from rowers.models import Workout, User, Rower
from rowingdata import rowingdata as rrdata

from rowingdata import rower as rrower
from rowingdata import main as rmain

from pandas import DataFrame,Series

import pandas as pd
import numpy as np

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):
    dt = datetime.timedelta(seconds=x)

    return dt

def rdata(file,rower=rrower()):
    try:
	res = rrdata(file,rower=rower)
    except IOError:
	res = 0

    return res

def getrowdata(id=0):

    # check if valid ID exists (workout exists)
    row = Workout.objects.get(id=id)

    f1 = row.csvfilename
	
    # get user

    r = row.user
    u = r.user

    rr = rrower(hrmax=r.max,hrut2=r.ut2,
		hrut1=r.ut1,hrat=r.at,
		hrtr=r.tr,hran=r.an)

    rowdata = rdata(f1,rower=rr)

    return rowdata,row

def dataprep(rowdatadf):
    rowdatadf.set_index([range(len(rowdatadf))],inplace=True)
    t = rowdatadf.ix[:,'TimeStamp (sec)']
    t = pd.Series(t-rowdatadf.ix[0,'TimeStamp (sec)'])

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

    p = rowdatadf.ix[:,' Stroke500mPace (sec/500m)']
    hr = rowdatadf.ix[:,' HRCur (bpm)']
    spm = rowdatadf.ix[:,' Cadence (stokes/min)']
    cumdist = rowdatadf.ix[:,'cum_dist']

    power = rowdatadf.ix[:,' Power (watts)']
    averageforce = rowdatadf.ix[:,' AverageDriveForce (lbs)']
    drivelength = rowdatadf.ix[:,' DriveLength (meters)']


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


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

    if windowsize > 3:
	spm = savgol_filter(spm,windowsize,3)
	hr = savgol_filter(hr,windowsize,3)
	drivelength = savgol_filter(drivelength,windowsize,3)

    t2 = t.fillna(method='ffill').apply(lambda x: timedeltaconv(x))

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


    drivespeed = drivelength/rowdatadf[' DriveTime (ms)']*1.0e3
    driveenergy = drivelength*averageforce*4.44822
    distance = rowdatadf.ix[:,'cum_dist']

    data = DataFrame(
	dict(
	    time = t2,
	    timesecs = t,
	    hr = hr,
	    pace = p2,
	    pseconds=p,
	    spm = spm,
	    cumdist = cumdist,
	    ftime = niceformat(t2),
	    fpace = nicepaceformat(p2),
	    driveenergy=driveenergy,
	    power=power,
	    averageforce=averageforce,
	    drivelength=drivelength,
	    peakforce=peakforce,
	    distance=distance,
	    drivespeed=drivespeed
	    )
	)

    return data