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hydpy-dev/hydpy | hydpy/core/objecttools.py | extract | def extract(values, types, skip=False):
"""Return a generator that extracts certain objects from `values`.
This function is thought for supporting the definition of functions
with arguments, that can be objects of of contain types or that can
be iterables containing these objects.
The following ex... | python | def extract(values, types, skip=False):
"""Return a generator that extracts certain objects from `values`.
This function is thought for supporting the definition of functions
with arguments, that can be objects of of contain types or that can
be iterables containing these objects.
The following ex... | [
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This function is thought for supporting the definition of functions
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The following examples show that function |extract|
basical... | [
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hydpy-dev/hydpy | hydpy/core/objecttools.py | enumeration | def enumeration(values, converter=str, default=''):
"""Return an enumeration string based on the given values.
The following four examples show the standard output of function
|enumeration|:
>>> from hydpy.core.objecttools import enumeration
>>> enumeration(('text', 3, []))
'text, 3, and []'
... | python | def enumeration(values, converter=str, default=''):
"""Return an enumeration string based on the given values.
The following four examples show the standard output of function
|enumeration|:
>>> from hydpy.core.objecttools import enumeration
>>> enumeration(('text', 3, []))
'text, 3, and []'
... | [
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... | Return an enumeration string based on the given values.
The following four examples show the standard output of function
|enumeration|:
>>> from hydpy.core.objecttools import enumeration
>>> enumeration(('text', 3, []))
'text, 3, and []'
>>> enumeration(('text', 3))
'text and 3'
>>> en... | [
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hydpy-dev/hydpy | hydpy/models/hland/hland_states.py | Ic.trim | def trim(self, lower=None, upper=None):
"""Trim upper values in accordance with :math:`IC \\leq ICMAX`.
>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(5)
>>> icmax(2.0)
>>> states.ic(-1.0, 0.0, 1.0, 2.0, 3.0)
>>> states.ic
ic(0.... | python | def trim(self, lower=None, upper=None):
"""Trim upper values in accordance with :math:`IC \\leq ICMAX`.
>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(5)
>>> icmax(2.0)
>>> states.ic(-1.0, 0.0, 1.0, 2.0, 3.0)
>>> states.ic
ic(0.... | [
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>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(5)
>>> icmax(2.0)
>>> states.ic(-1.0, 0.0, 1.0, 2.0, 3.0)
>>> states.ic
ic(0.0, 0.0, 1.0, 2.0, 2.0) | [
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hydpy-dev/hydpy | hydpy/models/hland/hland_states.py | SP.trim | def trim(self, lower=None, upper=None):
"""Trim values in accordance with :math:`WC \\leq WHC \\cdot SP`.
>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(7)
>>> whc(0.1)
>>> states.wc.values = -1.0, 0.0, 1.0, -1.0, 0.0, 0.5, 1.0
>>> stat... | python | def trim(self, lower=None, upper=None):
"""Trim values in accordance with :math:`WC \\leq WHC \\cdot SP`.
>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(7)
>>> whc(0.1)
>>> states.wc.values = -1.0, 0.0, 1.0, -1.0, 0.0, 0.5, 1.0
>>> stat... | [
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>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(7)
>>> whc(0.1)
>>> states.wc.values = -1.0, 0.0, 1.0, -1.0, 0.0, 0.5, 1.0
>>> states.sp(-1., 0., 0., 5., 5., 5., 5.)
>>> stat... | [
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hydpy-dev/hydpy | hydpy/models/hland/hland_states.py | WC.trim | def trim(self, lower=None, upper=None):
"""Trim values in accordance with :math:`WC \\leq WHC \\cdot SP`.
>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(7)
>>> whc(0.1)
>>> states.sp = 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0
>>> states.wc(-1.... | python | def trim(self, lower=None, upper=None):
"""Trim values in accordance with :math:`WC \\leq WHC \\cdot SP`.
>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(7)
>>> whc(0.1)
>>> states.sp = 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0
>>> states.wc(-1.... | [
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>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(7)
>>> whc(0.1)
>>> states.sp = 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0
>>> states.wc(-1.0, 0.0, 1.0, -1.0, 0.0, 0.5, 1.0)
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hydpy-dev/hydpy | hydpy/models/hland/hland_states.py | LZ.trim | def trim(self, lower=None, upper=None):
"""Trim negative value whenever there is no internal lake within
the respective subbasin.
>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(2)
>>> zonetype(FIELD, ILAKE)
>>> states.lz(-1.0)
>... | python | def trim(self, lower=None, upper=None):
"""Trim negative value whenever there is no internal lake within
the respective subbasin.
>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(2)
>>> zonetype(FIELD, ILAKE)
>>> states.lz(-1.0)
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>>> from hydpy.models.hland import *
>>> parameterstep('1d')
>>> nmbzones(2)
>>> zonetype(FIELD, ILAKE)
>>> states.lz(-1.0)
>>> states.lz
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | Sequences.load_data | def load_data(self, idx):
"""Call method |InputSequences.load_data| of all handled
|InputSequences| objects."""
for subseqs in self:
if isinstance(subseqs, abctools.InputSequencesABC):
subseqs.load_data(idx) | python | def load_data(self, idx):
"""Call method |InputSequences.load_data| of all handled
|InputSequences| objects."""
for subseqs in self:
if isinstance(subseqs, abctools.InputSequencesABC):
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | Sequences.save_data | def save_data(self, idx):
"""Call method `save_data|` of all handled |IOSequences|
objects registered under |OutputSequencesABC|."""
for subseqs in self:
if isinstance(subseqs, abctools.OutputSequencesABC):
subseqs.save_data(idx) | python | def save_data(self, idx):
"""Call method `save_data|` of all handled |IOSequences|
objects registered under |OutputSequencesABC|."""
for subseqs in self:
if isinstance(subseqs, abctools.OutputSequencesABC):
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | Sequences.conditions | def conditions(self) -> Dict[str, Dict[str, Union[float, numpy.ndarray]]]:
"""Nested dictionary containing the values of all condition
sequences.
See the documentation on property |HydPy.conditions| for further
information.
"""
conditions = {}
for subname in NAME... | python | def conditions(self) -> Dict[str, Dict[str, Union[float, numpy.ndarray]]]:
"""Nested dictionary containing the values of all condition
sequences.
