def test_save_data_data2d(tmp_path):
"""Does save_data work for Data2D?"""
from sherpa.astro.io import backend
y, x = np.mgrid[20:22, 10:13]
x = x.flatten()
y = y.flatten()
z = (x - 15)**2 + (y - 12)**2
ui.load_arrays(1, x, y, z, (2, 3), ui.Data2D)
out = tmp_path / "data.dat"
outfile = str(out)
ui.save_data(outfile)
cts = out.read_text()
# the output depends on the backend, and neither seems ideal
#
if backend.__name__ == 'sherpa.astro.io.crates_backend':
expected = ["#TEXT/SIMPLE", "# X0 X1 Y SHAPE"]
s = [2, 3, 0, 0, 0, 0]
for xi, yi, zi, si in zip(x, y, z, s):
expected.append(f"{xi} {yi} {zi} {si}")
expected = "\n".join(expected) + "\n"
elif backend.__name__ == 'sherpa.astro.io.pyfits_backend':
expected = "\n".join([str(zz) for zz in z]) + "\n"
else:
raise RuntimeError(f"UNKNOWN I/O BACKEND: {backend.__name__}")
assert cts == expected
def test_warnings_are_gone_arrays(self):
ui.load_arrays(1, [1, 2, 3], [4, 5, 6])
# We now have logic in conftest.py to catch white-listed warnings and fail on unexpected ones.
# We just need to make any warnings bubble up, here and in the following test.
with NamedTemporaryFile() as f:
ui.save_data(1, f.name, ascii=True, clobber=True)
with NamedTemporaryFile() as f:
ui.save_data(1, f.name, ascii=False, clobber=True)
def test_warnings_are_gone_pha(self):
pha = self.make_path("3c273.pi")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with NamedTemporaryFile() as f:
ui.load_pha(pha)
ui.save_data(1, f.name, ascii=False, clobber=True)
assert len(w) == 0
def test_warnings_are_gone_pha(self):
pha = self.make_path("3c273.pi")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with NamedTemporaryFile() as f:
ui.load_pha(pha)
ui.save_data(1, f.name, ascii=False, clobber=True)
assert len(w) == 0
def test_warnings_are_gone_arrays(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with NamedTemporaryFile() as f:
ui.load_arrays(1, [1, 2, 3], [4, 5, 6])
ui.save_data(1, f.name, ascii=True, clobber=True)
with NamedTemporaryFile() as f:
ui.save_data(1, f.name, ascii=False, clobber=True)
assert len(w) == 0
def test_warnings_are_gone_arrays(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with NamedTemporaryFile() as f:
ui.load_arrays(1, [1,2,3], [4,5,6])
ui.save_data(1, f.name, ascii=True, clobber=True)
with NamedTemporaryFile() as f:
ui.save_data(1, f.name, ascii=False, clobber=True)
assert len(w) == 0
def test_save_data_data1dint(tmp_path):
"""Does save_data work for Data1DInt?"""
ui.load_arrays(1, [1, 2, 4], [2, 3, 5], [5, 4, 3], ui.Data1DInt)
out = tmp_path / "data.dat"
outfile = str(out)
ui.save_data(outfile)
cts = out.read_text()
check_output(cts, ['XLO', 'XHI', 'Y'], [[1, 2, 5], [2, 3, 4], [4, 5, 3]])
def test_save_data_data1d_clobber(tmp_path):
"""save_data: does clobber=True work?"""
ui.load_arrays(1, [1], [5], ui.Data1D)
out = tmp_path / "data.dat"
outfile = str(out)
out.write_text('some text')
ui.save_data(outfile, clobber=True)
cts = out.read_text()
check_output(cts, ['X', 'Y'], [[1, 5]])
def test_save_data_data1d_fits(tmp_path):
"""Does save_data work for Data1D? FITS"""
from sherpa.astro.io import read_table_blocks
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.Data1D)
out = tmp_path / "data.dat"
outfile = str(out)
ui.save_data(outfile, ascii=False)
ans = read_table_blocks(outfile)
blocks = ans[1]
assert len(blocks) == 2
check_table(blocks[2], {'X': [1, 2, 3], 'Y': [5, 4, 3]})
def test_save_data_datapha(tmp_path):
"""Does save_data work for DataPHA?"""
# This import requires an I/O backend hence it is done here
#
from sherpa.astro.io.meta import Meta
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.DataPHA)
# The code requires DataPHA to have a "valid" header so
# fake one. Ideally we would not require it.
#
hdr = Meta()
ui.get_data().header = hdr
out = tmp_path / "data.dat"
outfile = str(out)
ui.save_data(outfile)
cts = out.read_text()
check_output(cts, ['CHANNEL', 'COUNTS'], [[1, 5], [2, 4], [3, 3]])
def test_save_data_dataimg(tmp_path):
"""Does save_data work for DataIMG? ASCII"""
# Can not write out an ASCII image with crates
from sherpa.astro.io import backend
if backend.__name__ == 'sherpa.astro.io.crates_backend':
pytest.skip('ASCII not supported for images with pycrates')
y, x = np.mgrid[0:2, 0:3]
x = x.flatten()
y = y.flatten()
z = (x - 1)**2 + y**2
ui.load_arrays(1, x, y, z, (2, 3), ui.DataIMG)
out = tmp_path / "data.dat"
outfile = str(out)
ui.save_data(outfile)
cts = out.read_text()
expected = "\n".join([str(zz) for zz in z]) + "\n"
assert cts == expected
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
def test_save_data_dataimg_fits(tmp_path):
"""Does save_data work for DataIMG? FITS"""
from sherpa.astro.io import read_image
y, x = np.mgrid[10:12, 20:23]
x = x.flatten()
y = y.flatten()
z = (x - 11)**2 + (y - 21)**2
ui.load_arrays(1, x, y, z, (2, 3), ui.DataIMG)
out = tmp_path / "data.dat"
outfile = str(out)
ui.save_data(outfile, ascii=False)
ans = read_image(outfile)
assert ans.shape == (2, 3) # Is this correct?
yl, xl = np.mgrid[1:3, 1:4]
xl = xl.flatten()
yl = yl.flatten()
assert ans.x0 == pytest.approx(xl)
assert ans.x1 == pytest.approx(yl)
assert ans.y == pytest.approx(z)
def test_warnings_are_gone_pha(self):
pha = self.make_path("3c273.pi")
ui.load_pha(pha)
with NamedTemporaryFile() as f:
ui.save_data(1, f.name, ascii=False, clobber=True)
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')
sau.save_model('background.fits.gz', clobber=True)
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')
sau.save_model('background.fits.gz', clobber=True)