python bokeh: update scatter plot colors on callback

Question

I only started to use Bokeh recently. I have a scatter plot in which I would like to color each marker according to a certain third property (say a quantity, while the x-axis is a date and the y-axis is a given value at that point in time).

Assuming my data is in a data frame, I managed to do this using a linear color map as follows:

min_q = df.quantity.min()
max_q = df.quantity.max()
mapper = linear_cmap(field_name='quantity', palette=palettes.Spectral6, low=min_q, high=max_q)
source = ColumnDataSource(data=get_data(df))

p = figure(x_axis_type="datetime")
p.scatter(x="date_column", y="value", marker="triangle", fill_color=mapper, line_color=None, source=source)
color_bar = ColorBar(color_mapper=mapper['transform'], width=8,  location=(0,0))
p.add_layout(color_bar, 'right')

This seems to work as expected. Below is the plot I get upon starting the bokeh server.

Then I have a callback function update() triggered upon changing value in some widget (a select or a time picker).

def update():
    # get new df (according to new date/select)
    df = get_df()
    # update min/max for colormap
    min_q = df.quantity.min()
    max_q = df.quantity.max()
    # I think I should not create a new mapper but doing so I get closer
    mapper = linear_cmap(field_name='quantity', palette=palettes.Spectral6 ,low=min_q, high=max_q)
    color_bar.color_mapper=mapper['transform'] 
    source.data = get_data(df)
    # etc

This is the closest I could get. The color map is updated with new values, but it seems that the colors of the marker still follow the original pattern. See picture below (given that quantity I would expect green, but it is blue as it still seen as < 4000 as in the map of the first plot before the callback).

Should I just add a "color" column to the data frame? I feel there is an easier/more convenient way to do that.

EDIT: Here is a minimal working example using the answer by bigreddot:

from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.plotting import figure
from bokeh.models import Button, ColumnDataSource, ColorBar, HoverTool
from bokeh.palettes import Spectral6
from bokeh.transform import linear_cmap
import numpy as np

x = [1,2,3,4,5,7,8,9,10]
y = [1,2,3,4,5,7,8,9,10]
z = [1,2,3,4,5,7,8,9,10]



source = ColumnDataSource(dict(x=x, y=y, z=z))

#Use the field name of the column source
mapper = linear_cmap(field_name='z', palette=Spectral6 ,low=min(y) ,high=max(y))

p = figure(plot_width=300, plot_height=300, title="Linear Color Map Based on Y")
p.circle(x='x', y='y', line_color=mapper,color=mapper, fill_alpha=1, size=12, source=source)

color_bar = ColorBar(color_mapper=mapper['transform'], width=8,  location=(0,0))
p.add_tools(HoverTool(tooltips="@z", show_arrow=False, point_policy='follow_mouse'))
p.add_layout(color_bar, 'right')

b = Button()

def update():
    new_z = np.exp2(z)
    mapper = linear_cmap(field_name='z', palette=Spectral6 ,low=min(new_z), high=max(new_z))
    color_bar.color_mapper=mapper['transform'] 
    source.data = dict(x=x, y=y, z=new_z)

b.on_click(update)

curdoc().add_root(column(b, p))

Upon update, the circles will be colored according to the original scale: everything bigger than 10 will be red. Instead, I would expect everything blue until the last 3 circle on tops that should be colored green yellow and red respectively.

Answer 1

It's possible that is a bug, feel free to open a GitHub issue.

That said, the above code does not represent best practices for Bokeh usage, which is: always make the smallest update possible. In this case, this means setting new property values on the existing color transform, rather than replacing the existing color transform.

Here is a complete working example (made with Bokeh 1.0.2) that demonstrates the glyph's colormapped colors updating in response to the data column changing:

from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.plotting import figure
from bokeh.models import Button, ColumnDataSource, ColorBar
from bokeh.palettes import Spectral6
from bokeh.transform import linear_cmap

x = [1,2,3,4,5,7,8,9,10]
y = [1,2,3,4,5,7,8,9,10]
z = [1,2,3,4,5,7,8,9,10]

#Use the field name of the column source
mapper = linear_cmap(field_name='z', palette=Spectral6 ,low=min(y) ,high=max(y))

source = ColumnDataSource(dict(x=x, y=y, z=z))

p = figure(plot_width=300, plot_height=300, title="Linear Color Map Based on Y")
p.circle(x='x', y='y', line_color=mapper,color=mapper, fill_alpha=1, size=12, source=source)

color_bar = ColorBar(color_mapper=mapper['transform'], width=8,  location=(0,0))
p.add_layout(color_bar, 'right')

b = Button()

def update():
    new_z = np.exp2(z)

    # update the existing transform
    mapper['transform'].low=min(new_z)
    mapper['transform'].high=max(new_z)

    source.data = dict(x=x, y=y, z=new_z)

b.on_click(update)

curdoc().add_root(column(b, p))

Here is the original plot:

And here is the update plot after clicking the button