snapshot...

modules/deeplearning/cloud_opd_fcn_abi.py

@@ -859,7 +859,7 @@ class SRCNN:
 
         cldy_frac_opd = self.run_inference_(bt, refl, refl_lo, refl_hi, refl_std, cp, opd)
 
-        cldy_frac_opd_out = np.zeros((y_len, x_len), dtype=np.int8)
+        cldy_frac_opd_out = np.zeros((y_len, x_len), dtype=np.float32)
         border = int((KERNEL_SIZE - 1) / 2)
         cldy_frac_opd_out[border:y_len - border, border:x_len - border] = cldy_frac_opd[0, :, :, 0]
 
@@ -886,7 +886,7 @@ class SRCNN:
             np.save(out_file, (cldy_frac_opd_out, bt, refl, cp))
         else:
             # return [cld_frac_out, bt, refl, cp, lons, lats]
-            return cldy_frac_opd_out
+            return cldy_frac_opd_out, opd
 
     def run_inference_full_disk(self, in_file, out_file):
         gc.collect()
@@ -928,7 +928,7 @@ class SRCNN:
         t1 = time.time()
         print('   inference time: ', (t1-t0))
 
-        cldy_frac_opd_out = np.zeros((y_len, x_len), dtype=np.int8)
+        cldy_frac_opd_out = np.zeros((y_len, x_len), dtype=np.float32)
         border = int((KERNEL_SIZE - 1) / 2)
         cldy_frac_opd_out[border:h_y_len, border:x_len - border] = cldy_frac_opd_nh[0, :, :, 0]
         cldy_frac_opd_out[h_y_len:y_len - border, border:x_len - border] = cldy_frac_opd_sh[0, :, :, 0]
@@ -956,7 +956,7 @@ class SRCNN:
             np.save(out_file, (cldy_frac_opd_out, bt, refl, cp))
         else:
             # return [cld_frac_out, bt, refl, cp, lons, lats]
-            return cldy_frac_opd_out
+            return cldy_frac_opd_out, opd
 
     def run_inference_(self, bt, refl, refl_lo, refl_hi, refl_std, cp, opd):
         bt = scale(bt, 'temp_11_0um_nom', mean_std_dct)