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solarPOT/Solar/write_solpos_to_file.py
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write_solpos_to_file.py
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import numpy as np
import pandas as pd
import xarray as xr
import os
import util
import csv
import sys
import shutil
def project_CW_N_to_ACW_E(angle):
# perform projection from clockwise angles with origin at north to anticlockwise angles with origin at east
projected_angle = 450 - angle
# truncate angle to the range of (0,360)
if projected_angle < 0:
return projected_angle + 360
elif projected_angle >= 360:
return projected_angle - 360
else:
return projected_angle
def solpos_file_to_directions(datafile, indexfile, outputfile):
# read data
index = pd.read_csv(indexfile)
mean_solpos = pd.read_csv(datafile, header = None, sep = ' ')
# merge index with data, drop duplicates, sort values and save to outputfile
mean_solpos = pd.DataFrame(data = mean_solpos.values, columns = index.columns.values)
mean_solpos.sort_values('direction').direction.drop_duplicates().to_csv(outputfile, sep = ' ', float_format = '%03.1f', header = False, index = False)
######################## INPUTS ####################
solpos_file = sys.argv[1]
output_path = sys.argv[2]
output_base = sys.argv[3]
mode = sys.argv[4]
################### DEFINITIONS #####################
projection = {
'horizon_grass' : lambda angle : project_CW_N_to_ACW_E(angle),
'hillshade_grass' : lambda angle : angle
}
orientation = {
'horizon_grass' : 'direction',
'hillshade_grass' : 'azimuth'
}
#######################################################
# create directory if needed
if os.path.isdir( os.path.join( output_path, output_base ) ):
shutil.rmtree( os.path.join( output_path, output_base ) )
os.mkdir( os.path.join( output_path, output_base ) )
#######################################################
sun_position = xr.open_dataset(solpos_file)
index_name = os.path.join( output_path, output_base, 'SOLPOS_index.csv')
util.write_row_to_csv(index_name, ['month', 'hour', orientation[mode], 'altitude'], initialize = True)
filename = os.path.join( output_path, output_base, 'SOLPOS.txt')
for month in range(1,13):
mean_solpos_month = sun_position.sel(timestamp = '2001-%02d-15' %month).mean(dim = ['x','y'])
filename_month = os.path.join( output_path, output_base, 'SOLPOS_%02d.txt' %month)
for t in range(len(mean_solpos_month.timestamp)):
# project azimuth angle to anticlockwise east frame and convert zenith angle to sun altitude
hour = pd.to_datetime(mean_solpos_month.timestamp.values[t]).hour
orientation = projection[mode]( mean_solpos_month.azimuth.values[t] )
altitude = min (90 - mean_solpos_month.apparent_zenith.values[t], 90) # just in case, set altitude to maximum of 90
# only calculate values where the sun is actually above the horizon (altitude > 0)
if altitude > 0:
util.write_row_to_csv(filename_month, [('%02d' %month), ('%02d' %hour), ('%03d' %orientation), ('%.3f' %altitude) ], delimiter = ' ')
util.write_row_to_csv(filename , [('%02d' %month), ('%02d' %hour), ('%03d' %orientation), ('%.3f' %altitude) ], delimiter = ' ')
print('Completed writing file %s' %filename_month)
if mode == 'horizon_grass':
# create file of directions (just for test purposes monthly is needed, does not actually 'make sense')
directions_file = os.path.join( output_path, output_base, 'SOLPOS_%02d_direction.txt' %month)
solpos_file_to_directions(filename_month, index_name, directions_file)
print('Completed writing file %s' %filename)
if mode == 'horizon_grass':
# do the same for all locations (this makes sense as duplicates can be found)
directions_file = os.path.join( output_path, output_base, 'SOLPOS_direction.txt')
solpos_file_to_directions(filename, index_name, directions_file)
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