class BiasCorrecter(object):
def __init__(self, dt, mp, cr):
self.transformer = TransformerWrapper(cr)
self.retrender = RetrenderWrapper(dt, mp)
self.detrender = DetrenderWrapper(dt, mp)
self.unrealizable = mp == 'false' and cr == 'mean-scale'
def correct(self, sim, obs, tsim, tobs):
""" Corrects simulated sequence sim using observations obs,
with tsim and tobs arrays of years """
tmin = max([tsim[0], tobs[0]])
tmax = min([tsim[-1], tobs[-1]])
odT, T = self.detrender.detrend(obs) # detrend observations
sdT, _ = self.detrender.detrend(sim) # detrend simulations
dT = masked_array(zeros(len(tsim)), mask = ones(len(tsim)))
rT = dT.copy()
if not self.unrealizable and not sdT.mask.all() and not odT.mask.all():
odT_c = self.__toverlap(odT, tobs, tmin, tmax) # transform distribution
sdT_c = self.__toverlap(sdT, tsim, tmin, tmax)
dT = self.transformer.transform(sdT, sdT_c, odT_c)
obs_c = self.__toverlap(obs, tobs, tmin, tmax) # combine
T_c = self.__toverlap(T, tobs, tmin, tmax)
dT_c = self.__toverlap(dT, tsim, tmin, tmax)
rT_c = self.retrender.retrend(T_c, dT_c, obs_c)
rT[logical_and(tsim >= tmin, tsim <= tmax)] = rT_c
return dT, rT
def __toverlap(self, x, tx, tmin, tmax):
return x[logical_and(tx >= tmin, tx <= tmax)]
class BiasCorrecter(object):
def __init__(self, dt, mp, cr):
self.transformer = TransformerWrapper(cr)
self.retrender = RetrenderWrapper(dt, mp)
self.detrender = DetrenderWrapper(dt, mp)
self.unrealizable = mp == 'false' and cr == 'mean-scale'
def correct(self, sim, obs, tsim, tobs):
""" Corrects simulated sequence sim using observations obs,
with tsim and tobs arrays of years """
tmin = max([tsim[0], tobs[0]])
tmax = min([tsim[-1], tobs[-1]])
odT, T = self.detrender.detrend(obs) # detrend observations
sdT, _ = self.detrender.detrend(sim) # detrend simulations
dT = masked_array(zeros(len(tsim)), mask=ones(len(tsim)))
rT = dT.copy()
if not self.unrealizable and not sdT.mask.all() and not odT.mask.all():
odT_c = self.__toverlap(odT, tobs, tmin,
tmax) # transform distribution
sdT_c = self.__toverlap(sdT, tsim, tmin, tmax)
dT = self.transformer.transform(sdT, sdT_c, odT_c)
obs_c = self.__toverlap(obs, tobs, tmin, tmax) # combine
T_c = self.__toverlap(T, tobs, tmin, tmax)
dT_c = self.__toverlap(dT, tsim, tmin, tmax)
rT_c = self.retrender.retrend(T_c, dT_c, obs_c)
rT[logical_and(tsim >= tmin, tsim <= tmax)] = rT_c
return dT, rT
def __toverlap(self, x, tx, tmin, tmax):
return x[logical_and(tx >= tmin, tx <= tmax)]
def __init__(self, dt, mp, cr):
self.transformer = TransformerWrapper(cr)
self.retrender = RetrenderWrapper(dt, mp)
self.detrender = DetrenderWrapper(dt, mp)
self.unrealizable = mp == 'false' and cr == 'mean-scale'
'mp',
zlib=True,
shuffle=False,
complevel=9)
mpvar[:] = range(1, nmp + 1)
mpvar.units = 'mapping'
mpvar.long_name = ', '.join(mp)
for v in variables:
var = fi.variables[v]
vardt = masked_array(zeros(sh), mask=ones(sh))
for latidx, lonidx in product(range(nlats), range(nlons)):
varmat = var[:, latidx, lonidx]
if not isMaskedArray(varmat) or not varmat.mask[0]:
for d, c in product(range(ndt), range(nmp)):
detrender = DetrenderWrapper(dt[d], mp[c])
vardt[:, latidx, lonidx, d, c] = detrender.detrend(varmat)[0]
