Instruction:
Write a Python 3 function for
calculating soil evaporation and update soil moisture.

Response:
def calc_ea_sm_v1(self):
    """Calculate soil evaporation and update soil moisture.

    Required control parameters:
      |NmbZones|
      |ZoneType|
      |FC|
      |LP|
      |ERed|

    Required fluxes sequences:
      |EPC|
      |EI|

    Required state sequence:
      |SP|

    Calculated flux sequence:
      |EA|

    Updated state sequence:
      |SM|

    Basic equations:
      :math:`\\frac{dSM}{dt} = - EA` \n
      :math:`EA_{temp} = \\biggl \\lbrace
      {
      {EPC \\cdot min\\left(\\frac{SM}{LP \\cdot FC}, 1\\right)
      \\ | \\ SP = 0}
      \\atop
      {0 \\ | \\ SP > 0}
      }` \n
      :math:`EA = EA_{temp} - max(ERED \\cdot (EA_{temp} + EI - EPC), 0)`

    Examples:

        Initialize seven zones of different types.  The field capacity
         of all fields and forests is set to 200mm, potential evaporation
         and interception evaporation are 2mm and 1mm respectively:

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> nmbzones(7)
        >>> zonetype(ILAKE, GLACIER, FIELD, FOREST, FIELD, FIELD, FIELD)
        >>> fc(200.0)
        >>> lp(0.0, 0.0, 0.5, 0.5, 0.0, 0.8, 1.0)
        >>> ered(0.0)
        >>> fluxes.epc = 2.0
        >>> fluxes.ei = 1.0
        >>> states.sp = 0.0

        Only fields and forests include soils; for glaciers and zones (the
        first two zones) no soil evaporation is performed.  For fields and
        forests, the underlying calculations are the same. In the following
        example, the relative soil moisture is 50% in all field and forest
        zones.  Hence, differences in soil evaporation are related to the
        different soil evaporation parameter values only:

        >>> states.sm = 100.0
        >>> model.calc_ea_sm_v1()
        >>> fluxes.ea
        ea(0.0, 0.0, 2.0, 2.0, 2.0, 1.25, 1.0)
        >>> states.sm
        sm(0.0, 0.0, 98.0, 98.0, 98.0, 98.75, 99.0)

        In the last example, evaporation values of 2mm have been calculated
        for some zones despite the fact, that these 2mm added to the actual
        interception evaporation of 1mm exceed potential evaporation.  This
        behaviour can be reduced...

        >>> states.sm = 100.0
        >>> ered(0.5)
        >>> model.calc_ea_sm_v1()
        >>> fluxes.ea
        ea(0.0, 0.0, 1.5, 1.5, 1.5, 1.125, 1.0)
        >>> states.sm
        sm(0.0, 0.0, 98.5, 98.5, 98.5, 98.875, 99.0)

        ...or be completely excluded:

        >>> states.sm = 100.0
        >>> ered(1.0)
        >>> model.calc_ea_sm_v1()
        >>> fluxes.ea
        ea(0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0)
        >>> states.sm
        sm(0.0, 0.0, 99.0, 99.0, 99.0, 99.0, 99.0)

        Any occurrence of a snow layer suppresses soil evaporation
        completely:

        >>> states.sp = 0.01
        >>> states.sm = 100.0
        >>> model.calc_ea_sm_v1()
        >>> fluxes.ea
        ea(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
        >>> states.sm
        sm(0.0, 0.0, 100.0, 100.0, 100.0, 100.0, 100.0)

        For (unrealistic) soils with zero field capacity, soil evaporation
        is always zero:

        >>> fc(0.0)
        >>> states.sm = 0.0
        >>> model.calc_ea_sm_v1()
        >>> fluxes.ea
        ea(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
        >>> states.sm
        sm(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
    """
    con = self.parameters.control.fastaccess
    flu = self.sequences.fluxes.fastaccess
    sta = self.sequences.states.fastaccess
    for k in range(con.nmbzones):
        if con.zonetype[k] in (FIELD, FOREST):
            if sta.sp[k] <= 0.:
                if (con.lp[k]*con.fc[k]) > 0.:
                    flu.ea[k] = flu.epc[k]*sta.sm[k]/(con.lp[k]*con.fc[k])
                    flu.ea[k] = min(flu.ea[k], flu.epc[k])
                else:
                    flu.ea[k] = flu.epc[k]
                flu.ea[k] -= max(con.ered[k] *
                                 (flu.ea[k]+flu.ei[k]-flu.epc[k]), 0.)
                flu.ea[k] = min(flu.ea[k], sta.sm[k])
            else:
                flu.ea[k] = 0.
            sta.sm[k] -= flu.ea[k]
        else:
            flu.ea[k] = 0.
            sta.sm[k] = 0.


Instruction:
Write a Python 3 function that can
accumulate the total inflow into the upper zone layer.

Response:
def calc_inuz_v1(self):
    """Accumulate the total inflow into the upper zone layer.

    Required control parameters:
      |NmbZones|
      |ZoneType|

    Required derived parameters:
      |RelLandZoneArea|

    Required fluxes sequences:
      |R|
      |CF|

    Calculated flux sequence:
      |InUZ|

    Basic equation:
      :math:`InUZ = R - CF`

    Examples:

        Initialize three zones of different relative `land sizes`
        (area related to the total size of the subbasin except lake areas):

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> nmbzones(3)
        >>> zonetype(FIELD, ILAKE, GLACIER)
        >>> derived.rellandzonearea = 2.0/3.0, 0.0, 1.0/3.0
        >>> fluxes.r = 6.0, 0.0, 2.0
        >>> fluxes.cf = 2.0, 0.0, 1.0
        >>> model.calc_inuz_v1()
        >>> fluxes.inuz
        inuz(3.0)

        Internal lakes do not contribute to the upper zone layer.  Hence
        for a subbasin consisting only of interal lakes a zero input
        value would be calculated:

        >>> zonetype(ILAKE, ILAKE, ILAKE)
        >>> model.calc_inuz_v1()
        >>> fluxes.inuz
        inuz(0.0)

    """
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    flu.inuz = 0.
    for k in range(con.nmbzones):
        if con.zonetype[k] != ILAKE:
            flu.inuz += der.rellandzonearea[k]*(flu.r[k]-flu.cf[k])


Instruction:
How would you explain what the following Python 3 function does
def calc_contriarea_v1(self):
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    sta = self.sequences.states.fastaccess
    if con.resparea and (der.relsoilarea > 0.):
        flu.contriarea = 0.
        for k in range(con.nmbzones):
            if con.zonetype[k] in (FIELD, FOREST):
                if con.fc[k] > 0.:
                    flu.contriarea += (der.relsoilzonearea[k] *
                                       (sta.sm[k]/con.fc[k])**con.beta[k])
                else:
                    flu.contriarea += der.relsoilzonearea[k]
    else:
        flu.contriarea = 1.

Response: Calculates the relative size of the contributing area of the whole subbasin.


Instruction:
Given the following Python 3 function, write the documentation
def calc_q0_perc_uz_v1(self):
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    sta = self.sequences.states.fastaccess
    flu.perc = 0.
    flu.q0 = 0.
    for dummy in range(con.recstep):
        # First state update related to the upper zone input.
        sta.uz += der.dt*flu.inuz
        # Second state update related to percolation.
        d_perc = min(der.dt*con.percmax*flu.contriarea, sta.uz)
        sta.uz -= d_perc
        flu.perc += d_perc
        # Third state update related to fast runoff response.
        if sta.uz > 0.:
            if flu.contriarea > 0.:
                d_q0 = (der.dt*con.k *
                        (sta.uz/flu.contriarea)**(1.+con.alpha))
                d_q0 = min(d_q0, sta.uz)
            else:
                d_q0 = sta.uz
            sta.uz -= d_q0
            flu.q0 += d_q0
        else:
            d_q0 = 0.

Response: This method calculates the fast response of the percolation of the current state of the current state of the current state of the current state of the current state of the current state of the current state of the current state of the current state of the current state of the current state of the current state of the current state.


Instruction:
How would you code a function in Python 3 to
calculate the lower zone layer in accordance with percolation from the upper groundwater to lower groundwater and in accordance with percolation from the lower groundwater and in accordance with percolation from the lower groundwater and in accordance with lake precipitation.

Response:
def calc_lz_v1(self):
    """Update the lower zone layer in accordance with percolation from
    upper groundwater to lower groundwater and/or in accordance with
    lake precipitation.

    Required control parameters:
      |NmbZones|
      |ZoneType|

    Required derived parameters:
      |RelLandArea|
      |RelZoneArea|

    Required fluxes sequences:
      |PC|
      |Perc|

    Updated state sequence:
      |LZ|

    Basic equation:
      :math:`\\frac{dLZ}{dt} = Perc + Pc`

    Examples:

        At first, a subbasin with two field zones is assumed (the zones
        could be of type forest or glacier as well).  In such zones,
        precipitation does not fall directly into the lower zone layer,
        hence the given precipitation of 2mm has no impact.  Only
        the actual percolation from the upper zone layer (underneath
        both field zones) is added to the lower zone storage:

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> nmbzones(2)
        >>> zonetype(FIELD, FIELD)
        >>> derived.rellandarea = 1.0
        >>> derived.relzonearea = 2.0/3.0, 1.0/3.0
        >>> fluxes.perc = 2.0
        >>> fluxes.pc = 5.0
        >>> states.lz = 10.0
        >>> model.calc_lz_v1()
        >>> states.lz
        lz(12.0)

        If the second zone is an internal lake, its precipitation falls
        on the lower zone layer directly.  Note that only 5/3mm
        precipitation are added, due to the relative size of the
        internal lake within the subbasin. Percolation from the upper
        zone layer increases the lower zone storage only by two thirds
        of its original value, due to the larger spatial extend of
        the lower zone layer:

        >>> zonetype(FIELD, ILAKE)
        >>> derived.rellandarea = 2.0/3.0
        >>> derived.relzonearea = 2.0/3.0, 1.0/3.0
        >>> states.lz = 10.0
        >>> model.calc_lz_v1()
        >>> states.lz
        lz(13.0)
    """
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    sta = self.sequences.states.fastaccess
    sta.lz += der.rellandarea*flu.perc
    for k in range(con.nmbzones):
        if con.zonetype[k] == ILAKE:
            sta.lz += der.relzonearea[k]*flu.pc[k]


Instruction:
Write a Python 3 function that can
calculate the internal lake evaporation for a single entry in the current state.

Response:
def calc_el_lz_v1(self):
    """Calculate lake evaporation.

    Required control parameters:
        |NmbZones|
        |ZoneType|
        |TTIce|

    Required derived parameters:
        |RelZoneArea|

    Required fluxes sequences:
        |TC|
        |EPC|

    Updated state sequence:
        |LZ|

    Basic equations:
        :math:`\\frac{dLZ}{dt} = -EL` \n
        :math:`EL = \\Bigl \\lbrace
        {
        {EPC \\ | \\ TC > TTIce}
        \\atop
        {0 \\ | \\ TC \\leq TTIce}
        }`

    Examples:

        Six zones of the same size are initialized.  The first three
        zones are no internal lakes, they can not exhibit any lake
        evaporation.  Of the last three zones, which are internal lakes,
        only the last one evaporates water.  For zones five and six,
        evaporation is suppressed due to an assumed ice layer, whenever
        the associated theshold temperature is not exceeded:

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> nmbzones(6)
        >>> zonetype(FIELD, FOREST, GLACIER, ILAKE, ILAKE, ILAKE)
        >>> ttice(-1.0)
        >>> derived.relzonearea = 1.0/6.0
        >>> fluxes.epc = 0.6
        >>> fluxes.tc = 0.0, 0.0, 0.0, 0.0, -1.0, -2.0
        >>> states.lz = 10.0
        >>> model.calc_el_lz_v1()
        >>> fluxes.el
        el(0.0, 0.0, 0.0, 0.6, 0.0, 0.0)
        >>> states.lz
        lz(9.9)

        Note that internal lakes always contain water.  Hence, the
        HydPy-H-Land model allows for negative values of the lower
        zone storage:

        >>> states.lz = 0.05
        >>> model.calc_el_lz_v1()
        >>> fluxes.el
        el(0.0, 0.0, 0.0, 0.6, 0.0, 0.0)
        >>> states.lz
        lz(-0.05)
    """
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    sta = self.sequences.states.fastaccess
    for k in range(con.nmbzones):
        if (con.zonetype[k] == ILAKE) and (flu.tc[k] > con.ttice[k]):
            flu.el[k] = flu.epc[k]
            sta.lz -= der.relzonearea[k]*flu.el[k]
        else:
            flu.el[k] = 0.


