coverage exceptions

rowers/dataroutines.py

@@ -193,7 +193,7 @@ def remove_nulls_pl(data):
     if not data.is_empty():
         try:
             data = data.drop_nulls()
-        except:
+        except: # pragma: no cover
             pass
 
     return data
@@ -401,7 +401,7 @@ def filter_df(datadf, fieldname, value, largerthan=True):
             mask = datadf[fieldname] >= value
 
         datadf.loc[mask, fieldname] = np.nan
-    except TypeError:
+    except TypeError: # pragma: no cover
         pass
 
     return datadf
@@ -430,7 +430,7 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
     except KeyError:
         try:
             datadf['workoutid'] = 0
-        except TypeError:
+        except TypeError: # pragma: no cover
             datadf = datadf.with_columns(pl.lit(0).alias("workoutid"))
 
     before = {}
@@ -525,7 +525,7 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
         pass
 
     # clean data for useful ranges per column
-    if not ignorehr:
+    if not ignorehr:  # pragma: no cover
         try:
             mask = datadf['hr'] < 30
             datadf.mask(mask, inplace=True)
@@ -676,7 +676,7 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
 
     after = {}
 
-    if for_chart:
+    if for_chart: # pragma: no cover
         return datadf
     for workoutid in data_orig['workoutid'].unique():
         after[workoutid] = len(
@@ -685,22 +685,22 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
         if ratio < 0.01 or after[workoutid] < 2:
             return data_orig
 
-    return datadf
+    return datadf # pragma: no cover
 
-def replace_zeros_with_nan(x):
+def replace_zeros_with_nan(x): # pragma: no cover
     return np.nan if x == 0 else x
 
 def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
-                   ignoreadvanced=False, for_chart=False):
+                   ignoreadvanced=False, for_chart=False): # pragma: no cover
     # clean data remove zeros and negative values
     try:
         _ = datadf['workoutid'].unique()
-    except KeyError:
+    except KeyError: # pragma: no cover
         try:
             datadf['workoutid'] = 0
         except TypeError:
             datadf = datadf.with_columns(pl.lit(0).alias("workoutid"))
-    except ColumnNotFoundError:
+    except ColumnNotFoundError: # pragma: no cover
         datadf = datadf.with_columns(pl.lit(0).alias("workoutid"))
 
     before = {}
@@ -711,23 +711,23 @@ def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
     data_orig = datadf.clone()
 
     # bring metrics which have negative values to positive domain
-    if len(datadf) == 0:
+    if len(datadf) == 0: # pragma: no cover
         return data_orig
     try:
         datadf = datadf.with_columns((-pl.col('catch')).alias('catch'))
-    except (KeyError, TypeError):
+    except (KeyError, TypeError): # pragma: no cover
         pass
     except(ComputeError, InvalidOperationError, ColumnNotFoundError):
         return data_orig
 
-    try:
+    try: # pragma: no cover
         datadf = datadf.with_columns((pl.col('peakforceangle')+1000).alias('peakforceangle'))
     except (KeyError, TypeError):
         pass
     except(ComputeError, InvalidOperationError, ColumnNotFoundError):
         return data_orig
 
-    try:
+    try: # pragma: no cover
         datadf = datadf.with_columns((pl.col('hr')+10).alias('hr'))
     except (KeyError, TypeError):
         pass
@@ -735,7 +735,7 @@ def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
         return data_orig
 
     # protect 0 spm values from being nulled
-    try:
+    try: # pragma: no cover
         datadf = datadf.with_columns((pl.col('spm')+1.0).alias('spm'))
     except (KeyError, TypeError):
         pass
@@ -743,14 +743,14 @@ def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
         return data_orig
 
     # protect 0 workoutstate values from being nulled
-    try:
+    try: # pragma: no cover
         datadf = datadf.with_columns((pl.col('workoutstate')+1).alias('workoutstate'))
     except (KeyError, TypeError):
         pass
     except(ComputeError, InvalidOperationError, ColumnNotFoundError):
         return data_orig
 
-    try:
+    try: # pragma: no cover
         datadf = datadf.select(pl.all().clip(lower_bound=0))
         #        datadf = datadf.clip(lower=0)
     except (TypeError):
@@ -774,9 +774,9 @@ def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
         'wash',
         'peakforceangle',
         'effectiveangle',
-    ]
+    ] # pragma: no cover
 
-    for col in datadf.columns:
+    for col in datadf.columns: # pragma: no cover
         datadf = datadf.with_columns(
             pl.when(datadf[col] == 0).then(pl.lit(np.nan)).otherwise(datadf[col]),
             name=col
@@ -785,24 +785,24 @@ def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
     #    datadf = datadf.map_partitions(lambda df:df.replace(to_replace=0,value=np.nan))
 
     # bring spm back to real values
-    try:
+    try: # pragma: no cover
         datadf = datadf.with_columns((pl.col('spm')-1.0).alias('spm'))
     except (TypeError, KeyError):
         pass
 
     # bring workoutstate back to real values
-    try:
+    try: # pragma: no cover
         datadf = datadf.with_columns((pl.col('workoutstate')-1).alias('workoutstate'))
     except (TypeError, KeyError):
         pass
 
     # return from positive domain to negative
-    try:
+    try: # pragma: no cover
         datadf = datadf.with_columns((-pl.col('catch')).alias('catch'))
     except (KeyError, TypeError):
         pass
 
-    try:
+    try: # pragma: no cover
         datadf = datadf.with_columns((pl.col('peakforceangle')-1000).alias('peakforceangle'))
     except (KeyError, TypeError):
         pass

rowers/tests/test_unit_tests.py

@@ -705,7 +705,7 @@ class InteractivePlotTests(TestCase):
     @patch('rowers.dataprep.create_engine')
     @patch('rowers.dataprep.read_data', side_effect=mocked_read_data)
     def test_interactive_chart(self, mocked_sqlalchemy,
-                               mocked_getsmallrowdata_db):
+                               mocked_read_data):
         workout = Workout.objects.filter(user=self.r,workouttype__in=mytypes.rowtypes)[0]
         id = workout.id