"""
.. _tutorial_comparison:
=================================
Comparison
=================================

This tutorial will guide you to creating your own comparison plots.
For a more in-depth view, please refer to the :ref:`how_tos` and the API docs :ref:`api-reference-api`

"""

# %%
# Required Imports
# ================
# First, let's add the required imports to the top of the script.

import pathlib as _pl

from pytrnsys_process import api


# %%
# Preparation
# =================================================
# First, you will need to download the
# `example data. <https://raw.githubusercontent.com/SPF-OST/pytrnsys_process/main/example_data/example_data.zip>`_
#
# Extract the data into a project folder you will use for this tutorial.
# You should end up with a folder structure looking like this.
#
# | root
# | ├─ example_data
# | ├─ tutorials
# |     ├─ tutorial_comparison.py
#
# For the rest of this tutorial, you should work in the last file: "tutorial_comparison.py"


# %%
# Defining comparison steps
# =========================
# This comparison step will loop through all simulations.
# It will use the hourly and monthly dataframes to do calculations.
# The results will be stored in the scalar dataframe that all simulations share.
# At the end we will return the modified :class:`pytrnsys_process.api.SimulationsData` object.
# Reason for this is mentioned in the next step.


def do_calc(simulations_data: api.SimulationsData):

    for sim_name, sim in simulations_data.simulations.items():

        hourly_data = sim.hourly
        max_ice_ratio = hourly_data["VIceRatio"].max()
        simulations_data.scalar.loc[sim_name, "VIceRatioMax"] = max_ice_ratio

        demand_columns = [
            "qSysOut_QSnk131Demand",
            "qSysOut_QSnk183Demand",
            "qSysOut_QSnk191Demand",
            "qSysOut_QSnk225Demand",
            "qSysOut_QSnk243Demand",
            "qSysOut_QSnk266Demand",
            "qSysOut_QSnk322Demand",
            "qSysOut_QSnk335Demand",
            "qSysOut_QSnk358Demand",
            "qSysOut_QSnk417Demand",
            "qSysOut_QSnk448Demand",
            "qSysOut_QSnk469Demand",
            "qSysOut_QSnk488Demand",
            "qSysOut_QSnk524Demand",
            "qSysOut_QSnk539Demand",
            "qSysOut_QSnk558Demand",
            "qSysOut_QSnk579Demand",
            "qSysOut_QSnk60Demand",
            "qSysOut_QSnk85Demand",
        ]

        # Unit conversion factor: kWh to GWh
        kwh_to_gwh = 1e-6

        # Process monthly data
        monthly_df = sim.monthly
        monthly_df["total_demand_GWh"] = (
            monthly_df[demand_columns].sum(axis=1) * kwh_to_gwh
        )

        # Calculate yearly total (excluding first 2 months)
        yearly_total = int(monthly_df["total_demand_GWh"].iloc[2::].sum())
        simulations_data.scalar.loc[sim_name, "yearly_demand_GWh"] = (
            yearly_total
        )
    return simulations_data


# %%
# Chain multiple comparison steps
# ===============================
# By chaining comparison steps together, we ensure that if a dependent step fails, the entire process fails.
# In this step, we call the previously defined step to validate our data before generating plots.
# This way, if an error occurs during the calculation, we avoid plotting incorrect data.


def plot_comparison(simulations_data: api.SimulationsData):
    simulations_data = do_calc(simulations_data)

    fig, _ = api.scalar_compare_plot(
        simulations_data.scalar,
        "VIceSscaled",
        "VIceRatioMax",
        group_by_color="yearly_demand_GWh",
        group_by_marker="ratioDHWtoSH_allSinks",
    )

    fig.show()


# %%
# Running comparison steps
# ===============================
# There are different ways of providing data to the do_comparison function.
# In this tutorial we will provide the path to our result folder.
# For other ways see :meth:`pytrnsys_process.api.do_comparison`
if __name__ == "__main__":
    path_to_sim = _pl.Path("../example_data/ice/")
    api.do_comparison([plot_comparison], results_folder=path_to_sim)

    # sphinx_gallery_start_ignore
    simulations_data = api.load_simulations_data_from_pickle(
        path_to_sim / "simulations_data.pickle"
    )
    plot_comparison(simulations_data)
    # sphinx_gallery_end_ignore