def planck_cmap(ncolors=256):
"""
Returns a color map similar to the one used for the "Planck CMB Map".
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
"""
segmentdata = {
"red": [(0.0, 0.00, 0.00), (0.1, 0.00, 0.00), (0.2, 0.00, 0.00),
(0.3, 0.00, 0.00), (0.4, 0.00, 0.00), (0.5, 1.00, 1.00),
(0.6, 1.00, 1.00), (0.7, 1.00, 1.00), (0.8, 0.83, 0.83),
(0.9, 0.67, 0.67), (1.0, 0.50, 0.50)],
"green": [(0.0, 0.00, 0.00), (0.1, 0.00, 0.00), (0.2, 0.00, 0.00),
(0.3, 0.30, 0.30), (0.4, 0.70, 0.70), (0.5, 1.00, 1.00),
(0.6, 0.70, 0.70), (0.7, 0.30, 0.30), (0.8, 0.00, 0.00),
(0.9, 0.00, 0.00), (1.0, 0.00, 0.00)],
"blue": [(0.0, 0.50, 0.50), (0.1, 0.67, 0.67), (0.2, 0.83, 0.83),
(0.3, 1.00, 1.00), (0.4, 1.00, 1.00), (0.5, 1.00, 1.00),
(0.6, 0.00, 0.00), (0.7, 0.00, 0.00), (0.8, 0.00, 0.00),
(0.9, 0.00, 0.00), (1.0, 0.00, 0.00)]
}
return cm("Planck-like", segmentdata, N=int(ncolors), gamma=1.0)
def he(ncolors=256):
"""
Returns a color map often used in High Energy Astronomy.
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
"""
segmentdata = {"red": [(0.000, 0.0, 0.0), (0.167, 0.0, 0.0),
(0.333, 0.5, 0.5), (0.500, 1.0, 1.0),
(0.667, 1.0, 1.0), (0.833, 1.0, 1.0),
(1.000, 1.0, 1.0)],
"green": [(0.000, 0.0, 0.0), (0.167, 0.0, 0.0),
(0.333, 0.0, 0.0), (0.500, 0.0, 0.0),
(0.667, 0.5, 0.5), (0.833, 1.0, 1.0),
(1.000, 1.0, 1.0)],
"blue": [(0.000, 0.0, 0.0), (0.167, 1.0, 1.0),
(0.333, 0.5, 0.5), (0.500, 0.0, 0.0),
(0.667, 0.0, 0.0), (0.833, 0.0, 0.0),
(1.000, 1.0, 1.0)]}
return cm("High Energy", segmentdata, N=int(ncolors), gamma=1.0)
def planck(ncolors=256):
from matplotlib.colors import LinearSegmentedColormap as cm
"""
Returns a color map similar to the one used for the "Planck CMB Map".
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
"""
segmentdata = {"red": [(0.0, 0.00, 0.00), (0.1, 0.00, 0.00),
(0.2, 0.00, 0.00), (0.3, 0.00, 0.00),
(0.4, 0.00, 0.00), (0.5, 1.00, 1.00),
(0.6, 1.00, 1.00), (0.7, 1.00, 1.00),
(0.8, 0.83, 0.83), (0.9, 0.67, 0.67),
(1.0, 0.50, 0.50)],
"green": [(0.0, 0.00, 0.00), (0.1, 0.00, 0.00),
(0.2, 0.00, 0.00), (0.3, 0.30, 0.30),
(0.4, 0.70, 0.70), (0.5, 1.00, 1.00),
(0.6, 0.70, 0.70), (0.7, 0.30, 0.30),
(0.8, 0.00, 0.00), (0.9, 0.00, 0.00),
(1.0, 0.00, 0.00)],
"blue": [(0.0, 0.50, 0.50), (0.1, 0.67, 0.67),
(0.2, 0.83, 0.83), (0.3, 1.00, 1.00),
(0.4, 1.00, 1.00), (0.5, 1.00, 1.00),
(0.6, 0.00, 0.00), (0.7, 0.00, 0.00),
(0.8, 0.00, 0.00), (0.9, 0.00, 0.00),
(1.0, 0.00, 0.00)]}
return cm("Planck-like", segmentdata, N=int(ncolors), gamma=1.0)
def pm(ncolors=256):
"""
Returns a color map useful for a zero-centerd range of values.
