holoviews.plotly Package#

`plotly` Package#

`annotation` Module#

class holoviews.plotting.plotly.annotation.LabelPlot(element, plot=None, **params)[source]#

Bases: ScatterPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

holoviews.plotting.plotly.chart.ScatterPlot: color_index

xoffset = param.Number(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Xoffset’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x30072a950>): Amount of offset to apply to labels along x-axis.
yoffset = param.Number(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Yoffset’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3007b3110>): Amount of offset to apply to labels along x-axis.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`callbacks` Module#

class holoviews.plotting.plotly.callbacks.PlotlyCallbackMetaClass(name, bases, attrs)[source]#

Bases: type

Metaclass for PlotlyCallback classes.

We want each callback class to keep track of all of the instances of the class. Using a meta class here lets us keep the logic for instance tracking in one place.

mro()#: Return a type’s method resolution order.

`chart` Module#

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

Bases: AreaMixin, ChartPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

padding = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘tuple’>), default=(0, 0.1), label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x300d08090>): 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.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

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

Bases: BarsMixin, ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

show_legend = param.Boolean(allow_refs=False, default=True, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17dcb27d0>): Whether to show legend for the plot.
multi_level = param.Boolean(allow_refs=False, default=True, label=’Multi level’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x300cbbfd0>): Whether the Bars should be grouped into a second categorical axis level.
stacked = param.Boolean(allow_refs=False, default=False, label=’Stacked’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x300cc1550>): Whether the bars should be stacked or grouped.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', **kwargs)[source]#: Make adjustments to plot extents by computing stacked bar heights, adjusting the bar baseline and forcing the x-axis to be categorical.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

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

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

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

Bases: ChartPlot, ColorbarPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

padding = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘tuple’>), default=(0, 0.1), label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x300de05d0>): 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 = param.Selector(allow_refs=False, default=’linear’, label=’Interpolation’, names={}, nested_refs=False, objects=[‘linear’, ‘steps-mid’, ‘steps-pre’, ‘steps-post’], rx=<param.reactive.reactive_ops object at 0x300de0e90>): Defines how the samples of the Curve are interpolated, default is ‘linear’, other options include ‘steps-mid’, ‘steps-pre’ and ‘steps-post’.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

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

Bases: ChartPlot, ColorbarPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

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

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

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

Bases: ChartPlot, ColorbarPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

color_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), label=’Color index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x300ed3b10>): Index of the dimension from which the color will the drawn

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

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

Bases: ChartPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

padding = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘tuple’>), default=(0, 0.1), label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x300f3d390>): 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.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`chart3d` Module#

class holoviews.plotting.plotly.chart3d.Chart3DPlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, invert_zaxis, labelled, logz, margins, responsive, zlabel

projection = param.String(allow_refs=False, default=’3d’, label=’Projection’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301b2ff50>): Allows supplying a custom projection to transform the axis coordinates during display. Example projections include ‘3d’ and ‘polar’ projections supported by some backends. Depending on the backend custom, projection objects may be supplied.

width = param.Integer(allow_refs=False, default=500, inclusive_bounds=(True, True), label=’Width’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301b35710>)

height = param.Integer(allow_refs=False, default=500, inclusive_bounds=(True, True), label=’Height’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301b35e10>)

aspect = param.Parameter(allow_refs=False, default=’cube’, label=’Aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3018478d0>): The aspect ratio mode of the plot. By default, a plot may select its own appropriate aspect ratio but sometimes it may be necessary to force a square aspect ratio (e.g. to display the plot as an element of a grid). The modes ‘auto’ and ‘equal’ correspond to the axis modes of the same name in matplotlib, a numeric value may also be passed.
zticks = param.Parameter(allow_None=True, allow_refs=False, label=’Zticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301b2ffd0>): Ticks along z-axis specified as an integer, explicit list of tick locations, list of tuples containing the locations.

