# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Solves optimal operation and capacity for a network with the option to
iteratively optimize while updating line reactances.

This script is used for optimizing the electrical network as well as the
sector coupled network.

Description
-----------

Total annual system costs are minimised with PyPSA. The full formulation of the
linear optimal power flow (plus investment planning
is provided in the
`documentation of PyPSA <https://pypsa.readthedocs.io/en/latest/optimal_power_flow.html#linear-optimal-power-flow>`_.

The optimization is based on the :func:`network.optimize` function.
Additionally, some extra constraints specified in :mod:`solve_network` are added.

.. note::

    The rules ``solve_elec_networks`` and ``solve_sector_networks`` run
    the workflow for all scenarios in the configuration file (``scenario:``)
    based on the rule :mod:`solve_network`.
"""
import logging
import re

import numpy as np
import pandas as pd
import pypsa
import xarray as xr
from _benchmark import memory_logger
from _helpers import configure_logging, update_config_with_sector_opts
from pypsa.descriptors import get_activity_mask

logger = logging.getLogger(__name__)
pypsa.pf.logger.setLevel(logging.WARNING)
from pypsa.descriptors import get_switchable_as_dense as get_as_dense

from prepare_sector_network import emission_sectors_from_opts


def add_land_use_constraint(n, planning_horizons, config):
    if "m" in snakemake.wildcards.clusters:
        _add_land_use_constraint_m(n, planning_horizons, config)
    else:
        _add_land_use_constraint(n)


def add_land_use_constraint_perfect(n):
    """
    Add global constraints for tech capacity limit.
    """
    logger.info("Add land-use constraint for perfect foresight")

    def compress_series(s):
        def process_group(group):
            if group.nunique() == 1:
                return pd.Series(group.iloc[0], index=[None])
            else:
                return group

        return s.groupby(level=[0, 1]).apply(process_group)

    def new_index_name(t):
        # Convert all elements to string and filter out None values
        parts = [str(x) for x in t if x is not None]
        # Join with space, but use a dash for the last item if not None
        return " ".join(parts[:2]) + (f"-{parts[-1]}" if len(parts) > 2 else "")

    def check_p_min_p_max(p_nom_max):
        p_nom_min = n.generators[ext_i].groupby(grouper).sum().p_nom_min
        p_nom_min = p_nom_min.reindex(p_nom_max.index)
        check = (
            p_nom_min.groupby(level=[0, 1]).sum()
            > p_nom_max.groupby(level=[0, 1]).min()
        )
        if check.sum():
            logger.warning(
                f"summed p_min_pu values at node larger than technical potential {check[check].index}"
            )

    grouper = [n.generators.carrier, n.generators.bus, n.generators.build_year]
    ext_i = n.generators.p_nom_extendable
    # get technical limit per node and investment period
    p_nom_max = n.generators[ext_i].groupby(grouper).min().p_nom_max
    # drop carriers without tech limit
    p_nom_max = p_nom_max[~p_nom_max.isin([np.inf, np.nan])]
    # carrier
    carriers = p_nom_max.index.get_level_values(0).unique()
    gen_i = n.generators[(n.generators.carrier.isin(carriers)) & (ext_i)].index
    n.generators.loc[gen_i, "p_nom_min"] = 0
    # check minimum capacities
    check_p_min_p_max(p_nom_max)
    # drop multi entries in case p_nom_max stays constant in different periods
    # p_nom_max = compress_series(p_nom_max)
    # adjust name to fit syntax of nominal constraint per bus
    df = p_nom_max.reset_index()
    df["name"] = df.apply(
        lambda row: f"nom_max_{row['carrier']}"
        + (f"_{row['build_year']}" if row["build_year"] is not None else ""),
        axis=1,
    )

    for name in df.name.unique():
        df_carrier = df[df.name == name]
        bus = df_carrier.bus
        n.buses.loc[bus, name] = df_carrier.p_nom_max.values

    return n


def _add_land_use_constraint(n):
    # warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'

    for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
        extendable_i = (n.generators.carrier == carrier) & n.generators.p_nom_extendable
        n.generators.loc[extendable_i, "p_nom_min"] = 0

        ext_i = (n.generators.carrier == carrier) & ~n.generators.p_nom_extendable
        existing = (
            n.generators.loc[ext_i, "p_nom"]
            .groupby(n.generators.bus.map(n.buses.location))
            .sum()
        )
        existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
        n.generators.loc[existing.index, "p_nom_max"] -= existing

