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#include <ctype.h>
#include <errno.h>
#include <getopt.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>
#include <zlib.h>
#include "quikr_functions.h"
#define USAGE "Usage:\n\tquikr_train [OPTION...] - to train a database for use with quikr.\n\nOptions:\n\n-i, --input\n\tthe database of sequences to create the sensing matrix (fasta format)\n\n-k, --kmer\n\tspecify what size of kmer to use. (default value is 6)\n\n-o, --output\n\tthe sensing matrix. (a gzip'd text file)\n\n-v, --verbose\n\tverbose mode."
int main(int argc, char **argv) {
char probabilities_command[512];
char kmers_file[256];
char *line = NULL;
char *val;
size_t len = 0;
int c;
int kmer = 6;
char *fasta_file = NULL;
char *output_file = NULL;
int x = 0;
int y = 0;
int verbose = 0;
gzFile output = NULL;
while (1) {
static struct option long_options[] = {
{"verbose", no_argument, 0, 'v'},
{"input", required_argument, 0, 'i'},
{"kmer", required_argument, 0, 'k'},
{"output", required_argument, 0, 'o'},
{0, 0, 0, 0}
};
int option_index = 0;
c = getopt_long (argc, argv, "i:o:k:hv", long_options, &option_index);
if (c == -1)
break;
switch (c) {
case 'i':
fasta_file = optarg;
break;
case 'k':
kmer = atoi(optarg);
break;
case 'o':
output_file = optarg;
break;
case 'v':
verbose = 1;
break;
case 'h':
printf("%s\n", USAGE);
exit(EXIT_SUCCESS);
break;
case '?':
/* getopt_long already printed an error message. */
break;
default:
exit(EXIT_FAILURE);
}
}
if(fasta_file == NULL) {
fprintf(stderr, "Error: input fasta file (-i) must be specified\n\n");
fprintf(stderr, "%s\n", USAGE);
exit(EXIT_FAILURE);
}
if(output_file == NULL) {
fprintf(stderr, "Error: output matrix file (-o) must be specified\n\n");
fprintf(stderr, "%s\n", USAGE);
exit(EXIT_FAILURE);
}
if(verbose) {
printf("kmer size: %d\n", kmer);
printf("fasta file: %s\n", fasta_file);
printf("output file: %s\n", output_file);
}
if(access (fasta_file, F_OK) == -1) {
fprintf(stderr, "Error: could not find %s\n", fasta_file);
exit(EXIT_FAILURE);
}
if(strcmp(&output_file[strlen(output_file) - 3], ".gz") != 0) {
char *temp = malloc(strlen(output_file) + 4);
if(temp == NULL) {
fprintf(stderr, "Could not allocate enough memory\n");
exit(EXIT_FAILURE);
}
sprintf(temp, "%s.gz", output_file);
output_file = temp;
printf("appending a .gz to our output file: %s\n", output_file);
}
// 4 ^ Kmer gives us the width, or the number of permutations of ACTG with kmer length
int width = pow(4, kmer);
int sequences = count_sequences(fasta_file);
if(sequences == 0) {
fprintf(stderr, "Error: %s contains 0 fasta sequences\n", fasta_file);
}
if(verbose)
printf("sequences: %d\nwidth: %d\n", sequences, width);
// Allocate our matrix with the appropriate size, just one row
double *trained_matrix = malloc(width*sizeof(double));
if(trained_matrix == NULL) {
fprintf(stderr, "Could not allocate enough memory\n");
exit(EXIT_FAILURE);
}
// call the probabilities-by-read command
sprintf(probabilities_command, "generate_kmers %d | probabilities-by-read %d %s /dev/stdin", kmer, kmer, fasta_file);
FILE *probabilities_output = popen(probabilities_command, "r");
if(probabilities_output == NULL) {
fprintf(stderr, "Error could not execute: %s\n", probabilities_command);
exit(EXIT_FAILURE);
}
// open our output file
output = gzopen(output_file, "w6");
if(output == NULL) {
fprintf(stderr, "Error: could not open output file, error code: %d", errno);
exit(EXIT_FAILURE);
}
if(verbose)
printf("Writing our sensing matrix to %s\n", output_file);
// read normalize and write our matrix in one go
for(x = 0; x < sequences; x++) {
getline(&line, &len, probabilities_output);
// Read our first element in outside of the loop
val = strtok(line,"\t\n\r");
trained_matrix[0] = atof(val);
// iterate through and load the array
for (y = 1; y < width; y++) {
val = strtok (NULL, "\t\n\r");
trained_matrix[y] = atof(val);
}
double row_sum = 0;
for( y = 0; y < width; y++) {
row_sum = row_sum + trained_matrix[y];
}
for( y = 0; y < width; y++) {
trained_matrix[y] = trained_matrix[y] / row_sum;
}
for( y = 0; y < width; y++) {
gzprintf(output, "%.10f\t", trained_matrix[y]);
}
gzprintf(output, "\n");
}
free(trained_matrix);
gzclose(output);
pclose(probabilities_output);
return 0;
}
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