Instruction:
Given the following Python 3 function, write the documentation
def mv_grid_topology(pypsa_network, configs, timestep=None,
                     line_color=None, node_color=None,
                     line_load=None, grid_expansion_costs=None,
                     filename=None, arrows=False,
                     grid_district_geom=True, background_map=True,
                     voltage=None, limits_cb_lines=None, limits_cb_nodes=None,
                     xlim=None, ylim=None, lines_cmap='inferno_r',
                     title='', scaling_factor_line_width=None):
    """
    Plot line loading as color on lines.

    Displays line loading relative to nominal capacity.

    Parameters
    ----------
    pypsa_network : :pypsa:`pypsa.Network<network>`
    configs : :obj:`dict`
        Dictionary with used configurations from config files. See
        :class:`~.grid.network.Config` for more information.
    timestep : :pandas:`pandas.Timestamp<timestamp>`
        Time step to plot analysis results for. If `timestep` is None maximum
        line load and if given, maximum voltage deviation, is used. In that
        case arrows cannot be drawn. Default: None.
    line_color : :obj:`str`
        Defines whereby to choose line colors (and implicitly size). Possible
        options are:

        * 'loading'
          Line color is set according to loading of the line. Loading of MV
          lines must be provided by parameter `line_load`.
        * 'expansion_costs'
          Line color is set according to investment costs of the line. This
          option also effects node colors and sizes by plotting investment in
          stations and setting `node_color` to 'storage_integration' in order
          to plot storage size of integrated storages. Grid expansion costs
          must be provided by parameter `grid_expansion_costs`.
        * None (default)
          Lines are plotted in black. Is also the fallback option in case of
          wrong input.

    node_color : :obj:`str`
        Defines whereby to choose node colors (and implicitly size). Possible
        options are:

        * 'technology'
          Node color as well as size is set according to type of node
          (generator, MV station, etc.).
        * 'voltage'
          Node color is set according to voltage deviation from 1 p.u..
          Voltages of nodes in MV grid must be provided by parameter `voltage`.
        * 'storage_integration'
          Only storages are plotted. Size of node corresponds to size of
          storage.
        * None (default)
          Nodes are not plotted. Is also the fallback option in case of wrong
          input.

    line_load : :pandas:`pandas.DataFrame<dataframe>`
        Dataframe with current results from power flow analysis in A. Index of
        the dataframe is a :pandas:`pandas.DatetimeIndex<datetimeindex>`,
        columns are the line representatives.
    grid_expansion_costs : :pandas:`pandas.DataFrame<dataframe>`
        Dataframe with grid expansion costs in kEUR. See `grid_expansion_costs`
        in :class:`~.grid.network.Results` for more information.
    filename : :obj:`str`
        Filename to save plot under. If not provided, figure is shown directly.
        Default: None.
    arrows : :obj:`Boolean`
        If True draws arrows on lines in the direction of the power flow. Does
        only work when `line_color` option 'loading' is used and a time step
        is given.
        Default: False.
    limits_cb_lines : :obj:`tuple`
        Tuple with limits for colorbar of line color. First entry is the
        minimum and second entry the maximum value. Default: None.
    limits_cb_nodes : :obj:`tuple`
        Tuple with limits for colorbar of nodes. First entry is the
        minimum and second entry the maximum value. Default: None.
    xlim : :obj:`tuple`
        Limits of x-axis. Default: None.
    ylim : :obj:`tuple`
        Limits of y-axis. Default: None.
    lines_cmap : :obj:`str`
        Colormap to use for lines in case `line_color` is 'loading' or
        'expansion_costs'. Default: 'inferno_r'.
    title : :obj:`str`
        Title of the plot. Default: ''.
    scaling_factor_line_width : :obj:`float` or None
        If provided line width is set according to the nominal apparent power
        of the lines. If line width is None a default line width of 2 is used
        for each line. Default: None.

    """

    def get_color_and_size(name, colors_dict, sizes_dict):
        if 'BranchTee' in name:
            return colors_dict['BranchTee'], sizes_dict['BranchTee']
        elif 'LVStation' in name:
            return colors_dict['LVStation'], sizes_dict['LVStation']
        elif 'GeneratorFluctuating' in name:
            return (colors_dict['GeneratorFluctuating'],
                    sizes_dict['GeneratorFluctuating'])
        elif 'Generator' in name:
            return colors_dict['Generator'], sizes_dict['Generator']
        elif 'DisconnectingPoint' in name:
            return (colors_dict['DisconnectingPoint'],
                    sizes_dict['DisconnectingPoint'])
        elif 'MVStation' in name:
            return colors_dict['MVStation'], sizes_dict['MVStation']
        elif 'Storage' in name:
            return colors_dict['Storage'], sizes_dict['Storage']
        else:
            return colors_dict['else'], sizes_dict['else']

    def nodes_by_technology(buses):
        bus_sizes = {}
        bus_colors = {}
        colors_dict = {'BranchTee': 'b',
                       'GeneratorFluctuating': 'g',
                       'Generator': 'k',
                       'LVStation': 'c',
                       'MVStation': 'r',
                       'Storage': 'y',
                       'DisconnectingPoint': '0.75',
                       'else': 'orange'}
        sizes_dict = {'BranchTee': 10,
                      'GeneratorFluctuating': 100,
                      'Generator': 100,
                      'LVStation': 50,
                      'MVStation': 120,
                      'Storage': 100,
                      'DisconnectingPoint': 50,
                      'else': 200}
        for bus in buses:
            bus_colors[bus], bus_sizes[bus] = get_color_and_size(
                bus, colors_dict, sizes_dict)
        return bus_sizes, bus_colors

    def nodes_by_voltage(buses, voltage):
        bus_colors = {}
        bus_sizes = {}
        for bus in buses:
            if 'primary' in bus:
                bus_tmp = bus[12:]
            else:
                bus_tmp = bus[4:]
            if timestep is not None:
                bus_colors[bus] = 100 * abs(1 - voltage.loc[timestep,
                                                      ('mv', bus_tmp)])
            else:
                bus_colors[bus] = 100 * max(abs(1 - voltage.loc[
                                                    :, ('mv', bus_tmp)]))
            bus_sizes[bus] = 50
        return bus_sizes, bus_colors

    def nodes_storage_integration(buses):
        bus_sizes = {}
        for bus in buses:
            if not 'storage' in bus:
                bus_sizes[bus] = 0
            else:
                tmp = bus.split('_')
                storage_repr = '_'.join(tmp[1:])
                bus_sizes[bus] = pypsa_network.storage_units.loc[
                           storage_repr, 'p_nom'] * 1000 / 3
        return bus_sizes

    def nodes_by_costs(buses, grid_expansion_costs):
        # sum costs for each station
        costs_lv_stations = grid_expansion_costs[
            grid_expansion_costs.index.str.contains("LVStation")]
        costs_lv_stations['station'] = \
            costs_lv_stations.reset_index()['index'].apply(
                lambda _: '_'.join(_.split('_')[0:2])).values
        costs_lv_stations = costs_lv_stations.groupby('station').sum()
        costs_mv_station = grid_expansion_costs[
            grid_expansion_costs.index.str.contains("MVStation")]
        costs_mv_station['station'] = \
            costs_mv_station.reset_index()['index'].apply(
                lambda _: '_'.join(_.split('_')[0:2])).values
        costs_mv_station = costs_mv_station.groupby('station').sum()

        bus_sizes = {}
        bus_colors = {}
        for bus in buses:
            if 'LVStation' in bus:
                try:
                    tmp = bus.split('_')
                    lv_st = '_'.join(tmp[2:])
                    bus_colors[bus] = costs_lv_stations.loc[
                        lv_st, 'total_costs']
                    bus_sizes[bus] = 100
                except:
                    bus_colors[bus] = 0
                    bus_sizes[bus] = 0
            elif 'MVStation' in bus:
                try:
                    tmp = bus.split('_')
                    mv_st = '_'.join(tmp[2:])
                    bus_colors[bus] = costs_mv_station.loc[
                        mv_st, 'total_costs']
                    bus_sizes[bus] = 100
                except:
                    bus_colors[bus] = 0
                    bus_sizes[bus] = 0
            else:
                bus_colors[bus] = 0
                bus_sizes[bus] = 0

        return bus_sizes, bus_colors

    # set font and font size
    font = {'family': 'serif',
            'size': 15}
    matplotlib.rc('font', **font)

    # create pypsa network only containing MV buses and lines
    pypsa_plot = PyPSANetwork()
    pypsa_plot.buses = pypsa_network.buses.loc[pypsa_network.buses.v_nom >= 10]
    # filter buses of aggregated loads and generators
    pypsa_plot.buses = pypsa_plot.buses[
        ~pypsa_plot.buses.index.str.contains("agg")]
    pypsa_plot.lines = pypsa_network.lines[
        pypsa_network.lines.bus0.isin(pypsa_plot.buses.index)][
        pypsa_network.lines.bus1.isin(pypsa_plot.buses.index)]