Instruction:
Can you implement a function in Python 3 that
calculates the slow response of the lower zone layer.

Response:
def calc_q1_lz_v1(self):
    """Calculate the slow response of the lower zone layer.

    Required control parameters:
        |K4|
        |Gamma|

    Calculated fluxes sequence:
        |Q1|

    Updated state sequence:
        |LZ|

    Basic equations:
        :math:`\\frac{dLZ}{dt} = -Q1` \n
        :math:`Q1 = \\Bigl \\lbrace
        {
        {K4 \\cdot LZ^{1+Gamma} \\ | \\ LZ > 0}
        \\atop
        {0 \\ | \\ LZ\\leq 0}
        }`

    Examples:

        As long as the lower zone storage is negative...

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> simulationstep('12h')
        >>> k4(0.2)
        >>> gamma(0.0)
        >>> states.lz = -2.0
        >>> model.calc_q1_lz_v1()
        >>> fluxes.q1
        q1(0.0)
        >>> states.lz
        lz(-2.0)

        ...or zero, no slow discharge response occurs:

        >>> states.lz = 0.0
        >>> model.calc_q1_lz_v1()
        >>> fluxes.q1
        q1(0.0)
        >>> states.lz
        lz(0.0)

        For storage values above zero the linear...

        >>> states.lz = 2.0
        >>> model.calc_q1_lz_v1()
        >>> fluxes.q1
        q1(0.2)
        >>> states.lz
        lz(1.8)

        ...or nonlinear storage routing equation applies:

        >>> gamma(1.)
        >>> states.lz = 2.0
        >>> model.calc_q1_lz_v1()
        >>> fluxes.q1
        q1(0.4)
        >>> states.lz
        lz(1.6)

        Note that the assumed length of the simulation step is only a
        half day. Hence the effective value of the storage coefficient
        is not 0.2 but 0.1:

        >>> k4
        k4(0.2)
        >>> k4.value
        0.1
    """
    con = self.parameters.control.fastaccess
    flu = self.sequences.fluxes.fastaccess
    sta = self.sequences.states.fastaccess
    if sta.lz > 0.:
        flu.q1 = con.k4*sta.lz**(1.+con.gamma)
    else:
        flu.q1 = 0.
    sta.lz -= flu.q1


Instruction:
Given the following Python 3 function, write the documentation
def calc_inuh_v1(self):
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    flu.inuh = der.rellandarea*flu.q0+flu.q1

Response: Calculate the unit hydrograph input.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def calc_outuh_quh_v1(self):
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    log = self.sequences.logs.fastaccess
    flu.outuh = der.uh[0]*flu.inuh+log.quh[0]
    for jdx in range(1, len(der.uh)):
        log.quh[jdx-1] = der.uh[jdx]*flu.inuh+log.quh[jdx]

Response: Calculates the output of a fast catchment response from the given log sequence and the remaining ordinates of the given log sequence.


Instruction:
Write a Python 3 script for
calculating the total discharge after possible abstractions.

Response:
def calc_qt_v1(self):
    """Calculate the total discharge after possible abstractions.

    Required control parameter:
      |Abstr|

    Required flux sequence:
      |OutUH|

    Calculated flux sequence:
      |QT|

    Basic equation:
        :math:`QT = max(OutUH - Abstr, 0)`

    Examples:

        Trying to abstract less then available, as much as available and
        less then available results in:

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> simulationstep('12h')
        >>> abstr(2.0)
        >>> fluxes.outuh = 2.0
        >>> model.calc_qt_v1()
        >>> fluxes.qt
        qt(1.0)
        >>> fluxes.outuh = 1.0
        >>> model.calc_qt_v1()
        >>> fluxes.qt
        qt(0.0)
        >>> fluxes.outuh = 0.5
        >>> model.calc_qt_v1()
        >>> fluxes.qt
        qt(0.0)

        Note that "negative abstractions" are allowed:

        >>> abstr(-2.0)
        >>> fluxes.outuh = 1.0
        >>> model.calc_qt_v1()
        >>> fluxes.qt
        qt(2.0)
    """
    con = self.parameters.control.fastaccess
    flu = self.sequences.fluxes.fastaccess
    flu.qt = max(flu.outuh-con.abstr, 0.)


Instruction:
Explain what the following Python 3 code does
def save(self, parameterstep=None, simulationstep=None):
        par = parametertools.Parameter
        for (modelname, var2aux) in self:
            for filename in var2aux.filenames:
                with par.parameterstep(parameterstep), \
                         par.simulationstep(simulationstep):
                    lines = [parametertools.get_controlfileheader(
                        modelname, parameterstep, simulationstep)]
                    for par in getattr(var2aux, filename):
                        lines.append(repr(par) + '\n')
                hydpy.pub.controlmanager.save_file(filename, ''.join(lines))

Response: Save all defined auxiliary control files.


Instruction:
Implement a function in Python 3 to
remove the defined variables from the current set of variables.

Response:
def remove(self, *values):
        """Remove the defined variables.

        The variables to be removed can be selected in two ways.  But the
        first example shows that passing nothing or an empty iterable to
        method |Variable2Auxfile.remove| does not remove any variable:

        >>> from hydpy import dummies
        >>> v2af = dummies.v2af
        >>> v2af.remove()
        >>> v2af.remove([])
        >>> from hydpy import print_values
        >>> print_values(v2af.filenames)
        file1, file2
        >>> print_values(v2af.variables, width=30)
        eqb(5000.0), eqb(10000.0),
        eqd1(100.0), eqd2(50.0),
        eqi1(2000.0), eqi2(1000.0)

        The first option is to pass auxiliary file names:

        >>> v2af.remove('file1')
        >>> print_values(v2af.filenames)
        file2
        >>> print_values(v2af.variables)
        eqb(10000.0), eqd1(100.0), eqd2(50.0)

        The second option is, to pass variables of the correct type
        and value:

        >>> v2af = dummies.v2af
        >>> v2af.remove(v2af.eqb[0])
        >>> print_values(v2af.filenames)
        file1, file2
        >>> print_values(v2af.variables)
        eqb(10000.0), eqd1(100.0), eqd2(50.0), eqi1(2000.0), eqi2(1000.0)

        One can pass multiple variables or iterables containing variables
        at once:

        >>> v2af = dummies.v2af
        >>> v2af.remove(v2af.eqb, v2af.eqd1, v2af.eqd2)
        >>> print_values(v2af.filenames)
        file1
        >>> print_values(v2af.variables)
        eqi1(2000.0), eqi2(1000.0)

        Passing an argument that equals neither a registered file name or a
        registered variable results in the following exception:

        >>> v2af.remove('test')
        Traceback (most recent call last):
        ...
        ValueError: While trying to remove the given object `test` of type \
`str` from the actual Variable2AuxFile object, the following error occurred: \
`'test'` is neither a registered filename nor a registered variable.
        """
        for value in objecttools.extract(values, (str, variabletools.Variable)):
            try:
                deleted_something = False
                for fn2var in list(self._type2filename2variable.values()):
                    for fn_, var in list(fn2var.items()):
                        if value in (fn_, var):
                            del fn2var[fn_]
                            deleted_something = True
                if not deleted_something:
                    raise ValueError(
                        f'`{repr(value)}` is neither a registered '
                        f'filename nor a registered variable.')
            except BaseException:
                objecttools.augment_excmessage(
                    f'While trying to remove the given object `{value}` '
                    f'of type `{objecttools.classname(value)}` from the '
                    f'actual Variable2AuxFile object')


Instruction:
How would you explain what the following Python 3 function does
def filenames(self):
        fns = set()
        for fn2var in self._type2filename2variable.values():
            fns.update(fn2var.keys())
        return sorted(fns)

Response: A list of all handled auxiliary file names.


Instruction:
Write a Python 3 script to
return the auxiliary file name of the given variable.

Response:
def get_filename(self, variable):
        """Return the auxiliary file name the given variable is allocated
        to or |None| if the given variable is not allocated to any
        auxiliary file name.

        >>> from hydpy import dummies
        >>> eqb = dummies.v2af.eqb[0]
        >>> dummies.v2af.get_filename(eqb)
        'file1'
        >>> eqb += 500.0
        >>> dummies.v2af.get_filename(eqb)
        """
        fn2var = self._type2filename2variable.get(type(variable), {})
        for (fn_, var) in fn2var.items():
            if var == variable:
                return fn_
        return None


Instruction:
How would you explain what the following Python 3 function does
def update(self):
        metapar = self.subpars.pars.control.remotedischargesafety
        self.shape = metapar.shape
        self(tuple(smoothtools.calc_smoothpar_logistic1(mp)
                   for mp in metapar.values))

Response: Calculate the smoothing parameter values.


Instruction:
Can you create a Python 3 function that
calculates the smoothing parameter value.

Response:
def update(self):
        """Calculate the smoothing parameter value.

        The following example is explained in some detail in module
        |smoothtools|:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> waterlevelminimumremotetolerance(0.0)
        >>> derived.waterlevelminimumremotesmoothpar.update()
        >>> from hydpy.cythons.smoothutils import smooth_logistic1
        >>> from hydpy import round_
        >>> round_(smooth_logistic1(0.1,
        ...        derived.waterlevelminimumremotesmoothpar))
        1.0
        >>> waterlevelminimumremotetolerance(2.5)
        >>> derived.waterlevelminimumremotesmoothpar.update()
        >>> round_(smooth_logistic1(2.5,
        ...        derived.waterlevelminimumremotesmoothpar))
        0.99
        """
        metapar = self.subpars.pars.control.waterlevelminimumremotetolerance
        self(smoothtools.calc_smoothpar_logistic1(metapar))


Instruction:
How would you implement a function in Python 3 that
calculates the smoothing parameter value for the current locale.

Response:
def update(self):
        """Calculate the smoothing parameter value.

