Instruction:
Write a Python 3 script for
integrating storage units in the grid.

Response:
def _integrate_storage(self, timeseries, position, params, voltage_level,
                           reactive_power_timeseries, **kwargs):
        """
        Integrate storage units in the grid.

        Parameters
        ----------
        timeseries : :obj:`str` or :pandas:`pandas.Series<series>`
            Parameter used to obtain time series of active power the storage
            storage is charged (negative) or discharged (positive) with. Can
            either be a given time series or an operation strategy. See class
            definition for more information
        position : :obj:`str` or :class:`~.grid.components.Station` or :class:`~.grid.components.BranchTee` or :class:`~.grid.components.Generator` or :class:`~.grid.components.Load`
            Parameter used to place the storage. See class definition for more
            information.
        params : :obj:`dict`
            Dictionary with storage parameters for one storage. See class
            definition for more information on what parameters must be
            provided.
        voltage_level : :obj:`str` or None
            `voltage_level` defines which side of the LV station the storage is
            connected to. Valid options are 'lv' and 'mv'. Default: None. See
            class definition for more information.
        reactive_power_timeseries : :pandas:`pandas.Series<series>` or None
            Reactive power time series in kvar (generator sign convention).
            Index of the series needs to be a
            :pandas:`pandas.DatetimeIndex<datetimeindex>`.

        """
        # place storage
        params = self._check_nominal_power(params, timeseries)
        if isinstance(position, Station) or isinstance(position, BranchTee) \
                or isinstance(position, Generator) \
                or isinstance(position, Load):
            storage = storage_integration.set_up_storage(
                node=position, parameters=params, voltage_level=voltage_level)
            line = storage_integration.connect_storage(storage, position)
        elif isinstance(position, str) \
                and position == 'hvmv_substation_busbar':
            storage, line = storage_integration.storage_at_hvmv_substation(
                self.edisgo.network.mv_grid, params)
        elif isinstance(position, str) \
                and position == 'distribute_storages_mv':
            # check active power time series
            if not isinstance(timeseries, pd.Series):
                raise ValueError(
                    "Storage time series needs to be a pandas Series if "
                    "`position` is 'distribute_storages_mv'.")
            else:
                timeseries = pd.DataFrame(data={'p': timeseries},
                                          index=timeseries.index)
                self._check_timeindex(timeseries)
            # check reactive power time series
            if reactive_power_timeseries is not None:
                self._check_timeindex(reactive_power_timeseries)
                timeseries['q'] = reactive_power_timeseries.loc[
                    timeseries.index]
            else:
                timeseries['q'] = 0
            # start storage positioning method
            storage_positioning.one_storage_per_feeder(
                edisgo=self.edisgo, storage_timeseries=timeseries,
                storage_nominal_power=params['nominal_power'], **kwargs)
            return
        else:
            message = 'Provided storage position option {} is not ' \
                      'valid.'.format(timeseries)
            logging.error(message)
            raise KeyError(message)

        # implement operation strategy (active power)
        if isinstance(timeseries, pd.Series):
            timeseries = pd.DataFrame(data={'p': timeseries},
                                      index=timeseries.index)
            self._check_timeindex(timeseries)
            storage.timeseries = timeseries
        elif isinstance(timeseries, str) and timeseries == 'fifty-fifty':
            storage_operation.fifty_fifty(self.edisgo.network, storage)
        else:
            message = 'Provided storage timeseries option {} is not ' \
                      'valid.'.format(timeseries)
            logging.error(message)
            raise KeyError(message)

        # reactive power
        if reactive_power_timeseries is not None:
            self._check_timeindex(reactive_power_timeseries)
            storage.timeseries = pd.DataFrame(
                {'p': storage.timeseries.p,
                 'q': reactive_power_timeseries.loc[storage.timeseries.index]},
                index=storage.timeseries.index)

        # update pypsa representation
        if self.edisgo.network.pypsa is not None:
            pypsa_io.update_pypsa_storage(
                self.edisgo.network.pypsa,
                storages=[storage], storages_lines=[line])


Instruction:
Can you tell what is the following Python 3 function doing
def _check_nominal_power(self, storage_parameters, timeseries):
        if storage_parameters.get('nominal_power', None) is None:
            try:
                storage_parameters['nominal_power'] = max(abs(timeseries))
            except:
                raise ValueError("Could not assign a nominal power to the "
                                 "storage. Please provide either a nominal "
                                 "power or an active power time series.")
        return storage_parameters

Response: Checks if nominal power is provided through storage_parameters and returns the absolute maximum of timeseries. Raises a ValueError if it cannot assign a nominal power to the storage.


Instruction:
How would you code a function in Python 3 to
raise an error if the time index of storage time series does not comply with the time index of load and feed - in time series.

Response:
def _check_timeindex(self, timeseries):
        """
        Raises an error if time index of storage time series does not
        comply with the time index of load and feed-in time series.

        Parameters
        -----------
        timeseries : :pandas:`pandas.DataFrame<dataframe>`
            DataFrame containing active power the storage is charged (negative)
            and discharged (positive) with in kW in column 'p' and
            reactive power in kVA in column 'q'.

        """
        try:
            timeseries.loc[self.edisgo.network.timeseries.timeindex]
        except:
            message = 'Time index of storage time series does not match ' \
                      'with load and feed-in time series.'
            logging.error(message)
            raise KeyError(message)


Instruction:
Implement a Python 3 function for
returning a pandas. DataFrame of the dispatchable generators.

Response:
def generation_dispatchable(self):
        """
        Get generation time series of dispatchable generators (only active
        power)

        Returns
        -------
        :pandas:`pandas.DataFrame<dataframe>`
            See class definition for details.

        """
        try:
            return self._generation_dispatchable.loc[[self.timeindex], :]
        except:
            return self._generation_dispatchable.loc[self.timeindex, :]


Instruction:
How would you explain what the following Python 3 function does
def generation_fluctuating(self):
        try:
            return self._generation_fluctuating.loc[[self.timeindex], :]
        except:
            return self._generation_fluctuating.loc[self.timeindex, :]

Response: Returns a pandas. DataFrame of the active generation time series of fluctuating renewables.


Instruction:
Write a Python 3 function that can
get load time series

Response:
def load(self):
        """
        Get load time series (only active power)

        Returns
        -------
        dict or :pandas:`pandas.DataFrame<dataframe>`
            See class definition for details.

        """
        try:
            return self._load.loc[[self.timeindex], :]
        except:
            return self._load.loc[self.timeindex, :]


Instruction:
Write a Python 3 function for
returning the curtailment time series of all curtailed generators and curtailed generators.

Response:
def curtailment(self):
        """
        Get curtailment time series of dispatchable generators (only active
        power)

        Parameters
        ----------
        curtailment : list or :pandas:`pandas.DataFrame<dataframe>`
            See class definition for details.

        Returns
        -------
        :pandas:`pandas.DataFrame<dataframe>`
            In the case curtailment is applied to all solar and wind generators
            curtailment time series either aggregated by technology type or by
            type and weather cell ID are returnded. In the first case columns
            of the DataFrame are 'solar' and 'wind'; in the second case columns
            need to be a :pandas:`pandas.MultiIndex<multiindex>` with the
            first level containing the type and the second level the weather
            cell ID.
            In the case curtailment is only applied to specific generators,
            curtailment time series of all curtailed generators, specified in
            by the column name are returned.

        """
        if self._curtailment is not None:
            if isinstance(self._curtailment, pd.DataFrame):
                try:
                    return self._curtailment.loc[[self.timeindex], :]
                except:
                    return self._curtailment.loc[self.timeindex, :]
            elif isinstance(self._curtailment, list):
                try:
                    curtailment = pd.DataFrame()
                    for gen in self._curtailment:
                        curtailment[gen] = gen.curtailment
                    return curtailment
                except:
                    raise
        else:
            return None


Instruction:
Here you have a function in Python 3, explain what it does
def curtailment(self):
        if self._curtailment is not None:
            result_dict = {}
            for key, gen_list in self._curtailment.items():
                curtailment_df = pd.DataFrame()
                for gen in gen_list:
                    curtailment_df[gen] = gen.curtailment
                result_dict[key] = curtailment_df
            return result_dict
        else:
            return None

Response: Returns a dictionary with curtailment assigned to each generator per curtailed power in the curtailed power in the curtailed power in the generator.


Instruction:
How would you explain what the following Python 3 function does
def storages(self):
        grids = [self.network.mv_grid] + list(self.network.mv_grid.lv_grids)
        storage_results = {}
        storage_results['storage_id'] = []
        storage_results['nominal_power'] = []
        storage_results['voltage_level'] = []
        storage_results['grid_connection_point'] = []
        for grid in grids:
            for storage in grid.graph.nodes_by_attribute('storage'):
                storage_results['storage_id'].append(repr(storage))
                storage_results['nominal_power'].append(storage.nominal_power)
                storage_results['voltage_level'].append(
                    'mv' if isinstance(grid, MVGrid) else 'lv')
                storage_results['grid_connection_point'].append(
                     grid.graph.neighbors(storage)[0])

        return pd.DataFrame(storage_results).set_index('storage_id')

Response: Returns a pandas. DataFrame containing all storages installed in MV and LV grids.


Instruction:
Write a Python 3 function for
returning a dataframe with storage time series.

Response:
def storages_timeseries(self):
        """
        Returns a dataframe with storage time series.

        Returns
        -------
        :pandas:`pandas.DataFrame<dataframe>`

            Dataframe containing time series of all storages installed in the
            MV grid and LV grids. Index of the dataframe is a
            :pandas:`pandas.DatetimeIndex<datetimeindex>`. Columns are the
            storage representatives.

