# -*- coding: utf-8 -*-

# Copyright (c) 2016-2022 by University of Kassel and Fraunhofer Institute for Energy Economics
# and Energy System Technology (IEE), Kassel. All rights reserved.


import os

import pandapower as pp
from pandapower import pp_dir


def _get_cases_path(filename=None):
    if filename:
        return os.path.join(pp_dir, "networks", "power_system_test_case_jsons", filename)
    else:
        return os.path.join(pp_dir, "networks", "power_system_test_case_jsons")


def _change_ref_bus(net, ref_bus_idx, ext_grid_p=0):
    """
    This function changes the current reference bus / buses, declared by net.ext_grid.bus towards \
    the given 'ref_bus_idx'
    If ext_grid_p is a list, it must be in the same order as net.ext_grid.index.
    """
    # cast ref_bus_idx and ext_grid_p as list
    if not isinstance(ref_bus_idx, list):
        ref_bus_idx = [ref_bus_idx]
    if not isinstance(ext_grid_p, list):
        ext_grid_p = [ext_grid_p]
    for i in ref_bus_idx:
        if i not in net.gen.bus.values and i not in net.ext_grid.bus.values:
            raise ValueError("Index %i is not in net.gen.bus or net.ext_grid.bus." % i)
    # determine indices of ext_grid and gen connected to ref_bus_idx
    gen_idx = net.gen.index[net.gen.bus.isin(ref_bus_idx)]
    ext_grid_idx = net.ext_grid.index[~net.ext_grid.bus.isin(ref_bus_idx)]
    # old ext_grid -> gen
    j = 0
    for i in ext_grid_idx:
        ext_grid_data = net.ext_grid.loc[i]
        net.ext_grid.drop(i, inplace=True)
        pp.create_gen(net, ext_grid_data.bus, ext_grid_p[j],
                      vm_pu=ext_grid_data.vm_pu, controllable=True,
                      min_q_mvar=ext_grid_data.min_q_mvar, max_q_mvar=ext_grid_data.max_q_mvar,
                      min_p_mw=ext_grid_data.min_p_mw, max_p_mw=ext_grid_data.max_p_mw)
        j += 1
    # old gen at ref_bus -> ext_grid (and sgen)
    for i in gen_idx:
        gen_data = net.gen.loc[i]
        net.gen.drop(i, inplace=True)
        if gen_data.bus not in net.ext_grid.bus.values:
            pp.create_ext_grid(net, gen_data.bus, vm_pu=gen_data.vm_pu, va_degree=0.,
                               min_q_mvar=gen_data.min_q_mvar, max_q_mvar=gen_data.max_q_mvar,
                               min_p_mw=gen_data.min_p_mw, max_p_mw=gen_data.max_p_mw)
        else:
            pp.create_sgen(net, gen_data.bus, p_mw=gen_data.p_mw,
                           min_q_mvar=gen_data.min_q_mvar, max_q_mvar=gen_data.max_q_mvar,
                           min_p_mw=gen_data.min_p_mw, max_p_mw=gen_data.max_p_mw)


def sorted_from_json(path, **kwargs):
    net = pp.from_json(path, **kwargs)
    for elm in pp.pp_elements():
        net[elm].sort_index(inplace=True)
    return net


def case4gs(**kwargs):
    """
    This is the 4 bus example from J. J. Grainger and W. D. Stevenson, Power system analysis. \
    McGraw-Hill, 1994. pp. 337-338. Its data origin is \
    `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_.

    OUTPUT:
         **net** - Returns the required ieee network case4gs

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case4gs()
    """
    case4gs = sorted_from_json(_get_cases_path("case4gs.json"), **kwargs)
    return case4gs


def case5(**kwargs):
    """
    This is the 5 bus example from F.Li and R.Bo, "Small Test Systems for Power System Economic \
    Studies" Its data origin is `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.

    OUTPUT:
         **net** - Returns the required ieee network case5

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case5()
    """
    case5 = sorted_from_json(_get_cases_path("case5.json", **kwargs))
    return case5


def case6ww(**kwargs):
    """
    Calls the json file case6ww.json which data origin is \
    `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_. It represents the 6 bus example from pp. \
    104, 112, 119, 123-124, 549 from A. J. Wood and B. F. Wollenberg, Power generation, operation, \
    and control. John Wiley & Sons, 2012..

