Instruction:
Can you generate a brief explanation for the following Python 3 code
def connect_lv_generators(network, allow_multiple_genos_per_load=True):

    # get predefined random seed and initialize random generator
    seed = int(network.config['grid_connection']['random_seed'])
    #random.seed(a=seed)
    random.seed(a=1234)
    # ToDo: Switch back to 'seed' as soon as line ids are finished, #58

    # get standard equipment
    std_line_type = network.equipment_data['lv_cables'].loc[
        network.config['grid_expansion_standard_equipment']['lv_line']]
    std_line_kind = 'cable'

    # # TEMP: DEBUG STUFF
    # lv_grid_stats = pd.DataFrame(columns=('lv_grid',
    #                                       'load_count',
    #                                       'geno_count',
    #                                       'more_genos_than_loads')
    #                             )

    # iterate over all LV grids
    for lv_grid in network.mv_grid.lv_grids:

        lv_loads = lv_grid.graph.nodes_by_attribute('load')

        # counter for genos in v_level 7
        log_geno_count_vlevel7 = 0

        # generate random list (without replacement => unique elements)
        # of loads (residential) to connect genos (P <= 30kW) to.
        lv_loads_res = sorted([lv_load for lv_load in lv_loads
                               if 'residential' in list(lv_load.consumption.keys())],
                              key=lambda _: repr(_))

        if len(lv_loads_res) > 0:
            lv_loads_res_rnd = set(random.sample(lv_loads_res,
                                                 len(lv_loads_res)))
        else:
            lv_loads_res_rnd = None

        # generate random list (without replacement => unique elements)
        # of loads (retail, industrial, agricultural) to connect genos
        # (30kW < P <= 100kW) to.
        lv_loads_ria = sorted([lv_load for lv_load in lv_loads
                               if any([_ in list(lv_load.consumption.keys())
                                       for _ in ['retail', 'industrial', 'agricultural']])],
                              key=lambda _: repr(_))

        if len(lv_loads_ria) > 0:
            lv_loads_ria_rnd = set(random.sample(lv_loads_ria,
                                                 len(lv_loads_ria)))
        else:
            lv_loads_ria_rnd = None

        for geno in sorted(lv_grid.graph.nodes_by_attribute('generator'), key=lambda x: repr(x)):
            if nx.is_isolate(lv_grid.graph, geno):

                lv_station = lv_grid.station

                # generator is of v_level 6 -> connect to LV station
                if geno.v_level == 6:

                    line_length = calc_geo_dist_vincenty(network=network,
                                                         node_source=geno,
                                                         node_target=lv_station)

                    line = Line(id=random.randint(10 ** 8, 10 ** 9),
                                length=line_length / 1e3,
                                quantity=1,
                                kind=std_line_kind,
                                type=std_line_type,
                                grid=lv_grid)

                    lv_grid.graph.add_edge(geno,
                                           lv_station,
                                           line=line,
                                           type='line')

                    # add line to equipment changes to track costs
                    _add_cable_to_equipment_changes(network=network,
                                                    line=line)

                # generator is of v_level 7 -> assign geno to load
                elif geno.v_level == 7:
                    # counter for genos in v_level 7
                    log_geno_count_vlevel7 += 1

                    # connect genos with P <= 30kW to residential loads, if available
                    if (geno.nominal_capacity <= 30) and (lv_loads_res_rnd is not None):
                        if len(lv_loads_res_rnd) > 0:
                            lv_load = lv_loads_res_rnd.pop()
                        # if random load list is empty, create new one
                        else:
                            lv_loads_res_rnd = set(random.sample(lv_loads_res,
                                                                 len(lv_loads_res))
                                                   )
                            lv_load = lv_loads_res_rnd.pop()

                        # get cable distributor of building
                        lv_conn_target = lv_grid.graph.neighbors(lv_load)[0]

                        if not allow_multiple_genos_per_load:
                            # check if there's an existing generator connected to the load
                            # if so, select next load. If no load is available, connect to station.
                            while any([isinstance(_, Generator)
                                       for _ in lv_grid.graph.neighbors(
                                    lv_grid.graph.neighbors(lv_load)[0])]):
                                if len(lv_loads_res_rnd) > 0:
                                    lv_load = lv_loads_res_rnd.pop()

                                    # get cable distributor of building
                                    lv_conn_target = lv_grid.graph.neighbors(lv_load)[0]
                                else:
                                    lv_conn_target = lv_grid.station

                                    logger.debug(
                                        'No valid conn. target found for {}. '
                                        'Connected to {}.'.format(
                                            repr(geno),
                                            repr(lv_conn_target)
                                        )
                                    )
                                    break

                    # connect genos with 30kW <= P <= 100kW to residential loads
                    # to retail, industrial, agricultural loads, if available
                    elif (geno.nominal_capacity > 30) and (lv_loads_ria_rnd is not None):
                        if len(lv_loads_ria_rnd) > 0:
                            lv_load = lv_loads_ria_rnd.pop()
                        # if random load list is empty, create new one
                        else:
                            lv_loads_ria_rnd = set(random.sample(lv_loads_ria,
                                                                 len(lv_loads_ria))
                                                   )
                            lv_load = lv_loads_ria_rnd.pop()

                        # get cable distributor of building
                        lv_conn_target = lv_grid.graph.neighbors(lv_load)[0]

                        if not allow_multiple_genos_per_load:
                            # check if there's an existing generator connected to the load
                            # if so, select next load. If no load is available, connect to station.
                            while any([isinstance(_, Generator)
                                       for _ in lv_grid.graph.neighbors(
                                    lv_grid.graph.neighbors(lv_load)[0])]):
                                if len(lv_loads_ria_rnd) > 0:
                                    lv_load = lv_loads_ria_rnd.pop()

                                    # get cable distributor of building
                                    lv_conn_target = lv_grid.graph.neighbors(lv_load)[0]
                                else:
                                    lv_conn_target = lv_grid.station

                                    logger.debug(
                                        'No valid conn. target found for {}. '
                                        'Connected to {}.'.format(
                                            repr(geno),
                                            repr(lv_conn_target)
                                        )
                                    )
                                    break

                    # fallback: connect to station
                    else:
                        lv_conn_target = lv_grid.station

                        logger.debug(
                            'No valid conn. target found for {}. '
                            'Connected to {}.'.format(
                                repr(geno),
                                repr(lv_conn_target)
                            )
                        )

                    line = Line(id=random.randint(10 ** 8, 10 ** 9),
                                length=1e-3,
                                quantity=1,
                                kind=std_line_kind,
                                type=std_line_type,
                                grid=lv_grid)

                    lv_grid.graph.add_edge(geno,
                                           lv_station,
                                           line=line,
                                           type='line')

                    # add line to equipment changes to track costs
                    _add_cable_to_equipment_changes(network=network,
                                                    line=line)

        # warn if there're more genos than loads in LV grid
        if log_geno_count_vlevel7 > len(lv_loads):
            logger.debug('The count of newly connected generators in voltage level 7 ({}) '
                         'exceeds the count of loads ({}) in LV grid {}.'
                         .format(str(log_geno_count_vlevel7),
                                 str(len(lv_loads)),
                                 repr(lv_grid)
                                 )
                         )

Response: Connect LV generators to existing grids.


Instruction:
How would you explain what the following Python 3 function does
def _add_cable_to_equipment_changes(network, line):
    network.results.equipment_changes = \
        network.results.equipment_changes.append(
            pd.DataFrame(
                {'iteration_step': [0],
                 'change': ['added'],
                 'equipment': [line.type.name],
                 'quantity': [1]
                 },
                index=[line]
            )
        )

Response: Adds cable to the equipment changes dataframe


Instruction:
Can you create a Python 3 function that
deletes a cable from the equipment changes if existing

Response:
def _del_cable_from_equipment_changes(network, line):
    """Delete cable from the equipment changes if existing

    This is needed if a cable was already added to network.results.equipment_changes
    but another node is connected later to this cable. Therefore, the cable needs to
    be split which changes the id (one cable id -> 2 new cable ids).

