Instruction:
How would you code a function in Python 3 to
return a list of modus switch disconnectors in MV grid.

Response:
def position_switch_disconnectors(mv_grid, mode='load', status='open'):
    """
    Determine position of switch disconnector in MV grid rings

    Determination of the switch disconnector location is motivated by placing
    it to minimized load flows in both parts of the ring (half-rings).
    The switch disconnecter will be installed to a LV station, unless none
    exists in a ring. In this case, a node of arbitrary type is chosen for the
    location of the switch disconnecter.

    Parameters
    ----------
    mv_grid : :class:`~.grid.grids.MVGrid`
        MV grid instance
    mode : str
        Define modus switch disconnector positioning: can be performed based of
        'load', 'generation' or both 'loadgen'. Defaults to 'load'
    status : str
        Either 'open' or 'closed'. Define which status is should be set
        initially. Defaults to 'open' (which refers to conditions of normal
        grid operation).

    Returns
    -------
    tuple
        A tuple of size 2 specifying their pair of nodes between which the
        switch disconnector is located. The first node specifies the node that
        actually includes the switch disconnector.

    Notes
    -----
    This function uses `nx.algorithms.find_cycle()` to identify nodes that are
    part of the MV grid ring(s). Make sure grid topology data that is provided
    has closed rings. Otherwise, no location for a switch disconnector can be
    identified.

    """

    def peak_load_gen_at_node(node):
        """Return peak load and peak generation capacity for ``node``

        Parameters
        ----------
        node : object
            Node instance in the grid topology graph

        Returns
        -------
        tuple
            Tuple of size two. First item is the peak load at ``node``; second
            parameters reflects peak generation capacity at ``node``.
            Returned peak_load and generation capacity is given as apparent
            power in kVA.

        """
        if isinstance(node, LVStation):
            node_peak_load = node.grid.peak_load
            node_peak_gen = node.grid.peak_generation
        elif isinstance(node, Generator):
            node_peak_load = 0
            node_peak_gen = node.nominal_capacity
        elif isinstance(node, Load):
            node_peak_load = node.peak_load.sum()
            node_peak_gen = 0
        elif isinstance(node, BranchTee):
            node_peak_load = 0
            node_peak_gen = 0

        return (node_peak_load / cos_phi_load, node_peak_gen / cos_phi_gen)

    def load_gen_from_subtree(graph, ring, node):
        """
        Accumulate load and generation capacity in branches to connecting node
        on the ring.

        Includes peak_load and generation capacity at ``node```itself.

        Parameters
        ----------
        graph : networkx.Graph
            The graph representing the MV grid topology
        ring : list
            A list of ring nodes
        node : networkx.Node
            A member of the ring

        Returns
        -------
        tuple
            Tuple of size two. First item is the peak load of subtree at
            ``node``; second parameter reflects peak generation capacity from
            subtree at ``node``.

        """
        ring_nodes_except_node = [_ for _ in ring if _ is not node]
        non_ring_nodes = [n for n in
                          [_ for _ in graph.nodes()
                           if _ is not mv_grid.station]
                          if n not in ring_nodes_except_node]
        subgraph = graph.subgraph(non_ring_nodes)

        nodes_subtree = nx.dfs_tree(subgraph, source=node)

        if len(nodes_subtree) > 1:
            peak_load_subtree = 0
            peak_gen_subtree = 0
            for n in nodes_subtree.nodes():
                peak_load_subtree_tmp, peak_gen_subtree_tmp = \
                    peak_load_gen_at_node(n)
                peak_load_subtree += peak_load_subtree_tmp
                peak_gen_subtree += peak_gen_subtree_tmp
            return (peak_load_subtree, peak_gen_subtree)
        else:
            return (0, 0)

    cos_phi_load = mv_grid.network.config['reactive_power_factor']['mv_load']
    cos_phi_gen = mv_grid.network.config['reactive_power_factor']['mv_gen']

    # Identify position of switch disconnector (SD)
    rings = nx.algorithms.cycle_basis(mv_grid.graph, root=mv_grid.station)

    for ring in rings:
        ring = [_ for _ in ring if _ is not mv_grid.station]

        node_peak_load = []
        node_peak_gen = []

        # Collect peak load and generation along the ring
        for node in ring:
            if len(mv_grid.graph.edges(nbunch=node)) > 2:
                peak_load, peak_gen = load_gen_from_subtree(
                    mv_grid.graph, ring, node)
            else:
                peak_load, peak_gen = peak_load_gen_at_node(node)

            node_peak_load.append(peak_load)
            node_peak_gen.append(peak_gen)

            # Choose if SD is placed 'load' or 'generation' oriented
            if mode == 'load':
                node_peak_data = node_peak_load
            elif mode == 'generation':
                node_peak_data = node_peak_gen
            elif mode == 'loadgen':
                node_peak_data = node_peak_load if sum(node_peak_load) > sum(
                    node_peak_gen) else node_peak_gen
            else:
                raise ValueError("Mode {mode} is not known!".format(mode=mode))

        # Set start value for difference in ring halfs
        diff_min = 10e9

        # if none of the nodes is of the type LVStation, a switch
        # disconnecter will be installed anyways.
        if any([isinstance(n, LVStation) for n in ring]):
            has_lv_station = True
        else:
            has_lv_station = False
            logging.debug("Ring {} does not have a LV station. "
                          "Switch disconnecter is installed at arbitrary "
                          "node.".format(ring))

        # Identify nodes where switch disconnector is located in between
        for ctr in range(len(node_peak_data)):
            # check if node that owns the switch disconnector is of type
            # LVStation
            if isinstance(ring[ctr - 2], LVStation) or not has_lv_station:
                # Iteratively split route and calc peak load difference
                route_data_part1 = sum(node_peak_data[0:ctr])
                route_data_part2 = sum(node_peak_data[ctr:len(node_peak_data)])
                diff = abs(route_data_part1 - route_data_part2)

                # stop walking through the ring when load/generation is almost
                # equal
                if diff <= diff_min:
                    diff_min = diff
                    position = ctr
                else:
                    break

        # find position of switch disconnector
        node1 = ring[position - 1]
        node2 = ring[position]

        implement_switch_disconnector(mv_grid, node1, node2)

    # open all switch disconnectors
    if status == 'open':
        for sd in mv_grid.graph.nodes_by_attribute('mv_disconnecting_point'):
            sd.open()
    elif status == 'close':
        for sd in mv_grid.graph.nodes_by_attribute('mv_disconnecting_point'):
            sd.close()


Instruction:
Can you generate the documentation for the following Python 3 function
def implement_switch_disconnector(mv_grid, node1, node2):
    # Get disconnecting point's location
    line = mv_grid.graph.edge[node1][node2]['line']

    length_sd_line = .75e-3 # in km

    x_sd = node1.geom.x + (length_sd_line / line.length) * (
        node1.geom.x - node2.geom.x)
    y_sd = node1.geom.y + (length_sd_line / line.length) * (
        node1.geom.y - node2.geom.y)

    # Instantiate disconnecting point
    mv_dp_number = len(mv_grid.graph.nodes_by_attribute(
        'mv_disconnecting_point'))
    disconnecting_point = MVDisconnectingPoint(
        id=mv_dp_number + 1,
        geom=Point(x_sd, y_sd),
        grid=mv_grid)
    mv_grid.graph.add_node(disconnecting_point, type='mv_disconnecting_point')

    # Replace original line by a new line
    new_line_attr = {
        'line': Line(
            id=line.id,
            type=line.type,
            length=line.length - length_sd_line,
            grid=mv_grid),
        'type': 'line'}
    mv_grid.graph.remove_edge(node1, node2)
    mv_grid.graph.add_edge(disconnecting_point, node2, new_line_attr)

    # Add disconnecting line segment
    switch_disconnector_line_attr = {
        'line': Line(
                  id="switch_disconnector_line_{}".format(
                      str(mv_dp_number + 1)),
                  type=line.type,
                  length=length_sd_line,
                  grid=mv_grid),
        'type': 'line'}

    mv_grid.graph.add_edge(node1, disconnecting_point,
                           switch_disconnector_line_attr)

    # Set line to switch disconnector
    disconnecting_point.line = mv_grid.graph.line_from_nodes(
        disconnecting_point, node1)

Response: This function creates a new switch disconnector in the MV grid.


Instruction:
Make a summary of the following Python 3 code
def select_cable(network, level, apparent_power):

    cable_count = 1

    if level == 'mv':

        available_cables = network.equipment_data['mv_cables'][
            network.equipment_data['mv_cables']['U_n'] ==
            network.mv_grid.voltage_nom]

        suitable_cables = available_cables[
            available_cables['I_max_th'] *
            network.mv_grid.voltage_nom > apparent_power]

        # increase cable count until appropriate cable type is found
        while suitable_cables.empty and cable_count < 20:
            cable_count += 1
            suitable_cables = available_cables[
                available_cables['I_max_th'] *
                network.mv_grid.voltage_nom *
                cable_count > apparent_power]
        if suitable_cables.empty and cable_count == 20:
            raise exceptions.MaximumIterationError(
                "Could not find a suitable cable for apparent power of "
                "{} kVA.".format(apparent_power))

        cable_type = suitable_cables.ix[suitable_cables['I_max_th'].idxmin()]

    elif level == 'lv':

        suitable_cables = network.equipment_data['lv_cables'][
            network.equipment_data['lv_cables']['I_max_th'] *
            network.equipment_data['lv_cables']['U_n'] > apparent_power]

        # increase cable count until appropriate cable type is found
        while suitable_cables.empty and cable_count < 20:
            cable_count += 1
            suitable_cables = network.equipment_data['lv_cables'][
                network.equipment_data['lv_cables']['I_max_th'] *
                network.equipment_data['lv_cables']['U_n'] *
                cable_count > apparent_power]
        if suitable_cables.empty and cable_count == 20:
            raise exceptions.MaximumIterationError(
                "Could not find a suitable cable for apparent power of "
                "{} kVA.".format(apparent_power))

        cable_type = suitable_cables.ix[suitable_cables['I_max_th'].idxmin()]

    else:
        raise ValueError('Please supply a level (either \'mv\' or \'lv\').')

    return cable_type, cable_count

Response: Selects an appropriate cable type and quantity using given apparent power.


Instruction:
Create a Python 3 function to
return all generators with some additional information.

Response:
def get_gen_info(network, level='mvlv', fluctuating=False):
    """
    Gets all the installed generators with some additional information.

    Parameters
    ----------
    network : :class:`~.grid.network.Network`
        Network object holding the grid data.
    level : :obj:`str`
        Defines which generators are returned. Possible options are:

        * 'mv'
          Only generators connected to the MV grid are returned.
        * 'lv'
          Only generators connected to the LV grids are returned.
        * 'mvlv'
          All generators connected to the MV grid and LV grids are returned.

