# -*- coding: utf-8 -*-
# pylint: disable=missing-module-docstring
# import...
# ...from site-packages
import numpy
# ...from HydPy
from hydpy.core.typingtools import *
from hydpy.core import sequencetools
[docs]
class Factor1DSequence(sequencetools.FactorSequence):
"""Base class for 1-dimensional factor subclasses that support aggregation with
respect to |ZoneArea|.
All |Factor1DSequence| subclasses must implement fitting mask objects individually.
The following example shows how the subclass |TC| works:
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(5)
>>> zonetype(FIELD, FOREST, GLACIER, ILAKE, SEALED)
>>> zonearea.values = 10.0, 20.0, 30.0, 35.0, 5.0
>>> factors.tc(5.0, 2.0, 4.0, 1.0, 6.0)
>>> from hydpy import round_
>>> round_(factors.tc.average_values())
2.75
"""
NDIM = 1
NUMERIC = False
@property
def refweights(self):
"""Alias for the associated instance of |ZoneArea| for calculating areal
values."""
return self.subseqs.seqs.model.parameters.control.zonearea
[docs]
class Factor2DSequence(sequencetools.FactorSequence):
"""Base class for 2-dimensional factor subclasses that support aggregation with
respect to |ZoneArea|.
All |Factor2DSequence| subclasses must implement fitting mask objects individually.
The following example shows how subclass |SWE| works, which implements mask |Land|:
>>> from hydpy.models.hland import *
>>> parameterstep()
>>> nmbzones(5)
>>> sclass(2)
>>> zonetype(FIELD, FOREST, GLACIER, ILAKE, SEALED)
>>> zonearea.values = 10.0, 20.0, 30.0, 40.0, 60.0
>>> factors.swe = [[40.0, 10.0, 30.0, nan, 0.0],
... [60.0, 30.0, 50.0, nan, 20.0]]
>>> from hydpy import round_
>>> round_(factors.swe.average_values())
22.5
"""
NDIM = 2
NUMERIC = False
@property
def refweights(self):
"""Alias for the associated instance of |ZoneArea| for calculating areal
values."""
return self.subseqs.seqs.model.parameters.control.zonearea
@property
def valuevector(self) -> VectorFloat:
"""Values of the individual zones; each entry is the average of the values of
all snow classes of a specific zone.
We take subclass |SWE| as an example:
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(3)
>>> sclass(2)
>>> zonetype(FIELD)
>>> zonearea.values = 1.0, 1.0, 1.0
>>> factors.swe = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
>>> from hydpy import print_vector
>>> print_vector(factors.swe.valuevector)
2.5, 3.5, 4.5
The definition of |Factor2DSequence.valuevector| of |Factor2DSequence| allows
applying method |Variable.average_values| like for the 1-dimensional
zone-related sequences of |hland|:
>>> print_vector([factors.swe.average_values()])
3.5
"""
return numpy.mean(self.value, axis=0)
@property
def seriesmatrix(self) -> MatrixFloat:
"""Time series of the values of the individual zones; each entry is the average
of the values of all snow classes of a specific zone.
We take subclass |Melt| as an example:
>>> from hydpy import pub
>>> pub.timegrids = "2000-01-01", "2000-01-05", "1d"
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(3)
>>> sclass(2)
>>> zonetype(FIELD)
>>> zonearea.values = 1.0, 1.0, 1.0
>>> factors.swe.prepare_series()
>>> factors.swe.series = [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
... [[2.0, 3.0, 4.0], [5.0, 6.0, 7.0]],
... [[3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],
... [[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]
>>> from hydpy import print_vector
>>> for values in factors.swe.seriesmatrix:
... print_vector(values)
2.5, 3.5, 4.5
3.5, 4.5, 5.5
4.5, 5.5, 6.5
5.5, 6.5, 7.5
The definition of |Factor2DSequence.seriesmatrix| of |Factor2DSequence| allows
applying method |IOSequence.average_series| like for the 1-dimensional
zone-related sequences of |hland|:
>>> print_vector(factors.swe.average_series())
3.5, 4.5, 5.5, 6.5
"""
return numpy.mean(self.series, axis=1)
[docs]
class Flux1DSequence(sequencetools.FluxSequence):
"""Base class for 1-dimensional flux subclasses that support aggregation with
respect to |ZoneArea|.