    Parameters
    ----------
    network : :class:`~.grid.network.Network`
        The eDisGo container object
    line : class:`~.grid.components.Line`
        Line instance which is to be deleted
    """
    if line in network.results.equipment_changes.index:
        network.results.equipment_changes = \
            network.results.equipment_changes.drop(line)


Instruction:
How would you explain what the following Python 3 function does
def _find_nearest_conn_objects(network, node, branches):

    # threshold which is used to determine if 2 objects are on the same position (see below for details on usage)
    conn_diff_tolerance = network.config['grid_connection'][
        'conn_diff_tolerance']

    conn_objects_min_stack = []

    node_shp = transform(proj2equidistant(network), node.geom)

    for branch in branches:
        stations = branch['adj_nodes']

        # create shapely objects for 2 stations and line between them, transform to equidistant CRS
        station1_shp = transform(proj2equidistant(network), stations[0].geom)
        station2_shp = transform(proj2equidistant(network), stations[1].geom)
        line_shp = LineString([station1_shp, station2_shp])

        # create dict with DING0 objects (line & 2 adjacent stations), shapely objects and distances
        conn_objects = {'s1': {'obj': stations[0],
                               'shp': station1_shp,
                               'dist': node_shp.distance(station1_shp) * 0.999},
                        's2': {'obj': stations[1],
                               'shp': station2_shp,
                               'dist': node_shp.distance(station2_shp) * 0.999},
                        'b': {'obj': branch,
                              'shp': line_shp,
                              'dist': node_shp.distance(line_shp)}}

        # Remove branch from the dict of possible conn. objects if it is too close to a node.
        # Without this solution, the target object is not unique for different runs (and so
        # were the topology)
        if (
                abs(conn_objects['s1']['dist'] - conn_objects['b']['dist']) < conn_diff_tolerance
             or abs(conn_objects['s2']['dist'] - conn_objects['b']['dist']) < conn_diff_tolerance
           ):
            del conn_objects['b']

        # remove MV station as possible connection point
        if isinstance(conn_objects['s1']['obj'], MVStation):
            del conn_objects['s1']
        elif isinstance(conn_objects['s2']['obj'], MVStation):
            del conn_objects['s2']

        # find nearest connection point on given triple dict (2 branch-adjacent stations + cable dist. on line)
        conn_objects_min = min(conn_objects.values(), key=lambda v: v['dist'])

        conn_objects_min_stack.append(conn_objects_min)

    # sort all objects by distance from node
    conn_objects_min_stack = [_ for _ in sorted(conn_objects_min_stack, key=lambda x: x['dist'])]

    return conn_objects_min_stack

Response: Searches all branches for nearest possible connection objects.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _connect_mv_node(network, node, target_obj):

    # get standard equipment
    std_line_type = network.equipment_data['mv_cables'].loc[
        network.config['grid_expansion_standard_equipment']['mv_line']]
    std_line_kind = 'cable'

    target_obj_result = None

    node_shp = transform(proj2equidistant(network), node.geom)

    # MV line is nearest connection point
    if isinstance(target_obj['shp'], LineString):

        adj_node1 = target_obj['obj']['adj_nodes'][0]
        adj_node2 = target_obj['obj']['adj_nodes'][1]

        # find nearest point on MV line
        conn_point_shp = target_obj['shp'].interpolate(target_obj['shp'].project(node_shp))
        conn_point_shp = transform(proj2conformal(network), conn_point_shp)

        line = network.mv_grid.graph.edge[adj_node1][adj_node2]

        # target MV line does currently not connect a load area of type aggregated
        if not line['type'] == 'line_aggr':

            # create branch tee and add it to grid
            branch_tee = BranchTee(geom=conn_point_shp,
                                   grid=network.mv_grid,
                                   in_building=False)
            network.mv_grid.graph.add_node(branch_tee,
                                           type='branch_tee')

            # split old branch into 2 segments
            # (delete old branch and create 2 new ones along cable_dist)
            # ==========================================================

            # backup kind and type of branch
            line_kind = line['line'].kind
            line_type = line['line'].type

            # remove line from graph
            network.mv_grid.graph.remove_edge(adj_node1, adj_node2)

            # delete line from equipment changes if existing
            _del_cable_from_equipment_changes(network=network,
                                              line=line['line'])

            line_length = calc_geo_dist_vincenty(network=network,
                                                 node_source=adj_node1,
                                                 node_target=branch_tee)
            line = Line(id=random.randint(10 ** 8, 10 ** 9),
                        length=line_length / 1e3,
                        quantity=1,
                        kind=line_kind,
                        type=line_type,
                        grid=network.mv_grid)

            network.mv_grid.graph.add_edge(adj_node1,
                                           branch_tee,
                                           line=line,
                                           type='line')

            # add line to equipment changes to track costs
            _add_cable_to_equipment_changes(network=network,
                                            line=line)

            line_length = calc_geo_dist_vincenty(network=network,
                                                 node_source=adj_node2,
                                                 node_target=branch_tee)
            line = Line(id=random.randint(10 ** 8, 10 ** 9),
                        length=line_length / 1e3,
                        quantity=1,
                        kind=line_kind,
                        type=line_type,
                        grid=network.mv_grid)

            network.mv_grid.graph.add_edge(adj_node2,
                                           branch_tee,
                                           line=line,
                                           type='line')

            # add line to equipment changes to track costs
            _add_cable_to_equipment_changes(network=network,
                                            line=line)

            # add new branch for new node (node to branch tee)
            # ================================================
            line_length = calc_geo_dist_vincenty(network=network,
                                                 node_source=node,
                                                 node_target=branch_tee)
            line = Line(id=random.randint(10 ** 8, 10 ** 9),
                        length=line_length / 1e3,
                        quantity=1,
                        kind=std_line_kind,
                        type=std_line_type,
                        grid=network.mv_grid)

            network.mv_grid.graph.add_edge(node,
                                           branch_tee,
                                           line=line,
                                           type='line')

            # add line to equipment changes to track costs
            _add_cable_to_equipment_changes(network=network,
                                            line=line)

            target_obj_result = branch_tee

    # node ist nearest connection point
    else:

        # what kind of node is to be connected? (which type is node of?)
        #   LVStation: Connect to LVStation or BranchTee
        #   Generator: Connect to LVStation, BranchTee or Generator
        if isinstance(node, LVStation):
            valid_conn_objects = (LVStation, BranchTee)
        elif isinstance(node, Generator):
            valid_conn_objects = (LVStation, BranchTee, Generator)
        else:
            raise ValueError('Oops, the node you are trying to connect is not a valid connection object')

        # if target is generator or Load, check if it is aggregated (=> connection not allowed)
        if isinstance(target_obj['obj'], (Generator, Load)):
            target_is_aggregated = any([_ for _ in network.mv_grid.graph.edge[target_obj['obj']].values()
                                        if _['type'] == 'line_aggr'])
        else:
            target_is_aggregated = False

        # target node is not a load area of type aggregated
        if isinstance(target_obj['obj'], valid_conn_objects) and not target_is_aggregated:

            # add new branch for satellite (station to station)
            line_length = calc_geo_dist_vincenty(network=network,
                                                 node_source=node,
                                                 node_target=target_obj['obj'])

            line = Line(id=random.randint(10 ** 8, 10 ** 9),
                        type=std_line_type,
                        kind=std_line_kind,
                        quantity=1,
                        length=line_length / 1e3,
                        grid=network.mv_grid)

            network.mv_grid.graph.add_edge(node,
                                           target_obj['obj'],
                                           line=line,
                                           type='line')

            # add line to equipment changes to track costs
            _add_cable_to_equipment_changes(network=network,
                                            line=line)

            target_obj_result = target_obj['obj']

    return target_obj_result

Response: Connects MV node to target object in MV grid


Instruction:
Make a summary of the following Python 3 code
def _get_griddistrict(ding0_filepath):
    grid_district = os.path.basename(ding0_filepath)
    grid_district_search = re.search('[_]+\d+', grid_district)
    if grid_district_search:
        grid_district = int(grid_district_search.group(0)[2:])
        return grid_district
    else:
        raise (KeyError('Grid District not found in '.format(grid_district)))

Response: Get the grid district number from the ding0 data file path


Instruction:
Create a Python 3 function to
run basic EDisGo algorithm.

