def setUp(self):
uncorr_map = accessors.open(uncorr_path)
corr_map = accessors.open(corr_path)
map_with_sources = accessors.open(deconv_path)
self.uncorr_image = image.ImageData(uncorr_map.data, uncorr_map.beam,
uncorr_map.wcs)
self.corr_image = image.ImageData(corr_map.data, uncorr_map.beam,
uncorr_map.wcs)
self.image_with_sources = image.ImageData(map_with_sources.data,
map_with_sources.beam,
map_with_sources.wcs)
def setUp(self):
# Beam here is a random beam, in this case the WENSS beam
# without the declination dependence.
fitsfile = tkp.accessors.fitsimage.FitsImage(corrected_fits,
beam=(54. / 3600,
54. / 3600, 0.))
self.my_im = image.ImageData(fitsfile.data, fitsfile.beam,
fitsfile.wcs)
def setUp(self):
# Beam here is a random beam, in this case the WENSS beam
# without the declination dependence.
fitsfile = tkp.accessors.fitsimage.FitsImage(corrected_fits,
beam=(54. / 3600,
54. / 3600, 0.))
self.image = image.ImageData(fitsfile.data, fitsfile.beam,
fitsfile.wcs)
self.results = self.image.extract(det=10.0, anl=3.0)
def setUp(self):
# This image is of the whole sequence, so obviously we won't see the
# transient varying. In fact, due to a glitch in the simulation
# process, it will appear smeared out & shouldn't be identified at
# all.
# Beam here is a random beam, in this case the WENSS beam
# without the declination dependence.
fitsfile = tkp.accessors.fitsimage.FitsImage(all_fits,
beam=(54. / 3600,
54. / 3600, 0.))
self.image = image.ImageData(fitsfile.data,
fitsfile.beam,
fitsfile.wcs,
radius=100)
self.results = self.image.extract(det=5, anl=3.0)
def setUp(self):
fitsfile = tkp.accessors.open(
os.path.join(DATAPATH,
'sourcefinder/simulations/deconvolved.fits'))
img = image.ImageData(fitsfile.data, fitsfile.beam, fitsfile.wcs)
# This is quite subtle. We bypass any possible flaws in the
# kappa, sigma clipping algorithm by supplying a background
# level and noise map. In this way we make sure that any
# possible biases in the measured source parameters cannot
# come from biases in the background level. The peak fluxes,
# in particular, can be biased low if the background levels
# are biased high. The background and noise levels supplied
# here are the true values.
extraction_results = img.extract(det=10.0,
anl=6.0,
noisemap=np.ma.array(BG_STD * np.ones(
(2048, 2048))),
bgmap=np.ma.array(BG_MEAN * np.ones(
(2048, 2048))))
self.number_sources = len(extraction_results)
peak_fluxes = []
deconv_smajaxes = []
deconv_sminaxes = []
deconv_bpas = []
for sources in extraction_results:
peak_fluxes.append([sources.peak.value, sources.peak.error])
deconv_smajaxes.append(
[sources.smaj_dc.value, sources.smaj_dc.error])
deconv_sminaxes.append(
[sources.smin_dc.value, sources.smin_dc.error])
deconv_bpas.append(
[sources.theta_dc.value, sources.theta_dc.error])
self.peak_fluxes = np.array(peak_fluxes)
self.deconv_smajaxes = np.array(deconv_smajaxes)
self.deconv_sminaxes = np.array(deconv_sminaxes)
self.deconv_bpas = np.array(deconv_bpas)