        The following example is explained in some detail in module
        |smoothtools|:

        >>> from hydpy.models.dam import *
        >>> parameterstep()
        >>> highestremotedischarge(1.0)
        >>> highestremotetolerance(0.0)
        >>> derived.highestremotesmoothpar.update()
        >>> from hydpy.cythons.smoothutils import smooth_min1
        >>> from hydpy import round_
        >>> round_(smooth_min1(-4.0, 1.5, derived.highestremotesmoothpar))
        -4.0
        >>> highestremotetolerance(2.5)
        >>> derived.highestremotesmoothpar.update()
        >>> round_(smooth_min1(-4.0, -1.5, derived.highestremotesmoothpar))
        -4.01

        Note that the example above corresponds to the example on function
        |calc_smoothpar_min1|, due to the value of parameter
        |HighestRemoteDischarge| being 1 m³/s.  Doubling the value of
        |HighestRemoteDischarge| also doubles the value of
        |HighestRemoteSmoothPar| proportional.  This leads to the following
        result:

        >>> highestremotedischarge(2.0)
        >>> derived.highestremotesmoothpar.update()
        >>> round_(smooth_min1(-4.0, 1.0, derived.highestremotesmoothpar))
        -4.02

        This relationship between |HighestRemoteDischarge| and
        |HighestRemoteSmoothPar| prevents from any smoothing when
        the value of |HighestRemoteDischarge| is zero:

        >>> highestremotedischarge(0.0)
        >>> derived.highestremotesmoothpar.update()
        >>> round_(smooth_min1(1.0, 1.0, derived.highestremotesmoothpar))
        1.0

        In addition, |HighestRemoteSmoothPar| is set to zero if
        |HighestRemoteDischarge| is infinity (because no actual value
        will ever come in the vicinit of infinity), which is why no
        value would be changed through smoothing anyway):

        >>> highestremotedischarge(inf)
        >>> derived.highestremotesmoothpar.update()
        >>> round_(smooth_min1(1.0, 1.0, derived.highestremotesmoothpar))
        1.0
        """
        control = self.subpars.pars.control
        if numpy.isinf(control.highestremotedischarge):
            self(0.0)
        else:
            self(control.highestremotedischarge *
                 smoothtools.calc_smoothpar_min1(control.highestremotetolerance)
                 )


Instruction:
Create a Python 3 function to
execute the given command in a new process.

Response:
def run_subprocess(command: str, verbose: bool = True, blocking: bool = True) \
        -> Optional[subprocess.Popen]:
    """Execute the given command in a new process.

    Only when both `verbose` and `blocking` are |True|, |run_subprocess|
    prints all responses to the current value of |sys.stdout|:

    >>> from hydpy import run_subprocess
    >>> import platform
    >>> esc = '' if 'windows' in platform.platform().lower() else '\\\\'
    >>> run_subprocess(f'python -c print{esc}(1+1{esc})')
    2

    With verbose being |False|, |run_subprocess| does never print out
    anything:

    >>> run_subprocess(f'python -c print{esc}(1+1{esc})', verbose=False)

    >>> process = run_subprocess('python', blocking=False, verbose=False)
    >>> process.kill()
    >>> _ = process.communicate()

    When `verbose` is |True| and `blocking` is |False|, |run_subprocess|
    prints all responses to the console ("invisible" for doctests):

    >>> process = run_subprocess('python', blocking=False)
    >>> process.kill()
    >>> _ = process.communicate()
    """
    if blocking:
        result1 = subprocess.run(
            command,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            encoding='utf-8',
            shell=True)
        if verbose:    # due to doctest replacing sys.stdout
            for output in (result1.stdout, result1.stderr):
                output = output.strip()
                if output:
                    print(output)
        return None
    stdouterr = None if verbose else subprocess.DEVNULL
    result2 = subprocess.Popen(
        command,
        stdout=stdouterr,
        stderr=stdouterr,
        encoding='utf-8',
        shell=True)
    return result2


Instruction:
Can you create a Python 3 function that
executes the given Python commands.

Response:
def exec_commands(commands: str, **parameters: Any) -> None:
    """Execute the given Python commands.

    Function |exec_commands| is thought for testing purposes only (see
    the main documentation on module |hyd|).  Seperate individual commands
    by semicolons and replaced whitespaces with underscores:

    >>> from hydpy.exe.commandtools import exec_commands
    >>> import sys
    >>> exec_commands("x_=_1+1;print(x)")
    Start to execute the commands ['x_=_1+1', 'print(x)'] for testing purposes.
    2

    |exec_commands| interprets double underscores as a single underscores:

    >>> exec_commands("x_=_1;print(x.____class____)")
    Start to execute the commands ['x_=_1', 'print(x.____class____)'] \
for testing purposes.
    <class 'int'>

    |exec_commands| evaluates additional keyword arguments before it
    executes the given commands:

    >>> exec_commands("e=x==y;print(e)", x=1, y=2)
    Start to execute the commands ['e=x==y', 'print(e)'] for testing purposes.
    False
    """
    cmdlist = commands.split(';')
    print(f'Start to execute the commands {cmdlist} for testing purposes.')
    for par, value in parameters.items():
        exec(f'{par} = {value}')
    for command in cmdlist:
        command = command.replace('__', 'temptemptemp')
        command = command.replace('_', ' ')
        command = command.replace('temptemptemp', '_')
        exec(command)


Instruction:
Can you write a function in Python 3 where it
prepares an empty log file eventually and returns its absolute path.

Response:
def prepare_logfile(filename: str) -> str:
    """Prepare an empty log file eventually and return its absolute path.

    When passing the "filename" `stdout`, |prepare_logfile| does not
    prepare any file and just returns `stdout`:

    >>> from hydpy.exe.commandtools import prepare_logfile
    >>> prepare_logfile('stdout')
    'stdout'

    When passing the "filename" `default`, |prepare_logfile| generates a
    filename containing the actual date and time, prepares an empty file
    on disk, and returns its path:

    >>> from hydpy import repr_, TestIO
    >>> from hydpy.core.testtools import mock_datetime_now
    >>> from datetime import datetime
    >>> with TestIO():
    ...     with mock_datetime_now(datetime(2000, 1, 1, 12, 30, 0)):
    ...         filepath = prepare_logfile('default')
    >>> import os
    >>> os.path.exists(filepath)
    True
    >>> repr_(filepath)    # doctest: +ELLIPSIS
    '...hydpy/tests/iotesting/hydpy_2000-01-01_12-30-00.log'

    For all other strings, |prepare_logfile| does not add any date or time
    information to the filename:

    >>> with TestIO():
    ...     with mock_datetime_now(datetime(2000, 1, 1, 12, 30, 0)):
    ...         filepath = prepare_logfile('my_log_file.txt')
    >>> os.path.exists(filepath)
    True
    >>> repr_(filepath)    # doctest: +ELLIPSIS
    '...hydpy/tests/iotesting/my_log_file.txt'
    """
    if filename == 'stdout':
        return filename
    if filename == 'default':
        filename = datetime.datetime.now().strftime(
            'hydpy_%Y-%m-%d_%H-%M-%S.log')
    with open(filename, 'w'):
        pass
    return os.path.abspath(filename)


Instruction:
How would you code a function in Python 3 to
execute a HydPy script function.

Response:
def execute_scriptfunction() -> None:
    """Execute a HydPy script function.

    Function |execute_scriptfunction| is indirectly applied and
    explained in the documentation on module |hyd|.
    """
    try:
        args_given = []
        kwargs_given = {}
        for arg in sys.argv[1:]:
            if len(arg) < 3:
                args_given.append(arg)
            else:
                try:
                    key, value = parse_argument(arg)
                    kwargs_given[key] = value
                except ValueError:
                    args_given.append(arg)
        logfilepath = prepare_logfile(kwargs_given.pop('logfile', 'stdout'))
        logstyle = kwargs_given.pop('logstyle', 'plain')
        try:
            funcname = str(args_given.pop(0))
        except IndexError:
            raise ValueError(
                'The first positional argument defining the function '
                'to be called is missing.')
        try:
            func = hydpy.pub.scriptfunctions[funcname]
        except KeyError:
            available_funcs = objecttools.enumeration(
                sorted(hydpy.pub.scriptfunctions.keys()))
            raise ValueError(
                f'There is no `{funcname}` function callable by `hyd.py`.  '
                f'Choose one of the following instead: {available_funcs}.')
        args_required = inspect.getfullargspec(func).args
        nmb_args_required = len(args_required)
        nmb_args_given = len(args_given)
        if nmb_args_given != nmb_args_required:
            enum_args_given = ''
            if nmb_args_given:
                enum_args_given = (
                    f' ({objecttools.enumeration(args_given)})')
            enum_args_required = ''
            if nmb_args_required:
                enum_args_required = (
                    f' ({objecttools.enumeration(args_required)})')
            raise ValueError(
                f'Function `{funcname}` requires `{nmb_args_required:d}` '
                f'positional arguments{enum_args_required}, but '
                f'`{nmb_args_given:d}` are given{enum_args_given}.')
        with _activate_logfile(logfilepath, logstyle, 'info', 'warning'):
            func(*args_given, **kwargs_given)
    except BaseException as exc:
        if logstyle not in LogFileInterface.style2infotype2string:
            logstyle = 'plain'
        with _activate_logfile(logfilepath, logstyle, 'exception', 'exception'):
            arguments = ', '.join(sys.argv)
            print(f'Invoking hyd.py with arguments `{arguments}` '
                  f'resulted in the following error:\n{str(exc)}\n\n'
                  f'See the following stack traceback for debugging:\n',
                  file=sys.stderr)
            traceback.print_tb(sys.exc_info()[2])


Instruction:
Write a Python 3 function for
parsing a string as a positional argument or a tuple containing a keyword and its value for a single argument.

Response:
def parse_argument(string: str) -> Union[str, Tuple[str, str]]:
    """Return a single value for a string understood as a positional
    argument or a |tuple| containing a keyword and its value for a
    string understood as a keyword argument.

    |parse_argument| is intended to be used as a helper function for
    function |execute_scriptfunction| only.  See the following
    examples to see which types of keyword arguments |execute_scriptfunction|
    covers:

    >>> from hydpy.exe.commandtools import parse_argument
    >>> parse_argument('x=3')
    ('x', '3')
    >>> parse_argument('"x=3"')
    '"x=3"'
    >>> parse_argument("'x=3'")
    "'x=3'"
    >>> parse_argument('x="3==3"')
    ('x', '"3==3"')
    >>> parse_argument("x='3==3'")
    ('x', "'3==3'")
    """
    idx_equal = string.find('=')
    if idx_equal == -1:
        return string
    idx_quote = idx_equal+1
    for quote in ('"', "'"):
        idx = string.find(quote)
        if -1 < idx < idx_quote:
            idx_quote = idx
    if idx_equal < idx_quote:
        return string[:idx_equal], string[idx_equal+1:]
    return string


Instruction:
Can you create a Python 3 function that
prints the given string and the current date and time.

Response:
def print_textandtime(text: str) -> None:
    """Print the given string and the current date and time with high
    precision for logging purposes.

    >>> from hydpy.exe.commandtools import print_textandtime
    >>> from hydpy.core.testtools import mock_datetime_now
    >>> from datetime import datetime
    >>> with mock_datetime_now(datetime(2000, 1, 1, 12, 30, 0, 123456)):
    ...     print_textandtime('something happens')
    something happens (2000-01-01 12:30:00.123456).
    """
    timestring = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
    print(f'{text} ({timestring}).')


Instruction:
Write a Python 3 function for
writing the given string as explained in the main documentation on class |LogFileInterface|.

Response:
def write(self, string: str) -> None:
        """Write the given string as explained in the main documentation
        on class |LogFileInterface|."""
        self.logfile.write('\n'.join(
            f'{self._string}{substring}' if substring else ''
            for substring in string.split('\n')))


Instruction:
Write a Python 3 function for
solving the differential equation of HydPy - L.

Response:
def solve_dv_dt_v1(self):
    """Solve the differential equation of HydPy-L.

    At the moment, HydPy-L only implements a simple numerical solution of
    its underlying ordinary differential equation.  To increase the accuracy
    (or sometimes even to prevent instability) of this approximation, one
    can set the value of parameter |MaxDT| to a value smaller than the actual
    simulation step size.  Method |solve_dv_dt_v1| then applies the methods
    related to the numerical approximation multiple times and aggregates
    the results.

    Note that the order of convergence is one only.  It is hard to tell how
    short the internal simulation step needs to be to ensure a certain degree
    of accuracy.  In most cases one hour or very often even one day should be
    sufficient to gain acceptable results.  However, this strongly depends on
    the given water stage-volume-discharge relationship.  Hence it seems
    advisable to always define a few test waves and apply the llake model with
    different |MaxDT| values.  Afterwards, select a |MaxDT| value  lower than
    one which results in acceptable approximations for all test waves.  The
    computation time of the llake mode per substep is rather small, so always
    include a safety factor.

