diff --git a/modules/icing/util.py b/modules/icing/util.py
index 98e9517b9ab50a714c75645a7d41593bce3f4dce..95c7724ade4b06e6e1f98d6b1e3a2c3b04f070e4 100644
--- a/modules/icing/util.py
+++ b/modules/icing/util.py
@@ -359,14 +359,14 @@ def prepare_evaluate1x1(h5f, name_list, satellite='GOES16', domain='FD', res_fac
ll = [(offset+j_0) + j*s_y for j in range(n_y)]
cc = [(offset+i_0) + i*s_x for i in range(n_x)]
- grd_dct_n = {name: [] for name in name_list}
+ grd_s = []
cnt_a = 0
for ds_name in name_list:
fill_value, fill_value_name = get_fill_attrs(ds_name)
gvals = get_grid_values(h5f, ds_name, j_0, i_0, None, num_j=ylen, num_i=xlen, fill_value_name=fill_value_name, fill_value=fill_value)
if gvals is not None:
- grd_dct_n[ds_name] = gvals.flatten()
+ grd_s.append(gvals.flatten())
cnt_a += 1
if cnt_a > 0 and cnt_a != len(name_list):
@@ -379,7 +379,9 @@ def prepare_evaluate1x1(h5f, name_list, satellite='GOES16', domain='FD', res_fac
satzen = satzen[0:n_y*s_y:s_y, 0:n_x*s_x:s_x]
cldmsk = cldmsk[0:n_y*s_y:s_y, 0:n_x*s_x:s_x]
- return grd_dct_n, solzen.flatten(), satzen.flatten(), cldmsk.flatten(), ll, cc
+ varX = np.stack(grd_s, axis=1)
+
+ return varX, solzen.flatten(), satzen.flatten(), cldmsk.flatten(), ll, cc
flt_level_ranges_str = {k: None for k in range(6)}
@@ -1492,8 +1494,10 @@ def run_icing_predict_image_1x1(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ou
# use_flight_altitude=use_flight_altitude)
# load parameter stats and model from disk
- stdSclr = joblib.load('/home/rink/stdSclr_4.pkl')
- model = joblib.load('/home/rink/icing_gbm.pkl')
+ stdSclr_day = joblib.load('/home/rink/stdSclr_4_day.pkl')
+ day_model = joblib.load('/home/rink/icing_gbm_day.pkl')
+ stdSclr_nght = joblib.load('/home/rink/stdSclr_4_nght.pkl')
+ nght_model = joblib.load('/home/rink/icing_gbm_nght.pkl')
if use_flight_altitude is True:
flight_levels = [0, 1, 2, 3, 4]
@@ -1518,7 +1522,8 @@ def run_icing_predict_image_1x1(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ou
# train_params = day_train_params
# elif day_night == 'NIGHT':
# train_params = nght_train_params
- train_params = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp']
+ day_train_params = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp']
+ nght_train_params = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
if satellite == 'H08':
clvrx_ds = CLAVRx_H08(clvrx_dir)
@@ -1551,62 +1556,38 @@ def run_icing_predict_image_1x1(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ou
keep_lons = None
keep_lats = None
- data_dct, solzen, satzen, cldmsk, ll, cc = prepare_evaluate1x1(h5f, name_list=train_params, satellite=satellite, domain=domain, offset=8)
+ varX_day, solzen, satzen, cldmsk, ll, cc = prepare_evaluate1x1(h5f, name_list=day_train_params, satellite=satellite, domain=domain, offset=8)
+ varX_ngth, solzen, satzen, cldmsk, ll, cc = prepare_evaluate1x1(h5f, name_list=nght_train_params, satellite=satellite, domain=domain, offset=8)
num_elems = len(cc)
num_lines = len(ll)
day_idxs = solzen < 80.0
- day_idxs = day_idxs.flatten()
num_day_tiles = np.sum(day_idxs)
nght_idxs = solzen > 100.0
- nght_idxs = nght_idxs.flatten()
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
+ cldy_idxs = cldmsk >= 2
+ num_cldy_tiles = np.sum(cldy_idxs)
+
+ fd_preds = np.zeros(num_lines * num_elems, dtype=np.int8)
+ fd_probs = np.zeros(num_lines * num_elems, dtype=np.float32)
+ fd_preds[:] = -1
+ fd_probs[:] = -1.0
if (day_night == 'AUTO' or day_night == 'DAY') and num_day_tiles > 0:
- for flvl in flight_levels:
- preds = preds_day_dct[flvl].flatten()
- probs = probs_day_dct[flvl].flatten()
- 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]
+ varX_std = stdSclr_day.transform(varX_day)
+ probs = day_model.predict_proba(varX_std)
+ fd_probs[:] = probs[day_idxs]
if (day_night == 'AUTO' or day_night == 'NIGHT') and num_nght_tiles > 0:
- for flvl in flight_levels:
- preds = preds_nght_dct[flvl].flatten()
- probs = probs_nght_dct[flvl].flatten()
- 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]
+ varX_std = stdSclr_nght.transform(varX_ngth)
+ probs = nght_model.predict_proba(varX_std)
+ fd_probs[:] = 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)
+ max_prob = fd_probs.reshape((num_lines, num_elems))
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)