    OUTPUT:
         **net** - Returns the required ieee network case6ww

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case6ww()
    """
    case6ww = sorted_from_json(_get_cases_path("case6ww.json", **kwargs))
    return case6ww


def case9(**kwargs):
    """
    Calls the json file case9.json which data origin is \
    `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_.
    This network was published in Anderson and Fouad's book 'Power System Control and Stability' \
    for the first time in 1980.

    OUTPUT:
         **net** - Returns the required ieee network case9

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case9()
    """
    case9 = sorted_from_json(_get_cases_path("case9.json", **kwargs))
    return case9


def case11_iwamoto(**kwargs):
    """
    Calls the json file case11_iwamoto.json which represents \
    the 11 bus example from `S. Iwamoto ; Y. Tamura , \
    A Load Flow Calculation Method for Ill-Conditioned Power Systems
    <https://ieeexplore.ieee.org/document/4110791/>'_ \
    IEEE Transactions on Power Apparatus and Systems

    Its data origin is the paper \
    `S.C. Tripathy, G. Durga Prasad, O.P. Malik, G.S. Hope, \
    Load-Flow Solutions for Ill-Conditioned Power Systems by a Newton-Like Method
    <https://ieeexplore.ieee.org/document/4111178/>`_\
    IEEE  IEEE Transactions on Power Apparatus and Systems, 1982.

    OUTPUT:
         **net** - Returns the required network case11_iwamoto

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case11_iwamoto()
    """
    case11 = sorted_from_json(_get_cases_path("case11_iwamoto.json", **kwargs))
    return case11


def case14(**kwargs):
    """
    Calls the json file case14.json which data origin is \
    `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_.
    This network was converted from IEEE Common Data Format (ieee14cdf.txt) on 20-Sep-2004 by
    cdf2matp, rev. 1.11, to matpower format and finally converted to pandapower format by
    pandapower.converter.from_ppc. The vn_kv was adapted considering the proposed voltage levels in
    `Washington case 14 <http://www2.ee.washington.edu/research/pstca/pf14/ieee14cdf.txt>`_

    OUTPUT:
         **net** - Returns the required ieee network case14

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case14()
    """
    case14 = sorted_from_json(_get_cases_path("case14.json", **kwargs))
    return case14


def case24_ieee_rts(**kwargs):
    """
    The IEEE 24-bus reliability test system was developed by the IEEE reliability subcommittee \
    and published in 1979.
    Some more information about this network are given by `Illinois University case 24 \
    <http://icseg.iti.illinois.edu/ieee-24-bus-system/>`_.
    The data origin for this network data is `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_.

    OUTPUT:
         **net** - Returns the required ieee network case24

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case24_ieee_rts()
    """
    case24 = sorted_from_json(_get_cases_path("case24_ieee_rts.json", **kwargs))
    return case24


def case30(**kwargs):
    """
    This function calls the json file case30.json which data origin is \
    `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_. The PYPOWER data are derived from
    `Washington 30 Bus Dynamic Test Case <https://www2.ee.washington.edu/research/pstca/dyn30/pg_tcadyn30.htm>`_.

    OUTPUT:
         **net** - Returns the required ieee network case30

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case30()
    """
    case30 = sorted_from_json(_get_cases_path("case30.json", **kwargs))
    return case30


def case_ieee30(**kwargs):
    """
    This function calls the json file case_ieee30.json which data origin is \
    `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_. The MATPOWER data are derived from
    `Washington IEEE 30 bus Case <http://www2.ee.washington.edu/research/pstca/pf30/pg_tca30bus.htm>`_.
    Additional information about this network are available at `Illinois University case 30 <http://icseg.iti.illinois.edu/ieee-30-bus-system/>`_.

    OUTPUT:
         **net** - Returns the required ieee network case30

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case_ieee30()
    """
    case_ieee30 = sorted_from_json(_get_cases_path("case_ieee30.json", **kwargs))
    return case_ieee30


def case33bw(**kwargs):
    """
    Calls the json file case33bw.json which data is provided by \
    `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin is the paper `M. Baran, F. Wu, Network reconfiguration in distribution systems \
    for loss reduction and load balancing \
    <http://ieeexplore.ieee.org/document/25627/>`_ IEEE Transactions on Power Delivery, 1989.