Response:
def run_edisgo_basic(ding0_filepath,
                     generator_scenario=None,
                     analysis='worst-case',
                     *edisgo_grid):
    """
    Analyze edisgo grid extension cost as reference scenario

    Parameters
    ----------
    ding0_filepath : str
        Path to ding0 data ending typically
        `/path/to/ding0_data/"ding0_grids__" + str(``grid_district``) + ".xxx"`

    analysis : str
        Either 'worst-case' or 'timeseries'

    generator_scenario : None or :obj:`str`
        If provided defines which scenario of future generator park to use
        and invokes import of these generators. Possible options are 'nep2035'
        and 'ego100'.

    Returns
    -------
    edisgo_grid : :class:`~.grid.network.EDisGo`
        eDisGo network container
    costs : :pandas:`pandas.Dataframe<dataframe>`
        Cost of grid extension
    grid_issues : dict
        Grids resulting in an error including error message

    """

    grid_district = _get_griddistrict(ding0_filepath)

    grid_issues = {}

    logging.info('Grid expansion for MV grid district {}'.format(grid_district))

    if edisgo_grid: # if an edisgo_grid is passed in arg then ignore everything else
        edisgo_grid = edisgo_grid[0]
    else:
        try:
            if 'worst-case' in analysis:
                edisgo_grid = EDisGo(ding0_grid=ding0_filepath,
                                     worst_case_analysis=analysis)
            elif 'timeseries' in analysis:
                edisgo_grid = EDisGo(ding0_grid=ding0_filepath,
                                     timeseries_generation_fluctuating='oedb',
                                     timeseries_load='demandlib')
        except FileNotFoundError as e:
            return None, pd.DataFrame(), {'grid': grid_district, 'msg': str(e)}

    # Import generators
    if generator_scenario:
        logging.info('Grid expansion for scenario \'{}\'.'.format(generator_scenario))
        edisgo_grid.import_generators(generator_scenario=generator_scenario)
    else:
        logging.info('Grid expansion with no generator imports based on scenario')

    try:
        # Do grid reinforcement
        edisgo_grid.reinforce()

        # Get costs
        costs_grouped = \
            edisgo_grid.network.results.grid_expansion_costs.groupby(
                ['type']).sum()
        costs = pd.DataFrame(costs_grouped.values,
                             columns=costs_grouped.columns,
                             index=[[edisgo_grid.network.id] * len(costs_grouped),
                                    costs_grouped.index]).reset_index()
        costs.rename(columns={'level_0': 'grid'}, inplace=True)

        grid_issues['grid'] = None
        grid_issues['msg'] = None

        logging.info('SUCCESS!')
    except MaximumIterationError:
        grid_issues['grid'] = edisgo_grid.network.id
        grid_issues['msg'] = str(edisgo_grid.network.results.unresolved_issues)
        costs = pd.DataFrame()
        logging.warning('Unresolved issues left after grid expansion.')
    except Exception as e:
        grid_issues['grid'] = edisgo_grid.network.id
        grid_issues['msg'] = repr(e)
        costs = pd.DataFrame()
        logging.exception()

    return edisgo_grid, costs, grid_issues


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

    # base case with no generator import
    edisgo_grid, \
    costs_before_geno_import, \
    grid_issues_before_geno_import = run_edisgo_basic(*run_args)

    if edisgo_grid:
        # clear the pypsa object and results from edisgo_grid
        edisgo_grid.network.results = Results(edisgo_grid.network)
        edisgo_grid.network.pypsa = None

        # case after generator import
        # run_args = [ding0_filename]
        # run_args.extend(run_args_opt)
        run_args.append(edisgo_grid)

        _, costs, \
        grid_issues = run_edisgo_basic(*run_args)

        return costs_before_geno_import, grid_issues_before_geno_import, \
               costs, grid_issues
    else:
        return costs_before_geno_import, grid_issues_before_geno_import, \
               costs_before_geno_import, grid_issues_before_geno_import

Response: Run the EDI - GO on same grid and return the results.


Instruction:
Write a Python 3 function for
running the eDisGo parallelization of a list of Ding0 grid files in parallel and return the cost of each grid imported.

Response:
def run_edisgo_pool(ding0_file_list, run_args_opt,
                    workers=mp.cpu_count(), worker_lifetime=1):
    """
    Use python multiprocessing toolbox for parallelization

    Several grids are analyzed in parallel.

    Parameters
    ----------
    ding0_file_list : list
        Ding0 grid data file names
    run_args_opt : list
        eDisGo options, see :func:`run_edisgo_basic` and
        :func:`run_edisgo_twice`
    workers: int
        Number of parallel process
    worker_lifetime : int
        Bunch of grids sequentially analyzed by a worker

    Returns
    -------
    all_costs_before_geno_import : list
        Grid extension cost before grid connection of new generators
    all_grid_issues_before_geno_import : list
        Remaining overloading or over-voltage issues in grid
    all_costs : list
        Grid extension cost due to grid connection of new generators
    all_grid_issues : list
        Remaining overloading or over-voltage issues in grid
    """
    def collect_pool_results(result):
        results.append(result)

    results = []

    pool = mp.Pool(workers,
                   maxtasksperchild=worker_lifetime)

    for file in ding0_file_list:
        edisgo_args = [file] + run_args_opt
        pool.apply_async(func=run_edisgo_twice,
                         args=(edisgo_args,),
                         callback=collect_pool_results)

    pool.close()
    pool.join()

    # process results data
    all_costs_before_geno_import = [r[0] for r in results]
    all_grid_issues_before_geno_import = [r[1] for r in results]
    all_costs = [r[2] for r in results]
    all_grid_issues = [r[3] for r in results]

    return all_costs_before_geno_import, all_grid_issues_before_geno_import, \
           all_costs, all_grid_issues


Instruction:
Can you generate the documentation for the following Python 3 function
def run_edisgo_pool_flexible(ding0_id_list, func, func_arguments,
                    workers=mp2.cpu_count(), worker_lifetime=1):
    """
    Use python multiprocessing toolbox for parallelization

    Several grids are analyzed in parallel based on your custom function that
    defines the specific application of eDisGo.

    Parameters
    ----------
    ding0_id_list : list of int
        List of ding0 grid data IDs (also known as HV/MV substation IDs)
    func : any function
        Your custom function that shall be parallelized
    func_arguments : tuple
        Arguments to custom function ``func``
    workers: int
        Number of parallel process
    worker_lifetime : int
        Bunch of grids sequentially analyzed by a worker

    Notes
    -----
    Please note, the following requirements for the custom function which is to
    be executed in parallel

    #. It must return an instance of the type :class:`~.edisgo.EDisGo`.
    #. The first positional argument is the MV grid district id (as int). It is
       prepended to the tuple of arguments ``func_arguments``

    Returns
    -------
    containers : dict of :class:`~.edisgo.EDisGo`
        Dict of EDisGo instances keyed by its ID
    """
    def collect_pool_results(result):
        """
        Store results from parallelized calculation in structured manner

        Parameters
        ----------
        result: :class:`~.edisgo.EDisGo`
        """
        results.update({result.network.id: result})

    results = {}

    pool = mp2.Pool(workers,
                   maxtasksperchild=worker_lifetime)

    def error_callback(key):
        return lambda o: results.update({key: o})

    for ding0_id in ding0_id_list:
        edisgo_args = (ding0_id, *func_arguments)
        pool.apply_async(func=func,
                         args=edisgo_args,
                         callback=collect_pool_results,
                         error_callback=error_callback(ding0_id))

    pool.close()
    pool.join()

    return results

Response: This function runs a custom function in parallel and returns a dict of all the containers in the main dict.


Instruction:
Given the following Python 3 function, write the documentation
def reinforce_grid(edisgo, timesteps_pfa=None, copy_graph=False,
                   max_while_iterations=10, combined_analysis=False):
    """
    Evaluates grid reinforcement needs and performs measures.

    This function is the parent function for all grid reinforcements.

