# Copyright (c) 2019-2021 by University of Kassel, Tu Dortmund, RWTH Aachen University and Fraunhofer
# Institute for Energy Economics and Energy System Technology (IEE) Kassel and individual
# contributors (see AUTHORS file for details). All rights reserved.

import numpy as np
import pandas as pd
import os
from copy import deepcopy
import pandapower as pp

from simbench import sb_dir
from simbench.networks.simbench_code import get_simbench_code_and_parameters
from simbench.networks.profiles import filter_unapplied_profiles
from simbench.networks.loadcases import filter_loadcases
from simbench import csv_data2pp, read_csv_data, csv_tablenames, idx_in_2nd_array, \
    ensure_iterability, pp_profile_names

try:
    import pandaplan.core.pplog as logging
except ImportError:
    import logging

logger = logging.getLogger(__name__)

__author__ = "smeinecke"


def complete_data_path(scenario, version=1, path_to_folders=None):
    """ Returns the path to all simbench grid csv files. """
    path_to_folders = path_to_folders if path_to_folders is not None else os.path.join(
        sb_dir, "networks")
    complete_data_path = os.path.join(path_to_folders, "%i-complete_data-mixed-all-%i-sw" % (
        int(version), int(scenario)))
    if int(scenario) not in [0, 1, 2]:
        logger.warning("Only scenario 0, 1 and 2 are known. However, %s is given." % str(scenario))
    if int(version) not in [1]:
        logger.warning("Only version 1 is known. However, %s is given." % str(version))
    return complete_data_path


def _grid_number_dict():
    return {"EHV": {"mixed": 1},
            "HV": {"mixed": 1, "urban": 2},
            "MV": {"rural": 1, "semiurb": 2, "urban": 3, "comm": 4},
            "LV": {"rural1": 1, "rural2": 2, "rural3": 3, "semiurb4": 4, "semiurb5": 5,
                   "urban6": 6}}


def get_bus_bus_switch_indices_from_csv(switch_table, node_table, error_type=ValueError):
    """ Returns a list of indices of bus-bus-switches from csv tables. """
    for nodeX in ["nodeA", "nodeB"]:
        missing_nodeX = switch_table[~switch_table[nodeX].isin(node_table.id)]
        if len(missing_nodeX):
            message = "At Switches " + str(["%s" % name for name in missing_nodeX.id]) + \
                " %s " % nodeX + str(["%s" % name for name in missing_nodeX[nodeX]]) + \
                " do not occur in Node table."
            raise ValueError(message)
    idx_nodeA = idx_in_2nd_array(switch_table.nodeA.values, node_table.id.values)
    idx_nodeB = idx_in_2nd_array(switch_table.nodeB.values, node_table.id.values)

    aux_nodeA = np.array(node_table.type[idx_nodeA] == "auxiliary")
    aux_nodeB = np.array(node_table.type[idx_nodeB] == "auxiliary")

    double_aux = switch_table.id[aux_nodeA & aux_nodeB]
    if len(double_aux):
        message = "Both side auxiliary nodes at Switches " + str([
            "%s" % name for name in double_aux])
        if error_type is None or error_type is False:
            logger.debug(message)
        else:
            raise error_type(message)

    return list(switch_table.index[~(aux_nodeA | aux_nodeB)])


def _simple_hv_subnet_determination(sb_code_parameters):
    """ Determines the hv_subnet, neglecting special cases of complete grid or complete dataset
        download. """
    hv_grid_number = _grid_number_dict()[sb_code_parameters[1]][sb_code_parameters[3]]
    hv_subnet = sb_code_parameters[1] + str(hv_grid_number)
    hv_subnet += ".101" if sb_code_parameters[1] in ["MV", "LV"] else ""
    hv_subnet = hv_subnet if hv_subnet not in ["LV5.101", "LV6.101"] else hv_subnet[:-3] + "201"
    return hv_subnet, hv_grid_number


def _simple_lv_subnets_determination(sb_code_parameters, hv_subnet, hv_grid_number, input_path):
    """ Determines the list of all lv_subnets which are connectable to given hv_subnet.
        This function neglects special cases of complete grid or complete dataset download. """
    if sb_code_parameters[2] == "":
        lv_subnets = []
    elif sb_code_parameters[2] == "HV":
        lv_subnet_list = ["HV1", "HV2"]
        lv_subnets = {"all": lv_subnet_list, 1: ["HV1"], 2: ["HV2"]}[sb_code_parameters[4]]
    else:
        load_data = pd.read_csv(os.path.join(input_path, "Load.csv"), sep=";")
        lv_types = load_data.loc[load_data.subnet.str.startswith(
            hv_subnet + "_" + sb_code_parameters[2])].profile.value_counts()
        filtered_lv_types = lv_types[
            (pd.Series(lv_types.index.str[:2]).str.upper() == sb_code_parameters[2]).values]

        lv_subnet_list = []
        for type_, number in filtered_lv_types.items():
            if type_[:2].upper() == sb_code_parameters[2]:
                if type_[3:] in _grid_number_dict()[sb_code_parameters[2]].keys():
                    lv_subnet_list += [sb_code_parameters[2] + str(_grid_number_dict()[
                        sb_code_parameters[2]][type_[3:]]) + ".%i" % (i+(hv_grid_number)*100) for
                        i in range(1, 1+number)]

