//
//
//  NLMSvariants.c
//
//  Created by Stefan Friese on 26.04.18.
//
//
#include <stdio.h>
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <string.h>
#include <float.h> // DBL_MAX

#define NUMBER_OF_SAMPLES 50
#define WINDOWSIZE 5
#define learnrate 0.8
#define RGB_COLOR 255
#if defined(_MSC_VER)
#include <BaseTsd.h>
typedef SSIZE_T ssize_t;
#endif

enum fileSuffix_t{ // used in conjunction with mkFileName()
    PURE_WEIGHTS,
    USED_WEIGHTS,
    DIRECT_PREDECESSOR,
    RESULTS,
    LOCAL_MEAN,
    TEST_VALUES,
    DIFFERENTIAL_PREDECESSOR
};

double xSamples[NUMBER_OF_SAMPLES] = { 0.0 };

/* *svg graph building* */
typedef struct {
	double xVal[7];
	double yVal[7];
}point_t;

point_t points[NUMBER_OF_SAMPLES]; // [0] = xActual, [1]=xpredicted from localMean, [2]=xpredicted from directPredecessor, [3] = xpredicted from differentialpredecessor, [4] = xError from localMean, [5] xError from directPredecessor, [6] xError from differentialPredecessor

/* *ppm read, copy, write* */
typedef struct {
	unsigned char red, green, blue;
}colorChannel_t;

typedef struct {
	int x, y;
	colorChannel_t *data;
}imagePixel_t;

static imagePixel_t * rdPPM(char *fileName); // read PPM file format
void mkPpmFile(char *fileName, imagePixel_t *image); // writes PPM file
int ppmColorChannel(FILE* fp, imagePixel_t *image, char *colorChannel); // writes colorChannel from PPM file to log file
void colorSamples(FILE* fp); // stores color channel values in xSamples[]

/* *file handling* */
char * mkFileName(char* buffer, size_t max_len, int suffixId);
char *fileSuffix(int id);
void myLogger(FILE* fp, point_t points[]);
void mkSvgGraph(point_t points[]);
//void weightsLogger(double *weights, int var);
/* *rand seed* */
double r2(void);
double rndm(void);

/* *args parser* */
void usage ( void ); 

/* *math* */
double sum_array(double x[], int length);
void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
//void differentialPredecessor(double *weights);
double *popNAN(double *xError); // Returns array without NAN values, if any exist
double windowXMean(int _arraylength, int xCount);// returns mean value of given window

int main( int argc, char **argv ) {
	char *colorChannel = (char *) malloc(sizeof(char)* 32);  
	char *inputfile = (char *)malloc(sizeof(char) * 32);
	unsigned int *seed = NULL;
	int i,k, xLength;
	unsigned int windowSize = 5;
	char *stdcolor = "green";
	colorChannel = stdcolor;
	unsigned int uint_buffer[1];
	
	while( (argc > 1) && (argv[1][0] == '-')  ) {	// Parses parameters from stdin
			switch( argv[1][1] ) {
				case 'i':
					inputfile = &argv[1][3];
					++argv;
					--argc;
					break;
				case 'w':
					sscanf(&argv[1][3], "%u", &windowSize);	
					++argv;
					--argc;
					break;
				case 'c':	
					colorChannel = &argv[1][3];	
					++argv;
					--argc;
					break;
				case 's':	
					sscanf(&argv[1][3], "%u", uint_buffer);
					seed = &uint_buffer[0];
					++argv;
					--argc;
					break;
				case 'h':
					printf("Program name: %s\n", argv[0]);
					usage();
					break;
				default:
					printf("Wrong Arguments: %s\n", argv[1]);
					usage();		
			}
		
