def test_list(self): """ Test that ModifiedJulianDate.get_list() gets results that are consistent with creating a list of ModifiedJulianDates by hand. """ rng = np.random.RandomState(88) tol = 10 # decimal place tolerance tai_list = 40000.0 + 10000.0 * rng.random_sample(20) tai_list = np.append(tai_list, 59580.0 + 10000.0 * rng.random_sample(20)) mjd_list = ModifiedJulianDate.get_list(TAI=tai_list) for tai, mjd in zip(tai_list, mjd_list): msg = "Offending TAI: %f" % tai control = ModifiedJulianDate(TAI=tai) self.assertAlmostEqual(mjd.TAI, tai, 11, msg=msg) self.assertAlmostEqual(mjd.TAI, control.TAI, tol, msg=msg) self.assertAlmostEqual(mjd.UTC, control.UTC, tol, msg=msg) self.assertAlmostEqual(mjd.UT1, control.UT1, tol, msg=msg) self.assertAlmostEqual(mjd.TT, control.TT, tol, msg=msg) self.assertAlmostEqual(mjd.TDB, control.TDB, tol, msg=msg) self.assertAlmostEqual(mjd.dut1, control.dut1, tol, msg=msg) utc_list = 40000.0 + 10000.0 * rng.random_sample(20) utc_list = np.append(utc_list, 59580.0 + 10000.0 * rng.random_sample(20)) mjd_list = ModifiedJulianDate.get_list(UTC=utc_list) for utc, mjd in zip(utc_list, mjd_list): msg = "Offending UTC: %f" % utc control = ModifiedJulianDate(UTC=utc) self.assertAlmostEqual(mjd.UTC, utc, tol, msg=msg) self.assertAlmostEqual(mjd.TAI, control.TAI, tol, msg=msg) self.assertAlmostEqual(mjd.UTC, control.UTC, tol, msg=msg) self.assertAlmostEqual(mjd.UT1, control.UT1, tol, msg=msg) self.assertAlmostEqual(mjd.TT, control.TT, tol, msg=msg) self.assertAlmostEqual(mjd.TDB, control.TDB, tol, msg=msg) self.assertAlmostEqual(mjd.dut1, control.dut1, tol, msg=msg) # Now test the case where we only have dates in the future (this # is an edge case since good_dexes in ModifiedJulianDate._get_ut1_from_utc # will have len = 0 tai_list = 60000.0 + 10000.0 * rng.random_sample(20) mjd_list = ModifiedJulianDate.get_list(TAI=tai_list) for tai, mjd in zip(tai_list, mjd_list): msg = "Offending TAI: %f" % tai control = ModifiedJulianDate(TAI=tai) self.assertAlmostEqual(mjd.TAI, tai, 11, msg=msg) self.assertAlmostEqual(mjd.TAI, control.TAI, tol, msg=msg) self.assertAlmostEqual(mjd.UTC, control.UTC, tol, msg=msg) self.assertAlmostEqual(mjd.UT1, control.UT1, tol, msg=msg) self.assertAlmostEqual(mjd.TT, control.TT, tol, msg=msg) self.assertAlmostEqual(mjd.TDB, control.TDB, tol, msg=msg) self.assertAlmostEqual(mjd.dut1, control.dut1, tol, msg=msg)
def test_alert_data_generation(self): dmag_cutoff = 0.005 mag_name_to_int = {'u': 0, 'g': 1, 'r': 2, 'i': 3, 'z' : 4, 'y': 5} _max_var_param_str = self.max_str_len class StarAlertTestDBObj(StellarAlertDBObjMixin, CatalogDBObject): objid = 'star_alert' tableid = 'stars' idColKey = 'simobjid' raColName = 'ra' decColName = 'dec' objectTypeId = 0 columns = [('raJ2000', 'ra*0.01745329252'), ('decJ2000', 'dec*0.01745329252'), ('parallax', 'px*0.01745329252/3600.0'), ('properMotionRa', 'pmra*0.01745329252/3600.0'), ('properMotionDec', 'pmdec*0.01745329252/3600.0'), ('radialVelocity', 'vrad'), ('variabilityParameters', 'varParamStr', str, _max_var_param_str)] class TestAlertsVarCatMixin(object): @register_method('alert_test') def applyAlertTest(self, valid_dexes, params, expmjd, variability_cache=None): if len(params) == 0: return np.array([[], [], [], [], [], []]) if isinstance(expmjd, numbers.Number): dmags_out = np.zeros((6, self.num_variable_obj(params))) else: dmags_out = np.zeros((6, self.num_variable_obj(params), len(expmjd))) for