holoviews.mpl Package#

`mpl` Package#

holoviews.plotting.mpl.set_style(key)[source]#: Select a style by name, e.g. set_style(‘default’). To revert to the previous style use the key ‘unset’ or False.

`annotation` Module#

class holoviews.plotting.mpl.annotation.ABLine2D(slope, intercept, *args, **kwargs)[source]#

Bases: Line2D

Draw a line based on its slope and y-intercept. Additional arguments are passed to the <matplotlib.lines.Line2D> constructor.

add_callback(func)[source]#

Add a callback function that will be called whenever one of the .Artist’s properties changes.

Parameters#

funccallable

The callback function. It must have the signature:

def func(artist: Artist) -> Any

where artist is the calling .Artist. Return values may exist but are ignored.

Returns#

int: The observer id associated with the callback. This id can be used for removing the callback with .remove_callback later.

Parameters#

mouseevent : ~matplotlib.backend_bases.MouseEvent

Returns#

containsbool

Whether any values are within the radius.

detailsdict

A dictionary {'ind': pointlist}, where pointlist is a list of points of the line that are within the pickradius around the event position.

TODO: sort returned indices by distance

convert_xunits(x)[source]#

Convert x using the unit type of the xaxis.

If the artist is not contained in an Axes or if the xaxis does not have units, x itself is returned.

convert_yunits(y)[source]#

Convert y using the unit type of the yaxis.

If the artist is not contained in an Axes or if the yaxis does not have units, y itself is returned.

draw(renderer)[source]#

Draw the Artist (and its children) using the given renderer.

This has no effect if the artist is not visible (.Artist.get_visible returns False).

Parameters#

renderer : ~matplotlib.backend_bases.RendererBase subclass.

Notes#

This method is overridden in the Artist subclasses.

findobj(match=None, include_self=True)[source]#

Find artist objects.

Recursively find all .Artist instances contained in the artist.

Parameters#

match

A filter criterion for the matches. This can be

None: Return all objects contained in artist.
A function with signature def match(artist: Artist) -> bool. The result will only contain artists for which the function returns True.
A class instance: e.g., .Line2D. The result will only contain artists of this class or its subclasses (isinstance check).

include_selfbool

Include self in the list to be checked for a match.

Returns#

list of .Artist

format_cursor_data(data)[source]#

Return a string representation of data.

Note

This method is intended to be overridden by artist subclasses. As an end-user of Matplotlib you will most likely not call this method yourself.

The default implementation converts ints and floats and arrays of ints and floats into a comma-separated string enclosed in square brackets, unless the artist has an associated colorbar, in which case scalar values are formatted using the colorbar’s formatter.

Parameters#

event : ~matplotlib.backend_bases.MouseEvent

Returns#

tuple or None

get_snap()[source]#

Return the snap setting.

See .set_snap for details.

get_solid_capstyle()[source]#

Return the .CapStyle for solid lines.

Parameters#

renderer~matplotlib.backend_bases.RendererBase subclass, optional: renderer that will be used to draw the figures (i.e. fig.canvas.get_renderer())

Returns#

.Bbox or None: The enclosing bounding box (in figure pixel coordinates). Returns None if clipping results in no intersection.

get_transform()[source]#: Return the .Transform instance used by this artist.

get_transformed_clip_path_and_affine()[source]#: Return the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation.

get_url()[source]#: Return the url.

get_visible()[source]#: Return the visibility.

get_window_extent(renderer=None)[source]#

Get the artist’s bounding box in display space.

The bounding box’ width and height are nonnegative.

Subclasses should override for inclusion in the bounding box “tight” calculation. Default is to return an empty bounding box at 0, 0.

Be careful when using this function, the results will not update if the artist window extent of the artist changes. The extent can change due to any changes in the transform stack, such as changing the Axes limits, the figure size, or the canvas used (as is done when saving a figure). This can lead to unexpected behavior where interactive figures will look fine on the screen, but will save incorrectly.

get_xdata(orig=True)[source]#

Return the xdata.

If orig is True, return the original data, else the processed data.

get_xydata()[source]#: Return the xy data as a (N, 2) array.

get_ydata(orig=True)[source]#

Return the ydata.

If orig is True, return the original data, else the processed data.

get_zorder()[source]#: Return the artist’s zorder.

have_units()[source]#: Return whether units are set on any axis.

is_dashed()[source]#

Return whether line has a dashed linestyle.

A custom linestyle is assumed to be dashed, we do not inspect the onoffseq directly.

Parameters#

filter_funccallable: A filter function, which takes a (m, n, depth) float array and a dpi value, and returns a (m, n, depth) array and two offsets from the bottom left corner of the image

set_alpha(alpha)[source]#

Set the alpha value used for blending - not supported on all backends.

Parameters#

alphascalar or None: alpha must be within the 0-1 range, inclusive.

set_animated(b)[source]#

Set whether the artist is intended to be used in an animation.

If True, the artist is excluded from regular drawing of the figure. You have to call .Figure.draw_artist / .Axes.draw_artist explicitly on the artist. This approach is used to speed up animations using blitting.

See also matplotlib.animation and blitting.

Parameters#

b : bool

set_antialiased(b)[source]#: Set whether to use antialiased rendering.

Parameters#

b : bool

set_c(color)[source]#: Alias for set_color.

set_clip_box(clipbox)[source]#

Set the artist’s clip .Bbox.

Parameters#

clipbox~matplotlib.transforms.BboxBase or None: Will typically be created from a .TransformedBbox. For instance, TransformedBbox(Bbox([[0, 0], [1, 1]]), ax.transAxes) is the default clipping for an artist added to an Axes.

set_clip_on(b)[source]#

Set whether the artist uses clipping.

When False, artists will be visible outside the Axes which can lead to unexpected results.

Parameters#

b : bool

set_clip_path(path, transform=None)[source]#

Set the artist’s clip path.

Parameters#

path~matplotlib.patches.Patch or .Path or .TransformedPath or None: The clip path. If given a .Path, transform must be provided as well. If None, a previously set clip path is removed.
transform~matplotlib.transforms.Transform, optional: Only used if path is a .Path, in which case the given .Path is converted to a .TransformedPath using transform.

Notes#

For efficiency, if path is a .Rectangle this method will set the clipping box to the corresponding rectangle and set the clipping path to None.

For technical reasons (support of ~.Artist.set), a tuple (path, transform) is also accepted as a single positional parameter.

set_color(color)[source]#: Set the color of the line.

Parameters#

color : :mpltype:`color`

set_dash_capstyle(s)[source]#

How to draw the end caps if the line is ~Line2D.is_dashed.

The default capstyle is :rc:`lines.dash_capstyle`.

Parameters#

s : .CapStyle or {‘butt’, ‘projecting’, ‘round’}

set_dash_joinstyle(s)[source]#

How to join segments of the line if it ~Line2D.is_dashed.

The default joinstyle is :rc:`lines.dash_joinstyle`.

Parameters#

s : .JoinStyle or {‘miter’, ‘round’, ‘bevel’}

set_dashes(seq)[source]#

Set the dash sequence.

The dash sequence is a sequence of floats of even length describing the length of dashes and spaces in points.

For example, (5, 2, 1, 2) describes a sequence of 5 point and 1 point dashes separated by 2 point spaces.

See also ~.Line2D.set_gapcolor, which allows those spaces to be filled with a color.

Parameters#

seqsequence of floats (on/off ink in points) or (None, None): If seq is empty or (None, None), the linestyle will be set to solid.

set_data(*args)[source]#: Set the x and y data.

Parameters#

*args : (2, N) array or two 1D arrays

See Also#

set_xdata set_ydata

set_drawstyle(drawstyle)[source]#

Set the drawstyle of the plot.

The drawstyle determines how the points are connected.

Parameters#

drawstyle{‘default’, ‘steps’, ‘steps-pre’, ‘steps-mid’, ‘steps-post’}, default: ‘default’

For ‘default’, the points are connected with straight lines.

The steps variants connect the points with step-like lines, i.e. horizontal lines with vertical steps. They differ in the location of the step:

‘steps-pre’: The step is at the beginning of the line segment, i.e. the line will be at the y-value of point to the right.
‘steps-mid’: The step is halfway between the points.
‘steps-post: The step is at the end of the line segment, i.e. the line will be at the y-value of the point to the left.
‘steps’ is equal to ‘steps-pre’ and is maintained for backward-compatibility.

For examples see /gallery/lines_bars_and_markers/step_demo.

set_ds(drawstyle)[source]#: Alias for set_drawstyle.

set_figure(fig)[source]#: Set the .Figure instance the artist belongs to.

Parameters#

fig : ~matplotlib.figure.Figure

set_fillstyle(fs)[source]#

Set the marker fill style.

Parameters#

fs{‘full’, ‘left’, ‘right’, ‘bottom’, ‘top’, ‘none’}

Possible values:

‘full’: Fill the whole marker with the markerfacecolor.
‘left’, ‘right’, ‘bottom’, ‘top’: Fill the marker half at the given side with the markerfacecolor. The other half of the marker is filled with markerfacecoloralt.
‘none’: No filling.

For examples see marker_fill_styles.

set_gapcolor(gapcolor)[source]#

Set a color to fill the gaps in the dashed line style.

Note

Striped lines are created by drawing two interleaved dashed lines. There can be overlaps between those two, which may result in artifacts when using transparency.

This functionality is experimental and may change.

Parameters#

gapcolor:mpltype:`color` or None: The color with which to fill the gaps. If None, the gaps are unfilled.

set_gid(gid)[source]#: Set the (group) id for the artist.

Parameters#

gid : str

set_in_layout(in_layout)[source]#: Set if artist is to be included in layout calculations, E.g. constrainedlayout_guide, .Figure.tight_layout(), and fig.savefig(fname, bbox_inches='tight').

Parameters#

in_layout : bool

set_label(s)[source]#

Set a label that will be displayed in the legend.

Parameters#

sobject: s will be converted to a string by calling str.

set_linestyle(ls)[source]#

Set the linestyle of the line.

Parameters#

ls{‘-’, ‘–’, ‘-.’, ‘:’, ‘’, (offset, on-off-seq), …}

Possible values:

A string:

linestyle

description

'-' or 'solid'

solid line

'--' or 'dashed'

dashed line

'-.' or 'dashdot'

dash-dotted line

':' or 'dotted'

dotted line

'none', 'None', ' ', or ''

draw nothing
Alternatively a dash tuple of the following form can be provided:
```
(offset, onoffseq)
```
where onoffseq is an even length tuple of on and off ink in points. See also set_dashes().

For examples see /gallery/lines_bars_and_markers/linestyles.

set_linewidth(w)[source]#

Set the line width in points.

Parameters#

wfloat: Line width, in points.

set_ls(ls)[source]#: Alias for set_linestyle.

set_lw(w)[source]#: Alias for set_linewidth.

set_marker(marker)[source]#

Set the line marker.

