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.

See Also#

remove_callback

property axes#

The ~.axes.Axes instance the artist resides in, or None.

contains(mouseevent)[source]#

Test whether mouseevent occurred on the line.

An event is deemed to have occurred “on” the line if it is less than self.pickradius (default: 5 points) away from it. Use ~.Line2D.get_pickradius or ~.Line2D.set_pickradius to get or set the pick radius.

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.

See Also#

get_cursor_data

get_aa()[source]#

Alias for get_antialiased.

get_agg_filter()[source]#

Return filter function to be used for agg filter.

get_alpha()[source]#

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

get_animated()[source]#

Return whether the artist is animated.

get_antialiased()[source]#

Return whether antialiased rendering is used.

get_bbox()[source]#

Get the bounding box of this line.

get_c()[source]#

Alias for get_color.

get_children()[source]#

Return a list of the child .Artists of this .Artist.

get_clip_box()[source]#

Return the clipbox.

get_clip_on()[source]#

Return whether the artist uses clipping.

get_clip_path()[source]#

Return the clip path.

get_color()[source]#

Return the line color.

See also ~.Line2D.set_color.

get_cursor_data(event)[source]#

Return the cursor data for a given event.

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.

Cursor data can be used by Artists to provide additional context information for a given event. The default implementation just returns None.

Subclasses can override the method and return arbitrary data. However, when doing so, they must ensure that .format_cursor_data can convert the data to a string representation.

The only current use case is displaying the z-value of an .AxesImage in the status bar of a plot window, while moving the mouse.

Parameters#

event : ~matplotlib.backend_bases.MouseEvent

See Also#

format_cursor_data

get_dash_capstyle()[source]#

Return the .CapStyle for dashed lines.

See also ~.Line2D.set_dash_capstyle.

get_dash_joinstyle()[source]#

Return the .JoinStyle for dashed lines.

See also ~.Line2D.set_dash_joinstyle.

get_data(orig=True)[source]#

Return the line data as an (xdata, ydata) pair.

If orig is True, return the original data.

get_drawstyle()[source]#

Return the drawstyle.

See also ~.Line2D.set_drawstyle.

get_ds()[source]#

Alias for get_drawstyle.

get_figure()[source]#

Return the .Figure instance the artist belongs to.

get_fillstyle()[source]#

Return the marker fill style.

See also ~.Line2D.set_fillstyle.

get_gapcolor()[source]#

Return the line gapcolor.

See also ~.Line2D.set_gapcolor.

get_gid()[source]#

Return the group id.

get_in_layout()[source]#

Return boolean flag, True if artist is included in layout calculations.

E.g. constrainedlayout_guide, .Figure.tight_layout(), and fig.savefig(fname, bbox_inches='tight').

get_label()[source]#

Return the label used for this artist in the legend.

get_linestyle()[source]#

Return the linestyle.

See also ~.Line2D.set_linestyle.

get_linewidth()[source]#

Return the linewidth in points.

See also ~.Line2D.set_linewidth.

get_ls()[source]#

Alias for get_linestyle.

get_lw()[source]#

Alias for get_linewidth.

get_marker()[source]#

Return the line marker.

See also ~.Line2D.set_marker.

get_markeredgecolor()[source]#

Return the marker edge color.

See also ~.Line2D.set_markeredgecolor.

get_markeredgewidth()[source]#

Return the marker edge width in points.

See also ~.Line2D.set_markeredgewidth.

get_markerfacecolor()[source]#

Return the marker face color.

See also ~.Line2D.set_markerfacecolor.

get_markerfacecoloralt()[source]#

Return the alternate marker face color.

See also ~.Line2D.set_markerfacecoloralt.

get_markersize()[source]#

Return the marker size in points.

See also ~.Line2D.set_markersize.

get_markevery()[source]#

Return the markevery setting for marker subsampling.

