diff --git a/modules/deeplearning/icing_cnn.py b/modules/deeplearning/icing_cnn.py
index e9cbd3a8501a2a936c2c379cb399c928c4e11d24..d4399867a2f2ebab5511d6d2f6a202e699ec5e8b 100644
--- a/modules/deeplearning/icing_cnn.py
+++ b/modules/deeplearning/icing_cnn.py
@@ -53,25 +53,24 @@ mean_std_dct.update(mean_std_dct_l1b)
mean_std_dct.update(mean_std_dct_l2)
# -- NIGHT L2 -----------------------------
-# 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']
-# -- DAY L2 -------------
-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', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
- 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+train_params_l2_night = ['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']
+# -- DAY L2 --------------------------------
+train_params_l2_day = ['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']
# -- DAY L1B --------------------------------
-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']
+train_params_l1b_day = ['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']
# -- NIGHT L1B -------------------------------
-# 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']
-# -- DAY LUNAR ---------------------
+train_params_l1b_night = ['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']
+# -- DAY LUNAR ---------------------------------
# train_params_l1b = ['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 = train_params_l2
+train_params = train_params_l1b_day + train_params_l2_day
# -- Zero out params (Experimentation Only) ------------
zero_out_params = ['cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
DO_ZERO_OUT = False
@@ -118,7 +117,17 @@ def build_residual_block(input, drop_rate, num_neurons, activation, block_name,
class IcingIntensityNN:
- def __init__(self, gpu_device=0, datapath=None):
+ def __init__(self, day_night='DAY', gpu_device=0, datapath=None):
+
+ if day_night == 'DAY':
+ self.train_params_l1b = train_params_l1b_day
+ self.train_params_l2 = train_params_l2_day
+ self.train_params = train_params_l1b_day + train_params_l2_day
+ else:
+ self.train_params_l1b = train_params_l1b_night
+ self.train_params_l2 = train_params_l2_night
+ self.train_params = train_params_l1b_night + train_params_l2_night
+
self.train_data = None
self.train_label = None
self.test_data = None
@@ -202,7 +211,7 @@ class IcingIntensityNN:
self.data_dct = None
- n_chans = len(train_params)
+ n_chans = len(self.train_params)
if TRIPLET:
n_chans *= 3
self.X_img = tf.keras.Input(shape=(img_width, img_width, n_chans))
@@ -252,7 +261,7 @@ class IcingIntensityNN:
nd_idxs = np.sort(nd_idxs)
data = []
- for param in train_params:
+ for param in self.train_params:
nda = self.get_parameter_data(param, nd_idxs, is_training)
# Manual Corruption Process. Better: see use of tf.keras.layers.GaussianNoise
# if NOISE_TRAINING and is_training:
@@ -295,12 +304,12 @@ class IcingIntensityNN:
def get_parameter_data(self, param, nd_idxs, is_training):
if is_training:
- if param in train_params_l1b:
+ if param in self.train_params_l1b:
h5f = self.h5f_l1b_trn
else:
h5f = self.h5f_l2_trn
else:
- if param in train_params_l1b:
+ if param in self.train_params_l1b:
h5f = self.h5f_l1b_tst
else:
h5f = self.h5f_l2_tst
@@ -363,7 +372,7 @@ class IcingIntensityNN:
nd_idxs = np.sort(nd_idxs)
data = []
- for param in train_params:
+ for param in self.train_params:
nda = self.data_dct[param][nd_idxs, ]
nda = normalize(nda, param, mean_std_dct)
data.append(nda)
@@ -542,7 +551,7 @@ class IcingIntensityNN:
activation = tf.nn.leaky_relu
momentum = 0.99
- num_filters = len(train_params) * 2
+ num_filters = len(self.train_params) * 2
if NOISE_TRAINING:
input_2d = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(self.inputs[0])
@@ -988,7 +997,7 @@ class IcingIntensityNN:
self.h5f_l2_tst.close()
def run_evaluate(self, filename, ckpt_dir):
- data_dct, ll, cc = make_for_full_domain_predict(filename, name_list=train_params)
+ data_dct, ll, cc = make_for_full_domain_predict(filename, name_list=self.train_params)
self.setup_eval_pipeline(data_dct, len(ll))
self.build_model()
self.build_training()
@@ -1081,7 +1090,7 @@ def run_evaluate_static(h5f, ckpt_dir_s_path, flight_level=4, prob_thresh=0.5, s
return ice_lons, ice_lats, preds_2d, lons_2d, lats_2d, x_rad, y_rad
-def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, flight_levels=[0, 1, 2, 3, 4], prob_thresh=0.5):
+def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, day_night='DAY', flight_levels=[0, 1, 2, 3, 4], prob_thresh=0.5):
ckpt_dir_s = os.listdir(ckpt_dir_s_path)
ckpt_dir = ckpt_dir_s[0]
@@ -1090,7 +1099,7 @@ def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, fli
preds_2d_dct = {flvl: None for flvl in flight_levels}
for flvl in flight_levels:
- nn = IcingIntensityNN()
+ nn = IcingIntensityNN(day_night=day_night)
nn.flight_level = flvl
nn.setup_eval_pipeline(data_dct, num_lines * num_elems)
nn.build_model()