    Parameters
    ----------
    edisgo : :class:`~.grid.network.EDisGo`
        The eDisGo API object
    timesteps_pfa : :obj:`str` or :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
        timesteps_pfa specifies for which time steps power flow analysis is
        conducted and therefore which time steps to consider when checking
        for over-loading and over-voltage issues.
        It defaults to None in which case all timesteps in
        timeseries.timeindex (see :class:`~.grid.network.TimeSeries`) are used.
        Possible options are:

        * None
          Time steps in timeseries.timeindex (see
          :class:`~.grid.network.TimeSeries`) are used.
        * 'snapshot_analysis'
          Reinforcement is conducted for two worst-case snapshots. See
          :meth:`edisgo.tools.tools.select_worstcase_snapshots()` for further
          explanation on how worst-case snapshots are chosen.
          Note: If you have large time series choosing this option will save
          calculation time since power flow analysis is only conducted for two
          time steps. If your time series already represents the worst-case
          keep the default value of None because finding the worst-case
          snapshots takes some time.
        * :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
          Use this option to explicitly choose which time steps to consider.

    copy_graph : :obj:`Boolean`
        If True reinforcement is conducted on a copied graph and discarded.
        Default: False.
    max_while_iterations : :obj:`int`
        Maximum number of times each while loop is conducted.
    combined_analysis : :obj:`Boolean`
        If True allowed voltage deviations for combined analysis of MV and LV
        grid are used. If False different allowed voltage deviations for MV
        and LV are used. See also config section
        `grid_expansion_allowed_voltage_deviations`. Default: False.

    Returns
    -------
    :class:`~.grid.network.Results`
        Returns the Results object holding grid expansion costs, equipment
        changes, etc.

    Notes
    -----
    See :ref:`features-in-detail` for more information on how grid
    reinforcement is conducted.

    """

    def _add_lines_changes_to_equipment_changes():
        equipment, index, quantity = [], [], []
        for line, number_of_lines in lines_changes.items():
            equipment.append(line.type.name)
            index.append(line)
            quantity.append(number_of_lines)
        edisgo_reinforce.network.results.equipment_changes = \
            edisgo_reinforce.network.results.equipment_changes.append(
                pd.DataFrame(
                    {'iteration_step': [iteration_step] * len(
                        lines_changes),
                     'change': ['changed'] * len(lines_changes),
                     'equipment': equipment,
                     'quantity': quantity},
                    index=index))

    def _add_transformer_changes_to_equipment_changes(mode):
        for station, transformer_list in transformer_changes[mode].items():
            edisgo_reinforce.network.results.equipment_changes = \
                edisgo_reinforce.network.results.equipment_changes.append(
                    pd.DataFrame(
                        {'iteration_step': [iteration_step] * len(
                            transformer_list),
                         'change': [mode] * len(transformer_list),
                         'equipment': transformer_list,
                         'quantity': [1] * len(transformer_list)},
                        index=[station] * len(transformer_list)))

    # assign MV feeder to every generator, LV station, load, and branch tee
    # to assign grid expansion costs to an MV feeder
    assign_mv_feeder_to_nodes(edisgo.network.mv_grid)

    # analyze for all time steps (advantage is that load and feed-in case can
    # be obtained more performant in case `timesteps_pfa` = 'snapshot_analysis'
    # plus edisgo and edisgo_reinforce will have pypsa representation in case
    # reinforcement needs to be conducted on a copied graph)
    edisgo.analyze()

    # in case reinforcement needs to be conducted on a copied graph the
    # edisgo object is deep copied
    if copy_graph is True:
        edisgo_reinforce = copy.deepcopy(edisgo)
    else:
        edisgo_reinforce = edisgo

    if timesteps_pfa is not None:
        # if timesteps_pfa = 'snapshot_analysis' get snapshots
        if (isinstance(timesteps_pfa, str) and
                    timesteps_pfa == 'snapshot_analysis'):
            snapshots = tools.select_worstcase_snapshots(
                edisgo_reinforce.network)
            # drop None values in case any of the two snapshots does not exist
            timesteps_pfa = pd.DatetimeIndex(data=[
                snapshots['load_case'], snapshots['feedin_case']]).dropna()
        # if timesteps_pfa is not of type datetime or does not contain
        # datetimes throw an error
        elif not isinstance(timesteps_pfa, datetime.datetime):
            if hasattr(timesteps_pfa, '__iter__'):
                if not all(isinstance(_, datetime.datetime)
                           for _ in timesteps_pfa):
                    raise ValueError(
                        'Input {} for timesteps_pfa is not valid.'.format(
                        timesteps_pfa))
            else:
                raise ValueError(
                    'Input {} for timesteps_pfa is not valid.'.format(
                        timesteps_pfa))

    iteration_step = 1
    edisgo_reinforce.analyze(timesteps=timesteps_pfa)

    # REINFORCE OVERLOADED TRANSFORMERS AND LINES

    logger.debug('==> Check station load.')
    overloaded_mv_station = checks.hv_mv_station_load(edisgo_reinforce.network)
    overloaded_lv_stations = checks.mv_lv_station_load(
        edisgo_reinforce.network)
    logger.debug('==> Check line load.')
    crit_lines_lv = checks.lv_line_load(edisgo_reinforce.network)
    crit_lines_mv = checks.mv_line_load(edisgo_reinforce.network)
    crit_lines = crit_lines_lv.append(crit_lines_mv)

    while_counter = 0
    while ((not overloaded_mv_station.empty or not overloaded_lv_stations.empty
                or not crit_lines.empty) and
            while_counter < max_while_iterations):

        if not overloaded_mv_station.empty:
            # reinforce substations
            transformer_changes = \
                reinforce_measures.extend_substation_overloading(
                    edisgo_reinforce.network, overloaded_mv_station)
            # write added and removed transformers to results.equipment_changes
            _add_transformer_changes_to_equipment_changes('added')
            _add_transformer_changes_to_equipment_changes('removed')

        if not overloaded_lv_stations.empty:
            # reinforce distribution substations
            transformer_changes = \
                reinforce_measures.extend_distribution_substation_overloading(
                    edisgo_reinforce.network, overloaded_lv_stations)
            # write added and removed transformers to results.equipment_changes
            _add_transformer_changes_to_equipment_changes('added')
            _add_transformer_changes_to_equipment_changes('removed')

        if not crit_lines.empty:
            # reinforce lines
            lines_changes = reinforce_measures.reinforce_branches_overloading(
                edisgo_reinforce.network, crit_lines)
            # write changed lines to results.equipment_changes
            _add_lines_changes_to_equipment_changes()

        # run power flow analysis again (after updating pypsa object) and check
        # if all over-loading problems were solved
        logger.debug('==> Run power flow analysis.')
        pypsa_io.update_pypsa_grid_reinforcement(
            edisgo_reinforce.network,
            edisgo_reinforce.network.results.equipment_changes[
                edisgo_reinforce.network.results.equipment_changes.
                    iteration_step==iteration_step])
        edisgo_reinforce.analyze(timesteps=timesteps_pfa)
        logger.debug('==> Recheck station load.')
        overloaded_mv_station = checks.hv_mv_station_load(
            edisgo_reinforce.network)
        overloaded_lv_stations = checks.mv_lv_station_load(
            edisgo_reinforce.network)
        logger.debug('==> Recheck line load.')
        crit_lines_lv = checks.lv_line_load(edisgo_reinforce.network)
        crit_lines_mv = checks.mv_line_load(edisgo_reinforce.network)
        crit_lines = crit_lines_lv.append(crit_lines_mv)

        iteration_step += 1
        while_counter += 1

    # check if all load problems were solved after maximum number of
    # iterations allowed
    if (while_counter == max_while_iterations and
            (not crit_lines.empty or not overloaded_mv_station.empty or
                 not overloaded_lv_stations.empty)):
        edisgo_reinforce.network.results.unresolved_issues.update(crit_lines)
        edisgo_reinforce.network.results.unresolved_issues.update(
            overloaded_lv_stations)
        edisgo_reinforce.network.results.unresolved_issues.update(
            overloaded_mv_station)
        raise exceptions.MaximumIterationError(
            "Overloading issues for the following lines could not be solved:"
            "{}".format(crit_lines))
    else:
        logger.info('==> Load issues were solved in {} iteration '
                    'step(s).'.format(while_counter))