Parameters#

markermarker style string, ~.path.Path or ~.markers.MarkerStyle: See ~matplotlib.markers for full description of possible arguments.

set_markeredgecolor(ec)[source]#: Set the marker edge color.

Parameters#

ec : :mpltype:`color`

set_markeredgewidth(ew)[source]#

Set the marker edge width in points.

Parameters#

ewfloat: Marker edge width, in points.

set_markerfacecolor(fc)[source]#: Set the marker face color.

Parameters#

fc : :mpltype:`color`

set_markerfacecoloralt(fc)[source]#: Set the alternate marker face color.

Parameters#

fc : :mpltype:`color`

set_markersize(sz)[source]#

Set the marker size in points.

Parameters#

szfloat: Marker size, in points.

set_markevery(every)[source]#

Set the markevery property to subsample the plot when using markers.

e.g., if every=5, every 5-th marker will be plotted.

Parameters#

everyNone or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]

Which markers to plot.

every=None: every point will be plotted.
every=N: every N-th marker will be plotted starting with marker 0.
every=(start, N): every N-th marker, starting at index start, will be plotted.
every=slice(start, end, N): every N-th marker, starting at index start, up to but not including index end, will be plotted.
every=[i, j, m, ...]: only markers at the given indices will be plotted.
every=[True, False, True, ...]: only positions that are True will be plotted. The list must have the same length as the data points.
every=0.1, (i.e. a float): markers will be spaced at approximately equal visual distances along the line; the distance along the line between markers is determined by multiplying the display-coordinate distance of the Axes bounding-box diagonal by the value of every.
every=(0.5, 0.1) (i.e. a length-2 tuple of float): similar to every=0.1 but the first marker will be offset along the line by 0.5 multiplied by the display-coordinate-diagonal-distance along the line.

For examples see /gallery/lines_bars_and_markers/markevery_demo.

Notes#

Setting markevery will still only draw markers at actual data points. While the float argument form aims for uniform visual spacing, it has to coerce from the ideal spacing to the nearest available data point. Depending on the number and distribution of data points, the result may still not look evenly spaced.

When using a start offset to specify the first marker, the offset will be from the first data point which may be different from the first the visible data point if the plot is zoomed in.

If zooming in on a plot when using float arguments then the actual data points that have markers will change because the distance between markers is always determined from the display-coordinates axes-bounding-box-diagonal regardless of the actual axes data limits.

set_mec(ec)[source]#: Alias for set_markeredgecolor.

set_mew(ew)[source]#: Alias for set_markeredgewidth.

set_mfc(fc)[source]#: Alias for set_markerfacecolor.

set_mfcalt(fc)[source]#: Alias for set_markerfacecoloralt.

set_mouseover(mouseover)[source]#: Set whether this artist is queried for custom context information when the mouse cursor moves over it.

Parameters#

mouseover : bool

See Also#

get_cursor_data .ToolCursorPosition .NavigationToolbar2

set_ms(sz)[source]#: Alias for set_markersize.

set_path_effects(path_effects)[source]#: Set the path effects.

Parameters#

path_effects : list of .AbstractPathEffect

set_picker(p)[source]#

Set the event picker details for the line.

Parameters#

pfloat or callable[[Artist, Event], tuple[bool, dict]]: If a float, it is used as the pick radius in points.

set_pickradius(pickradius)[source]#

Set the pick radius used for containment tests.

See .contains for more details.

Parameters#

pickradiusfloat: Pick radius, in points.

set_rasterized(rasterized)[source]#

Force rasterized (bitmap) drawing for vector graphics output.

Rasterized drawing is not supported by all artists. If you try to enable this on an artist that does not support it, the command has no effect and a warning will be issued.

This setting is ignored for pixel-based output.

See also /gallery/misc/rasterization_demo.

Parameters#

rasterized : bool

set_sketch_params(scale=None, length=None, randomness=None)[source]#

Set the sketch parameters.

Parameters#

scalefloat, optional

The amplitude of the wiggle perpendicular to the source line, in pixels. If scale is None, or not provided, no sketch filter will be provided.

lengthfloat, optional

The length of the wiggle along the line, in pixels (default 128.0)

randomnessfloat, optional

The scale factor by which the length is shrunken or expanded (default 16.0)

The PGF backend uses this argument as an RNG seed and not as described above. Using the same seed yields the same random shape.

set_snap(snap)[source]#

Set the snapping behavior.

Snapping aligns positions with the pixel grid, which results in clearer images. For example, if a black line of 1px width was defined at a position in between two pixels, the resulting image would contain the interpolated value of that line in the pixel grid, which would be a grey value on both adjacent pixel positions. In contrast, snapping will move the line to the nearest integer pixel value, so that the resulting image will really contain a 1px wide black line.

Snapping is currently only supported by the Agg and MacOSX backends.

Parameters#

snapbool or None

Possible values:

True: Snap vertices to the nearest pixel center.
False: Do not modify vertex positions.
None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center.

set_solid_capstyle(s)[source]#

How to draw the end caps if the line is solid (not ~Line2D.is_dashed)

The default capstyle is :rc:`lines.solid_capstyle`.

Parameters#

s : .CapStyle or {‘butt’, ‘projecting’, ‘round’}

set_solid_joinstyle(s)[source]#

How to join segments if the line is solid (not ~Line2D.is_dashed).

The default joinstyle is :rc:`lines.solid_joinstyle`.

Parameters#

s : .JoinStyle or {‘miter’, ‘round’, ‘bevel’}

set_transform(t)[source]#: Set the artist transform.

Parameters#

t : ~matplotlib.transforms.Transform

set_url(url)[source]#: Set the url for the artist.

Parameters#

url : str

set_visible(b)[source]#: Set the artist’s visibility.

Parameters#

b : bool

set_xdata(x)[source]#: Set the data array for x.

Parameters#

x : 1D array

See Also#

set_data set_ydata

set_ydata(y)[source]#: Set the data array for y.

Parameters#

y : 1D array

See Also#

set_data set_xdata

set_zorder(level)[source]#: Set the zorder for the artist. Artists with lower zorder values are drawn first.

Parameters#

level : float

property stale#: Whether the artist is ‘stale’ and needs to be re-drawn for the output to match the internal state of the artist.

property sticky_edges#

x and y sticky edge lists for autoscaling.

When performing autoscaling, if a data limit coincides with a value in the corresponding sticky_edges list, then no margin will be added–the view limit “sticks” to the edge. A typical use case is histograms, where one usually expects no margin on the bottom edge (0) of the histogram.

Moreover, margin expansion “bumps” against sticky edges and cannot cross them. For example, if the upper data limit is 1.0, the upper view limit computed by simple margin application is 1.2, but there is a sticky edge at 1.1, then the actual upper view limit will be 1.1.

This attribute cannot be assigned to; however, the x and y lists can be modified in place as needed.

Examples#

>>> artist.sticky_edges.x[:] = (xmin, xmax)
>>> artist.sticky_edges.y[:] = (ymin, ymax)

update(props)[source]#: Update this artist’s properties from the dict props.

Parameters#

props : dict

update_from(other)[source]#: Copy properties from other to self.

class holoviews.plotting.mpl.annotation.AnnotationPlot(annotation, **params)[source]#

Bases: ElementPlot

AnnotationPlot handles the display of all annotation elements.

Parameters inherited from:

show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x172955050>): Whether to show legend for the plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.ArrowPlot(annotation, **params)[source]#

Bases: AnnotationPlot

Draw an arrow using the information supplied to the Arrow annotation

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.HLinePlot(annotation, **params)[source]#

Bases: AnnotationPlot

Draw a horizontal line on the axis

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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.

draw_annotation(axis, position, opts)[source]#: Draw a horizontal line on the axis

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.HLinesAnnotationPlot(annotation, **params)[source]#

Bases: _SyntheticAnnotationPlot

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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=None, 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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

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

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.HSpanPlot(annotation, **params)[source]#

Bases: AnnotationPlot

Draw a horizontal span on the axis

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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.

draw_annotation(axis, positions, opts)[source]#: Draw a horizontal span on the axis

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.HSpansAnnotationPlot(annotation, **params)[source]#

Bases: _SyntheticAnnotationPlot

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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=None, 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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

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

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.LabelsPlot(*args, **kwargs)[source]#

Bases: ColorbarPlot

Parameters inherited from:

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 0x1725e4650>): Index of the dimension from which the color will the drawn
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 0x17324b290>): 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 0x17324b490>): Amount of offset to apply to labels along x-axis.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.SlopePlot(annotation, **params)[source]#

Bases: AnnotationPlot

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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.

draw_annotation(axis, position, opts)[source]#: Draw a horizontal line on the axis

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.SplinePlot(annotation, **params)[source]#

Bases: AnnotationPlot

Draw the supplied Spline annotation (see Spline docstring)

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.TextPlot(annotation, **params)[source]#

Bases: AnnotationPlot

Draw the Text annotation object

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.VLinePlot(annotation, **params)[source]#

Bases: AnnotationPlot

Draw a vertical line on the axis

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.VLinesAnnotationPlot(annotation, **params)[source]#

Bases: _SyntheticAnnotationPlot

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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=None, 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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

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

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.VSpanPlot(annotation, **params)[source]#

Bases: AnnotationPlot

Draw a vertical span on the axis

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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.

draw_annotation(axis, positions, opts)[source]#: Draw a vertical span on the axis

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.annotation.VSpansAnnotationPlot(annotation, **params)[source]#

Bases: _SyntheticAnnotationPlot

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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=None, 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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

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

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, annotation, ranges, style)[source]#: Update the elements of the plot.

`chart` Module#

class holoviews.plotting.mpl.chart.AreaPlot(element, **params)[source]#

Bases: AreaMixin, ChartPlot

Parameters inherited from:

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 0x17a7f2b10>): 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.
show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a7f3ed0>): Whether to show legend for the plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_data, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.BarPlot(*args, **kwargs)[source]#

Bases: BarsMixin, ColorbarPlot, LegendPlot

Parameters inherited from:

show_legend = param.Boolean(allow_refs=False, default=True, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a4fa090>): Whether to show legend for the plot.
bar_padding = param.Number(allow_refs=False, default=0.2, inclusive_bounds=(True, True), label=’Bar padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a7b7690>): Defines the padding between groups.
multi_level = param.Boolean(allow_refs=False, default=True, label=’Multi level’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a7b7390>): 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 0x17a5aaf10>): Whether the bars should be stacked or grouped.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.ChartPlot(element, **params)[source]#

Bases: ElementPlot

Baseclass to plot Chart elements.

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.CurvePlot(element, **params)[source]#

Bases: ChartPlot

CurvePlot can plot Curve and ViewMaps of Curve, which can be displayed as a single frame or animation. Axes, titles and legends are automatically generated from dim_info.

If the dimension is set to cyclic in the dim_info it will rotate the curve so that minimum y values are at the minimum x value to make the plots easier to interpret.

