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solarPOT/Solar/physical_potential.py
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physical_potential.py
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import numpy as np
import pandas as pd
import xarray as xr
import os
import util
from pvlib.irradiance import total_irrad, extraradiation, grounddiffuse
from pvlib.atmosphere import relativeairmass
import sys
def cosd(angle_in_degrees):
return np.cos(np.radians(angle_in_degrees))
def SISDIR_to_DNI( SISDIR , zenith_in_degrees ):
normal_incidence = SISDIR/cosd(zenith_in_degrees)
return( np.maximum( normal_incidence, 0 ) )
def shrink_temporal_dims(variables):
list_of_vars = []
for var in variables:
tt = util.Timer()
list_of_months = []
for month in range(len(var.date)):
print(month)
# select one month and convert hour to timestamp (with date)
monthly_var = var.isel(date = month)
monthly_var.coords['hour'] = util.to_timestamp(monthly_var.date.values, monthly_var.hour.values)
month_select = monthly_var.dropna('hour', how = 'all').drop('date')
# append this month's data to list
list_of_months.append(month_select)
# join listed timestamps
var_rearranged = xr.concat(list_of_months, dim = 'hour')
var_rearranged.rename({'hour' : 'timestamp'}, inplace = True)
list_of_vars.append(var_rearranged)
print('converted variable to 3-dimensional xarray')
tt.stop()
return xr.merge(list_of_vars)
################ INPUT (later argvs) #####################
# global horizontal irradiance
Gh_file = sys.argv[1]
# direct beam irradiance
Gb_file = sys.argv[2]
# netcdf dataset of solar_positions (obtained from solar_position.py)
sol_pos_file = sys.argv[3]
# file in which the final dataset of meteorological values should be stored
savefile = sys.argv[4]
###########################################################
Gh = xr.open_dataset(Gh_file)
Gb = xr.open_dataset(Gb_file)
solar_position = xr.open_dataset(sol_pos_file)
#########################################################
# Pre-process Gh and Gb (shrink temporal dimensions to one single dimension)
weather = shrink_temporal_dims( [Gh, Gb] )
##################################################################
# merge all information together
meteo = xr.merge([weather, solar_position]).transpose('x','y','timestamp')
print('merged weather data and solar position')
# compute additional variables
meteo['DHI'] = np.maximum((meteo['SIS'] - meteo['SISDIR']), 0)
print('computed DHI')
meteo['DNI'] = SISDIR_to_DNI(meteo['SISDIR'],meteo['apparent_zenith'])
print('computed DNI')
meteo['rel_airmass'] = xr.DataArray( relativeairmass(meteo['apparent_zenith']), coords = [meteo.x, meteo.y,meteo.timestamp])
extrarad = [extraradiation(t) for t in meteo.timestamp.values]
meteo['DNI_extra'] = xr.DataArray(extrarad, coords=[meteo.timestamp])
print('computed extraterrestrial components')
meteo.to_netcdf(savefile)
print('saved file as %s' %savefile)
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