#----------------------------------------------------------------------------- # 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. #----------------------------------------------------------------------------- ''' Helper functions for applying client-side computations such as transformations to data fields or ``ColumnDataSource`` expressions. ''' #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- import logging # isort:skip log = logging.getLogger(__name__) #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Bokeh imports from .core.properties import expr, field from .models.expressions import CumSum, Stack from .models.mappers import ( CategoricalColorMapper, CategoricalMarkerMapper, CategoricalPatternMapper, LinearColorMapper, LogColorMapper, ) from .models.transforms import Dodge, Jitter #----------------------------------------------------------------------------- # Globals and constants #----------------------------------------------------------------------------- __all__ = ( 'cumsum', 'dodge', 'factor_cmap', 'factor_hatch', 'factor_mark', 'jitter', 'linear_cmap', 'log_cmap', 'stack', 'transform', ) #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- [docs]def cumsum(field, include_zero=False): ''' Create a ``DataSpec`` dict to generate a ``CumSum`` expression for a ``ColumnDataSource``. Examples: .. code-block:: python p.wedge(start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'), ...) will generate a ``CumSum`` expressions that sum the ``"angle"`` column of a data source. For the ``start_angle`` value, the cumulative sums will start with a zero value. For ``start_angle``, no initial zero will be added (i.e. the sums will start with the first angle value, and include the last). ''' return expr(CumSum(field=field, include_zero=include_zero)) [docs]def dodge(field_name, value, range=None): ''' Create a ``DataSpec`` dict that applies a client-side ``Dodge`` transformation to a ``ColumnDataSource`` column. Args: field_name (str) : a field name to configure ``DataSpec`` with value (float) : the fixed offset to add to column data range (Range, optional) : a range to use for computing synthetic coordinates when necessary, e.g. a ``FactorRange`` when the column data is categorical (default: None) Returns: dict ''' return field(field_name, Dodge(value=value, range=range)) [docs]def factor_cmap(field_name, palette, factors, start=0, end=None, nan_color="gray"): ''' Create a ``DataSpec`` dict that applies a client-side ``CategoricalColorMapper`` transformation to a ``ColumnDataSource`` column. Args: field_name (str) : a field name to configure ``DataSpec`` with palette (seq[color]) : a list of colors to use for colormapping factors (seq) : a sequences of categorical factors corresponding to the palette start (int, optional) : a start slice index to apply when the column data has factors with multiple levels. (default: 0) end (int, optional) : an end slice index to apply when the column data has factors with multiple levels. (default: None) nan_color (color, optional) : a default color to use when mapping data from a column does not succeed (default: "gray") Returns: dict ''' return field(field_name, CategoricalColorMapper(palette=palette, factors=factors, start=start, end=end, nan_color=nan_color)) [docs]def factor_hatch(field_name, patterns, factors, start=0, end=None): ''' Create a ``DataSpec`` dict that applies a client-side ``CategoricalPatternMapper`` transformation to a ``ColumnDataSource`` column. Args: field_name (str) : a field name to configure ``DataSpec`` with patterns (seq[string]) : a list of hatch patterns to use to map to factors (seq) : a sequences of categorical factors corresponding to the palette start (int, optional) : a start slice index to apply when the column data has factors with multiple levels. (default: 0) end (int, optional) : an end slice index to apply when the column data has factors with multiple levels. (default: None) Returns: dict Added in version 1.1.1 ''' return field(field_name, CategoricalPatternMapper(patterns=patterns, factors=factors, start=start, end=end)) [docs]def factor_mark(field_name, markers, factors, start=0, end=None): ''' Create a ``DataSpec`` dict that applies a client-side ``CategoricalMarkerMapper`` transformation to a ``ColumnDataSource`` column. .. note:: This transform is primarily only useful with ``scatter``, which can be parameterized by glyph type. Args: field_name (str) : a field name to configure ``DataSpec`` with markers (seq[string]) : a list of markers to use to map to factors (seq) : a sequences of categorical factors corresponding to the palette start (int, optional) : a start slice index to apply when the column data has factors with multiple levels. (default: 0) end (int, optional) : an end slice index to apply when the column data has factors with multiple levels. (default: None) Returns: dict ''' return field(field_name, CategoricalMarkerMapper(markers=markers, factors=factors, start=start, end=end)) [docs]def jitter(field_name, width, mean=0, distribution="uniform", range=None): ''' Create a ``DataSpec`` dict that applies a client-side ``Jitter`` transformation to a ``ColumnDataSource`` column. Args: field_name (str) : a field name to configure ``DataSpec`` with width (float) : the width of the random distribution to apply mean (float, optional) : an offset to apply (default: 0) distribution (str, optional) : ``"uniform"`` or ``"normal"`` (default: ``"uniform"``) range (Range, optional) : a range to use for computing synthetic coordinates when necessary, e.g. a ``FactorRange`` when the column data is categorical (default: None) Returns: dict ''' return field(field_name, Jitter(mean=mean, width=width, distribution=distribution, range=range)) [docs]def linear_cmap(field_name, palette, low, high, low_color=None, high_color=None, nan_color="gray"): ''' Create a ``DataSpec`` dict that applyies a client-side ``LinearColorMapper`` transformation to a ``ColumnDataSource`` column. Args: field_name (str) : a field name to configure ``DataSpec`` with palette (seq[color]) : a list of colors to use for colormapping low (float) : a minimum value of the range to map into the palette. Values below this are clamped to ``low``. high (float) : a maximum value of the range to map into the palette. Values above this are clamped to ``high``. low_color (color, optional) : 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. (default: None) high_color (color, optional) : 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. (default: None) nan_color (color, optional) : a default color to use when mapping data from a column does not succeed (default: "gray") ''' return field(field_name, LinearColorMapper(palette=palette, low=low, high=high, nan_color=nan_color, low_color=low_color, high_color=high_color)) [docs]def log_cmap(field_name, palette, low, high, low_color=None, high_color=None, nan_color="gray"): ''' Create a ``DataSpec`` dict that applies a client-side ``LogColorMapper`` transformation to a ``ColumnDataSource`` column. Args: field_name (str) : a field name to configure ``DataSpec`` with palette (seq[color]) : a list of colors to use for colormapping low (float) : a minimum value of the range to map into the palette. Values below this are clamped to ``low``. high (float) : a maximum value of the range to map into the palette. Values above this are clamped to ``high``. low_color (color, optional) : 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. (default: None) high_color (color, optional) : 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. (default: None) nan_color (color, optional) : a default color to use when mapping data from a column does not succeed (default: "gray") ''' return field(field_name, LogColorMapper(palette=palette, low=low, high=high, nan_color=nan_color, low_color=low_color, high_color=high_color)) [docs]def stack(*fields): ''' Create a Create a ``DataSpec`` dict to generate a ``Stack`` expression for a ``ColumnDataSource``. Examples: .. code-block:: python p.vbar(bottom=stack("sales", "marketing"), ... will generate a ``Stack`` that sums the ``"sales"`` and ``"marketing"`` columns of a data source, and use those values as the ``top`` coordinate for a ``VBar``. ''' return expr(Stack(fields=fields)) [docs]def transform(field_name, transform): ''' Create a ``DataSpec`` dict that applies an arbitrary client-side ``Transform`` to a ``ColumnDataSource`` column. Args: field_name (str) : A field name to configure ``DataSpec`` with transform (Transform) : A transforms to apply to that field Returns: dict ''' return field(field_name, transform) #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------