import numpy as np
import deeplearning.icing_fcn as icing_fcn
import deeplearning.icing_cnn as icing_cnn
from icing.pirep_goes import setup, time_filter_3
from util.util import get_time_tuple_utc, is_day, check_oblique, homedir, write_icing_file_nc4,\
make_for_full_domain_predict, prepare_evaluate
from util.plot import make_icing_image
from util.geos_nav import get_navigation, get_lon_lat_2d_mesh
from util.setup import model_path_day, model_path_night
from aeolus.datasource import CLAVRx, CLAVRx_VIIRS, GOESL1B, CLAVRx_H08
import h5py
import datetime
def get_training_parameters(day_night='DAY', l1b_andor_l2='both'):
if day_night == 'DAY':
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
train_params_l1b = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
else:
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
train_params_l1b = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom']
if l1b_andor_l2 == 'both':
train_params = train_params_l1b + train_params_l2
elif l1b_andor_l2 == 'l1b':
train_params = train_params_l1b
elif l1b_andor_l2 == 'l2':
train_params = train_params_l2
return train_params
flt_level_ranges = {k: None for k in range(5)}
flt_level_ranges[0] = [0.0, 2000.0]
flt_level_ranges[1] = [2000.0, 4000.0]
flt_level_ranges[2] = [4000.0, 6000.0]
flt_level_ranges[3] = [6000.0, 8000.0]
flt_level_ranges[4] = [8000.0, 15000.0]
def run_make_images(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ckpt_dir_s_path='/Users/tomrink/tf_model/', prob_thresh=0.5, satellite='GOES16', domain='CONUS',
extent=[-105, -70, 15, 50],
pirep_file='/Users/tomrink/data/pirep/pireps_202109200000_202109232359.csv',
obs_lons=None, obs_lats=None, obs_times=None, obs_alt=None, flight_level=None,
use_flight_altitude=False, day_night='DAY', l1b_andor_l2='l2'):
if pirep_file is not None:
ice_dict, no_ice_dict, neg_ice_dict = setup(pirep_file)
if satellite == 'H08':
clvrx_ds = CLAVRx_H08(clvrx_dir)
else:
clvrx_ds = CLAVRx(clvrx_dir)
clvrx_files = clvrx_ds.flist
alt_lo, alt_hi = 0.0, 15000.0
if flight_level is not None:
alt_lo, alt_hi = flt_level_ranges[flight_level]
train_params = get_training_parameters(day_night=day_night, l1b_andor_l2=l1b_andor_l2)
for fidx, fname in enumerate(clvrx_files):
h5f = h5py.File(fname, 'r')
dto = clvrx_ds.get_datetime(fname)
ts = dto.timestamp()
clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
data_dct, ll, cc = make_for_full_domain_predict(h5f, name_list=train_params, satellite=satellite, domain=domain)
num_elems, num_lines = len(cc), len(ll)
dto, _ = get_time_tuple_utc(ts)
dto_0 = dto - datetime.timedelta(minutes=30)
dto_1 = dto + datetime.timedelta(minutes=30)
ts_0 = dto_0.timestamp()
ts_1 = dto_1.timestamp()
if pirep_file is not None:
_, keep_lons, keep_lats, _ = time_filter_3(ice_dict, ts_0, ts_1, alt_lo, alt_hi)
elif obs_times is not None:
keep = np.logical_and(obs_times >= ts_0, obs_times < ts_1)
keep = np.where(keep, np.logical_and(obs_alt >= alt_lo, obs_alt < alt_hi), False)
keep_lons = obs_lons[keep]
keep_lats = obs_lats[keep]
else:
keep_lons = None
keep_lats = None
ice_lons, ice_lats, preds_2d = icing_cnn.run_evaluate_static_avg(data_dct, ll, cc, ckpt_dir_s_path=ckpt_dir_s_path,
flight_level=flight_level, prob_thresh=prob_thresh,
satellite=satellite, domain=domain,
use_flight_altitude=use_flight_altitude)
make_icing_image(h5f, None, ice_lons, ice_lats, clvrx_str_time, satellite, domain,
ice_lons_vld=keep_lons, ice_lats_vld=keep_lats, extent=extent)