camera_angle = param.NumericTuple(allow_refs=False, default=(0.2, 0.5, 0.1, 0.2), label=’Camera angle’, length=4, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17fc27f10>)

camera_position = param.NumericTuple(allow_refs=False, default=(0.1, 0, -0.1), label=’Camera position’, length=3, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3019d8050>)

camera_zoom = param.Integer(allow_refs=False, default=3, inclusive_bounds=(True, True), label=’Camera zoom’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301b35b50>)

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.chart3d.Path3DPlot(element, plot=None, **params)[source]#

Bases: Chart3DPlot, CurvePlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, invert_zaxis, labelled, logz, margins, responsive, zlabel

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

holoviews.plotting.plotly.chart.CurvePlot: padding, interpolation

holoviews.plotting.plotly.chart3d.Chart3DPlot: projection, width, height, aspect, zticks, camera_angle, camera_position, camera_zoom

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.chart3d.Scatter3DPlot(element, plot=None, **params)[source]#

Bases: Chart3DPlot, ScatterPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, invert_zaxis, labelled, logz, margins, responsive, zlabel

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

holoviews.plotting.plotly.chart.ScatterPlot: color_index

holoviews.plotting.plotly.chart3d.Chart3DPlot: projection, width, height, aspect, zticks, camera_angle, camera_position, camera_zoom

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.chart3d.SurfacePlot(element, plot=None, **params)[source]#

Bases: Chart3DPlot, ColorbarPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, invert_zaxis, labelled, logz, margins, responsive, zlabel

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

holoviews.plotting.plotly.chart3d.Chart3DPlot: projection, width, height, aspect, zticks, camera_angle, camera_position, camera_zoom

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.chart3d.TriSurfacePlot(element, plot=None, **params)[source]#

Bases: Chart3DPlot, ColorbarPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, invert_zaxis, labelled, logz, margins, responsive, zlabel

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

holoviews.plotting.plotly.chart3d.Chart3DPlot: projection, width, height, aspect, zticks, camera_angle, camera_position, camera_zoom

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`dash` Module#

`element` Module#

class holoviews.plotting.plotly.element.ColorbarPlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

clim = param.NumericTuple(allow_refs=False, default=(nan, nan), label=’Clim’, length=2, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301e73790>): User-specified colorbar axis range limits for the plot, as a tuple (low,high). If specified, takes precedence over data and dimension ranges.
clim_percentile = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘bool’>), default=False, label=’Clim percentile’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x300fd3850>): Percentile value to compute colorscale robust to outliers. If True, uses 2nd and 98th percentile; otherwise uses the specified numerical percentile value.
colorbar = param.Boolean(allow_refs=False, default=False, label=’Colorbar’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301ddbf10>): Whether to display a colorbar.
color_levels = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘int’>, <class ‘list’>), label=’Color levels’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301e73d50>): Number of discrete colors to use when colormapping or a set of color intervals defining the range of values to map each color to.
colorbar_opts = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={}, label=’Colorbar opts’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301ea6050>): Allows setting including borderwidth, showexponent, nticks, outlinecolor, thickness, bgcolor, outlinewidth, bordercolor, ticklen, xpad, ypad, tickangle…
symmetric = param.Boolean(allow_refs=False, default=False, label=’Symmetric’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301e736d0>): Whether to make the colormap symmetric around zero.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.element.ElementPlot(element, plot=None, **params)[source]#

