From 8564a460c242d065cd1cb43cb6c7fcbf61262b24 Mon Sep 17 00:00:00 2001
From: Calvin <calvin@EESI>
Date: Wed, 15 May 2013 14:05:51 -0400
Subject: python: add some more descriptive comments, use lamb instead of
default lamb
---
src/python/quikr.py | 12 +++++++-----
1 file 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)
--
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