    # line colors
    if line_color == 'loading':
        # calculate relative line loading
        # get load factor
        residual_load = tools.get_residual_load_from_pypsa_network(
            pypsa_network)
        case = residual_load.apply(
                lambda _: 'feedin_case' if _ < 0 else 'load_case')
        if timestep is not None:
            timeindex = [timestep]
        else:
            timeindex = line_load.index
        load_factor = pd.DataFrame(
            data={'s_nom': [float(configs[
                                      'grid_expansion_load_factors'][
                                      'mv_{}_line'.format(case.loc[_])])
                            for _ in timeindex]},
            index=timeindex)
        # get allowed line load
        s_allowed = load_factor.dot(
            pypsa_plot.lines.s_nom.to_frame().T * 1e3)
        # get line load from pf
        line_colors = line_load.loc[:, pypsa_plot.lines.index].divide(
            s_allowed).max()
    elif line_color == 'expansion_costs':
        node_color = 'expansion_costs'
        line_costs = pypsa_plot.lines.join(
            grid_expansion_costs, rsuffix='costs', how='left')
        line_colors = line_costs.total_costs.fillna(0)
    else:
        line_colors = pd.Series('black', index=pypsa_plot.lines.index)

    # bus colors and sizes
    if node_color == 'technology':
        bus_sizes, bus_colors = nodes_by_technology(pypsa_plot.buses.index)
        bus_cmap = None
    elif node_color == 'voltage':
        bus_sizes, bus_colors = nodes_by_voltage(
            pypsa_plot.buses.index, voltage)
        bus_cmap = plt.cm.Blues
    elif node_color == 'storage_integration':
        bus_sizes = nodes_storage_integration(pypsa_plot.buses.index)
        bus_colors = 'orangered'
        bus_cmap = None
    elif node_color == 'expansion_costs':
        bus_sizes, bus_colors = nodes_by_costs(pypsa_plot.buses.index,
                                               grid_expansion_costs)
        bus_cmap = None
    elif node_color is None:
        bus_sizes = 0
        bus_colors = 'r'
        bus_cmap = None
    else:
        logging.warning('Choice for `node_color` is not valid. Default is '
                        'used instead.')
        bus_sizes = 0
        bus_colors = 'r'
        bus_cmap = None

    # convert bus coordinates to Mercator
    if contextily and background_map:
        inProj = Proj(init='epsg:4326')
        outProj = Proj(init='epsg:3857')
        x2, y2 = transform(inProj, outProj,
                           list(pypsa_plot.buses.loc[:, 'x']),
                           list(pypsa_plot.buses.loc[:, 'y']))
        pypsa_plot.buses.loc[:, 'x'] = x2
        pypsa_plot.buses.loc[:, 'y'] = y2

    # plot
    plt.figure(figsize=(12, 8))
    ax = plt.gca()

    # plot grid district
    if grid_district_geom and geopandas:
        try:
            subst = pypsa_network.buses[
                pypsa_network.buses.index.str.contains("MVStation")].index[0]
            subst_id = subst.split('_')[-1]
            projection = 3857 if contextily and background_map else 4326
            region = get_grid_district_polygon(configs, subst_id=subst_id,
                                               projection=projection)
            region.plot(ax=ax, color='white', alpha=0.2,
                        edgecolor='red', linewidth=2)
        except Exception as e:
            logging.warning("Grid district geometry could not be plotted due "
                            "to the following error: {}".format(e))

    # if scaling factor is given s_nom is plotted as line width
    if scaling_factor_line_width is not None:
        line_width = pypsa_plot.lines.s_nom * scaling_factor_line_width
    else:
        line_width = 2
    cmap = plt.cm.get_cmap(lines_cmap)
    ll = pypsa_plot.plot(line_colors=line_colors, line_cmap=cmap, ax=ax,
                         title=title, line_widths=line_width,
                         branch_components=['Line'], basemap=True,
                         bus_sizes=bus_sizes, bus_colors=bus_colors,
                         bus_cmap=bus_cmap)

    # color bar line loading
    if line_color == 'loading':
        if limits_cb_lines is None:
            limits_cb_lines = (min(line_colors), max(line_colors))
        v = np.linspace(limits_cb_lines[0], limits_cb_lines[1], 101)
        cb = plt.colorbar(ll[1], boundaries=v, ticks=v[0:101:10])
        cb.set_clim(vmin=limits_cb_lines[0], vmax=limits_cb_lines[1])
        cb.set_label('Line loading in p.u.')
    # color bar grid expansion costs
    elif line_color == 'expansion_costs':
        if limits_cb_lines is None:
            limits_cb_lines = (min(min(line_colors), min(bus_colors.values())),
                              max(max(line_colors), max(bus_colors.values())))
        v = np.linspace(limits_cb_lines[0], limits_cb_lines[1], 101)
        cb = plt.colorbar(ll[1], boundaries=v, ticks=v[0:101:10])
        cb.set_clim(vmin=limits_cb_lines[0], vmax=limits_cb_lines[1])
        cb.set_label('Grid expansion costs in kEUR')

    # color bar voltage
    if node_color == 'voltage':
        if limits_cb_nodes is None:
            limits_cb_nodes = (min(bus_colors.values()),
                                 max(bus_colors.values()))
        v_voltage = np.linspace(limits_cb_nodes[0], limits_cb_nodes[1], 101)
        cb_voltage = plt.colorbar(ll[0], boundaries=v_voltage,
                                  ticks=v_voltage[0:101:10])
        cb_voltage.set_clim(vmin=limits_cb_nodes[0],
                            vmax=limits_cb_nodes[1])
        cb_voltage.set_label('Voltage deviation in %')

    # storages
    if node_color == 'expansion_costs':
        ax.scatter(
            pypsa_plot.buses.loc[
                pypsa_network.storage_units.loc[:, 'bus'], 'x'],
            pypsa_plot.buses.loc[
                pypsa_network.storage_units.loc[:, 'bus'], 'y'],
            c='orangered',
            s=pypsa_network.storage_units.loc[:, 'p_nom'] * 1000 / 3)
    # add legend for storage size and line capacity
    if (node_color == 'storage_integration' or
        node_color == 'expansion_costs') and \
            pypsa_network.storage_units.loc[:, 'p_nom'].any() > 0:
        scatter_handle = plt.scatter(
            [], [], c='orangered', s=100, label='= 300 kW battery storage')
    else:
        scatter_handle = None
    if scaling_factor_line_width is not None:
        line_handle = plt.plot(
            [], [], c='black', linewidth=scaling_factor_line_width*10,
            label='= 10 MVA')
    else:
        line_handle = None
    if scatter_handle and line_handle:
        plt.legend(handles=[scatter_handle, line_handle[0]], labelspacing=1,
                   title='Storage size and line capacity', borderpad=0.5,
                   loc=2, framealpha=0.5, fontsize='medium')
    elif scatter_handle:
        plt.legend(handles=[scatter_handle], labelspacing=1,
                   title='Storage size', borderpad=0.5,
                   loc=2, framealpha=0.5, fontsize='medium')
    elif line_handle:
        plt.legend(handles=[line_handle[0]], labelspacing=1,
                   title='Line capacity', borderpad=0.5,
                   loc=2, framealpha=0.5, fontsize='medium')

    # axes limits
    if xlim is not None:
        ax.set_xlim(xlim[0], xlim[1])
    if ylim is not None:
        ax.set_ylim(ylim[0], ylim[1])

    # hide axes labels
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # draw arrows on lines
    if arrows and timestep and line_color=='loading':
        path = ll[1].get_segments()
        colors = cmap(ll[1].get_array() / 100)
        for i in range(len(path)):
            if pypsa_network.lines_t.p0.loc[timestep,
                                            line_colors.index[i]] > 0:
                arrowprops = dict(arrowstyle="->", color='b')#colors[i])
            else:
                arrowprops = dict(arrowstyle="<-", color='b')#colors[i])
            ax.annotate(
                "",
                xy=abs(
                    (path[i][0] - path[i][1]) * 0.51 - path[i][0]),
                xytext=abs(
                    (path[i][0] - path[i][1]) * 0.49 - path[i][0]),
                arrowprops=arrowprops,
                size=10)

    # plot map data in background
    if contextily and background_map:
        try:
            add_basemap(ax, zoom=12)
        except Exception as e:
            logging.warning("Background map could not be plotted due to the "
                            "following error: {}".format(e))

    if filename is None:
        plt.show()
    else:
        plt.savefig(filename)
        plt.close()

Response: Plots MV grid topology for given network and configurations.


Instruction:
Can you generate a brief explanation for the following Python 3 code
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_set = _pypsa_load_timeseries(
                network, mode=mode, timesteps=timesteps)

        if len(list(components['Generator'].index.values)) > 1:
            timeseries_gen_p_min, timeseries_gen_p_max = \
                _pypsa_generator_timeseries(
                    network, mode=mode, timesteps=timesteps)
            timeseries_storage_p_min, timeseries_storage_p_max = \
                _pypsa_storage_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)
    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_min,
                                     'Generator', 'p_min_pu')
        import_series_from_dataframe(pypsa_network, timeseries_gen_p_max,
                                     'Generator', 'p_max_pu')
        import_series_from_dataframe(pypsa_network, timeseries_storage_p_min,
                                     'Generator', 'p_min_pu')
        import_series_from_dataframe(pypsa_network, timeseries_storage_p_max,
                                     'Generator', 'p_max_pu')

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

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

    _check_integrity_of_pypsa(pypsa_network)

    return pypsa_network

Response: Translate graph based grid representation to PyPSA representation.


