I am trying to build an interactive graph in Bokeh. So far let's say I have a heatmap like below. In plain English:
- I am using rect to draw rectangles producing a heatmap.
- I am adding a RangeSlider.
- I am attaching a js_callback on changes of the range.
- In the custom Callback, I am able to retrieve the start and end range of the range slider.
What I am uncertain about is how to then select anything with it. This link (cb_obj.selected['1d'].indices) shows that one retrieve all selected data points. But how does one do the opposite?
In other words:
How do I select all the rectangles that fall between values a and b?
Below is the code with things I figured out already.
from math import pi
from bokeh.io import show
from bokeh.models import ColumnDataSource, HoverTool,
LinearColorMapper, CategoricalColorMapper, ColorBar, LogColorMapper,
LogTicker
from bokeh.plotting import figure
from bokeh.models.callbacks import CustomJS
col = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
row = ['A', 'B', 'C' , 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',
'N', 'O', 'P']
mapper = LogColorMapper(palette="Viridis256", low=min_value,
high=max_value)
source = ColumnDataSource(data = dict (
row = test['plate_row'],
col = test['plate_col'],
values = test['Melt Temp']))
TOOLS = "reset, tap,box_select, hover,save,pan,box_zoom,wheel_zoom"
p = figure(title="Plate Heatmap", x_range = (0.0,25.0), y_range =
list(reversed(row)),
x_axis_location="above", plot_width=650, plot_height=400,
tools=TOOLS)
r1 = p.rect(x="col", y="row", width=1, height=1,
source=source,
fill_color={'field': 'values', 'transform': mapper},
line_color=None)
callback = CustomJS(args=dict(source=source), code="""
var data = source.data;
var inds = cb_obj.selected['1d'].indices;
var lower_bound = cb_obj.start;
var upper_boudn = cb_obj.end;
// WHAT DO I DO NEXT?
source.trigger('change');
""")
range_slider = widgetbox(RangeSlider(start=min_value, end=max_value,
range= (min_value, max_value), step=0.1, title="Hit Threshold"))
range_slider.js_on_change('range', callback)
color_bar = ColorBar(color_mapper=mapper, ticker=LogTicker(),
label_standoff=12, border_line_color=None, location=
(0,0))
p.add_layout(color_bar, 'left')
layout = column(range_slider, p)
show(layout) # show the plot
While I was not able to do find a way to set what is selected or not, I found a way to achieve the same outcome. This is perhaps a prime example for how a Bokeh server could be used. To accomplish this same effect, one needs to do the following:
This information can be found here and represents the simplest way of deploying an interactive visualization. More complicated deployment scenarios may be necessary for your case. I am looking into this as well, but this will be another question for another day.