See also ~.Line2D.set_markevery.

get_mec()[source]#

Alias for get_markeredgecolor.

get_mew()[source]#

Alias for get_markeredgewidth.

get_mfc()[source]#

Alias for get_markerfacecolor.

get_mfcalt()[source]#

Alias for get_markerfacecoloralt.

get_mouseover()[source]#

Return whether this artist is queried for custom context information when the mouse cursor moves over it.

get_ms()[source]#

Alias for get_markersize.

get_path()[source]#

Return the ~matplotlib.path.Path associated with this line.

get_picker()[source]#

Return the picking behavior of the artist.

The possible values are described in .Artist.set_picker.

See Also#

.Artist.set_picker, .Artist.pickable, .Artist.pick

get_pickradius()[source]#

Return the pick radius used for containment tests.

See .contains for more details.

get_rasterized()[source]#

Return whether the artist is to be rasterized.

get_sketch_params()[source]#

Return the sketch parameters for the artist.

Returns#

tuple or None

A 3-tuple with the following elements:

  • scale: The amplitude of the wiggle perpendicular to the source line.

  • length: The length of the wiggle along the line.

  • randomness: The scale factor by which the length is shrunken or expanded.

Returns None if no sketch parameters were set.

get_snap()[source]#

Return the snap setting.

See .set_snap for details.

get_solid_capstyle()[source]#

Return the .CapStyle for solid lines.

See also ~.Line2D.set_solid_capstyle.

get_solid_joinstyle()[source]#

Return the .JoinStyle for solid lines.

See also ~.Line2D.set_solid_joinstyle.

get_tightbbox(renderer=None)[source]#

Like .Artist.get_window_extent, but includes any clipping.

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.

See also ~.Line2D.set_linestyle.

is_transform_set()[source]#

Return whether the Artist has an explicitly set transform.

This is True after .set_transform has been called.

property mouseover#

Return whether this artist is queried for custom context information when the mouse cursor moves over it.

pchanged()[source]#

Call all of the registered callbacks.

This function is triggered internally when a property is changed.

See Also#

add_callback remove_callback

pick(mouseevent)[source]#

Process a pick event.

Each child artist will fire a pick event if mouseevent is over the artist and the artist has picker set.

See Also#

.Artist.set_picker, .Artist.get_picker, .Artist.pickable

pickable()[source]#

Return whether the artist is pickable.

See Also#

.Artist.set_picker, .Artist.get_picker, .Artist.pick

property pickradius#

Return the pick radius used for containment tests.

See .contains for more details.

properties()[source]#

Return a dictionary of all the properties of the artist.

remove()[source]#

Remove the artist from the figure if possible.

The effect will not be visible until the figure is redrawn, e.g., with .FigureCanvasBase.draw_idle. Call ~.axes.Axes.relim to update the Axes limits if desired.

Note: ~.axes.Axes.relim will not see collections even if the collection was added to the Axes with autolim = True.

Note: there is no support for removing the artist’s legend entry.

remove_callback(oid)[source]#

Remove a callback based on its observer id.

See Also#

add_callback

set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, color=<UNSET>, dash_capstyle=<UNSET>, dash_joinstyle=<UNSET>, dashes=<UNSET>, data=<UNSET>, drawstyle=<UNSET>, fillstyle=<UNSET>, gapcolor=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, marker=<UNSET>, markeredgecolor=<UNSET>, markeredgewidth=<UNSET>, markerfacecolor=<UNSET>, markerfacecoloralt=<UNSET>, markersize=<UNSET>, markevery=<UNSET>, mouseover=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, pickradius=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, solid_capstyle=<UNSET>, solid_joinstyle=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, xdata=<UNSET>, ydata=<UNSET>, zorder=<UNSET>)[source]#

Set multiple properties at once.