Bases: PlotlyPlot, GenericElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

bgcolor = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘tuple’>), label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301f7c650>): If set bgcolor overrides the background color of the axis.
invert_axes = param.Selector(allow_refs=False, default=False, label=’Invert axes’, names={}, nested_refs=False, objects=[False], rx=<param.reactive.reactive_ops object at 0x301fb65d0>): Inverts the axes of the plot. Note that this parameter may not always be respected by all plots but should be respected by adjoined plots when appropriate.
invert_xaxis = param.Boolean(allow_refs=False, default=False, label=’Invert xaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb56d0>): Whether to invert the plot x-axis.
invert_yaxis = param.Boolean(allow_refs=False, default=False, label=’Invert yaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb6110>): Whether to invert the plot y-axis.
logx = param.Boolean(allow_refs=False, default=False, label=’Logx’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb5d50>): Whether to apply log scaling to the x-axis of the Chart.
logy = param.Boolean(allow_refs=False, default=False, label=’Logy’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb6650>): Whether to apply log scaling to the y-axis of the Chart.
show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb65d0>): Whether to show legend for the plot.
xaxis = param.Selector(allow_refs=False, default=’bottom’, label=’Xaxis’, names={}, nested_refs=False, objects=[‘top’, ‘bottom’, ‘bare’, ‘top-bare’, ‘bottom-bare’, None], rx=<param.reactive.reactive_ops object at 0x301fb6190>): 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 = param.Selector(allow_refs=False, default=’left’, label=’Yaxis’, names={}, nested_refs=False, objects=[‘left’, ‘right’, ‘bare’, ‘left-bare’, ‘right-bare’, None], rx=<param.reactive.reactive_ops object at 0x301fb59d0>): 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’.
xticks = param.Parameter(allow_None=True, allow_refs=False, label=’Xticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb48d0>): Ticks along x-axis specified as an integer, explicit list of tick locations, list of tuples containing the locations.
yticks = param.Parameter(allow_None=True, allow_refs=False, label=’Yticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb64d0>): Ticks along y-axis specified as an integer, explicit list of tick locations, list of tuples containing the locations.
aspect = param.Parameter(allow_refs=False, default=’cube’, label=’Aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb6650>): The aspect ratio mode of the plot. By default, a plot may select its own appropriate aspect ratio but sometimes it may be necessary to force a square aspect ratio (e.g. to display the plot as an element of a grid). The modes ‘auto’ and ‘equal’ correspond to the axis modes of the same name in matplotlib, a numeric value may also be passed.
invert_zaxis = param.Boolean(allow_refs=False, default=False, label=’Invert zaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb59d0>): Whether to invert the plot z-axis.
labelled = param.List(allow_refs=False, bounds=(0, None), default=[‘x’, ‘y’, ‘z’], label=’Labelled’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb4950>): Whether to label the ‘x’ and ‘y’ axes.
logz = param.Boolean(allow_refs=False, default=False, label=’Logz’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb6110>): Whether to apply log scaling to the y-axis of the Chart.
margins = param.NumericTuple(allow_refs=False, default=(50, 50, 50, 50), label=’Margins’, length=4, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb49d0>): Margins in pixel values specified as a tuple of the form (left, bottom, right, top).
responsive = param.Boolean(allow_refs=False, default=False, label=’Responsive’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb65d0>): Whether the plot should stretch to fill the available space.
zlabel = param.String(allow_None=True, allow_refs=False, label=’Zlabel’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb5f10>): An explicit override of the z-axis label, if set takes precedence over the dimension label.
zticks = param.Parameter(allow_None=True, allow_refs=False, label=’Zticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x301fb56d0>): Ticks along z-axis specified as an integer, explicit list of tick locations, list of tuples containing the locations.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.element.OverlayPlot(overlay, ranges=None, batched=True, keys=None, group_counter=None, **params)[source]#

Bases: GenericOverlayPlot, ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plot.GenericOverlayPlot: show_legend, batched, legend_limit, style_grouping

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(overlay, ranges, range_type='combined', dimension=None, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#

Set the plot(s) to the given frame number. Operates by manipulating the matplotlib objects held in the self._handles dictionary.

If n is greater than the number of available frames, update using the last available frame.