See the documentation on property |HydPy.conditions| for further
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"""
conditions = {}
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | Sequences.load_conditions | def load_conditions(self, filename=None):
"""Read the initial conditions from a file and assign them to the
respective |StateSequence| and/or |LogSequence| objects handled by
the actual |Sequences| object.
If no filename or dirname is passed, the ones defined by the
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respective |StateSequence| and/or |LogSequence| objects handled by
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | Sequences.save_conditions | def save_conditions(self, filename=None):
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If no filename or dirname is passed, the ones defined by the
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.dirpath_int | def dirpath_int(self):
"""Absolute path of the directory of the internal data file.
Normally, each sequence queries its current "internal" directory
path from the |SequenceManager| object stored in module |pub|:
>>> from hydpy import pub, repr_, TestIO
>>> from hydpy.core.filet... | python | def dirpath_int(self):
"""Absolute path of the directory of the internal data file.
Normally, each sequence queries its current "internal" directory
path from the |SequenceManager| object stored in module |pub|:
>>> from hydpy import pub, repr_, TestIO
>>> from hydpy.core.filet... | [
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.disk2ram | def disk2ram(self):
"""Move internal data from disk to RAM."""
values = self.series
self.deactivate_disk()
self.ramflag = True
self.__set_array(values)
self.update_fastaccess() | python | def disk2ram(self):
"""Move internal data from disk to RAM."""
values = self.series
self.deactivate_disk()
self.ramflag = True
self.__set_array(values)
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.ram2disk | def ram2disk(self):
"""Move internal data from RAM to disk."""
values = self.series
self.deactivate_ram()
self.diskflag = True
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self.update_fastaccess() | python | def ram2disk(self):
"""Move internal data from RAM to disk."""
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.seriesshape | def seriesshape(self):
"""Shape of the whole time series (time being the first dimension)."""
seriesshape = [len(hydpy.pub.timegrids.init)]
seriesshape.extend(self.shape)
return tuple(seriesshape) | python | def seriesshape(self):
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seriesshape.extend(self.shape)
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.series | def series(self) -> InfoArray:
"""Internal time series data within an |numpy.ndarray|."""
if self.diskflag:
array = self._load_int()
elif self.ramflag:
array = self.__get_array()
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.load_ext | def load_ext(self):
"""Read the internal data from an external data file."""
try:
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except AttributeError:
raise RuntimeError(
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.adjust_short_series | def adjust_short_series(self, timegrid, values):
"""Adjust a short time series to a longer timegrid.
Normally, time series data to be read from a external data files
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.check_completeness | def check_completeness(self):
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least one |numpy.nan| value, if option |Options.checkseries| is
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>>> pub.timegrids = '2000-01-01', '2000-01-11', '1d'
>>> from hydpy.core.sequ... | python | def check_completeness(self):
"""Raise a |RuntimeError| if the |IOSequence.series| contains at
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enabled.
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.save_ext | def save_ext(self):
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence._load_int | def _load_int(self):
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if self.NDIM > 0:
values = values.reshape(self.seriesshape)
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | IOSequence.average_series | def average_series(self, *args, **kwargs) -> InfoArray:
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | Sim.load_ext | def load_ext(self):
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | FastAccess.load_data | def load_data(self, idx):
"""Load the internal data of all sequences. Load from file if the
corresponding disk flag is activated, otherwise load from RAM."""
for name in self:
ndim = getattr(self, '_%s_ndim' % name)
diskflag = getattr(self, '_%s_diskflag' % name)
... | python | def load_data(self, idx):
"""Load the internal data of all sequences. Load from file if the
corresponding disk flag is activated, otherwise load from RAM."""
for name in self:
ndim = getattr(self, '_%s_ndim' % name)
diskflag = getattr(self, '_%s_diskflag' % name)
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | FastAccess.save_data | def save_data(self, idx):
"""Save the internal data of all sequences with an activated flag.
Write to file if the corresponding disk flag is activated; store
in working memory if the corresponding ram flag is activated."""
for name in self:
actual = getattr(self, name)
... | python | def save_data(self, idx):
"""Save the internal data of all sequences with an activated flag.
Write to file if the corresponding disk flag is activated; store
in working memory if the corresponding ram flag is activated."""
for name in self:
actual = getattr(self, name)
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | FastAccessNode.load_simdata | def load_simdata(self, idx: int) -> None:
"""Load the next sim sequence value (of the given index)."""
if self._sim_ramflag:
self.sim[0] = self._sim_array[idx]
elif self._sim_diskflag:
raw = self._sim_file.read(8)
self.sim[0] = struct.unpack('d', raw) | python | def load_simdata(self, idx: int) -> None:
"""Load the next sim sequence value (of the given index)."""
if self._sim_ramflag:
self.sim[0] = self._sim_array[idx]
elif self._sim_diskflag:
raw = self._sim_file.read(8)
self.sim[0] = struct.unpack('d', raw) | [
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | FastAccessNode.save_simdata | def save_simdata(self, idx: int) -> None:
"""Save the last sim sequence value (of the given index)."""
if self._sim_ramflag:
self._sim_array[idx] = self.sim[0]
elif self._sim_diskflag:
raw = struct.pack('d', self.sim[0])
self._sim_file.write(raw) | python | def save_simdata(self, idx: int) -> None:
"""Save the last sim sequence value (of the given index)."""
if self._sim_ramflag:
self._sim_array[idx] = self.sim[0]
elif self._sim_diskflag:
raw = struct.pack('d', self.sim[0])
self._sim_file.write(raw) | [
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hydpy-dev/hydpy | hydpy/core/sequencetools.py | FastAccessNode.load_obsdata | def load_obsdata(self, idx: int) -> None:
"""Load the next obs sequence value (of the given index)."""
if self._obs_ramflag:
self.obs[0] = self._obs_array[idx]
elif self._obs_diskflag:
raw = self._obs_file.read(8)
self.obs[0] = struct.unpack('d', raw) | python | def load_obsdata(self, idx: int) -> None:
"""Load the next obs sequence value (of the given index)."""
