fixed WMCN_tidescourse.py example script

hatyan/analysis_prediction.py

@@ -241,6 +241,7 @@ def analysis(ts, const_list, hatyan_settings=None, **kwargs): # nodalfactors=Tru
     metadata['fu_alltimes'] = hatyan_settings.fu_alltimes
     metadata['source'] = hatyan_settings.source
     COMP_mean_pd = metadata_add_to_obj(COMP_mean_pd, metadata)
+    COMP_all_pd = metadata_add_to_obj(COMP_all_pd, metadata)
 
     if hatyan_settings.return_allperiods:
         return COMP_mean_pd, COMP_all_pd

hatyan/timeseries.py

@@ -782,14 +782,16 @@ def write_tsnetcdf(ts, station, vertref, filename, ts_ext=None, tzone_hr=1, nosi
 
 def write_tsdia(ts, filename, headerformat='dia'):
     """
-    Writes the timeseries to an equidistant dia file. This is only supported 
+    Writes the timeseries to an equidistant dia file or the extremes to 
+    a non-equidistant dia file. This is only supported 
     for timeseries with a UTC+1 timestamp, since DONAR (and therefore dia)
     data is always in UTC+1 (MET/CET), also during summertime periods.
 
     Parameters
     ----------
     ts : pandas.DataFrame
-        The DataFrame should contain a 'values' column and a pd.DatetimeIndex as index, it contains the timeseries.
+        The DataFrame should contain a 'values' column and a pd.DatetimeIndex as index, it contains the timeseries. 
+        In case of extremes, the DataFrame should also contain a 'HWLW_code' column.
     station : TYPE
         DESCRIPTION.
     vertref : TYPE
@@ -807,7 +809,14 @@ def write_tsdia(ts, filename, headerformat='dia'):
     None.
 
     """
-    if "HWLWcode"in ts.columns:
+    if "HWLWcode" in ts.columns:
+        write_tsdia_HWLW(ts_ext=ts, filename=filename, headerformat=headerformat)
+    else:
+        write_tsdia_ts(ts=ts, filename=filename, headerformat=headerformat)
+
+
+def write_tsdia_ts(ts, filename, headerformat='dia'):
+    if "HWLWcode" in ts.columns:
         raise TypeError("a timeseries with extremes (HWLW) was passed to write_tsdia, use `write_tsdia_HWLW()` instead")
     metadata = metadata_from_obj(ts)
     waarnemingssoort = wns_from_metadata(metadata)
@@ -894,30 +903,6 @@ def write_tsdia(ts, filename, headerformat='dia'):
 
 
 def write_tsdia_HWLW(ts_ext, filename, headerformat='dia'):
-    """
-    writes the extremes timeseries to a non-equidistant dia file
-
-    Parameters
-    ----------
-    ts_ext : pandas.DataFrame
-        The DataFrame should contain a 'values' and 'HWLW_code' column and a pd.DatetimeIndex as index, it contains the times, values and codes of the timeseries that are extremes.
-    station : TYPE
-        DESCRIPTION.
-    vertref : TYPE
-        DESCRIPTION.
-    filename : TYPE
-        DESCRIPTION.
-
-    Raises
-    ------
-    Exception
-        DESCRIPTION.
-
-    Returns
-    -------
-    None.
-
-    """
 
     metadata = metadata_from_obj(ts_ext)
     waarnemingssoort = wns_from_metadata(metadata)

tests/examples/WMCN_tidescourse.py

@@ -32,6 +32,9 @@
         #component groups
         file_data_comp0 = os.path.join(dir_testdata,'predictie2019','%s_ana.txt'%(current_station))
         COMP_merged = hatyan.read_components(filename=file_data_comp0)
+        # overwrite metadata to avoid AssertionError from prediction
+        COMP_merged.attrs["xfac"] = False
+        COMP_merged.attrs["fu_alltimes"] = True
 
