holoviews.plotting.bokeh.chart module#

class holoviews.plotting.bokeh.chart.AreaPlot(element, plot=None, **params)[source]#

Bases: AreaMixin, SpreadPlot

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

selection_display

Parameter Definitions

Parameters inherited from:

padding = ClassSelector(class_=(<class 'int'>, <class 'float'>, <class 'tuple'>), default=(0, 0.1), label='Padding'): Fraction by which to increase auto-ranged extents to make datapoints more visible around borders. To compute padding, the axis whose screen size is largest is chosen, and the range of that axis is increased by the specified fraction along each axis. Other axes are then padded ensuring that the amount of screen space devoted to padding is equal for all axes. If specified as a tuple, the int or float values in the tuple will be used for padding in each axis, in order (x,y or x,y,z). For example, for padding=0.2 on a 800x800-pixel plot, an x-axis with the range [0,10] will be padded by 20% to be [-1,11], while a y-axis with a range [0,1000] will be padded to be [-100,1100], which should make the padding be approximately the same number of pixels. But if the same plot is changed to have a height of only 200, the y-range will then be [-400,1400] so that the y-axis padding will still match that of the x-axis. It is also possible to declare non-equal padding value for the lower and upper bound of an axis by supplying nested tuples, e.g. padding=(0.1, (0, 0.1)) will pad the x-axis lower and upper bound as well as the y-axis upper bound by a fraction of 0.1 while the y-axis lower bound is not padded at all.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.BarPlot(element, plot=None, **params)[source]#

Bases: BarsMixin, ColorbarPlot, LegendPlot

BarPlot allows generating single- or multi-category bar Charts, by selecting which key dimensions are mapped onto separate groups, categories and stacks.

Methods

`get_data`(element, ranges, style)	Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.
`get_stack`(xvals, yvals, baselines[, sign])	Iterates over a x- and y-values in a stack layer and appropriately offsets the layer on top of the previous layer.

selection_display

Parameter Definitions

Parameters inherited from:

default_tools = List(bounds=(0, None), default=['save', 'pan', 'wheel_zoom', 'box_zoom', 'reset', 'tap'], label='Default tools'): Default tools for BarPlot. Includes tap for click-to-select with link_selections.
baseline = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Baseline'): Value dimension naming the lower end of each bar, making bars float between two value dimensions instead of growing from zero. The first remaining value dimension is the upper end, so vdims=[‘Low’, ‘High’] with baseline=’Low’ spans Low to High. Supported for ungrouped and grouped Bars; combining it with stacked raises an error.
multi_level = Boolean(default=True, label='Multi level'): Whether the Bars should be grouped into a second categorical axis level.
stacked = Boolean(default=False, label='Stacked'): Whether the bars should be stacked or grouped.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

get_stack(xvals, yvals, baselines, sign='positive')[source]#: Iterates over a x- and y-values in a stack layer and appropriately offsets the layer on top of the previous layer.

class holoviews.plotting.bokeh.chart.CurvePlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

get_batched_data
selection_display

Parameter Definitions

Parameters inherited from:

padding = ClassSelector(class_=(<class 'int'>, <class 'float'>, <class 'tuple'>), default=(0, 0.1), label='Padding'): Fraction by which to increase auto-ranged extents to make datapoints more visible around borders. To compute padding, the axis whose screen size is largest is chosen, and the range of that axis is increased by the specified fraction along each axis. Other axes are then padded ensuring that the amount of screen space devoted to padding is equal for all axes. If specified as a tuple, the int or float values in the tuple will be used for padding in each axis, in order (x,y or x,y,z). For example, for padding=0.2 on a 800x800-pixel plot, an x-axis with the range [0,10] will be padded by 20% to be [-1,11], while a y-axis with a range [0,1000] will be padded to be [-100,1100], which should make the padding be approximately the same number of pixels. But if the same plot is changed to have a height of only 200, the y-range will then be [-400,1400] so that the y-axis padding will still match that of the x-axis. It is also possible to declare non-equal padding value for the lower and upper bound of an axis by supplying nested tuples, e.g. padding=(0.1, (0, 0.1)) will pad the x-axis lower and upper bound as well as the y-axis upper bound by a fraction of 0.1 while the y-axis lower bound is not padded at all.
interpolation = Selector(default='linear', label='Interpolation', names={}, objects=['linear', 'steps-mid', 'steps-pre', 'steps-post']): Defines how the samples of the Curve are interpolated, default is ‘linear’, other options include ‘steps-mid’, ‘steps-pre’ and ‘steps-post’.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.DonutPlot(*args, **kwargs)[source]#

Bases: DonutMixin, CompositeElementPlot, ColorbarPlot, LegendPlot

Renders a Donut (annular wedge) chart in Bokeh.

