diff --git a/modules/deeplearning/cloud_fraction_fcn.py b/modules/deeplearning/cloud_fraction_fcn.py
index a73c448262a20b26739204c9b6b28f661b8f06fb..ebdb0bbd8e465d86aeedf49b41026635ea4521ad 100644
--- a/modules/deeplearning/cloud_fraction_fcn.py
+++ b/modules/deeplearning/cloud_fraction_fcn.py
@@ -73,23 +73,13 @@ print('data_params_full: ', data_params_full)
print('label_param: ', label_param)
KERNEL_SIZE = 3 # target size: (128, 128)
-N = 1
+N_X = N_Y = 1
if KERNEL_SIZE == 3:
- # # slc_x = slice(2, N*128 + 4)
- # # slc_y = slice(2, N*128 + 4)
- # slc_x_2 = slice(1, N*128 + 6, 2)
- # slc_y_2 = slice(1, N*128 + 6, 2)
- # x_2 = np.arange(int((N*128)/2) + 3)
- # y_2 = np.arange(int((N*128)/2) + 3)
- # t = np.arange(0, int((N*128)/2) + 3, 0.5)
- # s = np.arange(0, int((N*128)/2) + 3, 0.5)
- # x_k = slice(1, N*128 + 3)
- # y_k = slice(1, N*128 + 3)
- slc_x = slice(1, int((N*128)/2) + 3)
- slc_y = slice(1, int((N*128)/2) + 3)
- x_128 = slice(4, N*128 + 4)
- y_128 = slice(4, N*128 + 4)
+ slc_x = slice(1, int((N_X*128)/2) + 3)
+ slc_y = slice(1, int((N_Y*128)/2) + 3)
+ x_128 = slice(4, N_X*128 + 4)
+ y_128 = slice(4, N_Y*128 + 4)
elif KERNEL_SIZE == 5:
slc_x = slice(3, 135)
slc_y = slice(3, 135)
@@ -127,13 +117,6 @@ def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.
return conv
-# def upsample(tmp):
-# tmp = tmp[:, slc_y_2, slc_x_2]
-# tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
-# tmp = tmp[:, y_k, x_k]
-# return tmp
-
-
def upsample_mean(grd):
bsize, ylen, xlen = grd.shape
up = np.zeros((bsize, ylen*2, xlen*2))
@@ -792,65 +775,41 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
def run_evaluate_static(in_file, out_file, ckpt_dir):
- N = 10
-
- slc_x = slice(2, N*128 + 4)
- slc_y = slice(2, N*128 + 4)
- slc_x_2 = slice(1, N*128 + 6, 2)
- slc_y_2 = slice(1, N*128 + 6, 2)
- x_2 = np.arange(int((N*128)/2) + 3)
- y_2 = np.arange(int((N*128)/2) + 3)
- t = np.arange(0, int((N*128)/2) + 3, 0.5)
- s = np.arange(0, int((N*128)/2) + 3, 0.5)
- x_k = slice(1, N*128 + 3)
- y_k = slice(1, N*128 + 3)
- x_128 = slice(3, N*128 + 3)
- y_128 = slice(3, N*128 + 3)
-
- sub_y, sub_x = (N * 128) + 10, (N * 128) + 10
- y_0, x_0, = 3232 - int(sub_y/2), 3200 - int(sub_x/2)
+
+ N_X = N_Y = 10
+
+ sub_y, sub_x = (N_Y * 128) + 10, (N_X * 128) + 10
+ y_0, x_0, = 3232 - int(sub_y/2), 1100 - int(sub_x/2)
+
+ slc_x = slice(1, int((N_X*128)/2) + 3)
+ slc_y = slice(1, int((N_Y*128)/2) + 3)
h5f = h5py.File(in_file, 'r')
- grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
- grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
- grd_a = grd_a.copy()
+ grd_a = get_grid_values_all(h5f, 'orig/temp_11_0um')
grd_a = np.where(np.isnan(grd_a), 0, grd_a)
- hr_grd_a = grd_a.copy()
- hr_grd_a = hr_grd_a[y_128, x_128]
- # Full res:
- # grd_a = grd_a[slc_y, slc_x]
- # Half res:
- grd_a = grd_a[slc_y_2, slc_x_2]
- grd_a = resample_2d_linear_one(x_2, y_2, grd_a, t, s)
- grd_a = grd_a[y_k, x_k]
+ grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
- # ------------------------------------------------------
- grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
- grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
- grd_b = grd_b.copy()
- grd_b = np.where(np.isnan(grd_b), 0, grd_b)
- hr_grd_b = grd_b.copy()
- hr_grd_b = hr_grd_b[y_128, x_128]
- grd_b = grd_b[slc_y, slc_x]
- grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
+ grd_a = grd_a[slc_y, slc_x]
- grd_c = get_grid_values_all(h5f, label_param)
- grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
- hr_grd_c = grd_c.copy()
- hr_grd_c = np.where(np.isnan(hr_grd_c), 0, grd_c)
- hr_grd_c = hr_grd_c[y_128, x_128]
- # hr_grd_c = smooth_2d_single(hr_grd_c, sigma=1.0)
+ grd_b = get_grid_values_all(h5f, 'super/refl_0_65um')
+ grd_b = np.where(np.isnan(grd_b), 0, grd_b)
+ grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
+ lo, hi, std, avg = get_min_max_std(grd_b)
+ # std = np.where(np.isnan(std), 0, std)
+ lo = normalize(lo, 'refl_0_65um_nom', mean_std_dct)
+ hi = normalize(hi, 'refl_0_65um_nom', mean_std_dct)
+ avg = normalize(avg, 'refl_0_65um_nom', mean_std_dct)
+ lo = lo[slc_y, slc_x]
+ hi = hi[slc_y, slc_x]
+ avg = avg[slc_y, slc_x]
+
+ grd_c = get_grid_values_all(h5f, 'orig/'+label_param)
grd_c = np.where(np.isnan(grd_c), 0, grd_c)
- grd_c = grd_c.copy()
- # grd_c = smooth_2d_single(grd_c, sigma=1.0)
- grd_c = grd_c[slc_y_2, slc_x_2]
- grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
- grd_c = grd_c[y_k, x_k]
- if label_param != 'cloud_probability':
- grd_c = normalize(grd_c, label_param, mean_std_dct)
-
- data = np.stack([grd_a, grd_b, grd_c], axis=2)
+ grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
+ grd_c = grd_c[slc_y, slc_x]
+
+ data = np.stack([grd_a, lo, hi, avg, grd_c], axis=2)
data = np.expand_dims(data, axis=0)
h5f.close()
@@ -858,9 +817,9 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
nn = SRCNN()
out_sr = nn.run_evaluate(data, ckpt_dir)
if out_file is not None:
- np.save(out_file, (out_sr[0, :, :, 0], hr_grd_a, hr_grd_b, hr_grd_c))
+ np.save(out_file, (out_sr[0, :, :, 0], grd_a, avg, grd_c))
else:
- return out_sr, hr_grd_a, hr_grd_b, hr_grd_c
+ return out_sr, grd_a, avg, grd_c
def analyze2(nda_m, nda_i):