		++argv;
		--argc;
	}
	imagePixel_t *image;	
	double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]; // = { { 0.0 }, {0.0} };
	char fileName[50];
	image = rdPPM(inputfile);
	mkFileName(fileName, sizeof(fileName), TEST_VALUES);
	FILE* fp5 = fopen(fileName, "w");
	xLength = ppmColorChannel(fp5, image, colorChannel); // Returns length of ppm input values
	printf("%d\n", xLength);
	FILE* fp6 = fopen(fileName, "r");
	colorSamples(fp6);

	if ( (seed != NULL) && (seed >= 0) ){ 
		srand( *seed );					// Seed for random number generating
		printf("srand is reproducable : %s");
	} else {
		srand( (unsigned int)time(NULL) );
		printf("srand from time");			// Default seed is time(NULL)
	}
	for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
		for (int k = 0; k < WINDOWSIZE; k++) {
			weights[k][i] = rndm(); 		// Init random weights
			printf("%lf\n", weights[k][i]);
		}
	}
	mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS);
	FILE *fp0 = fopen(fileName, "w");
	for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
		for (k = 0; k < WINDOWSIZE; k++) {
			fprintf(fp0, "[%d][%d]%lf\n", k, i, weights[k][i]);	// Save generated weights to to file
		}
	}
	fclose(fp0);

	// math magic
    	localMean(weights);
	directPredecessor(weights);
	differentialPredecessor(weights);
	
	mkSvgGraph(points);
	printf("\nDONE!\n");
}

/*
======================================================================================================

localMean

Variant (1/3), substract local mean.

======================================================================================================
*/
void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
	double (*local_weights)[WINDOWSIZE] =(double (*)[WINDOWSIZE]) malloc(sizeof(double) * (WINDOWSIZE+1) * (NUMBER_OF_SAMPLES+1));
	memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
	char fileName[50];
	double xError[2048]; // includes e(n)
	memset(xError, 0.0, NUMBER_OF_SAMPLES);// initialize xError-array with Zero
	int xCount = 0, i; // runtime var;
	mkFileName(fileName, sizeof(fileName), LOCAL_MEAN);
	FILE* fp4 = fopen(fileName, "w");
	fprintf(fp4, "\n===================================== LocalMean =====================================\nNo.\txPredicted\txActual\t\txError\n");

	double xMean = xSamples[0];
	double xSquared = 0.0;
	double xPredicted = 0.0;
	double xActual = 0.0;

	for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted

		int _arrayLength = ( xCount > WINDOWSIZE ) ? WINDOWSIZE + 1 : xCount;
		xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0;
		xPredicted = 0.0;
		xActual = xSamples[xCount + 1];
		//	weightedSum += _x[ xCount-1 ] * w[xCount][0];

		for (i = 1; i < _arrayLength; i++) { //get predicted value
			xPredicted += (local_weights[i][xCount] * (xSamples[xCount - i] - xMean));
		}
		xPredicted += xMean;
		xError[xCount] = xActual - xPredicted;
		points[xCount].xVal[1] = xCount;
		points[xCount].yVal[1] = xPredicted;
		points[xCount].xVal[4] = xCount;
		points[xCount].yVal[4] = xError[xCount];

		xSquared = 0.0;
		for (i = 1; i < _arrayLength; i++) { //get xSquared
			xSquared += pow(xSamples[xCount - i] - xMean, 2);
		}
		if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
			xSquared = 1.0;
		}
		for (i = 1; i < _arrayLength; i++) { //update weights
			local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xMean) / xSquared);
		}
		fprintf(fp4, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
	}
	double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
	double  xErrorLength = *xErrorPtr; // Watch popNAN()!
  	xErrorPtr[0] = 0.0;
//	printf("Xerrorl:%lf", xErrorLength);
	double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
	double deviation = 0.0;

	// Mean square
	for (i = 1; i < xErrorLength; i++) {
		deviation += pow(xError[i] - mean, 2);
	}
	deviation /= xErrorLength;
	printf("mean:%lf, devitation:%lf", mean, deviation);

	// write in file
	//mkFileName(fileName, sizeof(fileName), RESULTS);
	//FILE *fp2 = fopen(fileName, "w");
	fprintf(fp4, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
	//fclose(fp2);
	free(local_weights);
	fclose(fp4);