    # check if existing capacities are larger than technical potential
    existing_large = n.generators[
        n.generators["p_nom_min"] > n.generators["p_nom_max"]
    ].index
    if len(existing_large):
        logger.warning(
            f"Existing capacities larger than technical potential for {existing_large},\
                        adjust technical potential to existing capacities"
        )
        n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[
            existing_large, "p_nom_min"
        ]

    n.generators.p_nom_max.clip(lower=0, inplace=True)


def _add_land_use_constraint_m(n, planning_horizons, config):
    # if generators clustering is lower than network clustering, land_use accounting is at generators clusters

    grouping_years = config["existing_capacities"]["grouping_years"]
    current_horizon = snakemake.wildcards.planning_horizons

    for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
        existing = n.generators.loc[n.generators.carrier == carrier, "p_nom"]
        ind = list(
            {i.split(sep=" ")[0] + " " + i.split(sep=" ")[1] for i in existing.index}
        )

        previous_years = [
            str(y)
            for y in planning_horizons + grouping_years
            if y < int(snakemake.wildcards.planning_horizons)
        ]

        for p_year in previous_years:
            ind2 = [
                i for i in ind if i + " " + carrier + "-" + p_year in existing.index
            ]
            sel_current = [i + " " + carrier + "-" + current_horizon for i in ind2]
            sel_p_year = [i + " " + carrier + "-" + p_year for i in ind2]
            n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[
                sel_p_year
            ].rename(lambda x: x[:-4] + current_horizon)

    n.generators.p_nom_max.clip(lower=0, inplace=True)


def add_co2_sequestration_limit(n, config, limit=200):
    """
    Add a global constraint on the amount of Mt CO2 that can be sequestered.
    """
    n.carriers.loc["co2 stored", "co2_absorptions"] = -1
    n.carriers.co2_absorptions = n.carriers.co2_absorptions.fillna(0)

    limit = limit * 1e6
    for o in opts:
        if "seq" not in o:
            continue
        limit = float(o[o.find("seq") + 3 :]) * 1e6
        break

    if not n.investment_periods.empty:
        periods = n.investment_periods
        names = pd.Index([f"co2_sequestration_limit-{period}" for period in periods])
    else:
        periods = [np.nan]
        names = pd.Index(["co2_sequestration_limit"])

    n.madd(
        "GlobalConstraint",
        names,
        sense="<=",
        constant=limit,
        type="primary_energy",
        carrier_attribute="co2_absorptions",
        investment_period=periods,
    )


def add_carbon_constraint(n, snapshots):
    glcs = n.global_constraints.query('type == "co2_limit"')
    if glcs.empty:
        return
    for name, glc in glcs.iterrows():
        carattr = glc.carrier_attribute
        emissions = n.carriers.query(f"{carattr} != 0")[carattr]

        if emissions.empty:
            continue

        # stores
        n.stores["carrier"] = n.stores.bus.map(n.buses.carrier)
        stores = n.stores.query("carrier in @emissions.index and not e_cyclic")
        if not stores.empty:
            last = n.snapshot_weightings.reset_index().groupby("period").last()
            last_i = last.set_index([last.index, last.timestep]).index
            final_e = n.model["Store-e"].loc[last_i, stores.index]
            time_valid = int(glc.loc["investment_period"])
            time_i = pd.IndexSlice[time_valid, :]
            lhs = final_e.loc[time_i, :] - final_e.shift(snapshot=1).loc[time_i, :]

            rhs = glc.constant
            n.model.add_constraints(lhs <= rhs, name=f"GlobalConstraint-{name}")


def add_carbon_budget_constraint(n, snapshots):
    glcs = n.global_constraints.query('type == "Co2Budget"')
    if glcs.empty:
        return
    for name, glc in glcs.iterrows():
        carattr = glc.carrier_attribute
        emissions = n.carriers.query(f"{carattr} != 0")[carattr]

        if emissions.empty:
            continue

        # stores
        n.stores["carrier"] = n.stores.bus.map(n.buses.carrier)
        stores = n.stores.query("carrier in @emissions.index and not e_cyclic")
        if not stores.empty:
            last = n.snapshot_weightings.reset_index().groupby("period").last()
            last_i = last.set_index([last.index, last.timestep]).index
            final_e = n.model["Store-e"].loc[last_i, stores.index]
            time_valid = int(glc.loc["investment_period"])
            time_i = pd.IndexSlice[time_valid, :]
            weighting = n.investment_period_weightings.loc[time_valid, "years"]
            lhs = final_e.loc[time_i, :] * weighting

