diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 287aafa999daf2a3bbec36d38c3a05ef6f1bc21c..34b77bed8e9f42c3961700e277bc9d3c9d869f63 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -724,36 +724,44 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
def run_evaluate_static(in_file, out_file, ckpt_dir):
N = 8
- sub_y, sub_x = (N * 128) + 6, (N * 128) + 6
- y_0, x_0, = 2432 - int(sub_y/2), 2432 - int(sub_x/2)
- x_130 = slice(2, (N * 128) + 4)
- y_130 = slice(2, (N * 128) + 4)
+ 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)
- slc_y_2, slc_x_2 = slice(1, 128*N + 6, 2), slice(1, 128*N + 6, 2)
- y_2, x_2 = np.arange((128*N)/2 + 3), np.arange((128*N)/2 + 3)
- t, s = np.arange(1, (128*N)/2 + 2, 0.5), np.arange(1, (128*N)/2 + 2, 0.5)
+ sub_y, sub_x = (N * 128) + 10, (N * 128) + 10
+ y_0, x_0, = 2432 - int(sub_y/2), 2432 - int(sub_x/2)
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[y_130, x_130]
- bt = grd_a
- 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[y_130, x_130]
- refl = grd_b
- grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
+ # 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[y_130, x_130]
+ # bt = grd_a
+ # 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[y_130, x_130]
+ # refl = grd_b
+ # grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
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 = hr_grd_c[y_128, x_128]
grd_c = grd_c[slc_y_2, slc_x_2]
if label_param != 'cloud_probability':
grd_c = normalize(grd_c, label_param, mean_std_dct)
grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
- print(grd_a.shape, grd_b.shape, grd_c.shape)
+ grd_c = grd_c[y_k, x_k]
# data = np.stack([grd_a, grd_b, grd_c], axis=2)
data = np.stack([grd_c], axis=2)
@@ -766,40 +774,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
if out_file is not None:
np.save(out_file, [out_sr, hr_grd_c])
else:
- return out_sr, bt, refl
-
-
-def run_evaluate_static_2(in_file, out_file, ckpt_dir):
- nda = np.load(in_file)
-
- grd_a = nda[:, 0, :, :]
- grd_a = grd_a[:, slc_y_2, slc_x_2]
- grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
- grd_a = resample_2d_linear(x_2, y_2, grd_a, t, s)
-
- grd_b = nda[:, 2, :, :]
- grd_b = grd_b[:, slc_y_2, slc_x_2]
- grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
- grd_b = resample_2d_linear(x_2, y_2, grd_b, t, s)
-
- grd_c = nda[:, 3, :, :]
- grd_c = grd_c[:, slc_y_2, slc_x_2]
- if label_param != 'cloud_probability':
- grd_c = normalize(grd_c, label_param, mean_std_dct)
- grd_c = resample_2d_linear(x_2, y_2, grd_c, t, s)
-
- 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_probability':
- out_sr = denormalize(out_sr, label_param, mean_std_dct)
- pass
- if out_file is not None:
- np.save(out_file, out_sr)
- else:
- return out_sr
+ return out_sr, None, None
def analyze(fpath='/Users/tomrink/clavrx_snpp_viirs.A2019080.0100.001.2019080064252.uwssec_B00038315.level2.h5', param='cloud_probability'):