        # --- determine lv_subnets for single or all lv grids
        if sb_code_parameters[4] == "all":
            lv_subnets = lv_subnet_list
        elif isinstance(sb_code_parameters[4], str):
            if sb_code_parameters[2]+sb_code_parameters[4] not in lv_subnet_list:
                raise ValueError("'sb_code_parameters[4]' %s is " % sb_code_parameters[4] +
                                 "not in 'lv_subnet_list'.")
            lv_subnets = [sb_code_parameters[2]+sb_code_parameters[4]]
        else:
            raise ValueError("'sb_code_parameters[4]' must be a string, e.g. 'all' or 'MV1.1'" +
                             " (depending on the voltage level).")
    return lv_subnets


def get_relevant_subnets(sb_code_info, input_path):
    """ Determines a list of relevant subnet names of a parameter set, describing a SimBench grid
    selection. This list of subnets can be used to extract the requested SimBench grid from all
    grids data."""
    _, sb_code_parameters = get_simbench_code_and_parameters(sb_code_info)

    # --- in case of complete data download:
    if sb_code_parameters[1] == "complete_data":
        assert sb_code_parameters[2] == ""
        return sb_code_parameters[1], sb_code_parameters[2]

    # --- in case of complete grid download:
    if sb_code_parameters[2] == "HVMVLV":
        sb_code_parameters = deepcopy(sb_code_parameters)
        hv_subnets = []
        lv_subnets = []
        for hv_level, lv_level in zip(["EHV", "HV", "MV"], ["HV", "MV", "LV"]):
            sb_code_parameters[1] = hv_level
            sb_code_parameters[2] = lv_level
            for hv_type in _grid_number_dict()[hv_level].keys():
                sb_code_parameters[3] = hv_type
                hv_subnet, hv_grid_number = _simple_hv_subnet_determination(sb_code_parameters)
                hv_subnets += [hv_subnet]
                lv_subnets += _simple_lv_subnets_determination(
                    sb_code_parameters, hv_subnet, hv_grid_number, input_path)
        return hv_subnets, lv_subnets

    # --- in other cases
    # determine hv_subnet
    hv_subnet, hv_grid_number = _simple_hv_subnet_determination(sb_code_parameters)
    # determine lv_subnets
    lv_subnets = _simple_lv_subnets_determination(
        sb_code_parameters, hv_subnet, hv_grid_number, input_path)
    return hv_subnet, lv_subnets


def _extract_csv_table_by_subnet(csv_table, tablename, relevant_subnets, bus_bus_switches={}):
    """ Extracts csv table by subnet names.

        INPUT:
            **csv_table** (DataFrame)

            **tablename** (str)

            **relevant_subnets** (tuple) - first item is hv_subnet (str), second lv_subnets (list of
            strings)

        OPTIONAL:
            **bus_bus_switches** (set, {}) - indices of bus-bus-switches in csv DataFrame.
            Only used if tablename == "Switch".
    """
    hv_subnets = ensure_iterability(relevant_subnets[0])
    lv_subnets = relevant_subnets[1]
    if "complete_data" in hv_subnets or \
       isinstance(csv_table, pd.DataFrame) and not csv_table.shape[0]:
        return csv_table  # no extraction needed
    csv_table = deepcopy(csv_table)

    if isinstance(csv_table, pd.DataFrame) and "subnet" in csv_table.columns:
        logger.debug("Start extracting %s" % tablename)
        subnet_split = csv_table.subnet.str.split("_", expand=True)

        # --- hv_elms: all elements starting with hv_subnet
        hv_elms = set(subnet_split.index[subnet_split[0].isin(hv_subnets)])
        if tablename in ["Node", "Coordinates", "Measurement", "Switch", "Substation"]:
            # including elements that subnet data is hv_subnet between 1st "_" and 2nd "_" or end
            hv_elms_to_add = set(subnet_split.index[subnet_split[1].isin(hv_subnets)])
            if tablename == "Switch":
                hv_elms_to_add = hv_elms_to_add & bus_bus_switches
            elif tablename == "Measurement":
                bus_measurements = set(csv_table.index[
                    pd.isnull(csv_table[["element1", "element2"]]).any(axis=1)])
                hv_elms_to_add = hv_elms_to_add & bus_measurements
            hv_elms |= hv_elms_to_add

