more pl

rowers/dataroutines.py

@@ -773,55 +773,21 @@ def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
 
     # clean data for useful ranges per column
     if not ignorehr:
-        try:
-            datadf = datadf.filter(pl.col("hr")>=30)
-        except (KeyError, TypeError):  # pragma: no cover
-            pass
+        datadf = datadf.filter(pl.col("hr")>=30)
 
-    try:
-        datadf = datadf.filter(pl.col("spm") >=0)
-    except (KeyError, TypeError):
-        pass
+    
+    datadf = datadf.filter(
+        pl.col("spm") >=0,
+        pl.col("efficiency")<=200,
+        pl.col("spm")>=10,
+        pl.col("pace")<=300*1000.,
+        pl.col("efficiency")>=0,
+        pl.col("pace")>=60*1000,
+        pl.col("power")<=5000,
+        pl.col("spm")<=120,
+        pl.col("wash")>=1
+    )
 
-    try:
-        datadf = datadf.filter(pl.col("efficiency")<=200)
-    except (KeyError, TypeError):
-        pass
-
-    try:
-        datadf = datadf.filter(pl.col("spm")>=10)
-    except (KeyError, TypeError):
-        pass
-
-    try:
-        datadf = datadf.filter(pl.col("pace")<=300*1000.)
-    except (KeyError, TypeError):
-        pass
-
-    try:
-        datadf = datadf.filter(pl.col("efficiency")>=0)
-    except (KeyError, TypeError):
-        pass
-
-    try:
-        datadf = datadf.filter(pl.col("pace")>=60*1000)
-    except (KeyError, TypeError):
-        pass
-
-    try:
-        datadf = datadf.filter(pl.col("power")<=5000)
-    except (KeyError, TypeError):
-        pass
-
-    try:
-        datadf = datadf.filter(pl.col("spm")<=120)
-    except (KeyError, TypeError):
-        pass
-
-    try:
-        datadf = datadf.filter(pl.col("wash")>=1)
-    except (KeyError, TypeError):
-        pass
 
     # try to guess ignoreadvanced
     if not ignoreadvanced:
@@ -834,70 +800,25 @@ def clean_df_stats_pl(datadf, workstrokesonly=True, ignorehr=True,
                 pass
 
     if not ignoreadvanced:
-        try:
-            datadf = datadf.filter(pl.col("rhythm")>=0)
-        except (KeyError, TypeError):
-            pass
+        datadf = datadf.filter(pl.col("rhythm")>=0,
+                               pl.col("rhythm")<=70,
+                               pl.col("power")>=20,
+                               pl.col("drivelength")>=0.5,
+                               pl.col("forceratio")>=0.2,
+                               pl.col("forceratio")<=1.0,
+                               pl.col("drivespeed")>=0.5,
+                               pl.col("drivespeed")<=4,
+                               pl.col("driveenergy")<=2000,
+                               pl.col("driveenergy")>=100,
+                               pl.col("catch")<=-30)
 
-        try:
-            datadf = datadf.filter(pl.col("rhythm")<=70)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("power")>=20)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("drivelength")>=0.5)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("forceratio")>=0.2)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("forceratio")<=1.0)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("drivespeed")>=0.5)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("drivespeed")<=4)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("driveenergy")<=2000)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("driveenergy")>=100)
-        except (KeyError, TypeError):
-            pass
-
-        try:
-            datadf = datadf.filter(pl.col("catch")<=-30)
-        except (KeyError, TypeError):
-            pass
 
     # workoutstateswork = [1, 4, 5, 8, 9, 6, 7]
     workoutstatesrest = [3]
     # workoutstatetransition = [0, 2, 10, 11, 12, 13]
 
     if workstrokesonly == 'True' or workstrokesonly is True:
-        try:
-            datadf = datadf.filter(~pl.col("workoutstate").is_in(workoutstatesrest))
-        except:
-            pass
+        datadf = datadf.filter(~pl.col("workoutstate").is_in(workoutstatesrest))
 
     after = {}
 
@@ -1571,7 +1492,7 @@ def getsmallrowdata_pl(columns, ids=[], doclean=True, workstrokesonly=True, comp
 
         return data
 
-
+    df = df.fill_nan(None).drop_nulls()
     return df
 
     
@@ -2207,7 +2128,11 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
         df = dd.from_pandas(data, npartitions=1)
         if polars:
             pldf = pl.from_pandas(data)
-            pldf.write_parquet(filename, compression='gzip')
+            try:
+                pldf.write_parquet(filename, compression='gzip')
+            except IsADirectoryError:
+                shutil.rmtree(filename)
+                pldf.write_parquet(filename, compression='gzip')
         else:
             try:
                 df.to_parquet(filename, engine='fastparquet', compression='gzip')

rowers/interactiveplots.py

@@ -478,7 +478,7 @@ def interactive_forcecurve(theworkouts):
     rowdata.dropna(axis=0, how='any', inplace=True)
 
     if rowdata.empty:
-        return "", "No Valid Data Available", "", ""
+        return "", "No Valid Data Available"
 
     data_dict = rowdata.to_dict("records")
 
@@ -2312,7 +2312,6 @@ def interactive_cum_flex_chart2(theworkouts, promember=0,
     columns_basic = columns_basic + ['spm', 'driveenergy', 'distance', 'workoutstate']
 
     datadf = pd.DataFrame()
-    start = timezone.now()
     if promember:
         datadf = dataprep.getsmallrowdata_pl(columns, ids=ids, doclean=True,
                                              workstrokesonly=workstrokesonly, for_chart=True)
@@ -2320,7 +2319,6 @@ def interactive_cum_flex_chart2(theworkouts, promember=0,
         datadf = dataprep.getsmallrowdata_pl(columns_basic, ids=ids, doclean=True,
                                              workstrokesonly=workstrokesonly, for_chart=True)
 
-    print(timezone.now()-start)
     try:
         _ = datadf[yparam2]
     except (KeyError, ColumnNotFoundError):  # pragma: no cover
@@ -2331,9 +2329,7 @@ def interactive_cum_flex_chart2(theworkouts, promember=0,
     except (KeyError, ColumnNotFoundError):
         yparam1 = 'None'
 
-    datadf.drop_nulls()
-    #datadf.dropna(axis=1, how='all', inplace=True)
-    #datadf.dropna(axis=0, how='any', inplace=True)
+    datadf = datadf.fill_nan(None).drop_nulls()
 
     # test if we have drive energy
     try:  # pragma: no cover