Instruction:
Make a summary of the following Python 3 code
def move_dll(self):
        dirinfos = os.walk(self.buildpath)
        next(dirinfos)
        system_dependent_filename = None
        for dirinfo in dirinfos:
            for filename in dirinfo[2]:
                if (filename.startswith(self.cyname) and
                        filename.endswith(dllextension)):
                    system_dependent_filename = filename
                    break
            if system_dependent_filename:
                try:
                    shutil.move(os.path.join(dirinfo[0],
                                             system_dependent_filename),
                                os.path.join(self.cydirpath,
                                             self.cyname+dllextension))
                    break
                except BaseException:
                    prefix = ('After trying to cythonize module %s, when '
                              'trying to move the final cython module %s '
                              'from directory %s to directory %s'
                              % (self.pyname, system_dependent_filename,
                                 self.buildpath, self.cydirpath))
                    suffix = ('A likely error cause is that the cython module '
                              '%s does already exist in this directory and is '
                              'currently blocked by another Python process.  '
                              'Maybe it helps to close all Python processes '
                              'and restart the cyhonization afterwards.'
                              % self.cyname+dllextension)
                    objecttools.augment_excmessage(prefix, suffix)
        else:
            raise IOError('After trying to cythonize module %s, the resulting '
                          'file %s could neither be found in directory %s nor '
                          'its subdirectories.  The distul report should tell '
                          'whether the file has been stored somewhere else,'
                          'is named somehow else, or could not be build at '
                          'all.' % self.buildpath)

Response: Try to find the resulting dll file and to move it into the cythons package.


Instruction:
Here you have a function in Python 3, explain what it does
def parameters(self):
        lines = Lines()
        lines.add(0, '@cython.final')
        lines.add(0, 'cdef class Parameters(object):')
        for subpars in self.model.parameters:
            if subpars:
                lines.add(1, 'cdef public %s %s'
                             % (objecttools.classname(subpars), subpars.name))
        for subpars in self.model.parameters:
            if subpars:
                print('        - %s' % subpars.name)
                lines.add(0, '@cython.final')
                lines.add(0, 'cdef class %s(object):'
                             % objecttools.classname(subpars))
                for par in subpars:
                    try:
                        ctype = TYPE2STR[par.TYPE] + NDIM2STR[par.NDIM]
                    except KeyError:
                        ctype = par.TYPE + NDIM2STR[par.NDIM]
                    lines.add(1, 'cdef public %s %s' % (ctype, par.name))
        return lines

Response: Return a list of lines that define the parameters of the current object.


Instruction:
Write a Python 3 script for
returning a list of class Sequences.

Response:
def sequences(self):
        """Sequence declaration lines."""
        lines = Lines()
        lines.add(0, '@cython.final')
        lines.add(0, 'cdef class Sequences(object):')
        for subseqs in self.model.sequences:
            lines.add(1, 'cdef public %s %s'
                         % (objecttools.classname(subseqs), subseqs.name))
        if getattr(self.model.sequences, 'states', None) is not None:
            lines.add(1, 'cdef public StateSequences old_states')
            lines.add(1, 'cdef public StateSequences new_states')
        for subseqs in self.model.sequences:
            print('        - %s' % subseqs.name)
            lines.add(0, '@cython.final')
            lines.add(0, 'cdef class %s(object):'
                         % objecttools.classname(subseqs))
            for seq in subseqs:
                ctype = 'double' + NDIM2STR[seq.NDIM]
                if isinstance(subseqs, sequencetools.LinkSequences):
                    if seq.NDIM == 0:
                        lines.add(1, 'cdef double *%s' % seq.name)
                    elif seq.NDIM == 1:
                        lines.add(1, 'cdef double **%s' % seq.name)
                        lines.add(1, 'cdef public int len_%s' % seq.name)
                else:
                    lines.add(1, 'cdef public %s %s' % (ctype, seq.name))
                lines.add(1, 'cdef public int _%s_ndim' % seq.name)
                lines.add(1, 'cdef public int _%s_length' % seq.name)
                for idx in range(seq.NDIM):
                    lines.add(1, 'cdef public int _%s_length_%d'
                                 % (seq.name, idx))
                if seq.NUMERIC:
                    ctype_numeric = 'double' + NDIM2STR[seq.NDIM+1]
                    lines.add(1, 'cdef public %s _%s_points'
                                 % (ctype_numeric, seq.name))
                    lines.add(1, 'cdef public %s _%s_results'
                                 % (ctype_numeric, seq.name))
                    if isinstance(subseqs, sequencetools.FluxSequences):
                        lines.add(1, 'cdef public %s _%s_integrals'
                                     % (ctype_numeric, seq.name))
                        lines.add(1, 'cdef public %s _%s_sum'
                                     % (ctype, seq.name))
                if isinstance(subseqs, sequencetools.IOSequences):
                    lines.extend(self.iosequence(seq))
            if isinstance(subseqs, sequencetools.InputSequences):
                lines.extend(self.load_data(subseqs))
            if isinstance(subseqs, sequencetools.IOSequences):
                lines.extend(self.open_files(subseqs))
                lines.extend(self.close_files(subseqs))
                if not isinstance(subseqs, sequencetools.InputSequence):
                    lines.extend(self.save_data(subseqs))
            if isinstance(subseqs, sequencetools.LinkSequences):
                lines.extend(self.set_pointer(subseqs))
        return lines


Instruction:
Implement a function in Python 3 to
return a |Lines| object containing the |Reserved| and |Reserved| declaration lines for the given |IOSequence| object.

Response:
def iosequence(seq):
        """Special declaration lines for the given |IOSequence| object.
        """
        lines = Lines()
        lines.add(1, 'cdef public bint _%s_diskflag' % seq.name)
        lines.add(1, 'cdef public str _%s_path' % seq.name)
        lines.add(1, 'cdef FILE *_%s_file' % seq.name)
        lines.add(1, 'cdef public bint _%s_ramflag' % seq.name)
        ctype = 'double' + NDIM2STR[seq.NDIM+1]
        lines.add(1, 'cdef public %s _%s_array' % (ctype, seq.name))
        return lines


Instruction:
Can you generate a brief explanation for the following Python 3 code
def set_pointer(self, subseqs):
        lines = Lines()
        for seq in subseqs:
            if seq.NDIM == 0:
                lines.extend(self.set_pointer0d(subseqs))
            break
        for seq in subseqs:
            if seq.NDIM == 1:
                lines.extend(self.alloc(subseqs))
                lines.extend(self.dealloc(subseqs))
                lines.extend(self.set_pointer1d(subseqs))
            break
        return lines

Response: Set_pointer functions for link sequences.


Instruction:
Write a Python 3 function for
allocating memory for 1 - dimensional link sequences.

Response:
def alloc(subseqs):
        """Allocate memory for 1-dimensional link sequences."""
        print('            . setlength')
        lines = Lines()
        lines.add(1, 'cpdef inline alloc(self, name, int length):')
        for seq in subseqs:
            lines.add(2, 'if name == "%s":' % seq.name)
            lines.add(3, 'self._%s_length_0 = length' % seq.name)
            lines.add(3, 'self.%s = <double**> '
                         'PyMem_Malloc(length * sizeof(double*))' % seq.name)
        return lines


Instruction:
Here you have a function in Python 3, explain what it does
def set_pointer1d(subseqs):
        print('            . set_pointer1d')
        lines = Lines()
        lines.add(1, 'cpdef inline set_pointer1d'
                     '(self, str name, pointerutils.PDouble value, int idx):')
        for seq in subseqs:
            lines.add(2, 'if name == "%s":' % seq.name)
            lines.add(3, 'self.%s[idx] = value.p_value' % seq.name)
        return lines

Response: Set_pointer function for 1 - dimensional link sequences.


Instruction:
Make a summary of the following Python 3 code
def numericalparameters(self):
        lines = Lines()
        if self.model.NUMERICAL:
            lines.add(0, '@cython.final')
            lines.add(0, 'cdef class NumConsts(object):')
            for name in ('nmb_methods', 'nmb_stages'):
                lines.add(1, 'cdef public %s %s' % (TYPE2STR[int], name))
            for name in ('dt_increase', 'dt_decrease'):
                lines.add(1, 'cdef public %s %s' % (TYPE2STR[float], name))
            lines.add(1, 'cdef public configutils.Config pub')
            lines.add(1, 'cdef public double[:, :, :] a_coefs')
            lines.add(0, 'cdef class NumVars(object):')
            for name in ('nmb_calls', 'idx_method', 'idx_stage'):
                lines.add(1, 'cdef public %s %s' % (TYPE2STR[int], name))
            for name in ('t0', 't1', 'dt', 'dt_est',
                         'error', 'last_error', 'extrapolated_error'):
                lines.add(1, 'cdef public %s %s' % (TYPE2STR[float], name))
            lines.add(1, 'cdef public %s f0_ready' % TYPE2STR[bool])
        return lines

Response: Numeric parameter declaration lines.


Instruction:
Here you have a function in Python 3, explain what it does
def modeldeclarations(self):
        lines = Lines()
        lines.add(0, '@cython.final')
        lines.add(0, 'cdef class Model(object):')
        lines.add(1, 'cdef public int idx_sim')
        lines.add(1, 'cdef public Parameters parameters')
        lines.add(1, 'cdef public Sequences sequences')
        if hasattr(self.model, 'numconsts'):
            lines.add(1, 'cdef public NumConsts numconsts')
        if hasattr(self.model, 'numvars'):
            lines.add(1, 'cdef public NumVars numvars')
        return lines

Response: Attribute declarations of the model class.


Instruction:
Can you tell what is the following Python 3 function doing
def modelstandardfunctions(self):
        lines = Lines()
        lines.extend(self.doit)
        lines.extend(self.iofunctions)
        lines.extend(self.new2old)
        lines.extend(self.run)
        lines.extend(self.update_inlets)
        lines.extend(self.update_outlets)
        lines.extend(self.update_receivers)
        lines.extend(self.update_senders)
        return lines

Response: Standard functions of the model class.


Instruction:
Explain what the following Python 3 code does
def modelnumericfunctions(self):
        lines = Lines()
        lines.extend(self.solve)
        lines.extend(self.calculate_single_terms)
        lines.extend(self.calculate_full_terms)
        lines.extend(self.get_point_states)
        lines.extend(self.set_point_states)
        lines.extend(self.set_result_states)
        lines.extend(self.get_sum_fluxes)
        lines.extend(self.set_point_fluxes)
        lines.extend(self.set_result_fluxes)
        lines.extend(self.integrate_fluxes)
        lines.extend(self.reset_sum_fluxes)
        lines.extend(self.addup_fluxes)
        lines.extend(self.calculate_error)
        lines.extend(self.extrapolate_error)
        return lines

Response: Return a list of numeric functions of the model class.


Instruction:
Create a Python 3 function for
doing ( most of ) it function of the model class.

