def test_decimal_binnings(self):
values = np.array([
-0.2, -0.2, -0.6, 1.0, 0.2, -0.6, 0.6, 1.0, 0.4, -0.5, -0.4, -0.4,
-0.6, 0.6, 0.75, 0.4, -0.2, 0.2, 0.0, 0.0, -1.0, -0.6, -0.2, -0.6,
])
binning = decimal_binnings(values, factors=[0.2, 0.25, 0.5])
self.assertEqual(len(binning), 3)
np.testing.assert_array_equal(
binning[0].thresholds,
[-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1]
)
self.assertEqual(binning[0].width, 0.2)
np.testing.assert_array_equal(
binning[1].thresholds,
[-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1]
)
self.assertEqual(binning[1].width, 0.25)
np.testing.assert_array_equal(
binning[2].thresholds,
[-1, -0.5, 0, 0.5, 1]
)
self.assertEqual(binning[2].width, 0.5)
def set_color_bins(self):
if self.attr_color is None:
self.thresholds = self.colors = None
elif self.attr_color.is_discrete:
self.thresholds = None
self.colors = [QColor(*color) for color in self.attr_color.colors]
else:
col = self.data.get_column_view(self.attr_color)[0].astype(float)
self.thresholds = decimal_binnings(col, min_bins=4)[0][1:-1]
palette = ContinuousPaletteGenerator(*self.attr_color.colors)
nbins = len(self.thresholds) + 1
self.colors = [palette[i / (nbins - 1)] for i in range(nbins)]
def set_color_bins(self):
if self.attr_color is None:
self.thresholds = self.bin_labels = self.colors = None
elif self.attr_color.is_discrete:
self.thresholds = self.bin_labels = None
self.colors = self.attr_color.palette
else:
col = self.data.get_column_view(self.attr_color)[0].astype(float)
if self.attr_color.is_time:
binning = time_binnings(col, min_bins=4)[-1]
else:
binning = decimal_binnings(col, min_bins=4)[-1]
self.thresholds = binning.thresholds[1:-1]
self.bin_labels = (binning.labels[1:-1], binning.short_labels[1:-1])
palette = BinnedContinuousPalette.from_palette(
self.attr_color.palette, binning.thresholds)
self.colors = palette
def set_color_bins(self):
self.Warning.no_defined_colors.clear()
if self.attr_color is None:
self.thresholds = self.bin_labels = self.colors = None
return
if self.attr_color.is_discrete:
self.thresholds = self.bin_labels = None
self.colors = self.attr_color.palette
return
col = self.data.get_column_view(self.attr_color)[0].astype(float)
col = col[np.isfinite(col)]
if not col.size:
self.Warning.no_defined_colors(self.attr_color)
self.thresholds = self.bin_labels = self.colors = None
return
if self.attr_color.is_time:
binning = time_binnings(col, min_bins=4)[-1]
else:
binning = decimal_binnings(col, min_bins=4)[-1]
self.thresholds = binning.thresholds[1:-1]
self.bin_labels = (binning.labels[1:-1], binning.short_labels[1:-1])
if not self.bin_labels[0] and binning.labels:
# Nan's are already filtered out, but it doesn't hurt much
# to use nanmax/nanmin
if np.nanmin(col) == np.nanmax(col):
# Handle a degenerate case with a single value
# Use the second threshold (because value must be smaller),
# but the first threshold as label (because that's the
# actual value in the data.
self.thresholds = binning.thresholds[1:]
self.bin_labels = (binning.labels[:1],
binning.short_labels[:1])
palette = BinnedContinuousPalette.from_palette(self.attr_color.palette,
binning.thresholds)
self.colors = palette
