#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2020, Anaconda, Inc., and Bokeh Contributors. # All rights reserved. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- ''' Models for mapping values from one range or space to another in the client. Mappers (as opposed to scales) are not presumed to be invertible. ''' #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- import logging # isort:skip log = logging.getLogger(__name__) #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Standard library imports import warnings # Bokeh imports from .. import palettes from ..core.enums import Palette from ..core.has_props import abstract from ..core.properties import ( Color, Either, Enum, Float, HatchPatternType, Instance, Int, List, MarkerType, Seq, String, Tuple, ) from .transforms import Transform #----------------------------------------------------------------------------- # Globals and constants #----------------------------------------------------------------------------- __all__ = ( 'Mapper', 'ColorMapper', 'CategoricalMapper', 'CategoricalColorMapper', 'CategoricalMarkerMapper', 'CategoricalPatternMapper', 'ContinuousColorMapper', 'LinearColorMapper', 'LogColorMapper', 'EqHistColorMapper' ) #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- [docs]@abstract class Mapper(Transform): ''' Base class for mappers. ''' pass [docs]@abstract class ColorMapper(Mapper): ''' Base class for color mapper types. ''' palette = Seq(Color, help=""" A sequence of colors to use as the target palette for mapping. This property can also be set as a ``String``, to the name of any of the palettes shown in :ref:`bokeh.palettes`. """).accepts(Enum(Palette), lambda pal: getattr(palettes, pal)) nan_color = Color(default="gray", help=""" Color to be used if data is NaN or otherwise not mappable. (Default: 'gray') """) def __init__(self, palette=None, **kwargs): if palette is not None: kwargs['palette'] = palette super().__init__(**kwargs) [docs]@abstract class CategoricalMapper(Mapper): ''' Base class for mappers that map categorical factors to other values. ''' factors = Either(Seq(String), Seq(Tuple(String, String)), Seq(Tuple(String, String, String)), default=None, help=""" A sequence of factors / categories that map to the some target range. For example the following color mapper: .. code-block:: python mapper = CategoricalColorMapper(palette=["red", "blue"], factors=["foo", "bar"]) will map the factor ``"foo"`` to red and the factor ``"bar"`` to blue. """) start = Int(default=0, help=""" A start index to "slice" data factors with before mapping. For example, if the data to color map consists of 2-level factors such as ``["2016", "sales"]`` and ``["2016", "marketing"]``, then setting ``start=1`` will perform color mapping only based on the second sub-factor (i.e. in this case based on the department ``"sales"`` or ``"marketing"``) """) end = Int(help=""" A start index to "slice" data factors with before mapping. For example, if the data to color map consists of 2-level factors such as ``["2016", "sales"]`` and ``["2017", "marketing"]``, then setting ``end=1`` will perform color mapping only based on the first sub-factor (i.e. in this case based on the year ``"2016"`` or ``"2017"``) If ``None`` then all sub-factors from ``start`` to the end of the factor will be used for color mapping. """) [docs]class CategoricalColorMapper(CategoricalMapper, ColorMapper): ''' Map categorical factors to colors. Values that are passed to this mapper that are not in the factors list will be mapped to ``nan_color``. ''' def __init__(self, **kwargs): super().__init__(**kwargs) palette = self.palette factors = self.factors if palette is not None and factors is not None: if len(palette) < len(factors): extra_factors = factors[len(palette):] warnings.warn("Palette length does not match number of factors. %s will be assigned to `nan_color` %s" % (extra_factors, self.nan_color)) [docs]class CategoricalMarkerMapper(CategoricalMapper): ''' Map categorical factors to marker types. Values that are passed to this mapper that are not in the factors list will be mapped to ``default_value``. .. note:: This mappers is primarily only useful with the ``Scatter`` marker glyph that be parameterized by marker type. ''' markers = Seq(MarkerType, help=""" A sequence of marker types to use as the target for mapping. """) default_value = MarkerType(default="circle", help=""" A marker type to use in case an unrecognized factor is passed in to be mapped. """) [docs]class CategoricalPatternMapper(CategoricalMapper): ''' Map categorical factors to hatch fill patterns. Values that are passed to this mapper that are not in the factors list will be mapped to ``default_value``. Added in version 1.1.1 ''' patterns = Seq(HatchPatternType, help=""" A sequence of marker types to use as the target for mapping. """) default_value = HatchPatternType(default=" ", help=""" A hatch pattern to use in case an unrecognized factor is passed in to be mapped. """) [docs]@abstract class ContinuousColorMapper(ColorMapper): ''' Base class for continuous color mapper types. ''' domain = List(Tuple(Instance("bokeh.models.renderers.GlyphRenderer"), Either(String, List(String))), default=[], help=""" A collection of glyph renderers to pool data from for establishing data metrics. If empty, mapped data will be used instead. """) low = Float(default=None, help=""" The minimum value of the range to map into the palette. Values below this are clamped to ``low``. If ``None``, the value is inferred from data. """) high = Float(default=None, help=""" The maximum value of the range to map into the palette. Values above this are clamped to ``high``. If ``None``, the value is inferred from data. """) low_color = Color(default=None, help=""" Color to be used if data is lower than ``low`` value. If None, values lower than ``low`` are mapped to the first color in the palette. """) high_color = Color(default=None, help=""" Color to be used if data is higher than ``high`` value. If None, values higher than ``high`` are mapped to the last color in the palette. """) [docs]class LinearColorMapper(ContinuousColorMapper): ''' Map numbers in a range [*low*, *high*] linearly into a sequence of colors (a palette). For example, if the range is [0, 99] and the palette is ``['red', 'green', 'blue']``, the values would be mapped as follows:: x < 0 : 'red' # values < low are clamped 0 >= x < 33 : 'red' 33 >= x < 66 : 'green' 66 >= x < 99 : 'blue' 99 >= x : 'blue' # values > high are clamped ''' [docs]class LogColorMapper(ContinuousColorMapper): ''' Map numbers in a range [*low*, *high*] into a sequence of colors (a palette) on a natural logarithm scale. For example, if the range is [0, 25] and the palette is ``['red', 'green', 'blue']``, the values would be mapped as follows:: x < 0 : 'red' # values < low are clamped 0 >= x < 2.72 : 'red' # math.e ** 1 2.72 >= x < 7.39 : 'green' # math.e ** 2 7.39 >= x < 20.09 : 'blue' # math.e ** 3 20.09 >= x : 'blue' # values > high are clamped .. warning:: The ``LogColorMapper`` only works for images with scalar values that are non-negative. ''' [docs]class EqHistColorMapper(ContinuousColorMapper): bins = Int(default=256*256, help="Number of histogram bins") #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------