        Default: 'mvlv'.
    fluctuating : :obj:`bool`
        If True only returns fluctuating generators. Default: False.

    Returns
    --------
    :pandas:`pandas.DataFrame<dataframe>`
        Dataframe with all generators connected to the specified voltage
        level. Index of the dataframe are the generator objects of type
        :class:`~.grid.components.Generator`. Columns of the dataframe are:

        * 'gen_repr'
          The representative of the generator as :obj:`str`.
        * 'type'
          The generator type, e.g. 'solar' or 'wind' as :obj:`str`.
        * 'voltage_level'
          The voltage level the generator is connected to as :obj:`str`. Can
          either be 'mv' or 'lv'.
        * 'nominal_capacity'
          The nominal capacity of the generator as as :obj:`float`.
        * 'weather_cell_id'
          The id of the weather cell the generator is located in as :obj:`int`
          (only applies to fluctuating generators).

    """
    gens_w_id = []
    if 'mv' in level:
        gens = network.mv_grid.generators
        gens_voltage_level = ['mv']*len(gens)
        gens_type = [gen.type for gen in gens]
        gens_rating = [gen.nominal_capacity for gen in gens]
        for gen in gens:
            try:
                gens_w_id.append(gen.weather_cell_id)
            except AttributeError:
                gens_w_id.append(np.nan)
        gens_grid = [network.mv_grid]*len(gens)
    else:
        gens = []
        gens_voltage_level = []
        gens_type = []
        gens_rating = []
        gens_grid = []

    if 'lv' in level:
        for lv_grid in network.mv_grid.lv_grids:
            gens_lv = lv_grid.generators
            gens.extend(gens_lv)
            gens_voltage_level.extend(['lv']*len(gens_lv))
            gens_type.extend([gen.type for gen in gens_lv])
            gens_rating.extend([gen.nominal_capacity for gen in gens_lv])
            for gen in gens_lv:
                try:
                    gens_w_id.append(gen.weather_cell_id)
                except AttributeError:
                    gens_w_id.append(np.nan)
            gens_grid.extend([lv_grid] * len(gens_lv))

    gen_df = pd.DataFrame({'gen_repr': list(map(lambda x: repr(x), gens)),
                           'generator': gens,
                           'type': gens_type,
                           'voltage_level': gens_voltage_level,
                           'nominal_capacity': gens_rating,
                           'weather_cell_id': gens_w_id,
                           'grid': gens_grid})

    gen_df.set_index('generator', inplace=True, drop=True)

    # filter fluctuating generators
    if fluctuating:
        gen_df = gen_df.loc[(gen_df.type == 'solar') | (gen_df.type == 'wind')]

    return gen_df


Instruction:
Can you generate a brief explanation for the following Python 3 code
def assign_mv_feeder_to_nodes(mv_grid):
    mv_station_neighbors = mv_grid.graph.neighbors(mv_grid.station)
    # get all nodes in MV grid and remove MV station to get separate subgraphs
    mv_graph_nodes = mv_grid.graph.nodes()
    mv_graph_nodes.remove(mv_grid.station)
    subgraph = mv_grid.graph.subgraph(mv_graph_nodes)

    for neighbor in mv_station_neighbors:
        # determine feeder
        mv_feeder = mv_grid.graph.line_from_nodes(mv_grid.station, neighbor)
        # get all nodes in that feeder by doing a DFS in the disconnected
        # subgraph starting from the node adjacent to the MVStation `neighbor`
        subgraph_neighbor = nx.dfs_tree(subgraph, source=neighbor)
        for node in subgraph_neighbor.nodes():
            # in case of an LV station assign feeder to all nodes in that LV
            # grid
            if isinstance(node, LVStation):
                for lv_node in node.grid.graph.nodes():
                    lv_node.mv_feeder = mv_feeder
            else:
                node.mv_feeder = mv_feeder

Response: Assign a MV feeder to every generator LV station load and branch tee.


Instruction:
Make a summary of the following Python 3 code
def get_mv_feeder_from_line(line):
    try:
        # get nodes of line
        nodes = line.grid.graph.nodes_from_line(line)

        # get feeders
        feeders = {}
        for node in nodes:
            # if one of the nodes is an MV station the line is an MV feeder
            # itself
            if isinstance(node, MVStation):
                feeders[repr(node)] = None
            else:
                feeders[repr(node)] = node.mv_feeder

        # return feeder that is not None
        feeder_1 = feeders[repr(nodes[0])]
        feeder_2 = feeders[repr(nodes[1])]
        if not feeder_1 is None and not feeder_2 is None:
            if feeder_1 == feeder_2:
                return feeder_1
            else:
                logging.warning('Different feeders for line {}.'.format(line))
                return None
        else:
            return feeder_1 if feeder_1 is not None else feeder_2
    except Exception as e:
        logging.warning('Failed to get MV feeder: {}.'.format(e))
        return None

Response: Returns MV feeder identifier for the given line.


Instruction:
How would you explain what the following Python 3 function does
def disconnect_storage(network, storage):
    # does only remove from network.pypsa, not from network.pypsa_lopf
    # remove from pypsa (buses, storage_units, storage_units_t, lines)
    neighbor = storage.grid.graph.neighbors(storage)[0]
    if network.pypsa is not None:
        line = storage.grid.graph.line_from_nodes(storage, neighbor)
        network.pypsa.storage_units = network.pypsa.storage_units.loc[
                                      network.pypsa.storage_units.index.drop(
                                          repr(storage)), :]
        network.pypsa.storage_units_t.p_set.drop([repr(storage)], axis=1,
                                                 inplace=True)
        network.pypsa.storage_units_t.q_set.drop([repr(storage)], axis=1,
                                                 inplace=True)
        network.pypsa.buses = network.pypsa.buses.loc[
                              network.pypsa.buses.index.drop(
                                  '_'.join(['Bus', repr(storage)])), :]
        network.pypsa.lines = network.pypsa.lines.loc[
                              network.pypsa.lines.index.drop(
                                  repr(line)), :]
    # delete line
    neighbor = storage.grid.graph.neighbors(storage)[0]
    storage.grid.graph.remove_edge(storage, neighbor)
    # delete storage
    storage.grid.graph.remove_node(storage)

Response: Removes storage from network and pypsa representation.


Instruction:
Can you write a function in Python 3 where it
returns a list of weather cell ids contained in the grid.

Response:
def weather_cells(self):
        """
        Weather cells contained in grid

        Returns
        -------
        list
            list of weather cell ids contained in grid
        """
        if not self._weather_cells:

            # get all the weather cell ids
            self._weather_cells = []
            for gen in self.generators:
                if hasattr(gen, 'weather_cell_id'):
                    self._weather_cells.append(gen.weather_cell_id)

            # drop duplicates
            self._weather_cells = list(set(self._weather_cells))
            # no need to check for Nones in the list because None in
            # gen.weather_cell_id is kicked out by the if hasattr() before

        return self._weather_cells


Instruction:
Write a Python 3 function that can
return the total peak generation capacity of generators in this grid.

Response:
def peak_generation(self):
        """
        Cumulative peak generation capacity of generators of this grid

        Returns
        -------
        float
            Ad-hoc calculated or cached peak generation capacity
        """
        if self._peak_generation is None:
            self._peak_generation = sum(
                [gen.nominal_capacity
                 for gen in self.generators])

        return self._peak_generation


Instruction:
Here you have a function in Python 3, explain what it does
def peak_generation_per_technology(self):
        peak_generation = defaultdict(float)
        for gen in self.generators:
            peak_generation[gen.type] += gen.nominal_capacity

        return pd.Series(peak_generation)

Response: Returns a pandas. Series of peak generation of each technology in the grid.


Instruction:
Can you generate the documentation for the following Python 3 function
def peak_generation_per_technology_and_weather_cell(self):
        peak_generation = defaultdict(float)
        for gen in self.generators:
            if hasattr(gen, 'weather_cell_id'):
                if (gen.type, gen.weather_cell_id) in peak_generation.keys():
                    peak_generation[gen.type, gen.weather_cell_id] += gen.nominal_capacity
                else:
                    peak_generation[gen.type, gen.weather_cell_id] = gen.nominal_capacity
            else:
                message = 'No weather cell ID found for ' \
                          'generator {}.'.format(repr(gen))
                raise KeyError(message)

        series_index = pd.MultiIndex.from_tuples(list(peak_generation.keys()),
                                                 names=['type', 'weather_cell_id'])

        return pd.Series(peak_generation, index=series_index)

Response:
Peak generation of each technology and the
        corresponding weather cell in the grid

       .


Instruction:
Implement a Python 3 function for
returning the total peak load capacity of generators in this grid.

Response:
def peak_load(self):
        """
        Cumulative peak load capacity of generators of this grid

        Returns
        -------
        float
            Ad-hoc calculated or cached peak load capacity
        """
        if self._peak_load is None:
            self._peak_load = sum(
                [_.peak_load.sum()
                 for _ in self.graph.nodes_by_attribute('load')])

        return self._peak_load


Instruction:
Implement a Python 3 function for
returning a pandas. Series of all the load - level kWh per sector for whole grid

Response:
def consumption(self):
        """
        Consumption in kWh per sector for whole grid

        Returns
        -------
        :pandas:`pandas.Series<series>`
            Indexed by demand sector
        """
        consumption = defaultdict(float)
        for load in self.graph.nodes_by_attribute('load'):
            for sector, val in load.consumption.items():
                consumption[sector] += val

        return pd.Series(consumption)


Instruction:
Create a Python 3 function for
returning a list of Connected Generators within the grid.

Response:
def generators(self):
        """
        Connected Generators within the grid

        Returns
        -------
        list
            List of Generator Objects

        """
        if not self._generators:
            generators = list(self.graph.nodes_by_attribute('generator'))
            generators.extend(list(self.graph.nodes_by_attribute(
                'generator_aggr')))
            return generators
        else:
            return self._generators


Instruction:
Can you generate the documentation for the following Python 3 function
def draw(self):

        # get nodes' positions
        nodes_pos = {}
        for node in self.graph.nodes():
            nodes_pos[node] = (node.geom.x, node.geom.y)

        plt.figure()
        nx.draw_networkx(self.graph, nodes_pos, node_size=16, font_size=8)
        plt.show()

Response: Draw MV grid using the geo data stored in nodes


Instruction:
Here you have a function in Python 3, explain what it does
def nodes_from_line(self, line):

        return dict([(v, k) for k, v in
                     nx.get_edge_attributes(self, 'line').items()])[line]

Response: Returns a dict of nodes adjacent to a line.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def line_from_nodes(self, u, v):
        try:
            line = nx.get_edge_attributes(self, 'line')[(u, v)]
        except:
            try:
                line = nx.get_edge_attributes(self, 'line')[(v, u)]
            except:
                raise nx.NetworkXError('Line between ``u`` and ``v`` not '
                                       'included in the graph.')

        return line

Response: Get line between two nodes u and v.


