def trim(self, lower=None, upper=None):
"""Trim upper values in accordance with :math:`EQI2 \\leq EQI1`.
>>> from hydpy.models.lland import *
>>> parameterstep("1d")
>>> eqi2.value = 1.0
>>> eqi1(0.0)
>>> eqi1
eqi1(1.0)
>>> eqi1(1.0)
>>> eqi1
eqi1(1.0)
>>> eqi1(2.0)
>>> eqi1
eqi1(2.0)
"""
if lower is None:
lower = exceptiontools.getattr_(
self.subpars.eqi2,
"value",
None,
)
if upper is None:
upper = exceptiontools.getattr_(
self.subpars.eqb,
"value",
None,
)
super().trim(lower, upper)
def trim(self, lower=None, upper=None):
"""Trim values in accordance with :math:`PWP \\leq FK \\leq WMax`.
>>> from hydpy.models.lland import *
>>> parameterstep()
>>> nhru(3)
>>> lnk(ACKER)
>>> wmax(100.0)
>>> pwp(-10.0, 50.0, 110.0)
>>> pwp
pwp(0.0, 50.0, 100.0)
>>> fk.values = 80.0
>>> pwp.trim()
>>> pwp
pwp(0.0, 50.0, 80.0)
"""
if upper is None:
upper = exceptiontools.getattr_(
self.subpars.fk,
"value",
None,
)
if upper is None:
upper = exceptiontools.getattr_(
self.subpars.wmax,
"value",
None,
)
super().trim(lower, upper)
def __call__(self, *args, **kwargs):
try:
super().__call__(*args, **kwargs)
except NotImplementedError as exc:
counter = ("khq" in kwargs) + ("hq" in kwargs)
if counter == 0:
raise ValueError(
f"For parameter {objecttools.elementphrase(self)} a "
f"value can be set directly or indirectly by using "
f"the keyword arguments `khq` and `hq`."
) from exc
if counter == 1:
raise ValueError(
f"For the alternative calculation of parameter "
f"{objecttools.elementphrase(self)}, at least the "
f"keywords arguments `khq` and `hq` must be given."
) from exc
alpha = float(
kwargs.get(
"alpha",
exceptiontools.getattr_(self.subpars.alpha, "value", numpy.nan),
)
)
if numpy.isnan(alpha):
raise RuntimeError(
f"For the alternative calculation of parameter "
f"{objecttools.elementphrase(self)}, either the "
f"keyword argument `alpha` must be given or the value "
f"of parameter `alpha` must be defined beforehand."
) from exc
khq = float(kwargs["khq"])
hq = float(kwargs["hq"])
self(hq / ((hq / khq) ** (alpha + 1.0)))
def new(cls, variable: "typingtools.VariableProtocol", **kwargs):
"""Return a new |IndexMask| object of the same shape as the
parameter referenced by |property| |IndexMask.refindices|.
Entries are only |True|, if the integer values of the
respective entries of the referenced parameter are contained
in the |IndexMask| class attribute tuple `RELEVANT_VALUES`.
Before calling new (explicitly or implicitely), one must prepare
the variable returned by property |IndexMask.refindices|:
>>> from hydpy.models.hland import *
>>> parameterstep()
>>> states.sm.mask
Traceback (most recent call last):
...
RuntimeError: The mask of parameter `sm` of element `?` cannot be \
determined as long as parameter `zonetype` is not prepared properly.
>>> nmbzones(4)
>>> zonetype(FIELD, FOREST, ILAKE, GLACIER)
>>> states.sm.mask
Soil([ True, True, False, False])
"""
indices = cls.get_refindices(variable)
if numpy.min(exceptiontools.getattr_(indices, "values", 0)) < 1:
raise RuntimeError(
f"The mask of parameter {objecttools.elementphrase(variable)} "
f"cannot be determined as long as parameter `{indices.name}` "
f"is not prepared properly."
)
return cls.array2mask(numpy.in1d(indices.values, cls.RELEVANT_VALUES), **kwargs)
def __call__(self, *args, **kwargs) -> None:
self._keywordarguments = parametertools.KeywordArguments(False)
idx = self._find_kwargscombination(args, kwargs, (set(("lag", )), ))
if idx is None:
super().__call__(*args, **kwargs)
else:
lag = parametertools.trim_kwarg(self,
"lag",
kwargs["lag"],
lower=0.0)
lag /= self.get_timefactor()
self.value = int(round(lag))
self._keywordarguments = parametertools.KeywordArguments(lag=lag)
shape = self.value
model = self.subpars.pars.model
model.nmb_segments = shape
pars, seqs = model.parameters, model.sequences
for subvars in (
pars.control,
pars.derived,
seqs.factors,
seqs.fluxes,
seqs.states,
):
for variable in (var for var in subvars if var.NDIM == 1):
if variable.name == "coefficients":
continue
oldshape = exceptiontools.getattr_(variable, "shape", None)
if variable.name == "discharge":
if oldshape != (shape + 1, ):
variable.shape = (shape + 1, )
else:
if oldshape != (shape, ):
variable.shape = (shape, )
def _convertandtest(values, name):
try:
type_ = float if isinstance(values[0], float) else int
array = numpy.array(values, dtype=type_)
except BaseException:
objecttools.augment_excmessage(
f"While trying to assign a new `{name}` "
f"index array to an Indexer object"
)
if array.ndim != 1:
raise ValueError(
f"The `{name}` index array of an Indexer object must be "
f"1-dimensional. However, the given value has interpreted "
f"as a {array.ndim}-dimensional object."