Uses a CompositeElementPlot to draw both annular wedges and optional text labels as separate Bokeh glyphs. The center annotation is drawn as a manual Label outside the composite lifecycle.

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

selection_display

Parameter Definitions

Parameters inherited from:

show_frame = Boolean(default=False, label='Show frame'): Whether or not to show a complete frame around the plot.
center_label = String(allow_None=True, label='Center label'): Text to display in the center of the donut. If set to ‘total’, the sum of all values is shown. Any other string is used as a format template with access to ‘total’ and the vdim name as keys. None disables the center label.
center_text_align = Selector(default='center', label='Center text align', names={}, objects=['left', 'right', 'center']): Horizontal alignment of the center label.
center_text_baseline = Selector(default='middle', label='Center text baseline', names={}, objects=['top', 'middle', 'bottom', 'alphabetic', 'hanging']): Vertical alignment of the center label.
center_text_color = Color(allow_named=True, default='black', label='Center text color'): Color for the center label.
center_text_font_size = String(default='12pt', label='Center text font size'): Font size for the center label, as a CSS string (e.g. ‘16pt’).
inner_radius = Number(bounds=(0, 1), default=0.4, inclusive_bounds=(True, True), label='Inner radius'): Inner radius of the annulus. Set to 0 for a pie chart.
label_radius = Number(bounds=(0, None), default=0.7, inclusive_bounds=(True, True), label='Label radius'): Radial distance of wedge labels as a multiple of outer_radius. 1.0 places labels exactly at the outer edge; values above 1 push them outside the ring.
label_text_align = Selector(default='auto', label='Label text align', names={}, objects=['auto', 'left', 'right', 'center']): Horizontal alignment of wedge labels. ‘auto’ chooses left/right based on angular position and whether labels are inside or outside the ring.
outer_radius = Number(bounds=(0, None), default=1.0, inclusive_bounds=(True, True), label='Outer radius'): Outer radius of the annulus.
show_labels = ClassSelector(class_=(<class 'bool'>, <class 'str'>), default=False, label='Show labels'): Whether and how to draw text labels next to each wedge. Can be a boolean or a template string using dimension names (e.g. ‘{Category}: {Amount}’).
start_angle = Number(default=0, inclusive_bounds=(True, True), label='Start angle'): Rotation offset in radians for the first wedge.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.ErrorPlot(element, plot=None, **params)[source]#

Bases: ColorbarPlot

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

selection_display

Parameter Definitions

Parameters inherited from:

selected = List(allow_None=True, bounds=(0, None), label='Selected'): The current selection as a list of integers corresponding to the selected items.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.HistogramPlot(element, plot=None, **params)[source]#

Bases: ColorbarPlot

Methods

`get_data`(element, ranges, style)	Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.
`get_extents`(element, ranges[, range_type])	Gets the extents for the axes from the current Element.
`initialize_plot`([ranges, plot, plots, source])	Initializes a new plot object with the last available frame.

selection_display

Parameter Definitions

Parameters inherited from:

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

get_extents(element, ranges, range_type='combined', **kwargs)[source]#

Gets the extents for the axes from the current Element. The globally computed ranges can optionally override the extents.

The extents are computed by combining the data ranges, extents and dimension ranges. Each of these can be obtained individually by setting the range_type to one of:

‘data’: Just the data ranges
‘extents’: Element.extents
‘soft’: Dimension.soft_range values
‘hard’: Dimension.range values

To obtain the combined range, which includes range padding the default may be used:

‘combined’: All the range types combined and padding applied

This allows Overlay plots to obtain each range and combine them appropriately for all the objects in the overlay.

If lims_as_soft_ranges is set to True, the xlim and ylim will be treated as soft ranges instead of the default case as hard ranges while computing the extents. This is used e.g. when apply_hard_bounds is True and xlim/ylim is set, in which case we limit the initial viewable range to xlim/ylim, but allow navigation up to the abs max between the data range and xlim/ylim.

initialize_plot(ranges=None, plot=None, plots=None, source=None)[source]#: Initializes a new plot object with the last available frame.

class holoviews.plotting.bokeh.chart.PointPlot(element, plot=None, **params)[source]#

Bases: SizebarMixin, ColorbarPlot

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

get_batched_data
selection_display

Parameter Definitions

Parameters inherited from:

jitter = Number(allow_None=True, bounds=(0, None), inclusive_bounds=(True, True), label='Jitter'): The amount of jitter to apply to offset the points along the x-axis.
selected = List(allow_None=True, bounds=(0, None), label='Selected'): The current selection as a list of integers corresponding to the selected items.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.SideHistogramPlot(*args, **kwargs)[source]#

Bases: HistogramPlot

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

Parameter Definitions

Parameters inherited from:

show_title = Boolean(default=False, label='Show title'): Whether to display the plot title.
width = Integer(bounds=(0, None), default=125, inclusive_bounds=(True, True), label='Width'): The width of the plot
height = Integer(bounds=(0, None), default=125, inclusive_bounds=(True, True), label='Height'): The height of the plot
default_tools = List(bounds=(0, None), default=['save', 'pan', 'wheel_zoom', 'box_zoom', 'reset'], label='Default tools'): A list of plugin tools to use on the plot.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.SideSpikesPlot(element, plot=None, **params)[source]#

Bases: SpikesPlot

SpikesPlot with useful defaults for plotting adjoined rug plot.

