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Commit 71e0152d authored by tomrink's avatar tomrink
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...@@ -112,6 +112,11 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st ...@@ -112,6 +112,11 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st
total_num_not_missing = 0 total_num_not_missing = 0
hist_accum_valid_i = np.zeros(20, dtype=np.int64)
hist_accum_valid_m = np.zeros(20, dtype=np.int64)
hist_accum_train_i = np.zeros(20, dtype=np.int64)
hist_accum_train_m = np.zeros(20, dtype=np.int64)
for idx, data_f in enumerate(data_files): for idx, data_f in enumerate(data_files):
# if idx % 4 == 0: # if we want to skip some files # if idx % 4 == 0: # if we want to skip some files
if True: if True:
...@@ -177,6 +182,10 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st ...@@ -177,6 +182,10 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st
np.save(out_directory + 'valid_mres_' + str(cnt), valid_m) np.save(out_directory + 'valid_mres_' + str(cnt), valid_m)
np.save(out_directory + 'valid_ires_' + str(cnt), valid_i) np.save(out_directory + 'valid_ires_' + str(cnt), valid_i)
num_valid_samples = valid_m.shape[0] num_valid_samples = valid_m.shape[0]
h, b = np.histogram(valid_i.flatten(), bins=20, range=[0.0, 160.0])
hist_accum_valid_i += h
h, b = np.histogram(valid_m.flatten(), bins=20, range=[0.0, 160.0])
hist_accum_valid_m += h
num_train_samples = 0 num_train_samples = 0
if len(train_tiles_m) > 0: if len(train_tiles_m) > 0:
...@@ -185,6 +194,10 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st ...@@ -185,6 +194,10 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st
np.save(out_directory + 'train_ires_' + str(cnt), train_i) np.save(out_directory + 'train_ires_' + str(cnt), train_i)
np.save(out_directory + 'train_mres_' + str(cnt), train_m) np.save(out_directory + 'train_mres_' + str(cnt), train_m)
num_train_samples = train_m.shape[0] num_train_samples = train_m.shape[0]
h, b = np.histogram(train_i.flatten(), bins=20, range=[0.0, 160.0])
hist_accum_train_i += h
h, b = np.histogram(train_m.flatten(), bins=20, range=[0.0, 160.0])
hist_accum_train_m += h
print(' num_train_samples, num_valid_samples, progress % : ', num_train_samples, num_valid_samples, print(' num_train_samples, num_valid_samples, progress % : ', num_train_samples, num_valid_samples,
int((f_cnt / num_files) * 100)) int((f_cnt / num_files) * 100))
...@@ -195,6 +208,11 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st ...@@ -195,6 +208,11 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st
print('--------------------------------------------------') print('--------------------------------------------------')
print('** total_num_train_samples, total_num_valid_samples: ', total_num_train_samples, total_num_valid_samples) print('** total_num_train_samples, total_num_valid_samples: ', total_num_train_samples, total_num_valid_samples)
print('--------------------------------------------------')
print(hist_accum_train_i)
print(hist_accum_train_m)
print(hist_accum_valid_i)
print(hist_accum_valid_m)
# tile_width: Must be even! # tile_width: Must be even!
......
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