Instruction:
Given the following Python 3 function, write the documentation
def update(self):
        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)

Response: Update |RelSoilArea| based on |Area Area Zone Area and ZoneType|.


Instruction:
Make a summary of the following Python 3 code
def update(self):
        con = self.subpars.pars.control
        self(con.tt+con.dttm)

Response: Update |TTM| based on : math : TT + DTTM.


Instruction:
Create a Python 3 function for
updating |UH| based on |MaxBaz| and |QuH|.

Response:
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 triangle.  A value of
        |MaxBaz| being not larger than the simulation step size is
        identical with applying no unit hydrograph at all:

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> simulationstep('12h')
        >>> maxbaz(0.0)
        >>> derived.uh.update()
        >>> logs.quh.shape
        (1,)
        >>> derived.uh
        uh(1.0)

        Note that, due to difference of the parameter and the simulation
        step size in the given example, the largest assignment resulting
        in a `inactive` unit hydrograph is 1/2:

        >>> maxbaz(0.5)
        >>> derived.uh.update()
        >>> logs.quh.shape
        (1,)
        >>> derived.uh
        uh(1.0)

        When |MaxBaz| is in accordance with two simulation steps, both
        unit hydrograph ordinats must be 1/2 due to symmetry of the
        triangle:

        >>> maxbaz(1.0)
        >>> derived.uh.update()
        >>> logs.quh.shape
        (2,)
        >>> derived.uh
        uh(0.5)
        >>> derived.uh.values
        array([ 0.5,  0.5])

        A |MaxBaz| value in accordance with three simulation steps results
        in the ordinate values 2/9, 5/9, and 2/9:

        >>> maxbaz(1.5)
        >>> derived.uh.update()
        >>> logs.quh.shape
        (3,)
        >>> derived.uh
        uh(0.222222, 0.555556, 0.222222)

        And a final example, where the end of the triangle lies within
        a simulation step, resulting in the fractions 8/49, 23/49, 16/49,
        and 2/49:

        >>> maxbaz(1.75)
        >>> derived.uh.update()
        >>> logs.quh.shape
        (4,)
        >>> derived.uh
        uh(0.163265, 0.469388, 0.326531, 0.040816)
        """
        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.shape = 1
        else:
            # ...when MaxBaz is greater than the simulation time step.
            # Define some shortcuts for the following calculations.
            full = maxbaz
            # Now comes a terrible trick due to rounding problems coming from
            # the conversation of the SMHI parameter set to the HydPy
            # parameter set.  Time to get rid of it...
            if (full % 1.) < 1e-4:
                full //= 1.
            full_f = int(numpy.floor(full))
            full_c = int(numpy.ceil(full))
            half = full/2.
            half_f = int(numpy.floor(half))
            half_c = int(numpy.ceil(half))
            full_2 = full**2.
            # Calculate the triangle ordinate(s)...
            self.shape = full_c
            uh = self.values
            quh.shape = full_c
            # ...of the rising limb.
            points = numpy.arange(1, half_f+1)
            uh[:half_f] = (2.*points-1.)/(2.*full_2)
            # ...around the peak (if it exists).
            if numpy.mod(half, 1.) != 0.:
                uh[half_f] = (
                    (half_c-half)/full +
                    (2*half**2.-half_f**2.-half_c**2.)/(2.*full_2))
            # ...of the falling limb (eventually except the last one).
            points = numpy.arange(half_c+1., full_f+1.)
            uh[half_c:full_f] = 1./full-(2.*points-1.)/(2.*full_2)
            # ...at the end (if not already done).
            if numpy.mod(full, 1.) != 0.:
                uh[full_f] = (
                    (full-full_f)/full-(full_2-full_f**2.)/(2.*full_2))
            # Normalize the ordinates.
            self(uh/numpy.sum(uh))


Instruction:
Write a Python 3 script for
updating |QFactor| based on |Area| and the current simulation step size.

Response:
def update(self):
        """Update |QFactor| based on |Area| and the current simulation
        step size.

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> simulationstep('12h')
        >>> area(50.0)
        >>> derived.qfactor.update()
        >>> derived.qfactor
        qfactor(1.157407)
        """
        self(self.subpars.pars.control.area*1000. /
             self.subpars.qfactor.simulationstep.seconds)


Instruction:
Can you tell what is the following Python 3 function doing
def nmb_neurons(self) -> Tuple[int, ...]:
        return tuple(numpy.asarray(self._cann.nmb_neurons))

Response: Number of neurons of the hidden layers.


Instruction:
Write a Python 3 script to
shape of the array containing the activation of the hidden neurons.

Response:
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 number of connection between the
        hidden layers, the second integer value is maximum number of
        neurons of all hidden layers feeding information into another
        hidden layer (all except the last one), and the third integer
        value is the maximum number of the neurons of all hidden layers
        receiving information from another hidden layer (all except the
        first one):

        >>> from hydpy import ANN
        >>> ann = ANN(None)
        >>> ann(nmb_inputs=6, nmb_neurons=(4, 3, 2), nmb_outputs=6)
        >>> ann.shape_weights_hidden
        (2, 4, 3)
        >>> ann(nmb_inputs=6, nmb_neurons=(4,), nmb_outputs=6)
        >>> ann.shape_weights_hidden
        (0, 0, 0)
        """
        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


Instruction:
Can you tell what is the following Python 3 function doing
def nmb_weights_hidden(self) -> int:
        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

Response: Number of hidden weights.


Instruction:
Create a Python 3 function to
raise a |RuntimeError| if the network s shape is not defined completely.

Response:
def verify(self) -> None:
        """Raise a |RuntimeError| if the network's shape is not defined
        completely.

        >>> from hydpy import ANN
        >>> ANN(None).verify()
        Traceback (most recent call last):
        ...
        RuntimeError: The shape of the the artificial neural network \
parameter `ann` of element `?` has not been defined so far.
        """
        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))


Instruction:
How would you explain what the following Python 3 function does
def assignrepr(self, prefix) -> str:
        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)+',',
            objecttools.assignrepr_value(
                self.nmb_outputs, '%snmb_outputs=' % blanks)+',',
            objecttools.assignrepr_list2(
                self.weights_input, '%sweights_input=' % blanks)+',']
        if self.nmb_layers > 1:
            lines.append(objecttools.assignrepr_list3(
                self.weights_hidden, '%sweights_hidden=' % blanks)+',')
        lines.append(objecttools.assignrepr_list2(
            self.weights_output, '%sweights_output=' % blanks)+',')
        lines.append(objecttools.assignrepr_list2(
            self.intercepts_hidden, '%sintercepts_hidden=' % blanks)+',')
        lines.append(objecttools.assignrepr_list(
            self.intercepts_output, '%sintercepts_output=' % blanks)+')')
        return '\n'.join(lines)

Response: Return a string representation of the actual |anntools. ANN| object containing the given string.


Instruction:
Here you have a function in Python 3, explain what it does
def plot(self, xmin, xmax, idx_input=0, idx_output=0, points=100,
             **kwargs) -> None:
        """Plot the relationship between a certain input (`idx_input`) and a
        certain output (`idx_output`) variable described by the actual
        |anntools.ANN| object.

        Define the lower and the upper bound of the x axis via arguments
        `xmin` and `xmax`.  The number of plotting points can be modified
        by argument `points`.  Additional `matplotlib` plotting arguments
        can be passed as keyword arguments.
        """
        xs_ = numpy.linspace(xmin, xmax, points)
        ys_ = numpy.zeros(xs_.shape)
        for idx, x__ in enumerate(xs_):
            self.inputs[idx_input] = x__
            self.process_actual_input()
            ys_[idx] = self.outputs[idx_output]
        pyplot.plot(xs_, ys_, **kwargs)

Response: Plots the relationship between a certain input and a certain output.


Instruction:
Here you have a function in Python 3, explain what it does
def refresh(self) -> None:
        # 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 self._sann.nmb_anns > 1:
                    self._interp()
                else:
                    self._sann.ratios[:, 0] = 1.
                self.verify()
            else:
                self.__sann = None

Response: Refreshes the internal state of the SeasonalANN object for calculations.


Instruction:
Can you write a function in Python 3 where it
raises a |RuntimeError| and removes all handled neural networks and SeasonalANN objects from the internal cache.

Response:
def verify(self) -> None:
        """Raise a |RuntimeError| and removes all handled neural networks,
        if the they are defined inconsistently.

        Dispite all automated safety checks explained in the general
        documentation on class |anntools.SeasonalANN|, it is still possible
        to destroy the inner consistency of a |anntools.SeasonalANN| instance,
        as it stores its |anntools.ANN| objects by reference.  This is shown
        by the following example:

        >>> from hydpy import SeasonalANN, ann
        >>> seasonalann = SeasonalANN(None)
        >>> seasonalann.simulationstep = '1d'
        >>> jan = ann(nmb_inputs=1, nmb_neurons=(1,), nmb_outputs=1,
        ...           weights_input=0.0, weights_output=0.0,
        ...           intercepts_hidden=0.0, intercepts_output=1.0)
        >>> seasonalann(_1_1_12=jan)
        >>> jan.nmb_inputs, jan.nmb_outputs = 2, 3
        >>> jan.nmb_inputs, jan.nmb_outputs
        (2, 3)
        >>> seasonalann.nmb_inputs, seasonalann.nmb_outputs
        (1, 1)

        Due to the C level implementation of the mathematical core of both
        |anntools.ANN| and |anntools.SeasonalANN| in module |annutils|,
        such an inconsistency might result in a program crash without any
        informative error message.  Whenever you are afraid some
        inconsistency might have crept in, and you want to find out if this
        is actually the case, call method |anntools.SeasonalANN.verify|
        explicitly:

        >>> seasonalann.verify()
        Traceback (most recent call last):
        ...
        RuntimeError: The number of input and output values of all neural \
networks contained by a seasonal neural network collection must be \
identical and be known by the containing object.  But the seasonal \
neural network collection `seasonalann` of element `?` assumes `1` input \
and `1` output values, while the network corresponding to the time of \
year `toy_1_1_12_0_0` requires `2` input and `3` output values.