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

    generators = []
    loads = []
    branch_tees = []
    lines = []
    lv_stations = []
    storages = []

    for lv_grid in network.mv_grid.lv_grids:
        generators.extend(lv_grid.graph.nodes_by_attribute('generator'))
        loads.extend(lv_grid.graph.nodes_by_attribute('load'))
        branch_tees.extend(lv_grid.graph.nodes_by_attribute('branch_tee'))
        lines.extend(lv_grid.graph.lines())
        lv_stations.extend(lv_grid.graph.nodes_by_attribute('lv_station'))
        storages.extend(lv_grid.graph.nodes_by_attribute('storage'))

    omega = 2 * pi * 50

    generator = {'name': [],
                 'bus': [],
                 'control': [],
                 'p_nom': [],
                 'type': [],
                 'p_nom_extendable': [],
                 'p_nom_min': [],
                 'p_nom_max': [],
                 'capital_cost': []
                 }

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

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

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

    storage = {
        'name': [],
        'bus': [],
        'p_nom': [],
        'p_nom_extendable': [],
        'p_nom_min': [],
        'p_nom_max': [],
        'capital_cost': [],
        'max_hours': []}

    # create dictionary 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]))
        generator['p_nom_extendable'].append(False)
        generator['p_nom_min'].append(0)  # 0.3
        generator['p_nom_max'].append(0)
        generator['capital_cost'].append(0)

        bus['name'].append(bus_name)
        bus['v_nom'].append(gen.grid.voltage_nom)
        bus['x'].append(None)
        bus['y'].append(None)

    # create dictionary 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(None)
        bus['y'].append(None)

    # 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(None)
        bus['y'].append(None)

    # 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='lv')]))
        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='lv')]))
        else:
            line['bus1'].append('_'.join(['Bus', repr(l['adj_nodes'][1])]))

        line['type'].append("")
        line['x'].append(
            l['line'].type['L'] * omega / 1e3 * l['line'].length)
        line['r'].append(l['line'].type['R'] * l['line'].length)
        s_nom = sqrt(3) * l['line'].type['I_max_th'] * \
                l['line'].type['U_n'] / 1e3
        line['s_nom'].append(s_nom)
        line['s_nom_min'].append(s_nom)
        line['s_max_pu'].append(0.6)
        line['s_nom_extendable'].append(True)
        line['capital_cost'].append(100)
        line['length'].append(l['line'].length)

    lv_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'),
        'StorageUnit': pd.DataFrame(storage).set_index('name')}

    return lv_components

Response: Convert LV grid topology to PyPSA representation.


Instruction:
Write a Python 3 script to
return a pandas. DataFrame of load time series for the given timesteps and mode.

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 = []
    lv_load_timeseries_p = []

    # 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_p.append(load.pypsa_timeseries('p').rename(
                repr(load)).to_frame().loc[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'):
                for sector in list(load.consumption.keys()):
                    # for sector in list(list(load.consumption.keys())[0]):
                    # ToDo: remove consideration of only industrial sector
                    # now, if a load object has consumption in multiple sectors
                    # (like currently only industrial/retail) the consumption is
                    # implicitly assigned to the industrial sector when being
                    # exported to pypsa.
                    # ToDo: resolve this in the importer
                    if sector != 'retail':
                        # 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)

    return load_df_p


Instruction:
Write a Python 3 script to
return a pandas. DataFrame of generator time series for MV and LV grid level.

Response:
def _pypsa_generator_timeseries(network, timesteps, mode=None):
    """Timeseries in PyPSA compatible format for generator 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_gen_timeseries_p_min = []
    mv_gen_timeseries_p_max = []
    lv_gen_timeseries_p_min = []
    lv_gen_timeseries_p_max = []

    # MV generator timeseries
    if mode is 'mv' or mode is None:
        for gen in network.mv_grid.graph.nodes_by_attribute('generator') + \
                network.mv_grid.graph.nodes_by_attribute('generator_aggr'):
            mv_gen_timeseries_p_min.append(gen.pypsa_timeseries('p').rename(
                repr(gen)).to_frame().loc[timesteps] / gen.nominal_capacity)
            mv_gen_timeseries_p_max.append(gen.pypsa_timeseries('p').rename(
                repr(gen)).to_frame().loc[timesteps] / gen.nominal_capacity)

    # 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.graph.nodes_by_attribute('generator'):
                lv_gen_timeseries_p_min.append(gen.pypsa_timeseries('p').rename(
                    repr(gen)).to_frame().loc[timesteps] / gen.nominal_capacity)
                lv_gen_timeseries_p_max.append(gen.pypsa_timeseries('p').rename(
                    repr(gen)).to_frame().loc[timesteps] / gen.nominal_capacity)

    # Slack time series
    lv_gen_timeseries_p_min.append(
        pd.Series([-1] * len(timesteps), index=timesteps).rename(
            "Generator_slack").to_frame())
    lv_gen_timeseries_p_max.append(
        pd.Series([1] * len(timesteps), index=timesteps).rename(
            "Generator_slack").to_frame())

    gen_df_p_max = pd.concat(
        mv_gen_timeseries_p_max + lv_gen_timeseries_p_max, axis=1)
    gen_df_p_min = pd.concat(
        mv_gen_timeseries_p_min + lv_gen_timeseries_p_min, axis=1)

    return gen_df_p_min, gen_df_p_max


Instruction:
Given the following Python 3 function, write the documentation
def _pypsa_storage_timeseries(network, timesteps, mode=None):

    mv_storage_timeseries_p_min = []
    mv_storage_timeseries_p_max = []

    # 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_p_min.append(
                storage.timeseries.p.rename(repr(
                    storage)).to_frame().loc[timesteps])
            mv_storage_timeseries_p_max.append(
                storage.timeseries.p.rename(repr(
                    storage)).to_frame().loc[timesteps])

    storage_df_p_min = pd.concat(
        mv_storage_timeseries_p_min, axis=1)
    storage_df_p_max = pd.concat(
        mv_storage_timeseries_p_max, axis=1)

    return storage_df_p_min, storage_df_p_max

Response: Returns a pandas. DataFrame of time series for storage instances in pypsa compatible format.


Instruction:
Implement a Python 3 function for
returning the actual time series used in power flow analysis.

Response:
def timeseries(self):
        """
        Load time series

        It returns the actual time series used in power flow analysis. If
        :attr:`_timeseries` is not :obj:`None`, it is returned. Otherwise,
        :meth:`timeseries()` looks for time series of the according sector in
        :class:`~.grid.network.TimeSeries` object.

        Returns
        -------
        :pandas:`pandas.DataFrame<dataframe>`
            DataFrame containing active power in kW in column 'p' and
            reactive power in kVA in column 'q'.

        """
        if self._timeseries is None:

            if isinstance(self.grid, MVGrid):
                voltage_level = 'mv'
            elif isinstance(self.grid, LVGrid):
                voltage_level = 'lv'

            ts_total = None
            for sector in self.consumption.keys():
                consumption = self.consumption[sector]

                # check if load time series for MV and LV are differentiated
                try:
                    ts = self.grid.network.timeseries.load[
                        sector, voltage_level].to_frame('p')
                except KeyError:
                    try:
                        ts = self.grid.network.timeseries.load[
                            sector].to_frame('p')
                    except KeyError:
                        logger.exception(
                            "No timeseries for load of type {} "
                            "given.".format(sector))
                        raise
                ts = ts * consumption
                ts_q = self.timeseries_reactive
                if ts_q is not None:
                    ts['q'] = ts_q.q
                else:
                    ts['q'] = ts['p'] * self.q_sign * tan(
                        acos(self.power_factor))

                if ts_total is None:
                    ts_total = ts
                else:
                    ts_total.p += ts.p
                    ts_total.q += ts.q

            return ts_total
        else:
            return self._timeseries


Instruction:
Make a summary of the following Python 3 code
def timeseries_reactive(self):
        if self._timeseries_reactive is None:
            # if normalized reactive power time series are given, they are
            # scaled by the annual consumption; if none are given reactive
            # power time series are calculated timeseries getter using a given
            # power factor
            if self.grid.network.timeseries.load_reactive_power is not None:
                self.power_factor = 'not_applicable'
                self.reactive_power_mode = 'not_applicable'
                ts_total = None
                for sector in self.consumption.keys():
                    consumption = self.consumption[sector]

                    try:
                        ts = self.grid.network.timeseries.load_reactive_power[
                            sector].to_frame('q')
                    except KeyError:
                        logger.exception(
                            "No timeseries for load of type {} "
                            "given.".format(sector))
                        raise
                    ts = ts * consumption
                    if ts_total is None:
                        ts_total = ts
                    else:
                        ts_total.q += ts.q
                return ts_total
            else:
                return None

        else:
            return self._timeseries_reactive

Response: Returns a pandas. Series containing the time series of the Reactive power in kvar.


Instruction:
Explain what the following Python 3 code does
def peak_load(self):
        peak_load = pd.Series(self.consumption).mul(pd.Series(
            self.grid.network.config['peakload_consumption_ratio']).astype(
            float), fill_value=0)

        return peak_load

Response: Get sectoral peak load of the current user


Instruction:
How would you implement a function in Python 3 that
returns the power factor of load

Response:
def power_factor(self):
        """
        Power factor of load

        Parameters
        -----------
        power_factor : :obj:`float`
            Ratio of real power to apparent power.

        Returns
        --------
        :obj:`float`
            Ratio of real power to apparent power. If power factor is not set
            it is retrieved from the network config object depending on the
            grid level the load is in.

        """
        if self._power_factor is None:
            if isinstance(self.grid, MVGrid):
                self._power_factor = self.grid.network.config[
                    'reactive_power_factor']['mv_load']
            elif isinstance(self.grid, LVGrid):
                self._power_factor = self.grid.network.config[
                    'reactive_power_factor']['lv_load']
        return self._power_factor


Instruction:
Write a Python 3 script for
returning the reactive power mode of load.

