def colors(data, variable, palette=None):
if palette is None:
if is_discrete(variable):
palette = colorpalette.ColorPaletteGenerator(len(variable.values))
elif is_continuous(variable):
palette = colorpalette.ColorPaletteBW()
palette = colorpalette.ContinuousPaletteGenerator(
QtGui.QColor(220, 220, 220), QtGui.QColor(0, 0, 0), False)
else:
raise TypeError()
x = numpy.array(data[:, variable]).ravel()
if is_discrete(variable):
nvalues = len(variable.values)
x[numpy.isnan(x)] = nvalues
color_index = palette.getRGB(numpy.arange(nvalues + 1))
# Unknown values as gray
# TODO: This should already be a part of palette
color_index[nvalues] = (128, 128, 128)
colors = color_index[x.astype(int)]
else:
x, _ = scaled(x)
mask = numpy.isnan(x)
colors = numpy.empty((len(x), 3))
colors[mask] = (128, 128, 128)
colors[~mask] = [palette.getRGB(v) for v in x[~mask]]
# colors[~mask] = interpolate(palette, x[~mask], left=Qt.gray)
return colors
def create_image(contingencies, palette=None, scale=None):
# import scipy.signal
# import scipy.ndimage
if scale is None:
scale = lambda c: c / (contingencies.max() or 1)
P = scale(contingencies)
# if scale > 0:
# P = contingencies / scale
# else:
# P = contingencies
# nbins = node.xbins.shape[0] - 1
# smoothing = 32
# bandwidth = nbins / smoothing
if P.ndim == 3:
ncol = P.shape[-1]
if palette is None:
palette = colorpalette.ColorPaletteGenerator(ncol)
colors = [palette[i] for i in range(ncol)]
colors = np.array(
[[c.red(), c.green(), c.blue()] for c in colors]
)
# P = scipy.ndimage.filters.gaussian_filter(
# P, bandwidth, mode="constant")
# P /= P.max()
argmax = np.argmax(P, axis=2)
irow, icol = np.indices(argmax.shape)
P_max = P[irow, icol, argmax]
positive = P_max > 0
P_max = np.where(positive, P_max * 0.95 + 0.05, 0.0)
colors = 255 - colors[argmax.ravel()]
# XXX: Non linear intensity scaling
colors = colors * P_max.ravel().reshape(-1, 1)
colors = colors.reshape(P_max.shape + (3,))
colors = 255 - colors
elif P.ndim == 2:
palette = colorpalette.ColorPaletteBW()
mix = P
positive = mix > 0
mix = np.where(positive, mix * 0.99 + 0.01, 0.0)
# mix = scipy.ndimage.filters.gaussian_filter(
# mix, bandwidth, mode="constant")
# mix /= mix.max() if total else 1.0
colors = np.zeros((np.prod(mix.shape), 3)) + 255
colors = colors - mix.ravel().reshape(-1, 1) * 255
colors = colors.reshape(mix.shape + (3,))
return colors.astype(int)