        >>> seasonalann
        seasonalann()

        >>> seasonalann.verify()
        Traceback (most recent call last):
        ...
        RuntimeError: Seasonal artificial neural network collections need \
to handle at least one "normal" single neural network, but for the seasonal \
neural network `seasonalann` of element `?` none has been defined so far.
        """
        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 '
                '`%s` none has been defined so far.'
                % (self.name, objecttools.devicename(self)))
        for toy, ann_ in self:
            ann_.verify()
            if ((self.nmb_inputs != ann_.nmb_inputs) or
                    (self.nmb_outputs != ann_.nmb_outputs)):
                self._toy2ann.clear()
                raise RuntimeError(
                    'The number of input and output values of all neural '
                    'networks contained by a seasonal neural network '
                    'collection must be identical and be known by the '
                    'containing object.  But the seasonal neural '
                    'network collection `%s` of element `%s` assumes '
                    '`%d` input and `%d` output values, while the network '
                    'corresponding to the time of year `%s` requires '
                    '`%d` input and `%d` output values.'
                    % (self.name, objecttools.devicename(self),
                       self.nmb_inputs, self.nmb_outputs,
                       toy,
                       ann_.nmb_inputs, ann_.nmb_outputs))


Instruction:
Given the following Python 3 function, write the documentation
def shape(self) -> Tuple[int, ...]:
        return tuple(int(sub) for sub in self.ratios.shape)

Response: The shape of the array |anntools. SeasonalANN. ratios|.


Instruction:
Can you tell what is the following Python 3 function doing
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 the actual |anntools.SeasonalANN| object.
        """
        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()

Response: Plot the |ANN| object with the current |LEA| object.


Instruction:
Here you have a function in Python 3, explain what it does
def collect_variables(self, selections) -> None:
        self.insert_variables(self.device2target, self.targetspecs, selections)

Response: This method is used to collect the relevant target variables handled by the given |Selections| object.


Instruction:
Create a Python 3 function for
inserting the relevant target or base variables into the given device2variable dictionary.

Response:
def insert_variables(
            self, device2variable, exchangespec, selections) -> None:
        """Determine the relevant target or base variables (as defined by
        the given |ExchangeSpecification| object ) handled by the given
        |Selections| object and insert them into the given `device2variable`
        dictionary."""
        if self.targetspecs.master in ('node', 'nodes'):
            for node in selections.nodes:
                variable = self._query_nodevariable(node, exchangespec)
                device2variable[node] = variable
        else:
            for element in self._iter_relevantelements(selections):
                variable = self._query_elementvariable(element, exchangespec)
                device2variable[element] = variable


Instruction:
How would you implement a function in Python 3 that
assigns the given value to the given target or base variable.

Response:
def update_variable(self, variable, value) -> None:
        """Assign the given value(s) to the given target or base variable.

        If the assignment fails, |ChangeItem.update_variable| raises an
        error like the following:

        >>> from hydpy.core.examples import prepare_full_example_2
        >>> hp, pub, TestIO = prepare_full_example_2()
        >>> item = SetItem('alpha', 'hland_v1', 'control.alpha', 0)
        >>> item.collect_variables(pub.selections)
        >>> item.update_variables()    # doctest: +ELLIPSIS
        Traceback (most recent call last):
        ...
        TypeError: When trying to update a target variable of SetItem `alpha` \
with the value(s) `None`, the following error occurred: While trying to set \
the value(s) of variable `alpha` of element `...`, the following error \
occurred: The given value `None` cannot be converted to type `float`.
        """
        try:
            variable(value)
        except BaseException:
            objecttools.augment_excmessage(
                f'When trying to update a target variable of '
                f'{objecttools.classname(self)} `{self.name}` '
                f'with the value(s) `{value}`')


Instruction:
Here you have a function in Python 3, explain what it does
def update_variables(self) -> None:
        value = self.value
        for variable in self.device2target.values():
            self.update_variable(variable, value)

Response:
Assign the current values of the target variables to the values
        of the target variables.


Instruction:
Given the following Python 3 function, write the documentation
def collect_variables(self, selections) -> None:
        super().collect_variables(selections)
        self.insert_variables(self.device2base, self.basespecs, selections)

Response: Collect the base variables of the given |Selections| object and insert them into the base class.


Instruction:
Can you implement a function in Python 3 that
adds the general base base

Response:
def update_variables(self) -> None:
        """Add the general |ChangeItem.value| with the |Device| specific base
        variable and assign the result to the respective target variable.

        >>> from hydpy.core.examples import prepare_full_example_2
        >>> hp, pub, TestIO = prepare_full_example_2()
        >>> from hydpy.models.hland_v1 import FIELD
        >>> for element in hp.elements.catchment:
        ...     control = element.model.parameters.control
        ...     control.nmbzones(3)
        ...     control.zonetype(FIELD)
        ...     control.rfcf(1.1)
        >>> from hydpy.core.itemtools import AddItem
        >>> item = AddItem(
        ...     'sfcf', 'hland_v1', 'control.sfcf', 'control.rfcf', 1)
        >>> item.collect_variables(pub.selections)
        >>> land_dill = hp.elements.land_dill
        >>> land_dill.model.parameters.control.sfcf
        sfcf(?)
        >>> item.value = -0.1, 0.0, 0.1
        >>> item.update_variables()
        >>> land_dill.model.parameters.control.sfcf
        sfcf(1.0, 1.1, 1.2)

        >>> land_dill.model.parameters.control.rfcf.shape = 2
        >>> land_dill.model.parameters.control.rfcf = 1.1
        >>> item.update_variables()    # doctest: +ELLIPSIS
        Traceback (most recent call last):
        ...
        ValueError: When trying to add the value(s) `[-0.1  0.   0.1]` of \
AddItem `sfcf` and the value(s) `[ 1.1  1.1]` of variable `rfcf` of element \
`land_dill`, the following error occurred: operands could not be broadcast \
together with shapes (2,) (3,)...
        """
        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 the value(s) `{value}` of '
                    f'AddItem `{self.name}` and the value(s) `{base.value}` '
                    f'of variable {objecttools.devicephrase(base)}')
            self.update_variable(target, result)


Instruction:
Write a Python 3 function for
applying method |ExchangeItem. collect_variables| of the base class and determine the number of variables that are used in the current object.

Response:
def collect_variables(self, selections) -> None:
        """Apply method |ExchangeItem.collect_variables| of the base class
        |ExchangeItem| and determine the `ndim` attribute of the current
        |ChangeItem| object afterwards.

        The value of `ndim` depends on whether the values of the target
        variable or its time series is of interest:

        >>> from hydpy.core.examples import prepare_full_example_2
        >>> hp, pub, TestIO = prepare_full_example_2()
        >>> from hydpy.core.itemtools import SetItem
        >>> for target in ('states.lz', 'states.lz.series',
        ...                'states.sm', 'states.sm.series'):
        ...     item = GetItem('hland_v1', target)
        ...     item.collect_variables(pub.selections)
        ...     print(item, item.ndim)
        GetItem('hland_v1', 'states.lz') 0
        GetItem('hland_v1', 'states.lz.series') 1
        GetItem('hland_v1', 'states.sm') 1
        GetItem('hland_v1', 'states.sm.series') 2
        """
        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.series:
                self.ndim += 1
            break


Instruction:
How would you code a function in Python 3 to
return False if the location is closed or the OpeningHours object containing the specified attribute is currently open.

Response:
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


Instruction:
Write a Python 3 script for
returning True if the location is currently open.

Response:
def is_open_now(location=None, attr=None):
    """
    Returns False if the location is closed, or the OpeningHours object
    to show the location is currently open.
    Same as `is_open` but passes `now` to `utils.is_open` to bypass `get_now()`.
    """
    obj = utils.is_open(location, now=datetime.datetime.now())
    if obj is False:
        return False
    if attr is not None:
        return getattr(obj, attr)
    return obj


Instruction:
Here you have a function in Python 3, explain what it does
def opening_hours(location=None, concise=False):
    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 = utils.get_premises_model()
            ohrs = Location.objects.first().openinghours_set.all()
        except AttributeError:
            raise Exception("You must define some opening hours"
                            " to use the opening hours tags.")

    ohrs.order_by('weekday', 'from_hour')

    for o in ohrs:
        days.append({
            'day_number': o.weekday,
            'name': o.get_weekday_display(),
            'from_hour': o.from_hour,
            'to_hour': o.to_hour,
            'hours': '%s%s to %s%s' % (
                o.from_hour.strftime('%I:%M').lstrip('0'),
                o.from_hour.strftime('%p').lower(),
                o.to_hour.strftime('%I:%M').lstrip('0'),
                o.to_hour.strftime('%p').lower()
            )
        })

    open_days = [o.weekday for o in ohrs]
    for day_number, day_name in WEEKDAYS:
        if day_number not in open_days:
            days.append({
                'day_number': day_number,
                'name': day_name,
                'hours': 'Closed'
            })
    days = sorted(days, key=lambda k: k['day_number'])

    if concise:
        # [{'hours': '9:00am to 5:00pm', 'day_names': u'Monday to Friday'},
        #  {'hours':...
        template_name = 'openinghours/opening_hours_list_concise.html'
        concise_days = []
        current_set = {}
        for day in days:
            if 'hours' not in current_set.keys():
                current_set = {'day_names': [day['name']],
                               'hours': day['hours']}
            elif day['hours'] != current_set['hours']:
                concise_days.append(current_set)
                current_set = {'day_names': [day['name']],
                               'hours': day['hours']}
            else:
                current_set['day_names'].append(day['name'])
        concise_days.append(current_set)

        for day_set in concise_days:
            if len(day_set['day_names']) > 2:
                day_set['day_names'] = '%s to %s' % (day_set['day_names'][0],
                                                     day_set['day_names'][-1])
            elif len(day_set['day_names']) > 1:
                day_set['day_names'] = '%s and %s' % (day_set['day_names'][0],
                                                      day_set['day_names'][-1])
            else:
                day_set['day_names'] = '%s' % day_set['day_names'][0]

        days = concise_days

    template = get_template(template_name)
    return template.render({'days': days})

Response: Creates a rendered listing of opening hours.