Response:
def reactive_power_mode(self):
        """
        Power factor mode of Load.

        This information is necessary to make the load behave in an inductive
        or capacitive manner. Essentially this changes the sign of the reactive
        power.

        The convention used here in a load is that:
        - when `reactive_power_mode` is 'inductive' then Q is positive
        - when `reactive_power_mode` is 'capacitive' then Q is negative

        Parameters
        ----------
        reactive_power_mode : :obj:`str` or None
            Possible options are 'inductive', 'capacitive' and
            'not_applicable'. In the case of 'not_applicable' a reactive
            power time series must be given.

        Returns
        -------
        :obj:`str`
            In the case that this attribute is not set, it is retrieved from
            the network config object depending on the voltage level the load
            is in.

        """
        if self._reactive_power_mode is None:
            if isinstance(self.grid, MVGrid):
                self._reactive_power_mode = self.grid.network.config[
                    'reactive_power_mode']['mv_load']
            elif isinstance(self.grid, LVGrid):
                self._reactive_power_mode = self.grid.network.config[
                    'reactive_power_mode']['lv_load']

        return self._reactive_power_mode


Instruction:
Explain what the following Python 3 code does
def timeseries(self):
        if self._timeseries is None:
            # calculate time series for active and reactive power
            try:
                timeseries = \
                    self.grid.network.timeseries.generation_dispatchable[
                        self.type].to_frame('p')
            except KeyError:
                try:
                    timeseries = \
                        self.grid.network.timeseries.generation_dispatchable[
                            'other'].to_frame('p')
                except KeyError:
                    logger.exception("No time series for type {} "
                                     "given.".format(self.type))
                    raise

            timeseries = timeseries * self.nominal_capacity
            if self.timeseries_reactive is not None:
                timeseries['q'] = self.timeseries_reactive
            else:
                timeseries['q'] = timeseries['p'] * self.q_sign * tan(acos(
                    self.power_factor))

            return timeseries
        else:
            return self._timeseries.loc[
                   self.grid.network.timeseries.timeindex, :]

Response: Returns the actual dispatch time series used in power flow analysis.


Instruction:
Explain what the following Python 3 code does
def timeseries_reactive(self):
        if self._timeseries_reactive is None:
            if self.grid.network.timeseries.generation_reactive_power \
                    is not None:
                try:
                    timeseries = \
                        self.grid.network.timeseries.generation_reactive_power[
                            self.type].to_frame('q')
                except (KeyError, TypeError):
                    try:
                        timeseries = \
                            self.grid.network.timeseries.generation_reactive_power[
                                'other'].to_frame('q')
                    except:
                        logger.warning(
                            "No reactive power time series for type {} given. "
                            "Reactive power time series will be calculated from "
                            "assumptions in config files and active power "
                            "timeseries.".format(self.type))
                        return None
                self.power_factor = 'not_applicable'
                self.reactive_power_mode = 'not_applicable'
                return timeseries * self.nominal_capacity
            else:
                return None
        else:
            return self._timeseries_reactive.loc[
                   self.grid.network.timeseries.timeindex, :]

Response: Returns the time series in kvar that contains the reactive power.


Instruction:
Create a Python 3 function to
return the actual time series used in power flow analysis.

Response:
def timeseries(self):
        """
        Feed-in time series of generator

        It returns the actual time series used in power flow analysis. If
        :attr:`_timeseries` is not :obj:`None`, it is returned. Otherwise,
        :meth:`timeseries` looks for generation and curtailment time series
        of the according type of technology (and weather cell) in
        :class:`~.grid.network.TimeSeries`.

        Returns
        -------
        :pandas:`pandas.DataFrame<dataframe>`
            DataFrame containing active power in kW in column 'p' and
            reactive power in kVA in column 'q'.

        """
        if self._timeseries is None:

            # get time series for active power depending on if they are
            # differentiated by weather cell ID or not
            if isinstance(self.grid.network.timeseries.generation_fluctuating.
                                  columns, pd.MultiIndex):
                if self.weather_cell_id:
                    try:
                        timeseries = self.grid.network.timeseries.\
                            generation_fluctuating[
                            self.type, self.weather_cell_id].to_frame('p')
                    except KeyError:
                        logger.exception("No time series for type {} and "
                                         "weather cell ID {} given.".format(
                            self.type, self.weather_cell_id))
                        raise
                else:
                    logger.exception("No weather cell ID provided for "
                                     "fluctuating generator {}.".format(
                        repr(self)))
                    raise KeyError
            else:
                try:
                    timeseries = self.grid.network.timeseries.\
                        generation_fluctuating[self.type].to_frame('p')
                except KeyError:
                    logger.exception("No time series for type {} "
                                     "given.".format(self.type))
                    raise

            timeseries = timeseries * self.nominal_capacity

            # subtract curtailment
            if self.curtailment is not None:
                timeseries = timeseries.join(
                    self.curtailment.to_frame('curtailment'), how='left')
                timeseries.p = timeseries.p - timeseries.curtailment.fillna(0)

            if self.timeseries_reactive is not None:
                timeseries['q'] = self.timeseries_reactive
            else:
                timeseries['q'] = timeseries['p'] * self.q_sign * tan(acos(
                    self.power_factor))

            return timeseries
        else:
            return self._timeseries.loc[
                   self.grid.network.timeseries.timeindex, :]


Instruction:
Implement a Python 3 function for
returning a pandas. DataFrame containing the time series for the reactive power of the given generator.

Response:
def timeseries_reactive(self):
        """
        Reactive power time series in kvar.

        Parameters
        -------
        :pandas:`pandas.Series<series>`
            Series containing reactive power time series in kvar.

        Returns
        ----------
        :pandas:`pandas.DataFrame<dataframe>` or None
            Series containing reactive power time series in kvar. If it is not
            set it is tried to be retrieved from `generation_reactive_power`
            attribute of global TimeSeries object. If that is not possible
            None is returned.

        """

        if self._timeseries_reactive is None:
            # try to get time series for reactive power depending on if they
            # are differentiated by weather cell ID or not
            # raise warning if no time series for generator type (and weather
            # cell ID) can be retrieved
            if self.grid.network.timeseries.generation_reactive_power \
                    is not None:
                if isinstance(
                        self.grid.network.timeseries.generation_reactive_power.
                                columns, pd.MultiIndex):
                    if self.weather_cell_id:
                        try:
                            timeseries = self.grid.network.timeseries. \
                                generation_reactive_power[
                                self.type, self.weather_cell_id].to_frame('q')
                            return timeseries * self.nominal_capacity
                        except (KeyError, TypeError):
                            logger.warning("No time series for type {} and "
                                           "weather cell ID {} given. "
                                           "Reactive power time series will "
                                           "be calculated from assumptions "
                                           "in config files and active power "
                                           "timeseries.".format(
                                self.type, self.weather_cell_id))
                            return None
                    else:
                        raise ValueError(
                            "No weather cell ID provided for fluctuating "
                            "generator {}, but reactive power is given as a "
                            "MultiIndex suggesting that it is differentiated "
                            "by weather cell ID.".format(repr(self)))
                else:
                    try:
                        timeseries = self.grid.network.timeseries. \
                            generation_reactive_power[self.type].to_frame('q')
                        return timeseries * self.nominal_capacity
                    except (KeyError, TypeError):
                        logger.warning("No reactive power time series for "
                                       "type {} given. Reactive power time "
                                       "series will be calculated from "
                                       "assumptions in config files and "
                                       "active power timeseries.".format(
                            self.type))
                        return None
            else:
                return None
        else:
            return self._timeseries_reactive.loc[
                   self.grid.network.timeseries.timeindex, :]


Instruction:
Implement a Python 3 function for
returning the curtailment time series for the given technology type and weather cell ID.

Response:
def curtailment(self):
        """
        Parameters
        ----------
        curtailment_ts : :pandas:`pandas.Series<series>`
            See class definition for details.

        Returns
        -------
        :pandas:`pandas.Series<series>`
            If self._curtailment is set it returns that. Otherwise, if
            curtailment in :class:`~.grid.network.TimeSeries` for the
            corresponding technology type (and if given, weather cell ID)
            is set this is returned.

        """
        if self._curtailment is not None:
            return self._curtailment
        elif isinstance(self.grid.network.timeseries._curtailment,
                        pd.DataFrame):
            if isinstance(self.grid.network.timeseries.curtailment.
                                  columns, pd.MultiIndex):
                if self.weather_cell_id:
                    try:
                        return self.grid.network.timeseries.curtailment[
                            self.type, self.weather_cell_id]
                    except KeyError:
                        logger.exception("No curtailment time series for type "
                                         "{} and weather cell ID {} "
                                         "given.".format(self.type,
                                                         self.weather_cell_id))
                        raise
                else:
                    logger.exception("No weather cell ID provided for "
                                     "fluctuating generator {}.".format(
                        repr(self)))
                    raise KeyError
            else:
                try:
                    return self.grid.network.timeseries.curtailment[self.type]
                except KeyError:
                    logger.exception("No curtailment time series for type "
                                     "{} given.".format(self.type))
                    raise
        else:
            return None


Instruction:
Can you create a Python 3 function that
returns a pandas. DataFrame containing the time series of storage operation.

