from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

# This is Data prep used for testing purposes (no Django environment)
# Uses the debug SQLite database for stroke data
from rowingdata import rowingdata as rrdata
from rowingdata import make_cumvalues
from rowingdata import rower as rrower
from rowingdata import main as rmain
from rowingdata import empower_bug_correction,get_empower_rigging, get_file_type
from rowingdata.csvparsers import make_cumvalues_array
from time import strftime
from pandas import DataFrame,Series

import shutil
from shutil import copyfile
import pyarrow as pa

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

from rowsandall_app.settings import DATABASES
from rowsandall_app.settings_dev import DATABASES as DEV_DATABASES
from rowsandall_app.settings_dev import use_sqlite

from rowers.utils import lbstoN
import pytz
from timezonefinder import TimezoneFinder


try:
    user = DATABASES['default']['USER']
except KeyError:
    user = ''
try:
    password = DATABASES['default']['PASSWORD']
except KeyError:
    password = ''

try:
    database_name = DATABASES['default']['NAME']
except KeyError:
    database_name = ''
try:
    host = DATABASES['default']['HOST']
except KeyError:
    host = ''
try:
    port = DATABASES['default']['PORT']
except KeyError:
    port = ''

database_url = 'mysql://{user}:{password}@{host}:{port}/{database_name}'.format(
    user=user,
    password=password,
    database_name=database_name,
    host=host,
    port=port,
    )

database_name_dev = DEV_DATABASES['default']['NAME']

database_url_debug = database_url

if use_sqlite:
    database_url_debug = 'sqlite:///'+database_name_dev
    database_url = database_url_debug



# mapping the DB column names to the CSV file column names
columndict = {
    'time':'TimeStamp (sec)',
    'hr':' HRCur (bpm)',
    'velo': ' AverageBoatSpeed (m/s)',
    'pace':' Stroke500mPace (sec/500m)',
    'spm':' Cadence (stokes/min)',
    'power':' Power (watts)',
    'averageforce':' AverageDriveForce (lbs)',
    'drivelength':' DriveLength (meters)',
    'peakforce':' PeakDriveForce (lbs)',
    'distance':' Horizontal (meters)',
    'catch':'catch',
    'finish':'finish',
    'peakforceangle':'peakforceangle',
    'wash':'wash',
    'slip':'wash',
    'workoutstate':' WorkoutState',
    'cumdist':'cum_dist',
    }

from scipy.signal import savgol_filter

import datetime

def niceformat(values):
    out = []
    for v in values:
        formattedv = strfdelta(v)
        out.append(formattedv)

    return out

def strfdelta(tdelta):
    try:
        minutes,seconds = divmod(tdelta.seconds,60)
        tenths = int(tdelta.microseconds/1e5)
    except AttributeError:
        minutes,seconds = divmod(tdelta.view(np.int64),60e9)
        seconds,rest = divmod(seconds,1e9)
        tenths = int(rest/1e8)
    res = "{minutes:0>2}:{seconds:0>2}.{tenths:0>1}".format(
        minutes=minutes,
        seconds=seconds,
        tenths=tenths,
        )

    return res

def nicepaceformat(values):
    out = []
    for v in values:
        formattedv = strfdelta(v)
        out.append(formattedv)


    return out

def timedeltaconv(x):
    if not np.isnan(x):
        dt = datetime.timedelta(seconds=x)
    else:
        dt = datetime.timedelta(seconds=350.)


    return dt

def rdata(file,rower=rrower()):
    try:
        res = rrdata(csvfile=file,rower=rower)
    except IOError:
        try:
            res = rrdata(csvfile=file+'.gz',rower=rower)
        except IOError:
            res = 0

    return res

from rowers.utils import totaltime_sec_to_string
from rowers.metrics import dtypes


# Creates C2 stroke data
def create_c2_stroke_data_db(
        distance,duration,workouttype,
        workoutid,starttimeunix,csvfilename,debug=False):

    nr_strokes = int(distance/10.)