        # --- lv_elms: all elements starting with lv_subnet
        lv_elms = set(subnet_split.index[subnet_split[0].isin(lv_subnets)])

        lv_hv_elms = set()
        hv_lv_elms = set()
        if 1 in subnet_split.columns:

            # --- lv_hv_elms
            if tablename in ["Node", "Coordinates", "Measurement", "Switch", "Substation"]:
                # all elements with a higher voltage level before 1st "_" than after the first "_"
                subnet_split_level = pd.DataFrame(None, index=subnet_split.index,
                                                  columns=subnet_split.columns)
                subnet_split.loc[[0, 1]] = subnet_split.loc[[0, 1]].replace(
                    {"EHV": 1, "HV": 3, "MV": 5, "LV": 5}
                )
                # for col in [0, 1]:
                #     for level_str, level_int in zip(["EHV", "HV", "MV", "LV"], [1, 3, 5, 7]):
                #         subnet_split_level.loc[
                #             subnet_split[col].fillna("").str.contains(level_str), col] = level_int
                lv_hv_elms = set(subnet_split_level.index[
                    subnet_split_level[0] > subnet_split_level[1]])
            else:
                # all elements with lv_subnet before 1st "_"
                # and is hv_subnet between 1st "_" and 2nd or end
                lv_hv_elms = set(subnet_split.index[
                    (subnet_split[0].isin(lv_subnets)) & (subnet_split[1].isin(hv_subnets))])

            # --- hv_lv_elms: all elements with hv_subnet before 1st "_"
            # and is lv_subnet between 1st "_" and 2nd or end
            if tablename not in ["Node", "Coordinates", "Switch", "Substation"]:
                hv_lv_elms = set(subnet_split.index[
                    (subnet_split[0].isin(hv_subnets)) & (subnet_split[1].isin(lv_subnets))])
            if tablename == "Measurement":
                hv_lv_elms -= bus_measurements

        # --- determine indices to drop and examine dropping
        drop_idx = (set(csv_table.index) - hv_elms - lv_elms) | hv_lv_elms | lv_hv_elms
        csv_table.drop(drop_idx, inplace=True)
        no_extraction = False
    else:
        no_extraction = "Profile" not in tablename and "Type" not in tablename and \
            tablename != "StudyCases"
        if no_extraction:
            logger.warning("From %s no extraction can be made by 'subnet'." % tablename)
    return csv_table


def _get_extracted_csv_table(relevant_subnets, tablename, input_path, sep=";"):
    """ Returns extracted csv data of the requested SimBench grid. """
    csv_table = read_csv_data(input_path, sep=sep, tablename=tablename)
    if tablename == "Switch":
        node_table = read_csv_data(input_path, sep=sep, tablename="Node")
        bus_bus_switches = set(get_bus_bus_switch_indices_from_csv(csv_table, node_table))
    else:
        bus_bus_switches = {}
    extracted_csv_table = _extract_csv_table_by_subnet(csv_table, tablename, relevant_subnets,
                                                       bus_bus_switches=bus_bus_switches)
    return extracted_csv_table


def _get_extracted_csv_data_from_dict(csv_data, relevant_subnets):
    """ Returns extracted csv data of the requested SimBench grid from given csv data dict. """
    csv_data = deepcopy(csv_data)
    if "Node" in csv_data.keys() and "Switch" in csv_data.keys():
        bus_bus_switches = set(get_bus_bus_switch_indices_from_csv(
            csv_data["Switch"], csv_data["Node"]))
    else:
        bus_bus_switches = {}
    for key in csv_data.keys():
        csv_data[key] = _extract_csv_table_by_subnet(csv_data[key], key, relevant_subnets,
                                                     bus_bus_switches=bus_bus_switches)
    return csv_data


def get_extracted_csv_data(relevant_subnets, input_path, sep=";", **kwargs):
    """ Returns extracted csv data of the requested SimBench grid
    (per default from all SimBench grids csv data).
    **kwargs are ignored.
    """
    # --- import input data
    if 'complete_data' in relevant_subnets[0]:  # return complete data
        return read_csv_data(input_path, sep=sep)
    else:
        csv_data = dict()
        for tablename in csv_tablenames(['elements', 'profiles', 'types', 'cases']):
            csv_data[tablename] = _get_extracted_csv_table(relevant_subnets, tablename,
                                                           input_path=input_path)
    return csv_data


def _get_connected_buses_via_bus_bus_switch(net, buses):
    """ Returns a set of buses which are connected to 'buses' via bus-bus switches. """
    buses = set(ensure_iterability(buses))
    add_buses = [1]  # set anything to add_buses to start the while loop
    while(len(add_buses)):
        add_buses = pp.get_connected_buses(net, buses, consider=("s"))
        buses |= add_buses
    return buses


def generate_no_sw_variant(net):
    """ Drops all bus-bus switches and fuses buses which were connected by bus-bus switches.
        Furthermore drop all closed line and trafo switches. """
    # get bus-bus switch indices and close the switches to let get_connected_buses() find them
    bus_bus_sw = net.switch.index[net.switch.et == "b"]
    net.switch.loc[bus_bus_sw, "closed"] = True