Response:
def timeseries(self):
        """
        Time series of storage operation

        Parameters
        ----------
        ts : :pandas:`pandas.DataFrame<dataframe>`
            DataFrame containing active power the storage is charged (negative)
            and discharged (positive) with (on the grid side) in kW in column
            'p' and reactive power in kvar in column 'q'. When 'q' is positive,
            reactive power is supplied (behaving as a capacitor) and when 'q'
            is negative reactive power is consumed (behaving as an inductor).

        Returns
        -------
        :pandas:`pandas.DataFrame<dataframe>`
            See parameter `timeseries`.

        """
        # check if time series for reactive power is given, otherwise
        # calculate it
        if 'q' in self._timeseries.columns:
            return self._timeseries
        else:
            self._timeseries['q'] = abs(self._timeseries.p) * self.q_sign * \
                                    tan(acos(self.power_factor))
            return self._timeseries.loc[
                   self.grid.network.timeseries.timeindex, :]


Instruction:
How would you implement a function in Python 3 that
gets the sign reactive power based on the _reactive_power_mode attribute.

Response:
def q_sign(self):
        """
        Get the sign reactive power based on the
        :attr: `_reactive_power_mode`

        Returns
        -------
        :obj: `int` : +1 or -1
        """
        if self.reactive_power_mode.lower() == 'inductive':
            return -1
        elif self.reactive_power_mode.lower() == 'capacitive':
            return 1
        else:
            raise ValueError("Unknown value {} in reactive_power_mode".format(
                self.reactive_power_mode))


Instruction:
Can you tell what is the following Python 3 function doing
def open(self):
        if self._state != 'open':
            if self._line is not None:
                self._state = 'open'
                self._nodes = self.grid.graph.nodes_from_line(self._line)
                self.grid.graph.remove_edge(
                    self._nodes[0], self._nodes[1])
            else:
                raise ValueError('``line`` is not set')

Response: Toggle state to open switch disconnector


Instruction:
Write a Python 3 script for
toggling state to closed switch disconnector

Response:
def close(self):
        """Toggle state to closed switch disconnector"""
        self._state = 'closed'
        self.grid.graph.add_edge(
            self._nodes[0], self._nodes[1], {'line': self._line})


Instruction:
Can you write a function in Python 3 where it
provides : shallowely LineString object<linestrings > geometry of

Response:
def geom(self):
        """Provide :shapely:`Shapely LineString object<linestrings>` geometry of
        :class:`Line`"""
        adj_nodes = self._grid._graph.nodes_from_line(self)

        return LineString([adj_nodes[0].geom, adj_nodes[1].geom])


Instruction:
Can you tell what is the following Python 3 function doing
def wrap_context(func):
    @functools.wraps(func)
    def wrapper(self, request, context):
        return func(self, request, LogFieldsContext(context))

    return wrapper

Response: Wraps the provided servicer method by passing a wrapped context


Instruction:
Can you generate a brief explanation for the following Python 3 code
def add_log_fields(self, fields: Dict[str, Any]):
        self._log_fields.add_fields(fields)

Response: Add the provided log fields to the log_fields attribute of this object.


Instruction:
Can you create a Python 3 function that
packs the log fields and the invocation metadata into a new metadata list

Response:
def pack_metadata(self) -> List[Tuple[str, Any]]:
        """Packs the log fields and the invocation metadata into a new metadata

        The log fields are added in the new metadata with the key
        `LOG_FIELDS_KEY_META`.

        """
        metadata = [(k, v) for k, v in self._invocation_metadata.items()
                    if k != LOG_FIELDS_KEY_META]
        metadata.append((LOG_FIELDS_KEY_META, self._log_fields.dumps()))
        return metadata


Instruction:
Explain what the following Python 3 code does
def from_metadata(cls, metadata: Dict[str, Any]) -> 'LogFields':
        return cls(fields=json.loads(metadata.get(LOG_FIELDS_KEY_META, '{}')))

Response: Initialize the log fields from the provided metadata dictionary.


Instruction:
How would you code a function in Python 3 to
add the provided log fields to the log.

Response:
def add_fields(self, fields):
        """Add the provided log fields

        If a key is already present, then it is ignored.

        :param fields: the log fields to add

        """
        for k, v in fields.items():
            if k not in self._fields:
                self._fields[k] = v


Instruction:
Create a Python 3 function to
calculate the grid expansion costs for each reinforced transformer and line in kEUR.

Response:
def grid_expansion_costs(network, without_generator_import=False):
    """
    Calculates grid expansion costs for each reinforced transformer and line
    in kEUR.

    Attributes
    ----------
    network : :class:`~.grid.network.Network`
    without_generator_import : Boolean
        If True excludes lines that were added in the generator import to
        connect new generators to the grid from calculation of grid expansion
        costs. Default: False.

    Returns
    -------
    `pandas.DataFrame<dataframe>`
        DataFrame containing type and costs plus in the case of lines the
        line length and number of parallel lines of each reinforced
        transformer and line. Index of the DataFrame is the respective object
        that can either be a :class:`~.grid.components.Line` or a
        :class:`~.grid.components.Transformer`. Columns are the following:

        type: String
            Transformer size or cable name

        total_costs: float
            Costs of equipment in kEUR. For lines the line length and number of
            parallel lines is already included in the total costs.

        quantity: int
            For transformers quantity is always one, for lines it specifies the
            number of parallel lines.

        line_length: float
            Length of line or in case of parallel lines all lines in km.

        voltage_level : :obj:`str` {'lv' | 'mv' | 'mv/lv'}
            Specifies voltage level the equipment is in.

        mv_feeder : :class:`~.grid.components.Line`
            First line segment of half-ring used to identify in which
            feeder the grid expansion was conducted in.

    Notes
    -------
    Total grid expansion costs can be obtained through
    self.grid_expansion_costs.total_costs.sum().

    """
    def _get_transformer_costs(transformer):
        if isinstance(transformer.grid, LVGrid):
            return float(network.config['costs_transformers']['lv'])
        elif isinstance(transformer.grid, MVGrid):
            return float(network.config['costs_transformers']['mv'])

    def _get_line_costs(line, quantity):
        # get voltage level
        if isinstance(line.grid, LVGrid):
            voltage_level = 'lv'
        elif isinstance(line.grid, MVGrid):
            voltage_level = 'mv'
        else:
            raise KeyError("Grid must be LVGrid or MVGrid.")
        # get population density in people/km^2
        # transform area to calculate area in km^2
        projection = partial(
            pyproj.transform,
            pyproj.Proj(init='epsg:{}'.format(
                int(network.config['geo']['srid']))),
            pyproj.Proj(init='epsg:3035'))
        sqm2sqkm = 1e6
        population_density = (line.grid.grid_district['population'] /
                              (transform(projection,
                               line.grid.grid_district['geom']).area /
                               sqm2sqkm))
        if population_density <= 500:
            population_density = 'rural'
        else:
            population_density = 'urban'
        # get costs from config
        costs_cable = float(network.config['costs_cables']['{}_cable'.format(
            voltage_level)])
        costs_cable_earthwork = float(network.config['costs_cables'][
                                          '{}_cable_incl_earthwork_{}'.format(
                                              voltage_level,
                                              population_density)])
        return (costs_cable_earthwork * l.length +
                costs_cable * l.length * (quantity - 1))

    costs = pd.DataFrame()

    if without_generator_import:
        equipment_changes = network.results.equipment_changes.loc[
            network.results.equipment_changes.iteration_step > 0]
    else:
        equipment_changes = network.results.equipment_changes

    # costs for transformers
    if not equipment_changes.empty:
        transformers = equipment_changes[equipment_changes['equipment'].apply(
            isinstance, args=(Transformer,))]
        added_transformers = transformers[transformers['change'] == 'added']
        removed_transformers = transformers[
            transformers['change'] == 'removed']
        # check if any of the added transformers were later removed
        added_removed_transformers = added_transformers.loc[
            added_transformers['equipment'].isin(
                removed_transformers['equipment'])]
        added_transformers = added_transformers[
            ~added_transformers['equipment'].isin(
                added_removed_transformers.equipment)]
        # calculate costs for each transformer
        for t in added_transformers['equipment']:
            costs = costs.append(pd.DataFrame(
                {'type': t.type.name,
                 'total_costs': _get_transformer_costs(t),
                 'quantity': 1,
                 'voltage_level': 'mv/lv',
                 'mv_feeder': t.grid.station.mv_feeder if isinstance(
                     t.grid, LVGrid) else None},
                index=[t]))

        # costs for lines
        # get changed lines
        lines = equipment_changes.loc[equipment_changes.index[
            equipment_changes.reset_index()['index'].apply(
                isinstance, args=(Line,))]]
        # calculate costs for each reinforced line
        for l in list(lines.index.unique()):
            # check if line connects aggregated units
            aggr_lines = []
            aggr_lines_generator = l.grid.graph.lines_by_attribute('line_aggr')
            for aggr_line in aggr_lines_generator:
                aggr_lines.append(repr(aggr_line['line']))
            if not repr(l) in aggr_lines:
                number_lines_added = equipment_changes[
                    (equipment_changes.index == l) &
                    (equipment_changes.equipment ==
                     l.type.name)]['quantity'].sum()
                costs = costs.append(pd.DataFrame(
                    {'type': l.type.name,
                     'total_costs': _get_line_costs(l, number_lines_added),
                     'length': l.length * number_lines_added,
                     'quantity': number_lines_added,
                     'voltage_level': ('lv' if isinstance(l.grid, LVGrid)
                                       else 'mv'),
                     'mv_feeder': get_mv_feeder_from_line(l)},
                    index=[l]))

    # if no costs incurred write zero costs to DataFrame
    if costs.empty:
        costs = costs.append(pd.DataFrame(
            {'type': ['N/A'],
             'total_costs': [0],
             'length': [0],
             'quantity': [0],
             'voltage_level': '',
             'mv_feeder': ''
             },
            index=['No reinforced equipment.']))

    return costs


Instruction:
Given the following Python 3 function, write the documentation
def plot_mv_grid_topology(self, technologies=False, **kwargs):
        if self.network.pypsa is None:
            try:
                timesteps = self.network.timeseries.timeindex
                self.network.pypsa = pypsa_io.to_pypsa(
                    self.network, mode=None, timesteps=timesteps)
            except:
                logging.warning(
                    "pypsa representation of MV grid needed to plot MV "
                    "grid topology.")

        if self.network.pypsa is not None:
            plots.mv_grid_topology(
                self.network.pypsa, self.network.config,
                node_color='technology' if technologies is True else None,
                filename=kwargs.get('filename', None),
                grid_district_geom=kwargs.get('grid_district_geom', True),
                background_map=kwargs.get('background_map', True),
                xlim=kwargs.get('xlim', None), ylim=kwargs.get('ylim', None),
                title=kwargs.get('title', ''))

Response: Plots a MV grid topology.


