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def solve_dv_dt_v1(self):
"""Solve the differential equation of HydPy-L. At the moment, HydPy-L only implements a simple numerical solution of its underlying ord... |
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
old = self.sequences.states.fastaccess_old
new = self.sequences.states.fastaccess_new
aid = self.sequences.aides.fastaccess
flu.qa = 0.
aid.v = old.v
for _ in range(der.nmbsubsteps):
self.calc_vq()
... |
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def calc_vq_v1(self):
"""Calculate the auxiliary term. Required derived parameters: |Seconds| |NmbSubsteps| Required flux sequence: |QZ| Required aide sequence: ... |
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
aid = self.sequences.aides.fastaccess
aid.vq = 2.*aid.v+der.seconds/der.nmbsubsteps*flu.qz |
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def interp_qa_v1(self):
"""Calculate the lake outflow based on linear interpolation. Required control parameters: |N| |llake_control.Q| Required derived paramete... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
aid = self.sequences.aides.fastaccess
idx = der.toy[self.idx_sim]
for jdx in range(1, con.n):
if der.vq[idx, jdx] >= aid.vq:
break
aid.qa = ((aid.vq-der.vq[idx, jdx-1]) *
(con.q[i... |
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def calc_v_qa_v1(self):
"""Update the stored water volume based on the equation of continuity. Note that for too high outflow values, which would result in overd... |
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
aid = self.sequences.aides.fastaccess
aid.qa = min(aid.qa, flu.qz+der.nmbsubsteps/der.seconds*aid.v)
aid.v = max(aid.v+der.seconds/der.nmbsubsteps*(flu.qz-aid.qa), 0.) |
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def interp_w_v1(self):
"""Calculate the actual water stage based on linear interpolation. Required control parameters: |N| |llake_control.V| |llake_control.W| Re... |
con = self.parameters.control.fastaccess
new = self.sequences.states.fastaccess_new
for jdx in range(1, con.n):
if con.v[jdx] >= new.v:
break
new.w = ((new.v-con.v[jdx-1]) *
(con.w[jdx]-con.w[jdx-1]) /
(con.v[jdx]-con.v[jdx-1]) +
con.w[jdx-1]) |
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def corr_dw_v1(self):
"""Adjust the water stage drop to the highest value allowed and correct the associated fluxes. Note that method |corr_dw_v1| calls the meth... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
old = self.sequences.states.fastaccess_old
new = self.sequences.states.fastaccess_new
idx = der.toy[self.idx_sim]
if (con.maxdw[idx] > 0.) and ((old.w-new.w) > con.maxdw[... |
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def modify_qa_v1(self):
"""Add water to or remove water from the calculated lake outflow. Required control parameter: |Verzw| Required derived parameter: |llake_... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
idx = der.toy[self.idx_sim]
flu.qa = max(flu.qa-con.verzw[idx], 0.) |
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def thresholds(self):
"""Threshold values of the response functions.""" |
return numpy.array(
sorted(self._key2float(key) for key in self._coefs), dtype=float) |
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def prepare_arrays(sim=None, obs=None, node=None, skip_nan=False):
"""Prepare and return two |numpy| arrays based on the given arguments. Note that many function... |
if node:
if sim is not None:
raise ValueError(
'Values are passed to both arguments `sim` and `node`, '
'which is not allowed.')
if obs is not None:
raise ValueError(
'Values are passed to both arguments `obs` and `node`, '
... |
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def nse(sim=None, obs=None, node=None, skip_nan=False):
"""Calculate the efficiency criteria after Nash & Sutcliffe. If the simulated values predict the observed... |
sim, obs = prepare_arrays(sim, obs, node, skip_nan)
return 1.-numpy.sum((sim-obs)**2)/numpy.sum((obs-numpy.mean(obs))**2) |
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def bias_abs(sim=None, obs=None, node=None, skip_nan=False):
"""Calculate the absolute difference between the means of the simulated and the observed values. 0.0... |
sim, obs = prepare_arrays(sim, obs, node, skip_nan)
return numpy.mean(sim-obs) |
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def std_ratio(sim=None, obs=None, node=None, skip_nan=False):
"""Calculate the ratio between the standard deviation of the simulated and the observed values. 0.0... |
sim, obs = prepare_arrays(sim, obs, node, skip_nan)
return numpy.std(sim)/numpy.std(obs)-1. |
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def corr(sim=None, obs=None, node=None, skip_nan=False):
"""Calculate the product-moment correlation coefficient after Pearson. 1.0 -1.0 0.0 See the documentatio... |
sim, obs = prepare_arrays(sim, obs, node, skip_nan)
return numpy.corrcoef(sim, obs)[0, 1] |
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def hsepd_pdf(sigma1, sigma2, xi, beta, sim=None, obs=None, node=None, skip_nan=False):
"""Calculate the probability densities based on the heteroskedastic skewe... |
sim, obs = prepare_arrays(sim, obs, node, skip_nan)
sigmas = _pars_h(sigma1, sigma2, sim)
mu_xi, sigma_xi, w_beta, c_beta = _pars_sepd(xi, beta)
x, mu = obs, sim
a = (x-mu)/sigmas
a_xi = numpy.empty(a.shape)
idxs = mu_xi+sigma_xi*a < 0.