# preds_2d_dct, probs_2d_dct = run_evaluate_static(data_dct, num_lines, num_elems, day_night=day_night,
# ckpt_dir_s_path=ckpt_dir_s_path, prob_thresh=prob_thresh,
# flight_levels=[0],
# use_flight_altitude=use_flight_altitude)
#
# make_icing_image(None, probs_2d_dct[0], None, None, clvrx_str_time, satellite, domain,
# ice_lons_vld=keep_lons, ice_lats_vld=keep_lats, extent=extent)
h5f.close()
print('Done: ', clvrx_str_time)
def run_icing_predict(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output_dir=homedir,
day_model_path=model_path_day, night_model_path=model_path_night,
prob_thresh=0.5, satellite='GOES16', domain='CONUS', day_night='AUTO',
l1b_andor_l2='both', use_flight_altitude=True, res_fac=1, use_nan=False, model_type='CNN'):
if model_type == 'CNN':
model_module = icing_cnn
elif model_type == 'FCN':
model_module = icing_fcn
if use_flight_altitude is True:
flight_levels = [0, 1, 2, 3, 4]
else:
flight_levels = [0]
day_train_params = get_training_parameters(day_night='DAY', l1b_andor_l2=l1b_andor_l2)
nght_train_params = get_training_parameters(day_night='NIGHT', l1b_andor_l2=l1b_andor_l2)
if day_night == 'AUTO':
train_params = list(set(day_train_params + nght_train_params))
elif day_night == 'DAY':
train_params = day_train_params
elif day_night == 'NIGHT':
train_params = nght_train_params
if satellite == 'H08':
clvrx_ds = CLAVRx_H08(clvrx_dir)
else:
clvrx_ds = CLAVRx(clvrx_dir)
clvrx_files = clvrx_ds.flist
for fidx, fname in enumerate(clvrx_files):
h5f = h5py.File(fname, 'r')
dto = clvrx_ds.get_datetime(fname)
ts = dto.timestamp()
clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
data_dct, ll, cc = make_for_full_domain_predict(h5f, name_list=train_params, satellite=satellite, domain=domain, res_fac=res_fac)
if fidx == 0:
num_elems = len(cc)
num_lines = len(ll)
nav = get_navigation(satellite, domain)
lons_2d, lats_2d, x_rad, y_rad = get_lon_lat_2d_mesh(nav, ll, cc, offset=int(8 / res_fac))
ancil_data_dct, _, _ = make_for_full_domain_predict(h5f, name_list=
['solar_zenith_angle', 'sensor_zenith_angle', 'cld_height_acha', 'cld_geo_thick'],
satellite=satellite, domain=domain, res_fac=res_fac)
satzen = ancil_data_dct['sensor_zenith_angle']
solzen = ancil_data_dct['solar_zenith_angle']
day_idxs = []
nght_idxs = []
for j in range(num_lines):
for i in range(num_elems):
k = i + j*num_elems
if not check_oblique(satzen[k]):
continue
if is_day(solzen[k]):
day_idxs.append(k)
else:
nght_idxs.append(k)
num_tiles = num_lines * num_elems
num_day_tiles = len(day_idxs)
num_nght_tiles = len(nght_idxs)
# initialize output arrays
probs_2d_dct = {flvl: None for flvl in flight_levels}
preds_2d_dct = {flvl: None for flvl in flight_levels}
for flvl in flight_levels:
fd_preds = np.zeros(num_lines * num_elems, dtype=np.int8)
fd_preds[:] = -1
fd_probs = np.zeros(num_lines * num_elems, dtype=np.float32)
fd_probs[:] = -1.0
preds_2d_dct[flvl] = fd_preds
probs_2d_dct[flvl] = fd_probs
if (day_night == 'AUTO' or day_night == 'DAY') and num_day_tiles > 0:
day_data_dct = {name: [] for name in day_train_params}
for name in day_train_params:
for k in day_idxs:
day_data_dct[name].append(data_dct[name][k])
day_grd_dct = {name: None for name in day_train_params}
for ds_name in day_train_params:
day_grd_dct[ds_name] = np.stack(day_data_dct[ds_name])
preds_day_dct, probs_day_dct = model_module.run_evaluate_static(day_grd_dct, num_day_tiles, day_model_path,
day_night='DAY', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
flight_levels=flight_levels)
day_idxs = np.array(day_idxs)
for flvl in flight_levels:
day_preds = preds_day_dct[flvl]
day_probs = probs_day_dct[flvl]
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
fd_preds[day_idxs] = day_preds[:]
fd_probs[day_idxs] = day_probs[:]
if (day_night == 'AUTO' or day_night == 'NIGHT') and num_nght_tiles > 0:
nght_data_dct = {name: [] for name in nght_train_params}
for name in nght_train_params:
for k in nght_idxs:
nght_data_dct[name].append(data_dct[name][k])
nght_grd_dct = {name: None for name in nght_train_params}
for ds_name in nght_train_params:
nght_grd_dct[ds_name] = np.stack(nght_data_dct[ds_name])
preds_nght_dct, probs_nght_dct = model_module.run_evaluate_static(nght_grd_dct, num_nght_tiles, night_model_path,
day_night='NIGHT', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
flight_levels=flight_levels)
nght_idxs = np.array(nght_idxs)
for flvl in flight_levels:
nght_preds = preds_nght_dct[flvl]
nght_probs = probs_nght_dct[flvl]
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
fd_preds[nght_idxs] = nght_preds[:]
fd_probs[nght_idxs] = nght_probs[:]
for flvl in flight_levels:
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
preds_2d_dct[flvl] = fd_preds.reshape((num_lines, num_elems))
probs_2d_dct[flvl] = fd_probs.reshape((num_lines, num_elems))
write_icing_file_nc4(clvrx_str_time, output_dir, preds_2d_dct, probs_2d_dct,
x_rad, y_rad, lons_2d, lats_2d, cc, ll,
satellite=satellite, domain=domain, use_nan=use_nan, prob_thresh=prob_thresh)
print('Done: ', clvrx_str_time)
h5f.close()
def run_icing_predict_fcn(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output_dir=homedir,
day_model_path=model_path_day, night_model_path=model_path_night,
prob_thresh=0.5, satellite='GOES16', domain='CONUS', day_night='AUTO',
l1b_andor_l2='both', use_flight_altitude=False, res_fac=1, use_nan=False):
if use_flight_altitude is True:
flight_levels = [0, 1, 2, 3, 4]
else:
flight_levels = [0]
day_train_params = get_training_parameters(day_night='DAY', l1b_andor_l2=l1b_andor_l2)
nght_train_params = get_training_parameters(day_night='NIGHT', l1b_andor_l2=l1b_andor_l2)
if day_night == 'AUTO':
train_params = list(set(day_train_params + nght_train_params))
elif day_night == 'DAY':
train_params = day_train_params
elif day_night == 'NIGHT':
train_params = nght_train_params
if satellite == 'H08':
clvrx_ds = CLAVRx_H08(clvrx_dir)
else:
clvrx_ds = CLAVRx(clvrx_dir)
clvrx_files = clvrx_ds.flist
for fidx, fname in enumerate(clvrx_files):
h5f = h5py.File(fname, 'r')
dto = clvrx_ds.get_datetime(fname)
ts = dto.timestamp()
clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
data_dct, solzen, satzen, ll, cc = prepare_evaluate(h5f, name_list=train_params, satellite=satellite, domain=domain, offset=8)
num_elems = len(cc)
num_lines = len(ll)
if fidx == 0:
nav = get_navigation(satellite, domain)
lons_2d, lats_2d, x_rad, y_rad = get_lon_lat_2d_mesh(nav, ll, cc)
day_idxs = solzen < 80.0
num_day_tiles = np.sum(day_idxs)
nght_idxs = solzen > 100.0
num_nght_tiles = np.sum(nght_idxs)
# initialize output arrays
probs_2d_dct = {flvl: None for flvl in flight_levels}
preds_2d_dct = {flvl: None for flvl in flight_levels}
for flvl in flight_levels:
fd_preds = np.zeros(num_lines * num_elems, dtype=np.int8)
fd_preds[:] = -1
fd_probs = np.zeros(num_lines * num_elems, dtype=np.float32)
fd_probs[:] = -1.0
preds_2d_dct[flvl] = fd_preds
probs_2d_dct[flvl] = fd_probs
if (day_night == 'AUTO' or day_night == 'DAY') and num_day_tiles > 0:
preds_day_dct, probs_day_dct = icing_fcn.run_evaluate_static(data_dct, 1, day_model_path,
day_night='DAY', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
flight_levels=flight_levels)
for flvl in flight_levels:
preds = preds_day_dct[flvl]
probs = probs_day_dct[flvl]
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
fd_preds[day_idxs] = preds[day_idxs]
fd_probs[day_idxs] = probs[day_idxs]