Instruction:
Here you have a function in Python 3, explain what it does
def nodes_by_attribute(self, attr_val, attr='type'):

        temp_nodes = getattr(self, 'node')
        nodes = list(filter(None, map(lambda x: x if temp_nodes[x][attr] == attr_val else None,
                                      temp_nodes.keys())))

        return nodes

Response: Select Graph s nodes by attribute value


Instruction:
Create a Python 3 function to
return a generator for iterating over the lines in the network.

Response:
def lines_by_attribute(self, attr_val=None, attr='type'):
        """ Returns a generator for iterating over Graph's lines by attribute value.

        Get all lines that share the same attribute. By default, the attr 'type'
        is used to specify the lines' type (line, agg_line, etc.).

        The edge of a graph is described by the two adjacent nodes and the line
        object itself. Whereas the line object is used to hold all relevant
        power system parameters.

        Examples
        --------
        >>> import edisgo
        >>> G = edisgo.grids.Graph()
        >>> G.add_node(1, type='generator')
        >>> G.add_node(2, type='load')
        >>> G.add_edge(1, 2, type='line')
        >>> lines = G.lines_by_attribute('line')
        >>> list(lines)[0]
        <class 'tuple'>: ((node1, node2), line)

        Parameters
        ----------
        attr_val: str
            Value of the `attr` lines should be selected by
        attr: str, default: 'type'
            Attribute key which is 'type' by default

        Returns
        -------
        Generator of :obj:`dict`
            A list containing line elements that match the given attribute
            value

        Notes
        -----
        There are generator functions for nodes (`Graph.nodes()`) and edges
        (`Graph.edges()`) in NetworkX but unlike graph nodes, which can be
        represented by objects, branch objects can only be accessed by using an
        edge attribute ('line' is used here)

        To make access to attributes of the line objects simpler and more
        intuitive for the user, this generator yields a dictionary for each edge
        that contains information about adjacent nodes and the line object.

        Note, the construction of the dictionary highly depends on the structure
        of the in-going tuple (which is defined by the needs of networkX). If
        this changes, the code will break.

        Adapted from `Dingo <https://github.com/openego/dingo/blob/\
            ee237e37d4c228081e1e246d7e6d0d431c6dda9e/dingo/core/network/\
            __init__.py>`_.
        """

        # get all lines that have the attribute 'type' set
        lines_attributes = nx.get_edge_attributes(self, attr).items()

        # attribute value provided?
        if attr_val:
            # extract lines where 'type' == attr_val
            lines_attributes = [(k, self[k[0]][k[1]]['line'])
                                for k, v in lines_attributes if v == attr_val]
        else:
            # get all lines
            lines_attributes = [(k, self[k[0]][k[1]]['line'])
                                for k, v in lines_attributes]

        # sort them according to connected nodes
        lines_sorted = sorted(list(lines_attributes), key=lambda _: repr(_[1]))

        for line in lines_sorted:
            yield {'adj_nodes': line[0], 'line': line[1]}


Instruction:
Can you generate a brief explanation for the following Python 3 code
def intercept_service(self, continuation, handler_call_details):
        rpc_method_handler = self._get_rpc_handler(handler_call_details)
        if rpc_method_handler.response_streaming:
            if self._wrapped.is_streaming:
                # `self._wrapped` is a `StreamServerInterceptor`
                return self._wrapped.intercept_service(
                    continuation, handler_call_details)
        else:
            if not self._wrapped.is_streaming:
                # `self._wrapped` is a `UnaryServerInterceptor`
                return self._wrapped.intercept_service(
                    continuation, handler_call_details)

        # skip the interceptor due to type mismatch
        return continuation(handler_call_details)

Response: Intercepts incoming RPCs before handing them over to a handler


Instruction:
Can you tell what is the following Python 3 function doing
def to_pypsa(network, mode, timesteps):

    # check if timesteps is array-like, otherwise convert to list (necessary
    # to obtain a dataframe when using .loc in time series functions)
    if not hasattr(timesteps, "__len__"):
        timesteps = [timesteps]

    # get topology and time series data
    if mode is None:
        mv_components = mv_to_pypsa(network)
        lv_components = lv_to_pypsa(network)
        components = combine_mv_and_lv(mv_components, lv_components)

        if list(components['Load'].index.values):
            timeseries_load_p, timeseries_load_q = _pypsa_load_timeseries(
                network, mode=mode, timesteps=timesteps)

        if len(list(components['Generator'].index.values)) > 1:
            timeseries_gen_p, timeseries_gen_q = _pypsa_generator_timeseries(
                network, mode=mode, timesteps=timesteps)

        if list(components['Bus'].index.values):
            timeseries_bus_v_set = _pypsa_bus_timeseries(
                network, components['Bus'].index.tolist(), timesteps=timesteps)

        if len(list(components['StorageUnit'].index.values)) > 0:
            timeseries_storage_p, timeseries_storage_q = \
                _pypsa_storage_timeseries(
                    network, mode=mode, timesteps=timesteps)

    elif mode is 'mv':
        # the pypsa export works but NotImplementedError is raised since the
        # rest of edisgo (handling of results from pfa, grid expansion, etc.)
        # does not yet work
        raise NotImplementedError
        mv_components = mv_to_pypsa(network)
        components = add_aggregated_lv_components(network, mv_components)

        if list(components['Load'].index.values):
            timeseries_load_p, timeseries_load_q = _pypsa_load_timeseries(
                network, mode=mode, timesteps=timesteps)

        if len(list(components['Generator'].index.values)) > 1:
            timeseries_gen_p, timeseries_gen_q = _pypsa_generator_timeseries(
                network, mode=mode, timesteps=timesteps)

        if list(components['Bus'].index.values):
            timeseries_bus_v_set = _pypsa_bus_timeseries(
                network, components['Bus'].index.tolist(), timesteps=timesteps)

        if len(list(components['StorageUnit'].index.values)) > 0:
            timeseries_storage_p, timeseries_storage_q = \
                _pypsa_storage_timeseries(
                    network, mode=mode, timesteps=timesteps)

    elif mode is 'lv':
        raise NotImplementedError
        #lv_to_pypsa(network)
    else:
        raise ValueError("Provide proper mode or leave it empty to export "
                         "entire grid topology.")

    # check topology
    _check_topology(components)

    # create power flow problem
    pypsa_network = PyPSANetwork()
    pypsa_network.edisgo_mode = mode
    pypsa_network.set_snapshots(timesteps)

    # import grid topology to PyPSA network
    # buses are created first to avoid warnings
    pypsa_network.import_components_from_dataframe(components['Bus'], 'Bus')

    for k, comps in components.items():
        if k is not 'Bus' and not comps.empty:
            pypsa_network.import_components_from_dataframe(comps, k)

    # import time series to PyPSA network
    if len(list(components['Generator'].index.values)) > 1:
        import_series_from_dataframe(pypsa_network, timeseries_gen_p,
                                     'Generator', 'p_set')
        import_series_from_dataframe(pypsa_network, timeseries_gen_q,
                                     'Generator', 'q_set')

    if list(components['Load'].index.values):
        import_series_from_dataframe(pypsa_network, timeseries_load_p,
                                     'Load', 'p_set')
        import_series_from_dataframe(pypsa_network, timeseries_load_q,
                                     'Load', 'q_set')

    if list(components['Bus'].index.values):
        import_series_from_dataframe(pypsa_network, timeseries_bus_v_set,
                                     'Bus', 'v_mag_pu_set')

    if len(list(components['StorageUnit'].index.values)) > 0:
        import_series_from_dataframe(pypsa_network, timeseries_storage_p,
                                     'StorageUnit', 'p_set')
        import_series_from_dataframe(pypsa_network, timeseries_storage_q,
                                     'StorageUnit', 'q_set')

    _check_integrity_of_pypsa(pypsa_network)

    return pypsa_network

Response: Translate graph based grid representation to PyPSA representation.


Instruction:
Can you create a Python 3 function that
translates MV grid topology representation to PyPSA format.

Response:
def mv_to_pypsa(network):
    """Translate MV grid topology representation to PyPSA format

    MV grid topology translated here includes

    * MV station (no transformer, see :meth:`~.grid.network.EDisGo.analyze`)
    * Loads, Generators, Lines, Storages, Branch Tees of MV grid level as well
      as LV stations. LV stations do not have load and generation of LV level.

    Parameters
    ----------
    network : Network
        eDisGo grid container

    Returns
    -------
    dict of :pandas:`pandas.DataFrame<dataframe>`
        A DataFrame for each type of PyPSA components constituting the grid
        topology. Keys included

        * 'Generator'
        * 'Load'
        * 'Line'
        * 'BranchTee'
        * 'Transformer'
        * 'StorageUnit'

    .. warning::

        PyPSA takes resistance R and reactance X in p.u. The conversion from
        values in ohm to pu notation is performed by following equations

        .. math::

            r_{p.u.} = R_{\Omega} / Z_{B}

            x_{p.u.} = X_{\Omega} / Z_{B}

            with

            Z_{B} = V_B / S_B

        I'm quite sure, but its not 100 % clear if the base voltage V_B is
        chosen correctly. We take the primary side voltage of transformer as
        the transformers base voltage. See
        `#54 <https://github.com/openego/eDisGo/issues/54>`_ for discussion.
    """

    generators = network.mv_grid.generators
    loads = network.mv_grid.graph.nodes_by_attribute('load')
    branch_tees = network.mv_grid.graph.nodes_by_attribute('branch_tee')
    lines = network.mv_grid.graph.lines()
    lv_stations = network.mv_grid.graph.nodes_by_attribute('lv_station')
    mv_stations = network.mv_grid.graph.nodes_by_attribute('mv_station')
    disconnecting_points = network.mv_grid.graph.nodes_by_attribute(
        'mv_disconnecting_point')
    storages = network.mv_grid.graph.nodes_by_attribute(
        'storage')

    omega = 2 * pi * 50

    # define required dataframe columns for components
    generator = {'name': [],
                 'bus': [],
                 'control': [],
                 'p_nom': [],
                 'type': []}

    bus = {'name': [], 'v_nom': [], 'x': [], 'y': []}

    load = {'name': [], 'bus': []}

    line = {'name': [],
            'bus0': [],
            'bus1': [],
            'type': [],
            'x': [],
            'r': [],
            's_nom': [],
            'length': []}

    transformer = {'name': [],
                   'bus0': [],
                   'bus1': [],
                   'type': [],
                   'model': [],
                   'x': [],
                   'r': [],
                   's_nom': [],
                   'tap_ratio': []}

    storage = {
        'name': [],
        'bus': [],
        'p_nom': [],
        'state_of_charge_initial': [],
        'efficiency_store': [],
        'efficiency_dispatch': [],
        'standing_loss': []}

