def check_imgdata_convolved():
"""What is the behavior when we add the PSF to plot_pvalue?"""
r1 = ui.get_psf()
ui.plot_pvalue(c1, c1 + g1, conv_model=r1, num=40, bins=5)
tmp = ui.get_pvalue_results()
# these values are different to test_plot_pvalue_imgpsf_unconvolved
#
assert tmp.null == pytest.approx(2391.26963100235)
assert tmp.alt == pytest.approx(563.3992697080881)
assert tmp.lr == pytest.approx(1827.8703612942618)
assert tmp.samples.shape == (40, 1)
assert tmp.stats.shape == (40, 2)
assert tmp.ratios.shape == (40, )
tmp = ui.get_pvalue_plot()
assert tmp.lr == pytest.approx(1827.8703612942618)
assert tmp.xlabel == 'Likelihood Ratio'
assert tmp.ylabel == 'Frequency'
assert tmp.title == 'Likelihood Ratio Distribution'
assert tmp.ratios.shape == (40, )
assert tmp.xlo.shape == (6, )
assert tmp.xhi.shape == (6, )
assert tmp.y.shape == (6, )
def center_psf(self):
"""Set ``xpos`` and ``ypos`` of the PSF to the dataspace center."""
import sherpa.astro.ui as sau
try:
ny, nx = sau.get_data().shape
for par in sau.get_psf().kernel.pars:
if par.name is 'xpos':
par.val = (nx + 1) / 2.
elif par.name is 'ypos':
par.val = (ny + 1) / 2.
except:
raise Exception('PSF is not centered.')
def center_psf(self):
"""Set ``xpos`` and ``ypos`` of the PSF to the dataspace center."""
import sherpa.astro.ui as sau
try:
ny, nx = sau.get_data().shape
for par in sau.get_psf().kernel.pars:
if par.name is 'xpos':
par.val = (nx + 1) / 2.
elif par.name is 'ypos':
par.val = (ny + 1) / 2.
except:
raise Exception('PSF is not centered.')
def center_psf(self):
"""Set xpos and ypos of the PSF to the dataspace center"""
import sherpa.astro.ui as sau
try:
ny, nx = sau.get_data().shape
for par in sau.get_psf().kernel.pars:
if par.name is 'xpos':
par.val = (nx + 1) / 2.
elif par.name is 'ypos':
par.val = (ny + 1) / 2.
except:
logging.warning('PSF is not centered.')
def containment_fraction(self, theta, npix=1000):
"""Compute fraction of PSF contained inside theta."""
import sherpa.astro.ui as sau
sau.dataspace2d((npix, npix))
self.set()
# x_center = get_psf().kernel.pars.xpos
# y_center = get_psf().kernel.pars.ypos
x_center, y_center = sau.get_psf().model.center
x_center, y_center = x_center + 0.5, y_center + 0.5 # shift seen on image.
x, y = sau.get_data().x0, sau.get_data().x1
# Note: Here we have to use the source image, before I used
# get_model_image(), which returns the PSF-convolved PSF image,
# which is a factor of sqrt(2) ~ 1.4 too wide!!!
p = sau.get_source_image().y.flatten()
p /= np.nansum(p)
mask = (x - x_center)**2 + (y - y_center)**2 < theta**2
fraction = np.nansum(p[mask])
if 0: # debug
sau.get_data().y = p
sau.save_data('psf_sherpa.fits', clobber=True)
sau.get_data().y = mask.astype('int')
sau.save_data('mask_sherpa.fits', clobber=True)
return fraction
def containment_fraction(self, theta, npix=1000):
"""Compute fraction of PSF contained inside theta."""
import sherpa.astro.ui as sau
sau.dataspace2d((npix, npix))
self.set()
# x_center = get_psf().kernel.pars.xpos
# y_center = get_psf().kernel.pars.ypos
x_center, y_center = sau.get_psf().model.center
x_center, y_center = x_center + 0.5, y_center + 0.5 # shift seen on image.
x, y = sau.get_data().x0, sau.get_data().x1
# @note Here we have to use the source image, before I used
# get_model_image(), which returns the PSF-convolved PSF image,
# which is a factor of sqrt(2) ~ 1.4 too wide!!!
p = sau.get_source_image().y.flatten()
p /= np.nansum(p)
mask = (x - x_center) ** 2 + (y - y_center) ** 2 < theta ** 2
fraction = np.nansum(p[mask])
if 0: # debug
sau.get_data().y = p
sau.save_data('psf_sherpa.fits', clobber=True)
sau.get_data().y = mask.astype('int')
sau.save_data('mask_sherpa.fits', clobber=True)
return fraction