        """
        storages_p = pd.DataFrame()
        storages_q = pd.DataFrame()
        grids = [self.network.mv_grid] + list(self.network.mv_grid.lv_grids)
        for grid in grids:
            for storage in grid.graph.nodes_by_attribute('storage'):
                ts = storage.timeseries
                storages_p[repr(storage)] = ts.p
                storages_q[repr(storage)] = ts.q

        return storages_p, storages_q


Instruction:
Given the following Python 3 function, write the documentation
def s_res(self, components=None):

        if components is not None:
            labels_included = []
            labels_not_included = []
            labels = [repr(l) for l in components]
            for label in labels:
                if (label in list(self.pfa_p.columns) and
                        label in list(self.pfa_q.columns)):
                    labels_included.append(label)
                else:
                    labels_not_included.append(label)
            if labels_not_included:
                logging.warning(
                    "Apparent power for {lines} are not returned from "
                    "PFA".format(lines=labels_not_included))
        else:
            labels_included = self.pfa_p.columns

        s_res = ((self.pfa_p[labels_included] ** 2 + self.pfa_q[
            labels_included] ** 2)).applymap(sqrt)

        return s_res

Response: Returns the apparent power in kVA at a line or transformer.


Instruction:
Write a Python 3 script for
saving the results of the current curtailment to disk.

Response:
def save(self, directory, parameters='all'):
        """
        Saves results to disk.

        Depending on which results are selected and if they exist, the
        following directories and files are created:

        * `powerflow_results` directory

          * `voltages_pu.csv`

            See :py:attr:`~pfa_v_mag_pu` for more information.
          * `currents.csv`

            See :func:`~i_res` for more information.
          * `active_powers.csv`

            See :py:attr:`~pfa_p` for more information.
          * `reactive_powers.csv`

            See :py:attr:`~pfa_q` for more information.
          * `apparent_powers.csv`

            See :func:`~s_res` for more information.
          * `grid_losses.csv`

            See :py:attr:`~grid_losses` for more information.
          * `hv_mv_exchanges.csv`

            See :py:attr:`~hv_mv_exchanges` for more information.

        * `pypsa_network` directory

          See :py:func:`pypsa.Network.export_to_csv_folder`

        * `grid_expansion_results` directory

          * `grid_expansion_costs.csv`

            See :py:attr:`~grid_expansion_costs` for more information.
          * `equipment_changes.csv`

            See :py:attr:`~equipment_changes` for more information.
          * `unresolved_issues.csv`

            See :py:attr:`~unresolved_issues` for more information.

        * `curtailment_results` directory

          Files depend on curtailment specifications. There will be one file
          for each curtailment specification, that is for every key in
          :py:attr:`~curtailment` dictionary.

        * `storage_integration_results` directory

          * `storages.csv`

            See :func:`~storages` for more information.

        Parameters
        ----------
        directory : :obj:`str`
            Directory to save the results in.
        parameters : :obj:`str` or :obj:`list` of :obj:`str`
            Specifies which results will be saved. By default all results are
            saved. To only save certain results set `parameters` to one of the
            following options or choose several options by providing a list:

            * 'pypsa_network'
            * 'powerflow_results'
            * 'grid_expansion_results'
            * 'curtailment_results'
            * 'storage_integration_results'

        """
        def _save_power_flow_results(target_dir):
            if self.pfa_v_mag_pu is not None:
                # create directory
                os.makedirs(target_dir, exist_ok=True)

                # voltage
                self.pfa_v_mag_pu.to_csv(
                    os.path.join(target_dir, 'voltages_pu.csv'))

                # current
                self.i_res.to_csv(
                    os.path.join(target_dir, 'currents.csv'))

                # active power
                self.pfa_p.to_csv(
                    os.path.join(target_dir, 'active_powers.csv'))

                # reactive power
                self.pfa_q.to_csv(
                    os.path.join(target_dir, 'reactive_powers.csv'))

                # apparent power
                self.s_res().to_csv(
                    os.path.join(target_dir, 'apparent_powers.csv'))

                # grid losses
                self.grid_losses.to_csv(
                    os.path.join(target_dir, 'grid_losses.csv'))

                # grid exchanges
                self.hv_mv_exchanges.to_csv(os.path.join(
                    target_dir, 'hv_mv_exchanges.csv'))

        def _save_pypsa_network(target_dir):
            if self.network.pypsa:
                # create directory
                os.makedirs(target_dir, exist_ok=True)
                self.network.pypsa.export_to_csv_folder(target_dir)

        def _save_grid_expansion_results(target_dir):
            if self.grid_expansion_costs is not None:
                # create directory
                os.makedirs(target_dir, exist_ok=True)

                # grid expansion costs
                self.grid_expansion_costs.to_csv(os.path.join(
                    target_dir, 'grid_expansion_costs.csv'))

                # unresolved issues
                pd.DataFrame(self.unresolved_issues).to_csv(os.path.join(
                    target_dir, 'unresolved_issues.csv'))

                # equipment changes
                self.equipment_changes.to_csv(os.path.join(
                    target_dir, 'equipment_changes.csv'))

        def _save_curtailment_results(target_dir):
            if self.curtailment is not None:
                # create directory
                os.makedirs(target_dir, exist_ok=True)

                for key, curtailment_df in self.curtailment.items():
                    if type(key) == tuple:
                        type_prefix = '-'.join([key[0], str(key[1])])
                    elif type(key) == str:
                        type_prefix = key
                    else:
                        raise KeyError("Unknown key type {} for key {}".format(
                            type(key), key))

                    filename = os.path.join(
                        target_dir, '{}.csv'.format(type_prefix))

                    curtailment_df.to_csv(filename, index_label=type_prefix)

        def _save_storage_integration_results(target_dir):
            storages = self.storages
            if not storages.empty:
                # create directory
                os.makedirs(target_dir, exist_ok=True)

                # general storage information
                storages.to_csv(os.path.join(target_dir, 'storages.csv'))

                # storages time series
                ts_p, ts_q = self.storages_timeseries()
                ts_p.to_csv(os.path.join(
                    target_dir, 'storages_active_power.csv'))
                ts_q.to_csv(os.path.join(
                    target_dir, 'storages_reactive_power.csv'))

                if not self.storages_costs_reduction is None:
                    self.storages_costs_reduction.to_csv(
                        os.path.join(target_dir,
                                     'storages_costs_reduction.csv'))

        # dictionary with function to call to save each parameter
        func_dict = {
            'powerflow_results': _save_power_flow_results,
            'pypsa_network': _save_pypsa_network,
            'grid_expansion_results': _save_grid_expansion_results,
            'curtailment_results': _save_curtailment_results,
            'storage_integration_results': _save_storage_integration_results
        }

        # if string is given convert to list
        if isinstance(parameters, str):
            if parameters == 'all':
                parameters = ['powerflow_results', 'pypsa_network',
                              'grid_expansion_results', 'curtailment_results',
                              'storage_integration_results']
            else:
                parameters = [parameters]

        # save each parameter
        for parameter in parameters:
            try:
                func_dict[parameter](os.path.join(directory, parameter))
            except KeyError:
                message = "Invalid input {} for `parameters` when saving " \
                          "results. Must be any or a list of the following: " \
                          "'pypsa_network', 'powerflow_results', " \
                          "'grid_expansion_results', 'curtailment_results', " \
                          "'storage_integration_results'.".format(parameter)
                logger.error(message)
                raise KeyError(message)
            except:
                raise
        # save measures
        pd.DataFrame(data={'measure': self.measures}).to_csv(
            os.path.join(directory, 'measures.csv'))
        # save configs
        with open(os.path.join(directory, 'configs.csv'), 'w') as f:
            writer = csv.writer(f)
            rows = [
                ['{}'.format(key)] + [value for item in values.items()
                                      for value in item]
                for key, values in self.network.config._data.items()]
            writer.writerows(rows)


Instruction:
How would you explain what the following Python 3 function does
def v_res(self, nodes=None, level=None):
        # check if voltages are available:
        if hasattr(self, 'pfa_v_mag_pu'):
            self.pfa_v_mag_pu.sort_index(axis=1, inplace=True)
        else:
            message = "No voltage results available."
            raise AttributeError(message)

        if level is None:
            level = ['mv', 'lv']

        if nodes is None:
            return self.pfa_v_mag_pu.loc[:, (level, slice(None))]
        else:
            not_included = [_ for _ in nodes
                            if _ not in list(self.pfa_v_mag_pu[level].columns)]
            labels_included = [_ for _ in nodes if _ not in not_included]

            if not_included:
                logging.warning("Voltage levels for {nodes} are not returned "
                                "from PFA".format(nodes=not_included))
            return self.pfa_v_mag_pu[level][labels_included]

Response: Returns a pandas. DataFrame of voltage levels obtained at a given grid level.


Instruction:
Here you have a function in Python 3, explain what it does
def s_res(self, components=None):
        if components is None:
            return self.apparent_power
        else:
            not_included = [_ for _ in components
                            if _ not in self.apparent_power.index]
            labels_included = [_ for _ in components if _ not in not_included]

            if not_included:
                logging.warning(
                    "No apparent power results available for: {}".format(
                        not_included))
            return self.apparent_power.loc[:, labels_included]

Response: Returns the apparent power in kVA at lines and transformers.


Instruction:
Can you generate the documentation for the following Python 3 function
def storage_at_hvmv_substation(mv_grid, parameters, mode=None):
    storage = set_up_storage(node=mv_grid.station, parameters=parameters,
                             operational_mode=mode)
    line = connect_storage(storage, mv_grid.station)
    return storage, line

Response: Place storage at HV or MV substation bus bar.


Instruction:
Can you create a Python 3 function that
sets up a storage instance for the given node and parameters.

Response:
def set_up_storage(node, parameters,
                   voltage_level=None, operational_mode=None):
    """
    Sets up a storage instance.

    Parameters
    ----------
    node : :class:`~.grid.components.Station` or :class:`~.grid.components.BranchTee`
        Node the storage will be connected to.
    parameters : :obj:`dict`, optional
        Dictionary with storage parameters. Must at least contain
        'nominal_power'. See :class:`~.grid.network.StorageControl` for more
        information.
    voltage_level : :obj:`str`, optional
        This parameter only needs to be provided if `node` is of type
        :class:`~.grid.components.LVStation`. In that case `voltage_level`
        defines which side of the LV station the storage is connected to. Valid
        options are 'lv' and 'mv'. Default: None.
    operational_mode : :obj:`str`, optional
        Operational mode. See :class:`~.grid.network.StorageControl` for
        possible options and more information. Default: None.

