From 92948d16bf0ffd0b0b771f688b44b897de947c6a Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 31 Jan 2023 13:26:25 -0600
Subject: [PATCH] initial commit...
---
modules/util/viirs_surfrad.py | 17 ++++++-----------
1 file changed, 6 insertions(+), 11 deletions(-)
diff --git a/modules/util/viirs_surfrad.py b/modules/util/viirs_surfrad.py
index bdc2ff8b..a01e8c47 100644
--- a/modules/util/viirs_surfrad.py
+++ b/modules/util/viirs_surfrad.py
@@ -101,14 +101,12 @@ def process_cld_opd_(grd_k):
return grd_k
-def run_all(directory, out_directory, day_night='ANY', start=10):
+def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', start=10):
cnt = start
total_num_train_samples = 0
total_num_valid_samples = 0
num_keep_x_tiles = 8
- pattern = 'clavrx_VNP02MOD*.highres.nc.level2.nc'
- pattern = 'clavrx_*.nc'
path = directory + '**' + '/' + pattern
data_files = glob.glob(path, recursive=True)
@@ -120,7 +118,6 @@ def run_all(directory, out_directory, day_night='ANY', start=10):
f_cnt = 0
num_files = len(data_files)
-
print('Start, number of files: ', num_files)
for idx, data_f in enumerate(data_files):
@@ -138,13 +135,11 @@ def run_all(directory, out_directory, day_night='ANY', start=10):
except Exception as e:
print(e)
h5f.close()
- # label_h5f.close()
continue
print(data_f)
f_cnt += 1
h5f.close()
- # label_h5f.close()
if len(data_train_tiles) == 0:
continue
@@ -152,16 +147,16 @@ def run_all(directory, out_directory, day_night='ANY', start=10):
if (f_cnt % 5) == 0:
num_valid_samples = 0
if len(data_valid_tiles) > 0:
- # label_valid = np.stack(label_valid_tiles)
+ label_valid = np.stack(label_valid_tiles)
data_valid = np.stack(data_valid_tiles)
np.save(out_directory + 'data_valid_' + str(cnt), data_valid)
- # np.save(out_directory+'label_valid_' + str(cnt), label_valid)
+ np.save(out_directory + 'label_valid_' + str(cnt), label_valid)
num_valid_samples = data_valid.shape[0]
- # label_train = np.stack(label_train_tiles)
- # np.save(out_directory+'label_train_' + str(cnt), label_train)
+ label_train = np.stack(label_train_tiles)
data_train = np.stack(data_train_tiles)
- np.save(out_directory+'data_train_' + str(cnt), data_train)
+ np.save(out_directory + 'label_train_' + str(cnt), label_train)
+ np.save(out_directory + 'data_train_' + str(cnt), data_train)
num_train_samples = data_train.shape[0]
label_valid_tiles = []
--
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