    Of course, an adaptive step size determination would be much more
    convenient...

    Required derived parameter:
      |NmbSubsteps|

    Used aide sequence:
      |llake_aides.V|
      |llake_aides.QA|

    Updated state sequence:
      |llake_states.V|

    Calculated flux sequence:
      |llake_fluxes.QA|

    Note that method |solve_dv_dt_v1| calls the versions of `calc_vq`,
    `interp_qa` and `calc_v_qa` selected by the respective application model.
    Hence, also their parameter and sequence specifications need to be
    considered.

    Basic equation:
      :math:`\\frac{dV}{dt}= QZ - QA(V)`
    """
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    old = self.sequences.states.fastaccess_old
    new = self.sequences.states.fastaccess_new
    aid = self.sequences.aides.fastaccess
    flu.qa = 0.
    aid.v = old.v
    for _ in range(der.nmbsubsteps):
        self.calc_vq()
        self.interp_qa()
        self.calc_v_qa()
        flu.qa += aid.qa
    flu.qa /= der.nmbsubsteps
    new.v = aid.v


Instruction:
Can you write a function in Python 3 where it
calculates the auxiliary term v1.

Response:
def calc_vq_v1(self):
    """Calculate the auxiliary term.

    Required derived parameters:
      |Seconds|
      |NmbSubsteps|

    Required flux sequence:
      |QZ|

    Required aide sequence:
      |llake_aides.V|

    Calculated aide sequence:
      |llake_aides.VQ|

    Basic equation:
      :math:`VQ = 2 \\cdot V + \\frac{Seconds}{NmbSubsteps} \\cdot QZ`

    Example:

        The following example shows that the auxiliary term `vq` does not
        depend on the (outer) simulation step size but on the (inner)
        calculation step size defined by parameter `maxdt`:

        >>> from hydpy.models.llake import *
        >>> parameterstep('1d')
        >>> simulationstep('12h')
        >>> maxdt('6h')
        >>> derived.seconds.update()
        >>> derived.nmbsubsteps.update()
        >>> fluxes.qz = 2.
        >>> aides.v = 1e5
        >>> model.calc_vq_v1()
        >>> aides.vq
        vq(243200.0)
    """
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    aid = self.sequences.aides.fastaccess
    aid.vq = 2.*aid.v+der.seconds/der.nmbsubsteps*flu.qz


Instruction:
Can you generate the documentation for the following Python 3 function
def interp_qa_v1(self):
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    aid = self.sequences.aides.fastaccess
    idx = der.toy[self.idx_sim]
    for jdx in range(1, con.n):
        if der.vq[idx, jdx] >= aid.vq:
            break
    aid.qa = ((aid.vq-der.vq[idx, jdx-1]) *
              (con.q[idx, jdx]-con.q[idx, jdx-1]) /
              (der.vq[idx, jdx]-der.vq[idx, jdx-1]) +
              con.q[idx, jdx-1])
    aid.qa = max(aid.qa, 0.)

Response: Calculate the lake outflow based on linear interpolation.


Instruction:
Write a Python 3 function for
updating the stored water volume based on the equation of continuity.

Response:
def calc_v_qa_v1(self):
    """Update the stored water volume based on the equation of continuity.

    Note that for too high outflow values, which would result in overdraining
    the lake, the outflow is trimmed.

    Required derived parameters:
      |Seconds|
      |NmbSubsteps|

    Required flux sequence:
      |QZ|

    Updated aide sequences:
      |llake_aides.QA|
      |llake_aides.V|

    Basic Equation:
      :math:`\\frac{dV}{dt}= QZ - QA`

    Examples:

        Prepare a lake model with an initial storage of 100.000 m³ and an
        inflow of 2 m³/s and a (potential) outflow of 6 m³/s:

        >>> from hydpy.models.llake import *
        >>> parameterstep()
        >>> simulationstep('12h')
        >>> maxdt('6h')
        >>> derived.seconds.update()
        >>> derived.nmbsubsteps.update()
        >>> aides.v = 1e5
        >>> fluxes.qz = 2.
        >>> aides.qa = 6.

        Through calling method `calc_v_qa_v1` three times with the same inflow
        and outflow values, the storage is emptied after the second step and
        outflow is equal to inflow after the third step:

        >>> model.calc_v_qa_v1()
        >>> aides.v
        v(13600.0)
        >>> aides.qa
        qa(6.0)
        >>> model.new2old()
        >>> model.calc_v_qa_v1()
        >>> aides.v
        v(0.0)
        >>> aides.qa
        qa(2.62963)
        >>> model.new2old()
        >>> model.calc_v_qa_v1()
        >>> aides.v
        v(0.0)
        >>> aides.qa
        qa(2.0)

        Note that the results of method |calc_v_qa_v1| are not based
        depend on the (outer) simulation step size but on the (inner)
        calculation step size defined by parameter `maxdt`.
    """
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    aid = self.sequences.aides.fastaccess
    aid.qa = min(aid.qa, flu.qz+der.nmbsubsteps/der.seconds*aid.v)
    aid.v = max(aid.v+der.seconds/der.nmbsubsteps*(flu.qz-aid.qa), 0.)


Instruction:
Can you create a Python 3 function that
calculates the actual water stage based on linear interpolation.

Response:
def interp_w_v1(self):
    """Calculate the actual water stage based on linear interpolation.

    Required control parameters:
      |N|
      |llake_control.V|
      |llake_control.W|

    Required state sequence:
      |llake_states.V|

    Calculated state sequence:
      |llake_states.W|

    Examples:

        Prepare a model object:

        >>> from hydpy.models.llake import *
        >>> parameterstep('1d')
        >>> simulationstep('12h')

        For the sake of brevity, define a test function:

        >>> def test(*vs):
        ...     for v in vs:
        ...         states.v.new = v
        ...         model.interp_w_v1()
        ...         print(repr(states.v), repr(states.w))

        Define a simple `w`-`v` relationship consisting of three nodes and
        calculate the water stages for different volumes:

        >>> n(3)
        >>> v(0., 2., 4.)
        >>> w(-1., 1., 2.)

        Perform the interpolation for a few test points:

        >>> test(0., .5, 2., 3., 4., 5.)
        v(0.0) w(-1.0)
        v(0.5) w(-0.5)
        v(2.0) w(1.0)
        v(3.0) w(1.5)
        v(4.0) w(2.0)
        v(5.0) w(2.5)

        The reference water stage of the relationship can be selected
        arbitrarily.  Even negative water stages are returned, as is
        demonstrated by the first two calculations.  For volumes outside
        the range of the (`v`,`w`) pairs, the outer two highest pairs are
        used for linear extrapolation.
    """
    con = self.parameters.control.fastaccess
    new = self.sequences.states.fastaccess_new
    for jdx in range(1, con.n):
        if con.v[jdx] >= new.v:
            break
    new.w = ((new.v-con.v[jdx-1]) *
             (con.w[jdx]-con.w[jdx-1]) /
             (con.v[jdx]-con.v[jdx-1]) +
             con.w[jdx-1])


Instruction:
Write a Python 3 script for
adjusting the water stage drop to the highest value allowed and correct the associated fluxes.

Response:
def corr_dw_v1(self):
    """Adjust the water stage drop to the highest value allowed and correct
    the associated fluxes.

    Note that method |corr_dw_v1| calls the method `interp_v` of the
    respective application model.  Hence the requirements of the actual
    `interp_v` need to be considered additionally.

    Required control parameter:
      |MaxDW|

    Required derived parameters:
      |llake_derived.TOY|
      |Seconds|

    Required flux sequence:
      |QZ|

    Updated flux sequence:
      |llake_fluxes.QA|

    Updated state sequences:
      |llake_states.W|
      |llake_states.V|

    Basic Restriction:
      :math:`W_{old} - W_{new} \\leq MaxDW`

    Examples:

        In preparation for the following examples, define a short simulation
        time period with a simulation step size of 12 hours and initialize
        the required model object:

        >>> from hydpy import pub
        >>> pub.timegrids = '2000.01.01', '2000.01.04', '12h'
        >>> from hydpy.models.llake import *
        >>> parameterstep('1d')
        >>> derived.toy.update()
        >>> derived.seconds.update()

        Select the first half of the second day of January as the simulation
        step relevant for the following examples:

        >>> model.idx_sim = pub.timegrids.init['2000.01.02']

        The following tests are based on method |interp_v_v1| for the
        interpolation of the stored water volume based on the corrected
        water stage:

        >>> model.interp_v = model.interp_v_v1

        For the sake of simplicity, the underlying `w`-`v` relationship is
        assumed to be linear:

        >>> n(2.)
        >>> w(0., 1.)
        >>> v(0., 1e6)

        The maximum drop in water stage for the first half of the second
        day of January is set to 0.4 m/d.  Note that, due to the difference
        between the parameter step size and the simulation step size, the
        actual value used for calculation is 0.2 m/12h:

        >>> maxdw(_1_1_18=.1,
        ...       _1_2_6=.4,
        ...       _1_2_18=.1)
        >>> maxdw
        maxdw(toy_1_1_18_0_0=0.1,
              toy_1_2_6_0_0=0.4,
              toy_1_2_18_0_0=0.1)
        >>> from hydpy import round_
        >>> round_(maxdw.value[2])
        0.2

        Define old and new water stages and volumes in agreement with the
        given linear relationship:

        >>> states.w.old = 1.
        >>> states.v.old = 1e6
        >>> states.w.new = .9
        >>> states.v.new = 9e5

        Also define an inflow and an outflow value.  Note the that the latter
        is set to zero, which is inconsistent with the actual water stage drop
        defined above, but done for didactic reasons:

        >>> fluxes.qz = 1.
        >>> fluxes.qa = 0.

        Calling the |corr_dw_v1| method does not change the values of
        either of following sequences, as the actual drop (0.1 m/12h) is
        smaller than the allowed drop (0.2 m/12h):

        >>> model.corr_dw_v1()
        >>> states.w
        w(0.9)
        >>> states.v
        v(900000.0)
        >>> fluxes.qa
        qa(0.0)

        Note that the values given above are not recalculated, which can
        clearly be seen for the lake outflow, which is still zero.

        Through setting the new value of the water stage to 0.6 m, the actual
        drop (0.4 m/12h) exceeds the allowed drop (0.2 m/12h). Hence the
        water stage is trimmed and the other values are recalculated:

        >>> states.w.new = .6
        >>> model.corr_dw_v1()
        >>> states.w
        w(0.8)
        >>> states.v
        v(800000.0)
        >>> fluxes.qa
        qa(5.62963)

        Through setting the maximum water stage drop to zero, method
        |corr_dw_v1| is effectively disabled.  Regardless of the actual
        change in water stage, no trimming or recalculating is performed:

        >>> maxdw.toy_01_02_06 = 0.
        >>> states.w.new = .6
        >>> model.corr_dw_v1()
        >>> states.w
        w(0.6)
        >>> states.v
        v(800000.0)
        >>> fluxes.qa
        qa(5.62963)
    """
    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    old = self.sequences.states.fastaccess_old
    new = self.sequences.states.fastaccess_new
    idx = der.toy[self.idx_sim]
    if (con.maxdw[idx] > 0.) and ((old.w-new.w) > con.maxdw[idx]):
        new.w = old.w-con.maxdw[idx]
        self.interp_v()
        flu.qa = flu.qz+(old.v-new.v)/der.seconds


Instruction:
Can you tell what is the following Python 3 function doing
def modify_qa_v1(self):

    con = self.parameters.control.fastaccess
    der = self.parameters.derived.fastaccess
    flu = self.sequences.fluxes.fastaccess
    idx = der.toy[self.idx_sim]
    flu.qa = max(flu.qa-con.verzw[idx], 0.)

Response: Modify the flux sequence of the current model object to include or remove water from the calculated lake outflow.