`images` Module#

class holoviews.plotting.plotly.images.RGBPlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, is_geo=False, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`plot` Module#

class holoviews.plotting.plotly.plot.AdjointLayoutPlot(layout, layout_type, subplots, **params)[source]#

Bases: PlotlyPlot, GenericAdjointLayoutPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

initialize_plot(ranges=None, is_geo=False)[source]#: Plot all the views contained in the AdjointLayout Object using axes appropriate to the layout configuration. All the axes are supplied by LayoutPlot - the purpose of the call is to invoke subplots with correct options and styles and hide any empty axes as necessary.

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

class holoviews.plotting.plotly.plot.GridPlot(layout, ranges=None, layout_num=1, **params)[source]#

Bases: PlotlyPlot, GenericCompositePlot

Plot a group of elements in a grid layout based on a GridSpace element object.

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

hspacing = param.Number(allow_refs=False, bounds=(0, None), default=15, inclusive_bounds=(True, True), label=’Hspacing’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17df8c650>)

vspacing = param.Number(allow_refs=False, bounds=(0, None), default=15, inclusive_bounds=(True, True), label=’Vspacing’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3023107d0>)

shared_axes = param.Boolean(allow_refs=False, default=True, label=’Shared axes’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302205190>): Whether axes ranges should be shared across the layout, if disabled switches axiswise normalization option on globally.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

initialize_plot(ranges=None, is_geo=False)[source]#: Initialize the matplotlib figure.

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

class holoviews.plotting.plotly.plot.LayoutPlot(layout, **params)[source]#

Bases: PlotlyPlot, GenericLayoutPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericLayoutPlot: transpose

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

hspacing = param.Number(allow_refs=False, bounds=(0, None), default=120, inclusive_bounds=(True, True), label=’Hspacing’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302366490>)

vspacing = param.Number(allow_refs=False, bounds=(0, None), default=100, inclusive_bounds=(True, True), label=’Vspacing’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x30235c290>)

adjoint_spacing = param.Number(allow_refs=False, bounds=(0, None), default=20, inclusive_bounds=(True, True), label=’Adjoint spacing’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302366490>)

shared_axes = param.Boolean(allow_refs=False, default=True, label=’Shared axes’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302367990>): Whether axes ranges should be shared across the layout, if disabled switches axiswise normalization option on globally.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

initialize_plot(ranges=None, is_geo=False)[source]#: Initialize the matplotlib figure.

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

class holoviews.plotting.plotly.plot.PlotlyPlot(keys=None, dimensions=None, layout_dimensions=None, uniform=True, subplot=False, adjoined=None, layout_num=0, style=None, subplots=None, dynamic=False, **params)[source]#

Bases: DimensionedPlot, CallbackPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

width = param.Integer(allow_refs=False, default=400, inclusive_bounds=(True, True), label=’Width’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302391210>)

height = param.Integer(allow_refs=False, default=400, inclusive_bounds=(True, True), label=’Height’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3023922d0>)

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

initialize_plot(ranges=None, is_geo=False)[source]#: Initialize the matplotlib figure.

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

`raster` Module#

class holoviews.plotting.plotly.raster.HeatMapPlot(element, plot=None, **params)[source]#

Bases: HeatMapMixin, RasterPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

holoviews.plotting.plotly.raster.RasterPlot: padding, nodata

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.raster.QuadMeshPlot(element, plot=None, **params)[source]#

Bases: RasterPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

holoviews.plotting.plotly.raster.RasterPlot: padding

nodata = param.Integer(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Nodata’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302429a50>): Optional missing-data value for integer data. If non-None, data with this value will be replaced with NaN so that it is transparent (by default) when plotted.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.raster.RasterPlot(element, plot=None, **params)[source]#

Bases: ColorbarPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

padding = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘tuple’>), default=0, label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302577f10>): 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.
nodata = param.Integer(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Nodata’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302588c50>): Optional missing-data value for integer data. If non-None, data with this value will be replaced with NaN so that it is transparent (by default) when plotted.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`renderer` Module#

class holoviews.plotting.plotly.renderer.PlotlyRenderer(*, backend, center, css, dpi, fig, fps, holomap, mode, post_render_hooks, size, widget_location, widget_mode, info_fn, key_fn, name)[source]#

