def calc(self, *args, **kwargs):
if self.model is None:
raise PSFErr('nofold')
psf_space_evaluation = self.model.calc(*args, **kwargs)
if self._must_rebin:
return rebin_2d(psf_space_evaluation, self.psf_space, self.data_space).ravel()
else:
return psf_space_evaluation
def _set_origin(self, vals):
par = vals
if type(vals) in (str, numpy.string_):
raise PSFErr('notstr')
elif type(vals) not in (list, tuple, numpy.ndarray):
par = [vals]
self._origin = tuple(par)
if par is None:
self._origin = None
def get_kernel(self, data, subkernel=True):
indep, dep, kshape, lo, hi = self._get_kernel_data(data, subkernel)
ndim = len(kshape)
if ndim == 1:
dataset = Data1D('kernel', indep[0], dep)
elif ndim == 2:
dataset = Data2D('kernel', indep[0], indep[1], dep, kshape[::-1])
else:
raise PSFErr('ndim')
return dataset
def __call__(self, model, session=None):
if self.kernel is None:
raise PSFErr('notset')
kernel = self.kernel
if isinstance(kernel, Data):
kernel = numpy.asarray(kernel.get_dep())
if isinstance(model, string_types):
if session is None:
model = sherpa.astro.ui._session._eval_model_expression(model)
else:
model = session._eval_model_expression(model)
return ConvolutionModel(kernel, model, self)
def _get_kernel_data(self, data, subkernel=True):
self.fold(data)
if self.kernel is None:
raise PSFErr('notset')
kernel = self.kernel
dep = None
indep = None
lo = None
hi = None
if isinstance(kernel, Data):
dep = numpy.asarray(kernel.get_dep())
indep = kernel.get_indep()
elif callable(kernel):
dep = kernel(*self.model.args, **self.model.kwargs)
indep = self.model.args
kshape = self.model.kshape
if subkernel:
(dep, newshape) = self.model.init_kernel(dep)
if (numpy.array(kshape) != numpy.array(newshape)).any():
newindep = []
for axis in indep:
args = extract_kernel(axis,
self.model.kshape,
self.model.size,
self.model.center,
self.model.lo,
self.model.hi,
self.model.width,
self.model.radial)
newaxis = args[0]
lo = args[3] # subkernel offsets (lower bound)
hi = args[4] # subkernel offsets (upper bound)
newindep.append(newaxis)
indep = newindep
kshape = newshape
if self.model.frac is not None:
info('PSF frac: %s' % self.model.frac)
if numpy.isscalar(kshape):
kshape = [kshape]
return (indep, dep, kshape, lo, hi)
def get_kernel(self, data, subkernel=True):
indep, dep, kshape, lo, hi = self._get_kernel_data(data, subkernel)
# Use kernel data set WCS if available
eqpos = getattr(self.kernel, 'eqpos', None)
sky = getattr(self.kernel, 'sky', None)
# If kernel is a model, use WCS from data if available
if callable(self.kernel):
eqpos = getattr(data, 'eqpos', None)
sky = getattr(data, 'sky', None)
dataset = None
ndim = len(kshape)
if ndim == 1:
dataset = Data1D('kernel', indep[0], dep)
elif ndim == 2:
# Edit WCS to reflect the subkernel extraction in
# physical coordinates.
if (subkernel and sky is not None and lo is not None
and hi is not None):
if (WCS is not None):
sky = WCS(sky.name, sky.type, sky.crval, sky.crpix - lo,
sky.cdelt, sky.crota, sky.epoch, sky.equinox)
# FIXME: Support for WCS only (non-Chandra) coordinate
# transformations?
dataset = DataIMG('kernel',
indep[0],
indep[1],
dep,
kshape[::-1],
sky=sky,
eqpos=eqpos)
else:
raise PSFErr('ndim')
return dataset
def fold(self, data):
# FIXME how will we know the native dimensionality of the
# raveled model without the values?
kargs = {}
kshape = None
dshape = data.get_dims()
(size, center, origin, kargs['norm'],
radial) = (self.size, self.center, self.origin,
bool_cast(self.norm.val), int(self.radial.val))
kargs['size'] = size
kargs['center'] = center
kargs['origin'] = origin
kargs['is_model'] = False
kargs['do_pad'] = False
kargs['args'] = data.get_indep()
pixel_size_comparison = self._check_pixel_size(data)
if pixel_size_comparison == self.SAME_RESOLUTION: # Don't do anything special
self.data_space = EvaluationSpace2D(*data.get_indep())
self._must_rebin = False
elif pixel_size_comparison == self.BETTER_RESOLUTION: # Evaluate model in PSF space
self.data_space = EvaluationSpace2D(*data.get_indep())
self.psf_space = PSFSpace2D(self.data_space, self, data.sky.cdelt)
kargs['args'] = self.psf_space.grid
dshape = self.psf_space.shape
self._must_rebin = True
else: # PSF has worse resolution, error out
raise AttributeError(
"The PSF has a worse resolution than the data.")
