# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Helper functions and functions for plotting gamma-ray images."""
from matplotlib.colors import LinearSegmentedColormap
__all__ = ["colormap_hess", "colormap_milagro"]
[docs]
def colormap_hess(transition=0.5, width=0.1):
"""Colormap often used in H.E.S.S. collaboration publications.
This colormap goes black -> blue -> red -> yellow -> white.
A sharp blue -> red -> yellow transition is often used for significance images
with a value of red at ``transition ~ 5`` or ``transition ~ 7``
so that the following effect is achieved:
- black, blue: non-significant features, not well visible
- red: features at the detection threshold ``transition``
- yellow, white: significant features, very well visible
The transition parameter is defined between 0 and 1. To calculate the value
from data units an `~astropy.visualization.mpl_normalize.ImageNormalize`
instance should be used (see example below).
Parameters
----------
transition : float
Value of the transition to red (between 0 and 1). Default is 0.5.
width : float
Width of the blue-red color transition (between 0 and 1). Default is 0.5.
Returns
-------
colormap : `matplotlib.colors.LinearSegmentedColormap`
Colormap.
Examples
--------
>>> from gammapy.visualization import colormap_hess
>>> from astropy.visualization.mpl_normalize import ImageNormalize
>>> from astropy.visualization import LinearStretch
>>> normalize = ImageNormalize(vmin=-5, vmax=15, stretch=LinearStretch())
>>> transition = normalize(5)
>>> cmap = colormap_hess(transition=transition)
"""
# Compute normalised values (range 0 to 1) that
# correspond to red, blue, yellow.
red = float(transition)
if width > red:
blue = 0.1 * red
else:
blue = red - width
yellow = 2.0 / 3.0 * (1 - red) + red
black, white = 0, 1
# Create custom colormap
# List entries: (value, (R, G, B))
colors = [
(black, "k"),
(blue, (0, 0, 0.8)),
(red, "r"),
(yellow, (1.0, 1.0, 0)),
(white, "w"),
]
return LinearSegmentedColormap.from_list(name="hess", colors=colors)
[docs]
def colormap_milagro(transition=0.5, width=0.0001, huestart=0.6):
"""Colormap often used in Milagro collaboration publications.
This colormap is gray below ``transition`` and similar to the jet colormap above.
A sharp gray -> color transition is often used for significance images
with a transition value of ``transition ~ 5`` or ``transition ~ 7``,
so that the following effect is achieved:
- gray: non-significant features are not well visible
- color: significant features at the detection threshold ``transition``
Note that this colormap is often criticised for over-exaggerating small differences
in significance below and above the gray - color transition threshold.
The transition parameter is defined between 0 and 1. To calculate the value
from data units an `~astropy.visualization.mpl_normalize.ImageNormalize` instance should be
used (see example below).
Parameters
----------
transition : float
Transition value (below: gray, above: color). Default is 0.5.
width : float
Width of the transition. Default is 0.0001.
huestart : float
Hue of the color at ``transition``. Default is 0.6.
Returns
-------
colormap : `~matplotlib.colors.LinearSegmentedColormap`
Colormap.
Examples
--------
>>> from gammapy.visualization import colormap_milagro
>>> from astropy.visualization.mpl_normalize import ImageNormalize
>>> from astropy.visualization import LinearStretch
>>> normalize = ImageNormalize(vmin=-5, vmax=15, stretch=LinearStretch())
>>> transition = normalize(5)
>>> cmap = colormap_milagro(transition=transition)
"""
from colorsys import hls_to_rgb
# Compute normalised red, blue, yellow values
transition = float(transition)
# Create custom colormap
# List entries: (value, (H, L, S))
colors = [
(0, (1, 1, 0)),
(transition - width, (1, 0, 0)),
(transition, (huestart, 0.4, 0.5)),
(transition + width, (huestart, 0.4, 1)),
(0.99, (0, 0.6, 1)),
(1, (0, 1, 1)),
]
# Convert HLS values to RGB values
rgb_colors = [(val, hls_to_rgb(*hls)) for (val, hls) in colors]
return LinearSegmentedColormap.from_list(name="milagro", colors=rgb_colors)