vvar = fo.createVariable(v,
'f4', ('time', latname, lonname, 'dt', 'mp'),
zlib=True,
shuffle=False,
complevel=9,
fill_value=1e20)
vvar[:] = vardt
vvar.units = var.units if 'units' in var.ncattrs() else ''
vvar.long_name = var.long_name if 'long_name' in var.ncattrs() else ''
fi.close()
fo.close()
mpvar = fo.createVariable('mp',
'i4',
'mp',
zlib=True,
shuffle=False,
complevel=9)
mpvar[:] = range(1, nmp + 1)
mpvar.units = 'mapping'
mpvar.long_name = ', '.join(mp)
for cr in crops:
yld = fi.variables['yield_' + cr][:]
ylddt = masked_array(zeros(sh), mask=ones(sh))
for d, c in product(range(ndt), range(nmp)):
detrender = DetrenderWrapper(dt[d], mp[c])
for i in range(naggs):
ylddt[i, :, d, c] = detrender.detrend(yld[i, :])[0]
yvar = fo.createVariable('yield_' + cr,
'f4', (agglvl, 'time', 'dt', 'mp'),
zlib=True,
shuffle=False,
complevel=9,
fill_value=1e20)
yvar[:] = ylddt
yvar.units = fi.variables['yield_' + cr].units
yvar.long_name = fi.variables['yield_' + cr].long_name
fi.close()
fo.close()
def __init__(self, dt, mp, cr):
self.transformer = TransformerWrapper(cr)
self.retrender = RetrenderWrapper(dt, mp)
self.detrender = DetrenderWrapper(dt, mp)
self.unrealizable = mp == 'false' and cr == 'mean-scale'
dtvar[:] = range(1, ndt + 1)
dtvar.units = "mapping"
dtvar.long_name = ", ".join(dt)
fo.createDimension("mp", nmp)
mpvar = fo.createVariable("mp", "i4", "mp", zlib=True, shuffle=False, complevel=9)
mpvar[:] = range(1, nmp + 1)
mpvar.units = "mapping"
mpvar.long_name = ", ".join(mp)
for v in variables:
var = fi.variables[v]
vardt = masked_array(zeros(sh), mask=ones(sh))
for latidx, lonidx in product(range(nlats), range(nlons)):
varmat = var[:, latidx, lonidx]
if not isMaskedArray(varmat) or not varmat.mask[0]:
for d, c in product(range(ndt), range(nmp)):
detrender = DetrenderWrapper(dt[d], mp[c])
vardt[:, latidx, lonidx, d, c] = detrender.detrend(varmat)[0]
vvar = fo.createVariable(
v, "f4", ("time", latname, lonname, "dt", "mp"), zlib=True, shuffle=False, complevel=9, fill_value=1e20
)
vvar[:] = vardt
vvar.units = var.units if "units" in var.ncattrs() else ""
vvar.long_name = var.long_name if "long_name" in var.ncattrs() else ""
fi.close()
fo.close()
timevar.units = fi.variables['time'].units
timevar.long_name = 'time'
fo.createDimension('dt', ndt)
dtvar = fo.createVariable('dt', 'i4', 'dt', zlib = True, shuffle = False, complevel = 9)
dtvar[:] = range(1, ndt + 1)
dtvar.units = 'mapping'
dtvar.long_name = ', '.join(dt)
fo.createDimension('mp', nmp)
mpvar = fo.createVariable('mp', 'i4', 'mp', zlib = True, shuffle = False, complevel = 9)
mpvar[:] = range(1, nmp + 1)
mpvar.units = 'mapping'
mpvar.long_name = ', '.join(mp)
for cr in crops:
yld = fi.variables['yield_' + cr][:]
ylddt = masked_array(zeros(sh), mask = ones(sh))
for d, c in product(range(ndt), range(nmp)):
detrender = DetrenderWrapper(dt[d], mp[c])
for i in range(naggs):
ylddt[i, :, d, c] = detrender.detrend(yld[i, :])[0]
yvar = fo.createVariable('yield_' + cr, 'f4', (agglvl, 'time', 'dt', 'mp'), zlib = True, shuffle = False, complevel = 9, fill_value = 1e20)
yvar[:] = ylddt
yvar.units = fi.variables['yield_' + cr].units
yvar.long_name = fi.variables['yield_' + cr].long_name
fi.close()
fo.close()