	//weightsLogger( local_weights, USED_WEIGHTS );
}

/*
======================================================================================================

directPredecessor

Variant (2/3),
substract direct predecessor

======================================================================================================
*/
void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
	double (*local_weights)[WINDOWSIZE] = (double (*)[WINDOWSIZE]) malloc(sizeof(double) * (WINDOWSIZE+1) * (NUMBER_OF_SAMPLES+1));

	memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES );
	char fileName[512];
	double xError[2048];
	int xCount = 0, i;
	double xActual = 0.0;
	double xPredicted = 0.0;
	// File handling
	mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR);
	FILE *fp3 = fopen(fileName, "w");
	fprintf(fp3, "\n=====================================DirectPredecessor=====================================\nNo.\txPredicted\txAcutal\t\txError\n");


	for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
		int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
		xPredicted = 0.0;
		xActual = xSamples[xCount + 1];
		for (i = 1; i < _arrayLength; i++) {
			xPredicted += (local_weights[i][xCount] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
		}
		xPredicted += xSamples[xCount - 1];
		xError[xCount] = xActual - xPredicted;

		points[xCount].xVal[2] = xCount; 		// Fill point_t array for graph building
		points[xCount].yVal[2] = xPredicted;
		points[xCount].xVal[5] = xCount;
		points[xCount].yVal[5] = xError[xCount];

		double xSquared = 0.0;
		for (i = 1; i < _arrayLength; i++) {
			xSquared += pow(xSamples[xCount - 1] - xSamples[xCount - i - 1], 2); // substract direct predecessor
		}
		for (i = 1; i < _arrayLength; i++) {
			local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
		}
        fprintf(fp3, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
	}

	double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
	//printf("%lf", xErrorPtr[499]);
	double  xErrorLength = *xErrorPtr; // Watch popNAN()!
    xErrorPtr[0] = 0.0;
	//printf("Xerrorl:%lf", xErrorLength);

	double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
	double deviation = 0.0;

	// Mean square
	for (i = 1; i < xErrorLength; i++) {
		deviation += pow(xError[i] - mean, 2);
	}
	deviation /= xErrorLength;
	printf("mean:%lf, devitation:%lf", mean, deviation);

	// write in file
	//mkFileName(fileName, sizeof(fileName), RESULTS);
	//FILE *fp2 = fopen(fileName, "wa");
	fprintf(fp3, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
	fclose(fp3);
	//fclose(fp2);
	free(local_weights);

	//weightsLogger( local_weights, USED_WEIGHTS );
}

/*
======================================================================================================

differentialPredecessor

variant (3/3),
differential predecessor.

======================================================================================================
*/
void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
	double (*local_weights)[WINDOWSIZE] = (double (*)[WINDOWSIZE]) malloc(sizeof(double) * (WINDOWSIZE+1) * (NUMBER_OF_SAMPLES+1));
	memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES );
	char fileName[512];
	double xError[2048];
	int xCount = 0, i;
	double xPredicted = 0.0;
	double xActual = 0.0;

	// File handling
	mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR);
	FILE *fp6 = fopen(fileName, "w");
	fprintf(fp6, "\n=====================================DifferentialPredecessor=====================================\nNo.\txPredicted\txAcutal\t\txError\n");

		for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted

		int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
		xPredicted = 0.0;
		xActual = xSamples[xCount + 1];

		for (i = 1; i < _arrayLength; i++) {
			xPredicted += (local_weights[i][xCount] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
		}
		xPredicted += xSamples[xCount - 1];
		xError[xCount] = xActual - xPredicted;

		points[xCount].xVal[3] = xCount;
		points[xCount].yVal[3] = xPredicted;
		points[xCount].xVal[6] = xCount;
		points[xCount].yVal[6] = xError[xCount];
		double xSquared = 0.0;

		for (i = 1; i < _arrayLength; i++) {
			xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // substract direct predecessor
		}
		for (i = 1; i < _arrayLength; i++) {
			local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
		}
       		fprintf(fp6, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
	}

	double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
	//printf("%lf", xErrorPtr[499]);
	double  xErrorLength = *xErrorPtr; // Watch popNAN()!
    	xErrorPtr[0] = 0.0;
//	printf("Xerrorl:%lf", xErrorLength);

	double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
	double deviation = 0.0;

