#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2021, 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
#-----------------------------------------------------------------------------
from __future__ import annotations
import logging # isort:skip
log = logging.getLogger(__name__)
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Bokeh imports
from .. import palettes
from ..core.enums import Palette
from ..core.has_props import abstract
from ..core.properties import (
Color,
Either,
Enum,
FactorSeq,
Float,
HatchPatternType,
Instance,
Int,
List,
MarkerType,
Nullable,
Seq,
String,
Tuple,
)
from ..core.validation import warning
from ..core.validation.warnings import PALETTE_LENGTH_FACTORS_MISMATCH
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.
""")
def __init__(self, palette=None, **kwargs) -> None:
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 = FactorSeq(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 = Nullable(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``.
'''
@warning(PALETTE_LENGTH_FACTORS_MISMATCH)
def _check_palette_length(self):
palette = self.palette
factors = self.factors
if len(palette) < len(factors):
extra_factors = factors[len(palette):]
return f"{extra_factors} will be assigned to `nan_color` {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 = Nullable(Float, 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 = Nullable(Float, 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 = Nullable(Color, 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 = Nullable(Color, 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.
'''
@abstract
class ScanningColorMapper(ContinuousColorMapper):
pass
[docs]class EqHistColorMapper(ScanningColorMapper):
bins = Int(default=256*256, help="Number of histogram bins")
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------