def test_single_epoch_weak_transient(self):
"""
A weak (barely detected in blind extraction) transient appears at
field centre in one image, then disappears entirely.
Because it is a weak extraction, it will not be immediately marked
as transient, but it will get flagged up after forced-fitting due to
the variability search.
"""
im_params = self.im_params
transient_src = db_subs.MockSource(
template_extractedsource=db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
),
lightcurve={
im_params[2]['taustart_ts']: self.barely_detectable_flux
})
for img_pars in im_params[:3]:
image, _, forced_fits = insert_image_and_simulated_sources(
self.dataset, img_pars, [transient_src],
self.new_source_sigma_margin)
self.assertEqual(forced_fits, [])
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(newsources), 0)
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
# No variability yet
self.assertEqual(len(transients), 0)
# Now, the final, empty image:
image, blind_extractions, forced_fits = insert_image_and_simulated_sources(
self.dataset, im_params[3], [transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(blind_extractions), 0)
self.assertEqual(len(forced_fits), 1)
# No changes to newsource table
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(newsources), 0)
# But it now has high variability
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
self.assertEqual(len(transients), 1)
transient_properties = transients[0]
# Check that the bands for the images are the same as the transient's band
freq_bands = frequency_bands(self.dataset._id)
self.assertEqual(len(freq_bands), 1)
self.assertEqual(freq_bands[0], transient_properties['band'])
# Sanity check that the runcat is correctly matched
runcats = runcat_entries(self.dataset._id)
self.assertEqual(len(runcats), 1)
self.assertEqual(runcats[0]['runcat'],
transient_properties['runcat_id'])
def test_single_epoch_weak_transient(self):
"""
A weak (barely detected in blind extraction) transient appears at
field centre in one image, then disappears entirely.
Because it is a weak extraction, it will not be immediately marked
as transient, but it will get flagged up after forced-fitting due to
the variability search.
"""
im_params = self.im_params
transient_src = db_subs.MockSource(
template_extractedsource=db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
),
lightcurve={im_params[2]['taustart_ts'] :
self.barely_detectable_flux}
)
for img_pars in im_params[:3]:
image, _, forced_fits = insert_image_and_simulated_sources(
self.dataset, img_pars, [transient_src],
self.new_source_sigma_margin)
self.assertEqual(forced_fits, [])
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(newsources), 0)
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
# No variability yet
self.assertEqual(len(transients), 0)
# Now, the final, empty image:
image, blind_extractions, forced_fits = insert_image_and_simulated_sources(
self.dataset, im_params[3], [transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(blind_extractions), 0)
self.assertEqual(len(forced_fits), 1)
# No changes to newsource table
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(newsources), 0)
# But it now has high variability
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
self.assertEqual(len(transients), 1)
transient_properties = transients[0]
# Check that the bands for the images are the same as the transient's band
freq_bands = frequency_bands(self.dataset._id)
self.assertEqual(len(freq_bands), 1)
self.assertEqual(freq_bands[0], transient_properties['band'])
# Sanity check that the runcat is correctly matched
runcats = runcat_entries(self.dataset._id)
self.assertEqual(len(runcats), 1)
self.assertEqual(runcats[0]['runcat'], transient_properties['runcat_id'])
def test_single_epoch_bright_transient(self):
"""A bright transient appears at field centre in one image."""
im_params = self.im_params
transient_src = db_subs.MockSource(
template_extractedsource=db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
),
lightcurve={
im_params[2]['taustart_ts']: self.reliably_detectable_flux
})
for img_pars in im_params[:3]:
image, _, forced_fits = insert_image_and_simulated_sources(
self.dataset, img_pars, [transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(forced_fits), 0)
# Check the number of detected transients
transients = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(transients), 1)
newsrc_properties = transients[0]
# Check that the bands for the images are the same as the transient's band
freq_bands = frequency_bands(self.dataset._id)
self.assertEqual(len(freq_bands), 1)
self.assertEqual(freq_bands[0], newsrc_properties['band'])
# Sanity check that the runcat is correctly matched
runcats = runcat_entries(self.dataset._id)
self.assertEqual(len(runcats), 1)
self.assertEqual(runcats[0]['runcat'], newsrc_properties['runcat_id'])
# Since it is a single-epoch source, variability indices default to 0:
self.assertEqual(newsrc_properties['v_int'], 0)
self.assertEqual(newsrc_properties['eta_int'], 0)
# Bright 'new-source' / single-epoch transient; should have high sigmas:
self.assertTrue(newsrc_properties['low_thresh_sigma'] >
self.new_source_sigma_margin)
self.assertEqual(newsrc_properties['low_thresh_sigma'],
newsrc_properties['high_thresh_sigma'])
# Check the correct trigger xtrsrc was identified:
self.assertEqual(newsrc_properties['taustart_ts'],
transient_src.lightcurve.keys()[0])
# Ok, now add the last image and check that we get a correct forced-fit
# request:
image, _, forced_fits = insert_image_and_simulated_sources(
self.dataset, im_params[3], [transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(forced_fits), 1)
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
self.assertEqual(len(transients), 1)
transient_properties = transients[0]
# And now we should have a non-zero variability value
self.assertNotAlmostEqual(transient_properties['v_int'], 0)
self.assertNotAlmostEqual(transient_properties['eta_int'], 0)
def test_multi_epoch_source_flare_and_fade(self):
"""
A steady source (i.e. detected in first image) flares up,
then fades and finally disappears.
