def __init__(self,
loc_cst=False,
scale_cst=False,
shape_cst=True,
**kwargs): ##{{{
l_scale = kwargs.get("l_scale")
if l_scale is None: l_scale = sdt.ExpLink()
lparams = []
lparams.append({
"name": "loc",
"is_cst": loc_cst,
"link": kwargs.get("l_loc"),
"name_tex": r"\mu"
})
lparams.append({
"name": "scale",
"is_cst": scale_cst,
"link": l_scale,
"name_tex": r"\sigma"
})
lparams.append({
"name": "shape",
"is_cst": shape_cst,
"link": kwargs.get("l_shape"),
"name_tex": r"\xi"
})
AbstractModel.__init__(self, "GEV", sc.genextreme, sd.GEV, lparams,
**kwargs)
def default_arg(arg=None): ##{{{
"""
Dictionary of arguments of __init__
Parameters
----------
arg : None or dict
dictionary of arguments already fixed
Returns
-------
default: dict
Arguments of __init__, elements of "arg" are kept
"""
default = {
"tails": "upper",
"link_loc": sdt.IdLink(),
"link_scale": sdt.ExpLink(),
"link_shape": sdt.LogitLink(-0.5, 0.5),
"method": "MLE",
"no_test": True,
"verbose": False
}
if arg is not None:
for key in arg:
default[key] = arg[key]
return default
def __init__(self,
tails="upper",
link_loc=sdt.IdLink(),
link_scale=sdt.ExpLink(),
link_shape=sdt.LogitLink(-0.5, 0.5),
method="MLE",
no_test=True,
verbose=False): ##{{{
"""
"""
NSAbstractModel.__init__(self)
self._tails = 1 if tails == "upper" else -1
self._gev = sd.GEVLaw(method=method)
self._link = {
"loc": link_loc,
"scale": link_scale,
"shape": link_shape
}
self._no_test = no_test
self._verbose = verbose
self._loc0 = None
self._loc1 = None
self._scale0 = None
self._scale1 = None
self._shape = None
self.loc = None
self.scale = None
self.shape = None
self._loct = None
self._scalet = None
self._shapet = None
def __init__(self,
tails="upper",
link_scale=sdt.ExpLink(),
link_shape=sdt.LogitLink(-0.5, 0.5),
method="MLE",
verbose=False): ##{{{
"""
"""
NSAbstractModel.__init__(self)
self._tails = 1 if tails == "upper" else -1
self._lf_sc = link_fct_scale
self._lf_sh = link_fct_shape
self._verbose = verbose
self._method = method
self._loc0 = None
self._loc1 = None
self._scale0 = None
self._scale1 = None
self._shape0 = None
self._shape1 = None
self.loc = None
self.scale = None
self.shape = None
self._loct = None
self._scalet = None
self._shapet = None
def __init__(self, loc_cst=False, scale_cst=False, **kwargs):
l_scale = kwargs.get("l_scale")
if l_scale is None: l_scale = sdt.ExpLink()
lparams = []
lparams.append({
"name": "loc",
"is_cst": loc_cst,
"link": kwargs.get("l_loc"),
"name_tex": r"\mu"
})
lparams.append({
"name": "scale",
"is_cst": scale_cst,
"link": l_scale,
"name_tex": r"\sigma"
})
AbstractModel.__init__(self, "Normal", sc.norm, sd.Normal, lparams,
**kwargs)
def __init__(self,
link_loc=sdt.IdLink(),
link_scale=sdt.ExpLink(),
method="MLE",
verbose=False): ##{{{
"""
Initialization of the NS Gaussian Model
"""
NSAbstractModel.__init__(self)
self._norm = sd.Normal(method=method)
self._verbose = verbose
self._link = {"loc": link_loc, "scale": link_scale}
self.mu0 = None
self.mu1 = None
self.scale0 = None
self.scale1 = None
self._mut = None
self._scalet = None
def from_netcdf(ifile, ns_law=None): ##{{{
with nc.Dataset(ifile, "r") as ncFile:
## Extract dimensions
##===================
time = np.ma.getdata(ncFile.variables["time"][:])
sample = ncFile.variables["sample"][:]
forcing = ncFile.variables["forcing"][:]
models = ncFile.variables["models"][:]
ns_param = ncFile.variables["ns_param"][:]
stats = ncFile.variables["stat"][:]
reference = np.ma.getdata(ncFile.variables["reference"][:])
if ns_law is None:
## Extract ns_law attributes
##==========================
ns_law_attr = []
for p in ncFile.__dict__:
if "ns_law" in p: ns_law_attr.append(p)
## Transform ns_law attributes to ns_law params
##=============================================
ns_law_kwargs = {}
for p in ns_law_attr:
if "ns_law_param" in p:
pn, pk = p.split("_")[-2:]
if pk == "cst":
ns_law_kwargs[pn +
"_cst"] = ncFile.__dict__[p] == str(True)
if pk == "link":
if ncFile.__dict__[p] == str(sdt.ExpLink()):
ns_law_kwargs["l_" + pn] = sdt.ExpLink()
else:
ns_law_kwargs["l_" + pn] = sdt.IdLink()
ns_law = None
if ncFile.__dict__["ns_law_name"] == "Normal":
ns_law = Normal(**ns_law_kwargs)
elif ncFile.__dict__["ns_law_name"] == "GEV":
ns_law = GEV(**ns_law_kwargs)
elif ncFile.__dict__["ns_law_name"] == "GEVMin":
ns_law = GEVMin(**ns_law_kwargs)
## Set climatology
##=================
clim = Climatology(time, models, ns_law)
clim.X = xr.DataArray(np.ma.getdata(ncFile.variables["X"][:]),
coords=[time, sample, forcing, models],
dims=["time", "sample", "forcing", "models"])
clim.ns_params = xr.DataArray(np.ma.getdata(
ncFile.variables["ns_params"][:]),
coords=[ns_param, sample, models],
dims=["ns_params", "sample", "models"])
clim.stats = xr.DataArray(np.ma.getdata(ncFile.variables["stats"][:]),
coords=[time, sample, stats, models],
dims=["time", "sample", "stats", "models"])
clim.mm_params.mean = np.ma.getdata(ncFile.variables["mm_mean"][:])
clim.mm_params.cov = np.ma.getdata(ncFile.variables["mm_cov"][:])
## Set event
event = Event(ncFile.event_name,
reference.dtype.type(ncFile.event_time),
clim.X.dtype.type(ncFile.event_anomaly), reference,
ncFile.event_type, ncFile.event_side,
ncFile.event_variable, ncFile.event_unit)
return clim, event
##=====
basepath = os.path.dirname(os.path.abspath(__file__))
pathInp = os.path.join(basepath, "input/Normal")
pathOut = os.path.join(basepath, "output/Normal")
assert (os.path.exists(pathInp))
assert (os.path.exists(pathOut))
## Some global parameters
##=======================
time_period = np.arange(1850, 2101, 1, dtype=np.int)
time_reference = np.arange(1961, 1991, 1, dtype=np.int)
n_mcmc_drawn_min = 2500 if is_test else 5000
n_mcmc_drawn_max = 5000 if is_test else 10000
min_rate_accept = 0.05
n_sample = 1000 if not is_test else 10
ns_law = nsm.Normal(l_scale=sdt.ExpLink())
event = ns.Event("HW03",
2003,
None,
time_reference,
name_variable="T",
unit_variable="K")
verbose = True
ci = 0.05 if not is_test else 0.1
## Load models and observations
##=============================
models, lX, lY, Xo, Yo = load_models_obs(pathInp)
## Anomaly from observations
##==========================
