variables = list(set(variables))
variables.sort()
# number of variables, times, scenarios, aggregation levels
nvariables = len(variables)
ntimes = len(time)
naudata = len(audata)
nirrs = len(irr_levels) + 1
# preallocate final averages and areas
averages = masked_array(zeros((nvariables, naudata, ntimes, nirrs)),
mask=ones((nvariables, naudata, ntimes, nirrs)))
areas = masked_array(zeros((naudata, ntimes, 3)), mask=ones((naudata, ntimes, nirrs)))
# aggregator object
avobj = MeanAverager()
vunits = [''] * nvariables
for iidx in range(len(irr_levels)):
# Load planting file
plantingfile = findfile(files, 'plant-day')
if plantingfile:
with Dataset(plantingfile) as f:
planting_date = f.variables['plant-day_' + crop][0]
else:
planting_date = pdate[iidx]
# load harvest file
harvestfile = findfile(files, 'maty-day')
if harvestfile:
