Merge branch 'release/v10.40'

requirements.txt

@@ -19,6 +19,7 @@ certifi==2019.3.9
 cffi==1.12.2
 chardet==3.0.4
 Click==7.0
+cloudpickle==1.2.2
 colorama==0.4.1
 colorclass==2.2.0
 cookies==2.2.1
@@ -27,7 +28,7 @@ coreschema==0.0.4
 coverage==4.5.3
 cryptography==2.6.1
 cycler==0.10.0
-dask==1.1.4
+dask==2.6.0
 decorator==4.4.0
 defusedxml==0.5.0
 Django==2.1.7
@@ -39,7 +40,7 @@ django-cookie-law==2.0.1
 django-cors-headers==2.5.2
 django-countries==5.3.3
 django-datetime-widget==0.9.3
-django-debug-toolbar==1.11
+django-debug-toolbar==2.0
 django-extensions==2.1.6
 django-htmlmin==0.11.0
 django-leaflet==0.24.0
@@ -64,8 +65,10 @@ entrypoints==0.3
 execnet==1.5.0
 factory-boy==2.11.1
 Faker==1.0.4
+fastparquet==0.3.2
 fitparse==1.1.0
 Flask==1.0.2
+fsspec==0.5.2
 future==0.17.1
 geocoder==1.38.1
 geos==0.2.1
@@ -74,6 +77,7 @@ html5lib==1.0.1
 htmlmin==0.1.12
 HTMLParser==0.0.2
 httplib2==0.12.1
+hvplot==0.4.0
 icalendar==4.0.3
 idna==2.8
 image==1.5.27
@@ -99,10 +103,12 @@ jupyterlab-server==0.3.0
 keyring==18.0.0
 kiwisolver==1.0.1
 kombu==4.5.0
+llvmlite==0.30.0
 lxml==4.3.2
 Markdown==3.0.1
 MarkupSafe==1.1.1
 matplotlib==3.0.3
+minify==0.1.4
 MiniMockTest==0.5
 mistune==0.8.4
 mock==2.0.0
@@ -111,9 +117,11 @@ mpld3==0.3
 mysqlclient==1.4.2.post1
 nbconvert==5.4.1
 nbformat==4.4.0
+newrelic==5.2.1.129
 nose==1.3.7
 nose-parameterized==0.6.0
 notebook==5.7.6
+numba==0.46.0
 numpy==1.16.2
 oauth2==1.9.0.post1
 oauthlib==3.0.1
@@ -135,6 +143,7 @@ prompt-toolkit==2.0.9
 psycopg2==2.8.1
 ptyprocess==0.6.0
 py==1.8.0
+pyarrow==0.15.0
 pycparser==2.19
 Pygments==2.3.1
 pyparsing==2.3.1
@@ -160,7 +169,7 @@ ratelim==0.1.6
 redis==3.2.1
 requests==2.21.0
 requests-oauthlib==1.2.0
-rowingdata==2.5.4
+rowingdata==2.5.5
 rowingphysics==0.5.0
 rq==0.13.0
 scipy==1.2.1
@@ -179,7 +188,9 @@ terminado==0.8.1
 terminaltables==3.1.0
 testpath==0.4.2
 text-unidecode==1.2
+thrift==0.11.0
 timezonefinder==4.0.1
+toolz==0.10.0
 tornado==6.0.1
 tqdm==4.31.1
 traitlets==4.3.2
@@ -196,3 +207,4 @@ xlrd==1.2.0
 xmltodict==0.12.0
 yamjam==0.1.7
 yamllint==1.15.0
+yuicompressor==2.4.8

rowers/c2stuff.py

@@ -29,6 +29,70 @@ queue = django_rq.get_queue('default')
 queuelow = django_rq.get_queue('low')
 queuehigh = django_rq.get_queue('low')
 from rowers.utils import myqueue
+from rowers.models import C2WorldClassAgePerformance
+
+def getagegrouprecord(age,sex='male',weightcategory='hwt',
+                      distance=2000,duration=None,indf=pd.DataFrame()):
+
+    if not indf.empty:
+        if not duration:
+            df = indf[indf['distance'] == distance]
+        else:
+            duration = 60*int(duration)
+            df = indf[indf['duration'] == duration]
+    else:
+        if not duration:
+            df = pd.DataFrame(
+                list(
+                    C2WorldClassAgePerformance.objects.filter(
+                        distance=distance,
+                        sex=sex,
+                        weightcategory=weightcategory
+                    ).values()
+                )
+            )
+        else:
+            duration=60*int(duration)
+            df = pd.DataFrame(
+                list(
+                    C2WorldClassAgePerformance.objects.filter(
+                        duration=duration,
+                        sex=sex,
+                        weightcategory=weightcategory
+                    ).values()
+                )
+            )
+
+    if not df.empty:
+        ages = df['age']
+        powers = df['power']
+
+        #poly_coefficients = np.polyfit(ages,powers,6)
+        fitfunc = lambda pars, x: np.abs(pars[0])*(1-x/max(120,pars[1]))-np.abs(pars[2])*np.exp(-x/np.abs(pars[3]))+np.abs(pars[4])*(np.sin(np.pi*x/max(50,pars[5])))
+        errfunc = lambda pars, x,y: fitfunc(pars,x)-y
+
+        p0 = [700,120,700,10,100,100]
+
+        try:
+            p1, success = optimize.leastsq(errfunc,p0[:],
+                                       args = (ages,powers))
+        except:
+            p1 = p0
+            success = 0
+
+        if success:
+            power = fitfunc(p1, float(age))
+
+            #power = np.polyval(poly_coefficients,age)
+
+            power = 0.5*(np.abs(power)+power)
+        else:
+            power = 0
+    else:
+        power = 0
+
+    return power
+
 
 oauth_data = {
     'client_id': C2_CLIENT_ID,

rowers/dataprep.py

@@ -4,11 +4,10 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 
-
 # All the data preparation, data cleaning and data mangling should
 # be defined here
 from __future__ import unicode_literals, absolute_import
-from rowers.models import Workout, StrokeData,Team
+from rowers.models import Workout, Team
 
 import pytz
 
@@ -16,6 +15,7 @@ from rowingdata import rowingdata as rrdata
 
 from rowingdata import rower as rrower
 
+import shutil
 from shutil import copyfile
 
 from rowingdata import (
@@ -26,6 +26,10 @@ from rowers.tasks import handle_sendemail_unrecognized
 from rowers.tasks import handle_zip_file
 
 from pandas import DataFrame, Series
+import dask.dataframe as dd
+from dask.delayed import delayed
+import pyarrow.parquet as pq
+import pyarrow as pa
 
 from django.utils import timezone
 from django.utils.timezone import get_current_timezone
@@ -51,7 +55,7 @@ from rowingdata import (
 from rowingdata.csvparsers import HumonParser
 
 
-from rowers.metrics import axes,calc_trimp,rowingmetrics
+from rowers.metrics import axes,calc_trimp,rowingmetrics,dtypes
 from rowers.models import strokedatafields
 
 #allowedcolumns = [item[0] for item in rowingmetrics]
@@ -113,6 +117,7 @@ columndict = {
     'cumdist': 'cum_dist',
 }
 
+
 from scipy.signal import savgol_filter
 
 import datetime
@@ -348,22 +353,23 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
                    ignoreadvanced=False):
     # clean data remove zeros and negative values
 
+    
     # bring metrics which have negative values to positive domain
-    if datadf.empty:
+    if len(datadf)==0:
         return datadf
     try:
         datadf['catch'] = -datadf['catch']
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     try:
         datadf['peakforceangle'] = datadf['peakforceangle'] + 1000
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     try:
         datadf['hr'] = datadf['hr'] + 10
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     # protect 0 spm values from being nulled
@@ -378,6 +384,7 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
         pass
 
     datadf.replace(to_replace=0, value=np.nan, inplace=True)
+    #    datadf = datadf.map_partitions(lambda df:df.replace(to_replace=0,value=np.nan))
 
     # bring spm back to real values
     try:
@@ -388,141 +395,141 @@ def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
     # return from positive domain to negative
     try:
         datadf['catch'] = -datadf['catch']
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     try:
         datadf['peakforceangle'] = datadf['peakforceangle'] - 1000
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     try:
         datadf['hr'] = datadf['hr'] - 10
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     # clean data for useful ranges per column
     if not ignorehr:
         try:
             mask = datadf['hr'] < 30
-            datadf.loc[mask, 'hr'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
     try:
         mask = datadf['spm'] < 0
-        datadf.loc[mask,'spm'] = np.nan
+        datadf.mask(mask,inplace=True)
-    except KeyError:
+    except (KeyError,TypeError):
         pass
         
     try:
         mask = datadf['efficiency'] > 200.
-        datadf.loc[mask, 'efficiency'] = np.nan
+        datadf.mask(mask,inplace=True)
-    except KeyError:
+    except (KeyError,TypeError):
         pass
         
     try:
         mask = datadf['spm'] < 10
-        datadf.loc[mask, 'spm'] = np.nan
+        datadf.mask(mask,inplace=True)
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
 
     try:
         mask = datadf['pace'] / 1000. > 300.
-        datadf.loc[mask, 'pace'] = np.nan
+        datadf.mask(mask,inplace=True)
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     try:
         mask = datadf['efficiency'] < 0.
-        datadf.loc[mask, 'efficiency'] = np.nan
+        datadf.mask(mask,inplace=True)
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     try:
         mask = datadf['pace'] / 1000. < 60.
-        datadf.loc[mask, 'pace'] = np.nan
+        datadf.mask(mask,inplace=True)
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     try:
         mask = datadf['spm'] > 60
-        datadf.loc[mask, 'spm'] = np.nan
+        datadf.mask(mask,inplace=True)
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     try:
-        mask = datadf['wash'] < 1
+        mask = datadf['wash'] > 1
         datadf.loc[mask, 'wash'] = np.nan
-    except KeyError:
+    except (KeyError,TypeError):
         pass
 
     if not ignoreadvanced:
         try:
             mask = datadf['rhythm'] < 5
-            datadf.loc[mask, 'rhythm'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['rhythm'] > 70
-            datadf.loc[mask, 'rhythm'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['power'] < 20
-            datadf.loc[mask, 'power'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['drivelength'] < 0.5
-            datadf.loc[mask, 'drivelength'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['forceratio'] < 0.2
-            datadf.loc[mask, 'forceratio'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['forceratio'] > 1.0
-            datadf.loc[mask, 'forceratio'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['drivespeed'] < 0.5
-            datadf.loc[mask, 'drivespeed'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['drivespeed'] > 4
-            datadf.loc[mask, 'drivespeed'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['driveenergy'] > 2000
-            datadf.loc[mask, 'driveenergy'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['driveenergy'] < 100
-            datadf.loc[mask, 'driveenergy'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
         try:
             mask = datadf['catch'] > -30.
-            datadf.loc[mask, 'catch'] = np.nan
+            datadf.mask(mask,inplace=True)
-        except KeyError:
+        except (KeyError,TypeError):
             pass
 
     workoutstateswork = [1, 4, 5, 8, 9, 6, 7]
@@ -569,41 +576,6 @@ def getstatsfields():
 
     return fieldlist, fielddict
 
-def getstatsfields_old():
-    # Get field names and remove those that are not useful in stats
-    fields = StrokeData._meta.get_fields()
-
-    fielddict = {field.name: field.verbose_name for field in fields}
-
-    # fielddict.pop('workoutid')
-    fielddict.pop('ergpace')
-    fielddict.pop('hr_an')
-    fielddict.pop('hr_tr')
-    fielddict.pop('hr_at')
-    fielddict.pop('hr_ut2')
-    fielddict.pop('hr_ut1')
-    fielddict.pop('time')
-    fielddict.pop('distance')
-    fielddict.pop('nowindpace')
-    fielddict.pop('fnowindpace')
-    fielddict.pop('fergpace')
-    fielddict.pop('equivergpower')
-#    fielddict.pop('workoutstate')
-    fielddict.pop('fpace')
-    fielddict.pop('pace')
-    fielddict.pop('id')
-    fielddict.pop('ftime')
-    fielddict.pop('x_right')
-    fielddict.pop('hr_max')
-    fielddict.pop('hr_bottom')
-    fielddict.pop('cumdist')
-
-    try:
-        fieldlist = [field for field, value in fielddict.iteritems()]
-    except AttributeError:
-        fieldlist = [field for field, value in fielddict.items()]
-
-    return fieldlist, fielddict
 
