snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -268,7 +268,7 @@ class SRCNN:
         data_norm.append(tmp)
         # --------
         tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
-        if label_param != 'cloud_fraction':
+        if label_param != 'cloud_probability':
             tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
         else:
             tmp = np.where(np.isnan(tmp), 0, tmp)
@@ -280,7 +280,7 @@ class SRCNN:
         # -----------------------------------------------------
         # -----------------------------------------------------
         label = input_data[:, label_idx, slc_y, slc_x]
-        if label_param != 'cloud_fraction':
+        if label_param != 'cloud_probability':
             label = normalize(label, label_param, mean_std_dct)
         else:
             label = np.where(np.isnan(label), 0, label)
@@ -730,7 +730,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     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]
     grd_c = grd_c[slc_y_2, slc_x_2]
-    if label_param != 'cloud_fraction':
+    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)
 
@@ -739,7 +739,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
 
     nn = SRCNN()
     out_sr = nn.run_evaluate(data, ckpt_dir)
-    if label_param != 'cloud_fraction':
+    if label_param != 'cloud_probability':
         out_sr = denormalize(out_sr, label_param, mean_std_dct)
     if out_file is not None:
         np.save(out_file, out_sr)
@@ -762,7 +762,7 @@ def run_evaluate_static_2(in_file, out_file, ckpt_dir):
 
     grd_c = nda[:, 3, :, :]
     grd_c = grd_c[:, slc_y_2, slc_x_2]
-    if label_param != 'cloud_fraction':
+    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)
 
@@ -771,7 +771,7 @@ def run_evaluate_static_2(in_file, out_file, ckpt_dir):
 
     nn = SRCNN()
     out_sr = nn.run_evaluate(data, ckpt_dir)
-    if label_param != 'cloud_fraction':
+    if label_param != 'cloud_probability':
         out_sr = denormalize(out_sr, label_param, mean_std_dct)
         pass
     if out_file is not None:
@@ -780,7 +780,7 @@ def run_evaluate_static_2(in_file, out_file, ckpt_dir):
         return out_sr
 
 
-def analyze(fpath='/Users/tomrink/clavrx_snpp_viirs.A2019080.0100.001.2019080064252.uwssec_B00038315.level2.h5', param='cloud_fraction'):
+def analyze(fpath='/Users/tomrink/clavrx_snpp_viirs.A2019080.0100.001.2019080064252.uwssec_B00038315.level2.h5', param='cloud_probability'):
     h5f = h5py.File(fpath, 'r')
     grd = get_grid_values_all(h5f, param)
     grd = np.where(np.isnan(grd), 0, grd)
@@ -796,7 +796,7 @@ def analyze(fpath='/Users/tomrink/clavrx_snpp_viirs.A2019080.0100.001.2019080064
     leny, lenx = grd_lr.shape
     rnd = np.random.normal(loc=0, scale=0.001, size=grd_lr.size)
     grd_lr = grd_lr + rnd.reshape(grd_lr.shape)
-    if param == 'cloud_fraction':
+    if param == 'cloud_probability':
         grd_lr = np.where(grd_lr < 0, 0, grd_lr)
         grd_lr = np.where(grd_lr > 1, 1, grd_lr)