Follow-up fixes for bootstrap

@aaronspring, just to keep the follow-up on the recent PR merge in one place. I wanted to get it merged in since it’s been sitting there so long and got so large.

Some things that need to be followed-up on to fix it (you can cite this issue in a new PR):

• Decide if we want to maintain a separate significance level for init/uninit and persistence. If this is the case, a “quantile_persistence” and “quantile_ensemble” or something similar dimension distinction should be made to make plotting easy. The graphics plot was breaking in the notebook if the significance levels were different.
• Revise the perfect_model notebook with your new bootstrap functions so that the whole thing compiles as you see fit.
• Fix pytest to deal with datetime[ns]. (I think this was the problem you identified?)

Some links on the last one:

https://docs.scipy.org/doc/numpy/reference/arrays.datetime.html

https://stackoverflow.com/questions/22842314/numpy-datetime64-add-or-substract-date-interval

I’ve used those methods before to add intervals to a datetime. That might help with xr.sel().

[aaronspring]

Decide if we want to maintain a separate significance level for init/uninit and persistence. If this is the case, a “quantile_persistence” and “quantile_ensemble” or something similar dimension distinction should be made to make plotting easy. The graphics plot was breaking in the notebook if the significance levels were different.

I want to keep different significance levels for the calculation. For the plotting I havent implemented that. Now raises an error. I am unsure still how to gather all the results into a dataset nicely (I prefer here only the variable name as one data_var).

Fix pytest to deal with datetime[ns]. (I think this was the problem you identified?)

Changed compute_persistence_pm. Didnt adapt compute_persistence yet. But should be easily adaptable.

Revise the perfect_model notebook with your new bootstrap functions so that the whole thing compiles as you see fit.

Compiles.

[aaronspring]

concerning compute_persistence_pm I found a way: I transfer the initialisation time axis into the index values of control.time, then I can easily add lags to the index.

dim='time'
nlags=ds.time.size
init_month_index=0 # januar init, 10 for november
init_years = ds['initialization'].values
init_cftimes = []
for year in init_years:
    init_cftimes.append(control.sel(time=str(year)).isel(time=init_month_index).time)
init_cftimes=xr.concat(init_cftimes,'time')

init_index=[]
l=list(control.time.values)
for i, inits in enumerate(init_cftimes.time.values):
    init_index.append(l.index(init_cftimes[i]))

plag=[]
from xskillscore import pearson_r,rmse
metric=pearson_r
for lag in range(1, 1 + nlags):
    inits_index_plus_lag = [x+lag for x in inits_index]
    ref = control.isel({dim: inits_index_plus_lag})
    fct = control.isel({dim: inits_index})
    ref[dim] = fct[dim]
    plag.append(metric(ref, fct, dim=dim))
pers_new = xr.concat(plag, 'time')
pers_new['time'] = np.arange(1, 1 + nlags)