Parameter Definitions

Parameters inherited from:

xaxis = Selector(default='top-bare', label='Xaxis', names={}, objects=['top', 'bottom', 'bare', 'top-bare', 'bottom-bare', None]): Whether and where to display the xaxis, bare options allow suppressing all axis labels including ticks and xlabel. Valid options are ‘top’, ‘bottom’, ‘bare’, ‘top-bare’ and ‘bottom-bare’.
yaxis = Selector(default='right-bare', label='Yaxis', names={}, objects=['left', 'right', 'bare', 'left-bare', 'right-bare', None]): Whether and where to display the yaxis, bare options allow suppressing all axis labels including ticks and ylabel. Valid options are ‘left’, ‘right’, ‘bare’ ‘left-bare’ and ‘right-bare’.
width = Integer(bounds=(0, None), default=50, inclusive_bounds=(True, True), label='Width'): Width of plot
height = Integer(bounds=(0, None), default=50, inclusive_bounds=(True, True), label='Height'): Height of plot
border = Integer(default=5, inclusive_bounds=(True, True), label='Border'): Default borders on plot
selected = List(allow_None=True, bounds=(0, None), label='Selected'): The current selection as a list of integers corresponding to the selected items.

class holoviews.plotting.bokeh.chart.SizebarMixin(element, plot=None, **params)[source]#

Bases: LegendPlot

Parameter Definitions

Parameters inherited from:

sizebar = Boolean(default=False, label='Sizebar'): Whether to display a sizebar.
sizebar_location = Selector(default='below', label='Sizebar location', names={}, objects=['above', 'below', 'left', 'right', 'center']): Location anchor for positioning scale bar, default to ‘below’. The sizebar_location is only used if sizebar is True.
sizebar_orientation = Selector(default='horizontal', label='Sizebar orientation', names={}, objects=['horizontal', 'vertical']): Orientation of the sizebar, default to ‘horizontal’. The sizebar_orientation is only used if sizebar is True.
sizebar_color = String(default='black', label='Sizebar color'): Color of the glyph in the sizebar, default to ‘black’. The sizebar_color is only used if sizebar is True.
sizebar_alpha = Number(bounds=(0, 1), default=0.6, inclusive_bounds=(True, True), label='Sizebar alpha'): Alpha value of the glyph in the sizebar, default to 0.6. The sizebar_alpha is only used if sizebar is True.
sizebar_bounds = NumericTuple(allow_None=True, label='Sizebar bounds', length=2): Bounds of the sizebar, default to None which will automatically determine the bounds based on the data. The sizebar_bounds is only used if sizebar is True.
sizebar_opts = Dict(class_=<class 'dict'>, default={}, label='Sizebar opts'): Allows setting specific styling options for the sizebar. See https://docs.bokeh.org/en/latest/docs/reference/models/annotations.html#bokeh.models.SizeBar for more information. The sizebar_opts is only used if sizebar is True.

class holoviews.plotting.bokeh.chart.SpikesPlot(element, plot=None, **params)[source]#

Bases: SpikesMixin, ColorbarPlot

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

selection_display

Parameter Definitions

Parameters inherited from:

show_legend = Boolean(default=True, label='Show legend'): Whether to show legend for the plot.
spike_length = Number(default=0.5, inclusive_bounds=(True, True), label='Spike length'): The length of each spike if Spikes object is one dimensional.
position = Number(default=0.0, inclusive_bounds=(True, True), label='Position'): The position of the lower end of each spike.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.SpreadPlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

selection_display

Parameter Definitions

Parameters inherited from:

padding = ClassSelector(class_=(<class 'int'>, <class 'float'>, <class 'tuple'>), default=(0, 0.1), label='Padding'): Fraction by which to increase auto-ranged extents to make datapoints more visible around borders. To compute padding, the axis whose screen size is largest is chosen, and the range of that axis is increased by the specified fraction along each axis. Other axes are then padded ensuring that the amount of screen space devoted to padding is equal for all axes. If specified as a tuple, the int or float values in the tuple will be used for padding in each axis, in order (x,y or x,y,z). For example, for padding=0.2 on a 800x800-pixel plot, an x-axis with the range [0,10] will be padded by 20% to be [-1,11], while a y-axis with a range [0,1000] will be padded to be [-100,1100], which should make the padding be approximately the same number of pixels. But if the same plot is changed to have a height of only 200, the y-range will then be [-400,1400] so that the y-axis padding will still match that of the x-axis. It is also possible to declare non-equal padding value for the lower and upper bound of an axis by supplying nested tuples, e.g. padding=(0.1, (0, 0.1)) will pad the x-axis lower and upper bound as well as the y-axis upper bound by a fraction of 0.1 while the y-axis lower bound is not padded at all.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.VectorFieldPlot(element, plot=None, **params)[source]#