    # create dataframe representing generators and associated buses
    for gen in generators:
        bus_name = '_'.join(['Bus', repr(gen)])
        generator['name'].append(repr(gen))
        generator['bus'].append(bus_name)
        generator['control'].append('PQ')
        generator['p_nom'].append(gen.nominal_capacity / 1e3)
        generator['type'].append('_'.join([gen.type, gen.subtype]))

        bus['name'].append(bus_name)
        bus['v_nom'].append(gen.grid.voltage_nom)
        bus['x'].append(gen.geom.x)
        bus['y'].append(gen.geom.y)

    # create dataframe representing branch tees
    for bt in branch_tees:
        bus['name'].append('_'.join(['Bus', repr(bt)]))
        bus['v_nom'].append(bt.grid.voltage_nom)
        bus['x'].append(bt.geom.x)
        bus['y'].append(bt.geom.y)

    # create dataframes representing loads and associated buses
    for lo in loads:
        bus_name = '_'.join(['Bus', repr(lo)])
        load['name'].append(repr(lo))
        load['bus'].append(bus_name)

        bus['name'].append(bus_name)
        bus['v_nom'].append(lo.grid.voltage_nom)
        bus['x'].append(lo.geom.x)
        bus['y'].append(lo.geom.y)

    # create dataframe for lines
    for l in lines:
        line['name'].append(repr(l['line']))

        if l['adj_nodes'][0] in lv_stations:
            line['bus0'].append(
                '_'.join(['Bus', l['adj_nodes'][0].__repr__(side='mv')]))
        else:
            line['bus0'].append('_'.join(['Bus', repr(l['adj_nodes'][0])]))

        if l['adj_nodes'][1] in lv_stations:
            line['bus1'].append(
                '_'.join(['Bus', l['adj_nodes'][1].__repr__(side='mv')]))
        else:
            line['bus1'].append('_'.join(['Bus', repr(l['adj_nodes'][1])]))

        line['type'].append("")
        line['x'].append(
            l['line'].type['L'] / l['line'].quantity * omega / 1e3 *
            l['line'].length)
        line['r'].append(l['line'].type['R'] / l['line'].quantity *
                         l['line'].length)
        line['s_nom'].append(
            sqrt(3) * l['line'].type['I_max_th'] * l['line'].type['U_n'] *
            l['line'].quantity / 1e3)
        line['length'].append(l['line'].length)

    # create dataframe for LV stations incl. primary/secondary side bus
    for lv_st in lv_stations:
        transformer_count = 1
        # add primary side bus (bus0)
        bus0_name = '_'.join(['Bus', lv_st.__repr__(side='mv')])
        bus['name'].append(bus0_name)
        bus['v_nom'].append(lv_st.mv_grid.voltage_nom)
        bus['x'].append(lv_st.geom.x)
        bus['y'].append(lv_st.geom.y)

        # add secondary side bus (bus1)
        bus1_name = '_'.join(['Bus', lv_st.__repr__(side='lv')])
        bus['name'].append(bus1_name)
        bus['v_nom'].append(lv_st.transformers[0].voltage_op)
        bus['x'].append(None)
        bus['y'].append(None)

        # we choose voltage of transformers' primary side
        v_base = lv_st.mv_grid.voltage_nom

        for tr in lv_st.transformers:
            z_base = v_base ** 2 / tr.type.S_nom
            transformer['name'].append(
                '_'.join([repr(lv_st), 'transformer', str(transformer_count)]))
            transformer['bus0'].append(bus0_name)
            transformer['bus1'].append(bus1_name)
            transformer['type'].append("")
            transformer['model'].append('pi')
            transformer['r'].append(tr.type.R / z_base)
            transformer['x'].append(tr.type.X / z_base)
            transformer['s_nom'].append(tr.type.S_nom / 1e3)
            transformer['tap_ratio'].append(1)

            transformer_count += 1

    # create dataframe for MV stations (only secondary side bus)
    for mv_st in mv_stations:
        # add secondary side bus (bus1)
        bus1_name = '_'.join(['Bus', mv_st.__repr__(side='mv')])
        bus['name'].append(bus1_name)
        bus['v_nom'].append(mv_st.transformers[0].voltage_op)
        bus['x'].append(mv_st.geom.x)
        bus['y'].append(mv_st.geom.y)

    # create dataframe representing disconnecting points
    for dp in disconnecting_points:
        bus['name'].append('_'.join(['Bus', repr(dp)]))
        bus['v_nom'].append(dp.grid.voltage_nom)
        bus['x'].append(dp.geom.x)
        bus['y'].append(dp.geom.y)

    # create dataframe representing storages
    for sto in storages:
        bus_name = '_'.join(['Bus', repr(sto)])

        storage['name'].append(repr(sto))
        storage['bus'].append(bus_name)
        storage['p_nom'].append(sto.nominal_power / 1e3)
        storage['state_of_charge_initial'].append(sto.soc_initial)
        storage['efficiency_store'].append(sto.efficiency_in)
        storage['efficiency_dispatch'].append(sto.efficiency_out)
        storage['standing_loss'].append(sto.standing_loss)

        bus['name'].append(bus_name)
        bus['v_nom'].append(sto.grid.voltage_nom)
        bus['x'].append(sto.geom.x)
        bus['y'].append(sto.geom.y)

    # Add separate slack generator at MV station secondary side bus bar
    generator['name'].append("Generator_slack")
    generator['bus'].append(bus1_name)
    generator['control'].append('Slack')
    generator['p_nom'].append(0)
    generator['type'].append('Slack generator')

    components = {
        'Generator': pd.DataFrame(generator).set_index('name'),
        'Bus': pd.DataFrame(bus).set_index('name'),
        'Load': pd.DataFrame(load).set_index('name'),
        'Line': pd.DataFrame(line).set_index('name'),
        'Transformer': pd.DataFrame(transformer).set_index('name'),
        'StorageUnit': pd.DataFrame(storage).set_index('name')}

    return components


Instruction:
How would you implement a function in Python 3 that
combines MV and LV grid topology in PyPSA format

Response:
def combine_mv_and_lv(mv, lv):
    """Combine MV and LV grid topology in PyPSA format
    """

    combined = {
        c: pd.concat([mv[c], lv[c]], axis=0) for c in list(lv.keys())
    }

    combined['Transformer'] = mv['Transformer']

    return combined


Instruction:
Implement a Python 3 function for
adding aggregated LV load and generation at LV stations to the overall container.

Response:
def add_aggregated_lv_components(network, components):
    """
    Aggregates LV load and generation at LV stations

    Use this function if you aim for MV calculation only. The according
    DataFrames of `components` are extended by load and generators representing
    these aggregated respecting the technology type.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    components : dict of :pandas:`pandas.DataFrame<dataframe>`
        PyPSA components in tabular format

    Returns
    -------
    :obj:`dict` of :pandas:`pandas.DataFrame<dataframe>`
        The dictionary components passed to the function is returned altered.
    """
    generators = {}

    loads = {}

    # collect aggregated generation capacity by type and subtype
    # collect aggregated load grouped by sector
    for lv_grid in network.mv_grid.lv_grids:
        generators.setdefault(lv_grid, {})
        for gen in lv_grid.generators:
            generators[lv_grid].setdefault(gen.type, {})
            generators[lv_grid][gen.type].setdefault(gen.subtype, {})
            generators[lv_grid][gen.type][gen.subtype].setdefault(
                'capacity', 0)
            generators[lv_grid][gen.type][gen.subtype][
                'capacity'] += gen.nominal_capacity
            generators[lv_grid][gen.type][gen.subtype].setdefault(
                'name',
                '_'.join([gen.type,
                          gen.subtype,
                          'aggregated',
                          'LV_grid',
                          str(lv_grid.id)]))
        loads.setdefault(lv_grid, {})
        for lo in lv_grid.graph.nodes_by_attribute('load'):
            for sector, val in lo.consumption.items():
                loads[lv_grid].setdefault(sector, 0)
                loads[lv_grid][sector] += val

    # define dict for DataFrame creation of aggr. generation and load
    generator = {'name': [],
                 'bus': [],
                 'control': [],
                 'p_nom': [],
                 'type': []}

    load = {'name': [], 'bus': []}

    # fill generators dictionary for DataFrame creation
    for lv_grid_obj, lv_grid in generators.items():
        for _, gen_type in lv_grid.items():
            for _, gen_subtype in gen_type.items():
                generator['name'].append(gen_subtype['name'])
                generator['bus'].append(
                    '_'.join(['Bus', lv_grid_obj.station.__repr__('lv')]))
                generator['control'].append('PQ')
                generator['p_nom'].append(gen_subtype['capacity'])
                generator['type'].append("")

    # fill loads dictionary for DataFrame creation
    for lv_grid_obj, lv_grid in loads.items():
        for sector, val in lv_grid.items():
            load['name'].append('_'.join(['Load', sector, repr(lv_grid_obj)]))
            load['bus'].append(
                '_'.join(['Bus', lv_grid_obj.station.__repr__('lv')]))

    components['Generator'] = pd.concat(
        [components['Generator'], pd.DataFrame(generator).set_index('name')])
    components['Load'] = pd.concat(
        [components['Load'], pd.DataFrame(load).set_index('name')])

    return components


Instruction:
How would you implement a function in Python 3 that
returns a pandas. DataFrame of load time series for MV and LV grids.

Response:
def _pypsa_load_timeseries(network, timesteps, mode=None):
    """
    Time series in PyPSA compatible format for load instances

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    timesteps : array_like
        Timesteps is an array-like object with entries of type
        :pandas:`pandas.Timestamp<timestamp>` specifying which time steps
        to export to pypsa representation and use in power flow analysis.
    mode : str, optional
        Specifically retrieve load time series for MV or LV grid level or both.
        Either choose 'mv' or 'lv'.
        Defaults to None, which returns both timeseries for MV and LV in a
        single DataFrame.