    totalseconds = duration.hour*3600.
    totalseconds += duration.minute*60.
    totalseconds += duration.second
    totalseconds += duration.microsecond/1.e6

    try:
        spm = 60.*nr_strokes/totalseconds
    except ZeroDivisionError:
        spm = 20*np.zeros(nr_strokes)

    try:
        step = totalseconds/float(nr_strokes)
    except ZeroDivisionError:
        return 0

    elapsed = np.arange(nr_strokes)*totalseconds/(float(nr_strokes-1))

    dstep = distance/float(nr_strokes)

    d = np.arange(nr_strokes)*distance/(float(nr_strokes-1))

    unixtime = starttimeunix + elapsed

    pace = 500.*totalseconds/distance

    if workouttype in ['rower','slides','dynamic']:
        try:
            velo = distance/totalseconds
        except ZeroDivisionError:
            velo = 0
        power = 2.8*velo**3
    else:
        power = 0


    df = pd.DataFrame({
        'TimeStamp (sec)': unixtime,
        ' Horizontal (meters)': d,
        ' Cadence (stokes/min)': spm,
        ' Stroke500mPace (sec/500m)':pace,
        ' ElapsedTime (sec)':elapsed,
        ' Power (watts)':power,
        ' HRCur (bpm)':np.zeros(nr_strokes),
        ' longitude':np.zeros(nr_strokes),
        ' latitude':np.zeros(nr_strokes),
        ' DragFactor':np.zeros(nr_strokes),
        ' DriveLength (meters)':np.zeros(nr_strokes),
        ' StrokeDistance (meters)':np.zeros(nr_strokes),
        ' DriveTime (ms)':np.zeros(nr_strokes),
        ' StrokeRecoveryTime (ms)':np.zeros(nr_strokes),
        ' AverageDriveForce (lbs)':np.zeros(nr_strokes),
        ' PeakDriveForce (lbs)':np.zeros(nr_strokes),
        ' lapIdx':np.zeros(nr_strokes),
        'cum_dist': d
    })

    timestr = strftime("%Y%m%d-%H%M%S")

    df[' ElapsedTime (sec)'] = df['TimeStamp (sec)']

    res =  df.to_csv(csvfilename,index_label='index',
                     compression='gzip')

    data = dataprep(df,id=workoutid,bands=False,debug=debug)

    return data

# Saves C2 stroke data to CSV and database
def add_c2_stroke_data_db(strokedata,workoutid,starttimeunix,csvfilename,
                          debug=False,workouttype='rower'):

    res = make_cumvalues(0.1*strokedata['t'])
    cum_time = res[0]
    lapidx = res[1]

    unixtime = cum_time+starttimeunix
    # unixtime[0] = starttimeunix
    seconds = 0.1*strokedata.loc[:,'t']

    nr_rows = len(unixtime)

    try:
        latcoord = strokedata.loc[:,'lat']
        loncoord = strokedata.loc[:,'lon']
    except:
        latcoord = np.zeros(nr_rows)
        loncoord = np.zeros(nr_rows)


    try:
        strokelength = strokedata.loc[:,'strokelength']
    except:
        strokelength = np.zeros(nr_rows)

    dist2 = 0.1*strokedata.loc[:,'d']

    try:
        spm = strokedata.loc[:,'spm']
    except KeyError:
        spm = 0*dist2

    try:
        hr = strokedata.loc[:,'hr']
    except KeyError:
        hr = 0*spm

    pace = strokedata.loc[:,'p']/10.
    pace = np.clip(pace,0,1e4)
    pace = pace.replace(0,300)

    velo = 500./pace
    power = 2.8*velo**3
    if workouttype == 'bike':
        velo = 1000./pace