	// Mean square
	for (i = 1; i < xErrorLength; i++) {
		deviation += pow(xError[i] - mean, 2);
	}
	deviation /= xErrorLength;
	printf("mean:%lf, devitation:%lf", mean, deviation);

	// write in file
	//mkFileName(fileName, sizeof(fileName), RESULTS);
	//FILE *fp2 = fopen(fileName, "wa");
	fprintf(fp6, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
	//fclose(fp2);
	fclose(fp6);
	free(local_weights);

	//weightsLogger( local_weights, USED_WEIGHTS );
}

/*
======================================================================================================

mkFileName

Writes the current date plus the suffix with index suffixId
into the given buffer. If the total length is longer than max_len,
only max_len characters will be written.

======================================================================================================
*/
char *mkFileName(char* buffer, size_t max_len, int suffixId) {
	const char * format_str = "%Y-%m-%d_%H_%M_%S";
	size_t date_len;
	const char * suffix = fileSuffix(suffixId);
	time_t now = time(NULL);

	strftime(buffer, max_len, format_str, localtime(&now));
	date_len = strlen(buffer);
	strncat(buffer, suffix, max_len - date_len);
	return buffer;
}

/*
======================================================================================================

fileSuffix

Contains and returns every suffix for all existing filenames

======================================================================================================
*/
char * fileSuffix(int id) {
	char * suffix[] = { "_weights_pure.txt", "_weights_used.txt", "_direct_predecessor.txt", "_ergebnisse.txt", "_localMean.txt","_testvalues.txt", "_differential_predecessor.txt" };
	return suffix[id];
}

/*
======================================================================================================

myLogger

Logs x,y points to svg graph

======================================================================================================
*/
void weightsLogger (double weights[WINDOWSIZE], int val ) {
	char fileName[512];
	int i;
	mkFileName(fileName, sizeof(fileName), val);
	FILE* fp = fopen(fileName, "wa");
	for (i = 0; i < WINDOWSIZE; i++) {
	//	for (int k = 0; k < WINDOWSIZE; k++) {
			fprintf(fp, "[%d]%lf\n", i, weights[i]);
	//	}
	}
	fprintf(fp,"\n\n\n\n=====================NEXT=====================\n");
	fclose(fp);
}

/*
======================================================================================================

bufferLogger

formats output of mkSvgGraph -- Please open graphResults.html to see the output--

======================================================================================================
*/
void bufferLogger(char *buffer, point_t points[]) {
	int i;
	char _buffer[512] = "";

	for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) { // xActual
		sprintf(_buffer, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]);
		strcat(buffer, _buffer);
	}
	strcat(buffer, "\" fill=\"none\" id=\"svg_1\" stroke=\"black\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
	for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) { // xPredicted from localMean
		sprintf(_buffer, "L %f %f\n", points[i].xVal[1], points[i].yVal[1]);
		strcat(buffer, _buffer);
	}
	strcat(buffer, "\" fill=\"none\" id=\"svg_2\" stroke=\"green\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
	for (i = 0; i <= NUMBER_OF_SAMPLES - 1; i++) { //xPreddicted from directPredecessor
		sprintf(_buffer, "L %f %f\n", points[i].xVal[2], points[i].yVal[2]);
		strcat(buffer, _buffer);
	}
	strcat(buffer, "\" fill=\"none\" id=\"svg_3\" stroke=\"blue\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
	for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) { //xPredicted from diff Pred
		sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].yVal[3]);
		strcat(buffer, _buffer);
	}
	strcat(buffer, "\" fill=\"none\" id=\"svg_4\" stroke=\"red\" stroke-width=\"0.4px\"/>\n");
}

/*
======================================================================================================

sum_array

Sum of all elements in x within a defined length

======================================================================================================
*/
double sum_array(double x[], int xlength) {
	int i = 0;
	double sum = 0.0;

	if (xlength != 0) {
		for (i = 0; i < xlength; i++) {
			sum += x[i];
		}
	}
	return sum;
}

