Instruction:
Write a Python 3 function for
updating all master objects and slaves.

Response:
def update_masters(self):
        """Update all `master` |Substituter| objects.

        If a |Substituter| object is passed to the constructor of another
        |Substituter| object, they become `master` and `slave`:

        >>> from hydpy.core.autodoctools import Substituter
        >>> sub1 = Substituter()
        >>> from hydpy.core import devicetools
        >>> sub1.add_module(devicetools)
        >>> sub2 = Substituter(sub1)
        >>> sub3 = Substituter(sub2)
        >>> sub3.master.master is sub1
        True
        >>> sub2 in sub1.slaves
        True

        During initialization, all mappings handled by the master object
        are passed to its new slave:

        >>> sub3.find('Node|')
        |Node| :class:`~hydpy.core.devicetools.Node`
        |devicetools.Node| :class:`~hydpy.core.devicetools.Node`

        Updating a slave, does not affect its master directly:

        >>> from hydpy.core import hydpytools
        >>> sub3.add_module(hydpytools)
        >>> sub3.find('HydPy|')
        |HydPy| :class:`~hydpy.core.hydpytools.HydPy`
        |hydpytools.HydPy| :class:`~hydpy.core.hydpytools.HydPy`
        >>> sub2.find('HydPy|')

        Through calling |Substituter.update_masters|, the `medium2long`
        mappings are passed from the slave to its master:

        >>> sub3.update_masters()
        >>> sub2.find('HydPy|')
        |hydpytools.HydPy| :class:`~hydpy.core.hydpytools.HydPy`

        Then each master object updates its own master object also:

        >>> sub1.find('HydPy|')
        |hydpytools.HydPy| :class:`~hydpy.core.hydpytools.HydPy`

        In reverse, subsequent updates of master objects to not affect
        their slaves directly:

        >>> from hydpy.core import masktools
        >>> sub1.add_module(masktools)
        >>> sub1.find('Masks|')
        |Masks| :class:`~hydpy.core.masktools.Masks`
        |masktools.Masks| :class:`~hydpy.core.masktools.Masks`
        >>> sub2.find('Masks|')

        Through calling |Substituter.update_slaves|, the `medium2long`
        mappings are passed the master to all of its slaves:

        >>> sub1.update_slaves()
        >>> sub2.find('Masks|')
        |masktools.Masks| :class:`~hydpy.core.masktools.Masks`
        >>> sub3.find('Masks|')
        |masktools.Masks| :class:`~hydpy.core.masktools.Masks`
        """
        if self.master is not None:
            self.master._medium2long.update(self._medium2long)
            self.master.update_masters()


Instruction:
How would you code a function in Python 3 to
update all |Slave| objects.

Response:
def update_slaves(self):
        """Update all `slave` |Substituter| objects.

        See method |Substituter.update_masters| for further information.
        """
        for slave in self.slaves:
            slave._medium2long.update(self._medium2long)
            slave.update_slaves()


Instruction:
Here you have a function in Python 3, explain what it does
def get_commands(self, source=None):
        commands = []
        for key, value in self:
            if (source is None) or (key in source):
                commands.append('.. %s replace:: %s' % (key, value))
        return '\n'.join(commands)

Response: Return a string containing multiple reStructuredText commands with the substitutions currently defined.


Instruction:
Can you implement a function in Python 3 that
prints all substitutions that include the given text string.

Response:
def find(self, text):
        """Print all substitutions that include the given text string."""
        for key, value in self:
            if (text in key) or (text in value):
                print(key, value)


Instruction:
Implement a function in Python 3 to
add print commands time to the given function informing about execution time. To show how the |print_progress| decorator works, we need to modify the functions used by |print_progress| to gain system time information available in module |time|. First, we mock the functions |time.strftime| and |time.perf_counter|: >>> import time >>> from unittest import mock >>> strftime = time.strftime >>> perf_counter = time.perf_counter >>> strftime_mock = mock.MagicMock() >>> time.strftime = strftime_mock >>> time.perf_counter = mock.MagicMock() The mock of |time.strftime| shall respond to two calls, as if the first call to a decorated function occurs at quarter past eight, and the second one two seconds later: >>> time.strftime.side_effect = '20:15:00', '20:15:02' The mock of |time.perf_counter| shall respond to four calls, as if the subsequent calls by decorated functions occur at second 1, 3, 4, and 7: >>> time.perf_counter.side_effect = 1, 3, 4, 7 Now we decorate two test functions. The first one does nothing; the second one only calls the first one: >>> from hydpy.core.printtools import print_progress >>> @print_progress ... def test1(): ... pass >>> @print_progress ... def test2(): ... test1() The first example shows that the output is appropriately indented, tat the returned times are at the right place, that the calculated execution the is correct, and that the mock of |time.strftime| received a valid format string: >>> from hydpy import pub >>> pub.options.printprogress = True >>> test2() method test2 started at 20:15:00 method test1 started at 20:15:02 seconds elapsed: 1 seconds elapsed: 6 >>> strftime_mock.call_args call('%H:%M:%S') The second example verifies that resetting the indentation works: >>> time.strftime.side_effect = '20:15:00', '20:15:02' >>> time.perf_counter.side_effect = 1, 3, 4, 7 >>> test2() method test2 started at 20:15:00 method test1 started at 20:15:02 seconds elapsed: 1 seconds elapsed: 6 The last example shows that disabling the |Options.printprogress| option works as expected: >>> pub.options.printprogress = False >>> test2() >>> time.strftime = strftime >>> time.perf_counter = perf_counter

Response:
def print_progress(wrapped, _=None, args=None, kwargs=None):
    """Add print commands time to the given function informing about
     execution time.

    To show how the |print_progress| decorator works, we need to modify the
    functions used by |print_progress| to gain system time information
    available in module |time|.

    First, we mock the functions |time.strftime| and |time.perf_counter|:

    >>> import time
    >>> from unittest import mock
    >>> strftime = time.strftime
    >>> perf_counter = time.perf_counter
    >>> strftime_mock = mock.MagicMock()
    >>> time.strftime = strftime_mock
    >>> time.perf_counter = mock.MagicMock()

    The mock of |time.strftime| shall respond to two calls, as if the first
    call to a decorated function occurs at quarter past eight, and the second
    one two seconds later:

    >>> time.strftime.side_effect = '20:15:00', '20:15:02'

    The mock of |time.perf_counter| shall respond to four calls, as if the
    subsequent calls by decorated functions occur at second 1, 3, 4, and 7:

    >>> time.perf_counter.side_effect = 1, 3, 4, 7

    Now we decorate two test functions.  The first one does nothing; the
    second one only calls the first one:

    >>> from hydpy.core.printtools import print_progress
    >>> @print_progress
    ... def test1():
    ...     pass
    >>> @print_progress
    ... def test2():
    ...     test1()

    The first example shows that the output is appropriately indented,
    tat the returned times are at the right place, that the calculated
    execution the is correct, and that the mock of |time.strftime|
    received a valid format string:

    >>> from hydpy import pub
    >>> pub.options.printprogress = True
    >>> test2()
    method test2 started at 20:15:00
        method test1 started at 20:15:02
            seconds elapsed: 1
        seconds elapsed: 6
    >>> strftime_mock.call_args
    call('%H:%M:%S')

    The second example verifies that resetting the indentation works:

    >>> time.strftime.side_effect = '20:15:00', '20:15:02'
    >>> time.perf_counter.side_effect = 1, 3, 4, 7
    >>> test2()
    method test2 started at 20:15:00
        method test1 started at 20:15:02
            seconds elapsed: 1
        seconds elapsed: 6

    The last example shows that disabling the |Options.printprogress|
    option works as expected:

    >>> pub.options.printprogress = False
    >>> test2()

    >>> time.strftime = strftime
    >>> time.perf_counter = perf_counter
    """
    global _printprogress_indentation
    _printprogress_indentation += 4
    try:
        if hydpy.pub.options.printprogress:
            blanks = ' ' * _printprogress_indentation
            name = wrapped.__name__
            time_ = time.strftime('%H:%M:%S')
            with PrintStyle(color=34, font=1):
                print(f'{blanks}method {name} started at {time_}')
            seconds = time.perf_counter()
            sys.stdout.flush()
            wrapped(*args, **kwargs)
            blanks = ' ' * (_printprogress_indentation+4)
            seconds = time.perf_counter()-seconds
            with PrintStyle(color=34, font=1):
                print(f'{blanks}seconds elapsed: {seconds}')
            sys.stdout.flush()
        else:
            wrapped(*args, **kwargs)
    finally:
        _printprogress_indentation -= 4


Instruction:
Write a Python 3 function for
printing a simple progress bar while processing the given iterable.

Response:
def progressbar(iterable, length=23):
    """Print a simple progress bar while processing the given iterable.

    Function |progressbar| does print the progress bar when option
    `printprogress` is activted:

    >>> from hydpy import pub
    >>> pub.options.printprogress = True

    You can pass an iterable object.  Say you want to calculate the the sum
    of all integer values from 1 to 100 and print the progress of the
    calculation.  Using function |range| (which returns a list in Python 2
    and an iterator in Python3, but both are fine), one just has to
    interpose function |progressbar|:

    >>> from hydpy.core.printtools import progressbar
    >>> x_sum = 0
    >>> for x in progressbar(range(1, 101)):
    ...     x_sum += x
        |---------------------|
        ***********************
    >>> x_sum
    5050

    To prevent possible interim print commands from dismembering the status
    bar, they are delayed until the status bar is complete.  For intermediate
    print outs of each fiftieth calculation, the result looks as follows:

    >>> x_sum = 0
    >>> for x in progressbar(range(1, 101)):
    ...     x_sum += x
    ...     if not x % 50:
    ...         print(x, x_sum)
        |---------------------|
        ***********************
    50 1275
    100 5050

    The number of characters of the progress bar can be changed:

    >>> for i in progressbar(range(100), length=50):
    ...     continue
        |------------------------------------------------|
        **************************************************

    But its maximum number of characters is restricted by the length of the
    given iterable:

    >>> for i in progressbar(range(10), length=50):
    ...     continue
        |--------|
        **********

    The smallest possible progress bar has two characters:

    >>> for i in progressbar(range(2)):
    ...     continue
        ||
        **

    For iterables of length one or zero, no progress bar is plottet:

    >>> for i in progressbar(range(1)):
    ...     continue

    The same is True when the `printprogress` option is inactivated:

    >>> pub.options.printprogress = False
    >>> for i in progressbar(range(100)):
    ...     continue
    """
    if hydpy.pub.options.printprogress and (len(iterable) > 1):
        temp_name = os.path.join(tempfile.gettempdir(),
                                 'HydPy_progressbar_stdout')
        temp_stdout = open(temp_name, 'w')
        real_stdout = sys.stdout
        try:
            sys.stdout = temp_stdout
            nmbstars = min(len(iterable), length)
            nmbcounts = len(iterable)/nmbstars
            indentation = ' '*max(_printprogress_indentation, 0)
            with PrintStyle(color=36, font=1, file=real_stdout):
                print('    %s|%s|\n%s    ' % (indentation,
                                              '-'*(nmbstars-2),
                                              indentation),
                      end='',
                      file=real_stdout)
                counts = 1.
                for next_ in iterable:
                    counts += 1.
                    if counts >= nmbcounts:
                        print(end='*', file=real_stdout)
                        counts -= nmbcounts
                    yield next_
        finally:
            try:
                temp_stdout.close()
            except BaseException:
                pass
            sys.stdout = real_stdout
            print()
            with open(temp_name, 'r') as temp_stdout:
                sys.stdout.write(temp_stdout.read())
            sys.stdout.flush()
    else:
        for next_ in iterable:
            yield next_


Instruction:
Can you tell what is the following Python 3 function doing
def start_server(socket, projectname, xmlfilename: str) -> None:
    state.initialise(projectname, xmlfilename)
    server = http.server.HTTPServer(('', int(socket)), HydPyServer)
    server.serve_forever()

Response: Start the HydPy server using the given socket.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def await_server(port, seconds):
    now = time.perf_counter()
    end = now + float(seconds)
    while now <= end:
        try:
            urllib.request.urlopen(f'http://localhost:{port}/status')
            break
        except urllib.error.URLError:
            time.sleep(0.1)
            now = time.perf_counter()
    else:
        raise urllib.error.URLError(
            f'Waited for {seconds} seconds without response on port {port}.')