i_star in range(self.num_variable_obj(params)): if params['amp'][i_star] is not None: dmags = params['amp'][i_star]*np.cos(params['per'][i_star]*expmjd) for i_filter in range(6): dmags_out[i_filter][i_star] = dmags return dmags_out class TestAlertsVarCat(TestAlertsVarCatMixin, AlertStellarVariabilityCatalog): pass class TestAlertsTruthCat(TestAlertsVarCatMixin, CameraCoords, AstrometryStars, Variability, InstanceCatalog): column_outputs = ['uniqueId', 'chipName', 'dmagAlert', 'magAlert'] camera = obs_lsst_phosim.PhosimMapper().camera @compound('delta_umag', 'delta_gmag', 'delta_rmag', 'delta_imag', 'delta_zmag', 'delta_ymag') def get_TruthVariability(self): return self.applyVariability(self.column_by_name('varParamStr')) @cached def get_dmagAlert(self): return self.column_by_name('delta_%smag' % self.obs_metadata.bandpass) @cached def get_magAlert(self): return self.column_by_name('%smag' % self.obs_metadata.bandpass) + \ self.column_by_name('dmagAlert') star_db = StarAlertTestDBObj(database=self.star_db_name, driver='sqlite') # assemble the true light curves for each object; we need to figure out # if their np.max(dMag) ever goes over dmag_cutoff; then we will know if # we are supposed to simulate them true_lc_dict = {} true_lc_obshistid_dict = {} is_visible_dict = {} obs_dict = {} max_obshistid = -1 n_total_observations = 0 for obs in self.obs_list: obs_dict[obs.OpsimMetaData['obsHistID']] = obs obshistid = obs.OpsimMetaData['obsHistID'] if obshistid > max_obshistid: max_obshistid = obshistid cat = TestAlertsTruthCat(star_db, obs_metadata=obs) for line in cat.iter_catalog(): if line[1] is None: continue n_total_observations += 1 if line[0] not in true_lc_dict: true_lc_dict[line[0]] = {} true_lc_obshistid_dict[line[0]] = [] true_lc_dict[line[0]][obshistid] = line[2] true_lc_obshistid_dict[line[0]].append(obshistid) if line[0] not in is_visible_dict: is_visible_dict[line[0]] = False if line[3] <= self.obs_mag_cutoff[mag_name_to_int[obs.bandpass]]: is_visible_dict[line[0]] = True obshistid_bits = int(np.ceil(np.log(max_obshistid)/np.log(2))) skipped_due_to_mag = 0 objects_to_simulate = [] obshistid_unqid_set = set() for obj_id in true_lc_dict: dmag_max = -1.0 for obshistid in true_lc_dict[obj_id]: if np.abs(true_lc_dict[obj_id][obshistid]) > dmag_max: dmag_max = np.abs(true_lc_dict[obj_id][obshistid]) if dmag_max >= dmag_cutoff: if not is_visible_dict[obj_id]: skipped_due_to_mag += 1 continue objects_to_simulate.append(obj_id) for obshistid in true_lc_obshistid_dict[obj_id]: obshistid_unqid_set.add((obj_id << obshistid_bits) + obshistid) self.assertGreater(len(objects_to_simulate), 10) self.assertGreater(skipped_due_to_mag, 0) log_file_name = tempfile.mktemp(dir=self.output_dir, suffix='log.txt') alert_gen = AlertDataGenerator(testing=True) alert_gen.subdivide_obs(self.obs_list, htmid_level=6) for htmid in alert_gen.htmid_list: alert_gen.alert_data_from_htmid(htmid, star_db, photometry_class=TestAlertsVarCat, output_prefix='alert_test', output_dir=self.output_dir, dmag_cutoff=dmag_cutoff, log_file_name=log_file_name) dummy_sed = Sed() bp_dict = BandpassDict.loadTotalBandpassesFromFiles() phot_params = PhotometricParameters() # First, verify that the contents of the sqlite files are all correct n_tot_simulated = 0 alert_query = 'SELECT alert.uniqueId, alert.obshistId, meta.TAI, ' alert_query += 'meta.band, quiescent.flux, alert.dflux, ' alert_query += 'quiescent.snr, alert.snr, ' alert_query += 'alert.ra, alert.dec, alert.chipNum, ' alert_query += 'alert.xPix, alert.yPix, ast.pmRA, ast.pmDec, ' alert_query += 'ast.parallax ' alert_query += 'FROM alert_data AS alert ' alert_query += 'INNER JOIN metadata AS meta ON meta.obshistId=alert.obshistId ' alert_query += 'INNER JOIN quiescent_flux AS quiescent ' alert_query += 'ON quiescent.uniqueId=alert.uniqueId ' alert_query += 'AND quiescent.band=meta.band ' alert_query += 'INNER JOIN baseline_astrometry AS ast ' alert_query += 'ON ast.uniqueId=alert.uniqueId' alert_dtype = np.dtype([('uniqueId', int), ('obshistId', int), ('TAI', float), ('band', int), ('q_flux', float), ('dflux', float), ('q_snr', float), ('tot_snr', float), ('ra', float), ('dec', float), ('chipNum', int), ('xPix', float), ('yPix', float), ('pmRA', float), ('pmDec', float), ('parallax', float)]) sqlite_file_list = os.listdir(self.output_dir) n_tot_simulated = 0 obshistid_unqid_simulated_set = set() for file_name in sqlite_file_list: if not file_name.endswith('db'): continue full_name = os.path.join(self.output_dir, file_name) self.assertTrue(os.path.exists(full_name)) alert_db = DBObject(full_name, driver='sqlite') alert_data = alert_db.execute_arbitrary(alert_query, dtype=alert_dtype) if len(alert_data) == 0: continue mjd_list = ModifiedJulianDate.get_list(TAI=alert_data['TAI']) for i_obj in range(len(alert_data)): n_tot_simulated += 1 obshistid_unqid_simulated_set.add((alert_data['uniqueId'][i_obj] << obshistid_bits) + alert_data['obshistId'][i_obj]) unq = alert_data['uniqueId'][i_obj] obj_dex = (unq//1024)-1 self.assertAlmostEqual(self.pmra_truth[obj_dex], 0.001*alert_data['pmRA'][i_obj], 4) self.assertAlmostEqual(self.pmdec_truth[obj_dex], 0.001*alert_data['pmDec'][i_obj], 4) self.assertAlmostEqual(self.px_truth[obj_dex], 0.001*alert_data['parallax'][i_obj], 4) ra_truth, dec_truth = applyProperMotion(self.ra_truth[obj_dex], self.dec_truth[obj_dex], self.pmra_truth[obj_dex], self.pmdec_truth[obj_dex], self.px_truth[obj_dex], self.vrad_truth[obj_dex], mjd=mjd_list[i_obj]) distance = angularSeparation(ra_truth, dec_truth, alert_data['ra'][i_obj], alert_data['dec'][i_obj]) distance_arcsec = 3600.0*distance msg = '\ntruth: %e %e\nalert: %e %e\n' % (ra_truth, dec_truth, alert_data['ra'][i_obj], alert_data['dec'][i_obj]) self.assertLess(distance_arcsec, 0.0005, msg=msg) obs = obs_dict[alert_data['obshistId'][i_obj]] chipname = chipNameFromRaDec(self.ra_truth[obj_dex], self.dec_truth[obj_dex], pm_ra=self.pmra_truth[obj_dex], pm_dec=self.pmdec_truth[obj_dex], parallax=self.px_truth[obj_dex], v_rad=self.vrad_truth[obj_dex], obs_metadata=obs, camera=self.camera) chipnum = int(chipname.replace('R', '').replace('S', ''). replace(' ', '').replace(';', '').replace(',', ''). replace(':', '')) self.assertEqual(chipnum, alert_data['chipNum'][i_obj]) xpix, ypix = pixelCoordsFromRaDec(self.ra_truth[obj_dex], self.dec_truth[obj_dex], pm_ra=self.pmra_truth[obj_dex], pm_dec=self.pmdec_truth[obj_dex], parallax=self.px_truth[obj_dex], v_rad=self.vrad_truth[obj_dex], obs_metadata=obs, camera=self.camera) self.assertAlmostEqual(alert_data['xPix'][i_obj], xpix, 4) self.assertAlmostEqual(alert_data['yPix'][i_obj], ypix, 4) dmag_sim = -2.5*np.log10(1.0+alert_data['dflux'][i_obj]/alert_data['q_flux'][i_obj]) self.assertAlmostEqual(true_lc_dict[alert_data['uniqueId'][i_obj]][alert_data['obshistId'][i_obj]], dmag_sim, 