def test_probably_not_a_transient(self): """ No source at 250MHz, but we detect a source at 50MHz. Not necessarily a transient. Should trivially ignore 250MHz data when looking at a new 50MHz source. """ img_params = self.img_params img0 = img_params[0] # This time around, we just manually exclude the steady src from # the first image detections. steady_low_freq_src = MockSource( example_extractedsource_tuple(ra=img_params[0]['centre_ra'], dec=img_params[0]['centre_decl']), lightcurve=defaultdict(lambda: self.always_detectable_flux)) # Insert first image, no sources. tkp.db.Image(data=img_params[0], dataset=self.dataset) # Now set up second image. img1 = tkp.db.Image(data=img_params[1], dataset=self.dataset) xtr = steady_low_freq_src.simulate_extraction(img1, extraction_type='blind') insert_extracted_sources(img1._id, [xtr], 'blind') associate_extracted_sources(img1._id, deRuiter_r, self.new_source_sigma_margin) transients = get_newsources_for_dataset(self.dataset.id) # Should have no marked transients 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_marginal_transient(self): """ ( flux1 > (rms_min0*(det0 + margin) ) but ( flux1 < (rms_max0*(det0 + margin) ) --> Possible transient If it was in a region of rms_min, we would (almost certainly) have seen it in the first image. So new source --> Possible transient. But if it was in a region of rms_max, then perhaps we would have missed it. In which case, new source --> Just seeing deeper. Note that if we are tiling overlapping images, then the first time a field is processed with image-centre at the edge of the old field, we may get a bunch of unhelpful 'possible transients'. Furthermore, this will pick up fluctuating sources near the image-margins even with a fixed field of view. But without a more complex store of image-rms-per-position, we cannot do better. Hopefully we can use a 'distance from centre' feature to separate out the good and bad candidates in this case. """ img_params = self.img_params #Must pick flux value carefully to fire correct logic branch: marginal_transient_flux = self.reliably_detected_at_image_centre_flux marginal_transient = MockSource( example_extractedsource_tuple(ra=img_params[0]['centre_ra'], dec=img_params[0]['centre_decl']), lightcurve={img_params[1]['taustart_ts'] : marginal_transient_flux} ) #First, check that we've set up the test correctly: rms_min0 = img_params[0]['rms_min'] rms_max0 = img_params[0]['rms_max'] det0 = img_params[0]['detection_thresh'] self.assertTrue(marginal_transient_flux < rms_max0*(det0 + self.new_source_sigma_margin ) ) self.assertTrue(marginal_transient_flux > rms_min0*(det0 + self.new_source_sigma_margin ) ) for pars in self.img_params: img = tkp.db.Image(data=pars,dataset=self.dataset) xtr = marginal_transient.simulate_extraction(img, extraction_type='blind') if xtr is not None: img.insert_extracted_sources([xtr],'blind') img.associate_extracted_sources(deRuiter_r, self.new_source_sigma_margin) newsources = get_newsources_for_dataset(self.dataset.id) #Should have one 'possible' transient self.assertEqual(len(newsources),1) self.assertTrue( newsources[0]['low_thresh_sigma'] > self.new_source_sigma_margin) self.assertTrue( newsources[0]['high_thresh_sigma'] < self.new_source_sigma_margin)
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_probably_not_a_transient(self): """ ( flux1 < (rms_min0*(det0 + margin) ) --> Probably not a transient NB even if avg_source_flux == rms_min0*det0 + epsilon we might not detect it in the first image, due to noise fluctuations. So we provide the user-tunable marginal_detection_thresh, to ignore these 'noise' transients. """ img_params = self.img_params img0 = img_params[0] marginal_steady_src_flux = self.barely_detectable_flux # This time around, we just manually exclude the steady src from # the first image detections. marginal_steady_src = MockSource( example_extractedsource_tuple(ra=img_params[0]['centre_ra'], dec=img_params[0]['centre_decl'] ), lightcurve=defaultdict(lambda :marginal_steady_src_flux) ) #First, check that we've set up the test correctly: rms_min0 = img_params[0]['rms_min'] det0 = img_params[0]['detection_thresh'] self.assertTrue(marginal_steady_src_flux < rms_min0*(det0 + self.new_source_sigma_margin ) ) #Insert first image, no sources. tkp.db.Image(data=img_params[0],dataset=self.dataset) #Now set up second image. img1 = tkp.db.Image(data=img_params[1],dataset=self.dataset) xtr = marginal_steady_src.simulate_extraction(img1, extraction_type='blind') img1.insert_extracted_sources([xtr],'blind') img1.associate_extracted_sources(deRuiter_r, self.new_source_sigma_margin) newsources = get_newsources_for_dataset(self.dataset.id) #Should have no flagged new sources self.assertEqual(len(newsources),0)
