# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Helper functions and functions for plotting gamma-ray images."""
from __future__ import absolute_import, division, print_function, unicode_literals
from collections import OrderedDict
import numpy as np
from astropy.coordinates import Angle
__all__ = [
"colormap_hess",
"colormap_milagro",
"MapPanelPlotter",
"illustrate_colormap",
"grayify_colormap",
]
__doctest_requires__ = {("colormap_hess", "colormap_milagro"): ["matplotlib"]}
[docs]class MapPanelPlotter(object):
"""
Mape panel plotter class.
Given a `~matplotlib.pyplot.Figure` object this class creates axes objects
using `~matplotlib.gridspec.GridSpec` and plots a given sky map onto these.
Parameters
----------
figure : `~matplotlib.pyplot.figure.`
Figure instance.
xlim : `~astropy.coordinates.Angle`
Angle object specifying the longitude limits.
ylim : `~astropy.coordinates.Angle`
Angle object specifying the latitude limits.
npanels : int
Number of panels.
**kwargs : dict
Keyword arguments passed to `~matplotlib.gridspec.GridSpec`.
"""
def __init__(self, figure, xlim, ylim, npanels=4, **kwargs):
from matplotlib.gridspec import GridSpec
self.figure = figure
self.parameters = OrderedDict(xlim=xlim, ylim=ylim, npanels=npanels)
self.grid_spec = GridSpec(nrows=npanels, ncols=1, **kwargs)
def _get_ax_extend(self, ax, panel):
"""Get width and height of the axis in world coordinates"""
p = self.parameters
# compute aspect ratio of the axis
aspect = ax.bbox.width / ax.bbox.height
# compute width and height in world coordinates
height = np.abs(p["ylim"].diff())
width = aspect * height
left, bottom = p["xlim"][0].wrap_at("180d"), p["ylim"][0]
width_all = np.abs(p["xlim"].wrap_at("180d").diff())
xoverlap = ((p["npanels"] * width) - width_all) / (p["npanels"] - 1.0)
if xoverlap < 0:
raise ValueError(
"No overlap between panels. Please reduce figure "
"height or increase vertical space between the panels."
)
left = left - panel * (width - xoverlap)
return left[0], bottom, width, height
def _set_ax_fov(self, ax, panel):
left, bottom, width, height = self._get_ax_extend(ax, panel)
# set fov
xlim = Angle([left, left - width])
ylim = Angle([bottom, bottom + height])
xlim_pix, ylim_pix = ax.wcs.wcs_world2pix(xlim.deg, ylim.deg, 1)
ax.set_xlim(*xlim_pix)
ax.set_ylim(*ylim_pix)
return ax
[docs] def plot_panel(self, image, panel=1, panel_fov=None, **kwargs):
"""
Plot sky image on one panel.
Parameters
----------
map : `~gammapy.maps.WcsNDMap`
Map to plot.
panel : int
Which panel to plot on (counted from top).
"""
if panel_fov is None:
panel_fov = panel
spec = self.grid_spec[panel]
ax = self.figure.add_subplot(spec, projection=image.geom.wcs)
try:
ax = image.plot(ax=ax, **kwargs)[1]
except AttributeError:
ax = image.plot_rgb(ax=ax, **kwargs)
ax = self._set_ax_fov(ax, panel_fov)
return ax
[docs] def plot(self, image, **kwargs):
"""
Plot sky image on all panels.
Parameters
----------
map : `~gammapy.maps.WcsNDMap`
Map to plot.
"""
p = self.parameters
axes = []
for panel in range(p["npanels"]):
ax = self.plot_panel(image, panel=panel, **kwargs)
axes.append(ax)
return axes
[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 (default = 0.5)
Value of the transition to red (between 0 and 1).
width : float (default = 0.5)
Width of the blue-red color transition (between 0 and 1).
