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prepare_area_query.py
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Wed, Apr 30, 11:14

prepare_area_query.py
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import pandas as pd
import numpy as np
from rooftop_handling import get_tilt_category, get_orientation_category
import os
import sys
import time
###### GET DATA #########
ROOFS_info = sys.argv[1]
ROOFS_corners = sys.argv[2]
PANELS_ftr = sys.argv[3]
SHADE_ftr = sys.argv[4]
SHADE_cats = sys.argv[5]
OUTFILE = sys.argv[6]
############################
TILT_FIELD = 'NEIGUNG'
ORIENTATION_FIELD = 'AUSRICHTUN'
## == FUNCTION DEFINITIONS ==
def print_input():
print("")
print('Rooftop info file: %s' %ROOFS_info)
print('Roof corner file: %s' %ROOFS_corners)
print('Panelling feature file: %s' %PANELS_ftr)
print('Shading feature file: %s' %SHADE_ftr)
print('Shading categories file: %s' %SHADE_cats)
print('Output file: %s' %OUTFILE)
print("")
def replace_shade_NaNs(roofs, shade_cats):
# finds all rows in "roofs" with column "shaded_area_ratio_ftr" == NaN, and replaces these with the mean values
# in "shade_cats" based on the categorizations of "tilt_cat" and "orientation_cat" (specified in rooftop_handling.py)
# get columns with shaded area NaN and calculate tilt and orientation categories for these
shade_null_idx = roofs.shaded_area_ratio_ftr.isnull()
replacemnt_shade = roofs.loc[shade_null_idx, ['DF_UID', TILT_FIELD, ORIENTATION_FIELD]]
replacemnt_shade.rename( { TILT_FIELD : 'NEIGUNG', ORIENTATION_FIELD : 'AUSRICHTUNG' }, axis = 1, inplace = True)
replacemnt_shade['tilt_cat'] = replacemnt_shade.apply(get_tilt_category, axis = 1)
replacemnt_shade['orientation_cat'] = replacemnt_shade.apply(get_orientation_category, axis = 1)
# find mean values for all rows with shaded area ratio NaN and replace
replaced_shade = replacemnt_shade.merge(shade_cats.loc[:,['orientation_cat', 'tilt_cat', 'fully_shaded_ratio']], how = 'left', on = ['orientation_cat', 'tilt_cat'])
roofs.loc[shade_null_idx, 'shaded_area_ratio_ftr'] = replaced_shade.fully_shaded_ratio.values
return roofs
## == Load data ==
print_input()
timer = time.time()
roofs_info = pd.read_csv(ROOFS_info)
roofs_corners = pd.read_csv(ROOFS_corners)
panels_ftr = pd.read_csv(PANELS_ftr)
shade_ftr = pd.read_csv(SHADE_ftr, usecols = ['DF_UID', 'fully_shaded_ratio'])
shade_cats = pd.read_csv(SHADE_cats)
print('Read all data in %.3f seconds' %(time.time() - timer))
timer = time.time()
## == Merge roofs with shading info and roofs with panelled area info
## Step 1: merge information of number of vertices (roofs_corners) to rooftop information
roofs = roofs_info.merge(roofs_corners, on = 'DF_UID')
print('Merged rooftop information in %.3f seconds' %(time.time() - timer))
timer = time.time()
## Step 2: add panel information (available for all rooftops, based on Sonnendach roof polygons)
## NOTE: all roofs with panelled area == 0 can be excluded from analysis - they are unsuitable for PV installation
roofs_with_available_area = panels_ftr[panels_ftr.panel_count > 0]
set_panels_ftr = roofs_with_available_area.loc[:,['DF_UID', 'panel_tilt', 'best_align', 'panelled_area_ratio']]
# merge panelled area ratio for features
roofs_w_panels = roofs.merge(set_panels_ftr, how = 'inner', on = 'DF_UID')
roofs_w_panels.rename( {'panelled_area_ratio' : 'panelled_area_ratio_ftr'}, axis = 1, inplace = True)
print('Added panel fitting information in %.3f seconds' %(time.time() - timer))
timer = time.time()
## Step 3: add shading information (shaded area only)
## Not available for all roofs (missing approx. 20%) - unavailable data is filled using shade_cats
roofs_w_shade = roofs_w_panels.merge(shade_ftr, how = 'left', on = 'DF_UID')
roofs_w_shade.rename({ 'fully_shaded_ratio' : 'shaded_area_ratio_ftr' }, axis = 1, inplace = True)
print('Added shading information in %.3f seconds' %(time.time() - timer))
timer = time.time()
roofs_final = replace_shade_NaNs( roofs_w_shade, shade_cats )
print('Replaced NaNs of shading in %.3f seconds' %(time.time() - timer))
timer = time.time()
## == Save results as csv
roofs_final.to_csv(OUTFILE, index = False)
print('DONE - saved output to %s' %OUTFILE)

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