Response:
def doit(self):
        """Do (most of) it function of the model class."""
        print('                . doit')
        lines = Lines()
        lines.add(1, 'cpdef inline void doit(self, int idx) %s:' % _nogil)
        lines.add(2, 'self.idx_sim = idx')
        if getattr(self.model.sequences, 'inputs', None) is not None:
            lines.add(2, 'self.load_data()')
        if self.model.INLET_METHODS:
            lines.add(2, 'self.update_inlets()')
        if hasattr(self.model, 'solve'):
            lines.add(2, 'self.solve()')
        else:
            lines.add(2, 'self.run()')
            if getattr(self.model.sequences, 'states', None) is not None:
                lines.add(2, 'self.new2old()')
        if self.model.OUTLET_METHODS:
            lines.add(2, 'self.update_outlets()')
        return lines


Instruction:
Can you create a Python 3 function that
inputs and output functions of the model class.

Response:
def iofunctions(self):
        """Input/output functions of the model class."""
        lines = Lines()
        for func in ('open_files', 'close_files', 'load_data', 'save_data'):
            if ((func == 'load_data') and
                    (getattr(self.model.sequences, 'inputs', None) is None)):
                continue
            if ((func == 'save_data') and
                ((getattr(self.model.sequences, 'fluxes', None) is None) and
                 (getattr(self.model.sequences, 'states', None) is None))):
                continue
            print('            . %s' % func)
            nogil = func in ('load_data', 'save_data')
            idx_as_arg = func == 'save_data'
            lines.add(1, method_header(
                func, nogil=nogil, idx_as_arg=idx_as_arg))
            for subseqs in self.model.sequences:
                if func == 'load_data':
                    applyfuncs = ('inputs',)
                elif func == 'save_data':
                    applyfuncs = ('fluxes', 'states')
                else:
                    applyfuncs = ('inputs', 'fluxes', 'states')
                if subseqs.name in applyfuncs:
                    if func == 'close_files':
                        lines.add(2, 'self.sequences.%s.%s()'
                                     % (subseqs.name, func))
                    else:
                        lines.add(2, 'self.sequences.%s.%s(self.idx_sim)'
                                     % (subseqs.name, func))
        return lines


Instruction:
Create a Python 3 function for
lining of model method with the same name.

Response:
def calculate_single_terms(self):
        """Lines of model method with the same name."""
        lines = self._call_methods('calculate_single_terms',
                                   self.model.PART_ODE_METHODS)
        if lines:
            lines.insert(1, ('        self.numvars.nmb_calls ='
                             'self.numvars.nmb_calls+1'))
        return lines


Instruction:
Explain what the following Python 3 code does
def listofmodeluserfunctions(self):
        lines = []
        for (name, member) in vars(self.model.__class__).items():
            if (inspect.isfunction(member) and
                    (name not in ('run', 'new2old')) and
                    ('fastaccess' in inspect.getsource(member))):
                lines.append((name, member))
        run = vars(self.model.__class__).get('run')
        if run is not None:
            lines.append(('run', run))
        for (name, member) in vars(self.model).items():
            if (inspect.ismethod(member) and
                    ('fastaccess' in inspect.getsource(member))):
                lines.append((name, member))
        return lines

Response: List of user functions of the model class.


Instruction:
How would you code a function in Python 3 to
clean code lines. Implemented cleanups: * eventually remove method version * remove docstrings * remove comments * remove empty lines * remove line brackes within brackets * replace `modelutils` with nothing * remove complete lines containing `fastaccess` * replace shortcuts with complete references

Response:
def cleanlines(self):
        """Cleaned code lines.

        Implemented cleanups:
          * eventually remove method version
          * remove docstrings
          * remove comments
          * remove empty lines
          * remove line brackes within brackets
          * replace `modelutils` with nothing
          * remove complete lines containing `fastaccess`
          * replace shortcuts with complete references
        """
        code = inspect.getsource(self.func)
        code = '\n'.join(code.split('"""')[::2])
        code = code.replace('modelutils.', '')
        for (name, shortcut) in zip(self.collectornames,
                                    self.collectorshortcuts):
            code = code.replace('%s.' % shortcut, 'self.%s.' % name)
        code = self.remove_linebreaks_within_equations(code)
        lines = code.splitlines()
        self.remove_imath_operators(lines)
        lines[0] = 'def %s(self):' % self.funcname
        lines = [l.split('#')[0] for l in lines]
        lines = [l for l in lines if 'fastaccess' not in l]
        lines = [l.rstrip() for l in lines if l.rstrip()]
        return Lines(*lines)


Instruction:
How would you implement a function in Python 3 that
removes mathematical expressions that require Pythons global interpreter locking mechanism.

Response:
def remove_imath_operators(lines):
        """Remove mathematical expressions that require Pythons global
        interpreter locking mechanism.

        This is not a exhaustive test, but shows how the method works:

        >>> lines = ['    x += 1*1']
        >>> from hydpy.cythons.modelutils import FuncConverter
        >>> FuncConverter.remove_imath_operators(lines)
        >>> lines
        ['    x = x + (1*1)']
        """
        for idx, line in enumerate(lines):
            for operator in ('+=', '-=', '**=', '*=', '//=', '/=', '%='):
                sublines = line.split(operator)
                if len(sublines) > 1:
                    indent = line.count(' ') - line.lstrip().count(' ')
                    sublines = [sl.strip() for sl in sublines]
                    line = ('%s%s = %s %s (%s)'
                            % (indent*' ', sublines[0], sublines[0],
                               operator[:-1], sublines[1]))
                    lines[idx] = line


Instruction:
Can you tell what is the following Python 3 function doing
def calc_smoothpar_logistic2(metapar):
    if metapar <= 0.:
        return 0.
    return optimize.newton(_error_smoothpar_logistic2,
                           .3 * metapar**.84,
                           _smooth_logistic2_derivative,
                           args=(metapar,))

Response: Return the smoothing parameter corresponding to the given meta parameter when using |smooth_logistic2|.


Instruction:
Write a Python 3 script for
returning a |Date| instance based on a |numpy. ndarray| containing the date information of the first entry of the given |numpy. ndarray|.

Response:
def from_array(cls, array):
        """Return a |Date| instance based on date information (year,
        month, day, hour, minute, second) stored as the first entries of
        the successive rows of a |numpy.ndarray|.

        >>> from hydpy import Date
        >>> import numpy
        >>> array1d = numpy.array([1992, 10, 8, 15, 15, 42, 999])
        >>> Date.from_array(array1d)
        Date('1992-10-08 15:15:42')

        >>> array3d = numpy.zeros((7, 2, 2))
        >>> array3d[:, 0, 0] = array1d
        >>> Date.from_array(array3d)
        Date('1992-10-08 15:15:42')

        .. note::

           The date defined by the given |numpy.ndarray| cannot
           include any time zone information and corresponds to
           |Options.utcoffset|, which defaults to UTC+01:00.
        """
        intarray = numpy.array(array, dtype=int)
        for dummy in range(1, array.ndim):
            intarray = intarray[:, 0]
        return cls(datetime.datetime(*intarray[:6]))


Instruction:
Write a Python 3 script for
returning a 1 - dimensional |numpy| |numpy. ndarray| containing the actual date and time zone information.

Response:
def to_array(self):
        """Return a 1-dimensional |numpy| |numpy.ndarray|  with six entries
        defining the actual date (year, month, day, hour, minute, second).

        >>> from hydpy import Date
        >>> Date('1992-10-8 15:15:42').to_array()
        array([ 1992.,    10.,     8.,    15.,    15.,    42.])

        .. note::

           The date defined by the returned |numpy.ndarray| does not
           include any time zone information and corresponds to
           |Options.utcoffset|, which defaults to UTC+01:00.
        """
        return numpy.array([self.year, self.month, self.day, self.hour,
                            self.minute, self.second], dtype=float)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def from_cfunits(cls, units) -> 'Date':
        try:
            string = units[units.find('since')+6:]
            idx = string.find('.')
            if idx != -1:
                jdx = None
                for jdx, char in enumerate(string[idx+1:]):
                    if not char.isnumeric():
                        break
                    if char != '0':
                        raise ValueError(
                            'No other decimal fraction of a second '
                            'than "0" allowed.')
                else:
                    if jdx is None:
                        jdx = idx+1
                    else:
                        jdx += 1
                string = f'{string[:idx]}{string[idx+jdx+1:]}'
            return cls(string)
        except BaseException:
            objecttools.augment_excmessage(
                f'While trying to parse the date of the NetCDF-CF "units" '
                f'string `{units}`')

Response: Return a |Date| object representing the reference date of the current NetCDF - CF entry with the given units string.


Instruction:
Make a summary of the following Python 3 code
def to_cfunits(self, unit='hours', utcoffset=None):
        if utcoffset is None:
            utcoffset = hydpy.pub.options.utcoffset
        string = self.to_string('iso2', utcoffset)
        string = ' '.join((string[:-6], string[-6:]))
        return f'{unit} since {string}'

Response: Return a string agreeing with NetCDF - CF conventions.


Instruction:
Make a summary of the following Python 3 code
def _set_thing(self, thing, value):
        try:
            value = int(value)
        except (TypeError, ValueError):
            raise TypeError(
                f'Changing the {thing} of a `Date` instance is only '
                f'allowed via numbers, but the given value `{value}` '
                f'is of type `{type(value)}` instead.')
        kwargs = {}
        for unit in ('year', 'month', 'day', 'hour', 'minute', 'second'):
            kwargs[unit] = getattr(self, unit)
        kwargs[thing] = value
        self.datetime = datetime.datetime(**kwargs)

Response: Convenience method for _set_year _set_month...


Instruction:
Can you tell what is the following Python 3 function doing
def to_string(self, style=None, utcoffset=None):
        if not style:
            style = self.style
        if utcoffset is None:
            string = ''
            date = self.datetime
        else:
            sign = '+' if utcoffset >= 0 else '-'
            hours = abs(utcoffset // 60)
            minutes = abs(utcoffset % 60)
            string = f'{sign}{hours:02d}:{minutes:02d}'
            offset = utcoffset-hydpy.pub.options.utcoffset
            date = self.datetime + datetime.timedelta(minutes=offset)
        return date.strftime(self._formatstrings[style]) + string

Response:
Return a |str| object representing the actual date in
        accordance with the given style and the eventually given
        UTC offset.


Instruction:
Create a Python 3 function for
returning a |Period| instance based on a given number of seconds.

Response:
def fromseconds(cls, seconds):
        """Return a |Period| instance based on a given number of seconds."""
        try:
            seconds = int(seconds)
        except TypeError:
            seconds = int(seconds.flatten()[0])
        return cls(datetime.timedelta(0, int(seconds)))


Instruction:
Write a Python 3 script to
guess the unit of the period as the largest one which results in an integer duration.