Response: Block the current process until the hydpy server is responding on the given port or the given number of seconds elapsed.


Instruction:
Here you have a function in Python 3, explain what it does
def initialise(self, projectname: str, xmlfile: str) -> None:
        write = commandtools.print_textandtime
        write(f'Start HydPy project `{projectname}`')
        hp = hydpytools.HydPy(projectname)
        write(f'Read configuration file `{xmlfile}`')
        interface = xmltools.XMLInterface(xmlfile)
        write('Interpret the defined options')
        interface.update_options()
        write('Interpret the defined period')
        interface.update_timegrids()
        write('Read all network files')
        hp.prepare_network()
        write('Activate the selected network')
        hp.update_devices(interface.fullselection)
        write('Read the required control files')
        hp.init_models()
        write('Read the required condition files')
        interface.conditions_io.load_conditions()
        write('Read the required time series files')
        interface.series_io.prepare_series()
        interface.exchange.prepare_series()
        interface.series_io.load_series()
        self.hp = hp
        self.parameteritems = interface.exchange.parameteritems
        self.conditionitems = interface.exchange.conditionitems
        self.getitems = interface.exchange.getitems
        self.conditions = {}
        self.parameteritemvalues = collections.defaultdict(lambda: {})
        self.modifiedconditionitemvalues = collections.defaultdict(lambda: {})
        self.getitemvalues = collections.defaultdict(lambda: {})
        self.init_conditions = hp.conditions
        self.timegrids = {}

Response: Initialise a HydPy project based on the given XML configuration file.


Instruction:
Can you generate the documentation for the following Python 3 function
def POST_evaluate(self) -> None:
        for name, value in self._inputs.items():
            result = eval(value)
            self._outputs[name] = objecttools.flatten_repr(result)

Response:
Evaluate any valid Python expression with the * HydPy * server
        process and get its result.


Instruction:
Implement a function in Python 3 to
stop and close the HydPy server.

Response:
def GET_close_server(self) -> None:
        """Stop and close the *HydPy* server."""
        def _close_server():
            self.server.shutdown()
            self.server.server_close()
        shutter = threading.Thread(target=_close_server)
        shutter.deamon = True
        shutter.start()


Instruction:
Here you have a function in Python 3, explain what it does
def GET_parameteritemtypes(self) -> None:
        for item in state.parameteritems:
            self._outputs[item.name] = self._get_itemtype(item)

Response:
Get the types of all current exchange items supposed to change
        the values of |Parameter| objects.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def GET_conditionitemtypes(self) -> None:
        for item in state.conditionitems:
            self._outputs[item.name] = self._get_itemtype(item)

Response:
Get the types of all current exchange items supposed to change
        the values of StateSequence or LogSequence objects.


Instruction:
Here you have a function in Python 3, explain what it does
def GET_getitemtypes(self) -> None:
        for item in state.getitems:
            type_ = self._get_itemtype(item)
            for name, _ in item.yield_name2value():
                self._outputs[name] = type_

Response:
Get the types of all current exchange items supposed to return
        the values of Parameter or Sequence objects or the time series
        of IOSequence objects or the time series
        of IOParameter objects or the time series
        of IOSequence objects.


Instruction:
Can you create a Python 3 function that
changes the current simulation |Timegrid|.

Response:
def POST_timegrid(self) -> None:
        """Change the current simulation |Timegrid|."""
        init = hydpy.pub.timegrids.init
        sim = hydpy.pub.timegrids.sim
        sim.firstdate = self._inputs['firstdate']
        sim.lastdate = self._inputs['lastdate']
        state.idx1 = init[sim.firstdate]
        state.idx2 = init[sim.lastdate]


Instruction:
Can you tell what is the following Python 3 function doing
def GET_parameteritemvalues(self) -> None:
        for item in state.parameteritems:
            self._outputs[item.name] = item.value

Response: Get the values of all |ChangeItem| objects handling |Parameter| objects.


Instruction:
Can you write a function in Python 3 where it
gets the values of all |ChangeItem| objects handling |StateSequence| or |LogSequence| objects handling |StateSequence| or |LogSequence| objects handling |StateSequence| or |LogSequence| objects handling |StateSequence| or |LogSequence| objects.

Response:
def GET_conditionitemvalues(self) -> None:
        """Get the values of all |ChangeItem| objects handling |StateSequence|
        or |LogSequence| objects."""
        for item in state.conditionitems:
            self._outputs[item.name] = item.value


Instruction:
Make a summary of the following Python 3 code
def GET_getitemvalues(self) -> None:
        for item in state.getitems:
            for name, value in item.yield_name2value(state.idx1, state.idx2):
                self._outputs[name] = value

Response:
Get the values of all Variable objects observed by the
        current objects.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def GET_load_conditionvalues(self) -> None:
        try:
            state.hp.conditions = state.conditions[self._id][state.idx1]
        except KeyError:
            if state.idx1:
                self._statuscode = 500
                raise RuntimeError(
                    f'Conditions for ID `{self._id}` and time point '
                    f'`{hydpy.pub.timegrids.sim.firstdate}` are required, '
                    f'but have not been calculated so far.')
            else:
                state.hp.conditions = state.init_conditions

Response: Assign the |StateSequence| or |LogSequence| object values available to the current |HydPy| instance.


Instruction:
Make a summary of the following Python 3 code
def GET_save_conditionvalues(self) -> None:
        state.conditions[self._id] = state.conditions.get(self._id, {})
        state.conditions[self._id][state.idx2] = state.hp.conditions

Response: Save the |StateSequence| and |LogSequence| object values of the current simulation endpoint for the current simulation endpoint.


Instruction:
Create a Python 3 function to
save the values of those which are handling |Parameter| objects.

Response:
def GET_save_parameteritemvalues(self) -> None:
        """Save the values of those |ChangeItem| objects which are
        handling |Parameter| objects."""
        for item in state.parameteritems:
            state.parameteritemvalues[self._id][item.name] = item.value.copy()


Instruction:
Can you generate a brief explanation for the following Python 3 code
def GET_savedparameteritemvalues(self) -> None:
        dict_ = state.parameteritemvalues.get(self._id)
        if dict_ is None:
            self.GET_parameteritemvalues()
        else:
            for name, value in dict_.items():
                self._outputs[name] = value

Response: Get the previously saved values of those |ChangeItem| objects which are handling |Parameter| objects.


Instruction:
Make a summary of the following Python 3 code
def GET_save_modifiedconditionitemvalues(self) -> None:
        for item in state.conditionitems:
            state.modifiedconditionitemvalues[self._id][item.name] = \
                list(item.device2target.values())[0].value

Response: Save modified condition items values to the state.


Instruction:
How would you explain what the following Python 3 function does
def GET_savedmodifiedconditionitemvalues(self) -> None:
        dict_ = state.modifiedconditionitemvalues.get(self._id)
        if dict_ is None:
            self.GET_conditionitemvalues()
        else:
            for name, value in dict_.items():
                self._outputs[name] = value

Response: Get the modified condition items values.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def GET_save_getitemvalues(self) -> None:
        for item in state.getitems:
            for name, value in item.yield_name2value(state.idx1, state.idx2):
                state.getitemvalues[self._id][name] = value

Response: Save the values of all current |GetItem| objects.


Instruction:
Can you tell what is the following Python 3 function doing
def GET_savedgetitemvalues(self) -> None:
        dict_ = state.getitemvalues.get(self._id)
        if dict_ is None:
            self.GET_getitemvalues()
        else:
            for name, value in dict_.items():
                self._outputs[name] = value

Response: Get the previously saved values of all |GetItem| objects.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def GET_save_timegrid(self) -> None:
        state.timegrids[self._id] = copy.deepcopy(hydpy.pub.timegrids.sim)

Response: Save the current simulation period.


Instruction:
Can you generate the documentation for the following Python 3 function
def GET_savedtimegrid(self) -> None:
        try:
            self._write_timegrid(state.timegrids[self._id])
        except KeyError:
            self._write_timegrid(hydpy.pub.timegrids.init)

Response: Get the previously saved simulation period.