Instruction:
Write a Python 3 script for
plotting voltages in MV grid on grid topology plot.

Response:
def plot_mv_voltages(self, **kwargs):
        """
        Plots voltages in MV grid on grid topology plot.

        For more information see :func:`edisgo.tools.plots.mv_grid_topology`.

        """
        if self.network.pypsa is not None:
            try:
                v_res = self.network.results.v_res()
            except:
                logging.warning("Voltages `pfa_v_mag_pu` from power flow "
                                "analysis must be available to plot them.")
                return
            plots.mv_grid_topology(
                self.network.pypsa, self.network.config,
                timestep=kwargs.get('timestep', None),
                node_color='voltage',
                filename=kwargs.get('filename', None),
                grid_district_geom=kwargs.get('grid_district_geom', True),
                background_map=kwargs.get('background_map', True),
                voltage=v_res,
                limits_cb_nodes=kwargs.get('limits_cb_nodes', None),
                xlim=kwargs.get('xlim', None), ylim=kwargs.get('ylim', None),
                title=kwargs.get('title', ''))
        else:
            logging.warning("pypsa representation of MV grid needed to "
                            "plot voltages.")


Instruction:
How would you code a function in Python 3 to
plot relative line loading of MV lines.

Response:
def plot_mv_line_loading(self, **kwargs):
        """
        Plots relative line loading (current from power flow analysis to
        allowed current) of MV lines.

        For more information see :func:`edisgo.tools.plots.mv_grid_topology`.

        """
        if self.network.pypsa is not None and \
                self.network.results.i_res is not None:
            plots.mv_grid_topology(
                self.network.pypsa, self.network.config,
                timestep=kwargs.get('timestep', None),
                line_color='loading',
                node_color=kwargs.get('node_color', None),
                line_load=self.network.results.s_res(),
                filename=kwargs.get('filename', None),
                arrows=kwargs.get('arrows', None),
                grid_district_geom=kwargs.get('grid_district_geom', True),
                background_map=kwargs.get('background_map', True),
                voltage=self.network.results.v_res(),
                limits_cb_lines=kwargs.get('limits_cb_lines', None),
                limits_cb_nodes=kwargs.get('limits_cb_nodes', None),
                xlim=kwargs.get('xlim', None), ylim=kwargs.get('ylim', None),
                lines_cmap=kwargs.get('lines_cmap', 'inferno_r'),
                title=kwargs.get('title', ''),
                scaling_factor_line_width=kwargs.get(
                    'scaling_factor_line_width', None))
        else:
            if self.network.pypsa is None:
                logging.warning("pypsa representation of MV grid needed to "
                                "plot line loading.")
            if self.network.results.i_res is None:
                logging.warning("Currents `i_res` from power flow analysis "
                                "must be available to plot line loading.")


Instruction:
Here you have a function in Python 3, explain what it does
def plot_mv_grid_expansion_costs(self, **kwargs):
        if self.network.pypsa is not None and \
                self.network.results.grid_expansion_costs is not None:
            if isinstance(self, EDisGo):
                # convert index of grid expansion costs to str
                grid_expansion_costs = \
                    self.network.results.grid_expansion_costs.reset_index()
                grid_expansion_costs['index'] = \
                    grid_expansion_costs['index'].apply(lambda _: repr(_))
                grid_expansion_costs.set_index('index', inplace=True)
            else:
                grid_expansion_costs = \
                    self.network.results.grid_expansion_costs

            plots.mv_grid_topology(
                self.network.pypsa, self.network.config,
                line_color='expansion_costs',
                grid_expansion_costs=grid_expansion_costs,
                filename=kwargs.get('filename', None),
                grid_district_geom=kwargs.get('grid_district_geom', True),
                background_map=kwargs.get('background_map', True),
                limits_cb_lines=kwargs.get('limits_cb_lines', None),
                xlim=kwargs.get('xlim', None), ylim=kwargs.get('ylim', None),
                lines_cmap=kwargs.get('lines_cmap', 'inferno_r'),
                title=kwargs.get('title', ''),
                scaling_factor_line_width=kwargs.get(
                    'scaling_factor_line_width', None)
            )
        else:
            if self.network.pypsa is None:
                logging.warning("pypsa representation of MV grid needed to "
                                "plot grid expansion costs.")
            if self.network.results.grid_expansion_costs is None:
                logging.warning("Grid expansion cost results needed to plot "
                                "them.")

Response: Plots the expansion costs per MV line.


Instruction:
Make a summary of the following Python 3 code
def plot_mv_storage_integration(self, **kwargs):
        if self.network.pypsa is not None:
            plots.mv_grid_topology(
                self.network.pypsa, self.network.config,
                node_color='storage_integration',
                filename=kwargs.get('filename', None),
                grid_district_geom=kwargs.get('grid_district_geom', True),
                background_map=kwargs.get('background_map', True),
                xlim=kwargs.get('xlim', None), ylim=kwargs.get('ylim', None),
                title=kwargs.get('title', ''))
        else:
            if self.network.pypsa is None:
                logging.warning("pypsa representation of MV grid needed to "
                                "plot storage integration in MV grid.")

Response: Plots storage integration in MV grid.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def histogram_voltage(self, timestep=None, title=True, **kwargs):
        data = self.network.results.v_res()
        if title is True:
            if timestep is not None:
                title = "Voltage histogram for time step {}".format(timestep)
            else:
                title = "Voltage histogram \nfor time steps {} to {}".format(
                    data.index[0], data.index[-1])
        elif title is False:
            title = None
        plots.histogram(data=data, title=title, timeindex=timestep, **kwargs)

Response: Plots histogram of voltage for a specific time step.


Instruction:
Can you generate the documentation for the following Python 3 function
def histogram_relative_line_load(self, timestep=None, title=True,
                                     voltage_level='mv_lv', **kwargs):
        """
        Plots histogram of relative line loads.

        For more information see :func:`edisgo.tools.plots.histogram`.

        Parameters
        ----------
        Parameters
        ----------
        timestep : :pandas:`pandas.Timestamp<timestamp>` or None, optional
            Specifies time step histogram is plotted for. If timestep is None
            all time steps voltages are calculated for are used. Default: None.
        title : :obj:`str` or :obj:`bool`, optional
            Title for plot. If True title is auto generated. If False plot has
            no title. If :obj:`str`, the provided title is used. Default: True.
        voltage_level : :obj:`str`
            Specifies which voltage level to plot voltage histogram for.
            Possible options are 'mv', 'lv' and 'mv_lv'. 'mv_lv' is also the
            fallback option in case of wrong input. Default: 'mv_lv'

        """
        residual_load = tools.get_residual_load_from_pypsa_network(
            self.network.pypsa)
        case = residual_load.apply(
            lambda _: 'feedin_case' if _ < 0 else 'load_case')
        if timestep is not None:
            timeindex = [timestep]
        else:
            timeindex = self.network.results.s_res().index
        load_factor = pd.DataFrame(
            data={'s_nom': [float(self.network.config[
                                      'grid_expansion_load_factors'][
                                      'mv_{}_line'.format(case.loc[_])])
                            for _ in timeindex]},
            index=timeindex)
        if voltage_level == 'mv':
            lines = self.network.pypsa.lines.loc[
                self.network.pypsa.lines.v_nom > 1]
        elif voltage_level == 'lv':
            lines = self.network.pypsa.lines.loc[
                self.network.pypsa.lines.v_nom < 1]
        else:
            lines = self.network.pypsa.lines
        s_res = self.network.results.s_res().loc[
            timeindex, lines.index]
        # get allowed line load
        s_allowed = load_factor.dot(
            self.network.pypsa.lines.s_nom.to_frame().T * 1e3)
        # get line load from pf
        data = s_res.divide(s_allowed)

        if title is True:
            if timestep is not None:
                title = "Relative line load histogram for time step {}".format(
                    timestep)
            else:
                title = "Relative line load histogram \nfor time steps " \
                        "{} to {}".format(data.index[0], data.index[-1])
        elif title is False:
            title = None
        plots.histogram(data=data, title=title, **kwargs)

Response: Plots a histogram of relative line loads.