if self._obs_ramflag:
self.obs[0] = self._obs_array[idx]
elif self._obs_diskflag:
raw = self._obs_file.read(8)
self.obs[0] = struct.unpack('d', raw) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | AbsFHRU.update | def update(self):
"""Update |AbsFHRU| based on |FT| and |FHRU|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> ft(100.0)
>>> fhru(0.2, 0.8)
>>> derived.absfhru.update()
>>> derived.absfhru
absfh... | python | def update(self):
"""Update |AbsFHRU| based on |FT| and |FHRU|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> ft(100.0)
>>> fhru(0.2, 0.8)
>>> derived.absfhru.update()
>>> derived.absfhru
absfh... | [
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] | Update |AbsFHRU| based on |FT| and |FHRU|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> ft(100.0)
>>> fhru(0.2, 0.8)
>>> derived.absfhru.update()
>>> derived.absfhru
absfhru(20.0, 80.0) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | KInz.update | def update(self):
"""Update |KInz| based on |HInz| and |LAI|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> hinz(0.2)
>>> lai.acker_jun = 1.0
>>> lai.vers_dec = 2.0
>>> derived.kinz.update()
>>> from hydpy import rou... | python | def update(self):
"""Update |KInz| based on |HInz| and |LAI|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> hinz(0.2)
>>> lai.acker_jun = 1.0
>>> lai.vers_dec = 2.0
>>> derived.kinz.update()
>>> from hydpy import rou... | [
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] | Update |KInz| based on |HInz| and |LAI|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> hinz(0.2)
>>> lai.acker_jun = 1.0
>>> lai.vers_dec = 2.0
>>> derived.kinz.update()
>>> from hydpy import round_
>>> round_(derive... | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | WB.update | def update(self):
"""Update |WB| based on |RelWB| and |NFk|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> relwb(0.2)
>>> nfk(100.0, 200.0)
>>> derived.wb.update()
>>> derived.wb
wb(20.0, 40.0)... | python | def update(self):
"""Update |WB| based on |RelWB| and |NFk|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> relwb(0.2)
>>> nfk(100.0, 200.0)
>>> derived.wb.update()
>>> derived.wb
wb(20.0, 40.0)... | [
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] | Update |WB| based on |RelWB| and |NFk|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> relwb(0.2)
>>> nfk(100.0, 200.0)
>>> derived.wb.update()
>>> derived.wb
wb(20.0, 40.0) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | WZ.update | def update(self):
"""Update |WZ| based on |RelWZ| and |NFk|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> relwz(0.8)
>>> nfk(100.0, 200.0)
>>> derived.wz.update()
>>> derived.wz
wz(80.0, 160.0... | python | def update(self):
"""Update |WZ| based on |RelWZ| and |NFk|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> relwz(0.8)
>>> nfk(100.0, 200.0)
>>> derived.wz.update()
>>> derived.wz
wz(80.0, 160.0... | [
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] | Update |WZ| based on |RelWZ| and |NFk|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> nhru(2)
>>> lnk(ACKER)
>>> relwz(0.8)
>>> nfk(100.0, 200.0)
>>> derived.wz.update()
>>> derived.wz
wz(80.0, 160.0) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | KB.update | def update(self):
"""Update |KB| based on |EQB| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqb(10.0)
>>> tind.value = 10.0
>>> derived.kb.update()
>>> derived.kb
kb(100.0)
"""
con = self.subpars.pars.contr... | python | def update(self):
"""Update |KB| based on |EQB| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqb(10.0)
>>> tind.value = 10.0
>>> derived.kb.update()
>>> derived.kb
kb(100.0)
"""
con = self.subpars.pars.contr... | [
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] | Update |KB| based on |EQB| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqb(10.0)
>>> tind.value = 10.0
>>> derived.kb.update()
>>> derived.kb
kb(100.0) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | KI1.update | def update(self):
"""Update |KI1| based on |EQI1| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqi1(5.0)
>>> tind.value = 10.0
>>> derived.ki1.update()
>>> derived.ki1
ki1(50.0)
"""
con = self.subpars.pars.c... | python | def update(self):
"""Update |KI1| based on |EQI1| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqi1(5.0)
>>> tind.value = 10.0
>>> derived.ki1.update()
>>> derived.ki1
ki1(50.0)
"""
con = self.subpars.pars.c... | [
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] | Update |KI1| based on |EQI1| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqi1(5.0)
>>> tind.value = 10.0
>>> derived.ki1.update()
>>> derived.ki1
ki1(50.0) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | KI2.update | def update(self):
"""Update |KI2| based on |EQI2| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqi2(1.0)
>>> tind.value = 10.0
>>> derived.ki2.update()
>>> derived.ki2
ki2(10.0)
"""
con = self.subpars.pars.c... | python | def update(self):
"""Update |KI2| based on |EQI2| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqi2(1.0)
>>> tind.value = 10.0
>>> derived.ki2.update()
>>> derived.ki2
ki2(10.0)
"""
con = self.subpars.pars.c... | [
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] | Update |KI2| based on |EQI2| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqi2(1.0)
>>> tind.value = 10.0
>>> derived.ki2.update()
>>> derived.ki2
ki2(10.0) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | KD1.update | def update(self):
"""Update |KD1| based on |EQD1| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqd1(0.5)
>>> tind.value = 10.0
>>> derived.kd1.update()
>>> derived.kd1
kd1(5.0)
"""
con = self.subpars.pars.co... | python | def update(self):
"""Update |KD1| based on |EQD1| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqd1(0.5)
>>> tind.value = 10.0
>>> derived.kd1.update()
>>> derived.kd1
kd1(5.0)
"""
con = self.subpars.pars.co... | [
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] | Update |KD1| based on |EQD1| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqd1(0.5)
>>> tind.value = 10.0
>>> derived.kd1.update()
>>> derived.kd1
kd1(5.0) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | KD2.update | def update(self):
"""Update |KD2| based on |EQD2| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqd2(0.1)
>>> tind.value = 10.0
>>> derived.kd2.update()
>>> derived.kd2
kd2(1.0)
"""
con = self.subpars.pars.co... | python | def update(self):
"""Update |KD2| based on |EQD2| and |TInd|.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqd2(0.1)
>>> tind.value = 10.0
>>> derived.kd2.update()
>>> derived.kd2
kd2(1.0)
"""
con = self.subpars.pars.co... | [
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>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> eqd2(0.1)
>>> tind.value = 10.0
>>> derived.kd2.update()
>>> derived.kd2
kd2(1.0) | [
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hydpy-dev/hydpy | hydpy/models/lland/lland_derived.py | QFactor.update | def update(self):
"""Update |QFactor| based on |FT| and the current simulation step size.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> simulationstep('1d')
>>> ft(10.0)
>>> derived.qfactor.update()
>>> derived.qfactor
qfactor(0.115741)... | python | def update(self):
"""Update |QFactor| based on |FT| and the current simulation step size.