a_xi[idxs] = numpy.absolute(xi*(mu_xi+sigma_xi*a[i... |
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def calc_mean_time(timepoints, weights):
"""Return the weighted mean of the given timepoints. With equal given weights, the result is simply the mean of the give... |
timepoints = numpy.array(timepoints)
weights = numpy.array(weights)
validtools.test_equal_shape(timepoints=timepoints, weights=weights)
validtools.test_non_negative(weights=weights)
return numpy.dot(timepoints, weights)/numpy.sum(weights) |
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def calc_mean_time_deviation(timepoints, weights, mean_time=None):
"""Return the weighted deviation of the given timepoints from their mean time. With equal give... |
timepoints = numpy.array(timepoints)
weights = numpy.array(weights)
validtools.test_equal_shape(timepoints=timepoints, weights=weights)
validtools.test_non_negative(weights=weights)
if mean_time is None:
mean_time = calc_mean_time(timepoints, weights)
return (numpy.sqrt(numpy.dot(weight... |
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def evaluationtable(nodes, criteria, nodenames=None, critnames=None, skip_nan=False):
"""Return a table containing the results of the given evaluation criteria f... |
if nodenames:
if len(nodes) != len(nodenames):
raise ValueError(
'%d node objects are given which does not match with '
'number of given alternative names beeing %s.'
% (len(nodes), len(nodenames)))
else:
nodenames = [node.name for nod... |
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def set_primary_parameters(self, **kwargs):
"""Set all primary parameters at once.""" |
given = sorted(kwargs.keys())
required = sorted(self._PRIMARY_PARAMETERS)
if given == required:
for (key, value) in kwargs.items():
setattr(self, key, value)
else:
raise ValueError(
'When passing primary parameter values as initial... |
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def update(self):
"""Delete the coefficients of the pure MA model and also all MA and AR coefficients of the ARMA model. Also calculate or delete the values of a... |
del self.ma.coefs
del self.arma.ma_coefs
del self.arma.ar_coefs
if self.primary_parameters_complete:
self.calc_secondary_parameters()
else:
for secpar in self._SECONDARY_PARAMETERS.values():
secpar.__delete__(self) |
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def delay_response_series(self):
"""A tuple of two numpy arrays, which hold the time delays and the associated iuh values respectively.""" |
delays = []
responses = []
sum_responses = 0.
for t in itertools.count(self.dt_response/2., self.dt_response):
delays.append(t)
response = self(t)
responses.append(response)
sum_responses += self.dt_response*response
if (sum_re... |
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def plot(self, threshold=None, **kwargs):
"""Plot the instanteneous unit hydrograph. The optional argument allows for defining a threshold of the cumulative sum ... |
delays, responses = self.delay_response_series
pyplot.plot(delays, responses, **kwargs)
pyplot.xlabel('time')
pyplot.ylabel('response')
if threshold is not None:
threshold = numpy.clip(threshold, 0., 1.)