    # REINFORCE BRANCHES DUE TO VOLTAGE ISSUES
    iteration_step += 1

    # solve voltage problems in MV grid
    logger.debug('==> Check voltage in MV grid.')
    if combined_analysis:
        voltage_levels = 'mv_lv'
    else:
        voltage_levels = 'mv'
    crit_nodes = checks.mv_voltage_deviation(edisgo_reinforce.network,
                                             voltage_levels=voltage_levels)

    while_counter = 0
    while crit_nodes and while_counter < max_while_iterations:

        # reinforce lines
        lines_changes = reinforce_measures.reinforce_branches_overvoltage(
            edisgo_reinforce.network, edisgo_reinforce.network.mv_grid,
            crit_nodes[edisgo_reinforce.network.mv_grid])
        # write changed lines to results.equipment_changes
        _add_lines_changes_to_equipment_changes()

        # run power flow analysis again (after updating pypsa object) and check
        # if all over-voltage problems were solved
        logger.debug('==> Run power flow analysis.')
        pypsa_io.update_pypsa_grid_reinforcement(
            edisgo_reinforce.network,
            edisgo_reinforce.network.results.equipment_changes[
                edisgo_reinforce.network.results.equipment_changes.
                    iteration_step == iteration_step])
        edisgo_reinforce.analyze(timesteps=timesteps_pfa)
        logger.debug('==> Recheck voltage in MV grid.')
        crit_nodes = checks.mv_voltage_deviation(edisgo_reinforce.network,
                                                 voltage_levels=voltage_levels)

        iteration_step += 1
        while_counter += 1

    # check if all voltage problems were solved after maximum number of
    # iterations allowed
    if while_counter == max_while_iterations and crit_nodes:
        for k, v in crit_nodes.items():
            for node in v.index:
                edisgo_reinforce.network.results.unresolved_issues.update(
                    {repr(node): v.loc[node, 'v_mag_pu']})
        raise exceptions.MaximumIterationError(
            "Over-voltage issues for the following nodes in MV grid could "
            "not be solved: {}".format(crit_nodes))
    else:
        logger.info('==> Voltage issues in MV grid were solved in {} '
                    'iteration step(s).'.format(while_counter))

    # solve voltage problems at secondary side of LV stations
    logger.debug('==> Check voltage at secondary side of LV stations.')
    if combined_analysis:
        voltage_levels = 'mv_lv'
    else:
        voltage_levels = 'lv'
    crit_stations = checks.lv_voltage_deviation(edisgo_reinforce.network,
                                                mode='stations',
                                                voltage_levels=voltage_levels)

    while_counter = 0
    while crit_stations and while_counter < max_while_iterations:
        # reinforce distribution substations
        transformer_changes = \
            reinforce_measures.extend_distribution_substation_overvoltage(
                edisgo_reinforce.network, crit_stations)
        # write added transformers to results.equipment_changes
        _add_transformer_changes_to_equipment_changes('added')

        # run power flow analysis again (after updating pypsa object) and check
        # if all over-voltage problems were solved
        logger.debug('==> Run power flow analysis.')
        pypsa_io.update_pypsa_grid_reinforcement(
            edisgo_reinforce.network,
            edisgo_reinforce.network.results.equipment_changes[
                edisgo_reinforce.network.results.equipment_changes.
                    iteration_step == iteration_step])
        edisgo_reinforce.analyze(timesteps=timesteps_pfa)
        logger.debug('==> Recheck voltage at secondary side of LV stations.')
        crit_stations = checks.lv_voltage_deviation(
            edisgo_reinforce.network, mode='stations',
            voltage_levels=voltage_levels)

        iteration_step += 1
        while_counter += 1

    # check if all voltage problems were solved after maximum number of
    # iterations allowed
    if while_counter == max_while_iterations and crit_stations:
        for k, v in crit_stations.items():
            for node in v.index:
                edisgo_reinforce.network.results.unresolved_issues.update(
                    {repr(node): v.loc[node, 'v_mag_pu']})
        raise exceptions.MaximumIterationError(
            "Over-voltage issues at busbar could not be solved for the "
            "following LV grids: {}".format(crit_stations))
    else:
        logger.info('==> Voltage issues at busbars in LV grids were solved '
                    'in {} iteration step(s).'.format(while_counter))

    # solve voltage problems in LV grids
    logger.debug('==> Check voltage in LV grids.')
    crit_nodes = checks.lv_voltage_deviation(edisgo_reinforce.network,
                                             voltage_levels=voltage_levels)

    while_counter = 0
    while crit_nodes and while_counter < max_while_iterations:
        # for every grid in crit_nodes do reinforcement
        for grid in crit_nodes:
            # reinforce lines
            lines_changes = reinforce_measures.reinforce_branches_overvoltage(
                edisgo_reinforce.network, grid, crit_nodes[grid])
            # write changed lines to results.equipment_changes
            _add_lines_changes_to_equipment_changes()

        # run power flow analysis again (after updating pypsa object) and check
        # if all over-voltage problems were solved
        logger.debug('==> Run power flow analysis.')
        pypsa_io.update_pypsa_grid_reinforcement(
            edisgo_reinforce.network,
            edisgo_reinforce.network.results.equipment_changes[
                edisgo_reinforce.network.results.equipment_changes.
                    iteration_step == iteration_step])
        edisgo_reinforce.analyze(timesteps=timesteps_pfa)
        logger.debug('==> Recheck voltage in LV grids.')
        crit_nodes = checks.lv_voltage_deviation(edisgo_reinforce.network,
                                                 voltage_levels=voltage_levels)

        iteration_step += 1
        while_counter += 1

    # check if all voltage problems were solved after maximum number of
    # iterations allowed
    if while_counter == max_while_iterations and crit_nodes:
        for k, v in crit_nodes.items():
            for node in v.index:
                edisgo_reinforce.network.results.unresolved_issues.update(
                    {repr(node): v.loc[node, 'v_mag_pu']})
        raise exceptions.MaximumIterationError(
            "Over-voltage issues for the following nodes in LV grids could "
            "not be solved: {}".format(crit_nodes))
    else:
        logger.info(
            '==> Voltage issues in LV grids were solved '
            'in {} iteration step(s).'.format(while_counter))

    # RECHECK FOR OVERLOADED TRANSFORMERS AND LINES
    logger.debug('==> Recheck station load.')
    overloaded_mv_station = checks.hv_mv_station_load(edisgo_reinforce.network)
    overloaded_lv_stations = checks.mv_lv_station_load(
        edisgo_reinforce.network)
    logger.debug('==> Recheck line load.')
    crit_lines_lv = checks.lv_line_load(edisgo_reinforce.network)
    crit_lines_mv = checks.mv_line_load(edisgo_reinforce.network)
    crit_lines = crit_lines_lv.append(crit_lines_mv)

    while_counter = 0
    while ((not overloaded_mv_station.empty or not overloaded_lv_stations.empty
            or not crit_lines.empty) and while_counter < max_while_iterations):

        if not overloaded_mv_station.empty:
            # reinforce substations
            transformer_changes = \
                reinforce_measures.extend_substation_overloading(
                    edisgo_reinforce.network, overloaded_mv_station)
            # write added and removed transformers to results.equipment_changes
            _add_transformer_changes_to_equipment_changes('added')
            _add_transformer_changes_to_equipment_changes('removed')

        if not overloaded_lv_stations.empty:
            # reinforce substations
            transformer_changes = \
                reinforce_measures.extend_distribution_substation_overloading(
                    edisgo_reinforce.network, overloaded_lv_stations)
            # write added and removed transformers to results.equipment_changes
            _add_transformer_changes_to_equipment_changes('added')
            _add_transformer_changes_to_equipment_changes('removed')

        if not crit_lines.empty:
            # reinforce lines
            lines_changes = reinforce_measures.reinforce_branches_overloading(
                edisgo_reinforce.network, crit_lines)
            # write changed lines to results.equipment_changes
            _add_lines_changes_to_equipment_changes()

