Closed fanshi118 closed 3 years ago
We can ignore the coverage requirements for that new plotting function (i.e. set # pragma: no cover
on the function).
We can ignore the coverage requirements for that new plotting function (i.e. set
# pragma: no cover
on the function).
Just made the update: got rid of the wrapper and rearranged the commits.
@brandonwillard - just rebased. Moved the wrapper/styling code from utils.py
to the notebook, returning axes
in plot_predictive_histograms
.
I don't think we need to add the demo notebook when we merge this. It would be good to make it available independently as a Gist, though.
Removed; Gist added here.
Can you make the same plotting and out-of-sample updates to the other example notebook? That example should produce a much more interesting out-of-sample plot; one for which this new plotting function should really shine.
The time-varying model has time-dependent variables, which require the shape parameters to be reset as we sample the out-of-sample posterior predictives. This likely will lead to using the design matrix, for us to be able to update the time-dependent variables as we'd like to in OOS. I updated the notebook so that it drops the S_t
and P_t
, assumes the same shape, and truncates the series to the first week, just to show how the histogram plot would look. @brandonwillard , let me know what you think of this as opposed to potentially using the design matrix for this example.
Have you considered using pcolorfast
instead of fill_between
? I'm not sure if it would make for a good alternative, but it seems like it.
Have you considered using
pcolorfast
instead offill_between
? I'm not sure if it would make for a good alternative, but it seems like it.
Not yet. Seems interesting though. Will give it a shot and let you know.
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