def main():
database = tkp.db.Database()
dataset = tkp.db.DataSet(data={'description': "Banana test data"},
database=database)
n_images = 4
new_source_sigma_margin = 3
image_rms = 1e-3
detection_thresh = 10
reliably_detectable_flux = 1.01 * image_rms * (detection_thresh +
new_source_sigma_margin)
# 1mJy image RMS, 10-sigma detection threshold = 10mJy threshold.
test_specific_img_params = {'rms_qc': image_rms, 'rms_min': image_rms,
'rms_max': image_rms,
'detection_thresh': detection_thresh}
im_params = db_subs.generate_timespaced_dbimages_data(n_images,
**test_specific_img_params)
src_tuple = db_subs.example_extractedsource_tuple(ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],)
transient_src = db_subs.MockSource(
template_extractedsource=src_tuple,
lightcurve={im_params[2]['taustart_ts']:
reliably_detectable_flux}
)
for img_pars in im_params:
db_subs.insert_image_and_simulated_sources(dataset, img_pars,
[transient_src],
new_source_sigma_margin)
tkp.db.execute("insert into monitor values(1, 1, 1, 1, 1, 'bla')", commit=True)
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}
)
inserted_sources = []
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 = self.dataset.frequency_bands()
self.assertEqual(len(freq_bands), 1)
self.assertEqual(freq_bands[0], transient_properties['band'])
#Sanity check that the runcat is correctly matched
runcats = self.dataset.runcat_entries()
self.assertEqual(len(runcats), 1)
self.assertEqual(runcats[0]['runcat'], transient_properties['runcat_id'])
def test_null_detection_business_as_usual(self):
"""
If we do not blindly extract a steady source due to increased RMS,
then we expect a null-detection forced-fit to be triggered.
However, if the source properties are steady, this should not
result in the source being identified as transient.
"""
img0 = self.img_params[0]
steady_src_flux = self.barely_detectable_flux
steady_src = MockSource(
example_extractedsource_tuple(ra=img0['centre_ra'],
dec=img0['centre_decl']),
lightcurve=defaultdict(lambda: steady_src_flux))
image, blind_xtr, forced_fits = insert_image_and_simulated_sources(
self.dataset, self.img_params[0], [steady_src],
self.new_source_sigma_margin)
self.assertEqual(len(blind_xtr), 1)
self.assertEqual(len(forced_fits), 0)
image, blind_xtr, forced_fits = insert_image_and_simulated_sources(
self.dataset, self.img_params[1], [steady_src],
self.new_source_sigma_margin)
self.assertEqual(len(blind_xtr), 0)
self.assertEqual(len(forced_fits), 1)
get_sources_filtered_by_final_variability(dataset_id=self.dataset.id,
**self.search_params)
transients = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(transients), 0)
def test_null_detection_business_as_usual(self):
"""
If we do not blindly extract a steady source due to increased RMS,
then we expect a null-detection forced-fit to be triggered.
However, if the source properties are steady, this should not
result in the source being identified as transient.
"""
img0 = self.img_params[0]
steady_src_flux = self.barely_detectable_flux
steady_src = MockSource(
example_extractedsource_tuple(ra=img0['centre_ra'],
dec=img0['centre_decl']
),
lightcurve=defaultdict(lambda :steady_src_flux)
)
image, blind_xtr,forced_fits = insert_image_and_simulated_sources(
self.dataset,self.img_params[0],[steady_src],
self.new_source_sigma_margin)
self.assertEqual(len(blind_xtr),1)
self.assertEqual(len(forced_fits),0)
image, blind_xtr,forced_fits = insert_image_and_simulated_sources(
self.dataset,self.img_params[1],[steady_src],
self.new_source_sigma_margin)
self.assertEqual(len(blind_xtr),0)
self.assertEqual(len(forced_fits),1)
get_sources_filtered_by_final_variability(dataset_id=self.dataset.id,
**self.search_params)
transients=get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(transients),0)
def test_steady_source(self):
"""
Sanity check: Ensure we get no newsource table entries for a steady
source.
"""
im_params = self.im_params
steady_src = db_subs.MockSource(
template_extractedsource=db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
),
lightcurve=defaultdict(lambda: self.reliably_detectable_flux))
inserted_sources = []
for img_pars in im_params:
image, _, forced_fits = insert_image_and_simulated_sources(
self.dataset, img_pars, [steady_src],
self.new_source_sigma_margin)
#should not have any nulldetections
self.assertEqual(len(forced_fits), 0)
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
newsources = get_newsources_for_dataset(self.dataset.id)
#or newsources, high variability sources
self.assertEqual(len(transients), 0)
self.assertEqual(len(newsources), 0)
def test_steady_source(self):
"""
Sanity check: Ensure we get no newsource table entries for a steady
source.