    """

    # if node the storage is connected to is an LVStation voltage_level
    # defines which side the storage is connected to
    if isinstance(node, LVStation):
        if voltage_level == 'lv':
            grid = node.grid
        elif voltage_level == 'mv':
            grid = node.mv_grid
        else:
            raise ValueError(
                "{} is not a valid option for voltage_level.".format(
                    voltage_level))
    else:
        grid = node.grid

    return Storage(operation=operational_mode,
                   id='{}_storage_{}'.format(grid,
                                             len(grid.graph.nodes_by_attribute(
                                                 'storage')) + 1),
                   grid=grid,
                   geom=node.geom,
                   **parameters)


Instruction:
Can you implement a function in Python 3 that
connects storage to a given node.

Response:
def connect_storage(storage, node):
    """
    Connects storage to the given node.

    The storage is connected by a cable
    The cable the storage is connected with is selected to be able to carry
    the storages nominal power and equal amount of reactive power.
    No load factor is considered.

    Parameters
    ----------
    storage : :class:`~.grid.components.Storage`
        Storage instance to be integrated into the grid.
    node : :class:`~.grid.components.Station` or :class:`~.grid.components.BranchTee`
        Node the storage will be connected to.

    Returns
    -------
    :class:`~.grid.components.Line`
        Newly added line to connect storage.

    """

    # add storage itself to graph
    storage.grid.graph.add_node(storage, type='storage')

    # add 1m connecting line to node the storage is connected to
    if isinstance(storage.grid, MVGrid):
        voltage_level = 'mv'
    else:
        voltage_level = 'lv'

    # necessary apparent power the line must be able to carry is set to be
    # the storages nominal power and equal amount of reactive power devided by
    # the minimum load factor
    lf_dict = storage.grid.network.config['grid_expansion_load_factors']
    lf = min(lf_dict['{}_feedin_case_line'.format(voltage_level)],
             lf_dict['{}_load_case_line'.format(voltage_level)])
    apparent_power_line = sqrt(2) * storage.nominal_power / lf
    line_type, line_count = select_cable(storage.grid.network, voltage_level,
                                         apparent_power_line)
    line = Line(
        id=storage.id,
        type=line_type,
        kind='cable',
        length=1e-3,
        grid=storage.grid,
        quantity=line_count)

    storage.grid.graph.add_edge(node, storage, line=line, type='line')

    return line


Instruction:
Can you tell what is the following Python 3 function doing
def add_request_log_fields(
            self, log_fields: LogFields,
            call_details: Union[grpc.HandlerCallDetails,
                                grpc.ClientCallDetails]
    ):
        """Add log fields related to a request to the provided log fields

        :param log_fields: log fields instance to which to add the fields
        :param call_details: some information regarding the call

        """
        service, method = call_details.method[1:].split("/")
        log_fields.add_fields({
            "system":       "grpc",
            "span.kind":    self.KIND,
            "grpc.service": service,
            "grpc.method":  method,
        })

Response: Add log fields related to a request to the provided log fields instance.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def add_response_log_fields(self, log_fields: LogFields,
                                start_time: datetime, err: Exception):
        """Add log fields related to a response to the provided log fields

        :param log_fields: log fields instnace to which to add the fields
        :param start_time: start time of the request
        :param err: exception raised during the handling of the request.

        """
        code = "Unknown" if err is not None else "OK"
        duration = (datetime.utcnow() - start_time).total_seconds() * 1000
        log_fields.add_fields({
            "grpc.start_time": start_time.isoformat() + "Z",
            "grpc.code":       code,
            "duration":        "{duration}ms".format(duration=duration),
        })

Response: Add log fields related to a response to the provided log fields


Instruction:
Implement a Python 3 function for
selecting two worst - case snapshots from time series.

Response:
def select_worstcase_snapshots(network):
    """
    Select two worst-case snapshots from time series

    Two time steps in a time series represent worst-case snapshots. These are

    1. Load case: refers to the point in the time series where the
        (load - generation) achieves its maximum and is greater than 0.
    2. Feed-in case: refers to the point in the time series where the
        (load - generation) achieves its minimum and is smaller than 0.

    These two points are identified based on the generation and load time
    series. In case load or feed-in case don't exist None is returned.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`
        Network for which worst-case snapshots are identified.

    Returns
    -------
    :obj:`dict`
        Dictionary with keys 'load_case' and 'feedin_case'. Values are
        corresponding worst-case snapshots of type
        :pandas:`pandas.Timestamp<timestamp>` or None.

    """
    timeseries_load_feedin_case = network.timeseries.timesteps_load_feedin_case

    timestamp = {}
    timestamp['load_case'] = (
        timeseries_load_feedin_case.residual_load.idxmax()
        if max(timeseries_load_feedin_case.residual_load) > 0 else None)
    timestamp['feedin_case'] = (
        timeseries_load_feedin_case.residual_load.idxmin()
        if min(timeseries_load_feedin_case.residual_load) < 0 else None)
    return timestamp


Instruction:
Implement a Python 3 function for
calculating the residual load in MW in MV grid and underlying LV grids.

Response:
def get_residual_load_from_pypsa_network(pypsa_network):
    """
    Calculates residual load in MW in MV grid and underlying LV grids.

    Parameters
    ----------
    pypsa_network : :pypsa:`pypsa.Network<network>`
        The `PyPSA network
        <https://www.pypsa.org/doc/components.html#network>`_ container,
        containing load flow results.

    Returns
    -------
    :pandas:`pandas.Series<series>`
        Series with residual load in MW for each time step. Positiv values
        indicate a higher demand than generation and vice versa. Index of the
        series is a :pandas:`pandas.DatetimeIndex<datetimeindex>`

    """
    residual_load = \
        pypsa_network.loads_t.p_set.sum(axis=1) - (
                pypsa_network.generators_t.p_set.loc[
                :, pypsa_network.generators_t.p_set.columns !=
                   'Generator_slack'].sum(axis=1) +
                pypsa_network.storage_units_t.p_set.sum(axis=1))
    return residual_load


Instruction:
Make a summary of the following Python 3 code
def assign_load_feedin_case(network):

    if network.pypsa is not None:
        residual_load = get_residual_load_from_pypsa_network(network.pypsa) * \
                        1e3

    else:
        grids = [network.mv_grid] + list(network.mv_grid.lv_grids)

        gens = []
        loads = []
        for grid in grids:
            gens.extend(grid.generators)
            gens.extend(list(grid.graph.nodes_by_attribute('storage')))
            loads.extend(list(grid.graph.nodes_by_attribute('load')))

        generation_timeseries = pd.Series(
            0, index=network.timeseries.timeindex)
        for gen in gens:
            generation_timeseries += gen.timeseries.p

        load_timeseries = pd.Series(0, index=network.timeseries.timeindex)
        for load in loads:
            load_timeseries += load.timeseries.p

        residual_load = load_timeseries - generation_timeseries

    timeseries_load_feedin_case = residual_load.rename(
        'residual_load').to_frame()

    timeseries_load_feedin_case['case'] = \
        timeseries_load_feedin_case.residual_load.apply(
            lambda _: 'feedin_case' if _ < 0 else 'load_case')

    return timeseries_load_feedin_case

Response: Assign load - case to load - case.


Instruction:
How would you explain what the following Python 3 function does
def load_config(filename, config_dir=None, copy_default_config=True):
    if not config_dir:
        config_file = os.path.join(get_default_config_path(), filename)
    else:
        config_file = os.path.join(config_dir, filename)

        # config file does not exist -> copy default
        if not os.path.isfile(config_file):
            if copy_default_config:
                logger.info('Config file {} not found, I will create a '
                            'default version'.format(config_file))
                make_directory(config_dir)
                shutil.copy(os.path.join(package_path, 'config', filename.
                                         replace('.cfg', '_default.cfg')),
                            config_file)
            else:
                message = 'Config file {} not found.'.format(config_file)
                logger.error(message)
                raise FileNotFoundError(message)

    if len(cfg.read(config_file)) == 0:
        message = 'Config file {} not found or empty.'.format(config_file)
        logger.error(message)
        raise FileNotFoundError(message)
    global _loaded
    _loaded = True

Response: Loads the specified config file into the config_dir.


Instruction:
Make a summary of the following Python 3 code
def get_default_config_path():
    config_dir = get('user_dirs', 'config_dir')
    root_dir = get('user_dirs', 'root_dir')
    root_path = os.path.join(os.path.expanduser('~'), root_dir)
    config_path = os.path.join(root_path, config_dir)

    # root directory does not exist
    if not os.path.isdir(root_path):
        # create it
        logger.info('eDisGo root path {} not found, I will create it.'
                    .format(root_path))
        make_directory(root_path)

    # config directory does not exist
    if not os.path.isdir(config_path):
        # create it
        config_path = os.path.join(root_path, config_dir)
        make_directory(config_path)

        # copy default config files
        logger.info('eDisGo config path {} not found, I will create it.'
                    .format(config_path))

    # copy default config files if they don't exist
    internal_config_dir = os.path.join(package_path, 'config')
    for file in glob(os.path.join(internal_config_dir, '*.cfg')):
        filename = os.path.join(config_path,
                                os.path.basename(file).replace('_default', ''))
        if not os.path.isfile(filename):
            logger.info('I will create a default config file {} in {}'
                        .format(file, config_path))
            shutil.copy(file, filename)
    return config_path

Response: Returns the basic edisgo config path. If it does not exist it creates it and copies all default config files into it.


Instruction:
Can you implement a function in Python 3 that
makes directory if it does not exist.

Response:
def make_directory(directory):
    """
    Makes directory if it does not exist.

    Parameters
    -----------
    directory : :obj:`str`
        Directory path

    """
    if not os.path.isdir(directory):
        os.mkdir(directory)
        logger.info('Path {} not found, I will create it.'
                    .format(directory))


Instruction:
Create a Python 3 function to
check for over - loading issues in MV grid.

Response:
def mv_line_load(network):
    """
    Checks for over-loading issues in MV grid.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`

    Returns
    -------
    :pandas:`pandas.DataFrame<dataframe>`
        Dataframe containing over-loaded MV lines, their maximum relative
        over-loading and the corresponding time step.
        Index of the dataframe are the over-loaded lines of type
        :class:`~.grid.components.Line`. Columns are 'max_rel_overload'
        containing the maximum relative over-loading as float and 'time_index'
        containing the corresponding time step the over-loading occured in as
        :pandas:`pandas.Timestamp<timestamp>`.

