import os
from colorsys import hsv_to_rgb
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
cmap_path = os.path.join(os.path.dirname(__file__), "cmap_data")
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class Bunch(dict):
def __init__(self, **kw):
dict.__init__(self, kw)
self.__dict__ = self
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def get_color(color):
"""
https://stackoverflow.com/questions/10254207/color-and-line-writing-using-matplotlib
"""
for hue in range(color):
hue = 1.0 * hue / color
col = [int(x) for x in hsv_to_rgb(hue, 1.0, 230)]
yield "#{:02x}{:02x}{:02x}".format(*col)
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def cmat2cmpl(rgb, reverse=False):
"""
Convert RGB matplotlib colormap.
"""
rgb = np.asanyarray(rgb)
if reverse:
rgb = np.flipud(rgb)
return colors.ListedColormap(rgb)
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def phasemap_cm(m=256):
"""
Colormap periodic/circular data (phase).
"""
theta = 2 * np.pi * np.arange(0, m) / m
circ = np.exp(1j * theta)
# Vertices of colour triangle.
vred, vgreen, vblue = -2, 1 - np.sqrt(3) * 1j, 1 + np.sqrt(3) * 1j
vredc = vred - circ
vgreenc = vgreen - circ
vbluec = vblue - circ
red = np.abs(np.imag(vgreenc * np.conj(vbluec)))
green = np.abs(np.imag(vbluec * np.conj(vredc)))
blue = np.abs(np.imag(vredc * np.conj(vgreenc)))
return (
1.5
* np.c_[red, green, blue]
/ np.abs(np.imag((vred - vgreen) * np.conj(vred - vblue)))
)
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def zebra_cm(a=4, m=0.5, n=256):
"""
Zebra palette colormap with NBANDS broad bands and NENTRIES rows in
the color map.
The default is 4 broad bands
cmap = zebra(nbands, nentries)
References
----------
Hooker, S. B. et al, Detecting Dipole Ring Separatrices with Zebra
Palettes, IEEE Transactions on Geosciences and Remote Sensing, vol. 33,
1306-1312, 1995
Notes
-----
Saturation and value go from m to 1 don't use m = 0.
a = 4 -> there are this many large bands in the palette.
"""
from scipy.signal import sawtooth
x = np.arange(0, n)
hue = np.exp(-3.0 * x / n)
sat = m + (1.0 - m) * (0.5 * (1.0 + sawtooth(2.0 * np.pi * x / (n / a))))
val = m + (1.0 - m) * 0.5 * (1.0 + np.cos(2.0 * np.pi * x / (n / a / 2.0)))
return np.array([hsv_to_rgb(h, s, v) for h, s, v in zip(hue, sat, val)])
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def ctopo_pos_neg_cm(m=256):
"""
Colormap for positive/negative data with gray scale only
original from cushman-roisin book cd-rom.
"""
dx = 1.0 / (m - 1)
values = np.arange(0.0, 1.0, dx)
return np.c_[values, values, values]
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def avhrr_cm(m=256):
"""
AHVRR colormap used by NOAA Coastwatch.
"""
x = np.arange(0.0, m) / (m - 1)
xr = [0.0, 0.2, 0.4, 0.5, 0.6, 0.8, 1.0]
rr = [0.5, 1.0, 1.0, 0.5, 0.5, 0.0, 0.5]
xg = [0.0, 0.4, 0.6, 1.0]
gg = [0.0, 1.0, 1.0, 0.0]
xb = [0.0, 0.2, 0.4, 0.5, 0.6, 0.8, 1.0]
bb = [0.0, 0.0, 0.5, 0.5, 1.0, 1.0, 0.5]
r = np.interp(x, xr, rr)
g = np.interp(x, xg, gg)
b = np.interp(x, xb, bb)
return np.flipud(np.c_[r, g, b])
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def load_cmap(fname):
return np.loadtxt(fname, delimiter=",") / 255
# Functions colormaps.
arrays = {
"zebra": zebra_cm(),
"avhrr": avhrr_cm(),
"phasemap": phasemap_cm(),
"ctopo_pos_neg": ctopo_pos_neg_cm(),
}
# Data colormaps.
for fname in glob(f"{cmap_path}/*.dat"):
cmap = os.path.basename(fname).split(".")[0]
data = load_cmap(fname)
arrays.update({cmap: data})
cm = Bunch()
for key, value in arrays.items():
cm.update({key: cmat2cmpl(value)})
cm.update({f"{key}_r": cmat2cmpl(value, reverse=True)})
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def demo():
data = np.outer(np.arange(0, 1, 0.01), np.ones(10))
fig = plt.figure(figsize=(10, 5))
fig.subplots_adjust(top=0.8, bottom=0.05, left=0.01, right=0.99)
cmaps = sorted(m for m in cm.keys() if not m.endswith("_r"))
length = len(cmaps)
for k, cmap in enumerate(cmaps):
plt.subplot(1, length + 1, k + 1)
plt.axis("off")
plt.imshow(data, aspect="auto", cmap=cm.get(cmap), origin="lower")
plt.title(cmap, rotation=90, fontsize=10)
