weights = weights[:, logical_and(ltime >= y1, ltime <= y2)] # restrict range
else:
ltime = years.copy()
# restrict to overlapping years
yrsinrange = logical_and(years >= min(ltime), years <= max(ltime))
years = years[yrsinrange]
t0 = min(years) - y1 + 1
time = arange(t0, t0 + len(years))
# load aggregation mask
afile, avar = [a.strip() for a in agg.split(':')]
aggloader = AggMaskLoader(afile, avar)
adata = aggloader.data()[0]
audata = aggloader.udata()[0]
aname = aggloader.names()[0]
aunits = aggloader.units()[0]
alongname = aggloader.longnames()[0]
# load growing season file
with Dataset(gsfile) as f:
pdate = f.variables['planting_day'][:]
hdate = f.variables['growing_season_length'][:]
# get variables and scenarios
variables = []
for i in range(len(files)):
file_split = files[i].split('_')
variables.append(file_split[3])
if hasscen: slicer[yieldv.dimensions.index('scen')] = scenidx
yieldv = yieldv[slicer]
iridx, rfidx = irr.index('ir'), irr.index('rf')
if weightsf:
with nc(weightsf) as f:
rfweights = f.variables['rainfed'][:]
irweights = f.variables['irrigated'][:]
else:
rfweights = irweights = None
aggloader = AggMaskLoader(aggf, lats = lats, lons = lons)
adata = aggloader.data()
audata = aggloader.udata()
anames = aggloader.names()
aunits = aggloader.units()
alongnames = aggloader.longnames()
createAggFile(outputf, time, tunits, audata, anames, aunits, alongnames, scensel, irr, leaddim, hasscen)
f = nc(outputf, 'a')
avobj = MeanAverager()
for i in range(len(audata)):
if leaddim == 'scen':
dimsv = ('scen', 'time', anames[i], 'irr')
elif hasscen:
dimsv = ('time', 'scen', anames[i], 'irr')
else:
dimsv = ('time', anames[i], 'irr')
weights = weights[:, logical_and(ltime >= y1, ltime <= y2)] # restrict range
else:
ltime = years.copy()
# restrict to overlapping years
yrsinrange = logical_and(years >= min(ltime), years <= max(ltime))
years = years[yrsinrange]
t0 = min(years) - y1 + 1
time = arange(t0, t0 + len(years))
# load aggregation mask
afile, avar = [a.strip() for a in agg.split(':')]
aggloader = AggMaskLoader(afile, avar)
adata = aggloader.data()[0]
audata = aggloader.udata()[0]
aname = aggloader.names()[0]
aunits = aggloader.units()[0]
alongname = aggloader.longnames()[0]
# load growing season file
with nc(gsfile) as f:
pdate = f.variables['planting_day'][:]
hdate = f.variables['growing_season_length'][:]
# get variables and scenarios
variables = []; scens = []; scens_full = []
for i in range(len(files)):
fs = files[i].split('_')
if 'noirr' in fs:
fs.remove('noirr')
wlats, wlons = f.variables['lat'][:], f.variables['lon'][:]
sellat = logical_and(wlats >= llat - tol, wlats <= ulat + tol)
sellon = logical_and(wlons >= llon - tol, wlons <= ulon + tol)
wlats, wlons = wlats[sellat], wlons[sellon]
if abs(lats - wlats).max() > tol:
raise Exception('Latitudes in output file and weights mask do not agree!')
if abs(lons - wlons).max() > tol:
raise Exception('Longitudes in output file and weights mask do not agree!')
weights = f.variables['weights'][sellat][:, sellon]
aggloader = AggMaskLoader(aggf, lats = lats, lons = lons)
adata = aggloader.data()
audata = aggloader.udata()
anames = aggloader.names()
aunits = aggloader.units()
alongnames = aggloader.longnames()
alats = aggloader.latitudes()
alons = aggloader.longitudes()
if abs(lats - alats).max() > tol:
raise Exception('Latitudes in output file and aggregation mask do not agree!')
if abs(lons - alons).max() > tol:
raise Exception('Longitudes in output file and aggregation mask do not agree!')
createAggFile(outputf, time, tunits, audata, anames, aunits, alongnames, scensel, leaddim)
f = nc(outputf, 'a')
avobj = MeanAverager()
for i in range(len(audata)):