    # determine to_fuse dict
    # to_fuse is used to fuse buses which are connected via bus-bus switches
    to_fuse = dict()
    already_considered = set()
    for bbs in bus_bus_sw:
        bus1 = net.switch.bus.at[bbs]
        if bus1 not in already_considered:
            bus2 = net.switch.element.at[bbs]
            to_fuse[bus1] = _get_connected_buses_via_bus_bus_switch(net, {bus1, bus2}) - {bus1}
            already_considered |= {bus1}
            already_considered |= to_fuse[bus1]

    # drop all closed switches (which now also includes all bus-bus switches (see above))
    net.switch.drop(net.switch.index[net.switch.closed], inplace=True)

    # fuse buses which are connected via bus-bus switches
    for b1, b2 in to_fuse.items():
        pp.fuse_buses(net, b1, b2)

    # replace auxiliary type of buses with no switch connected anymore
    aux_buses = net.bus.index[net.bus.type == "auxiliary"]
    buses_at_sw = pd.concat([net.switch.bus, net.switch.element.loc[net.switch.et == "b"]])
    aux_buses_to_change_type = aux_buses[~aux_buses.isin(buses_at_sw)]
    net.bus.type.loc[aux_buses_to_change_type] = "b"


def get_simbench_net(sb_code_info, input_path=None):
    """
    Returns the simbench net, requested by a given SimBench code information. Please have a look
    into jupyter notebook tutorials to learn more about simbench grids and the meaning of SimBench
    codes.

    INPUT:
        **sb_code_info** (str or list) - simbench code which defines which simbench grid is
        requested, e.g. '1-MVLV-urban-all-0-sw' requests a grid with the urban MV grid and all
        connected LV grids, both of SimBench version 1, scenario zero and with full switch
        representation.

    OPTIONAL:
        **input_path** (path) - option to change the path to all simbench grid csv files. However,
        a change should not be necessary.

    OUTPUT:
        net (pandapowerNet)

    EXAMPLE:

        import simbench as sb

        net = sb.get_simbench_net('1-MVLV-urban-all-0-sw')
    """
    # --- get relevant subnets
    sb_code, sb_code_parameters = get_simbench_code_and_parameters(sb_code_info)
    input_path = input_path if input_path is not None else complete_data_path(sb_code_parameters[5])
    relevant_subnets = get_relevant_subnets(sb_code_parameters, input_path)

    # --- get_extracted_csv_data and convert this data to pandapower net
    csv_data = get_extracted_csv_data(relevant_subnets, input_path)
    filter_unapplied_profiles(csv_data)
    filter_loadcases(csv_data)
    net = csv_data2pp(csv_data)

    # --- remove switches if wanted by sb_code_info
    if not sb_code_parameters[6]:  # remove Switches
        generate_no_sw_variant(net)

    return net


def get_all_simbench_profiles(scenario, input_path=None, sep=";"):
    """
    Returns a dict of DataFrames with all simbench profiles of the given scenario.
    These include all profiles with the net data received by
    get_simbench_net("1-complete_data-mixed-all-%s-sw" % str(scenario)).

    INPUT:
        **scenario** (int) - defines to which scenario the requested profiles belong to. Should be
        within [0, 1, 2].

    OPTIONAL:
        **input_path** (path) - option to change the path to all simbench grid csv files. However,
        a change should not be necessary.

        **sep** (str, ";") - seperator of the csv files which contain the profiles information.

    OUTPUT:
        **profiles** (dict) - dict of DataFrames with all simbench profiles of the given scenario
    """
    input_path = input_path if input_path is not None else complete_data_path(scenario)
    csvtablenames = csv_tablenames(['profiles'])

    # read all profiles data
    profiles = read_csv_data(input_path, sep=sep, tablename=csvtablenames)

    # rename csv_tablenames by pandapower element names
    for csv_name, pp_name in zip(csvtablenames, pp_profile_names()):
        profiles[pp_name] = profiles.pop(csv_name)

    return profiles


if __name__ == '__main__':
    pass