cumsum = numpy.cumsum(responses)
idx = ... |
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def moment1(self):
"""The first time delay weighted statistical moment of the instantaneous unit hydrograph.""" |
delays, response = self.delay_response_series
return statstools.calc_mean_time(delays, response) |
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def moment2(self):
"""The second time delay weighted statistical momens of the instantaneous unit hydrograph.""" |
moment1 = self.moment1
delays, response = self.delay_response_series
return statstools.calc_mean_time_deviation(
delays, response, moment1) |
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def calc_secondary_parameters(self):
"""Determine the values of the secondary parameters `a` and `b`.""" |
self.a = self.x/(2.*self.d**.5)
self.b = self.u/(2.*self.d**.5) |
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def calc_secondary_parameters(self):
"""Determine the value of the secondary parameter `c`.""" |
self.c = 1./(self.k*special.gamma(self.n)) |
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def post(self, request, pk):
""" Clean the data and save opening hours in the database. Old opening hours are purged before new ones are saved. """ |
location = self.get_object()
# open days, disabled widget data won't make it into request.POST
present_prefixes = [x.split('-')[0] for x in request.POST.keys()]
day_forms = OrderedDict()
for day_no, day_name in WEEKDAYS:
for slot_no in (1, 2):
prefix ... |
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def get(self, request, pk):
""" Initialize the editing form 1. Build opening_hours, a lookup dictionary to populate the form slots: keys are day numbers, values ... |
location = self.get_object()
two_sets = False
closed = None
opening_hours = {}
for o in OpeningHours.objects.filter(company=location):
opening_hours.setdefault(o.weekday, []).append(o)
days = []
for day_no, day_name in WEEKDAYS:
if day_no ... |
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def calc_qjoints_v1(self):
"""Apply the routing equation. Required derived parameters: |NmbSegments| |C1| |C2| |C3| Updated state sequence: |QJoints| Basic equat... |
der = self.parameters.derived.fastaccess
new = self.sequences.states.fastaccess_new
old = self.sequences.states.fastaccess_old
for j in range(der.nmbsegments):
new.qjoints[j+1] = (der.c1*new.qjoints[j] +
der.c2*old.qjoints[j] +
der.c3*old.... |
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def pick_q_v1(self):
"""Assign the actual value of the inlet sequence to the upper joint of the subreach upstream.""" |
inl = self.sequences.inlets.fastaccess
new = self.sequences.states.fastaccess_new
new.qjoints[0] = 0.
for idx in range(inl.len_q):
new.qjoints[0] += inl.q[idx][0] |
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def pass_q_v1(self):
"""Assing the actual value of the lower joint of of the subreach downstream to the outlet sequence.""" |
der = self.parameters.derived.fastaccess
new = self.sequences.states.fastaccess_new
out = self.sequences.outlets.fastaccess
out.q[0] += new.qjoints[der.nmbsegments] |
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def _detect_encoding(data=None):
"""Return the default system encoding. If data is passed, try to decode the data with the default system encoding or from a shor... |
import locale
enc_list = ['utf-8', 'latin-1', 'iso8859-1', 'iso8859-2',
'utf-16', 'cp720']
code = locale.getpreferredencoding(False)
if data is None:
return code
if code.lower() not in enc_list:
enc_list.insert(0, code.lower())
for c in enc_list:
try:
... |
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def parameterstep(timestep=None):
"""Define a parameter time step size within a parameter control file. Argument: * timestep(|Period|):
Time step size. Function... |
if timestep is not None:
parametertools.Parameter.parameterstep(timestep)
namespace = inspect.currentframe().f_back.f_locals
model = namespace.get('model')
if model is None:
model = namespace['Model']()
namespace['model'] = model
if hydpy.pub.options.usecython and 'cytho... |
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def reverse_model_wildcard_import():
"""Clear the local namespace from a model wildcard import. Calling this method should remove the critical imports into the l... |
namespace = inspect.currentframe().f_back.f_locals
model = namespace.get('model')
if model is not None:
for subpars in model.parameters:
for par in subpars:
namespace.pop(par.name, None)
namespace.pop(objecttools.classname(par), None)
namespac... |
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def prepare_model(module: Union[types.ModuleType, str], timestep: PeriodABC.ConstrArg = None):
"""Prepare and return the model of the given module. In usual HydP... |
if timestep is not None:
parametertools.Parameter.parameterstep(timetools.Period(timestep))
try:
model = module.Model()
except AttributeError:
module = importlib.import_module(f'hydpy.models.{module}')
model = module.Model()
if hydpy.pub.options.usecython and hasattr(mod... |
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def simulationstep(timestep):
""" Define a simulation time step size for testing purposes within a parameter control file. Using |simulationstep| only affects th... |
if hydpy.pub.options.warnsimulationstep:
warnings.warn(
'Note that the applied function `simulationstep` is intended for '
'testing purposes only. When doing a HydPy simulation, parameter '
'values are initialised based on the actual simulation time step '
'... |
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def controlcheck(controldir='default', projectdir=None, controlfile=None):
"""Define the corresponding control file within a condition file. Function |controlche... |
namespace = inspect.currentframe().f_back.f_locals
model = namespace.get('model')
if model is None:
if not controlfile:
controlfile = os.path.split(namespace['__file__'])[-1]
if projectdir is None:
projectdir = (
os.path.split(
os.... |
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def update(self):
"""Update |RelSoilArea| based on |Area|, |ZoneArea|, and |ZoneType|. relsoilarea(0.3) """ |
con = self.subpars.pars.control
temp = con.zonearea.values.copy()
temp[con.zonetype.values == GLACIER] = 0.
