from rowers.utils import lbstoN
import numpy as np
import yaml
import pandas as pd
from scipy import optimize
from django.utils import timezone
from polars import String, Int32, Float64

from math import log10, log2
from rowers.mytypes import otwtypes, otetypes

nometrics = [
    'originalvelo',
    #    'cumdist',
    'strokes_slsh_min',
    ' WorkPerStroke (joules)',
    ' activityIdx',
    ' lapIdx',
    #    ' pointIdx',
    ' WorkoutType',
    ' IntervalType',
    ' WorkoutState',
    ' RowingState',
    ' WorkoutDurationType',
    ' WorkoutIntervalCount',
    'ergpace',
    'ref',
    'id',
    'deltat',
    'workoutid',
    'totalangle',
    'hr_bottom',
    'Position',
    'Extensions',
    'GPS Speed',
    'Split (IMP)',
    'Speed (IMP)',
    'driveenergy',
    'Distance/Stroke (IMP)',
    'Distance/Stroke (GPS)',
    'Distance (IMP)',
    'equivergpower',
    'cum_dist.1'
]

with open("rowingmetrics.yaml", "r") as file:
    td = yaml.safe_load(file)

rowingmetrics = tuple(
    (key, value)
    for key, value in td.items()
)



metricsdicts = {}
for key, dict in rowingmetrics:
    metricsdicts[key] = dict



metricsgroups = list(set([d['group'] for n, d in rowingmetrics]))

dtypes = {}

for name, d in rowingmetrics:
    if d['numtype'] == 'float':
        dtypes[name] = float
    elif d['numtype'] == 'int':  # pragma: no cover
        dtypes[name] = int

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}


def get_yaxminima(r, metric, mode):
    if metric == 'pace':
        if mode in otetypes:
            return r.slowpaceerg
        else:
            return r.slowpaceotw

    return yaxminima[metric]


def get_yaxmaxima(r, metric, mode):
    if metric == 'pace':
        if mode in otetypes:
            return r.fastpaceerg
        else:
            return r.fastpaceotw

    return yaxmaxima[metric]


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, hrftp):  # pragma: no cover
    if sex == 'male':
        f = 1.92
    else:
        f = 1.67

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

    hrr = (df['hr']-hrmin)/(hrmax-hrmin)
    hrrftp = (hrftp-hrmin)/float(hrmax-hrmin)
    trimp1hr = 60*hrrftp*0.64*np.exp(f*hrrftp)

    trimpdata = dt*hrr*0.64*np.exp(f*hrr)
    trimp = trimpdata.sum()

    hrtss = 100*trimp/trimp1hr

    return trimp, hrtss