diff options
Diffstat (limited to 'src/python')
| -rwxr-xr-x | src/python/quikr.py | 15 |
1 files changed, 5 insertions, 10 deletions
diff --git a/src/python/quikr.py b/src/python/quikr.py index 225ea9b..1fa27c7 100755 --- a/src/python/quikr.py +++ b/src/python/quikr.py @@ -23,10 +23,10 @@ def generate_kmers(kmer): return '\n'.join(''.join(x) for x in itertools.product('acgt', repeat=kmer)) -def isCompressed(filename): +def is_compressed(filename): """ This function checks to see if the file is gzipped - >>> boolean_value = isCompressed("/path/to/compressed/gzip/file") + >>> boolean_value = is_compressed("/path/to/compressed/gzip/file") >>> print boolean_value True @@ -39,7 +39,7 @@ def isCompressed(filename): try: f = open(filename, "rb") except IOError: - print "Warning: isCompressed could not find " + filename + print "Warning: is_compressed could not find " + filename return False # The first two bytes of a gzipped file are always '1f 8b' @@ -56,12 +56,6 @@ def train_matrix(input_file_location, kmer): Takes a input fasta file, and kmer, returns a custom trained matrix """ - kmer_file_name = str(kmer) + "mers.txt" - - if not os.path.isfile(kmer_file_name): - print "could not find kmer file" - exit() - input_file = Popen(["bash", "-c", "probabilities-by-read " + str(kmer) + " " + input_file_location + " <(generate_kmers 6)"], stdout=PIPE) # load and normalize the matrix by dividing each element by the sum of it's column. @@ -77,7 +71,7 @@ def train_matrix(input_file_location, kmer): def load_trained_matrix_from_file(trained_matrix_location): """ This is a helper function to load our trained matrix and run quikr """ - if isCompressed(trained_matrix_location): + if is_compressed(trained_matrix_location): trained_matrix_file = gzip.open(trained_matrix_location, "rb") else: trained_matrix_file = open(trained_matrix_location, "rb") @@ -103,6 +97,7 @@ def calculate_estimated_frequencies(input_fasta_location, trained_matrix, kmer, """ # We use the count program to count + count_input = Popen(["count-kmers", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE) # load the output of our count program and form a probability vector from the counts |