import pandas as pd
import datetime
import numpy as np
import plotly as pl
import plotly.graph_objs as go
import matplotlib.pyplot as plt
import math
import scipy
import random
import statistics 


import datetime 
import matplotlib.dates as mdates
import matplotlib.pyplot as plt

import mplfinance as mpf

import plotly
#import plotly.plotly as py
import plotly.graph_objs as go
# these two lines allow your code to show up in a notebook
from plotly.offline import init_notebook_mode, iplot
from plotly.subplots import make_subplots
init_notebook_mode()

class CoreMath:
    
    def __init__(self, base_df, params={
        'dataType':'ohcl',
        'colName':{
            'open':'open',
            'close':'close',
            'high':'high',
            'low':'low',
            'date':'date'
        },
        'action': None,
        'actionOptions':{}
    }
                ):
    
        self.base_df=base_df.reset_index(drop=True)
        self.params=params
        if self.params['dataType']=='ohcl':
            self.col=self.base_df[self.params['colName'][self.params['actionOptions']['valueType']]]
        elif self.params['dataType']=='series':
            self.col=self.base_df
        self.ans=self.getAns()
        
    
    def getAns(self):
        ans=None
        
        if self.params['action']=='findExt':
            ans = self.getExtremumValue()
        if self.params['action']=='findMean':
            ans = self.getMeanValue()
        
            
        return ans
    
    
    def getExtremumValue(self):
        ans=None
        '''
        actionOptions:
            'extremumtype':
                'min'
                'max'
            'valueType':
                'open'
                'close'
                'high'
                'low'
        '''
        if self.params['actionOptions']['extremumtype']=='max':
            ans=max(self.col)
            
        if self.params['actionOptions']['extremumtype']=='min':
            ans=min(self.col)

            
        return ans
     
        
    def getMeanValue(self):
        '''
        actionOptions:
            'MeanType':
                'MA'
                'SMA'
                'EMA'
                'WMA'
                --'SMMA'
            'valueType':
                'open'
                'close'
                'high'
                'low'
            'window'
            'span'
            'weights'
        '''
        ans=None
        if self.params['actionOptions']['MeanType']=='MA':
            ans = self.col.mean()
        if self.params['actionOptions']['MeanType']=='SMA':
            #ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
            ans=self.col.rolling(window=self.params['actionOptions']['window']).mean()
        if self.params['actionOptions']['MeanType']=='EMA':
            ans=self.col.ewm(span=elf.params['actionOptions']['span'], adjust=False).mean()
        if self.params['actionOptions']['MeanType']=='WMA':
            try:
                weights=self.params['actionOptions']['weights']
            except KeyError:
                weights=np.arange(1,self.params['actionOptions']['window']+1)
            ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False)

        
        
        return(ans)