variables.sort()
scens = list(set(scens))
scens.sort()
scens_full = list(set(scens_full))
scens_full.sort()
# number of variables, times, scenarios, aggregation levels
nv, nt, ns, na = len(variables), len(time), len(scens), len(audata)
# preallocate final averages and areas
averages = masked_array(zeros((nv, na, nt, ns, 3)),
mask=ones((nv, na, nt, ns, 3)))
areas = masked_array(zeros((na, nt, ns, 3)), mask=ones((na, nt, ns, 3)))
# aggregator object
avobj = MeanAverager()
vunits = [''] * nv
for i in range(len(scens_full)):
scen_irr = scens_full[i]
# find scenario and irr
scen_irr_split = scen_irr.split('_')
if len(scen_irr_split) == 2:
sidx = scens.index(scen_irr_split[0])
else:
sidx = scens.index('_'.join(scen_irr_split[0::2]))
iidx = int(scen_irr_split[1] != 'firr')
# load planting file
plantingfile = findfile(files, scen_irr, 'plant-day')
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:
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')
for j in range(nvars):
avev = f.createVariable(ivars[j] + '_' + anames[i], 'f4', dimsv, fill_value = 1e20, zlib = True, complevel = 9)
avev.units = vunits[j]
avev.long_name = 'average ' + anames[i] + ' ' + ivars[j]
slicer = [slice(0, n) for n in var[j].shape]
dimslist = list(dims[j])
else:
continue # irregular files
variables = list(set(variables)); variables.sort()
scens = list(set(scens)); scens.sort()
scens_full = list(set(scens_full)); scens_full.sort()
# number of variables, times, scenarios, aggregation levels
nv, nt, ns, na = len(variables), len(time), len(scens), len(audata)
# preallocate final averages and areas
averages = masked_array(zeros((nv, na, nt, ns, 3)), mask = ones((nv, na, nt, ns, 3)))
areas = masked_array(zeros((na, nt, ns, 3)), mask = ones((na, nt, ns, 3)))
# aggregator object
avobj = MeanAverager()
vunits = [''] * nv
for i in range(len(scens_full)):
scen_irr = scens_full[i]
# find scenario and irr
scen_irr_split = scen_irr.split('_')
if len(scen_irr_split) == 2:
sidx = scens.index(scen_irr_split[0])
else:
sidx = scens.index('_'.join(scen_irr_split[0 :: 2]))
iidx = int(scen_irr_split[1] != 'firr')
# load planting file
plantingfile = findfile(files, scen_irr, 'plant-day')
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)):
if leaddim == 'scen':
dimsv = ('scen', 'time', anames[i] + '_index')
else:
dimsv = ('time', 'scen', anames[i] + '_index')
for j in range(nvars):
avev = f.createVariable(ivars[j] + '_' + anames[i], 'f4', dimsv, fill_value = 1e20, zlib = True, complevel = 9)
avev.units = vunits[j]
avev.long_name = 'average ' + anames[i] + ' ' + ivars[j]
slicer = [slice(0, n) for n in var[j].shape]
dimslist = list(dims[j])
dimslist.remove('scen')
timeidx, latidx, lonidx = dimslist.index('time'), dimslist.index('lat'), dimslist.index('lon')
t = f.variables['time']
time = t[:]
tunits = t.units if 'units' in t.ncattrs() else ''
for i in range(nvars):
v = f.variables[ivars[i]]
var[i] = v[:]
var[i] = masked_where(isnan(var[i]), var[i]) # mask out NaNs
var[i].mask = resize(var[i].mask,
var[i].shape) # resize mask if necessary
vunits[i] = v.units if 'units' in v.ncattrs() else ''
createAggFile(options.output, time, tunits, audata, anames, aunits, alongnames)
f = nc(options.output, 'a') # open file for appending
if options.type == 'mean': # averager instance
avobj = MeanAverager()
else:
avobj = SumAverager()
for i in range(nmasks):
dims = (anames[i] + '_index', 'time')
for j in range(nvars):
avev = f.createVariable(ivars[j] + '_' + anames[i],
'f4',
dims,
fill_value=1e20,
zlib=True,
complevel=9)
avev[:] = avobj.av(var[j],
adata[i],
lats,
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)):
if leaddim == 'scen':
dimsv = ('scen', 'time', anames[i] + '_index')
else:
dimsv = ('time', 'scen', anames[i] + '_index')
for j in range(nvars):
avev = f.createVariable(ivars[j] + '_' + anames[i], 'f4', dimsv, fill_value = 1e20, zlib = True, complevel = 9)
avev.units = vunits[j]
avev.long_name = 'average ' + anames[i] + ' ' + ivars[j]
slicer = [slice(0, n) for n in var[j].shape]
dimslist = list(dims[j])
dimslist.remove('scen')
timeidx, latidx, lonidx = dimslist.index('time'), dimslist.index('lat'), dimslist.index('lon')
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, irr, leaddim)
f = nc(outputf, 'a')
avobj = MeanAverager()
for i in range(len(audata)):
if leaddim == 'scen':
dimsv = ('scen', 'time', anames[i] + '_index', 'irr')
else:
dimsv = ('time', 'scen', anames[i] + '_index', 'irr')
for j in range(nvars):
avev = f.createVariable(ivars[j] + '_' + anames[i],
'f4',
dimsv,
fill_value=1e20,
zlib=True,
complevel=9)
avev.units = vunits[j]
avev.long_name = 'average ' + anames[i] + ' ' + ivars[j]
ivars = [v.strip() for v in ivars.split(',')]
nvars = len(ivars)
var = [0] * nvars; vunits = [0] * nvars
with nc(ifile) as f:
lats = f.variables['lat'][:]
t = f.variables['time']
time = t[:]
tunits = t.units if 'units' in t.ncattrs() else ''
for i in range(nvars):
v = f.variables[ivars[i]]
var[i] = v[:]
var[i] = masked_where(isnan(var[i]), var[i]) # mask out NaNs
var[i].mask = resize(var[i].mask, var[i].shape) # resize mask if necessary
vunits[i] = v.units if 'units' in v.ncattrs() else ''
createAggFile(options.output, time, tunits, audata, anames, aunits, alongnames)
f = nc(options.output, 'a') # open file for appending
if options.type == 'mean': # averager instance
avobj = MeanAverager()
else:
avobj = SumAverager()
for i in range(nmasks):
dims = (anames[i] + '_index', 'time')
for j in range(nvars):
avev = f.createVariable(ivars[j] + '_' + anames[i], 'f4', dims, fill_value = 1e20, zlib = True, complevel = 9)
avev[:] = avobj.av(var[j], adata[i], lats, weights, calcarea = options.calcarea)
avev.units = vunits[j]
avev.long_name = ' '.join([options.type, anames[i], ivars[j]])
f.close()
