def check_integrals():
"""Check that Sherpa normed models integrate to 1."""
from sherpa.astro import ui
from sherpa.astro.ui import normgauss2d
from models import normdisk2d, normshell2d
ui.clean()
g = normgauss2d('g')
g.xpos, g.ypos, g.ampl, g.fwhm = 100, 100, 42, 5
d = normdisk2d('d')
d.xpos, d.ypos, d.ampl, d.r0 = 100, 100, 42, 50
s = normshell2d('s')
s.xpos, s.ypos, s.ampl, s.r0, s.width = 100, 100, 42, 30, 20
models = [g, d, s]
ui.dataspace2d((200, 200))
for model in models:
ui.set_model(model)
# In sherpa normed model values are flux per pixel area.
# So to get the total flux (represented by the `ampl` parameter)
# one can simply sum over all pixels, because a pixel has area 1 pix^2.
# :-)
integral = ui.get_model_image().y.sum()
print model.name, integral
def test_psf_model2d(self):
ui.dataspace2d([216, 261])
for model in self.models2d:
try:
ui.load_psf("psf2d", model + ".mdl")
ui.set_psf("psf2d")
mdl = ui.get_model_component("mdl")
self.assert_((numpy.array(mdl.get_center()) == numpy.array([108, 130])).all())
except:
print model
raise
def test_psf_model2d(self):
ui.dataspace2d([216, 261])
for model in self.models2d:
try:
ui.load_psf('psf2d', model + '.mdl')
ui.set_psf('psf2d')
mdl = ui.get_model_component('mdl')
self.assertTrue((numpy.array(mdl.get_center()) ==
numpy.array([108, 130])).all())
except:
print model
raise
def test_psf_model2d(self):
ui.dataspace2d([216,261])
for model in self.models2d:
try:
ui.load_psf('psf2d', model+'.mdl')
ui.set_psf('psf2d')
mdl = ui.get_model_component('mdl')
self.assert_( (numpy.array(mdl.get_center()) ==
numpy.array([108,130])).all() )
except:
print model
raise
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
from __future__ import print_function, division
import numpy as np
import sherpa.astro.ui as sau
# Define width of the source and the PSF
sigma_psf, sigma_source = 3, 4
# for relation of sigma and fwhm see
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
sigma_to_fwhm = np.sqrt(8 * np.log(2)) # ~ 2.35
sigma = np.sqrt(sigma_psf ** 2 + sigma_source ** 2)
fwhm = sigma_to_fwhm * sigma
# Seed the random number generator to make the output reproducible
np.random.seed(0)
sau.dataspace2d((200, 200))
sau.set_source('normgauss2d.source + const2d.background')
sau.set_par('source.xpos', 100)
sau.set_par('source.ypos', 100)
sau.set_par('source.ampl', 1e3)
sau.set_par('source.fwhm', fwhm)
sau.set_par('background.c0', 1)
sau.fake()
sau.save_model('model.fits.gz', clobber=True)
sau.save_data('counts.fits.gz', clobber=True)
sau.set_source('source')
sau.save_model('source.fits.gz', clobber=True)
sau.set_source('background')
from __future__ import print_function, division
import numpy as np
import sherpa.astro.ui as sau
# Define width of the source and the PSF
sigma_psf, sigma_source = 3, 4
# for relation of sigma and fwhm see
# http://cxc.harvard.edu/sherpa/ahelp/gauss2d.html
sigma_to_fwhm = np.sqrt(8 * np.log(2)) # ~ 2.35
sigma = np.sqrt(sigma_psf**2 + sigma_source**2)
fwhm = sigma_to_fwhm * sigma
# Seed the random number generator to make the output reproducible
np.random.seed(0)
sau.dataspace2d((200, 200))
sau.set_source('normgauss2d.source + const2d.background')
sau.set_par('source.xpos', 100)
sau.set_par('source.ypos', 100)
sau.set_par('source.ampl', 1e3)
sau.set_par('source.fwhm', fwhm)
sau.set_par('background.c0', 1)
sau.fake()
sau.save_model('model.fits.gz', clobber=True)
sau.save_data('counts.fits.gz', clobber=True)
sau.set_source('source')
sau.save_model('source.fits.gz', clobber=True)
sau.set_source('background')
