def applyRadiationDamageBiDir(data, nt_p, sigma_p, taur_p, nt_s, sigma_s, taur_s, iquadrant=0, rdose=1.6e10):
"""
:return: image that has been run through the CDM03 model
:rtype: ndarray
"""
#read in trap information
CTIed = cdm03bidir.cdm03(data,
iquadrant%2, iquadrant/2,
0.0, rdose,
nt_p, sigma_p, taur_p,
nt_s, sigma_s, taur_s,
[data.shape[0], data.shape[1], len(nt_p), len(nt_p)])
return np.asanyarray(CTIed)
def applyRadiationDamage(self, data, iquadrant=0):
"""
Apply radian damage based on FORTRAN CDM03 model. The method assumes that
input data covers only a single quadrant defined by the iquadrant integer.
:param data: imaging data to which the CDM03 model will be applied to.
:type data: ndarray
:param iquandrant: number of the quadrant to process
:type iquandrant: int
cdm03 - Function signature::
sout = cdm03(sinp,iflip,jflip,dob,rdose,in_nt,in_sigma,in_tr,[xdim,ydim,zdim])
Required arguments:
sinp : input rank-2 array('d') with bounds (xdim,ydim)
iflip : input int
jflip : input int
dob : input float
rdose : input float
in_nt : input rank-1 array('d') with bounds (zdim)
in_sigma : input rank-1 array('d') with bounds (zdim)
in_tr : input rank-1 array('d') with bounds (zdim)
Optional arguments:
xdim := shape(sinp,0) input int
ydim := shape(sinp,1) input int
zdim := len(in_nt) input int
Return objects:
sout : rank-2 array('d') with bounds (xdim,ydim)
.. Note:: Because Python/NumPy arrays are different row/column based, one needs
to be extra careful here. NumPy.asfortranarray will be called to get
an array laid out in Fortran order in memory. Before returning the
array will be laid out in memory in C-style (row-major order).
:return: image that has been run through the CDM03 model
:rtype: ndarray
""" ""
iflip = iquadrant / 2
jflip = iquadrant % 2
params = [
self.params['beta_p'], self.params['beta_s'], self.params['fwc'],
self.params['vth'], self.params['vg'], self.params['t'],
self.params['sfwc'], self.params['svg'], self.params['st'],
self.params['parallel'], self.params['serial']
]
if self.logger:
self.log.info('nt_p=' + str(self.nt_p))
self.log.info('nt_s=' + str(self.nt_s))
self.log.info('sigma_p= ' + str(self.sigma_p))
self.log.info('sigma_s= ' + str(self.sigma_s))
self.log.info('taur_p= ' + str(self.taur_p))
self.log.info('taur_s= ' + str(self.taur_s))
self.log.info('dob=%f' % self.values['dob'])
self.log.info('rdose=%e' % self.values['rdose'])
self.log.info('xsize=%i' % data.shape[1])
self.log.info('ysize=%i' % data.shape[0])
self.log.info('quadrant=%i' % iquadrant)
self.log.info('iflip=%i' % iflip)
self.log.info('jflip=%i' % jflip)
CTIed = cdm03bidir.cdm03(np.asfortranarray(data), jflip, iflip,
self.values['dob'], self.values['rdose'],
self.nt_p, self.sigma_p, self.taur_p,
self.nt_s, self.sigma_s, self.taur_s, params,
[
data.shape[0], data.shape[1],
len(self.nt_p),
len(self.nt_s),
len(self.params)
])
return np.asanyarray(CTIed)
def applyRadiationDamage(self, data, iquadrant=0):
"""
Apply radian damage based on FORTRAN CDM03 model. The method assumes that
input data covers only a single quadrant defined by the iquadrant integer.
:param data: imaging data to which the CDM03 model will be applied to.
:type data: ndarray
:param iquandrant: number of the quadrant to process
:type iquandrant: int
cdm03 - Function signature::
sout = cdm03(sinp,iflip,jflip,dob,rdose,in_nt,in_sigma,in_tr,[xdim,ydim,zdim])
Required arguments:
sinp : input rank-2 array('d') with bounds (xdim,ydim)
iflip : input int
jflip : input int
dob : input float
rdose : input float
in_nt : input rank-1 array('d') with bounds (zdim)
in_sigma : input rank-1 array('d') with bounds (zdim)
in_tr : input rank-1 array('d') with bounds (zdim)
Optional arguments:
xdim := shape(sinp,0) input int
ydim := shape(sinp,1) input int
zdim := len(in_nt) input int
Return objects:
sout : rank-2 array('d') with bounds (xdim,ydim)
.. Note:: Because Python/NumPy arrays are different row/column based, one needs
to be extra careful here. NumPy.asfortranarray will be called to get
an array laid out in Fortran order in memory. Before returning the
array will be laid out in memory in C-style (row-major order).
:return: image that has been run through the CDM03 model
:rtype: ndarray
"""""
iflip = iquadrant / 2
jflip = iquadrant % 2
params = [self.params['beta_p'], self.params['beta_s'], self.params['fwc'], self.params['vth'],
self.params['vg'], self.params['t'], self.params['sfwc'], self.params['svg'], self.params['st'],
self.params['parallel'], self.params['serial']]
if self.logger:
self.log.info('nt_p=' + str(self.nt_p))
self.log.info('nt_s=' + str(self.nt_s))
self.log.info('sigma_p= ' + str(self.sigma_p))
self.log.info('sigma_s= ' + str(self.sigma_s))
self.log.info('taur_p= ' + str(self.taur_p))
self.log.info('taur_s= ' + str(self.taur_s))
self.log.info('dob=%f' % self.values['dob'])
self.log.info('rdose=%e' % self.values['rdose'])
self.log.info('xsize=%i' % data.shape[1])
self.log.info('ysize=%i' % data.shape[0])
self.log.info('quadrant=%i' % iquadrant)
self.log.info('iflip=%i' % iflip)
self.log.info('jflip=%i' % jflip)
CTIed = cdm03bidir.cdm03(np.asfortranarray(data),
jflip, iflip,
self.values['dob'], self.values['rdose'],
self.nt_p, self.sigma_p, self.taur_p,
self.nt_s, self.sigma_s, self.taur_s,
params,
[data.shape[0], data.shape[1], len(self.nt_p), len(self.nt_s), len(self.params)])
return np.asanyarray(CTIed)