/*
======================================================================================================

popNanLength

returns length of new array without NAN values

======================================================================================================
*/
double *popNAN(double *xError) {
	int i, counter = 1;
	double tmpLength = 0.0;
	double *tmp = NULL;
	double *more_tmp = NULL;

	for ( i = 0; i < NUMBER_OF_SAMPLES; i++ ) {
		counter ++;
		more_tmp = (double *) realloc ( tmp, counter*(sizeof(double) ));
			if ( !isnan(xError[i]) ) {
				tmp = more_tmp;
				tmp[counter - 1] = xError[i];
				//printf("xERROR:%lf\n", tmp[counter - 1]);
				tmpLength++;
			}
	}
	counter += 1;
	more_tmp = (double *) realloc ( tmp, counter * sizeof(double) );
	tmp = more_tmp;
	*tmp = tmpLength; // Length of array has to be stored in tmp[0],
				    // Cause length is needed later on in the math functions.
				    // xError counting has to begin with 1 in the other functions !
	printf("tmpLength in tmp:%lf, %lf\n", tmp[counter-2], *tmp);
	return tmp;

}

/*
======================================================================================================

r2

returns a random double value between 0 and 1

======================================================================================================
*/
double r2(void) {
	return((rand() % 10000) / 10000.0);
}

/*
======================================================================================================

rndm

fills a double variable with random value and returns it

======================================================================================================
*/
double rndm(void) {
	double rndmval = r2();
	return rndmval;
}

/*
======================================================================================================

mkSvgGraph

parses template.svg and writes results in said template

======================================================================================================
*/
void mkSvgGraph(point_t points[]) {
	FILE *input = fopen("graphResults_template.html", "r");
	FILE *target = fopen("graphResults.html", "w");
	char line[512];
	char firstGraph[15] = { "<path d=\"M0 0" };

	if (input == NULL) {
		exit(EXIT_FAILURE);
	}

	char buffer[131072] = "";

	memset(buffer, '\0', sizeof(buffer));
	while (!feof(input)) {
		fgets(line, 512, input);
		strncat(buffer, line, strlen(line));
		if (strstr(line, firstGraph) != NULL) {
			bufferLogger(buffer, points);
		}

	}
	fprintf(target, buffer);
}

/*
======================================================================================================

rdPPM

reads data from file of type PPM, stores colorchannels in a struct in the
size of given picture

======================================================================================================
*/
static imagePixel_t *rdPPM(char *fileName) {
	char buffer[16];
	imagePixel_t *image;
	int c, rgbColor;

	FILE *fp = fopen(fileName, "rb");
	if (!fp) {
		exit(EXIT_FAILURE);
	}
	if (!fgets(buffer, sizeof(buffer), fp)) {
		perror(fileName);
		exit(EXIT_FAILURE);
	}
	if (buffer[0] != 'P' || buffer[1] != '6') {
		fprintf(stderr, "No PPM file format\n");
		exit(EXIT_FAILURE);
	}
	image = (imagePixel_t *)malloc(sizeof(imagePixel_t));
	if (!image) {
		fprintf(stderr, "malloc() failed");
	}
	c = getc(fp);
	while (c == '#') {
		while (getc(fp) != '\n');
		c = getc(fp);
	}
	ungetc(c, fp);
	if (fscanf(fp, "%d %d", &image->x, &image->y) != 2) {
		fprintf(stderr, "Invalid image size in %s\n", fileName);
		exit(EXIT_FAILURE);
	}
	if (fscanf(fp, "%d", &rgbColor) != 1) {
		fprintf(stderr, "Invalid rgb component in %s\n", fileName);
	}
	if (rgbColor != RGB_COLOR) {
		fprintf(stderr, "Invalid image color range in %s\n", fileName);
		exit(EXIT_FAILURE);
	}
	while (fgetc(fp) != '\n');
	image->data = (colorChannel_t *)malloc(image->x * image->y * sizeof(imagePixel_t));
	if (!image) {
		fprintf(stderr, "malloc() failed");
		exit(EXIT_FAILURE);
	}
	if (fread(image->data, 3 * image->x, image->y, fp) != image->y) {
		fprintf(stderr, "Loading image failed");
		exit(EXIT_FAILURE);
	}
	fclose(fp);
	return image;
}

