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_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 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 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])