def set_optim_nstns_tair_norm(stnda, path_xval_ds):
'''
Set the local optimal number of stations to be used for monthly
normal interpolation for each U.S. climate division based on
cross-validation mean absolute error.
Parameters
----------
stnda : twx.db.StationSerialDataDb
A StationSerialDataDb object pointing to the
database for which the local optimal number of
neighbors should be set.
path_xval_ds : str
Path where netCDF cross-validation MAE files from
create_climdiv_optim_nstns_db are located
'''
climdiv_stns = stnda.stns[CLIMDIV]
vars_optim = {}
for mth in np.arange(1, 13):
varname_optim = get_optim_varname(mth)
long_name = "Optimal number of neighbors to use for monthly normal interpolation for month %d" % mth
var_optim = stnda.add_stn_variable(
varname_optim,
long_name,
"",
'f8',
fill_value=netCDF4.default_fillvals['f8'])
vars_optim[mth] = var_optim
divs = np.unique(climdiv_stns[np.isfinite(climdiv_stns)])
stchk = StatusCheck(divs.size, 10)
for clim_div in divs:
fpath = os.path.join(
path_xval_ds,
"optim_nstns_%s_climdiv%d.nc" % (stnda.var_name, clim_div))
ds_climdiv = Dataset(fpath)
mae_climdiv = ds_climdiv.variables['mae'][:]
nnghs_climdiv = ds_climdiv.variables['min_nghs'][:]
climdiv_mask = np.nonzero(climdiv_stns == clim_div)[0]
for mth in np.arange(1, 13):
mae_climdiv_mth = mae_climdiv[mth - 1, :, :]
mmae = np.mean(mae_climdiv_mth, axis=1)
min_idx = np.argmin(mmae)
vars_optim[mth][climdiv_mask] = nnghs_climdiv[min_idx]
stchk.increment()
stnda.ds.sync()
def __get_nnghs(self, pt, mth, stns_rm=None):
self.stn_slct.set_ngh_stns(pt[LAT],
pt[LON],
DFLT_INIT_NNGHS,
load_obs=False,
stns_rm=stns_rm)
indomain_mask = np.isfinite(
self.stn_slct.ngh_stns[get_optim_varname(mth)])
domain_stns = self.stn_slct.ngh_stns[indomain_mask]
if domain_stns.size == 0:
raise Exception(
"Cannot determine the optimal # of neighbors to use!")
n_wgt = self.stn_slct.ngh_wgt[indomain_mask]
nnghs = np.int(
np.round(
np.average(domain_stns[get_optim_varname(mth)],
weights=n_wgt)))
return nnghs
def _get_rgn_nnghs_dict(stns):
rgns = np.unique(stns[CLIMDIV][np.isfinite(stns[CLIMDIV])])
nnghsAll = {}
for rgn in rgns:
rgn_mask = stns[CLIMDIV] == rgn
nnghsRgn = {}
for mth in np.arange(1, 13):
nnghsRgn[mth] = (stns[get_optim_varname(mth)][rgn_mask][0],
stns[get_optim_anom_varname(mth)][rgn_mask][0])
nnghsAll[rgn] = nnghsRgn
return nnghsAll
def build_empty_pt():
ptDtype = [(LON, np.float64), (LAT, np.float64), (ELEV, np.float64),
(TDI, np.float64), (CLIMDIV, np.float64), (MASK, np.float64)]
ptDtype.extend([("tmin%02d" % mth, np.float64)
for mth in np.arange(1, 13)])
ptDtype.extend([("tmax%02d" % mth, np.float64)
for mth in np.arange(1, 13)])
ptDtype.extend([(get_norm_varname(mth), np.float64)
for mth in np.arange(1, 13)])
ptDtype.extend([(get_optim_varname(mth), np.float64)
for mth in np.arange(1, 13)])
ptDtype.extend([(get_lst_varname(mth), np.float64)
for mth in np.arange(1, 13)])
ptDtype.extend([(get_optim_anom_varname(mth), np.float64)
for mth in np.arange(1, 13)])
a_pt = np.empty(1, dtype=ptDtype)
return a_pt[0]
def get_krig_params(self, pt, mth, rm_stnid=None):
'''
Get the moving window regression kriging variogram
parameters for a specific point and month. Currently
assumes an exponential variogram
Parameters
----------
pt : structured array
A structured array containing the point's latitude, longitude,
elevation, topographic dissection index, and average land skin
temperatures for each month. An empty point can be initialized
with build_empty_pt()
mth : int
The specific month as an integer (1-12)
rm_stnid : ndarray or str, optional
An array of station ids or a single station id for stations that
should not be considered neighbors for the specific point.
Returns
----------
nug : float
Exponential variogram nugget.
psill : float
Exponential variogram partial sill.
rng : float
Exponential variogram range.