Returns
-------
colormap : `matplotlib.colors.LinearSegmentedColormap`
Colormap
Examples
--------
>>> from gammapy.image 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)
.. plot::
from gammapy.image import colormap_hess, illustrate_colormap
import matplotlib.pyplot as plt
cmap = colormap_hess()
illustrate_colormap(cmap)
plt.show()
"""
from matplotlib.colors import LinearSegmentedColormap
# 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"),
]
cmap = LinearSegmentedColormap.from_list(name="hess", colors=colors)
return cmap
[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 (default = 0.5)
Transition value (below: gray, above: color).
width : float (default = 0.0001)
Width of the transition
huestart : float (default = 0.6)
Hue of the color at ``transition``
Returns
-------
colormap : `~matplotlib.colors.LinearSegmentedColormap`
Colormap
Examples
--------
>>> from gammapy.image 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)
.. plot::
from gammapy.image import colormap_milagro, illustrate_colormap
import matplotlib.pyplot as plt
cmap = colormap_milagro()
illustrate_colormap(cmap)
plt.show()
"""
from colorsys import hls_to_rgb
from matplotlib.colors import LinearSegmentedColormap
# 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]
cmap = LinearSegmentedColormap.from_list(name="milagro", colors=rgb_colors)
return cmap
[docs]def grayify_colormap(cmap, mode="hsp"):
"""
Return a grayscale version a the colormap.
The grayscale conversion of the colormap is bases on perceived luminance of
the colors. For the conversion either the `~skimage.color.rgb2gray` or a
generic method called ``hsp`` [1]_ can be used. The code is loosely based
on [2]_.
Parameters
----------
cmap : str or `~matplotlib.colors.Colormap`
Colormap name or instance.
mode : {'skimage, 'hsp'}
Grayscale conversion method. Either ``skimage`` or ``hsp``.
References
----------
.. [1] Darel Rex Finley, "HSP Color Model - Alternative to HSV (HSB) and HSL"
http://alienryderflex.com/hsp.html
.. [2] Jake VanderPlas, "How Bad Is Your Colormap?"
https://jakevdp.github.io/blog/2014/10/16/how-bad-is-your-colormap/
"""
import matplotlib.pyplot as plt
cmap = plt.cm.get_cmap(cmap)
colors = cmap(np.arange(cmap.N))
if mode == "skimage":
from skimage.color import rgb2gray # pylint:disable=import-error
luminance = rgb2gray(np.array([colors]))
colors[:, :3] = luminance[0][:, np.newaxis]
elif mode == "hsp":
rgb_weight = [0.299, 0.587, 0.114]
luminance = np.sqrt(np.dot(colors[:, :3] ** 2, rgb_weight))
colors[:, :3] = luminance[:, np.newaxis]
else:
raise ValueError("Not a valid grayscale conversion mode.")
return cmap.from_list(cmap.name + "_grayscale", colors, cmap.N)
[docs]def illustrate_colormap(cmap, **kwargs):
"""
Illustrate color distribution and perceived luminance of a colormap.
Parameters
----------
cmap : str or `~matplotlib.colors.Colormap`
Colormap name or instance.
kwargs : dicts
Keyword arguments passed to `grayify_colormap`.
"""
import matplotlib.pyplot as plt
cmap = plt.cm.get_cmap(cmap)
cmap_gray = grayify_colormap(cmap, **kwargs)
figure = plt.figure(figsize=(8, 6))
v = np.linspace(0, 1, 4 * cmap.N)
# Show colormap
show_cmap = figure.add_axes([0.1, 0.8, 0.8, 0.1])
im = np.outer(np.ones(50), v)
show_cmap.imshow(im, cmap=cmap, origin="lower")
show_cmap.set_xticklabels([])
show_cmap.set_yticklabels([])
show_cmap.set_yticks([])
show_cmap.set_title("RGB & Gray Luminance of colormap {}".format(cmap.name))
# Show colormap gray
show_cmap_gray = figure.add_axes([0.1, 0.72, 0.8, 0.09])
show_cmap_gray.imshow(im, cmap=cmap_gray, origin="lower")
show_cmap_gray.set_xticklabels([])
show_cmap_gray.set_yticklabels([])
show_cmap_gray.set_yticks([])
# Plot RGB profiles
plot_rgb = figure.add_axes([0.1, 0.1, 0.8, 0.6])
plot_rgb.plot(v, [cmap(_)[0] for _ in v], color="#A60628")
plot_rgb.plot(v, [cmap(_)[1] for _ in v], color="#467821")
plot_rgb.plot(v, [cmap(_)[2] for _ in v], color="#348ABD")
plot_rgb.plot(v, [cmap_gray(_)[0] for _ in v], color="k", linestyle="--")
plot_rgb.set_ylabel("Luminance")
plot_rgb.set_ylim(-0.005, 1.005)