Instruction:
How would you explain what the following Python 3 function does
def pass_q_v1(self):
    flu = self.sequences.fluxes.fastaccess
    out = self.sequences.outlets.fastaccess
    out.q[0] += flu.qa

Response: Update the link sequence.


Instruction:
Write a Python 3 script for
thresholding values of the response functions.

Response:
def thresholds(self):
        """Threshold values of the response functions."""
        return numpy.array(
            sorted(self._key2float(key) for key in self._coefs), dtype=float)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def prepare_arrays(sim=None, obs=None, node=None, skip_nan=False):
    if node:
        if sim is not None:
            raise ValueError(
                'Values are passed to both arguments `sim` and `node`, '
                'which is not allowed.')
        if obs is not None:
            raise ValueError(
                'Values are passed to both arguments `obs` and `node`, '
                'which is not allowed.')
        sim = node.sequences.sim.series
        obs = node.sequences.obs.series
    elif (sim is not None) and (obs is None):
        raise ValueError(
            'A value is passed to argument `sim` '
            'but no value is passed to argument `obs`.')
    elif (obs is not None) and (sim is None):
        raise ValueError(
            'A value is passed to argument `obs` '
            'but no value is passed to argument `sim`.')
    elif (sim is None) and (obs is None):
        raise ValueError(
            'Neither a `Node` object is passed to argument `node` nor '
            'are arrays passed to arguments `sim` and `obs`.')
    sim = numpy.asarray(sim)
    obs = numpy.asarray(obs)
    if skip_nan:
        idxs = ~numpy.isnan(sim) * ~numpy.isnan(obs)
        sim = sim[idxs]
        obs = obs[idxs]
    return sim, obs

Response: Prepare and return two |numpy| arrays based on the given arguments.


Instruction:
Here you have a function in Python 3, explain what it does
def nse(sim=None, obs=None, node=None, skip_nan=False):
    sim, obs = prepare_arrays(sim, obs, node, skip_nan)
    return 1.-numpy.sum((sim-obs)**2)/numpy.sum((obs-numpy.mean(obs))**2)

Response: Calculate the efficiency criteria after Nash & Sutcliffe.


Instruction:
Implement a Python 3 function for
calculating the absolute difference between the means of the simulated and observed values.

Response:
def bias_abs(sim=None, obs=None, node=None, skip_nan=False):
    """Calculate the absolute difference between the means of the simulated
    and the observed values.

    >>> from hydpy import round_
    >>> from hydpy import bias_abs
    >>> round_(bias_abs(sim=[2.0, 2.0, 2.0], obs=[1.0, 2.0, 3.0]))
    0.0
    >>> round_(bias_abs(sim=[5.0, 2.0, 2.0], obs=[1.0, 2.0, 3.0]))
    1.0
    >>> round_(bias_abs(sim=[1.0, 1.0, 1.0], obs=[1.0, 2.0, 3.0]))
    -1.0

    See the documentation on function |prepare_arrays| for some
    additional instructions for use of function |bias_abs|.
    """
    sim, obs = prepare_arrays(sim, obs, node, skip_nan)
    return numpy.mean(sim-obs)


Instruction:
Create a Python 3 function to
calculate the ratio between the standard deviation of the simulated and observed values.

Response:
def std_ratio(sim=None, obs=None, node=None, skip_nan=False):
    """Calculate the ratio between the standard deviation of the simulated
    and the observed values.

    >>> from hydpy import round_
    >>> from hydpy import std_ratio
    >>> round_(std_ratio(sim=[1.0, 2.0, 3.0], obs=[1.0, 2.0, 3.0]))
    0.0
    >>> round_(std_ratio(sim=[1.0, 1.0, 1.0], obs=[1.0, 2.0, 3.0]))
    -1.0
    >>> round_(std_ratio(sim=[0.0, 3.0, 6.0], obs=[1.0, 2.0, 3.0]))
    2.0

    See the documentation on function |prepare_arrays| for some
    additional instructions for use of function |std_ratio|.
    """
    sim, obs = prepare_arrays(sim, obs, node, skip_nan)
    return numpy.std(sim)/numpy.std(obs)-1.


Instruction:
Write a Python 3 script to
calculate the product - moment correlation coefficient after Pearson.

Response:
def corr(sim=None, obs=None, node=None, skip_nan=False):
    """Calculate the product-moment correlation coefficient after Pearson.

    >>> from hydpy import round_
    >>> from hydpy import corr
    >>> round_(corr(sim=[0.5, 1.0, 1.5], obs=[1.0, 2.0, 3.0]))
    1.0
    >>> round_(corr(sim=[4.0, 2.0, 0.0], obs=[1.0, 2.0, 3.0]))
    -1.0
    >>> round_(corr(sim=[1.0, 2.0, 1.0], obs=[1.0, 2.0, 3.0]))
    0.0

    See the documentation on function |prepare_arrays| for some
    additional instructions for use of function |corr|.
    """
    sim, obs = prepare_arrays(sim, obs, node, skip_nan)
    return numpy.corrcoef(sim, obs)[0, 1]


Instruction:
Can you tell what is the following Python 3 function doing
def hsepd_pdf(sigma1, sigma2, xi, beta,
              sim=None, obs=None, node=None, skip_nan=False):
    """Calculate the probability densities based on the
    heteroskedastic skewed exponential power distribution.

    For convenience, the required parameters of the probability density
    function as well as the simulated and observed values are stored
    in a dictonary:

    >>> import numpy
    >>> from hydpy import round_
    >>> from hydpy import hsepd_pdf
    >>> general = {'sigma1': 0.2,
    ...            'sigma2': 0.0,
    ...            'xi': 1.0,
    ...            'beta': 0.0,
    ...            'sim': numpy.arange(10.0, 41.0),
    ...            'obs': numpy.full(31, 25.0)}

    The following test function allows the variation of one parameter
    and prints some and plots all of probability density values
    corresponding to different simulated values:

    >>> def test(**kwargs):
    ...     from matplotlib import pyplot
    ...     special = general.copy()
    ...     name, values = list(kwargs.items())[0]
    ...     results = numpy.zeros((len(general['sim']), len(values)+1))
    ...     results[:, 0] = general['sim']
    ...     for jdx, value in enumerate(values):
    ...         special[name] = value
    ...         results[:, jdx+1] = hsepd_pdf(**special)
    ...         pyplot.plot(results[:, 0], results[:, jdx+1],
    ...                     label='%s=%.1f' % (name, value))
    ...     pyplot.legend()
    ...     for idx, result in enumerate(results):
    ...         if not (idx % 5):
    ...             round_(result)

    When varying parameter `beta`, the resulting probabilities correspond
    to the Laplace distribution (1.0), normal distribution (0.0), and the
    uniform distribution (-1.0), respectively.  Note that we use -0.99
    instead of -1.0 for approximating the uniform distribution to prevent
    from running into numerical problems, which are not solved yet:

    >>> test(beta=[1.0, 0.0, -0.99])
    10.0, 0.002032, 0.000886, 0.0
    15.0, 0.008359, 0.010798, 0.0
    20.0, 0.034382, 0.048394, 0.057739
    25.0, 0.141421, 0.079788, 0.057739
    30.0, 0.034382, 0.048394, 0.057739
    35.0, 0.008359, 0.010798, 0.0
    40.0, 0.002032, 0.000886, 0.0

    .. testsetup::

        >>> from matplotlib import pyplot
        >>> pyplot.close()

    When varying parameter `xi`, the resulting density is negatively
    skewed (0.2), symmetric (1.0), and positively skewed (5.0),
    respectively:

    >>> test(xi=[0.2, 1.0, 5.0])
    10.0, 0.0, 0.000886, 0.003175
    15.0, 0.0, 0.010798, 0.012957
    20.0, 0.092845, 0.048394, 0.036341
    25.0, 0.070063, 0.079788, 0.070063
    30.0, 0.036341, 0.048394, 0.092845
    35.0, 0.012957, 0.010798, 0.0
    40.0, 0.003175, 0.000886, 0.0

    .. testsetup::

        >>> from matplotlib import pyplot
        >>> pyplot.close()

    In the above examples, the actual `sigma` (5.0) is calculated by
    multiplying `sigma1` (0.2) with the mean simulated value (25.0),
    internally.  This can be done for modelling homoscedastic errors.
    Instead, `sigma2` is multiplied with the individual simulated values
    to account for heteroscedastic errors.  With increasing values of
    `sigma2`, the resulting densities are modified as follows:

    >>> test(sigma2=[0.0, 0.1, 0.2])
    10.0, 0.000886, 0.002921, 0.005737
    15.0, 0.010798, 0.018795, 0.022831
    20.0, 0.048394, 0.044159, 0.037988
    25.0, 0.079788, 0.053192, 0.039894
    30.0, 0.048394, 0.04102, 0.032708
    35.0, 0.010798, 0.023493, 0.023493
    40.0, 0.000886, 0.011053, 0.015771

    .. testsetup::

        >>> from matplotlib import pyplot
        >>> pyplot.close()
    """
    sim, obs = prepare_arrays(sim, obs, node, skip_nan)
    sigmas = _pars_h(sigma1, sigma2, sim)
    mu_xi, sigma_xi, w_beta, c_beta = _pars_sepd(xi, beta)
    x, mu = obs, sim
    a = (x-mu)/sigmas
    a_xi = numpy.empty(a.shape)
    idxs = mu_xi+sigma_xi*a < 0.
    a_xi[idxs] = numpy.absolute(xi*(mu_xi+sigma_xi*a[idxs]))
    a_xi[~idxs] = numpy.absolute(1./xi*(mu_xi+sigma_xi*a[~idxs]))
    ps = (2.*sigma_xi/(xi+1./xi)*w_beta *
          numpy.exp(-c_beta*a_xi**(2./(1.+beta))))/sigmas
    return ps

Response: This function calculates the probability densities based on the HSEPD distribution.


Instruction:
Can you create a Python 3 function that
calculates the mean of the logarithmised probability densities of the heteroskedastic skewed exponential power distribution.

Response:
def hsepd_manual(sigma1, sigma2, xi, beta,
                 sim=None, obs=None, node=None, skip_nan=False):
    """Calculate the mean of the logarithmised probability densities of the
    'heteroskedastic skewed exponential power distribution.

    The following examples are taken from the documentation of function
    |hsepd_pdf|, which is used by function |hsepd_manual|.  The first
    one deals with a heteroscedastic normal distribution:

    >>> from hydpy import round_
    >>> from hydpy import hsepd_manual
    >>> round_(hsepd_manual(sigma1=0.2, sigma2=0.2,
    ...                     xi=1.0, beta=0.0,
    ...                     sim=numpy.arange(10.0, 41.0),
    ...                     obs=numpy.full(31, 25.0)))
    -3.682842

    The second one is supposed to show to small zero probability density
    values are set to 1e-200 before calculating their logarithm (which
    means that the lowest possible value returned by function
    |hsepd_manual| is approximately -460):

    >>> round_(hsepd_manual(sigma1=0.2, sigma2=0.0,
    ...                     xi=1.0, beta=-0.99,
    ...                     sim=numpy.arange(10.0, 41.0),
    ...                     obs=numpy.full(31, 25.0)))
    -209.539335
    """
    sim, obs = prepare_arrays(sim, obs, node, skip_nan)
    return _hsepd_manual(sigma1, sigma2, xi, beta, sim, obs)


Instruction:
Write a Python 3 function for
calculating the mean of the logarithmised probability densities of the heteroskedastic skewed exponential power distribution.

Response:
def hsepd(sim=None, obs=None, node=None, skip_nan=False,
          inits=None, return_pars=False, silent=True):
    """Calculate the mean of the logarithmised probability densities of the
    'heteroskedastic skewed exponential power distribution.