Bases: Renderer

Parameters inherited from:

holoviews.plotting.renderer.Renderer: key_fn, info_fn, center, dpi, fps, mode, size, widget_location, widget_mode, css, post_render_hooks

backend = param.String(allow_refs=False, default=’plotly’, label=’Backend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302753c90>): The backend name.
fig = param.Selector(allow_refs=False, default=’auto’, label=’Fig’, names={}, nested_refs=False, objects=[‘html’, ‘png’, ‘svg’, ‘auto’], rx=<param.reactive.reactive_ops object at 0x3027616d0>): Output render format for static figures. If None, no figure rendering will occur.
holomap = param.Selector(allow_refs=False, default=’auto’, label=’Holomap’, names={}, nested_refs=False, objects=[‘scrubber’, ‘widgets’, ‘gif’, None, ‘auto’], rx=<param.reactive.reactive_ops object at 0x302761950>): Output render multi-frame (typically animated) format. If None, no multi-frame rendering will occur.

classmethod app(plot, show=False, new_window=False, websocket_origin=None, port=0)[source]#: Creates a bokeh app from a HoloViews object or plot. By default simply attaches the plot to bokeh’s curdoc and returns the Document, if show option is supplied creates an Application instance and displays it either in a browser window or inline if notebook extension has been loaded. Using the new_window option the app may be displayed in a new browser tab once the notebook extension has been loaded. A websocket origin is required when launching from an existing tornado server (such as the notebook) and it is not on the default port (‘localhost:8888’).

comm_manager[source]#: alias of CommManager

components(obj, fmt=None, comm=True, **kwargs)[source]#: Returns data and metadata dictionaries containing HTML and JS components to include render in app, notebook, or standalone document.

classmethod encode(entry)[source]#: Classmethod that applies conditional encoding based on mime-type. Given an entry as returned by __call__ return the data in the appropriate encoding.

classmethod export_widgets(obj, filename, fmt=None, template=None, json=False, json_path='', **kwargs)[source]#: Render and export object as a widget to a static HTML file. Allows supplying a custom template formatting string with fields to interpolate ‘js’, ‘css’ and the main ‘html’ containing the widget. Also provides options to export widget data to a json file in the supplied json_path (defaults to current path).

classmethod get_plot(obj, doc=None, renderer=None, comm=None, **kwargs)[source]#: Given a HoloViews Viewable return a corresponding plot instance.

get_plot_state(obj, doc=None, renderer=None, **kwargs)[source]#: Given a HoloViews Viewable return a corresponding figure dictionary. Allows cleaning the dictionary of any internal properties that were added

classmethod get_size(plot)[source]#

Return the display size associated with a plot before rendering to any particular format. Used to generate appropriate HTML display.

Returns a tuple of (width, height) in pixels.

html(obj, fmt=None, css=None, resources='CDN', **kwargs)[source]#: Renders plot or data structure and wraps the output in HTML. The comm argument defines whether the HTML output includes code to initialize a Comm, if the plot supplies one.

classmethod instance(**params)[source]#: Return an instance of this class, copying parameters from any existing instance provided.

classmethod load_nb(inline=True)[source]#: Loads the plotly notebook resources.

classmethod plot_options(obj, percent_size)[source]#

Given an object and a percentage size (as supplied by the %output magic) return all the appropriate plot options that would be used to instantiate a plot class for that element.

