from util.geos_nav import get_navigation from util.setup_cloud_fraction import model_path from aeolus.datasource import CLAVRx import os from deeplearning.cloud_fraction_fcn_abi import SRCNN, run_evaluate_static, run_evaluate_static_full_disk from util.util import get_cartopy_crs, write_cld_frac_file_nc4 import numpy as np def infer_cloud_fraction(clvrx_path, output_dir, full_disk=True): ckpt_dir_s = os.listdir(model_path) ckpt_dir = model_path + ckpt_dir_s[0] clvrx_ds = CLAVRx(clvrx_path) for fname, t_start, t_stop in clvrx_ds: dto = clvrx_ds.get_datetime(fname) clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M') out_file = output_dir + 'cloud_fraction_' + clvrx_str_time if full_disk: run_evaluate_static_full_disk(fname, out_file, ckpt_dir) else: run_evaluate_static(fname, out_file, ckpt_dir) def infer_cloud_fraction_new(clvrx_path, output_dir, full_disk=True, satellite='GOES16', domain='FD'): # location of the trained model ckpt_dir_s = os.listdir(model_path) ckpt_dir = model_path + ckpt_dir_s[0] # Navigation parameters geos, xlen, xmin, xmax, ylen, ymin, ymax = get_cartopy_crs(satellite, domain) nav = get_navigation(satellite, domain) cc = np.arange(xlen) ll = np.arange(ylen) x_rad = cc * nav.CFAC + nav.COFF y_rad = ll * nav.LFAC + nav.LOFF # Create a model instance and initialize with trained model above nn = SRCNN() nn.setup_inference(ckpt_dir) clvrx_ds = CLAVRx(clvrx_path) for fname, t_start, t_stop in clvrx_ds: dto = clvrx_ds.get_datetime(fname) clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M') out_file = output_dir + 'cloud_fraction_' + clvrx_str_time + '.nc' if full_disk: cld_frac = nn.run_inference_full_disk(fname, None) else: cld_frac = nn.run_inference(fname, None) write_cld_frac_file_nc4(clvrx_str_time, out_file, cld_frac, x_rad, y_rad, None, None, satellite=satellite, domain=domain, has_time=True)