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How do I make a bokeh DataTable respond to DoubleTap

I am trying to add a feature to recalculate the diff column.
Currently I using a button to trigger the callback but I would really like to trigger it by double clicking a row in the table.
I can only find solutions with single click implementation by using the code
source.selected.js_on_change('indices', callback).
Does anyone know how to get the DataTable to react to double clicks?
from bokeh.models import ColumnDataSource, TableColumn, DataTable, Div, CustomJS, Button
from bokeh.layouts import column
from bokeh.plotting import show
from bokeh import events
names = ['Alfa', 'Bravo', 'Charlie', 'Delta']
values = [150, 100, 125, 200]
difference = [0, 0, 0, 0]
data = dict(names=names, values=values, diff=difference)
source = ColumnDataSource(data)
columns = [TableColumn(field='names', title='Name', width=200),
TableColumn(field='values', title='Value (-)', width=200),
TableColumn(field='diff', title='Difference (%)', width=200)]
# create total table width value
table_width = 0
for col in columns:
table_width = table_width + col.width
header = Div(text=f'<b>Results<b>', style={'font-size': '150%'})
fig = DataTable(source=source, columns=columns, height=len(values) * 25 + 50, width=table_width, selectable=True)
# callback to change reference for (%) difference calculation
callback = CustomJS(args=dict(source=source), code="""
var idx = source.selected.indices[0]
if (typeof idx == "undefined") {
idx = 0
}
var ref_val = source.data['values'][idx]
console.log(ref_val)
var d = source.data['diff']
for (var i = 0; i < d.length; i++) {
value = source.data['values'][i]
source.data['diff'][i] = (100*(value/ref_val-1)).toFixed(2)
}
source.change.emit()
""")
button = Button()
button.label = 'Click HERE to change reference to selected row for Difference (%) calculation'
# source.selected.js_on_change('indices', callback)
source.selected.js_on_event(events.DoubleTap, callback)
button.js_on_event(events.ButtonClick, callback)
show(column([button, fig]))

My trick is to set 2 active rows for each row click.
lst_source_indices_old =[]
source = ColumnDataSource(df)
def callback(attr, old, new):
global lst_source_indices_old # declare it is a global variable
lst = source.selected.indices # selected row in DataTable
if(len(lst)==1):
nrow=lst[0]
if(lst==lst_source2_indices_old):
print("... DataTable : Double-click")
# Save
lst_source2_indices_old = [nrow]
source.selected.indices = [nrow, 9999] # s.t. every click will be triggered
source.selected.on_change('indices', callback)

Bokeh datatable filtering inconsistency

Apologies but I can't make any reproducible code for this question since it's pretty inconsistent.
So I have a bokeh data table, and I'm doing some filtering with it using 4 dropdown boxes. The data table updates based on dropdown box value, and the updates were written in JS. The filtering works as expected, but strangely enough for some very specific combinations of dropdown values it does not display anything in the data table. I was wondering if it was a problem with my data, but I coerced everything to strings and it still gave me the same problem.
The updates are written here:
combined_callback_code = """
var data = source.data;
var original_data = original_source.data;
var origin = origin_select_obj.value;
var classification = classification_select_obj.value;
var currency = currency_select_obj.value;
var grade = grade_select_obj.value;
for (var key in original_data) {
data[key] = [];
for (var i = 0; i < original_data['Origin'].length; ++i) {
if ((origin === "ALL" || original_data['Origin'][i] === origin) &&
(classification === "ALL" || original_data['Classification'][i] === classification) &&
(currency === "ALL" || original_data['Currency'][i] === currency) &&
(grade === "ALL" || original_data['BrokenPct'][i] === grade)){
data[key].push(original_data[key][i]);
}
}
}
target_obj.change.emit();
source.change.emit();
"""
# define the filter widgets, without callbacks for now
origin_list = ['ALL'] + df['Origin'].unique().tolist()
origin_select = Select(title="Origin:", value=origin_list[0], options=origin_list)
classification_list = ['ALL'] + df['Classification'].unique().tolist()
classification_select = Select(title="Classification:", value=classification_list[0], options=classification_list)
currency_list = ['ALL'] + df['Currency'].unique().tolist()
currency_select = Select(title = "Currency:", value=currency_list[0], options = currency_list)
grade_list = ["ALL"] + df['BrokenPct'].unique().tolist()
grade_select = Select(title = "Grade:", value = grade_list[0], options = grade_list)
# now define the callback objects now that the filter widgets exist
generic_callback = CustomJS(
args=dict(source=source,
original_source=original_source,
origin_select_obj=origin_select,
classification_select_obj=classification_select,
currency_select_obj = currency_select,
grade_select_obj = grade_select,
target_obj=data_table),
code=combined_callback_code
)
# finally, connect the callbacks to the filter widgets
origin_select.js_on_change('value', generic_callback)
classification_select.js_on_change('value', generic_callback)
currency_select.js_on_change('value', generic_callback)
grade_select.js_on_change('value', generic_callback)

