import io
import os

import pandas as pd
import streamlit as st
from openpyxl import Workbook
from openpyxl.utils.dataframe import dataframe_to_rows
from streamlit_extras.stylable_container import stylable_container

from WattFieldsCommon.constants import PATH_APP_FOLDER, PATH_IMG_FOLDER
from WattFieldsCommon.optim_analytics import (
    generate_df_block_temporal_analysis,
    generate_df_spatial_analysis,
    generate_df_temporal_analysis,
    generate_run_df_summary,
    generate_df_gstd_block
)


def setup_st_config_with_navbar(page_title, add_navbar=True):

    PATH_LOGO = os.path.join(PATH_IMG_FOLDER, "logo.png")
    st.logo(PATH_LOGO, icon_image=PATH_LOGO, size="large")

    st.set_page_config(layout="wide", page_title=page_title)
    st.markdown(
        """
        <style>
        /* Adjust font size for tabs */
        .stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
            font-size: 1.2rem;
        }
        /* Center the tab container */
        .stTabs [data-baseweb="tab-list"] {
            justify-content: center; /* Centers the tabs horizontally */
        }

        /* Optional: Add padding for the main container */
        .block-container {
            padding-top: 2.8rem;
            padding-bottom: 4rem;
            padding-left: 5rem;
            padding-right: 5rem;
        }
        </style>
        """,
        unsafe_allow_html=True,
    )

    if add_navbar:
        with stylable_container(
            "green",
            css_styles="""
            button {
                width: 100%;
                box-shadow: transparent 0 0 0 5px,rgba(18, 18, 18, .1) 0 6px 20px;
                color: #121212;
                line-height: 0.2;
                align-items: center;
                padding: 1rem 1.4rem;
                text-align: center;
                transition: box-shadow .2s,-webkit-box-shadow .2s;
            }""",
        ):
            col1, col2, col3 = st.columns(3)
            if col2.button("Revenir à la carte des sites"):
                st.switch_page(os.path.join(PATH_APP_FOLDER, "pages", "carte_sites.py"))


def get_first_end_index_anti_tracking(df_weekly_blocks):
    df_weekly_blocks_at = df_weekly_blocks[
        df_weekly_blocks["weekly_strategy_choice"] == "anti-tracking"
    ]
    return list(df_weekly_blocks_at["block_first_time_index"]), list(
        df_weekly_blocks_at["block_last_time_index"]
    )


@st.cache_data
def generate_dfs_and_excel_file(run, simu_config, ref_agri_run, ref_pv_run):
    # Generate all metrics / data associated to a specific run, cache them to gain some time

    ref_agri_yield = ref_agri_run["general_metrics"]["average_agri_yield"]
    ref_pv_energy = ref_pv_run["general_metrics"]["average_pv_energy"]

    PV_PNOM = simu_config["SolarFarmBifacial"]["panel_peak_power"]

    df_summary = generate_run_df_summary(
        dict_run=run,
        ref_agri_yield=ref_agri_yield,
        ref_pv_energy=ref_pv_energy,
        surface_panel=simu_config["SolarFarmBifacial"]["panel_surface"],
        pv_module_pnom=PV_PNOM,
        planting_date=simu_config["WeatherData"]["planting_date"],
    )
    df_temporal_analysis = generate_df_temporal_analysis(
        dict_run=run, dict_ref_agri_run=ref_agri_run
    )
    df_spatial_analysis = generate_df_spatial_analysis(dict_run=run, 
                                dict_ref_agri_run=ref_agri_run)

    df_block_temporal_analysis = generate_df_block_temporal_analysis(
        dict_run=run, ref_pv_run=ref_pv_run, pv_module_pnom=PV_PNOM
    )
    df_block_temporal_analysis["weekly_collected_ac_energy_per_peak_power"] = (
        df_block_temporal_analysis["weekly_collected_ac_energy"] / PV_PNOM
    )

    df_block_temporal_analysis_for_excel = df_block_temporal_analysis.copy()
    df_block_temporal_analysis_for_excel["block_first_time_index"] = (
        df_block_temporal_analysis_for_excel["block_first_time_index"].dt.tz_localize(None)
    )
    df_block_temporal_analysis_for_excel["block_middle_time_index"] = (
        df_block_temporal_analysis_for_excel["block_middle_time_index"].dt.tz_localize(None)
    )
    df_block_temporal_analysis_for_excel["block_last_time_index"] = (
        df_block_temporal_analysis_for_excel["block_last_time_index"].dt.tz_localize(None)
    )

