import pandas as pd
import numpy as np
from scipy.interpolate import griddata
from scipy import optimize

def updatecp(delta,cpvalues,r):
    cp2 = r.p0/(1+delta/r.p2)
    cp2 += r.p1/(1+delta/r.p3)

    delta = delta.append(delta)
    cp = cpvalues.append(cp2)

    powerdf = pd.DataFrame({
        'Delta':delta,
        'CP':cp,
        })

    powerdf.dropna(axis=0,inplace=True)
    powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)
    powerdf.drop_duplicates(subset='Delta',keep='first',inplace=True)
    

    res = cpfit(powerdf)
    p1 = res[0]

    r.p0 = p1[0]
    r.p1 = p1[1]
    r.p2 = p1[2]
    r.p3 = p1[3]

    r.save()

    return 1

def cpfit(powerdf):
    # Fit the data to thee parameter CP model
    fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
    errfunc = lambda pars,x,y: fitfunc(pars,x)-y
    
    p0 = [500,350,10,8000]

    p1 = p0

    thesecs = powerdf['Delta']
    theavpower = powerdf['CP']

    if len(thesecs)>=4:
	p1, success = optimize.leastsq(errfunc, p0[:], args = (thesecs,theavpower))
    else:
	factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
	p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]


    p1 = [abs(p) for p in p1]
    fitt = pd.Series(10**(4*np.arange(100)/100.))

    fitpower = fitfunc(p1,fitt)

    fitpoints = fitfunc(p1,thesecs)
    fitpoints0 = fitpoints.copy()
    dd = fitpoints-theavpower

    ddmin = dd.min()

    frac = abs(ddmin)/fitpoints.mean()

    while frac>0.0001:
        fitpoints = fitpoints*(fitpoints.mean()-ddmin)/(fitpoints.mean())
        dd = fitpoints-theavpower
        ddmin = dd.min()
        frac = abs(ddmin)/fitpoints.mean()
    
    ratio = fitpoints.mean()/fitpoints0.mean()
        
    return p1,fitt,fitpower,ratio

def getlogarr(maxt):
    maxlog10 = np.log10(maxt-5)
    logarr = np.log10(5.)+np.arange(50)*maxlog10/50.
    logarr = [int(10.**(la)) for la in logarr]
    logarr = pd.Series(logarr)
    logarr.drop_duplicates(keep='first',inplace=True)

    logarr = logarr.values
    return logarr

def getsinglecp(df):
    thesecs = df['TimeStamp (sec)'].max()-df['TimeStamp (sec)'].min()
    if thesecs != 0:
        maxt = 2*thesecs
    else:
        maxt = 1000.

    logarr = getlogarr(maxt)


    dfnew = pd.DataFrame({
        'time':1000*(df['TimeStamp (sec)']-df.ix[0,'TimeStamp (sec)']),
        'power':df[' Power (watts)']
    })
    
    dfnew['workoutid'] = 0
    
    dfgrouped = dfnew.groupby(['workoutid'])
    delta,cpvalue,avgpower = getcp(dfgrouped,logarr)

    return delta,cpvalue,avgpower

def getcp(dfgrouped,logarr):
    delta = []
    cpvalue = []
    avgpower = {}
    #avgpower[0] = 0

    for id,group in dfgrouped:
        tt = group['time'].copy()
        ww = group['power'].copy()

        tmax = tt.max()
        if tmax > 500000:
            newlen = int(tmax/2000.)
        else:
            newlen = len(tt)
        if newlen < len(tt):
            newt = np.arange(newlen)*tmax/float(newlen)
            ww = griddata(tt.values,
                          ww.values,
                          newt,method='linear',
                          rescale=True)

            tt = pd.Series(newt)
            ww = pd.Series(ww)

            
        try:
            avgpower[id] = int(ww.mean())
        except ValueError:
            avgpower[id] = '---'
        if not np.isnan(ww.mean()):
            length = len(ww)
            dt = []
            cpw = []
            for i in xrange(length-2):
                deltat,wmax = getmaxwattinterval(tt,ww,i)
                if not np.isnan(deltat) and not np.isnan(wmax):
                    dt.append(deltat)
                    cpw.append(wmax)


            
            dt = pd.Series(dt)
            cpw = pd.Series(cpw)
            if len(dt):
            
                cpvalues = griddata(dt.values,
                                    cpw.values,
                                    logarr,method='linear',
                                    rescale=True)

                for cpv in cpvalues:
                    cpvalue.append(cpv)
                for d in logarr:
                    delta.append(d)
            
    delta = pd.Series(delta,name='Delta')
    cpvalue = pd.Series(cpvalue,name='CP')
    return delta,cpvalue,avgpower

def getmaxwattinterval(tt,ww,i):
    w_roll = ww.rolling(i+2).mean().dropna()
    if len(w_roll):
        # now goes with # data points - should be fixed seconds
        indexmax = w_roll.idxmax(axis=1)
        try:
            t_0 = tt.ix[indexmax]
            t_1 = tt.ix[indexmax-i]
            deltat = 1.0e-3*(t_0-t_1)
            wmax = w_roll.ix[indexmax]
        except KeyError:
            pass
    else:
        wmax = 0
        deltat = 0

    return deltat,wmax