Default plot sizes at the plotting class level should be taken into account.

classmethod plotting_class(obj)[source]#: Given an object or Element class, return the suitable plotting class needed to render it with the current renderer.

classmethod save(obj, basename, fmt='auto', key=None, info=None, options=None, resources='inline', title=None, **kwargs)[source]#: Save a HoloViews object to file, either using an explicitly supplied format or to the appropriate default.

classmethod server_doc(obj, doc=None)[source]#: Get a bokeh Document with the plot attached. May supply an existing doc, otherwise bokeh.io.curdoc() is used to attach the plot to the global document instance.

classmethod state()[source]#: Context manager to handle global state for a backend, allowing Plot classes to temporarily override that state.

static_html(obj, fmt=None, template=None)[source]#: Generates a static HTML with the rendered object in the supplied format. Allows supplying a template formatting string with fields to interpolate ‘js’, ‘css’ and the main ‘html’.

classmethod validate(options)[source]#: Validate an options dictionary for the renderer.

`selection` Module#

class holoviews.plotting.plotly.selection.PlotlyOverlaySelectionDisplay(color_prop='color', is_cmap=False, supports_region=True)[source]#

Bases: OverlaySelectionDisplay

Overlay selection display subclass for use with plotly backend

`shapes` Module#

class holoviews.plotting.plotly.shapes.BoxShapePlot(element, plot=None, **params)[source]#

Bases: GeomMixin, ShapePlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', **kwargs)[source]#: Use first two key dimensions to set names, and all four to set the data range.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, is_geo=False, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.shapes.HVLinePlot(element, plot=None, **params)[source]#

Bases: ShapePlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

apply_ranges = param.Boolean(allow_refs=False, default=False, label=’Apply ranges’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3028ead10>): Whether to include the annotation in axis range calculations.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, is_geo=False, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.shapes.HVSpanPlot(element, plot=None, **params)[source]#

Bases: ShapePlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

apply_ranges = param.Boolean(allow_refs=False, default=False, label=’Apply ranges’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3029302d0>): Whether to include the annotation in axis range calculations.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, is_geo=False, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.shapes.PathShapePlot(element, plot=None, **params)[source]#

Bases: ShapePlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, is_geo=False, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.shapes.PathsPlot(element, plot=None, **params)[source]#

Bases: ShapePlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, is_geo=False, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.shapes.SegmentShapePlot(element, plot=None, **params)[source]#

Bases: GeomMixin, ShapePlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', **kwargs)[source]#: Use first two key dimensions to set names, and all four to set the data range.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, is_geo=False, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.shapes.ShapePlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, is_geo=False, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`stats` Module#

class holoviews.plotting.plotly.stats.BivariatePlot(element, plot=None, **params)[source]#

Bases: ChartPlot, ColorbarPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

holoviews.plotting.plotly.element.ColorbarPlot: clim, clim_percentile, colorbar, color_levels, colorbar_opts, symmetric

filled = param.Boolean(allow_refs=False, default=False, label=’Filled’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302d60310>)

ncontours = param.Integer(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Ncontours’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302d90a10>)

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.stats.BoxWhiskerPlot(element, plot=None, **params)[source]#

Bases: MultiDistributionPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

boxpoints = param.Selector(allow_refs=False, default=’outliers’, label=’Boxpoints’, names={}, nested_refs=False, objects=[‘all’, ‘outliers’, ‘suspectedoutliers’, False], rx=<param.reactive.reactive_ops object at 0x302d28790>): Which points to show, valid options are ‘all’, ‘outliers’, ‘suspectedoutliers’ and False
jitter = param.Number(allow_refs=False, default=0, inclusive_bounds=(True, True), label=’Jitter’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302e59cd0>): Sets the amount of jitter in the sample points drawn. If “0”, the sample points align along the distribution axis. If “1”, the sample points are drawn in a random jitter of width equal to the width of the box(es).
mean = param.Selector(allow_refs=False, default=False, label=’Mean’, names={}, nested_refs=False, objects=[True, False, ‘sd’], rx=<param.reactive.reactive_ops object at 0x302e58bd0>): If “True”, the mean of the box(es)’ underlying distribution is drawn as a dashed line inside the box(es). If “sd” the standard deviation is also drawn.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.stats.DistributionPlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

bandwidth = param.Number(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Bandwidth’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302ea9c90>): The bandwidth of the kernel for the density estimate.
cut = param.Number(allow_refs=False, default=3, inclusive_bounds=(True, True), label=’Cut’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302eaba50>): Draw the estimate to cut * bw from the extreme data points.
filled = param.Boolean(allow_refs=False, default=True, label=’Filled’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x302ea93d0>): Whether the bivariate contours should be filled.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.stats.MultiDistributionPlot(element, plot=None, **params)[source]#