"""
im_params = self.im_params
transient_src = db_subs.MockSource(
template_extractedsource=db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
),
lightcurve={
im_params[0]['taustart_ts']: self.barely_detectable_flux,
im_params[1]['taustart_ts']: 2 * self.barely_detectable_flux,
im_params[2]['taustart_ts']: self.barely_detectable_flux,
})
inserted_sources = []
for img_pars in im_params[:2]:
image, blind_xtr, forced_fits = insert_image_and_simulated_sources(
self.dataset, img_pars, [transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(forced_fits), 0)
inserted_sources.extend(blind_xtr)
#This should always be 0:
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(newsources), 0)
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
#We've seen a flare:
self.assertEqual(len(transients), 1)
transient_properties = transients[0]
# Check that the bands for the images are the same as the transient's band
freq_bands = frequency_bands(self.dataset._id)
self.assertEqual(len(freq_bands), 1)
self.assertEqual(freq_bands[0], transient_properties['band'])
#Sanity check that the runcat is correctly matched
runcats = runcat_entries(self.dataset._id)
self.assertEqual(len(runcats), 1)
self.assertEqual(runcats[0]['runcat'],
transient_properties['runcat_id'])
#Check we have sensible variability indices
# print "\n",transient_properties
metrics = db_subs.lightcurve_metrics(inserted_sources)
# print "\nAfter two images:"
for metric_name in 'v_int', 'eta_int':
# print metric_name, transient_properties[metric_name]
self.assertAlmostEqual(transient_properties[metric_name],
metrics[-1][metric_name])
#Add 3rd image (another blind detection), check everything is sane
image, blind_xtr, forced_fits = insert_image_and_simulated_sources(
self.dataset, im_params[2], [transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(forced_fits), 0)
inserted_sources.extend(blind_xtr)
self.assertEqual(len(get_newsources_for_dataset(self.dataset.id)), 0)
# Ok, now add the last image and check that we get a correct forced-fit
# request:
image, blind_xtr, forced_fits = insert_image_and_simulated_sources(
self.dataset, im_params[3], [transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(blind_xtr), 0)
self.assertEqual(len(forced_fits), 1)
inserted_sources.extend(forced_fits)
self.assertEqual(len(get_newsources_for_dataset(self.dataset.id)), 0)
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
# Variability indices should take non-detections into account
self.assertEqual(len(transients), 1)
transient_properties = transients[0]
metrics = db_subs.lightcurve_metrics(inserted_sources)
# print "\nAfter four images:"
for metric_name in 'v_int', 'eta_int':
# print metric_name, transient_properties[metric_name]
self.assertAlmostEqual(transient_properties[metric_name],
metrics[-1][metric_name])
def test_multi_epoch_source_flare_and_fade(self):
"""
A steady source (i.e. detected in first image) flares up,
then fades and finally disappears.
