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
for hydro, gcm in [(hydro, gcm) for hydro in HYDRO_MODEL for gcm in GCM_MODEL]:
haz_file = HAZARD_PTH +'flddph_' +hydro +'_' +gcm +'_' +RCP +'_flopros_gev_picontrol_2006_2300_0.1.nc'
haz_frac = HAZARD_PTH +'fldfrc_' +hydro +'_' +gcm +'_' +RCP +'_flopros_gev_picontrol_2006_2300_0.1.nc'
if haz_file not in HAZ_FILES:
for ssp, year in [(ssp, year) for ssp in SSP_MODEL for year in YEAR]:
exp = Exposures()
ssp_file = EXP_POP_PTH + ssp.upper(
) + '_1km/' + ssp + '_total_' + year + '.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 = int(year)
exp[INDICATOR_CENTR + HAZ_TYPE] = np.arange(len(exp), dtype=int)
exp[INDICATOR_IF + HAZ_TYPE] = np.ones(len(exp), dtype=int)
exp.check()
imp_model = [] # initite a list fro all models in one year
imp_save = None
for hydro, gcm in [(hydro, gcm) for hydro in HYDRO_MODEL
for gcm in GCM_MODEL]:
haz_file = HAZARD_PTH + 'flddph_' + hydro + '_' + gcm + '_' + RCP + '_flopros_gev_picontrol_2006_2300_0.1.nc'
haz_frac = HAZARD_PTH + 'fldfrc_' + hydro + '_' + gcm + '_' + RCP + '_flopros_gev_picontrol_2006_2300_0.1.nc'
if haz_file not in HAZ_FILES:
logging.error('no file: flddph_' + hydro + '_' + gcm + '_' + RCP +