temp[con.zonetype.values == ILAKE] = 0.
self(numpy.sum(temp)/con.area) |
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def update(self):
"""Update |UH| based on |MaxBaz|. .. note:: This method also updates the shape of log sequence |QUH|. |MaxBaz| determines the end point of the ... |
maxbaz = self.subpars.pars.control.maxbaz.value
quh = self.subpars.pars.model.sequences.logs.quh
# Determine UH parameters...
if maxbaz <= 1.:
# ...when MaxBaz smaller than or equal to the simulation time step.
self.shape = 1
self(1.)
quh.... |
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def update(self):
"""Update |QFactor| based on |Area| and the current simulation step size. qfactor(1.157407) """ |
self(self.subpars.pars.control.area*1000. /
self.subpars.qfactor.simulationstep.seconds) |
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"""Number of neurons of the hidden layers. (2, 1) (3,) Traceback (most recent call last):
hydpy.core.exceptiontools.AttributeNotReady: Attribute `nmb_neurons` \ ... |
return tuple(numpy.asarray(self._cann.nmb_neurons)) |
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def shape_weights_hidden(self) -> Tuple[int, int, int]: """Shape of the array containing the activation of the hidden neurons. The first integer value is the numb... |
if self.nmb_layers > 1:
nmb_neurons = self.nmb_neurons
return (self.nmb_layers-1,
max(nmb_neurons[:-1]),
max(nmb_neurons[1:]))
return 0, 0, 0 |
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def nmb_weights_hidden(self) -> int: """Number of hidden weights. 18 """ |
nmb = 0
for idx_layer in range(self.nmb_layers-1):
nmb += self.nmb_neurons[idx_layer] * self.nmb_neurons[idx_layer+1]
return nmb |
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def verify(self) -> None: """Raise a |RuntimeError| if the network's shape is not defined completely. Traceback (most recent call last):
RuntimeError: The shape ... |
if not self.__protectedproperties.allready(self):
raise RuntimeError(
'The shape of the the artificial neural network '
'parameter %s has not been defined so far.'
% objecttools.elementphrase(self)) |
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def assignrepr(self, prefix) -> str: """Return a string representation of the actual |anntools.ANN| object that is prefixed with the given string.""" |
prefix = '%s%s(' % (prefix, self.name)
blanks = len(prefix)*' '
lines = [
objecttools.assignrepr_value(
self.nmb_inputs, '%snmb_inputs=' % prefix)+',',
objecttools.assignrepr_tuple(
self.nmb_neurons, '%snmb_neurons=' % blanks)+',',
... |
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def refresh(self) -> None: """Prepare the actual |anntools.SeasonalANN| object for calculations. Dispite all automated refreshings explained in the general docume... |
# pylint: disable=unsupported-assignment-operation
if self._do_refresh:
if self.anns:
self.__sann = annutils.SeasonalANN(self.anns)
setattr(self.fastaccess, self.name, self._sann)
self._set_shape((None, self._sann.nmb_anns))
if... |
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def verify(self) -> None: """Raise a |RuntimeError| and removes all handled neural networks, if the they are defined inconsistently. Dispite all automated safety ... |
if not self.anns:
self._toy2ann.clear()
raise RuntimeError(
'Seasonal artificial neural network collections need '
'to handle at least one "normal" single neural network, '
'but for the seasonal neural network `%s` of element '
... |
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"""The shape of array |anntools.SeasonalANN.ratios|.""" |
return tuple(int(sub) for sub in self.ratios.shape) |
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def _set_shape(self, shape):
"""Private on purpose.""" |
try:
shape = (int(shape),)
except TypeError:
pass
shp = list(shape)
shp[0] = timetools.Period('366d')/self.simulationstep
shp[0] = int(numpy.ceil(round(shp[0], 10)))
getattr(self.fastaccess, self.name).ratios = numpy.zeros(
shp, dtype=... |
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Description:
"""A sorted |tuple| of all contained |TOY| objects.""" |
return tuple(toy for (toy, _) in self) |
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def plot(self, xmin, xmax, idx_input=0, idx_output=0, points=100, **kwargs) -> None: """Call method |anntools.ANN.plot| of all |anntools.ANN| objects handled by t... |
for toy, ann_ in self:
ann_.plot(xmin, xmax,
idx_input=idx_input, idx_output=idx_output,
points=points,
label=str(toy),
**kwargs)
pyplot.legend() |
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def specstring(self):
"""The string corresponding to the current values of `subgroup`, `state`, and `variable`. 'fluxes.qt' 'fluxes.qt.series' 'qt.series' """ |
if self.subgroup is None:
variable = self.variable
else:
variable = f'{self.subgroup}.{self.variable}'
if self.series:
variable = f'{variable}.series'
return variable |
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def collect_variables(self, selections) -> None: """Apply method |ExchangeItem.insert_variables| to collect the relevant target variables handled by the devices o... |
self.insert_variables(self.device2target, self.targetspecs, selections) |
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def update_variables(self) -> None: """Assign the current objects |ChangeItem.value| to the values of the target variables. We use the `LahnH` project in the foll... |
value = self.value
for variable in self.device2target.values():
self.update_variable(variable, value) |
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def collect_variables(self, selections) -> None: """Apply method |ChangeItem.collect_variables| of the base class |ChangeItem| and also apply method |ExchangeItem... |
super().collect_variables(selections)
self.insert_variables(self.device2base, self.basespecs, selections) |
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def update_variables(self) -> None: """Add the general |ChangeItem.value| with the |Device| specific base variable and assign the result to the respective target ... |
value = self.value
for device, target in self.device2target.items():
base = self.device2base[device]
try:
result = base.value + value
except BaseException:
raise objecttools.augment_excmessage(
f'When trying to add ... |
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def collect_variables(self, selections) -> None: """Apply method |ExchangeItem.collect_variables| of the base class |ExchangeItem| and determine the `ndim` attrib... |
super().collect_variables(selections)
for device in sorted(self.device2target.keys(), key=lambda x: x.name):
self._device2name[device] = f'{device.name}_{self.target}'
for target in self.device2target.values():
self.ndim = target.NDIM
if self.targetspecs.seri... |
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def yield_name2value(self, idx1=None, idx2=None) \ -> Iterator[Tuple[str, str]]: """Sequentially return name-value-pairs describing the current state of the targe... |
for device, name in self._device2name.items():
target = self.device2target[device]
if self.targetspecs.series:
values = target.series[idx1:idx2]
else:
values = target.values
if self.ndim == 0:
values = objecttools.r... |
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def iso_day_to_weekday(d):
""" Returns the weekday's name given a ISO weekday number; "today" if today is the same weekday. """ |
if int(d) == utils.get_now().isoweekday():
return _("today")
for w in WEEKDAYS:
if w[0] == int(d):
return w[1] |
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def is_open(location=None, attr=None):
""" Returns False if the location is closed, or the OpeningHours object to show the location is currently open. """ |
obj = utils.is_open(location)
if obj is False:
return False
if attr is not None:
return getattr(obj, attr)
return obj |
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def opening_hours(location=None, concise=False):
""" Creates a rendered listing of hours. """ |
template_name = 'openinghours/opening_hours_list.html'
days = [] # [{'hours': '9:00am to 5:00pm', 'name': u'Monday'}, {'hours...