    Function |hsepd| serves the same purpose as function |hsepd_manual|,
    but tries to estimate the parameters of the heteroscedastic skewed
    exponential distribution via an optimization algorithm.  This
    is shown by generating a random sample.  1000 simulated values
    are scattered around the observed (true) value of 10.0 with a
    standard deviation of 2.0:

    >>> import numpy
    >>> numpy.random.seed(0)
    >>> sim = numpy.random.normal(10.0, 2.0, 1000)
    >>> obs = numpy.full(1000, 10.0)

    First, as a reference, we calculate the "true" value based on
    function |hsepd_manual| and the correct distribution parameters:

    >>> from hydpy import round_
    >>> from hydpy import hsepd, hsepd_manual
    >>> round_(hsepd_manual(sigma1=0.2, sigma2=0.0,
    ...                     xi=1.0, beta=0.0,
    ...                     sim=sim, obs=obs))
    -2.100093

    When using function |hsepd|, the returned value is even a little
    "better":

    >>> round_(hsepd(sim=sim, obs=obs))
    -2.09983

    This is due to the deviation from the random sample to its
    theoretical distribution.  This is reflected by small differences
    between the estimated values and the theoretical values of
    `sigma1` (0.2), , `sigma2` (0.0), `xi` (1.0), and `beta` (0.0).
    The estimated values are returned in the mentioned order through
    enabling the `return_pars` option:

    >>> value, pars = hsepd(sim=sim, obs=obs, return_pars=True)
    >>> round_(pars, decimals=5)
    0.19966, 0.0, 0.96836, 0.0188

    There is no guarantee that the optimization numerical optimization
    algorithm underlying function |hsepd| will always find the parameters
    resulting in the largest value returned by function |hsepd_manual|.
    You can increase its robustness (and decrease computation time) by
    supplying good initial parameter values:

    >>> value, pars = hsepd(sim=sim, obs=obs, return_pars=True,
    ...                     inits=(0.2, 0.0, 1.0, 0.0))
    >>> round_(pars, decimals=5)
    0.19966, 0.0, 0.96836, 0.0188

    However, the following example shows a case when this strategie
    results in worse results:

    >>> value, pars = hsepd(sim=sim, obs=obs, return_pars=True,
    ...                     inits=(0.0, 0.2, 1.0, 0.0))
    >>> round_(value)
    -2.174492
    >>> round_(pars)
    0.0, 0.213179, 1.705485, 0.505112
    """

    def transform(pars):
        """Transform the actual optimization problem into a function to
        be minimized and apply parameter constraints."""
        sigma1, sigma2, xi, beta = constrain(*pars)
        return -_hsepd_manual(sigma1, sigma2, xi, beta, sim, obs)

    def constrain(sigma1, sigma2, xi, beta):
        """Apply constrains on the given parameter values."""
        sigma1 = numpy.clip(sigma1, 0.0, None)
        sigma2 = numpy.clip(sigma2, 0.0, None)
        xi = numpy.clip(xi, 0.1, 10.0)
        beta = numpy.clip(beta, -0.99, 5.0)
        return sigma1, sigma2, xi, beta

    sim, obs = prepare_arrays(sim, obs, node, skip_nan)
    if not inits:
        inits = [0.1, 0.2, 3.0, 1.0]
    values = optimize.fmin(transform, inits,
                           ftol=1e-12, xtol=1e-12,
                           disp=not silent)
    values = constrain(*values)
    result = _hsepd_manual(*values, sim=sim, obs=obs)
    if return_pars:
        return result, values
    return result


Instruction:
Can you tell what is the following Python 3 function doing
def calc_mean_time(timepoints, weights):
    timepoints = numpy.array(timepoints)
    weights = numpy.array(weights)
    validtools.test_equal_shape(timepoints=timepoints, weights=weights)
    validtools.test_non_negative(weights=weights)
    return numpy.dot(timepoints, weights)/numpy.sum(weights)

Response: Calculate the weighted mean time of the given timepoints.


Instruction:
Can you write a function in Python 3 where it
calculates the weighted deviation of the given timepoints from their mean time.

Response:
def calc_mean_time_deviation(timepoints, weights, mean_time=None):
    """Return the weighted deviation of the given timepoints from their mean
    time.

    With equal given weights, the is simply the standard deviation of the
    given time points:

    >>> from hydpy import calc_mean_time_deviation
    >>> calc_mean_time_deviation(timepoints=[3., 7.],
    ...                          weights=[2., 2.])
    2.0

    One can pass a precalculated or alternate mean time:

    >>> from hydpy import round_
    >>> round_(calc_mean_time_deviation(timepoints=[3., 7.],
    ...                                 weights=[2., 2.],
    ...                                 mean_time=4.))
    2.236068

    >>> round_(calc_mean_time_deviation(timepoints=[3., 7.],
    ...                                 weights=[1., 3.]))
    1.732051

    Or, in the most extreme case:

    >>> calc_mean_time_deviation(timepoints=[3., 7.],
    ...                          weights=[0., 4.])
    0.0

    There will be some checks for input plausibility perfomed, e.g.:

    >>> calc_mean_time_deviation(timepoints=[3., 7.],
    ...                          weights=[-2., 2.])
    Traceback (most recent call last):
    ...
    ValueError: While trying to calculate the weighted time deviation \
from mean time, the following error occurred: For the following objects, \
at least one value is negative: weights.
    """
    timepoints = numpy.array(timepoints)
    weights = numpy.array(weights)
    validtools.test_equal_shape(timepoints=timepoints, weights=weights)
    validtools.test_non_negative(weights=weights)
    if mean_time is None:
        mean_time = calc_mean_time(timepoints, weights)
    return (numpy.sqrt(numpy.dot(weights, (timepoints-mean_time)**2) /
                       numpy.sum(weights)))


Instruction:
Can you implement a function in Python 3 that
returns a table containing the results of the given evaluation criteria for the given |Node| objects.

Response:
def evaluationtable(nodes, criteria, nodenames=None,
                    critnames=None, skip_nan=False):
    """Return a table containing the results of the given evaluation
    criteria for the given |Node| objects.

    First, we define two nodes with different simulation and observation
    data (see function |prepare_arrays| for some explanations):

    >>> from hydpy import pub, Node, nan
    >>> pub.timegrids = '01.01.2000', '04.01.2000', '1d'
    >>> nodes = Node('test1'), Node('test2')
    >>> for node in nodes:
    ...     node.prepare_simseries()
    ...     node.sequences.sim.series = 1.0, 2.0, 3.0
    ...     node.sequences.obs.ramflag = True
    ...     node.sequences.obs.series = 4.0, 5.0, 6.0
    >>> nodes[0].sequences.sim.series = 1.0, 2.0, 3.0
    >>> nodes[0].sequences.obs.series = 4.0, 5.0, 6.0
    >>> nodes[1].sequences.sim.series = 1.0, 2.0, 3.0
    >>> with pub.options.checkseries(False):
    ...     nodes[1].sequences.obs.series = 3.0, nan, 1.0

    Selecting functions |corr| and |bias_abs| as evaluation criteria,
    function |evaluationtable| returns the following table (which is
    actually a pandas data frame):

    >>> from hydpy import evaluationtable, corr, bias_abs
    >>> evaluationtable(nodes, (corr, bias_abs))
           corr  bias_abs
    test1   1.0      -3.0
    test2   NaN       NaN

    One can pass alternative names for both the node objects and the
    criteria functions.  Also, `nan` values can be skipped:

    >>> evaluationtable(nodes, (corr, bias_abs),
    ...                 nodenames=('first node', 'second node'),
    ...                 critnames=('corrcoef', 'bias'),
    ...                 skip_nan=True)
                 corrcoef  bias
    first node        1.0  -3.0
    second node      -1.0   0.0

    The number of assigned node objects and criteria functions must
    match the number of givern alternative names:

    >>> evaluationtable(nodes, (corr, bias_abs),
    ...                 nodenames=('first node',))
    Traceback (most recent call last):
    ...
    ValueError: While trying to evaluate the simulation results of some \
node objects, the following error occurred: 2 node objects are given \
which does not match with number of given alternative names beeing 1.

    >>> evaluationtable(nodes, (corr, bias_abs),
    ...                 critnames=('corrcoef',))
    Traceback (most recent call last):
    ...
    ValueError: While trying to evaluate the simulation results of some \
node objects, the following error occurred: 2 criteria functions are given \
which does not match with number of given alternative names beeing 1.
    """
    if nodenames:
        if len(nodes) != len(nodenames):
            raise ValueError(
                '%d node objects are given which does not match with '
                'number of given alternative names beeing %s.'
                % (len(nodes), len(nodenames)))
    else:
        nodenames = [node.name for node in nodes]
    if critnames:
        if len(criteria) != len(critnames):
            raise ValueError(
                '%d criteria functions are given which does not match '
                'with number of given alternative names beeing %s.'
                % (len(criteria), len(critnames)))
    else:
        critnames = [crit.__name__ for crit in criteria]
    data = numpy.empty((len(nodes), len(criteria)), dtype=float)
    for idx, node in enumerate(nodes):
        sim, obs = prepare_arrays(None, None, node, skip_nan)
        for jdx, criterion in enumerate(criteria):
            data[idx, jdx] = criterion(sim, obs)
    table = pandas.DataFrame(
        data=data, index=nodenames, columns=critnames)
    return table


Instruction:
Implement a function in Python 3 to
set all primary parameters at once.

Response:
def set_primary_parameters(self, **kwargs):
        """Set all primary parameters at once."""
        given = sorted(kwargs.keys())
        required = sorted(self._PRIMARY_PARAMETERS)
        if given == required:
            for (key, value) in kwargs.items():
                setattr(self, key, value)
        else:
            raise ValueError(
                'When passing primary parameter values as initialization '
                'arguments of the instantaneous unit hydrograph class `%s`, '
                'or when using method `set_primary_parameters, one has to '
                'to define all values at once via keyword arguments.  '
                'But instead of the primary parameter names `%s` the '
                'following keywords were given: %s.'
                % (objecttools.classname(self),
                   ', '.join(required), ', '.join(given)))


Instruction:
Can you tell what is the following Python 3 function doing
def primary_parameters_complete(self):
        for primpar in self._PRIMARY_PARAMETERS.values():
            if primpar.__get__(self) is None:
                return False
        return True

Response: True if all primary parameters are defined or not.


Instruction:
How would you implement a function in Python 3 that
updates the values of the values of the MA and AR models.

Response:
def update(self):
        """Delete the coefficients of the pure MA model and also all MA and
        AR coefficients of the ARMA model.  Also calculate or delete the values
        of all secondary iuh parameters, depending on the completeness of the
        values of the primary parameters.
        """
        del self.ma.coefs
        del self.arma.ma_coefs
        del self.arma.ar_coefs
        if self.primary_parameters_complete:
            self.calc_secondary_parameters()
        else:
            for secpar in self._SECONDARY_PARAMETERS.values():
                secpar.__delete__(self)


Instruction:
Can you tell what is the following Python 3 function doing
def plot(self, threshold=None, **kwargs):
        delays, responses = self.delay_response_series
        pyplot.plot(delays, responses, **kwargs)
        pyplot.xlabel('time')
        pyplot.ylabel('response')
        if threshold is not None:
            threshold = numpy.clip(threshold, 0., 1.)
            cumsum = numpy.cumsum(responses)
            idx = numpy.where(cumsum >= threshold*cumsum[-1])[0][0]
            pyplot.xlim(0., delays[idx])

Response: Plot the instanteneous unit hydrograph.


Instruction:
Explain what the following Python 3 code does
def moment1(self):
        delays, response = self.delay_response_series
        return statstools.calc_mean_time(delays, response)

Response:
The first time delay weighted statistical moment of the
        instantaneous unit hydrograph.