    OUTPUT:
         **net** - Returns the required ieee network case33bw

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case33bw()
    """
    case33bw = sorted_from_json(_get_cases_path("case33bw.json", **kwargs))
    return case33bw


def case39(**kwargs):
    """
    Calls the json file case39.json which data origin is \
    `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_.
    This network was published the first time in G. Bills et al., On-line stability analysis \
    study, RP 90-1, E. P. R. I. North American Rockwell Corporation, Edison Electric Institute, \
    Ed. IEEE Press, Oct. 1970,. Some more information about this network are given by \
    `Illinois University case 39 <http://icseg.iti.illinois.edu/ieee-39-bus-system/>`_.
    Because the Pypower data origin proposes vn_kv=345 for all nodes the transformers connect \
    node of the same voltage level.

    OUTPUT:
         **net** - Returns the required ieee network case39

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case39()
    """
    case39 = sorted_from_json(_get_cases_path("case39.json", **kwargs))
    return case39


def case57(vn_kv_area1=115, vn_kv_area2=500, vn_kv_area3=138, vn_kv_area4=345, vn_kv_area5=230,
           vn_kv_area6=161, **kwargs):
    """
    This function provides the ieee case57 network with the data origin `PYPOWER case 57 \
    <https:/pypi.python.org/pypi/PYPOWER>`_.
    Some more information about this network are given by `Illinois University case 57 \
    <http://icseg.iti.illinois.edu/ieee-57-bus-system/>`_.
    Because the Pypower data origin proposes no vn_kv some assumption must be made. There are six \
    areas with coinciding voltage level. These are:

    - area 1 with coinciding voltage level comprises node 1-17
    - area 2 with coinciding voltage level comprises node 18-20
    - area 3 with coinciding voltage level comprises node 21-24 + 34-40 + 44-51
    - area 4 with coinciding voltage level comprises node 25 + 30-33
    - area 5 with coinciding voltage level comprises node 41-43 + 56-57
    - area 6 with coinciding voltage level comprises node 52-55 + 26-29

    OUTPUT:
         **net** - Returns the required ieee network case57

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case57()
    """
    case57 = sorted_from_json(_get_cases_path("case57.json", **kwargs))
    Idx_area1 = case57.bus[case57.bus.vn_kv == 110].index
    Idx_area2 = case57.bus[case57.bus.vn_kv == 120].index
    Idx_area3 = case57.bus[case57.bus.vn_kv == 125].index
    Idx_area4 = case57.bus[case57.bus.vn_kv == 130].index
    Idx_area5 = case57.bus[case57.bus.vn_kv == 140].index
    Idx_area6 = case57.bus[case57.bus.vn_kv == 150].index
    case57.bus.vn_kv.loc[Idx_area1] = vn_kv_area1  # default 115
    case57.bus.vn_kv.loc[Idx_area2] = vn_kv_area2  # default 500
    case57.bus.vn_kv.loc[Idx_area3] = vn_kv_area3  # default 138
    case57.bus.vn_kv.loc[Idx_area4] = vn_kv_area4  # default 345
    case57.bus.vn_kv.loc[Idx_area5] = vn_kv_area5  # default 230
    case57.bus.vn_kv.loc[Idx_area6] = vn_kv_area6  # default 161
    return case57


def case89pegase(**kwargs):
    """
    Calls the json file case89pegase.json which data is provided by \
    `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin are the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and \
    PEGASE <https://arxiv.org/abs/1603.01533>`_, 2016 and S. Fliscounakis, P. Panciatici, \
    F. Capitanescu, and L. Wehenkel, Contingency ranking with respect to overloads in very large \
    power systems taking into account uncertainty, preventive, and corrective actions, \
    IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

    OUTPUT:
         **net** - Returns the required ieee network case89pegase

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case89pegase()
    """
    case89pegase = sorted_from_json(_get_cases_path("case89pegase.json", **kwargs))
    return case89pegase


def case118(**kwargs):
    """
    Calls the json file case118.json which data origin is \
    `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_.
    Some more information about this network are given by `Washington case 118 \
    <http://www2.ee.washington.edu/research/pstca/pf118/pg_tca118bus.htm>`_ and \
    `Illinois University case 118 <http://icseg.iti.illinois.edu/ieee-118-bus-system/>`_.
    The PYPOWER case data are corrected at Vm of buses 68 and 116.

