diff options
| -rwxr-xr-x | src/python/quikr.py | 12 |
1 files changed, 7 insertions, 5 deletions
diff --git a/src/python/quikr.py b/src/python/quikr.py index cd6c8e0..d431f52 100755 --- a/src/python/quikr.py +++ b/src/python/quikr.py @@ -48,6 +48,9 @@ def train_matrix(input_file_location, kmer): Takes a input fasta file, and kmer, returns a custom sensing matrix returns an ndarray + + >>> sensing_matrix = train_matrix("/path/to/my/fasta.file", 6) + """ input_file = Popen(["bash", "-c", "probabilities-by-read " + str(kmer) + " " + input_file_location + " <(generate_kmers 6)"], stdout=PIPE) @@ -63,7 +66,7 @@ def train_matrix(input_file_location, kmer): def load_sensing_matrix_from_file(sensing_matrix_location): - """ This is a helper function to load our sensing matrix and run quikr """ + """ This is a helper function to load our sensing matrix from a file """ if is_compressed(sensing_matrix_location): sensing_matrix_file = gzip.open(sensing_matrix_location, "rb") @@ -75,12 +78,11 @@ def load_sensing_matrix_from_file(sensing_matrix_location): return sensing_matrix -def calculate_estimated_frequencies(input_fasta_location, sensing_matrix, kmer, default_lambda): +def calculate_estimated_frequencies(input_fasta_location, sensing_matrix, kmer, lamb): """ input_fasta is the input fasta file to find the estimated frequencies of sensing_matrix is the sensing matrix we are using to estimate the species kmer is the desired k-mer to use - default_lambda is inp returns the estimated requencies of bacteria present when given an input FASTA file of amplicon (454) reads. A k-mer based, L1 regularized, sparsity @@ -97,11 +99,11 @@ def calculate_estimated_frequencies(input_fasta_location, sensing_matrix, kmer, # load the output of our count program and form a probability vector from the counts counts = np.loadtxt(count_input.stdout) counts = counts / counts.sum(0) - counts = default_lambda * counts + counts = lamb * counts counts = np.concatenate([np.zeros(1), counts]) #form the k-mer sensing matrix - sensing_matrix = sensing_matrix * default_lambda; + sensing_matrix = sensing_matrix * lamb; sensing_matrix = np.vstack((np.ones(sensing_matrix.shape[1]), sensing_matrix)) xstar, rnorm = scipy.optimize.nnls(sensing_matrix, counts) |