        # run power flow analysis again (after updating pypsa object) and check
        # if all over-loading problems were solved
        logger.debug('==> Run power flow analysis.')
        pypsa_io.update_pypsa_grid_reinforcement(
            edisgo_reinforce.network,
            edisgo_reinforce.network.results.equipment_changes[
                edisgo_reinforce.network.results.equipment_changes.
                    iteration_step == iteration_step])
        edisgo_reinforce.analyze(timesteps=timesteps_pfa)
        logger.debug('==> Recheck station load.')
        overloaded_mv_station = checks.hv_mv_station_load(
            edisgo_reinforce.network)
        overloaded_lv_stations = checks.mv_lv_station_load(
            edisgo_reinforce.network)
        logger.debug('==> Recheck line load.')
        crit_lines_lv = checks.lv_line_load(edisgo_reinforce.network)
        crit_lines_mv = checks.mv_line_load(edisgo_reinforce.network)
        crit_lines = crit_lines_lv.append(crit_lines_mv)

        iteration_step += 1
        while_counter += 1

    # check if all load problems were solved after maximum number of
    # iterations allowed
    if (while_counter == max_while_iterations and
            (not crit_lines.empty or not overloaded_mv_station.empty or
                 not overloaded_lv_stations.empty)):
        edisgo_reinforce.network.results.unresolved_issues.update(crit_lines)
        edisgo_reinforce.network.results.unresolved_issues.update(
            overloaded_lv_stations)
        edisgo_reinforce.network.results.unresolved_issues.update(
            overloaded_mv_station)
        raise exceptions.MaximumIterationError(
            "Overloading issues (after solving over-voltage issues) for the"
            "following lines could not be solved: {}".format(crit_lines))
    else:
        logger.info(
            '==> Load issues were rechecked and solved '
            'in {} iteration step(s).'.format(while_counter))

    # final check 10% criteria
    checks.check_ten_percent_voltage_deviation(edisgo_reinforce.network)

    # calculate grid expansion costs
    edisgo_reinforce.network.results.grid_expansion_costs = \
        grid_expansion_costs(edisgo_reinforce.network)

    return edisgo_reinforce.network.results

Response: Evaluates grid reinforcement needs and performs measures.


Instruction:
Write a Python 3 script for
importing an eDisGo grid topology from a pickle file.

Response:
def import_from_ding0(file, network):
    """
    Import an eDisGo grid topology from
    `Ding0 data <https://github.com/openego/ding0>`_.

    This import method is specifically designed to load grid topology data in
    the format as `Ding0 <https://github.com/openego/ding0>`_ provides it via
    pickles.

    The import of the grid topology includes

        * the topology itself
        * equipment parameter
        * generators incl. location, type, subtype and capacity
        * loads incl. location and sectoral consumption

    Parameters
    ----------
    file: :obj:`str` or :class:`ding0.core.NetworkDing0`
        If a str is provided it is assumed it points to a pickle with Ding0
        grid data. This file will be read.
        If an object of the type :class:`ding0.core.NetworkDing0` data will be
        used directly from this object.
    network: :class:`~.grid.network.Network`
        The eDisGo data container object

    Notes
    -----
    Assumes :class:`ding0.core.NetworkDing0` provided by `file` contains
    only data of one mv_grid_district.

    """
    # when `file` is a string, it will be read by the help of pickle
    if isinstance(file, str):
        ding0_nd = load_nd_from_pickle(filename=file)
    # otherwise it is assumed the object is passed directly
    else:
        ding0_nd = file

    ding0_mv_grid = ding0_nd._mv_grid_districts[0].mv_grid

    # Make sure circuit breakers (respectively the rings) are closed
    ding0_mv_grid.close_circuit_breakers()

    # Import medium-voltage grid data
    network.mv_grid = _build_mv_grid(ding0_mv_grid, network)

    # Import low-voltage grid data
    lv_grids, lv_station_mapping, lv_grid_mapping = _build_lv_grid(
        ding0_mv_grid, network)

    # Assign lv_grids to network
    network.mv_grid.lv_grids = lv_grids

    # Integrate disconnecting points
    position_switch_disconnectors(network.mv_grid,
                                  mode=network.config['disconnecting_point'][
                                      'position'])

    # Check data integrity
    _validate_ding0_grid_import(network.mv_grid, ding0_mv_grid,
                                lv_grid_mapping)

    # Set data source
    network.set_data_source('grid', 'dingo')

    # Set more params
    network._id = network.mv_grid.id

    # Update the weather_cell_ids in mv_grid to include the ones in lv_grids
    # ToDo: maybe get a better solution to push the weather_cell_ids in lv_grids but not in mv_grid but into the
    # mv_grid.weather_cell_ids from within the Grid() object or the MVGrid() or LVGrid()
    mv_weather_cell_id = network.mv_grid.weather_cells
    for lvg in lv_grids:
        if lvg.weather_cells:
            for lv_w_id in lvg._weather_cells:
                if not (lv_w_id in mv_weather_cell_id):
                    network.mv_grid._weather_cells.append(lv_w_id)


Instruction:
Implement a Python 3 function for
building LV grids from Ding0 data and creates LV grids.

Response:
def _build_lv_grid(ding0_grid, network):
    """
    Build eDisGo LV grid from Ding0 data

    Parameters
    ----------
    ding0_grid: ding0.MVGridDing0
        Ding0 MV grid object

    Returns
    -------
    list of LVGrid
        LV grids
    dict
        Dictionary containing a mapping of LV stations in Ding0 to newly
        created eDisGo LV stations. This mapping is used to use the same
        instances of LV stations in the MV grid graph.
    """

    lv_station_mapping = {}
    lv_grids = []
    lv_grid_mapping = {}

    for la in ding0_grid.grid_district._lv_load_areas:
        for lvgd in la._lv_grid_districts:
            ding0_lv_grid = lvgd.lv_grid
            if not ding0_lv_grid.grid_district.lv_load_area.is_aggregated:

                # Create LV grid instance
                lv_grid = LVGrid(
                    id=ding0_lv_grid.id_db,
                    geom=ding0_lv_grid.grid_district.geo_data,
                    grid_district={
                        'geom': ding0_lv_grid.grid_district.geo_data,
                        'population': ding0_lv_grid.grid_district.population},
                    voltage_nom=ding0_lv_grid.v_level / 1e3,
                    network=network)

                station = {repr(_): _ for _ in
                           network.mv_grid.graph.nodes_by_attribute(
                               'lv_station')}['LVStation_' + str(
                                    ding0_lv_grid._station.id_db)]

                station.grid = lv_grid
                for t in station.transformers:
                    t.grid = lv_grid

                lv_grid.graph.add_node(station, type='lv_station')
                lv_station_mapping.update({ding0_lv_grid._station: station})

                # Create list of load instances and add these to grid's graph
                loads = {_: Load(
                    id=_.id_db,
                    geom=_.geo_data,
                    grid=lv_grid,
                    consumption=_.consumption) for _ in ding0_lv_grid.loads()}
                lv_grid.graph.add_nodes_from(loads.values(), type='load')

                # Create list of generator instances and add these to grid's
                # graph
                generators = {_: (GeneratorFluctuating(
                    id=_.id_db,
                    geom=_.geo_data,
                    nominal_capacity=_.capacity,
                    type=_.type,
                    subtype=_.subtype,
                    grid=lv_grid,
                    weather_cell_id=_.weather_cell_id,
                    v_level=_.v_level) if _.type in ['wind', 'solar'] else
                                  Generator(
                                      id=_.id_db,
                                      geom=_.geo_data,
                                      nominal_capacity=_.capacity,
                                      type=_.type,
                                      subtype=_.subtype,
                                      grid=lv_grid,
                                      v_level=_.v_level))
                              for _ in ding0_lv_grid.generators()}
                lv_grid.graph.add_nodes_from(generators.values(),
                                             type='generator')

                # Create list of branch tee instances and add these to grid's
                # graph
                branch_tees = {
                    _: BranchTee(id=_.id_db,
                                 geom=_.geo_data,
                                 grid=lv_grid,
                                 in_building=_.in_building)
                    for _ in ding0_lv_grid._cable_distributors}
                lv_grid.graph.add_nodes_from(branch_tees.values(),
                                              type='branch_tee')