The right way do update table data in your JS callback is this way:
var data = {};
//build your data here
source.data = data;
Where source is the Bokeh ColumnDataSource of you DataTable.
You don't need to use:
source.change.emit();
You do it only when you replace only a part of you data e.g. one table column.
And if data_table is your Bokeh DataTable object then also skip doing:
target_obj.change.emit();
The table date updates automatically when you update its ColumnDataSource.
See this simple example:
from bokeh.io import show
from bokeh.layouts import widgetbox
from bokeh.models import ColumnDataSource, Slider, DataTable, TableColumn, CustomJS
source = ColumnDataSource(dict(x = list(range(6)), y = [x ** 2 for x in range(6)]))
columns = [TableColumn(field = "x", title = "x"), TableColumn(field = "y", title = "x**2")]
table = DataTable(source = source, columns = columns, width = 320)
slider = Slider(start = 1, end = 20, value = 6, step = 1, title = "i", width = 300)
callback_code = """ i = slider.value;
new_data = {"x": [1, 2, 3, 4, 5], "y": [1, 4, 9, 16, 25]}
table.source.data = new_data
table.width = 320 + i * 25; """
callback = CustomJS(args = dict(slider = slider, table = table), code = callback_code)
slider.js_on_change('value', callback)
show(widgetbox(slider, table))

Changing colors on bokeh patches plot real time

I'm trying to create a bokeh plot of the US States, and color each of the state according to some data. Now using this tutorial I managed to create this, but I also want to enhance it, and add a slider to it, to change the values displayed. For example like displaying separate years.
With the help of this tutorial, I managed to add the slider, and the underlying data does change, according to the hover text, but the colors aren't recalculated, and so the visual representation does not match the values.
This is the code I've used, from a Jupyter notebook, so anybody who wants to try can reproduce
from bokeh.io import show, output_notebook
from bokeh.models import (
ColumnDataSource,
HoverTool,
LogColorMapper,
Range1d, CustomJS, Slider
)
from bokeh.palettes import Inferno256 as palette
from bokeh.plotting import figure
from bokeh.layouts import row, widgetbox
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.us_states import data as states
from bokeh.sampledata.unemployment import data as unemployment
import pandas as pd
import random
output_notebook()
palette.reverse()
states_accumulated ={}
available_state_codes = states.keys()
for key, value in counties.items():
state_name = value["state"].upper()
if state_name in states.keys() and "number" not in states[state_name]:
states[state_name]["number"] = key[0]
for key,state in states.items():
state["code"] = key
state_list = []
for key,state in states.items():
state_list.append(state)
unemployment_transf = []
for key,value in unemployment.items():
unemployment_transf.append({
"State":key[0],
"County":key[1],
"Value":value
})
unemp_df = pd.DataFrame(unemployment_transf)
unemp_sum = unemp_df.groupby("State").mean()["Value"]
unemp_sum = unemp_sum.sort_index()
unemp_sum_flat = {key:value for key, value in unemp_sum.items()}
for state in state_list:
state["value"] = unemp_sum_flat[state["number"]]
state_df = pd.DataFrame(state_list)
color_mapper = LogColorMapper(palette=palette)
state_xy = (list(state_df["lons"].values),list(state_df["lats"].values))
max_x = max([max(l) for l in state_xy[0]])
max_y = max([max(l) for l in state_xy[1]])
min_x = min([min(l) for l in state_xy[0]])
min_y = min([min(l) for l in state_xy[1]])
data=dict(
x=state_xy[0],
y=state_xy[1],
name=list(state_df["name"].values),
used = list(state_df["value"].values)
)
data['1999'] = list(state_df["value"].values)
data['2000'] = [random.randrange(0,10) for i in range(len(state_xy[0]))]
source = ColumnDataSource(data)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="States", tools=TOOLS,
x_axis_location=None, y_axis_location=None
)
p.width=450
p.height = 450
p.x_range= Range1d(-170,-60)
p.y_range = Range1d(min_y-10,max_y+10)
p.grid.grid_line_color = None
renderer = p.patches('x', 'y', source=source,
fill_color={'field': 'used', 'transform': color_mapper},
fill_alpha=0.7, line_color="white", line_width=0.5)
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Name", "#name"),
("Unemployment rate)", "#used%"),
("(Long, Lat)", "($x, $y)"),
]
callback = CustomJS(args=dict(source=source,plot=p,color_mapper = color_mapper,renderer = renderer), code="""
var data = source.data;
var year = year.value;
used = data['used']
should_be = data[String(year)]
for (i = 0; i < should_be.length; i++) {
used[i] = should_be[i];
}
""")
year_slider = Slider(start=1999, end=2000, value=1999, step=1,
title="year", callback=callback)
callback.args["year"] = year_slider
layout = row(
p,
widgetbox(year_slider),
)
show(layout)
Sample images of the plot:
What I would like to accomplish, is that when I change the slider, the colors on the plot should change. Now I think the JS callback should call some kind of redraw or recalculate, but I haven't found any documentation about it. Is there a way to do this?

