More time-efficient order in timeslice file creation

src/troute-network/troute/nhd_io.py

@@ -1085,6 +1085,63 @@ def _read_timeslice_file(f):
         observation_quality = pd.DataFrame()
     return timeslice_observations, observation_quality
 
+
+def _read_timeslice_file_numpy(f):
+
+    with netCDF4.Dataset(
+        filename = f,
+        mode = 'r',
+        format = "NETCDF4"
+    ) as ds:
+
+        #discharge = ds.variables['discharge'][:].data
+        discharge = ds.variables['discharge'][:].filled(fill_value = np.nan)
+        stns      = ds.variables['stationId'][:].filled(fill_value = np.nan)
+        t         = ds.variables['time'][:].filled(fill_value = np.nan)
+        qual      = ds.variables['discharge_quality'][:].filled(fill_value = np.nan)
+
+    return discharge, stns, t, qual
+
+
+def _read_timeslice_file(f):
+
+    with netCDF4.Dataset(
+        filename = f,
+        mode = 'r',
+        format = "NETCDF4"
+    ) as ds:
+
+        discharge = ds.variables['discharge'][:].filled(fill_value = np.nan)
+        stns      = ds.variables['stationId'][:].filled(fill_value = np.nan)
+        t         = ds.variables['time'][:].filled(fill_value = np.nan)
+        qual      = ds.variables['discharge_quality'][:].filled(fill_value = np.nan)
+
+    if discharge.size != 0 and stns.size != 0 and t.size != 0:        
+        stationId = np.apply_along_axis(''.join, 1, stns.astype(str))
+        time_str = np.apply_along_axis(''.join, 1, t.astype(str))
+        stationId = np.char.strip(stationId)
+
+        timeslice_observations = (pd.DataFrame({
+                                    'stationId' : stationId,
+                                    'datetime'  : time_str,
+                                    'discharge' : discharge
+                                }).
+                                set_index(['stationId', 'datetime']).
+                                unstack(1, fill_value = np.nan)['discharge'])
+
+        observation_quality = (pd.DataFrame({
+                                    'stationId' : stationId,
+                                    'datetime'  : time_str,
+                                    'quality'   : qual/100
+                                }).
+                                set_index(['stationId', 'datetime']).
+                                unstack(1, fill_value = np.nan)['quality'])
+    else:
+        timeslice_observations = pd.DataFrame()
+        observation_quality = pd.DataFrame()
+    return timeslice_observations, observation_quality
+
+
 def _interpolate_one(df, interpolation_limit, frequency):
 
     interp_out = (df.resample('min').
@@ -1098,6 +1155,7 @@ def _interpolate_one(df, interpolation_limit, frequency):
                        )
     return interp_out
 
+
 def get_obs_from_timeslices(
     crosswalk_df,
     crosswalk_gage_field,
@@ -1109,6 +1167,7 @@ def get_obs_from_timeslices(
     t0,
     cpu_pool, 
 ):
+
     """
     Read observations from TimeSlice files, interpolate available observations
     and organize into a Pandas DataFrame
@@ -1153,31 +1212,44 @@ def get_obs_from_timeslices(
     # - Parallelize this reading for speedup using netCDF4
     # - Generecize the function to read USACE or USGS data
     # - only return gages that are in the model domain (consider mask application)
-        
+
     # open TimeSlce files, organize data into dataframes
     with Parallel(n_jobs=cpu_pool) as parallel:
         jobs = []
         for f in timeslice_files:
-            jobs.append(delayed(_read_timeslice_file)(f))
-        timeslice_dataframes = parallel(jobs)
+            jobs.append(delayed(_read_timeslice_file_numpy)(f))
+        result = parallel(jobs)
+        discharge = [out[0] for out in result]
+        stns = [out[1] for out in result]
+        t = [out[2] for out in result]
+        qual = [out[3] for out in result]
+
+    if len(discharge) != 0 and len(stns) != 0 and len(t) != 0:     
+
+        stationIds = [np.apply_along_axis(''.join, 1, stn.astype(str)) for stn in stns]
+        stationIds = [np.char.strip(stn) for stn in stationIds]
+        time_strs = [np.apply_along_axis(''.join, 1, t1.astype(str)) for t1 in t]
+        timeAxis2 = [time_strs[i][0] for i in range(len(time_strs))]
+        timeAxis = [datetime.strptime(t,"%Y-%m-%d_%H:%M:%S") for t in timeAxis2]
+
+        dfList = [None]*len(timeAxis)
+        qList = [None]*len(timeAxis)
+
+        for i in range(len(stationIds)):
+            dfList[i]=(pd.DataFrame({'stationId': stationIds[i],'discharge':discharge[i]}).set_index(['stationId']))
+            qList[i]=(pd.DataFrame({'stationId': stationIds[i],'quality':qual[i]/100}).set_index(['stationId']))
+
+        # concatenate dataframes
+        timeslice_obs_df  = pd.concat(dfList, axis = 1)
+        timeslice_qual_df = pd.concat(qList, axis = 1)   
+
+        timeslice_obs_df.columns = timeAxis
+        timeslice_qual_df.columns = timeAxis
+
+    else:
+        timeslice_obs_df = pd.DataFrame()
+        timeslice_qual_df = pd.DataFrame()
 
-    all_empty = all(df.empty for tuple in timeslice_dataframes for df in tuple)
-    if all_empty:
-        LOG.debug(f'{crosswalk_gage_field} DataFrames is empty, check timeslice files.')
-        return pd.DataFrame()
-
-    # create lists of observations and obs quality dataframes returned 
-    # from _read_timeslice_file
-    timeslice_obs_frames = []
-    timeslice_qual_frames = []
-    for d in timeslice_dataframes:
-        timeslice_obs_frames.append(d[0])  # TimeSlice gage observation
-        timeslice_qual_frames.append(d[1]) # TimeSlice observation qual
-
-    # concatenate dataframes
-    timeslice_obs_df  = pd.concat(timeslice_obs_frames, axis = 1)
-    timeslice_qual_df = pd.concat(timeslice_qual_frames, axis = 1)   
-
     # Link <> gage crosswalk data
     df = crosswalk_df.reset_index()
     df[crosswalk_gage_field] = np.asarray(df[crosswalk_gage_field]).astype('<U15')
@@ -1209,10 +1281,11 @@ def get_obs_from_timeslices(
 
     # ---- Interpolate USGS observations to the input frequency (frequency_secs)
     observation_df_T = observation_df.transpose()             # transpose, making time the index
+
     observation_df_T.index = pd.to_datetime(
         observation_df_T.index, format = "%Y-%m-%d_%H:%M:%S"  # index variable as type datetime
     )
-    
+
     # specify resampling frequency 
     frequency = str(int(frequency_secs/60))+"min"    
 

test/LowerColorado_TX/test_AnA.yaml

@@ -31,7 +31,7 @@ compute_parameters:
     compute_kernel         : V02-structured
     assume_short_ts        : True
     subnetwork_target_size : 10000
-    cpu_pool               : 36
+    cpu_pool               : 1
     restart_parameters:
         #----------
         start_datetime: 2021-08-23_13:00