'''
# First determine the nnghs to use based on smoothed weighted average of
# the optimal nnghs bandwidth at each station point.
self.stn_slct.set_ngh_stns(pt[LAT],
pt[LON],
DFLT_INIT_NNGHS,
load_obs=False)
indomain_mask = np.isfinite(
self.stn_slct.ngh_stns[get_optim_varname(mth)])
domain_stns = self.stn_slct.ngh_stns[indomain_mask]
if domain_stns.size == 0:
raise Exception(
"Cannot determine the optimal # of neighbors to use!")
n_wgt = self.stn_slct.ngh_wgt[indomain_mask]
nnghs = np.int(
np.round(
np.average(domain_stns[get_optim_varname(mth)],
weights=n_wgt)))
# Now use the optimal nnghs to get the krig params for this mth
self.stn_slct.set_ngh_stns(pt[LAT], pt[LON], nnghs, load_obs=False)
nghs = self.stn_slct.ngh_stns
ngh_lon = ri.FloatSexpVector(nghs[LON])
ngh_lat = ri.FloatSexpVector(nghs[LAT])
ngh_elev = ri.FloatSexpVector(nghs[ELEV])
ngh_tdi = ri.FloatSexpVector(nghs[TDI])
ngh_lst = ri.FloatSexpVector(nghs[get_lst_varname(mth)])
ngh_tair = ri.FloatSexpVector(nghs[get_norm_varname(mth)])
ngh_wgt = ri.FloatSexpVector(self.stn_slct.ngh_wgt)
ngh_dists = ri.FloatSexpVector(self.stn_slct.ngh_dists)
rslt = self.r_func(ngh_lon, ngh_lat, ngh_elev, ngh_tdi, ngh_lst,
ngh_tair, ngh_wgt, ngh_dists)
nug = rslt[0]
psill = rslt[1]
rng = rslt[2]
return nug, psill, rng
def interp_pt(self, fix_invalid=True, stns_rm=None):
'''
Interpolate daily and monthly normal Tmin and Tmax values
for the current PtInterpTair.a_pt
Parameters
----------
fix_invalid : boolean, optional
If True, apply a fix on days where interpolated
Tmax > Tmin. Default: True.
stns_rm : ndarray or str, optional
An array of station ids or a single station id for stations that
should not be considered neighbors for the specific point.
Returns
----------
tmin_dly : ndarray
Daily interpolated Tmin
tmax_dly : ndarray
Daily interpolated Tmax
tmin_norms : ndarray
Interpolated monthly Tmin normals
tmax_norms : ndarray
Interpolated monthly Tmax normals
tmin_se : ndarray
Kriging standard error for monthly Tmin normals
tmax_se : ndarray
Kriging standard error for monthly Tmax normals
ninvalid : int
The number of days where Tmax > Tmin was fixed.
If fix_invalid is False, will be set to 0.
'''
# Set the monthly lst values and optim nnghs on the point
for mth in np.arange(1, 13):
self.a_pt[get_lst_varname(mth)] = self.a_pt["tmin%02d" % mth]
self.a_pt[get_optim_varname(mth)], self.a_pt[
get_optim_anom_varname(mth)] = self.nnghparams_tmin[
self.a_pt[CLIMDIV]][mth]
# Perform Tmin interpolation
tmin_dly, tmin_norms, tmin_se = self.interp_tmin.interp(
self.a_pt, stns_rm=stns_rm)
# Set the monthly lst values and optim nnghs on the point
for mth in np.arange(1, 13):
self.a_pt[get_lst_varname(mth)] = self.a_pt["tmax%02d" % mth]
self.a_pt[get_optim_varname(mth)], self.a_pt[
get_optim_anom_varname(mth)] = self.nnghparams_tmax[
self.a_pt[CLIMDIV]][mth]
# Perform Tmax interpolation
tmax_dly, tmax_norms, tmax_se = self.interp_tmax.interp(
self.a_pt, stns_rm=stns_rm)
ninvalid = 0
if fix_invalid:
tmin_dly, tmax_dly, ninvalid = tmin_tmax_fixer(tmin_dly, tmax_dly)
if ninvalid > 0:
tmin_dly_norm = np.take(tmin_dly, self.daysNormMask)
tmax_dly_norm = np.take(tmax_dly, self.daysNormMask)
tmin_mthly = np.array([
np.mean(np.take(tmin_dly_norm, amask))
for amask in self.yrMthsMasks
])
tmax_mthly = np.array([
np.mean(np.take(tmax_dly_norm, amask))
for amask in self.yrMthsMasks
])
tmin_norms = np.array([
np.mean(np.take(tmin_mthly, amask))
for amask in self.mth_masks
])
tmax_norms = np.array([
np.mean(np.take(tmax_mthly, amask))
for amask in self.mth_masks
])
return tmin_dly, tmax_dly, tmin_norms, tmax_norms, tmin_se, tmax_se, ninvalid