3) mag_name = ('u', 'g', 'r', 'i', 'z', 'y')[alert_data['band'][i_obj]] m5 = obs.m5[mag_name] q_mag = dummy_sed.magFromFlux(alert_data['q_flux'][i_obj]) self.assertAlmostEqual(self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex], q_mag, 4) snr, gamma = calcSNR_m5(self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex], bp_dict[mag_name], self.obs_mag_cutoff[alert_data['band'][i_obj]], phot_params) self.assertAlmostEqual(snr/alert_data['q_snr'][i_obj], 1.0, 4) tot_mag = self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex] + \ true_lc_dict[alert_data['uniqueId'][i_obj]][alert_data['obshistId'][i_obj]] snr, gamma = calcSNR_m5(tot_mag, bp_dict[mag_name], m5, phot_params) self.assertAlmostEqual(snr/alert_data['tot_snr'][i_obj], 1.0, 4) for val in obshistid_unqid_set: self.assertIn(val, obshistid_unqid_simulated_set) self.assertEqual(len(obshistid_unqid_set), len(obshistid_unqid_simulated_set)) astrometry_query = 'SELECT uniqueId, ra, dec, TAI ' astrometry_query += 'FROM baseline_astrometry' astrometry_dtype = np.dtype([('uniqueId', int), ('ra', float), ('dec', float), ('TAI', float)]) tai_list = [] for obs in self.obs_list: tai_list.append(obs.mjd.TAI) tai_list = np.array(tai_list) n_tot_ast_simulated = 0 for file_name in sqlite_file_list: if not file_name.endswith('db'): continue full_name = os.path.join(self.output_dir, file_name) self.assertTrue(os.path.exists(full_name)) alert_db = DBObject(full_name, driver='sqlite') astrometry_data = alert_db.execute_arbitrary(astrometry_query, dtype=astrometry_dtype) if len(astrometry_data) == 0: continue mjd_list = ModifiedJulianDate.get_list(TAI=astrometry_data['TAI']) for i_obj in range(len(astrometry_data)): n_tot_ast_simulated += 1 obj_dex = (astrometry_data['uniqueId'][i_obj]//1024) - 1 ra_truth, dec_truth = applyProperMotion(self.ra_truth[obj_dex], self.dec_truth[obj_dex], self.pmra_truth[obj_dex], self.pmdec_truth[obj_dex], self.px_truth[obj_dex], self.vrad_truth[obj_dex], mjd=mjd_list[i_obj]) distance = angularSeparation(ra_truth, dec_truth, astrometry_data['ra'][i_obj], astrometry_data['dec'][i_obj]) self.assertLess(3600.0*distance, 0.0005) del alert_gen gc.collect() self.assertGreater(n_tot_simulated, 10) self.assertGreater(len(obshistid_unqid_simulated_set), 10) self.assertLess(len(obshistid_unqid_simulated_set), n_total_observations) self.assertGreater(n_tot_ast_simulated, 0)
assert np.diff(obs_params['obsHistID']).min()>0 obsid_query = np.array(htmid_to_obs[htmid_query]) obs_dex = np.searchsorted(obs_params['obsHistID'].value, obsid_query) np.testing.assert_array_equal(obs_params['obsHistID'].value[obs_dex], obsid_query) ra_obs = obs_params['ra'].value[obs_dex] dec_obs = obs_params['dec'].value[obs_dex] mjd_obs = obs_params['mjd'].value[obs_dex] rotsky_obs = obs_params['rotSkyPos'].value[obs_dex] filter_obs = obs_params['filter'].value[obs_dex] m5_obs = obs_params['m5'].value[obs_dex] mjd_obj_list = ModifiedJulianDate.get_list(TAI=mjd_obs) obs_md_list = [] for ii in range(len(ra_obs)): obs = ObservationMetaData(pointingRA=ra_obs[ii], pointingDec=dec_obs[ii], mjd=mjd_obj_list[ii], rotSkyPos=rotsky_obs[ii], bandpassName='ugrizy'[filter_obs[ii]]) obs_md_list.append(obs) print('%d time steps' % len(filter_obs)) constraint = 'isvar=1 ' htmid_21_min = htmid_query<<2*(21-query_level) htmid_21_max = (htmid_query+1)<<2*(21-query_level) constraint += 'AND htmid>=%d AND htmid<%d' % (htmid_21_min, htmid_21_max)
