def create_rate_matrix(kappa, nt_distribution):
"""
@param kappa: adjusts for the transition rate differing from the transversion rate
@param nt_distribution: ordered ACGT nucleotide probabilities
@return: a rate matrix object with one expected nucleotide substitution per time unit
"""
# make some assertions about the distribution
for p in nt_distribution:
assert p >= 0
assert len(nt_distribution) == 4
assert RateMatrix.almost_equal(sum(nt_distribution), 1.0)
# define some intermediate variables
A, C, G, T = nt_distribution
R = float(A + G)
Y = float(C + T)
# make some more assertions about the distribution and about kappa
assert A + G > 0
assert C + T > 0
assert kappa > max(-Y, -R)
# get the normalization constant
normalization_constant = 4 * T * C * (1 + kappa / Y) + 4 * A * G * (
1 + kappa / R) + 4 * Y * R
# adjust the normalization constant to correct what might be an error in the paper
normalization_constant /= 2
# define the dictionary rate matrix
dict_rate_matrix = {}
for source_index, source in enumerate('ACGT'):
for sink_index, sink in enumerate('ACGT'):
key = (source, sink)
coefficient = 1.0
if key in g_transitions:
coefficient = 1 + kappa / (nt_distribution[source_index] +
nt_distribution[sink_index])
dict_rate_matrix[key] = coefficient * nt_distribution[
sink_index] / normalization_constant
for source in 'ACGT':
dict_rate_matrix[(source,
source)] = -sum(dict_rate_matrix[(source, sink)]
for sink in 'ACGT' if source != sink)
# convert the dictionary rate matrix to a row major rate matrix
row_major = MatrixUtil.dict_to_row_major(dict_rate_matrix, 'ACGT', 'ACGT')
# return the rate matrix object
rate_matrix_object = RateMatrix.RateMatrix(row_major, 'ACGT')
expected_rate = rate_matrix_object.get_expected_rate()
if not RateMatrix.almost_equal(expected_rate, 1.0):
assert False, 'the rate is %f but should be 1.0' % expected_rate
return rate_matrix_object
def create_rate_matrix(kappa, nt_distribution):
"""
@param kappa: adjusts for the transition rate differing from the transversion rate
@param nt_distribution: ordered ACGT nucleotide probabilities
@return: a rate matrix object with one expected nucleotide substitution per time unit
"""
# make some assertions about the distribution
for p in nt_distribution:
assert p >= 0
assert len(nt_distribution) == 4
assert RateMatrix.almost_equal(sum(nt_distribution), 1.0)
# define some intermediate variables
A, C, G, T = nt_distribution
R = float(A + G)
Y = float(C + T)
# make some more assertions about the distribution and about kappa
assert A+G > 0
assert C+T > 0
assert kappa > max(-Y, -R)
# get the normalization constant
normalization_constant = 4*T*C*(1 + kappa/Y) + 4*A*G*(1 + kappa/R) + 4*Y*R
# adjust the normalization constant to correct what might be an error in the paper
normalization_constant /= 2
# define the dictionary rate matrix
dict_rate_matrix = {}
for source_index, source in enumerate('ACGT'):
for sink_index, sink in enumerate('ACGT'):
key = (source, sink)
coefficient = 1.0
if key in g_transitions:
coefficient = 1 + kappa / (nt_distribution[source_index] + nt_distribution[sink_index])
dict_rate_matrix[key] = coefficient * nt_distribution[sink_index] / normalization_constant
for source in 'ACGT':
dict_rate_matrix[(source, source)] = -sum(dict_rate_matrix[(source, sink)] for sink in 'ACGT' if source != sink)
# convert the dictionary rate matrix to a row major rate matrix
row_major = MatrixUtil.dict_to_row_major(dict_rate_matrix, 'ACGT', 'ACGT')
# return the rate matrix object
rate_matrix_object = RateMatrix.RateMatrix(row_major, 'ACGT')
expected_rate = rate_matrix_object.get_expected_rate()
if not RateMatrix.almost_equal(expected_rate, 1.0):
assert False, 'the rate is %f but should be 1.0' % expected_rate
return rate_matrix_object