>>> from hydpy.models.lland import *
>>> parameterstep('1d')
>>> simulationstep('1d')
>>> ft(10.0)
>>> derived.qfactor.update()
>>> derived.qfactor
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hydpy-dev/hydpy | hydpy/auxs/networktools.py | RiverBasinNumbers2Selection._router_numbers | def _router_numbers(self):
"""A tuple of the numbers of all "routing" basins."""
return tuple(up for up in self._up2down.keys()
if up in self._up2down.values()) | python | def _router_numbers(self):
"""A tuple of the numbers of all "routing" basins."""
return tuple(up for up in self._up2down.keys()
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hydpy-dev/hydpy | hydpy/auxs/networktools.py | RiverBasinNumbers2Selection.supplier_elements | def supplier_elements(self):
"""A |Elements| collection of all "supplying" basins.
(All river basins are assumed to supply something to the downstream
basin.)
>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
... ... | python | def supplier_elements(self):
"""A |Elements| collection of all "supplying" basins.
(All river basins are assumed to supply something to the downstream
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>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
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hydpy-dev/hydpy | hydpy/auxs/networktools.py | RiverBasinNumbers2Selection.router_elements | def router_elements(self):
"""A |Elements| collection of all "routing" basins.
(Only river basins with a upstream basin are assumed to route
something to the downstream basin.)
>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
.... | python | def router_elements(self):
"""A |Elements| collection of all "routing" basins.
(Only river basins with a upstream basin are assumed to route
something to the downstream basin.)
>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
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hydpy-dev/hydpy | hydpy/auxs/networktools.py | RiverBasinNumbers2Selection.nodes | def nodes(self):
"""A |Nodes| collection of all required nodes.
>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
... (111, 113, 1129, 11269, 1125, 11261,
... 11262, 1123, 1124, 1122... | python | def nodes(self):
"""A |Nodes| collection of all required nodes.
>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
... (111, 113, 1129, 11269, 1125, 11261,
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hydpy-dev/hydpy | hydpy/auxs/networktools.py | RiverBasinNumbers2Selection.selection | def selection(self):
"""A complete |Selection| object of all "supplying" and "routing"
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>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
... (111, 113, 1129, 11269, 1125, 11261,
... | python | def selection(self):
"""A complete |Selection| object of all "supplying" and "routing"
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>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
... (111, 113, 1129, 11269, 1125, 11261,
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | str2chars | def str2chars(strings) -> numpy.ndarray:
"""Return |numpy.ndarray| containing the byte characters (second axis)
of all given strings (first axis).
>>> from hydpy.core.netcdftools import str2chars
>>> str2chars(['zeros', 'ones'])
array([[b'z', b'e', b'r', b'o', b's'],
[b'o', b'n', b'e', b... | python | def str2chars(strings) -> numpy.ndarray:
"""Return |numpy.ndarray| containing the byte characters (second axis)
of all given strings (first axis).
>>> from hydpy.core.netcdftools import str2chars
>>> str2chars(['zeros', 'ones'])
array([[b'z', b'e', b'r', b'o', b's'],
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>>> str2chars(['zeros', 'ones'])
array([[b'z', b'e', b'r', b'o', b's'],
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | chars2str | def chars2str(chars) -> List[str]:
"""Inversion function of function |str2chars|.
>>> from hydpy.core.netcdftools import chars2str
>>> chars2str([[b'z', b'e', b'r', b'o', b's'],
... [b'o', b'n', b'e', b's', b'']])
['zeros', 'ones']
>>> chars2str([])
[]
"""
strings = col... | python | def chars2str(chars) -> List[str]:
"""Inversion function of function |str2chars|.
>>> from hydpy.core.netcdftools import chars2str
>>> chars2str([[b'z', b'e', b'r', b'o', b's'],
... [b'o', b'n', b'e', b's', b'']])
['zeros', 'ones']
>>> chars2str([])
[]
"""
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | create_dimension | def create_dimension(ncfile, name, length) -> None:
"""Add a new dimension with the given name and length to the given
NetCDF file.
Essentially, |create_dimension| just calls the equally named method
of the NetCDF library, but adds information to possible error messages:
>>> from hydpy import Test... | python | def create_dimension(ncfile, name, length) -> None:
"""Add a new dimension with the given name and length to the given
NetCDF file.
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | create_variable | def create_variable(ncfile, name, datatype, dimensions) -> None:
"""Add a new variable with the given name, datatype, and dimensions
to the given NetCDF file.
Essentially, |create_variable| just calls the equally named method
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | query_variable | def query_variable(ncfile, name) -> netcdf4.Variable:
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Essentially, |query_variable| just performs a key assess via the
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>>> from hydpy.core.netcdftools import que... | python | def query_variable(ncfile, name) -> netcdf4.Variable:
"""Return the variable with the given name from the given NetCDF file.
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | query_timegrid | def query_timegrid(ncfile) -> timetools.Timegrid:
"""Return the |Timegrid| defined by the given NetCDF file.
>>> from hydpy.core.examples import prepare_full_example_1
>>> prepare_full_example_1()
>>> from hydpy import TestIO
>>> from hydpy.core.netcdftools import netcdf4
>>> from hydpy.core.ne... | python | def query_timegrid(ncfile) -> timetools.Timegrid:
"""Return the |Timegrid| defined by the given NetCDF file.
>>> from hydpy.core.examples import prepare_full_example_1
>>> prepare_full_example_1()
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>>> from hydpy.core.netcdftools import netcdf4
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | query_array | def query_array(ncfile, name) -> numpy.ndarray:
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NetCDF file.