         #prediction and validation
         bool_end1 = COMP_merged.index.astype(str).str.endswith('1')
@@ -298,7 +301,7 @@
     colors = plt.cm.jet(np.linspace(0,1,n_colors))
     for i_stat, current_station in enumerate(selected_stations):
         comp_frommeasurements_avg_group = hatyan.read_components(filename=os.path.join(dir_testdata,'predictie2019','%s_ana.txt'%(current_station)))
-        ts_prediction = hatyan.prediction(comp=comp_frommeasurements_avg_group, times=times_pred)
+        ts_prediction = hatyan.prediction(comp=comp_frommeasurements_avg_group, times=times_pred, xfac=True, fu_alltimes=False)
         vals_real = ts_prediction['values']
         times_real = ts_prediction.index
         ax1.plot(times_real, vals_real, label=current_station, color=colors[i_stat])
@@ -363,9 +366,3 @@
 
 
 
-
-
-
-
-
-

tests/examples/numbering_extremes.py

@@ -153,7 +153,7 @@
             pdrow['RDy'] = RDy/1000 #from m to km
         stats = pd.concat([stats,pdrow])
 
-        #hatyan.write_tsdia_HWLW(ts_ext=ts_ext_prediction, filename='prediction_HWLW_%im_%s.dia'%(times_step_pred, current_station))
+        #hatyan.write_tsdia(ts=ts_ext_prediction, filename='prediction_HWLW_%im_%s.dia'%(times_step_pred, current_station))
         #fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_ext=ts_ext_prediction, ts_ext_validation=ts_ext_validation)
         #fig.savefig('prediction_%im_%s_HWLW'%(times_step_pred, current_station))
         #fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_ext=ts_ext_prediction)

tests/examples/predictie_2019_19Ycomp4Ydia.py

@@ -119,7 +119,7 @@
         ts_ext_validation = None
     hatyan.write_tsdia(ts=ts_prediction, filename='prediction_%im_%s.dia'%(times_pred.step,current_station))
     ts_ext_prediction = hatyan.calc_HWLW(ts=ts_prediction)
-    hatyan.write_tsdia_HWLW(ts_ext=ts_ext_prediction, filename='prediction_HWLW_%im_%s.dia'%(times_pred.step, current_station))
+    hatyan.write_tsdia(ts=ts_ext_prediction, filename='prediction_HWLW_%im_%s.dia'%(times_pred.step, current_station))
     fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_ext=ts_ext_prediction, ts_ext_validation=ts_ext_validation)
     fig.savefig('prediction_%im_%s_HWLW'%(times_pred.step, current_station))
     fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_validation=ts_validation)

tests/examples/predictie_2019_19Ycomp4Ydia_VLISSGN.py

@@ -71,7 +71,7 @@
     ts_ext_validation = hatyan.readts_dia(filename=file_data_predvaliHWLW, station=current_station)
     hatyan.write_tsdia(ts=ts_prediction, filename='prediction_%im_%s.dia'%(times_pred.step,current_station))
     ts_ext_prediction1min = hatyan.calc_HWLW(ts=ts_prediction1min)
-    hatyan.write_tsdia_HWLW(ts_ext=ts_ext_prediction1min, filename='prediction_HWLW_%im_%s.dia'%(times_pred.step, current_station))
+    hatyan.write_tsdia(ts=ts_ext_prediction1min, filename='prediction_HWLW_%im_%s.dia'%(times_pred.step, current_station))
     fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_ext=ts_ext_prediction1min, ts_ext_validation=ts_ext_validation)
     fig.savefig('prediction_%im_%s_HWLW'%(times_pred.step, current_station))
     fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_validation=ts_validation)

tests/examples/predictie_2019_allfromdia.py

@@ -104,7 +104,7 @@
     #ts_ext_validation = hatyan.readts_dia(filename=file_data_predvaliHWLW, station=current_station)
     hatyan.write_tsdia(ts=ts_prediction, filename='prediction_%im_%s.dia'%(times_pred.step,current_station))
     #ts_ext_prediction = hatyan.calc_HWLW(ts=ts_prediction)
-    #hatyan.write_tsdia_HWLW(ts_ext=ts_ext_prediction, filename='prediction_HWLW_%im_%s.dia'%(times_pred.step, current_station))
+    #hatyan.write_tsdia(ts=ts_ext_prediction, filename='prediction_HWLW_%im_%s.dia'%(times_pred.step, current_station))
     #fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_ext=ts_ext_prediction, ts_ext_validation=ts_ext_validation)
     #fig.savefig('prediction_%im_%s_HWLW'%(times_pred.step, current_station))
     fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_validation=ts_validation)