    Notes
    -----
    Line over-load is determined based on allowed load factors for feed-in and
    load cases that are defined in the config file 'config_grid_expansion' in
    section 'grid_expansion_load_factors'.

    """

    crit_lines = pd.DataFrame()
    crit_lines = _line_load(network, network.mv_grid, crit_lines)

    if not crit_lines.empty:
        logger.debug('==> {} line(s) in MV grid has/have load issues.'.format(
            crit_lines.shape[0]))
    else:
        logger.debug('==> No line load issues in MV grid.')

    return crit_lines


Instruction:
Can you generate a brief explanation for the following Python 3 code
def lv_line_load(network):

    crit_lines = pd.DataFrame()

    for lv_grid in network.mv_grid.lv_grids:
        crit_lines = _line_load(network, lv_grid, crit_lines)

    if not crit_lines.empty:
        logger.debug('==> {} line(s) in LV grids has/have load issues.'.format(
            crit_lines.shape[0]))
    else:
        logger.debug('==> No line load issues in LV grids.')

    return crit_lines

Response: Checks for over - loading issues in LV grids.


Instruction:
Write a Python 3 function that can
check for over - loading issues of lines.

Response:
def _line_load(network, grid, crit_lines):
    """
    Checks for over-loading issues of lines.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`
    grid : :class:`~.grid.grids.LVGrid` or :class:`~.grid.grids.MVGrid`
    crit_lines : :pandas:`pandas.DataFrame<dataframe>`
        Dataframe containing over-loaded lines, their maximum relative
        over-loading and the corresponding time step.
        Index of the dataframe are the over-loaded lines of type
        :class:`~.grid.components.Line`. Columns are 'max_rel_overload'
        containing the maximum relative over-loading as float and 'time_index'
        containing the corresponding time step the over-loading occured in as
        :pandas:`pandas.Timestamp<timestamp>`.

    Returns
    -------
    :pandas:`pandas.DataFrame<dataframe>`
        Dataframe containing over-loaded lines, their maximum relative
        over-loading and the corresponding time step.
        Index of the dataframe are the over-loaded lines of type
        :class:`~.grid.components.Line`. Columns are 'max_rel_overload'
        containing the maximum relative over-loading as float and 'time_index'
        containing the corresponding time step the over-loading occured in as
        :pandas:`pandas.Timestamp<timestamp>`.

    """
    if isinstance(grid, LVGrid):
        grid_level = 'lv'
    else:
        grid_level = 'mv'

    for line in list(grid.graph.lines()):
        i_line_allowed_per_case = {}
        i_line_allowed_per_case['feedin_case'] = \
            line['line'].type['I_max_th'] * line['line'].quantity * \
            network.config['grid_expansion_load_factors'][
                '{}_feedin_case_line'.format(grid_level)]
        i_line_allowed_per_case['load_case'] = \
            line['line'].type['I_max_th'] * line['line'].quantity * \
            network.config['grid_expansion_load_factors'][
                '{}_load_case_line'.format(grid_level)]
        # maximum allowed line load in each time step
        i_line_allowed = \
            network.timeseries.timesteps_load_feedin_case.case.apply(
                lambda _: i_line_allowed_per_case[_])
        try:
            # check if maximum current from power flow analysis exceeds
            # allowed maximum current
            i_line_pfa = network.results.i_res[repr(line['line'])]
            if any((i_line_allowed - i_line_pfa) < 0):
                # find out largest relative deviation
                relative_i_res = i_line_pfa / i_line_allowed
                crit_lines = crit_lines.append(pd.DataFrame(
                    {'max_rel_overload': relative_i_res.max(),
                     'time_index': relative_i_res.idxmax()},
                    index=[line['line']]))
        except KeyError:
            logger.debug('No results for line {} '.format(str(line)) +
                         'to check overloading.')

    return crit_lines


Instruction:
Make a summary of the following Python 3 code
def hv_mv_station_load(network):
    crit_stations = pd.DataFrame()
    crit_stations = _station_load(network, network.mv_grid.station,
                                  crit_stations)
    if not crit_stations.empty:
        logger.debug('==> HV/MV station has load issues.')
    else:
        logger.debug('==> No HV/MV station load issues.')

    return crit_stations

Response: Checks for over - loading of HV or MV station.


Instruction:
Write a Python 3 script to
check for over - loading of MV and LV stations.

Response:
def mv_lv_station_load(network):
    """
    Checks for over-loading of MV/LV stations.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`

    Returns
    -------
    :pandas:`pandas.DataFrame<dataframe>`
        Dataframe containing over-loaded MV/LV stations, their apparent power
        at maximal over-loading and the corresponding time step.
        Index of the dataframe are the over-loaded stations of type
        :class:`~.grid.components.LVStation`. Columns are 's_pfa'
        containing the apparent power at maximal over-loading as float and
        'time_index' containing the corresponding time step the over-loading
        occured in as :pandas:`pandas.Timestamp<timestamp>`.

    Notes
    -----
    Over-load is determined based on allowed load factors for feed-in and
    load cases that are defined in the config file 'config_grid_expansion' in
    section 'grid_expansion_load_factors'.

    """

    crit_stations = pd.DataFrame()

    for lv_grid in network.mv_grid.lv_grids:
        crit_stations = _station_load(network, lv_grid.station,
                                      crit_stations)
    if not crit_stations.empty:
        logger.debug('==> {} MV/LV station(s) has/have load issues.'.format(
            crit_stations.shape[0]))
    else:
        logger.debug('==> No MV/LV station load issues.')

    return crit_stations


Instruction:
Can you create a Python 3 function that
checks for over - loading of stations.

Response:
def _station_load(network, station, crit_stations):
    """
    Checks for over-loading of stations.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`
    station : :class:`~.grid.components.LVStation` or :class:`~.grid.components.MVStation`
    crit_stations : :pandas:`pandas.DataFrame<dataframe>`
        Dataframe containing over-loaded stations, their apparent power at
        maximal over-loading and the corresponding time step.
        Index of the dataframe are the over-loaded stations either of type
        :class:`~.grid.components.LVStation` or
        :class:`~.grid.components.MVStation`. Columns are 's_pfa'
        containing the apparent power at maximal over-loading as float and
        'time_index' containing the corresponding time step the over-loading
        occured in as :pandas:`pandas.Timestamp<timestamp>`.

    Returns
    -------
    :pandas:`pandas.DataFrame<dataframe>`
        Dataframe containing over-loaded stations, their apparent power at
        maximal over-loading and the corresponding time step.
        Index of the dataframe are the over-loaded stations either of type
        :class:`~.grid.components.LVStation` or
        :class:`~.grid.components.MVStation`. Columns are 's_pfa'
        containing the apparent power at maximal over-loading as float and
        'time_index' containing the corresponding time step the over-loading
        occured in as :pandas:`pandas.Timestamp<timestamp>`.

    """
    if isinstance(station, LVStation):
        grid_level = 'lv'
    else:
        grid_level = 'mv'

    # maximum allowed apparent power of station for feed-in and load case
    s_station = sum([_.type.S_nom for _ in station.transformers])
    s_station_allowed_per_case = {}
    s_station_allowed_per_case['feedin_case'] = s_station * network.config[
        'grid_expansion_load_factors']['{}_feedin_case_transformer'.format(
        grid_level)]
    s_station_allowed_per_case['load_case'] = s_station * network.config[
        'grid_expansion_load_factors']['{}_load_case_transformer'.format(
        grid_level)]
    # maximum allowed apparent power of station in each time step
    s_station_allowed = \
        network.timeseries.timesteps_load_feedin_case.case.apply(
            lambda _: s_station_allowed_per_case[_])

    try:
        if isinstance(station, LVStation):
            s_station_pfa = network.results.s_res(
                station.transformers).sum(axis=1)
        else:
            s_station_pfa = network.results.s_res([station]).iloc[:, 0]
        s_res = s_station_allowed - s_station_pfa
        s_res = s_res[s_res < 0]
        # check if maximum allowed apparent power of station exceeds
        # apparent power from power flow analysis at any time step
        if not s_res.empty:
            # find out largest relative deviation
            load_factor = \
                network.timeseries.timesteps_load_feedin_case.case.apply(
                    lambda _: network.config[
                        'grid_expansion_load_factors'][
                        '{}_{}_transformer'.format(grid_level, _)])
            relative_s_res = load_factor * s_res
            crit_stations = crit_stations.append(pd.DataFrame(
                {'s_pfa': s_station_pfa.loc[relative_s_res.idxmin()],
                 'time_index': relative_s_res.idxmin()},
                index=[station]))

    except KeyError:
        logger.debug('No results for {} station to check overloading.'.format(
            grid_level.upper()))

    return crit_stations


Instruction:
Can you generate a brief explanation for the following Python 3 code
def mv_voltage_deviation(network, voltage_levels='mv_lv'):

    crit_nodes = {}

    v_dev_allowed_per_case = {}
    v_dev_allowed_per_case['feedin_case_lower'] = 0.9
    v_dev_allowed_per_case['load_case_upper'] = 1.1
    offset = network.config[
        'grid_expansion_allowed_voltage_deviations']['hv_mv_trafo_offset']
    control_deviation = network.config[
        'grid_expansion_allowed_voltage_deviations'][
        'hv_mv_trafo_control_deviation']
    if voltage_levels == 'mv_lv':
        v_dev_allowed_per_case['feedin_case_upper'] = \
            1 + offset + control_deviation + network.config[
                'grid_expansion_allowed_voltage_deviations'][
                'mv_lv_feedin_case_max_v_deviation']
        v_dev_allowed_per_case['load_case_lower'] = \
            1 + offset - control_deviation - network.config[
                'grid_expansion_allowed_voltage_deviations'][
                'mv_lv_load_case_max_v_deviation']
    elif voltage_levels == 'mv':
        v_dev_allowed_per_case['feedin_case_upper'] = \
            1 + offset + control_deviation + network.config[
                'grid_expansion_allowed_voltage_deviations'][
                'mv_feedin_case_max_v_deviation']
        v_dev_allowed_per_case['load_case_lower'] = \
            1 + offset - control_deviation - network.config[
                'grid_expansion_allowed_voltage_deviations'][
                'mv_load_case_max_v_deviation']
    else:
        raise ValueError(
            'Specified mode {} is not a valid option.'.format(voltage_levels))
    # maximum allowed apparent power of station in each time step
    v_dev_allowed_upper = \
        network.timeseries.timesteps_load_feedin_case.case.apply(
            lambda _: v_dev_allowed_per_case['{}_upper'.format(_)])
    v_dev_allowed_lower = \
        network.timeseries.timesteps_load_feedin_case.case.apply(
            lambda _: v_dev_allowed_per_case['{}_lower'.format(_)])

    nodes = network.mv_grid.graph.nodes()

    crit_nodes_grid = _voltage_deviation(
        network, nodes, v_dev_allowed_upper, v_dev_allowed_lower,
        voltage_level='mv')

    if not crit_nodes_grid.empty:
        crit_nodes[network.mv_grid] = crit_nodes_grid.sort_values(
            by=['v_mag_pu'], ascending=False)
        logger.debug(
            '==> {} node(s) in MV grid has/have voltage issues.'.format(
                crit_nodes[network.mv_grid].shape[0]))
    else:
        logger.debug('==> No voltage issues in MV grid.')

    return crit_nodes

Response: Returns a new MV grid with over - voltage issues.