Instruction:
Can you tell what is the following Python 3 function doing
def prepare_everything(self):
        self.prepare_network()
        self.init_models()
        self.load_conditions()
        with hydpy.pub.options.warnmissingobsfile(False):
            self.prepare_nodeseries()
        self.prepare_modelseries()
        self.load_inputseries()

Response: Convenience method to make the actual |HydPy| instance runable.


Instruction:
Implement a Python 3 function for
loading all network files and assign the complete selection to the HydPy object.

Response:
def prepare_network(self):
        """Load all network files as |Selections| (stored in module |pub|)
        and assign the "complete" selection to the |HydPy| object."""
        hydpy.pub.selections = selectiontools.Selections()
        hydpy.pub.selections += hydpy.pub.networkmanager.load_files()
        self.update_devices(hydpy.pub.selections.complete)


Instruction:
Write a Python 3 script to
call method |Elements.save_controls| of the |Elements| object currently handled by the |HydPy| object. We use the `LahnH` example project to demonstrate how to write a complete set parameter control files. For convenience, we let function |prepare_full_example_2| prepare a fully functional |HydPy| object, handling seven |Element| objects controlling four |hland_v1| and three |hstream_v1| application models: >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, TestIO = prepare_full_example_2() At first, there is only one control subfolder named "default", containing the seven control files used in the step above: >>> import os >>> with TestIO(): ... os.listdir('LahnH/control') ['default'] Next, we use the |ControlManager| to create a new directory and dump all control file into it: >>> with TestIO(): ... pub.controlmanager.currentdir = 'newdir' ... hp.save_controls() ... sorted(os.listdir('LahnH/control')) ['default', 'newdir'] We focus our examples on the (smaller) control files of application model |hstream_v1|. The values of parameter |hstream_control.Lag| and |hstream_control.Damp| for the river channel connecting the outlets of subcatchment `lahn_1` and `lahn_2` are 0.583 days and 0.0, respectively: >>> model = hp.elements.stream_lahn_1_lahn_2.model >>> model.parameters.control lag(0.583) damp(0.0) The corresponding written control file defines the same values: >>> dir_ = 'LahnH/control/newdir/' >>> with TestIO(): ... with open(dir_ + 'stream_lahn_1_lahn_2.py') as controlfile: ... print(controlfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy.models.hstream_v1 import * <BLANKLINE> simulationstep('1d') parameterstep('1d') <BLANKLINE> lag(0.583) damp(0.0) <BLANKLINE> Its name equals the element name and the time step information is taken for the |Timegrid| object available via |pub|: >>> pub.timegrids.stepsize Period('1d') Use the |Auxfiler| class To avoid redefining the same parameter values in multiple control files. Here, we prepare an |Auxfiler| object which handles the two parameters of the model discussed above: >>> from hydpy import Auxfiler >>> aux = Auxfiler() >>> aux += 'hstream_v1' >>> aux.hstream_v1.stream = model.parameters.control.damp >>> aux.hstream_v1.stream = model.parameters.control.lag When passing the |Auxfiler| object to |HydPy.save_controls|, both parameters the control file of element `stream_lahn_1_lahn_2` do not define their values on their own, but reference the auxiliary file `stream.py` instead: >>> with TestIO(): ... pub.controlmanager.currentdir = 'newdir' ... hp.save_controls(auxfiler=aux) ... with open(dir_ + 'stream_lahn_1_lahn_2.py') as controlfile: ... print(controlfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy.models.hstream_v1 import * <BLANKLINE> simulationstep('1d') parameterstep('1d') <BLANKLINE> lag(auxfile='stream') damp(auxfile='stream') <BLANKLINE> `stream.py` contains the actual value definitions: >>> with TestIO(): ... with open(dir_ + 'stream.py') as controlfile: ... print(controlfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy.models.hstream_v1 import * <BLANKLINE> simulationstep('1d') parameterstep('1d') <BLANKLINE> damp(0.0) lag(0.583) <BLANKLINE> The |hstream_v1| model of element `stream_lahn_2_lahn_3` defines the same value for parameter |hstream_control.Damp| but a different one for parameter |hstream_control.Lag|. Hence, only |hstream_control.Damp| can reference control file `stream.py` without distorting data: >>> with TestIO(): ... with open(dir_ + 'stream_lahn_2_lahn_3.py') as controlfile: ... print(controlfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy.models.hstream_v1 import * <BLANKLINE> simulationstep('1d') parameterstep('1d') <BLANKLINE> lag(0.417) damp(auxfile='stream') <BLANKLINE> Another option is to pass alternative step size information. The `simulationstep` information, which is not really required in control files but useful for testing them, has no impact on the written data. However, passing an alternative `parameterstep` information changes the written values of time dependent parameters both in the primary and the auxiliary control files, as to be expected: >>> with TestIO(): ... pub.controlmanager.currentdir = 'newdir' ... hp.save_controls( ... auxfiler=aux, parameterstep='2d', simulationstep='1h') ... with open(dir_ + 'stream_lahn_1_lahn_2.py') as controlfile: ... print(controlfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy.models.hstream_v1 import * <BLANKLINE> simulationstep('1h') parameterstep('2d') <BLANKLINE> lag(auxfile='stream') damp(auxfile='stream') <BLANKLINE> >>> with TestIO(): ... with open(dir_ + 'stream.py') as controlfile: ... print(controlfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy.models.hstream_v1 import * <BLANKLINE> simulationstep('1h') parameterstep('2d') <BLANKLINE> damp(0.0) lag(0.2915) <BLANKLINE> >>> with TestIO(): ... with open(dir_ + 'stream_lahn_2_lahn_3.py') as controlfile: ... print(controlfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy.models.hstream_v1 import * <BLANKLINE> simulationstep('1h') parameterstep('2d') <BLANKLINE> lag(0.2085) damp(auxfile='stream') <BLANKLINE>

Response:
def save_controls(self, parameterstep=None, simulationstep=None,
                      auxfiler=None):
        """Call method |Elements.save_controls| of the |Elements| object
        currently handled by the |HydPy| object.

        We use the `LahnH` example project to demonstrate how to write
        a complete set parameter control files.  For convenience, we let
        function |prepare_full_example_2| prepare a fully functional
        |HydPy| object, handling seven |Element| objects controlling
        four |hland_v1| and three |hstream_v1| application models:

        >>> from hydpy.core.examples import prepare_full_example_2
        >>> hp, pub, TestIO = prepare_full_example_2()

        At first, there is only one control subfolder named "default",
        containing the seven control files used in the step above:

        >>> import os
        >>> with TestIO():
        ...     os.listdir('LahnH/control')
        ['default']

        Next, we use the |ControlManager| to create a new directory
        and dump all control file into it:

        >>> with TestIO():
        ...     pub.controlmanager.currentdir = 'newdir'
        ...     hp.save_controls()
        ...     sorted(os.listdir('LahnH/control'))
        ['default', 'newdir']

        We focus our examples on the (smaller) control files of
        application model |hstream_v1|.  The values of parameter
        |hstream_control.Lag| and |hstream_control.Damp| for the
        river channel connecting the outlets of subcatchment `lahn_1`
        and `lahn_2` are 0.583 days and 0.0, respectively:

        >>> model = hp.elements.stream_lahn_1_lahn_2.model
        >>> model.parameters.control
        lag(0.583)
        damp(0.0)

        The corresponding written control file defines the same values:

        >>> dir_ = 'LahnH/control/newdir/'
        >>> with TestIO():
        ...     with open(dir_ + 'stream_lahn_1_lahn_2.py') as controlfile:
        ...         print(controlfile.read())
        # -*- coding: utf-8 -*-
        <BLANKLINE>
        from hydpy.models.hstream_v1 import *
        <BLANKLINE>
        simulationstep('1d')
        parameterstep('1d')
        <BLANKLINE>
        lag(0.583)
        damp(0.0)
        <BLANKLINE>

        Its name equals the element name and the time step information
        is taken for the |Timegrid| object available via |pub|:

        >>> pub.timegrids.stepsize
        Period('1d')

        Use the |Auxfiler| class To avoid redefining the same parameter
        values in multiple control files.  Here, we prepare an |Auxfiler|
        object which handles the two parameters of the model discussed
        above:

        >>> from hydpy import Auxfiler
        >>> aux = Auxfiler()
        >>> aux += 'hstream_v1'
        >>> aux.hstream_v1.stream = model.parameters.control.damp
        >>> aux.hstream_v1.stream = model.parameters.control.lag

        When passing the |Auxfiler| object to |HydPy.save_controls|,
        both parameters the control file of element `stream_lahn_1_lahn_2`
        do not define their values on their own, but reference the
        auxiliary file `stream.py` instead:

        >>> with TestIO():
        ...     pub.controlmanager.currentdir = 'newdir'
        ...     hp.save_controls(auxfiler=aux)
        ...     with open(dir_ + 'stream_lahn_1_lahn_2.py') as controlfile:
        ...         print(controlfile.read())
        # -*- coding: utf-8 -*-
        <BLANKLINE>
        from hydpy.models.hstream_v1 import *
        <BLANKLINE>
        simulationstep('1d')
        parameterstep('1d')
        <BLANKLINE>
        lag(auxfile='stream')
        damp(auxfile='stream')
        <BLANKLINE>

        `stream.py` contains the actual value definitions:

        >>> with TestIO():
        ...     with open(dir_ + 'stream.py') as controlfile:
        ...         print(controlfile.read())
        # -*- coding: utf-8 -*-
        <BLANKLINE>
        from hydpy.models.hstream_v1 import *
        <BLANKLINE>
        simulationstep('1d')
        parameterstep('1d')
        <BLANKLINE>
        damp(0.0)
        lag(0.583)
        <BLANKLINE>

        The |hstream_v1| model of element `stream_lahn_2_lahn_3` defines
        the same value for parameter |hstream_control.Damp| but a different
        one for parameter |hstream_control.Lag|.  Hence, only
        |hstream_control.Damp| can reference control file `stream.py`
        without distorting data:

        >>> with TestIO():
        ...     with open(dir_ + 'stream_lahn_2_lahn_3.py') as controlfile:
        ...         print(controlfile.read())
        # -*- coding: utf-8 -*-
        <BLANKLINE>
        from hydpy.models.hstream_v1 import *
        <BLANKLINE>
        simulationstep('1d')
        parameterstep('1d')
        <BLANKLINE>
        lag(0.417)
        damp(auxfile='stream')
        <BLANKLINE>

        Another option is to pass alternative step size information.
        The `simulationstep` information, which is not really required
        in control files but useful for testing them, has no impact
        on the written data.  However, passing an alternative
        `parameterstep` information changes the written values of
        time dependent parameters both in the primary and the auxiliary
        control files, as to be expected:

        >>> with TestIO():
        ...     pub.controlmanager.currentdir = 'newdir'
        ...     hp.save_controls(
        ...         auxfiler=aux, parameterstep='2d', simulationstep='1h')
        ...     with open(dir_ + 'stream_lahn_1_lahn_2.py') as controlfile:
        ...         print(controlfile.read())
        # -*- coding: utf-8 -*-
        <BLANKLINE>
        from hydpy.models.hstream_v1 import *
        <BLANKLINE>
        simulationstep('1h')
        parameterstep('2d')
        <BLANKLINE>
        lag(auxfile='stream')
        damp(auxfile='stream')
        <BLANKLINE>

        >>> with TestIO():
        ...     with open(dir_ + 'stream.py') as controlfile:
        ...         print(controlfile.read())
        # -*- coding: utf-8 -*-
        <BLANKLINE>
        from hydpy.models.hstream_v1 import *
        <BLANKLINE>
        simulationstep('1h')
        parameterstep('2d')
        <BLANKLINE>
        damp(0.0)
        lag(0.2915)
        <BLANKLINE>

        >>> with TestIO():
        ...     with open(dir_ + 'stream_lahn_2_lahn_3.py') as controlfile:
        ...         print(controlfile.read())
        # -*- coding: utf-8 -*-
        <BLANKLINE>
        from hydpy.models.hstream_v1 import *
        <BLANKLINE>
        simulationstep('1h')
        parameterstep('2d')
        <BLANKLINE>
        lag(0.2085)
        damp(auxfile='stream')
        <BLANKLINE>
        """
        self.elements.save_controls(parameterstep=parameterstep,
                                    simulationstep=simulationstep,
                                    auxfiler=auxfiler)


Instruction:
Given the following Python 3 function, write the documentation
def networkproperties(self):
        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.variables))

Response: Print out some properties of the network defined by the Node and Element objects currently handled by the HydPy object.


Instruction:
Explain what the following Python 3 code does
def endnodes(self):
        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

Response:
|Nodes| object containing all |Node| objects currently handled by
        the HydPy object which define a downstream end point of a network.


Instruction:
Can you create a Python 3 function that
returns a sorted list of strings summarizing all variables handled by the |Node| objects.

Response:
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)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def simindices(self):
        return (hydpy.pub.timegrids.init[hydpy.pub.timegrids.sim.firstdate],
                hydpy.pub.timegrids.init[hydpy.pub.timegrids.sim.lastdate])

Response:
Tuple containing the start and end indices of the simulation period
        regarding the initialization period defined by the object
        stored in module |pub|.


Instruction:
Make a summary of the following Python 3 code
def open_files(self, idx=0):
        self.elements.open_files(idx=idx)
        self.nodes.open_files(idx=idx)

Response: Call method |Devices. open_files| of the Nodes and Elements objects currently handled by the HydPy object.


Instruction:
Write a Python 3 function for
updating the order of the |Node| and |Element| objects currently handled by the HydPy| objects in the simulation time step.

Response:
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 processed during
        a simulation time step.  Optionally, a |Selection| object for defining
        new |Node| and |Element| objects can be passed."""
        if selection is not None:
            self.nodes = selection.nodes
            self.elements = selection.elements
        self._update_deviceorder()


Instruction:
Create a Python 3 function to
perform a simulation run over the actual simulation time period defined by the Timegrids object stored in module |pub|.

Response:
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()


Instruction:
Implement a function in Python 3 to
update the inlet link sequence.

Response:
def pic_inflow_v1(self):
    """Update the inlet link sequence.

    Required inlet sequence:
      |dam_inlets.Q|

    Calculated flux sequence:
      |Inflow|

    Basic equation:
      :math:`Inflow = Q`
    """
    flu = self.sequences.fluxes.fastaccess
    inl = self.sequences.inlets.fastaccess
    flu.inflow = inl.q[0]


Instruction:
Can you generate the documentation for the following Python 3 function
def pic_inflow_v2(self):
    flu = self.sequences.fluxes.fastaccess
    inl = self.sequences.inlets.fastaccess
    flu.inflow = inl.q[0]+inl.s[0]+inl.r[0]

Response: Update the inlet link sequences.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def pic_totalremotedischarge_v1(self):
    flu = self.sequences.fluxes.fastaccess
    rec = self.sequences.receivers.fastaccess
    flu.totalremotedischarge = rec.q[0]

Response: Update the total discharge of the receiver link sequence.


Instruction:
Given the following Python 3 function, write the documentation
def pic_loggedrequiredremoterelease_v1(self):
    log = self.sequences.logs.fastaccess
    rec = self.sequences.receivers.fastaccess
    log.loggedrequiredremoterelease[0] = rec.d[0]

Response: Update the log of the receiver link sequence.


Instruction:
Can you create a Python 3 function that
updates the log of the receiver link sequence.

Response:
def pic_loggedrequiredremoterelease_v2(self):
    """Update the receiver link sequence."""
    log = self.sequences.logs.fastaccess
    rec = self.sequences.receivers.fastaccess
    log.loggedrequiredremoterelease[0] = rec.s[0]


Instruction:
Create a Python 3 function to
update the log of the first receiver link sequence.

Response:
def pic_loggedallowedremoterelieve_v1(self):
    """Update the receiver link sequence."""
    log = self.sequences.logs.fastaccess
    rec = self.sequences.receivers.fastaccess
    log.loggedallowedremoterelieve[0] = rec.r[0]


Instruction:
Can you implement a function in Python 3 that
updates the logged total remote discharge at a cross section far downstream.

Response:
def update_loggedtotalremotedischarge_v1(self):
    """Log a new entry of discharge at a cross section far downstream.

    Required control parameter:
      |NmbLogEntries|

    Required flux sequence:
      |TotalRemoteDischarge|

    Calculated flux sequence:
      |LoggedTotalRemoteDischarge|

    Example:

        The following example shows that, with each new method call, the
        three memorized values are successively moved to the right and the
        respective new value is stored on the bare left position:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> nmblogentries(3)
        >>> logs.loggedtotalremotedischarge = 0.0
        >>> from hydpy import UnitTest
        >>> test = UnitTest(model, model.update_loggedtotalremotedischarge_v1,
        ...                 last_example=4,
        ...                 parseqs=(fluxes.totalremotedischarge,
        ...                          logs.loggedtotalremotedischarge))
        >>> test.nexts.totalremotedischarge = [1., 3., 2., 4]
        >>> del test.inits.loggedtotalremotedischarge
        >>> test()
        | ex. | totalremotedischarge |           loggedtotalremotedischarge |
        ---------------------------------------------------------------------
        |   1 |                  1.0 | 1.0  0.0                         0.0 |
        |   2 |                  3.0 | 3.0  1.0                         0.0 |
        |   3 |                  2.0 | 2.0  3.0                         1.0 |
        |   4 |                  4.0 | 4.0  2.0                         3.0 |
    """
    con = self.parameters.control.fastaccess
    flu = self.sequences.fluxes.fastaccess
    log = self.sequences.logs.fastaccess
    for idx in range(con.nmblogentries-1, 0, -1):
        log.loggedtotalremotedischarge[idx] = \
                                log.loggedtotalremotedischarge[idx-1]
    log.loggedtotalremotedischarge[0] = flu.totalremotedischarge


Instruction:
Write a Python 3 function for
calculating the water level based on an artificial neural network and aide.

Response:
def calc_waterlevel_v1(self):
    """Determine the water level based on an artificial neural network
    describing the relationship between water level and water stage.

    Required control parameter:
      |WaterVolume2WaterLevel|

    Required state sequence:
      |WaterVolume|

    Calculated aide sequence:
      |WaterLevel|

    Example:

        Prepare a dam model:

        >>> from hydpy.models.dam import *
        >>> parameterstep()

        Prepare a very simple relationship based on one single neuron:

        >>> watervolume2waterlevel(
        ...         nmb_inputs=1, nmb_neurons=(1,), nmb_outputs=1,
        ...         weights_input=0.5, weights_output=1.0,
        ...         intercepts_hidden=0.0, intercepts_output=-0.5)

        At least in the water volume range used in the following examples,
        the shape of the relationship looks acceptable:

        >>> from hydpy import UnitTest
        >>> test = UnitTest(
        ...     model, model.calc_waterlevel_v1,
        ...     last_example=10,
        ...     parseqs=(states.watervolume, aides.waterlevel))
        >>> test.nexts.watervolume = range(10)
        >>> test()
        | ex. | watervolume | waterlevel |
        ----------------------------------
        |   1 |         0.0 |        0.0 |
        |   2 |         1.0 |   0.122459 |
        |   3 |         2.0 |   0.231059 |
        |   4 |         3.0 |   0.317574 |
        |   5 |         4.0 |   0.380797 |
        |   6 |         5.0 |   0.424142 |
        |   7 |         6.0 |   0.452574 |
        |   8 |         7.0 |   0.470688 |
        |   9 |         8.0 |   0.482014 |
        |  10 |         9.0 |   0.489013 |

        For more realistic approximations of measured relationships between
        water level and volume, larger neural networks are required.
    """
    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]


Instruction:
Write a Python 3 function that can
calculate the allowed maximum relieve another location in the dam.

Response:
def calc_allowedremoterelieve_v2(self):
    """Calculate the allowed maximum relieve another location
    is allowed to discharge into the dam.