Response:
def _guessunit(self):
        """Guess the unit of the period as the largest one, which results in
        an integer duration.
        """
        if not self.days % 1:
            return 'd'
        elif not self.hours % 1:
            return 'h'
        elif not self.minutes % 1:
            return 'm'
        elif not self.seconds % 1:
            return 's'
        else:
            raise ValueError(
                'The stepsize is not a multiple of one '
                'second, which is not allowed.')


Instruction:
Implement a function in Python 3 to
return a |Timegrid| instance based on two date and one period getTaxID from a given |numpy. ndarray| object.

Response:
def from_array(cls, array):
        """Returns a |Timegrid| instance based on two date and one period
        information stored in the first 13 rows of a |numpy.ndarray| object.
        """
        try:
            return cls(Date.from_array(array[:6]),
                       Date.from_array(array[6:12]),
                       Period.fromseconds(array[12]))
        except IndexError:
            raise IndexError(
                f'To define a Timegrid instance via an array, 13 '
                f'numbers are required.  However, the given array '
                f'consist of {len(array)} entries/rows only.')


Instruction:
Can you write a function in Python 3 where it
returns a 1 - dimensional |numpy| |numpy. ndarray| with thirteen entries firstly defining the start date secondly defining the end date thirdly defining the step size in seconds.

Response:
def to_array(self):
        """Returns a 1-dimensional |numpy| |numpy.ndarray| with thirteen
        entries first defining the start date, secondly defining the end
        date and thirdly the step size in seconds.
        """
        values = numpy.empty(13, dtype=float)
        values[:6] = self.firstdate.to_array()
        values[6:12] = self.lastdate.to_array()
        values[12] = self.stepsize.seconds
        return values


Instruction:
Write a Python 3 script to
return a |Timegrid| object representing the given starting timepoints in relation to the given refdate.

Response:
def from_timepoints(cls, timepoints, refdate, unit='hours'):
        """Return a |Timegrid| object representing the given starting
        `timepoints` in relation to the given `refdate`.

        The following examples identical with the ones of
        |Timegrid.to_timepoints| but reversed.

        At least two given time points must be increasing and
        equidistant.  By default, they are assumed in hours since
        the given reference date:

        >>> from hydpy import Timegrid
        >>> Timegrid.from_timepoints(
        ...     [0.0, 6.0, 12.0, 18.0], '01.01.2000')
        Timegrid('01.01.2000 00:00:00',
                 '02.01.2000 00:00:00',
                 '6h')
        >>> Timegrid.from_timepoints(
        ...     [24.0, 30.0, 36.0, 42.0], '1999-12-31')
        Timegrid('2000-01-01 00:00:00',
                 '2000-01-02 00:00:00',
                 '6h')

        Other time units (`days` or `min`) must be passed explicitely
        (only the first character counts):

        >>> Timegrid.from_timepoints(
        ...     [0.0, 0.25, 0.5, 0.75], '01.01.2000', unit='d')
        Timegrid('01.01.2000 00:00:00',
                 '02.01.2000 00:00:00',
                 '6h')
        >>> Timegrid.from_timepoints(
        ...     [1.0, 1.25, 1.5, 1.75], '1999-12-31', unit='day')
        Timegrid('2000-01-01 00:00:00',
                 '2000-01-02 00:00:00',
                 '6h')
        """
        refdate = Date(refdate)
        unit = Period.from_cfunits(unit)
        delta = timepoints[1]-timepoints[0]
        firstdate = refdate+timepoints[0]*unit
        lastdate = refdate+(timepoints[-1]+delta)*unit
        stepsize = (lastdate-firstdate)/len(timepoints)
        return cls(firstdate, lastdate, stepsize)


Instruction:
Write a Python 3 function that can
return an |numpy. ndarray| representing the starting time points of the |Timegrid| object.

Response:
def to_timepoints(self, unit='hours', offset=None):
        """Return an |numpy.ndarray| representing the starting time points
        of the |Timegrid| object.

        The following examples identical with the ones of
        |Timegrid.from_timepoints| but reversed.

        By default, the time points are given in hours:

        >>> from hydpy import Timegrid
        >>> timegrid = Timegrid('2000-01-01', '2000-01-02', '6h')
        >>> timegrid.to_timepoints()
        array([  0.,   6.,  12.,  18.])

        Other time units (`days` or `min`) can be defined (only the first
        character counts):

        >>> timegrid.to_timepoints(unit='d')
        array([ 0.  ,  0.25,  0.5 ,  0.75])

        Additionally, one can pass an `offset` that must be of type |int|
        or an valid |Period| initialization argument:

        >>> timegrid.to_timepoints(offset=24)
        array([ 24.,  30.,  36.,  42.])
        >>> timegrid.to_timepoints(offset='1d')
        array([ 24.,  30.,  36.,  42.])
        >>> timegrid.to_timepoints(unit='day', offset='1d')
        array([ 1.  ,  1.25,  1.5 ,  1.75])
        """
        unit = Period.from_cfunits(unit)
        if offset is None:
            offset = 0.
        else:
            try:
                offset = Period(offset)/unit
            except TypeError:
                offset = offset
        step = self.stepsize/unit
        nmb = len(self)
        variable = numpy.linspace(offset, offset+step*(nmb-1), nmb)
        return variable


Instruction:
Given the following Python 3 function, write the documentation
def array2series(self, array):
        try:
            array = numpy.array(array, dtype=float)
        except BaseException:
            objecttools.augment_excmessage(
                'While trying to prefix timegrid information to the '
                'given array')
        if len(array) != len(self):
            raise ValueError(
                f'When converting an array to a sequence, the lengths of the '
                f'timegrid and the given array must be equal, but the length '
                f'of the timegrid object is `{len(self)}` and the length of '
                f'the array object is `{len(array)}`.')
        shape = list(array.shape)
        shape[0] += 13
        series = numpy.full(shape, numpy.nan)
        slices = [slice(0, 13)]
        subshape = [13]
        for dummy in range(1, series.ndim):
            slices.append(slice(0, 1))
            subshape.append(1)
        series[tuple(slices)] = self.to_array().reshape(subshape)
        series[13:] = array
        return series

Response: Given an array of timegrid objects and a timegrid object return a new array of timegrid objects.


Instruction:
Can you tell what is the following Python 3 function doing
def verify(self):
        if self.firstdate >= self.lastdate:
            raise ValueError(
                f'Unplausible timegrid. The first given date '
                f'{self.firstdate}, the second given date is {self.lastdate}.')
        if (self.lastdate-self.firstdate) % self.stepsize:
            raise ValueError(
                f'Unplausible timegrid. The period span between the given '
                f'dates {self.firstdate} and {self.lastdate} is not '
                f'a multiple of the given step size {self.stepsize}.')

Response:
Raise an |ValueError| if the dates or step size of the time
        frame are inconsistent.


Instruction:
Write a Python 3 function for
returning a |repr| string with an prefixed assignement.

Response:
def assignrepr(self, prefix, style=None, utcoffset=None):
        """Return a |repr| string with an prefixed assignement.

        Without option arguments given, printing the returned string
        looks like:

        >>> from hydpy import Timegrid
        >>> timegrid = Timegrid('1996-11-01 00:00:00',
        ...                     '1997-11-01 00:00:00',
        ...                     '1d')
        >>> print(timegrid.assignrepr(prefix='timegrid = '))
        timegrid = Timegrid('1996-11-01 00:00:00',
                            '1997-11-01 00:00:00',
                            '1d')

        The optional arguments are passed to method |Date.to_repr|
        without any modifications:

        >>> print(timegrid.assignrepr(
        ...     prefix='', style='iso1', utcoffset=120))
        Timegrid('1996-11-01T01:00:00+02:00',
                 '1997-11-01T01:00:00+02:00',
                 '1d')
        """
        skip = len(prefix) + 9
        blanks = ' ' * skip
        return (f"{prefix}Timegrid('"
                f"{self.firstdate.to_string(style, utcoffset)}',\n"
                f"{blanks}'{self.lastdate.to_string(style, utcoffset)}',\n"
                f"{blanks}'{str(self.stepsize)}')")


Instruction:
Here you have a function in Python 3, explain what it does
def verify(self):
        self.init.verify()
        self.sim.verify()
        if self.init.firstdate > self.sim.firstdate:
            raise ValueError(
                f'The first date of the initialisation period '
                f'({self.init.firstdate}) must not be later '
                f'than the first date of the simulation period '
                f'({self.sim.firstdate}).')
        elif self.init.lastdate < self.sim.lastdate:
            raise ValueError(
                f'The last date of the initialisation period '
                f'({self.init.lastdate}) must not be earlier '
                f'than the last date of the simulation period '
                f'({self.sim.lastdate}).')
        elif self.init.stepsize != self.sim.stepsize:
            raise ValueError(
                f'The initialization stepsize ({self.init.stepsize}) '
                f'must be identical with the simulation stepsize '
                f'({self.sim.stepsize}).')
        else:
            try:
                self.init[self.sim.firstdate]
            except ValueError:
                raise ValueError(
                    'The simulation time grid is not properly '
                    'alligned on the initialization time grid.')

Response: Raise an |ValueError| it the different time grids are not inconsistent.


Instruction:
Can you implement a function in Python 3 that
returns a |repr| string with a prefixed assignment.

Response:
def assignrepr(self, prefix):
        """Return a |repr| string with a prefixed assignment."""
        caller = 'Timegrids('
        blanks = ' ' * (len(prefix) + len(caller))
        prefix = f'{prefix}{caller}'
        lines = [f'{self.init.assignrepr(prefix)},']
        if self.sim != self.init:
            lines.append(f'{self.sim.assignrepr(blanks)},')
        lines[-1] = lines[-1][:-1] + ')'
        return '\n'.join(lines)


Instruction:
How would you implement a function in Python 3 that
amounts of time passed since the beginning of the year.

Response:
def seconds_passed(self):
        """Amount of time passed in seconds since the beginning of the year.

        In the first example, the year is only one minute and thirty seconds
        old:

        >>> from hydpy.core.timetools import TOY
        >>> TOY('1_1_0_1_30').seconds_passed
        90

        The second example shows that the 29th February is generally included:

        >>> TOY('3').seconds_passed
        5184000
        """
        return int((Date(self).datetime -
                    self._STARTDATE.datetime).total_seconds())


Instruction:
Write a Python 3 script for
returning the number of seconds left in the year.

Response:
def seconds_left(self):
        """Remaining part of the year in seconds.

        In the first example, only one minute and thirty seconds of the year
        remain:

        >>> from hydpy.core.timetools import TOY
        >>> TOY('12_31_23_58_30').seconds_left
        90

        The second example shows that the 29th February is generally included:

        >>> TOY('2').seconds_left
        28944000
        """
        return int((self._ENDDATE.datetime -
                    Date(self).datetime).total_seconds())


Instruction:
Can you create a Python 3 function that
returns a |Timegrid| object defining the central time points for the given simulation step.

Response:
def centred_timegrid(cls, simulationstep):
        """Return a |Timegrid| object defining the central time points
        of the year 2000 for the given simulation step.

