Details
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Type:
Story
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Status: Done
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Resolution: Done
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Fix Version/s: None
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Component/s: None
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Labels:None
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Story Points:4
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Epic Link:
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Team:SQuaRE
Description
Some improvements on the display of the threshold lines in the bokeh app
1) we want lines that extend to infinity independently of the zoom, pan
2) we dont want the hover tool on threshold lines
3) we want to turn them on/off to not hide subtle changes in the measurements (the y-axis range is automatically set to the threshold values if they are on)
4) we want to indicate regions below/above the thresholds
Attachments
Issue Links
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- Done
Activity
Bokeh annotations cannot be configured with data sources, after exchanging some emails with bokeh developers the trick is to use a JS callback to update the annotation parameters as the metric change, here is a minimal example for the record
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from bokeh.io import curdoc |
from bokeh.layouts import row, widgetbox |
from bokeh.models import Slider, Span, CustomJS, Label |
from bokeh.plotting import figure |
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slider = Slider(start=0, end=10, value=3, step=0.1, title='Slider') |
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plot = figure(width=700, height=250, x_range=(0,10), y_range=(-1, 1)) |
span = Span(location=slider.value, dimension='height') |
plot.add_layout(span)
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label = Label(x=slider.value, y=0, x_units='data', y_units='data', |
text="Minimum") |
plot.add_layout(label)
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slider.callback = CustomJS(args=dict(span=span, label=label, slider=slider), code=""" |
span.location = slider.value
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label.x = slider.value
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""") |
curdoc().add_root(row(plot, widgetbox(slider)))
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Another implementation using the same logic but updating the annotation parameters in the server side, avoiding the javascript callback. I think it is quite illustrative of bokeh's flexibility and worth to keep:
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from bokeh.io import curdoc |
from bokeh.layouts import row, widgetbox |
from bokeh.models import Slider, Span, Label |
from bokeh.plotting import figure |
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slider = Slider(start=0, end=10, value=3, step=0.1, title='Slider') |
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plot = figure(width=700, height=250, x_range=(0,10), y_range=(-1, 1)) |
span = Span(location=slider.value, dimension='height') |
plot.add_layout(span)
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label = Label(x=slider.value, y=0, x_units='data', y_units='data', |
text="Minimum") |
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plot.add_layout(label)
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def update_annotations(attr, old, new): |
span.location = new |
label.x = new |
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slider.on_change('value', update_annotations) |
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curdoc().add_root(row(plot, widgetbox(slider)))
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Added annotations for the mininum specification, design specification and stretch goals - it took me a while to figure out how to update the annotation parameters dynamically after changing the metric in the bokeh app.
Since the annotations are not interpreted as data, they do not affect the plot limits which is good. So the ability to turn annotations on/off is not required.
More info in the PR https://github.com/lsst-sqre/qa-dashboard/pull/14
Not reviewed.
Review can be done in DM-7612
Attempt to make bokeh spans annotation to work with a bokeh app.