class RSPModelPHA(RSPModel):
"""
RMF + ARF convolution model with associated PHA
"""
def __init__(self, arf, rmf, pha, model):
self.pha = pha
self._arf = arf
self._rmf = rmf
RSPModel.__init__(self, arf, rmf, model)
def filter(self):
RSPModel.filter(self)
pha = self.pha
# If PHA is a finer grid than RMF, evaluate model on PHA and
# rebin down to the granularity that the RMF expects.
if pha.bin_lo is not None and pha.bin_hi is not None:
bin_lo, bin_hi = pha.bin_lo, pha.bin_hi
# If PHA grid is in angstroms then convert to keV for
# consistency
if (bin_lo[0] > bin_lo[-1]) and (bin_hi[0] > bin_hi[-1]):
bin_lo = DataPHA._hc / pha.bin_hi
bin_hi = DataPHA._hc / pha.bin_lo
# FIXME: What about filtered option?? bin_lo, bin_hi are
# unfiltered??
# Compare disparate grids in energy space
self.arfargs = ((self.elo, self.ehi), (bin_lo, bin_hi))
# FIXME: Assumes ARF grid is finest
elo, ehi = self.rmf.get_indep()
# self.elo, self.ehi are from ARF
if len(elo) != len(self.elo) and len(ehi) != len(self.ehi):
self.rmfargs = ((elo, ehi), (self.elo, self.ehi))
# Assume energy as default spectral coordinates
self.xlo, self.xhi = self.elo, self.ehi
if self.pha.units == 'wavelength':
self.xlo, self.xhi = self.lo, self.hi
def startup(self):
arf = self._arf
rmf = self._rmf
# Create a view of original RMF
self.rmf = DataRMF(rmf.name, rmf.detchans, rmf.energ_lo, rmf.energ_hi,
rmf.n_grp, rmf.f_chan, rmf.n_chan, rmf.matrix,
rmf.offset, rmf.e_min, rmf.e_max, rmf.header)
# Create a view of original ARF
self.arf = DataARF(arf.name, arf.energ_lo, arf.energ_hi, arf.specresp,
arf.bin_lo, arf.bin_hi, arf.exposure, arf.header)
# Filter the view for current fitting session
_notice_resp(self.pha.get_noticed_channels(), self.arf, self.rmf)
self.filter()
# Assume energy as default spectral coordinates
self.xlo, self.xhi = self.elo, self.ehi
if self.pha.units == 'wavelength':
self.xlo, self.xhi = self.lo, self.hi
RSPModel.startup(self)
def teardown(self):
self.arf = self._arf # restore originals
self.rmf = self._rmf
self.filter()
RSPModel.teardown(self)
def calc(self, p, x, xhi=None, *args, **kwargs):
# x could be channels or x, xhi could be energy|wave
src = self.model.calc(p, self.xlo, self.xhi)
src = self.arf.apply_arf(src, *self.arfargs)
return self.rmf.apply_rmf(src, *self.rmfargs)
class RSPModelPHA(RSPModel):
"""RMF + ARF convolution model with associated PHA.
Notes
-----
Scaling by the AREASCAL setting (scalar or array) is included in
this model.
"""
def __init__(self, arf, rmf, pha, model):
self.pha = pha
self._arf = arf
self._rmf = rmf
RSPModel.__init__(self, arf, rmf, model)
def filter(self):
RSPModel.filter(self)
pha = self.pha
# If PHA is a finer grid than RMF, evaluate model on PHA and
# rebin down to the granularity that the RMF expects.
if pha.bin_lo is not None and pha.bin_hi is not None:
bin_lo, bin_hi = pha.bin_lo, pha.bin_hi
# If PHA grid is in angstroms then convert to keV for
# consistency
if (bin_lo[0] > bin_lo[-1]) and (bin_hi[0] > bin_hi[-1]):
bin_lo = DataPHA._hc / pha.bin_hi
bin_hi = DataPHA._hc / pha.bin_lo
# FIXME: What about filtered option?? bin_lo, bin_hi are
# unfiltered??
# Compare disparate grids in energy space
self.arfargs = ((self.elo, self.ehi), (bin_lo, bin_hi))
# FIXME: Assumes ARF grid is finest
elo, ehi = self.rmf.get_indep()
# self.elo, self.ehi are from ARF
if len(elo) != len(self.elo) and len(ehi) != len(self.ehi):
self.rmfargs = ((elo, ehi), (self.elo, self.ehi))
# Assume energy as default spectral coordinates
self.xlo, self.xhi = self.elo, self.ehi
if self.pha.units == 'wavelength':
self.xlo, self.xhi = self.lo, self.hi
def startup(self, cache):
arf = self._arf
rmf = self._rmf
# Create a view of original RMF
self.rmf = DataRMF(rmf.name, rmf.detchans, rmf.energ_lo, rmf.energ_hi,
rmf.n_grp, rmf.f_chan, rmf.n_chan, rmf.matrix,
rmf.offset, rmf.e_min, rmf.e_max, rmf.header)
# Create a view of original ARF
self.arf = DataARF(arf.name, arf.energ_lo, arf.energ_hi, arf.specresp,
arf.bin_lo, arf.bin_hi, arf.exposure, arf.header)
# Filter the view for current fitting session
_notice_resp(self.pha.get_noticed_channels(), self.arf, self.rmf)
self.filter()
# Assume energy as default spectral coordinates
self.xlo, self.xhi = self.elo, self.ehi
if self.pha.units == 'wavelength':
self.xlo, self.xhi = self.lo, self.hi
RSPModel.startup(self, cache)
def teardown(self):
self.arf = self._arf # restore originals
self.rmf = self._rmf
self.filter()
RSPModel.teardown(self)
def calc(self, p, x, xhi=None, *args, **kwargs):
# x could be channels or x, xhi could be energy|wave
src = self.model.calc(p, self.xlo, self.xhi)
src = self.arf.apply_arf(src, *self.arfargs)
src = self.rmf.apply_rmf(src, *self.rmfargs)