        >>> from hydpy.core.timetools import TOY
        >>> TOY.centred_timegrid('1d')
        Timegrid('2000-01-01 12:00:00',
                 '2001-01-01 12:00:00',
                 '1d')
        """
        simulationstep = Period(simulationstep)
        return Timegrid(
            cls._STARTDATE+simulationstep/2,
            cls._ENDDATE+simulationstep/2,
            simulationstep)


Instruction:
Can you implement a function in Python 3 that
returns the class name of the given object or class.

Response:
def classname(self):
    """Return the class name of the given instance object or class.

    >>> from hydpy.core.objecttools import classname
    >>> from hydpy import pub
    >>> print(classname(float))
    float
    >>> print(classname(pub.options))
    Options
    """
    if inspect.isclass(self):
        string = str(self)
    else:
        string = str(type(self))
    try:
        string = string.split("'")[1]
    except IndexError:
        pass
    return string.split('.')[-1]


Instruction:
Given the following Python 3 function, write the documentation
def name(self):
    cls = type(self)
    try:
        return cls.__dict__['_name']
    except KeyError:
        setattr(cls, '_name', instancename(self))
        return cls.__dict__['_name']

Response: Name of the class of the given instance in lower case letters.


Instruction:
Here you have a function in Python 3, explain what it does
def valid_variable_identifier(string):
    string = str(string)
    try:
        exec('%s = None' % string)
        if string in dir(builtins):
            raise SyntaxError()
    except SyntaxError:
        raise ValueError(
            'The given name string `%s` does not define a valid variable '
            'identifier.  Valid identifiers do not contain characters like '
            '`-` or empty spaces, do not start with numbers, cannot be '
            'mistaken with Python built-ins like `for`...)'
            % string)

Response:
Raises |ValueError| if the given string is not a valid Python
    identifier.


Instruction:
Explain what the following Python 3 code does
def augment_excmessage(prefix=None, suffix=None) -> NoReturn:
    exc_old = sys.exc_info()[1]
    message = str(exc_old)
    if prefix is not None:
        message = f'{prefix}, the following error occurred: {message}'
    if suffix is not None:
        message = f'{message} {suffix}'
    try:
        exc_new = type(exc_old)(message)
    except BaseException:
        exc_name = str(type(exc_old)).split("'")[1]
        exc_type = type(exc_name, (BaseException,), {})
        exc_type.__module = exc_old.__module__
        raise exc_type(message) from exc_old
    raise exc_new from exc_old

Response: Augment an exception message with additional information while keeping the original traceback.


Instruction:
Make a summary of the following Python 3 code
def excmessage_decorator(description) -> Callable:
    @wrapt.decorator
    def wrapper(wrapped, instance, args, kwargs):
        """Apply |augment_excmessage| when the wrapped function fails."""
        # pylint: disable=unused-argument
        try:
            return wrapped(*args, **kwargs)
        except BaseException:
            info = kwargs.copy()
            info['self'] = instance
            argnames = inspect.getfullargspec(wrapped).args
            if argnames[0] == 'self':
                argnames = argnames[1:]
            for argname, arg in zip(argnames, args):
                info[argname] = arg
            for argname in argnames:
                if argname not in info:
                    info[argname] = '?'
            message = eval(
                f"f'While trying to {description}'", globals(), info)
            augment_excmessage(message)
    return wrapper

Response: A decorator to apply an exception message to a non - numbered sequence of exceptions.


Instruction:
Implement a function in Python 3 to
print the given values in multiple lines with a certain maximum width.

Response:
def print_values(values, width=70):
    """Print the given values in multiple lines with a certain maximum width.

    By default, each line contains at most 70 characters:

    >>> from hydpy import print_values
    >>> print_values(range(21))
    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
    20

    You can change this default behaviour by passing an alternative
    number of characters:

    >>> print_values(range(21), width=30)
    0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
    10, 11, 12, 13, 14, 15, 16,
    17, 18, 19, 20
    """
    for line in textwrap.wrap(repr_values(values), width=width):
        print(line)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def assignrepr_values(values, prefix, width=None, _fakeend=0):
    ellipsis_ = hydpy.pub.options.ellipsis
    if (ellipsis_ > 0) and (len(values) > 2*ellipsis_):
        string = (repr_values(values[:ellipsis_]) +
                  ', ...,' +
                  repr_values(values[-ellipsis_:]))
    else:
        string = repr_values(values)
    blanks = ' '*len(prefix)
    if width is None:
        wrapped = [string]
        _fakeend = 0
    else:
        width -= len(prefix)
        wrapped = textwrap.wrap(string+'_'*_fakeend, width)
    if not wrapped:
        wrapped = ['']
    lines = []
    for (idx, line) in enumerate(wrapped):
        if idx == 0:
            lines.append('%s%s' % (prefix, line))
        else:
            lines.append('%s%s' % (blanks, line))
    string = '\n'.join(lines)
    return string[:len(string)-_fakeend]

Response: Return a prefixed and properly aligned string representation of the given values using function |repr|.


Instruction:
Create a Python 3 function to
return a prefixed and properly aligned string representation of the given 2 - dimensional value matrix using function |repr|.

Response:
def assignrepr_values2(values, prefix):
    """Return a prefixed and properly aligned string representation
    of the given 2-dimensional value matrix using function |repr|.

    >>> from hydpy.core.objecttools import assignrepr_values2
    >>> import numpy
    >>> print(assignrepr_values2(numpy.eye(3), 'test(') + ')')
    test(1.0, 0.0, 0.0,
         0.0, 1.0, 0.0,
         0.0, 0.0, 1.0)

    Functions |assignrepr_values2| works also on empty iterables:

    >>> print(assignrepr_values2([[]], 'test(') + ')')
    test()
    """
    lines = []
    blanks = ' '*len(prefix)
    for (idx, subvalues) in enumerate(values):
        if idx == 0:
            lines.append('%s%s,' % (prefix, repr_values(subvalues)))
        else:
            lines.append('%s%s,' % (blanks, repr_values(subvalues)))
    lines[-1] = lines[-1][:-1]
    return '\n'.join(lines)


Instruction:
Implement a Python 3 function for
returning a prefixed and properly aligned bracketed string representation of the given 2 - dimensional value matrix using function |repr|.

Response:
def _assignrepr_bracketed2(assignrepr_bracketed1, values, prefix, width=None):
    """Return a prefixed, wrapped and properly aligned bracketed string
    representation of the given 2-dimensional value matrix using function
    |repr|."""
    brackets = getattr(assignrepr_bracketed1, '_brackets')
    prefix += brackets[0]
    lines = []
    blanks = ' '*len(prefix)
    for (idx, subvalues) in enumerate(values):
        if idx == 0:
            lines.append(assignrepr_bracketed1(subvalues, prefix, width))
        else:
            lines.append(assignrepr_bracketed1(subvalues, blanks, width))
        lines[-1] += ','
    if (len(values) > 1) or (brackets != '()'):
        lines[-1] = lines[-1][:-1]
    lines[-1] += brackets[1]
    return '\n'.join(lines)


Instruction:
Write a Python 3 function that can
print values with a maximum number of digits in doctests.

Response:
def round_(values, decimals=None, width=0,
           lfill=None, rfill=None, **kwargs):
    """Prints values with a maximum number of digits in doctests.

    See the documentation on function |repr| for more details.  And
    note thate the option keyword arguments are passed to the print function.

    Usually one would apply function |round_| on a single or a vector
    of numbers:

    >>> from hydpy import round_
    >>> round_(1./3., decimals=6)
    0.333333
    >>> round_((1./2., 1./3., 1./4.), decimals=4)
    0.5, 0.3333, 0.25

    Additionally, one can supply a `width` and a `rfill` argument:
    >>> round_(1.0, width=6, rfill='0')
    1.0000

    Alternatively, one can use the `lfill` arguments, which
    might e.g. be usefull for aligning different strings:

    >>> round_('test', width=6, lfill='_')
    __test

    Using both the `lfill` and the `rfill` argument raises an error:

    >>> round_(1.0, lfill='_', rfill='0')
    Traceback (most recent call last):
    ...
    ValueError: For function `round_` values are passed for both \
arguments `lfill` and `rfill`.  This is not allowed.
    """
    if decimals is None:
        decimals = hydpy.pub.options.reprdigits
    with hydpy.pub.options.reprdigits(decimals):
        if isinstance(values, abctools.IterableNonStringABC):
            string = repr_values(values)
        else:
            string = repr_(values)
        if (lfill is not None) and (rfill is not None):
            raise ValueError(
                'For function `round_` values are passed for both arguments '
                '`lfill` and `rfill`.  This is not allowed.')
        if (lfill is not None) or (rfill is not None):
            width = max(width, len(string))
            if lfill is not None:
                string = string.rjust(width, lfill)
            else:
                string = string.ljust(width, rfill)
        print(string, **kwargs)


Instruction:
Given the following Python 3 function, write the documentation
def extract(values, types, skip=False):
    if isinstance(values, types):
        yield values
    elif skip and (values is None):
        return
    else:
        try:
            for value in values:
                for subvalue in extract(value, types, skip):
                    yield subvalue
        except TypeError as exc:
            if exc.args[0].startswith('The given value'):
                raise exc
            else:
                raise TypeError(
                    f'The given value `{repr(values)}` is neither iterable '
                    f'nor an instance of the following classes: '
                    f'{enumeration(types, converter=instancename)}.')

Response: Return a generator that extracts certain objects from values.


Instruction:
Can you generate the documentation for the following Python 3 function
def enumeration(values, converter=str, default=''):
    values = tuple(converter(value) for value in values)
    if not values:
        return default
    if len(values) == 1:
        return values[0]
    if len(values) == 2:
        return ' and '.join(values)
    return ', and '.join((', '.join(values[:-1]), values[-1]))

Response: Return an enumeration string based on the given values.


Instruction:
How would you code a function in Python 3 to
trim upper values in accordance with IC \\ leq ICMAX.

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

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> nmbzones(5)
        >>> icmax(2.0)
        >>> states.ic(-1.0, 0.0, 1.0, 2.0, 3.0)
        >>> states.ic
        ic(0.0, 0.0, 1.0, 2.0, 2.0)
        """
        if upper is None:
            control = self.subseqs.seqs.model.parameters.control
            upper = control.icmax
        hland_sequences.State1DSequence.trim(self, lower, upper)


Instruction:
How would you implement a function in Python 3 that
trims values in accordance with WC \\ leq WHC \\ cdot SP.

