From f4dbbaefe56cb9a4d8473089da0fd37345879223 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 25 Sep 2023 11:51:51 -0500
Subject: [PATCH] snapshot...
---
.../deeplearning/cloud_fraction_fcn_abi.py | 38 +++++++++++++------
1 file changed, 26 insertions(+), 12 deletions(-)
diff --git a/modules/deeplearning/cloud_fraction_fcn_abi.py b/modules/deeplearning/cloud_fraction_fcn_abi.py
index 9938f546..71b9b6d5 100644
--- a/modules/deeplearning/cloud_fraction_fcn_abi.py
+++ b/modules/deeplearning/cloud_fraction_fcn_abi.py
@@ -3,7 +3,7 @@ import tensorflow as tf
from util.plot_cm import confusion_matrix_values
from util.augment import augment_image
from util.setup_cloud_fraction import logdir, modeldir, now, ancillary_path
-from util.util import EarlyStop, normalize, denormalize, get_grid_values_all, make_tf_callable_generator
+from util.util import EarlyStop, normalize, denormalize, scale2, get_grid_values_all, make_tf_callable_generator
import glob
import os, datetime
import numpy as np
@@ -289,8 +289,7 @@ class SRCNN:
self.test_data_files = None
self.test_label_files = None
- # self.n_chans = len(data_params_half) + len(data_params_full) + 1
- self.n_chans = 6
+ self.n_chans = 5
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
@@ -327,15 +326,30 @@ class SRCNN:
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 = tmp[:, slc_y, 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)
+ # for param in sub_fields:
+ # idx = params.index(param)
+ # tmp = input_data[:, idx, :, :]
+ # tmp = tmp[:, slc_y, 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)
+
+ rlo = input_data[:, params.index('refl_submin_ch01'), :, :]
+ rlo = rlo[:, slc_y, slc_x]
+ rlo = normalize(rlo, 'refl_0_65um_nom', mean_std_dct)
+
+ rhi = input_data[:, params.index('refl_submax_ch01'), :, :]
+ rhi = rhi[:, slc_y, slc_x]
+ rhi = normalize(rhi, 'refl_0_65um_nom', mean_std_dct)
+ refl_rng = rhi - rlo
+ data_norm.append(refl_rng)
+
+ rstd = input_data[:, params.index('refl_substddev_ch01'), :, :]
+ rstd = rstd[:, slc_y, slc_x]
+ rstd = scale2(rstd, 0.0, 20.0)
+ data_norm.append(rstd)
tmp = input_label[:, label_idx_i, :, :]
tmp = get_grid_cell_mean(tmp)
--
GitLab