I’m trying to figure out the best way to link data points in one plot to a set of secondary plots, where each data point has it’s own secondary plot. For example, let say we have a two panel figure where one plot is a scatter plot and the other panel is a histogram summarizing the underlying distribution for a single data point in the scatter plot. When you select/hover over a data point from the scatter plot, the corresponding histogram for that data point is shown in the other panel. Any ideas on how to make this happen? Thanks!

There are several ways you can do that. Here is an example. You can put select_fig and hist_fig in separate panels if you want.

Basically in your ‘selection plot’ you should have a source and a taptool in order to have a callback that does something when you click on a point.

Then it’s just a matter of updating the data source of your secondary plots. It will become more involved if each point is supposed to bring up a different kind of plot.

Here I reworked the histogram example (https://bokeh.pydata.org/en/latest/docs/gallery/histogram.html) to select which plot to display using a scatter plot.

-- coding: utf-8 --

import numpy as np

import scipy.special

from bokeh.io import curdoc

from bokeh.layouts import gridplot

from bokeh.models import ColumnDataSource, LabelSet

from bokeh.plotting import figure

params = {

'normal':{'title':'Normal Distribution (μ=0, σ=0.5)',},

'log normal':{'title':'Log Normal Distribution (μ=0, σ=0.5)',},

'gamma':{'title':'Gamma Distribution (k=1, θ=2)',},

'weibull':{'title':'Weibull Distribution (λ=1, k=1.25)',},

}

def update_hist_fig(attr,old,new):

try:

	new_id = new['1d']['indices'][0]

except IndexError:

	return

select_points = curdoc().select_one({"name":"select_points"})

selected = select_points.data_source.data['txt'][new_id]

hist_fig = curdoc().select_one({"name":"hist_fig"})

hist_fig.title.text = params[selected]['title']

hist_data = params[selected]['hist_data']

hist = curdoc().select_one({"name":"hist"})

hist.data_source.data.update(hist_data)

lines_data = params[selected]['lines_data']

pdf_line = curdoc().select_one({"name":"pdf"})

pdf_line.data_source.data.update(lines_data)

cdf_line = curdoc().select_one({"name":"cdf"})

cdf_line.data_source.data.update(lines_data)

hist_fig = figure(title=“Normal Distribution (μ=0, σ=0.5)”,tools=“save”,background_fill_color=“#E8DDCB”,name=“hist_fig”)

mu, sigma = 0, 0.5

measured = np.random.normal(mu, sigma, 1000)

hist, edges = np.histogram(measured, density=True, bins=50)

x = np.linspace(-2, 2, 1000)

pdf = 1/(sigma * np.sqrt(2np.pi)) * np.exp(-(x-mu)**2 / (2sigma**2))

cdf = (1+scipy.special.erf((x-mu)/np.sqrt(2*sigma**2)))/2

params[‘normal’][‘hist_data’] = {‘top’:hist,‘left’:edges[:-1],‘right’:edges[1:]}

params[‘normal’][‘lines_data’] = {‘x’:x,‘pdf’:pdf,‘cdf’:cdf}

source = ColumnDataSource(data=params[‘normal’][‘hist_data’])

hist_fig.quad(top=‘top’, bottom=0, left=‘left’, right=‘right’,fill_color=“#036564”, line_color=“#033649”,name=‘hist’,source=source)

source = ColumnDataSource(data=params[‘normal’][‘lines_data’])

hist_fig.line(x=‘x’, y=‘pdf’, line_color=“#D95B43”, line_width=8, alpha=0.7, legend=“PDF”,source=source,name=‘pdf’)

hist_fig.line(x=‘x’, y=‘cdf’, line_color=“white”, line_width=2, alpha=0.7, legend=“CDF”,source=source,name=‘cdf’)

hist_fig.legend.location = “center_right”

hist_fig.legend.background_fill_color = “darkgrey”

hist_fig.xaxis.axis_label = ‘x’

hist_fig.yaxis.axis_label = ‘Pr(x)’

mu, sigma = 0, 0.5

measured = np.random.lognormal(mu, sigma, 1000)

hist, edges = np.histogram(measured, density=True, bins=50)

x = np.linspace(0.0001, 8.0, 1000)

pdf = 1/(x* sigma * np.sqrt(2np.pi)) * np.exp(-(np.log(x)-mu)**2 / (2sigma**2))

cdf = (1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2

params[‘log normal’][‘hist_data’] = {‘top’:hist,‘left’:edges[:-1],‘right’:edges[1:]}

params[‘log normal’][‘lines_data’] = {‘x’:x,‘pdf’:pdf,‘cdf’:cdf}

k, theta = 1.0, 2.0

measured = np.random.gamma(k, theta, 1000)

hist, edges = np.histogram(measured, density=True, bins=50)

x = np.linspace(0.0001, 20.0, 1000)

pdf = x**(k-1) * np.exp(-x/theta) / (theta**k * scipy.special.gamma(k))

cdf = scipy.special.gammainc(k, x/theta) / scipy.special.gamma(k)

params[‘gamma’][‘hist_data’] = {‘top’:hist,‘left’:edges[:-1],‘right’:edges[1:]}

params[‘gamma’][‘lines_data’] = {‘x’:x,‘pdf’:pdf,‘cdf’:cdf}

lam, k = 1, 1.25

measured = lam*(-np.log(np.random.uniform(0, 1, 1000)))**(1/k)

hist, edges = np.histogram(measured, density=True, bins=50)

x = np.linspace(0.0001, 8, 1000)

pdf = (k/lam)*(x/lam)**(k-1) * np.exp(-(x/lam)**k)

cdf = 1 - np.exp(-(x/lam)**k)

params[‘weibull’][‘hist_data’] = {‘top’:hist,‘left’:edges[:-1],‘right’:edges[1:]}

params[‘weibull’][‘lines_data’] = {‘x’:x,‘pdf’:pdf,‘cdf’:cdf}

select_fig = figure(tools=‘tap’,outline_line_alpha=0,min_border_top=20)

select_fig.renderers =

select_source = ColumnDataSource(data={‘x’:[1,1,1,1],‘y’:[0,1,2,3],‘txt’:[‘normal’,‘log normal’,‘gamma’,‘weibull’]})

select_fig.scatter(x=‘x’,y=‘y’,size=10,source=select_source,name=‘select_points’)

labset = LabelSet(x=‘x’,y=‘y’,text=‘txt’,y_offset=-5,x_offset=10,source=select_source)

select_fig.add_layout(labset)

select_source.on_change(‘selected’,update_hist_fig)

curdoc().add_root(gridplot(select_fig,hist_fig, ncols=2, plot_width=400, plot_height=400, toolbar_location=None))

``

Thanks Sébastien! This will definitely get the job done.