Supported properties are

Properties:

agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image alpha: scalar or None animated: bool antialiased or aa: bool clip_box: ~matplotlib.transforms.BboxBase or None clip_on: bool clip_path: Patch or (Path, Transform) or None color or c: :mpltype:`color` dash_capstyle: .CapStyle or {‘butt’, ‘projecting’, ‘round’} dash_joinstyle: .JoinStyle or {‘miter’, ‘round’, ‘bevel’} dashes: sequence of floats (on/off ink in points) or (None, None) data: (2, N) array or two 1D arrays drawstyle or ds: {‘default’, ‘steps’, ‘steps-pre’, ‘steps-mid’, ‘steps-post’}, default: ‘default’ figure: ~matplotlib.figure.Figure fillstyle: {‘full’, ‘left’, ‘right’, ‘bottom’, ‘top’, ‘none’} gapcolor: :mpltype:`color` or None gid: str in_layout: bool label: object linestyle or ls: {‘-’, ‘–’, ‘-.’, ‘:’, ‘’, (offset, on-off-seq), …} linewidth or lw: float marker: marker style string, ~.path.Path or ~.markers.MarkerStyle markeredgecolor or mec: :mpltype:`color` markeredgewidth or mew: float markerfacecolor or mfc: :mpltype:`color` markerfacecoloralt or mfcalt: :mpltype:`color` markersize or ms: float markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool] mouseover: bool path_effects: list of .AbstractPathEffect picker: float or callable[[Artist, Event], tuple[bool, dict]] pickradius: float rasterized: bool sketch_params: (scale: float, length: float, randomness: float) snap: bool or None solid_capstyle: .CapStyle or {‘butt’, ‘projecting’, ‘round’} solid_joinstyle: .JoinStyle or {‘miter’, ‘round’, ‘bevel’} transform: ~matplotlib.transforms.Transform url: str visible: bool xdata: 1D array ydata: 1D array zorder: float

set_aa(b)[source]#

Alias for set_antialiased.

set_agg_filter(filter_func)[source]#

Set the agg filter.

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

show_legend = param.Boolean(allow_refs=False, default=False, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x305fbf0d0>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

holoviews.plotting.mpl.annotation._SyntheticAnnotationPlot: apply_ranges

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

holoviews.plotting.mpl.annotation._SyntheticAnnotationPlot: apply_ranges

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

color_index = param.ClassSelector(allow_None=True, allow_refs=False, class_=(<class ‘str’>, <class ‘int’>), label=’Color index’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3067c7c10>)

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 0x17c68fbd0>)

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 0x17c68fc90>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

holoviews.plotting.mpl.annotation._SyntheticAnnotationPlot: apply_ranges

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.annotation.AnnotationPlot: show_legend

holoviews.plotting.mpl.annotation._SyntheticAnnotationPlot: apply_ranges

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

padding = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘tuple’>), default=(0, 0.1), label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306e9e150>)

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 0x306e7f450>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.LegendPlot: legend_cols, legend_labels, legend_position, legend_opts

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

show_legend = param.Boolean(allow_refs=False, default=True, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306e2a350>)

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 0x306ebed50>)

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 0x306e2a290>)

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 0x306ebe410>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

padding = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘tuple’>), default=(0, 0.1), label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306f10350>)

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 0x306f13690>)

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 0x306f12590>)

Enable axis grid.

autotick = param.Boolean(allow_refs=False, default=False, label=’Autotick’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306f13610>)

Whether to let matplotlib automatically compute tick marks or to allow the user to control tick marks.

interpolation = param.ObjectSelector(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 0x306f12590>)

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 0x306f103d0>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.LegendPlot: show_legend, legend_cols, legend_labels, legend_position, legend_opts

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

show_grid = param.Boolean(allow_refs=False, default=False, label=’Show grid’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306f58610>)

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 0x306f59e10>)

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 0x306f58710>)

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

scaling_method = param.ObjectSelector(allow_refs=False, default=’area’, label=’Scaling method’, names={}, nested_refs=False, objects=[‘width’, ‘area’], rx=<param.reactive.reactive_ops object at 0x306f59f10>)

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 0x306f5a450>)

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 0x306f59810>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.chart.AreaPlot: padding, show_legend

holoviews.plotting.mpl.plot.AdjoinedPlot: show_title, aspect, subplot_size, show_xlabel

bgcolor = param.Parameter(allow_refs=False, default=(1, 1, 1, 0), label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306f556d0>)

Make plot background invisible.

xaxis = param.ObjectSelector(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 0x306f77410>)

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.ObjectSelector(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 0x306f77710>)