The following example shows that |query_array| returns |nan| entries
to represent missing values even when the respective NetCDF variable
defines a different fill value... | python | def query_array(ncfile, name) -> numpy.ndarray:
"""Return the data of the variable with the given name from the given
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFInterface.log | def log(self, sequence, infoarray) -> None:
"""Prepare a |NetCDFFile| object suitable for the given |IOSequence|
object, when necessary, and pass the given arguments to its
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if isinstance(sequence, sequencetools.ModelSequence):
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFInterface.read | def read(self) -> None:
"""Call method |NetCDFFile.read| of all handled |NetCDFFile| objects.
"""
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFInterface.write | def write(self) -> None:
"""Call method |NetCDFFile.write| of all handled |NetCDFFile| objects.
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFFile.log | def log(self, sequence, infoarray) -> None:
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"""Pass the given |IoSequence| to a suitable instance of
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFFile.filepath | def filepath(self) -> str:
"""The NetCDF file path."""
return os.path.join(self._dirpath, self.name + '.nc') | python | def filepath(self) -> str:
"""The NetCDF file path."""
return os.path.join(self._dirpath, self.name + '.nc') | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFFile.read | def read(self) -> None:
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFFile.write | def write(self, timeunit, timepoints) -> None:
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self._... | python | def write(self, timeunit, timepoints) -> None:
"""Open a new NetCDF file temporarily and call method
|NetCDFVariableBase.write| of all handled |NetCDFVariableBase|
objects."""
with netcdf4.Dataset(self.filepath, "w") as ncfile:
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | Subdevice2Index.get_index | def get_index(self, name_subdevice) -> int:
"""Item access to the wrapped |dict| object with a specialized
error message."""
try:
return self.dict_[name_subdevice]
except KeyError:
raise OSError(
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... | python | def get_index(self, name_subdevice) -> int:
"""Item access to the wrapped |dict| object with a specialized
error message."""
try:
return self.dict_[name_subdevice]
except KeyError:
raise OSError(
'No data for sequence `%s` and (sub)device `%s` '
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableBase.log | def log(self, sequence, infoarray) -> None:
"""Log the given |IOSequence| object either for reading or writing
data.
The optional `array` argument allows for passing alternative data
in an |InfoArray| object replacing the series of the |IOSequence|
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"""Log the given |IOSequence| object either for reading or writing
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableBase.insert_subdevices | def insert_subdevices(self, ncfile) -> None:
"""Insert a variable of the names of the (sub)devices of the logged
sequences into the given NetCDF file
(1) We prepare a |NetCDFVariableBase| subclass with fixed
(sub)device names:
>>> from hydpy.core.netcdftools import NetCDFVariab... | python | def insert_subdevices(self, ncfile) -> None:
"""Insert a variable of the names of the (sub)devices of the logged
sequences into the given NetCDF file
(1) We prepare a |NetCDFVariableBase| subclass with fixed
(sub)device names:
>>> from hydpy.core.netcdftools import NetCDFVariab... | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableBase.query_subdevices | def query_subdevices(self, ncfile) -> List[str]:
"""Query the names of the (sub)devices of the logged sequences
from the given NetCDF file
(1) We apply function |NetCDFVariableBase.query_subdevices| on
an empty NetCDF file. The error message shows that the method
tries to query... | python | def query_subdevices(self, ncfile) -> List[str]:
"""Query the names of the (sub)devices of the logged sequences
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(1) We apply function |NetCDFVariableBase.query_subdevices| on
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableBase.query_subdevice2index | def query_subdevice2index(self, ncfile) -> Subdevice2Index:
"""Return a |Subdevice2Index| that maps the (sub)device names to
their position within the given NetCDF file.
Method |NetCDFVariableBase.query_subdevice2index| is based on
|NetCDFVariableBase.query_subdevices|. The returned
... | python | def query_subdevice2index(self, ncfile) -> Subdevice2Index:
"""Return a |Subdevice2Index| that maps the (sub)device names to
their position within the given NetCDF file.
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableBase.sort_timeplaceentries | def sort_timeplaceentries(self, timeentry, placeentry) -> Tuple[Any, Any]:
"""Return a |tuple| containing the given `timeentry` and `placeentry`
sorted in agreement with the currently selected `timeaxis`.
>>> from hydpy.core.netcdftools import NetCDFVariableBase
>>> from hydpy import ma... | python | def sort_timeplaceentries(self, timeentry, placeentry) -> Tuple[Any, Any]:
"""Return a |tuple| containing the given `timeentry` and `placeentry`
sorted in agreement with the currently selected `timeaxis`.
>>> from hydpy.core.netcdftools import NetCDFVariableBase
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableBase.get_timeplaceslice | def get_timeplaceslice(self, placeindex) -> \
Union[Tuple[slice, int], Tuple[int, slice]]:
"""Return a |tuple| for indexing a complete time series of a certain
location available in |NetCDFVariableBase.array|.
>>> from hydpy.core.netcdftools import NetCDFVariableBase
>>> fro... | python | def get_timeplaceslice(self, placeindex) -> \
Union[Tuple[slice, int], Tuple[int, slice]]:
"""Return a |tuple| for indexing a complete time series of a certain
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | DeepAndAggMixin.subdevicenames | def subdevicenames(self) -> Tuple[str, ...]:
"""A |tuple| containing the device names."""
self: NetCDFVariableBase
return tuple(self.sequences.keys()) | python | def subdevicenames(self) -> Tuple[str, ...]:
"""A |tuple| containing the device names."""
self: NetCDFVariableBase
return tuple(self.sequences.keys()) | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | DeepAndAggMixin.write | def write(self, ncfile) -> None:
"""Write the data to the given NetCDF file.
See the general documentation on classes |NetCDFVariableDeep|
and |NetCDFVariableAgg| for some examples.
"""
self: NetCDFVariableBase
self.insert_subdevices(ncfile)
dimensions = self.dim... | python | def write(self, ncfile) -> None:
"""Write the data to the given NetCDF file.
See the general documentation on classes |NetCDFVariableDeep|
and |NetCDFVariableAgg| for some examples.
"""
self: NetCDFVariableBase
self.insert_subdevices(ncfile)
dimensions = self.dim... | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | AggAndFlatMixin.dimensions | def dimensions(self) -> Tuple[str, ...]:
"""The dimension names of the NetCDF variable.
