def __init__(self, gbmModel, sequence, rawKinetics, m5Cclassifier):
MultiSiteCommon.__init__(self, gbmModel, sequence, rawKinetics)
models = np.genfromtxt(m5Cclassifier, delimiter=',' )
self.fwd_model = models[:,0]
self.rev_model = models[:,1]
def __init__(self,
gbmModel,
sequence,
rawKinetics,
identifyFlag,
modsToCall=['H', 'J', 'K']):
MultiSiteCommon.__init__(self, gbmModel, sequence, rawKinetics)
# FIXME: For debugging LDA, load in parameters for forward and reverse strands:
self.fwd_model = np.genfromtxt(
"/home/UNIXHOME/obanerjee/nat_fwd_model_expanded.csv",
delimiter=',')
self.rev_model = np.genfromtxt(
"/home/UNIXHOME/obanerjee/nat_rev_model_expanded.csv",
delimiter=',')
if identifyFlag:
if 'K' in modsToCall:
self.fwd_model = np.genfromtxt(
"/home/UNIXHOME/obanerjee/tet_fwd_model_expanded.csv",
delimiter=',')
self.rev_model = np.genfromtxt(
"/home/UNIXHOME/obanerjee/tet_rev_model_expanded.csv",
delimiter=',')
def __init__(self, gbmModel, sequence, rawKinetics):
MultiSiteCommon.__init__(self, gbmModel, sequence, rawKinetics)
self.fwd_model = np.genfromtxt("/home/UNIXHOME/obanerjee/initial_lr_model_weights_fwd.csv", delimiter=',')
self.rev_model = np.genfromtxt("/home/UNIXHOME/obanerjee/initial_lr_model_weights_rev.csv", delimiter=',')
self.fwd_model = np.squeeze(np.asarray(self.fwd_model))
self.rev_model = np.squeeze(np.asarray(self.rev_model))
def __init__(self, gbmModel, sequence, rawKinetics):
MultiSiteCommon.__init__(self, gbmModel, sequence, rawKinetics)
self.fwd_model = np.genfromtxt(
"/home/UNIXHOME/obanerjee/initial_lr_model_weights_fwd.csv",
delimiter=',')
self.rev_model = np.genfromtxt(
"/home/UNIXHOME/obanerjee/initial_lr_model_weights_rev.csv",
delimiter=',')
self.fwd_model = np.squeeze(np.asarray(self.fwd_model))
self.rev_model = np.squeeze(np.asarray(self.rev_model))
def __init__(self, gbmModel, sequence, rawKinetics, identifyFlag, modsToCall=['H', 'J', 'K']):
MultiSiteCommon.__init__(self, gbmModel, sequence, rawKinetics)
# FIXME: For debugging LDA, load in parameters for forward and reverse strands:
self.fwd_model = np.genfromtxt("/home/UNIXHOME/obanerjee/nat_fwd_model_expanded.csv", delimiter=',')
self.rev_model = np.genfromtxt("/home/UNIXHOME/obanerjee/nat_rev_model_expanded.csv", delimiter=',')
if identifyFlag:
if 'K' in modsToCall:
self.fwd_model = np.genfromtxt("/home/UNIXHOME/obanerjee/tet_fwd_model_expanded.csv", delimiter=',')
self.rev_model = np.genfromtxt("/home/UNIXHOME/obanerjee/tet_rev_model_expanded.csv", delimiter=',')
def __init__(self, gbmModel, sequence, rawKinetics, callBounds, methylMinCov, modsToCall=['H', 'J', 'K'], methylFractionFlag=False, useLDAFlag=False):
MultiSiteCommon.__init__(self, gbmModel, sequence, rawKinetics)
# Extents that we will attemp to call a modification
self.callStart = callBounds[0]
self.callEnd = callBounds[1]
self.callRange = xrange(self.callStart, self.callEnd)
self.methylMinCov = methylMinCov
self.modsToCall = modsToCall
self.methylFractionFlag = methylFractionFlag
self.useLDA = useLDAFlag
def __init__(self,
gbmModel,
sequence,
rawKinetics,
callBounds,
methylMinCov,
modsToCall=['H', 'J', 'K'],
methylFractionFlag=False,
useLDAFlag=False):
MultiSiteCommon.__init__(self, gbmModel, sequence, rawKinetics)
# Extents that we will attemp to call a modification
self.callStart = callBounds[0]
self.callEnd = callBounds[1]
self.callRange = xrange(self.callStart, self.callEnd)
self.methylMinCov = methylMinCov
self.modsToCall = modsToCall
self.methylFractionFlag = methylFractionFlag
self.useLDA = useLDAFlag