            rhs = glc.constant
            n.model.add_constraints(lhs <= rhs, name=f"GlobalConstraint-{name}")


def add_max_growth(n, config):
    """
    Add maximum growth rates for different carriers.
    """

    opts = snakemake.params["sector"]["limit_max_growth"]
    # take maximum yearly difference between investment periods since historic growth is per year
    factor = n.investment_period_weightings.years.max() * opts["factor"]
    for carrier in opts["max_growth"].keys():
        max_per_period = opts["max_growth"][carrier] * factor
        logger.info(
            f"set maximum growth rate per investment period of {carrier} to {max_per_period} GW."
        )
        n.carriers.loc[carrier, "max_growth"] = max_per_period * 1e3

    for carrier in opts["max_relative_growth"].keys():
        max_r_per_period = opts["max_relative_growth"][carrier]
        logger.info(
            f"set maximum relative growth per investment period of {carrier} to {max_r_per_period}."
        )
        n.carriers.loc[carrier, "max_relative_growth"] = max_r_per_period

    return n


def add_retrofit_gas_boiler_constraint(n, snapshots):
    """
    Allow retrofitting of existing gas boilers to H2 boilers.
    """
    c = "Link"
    logger.info("Add constraint for retrofitting gas boilers to H2 boilers.")
    # existing gas boilers
    mask = n.links.carrier.str.contains("gas boiler") & ~n.links.p_nom_extendable
    gas_i = n.links[mask].index
    mask = n.links.carrier.str.contains("retrofitted H2 boiler")
    h2_i = n.links[mask].index

    n.links.loc[gas_i, "p_nom_extendable"] = True
    p_nom = n.links.loc[gas_i, "p_nom"]
    n.links.loc[gas_i, "p_nom"] = 0

    # heat profile
    cols = n.loads_t.p_set.columns[
        n.loads_t.p_set.columns.str.contains("heat")
        & ~n.loads_t.p_set.columns.str.contains("industry")
        & ~n.loads_t.p_set.columns.str.contains("agriculture")
    ]
    profile = n.loads_t.p_set[cols].div(
        n.loads_t.p_set[cols].groupby(level=0).max(), level=0
    )
    # to deal if max value is zero
    profile.fillna(0, inplace=True)
    profile.rename(columns=n.loads.bus.to_dict(), inplace=True)
    profile = profile.reindex(columns=n.links.loc[gas_i, "bus1"])
    profile.columns = gas_i

    rhs = profile.mul(p_nom)

    dispatch = n.model["Link-p"]
    active = get_activity_mask(n, c, snapshots, gas_i)
    rhs = rhs[active]
    p_gas = dispatch.sel(Link=gas_i)
    p_h2 = dispatch.sel(Link=h2_i)

    lhs = p_gas + p_h2

    n.model.add_constraints(lhs == rhs, name="gas_retrofit")


def prepare_network(
    n,
    solve_opts=None,
    config=None,
    foresight=None,
    planning_horizons=None,
    co2_sequestration_potential=None,
):
    if "clip_p_max_pu" in solve_opts:
        for df in (
            n.generators_t.p_max_pu,
            n.generators_t.p_min_pu,
            n.storage_units_t.inflow,
        ):
            df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)

    if load_shedding := solve_opts.get("load_shedding"):
        # intersect between macroeconomic and surveybased willingness to pay
        # http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
        # TODO: retrieve color and nice name from config
        n.add("Carrier", "load", color="#dd2e23", nice_name="Load shedding")
        buses_i = n.buses.query("carrier == 'AC'").index
        if not np.isscalar(load_shedding):
            # TODO: do not scale via sign attribute (use Eur/MWh instead of Eur/kWh)
            load_shedding = 1e2  # Eur/kWh

        n.madd(
            "Generator",
            buses_i,
            " load",
            bus=buses_i,
            carrier="load",
            sign=1e-3,  # Adjust sign to measure p and p_nom in kW instead of MW
            marginal_cost=load_shedding,  # Eur/kWh
            p_nom=1e9,  # kW
        )

    if solve_opts.get("noisy_costs"):
        for t in n.iterate_components():
            # if 'capital_cost' in t.df:
            #    t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5)
            if "marginal_cost" in t.df:
                t.df["marginal_cost"] += 1e-2 + 2e-3 * (
                    np.random.random(len(t.df)) - 0.5
                )

        for t in n.iterate_components(["Line", "Link"]):
            t.df["capital_cost"] += (
                1e-1 + 2e-2 * (np.random.random(len(t.df)) - 0.5)
            ) * t.df["length"]