    df_summary_for_excel = df_summary.copy()
    df_summary_for_excel.loc["ref_agri_average_yield"] = ref_agri_run["general_metrics"][
        "average_agri_yield"
    ]
    df_summary_for_excel.loc["ref_pv_average_energy_per_peak"] = round(
        ref_pv_run["general_metrics"]["average_pv_energy"]
        / (simu_config["SolarFarmBifacial"]["panel_peak_power"] / 1000),
        2,
    )
    df_summary_for_excel.loc["PV"] = "- - - - - - - - - - - - - - - - - - - -"
    df_summary_for_excel.loc["AGRI"] = "- - - - - - - - - - - - - - - - - - - -"
    df_summary_for_excel.loc["AUTRES"] = "- - - - - - - - - - - - - - - - - - - -"
    df_summary_for_excel = df_summary_for_excel.loc[
        [ff for ff in df_summary_for_excel.index if ff not in ["collected_ac_energy"]]
    ]
    df_summary_for_excel = df_summary_for_excel.reset_index()

    df_gstd_blocks = generate_df_gstd_block(run, df_temporal_analysis = df_temporal_analysis, dict_ref_agri_run = ref_agri_run)

    description = {
        "PV": " - - - - - - - - - - - - - - - - - - - - - -",
        "normalized_collected_ac_energy": "Rendement agricole du run / Rendement agricole du run de reference (en %)",
        "collected_ac_energy_per_meter2": "Energie totale recoltee pour un metre carre de surface pv (en KWh / m2)",
        "collected_ac_energy_per_peak_power": "Energie totale recoltee divisee par la puissance crete du panneau (en kWh/kW-Crete)",
        "ref_pv_average_energy_per_peak": "REFERENCE PV : Energie AC recoltee pour un panneau avec une strategie full-backtracking (en kWh/kW-Crete)",
        "AGRI": " - - - - - - - - - - - - - - - - - - - - - -",
        "agri_average_yield": "Rendement moyen agricole (en Tonnes/Hectare)",
        "normalized_agri_average_yield": "Rendement agricole du run / Rendement agricole du run de reference (en %)",
        "ref_agri_average_yield": "REFERENCE AGRI : Rendement agricole de la simulation de reference (sans panneaux PV) (en Tonnes/Hectare)",
        "AUTRES": " - - - - - - - - - - - - - - - - - - - - - -",
        "run_duration": "Duree totale du run (= duree DSSAT la plus longue sur toutes les rangees) (en jours)",
        "nb_block_anti-tracking": "Nombre de semaines avec une strategie d'anti-tracking pour le panneau (en nombre de semaines)",
    }

    df_description = pd.Series(description).to_frame().reset_index()
    df_merged = df_description.merge(df_summary_for_excel, how="outer", on="index")
    right_order = list(description.keys())
    df_merged = df_merged.rename(
        columns={"index": "Nom de la variable", "0_x": "Description", "0_y": "Valeur"}
    )
    df_merged = df_merged.set_index("Nom de la variable").loc[right_order].reset_index()

    df_hourly_tracker_angle = run['df_hourly_orientation_planning']
    df_hourly_tracker_angle.fillna(0, inplace=True)
    df_hourly_tracker_angle_for_excel = df_hourly_tracker_angle.copy()
    df_hourly_tracker_angle_for_excel['REF_BACKTRACKING_tracker_theta'] = ref_pv_run['df_hourly_orientation_planning']['tracker_theta']
    df_hourly_tracker_angle_for_excel.index = df_hourly_tracker_angle_for_excel.index.strftime('%Y-%m-%dT%H:%M:%S')

    wb = Workbook()
    sheet1 = wb.active
    sheet1.title = "RECAP"
    for r in dataframe_to_rows(df_merged, index=True, header=True):
        sheet1.append(r)
    sheet2 = wb.create_sheet("ANALYSE_SPATIALE")
    for r in dataframe_to_rows(df_spatial_analysis, index=True, header=True):
        sheet2.append(r)
    sheet3 = wb.create_sheet("ANALYSE_TEMPORELLE")
    for r in dataframe_to_rows(df_temporal_analysis, index=True, header=True):
        sheet3.append(r)
    sheet4 = wb.create_sheet("PRODUCTION_ELECTRIQUE")
    for r in dataframe_to_rows(df_block_temporal_analysis_for_excel, index=False, header=True):
        sheet4.append(r)
    sheet5 = wb.create_sheet("ANGLE DU TRACKER")
    for r in dataframe_to_rows(df_hourly_tracker_angle_for_excel, index=True, header=True):
        sheet5.append(r)
    sheet6 = wb.create_sheet("GSTD BLOCKS")
    for r in dataframe_to_rows(df_gstd_blocks, index=True, header=True):
        sheet6.append(r)

    excel_file = io.BytesIO()
    wb.save(excel_file)
    excel_file.seek(0)

    return (
        excel_file,
        df_summary,
        df_temporal_analysis,
        df_spatial_analysis,
        df_block_temporal_analysis,
        df_hourly_tracker_angle
    )