Usually, the string defined by property |IOSequence.descr_sequence|
prefixes the first dimension name related to the location, which
allows storing different sequences types in one NetCDF file:
... | python | def dimensions(self) -> Tuple[str, ...]:
"""The dimension names of the NetCDF variable.
Usually, the string defined by property |IOSequence.descr_sequence|
prefixes the first dimension name related to the location, which
allows storing different sequences types in one NetCDF file:
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>>> from hydpy.core.examples import prepare_io_e... | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableDeep.get_slices | def get_slices(self, idx, shape) -> Tuple[IntOrSlice, ...]:
"""Return a |tuple| of one |int| and some |slice| objects to
accesses all values of a certain device within
|NetCDFVariableDeep.array|.
>>> from hydpy.core.netcdftools import NetCDFVariableDeep
>>> ncvar = NetCDFVariabl... | python | def get_slices(self, idx, shape) -> Tuple[IntOrSlice, ...]:
"""Return a |tuple| of one |int| and some |slice| objects to
accesses all values of a certain device within
|NetCDFVariableDeep.array|.
>>> from hydpy.core.netcdftools import NetCDFVariableDeep
>>> ncvar = NetCDFVariabl... | [
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|NetCDFVariableDeep.array|.
>>> from hydpy.core.netcdftools import NetCDFVariableDeep
>>> ncvar = NetCDFVariableDeep('test', isolate=False, timeaxis=1)
>>> ncvar.get_slices(2... | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableDeep.shape | def shape(self) -> Tuple[int, ...]:
"""Required shape of |NetCDFVariableDeep.array|.
For the default configuration, the first axis corresponds to the
number of devices, and the second one to the number of timesteps.
We show this for the 0-dimensional input sequence |lland_inputs.Nied|:
... | python | def shape(self) -> Tuple[int, ...]:
"""Required shape of |NetCDFVariableDeep.array|.
For the default configuration, the first axis corresponds to the
number of devices, and the second one to the number of timesteps.
We show this for the 0-dimensional input sequence |lland_inputs.Nied|:
... | [
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"=",... | Required shape of |NetCDFVariableDeep.array|.
For the default configuration, the first axis corresponds to the
number of devices, and the second one to the number of timesteps.
We show this for the 0-dimensional input sequence |lland_inputs.Nied|:
>>> from hydpy.core.examples import pr... | [
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] | train | https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/netcdftools.py#L1605-L1649 |
hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableDeep.array | def array(self) -> numpy.ndarray:
"""The series data of all logged |IOSequence| objects contained
in one single |numpy.ndarray|.
The documentation on |NetCDFVariableDeep.shape| explains how
|NetCDFVariableDeep.array| is structured. The first example
confirms that, for the defau... | python | def array(self) -> numpy.ndarray:
"""The series data of all logged |IOSequence| objects contained
in one single |numpy.ndarray|.
The documentation on |NetCDFVariableDeep.shape| explains how
|NetCDFVariableDeep.array| is structured. The first example
confirms that, for the defau... | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableDeep.dimensions | def dimensions(self) -> Tuple[str, ...]:
"""The dimension names of the NetCDF variable.
Usually, the string defined by property |IOSequence.descr_sequence|
prefixes all dimension names except the second one related to time,
which allows storing different sequences in one NetCDF file:
... | python | def dimensions(self) -> Tuple[str, ...]:
"""The dimension names of the NetCDF variable.
Usually, the string defined by property |IOSequence.descr_sequence|
prefixes all dimension names except the second one related to time,
which allows storing different sequences in one NetCDF file:
... | [
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Usually, the string defined by property |IOSequence.descr_sequence|
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which allows storing different sequences in one NetCDF file:
>>> from hydpy.core.examples import prepare_i... | [
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] | train | https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/netcdftools.py#L1708-L1745 |
hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableDeep.read | def read(self, ncfile, timegrid_data) -> None:
"""Read the data from the given NetCDF file.
The argument `timegrid_data` defines the data period of the
given NetCDF file.
See the general documentation on class |NetCDFVariableDeep|
for some examples.
"""
array = ... | python | def read(self, ncfile, timegrid_data) -> None:
"""Read the data from the given NetCDF file.
The argument `timegrid_data` defines the data period of the
given NetCDF file.
See the general documentation on class |NetCDFVariableDeep|
for some examples.
"""
array = ... | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableAgg.shape | def shape(self) -> Tuple[int, int]:
"""Required shape of |NetCDFVariableAgg.array|.
For the default configuration, the first axis corresponds to the
number of devices, and the second one to the number of timesteps.
We show this for the 1-dimensional input sequence |lland_fluxes.NKor|:
... | python | def shape(self) -> Tuple[int, int]:
"""Required shape of |NetCDFVariableAgg.array|.
For the default configuration, the first axis corresponds to the
number of devices, and the second one to the number of timesteps.
We show this for the 1-dimensional input sequence |lland_fluxes.NKor|:
... | [
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... | Required shape of |NetCDFVariableAgg.array|.
For the default configuration, the first axis corresponds to the
number of devices, and the second one to the number of timesteps.
We show this for the 1-dimensional input sequence |lland_fluxes.NKor|:
>>> from hydpy.core.examples import pre... | [
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".",
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] | train | https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/netcdftools.py#L1824-L1850 |
hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableAgg.array | def array(self) -> numpy.ndarray:
"""The aggregated data of all logged |IOSequence| objects contained
in one single |numpy.ndarray| object.
The documentation on |NetCDFVariableAgg.shape| explains how
|NetCDFVariableAgg.array| is structured. This first example
confirms that, und... | python | def array(self) -> numpy.ndarray:
"""The aggregated data of all logged |IOSequence| objects contained
in one single |numpy.ndarray| object.
The documentation on |NetCDFVariableAgg.shape| explains how
|NetCDFVariableAgg.array| is structured. This first example
confirms that, und... | [
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The documentation on |NetCDFVariableAgg.shape| explains how
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableFlat.shape | def shape(self) -> Tuple[int, int]:
"""Required shape of |NetCDFVariableFlat.array|.
For 0-dimensional sequences like |lland_inputs.Nied| and for the
default configuration (`timeaxis=1`), the first axis corresponds
to the number of devices, and the second one two the number of
t... | python | def shape(self) -> Tuple[int, int]:
"""Required shape of |NetCDFVariableFlat.array|.
