def setup_imgdata_model():
"""Use a model for the PSF"""
# Fake an image
#
x1, x0 = np.mgrid[0:8, 0:10]
ymod = 10 + 100 / ((x0 - 5.5)**2 + (x1 - 3.5)**2)
x0 = x0.flatten()
x1 = x1.flatten()
y = ymod.flatten()
y = y.astype(int) # convert to integers
ui.load_arrays(1, x0, x1, y, (8, 10), ui.DataIMG)
pmodel = ui.create_model_component('gauss2d', 'pmodel')
pmodel.xpos = 0
pmodel.ypos = 0
pmodel.fwhm = 3
ui.load_psf('psf', pmodel)
psf.size = [10, 10] # not sure if this is useful but leave in
psf.center = [5, 4]
ui.set_psf(psf)
setup_imgdata_source()
def test_psf_model2d(model, clean_astro_ui):
ui.dataspace2d([216, 261])
ui.load_psf('psf2d', model + '.mdl')
ui.set_psf('psf2d')
mdl = ui.get_model_component('mdl')
assert (numpy.array(mdl.get_center()) ==
numpy.array([108, 130])).all()
def set(self):
"""Set the PSF for Sherpa."""
import sherpa.astro.ui as sau
# from gammapy.image.models.utils import read_json
read_json(self.pars, sau.set_model)
sau.load_psf('psf', sau.get_model())
self.center_psf()
sau.set_psf('psf')
def set(self):
"""Set the PSF for Sherpa."""
import sherpa.astro.ui as sau
# from morphology.utils import read_json
read_json(self.pars, sau.set_model)
sau.load_psf('psf', sau.get_model())
sau.set_psf('psf')
self.center_psf()
def set(self):
"""Set the PSF for Sherpa."""
import sherpa.astro.ui as sau
from ..scripts.image_fit import read_json
read_json(self.pars, sau.set_model)
sau.load_psf('psf', sau.get_model())
self.center_psf()
sau.set_psf('psf')
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_model1d(self):
ui.dataspace1d(1, 10)
for model in self.models1d:
try:
ui.load_psf("psf1d", model + ".mdl")
ui.set_psf("psf1d")
mdl = ui.get_model_component("mdl")
self.assert_((numpy.array(mdl.get_center()) == numpy.array([4])).all())
except:
print model
raise
def test_psf_model1d(self):
ui.dataspace1d(1, 10)
for model in self.models1d:
try:
ui.load_psf('psf1d', model + '.mdl')
ui.set_psf('psf1d')
mdl = ui.get_model_component('mdl')
self.assertTrue(
(numpy.array(mdl.get_center()) == numpy.array([4])).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_model1d(self):
ui.dataspace1d(1, 10)
for model in self.models1d:
try:
ui.load_psf('psf1d', model + '.mdl')
ui.set_psf('psf1d')
mdl = ui.get_model_component('mdl')
self.assertTrue((numpy.array(mdl.get_center()) ==
numpy.array([4])).all())
except:
print model
raise
def setup(imgfile, emapfile):
shp.load_data(imgfile)
shp.load_table_model('emap', emapfile)
shp.freeze(emap.ampl)
# we're using an on axis psf provided in
# /packages/CALDB/data/nustar/fpm/bcf/psf/
shp.load_psf('psf', 'nuA2dpsf20100101v003_onaxis.fits')
shp.set_psf(psf)
psf.center = (500.0, 500.0)
psf.size = (200, 200)
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 setup_imgdata_file(tmp_path, recwarn):
"""Use a model for the PSF"""
# Fake an image
#
x1, x0 = np.mgrid[0:8, 0:10]
ymod = 10 + 100 / ((x0 - 5.5)**2 + (x1 - 3.5)**2)
x0 = x0.flatten()
x1 = x1.flatten()
y = ymod.flatten()
y = y.astype(int) # convert to integers
ui.load_arrays(1, x0, x1, y, (8, 10), ui.DataIMG)
# Sherpa can confuse astropy if you send in pathlib objects so
# convert to the raw filesystem path.
#
fname = tmp_path / "psf.fits"
infile = str(fname)
fake_psf_image(infile)
ui.load_psf('psf', infile)
psf.size = [10, 8]
psf.center = [5, 4]
# Just check where the warning comes from (pytest.warns didn't
# seem to remove the warning so it still triggered the general
# "check no warning" fixture, so use recwarn like this).
#
assert len(recwarn) == 0
ui.set_psf(psf)
assert len(recwarn) == 1
setup_imgdata_source()
assert len(recwarn) == 1
w = recwarn.pop()
assert issubclass(w.category, UserWarning)
wmsg = 'PSF Image does not have a pixel size. Sherpa ' + \
'will assume the pixel size is the same as the data'
assert str(w.message) == wmsg
def test_psf_model1d(model, center, clean_astro_ui):
ui.dataspace1d(1, 10)
ui.load_psf('psf1d', model + '.mdl')
ui.set_psf('psf1d')
mdl = ui.get_model_component('mdl')
assert mdl.get_center() == (center, )
def simulate_null_images(img_file: str,
psf_file: str,
n_null_sims: int,
no_core: bool = False,
mcmciter: int = 5000,
**kwargs) -> None:
"""
Simulates a specified number of baseline images for a given input observation and a psf file
:param img_file: Path to the input image file
:param psf_file: Path to the psf image file
:param n_null_sims: Number of baseline replicates to be simulated
:param no_core: Setting this to True will only generate baseline replicates with a flat background while the default value includes a point source at the location of the core
:param mcmciter: The number of MCMC samples to draw for simulating the baselines
"""
print("Creating the null file")
clean()
set_stat("cstat")
set_method("simplex")
load_image(img_file)
load_psf("mypsf", psf_file)
set_psf(mypsf)
if no_core:
set_model(const2d.c0)
set_par(c0.c0, min=0)
else:
set_model(gauss2d.q1 + const2d.c0)
set_par(c0.c0, min=0)
# set_par(q1.fwhm,max=0.5)
guess(q1)
fit()
results = get_fit_results()
save("core_source_fit.save", clobber=True)
save_source("null_q1_c1.fits", clobber=True)
covar()
if no_core:
for i in range(n_null_sims):
fake()
save_image("sim_null_{}.fits".format(i), clobber=True)
clean()
return
normgauss1d.g1
g1.pos = q1.fwhm
g1.fwhm = get_covar_results().parmaxes[0]
# check if there is a valid upper bound.
print(get_covar_results())
if (get_covar_results().parmaxes[0] is None
or get_covar_results().parmins[1] is None
or get_covar_results().parmins[0] is None):
for i in range(n_null_sims):
fake()
save_image("sim_null_{}.fits".format(i), clobber=True)
clean()
return
# if not go for the regular
set_prior(q1.fwhm, g1)
set_sampler_opt("defaultprior", False)
set_sampler_opt("priorshape", [True, False, False, False, False])
set_sampler_opt("originalscale", [True, True, True, True, True])
if mcmciter < n_null_sims * 100:
mcmciter = n_null_sims * 100
# the following code throws an error sometimes #bug
try:
stats, accept, params = get_draws(1, niter=mcmciter)
except:
params = [np.repeat(q1.fwhm.val, mcmciter)]
# print('Simulating the null files')
for i in range(n_null_sims):
set_par(q1.fwhm, params[0][(i + 1) * 100 - 1])
fake()
save_image("sim_null_{}.fits".format(i), clobber=True)
save_all(outfile="lira_input_baseline_sim.log", clobber=True)
clean()