Instruction:
Can you write a function in Python 3 where it
checks for voltage stability issues in LV grids.

Response:
def lv_voltage_deviation(network, mode=None, voltage_levels='mv_lv'):
    """
    Checks for voltage stability issues in LV grids.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`
    mode : None or String
        If None voltage at all nodes in LV grid is checked. If mode is set to
        'stations' only voltage at busbar is checked.
    voltage_levels : :obj:`str`
        Specifies which allowed voltage deviations to use. Possible options
        are:

        * 'mv_lv'
          This is the default. The allowed voltage deviation for nodes in the
          MV grid is the same as for nodes in the LV grid. Further load and
          feed-in case are not distinguished.
        * 'lv'
          Use this to handle allowed voltage deviations in the MV and LV grid
          differently. Here, load and feed-in case are differentiated as well.

    Returns
    -------
    :obj:`dict`
        Dictionary with :class:`~.grid.grids.LVGrid` as key and a
        :pandas:`pandas.DataFrame<dataframe>` with its critical nodes, sorted
        descending by voltage deviation, as value.
        Index of the dataframe are all nodes (of type
        :class:`~.grid.components.Generator`, :class:`~.grid.components.Load`,
        etc.) with over-voltage issues. Columns are 'v_mag_pu' containing the
        maximum voltage deviation as float and 'time_index' containing the
        corresponding time step the over-voltage occured in as
        :pandas:`pandas.Timestamp<timestamp>`.

    Notes
    -----
    Over-voltage is determined based on allowed voltage deviations defined in
    the config file 'config_grid_expansion' in section
    'grid_expansion_allowed_voltage_deviations'.

    """

    crit_nodes = {}

    v_dev_allowed_per_case = {}
    if voltage_levels == 'mv_lv':
        offset = network.config[
            'grid_expansion_allowed_voltage_deviations']['hv_mv_trafo_offset']
        control_deviation = network.config[
            'grid_expansion_allowed_voltage_deviations'][
            'hv_mv_trafo_control_deviation']
        v_dev_allowed_per_case['feedin_case_upper'] = \
            1 + offset + control_deviation + network.config[
                'grid_expansion_allowed_voltage_deviations'][
                'mv_lv_feedin_case_max_v_deviation']
        v_dev_allowed_per_case['load_case_lower'] = \
            1 + offset - control_deviation - network.config[
                'grid_expansion_allowed_voltage_deviations'][
                'mv_lv_load_case_max_v_deviation']

        v_dev_allowed_per_case['feedin_case_lower'] = 0.9
        v_dev_allowed_per_case['load_case_upper'] = 1.1

        v_dev_allowed_upper = \
                network.timeseries.timesteps_load_feedin_case.case.apply(
                lambda _: v_dev_allowed_per_case['{}_upper'.format(_)])
        v_dev_allowed_lower = \
            network.timeseries.timesteps_load_feedin_case.case.apply(
                lambda _: v_dev_allowed_per_case['{}_lower'.format(_)])
    elif voltage_levels == 'lv':
        pass
    else:
        raise ValueError(
            'Specified mode {} is not a valid option.'.format(voltage_levels))

    for lv_grid in network.mv_grid.lv_grids:

        if mode:
            if mode == 'stations':
                nodes = [lv_grid.station]
            else:
                raise ValueError(
                    "{} is not a valid option for input variable 'mode' in "
                    "function lv_voltage_deviation. Try 'stations' or "
                    "None".format(mode))
        else:
            nodes = lv_grid.graph.nodes()

        if voltage_levels == 'lv':
            if mode == 'stations':
                # get voltage at primary side to calculate upper bound for
                # feed-in case and lower bound for load case
                v_lv_station_primary = network.results.v_res(
                    nodes=[lv_grid.station], level='mv').iloc[:, 0]
                timeindex = v_lv_station_primary.index
                v_dev_allowed_per_case['feedin_case_upper'] = \
                    v_lv_station_primary + network.config[
                        'grid_expansion_allowed_voltage_deviations'][
                        'mv_lv_station_feedin_case_max_v_deviation']
                v_dev_allowed_per_case['load_case_lower'] = \
                    v_lv_station_primary - network.config[
                        'grid_expansion_allowed_voltage_deviations'][
                        'mv_lv_station_load_case_max_v_deviation']
            else:
                # get voltage at secondary side to calculate upper bound for
                # feed-in case and lower bound for load case
                v_lv_station_secondary = network.results.v_res(
                    nodes=[lv_grid.station], level='lv').iloc[:, 0]
                timeindex = v_lv_station_secondary.index
                v_dev_allowed_per_case['feedin_case_upper'] = \
                    v_lv_station_secondary + network.config[
                        'grid_expansion_allowed_voltage_deviations'][
                        'lv_feedin_case_max_v_deviation']
                v_dev_allowed_per_case['load_case_lower'] = \
                    v_lv_station_secondary - network.config[
                        'grid_expansion_allowed_voltage_deviations'][
                        'lv_load_case_max_v_deviation']
            v_dev_allowed_per_case['feedin_case_lower'] = pd.Series(
                0.9, index=timeindex)
            v_dev_allowed_per_case['load_case_upper'] = pd.Series(
                1.1, index=timeindex)
            # maximum allowed voltage deviation in each time step
            v_dev_allowed_upper = []
            v_dev_allowed_lower = []
            for t in timeindex:
                case = \
                    network.timeseries.timesteps_load_feedin_case.loc[
                        t, 'case']
                v_dev_allowed_upper.append(
                    v_dev_allowed_per_case[
                        '{}_upper'.format(case)].loc[t])
                v_dev_allowed_lower.append(
                    v_dev_allowed_per_case[
                        '{}_lower'.format(case)].loc[t])
            v_dev_allowed_upper = pd.Series(v_dev_allowed_upper,
                                            index=timeindex)
            v_dev_allowed_lower = pd.Series(v_dev_allowed_lower,
                                            index=timeindex)

        crit_nodes_grid = _voltage_deviation(
            network, nodes, v_dev_allowed_upper, v_dev_allowed_lower,
            voltage_level='lv')

        if not crit_nodes_grid.empty:
            crit_nodes[lv_grid] = crit_nodes_grid.sort_values(
                by=['v_mag_pu'], ascending=False)

    if crit_nodes:
        if mode == 'stations':
            logger.debug(
                '==> {} LV station(s) has/have voltage issues.'.format(
                    len(crit_nodes)))
        else:
            logger.debug(
                '==> {} LV grid(s) has/have voltage issues.'.format(
                    len(crit_nodes)))
    else:
        if mode == 'stations':
            logger.debug('==> No voltage issues in LV stations.')
        else:
            logger.debug('==> No voltage issues in LV grids.')

    return crit_nodes


Instruction:
Write a Python 3 script for
checking for voltage stability issues in LV grids.

Response:
def _voltage_deviation(network, nodes, v_dev_allowed_upper,
                       v_dev_allowed_lower, voltage_level):
    """
    Checks for voltage stability issues in LV grids.
    Parameters
    ----------
    network : :class:`~.grid.network.Network`
    nodes : :obj:`list`
        List of nodes (of type :class:`~.grid.components.Generator`,
        :class:`~.grid.components.Load`, etc.) to check voltage deviation for.
    v_dev_allowed_upper : :pandas:`pandas.Series<series>`
        Series with time steps (of type :pandas:`pandas.Timestamp<timestamp>`)
        power flow analysis was conducted for and the allowed upper limit of
        voltage deviation for each time step as float.
    v_dev_allowed_lower : :pandas:`pandas.Series<series>`
        Series with time steps (of type :pandas:`pandas.Timestamp<timestamp>`)
        power flow analysis was conducted for and the allowed lower limit of
        voltage deviation for each time step as float.
    voltage_levels : :obj:`str`
        Specifies which voltage level to retrieve power flow analysis results
        for. Possible options are 'mv' and 'lv'.

    Returns
    -------
    :pandas:`pandas.DataFrame<dataframe>`
        Dataframe with critical nodes, sorted descending by voltage deviation.
        Index of the dataframe are all nodes (of type
        :class:`~.grid.components.Generator`, :class:`~.grid.components.Load`,
        etc.) with over-voltage issues. Columns are 'v_mag_pu' containing the
        maximum voltage deviation as float and 'time_index' containing the
        corresponding time step the over-voltage occured in as
        :pandas:`pandas.Timestamp<timestamp>`.

