# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2022 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2022 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2022 UT-Battelle, LLC. All rights reserved.
#
# Additional copyright and license information can be found in the LICENSE file
# distributed with this code, or at
# https://raw.githubusercontent.com/MPAS-Dev/MPAS-Analysis/main/LICENSE
"""
Utilities for handling color maps and color bars
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani, Luke Van Roekel, Greg Streletz
import matplotlib.pyplot as plt
import matplotlib.colors as cols
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
import matplotlib
from xml.etree import ElementTree
import configparser
import cmocean
import importlib.resources
[docs]
def setup_colormap(config, configSectionName, suffix=''):
"""
Set up a colormap from the registry
Parameters
----------
config : instance of ConfigParser
the configuration, containing a [plot] section with options that
control plotting
configSectionName : str
name of config section
suffix: str, optional
suffix of colormap related options
Returns
-------
colormapDict : dict
A dictionary of colormap information.
'colormap' specifies the name of the new colormap
'norm' is a matplotlib norm object used to normalize the colormap
'levels' is an array of contour levels or ``None`` if not using indexed
color map
'ticks' is an array of values where ticks should be placed
'contours' is an array of contour values to plot or ``None`` if none
have been specified
'lineWidth' is the width of contour lines or ``None`` if not specified
'lineColor' is the color of contour lines or ``None`` if not specified
`arrowWidth` is the width of the arrows or ``None`` if not specified
`arrowSpacing` is the spacing between arrows or ``None`` if not
specified
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani, Greg Streletz
register_custom_colormaps()
option = f'colormapType{suffix}'
if config.has_option(configSectionName, option):
colormapType = config.get(configSectionName, option)
if colormapType == 'indexed':
(colormap, norm, levels, ticks) = _setup_indexed_colormap(
config, configSectionName, suffix=suffix)
elif colormapType == 'continuous':
(colormap, norm, ticks) = _setup_colormap_and_norm(
config, configSectionName, suffix=suffix)
levels = None
else:
raise ValueError(f'config section {configSectionName} option '
f'{option} is not "indexed" or "continuous"')
else:
colormap = None
norm = None
levels = None
ticks = None
contours = None
lineWidth = None
lineColor = None
arrowWidth = None
arrowSpacing = None
option = f'contourLevels{suffix}'
if config.has_option(configSectionName, option):
contours = config.getnumpy(configSectionName, option)
if isinstance(contours, str) and contours == 'none':
contours = None
option = f'contourThickness{suffix}'
if config.has_option(configSectionName, option):
lineWidth = config.getfloat(configSectionName, option)
option = f'contourColor{suffix}'
if config.has_option(configSectionName, option):
lineColor = config.get(configSectionName, option)
option = f'arrowWidth{suffix}'
if config.has_option(configSectionName, option):
arrowWidth = config.getexpression(configSectionName, option)
if isinstance(arrowWidth, str) and arrowWidth == 'None':
arrowWidth = None