    if solve_opts.get("nhours"):
        nhours = solve_opts["nhours"]
        n.set_snapshots(n.snapshots[:nhours])
        n.snapshot_weightings[:] = 8760.0 / nhours

    if foresight == "myopic":
        add_land_use_constraint(n, planning_horizons, config)

    if foresight == "perfect":
        n = add_land_use_constraint_perfect(n)
        if snakemake.params["sector"]["limit_max_growth"]["enable"]:
            n = add_max_growth(n, config)

    if n.stores.carrier.eq("co2 stored").any():
        limit = co2_sequestration_potential
        add_co2_sequestration_limit(n, config, limit=limit)

    return n


def add_CCL_constraints(n, config):
    """
    Add CCL (country & carrier limit) constraint to the network.

    Add minimum and maximum levels of generator nominal capacity per carrier
    for individual countries. Opts and path for agg_p_nom_minmax.csv must be defined
    in config.yaml. Default file is available at data/agg_p_nom_minmax.csv.

    Parameters
    ----------
    n : pypsa.Network
    config : dict

    Example
    -------
    scenario:
        opts: [Co2L-CCL-24H]
    electricity:
        agg_p_nom_limits: data/agg_p_nom_minmax.csv
    """
    agg_p_nom_minmax = pd.read_csv(
        config["electricity"]["agg_p_nom_limits"], index_col=[0, 1]
    )
    logger.info("Adding generation capacity constraints per carrier and country")
    p_nom = n.model["Generator-p_nom"]

    gens = n.generators.query("p_nom_extendable").rename_axis(index="Generator-ext")
    grouper = pd.concat([gens.bus.map(n.buses.country), gens.carrier])
    lhs = p_nom.groupby(grouper).sum().rename(bus="country")

    minimum = xr.DataArray(agg_p_nom_minmax["min"].dropna()).rename(dim_0="group")
    index = minimum.indexes["group"].intersection(lhs.indexes["group"])
    if not index.empty:
        n.model.add_constraints(
            lhs.sel(group=index) >= minimum.loc[index], name="agg_p_nom_min"
        )

    maximum = xr.DataArray(agg_p_nom_minmax["max"].dropna()).rename(dim_0="group")
    index = maximum.indexes["group"].intersection(lhs.indexes["group"])
    if not index.empty:
        n.model.add_constraints(
            lhs.sel(group=index) <= maximum.loc[index], name="agg_p_nom_max"
        )


def add_EQ_constraints(n, o, scaling=1e-1):
    """
    Add equity constraints to the network.

    Currently this is only implemented for the electricity sector only.

    Opts must be specified in the config.yaml.

    Parameters
    ----------
    n : pypsa.Network
    o : str

    Example
    -------
    scenario:
        opts: [Co2L-EQ0.7-24H]

    Require each country or node to on average produce a minimal share
    of its total electricity consumption itself. Example: EQ0.7c demands each country
    to produce on average at least 70% of its consumption; EQ0.7 demands
    each node to produce on average at least 70% of its consumption.
    """
    # TODO: Generalize to cover myopic and other sectors?
    float_regex = "[0-9]*\.?[0-9]+"
    level = float(re.findall(float_regex, o)[0])
    if o[-1] == "c":
        ggrouper = n.generators.bus.map(n.buses.country)
        lgrouper = n.loads.bus.map(n.buses.country)
        sgrouper = n.storage_units.bus.map(n.buses.country)
    else:
        ggrouper = n.generators.bus
        lgrouper = n.loads.bus
        sgrouper = n.storage_units.bus
    load = (
        n.snapshot_weightings.generators
        @ n.loads_t.p_set.groupby(lgrouper, axis=1).sum()
    )
    inflow = (
        n.snapshot_weightings.stores
        @ n.storage_units_t.inflow.groupby(sgrouper, axis=1).sum()
    )
    inflow = inflow.reindex(load.index).fillna(0.0)
    rhs = scaling * (level * load - inflow)
    p = n.model["Generator-p"]
    lhs_gen = (
        (p * (n.snapshot_weightings.generators * scaling))
        .groupby(ggrouper.to_xarray())
        .sum()
        .sum("snapshot")
    )
    # TODO: double check that this is really needed, why do have to subtract the spillage
    if not n.storage_units_t.inflow.empty:
        spillage = n.model["StorageUnit-spill"]
        lhs_spill = (
            (spillage * (-n.snapshot_weightings.stores * scaling))
            .groupby(sgrouper.to_xarray())
            .sum()
            .sum("snapshot")
        )
        lhs = lhs_gen + lhs_spill
    else:
        lhs = lhs_gen
    n.model.add_constraints(lhs >= rhs, name="equity_min")


def add_BAU_constraints(n, config):
    """
    Add a per-carrier minimal overall capacity.