Parameters inherited from:

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 0x17a8b4650>): 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.
show_legend = param.Boolean(allow_refs=False, default=True, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a90e4d0>): Whether to show legend for the plot.
show_grid = param.Boolean(allow_refs=False, default=False, label=’Show grid’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a90d890>): Enable axis grid.
autotick = param.Boolean(allow_refs=False, default=False, label=’Autotick’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a90e450>): Whether to let matplotlib automatically compute tick marks or to allow the user to control tick marks.
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 0x17a90dd90>): Defines how the samples of the Curve are interpolated, default is ‘linear’, other options include ‘steps-mid’, ‘steps-pre’ and ‘steps-post’.
relative_labels = param.Boolean(allow_refs=False, default=False, label=’Relative labels’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a90de90>): If plotted quantity is cyclic and center_cyclic is enabled, will compute tick labels relative to the center.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.ErrorPlot(*args, **kwargs)[source]#

Bases: ColorbarPlot

ErrorPlot plots the ErrorBar Element type and supporting both horizontal and vertical error bars via the ‘horizontal’ plot option.

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_data, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.HistogramPlot(histograms, **params)[source]#

Bases: ColorbarPlot

HistogramPlot can plot DataHistograms and ViewMaps of DataHistograms, which can be displayed as a single frame or animation.

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.PointPlot(*args, **kwargs)[source]#

Bases: ChartPlot, ColorbarPlot, LegendPlot

Note that the ‘cmap’, ‘vmin’ and ‘vmax’ style arguments control how point magnitudes are rendered to different colors.

Parameters inherited from:

show_grid = param.Boolean(allow_refs=False, default=False, label=’Show grid’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ab35c90>): Whether to draw grid lines at the tick positions.
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 0x17ab37550>): Deprecated in favor of color style mapping, e.g. color=dim(‘color’)
size_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), label=’Size index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ab35d90>): Deprecated in favor of size style mapping, e.g. size=dim(‘size’)
scaling_method = param.Selector(allow_refs=False, default=’area’, label=’Scaling method’, names={}, nested_refs=False, objects=[‘width’, ‘area’], rx=<param.reactive.reactive_ops object at 0x17ab375d0>): Deprecated in favor of size style mapping, e.g. size=dim(‘size’)**2.
scaling_factor = param.Number(allow_refs=False, bounds=(0, None), default=1, inclusive_bounds=(True, True), label=’Scaling factor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ab37b10>): Scaling factor which is applied to either the width or area of each point, depending on the value of scaling_method.
size_fn = param.Callable(allow_refs=False, default=<ufunc ‘absolute’>, label=’Size fn’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a65fe50>): Function applied to size values before applying scaling, to remove values lower than zero.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

size_fn = <ufunc 'absolute'>#

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

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.SideAreaPlot(element, **params)[source]#

Bases: AdjoinedPlot, AreaPlot

Parameters inherited from:

bgcolor = param.Parameter(allow_refs=False, default=(1, 1, 1, 0), label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17a548090>): Make plot background invisible.
xaxis = param.Selector(allow_refs=False, default=’bare’, label=’Xaxis’, names={}, nested_refs=False, objects=[‘top’, ‘bottom’, ‘bare’, ‘top-bare’, ‘bottom-bare’, None], rx=<param.reactive.reactive_ops object at 0x17ab8f6d0>): 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=’bare’, label=’Yaxis’, names={}, nested_refs=False, objects=[‘left’, ‘right’, ‘bare’, ‘left-bare’, ‘right-bare’, None], rx=<param.reactive.reactive_ops object at 0x17ab8f9d0>): 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’.
border_size = param.Number(allow_refs=False, default=0, inclusive_bounds=(True, True), label=’Border size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ab8fbd0>): The size of the border expressed as a fraction of the main plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_data, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.SideHistogramPlot(histograms, **params)[source]#

Bases: AdjoinedPlot, HistogramPlot

Parameters inherited from:

bgcolor = param.Parameter(allow_refs=False, default=(1, 1, 1, 0), label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17abfae10>): Make plot background invisible.
show_grid = param.Boolean(allow_refs=False, default=False, label=’Show grid’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17abfc550>): Whether to overlay a grid on the axis.
offset = param.Number(allow_refs=False, bounds=(0, 1), default=0.2, inclusive_bounds=(True, True), label=’Offset’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17abfca50>): Histogram value offset for a colorbar.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.SideSpikesPlot(*args, **kwargs)[source]#

Bases: AdjoinedPlot, SpikesPlot

Parameters inherited from:

show_frame = param.Boolean(allow_refs=False, default=True, label=’Show frame’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ac49210>): Whether or not to show a complete frame around the plot.
bgcolor = param.Parameter(allow_refs=False, default=(1, 1, 1, 0), label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ac53790>): Make plot background invisible.
xaxis = param.Selector(allow_refs=False, default=’bare’, label=’Xaxis’, names={}, nested_refs=False, objects=[‘top’, ‘bottom’, ‘bare’, ‘top-bare’, ‘bottom-bare’, None], rx=<param.reactive.reactive_ops object at 0x17ac53a90>): 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=’bare’, label=’Yaxis’, names={}, nested_refs=False, objects=[‘left’, ‘right’, ‘bare’, ‘left-bare’, ‘right-bare’, None], rx=<param.reactive.reactive_ops object at 0x17ac53d50>): 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’.
spike_length = param.Number(allow_refs=False, default=1, inclusive_bounds=(True, True), label=’Spike length’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ac53f50>): The length of each spike if Spikes object is one dimensional.
border_size = param.Number(allow_refs=False, default=0, inclusive_bounds=(True, True), label=’Border size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ac60350>): The size of the border expressed as a fraction of the main plot.
subplot_size = param.Number(allow_refs=False, default=0.1, inclusive_bounds=(True, True), label=’Subplot size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ac53f50>): The size subplots as expressed as a fraction of the main plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.SpikesPlot(*args, **kwargs)[source]#

Bases: SpikesMixin, PathPlot, ColorbarPlot

Parameters inherited from:

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 0x17acca010>): 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.
aspect = param.Parameter(allow_refs=False, default=’square’, label=’Aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17accb6d0>): The aspect ratio mode of the plot. Allows setting an explicit aspect ratio as width/height as well as ‘square’ and ‘equal’ options.
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 0x17acc9f50>): Index of the dimension from which the color will the drawn
spike_length = param.Number(allow_refs=False, default=0.1, inclusive_bounds=(True, True), label=’Spike length’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17accbbd0>): The length of each spike if Spikes object is one dimensional.
position = param.Number(allow_refs=False, default=0.0, inclusive_bounds=(True, True), label=’Position’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17acc4050>): The position of the lower end of each spike.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.SpreadPlot(element, **params)[source]#

Bases: AreaPlot

SpreadPlot plots the Spread Element type.

Parameters inherited from:

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 0x17ad28810>): 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.
show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ad29f10>): Whether to show legend for the plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_data, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart.VectorFieldPlot(*args, **kwargs)[source]#

Bases: ColorbarPlot

Renders vector fields in sheet coordinates. The vectors are expressed in polar coordinates and may be displayed according to angle alone (with some common, arbitrary arrow length) or may be true polar vectors.

The color or magnitude can be mapped onto any dimension using the color_index and size_index.

The length of the arrows is controlled by the ‘scale’ style option. The scaling of the arrows may also be controlled via the normalize_lengths and rescale_lengths plot option, which will normalize the lengths to a maximum of 1 and scale them according to the minimum distance respectively.

Parameters inherited from:

padding = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘tuple’>), default=0.05, label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ad7d610>): Fraction by which to increase auto-ranged extents to make datapoints more visible around borders. To compute padding, the axis whose screen size is largest is chosen, and the range of that axis is increased by the specified fraction along each axis. Other axes are then padded ensuring that the amount of screen space devoted to padding is equal for all axes. If specified as a tuple, the int or float values in the tuple will be used for padding in each axis, in order (x,y or x,y,z). For example, for padding=0.2 on a 800x800-pixel plot, an x-axis with the range [0,10] will be padded by 20% to be [-1,11], while a y-axis with a range [0,1000] will be padded to be [-100,1100], which should make the padding be approximately the same number of pixels. But if the same plot is changed to have a height of only 200, the y-range will then be [-400,1400] so that the y-axis padding will still match that of the x-axis. It is also possible to declare non-equal padding value for the lower and upper bound of an axis by supplying nested tuples, e.g. padding=(0.1, (0, 0.1)) will pad the x-axis lower and upper bound as well as the y-axis upper bound by a fraction of 0.1 while the y-axis lower bound is not padded at all.
arrow_heads = param.Boolean(allow_refs=False, default=True, label=’Arrow heads’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ad7ecd0>): Whether or not to draw arrow heads. If arrowheads are enabled, they may be customized with the ‘headlength’ and ‘headaxislength’ style options.
magnitude = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘holoviews.util.transform.dim’>), label=’Magnitude’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ad7d550>): Dimension or dimension value transform that declares the magnitude of each vector. Magnitude is expected to be scaled between 0-1, by default the magnitudes are rescaled relative to the minimum distance between vectors, this can be disabled with the rescale_lengths option.
rescale_lengths = param.Boolean(allow_refs=False, default=True, label=’Rescale lengths’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ad7ec90>): Whether the lengths will be rescaled to take into account the smallest non-zero distance between two vectors.
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 0x17ad7ee50>): Deprecated in favor of dimension value transform on color option, e.g. color=dim(‘Magnitude’).
size_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), label=’Size index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ad7ecd0>): Deprecated in favor of the magnitude option, e.g. magnitude=dim(‘Magnitude’).
normalize_lengths = param.Boolean(allow_refs=False, default=True, label=’Normalize lengths’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ad7d050>): Deprecated in favor of rescaling length using dimension value transforms using the magnitude option, e.g. dim(‘Magnitude’).norm().

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`chart3d` Module#

class holoviews.plotting.mpl.chart3d.Path3DPlot(*args, **kwargs)[source]#

Bases: Plot3D, PathPlot

Allows plotting paths on a 3D axis.

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart3d.Plot3D(*args, **kwargs)[source]#

Bases: ColorbarPlot

Plot3D provides a common baseclass for mplot3d based plots.