option = f'arrowSpacing{suffix}'
if config.has_option(configSectionName, option):
arrowSpacing = config.getexpression(configSectionName, option)
if isinstance(arrowSpacing, str) and arrowSpacing == 'None':
arrowSpacing = None
return {'colormap': colormap, 'norm': norm, 'levels': levels,
'ticks': ticks, 'contours': contours, 'lineWidth': lineWidth,
'lineColor': lineColor, 'arrowWidth': arrowWidth,
'arrowSpacing': arrowSpacing}
def register_custom_colormaps():
name = 'ferret'
backgroundColor = (0.9, 0.9, 0.9)
red = np.array([[0, 0.6],
[0.15, 1],
[0.35, 1],
[0.65, 0],
[0.8, 0],
[1, 0.75]])
green = np.array([[0, 0],
[0.1, 0],
[0.35, 1],
[1, 0]])
blue = np.array([[0, 0],
[0.5, 0],
[0.9, 0.9],
[1, 0.9]])
colorCount = 21
colorList = np.ones((colorCount, 4), float)
colorList[:, 0] = np.interp(np.linspace(0, 1, colorCount),
red[:, 0], red[:, 1])
colorList[:, 1] = np.interp(np.linspace(0, 1, colorCount),
green[:, 0], green[:, 1])
colorList[:, 2] = np.interp(np.linspace(0, 1, colorCount),
blue[:, 0], blue[:, 1])
colorList = colorList[::-1, :]
colorMap = cols.LinearSegmentedColormap.from_list(
name, colorList, N=255)
colorMap.set_bad(backgroundColor)
_register_colormap_and_reverse(name, colorMap)
name = 'erdc_iceFire_H'
colorArray = np.array([
[-1, 4.05432e-07, 0, 5.90122e-06],
[-0.87451, 0, 0.120401, 0.302675],
[-0.74902, 0, 0.216583, 0.524574],
[-0.623529, 0.0552475, 0.345025, 0.6595],
[-0.498039, 0.128047, 0.492588, 0.720288],
[-0.372549, 0.188955, 0.641309, 0.792092],
[-0.247059, 0.327673, 0.784935, 0.873434],
[-0.121569, 0.60824, 0.892164, 0.935547],
[0.00392157, 0.881371, 0.912178, 0.818099],
[0.129412, 0.951407, 0.835621, 0.449279],
[0.254902, 0.904481, 0.690489, 0],
[0.380392, 0.85407, 0.510864, 0],
[0.505882, 0.777093, 0.33018, 0.00088199],
[0.631373, 0.672862, 0.139087, 0.00269398],
[0.756863, 0.508815, 0, 0],
[0.882353, 0.299417, 0.000366289, 0.000547829],
[1, 0.0157519, 0.00332021, 4.55569e-08]], float)
colorCount = 255
colorList = np.ones((colorCount, 4), float)
x = colorArray[:, 0]
for cIndex in range(3):
colorList[:, cIndex] = np.interp(
np.linspace(-1., 1., colorCount),
x, colorArray[:, cIndex + 1])
colorMap = cols.LinearSegmentedColormap.from_list(
name, colorList, N=255)
_register_colormap_and_reverse(name, colorMap)
name = 'erdc_iceFire_L'
colorArray = np.array([
[-1, 0.870485, 0.913768, 0.832905],
[-0.87451, 0.586919, 0.887865, 0.934003],
[-0.74902, 0.31583, 0.776442, 0.867858],
[-0.623529, 0.18302, 0.632034, 0.787722],
[-0.498039, 0.117909, 0.484134, 0.713825],
[-0.372549, 0.0507239, 0.335979, 0.654741],
[-0.247059, 0, 0.209874, 0.511832],
[-0.121569, 0, 0.114689, 0.28935],
[0.00392157, 0.0157519, 0.00332021, 4.55569e-08],
[0.129412, 0.312914, 0, 0],
[0.254902, 0.520865, 0, 0],
[0.380392, 0.680105, 0.15255, 0.0025996],
[0.505882, 0.785109, 0.339479, 0.000797922],
[0.631373, 0.857354, 0.522494, 0],
[0.756863, 0.910974, 0.699774, 0],
[0.882353, 0.951921, 0.842817, 0.478545],
[1, 0.881371, 0.912178, 0.818099]], float)
colorCount = 255
colorList = np.ones((colorCount, 4), float)
x = colorArray[:, 0]
for cIndex in range(3):
colorList[:, cIndex] = np.interp(
np.linspace(-1., 1., colorCount),
x, colorArray[:, cIndex + 1])
colorMap = cols.LinearSegmentedColormap.from_list(
name, colorList, N=255)
_register_colormap_and_reverse(name, colorMap)