def test_certain_transient(self): """ flux1 > (rms_max0*(det0+margin) --> Definite transient Nice and bright, must be new - mark it definite transient. """ img_params = self.img_params bright_transient = 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 }) #First, check that we've set up the test correctly: rms_max0 = img_params[0]['rms_max'] det0 = img_params[0]['detection_thresh'] self.assertTrue(bright_transient.lightcurve.values()[0] > rms_max0 * (det0 + self.new_source_sigma_margin)) for pars in self.img_params: img = tkp.db.Image(data=pars, dataset=self.dataset) xtr = bright_transient.simulate_extraction(img, extraction_type='blind') if xtr is not None: insert_extracted_sources(img._id, [xtr], 'blind') associate_extracted_sources(img._id, deRuiter_r, self.new_source_sigma_margin) newsources = get_newsources_for_dataset(self.dataset.id) #Should have one 'definite' transient self.assertEqual(len(newsources), 1) self.assertTrue( newsources[0]['low_thresh_sigma'] > self.new_source_sigma_margin) self.assertTrue( newsources[0]['high_thresh_sigma'] > self.new_source_sigma_margin) self.assertTrue(newsources[0]['low_thresh_sigma'] > newsources[0] ['high_thresh_sigma'])
def setUp(self): self.database = tkp.db.Database() self.dataset = tkp.db.DataSet( data={'description': "Trans:" + self._testMethodName}, database=self.database) self.n_images = 8 self.image_rms = 1e-3 # 1mJy self.new_source_sigma_margin = 3 self.search_params = dict( eta_min=1, v_min=0.1, # minpoints=1, ) detection_thresh = 10 barely_detectable_flux = 1.01 * self.image_rms * (detection_thresh) reliably_detectable_flux = ( 1.01 * self.image_rms * (detection_thresh + self.new_source_sigma_margin)) test_specific_img_params = dict(rms_qc=self.image_rms, rms_min=self.image_rms, rms_max=self.image_rms, detection_thresh=detection_thresh) self.img_params = db_subs.generate_timespaced_dbimages_data( self.n_images, **test_specific_img_params) imgs = self.img_params first_img = imgs[0] centre_ra = first_img['centre_ra'] centre_decl = first_img['centre_decl'] xtr_radius = first_img['xtr_radius'] #At centre fixed_source = MockSource( example_extractedsource_tuple(ra=centre_ra, dec=centre_decl), lightcurve=defaultdict(lambda: barely_detectable_flux)) #How many transients should we know about after each image? self.n_transients_after_image = defaultdict(lambda: 0) self.n_newsources_after_image = defaultdict(lambda: 0) #shifted to +ve RA bright_fast_transient = MockSource( example_extractedsource_tuple(ra=centre_ra + xtr_radius * 0.5, dec=centre_decl), lightcurve={imgs[3]['taustart_ts']: reliably_detectable_flux}) #Detect immediately for img_idx in range(3, self.n_images): self.n_newsources_after_image[img_idx] += 1 #But only variable after non-detection for img_idx in range(4, self.n_images): self.n_transients_after_image[img_idx] += 1 # shifted to -ve RA weak_fast_transient = MockSource( example_extractedsource_tuple(ra=centre_ra - xtr_radius * 0.5, dec=centre_decl), lightcurve={imgs[3]['taustart_ts']: barely_detectable_flux}) # Not flagged as a newsource, could just be a weakly detected # steady-source at first. # But, shows high-variance after forced-fit in image[4] for img_idx in range(4, self.n_images): self.n_transients_after_image[img_idx] += 1 # shifted to +ve Dec weak_slow_transient = MockSource(example_extractedsource_tuple( ra=centre_ra, dec=centre_decl + xtr_radius * 0.5), lightcurve={ imgs[5]['taustart_ts']: barely_detectable_flux, imgs[6]['taustart_ts']: barely_detectable_flux * 0.95 }) # Not flagged as a newsource, could just be a weakly detected # steady-source at first. # Should not be flagged as transient until forced-fit in image[7] for img_idx in range(7, self.n_images): self.n_transients_after_image[img_idx] += 1 self.all_mock_sources = [ fixed_source, weak_slow_transient, bright_fast_transient, weak_fast_transient ]