From 9e3a820675dcb2657bbbc14054d2f3e437f32209 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 17 Oct 2022 15:13:38 -0500
Subject: [PATCH] normalize before upsampling (nans)
---
modules/deeplearning/srcnn_l1b_l2.py | 40 ++++++++++++++++++++++++++++
1 file changed, 40 insertions(+)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 899539f6..35bec4f6 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -718,6 +718,46 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
return out_sr
+def run_evaluate_static_2(in_file, out_file, ckpt_dir):
+ nda = np.load(in_file)
+ grd_a = nda[:, 0, :, :]
+ grd_a = grd_a[:, 3:131:2, 3:131:2]
+
+ grd_b = nda[:, 2, 3:131, 3:131]
+
+ grd_c = nda[:, 3, :, :]
+ grd_c = grd_c[:, 3:131:2, 3:131:2]
+
+ num, leny, lenx = grd_a.shape
+ x = np.arange(lenx)
+ y = np.arange(leny)
+ x_up = np.arange(0, lenx, 0.5)
+ y_up = np.arange(0, leny, 0.5)
+
+ grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
+ grd_a = resample_2d_linear(x, y, grd_a, x_up, y_up)
+
+ grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
+
+ if label_param == 'cloud_fraction':
+ grd_c = np.where(np.isnan(grd_c), 0, grd_c)
+ else:
+ grd_c = normalize(grd_c, label_param, mean_std_dct)
+ grd_c = resample_2d_linear(x, y, grd_c, x_up, y_up)
+
+ data = np.stack([grd_a, grd_b, grd_c], axis=3)
+ print(data.shape)
+
+ nn = SRCNN()
+ out_sr = nn.run_evaluate(data, ckpt_dir)
+ if label_param != 'cloud_fraction':
+ out_sr = denormalize(out_sr, label_param, mean_std_dct)
+ if out_file is not None:
+ np.save(out_file, out_sr)
+ else:
+ return out_sr
+
+
def analyze(fpath='/Users/tomrink/clavrx_snpp_viirs.A2019080.0100.001.2019080064252.uwssec_B00038315.level2.h5', param='cloud_fraction'):
h5f = h5py.File(fpath, 'r')
grd = get_grid_values_all(h5f, param)
--
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