Parameters inherited from:

projection = param.Selector(allow_refs=False, default=’3d’, label=’Projection’, names={}, nested_refs=False, objects=[‘3d’], rx=<param.reactive.reactive_ops object at 0x17b3869d0>): The projection of the matplotlib axis.
bgcolor = param.String(allow_refs=False, default=’white’, label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b386190>): Background color of the axis.
show_grid = param.Boolean(allow_refs=False, default=True, label=’Show grid’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17aff3f10>): Whether to draw a grid in the figure.
xaxis = param.Selector(allow_refs=False, default=’fixed’, label=’Xaxis’, names={}, nested_refs=False, objects=[‘fixed’, None], rx=<param.reactive.reactive_ops object at 0x17b386390>): Whether and where to display the xaxis.
yaxis = param.Selector(allow_refs=False, default=’fixed’, label=’Yaxis’, names={}, nested_refs=False, objects=[‘fixed’, None], rx=<param.reactive.reactive_ops object at 0x17b387250>): Whether and where to display the yaxis.
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 0x17b387cd0>): Whether to plot the ‘x’, ‘y’ and ‘z’ labels.
zaxis = param.Selector(allow_refs=False, default=’fixed’, label=’Zaxis’, names={}, nested_refs=False, objects=[‘fixed’, None], rx=<param.reactive.reactive_ops object at 0x17b386d90>): Whether and where to display the yaxis.
azimuth = param.Integer(allow_refs=False, bounds=(-180, 180), default=-60, inclusive_bounds=(True, True), label=’Azimuth’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b007f90>): Azimuth angle in the x,y plane.
elevation = param.Integer(allow_refs=False, bounds=(0, 180), default=30, inclusive_bounds=(True, True), label=’Elevation’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b3863d0>): Elevation angle in the z-axis.
distance = param.Integer(allow_refs=False, bounds=(7, 15), default=10, inclusive_bounds=(True, True), label=’Distance’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b41d710>): Distance from the plotted object.
disable_axes = param.Boolean(allow_refs=False, default=False, label=’Disable axes’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b387410>): Disable all axes.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart3d.Scatter3DPlot(*args, **kwargs)[source]#

Bases: Plot3D, PointPlot

Subclass of PointPlot allowing plotting of Points on a 3D axis, also allows mapping color and size onto a particular Dimension of the data.

Parameters inherited from:

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 0x17b496290>): Index of the dimension from which the color will the drawn
size_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), label=’Size index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b496b50>): Index of the dimension from which the sizes will the drawn.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

size_fn = <ufunc 'absolute'>#

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

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart3d.SurfacePlot(*args, **kwargs)[source]#

Bases: Plot3D

Plots surfaces wireframes and contours in 3D space. Provides options to switch the display type via the plot_type parameter has support for a number of styling options including strides and colors.

Parameters inherited from:

colorbar = param.Boolean(allow_refs=False, default=False, label=’Colorbar’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b4d5c10>): Whether to add a colorbar to the plot.
plot_type = param.Selector(allow_refs=False, default=’surface’, label=’Plot type’, names={}, nested_refs=False, objects=[‘surface’, ‘wireframe’, ‘contour’], rx=<param.reactive.reactive_ops object at 0x17b4cf790>): Specifies the type of visualization for the Surface object. Valid values are ‘surface’, ‘wireframe’ and ‘contour’.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_data, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.chart3d.TriSurfacePlot(*args, **kwargs)[source]#

Bases: Plot3D

Plots a trisurface given a TriSurface element, containing X, Y and Z coordinates.

Parameters inherited from:

colorbar = param.Boolean(allow_refs=False, default=False, label=’Colorbar’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b559dd0>): Whether to add a colorbar to the plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`element` Module#

class holoviews.plotting.mpl.element.ColorbarPlot(*args, **kwargs)[source]#

Bases: ElementPlot

Parameters inherited from:

clabel = param.String(allow_None=True, allow_refs=False, label=’Clabel’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b7d2890>): An explicit override of the color bar label, if set takes precedence over the title key in colorbar_opts.
clim = param.Tuple(allow_refs=False, default=(nan, nan), label=’Clim’, length=2, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b536410>): 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 0x17b7d2b90>): Percentile value to compute colorscale robust to outliers. If True, uses 2nd and 98th percentile; otherwise uses the specified numerical percentile value.
cformatter = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘matplotlib.ticker.Formatter’>, <class ‘function’>), label=’Cformatter’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b73c210>): Formatter for ticks along the colorbar axis.
colorbar = param.Boolean(allow_refs=False, default=False, label=’Colorbar’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b65bf10>): Whether to draw a colorbar.
colorbar_opts = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={}, label=’Colorbar opts’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b73cb50>): Allows setting specific styling options for the 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 0x17b536410>): Number of discrete colors to use when colormapping or a set of color intervals defining the range of values to map each color to.
cnorm = param.Selector(allow_refs=False, default=’linear’, label=’Cnorm’, names={}, nested_refs=False, objects=[‘linear’, ‘log’, ‘eq_hist’], rx=<param.reactive.reactive_ops object at 0x17b73cb90>): Color normalization to be applied during colormapping.
clipping_colors = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={}, label=’Clipping colors’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b536410>): Dictionary to specify colors for clipped values, allows setting color for NaN values and for values above and below the min and max value. The min, max or NaN color may specify an RGB(A) color as a color hex string of the form #FFFFFF or #FFFFFFFF or a length 3 or length 4 tuple specifying values in the range 0-1 or a named HTML color.
cbar_padding = param.Number(allow_refs=False, default=0.01, inclusive_bounds=(True, True), label=’Cbar padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17af9cbd0>): Padding between colorbar and other plots.
cbar_ticks = param.Parameter(allow_None=True, allow_refs=False, label=’Cbar ticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b250090>): Ticks along colorbar-axis specified as an integer, explicit list of tick locations, list of tuples containing the locations and labels or a matplotlib tick locator object. If set to None default matplotlib ticking behavior is applied.
cbar_width = param.Number(allow_refs=False, default=0.05, inclusive_bounds=(True, True), label=’Cbar width’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b65bf10>): Width of the colorbar as a fraction of the main plot
cbar_extend = param.Selector(allow_refs=False, label=’Cbar extend’, names={}, nested_refs=False, objects=[‘neither’, ‘both’, ‘min’, ‘max’], rx=<param.reactive.reactive_ops object at 0x17b7d2b90>): If not ‘neither’, make pointed end(s) for out-of- range values.
rescale_discrete_levels = param.Boolean(allow_refs=False, default=True, label=’Rescale discrete levels’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b7d2910>): If cnorm='eq_hist and there are only a few discrete values, then rescale_discrete_levels=True decreases the lower limit of the autoranged span so that the values are rendering towards the (more visible) top of the palette, thus avoiding washout of the lower values. Has no effect if cnorm!=`eq_hist. Set this value to False if you need to match historical unscaled behavior, prior to HoloViews 1.14.4.
symmetric = param.Boolean(allow_refs=False, default=False, label=’Symmetric’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x1733b5850>): Whether to make the colormap symmetric around zero.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.element.ElementPlot(element, **params)[source]#

Bases: GenericElementPlot, MPLPlot

Parameters inherited from:

apply_ticks = param.Boolean(allow_refs=False, default=True, label=’Apply ticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b7e4650>): Whether to apply custom ticks.
aspect = param.Parameter(allow_refs=False, default=’square’, label=’Aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8b4710>): 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 specifying the ratio between plot width and height may also be passed. To control the aspect ratio between the axis scales use the data_aspect option instead.
data_aspect = param.Number(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Data aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8b4c10>): Defines the aspect of the axis scaling, i.e. the ratio of y-unit to x-unit.
invert_zaxis = param.Boolean(allow_refs=False, default=False, label=’Invert zaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8b4650>): Whether to invert the plot z-axis.
labelled = param.List(allow_refs=False, bounds=(0, None), default=[‘x’, ‘y’], label=’Labelled’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8cd2d0>): Whether to plot the ‘x’ and ‘y’ labels.
logz = param.Boolean(allow_refs=False, default=False, label=’Logz’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8b4f90>): Whether to apply log scaling to the y-axis of the Chart.
xformatter = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘matplotlib.ticker.Formatter’>, <class ‘function’>), label=’Xformatter’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8cd2d0>): Formatter for ticks along the x-axis.
yformatter = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘matplotlib.ticker.Formatter’>, <class ‘function’>), label=’Yformatter’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8b4790>): Formatter for ticks along the y-axis.
zformatter = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘matplotlib.ticker.Formatter’>, <class ‘function’>), label=’Zformatter’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8cd2d0>): Formatter for ticks along the z-axis.
zaxis = param.Boolean(allow_refs=False, default=True, label=’Zaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8b4890>): Whether to display the z-axis.
zlabel = param.String(allow_None=True, allow_refs=False, label=’Zlabel’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8cdbd0>): An explicit override of the z-axis label, if set takes precedence over the dimension label.
zrotation = param.Integer(allow_refs=False, bounds=(0, 360), default=0, inclusive_bounds=(True, True), label=’Zrotation’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8b4790>): Rotation angle of the zticks.
zticks = param.Parameter(allow_None=True, allow_refs=False, label=’Zticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17b8cff90>): Ticks along z-axis specified as an integer, explicit list of tick locations, list of tuples containing the locations and labels or a matplotlib tick locator object. If set to None default matplotlib ticking behavior is applied.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.element.LegendPlot(element, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

show_legend = param.Boolean(allow_refs=False, default=True, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ba82550>): Whether to show legend for the plot.
legend_cols = param.Integer(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Legend cols’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ba58450>): Number of legend columns in the legend.
legend_labels = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={}, label=’Legend labels’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ba58c90>): A mapping that allows overriding legend labels.
legend_position = param.Selector(allow_refs=False, default=’inner’, label=’Legend position’, names={}, nested_refs=False, objects=[‘inner’, ‘right’, ‘bottom’, ‘top’, ‘left’, ‘best’, ‘top_right’, ‘top_left’, ‘bottom_left’, ‘bottom_right’], rx=<param.reactive.reactive_ops object at 0x17ba58650>): Allows selecting between a number of predefined legend position options. The predefined options may be customized in the legend_specs class attribute. By default, ‘inner’, ‘right’, ‘bottom’, ‘top’, ‘left’, ‘best’, ‘top_right’, ‘top_left’, ‘bottom_right’ and ‘bottom_left’ are supported.
legend_opts = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={}, label=’Legend opts’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ba58910>): Allows setting specific styling options for the colorbar.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.element.OverlayPlot(overlay, ranges=None, **params)[source]#

Bases: LegendPlot, GenericOverlayPlot

OverlayPlot supports compositors processing of Overlays across maps.

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`geometry` Module#

class holoviews.plotting.mpl.geometry.RectanglesPlot(*args, **kwargs)[source]#

Bases: GeomMixin, ColorbarPlot

Rectangles are polygons in 2D space where the key dimensions represent the bottom-left and top-right corner of the rectangle.

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.geometry.SegmentPlot(*args, **kwargs)[source]#

Bases: GeomMixin, ColorbarPlot

Segments are lines in 2D space where each two key dimensions specify a (x, y) node of the line.

