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solarPOT/Uncertainty/map_uncertainty_to_roofs.py
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map_uncertainty_to_roofs.py
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import numpy as np
import xarray as xr
import pandas as pd
import os
import time
import util
import sys
# USER INPUT
FILENAME = sys.argv[1] # FILENAME WITHOUT EXTENSION
OUTPATH = sys.argv[2]
# CONSTANTS
gt_path = '/work/hyenergy/output/solar_potential/physical_potential/results'
ROOF_fp = '/work/hyenergy/output/solar_potential/CH_ROOFS_pixel_link.csv'
SCRATCH = '/scratch/walch/scratch/tmp/'
OFFSET = 87.5 # this is the offset of the 200x200m pixels, i.e. the pixel coordinates (center of each pixel) are:
PIXEL = 200 # pixel size (in m)
# LOAD DATA
tot_unc = xr.open_dataset(os.path.join(gt_path, '%s.nc' %FILENAME) )
ROOFS = pd.read_csv( ROOF_fp )
#### Merge pixel data to rooftops
# Final data format: xarray dataset of dimensions [DF_UID, timestamp]
i = 0
tt = time.time()
for date in tot_unc.date:
for hour in tot_unc.hour:
timestamp = pd.to_datetime(date.values) + pd.Timedelta(int(hour), unit = 'h')
print(timestamp, time.time() - tt)
# get current timestamp and convert to pandas dataframe
unc_array = tot_unc.sel(date = date, hour = hour)
unc_df = unc_array.to_dataframe().dropna().reset_index().drop(columns = ['date', 'hour'])
unc_df['x'] = np.round(unc_df.x, 1)
unc_df['y'] = np.round(unc_df.y, 1)
if len(unc_df) == 0:
continue
merged_df = ROOFS.merge(unc_df, on = ['x', 'y'], how = 'left')
merged_df['timestamp'] = timestamp
# create xarray and save (to be opened and joined using mfdataset)
new_dataset = merged_df.set_index(['DF_UID', 'timestamp']).drop(columns = ['x', 'y']).to_xarray()
new_dataset.to_netcdf( os.path.join( SCRATCH , '%s_roofs_%d.nc' %(FILENAME, i)) )
i+=1
data_unc = []
for i in range(10):
data_unc.append(xr.open_mfdataset(os.path.join( SCRATCH , '%s_roofs_%d*' %(FILENAME, i)), chunks = {'DF_UID': 100000}))
data_unc = xr.concat(data_unc, dim = 'timestamp').sortby('timestamp')
print('finished processing; saving to file')
data_unc.to_netcdf( os.path.join( OUTPATH, FILENAME + '_roofs.nc' ))
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