import PySAM.Pvwattsv8 as pv
import glob
import PySAM.ResourceTools as tools
import csv
import itertools

kW_filename = open('C:/Dissertation/Chapter_1_SolarSiting/Data/CT_SAM_Analysis/Parcel_Locations.csv', 'r')
file = csv.DictReader(kW_filename)
Kw_list = []
for col in file:
    Kw_list.append(col['kW_per_acre'])
print('Kw per acre:', Kw_list)

weather_folder = "C:/Dissertation/Chapter_1_SolarSiting/Data/CT_SAM_Analysis/SAM_Weather_Data/"
weather_files = glob.glob(weather_folder + "/*.csv")

# load data from file into dictionaries
weather_data = [tools.SAM_CSV_to_solar_data(f) for f in weather_files]
steps_per_year = len(weather_data[0]['year'])
print(steps_per_year)

for (solar_resource, kW) in zip(weather_data, Kw_list):
    pvSingleOwner_model = pv.default("PVWattsSingleOwner")
    pvSingleOwner_model.value("system_capacity", kW)
    pvSingleOwner_model.SolarResource.solar_resource_data = solar_resource
    pvSingleOwner_model.execute(0)

ac_annual = pvSingleOwner_model.Outputs.ac_annual
print(ac_annual)
ac_monthly = pvSingleOwner_model.Outputs.ac_monthly
print(ac_monthly)
annual_energy = pvSingleOwner_model.Outputs.annual_energy
print(annual_energy)
lat = pvSingleOwner_model.Outputs.lat
print(lat)
long = pvSingleOwner_model.Outputs.lon
print(long)