# Without `location`, choose the first company.
if location:
ohrs = OpeningHours.objects.filter(company=location)
else:
try:
Location = util... |
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def prepare_everything(self):
"""Convenience method to make the actual |HydPy| instance runable.""" |
self.prepare_network()
self.init_models()
self.load_conditions()
with hydpy.pub.options.warnmissingobsfile(False):
self.prepare_nodeseries()
self.prepare_modelseries()
self.load_inputseries() |
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Description:
def save_controls(self, parameterstep=None, simulationstep=None, auxfiler=None):
"""Call method |Elements.save_controls| of the |Elements| object currently handl... |
self.elements.save_controls(parameterstep=parameterstep,
simulationstep=simulationstep,
auxfiler=auxfiler) |
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def networkproperties(self):
"""Print out some properties of the network defined by the |Node| and |Element| objects currently handled by the |HydPy| object.""" |
print('Number of nodes: %d' % len(self.nodes))
print('Number of elements: %d' % len(self.elements))
print('Number of end nodes: %d' % len(self.endnodes))
print('Number of distinct networks: %d' % len(self.numberofnetworks))
print('Applied node variables: %s' % ', '.join(self.var... |
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def numberofnetworks(self):
"""The number of distinct networks defined by the|Node| and |Element| objects currently handled by the |HydPy| object.""" |
sels1 = selectiontools.Selections()
sels2 = selectiontools.Selections()
complete = selectiontools.Selection('complete',
self.nodes, self.elements)
for node in self.endnodes:
sel = complete.copy(node.name).select_upstream(node)
... |
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def endnodes(self):
"""|Nodes| object containing all |Node| objects currently handled by the |HydPy| object which define a downstream end point of a network.""" |
endnodes = devicetools.Nodes()
for node in self.nodes:
for element in node.exits:
if ((element in self.elements) and
(node not in element.receivers)):
break
else:
endnodes += node
return endnodes |
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def variables(self):
"""Sorted list of strings summarizing all variables handled by the |Node| objects""" |
variables = set([])
for node in self.nodes:
variables.add(node.variable)
return sorted(variables) |
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def simindices(self):
"""Tuple containing the start and end index of the simulation period regarding the initialization period defined by the |Timegrids| object ... |
return (hydpy.pub.timegrids.init[hydpy.pub.timegrids.sim.firstdate],
hydpy.pub.timegrids.init[hydpy.pub.timegrids.sim.lastdate]) |
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def open_files(self, idx=0):
"""Call method |Devices.open_files| of the |Nodes| and |Elements| objects currently handled by the |HydPy| object.""" |
self.elements.open_files(idx=idx)
self.nodes.open_files(idx=idx) |
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def update_devices(self, selection=None):
"""Determines the order, in which the |Node| and |Element| objects currently handled by the |HydPy| objects need to be ... |
if selection is not None:
self.nodes = selection.nodes
self.elements = selection.elements
self._update_deviceorder() |
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def methodorder(self):
"""A list containing all methods of all |Node| and |Element| objects that need to be processed during a simulation time step in the order ... |
funcs = []
for node in self.nodes:
if node.deploymode == 'oldsim':
funcs.append(node.sequences.fastaccess.load_simdata)
elif node.deploymode == 'obs':
funcs.append(node.sequences.fastaccess.load_obsdata)
for node in self.nodes:
... |
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def doit(self):
"""Perform a simulation run over the actual simulation time period defined by the |Timegrids| object stored in module |pub|.""" |
idx_start, idx_end = self.simindices
self.open_files(idx_start)
methodorder = self.methodorder
for idx in printtools.progressbar(range(idx_start, idx_end)):
for func in methodorder:
func(idx)
self.close_files() |
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def pic_inflow_v1(self):
"""Update the inlet link sequence. Required inlet sequence: |dam_inlets.Q| Calculated flux sequence: |Inflow| Basic equation: :math:`Inf... |
flu = self.sequences.fluxes.fastaccess
inl = self.sequences.inlets.fastaccess
flu.inflow = inl.q[0] |
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def pic_inflow_v2(self):
"""Update the inlet link sequences. Required inlet sequences: |dam_inlets.Q| |dam_inlets.S| |dam_inlets.R| Calculated flux sequence: |In... |
flu = self.sequences.fluxes.fastaccess
inl = self.sequences.inlets.fastaccess
flu.inflow = inl.q[0]+inl.s[0]+inl.r[0] |
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def calc_waterlevel_v1(self):
"""Determine the water level based on an artificial neural network describing the relationship between water level and water stage.... |
con = self.parameters.control.fastaccess
new = self.sequences.states.fastaccess_new
aid = self.sequences.aides.fastaccess
con.watervolume2waterlevel.inputs[0] = new.watervolume
con.watervolume2waterlevel.process_actual_input()
aid.waterlevel = con.watervolume2waterlevel.outputs[0] |
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def calc_allowedremoterelieve_v2(self):
"""Calculate the allowed maximum relieve another location is allowed to discharge into the dam. Required control paramete... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
aid = self.sequences.aides.fastaccess
toy = der.toy[self.idx_sim]
flu.allowedremoterelieve = (
con.highestremoterelieve[toy] *
smoothutils.smooth_logistic1(
... |
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def calc_requiredremotesupply_v1(self):
"""Calculate the required maximum supply from another location that can be discharged into the dam. Required control para... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
aid = self.sequences.aides.fastaccess
toy = der.toy[self.idx_sim]
flu.requiredremotesupply = (
con.highestremotesupply[toy] *
smoothutils.smooth_logistic1(
... |
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def calc_naturalremotedischarge_v1(self):
"""Try to estimate the natural discharge of a cross section far downstream based on the last few simulation steps. Requ... |
con = self.parameters.control.fastaccess
flu = self.sequences.fluxes.fastaccess
log = self.sequences.logs.fastaccess
flu.naturalremotedischarge = 0.