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`graphs` Module#

class holoviews.plotting.mpl.graphs.ChordPlot(*args, **kwargs)[source]#

Bases: ChordMixin, GraphPlot

Parameters inherited from:

labels = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘holoviews.util.transform.dim’>), label=’Labels’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bd04e10>): The dimension or dimension value transform used to draw labels from.
label_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), label=’Label index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bd096d0>): Index of the dimension from which the node labels will be drawn

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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]#: A Chord plot is always drawn on a unit circle.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.graphs.GraphPlot(*args, **kwargs)[source]#

Bases: GraphMixin, ColorbarPlot

Parameters inherited from:

arrowhead_length = param.Number(allow_refs=False, default=0.025, inclusive_bounds=(True, True), label=’Arrowhead length’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bdd7d50>): If directed option is enabled this determines the length of the arrows as fraction of the overall extent of the graph.
directed = param.Boolean(allow_refs=False, default=False, label=’Directed’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bdb6790>): Whether to draw arrows on the graph edges to indicate the directionality of each edge.
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 0x17bdd7690>): Deprecated in favor of color style mapping, e.g. node_color=dim(‘color’)
edge_color_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), label=’Edge color index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bdd7e50>): Deprecated in favor of color style mapping, e.g. edge_color=dim(‘color’)

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.graphs.TriMeshPlot(*args, **kwargs)[source]#

Bases: GraphPlot

Parameters inherited from:

filled = param.Boolean(allow_refs=False, default=False, label=’Filled’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bdbf9d0>): Whether the triangles should be drawn as filled.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`heatmap` Module#

class holoviews.plotting.mpl.heatmap.HeatMapPlot(*args, **kwargs)[source]#

Bases: HeatMapMixin, QuadMeshPlot

Parameters inherited from:

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 0x17c00ca90>): 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.
xticks = param.Parameter(allow_refs=False, default=20, label=’Xticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bffac50>): Ticks along x-axis/segments specified as an integer, explicit list of ticks or function. If None, no ticks are shown.
yticks = param.Parameter(allow_refs=False, default=20, label=’Yticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bffaa10>): Ticks along y-axis/annulars specified as an integer, explicit list of ticks or function. If None, no ticks are shown.
clipping_colors = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={‘NaN’: ‘white’}, label=’Clipping colors’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ba79850>): Dictionary to specify colors for clipped values, allows setting color for NaN values and for values above and below the min and max value. The min, max or NaN color may specify an RGB(A) color as a color hex string of the form #FFFFFF or #FFFFFFFF or a length 3 or length 4 tuple specifying values in the range 0-1 or a named HTML color.
radial = param.Boolean(allow_refs=False, default=False, label=’Radial’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c00ca90>): Whether the HeatMap should be radial
show_values = param.Boolean(allow_refs=False, default=False, label=’Show values’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c00b5d0>): Whether to annotate each pixel with its value.
xmarks = param.Parameter(allow_None=True, allow_refs=False, label=’Xmarks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c00ca90>): Add separation lines to the heatmap for better readability. By default, does not show any separation lines. If parameter is of type integer, draws the given amount of separations lines spread across heatmap. If parameter is of type list containing integers, show separation lines at given indices. If parameter is of type tuple, draw separation lines at given categorical values. If parameter is of type function, draw separation lines where function returns True for passed heatmap category.
ymarks = param.Parameter(allow_None=True, allow_refs=False, label=’Ymarks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c00b5d0>): Add separation lines to the heatmap for better readability. By default, does not show any separation lines. If parameter is of type integer, draws the given amount of separations lines spread across heatmap. If parameter is of type list containing integers, show separation lines at given indices. If parameter is of type tuple, draw separation lines at given categorical values. If parameter is of type function, draw separation lines where function returns True for passed heatmap category.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.heatmap.RadialHeatMapPlot(*args, **kwargs)[source]#

Bases: ColorbarPlot

Parameters inherited from:

projection = param.Selector(allow_refs=False, default=’polar’, label=’Projection’, names={}, nested_refs=False, objects=[‘polar’], rx=<param.reactive.reactive_ops object at 0x17bfda110>): The projection of the plot axis, default of None is equivalent to 2D plot, ‘3d’ and ‘polar’ are also supported by matplotlib by default. May also supply a custom projection that is either a matplotlib projection type or implements the _as_mpl_axes method.
xticks = param.Parameter(allow_refs=False, default=4, label=’Xticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bfc9fd0>): Ticks along x-axis/segments specified as an integer, explicit list of ticks or function. If None, no ticks are shown.
yticks = param.Parameter(allow_refs=False, default=4, label=’Yticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bfe0490>): Ticks along y-axis/annulars specified as an integer, explicit list of ticks or function. If None, no ticks are shown.
start_angle = param.Number(allow_refs=False, default=1.5707963267948966, inclusive_bounds=(True, True), label=’Start angle’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bfc9c90>): Define starting angle of the first annulars. By default, beings at 12 o clock.
max_radius = param.Number(allow_refs=False, default=0.5, inclusive_bounds=(True, True), label=’Max radius’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17beff050>): Define the maximum radius which is used for the x and y range extents.
radius_inner = param.Number(allow_refs=False, bounds=(0, 0.5), default=0.1, inclusive_bounds=(True, True), label=’Radius inner’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bec6710>): Define the radius fraction of inner, empty space.
radius_outer = param.Number(allow_refs=False, bounds=(0, 1), default=0.05, inclusive_bounds=(True, True), label=’Radius outer’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bf712d0>): Define the radius fraction of outer space including the labels.
radial = param.Boolean(allow_refs=False, default=True, label=’Radial’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bfc8490>): Whether the HeatMap should be radial
show_values = param.Boolean(allow_refs=False, default=False, label=’Show values’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bec6310>): Whether to annotate each pixel with its value.
xmarks = param.Parameter(allow_None=True, allow_refs=False, label=’Xmarks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bfc8c10>): Add separation lines between segments for better readability. By default, does not show any separation lines. If parameter is of type integer, draws the given amount of separations lines spread across radial heatmap. If parameter is of type list containing integers, show separation lines at given indices. If parameter is of type tuple, draw separation lines at given segment values. If parameter is of type function, draw separation lines where function returns True for passed segment value.
ymarks = param.Parameter(allow_None=True, allow_refs=False, label=’Ymarks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17bfe07d0>): Add separation lines between annulars for better readability. By default, does not show any separation lines. If parameter is of type integer, draws the given amount of separations lines spread across radial heatmap. If parameter is of type list containing integers, show separation lines at given indices. If parameter is of type tuple, draw separation lines at given annular values. If parameter is of type function, draw separation lines where function returns True for passed annular value.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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(view, 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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`hex_tiles` Module#

class holoviews.plotting.mpl.hex_tiles.HexTilesPlot(*args, **kwargs)[source]#

Bases: ColorbarPlot

Parameters inherited from:

aggregator = param.Callable(allow_refs=False, default=<function size at 0x103fe1eb0>, label=’Aggregator’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c1f4690>): Aggregation function used to compute bin values. Any NumPy reduction is allowed, defaulting to np.size to count the number of values in each bin.
gridsize = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘tuple’>), default=50, label=’Gridsize’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c1f5f90>): Number of hexagonal bins along x- and y-axes. Defaults to uniform sampling along both axes when setting and integer but independent bin sampling can be specified a tuple of integers corresponding to the number of bins along each axis.
max_scale = param.Number(allow_refs=False, bounds=(0, None), default=0.9, inclusive_bounds=(True, True), label=’Max scale’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c1f6390>): When size_index is enabled this defines the maximum size of each bin relative to uniform tile size, i.e. for a value of 1, the largest bin will match the size of bins when scaling is disabled. Setting value larger than 1 will result in overlapping bins.
min_count = param.Number(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Min count’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c1f6890>): The display threshold before a bin is shown, by default bins with a count of less than 1 are hidden.

aggregator(axis=None)[source]#

Return the number of elements along a given axis.

Parameters#

aarray_like: Input data.
axisint, optional: Axis along which the elements are counted. By default, give the total number of elements.

Returns#

element_countint: Number of elements along the specified axis.

Examples#

>>> a = np.array([[1,2,3],[4,5,6]])
>>> np.size(a)
6
>>> np.size(a,1)
3
>>> np.size(a,0)
2

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`path` Module#

class holoviews.plotting.mpl.path.ContourPlot(*args, **kwargs)[source]#

Bases: PathPlot

Parameters inherited from:

color_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), default=0, label=’Color index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c2cac10>): Index of the dimension from which the color will the drawn

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.path.PathPlot(*args, **kwargs)[source]#

Bases: ColorbarPlot

Parameters inherited from:

show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c1b3f10>): Whether to show legend for the plot.
aspect = param.Parameter(allow_refs=False, default=’square’, label=’Aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c364b90>): PathPlots axes usually define single space so aspect of Paths follows aspect in data coordinates by default.
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 0x17c35f650>): Index of the dimension from which the color will the drawn

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.path.PolygonPlot(*args, **kwargs)[source]#

Bases: ContourPlot

PolygonPlot draws the polygon paths in the supplied Polygons object. If the Polygon has an associated value the color of Polygons will be drawn from the supplied cmap, otherwise the supplied facecolor will apply. Facecolor also determines the color for non-finite values.

Parameters inherited from:

show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c3b4890>): Whether to show legend for the plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`plot` Module#

class holoviews.plotting.mpl.plot.AdjoinedPlot(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

Parameters inherited from:

show_title = param.Boolean(allow_refs=False, default=False, label=’Show title’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c56c250>): Titles should be disabled on all SidePlots to avoid clutter.
aspect = param.Parameter(allow_refs=False, default=’auto’, label=’Aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c569790>): Aspect ratios on SideHistogramPlot should be determined by the AdjointLayoutPlot.
bgcolor = param.Parameter(allow_refs=False, default=(1, 1, 1, 0), label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c56c1d0>): Make plot background invisible.
border_size = param.Number(allow_refs=False, default=0.25, inclusive_bounds=(True, True), label=’Border size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c44cb10>): The size of the border expressed as a fraction of the main plot.
subplot_size = param.Number(allow_refs=False, default=0.25, inclusive_bounds=(True, True), label=’Subplot size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c56bd90>): The size subplots as expressed as a fraction of the main plot.
show_xlabel = param.Boolean(allow_refs=False, default=False, label=’Show xlabel’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c569810>): Whether to show the x-label of the plot. Disabled by default because plots are often too cramped to fit the title correctly.

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)[source]#: Initialize the matplotlib figure.

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.mpl.plot.AdjointLayoutPlot(layout, layout_type, subaxes, subplots, **params)[source]#

Bases: MPLPlot, GenericAdjointLayoutPlot

Initially, a AdjointLayoutPlot computes an appropriate layout based for the number of Views in the AdjointLayout object it has been given, but when embedded in a NdLayout, it can recompute the layout to match the number of rows and columns as part of a larger grid.

Parameters inherited from:

adjust_positions(redraw=True)[source]#

Make adjustments to the positions of subplots (if available) relative to the main plot axes as required.

This method is called by LayoutPlot after an initial pass used to position all the Layouts together. This method allows LayoutPlots to make final adjustments to the axis positions.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.mpl.plot.CompositePlot(layout, keys=None, dimensions=None, **params)[source]#

Bases: GenericCompositePlot, MPLPlot

CompositePlot provides a baseclass for plots coordinate multiple subplots to form a Layout.

