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