def __init__(self,
UnitigGraph,
breakpoint_penalty=1,
data_penalty=1,
trash_penalty=1,
expected_value_penalty=1,
infer_c=None,
infer_d=None,
default_ploidy=2):
SequenceGraphLpProblem.__init__(self)
self.graph = UnitigGraph
self.breakpoint_penalty = breakpoint_penalty
self.data_penalty = data_penalty
self.trash_penalty = trash_penalty
self.expected_value_penalty = expected_value_penalty
self.expected_ploidy = {}
for para in UnitigGraph.paralogs.iterkeys():
if para == "Notch2NL-C" and infer_c is not None:
self.expected_ploidy[para] = infer_c
elif para == "Notch2NL-D" and infer_d is not None:
self.expected_ploidy[para] = infer_d
else:
self.expected_ploidy[para] = default_ploidy
# list of Block objects
self.blocks = []
# maps Block objects to the paralogs they contain
self.block_map = {x: [] for x in UnitigGraph.paralogs.iterkeys()}
self._build_blocks(UnitigGraph, infer_c, infer_d)
def __init__(self, Avals, Bvals, windowSize, stepSize, breakpoint_penalty, data_penalty, deletion_penalty):
aStart = 146152644-3000
bStart = 148603586-3000
aStop = 146233816+4000
bStop = 148684557+4000
SequenceGraphLpProblem.__init__(self)
self.windows = []
for aPos, bPos in izip(xrange(aStart, aStop - windowSize, stepSize),
xrange(bStart, bStop - windowSize, stepSize)):
A = [y for x, y in Avals if x >= aPos and y < aPos]
B = [y for x, y in Bvals if x >= bPos and y < bPos]
self.windows.append([aPos, bPos, Window(A, B)])
self.build_model(breakpoint_penalty, data_penalty, deletion_penalty)
def __init__(self, Avals, Bvals, windowSize, stepSize, breakpoint_penalty,
data_penalty, deletion_penalty):
aStart = 146152644 - 3000
bStart = 148603586 - 3000
aStop = 146233816 + 4000
bStop = 148684557 + 4000
SequenceGraphLpProblem.__init__(self)
self.windows = []
for aPos, bPos in izip(xrange(aStart, aStop - windowSize, stepSize),
xrange(bStart, bStop - windowSize, stepSize)):
A = [y for x, y in Avals if x >= aPos and y < aPos]
B = [y for x, y in Bvals if x >= bPos and y < bPos]
self.windows.append([aPos, bPos, Window(A, B)])
self.build_model(breakpoint_penalty, data_penalty, deletion_penalty)
def __init__(self, deBruijnGraph, normalizing, breakpointPenalty=15, dataPenalty=1, tightness=1, inferC=None,
inferD=None, defaultPloidy=2.0):
SequenceGraphLpProblem.__init__(self)
self.blocks = []
self.block_map = {x[0]: [] for x in deBruijnGraph.paralogs}
self.offset_map = {x[0]: int(x[1]) for x in deBruijnGraph.paralogs}
self.normalizing = normalizing
self.breakpointPenalty = breakpointPenalty
self.dataPenalty = dataPenalty
self.tightness = tightness
self.inferC = inferC
self.inferD = inferD
self.defaultPloidy = defaultPloidy
self.buildBlocks(deBruijnGraph)
self.G = deBruijnGraph
def __init__(self, UnitigGraph, breakpoint_penalty=1, data_penalty=1, trash_penalty=1, expected_value_penalty=1,
infer_c=None, infer_d=None, default_ploidy=2):
SequenceGraphLpProblem.__init__(self)
self.graph = UnitigGraph
self.breakpoint_penalty = breakpoint_penalty
self.data_penalty = data_penalty
self.trash_penalty = trash_penalty
self.expected_value_penalty = expected_value_penalty
self.expected_ploidy = {}
for para in UnitigGraph.paralogs.iterkeys():
if para == "Notch2NL-C" and infer_c is not None:
self.expected_ploidy[para] = infer_c
elif para == "Notch2NL-D" and infer_d is not None:
self.expected_ploidy[para] = infer_d
else:
self.expected_ploidy[para] = default_ploidy
# list of Block objects
self.blocks = []
# maps Block objects to the paralogs they contain
self.block_map = {x: [] for x in UnitigGraph.paralogs.iterkeys()}
self._build_blocks(UnitigGraph, infer_c, infer_d)
def __init__(self,
deBruijnGraph,
normalizing,
breakpointPenalty=15,
dataPenalty=1,
tightness=1,
inferC=None,
inferD=None,
defaultPloidy=2.0):
SequenceGraphLpProblem.__init__(self)
self.blocks = []
self.block_map = {x[0]: [] for x in deBruijnGraph.paralogs}
self.offset_map = {x[0]: int(x[1]) for x in deBruijnGraph.paralogs}
self.normalizing = normalizing
self.breakpointPenalty = breakpointPenalty
self.dataPenalty = dataPenalty
self.tightness = tightness
self.inferC = inferC
self.inferD = inferD
self.defaultPloidy = defaultPloidy
self.buildBlocks(deBruijnGraph)
self.G = deBruijnGraph