 
 # A string representation for time deltas
@@ -1616,75 +1588,7 @@ def new_workout_from_df(r, df,
     return (id, message)
 
 
-# Compare the data from the CSV file and the database
-# Currently only calculates number of strokes. To be expanded with
-# more elaborate testing if needed
-def compare_data(id):
-    row = Workout.objects.get(id=id)
-    f1 = row.csvfilename
-    try:
-        rowdata = rdata(f1)
-        l1 = len(rowdata.df)
-    except AttributeError:
-        rowdata = 0
-        l1 = 0
 
-    engine = create_engine(database_url, echo=False)
-    query = sa.text('SELECT COUNT(*) FROM strokedata WHERE workoutid={id};'.format(
-        id=id,
-    ))
-    with engine.connect() as conn, conn.begin():
-        try:
-            res = conn.execute(query)
-            l2 = res.fetchall()[0][0]
-        except:
-            print("Database Locked")
-    conn.close()
-    engine.dispose()
-    lfile = l1
-    ldb = l2
-    return l1 == l2 and l1 != 0, ldb, lfile
-
-# Repair data for workouts where the CSV file is lost (or the DB entries
-# don't exist)
-
-
-def repair_data(verbose=False):
-    ws = Workout.objects.all()
-    for w in ws:
-        if verbose:
-            sys.stdout.write(".")
-        test, ldb, lfile = compare_data(w.id)
-        if not test:
-            if verbose:
-                print(w.id, lfile, ldb)
-            try:
-                rowdata = rdata(w.csvfilename)
-                if rowdata and len(rowdata.df):
-                    update_strokedata(w.id, rowdata.df)
-
-            except (IOError, AttributeError):
-                pass
-
-            if lfile == 0:
-                # if not ldb - delete workout
-
-                try:
-                    data = read_df_sql(w.id)
-                    try:
-                        datalength = len(data)
-                    except AttributeError:
-                        datalength = 0
-
-                    if datalength != 0:
-                        data.rename(columns=columndict, inplace=True)
-                        res = data.to_csv(w.csvfilename + '.gz',
-                                          index_label='index',
-                                          compression='gzip')
-                    else:
-                        w.delete()
-                except:
-                    pass
 
 # A wrapper around the rowingdata class, with some error catching
 
@@ -1710,17 +1614,11 @@ def rdata(file, rower=rrower()):
 
 
 def delete_strokedata(id):
-    engine = create_engine(database_url, echo=False)
+    dirname = 'media/strokedata_{id}.parquet.gz'.format(id=id)
-    query = sa.text('DELETE FROM strokedata WHERE workoutid={id};'.format(
+    try:
-        id=id,
+        shutil.rmtree(dirname)
-    ))
+    except FileNotFoundError:
-    with engine.connect() as conn, conn.begin():
+        pass
-        try:
-            result = conn.execute(query)
-        except:
-            print("Database Locked")
-    conn.close()
-    engine.dispose()
 
 # Replace stroke data in DB with data from CSV file
 
@@ -1747,7 +1645,6 @@ def testdata(time, distance, pace, spm):
 def getrowdata_db(id=0, doclean=False, convertnewtons=True,
                   checkefficiency=True):
     data = read_df_sql(id)
-    data['x_right'] = data['x_right'] / 1.0e6
     data['deltat'] = data['time'].diff()
 
     if data.empty:
@@ -1771,8 +1668,111 @@ def getrowdata_db(id=0, doclean=False, convertnewtons=True,
 
 # Fetch a subset of the data from the DB
 
+def getsmallrowdata_db(columns, ids=[], doclean=True,workstrokesonly=True,compute=True):
+    #    prepmultipledata(ids)
 