)
timegrids = exceptiontools.getattr_(hydpy.pub, "timegrids")
if timegrids is not None:
if len(array) != len(timegrids.init):
raise ValueError(
f"The `{name}` index array of an Indexer object must have "
f"a number of entries fitting to the initialization time "
f"period precisely. However, the given value has been "
f"interpreted to be of length `{len(array)}` and the "
f"length of the Timegrid object representing the actual "
f"initialisation period is `{len(timegrids.init)}`."
)
return array
def trim(self, lower=None, upper=None):
"""Trim values in accordance with :math:`AngstromFactor \\leq 1 -
AngstromConstant` or at least in accordance with :math:`AngstromFactor
\\leq 1`.
>>> from hydpy.models.lland import *
>>> parameterstep()
>>> angstromconstant(0.4, 0.4, nan, 0.4, 0.4, 0.4,
... 0.6, 0.8, 1.0, 1.0, nan, nan)
>>> angstromfactor(-0.2, 0.0, 0.2, 0.4, 0.6, 0.8,
... 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)
>>> angstromfactor
angstromfactor(jan=0.0, feb=0.0, mar=0.2, apr=0.4, may=0.6, jun=0.6,
jul=0.4, aug=0.2, sep=0.0, oct=0.0, nov=1.0, dec=1.0)
>>> angstromconstant(None)
>>> angstromfactor(0.6)
>>> angstromfactor
angstromfactor(0.6)
"""
if upper is None:
upper = exceptiontools.getattr_(
self.subpars.angstromconstant,
"values",
None,
)
if upper is not None:
upper = upper.copy()
idxs = numpy.isnan(upper)
upper[idxs] = 1.0
idxs = ~idxs
upper[idxs] = 1.0 - upper[idxs]
super().trim(lower, upper)
def trim(self, lower=None, upper=None):
r"""Trim |ThetaS| following :math:`1e^{-6} \leq ThetaS \leq 1.0` and,
if |ThetaR| exists for the relevant application model, also following
:math:`ThetaR \leq ThetaS`.
>>> from hydpy.models.wland import *
>>> parameterstep()
>>> thetas(0.0)
>>> thetas
thetas(0.000001)
>>> thetar.value = 0.5
>>> thetas(0.4)
>>> thetas
thetas(0.5)
>>> thetas(soil=SANDY_LOAM)
>>> thetas
thetas(0.5)
>>> thetas(1.01)
>>> thetas
thetas(1.0)
"""
if lower is None:
if exceptiontools.hasattr_(self.subpars, "thetar"):
lower = exceptiontools.getattr_(self.subpars.thetar, "value",
1e-6)
else:
lower = 1e-6
super().trim(lower, upper)
def _get_timegrids(func):
timegrids = exceptiontools.getattr_(hydpy.pub, "timegrids", None)
if timegrids is None:
name = func.__name__[1:]
raise exceptiontools.AttributeNotReady(
f"An Indexer object has been asked for an `{name}` array. "
f"Such an array has neither been determined yet nor can it "
f"be determined automatically at the moment. Either define "
f"an `{name}` array manually and pass it to the Indexer "
f"object, or make a proper Timegrids object available within "
f"the pub module."
)
return timegrids
def __call__(self, *args, **kwargs):
old = exceptiontools.getattr_(self, "value", None)
super().__call__(*args, **kwargs)
new = self.__hydpy__get_value__()
if new != old:
for subpars in self.subpars.pars.model.parameters:
for par in subpars:
if (par.NDIM == 1) and (not isinstance(
par, parametertools.MonthParameter)):
par.__hydpy__set_shape__(new)
for subseqs in self.subpars.pars.model.sequences:
for seq in subseqs:
if seq.NDIM == 1:
seq.__hydpy__set_shape__(new)
def trim(self, lower=None, upper=None):
r"""Trim |ThetaR| following :math:`1e^{-6} \leq ThetaR \leq ThetaS`.
>>> from hydpy.models.wland import *
>>> parameterstep()
>>> thetar(0.0)
>>> thetar
thetar(0.000001)
>>> thetas(0.41)
>>> thetar(0.42)
>>> thetar
thetar(0.41)
"""
if upper is None:
upper = exceptiontools.getattr_(self.subpars.thetas, "value", None)
super().trim(lower, upper)
def trim(self, lower=None, upper=None):
"""Trim upper values in accordance with :math:`DMax \\geq DMin`.