Instruction:
Can you tell what is the following Python 3 function doing
def curtail(self, methodology, curtailment_timeseries, **kwargs):
        CurtailmentControl(edisgo=self, methodology=methodology,
                           curtailment_timeseries=curtailment_timeseries,
                           **kwargs)

Response: Sets up curtailment time series.


Instruction:
Make a summary of the following Python 3 code
def import_from_ding0(self, file, **kwargs):
        import_from_ding0(file=file, network=self.network)

Response: Import grid data from DINGO file


Instruction:
Can you generate a brief explanation for the following Python 3 code
def import_generators(self, generator_scenario=None):
        if generator_scenario:
            self.network.generator_scenario = generator_scenario
        data_source = 'oedb'
        import_generators(network=self.network, data_source=data_source)

Response: Import generators from oedb.


Instruction:
Create a Python 3 function for
analyzing the grid by power flow analysis.

Response:
def analyze(self, mode=None, timesteps=None):
        """Analyzes the grid by power flow analysis

        Analyze the grid for violations of hosting capacity. Means, perform a
        power flow analysis and obtain voltages at nodes (load, generator,
        stations/transformers and branch tees) and active/reactive power at
        lines.

        The power flow analysis can currently only be performed for both grid
        levels MV and LV. See ToDos section for more information.

        A static `non-linear power flow analysis is performed using PyPSA
        <https://www.pypsa.org/doc/power_flow.html#full-non-linear-power-flow>`_.
        The high-voltage to medium-voltage transformer are not included in the
        analysis. The slack bus is defined at secondary side of these
        transformers assuming an ideal tap changer. Hence, potential
        overloading of the transformers is not studied here.

        Parameters
        ----------
        mode : str
            Allows to toggle between power flow analysis (PFA) on the whole
            grid topology (MV + LV), only MV or only LV. Defaults to None which
            equals power flow analysis for MV + LV which is the only
            implemented option at the moment. See ToDos section for
            more information.
        timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
            Timesteps specifies for which time steps to conduct the power flow
            analysis. It defaults to None in which case the time steps in
            timeseries.timeindex (see :class:`~.grid.network.TimeSeries`) are
            used.

        Notes
        -----
        The current implementation always translates the grid topology
        representation to the PyPSA format and stores it to
        :attr:`self.network.pypsa`.

        ToDos
        ------
        The option to export only the edisgo MV grid (mode = 'mv') to conduct
        a power flow analysis is implemented in
        :func:`~.tools.pypsa_io.to_pypsa` but NotImplementedError is raised
        since the rest of edisgo does not handle this option yet. The analyze
        function will throw an error since
        :func:`~.tools.pypsa_io.process_pfa_results`
        does not handle aggregated loads and generators in the LV grids. Also,
        grid reinforcement, pypsa update of time series, and probably other
        functionalities do not work when only the MV grid is analysed.

        Further ToDos are:
        * explain how power plants are modeled, if possible use a link
        * explain where to find and adjust power flow analysis defining
        parameters

        See Also
        --------
        :func:`~.tools.pypsa_io.to_pypsa`
            Translator to PyPSA data format

        """
        if timesteps is None:
            timesteps = self.network.timeseries.timeindex
        # check if timesteps is array-like, otherwise convert to list
        if not hasattr(timesteps, "__len__"):
            timesteps = [timesteps]

        if self.network.pypsa is None:
            # Translate eDisGo grid topology representation to PyPSA format
            self.network.pypsa = pypsa_io.to_pypsa(
                self.network, mode, timesteps)
        else:
            if self.network.pypsa.edisgo_mode is not mode:
                # Translate eDisGo grid topology representation to PyPSA format
                self.network.pypsa = pypsa_io.to_pypsa(
                    self.network, mode, timesteps)

        # check if all timesteps are in pypsa.snapshots, if not update time
        # series
        if False in [True if _ in self.network.pypsa.snapshots else False
                     for _ in timesteps]:
            pypsa_io.update_pypsa_timeseries(self.network, timesteps=timesteps)
        # run power flow analysis
        pf_results = self.network.pypsa.pf(timesteps)

        if all(pf_results['converged']['0'].tolist()):
            pypsa_io.process_pfa_results(
                self.network, self.network.pypsa, timesteps)
        else:
            raise ValueError("Power flow analysis did not converge.")


Instruction:
Here you have a function in Python 3, explain what it does
def analyze_lopf(self, mode=None, timesteps=None,
                     etrago_max_storage_size=None):
        """Analyzes the grid by power flow analysis

        Analyze the grid for violations of hosting capacity. Means, perform a
        power flow analysis and obtain voltages at nodes (load, generator,
        stations/transformers and branch tees) and active/reactive power at
        lines.

        The power flow analysis can currently only be performed for both grid
        levels MV and LV. See ToDos section for more information.

        A static `non-linear power flow analysis is performed using PyPSA
        <https://www.pypsa.org/doc/power_flow.html#full-non-linear-power-flow>`_.
        The high-voltage to medium-voltage transformer are not included in the
        analysis. The slack bus is defined at secondary side of these
        transformers assuming an ideal tap changer. Hence, potential
        overloading of the transformers is not studied here.

        Parameters
        ----------
        mode : str
            Allows to toggle between power flow analysis (PFA) on the whole
            grid topology (MV + LV), only MV or only LV. Defaults to None which
            equals power flow analysis for MV + LV which is the only
            implemented option at the moment. See ToDos section for
            more information.
        timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
            Timesteps specifies for which time steps to conduct the power flow
            analysis. It defaults to None in which case the time steps in
            timeseries.timeindex (see :class:`~.grid.network.TimeSeries`) are
            used.

        Notes
        -----
        The current implementation always translates the grid topology
        representation to the PyPSA format and stores it to
        :attr:`self.network.pypsa`.

        ToDos
        ------
        The option to export only the edisgo MV grid (mode = 'mv') to conduct
        a power flow analysis is implemented in
        :func:`~.tools.pypsa_io.to_pypsa` but NotImplementedError is raised
        since the rest of edisgo does not handle this option yet. The analyze
        function will throw an error since
        :func:`~.tools.pypsa_io.process_pfa_results`
        does not handle aggregated loads and generators in the LV grids. Also,
        grid reinforcement, pypsa update of time series, and probably other
        functionalities do not work when only the MV grid is analysed.

        Further ToDos are:
        * explain how power plants are modeled, if possible use a link
        * explain where to find and adjust power flow analysis defining
        parameters

        See Also
        --------
        :func:`~.tools.pypsa_io.to_pypsa`
            Translator to PyPSA data format

        """
        if timesteps is None:
            timesteps = self.network.timeseries.timeindex
        # check if timesteps is array-like, otherwise convert to list
        if not hasattr(timesteps, "__len__"):
            timesteps = [timesteps]

        # Translate eDisGo grid topology representation to PyPSA format
        logging.debug('Translate eDisGo grid topology representation to '
                      'PyPSA format.')
        self.network.pypsa_lopf = pypsa_io_lopf.to_pypsa(
            self.network, mode, timesteps)
        logging.debug('Translating eDisGo grid topology representation to '
                      'PyPSA format finished.')

        # add total storage capacity constraint
        def extra_functionality(network, snapshots):
            model = network.model
            # total installed capacity
            model.storages_p_nom = Constraint(
                rule=lambda model: sum(
                    model.generator_p_nom[s]
                    for s in self.network.pypsa_lopf.generators[
                        self.network.pypsa_lopf.generators.type ==
                        'Storage'].index) <= etrago_max_storage_size)

        # run power flow analysis
        self.network.pypsa_lopf.lopf(
            snapshots=timesteps, solver_name='cbc', keep_files=False,
            extra_functionality=extra_functionality,
            solver_options={'tee': True})

        # self.network.pypsa.model.write(
        #     io_options={'symbolic_solver_labels': True})

        print('objective: {}'.format(self.network.pypsa_lopf.objective))

        # relevant outputs
        # plot MV grid
        plots.storage_size(self.network.mv_grid, self.network.pypsa_lopf,
                           filename='storage_results_{}.pdf'.format(
                               self.network.id))

        storages = self.network.mv_grid.graph.nodes_by_attribute('storage')
        storages_repr = [repr(_) for _ in storages]
        print('Installed storage capacity: {} MW'.format(
            self.network.pypsa_lopf.generators.loc[
                storages_repr, 'p_nom_opt'].sum()))

        # export storage results (pypsa and path to storage)
        pypsa_storages_df = self.network.pypsa_lopf.generators.loc[
            storages_repr, :].sort_values(by=['p_nom_opt'], ascending=False)

        storage_repr = []
        storage_path = []
        for s in storages:
            storage_repr.append(repr(s))
            storage_path.append(nx.shortest_path(self.network.mv_grid.graph,
                                    self.network.mv_grid.station, s))
        graph_storages_df = pd.DataFrame({'path': storage_path},
                                         index=storage_repr)
        pypsa_storages_df.join(graph_storages_df).to_csv(
            'storage_results_{}.csv'.format(self.network.id))

        # take largest 8 storages and remove the rest
        keep_storages = pypsa_storages_df.iloc[:8, :].index
        remove_storages = pypsa_storages_df.iloc[8:, :].index
        # write time series to kept storages
        for s in keep_storages:
            keep_storage_obj = [_ for _ in storages if repr(_)==s][0]
            ts = self.network.pypsa_lopf.generators_t.p.loc[:, s]
            keep_storage_obj.timeseries = pd.DataFrame({'p': ts * 1000,
                                                        'q': [0] * len(ts)},
                                                        index=ts.index)
        # delete small storages
        for s in remove_storages:
            disconnect_storage(self.network,
                               [_ for _ in storages if repr(_)==s][0])

Response: Analyzes the grid by power flow analysis.