-def getsmallrowdata_db(columns, ids=[], doclean=True, workstrokesonly=True):
+    csvfilenames = ['media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
+    data = []
+    columns = [c for c in columns if c != 'None']
+    columns = list(set(columns))
+
+    if len(ids)>1:
+        for id,f in zip(ids,csvfilenames):
+            try:
+                #df = dd.read_parquet(f,columns=columns,engine='pyarrow')
+                df = pd.read_parquet(f,columns=columns)
+                data.append(df)
+            except OSError:
+                rowdata, row = getrowdata(id=id)
+                if rowdata and len(rowdata.df):
+                    datadf = dataprep(rowdata.df,id=id,bands=True,otwpower=True,barchart=True)
+                    #                    df = dd.read_parquet(f,columns=columns,engine='pyarrow')
+                    df = pd.read_parquet(f,columns=columns)
+                    data.append(df)
+
+        df = pd.concat(data,axis=0)
+        # df = dd.concat(data,axis=0)
+    
+    else:
+        try:
+            df = pd.read_parquet(csvfilenames[0],columns=columns)            
+        except OSError:
+            rowdata,row = getrowdata(id=ids[0])
+            if rowdata and len(rowdata.df):
+                data = dataprep(rowdata.df,id=ids[0],bands=True,otwpower=True,barchart=True)
+                df = pd.read_parquet(csvfilenames[0],columns=columns)
+                #                df = dd.read_parquet(csvfilenames[0],
+                #                                     column=columns,engine='pyarrow',
+                #                )
+
+                # df = df.loc[:,~df.columns.duplicated()]
+
+    
+
+    if compute:
+        data = df.copy()
+        if doclean:
+            data = clean_df_stats(data, ignorehr=True,
+                                workstrokesonly=workstrokesonly)
+        data.dropna(axis=1,how='all',inplace=True)
+        data.dropna(axis=0,how='any',inplace=True)
+        return data
+    
+    return df
+
+def getsmallrowdata_db_dask(columns, ids=[], doclean=True,workstrokesonly=True,compute=True):
+    #    prepmultipledata(ids)
+
+    csvfilenames = ['media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
+    data = []
+    columns = [c for c in columns if c != 'None']
+    columns = list(set(columns))
+
+    if len(ids)>1:
+        for id,f in zip(ids,csvfilenames):
+            try:
+                #df = dd.read_parquet(f,columns=columns,engine='pyarrow')
+                df = dd.read_parquet(f,columns=columns)
+                data.append(df)
+            except OSError:
+                rowdata, row = getrowdata(id=id)
+                if rowdata and len(rowdata.df):
+                    datadf = dataprep(rowdata.df,id=id,bands=True,otwpower=True,barchart=True)
+                    #                    df = dd.read_parquet(f,columns=columns,engine='pyarrow')
+                    df = dd.read_parquet(f,columns=columns)
+                    data.append(df)
+
+        df = dd.concat(data,axis=0)
+        # df = dd.concat(data,axis=0)
+    
+    else:
+        try:
+            df = dd.read_parquet(csvfilenames[0],columns=columns)            
+        except OSError:
+            rowdata,row = getrowdata(id=ids[0])
+            if rowdata and len(rowdata.df):
+                data = dataprep(rowdata.df,id=ids[0],bands=True,otwpower=True,barchart=True)
+                df = dd.read_parquet(csvfilenames[0],columns=columns)
+                #                df = dd.read_parquet(csvfilenames[0],
+                #                                     column=columns,engine='pyarrow',
+                #                )
+
+                # df = df.loc[:,~df.columns.duplicated()]
+
+    
+
+    if compute:
+        data = df.compute()
+        if doclean:
+            data = clean_df_stats(data, ignorehr=True,
+                                workstrokesonly=workstrokesonly)
+        data.dropna(axis=1,how='all',inplace=True)
+        data.dropna(axis=0,how='any',inplace=True)
+        return data
+    
+    return df
+
+def getsmallrowdata_db_old(columns, ids=[], doclean=True, workstrokesonly=True):
     prepmultipledata(ids)
     data,extracols = read_cols_df_sql(ids, columns)
     if extracols and len(ids)==1:
@@ -1850,31 +1850,20 @@ def getrowdata(id=0):
 # safety net for programming errors elsewhere in the app
 # Also used heavily when I moved from CSV file only to CSV+Stroke data
 
+import glob
 
 def prepmultipledata(ids, verbose=False):
-    query = sa.text('SELECT DISTINCT workoutid FROM strokedata')
+    filenames = glob.glob('media/*.parquet')
-    engine = create_engine(database_url, echo=False)
+    ids = [id for id in ids if 'media/strokedata_{id}.parquet.gz'.format(id=id) not in filenames]
 
-    with engine.connect() as conn, conn.begin():
+    for id in ids:
-        res = conn.execute(query)
-        res = list(itertools.chain.from_iterable(res.fetchall()))
-    conn.close()
-    engine.dispose()
-
-    try:
-        ids2 = [int(id) for id in ids]
-    except ValueError:
-        ids2 = ids
-
-    res = list(set(ids2) - set(res))
-    for id in res:
         rowdata, row = getrowdata(id=id)
         if verbose:
             print(id)
         if rowdata and len(rowdata.df):
             data = dataprep(rowdata.df, id=id, bands=True,
                             barchart=True, otwpower=True)
-    return res
+    return ids
 
 # Read a set of columns for a set of workout ids, returns data as a
 # pandas dataframe
@@ -1883,6 +1872,66 @@ def prepmultipledata(ids, verbose=False):
 def read_cols_df_sql(ids, columns, convertnewtons=True):
     # drop columns that are not in offical list
     #    axx = [ax[0] for ax in axes]
+
+    extracols = []
+
+    columns = list(columns) + ['distance', 'spm', 'workoutid']
+    columns = [x for x in columns if x != 'None']
+    columns = list(set(columns))
+    ids = [int(id) for id in ids]
+
+
+    if len(ids) == 0:
+        return pd.DataFrame(),extracols
+    elif len(ids) == 1:
+        try:
+            filename = 'media/strokedata_{id}.parquet.gz'.format(id=ids[0])
+            df = pd.read_parquet(filename,columns=columns)
+        except OSError:
+            rowdata,row = getrowdata(id=ids[0])
+            if rowdata and len(rowdata.df):
+                datadf = dataprep(rowdata.df,id=ids[0],bands=True,otwpower=True,barchart=True)
+                df = pd.read_parquet(filename,columns=columns)
+    else:
+        data = []
+        filenames = ['media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
+        for id,f in zip(ids,filenames):
+            try:
+                df = pd.read_parquet(f,columns=columns)
+                data.append(df)
+            except OSError:
+                rowdata,row = getrowdata(id=id)
+                if rowdata and len(rowdata.df):
+                    datadf = dataprep(rowdata.df,id=id,bands=True,otwpower=True,barchart=True)
+                    df = pd.read_parquet(f,columns=columns)
+                    data.append(df)
+
+        df = pd.concat(data,axis=0)
+                
+
+    df = df.fillna(value=0)
+
+    if 'peakforce' in columns:
+        funits = ((w.id, w.forceunit)
+                  for w in Workout.objects.filter(id__in=ids))
+        for id, u in funits:
+            if u == 'lbs':
+                mask = df['workoutid'] == id
+                df.loc[mask, 'peakforce'] = df.loc[mask, 'peakforce'] * lbstoN
+    if 'averageforce' in columns:
+        funits = ((w.id, w.forceunit)
+                  for w in Workout.objects.filter(id__in=ids))
+        for id, u in funits:
+            if u == 'lbs':
+                mask = df['workoutid'] == id
+                df.loc[mask, 'averageforce'] = df.loc[mask,
+                                                      'averageforce'] * lbstoN
+
+    return df,extracols
+
+def read_cols_df_sql_old(ids, columns, convertnewtons=True):
+    # drop columns that are not in offical list
+    #    axx = [ax[0] for ax in axes]
     prepmultipledata(ids)
     axx = [f.name for f in StrokeData._meta.get_fields()]
 