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

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> nmbzones(7)
        >>> whc(0.1)
        >>> states.wc.values = -1.0, 0.0, 1.0, -1.0, 0.0, 0.5, 1.0
        >>> states.sp(-1., 0., 0., 5., 5., 5., 5.)
        >>> states.sp
        sp(0.0, 0.0, 10.0, 5.0, 5.0, 5.0, 10.0)
        """
        whc = self.subseqs.seqs.model.parameters.control.whc
        wc = self.subseqs.wc
        if lower is None:
            if wc.values is not None:
                with numpy.errstate(divide='ignore', invalid='ignore'):
                    lower = numpy.clip(wc.values / whc.values, 0., numpy.inf)
            else:
                lower = 0.
        hland_sequences.State1DSequence.trim(self, lower, upper)


Instruction:
Implement a Python 3 function for
trimming values in accordance with WC \\ leq WHC \\ cdot SP.

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

        >>> from hydpy.models.hland import *
        >>> parameterstep('1d')
        >>> nmbzones(7)
        >>> whc(0.1)
        >>> states.sp = 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0
        >>> states.wc(-1.0, 0.0, 1.0, -1.0, 0.0, 0.5, 1.0)
        >>> states.wc
        wc(0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.5)
        """
        whc = self.subseqs.seqs.model.parameters.control.whc
        sp = self.subseqs.sp
        if (upper is None) and (sp.values is not None):
            upper = whc*sp
        hland_sequences.State1DSequence.trim(self, lower, upper)


Instruction:
Given the following Python 3 function, write the documentation
def trim(self, lower=None, upper=None):
        if upper is None:
            control = self.subseqs.seqs.model.parameters.control
            if not any(control.zonetype.values == ILAKE):
                lower = 0.
        sequencetools.StateSequence.trim(self, lower, upper)

Response:
Trim negative value whenever there is no internal lake within
        the respective subbasin.


Instruction:
Given the following Python 3 function, write the documentation
def load_data(self, idx):
        for subseqs in self:
            if isinstance(subseqs, abctools.InputSequencesABC):
                subseqs.load_data(idx)

Response:
Call method |InputSequences. load_data| of all handled
        |InputSequencesABC| objects.


Instruction:
Given the following Python 3 function, write the documentation
def save_data(self, idx):
        for subseqs in self:
            if isinstance(subseqs, abctools.OutputSequencesABC):
                subseqs.save_data(idx)

Response: Call method save_data| of all handled |IOSequences| objects registered under |OutputSequencesABC|.


Instruction:
Explain what the following Python 3 code does
def conditions(self) -> Dict[str, Dict[str, Union[float, numpy.ndarray]]]:
        conditions = {}
        for subname in NAMES_CONDITIONSEQUENCES:
            subseqs = getattr(self, subname, ())
            subconditions = {seq.name: copy.deepcopy(seq.values)
                             for seq in subseqs}
            if subconditions:
                conditions[subname] = subconditions
        return conditions

Response: Nested dictionary containing the values of all condition sequences.


Instruction:
Can you generate the documentation for the following Python 3 function
def load_conditions(self, filename=None):
        if self.hasconditions:
            if not filename:
                filename = self._conditiondefaultfilename
            namespace = locals()
            for seq in self.conditionsequences:
                namespace[seq.name] = seq
            namespace['model'] = self
            code = hydpy.pub.conditionmanager.load_file(filename)
            try:
                # ToDo: raises an escape sequence deprecation sometimes
                # ToDo: use runpy instead?
                # ToDo: Move functionality to filetools.py?
                exec(code)
            except BaseException:
                objecttools.augment_excmessage(
                    'While trying to gather initial conditions of element %s'
                    % objecttools.devicename(self))

Response: Load the initial conditions from a file and assign them to the state sequence and log sequence objects handled by the actual |Sequences| object.


Instruction:
Implement a Python 3 function for
saving the actual conditions of the state sequence and or log sequence objects in a file.

Response:
def save_conditions(self, filename=None):
        """Query the actual conditions of the |StateSequence| and/or
        |LogSequence| objects handled by the actual |Sequences| object and
        write them into a initial condition file.

        If no filename or dirname is passed, the ones defined by the
        |ConditionManager| stored in module |pub| are used.
        """
        if self.hasconditions:
            if filename is None:
                filename = self._conditiondefaultfilename
            con = hydpy.pub.controlmanager
            lines = ['# -*- coding: utf-8 -*-\n\n',
                     'from hydpy.models.%s import *\n\n' % self.model,
                     'controlcheck(projectdir="%s", controldir="%s")\n\n'
                     % (con.projectdir, con.currentdir)]
            for seq in self.conditionsequences:
                lines.append(repr(seq) + '\n')
            hydpy.pub.conditionmanager.save_file(filename, ''.join(lines))


Instruction:
Write a Python 3 function that can
move internal data from disk to RAM.

Response:
def disk2ram(self):
        """Move internal data from disk to RAM."""
        values = self.series
        self.deactivate_disk()
        self.ramflag = True
        self.__set_array(values)
        self.update_fastaccess()


Instruction:
Can you create a Python 3 function that
moves internal data from RAM to disk.

Response:
def ram2disk(self):
        """Move internal data from RAM to disk."""
        values = self.series
        self.deactivate_ram()
        self.diskflag = True
        self._save_int(values)
        self.update_fastaccess()


Instruction:
Explain what the following Python 3 code does
def seriesshape(self):
        seriesshape = [len(hydpy.pub.timegrids.init)]
        seriesshape.extend(self.shape)
        return tuple(seriesshape)

Response: Shape of the whole time series.


Instruction:
Write a Python 3 function for
shaping of the array of temporary values required for the numerical solver actually being selected.

Response:
def numericshape(self):
        """Shape of the array of temporary values required for the numerical
        solver actually being selected."""
        try:
            numericshape = [self.subseqs.seqs.model.numconsts.nmb_stages]
        except AttributeError:
            objecttools.augment_excmessage(
                'The `numericshape` of a sequence like `%s` depends on the '
                'configuration of the actual integration algorithm.  '
                'While trying to query the required configuration data '
                '`nmb_stages` of the model associated with element `%s`'
                % (self.name, objecttools.devicename(self)))
        # noinspection PyUnboundLocalVariable
        numericshape.extend(self.shape)
        return tuple(numericshape)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def series(self) -> InfoArray:
        if self.diskflag:
            array = self._load_int()
        elif self.ramflag:
            array = self.__get_array()
        else:
            raise AttributeError(
                f'Sequence {objecttools.devicephrase(self)} is not requested '
                f'to make any internal data available to the user.')
        return InfoArray(array, info={'type': 'unmodified'})

Response: Internal time series data within an |numpy. ndarray|.


Instruction:
How would you explain what the following Python 3 function does
def load_ext(self):
        try:
            sequencemanager = hydpy.pub.sequencemanager
        except AttributeError:
            raise RuntimeError(
                'The time series of sequence %s cannot be loaded.  Firstly, '
                'you have to prepare `pub.sequencemanager` correctly.'
                % objecttools.devicephrase(self))
        sequencemanager.load_file(self)

Response: Read the internal data from an external data file.


Instruction:
Write a Python 3 script to
adjust a short time series to a longer timegrid.

Response:
def adjust_short_series(self, timegrid, values):
        """Adjust a short time series to a longer timegrid.

        Normally, time series data to be read from a external data files
        should span (at least) the whole initialization time period of a
        HydPy project.  However, for some variables which are only used
        for comparison (e.g. observed runoff used for calibration),
        incomplete time series might also be helpful.  This method it
        thought for adjusting such incomplete series to the public
        initialization time grid stored in module |pub|.  It is
        automatically called in method |IOSequence.adjust_series| when
        necessary provided that the option |Options.checkseries| is
        disabled.

        Assume the initialization time period of a HydPy project spans
        five day:

        >>> from hydpy import pub
        >>> pub.timegrids = '2000.01.10', '2000.01.15', '1d'

        Prepare a node series object for observational data:

        >>> from hydpy.core.sequencetools import Obs
        >>> obs = Obs(None)

        Prepare a test function that expects the timegrid of the
        data and the data itself, which returns the ajdusted array by
        means of calling method |IOSequence.adjust_short_series|:

        >>> import numpy
        >>> def test(timegrid):
        ...     values = numpy.ones(len(timegrid))
        ...     return obs.adjust_short_series(timegrid, values)

        The following calls to the test function shows the arrays
        returned for different kinds of misalignments:

        >>> from hydpy import Timegrid
        >>> test(Timegrid('2000.01.05', '2000.01.20', '1d'))
        array([ 1.,  1.,  1.,  1.,  1.])
        >>> test(Timegrid('2000.01.12', '2000.01.15', '1d'))
        array([ nan,  nan,   1.,   1.,   1.])
        >>> test(Timegrid('2000.01.12', '2000.01.17', '1d'))
        array([ nan,  nan,   1.,   1.,   1.])
        >>> test(Timegrid('2000.01.10', '2000.01.13', '1d'))
        array([  1.,   1.,   1.,  nan,  nan])
        >>> test(Timegrid('2000.01.08', '2000.01.13', '1d'))
        array([  1.,   1.,   1.,  nan,  nan])
        >>> test(Timegrid('2000.01.12', '2000.01.13', '1d'))
        array([ nan,  nan,   1.,  nan,  nan])
        >>> test(Timegrid('2000.01.05', '2000.01.10', '1d'))
        array([ nan,  nan,  nan,  nan,  nan])
        >>> test(Timegrid('2000.01.05', '2000.01.08', '1d'))
        array([ nan,  nan,  nan,  nan,  nan])
        >>> test(Timegrid('2000.01.15', '2000.01.18', '1d'))
        array([ nan,  nan,  nan,  nan,  nan])
        >>> test(Timegrid('2000.01.16', '2000.01.18', '1d'))
        array([ nan,  nan,  nan,  nan,  nan])

        Through enabling option |Options.usedefaultvalues| the missing
        values are initialised with zero instead of nan:

        >>> with pub.options.usedefaultvalues(True):
        ...     test(Timegrid('2000.01.12', '2000.01.17', '1d'))
        array([ 0.,  0.,  1.,  1.,  1.])
        """
        idxs = [timegrid[hydpy.pub.timegrids.init.firstdate],
                timegrid[hydpy.pub.timegrids.init.lastdate]]
        valcopy = values
        values = numpy.full(self.seriesshape, self.initinfo[0])
        len_ = len(valcopy)
        jdxs = []
        for idx in idxs:
            if idx < 0:
                jdxs.append(0)
            elif idx <= len_:
                jdxs.append(idx)
            else:
                jdxs.append(len_)
        valcopy = valcopy[jdxs[0]:jdxs[1]]
        zdx1 = max(-idxs[0], 0)
        zdx2 = zdx1+jdxs[1]-jdxs[0]
        values[zdx1:zdx2] = valcopy
        return values


Instruction:
Here you have a function in Python 3, explain what it does
def check_completeness(self):
        if hydpy.pub.options.checkseries:
            isnan = numpy.isnan(self.series)
            if numpy.any(isnan):
                nmb = numpy.sum(isnan)
                valuestring = 'value' if nmb == 1 else 'values'
                raise RuntimeError(
                    f'The series array of sequence '
                    f'{objecttools.devicephrase(self)} contains '
                    f'{nmb} nan {valuestring}.')