Instruction:
Implement a Python 3 function for
trimming the value(s) of a |Variable| instance. Usually, users do not need to apply function |trim| directly. Instead, some |Variable| subclasses implement their own `trim` methods relying on function |trim|. Model developers should implement individual `trim` methods for their |Parameter| or |Sequence| subclasses when their boundary values depend on the actual project configuration (one example is soil moisture; its lowest possible value should possibly be zero in all cases, but its highest possible value could depend on another parameter defining the maximum storage capacity). For the following examples, we prepare a simple (not fully functional) |Variable| subclass, making use of function |trim| without any modifications. Function |trim| works slightly different for variables handling |float|, |int|, and |bool| values. We start with the most common content type |float|: >>> from hydpy.core.variabletools import trim, Variable >>> class Var(Variable): ... NDIM = 0 ... TYPE = float ... SPAN = 1.0, 3.0 ... trim = trim ... initinfo = 2.0, False ... __hydpy__connect_variable2subgroup__ = None First, we enable the printing of warning messages raised by function |trim|: >>> from hydpy import pub >>> pub.options.warntrim = True When not passing boundary values, function |trim| extracts them from class attribute `SPAN` of the given |Variable| instance, if available: >>> var = Var(None) >>> var.value = 2.0 >>> var.trim() >>> var var(2.0) >>> var.value = 0.0 >>> var.trim() Traceback (most recent call last): ... UserWarning: For variable `var` at least one value needed to be trimmed. \ The old and the new value(s) are `0.0` and `1.0`, respectively. >>> var var(1.0) >>> var.value = 4.0 >>> var.trim() Traceback (most recent call last): ... UserWarning: For variable `var` at least one value needed to be trimmed. \ The old and the new value(s) are `4.0` and `3.0`, respectively. >>> var var(3.0) In the examples above, outlier values are set to the respective boundary value, accompanied by suitable warning messages. For very tiny deviations, which might be due to precision problems only, outliers are trimmed but not reported: >>> var.value = 1.0 - 1e-15 >>> var == 1.0 False >>> trim(var) >>> var == 1.0 True >>> var.value = 3.0 + 1e-15 >>> var == 3.0 False >>> var.trim() >>> var == 3.0 True Use arguments `lower` and `upper` to override the (eventually) available `SPAN` entries: >>> var.trim(lower=4.0) Traceback (most recent call last): ... UserWarning: For variable `var` at least one value needed to be trimmed. \ The old and the new value(s) are `3.0` and `4.0`, respectively. >>> var.trim(upper=3.0) Traceback (most recent call last): ... UserWarning: For variable `var` at least one value needed to be trimmed. \ The old and the new value(s) are `4.0` and `3.0`, respectively. Function |trim| interprets both |None| and |numpy.nan| values as if no boundary value exists: >>> import numpy >>> var.value = 0.0 >>> var.trim(lower=numpy.nan) >>> var.value = 5.0 >>> var.trim(upper=numpy.nan) You can disable function |trim| via option |Options.trimvariables|: >>> with pub.options.trimvariables(False): ... var.value = 5.0 ... var.trim() >>> var var(5.0) Alternatively, you can omit the warning messages only: >>> with pub.options.warntrim(False): ... var.value = 5.0 ... var.trim() >>> var var(3.0) If a |Variable| subclass does not have (fixed) boundaries, give it either no `SPAN` attribute or a |tuple| containing |None| values: >>> del Var.SPAN >>> var.value = 5.0 >>> var.trim() >>> var var(5.0) >>> Var.SPAN = (None, None) >>> var.trim() >>> var var(5.0) The above examples deal with a 0-dimensional |Variable| subclass. The following examples repeat the most relevant examples for a 2-dimensional subclass: >>> Var.SPAN = 1.0, 3.0 >>> Var.NDIM = 2 >>> var.shape = 1, 3 >>> var.values = 2.0 >>> var.trim() >>> var.values = 0.0, 1.0, 2.0 >>> var.trim() Traceback (most recent call last): ... UserWarning: For variable `var` at least one value needed to be trimmed. \ The old and the new value(s) are `[[ 0. 1. 2.]]` and `[[ 1. 1. 2.]]`, \ respectively. >>> var var([[1.0, 1.0, 2.0]]) >>> var.values = 2.0, 3.0, 4.0 >>> var.trim() Traceback (most recent call last): ... UserWarning: For variable `var` at least one value needed to be trimmed. \ The old and the new value(s) are `[[ 2. 3. 4.]]` and `[[ 2. 3. 3.]]`, \ respectively. >>> var var([[2.0, 3.0, 3.0]]) >>> var.values = 1.0-1e-15, 2.0, 3.0+1e-15 >>> var.values == (1.0, 2.0, 3.0) array([[False, True, False]], dtype=bool) >>> var.trim() >>> var.values == (1.0, 2.0, 3.0) array([[ True, True, True]], dtype=bool) >>> var.values = 0.0, 2.0, 4.0 >>> var.trim(lower=numpy.nan, upper=numpy.nan) >>> var var([[0.0, 2.0, 4.0]]) >>> var.trim(lower=[numpy.nan, 3.0, 3.0]) Traceback (most recent call last): ... UserWarning: For variable `var` at least one value needed to be trimmed. \ The old and the new value(s) are `[[ 0. 2. 4.]]` and `[[ 0. 3. 3.]]`, \ respectively. >>> var.values = 0.0, 2.0, 4.0 >>> var.trim(upper=[numpy.nan, 1.0, numpy.nan]) Traceback (most recent call last): ... UserWarning: For variable `var` at least one value needed to be trimmed. \ The old and the new value(s) are `[[ 0. 2. 4.]]` and `[[ 1. 1. 4.]]`, \ respectively. For |Variable| subclasses handling |float| values, setting outliers to the respective boundary value might often be an acceptable approach. However, this is often not the case for subclasses handling |int| values, which often serve as option flags (e.g. to enable/disable a certain hydrological process for different land-use types). Hence, function |trim| raises an exception instead of a warning and does not modify the wrong |int| value: >>> Var.TYPE = int >>> Var.NDIM = 0 >>> Var.SPAN = 1, 3 >>> var.value = 2 >>> var.trim() >>> var var(2) >>> var.value = 0 >>> var.trim() Traceback (most recent call last): ... ValueError: The value `0` of parameter `var` of element `?` is not valid. >>> var var(0) >>> var.value = 4 >>> var.trim() Traceback (most recent call last): ... ValueError: The value `4` of parameter `var` of element `?` is not valid. >>> var var(4) >>> from hydpy import INT_NAN >>> var.value = 0 >>> var.trim(lower=0) >>> var.trim(lower=INT_NAN) >>> var.value = 4 >>> var.trim(upper=4) >>> var.trim(upper=INT_NAN) >>> Var.SPAN = 1, None >>> var.value = 0 >>> var.trim() Traceback (most recent call last): ... ValueError: The value `0` of parameter `var` of element `?` is not valid. >>> var var(0) >>> Var.SPAN = None, 3 >>> var.value = 0 >>> var.trim() >>> var.value = 4 >>> var.trim() Traceback (most recent call last): ... ValueError: The value `4` of parameter `var` of element `?` is not valid. >>> del Var.SPAN >>> var.value = 0 >>> var.trim() >>> var.value = 4 >>> var.trim() >>> Var.SPAN = 1, 3 >>> Var.NDIM = 2 >>> var.shape = (1, 3) >>> var.values = 2 >>> var.trim() >>> var.values = 0, 1, 2 >>> var.trim() Traceback (most recent call last): ... ValueError: At least one value of parameter `var` of element `?` \ is not valid. >>> var var([[0, 1, 2]]) >>> var.values = 2, 3, 4 >>> var.trim() Traceback (most recent call last): ... ValueError: At least one value of parameter `var` of element `?` \ is not valid. >>> var var([[2, 3, 4]]) >>> var.values = 0, 0, 2 >>> var.trim(lower=[0, INT_NAN, 2]) >>> var.values = 2, 4, 4 >>> var.trim(upper=[2, INT_NAN, 4]) For |bool| values, defining outliers does not make much sense, which is why function |trim| does nothing when applied on variables handling |bool| values: >>> Var.TYPE = bool >>> var.trim() If function |trim| encounters an unmanageable type, it raises an exception like the following: >>> Var.TYPE = str >>> var.trim() Traceback (most recent call last): ... NotImplementedError: Method `trim` can only be applied on parameters \ handling floating point, integer, or boolean values, but the "value type" \ of parameter `var` is `str`. >>> pub.options.warntrim = False

Response:
def trim(self: 'Variable', lower=None, upper=None) -> None:
    """Trim the value(s) of a |Variable| instance.

    Usually, users do not need to apply function |trim| directly.
    Instead, some |Variable| subclasses implement their own `trim`
    methods relying on function |trim|.  Model developers should
    implement individual `trim` methods for their |Parameter| or
    |Sequence| subclasses when their boundary values depend on the
    actual project configuration (one example is soil moisture;
    its lowest possible value should possibly be zero in all cases,
    but its highest possible value could depend on another parameter
    defining the maximum storage capacity).

    For the following examples, we prepare a simple (not fully
    functional) |Variable| subclass, making use of function |trim|
    without any modifications.  Function |trim| works slightly
    different for variables handling |float|, |int|, and |bool|
    values.  We start with the most common content type |float|:

    >>> from hydpy.core.variabletools import trim, Variable
    >>> class Var(Variable):
    ...     NDIM = 0
    ...     TYPE = float
    ...     SPAN = 1.0, 3.0
    ...     trim = trim
    ...     initinfo = 2.0, False
    ...     __hydpy__connect_variable2subgroup__ = None

    First, we enable the printing of warning messages raised by function
    |trim|:

    >>> from hydpy import pub
    >>> pub.options.warntrim = True

    When not passing boundary values, function |trim| extracts them from
    class attribute `SPAN` of the given |Variable| instance, if available:

    >>> var = Var(None)
    >>> var.value = 2.0
    >>> var.trim()
    >>> var
    var(2.0)

    >>> var.value = 0.0
    >>> var.trim()
    Traceback (most recent call last):
    ...
    UserWarning: For variable `var` at least one value needed to be trimmed.  \
The old and the new value(s) are `0.0` and `1.0`, respectively.
    >>> var
    var(1.0)

    >>> var.value = 4.0
    >>> var.trim()
    Traceback (most recent call last):
    ...
    UserWarning: For variable `var` at least one value needed to be trimmed.  \
The old and the new value(s) are `4.0` and `3.0`, respectively.
    >>> var
    var(3.0)

    In the examples above, outlier values are set to the respective
    boundary value, accompanied by suitable warning messages.  For very
    tiny deviations, which might be due to precision problems only,
    outliers are trimmed but not reported:

    >>> var.value = 1.0 - 1e-15
    >>> var == 1.0
    False
    >>> trim(var)
    >>> var == 1.0
    True

    >>> var.value = 3.0 + 1e-15
    >>> var == 3.0
    False
    >>> var.trim()
    >>> var == 3.0
    True

    Use arguments `lower` and `upper` to override the (eventually)
    available `SPAN` entries:

    >>> var.trim(lower=4.0)
    Traceback (most recent call last):
    ...
    UserWarning: For variable `var` at least one value needed to be trimmed.  \
The old and the new value(s) are `3.0` and `4.0`, respectively.

    >>> var.trim(upper=3.0)
    Traceback (most recent call last):
    ...
    UserWarning: For variable `var` at least one value needed to be trimmed.  \
The old and the new value(s) are `4.0` and `3.0`, respectively.