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
"""
segmentdata = {"red": [(0.0, 1.00, 1.00), (0.1, 0.96, 0.96),
(0.2, 0.84, 0.84), (0.3, 0.64, 0.64),
(0.4, 0.36, 0.36), (0.5, 0.00, 0.00),
(0.6, 0.00, 0.00), (0.7, 0.00, 0.00),
(0.8, 0.00, 0.00), (0.9, 0.00, 0.00),
(1.0, 0.00, 0.00)],
"green": [(0.0, 0.50, 0.50), (0.1, 0.32, 0.32),
(0.2, 0.18, 0.18), (0.3, 0.08, 0.08),
(0.4, 0.02, 0.02), (0.5, 0.00, 0.00),
(0.6, 0.02, 0.02), (0.7, 0.08, 0.08),
(0.8, 0.18, 0.18), (0.9, 0.32, 0.32),
(1.0, 0.50, 0.50)],
"blue": [(0.0, 0.00, 0.00), (0.1, 0.00, 0.00),
(0.2, 0.00, 0.00), (0.3, 0.00, 0.00),
(0.4, 0.00, 0.00), (0.5, 0.00, 0.00),
(0.6, 0.36, 0.36), (0.7, 0.64, 0.64),
(0.8, 0.84, 0.84), (0.9, 0.96, 0.96),
(1.0, 1.00, 1.00)]}
return cm("Plus Minus", segmentdata, N=int(ncolors), gamma=1.0)
def he(ncolors=256):
"""
Returns a color map often used in High Energy Astronomy.
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
"""
segmentdata = {
"red": [(0.000, 0.0, 0.0), (0.167, 0.0, 0.0), (0.333, 0.5, 0.5),
(0.500, 1.0, 1.0), (0.667, 1.0, 1.0), (0.833, 1.0, 1.0),
(1.000, 1.0, 1.0)],
"green": [(0.000, 0.0, 0.0), (0.167, 0.0, 0.0), (0.333, 0.0, 0.0),
(0.500, 0.0, 0.0), (0.667, 0.5, 0.5), (0.833, 1.0, 1.0),
(1.000, 1.0, 1.0)],
"blue": [(0.000, 0.0, 0.0), (0.167, 1.0, 1.0), (0.333, 0.5, 0.5),
(0.500, 0.0, 0.0), (0.667, 0.0, 0.0), (0.833, 0.0, 0.0),
(1.000, 1.0, 1.0)]
}
return cm("High Energy", segmentdata, N=int(ncolors), gamma=1.0)
def pm(ncolors=256):
"""
Returns a color map useful for a zero-centerd range of values.
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
"""
segmentdata = {
"red": [(0.0, 1.00, 1.00), (0.1, 0.96, 0.96), (0.2, 0.84, 0.84),
(0.3, 0.64, 0.64), (0.4, 0.36, 0.36), (0.5, 0.00, 0.00),
(0.6, 0.00, 0.00), (0.7, 0.00, 0.00), (0.8, 0.00, 0.00),
(0.9, 0.00, 0.00), (1.0, 0.00, 0.00)],
"green": [(0.0, 0.50, 0.50), (0.1, 0.32, 0.32), (0.2, 0.18, 0.18),
(0.3, 0.08, 0.08), (0.4, 0.02, 0.02), (0.5, 0.00, 0.00),
(0.6, 0.02, 0.02), (0.7, 0.08, 0.08), (0.8, 0.18, 0.18),
(0.9, 0.32, 0.32), (1.0, 0.50, 0.50)],
"blue": [(0.0, 0.00, 0.00), (0.1, 0.00, 0.00), (0.2, 0.00, 0.00),
(0.3, 0.00, 0.00), (0.4, 0.00, 0.00), (0.5, 0.00, 0.00),
(0.6, 0.36, 0.36), (0.7, 0.64, 0.64), (0.8, 0.84, 0.84),
(0.9, 0.96, 0.96), (1.0, 1.00, 1.00)]
}
return cm("Plus Minus", segmentdata, N=int(ncolors), gamma=1.0)
def plot_array_pil(x,
pil_object=True,
lims=None,
log_input=False,
log_clip=1e-7):
norm_x = np.squeeze(x)
cm = planck_cmap()
plt.register_cmap(cmap=cm)
img = Image.fromarray(cm(norm_x, bytes=True))
if pil_object:
return img
else:
return np.array(img)
def fu(ncolors=256):
"""
Returns a color map used for the "Faraday Map Uncertainty".
