snapshot

modules/deeplearning/amv_raob.py

@@ -25,7 +25,7 @@ gfs_date_format = '%y%m%d'
 
 h4_to_h5_path = home_dir + '/h4toh5convert'
 
-data_dir = '/data1/rink'
+data_dir = '/home/rink/data'
 converted_file_dir = data_dir + '/gfs_h5'
 CACHE_GFS = True
 
@@ -549,6 +549,7 @@ def get_bounding_gfs_files(timestamp):
     farr = np.array(filelist)
     farr = farr[sidxs]
     ftimes = tarr[sidxs]
+    idxs = np.arange(len(filelist))
 
     above = ftimes >= timestamp
     if not above.any():
@@ -559,16 +560,18 @@ def get_bounding_gfs_files(timestamp):
     if not below.any():
         return None, None, None, None
     tL = ftimes[below].max()
-
-    iL = np.searchsorted(ftimes, tL, 'left')
+    iL = idxs[below].max()
     iR = iL + 1
 
     fList = farr.tolist()
 
+    if timestamp == ftimes[iL]:
+        return fList[iL], ftimes[iL], None, None
+    else:
         return fList[iL], ftimes[iL], fList[iR], ftimes[iR]
 
 
-def get_profile(xr_dataset, fld_name, lons, lats, lon360=True):
+def get_profile(xr_dataset, fld_name, lons, lats, lon360=True, do_norm=False):
     if lon360:
         lons = np.where(lons < 0, lons + 360, lons) # convert -180,180 to 0,360
 
@@ -583,6 +586,9 @@ def get_profile(xr_dataset, fld_name, lons, lats, lon360=True):
     dim1 = xr.DataArray(lats, dims='k')
 
     intrp_fld = fld.interp(fakeDim1=dim1, fakeDim2=dim2, fakeDim0=plevs, method='linear')
+    intrp_fld = intrp_fld.values
+    if do_norm:
+        intrp_fld = normalize(intrp_fld, fld_name)
 
     return intrp_fld
 

modules/deeplearning/cloudheight.py

@@ -8,7 +8,7 @@ import xarray as xr
 import pickle
 
 from deeplearning.amv_raob import get_bounding_gfs_files, convert_file, get_images, get_interpolated_profile, \
-    split_matchup, shuffle_dict, get_interpolated_scalar, get_num_samples
+    split_matchup, shuffle_dict, get_interpolated_scalar, get_num_samples, get_time_tuple_utc, get_profile
 
 LOG_DEVICE_PLACEMENT = False
 
@@ -273,22 +273,30 @@ class CloudHeightNN:
                     label.append(tup[2])
                     sfc.append(tup[3])
                     continue
-            print('not found in cache, processing key: ', key)
 
             obs = self.matchup_dict.get(key)
             if obs is None:
                 print('no entry for: ', key)
             timestamp = obs[0][0]
+            print('not found in cache, processing key: ', key, get_time_tuple_utc(timestamp)[0])
 
             gfs_0, time_0, gfs_1, time_1 = get_bounding_gfs_files(timestamp)
-            if (gfs_0 is None) or (gfs_1 is None):
-                print('no GFS for: ', timestamp)
+            if (gfs_0 is None) and (gfs_1 is None):
+                print('no GFS for: ', get_time_tuple_utc(timestamp)[0])
                 continue
+            try:
                 gfs_0 = convert_file(gfs_0)
+                if gfs_1 is not None:
                     gfs_1 = convert_file(gfs_1)
+            except Exception as exc:
+                print(get_time_tuple_utc(timestamp)[0])
+                print(exc)
+                continue
 
+            ds_1 = None
             try:
                 ds_0 = xr.open_dataset(gfs_0)
+                if gfs_1 is not None:
                     ds_1 = xr.open_dataset(gfs_1)
             except Exception as exc:
                 print(exc)
@@ -334,20 +342,26 @@ class CloudHeightNN:
             lons = lons[common_idxs]
             lats = lats[common_idxs]
 
+            if ds_1 is not None:
                 ndb = get_interpolated_profile(ds_0, ds_1, time_0, time_1, 'temperature', timestamp, lons, lats, do_norm=True)
+            else:
+                ndb = get_profile(ds_0, 'temperature', lons, lats, do_norm=True)
             if ndb is None:
                 continue
 
+            if ds_1 is not None:
                 ndf = get_interpolated_profile(ds_0, ds_1, time_0, time_1, 'rh', timestamp, lons, lats, do_norm=False)
+            else:
+                ndf = get_profile(ds_0, 'rh', lons, lats, do_norm=False)
             if ndf is None:
                 continue
             ndf /= 100.0
             ndb = np.stack((ndb, ndf), axis=2)
 
-            ndd = get_interpolated_scalar(ds_0, ds_1, time_0, time_1, 'MSL pressure', timestamp, lons, lats, do_norm=False)
-            ndd /= 1000.0
+            #ndd = get_interpolated_scalar(ds_0, ds_1, time_0, time_1, 'MSL pressure', timestamp, lons, lats, do_norm=False)
+            #ndd /= 1000.0
 
-            nde = get_interpolated_scalar(ds_0, ds_1, time_0, time_1, 'surface temperature', timestamp, lons, lats, do_norm=True)
+            #nde = get_interpolated_scalar(ds_0, ds_1, time_0, time_1, 'surface temperature', timestamp, lons, lats, do_norm=True)
 
             # label/truth
             # Level of best fit (LBF)
@@ -369,7 +383,8 @@ class CloudHeightNN:
             images.append(nda)
             vprof.append(ndb)
             label.append(ndc)
-            nds = np.stack([ndd, nde], axis=1)
+            # nds = np.stack([ndd, nde], axis=1)
+            nds = np.zeros((len(lons), 2))
             sfc.append(nds)
 
             if not CACHE_GFS:
@@ -379,6 +394,7 @@ class CloudHeightNN:
                 self.in_mem_data_cache[key] = (nda, ndb, ndc, nds)
 
             ds_0.close()
+            if ds_1 is not None:
                ds_1.close()
 
         images = np.concatenate(images)