@@ -1911,12 +1960,12 @@ def read_cols_df_sql(ids, columns, convertnewtons=True):
     #            columns=cls,
     #        ))
     elif len(ids) == 1:
-        query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid={id}'.format(
+        query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid={id} ORDER BY time ASC'.format(
             id=ids[0],
             columns=cls,
         ))
     else:
-        query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid IN {ids}'.format(
+        query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid IN {ids} ORDER BY time ASC'.format(
             columns=cls,
             ids=tuple(ids),
         ))
@@ -1949,11 +1998,37 @@ def read_cols_df_sql(ids, columns, convertnewtons=True):
 
 # Read stroke data from the DB for a Workout ID. Returns a pandas dataframe
 
-
 def read_df_sql(id):
+    try:
+        f = 'media/strokedata_{id}.parquet.gz'.format(id=id)
+        df = pd.read_parquet(f)
+    except OSError:
+        rowdata,row = getrowdata(id=ids[0])
+        if rowdata and len(rowdata.df):
+            data = dataprep(rowdata.df,id=ids[0],bands=True,otwpower=True,barchart=True)
+            df = pd.read_parquet(f)
+
+    df = df.fillna(value=0)
+
+    funit = Workout.objects.get(id=id).forceunit
+
+    if funit == 'lbs':
+        try:
+            df['peakforce'] = df['peakforce'] * lbstoN
+        except KeyError:
+            pass
+
+        try:
+            df['averageforce'] = df['averageforce'] * lbstoN
+        except KeyError:
+            pass
+
+    return df
+
+def read_df_sql_old(id):
     engine = create_engine(database_url, echo=False)
 
-    df = pd.read_sql_query(sa.text('SELECT * FROM strokedata WHERE workoutid={id}'.format(
+    df = pd.read_sql_query(sa.text('SELECT * FROM strokedata WHERE workoutid={id} ORDER BY time ASC'.format(
         id=id)), engine)
 
     engine.dispose()
@@ -2142,14 +2217,13 @@ def add_efficiency(id=0):
     rowdata = rowdata.fillna(method='ffill')
     
     delete_strokedata(id)
+    
     if id != 0:
         rowdata['workoutid'] = id
-        engine = create_engine(database_url, echo=False)
+        filename = 'media/strokedata_{id}.parquet.gz'.format(id=id)
-        with engine.connect() as conn, conn.begin():
+        df = dd.from_pandas(rowdata,npartitions=1)
-            rowdata.to_sql('strokedata', engine,
+        df.to_parquet(filename,engine='fastparquet',compression='GZIP')
-                           if_exists='append', index=False)
+
-        conn.close()
-        engine.dispose()
     return rowdata
 
 # This is the main routine.
@@ -2292,19 +2366,6 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
     except KeyError:
         rowdatadf[' ElapsedTime (sec)'] = rowdatadf['TimeStamp (sec)']
 
-    if barchart:
-        # time increments for bar chart
-        time_increments = rowdatadf.loc[:, ' ElapsedTime (sec)'].diff()
-        try:
-            time_increments.iloc[0] = time_increments.iloc[1]
-        except (KeyError, IndexError):
-            time_increments.iloc[0] = 1.
-            
-        time_increments = 0.5 * time_increments + 0.5 * np.abs(time_increments)
-        x_right = (t2 + time_increments.apply(lambda x: timedeltaconv(x)))
-
-        data['x_right'] = x_right
-
     if empower:
         try:
             wash = rowdatadf.loc[:, 'wash']
@@ -2441,12 +2502,17 @@ def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
     # write data if id given
     if id != 0:
         data['workoutid'] = id
+        data.fillna(0,inplace=True)
+        data = data.astype(
+            dtype=dtypes,
+             )
+
+
+        filename = 'media/strokedata_{id}.parquet.gz'.format(id=id)
+        df = dd.from_pandas(data,npartitions=1)
+        df.to_parquet(filename,engine='fastparquet',compression='GZIP')
+
 
-        engine = create_engine(database_url, echo=False)
-        with engine.connect() as conn, conn.begin():
-            data.to_sql('strokedata', engine, if_exists='append', index=False)
-        conn.close()
-        engine.dispose()
     return data
 
 

rowers/dataprepnodjango.py

@@ -16,6 +16,8 @@ from pandas import DataFrame,Series
 import pandas as pd
 import numpy as np
 import itertools
+import dask.dataframe as dd
+from dask.delayed import delayed
 
 from sqlalchemy import create_engine
 import sqlalchemy as sa
@@ -145,6 +147,7 @@ def rdata(file,rower=rrower()):
     return res
 
 from rowers.utils import totaltime_sec_to_string
+from rowers.metrics import dtypes
 
 
 # Creates C2 stroke data
@@ -635,20 +638,11 @@ def new_workout_from_file(r,f2,
     return (id,message,f2)
 
 def delete_strokedata(id,debug=False):
-    if debug:
+    dirname = 'media/strokedata_{id}.parquet.gz'.format(id=id)
-        engine = create_engine(database_url_debug, echo=False)
+    try:
-    else:
+        shutil.rmtree(dirname)
-        engine = create_engine(database_url, echo=False)
+    except FileNotFoundError:
-    query = sa.text('DELETE FROM strokedata WHERE workoutid={id};'.format(
+        pass
-        id=id,
-    ))
-    with engine.connect() as conn, conn.begin():
-        try:
-            result = conn.execute(query)
-        except:
-            print("Database Locked")
-    conn.close()
-    engine.dispose()
 
 def update_strokedata(id,df,debug=False):
     delete_strokedata(id,debug=debug)
@@ -714,10 +708,25 @@ def testdata(time,distance,pace,spm):
 
 
 def getsmallrowdata_db(columns,ids=[],debug=False):
+    csvfilenames = ['media/strokedata_{id}.parquet'.format(id=id) for id in ids]
+    data = []
+    columns = [c for c in columns if c != 'None']
 
-    data = read_cols_df_sql(ids,columns,debug=debug)
+    if len(ids)>1:
+        for f in csvfilenames:
+            try:
+                df = pd.read_parquet(f,columns=columns,engine='pyarrow')
+                data.append(df)
+            except OSError:
+                pass
 
-    return data
+    
+        df = pd.concat(data,axis=0)
+    else:
+        df = pd.read_parquet(csvfilenames[0],columns=columns,engine='pyarrow')
+
+
+    return df
         
 def fitnessmetric_to_sql(m,table='powertimefitnessmetric',debug=False,
                          doclean=False):
@@ -761,51 +770,42 @@ def read_cols_df_sql(ids,columns,debug=False):
     columns = list(columns)+['distance','spm']
     columns = [x for x in columns if x != 'None']
     columns = list(set(columns))
-    cls = ''
+
     ids = [int(id) for id in ids]
-    if debug:
-        engine = create_engine(database_url_debug, echo=False)
-    else:
-        engine = create_engine(database_url, echo=False)
 
 
-    for column in columns:
-        cls += column+', '
-    cls = cls[:-2]
     if len(ids) == 0:
-        query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid=0'.format(
+        return pd.DataFrame()
-            columns = cls,
-            ))
     elif len(ids) == 1:
-        query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid={id}'.format(
+        try:
-            id = ids[0],
+            filename = 'media/strokedata_{id}.parquet.gz'.format(id=ids[0])
-            columns = cls,
+            df = pd.read_parquet(filename,columns=columns)
-            ))
+        except OSError:
+            pass
     else:
-        query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid IN {ids}'.format(
+        data = []
-            columns = cls,
+        filenames = ['media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
-            ids = tuple(ids),
+        for id,f in zip(ids,filenames):
-        ))
+            try:
+                df = pd.read_parquet(f,columns=columns)
+                data.append(df)
+            except OSError:
+                pass
+
+        df = pd.concat(data,axis=0)
 
-    df = pd.read_sql_query(query,engine)
-    engine.dispose()
     return df
         
 
 def read_df_sql(id,debug=False):
-    if debug:
+    try:
-        engine = create_engine(database_url_debug, echo=False)
+        f = 'media/strokedata_{id}.parquet.gz'.format(id=id)
-        print("read_df",id)
+        df = pd.read_parquet(f)
-        print(database_url_debug)
+    except OSError:
-    else:
+        pass
-        engine = create_engine(database_url, echo=False)
 
-    df = pd.read_sql_query(sa.text(
+    df = df.fillna(value=0)
-        'SELECT * FROM strokedata WHERE workoutid={id}'.format(
-            id=id
-        )), engine)
 
-    engine.dispose()
     return df
 
 def getcpdata_sql(rower_id,table='cpdata',debug=False):
@@ -1101,18 +1101,6 @@ def dataprep(rowdatadf,id=0,bands=True,barchart=True,otwpower=True,
     except KeyError:
         rowdatadf[' ElapsedTime (sec)'] = rowdatadf['TimeStamp (sec)']
         
-    if barchart:
-        # time increments for bar chart
-        time_increments = rowdatadf.loc[:,' ElapsedTime (sec)'].diff()
-        try:
-            time_increments.iloc[0] = time_increments.iloc[1]
-        except (KeyError, IndexError):
-            time_increments.iloc[1] = 1.
-            
-        time_increments = 0.5*time_increments+0.5*np.abs(time_increments)
-        x_right =  (t2+time_increments.apply(lambda x:timedeltaconv(x)))
-
-        data['x_right'] = x_right
 
     if empower:
         try:
@@ -1260,15 +1248,9 @@ 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)
+        filename = 'media/strokedata_{id}.parquet.gz'.format(id=id)
+        df = dd.from_pandas(data,npartitions=1)
+        df.to_parquet(filename,engine='fastparquet',compression='GZIP')
 
-        if debug:
-            engine = create_engine(database_url_debug, echo=False)
-        else:
-            engine = create_engine(database_url, echo=False)
-
-        with engine.connect() as conn, conn.begin():
-            data.to_sql('strokedata',engine,if_exists='append',index=False)
-                    
-        conn.close()
-        engine.dispose()    
     return data

rowers/interactiveplots.py

@@ -77,6 +77,7 @@ import rowers.stravastuff as stravastuff
 from rowers.dataprep import rdata
 import rowers.dataprep as dataprep
 import rowers.metrics as metrics
+import rowers.c2stuff as c2stuff
 
 from rowers.metrics import axes,axlabels,yaxminima,yaxmaxima
 
@@ -704,12 +705,20 @@ def interactive_forcecurve(theworkouts,workstrokesonly=True,plottype='scatter'):
         )
 
     if plottype == 'scatter':
-        sourcepoints = ColumnDataSource(
+        try:
-            data = dict(
+            sourcepoints = ColumnDataSource(
-                peakforceangle = rowdata['peakforceangle'],
+                data = dict(
-                peakforce = rowdata['peakforce']
+                    peakforceangle = rowdata['peakforceangle'],
+                    peakforce = rowdata['peakforce']
+                )
             )
-        )
+        except KeyError:
+            sourcepoints = ColumnDataSource(
+                data = dict(
+                    peakforceangle = [],
+                    peakforce = []
+                    )
+                )
     else:
         sourcepoints = ColumnDataSource(
             data = dict(
@@ -1807,7 +1816,7 @@ def interactive_agegroupcpchart(age,normalized=False):
     fhpower = []
     
     for distance in distances:
-        worldclasspower = metrics.getagegrouprecord(
+        worldclasspower = c2stuff.getagegrouprecord(
             age,
             sex='female',
             distance=distance,
@@ -1821,7 +1830,7 @@ def interactive_agegroupcpchart(age,normalized=False):
         except ZeroDivisionError:
             pass
     for duration in durations:
-        worldclasspower = metrics.getagegrouprecord(
+        worldclasspower = c2stuff.getagegrouprecord(
             age,
             sex='female',
             duration=duration,
@@ -1839,7 +1848,7 @@ def interactive_agegroupcpchart(age,normalized=False):
     flpower = []
     
     for distance in distances:
-        worldclasspower = metrics.getagegrouprecord(
+        worldclasspower = c2stuff.getagegrouprecord(
             age,
             sex='female',
             distance=distance,
@@ -1853,7 +1862,7 @@ def interactive_agegroupcpchart(age,normalized=False):
         except ZeroDivisionError:
             pass
     for duration in durations:
-        worldclasspower = metrics.getagegrouprecord(
+        worldclasspower = c2stuff.getagegrouprecord(
             age,
             sex='female',
             duration=duration,
@@ -1871,7 +1880,7 @@ def interactive_agegroupcpchart(age,normalized=False):
     mlpower = []
     