                # Merge node above defined above to a single dict
                nodes = {**loads,
                         **generators,
                         **branch_tees,
                         **{ding0_lv_grid._station: station}}

                edges = []
                edges_raw = list(nx.get_edge_attributes(
                    ding0_lv_grid._graph, 'branch').items())
                for edge in edges_raw:
                    edges.append({'adj_nodes': edge[0], 'branch': edge[1]})

                # Create list of line instances and add these to grid's graph
                lines = [(nodes[_['adj_nodes'][0]], nodes[_['adj_nodes'][1]],
                          {'line': Line(
                              id=_['branch'].id_db,
                              type=_['branch'].type,
                              length=_['branch'].length / 1e3,
                              kind=_['branch'].kind,
                              grid=lv_grid)
                          })
                         for _ in edges]
                # convert voltage from V to kV
                for line in lines:
                    # ToDo: remove work around once it's fixed in ding0
                    if line[2]['line'].type['U_n'] >= 400:
                        line[2]['line'].type['U_n'] = \
                            line[2]['line'].type['U_n'] / 1e3
                lv_grid.graph.add_edges_from(lines, type='line')

                # Add LV station as association to LV grid
                lv_grid._station = station

                # Add to lv grid mapping
                lv_grid_mapping.update({lv_grid: ding0_lv_grid})

                # Put all LV grid to a list of LV grids
                lv_grids.append(lv_grid)

    # ToDo: don't forget to adapt lv stations creation in MV grid
    return lv_grids, lv_station_mapping, lv_grid_mapping


Instruction:
Explain what the following Python 3 code does
def _determine_aggregated_nodes(la_centers):

    def aggregate_generators(gen, aggr):
        """Aggregate generation capacity per voltage level

        Parameters
        ----------
        gen: ding0.core.GeneratorDing0
            Ding0 Generator object
        aggr: dict
            Aggregated generation capacity. For structure see
            `_determine_aggregated_nodes()`.

        Returns
        -------

        """

        if gen.v_level not in aggr['generation']:
            aggr['generation'][gen.v_level] = {}
        if gen.type not in aggr['generation'][gen.v_level]:
            aggr['generation'][gen.v_level][gen.type] = {}
        if gen.subtype not in aggr['generation'][gen.v_level][gen.type]:
            aggr['generation'][gen.v_level][gen.type].update(
                     {gen.subtype: {'ids': [gen.id_db],
                                'capacity': gen.capacity}})
        else:
            aggr['generation'][gen.v_level][gen.type][gen.subtype][
                'ids'].append(gen.id_db)
            aggr['generation'][gen.v_level][gen.type][gen.subtype][
                'capacity'] += gen.capacity

        return aggr

    def aggregate_loads(la_center, aggr):
        """Aggregate consumption in load area per sector

        Parameters
        ----------
        la_center: LVLoadAreaCentreDing0
            Load area center object from Ding0

        Returns
        -------

        """
        for s in ['retail', 'industrial', 'agricultural', 'residential']:
            if s not in aggr['load']:
                aggr['load'][s] = 0

        aggr['load']['retail'] += sum(
            [_.sector_consumption_retail
             for _ in la_center.lv_load_area._lv_grid_districts])
        aggr['load']['industrial'] += sum(
            [_.sector_consumption_industrial
             for _ in la_center.lv_load_area._lv_grid_districts])
        aggr['load']['agricultural'] += sum(
            [_.sector_consumption_agricultural
             for _ in la_center.lv_load_area._lv_grid_districts])
        aggr['load']['residential'] += sum(
            [_.sector_consumption_residential
             for _ in la_center.lv_load_area._lv_grid_districts])

        return aggr

    aggregated = {}
    aggr_stations = []

    # ToDo: The variable generation_aggr is further used -> delete this code
    generation_aggr = {}
    for la in la_centers[0].grid.grid_district._lv_load_areas:
        for lvgd in la._lv_grid_districts:
            for gen in lvgd.lv_grid.generators():
                if la.is_aggregated:
                    generation_aggr.setdefault(gen.type, {})
                    generation_aggr[gen.type].setdefault(gen.subtype, {'ding0': 0})
                    generation_aggr[gen.type][gen.subtype].setdefault('ding0', 0)
                    generation_aggr[gen.type][gen.subtype]['ding0'] += gen.capacity

    dingo_import_data = pd.DataFrame(columns=('id',
                                              'capacity',
                                              'agg_geno')
                                     )

    for la_center in la_centers:
        aggr = {'generation': {}, 'load': {}, 'aggregates': []}

        # Determine aggregated generation in LV grid
        for lvgd in la_center.lv_load_area._lv_grid_districts:
            weather_cell_ids = {}
            for gen in lvgd.lv_grid.generators():
                aggr = aggregate_generators(gen, aggr)

                # Get the aggregated weather cell id of the area
                # b
                if isinstance(gen, GeneratorFluctuatingDing0):
                    if gen.weather_cell_id not in weather_cell_ids.keys():
                        weather_cell_ids[gen.weather_cell_id] = 1
                    else:
                        weather_cell_ids[gen.weather_cell_id] += 1

                dingo_import_data.loc[len(dingo_import_data)] = \
                    [int(gen.id_db),
                     gen.capacity,
                     None]

            # Get the weather cell id that occurs the most if there are any generators
            if not(list(lvgd.lv_grid.generators())):
                weather_cell_id = None
            else:
                if weather_cell_ids:
                    weather_cell_id = list(weather_cell_ids.keys())[
                        list(weather_cell_ids.values()).index(
                            max(weather_cell_ids.values()))]
                else:
                    weather_cell_id = None

            for v_level in aggr['generation']:
                for type in aggr['generation'][v_level]:
                    for subtype in aggr['generation'][v_level][type]:
                        # make sure to check if there are any generators before assigning
                        # a weather cell id
                        if not(list(lvgd.lv_grid.generators())):
                            pass
                        else:
                            aggr['generation'][v_level][type][subtype]['weather_cell_id'] = \
                                weather_cell_id

        # Determine aggregated load in MV grid
        # -> Implement once laods in Ding0 MV grids exist

        # Determine aggregated load in LV grid
        aggr = aggregate_loads(la_center, aggr)

        # Collect metadata of aggregated load areas
        aggr['aggregates'] = {
            'population': la_center.lv_load_area.zensus_sum,
            'geom': la_center.lv_load_area.geo_area}

        # Determine LV grids/ stations that are aggregated
        for _ in la_center.lv_load_area._lv_grid_districts:
            aggr_stations.append(_.lv_grid.station())

        # add elements to lists
        aggregated.update({la_center.id_db: aggr})

    return aggregated, aggr_stations, dingo_import_data

Response: This function determines the generation and load within load areas and aggregates the nodes in the LV grid.


Instruction:
Write a Python 3 function for
adding generators and loads to MV station representing aggregated load and generation capacity and load.