def test_alert_data_generation(self): dmag_cutoff = 0.005 mag_name_to_int = {'u': 0, 'g': 1, 'r': 2, 'i': 3, 'z' : 4, 'y': 5} _max_var_param_str = self.max_str_len class StarAlertTestDBObj(StellarAlertDBObjMixin, CatalogDBObject): objid = 'star_alert' tableid = 'stars' idColKey = 'simobjid' raColName = 'ra' decColName = 'dec' objectTypeId = 0 columns = [('raJ2000', 'ra*0.01745329252'), ('decJ2000', 'dec*0.01745329252'), ('parallax', 'px*0.01745329252/3600.0'), ('properMotionRa', 'pmra*0.01745329252/3600.0'), ('properMotionDec', 'pmdec*0.01745329252/3600.0'), ('radialVelocity', 'vrad'), ('variabilityParameters', 'varParamStr', str, _max_var_param_str)] class TestAlertsVarCatMixin(object): @register_method('alert_test') def applyAlertTest(self, valid_dexes, params, expmjd, variability_cache=None): if len(params) == 0: return np.array([[], [], [], [], [], []]) if isinstance(expmjd, numbers.Number): dmags_out = np.zeros((6, self.num_variable_obj(params))) else: dmags_out = np.zeros((6, self.num_variable_obj(params), len(expmjd))) for i_star in range(self.num_variable_obj(params)): if params['amp'][i_star] is not None: dmags = params['amp'][i_star]*np.cos(params['per'][i_star]*expmjd) for i_filter in range(6): dmags_out[i_filter][i_star] = dmags return dmags_out class TestAlertsVarCat(TestAlertsVarCatMixin, AlertStellarVariabilityCatalog): pass class TestAlertsTruthCat(TestAlertsVarCatMixin, CameraCoordsLSST, AstrometryStars, Variability, InstanceCatalog): column_outputs = ['uniqueId', 'chipName', 'dmagAlert', 'magAlert'] @compound('delta_umag', 'delta_gmag', 'delta_rmag', 'delta_imag', 'delta_zmag', 'delta_ymag') def get_TruthVariability(self): return self.applyVariability(self.column_by_name('varParamStr')) @cached def get_dmagAlert(self): return self.column_by_name('delta_%smag' % self.obs_metadata.bandpass) @cached def get_magAlert(self): return self.column_by_name('%smag' % self.obs_metadata.bandpass) + \ self.column_by_name('dmagAlert') star_db = StarAlertTestDBObj(database=self.star_db_name, driver='sqlite') # assemble the true light curves for each object; we need to figure out # if their np.max(dMag) ever goes over dmag_cutoff; then we will know if # we are supposed to simulate them true_lc_dict = {} true_lc_obshistid_dict = {} is_visible_dict = {} obs_dict = {} max_obshistid = -1 n_total_observations = 0 for obs in self.obs_list: obs_dict[obs.OpsimMetaData['obsHistID']] = obs obshistid = obs.OpsimMetaData['obsHistID'] if obshistid > max_obshistid: max_obshistid = obshistid cat = TestAlertsTruthCat(star_db, obs_metadata=obs) for line in cat.iter_catalog(): if line[1] is None: continue n_total_observations += 1 if line[0] not in true_lc_dict: true_lc_dict[line[0]] = {} true_lc_obshistid_dict[line[0]] = [] true_lc_dict[line[0]][obshistid] = line[2] true_lc_obshistid_dict[line[0]].append(obshistid) if line[0] not in is_visible_dict: is_visible_dict[line[0]] = False if line[3] <= self.obs_mag_cutoff[mag_name_to_int[obs.bandpass]]: is_visible_dict[line[0]] = True obshistid_bits = int(np.ceil(np.log(max_obshistid)/np.log(2))) skipped_due_to_mag = 0 objects_to_simulate = [] obshistid_unqid_set = set() for obj_id in true_lc_dict: dmag_max = -1.0 for obshistid in true_lc_dict[obj_id]: if np.abs(true_lc_dict[obj_id][obshistid]) > dmag_max: dmag_max = np.abs(true_lc_dict[obj_id][obshistid]) if dmag_max >= dmag_cutoff: if not