def len_scale(self, len_scale):
self._len_scale, anis = set_len_anis(self.dim, len_scale, self.anis)
if self.latlon:
self._anis = np.array((self.dim - 1) * [1], dtype=np.double)
else:
self._anis = anis
self.check_arg_bounds()
def anis(self, anis):
if self.latlon:
self._anis = np.array((self.dim - 1) * [1], dtype=np.double)
else:
self._len_scale, self._anis = set_len_anis(self.dim,
self.len_scale, anis)
self.check_arg_bounds()
def dim(self, dim):
# check if a fixed dimension should be used
if self.fix_dim() is not None:
print(self.name + ": using fixed dimension " + str(self.fix_dim()))
dim = self.fix_dim()
# set the dimension
if dim < 1 or dim > 3:
raise ValueError("Only dimensions of 1 <= d <= 3 are supported.")
self._dim = int(dim)
# create fourier transform just once (recreate for dim change)
self._sft = SFT(ndim=self.dim, **self.hankel_kw)
# recalculate dimension related parameters
if self._anis is not None:
self._len_scale, self._anis = set_len_anis(self.dim,
self._len_scale,
self._anis)
if self._angles is not None:
self._angles = set_angles(self.dim, self._angles)
def __init__(
self,
dim=3,
var=1.0,
len_scale=1.0,
nugget=0.0,
anis=1.0,
angles=0.0,
integral_scale=None,
rescale=None,
latlon=False,
var_raw=None,
hankel_kw=None,
**opt_arg,
):
# assert, that we use a subclass
# this is the case, if __init_subclass__ is called, which creates
# the "variogram"... so we check for that
if not hasattr(self, "variogram"):
raise TypeError("Don't instantiate 'CovModel' directly!")
# prepare dim setting
self._dim = None
self._hankel_kw = None
self._sft = None
# prepare parameters (they are checked in dim setting)
self._rescale = None
self._len_scale = None
self._anis = None
self._angles = None
# prepare parameters boundaries
self._var_bounds = None
self._len_scale_bounds = None
self._nugget_bounds = None
self._anis_bounds = None
self._opt_arg_bounds = {}
# Set latlon first
self._latlon = bool(latlon)
# SFT class will be created within dim.setter but needs hankel_kw
self.hankel_kw = hankel_kw
self.dim = dim
# optional arguments for the variogram-model
set_opt_args(self, opt_arg)
# set standard boundaries for variance, len_scale, nugget and opt_arg
bounds = self.default_arg_bounds()
bounds.update(self.default_opt_arg_bounds())
self.set_arg_bounds(check_args=False, **bounds)
# set parameters
self.rescale = rescale
self._nugget = float(nugget)
# set anisotropy and len_scale, disable anisotropy for latlon models
self._len_scale, anis = set_len_anis(self.dim, len_scale, anis)
if self.latlon:
self._anis = np.array((self.dim - 1) * [1], dtype=np.double)
self._angles = np.array(self.dim * [0], dtype=np.double)
else:
self._anis = anis
self._angles = set_angles(self.dim, angles)
# set var at last, because of the var_factor (to be right initialized)
if var_raw is None:
self._var = None
self.var = var
else:
self._var = float(var_raw)
self._integral_scale = None
self.integral_scale = integral_scale
# set var again, if int_scale affects var_factor
if var_raw is None:
self._var = None
self.var = var
else:
self._var = float(var_raw)
# final check for parameter bounds
self.check_arg_bounds()
# additional checks for the optional arguments (provided by user)
self.check_opt_arg()
# precision for printing
self._prec = 3
def len_scale(self, len_scale):
self._len_scale, self._anis = set_len_anis(self.dim, len_scale,
self.anis)
self.check_arg_bounds()
def __init__(self,
dim=3,
var=1.0,
len_scale=1.0,
nugget=0.0,
anis=1.0,
angles=0.0,
integral_scale=None,
var_raw=None,
hankel_kw=None,
**opt_arg):
# assert, that we use a subclass
# this is the case, if __init_subclass__ is called, which creates
# the "variogram"... so we check for that
if not hasattr(self, "variogram"):
raise TypeError("Don't instantiate 'CovModel' directly!")
# optional arguments for the variogram-model
# look up the defaults for the optional arguments (defined by the user)
default = self.default_opt_arg()
# add the default vaules if not specified
for def_arg in default:
if def_arg not in opt_arg:
opt_arg[def_arg] = default[def_arg]
# save names of the optional arguments
self._opt_arg = list(opt_arg.keys())
# add the optional arguments as attributes to the class
for opt_name in opt_arg:
if opt_name in dir(self): # "dir" also respects properties
raise ValueError(
"parameter '" + opt_name +
"' has a 'bad' name, since it is already present in " +
"the class. It could not be added to the model")
if opt_name not in self.default_opt_arg().keys():
warnings.warn(
"The given optional argument '{}' ".format(opt_name) +
"is unknown or has at least no defined standard value. " +
"Or you made a Typo... hehe.",
AttributeWarning,
)
# Magic happens here
setattr(self, opt_name, opt_arg[opt_name])
# set standard boundaries for variance, len_scale, nugget and opt_arg
self._var_bounds = None
self._len_scale_bounds = None
self._nugget_bounds = None
self._opt_arg_bounds = {}
bounds = self.default_arg_bounds()
bounds.update(self.default_opt_arg_bounds())
self.set_arg_bounds(**bounds)
# prepare dim setting
self._dim = None
self._len_scale = None
self._anis = None
self._angles = None
# SFT class will be created within dim.setter but needs hankel_kw
self._hankel_kw = None
self._sft = None
self.hankel_kw = hankel_kw
self.dim = dim
# set parameters
self._nugget = nugget
self._angles = set_angles(self.dim, angles)
self._len_scale, self._anis = set_len_anis(self.dim, len_scale, anis)
# set var at last, because of the var_factor (to be right initialized)
if var_raw is None:
self._var = None
self.var = var
else:
self._var = var_raw
self._integral_scale = None
self.integral_scale = integral_scale
# set var again, if int_scale affects var_factor
if var_raw is None:
self._var = None
self.var = var
else:
self._var = var_raw
# final check for parameter bounds
self.check_arg_bounds()
# additional checks for the optional arguments (provided by user)
self.check_opt_arg()
def anis(self, anis):
self._len_scale, self._anis = set_len_anis(self.dim, self.len_scale,
anis)
self.check_arg_bounds()