name = 'BuOr'
colors1 = plt.cm.PuOr(np.linspace(0., 1, 256))
colors2 = plt.cm.RdBu(np.linspace(0, 1, 256))
# combine them and build a new colormap, just the orange from the first
# and the blue from the second
colorList = np.vstack((colors1[0:128, :], colors2[128:256, :]))
# reverse the order
colorList = colorList[::-1, :]
colorMap = cols.LinearSegmentedColormap.from_list(name, colorList)
_register_colormap_and_reverse(name, colorMap)
name = 'Maximenko'
colorArray = np.array([
[-1, 0., 0.45882352941, 0.76470588235],
[-0.666667, 0., 0.70196078431, 0.90588235294],
[-0.333333, 0.3294117647, 0.87058823529, 1.],
[0., 0.76470588235, 0.94509803921, 0.98039215686],
[0.333333, 1., 1., 0.],
[0.666667, 1., 0.29411764705, 0.],
[1, 1., 0., 0.]], float)
colorCount = 255
colorList = np.ones((colorCount, 4), float)
x = colorArray[:, 0]
for cIndex in range(3):
colorList[:, cIndex] = np.interp(
np.linspace(-1., 1., colorCount),
x, colorArray[:, cIndex + 1])
colorMap = cols.LinearSegmentedColormap.from_list(
name, colorList, N=255)
_register_colormap_and_reverse(name, colorMap)
# add the cmocean color maps
map_names = list(cmocean.cm.cmapnames)
# don't bother with gray (already exists, I think)
map_names.pop(map_names.index('gray'))
for map_name in map_names:
_register_colormap_and_reverse(map_name, getattr(cmocean.cm, map_name))
# add ScientificColourMaps7 from
# http://www.fabiocrameri.ch/colourmaps.php
# https://doi.org/10.5281/zenodo.5501399
for map_name in ['acton', 'bam', 'bamako', 'bamO', 'batlow', 'batlowK',
'batlowW', 'berlin', 'bilbao', 'broc', 'brocO', 'buda',
'bukavu', 'cork', 'corkO', 'davos', 'devon', 'fes',
'grayC', 'hawaii', 'imola', 'lajolla', 'lapaz', 'lisbon',
'nuuk', 'oleron', 'oslo', 'roma', 'romaO', 'tofino',
'tokyo', 'turku', 'vanimo', 'vik', 'vikO']:
package = f'mpas_analysis.shared.plot.ScientificColourMaps7.{map_name}'
filename = f'{map_name}_PARAVIEW.xml'
with importlib.resources.path(package, filename) as xml_file:
_read_xml_colormap(xml_file, map_name)
# add SciVisColor colormaps from
# https://sciviscolor.org/home/colormaps/
for map_name in ['3wave-yellow-grey-blue', '3Wbgy5',
'4wave-grey-red-green-mgreen', '5wave-yellow-brown-blue',
'blue-1', 'blue-3', 'blue-6', 'blue-8', 'blue-orange-div',
'brown-2', 'brown-5', 'brown-8', 'green-1', 'green-4',
'green-7', 'green-8', 'orange-5', 'orange-6',
'orange-green-blue-gray', 'purple-7', 'purple-8', 'red-1',
'red-3', 'red-4', 'yellow-1', 'yellow-7']:
package = 'mpas_analysis.shared.plot.SciVisColorColormaps'
filename = f'{map_name}.xml'
with importlib.resources.path(package, filename) as xml_file:
_read_xml_colormap(xml_file, map_name)
# add SciVisColor colormaps created by hand using
# https://sciviscolor.org/color-moves-app/
for map_name in ['3wave-green-red-purple', '3wave-blue-red-brown',
'div-one-third-blue-two-thirds-red',
'div-one-third-green-two-thirds-red',]:
package = 'mpas_analysis.shared.plot.SciVisColorCustom'
filename = f'{map_name}.xml'
with importlib.resources.path(package, filename) as xml_file:
_read_xml_colormap(xml_file, map_name)
name = 'white_cmo_deep'
# modify cmo.deep to start at white
colors2 = plt.get_cmap('cmo.deep')(np.linspace(0, 1, 224))
colorCount = 32
colors1 = np.ones((colorCount, 4), float)
x = np.linspace(0., 1., colorCount+1)[0:-1]
white = [1., 1., 1., 1.]