if isinstance(self.kernel, Data):
kshape = self.kernel.get_dims()
# (kargs['lo'], kargs['hi'],
# kargs['width']) = _get_axis_info(self.kernel.get_indep(), kshape)
kargs['lo'] = [1] * len(kshape)
kargs['hi'] = kshape
kargs['width'] = [1] * len(kshape)
if center is None:
kargs['center'] = [int(dim / 2.) for dim in kshape]
# update center param to default
self.center = kargs['center']
if size is None:
kargs['size'] = kshape
# update size param to default
self.size = kargs['size']
else:
if (self.kernel is None) or (not callable(self.kernel)):
raise PSFErr('nopsf', self._name)
kshape = data.get_dims()
# (kargs['lo'], kargs['hi'],
# kargs['width']) = _get_axis_info(kargs['args'], dshape)
kargs['lo'] = [1] * len(kshape)
kargs['hi'] = kshape
kargs['width'] = [1] * len(kshape)
if center is None:
kargs['center'] = [int(dim / 2.) for dim in dshape]
# update center param to default
self.center = kargs['center']
if size is None:
kargs['size'] = dshape
# update size param to default
self.size = kargs['size']
kargs['is_model'] = True
if hasattr(self.kernel, 'pars'):
# freeze all PSF model parameters if not already.
for par in self.kernel.pars:
par.freeze()
if hasattr(self.kernel, 'thawedpars'):
kargs['frozen'] = (len(self.kernel.thawedpars) == 0)
is_kernel = (kargs['is_model'] and not kargs['norm']
and len(kshape) == 1)
# Handle noticed regions for convolution
if numpy.iterable(data.mask):
kargs['do_pad'] = True
kargs['pad_mask'] = data.mask
if is_kernel:
for id in ['is_model', 'lo', 'hi', 'width', 'size']:
kargs.pop(id)
self.model = Kernel(dshape, kshape, **kargs)
return
if radial:
self.model = RadialProfileKernel(dshape, kshape, **kargs)
return
self.model = PSFKernel(dshape, kshape, **kargs)
return
def __str__(self):
if self.kernel is None:
raise PSFErr('notset')
return "Convolution Kernel:\n" + self.kernel.__str__()
def fold(self, data):
# FIXME how will we know the native dimensionality of the
# raveled model without the values?
self._check_pixel_size(data)
kargs = {}
kshape = None
dshape = data.get_dims()
(size, center, origin,
kargs['norm'], radial) = (self.size, self.center, self.origin,
bool_cast(self.norm.val),
int(self.radial.val))
kargs['size'] = size
kargs['center'] = center
kargs['origin'] = origin
kargs['is_model'] = False
kargs['do_pad'] = False
kargs['args'] = data.get_indep()
if isinstance(self.kernel, Data):
kshape = self.kernel.get_dims()
# (kargs['lo'], kargs['hi'],
# kargs['width']) = _get_axis_info(self.kernel.get_indep(), kshape)
kargs['lo'] = [1] * len(kshape)
kargs['hi'] = kshape
kargs['width'] = [1] * len(kshape)
if center is None:
kargs['center'] = [int(dim / 2.) for dim in kshape]
# update center param to default
self.center = kargs['center']
if size is None:
kargs['size'] = kshape
# update size param to default
self.size = kargs['size']
else:
if (self.kernel is None) or (not callable(self.kernel)):
raise PSFErr('nopsf', self._name)
kshape = data.get_dims()
# (kargs['lo'], kargs['hi'],
# kargs['width']) = _get_axis_info(kargs['args'], dshape)
kargs['lo'] = [1] * len(kshape)
kargs['hi'] = kshape
kargs['width'] = [1] * len(kshape)
if center is None:
kargs['center'] = [int(dim / 2.) for dim in dshape]
# update center param to default
self.center = kargs['center']
if size is None:
kargs['size'] = dshape
# update size param to default
self.size = kargs['size']
kargs['is_model'] = True
if hasattr(self.kernel, 'pars'):
# freeze all PSF model parameters if not already.
for par in self.kernel.pars:
par.freeze()
if hasattr(self.kernel, 'thawedpars'):
kargs['frozen'] = (len(self.kernel.thawedpars) == 0)
# check size of self.size to ensure <= dshape for 2D
# if len(dshape) > 1:
# dsize = numpy.asarray(dshape)
# ksize = numpy.asarray(self.size)
# if True in (ksize>dsize):
# raise PSFErr('badsize', ksize, dsize)
is_kernel = (kargs['is_model'] and not kargs['norm'] and
len(kshape) == 1)
# Handle noticed regions for convolution
if numpy.iterable(data.mask):
kargs['do_pad'] = True
kargs['pad_mask'] = data.mask
if is_kernel:
for id in ['is_model', 'lo', 'hi', 'width', 'size']:
kargs.pop(id)
self.model = Kernel(dshape, kshape, **kargs)
return
if radial:
self.model = RadialProfileKernel(dshape, kshape, **kargs)
return
self.model = PSFKernel(dshape, kshape, **kargs)
return
def calc(self, *args, **kwargs):
if self.model is None:
raise PSFErr('nofold')
return self.model.calc(*args, **kwargs)