Response: Raise a |RuntimeError| if the |IOSequence. series| contains at least one |numpy. nan| value.


Instruction:
Explain what the following Python 3 code does
def save_ext(self):
        try:
            sequencemanager = hydpy.pub.sequencemanager
        except AttributeError:
            raise RuntimeError(
                'The time series of sequence %s cannot be saved.  Firstly,'
                'you have to prepare `pub.sequencemanager` correctly.'
                % objecttools.devicephrase(self))
        sequencemanager.save_file(self)

Response: Write the internal data into an external data file.


Instruction:
Explain what the following Python 3 code does
def _load_int(self):
        values = numpy.fromfile(self.filepath_int)
        if self.NDIM > 0:
            values = values.reshape(self.seriesshape)
        return values

Response: Load internal data from file and return it.


Instruction:
Implement a Python 3 function for
averaging the actual time series of the |Variable| object for all time points. Method |IOSequence.average_series| works similarly as method |Variable.average_values| of class |Variable|, from which we borrow some examples. However, firstly, we have to prepare a |Timegrids| object to define the |IOSequence.series| length: >>> from hydpy import pub >>> pub.timegrids = '2000-01-01', '2000-01-04', '1d' As shown for method |Variable.average_values|, for 0-dimensional |IOSequence| objects the result of |IOSequence.average_series| equals |IOSequence.series| itself: >>> from hydpy.core.sequencetools import IOSequence >>> class SoilMoisture(IOSequence): ... NDIM = 0 >>> sm = SoilMoisture(None) >>> sm.activate_ram() >>> import numpy >>> sm.series = numpy.array([190.0, 200.0, 210.0]) >>> sm.average_series() InfoArray([ 190., 200., 210.]) For |IOSequence| objects with an increased dimensionality, a weighting parameter is required, again: >>> SoilMoisture.NDIM = 1 >>> sm.shape = 3 >>> sm.activate_ram() >>> sm.series = ( ... [190.0, 390.0, 490.0], ... [200.0, 400.0, 500.0], ... [210.0, 410.0, 510.0]) >>> from hydpy.core.parametertools import Parameter >>> class Area(Parameter): ... NDIM = 1 ... shape = (3,) ... value = numpy.array([1.0, 1.0, 2.0]) >>> area = Area(None) >>> SoilMoisture.refweights = property(lambda self: area) >>> sm.average_series() InfoArray([ 390., 400., 410.]) The documentation on method |Variable.average_values| provides many examples on how to use different masks in different ways. Here we restrict ourselves to the first example, where a new mask enforces that |IOSequence.average_series| takes only the first two columns of the `series` into account: >>> from hydpy.core.masktools import DefaultMask >>> class Soil(DefaultMask): ... @classmethod ... def new(cls, variable, **kwargs): ... return cls.array2mask([True, True, False]) >>> SoilMoisture.mask = Soil() >>> sm.average_series() InfoArray([ 290., 300., 310.])

Response:
def average_series(self, *args, **kwargs) -> InfoArray:
        """Average the actual time series of the |Variable| object for all
        time points.

        Method |IOSequence.average_series| works similarly as method
        |Variable.average_values| of class |Variable|, from which we
        borrow some examples. However, firstly, we have to prepare a
        |Timegrids| object to define the |IOSequence.series| length:

        >>> from hydpy import pub
        >>> pub.timegrids = '2000-01-01', '2000-01-04', '1d'

        As shown for method |Variable.average_values|, for 0-dimensional
        |IOSequence| objects the result of |IOSequence.average_series|
        equals |IOSequence.series| itself:

        >>> from hydpy.core.sequencetools import IOSequence
        >>> class SoilMoisture(IOSequence):
        ...     NDIM = 0
        >>> sm = SoilMoisture(None)
        >>> sm.activate_ram()
        >>> import numpy
        >>> sm.series = numpy.array([190.0, 200.0, 210.0])
        >>> sm.average_series()
        InfoArray([ 190.,  200.,  210.])

        For |IOSequence| objects with an increased dimensionality, a
        weighting parameter is required, again:

        >>> SoilMoisture.NDIM = 1
        >>> sm.shape = 3
        >>> sm.activate_ram()
        >>> sm.series = (
        ...     [190.0, 390.0, 490.0],
        ...     [200.0, 400.0, 500.0],
        ...     [210.0, 410.0, 510.0])
        >>> from hydpy.core.parametertools import Parameter
        >>> class Area(Parameter):
        ...     NDIM = 1
        ...     shape = (3,)
        ...     value = numpy.array([1.0, 1.0, 2.0])
        >>> area = Area(None)
        >>> SoilMoisture.refweights = property(lambda self: area)
        >>> sm.average_series()
        InfoArray([ 390.,  400.,  410.])

        The documentation on method |Variable.average_values| provides
        many examples on how to use different masks in different ways.
        Here we restrict ourselves to the first example, where a new
        mask enforces that |IOSequence.average_series| takes only the
        first two columns of the `series` into account:

        >>> from hydpy.core.masktools import DefaultMask
        >>> class Soil(DefaultMask):
        ...     @classmethod
        ...     def new(cls, variable, **kwargs):
        ...         return cls.array2mask([True, True, False])
        >>> SoilMoisture.mask = Soil()
        >>> sm.average_series()
        InfoArray([ 290.,  300.,  310.])
        """
        try:
            if not self.NDIM:
                array = self.series
            else:
                mask = self.get_submask(*args, **kwargs)
                if numpy.any(mask):
                    weights = self.refweights[mask]
                    weights /= numpy.sum(weights)
                    series = self.series[:, mask]
                    axes = tuple(range(1, self.NDIM+1))
                    array = numpy.sum(weights*series, axis=axes)
                else:
                    return numpy.nan
            return InfoArray(array, info={'type': 'mean'})
        except BaseException:
            objecttools.augment_excmessage(
                'While trying to calculate the mean value of '
                'the internal time series of sequence %s'
                % objecttools.devicephrase(self))


Instruction:
Write a Python 3 script to
aggregate time series data based on the actual |FluxSequence.aggregation_ext| attribute of |IOSequence| subclasses. We prepare some nodes and elements with the help of method |prepare_io_example_1| and select a 1-dimensional flux sequence of type |lland_fluxes.NKor| as an example: >>> from hydpy.core.examples import prepare_io_example_1 >>> nodes, elements = prepare_io_example_1() >>> seq = elements.element3.model.sequences.fluxes.nkor If no |FluxSequence.aggregation_ext| is `none`, the original time series values are returned: >>> seq.aggregation_ext 'none' >>> seq.aggregate_series() InfoArray([[ 24., 25., 26.], [ 27., 28., 29.], [ 30., 31., 32.], [ 33., 34., 35.]]) If no |FluxSequence.aggregation_ext| is `mean`, function |IOSequence.aggregate_series| is called: >>> seq.aggregation_ext = 'mean' >>> seq.aggregate_series() InfoArray([ 25., 28., 31., 34.]) In case the state of the sequence is invalid: >>> seq.aggregation_ext = 'nonexistent' >>> seq.aggregate_series() Traceback (most recent call last): ... RuntimeError: Unknown aggregation mode `nonexistent` for \ sequence `nkor` of element `element3`. The following technical test confirms that all potential positional and keyword arguments are passed properly: >>> seq.aggregation_ext = 'mean' >>> from unittest import mock >>> seq.average_series = mock.MagicMock() >>> _ = seq.aggregate_series(1, x=2) >>> seq.average_series.assert_called_with(1, x=2)

Response:
def aggregate_series(self, *args, **kwargs) -> InfoArray:
        """Aggregates time series data based on the actual
        |FluxSequence.aggregation_ext| attribute of |IOSequence|
        subclasses.

        We prepare some nodes and elements with the help of
        method |prepare_io_example_1| and select a 1-dimensional
        flux sequence of type |lland_fluxes.NKor| as an example:

        >>> from hydpy.core.examples import prepare_io_example_1
        >>> nodes, elements = prepare_io_example_1()
        >>> seq = elements.element3.model.sequences.fluxes.nkor

        If no |FluxSequence.aggregation_ext| is `none`, the
        original time series values are returned:

        >>> seq.aggregation_ext
        'none'
        >>> seq.aggregate_series()
        InfoArray([[ 24.,  25.,  26.],
                   [ 27.,  28.,  29.],
                   [ 30.,  31.,  32.],
                   [ 33.,  34.,  35.]])

        If no |FluxSequence.aggregation_ext| is `mean`, function
        |IOSequence.aggregate_series| is called:

        >>> seq.aggregation_ext = 'mean'
        >>> seq.aggregate_series()
        InfoArray([ 25.,  28.,  31.,  34.])

        In case the state of the sequence is invalid:

        >>> seq.aggregation_ext = 'nonexistent'
        >>> seq.aggregate_series()
        Traceback (most recent call last):
        ...
        RuntimeError: Unknown aggregation mode `nonexistent` for \
sequence `nkor` of element `element3`.

        The following technical test confirms that all potential
        positional and keyword arguments are passed properly:
        >>> seq.aggregation_ext = 'mean'

        >>> from unittest import mock
        >>> seq.average_series = mock.MagicMock()
        >>> _ = seq.aggregate_series(1, x=2)
        >>> seq.average_series.assert_called_with(1, x=2)
        """
        mode = self.aggregation_ext
        if mode == 'none':
            return self.series
        elif mode == 'mean':
            return self.average_series(*args, **kwargs)
        else:
            raise RuntimeError(
                'Unknown aggregation mode `%s` for sequence %s.'
                % (mode, objecttools.devicephrase(self)))


Instruction:
Can you generate the documentation for the following Python 3 function
def old(self):
        value = getattr(self.fastaccess_old, self.name, None)
        if value is None:
            raise RuntimeError(
                'No value/values of sequence %s has/have '
                'not been defined so far.'
                % objecttools.elementphrase(self))
        else:
            if self.NDIM:
                value = numpy.asarray(value)
            return value

Response:
Assess to the state value at beginning of the time step which
        has been processed most recently.


Instruction:
Write a Python 3 script for
loading the external data of the time series of the given class.

Response:
def load_ext(self):
        """Read time series data like method |IOSequence.load_ext| of class
        |IOSequence|, but with special handling of missing data.

