yieldfile = findfile(files, 'yield')
if not yieldfile:
yvar = masked_array(zeros((ntimes,) + planting_date.shape), mask=zeros((ntimes,) + planting_date.shape))
else:
with Dataset(yieldfile) as f:
yvar = f.variables['yield_' + crop][:]
yvar = shiftdata(yvar, planting_date, harvest_date, yearthr) # shift data
yvar = masked_where(yvar < yieldthr1, yvar) # mask yields below threshold
yvar = masked_where(yvar > yieldthr2, yvar) # mask yields above threshold
yvar = yvar[yrsinrange] # restrict to overlapping years
ymsk = logical_not(yvar.mask)
# compute areas
print ymsk.sum(), ymsk.shape, adata.shape, weights[iidx].shape
areas[:, :, iidx] = avobj.areas(yvar, adata, lats, weights[iidx], calc_area)
for j in range(nvariables):
varfile = findfile(files, variables[j])
print "Reading %s" % varfile
if not varfile:
continue
with Dataset(varfile) as f:
var = f.variables[variables[j] + '_' + crop]
if not i:
vunits[j] = var.units if 'units' in var.ncattrs() else ''
var = var[:]
var[isnan(var)] = 0. # change NaNs to zero
var = shiftdata(var, planting_date, harvest_date, yearthr) # shift data
var = var[yrsinrange] # restrict to overlapping years
# load yield file
yieldfile = findfile(files, scen_irr, 'yield')
if yieldfile == []:
yvar = masked_array(zeros((nt,) + pd.shape), mask = zeros((nt,) + pd.shape))
else:
with nc(indir + sep + yieldfile) as f:
yvar = f.variables['yield_' + crop][:]
yvar = shiftdata(yvar, pd, hd, yearthr) # shift data
yvar = masked_where(yvar < yieldthr1, yvar) # mask yields below threshold
yvar = masked_where(yvar > yieldthr2, yvar) # mask yields above threshold
yvar = yvar[yrsinrange] # restrict to overlapping years
ymsk = logical_not(yvar.mask)
# compute areas
areas[:, :, sidx, iidx] = avobj.areas(yvar, adata, lats, weights[iidx], calcarea)
for j in range(nv):
# load variable
varfile = findfile(files, scen_irr, variables[j])
if not len(varfile): continue
with nc(indir + sep + varfile) as f:
var = f.variables[variables[j] + '_' + crop]
if not i: vunits[j] = var.units if 'units' in var.ncattrs() else ''
var = var[:]
var[isnan(var)] = 0. # change NaNs to zero
var = shiftdata(var, pd, hd, yearthr) # shift data
var = var[yrsinrange] # restrict to overlapping years
# aggregate
averages[j, :, :, sidx, iidx] = avobj.sum(var, adata, lats, weights[iidx], calcarea, ymsk)
