vforwater / hydrobox Goto Github PK

For #4 , this issue could collect some modlues that have already solved at lot of stuff. Whenever we can do the I/O via numpy or pandas, we should use their stuff instead of reprogramming it

• pastas for time series analysis - not sure though if this is 'just' a model / modeling framework

I want to move to github actions. Also, the CI is broken in some parts.

I have some plans to start developing into this package again.

My first plan is to check this list raoulcollenteur/Python-Hydrology-Tools

There are some well known and some not so well known, but awesome tools. I think some really fall into the scope of hydrobox and implementing some interfaces to these packages makes them usable with hydrobox.

@MarcusStrobl , this would also make them usable in V-FOR-WaTer, so it might be worth it to scan the list for you as well.?
Besides that, do you have general comments concerning hydrobox (maybe especially on a WPS_toolbox interface), before I dive into work?
Also, I think we could transfer hydrobox to V-FOR-WaTer organization. That can make it more visible in the context it is developed. We could also streamline developments better in GH issues on a project board.

I'm open to any suggestions!

I am thinking about implementing bokeh as an alternative for plotting. Similar to holoviews, the user could switch from the default matplotlib plotting backend to bokeh.
Plotting tools would return the bokeh.plotting.figure.Figure to let the user decide whether to use it in a notebook, save it into a file or create the Jsonfor bokehjs from it.
Similar to matplotlib, the functions would also accept a bokeh.Figure to plot on as an argument.

I think it is still not too much work to go for this way. Would help the lib, and might be helpful in V-FOR-WaTer portal.
@MarcusStrobl , what do you think about this?

Would it be appreciated and helpful in the Visualization implementation of V-FOR-WaTer?

I would like to plot the output from flow_duration_curve in Bokeh, though the output seems not to be usable for that.
According to the line for plotting in flow_duration_curve I should use sorted x and p values:

ax.plot(x[index], p[index], **kwargs)

sorted according to p:

index = np.argsort(p)

The output of flow_duration_curve is return p[index], but without the index I cannot create x[index].
So I guess a better output would be p instead of p[index], or a matching pair of x[index] and p[index].

To illustrate the problem I added some images. The result plot of the tool looks like this:

Naive plot of result from flow_duration_curve with x looks like:

Naive test to plot result from flow_duration_curve with x[np.argsort(x)] or x[np.argsort(p)] didn't work neither, of course.