import colorsys
import matplotlib.colors as mc
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
[docs]
def random_colors_list(nlabels, vmin=0.8, vmax=1.0, hsv=False):
"""
Generates a list of random HSV colors given the number of classes,
minimum and maximum values in the HSV spectrum.
Parameters
----------
nlabels: int
The number of classes or labels for which colors are generated.
vmin: float
The minimum value in the HSV spectrum. Default is 0.8.
vmax: float
The maximum value in the HSV spectrum. Default is 1.0.
hsv: bool
Indicates whether to return colors in HSV format (True) or convert them to RGB format (False).
Default is False.
Returns
-------
colors : list
A list of random HSV colors or RGB colors, depending on the value of `hsv`.
"""
randRGBcolors = []
randHSVcolors = [
(
np.random.uniform(low=0.0, high=0.95),
np.random.uniform(low=0.4, high=0.95),
np.random.uniform(low=vmin, high=vmax),
)
for i in range(nlabels)
]
# Convert HSV list to RGB
if hsv:
randRGBcolors = []
for HSVcolor in randHSVcolors:
randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))
return randHSVcolors
# Generate random colormap
[docs]
def rand_cmap(nlabels, type_color="soft"):
"""
It generates a categorical random color map, given the number of classes
Parameters
----------
nlabels: int
The number of categories to be coloured.
type_color: str {"soft", "bright"}
It defines whether using bright or soft pastel colors, by limiting the RGB spectrum.
Returns
-------
cmap: matplotlib.colors.LinearSegmentedColormap
The color map.
"""
if type_color not in ("bright", "soft"):
type_color = "bright"
# Generate color map for bright colors, based on hsv
if type_color == "bright":
randHSVcolors = [
(
np.random.uniform(low=0.0, high=0.8),
np.random.uniform(low=0.2, high=0.8),
np.random.uniform(low=0.9, high=1.0),
)
for i in range(nlabels)
]
# Convert HSV list to RGB
randRGBcolors = []
for HSVcolor in randHSVcolors:
randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))
random_colormap = LinearSegmentedColormap.from_list("new_map", randRGBcolors, N=nlabels)
# Generate soft pastel colors, by limiting the RGB spectrum
if type_color == "soft":
low = 0.6
high = 0.95
randRGBcolors = [
(
np.random.uniform(low=low, high=high),
np.random.uniform(low=low, high=high),
np.random.uniform(low=low, high=high),
)
for i in range(nlabels)
]
random_colormap = LinearSegmentedColormap.from_list("new_map", randRGBcolors, N=nlabels)
return random_colormap
[docs]
def kindlmann():
"""
It returns a Kindlmann color map. See https://ieeexplore.ieee.org/document/1183788
Returns
-------
cmap: matplotlib.colors.LinearSegmentedColormap
The color map.
"""
kindlmann_list = [
(0.00, 0.00, 0.00, 1),
(0.248, 0.0271, 0.569, 1),
(0.0311, 0.258, 0.646, 1),
(0.019, 0.415, 0.415, 1),
(0.025, 0.538, 0.269, 1),
(0.0315, 0.658, 0.103, 1),
(0.331, 0.761, 0.036, 1),
(0.768, 0.809, 0.039, 1),
(0.989, 0.862, 0.772, 1),
(1.0, 1.0, 1.0),
]
cmap = LinearSegmentedColormap.from_list("kindlmann", kindlmann_list)
return cmap
[docs]
def normalize(n, range1, range2):
"""
Normalizes a value `n` from one range to another range.
Parameters
----------
n: float or int
The value to be normalized.
range1: tuple or list
The original range of values [min, max] from which `n` is taken.
range2: tuple or list
The target range of values [min, max] to which `n` will be normalized.
Returns
-------
float or int
The normalized value of `n` in the target range.
"""
delta1 = range1[1] - range1[0]
delta2 = range2[1] - range2[0]
return (delta2 * (n - range1[0]) / delta1) + range2[0]
[docs]
def lighten_color(color, amount=0.5):
"""
Lightens the given color by multiplying (1-luminosity) by the given amount.
Parameters
----------
color: str or tuple
The color to be lightened. It can be a matplotlib color string, hex string, or RGB tuple.
amount: float, optional
The amount by which to lighten the color. Default value is 0.5.
Returns
-------
tuple
The lightened color in RGB format.
"""
try:
c = mc.cnames[color]
except KeyError:
c = color
c = colorsys.rgb_to_hls(*mc.to_rgb(c))
return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