/*
======================================================================================================

mkPpmFile

gets output from the result of rdPpmFile and writes a new PPM file. Best Case is a
carbon copy of the source image. Build for debugging

======================================================================================================
*/
void mkPpmFile(char *fileName, imagePixel_t *image) {
	FILE* fp = fopen(fileName, "wb");
	if (!fp) {
		fprintf(stderr, "Opening file failed.");
		exit(EXIT_FAILURE);
	}
	fprintf(fp, "P6\n");
	fprintf(fp, "%d %d\n", image->x, image->y);
	fprintf(fp, "%d\n", RGB_COLOR);
	fwrite(image->data, 3 * image->x, image->y, fp);
	fclose(fp);
}

/*
======================================================================================================

ppmColorChannel

gets one of the rgb color channels and writes them to a file

======================================================================================================
*/
int ppmColorChannel(FILE* fp, imagePixel_t *image, char *colorChannel) {
	// int length = (image->x * image->y) / 3;
	int i = 0;

	printf("colorChannel in Function: %s", colorChannel);
	if (image) {
	
		if (strcmp(colorChannel, "green") == 0){
			for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) {
				fprintf(fp, "%d\n", image->data[i].green);
				printf("|");
			}
		} else if (strcmp(colorChannel, "red") == 0){				
			for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) {
				fprintf(fp, "%d\n", image->data[i].red);
				printf(".");
			}	
			
		} else if (strcmp(colorChannel, "blue") == 0 ) {
			for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++ ) {
				fprintf(fp, "%d\n", image->data[i].blue);
				printf("/");
			}
		} else { 
			printf("Colorchannels are red, green and blue. Pick one of them!");
			exit(EXIT_FAILURE);
		}
	}
	fclose(fp);
	return NUMBER_OF_SAMPLES;
}

/*
======================================================================================================

colorSamples

reads colorChannel values from file and stores them in xSamples as well as in points datatype for
creating the SVG graph

======================================================================================================
*/
void colorSamples(FILE* fp) {
	int i = 0;
	char  buffer[NUMBER_OF_SAMPLES];

	while (!feof(fp)) {
		if (fgets(buffer, NUMBER_OF_SAMPLES, fp) != NULL) {
			sscanf(buffer, "%lf", &xSamples[i]);
			//printf("%lf\n", xSamples[i] );
			points[i].yVal[0] = xSamples[i];
			points[i].xVal[0] = i;
			++i;
		}
	}
	fclose(fp);
}

/*
======================================================================================================

windowXMean

returns mean value of given input, which has a length of WINDOWSIZE

======================================================================================================
*/
double windowXMean(int _arraylength, int xCount) {
	double sum = 0.0;
	double *ptr;

	for (ptr = &xSamples[xCount - _arraylength]; ptr != &xSamples[xCount]; ptr++) { //set ptr to beginning of window
        sum += *ptr;
	}
	return sum / (double)_arraylength;
}

/*
======================================================================================================

 usage 
 
 used in conjunction with the args parser. Returns help section of "-h"

======================================================================================================
*/
void usage ( void ) {
	printf("Usage: lms [POSIX style options] -i file ...\n");
	printf("POSIX options:\n");
	printf("\t-h\t\t\tDisplay this information.\n");
	printf("\t-i <filename>\t\tName of inputfile. Must be PPM image.\n");
	printf("\t-c <color>\t\tUse this color channel from inputfile.\n");
	printf("\t-w <digit>\t\tCount of used weights (windowSize).\n");
	printf("\t-s <digit>\t\tDigit for random seed generator.\n\t\t\t\tSame Digits produce same random values. Default is srand by time.");
	printf("\n\n");
	printf("lms compares prediction methods of least mean square methods.\nBy default it reads ppm file format and return logfiles as well\nas an svg graphs as an output of said least mean square methods.\n\nExample:\n\tlms -i myimage.ppm -w 3 -c green -s 5\n"); 
	exit(8);
}