All |Flux1DSequence| subclasses must implement fitting mask objects individually.
The following example shows how the subclass |PC| works:
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(5)
>>> zonetype(FIELD, FOREST, GLACIER, ILAKE, SEALED)
>>> zonearea.values = 10.0, 20.0, 30.0, 35.0, 5.0
>>> fluxes.pc(5.0, 2.0, 4.0, 1.0, 6.0)
>>> from hydpy import round_
>>> round_(fluxes.pc.average_values())
2.75
"""
NDIM = 1
NUMERIC = False
@property
def refweights(self):
"""Alias for the associated instance of |ZoneArea| for calculating areal
values."""
return self.subseqs.seqs.model.parameters.control.zonearea
[docs]
class Flux2DSequence(sequencetools.FluxSequence):
"""Base class for 2-dimensional flux subclasses that support aggregation with
respect to |ZoneArea|.
All |Flux2DSequence| subclasses must implement fitting mask objects individually.
The following example shows how subclass |Melt| works, which implements mask |Snow|:
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(5)
>>> sclass(2)
>>> zonetype(FIELD, FOREST, GLACIER, ILAKE, SEALED)
>>> zonearea.values = 10.0, 20.0, 30.0, 40.0, 60.0
>>> fluxes.melt = [[40.0, 10.0, 30.0, nan, 0.0],
... [60.0, 30.0, 50.0, nan, 20.0]]
>>> from hydpy import round_
>>> round_(fluxes.melt.average_values())
22.5
"""
NDIM = 2
NUMERIC = False
@property
def refweights(self):
"""Alias for the associated instance of |ZoneArea| for calculating areal
values."""
return self.subseqs.seqs.model.parameters.control.zonearea
@property
def valuevector(self) -> VectorFloat:
"""Values of the individual zones; each entry is the average of the values of
all snow classes of a specific zone.
We take subclass |Melt| as an example:
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(3)
>>> sclass(2)
>>> zonetype(FIELD)
>>> zonearea.values = 1.0, 1.0, 1.0
>>> fluxes.melt = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
>>> from hydpy import print_vector
>>> print_vector(fluxes.melt.valuevector)
2.5, 3.5, 4.5
The definition of |Flux2DSequence.valuevector| of |Flux2DSequence| allows
applying method |Variable.average_values| like for the 1-dimensional
zone-related sequences of |hland|:
>>> print_vector([fluxes.melt.average_values()])
3.5
"""
return numpy.mean(self.value, axis=0)
@property
def seriesmatrix(self) -> MatrixFloat:
"""Time series of the values of the individual zones; each entry is the average
of the values of all snow classes of a specific zone.
We take subclass |Melt| as an example:
>>> from hydpy import pub
>>> pub.timegrids = "2000-01-01", "2000-01-05", "1d"
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(3)
>>> sclass(2)
>>> zonetype(FIELD)
>>> zonearea.values = 1.0, 1.0, 1.0
>>> fluxes.melt.prepare_series()
>>> fluxes.melt.series = [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
... [[2.0, 3.0, 4.0], [5.0, 6.0, 7.0]],
... [[3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],
... [[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]
>>> from hydpy import print_vector
>>> for values in fluxes.melt.seriesmatrix:
... print_vector(values)
2.5, 3.5, 4.5
3.5, 4.5, 5.5
4.5, 5.5, 6.5
5.5, 6.5, 7.5
The definition of |Flux2DSequence.seriesmatrix| of |Flux2DSequence| allows
applying method |IOSequence.average_series| like for the 1-dimensional
zone-related sequences of |hland|:
>>> print_vector(fluxes.melt.average_series())
3.5, 4.5, 5.5, 6.5
"""
return numpy.mean(self.series, axis=1)
[docs]
class State1DSequence(sequencetools.StateSequence):
"""Base class for 1-dimensional state subclasses that support aggregation with
respect to |ZoneArea|.