"""
im_params = self.im_params
steady_src = db_subs.MockSource(
template_extractedsource=db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
),
lightcurve=defaultdict(lambda : self.reliably_detectable_flux)
)
inserted_sources = []
for img_pars in im_params:
image, _,forced_fits = insert_image_and_simulated_sources(
self.dataset,img_pars,[steady_src],
self.new_source_sigma_margin)
#should not have any nulldetections
self.assertEqual(len(forced_fits), 0)
transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
newsources = get_newsources_for_dataset(self.dataset.id)
#or newsources, high variability sources
self.assertEqual(len(transients), 0)
self.assertEqual(len(newsources), 0)
def setUp(self):
"""
create a fake transient. Taken from the transient test.
:return:
"""
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(
data={'description': "Augmented Runningcatalog test"},
database=self.database)
self.n_images = 4
self.new_source_sigma_margin = 3
image_rms = 1e-3
detection_thresh = 10
self.search_params = {'eta_min': 1, 'v_min': 0.1}
self.barely_detectable_flux = 1.01 * image_rms * detection_thresh
self.reliably_detectable_flux = 1.01 * image_rms * (
detection_thresh + self.new_source_sigma_margin)
# 1mJy image RMS, 10-sigma detection threshold = 10mJy threshold.
test_specific_img_params = {
'rms_qc': image_rms,
'rms_min': image_rms,
'rms_max': image_rms,
'detection_thresh': detection_thresh
}
self.im_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
im_params = self.im_params
src_tuple = db_subs.example_extractedsource_tuple(
ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],
)
transient_src = db_subs.MockSource(template_extractedsource=src_tuple,
lightcurve={
im_params[2]['taustart_ts']:
self.reliably_detectable_flux
})
for img_pars in im_params:
db_subs.insert_image_and_simulated_sources(
self.dataset, img_pars, [transient_src],
self.new_source_sigma_margin)
def setUp(self):
"""
create a fake transient. Taken from the transient test.
:return:
"""
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(data={'description':
"Augmented Runningcatalog test"},
database=self.database)
self.n_images = 4
self.new_source_sigma_margin = 3
image_rms = 1e-3
detection_thresh = 10
self.search_params = {'eta_min': 1, 'v_min': 0.1}
self.barely_detectable_flux = 1.01 * image_rms * detection_thresh
self.reliably_detectable_flux = 1.01 * image_rms * (detection_thresh +
self.new_source_sigma_margin)
# 1mJy image RMS, 10-sigma detection threshold = 10mJy threshold.
test_specific_img_params = {'rms_qc': image_rms, 'rms_min': image_rms,
'rms_max': image_rms,
'detection_thresh': detection_thresh}
self.im_params = db_subs.generate_timespaced_dbimages_data(
self.n_images, **test_specific_img_params)
im_params = self.im_params
src_tuple = db_subs.example_extractedsource_tuple(ra=im_params[0]['centre_ra'],
dec=im_params[0]['centre_decl'],)
transient_src = db_subs.MockSource(
template_extractedsource=src_tuple,
lightcurve={im_params[2]['taustart_ts']:
self.reliably_detectable_flux}
)
for img_pars in im_params:
db_subs.insert_image_and_simulated_sources(self.dataset, img_pars,
[transient_src],
self.new_source_sigma_margin)
def setUp(self):
self.database = tkp.db.Database()
self.dataset = tkp.db.DataSet(database=self.database,
data={'description':"Median test"
+ self._testMethodName})
self.n_images = 5
self.im_params = db_subs.generate_timespaced_dbimages_data(self.n_images)
for idx, impar in enumerate(self.im_params):
impar['rms_max'] = (idx+1)*1e-4
self.image_ids = []
for img_pars in self.im_params:
image,_,_ = db_subs.insert_image_and_simulated_sources(
self.dataset,img_pars,[],
new_source_sigma_margin=3)
self.image_ids.append(image.id)
def test_full_transient_search_routine(self):
inserted_imgs = []
for img_idx in xrange(self.n_images):
image, _,_ = insert_image_and_simulated_sources(
self.dataset,self.img_params[img_idx],self.all_mock_sources,
self.new_source_sigma_margin)
inserted_imgs.append(image)
transients = get_sources_filtered_by_final_variability(dataset_id=self.dataset.id,
**self.search_params)
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(transients),
self.n_transients_after_image[img_idx])
