From 45cd28cec20d53fb90ccd4007dd6962bb7f1dd4a Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 26 Jun 2023 13:33:17 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 42 +++++++++++----------
1 file changed, 22 insertions(+), 20 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 7719ad33..fb5c726a 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -58,8 +58,8 @@ params = ['temp_11_0um_nom', 'refl_0_65um_nom', 'refl_submin_ch01', 'refl_submax
params_i = ['temp_11_0um_nom', 'refl_0_65um_nom', 'temp_stddev3x3_ch31', 'refl_stddev3x3_ch01', label_param]
data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
data_params_full = ['refl_0_65um_nom']
-# sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
-sub_fields = ['refl_stddev3x3_ch01']
+sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
+# sub_fields = ['refl_stddev3x3_ch01']
label_idx_i = params_i.index(label_param)
label_idx = params.index(label_param)
@@ -209,7 +209,7 @@ class SRCNN:
self.test_label_files = None
# self.n_chans = len(data_params_half) + len(data_params_full) + 1
- self.n_chans = 4
+ self.n_chans = 6
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
@@ -265,38 +265,40 @@ class SRCNN:
tmp = np.where(np.isnan(tmp), 0.0, tmp)
tmp = tmp[:, self.slc_y_m, self.slc_x_m]
tmp = self.upsample(tmp)
+ tmp = smooth_2d(tmp)
tmp = normalize(tmp, param, mean_std_dct)
data_norm.append(tmp)
- for param in sub_fields:
- idx = params.index(param)
- tmp = input_data[:, idx, :, :]
- tmp = np.where(np.isnan(tmp), 0.0, tmp)
- tmp = tmp[:, self.slc_y_m, self.slc_x_m]
- tmp = self.upsample(tmp)
- # if param != 'refl_substddev_ch01':
- if False:
- tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
- else:
- tmp = np.where(np.isnan(tmp), 0.0, tmp)
- data_norm.append(tmp)
-
# for param in sub_fields:
# idx = params.index(param)
# tmp = input_data[:, idx, :, :]
- # tmp = upsample_nearest(tmp)
- # tmp = tmp[:, self.slc_y, self.slc_x]
- # if param != 'refl_substddev_ch01':
+ # tmp = np.where(np.isnan(tmp), 0.0, tmp)
+ # tmp = tmp[:, self.slc_y_m, self.slc_x_m]
+ # tmp = self.upsample(tmp)
+ # # if param != 'refl_substddev_ch01':
+ # if False:
# tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
# else:
- # tmp = np.where(np.isnan(tmp), 0, tmp)
+ # tmp = np.where(np.isnan(tmp), 0.0, tmp)
# data_norm.append(tmp)
+
+ for param in sub_fields:
+ idx = params.index(param)
+ tmp = input_data[:, idx, :, :]
+ tmp = upsample_nearest(tmp)
+ tmp = tmp[:, self.slc_y, self.slc_x]
+ if param != 'refl_substddev_ch01':
+ tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+ else:
+ tmp = np.where(np.isnan(tmp), 0, tmp)
+ data_norm.append(tmp)
# ---------------------------------------------------
tmp = input_label[:, label_idx_i, ::2, ::2]
tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0.0, tmp)
tmp = tmp[:, self.slc_y_2, self.slc_x_2]
tmp = self.upsample(tmp)
+ tmp = smooth_2d(tmp)
tmp = normalize(tmp, label_param, mean_std_dct)
data_norm.append(tmp)
# ---------
--
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