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 0x306f77910>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

holoviews.plotting.mpl.plot.AdjoinedPlot: show_title, aspect, border_size, subplot_size, show_xlabel

bgcolor = param.Parameter(allow_refs=False, default=(1, 1, 1, 0), label=’Bgcolor’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306f89710>)

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 0x306f8ae10>)

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 0x306f8b310>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

holoviews.plotting.mpl.path.PathPlot: show_legend

holoviews.plotting.mpl.chart.SpikesPlot: padding, color_index, position

holoviews.plotting.mpl.plot.AdjoinedPlot: show_title, aspect, show_xlabel

show_frame = param.Boolean(allow_refs=False, default=True, label=’Show frame’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306fa1dd0>)

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 0x306fa34d0>)

Make plot background invisible.

xaxis = param.ObjectSelector(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 0x306fa37d0>)

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.ObjectSelector(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 0x306fa3ad0>)

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 0x306fa3cd0>)

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 0x306f980d0>)

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 0x306fa3e10>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

holoviews.plotting.mpl.path.PathPlot: show_legend

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 0x306fbb610>)

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 0x306fc0d50>)

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 0x306fbb550>)

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 0x306fc1210>)

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 0x306fc1650>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

padding = param.ClassSelector(allow_refs=False, class_=(<class ‘int’>, <class ‘float’>, <class ‘tuple’>), default=(0, 0.1), label=’Padding’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x306fc85d0>)

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 0x306fc9cd0>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

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 0x306fe4390>)

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 0x306fe5a10>)

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 0x306fe4050>)

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 0x306fe5990>)

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 0x306fe5b90>)

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 0x306fe5a10>)

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 0x306fe4810>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, data_aspect, invert_zaxis, logz, xformatter, yformatter, zformatter, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

holoviews.plotting.mpl.path.PathPlot: show_legend, aspect, color_index

holoviews.plotting.mpl.chart3d.Plot3D: projection, bgcolor, show_grid, xaxis, yaxis, labelled, zaxis, azimuth, elevation, distance, disable_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.Plot3D(*args, **kwargs)[source]#

Bases: ColorbarPlot

Plot3D provides a common baseclass for mplot3d based plots.

Parameters inherited from:

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

holoviews.plotting.mpl.plot.MPLPlot: backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, invert_axes, invert_xaxis, invert_yaxis, logx, logy, padding, show_legend, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation, xticks, yticks

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, logz, xformatter, yformatter, zformatter, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

projection = param.ObjectSelector(allow_refs=False, default=’3d’, label=’Projection’, names={}, nested_refs=False, objects=[‘3d’], rx=<param.reactive.reactive_ops object at 0x3075784d0>)

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 0x30757b910>)

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 0x30757a3d0>)

Whether to draw a grid in the figure.

xaxis = param.ObjectSelector(allow_refs=False, default=’fixed’, label=’Xaxis’, names={}, nested_refs=False, objects=[‘fixed’, None], rx=<param.reactive.reactive_ops object at 0x30757b990>)

Whether and where to display the xaxis.

yaxis = param.ObjectSelector(allow_refs=False, default=’fixed’, label=’Yaxis’, names={}, nested_refs=False, objects=[‘fixed’, None], rx=<param.reactive.reactive_ops object at 0x30757bb10>)

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 0x30757b810>)

Whether to plot the ‘x’, ‘y’ and ‘z’ labels.

zaxis = param.ObjectSelector(allow_refs=False, default=’fixed’, label=’Zaxis’, names={}, nested_refs=False, objects=[‘fixed’, None], rx=<param.reactive.reactive_ops object at 0x30757bad0>)

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 0x30757bc90>)

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 0x30757c090>)

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 0x30757bd90>)

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 0x3075784d0>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, logz, xformatter, yformatter, zformatter, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.LegendPlot: show_legend, legend_cols, legend_labels, legend_position, legend_opts

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

holoviews.plotting.mpl.chart.PointPlot: scaling_method, scaling_factor, size_fn

holoviews.plotting.mpl.chart3d.Plot3D: projection, bgcolor, show_grid, xaxis, yaxis, labelled, zaxis, azimuth, elevation, distance, disable_axes

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 0x30759a9d0>)