For 0-dimensional sequences like |lland_inputs.Nied| and for the
default configuration (`timeaxis=1`), the first axis corresponds
to the number of devices, and the second one two the number of
t... | [
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For 0-dimensional sequences like |lland_inputs.Nied| and for the
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>>> from hydpy.core.examples... | [
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] | train | https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/netcdftools.py#L1979-L2019 |
hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableFlat.array | def array(self) -> numpy.ndarray:
"""The series data of all logged |IOSequence| objects contained in
one single |numpy.ndarray| object.
The documentation on |NetCDFVariableAgg.shape| explains how
|NetCDFVariableAgg.array| is structured. The first example
confirms that, under de... | python | def array(self) -> numpy.ndarray:
"""The series data of all logged |IOSequence| objects contained in
one single |numpy.ndarray| object.
The documentation on |NetCDFVariableAgg.shape| explains how
|NetCDFVariableAgg.array| is structured. The first example
confirms that, under de... | [
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The documentation on |NetCDFVariableAgg.shape| explains how
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confirms that, under default configuration (`timeaxis=1`), the
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableFlat.subdevicenames | def subdevicenames(self) -> Tuple[str, ...]:
"""A |tuple| containing the (sub)device names.
Property |NetCDFVariableFlat.subdevicenames| clarifies which
row of |NetCDFVariableAgg.array| contains which time series.
For 0-dimensional series like |lland_inputs.Nied|, the plain
devi... | python | def subdevicenames(self) -> Tuple[str, ...]:
"""A |tuple| containing the (sub)device names.
Property |NetCDFVariableFlat.subdevicenames| clarifies which
row of |NetCDFVariableAgg.array| contains which time series.
For 0-dimensional series like |lland_inputs.Nied|, the plain
devi... | [
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Property |NetCDFVariableFlat.subdevicenames| clarifies which
row of |NetCDFVariableAgg.array| contains which time series.
For 0-dimensional series like |lland_inputs.Nied|, the plain
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>>> from hydpy.core.examp... | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableFlat._product | def _product(shape) -> Iterator[Tuple[int, ...]]:
"""Should return all "subdevice index combinations" for sequences
with arbitrary dimensions:
>>> from hydpy.core.netcdftools import NetCDFVariableFlat
>>> _product = NetCDFVariableFlat.__dict__['_product'].__func__
>>> for comb i... | python | def _product(shape) -> Iterator[Tuple[int, ...]]:
"""Should return all "subdevice index combinations" for sequences
with arbitrary dimensions:
>>> from hydpy.core.netcdftools import NetCDFVariableFlat
>>> _product = NetCDFVariableFlat.__dict__['_product'].__func__
>>> for comb i... | [
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>>> from hydpy.core.netcdftools import NetCDFVariableFlat
>>> _product = NetCDFVariableFlat.__dict__['_product'].__func__
>>> for comb in _product([1, 2, 3]):
... print(comb)
(0... | [
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableFlat.read | def read(self, ncfile, timegrid_data) -> None:
"""Read the data from the given NetCDF file.
The argument `timegrid_data` defines the data period of the
given NetCDF file.
See the general documentation on class |NetCDFVariableFlat|
for some examples.
"""
array = ... | python | def read(self, ncfile, timegrid_data) -> None:
"""Read the data from the given NetCDF file.
The argument `timegrid_data` defines the data period of the
given NetCDF file.
See the general documentation on class |NetCDFVariableFlat|
for some examples.
"""
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hydpy-dev/hydpy | hydpy/core/netcdftools.py | NetCDFVariableFlat.write | def write(self, ncfile) -> None:
"""Write the data to the given NetCDF file.
See the general documentation on class |NetCDFVariableFlat|
for some examples.
"""
self.insert_subdevices(ncfile)
create_variable(ncfile, self.name, 'f8', self.dimensions)
ncfile[self.na... | python | def write(self, ncfile) -> None:
"""Write the data to the given NetCDF file.
See the general documentation on class |NetCDFVariableFlat|
for some examples.
"""
self.insert_subdevices(ncfile)
create_variable(ncfile, self.name, 'f8', self.dimensions)
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hydpy-dev/hydpy | hydpy/models/llake/llake_derived.py | NmbSubsteps.update | def update(self):
"""Determine the number of substeps.
Initialize a llake model and assume a simulation step size of 12 hours:
>>> from hydpy.models.llake import *
>>> parameterstep('1d')
>>> simulationstep('12h')
If the maximum internal step size is also set to 12 hou... | python | def update(self):
"""Determine the number of substeps.
Initialize a llake model and assume a simulation step size of 12 hours:
>>> from hydpy.models.llake import *
>>> parameterstep('1d')
>>> simulationstep('12h')
If the maximum internal step size is also set to 12 hou... | [
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hydpy-dev/hydpy | hydpy/models/llake/llake_derived.py | VQ.update | def update(self):
"""Calulate the auxilary term.
>>> from hydpy.models.llake import *
>>> parameterstep('1d')
>>> simulationstep('12h')
>>> n(3)
>>> v(0., 1e5, 1e6)
>>> q(_1=[0., 1., 2.], _7=[0., 2., 5.])
>>> maxdt('12h')
>>> derived.seconds.updat... | python | def update(self):
"""Calulate the auxilary term.
>>> from hydpy.models.llake import *
>>> parameterstep('1d')
>>> simulationstep('12h')
>>> n(3)
>>> v(0., 1e5, 1e6)
>>> q(_1=[0., 1., 2.], _7=[0., 2., 5.])
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hydpy-dev/hydpy | hydpy/core/examples.py | prepare_io_example_1 | def prepare_io_example_1() -> Tuple[devicetools.Nodes, devicetools.Elements]:
# noinspection PyUnresolvedReferences
"""Prepare an IO example configuration.
>>> from hydpy.core.examples import prepare_io_example_1
>>> nodes, elements = prepare_io_example_1()
(1) Prepares a short initialisation peri... | python | def prepare_io_example_1() -> Tuple[devicetools.Nodes, devicetools.Elements]:
# noinspection PyUnresolvedReferences
"""Prepare an IO example configuration.