All |State1DSequence| subclasses must implement fitting mask objects individually.
The following example shows how subclass |SM| works, which implements mask |Soil|:
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(5)
>>> zonetype(FIELD, FOREST, GLACIER, ILAKE, SEALED)
>>> zonearea.values = 10.0, 20.0, 30.0, 40.0, 50.0
>>> fc(100.0)
>>> states.sm = 50.0, 20.0, 40.0, 10.0, nan
>>> from hydpy import round_
>>> round_(states.sm.average_values())
30.0
"""
NDIM = 1
NUMERIC = False
@property
def refweights(self):
"""Alias for the associated instance of |ZoneArea| for calculating
areal values."""
return self.subseqs.seqs.model.parameters.control.zonearea
[docs]
class State2DSequence(sequencetools.StateSequence):
"""Base class for 2-dimensional state subclasses that support aggregation with
respect to |ZoneArea|.
All |State2DSequence| subclasses must implement fitting mask objects individually.
The following example shows how subclass |SP| works, which implements mask |Snow|:
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(5)
>>> sclass(2)
>>> zonetype(FIELD, FOREST, GLACIER, ILAKE, SEALED)
>>> zonearea.values = 10.0, 20.0, 30.0, 40.0, 60.0
>>> states.sp = [[40.0, 10.0, 30.0, nan, 0.0],
... [60.0, 30.0, 50.0, nan, 20.0]]
>>> from hydpy import round_
>>> round_(states.sp.average_values())
22.5
"""
NDIM = 2
NUMERIC = False
@property
def refweights(self):
"""Alias for the associated instance of |ZoneArea| for calculating areal
values."""
return self.subseqs.seqs.model.parameters.control.zonearea
@property
def valuevector(self) -> VectorFloat:
"""Values of the individual zones; each entry is the average of the values of
all snow classes of a specific zone.
We take subclass |SP| as an example:
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(3)
>>> sclass(2)
>>> zonetype(FIELD)
>>> zonearea.values = 1.0, 1.0, 1.0
>>> states.sp = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
>>> from hydpy import print_vector
>>> print_vector(states.sp.valuevector)
2.5, 3.5, 4.5
The definition of |State2DSequence.valuevector| of |State2DSequence| allows
applying method |Variable.average_values| like for the 1-dimensional
zone-related sequences of |hland|:
>>> print_vector([states.sp.average_values()])
3.5
"""
return numpy.mean(self.value, axis=0)
@property
def seriesmatrix(self) -> MatrixFloat:
"""Time series of the values of the individual zones; each entry is the average
of the values of all snow classes of a specific zone.
We take subclass |SP| as an example:
>>> from hydpy import pub
>>> pub.timegrids = "2000-01-01", "2000-01-05", "1d"
>>> from hydpy.models.hland import *
>>> parameterstep("1d")
>>> nmbzones(3)
>>> sclass(2)
>>> zonetype(FIELD)
>>> zonearea.values = 1.0, 1.0, 1.0
>>> states.sp.prepare_series()
>>> states.sp.series = [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
... [[2.0, 3.0, 4.0], [5.0, 6.0, 7.0]],
... [[3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],
... [[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]
>>> from hydpy import print_vector
>>> for values in states.sp.seriesmatrix:
... print_vector(values)
2.5, 3.5, 4.5
3.5, 4.5, 5.5
4.5, 5.5, 6.5
5.5, 6.5, 7.5
The definition of |State2DSequence.seriesmatrix| of |State2DSequence| allows
applying method |IOSequence.average_series| like for the 1-dimensional
zone-related sequences of |hland|:
>>> print_vector(states.sp.average_series())
3.5, 4.5, 5.5, 6.5
"""
return numpy.mean(self.series, axis=1)
