Merge branch 'develop' into feature/embeddedvideo

rowers/dataprep.py

@@ -2564,9 +2564,11 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
     if id != 0:
         data['workoutid'] = id
         data.fillna(0,inplace=True)
-        data = data.astype(
-            dtype=dtypes,
-             )
+        for k, v in dtypes.items():
+            try:
+                data[k] = data[k].astype(v)
+            except KeyError:
+                pass
 
 
         filename = 'media/strokedata_{id}.parquet.gz'.format(id=id)

rowers/dataprepnodjango.py

@@ -1254,7 +1254,13 @@ def dataprep(rowdatadf,id=0,bands=True,barchart=True,otwpower=True,
     # write data if id given
     if id != 0:
         data['workoutid'] = id
-        data = data.astype(dtype=dtypes)
+        data.fillna(0,inplace=True)
+        for k, v in dtypes.items():
+            try:
+                data[k] = data[k].astype(v) 
+            except KeyError:
+                pass
+
         filename = 'media/strokedata_{id}.parquet.gz'.format(id=id)
         df = dd.from_pandas(data,npartitions=1)
         df.to_parquet(filename,engine='fastparquet',compression='GZIP')

rowers/views/workoutviews.py

@@ -2796,18 +2796,21 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
         pass
 
     for field,verbosename in fielddict.items():
-        thedict = {
-            'mean':datadf[field].mean(),
-            'wmean': wavg(datadf, field, 'deltat'),
-            'min': datadf[field].min(),
-            'std': datadf[field].std(),
-            'max': datadf[field].max(),
-            'median': datadf[field].median(),
-            'firstq':datadf[field].quantile(q=0.25),
-            'thirdq':datadf[field].quantile(q=0.75),
-            'verbosename':verbosename,
-        }
-        stats[field] = thedict
+        try:
+            thedict = {
+                'mean':datadf[field].mean(),
+                'wmean': wavg(datadf, field, 'deltat'),
+                'min': datadf[field].min(),
+                'std': datadf[field].std(),
+                'max': datadf[field].max(),
+                'median': datadf[field].median(),
+                'firstq':datadf[field].quantile(q=0.25),
+                'thirdq':datadf[field].quantile(q=0.75),
+                'verbosename':verbosename,
+            }
+            stats[field] = thedict
+        except KeyError:
+            pass
 
     # Create a dict with correlation values
     cor = datadf.corr(method='spearman')