# Assume any issues with the binning (between AREASCAL
# and src) is related to the RMF rather than the ARF.
return apply_areascal(src, self.pha, "RMF: {}".format(self.rmf.name))
class ARFModelPHA(ARFModel):
"""
ARF convolution model with associated PHA
"""
def __init__(self, arf, pha, model):
self.pha = pha
self._arf = arf # store a reference to original
ARFModel.__init__(self, arf, model)
def filter(self):
ARFModel.filter(self)
pha = self.pha
# If PHA is a finer grid than ARF, evaluate model on PHA and
# rebin down to the granularity that the ARF expects.
if pha.bin_lo is not None and pha.bin_hi is not None:
bin_lo, bin_hi = pha.bin_lo, pha.bin_hi
# If PHA grid is in angstroms then convert to keV for
# consistency
if (bin_lo[0] > bin_lo[-1]) and (bin_hi[0] > bin_hi[-1]):
bin_lo = DataPHA._hc / pha.bin_hi
bin_hi = DataPHA._hc / pha.bin_lo
# FIXME: What about filtered option?? bin_lo, bin_hi are
# unfiltered??
# Compare disparate grids in energy space
self.arfargs = ((self.elo, self.ehi), (bin_lo, bin_hi))
# FIXME: Assumes ARF grid is finest
# Assume energy as default spectral coordinates
self.xlo, self.xhi = self.elo, self.ehi
if self.pha.units == 'wavelength':
self.xlo, self.xhi = self.lo, self.hi
def startup(self):
arf = self._arf # original
pha = self.pha
# Create a view of original ARF
self.arf = DataARF(arf.name, arf.energ_lo, arf.energ_hi, arf.specresp,
arf.bin_lo, arf.bin_hi, arf.exposure, arf.header)
# Filter the view for current fitting session
if numpy.iterable(pha.mask):
mask = pha.get_mask()
if len(mask) == len(self.arf.specresp):
self.arf.notice(mask)
self.filter()
# Assume energy as default spectral coordinates
self.xlo, self.xhi = self.elo, self.ehi
if pha.units == 'wavelength':
self.xlo, self.xhi = self.lo, self.hi
ARFModel.startup(self)
def teardown(self):
self.arf = self._arf # restore original
self.filter()
ARFModel.teardown(self)
def calc(self, p, x, xhi=None, *args, **kwargs):
# x could be channels or x, xhi could be energy|wave
src = self.model.calc(p, self.xlo, self.xhi)
return self.arf.apply_arf(src, *self.arfargs)
class ARFModelPHA(ARFModel):
"""ARF convolution model with associated PHA data set.
Notes
-----
Scaling by the AREASCAL setting (scalar or array) is included in
this model. It is not yet clear if this is handled correctly.
"""
def __init__(self, arf, pha, model):
self.pha = pha
self._arf = arf # store a reference to original
ARFModel.__init__(self, arf, model)
def filter(self):
ARFModel.filter(self)
pha = self.pha
# If PHA is a finer grid than ARF, evaluate model on PHA and
# rebin down to the granularity that the ARF expects.
if pha.bin_lo is not None and pha.bin_hi is not None:
bin_lo, bin_hi = pha.bin_lo, pha.bin_hi
# If PHA grid is in angstroms then convert to keV for
# consistency
if (bin_lo[0] > bin_lo[-1]) and (bin_hi[0] > bin_hi[-1]):
bin_lo = DataPHA._hc / pha.bin_hi
bin_hi = DataPHA._hc / pha.bin_lo
# FIXME: What about filtered option?? bin_lo, bin_hi are
# unfiltered??
# Compare disparate grids in energy space
self.arfargs = ((self.elo, self.ehi), (bin_lo, bin_hi))
# FIXME: Assumes ARF grid is finest
# Assume energy as default spectral coordinates
self.xlo, self.xhi = self.elo, self.ehi
if self.pha.units == 'wavelength':
self.xlo, self.xhi = self.lo, self.hi
def startup(self, cache):
arf = self._arf # original
pha = self.pha
# Create a view of original ARF
self.arf = DataARF(arf.name, arf.energ_lo, arf.energ_hi, arf.specresp,
arf.bin_lo, arf.bin_hi, arf.exposure, arf.header)
# Filter the view for current fitting session
if numpy.iterable(pha.mask):
mask = pha.get_mask()
if len(mask) == len(self.arf.specresp):
self.arf.notice(mask)
self.filter()
# Assume energy as default spectral coordinates
self.xlo, self.xhi = self.elo, self.ehi
if pha.units == 'wavelength':
self.xlo, self.xhi = self.lo, self.hi
ARFModel.startup(self, cache)
def teardown(self):
self.arf = self._arf # restore original
self.filter()
ARFModel.teardown(self)
def calc(self, p, x, xhi=None, *args, **kwargs):
# x could be channels or x, xhi could be energy|wave
src = self.model.calc(p, self.xlo, self.xhi)
src = self.arf.apply_arf(src, *self.arfargs)
return apply_areascal(src, self.pha, "ARF: {}".format(self.arf.name))