is_visible_dict[obj_id]: skipped_due_to_mag += 1 continue objects_to_simulate.append(obj_id) for obshistid in true_lc_obshistid_dict[obj_id]: obshistid_unqid_set.add((obj_id << obshistid_bits) + obshistid) self.assertGreater(len(objects_to_simulate), 10) self.assertGreater(skipped_due_to_mag, 0) log_file_name = tempfile.mktemp(dir=self.output_dir, suffix='log.txt') alert_gen = AlertDataGenerator(testing=True) alert_gen.subdivide_obs(self.obs_list, htmid_level=6) for htmid in alert_gen.htmid_list: alert_gen.alert_data_from_htmid(htmid, star_db, photometry_class=TestAlertsVarCat, output_prefix='alert_test', output_dir=self.output_dir, dmag_cutoff=dmag_cutoff, log_file_name=log_file_name) dummy_sed = Sed() bp_dict = BandpassDict.loadTotalBandpassesFromFiles() phot_params = PhotometricParameters() # First, verify that the contents of the sqlite files are all correct n_tot_simulated = 0 alert_query = 'SELECT alert.uniqueId, alert.obshistId, meta.TAI, ' alert_query += 'meta.band, quiescent.flux, alert.dflux, ' alert_query += 'quiescent.snr, alert.snr, ' alert_query += 'alert.ra, alert.dec, alert.chipNum, ' alert_query += 'alert.xPix, alert.yPix, ast.pmRA, ast.pmDec, ' alert_query += 'ast.parallax ' alert_query += 'FROM alert_data AS alert ' alert_query += 'INNER JOIN metadata AS meta ON meta.obshistId=alert.obshistId ' alert_query += 'INNER JOIN quiescent_flux AS quiescent ' alert_query += 'ON quiescent.uniqueId=alert.uniqueId ' alert_query += 'AND quiescent.band=meta.band ' alert_query += 'INNER JOIN baseline_astrometry AS ast ' alert_query += 'ON ast.uniqueId=alert.uniqueId' alert_dtype = np.dtype([('uniqueId', int), ('obshistId', int), ('TAI', float), ('band', int), ('q_flux', float), ('dflux', float), ('q_snr', float), ('tot_snr', float), ('ra', float), ('dec', float), ('chipNum', int), ('xPix', float), ('yPix', float), ('pmRA', float), ('pmDec', float), ('parallax', float)]) sqlite_file_list = os.listdir(self.output_dir) n_tot_simulated = 0 obshistid_unqid_simulated_set = set() for file_name in sqlite_file_list: if not file_name.endswith('db'): continue full_name = os.path.join(self.output_dir, file_name) self.assertTrue(os.path.exists(full_name)) alert_db = DBObject(full_name, driver='sqlite') alert_data = alert_db.execute_arbitrary(alert_query, dtype=alert_dtype) if len(alert_data) == 0: continue mjd_list = ModifiedJulianDate.get_list(TAI=alert_data['TAI']) for i_obj in range(len(alert_data)): n_tot_simulated += 1 obshistid_unqid_simulated_set.add((alert_data['uniqueId'][i_obj] << obshistid_bits) + alert_data['obshistId'][i_obj]) unq = alert_data['uniqueId'][i_obj] obj_dex = (unq//1024)-1 self.assertAlmostEqual(self.pmra_truth[obj_dex], 0.001*alert_data['pmRA'][i_obj], 4) self.assertAlmostEqual(self.pmdec_truth[obj_dex], 0.001*alert_data['pmDec'][i_obj], 4) self.assertAlmostEqual(self.px_truth[obj_dex], 0.001*alert_data['parallax'][i_obj], 4) ra_truth, dec_truth = applyProperMotion(self.ra_truth[obj_dex], self.dec_truth[obj_dex], self.pmra_truth[obj_dex], self.pmdec_truth[obj_dex], self.px_truth[obj_dex], self.vrad_truth[obj_dex], mjd=mjd_list[i_obj]) distance = angularSeparation(ra_truth, dec_truth, alert_data['ra'][i_obj], alert_data['dec'][i_obj]) distance_arcsec = 3600.0*distance msg = '\ntruth: %e %e\nalert: %e %e\n' % (ra_truth, dec_truth, alert_data['ra'][i_obj], alert_data['dec'][i_obj]) self.assertLess(distance_arcsec, 0.0005, msg=msg) obs = obs_dict[alert_data['obshistId'][i_obj]] chipname = chipNameFromRaDecLSST(self.ra_truth[obj_dex], self.dec_truth[obj_dex], pm_ra=self.pmra_truth[obj_dex], pm_dec=self.pmdec_truth[obj_dex], parallax=self.px_truth[obj_dex], v_rad=self.vrad_truth[obj_dex], obs_metadata=obs, band=obs.bandpass) chipnum = int(chipname.replace('R', '').replace('S', ''). replace(' ', '').replace(';', '').replace(',', ''). replace(':', '')) self.assertEqual(chipnum, alert_data['chipNum'][i_obj]) xpix, ypix = pixelCoordsFromRaDecLSST(self.ra_truth[obj_dex], self.dec_truth[obj_dex], pm_ra=self.pmra_truth[obj_dex], pm_dec=self.pmdec_truth[obj_dex], parallax=self.px_truth[obj_dex], v_rad=self.vrad_truth[obj_dex], obs_metadata=obs, band=obs.bandpass) self.assertAlmostEqual(alert_data['xPix'][i_obj], xpix, 4) self.assertAlmostEqual(alert_data['yPix'][i_obj], ypix, 4) dmag_sim = -2.5*np.log10(1.0+alert_data['dflux'][i_obj]/alert_data['q_flux'][i_obj]) self.assertAlmostEqual(true_lc_dict[alert_data['uniqueId'][i_obj]][alert_data['obshistId'][i_obj]], dmag_sim, 3) mag_name = ('u', 'g', 'r', 'i', 'z', 'y')[alert_data['band'][i_obj]] m5 = obs.m5[mag_name] q_mag = dummy_sed.magFromFlux(alert_data['q_flux'][i_obj]) self.assertAlmostEqual(self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex], q_mag, 4) snr, gamma = calcSNR_m5(self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex], bp_dict[mag_name], self.obs_mag_cutoff[alert_data['band'][i_obj]], phot_params) self.assertAlmostEqual(snr/alert_data['q_snr'][i_obj], 1.0, 4) tot_mag = self.mag0_truth_dict[alert_data['band'][i_obj]][obj_dex] + \ true_lc_dict[alert_data['uniqueId'][i_obj]][alert_data['obshistId'][i_obj]] snr, gamma = calcSNR_m5(tot_mag, bp_dict[mag_name], m5, phot_params) self.assertAlmostEqual(snr/alert_data['tot_snr'][i_obj], 1.0, 4) for val in obshistid_unqid_set: self.assertIn(val, obshistid_unqid_simulated_set) self.assertEqual(len(obshistid_unqid_set), len(obshistid_unqid_simulated_set)) astrometry_query = 'SELECT uniqueId, ra, dec, TAI ' astrometry_query += 'FROM baseline_astrometry' astrometry_dtype = np.dtype([('uniqueId', int), ('ra', float), ('dec', float), ('TAI', float)]) tai_list = [] for obs in self.obs_list: tai_list.append(obs.mjd.TAI) tai_list = np.array(tai_list) n_tot_ast_simulated = 0 for file_name in sqlite_file_list: if not file_name.endswith('db'): continue full_name = os.path.join(self.output_dir, file_name) self.assertTrue(os.path.exists(full_name)) alert_db = DBObject(full_name, driver='sqlite') astrometry_data = alert_db.execute_arbitrary(astrometry_query, dtype=astrometry_dtype) if len(astrometry_data) == 0: continue mjd_list = ModifiedJulianDate.get_list(TAI=astrometry_data['TAI']) for i_obj in range(len(astrometry_data)): n_tot_ast_simulated += 1 obj_dex = (astrometry_data['uniqueId'][i_obj]//1024) - 1 ra_truth, dec_truth = applyProperMotion(self.ra_truth[obj_dex], self.dec_truth[obj_dex], self.pmra_truth[obj_dex], self.pmdec_truth[obj_dex], self.px_truth[obj_dex], self.vrad_truth[obj_dex], mjd=mjd_list[i_obj]) distance = angularSeparation(ra_truth, dec_truth, astrometry_data['ra'][i_obj], astrometry_data['dec'][i_obj]) self.assertLess(3600.0*distance, 0.0005) del alert_gen gc.collect() self.assertGreater(n_tot_simulated, 10) self.assertGreater(len(obshistid_unqid_simulated_set), 10) self.assertLess(len(obshistid_unqid_simulated_set), n_total_observations) self.assertGreater(n_tot_ast_simulated, 0)