    Function |trim| interprets both |None| and |numpy.nan| values as if
    no boundary value exists:

    >>> import numpy
    >>> var.value = 0.0
    >>> var.trim(lower=numpy.nan)
    >>> var.value = 5.0
    >>> var.trim(upper=numpy.nan)

    You can disable function |trim| via option |Options.trimvariables|:

    >>> with pub.options.trimvariables(False):
    ...     var.value = 5.0
    ...     var.trim()
    >>> var
    var(5.0)

    Alternatively, you can omit the warning messages only:

    >>> with pub.options.warntrim(False):
    ...     var.value = 5.0
    ...     var.trim()
    >>> var
    var(3.0)

    If a |Variable| subclass does not have (fixed) boundaries, give it
    either no `SPAN` attribute or a |tuple| containing |None| values:

    >>> del Var.SPAN
    >>> var.value = 5.0
    >>> var.trim()
    >>> var
    var(5.0)

    >>> Var.SPAN = (None, None)
    >>> var.trim()
    >>> var
    var(5.0)

    The above examples deal with a 0-dimensional |Variable| subclass.
    The following examples repeat the most relevant examples for a
    2-dimensional subclass:

    >>> Var.SPAN = 1.0, 3.0
    >>> Var.NDIM = 2
    >>> var.shape = 1, 3
    >>> var.values = 2.0
    >>> var.trim()

    >>> var.values = 0.0, 1.0, 2.0
    >>> var.trim()
    Traceback (most recent call last):
    ...
    UserWarning: For variable `var` at least one value needed to be trimmed.  \
The old and the new value(s) are `[[ 0.  1.  2.]]` and `[[ 1.  1.  2.]]`, \
respectively.
    >>> var
    var([[1.0, 1.0, 2.0]])

    >>> var.values = 2.0, 3.0, 4.0
    >>> var.trim()
    Traceback (most recent call last):
    ...
    UserWarning: For variable `var` at least one value needed to be trimmed.  \
The old and the new value(s) are `[[ 2.  3.  4.]]` and `[[ 2.  3.  3.]]`, \
respectively.
    >>> var
    var([[2.0, 3.0, 3.0]])

    >>> var.values = 1.0-1e-15, 2.0, 3.0+1e-15
    >>> var.values == (1.0, 2.0, 3.0)
    array([[False,  True, False]], dtype=bool)
    >>> var.trim()
    >>> var.values == (1.0, 2.0, 3.0)
    array([[ True,  True,  True]], dtype=bool)

    >>> var.values = 0.0, 2.0, 4.0
    >>> var.trim(lower=numpy.nan, upper=numpy.nan)
    >>> var
    var([[0.0, 2.0, 4.0]])

    >>> var.trim(lower=[numpy.nan, 3.0, 3.0])
    Traceback (most recent call last):
    ...
    UserWarning: For variable `var` at least one value needed to be trimmed.  \
The old and the new value(s) are `[[ 0.  2.  4.]]` and `[[ 0.  3.  3.]]`, \
respectively.

    >>> var.values = 0.0, 2.0, 4.0
    >>> var.trim(upper=[numpy.nan, 1.0, numpy.nan])
    Traceback (most recent call last):
    ...
    UserWarning: For variable `var` at least one value needed to be trimmed.  \
The old and the new value(s) are `[[ 0.  2.  4.]]` and `[[ 1.  1.  4.]]`, \
respectively.

    For |Variable| subclasses handling |float| values, setting outliers
    to the respective boundary value might often be an acceptable approach.
    However, this is often not the case for subclasses handling |int|
    values, which often serve as option flags (e.g. to enable/disable
    a certain hydrological process for different land-use types). Hence,
    function |trim| raises an exception instead of a warning and does
    not modify the wrong |int| value:

    >>> Var.TYPE = int
    >>> Var.NDIM = 0
    >>> Var.SPAN = 1, 3

    >>> var.value = 2
    >>> var.trim()
    >>> var
    var(2)

    >>> var.value = 0
    >>> var.trim()
    Traceback (most recent call last):
    ...
    ValueError: The value `0` of parameter `var` of element `?` is not valid.
    >>> var
    var(0)
    >>> var.value = 4
    >>> var.trim()
    Traceback (most recent call last):
    ...
    ValueError: The value `4` of parameter `var` of element `?` is not valid.
    >>> var
    var(4)

    >>> from hydpy import INT_NAN
    >>> var.value = 0
    >>> var.trim(lower=0)
    >>> var.trim(lower=INT_NAN)

    >>> var.value = 4
    >>> var.trim(upper=4)
    >>> var.trim(upper=INT_NAN)

    >>> Var.SPAN = 1, None
    >>> var.value = 0
    >>> var.trim()
    Traceback (most recent call last):
    ...
    ValueError: The value `0` of parameter `var` of element `?` is not valid.
    >>> var
    var(0)

    >>> Var.SPAN = None, 3
    >>> var.value = 0
    >>> var.trim()
    >>> var.value = 4
    >>> var.trim()
    Traceback (most recent call last):
    ...
    ValueError: The value `4` of parameter `var` of element `?` is not valid.

    >>> del Var.SPAN
    >>> var.value = 0
    >>> var.trim()
    >>> var.value = 4
    >>> var.trim()

    >>> Var.SPAN = 1, 3
    >>> Var.NDIM = 2
    >>> var.shape = (1, 3)
    >>> var.values = 2
    >>> var.trim()

    >>> var.values = 0, 1, 2
    >>> var.trim()
    Traceback (most recent call last):
    ...
    ValueError: At least one value of parameter `var` of element `?` \
is not valid.
    >>> var
    var([[0, 1, 2]])
    >>> var.values = 2, 3, 4
    >>> var.trim()
    Traceback (most recent call last):
     ...
    ValueError: At least one value of parameter `var` of element `?` \
is not valid.
    >>> var
    var([[2, 3, 4]])

    >>> var.values = 0, 0, 2
    >>> var.trim(lower=[0, INT_NAN, 2])

    >>> var.values = 2, 4, 4
    >>> var.trim(upper=[2, INT_NAN, 4])

    For |bool| values, defining outliers does not make much sense,
    which is why function |trim| does nothing when applied on
    variables handling |bool| values:

    >>> Var.TYPE = bool
    >>> var.trim()

    If function |trim| encounters an unmanageable type, it raises an
    exception like the following:

    >>> Var.TYPE = str
    >>> var.trim()
    Traceback (most recent call last):
    ...
    NotImplementedError: Method `trim` can only be applied on parameters \
handling floating point, integer, or boolean values, but the "value type" \
of parameter `var` is `str`.

    >>> pub.options.warntrim = False
    """
    if hydpy.pub.options.trimvariables:
        if lower is None:
            lower = self.SPAN[0]
        if upper is None:
            upper = self.SPAN[1]
        type_ = getattr(self, 'TYPE', float)
        if type_ is float:
            if self.NDIM == 0:
                _trim_float_0d(self, lower, upper)
            else:
                _trim_float_nd(self, lower, upper)
        elif type_ is int:
            if self.NDIM == 0:
                _trim_int_0d(self, lower, upper)
            else:
                _trim_int_nd(self, lower, upper)
        elif type_ is bool:
            pass
        else:
            raise NotImplementedError(
                f'Method `trim` can only be applied on parameters '
                f'handling floating point, integer, or boolean values, '
                f'but the "value type" of parameter `{self.name}` is '
                f'`{objecttools.classname(self.TYPE)}`.')


Instruction:
Make a summary of the following Python 3 code
def _get_tolerance(values):
    tolerance = numpy.abs(values*1e-15)
    if hasattr(tolerance, '__setitem__'):
        tolerance[numpy.isinf(tolerance)] = 0.
    elif numpy.isinf(tolerance):
        tolerance = 0.
    return tolerance

Response:
Return some numerical accuracy to be expected for the
    given floating point values.


Instruction:
Write a Python 3 function for
returning a function usable as a comparison method for class |Variable|.

Response:
def _compare_variables_function_generator(
        method_string, aggregation_func):
    """Return a function usable  as a comparison method for class |Variable|.

    Pass the specific method (e.g. `__eq__`) and the corresponding
    operator (e.g. `==`) as strings.  Also pass either |numpy.all| or
    |numpy.any| for aggregating multiple boolean values.
    """
    def comparison_function(self, other):
        """Wrapper for comparison functions for class |Variable|."""
        if self is other:
            return method_string in ('__eq__', '__le__', '__ge__')
        method = getattr(self.value, method_string)
        try:
            if hasattr(type(other), '__hydpy__get_value__'):
                other = other.__hydpy__get_value__()
            result = method(other)
            if result is NotImplemented:
                return result
            return aggregation_func(result)
        except BaseException:
            objecttools.augment_excmessage(
                f'While trying to compare variable '
                f'{objecttools.elementphrase(self)} with object '
                f'`{other}` of type `{objecttools.classname(other)}`')
    return comparison_function


Instruction:
Can you write a function in Python 3 where it
returns a valid string representation for the given |Variable| object.

Response:
def to_repr(self: Variable, values, brackets1d: Optional[bool] = False) \
        -> str:
    """Return a valid string representation for the given |Variable|
    object.

    Function |to_repr| it thought for internal purposes only, more
    specifically for defining string representations of subclasses
    of class |Variable| like the following:

    >>> from hydpy.core.variabletools import to_repr, Variable
    >>> class Var(Variable):
    ...     NDIM = 0
    ...     TYPE = int
    ...     __hydpy__connect_variable2subgroup__ = None
    ...     initinfo = 1.0, False
    >>> var = Var(None)
    >>> var.value = 2
    >>> var
    var(2)

    The following examples demonstrate all covered cases.  Note that
    option `brackets1d` allows choosing between a "vararg" and an
    "iterable" string representation for 1-dimensional variables
    (the first one being the default):

    >>> print(to_repr(var, 2))
    var(2)

    >>> Var.NDIM = 1
    >>> var = Var(None)
    >>> var.shape = 3
    >>> print(to_repr(var, range(3)))
    var(0, 1, 2)
    >>> print(to_repr(var, range(3), True))
    var([0, 1, 2])
    >>> print(to_repr(var, range(30)))
    var(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
        19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29)
    >>> print(to_repr(var, range(30), True))
    var([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
         19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])

    >>> Var.NDIM = 2
    >>> var = Var(None)
    >>> var.shape = (2, 3)
    >>> print(to_repr(var, [range(3), range(3, 6)]))
    var([[0, 1, 2],
         [3, 4, 5]])
    >>> print(to_repr(var, [range(30), range(30, 60)]))
    var([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
          19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
         [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
          46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]])
    """
    prefix = f'{self.name}('
    if isinstance(values, str):
        string = f'{self.name}({values})'
    elif self.NDIM == 0:
        string = f'{self.name}({objecttools.repr_(values)})'
    elif self.NDIM == 1:
        if brackets1d:
            string = objecttools.assignrepr_list(values, prefix, 72) + ')'
        else:
            string = objecttools.assignrepr_values(
                values, prefix, 72) + ')'
    else:
        string = objecttools.assignrepr_list2(values, prefix, 72) + ')'
    return '\n'.join(self.commentrepr + [string])


Instruction:
Implement a function in Python 3 to
raise a |RuntimeError| if at least one of the required values of a variable object is not set.

Response:
def verify(self) -> None:
        """Raises a |RuntimeError| if at least one of the required values
        of a |Variable| object is |None| or |numpy.nan|. The descriptor
        `mask` defines, which values are considered to be necessary.

        Example on a 0-dimensional |Variable|:

        >>> from hydpy.core.variabletools import Variable
        >>> class Var(Variable):
        ...     NDIM = 0
        ...     TYPE = float
        ...     __hydpy__connect_variable2subgroup__ = None
        ...     initinfo = 0.0, False
        >>> var = Var(None)
        >>> import numpy
        >>> var.shape = ()
        >>> var.value = 1.0
        >>> var.verify()
        >>> var.value = numpy.nan
        >>> var.verify()
        Traceback (most recent call last):
        ...
        RuntimeError: For variable `var`, 1 required value has not been set yet.

