holoviews.plotting.bokeh.stats module#

class holoviews.plotting.bokeh.stats.BivariatePlot(*args, **params)[source]#

Bases: PolygonPlot

Bivariate plot visualizes two-dimensional kernel density estimates. Additionally, by enabling the joint option, the marginals distributions can be plotted alongside each axis (does not animate or compose).

Parameter Definitions

Parameters inherited from:

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

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, bgcolor, default_span, hooks, invert_axes, invert_xaxis, invert_yaxis, logx, logy, padding, show_grid, xaxis, yaxis, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.bokeh.plot.BokehPlot: title, shared_datasource, title_format

holoviews.plotting.bokeh.element.ElementPlot: fontsize, xticks, yticks, toolbar, width, height, active_tools, align, apply_hard_bounds, autorange, border, aspect, backend_opts, data_aspect, frame_width, frame_height, min_width, min_height, max_width, max_height, margin, multi_y, scalebar, scalebar_range, scalebar_unit, scalebar_location, scalebar_label, scalebar_tool, scalebar_opts, subcoordinate_y, subcoordinate_scale, responsive, gridstyle, labelled, lod, show_frame, shared_axes, default_tools, tools, hover_tooltips, hover_formatters, hover_mode, xformatter, yformatter

holoviews.plotting.bokeh.element.ColorbarPlot: color_levels, cformatter, clabel, clim, clim_percentile, cnorm, colorbar, colorbar_position, colorbar_opts, clipping_colors, cticks, logz, rescale_discrete_levels, symmetric

holoviews.plotting.bokeh.element.LegendPlot: legend_cols, legend_labels, legend_muted, legend_offset, legend_position, legend_opts

holoviews.plotting.bokeh.path.ContourPlot: show_legend, selected, color_index

bandwidth = Number(allow_None=True, inclusive_bounds=(True, True), label='Bandwidth')

The bandwidth of the kernel for the density estimate.

cut = Number(default=3, inclusive_bounds=(True, True), label='Cut')

Draw the estimate to cut * bw from the extreme data points.

filled = Boolean(default=False, label='Filled')

Whether the bivariate contours should be filled.

levels = ClassSelector(class_=(<class 'list'>, <class 'int'>), default=10, label='Levels')

A list of scalar values used to specify the contour levels.

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

Bases: MultiDistributionMixin, CompositeElementPlot, ColorbarPlot, LegendPlot

Parameter Definitions

Parameters inherited from:

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

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, bgcolor, default_span, hooks, invert_axes, invert_xaxis, invert_yaxis, logx, logy, padding, show_grid, xaxis, yaxis, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.bokeh.plot.BokehPlot: title, shared_datasource, title_format

holoviews.plotting.bokeh.element.ElementPlot: fontsize, xticks, yticks, toolbar, width, height, active_tools, align, apply_hard_bounds, autorange, border, aspect, backend_opts, data_aspect, frame_width, frame_height, min_width, min_height, max_width, max_height, margin, multi_y, scalebar, scalebar_range, scalebar_unit, scalebar_location, scalebar_label, scalebar_tool, scalebar_opts, subcoordinate_y, subcoordinate_scale, responsive, gridstyle, labelled, lod, show_frame, shared_axes, default_tools, tools, hover_tooltips, hover_formatters, hover_mode, xformatter, yformatter

holoviews.plotting.bokeh.element.LegendPlot: legend_cols, legend_labels, legend_muted, legend_offset, legend_position, legend_opts

holoviews.plotting.bokeh.element.ColorbarPlot: color_levels, cformatter, clabel, clim, clim_percentile, cnorm, colorbar, colorbar_position, colorbar_opts, clipping_colors, cticks, logz, rescale_discrete_levels, symmetric

show_legend = Boolean(default=False, label='Show legend')

Whether to show legend for the plot.

outlier_radius = Number(default=0.01, inclusive_bounds=(True, True), label='Outlier radius')

The radius of the circle marker for the outliers.

color_index = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Color index')

Deprecated in favor of color style mapping, e.g. box_color=dim(‘color’)

get_data(element, ranges, style)[source]#

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

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

Bases: AreaPlot

DistributionPlot visualizes a distribution of values as a KDE.