Parameters inherited from:

shared_axes = param.Boolean(allow_refs=False, default=True, label=’Shared axes’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c60bb10>): Whether axes ranges should be shared across the layout, if disabled switches axiswise normalization option on globally.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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)[source]#: Initialize the matplotlib figure.

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.mpl.plot.GridPlot(layout, axis=None, create_axes=True, ranges=None, layout_num=1, keys=None, **params)[source]#

Bases: CompositePlot

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

Parameters inherited from:

aspect = param.Parameter(allow_refs=False, default=’equal’, label=’Aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c63bc10>): Aspect ratios on GridPlot should be automatically determined.
padding = param.Number(allow_refs=False, default=0.1, inclusive_bounds=(True, True), label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c649a90>): The amount of padding as a fraction of the total Grid size
shared_xaxis = param.Boolean(allow_refs=False, default=False, label=’Shared xaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c63af50>): If enabled the x-axes of the GridSpace will be drawn from the objects inside the Grid rather than the GridSpace dimensions.
shared_yaxis = param.Boolean(allow_refs=False, default=False, label=’Shared yaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c63b990>): If enabled the x-axes of the GridSpace will be drawn from the objects inside the Grid rather than the GridSpace dimensions.
show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c63af50>): Legends add to much clutter in a grid and are disabled by default.
xaxis = param.Selector(allow_refs=False, default=’bottom’, label=’Xaxis’, names={}, nested_refs=False, objects=[‘bottom’, ‘top’, None], rx=<param.reactive.reactive_ops object at 0x17c63ae10>): Whether and where to display the xaxis, supported options are ‘bottom’, ‘top’ and None.
yaxis = param.Selector(allow_refs=False, default=’left’, label=’Yaxis’, names={}, nested_refs=False, objects=[‘left’, ‘right’, None], rx=<param.reactive.reactive_ops object at 0x17c648250>): Whether and where to display the yaxis, supported options are ‘left’, ‘right’ and None.
xrotation = param.Integer(allow_refs=False, bounds=(0, 360), default=0, inclusive_bounds=(True, True), label=’Xrotation’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c0e7f90>): Rotation angle of the xticks.
yrotation = param.Integer(allow_refs=False, bounds=(0, 360), default=0, inclusive_bounds=(True, True), label=’Yrotation’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c649e90>): Rotation angle of the yticks.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.mpl.plot.LayoutPlot(layout, keys=None, **params)[source]#

Bases: GenericLayoutPlot, CompositePlot

A LayoutPlot accepts either a Layout or a NdLayout and displays the elements in a cartesian grid in scanline order.

Parameters inherited from:

fontsize = param.Parameter(allow_None=True, allow_refs=False, default={‘title’: 16}, label=’Fontsize’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c67e390>): Specifies various font sizes of the displayed text. Finer control is available by supplying a dictionary where any unmentioned keys revert to the default sizes, e.g: {‘ticks’:20, ‘title’:15, ‘ylabel’:5, ‘xlabel’:5, ‘zlabel’:5, ‘legend’:8, ‘legend_title’:13} You can set the font size of ‘zlabel’, ‘ylabel’ and ‘xlabel’ together using the ‘labels’ key.
fig_bounds = param.NumericTuple(allow_refs=False, default=(0.05, 0.05, 0.95, 0.95), label=’Fig bounds’, length=4, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c67fb10>): The bounds of the figure as a 4-tuple of the form (left, bottom, right, top), defining the size of the border around the subplots.
absolute_scaling = param.Selector(allow_refs=False, default=False, label=’Absolute scaling’, names={}, nested_refs=False, objects=[False], rx=<param.reactive.reactive_ops object at 0x17c67fd90>): If aspect_weight is enabled absolute_scaling determines whether axes are scaled relative to the widest plot or whether the aspect scales the axes in absolute terms.
aspect_weight = param.Number(allow_refs=False, default=0, inclusive_bounds=(True, True), label=’Aspect weight’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c684250>): Weighting of the individual aspects when computing the Layout grid aspects and overall figure size.
tight = param.Boolean(allow_refs=False, default=False, label=’Tight’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c67e050>): Tightly fit the axes in the layout within the fig_bounds and tight_padding.
tight_padding = param.Parameter(allow_refs=False, default=3, label=’Tight padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c67fa90>): Integer or tuple specifying the padding in inches in a tight layout.
hspace = param.Number(allow_refs=False, default=0.5, inclusive_bounds=(True, True), label=’Hspace’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c67fe90>): Specifies the space between horizontally adjacent elements in the grid. Default value is set conservatively to avoid overlap of subplots.
vspace = param.Number(allow_refs=False, default=0.3, inclusive_bounds=(True, True), label=’Vspace’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c684650>): Specifies the space between vertically adjacent elements in the grid. Default value is set conservatively to avoid overlap of subplots.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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.

grid_situate(current_idx, layout_type, subgrid_width)[source]#

Situate the current AdjointLayoutPlot in a LayoutPlot. The LayoutPlot specifies a layout_type into which the AdjointLayoutPlot must be embedded. This enclosing layout is guaranteed to have enough cells to display all the views.

Based on this enforced layout format, a starting index supplied by LayoutPlot (indexing into a large gridspec arrangement) is updated to the appropriate embedded value. It will also return a list of gridspec indices associated with the all the required layout axes.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.mpl.plot.MPLPlot(fig=None, axis=None, **params)[source]#

Bases: DimensionedPlot

An MPLPlot object draws a matplotlib figure object when called or indexed but can also return a matplotlib animation object as appropriate. MPLPlots take element objects such as Image, Contours or Points as inputs and plots them in the appropriate format using matplotlib. As HoloMaps are supported, all plots support animation via the anim() method.

Parameters inherited from:

projection = param.Parameter(allow_None=True, allow_refs=False, label=’Projection’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6c77d0>): The projection of the plot axis, default of None is equivalent to 2D plot, ‘3d’ and ‘polar’ are also supported by matplotlib by default. May also supply a custom projection that is either a matplotlib projection type or implements the _as_mpl_axes method.
backend_opts = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={}, label=’Backend opts’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b9050>): A dictionary of custom options to apply to the plot or subcomponents of the plot. The keys in the dictionary mirror attribute access on the underlying models stored in the plot’s handles, e.g. {‘colorbar.margin’: 10} will index the colorbar in the Plot.handles and then set the margin to 10.
fig_alpha = param.Number(allow_refs=False, bounds=(0, 1), default=1.0, inclusive_bounds=(True, True), label=’Fig alpha’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b9550>): Alpha of the overall figure background.
fig_bounds = param.NumericTuple(allow_refs=False, default=(0.15, 0.15, 0.85, 0.85), label=’Fig bounds’, length=4, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b8d50>): The bounds of the overall figure as a 4-tuple of the form (left, bottom, right, top), defining the size of the border around the subplots.
fig_inches = param.Parameter(allow_refs=False, default=4, label=’Fig inches’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6c7710>): The overall matplotlib figure size in inches. May be set as an integer in which case it will be used to autocompute a size. Alternatively may be set with an explicit tuple or list, in which case it will be applied directly after being scaled by fig_size. If either the width or height is set to None, it will be computed automatically.
fig_latex = param.Boolean(allow_refs=False, default=False, label=’Fig latex’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b8ad0>): Whether to use LaTeX text in the overall figure.
fig_rcparams = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={}, label=’Fig rcparams’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6c77d0>): matplotlib rc parameters to apply to the overall figure.
fig_size = param.Number(allow_refs=False, bounds=(1, None), default=100.0, inclusive_bounds=(True, True), label=’Fig size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b9550>): Size relative to the supplied overall fig_inches in percent.
initial_hooks = param.HookList(allow_refs=False, bounds=(0, None), default=[], label=’Initial hooks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6c7710>): Optional list of hooks called before plotting the data onto the axis (now marked for deprecation). The hook is passed the plot object and the displayed object; other plotting handles can be accessed via plot.handles.
sublabel_format = param.String(allow_None=True, allow_refs=False, label=’Sublabel format’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b8fd0>): Allows labeling the subaxes in each plot with various formatters including {Alpha}, {alpha}, {numeric} and {roman}.
sublabel_offset = param.Number(allow_refs=False, bounds=(0, None), default=0, inclusive_bounds=(True, True), label=’Sublabel offset’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b92d0>): Allows offsetting the sublabel index.
sublabel_position = param.NumericTuple(allow_refs=False, default=(-0.35, 0.85), label=’Sublabel position’, length=2, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b8a50>): Position relative to the plot for placing the optional subfigure label.
sublabel_size = param.Number(allow_refs=False, default=18, inclusive_bounds=(True, True), label=’Sublabel size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b92d0>): Size of optional subfigure label.
sublabel_skip = param.List(allow_None=True, allow_refs=False, bounds=(0, None), class_=<class ‘int’>, item_type=<class ‘int’>, label=’Sublabel skip’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6b8950>): List of elements to skip when labeling subplots. Numbering starts at 1.
show_frame = param.Boolean(allow_refs=False, default=False, label=’Show frame’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c6c7490>): Whether or not to show a complete frame around the plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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)[source]#: Initialize the matplotlib figure.

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.

holoviews.plotting.mpl.plot.mpl_rc_context(f)[source]#: Decorator for MPLPlot methods applying the matplotlib rc params in the plots fig_rcparams while when method is called.

`raster` Module#

class holoviews.plotting.mpl.raster.QuadMeshPlot(*args, **kwargs)[source]#

Bases: ColorbarPlot

Parameters inherited from:

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 0x17c8d4950>): 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.
show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c86bf10>): Whether to show legend for the plot.
clipping_colors = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={‘NaN’: ‘transparent’}, label=’Clipping colors’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c8d4a50>): Dictionary to specify colors for clipped values, allows setting color for NaN values and for values above and below the min and max value. The min, max or NaN color may specify an RGB(A) color as a color hex string of the form #FFFFFF or #FFFFFFFF or a length 3 or length 4 tuple specifying values in the range 0-1 or a named HTML color.
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 0x17c8f5450>): 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.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.raster.RGBPlot(element, **params)[source]#

Bases: RasterBasePlot, LegendPlot

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.raster.RasterBasePlot(element, **params)[source]#

Bases: ElementPlot

Parameters inherited from:

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 0x17c8f3d90>): 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.
show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c85a710>): Whether to show legend for the plot.
aspect = param.Parameter(allow_refs=False, default=’equal’, label=’Aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c813490>): Raster elements respect the aspect ratio of the Images by default but may be set to an explicit aspect ratio or to ‘square’.
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 0x17c85a290>): 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.
situate_axes = param.Boolean(allow_refs=False, default=True, label=’Situate axes’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c842550>): Whether to situate the image relative to other plots.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.raster.RasterGridPlot(layout, keys=None, dimensions=None, create_axes=False, ranges=None, layout_num=1, **params)[source]#

Bases: GridPlot, OverlayPlot

RasterGridPlot evenly spaces out plots of individual projections on a grid, even when they differ in size. Since this class uses a single axis to generate all the individual plots it is much faster than the equivalent using subplots.