    Required control parameters:
      |HighestRemoteRelieve|
      |WaterLevelRelieveThreshold|

    Required derived parameter:
      |WaterLevelRelieveSmoothPar|

    Required aide sequence:
      |WaterLevel|

    Calculated flux sequence:
      |AllowedRemoteRelieve|

    Basic equation:
      :math:`ActualRemoteRelease = HighestRemoteRelease \\cdot
      smooth_{logistic1}(WaterLevelRelieveThreshold-WaterLevel,
      WaterLevelRelieveSmoothPar)`

    Used auxiliary method:
      |smooth_logistic1|

    Examples:

        All control parameters that are involved in the calculation of
        |AllowedRemoteRelieve| are derived from |SeasonalParameter|.
        This allows to simulate seasonal dam control schemes.
        To show how this works, we first define a short simulation
        time period of only two days:

        >>> from hydpy import pub
        >>> pub.timegrids = '2001.03.30', '2001.04.03', '1d'

        Now we prepare the dam model and define two different control
        schemes for the hydrological summer (April to October) and
        winter month (November to May)

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> highestremoterelieve(_11_1_12=1.0, _03_31_12=1.0,
        ...                      _04_1_12=2.0, _10_31_12=2.0)
        >>> waterlevelrelievethreshold(_11_1_12=3.0, _03_31_12=2.0,
        ...                            _04_1_12=4.0, _10_31_12=4.0)
        >>> waterlevelrelievetolerance(_11_1_12=0.0, _03_31_12=0.0,
        ...                            _04_1_12=1.0, _10_31_12=1.0)
        >>> derived.waterlevelrelievesmoothpar.update()
        >>> derived.toy.update()

        The following test function is supposed to calculate
        |AllowedRemoteRelieve| for values of |WaterLevel| ranging
        from 0 and 8 m:

        >>> from hydpy import UnitTest
        >>> test = UnitTest(model,
        ...                 model.calc_allowedremoterelieve_v2,
        ...                 last_example=9,
        ...                 parseqs=(aides.waterlevel,
        ...                          fluxes.allowedremoterelieve))
        >>> test.nexts.waterlevel = range(9)

        On March 30 (which is the last day of the winter month and the
        first day of the simulation period), the value of
        |WaterLevelRelieveSmoothPar| is zero.  Hence, |AllowedRemoteRelieve|
        drops abruptly from 1 m³/s (the value of |HighestRemoteRelieve|) to
        0 m³/s, as soon as |WaterLevel| reaches 3 m (the value
        of |WaterLevelRelieveThreshold|):

        >>> model.idx_sim = pub.timegrids.init['2001.03.30']
        >>> test(first_example=2, last_example=6)
        | ex. | waterlevel | allowedremoterelieve |
        -------------------------------------------
        |   3 |        1.0 |                  1.0 |
        |   4 |        2.0 |                  1.0 |
        |   5 |        3.0 |                  0.0 |
        |   6 |        4.0 |                  0.0 |

        On April 1 (which is the first day of the sommer month and the
        last day of the simulation period), all parameter values are
        increased.  The value of parameter |WaterLevelRelieveSmoothPar|
        is 1 m.  Hence, loosely speaking, |AllowedRemoteRelieve| approaches
        the "discontinuous extremes (2 m³/s -- which is the value of
        |HighestRemoteRelieve| -- and 0 m³/s) to 99 % within a span of
        2 m³/s around the original threshold value of 4 m³/s defined by
        |WaterLevelRelieveThreshold|:

        >>> model.idx_sim = pub.timegrids.init['2001.04.01']
        >>> test()
        | ex. | waterlevel | allowedremoterelieve |
        -------------------------------------------
        |   1 |        0.0 |                  2.0 |
        |   2 |        1.0 |             1.999998 |
        |   3 |        2.0 |             1.999796 |
        |   4 |        3.0 |                 1.98 |
        |   5 |        4.0 |                  1.0 |
        |   6 |        5.0 |                 0.02 |
        |   7 |        6.0 |             0.000204 |
        |   8 |        7.0 |             0.000002 |
        |   9 |        8.0 |                  0.0 |
    """
    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(
            con.waterlevelrelievethreshold[toy]-aid.waterlevel,
            der.waterlevelrelievesmoothpar[toy]))


Instruction:
Can you generate a brief explanation for the following Python 3 code
def calc_requiredremotesupply_v1(self):
    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(
            con.waterlevelsupplythreshold[toy]-aid.waterlevel,
            der.waterlevelsupplysmoothpar[toy]))

Response:
Calculates the required maximum supply from another location
    that can be discharged into the dam.


Instruction:
Make a summary of the following Python 3 code
def calc_naturalremotedischarge_v1(self):
    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])
    if flu.naturalremotedischarge > 0.:
        flu.naturalremotedischarge /= con.nmblogentries
    else:
        flu.naturalremotedischarge = 0.

Response:
Estimate the natural discharge of a cross section far downstream
   .


Instruction:
Can you create a Python 3 function that
calculates the remote discharge demand of a cross section far downstream.

Response:
def calc_remotedemand_v1(self):
    """Estimate the discharge demand of a cross section far downstream.

    Required control parameter:
      |RemoteDischargeMinimum|

    Required derived parameters:
      |dam_derived.TOY|

    Required flux sequence:
      |dam_derived.TOY|

    Calculated flux sequence:
      |RemoteDemand|

    Basic equation:
      :math:`RemoteDemand =
      max(RemoteDischargeMinimum - NaturalRemoteDischarge, 0`

    Examples:

        Low water elevation is often restricted to specific month of the year.
        Sometimes the pursued lowest discharge value varies over the year
        to allow for a low flow variability that is in some agreement with
        the natural flow regime.  The HydPy-Dam model supports such
        variations.  Hence we define a short simulation time period first.
        This enables us to show how the related parameters values can be
        defined and how the calculation of the `remote` water demand
        throughout the year actually works:

        >>> from hydpy import pub
        >>> pub.timegrids = '2001.03.30', '2001.04.03', '1d'

        Prepare the dam model:

        >>> from hydpy.models.dam import *
        >>> parameterstep()

        Assume the required discharge at a gauge downstream being 2 m³/s
        in the hydrological summer half-year (April to October).  In the
        winter month (November to May), there is no such requirement:

        >>> remotedischargeminimum(_11_1_12=0.0, _03_31_12=0.0,
        ...                        _04_1_12=2.0, _10_31_12=2.0)
        >>> derived.toy.update()

        Prepare a test function, that calculates the remote discharge demand
        based on the parameter values defined above and for natural remote
        discharge values ranging between 0 and 3 m³/s:

        >>> from hydpy import UnitTest
        >>> test = UnitTest(model, model.calc_remotedemand_v1, last_example=4,
        ...                 parseqs=(fluxes.naturalremotedischarge,
        ...                          fluxes.remotedemand))
        >>> test.nexts.naturalremotedischarge = range(4)

        On April 1, the required discharge is 2 m³/s:

        >>> model.idx_sim = pub.timegrids.init['2001.04.01']
        >>> test()
        | ex. | naturalremotedischarge | remotedemand |
        -----------------------------------------------
        |   1 |                    0.0 |          2.0 |
        |   2 |                    1.0 |          1.0 |
        |   3 |                    2.0 |          0.0 |
        |   4 |                    3.0 |          0.0 |

        On May 31, the required discharge is 0 m³/s:

        >>> model.idx_sim = pub.timegrids.init['2001.03.31']
        >>> test()
        | ex. | naturalremotedischarge | remotedemand |
        -----------------------------------------------
        |   1 |                    0.0 |          0.0 |
        |   2 |                    1.0 |          0.0 |
        |   3 |                    2.0 |          0.0 |
        |   4 |                    3.0 |          0.0 |
    """
    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.)


Instruction:
Write a Python 3 script for
estimating the shortfall of actual discharge under the required discharge at the cross section far downstream.

Response:
def calc_remotefailure_v1(self):
    """Estimate the shortfall of actual discharge under the required discharge
    of a cross section far downstream.

    Required control parameters:
      |NmbLogEntries|
      |RemoteDischargeMinimum|

    Required derived parameters:
      |dam_derived.TOY|

    Required log sequence:
      |LoggedTotalRemoteDischarge|

    Calculated flux sequence:
      |RemoteFailure|

    Basic equation:
      :math:`RemoteFailure =
      \\frac{\\Sigma(LoggedTotalRemoteDischarge)}{NmbLogEntries} -
      RemoteDischargeMinimum`

    Examples:

        As explained in the documentation on method |calc_remotedemand_v1|,
        we have to define a simulation period first:

        >>> from hydpy import pub
        >>> pub.timegrids = '2001.03.30', '2001.04.03', '1d'

        Now we prepare a dam model with log sequences memorizing three values:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> nmblogentries(3)

        Again, the required discharge is 2 m³/s in summer and 0 m³/s in winter:

        >>> remotedischargeminimum(_11_1_12=0.0, _03_31_12=0.0,
        ...                        _04_1_12=2.0, _10_31_12=2.0)
        >>> derived.toy.update()

        Let it be supposed that the actual discharge at the remote
        cross section droped from 2 m³/s to 0  m³/s over the last three days:

        >>> logs.loggedtotalremotedischarge(0.0, 1.0, 2.0)

        This means that for the April 1 there would have been an averaged
        shortfall of 1 m³/s:

        >>> model.idx_sim = pub.timegrids.init['2001.04.01']
        >>> model.calc_remotefailure_v1()
        >>> fluxes.remotefailure
        remotefailure(1.0)

        Instead for May 31 there would have been an excess of 1 m³/s, which
        is interpreted to be a "negative failure":

        >>> model.idx_sim = pub.timegrids.init['2001.03.31']
        >>> model.calc_remotefailure_v1()
        >>> fluxes.remotefailure
        remotefailure(-1.0)
    """
    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.remotefailure /= con.nmblogentries
    flu.remotefailure += con.remotedischargeminimum[der.toy[self.idx_sim]]


Instruction:
Can you tell what is the following Python 3 function doing
def calc_requiredremoterelease_v1(self):
    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,
            der.remotedischargesmoothpar[der.toy[self.idx_sim]]))

Response: Calculates the required release needed to not fall below the threshold.


Instruction:
Given the following Python 3 function, write the documentation
def calc_requiredremoterelease_v2(self):
    flu = self.sequences.fluxes.fastaccess
    log = self.sequences.logs.fastaccess
    flu.requiredremoterelease = log.loggedrequiredremoterelease[0]

Response: Calculate the required remote release of the last simulation step.