     for distance in distances:
-        worldclasspower = metrics.getagegrouprecord(
+        worldclasspower = c2stuff.getagegrouprecord(
             age,
             sex='male',
             distance=distance,
@@ -1885,7 +1894,7 @@ def interactive_agegroupcpchart(age,normalized=False):
         except ZeroDivisionError:
             pass
     for duration in durations:
-        worldclasspower = metrics.getagegrouprecord(
+        worldclasspower = c2stuff.getagegrouprecord(
             age,
             sex='male',
             duration=duration,
@@ -1904,7 +1913,7 @@ def interactive_agegroupcpchart(age,normalized=False):
     mhpower = []
     
     for distance in distances:
-        worldclasspower = metrics.getagegrouprecord(
+        worldclasspower = c2stuff.getagegrouprecord(
             age,
             sex='male',
             distance=distance,
@@ -1918,7 +1927,7 @@ def interactive_agegroupcpchart(age,normalized=False):
         except ZeroDivisionError:
             pass
     for duration in durations:
-        worldclasspower = metrics.getagegrouprecord(
+        worldclasspower = c2stuff.getagegrouprecord(
             age,
             sex='male',
             duration=duration,
@@ -2691,11 +2700,11 @@ def interactive_chart(id=0,promember=0,intervaldata = {}):
     row = Workout.objects.get(id=id)
     if datadf.empty:
         return "","No Valid Data Available"
-    else:
+    #else:
-        try:
+    #    try:
-            datadf.sort_values(by='time',ascending=True,inplace=True)
+    #        datadf.sort_values(by='time',ascending=True,inplace=True)
-        except KeyError:
+    #    except KeyError:
-            return "","No valid data available"
+    #        return "","No valid data available"
 
     try:
         spm = datadf['spm']
@@ -2707,10 +2716,6 @@ def interactive_chart(id=0,promember=0,intervaldata = {}):
     except KeyError:
         datadf['pace'] = 0
         
-    #datadf,row = dataprep.getrowdata_db(id=id)
-    #if datadf.empty:
-        #return "","No Valid Data Available"
-
     source = ColumnDataSource(
         datadf
         )
@@ -3518,11 +3523,11 @@ def interactive_flex_chart2(id=0,promember=0,
     row = Workout.objects.get(id=id)
     if rowdata.empty:
         return "","No valid data",'','',workstrokesonly
-    else:
+    #else:
-        try:
+    #    try:
-            rowdata.sort_values(by='time',ascending=True,inplace=True)
+    #        rowdata.sort_values(by='time',ascending=True,inplace=True)
-        except KeyError:
+    #    except KeyError:
-            pass
+    #        pass
 
     workoutstateswork = [1,4,5,8,9,6,7]
     workoutstatesrest = [3]
@@ -4056,15 +4061,7 @@ def thumbnails_set(r,id,favorites):
     columns += [f.yparam2 for f in favorites]
 
     columns += ['time']
-    try:
+    rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],doclean=True)
-        rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],doclean=True)
-    except:
-        return [
-            {'script':"",
-             'div':"",
-             'notes':""
-             }]
-        
         
     rowdata.dropna(axis=1,how='all',inplace=True)
 
@@ -4086,11 +4083,11 @@ def thumbnails_set(r,id,favorites):
              'notes':""
              }]
         
-    else:
+    #    else:
-        try:
+    #        try:
-            rowdata.sort_values(by='time',ascending=True,inplace=True)
+    #            rowdata.sort_values(by='time',ascending=True,inplace=True)
-        except KeyError:
+    #        except KeyError:
-            pass
+    #            pass
 
     l = len(rowdata)
     maxlength = 50
@@ -4665,13 +4662,13 @@ def interactive_comparison_chart(id1=0,id2=0,xparam='distance',yparam='spm',
 
     if rowdata1.empty:
         return "","No Valid Data Available"
-    else:
+#    else:
-        rowdata1.sort_values(by='time',ascending=True,inplace=True)
+#        rowdata1.sort_values(by='time',ascending=True,inplace=True)
     
     if rowdata2.empty:
         return "","No Valid Data Available"
-    else:
+#    else:
-        rowdata2.sort_values(by='time',ascending=True,inplace=True)
+#        rowdata2.sort_values(by='time',ascending=True,inplace=True)
 
     try:
         x1 = rowdata1.loc[:,xparam]

rowers/metrics.py

@@ -6,7 +6,7 @@ from __future__ import unicode_literals
 from __future__ import absolute_import
 from rowers.utils import lbstoN
 import numpy as np
-from rowers.models import C2WorldClassAgePerformance
+
 import pandas as pd
 from scipy import optimize
 from django.utils import timezone
@@ -290,7 +290,13 @@ rowingmetrics = (
 
     )
     
+dtypes = {}
 
+for name,d in rowingmetrics:
+    if d['numtype'] == 'float':
+        dtypes[name] = float
+    elif d['numtype'] == 'int':
+        dtypes[name] = int
         
 axesnew = [
     (name,d['verbose_name'],d['ax_min'],d['ax_max'],d['type']) for name,d in rowingmetrics
@@ -391,64 +397,3 @@ def calc_trimp(df,sex,hrmax,hrmin,hrftp):
     return trimp,hrtss
 