Parameters inherited from:

apply_ranges = param.Parameter(allow_None=True, allow_refs=False, default=True, label=’Apply ranges’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9e1bd0>): Whether to compute the plot bounds from the data itself.
apply_extents = param.Parameter(allow_None=True, allow_refs=False, default=True, label=’Apply extents’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08c50>): Whether to apply extent overrides on the Elements
bgcolor = param.Parameter(allow_None=True, allow_refs=False, label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9e1bd0>): If set bgcolor overrides the background color of the axis.
default_span = param.Parameter(allow_None=True, allow_refs=False, default=2.0, label=’Default span’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08910>): Defines the span of an axis if the axis range is zero, i.e. if the lower and upper end of an axis are equal or no range is defined at all. For example if there is a single datapoint at 0 a default_span of 2.0 will result in axis ranges spanning from -1 to 1.
hooks = param.Parameter(allow_None=True, allow_refs=False, default=[], label=’Hooks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Optional list of hooks called when finalizing a plot. The hook is passed the plot object and the displayed element, and other plotting handles can be accessed via plot.handles.
invert_axes = param.Parameter(allow_None=True, allow_refs=False, default=False, label=’Invert axes’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08e10>): Whether to invert the x- and y-axis
invert_xaxis = param.Parameter(allow_None=True, allow_refs=False, default=False, label=’Invert xaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Whether to invert the plot x-axis.
invert_yaxis = param.Parameter(allow_None=True, allow_refs=False, default=False, label=’Invert yaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08cd0>): Whether to invert the plot y-axis.
logx = param.Parameter(allow_None=True, allow_refs=False, default=False, label=’Logx’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Whether the x-axis of the plot will be a log axis.
logy = param.Parameter(allow_None=True, allow_refs=False, default=False, label=’Logy’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08d50>): Whether the y-axis of the plot will be a log axis.
padding = param.Number(allow_refs=False, default=0.1, inclusive_bounds=(True, True), label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca09050>): The amount of padding as a fraction of the total Grid size
show_grid = param.Parameter(allow_None=True, allow_refs=False, default=False, label=’Show grid’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08bd0>): Whether to show a Cartesian grid on the plot.
xlabel = param.Parameter(allow_None=True, allow_refs=False, label=’Xlabel’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3b50>): An explicit override of the x-axis label, if set takes precedence over the dimension label.
ylabel = param.Parameter(allow_None=True, allow_refs=False, label=’Ylabel’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08690>): An explicit override of the y-axis label, if set takes precedence over the dimension label.
xlim = param.Parameter(allow_None=True, allow_refs=False, default=(nan, nan), label=’Xlim’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): User-specified x-axis range limits for the plot, as a tuple (low,high). If specified, takes precedence over data and dimension ranges.
ylim = param.Parameter(allow_None=True, allow_refs=False, default=(nan, nan), label=’Ylim’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08610>): User-specified y-axis range limits for the plot, as a tuple (low,high). If specified, takes precedence over data and dimension ranges.
zlim = param.Parameter(allow_None=True, allow_refs=False, default=(nan, nan), label=’Zlim’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): User-specified z-axis range limits for the plot, as a tuple (low,high). If specified, takes precedence over data and dimension ranges.
xticks = param.Parameter(allow_None=True, allow_refs=False, label=’Xticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca093d0>): Ticks along x-axis specified as an integer, explicit list of tick locations. If set to None default ticking behavior is applied.
yticks = param.Parameter(allow_None=True, allow_refs=False, label=’Yticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Ticks along y-axis specified as an integer, explicit list of tick locations. If set to None default ticking behavior is applied.
batched = param.Parameter(allow_None=True, allow_refs=False, default=True, label=’Batched’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08710>): Whether to plot Elements NdOverlay in a batched plotting call if possible. Disables legends and zorder may not be preserved.
legend_limit = param.Parameter(allow_None=True, allow_refs=False, default=25, label=’Legend limit’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Number of rendered glyphs before legends are disabled.
style_grouping = param.Parameter(allow_None=True, allow_refs=False, default=2, label=’Style grouping’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08bd0>): The length of the type.group.label spec that will be used to group Elements into style groups. A style_grouping value of 1 will group just by type, a value of 2 will group by type and group, and a value of 3 will group by the full specification.
apply_ticks = param.Parameter(allow_None=True, allow_refs=False, default=True, label=’Apply ticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Whether to apply custom ticks.
data_aspect = param.Parameter(allow_None=True, allow_refs=False, label=’Data aspect’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08610>): Defines the aspect of the axis scaling, i.e. the ratio of y-unit to x-unit.
invert_zaxis = param.Parameter(allow_None=True, allow_refs=False, default=False, label=’Invert zaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Whether to invert the plot z-axis.
labelled = param.Parameter(allow_None=True, allow_refs=False, default=[‘x’, ‘y’], label=’Labelled’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08c50>): Whether to plot the ‘x’ and ‘y’ labels.
logz = param.Parameter(allow_None=True, allow_refs=False, default=False, label=’Logz’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Whether to apply log scaling to the y-axis of the Chart.
xformatter = param.Parameter(allow_None=True, allow_refs=False, label=’Xformatter’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca093d0>): Formatter for ticks along the x-axis.
yformatter = param.Parameter(allow_None=True, allow_refs=False, label=’Yformatter’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Formatter for ticks along the y-axis.
zformatter = param.Parameter(allow_None=True, allow_refs=False, label=’Zformatter’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08710>): Formatter for ticks along the z-axis.
zaxis = param.Parameter(allow_None=True, allow_refs=False, default=True, label=’Zaxis’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Whether to display the z-axis.
zlabel = param.Parameter(allow_None=True, allow_refs=False, label=’Zlabel’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08610>): An explicit override of the z-axis label, if set takes precedence over the dimension label.
zrotation = param.Parameter(allow_None=True, allow_refs=False, default=0, label=’Zrotation’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Rotation angle of the zticks.
zticks = param.Parameter(allow_None=True, allow_refs=False, label=’Zticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08c50>): Ticks along z-axis specified as an integer, explicit list of tick locations, list of tuples containing the locations and labels or a matplotlib tick locator object. If set to None default matplotlib ticking behavior is applied.
legend_cols = param.Parameter(allow_None=True, allow_refs=False, label=’Legend cols’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Number of legend columns in the legend.
legend_labels = param.Parameter(allow_None=True, allow_refs=False, default={}, label=’Legend labels’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca093d0>): A mapping that allows overriding legend labels.
legend_position = param.Parameter(allow_None=True, allow_refs=False, default=’inner’, label=’Legend position’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17c9f3bd0>): Allows selecting between a number of predefined legend position options. The predefined options may be customized in the legend_specs class attribute. By default, ‘inner’, ‘right’, ‘bottom’, ‘top’, ‘left’, ‘best’, ‘top_right’, ‘top_left’, ‘bottom_right’ and ‘bottom_left’ are supported.
legend_opts = param.Parameter(allow_None=True, allow_refs=False, default={}, label=’Legend opts’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca08710>): Allows setting specific styling options for the colorbar.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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(view, 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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.raster.RasterPlot(*args, **kwargs)[source]#

Bases: RasterBasePlot, ColorbarPlot

Parameters inherited from:

clipping_colors = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={‘NaN’: ‘transparent’}, label=’Clipping colors’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ca70750>): Dictionary to specify colors for clipped values, allows setting color for NaN values and for values above and below the min and max value. The min, max or NaN color may specify an RGB(A) color as a color hex string of the form #FFFFFF or #FFFFFFFF or a length 3 or length 4 tuple specifying values in the range 0-1 or a named HTML color.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`renderer` Module#

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

Bases: Renderer

Exporter used to render data from matplotlib, either to a stream or directly to file.

The __call__ method renders an HoloViews component to raw data of a specified matplotlib format. The save method is the corresponding method for saving a HoloViews objects to disk.

The save_fig and save_anim methods are used to save matplotlib figure and animation objects. These match the two primary return types of plotting class implemented with matplotlib.

Parameters inherited from:

backend = param.String(allow_refs=False, default=’matplotlib’, label=’Backend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17cd545d0>): The backend name.
dpi = param.Integer(allow_refs=False, default=72, inclusive_bounds=(True, True), label=’Dpi’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17cd49850>): The render resolution in dpi (dots per inch)
fig = param.Selector(allow_refs=False, default=’auto’, label=’Fig’, names={}, nested_refs=False, objects=[‘png’, ‘svg’, ‘pdf’, ‘pgf’, ‘html’, None, ‘auto’], rx=<param.reactive.reactive_ops object at 0x17cd26c50>): 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=[‘widgets’, ‘scrubber’, ‘webm’, ‘mp4’, ‘gif’, None, ‘auto’], rx=<param.reactive.reactive_ops object at 0x17cd26990>): Output render multi-frame (typically animated) format. If None, no multi-frame rendering will occur.
mode = param.Selector(allow_refs=False, default=’default’, label=’Mode’, names={}, nested_refs=False, objects=[‘default’], rx=<param.reactive.reactive_ops object at 0x17cd26b50>): Whether to render the object in regular or server mode. In server mode a bokeh Document will be returned which can be served as a bokeh server app. By default renders all output is rendered to HTML.
interactive = param.Boolean(allow_refs=False, default=False, label=’Interactive’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17cd26a90>): Whether to enable interactive plotting allowing interactive plotting with explicitly calling show.

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.

classmethod get_plot_state(obj, renderer=None, **kwargs)[source]#: Given a HoloViews Viewable return a corresponding plot state.

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]#: Initialize matplotlib backend

classmethod plot_options(obj, percent_size)[source]#

Given a holoviews object and a percentage size, apply heuristics to compute a suitable figure size. For instance, scaling layouts and grids linearly can result in unwieldy figure sizes when there are a large number of elements. As ad hoc heuristics are used, this functionality is kept separate from the plotting classes themselves.

Used by the IPython Notebook display hooks and the save utility. Note that this can be overridden explicitly per object using the fig_size and size plot options.

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.

show(obj)[source]#: Renders the supplied object and displays it using the active GUI backend.