    # save csv
    # Create data frame with all necessary data to write to csv
    df = pd.DataFrame({'TimeStamp (sec)':unixtime,
                       ' Horizontal (meters)': dist2,
                       ' Cadence (stokes/min)':spm,
                       ' HRCur (bpm)':hr,
                       ' longitude':loncoord,
                       ' latitude':latcoord,
                       ' Stroke500mPace (sec/500m)':pace,
                       ' Power (watts)':power,
                       ' DragFactor':np.zeros(nr_rows),
                       ' DriveLength (meters)':np.zeros(nr_rows),
                       ' StrokeDistance (meters)':strokelength,
                       ' DriveTime (ms)':np.zeros(nr_rows),
                       ' StrokeRecoveryTime (ms)':np.zeros(nr_rows),
                       ' AverageDriveForce (lbs)':np.zeros(nr_rows),
                       ' PeakDriveForce (lbs)':np.zeros(nr_rows),
                       ' lapIdx':lapidx,
                       ' WorkoutState': 4,
                       ' ElapsedTime (sec)':seconds,
                       'cum_dist': dist2
                       })


    df.sort_values(by='TimeStamp (sec)',ascending=True)

    timestr = strftime("%Y%m%d-%H%M%S")


    # Create CSV file name and save data to CSV file

    res =  df.to_csv(csvfilename,index_label='index',
                     compression='gzip')


    try:
        data = dataprep(df,id=workoutid,bands=False,debug=debug)
    except:
        return 0

    return data


def handle_nonpainsled(f2,fileformat,summary=''):
    oarlength = 2.89
    inboard = 0.88
    # handle RowPro:
    if (fileformat == 'rp'):
        row = RowProParser(f2)
        # handle TCX
    if (fileformat ==  'tcx'):
        row = TCXParser(f2)

    # handle Mystery
    if (fileformat == 'mystery'):
        row = MysteryParser(f2)

    # handle RowPerfect
    if (fileformat == 'rowperfect3'):
        row = RowPerfectParser(f2)

    # handle ErgData
    if (fileformat == 'ergdata'):
        row = ErgDataParser(f2)

    # handle CoxMate
    if (fileformat == 'coxmate'):
        row = CoxMateParser(f2)

    # handle Mike
    if (fileformat == 'bcmike'):
        row = BoatCoachAdvancedParser(f2)

    # handle BoatCoach OTW
    if (fileformat == 'boatcoachotw'):
        row = BoatCoachOTWParser(f2)

    # handle BoatCoach
    if (fileformat == 'boatcoach'):
        row = BoatCoachParser(f2)

    # handle painsled desktop
    if (fileformat == 'painsleddesktop'):
        row = painsledDesktopParser(f2)

    # handle speed coach GPS
    if (fileformat == 'speedcoach'):
        row = speedcoachParser(f2)

    # handle speed coach GPS 2
    if (fileformat == 'speedcoach2'):
        row = SpeedCoach2Parser(f2)
        try:
            oarlength,inboard = get_empower_rigging(f2)
            summary = row.allstats()
        except:
            pass


    # handle ErgStick
    if (fileformat == 'ergstick'):
        row = ErgStickParser(f2)

    # handle FIT
    if (fileformat == 'fit'):
        row = FITParser(f2)
        s = fitsummarydata(f2)
        s.setsummary()
        summary = s.summarytext


    f_to_be_deleted = f2
    # should delete file
    f2 = f2[:-4]+'o.csv'
    row.write_csv(f2,gzip=True)

    #os.remove(f2)
    try:
        os.remove(f_to_be_deleted)
    except:
        os.remove(f_to_be_deleted+'.gz')

    return (f2,summary,oarlength,inboard)



def delete_strokedata(id,debug=False):
    dirname = 'media/strokedata_{id}.parquet.gz'.format(id=id)
    try:
        shutil.rmtree(dirname)
    except FileNotFoundError:
        pass

def update_strokedata(id,df,debug=False):
    delete_strokedata(id,debug=debug)
    if debug:
        print("updating ",id)
    rowdata = dataprep(df,id=id,bands=True,barchart=True,otwpower=True,
                       debug=debug)

    return rowdata

def update_empower(id, inboard, oarlength, boattype, df, f1, debug=False):

    corr_factor = 1.0
    if 'x' in boattype:
        # sweep
        a = 0.06
        b = 0.275
    else:
        # scull
        a = 0.15
        b = 0.275

    corr_factor = empower_bug_correction(oarlength,inboard,a,b)