if (day_night == 'AUTO' or day_night == 'NIGHT') and num_nght_tiles > 0:
preds_nght_dct, probs_nght_dct = icing_fcn.run_evaluate_static(data_dct, 1, night_model_path,
day_night='NIGHT', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
flight_levels=flight_levels)
for flvl in flight_levels:
preds = preds_nght_dct[flvl]
probs = probs_nght_dct[flvl]
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
fd_preds[nght_idxs] = preds[nght_idxs]
fd_probs[nght_idxs] = probs[nght_idxs]
for flvl in flight_levels:
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
preds_2d_dct[flvl] = fd_preds.reshape((num_lines, num_elems))
probs_2d_dct[flvl] = fd_probs.reshape((num_lines, num_elems))
write_icing_file_nc4(clvrx_str_time, output_dir, preds_2d_dct, probs_2d_dct,
x_rad, y_rad, lons_2d, lats_2d, cc, ll,
satellite=satellite, domain=domain, use_nan=use_nan, prob_thresh=prob_thresh)
print('Done: ', clvrx_str_time)
h5f.close()
def run_icing_predict_image(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output_dir=homedir,
day_model_path=model_path_day, night_model_path=model_path_night,
prob_thresh=0.5, satellite='GOES16', domain='CONUS', day_night='AUTO',
l1b_andor_l2='BOTH', use_flight_altitude=True, res_fac=1, model_type='CNN',
extent=[-105, -70, 15, 50],
pirep_file='/Users/tomrink/data/pirep/pireps_202109200000_202109232359.csv',
obs_lons=None, obs_lats=None, obs_times=None, obs_alt=None, flight_level=None):
if model_type == 'CNN':
model_module = icing_cnn
elif model_type == 'FCN':
model_module = icing_fcn
if use_flight_altitude is True:
flight_levels = [0, 1, 2, 3, 4]
else:
flight_levels = [0]
if pirep_file is not None:
ice_dict, no_ice_dict, neg_ice_dict = setup(pirep_file)
alt_lo, alt_hi = 0.0, 15000.0
if flight_level is not None:
alt_lo, alt_hi = flt_level_ranges[flight_level]
day_train_params = get_training_parameters(day_night='DAY', l1b_andor_l2=l1b_andor_l2)
nght_train_params = get_training_parameters(day_night='NIGHT', l1b_andor_l2=l1b_andor_l2)
if day_night == 'AUTO':
train_params = list(set(day_train_params + nght_train_params))
elif day_night == 'DAY':
train_params = day_train_params
elif day_night == 'NIGHT':
train_params = nght_train_params
if satellite == 'H08':
clvrx_ds = CLAVRx_H08(clvrx_dir)
else:
clvrx_ds = CLAVRx(clvrx_dir)
clvrx_files = clvrx_ds.flist
for fidx, fname in enumerate(clvrx_files):
h5f = h5py.File(fname, 'r')
dto = clvrx_ds.get_datetime(fname)
ts = dto.timestamp()
clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
data_dct, ll, cc = make_for_full_domain_predict(h5f, name_list=train_params, satellite=satellite, domain=domain, res_fac=res_fac)
if fidx == 0:
num_elems = len(cc)
num_lines = len(ll)
nav = get_navigation(satellite, domain)
ancil_data_dct, _, _ = make_for_full_domain_predict(h5f, name_list=
['solar_zenith_angle', 'sensor_zenith_angle', 'cld_height_acha', 'cld_geo_thick'],
satellite=satellite, domain=domain, res_fac=res_fac)
satzen = ancil_data_dct['sensor_zenith_angle']
solzen = ancil_data_dct['solar_zenith_angle']
day_idxs = []
nght_idxs = []
for j in range(num_lines):
for i in range(num_elems):
k = i + j*num_elems
if not check_oblique(satzen[k]):
continue
if is_day(solzen[k]):
day_idxs.append(k)
else:
nght_idxs.append(k)
num_tiles = num_lines * num_elems
num_day_tiles = len(day_idxs)
num_nght_tiles = len(nght_idxs)
# initialize output arrays
probs_2d_dct = {flvl: None for flvl in flight_levels}
preds_2d_dct = {flvl: None for flvl in flight_levels}
for flvl in flight_levels:
fd_preds = np.zeros(num_lines * num_elems, dtype=np.int8)
fd_preds[:] = -1
fd_probs = np.zeros(num_lines * num_elems, dtype=np.float32)
fd_probs[:] = -1.0