    OUTPUT:
         **net** - Returns the required ieee network case118

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case118()
    """
    case118 = sorted_from_json(_get_cases_path("case118.json", **kwargs))
    return case118


def case145(**kwargs):
    """
    Calls the json file case145.json which data origin is \
    `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    This data is converted by MATPOWER 5.1 using CDF2MPC on 18-May-2016 from 'dd50cdf.txt'.

    OUTPUT:
         **net** - Returns the required ieee network case145

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case145()
    """
    case145 = sorted_from_json(_get_cases_path("case145.json", **kwargs))
    return case145


def case_illinois200(**kwargs):
    """
    This function calls the json file case_illinois200.json which data origin is \
    `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_. This network was published in \
    A.B. Birchfield, T. Xu, K.M. Gegner, K.S. Shetye, T.J. Overbye, "Grid Structural Characteristics as Validation Criteria for Synthetic Networks," IEEE Transactions on Power Systems, 2017.
    Some additional information about this network are available at `Illinois University Illinois 200 <http://icseg.iti.illinois.edu/illinois200/>`_.

    OUTPUT:
         **net** - Returns the required ieee network case30

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case_illinois200()
    """
    case_illinois200 = sorted_from_json(_get_cases_path("case_illinois200.json", **kwargs))
    return case_illinois200


def case300(**kwargs):
    """
    Calls the json file case300.json which data origin is \
    `PYPOWER <https:/pypi.python.org/pypi/PYPOWER>`_.
    Some more information about this network are given by \
    `Washington case 300 <http://www2.ee.washington.edu/research/pstca/pf300/pg_tca300bus.htm>`_ \
    and `Illinois University case 300 <http://icseg.iti.illinois.edu/ieee-300-bus-system/>`_.

    OUTPUT:
         **net** - Returns the required ieee network case300

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case300()
    """
    case300 = sorted_from_json(_get_cases_path("case300.json", **kwargs))
    return case300


def case1354pegase(**kwargs):
    """
    This grid represents a part of the European high voltage transmission network. The data is \
    provided by `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin are the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE \
    <https://arxiv.org/abs/1603.01533>`_, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, \
    and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems \
    taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on \
    Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

    OUTPUT:
         **net** - Returns the required ieee network case1354pegase

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case1354pegase()
    """
    case1354pegase = sorted_from_json(_get_cases_path("case1354pegase.json", **kwargs))
    return case1354pegase


def case1888rte(ref_bus_idx=1246, **kwargs):
    """
    This case accurately represents the size and complexity of French very high voltage and high \
    voltage transmission network. The data is provided by `MATPOWER \
    <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin is the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE \
    <https://arxiv.org/abs/1603.01533>`_, 2016.

    OPTIONAL:

        **ref_bus_idx** - Since the MATPOWER case provides a reference bus without connected \
            generator, because a distributed slack is assumed, to convert the data to pandapower, \
            another bus has been assumed as reference bus. Via 'ref_bus_idx' the User can choose a \
            reference bus, which should have a generator connected to. Please be aware that by \
            changing the reference bus to another bus than the proposed default value, maybe a \
            powerflow does not converge anymore!

    OUTPUT:
         **net** - Returns the required ieee network case1888rte

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case1888rte()
    """
    case1888rte = sorted_from_json(_get_cases_path("case1888rte.json", **kwargs))
    case1888rte.ext_grid.loc[0, ['min_p_mw',  'max_p_mw',  'min_q_mvar', 'max_q_mvar']] *= 2

    if ref_bus_idx != 1246:  # change reference bus
        _change_ref_bus(case1888rte, ref_bus_idx, ext_grid_p=[-89.5])
    return case1888rte


def case2848rte(ref_bus_idx=271, **kwargs):
    """
    This case accurately represents the size and complexity of French very high voltage and high \
    voltage transmission network. The data is provided by \
    `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin is the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE \
    <https://arxiv.org/abs/1603.01533>`_, 2016.