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
References
----------
.. [#] N. Opermann et. al.,
"An improved map of the Galactic Faraday sky",
Astronomy & Astrophysics, Volume 542, id.A93, 06/2012;
`arXiv:1111.6186 <http://www.arxiv.org/abs/1111.6186>`_
"""
segmentdata = {
"red": [(0.000, 1.00, 1.00), (0.100, 0.80, 0.80),
(0.200, 0.65, 0.65), (0.410, 0.60, 0.60),
(0.500, 0.70, 0.70), (0.560, 0.96, 0.96),
(0.590, 1.00, 1.00), (0.740, 0.80, 0.80),
(0.800, 0.80, 0.80), (0.900, 0.50, 0.50),
(1.000, 0.40, 0.40)],
"green": [(0.000, 0.90, 0.90), (0.200, 0.65, 0.65),
(0.362, 0.95, 0.95), (0.500, 1.00, 1.00),
(0.638, 0.88, 0.88), (0.800, 0.25, 0.25),
(0.900, 0.30, 0.30), (1.000, 0.20, 0.20)],
"blue": [(0.000, 1.00, 1.00), (0.100, 0.80, 0.80),
(0.200, 1.00, 1.00), (0.410, 1.00, 1.00),
(0.440, 0.96, 0.96), (0.500, 0.70, 0.70),
(0.590, 0.42, 0.42), (0.800, 0.00, 0.00),
(1.000, 0.00, 0.00)]
}
return cm("Faraday Uncertainty",
segmentdata,
N=int(ncolors),
gamma=1.0)
def fu(ncolors=256):
"""
Returns a color map used for the "Faraday Map Uncertainty".
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
References
----------
.. [#] N. Opermann et. al.,
"An improved map of the Galactic Faraday sky",
Astronomy & Astrophysics, Volume 542, id.A93, 06/2012;
`arXiv:1111.6186 <http://www.arxiv.org/abs/1111.6186>`_
"""
segmentdata = {"red": [(0.000, 1.00, 1.00), (0.100, 0.80, 0.80),
(0.200, 0.65, 0.65), (0.410, 0.60, 0.60),
(0.500, 0.70, 0.70), (0.560, 0.96, 0.96),
(0.590, 1.00, 1.00), (0.740, 0.80, 0.80),
(0.800, 0.80, 0.80), (0.900, 0.50, 0.50),
(1.000, 0.40, 0.40)],
"green": [(0.000, 0.90, 0.90), (0.200, 0.65, 0.65),
(0.362, 0.95, 0.95), (0.500, 1.00, 1.00),
(0.638, 0.88, 0.88), (0.800, 0.25, 0.25),
(0.900, 0.30, 0.30), (1.000, 0.20, 0.20)],
"blue": [(0.000, 1.00, 1.00), (0.100, 0.80, 0.80),
(0.200, 1.00, 1.00), (0.410, 1.00, 1.00),
(0.440, 0.96, 0.96), (0.500, 0.70, 0.70),
(0.590, 0.42, 0.42), (0.800, 0.00, 0.00),
(1.000, 0.00, 0.00)]}
return cm("Faraday Uncertainty", segmentdata, N=int(ncolors),
gamma=1.0)
import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap as cm import numpy as np arr = np.random.randint(0,2,size=(10,20)).reshape(10,20) #fig = plt.figure(figsize=(4, 4)) im = plt.imshow(arr, interpolation='none', vmin = 0, vmax = 1, cmap = cm(['white','black'])) plt.colorbar(im, use_gridspec=True) plt.show()
