def get_event_fc_loop(data_fc, events):
event_fc={}
for event in events:
event_fc[event]={}
event_pm=event[0]
event_thres=convert_fracstr_to_flt(event[1:])
for start_date in start_dates:
event_fc[event][start_date]={}
for model in data_fc[start_date]:
event_fc[event][start_date][model]=get_event_fc_rel(np.rollaxis(np.rollaxis(data_fc[start_date][model]['data'],2),2),event_pm,event_thres) #rollaxis commands to get ens_member and start_year dimensions at the front
event_fc[event][start_date][model]=np.rollaxis(event_fc[event][start_date][model],2)
return event_fc
#Find whether an event occurred in observations on each forecast date
if args.events:
event_fc_veri={}
for event in args.events:
event_fc_veri[event]={}
event_pm=event[0]
event_thres=convert_fracstr_to_flt(event[1:])
for veri in args.veri:
event_fc_veri[event][veri]={}
for start_date in start_dates:
event_fc_veri[event][veri][start_date]={}
for model in data_fc[start_date]:
event_fc_veri[event][veri][start_date][model]=get_event_fc_rel(fc_veri[veri][start_date][model].T[np.newaxis,...], event_pm,event_thres).T[...,0] #transpose fc_veri arrays and add axis at the front in the function call so their first two dimensions correspond to the ensemble members and different start dates, as expected by get_event_fc_rel()
#Plotting diagnostics as a function of lead time
#Assign linecolour and style to each model
linecolors=['k','b','g','r','c','m','y','brown','gray']
ncols=len(linecolors)
linestyles=['-', ':','-.'] #linestyles to cycle through for each set of colours - don't use '--' as I use this for plotting model RMSS below.
model_lines={}
model_no=0
for model in model_list:
model_lines[model]={}
model_lines[model]['color']=linecolors[model_no % ncols]
model_lines[model]['style']=linestyles[model_no/ncols]
model_no+=1