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solarPOT/Potential_hourly/scripts/merge_commune_batch.py
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merge_commune_batch.py
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import numpy as np
import pandas as pd
import xarray as xr
import sys
import os
import time
# # Define file paths
COMM_FP = '/Users/alinawalch/Documents/EPFL/data/EMPA_data/Sonnendach_Gemeinden_Dachflaechen.csv'
ROOF_FP = '/Volumes/Seagate Backup Plus Drive/DOKUMENTE/EPFL/data/Results/PV_w_uncertainty/CH_ROOFS_all_replaced.csv'
AREA_FP = '/Volumes/Seagate Backup Plus Drive/DOKUMENTE/EPFL/data/Results/PV_w_uncertainty/PRED_available_area_unc_filled.csv'
PV_PATH = sys.argv[1]
GT_PATH = sys.argv[2]
OUTPATH = sys.argv[3]
## CONSTANTS
FLAT_TILT = 10
MAX_ASPECT = 90
# Print file paths
print('\nCommune mapping file path: %s' %COMM_FP)
print('Available area file path: %s' %AREA_FP)
print('Roof surfaces file path: %s' %ROOF_FP)
print('PV input path: %s' %PV_PATH)
print('Gt input path: %s' %GT_PATH)
print('Output path: %s\n\n' %OUTPATH)
# # Load commune map
tt = time.time()
comm_map = pd.read_csv(COMM_FP, encoding = 'cp1250', usecols = ['DF_UID', 'BFS_NUMMER', 'NAME'] )
# # Load roof data
roofs = pd.read_csv(ROOF_FP, usecols = ['DF_UID', 'NEIGUNG', 'AUSRICHTUNG'])
av_area = pd.read_csv(AREA_FP, usecols = ['DF_UID', 'available_area', 'available_area_std'])
roof_info = roofs.merge(av_area, on = 'DF_UID').merge(comm_map, on = 'DF_UID', how = 'left')
print('Loaded roof info in %.2fs' %(time.time()-tt))
# # Get suitable roofs
tt = time.time()
roof_info['suitable'] = ( ( (roof_info.NEIGUNG <= FLAT_TILT) | (abs(roof_info.AUSRICHTUNG) <= MAX_ASPECT) )
& (roof_info.available_area >= 8) ).astype(int)
# # Load prediction
tilted_irrad = xr.open_mfdataset( GT_PATH )
pv_potential = xr.open_mfdataset( PV_PATH )
# # Group suitable potential by communes
tt = time.time()
time_xr = pv_potential.time
roof_sel = roof_info.loc[ roof_info.DF_UID.isin(pv_potential.DF_UID.values),
['DF_UID', 'suitable', 'available_area', 'BFS_NUMMER'] ]
roof_xr = roof_sel.set_index('DF_UID').to_xarray()
hourly_suitable = xr.merge([pv_potential.pv_potential, tilted_irrad.tilted_irradiance, roof_xr.available_area]) * roof_xr.suitable
hourly_suitable.coords['BFS_NUMMER'] = roof_xr.BFS_NUMMER.astype('int')
pv_communes_hourly = hourly_suitable.pv_potential.groupby('BFS_NUMMER').sum().astype('float32') / 1000
gt_communes_hourly = ( hourly_suitable.tilted_irradiance.where( hourly_suitable.tilted_irradiance > 0 )
.groupby('BFS_NUMMER').mean().astype('float32') )
gt_weighted_sum = ((hourly_suitable.tilted_irradiance * hourly_suitable.available_area )
.groupby('BFS_NUMMER').sum().astype('float32').rename('gt_weighted_by_APV'))
weights_sum = (hourly_suitable.available_area * roof_xr.suitable ).groupby('BFS_NUMMER').sum().rename('APV_weight_sum')
communes_output = xr.merge([pv_communes_hourly, gt_communes_hourly, gt_weighted_sum, weights_sum, time_xr]) # gt_communes_hourly
print('\nCreated hourly commune potential in %.2fs\n' %(time.time()-tt))
print(communes_output)
# ### Save intermediate files
tt = time.time()
communes_output.to_netcdf( OUTPATH )
print('\nComputed hourly commune potential and saved output in %.2fs' %(time.time()-tt))
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