bokeh.transform#

Helper functions for applying client-side computations such as transformations to data fields or ColumnDataSource expressions.

cumsum(field_name: str, include_zero: bool = False) → Expr[source]#

Create a DataSpec dict to generate a CumSum expression for a ColumnDataSource.

Parameters:

field_name (str) – a field name to configure CumSum with
include_zero (bool, optional) – whether to include zero in the sum (default: False)

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 the start_angle value, the cumulative sums will start with a zero value. For end_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: str, value: float, range: Range | None = None) → Field[source]#

Create a DataSpec dict that applies a client-side Dodge transformation to a ColumnDataSource column.

Parameters:

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:

Field

factor_cmap(field_name: str, palette: Sequence[ColorLike], factors: Factors, start: float = 0, end: float | None = None, nan_color: ColorLike = 'gray') → Field[source]#

Create a DataSpec dict that applies a client-side CategoricalColorMapper transformation to a ColumnDataSource column.

Parameters:

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:

Field

factor_hatch(field_name: str, patterns: Sequence[str], factors: Factors, start: float = 0, end: float | None = None) → Field[source]#

Create a DataSpec dict that applies a client-side CategoricalPatternMapper transformation to a ColumnDataSource column.

Parameters:

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:

Field

Added in version 1.1.1

factor_mark(field_name: str, markers: Sequence[str], factors: Factors, start: float = 0, end: float | None = None) → Field[source]#

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.

Parameters:

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:

Field

jitter(field_name: str, width: float, mean: float = 0, distribution: JitterRandomDistributionType = 'uniform', range: Range | None = None) → Field[source]#

Create a DataSpec dict that applies a client-side Jitter transformation to a ColumnDataSource column.

Parameters:

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:

Field

linear_cmap(field_name: str, palette: Sequence[ColorLike], low: float, high: float, low_color: ColorLike | None = None, high_color: ColorLike | None = None, nan_color: ColorLike = 'gray') → Field[source]#

Create a DataSpec dict that applyies a client-side LinearColorMapper transformation to a ColumnDataSource column.

Parameters:

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”)

log_cmap(field_name: str, palette: Sequence[ColorLike], low: float, high: float, low_color: ColorLike | None = None, high_color: ColorLike | None = None, nan_color: ColorLike = 'gray') → Field[source]#

Create a DataSpec dict that applies a client-side LogColorMapper transformation to a ColumnDataSource column.

Parameters:

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”)

stack(*fields: str) → Expr[source]#

Create a Create a DataSpec dict to generate a Stack expression for a ColumnDataSource.

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 the top coordinate for a VBar.

transform(field_name: str, transform: Transform) → Field[source]#

Create a DataSpec dict that applies an arbitrary client-side Transform to a ColumnDataSource column.

Parameters:

field_name (str) – A field name to configure DataSpec with
transform (Transform) – A transforms to apply to that field

Returns:

Field