if fix_zeros:
exposure_tmp.value[exposure_tmp.value<1] = 1
# exposure_tmp.plot_raster(res=RES_ARCSEC/3600, save_tiff=\
# os.path.join(RES_DIR, '%s_%ias.tiff' %(fi[0:-4], RES_ARCSEC)))
exposure_tmp.plot_raster(res=RES_ARCSEC/3600, raster_res=res_target/3600, save_tiff=\
os.path.join(RES_DIR, '%s_%ias.tiff' %(fi[0:-4]+fadd, res_target)))
"""COMBINE AND PLOT EXPOSURE AT TARGET RESOLUTION:"""
print('\n' + '\x1b[1;03;30;30m' + 'COMBINE AND PLOT EXPOSURE AT TARGET RESOLUTION' + '\x1b[0m')
for res_target in res_targets:
exposure_data = Exposures()
for idx, fi in enumerate(files):
exposure_tmp = Exposures()
if try_read_from_tiff and os.path.exists(os.path.join(RES_DIR, '%s_%ias.tiff' %(fi[0:-4]+fadd, res_target))):
print('\n' + '\x1b[1;03;30;30m' + 'Loading: %s_%ias.tiff' %(fi[0:-4]+fadd, res_target) + '\x1b[0m')
exposure_tmp.set_from_raster(os.path.join(RES_DIR, '%s_%ias.tiff' %(fi[0:-4]+fadd, res_target)))
exposure_tmp = Exposures(exposure_tmp)
exposure_tmp.set_geometry_points() # set geometry attribute (shapely Points) from GeoDataFrame from latitude and longitude
exposure_tmp.check() # puts metadata that has not been assigned
exposure_data = exposure_data.append(exposure_tmp)
if fix_zeros:
exposure_tmp.value[exposure_tmp.value<plot_minimum] = plot_minimum
else:
print('\n' + '\x1b[1;03;30;30m' + 'ERROR Loading: %s_%ias.tiff' %(fi[0:-4]+fadd, res_target) + '\x1b[0m')
print('\n' + '\x1b[1;03;30;30m' + 'Checking combined data...' + '\x1b[0m')
exposure_data.check()
if write_to_tiff:
print('\n' + '\x1b[1;03;30;30m' + 'Writing combined data to TIFF...' + '\x1b[0m')
exposure_data.plot_raster(res=res_target/3600, raster_res=res_target/3600, save_tiff=\
os.path.join(RES_DIR, '%s_000_%ias.tiff' %(files[0][0:-8]+fadd, res_target)))
if_fl.tag.description = '1m step function'
if_fl.append(if_1m)
return if_fl
ifs_step = iffl()
yy_start = 106
yy_end = 116
idx_band = 1
exp = Exposures()
ssp_file = EXP_POP_PTH +'baseYr_total_2000.tif'
exp.set_from_raster(ssp_file, transform=DST_META['transform'], height=DST_META['height'],
width=DST_META['width'], resampling=Resampling.average)
exp.value *= 25 # sum of the grids after upscaling
if np.any(exp.value<0) == True:
raise ValueError
exp.value_unit = 'N people per pixel'
exp.ref_year = 2000
exp[INDICATOR_CENTR+HAZ_TYPE] = np.arange(len(exp), dtype=int)
exp[INDICATOR_IF+HAZ_TYPE] = np.ones(len(exp), dtype=int)
exp.check()
for year in YEAR:
imp_model = [] # initite a list fro all models in one year
imp_save = None