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.

class holoviews.plotting.mpl.renderer.OutputWarning(*, name)[source]#: Bases: Parameterized

`sankey` Module#

class holoviews.plotting.mpl.sankey.SankeyPlot(*args, **kwargs)[source]#

Bases: GraphPlot

Parameters inherited from:

color_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), default=2, label=’Color index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ce4f350>): Index of the dimension from which the node labels will be drawn
labels = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘holoviews.util.transform.dim’>), label=’Labels’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ce54c50>): The dimension or dimension value transform used to draw labels from.
show_values = param.Boolean(allow_refs=False, default=True, label=’Show values’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ce4ef50>): Whether to show the values.
label_position = param.Selector(allow_refs=False, default=’right’, label=’Label position’, names={}, nested_refs=False, objects=[‘left’, ‘right’], rx=<param.reactive.reactive_ops object at 0x17ce54910>): Whether node labels should be placed to the left or right.
node_width = param.Number(allow_refs=False, default=15, inclusive_bounds=(True, True), label=’Node width’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ce55050>): Width of the nodes.
node_padding = param.Integer(allow_None=True, allow_refs=False, inclusive_bounds=(True, True), label=’Node padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ce55310>): Number of pixels of padding relative to the bounds.
iterations = param.Integer(allow_refs=False, default=32, inclusive_bounds=(True, True), label=’Iterations’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ce54e10>): Number of iterations to run the layout algorithm.
node_sort = param.Boolean(allow_refs=False, default=True, label=’Node sort’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ce54510>): Sort nodes in ascending breadth.
label_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), default=2, label=’Label index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17ce13e90>): Index of the dimension from which the node labels will be drawn

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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]#: A Chord plot is always drawn on a unit circle.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`stats` Module#

class holoviews.plotting.mpl.stats.BivariatePlot(*args, **kwargs)[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).

Parameters inherited from:

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 0x17cf7a710>): 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 0x17cf80510>): Draw the estimate to cut * bw from the extreme data points.
filled = param.Boolean(allow_refs=False, default=False, label=’Filled’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17cf79d50>): Whether the bivariate contours should be filled.
levels = param.ClassSelector(allow_refs=False, class_=(<class ‘list’>, <class ‘int’>), default=10, label=’Levels’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17cf7bd10>): A list of scalar values used to specify the contour levels.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.stats.BoxPlot(element, **params)[source]#

Bases: MultiDistributionMixin, ChartPlot

BoxPlot plots the ErrorBar Element type and supporting both horizontal and vertical error bars via the ‘horizontal’ plot option.

Parameters inherited from:

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.stats.DistributionPlot(element, **params)[source]#

Bases: AreaPlot

DistributionPlot visualizes a distribution of values as a KDE.

Parameters inherited from:

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 0x17d02b3d0>): 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 0x17d02d1d0>): 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 0x17d02ab10>): Whether the bivariate contours should be filled.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_data, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.stats.SideBoxPlot(*args, **kwargs)[source]#

Bases: AdjoinedPlot, BoxPlot

Parameters inherited from:

bgcolor = param.Parameter(allow_refs=False, default=(1, 1, 1, 0), label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17d090c90>): Make plot background invisible.
xaxis = param.Selector(allow_refs=False, default=’bare’, label=’Xaxis’, names={}, nested_refs=False, objects=[‘top’, ‘bottom’, ‘bare’, ‘top-bare’, ‘bottom-bare’, None], rx=<param.reactive.reactive_ops object at 0x17d070b50>): 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=’bare’, label=’Yaxis’, names={}, nested_refs=False, objects=[‘left’, ‘right’, ‘bare’, ‘left-bare’, ‘right-bare’, None], rx=<param.reactive.reactive_ops object at 0x17d05e490>): 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’.
border_size = param.Number(allow_refs=False, default=0, inclusive_bounds=(True, True), label=’Border size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17d090250>): The size of the border expressed as a fraction of the main plot.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

class holoviews.plotting.mpl.stats.ViolinPlot(element, **params)[source]#

Bases: BoxPlot

BoxPlot plots the ErrorBar Element type and supporting both horizontal and vertical error bars via the ‘horizontal’ plot option.

Parameters inherited from:

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 0x17d0d02d0>): Allows supplying explicit bandwidth value rather than relying on scott or silverman method.
inner = param.Selector(allow_refs=False, default=’box’, label=’Inner’, names={}, nested_refs=False, objects=[‘box’, ‘medians’, None], rx=<param.reactive.reactive_ops object at 0x17d0eb250>): Inner visual indicator for distribution values: * box - A small box plot * stick - Lines indicating each sample value * quartiles - Indicates first, second and third quartiles

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axis, element, ranges, style)[source]#: Update the elements of the plot.

`tabular` Module#

class holoviews.plotting.mpl.tabular.TablePlot(table, **params)[source]#

Bases: ElementPlot

A TablePlot can plot both TableViews and ViewMaps which display as either a single static table or as an animated table respectively.

Parameters inherited from:

border = param.Number(allow_refs=False, bounds=(0.0, 0.5), default=0.05, inclusive_bounds=(True, True), label=’Border’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17d479c90>): The fraction of the plot that should be empty around the edges.
float_precision = param.Integer(allow_refs=False, default=3, inclusive_bounds=(True, True), label=’Float precision’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17d433550>): The floating point precision to use when printing float numeric data types.
max_value_len = param.Integer(allow_refs=False, default=20, inclusive_bounds=(True, True), label=’Max value len’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17d430190>): The maximum allowable string length of a value shown in any table cell. Any strings longer than this length will be truncated.
max_font_size = param.Integer(allow_refs=False, default=12, inclusive_bounds=(True, True), label=’Max font size’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17d4334d0>): The largest allowable font size for the text in each table cell.
max_rows = param.Integer(allow_refs=False, default=15, inclusive_bounds=(True, True), label=’Max rows’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17d430250>): The maximum number of Table rows before the table is summarized.
font_types = param.Dict(allow_refs=False, class_=<class ‘dict’>, default={‘heading’: <matplotlib.font_manager.FontProperties object at 0x17161a690>}, label=’Font types’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x17d45dc50>): The font style used for heading labels used for emphasis.

anim(start=0, stop=None, fps=30)[source]#: Method to return a matplotlib animation. The start and stop frames may be specified as well as the fps.

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_artists(ax, plot_args, plot_kwargs)[source]#: Initializes the artist based on the plot method declared on the plot.

initialize_plot(*args, **kwargs)[source]#: Initialize the matplotlib figure.

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.

teardown_handles()[source]#: If no custom update_handles method is supplied this method is called to tear down any previous handles before replacing them.

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(*args, **kwargs)[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.

update_handles(key, axes, element, ranges, style)[source]#: Update the elements of the plot.

`util` Module#

class holoviews.plotting.mpl.util.CFTimeConverter(*, interval_multiples=True)[source]#

Bases: DateConverter

Defines conversions for cftime types by extending nc_time_axis.

axisinfo(unit, axis)[source]#

Return the ~matplotlib.units.AxisInfo for unit.

unit is a ~datetime.tzinfo instance or None. The axis argument is required but not used.

classmethod convert(value, unit, axis)[source]#

If value is not already a number or sequence of numbers, convert it with date2num.

The unit and axis arguments are not used.

static default_units(x, axis)[source]#: Return the ~datetime.tzinfo instance of x or of its first element, or None

class holoviews.plotting.mpl.util.EqHistNormalize(vmin=None, vmax=None, clip=False, rescale_discrete_levels=True, nbins=65536, ncolors=256)[source]#

Bases: Normalize

autoscale(A)[source]#: Set vmin, vmax to min, max of A.

autoscale_None(A)[source]#: If vmin or vmax are not set, use the min/max of A to set them.

inverse(value)[source]#

Maps the normalized value (i.e., index in the colormap) back to image data value.

Parameters#

value: Normalized value.

process_value(data)[source]#

Homogenize the input value for easy and efficient normalization.

value can be a scalar or sequence.

Parameters#

value: Data to normalize.

Returns#

resultmasked array: Masked array with the same shape as value.
is_scalarbool: Whether value is a scalar.

Notes#

Float dtypes are preserved; integer types with two bytes or smaller are converted to np.float32, and larger types are converted to np.float64. Preserving float32 when possible, and using in-place operations, greatly improves speed for large arrays.

scaled()[source]#: Return whether vmin and vmax are both set.

holoviews.plotting.mpl.util.axis_overlap(ax1, ax2)[source]#: Tests whether two axes overlap vertically

holoviews.plotting.mpl.util.filter_styles(style, group, other_groups, blacklist=None)[source]#

Filters styles which are specific to a particular artist, e.g. for a GraphPlot this will filter options specific to the nodes and edges.

Arguments#

style: dict: Dictionary of styles and values
group: str: Group within the styles to filter for
other_groups: list: Other groups to filter out
blacklist: list (optional): List of options to filter out

Returns#

filtered: dict: Filtered dictionary of styles

holoviews.plotting.mpl.util.fix_aspect(fig, nrows, ncols, title=None, extra_artists=None, vspace=0.2, hspace=0.2)[source]#: Calculate heights and widths of axes and adjust the size of the figure to match the aspect.

holoviews.plotting.mpl.util.get_raster_array(image)[source]#: Return the array data from any Raster or Image type

holoviews.plotting.mpl.util.get_tight_bbox(fig, bbox_extra_artists=None, pad=None)[source]#: Compute a tight bounding box around all the artists in the figure.

holoviews.plotting.mpl.util.is_color(color)[source]#: Checks if supplied object is a valid color spec.

holoviews.plotting.mpl.util.polygons_to_path_patches(element)[source]#: Converts Polygons into list of lists of matplotlib.patches.PathPatch objects including any specified holes. Each list represents one (multi-)polygon.

holoviews.plotting.mpl.util.resolve_rows(rows)[source]#: Recursively iterate over lists of axes merging them by their vertical overlap leaving a list of rows.

holoviews.plotting.mpl.util.ring_coding(array)[source]#: Produces matplotlib Path codes for exterior and interior rings of a polygon geometry.

holoviews.plotting.mpl.util.validate(style, value, vectorized=True)[source]#

Validates a style and associated value.

Arguments#

style: str: The style to validate (e.g. ‘color’, ‘size’ or ‘marker’)
value:: The style value to validate
vectorized: bool: Whether validator should allow vectorized setting

Returns#

valid: boolean or None: If validation is supported returns boolean, otherwise None

holoviews.plotting.mpl.util.wrap_formatter(formatter)[source]#: Wraps formatting function or string in appropriate matplotlib formatter type.

linestyle	description
`'-'` or `'solid'`	solid line
`'--'` or `'dashed'`	dashed line
`'-.'` or `'dashdot'`	dash-dotted line
`':'` or `'dotted'`	dotted line
`'none'`, `'None'`, `' '`, or `''`	draw nothing

holoviews.mpl Package#

mpl Package#

annotation Module#

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chart Module#

chart3d Module#

element Module#

geometry Module#

graphs Module#

heatmap Module#

`mpl` Package#

`annotation` Module#

`chart` Module#

`chart3d` Module#

`element` Module#

`geometry` Module#

`graphs` Module#

`heatmap` Module#

`hex_tiles` Module#

`path` Module#

`plot` Module#

`raster` Module#

`renderer` Module#

`sankey` Module#

`stats` Module#

`tabular` Module#

`util` Module#