    Returns
    -------
    :pandas:`pandas.DataFrame<dataframe>`
        Time series table in PyPSA format
    """
    mv_load_timeseries_p = []
    mv_load_timeseries_q = []
    lv_load_timeseries_p = []
    lv_load_timeseries_q = []

    # add MV grid loads
    if mode is 'mv' or mode is None:
        for load in network.mv_grid.graph.nodes_by_attribute('load'):
            mv_load_timeseries_q.append(load.pypsa_timeseries('q').rename(
                repr(load)).to_frame().loc[timesteps])
            mv_load_timeseries_p.append(load.pypsa_timeseries('p').rename(
                repr(load)).to_frame().loc[timesteps])
        if mode is 'mv':
            lv_load_timeseries_p, lv_load_timeseries_q = \
                _pypsa_load_timeseries_aggregated_at_lv_station(
                    network, timesteps)

    # add LV grid's loads
    if mode is 'lv' or mode is None:
        for lv_grid in network.mv_grid.lv_grids:
            for load in lv_grid.graph.nodes_by_attribute('load'):
                lv_load_timeseries_q.append(load.pypsa_timeseries('q').rename(
                    repr(load)).to_frame().loc[timesteps])
                lv_load_timeseries_p.append(load.pypsa_timeseries('p').rename(
                    repr(load)).to_frame().loc[timesteps])

    load_df_p = pd.concat(mv_load_timeseries_p + lv_load_timeseries_p, axis=1)
    load_df_q = pd.concat(mv_load_timeseries_q + lv_load_timeseries_q, axis=1)

    return load_df_p, load_df_q


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _pypsa_generator_timeseries(network, timesteps, mode=None):

    mv_gen_timeseries_q = []
    mv_gen_timeseries_p = []
    lv_gen_timeseries_q = []
    lv_gen_timeseries_p = []

    # MV generator timeseries
    if mode is 'mv' or mode is None:
        for gen in network.mv_grid.generators:
            mv_gen_timeseries_q.append(gen.pypsa_timeseries('q').rename(
                repr(gen)).to_frame().loc[timesteps])
            mv_gen_timeseries_p.append(gen.pypsa_timeseries('p').rename(
                repr(gen)).to_frame().loc[timesteps])
        if mode is 'mv':
            lv_gen_timeseries_p, lv_gen_timeseries_q = \
                _pypsa_generator_timeseries_aggregated_at_lv_station(
                    network, timesteps)

    # LV generator timeseries
    if mode is 'lv' or mode is None:
        for lv_grid in network.mv_grid.lv_grids:
            for gen in lv_grid.generators:
                lv_gen_timeseries_q.append(gen.pypsa_timeseries('q').rename(
                    repr(gen)).to_frame().loc[timesteps])
                lv_gen_timeseries_p.append(gen.pypsa_timeseries('p').rename(
                    repr(gen)).to_frame().loc[timesteps])

    gen_df_p = pd.concat(mv_gen_timeseries_p + lv_gen_timeseries_p, axis=1)
    gen_df_q = pd.concat(mv_gen_timeseries_q + lv_gen_timeseries_q, axis=1)

    return gen_df_p, gen_df_q

Response: Returns a pandas. DataFrame of generator time series for the given number of timesteps.


Instruction:
Write a Python 3 script to
return a pandas. DataFrame of time series for storage instances in pypsa compatible format.

Response:
def _pypsa_storage_timeseries(network, timesteps, mode=None):
    """
    Timeseries in PyPSA compatible format for storage instances

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    timesteps : array_like
        Timesteps is an array-like object with entries of type
        :pandas:`pandas.Timestamp<timestamp>` specifying which time steps
        to export to pypsa representation and use in power flow analysis.
    mode : str, optional
        Specifically retrieve generator time series for MV or LV grid level or
        both. Either choose 'mv' or 'lv'.
        Defaults to None, which returns both timeseries for MV and LV in a
        single DataFrame.

    Returns
    -------
    :pandas:`pandas.DataFrame<dataframe>`
        Time series table in PyPSA format
    """

    mv_storage_timeseries_q = []
    mv_storage_timeseries_p = []
    lv_storage_timeseries_q = []
    lv_storage_timeseries_p = []

    # MV storage time series
    if mode is 'mv' or mode is None:
        for storage in network.mv_grid.graph.nodes_by_attribute('storage'):
            mv_storage_timeseries_q.append(
                storage.pypsa_timeseries('q').rename(
                    repr(storage)).to_frame().loc[timesteps])
            mv_storage_timeseries_p.append(
                storage.pypsa_timeseries('p').rename(
                    repr(storage)).to_frame().loc[timesteps])

    # LV storage time series
    if mode is 'lv' or mode is None:
        for lv_grid in network.mv_grid.lv_grids:
            for storage in lv_grid.graph.nodes_by_attribute('storage'):
                lv_storage_timeseries_q.append(
                    storage.pypsa_timeseries('q').rename(
                        repr(storage)).to_frame().loc[timesteps])
                lv_storage_timeseries_p.append(
                    storage.pypsa_timeseries('p').rename(
                        repr(storage)).to_frame().loc[timesteps])

    storage_df_p = pd.concat(
        mv_storage_timeseries_p + lv_storage_timeseries_p, axis=1)
    storage_df_q = pd.concat(
        mv_storage_timeseries_q + lv_storage_timeseries_q, axis=1)

    return storage_df_p, storage_df_q


Instruction:
Write a Python 3 script for
returning a Pandas dataframe of time series for each bus in timesteps.

Response:
def _pypsa_bus_timeseries(network, buses, timesteps):
    """
    Time series in PyPSA compatible format for bus instances

    Set all buses except for the slack bus to voltage of 1 pu (it is assumed
    this setting is entirely ignored during solving the power flow problem).
    This slack bus is set to an operational voltage which is typically greater
    than nominal voltage plus a control deviation.
    The control deviation is always added positively to the operational voltage.
    For example, the operational voltage (offset) is set to 1.025 pu plus the
    control deviation of 0.015 pu. This adds up to a set voltage of the slack
    bus of 1.04 pu.

    .. warning::

        Voltage settings for the slack bus defined by this function assume the
        feedin case (reverse power flow case) as the worst-case for the power
        system. Thus, the set point for the slack is always greater 1.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    timesteps : array_like
        Timesteps is an array-like object with entries of type
        :pandas:`pandas.Timestamp<timestamp>` specifying which time steps
        to export to pypsa representation and use in power flow analysis.
    buses : list
        Buses names

    Returns
    -------
    :pandas:`pandas.DataFrame<dataframe>`
        Time series table in PyPSA format
    """

    # get slack bus label
    slack_bus = '_'.join(
        ['Bus', network.mv_grid.station.__repr__(side='mv')])

    # set all buses (except slack bus) to nominal voltage
    v_set_dict = {bus: 1 for bus in buses if bus != slack_bus}

    # Set slack bus to operational voltage (includes offset and control
    # deviation
    control_deviation = network.config[
        'grid_expansion_allowed_voltage_deviations'][
        'hv_mv_trafo_control_deviation']
    if control_deviation != 0:
        control_deviation_ts = \
            network.timeseries.timesteps_load_feedin_case.case.apply(
                lambda _: control_deviation if _ == 'feedin_case'
                                            else -control_deviation)
    else:
        control_deviation_ts = 0

    slack_voltage_pu = control_deviation_ts + 1 + \
                       network.config[
                           'grid_expansion_allowed_voltage_deviations'][
                           'hv_mv_trafo_offset']

    v_set_dict.update({slack_bus: slack_voltage_pu})

    # Convert to PyPSA compatible dataframe
    v_set_df = pd.DataFrame(v_set_dict, index=timesteps)

    return v_set_df


Instruction:
Here you have a function in Python 3, explain what it does
def _pypsa_generator_timeseries_aggregated_at_lv_station(network, timesteps):

    generation_p = []
    generation_q = []

    for lv_grid in network.mv_grid.lv_grids:
        # Determine aggregated generation at LV stations
        generation = {}
        for gen in lv_grid.generators:
            # for type in gen.type:
            #     for subtype in gen.subtype:
            gen_name = '_'.join([gen.type,
                                 gen.subtype,
                                 'aggregated',
                                 'LV_grid',
                                 str(lv_grid.id)])

            generation.setdefault(gen.type, {})
            generation[gen.type].setdefault(gen.subtype, {})
            generation[gen.type][gen.subtype].setdefault('timeseries_p', [])
            generation[gen.type][gen.subtype].setdefault('timeseries_q', [])
            generation[gen.type][gen.subtype]['timeseries_p'].append(
                gen.pypsa_timeseries('p').rename(gen_name).to_frame().loc[
                    timesteps])
            generation[gen.type][gen.subtype]['timeseries_q'].append(
                gen.pypsa_timeseries('q').rename(gen_name).to_frame().loc[
                    timesteps])

        for k_type, v_type in generation.items():
            for k_type, v_subtype in v_type.items():
                col_name = v_subtype['timeseries_p'][0].columns[0]
                generation_p.append(
                    pd.concat(v_subtype['timeseries_p'],
                              axis=1).sum(axis=1).rename(col_name).to_frame())
                generation_q.append(
                    pd.concat(v_subtype['timeseries_q'], axis=1).sum(
                        axis=1).rename(col_name).to_frame())

    return generation_p, generation_q

Response: Aggregates generator time series per generator subtype and LV grid.


Instruction:
Create a Python 3 function to
aggregate load time series per sector and LV grid. Parameters ---------- network : Network The eDisGo grid topology model overall container timesteps : array_like Timesteps is an array-like object with entries of type :pandas:`pandas.Timestamp<timestamp>` specifying which time steps to export to pypsa representation and use in power flow analysis. Returns ------- tuple of :pandas:`pandas.DataFrame<dataframe>` Tuple of size two containing DataFrames that represent 1. 'p_set' of aggregated Load per sector at each LV station 2. 'q_set' of aggregated Load per sector at each LV station

Response:
def _pypsa_load_timeseries_aggregated_at_lv_station(network, timesteps):
    """
    Aggregates load time series per sector and LV grid.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    timesteps : array_like
        Timesteps is an array-like object with entries of type
        :pandas:`pandas.Timestamp<timestamp>` specifying which time steps
        to export to pypsa representation and use in power flow analysis.

