diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index a3b24a18882a3bd21fca8e7339cdccfe0bc787d7..800b29751eb717953e75571fc401d71740a9299d 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -1590,61 +1590,61 @@ def tile_extract(icing_files, no_icing_files, trnfile='/home/rink/tiles_train.h5
     ice_flt_alt_s = np.array(ice_flt_alt_s)
     num_ice = icing_int_s.shape[0]
 
-    # # No icing  ------------------------------------------------------------
-    # num_no_ice = 0
-    # for fidx in range(len(no_icing_files)):
-    #     fname = no_icing_files[fidx]
-    #     f = h5py.File(fname, 'r')
-    #     h5_s_no_icing.append(f)
-    #
-    #     times = f['time']
-    #     num_obs = len(times)
-    #     lons = f['longitude']
-    #     lats = f['latitude']
-    #     flt_altitude = f['icing_altitude'][:]
-    #
-    #     for i in range(num_obs):
-    #         cld_msk = f[cld_mask_name][i, n_a:n_b, m_a:m_b]
-    #         for ds_name in params:
-    #             dat = f[ds_name][i, n_a:n_b, m_a:m_b]
-    #             if L1B_or_L2 == 'L2':
-    #                 keep = np.logical_or(cld_msk == 2, cld_msk == 3)  # cloudy
-    #                 np.where(keep, dat, np.nan)
-    #             no_icing_data_dct[ds_name].append(dat)
-    #         num_no_ice += 1
-    #         no_ice_time_s.append(times[i])
-    #         no_ice_lon_s.append(lons[i])
-    #         no_ice_lat_s.append(lats[i])
-    #         no_ice_flt_alt_s.append(flt_altitude[i])
-    #
-    #     print(fname)
-    #
-    # for ds_name in params:
-    #     lst = no_icing_data_dct[ds_name]
-    #     no_icing_data_dct[ds_name] = np.stack(lst, axis=0)
-    # no_icing_int_s = np.full(num_no_ice, -1)
-    # no_ice_time_s = np.array(no_ice_time_s)
-    # no_ice_lon_s = np.array(no_ice_lon_s)
-    # no_ice_lat_s = np.array(no_ice_lat_s)
-    # no_ice_flt_alt_s = np.array(no_ice_flt_alt_s)
-    #
-    # icing_intensity = np.concatenate([icing_int_s, no_icing_int_s])
-    # icing_times = np.concatenate([ice_time_s, no_ice_time_s])
-    # icing_lons = np.concatenate([ice_lon_s, no_ice_lon_s])
-    # icing_lats = np.concatenate([ice_lat_s, no_ice_lat_s])
-    # icing_alt = np.concatenate([ice_flt_alt_s, no_ice_flt_alt_s])
-
-    icing_intensity = icing_int_s
-    icing_times = ice_time_s
-    icing_lons = ice_lon_s
-    icing_lats = ice_lat_s
-    icing_alt = ice_flt_alt_s
+    # No icing  ------------------------------------------------------------
+    num_no_ice = 0
+    for fidx in range(len(no_icing_files)):
+        fname = no_icing_files[fidx]
+        f = h5py.File(fname, 'r')
+        h5_s_no_icing.append(f)
+
+        times = f['time']
+        num_obs = len(times)
+        lons = f['longitude']
+        lats = f['latitude']
+        flt_altitude = f['icing_altitude'][:]
+
+        for i in range(num_obs):
+            cld_msk = f[cld_mask_name][i, n_a:n_b, m_a:m_b]
+            for ds_name in params:
+                dat = f[ds_name][i, n_a:n_b, m_a:m_b]
+                if L1B_or_L2 == 'L2':
+                    keep = np.logical_or(cld_msk == 2, cld_msk == 3)  # cloudy
+                    np.where(keep, dat, np.nan)
+                no_icing_data_dct[ds_name].append(dat)
+            num_no_ice += 1
+            no_ice_time_s.append(times[i])
+            no_ice_lon_s.append(lons[i])
+            no_ice_lat_s.append(lats[i])
+            no_ice_flt_alt_s.append(flt_altitude[i])
+
+        print(fname)
+
+    for ds_name in params:
+        lst = no_icing_data_dct[ds_name]
+        no_icing_data_dct[ds_name] = np.stack(lst, axis=0)
+    no_icing_int_s = np.full(num_no_ice, -1)
+    no_ice_time_s = np.array(no_ice_time_s)
+    no_ice_lon_s = np.array(no_ice_lon_s)
+    no_ice_lat_s = np.array(no_ice_lat_s)
+    no_ice_flt_alt_s = np.array(no_ice_flt_alt_s)
+
+    icing_intensity = np.concatenate([icing_int_s, no_icing_int_s])
+    icing_times = np.concatenate([ice_time_s, no_ice_time_s])
+    icing_lons = np.concatenate([ice_lon_s, no_ice_lon_s])
+    icing_lats = np.concatenate([ice_lat_s, no_ice_lat_s])
+    icing_alt = np.concatenate([ice_flt_alt_s, no_ice_flt_alt_s])
+
+    # icing_intensity = icing_int_s
+    # icing_times = ice_time_s
+    # icing_lons = ice_lon_s
+    # icing_lats = ice_lat_s
+    # icing_alt = ice_flt_alt_s
 
     data_dct = {}
-    # for ds_name in params:
-    #     data_dct[ds_name] = np.concatenate([icing_data_dct[ds_name], no_icing_data_dct[ds_name]])
     for ds_name in params:
-        data_dct[ds_name] = icing_data_dct[ds_name]
+        data_dct[ds_name] = np.concatenate([icing_data_dct[ds_name], no_icing_data_dct[ds_name]])
+    # for ds_name in params:
+    #     data_dct[ds_name] = icing_data_dct[ds_name]
 
     # do sort -------------------------------------
     ds_indexes = np.argsort(icing_times)
@@ -1789,8 +1789,8 @@ def tile_extract(icing_files, no_icing_files, trnfile='/home/rink/tiles_train.h5
     for h5f in h5_s_icing:
         h5f.close()
 
-    # for h5f in h5_s_no_icing:
-    #     h5f.close()
+    for h5f in h5_s_no_icing:
+        h5f.close()
 
 
 def write_file(outfile, params, param_types, data_dct, icing_intensity, icing_times, icing_lons, icing_lats, icing_alt):