def prepareToCorrelation(self,df,by):
"""
prepare dataframe to correlation algorithm
:param df: input df {pd.DataFrame}
:param by: group by column
:return: df : input dataframe from
originFrame : frames to move
analyzedFrameUpdated : stable main frame contains the most really coordinates
fewDataFrame : lack of data frame
readyToCorr : if data frame is ready to be pushed to correlation algorithm
"""
_df = df.copy()
fewDataFrame = pd.DataFrame()
if by == 'User':
self.startPoint,noises = Filters.noisyUser(_df,by,col = 'rawPower')
#df = self.filters.rollingMean(df,'Power',by = by,noises = noises)
if by == 'race_id':
self.startPoint = _df['race_id'].iloc[0]
analyzedFrame,originFrame = self.splitFrameByMinLen(_df,by)
analyzedFrameUpdated,readyToCorr = self.checkBoundaries(originFrame,analyzedFrame,by)
if (not readyToCorr):
_df.drop(analyzedFrame.index,inplace=True)
fewDataFrame = analyzedFrame
return _df,originFrame,analyzedFrameUpdated,fewDataFrame,readyToCorr
