bokeh.transform¶
Helper functions for applying client-side computations such as
transformations to data fields or ColumnDataSource
expressions.
-
cumsum
(field, include_zero=False)[source]¶ Create a Create a
DataSpec
dict to generate aCumSum
expression for aColumnDataSource
.Examples
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 thestart_angle
value, the cumulative sums will start with a zero value. Forstart_angle
, no initial zero will be added (i.e. the sums will start with the first angle value, and include the last).
-
dodge
(field_name, value, range=None)[source]¶ Create a
DataSpec
dict that applies a client-sideJitter
transformation to aColumnDataSource
column.- Parameters
- Returns
dict
-
factor_cmap
(field_name, palette, factors, start=0, end=None, nan_color='gray')[source]¶ Create a
DataSpec
dict that applies a client-sideCategoricalColorMapper
transformation to aColumnDataSource
column.- Parameters
field_name (str) – a field name to configure
DataSpec
withpalette (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
-
factor_hatch
(field_name, patterns, factors, start=0, end=None)[source]¶ Create a
DataSpec
dict that applies a client-sideCategoricalPatternMapper
transformation to aColumnDataSource
column.- Parameters
field_name (str) – a field name to configure
DataSpec
withpatterns (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
-
factor_mark
(field_name, markers, factors, start=0, end=None)[source]¶ Create a
DataSpec
dict that applies a client-sideCategoricalMarkerMapper
transformation to aColumnDataSource
column.Note
This transform is primarily only useful with
scatter
, which can be parameterized by glyph type.- Parameters
field_name (str) – a field name to configure
DataSpec
withmarkers (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
-
jitter
(field_name, width, mean=0, distribution='uniform', range=None)[source]¶ Create a
DataSpec
dict that applies a client-sideJitter
transformation to aColumnDataSource
column.- Parameters
field_name (str) – a field name to configure
DataSpec
withwidth (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
-
linear_cmap
(field_name, palette, low, high, low_color=None, high_color=None, nan_color='gray')[source]¶ Create a
DataSpec
dict that applyies a client-sideLinearColorMapper
transformation to aColumnDataSource
column.- Parameters
field_name (str) – a field name to configure
DataSpec
withpalette (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 thanlow
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 thanhigh
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”)
-
log_cmap
(field_name, palette, low, high, low_color=None, high_color=None, nan_color='gray')[source]¶ Create a
DataSpec
dict that applies a client-sideLogColorMapper
transformation to aColumnDataSource
column.- Parameters
field_name (str) – a field name to configure
DataSpec
withpalette (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 thanlow
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 thanhigh
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”)
-
stack
(*fields)[source]¶ Create a Create a
DataSpec
dict to generate aStack
expression for aColumnDataSource
.Examples
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 thetop
coordinate for aVBar
.