Response:
def _attach_aggregated(network, grid, aggregated, ding0_grid):
    """Add Generators and Loads to MV station representing aggregated generation
    capacity and load

    Parameters
    ----------
    grid: MVGrid
        MV grid object
    aggregated: dict
        Information about aggregated load and generation capacity. For
        information about the structure of the dict see ... .
    ding0_grid: ding0.Network
        Ding0 network container
    Returns
    -------
    MVGrid
        Altered instance of MV grid including aggregated load and generation
    """

    aggr_line_type = ding0_grid.network._static_data['MV_cables'].iloc[
        ding0_grid.network._static_data['MV_cables']['I_max_th'].idxmax()]

    for la_id, la in aggregated.items():
        # add aggregated generators
        for v_level, val in la['generation'].items():
            for type, val2 in val.items():
                for subtype, val3 in val2.items():
                    if type in ['solar', 'wind']:
                        gen = GeneratorFluctuating(
                            id='agg-' + str(la_id) + '-' + '_'.join(
                                [str(_) for _ in val3['ids']]),
                            nominal_capacity=val3['capacity'],
                            weather_cell_id=val3['weather_cell_id'],
                            type=type,
                            subtype=subtype,
                            geom=grid.station.geom,
                            grid=grid,
                            v_level=4)
                    else:
                        gen = Generator(
                            id='agg-' + str(la_id) + '-' + '_'.join(
                                [str(_) for _ in val3['ids']]),
                            nominal_capacity=val3['capacity'],
                            type=type,
                            subtype=subtype,
                            geom=grid.station.geom,
                            grid=grid,
                            v_level=4)
                    grid.graph.add_node(gen, type='generator_aggr')

                    # backup reference of geno to LV geno list (save geno
                    # where the former LV genos are aggregated in)
                    network.dingo_import_data.set_value(network.dingo_import_data['id'].isin(val3['ids']),
                                                        'agg_geno',
                                                        gen)

                    # connect generator to MV station
                    line = Line(id='line_aggr_generator_la_' + str(la_id) + '_vlevel_{v_level}_'
                                '{subtype}'.format(
                                 v_level=v_level,
                                 subtype=subtype),
                                 type=aggr_line_type,
                                 kind='cable',
                                 length=1e-3,
                                 grid=grid)
                    grid.graph.add_edge(grid.station,
                                        gen,
                                        line=line,
                                        type='line_aggr')

        for sector, sectoral_load in la['load'].items():
            load = Load(
                geom=grid.station.geom,
                consumption={sector: sectoral_load},
                grid=grid,
                id='_'.join(['Load_aggregated', sector, repr(grid), str(la_id)]))

            grid.graph.add_node(load, type='load')

            # connect aggregated load to MV station
            line = Line(id='_'.join(['line_aggr_load_la_' + str(la_id), sector, str(la_id)]),
                        type=aggr_line_type,
                        kind='cable',
                        length=1e-3,
                        grid=grid)

            grid.graph.add_edge(grid.station,
                                load,
                                line=line,
                                type='line_aggr')


Instruction:
How would you explain what the following Python 3 function does
def _validate_ding0_grid_import(mv_grid, ding0_mv_grid, lv_grid_mapping):

    # Check number of components in MV grid
    _validate_ding0_mv_grid_import(mv_grid, ding0_mv_grid)

    # Check number of components in LV grid
    _validate_ding0_lv_grid_import(mv_grid.lv_grids, ding0_mv_grid,
                                   lv_grid_mapping)

    # Check cumulative load and generation in MV grid district
    _validate_load_generation(mv_grid, ding0_mv_grid)

Response: Cross - check imported data with original data source


Instruction:
Here you have a function in Python 3, explain what it does
def _validate_ding0_mv_grid_import(grid, ding0_grid):

    integrity_checks = ['branch_tee',
                        'disconnection_point', 'mv_transformer',
                        'lv_station'#,'line',
                        ]

    data_integrity = {}
    data_integrity.update({_: {'ding0': None, 'edisgo': None, 'msg': None}
                           for _ in integrity_checks})

    # Check number of branch tees
    data_integrity['branch_tee']['ding0'] = len(ding0_grid._cable_distributors)
    data_integrity['branch_tee']['edisgo'] = len(
        grid.graph.nodes_by_attribute('branch_tee'))

    # Check number of disconnecting points
    data_integrity['disconnection_point']['ding0'] = len(
        ding0_grid._circuit_breakers)
    data_integrity['disconnection_point']['edisgo'] = len(
        grid.graph.nodes_by_attribute('mv_disconnecting_point'))

    # Check number of MV transformers
    data_integrity['mv_transformer']['ding0'] = len(
        list(ding0_grid.station().transformers()))
    data_integrity['mv_transformer']['edisgo'] = len(
        grid.station.transformers)

    # Check number of LV stations in MV grid (graph)
    data_integrity['lv_station']['edisgo'] = len(grid.graph.nodes_by_attribute(
        'lv_station'))
    data_integrity['lv_station']['ding0'] = len(
        [_ for _ in ding0_grid._graph.nodes()
         if (isinstance(_, LVStationDing0) and
             not _.grid.grid_district.lv_load_area.is_aggregated)])

    # Check number of lines outside aggregated LA
    # edges_w_la = grid.graph.lines()
    # data_integrity['line']['edisgo'] = len([_ for _ in edges_w_la
    #          if not (_['adj_nodes'][0] == grid.station or
    #                  _['adj_nodes'][1] == grid.station) and
    #          _['line']._length > .5])
    # data_integrity['line']['ding0'] = len(
    #     [_ for _ in ding0_grid.lines()
    #      if not _['branch'].connects_aggregated])

    # raise an error if data does not match
    for c in integrity_checks:
        if data_integrity[c]['edisgo'] != data_integrity[c]['ding0']:
            raise ValueError(
                'Unequal number of objects for {c}. '
                '\n\tDing0:\t{ding0_no}'
                '\n\teDisGo:\t{edisgo_no}'.format(
                    c=c,
                    ding0_no=data_integrity[c]['ding0'],
                    edisgo_no=data_integrity[c]['edisgo']))

    return data_integrity

Response: Verify imported data with original data from MV grid


Instruction:
Make a summary of the following Python 3 code
def _validate_ding0_lv_grid_import(grids, ding0_grid, lv_grid_mapping):

    integrity_checks = ['branch_tee', 'lv_transformer',
                        'generator', 'load','line']

    data_integrity = {}

    for grid in grids:

        data_integrity.update({grid:{_: {'ding0': None, 'edisgo': None, 'msg': None}
                           for _ in integrity_checks}})

        # Check number of branch tees
        data_integrity[grid]['branch_tee']['ding0'] = len(
            lv_grid_mapping[grid]._cable_distributors)
        data_integrity[grid]['branch_tee']['edisgo'] = len(
            grid.graph.nodes_by_attribute('branch_tee'))

        # Check number of LV transformers
        data_integrity[grid]['lv_transformer']['ding0'] = len(
            list(lv_grid_mapping[grid].station().transformers()))
        data_integrity[grid]['lv_transformer']['edisgo'] = len(
            grid.station.transformers)

        # Check number of generators
        data_integrity[grid]['generator']['edisgo'] = len(
            grid.generators)
        data_integrity[grid]['generator']['ding0'] = len(
            list(lv_grid_mapping[grid].generators()))

        # Check number of loads
        data_integrity[grid]['load']['edisgo'] = len(
            grid.graph.nodes_by_attribute('load'))
        data_integrity[grid]['load']['ding0'] = len(
            list(lv_grid_mapping[grid].loads()))

        # Check number of lines outside aggregated LA
        data_integrity[grid]['line']['edisgo'] = len(
            list(grid.graph.lines()))
        data_integrity[grid]['line']['ding0'] = len(
            [_ for _ in lv_grid_mapping[grid].graph_edges()
             if not _['branch'].connects_aggregated])

    # raise an error if data does not match
    for grid in grids:
        for c in integrity_checks:
            if data_integrity[grid][c]['edisgo'] != data_integrity[grid][c]['ding0']:
                raise ValueError(
                    'Unequal number of objects in grid {grid} for {c}. '
                    '\n\tDing0:\t{ding0_no}'
                    '\n\teDisGo:\t{edisgo_no}'.format(
                        grid=grid,
                        c=c,
                        ding0_no=data_integrity[grid][c]['ding0'],
                        edisgo_no=data_integrity[grid][c]['edisgo']))

Response: Verify imported data with original data from LV grid


Instruction:
Can you generate the documentation for the following Python 3 function
def import_generators(network, data_source=None, file=None):

    if data_source == 'oedb':
        logging.warning('Right now only solar and wind generators can be '
                        'imported from the oedb.')
        _import_genos_from_oedb(network=network)
        network.mv_grid._weather_cells = None
        if network.pypsa is not None:
            pypsa_io.update_pypsa_generator_import(network)
    elif data_source == 'pypsa':
        _import_genos_from_pypsa(network=network, file=file)
    else:
        logger.error("Invalid option {} for generator import. Must either be "
                     "'oedb' or 'pypsa'.".format(data_source))
        raise ValueError('The option you specified is not supported.')

Response: Imports the generator data from source.