        The method's "special handling" is to convert errors to warnings.
        We explain the reasons in the documentation on method |Obs.load_ext|
        of class |Obs|, from which we borrow the following examples.
        The only differences are that method |Sim.load_ext| of class |Sim|
        does not disable property |IOSequence.memoryflag| and uses option
        |Options.warnmissingsimfile| instead of |Options.warnmissingobsfile|:

        >>> from hydpy.core.examples import prepare_full_example_1
        >>> prepare_full_example_1()
        >>> from hydpy import HydPy, pub, TestIO
        >>> hp = HydPy('LahnH')
        >>> pub.timegrids = '1996-01-01', '1996-01-06', '1d'
        >>> with TestIO():
        ...     hp.prepare_network()
        ...     hp.init_models()
        ...     hp.prepare_simseries()
        >>> sim = hp.nodes.dill.sequences.sim
        >>> with TestIO():
        ...     sim.load_ext()    # doctest: +ELLIPSIS
        Traceback (most recent call last):
        ...
        UserWarning: While trying to load the external data of sequence \
`sim` of node `dill`, the following error occurred: [Errno 2] No such file \
or directory: '...dill_sim_q.asc'
        >>> sim.series
        InfoArray([ nan,  nan,  nan,  nan,  nan])

        >>> sim.series = 1.0
        >>> with TestIO():
        ...     sim.save_ext()
        >>> sim.series = 0.0
        >>> with TestIO():
        ...     sim.load_ext()
        >>> sim.series
        InfoArray([ 1.,  1.,  1.,  1.,  1.])

        >>> import numpy
        >>> sim.series[2] = numpy.nan
        >>> with TestIO():
        ...     pub.sequencemanager.nodeoverwrite = True
        ...     sim.save_ext()
        >>> with TestIO():
        ...     sim.load_ext()
        Traceback (most recent call last):
        ...
        UserWarning: While trying to load the external data of sequence `sim` \
of node `dill`, the following error occurred: The series array of sequence \
`sim` of node `dill` contains 1 nan value.
        >>> sim.series
        InfoArray([  1.,   1.,  nan,   1.,   1.])

        >>> sim.series = 0.0
        >>> with TestIO():
        ...     with pub.options.warnmissingsimfile(False):
        ...         sim.load_ext()
        >>> sim.series
        InfoArray([  1.,   1.,  nan,   1.,   1.])
        """
        try:
            super().load_ext()
        except BaseException:
            if hydpy.pub.options.warnmissingsimfile:
                warnings.warn(str(sys.exc_info()[1]))


Instruction:
Create a Python 3 function to
read external time series data from file.

Response:
def load_ext(self):
        """Read time series data like method |IOSequence.load_ext| of class
        |IOSequence|, but with special handling of missing data.

        When reading incomplete time series data, *HydPy* usually raises
        a |RuntimeError| to prevent from performing erroneous calculations.
        For instance, this makes sense for meteorological input data, being a
        definite requirement for hydrological simulations.  However, the
        same often does not hold for the time series of |Obs| sequences,
        e.g. representing measured discharge. Measured discharge is often
        handled as an optional input value, or even used for comparison
        purposes only.

        According to this reasoning, *HydPy* raises (at most) a |UserWarning|
        in case of missing or incomplete external time series data of |Obs|
        sequences.  The following examples show this based on the `LahnH`
        project, mainly focussing on the |Obs| sequence of node `dill`,
        which is ready for handling time series data at the end of the
        following steps:

        >>> from hydpy.core.examples import prepare_full_example_1
        >>> prepare_full_example_1()
        >>> from hydpy import HydPy, pub, TestIO
        >>> hp = HydPy('LahnH')
        >>> pub.timegrids = '1996-01-01', '1996-01-06', '1d'
        >>> with TestIO():
        ...     hp.prepare_network()
        ...     hp.init_models()
        ...     hp.prepare_obsseries()
        >>> obs = hp.nodes.dill.sequences.obs
        >>> obs.ramflag
        True

        Trying to read non-existing data raises the following warning
        and disables the sequence's ability to handle time series data:

        >>> with TestIO():
        ...     hp.load_obsseries()    # doctest: +ELLIPSIS
        Traceback (most recent call last):
        ...
        UserWarning: The `memory flag` of sequence `obs` of node `dill` had \
to be set to `False` due to the following problem: While trying to load the \
external data of sequence `obs` of node `dill`, the following error occurred: \
[Errno 2] No such file or directory: '...dill_obs_q.asc'
        >>> obs.ramflag
        False

        After writing a complete external data fine, everything works fine:

        >>> obs.activate_ram()
        >>> obs.series = 1.0
        >>> with TestIO():
        ...     obs.save_ext()
        >>> obs.series = 0.0
        >>> with TestIO():
        ...     obs.load_ext()
        >>> obs.series
        InfoArray([ 1.,  1.,  1.,  1.,  1.])

        Reading incomplete data also results in a warning message, but does
        not disable the |IOSequence.memoryflag|:

        >>> import numpy
        >>> obs.series[2] = numpy.nan
        >>> with TestIO():
        ...     pub.sequencemanager.nodeoverwrite = True
        ...     obs.save_ext()
        >>> with TestIO():
        ...     obs.load_ext()
        Traceback (most recent call last):
        ...
        UserWarning: While trying to load the external data of sequence `obs` \
of node `dill`, the following error occurred: The series array of sequence \
`obs` of node `dill` contains 1 nan value.
        >>> obs.memoryflag
        True

        Option |Options.warnmissingobsfile| allows disabling the warning
        messages without altering the functionalities described above:

        >>> hp.prepare_obsseries()
        >>> with TestIO():
        ...     with pub.options.warnmissingobsfile(False):
        ...         hp.load_obsseries()
        >>> obs.series
        InfoArray([  1.,   1.,  nan,   1.,   1.])
        >>> hp.nodes.lahn_1.sequences.obs.memoryflag
        False
        """
        try:
            super().load_ext()
        except OSError:
            del self.memoryflag
            if hydpy.pub.options.warnmissingobsfile:
                warnings.warn(
                    f'The `memory flag` of sequence '
                    f'{objecttools.nodephrase(self)} had to be set to `False` '
                    f'due to the following problem: {sys.exc_info()[1]}')
        except BaseException:
            if hydpy.pub.options.warnmissingobsfile:
                warnings.warn(str(sys.exc_info()[1]))


Instruction:
Given the following Python 3 function, write the documentation
def open_files(self, idx):
        for name in self:
            if getattr(self, '_%s_diskflag' % name):
                path = getattr(self, '_%s_path' % name)
                file_ = open(path, 'rb+')
                ndim = getattr(self, '_%s_ndim' % name)
                position = 8*idx
                for idim in range(ndim):
                    length = getattr(self, '_%s_length_%d' % (name, idim))
                    position *= length
                file_.seek(position)
                setattr(self, '_%s_file' % name, file_)

Response: Open all files with an activated disk flag.


Instruction:
Can you tell what is the following Python 3 function doing
def close_files(self):
        for name in self:
            if getattr(self, '_%s_diskflag' % name):
                file_ = getattr(self, '_%s_file' % name)
                file_.close()

Response: Close all files with an activated disk flag.


Instruction:
Can you implement a function in Python 3 that
loads the internal data of all sequences. If the disk flag is activated then load from RAM.

Response:
def load_data(self, idx):
        """Load the internal data of all sequences.  Load from file if the
        corresponding disk flag is activated, otherwise load from RAM."""
        for name in self:
            ndim = getattr(self, '_%s_ndim' % name)
            diskflag = getattr(self, '_%s_diskflag' % name)
            ramflag = getattr(self, '_%s_ramflag' % name)
            if diskflag:
                file_ = getattr(self, '_%s_file' % name)
                length_tot = 1
                shape = []
                for jdx in range(ndim):
                    length = getattr(self, '_%s_length_%s' % (name, jdx))
                    length_tot *= length
                    shape.append(length)
                raw = file_.read(length_tot*8)
                values = struct.unpack(length_tot*'d', raw)
                if ndim:
                    values = numpy.array(values).reshape(shape)
                else:
                    values = values[0]
            elif ramflag:
                array = getattr(self, '_%s_array' % name)
                values = array[idx]
            if diskflag or ramflag:
                if ndim == 0:
                    setattr(self, name, values)
                else:
                    getattr(self, name)[:] = values


Instruction:
Can you generate the documentation for the following Python 3 function
def save_data(self, idx):
        for name in self:
            actual = getattr(self, name)
            diskflag = getattr(self, '_%s_diskflag' % name)
            ramflag = getattr(self, '_%s_ramflag' % name)
            if diskflag:
                file_ = getattr(self, '_%s_file' % name)
                ndim = getattr(self, '_%s_ndim' % name)
                length_tot = 1
                for jdx in range(ndim):
                    length = getattr(self, '_%s_length_%s' % (name, jdx))
                    length_tot *= length
                if ndim:
                    raw = struct.pack(length_tot*'d', *actual.flatten())
                else:
                    raw = struct.pack('d', actual)
                file_.write(raw)
            elif ramflag:
                array = getattr(self, '_%s_array' % name)
                array[idx] = actual

Response:
Save the internal data of all sequences with an activated flag. Write to file if the corresponding disk flag is activated ; store
        in working memory if the corresponding ram flag is activated.


Instruction:
Implement a function in Python 3 to
load the next sim sequence value of the given index.

Response:
def load_simdata(self, idx: int) -> None:
        """Load the next sim sequence value (of the given index)."""
        if self._sim_ramflag:
            self.sim[0] = self._sim_array[idx]
        elif self._sim_diskflag:
            raw = self._sim_file.read(8)
            self.sim[0] = struct.unpack('d', raw)


Instruction:
Make a summary of the following Python 3 code
def save_simdata(self, idx: int) -> None:
        if self._sim_ramflag:
            self._sim_array[idx] = self.sim[0]
        elif self._sim_diskflag:
            raw = struct.pack('d', self.sim[0])
            self._sim_file.write(raw)

Response: Save the last sim sequence value of the given index.


Instruction:
Create a Python 3 function to
load the next observation sequence value of the given index.

Response:
def load_obsdata(self, idx: int) -> None:
        """Load the next obs sequence value (of the given index)."""
        if self._obs_ramflag:
            self.obs[0] = self._obs_array[idx]
        elif self._obs_diskflag:
            raw = self._obs_file.read(8)
            self.obs[0] = struct.unpack('d', raw)


Instruction:
Can you tell what is the following Python 3 function doing
def update(self):
        control = self.subpars.pars.control
        self(control.ft*control.fhru)

Response: Update |AbsFHRU| based on |FT| and |FHRU|.


Instruction:
Can you tell what is the following Python 3 function doing
def update(self):
        con = self.subpars.pars.control
        self(con.hinz*con.lai)

Response: Update |KInz| based on |HInz| and |LAI|.


Instruction:
Can you tell what is the following Python 3 function doing
def update(self):
        con = self.subpars.pars.control
        self(con.relwb*con.nfk)

Response: Update |WB| based on |RelWB| and |NFk|.


Instruction:
How would you code a function in Python 3 to
update |WZ| based on |RelWZ| and |NFk|.

Response:
def update(self):
        """Update |WZ| based on |RelWZ| and |NFk|.

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> nhru(2)
        >>> lnk(ACKER)
        >>> relwz(0.8)
        >>> nfk(100.0, 200.0)
        >>> derived.wz.update()
        >>> derived.wz
        wz(80.0, 160.0)
        """
        con = self.subpars.pars.control
        self(con.relwz*con.nfk)


Instruction:
Make a summary of the following Python 3 code
def update(self):
        con = self.subpars.pars.control
        self(con.eqb*con.tind)

Response: Update |KB| based on |EQB| and |TInd|.