        Example on a 2-dimensional |Variable|:

        >>> Var.NDIM = 2
        >>> var = Var(None)
        >>> var.shape = (2, 3)
        >>> var.value = numpy.ones((2,3))
        >>> var.value[:, 1] = numpy.nan
        >>> var.verify()
        Traceback (most recent call last):
        ...
        RuntimeError: For variable `var`, 2 required values \
have not been set yet.

        >>> Var.mask = var.mask
        >>> Var.mask[0, 1] = False
        >>> var.verify()
        Traceback (most recent call last):
        ...
        RuntimeError: For variable `var`, 1 required value has not been set yet.

        >>> Var.mask[1, 1] = False
        >>> var.verify()
        """
        nmbnan: int = numpy.sum(numpy.isnan(
            numpy.array(self.value)[self.mask]))
        if nmbnan:
            if nmbnan == 1:
                text = 'value has'
            else:
                text = 'values have'
            raise RuntimeError(
                f'For variable {objecttools.devicephrase(self)}, '
                f'{nmbnan} required {text} not been set yet.')


Instruction:
How would you explain what the following Python 3 function does
def average_values(self, *args, **kwargs) -> float:
        try:
            if not self.NDIM:
                return self.value
            mask = self.get_submask(*args, **kwargs)
            if numpy.any(mask):
                weights = self.refweights[mask]
                return numpy.sum(weights*self[mask])/numpy.sum(weights)
            return numpy.nan
        except BaseException:
            objecttools.augment_excmessage(
                f'While trying to calculate the mean value of variable '
                f'{objecttools.devicephrase(self)}')

Response: A method that calculates the actual values of the |Variable| object.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def get_submask(self, *args, **kwargs) -> masktools.CustomMask:
        if args or kwargs:
            masks = self.availablemasks
            mask = masktools.CustomMask(numpy.full(self.shape, False))
            for arg in args:
                mask = mask + self._prepare_mask(arg, masks)
            for key, value in kwargs.items():
                mask = mask + self._prepare_mask(key, masks, **value)
            if mask not in self.mask:
                raise ValueError(
                    f'Based on the arguments `{args}` and `{kwargs}` '
                    f'the mask `{repr(mask)}` has been determined, '
                    f'which is not a submask of `{repr(self.mask)}`.')
        else:
            mask = self.mask
        return mask

Response: Get a sub - mask of the object.


Instruction:
Given the following Python 3 function, write the documentation
def commentrepr(self) -> List[str]:
        if hydpy.pub.options.reprcomments:
            return [f'# {line}' for line in
                    textwrap.wrap(objecttools.description(self), 72)]
        return []

Response:
A list with comments for making string representations
            more informative.


Instruction:
Implement a function in Python 3 to
add a table into a worksheet.

Response:
def AddTable(p_workSheet = None, p_headerDict = None, p_startColumn = 1, p_startRow = 1, p_headerHeight = None, p_data = None, p_mainTable = False, p_conditionalFormatting = None, p_tableStyleInfo = None, p_withFilters = True):
    """Insert a table in a given worksheet.

        Args:
            p_workSheet (openpyxl.worksheet.worksheet.Worksheet): the worksheet where the table will be inserted. Defaults to None.
            p_headerDict (collections.OrderedDict): an ordered dict that contains table header columns.
                Notes:
                    Each entry is in the following form:
                        Key: Name of the column to be searched in p_data.Columns.
                        Value: Spartacus.Report.Field instance.
                Examples:
                    p_headerDict = collections.OrderedDict([
                        (
                            'field_one',
                            Field(
                                p_name = 'Code',
                                p_width = 15,
                                p_data = Data(
                                    p_type = 'int'
                                )
                            )
                        ),
                        (
                            'field_two',
                            Field(
                                p_name = 'Result',
                                p_width = 15,
                                p_data = Data(
                                    p_type = 'int_formula'
                                )
                            )
                        )
                    ])
            p_startColumn (int): the column number where the table should start. Defaults to 1.
                Notes:
                    Must be a positive integer.
            p_startRow (int): the row number where the table should start. Defaults to 1.
                Notes:
                    Must be a positive integer.
            p_headerHeight (float): the header row height in pt. Defaults to None.
                Notes:
                    Must be a non-negative number or None.
            p_data (Spartacus.Database.DataTable): the datatable that contains the data that will be inserted into the excel table. Defaults to None.
                Notes:
                    If the corresponding column data type in p_headerDict is some kind of formula, then below wildcards can be used:
                        #row#: the current row.
                        #column_columname#: will be replaced by the letter of the column.
                Examples:
                    p_data = Spartacus.Database.DataTable that contains:
                        Columns: ['field_one', 'field_two'].
                        Rows: [
                            [
                                'HAHAHA',
                                '=if(#column_field_one##row# = "HAHAHA", 1, 0)'
                            ],
                            [
                                'HEHEHE',
                                '=if(#column_field_one##row# = "HAHAHA", 1, 0)'
                            ]
                        ]
            p_mainTable (bool): if this table is the main table of the current worksheet. Defaults to False.
                Notes:
                    If it's the main table, then it will consider p_width, p_hidden and freeze panes in the first table row. The 3 parameters are ignored otherwise.
            p_conditionalFormatting (Spartacus.Report.ConditionalFormatting): a conditional formatting that should be applied to data rows. Defaults to None.
                Notes:
                    Will be applied to all data rows of this table.
                    A wildcard can be used and be replaced properly:
                        #row#: the current data row.
                        #column_columname#: will be replaced by the letter of the column.
                Examples:
                    p_conditionalFormatting = ConditionalFormatting(
                        p_formula = '$Y#row# = 2',
                        p_differentialStyle = openpyxl.styles.differential.DifferentialStyle(
                            fill = openpyxl.styles.PatternFill(
                                bgColor = 'D3D3D3'
                            )
                        )
                    )
            p_tableStyleInfo (openpyxl.worksheet.table.TableStyleInfo): a style to be applied to this table. Defaults to None.
                Notes:
                    Will not be applied to summaries, if any.
                Examples:
                    p_tableStyleInfo = openpyxl.worksheet.table.TableStyleInfo(
                        name = 'TableStyleMedium23',
                        showFirstColumn = True,
                        showLastColumn = True,
                        showRowStripes = True,
                        showColumnStripes = False
                    )

            p_withFilters (bool): if the table must contain auto-filters.

        Yields:
            int: Every 1000 lines inserted into the table, yields actual line number.

        Raises:
            Spartacus.Report.Exception: custom exceptions occurred in this script.
    """

    if not isinstance(p_workSheet, openpyxl.worksheet.worksheet.Worksheet):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_workSheet" must be of type "openpyxl.worksheet.worksheet.Worksheet".')

    if not isinstance(p_headerDict, collections.OrderedDict):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_headerDict" must be of type "collections.OrderedDict".')

    if not isinstance(p_startColumn, int):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_startColumn" must be of type "int".')

    if p_startColumn < 1:
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_startColumn" must be a positive integer.')

    if not isinstance(p_startRow, int):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_startRow" must be of type "int".')

    if p_startRow < 1:
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_startRow" must be a positive integer.')

    if p_headerHeight is not None and not isinstance(p_headerHeight, int) and not isinstance(p_headerHeight, float):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_headerHeight" must be None or of type "int" or "float".')

    if not isinstance(p_data, Spartacus.Database.DataTable):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_data" must be of type "Spartacus.Database.DataTable".')

    if not isinstance(p_mainTable, bool):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_mainTable" must be of type "bool".')

    if p_conditionalFormatting is not None and not isinstance(p_conditionalFormatting, ConditionalFormatting):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_conditionalFormatting" must be None or of type "Spartacus.Report.ConditionalFormatting".')

    if p_tableStyleInfo is not None and not isinstance(p_tableStyleInfo, openpyxl.worksheet.table.TableStyleInfo):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_tableStyleInfo" must be None or of type "openpyxl.worksheet.table.TableStyleInfo".')

    if p_withFilters is not None and not isinstance(p_withFilters, bool):
        raise Spartacus.Report.Exception('Error during execution of method "Static.AddTable": Parameter "p_withFilters" must be None or of type "bool".')

    #Format Header
    if p_headerHeight is not None:
        p_workSheet.row_dimensions[p_startRow].height = p_headerHeight

    v_headerList = list(p_headerDict.keys())

    for i in range(len(v_headerList)):
        v_header = p_headerDict[v_headerList[i]]
        v_letter = openpyxl.utils.get_column_letter(i + p_startColumn)

        v_cell = p_workSheet['{0}{1}'.format(v_letter, p_startRow)]
        v_cell.value = v_header.name

        if p_mainTable:
            p_workSheet.column_dimensions[v_letter].width = v_header.width
            p_workSheet.column_dimensions[v_letter].hidden = v_header.hidden

        if v_header.comment is not None:
            v_cell.comment = v_header.comment

        if v_header.border is not None:
            v_cell.border = v_header.border

        if v_header.font is not None:
            v_cell.font = v_header.font

        if v_header.fill is not None:
            v_cell.fill = v_header.fill

        if v_header.alignment is not None:
            v_cell.alignment = v_header.alignment

    if p_mainTable:
        p_workSheet.freeze_panes = 'A{0}'.format(p_startRow + 1)

    #used in formula fields, if it's the case
    v_pattern = re.compile(r'#column_[^\n\r#]*#')

    v_line = 0

    #Fill content
    for v_row in p_data.Rows:
        v_line += 1

        for i in range(len(v_headerList)):
            v_headerData = p_headerDict[v_headerList[i]].data
            v_letter = openpyxl.utils.get_column_letter(i + p_startColumn)

            v_cell = p_workSheet['{0}{1}'.format(v_letter, v_line + p_startRow)] #Plus p_startRow to "jump" report header lines

            if v_headerData.border is not None:
                v_cell.border = v_headerData.border

            if v_headerData.font is not None:
                v_cell.font = v_headerData.font

            if v_headerData.fill is not None:
                v_cell.fill = v_headerData.fill

            if v_headerData.alignment is not None:
                v_cell.alignment = v_headerData.alignment

            if v_headerData.type == 'int':
                v_key = str(v_row[v_headerList[i]])

                if v_key in v_headerData.valueMapping:
                    v_cell.value = v_headerData.valueMapping[v_key]
                else:
                    try:
                        v_cell.value = int(v_row[v_headerList[i]])
                    except (Exception, TypeError, ValueError):
                        v_cell.value = v_row[v_headerList[i]] if v_row[v_headerList[i]] is not None else ''

                v_cell.number_format = '0'
            elif v_headerData.type == 'float':
                v_key = str(v_row[v_headerList[i]])

                if v_key in v_headerData.valueMapping:
                    v_cell.value = v_headerData.valueMapping[v_key]
                else:
                    try:
                        v_cell.value = float(v_row[v_headerList[i]])
                    except (Exception, TypeError, ValueError):
                        v_cell.value = v_row[v_headerList[i]] if v_row[v_headerList[i]] is not None else ''

                v_cell.number_format = '#,##0.00'
            elif v_headerData.type == 'float4':
                v_key = str(v_row[v_headerList[i]])