append source.change.emit() to the Javascipt code to trigger the change event.

Appending source.trigger("change"); to the CustomJS seems to solve the problem, now as the slider changes, the colors change.

Bokeh Plot Update Using Slider

I am trying to use a slider to update my Bokeh Plot. I am finding it difficult to achieve it using pandas dataframe(did not find any examples so far).
The other way is to use the "columndatasource" (found some examples over forums) but still not able to achieve the functionality.
So I have two columns, X axis is date and the Y axis is Volume. I want to change my Y values based on slider input. I am able to see the plot but the slider functionality is not working
Any help will be very much appreciable.
source = ColumnDataSource(data=dict(x=df2['Date'],y=df2['Vol']))
S1 = figure(plot_width=400,plot_height=400,tools=TOOLS1,title="Volume Per Day",x_axis_type="datetime")
S1.line('x','y',source=source)
callback_test = CustomJS(args=dict(source=source), code="""
var data = source.get('data');
var s_val = cb_obj.value
x = data['x']
y = data['y']
console.log(cb_obj)
for (i = 0; i < s_val; i++) {
y[i] = y[i]
}
source.trigger('change');
""")
slider = Slider(start=0, end= max_Vol, value=1, step=100,title="Vol Per Day",callback=callback_test)

You are trying to update the range of data that is plotted using a slider.
When you do:
y = data['y']
for (i = 0; i < s_val; i++) {
y[i] = y[i]
}
the python equivalent would be, if y is some array with length>s_val:
for i in range(s_val):
y[i] = y[i]
This just replaces the elements from 0 to s_val-1 by themselves and doesn't change the rest of the list.
You can do two things:
update the displayed axis range directly
use an empty source that you will fill from your existing source based on the slider value
.
source = ColumnDataSource(data=dict(x=df2['Date'],y=df2['Vol']))
fill_source = ColumnDataSource(data=dict(x=[],y=[]))
S1 = figure(plot_width=400,plot_height=400,tools=TOOLS1,title="Volume Per Day",x_axis_type="datetime")
S1.line('x','y',source=fill_source)
callback_test = CustomJS(args=dict(source=source,fill_source=fill_source), code="""
var data = source.data;
var fill_data = fill_source.data;
var s_val = cb_obj.value;
fill_data['x']=[];
fill_data['y']=[];
for (i = 0; i < s_val; i++) {
fill_data['y'][i].push(data['y'][i]);
fill_data['x'][i].push(data['x'][i]);
}
fill_source.trigger('change');
""")