    success = False

    try:
        df['power empower old'] = df[' Power (watts)']
        df[' Power (watts)'] = df[' Power (watts)'] * corr_factor
        df['driveenergy empower old'] = df['driveenergy']
        df['driveenergy'] = df['driveenergy'] * corr_factor
        success = True
    except KeyError:
        pass

    if success:
        delete_strokedata(id,debug=debug)
        if debug:
            print("updated ",id)
            print("correction ",corr_factor)
    else:
        if debug:
            print("not updated ",id)


    rowdata = dataprep(df,id=id,bands=True,barchart=True,otwpower=True,
                       debug=debug)

    row = rrdata(df=df)
    row.write_csv(f1,gzip=True)

    return success


def testdata(time,distance,pace,spm):
    t1 = np.issubdtype(time,np.number)
    t2 = np.issubdtype(distance,np.number)
    t3 = np.issubdtype(pace,np.number)
    t4 = np.issubdtype(spm,np.number)

    return t1 and t2 and t3 and t4



def getsmallrowdata_db(columns,ids=[],debug=False):
    csvfilenames = ['media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
    data = []
    columns = [c for c in columns if c != 'None']

    df = pd.DataFrame()

    if len(ids)>1:
        for id, f in zip(ids,csvfilenames):
            try:
                df = pd.read_parquet(f,columns=columns,engine='pyarrow')
                data.append(df)
            except OSError:
                pass
            except pa.lib.ArrowInvalid:
                pass

        try:
            df =  pd.concat(data,axis=0)
        except ValueError:
            df = pd.DataFrame()
    elif len(ids)==1:
        try:
            df = pd.read_parquet(csvfilenames[0],columns=columns,engine='pyarrow')
        except (OSError,IndexError):
            df = pd.DataFrame()
    else:
        df = pd.DataFrame()


    return df

def update_workout_field_sql(workoutid,fieldname,value,debug=False):
    if debug:
        engine = create_engine(database_url_debug, echo=False)
    else:
        engine = create_engine(database_url, echo=False)

    table = 'rowers_workout'

    query = "UPDATE %s SET %s = '%s' WHERE `id` = %s;" % (table,fieldname,value,workoutid)


    with engine.connect() as conn, conn.begin():
        result = conn.execute(query)

    conn.close()
    engine.dispose()

    return 1

def update_c2id_sql(id,c2id):
    engine = create_engine(database_url, echo=False)
    table = 'rowers_workout'

    query = "UPDATE %s SET uploadedtoc2 = %s WHERE `id` = %s;" % (table,c2id,id)

    with engine.connect() as conn, conn.begin():
        result = conn.execute(query)

    conn.close()
    engine.dispose()

    return 1





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))

    ids = [int(id) for id in ids]


    if len(ids) == 0:
        return pd.DataFrame()
    elif len(ids) == 1:
        try:
            filename = 'media/strokedata_{id}.parquet.gz'.format(id=ids[0])
            df = pd.read_parquet(filename,columns=columns)
        except OSError:
            pass
    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:
                pass

        df = pd.concat(data,axis=0)

    return df


def read_df_sql(id,debug=False):
    try:
        f = 'media/strokedata_{id}.parquet.gz'.format(id=id)
        df = pd.read_parquet(f)
    except OSError:
        pass

    df = df.fillna(value=0)

    return df

def getcpdata_sql(rower_id,table='cpdata',debug=False):
    if debug:
        engine = create_engine(database_url_debug, echo=False)
    else:
        engine = create_engine(database_url, echo=False)

    query = sa.text('SELECT * from {table} WHERE user={rower_id};'.format(
        rower_id=rower_id,
        table=table,
        ))
    connection = engine.raw_connection()
    df = pd.read_sql_query(query, engine)

    return df

def deletecpdata_sql(rower_id,table='cpdata',debug=False):
    if debug:
        engine = create_engine(database_url_debug, echo=False)
    else:
        engine = create_engine(database_url, echo=False)

    query = sa.text('DELETE from {table} WHERE user={rower_id};'.format(
        rower_id=rower_id,
        table=table,
        ))
    with engine.connect() as conn, conn.begin():
        try:
            result = conn.execute(query)
        except:
            print("Database locked")
    conn.close()
    engine.dispose()