Parameter Definitions

Parameters inherited from:

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

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, bgcolor, default_span, hooks, invert_axes, invert_xaxis, invert_yaxis, logx, logy, show_legend, show_grid, xaxis, yaxis, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.bokeh.plot.BokehPlot: title, shared_datasource, title_format

holoviews.plotting.bokeh.element.ElementPlot: fontsize, xticks, yticks, toolbar, width, height, active_tools, align, apply_hard_bounds, autorange, border, aspect, backend_opts, data_aspect, frame_width, frame_height, min_width, min_height, max_width, max_height, margin, multi_y, scalebar, scalebar_range, scalebar_unit, scalebar_location, scalebar_label, scalebar_tool, scalebar_opts, subcoordinate_y, subcoordinate_scale, responsive, gridstyle, labelled, lod, show_frame, shared_axes, default_tools, tools, hover_tooltips, hover_formatters, hover_mode, xformatter, yformatter

holoviews.plotting.bokeh.chart.AreaPlot: padding

bandwidth = Number(allow_None=True, inclusive_bounds=(True, True), label='Bandwidth')

The bandwidth of the kernel for the density estimate.

cut = Number(default=3, inclusive_bounds=(True, True), label='Cut')

Draw the estimate to cut * bw from the extreme data points.

filled = Boolean(default=True, label='Filled')

Whether the bivariate contours should be filled.

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

Bases: BoxWhiskerPlot

Parameter Definitions

Parameters inherited from:

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

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, bgcolor, default_span, hooks, invert_axes, invert_xaxis, invert_yaxis, logx, logy, padding, show_grid, xaxis, yaxis, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation

holoviews.plotting.bokeh.plot.BokehPlot: title, shared_datasource, title_format

holoviews.plotting.bokeh.element.ElementPlot: fontsize, xticks, yticks, toolbar, width, height, active_tools, align, apply_hard_bounds, autorange, border, aspect, backend_opts, data_aspect, frame_width, frame_height, min_width, min_height, max_width, max_height, margin, multi_y, scalebar, scalebar_range, scalebar_unit, scalebar_location, scalebar_label, scalebar_tool, scalebar_opts, subcoordinate_y, subcoordinate_scale, responsive, gridstyle, labelled, lod, show_frame, shared_axes, default_tools, tools, hover_tooltips, hover_formatters, hover_mode, xformatter, yformatter

holoviews.plotting.bokeh.element.LegendPlot: legend_cols, legend_labels, legend_muted, legend_offset, legend_position, legend_opts

holoviews.plotting.bokeh.element.ColorbarPlot: color_levels, cformatter, clabel, clim, clim_percentile, cnorm, colorbar, colorbar_position, colorbar_opts, clipping_colors, cticks, logz, rescale_discrete_levels, symmetric

holoviews.plotting.bokeh.stats.BoxWhiskerPlot: show_legend, outlier_radius

color_index = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'int'>), label='Color index')

Deprecated in favor of color style mapping, e.g. violin_color=dim(‘color’)

bandwidth = Number(allow_None=True, inclusive_bounds=(True, True), label='Bandwidth')

Allows supplying explicit bandwidth value rather than relying on scott or silverman method.

clip = NumericTuple(allow_None=True, label='Clip', length=2)

A tuple of a lower and upper bound to clip the violin at.

cut = Number(default=5, inclusive_bounds=(True, True), label='Cut')

Draw the estimate to cut * bw from the extreme data points.

inner = Selector(default='box', label='Inner', names={}, objects=['box', 'quartiles', 'stick', None])

Inner visual indicator for distribution values: * box - A small box plot * stick - Lines indicating each sample value * quartiles - Indicates first, second and third quartiles

split = ClassSelector(allow_None=True, class_=(<class 'str'>, <class 'holoviews.util.transform.dim'>), label='Split')

The dimension to split the Violin on.

violin_width = Number(default=0.8, inclusive_bounds=(True, True), label='Violin width')

Relative width of the violin

get_data(element, ranges, style)[source]#

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