snapshot...

modules/deeplearning/cloud_fraction_fcn.py

@@ -774,15 +774,11 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
 
 def run_evaluate_static(in_file, out_file, ckpt_dir):
 
-    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)
-
     h5f = h5py.File(in_file, 'r')
 
     grd_a = get_grid_values_all(h5f, 'orig/temp_11_0um')
     grd_a = np.where(np.isnan(grd_a), 0, grd_a)
     grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
-    grd_a = grd_a[2:3230, 2:1098]
 
     grd_b = get_grid_values_all(h5f, 'super/refl_0_65um')
     grd_b = np.where(np.isnan(grd_b), 0, grd_b)
@@ -791,13 +787,9 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     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[0, 2:3230, 2:1098]
-    hi = hi[0, 2:3230, 2:1098]
-    avg = avg[0, 2:3230, 2:1098]
 
     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[2:3230, 2:1098]
 
     data = np.stack([grd_a, lo, hi, avg, grd_c], axis=2)
     data = np.expand_dims(data, axis=0)