group_PV_to_buildings.py
321a85653075master

group_PV_to_buildings.py
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			import numpy as np
			import pandas as pd
			import geopandas as gpd

			import os
			import sys
			import time

			#### Variable definitions

			## INPUTS
			PV_FP = sys.argv[1] # '/scratch/walch/pv_linking/PV_cleaned.csv'

			OUT_ROOF = sys.argv[2]
			OUT_BLD = sys.argv[3]

			if len(sys.argv) > 4:
			MIN_SIZE = float(sys.argv[4])
			else:
			MIN_SIZE = None

			print('\nLoad PV pixels from %s' %PV_FP)
			print('Save output per roof to %s' %OUT_ROOF)
			print('Save output per installation to %s\n' %OUT_BLD)

			## Constants

			# Fields in the roof
			PROPERTIES = ['ROOF_AREA', 'ROOF_TILT', 'ROOF_ASPECT'] # MUST BE IN PV_FP

			#### Load data

			all_pixels = pd.read_csv( PV_FP )

			#### Process data

			# add a dummy variable for pixel count
			all_pixels['pixel_count'] = 1

			## Roof pixels, grouped by DF_UID

			# group by the rooftop ID
			pixel_groups = all_pixels.dropna(subset = ['DF_UID']).groupby('DF_UID')

			# Get the first value for the roof columns (either "first" or "mean" is correct, there is only one value per DF_UID),
			# and sum the pixel area (projected area of detected pixels) and PV area (real area that accounts for roof tilt)
			roof_with_PV = pd.concat( [pixel_groups.first() [ PROPERTIES ],
			pixel_groups.sum() [[ 'pixel_area', 'PV_area' ]],
			pixel_groups.count()[[ 'pixel_count' ]] ], axis = 1 )

			## Building pixels, grouped by BLD_ID

			# group by the building ID
			pixel_groups = all_pixels[all_pixels['DF_UID'].isna()].groupby('BLD_ID')

			# Get the first value for the roof columns (either "first" or "mean" is correct, there is only one value per DF_UID),
			# and sum the pixel area (projected area of detected pixels) and PV area (real area that accounts for roof tilt)
			bld_with_PV = pd.concat( [pixel_groups.first() [ PROPERTIES ],
			pixel_groups.sum() [[ 'pixel_area', 'PV_area' ]],
			pixel_groups.count()[[ 'pixel_count' ]] ], axis = 1 ).reset_index()

			all_with_PV = pd.concat([roof_with_PV.reset_index(), bld_with_PV], sort = False)

			# Aggregate to installations
			installations = all_with_PV.groupby('BLD_ID').sum()[['pixel_area',
			'PV_area', 'pixel_count']].reset_index()

			all_with_PV.to_csv( OUT_ROOF, index = False )
			print('Saved %s' %OUT_ROOF)

			installations.to_csv( OUT_BLD, index = False )
			print('Saved %s' %OUT_BLD)

			if MIN_SIZE is not None:
			# Drop all the installations with a total PV area of less than MIN_SIZE
			installations_minSize = installations[ installations.pixel_area >= MIN_SIZE ]

			# Drop all the pixels on buildings with an installation size below min_size
			pixels_installations = all_pixels[ all_pixels.BLD_ID.isin( installations_minSize.BLD_ID.values ) ]

			print(len(pixels_installations))
			print(len(all_pixels))

			save_fp = os.path.splitext( PV_FP )[0] + '_minInstall%dm2' %MIN_SIZE + os.path.splitext( PV_FP )[1]
			pixels_installations.drop(columns = ['pixel_count']).to_csv(save_fp, index = False)
			print('Saved %s' %save_fp)

			save_fp = os.path.splitext( OUT_BLD )[0] + '_minInstall%dm2' %MIN_SIZE + os.path.splitext( OUT_BLD )[1]
			installations_minSize.to_csv(save_fp, index = False)

			print('Saved %s - DONE' %save_fp)

solarPV_identification/PV_postprocessing/group_PV_to_buildings.py321a85653075master

group_PV_to_buildings.pyView Options

solarPV_identification/PV_postprocessing/group_PV_to_buildings.py
321a85653075master

group_PV_to_buildings.py
View Options