    BAU_mincapacities and opts must be adjusted in the config.yaml.

    Parameters
    ----------
    n : pypsa.Network
    config : dict

    Example
    -------
    scenario:
        opts: [Co2L-BAU-24H]
    electricity:
        BAU_mincapacities:
            solar: 0
            onwind: 0
            OCGT: 100000
            offwind-ac: 0
            offwind-dc: 0
    Which sets minimum expansion across all nodes e.g. in Europe to 100GW.
    OCGT bus 1 + OCGT bus 2 + ... > 100000
    """
    mincaps = pd.Series(config["electricity"]["BAU_mincapacities"])
    p_nom = n.model["Generator-p_nom"]
    ext_i = n.generators.query("p_nom_extendable")
    ext_carrier_i = xr.DataArray(ext_i.carrier.rename_axis("Generator-ext"))
    lhs = p_nom.groupby(ext_carrier_i).sum()
    index = mincaps.index.intersection(lhs.indexes["carrier"])
    rhs = mincaps[index].rename_axis("carrier")
    n.model.add_constraints(lhs >= rhs, name="bau_mincaps")


# TODO: think about removing or make per country
def add_SAFE_constraints(n, config):
    """
    Add a capacity reserve margin of a certain fraction above the peak demand.
    Renewable generators and storage do not contribute. Ignores network.

    Parameters
    ----------
        n : pypsa.Network
        config : dict

    Example
    -------
    config.yaml requires to specify opts:

    scenario:
        opts: [Co2L-SAFE-24H]
    electricity:
        SAFE_reservemargin: 0.1
    Which sets a reserve margin of 10% above the peak demand.
    """
    peakdemand = n.loads_t.p_set.sum(axis=1).max()
    margin = 1.0 + config["electricity"]["SAFE_reservemargin"]
    reserve_margin = peakdemand * margin
    conventional_carriers = config["electricity"]["conventional_carriers"]
    ext_gens_i = n.generators.query(
        "carrier in @conventional_carriers & p_nom_extendable"
    ).index
    p_nom = n.model["Generator-p_nom"].loc[ext_gens_i]
    lhs = p_nom.sum()
    exist_conv_caps = n.generators.query(
        "~p_nom_extendable & carrier in @conventional_carriers"
    ).p_nom.sum()
    rhs = reserve_margin - exist_conv_caps
    n.model.add_constraints(lhs >= rhs, name="safe_mintotalcap")


def add_operational_reserve_margin(n, sns, config):
    """
    Build reserve margin constraints based on the formulation given in
    https://genxproject.github.io/GenX/dev/core/#Reserves.

    Parameters
    ----------
        n : pypsa.Network
        sns: pd.DatetimeIndex
        config : dict

    Example:
    --------
    config.yaml requires to specify operational_reserve:
    operational_reserve: # like https://genxproject.github.io/GenX/dev/core/#Reserves
        activate: true
        epsilon_load: 0.02 # percentage of load at each snapshot
        epsilon_vres: 0.02 # percentage of VRES at each snapshot
        contingency: 400000 # MW
    """
    reserve_config = config["electricity"]["operational_reserve"]
    EPSILON_LOAD = reserve_config["epsilon_load"]
    EPSILON_VRES = reserve_config["epsilon_vres"]
    CONTINGENCY = reserve_config["contingency"]

    # Reserve Variables
    n.model.add_variables(
        0, np.inf, coords=[sns, n.generators.index], name="Generator-r"
    )
    reserve = n.model["Generator-r"]
    summed_reserve = reserve.sum("Generator")

    # Share of extendable renewable capacities
    ext_i = n.generators.query("p_nom_extendable").index
    vres_i = n.generators_t.p_max_pu.columns
    if not ext_i.empty and not vres_i.empty:
        capacity_factor = n.generators_t.p_max_pu[vres_i.intersection(ext_i)]
        p_nom_vres = (
            n.model["Generator-p_nom"]
            .loc[vres_i.intersection(ext_i)]
            .rename({"Generator-ext": "Generator"})
        )
        lhs = summed_reserve + (p_nom_vres * (-EPSILON_VRES * capacity_factor)).sum(
            "Generator"
        )

