILAGRO-style colormap')
plt.show()
from gammapy.image import colormap_hess, illustrate_colormap import matplotlib.pyplot as plt cmap = colormap_hess() illustrate_colormap(cmap) plt.show()