Bases: MultiDistributionMixin, ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

class holoviews.plotting.plotly.stats.ViolinPlot(element, plot=None, **params)[source]#

Bases: MultiDistributionPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

box = param.Boolean(allow_refs=False, default=True, label=’Box’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x303052dd0>): Whether to draw a boxplot inside the violin
meanline = param.Boolean(allow_refs=False, default=False, label=’Meanline’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x30304c510>): If “True”, the mean of the box(es)’ underlying distribution is drawn as a dashed line inside the box(es). If “sd” the standard deviation is also drawn.

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`tabular` Module#

class holoviews.plotting.plotly.tabular.TablePlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

width = param.Number(allow_refs=False, default=400, inclusive_bounds=(True, True), label=’Width’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3032eee10>)

height = param.Number(allow_refs=False, default=400, inclusive_bounds=(True, True), label=’Height’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3032fe410>)

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

get_extents(element, ranges, range_type='combined', dimension=None, xdim=None, ydim=None, zdim=None, lims_as_soft_ranges=False, **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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`tiles` Module#

class holoviews.plotting.plotly.tiles.TilePlot(element, plot=None, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

holoviews.plotting.plot.DimensionedPlot: fontsize, fontscale, show_title, title, normalize, projection

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, padding, show_grid, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.plotly.plot.PlotlyPlot: width, height

holoviews.plotting.plotly.element.ElementPlot: bgcolor, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, xaxis, yaxis, xticks, yticks, aspect, invert_zaxis, labelled, logz, margins, responsive, zlabel, zticks

cleanup()[source]#: Cleans up references to the plot on the attached Stream subscribers.

compute_ranges(obj, key, ranges)[source]#: Given an object, a specific key, and the normalization options, this method will find the specified normalization options on the appropriate OptionTree, group the elements according to the selected normalization option (i.e. either per frame or over the whole animation) and finally compute the dimension ranges in each group. The new set of ranges is returned.

generate_plot(key, ranges, element=None, is_geo=False)[source]#: Override to force is_geo to True

get_aspect(xspan, yspan)[source]#: Computes the aspect ratio of the plot

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.

get_padding(obj, extents)[source]#: Computes padding along the axes taking into account the plot aspect.

get_zorder(overlay, key, el)[source]#: Computes the z-order of element in the NdOverlay taking into account possible batching of elements.

init_graph(datum, options, index=0, **kwargs)[source]#

Initialize the plotly components that will represent the element

Parameters#

datum: dict: An element of the data list returned by the get_data method
options: dict: Graph options that were returned by the graph_options method
index: int: Index of datum in the original list returned by the get_data method

Returns#

dict

Dictionary of the plotly components that represent the element. Keys may include:

‘traces’: List of trace dicts

‘annotations’: List of annotations dicts

‘images’: List of image dicts

‘shapes’: List of shape dicts

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

property link_sources#: Returns potential Link or Stream sources.

matches(spec)[source]#: Matches a specification against the current Plot.

push()[source]#: Pushes plot updates to the frontend.

refresh(**kwargs)[source]#: Refreshes the plot by rerendering it and then pushing the updated data if the plot has an associated Comm.

set_root(root)[source]#: Sets the root model on all subplots.

property state#: The plotting state that gets updated via the update method and used by the renderer to generate output.

traverse(fn=None, specs=None, full_breadth=True)[source]#: Traverses any nested DimensionedPlot returning a list of all plots that match the specs. The specs should be supplied as a list of either Plot types or callables, which should return a boolean given the plot class.

update(key)[source]#: Update the internal state of the Plot to represent the given key tuple (where integers represent frames). Returns this state.

update_frame(key, ranges=None, element=None, is_geo=False)[source]#: Updates an existing plot with data corresponding to the key.

