from utils import lbstoN
import numpy as np
from models import C2WorldClassAgePerformance
import pandas as pd
from scipy import optimize
from django.utils import timezone

rowingmetrics = (
    ('time',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Time',
        'ax_min': 0,
        'ax_max': 1e5,
        'mode':'both',
        'type': 'basic'}),
    
    ('hr',{
        'numtype':'integer',
        'null':True,
        'verbose_name': 'Heart Rate (bpm)',
        'ax_min': 100,
        'ax_max': 200,
        'mode':'both',
        'type': 'basic'}),

    ('pace',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Pace (/500m)',
        'ax_min': 1.0e3*210,
        'ax_max': 1.0e3*75,
        'mode':'both',
        'type': 'basic'}),

    ('spm',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Stroke Rate (spm)',
        'ax_min': 15,
        'ax_max': 45,
        'mode':'both',
        'type': 'basic'}),
    
    ('driveenergy',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Work Per Stroke (J)',
        'ax_min': 0,
        'ax_max': 1000,
        'mode':'both',
        'type': 'pro'}),
    
    ('power',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Power (W)',
        'ax_min': 0,
        'ax_max': 600,
        'mode':'both',
        'type': 'basic'}),
    
    ('averageforce',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Average Drive Force (N)',
        'ax_min': 0,
        'ax_max': 1200,
        'mode':'both',
        'type': 'pro'}),
    
    ('peakforce',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Peak Drive Force (N)',
        'ax_min': 0,
        'ax_max': 1500,
        'mode':'both',
        'type': 'pro'}),
    
    ('drivelength',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Drive Length (m)',
        'ax_min': 0,
        'ax_max': 2.5,
        'mode':'both',
        'type': 'pro'}),

    ('forceratio',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Average/Peak Force Ratio',
        'ax_min': 0,
        'ax_max': 1,
        'mode':'both',
        'type': 'pro'}),

    ('distance',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Distance (m)',
        'ax_min': 0,
        'ax_max': 1e5,
        'mode':'both',
        'type': 'basic'}),

    ('cumdist',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Cumulative Distance (m)',
        'ax_min': 0,
        'ax_max': 1e5,
        'mode':'both',
        'type': 'basic'}),

    ('drivespeed',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Drive Speed (m/s)',
        'ax_min': 0,
        'ax_max': 4,
        'default': 0,
        'mode':'both',
        'type': 'pro'}),

    ('catch',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Catch Angle (degrees)',
        'ax_min': -40,
        'ax_max': -75,
        'default': 0,
        'mode':'water',
        'type': 'pro'}),

    ('slip',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Slip (degrees)',
        'ax_min': 0,
        'ax_max': 20,
        'default': 0,
        'mode':'water',
        'type': 'pro'}),

    ('finish',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Finish Angle (degrees)',
        'ax_min': 20,
        'ax_max': 55,
        'default': 0,
        'mode':'water',
        'type': 'pro'}),

    ('wash',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Wash (degrees)',
        'ax_min': 0,
        'ax_max': 30,
        'default': 0,
        'mode':'water',
        'type': 'pro'}),

    ('peakforceangle',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Peak Force Angle',
        'ax_min': -20,
        'ax_max': 20,
        'default': 0,
        'mode':'water',
        'type': 'pro'}),

    
    ('totalangle',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Drive Length (deg)',
        'ax_min': 40,
        'ax_max': 140,
        'default': 0,
        'mode':'water',
        'type': 'pro'}),

    
    ('effectiveangle',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Effective Drive Length (deg)',
        'ax_min': 40,
        'ax_max': 140,
        'default': 0,
        'mode':'water',
        'type': 'pro'}),

    ('rhythm',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Stroke Rhythm',
        'ax_min': 20,
        'ax_max': 55,
        'default': 1.0,
        'mode':'erg',
        'type': 'pro'}),

    ('efficiency',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'OTW Efficiency (%)',
        'ax_min': 0,
        'ax_max': 110,
        'default': 0,
        'mode':'water',
        'type': 'pro'}),

    ('distanceperstroke',{
        'numtype':'float',
        'null':True,
        'verbose_name': 'Distance per Stroke (m)',
        'ax_min': 0,
        'ax_max': 15,
        'default': 0,
        'mode':'both',
        'type': 'basic'}),

    )
    
    
    
axesnew = [
    (name,d['verbose_name'],d['ax_min'],d['ax_max'],d['type']) for name,d in rowingmetrics
    ]
    
axes = tuple(axesnew+[('None','None',0,1,'basic')])

axlabels = {ax[0]:ax[1] for ax in axes}

yaxminima = {ax[0]:ax[2] for ax in axes}

yaxmaxima = {ax[0]:ax[3] for ax in axes}

defaultfavoritecharts = (
    {
        'yparam1':'pace',
        'yparam2':'spm',
        'xparam':'time',
        'plottype':'line',
        'workouttype':'both',
        'reststrokes':True,
        'notes':"""This chart shows your pace and stroke rate versus time. """,
        },
    {
        'yparam1':'pace',
        'yparam2':'hr',
        'xparam':'time',
        'plottype':'line',
        'workouttype':'both',
        'reststrokes':True,
        'notes':"""This chart shows your pace and heart rate versus time.
Heart rate values will be shown only when it is in your data, i.e. 
in case you recorded your heart rate during your workout""",
        },
    {
        'yparam1':'distanceperstroke',
        'yparam2':'hr',
        'xparam':'time',
        'plottype':'line',
        'workouttype':'otw',
        'reststrokes':True,
        'notes':"""This chart shows the Distance covered per stroke, and your
heart rate versus time. """,
        },
    {
        'yparam1':'driveenergy',
        'yparam2':'hr',
        'xparam':'time',
        'plottype':'line',
        'workouttype':'ote',
        'reststrokes':True,
        'notes':"""This chart shows the Work per Stroke and your heart rate
plotted versus time. """,
        },
    {
        'yparam1':'distanceperstroke',
        'yparam2':'None',
        'xparam':'spm',
        'plottype':'scatter',
        'workouttype':'otw',
        'reststrokes':True,
        'notes':"""This chart shows the Distance per Stroke versus stroke 
stroke rate. You should see a steady decline of the Distance per Stroke
as you increase stroke rate. Typical values are > 10m for steady state 
dropping to 8m for race pace in the single.""",
        },
    {
        'yparam1':'driveenergy',
        'yparam2':'None',
        'xparam':'spm',
        'plottype':'scatter',
        'workouttype':'ote',
        'reststrokes':True,
        'notes':"""This chart shows the Work per Stroke versus Stroke Rate.
This value should be fairly constant across all stroke rates.""",
        },
    )


def calc_trimp(df,sex,hrmax,hrmin):
    if sex == 'male':
        f = 1.92
    else:
        f = 1.67

    dt = df['time'].diff()/6.e4

    hrr = (df['hr']-hrmin)/(hrmax-hrmin)
    trimpdata = dt*hrr*0.64*np.exp(f*hrr)
    trimp = trimpdata.sum()

    return trimp


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