    Returns
    -------
    tuple of :pandas:`pandas.DataFrame<dataframe>`
        Tuple of size two containing DataFrames that represent

            1. 'p_set' of aggregated Load per sector at each LV station
            2. 'q_set' of aggregated Load per sector at each LV station

    """
    # ToDo: Load.pypsa_timeseries is not differentiated by sector so this
    # function will not work (either change here and in
    # add_aggregated_lv_components or in Load class)

    load_p = []
    load_q = []

    for lv_grid in network.mv_grid.lv_grids:
        # Determine aggregated load at LV stations
        load = {}
        for lo in lv_grid.graph.nodes_by_attribute('load'):
            for sector, val in lo.consumption.items():
                load.setdefault(sector, {})
                load[sector].setdefault('timeseries_p', [])
                load[sector].setdefault('timeseries_q', [])

                load[sector]['timeseries_p'].append(
                    lo.pypsa_timeseries('p').rename(repr(lo)).to_frame().loc[
                        timesteps])
                load[sector]['timeseries_q'].append(
                    lo.pypsa_timeseries('q').rename(repr(lo)).to_frame().loc[
                        timesteps])

        for sector, val in load.items():
            load_p.append(
                pd.concat(val['timeseries_p'], axis=1).sum(axis=1).rename(
                    '_'.join(['Load', sector, repr(lv_grid)])).to_frame())
            load_q.append(
                pd.concat(val['timeseries_q'], axis=1).sum(axis=1).rename(
                    '_'.join(['Load', sector, repr(lv_grid)])).to_frame())

    return load_p, load_q


Instruction:
Can you tell what is the following Python 3 function doing
def process_pfa_results(network, pypsa, timesteps):
    # get the absolute losses in the system
    # subtracting total generation (including slack) from total load
    grid_losses = {'p': 1e3 * (pypsa.generators_t['p'].sum(axis=1) -
                               pypsa.loads_t['p'].sum(axis=1)),
                   'q': 1e3 * (pypsa.generators_t['q'].sum(axis=1) -
                               pypsa.loads_t['q'].sum(axis=1))}

    network.results.grid_losses = pd.DataFrame(grid_losses).loc[timesteps, :]

    # get slack results
    grid_exchanges = {'p': 1e3 * (pypsa.generators_t['p']['Generator_slack']),
                      'q': 1e3 * (pypsa.generators_t['q']['Generator_slack'])}

    network.results.hv_mv_exchanges = pd.DataFrame(grid_exchanges).loc[timesteps, :]

    # get p and q of lines, LV transformers and MV Station (slack generator)
    # in absolute values
    q0 = pd.concat(
        [np.abs(pypsa.lines_t['q0']),
         np.abs(pypsa.transformers_t['q0']),
         np.abs(pypsa.generators_t['q']['Generator_slack'].rename(
             repr(network.mv_grid.station)))], axis=1).loc[timesteps, :]
    q1 = pd.concat(
        [np.abs(pypsa.lines_t['q1']),
         np.abs(pypsa.transformers_t['q1']),
         np.abs(pypsa.generators_t['q']['Generator_slack'].rename(
             repr(network.mv_grid.station)))], axis=1).loc[timesteps, :]
    p0 = pd.concat(
        [np.abs(pypsa.lines_t['p0']),
         np.abs(pypsa.transformers_t['p0']),
         np.abs(pypsa.generators_t['p']['Generator_slack'].rename(
            repr(network.mv_grid.station)))], axis=1).loc[timesteps, :]
    p1 = pd.concat(
        [np.abs(pypsa.lines_t['p1']),
         np.abs(pypsa.transformers_t['p1']),
         np.abs(pypsa.generators_t['p']['Generator_slack'].rename(
             repr(network.mv_grid.station)))], axis=1).loc[timesteps, :]

    # determine apparent power and line endings/transformers' side
    s0 = np.hypot(p0, q0)
    s1 = np.hypot(p1, q1)

    # choose p and q from line ending with max(s0,s1)
    network.results.pfa_p = p0.where(s0 > s1, p1) * 1e3
    network.results.pfa_q = q0.where(s0 > s1, q1) * 1e3

    lines_bus0 = pypsa.lines['bus0'].to_dict()
    bus0_v_mag_pu = pypsa.buses_t['v_mag_pu'].T.loc[
                    list(lines_bus0.values()), :].copy()
    bus0_v_mag_pu.index = list(lines_bus0.keys())

    lines_bus1 = pypsa.lines['bus1'].to_dict()
    bus1_v_mag_pu = pypsa.buses_t['v_mag_pu'].T.loc[
                    list(lines_bus1.values()), :].copy()
    bus1_v_mag_pu.index = list(lines_bus1.keys())

    line_voltage_avg = 0.5 * (bus0_v_mag_pu.loc[:, timesteps] +
                              bus1_v_mag_pu.loc[:, timesteps])

    # Get voltage levels at line (avg. of buses at both sides)
    network.results._i_res = \
        network.results.s_res()[pypsa.lines_t['q0'].columns].truediv(
            pypsa.lines['v_nom'] * line_voltage_avg.T,
            axis='columns') * sqrt(3)
    # process results at nodes
    generators_names = [repr(g) for g in network.mv_grid.generators]
    generators_mapping = {v: k for k, v in
                          pypsa.generators.loc[generators_names][
                              'bus'].to_dict().items()}
    storages_names = [repr(g) for g in
                      network.mv_grid.graph.nodes_by_attribute('storage')]
    storages_mapping = {v: k for k, v in
                        pypsa.storage_units.loc[storages_names][
                            'bus'].to_dict().items()}
    branch_t_names = [repr(bt) for bt in
                      network.mv_grid.graph.nodes_by_attribute('branch_tee')]
    branch_t_mapping = {'_'.join(['Bus', v]): v for v in branch_t_names}
    mv_station_names = [repr(m) for m in
                        network.mv_grid.graph.nodes_by_attribute('mv_station')]
    mv_station_mapping_sec = {'_'.join(['Bus', v]): v for v in
                              mv_station_names}
    mv_switch_disconnector_names = [repr(sd) for sd in
                                    network.mv_grid.graph.nodes_by_attribute(
                                        'mv_disconnecting_point')]
    mv_switch_disconnector_mapping = {'_'.join(['Bus', v]): v for v in
                                      mv_switch_disconnector_names}

    lv_station_mapping_pri = {
        '_'.join(['Bus', l.__repr__('mv')]): repr(l)
        for l in network.mv_grid.graph.nodes_by_attribute('lv_station')}
    lv_station_mapping_sec = {
        '_'.join(['Bus', l.__repr__('lv')]): repr(l)
        for l in network.mv_grid.graph.nodes_by_attribute('lv_station')}
    loads_names = [repr(lo) for lo in
                   network.mv_grid.graph.nodes_by_attribute('load')]
    loads_mapping = {v: k for k, v in
                     pypsa.loads.loc[loads_names][
                         'bus'].to_dict().items()}

    lv_generators_names = []
    lv_storages_names = []
    lv_branch_t_names = []
    lv_loads_names = []
    for lv_grid in network.mv_grid.lv_grids:
        lv_generators_names.extend([repr(g) for g in
                                    lv_grid.graph.nodes_by_attribute(
                                        'generator')])
        lv_storages_names.extend([repr(g) for g in
                                  lv_grid.graph.nodes_by_attribute(
                                      'storage')])
        lv_branch_t_names.extend([repr(bt) for bt in
                             lv_grid.graph.nodes_by_attribute('branch_tee')])
        lv_loads_names.extend([repr(lo) for lo in
                          lv_grid.graph.nodes_by_attribute('load')])

    lv_generators_mapping = {v: k for k, v in
                             pypsa.generators.loc[lv_generators_names][
                                 'bus'].to_dict().items()}
    lv_storages_mapping = {v: k for k, v in
                           pypsa.storage_units.loc[lv_storages_names][
                               'bus'].to_dict().items()}
    lv_branch_t_mapping = {'_'.join(['Bus', v]): v for v in lv_branch_t_names}
    lv_loads_mapping = {v: k for k, v in pypsa.loads.loc[lv_loads_names][
        'bus'].to_dict().items()}

    names_mapping = {
        **generators_mapping,
        **storages_mapping,
        **branch_t_mapping,
        **mv_station_mapping_sec,
        **lv_station_mapping_pri,
        **lv_station_mapping_sec,
        **mv_switch_disconnector_mapping,
        **loads_mapping,
        **lv_generators_mapping,
        **lv_storages_mapping,
        **lv_loads_mapping,
        **lv_branch_t_mapping
    }

    # write voltage levels obtained from power flow to results object
    pfa_v_mag_pu_mv = (pypsa.buses_t['v_mag_pu'][
        list(generators_mapping) +
        list(storages_mapping) +
        list(branch_t_mapping) +
        list(mv_station_mapping_sec) +
        list(mv_switch_disconnector_mapping) +
        list(lv_station_mapping_pri) +
        list(loads_mapping)]).rename(columns=names_mapping)
    pfa_v_mag_pu_lv = (pypsa.buses_t['v_mag_pu'][
        list(lv_station_mapping_sec) +
        list(lv_generators_mapping) +
        list(lv_storages_mapping) +
        list(lv_branch_t_mapping) +
        list(lv_loads_mapping)]).rename(columns=names_mapping)
    network.results.pfa_v_mag_pu = pd.concat(
        {'mv': pfa_v_mag_pu_mv.loc[timesteps, :],
         'lv': pfa_v_mag_pu_lv.loc[timesteps, :]}, axis=1)

Response: Processes the results of a single PFA and returns a new set of results.


Instruction:
Explain what the following Python 3 code does
def update_pypsa_generator_import(network):

    # get topology and time series data
    if network.pypsa.edisgo_mode is None:
        mv_components = mv_to_pypsa(network)
        lv_components = lv_to_pypsa(network)
        components = combine_mv_and_lv(mv_components, lv_components)
    elif network.pypsa.edisgo_mode is 'mv':
        raise NotImplementedError
    elif network.pypsa.edisgo_mode is 'lv':
        raise NotImplementedError
    else:
        raise ValueError("Provide proper mode or leave it empty to export "
                         "entire grid topology.")

    # check topology
    _check_topology(components)

    # create power flow problem
    pypsa_network = PyPSANetwork()
    pypsa_network.edisgo_mode = network.pypsa.edisgo_mode
    pypsa_network.set_snapshots(network.pypsa.snapshots)

    # import grid topology to PyPSA network
    # buses are created first to avoid warnings
    pypsa_network.import_components_from_dataframe(components['Bus'], 'Bus')

    for k, comps in components.items():
        if k is not 'Bus' and not comps.empty:
            pypsa_network.import_components_from_dataframe(comps, k)

    # import time series to PyPSA network
    pypsa_network.generators_t.p_set = network.pypsa.generators_t.p_set
    pypsa_network.generators_t.q_set = network.pypsa.generators_t.q_set
    pypsa_network.loads_t.p_set = network.pypsa.loads_t.p_set
    pypsa_network.loads_t.q_set = network.pypsa.loads_t.q_set
    pypsa_network.storage_units_t.p_set = network.pypsa.storage_units_t.p_set
    pypsa_network.storage_units_t.q_set = network.pypsa.storage_units_t.q_set
    pypsa_network.buses_t.v_mag_pu_set = network.pypsa.buses_t.v_mag_pu_set

    network.pypsa = pypsa_network

    if len(list(components['Generator'].index.values)) > 1:
        update_pypsa_generator_timeseries(network)
    if list(components['Bus'].index.values):
        update_pypsa_bus_timeseries(network)
    if len(list(components['StorageUnit'].index.values)) > 0:
        update_pypsa_storage_timeseries(network)

    _check_integrity_of_pypsa(pypsa_network)

Response: Update PyPSA generator import code for a given network.