tests/examples/predictie_2019_frommergedcomp.py

@@ -67,7 +67,7 @@
     #ts_ext_validation = hatyan.readts_dia(filename=file_data_predvaliHWLW, station=current_station)
     hatyan.write_tsdia(ts=ts_prediction, filename='prediction_%im_%s.dia'%(times_pred.step,current_station))
     #ts_ext_prediction = hatyan.calc_HWLW(ts=ts_prediction)
-    #hatyan.write_tsdia_HWLW(ts_ext=ts_ext_prediction, filename='prediction_HWLW_%im_%s.dia'%(times_pred.step, current_station))
+    #hatyan.write_tsdia(ts=ts_ext_prediction, filename='prediction_HWLW_%im_%s.dia'%(times_pred.step, current_station))
     #fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_ext=ts_ext_prediction, ts_ext_validation=ts_ext_validation)
     #fig.savefig('prediction_%im_%s_HWLW'%(times_pred.step, current_station))
     fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_validation=ts_validation)

tests/examples/predictie_2019_frommergedcomp_WSdwarsstroming.py

@@ -57,8 +57,8 @@
     ax.grid()
     ax.set_xlabel('Tijd')
     ax.set_ylabel('astro verval %d [m] (TERNZN-HANSWT)'%yr)
-    #hatyan.write_tsdia_HWLW(ts_ext=ts_ext_prediction_main, station=current_station, vertref='NAP', filename='prediction_HWLW_%im_%s_main.dia'%(times_step_pred, current_station))
-    #hatyan.write_tsdia_HWLW(ts_ext=ts_ext_prediction_clean, station=current_station, vertref='NAP', filename='prediction_HWLW_%im_%s_agger345.dia'%(times_step_pred, current_station))
+    #hatyan.write_tsdia(ts=ts_ext_prediction_main, station=current_station, vertref='NAP', filename='prediction_HWLW_%im_%s_main.dia'%(times_step_pred, current_station))
+    #hatyan.write_tsdia(ts=ts_ext_prediction_clean, station=current_station, vertref='NAP', filename='prediction_HWLW_%im_%s_agger345.dia'%(times_step_pred, current_station))
 fig.tight_layout()
 fig.savefig('prediction_WSdwarsstroming')
 

tests/test_timeseries.py

@@ -40,7 +40,7 @@ def test_readwrite_tsdia_ext_rounding():
     for diff in [0, 0.004, -0.004]:
         ts_pred_rounddiff = hatyan.readts_dia(file_pred)
         ts_pred_rounddiff['values'] = ts_pred['values'] + diff
-        hatyan.write_tsdia_HWLW(ts_ext=ts_pred_rounddiff, filename=file_new)
+        hatyan.write_tsdia(ts=ts_pred_rounddiff, filename=file_new)
         ts_new = hatyan.readts_dia(file_new)
 
         assert np.allclose(ts_pred, ts_new)
@@ -211,8 +211,8 @@ def test_readwrite_diawia():
 
         #write to files
         if 'HWLWcode' in ts_dia.columns:
-            hatyan.write_tsdia_HWLW(ts_ext=ts_dia, filename=file_dia_out)
-            hatyan.write_tsdia_HWLW(ts_ext=ts_wia, filename=file_wia_out, headerformat='wia')
+            hatyan.write_tsdia(ts=ts_dia, filename=file_dia_out)
+            hatyan.write_tsdia(ts=ts_wia, filename=file_wia_out, headerformat='wia')
         else:
             hatyan.write_tsdia(ts=ts_dia, filename=file_dia_out)
             hatyan.write_tsdia(ts=ts_wia, filename=file_wia_out, headerformat='wia')