def make_psf(configuration):
# The psf parameters in terms of the input parameters. To simulate the different pixel size
# we set the sigma of the psf to be a multiple (according to the ratio input)
# of the actual sigma in data pixel units.
# This should correspond, when ratio>1 to the case when the PSF's resolution is bigger
# than the image. If the ratio is 1 than they have the same pixel size.
psf_sigma = configuration.psf_sigma * configuration.resolution_ratio
psf_amplitude = configuration.psf_amplitude
psf_position = configuration.psf_position
# We model the PSF as a gaussian as well. Note we are using the psf_sigma variable,
# that is the one which is scaled through the ration. In other terms, we are working in
# units of Data Pixels to simulate the conditions of the bug, when the ratio != 1.
psf, psf_x, psf_y = symmetric_gaussian_image(amplitude=psf_amplitude, sigma=psf_sigma,
position=psf_position, n_bins=configuration.psf_size)
# Normalize PSF
norm_psf = psf / psf.sum()
cdelt = DATA_PIXEL_SIZE / configuration.resolution_ratio
psf_wcs = WcsStub([cdelt, cdelt])
# Create a Sherpa PSF model object using the psf arrays
sherpa_kernel = DataIMG('kernel_data',
psf_x, psf_y, norm_psf,
shape=(configuration.psf_size, configuration.psf_size),
sky=psf_wcs
)
psf_model = PSFModel('psf_model', kernel=sherpa_kernel)
psf_model.norm = 1
psf_model.origin = (psf_position + 1, psf_position + 1)
return psf, psf_model
def make_data(data_class):
"""Create a test data object of the given class.
Using a string means it is easier to support the various PHA
"types" - eg basic, grouping, grouping+quality.
"""
x0 = np.asarray([1, 3, 7, 12])
y = np.asarray([2, 3, 4, 5])
if data_class == "1d":
return Data1D('x1', x0, y)
if data_class == "1dint":
return Data1DInt('xint1', x0, np.asarray([3, 5, 8, 15]), y)
chans = np.arange(1, 5)
if data_class == "pha":
return DataPHA('pha', chans, y)
# We want to provide PHA tests that check out the grouping and
# quality handling (but it is not worth trying all different
# variants), so we have "grp" for grouping and no quality [*], and
# "qual" for grouping and quality.
#
# [*] by which I mean we have not called ignore_bad, not that
# there is no quality array.
#
grp = np.asarray([1, -1, 1, 1])
qual = np.asarray([0, 0, 2, 0])
pha = DataPHA('pha', chans, y, grouping=grp, quality=qual)
if data_class == "grp":
return pha
if data_class == "qual":
pha.ignore_bad()
return pha
x0 = np.asarray([1, 2, 3] * 2)
x1 = np.asarray([1, 1, 1, 2, 2, 2])
y = np.asarray([2, 3, 4, 5, 6, 7])
if data_class == "2d":
return Data2D('x2', x0, x1, y, shape=(2, 3))
if data_class == "2dint":
return Data2DInt('xint2', x0, x1, x0 + 1, x1 + 1, y, shape=(2, 3))
if data_class == "img":
return DataIMG('img', x0, x1, y, shape=(2, 3))
if data_class == "imgint":
return DataIMGInt('imgi', x0, x1, x0 + 1, x1 + 1, y, shape=(2, 3))
assert False
def make_image(configuration):
source_position = configuration.source_position
# The convolution of two gaussians is a gaussian with a stddev which is the sum
# of those convolved, so we model the image as a Gaussian itself.
image_sigma = sqrt(configuration.source_sigma ** 2 + configuration.psf_sigma ** 2)
image_amplitude = configuration.source_amplitude * configuration.psf_amplitude
image, image_x, image_y = symmetric_gaussian_image(amplitude=image_amplitude, sigma=image_sigma,
position=source_position, n_bins=configuration.image_size)
data_image = DataIMG("image", image_x, image_y, image,
shape=(configuration.image_size, configuration.image_size),
sky=WcsStub([DATA_PIXEL_SIZE, DATA_PIXEL_SIZE])
)
return data_image
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 make_test_image():
"""A simple image
Note that normally you'd have logical axes of 1:31,
1:21 here and then a WCS, but I've decided to number
the axes differently (in physical units) as there is
no requirement that the logical units are 1:nx/ny.
"""
x1, x0 = np.mgrid[3830:3850, 4245:4275]
# What is the ordering of shape? At the moment going for
# NumPy style (ny, nx), but there is no credible documentation
# (any documentation was added to describe the behavior we
# have now).
#
shape = x0.shape
x0 = x0.flatten()
x1 = x1.flatten()
y = np.ones(x0.size)
return DataIMG('d', x0, x1, y, shape=shape)