def bokeh_palette_to_palette(cmap, ncolors=None, categorical=False):
from bokeh import palettes
# Handle categorical colormaps to avoid interpolation
categories = [
'accent', 'category', 'dark', 'colorblind', 'pastel', 'set1', 'set2',
'set3', 'paired'
]
cmap_categorical = any(cat in cmap.lower() for cat in categories)
reverse = False
if cmap.endswith('_r'):
cmap = cmap[:-2]
reverse = True
# Some colormaps are inverted compared to matplotlib
inverted = (not cmap_categorical and not cmap.capitalize() in palettes.mpl)
if inverted:
reverse = not reverse
ncolors = ncolors or 256
# Alias mpl tab cmaps with bokeh Category cmaps
if cmap.startswith('tab'):
cmap = cmap.replace('tab', 'Category')
# Process as bokeh palette
palette = getattr(palettes, cmap, getattr(palettes, cmap.capitalize(),
None))
if palette is None:
raise ValueError("Supplied palette %s not found among bokeh palettes" %
cmap)
elif isinstance(palette, dict) and (cmap in palette
or cmap.capitalize() in palette):
# Some bokeh palettes are doubly nested
palette = palette.get(cmap, palette.get(cmap.capitalize()))
if isinstance(palette, dict):
palette = palette[max(palette)]
if not cmap_categorical:
if len(palette) < ncolors:
palette = polylinear_gradient(palette, ncolors)
elif callable(palette):
palette = palette(ncolors)
if reverse: palette = palette[::-1]
if len(palette) != ncolors:
if categorical and cmap_categorical:
palette = [palette[i % len(palette)] for i in range(ncolors)]
else:
lpad, rpad = -0.5, 0.49999999999
indexes = np.linspace(lpad, (len(palette) - 1) + rpad, ncolors)
palette = [palette[int(np.round(v))] for v in indexes]
return palette
def bokeh_palette_to_palette(cmap, ncolors=None, categorical=False):
from bokeh import palettes
# Handle categorical colormaps to avoid interpolation
categories = ['accent', 'category', 'dark', 'colorblind', 'pastel',
'set1', 'set2', 'set3', 'paired']
cmap_categorical = any(cat in cmap.lower() for cat in categories)
reverse = False
if cmap.endswith('_r'):
cmap = cmap[:-2]
reverse = True
# Some colormaps are inverted compared to matplotlib
inverted = (not cmap_categorical and not cmap.capitalize() in palettes.mpl)
if inverted:
reverse=not reverse
ncolors = ncolors or 256
# Alias mpl tab cmaps with bokeh Category cmaps
if cmap.startswith('tab'):
cmap = cmap.replace('tab', 'Category')
# Process as bokeh palette
palette = getattr(palettes, cmap, getattr(palettes, cmap.capitalize(), None))
if palette is None:
raise ValueError("Supplied palette %s not found among bokeh palettes" % cmap)
elif isinstance(palette, dict) and (cmap in palette or cmap.capitalize() in palette):
# Some bokeh palettes are doubly nested
palette = palette.get(cmap, palette.get(cmap.capitalize()))
if isinstance(palette, dict):
palette = palette[max(palette)]
if not cmap_categorical:
if len(palette) < ncolors:
palette = polylinear_gradient(palette, ncolors)
elif callable(palette):
palette = palette(ncolors)
if reverse: palette = palette[::-1]
if len(palette) != ncolors:
if categorical and cmap_categorical:
palette = [palette[i%len(palette)] for i in range(ncolors)]
else:
lpad, rpad = -0.5, 0.49999999999
indexes = np.linspace(lpad, (len(palette)-1)+rpad, ncolors)
palette = [palette[int(np.round(v))] for v in indexes]
return palette