    OPTIONAL:

        **ref_bus_idx** - Since the MATPOWER case provides a reference bus without connected \
            generator, because a distributed slack is assumed, to convert the data to pandapower, \
            another bus has been assumed as reference bus. Via 'ref_bus_idx' the User can choose a \
            reference bus, which should have a generator connected to. Please be aware that by \
            changing the reference bus to another bus than the proposed default value, maybe a \
            powerflow does not converge anymore!

    OUTPUT:
         **net** - Returns the required ieee network case2848rte

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case2848rte()
    """
    case2848rte = sorted_from_json(_get_cases_path("case2848rte.json", **kwargs))
    if ref_bus_idx != 271:  # change reference bus
        _change_ref_bus(case2848rte, ref_bus_idx, ext_grid_p=[44.01])
    return case2848rte


def case2869pegase(**kwargs):
    """
    This grid represents a part of the European high voltage transmission network. The data is \
    provided by `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin i the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE \
    <https://arxiv.org/abs/1603.01533>`_, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, \
    and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems \
    taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on \
    Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

    OUTPUT:
         **net** - Returns the required ieee network case2869pegase

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case2869pegase()
    """
    case2869pegase = sorted_from_json(_get_cases_path("case2869pegase.json", **kwargs))
    return case2869pegase


def case3120sp(**kwargs):
    """
    This case represents the Polish 400, 220 and 110 kV networks during summer 2008 morning peak \
    conditions. The data was provided by Roman Korab <roman.korab@polsl.pl> and to pandapower \
    converted from `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.

    OUTPUT:
         **net** - Returns the required ieee network case3120sp

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case3120sp()
    """
    case3120sp = sorted_from_json(_get_cases_path("case3120sp.json", **kwargs))
    return case3120sp


def case6470rte(ref_bus_idx=5988, **kwargs):
    """
    This case accurately represents the size and complexity of French very high voltage and high \
    voltage transmission network. The data is provided by \
    `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin is the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE \
    <https://arxiv.org/abs/1603.01533>`_, 2016.

    OPTIONAL:

        **ref_bus_idx** - Since the MATPOWER case provides a reference bus without connected \
            generator, because a distributed slack is assumed, to convert the data to pandapower, \
            another bus has been assumed as reference bus. Via 'ref_bus_idx' the User can choose a \
            reference bus, which should have a generator connected to. Please be aware that by \
            changing the reference bus to another bus than the proposed default value, maybe a \
            powerflow does not converge anymore!

    OUTPUT:
         **net** - Returns the required ieee network case6470rte

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case6470rte()
    """
    case6470rte = sorted_from_json(_get_cases_path("case6470rte.json", **kwargs))
    case6470rte.ext_grid.loc[0, ['min_p_mw',  'max_p_mw',  'min_q_mvar', 'max_q_mvar']] *= 2
    if ref_bus_idx != 5988:  # change reference bus
        _change_ref_bus(case6470rte, ref_bus_idx, ext_grid_p=[-169.41])
    return case6470rte


def case6495rte(ref_bus_idx=None, **kwargs):
    """
    This case accurately represents the size and complexity of French very high voltage and high \
    voltage transmission network. The data is provided by \
    `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin is the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE \
    <https://arxiv.org/abs/1603.01533>`_, 2016.

    OPTIONAL:

        **ref_bus_idx** - Since the MATPOWER case provides a reference bus without connected \
            generator, because a distributed slack is assumed, to convert the data to pandapower, \
            another buses (6077, 6161, 6305, 6306, 6307, 6308) has been assumed as reference bus. \
            Via 'ref_bus_idx' the User can choose a \
            reference bus, which should have a generator connected to. Please be aware that by \
            changing the reference bus to another bus than the proposed default value, maybe a \
            powerflow does not converge anymore!