                if v_key in v_headerData.valueMapping:
                    v_cell.value = v_headerData.valueMapping[v_key]
                else:
                    try:
                        v_cell.value = float(v_row[v_headerList[i]])
                    except (Exception, TypeError, ValueError):
                        v_cell.value = v_row[v_headerList[i]] if v_row[v_headerList[i]] is not None else ''

                v_cell.number_format = '#,##0.0000'
            elif v_headerData.type == 'percent':
                v_key = str(v_row[v_headerList[i]])

                if v_key in v_headerData.valueMapping:
                    v_cell.value = v_headerData.valueMapping[v_key]
                else:
                    try:
                        v_cell.value = float(v_row[v_headerList[i]])
                    except (Exception, TypeError, ValueError):
                        v_cell.value = v_row[v_headerList[i]] if v_row[v_headerList[i]] is not None else ''

                v_cell.number_format = '0.00%'
            elif v_headerData.type == 'date':
                v_key = str(v_row[v_headerList[i]])

                if v_key in v_headerData.valueMapping:
                    v_cell.value = v_headerData.valueMapping[v_key]
                else:
                    v_cell.value = v_row[v_headerList[i]] if v_row[v_headerList[i]] is not None else ''

                v_cell.number_format = 'DD/MM/YYYY'
            elif v_headerData.type == 'str':
                v_key = str(v_row[v_headerList[i]])

                if v_key in v_headerData.valueMapping:
                    v_cell.value = v_headerData.valueMapping[v_key]
                else:
                    v_cell.value = v_row[v_headerList[i]] if v_row[v_headerList[i]] is not None else ''
            elif v_headerData.type == 'bool':
                v_key = str(v_row[v_headerList[i]])

                if v_key in v_headerData.valueMapping:
                    v_cell.value = v_headerData.valueMapping[v_key]
                else:
                    try:
                        v_cell.value = bool(v_row[v_headerList[i]]) if v_row[v_headerList[i]] is not None and str(v_row[v_headerList[i]]).strip() != '' else ''
                    except (Exception, TypeError, ValueError):
                        v_cell.value = v_row[v_headerList[i]] if v_row[v_headerList[i]] is not None else ''
            if v_headerData.type == 'int_formula':
                v_value = v_row[v_headerList[i]].replace('#row#', str(p_startRow + v_line))
                v_match = re.search(v_pattern, v_value)

                while v_match is not None:
                    v_start = v_match.start()
                    v_end = v_match.end()
                    v_matchColumn = openpyxl.utils.get_column_letter(p_startColumn + v_headerList.index(v_value[v_start + 8 : v_end - 1])) #Discard starting #column_ and ending # in match
                    v_value = v_value[:v_start] + v_matchColumn + v_value[v_end:]
                    v_match = re.search(v_pattern, v_value)

                v_cell.value = v_value
                v_cell.number_format = '0'
            elif v_headerData.type == 'float_formula':
                v_value = v_row[v_headerList[i]].replace('#row#', str(p_startRow + v_line))
                v_match = re.search(v_pattern, v_value)

                while v_match is not None:
                    v_start = v_match.start()
                    v_end = v_match.end()
                    v_matchColumn = openpyxl.utils.get_column_letter(p_startColumn + v_headerList.index(v_value[v_start + 8 : v_end - 1])) #Discard starting #column_ and ending # in match
                    v_value = v_value[:v_start] + v_matchColumn + v_value[v_end:]
                    v_match = re.search(v_pattern, v_value)

                v_cell.value = v_value
                v_cell.number_format = '#,##0.00'
            elif v_headerData.type == 'float4_formula':
                v_value = v_row[v_headerList[i]].replace('#row#', str(p_startRow + v_line))
                v_match = re.search(v_pattern, v_value)

                while v_match is not None:
                    v_start = v_match.start()
                    v_end = v_match.end()
                    v_matchColumn = openpyxl.utils.get_column_letter(p_startColumn + v_headerList.index(v_value[v_start + 8 : v_end - 1])) #Discard starting #column_ and ending # in match
                    v_value = v_value[:v_start] + v_matchColumn + v_value[v_end:]
                    v_match = re.search(v_pattern, v_value)

                v_cell.value = v_value
                v_cell.number_format = '#,##0.0000'
            elif v_headerData.type == 'percent_formula':
                v_value = v_row[v_headerList[i]].replace('#row#', str(p_startRow + v_line))
                v_match = re.search(v_pattern, v_value)

                while v_match is not None:
                    v_start = v_match.start()
                    v_end = v_match.end()
                    v_matchColumn = openpyxl.utils.get_column_letter(p_startColumn + v_headerList.index(v_value[v_start + 8 : v_end - 1])) #Discard starting #column_ and ending # in match
                    v_value = v_value[:v_start] + v_matchColumn + v_value[v_end:]
                    v_match = re.search(v_pattern, v_value)

                v_cell.value = v_value
                v_cell.number_format = '0.00%'
            elif v_headerData.type == 'date_formula':
                v_value = v_row[v_headerList[i]].replace('#row#', str(p_startRow + v_line))
                v_match = re.search(v_pattern, v_value)

                while v_match is not None:
                    v_start = v_match.start()
                    v_end = v_match.end()
                    v_matchColumn = openpyxl.utils.get_column_letter(p_startColumn + v_headerList.index(v_value[v_start + 8 : v_end - 1])) #Discard starting #column_ and ending # in match
                    v_value = v_value[:v_start] + v_matchColumn + v_value[v_end:]
                    v_match = re.search(v_pattern, v_value)

                v_cell.value = v_value
                v_cell.number_format = 'DD/MM/YYYY'
            elif v_headerData.type == 'str_formula':
                v_value = v_row[v_headerList[i]].replace('#row#', str(p_startRow + v_line))
                v_match = re.search(v_pattern, v_value)

                while v_match is not None:
                    v_start = v_match.start()
                    v_end = v_match.end()
                    v_matchColumn = openpyxl.utils.get_column_letter(p_startColumn + v_headerList.index(v_value[v_start + 8 : v_end - 1])) #Discard starting #column_ and ending # in match
                    v_value = v_value[:v_start] + v_matchColumn + v_value[v_end:]
                    v_match = re.search(v_pattern, v_value)

                v_cell.value = v_value

        if v_line % 1000 == 0:
            yield v_line

    v_lastLine = len(p_data.Rows) + p_startRow

    #Apply conditional formatting, if any
    if p_conditionalFormatting is not None:
        v_startLetter = openpyxl.utils.get_column_letter(p_startColumn)
        v_finalLetter = openpyxl.utils.get_column_letter(len(v_headerList) + p_startColumn - 1)

        v_formula = p_conditionalFormatting.formula.replace('#row#', str(p_startRow + 1))

        v_match = re.search(v_pattern, v_formula)

        while v_match is not None:
            v_start = v_match.start()
            v_end = v_match.end()
            v_matchColumn = openpyxl.utils.get_column_letter(p_startColumn + v_headerList.index(v_formula[v_start + 8 : v_end - 1])) #Discard starting #column_ and ending # in match
            v_formula = v_formula[:v_start] + v_matchColumn + v_formula[v_end:]
            v_match = re.search(v_pattern, v_formula)

        v_rule = openpyxl.formatting.rule.Rule(
            type = 'expression',
            formula = [v_formula],
            dxf = p_conditionalFormatting.differentialStyle
        )

        p_workSheet.conditional_formatting.add(
            '{0}{1}:{2}{3}'.format(v_startLetter, p_startRow + 1, v_finalLetter, v_lastLine),
            v_rule
        )

    #Build Summary
    for i in range(len(v_headerList)):
        v_headerSummaryList = p_headerDict[v_headerList[i]].summaryList

        for v_headerSummary in v_headerSummaryList:
            v_letter = openpyxl.utils.get_column_letter(i + p_startColumn)

            v_index = p_startRow - 1

            if v_headerSummary.index < 0:
                v_index = p_startRow + v_headerSummary.index
            elif v_headerSummary.index > 0:
                v_index = v_lastLine + v_headerSummary.index

            v_value = v_headerSummary.function.replace('#column#', v_letter).replace('#start_row#', str(p_startRow + 1)).replace('#end_row#', str(v_lastLine))

            v_match = re.search(v_pattern, v_value)

            while v_match is not None:
                v_start = v_match.start()
                v_end = v_match.end()
                v_matchColumn = openpyxl.utils.get_column_letter(p_startColumn + v_headerList.index(v_value[v_start + 8 : v_end - 1])) #Discard starting #column_ and ending # in match
                v_value = v_value[:v_start] + v_matchColumn + v_value[v_end:]
                v_match = re.search(v_pattern, v_value)

            v_cell = p_workSheet['{0}{1}'.format(v_letter, v_index)]
            v_cell.value = v_value

            if v_headerSummary.border is not None:
                v_cell.border = v_headerSummary.border

            if v_headerSummary.font is not None:
                v_cell.font = v_headerSummary.font

            if v_headerSummary.fill is not None:
                v_cell.fill = v_headerSummary.fill

            if v_headerSummary.type == 'int':
                v_cell.number_format = '0'
            elif v_headerSummary.type == 'float':
                v_cell.number_format = '#,##0.00'
            elif v_headerSummary.type == 'float4':
                v_cell.number_format = '#,##0.0000'
            elif v_headerSummary.type == 'percent':
                v_cell.number_format = '0.00%'

    #Create a new table and add it to worksheet
    v_name = 'Table_{0}_{1}'.format(p_workSheet.title.replace(' ', ''), len(p_workSheet._tables) + 1) #excel doesn't accept same displayName in more than one table.
    v_name = ''.join([c for c in v_name if c.isalnum()]) #Excel doesn't accept non-alphanumeric characters.

    v_table = openpyxl.worksheet.table.Table(
        displayName = v_name,
        ref = '{0}{1}:{2}{3}'.format(
            openpyxl.utils.get_column_letter(p_startColumn),
            p_startRow,
            openpyxl.utils.get_column_letter(p_startColumn + len(v_headerList) - 1),
            v_lastLine
        )
    )

    if p_tableStyleInfo is not None:
        v_table.tableStyleInfo = p_tableStyleInfo

    if not p_withFilters:
        v_table.headerRowCount = 0

    p_workSheet.add_table(v_table)


Instruction:
Here you have a function in Python 3, explain what it does
def get_controlfileheader(
        model: Union[str, 'modeltools.Model'],
        parameterstep: timetools.PeriodConstrArg = None,
        simulationstep: timetools.PeriodConstrArg = None) -> str:
    """Return the header of a regular or auxiliary parameter control file.

    The header contains the default coding information, the import command
    for the given model and the actual parameter and simulation step sizes.