def delete_agegroup_db(age,sex,weightcategory,debug=False):
    if debug:
        engine = create_engine(database_url_debug, echo=False)
    else:
        engine = create_engine(database_url, echo=False)

    query = sa.text('DELETE from {table} WHERE age={age} and weightcategory = {weightcategory} and sex={sex};'.format(
        sex=sex,
        age=age,
        weightcategory=weightcategory,
        table='calcagegrouprecords'
        ))
    with engine.connect() as conn, conn.begin():
        try:
            result = conn.execute(query)
        except:
            print("Database locked")
    conn.close()
    engine.dispose()

def update_agegroup_db(age,sex,weightcategory,wcdurations,wcpower,
                       debug=False):

    delete_agegroup_db(age,sex,weightcategory,debug=debug)

    wcdurations = [None if type(y) is float and np.isnan(y) else y for y in wcdurations]
    wcpower = [None if type(y) is float and np.isnan(y) else y for y in wcpower]

    df = pd.DataFrame(
        {
            'duration':wcdurations,
            'power':wcpower,
         }
    )

    df['sex'] = sex
    df['age'] = age
    df['weightcategory'] = weightcategory
    df.replace([np.inf,-np.inf],np.nan,inplace=True)
    df.dropna(axis=0,inplace=True)

    if debug:
        engine = create_engine(database_url_debug, echo=False)
    else:
        engine = create_engine(database_url, echo=False)

    table = 'calcagegrouprecords'
    with engine.connect() as conn, conn.begin():
        df.to_sql(table, engine, if_exists='append', index=False)
    conn.close()
    engine.dispose()


def updatecpdata_sql(rower_id,delta,cp,table='cpdata',distance=pd.Series([]),debug=False):
    deletecpdata_sql(rower_id,table=table,debug=debug)
    df = pd.DataFrame(
        {
            'delta':delta,
            'cp':cp,
            'user':rower_id
        }
    )


    if not distance.empty:
        df['distance'] = distance

    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():
        df.to_sql(table, engine, if_exists='append', index=False)
    conn.close()
    engine.dispose()






def smalldataprep(therows,xparam,yparam1,yparam2):
    df = pd.DataFrame()
    if yparam2 == 'None':
        yparam2 = 'power'
    df[xparam] = []
    df[yparam1] = []
    df[yparam2] = []
    df['distance'] = []
    df['spm'] = []
    for workout in therows:
        f1 = workout.csvfilename

        try:
            rowdata = dataprep(rrdata(csvfile=f1).df)

            rowdata = pd.DataFrame({xparam: rowdata[xparam],
                                    yparam1: rowdata[yparam1],
                                    yparam2: rowdata[yparam2],
                                    'distance': rowdata['distance'],
                                    'spm': rowdata['spm'],
            }
            )
            df = pd.concat([df,rowdata],ignore_index=True)
        except IOError:
            try:
                rowdata = dataprep(rrdata(csvfile=f1+'.gz').df)
                rowdata = pd.DataFrame({xparam: rowdata[xparam],
                                        yparam1: rowdata[yparam1],
                                        yparam2: rowdata[yparam2],
                                        'distance': rowdata['distance'],
                                        'spm': rowdata['spm'],
                }
                )
                df = pd.concat([df,rowdata],ignore_index=True)
            except IOError:
                pass

    return df


def dataprep(rowdatadf,id=0,bands=True,barchart=True,otwpower=True,
             empower=True,debug=False,inboard=0.88,forceunit='lbs'):

    if rowdatadf.empty:
        if debug:
            print("empty")
        return 0

    if debug:
        print("dataprep",id)

    # rowdatadf.set_index([range(len(rowdatadf))],inplace=True)
    t = rowdatadf.loc[:,'TimeStamp (sec)']
    t = pd.Series(t-rowdatadf.loc[:,'TimeStamp (sec)'].iloc[0])

    row_index = rowdatadf.loc[:,' Stroke500mPace (sec/500m)'] > 3000
    rowdatadf.loc[row_index,' Stroke500mPace (sec/500m)'] = 3000.

