def __init__(self, *args, **kwargs): # pylint: disable=unused-argument
"""
Initialize ReadAligner.
HMM state notation abbreviations:
M_t - trusted match; M_u - untrusted match
Ir_t - trusted read insert; Ir_u - untrusted read insert
Ig_t - trusted graph insert; Ig_u - untrusted graph insert
Keyword arguments:
filename - a path to a JSON encoded file providing the scoring matrix
for the HMM in an entry named 'scoring_matrix' and the transition
probabilities for the HMM in an entry named
'transition_probabilities'. If provided the remaining keyword
arguments are ignored. (default: None)
scoring_matrix - a list of floats: trusted match, trusted mismatch,
unstrusted match, untrusted mismatch. (default:
ReadAligner.defaultScoringMatrix)
transition_probabilities - A sparse matrix as a tuple of six tuples.
The inner tuples contain 6, 4, 4, 6, 4, and 4 floats respectively.
Transition are notated as 'StartState-NextState':
(
( M_t-M_t, M_t-Ir_t, M_t-Ig_t, M_t-M_u, M_t-Ir_u, M_t-Ig_u),
(Ir_t-M_t, Ir_t-Ir_t, Ir_t-M_u, Ir_t-Ir_u ),
(Ig_t-M_t, , Ig_t-Ig_t, Ig_t-M_u, Ig_t-Ig_u),
( M_u-M_t, M_u-Ir_t, M_u-Ig_t, M_u-M_u, M_u-Ir_u, M_u-Ig_u),
(Ir_u-M_t, Ir_u-Ir_t, Ir_u-M_u, Ir_u-Ir_u ),
(Ig_u-M_t, , Ig_u-Ig_t, Ig_u-M_u, Ig_u-Ig_u)
)
(default: ReadAligner.defaultTransitionProbabilities)
Note: the underlying CPython implementation creates the ReadAligner
during the __new__ process and so the class initialization actually
occurs there. Instatiation is documented here in __init__ as this is
the traditional way.
"""
_ReadAligner.__init__(self)
def __init__(self, *args, **kwargs): # pylint: disable=unused-argument
"""
Initialize ReadAligner.
HMM state notation abbreviations:
M_t - trusted match; M_u - untrusted match
Ir_t - trusted read insert; Ir_u - untrusted read insert
Ig_t - trusted graph insert; Ig_u - untrusted graph insert
Keyword arguments:
filename - a path to a JSON encoded file providing the scoring matrix
for the HMM in an entry named 'scoring_matrix' and the transition
probabilities for the HMM in an entry named
'transition_probabilities'. If provided the remaining keyword
arguments are ignored. (default: None)
scoring_matrix - a list of floats: trusted match, trusted mismatch,
unstrusted match, untrusted mismatch. (default:
ReadAligner.defaultScoringMatrix)
transition_probabilities - A sparse matrix as a tuple of six tuples.
The inner tuples contain 6, 4, 4, 6, 4, and 4 floats respectively.
Transition are notated as 'StartState-NextState':
(
( M_t-M_t, M_t-Ir_t, M_t-Ig_t, M_t-M_u, M_t-Ir_u, M_t-Ig_u),
(Ir_t-M_t, Ir_t-Ir_t, Ir_t-M_u, Ir_t-Ir_u ),
(Ig_t-M_t, , Ig_t-Ig_t, Ig_t-M_u, Ig_t-Ig_u),
( M_u-M_t, M_u-Ir_t, M_u-Ig_t, M_u-M_u, M_u-Ir_u, M_u-Ig_u),
(Ir_u-M_t, Ir_u-Ir_t, Ir_u-M_u, Ir_u-Ir_u ),
(Ig_u-M_t, , Ig_u-Ig_t, Ig_u-M_u, Ig_u-Ig_u)
)
(default: ReadAligner.defaultTransitionProbabilities)
Note: the underlying CPython implementation creates the ReadAligner
during the __new__ process and so the class initialization actually
occurs there. Instatiation is documented here in __init__ as this is
the traditional way.
"""
_ReadAligner.__init__(self)
def __new__(cls, count_graph, trusted_cov_cutoff, bits_theta, **kwargs):
if 'filename' in kwargs:
with open(kwargs.pop('filename')) as paramfile:
params = json.load(paramfile)
scoring_matrix = params['scoring_matrix']
transition_probabilities = params['transition_probabilities']
else:
if 'scoring_matrix' in kwargs:
scoring_matrix = kwargs.pop('scoring_matrix')
else:
scoring_matrix = ReadAligner.defaultScoringMatrix
if 'transition_probabilities' in kwargs:
transition_probabilities = kwargs.pop(
'transition_probabilities')
else:
transition_probabilities = \
ReadAligner.defaultTransitionProbabilities
r = _ReadAligner.__new__(cls, count_graph, trusted_cov_cutoff,
bits_theta, scoring_matrix,
transition_probabilities)
r.graph = count_graph
return r
def __new__(cls, count_graph, trusted_cov_cutoff, bits_theta,
**kwargs):
if 'filename' in kwargs:
with open(kwargs.pop('filename')) as paramfile:
params = json.load(paramfile)
scoring_matrix = params['scoring_matrix']
transition_probabilities = params['transition_probabilities']
else:
if 'scoring_matrix' in kwargs:
scoring_matrix = kwargs.pop('scoring_matrix')
else:
scoring_matrix = ReadAligner.defaultScoringMatrix
if 'transition_probabilities' in kwargs:
transition_probabilities = kwargs.pop(
'transition_probabilities')
else:
transition_probabilities = \
ReadAligner.defaultTransitionProbabilities
readaligner = _ReadAligner.__new__(
cls, count_graph, trusted_cov_cutoff, bits_theta, scoring_matrix,
transition_probabilities)
readaligner.graph = count_graph
return readaligner