# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Creates plots for optimised power network topologies and regional generation,
storage and conversion capacities built.
"""

import logging

import cartopy.crs as ccrs
import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd
import pypsa
from _helpers import configure_logging, set_scenario_config
from plot_summary import preferred_order, rename_techs
from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches

logger = logging.getLogger(__name__)


def rename_techs_tyndp(tech):
    tech = rename_techs(tech)
    if "heat pump" in tech or "resistive heater" in tech:
        return "power-to-heat"
    elif tech in ["H2 Electrolysis", "methanation", "H2 liquefaction"]:
        return "power-to-gas"
    elif tech == "H2":
        return "H2 storage"
    elif tech in ["NH3", "Haber-Bosch", "ammonia cracker", "ammonia store"]:
        return "ammonia"
    elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
        return "gas-to-power/heat"
    # elif "solar" in tech:
    #     return "solar"
    elif tech in ["Fischer-Tropsch", "methanolisation"]:
        return "power-to-liquid"
    elif "offshore wind" in tech:
        return "offshore wind"
    elif "CC" in tech or "sequestration" in tech:
        return "CCS"
    else:
        return tech


def assign_location(n):
    for c in n.iterate_components(n.one_port_components | n.branch_components):
        ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
        for i in ifind.value_counts().index:
            # these have already been assigned defaults
            if i == -1:
                continue
            names = ifind.index[ifind == i]
            c.df.loc[names, "location"] = names.str[:i]


def load_projection(plotting_params):
    proj_kwargs = plotting_params.get("projection", dict(name="EqualEarth"))
    proj_func = getattr(ccrs, proj_kwargs.pop("name"))
    return proj_func(**proj_kwargs)


def plot_map(
    n,
    components=["links", "stores", "storage_units", "generators"],
    bus_size_factor=2e10,
    transmission=False,
    with_legend=True,
):
    tech_colors = snakemake.params.plotting["tech_colors"]

    assign_location(n)
    # Drop non-electric buses so they don't clutter the plot
    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)

    costs = pd.DataFrame(index=n.buses.index)

    for comp in components:
        df_c = getattr(n, comp)

        if df_c.empty:
            continue

        df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)

        attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"

        costs_c = (
            (df_c.capital_cost * df_c[attr])
            .groupby([df_c.location, df_c.nice_group])
            .sum()
            .unstack()
            .fillna(0.0)
        )
        costs = pd.concat([costs, costs_c], axis=1)

        logger.debug(f"{comp}, {costs}")

    costs = costs.T.groupby(costs.columns).sum().T

    costs.drop(list(costs.columns[(costs == 0.0).all()]), axis=1, inplace=True)

    new_columns = preferred_order.intersection(costs.columns).append(
        costs.columns.difference(preferred_order)
    )
    costs = costs[new_columns]

    for item in new_columns:
        if item not in tech_colors:
            logger.warning(f"{item} not in config/plotting/tech_colors")

    costs = costs.stack()  # .sort_index()

    # hack because impossible to drop buses...
    eu_location = snakemake.params.plotting.get("eu_node_location", dict(x=-5.5, y=46))
    n.buses.loc["EU gas", "x"] = eu_location["x"]
    n.buses.loc["EU gas", "y"] = eu_location["y"]

    n.links.drop(
        n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")],
        inplace=True,
    )

    # drop non-bus
    to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
    if len(to_drop) != 0:
        logger.info(f"Dropping non-buses {to_drop.tolist()}")
        costs.drop(to_drop, level=0, inplace=True, axis=0, errors="ignore")

    # make sure they are removed from index
    costs.index = pd.MultiIndex.from_tuples(costs.index.values)

    threshold = 100e6  # 100 mEUR/a
    carriers = costs.groupby(level=1).sum()
    carriers = carriers.where(carriers > threshold).dropna()
    carriers = list(carriers.index)

    # PDF has minimum width, so set these to zero
    line_lower_threshold = 500.0
    line_upper_threshold = 1e4
    linewidth_factor = 4e3
    ac_color = "rosybrown"
    dc_color = "darkseagreen"

    title = "added grid"

    if snakemake.wildcards["ll"] == "v1.0":
        # should be zero
        line_widths = n.lines.s_nom_opt - n.lines.s_nom
        link_widths = n.links.p_nom_opt - n.links.p_nom
        if transmission:
            line_widths = n.lines.s_nom_opt
            link_widths = n.links.p_nom_opt
            linewidth_factor = 2e3
            line_lower_threshold = 0.0
            title = "current grid"
    else:
        line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
        link_widths = n.links.p_nom_opt - n.links.p_nom_min
        if transmission:
            line_widths = n.lines.s_nom_opt
            link_widths = n.links.p_nom_opt
            title = "total grid"

    line_widths = line_widths.clip(line_lower_threshold, line_upper_threshold)
    link_widths = link_widths.clip(line_lower_threshold, line_upper_threshold)

    line_widths = line_widths.replace(line_lower_threshold, 0)
    link_widths = link_widths.replace(line_lower_threshold, 0)

    fig, ax = plt.subplots(subplot_kw={"projection": proj})
    fig.set_size_inches(7, 6)

    n.plot(
        bus_sizes=costs / bus_size_factor,
        bus_colors=tech_colors,
        line_colors=ac_color,
        link_colors=dc_color,
        line_widths=line_widths / linewidth_factor,
        link_widths=link_widths / linewidth_factor,
        ax=ax,
        **map_opts,
    )

    sizes = [20, 10, 5]
    labels = [f"{s} bEUR/a" for s in sizes]
    sizes = [s / bus_size_factor * 1e9 for s in sizes]

    legend_kw = dict(
        loc="upper left",
        bbox_to_anchor=(0.01, 1.06),
        labelspacing=0.8,
        frameon=False,
        handletextpad=0,
        title="system cost",
    )

    add_legend_circles(
        ax,
        sizes,
        labels,
        srid=n.srid,
        patch_kw=dict(facecolor="lightgrey"),
        legend_kw=legend_kw,
    )

    sizes = [10, 5]
    labels = [f"{s} GW" for s in sizes]
    scale = 1e3 / linewidth_factor
    sizes = [s * scale for s in sizes]

    legend_kw = dict(
        loc="upper left",
        bbox_to_anchor=(0.27, 1.06),
        frameon=False,
        labelspacing=0.8,
        handletextpad=1,
        title=title,
    )

    add_legend_lines(
        ax, sizes, labels, patch_kw=dict(color="lightgrey"), legend_kw=legend_kw
    )

    legend_kw = dict(
        bbox_to_anchor=(1.52, 1.04),
        frameon=False,
    )

    if with_legend:
        colors = [tech_colors[c] for c in carriers] + [ac_color, dc_color]
        labels = carriers + ["HVAC line", "HVDC link"]

        add_legend_patches(
            ax,
            colors,
            labels,
            legend_kw=legend_kw,
        )

    fig.savefig(snakemake.output.map, bbox_inches="tight")


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

        snakemake = mock_snakemake(
            "plot_power_network",
            simpl="",
            opts="",
            clusters="37",
            ll="v1.0",
            sector_opts="4380H-T-H-B-I-A-dist1",
        )

    configure_logging(snakemake)
    set_scenario_config(snakemake)

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

    regions = gpd.read_file(snakemake.input.regions).set_index("name")

    map_opts = snakemake.params.plotting["map"]

    if map_opts["boundaries"] is None:
        map_opts["boundaries"] = regions.total_bounds[[0, 2, 1, 3]] + [-1, 1, -1, 1]

    proj = load_projection(snakemake.params.plotting)

    plot_map(n)
