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instrument.py
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instrument.py
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from __future__ import division
#
# Copyright (C) 2008, 2016, 2018 Smithsonian Astrophysical Observatory
#
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
import math
import warnings
import numpy
import logging
from six import string_types
from sherpa.astro.data import DataIMG
from sherpa.data import Data, Data1D, Data2D
from sherpa.models import ArithmeticModel, ArithmeticConstantModel, \
ArithmeticFunctionModel, CompositeModel, Model
from sherpa.models.parameter import Parameter
from sherpa.models.regrid import EvaluationSpace2D, rebin_2d
from sherpa.utils import bool_cast, NoNewAttributesAfterInit
from sherpa.utils.err import PSFErr
from sherpa.utils._psf import extract_kernel, get_padsize, normalize, \
pad_data, set_origin, tcdData, unpad_data
import sherpa
info = logging.getLogger(__name__).info
__all__ = ('Kernel', 'PSFKernel', 'RadialProfileKernel', 'PSFModel',
'ConvolutionModel', 'PSFSpace2D')
class ConvolutionModel(CompositeModel, ArithmeticModel):
@staticmethod
def wrapobj(obj):
if isinstance(obj, ArithmeticModel):
return obj
return ArithmeticFunctionModel(obj)
@staticmethod
def wrapkern(obj):
if isinstance(obj, ArithmeticModel):
return obj
elif callable(obj):
return ArithmeticFunctionModel(obj)
return ArithmeticConstantModel(obj, 'kernel')
def __init__(self, lhs, rhs, psf):
self.lhs = self.wrapkern(lhs)
self.rhs = self.wrapobj(rhs)
self.psf = psf
CompositeModel.__init__(self,
('%s(%s)' %
(self.psf.name, self.rhs.name)),
(self.psf, self.lhs, self.rhs))
def calc(self, p, *args, **kwargs):
nlhs = len(self.lhs.pars)
return self.psf.calc(p[:nlhs], p[nlhs:],
self.lhs.calc, self.rhs.calc, *args, **kwargs)
class Kernel(NoNewAttributesAfterInit):
"Base class for convolution kernels"
def __init__(self, dshape, kshape, norm=False, frozen=True,
center=None, args=[], kwargs={},
do_pad=False, pad_mask=None, origin=None):
if origin is None:
origin = numpy.zeros(len(kshape))
self.dshape = dshape
self.kshape = kshape
self.kernel = None
self.skshape = None
self.norm = norm
self.origin = origin
self.frozen = frozen
self.center = center
self.args = args
self.kwargs = kwargs
self.renorm_shape = None
self.renorm = None
self.do_pad = do_pad
self.pad_mask = pad_mask
self.frac = None
self._tcd = tcdData()
NoNewAttributesAfterInit.__init__(self)
def __setstate__(self, state):
state['_tcd'] = tcdData()
self.__dict__.update(state)
def __getstate__(self):
state = self.__dict__.copy()
state.pop('_tcd')
return state
def __repr__(self):
return "<%s kernel instance>" % type(self).__name__
def __str__(self):
ss = [
'dshape = %s' % str(self.dshape),
'kshape = %s' % str(self.kshape),
# 'kernel = %s' % type(self.kernel).__name__,
'skshape = %s' % str(self.skshape),
'norm = %s' % str(self.norm),
'origin = %s' % str(self.origin),
'frozen = %s' % str(self.frozen),
'center = %s' % str(self.center),
'args = %s' % str(self.args),
'kwargs = %s' % str(self.kwargs),
'renorm_shape = %s' % str(self.renorm_shape),
'renorm = %s' % str(self.renorm),
'do_pad = %s' % str(self.do_pad),
'pad_mask = %s' % str(self.pad_mask),
'frac = %s' % str(self.frac)
]
return '\n'.join(ss)
def init_kernel(self, kernel):
if not self.frozen:
self._tcd.clear_kernel_fft()
renorm_shape = []
for axis in self.dshape:
renorm_shape.append(get_padsize(2 * axis))
self.renorm_shape = tuple(renorm_shape)
kernpad = pad_data(kernel, self.dshape, self.renorm_shape)
self.renorm = self._tcd.convolve(numpy.ones(len(kernel)), kernpad,