Instruction:
Write a Python 3 function that can
reinforce the object and calculates grid expansion costs.

Response:
def reinforce(self, **kwargs):
        """
        Reinforces the grid and calculates grid expansion costs.

        See :meth:`edisgo.flex_opt.reinforce_grid` for more information.

        """
        results = reinforce_grid(
            self, max_while_iterations=kwargs.get(
                'max_while_iterations', 10),
            copy_graph=kwargs.get('copy_graph', False),
            timesteps_pfa=kwargs.get('timesteps_pfa', None),
            combined_analysis=kwargs.get('combined_analysis', False))

        # add measure to Results object
        if not kwargs.get('copy_graph', False):
            self.network.results.measures = 'grid_expansion'

        return results


Instruction:
Create a Python 3 function to
integrate storage into grid.

Response:
def integrate_storage(self, timeseries, position, **kwargs):
        """
        Integrates storage into grid.

        See :class:`~.grid.network.StorageControl` for more information.

        """
        StorageControl(edisgo=self, timeseries=timeseries,
                       position=position, **kwargs)


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

        package_path = edisgo.__path__[0]
        equipment_dir = self.config['system_dirs']['equipment_dir']

        data = {}
        equipment = {'mv': ['trafos', 'lines', 'cables'],
                     'lv': ['trafos', 'cables']}

        for voltage_level, eq_list in equipment.items():
            for i in eq_list:
                equipment_parameters = self.config['equipment'][
                    'equipment_{}_parameters_{}'.format(voltage_level, i)]
                data['{}_{}'.format(voltage_level, i)] = pd.read_csv(
                    os.path.join(package_path, equipment_dir,
                                 equipment_parameters),
                    comment='#', index_col='name',
                    delimiter=',', decimal='.')

        return data

Response: Load the data for transformers cables etc.


Instruction:
Explain what the following Python 3 code does
def _load_config(config_path=None):

        config_files = ['config_db_tables', 'config_grid',
                        'config_grid_expansion', 'config_timeseries']

        # load configs
        if isinstance(config_path, dict):
            for conf in config_files:
                config.load_config(filename='{}.cfg'.format(conf),
                                   config_dir=config_path[conf],
                                   copy_default_config=False)
        else:
            for conf in config_files:
                config.load_config(filename='{}.cfg'.format(conf),
                                   config_dir=config_path)

        config_dict = config.cfg._sections

        # convert numeric values to float
        for sec, subsecs in config_dict.items():
            for subsec, val in subsecs.items():
                # try str -> float conversion
                try:
                    config_dict[sec][subsec] = float(val)
                except:
                    pass

        # convert to time object
        config_dict['demandlib']['day_start'] = datetime.datetime.strptime(
            config_dict['demandlib']['day_start'], "%H:%M")
        config_dict['demandlib']['day_start'] = datetime.time(
            config_dict['demandlib']['day_start'].hour,
            config_dict['demandlib']['day_start'].minute)
        config_dict['demandlib']['day_end'] = datetime.datetime.strptime(
            config_dict['demandlib']['day_end'], "%H:%M")
        config_dict['demandlib']['day_end'] = datetime.time(
            config_dict['demandlib']['day_end'].hour,
            config_dict['demandlib']['day_end'].minute)

        return config_dict

Response: Load config files and return a dictionary of the configuration data.


Instruction:
Can you tell what is the following Python 3 function doing
def _check_timeindex(self):
        try:
            self.timeseries.generation_fluctuating
            self.timeseries.generation_dispatchable
            self.timeseries.load
            self.timeseries.generation_reactive_power
            self.timeseries.load_reactive_power
        except:
            message = 'Time index of feed-in and load time series does ' \
                      'not match.'
            logging.error(message)
            raise KeyError(message)

Response: Check if all feed - in and load time series contain the specified time index.


Instruction:
Write a Python 3 function for
defining time series for worst case generation.

Response:
def _worst_case_generation(self, worst_case_scale_factors, modes):
        """
        Define worst case generation time series for fluctuating and
        dispatchable generators.

        Parameters
        ----------
        worst_case_scale_factors : dict
            Scale factors defined in config file 'config_timeseries.cfg'.
            Scale factors describe actual power to nominal power ratio of in
            worst-case scenarios.
        modes : list
            List with worst-cases to generate time series for. Can be
            'feedin_case', 'load_case' or both.

        """

        self.timeseries.generation_fluctuating = pd.DataFrame(
            {'solar': [worst_case_scale_factors[
                           '{}_feedin_pv'.format(mode)] for mode in modes],
             'wind': [worst_case_scale_factors[
                          '{}_feedin_other'.format(mode)] for mode in modes]},
            index=self.timeseries.timeindex)

        self.timeseries.generation_dispatchable = pd.DataFrame(
            {'other': [worst_case_scale_factors[
                           '{}_feedin_other'.format(mode)] for mode in modes]},
            index=self.timeseries.timeindex)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _worst_case_load(self, worst_case_scale_factors,
                         peakload_consumption_ratio, modes):
        """
        Define worst case load time series for each sector.

        Parameters
        ----------
        worst_case_scale_factors : dict
            Scale factors defined in config file 'config_timeseries.cfg'.
            Scale factors describe actual power to nominal power ratio of in
            worst-case scenarios.
        peakload_consumption_ratio : dict
            Ratios of peak load to annual consumption per sector, defined in
            config file 'config_timeseries.cfg'
        modes : list
            List with worst-cases to generate time series for. Can be
            'feedin_case', 'load_case' or both.

        """

        sectors = ['residential', 'retail', 'industrial', 'agricultural']
        lv_power_scaling = np.array(
            [worst_case_scale_factors['lv_{}_load'.format(mode)]
             for mode in modes])
        mv_power_scaling = np.array(
            [worst_case_scale_factors['mv_{}_load'.format(mode)]
             for mode in modes])

        lv = {(sector, 'lv'): peakload_consumption_ratio[sector] *
                              lv_power_scaling
              for sector in sectors}
        mv = {(sector, 'mv'): peakload_consumption_ratio[sector] *
                              mv_power_scaling
              for sector in sectors}
        self.timeseries.load = pd.DataFrame({**lv, **mv},
                                            index=self.timeseries.timeindex)

Response: Generate worst case load time series for each sector.


Instruction:
Given the following Python 3 function, write the documentation
def _check_timeindex(self, curtailment_timeseries, network):
        if curtailment_timeseries is None:
            message = 'No curtailment given.'
            logging.error(message)
            raise KeyError(message)
        try:
            curtailment_timeseries.loc[network.timeseries.timeindex]
        except:
            message = 'Time index of curtailment time series does not match ' \
                      'with load and feed-in time series.'
            logging.error(message)
            raise KeyError(message)

Response: Raises an error if time index of curtailment time series does not comply with the time index of load and feed - in time series.


Instruction:
Write a Python 3 script to
check that the curtailment at any time step exceeds the total feed - in of all generators and curtailment can be distributed among all generators. Raises an error if the curtailment at any time step exceeds the total feed - in of all generators and curtailment can be distributed among all generators.

Response:
def _precheck(self, curtailment_timeseries, feedin_df, curtailment_key):
        """
        Raises an error if the curtailment at any time step exceeds the
        total feed-in of all generators curtailment can be distributed among
        at that time.

        Parameters
        -----------
        curtailment_timeseries : :pandas:`pandas.Series<series>`
            Curtailment time series in kW for the technology (and weather
            cell) specified in `curtailment_key`.
        feedin_df : :pandas:`pandas.Series<series>`
            Feed-in time series in kW for all generators of type (and in
            weather cell) specified in `curtailment_key`.
        curtailment_key : :obj:`str` or :obj:`tuple` with :obj:`str`
            Technology (and weather cell) curtailment is given for.

        """
        if not feedin_df.empty:
            feedin_selected_sum = feedin_df.sum(axis=1)
            diff = feedin_selected_sum - curtailment_timeseries
            # add tolerance (set small negative values to zero)
            diff[diff.between(-1, 0)] = 0
            if not (diff >= 0).all():
                bad_time_steps = [_ for _ in diff.index
                                  if diff[_] < 0]
                message = 'Curtailment demand exceeds total feed-in in time ' \
                          'steps {}.'.format(bad_time_steps)
                logging.error(message)
                raise ValueError(message)
        else:
            bad_time_steps = [_ for _ in curtailment_timeseries.index
                              if curtailment_timeseries[_] > 0]
            if bad_time_steps:
                message = 'Curtailment given for time steps {} but there ' \
                          'are no generators to meet the curtailment target ' \
                          'for {}.'.format(bad_time_steps, curtailment_key)
                logging.error(message)
                raise ValueError(message)


Instruction:
Given the following Python 3 function, write the documentation
def _postcheck(self, network, feedin):
        curtailment = network.timeseries.curtailment
        gen_repr = [repr(_) for _ in curtailment.columns]
        feedin_repr = feedin.loc[:, gen_repr]
        curtailment_repr = curtailment
        curtailment_repr.columns = gen_repr
        if not ((feedin_repr - curtailment_repr) > -1e-1).all().all():
            message = 'Curtailment exceeds feed-in.'
            logging.error(message)
            raise TypeError(message)

Response: Checks that the curtailment of a generator exceeds the curtailment of a feed - in of a generator at any time step. Raises an error if the curtailment exceeds the feed - in of that generator at any time step.