 
-def getagegrouprecord(age,sex='male',weightcategory='hwt',
-                      distance=2000,duration=None,indf=pd.DataFrame()):
-
-    if not indf.empty:
-        if not duration:
-            df = indf[indf['distance'] == distance]
-        else:
-            duration = 60*int(duration)
-            df = indf[indf['duration'] == duration]
-    else:
-        if not duration:
-            df = pd.DataFrame(
-                list(
-                    C2WorldClassAgePerformance.objects.filter(
-                        distance=distance,
-                        sex=sex,
-                        weightcategory=weightcategory
-                    ).values()
-                )
-            )
-        else:
-            duration=60*int(duration)
-            df = pd.DataFrame(
-                list(
-                    C2WorldClassAgePerformance.objects.filter(
-                        duration=duration,
-                        sex=sex,
-                        weightcategory=weightcategory
-                    ).values()
-                )
-            )
-
-    if not df.empty:
-        ages = df['age']
-        powers = df['power']
-
-        #poly_coefficients = np.polyfit(ages,powers,6)
-        fitfunc = lambda pars, x: np.abs(pars[0])*(1-x/max(120,pars[1]))-np.abs(pars[2])*np.exp(-x/np.abs(pars[3]))+np.abs(pars[4])*(np.sin(np.pi*x/max(50,pars[5])))
-        errfunc = lambda pars, x,y: fitfunc(pars,x)-y
-
-        p0 = [700,120,700,10,100,100]
-
-        try:
-            p1, success = optimize.leastsq(errfunc,p0[:],
-                                       args = (ages,powers))
-        except:
-            p1 = p0
-            success = 0
-
-        if success:
-            power = fitfunc(p1, float(age))
-
-            #power = np.polyval(poly_coefficients,age)
-
-            power = 0.5*(np.abs(power)+power)
-        else:
-            power = 0
-    else:
-        power = 0
-
-    return power

rowers/models.py

@@ -28,6 +28,8 @@ from django_countries.fields import CountryField
 from scipy.interpolate import splprep, splev, CubicSpline
 import numpy as np
 
+import shutil
+
 from django.conf import settings
 from sqlalchemy import create_engine
 import sqlalchemy as sa
@@ -2805,6 +2807,12 @@ def auto_delete_file_on_delete(sender, instance, **kwargs):
     if instance.csvfilename+'.gz':
         if os.path.isfile(instance.csvfilename+'.gz'):
             os.remove(instance.csvfilename+'.gz')
+    # remove parquet file
+    try:
+        dirname = 'media/strokedata_{id}.parquet.gz'.format(id=instance.id)
+        shutil.rmtree(dirname)
+    except FileNotFoundError:
+        pass
 
 @receiver(models.signals.post_delete,sender=Workout)
 def update_duplicates_on_delete(sender, instance, **kwargs):
@@ -2842,20 +2850,20 @@ def update_duplicates_on_delete(sender, instance, **kwargs):
         
     
 # Delete stroke data from the database when a workout is deleted
-@receiver(models.signals.post_delete,sender=Workout)
+#@receiver(models.signals.post_delete,sender=Workout)
-def auto_delete_strokedata_on_delete(sender, instance, **kwargs):
+#def auto_delete_strokedata_on_delete(sender, instance, **kwargs):
-    if instance.id:
+#    if instance.id:
-        query = sa.text('DELETE FROM strokedata WHERE workoutid={id};'.format(
+#        query = sa.text('DELETE FROM strokedata WHERE workoutid={id};'.format(
-            id=instance.id,
+#            id=instance.id,
-            ))
+#            ))
-        engine = create_engine(database_url, echo=False)
+#        engine = create_engine(database_url, echo=False)
-        with engine.connect() as conn, conn.begin():
+#        with engine.connect() as conn, conn.begin():
-            try:
+#            try:
-                result = conn.execute(query)
+#                result = conn.execute(query)
-            except:
+#            except:
-                print("Database Locked")
+#                print("Database Locked")
-        conn.close()
+#        conn.close()
-        engine.dispose()
+#        engine.dispose()
 
 # Virtual Race results (for keeping results when workouts are deleted)
 @python_2_unicode_compatible

rowers/serializers.py

@@ -7,7 +7,7 @@ from __future__ import unicode_literals
 # Also optionally define POST, PATCH methods (create, update)
 
 from rest_framework import serializers
-from rowers.models import Workout,Rower,StrokeData,FavoriteChart
+from rowers.models import Workout,Rower,FavoriteChart
 
 import datetime
 

rowers/urls.py

@@ -7,7 +7,7 @@ from django.conf.urls import url, include
 from django.urls import path, re_path
 from django.contrib.auth.models import User
 from django.contrib.auth.decorators import login_required, permission_required
-from rowers.models import Workout,Rower,StrokeData,FavoriteChart
+from rowers.models import Workout,Rower,FavoriteChart
 
 from rest_framework import routers, serializers, viewsets,permissions
 from rest_framework.urlpatterns import format_suffix_patterns

rowers/views/statements.py

@@ -99,7 +99,7 @@ from rowers.models import (
     )
 from rowers.models import (
     RowerPowerForm,RowerForm,GraphImage,AdvancedWorkoutForm,
-    RowerPowerZonesForm,AccountRowerForm,UserForm,StrokeData,
+    RowerPowerZonesForm,AccountRowerForm,UserForm,
     Team,TeamForm,TeamInviteForm,TeamInvite,TeamRequest,
     WorkoutComment,WorkoutCommentForm,RowerExportForm,
     CalcAgePerformance,
@@ -256,19 +256,17 @@ def getfavorites(r,row):
     if 'speedcoach2' in row.workoutsource:
         workoutsource = 'speedcoach2'
         
-    try:
+    favorites = FavoriteChart.objects.filter(user=r,
-        favorites = FavoriteChart.objects.filter(user=r,
+                                             workouttype__in=matchworkouttypes).order_by("id")
-                                                 workouttype__in=matchworkouttypes).order_by("id")
+    favorites2 = FavoriteChart.objects.filter(user=r,
-        favorites2 = FavoriteChart.objects.filter(user=r,
+                                              workouttype__in=[workoutsource]).order_by("id")
-                                                  workouttype__in=[workoutsource]).order_by("id")
-
-        favorites = favorites | favorites2
 
 
-        maxfav = len(favorites)-1
+
-    except:
+    favorites = favorites | favorites2
-        favorites = None
+    
-        maxfav = 0
+    maxfav = len(favorites)-1
+
 
     return favorites,maxfav
     

rowers/views/workoutviews.py

@@ -2768,7 +2768,6 @@ def workout_workflow_view(request,id):
 
     favorites,maxfav = getfavorites(r,row)
 
-
     charts = get_call()