    The first example shows that, if you pass the model argument as a
    string, you have to take care that this string makes sense:

    >>> from hydpy.core.parametertools import get_controlfileheader, Parameter
    >>> from hydpy import Period, prepare_model, pub, Timegrids, Timegrid
    >>> print(get_controlfileheader(model='no model class',
    ...                          parameterstep='-1h',
    ...                          simulationstep=Period('1h')))
    # -*- coding: utf-8 -*-
    <BLANKLINE>
    from hydpy.models.no model class import *
    <BLANKLINE>
    simulationstep('1h')
    parameterstep('-1h')
    <BLANKLINE>
    <BLANKLINE>

    The second example shows the saver option to pass the proper model
    object.  It also shows that function |get_controlfileheader| tries
    to gain the parameter and simulation step sizes from the global
    |Timegrids| object contained in the module |pub| when necessary:

    >>> model = prepare_model('lland_v1')
    >>> _ = Parameter.parameterstep('1d')
    >>> pub.timegrids = '2000.01.01', '2001.01.01', '1h'
    >>> print(get_controlfileheader(model=model))
    # -*- coding: utf-8 -*-
    <BLANKLINE>
    from hydpy.models.lland_v1 import *
    <BLANKLINE>
    simulationstep('1h')
    parameterstep('1d')
    <BLANKLINE>
    <BLANKLINE>
    """
    with Parameter.parameterstep(parameterstep):
        if simulationstep is None:
            simulationstep = Parameter.simulationstep
        else:
            simulationstep = timetools.Period(simulationstep)
        return (f"# -*- coding: utf-8 -*-\n\n"
                f"from hydpy.models.{model} import *\n\n"
                f"simulationstep('{simulationstep}')\n"
                f"parameterstep('{Parameter.parameterstep}')\n\n")

Response: Return the header of a regular or auxiliary parameter control file.


Instruction:
Write a Python 3 script for
assigning docstrings to the constants handled by |Constants| to make them available in the interactive mode of Python.

Response:
def _prepare_docstrings(self, frame):
        """Assign docstrings to the constants handled by |Constants|
        to make them available in the interactive mode of Python."""
        if config.USEAUTODOC:
            filename = inspect.getsourcefile(frame)
            with open(filename) as file_:
                sources = file_.read().split('"""')
            for code, doc in zip(sources[::2], sources[1::2]):
                code = code.strip()
                key = code.split('\n')[-1].split()[0]
                value = self.get(key)
                if value:
                    value.__doc__ = doc


Instruction:
Write a Python 3 function that can
update the internal state of the secondary parameters.

Response:
def update(self) -> None:
        """Call method |Parameter.update| of all "secondary" parameters.

        Directly after initialisation, neither the primary (`control`)
        parameters nor the secondary (`derived`)  parameters of
        application model |hstream_v1| are ready for usage:

        >>> from hydpy.models.hstream_v1 import *
        >>> parameterstep('1d')
        >>> simulationstep('1d')
        >>> derived
        nmbsegments(?)
        c1(?)
        c3(?)
        c2(?)

        Trying to update the values of the secondary parameters while the
        primary ones are still not defined, raises errors like the following:

        >>> model.parameters.update()
        Traceback (most recent call last):
        ...
        AttributeError: While trying to update parameter ``nmbsegments` \
of element `?``, the following error occurred: For variable `lag`, \
no value has been defined so far.

        With proper values both for parameter |hstream_control.Lag| and
        |hstream_control.Damp|, updating the derived parameters succeeds:

        >>> lag(0.0)
        >>> damp(0.0)
        >>> model.parameters.update()
        >>> derived
        nmbsegments(0)
        c1(0.0)
        c3(0.0)
        c2(1.0)
        """
        for subpars in self.secondary_subpars:
            for par in subpars:
                try:
                    par.update()
                except BaseException:
                    objecttools.augment_excmessage(
                        f'While trying to update parameter '
                        f'`{objecttools.elementphrase(par)}`')


Instruction:
Here you have a function in Python 3, explain what it does
def save_controls(self, filepath: Optional[str] = None,
                      parameterstep: timetools.PeriodConstrArg = None,
                      simulationstep: timetools.PeriodConstrArg = None,
                      auxfiler: 'auxfiletools.Auxfiler' = None):
        """Write the control parameters to file.

        Usually, a control file consists of a header (see the documentation
        on the method |get_controlfileheader|) and the string representations
        of the individual |Parameter| objects handled by the `control`
        |SubParameters| object.

        The main functionality of method |Parameters.save_controls| is
        demonstrated in the documentation on the method |HydPy.save_controls|
        of class |HydPy|, which one would apply to write the parameter
        information of complete *HydPy* projects.  However, to call
        |Parameters.save_controls| on individual |Parameters| objects
        offers the advantage to choose an arbitrary file path, as shown
        in the following example:

        >>> from hydpy.models.hstream_v1 import *
        >>> parameterstep('1d')
        >>> simulationstep('1h')
        >>> lag(1.0)
        >>> damp(0.5)

        >>> from hydpy import Open
        >>> with Open():
        ...     model.parameters.save_controls('otherdir/otherfile.py')
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        otherdir/otherfile.py
        -------------------------------------
        # -*- coding: utf-8 -*-
        <BLANKLINE>
        from hydpy.models.hstream_v1 import *
        <BLANKLINE>
        simulationstep('1h')
        parameterstep('1d')
        <BLANKLINE>
        lag(1.0)
        damp(0.5)
        <BLANKLINE>
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

        Without a given file path and a proper project configuration,
        method |Parameters.save_controls| raises the following error:

        >>> model.parameters.save_controls()
        Traceback (most recent call last):
        ...
        RuntimeError: To save the control parameters of a model to a file, \
its filename must be known.  This can be done, by passing a filename to \
function `save_controls` directly.  But in complete HydPy applications, \
it is usally assumed to be consistent with the name of the element \
handling the model.
        """
        if self.control:
            variable2auxfile = getattr(auxfiler, str(self.model), None)
            lines = [get_controlfileheader(
                self.model, parameterstep, simulationstep)]
            with Parameter.parameterstep(parameterstep):
                for par in self.control:
                    if variable2auxfile:
                        auxfilename = variable2auxfile.get_filename(par)
                        if auxfilename:
                            lines.append(
                                f"{par.name}(auxfile='{auxfilename}')\n")
                            continue
                    lines.append(repr(par) + '\n')
            text = ''.join(lines)
            if filepath:
                with open(filepath, mode='w', encoding='utf-8') as controlfile:
                    controlfile.write(text)
            else:
                filename = objecttools.devicename(self)
                if filename == '?':
                    raise RuntimeError(
                        'To save the control parameters of a model to a file, '
                        'its filename must be known.  This can be done, by '
                        'passing a filename to function `save_controls` '
                        'directly.  But in complete HydPy applications, it is '
                        'usally assumed to be consistent with the name of the '
                        'element handling the model.')
                hydpy.pub.controlmanager.save_file(filename, text)

Response: Write the control parameters to file.


Instruction:
Can you implement a function in Python 3 that
tries to return the parameter values from the auxiliary control file with the given name.

Response:
def _get_values_from_auxiliaryfile(self, auxfile):
        """Try to return the parameter values from the auxiliary control file
        with the given name.

        Things are a little complicated here.  To understand this method, you
        should first take a look at the |parameterstep| function.
        """
        try:
            frame = inspect.currentframe().f_back.f_back
            while frame:
                namespace = frame.f_locals
                try:
                    subnamespace = {'model': namespace['model'],
                                    'focus': self}
                    break
                except KeyError:
                    frame = frame.f_back
            else:
                raise RuntimeError(
                    'Cannot determine the corresponding model.  Use the '
                    '`auxfile` keyword in usual parameter control files only.')
            filetools.ControlManager.read2dict(auxfile, subnamespace)
            try:
                subself = subnamespace[self.name]
            except KeyError:
                raise RuntimeError(
                    f'The selected file does not define value(s) for '
                    f'parameter {self.name}')
            return subself.values
        except BaseException:
            objecttools.augment_excmessage(
                f'While trying to extract information for parameter '
                f'`{self.name}` from file `{auxfile}`')


Instruction:
How would you explain what the following Python 3 function does
def initinfo(self) -> Tuple[Union[float, int, bool], bool]:
        init = self.INIT
        if (init is not None) and hydpy.pub.options.usedefaultvalues:
            with Parameter.parameterstep('1d'):
                return self.apply_timefactor(init), True
        return variabletools.TYPE2MISSINGVALUE[self.TYPE], False

Response: The actual initial value of the given parameter.


Instruction:
Write a Python 3 script to
factor to adjust a new value of a time-dependent parameter. For a time-dependent parameter, its effective value depends on the simulation step size. Method |Parameter.get_timefactor| returns the fraction between the current simulation step size and the current parameter step size. .. testsetup:: >>> from hydpy import pub >>> del pub.timegrids >>> from hydpy.core.parametertools import Parameter >>> Parameter.simulationstep.delete() Period() Method |Parameter.get_timefactor| raises the following error when time information is not available: >>> from hydpy.core.parametertools import Parameter >>> Parameter.get_timefactor() Traceback (most recent call last): ... RuntimeError: To calculate the conversion factor for adapting the \ values of the time-dependent parameters, you need to define both a \ parameter and a simulation time step size first. One can define both time step sizes directly: >>> _ = Parameter.parameterstep('1d') >>> _ = Parameter.simulationstep('6h') >>> Parameter.get_timefactor() 0.25 As usual, the "global" simulation step size of the |Timegrids| object of module |pub| is prefered: >>> from hydpy import pub >>> pub.timegrids = '2000-01-01', '2001-01-01', '12h' >>> Parameter.get_timefactor() 0.5

Response:
def get_timefactor(cls) -> float:
        """Factor to adjust a new value of a time-dependent parameter.

        For a time-dependent parameter, its effective value depends on the
        simulation step size.  Method |Parameter.get_timefactor| returns
        the fraction between the current simulation step size and the
        current parameter step size.

        .. testsetup::

            >>> from hydpy import pub
            >>> del pub.timegrids
            >>> from hydpy.core.parametertools import Parameter
            >>> Parameter.simulationstep.delete()
            Period()

        Method |Parameter.get_timefactor| raises the following error
        when time information is not available:

        >>> from hydpy.core.parametertools import Parameter
        >>> Parameter.get_timefactor()
        Traceback (most recent call last):
        ...
        RuntimeError: To calculate the conversion factor for adapting the \
values of the time-dependent parameters, you need to define both a \
parameter and a simulation time step size first.

        One can define both time step sizes directly:

        >>> _ = Parameter.parameterstep('1d')
        >>> _ = Parameter.simulationstep('6h')
        >>> Parameter.get_timefactor()
        0.25

        As usual, the "global" simulation step size of the |Timegrids|
        object of module |pub| is prefered:

        >>> from hydpy import pub
        >>> pub.timegrids = '2000-01-01', '2001-01-01', '12h'
        >>> Parameter.get_timefactor()
        0.5
        """
        try:
            parfactor = hydpy.pub.timegrids.parfactor
        except RuntimeError:
            if not (cls.parameterstep and cls.simulationstep):
                raise RuntimeError(
                    f'To calculate the conversion factor for adapting '
                    f'the values of the time-dependent parameters, '
                    f'you need to define both a parameter and a simulation '
                    f'time step size first.')
            else:
                date1 = timetools.Date('2000.01.01')
                date2 = date1 + cls.simulationstep
                parfactor = timetools.Timegrids(timetools.Timegrid(
                    date1, date2, cls.simulationstep)).parfactor
        return parfactor(cls.parameterstep)


Instruction:
Can you implement a function in Python 3 that
changes and return the given value in accordance with the type of time - dependentence and the type of time - dependentence .