for cIndex in range(4):
colors1[:, cIndex] = np.interp(x, [0., 1.],
[white[cIndex], colors2[0, cIndex]])
colors = np.vstack((colors1, colors2))
# generating a smoothly-varying LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list(name, colors)
_register_colormap_and_reverse(name, cmap)
def _setup_colormap_and_norm(config, configSectionName, suffix=''):
"""
Set up a colormap from the registry
Parameters
----------
config : instance of ConfigParser
the configuration, containing a [plot] section with options that
control plotting
configSectionName : str
name of config section
suffix: str, optional
suffix of colormap related options
Returns
-------
colormap : srt
new colormap
norm : matplotlib.colors.Normalize
the norm used to normalize the colormap
ticks : array of float
the tick marks on the colormap
"""
# Authors
# -------
# Xylar Asay-Davis
register_custom_colormaps()
colormap = plt.get_cmap(config.get(configSectionName,
f'colormapName{suffix}'))
normType = config.get(configSectionName, f'normType{suffix}')
kwargs = config.getexpression(configSectionName,
f'normArgs{suffix}')
if normType == 'symLog':
norm = cols.SymLogNorm(**kwargs)
elif normType == 'log':
norm = cols.LogNorm(**kwargs)
elif normType == 'linear':
norm = cols.Normalize(**kwargs)
else:
raise ValueError(f'Unsupported norm type {normType} in section '
f'{configSectionName}')
try:
ticks = config.getnumpy(configSectionName, f'colorbarTicks{suffix}')
except configparser.NoOptionError:
ticks = None
return colormap, norm, ticks
def _setup_indexed_colormap(config, configSectionName, suffix=''):
"""
Set up a colormap from the registry
Parameters
----------
config : instance of ConfigParser
the configuration, containing a [plot] section with options that
control plotting
configSectionName : str
name of config section
suffix: str, optional
suffix of colormap related options
Returns
-------
colormap : srt
new colormap
norm : matplotlib.colors.Normalize
the norm used to normalize the colormap
ticks : array of float
the tick marks on the colormap
"""
# Authors
# -------
# Xylar Asay-Davis, Milena Veneziani, Greg Streletz
colormap = plt.get_cmap(config.get(configSectionName,
f'colormapName{suffix}'))
indices = config.getnumpy(configSectionName, f'colormapIndices{suffix}')
try:
levels = config.getnumpy(configSectionName, f'colorbarLevels{suffix}')
except configparser.NoOptionError:
levels = None
if levels is not None:
# set under/over values based on the first/last indices in the colormap
underColor = colormap(indices[0])
overColor = colormap(indices[-1])
if len(levels) + 1 == len(indices):
# we have 2 extra values for the under/over so make the colormap
# without these values
indices = indices[1:-1]
elif len(levels) - 1 != len(indices):
# indices list must be either one element shorter
# or one element longer than colorbarLevels list
raise ValueError('length mismatch between indices and '
'colorbarLevels')
colormap = cols.ListedColormap(colormap(indices),
f'colormapName{suffix}')
colormap.set_under(underColor)
colormap.set_over(overColor)
norm = cols.BoundaryNorm(levels, colormap.N)
try:
ticks = config.getnumpy(configSectionName, f'colorbarTicks{suffix}')
except configparser.NoOptionError:
ticks = levels
return colormap, norm, levels, ticks
def _read_xml_colormap(xmlFile, map_name):
"""Read in an XML colormap"""
xml = ElementTree.parse(xmlFile)
root = xml.getroot()
colormap = root.findall('ColorMap')
if len(colormap) > 0:
colormap = colormap[0]
colorDict = {'red': [], 'green': [], 'blue': []}
for point in colormap.findall('Point'):
x = float(point.get('x'))
color = [float(point.get('r')), float(point.get('g')),
float(point.get('b'))]
colorDict['red'].append((x, color[0], color[0]))
colorDict['green'].append((x, color[1], color[1]))
colorDict['blue'].append((x, color[2], color[2]))
cmap = LinearSegmentedColormap(map_name, colorDict, 256)
_register_colormap_and_reverse(map_name, cmap)
def _register_colormap_and_reverse(map_name, cmap):
if map_name not in matplotlib.colormaps:
matplotlib.colormaps.register(cmap, name=map_name)
matplotlib.colormaps.register(cmap.reversed(), name=f'{map_name}_r')
def _plot_color_gradients():
"""from https://matplotlib.org/tutorials/colors/colormaps.html"""
cmap_list = [m for m in plt.colormaps() if not m.endswith("_r")]
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
nrows = len(cmap_list)
fig, axes = plt.subplots(figsize=(7.2, 0.25 * nrows), nrows=nrows)
fig.subplots_adjust(top=0.99, bottom=0.01, left=0.35, right=0.99)
for ax, name in zip(axes, cmap_list):
ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
pos = list(ax.get_position().bounds)
x_text = pos[0] - 0.01
y_text = pos[1] + pos[3] / 2.
fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axes:
ax.set_axis_off()
plt.savefig('colormaps.png', dpi=100)