"""Towerpy: an open-source toolbox for processing polarimetric radar data."""
import warnings
import numpy as np
import matplotlib as mpl
import matplotlib.colors as mcolors
from .tpy_colors import towerpy_colours
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore", category=UserWarning)
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def plot_color_gradients(category, cmap_list):
"""
Plot data with the associated colormaps.
Parameters
----------
category : TYPE
DESCRIPTION.
cmap_list : TYPE
DESCRIPTION.
Returns
-------
None.
"""
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
cmaps = {}
# Create figure and adjust figure height to number of colormaps
nrows = len(cmap_list)
figh = 0.35 + 0.15 + (nrows + (nrows - 1) * 0.1) * 0.22
fig, axs = plt.subplots(nrows=nrows + 1, figsize=(9.4, figh))
fig.subplots_adjust(top=1 - 0.35 / figh, bottom=0.15 / figh,
left=0.2, right=0.99)
axs[0].set_title(f'{category} colormaps', fontsize=14)
for ax, name in zip(axs, cmap_list):
ax.imshow(gradient, aspect='auto', cmap=mpl.colormaps[name])
ax.text(-0.01, 0.5, name, va='center', ha='right', fontsize=10,
transform=ax.transAxes)
# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axs:
ax.set_axis_off()
# Save colormap list for later.
cmaps[category] = cmap_list
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def plot_examples(colormaps, vmin=-5, vmax=5):
"""
Plot random data to depict the colormaps.
Parameters
----------
colormaps : TYPE
DESCRIPTION.
vmin : TYPE, optional
DESCRIPTION. The default is -5.
vmax : TYPE, optional
DESCRIPTION. The default is 5.
Returns
-------
None.
"""
np.random.seed(19680801)
data = np.random.randn(50, 50)
# n = len(colormaps)
nitems = len(colormaps)
if nitems > 5:
ncols = int(nitems**0.5)
nrows = nitems // ncols
# EDIT for correct number of rows:
# If one additional row is necessary -> add one:
if nitems % ncols != 0:
nrows += 1
fsize = (9, 9)
else:
ncols = nitems
nrows = 1
fsize = (nitems * 2 + 2, 3)
fig, axs = plt.subplots(nrows, ncols, figsize=fsize,
constrained_layout=True, squeeze=False)
for [ax, cmap] in zip(axs.flat, colormaps):
psm = ax.pcolormesh(data, cmap=cmap, rasterized=True,
vmin=vmin, vmax=vmax)
fig.colorbar(psm, ax=ax)
plt.show()
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def plot_color_gradients_grayscale(cmap_list):
"""
Plot random data to depict the colormaps in grayscale.
Parameters
----------
cmap_list : TYPE
DESCRIPTION.
Returns
-------
None.
"""
from colorspacious import cspace_converter
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
x = np.linspace(0.0, 1.0, 100)
fig, axs = plt.subplots(nrows=len(cmap_list), ncols=2)
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99,
wspace=0.05)
fig.suptitle(' colormaps', fontsize=14, y=1.0, x=0.6)
for ax, name in zip(axs, cmap_list):
# Get RGB values for colormap.
rgb = mpl.colormaps[name](x)[np.newaxis, :, :3]
# Get colormap in CAM02-UCS colorspace. We want the lightness.
lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
L = lab[0, :, 0]
L = np.float32(np.vstack((L, L, L)))
ax[0].imshow(gradient, aspect='auto', cmap=mpl.colormaps[name])
ax[1].imshow(L, aspect='auto', cmap='binary_r', vmin=0., vmax=100.)
pos = list(ax[0].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 axs.flat:
ax.set_axis_off()
plt.show()
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def plot_color_gradients_cvd(cmap_list, cvd_type='protanomaly',
severity=100):
"""
Plot random data to depict the colormaps for CVD.
Parameters
----------
cmap_list : list
DESCRIPTION.
cvd_type : str, optional
DESCRIPTION. The default is 'protanomaly'.
severity : float or int, optional
DESCRIPTION. The default is 100.
Returns
-------
None.
"""
from colorspacious import cspace_convert
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
cvd_space = {'name': 'sRGB1+CVD', 'cvd_type': cvd_type,
'severity': severity}
x = np.linspace(0.0, 1.0, 100)
fig, axs = plt.subplots(nrows=len(cmap_list), ncols=2, figsize=(10, 6))
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.11, right=0.99,
wspace=0.05)
fig.suptitle(f'Color vision deficiency simulation [{cvd_type}'
+ f' - severity:{severity}]',
fontsize=14, y=1.0, x=0.6)
for ax, name in zip(axs, cmap_list):
# Get RGB values for colormap.
rgb = mpl.colormaps[name](x)[np.newaxis, :, :3]
# Get colormap in CAM02-UCS colorspace. We want the lightness.
# lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
# hopper_deuteranomaly_sRGB
lab = cspace_convert(rgb, cvd_space, "sRGB1")
L = np.clip(lab, 0, 1)
# L = lab[0, :, 0]
# L = np.float32(np.vstack((L, L, L)))
ax[0].imshow(gradient, aspect='auto', cmap=mpl.colormaps[name])
ax[1].imshow(L, aspect='auto', cmap='binary_r', vmin=0., vmax=100.)
pos = list(ax[0].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 axs.flat:
ax.set_axis_off()
plt.show()
# This part creates colour maps from a dictionary listing colour gradients.
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tpy_LSC = {cname: mcolors.LinearSegmentedColormap.from_list(cname, colrs)
for cname, colrs in towerpy_colours.items()}
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tpy_LC = {cname: mcolors.ListedColormap(colrs)
for cname, colrs in towerpy_colours.items()}
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tpy_LSC_r = {cname+'_r': clmap.reversed()
for cname, clmap in tpy_LSC.items()}
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tpy_LC_r = {cname+'_r': clmap.reversed()
for cname, clmap in tpy_LC.items()}
locals().update(tpy_LSC)
locals().update(tpy_LC)
locals().update(tpy_LSC_r)
locals().update(tpy_LC_r)
for name, cmap in tpy_LSC.items():
fname = 'tpylsc_' + name
mpl.colormaps.register(name=fname, cmap=cmap, force=True)
for name, cmap in tpy_LC.items():
fname = 'tpylc_' + name
mpl.colormaps.register(name=fname, cmap=cmap, force=True)
for name, cmap in tpy_LSC_r.items():
fname = 'tpylsc_' + name
mpl.colormaps.register(name=fname, cmap=cmap, force=True)
for name, cmap in tpy_LC_r.items():
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fname = 'tpylc_' + name
mpl.colormaps.register(name=fname, cmap=cmap, force=True)
