//
//  FileInputArray.cpp
//  Project
//
//  Created by Pavlos Charalambides on 17/01/2017.
//  Copyright © 2017 Pavlos Charlambides. All rights reserved.
//

#include <iostream>
//my added libraries
#include <fstream> //input output class to operate on files
#include <sstream> //stream class to operate on strings
#include <string> //string class
#include <vector>
#include "InputRead.hpp"
//for fft
#include "preFFT.hpp"

using namespace std;

int main(int argc, const char * argv[]) {
	string filename = "/Users/PavlosCh/Desktop/C++/Project/Project/test.txt";
	float data [20000];
	float data2D [20][512]; // length would vary based on sampling frequency
	readInput(filename, data);

	//assume we only need 2seconds, would leave us with 10000 data points
	int dataLength = 10240;
	float dataReduced [dataLength];

	int startSignal = 7500; //Sam has to calculate this threshold
	for (int i=0; i<dataLength; i++){
		dataReduced[i] = data[startSignal+i];
	}
	//DATA ARRAY SHOULD BE DELETED at this point

	//deviding the signal into bins of 512, padding last one with zero
	preFFT(dataReduced, data2D);
	//performing FFT on every row of array above

	/*
	#include "dftComplex.hpp"
	const int N = 512;
	for (int i=0; i<20; i++) {
		Complex y1[N], y2[N/2 + 1];
		DftComplex(data2D[i], y1, N); //on receiver side have to add DftComplex(float *arr, Complex y[], int nFft)
		FFTReal fft(N);
		fft.Execute(data2D[i], y2);
	}
	*/

	//Normalising

	for (int i=0; i<20; i++) {
		for (int j = 0; j<512; j++){
			float sumFFTrow = 0;
			for (int k=0; k<512; k++) {
				sumFFTrow = sumFFTrow + data2D[i][k];
			}
			data2D[i][j] = data2D[i][j]/sumFFTrow;
		}
	}
	//feature vector
	float featureVector[20];
	float avg = 0;
	for (int i=0; i<20; i++) {
		for (int j=0; j<512; j++){
			avg = avg + data2D[i][j];
		}
		featureVector[i] = avg/512;
	}




	return 0;
}