Here is the changes I have made to make it work with Bokeh last version
Some syntax error in the JavaScript part have been corrected, the method to trigger change is now change.emit, and the callback for a stand alone document is set after the Slider definition thanks to js_on_change
I have added all the import commands to give a complete example
I have also changed the visualization to show only data below the number of flight set by the slider (for more comprehension when moving the Slider towards lower values)
Below is the resulting code:
from bokeh.layouts import column, widgetbox
from bokeh.models import ColumnDataSource, CustomJS
from bokeh.models.widgets import Slider
from bokeh.plotting import Figure
import pandas as pd
from datetime import datetime, date, timedelta
from bokeh.plotting import show
from random import randint
today = date.today()
random_data = [[today + timedelta(days = i), randint(0, 10000)] for i in range(10)]
df2 = pd.DataFrame(random_data, columns = ['Date', 'Vol'])
source = ColumnDataSource(data = dict(x = df2['Date'], y = df2['Vol']))
fill_source = ColumnDataSource(data = dict(x = df2['Date'], y = df2['Vol'])) # set the graph to show all data at loading
TOOLS1 = []
S1 = Figure(plot_width = 400, plot_height = 400, tools = TOOLS1, title = "Volume Per Day", x_axis_type = "datetime")
S1.line('x', 'y', source = fill_source)
callback_test = CustomJS(args = dict(source = source, fill_source = fill_source), code = """
var data = source.data;
var fill_data = fill_source.data;
var s_val = cb_obj.value;
fill_data['x']=[];
fill_data['y']=[];
for (var i = 0; i <= data.x.length; i++) { // added "var" declaration of variable "i"
if (data['y'][i] <= s_val) { // more meaningful visualization: assuming you want to focuss on dates with less number of flights
fill_data['y'].push(data['y'][i]); // [i] index on left side of assignment removed
}
else {
fill_data['y'].push(0);
}
fill_data['x'].push(data['x'][i]);
}
fill_source.change.emit() ; // "trigger" method replaced by "change.emit"
""")
max_Vol = df2['Vol'].max()
slider = Slider(start = 0, end = max_Vol, value = max_Vol, step = 100, title = "Vol Per Day") # Remove attribute "callback = callback_test"
slider.js_on_change('value', callback_test) # new way of defining event listener
controls = widgetbox(slider)
layout = column(controls, S1)
show(layout)
Would be nice if I could embbed the resulting (HTML) visualization directly in this answer, let me now if it's possible ;)

Get selected data contained within box select tool in Bokeh

If I have a scatter plot in bokeh and I've enabled the Box Select Tool, suppose I select a few points with the Box Select Tool. How can I access the (x,y) position location information of the points that I've selected?
%matplotlib inline
import numpy as np
from random import choice
from string import ascii_lowercase
from bokeh.models.tools import *
from bokeh.plotting import *
output_notebook()
TOOLS="pan,wheel_zoom,reset,hover,poly_select,box_select"
p = figure(title = "My chart", tools=TOOLS)
p.xaxis.axis_label = 'X'
p.yaxis.axis_label = 'Y'
source = ColumnDataSource(
data=dict(
xvals=list(range(0, 10)),
yvals=list(np.random.normal(0, 1, 10)),
letters = [choice(ascii_lowercase) for _ in range(10)]
)
)
p.scatter("xvals", "yvals",source=source,fill_alpha=0.2, size=5)
select_tool = p.select(dict(type=BoxSelectTool))[0]
show(p)
# How can I know which points are contained in the Box Select Tool?
I can't call the "callback" attribute and the "dimensions" attribute just returns a list ["width", "height"]. If I can just get the dimensions and the location of the Selected Box, I can figure out which points are in my dataset from there.

You can use a callback on the ColumnDataSource that updates a Python variable with the indices of the selected data:
%matplotlib inline
import numpy as np
from random import choice
from string import ascii_lowercase
from bokeh.models.tools import *
from bokeh.plotting import *
from bokeh.models import CustomJS
output_notebook()
TOOLS="pan,wheel_zoom,reset,hover,poly_select,box_select"
p = figure(title = "My chart", tools=TOOLS)
p.xaxis.axis_label = 'X'
p.yaxis.axis_label = 'Y'
source = ColumnDataSource(
data=dict(
xvals=list(range(0, 10)),
yvals=list(np.random.normal(0, 1, 10)),
letters = [choice(ascii_lowercase) for _ in range(10)]
)
)
p.scatter("xvals", "yvals",source=source,fill_alpha=0.2, size=5)
select_tool = p.select(dict(type=BoxSelectTool))[0]
source.callback = CustomJS(args=dict(p=p), code="""
var inds = cb_obj.get('selected')['1d'].indices;
var d1 = cb_obj.get('data');
console.log(d1)
var kernel = IPython.notebook.kernel;
IPython.notebook.kernel.execute("inds = " + inds);
"""
)
show(p)
Then you can access the desired data attributes using something like
zip([source.data['xvals'][i] for i in inds],
[source.data['yvals'][i] for i in inds])