    """

    def _append_crit_node(series):
        return pd.DataFrame({'v_mag_pu': series.max(),
                             'time_index': series.idxmax()},
                            index=[node])

    crit_nodes_grid = pd.DataFrame()

    v_mag_pu_pfa = network.results.v_res(nodes=nodes, level=voltage_level)

    for node in nodes:
        # check for over- and under-voltage
        overvoltage = v_mag_pu_pfa[repr(node)][
            (v_mag_pu_pfa[repr(node)] > (v_dev_allowed_upper.loc[
                v_mag_pu_pfa.index]))]
        undervoltage = v_mag_pu_pfa[repr(node)][
            (v_mag_pu_pfa[repr(node)] < (v_dev_allowed_lower.loc[
                v_mag_pu_pfa.index]))]

        # write greatest voltage deviation to dataframe
        if not overvoltage.empty:
            overvoltage_diff = overvoltage - v_dev_allowed_upper.loc[
                overvoltage.index]
            if not undervoltage.empty:
                undervoltage_diff = v_dev_allowed_lower.loc[
                    undervoltage.index] - undervoltage
                if overvoltage_diff.max() > undervoltage_diff.max():
                    crit_nodes_grid = crit_nodes_grid.append(
                        _append_crit_node(overvoltage_diff))
                else:
                    crit_nodes_grid = crit_nodes_grid.append(
                        _append_crit_node(undervoltage_diff))
            else:
                crit_nodes_grid = crit_nodes_grid.append(
                    _append_crit_node(overvoltage_diff))
        elif not undervoltage.empty:
            undervoltage_diff = v_dev_allowed_lower.loc[
                                    undervoltage.index] - undervoltage
            crit_nodes_grid = crit_nodes_grid.append(
                _append_crit_node(undervoltage_diff))

    return crit_nodes_grid


Instruction:
Create a Python 3 function to
check if 10% criteria is exceeded.

Response:
def check_ten_percent_voltage_deviation(network):
    """
    Checks if 10% criteria is exceeded.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`

    """

    v_mag_pu_pfa = network.results.v_res()
    if (v_mag_pu_pfa > 1.1).any().any() or (v_mag_pu_pfa < 0.9).any().any():
        message = "Maximum allowed voltage deviation of 10% exceeded."
        raise ValueError(message)


Instruction:
Given the following Python 3 function, write the documentation
def proj2equidistant(network):
    srid = int(network.config['geo']['srid'])

    return partial(pyproj.transform,
                   pyproj.Proj(init='epsg:{}'
                               .format(str(srid))),  # source coordinate system
                   pyproj.Proj(init='epsg:3035')  # destination coordinate system
                   )

Response: Defines conformal ( e. g. WGS84 to ETRS


Instruction:
Implement a function in Python 3 to
calculate lines in nodes associated graph that are at least partly partly within buffer of radius from node.

Response:
def calc_geo_lines_in_buffer(network, node, grid, radius, radius_inc):
    """Determines lines in nodes' associated graph that are at least partly
    within buffer of radius from node. If there are no lines, the buffer is
    successively extended by radius_inc until lines are found.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`
        The eDisGo container object
    node : :class:`~.grid.components.Component`
        Origin node the buffer is created around (e.g. :class:`~.grid.components.Generator`).
        Node must be a member of grid's graph (grid.graph)
    grid : :class:`~.grid.grids.Grid`
        Grid whose lines are searched
    radius : :obj:`float`
        Buffer radius in m
    radius_inc : :obj:`float`
        Buffer radius increment in m

    Returns
    -------
    :obj:`list` of :class:`~.grid.components.Line`
        Sorted (by repr()) list of lines

    Notes
    -----
    Adapted from `Ding0 <https://github.com/openego/ding0/blob/\
        21a52048f84ec341fe54e0204ac62228a9e8a32a/\
        ding0/tools/geo.py#L53>`_.
    """

    lines = []

    while not lines:
        node_shp = transform(proj2equidistant(network), node.geom)
        buffer_zone_shp = node_shp.buffer(radius)
        for line in grid.graph.lines():
            nodes = line['adj_nodes']
            branch_shp = transform(proj2equidistant(network), LineString([nodes[0].geom, nodes[1].geom]))
            if buffer_zone_shp.intersects(branch_shp):
                lines.append(line)
        radius += radius_inc

    return sorted(lines, key=lambda _: repr(_))


Instruction:
Can you generate a brief explanation for the following Python 3 code
def calc_geo_dist_vincenty(network, node_source, node_target):

    branch_detour_factor = network.config['grid_connection'][
        'branch_detour_factor']

    # notice: vincenty takes (lat,lon)
    branch_length = branch_detour_factor * vincenty((node_source.geom.y, node_source.geom.x),
                                                    (node_target.geom.y, node_target.geom.x)).m

    # ========= BUG: LINE LENGTH=0 WHEN CONNECTING GENERATORS ===========
    # When importing generators, the geom_new field is used as position. If it is empty, EnergyMap's geom
    # is used and so there are a couple of generators at the same position => length of interconnecting
    # line is 0. See issue #76
    if branch_length == 0:
        branch_length = 1
        logger.debug('Geo distance is zero, check objects\' positions. '
                     'Distance is set to 1m')
    # ===================================================================

    return branch_length

Response: Calculates the geodesic distance between node_source and node_target and returns the distance in m


Instruction:
Can you tell what is the following Python 3 function doing
def get_username(details, backend, response, *args, **kwargs):

    user = details.get('user')
    if not user:
        user_uuid = kwargs.get('uid')
        if not user_uuid:
            return

        username = uuid_to_username(user_uuid)
    else:
        username = user.username

    return {
        'username': username
    }

Response: Sets the username argument.


Instruction:
How would you code a function in Python 3 to
update user based on token information.

Response:
def pre_social_login(self, request, sociallogin):
        """Update user based on token information."""

        user = sociallogin.user
        # If the user hasn't been saved yet, it will be updated
        # later on in the sign-up flow.
        if not user.pk:
            return

        data = sociallogin.account.extra_data
        oidc = sociallogin.account.provider == 'helsinki_oidc'
        update_user(user, data, oidc)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def sync_groups_from_ad(self):

        ad_list = ADGroupMapping.objects.values_list('ad_group', 'group')
        mappings = {ad_group: group for ad_group, group in ad_list}

        user_ad_groups = set(self.ad_groups.filter(groups__isnull=False).values_list(flat=True))
        all_mapped_groups = set(mappings.values())
        old_groups = set(self.groups.filter(id__in=all_mapped_groups).values_list(flat=True))
        new_groups = set([mappings[x] for x in user_ad_groups])

        groups_to_delete = old_groups - new_groups
        if groups_to_delete:
            self.groups.remove(*groups_to_delete)
        groups_to_add = new_groups - old_groups
        if groups_to_add:
            self.groups.add(*groups_to_add)

Response: Determine which Django groups to add or remove based on AD groups.


Instruction:
How would you code a function in Python 3 to
patch rest_framework_jwt authentication settings from allauth

Response:
def patch_jwt_settings():
    """Patch rest_framework_jwt authentication settings from allauth"""
    defaults = api_settings.defaults
    defaults['JWT_PAYLOAD_GET_USER_ID_HANDLER'] = (
        __name__ + '.get_user_id_from_payload_handler')

    if 'allauth.socialaccount' not in settings.INSTALLED_APPS:
        return

    from allauth.socialaccount.models import SocialApp
    try:
        app = SocialApp.objects.get(provider='helsinki')
    except SocialApp.DoesNotExist:
        return

    defaults['JWT_SECRET_KEY'] = app.secret
    defaults['JWT_AUDIENCE'] = app.client_id


Instruction:
Can you tell what is the following Python 3 function doing
def uuid_to_username(uuid):
    uuid_data = getattr(uuid, 'bytes', None) or UUID(uuid).bytes
    b32coded = base64.b32encode(uuid_data)
    return 'u-' + b32coded.decode('ascii').replace('=', '').lower()

Response: Convert UUID to username.


Instruction:
Implement a function in Python 3 to
convert username to UUID.

Response:
def username_to_uuid(username):
    """
    Convert username to UUID.

    >>> username_to_uuid('u-ad52zgilvnpgnduefzlh5jgr6y')
    UUID('00fbac99-0bab-5e66-8e84-2e567ea4d1f6')
    """
    if not username.startswith('u-') or len(username) != 28:
        raise ValueError('Not an UUID based username: %r' % (username,))
    decoded = base64.b32decode(username[2:].upper() + '======')
    return UUID(bytes=decoded)


Instruction:
How would you code a function in Python 3 to
map OIDC claims to Django user fields.

Response:
def oidc_to_user_data(payload):
    """
    Map OIDC claims to Django user fields.
    """
    payload = payload.copy()

    field_map = {
        'given_name': 'first_name',
        'family_name': 'last_name',
        'email': 'email',
    }
    ret = {}
    for token_attr, user_attr in field_map.items():
        if token_attr not in payload:
            continue
        ret[user_attr] = payload.pop(token_attr)
    ret.update(payload)

    return ret


Instruction:
Can you generate the documentation for the following Python 3 function
def has_api_scopes(self, *api_scopes):
        if self._authorized_api_scopes is None:
            return None
        return all((x in self._authorized_api_scopes) for x in api_scopes)

Response: Test if all given API scopes are authorized.


Instruction:
Can you write a function in Python 3 where it
tests if there is an API scope with the given prefix.

Response:
def has_api_scope_with_prefix(self, prefix):
        """
        Test if there is an API scope with the given prefix.

        :rtype: bool|None
        """
        if self._authorized_api_scopes is None:
            return None
        return any(
            x == prefix or x.startswith(prefix + '.')
            for x in self._authorized_api_scopes)


Instruction:
Can you tell what is the following Python 3 function doing
def get_function_by_path(dotted_function_module_path):
    # Separate the module path from the function name.
    module_path, function_name = tuple(dotted_function_module_path.rsplit('.', 1))

    module = import_module(module_path)
    return getattr(module, function_name)

Response: Returns the function for a given path.


Instruction:
Write a Python 3 script to
write a waypoint to the specified IANA top level domain.