#Sanity check that everything went into one band
bands = self.dataset.frequency_bands()
self.assertEqual(len(bands), 1)
all_transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
# for t in all_transients:
# print "V_int:", t['v_int'], " eta_int:", t['eta_int']
#Now test thresholding:
more_highly_variable = sum(t['v_int'] > 2.0 for t in all_transients)
very_non_flat = sum(t['eta_int'] > 100.0 for t in all_transients)
high_v_transients = get_sources_filtered_by_final_variability(
eta_min=1.1,
v_min=2.0,
dataset_id=self.dataset.id,
# minpoints=1
)
self.assertEqual(len(high_v_transients), more_highly_variable)
high_eta_transients = get_sources_filtered_by_final_variability(
eta_min=100,
v_min=0.01,
dataset_id=self.dataset.id,
# minpoints=1
)
self.assertEqual(len(high_eta_transients), very_non_flat)
def test_full_transient_search_routine(self):
inserted_imgs = []
for img_idx in xrange(self.n_images):
image, _,_ = insert_image_and_simulated_sources(
self.dataset,self.img_params[img_idx],self.all_mock_sources,
self.new_source_sigma_margin)
inserted_imgs.append(image)
transients = get_sources_filtered_by_final_variability(dataset_id=self.dataset.id,
**self.search_params)
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(transients),
self.n_transients_after_image[img_idx])
#Sanity check that everything went into one band
bands = frequency_bands(self.dataset._id)
self.assertEqual(len(bands), 1)
all_transients = get_sources_filtered_by_final_variability(
dataset_id=self.dataset.id, **self.search_params)
# for t in all_transients:
# print "V_int:", t['v_int'], " eta_int:", t['eta_int']
#Now test thresholding:
more_highly_variable = sum(t['v_int'] > 2.0 for t in all_transients)
very_non_flat = sum(t['eta_int'] > 100.0 for t in all_transients)
high_v_transients = get_sources_filtered_by_final_variability(
eta_min=1.1,
v_min=2.0,
dataset_id=self.dataset.id,
# minpoints=1
)
self.assertEqual(len(high_v_transients), more_highly_variable)
high_eta_transients = get_sources_filtered_by_final_variability(
eta_min=100,
v_min=0.01,
dataset_id=self.dataset.id,
# minpoints=1
)
self.assertEqual(len(high_eta_transients), very_non_flat)
def test_previous_image_id(self):
img_params = self.img_params
mock_sources = []
mock_sources.append(
MockSource(example_extractedsource_tuple(
ra=img_params[0]['centre_ra'],
dec=img_params[0]['centre_decl']),
lightcurve={
img_params[-1]['taustart_ts']:
self.always_detectable_flux
}))
mock_sources.append(
MockSource(example_extractedsource_tuple(
ra=img_params[0]['centre_ra'] + 1,
dec=img_params[0]['centre_decl']),
lightcurve={
img_params[-1]['taustart_ts']:
self.always_detectable_flux
}))
image_ids = {}
for img_idx in xrange(self.n_images):
image, _, _ = insert_image_and_simulated_sources(
self.dataset, self.img_params[img_idx], mock_sources,
self.new_source_sigma_margin)
image_ids[img_idx] = image.id
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(newsources), len(mock_sources))
newsource_properties = newsources[0]
print "Image IDs:", image_ids
self.assertEqual(newsource_properties['previous_limits_image'],
image_ids[4])
def test_previous_image_id(self):
img_params = self.img_params
mock_sources=[]
mock_sources.append( MockSource(
example_extractedsource_tuple(ra=img_params[0]['centre_ra'],
dec=img_params[0]['centre_decl']),
lightcurve={img_params[-1]['taustart_ts']:
self.always_detectable_flux}
))
mock_sources.append( MockSource(
example_extractedsource_tuple(ra=img_params[0]['centre_ra']+1,
dec=img_params[0]['centre_decl']),
lightcurve={img_params[-1]['taustart_ts']:
self.always_detectable_flux}
))
image_ids={}
for img_idx in xrange(self.n_images):
image, _,_ = insert_image_and_simulated_sources(
self.dataset,self.img_params[img_idx],mock_sources,
self.new_source_sigma_margin)
image_ids[img_idx]=image.id
newsources = get_newsources_for_dataset(self.dataset.id)
self.assertEqual(len(newsources),len(mock_sources))
newsource_properties = newsources[0]
print "Image IDs:", image_ids
self.assertEqual(newsource_properties['previous_limits_image'],
image_ids[4])
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)
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)
