# -*- coding: utf-8 -*-
"""
Created on Wed Jun 16 09:34:35 2021

@author: -
"""
import osa
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import timedelta
from scipy.optimize import curve_fit

# Formulas

def ReadCSV(filename):
    df = pd.read_csv(filename, names=['Datetime', 'Acc X','Acc Y', 'Acc Z'], infer_datetime_format=True)
    df['Datetime'] = pd.to_datetime(df['Datetime'])
    df['Date'] = [d.date() for d in df['Datetime']]
    df = df.reindex(columns=['Datetime','Date','Time','Acc X','Acc Y', 'Acc Z'])
    return df

def CreateDays(x, filename, path):
    
    savename = filename.replace('.csv','')
    savepath = path + savename
    os.makedirs(savepath)
    os.chdir(savepath)
    
    startdate = x['Date'].iloc[0]
    week = range(1,8)
    
    for i in week:
        weekdayindex = i-1
        
        day = startdate + timedelta(days=weekdayindex)
        daydate = x['Date'] == startdate + timedelta(days=weekdayindex)
        dataday = x[daydate]
        totalweek = {day:dataday}
        
        savefile = totalweek[day]
        varname = filename.replace('.csv','-') + str(day) + '.csv'
        savefile.to_csv(varname)
        
        print(varname +' saved')
    
    return(totalweek)


def SVMEpoch(DF,ResampRate, ResampData):
    newDF = pd.DataFrame(DF)
    newDF['X2'] = np.power(newDF['Acc X'], 2)
    newDF['Y2'] = np.power(newDF['Acc Y'], 2)
    newDF['Z2'] = np.power(newDF['Acc Z'], 2)
    newDF['SVM'] = np.sqrt(newDF[['X2', 'Y2', 'Z2']].sum(axis=1))
    newDF['Datetime'] = pd.to_datetime(newDF['Datetime'])
   
    EpochSVM = newDF.resample(ResampRate, on = ResampData).mean()
    return(newDF, EpochSVM)

def func(x, a, b, c):
    return a * np.exp(-b*x) + c

def SlopeWeeker(Keylist, Dict):
    try:    
        SlopeWeek = pd.DataFrame(columns=['a','b', 'c', 'Name'])
        SlopeWeek = SlopeWeek.set_index('Name')
            
        for key in Keylist:
            newDF, EpochSVM = SVMEpoch(Dict[key], '60S', 'Datetime')
           
            ENMO = EpochSVM['SVM']-1
            ENMO = ENMO*1000
           
            for value in ENMO:
                if value < 0:
                    value = 0
           
            BinSize = 5 
           
            ENMOmax = int(ENMO.max())
            
            if ENMOmax % BinSize == 0:
                ENMOmax = ENMOmax+1 #to make sure that interference with binsize is impossible
            
            MaxBin = int(ENMOmax/BinSize)+1
            ENMO = ENMO.astype(int)
           
            Counter = pd.DataFrame(np.zeros((1,MaxBin)))
           
            for x in Counter:
                Count = (x+1)*BinSize
                Start = Count - BinSize
                Number = ENMO.between(Start, Count).sum()
                Counter[x] = Number
            
            Counter = Counter.to_numpy()
            Counter = Counter.astype(float)
            Counter = Counter.flatten()
            
            Xscale = np.arange(0,ENMOmax, BinSize)
            Xscale = Xscale.astype(float)
            
            popt, _ = curve_fit(func, Xscale, Counter, p0=None) # fit curve through points 
            a, b, c = popt
            
            Trendline = func(Xscale, a, b, c)
           
            SlopeWeek.loc[key, 'a'] = a
            SlopeWeek.loc[key, 'b'] = b
            SlopeWeek.loc[key, 'c'] = c
            SlopeWeek.loc[key, 'ENMOmax'] = ENMOmax
           
            PtName = key.replace('35694_00000', '')
            PtName = PtName.replace('resampled-','') 
            PtName = PtName.replace('.csv','')
            
            plt.figure()
            plt.ylim(0,1440)
            plt.xlim(0,(ENMOmax+10))
            plt.title('Intensity plot ' + PtName)
            plt.xlabel('Movement intensity [bins of ' + str(BinSize) + ' mg]')
            plt.ylabel('Amount of time spend at intensity [min]')
            plt.grid()
            plt.scatter(Xscale, y=Counter)
            plt.plot(Xscale, Trendline, 'r--')
            
            PtName = (PtName + '.png')
            plt.savefig(fname=PtName)

            plt.show()
            
    except:
        print(PtName + ' could not be used')

    return SlopeWeek