Instruction:
Given the following Python 3 function, write the documentation
def update_pypsa_grid_reinforcement(network, equipment_changes):

    # Step 1: Update transformers
    transformers = equipment_changes[
        equipment_changes['equipment'].apply(isinstance, args=(Transformer,))]
    # HV/MV transformers are excluded because it's not part of the PFA
    removed_transformers = [repr(_) for _ in
                            transformers[transformers['change'] == 'removed'][
                                'equipment'].tolist() if _.voltage_op < 10]
    added_transformers = transformers[transformers['change'] == 'added']

    transformer = {'name': [],
                   'bus0': [],
                   'bus1': [],
                   'type': [],
                   'model': [],
                   'x': [],
                   'r': [],
                   's_nom': [],
                   'tap_ratio': []}

    for idx, row in added_transformers.iterrows():

        if isinstance(idx, LVStation):
            # we choose voltage of transformers' primary side
            v_base = idx.mv_grid.voltage_nom
            z_base = v_base ** 2 / row['equipment'].type.S_nom

            transformer['bus0'].append('_'.join(['Bus', idx.__repr__(
                side='mv')]))
            transformer['bus1'].append('_'.join(['Bus', idx.__repr__(
                side='lv')]))
            transformer['name'].append(repr(row['equipment']))
            transformer['type'].append("")
            transformer['model'].append('pi')
            transformer['r'].append(row['equipment'].type.R / z_base)
            transformer['x'].append(row['equipment'].type.X / z_base)
            transformer['s_nom'].append(row['equipment'].type.S_nom / 1e3)
            transformer['tap_ratio'].append(1)

    network.pypsa.transformers.drop(removed_transformers, inplace=True)

    if transformer['name']:
        network.pypsa.import_components_from_dataframe(
            pd.DataFrame(transformer).set_index('name'), 'Transformer')

    # Step 2: Update lines
    lines = equipment_changes.loc[equipment_changes.index[
        equipment_changes.reset_index()['index'].apply(
            isinstance, args=(Line,))]]
    changed_lines = lines[lines['change'] == 'changed']

    lv_stations = network.mv_grid.graph.nodes_by_attribute('lv_station')

    omega = 2 * pi * 50

    for idx, row in changed_lines.iterrows():
        # Update line parameters
        network.pypsa.lines.loc[repr(idx), 'r'] = (
            idx.type['R'] / idx.quantity * idx.length)
        network.pypsa.lines.loc[repr(idx), 'x'] = (
            idx.type['L'] / 1e3 * omega / idx.quantity * idx.length)
        network.pypsa.lines.loc[repr(idx), 's_nom'] = (
            sqrt(3) * idx.type['I_max_th'] * idx.type[
                'U_n'] * idx.quantity / 1e3)
        network.pypsa.lines.loc[repr(idx), 'length'] = idx.length

        # Update buses line is connected to
        adj_nodes = idx.grid.graph.nodes_from_line(idx)

        if adj_nodes[0] in lv_stations:
            side = 'lv' if isinstance(idx.grid, LVGrid) else 'mv'
            bus0 = '_'.join(['Bus', adj_nodes[0].__repr__(side=side)])
        else:
            bus0 = '_'.join(['Bus', repr(adj_nodes[0])])

        if adj_nodes[1] in lv_stations:
            side = 'lv' if isinstance(idx.grid, LVGrid) else 'mv'
            bus1 = '_'.join(['Bus', adj_nodes[1].__repr__(side=side)])
        else:
            bus1 = '_'.join(['Bus', repr(adj_nodes[1])])

        network.pypsa.lines.loc[repr(idx), 'bus0'] = bus0
        network.pypsa.lines.loc[repr(idx), 'bus1'] = bus1

Response: Updates the data of the current eDisGo grid with the changes in equipment_changes.


Instruction:
Write a Python 3 function that can
update the pypsa representation of the edisgo graph with the given storages and lines.

Response:
def update_pypsa_storage(pypsa, storages, storages_lines):
    """
    Adds storages and their lines to pypsa representation of the edisgo graph.

    This function effects the following attributes of the pypsa network:
    components ('StorageUnit'), storage_units, storage_units_t (p_set, q_set),
    buses, lines

    Parameters
    -----------
    pypsa : :pypsa:`pypsa.Network<network>`
    storages : :obj:`list`
        List with storages of type :class:`~.grid.components.Storage` to add
        to pypsa network.
    storages_lines : :obj:`list`
        List with lines of type :class:`~.grid.components.Line` that connect
        storages to the grid.

    """
    bus = {'name': [], 'v_nom': [], 'x': [], 'y': []}

    line = {'name': [],
            'bus0': [],
            'bus1': [],
            'type': [],
            'x': [],
            'r': [],
            's_nom': [],
            'length': []}

    storage = {
        'name': [],
        'bus': [],
        'p_nom': [],
        'state_of_charge_initial': [],
        'efficiency_store': [],
        'efficiency_dispatch': [],
        'standing_loss': []}

    for s in storages:
        bus_name = '_'.join(['Bus', repr(s)])

        storage['name'].append(repr(s))
        storage['bus'].append(bus_name)
        storage['p_nom'].append(s.nominal_power / 1e3)
        storage['state_of_charge_initial'].append(s.soc_initial)
        storage['efficiency_store'].append(s.efficiency_in)
        storage['efficiency_dispatch'].append(s.efficiency_out)
        storage['standing_loss'].append(s.standing_loss)

        bus['name'].append(bus_name)
        bus['v_nom'].append(s.grid.voltage_nom)
        bus['x'].append(s.geom.x)
        bus['y'].append(s.geom.y)

    omega = 2 * pi * 50
    for l in storages_lines:
        line['name'].append(repr(l))

        adj_nodes = l.grid.graph.nodes_from_line(l)
        if isinstance(l.grid, LVGrid):
            if isinstance(adj_nodes[0], LVStation):
                line['bus0'].append(
                    '_'.join(['Bus', adj_nodes[0].__repr__(side='lv')]))
            else:
                line['bus0'].append('_'.join(['Bus', repr(adj_nodes[0])]))

            if isinstance(adj_nodes[1], LVStation):
                line['bus1'].append(
                    '_'.join(['Bus', adj_nodes[1].__repr__(side='lv')]))
            else:
                line['bus1'].append('_'.join(['Bus', repr(adj_nodes[1])]))
        else:
            if isinstance(adj_nodes[0], LVStation):
                line['bus0'].append(
                    '_'.join(['Bus', adj_nodes[0].__repr__(side='mv')]))
            elif isinstance(adj_nodes[0], MVStation):
                line['bus0'].append(
                    '_'.join(['Bus', adj_nodes[0].__repr__(side='lv')]))
            else:
                line['bus0'].append('_'.join(['Bus', repr(adj_nodes[0])]))

            if isinstance(adj_nodes[1], LVStation):
                line['bus1'].append(
                    '_'.join(['Bus', adj_nodes[1].__repr__(side='mv')]))
            elif isinstance(adj_nodes[1], MVStation):
                line['bus1'].append(
                    '_'.join(['Bus', adj_nodes[1].__repr__(side='lv')]))
            else:
                line['bus1'].append('_'.join(['Bus', repr(adj_nodes[1])]))

        line['type'].append("")
        line['x'].append(l.type['L'] * omega / 1e3 * l.length)
        line['r'].append(l.type['R'] * l.length)
        line['s_nom'].append(
            sqrt(3) * l.type['I_max_th'] * l.type['U_n'] / 1e3)
        line['length'].append(l.length)

    # import new components to pypsa
    pypsa.import_components_from_dataframe(
        pd.DataFrame(bus).set_index('name'), 'Bus')
    pypsa.import_components_from_dataframe(
        pd.DataFrame(storage).set_index('name'), 'StorageUnit')
    pypsa.import_components_from_dataframe(
        pd.DataFrame(line).set_index('name'), 'Line')

    # import time series of storages and buses to pypsa
    timeseries_storage_p = pd.DataFrame()
    timeseries_storage_q = pd.DataFrame()
    for s in storages:
        timeseries_storage_p[repr(s)] = s.pypsa_timeseries('p').loc[
            pypsa.storage_units_t.p_set.index]
        timeseries_storage_q[repr(s)] = s.pypsa_timeseries('q').loc[
            pypsa.storage_units_t.q_set.index]

    import_series_from_dataframe(pypsa, timeseries_storage_p,
                                 'StorageUnit', 'p_set')
    import_series_from_dataframe(pypsa, timeseries_storage_q,
                                 'StorageUnit', 'q_set')


Instruction:
Implement a Python 3 function for
updating load generator storage and bus time series in pypsa representation.

Response:
def update_pypsa_timeseries(network, loads_to_update=None,
                            generators_to_update=None, storages_to_update=None,
                            timesteps=None):
    """
    Updates load, generator, storage and bus time series in pypsa network.

    See functions :func:`update_pypsa_load_timeseries`,
    :func:`update_pypsa_generator_timeseries`,
    :func:`update_pypsa_storage_timeseries`, and
    :func:`update_pypsa_bus_timeseries` for more information.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    loads_to_update : :obj:`list`, optional
        List with all loads (of type :class:`~.grid.components.Load`) that need
        to be updated. If None all loads are updated depending on mode. See
        :meth:`~.tools.pypsa_io.to_pypsa` for more information.
    generators_to_update : :obj:`list`, optional
        List with all generators (of type :class:`~.grid.components.Generator`)
        that need to be updated. If None all generators are updated depending
        on mode. See :meth:`~.tools.pypsa_io.to_pypsa` for more information.
    storages_to_update : :obj:`list`, optional
        List with all storages (of type :class:`~.grid.components.Storage`)
        that need to be updated. If None all storages are updated depending on
        mode. See :meth:`~.tools.pypsa_io.to_pypsa` for more information.
    timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
        Timesteps specifies which time steps of the load time series to export
        to pypsa representation and use in power flow analysis.
        If None all time steps currently existing in pypsa representation are
        updated. If not None current time steps are overwritten by given
        time steps. Default: None.

    """
    update_pypsa_load_timeseries(
        network, loads_to_update=loads_to_update, timesteps=timesteps)
    update_pypsa_generator_timeseries(
        network, generators_to_update=generators_to_update,
        timesteps=timesteps)
    update_pypsa_storage_timeseries(
        network, storages_to_update=storages_to_update, timesteps=timesteps)
    update_pypsa_bus_timeseries(network, timesteps=timesteps)
    # update pypsa snapshots
    if timesteps is None:
        timesteps = network.pypsa.buses_t.v_mag_pu_set.index
    network.pypsa.set_snapshots(timesteps)


Instruction:
Create a Python 3 function for
updating load time series in pypsa representation.

