diff --git a/modules/util/viirs_surfrad.py b/modules/util/viirs_surfrad.py
index 97a24f4753dffa1f6041978dcf5efa0f80479a86..d158de282b725f03d26afc74570b45033220153a 100644
--- a/modules/util/viirs_surfrad.py
+++ b/modules/util/viirs_surfrad.py
@@ -25,40 +25,40 @@ def keep_tile(param, param_s, tile):
grd_k = tile[k, ].copy()
if target_param == 'cloud_probability':
- grd_k = process_cld_prob_(grd_k)
+ grd_k, bflag = process_cld_prob_(grd_k)
elif target_param == 'cld_opd_dcomp':
- grd_k = process_cld_opd_(grd_k)
+ grd_k, bflag = process_cld_opd_(grd_k)
if grd_k is not None:
tile[k, ] = grd_k
- return tile
+ return tile, bflag
else:
- return None
+ return None, bflag
def process_cld_prob_(grd_k):
keep = np.invert(np.isnan(grd_k))
num_keep = np.sum(keep)
if num_keep / grd_k.size < 0.98:
- return None
+ return None, True
keep_clr = np.where(keep, grd_k < 0.20, False)
frac_keep = np.sum(keep_clr)/num_keep
- if not (0.38 < frac_keep < 0.62):
- return None
+ if not (0.40 < frac_keep < 0.60):
+ return None, False
grd_k = np.where(np.invert(keep), 0, grd_k) # Convert NaNs to 0
- return grd_k
+ return grd_k, False
def process_cld_opd_(grd_k):
keep = np.invert(np.isnan(grd_k))
num_keep = np.sum(keep)
if num_keep / grd_k.size < 0.98:
- return None
+ return None, True
grd_k = np.where(np.invert(keep), 0, grd_k)
keep = np.where(keep, np.logical_and(0.1 < grd_k, grd_k < 158.0), False)
if np.sum(keep)/num_keep < 0.50:
- return None
- return grd_k
+ return None, False
+ return grd_k, False
def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', start=10):
@@ -80,6 +80,8 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st
num_files = len(data_files)
print('Start, number of files: ', num_files)
+ total_num_not_missing = 0
+
for idx, data_f in enumerate(data_files):
# if idx % 4 == 0: # if we want to skip some files
if True:
@@ -90,9 +92,9 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st
continue
try:
- run(h5f, data_params, data_train_tiles, data_valid_tiles,
- label_params, label_train_tiles, label_valid_tiles,
- num_keep_x_tiles=num_keep_x_tiles, tile_width=64, kernel_size=7, day_night=day_night)
+ num_not_missing = run(h5f, data_params, data_train_tiles, data_valid_tiles,
+ label_params, label_train_tiles, label_valid_tiles,
+ num_keep_x_tiles=num_keep_x_tiles, tile_width=64, kernel_size=7, day_night=day_night)
except Exception as e:
print(e)
h5f.close()
@@ -129,7 +131,9 @@ def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', st
print(' num_train_samples, num_valid_samples, progress % : ', num_train_samples, num_valid_samples, int((f_cnt/num_files)*100))
total_num_train_samples += num_train_samples
total_num_valid_samples += num_valid_samples
- print('total_num_train_samples, total_num_valid_samples: ', total_num_train_samples, total_num_valid_samples)
+ total_num_not_missing += num_not_missing
+ print('total_num_train_samples, total_num_valid_samples, total_num_not_missing: ', total_num_train_samples,
+ total_num_valid_samples, total_num_not_missing)
print('--------------------------------------------------')
cnt += 1
@@ -179,12 +183,13 @@ def run(h5f, param_s, train_tiles, valid_tiles, lbl_param_s, lbl_train_tiles, lb
i_start = int(num_pixels / 2) - int((num_keep_x_tiles * tile_width) / 2)
j_start = 0
- num_keep_y_tiles = int(num_lines / tile_width) - 3
+ num_y_tiles = int(num_lines / tile_width) - 1
- num_y_valid = int(num_keep_y_tiles * 0.15) + 1
- num_y_train = num_keep_y_tiles - num_y_valid - 1
+ data_tiles = []
+ lbl_tiles = []
+ num_not_missing = 0
- for j in range(num_y_train):
+ for j in range(num_y_tiles):
j_a = j_start + j * j_skip
j_b = j_a + tile_width
@@ -199,31 +204,23 @@ def run(h5f, param_s, train_tiles, valid_tiles, lbl_param_s, lbl_train_tiles, lb
nda = data[:, j_a:j_b, i_a:i_b]
nda_lbl = label[:, j_a*2:j_b*2, i_a*2:i_b*2]
- nda_lbl = keep_tile(group_name_i+target_param, lbl_param_s, nda_lbl)
+ nda_lbl, missing_flag = keep_tile(group_name_i+target_param, lbl_param_s, nda_lbl)
+ if not missing_flag:
+ num_not_missing += 1
if nda_lbl is not None:
- train_tiles.append(nda)
- lbl_train_tiles.append(nda_lbl)
-
- j_start = num_y_train * tile_width + 2*tile_width
- for j in range(num_y_valid):
- j_a = j_start + j * j_skip
- j_b = j_a + tile_width
+ data_tiles.append(nda)
+ lbl_tiles.append(nda_lbl)
- for i in range(num_keep_x_tiles):
- i_a = i_start + i * i_skip
- i_b = i_a + tile_width
+ num_tiles = len(lbl_tiles)
+ num_valid = int(num_tiles * 0.10)
+ num_train = num_tiles - num_valid
- if day_night == 'DAY' and not is_day(solzen[j_a:j_b, i_a:i_b]):
- continue
- elif day_night == 'NIGHT' and is_day(solzen[j_a:j_b, i_a:i_b]):
- continue
-
- nda = data[:, j_a:j_b, i_a:i_b]
- nda_lbl = label[:, j_a * 2:j_b * 2, i_a * 2:i_b * 2]
- nda_lbl = keep_tile(group_name_i+target_param, lbl_param_s, nda_lbl)
-
- if nda_lbl is not None:
- valid_tiles.append(nda)
- lbl_valid_tiles.append(nda_lbl)
+ for k in range(num_train):
+ train_tiles.append(data_tiles[k])
+ lbl_train_tiles.append(lbl_tiles[k])
+ for k in range(num_valid):
+ valid_tiles.append(data_tiles[num_train + k])
+ lbl_valid_tiles.append(lbl_tiles[num_train + k])
+ return num_not_missing