Instruction:
How would you explain what the following Python 3 function does
def moment2(self):
        moment1 = self.moment1
        delays, response = self.delay_response_series
        return statstools.calc_mean_time_deviation(
            delays, response, moment1)

Response:
The second time delay weighted statistical momens of the
            instantaneous unit hydrograph.


Instruction:
How would you explain what the following Python 3 function does
def calc_secondary_parameters(self):
        self.a = self.x/(2.*self.d**.5)
        self.b = self.u/(2.*self.d**.5)

Response: Determine the values of the secondary parameters a and b.


Instruction:
Can you generate the documentation for the following Python 3 function
def calc_secondary_parameters(self):
        self.c = 1./(self.k*special.gamma(self.n))

Response: Determine the value of the secondary parameter c.


Instruction:
Write a Python 3 script to
trim values in accordance with WAeS \\ leq PWMax \\ cdot WATS.

Response:
def trim(self, lower=None, upper=None):
        """Trim values in accordance with :math:`WAeS \\leq PWMax \\cdot WATS`,
        or at least in accordance with if :math:`WATS \\geq 0`.

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> nhru(7)
        >>> pwmax(2.0)
        >>> states.waes = -1., 0., 1., -1., 5., 10., 20.
        >>> states.wats(-1., 0., 0., 5., 5., 5., 5.)
        >>> states.wats
        wats(0.0, 0.0, 0.5, 5.0, 5.0, 5.0, 10.0)
        """
        pwmax = self.subseqs.seqs.model.parameters.control.pwmax
        waes = self.subseqs.waes
        if lower is None:
            lower = numpy.clip(waes/pwmax, 0., numpy.inf)
            lower[numpy.isnan(lower)] = 0.0
        lland_sequences.State1DSequence.trim(self, lower, upper)


Instruction:
Write a Python 3 function that can
trim values in accordance with WAeS \\ leq PWMax \\ cdot WATS.

Response:
def trim(self, lower=None, upper=None):
        """Trim values in accordance with :math:`WAeS \\leq PWMax \\cdot WATS`.

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> nhru(7)
        >>> pwmax(2.)
        >>> states.wats = 0., 0., 0., 5., 5., 5., 5.
        >>> states.waes(-1., 0., 1., -1., 5., 10., 20.)
        >>> states.waes
        waes(0.0, 0.0, 0.0, 0.0, 5.0, 10.0, 10.0)
        """
        pwmax = self.subseqs.seqs.model.parameters.control.pwmax
        wats = self.subseqs.wats
        if upper is None:
            upper = pwmax*wats
        lland_sequences.State1DSequence.trim(self, lower, upper)


Instruction:
Write a Python 3 function for
trimming values in accordance with Bowa \\ leq NFk.

Response:
def trim(self, lower=None, upper=None):
        """Trim values in accordance with :math:`BoWa \\leq NFk`.

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> nhru(5)
        >>> nfk(200.)
        >>> states.bowa(-100.,0., 100., 200., 300.)
        >>> states.bowa
        bowa(0.0, 0.0, 100.0, 200.0, 200.0)
        """
        if upper is None:
            upper = self.subseqs.seqs.model.parameters.control.nfk
        lland_sequences.State1DSequence.trim(self, lower, upper)


Instruction:
Can you tell what is the following Python 3 function doing
def post(self, request, pk):
        location = self.get_object()
        # open days, disabled widget data won't make it into request.POST
        present_prefixes = [x.split('-')[0] for x in request.POST.keys()]
        day_forms = OrderedDict()
        for day_no, day_name in WEEKDAYS:
            for slot_no in (1, 2):
                prefix = self.form_prefix(day_no, slot_no)
                # skip closed day as it would be invalid form due to no data
                if prefix not in present_prefixes:
                    continue
                day_forms[prefix] = (day_no, Slot(request.POST, prefix=prefix))

        if all([day_form[1].is_valid() for pre, day_form in day_forms.items()]):
            OpeningHours.objects.filter(company=location).delete()
            for prefix, day_form in day_forms.items():
                day, form = day_form
                opens, shuts = [str_to_time(form.cleaned_data[x])
                                for x in ('opens', 'shuts')]
                if opens != shuts:
                    OpeningHours(from_hour=opens, to_hour=shuts,
                                 company=location, weekday=day).save()
        return redirect(request.path_info)

Response: Clean the data and save opening hours in the database.


Instruction:
Can you create a Python 3 function that
initializes the editing form 1. Build opening_hours, a lookup dictionary to populate the form slots: keys are day numbers, values are lists of opening hours for that day. 2. Build days, a list of days with 2 slot forms each. 3. Build form initials for the 2 slots padding/trimming opening_hours to end up with exactly 2 slots even if it's just None values.

Response:
def get(self, request, pk):
        """ Initialize the editing form

        1. Build opening_hours, a lookup dictionary to populate the form
           slots: keys are day numbers, values are lists of opening
           hours for that day.
        2. Build days, a list of days with 2 slot forms each.
        3. Build form initials for the 2 slots padding/trimming
           opening_hours to end up with exactly 2 slots even if it's
           just None values.
        """
        location = self.get_object()
        two_sets = False
        closed = None
        opening_hours = {}
        for o in OpeningHours.objects.filter(company=location):
            opening_hours.setdefault(o.weekday, []).append(o)
        days = []
        for day_no, day_name in WEEKDAYS:
            if day_no not in opening_hours.keys():
                if opening_hours:
                    closed = True
                ini1, ini2 = [None, None]
            else:
                closed = False
                ini = [{'opens': time_to_str(oh.from_hour),
                        'shuts': time_to_str(oh.to_hour)}
                       for oh in opening_hours[day_no]]
                ini += [None] * (2 - len(ini[:2]))  # pad
                ini1, ini2 = ini[:2]  # trim
                if ini2:
                    two_sets = True
            days.append({
                'name': day_name,
                'number': day_no,
                'slot1': Slot(prefix=self.form_prefix(day_no, 1), initial=ini1),
                'slot2': Slot(prefix=self.form_prefix(day_no, 2), initial=ini2),
                'closed': closed
            })
        return render(request, self.template_name, {
            'days': days,
            'two_sets': two_sets,
            'location': location,
        })


Instruction:
Can you tell what is the following Python 3 function doing
def calc_qjoints_v1(self):
    der = self.parameters.derived.fastaccess
    new = self.sequences.states.fastaccess_new
    old = self.sequences.states.fastaccess_old
    for j in range(der.nmbsegments):
        new.qjoints[j+1] = (der.c1*new.qjoints[j] +
                            der.c2*old.qjoints[j] +
                            der.c3*old.qjoints[j+1])

Response: Calculates the routing equation for the current state sequence.


Instruction:
Create a Python 3 function for
assigning the actual value of the inlet sequence to the upper joint of the subreach upstream.

Response:
def pick_q_v1(self):
    """Assign the actual value of the inlet sequence to the upper joint
    of the subreach upstream."""
    inl = self.sequences.inlets.fastaccess
    new = self.sequences.states.fastaccess_new
    new.qjoints[0] = 0.
    for idx in range(inl.len_q):
        new.qjoints[0] += inl.q[idx][0]


Instruction:
Can you generate the documentation for the following Python 3 function
def pass_q_v1(self):
    der = self.parameters.derived.fastaccess
    new = self.sequences.states.fastaccess_new
    out = self.sequences.outlets.fastaccess
    out.q[0] += new.qjoints[der.nmbsegments]

Response:
Assing the actual value of the lower joint of the subreach
 downstream to the outlet sequence.


Instruction:
Can you write a function in Python 3 where it
tries to detect the encoding of the data. If data is passed try to decode the data with the default system encoding. If data is not passed try to decode the data with the default system encoding.

Response:
def _detect_encoding(data=None):
    """Return the default system encoding. If data is passed, try
    to decode the data with the default system encoding or from a short
    list of encoding types to test.

    Args:
        data - list of lists
    Returns:
        enc - system encoding
    """
    import locale
    enc_list = ['utf-8', 'latin-1', 'iso8859-1', 'iso8859-2',
                'utf-16', 'cp720']
    code = locale.getpreferredencoding(False)
    if data is None:
        return code
    if code.lower() not in enc_list:
        enc_list.insert(0, code.lower())
    for c in enc_list:
        try:
            for line in data:
                line.decode(c)
        except (UnicodeDecodeError, UnicodeError, AttributeError):
            continue
        return c
    print("Encoding not detected. Please pass encoding value manually")


Instruction:
Here you have a function in Python 3, explain what it does
def parameterstep(timestep=None):
    if timestep is not None:
        parametertools.Parameter.parameterstep(timestep)
    namespace = inspect.currentframe().f_back.f_locals
    model = namespace.get('model')
    if model is None:
        model = namespace['Model']()
        namespace['model'] = model
        if hydpy.pub.options.usecython and 'cythonizer' in namespace:
            cythonizer = namespace['cythonizer']
            namespace['cythonmodule'] = cythonizer.cymodule
            model.cymodel = cythonizer.cymodule.Model()
            namespace['cymodel'] = model.cymodel
            model.cymodel.parameters = cythonizer.cymodule.Parameters()
            model.cymodel.sequences = cythonizer.cymodule.Sequences()
            for numpars_name in ('NumConsts', 'NumVars'):
                if hasattr(cythonizer.cymodule, numpars_name):
                    numpars_new = getattr(cythonizer.cymodule, numpars_name)()
                    numpars_old = getattr(model, numpars_name.lower())
                    for (name_numpar, numpar) in vars(numpars_old).items():
                        setattr(numpars_new, name_numpar, numpar)
                    setattr(model.cymodel, numpars_name.lower(), numpars_new)
            for name in dir(model.cymodel):
                if (not name.startswith('_')) and hasattr(model, name):
                    setattr(model, name, getattr(model.cymodel, name))
        if 'Parameters' not in namespace:
            namespace['Parameters'] = parametertools.Parameters
        model.parameters = namespace['Parameters'](namespace)
        if 'Sequences' not in namespace:
            namespace['Sequences'] = sequencetools.Sequences
        model.sequences = namespace['Sequences'](**namespace)
        namespace['parameters'] = model.parameters
        for pars in model.parameters:
            namespace[pars.name] = pars
        namespace['sequences'] = model.sequences
        for seqs in model.sequences:
            namespace[seqs.name] = seqs
        if 'Masks' in namespace:
            model.masks = namespace['Masks'](model)
            namespace['masks'] = model.masks
    try:
        namespace.update(namespace['CONSTANTS'])
    except KeyError:
        pass
    focus = namespace.get('focus')
    for par in model.parameters.control:
        try:
            if (focus is None) or (par is focus):
                namespace[par.name] = par
            else:
                namespace[par.name] = lambda *args, **kwargs: None
        except AttributeError:
            pass

Response: Define a parameter time step size within a parameter control file.


Instruction:
Can you create a Python 3 function that
clears the local namespace from a wildcard import of HydPy - L - Land_v1 model.

Response:
def reverse_model_wildcard_import():
    """Clear the local namespace from a model wildcard import.

    Calling this method should remove the critical imports into the local
    namespace due the last wildcard import of a certain application model.
    It is thought for securing the successive preperation of different
    types of models via wildcard imports.  See the following example, on
    how it can be applied.

    >>> from hydpy import reverse_model_wildcard_import

    Assume you wildcard import the first version of HydPy-L-Land (|lland_v1|):

    >>> from hydpy.models.lland_v1 import *

    This for example adds the collection class for handling control
    parameters of `lland_v1` into the local namespace:

    >>> print(ControlParameters(None).name)
    control

    Calling function |parameterstep| for example prepares the control
    parameter object |lland_control.NHRU|:

    >>> parameterstep('1d')
    >>> nhru
    nhru(?)