    # Total demand per t
    demand = get_as_dense(n, "Load", "p_set").sum(axis=1)

    # VRES potential of non extendable generators
    capacity_factor = n.generators_t.p_max_pu[vres_i.difference(ext_i)]
    renewable_capacity = n.generators.p_nom[vres_i.difference(ext_i)]
    potential = (capacity_factor * renewable_capacity).sum(axis=1)

    # Right-hand-side
    rhs = EPSILON_LOAD * demand + EPSILON_VRES * potential + CONTINGENCY

    n.model.add_constraints(lhs >= rhs, name="reserve_margin")

    # additional constraint that capacity is not exceeded
    gen_i = n.generators.index
    ext_i = n.generators.query("p_nom_extendable").index
    fix_i = n.generators.query("not p_nom_extendable").index

    dispatch = n.model["Generator-p"]
    reserve = n.model["Generator-r"]

    capacity_variable = n.model["Generator-p_nom"].rename(
        {"Generator-ext": "Generator"}
    )
    capacity_fixed = n.generators.p_nom[fix_i]

    p_max_pu = get_as_dense(n, "Generator", "p_max_pu")

    lhs = dispatch + reserve - capacity_variable * p_max_pu[ext_i]

    rhs = (p_max_pu[fix_i] * capacity_fixed).reindex(columns=gen_i, fill_value=0)

    n.model.add_constraints(lhs <= rhs, name="Generator-p-reserve-upper")


def add_battery_constraints(n):
    """
    Add constraint ensuring that charger = discharger, i.e.
    1 * charger_size - efficiency * discharger_size = 0
    """
    if not n.links.p_nom_extendable.any():
        return

    discharger_bool = n.links.index.str.contains("battery discharger")
    charger_bool = n.links.index.str.contains("battery charger")

    dischargers_ext = n.links[discharger_bool].query("p_nom_extendable").index
    chargers_ext = n.links[charger_bool].query("p_nom_extendable").index

    eff = n.links.efficiency[dischargers_ext].values
    lhs = (
        n.model["Link-p_nom"].loc[chargers_ext]
        - n.model["Link-p_nom"].loc[dischargers_ext] * eff
    )

    n.model.add_constraints(lhs == 0, name="Link-charger_ratio")


def add_chp_constraints(n):
    electric = (
        n.links.index.str.contains("urban central")
        & n.links.index.str.contains("CHP")
        & n.links.index.str.contains("electric")
    )
    heat = (
        n.links.index.str.contains("urban central")
        & n.links.index.str.contains("CHP")
        & n.links.index.str.contains("heat")
    )

    electric_ext = n.links[electric].query("p_nom_extendable").index
    heat_ext = n.links[heat].query("p_nom_extendable").index

    electric_fix = n.links[electric].query("~p_nom_extendable").index
    heat_fix = n.links[heat].query("~p_nom_extendable").index

    p = n.model["Link-p"]  # dimension: [time, link]

    # output ratio between heat and electricity and top_iso_fuel_line for extendable
    if not electric_ext.empty:
        p_nom = n.model["Link-p_nom"]

        lhs = (
            p_nom.loc[electric_ext]
            * (n.links.p_nom_ratio * n.links.efficiency)[electric_ext].values
            - p_nom.loc[heat_ext] * n.links.efficiency[heat_ext].values
        )
        n.model.add_constraints(lhs == 0, name="chplink-fix_p_nom_ratio")

        rename = {"Link-ext": "Link"}
        lhs = (
            p.loc[:, electric_ext]
            + p.loc[:, heat_ext]
            - p_nom.rename(rename).loc[electric_ext]
        )
        n.model.add_constraints(lhs <= 0, name="chplink-top_iso_fuel_line_ext")

    # top_iso_fuel_line for fixed
    if not electric_fix.empty:
        lhs = p.loc[:, electric_fix] + p.loc[:, heat_fix]
        rhs = n.links.p_nom[electric_fix]
        n.model.add_constraints(lhs <= rhs, name="chplink-top_iso_fuel_line_fix")

    # back-pressure
    if not electric.empty:
        lhs = (
            p.loc[:, heat] * (n.links.efficiency[heat] * n.links.c_b[electric].values)
            - p.loc[:, electric] * n.links.efficiency[electric]
        )
        n.model.add_constraints(lhs <= rhs, name="chplink-backpressure")


def add_pipe_retrofit_constraint(n):
    """
    Add constraint for retrofitting existing CH4 pipelines to H2 pipelines.
    """
    gas_pipes_i = n.links.query("carrier == 'gas pipeline' and p_nom_extendable").index
    h2_retrofitted_i = n.links.query(
        "carrier == 'H2 pipeline retrofitted' and p_nom_extendable"
    ).index

    if h2_retrofitted_i.empty or gas_pipes_i.empty:
        return

    p_nom = n.model["Link-p_nom"]