>>> from hydpy.core.examples import prepare_io_example_1
>>> nodes, elements = prepare_io_example_1()
(1) Prepares a short initialisation peri... | [
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>>> nodes, elements = prepare_io_example_1()
(1) Prepares a short initialisation period of five days:
>>> from hydpy import pub
>>> pub.timegrids
Timegrids(Timegrid('2000-01-01 00:00:00',
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hydpy-dev/hydpy | hydpy/core/examples.py | prepare_full_example_1 | def prepare_full_example_1() -> None:
"""Prepare the complete `LahnH` project for testing.
>>> from hydpy.core.examples import prepare_full_example_1
>>> prepare_full_example_1()
>>> from hydpy import TestIO
>>> import os
>>> with TestIO():
... print('root:', *sorted(os.listdir('.')))
... | python | def prepare_full_example_1() -> None:
"""Prepare the complete `LahnH` project for testing.
>>> from hydpy.core.examples import prepare_full_example_1
>>> prepare_full_example_1()
>>> from hydpy import TestIO
>>> import os
>>> with TestIO():
... print('root:', *sorted(os.listdir('.')))
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>>> prepare_full_example_1()
>>> from hydpy import TestIO
>>> import os
>>> with TestIO():
... print('root:', *sorted(os.listdir('.')))
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hydpy-dev/hydpy | hydpy/core/examples.py | prepare_full_example_2 | def prepare_full_example_2(lastdate='1996-01-05') -> (
hydpytools.HydPy, hydpy.pub, testtools.TestIO):
"""Prepare the complete `LahnH` project for testing.
|prepare_full_example_2| calls |prepare_full_example_1|, but also
returns a readily prepared |HydPy| instance, as well as module
|pub| and ... | python | def prepare_full_example_2(lastdate='1996-01-05') -> (
hydpytools.HydPy, hydpy.pub, testtools.TestIO):
"""Prepare the complete `LahnH` project for testing.
|prepare_full_example_2| calls |prepare_full_example_1|, but also
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inkjet/pypostalcode | pypostalcode/__init__.py | PostalCodeDatabase.get_postalcodes_around_radius | def get_postalcodes_around_radius(self, pc, radius):
postalcodes = self.get(pc)
if postalcodes is None:
raise PostalCodeNotFoundException("Could not find postal code you're searching for.")
else:
pc = postalcodes[0]
radius = float(radius)
... | python | def get_postalcodes_around_radius(self, pc, radius):
postalcodes = self.get(pc)
if postalcodes is None:
raise PostalCodeNotFoundException("Could not find postal code you're searching for.")
else:
pc = postalcodes[0]
radius = float(radius)
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savvastj/nbashots | nbashots/api.py | get_all_player_ids | def get_all_player_ids(ids="shots"):
"""
Returns a pandas DataFrame containing the player IDs used in the
stats.nba.com API.
Parameters
----------
ids : { "shots" | "all_players" | "all_data" }, optional
Passing in "shots" returns a DataFrame that contains the player IDs of
all ... | python | def get_all_player_ids(ids="shots"):
"""
Returns a pandas DataFrame containing the player IDs used in the
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Parameters
----------
ids : { "shots" | "all_players" | "all_data" }, optional
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savvastj/nbashots | nbashots/api.py | get_player_id | def get_player_id(player):
"""
Returns the player ID(s) associated with the player name that is passed in.
There are instances where players have the same name so there are multiple
player IDs associated with it.
Parameters
----------
player : str
The desired player's name in 'Last... | python | def get_player_id(player):
"""
Returns the player ID(s) associated with the player name that is passed in.
There are instances where players have the same name so there are multiple
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player : str
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savvastj/nbashots | nbashots/api.py | get_all_team_ids | def get_all_team_ids():
"""Returns a pandas DataFrame with all Team IDs"""
df = get_all_player_ids("all_data")
df = pd.DataFrame({"TEAM_NAME": df.TEAM_NAME.unique(),
"TEAM_ID": df.TEAM_ID.unique()})
return df | python | def get_all_team_ids():
"""Returns a pandas DataFrame with all Team IDs"""
df = get_all_player_ids("all_data")
df = pd.DataFrame({"TEAM_NAME": df.TEAM_NAME.unique(),
"TEAM_ID": df.TEAM_ID.unique()})
return df | [
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savvastj/nbashots | nbashots/api.py | get_team_id | def get_team_id(team_name):
""" Returns the team ID associated with the team name that is passed in.
Parameters
----------
team_name : str
The team name whose ID we want. NOTE: Only pass in the team name
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""" Returns the team ID associated with the team name that is passed in.
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savvastj/nbashots | nbashots/api.py | get_player_img | def get_player_img(player_id):
"""
Returns the image of the player from stats.nba.com as a numpy array and
saves the image as PNG file in the current directory.
Parameters
----------
player_id: int
The player ID used to find the image.
Returns
-------
player_img: ndarray
... | python | def get_player_img(player_id):
"""
Returns the image of the player from stats.nba.com as a numpy array and
saves the image as PNG file in the current directory.
Parameters
----------
player_id: int
The player ID used to find the image.
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player_img: ndarray
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savvastj/nbashots | nbashots/api.py | TeamLog.get_game_logs | def get_game_logs(self):
"""Returns team game logs as a pandas DataFrame"""
logs = self.response.json()['resultSets'][0]['rowSet']
headers = self.response.json()['resultSets'][0]['headers']
df = pd.DataFrame(logs, columns=headers)
df.GAME_DATE = pd.to_datetime(df.GAME_DATE)
... | python | def get_game_logs(self):
"""Returns team game logs as a pandas DataFrame"""
logs = self.response.json()['resultSets'][0]['rowSet']
headers = self.response.json()['resultSets'][0]['headers']
df = pd.DataFrame(logs, columns=headers)
df.GAME_DATE = pd.to_datetime(df.GAME_DATE)
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savvastj/nbashots | nbashots/api.py | TeamLog.get_game_id | def get_game_id(self, date):
"""Returns the Game ID associated with the date that is passed in.
Parameters
----------
date : str
The date associated with the game whose Game ID. The date that is
passed in can take on a numeric format of MM/DD/YY (like "01/06/16"
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"""Returns the Game ID associated with the date that is passed in.
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date : str
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