    p = rowdatadf.loc[:,' Stroke500mPace (sec/500m)']
    try:
        velo = rowdatadf.loc[:,' AverageBoatSpeed (m/s)']
    except KeyError:
        velo = 500./p

    hr = rowdatadf.loc[:,' HRCur (bpm)']
    spm = rowdatadf.loc[:,' Cadence (stokes/min)']
    cumdist = rowdatadf.loc[:,'cum_dist']

    power = rowdatadf.loc[:,' Power (watts)']
    averageforce = rowdatadf.loc[:,' AverageDriveForce (lbs)']
    drivelength = rowdatadf.loc[:,' DriveLength (meters)']
    try:
        workoutstate = rowdatadf.loc[:,' WorkoutState']
    except KeyError:
        workoutstate = 0*hr

    peakforce = rowdatadf.loc[:,' PeakDriveForce (lbs)']

    forceratio = averageforce/peakforce
    forceratio = forceratio.fillna(value=0)

    try:
        drivetime = rowdatadf.loc[:,' DriveTime (ms)']
        recoverytime = rowdatadf.loc[:,' StrokeRecoveryTime (ms)']
        rhythm = 100.*drivetime/(recoverytime+drivetime)
        rhythm = rhythm.fillna(value=0)
    except:
        rhythm = 0.0*forceratio

    f = rowdatadf['TimeStamp (sec)'].diff().mean()
    if f != 0:
        try:
            windowsize = 2*(int(10./(f)))+1
        except ValueError:
            windowsize = 1
    else:
        windowsize = 1
    if windowsize <= 3:
        windowsize = 5

    if windowsize > 3 and windowsize<len(hr):
        spm = savgol_filter(spm,windowsize,3)
        hr = savgol_filter(hr,windowsize,3)
        drivelength = savgol_filter(drivelength,windowsize,3)
        forceratio = savgol_filter(forceratio,windowsize,3)

    try:
        t2 = t.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
    except TypeError:
        t2 = 0*t


    p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x))

    try:
        drivespeed = drivelength/rowdatadf[' DriveTime (ms)']*1.0e3
    except KeyError:
        drivespeed = 0.0*rowdatadf['TimeStamp (sec)']
    except TypeError:
        drivespeed = 0.0*rowdatadf['TimeStamp (sec)']

    drivespeed = drivespeed.fillna(value=0)

    try:
        driveenergy = rowdatadf['driveenergy']
    except KeyError:
        if forceunit == 'lbs':
            driveenergy = drivelength*averageforce*lbstoN
        else:
            drivenergy = drivelength*averageforce

    distance = rowdatadf.loc[:,'cum_dist']

    velo = 500./p

    distanceperstroke = 60.*velo/spm

    if forceunit == 'lbs':
        averageforce *= lbstoN
        peakforce *= lbstoN


    data = DataFrame(
        dict(
            time=t * 1e3,
            hr=hr,
            pace=p * 1e3,
            spm=spm,
            velo=velo,
            cumdist=cumdist,
            ftime=niceformat(t2),
            fpace=nicepaceformat(p2),
            driveenergy=driveenergy,
            power=power,
            workoutstate=workoutstate,
            averageforce=averageforce,
            drivelength=drivelength,
            peakforce=peakforce,
            forceratio=forceratio,
            distance=distance,
            drivespeed=drivespeed,
            rhythm=rhythm,
            distanceperstroke=distanceperstroke,
        )
    )

    if bands:
        # HR bands
        data['hr_ut2'] = rowdatadf.loc[:,'hr_ut2']
        data['hr_ut1'] = rowdatadf.loc[:,'hr_ut1']
        data['hr_at'] = rowdatadf.loc[:,'hr_at']
        data['hr_tr'] = rowdatadf.loc[:,'hr_tr']
        data['hr_an'] = rowdatadf.loc[:,'hr_an']
        data['hr_max'] = rowdatadf.loc[:,'hr_max']
        data['hr_bottom'] = 0.0*data['hr']


    try:
        tel = rowdatadf.loc[:,' ElapsedTime (sec)']
    except KeyError:
        rowdatadf[' ElapsedTime (sec)'] = rowdatadf['TimeStamp (sec)']