for idx in range(con.nmblogentries):
flu.naturalremotedischarge += (
log.loggedtotalremotedischarge[idx] - log.loggedoutflow[idx])... |
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def calc_remotedemand_v1(self):
"""Estimate the discharge demand of a cross section far downstream. Required control parameter: |RemoteDischargeMinimum| Required... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
flu.remotedemand = max(con.remotedischargeminimum[der.toy[self.idx_sim]] -
flu.naturalremotedischarge, 0.) |
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def calc_remotefailure_v1(self):
"""Estimate the shortfall of actual discharge under the required discharge of a cross section far downstream. Required control p... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
log = self.sequences.logs.fastaccess
flu.remotefailure = 0
for idx in range(con.nmblogentries):
flu.remotefailure -= log.loggedtotalremotedischarge[idx]
flu.remot... |
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def calc_requiredremoterelease_v1(self):
"""Guess the required release necessary to not fall below the threshold value at a cross section far downstream with a c... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
flu.requiredremoterelease = (
flu.remotedemand+con.remotedischargesafety[der.toy[self.idx_sim]] *
smoothutils.smooth_logistic1(
flu.remotefailure,
... |
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def calc_requiredremoterelease_v2(self):
"""Get the required remote release of the last simulation step. Required log sequence: |LoggedRequiredRemoteRelease| Cal... |
flu = self.sequences.fluxes.fastaccess
log = self.sequences.logs.fastaccess
flu.requiredremoterelease = log.loggedrequiredremoterelease[0] |
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def calc_allowedremoterelieve_v1(self):
"""Get the allowed remote relieve of the last simulation step. Required log sequence: |LoggedAllowedRemoteRelieve| Calcul... |
flu = self.sequences.fluxes.fastaccess
log = self.sequences.logs.fastaccess
flu.allowedremoterelieve = log.loggedallowedremoterelieve[0] |
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def calc_possibleremoterelieve_v1(self):
"""Calculate the highest possible water release that can be routed to a remote location based on an artificial neural ne... |
con = self.parameters.control.fastaccess
flu = self.sequences.fluxes.fastaccess
aid = self.sequences.aides.fastaccess
con.waterlevel2possibleremoterelieve.inputs[0] = aid.waterlevel
con.waterlevel2possibleremoterelieve.process_actual_input()
flu.possibleremoterelieve = con.waterlevel2possiblere... |
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def calc_actualremoterelieve_v1(self):
"""Calculate the actual amount of water released to a remote location to relieve the dam during high flow conditions. Requ... |
con = self.parameters.control.fastaccess
flu = self.sequences.fluxes.fastaccess
d_smoothpar = con.remoterelievetolerance*flu.allowedremoterelieve
flu.actualremoterelieve = smoothutils.smooth_min1(
flu.possibleremoterelieve, flu.allowedremoterelieve, d_smoothpar)
for dummy in range(5):
... |
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def calc_targetedrelease_v1(self):
"""Calculate the targeted water release for reducing drought events, taking into account both the required water release and t... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
if con.restricttargetedrelease:
flu.targetedrelease = smoothutils.smooth_logistic1(
flu.inflow-con.neardischargeminimumthreshold[
der.toy[self.idx... |
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def calc_actualrelease_v1(self):
"""Calculate the actual water release that can be supplied by the dam considering the targeted release and the given water level... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
aid = self.sequences.aides.fastaccess
flu.actualrelease = (flu.targetedrelease *
smoothutils.smooth_logistic1(
aid.waterleve... |
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def calc_missingremoterelease_v1(self):
"""Calculate the portion of the required remote demand that could not be met by the actual discharge release. Required fl... |
flu = self.sequences.fluxes.fastaccess
flu.missingremoterelease = max(
flu.requiredremoterelease-flu.actualrelease, 0.) |
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def calc_actualremoterelease_v1(self):