Response:
def apply_timefactor(cls, values):
        """Change and return the given value(s) in accordance with
        |Parameter.get_timefactor| and the type of time-dependence
        of the actual parameter subclass.

        .. testsetup::

            >>> from hydpy import pub
            >>> del pub.timegrids

        For the same conversion factor returned by method
        |Parameter.get_timefactor|, method |Parameter.apply_timefactor|
        behaves differently depending on the `TIME` attribute of the
        respective |Parameter| subclass.  We first prepare a parameter
        test class and define both the parameter and simulation step size:

        >>> from hydpy.core.parametertools import Parameter
        >>> class Par(Parameter):
        ...     TIME = None
        >>> Par.parameterstep = '1d'
        >>> Par.simulationstep = '6h'

        |None| means the value(s) of the parameter are not time-dependent
        (e.g. maximum storage capacity).  Hence, |Parameter.apply_timefactor|
        returns the original value(s):

        >>> Par.apply_timefactor(4.0)
        4.0

        |True| means the effective parameter value is proportional to
        the simulation step size (e.g. travel time). Hence,
        |Parameter.apply_timefactor| returns a reduced value in the
        next example (where the simulation step size is smaller than
        the parameter step size):

        >>> Par.TIME = True
        >>> Par.apply_timefactor(4.0)
        1.0

        |False| means the effective parameter value is inversely
        proportional to the simulation step size (e.g. storage
        coefficient). Hence, |Parameter.apply_timefactor| returns
        an increased value in the next example:

        >>> Par.TIME = False
        >>> Par.apply_timefactor(4.0)
        16.0
        """
        if cls.TIME is True:
            return values * cls.get_timefactor()
        if cls.TIME is False:
            return values / cls.get_timefactor()
        return values


Instruction:
Can you tell what is the following Python 3 function doing
def revert_timefactor(cls, values):
        if cls.TIME is True:
            return values / cls.get_timefactor()
        if cls.TIME is False:
            return values * cls.get_timefactor()
        return values

Response: This method reverts the timefactor of a parameter to a new value.


Instruction:
Given the following Python 3 function, write the documentation
def compress_repr(self) -> Optional[str]:
        if not hasattr(self, 'value'):
            return '?'
        if not self:
            return f"{self.NDIM * '['}{self.NDIM * ']'}"
        unique = numpy.unique(self[self.mask])
        if sum(numpy.isnan(unique)) == len(unique.flatten()):
            unique = numpy.array([numpy.nan])
        else:
            unique = self.revert_timefactor(unique)
        if len(unique) == 1:
            return objecttools.repr_(unique[0])
        return None

Response: Return the compressed representation of the current parameter value.


Instruction:
Can you create a Python 3 function that
works as |Parameter. compress_repr| but returns a string with constant names instead of constant values.

Response:
def compress_repr(self) -> str:
        """Works as |Parameter.compress_repr|, but returns a
        string with constant names instead of constant values.

        See the main documentation on class |NameParameter| for
        further information.
        """
        string = super().compress_repr()
        if string in ('?', '[]'):
            return string
        if string is None:
            values = self.values
        else:
            values = [int(string)]
        invmap = {value: key for key, value in
                  self.CONSTANTS.items()}
        result = ', '.join(
            invmap.get(value, repr(value)) for value in values)
        if len(self) > 255:
            result = f'[{result}]'
        return result


Instruction:
How would you explain what the following Python 3 function does
def compress_repr(self) -> Optional[str]:
        string = super().compress_repr()
        if string is not None:
            return string
        results = []
        mask = self.mask
        refindices = mask.refindices.values
        for (key, value) in self.MODEL_CONSTANTS.items():
            if value in mask.RELEVANT_VALUES:
                unique = numpy.unique(self.values[refindices == value])
                unique = self.revert_timefactor(unique)
                length = len(unique)
                if length == 1:
                    results.append(
                        f'{key.lower()}={objecttools.repr_(unique[0])}')
                elif length > 1:
                    return None
        return ', '.join(sorted(results))

Response: Returns a string representation of the object.


Instruction:
How would you implement a function in Python 3 that
refreshes the actual simulation values based on the toy - value pairs.

Response:
def refresh(self) -> None:
        """Update the actual simulation values based on the toy-value pairs.

        Usually, one does not need to call refresh explicitly.  The
        "magic" methods __call__, __setattr__, and __delattr__ invoke
        it automatically, when required.

        Instantiate a 1-dimensional |SeasonalParameter| object:

        >>> from hydpy.core.parametertools import SeasonalParameter
        >>> class Par(SeasonalParameter):
        ...     NDIM = 1
        ...     TYPE = float
        ...     TIME = None
        >>> par = Par(None)
        >>> par.simulationstep = '1d'
        >>> par.shape = (None,)

        When a |SeasonalParameter| object does not contain any toy-value
        pairs yet, the method |SeasonalParameter.refresh| sets all actual
        simulation values to zero:

        >>> par.values = 1.
        >>> par.refresh()
        >>> par.values[0]
        0.0

        When there is only one toy-value pair, its values are relevant
        for all actual simulation values:

        >>> par.toy_1 = 2. # calls refresh automatically
        >>> par.values[0]
        2.0

        Method |SeasonalParameter.refresh| performs a linear interpolation
        for the central time points of each simulation time step.  Hence,
        in the following example, the original values of the toy-value
        pairs do not show up:

        >>> par.toy_12_31 = 4.
        >>> from hydpy import round_
        >>> round_(par.values[0])
        2.00274
        >>> round_(par.values[-2])
        3.99726
        >>> par.values[-1]
        3.0

        If one wants to preserve the original values in this example, one
        would have to set the corresponding toy instances in the middle of
        some simulation step intervals:

        >>> del par.toy_1
        >>> del par.toy_12_31
        >>> par.toy_1_1_12 = 2
        >>> par.toy_12_31_12 = 4.
        >>> par.values[0]
        2.0
        >>> round_(par.values[1])
        2.005479
        >>> round_(par.values[-2])
        3.994521
        >>> par.values[-1]
        4.0
        """
        if not self:
            self.values[:] = 0.
        elif len(self) == 1:
            values = list(self._toy2values.values())[0]
            self.values[:] = self.apply_timefactor(values)
        else:
            for idx, date in enumerate(
                    timetools.TOY.centred_timegrid(self.simulationstep)):
                values = self.interp(date)
                self.values[idx] = self.apply_timefactor(values)


Instruction:
Given the following Python 3 function, write the documentation
def interp(self, date: timetools.Date) -> float:
        xnew = timetools.TOY(date)
        xys = list(self)
        for idx, (x_1, y_1) in enumerate(xys):
            if x_1 > xnew:
                x_0, y_0 = xys[idx-1]
                break
        else:
            x_0, y_0 = xys[-1]
            x_1, y_1 = xys[0]
        return y_0+(y_1-y_0)/(x_1-x_0)*(xnew-x_0)

Response: Perform a linear value interpolation for the given date and return the result.


Instruction:
Can you generate the documentation for the following Python 3 function
def update(self) -> None:
        mask = self.mask
        weights = self.refweights[mask]
        self[~mask] = numpy.nan
        self[mask] = weights/numpy.sum(weights)

Response: Update subclass of |RelSubweightsMixin| based on refweights.


Instruction:
How would you explain what the following Python 3 function does
def alternative_initvalue(self) -> Union[bool, int, float]:
        if self._alternative_initvalue is None:
            raise AttributeError(
                f'No alternative initial value for solver parameter '
                f'{objecttools.elementphrase(self)} has been defined so far.')
        else:
            return self._alternative_initvalue

Response:
A user - defined value to be used instead of the value of class
        constant INIT.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def update(self) -> None:
        indexarray = hydpy.pub.indexer.timeofyear
        self.shape = indexarray.shape
        self.values = indexarray

Response: Update |Indexer. timeofyear| array of the |Indexer| object available in module |pub|.


Instruction:
Write a Python 3 script for
returning the premises model that is installed with the given app_label. model_name.

Response:
def get_premises_model():
    """
    Support for custom company premises model
    with developer friendly validation.
    """
    try:
        app_label, model_name = PREMISES_MODEL.split('.')
    except ValueError:
        raise ImproperlyConfigured("OPENINGHOURS_PREMISES_MODEL must be of the"
                                   " form 'app_label.model_name'")
    premises_model = get_model(app_label=app_label, model_name=model_name)
    if premises_model is None:
        raise ImproperlyConfigured("OPENINGHOURS_PREMISES_MODEL refers to"
                                   " model '%s' that has not been installed"
                                   % PREMISES_MODEL)
    return premises_model


Instruction:
Can you create a Python 3 function that
returns a datetime. datetime object for the current request.

Response:
def get_now():
    """
    Allows to access global request and read a timestamp from query.
    """
    if not get_current_request:
        return datetime.datetime.now()
    request = get_current_request()
    if request:
        openinghours_now = request.GET.get('openinghours-now')
        if openinghours_now:
            return datetime.datetime.strptime(openinghours_now, '%Y%m%d%H%M%S')
    return datetime.datetime.now()


Instruction:
Given the following Python 3 function, write the documentation
def get_closing_rule_for_now(location):
    now = get_now()

    if location:
        return ClosingRules.objects.filter(company=location,
                                           start__lte=now, end__gte=now)

    return Company.objects.first().closingrules_set.filter(start__lte=now,
                                                           end__gte=now)

Response: Returns QuerySet of ClosingRules that are currently valid for the given location.


Instruction:
Implement a Python 3 function for
returning True if company is currently open.

Response:
def is_open(location, now=None):
    """
    Is the company currently open? Pass "now" to test with a specific
    timestamp. Can be used stand-alone or as a helper.
    """
    if now is None:
        now = get_now()

    if has_closing_rule_for_now(location):
        return False

    now_time = datetime.time(now.hour, now.minute, now.second)

    if location:
        ohs = OpeningHours.objects.filter(company=location)
    else:
        ohs = Company.objects.first().openinghours_set.all()
    for oh in ohs:
        is_open = False
        # start and end is on the same day
        if (oh.weekday == now.isoweekday() and
                oh.from_hour <= now_time and
                now_time <= oh.to_hour):
            is_open = oh

        # start and end are not on the same day and we test on the start day
        if (oh.weekday == now.isoweekday() and
                oh.from_hour <= now_time and
                ((oh.to_hour < oh.from_hour) and
                    (now_time < datetime.time(23, 59, 59)))):
            is_open = oh

        # start and end are not on the same day and we test on the end day
        if (oh.weekday == (now.isoweekday() - 1) % 7 and
                oh.from_hour >= now_time and
                oh.to_hour >= now_time and
                oh.to_hour < oh.from_hour):
            is_open = oh
            # print " 'Special' case after midnight", oh

        if is_open is not False:
            return oh
    return False