    OUTPUT:
         **net** - Returns the required ieee network case6495rte

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case6495rte()
    """
    ref_bus_idx = ref_bus_idx or [6077, 6161, 6305, 6306, 6307, 6308]
    case6495rte = sorted_from_json(_get_cases_path("case6495rte.json", **kwargs))
    if ref_bus_idx != [6077, 6161, 6305, 6306, 6307, 6308]:  # change reference bus
        _change_ref_bus(case6495rte, ref_bus_idx, ext_grid_p=[1382.35, 2894.13, 1498.32,
                                                              1498.32, 1493.11, 1493.12])
    return case6495rte


def case6515rte(ref_bus_idx=6171, **kwargs):
    """
    This case accurately represents the size and complexity of French very high voltage and high \
    voltage transmission network. The data is provided by `MATPOWER \
    <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin is the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE \
    <https://arxiv.org/abs/1603.01533>`_, 2016.

    OPTIONAL:

        **ref_bus_idx** - Since the MATPOWER case provides a reference bus without connected \
            generator, because a distributed slack is assumed, to convert the data to pandapower, \
            another bus has been assumed as reference bus. Via 'ref_bus_idx' the User can choose a \
            reference bus, which should have a generator connected to. Please be aware that by \
            changing the reference bus to another bus than the proposed default value, maybe a \
            powerflow does not converge anymore!

    OUTPUT:
         **net** - Returns the required ieee network case6515rte

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case6515rte()
    """
    case6515rte = sorted_from_json(_get_cases_path("case6515rte.json", **kwargs))
    if ref_bus_idx != 6171:  # change reference bus
        _change_ref_bus(case6515rte, ref_bus_idx, ext_grid_p=2850.78)
    return case6515rte


def case9241pegase(**kwargs):
    """
    This grid represents a part of the European high voltage transmission network. The data is \
    provided by `MATPOWER <http://www.pserc.cornell.edu/matpower/>`_.
    The data origin are the paper `C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power \
    flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE \
    <https://arxiv.org/abs/1603.01533>`_, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, \
    and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems \
    taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on \
    Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

    OUTPUT:
         **net** - Returns the required ieee network case9241pegase

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.case9241pegase()
    """
    case9241pegase = sorted_from_json(_get_cases_path("case9241pegase.json", **kwargs))
    return case9241pegase


def GBreducednetwork(**kwargs):
    """
    Calls the json file GBreducednetwork.json which data is provided by `W. A. Bukhsh, Ken \
    McKinnon, Network data of real transmission networks, April 2013  \
    <http://www.maths.ed.ac.uk/optenergy/NetworkData/reducedGB/>`__.
    This data is a representative model of electricity transmission network in Great Britain (GB). \
    It was originally developed at the University of Strathclyde in 2010.

    OUTPUT:
         **net** - Returns the required ieee network GBreducednetwork

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.GBreducednetwork()
    """
    GBreducednetwork = sorted_from_json(_get_cases_path("GBreducednetwork.json", **kwargs))
    return GBreducednetwork


def GBnetwork(**kwargs):
    """
    Calls the json file GBnetwork.json which data is provided by `W. A. Bukhsh, Ken McKinnon, \
    Network data of real transmission networks, April 2013  \
    <http://www.maths.ed.ac.uk/optenergy/NetworkData/fullGB/>`_.
    This data represents detailed model of electricity transmission network of Great Britian (GB). \
    It consists of 2224 nodes, 3207 branches and 394 generators. This data is obtained from \
    publically available data on National grid website. The data was originally pointing out by \
    Manolis Belivanis, University of Strathclyde.

    OUTPUT:
         **net** - Returns the required ieee network GBreducednetwork

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.GBnetwork()
    """
    GBnetwork = sorted_from_json(_get_cases_path("GBnetwork.json", **kwargs))
    return GBnetwork


def iceland(**kwargs):
    """
    Calls the json file iceland.json which data is provided by `W. A. Bukhsh, Ken McKinnon, Network \
    data of real transmission networks, April 2013  \
    <http://www.maths.ed.ac.uk/optenergy/NetworkData/iceland/>`__.
    This data represents electricity transmission network of Iceland. It consists of 118 nodes, \
    206 branches and 35 generators. It was originally developed in PSAT format by Patrick McNabb, \
    Durham University in January 2011.

    OUTPUT:
         **net** - Returns the required ieee network iceland

    EXAMPLE:
         import pandapower.networks as pn

         net = pn.iceland()
    """
    iceland = sorted_from_json(_get_cases_path("iceland.json", **kwargs))
    return iceland