Response:
def write_waypoint(
            self, name, shortname, country, latitude, longitude, elevation=u'',
            style=WaypointStyle.NORMAL, runway_direction=u'', runway_length=u'',
            frequency=u'', description=u''):

        """
        Write a waypoint::

            writer.write_waypoint(
                'Meiersberg',
                'MEIER',
                'DE',
                (51 + 7.345 / 60.),
                (6 + 24.765 / 60.),
            )
            # -> "Meiersberg","MEIER",DE,5107.345N,00624.765E,,1,,,,

        :param name: name of the waypoint (must not be empty)
        :param shortname: short name for depending GPS devices
        :param country: IANA top level domain country code (see
            http://www.iana.org/cctld/cctld-whois.htm)
        :param latitude: latitude of the point (between -90 and 90 degrees)
        :param longitude: longitude of the point (between -180 and 180 degrees)
        :param elevation: elevation of the waypoint in meters or as
            ``(elevation, unit)`` tuple
        :param style: the waypoint type (see official specification for the
            list of valid styles, defaults to "Normal")
        :param runway_direction: heading of the runway in degrees if the
            waypoint is landable
        :param runway_length: length of the runway in meters or as ``(length,
            unit)`` tuple if the waypoint is landable
        :param frequency: radio frequency of the airport
        :param description: optional description of the waypoint (no length
            limit)
        """

        if self.in_task_section:
            raise RuntimeError(u'Waypoints must be written before any tasks')

        if not name:
            raise ValueError(u'Waypoint name must not be empty')

        fields = [
            self.escape(name),
            self.escape(shortname),
            country,
            self.format_latitude(latitude),
            self.format_longitude(longitude),
            self.format_distance(elevation),
            str(style),
            str(runway_direction),
            self.format_distance(runway_length),
            self.escape(frequency),
            self.escape(description),
        ]

        self.write_fields(fields)

        self.wps.add(name)


Instruction:
Can you write a function in Python 3 where it
writes a task definition to file.

Response:
def write_task(self, description, waypoints):

        """
        Write a task definition::

            writer.write_task('500 km FAI', [
                'MEIER',
                'BRILO',
                'AILER',
                'MEIER',
            ])
            # -> "500 km FAI","MEIER","BRILO","AILER","MEIER"

        Make sure that the referenced waypoints have been written with
        :meth:`~aerofiles.seeyou.Writer.write_waypoint` before writing the
        task. The task section divider will be written to automatically when
        :meth:`~aerofiles.seeyou.Writer.write_task` is called the first time.
        After the first task is written
        :meth:`~aerofiles.seeyou.Writer.write_waypoint` must not be called
        anymore.

        :param description: description of the task (may be blank)
        :param waypoints: list of waypoints in the task (names must match the
            long names of previously written waypoints)
        """

        if not self.in_task_section:
            self.write_line()
            self.write_line(self.DIVIDER)
            self.in_task_section = True

        fields = [self.escape(description)]

        for waypoint in waypoints:
            if waypoint not in self.wps:
                raise ValueError(u'Waypoint "%s" was not found' % waypoint)

            fields.append(self.escape(waypoint))

        self.write_fields(fields)


Instruction:
Can you implement a function in Python 3 that
writes an options line for a specific task.

Response:
def write_task_options(self, **kw):

        """
        Write an options line for a task definition::

            writer.write_task_options(
                start_time=time(12, 34, 56),
                task_time=timedelta(hours=1, minutes=45, seconds=12),
                waypoint_distance=False,
                distance_tolerance=(0.7, 'km'),
                altitude_tolerance=300.0,
            )
            # -> Options,NoStart=12:34:56,TaskTime=01:45:12,WpDis=False,NearDis=0.7km,NearAlt=300.0m

        :param start_time: opening time of the start line as
            :class:`datetime.time` or string
        :param task_time: designated time for the task as
            :class:`datetime.timedelta` or string
        :param waypoint_distance: task distance calculation (``False``: use
            fixes, ``True``: use waypoints)
        :param distance_tolerance: distance tolerance in meters or as
            ``(distance, unit)`` tuple
        :param altitude_tolerance: altitude tolerance in meters or as
            ``(distance, unit)`` tuple
        :param min_distance: "uncompleted leg (``False``: calculate maximum
            distance from last observation zone)"
        :param random_order: if ``True``, then Random order of waypoints is
            checked
        :param max_points: maximum number of points
        :param before_points: number of mandatory waypoints at the beginning.
            ``1`` means start line only, ``2`` means start line plus first
            point in task sequence (Task line).
        :param after_points: number of mandatory waypoints at the end. ``1``
            means finish line only, ``2`` means finish line and one point
            before finish in task sequence (Task line).
        :param bonus: bonus for crossing the finish line
        """

        if not self.in_task_section:
            raise RuntimeError(
                u'Task options have to be written in task section')

        fields = ['Options']

        if 'start_time' in kw:
            fields.append(u'NoStart=' + self.format_time(kw['start_time']))

        if 'task_time' in kw:
            fields.append(u'TaskTime=' + self.format_timedelta(kw['task_time']))

        if 'waypoint_distance' in kw:
            fields.append(u'WpDis=%s' % kw['waypoint_distance'])

        if 'distance_tolerance' in kw:
            fields.append(
                u'NearDis=' + self.format_distance(kw['distance_tolerance']))

        if 'altitude_tolerance' in kw:
            fields.append(
                u'NearAlt=' + self.format_distance(kw['altitude_tolerance']))

        if 'min_distance' in kw:
            fields.append(u'MinDis=%s' % kw['min_distance'])

        if 'random_order' in kw:
            fields.append(u'RandomOrder=%s' % kw['random_order'])

        if 'max_points' in kw:
            fields.append(u'MaxPts=%d' % kw['max_points'])

        if 'before_points' in kw:
            fields.append(u'BeforePts=%d' % kw['before_points'])

        if 'after_points' in kw:
            fields.append(u'AfterPts=%d' % kw['after_points'])

        if 'bonus' in kw:
            fields.append(u'Bonus=%d' % kw['bonus'])

        self.write_fields(fields)


Instruction:
How would you explain what the following Python 3 function does
def write_observation_zone(self, num, **kw):

        """
        Write observation zone information for a taskpoint::

            writer.write_task_options(
                start_time=time(12, 34, 56),
                task_time=timedelta(hours=1, minutes=45, seconds=12),
                waypoint_distance=False,
                distance_tolerance=(0.7, 'km'),
                altitude_tolerance=300.0,
            )
            # -> Options,NoStart=12:34:56,TaskTime=01:45:12,WpDis=False,NearDis=0.7km,NearAlt=300.0m

        :param num: consecutive number of a waypoint (``0``: Start)
        :param style: direction (``0``: Fixed value, ``1``: Symmetrical, ``2``:
            To next point, ``3``: To previous point, ``4``: To start point
        :param radius: radius 1 in meter or as ``(radius, unit)`` tuple
        :param angle: angle 1 in degrees
        :param radius2: radius 2 in meter or as ``(radius, unit)`` tuple
        :param angle 2: angle 2 in degrees
        :param angle12: angle 12 in degress
        :param line: should be ``True`` if start or finish line
        """

        if not self.in_task_section:
            raise RuntimeError(
                u'Observation zones have to be written in task section')

        fields = [u'ObsZone=%d' % num]

        if 'style' in kw:
            fields.append(u'Style=%d' % kw['style'])

        if 'radius' in kw:
            fields.append(u'R1=' + self.format_distance(kw['radius']))

        if 'angle' in kw:
            fields.append(u'A1=' + self.format_angle(kw['angle']))

        if 'radius2' in kw:
            fields.append(u'R2=' + self.format_distance(kw['radius2']))

        if 'angle2' in kw:
            fields.append(u'A2=' + self.format_angle(kw['angle2']))

        if 'angle12' in kw:
            fields.append(u'A12=' + self.format_angle(kw['angle12']))

        if 'line' in kw:
            fields.append(u'Line=' + ('1' if kw['line'] else '0'))

        self.write_fields(fields)

Response: Write observation zone information for a taskpoint.


Instruction:
Here you have a function in Python 3, explain what it does
def register_site(self):
        if self.oxd_id:
            logger.info('Client is already registered. ID: %s', self.oxd_id)
            return self.oxd_id

        # add required params for the command
        params = {
            "authorization_redirect_uri": self.authorization_redirect_uri,
			"oxd_rp_programming_language": "python",
            }

        # add other optional params if they exist in config
        for op in self.opt_params:
            if self.config.get("client", op):
                params[op] = self.config.get("client", op)

        for olp in self.opt_list_params:
            if self.config.get("client", olp):
                params[olp] = self.config.get("client", olp).split(",")

        logger.debug("Sending command `register_site` with params %s", params)
        response = self.msgr.request("register_site", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])

        self.oxd_id = response["data"]["oxd_id"]
        self.config.set("oxd", "id", self.oxd_id)
        logger.info("Site registration successful. Oxd ID: %s", self.oxd_id)
        return self.oxd_id

Response: Function to register the site and generate a unique ID for the site.


Instruction:
Create a Python 3 function for
functioning to get the authorization url that the user must access for authentication and authentication.

Response:
def get_authorization_url(self, acr_values=None, prompt=None, scope=None,
                              custom_params=None):
        """Function to get the authorization url that can be opened in the
        browser for the user to provide authorization and authentication

        Parameters:
            * **acr_values (list, optional):** acr values in the order of priority
            * **prompt (string, optional):** prompt=login is required if you want to force alter current user session (in case user is already logged in from site1 and site2 constructs authorization request and want to force alter current user session)
            * **scope (list, optional):** scopes required, takes the one provided during site registrations by default
            * **custom_params (dict, optional):** Any custom arguments that the client wishes to pass on to the OP can be passed on as extra parameters to the function

        Returns:
            **string:** The authorization url that the user must access for authentication and authorization

        Raises:
            **OxdServerError:** If the oxd throws an error for any reason.
        """
        params = {"oxd_id": self.oxd_id}

        if scope and isinstance(scope, list):
            params["scope"] = scope

        if acr_values and isinstance(acr_values, list):
            params["acr_values"] = acr_values

        if prompt and isinstance(prompt, str):
            params["prompt"] = prompt

        if custom_params:
            params["custom_parameters"] = custom_params

        logger.debug("Sending command `get_authorization_url` with params %s",
                     params)
        response = self.msgr.request("get_authorization_url", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])
        return response['data']['authorization_url']


Instruction:
Explain what the following Python 3 code does
def get_tokens_by_code(self, code, state):
        params = dict(oxd_id=self.oxd_id, code=code, state=state)

        logger.debug("Sending command `get_tokens_by_code` with params %s",
                     params)
        response = self.msgr.request("get_tokens_by_code", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])
        return response['data']

Response: Function to get the user details from the user details.