self.dshape, renorm_shape,
self.origin)
self.renorm = unpad_data(self.renorm, renorm_shape, self.dshape)
return (kernel, self.dshape)
def init_data(self, data):
if self.renorm_shape is None:
renorm_shape = []
for axis in self.dshape:
renorm_shape.append(get_padsize(2 * axis))
self.renorm_shape = tuple(renorm_shape)
# pad the data and convolve with unpadded kernel
datapad = pad_data(data, self.dshape, self.renorm_shape)
return (datapad, self.renorm_shape)
def deinit(self, vals):
if self.renorm is not None:
vals = unpad_data(vals, self.renorm_shape, self.dshape)
vals = vals / self.renorm
if self.do_pad:
vals = vals[self.pad_mask]
return vals
def convolve(self, data, dshape, kernel, kshape):
return self._tcd.convolve(data, kernel, dshape, kshape, self.origin)
def calc(self, pl, pr, lhs, rhs, *args, **kwargs):
if self.do_pad and len(args[0]) == numpy.prod(self.dshape):
self.do_pad = False
data = rhs(pr, *self.args, **self.kwargs)
(data, dshape) = self.init_data(data)
if self.kernel is None or not self.frozen:
kernel = lhs(pl, *self.args, **self.kwargs)
(self.kernel, self.skshape) = self.init_kernel(kernel)
vals = self.convolve(data, dshape, self.kernel, self.skshape)
return self.deinit(vals)
class ConvolutionKernel(Model):
def __init__(self, kernel, name='conv'):
self.kernel = kernel
self.name = name
self._tcd = tcdData()
Model.__init__(self, name)
def __setstate__(self, state):
state['_tcd'] = tcdData()
self.__dict__.update(state)
def __getstate__(self):
state = self.__dict__.copy()
state.pop('_tcd')
return state
def __repr__(self):
return "<%s kernel instance>" % type(self).__name__
def __str__(self):
if self.kernel is None:
raise PSFErr('notset')
return "Convolution Kernel:\n" + self.kernel.__str__()
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 set_kernel(self, kernel):
self.kernel = kernel
def calc(self, pl, pr, lhs, rhs, *args, **kwargs):
self._tcd.clear_kernel_fft()
data = numpy.asarray(rhs(pr, *args, **kwargs))
kern = numpy.asarray(lhs(pl, *args, **kwargs))
size = data.size
return self._tcd.convolve(data, kern, size, kern.size,
int(size / 2))[:size]
class PSFKernel(Kernel):
"class for PSF convolution kernels"
def __init__(self, dshape, kshape, is_model=False, norm=True, frozen=True,
center=None, size=None, lo=None, hi=None, width=None,
args=[], kwargs={},
pad_mask=None, do_pad=False, origin=None):
self.is_model = is_model
self.size = size
self.lo = lo
self.hi = hi
self.width = width
self.radial = 0
Kernel.__init__(self, dshape, kshape, norm, frozen,
center, args, kwargs,
do_pad, pad_mask, origin)
self.origin = origin
def __str__(self):
ss = [
'is_model = %s' % str(self.is_model),
'size = %s' % str(self.size),
'lo = %s' % str(self.lo),
'hi = %s' % str(self.hi),
'width = %s' % str(self.width),
'radial = %s' % str(self.radial)
]
return Kernel.__str__(self) + '\n' + '\n'.join(ss)
def init_kernel(self, kernel):
# If PSF dataset, normalize before kernel extraction
# if not self.is_model and self.norm:
if self.norm:
kernel = normalize(kernel)
(kernel, kshape, self.frac,
lo, hi) = extract_kernel(kernel, self.kshape, self.size, self.center,
self.lo, self.hi, self.width, self.radial)
# If PSF model, then normalize integrated volume to 1, after
# kernel extraction
# if self.is_model and self.norm:
# self.frac = 1.0
# kernel = normalize(kernel)
# Find brightest pixel of PSF--assume that is the origin
# Just assuming that the origin is half of szs1 can lead to
# unwanted pixel shifts--but this assumes that origin should
# be centered on brightest pixel.
brightPixel = list(numpy.where(kernel == kernel.max())).pop()