"""
im_params = self.im_params
transient_src = db_subs.MockSource(
template_extractedsource=db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
),
lightcurve={
im_params[0]['taustart_ts'] : self.barely_detectable_flux,
im_params[1]['taustart_ts'] : 2*self.barely_detectable_flux,
im_params[2]['taustart_ts'] : self.barely_detectable_flux,
}
)
inserted_sources = []
for img_pars in im_params[:2]:
image, blind_xtr,forced_fits = insert_image_and_simulated_sources(
self.dataset,img_pars,[transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(forced_fits), 0)
inserted_sources.extend(blind_xtr)
#This should always be 0:
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(newsources), 0)
transients = get_sources_filtered_by_final_variability(dataset_id=self.dataset.id,
**self.search_params)
#We've seen a flare:
self.assertEqual(len(transients), 1)
transient_properties = transients[0]
# Check that the bands for the images are the same as the transient's band
freq_bands = frequency_bands(self.dataset._id)
self.assertEqual(len(freq_bands), 1)
self.assertEqual(freq_bands[0], transient_properties['band'])
#Sanity check that the runcat is correctly matched
runcats = runcat_entries(self.dataset._id)
self.assertEqual(len(runcats), 1)
self.assertEqual(runcats[0]['runcat'], transient_properties['runcat_id'])
#Check we have sensible variability indices
# print "\n",transient_properties
metrics = db_subs.lightcurve_metrics(inserted_sources)
# print "\nAfter two images:"
for metric_name in 'v_int', 'eta_int':
# print metric_name, transient_properties[metric_name]
self.assertAlmostEqual(transient_properties[metric_name],
metrics[-1][metric_name])
#Add 3rd image (another blind detection), check everything is sane
image, blind_xtr,forced_fits = insert_image_and_simulated_sources(
self.dataset,im_params[2],[transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(forced_fits), 0)
inserted_sources.extend(blind_xtr)
self.assertEqual(len(get_newsources_for_dataset(self.dataset.id)),0)
# Ok, now add the last image and check that we get a correct forced-fit
# request:
image, blind_xtr,forced_fits = insert_image_and_simulated_sources(
self.dataset,im_params[3],[transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(blind_xtr),0)
self.assertEqual(len(forced_fits),1)
inserted_sources.extend(forced_fits)
self.assertEqual(len(get_newsources_for_dataset(self.dataset.id)),0)
transients = get_sources_filtered_by_final_variability(dataset_id=self.dataset.id,
**self.search_params)
# Variability indices should take non-detections into account
self.assertEqual(len(transients), 1)
transient_properties = transients[0]
metrics = db_subs.lightcurve_metrics(inserted_sources)
# print "\nAfter four images:"
for metric_name in 'v_int', 'eta_int':
# print metric_name, transient_properties[metric_name]
self.assertAlmostEqual(transient_properties[metric_name],
metrics[-1][metric_name])
def test_single_epoch_bright_transient(self):
"""A bright transient appears at field centre in one image."""
im_params = self.im_params
transient_src = db_subs.MockSource(
template_extractedsource=db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
),
lightcurve={im_params[2]['taustart_ts'] :
self.reliably_detectable_flux}
)
for img_pars in im_params[:3]:
image, _,forced_fits = insert_image_and_simulated_sources(
self.dataset,img_pars,[transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(forced_fits), 0)
# Check the number of detected transients
transients = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(transients), 1)
newsrc_properties = transients[0]
# Check that the bands for the images are the same as the transient's band
freq_bands = frequency_bands(self.dataset._id)
self.assertEqual(len(freq_bands), 1)
self.assertEqual(freq_bands[0], newsrc_properties['band'])
# Sanity check that the runcat is correctly matched
runcats = runcat_entries(self.dataset._id)
self.assertEqual(len(runcats), 1)
self.assertEqual(runcats[0]['runcat'], newsrc_properties['runcat_id'])
# Since it is a single-epoch source, variability indices default to 0:
self.assertEqual(newsrc_properties['v_int'],0)
self.assertEqual(newsrc_properties['eta_int'],0)
# Bright 'new-source' / single-epoch transient; should have high sigmas:
self.assertTrue(
newsrc_properties['low_thresh_sigma']> self.new_source_sigma_margin)
self.assertEqual(newsrc_properties['low_thresh_sigma'],
newsrc_properties['high_thresh_sigma'])
# Check the correct trigger xtrsrc was identified:
self.assertEqual(newsrc_properties['taustart_ts'],
transient_src.lightcurve.keys()[0])
# Ok, now add the last image and check that we get a correct forced-fit
# request:
image, _,forced_fits = insert_image_and_simulated_sources(
self.dataset,im_params[3],[transient_src],
self.new_source_sigma_margin)
self.assertEqual(len(forced_fits),1)
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id,**self.search_params)
self.assertEqual(len(transients), 1)
transient_properties = transients[0]
# And now we should have a non-zero variability value
self.assertNotAlmostEqual(transient_properties['v_int'], 0)
self.assertNotAlmostEqual(transient_properties['eta_int'], 0)