from src.Functions_Energy_Model import *
from src.Functions_General import *
import subprocess
import signal
import time

location_ita = 'Biella' # location in Italian
capacity = 100 # kWp
tilt_angle = 30 # gradi
azimuth = 0 # gradi
num_years = 1 # anni di simulazione
derating_factor_percent = 1 # derating factor that reduce the efficiency of the modules in percentage [%]

config = yaml.safe_load(open("config.yml", 'r'))
path_export = str(config['filename_output_csv_gen_pv']) # percorso del file di output

# IGNORE THIS
isJoe = config['joe_fight']
joePlaying = False
if(isJoe == True):
    if shutil.which('ffplay') is not None:
        joePlaying = True
        joe = subprocess.Popen(['ffplay', '-fs', 'assets/joefight.mp4'])
        time.sleep(25) # deve arrivare il drop
    else:
        print("ffplay non installato, no joe fight.")

# INIZIALIZZAZIONE PARAMETRI PER SIMULAZIONE
location_eng = location_italian_to_english(location_ita)
path = "config.yml"
filename = ""
key  = "project_lifetime_yrs"
value = num_years
add_to_file_yml(path, filename, key, value)
suppress_printing(generate_calendar) # generazione del calendario
coordinates_dataset = suppress_printing(create_coordinates_dataset, [location_eng]) # crea un dataset con le coordinate di tutte le posizioni
derating_factor = derating_factor_percent / 100

# CALCOLO PRODUTTIVITA'
result_ac_energies_resampled = suppress_printing(simulate_1_kWp_generators, coordinates_dataset, tilt_angle, azimuth)

# SCALING PRODUTTIVITA'
result_ac_energies_gens = {} # initialization of the output dictionary
result_ac_energies_gens['gen_pv_' + str(capacity) + '_kWp'] = result_ac_energies_resampled[location_eng] * capacity

# DERATING PRODUTTIVITA'
result_ac_energies_gens_derated = suppress_printing(simulate_gens_derated_productivity, derating_factor, result_ac_energies_gens)

# GENERAZIONE DATAFRAME
result_ac_energies_to_csv_df = suppress_printing(simulate_unstacked_productivity, result_ac_energies_gens_derated) # create two unstacked dataframe (the other functions work with dictionaries)

# ESPORTAZIONE RISULTATI IN CSV
print("Valore massimo:",max(result_ac_energies_to_csv_df.gen_pv_100_kWp)) # print the maximum value of the generated energy
print("Somma totale:",sum(result_ac_energies_to_csv_df.gen_pv_100_kWp)) # print the total sum of the generated energy
result_ac_energies_to_csv_df.to_csv(path_export, encoding='utf-8')

# GENERAZIONE GRAFICO
import plotly.express as px
# Supponendo che la colonna di interesse sia 'gen_pv_100_kWp'
fig = px.line(
    result_ac_energies_to_csv_df,
    x=result_ac_energies_to_csv_df.index,
    y='gen_pv_100_kWp',
    title='Produttività Fotovoltaica nel Tempo',
    labels={'gen_pv_100_kWp': 'Energia [kWh]', 'index': 'Data/Ora'}
)
fig.show()

if joePlaying:
    joe.terminate()
    joe.kill()