Instruction:
Explain what the following Python 3 code does
def calc_allowedremoterelieve_v1(self):
    flu = self.sequences.fluxes.fastaccess
    log = self.sequences.logs.fastaccess
    flu.allowedremoterelieve = log.loggedallowedremoterelieve[0]

Response: Calculate the allowed remote relieve of the last simulation step.


Instruction:
Can you implement a function in Python 3 that
calculates the total water release required for reducing drought events.

Response:
def calc_requiredrelease_v1(self):
    """Calculate the total water release (immediately and far downstream)
    required for reducing drought events.

    Required control parameter:
      |NearDischargeMinimumThreshold|

    Required derived parameters:
      |NearDischargeMinimumSmoothPar2|
      |dam_derived.TOY|

    Required flux sequence:
      |RequiredRemoteRelease|

    Calculated flux sequence:
      |RequiredRelease|

    Basic equation:
      :math:`RequiredRelease = RequiredRemoteRelease
      \\cdot smooth_{logistic2}(
      RequiredRemoteRelease-NearDischargeMinimumThreshold,
      NearDischargeMinimumSmoothPar2)`

    Used auxiliary method:
      |smooth_logistic2|

    Examples:

        As in the examples above, define a short simulation time period first:

        >>> from hydpy import pub
        >>> pub.timegrids = '2001.03.30', '2001.04.03', '1d'

        Prepare the dam model:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> derived.toy.update()

        Define a minimum discharge value for a cross section immediately
        downstream of 4 m³/s for the summer months and of 0 m³/s for the
        winter months:

        >>> neardischargeminimumthreshold(_11_1_12=0.0, _03_31_12=0.0,
        ...                               _04_1_12=4.0, _10_31_12=4.0)

        Also define related tolerance values that are 1 m³/s in summer and
        0 m³/s in winter:

        >>> neardischargeminimumtolerance(_11_1_12=0.0, _03_31_12=0.0,
        ...                               _04_1_12=1.0, _10_31_12=1.0)
        >>> derived.neardischargeminimumsmoothpar2.update()

        Prepare a test function, that calculates the required total discharge
        based on the parameter values defined above and for a required value
        for a cross section far downstream ranging from 0 m³/s to 8 m³/s:

        >>> from hydpy import UnitTest
        >>> test = UnitTest(model, model.calc_requiredrelease_v1,
        ...                 last_example=9,
        ...                 parseqs=(fluxes.requiredremoterelease,
        ...                          fluxes.requiredrelease))
        >>> test.nexts.requiredremoterelease = range(9)

        On May 31, both the threshold and the tolerance value are 0 m³/s.
        Hence the required total and the required remote release are equal:

        >>> model.idx_sim = pub.timegrids.init['2001.03.31']
        >>> test()
        | ex. | requiredremoterelease | requiredrelease |
        -------------------------------------------------
        |   1 |                   0.0 |             0.0 |
        |   2 |                   1.0 |             1.0 |
        |   3 |                   2.0 |             2.0 |
        |   4 |                   3.0 |             3.0 |
        |   5 |                   4.0 |             4.0 |
        |   6 |                   5.0 |             5.0 |
        |   7 |                   6.0 |             6.0 |
        |   8 |                   7.0 |             7.0 |
        |   9 |                   8.0 |             8.0 |

        On April 1, the threshold value is 4 m³/s and the tolerance value
        is 1 m³/s.  For low values of the required remote release, the
        required total release approximates the threshold value. For large
        values, it approximates the required remote release itself.  Around
        the threshold value, due to the tolerance value of 1 m³/s, the
        required total release is a little larger than both the treshold
        value and the required remote release value:

        >>> model.idx_sim = pub.timegrids.init['2001.04.01']
        >>> test()
        | ex. | requiredremoterelease | requiredrelease |
        -------------------------------------------------
        |   1 |                   0.0 |             4.0 |
        |   2 |                   1.0 |        4.000012 |
        |   3 |                   2.0 |        4.000349 |
        |   4 |                   3.0 |            4.01 |
        |   5 |                   4.0 |        4.205524 |
        |   6 |                   5.0 |            5.01 |
        |   7 |                   6.0 |        6.000349 |
        |   8 |                   7.0 |        7.000012 |
        |   9 |                   8.0 |             8.0 |

    """
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    flu.requiredrelease = con.neardischargeminimumthreshold[
        der.toy[self.idx_sim]]
    flu.requiredrelease = (
        flu.requiredrelease +
        smoothutils.smooth_logistic2(
            flu.requiredremoterelease-flu.requiredrelease,
            der.neardischargeminimumsmoothpar2[
                der.toy[self.idx_sim]]))


Instruction:
Write a Python 3 script for
calculating the required water release for a single base on the next stage of the water release.

Response:
def calc_requiredrelease_v2(self):
    """Calculate the water release (immediately downstream) required for
    reducing drought events.

    Required control parameter:
      |NearDischargeMinimumThreshold|

    Required derived parameter:
      |dam_derived.TOY|

    Calculated flux sequence:
      |RequiredRelease|

    Basic equation:
      :math:`RequiredRelease = NearDischargeMinimumThreshold`

    Examples:

        As in the examples above, define a short simulation time period first:

        >>> from hydpy import pub
        >>> pub.timegrids = '2001.03.30', '2001.04.03', '1d'

        Prepare the dam model:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> derived.toy.update()

        Define a minimum discharge value for a cross section immediately
        downstream of 4 m³/s for the summer months and of 0 m³/s for the
        winter months:

        >>> neardischargeminimumthreshold(_11_1_12=0.0, _03_31_12=0.0,
        ...                               _04_1_12=4.0, _10_31_12=4.0)

        As to be expected, the calculated required release is 0.0 m³/s
        on May 31 and 4.0 m³/s on April 1:

        >>> model.idx_sim = pub.timegrids.init['2001.03.31']
        >>> model.calc_requiredrelease_v2()
        >>> fluxes.requiredrelease
        requiredrelease(0.0)
        >>> model.idx_sim = pub.timegrids.init['2001.04.01']
        >>> model.calc_requiredrelease_v2()
        >>> fluxes.requiredrelease
        requiredrelease(4.0)
    """
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    flu.requiredrelease = con.neardischargeminimumthreshold[
        der.toy[self.idx_sim]]


Instruction:
Write a Python 3 function that can
calculate the highest possible water release that can be routed to another location based on an artificial network describing the possible remote relieve between the two possible release and the water stage.

Response:
def calc_possibleremoterelieve_v1(self):
    """Calculate the highest possible water release that can be routed to
    a remote location based on an artificial neural network describing the
    relationship between possible release and water stage.

    Required control parameter:
      |WaterLevel2PossibleRemoteRelieve|

    Required aide sequence:
      |WaterLevel|

    Calculated flux sequence:
      |PossibleRemoteRelieve|

    Example:

        For simplicity, the example of method |calc_flooddischarge_v1|
        is reused.  See the documentation on the mentioned method for
        further information:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> waterlevel2possibleremoterelieve(
        ...     nmb_inputs=1,
        ...     nmb_neurons=(2,),
        ...     nmb_outputs=1,
        ...     weights_input=[[50., 4]],
        ...     weights_output=[[2.], [30]],
        ...     intercepts_hidden=[[-13000, -1046]],
        ...     intercepts_output=[0.])
        >>> from hydpy import UnitTest
        >>> test = UnitTest(
        ...     model, model.calc_possibleremoterelieve_v1,
        ...     last_example=21,
        ...     parseqs=(aides.waterlevel, fluxes.possibleremoterelieve))
        >>> test.nexts.waterlevel = numpy.arange(257, 261.1, 0.2)
        >>> test()
        | ex. | waterlevel | possibleremoterelieve |
        --------------------------------------------
        |   1 |      257.0 |                   0.0 |
        |   2 |      257.2 |              0.000001 |
        |   3 |      257.4 |              0.000002 |
        |   4 |      257.6 |              0.000005 |
        |   5 |      257.8 |              0.000011 |
        |   6 |      258.0 |              0.000025 |
        |   7 |      258.2 |              0.000056 |
        |   8 |      258.4 |              0.000124 |
        |   9 |      258.6 |              0.000275 |
        |  10 |      258.8 |              0.000612 |
        |  11 |      259.0 |              0.001362 |
        |  12 |      259.2 |              0.003031 |
        |  13 |      259.4 |              0.006745 |
        |  14 |      259.6 |              0.015006 |
        |  15 |      259.8 |              0.033467 |
        |  16 |      260.0 |              1.074179 |
        |  17 |      260.2 |              2.164498 |
        |  18 |      260.4 |              2.363853 |
        |  19 |      260.6 |               2.79791 |
        |  20 |      260.8 |              3.719725 |
        |  21 |      261.0 |              5.576088 |
    """
    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.waterlevel2possibleremoterelieve.outputs[0]


Instruction:
Make a summary of the following Python 3 code
def calc_actualremoterelieve_v1(self):
    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):
        d_smoothpar /= 5.
        flu.actualremoterelieve = smoothutils.smooth_max1(
            flu.actualremoterelieve, 0., d_smoothpar)
        d_smoothpar /= 5.
        flu.actualremoterelieve = smoothutils.smooth_min1(
            flu.actualremoterelieve, flu.possibleremoterelieve, d_smoothpar)
    flu.actualremoterelieve = min(flu.actualremoterelieve,
                                  flu.possibleremoterelieve)
    flu.actualremoterelieve = min(flu.actualremoterelieve,
                                  flu.allowedremoterelieve)
    flu.actualremoterelieve = max(flu.actualremoterelieve, 0.)

Response: Calculates the actual amount of water released to a remote location.


Instruction:
Explain what the following Python 3 code does
def calc_targetedrelease_v1(self):
    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_sim]],
            der.neardischargeminimumsmoothpar1[der.toy[self.idx_sim]])
        flu.targetedrelease = (flu.targetedrelease * flu.requiredrelease +
                               (1.-flu.targetedrelease) * flu.inflow)
    else:
        flu.targetedrelease = flu.requiredrelease

Response: Calculates targeted water release for reducing drought events in a single low flow and the actual water release.


