From 2135ff255ab2f523fcdb1f8b427293031978c744 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 15 Jun 2023 12:00:26 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 11 +++++------
1 file changed, 5 insertions(+), 6 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 6d3cba99..ddfe51c6 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -261,7 +261,7 @@ class SRCNN:
for param in data_params_half:
idx = params.index(param)
tmp = input_data[:, idx, :, :]
- tmp = np.where(np.isnan(tmp), 0, tmp)
+ tmp = np.where(np.isnan(tmp), 0.0, tmp)
tmp = tmp[:, self.slc_y_m, self.slc_x_m]
tmp = self.upsample(tmp)
tmp = normalize(tmp, param, mean_std_dct)
@@ -270,13 +270,13 @@ class SRCNN:
for param in sub_fields:
idx = params.index(param)
tmp = input_data[:, idx, :, :]
- tmp = np.where(np.isnan(tmp), 0, tmp)
+ 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':
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:
@@ -292,7 +292,7 @@ class SRCNN:
# ---------------------------------------------------
tmp = input_label[:, label_idx_i, ::2, ::2]
tmp = tmp.copy()
- tmp = np.where(np.isnan(tmp), 0, tmp)
+ tmp = np.where(np.isnan(tmp), 0.0, tmp)
tmp = tmp[:, self.slc_y_2, self.slc_x_2]
tmp = self.upsample(tmp)
tmp = normalize(tmp, label_param, mean_std_dct)
@@ -300,7 +300,6 @@ class SRCNN:
# ---------
data = np.stack(data_norm, axis=3)
data = data.astype(np.float32)
- print(data.shape)
# -----------------------------------------------------
# -----------------------------------------------------
@@ -310,7 +309,7 @@ class SRCNN:
label = scale(label, label_param, mean_std_dct)
label = label[:, self.y_128, self.x_128]
- label = np.where(np.isnan(label), 0, label)
+ label = np.where(np.isnan(label), 0.0, label)
label = np.expand_dims(label, axis=3)
data = data.astype(np.float32)
--
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