Response:
def update_pypsa_load_timeseries(network, loads_to_update=None,
                                 timesteps=None):
    """
    Updates load time series in pypsa representation.

    This function overwrites p_set and q_set of loads_t attribute of
    pypsa network.
    Be aware that if you call this function with `timesteps` and thus overwrite
    current time steps it may lead to inconsistencies in the pypsa network
    since only load time series are updated but none of the other time series
    or the snapshots attribute of the pypsa network. Use the function
    :func:`update_pypsa_timeseries` to change the time steps you want to
    analyse in the power flow analysis.
    This function will also raise an error when a load that is currently not
    in the pypsa representation is added.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    loads_to_update : :obj:`list`, optional
        List with all loads (of type :class:`~.grid.components.Load`) that need
        to be updated. If None all loads are updated depending on mode. See
        :meth:`~.tools.pypsa_io.to_pypsa` for more information.
    timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
        Timesteps specifies which time steps of the load time series to export
        to pypsa representation. If None all time steps currently existing in
        pypsa representation are updated. If not None current time steps are
        overwritten by given time steps. Default: None.

    """
    _update_pypsa_timeseries_by_type(
        network, type='load', components_to_update=loads_to_update,
        timesteps=timesteps)


Instruction:
Can you generate the documentation for the following Python 3 function
def update_pypsa_generator_timeseries(network, generators_to_update=None,
                                      timesteps=None):
    """
    Updates generator time series in pypsa representation.

    This function overwrites p_set and q_set of generators_t attribute of
    pypsa network.
    Be aware that if you call this function with `timesteps` and thus overwrite
    current time steps it may lead to inconsistencies in the pypsa network
    since only generator time series are updated but none of the other time
    series or the snapshots attribute of the pypsa network. Use the function
    :func:`update_pypsa_timeseries` to change the time steps you want to
    analyse in the power flow analysis.
    This function will also raise an error when a generator that is currently
    not in the pypsa representation is added.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    generators_to_update : :obj:`list`, optional
        List with all generators (of type :class:`~.grid.components.Generator`)
        that need to be updated. If None all generators are updated depending
        on mode. See :meth:`~.tools.pypsa_io.to_pypsa` for more information.
    timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
        Timesteps specifies which time steps of the generator time series to
        export to pypsa representation. If None all time steps currently
        existing in pypsa representation are updated. If not None current time
        steps are overwritten by given time steps. Default: None.

    """
    _update_pypsa_timeseries_by_type(
        network, type='generator', components_to_update=generators_to_update,
        timesteps=timesteps)

Response: Updates generator time series in pypsa representation.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def update_pypsa_storage_timeseries(network, storages_to_update=None,
                                    timesteps=None):
    """
    Updates storage time series in pypsa representation.

    This function overwrites p_set and q_set of storage_unit_t attribute of
    pypsa network.
    Be aware that if you call this function with `timesteps` and thus overwrite
    current time steps it may lead to inconsistencies in the pypsa network
    since only storage time series are updated but none of the other time
    series or the snapshots attribute of the pypsa network. Use the function
    :func:`update_pypsa_timeseries` to change the time steps you want to
    analyse in the power flow analysis.
    This function will also raise an error when a storage that is currently
    not in the pypsa representation is added.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    storages_to_update : :obj:`list`, optional
        List with all storages (of type :class:`~.grid.components.Storage`)
        that need to be updated. If None all storages are updated depending on
        mode. See :meth:`~.tools.pypsa_io.to_pypsa` for more information.
    timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
        Timesteps specifies which time steps of the storage time series to
        export to pypsa representation. If None all time steps currently
        existing in pypsa representation are updated. If not None current time
        steps are overwritten by given time steps. Default: None.

    """
    _update_pypsa_timeseries_by_type(
        network, type='storage', components_to_update=storages_to_update,
        timesteps=timesteps)

Response: Updates storage time series in pypsa representation.


Instruction:
How would you implement a function in Python 3 that
updates the time series of the buses voltage in pypsa representation.

Response:
def update_pypsa_bus_timeseries(network, timesteps=None):
    """
    Updates buses voltage time series in pypsa representation.

    This function overwrites v_mag_pu_set of buses_t attribute of
    pypsa network.
    Be aware that if you call this function with `timesteps` and thus overwrite
    current time steps it may lead to inconsistencies in the pypsa network
    since only bus time series are updated but none of the other time
    series or the snapshots attribute of the pypsa network. Use the function
    :func:`update_pypsa_timeseries` to change the time steps you want to
    analyse in the power flow analysis.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
        Timesteps specifies which time steps of the time series to
        export to pypsa representation. If None all time steps currently
        existing in pypsa representation are updated. If not None current
        time steps are overwritten by given time steps. Default: None.

    """
    if timesteps is None:
        timesteps = network.pypsa.buses_t.v_mag_pu_set.index
    # check if timesteps is array-like, otherwise convert to list
    if not hasattr(timesteps, "__len__"):
        timesteps = [timesteps]
    buses = network.pypsa.buses.index
    v_mag_pu_set = _pypsa_bus_timeseries(network, buses, timesteps)
    network.pypsa.buses_t.v_mag_pu_set = v_mag_pu_set


Instruction:
Given the following Python 3 function, write the documentation
def _update_pypsa_timeseries_by_type(network, type, components_to_update=None,
                                     timesteps=None):
    """
    Updates time series of specified component in pypsa representation.

    Be aware that if you call this function with `timesteps` and thus overwrite
    current time steps it may lead to inconsistencies in the pypsa network
    since only time series of the specified component are updated but none of
    the other time series or the snapshots attribute of the pypsa network.
    Use the function :func:`update_pypsa_timeseries` to change the time steps
    you want to analyse in the power flow analysis.
    This function will raise an error when a component that is currently not in
    the pypsa representation is added.

    Parameters
    ----------
    network : Network
        The eDisGo grid topology model overall container
    type : :obj:`str`
        Type specifies the type of component (load, generator or storage)
        that is updated.
    components_to_update : :obj:`list`, optional
        List with all components (either of type
        :class:`~.grid.components.Load`, :class:`~.grid.components.Generator`
        or :class:`~.grid.components.Storage`) that need to be updated.
        Possible options are 'load', 'generator' and 'storage'.
        Components in list must all be of the same type. If None all components
        specified by `type` are updated depending on the mode. See
        :meth:`~.tools.pypsa_io.to_pypsa` for more information on mode.
    timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
        Timesteps specifies which time steps of the time series to
        export to pypsa representation. If None all time steps currently
        existing in pypsa representation are updated. If not None current
        time steps are overwritten by given time steps. Default: None.

    """

    # pypsa dataframe to update
    if type == 'load':
        pypsa_ts = network.pypsa.loads_t
        components_in_pypsa = network.pypsa.loads.index
    elif type == 'generator':
        pypsa_ts = network.pypsa.generators_t
        components_in_pypsa = network.pypsa.generators.index
    elif type == 'storage':
        pypsa_ts = network.pypsa.storage_units_t
        components_in_pypsa = network.pypsa.storage_units.index
    else:
        raise ValueError('{} is not a valid type.'.format(type))

    # MV and LV loads
    if network.pypsa.edisgo_mode is None:
        # if no components are specified get all components of specified type
        # in whole grid
        if components_to_update is None:
            grids = [network.mv_grid] + list(network.mv_grid.lv_grids)
            if type == 'generator':
                components_to_update = list(itertools.chain(
                    *[grid.generators for grid in grids]))
            else:
                components_to_update = list(itertools.chain(
                    *[grid.graph.nodes_by_attribute(type) for grid in grids]))
        # if no time steps are specified update all time steps currently
        # contained in pypsa representation
        if timesteps is None:
            timesteps = pypsa_ts.p_set.index
        # check if timesteps is array-like, otherwise convert to list
        # (necessary to avoid getting a scalar using .loc)
        if not hasattr(timesteps, "__len__"):
            timesteps = [timesteps]

        p_set = pd.DataFrame()
        q_set = pd.DataFrame()
        for comp in components_to_update:
            if repr(comp) in components_in_pypsa:
                p_set[repr(comp)] = comp.pypsa_timeseries('p').loc[timesteps]
                q_set[repr(comp)] = comp.pypsa_timeseries('q').loc[timesteps]
            else:
                raise KeyError("Tried to update component {} but could not "
                               "find it in pypsa network.".format(comp))
        # overwrite pypsa time series
        pypsa_ts.p_set = p_set
        pypsa_ts.q_set = q_set

    # MV and aggregated LV loads
    elif network.pypsa.edisgo_mode is 'mv':
        raise NotImplementedError

    # LV only
    elif network.pypsa.edisgo_mode is 'lv':
        raise NotImplementedError

Response: Updates time series of specified type in pypsa representation.


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

    # get params from config
    buffer_radius = int(network.config[
                            'grid_connection']['conn_buffer_radius'])
    buffer_radius_inc = int(network.config[
                                'grid_connection']['conn_buffer_radius_inc'])

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

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

            # ===== voltage level 4: generator has to be connected to MV station =====
            if geno.v_level == 4:

                line_length = calc_geo_dist_vincenty(network=network,
                                                     node_source=geno,
                                                     node_target=network.mv_grid.station)

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

                network.mv_grid.graph.add_edge(network.mv_grid.station,
                                               geno,
                                               line=line,
                                               type='line')

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

            # ===== voltage level 5: generator has to be connected to MV grid (next-neighbor) =====
            elif geno.v_level == 5:

                # get branches within a the predefined radius `generator_buffer_radius`
                branches = calc_geo_lines_in_buffer(network=network,
                                                    node=geno,
                                                    grid=network.mv_grid,
                                                    radius=buffer_radius,
                                                    radius_inc=buffer_radius_inc)

                # calc distance between generator and grid's lines -> find nearest line
                conn_objects_min_stack = _find_nearest_conn_objects(network=network,
                                                                    node=geno,
                                                                    branches=branches)

                # connect!
                # go through the stack (from nearest to most far connection target object)
                generator_connected = False
                for dist_min_obj in conn_objects_min_stack:
                    target_obj_result = _connect_mv_node(network=network,
                                                         node=geno,
                                                         target_obj=dist_min_obj)

                    if target_obj_result is not None:
                        generator_connected = True
                        break

                if not generator_connected:
                    logger.debug(
                        'Generator {0} could not be connected, try to '
                        'increase the parameter `conn_buffer_radius` in '
                        'config file `config_grid.cfg` to gain more possible '
                        'connection points.'.format(geno))

Response: Connect MV generators to existing grids.