preds_2d_dct[flvl] = fd_preds
probs_2d_dct[flvl] = fd_probs
if (day_night == 'AUTO' or day_night == 'DAY') and num_day_tiles > 0:
day_data_dct = {name: [] for name in day_train_params}
for name in day_train_params:
for k in day_idxs:
day_data_dct[name].append(data_dct[name][k])
day_grd_dct = {name: None for name in day_train_params}
for ds_name in day_train_params:
day_grd_dct[ds_name] = np.stack(day_data_dct[ds_name])
preds_day_dct, probs_day_dct = model_module.run_evaluate_static(day_grd_dct, num_day_tiles, day_model_path,
day_night='DAY', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
flight_levels=flight_levels)
day_idxs = np.array(day_idxs)
for flvl in flight_levels:
day_preds = preds_day_dct[flvl]
day_probs = probs_day_dct[flvl]
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
fd_preds[day_idxs] = day_preds[:]
fd_probs[day_idxs] = day_probs[:]
if (day_night == 'AUTO' or day_night == 'NIGHT') and num_nght_tiles > 0:
nght_data_dct = {name: [] for name in nght_train_params}
for name in nght_train_params:
for k in nght_idxs:
nght_data_dct[name].append(data_dct[name][k])
nght_grd_dct = {name: None for name in nght_train_params}
for ds_name in nght_train_params:
nght_grd_dct[ds_name] = np.stack(nght_data_dct[ds_name])
preds_nght_dct, probs_nght_dct = model_module.run_evaluate_static(nght_grd_dct, num_nght_tiles, night_model_path,
day_night='NIGHT', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
flight_levels=flight_levels)
nght_idxs = np.array(nght_idxs)
for flvl in flight_levels:
nght_preds = preds_nght_dct[flvl]
nght_probs = probs_nght_dct[flvl]
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
fd_preds[nght_idxs] = nght_preds[:]
fd_probs[nght_idxs] = nght_probs[:]
for flvl in flight_levels:
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
preds_2d_dct[flvl] = fd_preds.reshape((num_lines, num_elems))
probs_2d_dct[flvl] = fd_probs.reshape((num_lines, num_elems))
dto, _ = get_time_tuple_utc(ts)
dto_0 = dto - datetime.timedelta(minutes=30)
dto_1 = dto + datetime.timedelta(minutes=30)
ts_0 = dto_0.timestamp()
ts_1 = dto_1.timestamp()
if pirep_file is not None:
_, keep_lons, keep_lats, _ = time_filter_3(ice_dict, ts_0, ts_1, alt_lo, alt_hi)
elif obs_times is not None:
keep = np.logical_and(obs_times >= ts_0, obs_times < ts_1)
keep = np.where(keep, np.logical_and(obs_alt >= alt_lo, obs_alt < alt_hi), False)
keep_lons = obs_lons[keep]
keep_lats = obs_lats[keep]
else:
keep_lons = None
keep_lats = None
prob_s = []
for flvl in flight_levels:
probs = probs_2d_dct[flvl]
prob_s.append(probs)
prob_s = np.stack(prob_s, axis=-1)
max_prob = np.max(prob_s, axis=2)
max_prob = np.where(max_prob < 0.5, np.nan, max_prob)
make_icing_image(h5f, max_prob, None, None, clvrx_str_time, satellite, domain,
ice_lons_vld=keep_lons, ice_lats_vld=keep_lats, extent=extent)
print('Done: ', clvrx_str_time)
h5f.close()
def run_icing_predict_image_fcn(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output_dir=homedir,
day_model_path=model_path_day, night_model_path=model_path_night,
prob_thresh=0.5, satellite='GOES16', domain='CONUS', day_night='AUTO',
l1b_andor_l2='BOTH', use_flight_altitude=True, res_fac=1,
extent=[-105, -70, 15, 50],
pirep_file='/Users/tomrink/data/pirep/pireps_202109200000_202109232359.csv',
obs_lons=None, obs_lats=None, obs_times=None, obs_alt=None, flight_level=None):
if use_flight_altitude is True:
flight_levels = [0, 1, 2, 3, 4]
else:
flight_levels = [0]
if pirep_file is not None:
ice_dict, no_ice_dict, neg_ice_dict = setup(pirep_file)
alt_lo, alt_hi = 0.0, 15000.0
if flight_level is not None:
alt_lo, alt_hi = flt_level_ranges[flight_level]