Here is a working example with Python 3.7.5 and Bokeh 1.4.0
public github link to this jupyter notebook:
https://github.com/surfaceowl-ai/python_visualizations/blob/master/notebooks/bokeh_save_linked_plot_data.ipynb
environment report:
virtual env python version: Python 3.7.5
virtual env ipython version: 7.9.0
watermark package reports:
bokeh 1.4.0
jupyter 1.0.0
numpy 1.17.4
pandas 0.25.3
rise 5.6.0
watermark 2.0.2
# Generate linked plots + TABLE displaying data + save button to export cvs of selected data
from random import random
from bokeh.io import output_notebook # prevent opening separate tab with graph
from bokeh.io import show
from bokeh.layouts import row
from bokeh.layouts import grid
from bokeh.models import CustomJS, ColumnDataSource
from bokeh.models import Button # for saving data
from bokeh.models.widgets import DataTable, DateFormatter, TableColumn
from bokeh.models import HoverTool
from bokeh.plotting import figure
# create data
x = [random() for x in range(500)]
y = [random() for y in range(500)]
# create first subplot
plot_width = 400
plot_height = 400
s1 = ColumnDataSource(data=dict(x=x, y=y))
fig01 = figure(
plot_width=plot_width,
plot_height=plot_height,
tools=["lasso_select", "reset", "save"],
title="Select Here",
)
fig01.circle("x", "y", source=s1, alpha=0.6)
# create second subplot
s2 = ColumnDataSource(data=dict(x=[], y=[]))
# demo smart error msg: `box_zoom`, vs `BoxZoomTool`
fig02 = figure(
plot_width=400,
plot_height=400,
x_range=(0, 1),
y_range=(0, 1),
tools=["box_zoom", "wheel_zoom", "reset", "save"],
title="Watch Here",
)
fig02.circle("x", "y", source=s2, alpha=0.6, color="firebrick")
# create dynamic table of selected points
columns = [
TableColumn(field="x", title="X axis"),
TableColumn(field="y", title="Y axis"),
]
table = DataTable(
source=s2,
columns=columns,
width=400,
height=600,
sortable=True,
selectable=True,
editable=True,
)
# fancy javascript to link subplots
# js pushes selected points into ColumnDataSource of 2nd plot
# inspiration for this from a few sources:
# credit: https://stackoverflow.com/users/1097752/iolsmit via: https://stackoverflow.com/questions/48982260/bokeh-lasso-select-to-table-update
# credit: https://stackoverflow.com/users/8412027/joris via: https://stackoverflow.com/questions/34164587/get-selected-data-contained-within-box-select-tool-in-bokeh
s1.selected.js_on_change(
"indices",
CustomJS(
args=dict(s1=s1, s2=s2, table=table),
code="""
var inds = cb_obj.indices;
var d1 = s1.data;
var d2 = s2.data;
d2['x'] = []
d2['y'] = []
for (var i = 0; i < inds.length; i++) {
d2['x'].push(d1['x'][inds[i]])
d2['y'].push(d1['y'][inds[i]])
}
s2.change.emit();
table.change.emit();
var inds = source_data.selected.indices;
var data = source_data.data;
var out = "x, y\\n";
for (i = 0; i < inds.length; i++) {
out += data['x'][inds[i]] + "," + data['y'][inds[i]] + "\\n";
}
var file = new Blob([out], {type: 'text/plain'});
""",
),
)
# create save button - saves selected datapoints to text file onbutton
# inspriation for this code:
# credit: https://stackoverflow.com/questions/31824124/is-there-a-way-to-save-bokeh-data-table-content
# note: savebutton line `var out = "x, y\\n";` defines the header of the exported file, helpful to have a header for downstream processing
savebutton = Button(label="Save", button_type="success")
savebutton.callback = CustomJS(
args=dict(source_data=s1),
code="""
var inds = source_data.selected.indices;
var data = source_data.data;
var out = "x, y\\n";
for (i = 0; i < inds.length; i++) {
out += data['x'][inds[i]] + "," + data['y'][inds[i]] + "\\n";
}
var file = new Blob([out], {type: 'text/plain'});
var elem = window.document.createElement('a');
elem.href = window.URL.createObjectURL(file);
elem.download = 'selected-data.txt';
document.body.appendChild(elem);
elem.click();
document.body.removeChild(elem);
""",
)
# add Hover tool
# define what is displayed in the tooltip
tooltips = [
("X:", "#x"),
("Y:", "#y"),
("static text", "static text"),
]
fig02.add_tools(HoverTool(tooltips=tooltips))
# display results
# demo linked plots
# demo zooms and reset
# demo hover tool
# demo table
# demo save selected results to file
layout = grid([fig01, fig02, table, savebutton], ncols=3)
output_notebook()
show(layout)
# things to try:
# select random shape of blue dots with lasso tool in 'Select Here' graph
# only selected points appear as red dots in 'Watch Here' graph -- try zooming, saving that graph separately
# selected points also appear in the table, which is sortable
# click the 'Save' button to export a csv
# TODO: export from Bokeh to pandas dataframe