origin = None
# if more than one pixel qualifies as brightest, such as const2D
# use the middle of subkernel -- assumes the user provided center at
# time of kernel extraction, so that should be middle of subkernel.
if (not numpy.isscalar(brightPixel)) and len(brightPixel) != 1:
origin = set_origin(kshape)
else:
origin = set_origin(kshape, brightPixel)
if self.origin is None:
self.origin = origin
if self.is_model and not self.frozen:
# if the kernel model has thawed parameters, clear the old FFT and
# recompute the kernel FFT at each model evaluation
self._tcd.clear_kernel_fft()
return (kernel, kshape)
def init_data(self, data):
return (data, self.dshape)
class RadialProfileKernel(PSFKernel):
"class for 1D radial profile PSF convolution kernels"
def __init__(self, dshape, kshape, is_model=False,
norm=True, frozen=True,
center=None, size=None, lo=None, hi=None, width=None,
args=[], kwargs={},
pad_mask=None, do_pad=False, origin=None):
self.radialsize = None
PSFKernel.__init__(self, dshape, kshape, is_model, norm,
frozen, center, size, lo, hi, width, args, kwargs,
pad_mask, do_pad, origin)
self.radial = 1
def __str__(self):
return (PSFKernel.__str__(self) + '\n' +
'radialsize = %s' % str(self.radialsize))
def init_data(self, data):
data, dshape = PSFKernel.init_data(self, data)
# NOTICE: radial profile is 1D only!
if self.radialsize is None:
self.radialsize = self.dshape[0]
return data, dshape
def deinit(self, vals):
# NOTICE: radial profile is 1D only!
vals = vals[:self.radialsize]
return PSFKernel.deinit(self, vals)
def convolve(self, data, dshape, kernel, kshape):
origin = self.origin
if self.radialsize is not None:
origin = self.origin + (numpy.asarray(dshape) -
numpy.asarray(self.radialsize))
return self._tcd.convolve(data, kernel, dshape, kshape, origin)
def calc(self, pl, pr, lhs, rhs, *args, **kwargs):
if self.do_pad and len(args[0]) == numpy.prod(self.dshape):
self.do_pad = False
data = rhs(pr, *self.args, **self.kwargs)
(data, dshape) = self.init_data(data)
# NOTICE: radial profile is 1D only!
# old sherpa source model grid extension to zero
# (radial profile add core)
tail_grid = _create_tail_grid(self.args)
if tail_grid is not None:
tail = rhs(pr, *tail_grid, **self.kwargs)
data = numpy.concatenate([tail, data])
dshape = (len(data),)
if self.kernel is None or not self.frozen:
kernel = lhs(pl, *self.args, **self.kwargs)
(self.kernel, self.skshape) = self.init_kernel(kernel)
vals = self.convolve(data, dshape, self.kernel, self.skshape)
return self.deinit(vals)
def _create_tail_grid(axis_list):
if len(axis_list) == 1:
# non-binned axis
grid = axis_list[0]
# origsize = len(grid)
width = grid[1] - grid[0]
tail = numpy.arange(grid[0] - width, 0., -width)[::-1]
return (tail,)
elif len(axis_list) == 2:
# binned axis
gridlo, gridhi = axis_list
# origsize = len(gridlo)
width = (gridhi[0] - gridlo[0])
mid = (gridlo[0] + gridhi[0]) / 2.
mids = numpy.arange(mid, 0., -width)[::-1]
taillo = mids - width / 2.
tailhi = mids + width / 2.