    CH4_per_H2 = 1 / n.config["sector"]["H2_retrofit_capacity_per_CH4"]
    lhs = p_nom.loc[gas_pipes_i] + CH4_per_H2 * p_nom.loc[h2_retrofitted_i]
    rhs = n.links.p_nom[gas_pipes_i].rename_axis("Link-ext")

    n.model.add_constraints(lhs == rhs, name="Link-pipe_retrofit")


def add_co2limit_country(n, limit_countries, nyears=1.0):
    """
    Add a set of emissions limit constraints for specified countries.

    The countries and emissions limits are specified in the config file entry 'co2_budget_country_{investment_year}'.

    Parameters
    ----------
    n : pypsa.Network
    config : dict
    limit_countries : dict
    nyears: float, optional
        Used to scale the emissions constraint to the number of snapshots of the base network.
    """
    logger.info(f"Adding CO2 budget limit for each country as per unit of 1990 levels")

    countries = n.config["countries"]

    # TODO: import function from prepare_sector_network? Move to common place?
    sectors = emission_sectors_from_opts(opts)

    # convert Mt to tCO2
    co2_totals = 1e6 * pd.read_csv(snakemake.input.co2_totals_name, index_col=0)

    co2_limit_countries = co2_totals.loc[countries, sectors].sum(axis=1)
    co2_limit_countries = co2_limit_countries.loc[co2_limit_countries.index.isin(limit_countries.keys())]

    co2_limit_countries *= co2_limit_countries.index.map(limit_countries) * nyears

    p = n.model["Link-p"]  # dimension: (time, component)

    # NB: Most country-specific links retain their locational information in bus1 (except for DAC, where it is in bus2)
    country = n.links.bus1.map(n.buses.location).map(n.buses.country)
    country_DAC = (
        n.links[n.links.carrier == "DAC"]
        .bus2.map(n.buses.location)
        .map(n.buses.country)
    )
    country[country_DAC.index] = country_DAC

    lhs = []
    for port in [col[3:] for col in n.links if col.startswith("bus")]:
        if port == str(0):
            efficiency = (
                n.links["efficiency"].apply(lambda x: 1.0).rename("efficiency0")
            )
        elif port == str(1):
            efficiency = n.links["efficiency"]
        else:
            efficiency = n.links[f"efficiency{port}"]
        mask = n.links[f"bus{port}"].map(n.buses.carrier).eq("co2")

        idx = n.links[mask].index

        grouping = country.loc[idx]

        if not grouping.isnull().all():
            expr = (
                (p.loc[:, idx] * efficiency[idx])
                .groupby(grouping, axis=1)
                .sum()
                .sum(dims="snapshot")
            )
            lhs.append(expr)

    lhs = sum(lhs)  # dimension: (country)
    lhs = lhs.rename({list(lhs.dims.keys())[0]: "country"})
    rhs = pd.Series(co2_limit_countries)  # dimension: (country)

    for ct in lhs.indexes["country"]:
        n.model.add_constraints(
            lhs.loc[ct] <= rhs[ct],
            name=f"GlobalConstraint-co2_limit_per_country{ct}",
        )
        n.add(
            "GlobalConstraint",
            f"co2_limit_per_country{ct}",
            constant=rhs[ct],
            sense="<=",
            type="",
        )


def extra_functionality(n, snapshots):
    """
    Collects supplementary constraints which will be passed to
    ``pypsa.optimization.optimize``.

