def main():
# create the alignment object
print 'creating the alignment...'
alignment_string = Fasta.brown_example_alignment.strip()
alignment = Fasta.Alignment(StringIO(alignment_string))
# create a tree object
print 'creating the tree...'
tree_string = Newick.brown_example_tree
tree = Newick.parse(tree_string, Newick.NewickTree)
# create a rate matrix object
print 'creating the rate matrix object...'
distribution = {'A': .25, 'C': .25, 'G': .25, 'T': .25}
kappa = 2.0
row_major_rate_matrix = RateMatrix.get_unscaled_hky85_rate_matrix(
distribution, kappa).get_row_major_rate_matrix()
rate_matrix = RateMatrix.FastRateMatrix(row_major_rate_matrix,
list('ACGT'))
rate_matrix.normalize()
# get the mle_rates
print 'getting the mle rates...'
mle_rates = get_mle_rates(tree, alignment, rate_matrix)
print 'mle rates:'
print mle_rates
print 'stockholm string:'
print get_stockholm_string(tree, alignment, mle_rates)
def get_response_content(fs):
# get the tree
tree = Newick.parse(fs.tree, Newick.NewickTree)
tree.assert_valid()
# get the nucleotide distribution
distribution = SnippetUtil.get_distribution(fs.weights, 'nucleotide',
list('ACGT'))
# get the nucleotide alignment
try:
alignment = Fasta.Alignment(StringIO(fs.alignment))
alignment.force_nucleotide()
except Fasta.AlignmentError as e:
raise HandlingError(e)
# get the rate matrix defined by the nucleotide distribution and kappa
row_major_rate_matrix = RateMatrix.get_unscaled_hky85_rate_matrix(
distribution, fs.kappa).get_row_major_rate_matrix()
rate_matrix = RateMatrix.FastRateMatrix(row_major_rate_matrix,
list('ACGT'))
rate_matrix.normalize()
# get the mle rates
mle_rates = get_mle_rates(tree, alignment, rate_matrix)
# return the response
return get_stockholm_string(tree, alignment, mle_rates) + '\n'
def get_response_content(fs):
# read the matrix from the form data
R = fs.matrix
matrix_size = len(R)
# enforce constraints on the initial and final states
if fs.initial >= matrix_size:
msg = 'the initial state must be less than the size of the matrix'
raise HandlingError(msg)
if fs.final >= matrix_size:
msg = 'the final state must be less than the size of the matrix'
raise HandlingError(msg)
# convert the row major rate matrix to a rate matrix object
arbitrary_states = list(str(x) for x in range(matrix_size))
rate_matrix_object = RateMatrix.FastRateMatrix(
R.tolist(), arbitrary_states)
# get the expected time spent in each state
expected_wait_times = rate_matrix_object.get_expected_times(
fs.initial, fs.final, fs.time)
# create the response text
out = StringIO()
for state_index, expected_wait_time in enumerate(expected_wait_times):
print >> out, state_index, ':', expected_wait_time / fs.time
# return the response
return out.getvalue()