Instruction:
Make a summary of the following Python 3 code
def calc_actualrelease_v1(self):
    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.waterlevel-con.waterlevelminimumthreshold,
                             der.waterlevelminimumsmoothpar))

Response: Calculate the actual water release that can be supplied by the aide model considering the targeted release and the given water level.


Instruction:
Implement a function in Python 3 to
calculate the portion of the required remote demand that could not be met by the actual discharge release.

Response:
def calc_missingremoterelease_v1(self):
    """Calculate the portion of the required remote demand that could not
    be met by the actual discharge release.

    Required flux sequences:
      |RequiredRemoteRelease|
      |ActualRelease|

    Calculated flux sequence:
      |MissingRemoteRelease|

    Basic equation:
      :math:`MissingRemoteRelease = max(
      RequiredRemoteRelease-ActualRelease, 0)`

    Example:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> fluxes.requiredremoterelease = 2.0
        >>> fluxes.actualrelease = 1.0
        >>> model.calc_missingremoterelease_v1()
        >>> fluxes.missingremoterelease
        missingremoterelease(1.0)
        >>> fluxes.actualrelease = 3.0
        >>> model.calc_missingremoterelease_v1()
        >>> fluxes.missingremoterelease
        missingremoterelease(0.0)
    """
    flu = self.sequences.fluxes.fastaccess
    flu.missingremoterelease = max(
        flu.requiredremoterelease-flu.actualrelease, 0.)


Instruction:
Here you have a function in Python 3, explain what it does
def calc_actualremoterelease_v1(self):
    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.waterlevelminimumremotethreshold,
            der.waterlevelminimumremotesmoothpar))

Response: Calculate the actual remote water release that can be supplied by the specified remote release and the given water level.


Instruction:
Can you write a function in Python 3 where it
constrains the actual relieve discharge to a remote location.

Response:
def update_actualremoterelieve_v1(self):
    """Constrain the actual relieve discharge to a remote location.

    Required control parameter:
      |HighestRemoteDischarge|

    Required derived parameter:
      |HighestRemoteSmoothPar|

    Updated flux sequence:
      |ActualRemoteRelieve|

    Basic equation - discontinous:
      :math:`ActualRemoteRelieve = min(ActualRemoteRelease,
      HighestRemoteDischarge)`

    Basic equation - continous:
      :math:`ActualRemoteRelieve = smooth_min1(ActualRemoteRelieve,
      HighestRemoteDischarge, HighestRemoteSmoothPar)`

    Used auxiliary methods:
      |smooth_min1|
      |smooth_max1|

    Note that the given continous basic equation is a simplification of
    the complete algorithm to update |ActualRemoteRelieve|, which also
    makes use of |smooth_max1| to prevent from gaining negative values
    in a smooth manner.

    Examples:

        Prepare a dam model:

        >>> from hydpy.models.dam import *
        >>> parameterstep()

        Prepare a test function object that performs eight examples with
        |ActualRemoteRelieve| ranging from 0 to 8 m³/s and a fixed
        initial value of parameter |HighestRemoteDischarge| of 4 m³/s:

        >>> highestremotedischarge(4.0)
        >>> from hydpy import UnitTest
        >>> test = UnitTest(model, model.update_actualremoterelieve_v1,
        ...                 last_example=8,
        ...                 parseqs=(fluxes.actualremoterelieve,))
        >>> test.nexts.actualremoterelieve = range(8)

        Through setting the value of |HighestRemoteTolerance| to the
        lowest possible value, there is no smoothing.  Instead, the
        shown relationship agrees with a combination of the discontinuous
        minimum and maximum function:

        >>> highestremotetolerance(0.0)
        >>> derived.highestremotesmoothpar.update()
        >>> test()
        | ex. | actualremoterelieve |
        -----------------------------
        |   1 |                 0.0 |
        |   2 |                 1.0 |
        |   3 |                 2.0 |
        |   4 |                 3.0 |
        |   5 |                 4.0 |
        |   6 |                 4.0 |
        |   7 |                 4.0 |
        |   8 |                 4.0 |

        Setting a sensible |HighestRemoteTolerance| value results in
        a moderate smoothing:

        >>> highestremotetolerance(0.1)
        >>> derived.highestremotesmoothpar.update()
        >>> test()
        | ex. | actualremoterelieve |
        -----------------------------
        |   1 |                 0.0 |
        |   2 |            0.999999 |
        |   3 |             1.99995 |
        |   4 |            2.996577 |
        |   5 |            3.836069 |
        |   6 |            3.991578 |
        |   7 |            3.993418 |
        |   8 |            3.993442 |

        Method |update_actualremoterelieve_v1| is defined in a similar
        way as method |calc_actualremoterelieve_v1|.  Please read the
        documentation on |calc_actualremoterelieve_v1| for further
        information.
    """
    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 dummy in range(5):
        d_smooth /= 5.
        d_value = smoothutils.smooth_max1(
            d_value, 0., d_smooth)
        d_smooth /= 5.
        d_value = smoothutils.smooth_min1(
            d_value, d_highest, d_smooth)
    d_value = min(d_value, flu.actualremoterelieve)
    d_value = min(d_value, d_highest)
    flu.actualremoterelieve = max(d_value, 0.)


Instruction:
Implement a Python 3 function for
calculating the discharge during and after a flood event based on the aide sequence of the next stage and the next stage.

Response:
def calc_flooddischarge_v1(self):
    """Calculate the discharge during and after a flood event based on an
    |anntools.SeasonalANN| describing the relationship(s) between discharge
    and water stage.

    Required control parameter:
      |WaterLevel2FloodDischarge|

    Required derived parameter:
      |dam_derived.TOY|

    Required aide sequence:
      |WaterLevel|

    Calculated flux sequence:
      |FloodDischarge|

    Example:

        The control parameter |WaterLevel2FloodDischarge| is derived from
        |SeasonalParameter|.  This allows to simulate different seasonal
        dam control schemes.  To show that the seasonal selection mechanism
        is implemented properly, we define a short simulation period of
        three days:

        >>> from hydpy import pub
        >>> pub.timegrids = '2001.01.01', '2001.01.04', '1d'

        Now we prepare a dam model and define two different relationships
        between water level and flood discharge.  The first relatively
        simple relationship (for January, 2) is based on two neurons
        contained in a single hidden layer and is used in the following
        example.  The second neural network (for January, 3) is not
        applied at all, which is why we do not need to assign any parameter
        values to it:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> waterlevel2flooddischarge(
        ...     _01_02_12 = ann(nmb_inputs=1,
        ...                     nmb_neurons=(2,),
        ...                     nmb_outputs=1,
        ...                     weights_input=[[50., 4]],
        ...                     weights_output=[[2.], [30]],
        ...                     intercepts_hidden=[[-13000, -1046]],
        ...                     intercepts_output=[0.]),
        ...     _01_03_12 = ann(nmb_inputs=1,
        ...                     nmb_neurons=(2,),
        ...                     nmb_outputs=1))
        >>> derived.toy.update()
        >>> model.idx_sim = pub.timegrids.sim['2001.01.02']

        The following example shows two distinct effects of both neurons
        in the first network.  One neuron describes a relatively sharp
        increase between 259.8 and 260.2 meters from about 0 to 2 m³/s.
        This could describe a release of water through a bottom outlet
        controlled by a valve.  The add something like an exponential
        increase between 260 and 261 meters, which could describe the
        uncontrolled flow over a spillway:

        >>> from hydpy import UnitTest
        >>> test = UnitTest(model, model.calc_flooddischarge_v1,
        ...                 last_example=21,
        ...                 parseqs=(aides.waterlevel,
        ...                          fluxes.flooddischarge))
        >>> test.nexts.waterlevel = numpy.arange(257, 261.1, 0.2)
        >>> test()
        | ex. | waterlevel | flooddischarge |
        -------------------------------------
        |   1 |      257.0 |            0.0 |
        |   2 |      257.2 |       0.000001 |
        |   3 |      257.4 |       0.000002 |
        |   4 |      257.6 |       0.000005 |
        |   5 |      257.8 |       0.000011 |
        |   6 |      258.0 |       0.000025 |
        |   7 |      258.2 |       0.000056 |
        |   8 |      258.4 |       0.000124 |
        |   9 |      258.6 |       0.000275 |
        |  10 |      258.8 |       0.000612 |
        |  11 |      259.0 |       0.001362 |
        |  12 |      259.2 |       0.003031 |
        |  13 |      259.4 |       0.006745 |
        |  14 |      259.6 |       0.015006 |
        |  15 |      259.8 |       0.033467 |
        |  16 |      260.0 |       1.074179 |
        |  17 |      260.2 |       2.164498 |
        |  18 |      260.4 |       2.363853 |
        |  19 |      260.6 |        2.79791 |
        |  20 |      260.8 |       3.719725 |
        |  21 |      261.0 |       5.576088 |
    """
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    aid = self.sequences.aides.fastaccess
    con.waterlevel2flooddischarge.inputs[0] = aid.waterlevel
    con.waterlevel2flooddischarge.process_actual_input(der.toy[self.idx_sim])
    flu.flooddischarge = con.waterlevel2flooddischarge.outputs[0]


Instruction:
Can you generate a brief explanation for the following Python 3 code
def calc_outflow_v1(self):
    flu = self.sequences.fluxes.fastaccess
    flu.outflow = max(flu.actualrelease + flu.flooddischarge, 0.)

Response: Calculate the total outflow of the dam.


Instruction:
How would you code a function in Python 3 to
update the actual water volume.

Response:
def update_watervolume_v1(self):
    """Update the actual water volume.

    Required derived parameter:
      |Seconds|

    Required flux sequences:
      |Inflow|
      |Outflow|

    Updated state sequence:
      |WaterVolume|

    Basic equation:
      :math:`\\frac{d}{dt}WaterVolume = 1e-6 \\cdot (Inflow-Outflow)`

    Example:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> derived.seconds = 2e6
        >>> states.watervolume.old = 5.0
        >>> fluxes.inflow = 2.0
        >>> fluxes.outflow = 3.0
        >>> model.update_watervolume_v1()
        >>> states.watervolume
        watervolume(3.0)
    """
    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)


Instruction:
Can you implement a function in Python 3 that
updates the outlet link sequence |dam_outlets. Q|.