    If you want to enforce additional custom constraints, this is a good
    location to add them. The arguments ``opts`` and
    ``snakemake.config`` are expected to be attached to the network.
    """
    opts = n.opts
    config = n.config
    if "BAU" in opts and n.generators.p_nom_extendable.any():
        add_BAU_constraints(n, config)
    if "SAFE" in opts and n.generators.p_nom_extendable.any():
        add_SAFE_constraints(n, config)
    if "CCL" in opts and n.generators.p_nom_extendable.any():
        add_CCL_constraints(n, config)
    reserve = config["electricity"].get("operational_reserve", {})
    if reserve.get("activate"):
        add_operational_reserve_margin(n, snapshots, config)
    for o in opts:
        if "EQ" in o:
            add_EQ_constraints(n, o)
    add_battery_constraints(n)
    add_pipe_retrofit_constraint(n)
    if n._multi_invest:
        add_carbon_constraint(n, snapshots)
        add_carbon_budget_constraint(n, snapshots)
        add_retrofit_gas_boiler_constraint(n, snapshots)

    if n.config["sector"]["co2_budget_national"]:
        # prepare co2 constraint
        nhours = n.snapshot_weightings.generators.sum()
        nyears = nhours / 8760
        investment_year = int(snakemake.wildcards.planning_horizons[-4:])
        limit_countries = snakemake.config["co2_budget_national"][investment_year]

        # add co2 constraint for each country
        logger.info(f"Add CO2 limit for each country")
        add_co2limit_country(n, limit_countries, nyears)


def solve_network(n, config, solving, opts="", **kwargs):
    set_of_options = solving["solver"]["options"]
    cf_solving = solving["options"]

    kwargs["multi_investment_periods"] = config["foresight"] == "perfect"
    kwargs["solver_options"] = (
        solving["solver_options"][set_of_options] if set_of_options else {}
    )
    kwargs["solver_name"] = solving["solver"]["name"]
    kwargs["extra_functionality"] = extra_functionality
    kwargs["transmission_losses"] = cf_solving.get("transmission_losses", False)
    kwargs["linearized_unit_commitment"] = cf_solving.get(
        "linearized_unit_commitment", False
    )
    kwargs["assign_all_duals"] = cf_solving.get("assign_all_duals", False)

    rolling_horizon = cf_solving.pop("rolling_horizon", False)
    skip_iterations = cf_solving.pop("skip_iterations", False)
    if not n.lines.s_nom_extendable.any():
        skip_iterations = True
        logger.info("No expandable lines found. Skipping iterative solving.")

    # add to network for extra_functionality
    n.config = config
    n.opts = opts

    if rolling_horizon:
        kwargs["horizon"] = cf_solving.get("horizon", 365)
        kwargs["overlap"] = cf_solving.get("overlap", 0)
        n.optimize.optimize_with_rolling_horizon(**kwargs)
        status, condition = "", ""
    elif skip_iterations:
        status, condition = n.optimize(**kwargs)
    else:
        kwargs["track_iterations"] = (cf_solving.get("track_iterations", False),)
        kwargs["min_iterations"] = (cf_solving.get("min_iterations", 4),)
        kwargs["max_iterations"] = (cf_solving.get("max_iterations", 6),)
        status, condition = n.optimize.optimize_transmission_expansion_iteratively(
            **kwargs
        )

    if status != "ok" and not rolling_horizon:
        logger.warning(
            f"Solving status '{status}' with termination condition '{condition}'"
        )
    if "infeasible" in condition:
        raise RuntimeError("Solving status 'infeasible'")

    return n


if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

        snakemake = mock_snakemake(
            "solve_sector_network_perfect",
            configfiles="../config/test/config.perfect.yaml",
            simpl="",
            opts="",
            clusters="5",
            ll="v1.5",
            sector_opts="8760H-T-H-B-I-A-solar+p3-dist1",
            planning_horizons="2030",
        )
    configure_logging(snakemake)
    if "sector_opts" in snakemake.wildcards.keys():
        update_config_with_sector_opts(
            snakemake.config, snakemake.wildcards.sector_opts
        )

    opts = snakemake.wildcards.opts
    if "sector_opts" in snakemake.wildcards.keys():
        opts += "-" + snakemake.wildcards.sector_opts
    opts = [o for o in opts.split("-") if o != ""]
    solve_opts = snakemake.params.solving["options"]

    np.random.seed(solve_opts.get("seed", 123))

    n = pypsa.Network(snakemake.input.network)

    n = prepare_network(
        n,
        solve_opts,
        config=snakemake.config,
        foresight=snakemake.params.foresight,
        planning_horizons=snakemake.params.planning_horizons,
        co2_sequestration_potential=snakemake.params["co2_sequestration_potential"],
    )

    with memory_logger(
        filename=getattr(snakemake.log, "memory", None), interval=30.0
    ) as mem:
        n = solve_network(
            n,
            config=snakemake.config,
            solving=snakemake.params.solving,
            opts=opts,
            log_fn=snakemake.log.solver,
        )

    logger.info(f"Maximum memory usage: {mem.mem_usage}")

    n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
    n.export_to_netcdf(snakemake.output[0])