# Copyright 2025 ISAE-SUPAERO, https://www.isae-supaero.fr/en/
# Copyright 2025 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

"""
Baseline comparison across SSP pathways
=======================================
"""

from noads.application.examples import single_policy_scenario_optimization
from noads.application.visualization import plot_tech_scenarios_trends

# %%
# Cross-scenario baseline comparison
# ----------------------------------
# Gather baseline (fossil kerosene only) results from all three SSP pathways and
# three technology levels, then plot the technology scenario trends side-by-side.
# This shows how emissions, final energy, and cost differ across climate futures
# when no mitigation action is taken.


def load_baseline_results(global_scenario_name, technology_index):
    """Load previously saved baseline optimization results."""
    return single_policy_scenario_optimization(
        global_scenario_name=global_scenario_name,
        carbon_budget_percent=3.0,
        technology_index=technology_index,
        drop_in_only=True,
        fossil_kerosene_only=True,
        low_demand_formulation=False,
        preferential_energy=False,
        load_optimum=True,
        plot_optimum=False,
        save_optimum=False,
        save_figs=False,
        save_history_view=False,
    )


# %%
# Load results for each background scenario
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
background_scenarios = ["SSP1-19", "SSP2-26", "SSP5-45"]
scenario_outputs = {}

for scenario in background_scenarios:
    outputs_per_tech = [
        load_baseline_results(scenario, tech_idx) for tech_idx in range(3)
    ]
    scenario_outputs[scenario.split("-")[0]] = outputs_per_tech

# %%
# Plot comparison
# ^^^^^^^^^^^^^^^
plot_tech_scenarios_trends(
    scenario_outputs=scenario_outputs,
    colors=["#BCBD22", "#7F7F7F", "#8C564B"],
    save_fig=False,
)