Instruction:
Make a summary of the following Python 3 code
def get_access_token_by_refresh_token(self, refresh_token, scope=None):
        params = {
            "oxd_id": self.oxd_id,
            "refresh_token": refresh_token
        }

        if scope:
            params['scope'] = scope

        logger.debug("Sending command `get_access_token_by_refresh_token` with"
                     " params %s", params)
        response = self.msgr.request("get_access_token_by_refresh_token",
                                     **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])
        return response['data']

Response: Function that is used to get a new access token using a refresh token.


Instruction:
How would you implement a function in Python 3 that
functions to get the user info from the OP

Response:
def get_user_info(self, access_token):
        """Function to get the information about the user using the access code
        obtained from the OP

        Note:
            Refer to the /.well-known/openid-configuration URL of your OP for
            the complete list of the claims for different scopes.

        Parameters:
            * **access_token (string):** access token from the get_tokens_by_code function

        Returns:
            **dict:** The user data claims that are returned by the OP in format

            Example response::

                {
                    "sub": ["248289761001"],
                    "name": ["Jane Doe"],
                    "given_name": ["Jane"],
                    "family_name": ["Doe"],
                    "preferred_username": ["j.doe"],
                    "email": ["janedoe@example.com"],
                    "picture": ["http://example.com/janedoe/me.jpg"]
                }

        Raises:
            **OxdServerError:** If the param access_token is empty OR if the oxd
                Server returns an error.
        """
        params = dict(oxd_id=self.oxd_id, access_token=access_token)
        params["access_token"] = access_token
        logger.debug("Sending command `get_user_info` with params %s",
                     params)
        response = self.msgr.request("get_user_info", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])
        return response['data']['claims']


Instruction:
How would you implement a function in Python 3 that
functions to logout the user.

Response:
def get_logout_uri(self, id_token_hint=None, post_logout_redirect_uri=None,
                       state=None, session_state=None):
        """Function to logout the user.

        Parameters:
            * **id_token_hint (string, optional):** oxd server will use last used ID Token, if not provided
            * **post_logout_redirect_uri (string, optional):** URI to redirect, this uri would override the value given in the site-config
            * **state (string, optional):** site state
            * **session_state (string, optional):** session state

        Returns:
            **string:** The URI to which the user must be directed in order to
            perform the logout
        """
        params = {"oxd_id": self.oxd_id}
        if id_token_hint:
            params["id_token_hint"] = id_token_hint

        if post_logout_redirect_uri:
            params["post_logout_redirect_uri"] = post_logout_redirect_uri

        if state:
            params["state"] = state

        if session_state:
            params["session_state"] = session_state

        logger.debug("Sending command `get_logout_uri` with params %s", params)
        response = self.msgr.request("get_logout_uri", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])
        return response['data']['uri']


Instruction:
Given the following Python 3 function, write the documentation
def update_site(self, client_secret_expires_at=None):
        params = {
            "oxd_id": self.oxd_id,
            "authorization_redirect_uri": self.authorization_redirect_uri
        }

        if client_secret_expires_at:
            params["client_secret_expires_at"] = client_secret_expires_at

        for param in self.opt_params:
            if self.config.get("client", param):
                value = self.config.get("client", param)
                params[param] = value

        for param in self.opt_list_params:
            if self.config.get("client", param):
                value = self.config.get("client", param).split(",")
                params[param] = value

        logger.debug("Sending `update_site` with params %s",
                     params)
        response = self.msgr.request("update_site", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])

        return True

Response: Function to update the site s information with OpenID Provider. This function should be called after changing the values in the cfg file.


Instruction:
Write a Python 3 function for
functioning to be used in a UMA Resource Server to protect resources.

Response:
def uma_rs_protect(self, resources, overwrite=None):
        """Function to be used in a UMA Resource Server to protect resources.

        Parameters:
            * **resources (list):** list of resource to protect. See example at `here <https://gluu.org/docs/oxd/3.1.2/api/#uma-rs-protect-resources>`_
            * **overwrite (bool):** If true, Allows to update existing resources

        Returns:
            **bool:** The status of the request.
        """
        params = dict(oxd_id=self.oxd_id, resources=resources)

        if overwrite:
            params["overwrite"] = overwrite

        logger.debug("Sending `uma_rs_protect` with params %s", params)
        response = self.msgr.request("uma_rs_protect", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])
        return True


Instruction:
Create a Python 3 function for
functioning to be used in a UMA Resource Server to check access.

Response:
def uma_rs_check_access(self, rpt, path, http_method):
        """Function to be used in a UMA Resource Server to check access.

        Parameters:
            * **rpt (string):** RPT or blank value if absent (not send by RP)
            * **path (string):** Path of resource (e.g. for http://rs.com/phones, /phones should be passed)
            * **http_method (string):** Http method of RP request (GET, POST, PUT, DELETE)

        Returns:
            **dict:** The access information received in the format below.

            If the access is granted::

                { "access": "granted" }

            If the access is denied with ticket response::

                {
                    "access": "denied",
                    "www-authenticate_header": "UMA realm='example',
                        as_uri='https://as.example.com',
                        error='insufficient_scope',
                        ticket='016f84e8-f9b9-11e0-bd6f-0021cc6004de'",
                    "ticket": "016f84e8-f9b9-11e0-bd6f-0021cc6004de"
                }

            If the access is denied without ticket response::

                { "access": "denied" }

        Raises:
            ``oxdpython.exceptions.InvalidRequestError`` if the resource is not
                protected
        """
        params = {"oxd_id": self.oxd_id,
                  "rpt": rpt,
                  "path": path,
                  "http_method": http_method}

        logger.debug("Sending command `uma_rs_check_access` with params %s",
                     params)
        response = self.msgr.request("uma_rs_check_access", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            if response['data']['error'] == 'invalid_request':
                raise InvalidRequestError(response['data'])
            else:
                raise OxdServerError(response['data'])
        return response['data']


Instruction:
Given the following Python 3 function, write the documentation
def uma_rp_get_rpt(self, ticket, claim_token=None, claim_token_format=None,
                       pct=None, rpt=None, scope=None, state=None):
        """Function to be used by a UMA Requesting Party to get RPT token.

        Parameters:
            * **ticket (str, REQUIRED):** ticket
            * **claim_token (str, OPTIONAL):** claim token
            * **claim_token_format (str, OPTIONAL):** claim token format
            * **pct (str, OPTIONAL):** pct
            * **rpt (str, OPTIONAL):** rpt
            * **scope (list, OPTIONAL):** scope
            * **state (str, OPTIONAL):** state that is returned from `uma_rp_get_claims_gathering_url` command

        Returns:
            **dict:** The response from the OP.

            Success response::

                {
                    "status":"ok",
                    "data":{
                        "access_token":"SSJHBSUSSJHVhjsgvhsgvshgsv",
                        "token_type":"Bearer",
                        "pct":"c2F2ZWRjb25zZW50",
                        "upgraded":true
                    }
                }

            NeedInfoError response::

                {
					"error":"need_info",
					"ticket":"ZXJyb3JfZGV0YWlscw==",
					"required_claims":[
						{
							"claim_token_format":[
								"http://openid.net/specs/openid-connect-core-1_0.html#IDToken"
							],
							"claim_type":"urn:oid:0.9.2342.19200300.100.1.3",
							"friendly_name":"email",
							"issuer":["https://example.com/idp"],
							"name":"email23423453ou453"
						}
					],
					"redirect_user":"https://as.example.com/rqp_claims?id=2346576421"
				}

        Raises:
            **OxdServerError:** When oxd-server reports a generic internal_error
            **InvalidTicketError:** When the oxd server returns a "invalid_ticket" error

        """
        params = {
            "oxd_id": self.oxd_id,
            "ticket": ticket
            }
        if claim_token:
            params["claim_token"] = claim_token
        if claim_token_format:
            params["claim_token_format"] = claim_token_format
        if pct:
            params["pct"] = pct
        if rpt:
            params["rpt"] = rpt
        if scope:
            params["scope"] = scope
        if state:
            params["state"] = state

        logger.debug("Sending command `uma_rp_get_rpt` with params %s", params)
        response = self.msgr.request("uma_rp_get_rpt", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'ok':
            return response['data']

        if response['data']['error'] == 'internal_error':
            raise OxdServerError(response['data'])

        if response['data']['error'] == 'need_info':
            return response['data']

        if response['data']['error'] == 'invalid_ticket':
            raise InvalidTicketError(response['data'])

Response: Function to be used by a UMA Requesting Party to get a RPT token.


Instruction:
Can you tell what is the following Python 3 function doing
def setup_client(self):
        # add required params for the command
        params = {
            "authorization_redirect_uri": self.authorization_redirect_uri,
			"oxd_rp_programming_language": "python",
            }

        # add other optional params if they exist in config
        for op in self.opt_params:
            if self.config.get("client", op):
                params[op] = self.config.get("client", op)

        for olp in self.opt_list_params:
            if self.config.get("client", olp):
                params[olp] = self.config.get("client", olp).split(",")

        logger.debug("Sending command `setup_client` with params %s", params)

        response = self.msgr.request("setup_client", **params)
        logger.debug("Received response: %s", response)

        if response['status'] == 'error':
            raise OxdServerError(response['data'])
        data = response["data"]

        self.oxd_id = data["oxd_id"]
        self.config.set("oxd", "id", data["oxd_id"])
        self.config.set("client", "client_id", data["client_id"])
        self.config.set("client", "client_secret", data["client_secret"])
        if data["client_registration_access_token"]:
            self.config.set("client", "client_registration_access_token",
                            data["client_registration_access_token"])
        if data["client_registration_client_uri"]:
            self.config.set("client", "client_registration_client_uri",
                            data["client_registration_client_uri"])
        self.config.set("client", "client_id_issued_at",
                        str(data["client_id_issued_at"]))

        return data

Response: This command registers the client for communication protection.