>>> from hydpy.models.lland import *
>>> parameterstep("1d")
>>> simulationstep("12h")
>>> nhru(3)
>>> lnk(ACKER)
>>> dmin.values = 2.0
>>> dmax(2.0, 4.0, 6.0)
>>> dmax
dmax(4.0, 4.0, 6.0)
"""
if lower is None:
lower = exceptiontools.getattr_(
self.subpars.dmin,
"value",
None,
)
super().trim(lower, upper)
def trim(self, lower=None, upper=None):
"""Trim upper values in accordance with :math:`EQD2 \\leq EQD1`.
>>> from hydpy.models.lland import *
>>> parameterstep("1d")
>>> eqd1.value = 3.0
>>> eqd2(2.0)
>>> eqd2
eqd2(2.0)
>>> eqd2(3.0)
>>> eqd2
eqd2(3.0)
>>> eqd2(4.0)
>>> eqd2
eqd2(3.0)
"""
if upper is None:
upper = exceptiontools.getattr_(
self.subpars.eqd1,
"value",
None,
)
super().trim(lower, upper)
def trim(self, lower=None, upper=None):
"""Trim upper values in accordance with :math:`GSBGrad1 \\leq GSBGrad2`.
>>> from hydpy.models.lland import *
>>> simulationstep("1h")
>>> parameterstep("1d")
>>> gsbgrad1(1.0)
>>> gsbgrad2(2.0)
>>> gsbgrad2
gsbgrad2(2.0)
>>> gsbgrad2(1.0)
>>> gsbgrad2
gsbgrad2(1.0)
>>> gsbgrad2(0.0)
>>> gsbgrad2
gsbgrad2(1.0)
"""
if lower is None:
lower = exceptiontools.getattr_(
self.subpars.gsbgrad1,
"value",
None,
)
super().trim(lower, upper)
def _fset(self, values):
self.values = self._convertandtest(values, self.name)
self.timegrids = copy.deepcopy(exceptiontools.getattr_(hydpy.pub, "timegrids"))
def call_fget(self, obj) -> NDArrayFloat:
timegrids = exceptiontools.getattr_(hydpy.pub, "timegrids", None)
if (self.values is None) or (self.timegrids != timegrids):
self.values = self._calcidxs(self.fget(obj))
self.timegrids = copy.deepcopy(timegrids)
return self.values
def update(self):
"""Calculate the number of entries and adjust the shape of all relevant log
sequences.
The aimed memory duration is one day. Hence, the number of the required log
entries depends on the simulation step size:
>>> from hydpy.models.lland import *
>>> parameterstep()
>>> from hydpy import pub
>>> pub.timegrids = "2000-01-01", "2000-01-02", "1h"
>>> derived.nmblogentries.update()
>>> derived.nmblogentries
nmblogentries(24)
>>> logs
wet0(nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan)
loggedteml(nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan)
loggedrelativehumidity(nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan)
loggedsunshineduration(nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan)
loggedpossiblesunshineduration(nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan)
loggedglobalradiation(nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan)
loggedwindspeed2m(nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan)
To prevent from loosing information, updating parameter |NmbLogEntries| resets
the shape of the relevant log sequences only when necessary:
>>> logs.wet0 = 1.0
>>> logs.loggedteml = 2.0
>>> logs.loggedrelativehumidity.shape = (6,)
>>> logs.loggedrelativehumidity = 3.0
>>> derived.nmblogentries.update()
>>> logs # doctest: +ELLIPSIS
wet0(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)
loggedteml(2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0)
loggedrelativehumidity(nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan)
...
There is an explicit check for inappropriate simulation step sizes:
>>> pub.timegrids = "2000-01-01 00:00", "2000-01-01 10:00", "5h"
>>> derived.nmblogentries.update()
Traceback (most recent call last):
...
ValueError: The value of parameter `nmblogentries` of element `?` cannot be \
determined for a the current simulation step size. The fraction of the memory period \
(1d) and the simulation step size (5h) leaves a remainder.
.. testsetup::
>>> del pub.timegrids
"""
nmb = "1d" / hydpy.pub.options.simulationstep
if nmb % 1:
raise ValueError(
f"The value of parameter {objecttools.elementphrase(self)} cannot be "
f"determined for a the current simulation step size. The fraction of "
f"the memory period (1d) and the simulation step size "
f"({hydpy.pub.timegrids.stepsize}) leaves a remainder.")
self(nmb)
nmb = int(nmb)
logs = self.subpars.pars.model.sequences.logs
for seq in logs:
shape = exceptiontools.getattr_(seq, "shape", (None, ))
if nmb != shape[-1]:
seq.shape = nmb
