holoviews.plotting.bokeh.chart module#

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

Bases: AreaMixin, SpreadPlot

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.

Parameter Definitions

Parameters inherited from:

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.
color_index = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Color index'): Deprecated in favor of color style mapping, e.g. color=dim(‘color’)

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

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.ErrorPlot(element, plot=None, **params)[source]#

Bases: ColorbarPlot

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

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.

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

Bases: LegendPlot, ColorbarPlot

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.
color_index = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Color index'): Deprecated in favor of color style mapping, e.g. color=dim(‘color’)
size_index = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Size index'): Deprecated in favor of size style mapping, e.g. size=dim(‘size’)
scaling_method = Selector(default='area', label='Scaling method', names={}, objects=['width', 'area']): Deprecated in favor of size style mapping, e.g. size=dim(‘size’)**2.
scaling_factor = Number(bounds=(0, None), default=1, inclusive_bounds=(True, True), label='Scaling factor'): Scaling factor which is applied to either the width or area of each point, depending on the value of scaling_method.
size_fn = Callable(default=<ufunc 'absolute'>, label='Size fn'): Function applied to size values before applying scaling, to remove values lower than zero.

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.

size_fn = <ufunc 'absolute'>#

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

Bases: HistogramPlot

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.SpikesPlot(element, plot=None, **params)[source]#

Bases: SpikesMixin, ColorbarPlot

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.
color_index = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Color index'): Deprecated in favor of color style mapping, e.g. color=dim(‘color’)

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

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

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.
color_index = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Color index'): Deprecated in favor of dimension value transform on color option, e.g. color=dim(‘Magnitude’).
size_index = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Size index'): Deprecated in favor of the magnitude option, e.g. magnitude=dim(‘Magnitude’).
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.

holoviews.plotting.bokeh.chart module#

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