    if empower:
        try:
            wash = rowdatadf.loc[:,'wash']
        except KeyError:
            wash = 0*t

        try:
            catch = rowdatadf.loc[:,'catch']
        except KeyError:
            catch = 0*t

        try:
            finish = rowdatadf.loc[:,'finish']
        except KeyError:
            finish = 0*t

        try:
            peakforceangle = rowdatadf.loc[:,'peakforceangle']
        except KeyError:
            peakforceangle = 0*t


        if data['driveenergy'].mean() == 0:
            try:
                driveenergy = rowdatadf.loc[:,'driveenergy']
            except KeyError:
                driveenergy = power*60/spm
        else:
            driveenergy = data['driveenergy']


        arclength = (inboard-0.05)*(np.radians(finish)-np.radians(catch))
        if arclength.mean()>0:
            drivelength = arclength
        elif drivelength.mean() == 0:
            drivelength = driveenergy/(averageforce*4.44822)

        try:
            slip = rowdatadf.loc[:,'slip']
        except KeyError:
            slip = 0*t

        try:
            totalangle = finish-catch
            effectiveangle = finish-wash-catch-slip
        except ValueError:
            totalangle = 0*t
            effectiveangle = 0*t


        if windowsize > 3 and windowsize<len(slip):
            try:
                wash = savgol_filter(wash,windowsize,3)
            except TypeError:
                pass
            try:
                slip = savgol_filter(slip,windowsize,3)
            except TypeError:
                pass
            try:
                catch = savgol_filter(catch,windowsize,3)
            except TypeError:
                pass
            try:
                finish = savgol_filter(finish,windowsize,3)
            except TypeError:
                pass
            try:
                peakforceangle = savgol_filter(peakforceangle,windowsize,3)
            except TypeError:
                pass
            try:
                driveenergy = savgol_filter(driveenergy,windowsize,3)
            except TypeError:
                pass
            try:
                drivelength = savgol_filter(drivelength,windowsize,3)
            except TypeError:
                pass
            try:
                totalangle = savgol_filter(totalangle,windowsize,3)
            except TypeError:
                pass
            try:
                effectiveangle = savgol_filter(effectiveangle,windowsize,3)
            except TypeError:
                pass

        velo = 500./p

        ergpw = 2.8*velo**3
        efficiency = 100.*ergpw/power

        efficiency = efficiency.replace([-np.inf,np.inf],np.nan)
        efficiency.fillna(method='ffill')

        try:
            data['wash'] = wash
            data['catch'] = catch
            data['slip'] = slip
            data['finish'] = finish
            data['peakforceangle'] = peakforceangle
            data['driveenergy'] = driveenergy
            data['drivelength'] = drivelength
            data['totalangle'] = totalangle
            data['effectiveangle'] = effectiveangle
            data['efficiency'] = efficiency
        except ValueError:
            pass

    if otwpower:
        try:
            nowindpace = rowdatadf.loc[:,'nowindpace']
        except KeyError:
            nowindpace = p
        try:
            equivergpower = rowdatadf.loc[:,'equivergpower']
        except KeyError:
            equivergpower = 0*p+50.

        nowindpace2 = nowindpace.apply(lambda x: timedeltaconv(x))
        ergvelo = (equivergpower/2.8)**(1./3.)

        ergpace = 500./ergvelo
        ergpace[ergpace == np.inf] = 240.
        ergpace2 = ergpace.apply(lambda x: timedeltaconv(x))



        data['ergpace'] = ergpace*1.e3
        data['nowindpace'] = nowindpace*1.e3
        data['equivergpower'] = equivergpower
        data['fergpace'] = nicepaceformat(ergpace2)
        data['fnowindpace'] = nicepaceformat(nowindpace2)
        data['efficiency'] = efficiency


    data = data.replace([-np.inf,np.inf],np.nan)
    data = data.fillna(method='ffill')

    data.dropna(axis=0,inplace=True,how='all')
    data.dropna(axis=1,inplace=True,how='any')

    # write data if id given
    if id != 0:
        data['workoutid'] = id
        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')

    return data