def run_sims(maxscale=3):
for scale in range(1,maxscale):
synthetically_image_fitsfile(paths.simpath("simimage_scale{0}_gaussian.fits".format(scale)),
base_name='casa_simimage_scale{0}_gaussian'.format(scale),
cleanup=True)
synthetically_image_fitsfile(paths.simpath("simimage_scale{0}_tophat.fits".format(scale)),
base_name='casa_simimage_scale{0}_tophat'.format(scale),
cleanup=True)
def run_sims(maxscale=3):
for scale in range(1, maxscale):
synthetically_image_fitsfile(
paths.simpath("simimage_scale{0}_gaussian.fits".format(scale)),
base_name='casa_simimage_scale{0}_gaussian'.format(scale),
cleanup=True)
synthetically_image_fitsfile(
paths.simpath("simimage_scale{0}_tophat.fits".format(scale)),
base_name='casa_simimage_scale{0}_tophat'.format(scale),
cleanup=True)
def ridiculous_tests():
for unit in ('Jy/beam', 'Jy/pixel', 'Jy', 'K'):
fh = simulate_grid_for_fitsfile(paths.dpath("w51_te_continuum_best.fits"),
separation=45*3, amplitude_range=[0.001,0.1],
scale=3,
random_offset=20)
assert fh[0].data.max() > 0
fh[0].header['BUNIT'] = unit
fh.writeto(paths.simpath("stupidtest_{0}.fits".format(unit.replace("/","_"))),
clobber=True)
synthetically_image_fitsfile(paths.simpath("stupidtest_{0}.fits".format(unit.replace("/","_"))),
base_name="casa_stupidtest_{0}".format(unit.replace("/","_")),
cleanup=True
)
def setup_sims(maxscale=3):
for scale in range(1,maxscale):
fh = simulate_grid_for_fitsfile(paths.dpath("w51_te_continuum_best.fits"),
separation=45*scale, amplitude_range=[0.001,0.1],
scale=scale,
random_offset=20)
fh[0].header['BUNIT'] = 'Jy/beam'
fh.writeto(paths.simpath("simimage_scale{0}_gaussian.fits".format(scale)),
clobber=True)
fh = simulate_grid_for_fitsfile(paths.dpath("w51_te_continuum_best.fits"),
separation=45*scale, amplitude_range=[0.001,0.1],
kernel=Tophat2DKernel,
scale=scale,
random_offset=20)
fh[0].header['BUNIT'] = 'Jy/beam'
fh.writeto(paths.simpath("simimage_scale{0}_tophat.fits".format(scale)),
clobber=True)
def ridiculous_tests():
for unit in ('Jy/beam', 'Jy/pixel', 'Jy', 'K'):
fh = simulate_grid_for_fitsfile(
paths.dpath("w51_te_continuum_best.fits"),
separation=45 * 3,
amplitude_range=[0.001, 0.1],
scale=3,
random_offset=20)
assert fh[0].data.max() > 0
fh[0].header['BUNIT'] = unit
fh.writeto(paths.simpath("stupidtest_{0}.fits".format(
unit.replace("/", "_"))),
clobber=True)
synthetically_image_fitsfile(
paths.simpath("stupidtest_{0}.fits".format(unit.replace("/",
"_"))),
base_name="casa_stupidtest_{0}".format(unit.replace("/", "_")),
cleanup=True)
def setup_sims(maxscale=3):
for scale in range(1, maxscale):
fh = simulate_grid_for_fitsfile(
paths.dpath("w51_te_continuum_best.fits"),
separation=45 * scale,
amplitude_range=[0.001, 0.1],
scale=scale,
random_offset=20)
fh[0].header['BUNIT'] = 'Jy/beam'
fh.writeto(paths.simpath(
"simimage_scale{0}_gaussian.fits".format(scale)),
clobber=True)
fh = simulate_grid_for_fitsfile(
paths.dpath("w51_te_continuum_best.fits"),
separation=45 * scale,
amplitude_range=[0.001, 0.1],
kernel=Tophat2DKernel,
scale=scale,
random_offset=20)
fh[0].header['BUNIT'] = 'Jy/beam'
fh.writeto(paths.simpath(
"simimage_scale{0}_tophat.fits".format(scale)),
clobber=True)
