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# Copyright (c) 2012 - 2018, Anaconda, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with this software.
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''' Helper functions for applying client-side computations such as
transformations to data fields or ``ColumnDataSource`` expressions.
'''
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# Boilerplate
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from __future__ import absolute_import, division, print_function, unicode_literals
import logging
log = logging.getLogger(__name__)
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# Imports
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# Standard library imports
# External imports
# Bokeh imports
from .core.properties import expr, field
from .models.expressions import CumSum, Stack
from .models.mappers import CategoricalColorMapper, CategoricalMarkerMapper, LinearColorMapper, LogColorMapper
from .models.transforms import Dodge, Jitter
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# Globals and constants
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__all__ = (
'cumsum',
'dodge',
'factor_cmap',
'factor_mark',
'jitter',
'linear_cmap',
'log_cmap',
'stack',
'transform',
)
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# General API
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[docs]def cumsum(field, include_zero=False):
''' Create a 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 to apply a client-side ``Jitter``
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 to apply 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_mark(field_name, markers, factors, start=0, end=None):
''' Create a ``DataSpec`` dict to apply 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 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 to apply 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 to apply 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 to apply 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))
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# Dev API
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# Private API
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#-----------------------------------------------------------------------------
# Code
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