Bases: ColorbarPlot

Methods

get_data(element, ranges, style)

Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

selection_display

Parameter Definitions

Parameters inherited from:

padding = ClassSelector(class_=(<class 'int'>, <class 'float'>, <class 'tuple'>), default=0.05, label='Padding'): Fraction by which to increase auto-ranged extents to make datapoints more visible around borders. To compute padding, the axis whose screen size is largest is chosen, and the range of that axis is increased by the specified fraction along each axis. Other axes are then padded ensuring that the amount of screen space devoted to padding is equal for all axes. If specified as a tuple, the int or float values in the tuple will be used for padding in each axis, in order (x,y or x,y,z). For example, for padding=0.2 on a 800x800-pixel plot, an x-axis with the range [0,10] will be padded by 20% to be [-1,11], while a y-axis with a range [0,1000] will be padded to be [-100,1100], which should make the padding be approximately the same number of pixels. But if the same plot is changed to have a height of only 200, the y-range will then be [-400,1400] so that the y-axis padding will still match that of the x-axis. It is also possible to declare non-equal padding value for the lower and upper bound of an axis by supplying nested tuples, e.g. padding=(0.1, (0, 0.1)) will pad the x-axis lower and upper bound as well as the y-axis upper bound by a fraction of 0.1 while the y-axis lower bound is not padded at all.
arrow_heads = Boolean(default=True, label='Arrow heads'): Whether or not to draw arrow heads.
magnitude = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'holoviews.util.transform.dim'>), label='Magnitude'): Dimension or dimension value transform that declares the magnitude of each vector. Magnitude is expected to be scaled between 0-1, by default the magnitudes are rescaled relative to the minimum distance between vectors, this can be disabled with the rescale_lengths option.
pivot = Selector(default='mid', label='Pivot', names={}, objects=['mid', 'tip', 'tail']): The point around which the arrows should pivot valid options include ‘mid’, ‘tip’ and ‘tail’.
rescale_lengths = Boolean(default=True, label='Rescale lengths'): Whether the lengths will be rescaled to take into account the smallest non-zero distance between two vectors.
normalize_lengths = Boolean(default=True, label='Normalize lengths'): Deprecated in favor of rescaling length using dimension value transforms using the magnitude option, e.g. dim(‘Magnitude’).norm().

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

class holoviews.plotting.bokeh.chart.WaterfallPlot(element, plot=None, **params)[source]#

Bases: WaterfallMixin, ColorbarPlot, LegendPlot

Renders a Waterfall chart with floating bars colored by direction (positive / negative / total).

Methods

`get_data`(element, ranges, style)	Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.
`initialize_plot`([ranges, plot, plots, source])	Initializes a new plot object with the last available frame.
`update_handles`(key, axis, element, ranges, style)	Refresh connector source when data changes.

selection_display

Parameter Definitions

Parameters inherited from:

connector_line_color = String(default='gray', label='Connector line color'): Color for connector lines between bars.
connector_line_dash = String(default='dashed', label='Connector line dash'): Line dash style for connector lines between bars.
connector_line_width = Number(default=1, inclusive_bounds=(True, True), label='Connector line width'): Width of connector lines between bars.
negative_color = String(default='crimson', label='Negative color'): Color for bars representing negative changes.
positive_color = String(default='limegreen', label='Positive color'): Color for bars representing positive changes.
show_connectors = Boolean(default=True, label='Show connectors'): Whether to draw horizontal connector lines between bars.
show_total = Boolean(default=True, label='Show total'): Whether to append a final total bar running from 0 to the cumulative sum.
start_color = String(allow_None=True, default='steelblue', label='Start color'): Color for the first bar (the starting absolute value). If None, the first bar is colored by its sign using positive_color or negative_color.
total_color = String(allow_None=True, label='Total color'): Color for the summary total bar. If None, inherits the resolved start_color.
total_label = String(default='Total', label='Total label'): Label used for the auto-appended total bar.

get_data(element, ranges, style)[source]#: Returns the data from an element in the appropriate format for initializing or updating a ColumnDataSource and a dictionary which maps the expected keywords arguments of a glyph to the column in the datasource.

initialize_plot(ranges=None, plot=None, plots=None, source=None)[source]#: Initializes a new plot object with the last available frame.

update_handles(key, axis, element, ranges, style)[source]#: Refresh connector source when data changes.