HoloViews#
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import panel as pn
pn.extension('plotly')
The HoloViews
pane renders HoloViews plots with one of the plotting backends supported by HoloViews. It supports the regular HoloViews widgets for exploring the key dimensions of a HoloMap
or DynamicMap
, but is more flexible than the native HoloViews widgets since it also allows customizing widget types and their position relative to the plot.
Parameters:#
For layout and styling related parameters see the customization user guide.
backend
(str): Any of the supported HoloViews backends (‘bokeh’, ‘matplotlib’, or ‘plotly’)center
(boolean, default=False): Whether to center the plotlinked_axes
(boolean, default=True): Whether to link axes across plots in a panel layoutobject
(object): The HoloViews object being displayedwidget_location
(str): Where to lay out the widget relative to the plotwidget_layout
(ListPanel type): The object to lay the widgets out in, one ofRow
,Column
orWidgetBox
widget_type
(str): Whether to generate individual widgets for each dimension, or to use a global linear scrubber with dimensions concatenated.widgets
(dict): A mapping from dimension name to a widget class, instance, or dictionary of overrides to modify the default widgets.
Display#
default_layout
(pn.layout.Panel, default=Row): Layout to wrap the plot and widgets in
The panel
function will automatically convert any HoloViews
object into a displayable panel, while keeping all of its interactive features:
import numpy as np
import holoviews as hv
box = hv.BoxWhisker((np.random.randint(0, 10, 100), np.random.randn(100)), 'Group').sort()
hv_layout = pn.panel(box)
hv_layout
By setting the pane’s object
the plot can be updated like all other pane objects:
hv_layout.object = hv.Violin(box).opts(violin_color='Group', cmap='Category20')
Widgets#
HoloViews natively renders plots with widgets if a HoloMap or DynamicMap declares any key dimensions. Unlike Panel’s interact
functionality, this approach efficiently updates just the data inside a plot instead of replacing it entirely. Calling pn.panel
on the DynamicMap will return a Row
layout (configurable via the default_layout
option), which is equivalent to calling pn.pane.HoloViews(dmap).layout
:
import pandas as pd
import hvplot.pandas
import holoviews.plotting.bokeh
def sine(frequency=1.0, amplitude=1.0, function='sin'):
xs = np.arange(200)/200*20.0
ys = amplitude*getattr(np, function)(frequency*xs)
return pd.DataFrame(dict(y=ys), index=xs).hvplot()
dmap = hv.DynamicMap(sine, kdims=['frequency', 'amplitude', 'function']).redim.range(
frequency=(0.1, 10), amplitude=(1, 10)).redim.values(function=['sin', 'cos', 'tan'])
hv_panel = pn.panel(dmap)
print(hv_panel)