Neptune-bokeh IntegrationΒΆ
This integration lets you log interactive charts generated in bokeh, like confusion matrix or distribution, in Neptune.

Follow these steps:
Create an experiment:
import neptune neptune.init(api_token='ANONYMOUS',project_qualified_name='shared/showroom') neptune.create_experiment()
Create and log bokeh charts into Neptune:
from bokeh.models import LogColorMapper from bokeh.palettes import Viridis6 as palette from bokeh.plotting import figure from bokeh.sampledata.unemployment import data as unemployment from bokeh.sampledata.us_counties import data as counties palette = tuple(reversed(palette)) counties = { code: county for code, county in counties.items() if county["state"] == "tx" } county_xs = [county["lons"] for county in counties.values()] county_ys = [county["lats"] for county in counties.values()] county_names = [county['name'] for county in counties.values()] county_rates = [unemployment[county_id] for county_id in counties] color_mapper = LogColorMapper(palette=palette) data=dict( x=county_xs, y=county_ys, name=county_names, rate=county_rates, ) TOOLS = "pan,wheel_zoom,reset,hover,save" p = figure( title="Texas Unemployment, 2009", tools=TOOLS, x_axis_location=None, y_axis_location=None, tooltips=[ ("Name", "@name"), ("Unemployment rate", "@rate%"), ("(Long, Lat)", "($x, $y)") ]) p.grid.grid_line_color = None p.hover.point_policy = "follow_mouse" p.patches('x', 'y', source=data, fill_color={'field': 'rate', 'transform': color_mapper}, fill_alpha=0.7, line_color="white", line_width=0.5)
from neptunecontrib.api import log_chart log_chart(name='bokeh_figure', chart=p)
Explore the results in the Neptune dashboard:
Check out this experiment in the app.