"""Calculate the actual remote water release that can be supplied by the dam considering the required remote release and t... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
aid = self.sequences.aides.fastaccess
flu.actualremoterelease = (
flu.requiredremoterelease *
smoothutils.smooth_logistic1(
aid.waterlevel-con.waterle... |
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def update_actualremoterelieve_v1(self):
"""Constrain the actual relieve discharge to a remote location. Required control parameter: |HighestRemoteDischarge| Req... |
con = self.parameters.control.fastaccess
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
d_smooth = der.highestremotesmoothpar
d_highest = con.highestremotedischarge
d_value = smoothutils.smooth_min1(
flu.actualremoterelieve, d_highest, d_smooth)
for ... |
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def calc_outflow_v1(self):
"""Calculate the total outflow of the dam. Note that the maximum function is used to prevent from negative outflow values, which could... |
flu = self.sequences.fluxes.fastaccess
flu.outflow = max(flu.actualrelease + flu.flooddischarge, 0.) |
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def update_watervolume_v1(self):
"""Update the actual water volume. Required derived parameter: |Seconds| Required flux sequences: |Inflow| |Outflow| Updated sta... |
der = self.parameters.derived.fastaccess
flu = self.sequences.fluxes.fastaccess
old = self.sequences.states.fastaccess_old
new = self.sequences.states.fastaccess_new
new.watervolume = (old.watervolume +
der.seconds*(flu.inflow-flu.outflow)/1e6) |
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def pass_outflow_v1(self):
"""Update the outlet link sequence |dam_outlets.Q|.""" |
flu = self.sequences.fluxes.fastaccess
out = self.sequences.outlets.fastaccess
out.q[0] += flu.outflow |
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def pass_missingremoterelease_v1(self):
"""Update the outlet link sequence |dam_senders.D|.""" |
flu = self.sequences.fluxes.fastaccess
sen = self.sequences.senders.fastaccess
sen.d[0] += flu.missingremoterelease |
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def moments(self):
"""The first two time delay weighted statistical moments of the MA coefficients.""" |
moment1 = statstools.calc_mean_time(self.delays, self.coefs)
moment2 = statstools.calc_mean_time_deviation(
self.delays, self.coefs, moment1)
return numpy.array([moment1, moment2]) |
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def effective_max_ar_order(self):
"""The maximum number of AR coefficients that shall or can be determined. It is the minimum of |ARMA.max_ar_order| and the numb... |
return min(self.max_ar_order, self.ma.order-self.ma.turningpoint[0]-1) |
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def update_ar_coefs(self):
"""Determine the AR coefficients. The number of AR coefficients is subsequently increased until the required precision |ARMA.max_rel_r... |
del self.ar_coefs
for ar_order in range(1, self.effective_max_ar_order+1):
self.calc_all_ar_coefs(ar_order, self.ma)
if self._rel_rmse < self.max_rel_rmse:
break
else:
with hydpy.pub.options.reprdigits(12):
raise RuntimeError(
... |
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def dev_moments(self):
"""Sum of the absolute deviations between the central moments of the instantaneous unit hydrograph and the ARMA approximation.""" |
return numpy.sum(numpy.abs(self.moments-self.ma.moments)) |
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def norm_coefs(self):
"""Multiply all coefficients by the same factor, so that their sum becomes one.""" |
sum_coefs = self.sum_coefs
self.ar_coefs /= sum_coefs
self.ma_coefs /= sum_coefs |
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def sum_coefs(self):
"""The sum of all AR and MA coefficients""" |
return numpy.sum(self.ar_coefs) + numpy.sum(self.ma_coefs) |
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def calc_all_ar_coefs(self, ar_order, ma_model):
"""Determine the AR coeffcients based on a least squares approach. The argument `ar_order` defines the number of... |
turning_idx, _ = ma_model.turningpoint
values = ma_model.coefs[turning_idx:]
self.ar_coefs, residuals = numpy.linalg.lstsq(
self.get_a(values, ar_order),
self.get_b(values, ar_order),
rcond=-1)[:2]
if len(residuals) == 1:
self._rel_rmse = ... |
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