    Calling function |reverse_model_wildcard_import| removes both
    objects (and many more, but not all) from the local namespace:

    >>> reverse_model_wildcard_import()

    >>> ControlParameters
    Traceback (most recent call last):
    ...
    NameError: name 'ControlParameters' is not defined

    >>> nhru
    Traceback (most recent call last):
    ...
    NameError: name 'nhru' is not defined
    """
    namespace = inspect.currentframe().f_back.f_locals
    model = namespace.get('model')
    if model is not None:
        for subpars in model.parameters:
            for par in subpars:
                namespace.pop(par.name, None)
                namespace.pop(objecttools.classname(par), None)
            namespace.pop(subpars.name, None)
            namespace.pop(objecttools.classname(subpars), None)
        for subseqs in model.sequences:
            for seq in subseqs:
                namespace.pop(seq.name, None)
                namespace.pop(objecttools.classname(seq), None)
            namespace.pop(subseqs.name, None)
            namespace.pop(objecttools.classname(subseqs), None)
        for name in ('parameters', 'sequences', 'masks', 'model',
                     'Parameters', 'Sequences', 'Masks', 'Model',
                     'cythonizer', 'cymodel', 'cythonmodule'):
            namespace.pop(name, None)
        for key in list(namespace.keys()):
            try:
                if namespace[key].__module__ == model.__module__:
                    del namespace[key]
            except AttributeError:
                pass


Instruction:
Can you generate a brief explanation for the following Python 3 code
def prepare_model(module: Union[types.ModuleType, str],
                  timestep: PeriodABC.ConstrArg = None):
    """Prepare and return the model of the given module.

    In usual HydPy projects, each hydrological model instance is prepared
    in an individual control file.  This allows for "polluting" the
    namespace with different model attributes.  There is no danger of
    name conflicts, as long as no other (wildcard) imports are performed.

    However, there are situations when different models are to be loaded
    into the same namespace.  Then it is advisable to use function
    |prepare_model|, which just returns a reference to the model
    and nothing else.

    See the documentation of |dam_v001| on how to apply function
    |prepare_model| properly.
    """
    if timestep is not None:
        parametertools.Parameter.parameterstep(timetools.Period(timestep))
    try:
        model = module.Model()
    except AttributeError:
        module = importlib.import_module(f'hydpy.models.{module}')
        model = module.Model()
    if hydpy.pub.options.usecython and hasattr(module, 'cythonizer'):
        cymodule = module.cythonizer.cymodule
        cymodel = cymodule.Model()
        cymodel.parameters = cymodule.Parameters()
        cymodel.sequences = cymodule.Sequences()
        model.cymodel = cymodel
        for numpars_name in ('NumConsts', 'NumVars'):
            if hasattr(cymodule, numpars_name):
                numpars_new = getattr(cymodule, numpars_name)()
                numpars_old = getattr(model, numpars_name.lower())
                for (name_numpar, numpar) in vars(numpars_old).items():
                    setattr(numpars_new, name_numpar, numpar)
                setattr(cymodel, numpars_name.lower(), numpars_new)
        for name in dir(cymodel):
            if (not name.startswith('_')) and hasattr(model, name):
                setattr(model, name, getattr(cymodel, name))
        dict_ = {'cythonmodule': cymodule,
                 'cymodel': cymodel}
    else:
        dict_ = {}
    dict_.update(vars(module))
    dict_['model'] = model
    if hasattr(module, 'Parameters'):
        model.parameters = module.Parameters(dict_)
    else:
        model.parameters = parametertools.Parameters(dict_)
    if hasattr(module, 'Sequences'):
        model.sequences = module.Sequences(**dict_)
    else:
        model.sequences = sequencetools.Sequences(**dict_)
    if hasattr(module, 'Masks'):
        model.masks = module.Masks(model)
    return model

Response: Prepare and return the model of the given module.


Instruction:
Write a Python 3 function for
defining a simulation time step size for testing purposes within a single parameter control file.

Response:
def simulationstep(timestep):
    """ Define a simulation time step size for testing purposes within a
    parameter control file.

    Using |simulationstep| only affects the values of time dependent
    parameters, when `pub.timegrids.stepsize` is not defined.  It thus has
    no influence on usual hydpy simulations at all.  Use it just to check
    your parameter control files.  Write it in a line immediately behind
    the one calling |parameterstep|.

    To clarify its purpose, executing raises a warning, when executing
    it from within a control file:

    >>> from hydpy import pub
    >>> with pub.options.warnsimulationstep(True):
    ...     from hydpy.models.hland_v1 import *
    ...     parameterstep('1d')
    ...     simulationstep('1h')
    Traceback (most recent call last):
    ...
    UserWarning: Note that the applied function `simulationstep` is intended \
for testing purposes only.  When doing a HydPy simulation, parameter values \
are initialised based on the actual simulation time step as defined under \
`pub.timegrids.stepsize` and the value given to `simulationstep` is ignored.
    >>> k4.simulationstep
    Period('1h')
    """
    if hydpy.pub.options.warnsimulationstep:
        warnings.warn(
            'Note that the applied function `simulationstep` is intended for '
            'testing purposes only.  When doing a HydPy simulation, parameter '
            'values are initialised based on the actual simulation time step '
            'as defined under `pub.timegrids.stepsize` and the value given '
            'to `simulationstep` is ignored.')
    parametertools.Parameter.simulationstep(timestep)


Instruction:
Implement a Python 3 function for
defining the corresponding control file within a condition file. Function |controlcheck| serves similar purposes as function |parameterstep|. It is the reason why one can interactively access the state and/or the log sequences within condition files as `land_dill.py` of the example project `LahnH`. It is called `controlcheck` due to its implicite feature to check upon the execution of the condition file if eventual specifications within both files disagree. The following test, where we write a number of soil moisture values (|hland_states.SM|) into condition file `land_dill.py` which does not agree with the number of hydrological response units (|hland_control.NmbZones|) defined in control file `land_dill.py`, verifies that this actually works within a new Python process: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> import os, subprocess >>> from hydpy import TestIO >>> cwd = os.path.join('LahnH', 'conditions', 'init_1996_01_01') >>> with TestIO(): ... os.chdir(cwd) ... with open('land_dill.py') as file_: ... lines = file_.readlines() ... lines[10:12] = 'sm(185.13164, 181.18755)', '' ... with open('land_dill.py', 'w') as file_: ... _ = file_.write('\\n'.join(lines)) ... result = subprocess.run( ... 'python land_dill.py', ... stdout=subprocess.PIPE, ... stderr=subprocess.PIPE, ... universal_newlines=True, ... shell=True) >>> print(result.stderr.split('ValueError:')[-1].strip()) While trying to set the value(s) of variable `sm`, the following error \ occurred: While trying to convert the value(s) `(185.13164, 181.18755)` to \ a numpy ndarray with shape `(12,)` and type `float`, the following error \ occurred: could not broadcast input array from shape (2) into shape (12) With a little trick, we can fake to be "inside" condition file `land_dill.py`. Calling |controlcheck| then e.g. prepares the shape of sequence |hland_states.Ic| as specified by the value of parameter |hland_control.NmbZones| given in the corresponding control file: >>> from hydpy.models.hland_v1 import * >>> __file__ = 'land_dill.py' # ToDo: undo? >>> with TestIO(): ... os.chdir(cwd) ... controlcheck() >>> ic.shape (12,) In the above example, the standard names for the project directory (the one containing the executed condition file) and the control directory (`default`) are used. The following example shows how to change them: >>> del model >>> with TestIO(): # doctest: +ELLIPSIS ... os.chdir(cwd) ... controlcheck(projectdir='somewhere', controldir='nowhere') Traceback (most recent call last): ... FileNotFoundError: While trying to load the control file \ `...hydpy...tests...iotesting...control...nowhere...land_dill.py`, the \ following error occurred: [Errno 2] No such file or directory: '...land_dill.py' Note that the functionalities of function |controlcheck| are disabled when there is already a `model` variable in the namespace, which is the case when a condition file is executed within the context of a complete HydPy project.

Response:
def controlcheck(controldir='default', projectdir=None, controlfile=None):
    """Define the corresponding control file within a condition file.

    Function |controlcheck| serves similar purposes as function
    |parameterstep|.  It is the reason why one can interactively
    access the state and/or the log sequences within condition files
    as `land_dill.py` of the example project `LahnH`.  It is called
    `controlcheck` due to its implicite feature to check upon the execution
    of the condition file if eventual specifications within both files
    disagree.  The following test, where we write a number of soil moisture
    values (|hland_states.SM|) into condition file `land_dill.py` which
    does not agree with the number of hydrological response units
    (|hland_control.NmbZones|) defined in control file `land_dill.py`,
    verifies that this actually works within a new Python process:

    >>> from hydpy.core.examples import prepare_full_example_1
    >>> prepare_full_example_1()

    >>> import os, subprocess
    >>> from hydpy import TestIO
    >>> cwd = os.path.join('LahnH', 'conditions', 'init_1996_01_01')
    >>> with TestIO():
    ...     os.chdir(cwd)
    ...     with open('land_dill.py') as file_:
    ...         lines = file_.readlines()
    ...     lines[10:12] = 'sm(185.13164, 181.18755)', ''
    ...     with open('land_dill.py', 'w') as file_:
    ...         _ = file_.write('\\n'.join(lines))
    ...     result = subprocess.run(
    ...         'python land_dill.py',
    ...         stdout=subprocess.PIPE,
    ...         stderr=subprocess.PIPE,
    ...         universal_newlines=True,
    ...         shell=True)
    >>> print(result.stderr.split('ValueError:')[-1].strip())
    While trying to set the value(s) of variable `sm`, the following error \
occurred: While trying to convert the value(s) `(185.13164, 181.18755)` to \
a numpy ndarray with shape `(12,)` and type `float`, the following error \
occurred: could not broadcast input array from shape (2) into shape (12)

    With a little trick, we can fake to be "inside" condition file
    `land_dill.py`.  Calling |controlcheck| then e.g. prepares the shape
    of sequence |hland_states.Ic| as specified by the value of parameter
    |hland_control.NmbZones| given in the corresponding control file:

    >>> from hydpy.models.hland_v1 import *
    >>> __file__ = 'land_dill.py'   # ToDo: undo?
    >>> with TestIO():
    ...     os.chdir(cwd)
    ...     controlcheck()
    >>> ic.shape
    (12,)

    In the above example, the standard names for the project directory
    (the one containing the executed condition file) and the control
    directory (`default`) are used.  The following example shows how
    to change them:

    >>> del model
    >>> with TestIO():   # doctest: +ELLIPSIS
    ...     os.chdir(cwd)
    ...     controlcheck(projectdir='somewhere', controldir='nowhere')
    Traceback (most recent call last):
    ...
    FileNotFoundError: While trying to load the control file \
`...hydpy...tests...iotesting...control...nowhere...land_dill.py`, the \
following error occurred: [Errno 2] No such file or directory: '...land_dill.py'

    Note that the functionalities of function |controlcheck| are disabled
    when there is already a `model` variable in the namespace, which is
    the case when a condition file is executed within the context of a
    complete HydPy project.
    """
    namespace = inspect.currentframe().f_back.f_locals
    model = namespace.get('model')
    if model is None:
        if not controlfile:
            controlfile = os.path.split(namespace['__file__'])[-1]
        if projectdir is None:
            projectdir = (
                os.path.split(
                    os.path.split(
                        os.path.split(os.getcwd())[0])[0])[-1])
        dirpath = os.path.abspath(os.path.join(
            '..', '..', '..', projectdir, 'control', controldir))

        class CM(filetools.ControlManager):
            currentpath = dirpath

        model = CM().load_file(filename=controlfile)['model']
        model.parameters.update()
        namespace['model'] = model
        for name in ('states', 'logs'):
            subseqs = getattr(model.sequences, name, None)
            if subseqs is not None:
                for seq in subseqs:
                    namespace[seq.name] = seq