Instruction:
Can you create a Python 3 function that
updates |KI1| based on |EQI1| and |TInd|.

Response:
def update(self):
        """Update |KI1| based on |EQI1| and |TInd|.

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> eqi1(5.0)
        >>> tind.value = 10.0
        >>> derived.ki1.update()
        >>> derived.ki1
        ki1(50.0)
        """
        con = self.subpars.pars.control
        self(con.eqi1*con.tind)


Instruction:
How would you code a function in Python 3 to
update |KI2| based on |EQI2| and |TInd|.

Response:
def update(self):
        """Update |KI2| based on |EQI2| and |TInd|.

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> eqi2(1.0)
        >>> tind.value = 10.0
        >>> derived.ki2.update()
        >>> derived.ki2
        ki2(10.0)
        """
        con = self.subpars.pars.control
        self(con.eqi2*con.tind)


Instruction:
Write a Python 3 script to
update |KD1| based on |EQD1| and |TInd|.

Response:
def update(self):
        """Update |KD1| based on |EQD1| and |TInd|.

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> eqd1(0.5)
        >>> tind.value = 10.0
        >>> derived.kd1.update()
        >>> derived.kd1
        kd1(5.0)
        """
        con = self.subpars.pars.control
        self(con.eqd1*con.tind)


Instruction:
Write a Python 3 script to
update |KD2| based on |EQD2| and |TInd|.

Response:
def update(self):
        """Update |KD2| based on |EQD2| and |TInd|.

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> eqd2(0.1)
        >>> tind.value = 10.0
        >>> derived.kd2.update()
        >>> derived.kd2
        kd2(1.0)
        """
        con = self.subpars.pars.control
        self(con.eqd2*con.tind)


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

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

        >>> from hydpy.models.lland import *
        >>> parameterstep('1d')
        >>> simulationstep('1d')
        >>> ft(10.0)
        >>> derived.qfactor.update()
        >>> derived.qfactor
        qfactor(0.115741)
        """
        con = self.subpars.pars.control
        self(con.ft*1000./self.simulationstep.seconds)


Instruction:
Can you generate the documentation for the following Python 3 function
def _router_numbers(self):
        return tuple(up for up in self._up2down.keys()
                     if up in self._up2down.values())

Response: A tuple of the numbers of all routing basins.


Instruction:
How would you explain what the following Python 3 function does
def supplier_elements(self):
        elements = devicetools.Elements()
        for supplier in self._supplier_numbers:
            element = self._get_suppliername(supplier)
            try:
                outlet = self._get_nodename(self._up2down[supplier])
            except TypeError:
                outlet = self.last_node
            elements += devicetools.Element(element, outlets=outlet)
        return elements

Response: A |Elements| collection of all supplying basins.


Instruction:
Explain what the following Python 3 code does
def nodes(self):
        return (
            devicetools.Nodes(
                self.node_prefix+routers for routers in self._router_numbers) +
            devicetools.Node(self.last_node))

Response: A |Nodes| collection of all required nodes.


Instruction:
Can you tell what is the following Python 3 function doing
def selection(self):
        return selectiontools.Selection(
            self.selection_name, self.nodes, self.elements)

Response: A |Selection| object of all supplying and routing nodes and required elements and required nodes.


Instruction:
Can you tell what is the following Python 3 function doing
def str2chars(strings) -> numpy.ndarray:
    maxlen = 0
    for name in strings:
        maxlen = max(maxlen, len(name))
    # noinspection PyTypeChecker
    chars = numpy.full(
        (len(strings), maxlen), b'', dtype='|S1')
    for idx, name in enumerate(strings):
        for jdx, char in enumerate(name):
            chars[idx, jdx] = char.encode('utf-8')
    return chars

Response: Return |numpy. ndarray| containing the byte characters of all given strings.


Instruction:
Here you have a function in Python 3, explain what it does
def chars2str(chars) -> List[str]:
    strings = collections.deque()
    for subchars in chars:
        substrings = collections.deque()
        for char in subchars:
            if char:
                substrings.append(char.decode('utf-8'))
            else:
                substrings.append('')
        strings.append(''.join(substrings))
    return list(strings)

Response: Inversion function of function |str2chars|.


Instruction:
How would you explain what the following Python 3 function does
def create_dimension(ncfile, name, length) -> None:
    try:
        ncfile.createDimension(name, length)
    except BaseException:
        objecttools.augment_excmessage(
            'While trying to add dimension `%s` with length `%d` '
            'to the NetCDF file `%s`'
            % (name, length, get_filepath(ncfile)))

Response: Create a new dimension with the given name and length.


Instruction:
How would you code a function in Python 3 to
create a new variable with the given name datatype and dimensions.

Response:
def create_variable(ncfile, name, datatype, dimensions) -> None:
    """Add a new variable with the given name, datatype, and dimensions
    to the given NetCDF file.

    Essentially, |create_variable| just calls the equally named method
    of the NetCDF library, but adds information to possible error messages:

    >>> from hydpy import TestIO
    >>> from hydpy.core.netcdftools import netcdf4
    >>> with TestIO():
    ...     ncfile = netcdf4.Dataset('test.nc', 'w')
    >>> from hydpy.core.netcdftools import create_variable
    >>> try:
    ...     create_variable(ncfile, 'var1', 'f8', ('dim1',))
    ... except BaseException as exc:
    ...     print(str(exc).strip('"'))    # doctest: +ELLIPSIS
    While trying to add variable `var1` with datatype `f8` and \
dimensions `('dim1',)` to the NetCDF file `test.nc`, the following error \
occurred: ...

    >>> from hydpy.core.netcdftools import create_dimension
    >>> create_dimension(ncfile, 'dim1', 5)
    >>> create_variable(ncfile, 'var1', 'f8', ('dim1',))
    >>> import numpy
    >>> numpy.array(ncfile['var1'][:])
    array([ nan,  nan,  nan,  nan,  nan])

    >>> ncfile.close()
    """
    default = fillvalue if (datatype == 'f8') else None
    try:
        ncfile.createVariable(
            name, datatype, dimensions=dimensions, fill_value=default)
        ncfile[name].long_name = name
    except BaseException:
        objecttools.augment_excmessage(
            'While trying to add variable `%s` with datatype `%s` '
            'and dimensions `%s` to the NetCDF file `%s`'
            % (name, datatype, dimensions, get_filepath(ncfile)))


Instruction:
Here you have a function in Python 3, explain what it does
def query_variable(ncfile, name) -> netcdf4.Variable:
    try:
        return ncfile[name]
    except (IndexError, KeyError):
        raise OSError(
            'NetCDF file `%s` does not contain variable `%s`.'
            % (get_filepath(ncfile), name))

Response: Query the variable with the given name from the given NetCDF file.


Instruction:
Given the following Python 3 function, write the documentation
def query_timegrid(ncfile) -> timetools.Timegrid:
    timepoints = ncfile[varmapping['timepoints']]
    refdate = timetools.Date.from_cfunits(timepoints.units)
    return timetools.Timegrid.from_timepoints(
        timepoints=timepoints[:],
        refdate=refdate,
        unit=timepoints.units.strip().split()[0])

Response: Query the |Timegrid| defined by the given NetCDF file.


Instruction:
Can you create a Python 3 function that
queries the data of the given name from the given NetCDF file.

Response:
def query_array(ncfile, name) -> numpy.ndarray:
    """Return the data of the variable with the given name from the given
    NetCDF file.

    The following example shows that |query_array| returns |nan| entries
    to represent missing values even when the respective NetCDF variable
    defines a different fill value:

    >>> from hydpy import TestIO
    >>> from hydpy.core.netcdftools import netcdf4
    >>> from hydpy.core import netcdftools
    >>> netcdftools.fillvalue = -999.0
    >>> with TestIO():
    ...     with netcdf4.Dataset('test.nc', 'w') as ncfile:
    ...         netcdftools.create_dimension(ncfile, 'dim1', 5)
    ...         netcdftools.create_variable(ncfile, 'var1', 'f8', ('dim1',))
    ...     ncfile = netcdf4.Dataset('test.nc', 'r')
    >>> netcdftools.query_variable(ncfile, 'var1')[:].data
    array([-999., -999., -999., -999., -999.])
    >>> netcdftools.query_array(ncfile, 'var1')
    array([ nan,  nan,  nan,  nan,  nan])
    >>> import numpy
    >>> netcdftools.fillvalue = numpy.nan
    """
    variable = query_variable(ncfile, name)
    maskedarray = variable[:]
    fillvalue_ = getattr(variable, '_FillValue', numpy.nan)
    if not numpy.isnan(fillvalue_):
        maskedarray[maskedarray.mask] = numpy.nan
    return maskedarray.data


Instruction:
Create a Python 3 function for
logging the given |IOSequence| object to its |NetCDFFile. log| method.

Response:
def log(self, sequence, infoarray) -> None:
        """Prepare a |NetCDFFile| object suitable for the given |IOSequence|
        object, when necessary, and pass the given arguments to its
        |NetCDFFile.log| method."""
        if isinstance(sequence, sequencetools.ModelSequence):
            descr = sequence.descr_model
        else:
            descr = 'node'
        if self._isolate:
            descr = '%s_%s' % (descr, sequence.descr_sequence)
            if ((infoarray is not None) and
                    (infoarray.info['type'] != 'unmodified')):
                descr = '%s_%s' % (descr, infoarray.info['type'])
        dirpath = sequence.dirpath_ext
        try:
            files = self.folders[dirpath]
        except KeyError:
            files: Dict[str, 'NetCDFFile'] = collections.OrderedDict()
            self.folders[dirpath] = files
        try:
            file_ = files[descr]
        except KeyError:
            file_ = NetCDFFile(
                name=descr,
                flatten=self._flatten,
                isolate=self._isolate,
                timeaxis=self._timeaxis,
                dirpath=dirpath)
            files[descr] = file_
        file_.log(sequence, infoarray)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def read(self) -> None:
        for folder in self.folders.values():
            for file_ in folder.values():
                file_.read()

Response: Call method |NetCDFFile. read| of all handled |NetCDFFile objects.


Instruction:
How would you explain what the following Python 3 function does
def write(self) -> None:
        if self.folders:
            init = hydpy.pub.timegrids.init
            timeunits = init.firstdate.to_cfunits('hours')
            timepoints = init.to_timepoints('hours')
            for folder in self.folders.values():
                for file_ in folder.values():
                    file_.write(timeunits, timepoints)

Response: Write all |NetCDFFile| objects in |NetCDFFolder| objects in |NetCDFFile| objects in |NetCDFFolder| objects.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def filenames(self) -> Tuple[str, ...]:
        return tuple(sorted(set(itertools.chain(
            *(_.keys() for _ in self.folders.values())))))

Response: A |tuple| of names of all handled |NetCDFFile| objects.