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
