Add data plot to visualization #73
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Noting related FLORIS pull: NREL/floris#606 |
flasc/visualization.py
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| # Small little demo of data plot, can delete | ||
| if __name__ == "__main__": |
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This is a nice example. It would be good to apply this plot somewhere in the flasc_cookiecutter_template repository on actual data so that users are made aware of its existence, perhaps?
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That is a good idea, just gave myself a todo item to do that, maybe also we can use someplace in flasc's examples...
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Definitely helps for showing the usage of the function! In my opinion, this should probably be removed before the code is merged, since it is kind of a toy example. An option would be to add a more realistic usage somewhere in the examples, but perhaps just adding to the cookiecutter template at a later date is fine.
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^ Took too long writing my review, and Paul beat me to it :)
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Agree, thinking that could be a seperate issue in the cookie_cutter repo? (To add an example?). I made a small one in the wake steering example but I think we have a broader discussion of where examples go so this could ultimately join that
flasc/visualization.py
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| # Small little demo of data plot, can delete | ||
| if __name__ == "__main__": |
There was a problem hiding this comment.
Definitely helps for showing the usage of the function! In my opinion, this should probably be removed before the code is merged, since it is kind of a toy example. An option would be to add a more realistic usage somewhere in the examples, but perhaps just adding to the cookiecutter template at a later date is fine.
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| # If x_edges not provided, use 50 bins over range of x | ||
| if x_edges is None: | ||
| x_edges = np.linspace(df["x"].min()*.98, df["x"].max()*1.02, 50) |
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@paulf81 I changed the lower and upper edges from min*0.95, max*1.05 to min*0.98, max*1.02 to avoid numpy warnings that get caused because, combined the 50 bins, there will always be empty bins. An alternative, if you'd prefer, is to leave the edges at 0.95 and 1.05 but lower the bin count to 20, i.e.
x_edges = np.linspace(df["x"].min()*.95, df["x"].max()*1.05, 20)
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I do see that the warnings that I was trying to avoid are still being raised in the example, I think because the data isn't really spread along the x axis in a realistic way. For now, I don't think that's really a problem.
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If you are using the example dataset, absolutely the data is mostly in really low wind speeds, indeed unrealistic. This has been an annoyance for me while working with this data, for sure. I wasn't sure if we wanted to fix this or just wait for the revision of the examples to finish.
| df_agg["y_ci_lower"], df_agg["y_ci_upper"] = st.t.interval( | ||
| confidence_level, | ||
| df_agg["y_count"]-1, | ||
| loc=df_agg["y_mean"], | ||
| scale=df_agg["y_sem"] | ||
| ) |
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@paulf81 I think what you have, using the t-distribution, is good (it's a little more correct than the z-score approach, which assumes the data set is large enough that the means can be treated as normal---the t-distribution doesn't make that assumption, and approaches the z-score method as the number of samples becomes large). I've rearranged the code a little in my commits, but have not changed the method.
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Hi @Bartdoekemeijer and @misi9170 what do you think, good to merge this one? I know we have some open conversations but unless I misread we can put off full solutions to these to other activities? |
@paulf81, good by me. |
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Squashing and a'merging |
Yes, maybe we can delete demo code after you approve and before merging
Feature or improvement description
data_plot was a function I'd found useful in the past for having an on hand way to represent noisy SCADA data (typical example might be achieved offset). It exists in a pretty out-dated version in FLORIS, so I've added this updated version to FLASC, and will submit another pull request to FLORIS to remove the outdate code
Impacted areas of the software
visualization.py