`util` Module#

holoviews.plotting.plotly.util.clean_internal_figure_properties(fig)[source]#

Remove all HoloViews internal properties (those with leading underscores) from the input figure.

Note: This function mutates the input figure

Parameters#

fig: dict: The figure dictionary to process.

holoviews.plotting.plotly.util.configure_matching_axes_from_dims(fig, matching_prop='_dim')[source]#

Configure matching axes for a figure

Note: This function mutates the input figure

Parameters#

fig: dict: The figure dictionary to process.
matching_prop: str: The name of the axis property that should be used to determine that two axes should be matched together. If the property is missing or None, axes will not be matched

holoviews.plotting.plotly.util.figure_grid(figures_grid, row_spacing=50, column_spacing=50, share_xaxis=False, share_yaxis=False, width=None, height=None)[source]#

Construct a figure from a 2D grid of sub-figures

Parameters#

figures_grid: list of list of (dict or None): 2D list of plotly figure dicts that will be combined in a grid to produce the resulting figure. None values maybe used to leave empty grid cells
row_spacing: float (default 50): Vertical spacing between rows in the grid in pixels
column_spacing: float (default 50): Horizontal spacing between columns in the grid in pixels coordinates
share_xaxis: bool (default False): Share x-axis between sub-figures in the same column. Also link all x-axes in the figure. This will only work if each sub-figure has a single x-axis
share_yaxis: bool (default False): Share y-axis between sub-figures in the same row. Also link all y-axes in the figure. This will only work if each subfigure has a single y-axis
width: int (default None): Final figure width. If not specified, width is the sum of the max width of the figures in each column
height: int (default None): Final figure width. If not specified, height is the sum of the max height of the figures in each row

Returns#

dict: A plotly figure dict

holoviews.plotting.plotly.util.get_colorscale(cmap, levels=None, cmin=None, cmax=None)[source]#

Converts a cmap spec to a plotly colorscale

Args:: cmap: A recognized colormap by name or list of colors levels: A list or integer declaring the color-levels cmin: The lower bound of the color range cmax: The upper bound of the color range
Returns:: A valid plotly colorscale

holoviews.plotting.plotly.util.merge_figure(fig, subfig)[source]#

Merge a sub-figure into a parent figure

Note: This function mutates the input fig dict, but it does not mutate the subfig dict

Parameters#

fig: dict: The plotly figure dict into which the sub figure will be merged
subfig: dict: The plotly figure dict that will be copied and then merged into fig

holoviews.plotting.plotly.util.merge_layout(obj, subobj)[source]#

Merge layout objects recursively

Note: This function mutates the input obj dict, but it does not mutate the subobj dict

Parameters#

obj: dict: dict into which the sub-figure dict will be merged
subobj: dict: dict that sill be copied and merged into obj

holoviews.plotly Package#

plotly Package#

annotation Module#

Parameters#

Returns#

callbacks Module#

chart Module#

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chart3d Module#

Parameters#

Returns#

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Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

dash Module#

element Module#

Parameters#

Returns#

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images Module#

Parameters#

Returns#

plot Module#

raster Module#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

renderer Module#

selection Module#

shapes Module#

Parameters#

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stats Module#

Parameters#

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Returns#

Parameters#

Returns#

Parameters#

`plotly` Package#

`annotation` Module#

`callbacks` Module#

`chart` Module#

`chart3d` Module#

`dash` Module#

`element` Module#

`images` Module#

`plot` Module#

`raster` Module#

`renderer` Module#

`selection` Module#

`shapes` Module#

`stats` Module#

`tabular` Module#

`tiles` Module#

`util` Module#