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 0x3075a49d0>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, invert_axes, invert_xaxis, invert_yaxis, logx, logy, padding, show_legend, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation, xticks, yticks

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, logz, xformatter, yformatter, zformatter, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

holoviews.plotting.mpl.chart3d.Plot3D: projection, bgcolor, show_grid, xaxis, yaxis, labelled, zaxis, azimuth, elevation, distance, disable_axes

colorbar = param.Boolean(allow_refs=False, default=False, label=’Colorbar’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x30756ecd0>)

Whether to add a colorbar to the plot.

plot_type = param.ObjectSelector(allow_refs=False, default=’surface’, label=’Plot type’, names={}, nested_refs=False, objects=[‘surface’, ‘wireframe’, ‘contour’], rx=<param.reactive.reactive_ops object at 0x3075457d0>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

holoviews.plotting.plot.GenericElementPlot: apply_ranges, apply_extents, default_span, hooks, invert_axes, invert_xaxis, invert_yaxis, logx, logy, padding, show_legend, xlabel, ylabel, xlim, ylim, zlim, xrotation, yrotation, xticks, yticks

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, logz, xformatter, yformatter, zformatter, zlabel, zrotation, zticks

holoviews.plotting.mpl.element.ColorbarPlot: clabel, clim, clim_percentile, cformatter, colorbar_opts, color_levels, cnorm, clipping_colors, cbar_padding, cbar_ticks, cbar_width, cbar_extend, rescale_discrete_levels, symmetric

holoviews.plotting.mpl.chart3d.Plot3D: projection, bgcolor, show_grid, xaxis, yaxis, labelled, zaxis, azimuth, elevation, distance, disable_axes

colorbar = param.Boolean(allow_refs=False, default=False, label=’Colorbar’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x3075be0d0>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

clabel = param.String(allow_None=True, allow_refs=False, label=’Clabel’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x307946810>)

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 0x3078bbc50>)

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 0x307946810>)

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 0x307968050>)

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 0x3075cf310>)

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 0x307945190>)

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 0x307945fd0>)

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.ObjectSelector(allow_refs=False, default=’linear’, label=’Cnorm’, names={}, nested_refs=False, objects=[‘linear’, ‘log’, ‘eq_hist’], rx=<param.reactive.reactive_ops object at 0x307945190>)

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 0x307945fd0>)

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 0x307977f50>)

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 0x307945bd0>)

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 0x307976950>)

Width of the colorbar as a fraction of the main plot

cbar_extend = param.ObjectSelector(allow_refs=False, label=’Cbar extend’, names={}, nested_refs=False, objects=[‘neither’, ‘both’, ‘min’, ‘max’], rx=<param.reactive.reactive_ops object at 0x307977150>)

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 0x307946dd0>)

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 0x307945fd0>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

apply_ticks = param.Boolean(allow_refs=False, default=True, label=’Apply ticks’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x307924910>)

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 0x307959510>)

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 0x3079583d0>)

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 0x307958bd0>)

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 0x3079082d0>)

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 0x307958c90>)

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 0x30790b150>)

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 0x307958e90>)

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 0x30790b150>)

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 0x307958b10>)

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 0x3079082d0>)

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 0x307959610>)

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 0x307924610>)

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:

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

holoviews.plotting.mpl.plot.MPLPlot: projection, backend_opts, fig_alpha, fig_bounds, fig_inches, fig_latex, fig_rcparams, fig_size, initial_hooks, sublabel_format, sublabel_position, sublabel_size, show_frame

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

holoviews.plotting.mpl.element.ElementPlot: apply_ticks, aspect, data_aspect, invert_zaxis, labelled, logz, xformatter, yformatter, zformatter, zaxis, zlabel, zrotation, zticks

show_legend = param.Boolean(allow_refs=False, default=True, label=’Show legend’, nested_refs=False, rx=<param.reactive.reactive_ops object at 0x307b957d0>)

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 0x307b95690>)

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 0x307b97350>)

A mapping that allows overriding legend labels.

legend_position = param.ObjectSelector(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 0x307b952d0>)

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 0x307b971d0>)

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.