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_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_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 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_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_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)