def get_default_cdict():
colors = get_default_colors(norm=True)
outp = {'red': [], 'green': [], 'blue': []}
for k, (h, r, g, b) in colors.items():
outp['red'].append((h, r, r))
outp['green'].append((h, g, g))
outp['blue'].append((h, b, b))
return outp
def get_default_colors(norm=False):
scale = 1.0 / 255.0 if norm else 1.0
colors = {
# Labeled tuples of (height, r, g, b)
'min': (0, 45, 15, 128),
'ocean_depth_boundary': (0, 45, 15, 128),
'ocean_shallow': (70, 87, 63, 153),
'ocean_coastal': (105, 175, 238, 238),
'coastline': (110, 194, 178, 128),
'grass': (120, 65, 152, 10),
'plains': (160, 130, 123, 69),
'stone': (200, 136, 140, 141),
'snow': (230, 240, 240, 240),
'max': (255, 255, 255, 255)
}
return {k: tuple(x * scale for x in y) for k, y in colors.items()}
def change_catalogue(self, catalogue):
self.params.blockSignals(True)
with self.mutex:
self.catalogue = catalogue
colors = make_color_dict(self.catalogue['clusters'])
self.qcolors = {}
for k, color in colors.items():
r, g, b = color
self.qcolors[k] = QT.QColor(r * 255, g * 255, b * 255)
self.all_plotted_labels = self.catalogue['cluster_labels'].tolist(
) + [LABEL_UNCLASSIFIED]
centers0 = self.catalogue['centers0']
if centers0.shape[0] > 0:
self.params['bin_min'] = np.min(centers0) * 1.5
self.params['bin_max'] = np.max(centers0) * 1.5
bin_min, bin_max = self.params['bin_min'], self.params['bin_max']
bin_size = self.params['bin_size']
self.bins = np.arange(bin_min, bin_max, self.params['bin_size'])
self.combobox.clear()
self.combobox.addItems([str(k) for k in self.all_plotted_labels])
self.on_clear()
self._max = 10
_, peak_width, nb_chan = self.catalogue['centers0'].shape
x, y = [], []
for c in range(1, nb_chan):
x.extend([c * peak_width, c * peak_width, np.nan])
y.extend([-1000, 1000, np.nan])
self.curve_limit.setData(x=x, y=y, connect='finite')
self.params.blockSignals(False)
['pellet'], ['insured pellet']]
colors = {
'r1_in': 'red',
'r1_out': 'lightsalmon',
'r2_in': 'orange',
'r2_out': 'bisque',
'r3_in': 'green',
'r3_out': 'lightgreen',
'r4_in': 'blue',
'r4_out': 'lightblue',
'pellet': 'aqua',
'insured pellet': 'brown'
}
for k, v in colors.items():
col = matplotlib.colors.to_rgba_array(v)[0] * 255
colors[k] = np.array([int(c) for c in col])
sizes = {
'r1_in': 1,
'r1_out': 1,
'r2_in': 1,
'r2_out': 1,
'r3_in': 1,
'r3_out': 1,
'r4_in': 1,
'r4_out': 1,
'pellet': 3,
'insured pellet': 3
}
def colors_overview(colors: Dict, ncols: int = 2, figsize: Tuple[int, int] = (8, 5), save: str = None):
"""
Draw an overview plot of all used colors
Args:
colors: Dictionary of color name and color
ncols: How many columns for the plot
figsize: Size of the figure as specified in matplotlib
save: Path to save the plot to
Example:
.. image:: /_images/colors.png
"""
from matplotlib import colors as mcolors
# Sort colors by hue, saturation, value and name.
by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name) for name, color in colors.items())
sorted_names = [name for hsv, name in by_hsv]
n = len(sorted_names)
nrows = n // ncols + 1
fig, ax = plt.subplots(figsize=figsize)
# Get height and width
x, y = fig.get_dpi() * fig.get_size_inches()
h = y / (nrows + 1)
w = x / ncols
for i, name in enumerate(sorted_names):
col = i % ncols
row = i // ncols
y = y - (row * h) - h
xi_line = w * (col + 0.05)
xf_line = w * (col + 0.25)
xi_text = w * (col + 0.3)
ax.text(
xi_text,
y,
"%s %s" % (name, colors[name]),
fontsize=(h * 0.4),
horizontalalignment="left",
verticalalignment="center",
)
ax.hlines(y + h * 0.1, xi_line, xf_line, color=colors[name], linewidth=(h * 0.6))
ax.set_xlim(0, x)
ax.set_ylim(0, y)
ax.set_axis_off()
fig.subplots_adjust(left=0, right=1, top=1, bottom=0, hspace=0, wspace=0)
if save:
print(f"Saving to {save}")
plt.savefig(save, bbox_to_anchor="tight")
plt.show()