Response:
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


Instruction:
How would you implement a function in Python 3 that
updates the actual remoterelease sequence |dam_outlets. S|.

Response:
def pass_actualremoterelease_v1(self):
    """Update the outlet link sequence |dam_outlets.S|."""
    flu = self.sequences.fluxes.fastaccess
    out = self.sequences.outlets.fastaccess
    out.s[0] += flu.actualremoterelease


Instruction:
Write a Python 3 script for
updating the actual remoterelieve sequence |dam_outlets. R|.

Response:
def pass_actualremoterelieve_v1(self):
    """Update the outlet link sequence |dam_outlets.R|."""
    flu = self.sequences.fluxes.fastaccess
    out = self.sequences.outlets.fastaccess
    out.r[0] += flu.actualremoterelieve


Instruction:
Can you generate a brief explanation for the following Python 3 code
def pass_missingremoterelease_v1(self):
    flu = self.sequences.fluxes.fastaccess
    sen = self.sequences.senders.fastaccess
    sen.d[0] += flu.missingremoterelease

Response: Update the outlet link sequence |dam_senders. D|.


Instruction:
How would you code a function in Python 3 to
update the outlet link sequence |dam_outlets. R|.

Response:
def pass_allowedremoterelieve_v1(self):
    """Update the outlet link sequence |dam_outlets.R|."""
    flu = self.sequences.fluxes.fastaccess
    sen = self.sequences.senders.fastaccess
    sen.r[0] += flu.allowedremoterelieve


Instruction:
Create a Python 3 function for
updating the outlet link sequence |dam_outlets. S|.

Response:
def pass_requiredremotesupply_v1(self):
    """Update the outlet link sequence |dam_outlets.S|."""
    flu = self.sequences.fluxes.fastaccess
    sen = self.sequences.senders.fastaccess
    sen.s[0] += flu.requiredremotesupply


Instruction:
How would you explain what the following Python 3 function does
def update_loggedoutflow_v1(self):
    con = self.parameters.control.fastaccess
    flu = self.sequences.fluxes.fastaccess
    log = self.sequences.logs.fastaccess
    for idx in range(con.nmblogentries-1, 0, -1):
        log.loggedoutflow[idx] = log.loggedoutflow[idx-1]
    log.loggedoutflow[0] = flu.outflow

Response: Update the logged outflow sequence with the new entry of discharge at a cross section far downstream.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def turningpoint(self):
        coefs = self.coefs
        old_dc = coefs[1]-coefs[0]
        for idx in range(self.order-2):
            new_dc = coefs[idx+2]-coefs[idx+1]
            if (old_dc < 0.) and (new_dc > old_dc):
                return idx, coefs[idx]
            old_dc = new_dc
        raise RuntimeError(
            'Not able to detect a turning point in the impulse response '
            'defined by the MA coefficients %s.'
            % objecttools.repr_values(coefs))

Response: Return the index and value tuple of the recession part of the instantaneous unit hydrograph.


Instruction:
Implement a function in Python 3 to
determine the AR coefficients.

Response:
def update_ar_coefs(self):
        """Determine the AR coefficients.

        The number of AR coefficients is subsequently increased until the
        required precision |ARMA.max_rel_rmse| is reached.  Otherwise,
        a |RuntimeError| is raised.
        """
        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(
                    f'Method `update_ar_coefs` is not able to determine '
                    f'the AR coefficients of the ARMA model with the desired '
                    f'accuracy.  You can either set the tolerance value '
                    f'`max_rel_rmse` to a higher value or increase the '
                    f'allowed `max_ar_order`.  An accuracy of `'
                    f'{objecttools.repr_(self._rel_rmse)}` has been reached '
                    f'using `{self.effective_max_ar_order}` coefficients.')


Instruction:
Write a Python 3 script for
summing of the absolute deviations between the central moments of the instantaneous unit hydrograph and the ARMA approximation.

Response:
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))


Instruction:
Write a Python 3 script for
multiplying all coefficients by the same factor so that their sum becomes one.

Response:
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


Instruction:
Write a Python 3 function that can
calculate the AR coefficients of all the entries in the current set of all the entries in the set of all the entries in the set of all the entries in the set of all the entries in the set.

Response:
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 AR coefficients to be
        determined.  The argument `ma_order` defines a pure |MA| model.
        The least squares approach is applied on all those coefficents of the
        pure MA model, which are associated with the part of the recession
        curve behind its turning point.

        The attribute |ARMA.rel_rmse| is updated with the resulting
        relative root mean square error.
        """
        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 = numpy.sqrt(residuals[0])/numpy.sum(values)
        else:
            self._rel_rmse = 0.


Instruction:
Can you tell what is the following Python 3 function doing
def get_a(values, n):
        m = len(values)-n
        a = numpy.empty((m, n), dtype=float)
        for i in range(m):
            i0 = i-1 if i > 0 else None
            i1 = i+n-1
            a[i] = values[i1:i0:-1]
        return numpy.array(a)

Response:
Extract the independent variables of the given values and return
        them as a matrix with n columns.


Instruction:
Make a summary of the following Python 3 code
def update_ma_coefs(self):
        self.ma_coefs = []
        for ma_order in range(1, self.ma.order+1):
            self.calc_next_ma_coef(ma_order, self.ma)
            if self.dev_coefs < self.max_dev_coefs:
                self.norm_coefs()
                break
        else:
            with hydpy.pub.options.reprdigits(12):
                raise RuntimeError(
                    f'Method `update_ma_coefs` is not able to determine the '
                    f'MA coefficients of the ARMA model with the desired '
                    f'accuracy.  You can set the tolerance value '
                    f'´max_dev_coefs` to a higher value.  An accuracy of '
                    f'`{objecttools.repr_(self.dev_coefs)}` has been reached '
                    f'using `{self.ma.order}` MA coefficients.')
        if numpy.min(self.response) < 0.:
            warnings.warn(
                'Note that the smallest response to a standard impulse of the '
                'determined ARMA model is negative (`%s`).'
                % objecttools.repr_(numpy.min(self.response)))

Response: Determine the MA coefficients.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def calc_next_ma_coef(self, ma_order, ma_model):
        idx = ma_order-1
        coef = ma_model.coefs[idx]
        for jdx, ar_coef in enumerate(self.ar_coefs):
            zdx = idx-jdx-1
            if zdx >= 0:
                coef -= ar_coef*ma_model.coefs[zdx]
        self.ma_coefs = numpy.concatenate((self.ma_coefs, [coef]))

Response: Calculates the next MA coefficient for the given MA model based on its AR coefficients and the MA ordinates of the given MA model.


Instruction:
Implement a function in Python 3 to
return the response to a standard dt impulse.

Response:
def response(self):
        """Return the response to a standard dt impulse."""
        values = []
        sum_values = 0.
        ma_coefs = self.ma_coefs
        ar_coefs = self.ar_coefs
        ma_order = self.ma_order
        for idx in range(len(self.ma.delays)):
            value = 0.
            if idx < ma_order:
                value += ma_coefs[idx]
            for jdx, ar_coef in enumerate(ar_coefs):
                zdx = idx-jdx-1
                if zdx >= 0:
                    value += ar_coef*values[zdx]
            values.append(value)
            sum_values += value
        return numpy.array(values)


Instruction:
Explain what the following Python 3 code does
def method_header(method_name, nogil=False, idx_as_arg=False):
    if not config.FASTCYTHON:
        nogil = False
    header = 'cpdef inline void %s(self' % method_name
    header += ', int idx)' if idx_as_arg else ')'
    header += ' nogil:' if nogil else ':'
    return header

Response:
Returns the Cython method header for methods without arguments except
    self.


Instruction:
Given the following Python 3 function, write the documentation
def add(self, indent, line):
        if isinstance(line, str):
            list.append(self, indent*4*' ' + line)
        else:
            for subline in line:
                list.append(self, indent*4*' ' + subline)

Response:
Appends the given text line with prefixed spaces in accordance with
        the given number of indentation levels.


Instruction:
Write a Python 3 function that can
name of the compiled module.

Response:
def pyname(self):
        """Name of the compiled module."""
        if self.pymodule.endswith('__init__'):
            return self.pymodule.split('.')[-2]
        else:
            return self.pymodule.split('.')[-1]


Instruction:
Can you generate the documentation for the following Python 3 function
def pyxwriter(self):
        model = self.Model()
        if hasattr(self, 'Parameters'):
            model.parameters = self.Parameters(vars(self))
        else:
            model.parameters = parametertools.Parameters(vars(self))
        if hasattr(self, 'Sequences'):
            model.sequences = self.Sequences(model=model, **vars(self))
        else:
            model.sequences = sequencetools.Sequences(model=model,
                                                      **vars(self))
        return PyxWriter(self, model, self.pyxfilepath)

Response: Update the pyx file.


Instruction:
Given the following Python 3 function, write the documentation
def outdated(self):
        if hydpy.pub.options.forcecompiling:
            return True
        if os.path.split(hydpy.__path__[0])[-2].endswith('-packages'):
            return False
        if not os.path.exists(self.dllfilepath):
            return True
        cydate = os.stat(self.dllfilepath).st_mtime
        for pysourcefile in self.pysourcefiles:
            pydate = os.stat(pysourcefile).st_mtime
            if pydate > cydate:
                return True
        return False

Response: True if at least one of the pysourcefiles is newer than the compiled file under |Cythonizer. pyxfilepath| False otherwise.


Instruction:
Here you have a function in Python 3, explain what it does
def compile_(self):
        from Cython import Build
        argv = copy.deepcopy(sys.argv)
        sys.argv = [sys.argv[0], 'build_ext', '--build-lib='+self.buildpath]
        exc_modules = [
                distutils.extension.Extension(
                        'hydpy.cythons.autogen.'+self.cyname,
                        [self.pyxfilepath], extra_compile_args=['-O2'])]
        distutils.core.setup(ext_modules=Build.cythonize(exc_modules),
                             include_dirs=[numpy.get_include()])
        sys.argv = argv

Response: Translate cython code to C code and compile it.