release1.0

1 files changed, 176 insertions, 0 deletions

diff --git a/src/c/quikr_train.c b/src/c/quikr_train.c
new file mode 100644
index 0000000..d2a83ef
--- /dev/null
+++ b/src/c/quikr_train.c
@@ -0,0 +1,176 @@
+#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 AWK_KMER_PERMUTATIONS "awk 'function p(l,v,i){for(i in A) {if(l<%d) p(l+1, (v?v\"\":x)i); else print v\"\"i;}} {A[$0]} END {p(1);} ' <<<$'A\nC\nG\nT'"
+#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 = 0;
+
+  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 directory (-o) must be specified\n\n");
+    fprintf(stderr, "%s\n", USAGE);
+    exit(EXIT_FAILURE);
+  }
+
+  if(kmer == 0)
+    kmer = 6;
+
+  if(verbose) {
+    printf("kmer size: %d\n", kmer);
+    printf("fasta file: %s\n", fasta_file);
+    printf("output file: %s\n", output_file);
+  }
+
+  if(strcmp(&output_file[strlen(output_file) - 3], ".gz") != 0) {
+    char *temp = malloc(sizeof(strlen(output_file) + 4));
+    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(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 allocated enough memory\n");
+    exit(EXIT_FAILURE);
+  }
+
+  // call the probabilities-by-read command
+  sprintf(kmers_file, AWK_KMER_PERMUTATIONS, kmer);
+  sprintf(probabilities_command, "%s | probabilities-by-read %d %s /dev/stdin", kmers_file, 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;
+}