day_train_params = get_training_parameters(day_night='DAY', l1b_andor_l2=l1b_andor_l2)
nght_train_params = get_training_parameters(day_night='NIGHT', l1b_andor_l2=l1b_andor_l2)
if day_night == 'AUTO':
train_params = list(set(day_train_params + nght_train_params))
elif day_night == 'DAY':
train_params = day_train_params
elif day_night == 'NIGHT':
train_params = nght_train_params
if satellite == 'H08':
clvrx_ds = CLAVRx_H08(clvrx_dir)
else:
clvrx_ds = CLAVRx(clvrx_dir)
clvrx_files = clvrx_ds.flist
for fidx, fname in enumerate(clvrx_files):
h5f = h5py.File(fname, 'r')
dto = clvrx_ds.get_datetime(fname)
ts = dto.timestamp()
clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
dto, _ = get_time_tuple_utc(ts)
dto_0 = dto - datetime.timedelta(minutes=30)
dto_1 = dto + datetime.timedelta(minutes=30)
ts_0 = dto_0.timestamp()
ts_1 = dto_1.timestamp()
if pirep_file is not None:
_, keep_lons, keep_lats, _ = time_filter_3(ice_dict, ts_0, ts_1, alt_lo, alt_hi)
elif obs_times is not None:
keep = np.logical_and(obs_times >= ts_0, obs_times < ts_1)
keep = np.where(keep, np.logical_and(obs_alt >= alt_lo, obs_alt < alt_hi), False)
keep_lons = obs_lons[keep]
keep_lats = obs_lats[keep]
else:
keep_lons = None
keep_lats = None
data_dct, solzen, satzen, ll, cc = prepare_evaluate(h5f, name_list=train_params, satellite=satellite, domain=domain, offset=8)
num_elems = len(cc)
num_lines = len(ll)
if fidx == 0:
nav = get_navigation(satellite, domain)
lons_2d, lats_2d, x_rad, y_rad = get_lon_lat_2d_mesh(nav, ll, cc)
day_idxs = solzen < 80.0
num_day_tiles = np.sum(day_idxs)
nght_idxs = solzen > 100.0
num_nght_tiles = np.sum(nght_idxs)
# initialize output arrays
probs_2d_dct = {flvl: None for flvl in flight_levels}
preds_2d_dct = {flvl: None for flvl in flight_levels}
for flvl in flight_levels:
fd_preds = np.zeros(num_lines * num_elems, dtype=np.int8)
fd_preds[:] = -1
fd_probs = np.zeros(num_lines * num_elems, dtype=np.float32)
fd_probs[:] = -1.0
preds_2d_dct[flvl] = fd_preds
probs_2d_dct[flvl] = fd_probs
if (day_night == 'AUTO' or day_night == 'DAY') and num_day_tiles > 0:
preds_day_dct, probs_day_dct = icing_fcn.run_evaluate_static(data_dct, day_model_path,
day_night='DAY', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
flight_levels=flight_levels)
for flvl in flight_levels:
preds = preds_day_dct[flvl]
probs = probs_day_dct[flvl]
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
fd_preds[day_idxs] = preds[day_idxs]
fd_probs[day_idxs] = probs[day_idxs]
if (day_night == 'AUTO' or day_night == 'NIGHT') and num_nght_tiles > 0:
preds_nght_dct, probs_nght_dct = icing_fcn.run_evaluate_static_fcn(data_dct, night_model_path,
day_night='NIGHT', l1b_or_l2=l1b_andor_l2,
prob_thresh=prob_thresh,
use_flight_altitude=use_flight_altitude,
flight_levels=flight_levels)
for flvl in flight_levels:
preds = preds_nght_dct[flvl]
probs = probs_nght_dct[flvl]
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
fd_preds[nght_idxs] = preds[nght_idxs]
fd_probs[nght_idxs] = probs[nght_idxs]
for flvl in flight_levels:
fd_preds = preds_2d_dct[flvl]
fd_probs = probs_2d_dct[flvl]
preds_2d_dct[flvl] = fd_preds.reshape((num_lines, num_elems))
probs_2d_dct[flvl] = fd_probs.reshape((num_lines, num_elems))
prob_s = []
for flvl in flight_levels:
probs = probs_2d_dct[flvl]
prob_s.append(probs)
prob_s = np.stack(prob_s, axis=-1)
max_prob = np.max(prob_s, axis=2)
max_prob = np.where(max_prob < 0.5, np.nan, max_prob)
make_icing_image(h5f, max_prob, None, None, clvrx_str_time, satellite, domain,
ice_lons_vld=keep_lons, ice_lats_vld=keep_lats, extent=extent)
print('Done: ', clvrx_str_time)
h5f.close()