return (taillo, tailhi)
return None
class PSFModel(Model):
def __init__(self, name='psfmodel', kernel=None):
self._name = name
self._size = None
self._origin = None
self._center = None
self._must_rebin = False
self.radial = Parameter(name, 'radial', 0, 0, 1, hard_min=0,
hard_max=1, alwaysfrozen=True)
self.norm = Parameter(name, 'norm', 1, 0, 1, hard_min=0, hard_max=1,
alwaysfrozen=True)
self.kernel = kernel
self.model = None
self.data_space = None
self.psf_space = None
Model.__init__(self, name)
def _get_center(self):
if self._center is not None:
if len(self._center) == 1:
return self._center[0]
return self._center
def _set_center(self, vals):
par = vals
if type(vals) in (str, numpy.string_):
raise PSFErr('nostr')
elif type(vals) not in (list, tuple, numpy.ndarray):
par = [vals]
self._center = tuple(par)
if par is None:
self._center = None
center = property(_get_center, _set_center, doc='array of size parameters')
def _get_size(self):
if self._size is not None:
if len(self._size) == 1:
return self._size[0]
return self._size
def _set_size(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._size = tuple(par)
if par is None:
self._size = None
size = property(_get_size, _set_size, doc='array of size parameters')
def _get_origin(self):
if self._origin is not None:
if len(self._origin) == 1:
return self._origin[0]
return self._origin
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
origin = property(_get_origin, _set_origin, doc='FFT origin')
def _get_str(self):
s = ''
if self.kernel is not None:
s += ('\n %-12s %-6s %12s' %
('%s.kernel' % self._name, 'frozen',
self.kernel.name))
if self.size is not None:
s += ('\n %-12s %-6s %12s %12s %12s' %
('%s.size' % self._name, 'frozen',
self.size, self.size, self.size))
if self.center is not None:
s += ('\n %-12s %-6s %12s %12s %12s' %
('%s.center' % self._name, 'frozen',
self.center, self.center, self.center))
if self.origin is not None:
s += ('\n %-12s %-6s %12s %12s %12s' %
('%s.origin' % self._name, 'frozen',
self.origin, self.origin, self.origin))
for p in [self.radial, self.norm]:
s += ('\n %-12s %-6s %12g %12g %12g %10s' %
(p.fullname, 'frozen', p.val, p.min, p.max, p.units))
return s
def __str__(self):
s = self.name
hfmt = '\n %-12s %-6s %12s %12s %12s %10s'
s += hfmt % ('Param', 'Type', 'Value', 'Min', 'Max', 'Units')
s += hfmt % ('-' * 5, '-' * 4, '-' * 5, '-' * 3, '-' * 3, '-' * 5)
s += self._get_str()
return s
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 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 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 _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)
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 _check_pixel_size(self, data):
"""
If the data and PSF dot not have WCS information, assume the PSF has the same resolution
as the image.
Otherwise check the WCS information to determine the relative resolutions.
We only check the resolution in one dimention and assume they are the same
"""
if hasattr(self.kernel, "sky"):
# This corresponds to the case when the kernel is actually a psf image, not just a model.
try:
psf_pixel_size = self.kernel.sky.cdelt
except AttributeError: # If the kernel does not have a pixel size, issue a warning and keep going
warnings.warn("PSF Image does not have a pixel size. Sherpa will assume the pixel size is the same"
"as the data")
return self.SAME_RESOLUTION
try:
data_pixel_size = data.sky.cdelt
except AttributeError:
warnings.warn("Data Image does not have a pixel size. Sherpa will assume the pixel size is the same"
"as the PSF")
return self.SAME_RESOLUTION
if numpy.allclose(psf_pixel_size, data_pixel_size):
return self.SAME_RESOLUTION
if psf_pixel_size[0] < data_pixel_size[0]:
return self.BETTER_RESOLUTION
if psf_pixel_size[0] > data_pixel_size[0]:
return self.WORSE_RESOLUTION
return self.SAME_RESOLUTION
SAME_RESOLUTION = 0
BETTER_RESOLUTION = 1
WORSE_RESOLUTION = -1
class PSFSpace2D(EvaluationSpace2D):
"""
This class defines a special evaluation space that has the same boundaries as the data space but a number of pixels
consistent with the pixel size of the PSF. This space is used when the data image and the PSF have a different
pixel size so the model is evaluated on the "PSF space" and then rebinned back to the data space for the calculation
of the statistic during a fit.
"""
def __init__(self, data_space, psf_model, data_pixel_size=None):
if data_pixel_size is None:
data_pixel_size = [1, 1]
x_start, y_start = data_space.start
x_end, y_end = data_space.end
psf_pixel_size_axis0 = psf_model.kernel.sky.cdelt[0]
psf_pixel_size_axis1 = psf_model.kernel.sky.cdelt[1]
data_pixel_size_axis0 = data_pixel_size[0]
data_pixel_size_axis1 = data_pixel_size[1]
step_x = psf_pixel_size_axis0 / data_pixel_size_axis0
step_y = psf_pixel_size_axis1 / data_pixel_size_axis1
x_range_end, y_range_end = x_end + 1, y_end + 1
x = numpy.arange(x_start, x_range_end, step_x)
y = numpy.arange(y_start, y_range_end, step_y)
self.data_2_psf_pixel_size_ratio = (step_x, step_y)
super(PSFSpace2D, self).__init__(x, y)