Crossfiltering with HoloViews

bokeh
hugging face
pyodide
timeseries
Apply a selection in one plot as a filter in other plots
Author

Marc Skov Madsen

Published

January 8, 2024

Introduction

Crossfiltering lets you interact with one chart and apply that interaction as a filter to other charts in the report.

With HoloViews you can add crossfiltering to your hvPlot or Holoviews plots. Check out the Linked Brushing Reference Guide.

App

Bokeh

Plotly

For the Plotly backend I cannot get responsive plots working. That is why I use fixed sizes. See Panel #6173.

Open in a new window | Open in Hugging Face Spaces

Code

Show Bokeh 
"""*Linked Brushing* is a very powerful technique. It's also often called
*Linked Selections* or *Crossfiltering*.

This example is inspired by the HoloViews [Linked Brushing Reference Guide]\
(http://holoviews.org/user_guide/Linked_Brushing.html) and the Plotly blog post
[Introducing Dash HoloViews]\
(https://medium.com/plotly/introducing-dash-holoviews-6a05c088ebe5).

This example uses the *Iris* dataset.
"""
from typing import Tuple

import holoviews as hv
import pandas as pd
import panel as pn
from holoviews import opts
from panel.template import FastListTemplate


@pn.cache
def get_iris_data():
    return pd.read_csv("https://cdn.awesome-panel.org/resources/crossfiltering_holoviews/iris.csv.gz")


ACCENT = "#F08080"

CSS = """
.main .card-margin.stretch_both {
    height: calc(50vh - 65px) !important;
}
"""
if not CSS in pn.config.raw_css:
    pn.config.raw_css.append(CSS)

BOKEH_TOOLS = {
    "tools": ["hover"], "active_tools": ["box_select"]
}


def get_linked_plots() -> Tuple:
    """Returns a tuple (scatter, hist) of linked plots
    
    See http://holoviews.org/user_guide/Linked_Brushing.html
    """

    dataset = hv.Dataset(get_iris_data())

    scatter = hv.Scatter(dataset, kdims=["sepal_length"], vdims=["sepal_width"])
    hist = hv.operation.histogram(dataset, dimension="petal_width", normed=False)

    # pylint: disable=no-value-for-parameter
    selection_linker = hv.selection.link_selections.instance()
    # pylint: disable=no-member
    scatter = selection_linker(scatter).opts(
        opts.Scatter(color=ACCENT, responsive=True, size=10, **BOKEH_TOOLS),
    )
    hist = selection_linker(hist).opts(
        opts.Histogram(color=ACCENT, responsive=True, **BOKEH_TOOLS)
    )

    return scatter, hist


def create_app():
    """Returns the app in a nice FastListTemplate"""
    scatter, hist = get_linked_plots()
    scatter_panel = pn.pane.HoloViews(scatter, sizing_mode="stretch_both")
    hist_panel = pn.pane.HoloViews(hist, sizing_mode="stretch_both")
    
    template = FastListTemplate(
        site="Awesome Panel",
        site_url="https://awesome-panel.org",
        title="Crossfiltering with HoloViews and Bokeh",
        accent=ACCENT,
        main=[
            # We need to wrap in Columns to get them to stretch properly
            pn.Column(scatter_panel, sizing_mode="stretch_both"),
            pn.Column(hist_panel, sizing_mode="stretch_both"),
        ],
    )
    return template

pn.extension()
hv.extension("bokeh")
create_app().servable()
Show Plotly 
"""*Linked Brushing* is a very powerful technique. It's also often called
*Linked Selections* or *Crossfiltering*.

This example is inspired by the HoloViews [Linked Brushing Reference Guide]\
(http://holoviews.org/user_guide/Linked_Brushing.html) and the Plotly blog post
[Introducing Dash HoloViews]\
(https://medium.com/plotly/introducing-dash-holoviews-6a05c088ebe5).

This example uses the *Iris* dataset.
"""
from typing import Tuple

import holoviews as hv
import panel as pn
from holoviews import opts
from panel.template import FastListTemplate
import plotly.io as pio
import pandas as pd

@pn.cache
def get_iris_data():
    return pd.read_csv("https://cdn.awesome-panel.org/resources/crossfiltering_holoviews/iris.csv.gz")


ACCENT = "#F08080"

CSS = """
.main .card-margin.stretch_both {
    height: calc(100vh - 125px) !important;
}
"""

def _plotly_hooks(plot, element):
    """Used by HoloViews to give plots plotly plots special treatment"""
    fig = plot.state
    
    fig["layout"]["dragmode"] = "select"
    fig["config"]["displayModeBar"] = True
    if isinstance(element, hv.Histogram):
        # Constrain histogram selection direction to horizontal
        fig["layout"]["selectdirection"] = "h"


def get_linked_plots() -> Tuple:
    """Returns a tuple (scatter, hist) of linked plots
    
    See http://holoviews.org/user_guide/Linked_Brushing.html
    """

    dataset = hv.Dataset(get_iris_data())

    scatter = hv.Scatter(dataset, kdims=["sepal_length"], vdims=["sepal_width"])
    hist = hv.operation.histogram(dataset, dimension="petal_width", normed=False)

    # pylint: disable=no-value-for-parameter
    selection_linker = hv.selection.link_selections.instance()
    # pylint: disable=no-member
    scatter = selection_linker(scatter).opts(
        opts.Scatter(color=ACCENT, size=10, hooks=[_plotly_hooks], width=700, height=400),
    )
    hist = selection_linker(hist).opts(
        opts.Histogram(color=ACCENT, hooks=[_plotly_hooks], width=700, height=400)
    )

    return scatter, hist


def create_app():
    """Returns the app in a nice FastListTemplate"""
    if pn.config.theme == "dark":
        pio.templates.default = "plotly_dark"
    else:
        pio.templates.default = "plotly_white"
    scatter, hist = get_linked_plots()
    scatter_panel = pn.pane.HoloViews(scatter, sizing_mode="stretch_both", backend="plotly")
    hist_panel = pn.pane.HoloViews(hist, sizing_mode="stretch_both", backend="plotly")

    def reset(event):
        scatter, hist = get_linked_plots()
        scatter_panel.object=scatter
        hist_panel.object=hist

    reset_button = pn.widgets.Button(name="RESET PLOTS", on_click=reset, description="Resets the plots. Plotly does not have a built in way to do this.")
    
    template = FastListTemplate(
        site="Awesome Panel",
        site_url="https://awesome-panel.org",
        title="Crossfiltering with HoloViews and Plotly",
        accent=ACCENT,
        main=[
            # We need to wrap in Columns to get them to stretch properly
            pn.Column(reset_button, scatter_panel, pn.layout.Spacer(height=20), hist_panel, height=870, sizing_mode="stretch_width"),
        ],
        main_max_width="850px",
    )
    return template

pn.extension("plotly", raw_css=[CSS])
hv.extension("plotly")
create_app().servable()

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